diff --git a/.flake8 b/.flake8 index 5cf65b68b..dea01af93 100644 --- a/.flake8 +++ b/.flake8 @@ -19,3 +19,5 @@ exclude = .venv build tutorials_and_tools/tutorials_and_tools/*.py + tutorials/**/scripts/*.py + tools/**/scripts/*.py diff --git a/.github/workflows/deploy-books.yml b/.github/workflows/deploy-books.yml index e64189fe5..69db8c54f 100644 --- a/.github/workflows/deploy-books.yml +++ b/.github/workflows/deploy-books.yml @@ -5,6 +5,11 @@ on: push: branches: - main + - development + pull_request: + branches: + - development + - main # If your git repository has the Jupyter Book within some-subfolder next to # unrelated files, you can make this run only if a file within that specific # folder has been modified. @@ -16,9 +21,8 @@ on: jobs: deploy-book: runs-on: ubuntu-latest - environment: - name: github-pages permissions: + contents: write pages: write id-token: write steps: @@ -47,15 +51,14 @@ jobs: # Build the book - name: Build the book run: | - jupyter-book build tutorials_and_tools + jupyter-book build tutorials - # Upload the book's HTML as an artifact - - name: Upload artifact - uses: actions/upload-pages-artifact@v3 - with: - path: "tutorials_and_tools/_build/html" - - # Deploy the book's HTML to GitHub Pages + # Deploy to gh-pages using peaceiris action for all branches - name: Deploy to GitHub Pages - id: deployment - uses: actions/deploy-pages@v4 + uses: peaceiris/actions-gh-pages@v3 + with: + github_token: ${{ secrets.GITHUB_TOKEN }} + publish_dir: tutorials/_build/html + publish_branch: gh-pages + destination_dir: ${{ github.ref == 'refs/heads/main' && '.' || 'dev-preview' }} + keep_files: ${{ github.ref != 'refs/heads/main' }} diff --git a/.github/workflows/flake8.yaml b/.github/workflows/flake8.yaml index 37893327f..503cfbde5 100644 --- a/.github/workflows/flake8.yaml +++ b/.github/workflows/flake8.yaml @@ -28,4 +28,4 @@ jobs: run: echo '::add-matcher::.github/workflows/flake8-linter-matcher.json' - name: Run flake8 on changed files only run: | - git diff origin/$GITHUB_BASE_REF HEAD -- "*.py" | flake8 --config=.flake8 --diff + git diff origin/$GITHUB_BASE_REF HEAD --name-only -- "*.py" | grep -v -E "(tutorials/.*/scripts/|tools/.*/scripts/)" | xargs -r git diff origin/$GITHUB_BASE_REF HEAD -- | flake8 --config=.flake8 --diff diff --git a/.github/workflows/nbval.yaml b/.github/workflows/nbval.yaml index 501d5fded..48f23b9e0 100644 --- a/.github/workflows/nbval.yaml +++ b/.github/workflows/nbval.yaml @@ -15,22 +15,20 @@ jobs: env: # Notebooks with estimated runtime in seconds (format: "notebook:runtime") ALL_NOTEBOOKS: | - "1 - Basics, Humidity, Design.ipynb:10" - "2 - Degradation.ipynb:5" - "3 - Spectral Degradation.ipynb:5" - "4 - Weather Database Access.ipynb:15" - "ASTM Live Demo.ipynb:10" - "B-O LID - Accelerated Test.ipynb:10" - "Custom-Functions-Nopython.ipynb:30" - "LETID - Accelerated Test.ipynb:120" - "LETID - Outdoor LETID.ipynb:30" - "LETID - Outdoor Scenario Based on Accelerated Test Result.ipynb:45" - "LETID - Passivated Wafer.ipynb:80" - "Monte Carlo - Arrhenius.ipynb:180" - "Monte Carlo - Standoff.ipynb:30" - "Tools - Edge Seal Oxygen Ingress.ipynb:15" - "Van't Hoff Degradation Model.ipynb:5" - "Geospatial - Local Scenario.ipynb:1" + "01_basics/01_basics_humidity_design.ipynb:10" + "01_basics/02_degradation.ipynb:5" + "01_basics/03_spectral_degradation.ipynb:5" + "01_basics/04_weather_database_access.ipynb:15" + "02_degradation/01_bo_lid_accelerated_test.ipynb:10" + "02_degradation/02_letid_accelerated_test.ipynb:120" + "02_degradation/03_letid_outdoor.ipynb:30" + "02_degradation/04_letid_outdoor_scenario.ipynb:45" + "02_degradation/05_letid_passivated_wafer.ipynb:80" + "02_degradation/06_vant_hoff_degradation_model.ipynb:5" + "03_monte_carlo/01_arrhenius.ipynb:180" + "03_monte_carlo/02_standoff.ipynb:30" + "06_advanced/01_custom_functions_nopython.ipynb:30" + "10_workshop_demos/01_astm_live_demo.ipynb:10" steps: - name: Calculate batch matrix id: set-matrix @@ -150,7 +148,7 @@ jobs: fail-fast: false matrix: ${{ fromJson(needs.calculate-batches.outputs.matrix) }} env: - NOTEBOOK_DIR: "tutorials_and_tools/tutorials_and_tools" + NOTEBOOK_DIR: "tutorials" SANITIZE_CFG: "nbval_sanitization_rules.cfg" ALL_NOTEBOOKS: ${{ needs.calculate-batches.outputs.notebooks }} BATCH_ASSIGNMENTS: ${{ needs.calculate-batches.outputs.batch_assignments }} @@ -167,14 +165,14 @@ jobs: uses: actions/cache@v4 with: path: ~/.cache/pip - key: ${{ runner.os }}-pip-${{ hashFiles('tutorials_and_tools/requirements.txt', 'pyproject.toml') }} + key: ${{ runner.os }}-pip-${{ hashFiles('tutorials/requirements.txt', 'pyproject.toml') }} restore-keys: | ${{ runner.os }}-pip- - name: Install notebook environment run: | python -m pip install --upgrade pip wheel - pip install --timeout=300 -r tutorials_and_tools/requirements.txt + pip install --timeout=300 -r tutorials/requirements.txt pip install --timeout=300 -e .[test] pip install nbval global_land_mask dotenv imageio nrel-pysam pip install nbconvert[webpdf] diff --git a/.github/workflows/publish-to-pypi.yml b/.github/workflows/publish-to-pypi.yml index 78887af39..597e9fc30 100644 --- a/.github/workflows/publish-to-pypi.yml +++ b/.github/workflows/publish-to-pypi.yml @@ -1,6 +1,9 @@ name: Publish Python 🐍 distribution πŸ“¦ to PyPI and TestPyPI -on: push +on: + push: + tags: + - '*' jobs: build: @@ -9,6 +12,8 @@ jobs: steps: - uses: actions/checkout@v4 + with: + fetch-depth: 0 # Fetch all history for setuptools_scm - name: Set up Python uses: actions/setup-python@v5 with: @@ -27,10 +32,35 @@ jobs: name: python-package-distributions path: dist/ + publish-to-testpypi: + name: Publish Python 🐍 distribution πŸ“¦ to TestPyPI + if: startsWith(github.ref, 'refs/tags/') + needs: + - build + runs-on: ubuntu-latest + environment: + name: testpypi + url: https://test.pypi.org/p/pvdeg + permissions: + id-token: write # IMPORTANT: mandatory for trusted publishing + + steps: + - name: Download all the dists + uses: actions/download-artifact@v4 + with: + name: python-package-distributions + path: dist/ + - name: Publish distribution πŸ“¦ to TestPyPI + uses: pypa/gh-action-pypi-publish@release/v1 + with: + repository-url: https://test.pypi.org/legacy/ + skip-existing: true + publish-to-pypi: name: >- Publish Python 🐍 distribution πŸ“¦ to PyPI - if: startsWith(github.ref, 'refs/tags/') # only publish to PyPI on tag pushes + # Only publish to PyPI on final release tags (not rc, alpha, beta, etc.) + if: startsWith(github.ref, 'refs/tags/') && !contains(github.ref, 'rc') && !contains(github.ref, 'alpha') && !contains(github.ref, 'beta') && !contains(github.ref, 'a') && !contains(github.ref, 'b') needs: - build runs-on: ubuntu-latest diff --git a/.github/workflows/testbook.yaml b/.github/workflows/testbook.yaml index 161dc41ed..8cb2a8f7e 100644 --- a/.github/workflows/testbook.yaml +++ b/.github/workflows/testbook.yaml @@ -6,17 +6,16 @@ jobs: notebook-testbook-check: runs-on: ubuntu-latest env: - NOTEBOOK_DIR: "tutorials_and_tools/tutorials_and_tools" + NOTEBOOK_DIR: "tutorials" TESTBOOK_NOTEBOOK_LIST: | - "API Call Required - PYSam.ipynb" - "API Call Required - load_nsrdb_distributed.ipynb" - "HPC Connection Required - Scenario - Geographical Features.ipynb" - "HPC Connection Required - Scenario - Geospatial.ipynb" - "HPC Connection Required - Scenario - Non-uniform Mountain Prefferential Downselect.ipynb" - "HPC Connection Required - Scenario - Single Location.ipynb" - "HPC Connection Required - Scenario - Temperature.ipynb" - "HPC Connection Required - Tools - Module Standoff for IEC TS 63126.ipynb" - "HPC Connection Required - Geospatial Templates.ipynb" + "06_advanced/03_pysam_api.ipynb" + "06_advanced/04_nsrdb_distributed_api.ipynb" + "04_scenario/01_scenario_temperature.ipynb" + "04_scenario/02_scenario_single_location.ipynb" + "04_scenario/03_scenario_mountain_downselect.ipynb" + "05_geospatial/01_geospatial_templates.ipynb" + "05_geospatial/04_scenario_geographical_features.ipynb" + "05_geospatial/05_scenario_geospatial.ipynb" steps: - uses: actions/checkout@v4 @@ -27,7 +26,7 @@ jobs: - name: Install notebook environment run: | python -m pip install --upgrade pip wheel - pip install --timeout=300 -r tutorials_and_tools/requirements.txt + pip install --timeout=300 -r tutorials/requirements.txt pip install --timeout=300 -e .[test] pip install testbook pip install global_land_mask dotenv imageio nrel-pysam diff --git a/.gitignore b/.gitignore index a4c303f76..704282f8b 100644 --- a/.gitignore +++ b/.gitignore @@ -69,7 +69,10 @@ instance/ docs/sphinx/_build/ # tutorial jupyter-book +_build/ pvdeg_tutorials/_build/ +tutorials/_build/ +tutorials_and_tools/_build/ # PyBuilder target/ diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index f641d7edb..246e75b44 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -1,6 +1,6 @@ # See https://pre-commit.com for more information # See https://pre-commit.com/hooks.html for more hooks -exclude: '^(docs/source/|tutorials_and_tools/_build/)' +exclude: '^(docs/source/|tutorials/_build/)' repos: - repo: https://github.com/pre-commit/pre-commit-hooks rev: v6.0.0 @@ -14,9 +14,17 @@ repos: rev: 25.9.0 hooks: - id: black - exclude: '^tutorials_and_tools/' + exclude: '^tutorials/' - repo: https://github.com/PyCQA/flake8 rev: 7.3.0 # Use the desired flake8 version hooks: - id: flake8 - exclude: '^tutorials_and_tools/' + exclude: '^tutorials/' +- repo: https://github.com/mwouts/jupytext + rev: v1.18.1 + hooks: + - id: jupytext + args: [--sync, --pipe, black] + files: '^tutorials/.*\.ipynb$' + additional_dependencies: + - black==25.9.0 diff --git a/.readthedocs.yaml b/.readthedocs.yaml index 4a29a4b83..1911d78d1 100644 --- a/.readthedocs.yaml +++ b/.readthedocs.yaml @@ -10,19 +10,11 @@ build: os: ubuntu-22.04 tools: python: "3.11" - # You can also specify other tool versions: - # nodejs: "19" - # rust: "1.64" - # golang: "1.19" # Build documentation in the docs/ directory with Sphinx sphinx: configuration: docs/source/conf.py -# If using Sphinx, optionally build your docs in additional formats such as PDF -# formats: -# - pdf - # Optionally declare the Python requirements required to build your docs python: install: @@ -30,3 +22,8 @@ python: path: . extra_requirements: - docs + +# Note: Branch-to-version mapping is configured in ReadTheDocs dashboard: +# - 'main' branch -> 'stable' version (default) +# - 'development' branch -> 'latest' version +# To configure: Project Settings > Versions > Activate/set as default diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md new file mode 100644 index 000000000..d65ceaca0 --- /dev/null +++ b/CONTRIBUTING.md @@ -0,0 +1,120 @@ +# Contributing to PVDeg + +Thank you for your interest in contributing to PVDeg! We welcome contributions from the community. + +πŸ“– **For comprehensive contributing guidelines, see the [Contributing Guide](https://pvdegradationtools.readthedocs.io/en/latest/user_guide/contributing.html) in our documentation.** + +This document provides a quick reference for getting started. + +## Quick Start for Developers + +```bash +# 1. Fork and clone +git clone https://github.com/YOUR-USERNAME/PVDegradationTools.git +cd PVDegradationTools + +# 2. Create environment (choose venv or conda) +python -m venv pvdeg-dev +source pvdeg-dev/bin/activate # Windows: pvdeg-dev\Scripts\activate + +# 3. Install in editable mode with all dependencies +pip install -e .[all] + +# 4. Install pre-commit hooks +pre-commit install + +# 5. (Optional, required for HPC) Register Jupyter kernel +python -m ipykernel install --user --name=pvdeg-dev +``` + +πŸ‘‰ **See detailed installation instructions, environment options, and troubleshooting in the [documentation](https://pvdegradationtools.readthedocs.io/en/latest/user_guide/contributing.html#getting-started).** + +## Pre-commit Hooks + +Pre-commit hooks automatically run code quality checks (black, flake8, jupytext, etc.) before each commit. + +**Manual runs:** +```bash +pre-commit run --all-files # All hooks on all files +pre-commit run --files path/to/file.py # Specific files +``` + +πŸ‘‰ **See [Pre-commit Hooks documentation](https://pvdegradationtools.readthedocs.io/en/latest/user_guide/contributing.html#pre-commit-hooks) for details on what each hook does and jupytext synchronization.** + +## Validating Notebook Outputs + +When contributing notebook changes, validate outputs cleanly to avoid environment-specific formatting issues: + +**Single notebook:** +```bash +jupyter nbconvert --to notebook --execute --inplace "tutorials/01_basics/01_basics_humidity_design.ipynb" +``` + +**All notebooks in a category:** +```bash +jupyter nbconvert --to notebook --execute --inplace "tutorials/01_basics/*.ipynb" +``` + +## Ways to Contribute + +### Code Contributions + +- Follow PEP 8 style (88 character line length) +- Include tests for new functionality +- Add/update docstrings (numpydoc format) +- Update documentation as needed + +πŸ‘‰ **See [Code Contributions](https://pvdegradationtools.readthedocs.io/en/latest/user_guide/contributing.html#code-contributions) for detailed guidelines, examples, and testing instructions.** + +### Database Contributions + +PVDeg maintains community-driven open datasets for PV degradation modeling. We welcome contributions of validated material properties and degradation parameters! + +**Databases in `pvdeg/data/`:** +- **DegradationDatabase.json**: Kinetic parameters, activation energies +- **H2Opermeation.json**: Water vapor permeation for encapsulants/backsheets +- **O2permeation.json**: Oxygen permeation properties +- **AApermeation.json**: Acetic acid permeation data +- **albedo.json**: UV-albedo for different surface types + +**Quick guidelines:** +- Use peer-reviewed sources +- Follow existing JSON structure +- Include proper citations (DOI preferred) +- Specify units and measurement conditions +- Validate JSON syntax before submitting + +πŸ‘‰ **See [Database Contributions](https://pvdegradationtools.readthedocs.io/en/latest/user_guide/contributing.html#database-contributions) for detailed guidelines, data quality requirements, and JSON examples.** + +## Submitting Changes + +```bash +# 1. Create feature branch +git checkout -b feature/my-feature + +# 2. Make changes and commit (pre-commit runs automatically) +git add . +git commit -m "Description of changes" + +# 3. Push and open Pull Request +git push origin feature/my-feature +``` + +**Pull Request Checklist:** +- [ ] Tests pass (`pytest`) +- [ ] Pre-commit hooks pass +- [ ] Documentation updated +- [ ] Changelog entry added (if user-facing change) + +πŸ‘‰ **See [Submitting Changes](https://pvdegradationtools.readthedocs.io/en/latest/user_guide/contributing.html#submitting-changes) for the complete checklist and review process.** + +## Additional Resources + +- πŸ“– [Full Contributing Guide](https://pvdegradationtools.readthedocs.io/en/latest/user_guide/contributing.html) - Comprehensive documentation +- πŸ“¦ [Installation Guide](https://pvdegradationtools.readthedocs.io/en/latest/user_guide/installation.html) - Detailed setup instructions +- πŸ› [GitHub Issues](https://github.com/NREL/PVDegradationTools/issues) - Report bugs or request features +- πŸ’¬ [GitHub Discussions](https://github.com/NREL/PVDegradationTools/discussions) - Ask questions + +## Contributor License Agreement + +First-time contributors must sign the [Contributor License Agreement](cla-1.0.md). When you submit your first pull request, a bot will comment with instructions. diff --git a/LICENSE.md b/LICENSE.md index 513847a02..29c8c6ffd 100644 --- a/LICENSE.md +++ b/LICENSE.md @@ -1,6 +1,6 @@ BSD 3-Clause License -Copyright 2020-2023 Alliance for Sustainable Energy, LLC +Copyright 2020 Alliance for Sustainable Energy, LLC Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: diff --git a/README.md b/README.md index cd2749910..2f8bcf955 100644 --- a/README.md +++ b/README.md @@ -13,7 +13,7 @@ Publications - DOI + DOI @@ -39,94 +39,111 @@ -# PV Degradation Tools (pvdeg) +# PVDeg: Python Package for Modeling Degradation in Photovoltaic Systems -This repository contains functions for calculating degradation of photovoltaic modules. For example, functions to calculate front and rear relative Humidity, as well as Acceleration Factors. A degradation calculation function is also being developed, considering humidity and spectral irradiances models. +PVDeg is an open-source Python package for modeling photovoltaic (PV) degradation, developed at the National Renewable Energy Laboratory (NREL) and supported by the Durable Module Materials (DuraMAT) consortium. It provides modular functions, materials databases, and calculation workflows for simulating degradation mechanisms (e.g., LeTID, hydrolysis, UV exposure) using weather data from the National Solar Radiation Database (NSRDB) and the Photovoltaic Geographical Information System (PVGIS). By integrating Monte Carlo uncertainty propagation and geospatial processing, PVDeg enables field-relevant predictions and uncertainty quantification of module reliability and lifetime. +## Key Features -Tutorials -========= - -### Jupyter Book +- **Core Degradation Functions**: Dedicated functions for physical degradation mechanisms including moisture ingress, LeTID, UV exposure, and thermal stress +- **Scenario Class**: Simplified workflow interface for complex multi-parameter degradation studies +- **Geospatial Analysis**: Large-scale spatial analyses with parallel processing across geographic regions +- **Monte Carlo Framework**: Uncertainty quantification through parameter distribution sampling +- **Material Databases**: Curated degradation parameters, kinetic coefficients, and material properties +- **Weather Data Integration**: Seamless access to NSRDB and PVGIS meteorological data +- **Standards Support**: Contributions to IEC TS 63126 and other standardization efforts +## Example Applications -For in depth Tutorials you can run online, see our [jupyter-book](https://nrel.github.io/PVDegradationTools/intro.html) [![Jupyter Book Badge](https://jupyterbook.org/badge.svg)](https://nrel.github.io/PVDegradationTools/intro.html) +PVDeg has been adopted in multiple studies across the PV reliability community: -Clicking on the rocket-icon on the top allows you to launch the journals on [Google Colaboratory](https://colab.research.google.com/) for interactive mode. -Just uncomment the first line `pip install ...` to install the environment on each journal if you follow this mode. - -### Binder +- **Thermal Stability and IEC TS 63126 Compliance**: Calculate effective standoff distances and generate public maps supporting the IEC TS 63126 standard +- **Light and Elevated Temperature Induced Degradation (LeTID)**: Integrated into international interlaboratory comparison studies and field-aged array analyses +- **Geospatial Performance Modeling**: Coupled with GeoGridFusion to streamline weather-data storage and spatial queries for large-scale degradation simulations +- **Agrivoltaics and System-Level Modeling**: Combined with PySAM to assess degradation-driven yield losses in dual-use agrivoltaic systems +- **Material-Property Parameterization**: Studies of UV-induced polymer degradation and moisture-related failures in encapsulants and backsheets -To run these tutorials in Binder, you can click here: -[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/NREL/PVDegradationTools/main) -It takes a minute to load the environment. +Documentation +============= -### Locally +Full documentation is available at **[ReadTheDocs](https://pvdegradationtools.readthedocs.io)** including: -You can also run the tutorial locally in a virtual environment, i.e., `venv` or -[miniconda](https://docs.conda.io/en/latest/miniconda.html). +- πŸ“š [API Reference](https://pvdegradationtools.readthedocs.io/en/latest/api.html) - Complete function and class documentation +- πŸ“– [User Guide](https://pvdegradationtools.readthedocs.io/en/latest/user_guide/installation.html) - Installation, tutorials, and usage guides +- πŸ”§ [Contributing Guide](https://pvdegradationtools.readthedocs.io/en/latest/user_guide/contributing.html) - Development setup and guidelines +- πŸ“° [What's New](https://pvdegradationtools.readthedocs.io/en/latest/whatsnew/index.html) - Release notes and changelogs -1. Create and activate a new environment, e.g., on Mac/Linux terminal with `venv`: - ``` - python -m venv pvdeg - . pvdeg/bin/activate - ``` - or with `conda`: - ``` - conda create -n pvdeg - conda activate pvdeg - ``` +Installation +============ -1. Install `pvdeg` into the new environment with `pip`: - ``` - python -m pip install pvdeg - ``` +PVDeg releases may be installed using `pip` and `conda` tools. Compatible with Python 3.10 and above. -1. Start a Jupyter session: +**Quick Install:** - ``` - jupyter notebook - ``` +```bash +pip install pvdeg +``` -1. Use the file explorer in Jupyter lab to browse to `tutorials` - and start the first Tutorial. +**With optional dependencies:** +| Group | Install Command | Purpose | +|-------|----------------|---------| +| **sam** | `pip install pvdeg[sam]` | PySAM support for system modeling | +| **docs** | `pip install pvdeg[docs]` | Sphinx documentation tools | +| **test** | `pip install pvdeg[test]` | Testing and validation tools | +| **books** | `pip install pvdeg[books]` | Jupyter Book publishing | +| **all** | `pip install pvdeg[all]` | All optional dependencies | -Documentation -============= +**Developer Installation:** -Documentation is available in [ReadTheDocs](https://PVDegradationTools.readthedocs.io) where you can find more details on the API functions. +```bash +git clone https://github.com/NREL/PVDegradationTools.git +cd PVDegradationTools +pip install -e .[all] +``` +πŸ“– **For detailed installation instructions including conda environments, HPC setup, troubleshooting, and version compatibility, see the [Installation Guide](https://pvdegradationtools.readthedocs.io/en/latest/user_guide/installation.html).** -Installation -============ +Tutorials +========= -Relative Humidity and Acceleration Factors for Solar Modules releases may be installed using the ``pip`` and ``conda`` tools. Compatible with Python 3.5 and above. +PVDeg provides comprehensive tutorials organized by topic. Choose your preferred environment: -Install with: +### Jupyter Book (Recommended) - pip install pvdeg +Interactive tutorials with live execution: [**PVDeg Jupyter Book**](https://nrel.github.io/PVDegradationTools/intro.html) -For developer installation, clone the repository, navigate to the folder location and install as: +- Click the πŸš€ rocket icon to launch notebooks in [Google Colab](https://colab.research.google.com/) +- **Development Preview:** See latest changes at [dev-preview](https://nrel.github.io/PVDegradationTools/dev-preview/intro.html) - pip install -e .[all] +### Binder -Running jupyter notebooks using anaconda prompt -=============================================== +Run tutorials in your browser without installation: [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/NREL/PVDegradationTools/main) -Note that in order to run notebooks cleanly and validate outputs, use the following -commands to run either one notebook: +### Local Installation - jupyter nbconvert --to notebook --execute --inplace "tutorials_and_tools/ - tutorials_and_tools/Monte Carlo - Arrhenius.ipynb" +1. **Install PVDeg** (see [Installation](#installation)) -or all notebooks inside the tutorials and tools folder: +2. **Clone the repository** to access tutorial notebooks: + ```bash + git clone https://github.com/NREL/PVDegradationTools.git + cd PVDegradationTools + ``` - jupyter nbconvert --to notebook --execute --inplace "tutorials_and_tools/ - tutorials_and_tools/*.ipynb" +3. **Start Jupyter:** + ```bash + jupyter notebook + ``` -This avoids formatting issues that may arise depending on your own local environment -or IDE. +4. **Navigate to tutorials** organized by category: + - `01_basics/` - Introduction to PVDeg fundamentals + - `02_degradation/` - Degradation mechanism modeling + - `03_monte_carlo/` - Monte Carlo uncertainty analysis + - `04_geospatial/` - Geospatial and HPC scenarios + - `05_advanced/` - Advanced topics and API access + - `10_workshop_demos/` - Workshop demonstrations + - `tools/` - Standalone analysis tools +πŸ“– **For more information on running and validating notebooks, see the [documentation](https://pvdegradationtools.readthedocs.io).** License ======= @@ -135,26 +152,48 @@ License Contributing -======= +============ + +We welcome contributions to this software! Please see [CONTRIBUTING.md](CONTRIBUTING.md) for detailed instructions on: +- Setting up your development environment +- Installing and using pre-commit hooks +- Code style guidelines +- Testing and documentation requirements +- **Contributing to material property and degradation parameter databases** +- Submitting pull requests + +Please read the copyright license agreement (cla-1.0.md), with instructions on signing it in sign-CLA.md. -We welcome contributiosn to this software, but please read the copyright license agreement (cla-1.0.md), with instructions on signing it in sign-CLA.md. For questions, email us. +All code, documentation, and discussion contributors are acknowledged for their contributions to the PVDeg project. Getting support =============== If you suspect that you may have discovered a bug or if you'd like to -change something about pvdeg, then please make an issue on our -[GitHub issues page](hhttps://github.com/NREL/PVDegradationTools/issues). +change something about PVDeg, then please make an issue on our +[GitHub issues page](https://github.com/NREL/PVDegradationTools/issues). Citing ====== -If you use this functions in a published work, please cite: +If you use PVDeg in a published work, please cite: + +**JOSS Paper (In Review):** + + Daxini, R., Ovaitt, S., Springer, M., Ford, T., & Kempe, M. (2025). PVDeg: a python package for modeling degradation on solar photovoltaic systems. Journal of Open Source Software (In Review). + +**Latest Release:** + +[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.8088578.svg)](https://zenodo.org/records/8088578/latest) + +**Software Record:** Holsapple, Derek, Ayala Pelaez, Silvana, Kempe, Michael. "PV Degradation Tools", NREL Github 2020, Software Record SWR-20-71. -And/or the specific release from Zenodo: - Martin Springer, Tobin Ford, Rajiv Daxini, Matthew Brown, Silvana Ovaitt, Joseph Karas, Mark Campanelli, Derek M Holsapple, Kevin Anderson, Michael Kempe. (2025). NREL/PVDegradationTools: 0.6.1 (0.6.1). Zenodo. https://doi.org/10.5281/zenodo.17265988 +Acknowledgements +================ + +This work was authored by the National Renewable Energy Laboratory, operated by Alliance for Sustainable Energy, LLC, for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308. Funding provided as part of the Durable Modules Materials Consortium (DuraMAT), an Energy Materials Network Consortium funded by the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, Solar Energy Technologies Office Agreement Number 32509. The research was performed using computational resources sponsored by the Department of Energy's Office of Energy Efficiency and Renewable Energy and located at the National Renewable Energy Laboratory. The views expressed in the article do not necessarily represent the views of the DOE or the U.S. Government. The U.S. Government retains and the publisher, by accepting the article for publication, acknowledges that the U.S. Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this work, or allow others to do so, for U.S. Government purposes. diff --git a/docs/source/index.rst b/docs/source/index.rst index 5eef84de2..878df391b 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -12,19 +12,7 @@ Welcome to pvdeg! ============================================================== -This PVDegradation Tools Model is a first-of-its-kind detailed spatio-temporal -modeling assessment tool that empowers users to calculate various PV degradation -modes, for different PV technologies and materials in its database. - -NREL is developing the pvdeg model to help PV Researchers, Module manufacturers, -and other PV stakeholders assess different materials and technologies for -different degradation modes in all locations throughout the world. Available as -open source, the PVDegradation model currently supports PV energy calculations -for water vapor pressure, ingress rate, edge seal width, Van Hofft Irradiance -Degradation, Weighted Average Irradiance, Arrehnius Acceleration Factor, -relative humidity in the outside, front and back encapsulant and backsheet, -spectral degradation, and solder fatigue. More functions for standards and -other degradation profiles are in the works. +PVDeg is an open-source Python package for modeling photovoltaic (PV) degradation, developed at the National Renewable Energy Laboratory (NREL) and supported by the Durable Module Materials (DuraMAT) consortium. It provides modular functions, materials databases, and calculation workflows for simulating degradation mechanisms (e.g., LeTID, hydrolysis, UV exposure) using weather data from the National Solar Radiation Database (NSRDB) and the Photovoltaic Geographical Information System (PVGIS). By integrating Monte Carlo uncertainty propagation and geospatial processing, PVDeg enables field-relevant predictions and uncertainty quantification of module reliability and lifetime. The source code for pvdeg is hosted on `github `_. Please see the :ref:`installation` page for installation help. @@ -35,35 +23,49 @@ See :ref:`tutorials` to learn how to use and experiment with various functionali :alt: PVDeg-Flow diagram. +Key Features +============ + +- **Core Degradation Functions**: Dedicated functions for physical degradation mechanisms including moisture ingress, LeTID, UV exposure, and thermal stress +- **Scenario Class**: Simplified workflow interface for complex multi-parameter degradation studies +- **Geospatial Analysis**: Large-scale spatial analyses with parallel processing across geographic regions +- **Monte Carlo Framework**: Uncertainty quantification through parameter distribution sampling +- **Material Databases**: Curated degradation parameters, kinetic coefficients, and material properties +- **Weather Data Integration**: Seamless access to NSRDB and PVGIS meteorological data +- **Standards Support**: Contributions to IEC TS 63126 and other standardization efforts + How the Model Works =================== -Coupled with pvlib for module performan and weather/irradiance calculations, -PVDegradation Tool estimates degradations, and accelerated factors on -user-defined parameters. The `Data Library` is under development as part of the -PVDegradationTool project, compiling literature parameters and functions. +PVDeg's core API provides dedicated functions for calculating physical degradation mechanisms, accessing material properties and environmental stressors. These functions rely on standardized environmental stressors such as temperature, irradiance, and humidity, and can be chained to produce lifetime predictions under realistic field conditions. + +To simplify complex workflows, PVDeg wraps its core functions into a ``Scenario`` class that defines locations, module configurations, and degradation mechanisms. This enables user-friendly workflows, simplifying the setup and execution of complex multi-parameter degradation studies. + +The geospatial analysis layer enables large-scale spatial analyses by automatically distributing degradation calculations across geographic regions using parallel processing and advanced data structures. It integrates environmental data from NSRDB and PVGIS and automates sampling across latitude-longitude grids to produce maps, such as standoff distance distribution used in IEC TS 63126 compliance studies. -The PVDegradationTool simulatineously reads tens of terabytes of time-series -solar data from state-of-art resource data set National Solar Radiation Database -(NSRDB), publicly available on the cloud, enabling the execution of pvdeg -beyond the confines of NREL's high-performance computing capabilities. +PVDeg's Monte Carlo engine samples parameter distributions and their correlations to generate thousands of realizations, producing confidence intervals on degradation rates rather than single deterministic values. This capability can help quantify uncertainty in complex and non-linear module lifetime predictions, and identify which parameters most strongly affect reliability risk. Citing PVDegradation Tools ========================== -If you use this calculator in a published work, please cite: +If you use PVDeg in a published work, please cite: + +**JOSS Paper (In Review):** .. code-block:: - Holsapple, Derek, Ayala Pelaez, Silvana, Kempe, Michael. "PV Degradation Tools", NREL Github 2020, Software Record SWR-20-71. + Daxini, R., Ovaitt, S., Springer, M., Ford, T., & Kempe, M. (2025). PVDeg: a python package for modeling degradation on solar photovoltaic systems. Journal of Open Source Software (In Review). +**Latest Release:** -Please also cite the DOI corresponding to the specific version that you used. -DOIs are listed at Zenodo.org. `linked here `_ +Please cite the DOI corresponding to the specific version that you used. +DOIs are listed at `Zenodo.org `_ + +**Software Record:** .. code-block:: - Martin Springer, Tobin Ford, Matt, MDKempe, Silvana Ovaitt, AidanWesley, joe karas, Mark Campanelli, Derek M Holsapple, and Kevin Anderson. β€œNREL/PVDegradationTools: 0.4.2.” Zenodo, 2024. doi:10.5281/zenodo.13760911. + Holsapple, Derek, Ayala Pelaez, Silvana, Kempe, Michael. "PV Degradation Tools", NREL Github 2020, Software Record SWR-20-71. .. toctree:: diff --git a/docs/source/user_guide/contributing.rst b/docs/source/user_guide/contributing.rst index 67a13cf6c..ec692cc2c 100644 --- a/docs/source/user_guide/contributing.rst +++ b/docs/source/user_guide/contributing.rst @@ -3,273 +3,588 @@ Contributing ============ -.. note:: - This contributing document is heavily based on pvlib-python - contribution guidelines. This is still a work in progress +We welcome contributions to PVDeg! Whether you're fixing bugs, adding features, +improving documentation, or contributing to our material property databases, your +help is valuable to the PV community. -Encouraging more people to help develop Pvdeg is essential to our -success. Therefore, we want to make it easy and rewarding for you to -contribute. +For a quick overview, see `CONTRIBUTING.md `_ on GitHub. -There is a lot of material in this section, aimed at a variety of -contributors from novice to expert. Don't worry if you don't (yet) -understand parts of it. +This guide provides comprehensive details for contributors. -Easy ways to contribute +Easy Ways to Contribute ~~~~~~~~~~~~~~~~~~~~~~~ -Here are a few ideas for how you can contribute, even if you are new to -bifacial_radiance, git, or Python: +Here are ways to contribute, even if you're new to PVDeg, git, or Python: -* Ask and answer `pvdeg questions on StackOverflow `_. -* Make `GitHub issues `_ - and contribute to the conversations about how to resolve them. -* Read issues and pull requests that other people created and - contribute to the conversation about how to resolve them. -* Improve the documentation and the unit tests. -* Improve the IPython/Jupyter Notebook tutorials or write new ones that - demonstrate how to use Pvdeg in your area of expertise. -* Tell your friends and colleagues about pvdeg -* Add your project to our - `Projects and publications that use pvdeg wiki - `_. +* **Report bugs or request features** via `GitHub issues `_ +* **Join discussions** on existing issues and pull requests +* **Improve documentation** - fix typos, clarify explanations, add examples +* **Enhance unit tests** - increase coverage or improve test quality +* **Create or improve tutorials** - demonstrate PVDeg in your area of expertise +* **Contribute to material databases** - add validated degradation parameters and properties +* **Share your work** - add your project to our `wiki `_ +* **Spread the word** - tell colleagues about PVDeg +Getting Started +~~~~~~~~~~~~~~~ -How to contribute new code -~~~~~~~~~~~~~~~~~~~~~~~~~~ +Development Environment Setup +------------------------------ -The basics +1. **Fork and clone the repository**: + + .. code-block:: bash + + git clone https://github.com/YOUR-USERNAME/PVDegradationTools.git + cd PVDegradationTools + +2. **Create a virtual environment**: + + Using venv: + + .. code-block:: bash + + python -m venv pvdeg-dev + source pvdeg-dev/bin/activate # Windows: pvdeg-dev\\Scripts\\activate + + Or using conda: + + .. code-block:: bash + + conda create -n pvdeg-dev python=3.11 + conda activate pvdeg-dev + +3. **Install in editable mode with all dependencies**: + + .. code-block:: bash + + pip install -e .[all] + +4. **Install pre-commit hooks**: + + .. code-block:: bash + + pre-commit install + + Pre-commit hooks automatically enforce code quality standards before each commit. + +5. **(Optional but required for HPC) Register Jupyter kernel**: + + .. code-block:: bash + + python -m ipykernel install --user --name=pvdeg-dev + + This is especially important when working on HPC systems like NREL's Kestrel. + +Pre-commit Hooks +~~~~~~~~~~~~~~~~ + +We use pre-commit hooks to maintain code quality and consistency. + +What the Hooks Do +----------------- + +Our ``.pre-commit-config.yaml`` configuration runs: + +* **black** - Python code formatter (line length: 88 characters) +* **flake8** - Python linter for style guide enforcement +* **jupytext** - Syncs Jupyter notebooks with Python scripts for version control +* **trailing-whitespace** - Removes trailing whitespace +* **end-of-file-fixer** - Ensures files end with newline +* **check-yaml** - Validates YAML syntax +* **check-added-large-files** - Prevents committing large files + +Running Pre-commit Manually +---------------------------- + +Run all hooks on all files: + +.. code-block:: bash + + pre-commit run --all-files + +Run hooks on specific files: + +.. code-block:: bash + + pre-commit run --files path/to/file.py + +Skip hooks for a specific commit (use sparingly): + +.. code-block:: bash + + git commit --no-verify -m "commit message" + +Jupytext Synchronization +------------------------- + +Notebooks in ``tutorials/`` are automatically synced with Python scripts in ``scripts/`` folders. + +When you edit a notebook: + +1. Pre-commit hook creates/updates a ``.py`` script version +2. Both files are staged for commit +3. Version control works well with notebooks (diffs show code changes, not JSON) + +Manual sync: + +.. code-block:: bash + + jupytext --sync tutorials/**/*.ipynb + +Code Contributions +~~~~~~~~~~~~~~~~~~ + +Code Style ---------- -Contributors to pvdeg use GitHub's pull requests to add/modify -its source code. The GitHub pull request process can be intimidating for -new users, but you'll find that it becomes straightforward once you use -it a few times. Please let us know if you get stuck at any point in the -process. Here's an outline of the process: - -#. Create a GitHub issue and get initial feedback from users and - maintainers. If the issue is a bug report, please include the - code needed to reproduce the problem. -#. Obtain the latest version of pvdeg: Fork the pvdeg - project to your GitHub account, ``git clone`` your fork to your computer. -#. Make some or all of your changes/additions and ``git commit`` them to - your local repository. -#. Share your changes with us via a pull request: ``git push`` your - local changes to your GitHub fork, then go to GitHub make a pull - request. - -The Pandas project maintains an excellent `contributing page -`_ that goes -into detail on each of these steps. Also see GitHub's `Set Up Git -`_ and `Using Pull -Requests `_. - -We strongly recommend using virtual environments for development. -Virtual environments make it trivial to switch between different -versions of software. This `astropy guide -`_ is a good reference for virtual environments. If -this is your first pull request, don't worry about using a virtual -environment. - -You must include documentation and unit tests for any new or improved -code. We can provide help and advice on this after you start the pull -request. See the Testing section below. - - -.. _pull-request-scope: - -Pull request scope ------------------- +We follow standard Python conventions: -This section can be summed up as "less is more". - -A pull request can quickly become unmanageable if too many lines are -added or changed. "Too many" is hard to define, but as a rule of thumb, -we encourage contributions that contain less than 50 lines of primary code. -50 lines of primary code will typically need at least 250 lines -of documentation and testing. This is about the limit of what the -maintainers can review on a regular basis. - -A pull request can also quickly become unmanageable if it proposes -changes to the API in order to implement another feature. Consider -clearly and concisely documenting all proposed API changes before -implementing any code. - -Questions about related issues frequently come up in the process of -addressing implementing code for a pull request. Please try to avoid -expanding the scope of your pull request (this also applies to -reviewers!). We'd rather see small, well-documented additions to the -project's technical debt than see a pull request languish because its -scope expanded beyond what the reviewer community is capable of -processing. - -Of course, sometimes it is necessary to make a large pull request. We -only ask that you take a few minutes to consider how to break it into -smaller chunks before proceeding. - -When should I submit a pull request? ------------------------------------- - -The short answer: anytime. - -The long answer: it depends. If in doubt, go ahead and submit. You do -not need to make all of your changes before creating a pull request. -Your pull requests will automatically be updated when you commit new -changes and push them to GitHub. - -There are pros and cons to submitting incomplete pull-requests. On the -plus side, it gives everybody an easy way to comment on the code and can -make the process more efficient. On the minus side, it's easy for an -incomplete pull request to grow into a multi-month saga that leaves -everyone unhappy. If you submit an incomplete pull request, please be -very clear about what you would like feedback on and what we should -ignore. Alternatives to incomplete pull requests include creating a -`gist `_ or experimental branch and linking to -it in the corresponding issue. - -The best way to ensure that a pull request will be reviewed and merged in -a timely manner is to: - -#. Start by creating an issue. The issue should be well-defined and - actionable. -#. Ask the maintainers to tag the issue with the appropriate milestone. -#. Tag pvdeg community members or ``@pvdeg/maintainer`` when the pull - request is ready for review. (see :ref:`pull-request-reviews`) - - -.. _pull-request-reviews: - -Pull request reviews --------------------- +* **PEP 8** style guide with 88 character line length (black default) +* **Google-style docstrings** for functions and classes (numpydoc format) +* Type hints encouraged for function signatures +* Descriptive variable names (avoid single letters except in loops) +* Code must be compatible with Python 3.10 and above +* Remove ``print`` statements and ``logging`` calls before committing (``warning`` is acceptable) +* Set your editor to strip trailing whitespace + +**Variable naming**: PVDeg uses a mix of full and abbreviated names. Prefer full names for new contributions, especially in the API. Abbreviations can be used within functions to improve readability of formulae. + +Example docstring: -The pvdeg community and maintainers will review your pull request in a -timely fashion. Please "ping" ``@pvdeg/maintainer`` if it seems that -your pull request has been forgotten at any point in the pull request -process. +.. code-block:: python + + def calculate_degradation(temp, humidity, irradiance): + """ + Calculate degradation rate based on environmental conditions. + + Parameters + ---------- + temp : float or array-like + Temperature in degrees Celsius. + humidity : float or array-like + Relative humidity as percentage (0-100). + irradiance : float or array-like + Irradiance in W/mΒ². + + Returns + ------- + float or array-like + Degradation rate in %/year. + + Notes + ----- + Based on the Arrhenius equation with humidity acceleration factor. + See [1]_ for detailed methodology. + + References + ---------- + .. [1] Author et al. (2020). "Title." Journal. DOI. + """ + pass + +**Generic type descriptors** used in PVDeg documentation: + +* **dict-like**: dict, OrderedDict, pd.Series +* **numeric**: scalar, np.array, pd.Series (typically int or float dtype) +* **array-like**: np.array, pd.Series (typically int or float dtype) + +Parameters that specify a specific type require that exact input type. + +Testing +------- -Keep in mind that the PV modeling community is diverse and each pvdeg -community member brings a different perspective when reviewing code. -Some reviewers bring years of expertise in the sub-field that your code -contributes to and will focus on the details of the algorithm. Other -reviewers will be more focused on integrating your code with the rest of -pvdeg, ensuring that it is feasible to maintain, that it meets the -:ref:`code style ` guidelines, and that it is -:ref:`comprehensively tested `. Limiting the scope of the pull -request makes it much more likely that all of these reviews can be -conducted and any issues can be resolved in a timely fashion. +All code contributions should include unit tests. -Sometimes it's hard for reviewers to be immediately available, so the -right amount of patience is to be expected. That said, interested -reviewers should do their best to not wait until the last minute to put -in their two cents. +**Running the test suite**: +.. code-block:: bash -.. _code-style: + pytest tests/ -Code style -~~~~~~~~~~ +**Run tests with coverage report**: -pvdeg generally follows the `PEP 8 -- Style Guide for Python Code -`_. Maximum line length for code -is 88 characters. +.. code-block:: bash -Code must be compatible with Python 3.10 and above. + pytest --cov=pvdeg --cov-report=html tests/ -*ALIGN WITH PROJECT VARIABLE NAMES* -pvdeg uses a mix of full and abbreviated variable names. See -:ref:`variables_style_rules`. We could be better about consistency. -Prefer full names for new contributions. This is especially important -for the API. Abbreviations can be used within a function to improve the -readability of formulae. +**Test specific modules**: -Set your editor to strip extra whitespace from line endings. This -prevents the git commit history from becoming cluttered with whitespace -changes. +.. code-block:: bash -Please see :ref:`API` for information specific to documentation -style. + pytest tests/test_spectral.py -Remove any ``logging`` calls and ``print`` statements that you added -during development. ``warning`` is ok. +**Test notebooks** (requires nbval): -We typically use GitHub's -"`squash and merge `_" -feature to merge your pull request into pvdeg. GitHub will condense the -commit history of your branch into a single commit when merging into -pvdeg/main (the commit history on your branch remains -unchanged). Therefore, you are free to make commits that are as big or -small as you'd like while developing your pull request. +.. code-block:: bash + pytest --nbval tutorials/ -.. _documentation: +**Writing tests**: + +* Place tests in ``tests/`` directory +* Name test files ``test_.py`` +* Use descriptive test function names: ``test__`` +* Include edge cases and error conditions +* Use pytest fixtures for shared test data +* Mock external dependencies (weather APIs, file downloads) +* New test files must be included in ``tests/__init__.py`` + +**Debugging tests**: Use pytest's ``--pdb`` flag to drop into the debugger at test failures: + +.. code-block:: bash + + pytest tests/test_spectral.py --pdb + +This is preferred over adding ``print`` or ``logging`` statements. + +Example test: + +.. code-block:: python + + import pytest + import numpy as np + from pvdeg import spectral + + def test_spectral_degradation_basic(): + """Test spectral degradation with simple inputs.""" + wavelengths = np.array([300, 400, 500, 600, 700]) + spectrum = np.array([0.1, 0.3, 0.5, 0.4, 0.2]) + + result = spectral.calc_degradation(wavelengths, spectrum) + + assert isinstance(result, float) + assert result > 0 + assert result < 100 # Reasonable degradation range + + def test_spectral_degradation_with_nan(): + """Test spectral degradation handles NaN values.""" + wavelengths = np.array([300, 400, np.nan, 600, 700]) + spectrum = np.array([0.1, 0.3, 0.5, 0.4, 0.2]) + + with pytest.raises(ValueError, match="NaN values"): + spectral.calc_degradation(wavelengths, spectrum) Documentation -~~~~~~~~~~~~~ +------------- -Documentation must be written in -`numpydoc format `_ format which is rendered -using the `Sphinx Napoleon extension -`_. +**Building documentation locally**: -The numpydoc format includes a specification for the allowable input -types. Python's `duck typing `_ -allows for multiple input types to work for many parameters. pvdeg uses -the following generic descriptors as short-hand to indicate which -specific types may be used: +1. Install documentation dependencies: -* dict-like : dict, OrderedDict, pd.Series -* numeric : scalar, np.array, pd.Series. Typically int or float dtype. -* array-like : np.array, pd.Series. Typically int or float dtype. + .. code-block:: bash -Parameters that specify a specific type require that specific input type. + pip install -e .[docs] -Read the Docs will automatically build the documentation for each pull -request. Please confirm the documentation renders correctly by following -the ``continuous-documentation/read-the-docs`` link within the checks -status box at the bottom of the pull request. +2. Build the docs: -.. _testing: + .. code-block:: bash -Testing -~~~~~~~ + cd docs + make html + +3. View in browser: + + .. code-block:: bash + + # Open docs/build/html/index.html + +**Verifying documentation**: Read the Docs automatically builds documentation for each pull request. Confirm it renders correctly by following the ``continuous-documentation/read-the-docs`` link in the PR checks. + +**Documentation types**: -Developers **must** include comprehensive tests for any additions or -modifications to pvdeg. New unit test code should be placed in the corresponding test module in the pvdeg/test directory. -If you create a new testing file, remember to include it in ``__init__.py``. +* **API documentation**: Automatically generated from docstrings +* **User guide**: RST files in ``docs/source/user_guide/`` +* **Tutorials**: Jupyter notebooks in ``tutorials/`` +* **What's new**: Release notes in ``docs/source/whatsnew/`` -A pull request will automatically run the tests for you on Linux platform and python versions 2.7 and 3.6. However, it is typically more efficient to run and debug the tests in your own local -environment. +**Tutorial guidelines**: -pvdeg's unit tests can easily be run by executing ``pytest`` on the -pvdeg directory: +* Place tutorials in appropriate ``tutorials//`` folder +* Use descriptive names: ``01_intro_to_topic.ipynb`` +* Include markdown cells explaining concepts +* Use local data files when possible (avoid API dependencies for reproducibility) +* Keep execution time under 5 minutes +* Clear outputs before committing (pre-commit will help) -``pytest pvdeg`` +Notebook Validation +------------------- -or, for a single module: +When contributing notebook changes, it's important to validate that notebooks execute correctly +and produce consistent outputs across different environments. -``pytest pvdeg/test/spectral.py`` +**Why validate notebooks?** -or, for a single test: +* Ensures reproducibility across different systems and environments +* Catches environment-specific bugs before they reach users +* Maintains consistent output formatting in version control +* Verifies that all cells execute in the correct order +* Confirms that notebooks work with current dependencies -``pytest pvdeg/test/spectral.py::test_poa_irradiance`` +**Using nbconvert for validation** -We suggest using pytest's ``--pdb`` flag to debug test failures rather -than using ``print`` or ``logging`` calls. For example: +The ``jupyter nbconvert`` command executes notebooks in a clean kernel and validates outputs. +This is the recommended approach for testing notebook changes. -``pytest pvdeg/test/spectral.py --pdb`` +Execute a single notebook: -will drop you into the -`pdb debugger `_ at the -location of a test failure. As described in :ref:`code-style`, pvdeg -code does not use ``print`` or ``logging`` calls, and this also applies -to the test suite (with rare exceptions). +.. code-block:: bash -This documentation + jupyter nbconvert --to notebook --execute --inplace "tutorials/01_basics/01_basics_humidity_design.ipynb" + +Execute all notebooks in a category: + +.. code-block:: bash + + jupyter nbconvert --to notebook --execute --inplace "tutorials/01_basics/*.ipynb" + +Execute all tutorial notebooks (use with caution - may take a long time): + +.. code-block:: bash + + jupyter nbconvert --to notebook --execute --inplace "tutorials/**/*.ipynb" + +**Understanding the flags**: + +* ``--to notebook``: Converts back to notebook format (not HTML or other formats) +* ``--execute``: Executes all cells in order with a fresh kernel +* ``--inplace``: Overwrites the original notebook with execution results + +**Using pytest with nbval** + +For automated testing, use pytest with the nbval plugin to validate notebooks: + +.. code-block:: bash + + # Test all notebooks + pytest --nbval tutorials/ + + # Test specific notebook + pytest --nbval tutorials/01_basics/01_basics_humidity_design.ipynb + + # Skip output comparison (only check execution) + pytest --nbval --nbval-lax tutorials/ + +The ``--nbval-lax`` flag is useful when outputs may vary slightly (e.g., timestamps, random numbers) +but you still want to verify the notebook executes without errors. + +**Best practices for notebook contributions**: + +1. **Clear outputs before committing**: Use ``jupyter nbconvert --ClearOutputPreprocessor.enabled=True --inplace notebook.ipynb`` + or let pre-commit hooks handle it automatically + +2. **Test locally first**: Always run notebooks locally before pushing to verify they work + +3. **Use relative paths**: Ensure data paths work from the repository root + +4. **Document dependencies**: If a notebook requires optional dependencies (e.g., PySAM), + note this in a markdown cell at the top + +5. **Handle API keys gracefully**: Use commented-out API sections with local data alternatives, + following the pattern in existing notebooks + +6. **Keep execution time reasonable**: Aim for under 5 minutes per notebook. For longer analyses, + consider pre-computing results or creating separate demo notebooks + +7. **Version control friendly**: Clear all outputs before committing (jupytext and pre-commit help with this) + +**Troubleshooting notebook validation failures**: + +* **Import errors**: Verify all dependencies are installed in your environment +* **Path errors**: Check that data files exist at expected locations +* **Kernel errors**: Ensure your Jupyter kernel is properly registered (see installation guide) +* **Timeout errors**: Increase timeout with ``--ExecutePreprocessor.timeout=600`` (seconds) +* **API failures**: For notebooks with API calls, use local data or mock responses in tests + +Database Contributions +~~~~~~~~~~~~~~~~~~~~~~ + +One of the most valuable contributions you can make is adding validated material +property data and degradation parameters to our databases. + +What to Contribute +------------------ + +We maintain several JSON databases in ``pvdeg/data/``: + +* **DegradationDatabase.json**: Degradation rates, activation energies, temperature coefficients +* **H2Opermeation.json**: Water vapor transmission rates for encapsulants and barriers +* **O2permeation.json**: Oxygen permeation rates +* **AApermeation.json**: Acetic acid permeation data +* **albedo.json**: Ground albedo values for different surfaces + +How to Contribute +----------------- + +1. **Locate peer-reviewed data**: Published journal articles, conference proceedings, or technical reports +2. **Prepare structured data**: Follow the JSON format in existing entries +3. **Document metadata**: Include measurement conditions, equipment, uncertainties +4. **Submit via pull request**: Add your data to the appropriate JSON file with citation + +Data Quality Guidelines +----------------------- + +* **Peer-reviewed sources**: Prefer published research over manufacturer specs +* **Complete metadata**: Temperature, humidity, test conditions, sample details +* **Uncertainty quantification**: Include error bars or confidence intervals when available +* **Clear units**: Explicitly state all units +* **Proper citations**: Full reference with DOI when possible + +Example JSON Entry +------------------ + +.. code-block:: json + + { + "material": "EVA", + "property": "activation_energy", + "value": 0.87, + "units": "eV", + "conditions": { + "temperature_range": "60-85 C", + "test_method": "Arrhenius analysis", + "sample_thickness": "0.46 mm" + }, + "uncertainty": 0.05, + "reference": { + "authors": "Smith et al.", + "title": "Degradation study of EVA encapsulants", + "journal": "Solar Energy Materials", + "year": 2020, + "doi": "10.1016/j.solmat.2020.xxxxx" + } + } + +Submitting Changes ~~~~~~~~~~~~~~~~~~ -If this documentation is unclear, help us improve it! Consider looking -at the `pandas -documentation `_ for inspiration. \ No newline at end of file +Pull Request Process +-------------------- + +1. **Create a feature branch**: + + .. code-block:: bash + + git checkout -b feature/descriptive-name + + Use prefixes: ``feature/``, ``bugfix/``, ``docs/``, ``refactor/`` + +2. **Make changes and commit**: + + .. code-block:: bash + + git add . + git commit -m "Add descriptive commit message" + + Write clear commit messages explaining *why* the change was made. + +3. **Push to your fork**: + + .. code-block:: bash + + git push origin feature/descriptive-name + +4. **Open a pull request** on GitHub: + + * Provide a clear description of changes + * Reference related issues (e.g., "Closes #123") + * Ensure all CI checks pass (tests, pre-commit hooks) + * Request review from maintainers or tag ``@NREL/pvdeg-maintainers`` + +**Best practices**: + +* Keep pull requests focused and manageable (preferably <50 lines of primary code) +* Consider breaking large changes into smaller, incremental pull requests +* Submit pull requests early if you want feedback (mark as draft if incomplete) +* Be patient - reviewers bring diverse expertise and perspectives +* GitHub will automatically "squash and merge" your commits into a single commit + +Pull Request Checklist +---------------------- + +Before submitting your pull request, verify: + +- [ ] Code follows PEP 8 style (black formatting applied) +- [ ] All tests pass locally (``pytest tests/``) +- [ ] New functionality includes unit tests +- [ ] Docstrings added/updated for new/modified functions +- [ ] Type hints included for function signatures +- [ ] Pre-commit hooks pass (``pre-commit run --all-files``) +- [ ] Documentation updated if adding features +- [ ] What's new entry added if significant change +- [ ] Notebooks clear outputs and sync with ``.py`` scripts +- [ ] Database entries include proper citations and metadata + +Code Review Process +------------------- + +* Maintainers will review your pull request within a few days +* Discussion and iteration are normal - don't be discouraged +* Address feedback by pushing new commits to your branch +* Ping one of the pvdeg maintainers if your pull request seems forgotten +* Once approved, maintainers will merge your changes +* Your contribution will be included in the next release + +**Understanding reviews**: The PV modeling community is diverse. Reviewers may focus on: + +* Algorithm details and scientific accuracy +* Integration with existing PVDeg code +* Code maintainability and style +* Test coverage and documentation quality + +Contributor License Agreement +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +First-time contributors must sign the `Contributor License Agreement (CLA) +`_. +This protects both you and the project. + +When you submit your first pull request, a bot will comment with instructions +to sign the CLA. Simply follow the provided link. + +Release Process +~~~~~~~~~~~~~~~ + +For maintainers and contributors interested in the release workflow: + +**Versioning**: We follow `semantic versioning `_ (MAJOR.MINOR.PATCH) + +* MAJOR: Incompatible API changes +* MINOR: New features (backward compatible) +* PATCH: Bug fixes (backward compatible) + +**Testing releases**: + +* Pre-release tags (``v0.7.0-rc1``, ``v0.7.0-alpha1``) β†’ TestPyPI only +* Final tags (``v0.7.0``) β†’ TestPyPI and PyPI + +**What's new**: Update ``docs/source/whatsnew/releases/vX.Y.Z.rst`` with: + +* Enhancements +* Breaking changes +* Deprecations +* Bug fixes +* Dependency updates +* Issue references (#123) + +Getting Help +~~~~~~~~~~~~ + +If you have questions or need help: + +* **Ask on GitHub Discussions**: ``_ +* **Open an issue**: For bugs or feature requests +* **Check the documentation**: ``_ +* **Review existing PRs**: See how others approached similar problems + +Community Guidelines +~~~~~~~~~~~~~~~~~~~~ + +* Be respectful and professional +* Assume positive intent in all interactions +* Focus on constructive feedback +* Welcome newcomers and help them learn +* Give credit where it's due + +Thank you for contributing to PVDeg and supporting the PV research community! 🌞 \ No newline at end of file diff --git a/docs/source/user_guide/installation.rst b/docs/source/user_guide/installation.rst index dfe562ff0..4373ebceb 100644 --- a/docs/source/user_guide/installation.rst +++ b/docs/source/user_guide/installation.rst @@ -3,68 +3,195 @@ Installation ============ -pvdeg releases may be installed using the pip and conda tools. +PVDeg releases may be installed using pip. Compatible with Python 3.10 and above. -Base Install: +For a quick start, see the **Installation** section in our `README.md `_. + +Base Install +------------ + +To install PVDeg with core functionality: + +.. code-block:: bash + + pip install pvdeg + +This installs PVDeg with all required dependencies for basic degradation modeling, including: + +* Core scientific computing libraries (numpy, pandas, scipy) +* PV modeling with pvlib +* Weather data access (NREL-rex) +* Geospatial tools (cartopy, geopy) +* Jupyter notebook support (jupyterlab, notebook) +* Pre-commit hooks for development + +Optional Dependencies --------------------- -To get PVDeg the simplest way use: -.. code:: +PVDeg offers optional dependency groups for specific use cases. You can install these using the bracket syntax: - pip install pvdeg +.. code-block:: bash -Optional Install: ----------------- -PVDeg provides optional installs for testing, and documenation. We can specify these using `[]` syntax. + pip install pvdeg[group_name] +Available Optional Groups +~~~~~~~~~~~~~~~~~~~~~~~~~ -.. list-table:: Extra Installs in PVDeg - :widths: 30 70 - :header-rows: 1 +**sam** - System Advisor Model Integration + Adds NREL-PySAM for detailed system performance modeling and integration with SAM. + Required for agrivoltaics and advanced system modeling workflows. - * - **Extra Install Name** - - **Explanation** - * - ``docs`` - - Documentation dependencies - * - ``test`` - - Testing dependencies - * - ``books`` - - Jupyter utilities - * - ``sam`` - - NREL System Advisor Model dependencies - * - ``all`` - - all of the above names combined + .. code-block:: bash -To download packages required for testing, run: + pip install pvdeg[sam] -.. code:: +**docs** - Documentation Building + Installs tools for building Sphinx documentation including: + + * sphinx and themes + * nbsphinx for notebook integration + * sphinx-gallery for example galleries + + .. code-block:: bash + + pip install pvdeg[docs] + +**test** - Testing and Validation + Includes pytest, pytest-cov, nbval for notebook validation, and scikit-learn for testing. + Essential for contributors running the test suite. + + .. code-block:: bash pip install pvdeg[test] -To download all optional packages, run: +**books** - Jupyter Book Publishing + Adds jupyter-book for building and publishing tutorial documentation. + + .. code-block:: bash -.. code:: + pip install pvdeg[books] + +**all** - Everything + Installs all optional dependencies above. Recommended for developers. + + .. code-block:: bash pip install pvdeg[all] +Using Conda Environments +------------------------- + +While PVDeg is installed via pip, you can use conda to manage your Python environment: + +1. Create a new conda environment: + + .. code-block:: bash + + conda create -n pvdeg python=3.11 + conda activate pvdeg + +2. Install PVDeg with pip: + + .. code-block:: bash + + pip install pvdeg[all] + +3. Register the environment as a Jupyter kernel (important for HPC systems): + + .. code-block:: bash + + python -m ipykernel install --user --name=pvdeg + + This allows you to select the ``pvdeg`` kernel when running Jupyter notebooks, + especially important on HPC systems like NREL's Kestrel. Developer Installation ---------------------- +If you want to contribute to PVDeg or modify the source code, install in editable mode: -For developer installation, use the following steps. +1. Fork the repository on GitHub and clone your fork: + .. code-block:: bash -1. clone the reposityory using + git clone https://github.com/YOUR-USERNAME/PVDegradationTools.git + cd PVDegradationTools -.. code:: +2. Create and activate a virtual environment: - git clone https://github.com/NREL/PVDegradationTools.git + Using venv (Python standard library): -2. Navigate the the root of the repository such that you are in ``path/to/PVDegradationTools``, then run the following command. + .. code-block:: bash -.. code:: + python -m venv pvdeg-dev + source pvdeg-dev/bin/activate # On Windows: pvdeg-dev\Scripts\activate + + Or using conda: + + .. code-block:: bash + + conda create -n pvdeg-dev python=3.11 + conda activate pvdeg-dev + +3. Install in editable mode with all dependencies: + + .. code-block:: bash + + pip install -e .[all] + + The ``-e`` flag installs the package in "editable" mode, meaning changes you make + to the source code are immediately reflected without reinstalling. + +4. Install pre-commit hooks for code quality checks: + + .. code-block:: bash + + pre-commit install + + This will automatically run code formatting (black) and linting (flake8) before each commit. + +5. (Optional but recommended for HPC) Register the kernel for Jupyter: + + .. code-block:: bash + + python -m ipykernel install --user --name=pvdeg-dev + +6. Verify your installation by running the test suite: + + .. code-block:: bash + + pytest pvdeg + +For complete developer guidelines including contribution workflow, code style, +and testing requirements, see :ref:`contributing`. + +Troubleshooting +--------------- + +**Import errors after installation** + Make sure you're in the correct environment and that the installation completed successfully. + Try ``pip list | grep pvdeg`` to verify the package is installed. + +**HPC/Kestrel Jupyter kernel issues** + If your environment doesn't appear in Jupyter on HPC systems, ensure you've run: + ``python -m ipykernel install --user --name=your-env-name`` + +**Dependency conflicts** + Use a fresh virtual environment if you encounter version conflicts with existing packages. + +**Cartopy installation issues** + Cartopy requires GEOS and PROJ libraries. On some systems, you may need to install these + via conda: ``conda install -c conda-forge cartopy`` + +Version Compatibility +--------------------- - pip install -e . +* **Python**: 3.10, 3.11, 3.12, 3.13 +* **Operating Systems**: Linux, macOS, Windows +* **Key Dependencies**: + * pvlib >= 0.12.0 + * numpy >= 1.19.3 + * pandas (compatible versions) + * h5py <= 3.14.0 (pinned due to compatibility issues) -Compatible with Python 3.10 and above. +For the most up-to-date compatibility information, see the ``pyproject.toml`` file in the repository. diff --git a/docs/source/whatsnew/index.rst b/docs/source/whatsnew/index.rst index e03221e40..a0c07b48e 100644 --- a/docs/source/whatsnew/index.rst +++ b/docs/source/whatsnew/index.rst @@ -2,7 +2,14 @@ What's New ========== -PVDegradationTools (pvdeg) change log: + +This page contains the release notes for PVDeg, documenting new features, breaking changes, +bug fixes, and other improvements in each version. + +For information on contributing to future releases, see :ref:`contributing`. + +Release Notes +------------- .. include:: releases/v0.7.0.rst .. include:: releases/v0.6.1.rst diff --git a/docs/source/whatsnew/releases/v0.6.2.rst b/docs/source/whatsnew/releases/v0.6.2.rst deleted file mode 100644 index 90a8f927e..000000000 --- a/docs/source/whatsnew/releases/v0.6.2.rst +++ /dev/null @@ -1,41 +0,0 @@ -v0.6.2 (2025-12-04) -=================== - -Breaking Changes ----------------- -- Update NSRDB weather data retrieval to use PSM v4 instead of - PSM v3. Instances of ``pvlib.iotools.get_psm3()`` replaced with - ``pvlib.iotools.get_nsrdb_psm4_tmy()``. (:issue:`286`, :pull:`287`) - - -Enhancements ------------- -- Add Python 3.13 support. (:issue:`279`, :pull:`281`) -- Remove `kinetic_parameters.json` and merged parameters into - 'DegradationDatabase.json`. (:issue:`176`, :pull:`248`) - - -Deprecations -------------- -- Drop Python 3.9 support. The minimum supported Python version is now 3.10. - - -Bug Fixes ---------- -- Update MD5 checksum references in documentation from old hash to new hash - - -Dependencies ------------- -- Pin h5py<=3.14.0 (:issue:`278`, :pull:`277`) - - -Testing -------- - - -Contributors ------------- -- Martin Springer (:ghuser:`martin-springer`) -- Rajiv Daxini (:ghuser:`RDaxini`) - diff --git a/docs/source/whatsnew/releases/v0.7.0.rst b/docs/source/whatsnew/releases/v0.7.0.rst index e0819d0ac..4bfb18126 100644 --- a/docs/source/whatsnew/releases/v0.7.0.rst +++ b/docs/source/whatsnew/releases/v0.7.0.rst @@ -1,8 +1,52 @@ -v0.7.0 (2025-10-22) +v0.7.0 (2025-12-05) =================== +Breaking Changes +---------------- +- Update NSRDB weather data retrieval to use PSM v4 instead of + PSM v3. Instances of ``pvlib.iotools.get_psm3()`` replaced with + ``pvlib.iotools.get_nsrdb_psm4_tmy()``. (:issue:`286`, :pull:`287`) +- Enable multi-material handling in the Scenario class: + - Allow `material` parameter in `scenario.addModule()` to be a nested dictionary of + multiple materials from different materials files + - Update `utilities._add_material()` and `scenario.add_material()` to support this + functionality. + + Enhancements ------------ +- Add Python 3.13 support. (:issue:`279`, :pull:`281`) +- Remove `kinetic_parameters.json` and merged parameters into + 'DegradationDatabase.json`. (:issue:`176`, :pull:`248`) +- Update citation references in documentation homepage (``index.rst``) and README with + current Zenodo DOI link, JOSS paper (in review), and software record. (:issue:`283`, + :pull:`302`) +- Add CONTRIBUTING.md with comprehensive guidelines for code contributions, pre-commit + hooks, and database contributions. Highlights community-driven material property and + degradation parameter databases. Includes detailed pre-commit installation and usage + documentation, and ipykernel setup instructions for conda environments. + (:issue:`145`, :issue:`293`, :issue:`296`, :pull:`302`) +- Configure ReadTheDocs to build documentation on every pull request for preview and + review. (:issue:`258`, :pull:`302`) +- Restructure tutorial notebooks and migrate from ``tutorials_and_tools/`` to new + ``tutorials/`` directory with improved organization and naming conventions. + (:issue:`232`, :issue:`271`, :pull:`302`) + - Reorganize tutorials into logical themed folders: ``01_basics`` (4 notebooks), + ``02_degradation`` (6 notebooks), ``03_monte_carlo`` (2 notebooks), + ``04_scenario`` (3 notebooks), ``05_geospatial`` (5 notebooks), + ``06_advanced`` (4 notebooks), ``10_workshop_demos`` (2 notebooks), and + ``tools`` (3 tool notebooks). + - Add newer notebooks to Jupyter Book build and update table of contents. + - Update notebooks to load pre-saved weather data by default to avoid API rate + limits: ``03_monte_carlo/01_arrhenius.ipynb`` (Miami), + ``01_basics/04_weather_database_access.ipynb`` (Golden, CO), + ``03_monte_carlo/02_standoff.ipynb`` (NYC). + - Add jupytext configuration and Python script versions for all notebooks with + pre-commit hook synchronization for version control. + - Update Eagle HPC references to Kestrel (NREL's current HPC system). + - Update all configuration files (``_toc.yml``, ``_config.yml``, ``myst.yml``), + GitHub Actions workflows (``nbval.yaml``, ``testbook.yaml``, + ``deploy-books.yml``), and CI/CD pipelines for new structure. - Add pysam module to support InSPIRE Agrivoltaics geospatial simulations. Currently our pysam wrapper implementation is largely project specific. We aim to generalize this module in the future to support out of the box geospatial pysam calculations for all @@ -23,6 +67,24 @@ Enhancements - :py:mod:`inspire_ground_irradiance()` to calculate inter-row ground irradiance for the InSPIRE agrivoltaics irradiance dataset. - :py:mod:`solar_resource_dict()` to create solar_resource collections for SAM input from PVDeg weather and metadata. + +Deprecations +------------- +- Drop Python 3.9 support. The minimum supported Python version is now 3.10. (:issue:`279`, :pull:`281`) + + +Bug Fixes +--------- +- Update MD5 checksum references in documentation from old hash to new hash (:pull:`276`) + + +Dependencies +------------ +- Pin h5py<=3.14.0 (:issue:`278`, :pull:`277`) + + Contributors ------------ +- Rajiv Daxini (:ghuser:`RDaxini`) +- Martin Springer (:ghuser:`martin-springer`) - Tobin Ford (:ghuser:`tobin-ford`) diff --git a/jupytext.toml b/jupytext.toml new file mode 100644 index 000000000..5345bcdcc --- /dev/null +++ b/jupytext.toml @@ -0,0 +1,9 @@ +# Jupytext configuration +# Sync notebooks to scripts/ subdirectories as Python files with percent format + +# Default formats for notebooks +formats = "ipynb,scripts//py:percent" + +# Notebook metadata filter (reduce diff noise) +notebook_metadata_filter = "-all" +cell_metadata_filter = "-all" diff --git a/pvdeg/pysam.py b/pvdeg/pysam.py index 69dedbdac..b4b72d09e 100644 --- a/pvdeg/pysam.py +++ b/pvdeg/pysam.py @@ -78,8 +78,8 @@ def pysam( pv_model: str choose pySam photovoltaic system model. Some models are less thorough and run faster. - pvwatts8 is ~50x faster than pysamv1 but only calculates 46 parameters while - pysamv1 calculates 195. + pvwatts8 is ~50x faster than pvsamv1 but only calculates 46 parameters while + pvsamv1 calculates 195. options: ``pvwatts8``, ``pvsamv1`` diff --git a/pyproject.toml b/pyproject.toml index 560f86562..07a004d74 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -57,6 +57,7 @@ dependencies = [ "pre-commit", "sympy", "zarr", + "jupytext", ] dynamic = ["version"] @@ -79,7 +80,7 @@ test = [ "NREL-PySAM", ] books = [ - "jupyter-book", + "jupyter-book<2.0", ] sam = [ "NREL-PySAM", diff --git a/tutorials_and_tools/tutorials_and_tools/1 - Basics, Humidity, Design.ipynb b/tutorials/01_basics/01_basics_humidity_design.ipynb similarity index 98% rename from tutorials_and_tools/tutorials_and_tools/1 - Basics, Humidity, Design.ipynb rename to tutorials/01_basics/01_basics_humidity_design.ipynb index ac2fdd503..889b33be0 100644 --- a/tutorials_and_tools/tutorials_and_tools/1 - Basics, Humidity, Design.ipynb +++ b/tutorials/01_basics/01_basics_humidity_design.ipynb @@ -4,8 +4,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# 1 - Basics, Humidity, Design\n", - "### Module Humidity and Edge Seal Width\n", + "# Humidity Design Basics\n", + "## Module Humidity and Edge Seal Width\n", "\n", "\n", "**Requirements:**\n", @@ -20,7 +20,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2025-10-03T22:55:12.489520Z", @@ -32,7 +32,7 @@ "outputs": [], "source": [ "# if running on google colab, uncomment the next line and execute this cell to install the dependencies and prevent \"ModuleNotFoundError\" in later cells:\n", - "# !pip install pvdeg==0.3.3" + "# !pip install pvdeg" ] }, { @@ -93,7 +93,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 1. Reading in Weather Data and Site Meta-Data\n", + "# 1. Reading in Weather Data and Site Meta-Data\n", "Most `pvdeg` functions have been standardized to operate from popular weather files such as TMY3, EPW, and PSM3. For high-performance computer users, NSRDB and other database fetching tools can be used. For these tutorials, we will use local weather files. Unless otherwise stated, functions require the following fields within a weather file:\n", "\n", "- date-time index\n", @@ -1175,7 +1175,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 2. Solar Position, POA, and Module Temperature\n", + "# 2. Solar Position, POA, and Module Temperature\n", "\n", "Many operations with `PVDEG` require solar position, POA irradiance, and module temperature. Usually a `pvdeg` method will calculate these data sets automatically when it is required, however it will not keep the data for external use. For now, we will calculate them directly. This is helpful when you need to use the data for an external calculation. Here, we generate the solar position (azimuth, elevation, etc), plane of array irradiance (POA), and module temperature. `PVDEG` has wrappers for quickly using `PVLIB` to generate these figures with minimal input" ] @@ -1585,15 +1585,15 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 3. Module Humidities\n", - "`PVDEG` can be used to calculate the relative humidity of several layers within a PV module: the outside surface of the module, front ecapsulant, back encapsulant, and backsheet. This can be done with 2 techniques: Automatically and Manually. " + "# 3. Module Humidities\n", + "`PVDEG` can be used to calculate the relative humidity of several layers within a PV module: the outside surface of the module, front ecapsulant, back encapsulant, and backsheet. This can be done with 2 techniques: Automatically and Manually." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### 3.a. Automatic\n", + "# 3.a. Automatic\n", "Use the function `humidity.module`. This method does all of the calculations behind the scenes. It requires only 2 inputs (weather data and meta data). It will return a dataframe with all the layers of the module." ] }, @@ -1711,7 +1711,9 @@ } ], "source": [ - "rh_module = pvdeg.humidity.module(weather_df=WEATHER, meta=META, backsheet_thickness=0.3, back_encap_thickness=0.46)\n", + "rh_module = pvdeg.humidity.module(\n", + " weather_df=WEATHER, meta=META, backsheet_thickness=0.3, back_encap_thickness=0.46\n", + ")\n", "rh_module.head()" ] }, @@ -1719,7 +1721,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### 3.b. Manual\n", + "# 3.b. Manual\n", "Use the individual functions to calculate the RH in each layer. These are named and require more extensive input parameters. This must be done in the correct order:\n", "1. Surface Outside\n", "2. Front Encapsulant\n", @@ -1740,7 +1742,7 @@ }, "outputs": [], "source": [ - "surface_relative_humidity=WEATHER[\"relative_humidity\"]\n", + "surface_relative_humidity = WEATHER[\"relative_humidity\"]\n", "temperature_ambient = WEATHER[\"temp_air\"]\n", "\n", "rh_surface_relative = pvdeg.humidity.surface_relative(\n", @@ -1770,7 +1772,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 4. Design: Edge Seal Width\n", + "# 4. Design: Edge Seal Width\n", "`pvdeg.design.edge_seal_width` calculates the width [cm] required for several years of water ingress. If you do not specify the number of years, it will use the default value of 25. As with most `pvdeg` functions, we can automatically generate all necessary data if we pass the two arguments (weather_df, meta)." ] }, @@ -1832,7 +1834,9 @@ } ], "source": [ - "water_saturation_pressure, avg_water_saturation_pressure = pvdeg.humidity.water_saturation_pressure(WEATHER[\"temp_air\"])\n", + "water_saturation_pressure, avg_water_saturation_pressure = (\n", + " pvdeg.humidity.water_saturation_pressure(WEATHER[\"temp_air\"])\n", + ")\n", "\n", "k = pvdeg.design.edge_seal_ingress_rate(avg_water_saturation_pressure)\n", "\n", @@ -1847,7 +1851,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Alternate Edge Seal Width\n", + "# Alternate Edge Seal Width\n", "If you have the dew point, or wet-bulb temperature, you can calculate the edge seal width directly." ] }, diff --git a/tutorials_and_tools/tutorials_and_tools/2 - Degradation.ipynb b/tutorials/01_basics/02_degradation.ipynb similarity index 96% rename from tutorials_and_tools/tutorials_and_tools/2 - Degradation.ipynb rename to tutorials/01_basics/02_degradation.ipynb index a6a909a36..44bfb329b 100644 --- a/tutorials_and_tools/tutorials_and_tools/2 - Degradation.ipynb +++ b/tutorials/01_basics/02_degradation.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# 2 - Degradation\n", + "# Degradation Overview\n", "Degradation and Solder Fatigue\n", "\n", "**Requirements**:\n", @@ -26,7 +26,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2025-10-03T22:55:24.458885Z", @@ -38,7 +38,7 @@ "outputs": [], "source": [ "# if running on google colab, uncomment the next line and execute this cell to install the dependencies and prevent \"ModuleNotFoundError\" in later cells:\n", - "# !pip install pvdeg==0.3.3" + "# !pip install pvdeg" ] }, { @@ -99,7 +99,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 1. Read In the Weather File\n", + "# 1. Read In the Weather File\n", "\n", "This is usually the first step. Use a PSM3, TMY3, or EPW file. For this demo, use the provided PSM3 weather file." ] @@ -125,7 +125,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 2. Generate Basic Modeling Data\n", + "# 2. Generate Basic Modeling Data\n", "\n", "For this tutorial we will need solar position, POA, PV cell and module temperature. Let's gernate those individually with their respective functions." ] @@ -167,7 +167,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 3. VantHoff Degradation\n", + "# 3. VantHoff Degradation\n", "\n", "Van 't Hoff Irradiance Degradation\n", "\n", @@ -214,7 +214,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 4. Arrhenius\n", + "# 4. Arrhenius\n", "Calculate the Acceleration Factor between the rate of degredation of a modeled environmnet versus a modeled controlled environmnet\n", "\n", "Example: \"If the AF=25 then 1 year of Controlled Environment exposure is equal to 25 years in the field\"\n", @@ -278,7 +278,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 5. Quick Method (Degradation)\n", + "# 5. Quick Method (Degradation)\n", "\n", "For quick calculations, you can omit POA and both module and cell temperature. The function will calculate these figures as needed using the available weather data with the default options for PV module configuration." ] @@ -371,9 +371,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 6. Solder Fatigue\n", + "# 6. Solder Fatigue\n", "\n", - "Estimate the thermomechanical fatigue of flat plate photovoltaic module solder joints over the time range given using estimated cell temperature. Like other `pvdeg` funcitons, the minimal parameters are (weather_df, meta). Running the function with only these two inputs will use default PV module configurations ( open_rack_glass_polymer ) and the 'sapm' temperature model over the entire length of the weather data. " + "Estimate the thermomechanical fatigue of flat plate photovoltaic module solder joints over the time range given using estimated cell temperature. Like other `pvdeg` funcitons, the minimal parameters are (weather_df, meta). Running the function with only these two inputs will use default PV module configurations ( open_rack_glass_polymer ) and the 'sapm' temperature model over the entire length of the weather data." ] }, { @@ -440,9 +440,12 @@ "\n", "# calculate cell temperature over our selected date-time range.\n", "# specify the module configuration\n", - "temp_cell = pvdeg.temperature.cell(weather_df=WEATHER.loc[time_range],\n", - " meta=META,temp_model=\"sapm\",\n", - " conf=\"insulated_back_glass_polymer\")\n", + "temp_cell = pvdeg.temperature.cell(\n", + " weather_df=WEATHER.loc[time_range],\n", + " meta=META,\n", + " temp_model=\"sapm\",\n", + " conf=\"insulated_back_glass_polymer\",\n", + ")\n", "\n", "fatigue = pvdeg.fatigue.solder_fatigue(\n", " weather_df=WEATHER, meta=META, time_range=time_range, temp_cell=temp_cell\n", diff --git a/tutorials_and_tools/tutorials_and_tools/3 - Spectral Degradation.ipynb b/tutorials/01_basics/03_spectral_degradation.ipynb similarity index 80% rename from tutorials_and_tools/tutorials_and_tools/3 - Spectral Degradation.ipynb rename to tutorials/01_basics/03_spectral_degradation.ipynb index 5a3e8c44c..7ff5631c6 100644 --- a/tutorials_and_tools/tutorials_and_tools/3 - Spectral Degradation.ipynb +++ b/tutorials/01_basics/03_spectral_degradation.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# 3 - Spectral Degradation\n", + "# Spectral Degradation\n", "\n", "**Requirements:**\n", "- spectral irradiance (measured or simulated)\n", @@ -20,19 +20,19 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { "execution": { - "iopub.execute_input": "2025-10-03T22:55:34.434579Z", - "iopub.status.busy": "2025-10-03T22:55:34.434579Z", - "iopub.status.idle": "2025-10-03T22:55:34.439105Z", - "shell.execute_reply": "2025-10-03T22:55:34.439105Z" + "iopub.execute_input": "2025-11-21T20:32:56.206250Z", + "iopub.status.busy": "2025-11-21T20:32:56.205909Z", + "iopub.status.idle": "2025-11-21T20:32:56.213170Z", + "shell.execute_reply": "2025-11-21T20:32:56.212214Z" } }, "outputs": [], "source": [ "# if running on google colab, uncomment the next line and execute this cell to install the dependencies and prevent \"ModuleNotFoundError\" in later cells:\n", - "# !pip install pvdeg==0.3.3" + "# !pip install pvdeg" ] }, { @@ -40,10 +40,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2025-10-03T22:55:34.441111Z", - "iopub.status.busy": "2025-10-03T22:55:34.441111Z", - "iopub.status.idle": "2025-10-03T22:55:40.321316Z", - "shell.execute_reply": "2025-10-03T22:55:40.321316Z" + "iopub.execute_input": "2025-11-21T20:32:56.215780Z", + "iopub.status.busy": "2025-11-21T20:32:56.215510Z", + "iopub.status.idle": "2025-11-21T20:33:01.704879Z", + "shell.execute_reply": "2025-11-21T20:33:01.703248Z" } }, "outputs": [], @@ -55,7 +55,9 @@ "import pvdeg\n", "from pvdeg import DATA_DIR\n", "\n", - "warnings.filterwarnings('ignore', message='.*Pyarrow will become a required dependency.*')" + "warnings.filterwarnings(\n", + " \"ignore\", message=\".*Pyarrow will become a required dependency.*\"\n", + ")" ] }, { @@ -63,10 +65,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2025-10-03T22:55:40.325322Z", - "iopub.status.busy": "2025-10-03T22:55:40.324332Z", - "iopub.status.idle": "2025-10-03T22:55:40.332481Z", - "shell.execute_reply": "2025-10-03T22:55:40.332481Z" + "iopub.execute_input": "2025-11-21T20:33:01.708239Z", + "iopub.status.busy": "2025-11-21T20:33:01.707394Z", + "iopub.status.idle": "2025-11-21T20:33:01.713731Z", + "shell.execute_reply": "2025-11-21T20:33:01.712494Z" } }, "outputs": [ @@ -75,9 +77,9 @@ "output_type": "stream", "text": [ "Working on a Windows 11\n", - "Python version 3.12.9 | packaged by Anaconda, Inc. | (main, Feb 6 2025, 18:49:16) [MSC v.1929 64 bit (AMD64)]\n", - "Pandas version 2.2.3\n", - "pvdeg version 0.5.1.dev623+g51cc68b8e.d20250905\n" + "Python version 3.13.5 | packaged by Anaconda, Inc. | (main, Jun 12 2025, 16:37:03) [MSC v.1929 64 bit (AMD64)]\n", + "Pandas version 2.3.3\n", + "pvdeg version 0.5.1.dev857+g1a511e445.d20251004\n" ] } ], @@ -96,18 +98,18 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 1. Read in spectral irradiance data\n", + "# 1. Read in spectral irradiance data\n", "\n", "Spectral degradation has 4 main requirements:\n", "- Spectral Irradiance [W/m^2 nm]\n", - "- Wavelength [nm] \n", + "- Wavelength [nm]\n", "- Module Relative Humidity [%]\n", "- Module Temperature [C]\n", "\n", "For more advanced scenarios, you may want to calculate the degradation of a particular layer within the module. Below, we are using *backside* irradiance and therefore a slightly different temperature and humidity have been calculated. To calculate degradation on the backside, we used `pvdeg.humidity.rh_backsheet`. For the the front side, you should use `pvdeg.humidity.rh_surface_outside` or `rh_front_encap`\n", "\n", "\n", - "For this tutorial we are using pre-generated data from a ray-tracing simulation. To calculate the degradation rate, we will need the wavelengths used in the simulation. " + "For this tutorial we are using pre-generated data from a ray-tracing simulation. To calculate the degradation rate, we will need the wavelengths used in the simulation." ] }, { @@ -115,10 +117,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2025-10-03T22:55:40.338303Z", - "iopub.status.busy": "2025-10-03T22:55:40.338303Z", - "iopub.status.idle": "2025-10-03T22:55:40.366478Z", - "shell.execute_reply": "2025-10-03T22:55:40.365472Z" + "iopub.execute_input": "2025-11-21T20:33:01.745463Z", + "iopub.status.busy": "2025-11-21T20:33:01.744998Z", + "iopub.status.idle": "2025-11-21T20:33:01.761746Z", + "shell.execute_reply": "2025-11-21T20:33:01.760709Z" } }, "outputs": [ @@ -223,7 +225,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### 2. Calculate Degradation\n", + "# 2. Calculate Degradation\n", "\n", "The spectral degradation function has several optional paramters. For more information, refer to the documentation. Below is a function call with the minimum required information." ] @@ -233,10 +235,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2025-10-03T22:55:40.369480Z", - "iopub.status.busy": "2025-10-03T22:55:40.369480Z", - "iopub.status.idle": "2025-10-03T22:55:40.379780Z", - "shell.execute_reply": "2025-10-03T22:55:40.379780Z" + "iopub.execute_input": "2025-11-21T20:33:01.764386Z", + "iopub.status.busy": "2025-11-21T20:33:01.764062Z", + "iopub.status.idle": "2025-11-21T20:33:01.771531Z", + "shell.execute_reply": "2025-11-21T20:33:01.770906Z" } }, "outputs": [ @@ -254,7 +256,7 @@ " rh=SPECTRA[\"RH\"],\n", " temp=SPECTRA[\"Temperature\"],\n", " wavelengths=wavelengths,\n", - " time=SPECTRA.index\n", + " time=SPECTRA.index,\n", ")" ] } @@ -275,7 +277,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.9" + "version": "3.13.5" } }, "nbformat": 4, diff --git a/tutorials_and_tools/tutorials_and_tools/4 - Weather Database Access.ipynb b/tutorials/01_basics/04_weather_database_access.ipynb similarity index 77% rename from tutorials_and_tools/tutorials_and_tools/4 - Weather Database Access.ipynb rename to tutorials/01_basics/04_weather_database_access.ipynb index 4e52db21d..6251ae297 100644 --- a/tutorials_and_tools/tutorials_and_tools/4 - Weather Database Access.ipynb +++ b/tutorials/01_basics/04_weather_database_access.ipynb @@ -4,23 +4,23 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# 4 - Weather Database Access\n", + "# Weather Database Access\n", "\n", "**Requirements:**\n", "- Internet access\n", "- NSRDB API key. API keys are free. You can request and obtain an API key in about 5 minutes. To get your own key, visit https://developer.nrel.gov/signup/\n", - "- Step **1.** is for Eagle HPC users. You will need an account with NREL's Eagle computer for this method.\n", + "- Step **1.** is for Kestrel HPC users. You will need an account with NREL's Kestrel computer for this method.\n", "\n", "**Objectives:**\n", "\n", "Using direct access to large scale weather databases, we're going to estimate the minimum standoff distance for a roof mounted PV system. We'll do this in 3 ways using both the NSRDB and PVGIS database.\n", - "1. Single Location, NSRDB via NREL's high performance computer, Eagle.\n", + "1. Single Location, NSRDB via NREL's high performance computer, Kestrel.\n", "2. Single Location via NSRDB public API key.\n", "3. Single Location via the PVGIS public database\n", "\n", "**Background:**\n", "\n", - "This journal will demonstrate all existing built-in methods for directly accessing public weather databases. Some methods are restriced to certain user groups. For general users, see methods **2** and **3**. For users with an active Eagle HPC account, you may use method **1** as well as **2** and **3**.\n", + "This journal will demonstrate all existing built-in methods for directly accessing public weather databases. Some methods are restriced to certain user groups. For general users, see methods **2** and **3**. For users with an active Kestrel HPC account, you may use method **1** as well as **2** and **3**.\n", "\n", "For all users and all steps: This journal will run significantly longer than other tutorials and have significant internet traffic as you fetch large datasets." ] @@ -42,7 +42,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Single location example" + "# Single location example" ] }, { @@ -50,16 +50,16 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2025-10-21T16:58:18.254749Z", - "iopub.status.busy": "2025-10-21T16:58:18.253749Z", - "iopub.status.idle": "2025-10-21T16:58:18.271560Z", - "shell.execute_reply": "2025-10-21T16:58:18.268549Z" + "iopub.execute_input": "2025-12-03T15:42:13.626699Z", + "iopub.status.busy": "2025-12-03T15:42:13.626456Z", + "iopub.status.idle": "2025-12-03T15:42:13.632347Z", + "shell.execute_reply": "2025-12-03T15:42:13.631558Z" } }, "outputs": [], "source": [ "# if running on google colab, uncomment the next line and execute this cell to install the dependencies and prevent \"ModuleNotFoundError\" in later cells:\n", - "# !pip install pvdeg==0.3.3" + "# !pip install pvdeg" ] }, { @@ -67,16 +67,17 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2025-10-21T16:58:18.278648Z", - "iopub.status.busy": "2025-10-21T16:58:18.278648Z", - "iopub.status.idle": "2025-10-21T16:58:33.524665Z", - "shell.execute_reply": "2025-10-21T16:58:33.522654Z" + "iopub.execute_input": "2025-12-03T15:42:13.634556Z", + "iopub.status.busy": "2025-12-03T15:42:13.634241Z", + "iopub.status.idle": "2025-12-03T15:42:17.912055Z", + "shell.execute_reply": "2025-12-03T15:42:17.910698Z" }, "scrolled": true }, "outputs": [], "source": [ - "import pvdeg" + "import pvdeg\n", + "import pandas as pd" ] }, { @@ -84,10 +85,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2025-10-21T16:58:33.534992Z", - "iopub.status.busy": "2025-10-21T16:58:33.533159Z", - "iopub.status.idle": "2025-10-21T16:58:33.560137Z", - "shell.execute_reply": "2025-10-21T16:58:33.558126Z" + "iopub.execute_input": "2025-12-03T15:42:17.915654Z", + "iopub.status.busy": "2025-12-03T15:42:17.914551Z", + "iopub.status.idle": "2025-12-03T15:42:17.921986Z", + "shell.execute_reply": "2025-12-03T15:42:17.920956Z" } }, "outputs": [ @@ -96,8 +97,8 @@ "output_type": "stream", "text": [ "Working on a Windows 11\n", - "Python version 3.12.9 | packaged by Anaconda, Inc. | (main, Feb 6 2025, 18:49:16) [MSC v.1929 64 bit (AMD64)]\n", - "pvdeg version 0.5.1.dev623+g51cc68b8e.d20250905\n" + "Python version 3.13.5 | packaged by Anaconda, Inc. | (main, Jun 12 2025, 16:37:03) [MSC v.1929 64 bit (AMD64)]\n", + "pvdeg version 0.5.1.dev857+g1a511e445.d20251004\n" ] } ], @@ -115,9 +116,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 1. NSRDB - HSDS on Kestrel\n", + "# 1. NSRDB - HSDS on Kestrel\n", "\n", - "This method requires a direct connection to NREL's high performance computer \"Eagle\". If you are not running this journal from Eagle, skip this section and proceed to section **2.**\n", + "This method requires a direct connection to NREL's high performance computer \"Kestrel\". If you are not running this journal from Kestrel, skip this section and proceed to section **2.**\n", "\n", "In this step:\n", "\n", @@ -131,10 +132,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2025-10-21T16:58:33.662193Z", - "iopub.status.busy": "2025-10-21T16:58:33.660193Z", - "iopub.status.idle": "2025-10-21T16:58:33.671042Z", - "shell.execute_reply": "2025-10-21T16:58:33.670034Z" + "iopub.execute_input": "2025-12-03T15:42:17.959558Z", + "iopub.status.busy": "2025-12-03T15:42:17.959029Z", + "iopub.status.idle": "2025-12-03T15:42:17.966043Z", + "shell.execute_reply": "2025-12-03T15:42:17.964813Z" } }, "outputs": [], @@ -182,7 +183,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 2. NSRDB - API\n", + "# 2. NSRDB - API\n", "\n", "To access the NREL NSRDB, you will need an API key. Key's are free, but require you to set up an account. Without an API key, you can use a demonstration API which is severely limited. To set up an account and get your API key, visit https://developer.nrel.gov/signup/\n", "\n", @@ -198,10 +199,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2025-10-21T16:58:33.680397Z", - "iopub.status.busy": "2025-10-21T16:58:33.679397Z", - "iopub.status.idle": "2025-10-21T16:58:38.584652Z", - "shell.execute_reply": "2025-10-21T16:58:38.583645Z" + "iopub.execute_input": "2025-12-03T15:42:17.969026Z", + "iopub.status.busy": "2025-12-03T15:42:17.968679Z", + "iopub.status.idle": "2025-12-03T15:42:18.182705Z", + "shell.execute_reply": "2025-12-03T15:42:18.181410Z" } }, "outputs": [ @@ -209,9 +210,6 @@ "name": "stdout", "output_type": "stream", "text": [ - "\r", - " Column \"relative_humidity\" not found in DataFrame. Calculating...\r", - " \r", "The array surface_tilt angle was not provided, therefore the latitude of 39.7 was used.\n", "The estimated Tβ‚‰β‚ˆ of an insulated-back module is 75.9Β°C. \n", "The estimated Tβ‚‰β‚ˆ of an open-rack module is 51.6Β°C. \n", @@ -223,19 +221,27 @@ } ], "source": [ - "API_KEY = \"your_api_key_here\"\n", - "# The example API key here is for demonstation and is rate-limited per IP.\n", - "# To get your own API key, visit https://developer.nrel.gov/signup/\n", + "# Load pre-saved weather data for this tutorial\n", + "# This avoids API rate limits during testing and builds\n", + "import json\n", "\n", - "weather_db = \"PSM4\"\n", - "weather_id = (39.741931, -105.169891)\n", - "weather_arg = {\n", - " \"api_key\": \"DEMO_KEY\",\n", - " \"email\": \"user@mail.com\",\n", - " \"map_variables\": True,\n", - "}\n", + "weather_df = pd.read_csv(\"../data/psm4_golden.csv\", index_col=0, parse_dates=True)\n", + "with open(\"../data/meta_golden.json\", \"r\") as f:\n", + " meta = json.load(f)\n", + "\n", + "# Uncomment below to fetch fresh data with your own API key:\n", + "# API_KEY = \"your_api_key_here\"\n", + "# # The example API key here is for demonstation and is rate-limited per IP.\n", + "# # To get your own API key, visit https://developer.nrel.gov/signup/\n", + "# weather_db = \"PSM4\"\n", + "# weather_id = (39.741931, -105.169891)\n", + "# weather_arg = {\n", + "# \"api_key\": \"DEMO_KEY\",\n", + "# \"email\": \"user@mail.com\",\n", + "# \"map_variables\": True,\n", + "# }\n", + "# weather_df, meta = pvdeg.weather.get(weather_db, weather_id, **weather_arg)\n", "\n", - "weather_df, meta = pvdeg.weather.get(weather_db, weather_id, **weather_arg)\n", "# Perform calculation and output interpretation or results\n", "res = pvdeg.standards.standoff(\n", " weather_df=weather_df,\n", @@ -253,7 +259,7 @@ "print(pvdeg.standards.interpret_standoff(res))\n", "\n", "# Clean metadata for consistent output\n", - "meta_clean = {k: v for k, v in meta.items() if k not in ['irradiance_time_offset']}\n", + "meta_clean = {k: v for k, v in meta.items() if k not in [\"irradiance_time_offset\"]}\n", "print(meta_clean)" ] }, @@ -261,7 +267,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 3. PVGIS\n", + "# 3. PVGIS\n", "\n", "This method uses the PVGIS database, a public resource. It requires no API key or user account." ] @@ -271,10 +277,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2025-10-21T16:58:38.593342Z", - "iopub.status.busy": "2025-10-21T16:58:38.592172Z", - "iopub.status.idle": "2025-10-21T16:58:42.662810Z", - "shell.execute_reply": "2025-10-21T16:58:42.661276Z" + "iopub.execute_input": "2025-12-03T15:42:18.187172Z", + "iopub.status.busy": "2025-12-03T15:42:18.186692Z", + "iopub.status.idle": "2025-12-03T15:42:20.552763Z", + "shell.execute_reply": "2025-12-03T15:42:20.551416Z" } }, "outputs": [ @@ -318,21 +324,14 @@ "print(pvdeg.standards.interpret_standoff(res))\n", "\n", "# Clean metadata for consistent output (remove variable fields)\n", - "meta_clean = {k: v for k, v in meta.items() if k not in ['irradiance_time_offset']}\n", + "meta_clean = {k: v for k, v in meta.items() if k not in [\"irradiance_time_offset\"]}\n", "print(meta_clean)" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { "kernelspec": { - "display_name": "pvdeg", + "display_name": "pvdeg_313", "language": "python", "name": "python3" }, @@ -346,7 +345,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.9" + "version": "3.13.5" } }, "nbformat": 4, diff --git a/tutorials/01_basics/README.md b/tutorials/01_basics/README.md new file mode 100644 index 000000000..00b4fd4a0 --- /dev/null +++ b/tutorials/01_basics/README.md @@ -0,0 +1,16 @@ +# Basics Tutorials + +Introduction to PVDeg covering fundamental concepts and workflows. + +## Tutorials + +1. **01_basics_humidity_design.ipynb** - Introduction to basic concepts, humidity calculations, and design parameters +2. **02_degradation.ipynb** - Overview of degradation modeling and calculations +3. **03_spectral_degradation.ipynb** - Spectral degradation analysis +4. **04_weather_database_access.ipynb** - Accessing and working with weather databases + +## Getting Started + +These tutorials are designed to be completed in sequence. Start with tutorial 01 and progress through to 04. + +All tutorials can be run locally or in Google Colab by clicking the rocket icon at the top of each notebook. diff --git a/tutorials_and_tools/tutorials_and_tools/1 - Basics, Humidity, Design.py b/tutorials/01_basics/scripts/01_basics_humidity_design.py similarity index 81% rename from tutorials_and_tools/tutorials_and_tools/1 - Basics, Humidity, Design.py rename to tutorials/01_basics/scripts/01_basics_humidity_design.py index 66842b3ca..4b3de7fc3 100644 --- a/tutorials_and_tools/tutorials_and_tools/1 - Basics, Humidity, Design.py +++ b/tutorials/01_basics/scripts/01_basics_humidity_design.py @@ -1,8 +1,6 @@ -#!/usr/bin/env python -# coding: utf-8 - -# # 1 - Basics, Humidity, Design -# ### Module Humidity and Edge Seal Width +# %% [markdown] +# # Humidity Design Basics +# ## Module Humidity and Edge Seal Width # # # **Requirements:** @@ -14,26 +12,18 @@ # 3. Generate module humidities # 4. Calculate edge seal width -# In[1]: - - +# %% # if running on google colab, uncomment the next line and execute this cell to install the dependencies and prevent "ModuleNotFoundError" in later cells: -# !pip install pvdeg==0.3.3 - - -# In[1]: - +# # !pip install pvdeg +# %% import os import pandas as pd import pvdeg from pvdeg import DATA_DIR - -# In[ ]: - - +# %% # This information helps with debugging and getting support :) import sys import platform @@ -43,8 +33,8 @@ print("Pandas version ", pd.__version__) print("pvdeg version ", pvdeg.__version__) - -# ## 1. Reading in Weather Data and Site Meta-Data +# %% [markdown] +# # 1. Reading in Weather Data and Site Meta-Data # Most `pvdeg` functions have been standardized to operate from popular weather files such as TMY3, EPW, and PSM3. For high-performance computer users, NSRDB and other database fetching tools can be used. For these tutorials, we will use local weather files. Unless otherwise stated, functions require the following fields within a weather file: # # - date-time index @@ -62,30 +52,24 @@ # - Longitude # - Altitude -# In[2]: - - +# %% PSM_FILE = os.path.join(DATA_DIR, "psm3_demo.csv") WEATHER, META = pvdeg.weather.read(PSM_FILE, "psm") - +# %% [markdown] # Let's take a closer look at the dataframe (weather) and dictionary (meta) imported above. The structure will be used by most `pvdeg` functions. -# In[3]: - - +# %% WEATHER.head() - +# %% [markdown] # Meta-Data will vary greatly between weather files. However, at a minimum they should all contain the fields required by `pvdeg` listed above. Let's look at the meta-data generated by our PSM3 weather file. It provides much more information that we need, but at the very end are the three most important fields. -# In[4]: - - +# %% META - +# %% [markdown] # We'll be working with a lot of time series data, generally with a date-time index. If you're # processing an entire year of data, you don't need to sort or filter the data. However, you may want # to filter the data by a few different criteria. Below are some common examples. @@ -94,38 +78,28 @@ # - range of months # - Filter by sun-up hours -# In[5]: - - +# %% # Let's select the month of June june_weather = WEATHER[WEATHER.index.month == 6] june_weather - -# In[6]: - - +# %% # Let's filter the summer months for the northern hemisphere summer_months = [6, 7, 8, 9] summer_weather = WEATHER[WEATHER.index.month.isin(summer_months)] summer_weather - -# In[7]: - - +# %% # lets just select hours from the year where the sun is above the horizon sunup_weather = WEATHER[WEATHER["ghi"] > 0] sunup_weather - -# ## 2. Solar Position, POA, and Module Temperature +# %% [markdown] +# # 2. Solar Position, POA, and Module Temperature # # Many operations with `PVDEG` require solar position, POA irradiance, and module temperature. Usually a `pvdeg` method will calculate these data sets automatically when it is required, however it will not keep the data for external use. For now, we will calculate them directly. This is helpful when you need to use the data for an external calculation. Here, we generate the solar position (azimuth, elevation, etc), plane of array irradiance (POA), and module temperature. `PVDEG` has wrappers for quickly using `PVLIB` to generate these figures with minimal input -# In[8]: - - +# %% sol_pos = pvdeg.spectral.solar_position(weather_df=WEATHER, meta=META) poa_df = pvdeg.spectral.poa_irradiance( @@ -134,7 +108,7 @@ temp_mod = pvdeg.temperature.module(weather_df=WEATHER, meta=META) # , poa=poa_df) - +# %% [markdown] # For more advanced usage of these functions, refer to the documentation for `pvdeg` and `pvlib`. # # Lets inspect the output from each of these functions @@ -142,20 +116,16 @@ # `solar_position` returns a datetime-indexed dataframe of solar position for the length and frequency given by the weather file. # -# In[9]: - - +# %% sol_pos - +# %% [markdown] # `poa_irradiance` returns a datetime-indexed dataframe as well. Each column contains either the global plane of array irradiance or a particular contribition. Lets look at a time in the index we expect to have daylight. -# In[10]: - - +# %% poa_df.loc["1999-01-01 13:30:00-07:00":"1999-01-01 17:30:00-07:00"] - +# %% [markdown] # `temperature.module` has several optional parameters. Below is an example which explicity uses all of the default values. This function will return a datetime-indexed series matching the index of the given weather file. For more options, see the pvlib documentation for temperature models. # # https://pvlib-python.readthedocs.io/en/stable/reference/pv_modeling/temperature.html @@ -166,87 +136,84 @@ # - conf : choose a recognized module configuration # - wind_speed_factor : change the empirical wind speed fit paramter -# In[11]: - - +# %% temp_mod = pvdeg.temperature.module( weather_df=WEATHER, meta=META, poa=poa_df, temp_model="sapm", conf="open_rack_glass_polymer", - wind_speed_factor=1, ) temp_mod.loc["1999-01-01 13:30:00-07:00":"1999-01-01 17:30:00-07:00"] - -# ## 3. Module Humidities +# %% [markdown] +# # 3. Module Humidities # `PVDEG` can be used to calculate the relative humidity of several layers within a PV module: the outside surface of the module, front ecapsulant, back encapsulant, and backsheet. This can be done with 2 techniques: Automatically and Manually. -# ### 3.a. Automatic +# %% [markdown] +# # 3.a. Automatic # Use the function `humidity.module`. This method does all of the calculations behind the scenes. It requires only 2 inputs (weather data and meta data). It will return a dataframe with all the layers of the module. -# In[12]: - - -rh_module = pvdeg.humidity.module(weather_df=WEATHER, meta=META) +# %% +rh_module = pvdeg.humidity.module( + weather_df=WEATHER, meta=META, backsheet_thickness=0.3, back_encap_thickness=0.46 +) rh_module.head() - -# ### 3.b. Manual +# %% [markdown] +# # 3.b. Manual # Use the individual functions to calculate the RH in each layer. These are named and require more extensive input parameters. This must be done in the correct order: # 1. Surface Outside # 2. Front Encapsulant # 2. Back Encapsulant # 3. Backsheet -# In[13]: +# %% +surface_relative_humidity = WEATHER["relative_humidity"] +temperature_ambient = WEATHER["temp_air"] - -rh_surface_outside = pvdeg.humidity.surface_relative( - rh_ambient=WEATHER["relative_humidity"], - temp_ambient=WEATHER["temp_air"], +rh_surface_relative = pvdeg.humidity.surface_relative( + rh_ambient=surface_relative_humidity, + temp_ambient=temperature_ambient, temp_module=temp_mod, ) -rh_front_encap = pvdeg.humidity.front_encapsulant( - rh_ambient=rh_surface_outside, - temp_ambient=WEATHER["temp_air"], +rh_front_encapsulant = pvdeg.humidity.front_encapsulant( + rh_ambient=surface_relative_humidity, + temp_ambient=temperature_ambient, temp_module=temp_mod, ) -rh_back_encap = pvdeg.humidity.back_encapsulant_water_concentration( - rh_ambient=rh_surface_relative, - temp_ambient=WEATHER["temp_air"], +rh_back_encap = pvdeg.humidity.back_encapsulant( + rh_ambient=surface_relative_humidity, + temp_ambient=temperature_ambient, temp_module=temp_mod, ) rh_backsheet = pvdeg.humidity.backsheet_from_encap( - rh_back_encap=rh_back_encap, rh_surface_outside=WEATHER["relative_humidity"] + rh_back_encap=rh_back_encap, rh_surface_outside=surface_relative_humidity ) - -# ## 4. Design: Edge Seal Width +# %% [markdown] +# # 4. Design: Edge Seal Width # `pvdeg.design.edge_seal_width` calculates the width [cm] required for several years of water ingress. If you do not specify the number of years, it will use the default value of 25. As with most `pvdeg` functions, we can automatically generate all necessary data if we pass the two arguments (weather_df, meta). -# In[14]: - - +# %% edge_seal_width = pvdeg.design.edge_seal_width(weather_df=WEATHER, meta=META) edge_seal_width - +# %% [markdown] # Lets look at the calculation step by step. To get the estimated edge seal width, we need the following: # 1. Humidity Saturation Point # 2. Edge seal ingress rate ( k ) or the estimated rate of water ingress through edge seal in [cm/hour^0.5] -# In[15]: - - -psat, psat_avg = pvdeg.humidity.psat(WEATHER["temp_air"]) +# %% +water_saturation_pressure, avg_water_saturation_pressure = ( + pvdeg.humidity.water_saturation_pressure(WEATHER["temp_air"]) +) -k = pvdeg.design.edge_seal_ingress_rate(avg_psat=psat_avg) +k = pvdeg.design.edge_seal_ingress_rate(avg_water_saturation_pressure) edge_seal_width = pvdeg.design.edge_seal_width( weather_df=WEATHER, meta=META, k=k, years=25 @@ -254,13 +221,11 @@ print(edge_seal_width) - -# ### Alternate Edge Seal Width +# %% [markdown] +# # Alternate Edge Seal Width # If you have the dew point, or wet-bulb temperature, you can calculate the edge seal width directly. -# In[17]: - - +# %% edge_seal_width = pvdeg.design.edge_seal_width( weather_df=WEATHER, meta=META, from_dew_point=True ) diff --git a/tutorials_and_tools/tutorials_and_tools/2 - Degradation.py b/tutorials/01_basics/scripts/02_degradation.py similarity index 92% rename from tutorials_and_tools/tutorials_and_tools/2 - Degradation.py rename to tutorials/01_basics/scripts/02_degradation.py index 6da786f50..b6db33e1c 100644 --- a/tutorials_and_tools/tutorials_and_tools/2 - Degradation.py +++ b/tutorials/01_basics/scripts/02_degradation.py @@ -1,7 +1,5 @@ -#!/usr/bin/env python -# coding: utf-8 - -# # 2 - Degradation +# %% [markdown] +# # Degradation Overview # Degradation and Solder Fatigue # # **Requirements**: @@ -20,26 +18,18 @@ # 5. Quick Method # 5. Solder Fatigue -# In[1]: - - +# %% # if running on google colab, uncomment the next line and execute this cell to install the dependencies and prevent "ModuleNotFoundError" in later cells: -# !pip install pvdeg==0.3.3 - - -# In[2]: - +# # !pip install pvdeg +# %% import os import pandas as pd import pvdeg from pvdeg import DATA_DIR - -# In[3]: - - +# %% # This information helps with debugging and getting support :) import sys import platform @@ -49,25 +39,21 @@ print("Pandas version ", pd.__version__) print("pvdeg version ", pvdeg.__version__) - -# ## 1. Read In the Weather File +# %% [markdown] +# # 1. Read In the Weather File # # This is usually the first step. Use a PSM3, TMY3, or EPW file. For this demo, use the provided PSM3 weather file. -# In[2]: - - +# %% PSM_FILE = os.path.join(DATA_DIR, "psm3_demo.csv") WEATHER, META = pvdeg.weather.read(PSM_FILE, "psm") - -# ## 2. Generate Basic Modeling Data +# %% [markdown] +# # 2. Generate Basic Modeling Data # # For this tutorial we will need solar position, POA, PV cell and module temperature. Let's gernate those individually with their respective functions. -# In[3]: - - +# %% sol_pos = pvdeg.spectral.solar_position(weather_df=WEATHER, meta=META) poa_df = pvdeg.spectral.poa_irradiance( @@ -78,8 +64,8 @@ temp_module = pvdeg.temperature.module(weather_df=WEATHER, meta=META, poa=poa_df) - -# ## 3. VantHoff Degradation +# %% [markdown] +# # 3. VantHoff Degradation # # Van 't Hoff Irradiance Degradation # @@ -87,9 +73,7 @@ # # As with most `pvdeg` functions, the following functions will always require two arguments (weather_df and meta) -# In[4]: - - +# %% # chamber irradiance (W/m^2) I_chamber = 1000 # chamber temperature (C) @@ -102,16 +86,16 @@ I_chamber=I_chamber, temp_chamber=temp_chamber, poa=poa_df, - temp_cell=temp_cell, + temp=temp_cell, ) # calculate the VantHoff weighted irradiance irr_weighted_avg_v = pvdeg.degradation.IwaVantHoff( - weather_df=WEATHER, meta=META, poa=poa_df, temp_cell=temp_cell + weather_df=WEATHER, meta=META, poa=poa_df, temp=temp_cell ) - -# ## 4. Arrhenius +# %% [markdown] +# # 4. Arrhenius # Calculate the Acceleration Factor between the rate of degredation of a modeled environmnet versus a modeled controlled environmnet # # Example: "If the AF=25 then 1 year of Controlled Environment exposure is equal to 25 years in the field" @@ -124,9 +108,7 @@ # Equation: # $$ I_{WA} = [ \frac{ \Sigma (I_{outdoor}^x * RH_{outdoor}^n e^{\frac{-E_a}{k T_{outdood}}}) }{ N * RH_{WA}^n * e^{- \frac{E_a}{k T_eq}} } ]^{\frac{1}{x}} $$ -# In[6]: - - +# %% # relative humidity within chamber (%) rh_chamber = 15 # arrhenius activation energy (kj/mol) @@ -146,7 +128,7 @@ rh_chamber=rh_chamber, temp_chamber=temp_chamber, poa=poa_df, - temp_cell=temp_cell, + temp=temp_cell, Ea=Ea, ) @@ -155,18 +137,16 @@ meta=META, poa=poa_df, rh_outdoor=WEATHER["relative_humidity"], - temp_cell=temp_cell, + temp=temp_cell, Ea=Ea, ) - -# ## 5. Quick Method (Degradation) +# %% [markdown] +# # 5. Quick Method (Degradation) # # For quick calculations, you can omit POA and both module and cell temperature. The function will calculate these figures as needed using the available weather data with the default options for PV module configuration. -# In[7]: - - +# %% # chamber settings I_chamber = 1000 temp_chamber = 60 @@ -181,10 +161,7 @@ irr_weighted_avg_v = pvdeg.degradation.IwaVantHoff(weather_df=WEATHER, meta=META) - -# In[8]: - - +# %% rh_surface = pvdeg.humidity.surface_relative( rh_ambient=WEATHER["relative_humidity"], temp_ambient=WEATHER["temp_air"], @@ -205,17 +182,15 @@ weather_df=WEATHER, meta=META, rh_outdoor=WEATHER["relative_humidity"], Ea=Ea ) - -# ## 6. Solder Fatigue +# %% [markdown] +# # 6. Solder Fatigue # # Estimate the thermomechanical fatigue of flat plate photovoltaic module solder joints over the time range given using estimated cell temperature. Like other `pvdeg` funcitons, the minimal parameters are (weather_df, meta). Running the function with only these two inputs will use default PV module configurations ( open_rack_glass_polymer ) and the 'sapm' temperature model over the entire length of the weather data. -# In[9]: - - +# %% fatigue = pvdeg.fatigue.solder_fatigue(weather_df=WEATHER, meta=META) - +# %% [markdown] # If you wish to reduce the span of time or use a non-default temperature model, you may specify the parameters manually. Let's try an explicit example. # We want the solder fatigue estimated over the month of June for a roof mounted glass-front polymer-back module. # @@ -223,9 +198,7 @@ # 2. Next, generate the cell temperature. Make sure to explicity restrict the weather data to our dt-index for June. Next, declare the PV module configuration. # 3. Calculate the fatigue. Explicity specify the time_range (our dt-index for June from step 1) and the cell temperature as we caculated in step 2 -# In[ ]: - - +# %% # select the month of June time_range = WEATHER.index[WEATHER.index.month == 6] @@ -238,7 +211,6 @@ conf="insulated_back_glass_polymer", ) - fatigue = pvdeg.fatigue.solder_fatigue( weather_df=WEATHER, meta=META, time_range=time_range, temp_cell=temp_cell ) diff --git a/tutorials_and_tools/tutorials_and_tools/3 - Spectral Degradation.py b/tutorials/01_basics/scripts/03_spectral_degradation.py similarity index 81% rename from tutorials_and_tools/tutorials_and_tools/3 - Spectral Degradation.py rename to tutorials/01_basics/scripts/03_spectral_degradation.py index 36f5db8b6..f9ba0bed4 100644 --- a/tutorials_and_tools/tutorials_and_tools/3 - Spectral Degradation.py +++ b/tutorials/01_basics/scripts/03_spectral_degradation.py @@ -1,7 +1,5 @@ -#!/usr/bin/env python -# coding: utf-8 - -# # 3 - Spectral Degradation +# %% [markdown] +# # Spectral Degradation # # **Requirements:** # - spectral irradiance (measured or simulated) @@ -14,26 +12,23 @@ # 1. Read in spectral irradiance # 2. Calculate spectral degradation -# In[1]: - - +# %% # if running on google colab, uncomment the next line and execute this cell to install the dependencies and prevent "ModuleNotFoundError" in later cells: -# !pip install pvdeg==0.3.3 - - -# In[1]: - +# # !pip install pvdeg +# %% import os +import warnings import pandas as pd import numpy as np import pvdeg from pvdeg import DATA_DIR +warnings.filterwarnings( + "ignore", message=".*Pyarrow will become a required dependency.*" +) -# In[ ]: - - +# %% # This information helps with debugging and getting support :) import sys import platform @@ -43,8 +38,8 @@ print("Pandas version ", pd.__version__) print("pvdeg version ", pvdeg.__version__) - -# ## 1. Read in spectral irradiance data +# %% [markdown] +# # 1. Read in spectral irradiance data # # Spectral degradation has 4 main requirements: # - Spectral Irradiance [W/m^2 nm] @@ -57,25 +52,22 @@ # # For this tutorial we are using pre-generated data from a ray-tracing simulation. To calculate the degradation rate, we will need the wavelengths used in the simulation. -# In[2]: - - +# %% wavelengths = np.array(range(280, 420, 20)) SPECTRA = pd.read_csv(os.path.join(DATA_DIR, "spectra.csv"), header=0, index_col=0) SPECTRA.head() - -# ### 2. Calculate Degradation +# %% [markdown] +# # 2. Calculate Degradation # # The spectral degradation function has several optional paramters. For more information, refer to the documentation. Below is a function call with the minimum required information. -# In[3]: - - -degradation = pvdeg.degradation.degradation( +# %% +degradation = pvdeg.degradation.degradation_spectral( spectra=SPECTRA["Spectra"], - rh_module=SPECTRA["RH"], - temp_module=SPECTRA["Temperature"], + rh=SPECTRA["RH"], + temp=SPECTRA["Temperature"], wavelengths=wavelengths, + time=SPECTRA.index, ) diff --git a/tutorials_and_tools/tutorials_and_tools/4 - Weather Database Access.py b/tutorials/01_basics/scripts/04_weather_database_access.py similarity index 71% rename from tutorials_and_tools/tutorials_and_tools/4 - Weather Database Access.py rename to tutorials/01_basics/scripts/04_weather_database_access.py index a2406aad9..a44296faa 100644 --- a/tutorials_and_tools/tutorials_and_tools/4 - Weather Database Access.py +++ b/tutorials/01_basics/scripts/04_weather_database_access.py @@ -1,26 +1,25 @@ -#!/usr/bin/env python -# coding: utf-8 - -# # 4 - Weather Database Access +# %% [markdown] +# # Weather Database Access # # **Requirements:** # - Internet access # - NSRDB API key. API keys are free. You can request and obtain an API key in about 5 minutes. To get your own key, visit https://developer.nrel.gov/signup/ -# - Step **1.** is for Eagle HPC users. You will need an account with NREL's Eagle computer for this method. +# - Step **1.** is for Kestrel HPC users. You will need an account with NREL's Kestrel computer for this method. # # **Objectives:** # # Using direct access to large scale weather databases, we're going to estimate the minimum standoff distance for a roof mounted PV system. We'll do this in 3 ways using both the NSRDB and PVGIS database. -# 1. Single Location, NSRDB via NREL's high performance computer, Eagle. +# 1. Single Location, NSRDB via NREL's high performance computer, Kestrel. # 2. Single Location via NSRDB public API key. # 3. Single Location via the PVGIS public database # # **Background:** # -# This journal will demonstrate all existing built-in methods for directly accessing public weather databases. Some methods are restriced to certain user groups. For general users, see methods **2** and **3**. For users with an active Eagle HPC account, you may use method **1** as well as **2** and **3**. +# This journal will demonstrate all existing built-in methods for directly accessing public weather databases. Some methods are restriced to certain user groups. For general users, see methods **2** and **3**. For users with an active Kestrel HPC account, you may use method **1** as well as **2** and **3**. # # For all users and all steps: This journal will run significantly longer than other tutorials and have significant internet traffic as you fetch large datasets. +# %% [markdown] # This example demonstrates the calculation of a minimum standoff distance necessary for roof-mounted PV modules to ensure that the $T_{98}$ operational temperature remains under 70Β°C, in which case the more rigorous thermal stability testing requirements of IEC TS 63126 would not needed to be considered. We use data from [Fuentes, 1987] to model the approximate exponential decay in temperature, $T(X)$, with increasing standoff distance, $X$, as, # # $$ X = -X_0 \ln\left(1-\frac{T_0-T}{\Delta T}\right)$$ @@ -29,24 +28,18 @@ # # The following figure showcases this calulation for the entire United States. We used pvlib and data from the National Solar Radiation Database (NSRDB) to calculate the module temperatures for different mounting configuration and applied our model to obtain the standoff distance for roof-mounted PV systems. -# ## Single location example - -# In[1]: - +# %% [markdown] +# # Single location example +# %% # if running on google colab, uncomment the next line and execute this cell to install the dependencies and prevent "ModuleNotFoundError" in later cells: -# !pip install pvdeg==0.3.3 - - -# In[2]: - +# # !pip install pvdeg +# %% import pvdeg +import pandas as pd - -# In[3]: - - +# %% # This information helps with debugging and getting support :) import sys import platform @@ -55,10 +48,10 @@ print("Python version ", sys.version) print("pvdeg version ", pvdeg.__version__) - -# ## 1. NSRDB - HSDS on Eagle +# %% [markdown] +# # 1. NSRDB - HSDS on Kestrel # -# This method requires a direct connection to NREL's high performance computer "Eagle". If you are not running this journal from Eagle, skip this section and proceed to section **2.** +# This method requires a direct connection to NREL's high performance computer "Kestrel". If you are not running this journal from Kestrel, skip this section and proceed to section **2.** # # In this step: # @@ -66,15 +59,12 @@ # # Next, we want to select a satellite, named dataset (year of data), and what weather attributes we want to fetch. For further options, see the documentation for `pvdeg.weather.get` -# In[ ]: - - +# %% # Get weather data weather_db = "NSRDB" # Latitude and Longitude weather_id = (33.448376, -112.074036) -weather_id = (39.741931, -105.169891) # weather_id = 1933572 weather_arg = { "satellite": "GOES", @@ -90,34 +80,22 @@ ], } -weather_df, meta = pvdeg.weather.get(weather_db, weather_id, **weather_arg) +# Uncomment the following when working on NREL Kestrel + +# weather_df, meta = pvdeg.weather.get(weather_db, weather_id, **weather_arg) +# res = pvdeg.standards.standoff(weather_df=weather_df, meta=meta, tilt=None, azimuth=180, sky_model='isotropic', temp_model='sapm', +# conf_0='insulated_back_glass_polymer', conf_inf='open_rack_glass_polymer', T98=70, x_0=6.5, wind_factor=0.33) +# print(pvdeg.standards.interpret_standoff(res)) +# print(meta) +# %% [markdown] # `pvdeg.weather.get` returns the same variables as `weather.read` which we have used in each journal before this. We get a weather DataFrame and a meta-data dicitonary. Each contains a minimum of consistent fields, but may have additional fields based on the database accessed or the attributes requested. # # Lets verify the weather data we fetched by running a familiar calculation; standoff distance. -# In[ ]: - - -res = pvdeg.standards.standoff( - weather_df=weather_df, - meta=meta, - tilt=None, - azimuth=180, - sky_model="isotropic", - temp_model="sapm", - conf_0="insulated_back_glass_polymer", - conf_inf="open_rack_glass_polymer", - T98=70, - x_0=6.5, - wind_factor=0.33, -) -print(pvdeg.standards.interpret_standoff(res)) -print(meta) - - -# ## 2. NSRDB - API +# %% [markdown] +# # 2. NSRDB - API # # To access the NREL NSRDB, you will need an API key. Key's are free, but require you to set up an account. Without an API key, you can use a demonstration API which is severely limited. To set up an account and get your API key, visit https://developer.nrel.gov/signup/ # @@ -127,24 +105,28 @@ # - names = 'tmy' : generate a TMY-like weather dataframe aggregate. This will calculate the relative humidity from temperature and dew point. # - names = '2019' : collect a weather dataframe including measured relative humidity. -# In[ ]: - +# %% +# Load pre-saved weather data for this tutorial +# This avoids API rate limits during testing and builds +import json -API_KEY = "your_api_key_here" -# The example API key here is for demonstation and is rate-limited per IP. -# To get your own API key, visit https://developer.nrel.gov/signup/ +weather_df = pd.read_csv("../data/psm4_golden.csv", index_col=0, parse_dates=True) +with open("../data/meta_golden.json", "r") as f: + meta = json.load(f) -weather_db = "PSM3" -weather_id = (39.741931, -105.169891) -weather_arg = { - "api_key": "DEMO_KEY", - "email": "user@mail.com", - "names": "tmy", - "attributes": [], - "map_variables": True, -} +# Uncomment below to fetch fresh data with your own API key: +# API_KEY = "your_api_key_here" +# # The example API key here is for demonstation and is rate-limited per IP. +# # To get your own API key, visit https://developer.nrel.gov/signup/ +# weather_db = "PSM4" +# weather_id = (39.741931, -105.169891) +# weather_arg = { +# "api_key": "DEMO_KEY", +# "email": "user@mail.com", +# "map_variables": True, +# } +# weather_df, meta = pvdeg.weather.get(weather_db, weather_id, **weather_arg) -weather_df, meta = pvdeg.weather.get(weather_db, weather_id, **weather_arg) # Perform calculation and output interpretation or results res = pvdeg.standards.standoff( weather_df=weather_df, @@ -160,16 +142,17 @@ wind_factor=0.33, ) print(pvdeg.standards.interpret_standoff(res)) -print(meta) +# Clean metadata for consistent output +meta_clean = {k: v for k, v in meta.items() if k not in ["irradiance_time_offset"]} +print(meta_clean) -# ## 3. PVGIS +# %% [markdown] +# # 3. PVGIS # # This method uses the PVGIS database, a public resource. It requires no API key or user account. -# In[ ]: - - +# %% weather_db = "PVGIS" # weather_id = (39.741931, -105.169891) weather_id = (24.7136, 46.6753) # Riyadh, Saudi Arabia @@ -193,7 +176,7 @@ wind_factor=0.33, ) print(pvdeg.standards.interpret_standoff(res)) -print(meta) - -# In[ ]: +# Clean metadata for consistent output (remove variable fields) +meta_clean = {k: v for k, v in meta.items() if k not in ["irradiance_time_offset"]} +print(meta_clean) diff --git a/tutorials_and_tools/tutorials_and_tools/B-O LID - Accelerated Test.ipynb b/tutorials/02_degradation/01_bo_lid_accelerated_test.ipynb similarity index 99% rename from tutorials_and_tools/tutorials_and_tools/B-O LID - Accelerated Test.ipynb rename to tutorials/02_degradation/01_bo_lid_accelerated_test.ipynb index 17812df26..87d53755d 100644 --- a/tutorials_and_tools/tutorials_and_tools/B-O LID - Accelerated Test.ipynb +++ b/tutorials/02_degradation/01_bo_lid_accelerated_test.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# B-O LID - Accelerated Test\n", + "# B-O LID Accelerated Test\n", "\n", "## Example: Boron-oxygen light-induced degradation (B-O LID) progression in accelerated tests\n", "\n", @@ -18,14 +18,12 @@ "2. Define necessary degradation parameters: degraded lifetime and defect states, this time using B-O LID kinetics\n", "3. Create timeseries of temperature and current injection\n", "4. Run through timeseries, calculating defect states\n", - "5. Calculate device degradation and plot\n", - "\n", - "\n" + "5. Calculate device degradation and plot" ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2025-11-13T21:54:07.846379Z", @@ -37,7 +35,7 @@ "outputs": [], "source": [ "# if running on google colab, uncomment the next line and execute this cell to install the dependencies and prevent \"ModuleNotFoundError\" in later cells:\n", - "# !pip install pvdeg==0.3.3" + "# !pip install pvdeg" ] }, { @@ -103,7 +101,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Device parameters\n", + "# Device parameters\n", "To define a device, we need to define several important quantities about the device: wafer thickness (in $\\mu m$), rear surface recombination velocity (in cm/s), and cell area (in cm2)." ] }, @@ -160,7 +158,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Degradation parameters\n", + "# Degradation parameters\n", "To model the device's degradation, we need to define several more important quantities about the degradation the device will experience. These include undegraded and degraded lifetime (in $\\mu s$)." ] }, @@ -330,22 +328,26 @@ "iopub.status.busy": "2025-11-13T21:54:16.954627Z", "iopub.status.idle": "2025-11-13T21:54:16.958966Z", "shell.execute_reply": "2025-11-13T21:54:16.958966Z" - } + }, + "lines_to_next_cell": 2 }, "outputs": [], "source": [ "mechanism_params = utilities.get_kinetics(\"D045\")\n", "\n", "# Flatten the nested dictionary to get only the \"value\" for each parameter\n", - "mechanism_params = {k: v[\"value\"] if isinstance(v, dict) and \"value\" in v else v for k, v in mechanism_params.items()}\n" + "mechanism_params = {\n", + " k: v[\"value\"] if isinstance(v, dict) and \"value\" in v else v\n", + " for k, v in mechanism_params.items()\n", + "}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Set up timeseries\n", - "In this example, we are going to model test with constant temperature and injection. IEC 61215 MQT 19.1 prescribes 10 $kWh/m^2$ of 1-sun illumination (i.e., 10 hours of 1-sun) with maximum power point tracking at $50\\degree C$. For most typical c-Si modules, MPP injection is roughly $I_{sc}-I_{mp}$, or roughly equal to $0.05\\times I_{sc}$. So we will set injection equal to 0.05 \"suns\" of injection. \n", + "# Set up timeseries\n", + "In this example, we are going to model test with constant temperature and injection. IEC 61215 MQT 19.1 prescribes 10 $kWh/m^2$ of 1-sun illumination (i.e., 10 hours of 1-sun) with maximum power point tracking at $50\\degree C$. For most typical c-Si modules, MPP injection is roughly $I_{sc}-I_{mp}$, or roughly equal to $0.05\\times I_{sc}$. So we will set injection equal to 0.05 \"suns\" of injection.\n", "\n", "We will create a pandas datetime series and calculate the changes in defect states for each timestep. As B-O LID can initially proceed quickly, we will create a timeseries with 1-second intervals for the first 10 minutes, then proceed with 1-minute intervals" ] @@ -497,7 +499,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Finish calculating degraded device parameters.\n", + "# Finish calculating degraded device parameters.\n", "Now that we have calculated defect states, we can calculate all the quantities that depend on defect states." ] }, @@ -831,7 +833,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Plot the results\n", + "# Plot the results\n", "\n" ] }, diff --git a/tutorials_and_tools/tutorials_and_tools/LETID - Accelerated Test.ipynb b/tutorials/02_degradation/02_letid_accelerated_test.ipynb similarity index 99% rename from tutorials_and_tools/tutorials_and_tools/LETID - Accelerated Test.ipynb rename to tutorials/02_degradation/02_letid_accelerated_test.ipynb index 319acd760..e8578f837 100644 --- a/tutorials_and_tools/tutorials_and_tools/LETID - Accelerated Test.ipynb +++ b/tutorials/02_degradation/02_letid_accelerated_test.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# LETID - Accelerated Tests\n", + "# LETID Accelerated Test\n", "\n", "\n", "Several standardized accelerated tests have been developed for LETID. These include [IEC TS 63342](https://webstore.iec.ch/publication/67332) for c-Si photovoltaic modules, and [IEC TS 63202-4](https://webstore.iec.ch/publication/67612) for c-Si photovoltaic cells.\n", @@ -18,13 +18,12 @@ "2. Define necessary degradation parameters: degraded lifetime and defect states\n", "3. Create timeseries of temperature and current injection\n", "4. Run through timeseries, calculating defect states\n", - "5. Calculate device degradation and plot\n", - "\n" + "5. Calculate device degradation and plot" ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2025-11-13T21:55:18.751013Z", @@ -36,7 +35,7 @@ "outputs": [], "source": [ "# if running on google colab, uncomment the next line and execute this cell to install the dependencies and prevent \"ModuleNotFoundError\" in later cells:\n", - "# !pip install pvdeg==0.3.3" + "# !pip install pvdeg" ] }, { @@ -100,7 +99,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Device parameters\n", + "# Device parameters\n", "To define a device, we need to define several important quantities about the device: wafer thickness (in $\\mu m$), rear surface recombination velocity (in cm/s), and cell area (in cm2). The values defined below are representative of a typical PERC solar cell." ] }, @@ -126,7 +125,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "##### Other device parameters\n", + "# Other device parameters\n", "Other required device parameters: base diffusivity (in $cm^2/s$), and optical generation profile, which allow us to estimate current collection in the device." ] }, @@ -156,7 +155,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Degradation parameters\n", + "# Degradation parameters\n", "To model the device's degradation, we need to define several more important quantities about the degradation the device will experience. These include undegraded and degraded lifetime (in $\\mu s$)." ] }, @@ -181,7 +180,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Let's see how much maximum power degradation these parameters will result in: " + "Let's see how much maximum power degradation these parameters will result in:" ] }, { @@ -376,8 +375,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Set up timeseries\n", - "In this example, we are going to model test with constant temperature and current injection. IEC TS 63342 prescribes two to three weeks of injection equivalent to $2\\times(I_{sc}-I_{mp})$, at $75\\degree C$. For most typical c-Si modules, $2\\times(I_{sc}-I_{mp})$ is roughly equal to $0.1\\times I_{sc}$. So we will set injection equal to 0.1 \"suns\" of injection. \n", + "# Set up timeseries\n", + "In this example, we are going to model test with constant temperature and current injection. IEC TS 63342 prescribes two to three weeks of injection equivalent to $2\\times(I_{sc}-I_{mp})$, at $75\\degree C$. For most typical c-Si modules, $2\\times(I_{sc}-I_{mp})$ is roughly equal to $0.1\\times I_{sc}$. So we will set injection equal to 0.1 \"suns\" of injection.\n", "\n", "We will create a pandas datetime series and calculate the changes in defect states for each timestep." ] @@ -617,7 +616,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Run through timesteps\n", + "# Run through timesteps\n", "Since each timestep depends on the preceding timestep, we need to calculate in a loop. This will take a few minutes depending on the length of the timeseries." ] }, @@ -719,7 +718,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Finish calculating degraded device parameters.\n", + "# Finish calculating degraded device parameters.\n", "Now that we have calculated defect states, we can calculate all the quantities that depend on defect states." ] }, @@ -1053,7 +1052,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Plot the results\n", + "# Plot the results\n", "\n" ] }, @@ -1191,8 +1190,17 @@ "source": [ "FIXED_START_DATE = dt.datetime(2025, 9, 9, 0, 0)\n", "result = letid.calc_letid_lab(\n", - " tau_0, tau_deg, wafer_thickness, s_rear, nA_0, nB_0, nC_0, 0.1, 75, \"D037\",\n", - " start=FIXED_START_DATE\n", + " tau_0,\n", + " tau_deg,\n", + " wafer_thickness,\n", + " s_rear,\n", + " nA_0,\n", + " nB_0,\n", + " nC_0,\n", + " 0.1,\n", + " 75,\n", + " \"D037\",\n", + " start=FIXED_START_DATE,\n", ")\n", "result[\"Datetime\"] = result[\"Datetime\"].dt.round(\"s\")\n", "print(result)" diff --git a/tutorials_and_tools/tutorials_and_tools/LETID - Outdoor LETID.ipynb b/tutorials/02_degradation/03_letid_outdoor.ipynb similarity index 99% rename from tutorials_and_tools/tutorials_and_tools/LETID - Outdoor LETID.ipynb rename to tutorials/02_degradation/03_letid_outdoor.ipynb index d705afbdb..e072015af 100644 --- a/tutorials_and_tools/tutorials_and_tools/LETID - Outdoor LETID.ipynb +++ b/tutorials/02_degradation/03_letid_outdoor.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# LETID - Outdoor Environments \n", + "# LETID Outdoor\n", "\n", "This is an example on how to model LETID progression in outdoor environments\n", "\n", @@ -21,12 +21,12 @@ "2. Define necessary solar cell device parameters\n", "3. Define necessary degradation parameters: degraded lifetime and defect states\n", "4. Run through timeseries, calculating defect states\n", - "5. Calculate device degradation and plot\n" + "5. Calculate device degradation and plot" ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2025-10-03T23:00:55.203351Z", @@ -38,7 +38,7 @@ "outputs": [], "source": [ "# if running on google colab, uncomment the next line and execute this cell to install the dependencies and prevent \"ModuleNotFoundError\" in later cells:\n", - "# !pip install pvdeg==0.3.3" + "# !pip install pvdeg" ] }, { @@ -799,7 +799,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Set up timeseries\n", + "# Set up timeseries\n", "In this example, injection is a function of both the operating point of the module (which we will assume is maximum power point) and irradiance. Maximum power point injection is equivalent to $(I_{sc}-I_{mp})/I_{sc}\\times Ee$, where $Ee$ is effective irradiance, the irradiance absorbed by the module's cells. We normalize it to 1-sun irradiance, 1000 $W/m^2$.\n", "\n", "We will use the irradiance, DC operating point, and cell temperature from the pvlib modelchain results." @@ -990,7 +990,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Device parameters\n", + "# Device parameters\n", "To define a device, we need to define several important quantities about the device: wafer thickness (in $\\mu m$), rear surface recombination velocity (in cm/s), and cell area (in cm2)." ] }, @@ -1046,7 +1046,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Degradation parameters\n", + "# Degradation parameters\n", "To model the device's degradation, we need to define several more important quantities about the degradation the device will experience. These include undegraded and degraded lifetime (in $\\mu s$)." ] }, @@ -1116,7 +1116,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Run through timesteps\n", + "# Run through timesteps\n", "Since each timestep depends on the preceding timestep, we need to calculate in a loop. This will take a few minutes depending on the length of the timeseries." ] }, @@ -1232,7 +1232,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Finish calculating degraded device parameters.\n", + "# Finish calculating degraded device parameters.\n", "Now that we have calculated defect states, we can calculate all the quantities that depend on defect states." ] }, @@ -1559,7 +1559,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Plot the results" + "# Plot the results" ] }, { @@ -1628,7 +1628,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "##### The function `calc_letid_outdoors` wraps all of the steps above into a single function:" + "# The function `calc_letid_outdoors` wraps all of the steps above into a single function:" ] }, { diff --git a/tutorials_and_tools/tutorials_and_tools/LETID - Outdoor Scenario Based on Accelerated Test Result.ipynb b/tutorials/02_degradation/04_letid_outdoor_scenario.ipynb similarity index 99% rename from tutorials_and_tools/tutorials_and_tools/LETID - Outdoor Scenario Based on Accelerated Test Result.ipynb rename to tutorials/02_degradation/04_letid_outdoor_scenario.ipynb index efc47700a..8b7b284bd 100644 --- a/tutorials_and_tools/tutorials_and_tools/LETID - Outdoor Scenario Based on Accelerated Test Result.ipynb +++ b/tutorials/02_degradation/04_letid_outdoor_scenario.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# LETID - Outdoor Scenario Based on Accelerated Test\n", + "# LETID Outdoor Scenario\n", "\n", "This is an example for using a test result to model LETID progression in outdoor environments\n", "\n", @@ -25,12 +25,12 @@ "3. Define necessary solar cell device parameters\n", "4. Define necessary degradation parameters: degraded lifetime and defect states\n", "5. Run through timeseries, calculating defect states\n", - "6. Calculate device degradation and plot\n" + "6. Calculate device degradation and plot" ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2025-10-03T23:01:43.978222Z", @@ -42,7 +42,7 @@ "outputs": [], "source": [ "# if running on google colab, uncomment the next line and execute this cell to install the dependencies and prevent \"ModuleNotFoundError\" in later cells:\n", - "# !pip install pvdeg==0.3.3" + "# !pip install pvdeg" ] }, { @@ -1067,7 +1067,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Set up PVlib model\n", + "# Set up PVlib model\n", "Note that the module we select here is NOT the same \"Type S\" module that was tested for LETID. I'm simply trying to find a module in the CEC database with I-V characteristics that are reasonably close to the tested module, so the pvlib calculated DC results are close to how our Type S module might behave in the field." ] }, @@ -1558,7 +1558,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Set up timeseries\n", + "# Set up timeseries\n", "In this example, injection is a function of both the operating point of the module (which we will assume is maximum power point) and irradiance. Maximum power point injection is equivalent to $(I_{sc}-I_{mp})/I_{sc}\\times Ee$, where $Ee$ is effective irradiance, the irradiance absorbed by the module's cells. We normalize it to 1-sun irradiance, 1000 $W/m^2$.\n", "\n", "We will use the irradiance, DC operating point, and cell temperature from the pvlib modelchain results." @@ -1596,7 +1596,7 @@ "metadata": {}, "source": [ "\n", - "##### Remaining degradation parameters:\n", + "# Remaining degradation parameters:\n", "We've already set our important device parameters: ```tau_0```, ```tau_deg```, ```wafer_thickness```, ```srv_rear```, ```cell_area```, etc, but we need a few more: generation profile and carrier diffusivity. These are necessary for calculating current collection, and the \"default\" values provided here should be sufficient for most use cases.\n", "\n", "The rest of the quantities to define are: the initial percentage of defects in each state (A, B, and C), and the dictionary of mechanism parameters.\n", @@ -1848,7 +1848,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Run through timesteps\n", + "# Run through timesteps\n", "Since each timestep depends on the preceding timestep, we need to calculate in a loop. This will take a few minutes depending on the length of the timeseries." ] }, @@ -2668,7 +2668,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Plot the results" + "# Plot the results" ] }, { @@ -2958,7 +2958,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "##### The function `calc_letid_outdoors` wraps all of the steps above into a single function:" + "# The function `calc_letid_outdoors` wraps all of the steps above into a single function:" ] }, { diff --git a/tutorials_and_tools/tutorials_and_tools/LETID - Passivated Wafer.ipynb b/tutorials/02_degradation/05_letid_passivated_wafer.ipynb similarity index 99% rename from tutorials_and_tools/tutorials_and_tools/LETID - Passivated Wafer.ipynb rename to tutorials/02_degradation/05_letid_passivated_wafer.ipynb index 7747a2a65..80ccf66c2 100644 --- a/tutorials_and_tools/tutorials_and_tools/LETID - Passivated Wafer.ipynb +++ b/tutorials/02_degradation/05_letid_passivated_wafer.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# LETID - Passivated Wafer\n", + "# LETID Passivated Wafer\n", "\n", "This example shows how to simulate an accelerated test performed on a well-passivated Si wafer, rather than a solar cell.\n", "In a well-passivated wafer, carrier injection ($\\Delta n$) is linearly proportional to carrier lifetime, assuming surface recombination velocity can be approximated to be zero.\n", @@ -19,12 +19,12 @@ "2. Define necessary degradation parameters: degraded lifetime and defect states\n", "3. Create timeseries of temperature and injection\n", "4. Run through timeseries, calculating defect states\n", - "5. Calculate device degradation and plot\n" + "5. Calculate device degradation and plot" ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2025-10-22T16:59:10.302053Z", @@ -36,7 +36,7 @@ "outputs": [], "source": [ "# if running on google colab, uncomment the next line and execute this cell to install the dependencies and prevent \"ModuleNotFoundError\" in later cells:\n", - "# !pip install pvdeg==0.3.3" + "# !pip install pvdeg" ] }, { @@ -98,8 +98,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Device parameters\n", - "For modeling a wafer, we don't need as many device parameters. Wafer thickness, lifetime, and an optical generation profile, which allows us to calculate generation current, which we take to be device current assuming surface recombination is very low. " + "# Device parameters\n", + "For modeling a wafer, we don't need as many device parameters. Wafer thickness, lifetime, and an optical generation profile, which allows us to calculate generation current, which we take to be device current assuming surface recombination is very low." ] }, { @@ -131,7 +131,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Degradation parameters\n", + "# Degradation parameters\n", "Here we'll model LETID under 0.5-sun, open-circuit injection. In a passivated wafer, recombination per volume ($\\frac{\\Delta n}{\\tau}$) must equal generation per volume ($\\frac{Jq}{W}$), which gives $\\Delta n = \\frac{Jq\\tau}{W}$\n", "\n", "The term $\\Delta n^{x_{ij}}$ is calculated for wafers by the function ```letid.carrier_factor_wafer```, which adjusts the rate of the transition $i \\rightarrow j$ for $\\Delta n$.\n", @@ -162,7 +162,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Demonstrate calculation of dn^x_ij\n", + "# Demonstrate calculation of dn^x_ij\n", "1. Calculate $\\Delta n$ of the wafer" ] }, @@ -325,7 +325,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Degradation parameters\n", + "# Degradation parameters\n", "To model the device's degradation, we need to define several more important quantities about the degradation the device will experience. These include undegraded and degraded lifetime (in $\\mu s$), and starting defect state percentages" ] }, @@ -380,7 +380,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Set up timeseries\n", + "# Set up timeseries\n", "In this example, we are going to model a wafer exposed to 0.5 suns illumination at $125\\degree C$ for 24 hours, and compare to experimental data from [Wyller *et al.* 2021](https://doi.org/10.1109/JPHOTOV.2021.3078367)." ] }, @@ -613,7 +613,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Run through timesteps\n", + "# Run through timesteps\n", "Since each timestep depends on the preceding timestep, we need to calculate in a loop. This will take a few minutes depending on the length of the timeseries." ] }, @@ -693,7 +693,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Finish calculating degraded device parameters.\n", + "# Finish calculating degraded device parameters.\n", "Now that we have calculated defect states, we can calculate all the quantities that depend on defect states. Since this is a wafer, device parameters like Jsc, Voc, FF, etc., aren't really appropriate\n", "\n", "For wafers, results are often presented in terms of normalized defect density (NDD), where\n", @@ -948,7 +948,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Plot the results" + "# Plot the results" ] }, { @@ -1021,7 +1021,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Compare model results to experimental data\n", + "# Compare model results to experimental data\n", "Experimental data (0.5 suns, 125°C, 150°C, 175°C) from [Wyller 2021](https://doi.org/10.1109/JPHOTOV.2021.3078367).\n", "Comparison with experimental data requires normalization constant ($C_{norm}$), which relates NDD to $N_B$ as described in [Repins 2023](https://doi.org/10.1557/s43577-022-00438-8)." ] diff --git a/tutorials_and_tools/tutorials_and_tools/Van't Hoff Degradation Model.ipynb b/tutorials/02_degradation/06_vant_hoff_degradation_model.ipynb similarity index 97% rename from tutorials_and_tools/tutorials_and_tools/Van't Hoff Degradation Model.ipynb rename to tutorials/02_degradation/06_vant_hoff_degradation_model.ipynb index e87a3c92f..69020647f 100644 --- a/tutorials_and_tools/tutorials_and_tools/Van't Hoff Degradation Model.ipynb +++ b/tutorials/02_degradation/06_vant_hoff_degradation_model.ipynb @@ -4,13 +4,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# A - Van't Hoff Degradation\n", - "### Calculate site specific degradation according to the Van't Hoff equation\n", - "***\n", + "# Van't Hoff Degradation Model\n", + "## Calculate site specific degradation according to the Van't Hoff equation\n", + "\n", "Michael Kempe\n", "\n", "2023.08.31\n", - "***\n", "\n", "**Requirements**:\n", "- compatible weather file (PSM3, TMY3, EPW) or lattitude and longitude of desired site\n", @@ -28,7 +27,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2025-10-03T23:13:33.974180Z", @@ -40,7 +39,7 @@ "outputs": [], "source": [ "# if running on google colab, uncomment the next line and execute this cell to install the dependencies and prevent \"ModuleNotFoundError\" in later cells:\n", - "# pip install pvdeg==0.1.1" + "# pip install pvdeg" ] }, { @@ -66,7 +65,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 1. Read In the Weather File\n", + "# 1. Read In the Weather File\n", "\n", "This is usually the first step. Use a PSM3, TMY3, or EPW file. For this demo, use the provided PSM3 weather file." ] @@ -108,7 +107,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 2. Generate Basic Modeling Data\n", + "# 2. Generate Basic Modeling Data\n", "\n", "For this tutorial we will need solar position, POA, PV cell and module temperature. Let's gernate those individually with their respective functions." ] @@ -150,7 +149,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 3. VantHoff Degradation\n", + "# 3. VantHoff Degradation\n", "\n", "Van't Hoff Irradiance Degradation Equation:\n", "$$ R_o = R_D Β· G^p Β· T_f^{\\frac{T}{10} }$$\n", @@ -221,7 +220,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 4. Arrhenius\n", + "# 4. Arrhenius\n", "Calculate the Acceleration Factor between the rate of degredation of a modeled environmnet versus a modeled controlled environmnet\n", "\n", "Example: \"If the *AF*=25 then 1 year of Controlled Environment exposure is equal to 25 years in the field\"\n", @@ -286,7 +285,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 5. Quick Method (Degradation)\n", + "# 5. Quick Method (Degradation)\n", "\n", "For quick calculations, you can omit POA and both module and cell temperature. The function will calculate these figures as needed using the available weather data with the default options for PV module configuration." ] @@ -381,9 +380,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 6. Solder Fatigue\n", + "# 6. Solder Fatigue\n", "\n", - "Estimate the thermomechanical fatigue of flat plate photovoltaic module solder joints over the time range given using estimated cell temperature. Like other `pvdeg` funcitons, the minimal parameters are (weather_df, meta). Running the function with only these two inputs will use default PV module configurations ( open_rack_glass_polymer ) and the 'sapm' temperature model over the entire length of the weather data. " + "Estimate the thermomechanical fatigue of flat plate photovoltaic module solder joints over the time range given using estimated cell temperature. Like other `pvdeg` funcitons, the minimal parameters are (weather_df, meta). Running the function with only these two inputs will use default PV module configurations ( open_rack_glass_polymer ) and the 'sapm' temperature model over the entire length of the weather data." ] }, { diff --git a/tutorials/02_degradation/README.md b/tutorials/02_degradation/README.md new file mode 100644 index 000000000..3e4e27e6a --- /dev/null +++ b/tutorials/02_degradation/README.md @@ -0,0 +1,20 @@ +# Degradation Tutorials + +Advanced tutorials focused on specific degradation mechanisms including LID and LETID. + +## Tutorials + +1. **01_bo_lid_accelerated_test.ipynb** - Boron-Oxygen Light Induced Degradation (B-O LID) accelerated testing +2. **02_letid_accelerated_test.ipynb** - Light and Elevated Temperature Induced Degradation (LETID) accelerated testing +3. **03_letid_outdoor.ipynb** - LETID modeling for outdoor conditions +4. **04_letid_outdoor_scenario.ipynb** - LETID outdoor scenarios based on accelerated test results +5. **05_letid_passivated_wafer.ipynb** - LETID analysis for passivated wafer technologies +6. **06_vant_hoff_degradation_model.ipynb** - Van't Hoff degradation modeling approach + +## Topics Covered + +- Light Induced Degradation (LID) +- Light and Elevated Temperature Induced Degradation (LETID) +- Accelerated testing methods +- Outdoor degradation prediction +- Degradation kinetics diff --git a/tutorials_and_tools/tutorials_and_tools/B-O LID - Accelerated Test.py b/tutorials/02_degradation/scripts/01_bo_lid_accelerated_test.py similarity index 93% rename from tutorials_and_tools/tutorials_and_tools/B-O LID - Accelerated Test.py rename to tutorials/02_degradation/scripts/01_bo_lid_accelerated_test.py index b2f05a485..bbfc59d20 100644 --- a/tutorials_and_tools/tutorials_and_tools/B-O LID - Accelerated Test.py +++ b/tutorials/02_degradation/scripts/01_bo_lid_accelerated_test.py @@ -1,7 +1,5 @@ -#!/usr/bin/env python -# coding: utf-8 - -# # B-O LID - Accelerated Test +# %% [markdown] +# # B-O LID Accelerated Test # # ## Example: Boron-oxygen light-induced degradation (B-O LID) progression in accelerated tests # @@ -16,31 +14,21 @@ # 3. Create timeseries of temperature and current injection # 4. Run through timeseries, calculating defect states # 5. Calculate device degradation and plot -# -# -# - -# In[1]: - +# %% # if running on google colab, uncomment the next line and execute this cell to install the dependencies and prevent "ModuleNotFoundError" in later cells: -# !pip install pvdeg==0.3.3 - - -# In[2]: - +# # !pip install pvdeg +# %% from pvdeg import letid, collection, utilities, DATA_DIR import os import pandas as pd import numpy as np import matplotlib.pyplot as plt +import pvlib - -# In[ ]: - - +# %% # This information helps with debugging and getting support :) import sys import platform @@ -52,25 +40,21 @@ print("pvlib version ", pvlib.__version__) print("pvdeg version ", pvdeg.__version__) - -# ## Device parameters +# %% [markdown] +# # Device parameters # To define a device, we need to define several important quantities about the device: wafer thickness (in $\mu m$), rear surface recombination velocity (in cm/s), and cell area (in cm2). -# In[3]: - - +# %% wafer_thickness = 180 # um s_rear = 46 # cm/s cell_area = 243 # cm^2 - +# %% [markdown] # Other device parameters # # Other required parameters are base diffusivity (in $cm^2/s$), and optical generation profile, which allow us to estimate current collection in the device. -# In[4]: - - +# %% generation_df = pd.read_excel( os.path.join(DATA_DIR, "PVL_GenProfile.xlsx"), header=0 ) # this is an optical generation profile generated by PVLighthouse's OPAL2 default model for 1-sun, normal incident AM1.5 sunlight on a 180-um thick SiNx-coated, pyramid-textured wafer. @@ -79,69 +63,61 @@ d_base = 27 # cm^2/s electron diffusivity. See https://www2.pvlighthouse.com.au/calculators/mobility%20calculator/mobility%20calculator.aspx for details - -# ## Degradation parameters +# %% [markdown] +# # Degradation parameters # To model the device's degradation, we need to define several more important quantities about the degradation the device will experience. These include undegraded and degraded lifetime (in $\mu s$). -# In[5]: - - +# %% tau_0 = 115 # us, carrier lifetime in non-degraded states, e.g. LETID/LID states A or C tau_deg = 55 # us, carrier lifetime in fully-degraded state, e.g. LETID/LID state B - +# %% [markdown] # Let's see how much maximum power degradation these parameters will result in: -# In[6]: - - +# %% letid.calc_pmp_loss_from_tau_loss( tau_0, tau_deg, cell_area, wafer_thickness, s_rear ) # returns % power loss, pmp_0, pmp_deg - +# %% [markdown] # Remaining degradation parameters: # # The rest of the quantities to define are: the initial percentage of defects in each state (A, B, and C), and the dictionary of mechanism parameters. # # In this example, we'll assume the device starts in the fully-undegraded state (100% state A), and we'll use the parameters for B-O LID -# In[7]: - - +# %% # starting defect state percentages nA_0 = 100 nB_0 = 0 nC_0 = 0 - -# In[8]: - - +# %% # Here's a list of the possible sets of kinetic parameters from kinetic_parameters.json: utilities.get_kinetics() +# %% +mechanism_params = utilities.get_kinetics("D045") -# In[9]: +# Flatten the nested dictionary to get only the "value" for each parameter +mechanism_params = { + k: v["value"] if isinstance(v, dict) and "value" in v else v + for k, v in mechanism_params.items() +} -mechanism_params = utilities.get_kinetics("D054") -print(mechanism_params) - - -# ## Set up timeseries +# %% [markdown] +# # Set up timeseries # In this example, we are going to model test with constant temperature and injection. IEC 61215 MQT 19.1 prescribes 10 $kWh/m^2$ of 1-sun illumination (i.e., 10 hours of 1-sun) with maximum power point tracking at $50\degree C$. For most typical c-Si modules, MPP injection is roughly $I_{sc}-I_{mp}$, or roughly equal to $0.05\times I_{sc}$. So we will set injection equal to 0.05 "suns" of injection. # # We will create a pandas datetime series and calculate the changes in defect states for each timestep. As B-O LID can initially proceed quickly, we will create a timeseries with 1-second intervals for the first 10 minutes, then proceed with 1-minute intervals -# In[10]: - - +# %% temperature = 50 # degrees celsius suns = 0.05 # "suns" of injection, e.g 1-sun illumination at open circuit would be 1; dark current injection is given as a fraction of Isc, e.g., injecting Isc would be 1. For this example we assume injection is 0.05*Isc. timesteps_initial = pd.date_range( - start="2022-01-01 00:00:00", end="2022-01-01 00:10:00", freq="S" + start="2022-01-01 00:00:00", end="2022-01-01 00:10:00", freq="s" ) # 10 minutes of 1-second interval timesteps. In general, we should select small timesteps unless we are sure defect reactions are proceeding very slowly timesteps = pd.date_range( start="2022-01-01 00:10:00", end="2022-01-01 10:00:00", freq="min" @@ -172,10 +148,7 @@ tau_0, tau_deg, nB_0 ) # calculate tau for the first timestep - -# In[11]: - - +# %% for index, timestep in timesteps.iterrows(): # first row tau has already been assigned if index == 0: @@ -256,13 +229,11 @@ timesteps.at[index, "NB"] = n_B + dN_Bdt * t_step timesteps.at[index, "NC"] = n_C + dN_Cdt * t_step - -# ## Finish calculating degraded device parameters. +# %% [markdown] +# # Finish calculating degraded device parameters. # Now that we have calculated defect states, we can calculate all the quantities that depend on defect states. -# In[12]: - - +# %% timesteps["tau"] = letid.tau_now(tau_0, tau_deg, timesteps["NB"]) # calculate device Jsc for every timestep. Unfortunately this requires an integration so I think we have to run through a loop. Device Jsc allows calculation of device Voc. @@ -275,10 +246,7 @@ timesteps.at[index, "tau"], wafer_thickness, s_rear, jsc_now, temperature=25 ) - -# In[13]: - - +# %% # this function quickly calculates the rest of the device parameters: Isc, FF, max power, and normalized max power timesteps = letid.calc_device_params(timesteps, cell_area=243) timesteps["time (days)"] = ( @@ -287,14 +255,12 @@ timesteps - -# ## Plot the results +# %% [markdown] +# # Plot the results # # -# In[14]: - - +# %% from cycler import cycler plt.style.use("default") @@ -327,6 +293,3 @@ ) plt.show() - - -# In[ ]: diff --git a/tutorials_and_tools/tutorials_and_tools/LETID - Accelerated Test.py b/tutorials/02_degradation/scripts/02_letid_accelerated_test.py similarity index 91% rename from tutorials_and_tools/tutorials_and_tools/LETID - Accelerated Test.py rename to tutorials/02_degradation/scripts/02_letid_accelerated_test.py index 4d959807e..df025bfc3 100644 --- a/tutorials_and_tools/tutorials_and_tools/LETID - Accelerated Test.py +++ b/tutorials/02_degradation/scripts/02_letid_accelerated_test.py @@ -1,7 +1,5 @@ -#!/usr/bin/env python -# coding: utf-8 - -# # LETID - Accelerated Tests +# %% [markdown] +# # LETID Accelerated Test # # # Several standardized accelerated tests have been developed for LETID. These include [IEC TS 63342](https://webstore.iec.ch/publication/67332) for c-Si photovoltaic modules, and [IEC TS 63202-4](https://webstore.iec.ch/publication/67612) for c-Si photovoltaic cells. @@ -16,30 +14,22 @@ # 3. Create timeseries of temperature and current injection # 4. Run through timeseries, calculating defect states # 5. Calculate device degradation and plot -# -# - -# In[1]: - +# %% # if running on google colab, uncomment the next line and execute this cell to install the dependencies and prevent "ModuleNotFoundError" in later cells: -# !pip install pvdeg==0.3.3 - - -# In[1]: - +# # !pip install pvdeg +# %% from pvdeg import letid, collection, utilities, DATA_DIR import os import pandas as pd import numpy as np import matplotlib.pyplot as plt +import pvdeg +import datetime as dt - -# In[ ]: - - +# %% # This information helps with debugging and getting support :) import sys import platform @@ -49,24 +39,20 @@ print("Pandas version ", pd.__version__) print("pvdeg version ", pvdeg.__version__) - -# ## Device parameters +# %% [markdown] +# # Device parameters # To define a device, we need to define several important quantities about the device: wafer thickness (in $\mu m$), rear surface recombination velocity (in cm/s), and cell area (in cm2). The values defined below are representative of a typical PERC solar cell. -# In[2]: - - +# %% wafer_thickness = 180 # um s_rear = 46 # cm/s cell_area = 243 # cm^2 - -# ##### Other device parameters +# %% [markdown] +# # Other device parameters # Other required device parameters: base diffusivity (in $cm^2/s$), and optical generation profile, which allow us to estimate current collection in the device. -# In[3]: - - +# %% generation_df = pd.read_excel( os.path.join(DATA_DIR, "PVL_GenProfile.xlsx"), header=0 ) # this is an optical generation profile generated by PVLighthouse's OPAL2 default model for 1-sun, normal incident AM1.5 sunlight on a 180-um thick SiNx-coated, pyramid-textured wafer. @@ -75,76 +61,60 @@ d_base = 27 # cm^2/s electron diffusivity. See https://www2.pvlighthouse.com.au/calculators/mobility%20calculator/mobility%20calculator.aspx for details - -# ## Degradation parameters +# %% [markdown] +# # Degradation parameters # To model the device's degradation, we need to define several more important quantities about the degradation the device will experience. These include undegraded and degraded lifetime (in $\mu s$). -# In[4]: - - +# %% tau_0 = 115 # us, carrier lifetime in non-degraded states, e.g. LETID/LID states A or C tau_deg = 55 # us, carrier lifetime in fully-degraded state, e.g. LETID/LID state B - +# %% [markdown] # Let's see how much maximum power degradation these parameters will result in: -# In[5]: - - +# %% loss, pmp_0, pmp_deg = letid.calc_pmp_loss_from_tau_loss( tau_0, tau_deg, cell_area, wafer_thickness, s_rear ) # returns % power loss, pmp_0, pmp_deg print(loss) - +# %% [markdown] # Check to see the device's current collection -# In[6]: - - +# %% jsc_0 = collection.calculate_jsc_from_tau_cp( tau_0, wafer_thickness, d_base, s_rear, generation, depth ) # returns short-circuit current (Jsc) in mA/cm^2 given required cell parameters print(jsc_0) - +# %% [markdown] # Remaining degradation parameters: # # The rest of the quantities to define are: the initial percentage of defects in each state (A, B, and C), and the dictionary of mechanism parameters. # # In this example, we'll assume the device starts in the fully-undegraded state (100% state A), and we'll use the parameters for LETID degradation from Repins. -# In[7]: - - +# %% # starting defect state percentages nA_0 = 100 nB_0 = 0 nC_0 = 0 - -# In[10]: - - -# Here's a list of the possible sets of kinetic parameters from kinetic_parameters.json: +# %% +# Here's a list of the possible sets of kinetic parameters from DegradationDatabase.json: utilities.get_kinetics() - -# In[12]: - - -mechanism_params = utilities.get_kinetics('D037') +# %% +mechanism_params = utilities.get_kinetics("D037") print(mechanism_params) - -# ## Set up timeseries +# %% [markdown] +# # Set up timeseries # In this example, we are going to model test with constant temperature and current injection. IEC TS 63342 prescribes two to three weeks of injection equivalent to $2\times(I_{sc}-I_{mp})$, at $75\degree C$. For most typical c-Si modules, $2\times(I_{sc}-I_{mp})$ is roughly equal to $0.1\times I_{sc}$. So we will set injection equal to 0.1 "suns" of injection. # # We will create a pandas datetime series and calculate the changes in defect states for each timestep. -# In[13]: - - +# %% temperature = 75 # degrees celsius suns = 0.1 # "suns" of injection, e.g 1-sun illumination at open circuit would be 1; dark current injection is given as a fraction of Isc, e.g., injecting Isc would be 1. For this example we assume injection is 0.1*Isc. @@ -178,19 +148,14 @@ tau_0, tau_deg, nB_0 ) # calculate tau for the first timestep - -# In[14]: - - +# %% timesteps - -# ## Run through timesteps +# %% [markdown] +# # Run through timesteps # Since each timestep depends on the preceding timestep, we need to calculate in a loop. This will take a few minutes depending on the length of the timeseries. -# In[15]: - - +# %% for index, timestep in timesteps.iterrows(): # first row tau has already been assigned if index == 0: @@ -271,13 +236,11 @@ timesteps.at[index, "NB"] = n_B + dN_Bdt * t_step timesteps.at[index, "NC"] = n_C + dN_Cdt * t_step - -# ## Finish calculating degraded device parameters. +# %% [markdown] +# # Finish calculating degraded device parameters. # Now that we have calculated defect states, we can calculate all the quantities that depend on defect states. -# In[16]: - - +# %% timesteps["tau"] = letid.tau_now(tau_0, tau_deg, timesteps["NB"]) # calculate device Jsc for every timestep. Unfortunately this requires an integration so I think we have to run through a loop. Device Jsc allows calculation of device Voc. @@ -290,10 +253,7 @@ timesteps.at[index, "tau"], wafer_thickness, s_rear, jsc_now, temperature=25 ) - -# In[17]: - - +# %% # this function quickly calculates the rest of the device parameters: Isc, FF, max power, and normalized max power timesteps = letid.calc_device_params(timesteps, cell_area=243) timesteps["time (days)"] = ( @@ -302,14 +262,12 @@ timesteps - -# ## Plot the results +# %% [markdown] +# # Plot the results # # -# In[18]: - - +# %% from cycler import cycler plt.style.use("default") @@ -350,12 +308,23 @@ plt.show() - +# %% [markdown] # The function `calc_letid_lab` wraps all of the steps above into a single function: -# In[11]: - - -letid.calc_letid_lab( - tau_0, tau_deg, wafer_thickness, s_rear, nA_0, nB_0, nC_0, 0.1, 75, 'D037' +# %% +FIXED_START_DATE = dt.datetime(2025, 9, 9, 0, 0) +result = letid.calc_letid_lab( + tau_0, + tau_deg, + wafer_thickness, + s_rear, + nA_0, + nB_0, + nC_0, + 0.1, + 75, + "D037", + start=FIXED_START_DATE, ) +result["Datetime"] = result["Datetime"].dt.round("s") +print(result) diff --git a/tutorials_and_tools/tutorials_and_tools/LETID - Outdoor LETID.py b/tutorials/02_degradation/scripts/03_letid_outdoor.py similarity index 93% rename from tutorials_and_tools/tutorials_and_tools/LETID - Outdoor LETID.py rename to tutorials/02_degradation/scripts/03_letid_outdoor.py index 221f8a6df..66ebd270d 100644 --- a/tutorials_and_tools/tutorials_and_tools/LETID - Outdoor LETID.py +++ b/tutorials/02_degradation/scripts/03_letid_outdoor.py @@ -1,7 +1,5 @@ -#!/usr/bin/env python -# coding: utf-8 - -# # LETID - Outdoor Environments +# %% [markdown] +# # LETID Outdoor # # This is an example on how to model LETID progression in outdoor environments # @@ -19,18 +17,12 @@ # 3. Define necessary degradation parameters: degraded lifetime and defect states # 4. Run through timeseries, calculating defect states # 5. Calculate device degradation and plot -# - -# In[1]: - +# %% # if running on google colab, uncomment the next line and execute this cell to install the dependencies and prevent "ModuleNotFoundError" in later cells: -# !pip install pvdeg==0.3.3 - - -# In[1]: - +# # !pip install pvdeg +# %% from pvdeg import letid, collection, utilities, DATA_DIR import pvlib @@ -38,11 +30,9 @@ import pandas as pd import numpy as np import matplotlib.pyplot as plt +import pvdeg - -# In[ ]: - - +# %% # This information helps with debugging and getting support :) import sys import platform @@ -53,40 +43,29 @@ print("pvlib version ", pvlib.__version__) print("pvdeg version ", pvdeg.__version__) - +# %% [markdown] # First, we'll use pvlib to create and run a model system, and use the irradiance, temperature, and operating point of that model to set up our LETID model # For this example, we'll model a fixed latitude tilt system at NREL, in Golden, CO, USA, using [NSRDB](https://nsrdb.nrel.gov/) hourly PSM weather data, SAPM temperature models, and module and inverter models from the CEC database. -# In[2]: - - +# %% # load weather and location data, use pvlib read_psm3 function with map_variables = True sam_file = "psm3.csv" -weather, meta = pvlib.iotools.read_psm3( - os.path.join(DATA_DIR, sam_file), map_variables=True +weather, meta = pvdeg.weather.read( + os.path.join(DATA_DIR, sam_file), file_type="PSM3", map_variables=True ) - -# In[3]: - - +# %% weather - -# In[4]: - - +# %% # if our weather file doesn't have precipitable water, calculate it with pvlib if "precipitable_water" not in weather.columns: weather["precipitable_water"] = pvlib.atmosphere.gueymard94_pw( weather["temp_air"], weather["relative_humidity"] ) - -# In[5]: - - +# %% # rename some columns for pvlib if they haven't been already weather.rename( columns={ @@ -112,16 +91,10 @@ ] ] - -# In[6]: - - +# %% weather - -# In[7]: - - +# %% # import pvlib stuff and pick a module and inverter. Choice of these things will slightly affect the pvlib results which we later use to calculate injection. # we'll use the SAPM temperature model open-rack glass/polymer coeffecients. @@ -140,14 +113,11 @@ "open_rack_glass_polymer" ] - -# In[8]: - - +# %% # set up system in pvlib lat = meta["latitude"] lon = meta["longitude"] -tz = meta["Local Time Zone"] +tz = meta["tz"] elevation = meta["altitude"] surface_tilt = lat # fixed, latitude tilt surface_azimuth = 180 # south-facing @@ -162,23 +132,18 @@ temperature_model_parameters=temperature_model_parameters, ) - -# In[9]: - - +# %% # create and run pvlib modelchain mc = ModelChain(system, location, aoi_model="physical") mc.run_model(weather) - -# ## Set up timeseries +# %% [markdown] +# # Set up timeseries # In this example, injection is a function of both the operating point of the module (which we will assume is maximum power point) and irradiance. Maximum power point injection is equivalent to $(I_{sc}-I_{mp})/I_{sc}\times Ee$, where $Ee$ is effective irradiance, the irradiance absorbed by the module's cells. We normalize it to 1-sun irradiance, 1000 $W/m^2$. # # We will use the irradiance, DC operating point, and cell temperature from the pvlib modelchain results. -# In[10]: - - +# %% ee = mc.results.effective_irradiance # injection = (mc.results.dc['i_sc']-mc.results.dc['i_mp'])/(mc.results.dc['i_sc'])*(ee/1000) injection = letid.calc_injection_outdoors(mc.results) @@ -192,38 +157,28 @@ ) # reset the index so datetime is a column. I prefer integer indexing. timesteps.rename(columns={"index": "Datetime"}, inplace=True) - -# In[11]: - - +# %% # filter out times when injection is NaN, these won't progress LETID, and it'll make the calculations below run faster timesteps = timesteps[timesteps["Injection"].notnull()] timesteps.reset_index(inplace=True, drop=True) - -# In[12]: - - +# %% timesteps - -# ## Device parameters +# %% [markdown] +# # Device parameters # To define a device, we need to define several important quantities about the device: wafer thickness (in $\mu m$), rear surface recombination velocity (in cm/s), and cell area (in cm2). -# In[1]: - - +# %% wafer_thickness = 180 # um s_rear = 46 # cm/s cell_area = 243 # cm^2 - +# %% [markdown] # Other device parameters # Other required device parameters: base diffusivity (in cm2/s), and optical generation profile, which allow us to estimate current collection in the device. -# In[14]: - - +# %% generation_df = pd.read_excel( os.path.join(DATA_DIR, "PVL_GenProfile.xlsx"), header=0 ) # this is an optical generation profile generated by PVLighthouse's OPAL2 default model for 1-sun, normal incident AM1.5 sunlight on a 180-um thick SiNx-coated, pyramid-textured wafer. @@ -232,32 +187,28 @@ d_base = 27 # cm^2/s electron diffusivity. See https://www2.pvlighthouse.com.au/calculators/mobility%20calculator/mobility%20calculator.aspx for details - -# ## Degradation parameters +# %% [markdown] +# # Degradation parameters # To model the device's degradation, we need to define several more important quantities about the degradation the device will experience. These include undegraded and degraded lifetime (in $\mu s$). -# In[15]: - - +# %% tau_0 = 115 # us, carrier lifetime in non-degraded states, e.g. LETID/LID states A or C tau_deg = 55 # us, carrier lifetime in fully-degraded state, e.g. LETID/LID state B - +# %% [markdown] # Remaining degradation parameters: # # The rest of the quantities to define are: the initial percentage of defects in each state (A, B, and C), and the dictionary of mechanism parameters. # # In this example, we'll assume the device starts in the fully-undegraded state (100% state A), and we'll use the kinetic parameters for LETID degradation from Repins. -# In[16]: - - +# %% # starting defect state percentages nA_0 = 100 nB_0 = 0 nC_0 = 0 -mechanism_params = utilities.get_kinetics('D037') +mechanism_params = utilities.get_kinetics("D037") timesteps[["NA", "NB", "NC", "tau"]] = ( np.nan @@ -272,13 +223,11 @@ tau_0, tau_deg, nB_0 ) # calculate tau for the first timestep - -# ## Run through timesteps +# %% [markdown] +# # Run through timesteps # Since each timestep depends on the preceding timestep, we need to calculate in a loop. This will take a few minutes depending on the length of the timeseries. -# In[17]: - - +# %% for index, timestep in timesteps.iterrows(): # first row tau has already been assigned if index == 0: @@ -373,13 +322,11 @@ tau, wafer_thickness, s_rear, jsc, temperature=25 ) - -# ## Finish calculating degraded device parameters. +# %% [markdown] +# # Finish calculating degraded device parameters. # Now that we have calculated defect states, we can calculate all the quantities that depend on defect states. -# In[18]: - - +# %% timesteps["tau"] = letid.tau_now(tau_0, tau_deg, timesteps["NB"]) # calculate device Jsc for every timestep. Unfortunately this requires an integration so I think we have to run through a loop. Device Jsc allows calculation of device Voc. @@ -392,24 +339,20 @@ timesteps.at[index, "tau"], wafer_thickness, s_rear, jsc_now, temperature=25 ) - -# In[19]: - - +# %% timesteps = letid.calc_device_params( timesteps, cell_area=243 ) # this function quickly calculates the rest of the device parameters: Isc, FF, max power, and normalized max power timesteps - +# %% [markdown] # Note of course that all these calculated device parameters are modeled STC device parameters, not the instantaneous, weather-dependent values. This isn't a robust performance model of a degraded module. -# ## Plot the results - -# In[20]: - +# %% [markdown] +# # Plot the results +# %% from cycler import cycler plt.style.use("default") @@ -440,18 +383,17 @@ plt.show() - +# %% [markdown] # The example data provided for Golden, CO, shows how $N_A$ increases in cold weather, and power temporarily recovers, due to temporary recovery of LETID (B->A). -# ##### The function `calc_letid_outdoors` wraps all of the steps above into a single function: - -# In[21]: - +# %% [markdown] +# # The function `calc_letid_outdoors` wraps all of the steps above into a single function: +# %% nA_0 = 100 nB_0 = 0 nC_0 = 0 -mechanism_params = 'D037' +mechanism_params = "D037" letid.calc_letid_outdoors( tau_0, @@ -468,5 +410,4 @@ module_parameters=cec_module, ) - -# In[ ]: +# %% diff --git a/tutorials_and_tools/tutorials_and_tools/LETID - Outdoor Scenario Based on Accelerated Test Result.py b/tutorials/02_degradation/scripts/04_letid_outdoor_scenario.py similarity index 95% rename from tutorials_and_tools/tutorials_and_tools/LETID - Outdoor Scenario Based on Accelerated Test Result.py rename to tutorials/02_degradation/scripts/04_letid_outdoor_scenario.py index 91e7ee2f2..1279bb96f 100644 --- a/tutorials_and_tools/tutorials_and_tools/LETID - Outdoor Scenario Based on Accelerated Test Result.py +++ b/tutorials/02_degradation/scripts/04_letid_outdoor_scenario.py @@ -1,7 +1,5 @@ -#!/usr/bin/env python -# coding: utf-8 - -# # LETID - Outdoor Scenario Based on Accelerated Test +# %% [markdown] +# # LETID Outdoor Scenario # # This is an example for using a test result to model LETID progression in outdoor environments # @@ -23,18 +21,12 @@ # 4. Define necessary degradation parameters: degraded lifetime and defect states # 5. Run through timeseries, calculating defect states # 6. Calculate device degradation and plot -# - -# In[1]: - +# %% # if running on google colab, uncomment the next line and execute this cell to install the dependencies and prevent "ModuleNotFoundError" in later cells: -# !pip install pvdeg==0.3.3 - - -# In[1]: - +# # !pip install pvdeg +# %% from pvdeg import letid, collection, utilities, DATA_DIR import pvdeg @@ -45,10 +37,7 @@ import matplotlib.pyplot as plt - -# In[ ]: - - +# %% # This information helps with debugging and getting support :) import sys import platform @@ -58,30 +47,23 @@ print("Pandas version ", pd.__version__) print("pvdeg version ", pvdeg.__version__) - +# %% [markdown] # First, we'll load some data taken from an accelerated test. See [Karas *et al.* 2022](https://onlinelibrary.wiley.com/doi/10.1002/pip.3573) for full details. This data is the average of "Type S" modules from Lab 3. Type S modules were prototype modules made with 48 monocrystalline cells, and degraded about 4-5% in LETID testing. Data throughout testing is shown below: -# In[2]: - - +# %% cell_area = 243 # cm^2 - -# In[3]: - - +# %% df = pd.read_csv(os.path.join(DATA_DIR, "module test data.csv")) df["cell Voc"] = df["Voc"] / 48 df["Jsc"] = df["Isc"] / cell_area * 1000 df["% degradation"] = (df["Pmp"] - df["Pmp"].iloc[0]) / df["Pmp"] * 100 df - +# %% [markdown] # The module parameter that is most sensitve to our device input parameters is cell open-circuit voltage, which in this case started at about 0.664 V/cell. We will select reasonable values for solar cell input parameters, and use ```letid.calc_voc_from_tau()``` to check if those parameters match the cell Voc of the device we're trying to model. The important quantities here are bulk lifetime in the initial state (```tau_0```), wafer thickness, and rear surface recombination velocity. -# In[4]: - - +# %% tau_0 = 120 # [us] reasonable bulk lifetime for commercial-quality p-type silicon, maybe a little on the low side for typical wafer lifetimes, which could range from 100-500 us. wafer_thickness = 180 # [um] a reasonable wafer thickness for typical commercial silicon solar cells. Wafer thicknesses for Si solar cells are typically 160-180 um. @@ -102,10 +84,7 @@ voc_0 = letid.calc_voc_from_tau(tau_0, wafer_thickness, srv_rear, jsc_0, temperature) voc_0 - -# In[5]: - - +# %% generation_df = pd.read_excel( os.path.join(DATA_DIR, "PVL_GenProfile.xlsx"), header=0 ) # this is an optical generation profile generated by PVLighthouse's OPAL2 default model for 1-sun, normal incident AM1.5 sunlight on a 180-um thick SiNx-coated, pyramid-textured wafer. @@ -114,12 +93,10 @@ d_base = 27 # cm^2/s electron diffusivity. See https://www2.pvlighthouse.com.au/calculators/mobility%20calculator/mobility%20calculator.aspx for details - +# %% [markdown] # Pretty close! -# In[6]: - - +# %% # check to make sure power is close to the measured Week 0 power ff_0 = df.query("Week == 0")["FF"].item() # [%] fill factor @@ -127,12 +104,10 @@ pmp_0 = (voc_0 * 48) * (jsc_0 * cell_area / 1000) * ff_0 / 100 # [W] maximum power pmp_0, df.query("Week == 0")["Pmp"].item() - +# %% [markdown] # Now we do the same thing for the degraded state to determine ```tau_deg```, the bulk lifetime when the module is in its most degraded state. So here the cell Voc target is the roughly 0.656 V measured after 4 weeks of testing. -# In[7]: - - +# %% tau_deg = 80 # [us] degraded bulk lifetime isc_deg = df.query("Week == 4")[ @@ -145,10 +120,7 @@ ) voc_deg - -# In[8]: - - +# %% # check to make sure power is close to the measured Week 4 power ff_deg = df.query("Week == 4")["FF"].item() # [%] fill factor @@ -158,49 +130,35 @@ ) # [W] maximum power pmp_deg, df.query("Week == 4")["Pmp"].item() - -# In[9]: - - +# %% (pmp_0 - pmp_deg) / pmp_0 - +# %% [markdown] # So for modeling this module, we will use ```tau_0``` = 120 $\mu s$, ```tau_deg``` = 80 $\mu s$, with ```wafer_thickness``` = 180 $\mu m$ and ```srv_rear``` = 100 cm/s. # # Great! # # The example proceeds below in similar fashion to the outdoor example, using a fixed latitude tilt system at NREL, in Golden, CO, USA, using [NSRDB](https://nsrdb.nrel.gov/) hourly PSM weather data, SAPM temperature models, and module and inverter models from the CEC database. -# In[10]: - - +# %% # load weather and location data, use pvlib read_psm3 function sam_file = "psm3.csv" -weather, meta = pvlib.iotools.read_psm3( - os.path.join(DATA_DIR, sam_file), map_variables=True +weather, meta = pvdeg.weather.read( + os.path.join(DATA_DIR, sam_file), file_type="PSM3", map_variables=True ) - -# In[11]: - - +# %% weather - -# In[12]: - - +# %% # if our weather file doesn't have precipitable water, calculate it with pvlib if "precipitable_water" not in weather.columns: weather["precipitable_water"] = pvlib.atmosphere.gueymard94_pw( weather["temp_air"], weather["relative_humidity"] ) - -# In[13]: - - +# %% # drop unneeded columns weather = weather[ [ @@ -214,35 +172,25 @@ ] ] - -# In[14]: - - +# %% weather - -# ### Set up PVlib model +# %% [markdown] +# # Set up PVlib model # Note that the module we select here is NOT the same "Type S" module that was tested for LETID. I'm simply trying to find a module in the CEC database with I-V characteristics that are reasonably close to the tested module, so the pvlib calculated DC results are close to how our Type S module might behave in the field. -# In[15]: - - +# %% cec_modules = pvlib.pvsystem.retrieve_sam("CECMod").T cec_modules[cec_modules["STC"].between(220, 250) & (cec_modules["N_s"] == 48)] - +# %% [markdown] # The LG ones look close to the module we're trying to model. Pmp around 235W, Isc around 9.9A. Let's go with 'LG_Electronics_Inc__LG235N8K_G4' -# In[16]: - - +# %% cec_modules = cec_modules.T cec_module = cec_modules["LG_Electronics_Inc__LG235N8K_G4"] - -# In[17]: - - +# %% # import the rest of the pvlib stuff # we'll use the SAPM temperature model open-rack glass/polymer coeffecients. @@ -258,14 +206,11 @@ "open_rack_glass_polymer" ] - -# In[18]: - - +# %% # set up system in pvlib lat = meta["latitude"] lon = meta["longitude"] -tz = meta["Local Time Zone"] +tz = meta["tz"] elevation = meta["altitude"] surface_tilt = lat # fixed, latitude tilt surface_azimuth = 180 # south-facing @@ -280,23 +225,18 @@ temperature_model_parameters=temperature_model_parameters, ) - -# In[19]: - - +# %% # create and run pvlib modelchain mc = ModelChain(system, location, aoi_model="physical") mc.run_model(weather) - -# ## Set up timeseries +# %% [markdown] +# # Set up timeseries # In this example, injection is a function of both the operating point of the module (which we will assume is maximum power point) and irradiance. Maximum power point injection is equivalent to $(I_{sc}-I_{mp})/I_{sc}\times Ee$, where $Ee$ is effective irradiance, the irradiance absorbed by the module's cells. We normalize it to 1-sun irradiance, 1000 $W/m^2$. # # We will use the irradiance, DC operating point, and cell temperature from the pvlib modelchain results. -# In[21]: - - +# %% ee = mc.results.effective_irradiance # injection = (mc.results.dc['i_sc']-mc.results.dc['i_mp'])/(mc.results.dc['i_sc'])*(ee/1000) injection = letid.calc_injection_outdoors(mc.results) @@ -310,18 +250,16 @@ ) # reset the index so datetime is a column. I prefer integer indexing. timesteps.rename(columns={"index": "Datetime"}, inplace=True) - +# %% [markdown] # -# ##### Remaining degradation parameters: +# # Remaining degradation parameters: # We've already set our important device parameters: ```tau_0```, ```tau_deg```, ```wafer_thickness```, ```srv_rear```, ```cell_area```, etc, but we need a few more: generation profile and carrier diffusivity. These are necessary for calculating current collection, and the "default" values provided here should be sufficient for most use cases. # # The rest of the quantities to define are: the initial percentage of defects in each state (A, B, and C), and the dictionary of mechanism parameters. # # In this example, we'll assume the device starts in the fully-undegraded state (100% state A), and we'll use the parameters for LETID degradation from Repins. -# In[22]: - - +# %% generation_df = pd.read_excel( os.path.join(DATA_DIR, "PVL_GenProfile.xlsx"), header=0 ) # this is an optical generation profile generated by PVLighthouse's OPAL2 default model for 1-sun, normal incident AM1.5 sunlight on a 180-um thick SiNx-coated, pyramid-textured wafer. @@ -330,16 +268,13 @@ d_base = 27 # cm^2/s electron diffusivity. See https://www2.pvlighthouse.com.au/calculators/mobility%20calculator/mobility%20calculator.aspx for details - -# In[29]: - - +# %% # starting defect state percentages nA_0 = 100 nB_0 = 0 nC_0 = 0 -mechanism_params = utilities.get_kinetics('D037') +mechanism_params = utilities.get_kinetics("D037") timesteps[["NA", "NB", "NC", "tau"]] = ( np.nan @@ -354,19 +289,14 @@ tau_0, tau_deg, nB_0 ) # calculate tau for the first timestep - -# In[30]: - - +# %% timesteps - -# ## Run through timesteps +# %% [markdown] +# # Run through timesteps # Since each timestep depends on the preceding timestep, we need to calculate in a loop. This will take a few minutes depending on the length of the timeseries. -# In[31]: - - +# %% for index, timestep in timesteps.iterrows(): # first row tau has already been assigned if index == 0: @@ -447,10 +377,7 @@ timesteps.at[index, "NB"] = n_B + dN_Bdt * t_step timesteps.at[index, "NC"] = n_C + dN_Cdt * t_step - -# In[32]: - - +# %% timesteps["tau"] = letid.tau_now(tau_0, tau_deg, timesteps["NB"]) # calculate device Jsc for every timestep. Unfortunately this requires an integration so I think we have to run through a loop. Device Jsc allows calculation of device Voc. @@ -463,22 +390,17 @@ timesteps.at[index, "tau"], wafer_thickness, srv_rear, jsc_now, temperature=25 ) - -# In[33]: - - +# %% timesteps = letid.calc_device_params( timesteps, cell_area ) # this function quickly calculates the rest of the device parameters: Isc, FF, max power, and normalized max power timesteps - +# %% [markdown] # Note of course that all these calculated device parameters are modeled STC device parameters, not the instantaneous, weather-dependent values. We'll merge back in the pvlib results for convenience, but these don't reflect the device degradation. We'll calculate energy loss next -# In[34]: - - +# %% timesteps = timesteps.merge(mc.results.dc, left_on="Datetime", right_index=True) timesteps = timesteps.merge( pd.DataFrame(mc.results.effective_irradiance, columns=["Effective irradiance"]), @@ -489,12 +411,10 @@ timesteps +# %% [markdown] +# # Plot the results -# ### Plot the results - -# In[35]: - - +# %% from cycler import cycler plt.style.use("default") @@ -528,10 +448,7 @@ plt.show() - -# In[36]: - - +# %% import matplotlib.dates as mdates plt.style.use("default") @@ -564,10 +481,7 @@ plt.show() - -# In[37]: - - +# %% import matplotlib.dates as mdates from scipy.integrate import cumulative_trapezoid, simpson @@ -689,13 +603,11 @@ plt.show() +# %% [markdown] +# # The function `calc_letid_outdoors` wraps all of the steps above into a single function: -# ##### The function `calc_letid_outdoors` wraps all of the steps above into a single function: - -# In[39]: - - -mechanism_params = 'D037' +# %% +mechanism_params = "D037" letid.calc_letid_outdoors( tau_0, @@ -712,11 +624,7 @@ module_parameters=cec_module, ) - -# In[38]: - - +# %% timesteps - -# In[ ]: +# %% diff --git a/tutorials_and_tools/tutorials_and_tools/LETID - Passivated Wafer.py b/tutorials/02_degradation/scripts/05_letid_passivated_wafer.py similarity index 94% rename from tutorials_and_tools/tutorials_and_tools/LETID - Passivated Wafer.py rename to tutorials/02_degradation/scripts/05_letid_passivated_wafer.py index d8b3ebb86..be149965b 100644 --- a/tutorials_and_tools/tutorials_and_tools/LETID - Passivated Wafer.py +++ b/tutorials/02_degradation/scripts/05_letid_passivated_wafer.py @@ -1,7 +1,5 @@ -#!/usr/bin/env python -# coding: utf-8 - -# # LETID - Passivated Wafer +# %% [markdown] +# # LETID Passivated Wafer # # This example shows how to simulate an accelerated test performed on a well-passivated Si wafer, rather than a solar cell. # In a well-passivated wafer, carrier injection ($\Delta n$) is linearly proportional to carrier lifetime, assuming surface recombination velocity can be approximated to be zero. @@ -17,18 +15,12 @@ # 3. Create timeseries of temperature and injection # 4. Run through timeseries, calculating defect states # 5. Calculate device degradation and plot -# - -# In[1]: - +# %% # if running on google colab, uncomment the next line and execute this cell to install the dependencies and prevent "ModuleNotFoundError" in later cells: -# !pip install pvdeg==0.3.3 - - -# In[1]: - +# # !pip install pvdeg +# %% from pvdeg import letid, collection, utilities, DATA_DIR import pvdeg import os @@ -36,10 +28,7 @@ import numpy as np import matplotlib.pyplot as plt - -# In[ ]: - - +# %% # This information helps with debugging and getting support :) import sys import platform @@ -49,13 +38,11 @@ print("Pandas version ", pd.__version__) print("pvdeg version ", pvdeg.__version__) - -# ## Device parameters +# %% [markdown] +# # Device parameters # For modeling a wafer, we don't need as many device parameters. Wafer thickness, lifetime, and an optical generation profile, which allows us to calculate generation current, which we take to be device current assuming surface recombination is very low. -# In[2]: - - +# %% wafer_thickness = 180 # um tau = 350 # us, lifetime of the wafer for demonstration purposes @@ -67,8 +54,8 @@ j_gen = collection.generation_current(generation, depth) - -# ## Degradation parameters +# %% [markdown] +# # Degradation parameters # Here we'll model LETID under 0.5-sun, open-circuit injection. In a passivated wafer, recombination per volume ($\frac{\Delta n}{\tau}$) must equal generation per volume ($\frac{Jq}{W}$), which gives $\Delta n = \frac{Jq\tau}{W}$ # # The term $\Delta n^{x_{ij}}$ is calculated for wafers by the function ```letid.carrier_factor_wafer```, which adjusts the rate of the transition $i \rightarrow j$ for $\Delta n$. @@ -77,19 +64,15 @@ # # See [Repins *et al.* 2023](https://doi.org/10.1557/s43577-022-00438-8) for more details. -# In[3]: - - -mechanism_params = utilities.get_kinetics('D037') +# %% +mechanism_params = utilities.get_kinetics("D037") injection = 0.5 # - -# ### Demonstrate calculation of dn^x_ij +# %% [markdown] +# # Demonstrate calculation of dn^x_ij # 1. Calculate $\Delta n$ of the wafer -# In[4]: - - +# %% from scipy.constants import elementary_charge as q # Elementary charge, C dn = ( @@ -97,17 +80,16 @@ ) # calculate excess carrier density dn - +# %% [markdown] # 2. Calculate $\Delta n_{lit}$, the excess carrier density from the literature experiments where kinetic parameters were determined. # Assuming literature experiments were carried out on cells, this requires using the `calc_dn` function -# In[5]: - - +# %% from scipy.constants import convert_temperature transition = "bc" # for this example we'll look at the B -> C transition +# mechanism_params now contains extracted numeric values meas_tau = mechanism_params[f"tau_{transition}"] meas_temp = mechanism_params[f"temperature_{transition}"] meas_temp = convert_temperature(meas_temp, "K", "C") @@ -127,69 +109,48 @@ ) dn_lit - +# %% [markdown] # 3. Return the exponent $x_{ij}$ of the transition in question # -# In[6]: - - +# %% exponent = mechanism_params[f"x_{transition}"] -exponent - - -# 4. Calculate $(\frac{\Delta n}{\Delta n_{lit}})^{x_{ij}}$. This is the acceleration factor of the transition due to excess carriers. -# - -# In[7]: - - (dn / dn_lit) ** exponent - -# In[8]: - - +# %% # the "carrier_factor_wafer" function yields the same result letid.carrier_factor_wafer( tau, transition, injection, j_gen, wafer_thickness, mechanism_params ) - -# ## Degradation parameters +# %% [markdown] +# # Degradation parameters # To model the device's degradation, we need to define several more important quantities about the degradation the device will experience. These include undegraded and degraded lifetime (in $\mu s$), and starting defect state percentages -# In[9]: - - +# %% tau_0 = 350 # us, carrier lifetime in non-degraded states, e.g. LETID/LID states A or C tau_deg = 41 # us, carrier lifetime in fully-degraded state, e.g. LETID/LID state B - -# In[10]: - - +# %% # starting defect state percentages nA_0 = 100 nB_0 = 0 nC_0 = 0 -mechanism_params = utilities.get_kinetics('D037') +mechanism_params = utilities.get_kinetics("D037") print(mechanism_params) - -# ## Set up timeseries +# %% [markdown] +# # Set up timeseries # In this example, we are going to model a wafer exposed to 0.5 suns illumination at $125\degree C$ for 24 hours, and compare to experimental data from [Wyller *et al.* 2021](https://doi.org/10.1109/JPHOTOV.2021.3078367). -# In[11]: - - +# %% temperature = 125 # degrees celsius suns = 0.5 # "suns" of injection, e.g 1-sun illumination at open circuit would be 1; dark current injection is given as a fraction of Isc, e.g., injecting Isc would be 1. For this example we assume injection is 0.1*Isc. timesteps = pd.date_range( - start="2022-01-01 00:00:00", end="2022-01-02 00:00:00", freq="S" + start="2022-01-01 00:00:00", end="2022-01-02 00:00:00", freq="s" ) # three weeks of 1-minute interval timesteps. In general, we should select small timesteps unless we are sure defect reactions are proceeding very slowly timesteps = pd.DataFrame(timesteps, columns=["Datetime"]) @@ -212,19 +173,14 @@ tau_0, tau_deg, nB_0 ) # calculate tau for the first timestep - -# In[12]: - - +# %% timesteps - -# ## Run through timesteps +# %% [markdown] +# # Run through timesteps # Since each timestep depends on the preceding timestep, we need to calculate in a loop. This will take a few minutes depending on the length of the timeseries. -# In[13]: - - +# %% for index, timestep in timesteps.iterrows(): # first row tau has already been assigned if index == 0: @@ -283,17 +239,15 @@ timesteps.at[index, "NB"] = n_B + dN_Bdt * t_step timesteps.at[index, "NC"] = n_C + dN_Cdt * t_step - -# ## Finish calculating degraded device parameters. +# %% [markdown] +# # Finish calculating degraded device parameters. # Now that we have calculated defect states, we can calculate all the quantities that depend on defect states. Since this is a wafer, device parameters like Jsc, Voc, FF, etc., aren't really appropriate # # For wafers, results are often presented in terms of normalized defect density (NDD), where # # $NDD(t) = \frac{1}{\tau (t)} - \frac{1}{\tau _0}$ -# In[14]: - - +# %% timesteps["tau"] = letid.tau_now(tau_0, tau_deg, timesteps["NB"]) timesteps["NDD"] = letid.calc_ndd(timesteps["tau"].iloc[0], timesteps["tau"]) @@ -302,18 +256,13 @@ timesteps["Datetime"] - timesteps.iloc[0]["Datetime"] ).dt.total_seconds() # create a column for seconds elapsed - -# In[15]: - - +# %% timesteps +# %% [markdown] +# # Plot the results -# ## Plot the results - -# In[18]: - - +# %% from cycler import cycler plt.style.use("default") @@ -355,31 +304,23 @@ plt.show() - -# ## Compare model results to experimental data +# %% [markdown] +# # Compare model results to experimental data # Experimental data (0.5 suns, 125°C, 150°C, 175°C) from [Wyller 2021](https://doi.org/10.1109/JPHOTOV.2021.3078367). # Comparison with experimental data requires normalization constant ($C_{norm}$), which relates NDD to $N_B$ as described in [Repins 2023](https://doi.org/10.1557/s43577-022-00438-8). -# In[19]: - - +# %% temperatures = [125, 150, 175] # degrees celsius - -# In[20]: - - +# %% timesteps125 = timesteps # we already modeled the 125C experiment above - -# In[21]: - - +# %% temperature = temperatures[1] # degrees celsius suns = 0.5 # "suns" of injection, e.g 1-sun illumination at open circuit would be 1; dark current injection is given as a fraction of Isc, e.g., injecting Isc would be 1. For this example we assume injection is 0.1*Isc. timesteps = pd.date_range( - start="2022-01-01 00:00:00", end="2022-01-02 00:00:00", freq="S" + start="2022-01-01 00:00:00", end="2022-01-02 00:00:00", freq="s" ) # three weeks of 1-minute interval timesteps. In general, we should select small timesteps unless we are sure defect reactions are proceeding very slowly timesteps = pd.DataFrame(timesteps, columns=["Datetime"]) @@ -402,10 +343,7 @@ tau_0, tau_deg, nB_0 ) # calculate tau for the first timestep - -# In[22]: - - +# %% for index, timestep in timesteps.iterrows(): # first row tau has already been assigned if index == 0: @@ -464,31 +402,22 @@ timesteps.at[index, "NB"] = n_B + dN_Bdt * t_step timesteps.at[index, "NC"] = n_C + dN_Cdt * t_step - -# In[23]: - - +# %% timesteps["tau"] = letid.tau_now(tau_0, tau_deg, timesteps["NB"]) timesteps["NDD"] = letid.calc_ndd(timesteps["tau"].iloc[0], timesteps["tau"]) timesteps["time (s)"] = ( timesteps["Datetime"] - timesteps.iloc[0]["Datetime"] ).dt.total_seconds() # create a column for seconds elapsed - -# In[24]: - - +# %% timesteps150 = timesteps - -# In[25]: - - +# %% temperature = temperatures[2] # degrees celsius suns = 0.5 # "suns" of injection, e.g 1-sun illumination at open circuit would be 1; dark current injection is given as a fraction of Isc, e.g., injecting Isc would be 1. For this example we assume injection is 0.1*Isc. timesteps = pd.date_range( - start="2022-01-01 00:00:00", end="2022-01-02 00:00:00", freq="S" + start="2022-01-01 00:00:00", end="2022-01-02 00:00:00", freq="s" ) # three weeks of 1-minute interval timesteps. In general, we should select small timesteps unless we are sure defect reactions are proceeding very slowly timesteps = pd.DataFrame(timesteps, columns=["Datetime"]) @@ -511,10 +440,7 @@ tau_0, tau_deg, nB_0 ) # calculate tau for the first timestep - -# In[26]: - - +# %% for index, timestep in timesteps.iterrows(): # first row tau has already been assigned if index == 0: @@ -573,32 +499,20 @@ timesteps.at[index, "NB"] = n_B + dN_Bdt * t_step timesteps.at[index, "NC"] = n_C + dN_Cdt * t_step - -# In[27]: - - +# %% timesteps["tau"] = letid.tau_now(tau_0, tau_deg, timesteps["NB"]) timesteps["NDD"] = letid.calc_ndd(timesteps["tau"].iloc[0], timesteps["tau"]) timesteps["time (s)"] = ( timesteps["Datetime"] - timesteps.iloc[0]["Datetime"] ).dt.total_seconds() # create a column for seconds elapsed - -# In[28]: - - +# %% timesteps175 = timesteps - -# In[29]: - - +# %% timesteps = pd.concat([timesteps125, timesteps150, timesteps175]) - -# In[30]: - - +# %% normalization_constants = { 125: 1.35, 150: 0.6, @@ -610,16 +524,10 @@ ) timesteps["NDD-normalized"] = timesteps["NDD"] * timesteps["Normalization constant"] - -# In[31]: - - +# %% grouped_model = timesteps.groupby("Temperature") - -# In[32]: - - +# %% literature_data_file = os.path.join(DATA_DIR, "wyller data.csv") data = pd.read_csv(literature_data_file, header=0) @@ -628,16 +536,10 @@ # data['Normalization constant'] = data['Series'].map(normalization_constants) # data['Y-normalized'] = data['Y']*data['Normalization constant'] - -# In[33]: - - +# %% grouped_data = data.groupby("Temperature") - -# In[34]: - - +# %% import itertools fig, ax = plt.subplots() @@ -669,10 +571,7 @@ plt.show() - -# In[35]: - - +# %% import itertools fig, ax = plt.subplots() @@ -718,6 +617,3 @@ # fig.savefig('wafer data.png', dpi = 600) plt.show() - - -# In[ ]: diff --git a/tutorials/02_degradation/scripts/06_vant_hoff_degradation_model.py b/tutorials/02_degradation/scripts/06_vant_hoff_degradation_model.py new file mode 100644 index 000000000..3f139c7f7 --- /dev/null +++ b/tutorials/02_degradation/scripts/06_vant_hoff_degradation_model.py @@ -0,0 +1,230 @@ +# %% [markdown] +# # Van't Hoff Degradation Model +# ## Calculate site specific degradation according to the Van't Hoff equation +# +# Michael Kempe +# +# 2023.08.31 +# +# **Requirements**: +# - compatible weather file (PSM3, TMY3, EPW) or lattitude and longitude of desired site +# - Accelerated testing chamber parameters +# - chamber irradiance [W/m^2] +# - chamber temperature [Β°C] +# - 10Β°C acceleration factor +# +# **Steps**: +# 1. Read/find the weather data +# 2. Generate basic modeling data +# 3. Calculate VantHoff degradation acceleration factor +# 4. Expand calculations to a region + +# %% +# if running on google colab, uncomment the next line and execute this cell to install the dependencies and prevent "ModuleNotFoundError" in later cells: +# pip install pvdeg + +# %% +import os + +import pvdeg +from pvdeg import DATA_DIR + +# %% [markdown] +# # 1. Read In the Weather File +# +# This is usually the first step. Use a PSM3, TMY3, or EPW file. For this demo, use the provided PSM3 weather file. + +# %% +PSM_FILE = os.path.join(DATA_DIR, "psm3_demo.csv") +WEATHER, META = pvdeg.weather.read(PSM_FILE, "psm") +print( + "Latitude =", + META["latitude"], + "Longitude =", + META["longitude"], + META["Country"], + META["City"], +) + +# %% [markdown] +# # 2. Generate Basic Modeling Data +# +# For this tutorial we will need solar position, POA, PV cell and module temperature. Let's gernate those individually with their respective functions. + +# %% +sol_pos = pvdeg.spectral.solar_position(weather_df=WEATHER, meta=META) + +poa_df = pvdeg.spectral.poa_irradiance( + weather_df=WEATHER, meta=META, sol_position=sol_pos +) + +temp_cell = pvdeg.temperature.cell(weather_df=WEATHER, meta=META, poa=poa_df) + +temp_module = pvdeg.temperature.module(weather_df=WEATHER, meta=META, poa=poa_df) + +# %% [markdown] +# # 3. VantHoff Degradation +# +# Van't Hoff Irradiance Degradation Equation: +# $$ R_o = R_D Β· G^p Β· T_f^{\frac{T}{10} }$$ +# +# For the yearly average degredation outdoors to be the same as the controlled environmnet, the lamp settings will need to be set to *G$_{WA}$* and the temperature set to *T$_{oeq}$*. +# +# As with most `pvdeg` functions, the following functions will always require two arguments (weather_df and meta) + +# %% +# chamber irradiance (W/mΒ²) +I_chamber = 1600 +# chamber temperature (Β°C) +temp_chamber = 85 +# Schwartzchild Coefficient +p = 0.64 +# Acceleration factor for every 10Β°C +Tf = 1.41 + +# calculate the Van't Hoff Acceleration factor +vantHoff_deg = pvdeg.degradation.vantHoff_deg( + weather_df=WEATHER, + meta=META, + I_chamber=I_chamber, + temp_chamber=temp_chamber, + poa=poa_df, + temp=temp_cell, + p=p, + Tf=Tf, +) + +# calculate the Van't Hoff weighted irradiance +irr_weighted_avg_v = pvdeg.degradation.IwaVantHoff( + weather_df=WEATHER, meta=META, poa=poa_df, temp=temp_cell, p=p, Tf=Tf +) + +print( + "AF =", + round(vantHoff_deg, 1), + "(Β°C) , and G_WA =", + round(irr_weighted_avg_v), + "(W/mΒ²)", +) + +# %% [markdown] +# # 4. Arrhenius +# Calculate the Acceleration Factor between the rate of degredation of a modeled environmnet versus a modeled controlled environmnet +# +# Example: "If the *AF*=25 then 1 year of Controlled Environment exposure is equal to 25 years in the field" +# +# Equation: +# $$ AF = N Β· \frac{ G_{chamber}^x Β· RH_{chamber}^n Β· e^{\frac{- E_a}{k T_{chamber}}} }{ \Sigma (G_{POA}^x Β· RH_{outdoor}^n Β· e^{\frac{-E_a}{k T_outdoor}}) }$$ +# +# Function to calculate *G$_{WA}$*, the Environment Characterization (W/mΒ²). If the controlled environmnet lamp settings are set at *G$_{WA}$*, and the temperature set to *T$_{eq}$*, then the degradation will be the same as the yearly average outdoors. +# +# Equation: +# $$ G_{WA} = [ \frac{ \Sigma (G_{outdoor}^x Β· RH_{outdoor}^n e^{\frac{-E_a}{k T_{outdood}}}) }{ N Β· RH_{WA}^n Β· e^{- \frac{E_a}{k T_eq}} } ]^{\frac{1}{x}} $$ + +# %% +# relative humidity within chamber (%) +rh_chamber = 15 +# arrhenius activation energy (kj/mol) +Ea = 40 + +rh_surface = pvdeg.humidity.surface_relative( + rh_ambient=WEATHER["relative_humidity"], + temp_ambient=WEATHER["temp_air"], + temp_module=temp_module, +) + +arrhenius_deg = pvdeg.degradation.arrhenius_deg( + weather_df=WEATHER, + meta=META, + rh_outdoor=rh_surface, + I_chamber=I_chamber, + rh_chamber=rh_chamber, + temp_chamber=temp_chamber, + poa=poa_df, + temp=temp_cell, + Ea=Ea, +) + +irr_weighted_avg_a = pvdeg.degradation.IwaArrhenius( + weather_df=WEATHER, + meta=META, + poa=poa_df, + rh_outdoor=WEATHER["relative_humidity"], + temp=temp_cell, + Ea=Ea, +) + +# %% [markdown] +# # 5. Quick Method (Degradation) +# +# For quick calculations, you can omit POA and both module and cell temperature. The function will calculate these figures as needed using the available weather data with the default options for PV module configuration. + +# %% +# chamber settings +I_chamber = 1000 +temp_chamber = 60 +rh_chamber = 15 + +# activation energy +Ea = 40 + +vantHoff_deg = pvdeg.degradation.vantHoff_deg( + weather_df=WEATHER, meta=META, I_chamber=I_chamber, temp_chamber=temp_chamber +) + +irr_weighted_avg_v = pvdeg.degradation.IwaVantHoff(weather_df=WEATHER, meta=META) + +# %% +rh_surface = pvdeg.humidity.surface_relative( + rh_ambient=WEATHER["relative_humidity"], + temp_ambient=WEATHER["temp_air"], + temp_module=temp_module, +) + +arrhenius_deg = pvdeg.degradation.arrhenius_deg( + weather_df=WEATHER, + meta=META, + rh_outdoor=rh_surface, + I_chamber=I_chamber, + rh_chamber=rh_chamber, + temp_chamber=temp_chamber, + Ea=Ea, +) + +irr_weighted_avg_a = pvdeg.degradation.IwaArrhenius( + weather_df=WEATHER, meta=META, rh_outdoor=WEATHER["relative_humidity"], Ea=Ea +) + +# %% [markdown] +# # 6. Solder Fatigue +# +# Estimate the thermomechanical fatigue of flat plate photovoltaic module solder joints over the time range given using estimated cell temperature. Like other `pvdeg` funcitons, the minimal parameters are (weather_df, meta). Running the function with only these two inputs will use default PV module configurations ( open_rack_glass_polymer ) and the 'sapm' temperature model over the entire length of the weather data. + +# %% +fatigue = pvdeg.fatigue.solder_fatigue(weather_df=WEATHER, meta=META) + +# %% [markdown] +# If you wish to reduce the span of time or use a non-default temperature model, you may specify the parameters manually. Let's try an explicit example. +# We want the solder fatigue estimated over the month of June for a roof mounted glass-front polymer-back module. +# +# 1. Lets create a datetime-index for the month of June. +# 2. Next, generate the cell temperature. Make sure to explicity restrict the weather data to our dt-index for June. Next, declare the PV module configuration. +# 3. Calculate the fatigue. Explicity specify the time_range (our dt-index for June from step 1) and the cell temperature as we caculated in step 2 + +# %% +# select the month of June +time_range = WEATHER.index[WEATHER.index.month == 6] + +# calculate cell temperature over our selected date-time range. +# specify the module configuration +temp_cell = pvdeg.temperature.cell( + weather_df=WEATHER.loc[time_range], + meta=META, + temp_model="sapm", + conf="insulated_back_glass_polymer", +) + + +fatigue = pvdeg.fatigue.solder_fatigue( + weather_df=WEATHER, meta=META, time_range=time_range, temp_cell=temp_cell +) diff --git a/tutorials_and_tools/tutorials_and_tools/Monte Carlo - Arrhenius.ipynb b/tutorials/03_monte_carlo/01_arrhenius.ipynb similarity index 91% rename from tutorials_and_tools/tutorials_and_tools/Monte Carlo - Arrhenius.ipynb rename to tutorials/03_monte_carlo/01_arrhenius.ipynb index b5792cd44..756a22dad 100644 --- a/tutorials_and_tools/tutorials_and_tools/Monte Carlo - Arrhenius.ipynb +++ b/tutorials/03_monte_carlo/01_arrhenius.ipynb @@ -4,10 +4,10 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Monte Carlo - Arrhenius Degradation \n", + "# Arrhenius Monte Carlo\n", "\n", "\n", - "A monte carlo simulation can be used to predict results of an event with a certain amount of uncertainty. This will be introduced to our use case via mean and standard deviation for each modeling constant. Correlated multivariate monte carlo simulations expand on this by linking the behavior of multiple input variables together with correlation data, in our case we will use correlation coefficients but \n", + "A monte carlo simulation can be used to predict results of an event with a certain amount of uncertainty. This will be introduced to our use case via mean and standard deviation for each modeling constant. Correlated multivariate monte carlo simulations expand on this by linking the behavior of multiple input variables together with correlation data, in our case we will use correlation coefficients but\n", "\n", "**Objectives**\n", "1. Define necessary monte carlo simulation parameters : correlation coefficients, mean and standard standard deviation, number of trials, function to apply, requried function input\n", @@ -20,16 +20,16 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2025-10-22T01:42:48.748059Z", - "iopub.status.busy": "2025-10-22T01:42:48.748059Z", - "iopub.status.idle": "2025-10-22T01:42:48.753627Z", - "shell.execute_reply": "2025-10-22T01:42:48.753627Z" + "iopub.execute_input": "2025-12-03T15:31:15.032282Z", + "iopub.status.busy": "2025-12-03T15:31:15.031913Z", + "iopub.status.idle": "2025-12-03T15:31:15.040663Z", + "shell.execute_reply": "2025-12-03T15:31:15.039288Z" } }, "outputs": [], "source": [ "# if running on google colab, uncomment the next line and execute this cell to install the dependencies and prevent \"ModuleNotFoundError\" in later cells:\n", - "# !pip install pvdeg==0.3.3" + "# !pip install pvdeg" ] }, { @@ -37,10 +37,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2025-10-22T01:42:48.757636Z", - "iopub.status.busy": "2025-10-22T01:42:48.756636Z", - "iopub.status.idle": "2025-10-22T01:42:59.270706Z", - "shell.execute_reply": "2025-10-22T01:42:59.269169Z" + "iopub.execute_input": "2025-12-03T15:31:15.044488Z", + "iopub.status.busy": "2025-12-03T15:31:15.043962Z", + "iopub.status.idle": "2025-12-03T15:31:20.038842Z", + "shell.execute_reply": "2025-12-03T15:31:20.037554Z" } }, "outputs": [], @@ -57,10 +57,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2025-10-22T01:42:59.277622Z", - "iopub.status.busy": "2025-10-22T01:42:59.276619Z", - "iopub.status.idle": "2025-10-22T01:42:59.294472Z", - "shell.execute_reply": "2025-10-22T01:42:59.293459Z" + "iopub.execute_input": "2025-12-03T15:31:20.042026Z", + "iopub.status.busy": "2025-12-03T15:31:20.041346Z", + "iopub.status.idle": "2025-12-03T15:31:20.047578Z", + "shell.execute_reply": "2025-12-03T15:31:20.046538Z" } }, "outputs": [ @@ -69,10 +69,10 @@ "output_type": "stream", "text": [ "Working on a Windows 11\n", - "Python version 3.12.9 | packaged by Anaconda, Inc. | (main, Feb 6 2025, 18:49:16) [MSC v.1929 64 bit (AMD64)]\n", - "Pandas version 2.2.3\n", + "Python version 3.13.5 | packaged by Anaconda, Inc. | (main, Jun 12 2025, 16:37:03) [MSC v.1929 64 bit (AMD64)]\n", + "Pandas version 2.3.3\n", "Pvlib version 0.13.0\n", - "pvdeg version 0.5.1.dev623+g51cc68b8e.d20250905\n" + "pvdeg version 0.5.1.dev857+g1a511e445.d20251004\n" ] } ], @@ -92,13 +92,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Correlated Monte Carlo Simulation (parameters)\n", + "# Correlated Monte Carlo Simulation (parameters)\n", "\n", "For this simulation we will be using an arrhenius equation to calculate degredation rate given by $R_D = R_0 * I ^ X * e ^ {\\frac{-Ea}{kT}}$, where R0 is prefactor degredation, I is irradiance, X is the irridiance relation, Ea is activation energy and T is degrees K\n", "\n", "We will use R0, X and Ea to preform a 3 variable monte carlo simulation to calculate degredation.\n", "\n", - "### Required inputs\n", + "## Required inputs\n", "To run a monte carlo simulation with pvdeg.montecarlo the following inputs will be required\n", "- function (currently only works with pvdeg.montecarlo.vecArrhenius() but will eventually work with most pvdeg calculation functions)\n", "- function arguments (ex: metadata, weather, cell temperature, solar position, etc.)\n", @@ -111,7 +111,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Defining Correlation Coefficients\n", + "# Defining Correlation Coefficients\n", "pvdeg.montecarlo stores correlation coefficients in a ``Corr`` object. To represent a given correlation coefficient follow the given syntax below, replacing the values in the brackets with your correlation coefficients\n", "\n", " {my_correlation_object} = Corr('{variable1}', '{variable2}', {correlation coefficient})\n", @@ -126,10 +126,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2025-10-22T01:42:59.379732Z", - "iopub.status.busy": "2025-10-22T01:42:59.379732Z", - "iopub.status.idle": "2025-10-22T01:42:59.387248Z", - "shell.execute_reply": "2025-10-22T01:42:59.386237Z" + "iopub.execute_input": "2025-12-03T15:31:20.097200Z", + "iopub.status.busy": "2025-12-03T15:31:20.096701Z", + "iopub.status.idle": "2025-12-03T15:31:20.103960Z", + "shell.execute_reply": "2025-12-03T15:31:20.102520Z" } }, "outputs": [], @@ -146,10 +146,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2025-10-22T01:42:59.392251Z", - "iopub.status.busy": "2025-10-22T01:42:59.392251Z", - "iopub.status.idle": "2025-10-22T01:42:59.402244Z", - "shell.execute_reply": "2025-10-22T01:42:59.401235Z" + "iopub.execute_input": "2025-12-03T15:31:20.107319Z", + "iopub.status.busy": "2025-12-03T15:31:20.106724Z", + "iopub.status.idle": "2025-12-03T15:31:20.117397Z", + "shell.execute_reply": "2025-12-03T15:31:20.116034Z" } }, "outputs": [ @@ -172,7 +172,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Defining Mean and Standard Deviation\n", + "# Defining Mean and Standard Deviation\n", "We will store the mean and correlation for each variable, expressed when we defined the correlation cefficients. If a variable is left out at this stage, the monte carlo simulation will throw errors." ] }, @@ -181,10 +181,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2025-10-22T01:42:59.408509Z", - "iopub.status.busy": "2025-10-22T01:42:59.407525Z", - "iopub.status.idle": "2025-10-22T01:42:59.413056Z", - "shell.execute_reply": "2025-10-22T01:42:59.412498Z" + "iopub.execute_input": "2025-12-03T15:31:20.120132Z", + "iopub.status.busy": "2025-12-03T15:31:20.119666Z", + "iopub.status.idle": "2025-12-03T15:31:20.125912Z", + "shell.execute_reply": "2025-12-03T15:31:20.124292Z" } }, "outputs": [], @@ -203,7 +203,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Generating Monte Carlo Input Data\n", + "# Generating Monte Carlo Input Data\n", "Next we will use the information collected above to generate correlated data from our modeling constant correlations, means and standard deviations." ] }, @@ -212,10 +212,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2025-10-22T01:42:59.417598Z", - "iopub.status.busy": "2025-10-22T01:42:59.417598Z", - "iopub.status.idle": "2025-10-22T01:42:59.436272Z", - "shell.execute_reply": "2025-10-22T01:42:59.435765Z" + "iopub.execute_input": "2025-12-03T15:31:20.129997Z", + "iopub.status.busy": "2025-12-03T15:31:20.129316Z", + "iopub.status.idle": "2025-12-03T15:31:20.146975Z", + "shell.execute_reply": "2025-12-03T15:31:20.145783Z" } }, "outputs": [ @@ -241,7 +241,7 @@ } ], "source": [ - "np.random.seed(42) # for reproducibility\n", + "np.random.seed(42) # for reproducibility\n", "mc_inputs = pvdeg.montecarlo.generateCorrelatedSamples(\n", " corr=corr_coeff, stats=stats_dict, n=n\n", ")\n", @@ -252,7 +252,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### Sanity Check\n", + "# Sanity Check\n", "We can observe the mean and standard deviation of our newly correlated samples before using them for calculations to ensure that we have not incorrectly altered the data. The mean and standard deviation should be the similar (within a range) to your original input (the error comes from the standard distribution of generated random numbers)\n", "\n", "This also applies to the correlation coefficients originally inputted, they should be witin the same range as those orginally supplied." @@ -263,10 +263,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2025-10-22T01:42:59.440011Z", - "iopub.status.busy": "2025-10-22T01:42:59.440011Z", - "iopub.status.idle": "2025-10-22T01:42:59.458649Z", - "shell.execute_reply": "2025-10-22T01:42:59.457280Z" + "iopub.execute_input": "2025-12-03T15:31:20.150007Z", + "iopub.status.busy": "2025-12-03T15:31:20.149630Z", + "iopub.status.idle": "2025-12-03T15:31:20.160415Z", + "shell.execute_reply": "2025-12-03T15:31:20.158947Z" } }, "outputs": [ @@ -287,7 +287,9 @@ "source": [ "# mean and standard deviation match inputs\n", "for col in mc_inputs.columns:\n", - " print(f\"{col} : mean {mc_inputs[col].mean():.10f}, stdev {mc_inputs[col].std():.10f}\")\n", + " print(\n", + " f\"{col} : mean {mc_inputs[col].mean():.10f}, stdev {mc_inputs[col].std():.10f}\"\n", + " )\n", "\n", "print()\n", "\n", @@ -300,11 +302,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Other Function Requirements \n", + "# Other Function Requirements\n", "Based on the function chosen to run in the monte carlo simulation, various other data will be required. In this case we will need cell temperature and total plane of array irradiance.\n", "\n", "
\n", - "Please use your own API key: The block below makes an NSRDB API to get weather and meta data and then calculate cell temperature and global poa irradiance. This tutorial will work with the DEMO Key provided, but it will take you less than 3 minutes to obtain your own at https://developer.nrel.gov/signup/ so register now.) \n", + "Please use your own API key: The block below makes an NSRDB API to get weather and meta data and then calculate cell temperature and global poa irradiance. This tutorial will work with the DEMO Key provided, but it will take you less than 3 minutes to obtain your own at https://developer.nrel.gov/signup/ so register now.)\n", "
" ] }, @@ -313,40 +315,38 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2025-10-22T01:42:59.469961Z", - "iopub.status.busy": "2025-10-22T01:42:59.464663Z", - "iopub.status.idle": "2025-10-22T01:43:02.531020Z", - "shell.execute_reply": "2025-10-22T01:43:02.530008Z" + "iopub.execute_input": "2025-12-03T15:31:20.163198Z", + "iopub.status.busy": "2025-12-03T15:31:20.162833Z", + "iopub.status.idle": "2025-12-03T15:31:20.360649Z", + "shell.execute_reply": "2025-12-03T15:31:20.358431Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r", - " Column \"relative_humidity\" not found in DataFrame. Calculating...\r", - " \r" - ] - } - ], + "outputs": [], "source": [ - "weather_db = \"PSM4\"\n", - "weather_id = (25.783388, -80.189029)\n", - "weather_arg = {\n", - " \"api_key\": \"DEMO_KEY\",\n", - " \"email\": \"user@mail.com\",\n", - " \"map_variables\": True,\n", - "}\n", + "# Load pre-saved weather data for this tutorial\n", + "# This avoids API rate limits during testing and builds\n", + "import json\n", + "\n", + "weather_df = pd.read_csv(\"../data/psm4_miami.csv\", index_col=0, parse_dates=True)\n", + "with open(\"../data/meta_miami.json\", \"r\") as f:\n", + " meta = json.load(f)\n", "\n", - "weather_df, meta = pvdeg.weather.get(weather_db, weather_id, **weather_arg)" + "# Uncomment below to fetch fresh data with your own API key:\n", + "# weather_db = \"PSM4\"\n", + "# weather_id = (25.783388, -80.189029)\n", + "# weather_arg = {\n", + "# \"api_key\": \"YOUR_API_KEY\", # Get your key at https://developer.nrel.gov/signup/\n", + "# \"email\": \"your.email@example.com\",\n", + "# \"map_variables\": True,\n", + "# }\n", + "# weather_df, meta = pvdeg.weather.get(weather_db, weather_id, **weather_arg)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Calculate the sun position, poa irradiance, and module temperature. " + "Calculate the sun position, poa irradiance, and module temperature." ] }, { @@ -354,10 +354,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2025-10-22T01:43:02.537021Z", - "iopub.status.busy": "2025-10-22T01:43:02.535021Z", - "iopub.status.idle": "2025-10-22T01:43:02.773887Z", - "shell.execute_reply": "2025-10-22T01:43:02.772605Z" + "iopub.execute_input": "2025-12-03T15:31:20.364493Z", + "iopub.status.busy": "2025-12-03T15:31:20.364050Z", + "iopub.status.idle": "2025-12-03T15:31:20.457855Z", + "shell.execute_reply": "2025-12-03T15:31:20.456610Z" } }, "outputs": [ @@ -387,10 +387,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2025-10-22T01:43:02.779899Z", - "iopub.status.busy": "2025-10-22T01:43:02.778899Z", - "iopub.status.idle": "2025-10-22T01:43:02.786236Z", - "shell.execute_reply": "2025-10-22T01:43:02.784898Z" + "iopub.execute_input": "2025-12-03T15:31:20.461103Z", + "iopub.status.busy": "2025-12-03T15:31:20.460643Z", + "iopub.status.idle": "2025-12-03T15:31:20.465621Z", + "shell.execute_reply": "2025-12-03T15:31:20.464493Z" } }, "outputs": [], @@ -403,7 +403,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Runs monte carlo simulation for the example `pvdeg.montecarlo.vecArrhenius` function, using the correlated data dataframe created above and the required function arguments. \n", + "Runs monte carlo simulation for the example `pvdeg.montecarlo.vecArrhenius` function, using the correlated data dataframe created above and the required function arguments.\n", "\n", "We can see the necessary inputs by using the help command:" ] @@ -413,10 +413,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2025-10-22T01:43:02.790833Z", - "iopub.status.busy": "2025-10-22T01:43:02.789829Z", - "iopub.status.idle": "2025-10-22T01:43:02.798688Z", - "shell.execute_reply": "2025-10-22T01:43:02.797680Z" + "iopub.execute_input": "2025-12-03T15:31:20.468825Z", + "iopub.status.busy": "2025-12-03T15:31:20.468211Z", + "iopub.status.idle": "2025-12-03T15:31:20.474781Z", + "shell.execute_reply": "2025-12-03T15:31:20.473547Z" } }, "outputs": [ @@ -426,7 +426,11 @@ "text": [ "Help on function simulate in module pvdeg.montecarlo:\n", "\n", - "simulate(func: Callable, correlated_samples: pandas.core.frame.DataFrame, **function_kwargs) -> pandas.core.series.Series\n", + "simulate(\n", + " func: Callable,\n", + " correlated_samples: pandas.core.frame.DataFrame,\n", + " **function_kwargs\n", + ") -> pandas.core.series.Series\n", " Apply a target function to data to preform a monte carlo simulation.\n", "\n", " If you get\n", @@ -462,10 +466,10 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Running the Monte Carlo Simulation\n", - "We will pass the target function, `pvdeg.degredation.vecArrhenius()`, its required arguments via the correlated_samples and func_kwargs. Our fixed arguments will be passed in the form of a dictionary while the randomized monte carlo input data will be contained in a DataFrame. \n", + "# Running the Monte Carlo Simulation\n", + "We will pass the target function, `pvdeg.degredation.vecArrhenius()`, its required arguments via the correlated_samples and func_kwargs. Our fixed arguments will be passed in the form of a dictionary while the randomized monte carlo input data will be contained in a DataFrame.\n", "\n", - "All required target function arguments should be contained between the column names of the randomized input data and fixed argument dictionary, \n", + "All required target function arguments should be contained between the column names of the randomized input data and fixed argument dictionary,\n", "\n", "(You can use any data you want here as long as the DataFrame's column names match the required target function's parameter names NOT included in the kwargs)" ] @@ -475,10 +479,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2025-10-22T01:43:02.803117Z", - "iopub.status.busy": "2025-10-22T01:43:02.803117Z", - "iopub.status.idle": "2025-10-22T01:53:49.409597Z", - "shell.execute_reply": "2025-10-22T01:53:49.408576Z" + "iopub.execute_input": "2025-12-03T15:31:20.477332Z", + "iopub.status.busy": "2025-12-03T15:31:20.477063Z", + "iopub.status.idle": "2025-12-03T15:35:18.449107Z", + "shell.execute_reply": "2025-12-03T15:35:18.447068Z" } }, "outputs": [], @@ -492,7 +496,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Viewing Our Data\n", + "# Viewing Our Data\n", "Let's plot the results using a histogram" ] }, @@ -501,16 +505,16 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2025-10-22T01:53:49.416594Z", - "iopub.status.busy": "2025-10-22T01:53:49.416594Z", - "iopub.status.idle": "2025-10-22T01:53:50.219396Z", - "shell.execute_reply": "2025-10-22T01:53:50.217864Z" + "iopub.execute_input": "2025-12-03T15:35:18.455337Z", + "iopub.status.busy": "2025-12-03T15:35:18.454707Z", + "iopub.status.idle": "2025-12-03T15:35:18.719442Z", + "shell.execute_reply": "2025-12-03T15:35:18.717883Z" } }, "outputs": [ { "data": { - "image/png": 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" ] @@ -550,7 +554,7 @@ ], "metadata": { "kernelspec": { - "display_name": "pvdeg", + "display_name": "pvdeg_313", "language": "python", "name": "python3" }, @@ -564,7 +568,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.9" + "version": "3.13.5" } }, "nbformat": 4, diff --git a/tutorials/03_monte_carlo/02_standoff.ipynb b/tutorials/03_monte_carlo/02_standoff.ipynb new file mode 100644 index 000000000..03d2cee16 --- /dev/null +++ b/tutorials/03_monte_carlo/02_standoff.ipynb @@ -0,0 +1,447 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Standoff Monte Carlo\n", + "\n", + "See Monte Carlo - Arrhenius Degredation for a more in depth guide. Steps will be shortened for brevity.\n", + "This journal applies a Monte Carlo to the Standoff Calculation" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2025-12-03T15:54:33.351632Z", + "iopub.status.busy": "2025-12-03T15:54:33.351415Z", + "iopub.status.idle": "2025-12-03T15:54:33.358225Z", + "shell.execute_reply": "2025-12-03T15:54:33.357110Z" + } + }, + "outputs": [], + "source": [ + "# if running on google colab, uncomment the next line and execute this cell to install the dependencies and prevent \"ModuleNotFoundError\" in later cells:\n", + "# !pip install pvdeg" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2025-12-03T15:54:33.361134Z", + "iopub.status.busy": "2025-12-03T15:54:33.360799Z", + "iopub.status.idle": "2025-12-03T15:54:37.373528Z", + "shell.execute_reply": "2025-12-03T15:54:37.372335Z" + } + }, + "outputs": [], + "source": [ + "import pvlib\n", + "import numpy as np\n", + "import pandas as pd\n", + "import json\n", + "import pvdeg\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2025-12-03T15:54:37.376756Z", + "iopub.status.busy": "2025-12-03T15:54:37.376236Z", + "iopub.status.idle": "2025-12-03T15:54:37.382012Z", + "shell.execute_reply": "2025-12-03T15:54:37.381258Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Working on a Windows 11\n", + "Python version 3.13.5 | packaged by Anaconda, Inc. | (main, Jun 12 2025, 16:37:03) [MSC v.1929 64 bit (AMD64)]\n", + "Pandas version 2.3.3\n", + "Pvlib version 0.13.0\n", + "Pvdeg version 0.5.1.dev857+g1a511e445.d20251004\n" + ] + } + ], + "source": [ + "# This information helps with debugging and getting support :)\n", + "import sys\n", + "import platform\n", + "\n", + "print(\"Working on a \", platform.system(), platform.release())\n", + "print(\"Python version \", sys.version)\n", + "print(\"Pandas version \", pd.__version__)\n", + "print(\"Pvlib version \", pvlib.__version__)\n", + "print(\"Pvdeg version \", pvdeg.__version__)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Simple Standoff Calculation\n", + "\n", + "This is copied from another tutorial called `4 - Standards.ipynb`, please visit this page for a more in depth explanation of the process for a single standoff calculation.\n", + "\n", + "
\n", + "Please use your own API key: The block below makes an NSRDB API to get weather and meta data. This tutorial will work with the DEMO Key provided, but it will take you less than 3 minutes to obtain your own at https://developer.nrel.gov/signup/ so register now.)\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2025-12-03T15:54:37.410494Z", + "iopub.status.busy": "2025-12-03T15:54:37.410133Z", + "iopub.status.idle": "2025-12-03T15:54:37.529945Z", + "shell.execute_reply": "2025-12-03T15:54:37.528812Z" + } + }, + "outputs": [], + "source": [ + "# Load weather data from locally saved files to avoid API rate limits\n", + "WEATHER = pd.read_csv(\"../data/psm4_nyc.csv\", index_col=0, parse_dates=True)\n", + "with open(\"../data/meta_nyc.json\", \"r\") as f:\n", + " META = json.load(f)\n", + "\n", + "# To use the NSRDB API instead, uncomment the lines below and add your API key\n", + "# Get your API key at: https://developer.nrel.gov/signup/\n", + "# weather_db = \"PSM4\"\n", + "# weather_id = (40.633365593159226, -73.9945801019899) # Manhattan, NYC\n", + "# weather_arg = {\n", + "# \"api_key\": \"YOUR_API_KEY\",\n", + "# \"email\": \"user@mail.com\",\n", + "# \"map_variables\": True,\n", + "# }\n", + "# WEATHER, META = pvdeg.weather.get(weather_db, weather_id, **weather_arg)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2025-12-03T15:54:37.532496Z", + "iopub.status.busy": "2025-12-03T15:54:37.532108Z", + "iopub.status.idle": "2025-12-03T15:54:37.606204Z", + "shell.execute_reply": "2025-12-03T15:54:37.605328Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The array surface_tilt angle was not provided, therefore the latitude of 40.6 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The array surface_tilt angle was not provided, therefore the latitude of 40.6 was used.\n", + " x T98_0 T98_inf\n", + "0 0.522443 71.778148 48.754265\n", + " x T98_0 T98_inf\n", + "0 0.490293 71.778148 48.754265\n" + ] + } + ], + "source": [ + "# simple standoff calculation\n", + "height1 = pvdeg.standards.standoff(weather_df=WEATHER, meta=META)\n", + "\n", + "# more arguments standoff calculation\n", + "height2 = pvdeg.standards.standoff(\n", + " weather_df=WEATHER,\n", + " meta=META,\n", + " tilt=None,\n", + " azimuth=180,\n", + " sky_model=\"isotropic\",\n", + " temp_model=\"sapm\",\n", + " x_0=6.1,\n", + " wind_factor=0.33, # default\n", + ")\n", + "\n", + "print(height1)\n", + "print(height2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Defining Correlation Coefficients, Mean and Standard Deviation For Monte Carlo Simulation\n", + "\n", + "We will leave the list of correlations blank because our variables are not correlated. For a correlated use case visit the `Monte Carlo - Arrhenius.ipynb` tutorial.\n", + "\n", + "Mean and standard deviation must always be populated if being used to create a dataset. However, you can feed your own correlated or uncorrelated data into the simulate function but column names must be consistent." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "execution": { + "iopub.execute_input": "2025-12-03T15:54:37.609055Z", + "iopub.status.busy": "2025-12-03T15:54:37.608759Z", + "iopub.status.idle": "2025-12-03T15:54:37.615160Z", + "shell.execute_reply": "2025-12-03T15:54:37.613645Z" + } + }, + "outputs": [], + "source": [ + "# These numbers may not make sense in the context of the problem but work for demonstraiting the process\n", + "stats = {\"X_0\": {\"mean\": 5, \"stdev\": 3}, \"wind_factor\": {\"mean\": 0.33, \"stdev\": 0.5}}\n", + "\n", + "corr_coeff = []\n", + "\n", + "samples = pvdeg.montecarlo.generateCorrelatedSamples(corr_coeff, stats, 500)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "execution": { + "iopub.execute_input": "2025-12-03T15:54:37.617936Z", + "iopub.status.busy": "2025-12-03T15:54:37.617529Z", + "iopub.status.idle": "2025-12-03T15:54:37.623553Z", + "shell.execute_reply": "2025-12-03T15:54:37.622813Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " X_0 wind_factor\n", + "0 5.469355 0.077734\n", + "1 8.941992 1.292381\n", + "2 4.864771 -0.179995\n", + "3 5.254502 -0.590417\n", + "4 0.004934 0.041089\n", + ".. ... ...\n", + "495 10.638711 0.783493\n", + "496 8.275310 -0.505200\n", + "497 5.299550 1.056922\n", + "498 4.667034 0.124726\n", + "499 7.426355 0.389194\n", + "\n", + "[500 rows x 2 columns]\n" + ] + } + ], + "source": [ + "print(samples)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Standoff Monte Carlo Inputs\n", + "\n", + "When using the pvdeg.montecarlo.simulate() function on a target function all of the target function's required arguments must still be given. Our non-changing arguments will be stored in a dictionary. The randomized monte carlo input data will also be passed to the target function via the simulate function. All required target function arguments should be contained between the column names of the randomized input data and fixed argument dictionary," + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "execution": { + "iopub.execute_input": "2025-12-03T15:54:37.625786Z", + "iopub.status.busy": "2025-12-03T15:54:37.625517Z", + "iopub.status.idle": "2025-12-03T15:54:54.342566Z", + "shell.execute_reply": "2025-12-03T15:54:54.341196Z" + } + }, + "outputs": [], + "source": [ + "# defining arguments to pass to the target function, standoff() in this case\n", + "function_kwargs = {\n", + " \"weather_df\": WEATHER,\n", + " \"meta\": META,\n", + " \"azimuth\": 180,\n", + " \"tilt\": 0,\n", + " \"temp_model\": \"sapm\",\n", + " \"sky_model\": \"isotropic\",\n", + " \"conf_0\": \"insulated_back_glass_polymer\",\n", + " \"conf_inf\": \"open_rack_glass_polymer\",\n", + " \"T98\": 70,\n", + " \"irradiance_kwarg\": {},\n", + " \"conf_0_kwarg\": {},\n", + " \"conf_inf_kwarg\": {},\n", + " \"model_kwarg\": {},\n", + "}\n", + "\n", + "# notice how we left off parts we want to use in the monte carlo simulation because they are already contained in the dataframe\n", + "\n", + "results = pvdeg.montecarlo.simulate(\n", + " func=pvdeg.standards.standoff,\n", + " correlated_samples=samples,\n", + " **function_kwargs,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Dealing With Series\n", + "Notice how our results are contained in a pandas series instead of a dataframe.\n", + "\n", + "This means we have to do an extra step to view our results. Run the block below to confirm that our results are indeed contained in a series. And convert them into a simpler dataframe." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "execution": { + "iopub.execute_input": "2025-12-03T15:54:54.345918Z", + "iopub.status.busy": "2025-12-03T15:54:54.345619Z", + "iopub.status.idle": "2025-12-03T15:54:54.357505Z", + "shell.execute_reply": "2025-12-03T15:54:54.356469Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "print(type(results))\n", + "\n", + "# Convert from pandas Series to pandas DataFrame\n", + "results_df = pd.concat(results.tolist()).reset_index(drop=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "execution": { + "iopub.execute_input": "2025-12-03T15:54:54.359788Z", + "iopub.status.busy": "2025-12-03T15:54:54.359522Z", + "iopub.status.idle": "2025-12-03T15:54:54.364813Z", + "shell.execute_reply": "2025-12-03T15:54:54.364026Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " x T98_0 T98_inf\n", + "0 0.522708 72.043832 49.619994\n", + "1 0.000000 53.514578 38.261491\n", + "2 0.913508 73.973125 50.766052\n", + "3 1.433251 75.649225 51.985503\n", + "4 0.000535 72.306730 49.869504\n", + ".. ... ... ...\n", + "495 0.000000 63.937194 43.917086\n", + "496 2.156556 75.422519 51.785908\n", + "497 0.000000 58.789809 40.917786\n", + "498 0.368321 71.697995 49.322339\n", + "499 0.000000 68.986831 47.547938\n", + "\n", + "[500 rows x 3 columns]\n" + ] + } + ], + "source": [ + "print(results_df)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Viewing Our Data\n", + "Let's plot the results using a histogram" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "execution": { + "iopub.execute_input": "2025-12-03T15:54:54.366762Z", + "iopub.status.busy": "2025-12-03T15:54:54.366482Z", + "iopub.status.idle": "2025-12-03T15:54:54.491917Z", + "shell.execute_reply": "2025-12-03T15:54:54.490949Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "bin_edges = np.arange(results_df[\"x\"].min(), results_df[\"x\"].max() + 0.1, 0.05)\n", + "plt.figure(figsize=(8, 6))\n", + "plt.hist(\n", + " results_df[\"x\"],\n", + " bins=bin_edges,\n", + " edgecolor=\"blue\",\n", + " histtype=\"step\",\n", + " linewidth=1,\n", + " label=\"Standoff Distance\",\n", + ")\n", + "plt.ylabel(\"Counts (out of n trials)\")\n", + "plt.xlabel(\"standoff distance [m]\")\n", + "plt.axvline(np.mean(results_df[\"x\"]), color=\"red\", label=\"mean\")\n", + "plt.axvline(np.median(results_df[\"x\"]), linestyle=\"--\", label=\"median\")\n", + "\n", + "plt.legend()\n", + "plt.grid(True)\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "pvdeg_313", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tutorials/03_monte_carlo/README.md b/tutorials/03_monte_carlo/README.md new file mode 100644 index 000000000..1faa73b17 --- /dev/null +++ b/tutorials/03_monte_carlo/README.md @@ -0,0 +1,16 @@ +# Monte Carlo Simulations + +Tutorials on using Monte Carlo methods for uncertainty analysis and probabilistic modeling. + +## Tutorials + +1. **01_arrhenius.ipynb** - Monte Carlo simulations with Arrhenius degradation models +2. **02_standoff.ipynb** - Monte Carlo analysis for module standoff parameters + +## Overview + +These tutorials demonstrate how to: +- Incorporate parameter uncertainty into degradation predictions +- Perform probabilistic risk assessments +- Analyze sensitivity to input parameters +- Generate confidence intervals for degradation estimates diff --git a/tutorials_and_tools/tutorials_and_tools/Monte Carlo - Arrhenius.py b/tutorials/03_monte_carlo/scripts/01_arrhenius.py similarity index 81% rename from tutorials_and_tools/tutorials_and_tools/Monte Carlo - Arrhenius.py rename to tutorials/03_monte_carlo/scripts/01_arrhenius.py index f6e533ae0..6e36ec32a 100644 --- a/tutorials_and_tools/tutorials_and_tools/Monte Carlo - Arrhenius.py +++ b/tutorials/03_monte_carlo/scripts/01_arrhenius.py @@ -1,7 +1,5 @@ -#!/usr/bin/env python -# coding: utf-8 - -# # Monte Carlo - Arrhenius Degradation +# %% [markdown] +# # Arrhenius Monte Carlo # # # A monte carlo simulation can be used to predict results of an event with a certain amount of uncertainty. This will be introduced to our use case via mean and standard deviation for each modeling constant. Correlated multivariate monte carlo simulations expand on this by linking the behavior of multiple input variables together with correlation data, in our case we will use correlation coefficients but @@ -11,26 +9,18 @@ # 2. Define process for creating and utilizing modeling constant correlation data # 3. Preform simple monte carlo simulation using arrhenius equation to calculate degredation and plot -# In[ ]: - - +# %% # if running on google colab, uncomment the next line and execute this cell to install the dependencies and prevent "ModuleNotFoundError" in later cells: -# !pip install pvdeg==0.3.3 - - -# In[1]: - +# # !pip install pvdeg +# %% import pvlib import numpy as np import pandas as pd import pvdeg import matplotlib.pyplot as plt - -# In[7]: - - +# %% # This information helps with debugging and getting support :) import sys import platform @@ -41,14 +31,14 @@ print("Pvlib version ", pvlib.__version__) print("pvdeg version ", pvdeg.__version__) - -# ## Correlated Monte Carlo Simulation (parameters) +# %% [markdown] +# # Correlated Monte Carlo Simulation (parameters) # # For this simulation we will be using an arrhenius equation to calculate degredation rate given by $R_D = R_0 * I ^ X * e ^ {\frac{-Ea}{kT}}$, where R0 is prefactor degredation, I is irradiance, X is the irridiance relation, Ea is activation energy and T is degrees K # # We will use R0, X and Ea to preform a 3 variable monte carlo simulation to calculate degredation. # -# ### Required inputs +# ## Required inputs # To run a monte carlo simulation with pvdeg.montecarlo the following inputs will be required # - function (currently only works with pvdeg.montecarlo.vecArrhenius() but will eventually work with most pvdeg calculation functions) # - function arguments (ex: metadata, weather, cell temperature, solar position, etc.) @@ -56,7 +46,8 @@ # - correlation constants (if not entered, default = 0) # - number of trials to run -# ### Defining Correlation Coefficients +# %% [markdown] +# # Defining Correlation Coefficients # pvdeg.montecarlo stores correlation coefficients in a ``Corr`` object. To represent a given correlation coefficient follow the given syntax below, replacing the values in the brackets with your correlation coefficients # # {my_correlation_object} = Corr('{variable1}', '{variable2}', {correlation coefficient}) @@ -65,28 +56,21 @@ # # After defining the all known correlations add them to a list which we will feed into our simulation later -# In[10]: - - +# %% corr_Ea_X = pvdeg.montecarlo.Corr("Ea", "X", 0.0269) corr_Ea_LnR0 = pvdeg.montecarlo.Corr("Ea", "LnR0", -0.9995) corr_X_LnR0 = pvdeg.montecarlo.Corr("X", "LnR0", -0.0400) corr_coeff = [corr_Ea_X, corr_Ea_LnR0, corr_X_LnR0] - -# In[11]: - - +# %% type(corr_Ea_X) - -# ### Defining Mean and Standard Deviation +# %% [markdown] +# # Defining Mean and Standard Deviation # We will store the mean and correlation for each variable, expressed when we defined the correlation cefficients. If a variable is left out at this stage, the monte carlo simulation will throw errors. -# In[12]: - - +# %% stats_dict = { "Ea": {"mean": 62.08, "stdev": 7.3858}, "LnR0": {"mean": 13.7223084, "stdev": 2.47334772}, @@ -96,67 +80,67 @@ # and number of monte carlo trials to run n = 20000 - -# ### Generating Monte Carlo Input Data +# %% [markdown] +# # Generating Monte Carlo Input Data # Next we will use the information collected above to generate correlated data from our modeling constant correlations, means and standard deviations. -# In[15]: - - +# %% +np.random.seed(42) # for reproducibility mc_inputs = pvdeg.montecarlo.generateCorrelatedSamples( corr=corr_coeff, stats=stats_dict, n=n ) print(mc_inputs) - -# #### Sanity Check +# %% [markdown] +# # Sanity Check # We can observe the mean and standard deviation of our newly correlated samples before using them for calculations to ensure that we have not incorrectly altered the data. The mean and standard deviation should be the similar (within a range) to your original input (the error comes from the standard distribution of generated random numbers) # # This also applies to the correlation coefficients originally inputted, they should be witin the same range as those orginally supplied. -# In[16]: - - +# %% # mean and standard deviation match inputs for col in mc_inputs.columns: - print(f"{col} : mean {mc_inputs[col].mean()}, stdev {mc_inputs[col].std()}") + print( + f"{col} : mean {mc_inputs[col].mean():.10f}, stdev {mc_inputs[col].std():.10f}" + ) print() -# come up with a better way of checking -print("Ea_X", np.corrcoef(mc_inputs["Ea"], mc_inputs["X"])[0][1]) -print("Ea_lnR0", np.corrcoef(mc_inputs["Ea"], mc_inputs["LnR0"])[0][1]) -print("X_lnR0", np.corrcoef(mc_inputs["X"], mc_inputs["LnR0"])[0][1]) - +print("Ea_X", round(np.corrcoef(mc_inputs["Ea"], mc_inputs["X"])[0][1], 3)) +print("Ea_lnR0", round(np.corrcoef(mc_inputs["Ea"], mc_inputs["LnR0"])[0][1], 3)) +print("X_lnR0", round(np.corrcoef(mc_inputs["X"], mc_inputs["LnR0"])[0][1], 3)) -# ### Other Function Requirements +# %% [markdown] +# # Other Function Requirements # Based on the function chosen to run in the monte carlo simulation, various other data will be required. In this case we will need cell temperature and total plane of array irradiance. # #
# Please use your own API key: The block below makes an NSRDB API to get weather and meta data and then calculate cell temperature and global poa irradiance. This tutorial will work with the DEMO Key provided, but it will take you less than 3 minutes to obtain your own at https://developer.nrel.gov/signup/ so register now.) #
-# In[17]: - - -weather_db = "PSM3" -weather_id = (25.783388, -80.189029) -weather_arg = { - "api_key": "DEMO_KEY", - "email": "user@mail.com", - "names": "tmy", - "attributes": [], - "map_variables": True, -} - -weather_df, meta = pvdeg.weather.get(weather_db, weather_id, **weather_arg) - - +# %% +# Load pre-saved weather data for this tutorial +# This avoids API rate limits during testing and builds +import json + +weather_df = pd.read_csv("../data/psm4_miami.csv", index_col=0, parse_dates=True) +with open("../data/meta_miami.json", "r") as f: + meta = json.load(f) + +# Uncomment below to fetch fresh data with your own API key: +# weather_db = "PSM4" +# weather_id = (25.783388, -80.189029) +# weather_arg = { +# "api_key": "YOUR_API_KEY", # Get your key at https://developer.nrel.gov/signup/ +# "email": "your.email@example.com", +# "map_variables": True, +# } +# weather_df, meta = pvdeg.weather.get(weather_db, weather_id, **weather_arg) + +# %% [markdown] # Calculate the sun position, poa irradiance, and module temperature. -# In[18]: - - +# %% sol_pos = pvdeg.spectral.solar_position(weather_df, meta) poa_irradiance = pvdeg.spectral.poa_irradiance(weather_df, meta) temp_mod = pvdeg.temperature.module( @@ -167,45 +151,37 @@ poa_global = poa_irradiance["poa_global"].to_numpy() cell_temperature = temp_mod.to_numpy() - -# In[24]: - - +# %% # must already be numpy arrays function_kwargs = {"poa_global": poa_global, "module_temp": cell_temperature} - +# %% [markdown] # Runs monte carlo simulation for the example `pvdeg.montecarlo.vecArrhenius` function, using the correlated data dataframe created above and the required function arguments. # # We can see the necessary inputs by using the help command: -# In[28]: - - +# %% +# NBVAL_SKIP help(pvdeg.montecarlo.simulate) - -# ### Running the Monte Carlo Simulation +# %% [markdown] +# # Running the Monte Carlo Simulation # We will pass the target function, `pvdeg.degredation.vecArrhenius()`, its required arguments via the correlated_samples and func_kwargs. Our fixed arguments will be passed in the form of a dictionary while the randomized monte carlo input data will be contained in a DataFrame. # # All required target function arguments should be contained between the column names of the randomized input data and fixed argument dictionary, # # (You can use any data you want here as long as the DataFrame's column names match the required target function's parameter names NOT included in the kwargs) -# In[31]: - - +# %% results = pvdeg.montecarlo.simulate( func=pvdeg.degradation.vecArrhenius, correlated_samples=mc_inputs, **function_kwargs ) - -# ### Viewing Our Data +# %% [markdown] +# # Viewing Our Data # Let's plot the results using a histogram -# In[41]: - - +# %% lnDeg = np.log10(results) percentile_2p5 = np.percentile(lnDeg, 2.5) percentile_97p5 = np.percentile(lnDeg, 97.5) diff --git a/tutorials_and_tools/tutorials_and_tools/Monte Carlo - Standoff.py b/tutorials/03_monte_carlo/scripts/02_standoff.py similarity index 77% rename from tutorials_and_tools/tutorials_and_tools/Monte Carlo - Standoff.py rename to tutorials/03_monte_carlo/scripts/02_standoff.py index bd027def5..b2c24e045 100644 --- a/tutorials_and_tools/tutorials_and_tools/Monte Carlo - Standoff.py +++ b/tutorials/03_monte_carlo/scripts/02_standoff.py @@ -1,32 +1,22 @@ -#!/usr/bin/env python -# coding: utf-8 - -# # Monte Carlo - Standoff Calculation +# %% [markdown] +# # Standoff Monte Carlo # # See Monte Carlo - Arrhenius Degredation for a more in depth guide. Steps will be shortened for brevity. # This journal applies a Monte Carlo to the Standoff Calculation -# - -# In[ ]: - +# %% # if running on google colab, uncomment the next line and execute this cell to install the dependencies and prevent "ModuleNotFoundError" in later cells: -# !pip install pvdeg==0.3.3 - - -# In[1]: - +# # !pip install pvdeg +# %% import pvlib import numpy as np import pandas as pd +import json import pvdeg import matplotlib.pyplot as plt - -# In[2]: - - +# %% # This information helps with debugging and getting support :) import sys import platform @@ -37,8 +27,8 @@ print("Pvlib version ", pvlib.__version__) print("Pvdeg version ", pvdeg.__version__) - -# ### Simple Standoff Calculation +# %% [markdown] +# # Simple Standoff Calculation # # This is copied from another tutorial called `4 - Standards.ipynb`, please visit this page for a more in depth explanation of the process for a single standoff calculation. # @@ -46,25 +36,24 @@ # Please use your own API key: The block below makes an NSRDB API to get weather and meta data. This tutorial will work with the DEMO Key provided, but it will take you less than 3 minutes to obtain your own at https://developer.nrel.gov/signup/ so register now.) # -# In[3]: - - -weather_db = "PSM3" -weather_id = (40.633365593159226, -73.9945801019899) # Manhattan, NYC -weather_arg = { - "api_key": "DEMO_KEY", - "email": "user@mail.com", - "names": "tmy", - "attributes": [], - "map_variables": True, -} - -WEATHER, META = pvdeg.weather.get(weather_db, weather_id, **weather_arg) - - -# In[4]: - - +# %% +# Load weather data from locally saved files to avoid API rate limits +WEATHER = pd.read_csv("../data/psm4_nyc.csv", index_col=0, parse_dates=True) +with open("../data/meta_nyc.json", "r") as f: + META = json.load(f) + +# To use the NSRDB API instead, uncomment the lines below and add your API key +# Get your API key at: https://developer.nrel.gov/signup/ +# weather_db = "PSM4" +# weather_id = (40.633365593159226, -73.9945801019899) # Manhattan, NYC +# weather_arg = { +# "api_key": "YOUR_API_KEY", +# "email": "user@mail.com", +# "map_variables": True, +# } +# WEATHER, META = pvdeg.weather.get(weather_db, weather_id, **weather_arg) + +# %% # simple standoff calculation height1 = pvdeg.standards.standoff(weather_df=WEATHER, meta=META) @@ -83,16 +72,14 @@ print(height1) print(height2) - -# ### Defining Correlation Coefficients, Mean and Standard Deviation For Monte Carlo Simulation +# %% [markdown] +# # Defining Correlation Coefficients, Mean and Standard Deviation For Monte Carlo Simulation # # We will leave the list of correlations blank because our variables are not correlated. For a correlated use case visit the `Monte Carlo - Arrhenius.ipynb` tutorial. # # Mean and standard deviation must always be populated if being used to create a dataset. However, you can feed your own correlated or uncorrelated data into the simulate function but column names must be consistent. -# In[5]: - - +# %% # These numbers may not make sense in the context of the problem but work for demonstraiting the process stats = {"X_0": {"mean": 5, "stdev": 3}, "wind_factor": {"mean": 0.33, "stdev": 0.5}} @@ -100,20 +87,15 @@ samples = pvdeg.montecarlo.generateCorrelatedSamples(corr_coeff, stats, 500) - -# In[6]: - - +# %% print(samples) - -# #### Standoff Monte Carlo Inputs +# %% [markdown] +# # Standoff Monte Carlo Inputs # # When using the pvdeg.montecarlo.simulate() function on a target function all of the target function's required arguments must still be given. Our non-changing arguments will be stored in a dictionary. The randomized monte carlo input data will also be passed to the target function via the simulate function. All required target function arguments should be contained between the column names of the randomized input data and fixed argument dictionary, -# In[28]: - - +# %% # defining arguments to pass to the target function, standoff() in this case function_kwargs = { "weather_df": WEATHER, @@ -122,46 +104,43 @@ "tilt": 0, "temp_model": "sapm", "sky_model": "isotropic", - "conf_0": "insulated_back_glass_polymer", # or is it conf_inf + "conf_0": "insulated_back_glass_polymer", "conf_inf": "open_rack_glass_polymer", "T98": 70, + "irradiance_kwarg": {}, + "conf_0_kwarg": {}, + "conf_inf_kwarg": {}, + "model_kwarg": {}, } # notice how we left off parts we want to use in the monte carlo simulation because they are already contained in the dataframe results = pvdeg.montecarlo.simulate( func=pvdeg.standards.standoff, - correlated_samples=samples, # in this case correlated_samples is a misnomer, they are not required to be correlated + correlated_samples=samples, **function_kwargs, ) - -# #### Dealing With Series +# %% [markdown] +# # Dealing With Series # Notice how our results are contained in a pandas series instead of a dataframe. # # This means we have to do an extra step to view our results. Run the block below to confirm that our results are indeed contained in a series. And convert them into a simpler dataframe. -# In[29]: - - +# %% print(type(results)) # Convert from pandas Series to pandas DataFrame results_df = pd.concat(results.tolist()).reset_index(drop=True) - -# In[30]: - - +# %% print(results_df) - -# ### Viewing Our Data +# %% [markdown] +# # Viewing Our Data # Let's plot the results using a histogram -# In[34]: - - +# %% bin_edges = np.arange(results_df["x"].min(), results_df["x"].max() + 0.1, 0.05) plt.figure(figsize=(8, 6)) plt.hist( diff --git a/tutorials/04_geospatial/02_geospatial_templates.ipynb b/tutorials/04_geospatial/02_geospatial_templates.ipynb new file mode 100644 index 000000000..8a44eaa57 --- /dev/null +++ b/tutorials/04_geospatial/02_geospatial_templates.ipynb @@ -0,0 +1,6613 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Geospatial Templates (HPC)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2025-10-17T21:51:06.498163Z", + "iopub.status.busy": "2025-10-17T21:51:06.498163Z", + "iopub.status.idle": "2025-10-17T21:51:15.430153Z", + "shell.execute_reply": "2025-10-17T21:51:15.429145Z" + }, + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "import pvdeg\n", + "from pvdeg import TEST_DATA_DIR\n", + "import pandas as pd\n", + "import os\n", + "import xarray as xr" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Geospatial Templates\n", + "\n", + "When running a geospatial analysis using `pvdeg.geospatial.analysis` on arbitary `pvdeg` functions you will need to specify a template for the shape of the output data. This is because the input data comes with dimensions of gid and time while the output will have data in a different shape usually corresonding to coordinates.\n", + "- gid, identification number corresponding to an NSRDB datapoint's location\n", + "- time, timeseries corresponding to the hourly time indicies of NSRDB datapoint's yearly meteorological data.\n", + "\n", + "Follow the steps below to see how we generate templates before running the analysis.\n", + "\n", + "The only functions where this is not required are currently `pvdeg.standards.standoff`, `pvdeg.humidity.moduke` and, `letid.calc_letid_outdoors` as they are predefined within the package." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Loading Geospatial Data\n", + "\n", + "This step skips over making the `pvdeg.weather.get` call with `geospatial == True`. See the [Duramat Demo](../10_workshop_demos/02_duramat_live_demo.ipynb) for information on how to do this traditionally.\n", + "\n", + "We can also use a `GeospatialScenario` object. See the [Geospatial Scenario Tutorial](./04_scenario_geospatial.ipynb) for more information on how to use this approach.\n", + "\n", + "*The cell below loads a pickled xarray object, this is not the best way to do this. xarray datasets should be stored as `.nc` - netcdf files*" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2025-10-17T21:51:15.436021Z", + "iopub.status.busy": "2025-10-17T21:51:15.430153Z", + "iopub.status.idle": "2025-10-17T21:51:15.759623Z", + "shell.execute_reply": "2025-10-17T21:51:15.759623Z" + }, + "lines_to_next_cell": 2 + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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<xarray.Dataset> Size: 9MB\n",
+       "Dimensions:            (time: 17520, gid: 11)\n",
+       "Coordinates:\n",
+       "  * gid                (gid) int64 88B 449211 452064 453020 ... 460613 462498\n",
+       "  * time               (time) datetime64[ns] 140kB 2022-01-01 ... 2022-12-31T...\n",
+       "Data variables:\n",
+       "    temp_air           (time, gid) float64 2MB ...\n",
+       "    wind_speed         (time, gid) float64 2MB ...\n",
+       "    dhi                (time, gid) float64 2MB ...\n",
+       "    ghi                (time, gid) float64 2MB ...\n",
+       "    dni                (time, gid) float64 2MB ...\n",
+       "    relative_humidity  (time, gid) float64 2MB ...
" + ], + "text/plain": [ + " Size: 9MB\n", + "Dimensions: (time: 17520, gid: 11)\n", + "Coordinates:\n", + " * gid (gid) int64 88B 449211 452064 453020 ... 460613 462498\n", + " * time (time) datetime64[ns] 140kB 2022-01-01 ... 2022-12-31T...\n", + "Data variables:\n", + " temp_air (time, gid) float64 2MB ...\n", + " wind_speed (time, gid) float64 2MB ...\n", + " dhi (time, gid) float64 2MB ...\n", + " ghi (time, gid) float64 2MB ...\n", + " dni (time, gid) float64 2MB ...\n", + " relative_humidity (time, gid) float64 2MB ..." + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "geo_meta = pd.read_csv(os.path.join(TEST_DATA_DIR, \"summit-meta.csv\"), index_col=0)\n", + "\n", + "# Use xarray to open NetCDF file instead of pickle\n", + "geo_weather = xr.open_dataset(os.path.join(TEST_DATA_DIR, \"summit-weather.nc\"))\n", + "\n", + "geo_weather" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Creating Templates Manually\n", + "\n", + "`pvdeg.geospatial.ouput_template` we can produce a template for our result data.\n", + "\n", + "We need to do this because different functions return different types of values, some return multiple values as tuples, some return only single numerics, others return timeseries results. We need to specify the shape of our data to create an output xarray dataset." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Examples\n", + "\n", + "## 98α΅—Κ° module percential temperature at Standoff Height\n", + "\n", + "Say we want to estimate the 98α΅—Κ° percential temperature for the module at the given tilt, azimuth, and x_eff. `PVDeg` has a function to do this, `pvdeg.standards.T98_estimate` BUT it doesn't have a preset geospatial template. We will need to make one.\n", + "\n", + "- look at the function return values.\n", + "From the docstring we can see that `T98_estimate` only has one return value. IMPORTANT, this value is a single float, NOT a timeseries. This means our output shape will only be dependent on the input identifier and NOT time.\n", + "\n", + "Therefore we will map the output variable `T98` to the location identifier `gid` using a dictionary with `str: tuple` mappings.\n", + "\n", + " *IMPORTANT: you must use the syntax below where the variable maps to a tuple of the coordinates. in this case there needs to be a trailing comma in the tuple or python will iterate over the characters in the tuple instead of the elements. See further examples to alleviate confusion.*" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2025-10-17T21:51:15.833252Z", + "iopub.status.busy": "2025-10-17T21:51:15.833252Z", + "iopub.status.idle": "2025-10-17T21:51:15.846483Z", + "shell.execute_reply": "2025-10-17T21:51:15.846483Z" + } + }, + "outputs": [], + "source": [ + "# define output shape\n", + "shapes = {\n", + " \"T98\": (\n", + " \"gid\",\n", + " ) # one return value at each datapoint, only dependent on datapoint, not time\n", + "}\n", + "\n", + "# create xarray template for output to be populated when analysis is run\n", + "template = pvdeg.geospatial.output_template(\n", + " ds_gids=geo_weather,\n", + " shapes=shapes,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2025-10-17T21:51:15.849012Z", + "iopub.status.busy": "2025-10-17T21:51:15.849012Z", + "iopub.status.idle": "2025-10-17T21:51:18.307866Z", + "shell.execute_reply": "2025-10-17T21:51:18.307866Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The array surface_tilt angle was not provided, therefore the latitude of 39.9 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The array surface_tilt angle was not provided, therefore the latitude of 39.9 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", + "The array surface_tilt angle was not provided, therefore the latitude of 39.7 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The array surface_tilt angle was not provided, therefore the latitude of 39.8 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", + "The array surface_tilt angle was not provided, therefore the latitude of 39.8 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The array surface_tilt angle was not provided, therefore the latitude of 39.4 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The array surface_tilt angle was not provided, therefore the latitude of 39.5 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The array surface_tilt angle was not provided, therefore the latitude of 39.4 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The array surface_tilt angle was not provided, therefore the latitude of 39.6 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The array surface_tilt angle was not provided, therefore the latitude of 39.5 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The array surface_tilt angle was not provided, therefore the latitude of 39.6 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n" + ] + } + ], + "source": [ + "geo_estimate_temp = pvdeg.geospatial.analysis(\n", + " weather_ds=geo_weather,\n", + " meta_df=geo_meta,\n", + " func=pvdeg.standards.T98_estimate,\n", + " template=template,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Glass Glass Estimated Module Temperature\n", + "\n", + "Now we want to calculate geospatial timeseries temperature values for a module using `pvdeg.temperature.module`. This is not super practical because all `pvdeg` functions that need to use tempeature for their calculations preform the temperature calculation internally, this is just for show.\n", + "\n", + "This calculation differs from the above because the temperature functions return the module temperature in a timeseries format. So we care about 2 dimensions, location identifier and TIME." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2025-10-17T21:51:18.314097Z", + "iopub.status.busy": "2025-10-17T21:51:18.311873Z", + "iopub.status.idle": "2025-10-17T21:51:18.325839Z", + "shell.execute_reply": "2025-10-17T21:51:18.325839Z" + } + }, + "outputs": [], + "source": [ + "# define output shape\n", + "shapes = {\n", + " \"module_temperature\": (\n", + " \"gid\",\n", + " \"time\",\n", + " ) # one return value at each datapoint, only dependent on datapoint, not time\n", + "}\n", + "\n", + "# create xarray template for output to be populated when analysis is run\n", + "temperature_template = pvdeg.geospatial.output_template(\n", + " ds_gids=geo_weather,\n", + " shapes=shapes,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "execution": { + "iopub.execute_input": "2025-10-17T21:51:18.330857Z", + "iopub.status.busy": "2025-10-17T21:51:18.330857Z", + "iopub.status.idle": "2025-10-17T21:51:20.707569Z", + "shell.execute_reply": "2025-10-17T21:51:20.707569Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The array surface_tilt angle was not provided, therefore the latitude of 39.9 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The array surface_tilt angle was not provided, therefore the latitude of 39.9 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The array surface_tilt angle was not provided, therefore the latitude of 39.7 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", + "The array surface_tilt angle was not provided, therefore the latitude of 39.8 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The array surface_tilt angle was not provided, therefore the latitude of 39.8 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", + "The array surface_tilt angle was not provided, therefore the latitude of 39.4 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The array surface_tilt angle was not provided, therefore the latitude of 39.5 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The array surface_tilt angle was not provided, therefore the latitude of 39.4 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", + "The array surface_tilt angle was not provided, therefore the latitude of 39.6 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The array surface_tilt angle was not provided, therefore the latitude of 39.5 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The array surface_tilt angle was not provided, therefore the latitude of 39.6 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n" + ] + } + ], + "source": [ + "geo_temperature_res = pvdeg.geospatial.analysis(\n", + " weather_ds=geo_weather,\n", + " meta_df=geo_meta,\n", + " func=pvdeg.temperature.module,\n", + " template=temperature_template, # use the template we created\n", + " conf=\"open_rack_glass_glass\", # provide kwargs for function here\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "execution": { + "iopub.execute_input": "2025-10-17T21:51:20.709329Z", + "iopub.status.busy": "2025-10-17T21:51:20.709329Z", + "iopub.status.idle": "2025-10-17T21:51:21.143541Z", + "shell.execute_reply": "2025-10-17T21:51:21.142530Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot the temperature at ONE of the geospatial analysis result locations but we have calculated all of these.\n", + "import matplotlib.pyplot as plt\n", + "\n", + "module_temps = (\n", + " geo_temperature_res[\"module\"].sel(latitude=39.89, longitude=\"-106.42\").values\n", + ")\n", + "\n", + "plt.plot(module_temps)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Self Explaining Code\n", + "\n", + "If we are looking at adding templates for other functions, we can also look at the 3 presaved templates for existing `pvdeg` functions. Visit [pvdeg.geospatial.template_parameters](../../pvdeg/geospatial.py) and inspect this function to see how these different target functions utilize templates and shapes." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Creating Templates Programatically\n", + "\n", + "We can use `pvdeg.geospatial.autotemplate` to generate a template for a given function. This can return a bad result which will fail or work improperly when running `pvdeg.geospatial.analysis` with the generated template. Results should be scrutinized to make sure they are the right format." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Examples Below\n", + "Steps\n", + "- Create template using autotemplating function. Pulls in information about function to determine shape of output. Not usable on functions with ambigious return types.\n", + "- Call geospatial analysis function using template" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Geospatial Cell Temperature Calculation\n", + "As shown below, we have two options, we can choose to provide a template that is generated by a function which supports autotemplating. Or we can provide the function to `geospatial.analysis` and let it generate a template internally.\n", + "\n", + "## Providing a Template with `Geospatial.auto_template`" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "execution": { + "iopub.execute_input": "2025-10-17T21:51:21.143541Z", + "iopub.status.busy": "2025-10-17T21:51:21.143541Z", + "iopub.status.idle": "2025-10-17T21:51:23.543854Z", + "shell.execute_reply": "2025-10-17T21:51:23.542840Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The array surface_tilt angle was not provided, therefore the latitude of 39.9 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The array surface_tilt angle was not provided, therefore the latitude of 39.9 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The array surface_tilt angle was not provided, therefore the latitude of 39.7 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The array surface_tilt angle was not provided, therefore the latitude of 39.8 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The array surface_tilt angle was not provided, therefore the latitude of 39.8 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", + "The array surface_tilt angle was not provided, therefore the latitude of 39.4 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The array surface_tilt angle was not provided, therefore the latitude of 39.5 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The array surface_tilt angle was not provided, therefore the latitude of 39.4 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", + "The array surface_tilt angle was not provided, therefore the latitude of 39.6 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The array surface_tilt angle was not provided, therefore the latitude of 39.5 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The array surface_tilt angle was not provided, therefore the latitude of 39.6 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n" + ] + }, + { + "data": { + "text/html": [ + "
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<xarray.Dataset> Size: 11MB\n",
+       "Dimensions:    (latitude: 8, longitude: 10, time: 17520)\n",
+       "Coordinates:\n",
+       "  * latitude   (latitude) float64 64B 39.41 39.45 39.53 ... 39.69 39.81 39.89\n",
+       "  * longitude  (longitude) float64 80B -106.4 -106.3 -106.3 ... -105.9 -105.9\n",
+       "  * time       (time) datetime64[ns] 140kB 2022-01-01 ... 2022-12-31T23:30:00\n",
+       "Data variables:\n",
+       "    cell       (time, latitude, longitude) float64 11MB nan nan nan ... nan nan
" + ], + "text/plain": [ + " Size: 11MB\n", + "Dimensions: (latitude: 8, longitude: 10, time: 17520)\n", + "Coordinates:\n", + " * latitude (latitude) float64 64B 39.41 39.45 39.53 ... 39.69 39.81 39.89\n", + " * longitude (longitude) float64 80B -106.4 -106.3 -106.3 ... -105.9 -105.9\n", + " * time (time) datetime64[ns] 140kB 2022-01-01 ... 2022-12-31T23:30:00\n", + "Data variables:\n", + " cell (time, latitude, longitude) float64 11MB nan nan nan ... nan nan" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# create a template using auto_template for the desired function\n", + "cell_temp_template = pvdeg.geospatial.auto_template(\n", + " func=pvdeg.temperature.cell, ds_gids=geo_weather\n", + ")\n", + "\n", + "# run the geospatial analysis with the template\n", + "pvdeg.geospatial.analysis(\n", + " weather_ds=geo_weather,\n", + " meta_df=geo_meta,\n", + " func=pvdeg.temperature.cell,\n", + " template=cell_temp_template,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Analysis Without Providing a Template\n", + "\n", + "If a function is supported by `geospatial.auto_template` we do not need to create a template outside of the function as shown in the cell above. We can simply pass the function to `geospatial.analysis` and it will create a template for us." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "execution": { + "iopub.execute_input": "2025-10-17T21:51:23.543854Z", + "iopub.status.busy": "2025-10-17T21:51:23.543854Z", + "iopub.status.idle": "2025-10-17T21:51:25.796526Z", + "shell.execute_reply": "2025-10-17T21:51:25.795514Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The array surface_tilt angle was not provided, therefore the latitude of 39.9 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The array surface_tilt angle was not provided, therefore the latitude of 39.9 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", + "The array surface_tilt angle was not provided, therefore the latitude of 39.7 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The array surface_tilt angle was not provided, therefore the latitude of 39.8 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", + "The array surface_tilt angle was not provided, therefore the latitude of 39.8 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The array surface_tilt angle was not provided, therefore the latitude of 39.4 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", + "The array surface_tilt angle was not provided, therefore the latitude of 39.5 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The array surface_tilt angle was not provided, therefore the latitude of 39.4 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", + "The array surface_tilt angle was not provided, therefore the latitude of 39.6 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The array surface_tilt angle was not provided, therefore the latitude of 39.5 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", + "The array surface_tilt angle was not provided, therefore the latitude of 39.6 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n" + ] + }, + { + "data": { + "text/html": [ + "
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<xarray.Dataset> Size: 11MB\n",
+       "Dimensions:    (latitude: 8, longitude: 10, time: 17520)\n",
+       "Coordinates:\n",
+       "  * latitude   (latitude) float64 64B 39.41 39.45 39.53 ... 39.69 39.81 39.89\n",
+       "  * longitude  (longitude) float64 80B -106.4 -106.3 -106.3 ... -105.9 -105.9\n",
+       "  * time       (time) datetime64[ns] 140kB 2022-01-01 ... 2022-12-31T23:30:00\n",
+       "Data variables:\n",
+       "    cell       (time, latitude, longitude) float64 11MB nan nan nan ... nan nan
" + ], + "text/plain": [ + " Size: 11MB\n", + "Dimensions: (latitude: 8, longitude: 10, time: 17520)\n", + "Coordinates:\n", + " * latitude (latitude) float64 64B 39.41 39.45 39.53 ... 39.69 39.81 39.89\n", + " * longitude (longitude) float64 80B -106.4 -106.3 -106.3 ... -105.9 -105.9\n", + " * time (time) datetime64[ns] 140kB 2022-01-01 ... 2022-12-31T23:30:00\n", + "Data variables:\n", + " cell (time, latitude, longitude) float64 11MB nan nan nan ... nan nan" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pvdeg.geospatial.analysis(\n", + " weather_ds=geo_weather,\n", + " meta_df=geo_meta,\n", + " func=pvdeg.temperature.cell,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Geospatial Module Temperature Calculation" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "execution": { + "iopub.execute_input": "2025-10-17T21:51:25.803558Z", + "iopub.status.busy": "2025-10-17T21:51:25.802547Z", + "iopub.status.idle": "2025-10-17T21:51:27.987729Z", + "shell.execute_reply": "2025-10-17T21:51:27.986717Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The array surface_tilt angle was not provided, therefore the latitude of 39.9 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The array surface_tilt angle was not provided, therefore the latitude of 39.9 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The array surface_tilt angle was not provided, therefore the latitude of 39.7 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", + "The array surface_tilt angle was not provided, therefore the latitude of 39.8 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The array surface_tilt angle was not provided, therefore the latitude of 39.8 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", + "The array surface_tilt angle was not provided, therefore the latitude of 39.4 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The array surface_tilt angle was not provided, therefore the latitude of 39.5 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The array surface_tilt angle was not provided, therefore the latitude of 39.4 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", + "The array surface_tilt angle was not provided, therefore the latitude of 39.6 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The array surface_tilt angle was not provided, therefore the latitude of 39.5 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", + "The array surface_tilt angle was not provided, therefore the latitude of 39.6 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n" + ] + }, + { + "data": { + "text/html": [ + "
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<xarray.Dataset> Size: 11MB\n",
+       "Dimensions:    (latitude: 8, longitude: 10, time: 17520)\n",
+       "Coordinates:\n",
+       "  * latitude   (latitude) float64 64B 39.41 39.45 39.53 ... 39.69 39.81 39.89\n",
+       "  * longitude  (longitude) float64 80B -106.4 -106.3 -106.3 ... -105.9 -105.9\n",
+       "  * time       (time) datetime64[ns] 140kB 2022-01-01 ... 2022-12-31T23:30:00\n",
+       "Data variables:\n",
+       "    module     (time, latitude, longitude) float64 11MB nan nan nan ... nan nan
" + ], + "text/plain": [ + " Size: 11MB\n", + "Dimensions: (latitude: 8, longitude: 10, time: 17520)\n", + "Coordinates:\n", + " * latitude (latitude) float64 64B 39.41 39.45 39.53 ... 39.69 39.81 39.89\n", + " * longitude (longitude) float64 80B -106.4 -106.3 -106.3 ... -105.9 -105.9\n", + " * time (time) datetime64[ns] 140kB 2022-01-01 ... 2022-12-31T23:30:00\n", + "Data variables:\n", + " module (time, latitude, longitude) float64 11MB nan nan nan ... nan nan" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "module_temp_template = pvdeg.geospatial.auto_template(\n", + " func=pvdeg.temperature.module, ds_gids=geo_weather\n", + ")\n", + "\n", + "pvdeg.geospatial.analysis(\n", + " weather_ds=geo_weather,\n", + " meta_df=geo_meta,\n", + " func=pvdeg.temperature.module,\n", + " template=module_temp_template,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Geospatial Solar Position Calculation" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "execution": { + "iopub.execute_input": "2025-10-17T21:51:27.991649Z", + "iopub.status.busy": "2025-10-17T21:51:27.990736Z", + "iopub.status.idle": "2025-10-17T21:51:29.908535Z", + "shell.execute_reply": "2025-10-17T21:51:29.906378Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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<xarray.Dataset> Size: 67MB\n",
+       "Dimensions:             (latitude: 8, longitude: 10, time: 17520)\n",
+       "Coordinates:\n",
+       "  * latitude            (latitude) float64 64B 39.41 39.45 39.53 ... 39.81 39.89\n",
+       "  * longitude           (longitude) float64 80B -106.4 -106.3 ... -105.9 -105.9\n",
+       "  * time                (time) datetime64[ns] 140kB 2022-01-01 ... 2022-12-31...\n",
+       "Data variables:\n",
+       "    apparent_zenith     (time, latitude, longitude) float64 11MB nan nan ... nan\n",
+       "    zenith              (time, latitude, longitude) float64 11MB nan nan ... nan\n",
+       "    apparent_elevation  (time, latitude, longitude) float64 11MB nan nan ... nan\n",
+       "    elevation           (time, latitude, longitude) float64 11MB nan nan ... nan\n",
+       "    azimuth             (time, latitude, longitude) float64 11MB nan nan ... nan\n",
+       "    equation_of_time    (time, latitude, longitude) float64 11MB nan nan ... nan
" + ], + "text/plain": [ + " Size: 67MB\n", + "Dimensions: (latitude: 8, longitude: 10, time: 17520)\n", + "Coordinates:\n", + " * latitude (latitude) float64 64B 39.41 39.45 39.53 ... 39.81 39.89\n", + " * longitude (longitude) float64 80B -106.4 -106.3 ... -105.9 -105.9\n", + " * time (time) datetime64[ns] 140kB 2022-01-01 ... 2022-12-31...\n", + "Data variables:\n", + " apparent_zenith (time, latitude, longitude) float64 11MB nan nan ... nan\n", + " zenith (time, latitude, longitude) float64 11MB nan nan ... nan\n", + " apparent_elevation (time, latitude, longitude) float64 11MB nan nan ... nan\n", + " elevation (time, latitude, longitude) float64 11MB nan nan ... nan\n", + " azimuth (time, latitude, longitude) float64 11MB nan nan ... nan\n", + " equation_of_time (time, latitude, longitude) float64 11MB nan nan ... nan" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "solar_position_template = pvdeg.geospatial.auto_template(\n", + " func=pvdeg.spectral.solar_position, ds_gids=geo_weather\n", + ")\n", + "\n", + "pvdeg.geospatial.analysis(\n", + " weather_ds=geo_weather,\n", + " meta_df=geo_meta,\n", + " func=pvdeg.spectral.solar_position,\n", + " template=solar_position_template,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Geospatial POA Irradiance Calculation" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "execution": { + "iopub.execute_input": "2025-10-17T21:51:29.916050Z", + "iopub.status.busy": "2025-10-17T21:51:29.915045Z", + "iopub.status.idle": "2025-10-17T21:51:32.133529Z", + "shell.execute_reply": "2025-10-17T21:51:32.132519Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The array surface_tilt angle was not provided, therefore the latitude of 39.9 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", + "The array surface_tilt angle was not provided, therefore the latitude of 39.9 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The array surface_tilt angle was not provided, therefore the latitude of 39.7 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", + "The array surface_tilt angle was not provided, therefore the latitude of 39.8 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The array surface_tilt angle was not provided, therefore the latitude of 39.8 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", + "The array surface_tilt angle was not provided, therefore the latitude of 39.4 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The array surface_tilt angle was not provided, therefore the latitude of 39.5 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", + "The array surface_tilt angle was not provided, therefore the latitude of 39.4 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The array surface_tilt angle was not provided, therefore the latitude of 39.6 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", + "The array surface_tilt angle was not provided, therefore the latitude of 39.5 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The array surface_tilt angle was not provided, therefore the latitude of 39.6 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n" + ] + }, + { + "data": { + "text/html": [ + "
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<xarray.Dataset> Size: 56MB\n",
+       "Dimensions:             (latitude: 8, longitude: 10, time: 17520)\n",
+       "Coordinates:\n",
+       "  * latitude            (latitude) float64 64B 39.41 39.45 39.53 ... 39.81 39.89\n",
+       "  * longitude           (longitude) float64 80B -106.4 -106.3 ... -105.9 -105.9\n",
+       "  * time                (time) datetime64[ns] 140kB 2022-01-01 ... 2022-12-31...\n",
+       "Data variables:\n",
+       "    poa_global          (time, latitude, longitude) float64 11MB nan nan ... nan\n",
+       "    poa_direct          (time, latitude, longitude) float64 11MB nan nan ... nan\n",
+       "    poa_diffuse         (time, latitude, longitude) float64 11MB nan nan ... nan\n",
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" + ], + "text/plain": [ + " Size: 56MB\n", + "Dimensions: (latitude: 8, longitude: 10, time: 17520)\n", + "Coordinates:\n", + " * latitude (latitude) float64 64B 39.41 39.45 39.53 ... 39.81 39.89\n", + " * longitude (longitude) float64 80B -106.4 -106.3 ... -105.9 -105.9\n", + " * time (time) datetime64[ns] 140kB 2022-01-01 ... 2022-12-31...\n", + "Data variables:\n", + " poa_global (time, latitude, longitude) float64 11MB nan nan ... nan\n", + " poa_direct (time, latitude, longitude) float64 11MB nan nan ... nan\n", + " poa_diffuse (time, latitude, longitude) float64 11MB nan nan ... nan\n", + " poa_sky_diffuse (time, latitude, longitude) float64 11MB nan nan ... nan\n", + " poa_ground_diffuse (time, latitude, longitude) float64 11MB nan nan ... nan" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "poa_irradiance_template = pvdeg.geospatial.auto_template(\n", + " func=pvdeg.spectral.poa_irradiance, ds_gids=geo_weather\n", + ")\n", + "\n", + "pvdeg.geospatial.analysis(\n", + " weather_ds=geo_weather,\n", + " meta_df=geo_meta,\n", + " func=pvdeg.spectral.poa_irradiance,\n", + " template=poa_irradiance_template,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Geospatial 98th Percentile Operating Temperature Calculation" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "execution": { + "iopub.execute_input": "2025-10-17T21:51:32.137524Z", + "iopub.status.busy": "2025-10-17T21:51:32.136524Z", + "iopub.status.idle": "2025-10-17T21:51:34.270080Z", + "shell.execute_reply": "2025-10-17T21:51:34.270080Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The array surface_tilt angle was not provided, therefore the latitude of 39.9 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The array surface_tilt angle was not provided, therefore the latitude of 39.9 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", + "The array surface_tilt angle was not provided, therefore the latitude of 39.7 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The array surface_tilt angle was not provided, therefore the latitude of 39.8 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", + "The array surface_tilt angle was not provided, therefore the latitude of 39.8 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The array surface_tilt angle was not provided, therefore the latitude of 39.4 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", + "The array surface_tilt angle was not provided, therefore the latitude of 39.5 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The array surface_tilt angle was not provided, therefore the latitude of 39.4 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", + "The array surface_tilt angle was not provided, therefore the latitude of 39.6 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The array surface_tilt angle was not provided, therefore the latitude of 39.5 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", + "The array surface_tilt angle was not provided, therefore the latitude of 39.6 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n" + ] + }, + { + "data": { + "text/html": [ + "
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<xarray.Dataset> Size: 784B\n",
+       "Dimensions:       (latitude: 8, longitude: 10)\n",
+       "Coordinates:\n",
+       "  * latitude      (latitude) float64 64B 39.41 39.45 39.53 ... 39.69 39.81 39.89\n",
+       "  * longitude     (longitude) float64 80B -106.4 -106.3 -106.3 ... -105.9 -105.9\n",
+       "Data variables:\n",
+       "    T98_estimate  (latitude, longitude) float64 640B nan nan nan ... nan nan nan
" + ], + "text/plain": [ + " Size: 784B\n", + "Dimensions: (latitude: 8, longitude: 10)\n", + "Coordinates:\n", + " * latitude (latitude) float64 64B 39.41 39.45 39.53 ... 39.69 39.81 39.89\n", + " * longitude (longitude) float64 80B -106.4 -106.3 -106.3 ... -105.9 -105.9\n", + "Data variables:\n", + " T98_estimate (latitude, longitude) float64 640B nan nan nan ... nan nan nan" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "standoff_template = pvdeg.geospatial.auto_template(\n", + " func=pvdeg.standards.T98_estimate, ds_gids=geo_weather\n", + ")\n", + "\n", + "pvdeg.geospatial.analysis(\n", + " weather_ds=geo_weather,\n", + " meta_df=geo_meta,\n", + " func=pvdeg.standards.T98_estimate,\n", + " template=standoff_template,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Geospatial Module Humidity Calculation" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "execution": { + "iopub.execute_input": "2025-10-17T21:51:34.274974Z", + "iopub.status.busy": "2025-10-17T21:51:34.274974Z", + "iopub.status.idle": "2025-10-17T21:51:38.344783Z", + "shell.execute_reply": "2025-10-17T21:51:38.343258Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The array surface_tilt angle was not provided, therefore the latitude of 39.9 was used.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The array surface_tilt angle was not provided, therefore the latitude of 39.9 was used.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The array surface_tilt angle was not provided, therefore the latitude of 39.7 was used.\n", + "The array surface_tilt angle was not provided, therefore the latitude of 39.8 was used.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The array surface_tilt angle was not provided, therefore the latitude of 39.8 was used.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The array surface_tilt angle was not provided, therefore the latitude of 39.4 was used.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The array surface_tilt angle was not provided, therefore the latitude of 39.5 was used.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The array surface_tilt angle was not provided, therefore the latitude of 39.4 was used.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The array surface_tilt angle was not provided, therefore the latitude of 39.6 was used.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The array surface_tilt angle was not provided, therefore the latitude of 39.5 was used.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The array surface_tilt angle was not provided, therefore the latitude of 39.6 was used.\n" + ] + }, + { + "data": { + "text/html": [ + "
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<xarray.Dataset> Size: 56MB\n",
+       "Dimensions:             (latitude: 8, longitude: 10, time: 17520)\n",
+       "Coordinates:\n",
+       "  * latitude            (latitude) float64 64B 39.41 39.45 39.53 ... 39.81 39.89\n",
+       "  * longitude           (longitude) float64 80B -106.4 -106.3 ... -105.9 -105.9\n",
+       "  * time                (time) datetime64[ns] 140kB 2022-01-01 ... 2022-12-31...\n",
+       "Data variables:\n",
+       "    RH_surface_outside  (time, latitude, longitude) float64 11MB nan nan ... nan\n",
+       "    RH_front_encap      (time, latitude, longitude) float64 11MB nan nan ... nan\n",
+       "    RH_back_encap       (time, latitude, longitude) float64 11MB nan nan ... nan\n",
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" + ], + "text/plain": [ + " Size: 56MB\n", + "Dimensions: (latitude: 8, longitude: 10, time: 17520)\n", + "Coordinates:\n", + " * latitude (latitude) float64 64B 39.41 39.45 39.53 ... 39.81 39.89\n", + " * longitude (longitude) float64 80B -106.4 -106.3 ... -105.9 -105.9\n", + " * time (time) datetime64[ns] 140kB 2022-01-01 ... 2022-12-31...\n", + "Data variables:\n", + " RH_surface_outside (time, latitude, longitude) float64 11MB nan nan ... nan\n", + " RH_front_encap (time, latitude, longitude) float64 11MB nan nan ... nan\n", + " RH_back_encap (time, latitude, longitude) float64 11MB nan nan ... nan\n", + " Ce_back_encap (time, latitude, longitude) float64 11MB nan nan ... nan\n", + " RH_backsheet (time, latitude, longitude) float64 11MB nan nan ... nan" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "humidity_template = pvdeg.geospatial.auto_template(\n", + " func=pvdeg.humidity.module, ds_gids=geo_weather\n", + ")\n", + "\n", + "pvdeg.geospatial.analysis(\n", + " weather_ds=geo_weather,\n", + " meta_df=geo_meta,\n", + " func=pvdeg.humidity.module,\n", + " template=humidity_template,\n", + " backsheet_thickness=0.3,\n", + " back_encap_thickness=0.5,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Geospatial IwaVantHoff Environment Characterization Calculation" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "execution": { + "iopub.execute_input": "2025-10-17T21:51:38.349873Z", + "iopub.status.busy": "2025-10-17T21:51:38.349873Z", + "iopub.status.idle": "2025-10-17T21:51:40.570094Z", + "shell.execute_reply": "2025-10-17T21:51:40.570094Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The array surface_tilt angle was not provided, therefore the latitude of 39.9 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The array surface_tilt angle was not provided, therefore the latitude of 39.9 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", + "The array surface_tilt angle was not provided, therefore the latitude of 39.7 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The array surface_tilt angle was not provided, therefore the latitude of 39.8 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", + "The array surface_tilt angle was not provided, therefore the latitude of 39.8 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The array surface_tilt angle was not provided, therefore the latitude of 39.4 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", + "The array surface_tilt angle was not provided, therefore the latitude of 39.5 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The array surface_tilt angle was not provided, therefore the latitude of 39.4 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", + "The array surface_tilt angle was not provided, therefore the latitude of 39.6 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The array surface_tilt angle was not provided, therefore the latitude of 39.5 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", + "The array surface_tilt angle was not provided, therefore the latitude of 39.6 was used.\n", + "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n" + ] + }, + { + "data": { + "text/html": [ + "
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<xarray.Dataset> Size: 784B\n",
+       "Dimensions:      (latitude: 8, longitude: 10)\n",
+       "Coordinates:\n",
+       "  * latitude     (latitude) float64 64B 39.41 39.45 39.53 ... 39.69 39.81 39.89\n",
+       "  * longitude    (longitude) float64 80B -106.4 -106.3 -106.3 ... -105.9 -105.9\n",
+       "Data variables:\n",
+       "    IwaVantHoff  (latitude, longitude) float64 640B nan nan nan ... nan nan nan
" + ], + "text/plain": [ + " Size: 784B\n", + "Dimensions: (latitude: 8, longitude: 10)\n", + "Coordinates:\n", + " * latitude (latitude) float64 64B 39.41 39.45 39.53 ... 39.69 39.81 39.89\n", + " * longitude (longitude) float64 80B -106.4 -106.3 -106.3 ... -105.9 -105.9\n", + "Data variables:\n", + " IwaVantHoff (latitude, longitude) float64 640B nan nan nan ... nan nan nan" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "iwa_vant_hoff_template = pvdeg.geospatial.auto_template(\n", + " func=pvdeg.degradation.IwaVantHoff, ds_gids=geo_weather\n", + ")\n", + "\n", + "pvdeg.geospatial.analysis(\n", + " weather_ds=geo_weather,\n", + " meta_df=geo_meta,\n", + " func=pvdeg.degradation.IwaVantHoff,\n", + " template=iwa_vant_hoff_template,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Geospatial Edge Seal Width Calculation" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "execution": { + "iopub.execute_input": "2025-10-17T21:51:40.577856Z", + "iopub.status.busy": "2025-10-17T21:51:40.576777Z", + "iopub.status.idle": "2025-10-17T21:51:40.853579Z", + "shell.execute_reply": "2025-10-17T21:51:40.853579Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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<xarray.Dataset> Size: 784B\n",
+       "Dimensions:          (latitude: 8, longitude: 10)\n",
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+       "  * longitude        (longitude) float64 80B -106.4 -106.3 ... -105.9 -105.9\n",
+       "Data variables:\n",
+       "    edge_seal_width  (latitude, longitude) float64 640B nan nan nan ... nan nan
" + ], + "text/plain": [ + " Size: 784B\n", + "Dimensions: (latitude: 8, longitude: 10)\n", + "Coordinates:\n", + " * latitude (latitude) float64 64B 39.41 39.45 39.53 ... 39.81 39.89\n", + " * longitude (longitude) float64 80B -106.4 -106.3 ... -105.9 -105.9\n", + "Data variables:\n", + " edge_seal_width (latitude, longitude) float64 640B nan nan nan ... nan nan" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "edge_seal_template = pvdeg.geospatial.auto_template(\n", + " func=pvdeg.design.edge_seal_width, ds_gids=geo_weather\n", + ")\n", + "\n", + "pvdeg.geospatial.analysis(\n", + " weather_ds=geo_weather,\n", + " meta_df=geo_meta,\n", + " func=pvdeg.design.edge_seal_width,\n", + " template=edge_seal_template,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "execution": { + "iopub.execute_input": "2025-10-17T21:51:40.861021Z", + "iopub.status.busy": "2025-10-17T21:51:40.860019Z", + "iopub.status.idle": "2025-10-17T21:51:40.883596Z", + "shell.execute_reply": "2025-10-17T21:51:40.883596Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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<xarray.Dataset> Size: 176B\n",
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+       "Coordinates:\n",
+       "  * gid      (gid) int64 88B 449211 452064 453020 ... 459670 460613 462498\n",
+       "Data variables:\n",
+       "    width    (gid) float64 88B dask.array<chunksize=(11,), meta=np.ndarray>
" + ], + "text/plain": [ + " Size: 176B\n", + "Dimensions: (gid: 11)\n", + "Coordinates:\n", + " * gid (gid) int64 88B 449211 452064 453020 ... 459670 460613 462498\n", + "Data variables:\n", + " width (gid) float64 88B dask.array" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "edge_seal_template" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "pvdeg", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.9" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/tutorials/04_geospatial/08_module_standoff_iec63126.ipynb b/tutorials/04_geospatial/08_module_standoff_iec63126.ipynb new file mode 100644 index 000000000..99add4da1 --- /dev/null +++ b/tutorials/04_geospatial/08_module_standoff_iec63126.ipynb @@ -0,0 +1,723 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Tools - Module Standoff for IEC TS 63126\n", + "\n", + "## Calculation of module standoff distance according to IEC TS 63126\n", + "\n", + "**Requirements:**\n", + "- Local weather data file or site longitude and latittude\n", + "\n", + "**Objectives:**\n", + "1. Import weather data.\n", + "2. Calculate installation standoff - Level 1 and Level 2.\n", + "3. Calculate $X_{eff}$ from provided module temperature data.\n", + "4. Calculate $T_{98}$ for a given azimuth, tilt, and $X_{eff}$.\n", + "5. Plot $X_{min}$ for all azimuth and tilt for a given $T_{98}$.\n", + "6. Plot $X_{min}$ for Level 1, Level 2, or a $T_{98}$ for a given region.\n", + "\n", + "**Background:**\n", + "\n", + "This notebook calculates the a minimum effective standoff distance ($X_{eff}$) necessary for roof-mounted PV modules to ensure that the $98^{th}$ percentile operating temperature, $T_{98}$, remains under 70Β°C for compliance to IEC 61730 and IEC 61215. For higher $T_{98}$ values above 70Β°C or 80Β°C testing must be done to the specifications for Level 1 and Level 2 of IEC TS 63126. This method is outlined in the appendix of IEC TS 63126 and is based on the model from *[King 2004] and data from **[Fuentes, 1987] to model the approximate exponential decay in temperature, $T(X)$, with increasing standoff distance, $X$, as,\n", + "\n", + "$$ X = -X_0 \\ln\\left(1-\\frac{T_0-T}{\\Delta T}\\right), Equation 1 $$\n", + "\n", + "where $T_0$ is the temperature for $X=0$ (insulated-back) and $\\Delta T$ is the temperature difference between an insulated-back ($X=0$) and open-rack mounting configuration ($X=\\infty)$.\n", + "\n", + " We used pvlib and data from the National Solar Radiation Database (NSRDB) to calculate the module temperatures for the insulated-back and open-rack mounting configurations and apply our model to obtain the minimum standoff distance for roof-mounted PV systems to achieve a temperature lower than a specified $T_{98}$. The following figure showcases this calulation for the entire world for an $X_{eff}$ that results in $T_{98}$=70Β°C. Values of $X_{eff}$ higher than this will require Level 1 or Level 2 certification.\n", + "\n", + "$*$ D. L. King, W. E. Boyson, and J. A. Kratochvil, \"Photovoltaic array performance model,\" SAND2004-3535, Sandia National Laboratories, Albuquerque, NM, 2004. '\\\n", + "$**$ M. K. Fuentes, \"A simplified thermal model for Flat-Plate photovoltaic arrays,\" United States, 1987-05-01 1987. https://www.osti.gov/biblio/6802914\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![alt text](images/T98_70C_standoff_Map.png)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# if running on google colab, uncomment the next line and execute this cell to install the dependencies\n", + "# and prevent \"ModuleNotFoundError\" in later cells:\n", + "#!pip install pvdeg" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import pvdeg\n", + "import pandas as pd\n", + "from pvdeg import DATA_DIR\n", + "import dask\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import math\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# This information helps with debugging and getting support :)\n", + "import sys\n", + "import platform\n", + "\n", + "print(\"Working on a \", platform.system(), platform.release())\n", + "print(\"Python version \", sys.version)\n", + "print(\"Pandas version \", pd.__version__)\n", + "print(\"pvdeg version \", pvdeg.__version__)\n", + "print(\"dask version\", dask.__version__)\n", + "print(DATA_DIR)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 1. Import Weather Data\n", + "\n", + "The function has these minimum requirements when using a weather data file:\n", + "- Weather data containing (at least) DNI, DHI, GHI, Temperature, RH, and Wind-Speed data at module level.\n", + "- Site meta-data containing (at least) latitude, longitude, and time zone\n", + "\n", + "Alternatively one may can get meterological data from the NSRDB or PVGIS with just the longitude and latitude. This function for the NSRDB (via NSRDB 'PSM3') works primarily for most of North America and South America. PVGIS works for most of the rest of the world (via SARAH 'PVGIS'). See the tutorial \"Weather Database Access.ipynb\" tutorial on PVdeg or Jensen et al. https://doi.org/10.1016/j.solener.2023.112092 for satellite coverage information.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Get data from a supplied data file (Do not use the next box of code if using your own file)\n", + "weather_file = os.path.join(DATA_DIR, \"psm3_demo.csv\")\n", + "WEATHER_df, META = pvdeg.weather.read(weather_file, \"csv\")\n", + "print(META)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# This routine will get a meteorological dataset from anywhere in the world where it is available\n", + "# weather_id = (24.7136, 46.6753) #Riyadh, Saudi Arabia\n", + "# weather_id = (35.6754, 139.65) #Tokyo, Japan\n", + "# weather_id = (-43.52646, 172.62165) #Christchurch, New Zealand\n", + "# weather_id = (64.84031, -147.73836) #Fairbanks, Alaska\n", + "# weather_id = (65.14037, -21.91633) #Reykjavik, Iceland\n", + "# weather_id = (33.4152, -111.8315) #Mesa, Arizona\n", + "# WEATHER_df, META = pvdeg.weather.get_anywhere(id=weather_id)\n", + "# print(META)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2. Calculate Installation Standoff Minimum - Level 1 and Level 2\n", + "\n", + "According to IEC TS 63126, Level 0, Level 1 and Level 2 certification is limited to Tβ‚‰β‚ˆ<70Β°C, <80Β°C and <90Β°C, respectively. Level 0 certification is essentially compliance to IEC 61730 and IEC 61215. The default value of Tβ‚‰β‚ˆ<70Β°C represents the minimium gap to avoid higher temperature certification according to IEC TS 63126. This minimum standoff ($x_{min}$) is the distance between the bottom of the module frame and the roof and can be extimated for a given environment as,\n", + "\n", + "$$ X_{min} = -X_0 \\ln\\left(1-\\frac{T_{98,0}-T}{ T_{98,0}- T_{98,inf}}\\right), Equation 2 $$\n", + "\n", + "where $T_{98,0}$ is the $98^{th}$ percentile temperature for an insulated back module and $T_{98,inf}$ is the $98^{th}$ percentile temperature for an open rack mounted module.\n", + "\n", + "Once the meterological data has been obtained, the input parameter possibilities are:\n", + "\n", + "- Tβ‚‰β‚ˆ : Does not necessarily need to be set at 70Β°C or 80Β°C for IEC TS 63216, you might want to use a different number to compensate for a thermal aspect of the particular system you are considering. The default is 70Β°C.\n", + "- tilt : tilt from horizontal of PV module. The default is 0Β°.\n", + "- azimuth : azimuth in degrees from North. The default is 180Β° for south facing.\n", + "- sky_model : pvlib compatible model for generating sky characteristics (Options: 'isotropic', 'klucher', 'haydavies', 'reindl', 'king', 'perez'). The default is 'isotropic'.\n", + "- temp_model : pvlib compatible module temperature model. (Options: 'sapm', 'pvsyst', 'faiman', 'sandia'). The default is 'sapm'.\n", + "- conf_0 : Temperature model for hotest mounting configuration. Default is \"insulated_back_glass_polymer\".\n", + "- conf_inf : Temperature model for open rack mounting. Default is \"open_rack_glass_polymer\".\n", + "- x_0 : thermal decay constant [cm] (see documentation). The default is 6.5 cm.\n", + "- wind_factor : Wind speed power law correction factor to account for different wind speed measurement heights between weather database (e.g. NSRDB) and the tempeature model (e.g. SAPM). The default is 0.33." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The following is the minimum function call. It defaults to horizontal tilt and Tβ‚‰β‚ˆ=70Β°C." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "standoff = pvdeg.standards.standoff(weather_df=WEATHER_df, meta=META)\n", + "print(pvdeg.standards.interpret_standoff(standoff))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The following is a full function call for both Tβ‚‰β‚ˆ=70Β°C and 80Β°C separately even though the second standoff distance can be calculated using only T98_0 and T98_inf. With this function, one may also want to change the tilt, azimuth, or T98." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "standoff_1 = pvdeg.standards.standoff(\n", + " weather_df=WEATHER_df,\n", + " meta=META,\n", + " T98=70,\n", + " tilt=META[\"latitude\"],\n", + " azimuth=None,\n", + " sky_model=\"isotropic\",\n", + " temp_model=\"sapm\",\n", + " conf_0=\"insulated_back_glass_polymer\",\n", + " conf_inf=\"open_rack_glass_polymer\",\n", + " x_0=6.5,\n", + " wind_factor=0.33,\n", + ")\n", + "print(\"First calculation standoff = \", \"%.1f\" % standoff_1[\"x\"].iloc[0], \" cm.\")\n", + "standoff_2 = pvdeg.standards.standoff(\n", + " weather_df=WEATHER_df,\n", + " meta=META,\n", + " T98=80,\n", + " tilt=META[\"latitude\"],\n", + " azimuth=None,\n", + " sky_model=\"isotropic\",\n", + " temp_model=\"sapm\",\n", + " conf_0=\"insulated_back_glass_polymer\",\n", + " conf_inf=\"open_rack_glass_polymer\",\n", + " x_0=6.5,\n", + " wind_factor=0.33,\n", + ")\n", + "print(\"Second calculation standoff = \", \"%.1f\" % standoff_2[\"x\"].iloc[0], \" cm.\")\n", + "print(pvdeg.standards.interpret_standoff(standoff_1=standoff_1, standoff_2=standoff_2))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 3. Calculate $X_{eff}$ from provided module temperature data.\n", + "\n", + "To do this calculation, one must use a set of data with:\n", + " - meterological irradiance data sufficient to calculate the POA irradiance (DHI, GHI, and DNI),\n", + " - ambient temperature data,\n", + " - wind speed at module height, (wind_factor=0.33 will be used unless otherwise specified)\n", + " - temperature measurements of the module in the test system. Ideally this would be measured under a worst case scenario that maximizes the module temperature for a given site,\n", + " - geographic meta data including longitude and latitude,\n", + "\n", + "To create a weather file of your own, copy the format of the example file 'xeff_demo.csv'. This is formatted with the first row containing meta data variable names, the second row containing the corresponding values, the third row containing meteorological data headers, and all the remaining rows containing the meteorological data.\n", + "\n", + "To do this calculation, one should also filter the data to remove times when the sun is not shining or when snow is likely to be on the module. The recommendations and programmed defaults are to use poa_min=100 W/mΒ² and data when the minimum ambient temperature t_amb_min=0." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Read the weather file\n", + "weather_file = os.path.join(DATA_DIR, \"xeff_demo.csv\")\n", + "xeff_weather, xeff_meta = pvdeg.weather.read(weather_file, \"csv\")\n", + "# Pull measured temperature and calculate theoretical insulated back module temperature and open rack module temperature\n", + "T_0, T_inf, xeff_poa = pvdeg.standards.eff_gap_parameters(\n", + " weather_df=xeff_weather,\n", + " meta=xeff_meta,\n", + " sky_model=\"isotropic\",\n", + " temp_model=\"sapm\",\n", + " conf_0=\"insulated_back_glass_polymer\",\n", + " conf_inf=\"open_rack_glass_polymer\",\n", + " wind_factor=0.33,\n", + ")\n", + "# Now calculate X_eff.\n", + "x_eff = pvdeg.standards.eff_gap(\n", + " T_0,\n", + " T_inf,\n", + " xeff_weather[\"module_temperature\"],\n", + " xeff_weather[\"temp_air\"],\n", + " xeff_poa[\"poa_global\"],\n", + " x_0=6.5,\n", + " poa_min=100,\n", + " t_amb_min=0,\n", + ")\n", + "print(\"The effective standoff for this system is\", \"%.1f\" % x_eff, \"cm.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 4. Calculate $T_{98}$ for a given azimuth, tilt, and $X_{eff}$.\n", + "\n", + "Equation 2 can be reorganized as,\n", + "\n", + "$$ T_{98} = T_{98,0} -( T_{98,0}- T_{98,inf}) \\left(1-e^{-\\frac{x_{eff}}{x_{0}}}\\right), Equation 3 $$\n", + "\n", + "and used to calculate the $98^{th}$ percential temperature, $T_{98}$, for a PV system having a given effective standoff height, $X_{eff}$, for an arbitrarily oriented module. Here, $T_{98,0}$ is the $98^{th}$ percentile for an insulated-back module and $T_{98,inf}$ is the $98^{th}$ percentile for a rack-mounted module. The input parameter possibilities are the same as shown in Objective #2 above, but the example below uses the default parameters. The actual tilt [degrees], azimuth [degrees] and $X_{eff}$ [cm] can be modifed as desired." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# This is the minimal function call using the common default settings to estimate Tβ‚‰β‚ˆ.\n", + "T_98 = pvdeg.standards.T98_estimate(\n", + " weather_df=WEATHER_df, meta=META, tilt=-META[\"latitude\"], azimuth=None, x_eff=10\n", + ")\n", + "print(\"The 98α΅—Κ° percential temperature is estimated to be\", \"%.1f\" % T_98, \"Β°C.\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# This code will calculate the temperature for an arbitrary x_eff distance. Either set of kwargs can be modified and use.\n", + "# irradiance_kwarg ={\n", + "# \"axis_tilt\": None,\n", + "# \"axis_azimuth\": None,\n", + "# \"x_eff\": 10,\n", + "# \"module_mount\": '1_axis'}\n", + "irradiance_kwarg = {\n", + " \"tilt\": META[\"latitude\"],\n", + " \"azimuth\": None,\n", + " \"x_eff\": 10,\n", + " \"module_mount\": \"fixed\",\n", + "}\n", + "\n", + "T_xeff = pvdeg.standards.x_eff_temperature_estimate(\n", + " weather_df=WEATHER_df, meta=META, **irradiance_kwarg\n", + ")\n", + "\n", + "print(\n", + " \"The 98α΅—Κ° percential temperature is estimated to be\",\n", + " \"%.1f\" % np.percentile(T_xeff, 98),\n", + " \"Β°C.\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 5. Plot $X_{min}$ for all azimuth and tilt for a given $T_{98}$.\n", + "\n", + "The temperature of a system is affected by the orientation. This section will scan all possible tilts and azimuths calculating the minimum standoff distance for a given $T_{98}$. Similar additional factors as above can also be modified but are not included here for simplicity. The tilt_step and azimuth_step are the number of degrees for each step for the 90Β° and 180Β° tilt and azimuth spans, respectively. The default for this calculation is for $T_{98}$=70Β°C, the boundary between Level 0 and Level 1 requirements. The temperature model information given below is unnecessary as these are default values that would get populated automatically. However, they were included here for clarity into a standard practice as per IEC TS 63126.\n", + "\n", + "$$ X_{min} = -X_0 \\ln\\left(1-\\frac{T_{98,0}-T}{ T_{98,0}- T_{98,inf}}\\right), Equation 2 $$" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Scans through all the azimuth and tilt running the minimum standoff calculation\n", + "# Set up keyword parameters for the calculation\n", + "\n", + "kwarg_x = dict(\n", + " sky_model=\"isotropic\",\n", + " temp_model=\"sapm\",\n", + " conf_0=\"insulated_back_glass_polymer\",\n", + " conf_inf=\"open_rack_glass_polymer\",\n", + " T98=70,\n", + " x_0=6.5,\n", + " wind_factor=0.33,\n", + ")\n", + "# Run the calculation\n", + "x_azimuth_step = 10\n", + "x_tilt_step = 10\n", + "standoff_series = pvdeg.utilities.tilt_azimuth_scan(\n", + " weather_df=WEATHER_df,\n", + " meta=META,\n", + " tilt_step=x_tilt_step,\n", + " azimuth_step=x_azimuth_step,\n", + " func=pvdeg.standards.standoff_x,\n", + " **kwarg_x,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The next cell creates a plot of the calculated data. Some of the things you may want to change are:\n", + "- cmap=\"Spectral_r\": Change to have different colors\n", + "- plt.title : This will change the plot title.\n", + "- figsize=(16,4) : Change the plot dimensions and/or aspect ratio.\n", + "- vmax=None : This can be set to a numeric value to control the depth scale maximum\n", + "- vmin=0 : This controls the minimum of the depth scale.\n", + "- v_ticks=37 : This changes the number of vertical tick marks\n", + "- h_ticks=10 : This changes the number of horizontal tick marks\n", + "- Unblock the last two lines to ouput the plot as an *.png image file" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "standoff_series_df = pd.DataFrame(\n", + " {\n", + " \"Tilt\": standoff_series[:, 0],\n", + " \"Azimuth\": standoff_series[:, 1],\n", + " \"Xβ‚˜α΅’β‚™\": standoff_series[:, 2],\n", + " }\n", + ")\n", + "x_fig = plt.figure(figsize=(16, 4))\n", + "plt.title(\n", + " r\"Plot of $\\it{Xβ‚˜α΅’β‚™}$ for all orientations for $\\it{Tβ‚‰β‚ˆ}$=\"\n", + " + \"%.0f\" % kwarg_x[\"T98\"]\n", + " + \"Β°C.\",\n", + " fontsize=15,\n", + " y=1.08,\n", + ")\n", + "x_fig = sns.heatmap(\n", + " standoff_series_df.pivot(index=\"Tilt\", columns=\"Azimuth\", values=\"Xβ‚˜α΅’β‚™\"),\n", + " cbar_kws={\"label\": \"Xβ‚˜α΅’β‚™\", \"format\": \"%.0f\", \"pad\": 0.02},\n", + " cmap=\"Spectral_r\",\n", + " vmin=0,\n", + " vmax=None,\n", + ")\n", + "\n", + "h_ticks = 37\n", + "x_number = math.ceil(360 / x_azimuth_step) + 1\n", + "x_ticks = [\n", + " (x * (360 / (h_ticks - 1)) / x_azimuth_step + 0.5) for x in range(h_ticks - 1)\n", + "]\n", + "x_labels = [(\"%.0f\" % (360 / (h_ticks - 1) * x)) for x in range(h_ticks)]\n", + "x_ticks.append(x_number - 0.5)\n", + "x_fig.set_xticks(x_ticks)\n", + "x_fig.set_xticklabels(x_labels, rotation=90)\n", + "\n", + "v_ticks = 10\n", + "y_number = math.ceil(90 / x_tilt_step) + 1\n", + "y_ticks = [(x * (90 / (v_ticks - 1)) / x_tilt_step + 0.5) for x in range(v_ticks - 1)]\n", + "y_labels = [(\"%.0f\" % (90 / (v_ticks - 1) * x)) for x in range(v_ticks)]\n", + "y_ticks.append(y_number - 0.5)\n", + "x_fig.set_yticks(y_ticks)\n", + "x_fig.set_yticklabels(y_labels, rotation=0)\n", + "\n", + "x_fig.set_xlabel(\"Azimuth [Β°]\", fontsize=15, labelpad=10)\n", + "x_fig.set_ylabel(\"Tilt [Β°]\", fontsize=15)\n", + "x_fig.figure.axes[-1].set_ylabel(r\"$\\it{Xβ‚˜α΅’β‚™}$ [cm]\", size=15)\n", + "x_fig.invert_yaxis()\n", + "\n", + "output_folder = os.path.join(\n", + " os.path.dirname(os.path.dirname(os.getcwd())), \"TEMP\", \"results\"\n", + ")\n", + "try:\n", + " os.makedirs(output_folder)\n", + "except OSError as error:\n", + " print(error)\n", + "\n", + "plt.savefig(\n", + " os.path.join(output_folder, \"Standoff_Scan.png\"), bbox_inches=\"tight\"\n", + ") # Creates an image file of the standoff plot\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 6. Plot $T_{98}$ for all azimuth and tilt for a given $X_{eff}$.\n", + "\n", + "The temperature of a system is affected by the orientation and the effective standoff, $X_{eff}$, of the system. This section will scan all possible tilts and azimuths calculating the $T_{98}$ for a given $X_{eff}$. As above, additional factors can be modified but are not included here for simplicity. The tilt_step and azimuth_step are the number of degrees for each step for the 90Β° and 180Β° tilt and azimuth spans, respectively. The default for this calculation is for $X_{eff}$=10 cm, a common effective standoff distance on a rooftop system. A value of $X_{eff}$=None will run the calculations for an open rack system and $X_{eff}$=0 for an insulated-back system." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Scans through all the azimuth and tilt running the 98α΅—Κ° percentile temperature calculation.\n", + "# Set up keyword parameters for the calculation\n", + "kwarg_T = dict(\n", + " sky_model=\"isotropic\",\n", + " temp_model=\"sapm\",\n", + " conf_0=\"insulated_back_glass_polymer\",\n", + " conf_inf=\"open_rack_glass_polymer\",\n", + " x_eff=5,\n", + " x_0=6.5,\n", + " wind_factor=0.33,\n", + ")\n", + "# Run the calculation\n", + "T_azimuth_step = 10\n", + "T_tilt_step = 10\n", + "T98_series = pvdeg.utilities.tilt_azimuth_scan(\n", + " weather_df=WEATHER_df,\n", + " meta=META,\n", + " tilt_step=T_tilt_step,\n", + " azimuth_step=T_azimuth_step,\n", + " func=pvdeg.standards.T98_estimate,\n", + " **kwarg_T,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The next cell creates a plot of the calculated data. Some of the things you may want to change are:\n", + "- cmap=\"Spectral_r\": Change to have different colors\n", + "- plt.title : This will change the plot title.\n", + "- figsize=(16,4) : Change the plot dimensions and/or aspect ratio.\n", + "- vmax=None : This can be set to a numeric value to control the depth scale maximum\n", + "- vmin=None : This controls the minimum of the depth scale.\n", + "- v_ticks=37 : This changes the number of vertical tick marks\n", + "- h_ticks=10 : This changes the number of horizontal tick marks\n", + "- Unblock the last two lines to ouput the plot as an *.png image file" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# This produces the plot of the data\n", + "T98_series_df = pd.DataFrame(\n", + " {\"Tilt\": T98_series[:, 0], \"Azimuth\": T98_series[:, 1], \"Tβ‚‰β‚ˆ\": T98_series[:, 2]}\n", + ")\n", + "T98_fig = plt.figure(figsize=(16, 4))\n", + "if kwarg_T[\"x_eff\"] == None:\n", + " plt.title(\n", + " r\"Plot of $\\it{Tβ‚‰β‚ˆ}$ for all orientations for an open-rack mounting.\",\n", + " fontsize=15,\n", + " y=1.08,\n", + " )\n", + "else:\n", + " plt.title(\n", + " r\"Plot of $\\it{Tβ‚‰β‚ˆ}$ for all orientations for $X_{eff}$=\"\n", + " + \"%.0f\" % kwarg_T[\"x_eff\"]\n", + " + \" cm.\",\n", + " fontsize=15,\n", + " y=1.08,\n", + " )\n", + "T98_fig = sns.heatmap(\n", + " T98_series_df.pivot(index=\"Tilt\", columns=\"Azimuth\", values=\"Tβ‚‰β‚ˆ\"),\n", + " cbar_kws={\"label\": \"Xβ‚˜α΅’β‚™\", \"format\": \"%.0f\", \"pad\": 0.02},\n", + " cmap=\"Spectral_r\",\n", + " vmin=None,\n", + " vmax=None,\n", + ")\n", + "\n", + "h_ticks = 37\n", + "x_number = math.ceil(360 / T_azimuth_step) + 1\n", + "x_ticks = [\n", + " (x * (360 / (h_ticks - 1)) / T_azimuth_step + 0.5) for x in range(h_ticks - 1)\n", + "]\n", + "x_labels = [(\"%.0f\" % (360 / (h_ticks - 1) * x)) for x in range(h_ticks)]\n", + "x_ticks.append(x_number - 0.5)\n", + "T98_fig.set_xticks(x_ticks)\n", + "T98_fig.set_xticklabels(x_labels, rotation=90)\n", + "\n", + "v_ticks = 10\n", + "y_number = math.ceil(90 / T_tilt_step) + 1\n", + "y_ticks = [(x * (90 / (v_ticks - 1)) / T_tilt_step + 0.5) for x in range(v_ticks - 1)]\n", + "y_labels = [(\"%.0f\" % (90 / (v_ticks - 1) * x)) for x in range(v_ticks)]\n", + "y_ticks.append(y_number - 0.5)\n", + "T98_fig.set_yticks(y_ticks)\n", + "T98_fig.set_yticklabels(y_labels, rotation=0)\n", + "\n", + "T98_fig.set_xlabel(\"Azimuth [Β°]\", fontsize=15, labelpad=10)\n", + "T98_fig.set_ylabel(\"Tilt [Β°]\", fontsize=15)\n", + "T98_fig.figure.axes[-1].set_ylabel(r\"$\\it{Tβ‚‰β‚ˆ}$ [Β°C]\", size=15)\n", + "T98_fig.invert_yaxis()\n", + "\n", + "plt.savefig(\n", + " os.path.join(output_folder, \"T98_Scan.png\"), bbox_inches=\"tight\"\n", + ") # Creates an image file of the standoff plot\n", + "plt.show(T98_fig)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 7. Plot $X_{min}$ for a $T_{98}$, and plot $T_{98}$ for a given region.\n", + "\n", + "This last Objective is much more complicated and is set up to utilize acess to a lot of computational power to run many sites simultaneously to create a regional map of standoff distance.\n", + "For more in-depth instructions on doing this, look at the tutorial \"04_scenario_geospatial.ipynb\" here in PVDeg.\n", + "\n", + "Step #1: Create an object, \"geospatial_standoff_scenario\" to be used to run the computations." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "geospatial_standoff_scenario = pvdeg.GeospatialScenario(\n", + " name=\"standoff geospatial\",\n", + " geospatial=True,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Step #2: Identifies a subset of locations from the database to run the computations.\n", + "Specifically all are from the NSRDB." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "geospatial_standoff_scenario.addLocation(\n", + " state=\"Colorado\", county=\"Summit\"\n", + ") # Identifies a subset of locations from the database to run the computations. Specifically all are from the NSRDB." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Step #3: indicate which function will be run. Here the default is the standoff calculation, but it could be any other function with a key word argument dictionary.\n", + "Here the 98th percential temperature is defined as 70C, but any arbitrary value can be specified." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "geospatial_standoff_scenario.addJob(\n", + " func=pvdeg.standards.standoff, func_params={\"T98\": 70}\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Step #4: Run the scenario" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "geospatial_standoff_scenario.run()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Step #5: Create a plot of the standoff calculation." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "geospatial_standoff_scenario.plot_world(\"x\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "geospatial_standoff_scenario.plot_world(\"T98_inf\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 8. Save data outputs.\n", + "\n", + "This cell contains a number of pre-scripted commands for exporting and saving data. The code to save plots is located after the plot creation and is blocked by default. First check that the output folder exists, then unblock the code for data you would like to save." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "print(\"Your results will be stored in %s\" % output_folder)\n", + "print(\"The folder must already exist or the file will not be created\")\n", + "\n", + "pvdeg.weather.write(\n", + " data_df=WEATHER_df,\n", + " metadata=META,\n", + " savefile=os.path.join(output_folder, \"WeatherFile.csv\"),\n", + ") # Writes the meterological data to an *.csv file.\n", + "\n", + "pd.DataFrame(standoff_series_df).to_csv(\n", + " os.path.join(output_folder, \"Standoff_Scan.csv\")\n", + ") # Writes a file with the Tilt and Azimuth scan calculations of standoff.\n", + "\n", + "pd.DataFrame(T98_series_df).to_csv(\n", + " os.path.join(output_folder, \"T98_Scan.csv\")\n", + ") # Writes a file with the Tilt and Azimuth scan calculations of T98." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "pvdeg", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.9" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/tutorials/04_geospatial/scripts/02_geospatial_templates.py b/tutorials/04_geospatial/scripts/02_geospatial_templates.py new file mode 100644 index 000000000..730ec803c --- /dev/null +++ b/tutorials/04_geospatial/scripts/02_geospatial_templates.py @@ -0,0 +1,282 @@ +# %% [markdown] +# # Geospatial Templates (HPC) +# + +# %% +import pvdeg +from pvdeg import TEST_DATA_DIR +import pandas as pd +import os +import xarray as xr + + +# %% [markdown] +# # Geospatial Templates +# +# When running a geospatial analysis using `pvdeg.geospatial.analysis` on arbitary `pvdeg` functions you will need to specify a template for the shape of the output data. This is because the input data comes with dimensions of gid and time while the output will have data in a different shape usually corresonding to coordinates. +# - gid, identification number corresponding to an NSRDB datapoint's location +# - time, timeseries corresponding to the hourly time indicies of NSRDB datapoint's yearly meteorological data. +# +# Follow the steps below to see how we generate templates before running the analysis. +# +# The only functions where this is not required are currently `pvdeg.standards.standoff`, `pvdeg.humidity.moduke` and, `letid.calc_letid_outdoors` as they are predefined within the package. + +# %% [markdown] +# # Loading Geospatial Data +# +# This step skips over making the `pvdeg.weather.get` call with `geospatial == True`. See the [Duramat Demo](../10_workshop_demos/02_duramat_live_demo.ipynb) for information on how to do this traditionally. +# +# We can also use a `GeospatialScenario` object. See the [Geospatial Scenario Tutorial](./04_scenario_geospatial.ipynb) for more information on how to use this approach. +# +# *The cell below loads a pickled xarray object, this is not the best way to do this. xarray datasets should be stored as `.nc` - netcdf files* + +# %% +geo_meta = pd.read_csv(os.path.join(TEST_DATA_DIR, "summit-meta.csv"), index_col=0) + +# Use xarray to open NetCDF file instead of pickle +geo_weather = xr.open_dataset(os.path.join(TEST_DATA_DIR, "summit-weather.nc")) + +geo_weather + + +# %% [markdown] +# # Creating Templates Manually +# +# `pvdeg.geospatial.ouput_template` we can produce a template for our result data. +# +# We need to do this because different functions return different types of values, some return multiple values as tuples, some return only single numerics, others return timeseries results. We need to specify the shape of our data to create an output xarray dataset. + +# %% [markdown] +# # Examples +# +# ## 98α΅—Κ° module percential temperature at Standoff Height +# +# Say we want to estimate the 98α΅—Κ° percential temperature for the module at the given tilt, azimuth, and x_eff. `PVDeg` has a function to do this, `pvdeg.standards.T98_estimate` BUT it doesn't have a preset geospatial template. We will need to make one. +# +# - look at the function return values. +# From the docstring we can see that `T98_estimate` only has one return value. IMPORTANT, this value is a single float, NOT a timeseries. This means our output shape will only be dependent on the input identifier and NOT time. +# +# Therefore we will map the output variable `T98` to the location identifier `gid` using a dictionary with `str: tuple` mappings. +# +# *IMPORTANT: you must use the syntax below where the variable maps to a tuple of the coordinates. in this case there needs to be a trailing comma in the tuple or python will iterate over the characters in the tuple instead of the elements. See further examples to alleviate confusion.* + +# %% +# define output shape +shapes = { + "T98": ( + "gid", + ) # one return value at each datapoint, only dependent on datapoint, not time +} + +# create xarray template for output to be populated when analysis is run +template = pvdeg.geospatial.output_template( + ds_gids=geo_weather, + shapes=shapes, +) + +# %% +geo_estimate_temp = pvdeg.geospatial.analysis( + weather_ds=geo_weather, + meta_df=geo_meta, + func=pvdeg.standards.T98_estimate, + template=template, +) + +# %% [markdown] +# # Glass Glass Estimated Module Temperature +# +# Now we want to calculate geospatial timeseries temperature values for a module using `pvdeg.temperature.module`. This is not super practical because all `pvdeg` functions that need to use tempeature for their calculations preform the temperature calculation internally, this is just for show. +# +# This calculation differs from the above because the temperature functions return the module temperature in a timeseries format. So we care about 2 dimensions, location identifier and TIME. + +# %% +# define output shape +shapes = { + "module_temperature": ( + "gid", + "time", + ) # one return value at each datapoint, only dependent on datapoint, not time +} + +# create xarray template for output to be populated when analysis is run +temperature_template = pvdeg.geospatial.output_template( + ds_gids=geo_weather, + shapes=shapes, +) + +# %% +geo_temperature_res = pvdeg.geospatial.analysis( + weather_ds=geo_weather, + meta_df=geo_meta, + func=pvdeg.temperature.module, + template=temperature_template, # use the template we created + conf="open_rack_glass_glass", # provide kwargs for function here +) + +# %% +# plot the temperature at ONE of the geospatial analysis result locations but we have calculated all of these. +import matplotlib.pyplot as plt + +module_temps = ( + geo_temperature_res["module"].sel(latitude=39.89, longitude="-106.42").values +) + +plt.plot(module_temps) + +# %% [markdown] +# # Self Explaining Code +# +# If we are looking at adding templates for other functions, we can also look at the 3 presaved templates for existing `pvdeg` functions. Visit [pvdeg.geospatial.template_parameters](../../pvdeg/geospatial.py) and inspect this function to see how these different target functions utilize templates and shapes. + +# %% [markdown] +# # Creating Templates Programatically +# +# We can use `pvdeg.geospatial.autotemplate` to generate a template for a given function. This can return a bad result which will fail or work improperly when running `pvdeg.geospatial.analysis` with the generated template. Results should be scrutinized to make sure they are the right format. + +# %% [markdown] +# # Examples Below +# Steps +# - Create template using autotemplating function. Pulls in information about function to determine shape of output. Not usable on functions with ambigious return types. +# - Call geospatial analysis function using template + +# %% [markdown] +# # Geospatial Cell Temperature Calculation +# As shown below, we have two options, we can choose to provide a template that is generated by a function which supports autotemplating. Or we can provide the function to `geospatial.analysis` and let it generate a template internally. +# +# ## Providing a Template with `Geospatial.auto_template` + +# %% +# create a template using auto_template for the desired function +cell_temp_template = pvdeg.geospatial.auto_template( + func=pvdeg.temperature.cell, ds_gids=geo_weather +) + +# run the geospatial analysis with the template +pvdeg.geospatial.analysis( + weather_ds=geo_weather, + meta_df=geo_meta, + func=pvdeg.temperature.cell, + template=cell_temp_template, +) + +# %% [markdown] +# # Analysis Without Providing a Template +# +# If a function is supported by `geospatial.auto_template` we do not need to create a template outside of the function as shown in the cell above. We can simply pass the function to `geospatial.analysis` and it will create a template for us. + +# %% +pvdeg.geospatial.analysis( + weather_ds=geo_weather, + meta_df=geo_meta, + func=pvdeg.temperature.cell, +) + +# %% [markdown] +# # Geospatial Module Temperature Calculation + +# %% +module_temp_template = pvdeg.geospatial.auto_template( + func=pvdeg.temperature.module, ds_gids=geo_weather +) + +pvdeg.geospatial.analysis( + weather_ds=geo_weather, + meta_df=geo_meta, + func=pvdeg.temperature.module, + template=module_temp_template, +) + +# %% [markdown] +# # Geospatial Solar Position Calculation + +# %% +solar_position_template = pvdeg.geospatial.auto_template( + func=pvdeg.spectral.solar_position, ds_gids=geo_weather +) + +pvdeg.geospatial.analysis( + weather_ds=geo_weather, + meta_df=geo_meta, + func=pvdeg.spectral.solar_position, + template=solar_position_template, +) + +# %% [markdown] +# # Geospatial POA Irradiance Calculation + +# %% +poa_irradiance_template = pvdeg.geospatial.auto_template( + func=pvdeg.spectral.poa_irradiance, ds_gids=geo_weather +) + +pvdeg.geospatial.analysis( + weather_ds=geo_weather, + meta_df=geo_meta, + func=pvdeg.spectral.poa_irradiance, + template=poa_irradiance_template, +) + +# %% [markdown] +# # Geospatial 98th Percentile Operating Temperature Calculation + +# %% +standoff_template = pvdeg.geospatial.auto_template( + func=pvdeg.standards.T98_estimate, ds_gids=geo_weather +) + +pvdeg.geospatial.analysis( + weather_ds=geo_weather, + meta_df=geo_meta, + func=pvdeg.standards.T98_estimate, + template=standoff_template, +) + +# %% [markdown] +# # Geospatial Module Humidity Calculation + +# %% +humidity_template = pvdeg.geospatial.auto_template( + func=pvdeg.humidity.module, ds_gids=geo_weather +) + +pvdeg.geospatial.analysis( + weather_ds=geo_weather, + meta_df=geo_meta, + func=pvdeg.humidity.module, + template=humidity_template, + backsheet_thickness=0.3, + back_encap_thickness=0.5, +) + +# %% [markdown] +# # Geospatial IwaVantHoff Environment Characterization Calculation + +# %% +iwa_vant_hoff_template = pvdeg.geospatial.auto_template( + func=pvdeg.degradation.IwaVantHoff, ds_gids=geo_weather +) + +pvdeg.geospatial.analysis( + weather_ds=geo_weather, + meta_df=geo_meta, + func=pvdeg.degradation.IwaVantHoff, + template=iwa_vant_hoff_template, +) + +# %% [markdown] +# # Geospatial Edge Seal Width Calculation + +# %% +edge_seal_template = pvdeg.geospatial.auto_template( + func=pvdeg.design.edge_seal_width, ds_gids=geo_weather +) + +pvdeg.geospatial.analysis( + weather_ds=geo_weather, + meta_df=geo_meta, + func=pvdeg.design.edge_seal_width, + template=edge_seal_template, +) + +# %% +edge_seal_template diff --git a/tutorials/04_geospatial/scripts/08_module_standoff_iec63126.py b/tutorials/04_geospatial/scripts/08_module_standoff_iec63126.py new file mode 100644 index 000000000..f87f716b0 --- /dev/null +++ b/tutorials/04_geospatial/scripts/08_module_standoff_iec63126.py @@ -0,0 +1,499 @@ +# %% [markdown] +# # Tools - Module Standoff for IEC TS 63126 +# +# ## Calculation of module standoff distance according to IEC TS 63126 +# +# **Requirements:** +# - Local weather data file or site longitude and latittude +# +# **Objectives:** +# 1. Import weather data. +# 2. Calculate installation standoff - Level 1 and Level 2. +# 3. Calculate $X_{eff}$ from provided module temperature data. +# 4. Calculate $T_{98}$ for a given azimuth, tilt, and $X_{eff}$. +# 5. Plot $X_{min}$ for all azimuth and tilt for a given $T_{98}$. +# 6. Plot $X_{min}$ for Level 1, Level 2, or a $T_{98}$ for a given region. +# +# **Background:** +# +# This notebook calculates the a minimum effective standoff distance ($X_{eff}$) necessary for roof-mounted PV modules to ensure that the $98^{th}$ percentile operating temperature, $T_{98}$, remains under 70Β°C for compliance to IEC 61730 and IEC 61215. For higher $T_{98}$ values above 70Β°C or 80Β°C testing must be done to the specifications for Level 1 and Level 2 of IEC TS 63126. This method is outlined in the appendix of IEC TS 63126 and is based on the model from *[King 2004] and data from **[Fuentes, 1987] to model the approximate exponential decay in temperature, $T(X)$, with increasing standoff distance, $X$, as, +# +# $$ X = -X_0 \ln\left(1-\frac{T_0-T}{\Delta T}\right), Equation 1 $$ +# +# where $T_0$ is the temperature for $X=0$ (insulated-back) and $\Delta T$ is the temperature difference between an insulated-back ($X=0$) and open-rack mounting configuration ($X=\infty)$. +# +# We used pvlib and data from the National Solar Radiation Database (NSRDB) to calculate the module temperatures for the insulated-back and open-rack mounting configurations and apply our model to obtain the minimum standoff distance for roof-mounted PV systems to achieve a temperature lower than a specified $T_{98}$. The following figure showcases this calulation for the entire world for an $X_{eff}$ that results in $T_{98}$=70Β°C. Values of $X_{eff}$ higher than this will require Level 1 or Level 2 certification. +# +# $*$ D. L. King, W. E. Boyson, and J. A. Kratochvil, "Photovoltaic array performance model," SAND2004-3535, Sandia National Laboratories, Albuquerque, NM, 2004. '\ +# $**$ M. K. Fuentes, "A simplified thermal model for Flat-Plate photovoltaic arrays," United States, 1987-05-01 1987. https://www.osti.gov/biblio/6802914 +# + +# %% [markdown] +# ![alt text](images/T98_70C_standoff_Map.png) + +# %% +# if running on google colab, uncomment the next line and execute this cell to install the dependencies +# and prevent "ModuleNotFoundError" in later cells: +# #!pip install pvdeg + +# %% +import os +import pvdeg +import pandas as pd +from pvdeg import DATA_DIR +import dask +import matplotlib.pyplot as plt +import seaborn as sns +import math +import numpy as np + +# %% +# This information helps with debugging and getting support :) +import sys +import platform + +print("Working on a ", platform.system(), platform.release()) +print("Python version ", sys.version) +print("Pandas version ", pd.__version__) +print("pvdeg version ", pvdeg.__version__) +print("dask version", dask.__version__) +print(DATA_DIR) + +# %% [markdown] +# # 1. Import Weather Data +# +# The function has these minimum requirements when using a weather data file: +# - Weather data containing (at least) DNI, DHI, GHI, Temperature, RH, and Wind-Speed data at module level. +# - Site meta-data containing (at least) latitude, longitude, and time zone +# +# Alternatively one may can get meterological data from the NSRDB or PVGIS with just the longitude and latitude. This function for the NSRDB (via NSRDB 'PSM3') works primarily for most of North America and South America. PVGIS works for most of the rest of the world (via SARAH 'PVGIS'). See the tutorial "Weather Database Access.ipynb" tutorial on PVdeg or Jensen et al. https://doi.org/10.1016/j.solener.2023.112092 for satellite coverage information. +# + +# %% +# Get data from a supplied data file (Do not use the next box of code if using your own file) +weather_file = os.path.join(DATA_DIR, "psm3_demo.csv") +WEATHER_df, META = pvdeg.weather.read(weather_file, "csv") +print(META) + +# %% +# This routine will get a meteorological dataset from anywhere in the world where it is available +# weather_id = (24.7136, 46.6753) #Riyadh, Saudi Arabia +# weather_id = (35.6754, 139.65) #Tokyo, Japan +# weather_id = (-43.52646, 172.62165) #Christchurch, New Zealand +# weather_id = (64.84031, -147.73836) #Fairbanks, Alaska +# weather_id = (65.14037, -21.91633) #Reykjavik, Iceland +# weather_id = (33.4152, -111.8315) #Mesa, Arizona +# WEATHER_df, META = pvdeg.weather.get_anywhere(id=weather_id) +# print(META) + +# %% [markdown] +# # 2. Calculate Installation Standoff Minimum - Level 1 and Level 2 +# +# According to IEC TS 63126, Level 0, Level 1 and Level 2 certification is limited to Tβ‚‰β‚ˆ<70Β°C, <80Β°C and <90Β°C, respectively. Level 0 certification is essentially compliance to IEC 61730 and IEC 61215. The default value of Tβ‚‰β‚ˆ<70Β°C represents the minimium gap to avoid higher temperature certification according to IEC TS 63126. This minimum standoff ($x_{min}$) is the distance between the bottom of the module frame and the roof and can be extimated for a given environment as, +# +# $$ X_{min} = -X_0 \ln\left(1-\frac{T_{98,0}-T}{ T_{98,0}- T_{98,inf}}\right), Equation 2 $$ +# +# where $T_{98,0}$ is the $98^{th}$ percentile temperature for an insulated back module and $T_{98,inf}$ is the $98^{th}$ percentile temperature for an open rack mounted module. +# +# Once the meterological data has been obtained, the input parameter possibilities are: +# +# - Tβ‚‰β‚ˆ : Does not necessarily need to be set at 70Β°C or 80Β°C for IEC TS 63216, you might want to use a different number to compensate for a thermal aspect of the particular system you are considering. The default is 70Β°C. +# - tilt : tilt from horizontal of PV module. The default is 0Β°. +# - azimuth : azimuth in degrees from North. The default is 180Β° for south facing. +# - sky_model : pvlib compatible model for generating sky characteristics (Options: 'isotropic', 'klucher', 'haydavies', 'reindl', 'king', 'perez'). The default is 'isotropic'. +# - temp_model : pvlib compatible module temperature model. (Options: 'sapm', 'pvsyst', 'faiman', 'sandia'). The default is 'sapm'. +# - conf_0 : Temperature model for hotest mounting configuration. Default is "insulated_back_glass_polymer". +# - conf_inf : Temperature model for open rack mounting. Default is "open_rack_glass_polymer". +# - x_0 : thermal decay constant [cm] (see documentation). The default is 6.5 cm. +# - wind_factor : Wind speed power law correction factor to account for different wind speed measurement heights between weather database (e.g. NSRDB) and the tempeature model (e.g. SAPM). The default is 0.33. + +# %% [markdown] +# The following is the minimum function call. It defaults to horizontal tilt and Tβ‚‰β‚ˆ=70Β°C. + +# %% +standoff = pvdeg.standards.standoff(weather_df=WEATHER_df, meta=META) +print(pvdeg.standards.interpret_standoff(standoff)) + +# %% [markdown] +# The following is a full function call for both Tβ‚‰β‚ˆ=70Β°C and 80Β°C separately even though the second standoff distance can be calculated using only T98_0 and T98_inf. With this function, one may also want to change the tilt, azimuth, or T98. + +# %% +standoff_1 = pvdeg.standards.standoff( + weather_df=WEATHER_df, + meta=META, + T98=70, + tilt=META["latitude"], + azimuth=None, + sky_model="isotropic", + temp_model="sapm", + conf_0="insulated_back_glass_polymer", + conf_inf="open_rack_glass_polymer", + x_0=6.5, + wind_factor=0.33, +) +print("First calculation standoff = ", "%.1f" % standoff_1["x"].iloc[0], " cm.") +standoff_2 = pvdeg.standards.standoff( + weather_df=WEATHER_df, + meta=META, + T98=80, + tilt=META["latitude"], + azimuth=None, + sky_model="isotropic", + temp_model="sapm", + conf_0="insulated_back_glass_polymer", + conf_inf="open_rack_glass_polymer", + x_0=6.5, + wind_factor=0.33, +) +print("Second calculation standoff = ", "%.1f" % standoff_2["x"].iloc[0], " cm.") +print(pvdeg.standards.interpret_standoff(standoff_1=standoff_1, standoff_2=standoff_2)) + +# %% [markdown] +# # 3. Calculate $X_{eff}$ from provided module temperature data. +# +# To do this calculation, one must use a set of data with: +# - meterological irradiance data sufficient to calculate the POA irradiance (DHI, GHI, and DNI), +# - ambient temperature data, +# - wind speed at module height, (wind_factor=0.33 will be used unless otherwise specified) +# - temperature measurements of the module in the test system. Ideally this would be measured under a worst case scenario that maximizes the module temperature for a given site, +# - geographic meta data including longitude and latitude, +# +# To create a weather file of your own, copy the format of the example file 'xeff_demo.csv'. This is formatted with the first row containing meta data variable names, the second row containing the corresponding values, the third row containing meteorological data headers, and all the remaining rows containing the meteorological data. +# +# To do this calculation, one should also filter the data to remove times when the sun is not shining or when snow is likely to be on the module. The recommendations and programmed defaults are to use poa_min=100 W/mΒ² and data when the minimum ambient temperature t_amb_min=0. + +# %% +# Read the weather file +weather_file = os.path.join(DATA_DIR, "xeff_demo.csv") +xeff_weather, xeff_meta = pvdeg.weather.read(weather_file, "csv") +# Pull measured temperature and calculate theoretical insulated back module temperature and open rack module temperature +T_0, T_inf, xeff_poa = pvdeg.standards.eff_gap_parameters( + weather_df=xeff_weather, + meta=xeff_meta, + sky_model="isotropic", + temp_model="sapm", + conf_0="insulated_back_glass_polymer", + conf_inf="open_rack_glass_polymer", + wind_factor=0.33, +) +# Now calculate X_eff. +x_eff = pvdeg.standards.eff_gap( + T_0, + T_inf, + xeff_weather["module_temperature"], + xeff_weather["temp_air"], + xeff_poa["poa_global"], + x_0=6.5, + poa_min=100, + t_amb_min=0, +) +print("The effective standoff for this system is", "%.1f" % x_eff, "cm.") + +# %% [markdown] +# # 4. Calculate $T_{98}$ for a given azimuth, tilt, and $X_{eff}$. +# +# Equation 2 can be reorganized as, +# +# $$ T_{98} = T_{98,0} -( T_{98,0}- T_{98,inf}) \left(1-e^{-\frac{x_{eff}}{x_{0}}}\right), Equation 3 $$ +# +# and used to calculate the $98^{th}$ percential temperature, $T_{98}$, for a PV system having a given effective standoff height, $X_{eff}$, for an arbitrarily oriented module. Here, $T_{98,0}$ is the $98^{th}$ percentile for an insulated-back module and $T_{98,inf}$ is the $98^{th}$ percentile for a rack-mounted module. The input parameter possibilities are the same as shown in Objective #2 above, but the example below uses the default parameters. The actual tilt [degrees], azimuth [degrees] and $X_{eff}$ [cm] can be modifed as desired. + +# %% +# This is the minimal function call using the common default settings to estimate Tβ‚‰β‚ˆ. +T_98 = pvdeg.standards.T98_estimate( + weather_df=WEATHER_df, meta=META, tilt=-META["latitude"], azimuth=None, x_eff=10 +) +print("The 98α΅—Κ° percential temperature is estimated to be", "%.1f" % T_98, "Β°C.") + +# %% +# This code will calculate the temperature for an arbitrary x_eff distance. Either set of kwargs can be modified and use. +# irradiance_kwarg ={ +# "axis_tilt": None, +# "axis_azimuth": None, +# "x_eff": 10, +# "module_mount": '1_axis'} +irradiance_kwarg = { + "tilt": META["latitude"], + "azimuth": None, + "x_eff": 10, + "module_mount": "fixed", +} + +T_xeff = pvdeg.standards.x_eff_temperature_estimate( + weather_df=WEATHER_df, meta=META, **irradiance_kwarg +) + +print( + "The 98α΅—Κ° percential temperature is estimated to be", + "%.1f" % np.percentile(T_xeff, 98), + "Β°C.", +) + +# %% [markdown] +# # 5. Plot $X_{min}$ for all azimuth and tilt for a given $T_{98}$. +# +# The temperature of a system is affected by the orientation. This section will scan all possible tilts and azimuths calculating the minimum standoff distance for a given $T_{98}$. Similar additional factors as above can also be modified but are not included here for simplicity. The tilt_step and azimuth_step are the number of degrees for each step for the 90Β° and 180Β° tilt and azimuth spans, respectively. The default for this calculation is for $T_{98}$=70Β°C, the boundary between Level 0 and Level 1 requirements. The temperature model information given below is unnecessary as these are default values that would get populated automatically. However, they were included here for clarity into a standard practice as per IEC TS 63126. +# +# $$ X_{min} = -X_0 \ln\left(1-\frac{T_{98,0}-T}{ T_{98,0}- T_{98,inf}}\right), Equation 2 $$ + +# %% +# Scans through all the azimuth and tilt running the minimum standoff calculation +# Set up keyword parameters for the calculation + +kwarg_x = dict( + sky_model="isotropic", + temp_model="sapm", + conf_0="insulated_back_glass_polymer", + conf_inf="open_rack_glass_polymer", + T98=70, + x_0=6.5, + wind_factor=0.33, +) +# Run the calculation +x_azimuth_step = 10 +x_tilt_step = 10 +standoff_series = pvdeg.utilities.tilt_azimuth_scan( + weather_df=WEATHER_df, + meta=META, + tilt_step=x_tilt_step, + azimuth_step=x_azimuth_step, + func=pvdeg.standards.standoff_x, + **kwarg_x, +) + +# %% [markdown] +# The next cell creates a plot of the calculated data. Some of the things you may want to change are: +# - cmap="Spectral_r": Change to have different colors +# - plt.title : This will change the plot title. +# - figsize=(16,4) : Change the plot dimensions and/or aspect ratio. +# - vmax=None : This can be set to a numeric value to control the depth scale maximum +# - vmin=0 : This controls the minimum of the depth scale. +# - v_ticks=37 : This changes the number of vertical tick marks +# - h_ticks=10 : This changes the number of horizontal tick marks +# - Unblock the last two lines to ouput the plot as an *.png image file + +# %% +standoff_series_df = pd.DataFrame( + { + "Tilt": standoff_series[:, 0], + "Azimuth": standoff_series[:, 1], + "Xβ‚˜α΅’β‚™": standoff_series[:, 2], + } +) +x_fig = plt.figure(figsize=(16, 4)) +plt.title( + r"Plot of $\it{Xβ‚˜α΅’β‚™}$ for all orientations for $\it{Tβ‚‰β‚ˆ}$=" + + "%.0f" % kwarg_x["T98"] + + "Β°C.", + fontsize=15, + y=1.08, +) +x_fig = sns.heatmap( + standoff_series_df.pivot(index="Tilt", columns="Azimuth", values="Xβ‚˜α΅’β‚™"), + cbar_kws={"label": "Xβ‚˜α΅’β‚™", "format": "%.0f", "pad": 0.02}, + cmap="Spectral_r", + vmin=0, + vmax=None, +) + +h_ticks = 37 +x_number = math.ceil(360 / x_azimuth_step) + 1 +x_ticks = [ + (x * (360 / (h_ticks - 1)) / x_azimuth_step + 0.5) for x in range(h_ticks - 1) +] +x_labels = [("%.0f" % (360 / (h_ticks - 1) * x)) for x in range(h_ticks)] +x_ticks.append(x_number - 0.5) +x_fig.set_xticks(x_ticks) +x_fig.set_xticklabels(x_labels, rotation=90) + +v_ticks = 10 +y_number = math.ceil(90 / x_tilt_step) + 1 +y_ticks = [(x * (90 / (v_ticks - 1)) / x_tilt_step + 0.5) for x in range(v_ticks - 1)] +y_labels = [("%.0f" % (90 / (v_ticks - 1) * x)) for x in range(v_ticks)] +y_ticks.append(y_number - 0.5) +x_fig.set_yticks(y_ticks) +x_fig.set_yticklabels(y_labels, rotation=0) + +x_fig.set_xlabel("Azimuth [Β°]", fontsize=15, labelpad=10) +x_fig.set_ylabel("Tilt [Β°]", fontsize=15) +x_fig.figure.axes[-1].set_ylabel(r"$\it{Xβ‚˜α΅’β‚™}$ [cm]", size=15) +x_fig.invert_yaxis() + +output_folder = os.path.join( + os.path.dirname(os.path.dirname(os.getcwd())), "TEMP", "results" +) +try: + os.makedirs(output_folder) +except OSError as error: + print(error) + +plt.savefig( + os.path.join(output_folder, "Standoff_Scan.png"), bbox_inches="tight" +) # Creates an image file of the standoff plot +plt.show() + +# %% [markdown] +# # 6. Plot $T_{98}$ for all azimuth and tilt for a given $X_{eff}$. +# +# The temperature of a system is affected by the orientation and the effective standoff, $X_{eff}$, of the system. This section will scan all possible tilts and azimuths calculating the $T_{98}$ for a given $X_{eff}$. As above, additional factors can be modified but are not included here for simplicity. The tilt_step and azimuth_step are the number of degrees for each step for the 90Β° and 180Β° tilt and azimuth spans, respectively. The default for this calculation is for $X_{eff}$=10 cm, a common effective standoff distance on a rooftop system. A value of $X_{eff}$=None will run the calculations for an open rack system and $X_{eff}$=0 for an insulated-back system. + +# %% +# Scans through all the azimuth and tilt running the 98α΅—Κ° percentile temperature calculation. +# Set up keyword parameters for the calculation +kwarg_T = dict( + sky_model="isotropic", + temp_model="sapm", + conf_0="insulated_back_glass_polymer", + conf_inf="open_rack_glass_polymer", + x_eff=5, + x_0=6.5, + wind_factor=0.33, +) +# Run the calculation +T_azimuth_step = 10 +T_tilt_step = 10 +T98_series = pvdeg.utilities.tilt_azimuth_scan( + weather_df=WEATHER_df, + meta=META, + tilt_step=T_tilt_step, + azimuth_step=T_azimuth_step, + func=pvdeg.standards.T98_estimate, + **kwarg_T, +) + +# %% [markdown] +# The next cell creates a plot of the calculated data. Some of the things you may want to change are: +# - cmap="Spectral_r": Change to have different colors +# - plt.title : This will change the plot title. +# - figsize=(16,4) : Change the plot dimensions and/or aspect ratio. +# - vmax=None : This can be set to a numeric value to control the depth scale maximum +# - vmin=None : This controls the minimum of the depth scale. +# - v_ticks=37 : This changes the number of vertical tick marks +# - h_ticks=10 : This changes the number of horizontal tick marks +# - Unblock the last two lines to ouput the plot as an *.png image file + +# %% +# This produces the plot of the data +T98_series_df = pd.DataFrame( + {"Tilt": T98_series[:, 0], "Azimuth": T98_series[:, 1], "Tβ‚‰β‚ˆ": T98_series[:, 2]} +) +T98_fig = plt.figure(figsize=(16, 4)) +if kwarg_T["x_eff"] == None: + plt.title( + r"Plot of $\it{Tβ‚‰β‚ˆ}$ for all orientations for an open-rack mounting.", + fontsize=15, + y=1.08, + ) +else: + plt.title( + r"Plot of $\it{Tβ‚‰β‚ˆ}$ for all orientations for $X_{eff}$=" + + "%.0f" % kwarg_T["x_eff"] + + " cm.", + fontsize=15, + y=1.08, + ) +T98_fig = sns.heatmap( + T98_series_df.pivot(index="Tilt", columns="Azimuth", values="Tβ‚‰β‚ˆ"), + cbar_kws={"label": "Xβ‚˜α΅’β‚™", "format": "%.0f", "pad": 0.02}, + cmap="Spectral_r", + vmin=None, + vmax=None, +) + +h_ticks = 37 +x_number = math.ceil(360 / T_azimuth_step) + 1 +x_ticks = [ + (x * (360 / (h_ticks - 1)) / T_azimuth_step + 0.5) for x in range(h_ticks - 1) +] +x_labels = [("%.0f" % (360 / (h_ticks - 1) * x)) for x in range(h_ticks)] +x_ticks.append(x_number - 0.5) +T98_fig.set_xticks(x_ticks) +T98_fig.set_xticklabels(x_labels, rotation=90) + +v_ticks = 10 +y_number = math.ceil(90 / T_tilt_step) + 1 +y_ticks = [(x * (90 / (v_ticks - 1)) / T_tilt_step + 0.5) for x in range(v_ticks - 1)] +y_labels = [("%.0f" % (90 / (v_ticks - 1) * x)) for x in range(v_ticks)] +y_ticks.append(y_number - 0.5) +T98_fig.set_yticks(y_ticks) +T98_fig.set_yticklabels(y_labels, rotation=0) + +T98_fig.set_xlabel("Azimuth [Β°]", fontsize=15, labelpad=10) +T98_fig.set_ylabel("Tilt [Β°]", fontsize=15) +T98_fig.figure.axes[-1].set_ylabel(r"$\it{Tβ‚‰β‚ˆ}$ [Β°C]", size=15) +T98_fig.invert_yaxis() + +plt.savefig( + os.path.join(output_folder, "T98_Scan.png"), bbox_inches="tight" +) # Creates an image file of the standoff plot +plt.show(T98_fig) + +# %% [markdown] +# # 7. Plot $X_{min}$ for a $T_{98}$, and plot $T_{98}$ for a given region. +# +# This last Objective is much more complicated and is set up to utilize acess to a lot of computational power to run many sites simultaneously to create a regional map of standoff distance. +# For more in-depth instructions on doing this, look at the tutorial "04_scenario_geospatial.ipynb" here in PVDeg. +# +# Step #1: Create an object, "geospatial_standoff_scenario" to be used to run the computations. + +# %% +geospatial_standoff_scenario = pvdeg.GeospatialScenario( + name="standoff geospatial", + geospatial=True, +) + +# %% [markdown] +# Step #2: Identifies a subset of locations from the database to run the computations. +# Specifically all are from the NSRDB. + +# %% +geospatial_standoff_scenario.addLocation( + state="Colorado", county="Summit" +) # Identifies a subset of locations from the database to run the computations. Specifically all are from the NSRDB. + +# %% [markdown] +# Step #3: indicate which function will be run. Here the default is the standoff calculation, but it could be any other function with a key word argument dictionary. +# Here the 98th percential temperature is defined as 70C, but any arbitrary value can be specified. + +# %% +geospatial_standoff_scenario.addJob( + func=pvdeg.standards.standoff, func_params={"T98": 70} +) + +# %% [markdown] +# Step #4: Run the scenario + +# %% +geospatial_standoff_scenario.run() + +# %% [markdown] +# Step #5: Create a plot of the standoff calculation. + +# %% +geospatial_standoff_scenario.plot_world("x") + +# %% +geospatial_standoff_scenario.plot_world("T98_inf") + +# %% [markdown] +# # 8. Save data outputs. +# +# This cell contains a number of pre-scripted commands for exporting and saving data. The code to save plots is located after the plot creation and is blocked by default. First check that the output folder exists, then unblock the code for data you would like to save. + +# %% +print("Your results will be stored in %s" % output_folder) +print("The folder must already exist or the file will not be created") + +pvdeg.weather.write( + data_df=WEATHER_df, + metadata=META, + savefile=os.path.join(output_folder, "WeatherFile.csv"), +) # Writes the meterological data to an *.csv file. + +pd.DataFrame(standoff_series_df).to_csv( + os.path.join(output_folder, "Standoff_Scan.csv") +) # Writes a file with the Tilt and Azimuth scan calculations of standoff. + +pd.DataFrame(T98_series_df).to_csv( + os.path.join(output_folder, "T98_Scan.csv") +) # Writes a file with the Tilt and Azimuth scan calculations of T98. diff --git a/tutorials_and_tools/tutorials_and_tools/HPC Connection Required - Scenario - Temperature.ipynb b/tutorials/04_scenario/01_scenario_temperature.ipynb similarity index 99% rename from tutorials_and_tools/tutorials_and_tools/HPC Connection Required - Scenario - Temperature.ipynb rename to tutorials/04_scenario/01_scenario_temperature.ipynb index 275218ce7..10969cb1f 100644 --- a/tutorials_and_tools/tutorials_and_tools/HPC Connection Required - Scenario - Temperature.ipynb +++ b/tutorials/04_scenario/01_scenario_temperature.ipynb @@ -1,8 +1,15 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Temperature Scenario\n" + ] + }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2025-11-04T16:11:02.863972Z", @@ -14,7 +21,7 @@ "outputs": [], "source": [ "# if running on google colab, uncomment the next line and execute this cell to install the dependencies and prevent \"ModuleNotFoundError\" in later cells:\n", - "# !pip install pvdeg==0.3.3" + "# !pip install pvdeg" ] }, { @@ -70,7 +77,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Adding Modules and Pipeline Jobs (Run Functions on Scenario Object)\n", + "# Adding Modules and Pipeline Jobs (Run Functions on Scenario Object)\n", "\n", "Material: `OX003` corresponds to a set of EVA material parameters from the default file `O2Permeation.json` in the `pvdeg/data` directory. Look in these files to see available options." ] @@ -91,8 +98,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "\r", - " Column \"relative_humidity\" not found in DataFrame. Calculating...\r", + "\r\n", + " Column \"relative_humidity\" not found in DataFrame. Calculating...\r\n", " \r" ] }, @@ -179,14 +186,16 @@ " func_kwarg={\"I_chamber\": 1000, \"temp_chamber\": 30},\n", ")\n", "\n", - "scene_temp.addJob(func=pvdeg.degradation.IwaVantHoff,)" + "scene_temp.addJob(\n", + " func=pvdeg.degradation.IwaVantHoff,\n", + ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Run and View Scenario Results" + "# Run and View Scenario Results" ] }, { @@ -1507,7 +1516,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Plotting and Extracting Results\n", + "# Plotting and Extracting Results\n", "These methods are independent of one another (i.e. you do not need to extract before plotting but both are shown below.)" ] }, @@ -1538,7 +1547,7 @@ "tf = datetime.datetime(1970, 1, 1, 23, 59)\n", "\n", "# Get the first function result dynamically\n", - "function_ids = [key[1] for key in scene_temp.results.keys() if key[0] == 'function']\n", + "function_ids = [key[1] for key in scene_temp.results.keys() if key[0] == \"function\"]\n", "if function_ids:\n", " temp_df = scene_temp.extract(\n", " (\"function\", function_ids[0]), tmy=True, start_time=t0, end_time=tf\n", @@ -1570,7 +1579,7 @@ ], "source": [ "# Get the first function result dynamically for plotting\n", - "function_ids = [key[1] for key in scene_temp.results.keys() if key[0] == 'function']\n", + "function_ids = [key[1] for key in scene_temp.results.keys() if key[0] == \"function\"]\n", "if function_ids:\n", " scene_temp.plot(\n", " (\"function\", function_ids[0]),\n", @@ -1587,7 +1596,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Create a Copy of a Scenario" + "# Create a Copy of a Scenario" ] }, { @@ -1613,8 +1622,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "\r", - " Column \"relative_humidity\" not found in DataFrame. Calculating...\r", + "\r\n", + " Column \"relative_humidity\" not found in DataFrame. Calculating...\r\n", " \r" ] }, @@ -2157,12 +2166,7 @@ "from pathlib import Path\n", "\n", "parent_dir = Path(pvdeg.PVDEG_DIR).parent\n", - "new_path = (\n", - " parent_dir\n", - " / \"tutorials_and_tools\"\n", - " / \"tutorials_and_tools\"\n", - " / \"temperature and degradation.json\"\n", - ")\n", + "new_path = parent_dir / \"tutorials\" / \"data\" / \"temperature_and_degradation.json\"\n", "\n", "copy = pvdeg.scenario.Scenario.load_json(\n", " file_path=str(new_path),\n", diff --git a/tutorials_and_tools/tutorials_and_tools/HPC Connection Required - Scenario - Single Location.ipynb b/tutorials/04_scenario/02_scenario_single_location.ipynb similarity index 98% rename from tutorials_and_tools/tutorials_and_tools/HPC Connection Required - Scenario - Single Location.ipynb rename to tutorials/04_scenario/02_scenario_single_location.ipynb index 39c5c3490..27e4189e7 100644 --- a/tutorials_and_tools/tutorials_and_tools/HPC Connection Required - Scenario - Single Location.ipynb +++ b/tutorials/04_scenario/02_scenario_single_location.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Scenario Objects - OOP Approach to PVDEG\n", + "# Single Location\n", "\n", "Author: Tobin Ford | tobin.ford@nrel.gov\n", "\n", @@ -16,7 +16,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2025-10-22T23:46:56.507973Z", @@ -28,7 +28,7 @@ "outputs": [], "source": [ "# if running on google colab, uncomment the next line and execute this cell to install the dependencies and prevent \"ModuleNotFoundError\" in later cells:\n", - "# !pip install pvdeg==0.3.3" + "# !pip install pvdeg" ] }, { @@ -84,13 +84,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Define Single Point Scenario Object\n", + "# Define Single Point Scenario Object\n", "Scenario is a general class that can be used to replace the legacy functional pvdeg analysis approach with an object orented one. ``Scenario`` can preform single location or geospatial analysis. The scenario constructor takes many arguments but the only required one for the following use cases is the ``name`` attribute. It is visible in when we display the entire scenario and is present in the file of saved information about the scenario. We also need to provide the class constructor with our API key and email.\n", "\n", "A way around this is to provide the weather and metadata in the pipeline job arguments or you can load data from somewhere else and provide it in the same fashion.\n", "\n", "
\n", - "Please use your own API key: The block below makes an NSRDB API to get weather and meta data. This tutorial will work with the DEMO Key provided, but it will take you less than 3 minutes to obtain your own at https://developer.nrel.gov/signup/ so register now.) \n", + "Please use your own API key: The block below makes an NSRDB API to get weather and meta data. This tutorial will work with the DEMO Key provided, but it will take you less than 3 minutes to obtain your own at https://developer.nrel.gov/signup/ so register now.)\n", "
" ] }, @@ -116,10 +116,10 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Adding A Location\n", - "To add a single point using data from the Physical Solar Model (PSM3), simply feed the scenario a single coordinate in tuple form via the ``addLocation`` method. Currently this is the only way to add a location to a non-geospatial scenario, all of the other arguments are unusable when ``Scenario.geospatial == False``. \n", + "# Adding A Location\n", + "To add a single point using data from the Physical Solar Model (PSM3), simply feed the scenario a single coordinate in tuple form via the ``addLocation`` method. Currently this is the only way to add a location to a non-geospatial scenario, all of the other arguments are unusable when ``Scenario.geospatial == False``.\n", "\n", - "Attempting to add a second location by calling the method again with a different coordinate pair will overwrite the old location data stored in the class instance. " + "Attempting to add a second location by calling the method again with a different coordinate pair will overwrite the old location data stored in the class instance." ] }, { @@ -138,8 +138,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "\r", - " Column \"relative_humidity\" not found in DataFrame. Calculating...\r", + "\r\n", + " Column \"relative_humidity\" not found in DataFrame. Calculating...\r\n", " \r" ] } @@ -460,9 +460,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Scenario Pipelines\n", + "# Scenario Pipelines\n", "\n", - "The pipeline is a list of tasks called jobs for the scenario to run. We will populate the pipeline with a list of jobs before executing them all at once. \n", + "The pipeline is a list of tasks called jobs for the scenario to run. We will populate the pipeline with a list of jobs before executing them all at once.\n", "\n", "To add a job to the pipeline use the ``updatePipeline`` method. Two examples of adding functions to the pipeline will be shown below." ] @@ -471,7 +471,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Adding a job without function arguments\n", + "# Adding a job without function arguments\n", "\n", "The simplest case of adding a job to the pipeline is when it only requires us to provide simple weather and metadata. In the function definition and docstring these appear as ``weather_df`` and ``meta``. Since these attributes are contained in our scenario class instance we do not have to worry about them. We can simply add the function as shown below." ] @@ -496,7 +496,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Adding a job with function arguments\n", + "# Adding a job with function arguments\n", "\n", "When adding a job that contains a function requiring other arguments such as ``solder_fatigue`` which requires a value for ``wind_factor``, we will need to provide it. The most straightforeward way to do this is using a kwargs dictionary and passing it to the function. We do not unpack the dictionary before passing it. This is done inside of the scenario at pipeline runtime (when ``runPipeline`` is called)." ] @@ -516,17 +516,15 @@ "source": [ "kwargs = {\"wind_factor\": 0.33}\n", "\n", - "simple_scenario.addJob(\n", - " func=pvdeg.fatigue.solder_fatigue, func_kwarg=kwargs\n", - " )" + "simple_scenario.addJob(func=pvdeg.fatigue.solder_fatigue, func_kwarg=kwargs)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Adding a job with weather and metadata from outside of the class\n", - "### Not functional \n", + "# Adding a job with weather and metadata from outside of the class\n", + "## Not functional\n", "\n", "could just directly set weather data with scenario.weather_data = weather and scenario.meta_data = meta but that would only work for all of the jobs in the pipeline\n", "\n", @@ -561,7 +559,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## View Scenario\n", + "# View Scenario\n", "\n", "The ``viewScenario`` method provides an overview of the information contained within your scenario object. Here you can see if it contains the location weather and metadata. As well as the jobs in the pipeline and their arguments." ] @@ -667,7 +665,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Display\n", + "# Display\n", "\n", "The fancier cousin of viewScenario. Only works in a jupyter environemnt as it uses a special ipython backend to render the html and javascript.\n", "\n", @@ -1028,7 +1026,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Executing Pipeline Jobs\n", + "# Executing Pipeline Jobs\n", "To run the pipeline after we have populated it with the desired jobs call the ``runPipeline`` method on our scenario instance. This will run all of the jobs we have previously added. The functions that need weather and metadata will grab it from the scenario instance using the correct location added above. The pipeline jobs results will be saved to the scenario instance." ] }, @@ -1071,7 +1069,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Results Series ##\n", + "# Results Series ##\n", "We will use a series to store the various return values of functions run in our pipeline. These can partially obfuscate the dataframes within them so to access the dataframes, use the function name to access it. To get one of the results we can index it using dictionary syntax. If the job was called `'KSDJQ'` do `'simple_scenario.results['KSDJQ']` to directly access the result for that job" ] }, @@ -1270,7 +1268,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Cleaning Up the Scenario\n", + "# Cleaning Up the Scenario\n", "\n", "Each scenario object creates a directory named ``pvd_job_...`` that contains information about the scenario instance. To remove the directory and all of its information call ``clean`` on the scenario. This will permanently delete the directory created by the scenario." ] diff --git a/tutorials_and_tools/tutorials_and_tools/HPC Connection Required - Scenario - Non-uniform Mountain Prefferential Downselect.ipynb b/tutorials/04_scenario/03_scenario_mountain_downselect.ipynb similarity index 93% rename from tutorials_and_tools/tutorials_and_tools/HPC Connection Required - Scenario - Non-uniform Mountain Prefferential Downselect.ipynb rename to tutorials/04_scenario/03_scenario_mountain_downselect.ipynb index 81b3771c6..60f6b2297 100644 --- a/tutorials_and_tools/tutorials_and_tools/HPC Connection Required - Scenario - Non-uniform Mountain Prefferential Downselect.ipynb +++ b/tutorials/04_scenario/03_scenario_mountain_downselect.ipynb @@ -1,5 +1,12 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Mountain Downselect\n" + ] + }, { "cell_type": "code", "execution_count": null, @@ -14,7 +21,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Adding Points\n", + "# Adding Points\n", "\n", "We are going to add all of the points in the American West to the scenario and downsample by a factor of 1. This will include only half of the points in the latitude axis and half in the longitude axis." ] @@ -37,7 +44,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Preview The Scenario's Points\n", + "# Preview The Scenario's Points\n", "\n", "Use `plot_cords` to get a quick snapshot of all coordinates included in the scenario's metadata." ] @@ -68,13 +75,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Downselecting\n", + "# Downselecting\n", "\n", "Using weighted random choices based on elevation we will create a sparse grid from the full metadata for fast calculations. This requires sklearn to be installed but this is not in the `pvdeg` dependency list to you will have to install it seperately.\n", "\n", - "### Normalization\n", + "## Normalization\n", "\n", - "At each metadata point in our dataset we will calculate a weight based on its changes in elevation compared to its neighbors. The higher the weight, the greater the change in elevation from a point's immediate neighbors. The downselection methods and functions use these weights to randomly select a subset of the datapoints, prefferentially selecting those with higher weights. \n", + "At each metadata point in our dataset we will calculate a weight based on its changes in elevation compared to its neighbors. The higher the weight, the greater the change in elevation from a point's immediate neighbors. The downselection methods and functions use these weights to randomly select a subset of the datapoints, prefferentially selecting those with higher weights.\n", "\n", "We have some control over which points get selected because all points' weights must be normalized (mapped from 0 to 1) before downselecting. We can apply a function such as $e^x$ or $\\log x$ to the weights during normalization. This could help change the distribution of weights that are chosen. This could remove points from the mountains and add them to areas with fewer changes in elevation, or vice versa.\n", "\n", @@ -85,9 +92,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Providing a KdTree\n", + "# Providing a KdTree\n", "\n", - "As shown below the lines to create a kdtree are commented out. \n" + "As shown below the lines to create a kdtree are commented out.\n" ] }, { @@ -121,7 +128,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Extracting from Scenario\n", + "# Extracting from Scenario\n", "\n", "Scenarios provide an easy way to select and downsample geospatial data but we can easily pull out the data to use other `pvdeg` functions on it. In the cell below, we extract the weather data and meta data from the scenario and take only the matching entries from the weather. Then we load the xarray dataset into memory. Previously, it was stored lazily out of memory but we want to do operations on it. (Chunking causes issues when calculating so this eliminates any chunks)" ] @@ -145,7 +152,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Geospatial Calculation\n", + "# Geospatial Calculation\n", "\n", "Run a standoff calculation on the extracted scenario weather data and scenario meta data." ] @@ -171,7 +178,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Viewing Results\n", + "# Viewing Results\n", "\n", "Inspecting the xarray dataset below shows us that we have many Not a Number (NaN) entries. These occur because we did not provide weather data at every point in the grid of possile latitude-longitude pairs. Expanding the `x` datavariable shows that there are some valid results but these are uncommon." ] @@ -189,7 +196,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Plotting Sparse Data I\n", + "# Plotting Sparse Data I\n", "\n", "If we try to plot existing data with the current plotting methods exposed by `pvdeg` we will encounter issues. This will produce weak plotting results." ] diff --git a/tutorials/04_scenario/README.md b/tutorials/04_scenario/README.md new file mode 100644 index 000000000..9191cd805 --- /dev/null +++ b/tutorials/04_scenario/README.md @@ -0,0 +1,20 @@ +# Scenario Analysis + +Tutorials for scenario-based modeling and analysis workflows. + +## Tutorials + +1. **01_scenario_temperature.ipynb** - Temperature-based scenario modeling +2. **02_scenario_single_location.ipynb** - Detailed single-location scenario analysis +3. **03_scenario_mountain_downselect.ipynb** - Non-uniform mountain terrain downselection + +## Notes + +All tutorials can run on standard hardware. Tutorial 3 uses cached outputs in the documentation build due to longer execution time, but you can execute it locally in your own environment. + +## Topics Covered + +- Temperature scenario analysis +- Single-site scenario generation +- Climate-based scenario development +- Terrain-specific modeling diff --git a/tutorials/04_scenario/scripts/01_local_scenario.py b/tutorials/04_scenario/scripts/01_local_scenario.py new file mode 100644 index 000000000..8b0ed0baa --- /dev/null +++ b/tutorials/04_scenario/scripts/01_local_scenario.py @@ -0,0 +1,18 @@ +# %% [markdown] +# # Local Scenario +# +# Author: Tobin Ford +# Email : tobin.ford@nrel.gov + +# %% [markdown] +# # Use Case +# - If you are unable to access the NSRDB via NREL HPC or AWS + +# %% [markdown] +# # Sourcing Data ## +# +# On the [NSRDB Dataviewer](https://nsrdb.nrel.gov/data-viewer), you can use the area querys to collect information from the area you want. Only tested with USA & Americas - Typical Meteorological Year so far. Use the dropdowns to select the attributes you will need to run calculations. Select a year and follow the instructions to get the zip file containing the weather data csv's for each location in the selected region. +# +# + +# %% diff --git a/tutorials/04_scenario/scripts/01_scenario_temperature.py b/tutorials/04_scenario/scripts/01_scenario_temperature.py new file mode 100644 index 000000000..562449ba2 --- /dev/null +++ b/tutorials/04_scenario/scripts/01_scenario_temperature.py @@ -0,0 +1,163 @@ +# %% [markdown] +# # Temperature Scenario +# + +# %% +# if running on google colab, uncomment the next line and execute this cell to install the dependencies and prevent "ModuleNotFoundError" in later cells: +# # !pip install pvdeg + +# %% +import pvdeg +import os + +# %% +# This information helps with debugging and getting support :) +import sys +import platform + +print("Working on a ", platform.system(), platform.release()) +print("Python version ", sys.version) +print("pvdeg version ", pvdeg.__version__) + +# %% [markdown] +# # Adding Modules and Pipeline Jobs (Run Functions on Scenario Object) +# +# Material: `OX003` corresponds to a set of EVA material parameters from the default file `O2Permeation.json` in the `pvdeg/data` directory. Look in these files to see available options. + +# %% +scene_temp = pvdeg.Scenario( + name="temperature and degradation", + api_key="DEMO_KEY", + email="user@mail.com", +) + +scene_temp.addLocation( + lat_long=(25.783388, -80.189029), +) + +# this module will be overwritten because another with the same name is added afterwards +scene_temp.addModule(module_name="sapm_1", temperature_model="sapm") + +scene_temp.addModule( + module_name="sapm_1", + racking="open_rack_glass_polymer", + materials="OX003", + temperature_model="sapm", + irradiance_kwarg={"azimuth": 120, "tilt": 30}, + model_kwarg={"irrad_ref": 1100}, +) + +scene_temp.addModule( + module_name="pvsyst_1", + racking="freestanding", + materials="OX003", + temperature_model="pvsyst", + irradiance_kwarg={"azimuth": 180, "tilt": 0}, + model_kwarg={"module_efficiency": 0.15}, +) +scene_temp.addModule( + module_name="sapm_2", + racking="open_rack_glass_polymer", + materials="OX003", + temperature_model="sapm", + irradiance_kwarg={"azimuth": 120, "tilt": 30}, + model_kwarg={"irrad_ref": 1000}, +) +scene_temp.addModule( + module_name="sapm_3", + racking="open_rack_glass_polymer", + materials="OX003", + temperature_model="sapm", + irradiance_kwarg={"azimuth": 180, "tilt": 0}, + model_kwarg={"irrad_ref": 1000}, +) + +scene_temp.addModule( + module_name="pvsyst_2", + racking="freestanding", + materials="OX003", + temperature_model="pvsyst", + irradiance_kwarg={"azimuth": 180, "tilt": 0}, + model_kwarg={"module_efficiency": 0.2}, +) + +scene_temp.addJob( + func=pvdeg.temperature.temperature, + func_kwarg={"cell_or_mod": "cell"}, +) + +scene_temp.addJob( + func=pvdeg.degradation.vantHoff_deg, + func_kwarg={"I_chamber": 1000, "temp_chamber": 25}, +) + +scene_temp.addJob( + func=pvdeg.degradation.vantHoff_deg, + func_kwarg={"I_chamber": 1000, "temp_chamber": 30}, +) + +scene_temp.addJob( + func=pvdeg.degradation.IwaVantHoff, +) + +# %% [markdown] +# # Run and View Scenario Results + +# %% +scene_temp.run() + +scene_temp + +# %% +scene_temp.dump() + +# %% [markdown] +# # Plotting and Extracting Results +# These methods are independent of one another (i.e. you do not need to extract before plotting but both are shown below.) + +# %% +import datetime + +t0 = datetime.datetime(1970, 1, 1, 0, 0) +tf = datetime.datetime(1970, 1, 1, 23, 59) + +# Get the first function result dynamically +function_ids = [key[1] for key in scene_temp.results.keys() if key[0] == "function"] +if function_ids: + temp_df = scene_temp.extract( + ("function", function_ids[0]), tmy=True, start_time=t0, end_time=tf + ) + display(temp_df) +else: + print("No function results found") + +# %% +# Get the first function result dynamically for plotting +function_ids = [key[1] for key in scene_temp.results.keys() if key[0] == "function"] +if function_ids: + scene_temp.plot( + ("function", function_ids[0]), + tmy=True, + start_time=t0, + end_time=tf, + title="single day cell temperature", + ) +else: + print("No function results found") + +# %% [markdown] +# # Create a Copy of a Scenario + +# %% +from pathlib import Path + +parent_dir = Path(pvdeg.PVDEG_DIR).parent +new_path = parent_dir / "tutorials" / "data" / "temperature_and_degradation.json" + +copy = pvdeg.scenario.Scenario.load_json( + file_path=str(new_path), + email="user@mail.com", + api_key="DEMO_KEY", +) + +# copy diff --git a/tutorials/04_scenario/scripts/02_geospatial_templates.py b/tutorials/04_scenario/scripts/02_geospatial_templates.py new file mode 100644 index 000000000..56a33c6f8 --- /dev/null +++ b/tutorials/04_scenario/scripts/02_geospatial_templates.py @@ -0,0 +1,282 @@ +# %% [markdown] +# # Geospatial Templates (HPC) +# + +# %% +import pvdeg +from pvdeg import TEST_DATA_DIR +import pandas as pd +import os +import xarray as xr + + +# %% [markdown] +# # Geospatial Templates +# +# When running a geospatial analysis using `pvdeg.geospatial.analysis` on arbitary `pvdeg` functions you will need to specify a template for the shape of the output data. This is because the input data comes with dimensions of gid and time while the output will have data in a different shape usually corresonding to coordinates. +# - gid, identification number corresponding to an NSRDB datapoint's location +# - time, timeseries corresponding to the hourly time indicies of NSRDB datapoint's yearly meteorological data. +# +# Follow the steps below to see how we generate templates before running the analysis. +# +# The only functions where this is not required are currently `pvdeg.standards.standoff`, `pvdeg.humidity.moduke` and, `letid.calc_letid_outdoors` as they are predefined within the package. + +# %% [markdown] +# # Loading Geospatial Data +# +# This step skips over making the `pvdeg.weather.get` call with `geospatial == True`. See the [Duramat Demo](./DuraMAT%20Live%20Demo.ipynb) for information on how to do this traditionally. +# +# We can also use a `GeospatialScenario` object. See the [Geospatial Scenario Tutorial](./Scenario%20-%20Geospatial.ipynb) for more information on how to use this approach. +# +# *The cell below loads a pickled xarray object, this is not the best way to do this. xarray datasets should be stored as `.nc` - netcdf files* + +# %% +geo_meta = pd.read_csv(os.path.join(TEST_DATA_DIR, "summit-meta.csv"), index_col=0) + +# Use xarray to open NetCDF file instead of pickle +geo_weather = xr.open_dataset(os.path.join(TEST_DATA_DIR, "summit-weather.nc")) + +geo_weather + + +# %% [markdown] +# # Creating Templates Manually +# +# `pvdeg.geospatial.ouput_template` we can produce a template for our result data. +# +# We need to do this because different functions return different types of values, some return multiple values as tuples, some return only single numerics, others return timeseries results. We need to specify the shape of our data to create an output xarray dataset. + +# %% [markdown] +# # Examples +# +# ## 98α΅—Κ° module percential temperature at Standoff Height +# +# Say we want to estimate the 98α΅—Κ° percential temperature for the module at the given tilt, azimuth, and x_eff. `PVDeg` has a function to do this, `pvdeg.standards.T98_estimate` BUT it doesn't have a preset geospatial template. We will need to make one. +# +# - look at the function return values. +# From the docstring we can see that `T98_estimate` only has one return value. IMPORTANT, this value is a single float, NOT a timeseries. This means our output shape will only be dependent on the input identifier and NOT time. +# +# Therefore we will map the output variable `T98` to the location identifier `gid` using a dictionary with `str: tuple` mappings. +# +# *IMPORTANT: you must use the syntax below where the variable maps to a tuple of the coordinates. in this case there needs to be a trailing comma in the tuple or python will iterate over the characters in the tuple instead of the elements. See further examples to alleviate confusion.* + +# %% +# define output shape +shapes = { + "T98": ( + "gid", + ) # one return value at each datapoint, only dependent on datapoint, not time +} + +# create xarray template for output to be populated when analysis is run +template = pvdeg.geospatial.output_template( + ds_gids=geo_weather, + shapes=shapes, +) + +# %% +geo_estimate_temp = pvdeg.geospatial.analysis( + weather_ds=geo_weather, + meta_df=geo_meta, + func=pvdeg.standards.T98_estimate, + template=template, +) + +# %% [markdown] +# # Glass Glass Estimated Module Temperature +# +# Now we want to calculate geospatial timeseries temperature values for a module using `pvdeg.temperature.module`. This is not super practical because all `pvdeg` functions that need to use tempeature for their calculations preform the temperature calculation internally, this is just for show. +# +# This calculation differs from the above because the temperature functions return the module temperature in a timeseries format. So we care about 2 dimensions, location identifier and TIME. + +# %% +# define output shape +shapes = { + "module_temperature": ( + "gid", + "time", + ) # one return value at each datapoint, only dependent on datapoint, not time +} + +# create xarray template for output to be populated when analysis is run +temperature_template = pvdeg.geospatial.output_template( + ds_gids=geo_weather, + shapes=shapes, +) + +# %% +geo_temperature_res = pvdeg.geospatial.analysis( + weather_ds=geo_weather, + meta_df=geo_meta, + func=pvdeg.temperature.module, + template=temperature_template, # use the template we created + conf="open_rack_glass_glass", # provide kwargs for function here +) + +# %% +# plot the temperature at ONE of the geospatial analysis result locations but we have calculated all of these. +import matplotlib.pyplot as plt + +module_temps = ( + geo_temperature_res["module"].sel(latitude=39.89, longitude="-106.42").values +) + +plt.plot(module_temps) + +# %% [markdown] +# # Self Explaining Code +# +# If we are looking at adding templates for other functions, we can also look at the 3 presaved templates for existing `pvdeg` functions. Visit [pvdeg.geospatial.template_parameters](../../pvdeg/geospatial.py) and inspect this function to see how these different target functions utilize templates and shapes. + +# %% [markdown] +# # Creating Templates Programatically +# +# We can use `pvdeg.geospatial.autotemplate` to generate a template for a given function. This can return a bad result which will fail or work improperly when running `pvdeg.geospatial.analysis` with the generated template. Results should be scrutinized to make sure they are the right format. + +# %% [markdown] +# # Examples Below +# Steps +# - Create template using autotemplating function. Pulls in information about function to determine shape of output. Not usable on functions with ambigious return types. +# - Call geospatial analysis function using template + +# %% [markdown] +# # Geospatial Cell Temperature Calculation +# As shown below, we have two options, we can choose to provide a template that is generated by a function which supports autotemplating. Or we can provide the function to `geospatial.analysis` and let it generate a template internally. +# +# ## Providing a Template with `Geospatial.auto_template` + +# %% +# create a template using auto_template for the desired function +cell_temp_template = pvdeg.geospatial.auto_template( + func=pvdeg.temperature.cell, ds_gids=geo_weather +) + +# run the geospatial analysis with the template +pvdeg.geospatial.analysis( + weather_ds=geo_weather, + meta_df=geo_meta, + func=pvdeg.temperature.cell, + template=cell_temp_template, +) + +# %% [markdown] +# # Analysis Without Providing a Template +# +# If a function is supported by `geospatial.auto_template` we do not need to create a template outside of the function as shown in the cell above. We can simply pass the function to `geospatial.analysis` and it will create a template for us. + +# %% +pvdeg.geospatial.analysis( + weather_ds=geo_weather, + meta_df=geo_meta, + func=pvdeg.temperature.cell, +) + +# %% [markdown] +# # Geospatial Module Temperature Calculation + +# %% +module_temp_template = pvdeg.geospatial.auto_template( + func=pvdeg.temperature.module, ds_gids=geo_weather +) + +pvdeg.geospatial.analysis( + weather_ds=geo_weather, + meta_df=geo_meta, + func=pvdeg.temperature.module, + template=module_temp_template, +) + +# %% [markdown] +# # Geospatial Solar Position Calculation + +# %% +solar_position_template = pvdeg.geospatial.auto_template( + func=pvdeg.spectral.solar_position, ds_gids=geo_weather +) + +pvdeg.geospatial.analysis( + weather_ds=geo_weather, + meta_df=geo_meta, + func=pvdeg.spectral.solar_position, + template=solar_position_template, +) + +# %% [markdown] +# # Geospatial POA Irradiance Calculation + +# %% +poa_irradiance_template = pvdeg.geospatial.auto_template( + func=pvdeg.spectral.poa_irradiance, ds_gids=geo_weather +) + +pvdeg.geospatial.analysis( + weather_ds=geo_weather, + meta_df=geo_meta, + func=pvdeg.spectral.poa_irradiance, + template=poa_irradiance_template, +) + +# %% [markdown] +# # Geospatial 98th Percentile Operating Temperature Calculation + +# %% +standoff_template = pvdeg.geospatial.auto_template( + func=pvdeg.standards.T98_estimate, ds_gids=geo_weather +) + +pvdeg.geospatial.analysis( + weather_ds=geo_weather, + meta_df=geo_meta, + func=pvdeg.standards.T98_estimate, + template=standoff_template, +) + +# %% [markdown] +# # Geospatial Module Humidity Calculation + +# %% +humidity_template = pvdeg.geospatial.auto_template( + func=pvdeg.humidity.module, ds_gids=geo_weather +) + +pvdeg.geospatial.analysis( + weather_ds=geo_weather, + meta_df=geo_meta, + func=pvdeg.humidity.module, + template=humidity_template, + backsheet_thickness=0.3, + back_encap_thickness=0.5, +) + +# %% [markdown] +# # Geospatial IwaVantHoff Environment Characterization Calculation + +# %% +iwa_vant_hoff_template = pvdeg.geospatial.auto_template( + func=pvdeg.degradation.IwaVantHoff, ds_gids=geo_weather +) + +pvdeg.geospatial.analysis( + weather_ds=geo_weather, + meta_df=geo_meta, + func=pvdeg.degradation.IwaVantHoff, + template=iwa_vant_hoff_template, +) + +# %% [markdown] +# # Geospatial Edge Seal Width Calculation + +# %% +edge_seal_template = pvdeg.geospatial.auto_template( + func=pvdeg.design.edge_seal_width, ds_gids=geo_weather +) + +pvdeg.geospatial.analysis( + weather_ds=geo_weather, + meta_df=geo_meta, + func=pvdeg.design.edge_seal_width, + template=edge_seal_template, +) + +# %% +edge_seal_template diff --git a/tutorials/04_scenario/scripts/02_scenario_single_location.py b/tutorials/04_scenario/scripts/02_scenario_single_location.py new file mode 100644 index 000000000..1200adb84 --- /dev/null +++ b/tutorials/04_scenario/scripts/02_scenario_single_location.py @@ -0,0 +1,154 @@ +# %% [markdown] +# # Single Location +# +# Author: Tobin Ford | tobin.ford@nrel.gov +# +# 2024 +# **** +# +# A simple object orented workflow walkthrough using pvdeg. + +# %% +# if running on google colab, uncomment the next line and execute this cell to install the dependencies and prevent "ModuleNotFoundError" in later cells: +# # !pip install pvdeg + +# %% +import pvdeg +import os + +# %% +# This information helps with debugging and getting support :) +import sys +import platform + +print("Working on a ", platform.system(), platform.release()) +print("Python version ", sys.version) +print("pvdeg version ", pvdeg.__version__) + +# %% [markdown] +# # Define Single Point Scenario Object +# Scenario is a general class that can be used to replace the legacy functional pvdeg analysis approach with an object orented one. ``Scenario`` can preform single location or geospatial analysis. The scenario constructor takes many arguments but the only required one for the following use cases is the ``name`` attribute. It is visible in when we display the entire scenario and is present in the file of saved information about the scenario. We also need to provide the class constructor with our API key and email. +# +# A way around this is to provide the weather and metadata in the pipeline job arguments or you can load data from somewhere else and provide it in the same fashion. +# +#
+# Please use your own API key: The block below makes an NSRDB API to get weather and meta data. This tutorial will work with the DEMO Key provided, but it will take you less than 3 minutes to obtain your own at https://developer.nrel.gov/signup/ so register now.) +#
+ +# %% +simple_scenario = pvdeg.Scenario( + name="Point Minimum Standoff", email="user@mail.com", api_key="DEMO_KEY" +) + +# %% [markdown] +# # Adding A Location +# To add a single point using data from the Physical Solar Model (PSM3), simply feed the scenario a single coordinate in tuple form via the ``addLocation`` method. Currently this is the only way to add a location to a non-geospatial scenario, all of the other arguments are unusable when ``Scenario.geospatial == False``. +# +# Attempting to add a second location by calling the method again with a different coordinate pair will overwrite the old location data stored in the class instance. + +# %% +simple_scenario.addLocation( + lat_long=(25.783388, -80.189029), +) + +# %% +simple_scenario.weather_data + +# %% [markdown] +# # Scenario Pipelines +# +# The pipeline is a list of tasks called jobs for the scenario to run. We will populate the pipeline with a list of jobs before executing them all at once. +# +# To add a job to the pipeline use the ``updatePipeline`` method. Two examples of adding functions to the pipeline will be shown below. + +# %% [markdown] +# # Adding a job without function arguments +# +# The simplest case of adding a job to the pipeline is when it only requires us to provide simple weather and metadata. In the function definition and docstring these appear as ``weather_df`` and ``meta``. Since these attributes are contained in our scenario class instance we do not have to worry about them. We can simply add the function as shown below. + +# %% +simple_scenario.addJob(func=pvdeg.standards.standoff) + +# %% [markdown] +# # Adding a job with function arguments +# +# When adding a job that contains a function requiring other arguments such as ``solder_fatigue`` which requires a value for ``wind_factor``, we will need to provide it. The most straightforeward way to do this is using a kwargs dictionary and passing it to the function. We do not unpack the dictionary before passing it. This is done inside of the scenario at pipeline runtime (when ``runPipeline`` is called). + +# %% +kwargs = {"wind_factor": 0.33} + +simple_scenario.addJob(func=pvdeg.fatigue.solder_fatigue, func_kwarg=kwargs) + +# %% [markdown] +# # Adding a job with weather and metadata from outside of the class +# ## Not functional +# +# could just directly set weather data with scenario.weather_data = weather and scenario.meta_data = meta but that would only work for all of the jobs in the pipeline +# +# Say local weather data is available or other, if we want to use this rather than the PSM3 data at a latitude and longitude we can also provide the weather and metadata in the function arguments. This is probably the best if avoided but follows the same syntax as providing other function arguments. See the example below. + +# %% +PSM_FILE = os.path.join(pvdeg.DATA_DIR, "psm3_demo.csv") +weather, meta = pvdeg.weather.read(PSM_FILE, "psm") + +kwargs = {"weather_df": weather, "meta": meta} + +simple_scenario.addJob(func=pvdeg.standards.standoff, func_kwarg=kwargs) + +# FIX THIS CASE IN SCENARIO CLASS +# (simple_scenario.pipeline[1]['job'])(**simple_scenario.pipeline[1]['params']) + +# %% [markdown] +# # View Scenario +# +# The ``viewScenario`` method provides an overview of the information contained within your scenario object. Here you can see if it contains the location weather and metadata. As well as the jobs in the pipeline and their arguments. + +# %% +simple_scenario.viewScenario() + +# %% [markdown] +# # Display +# +# The fancier cousin of viewScenario. Only works in a jupyter environemnt as it uses a special ipython backend to render the html and javascript. +# +# It can be called with just the Scenario instance as follows +# `simple_scenario` +# +# or using the display function +# `display(simple_scenario)` + +# %% +simple_scenario + +# %% [markdown] +# # Executing Pipeline Jobs +# To run the pipeline after we have populated it with the desired jobs call the ``runPipeline`` method on our scenario instance. This will run all of the jobs we have previously added. The functions that need weather and metadata will grab it from the scenario instance using the correct location added above. The pipeline jobs results will be saved to the scenario instance. + +# %% +simple_scenario.run() + +# %% [markdown] +# # Results Series ## +# We will use a series to store the various return values of functions run in our pipeline. These can partially obfuscate the dataframes within them so to access the dataframes, use the function name to access it. To get one of the results we can index it using dictionary syntax. If the job was called `'KSDJQ'` do `'simple_scenario.results['KSDJQ']` to directly access the result for that job + +# %% +print(simple_scenario.results) +print("We can't see out data in here so we need to do another step", end="\n\n") + +# to see all available ouputs of results do +print( + f"this is the list of all available frames in results : {simple_scenario.results.index}\n" +) + +# loop over all results and display +for keys, results in simple_scenario.results.items(): + print(keys) + display(results) + +# %% [markdown] +# # Cleaning Up the Scenario +# +# Each scenario object creates a directory named ``pvd_job_...`` that contains information about the scenario instance. To remove the directory and all of its information call ``clean`` on the scenario. This will permanently delete the directory created by the scenario. + +# %% +simple_scenario.clean() diff --git a/tutorials/04_scenario/scripts/03_scenario_geographical_features.py b/tutorials/04_scenario/scripts/03_scenario_geographical_features.py new file mode 100644 index 000000000..17459eaea --- /dev/null +++ b/tutorials/04_scenario/scripts/03_scenario_geographical_features.py @@ -0,0 +1,116 @@ +# %% [markdown] +# # Geographical Features (HPC) +# + +# %% +import pvdeg +import numpy as np + +# %% [markdown] +# # Create Scenario and Populate Location +# The default geospatial datapoints for addLocation are from the Americas satellite. This is the only data we are interested in for this case so don't specify any location arguments. +# To speed up calculations we will downsample to get coarser data. The downsampling function is not linear. A downsample factor of 10 takes us from 2018267 entries to only 5109. + +# %% +features = pvdeg.GeospatialScenario(name="finding-features") + +features.addLocation(downsample_factor=10) + +# %% [markdown] +# # Intro to KDTrees +# +# A K dimensional tree is a datastructure for organizing n points in a k dimensional space. They are often used for nearest neighbors searches and many ML algorithms. Letting k = 2, our dimensions will be latitude and longitude. This is much faster than iterating over tabular data structures to find neighbors. The function to create a kdtree below requires scikit learn (also known as sklearn), a python machine learning library which is not included in the pvdeg dependency list so you will have to install it independently. Scipy also has a kdtree class but it is much slower. Depending on the number of datapoints the tree build time could be quite long but for our purposes, the cell below will be quick. + +# %% +tree = pvdeg.geospatial.meta_KDtree(meta_df=features.meta_data) + +# %% [markdown] +# # Remaining Points After Downsampling +# +# Use `plot_coords` to see all latitude longitude coordinate pairs included in a scenario's metadata. You can provide corners of a bounding box to change the extent of the matplotlib plot. + +# %% +features.plot_coords() + +# %% [markdown] +# # Identifying Mountains from Geospatial Data I +# +# Identify moutains by using the function below. There are currently 2 methods to identify mountains from our data. Both will add a column to our `GeospatialScenario.meta_data` attribute called `mountain` containing boolean values that represent if a data point is in/near mountains or not near mountains. +# +# - `classify_mountains_weights` uses k nearest neighbors search and calculates local changes in height with different methods, the default is the `mean` absolute change compared to a points neighbors, then it assigns a weight to each point. If the remaining points are above the threshold then the percentile argument determines sensitivity to classifying points as mountains (higher weight, more mountainous). Much better than the following option. +# - `classify_mountains_radii` uses 2 nearest neighbors radius searches at each point and compares the average elevation of the inner circle to the avg radius of the circle. + +# %% +features.classify_mountains_weights(kdtree=tree) + +# %% [markdown] +# # Identifing Mountains from Geospatial Data II +# +# We do not need to provide a `kdtree` when we call this method. We can provide a `kdtree` if we create one first with `pvdeg.geospatial.meta_kdtree` but it will work without supplying it. +# GeospatialScenario will create a kdtree internally for us. So the method can simply be called as shown below. + +# %% +# this will work without running the above cells to create a kdtree or the cell directly above where we utilize the created kdtree. +features.classify_mountains_weights() + +# %% [markdown] +# # Convenience Plotting for Binary Classifications +# +# the `plot_meta_classification` method allows you to plot your data with respect to a boolean classifier present in the `scenario.meta_data` dataframe columns. + +# %% +features.plot_meta_classification(col_name="mountain") + +# %% [markdown] +# # Identifying mountains within a bounding box. +# +# Sometimes we only want information from one area. To allow for more control over the selection process. To pick only one area from an existing superset of data we can use a bounding box. +# Either provide the top left and bottom right (latitude-longitude) pairs or a 2d numpy array of these pairs which will use the most extreme entries to form the bounding box. + +# %% +features.classify_mountains_radii( + kdtree=tree, + elevation_floor=0, + bbox_kwarg={ + "coords": np.array( + [ + [47.960502, -115.048828], + [47.842658, -101.118164], + [36.738884, -113.686523], + [36.633162, -100.283203], + ] + ) + }, +) + +# %% +features.plot_meta_classification(col_name="mountain") + +# %% [markdown] +# # Identifying Coastlines from Geospatial Data +# +# To identify points within a search radius of any [natural earth features](https://www.naturalearthdata.com/features/). The following examples show the process of identifying coastline datapoints. + +# %% +features.classify_feature( + kdtree=tree, + feature_name="coastline", + resolution="10m", + radius=0.5, # change this to get a wider band of selected data near the coastline +) + +# %% +features.plot_meta_classification(col_name="coastline") + +# %% [markdown] +# # Finding Rivers +# +# Above we saw how to find points near coastlines, now lets do points near rivers. It is very similar + +# %% +features.classify_feature( + kdtree=tree, feature_name="rivers_lake_centerlines", resolution="10m", radius=0.2 +) + +# %% +features.plot_meta_classification(col_name="rivers_lake_centerlines") diff --git a/tutorials/04_scenario/scripts/03_scenario_mountain_downselect.py b/tutorials/04_scenario/scripts/03_scenario_mountain_downselect.py new file mode 100644 index 000000000..8d0b0e509 --- /dev/null +++ b/tutorials/04_scenario/scripts/03_scenario_mountain_downselect.py @@ -0,0 +1,124 @@ +# %% [markdown] +# # Mountain Downselect +# + +# %% +import pvdeg +import numpy as np + +# %% [markdown] +# # Adding Points +# +# We are going to add all of the points in the American West to the scenario and downsample by a factor of 1. This will include only half of the points in the latitude axis and half in the longitude axis. + +# %% +dynamic_points = pvdeg.GeospatialScenario(name="dynamic-selection") + +dynamic_points.addLocation( + state=["CO", "UT"], # , 'NM', 'NV', 'ID', 'WY', 'AZ', 'CA', 'OR', 'WA'], + downsample_factor=1, +) + +# %% [markdown] +# # Preview The Scenario's Points +# +# Use `plot_cords` to get a quick snapshot of all coordinates included in the scenario's metadata. + +# %% +dynamic_points.plot_coords( + coord_1=[48.574790, -130.253906], # uncomment to see Larger scale view + coord_2=[25.482951, -68.027344], + size=0.005, +) + +# %% +dynamic_points.meta_data + +# %% [markdown] +# # Downselecting +# +# Using weighted random choices based on elevation we will create a sparse grid from the full metadata for fast calculations. This requires sklearn to be installed but this is not in the `pvdeg` dependency list to you will have to install it seperately. +# +# ## Normalization +# +# At each metadata point in our dataset we will calculate a weight based on its changes in elevation compared to its neighbors. The higher the weight, the greater the change in elevation from a point's immediate neighbors. The downselection methods and functions use these weights to randomly select a subset of the datapoints, prefferentially selecting those with higher weights. +# +# We have some control over which points get selected because all points' weights must be normalized (mapped from 0 to 1) before downselecting. We can apply a function such as $e^x$ or $\log x$ to the weights during normalization. This could help change the distribution of weights that are chosen. This could remove points from the mountains and add them to areas with fewer changes in elevation, or vice versa. +# +# *Note: `pvdeg`'s downselection functions use `numpy.random`, the random seed is not fixed so the result will change between runs.* + +# %% [markdown] +# # Providing a KdTree +# +# As shown below the lines to create a kdtree are commented out. +# + +# %% +# Set random seed for reproducible results +np.random.seed(42) + +# west_tree = pvdeg.geospatial.meta_KDtree(meta_df=dynamic_points.meta_data) + +dynamic_points.downselect_elevation_stochastic( + # kdtree=west_tree, + downselect_prop=0.5, + normalization="linear", +) + +# %% +dynamic_points.plot_coords() + +# %% [markdown] +# # Extracting from Scenario +# +# Scenarios provide an easy way to select and downsample geospatial data but we can easily pull out the data to use other `pvdeg` functions on it. In the cell below, we extract the weather data and meta data from the scenario and take only the matching entries from the weather. Then we load the xarray dataset into memory. Previously, it was stored lazily out of memory but we want to do operations on it. (Chunking causes issues when calculating so this eliminates any chunks) + +# %% +weather = dynamic_points.weather_data + +sub_weather = weather.sel( + gid=dynamic_points.meta_data.index +) # downselect weather using restricted metadata set + +sub_weather = sub_weather.compute() # load into memory + +# %% [markdown] +# # Geospatial Calculation +# +# Run a standoff calculation on the extracted scenario weather data and scenario meta data. + +# %% +# geospatial analysis now + +geo = { + "func": pvdeg.standards.standoff, + "weather_ds": sub_weather, + "meta_df": dynamic_points.meta_data, +} + +analysis_result = pvdeg.geospatial.analysis(**geo) + +# %% [markdown] +# # Viewing Results +# +# Inspecting the xarray dataset below shows us that we have many Not a Number (NaN) entries. These occur because we did not provide weather data at every point in the grid of possile latitude-longitude pairs. Expanding the `x` datavariable shows that there are some valid results but these are uncommon. + +# %% +analysis_result + +# %% [markdown] +# # Plotting Sparse Data I +# +# If we try to plot existing data with the current plotting methods exposed by `pvdeg` we will encounter issues. This will produce weak plotting results. + +# %% +# This cell demonstrates the issue with plotting sparse data directly +# It will raise a TypeError because there's no numeric data to plot +try: + pvdeg.geospatial.plot_USA(analysis_result["x"]) +except TypeError as e: + print(f"Expected error when plotting sparse data: {e}") + print("This is why we need to use plot_sparse_analysis() instead (see next cell)") + +# %% +pvdeg.geospatial.plot_sparse_analysis(analysis_result, data_var="x", method="linear") diff --git a/tutorials/04_scenario/scripts/04_scenario_geospatial.py b/tutorials/04_scenario/scripts/04_scenario_geospatial.py new file mode 100644 index 000000000..2438baf0c --- /dev/null +++ b/tutorials/04_scenario/scripts/04_scenario_geospatial.py @@ -0,0 +1,96 @@ +# %% [markdown] +# # Scenario Geospatial (HPC) +# + +# %% +import pvdeg + +# %% [markdown] +# # Define Geospatial Scenario Object +# +# To preform geospatial analysis we can create a `GeospatialScenario` object. Alternatively, to preform single location analysis use `Scenario`. Scenario and GeospatialScenario are generalized classes that can be used to replace the legacy functional pvdeg analysis approach with an object orented one. + +# %% +geospatial_standoff_scenario = pvdeg.GeospatialScenario( + name="standoff geospatial", +) + +# %% [markdown] +# # Add Location +# To add locations for geospatial analysis we will use the ``.addLocation`` method. We can choose downselect from the NSRDB to a country, state and county in that order. *Support for multiple of each category in list form soon.* The ``see_added`` flag allows us to see the gids we have added to the scenario. + +# %% +geospatial_standoff_scenario.addLocation( + state="Colorado", county="Summit", see_added=True, downsample_factor=3 +) + +# %% [markdown] +# # Add Functions to the Pipeline +# The scenario has a queue of jobs to preform. These are stored in an attribute called ``pipeline``, you can directly update the pipeline but this will bypass the assistance given in creating the job function and parameters. The easiest way to add a job to the pipeline is the ``.updatePipeline`` method. For geospatial analysis, weather and metadata is collected and stored in the scenario at the time of the ``.addLocation`` method call so we do not need to include it below, but if we have other function kwargs to include, they should go in the ``func_params`` argument. +# +# Only a few pvdeg functions are currently supported for geospatial analysis. See the docstring for ``.updatePipeline`` to view currently supported functions. ``updatePipeline`` will not let you add unsupported geospatial functions. The ``see_added`` flag allows us to see the new job added to the pipeline. + +# %% +geospatial_standoff_scenario.addJob(func=pvdeg.standards.standoff, see_added=True) + +# %% +geospatial_standoff_scenario + +# %% [markdown] +# # Run the job in the pipeline +# +# Currently ``scenario`` only supports one geospatial analysis at a time. We cannot have two geospatial jobs at the same time. + +# %% +geospatial_standoff_scenario.run() + +# %% [markdown] +# # Directly Access Results Attribute +# +# We can either view the results of the scenario pipeline using ``.viewScenario`` as shown above. The results will be displayed only if the pipeline has been run. Alternatively, we can directly view the ``results`` atribute of the scenario. + +# %% +geospatial_standoff_scenario.results + +# %% [markdown] +# # Cleanup +# +# The scenario object will store its attributes in a file the python script's current working directory. If we want to delete this file when we are done with the scenario instance we can use the ``.clean()`` method as shown below. + +# %% +geospatial_standoff_scenario.clean() + +# %% [markdown] +# # Example Geospatial Functionality +# Many functions are supported for geospatial analysis, here are a few. +# - ``pvdeg.standards.standoff`` +# - ``pvdeg.humidity.module`` +# - ``pvdeg.letid.calc_letid_outdoors`` +# +# See the Geospatial Templates tutorial for an example on this. + +# %% +geospatial_humidity_scenario = pvdeg.GeospatialScenario( + name="humidity scenario", geospatial=True +) + +geospatial_humidity_scenario.addLocation( + state="Colorado", county="Jefferson", see_added=True +) + +geospatial_humidity_scenario.addJob( + func=pvdeg.humidity.module, + func_params={ + "backsheet_thickness": 0.3, # mm, thickness of PET backsheet + "back_encap_thickness": 0.46, # mm, thickness of EVA backside encapsulant + "encapsulant": "W001", # EVA encapsulant + "backsheet": "W017", # PET backsheet + }, + see_added=True, +) + +# %% +geospatial_humidity_scenario.run() + +# %% +geospatial_humidity_scenario.results diff --git a/tutorials/04_scenario/scripts/05_scenario_mountain_downselect.py b/tutorials/04_scenario/scripts/05_scenario_mountain_downselect.py new file mode 100644 index 000000000..4068b5321 --- /dev/null +++ b/tutorials/04_scenario/scripts/05_scenario_mountain_downselect.py @@ -0,0 +1,124 @@ +# %% [markdown] +# # Mountain Downselect (HPC) +# + +# %% +import pvdeg +import numpy as np + +# %% [markdown] +# # Adding Points +# +# We are going to add all of the points in the American West to the scenario and downsample by a factor of 1. This will include only half of the points in the latitude axis and half in the longitude axis. + +# %% +dynamic_points = pvdeg.GeospatialScenario(name="dynamic-selection") + +dynamic_points.addLocation( + state=["CO", "UT"], # , 'NM', 'NV', 'ID', 'WY', 'AZ', 'CA', 'OR', 'WA'], + downsample_factor=1, +) + +# %% [markdown] +# # Preview The Scenario's Points +# +# Use `plot_cords` to get a quick snapshot of all coordinates included in the scenario's metadata. + +# %% +dynamic_points.plot_coords( + coord_1=[48.574790, -130.253906], # uncomment to see Larger scale view + coord_2=[25.482951, -68.027344], + size=0.005, +) + +# %% +dynamic_points.meta_data + +# %% [markdown] +# # Downselecting +# +# Using weighted random choices based on elevation we will create a sparse grid from the full metadata for fast calculations. This requires sklearn to be installed but this is not in the `pvdeg` dependency list to you will have to install it seperately. +# +# ## Normalization +# +# At each metadata point in our dataset we will calculate a weight based on its changes in elevation compared to its neighbors. The higher the weight, the greater the change in elevation from a point's immediate neighbors. The downselection methods and functions use these weights to randomly select a subset of the datapoints, prefferentially selecting those with higher weights. +# +# We have some control over which points get selected because all points' weights must be normalized (mapped from 0 to 1) before downselecting. We can apply a function such as $e^x$ or $\log x$ to the weights during normalization. This could help change the distribution of weights that are chosen. This could remove points from the mountains and add them to areas with fewer changes in elevation, or vice versa. +# +# *Note: `pvdeg`'s downselection functions use `numpy.random`, the random seed is not fixed so the result will change between runs.* + +# %% [markdown] +# # Providing a KdTree +# +# As shown below the lines to create a kdtree are commented out. +# + +# %% +# Set random seed for reproducible results +np.random.seed(42) + +# west_tree = pvdeg.geospatial.meta_KDtree(meta_df=dynamic_points.meta_data) + +dynamic_points.downselect_elevation_stochastic( + # kdtree=west_tree, + downselect_prop=0.5, + normalization="linear", +) + +# %% +dynamic_points.plot_coords() + +# %% [markdown] +# # Extracting from Scenario +# +# Scenarios provide an easy way to select and downsample geospatial data but we can easily pull out the data to use other `pvdeg` functions on it. In the cell below, we extract the weather data and meta data from the scenario and take only the matching entries from the weather. Then we load the xarray dataset into memory. Previously, it was stored lazily out of memory but we want to do operations on it. (Chunking causes issues when calculating so this eliminates any chunks) + +# %% +weather = dynamic_points.weather_data + +sub_weather = weather.sel( + gid=dynamic_points.meta_data.index +) # downselect weather using restricted metadata set + +sub_weather = sub_weather.compute() # load into memory + +# %% [markdown] +# # Geospatial Calculation +# +# Run a standoff calculation on the extracted scenario weather data and scenario meta data. + +# %% +# geospatial analysis now + +geo = { + "func": pvdeg.standards.standoff, + "weather_ds": sub_weather, + "meta_df": dynamic_points.meta_data, +} + +analysis_result = pvdeg.geospatial.analysis(**geo) + +# %% [markdown] +# # Viewing Results +# +# Inspecting the xarray dataset below shows us that we have many Not a Number (NaN) entries. These occur because we did not provide weather data at every point in the grid of possile latitude-longitude pairs. Expanding the `x` datavariable shows that there are some valid results but these are uncommon. + +# %% +analysis_result + +# %% [markdown] +# # Plotting Sparse Data I +# +# If we try to plot existing data with the current plotting methods exposed by `pvdeg` we will encounter issues. This will produce weak plotting results. + +# %% +# This cell demonstrates the issue with plotting sparse data directly +# It will raise a TypeError because there's no numeric data to plot +try: + pvdeg.geospatial.plot_USA(analysis_result["x"]) +except TypeError as e: + print(f"Expected error when plotting sparse data: {e}") + print("This is why we need to use plot_sparse_analysis() instead (see next cell)") + +# %% +pvdeg.geospatial.plot_sparse_analysis(analysis_result, data_var="x", method="linear") diff --git a/tutorials/04_scenario/scripts/06_scenario_single_location.py b/tutorials/04_scenario/scripts/06_scenario_single_location.py new file mode 100644 index 000000000..dd9daa366 --- /dev/null +++ b/tutorials/04_scenario/scripts/06_scenario_single_location.py @@ -0,0 +1,154 @@ +# %% [markdown] +# # Single Location (HPC) +# +# Author: Tobin Ford | tobin.ford@nrel.gov +# +# 2024 +# **** +# +# A simple object orented workflow walkthrough using pvdeg. + +# %% +# if running on google colab, uncomment the next line and execute this cell to install the dependencies and prevent "ModuleNotFoundError" in later cells: +# # !pip install pvdeg + +# %% +import pvdeg +import os + +# %% +# This information helps with debugging and getting support :) +import sys +import platform + +print("Working on a ", platform.system(), platform.release()) +print("Python version ", sys.version) +print("pvdeg version ", pvdeg.__version__) + +# %% [markdown] +# # Define Single Point Scenario Object +# Scenario is a general class that can be used to replace the legacy functional pvdeg analysis approach with an object orented one. ``Scenario`` can preform single location or geospatial analysis. The scenario constructor takes many arguments but the only required one for the following use cases is the ``name`` attribute. It is visible in when we display the entire scenario and is present in the file of saved information about the scenario. We also need to provide the class constructor with our API key and email. +# +# A way around this is to provide the weather and metadata in the pipeline job arguments or you can load data from somewhere else and provide it in the same fashion. +# +#
+# Please use your own API key: The block below makes an NSRDB API to get weather and meta data. This tutorial will work with the DEMO Key provided, but it will take you less than 3 minutes to obtain your own at https://developer.nrel.gov/signup/ so register now.) +#
+ +# %% +simple_scenario = pvdeg.Scenario( + name="Point Minimum Standoff", email="user@mail.com", api_key="DEMO_KEY" +) + +# %% [markdown] +# # Adding A Location +# To add a single point using data from the Physical Solar Model (PSM3), simply feed the scenario a single coordinate in tuple form via the ``addLocation`` method. Currently this is the only way to add a location to a non-geospatial scenario, all of the other arguments are unusable when ``Scenario.geospatial == False``. +# +# Attempting to add a second location by calling the method again with a different coordinate pair will overwrite the old location data stored in the class instance. + +# %% +simple_scenario.addLocation( + lat_long=(25.783388, -80.189029), +) + +# %% +simple_scenario.weather_data + +# %% [markdown] +# # Scenario Pipelines +# +# The pipeline is a list of tasks called jobs for the scenario to run. We will populate the pipeline with a list of jobs before executing them all at once. +# +# To add a job to the pipeline use the ``updatePipeline`` method. Two examples of adding functions to the pipeline will be shown below. + +# %% [markdown] +# # Adding a job without function arguments +# +# The simplest case of adding a job to the pipeline is when it only requires us to provide simple weather and metadata. In the function definition and docstring these appear as ``weather_df`` and ``meta``. Since these attributes are contained in our scenario class instance we do not have to worry about them. We can simply add the function as shown below. + +# %% +simple_scenario.addJob(func=pvdeg.standards.standoff) + +# %% [markdown] +# # Adding a job with function arguments +# +# When adding a job that contains a function requiring other arguments such as ``solder_fatigue`` which requires a value for ``wind_factor``, we will need to provide it. The most straightforeward way to do this is using a kwargs dictionary and passing it to the function. We do not unpack the dictionary before passing it. This is done inside of the scenario at pipeline runtime (when ``runPipeline`` is called). + +# %% +kwargs = {"wind_factor": 0.33} + +simple_scenario.addJob(func=pvdeg.fatigue.solder_fatigue, func_kwarg=kwargs) + +# %% [markdown] +# # Adding a job with weather and metadata from outside of the class +# ## Not functional +# +# could just directly set weather data with scenario.weather_data = weather and scenario.meta_data = meta but that would only work for all of the jobs in the pipeline +# +# Say local weather data is available or other, if we want to use this rather than the PSM3 data at a latitude and longitude we can also provide the weather and metadata in the function arguments. This is probably the best if avoided but follows the same syntax as providing other function arguments. See the example below. + +# %% +PSM_FILE = os.path.join(pvdeg.DATA_DIR, "psm3_demo.csv") +weather, meta = pvdeg.weather.read(PSM_FILE, "psm") + +kwargs = {"weather_df": weather, "meta": meta} + +simple_scenario.addJob(func=pvdeg.standards.standoff, func_kwarg=kwargs) + +# FIX THIS CASE IN SCENARIO CLASS +# (simple_scenario.pipeline[1]['job'])(**simple_scenario.pipeline[1]['params']) + +# %% [markdown] +# # View Scenario +# +# The ``viewScenario`` method provides an overview of the information contained within your scenario object. Here you can see if it contains the location weather and metadata. As well as the jobs in the pipeline and their arguments. + +# %% +simple_scenario.viewScenario() + +# %% [markdown] +# # Display +# +# The fancier cousin of viewScenario. Only works in a jupyter environemnt as it uses a special ipython backend to render the html and javascript. +# +# It can be called with just the Scenario instance as follows +# `simple_scenario` +# +# or using the display function +# `display(simple_scenario)` + +# %% +simple_scenario + +# %% [markdown] +# # Executing Pipeline Jobs +# To run the pipeline after we have populated it with the desired jobs call the ``runPipeline`` method on our scenario instance. This will run all of the jobs we have previously added. The functions that need weather and metadata will grab it from the scenario instance using the correct location added above. The pipeline jobs results will be saved to the scenario instance. + +# %% +simple_scenario.run() + +# %% [markdown] +# # Results Series ## +# We will use a series to store the various return values of functions run in our pipeline. These can partially obfuscate the dataframes within them so to access the dataframes, use the function name to access it. To get one of the results we can index it using dictionary syntax. If the job was called `'KSDJQ'` do `'simple_scenario.results['KSDJQ']` to directly access the result for that job + +# %% +print(simple_scenario.results) +print("We can't see out data in here so we need to do another step", end="\n\n") + +# to see all available ouputs of results do +print( + f"this is the list of all available frames in results : {simple_scenario.results.index}\n" +) + +# loop over all results and display +for keys, results in simple_scenario.results.items(): + print(keys) + display(results) + +# %% [markdown] +# # Cleaning Up the Scenario +# +# Each scenario object creates a directory named ``pvd_job_...`` that contains information about the scenario instance. To remove the directory and all of its information call ``clean`` on the scenario. This will permanently delete the directory created by the scenario. + +# %% +simple_scenario.clean() diff --git a/tutorials/04_scenario/scripts/07_scenario_temperature.py b/tutorials/04_scenario/scripts/07_scenario_temperature.py new file mode 100644 index 000000000..607d2a780 --- /dev/null +++ b/tutorials/04_scenario/scripts/07_scenario_temperature.py @@ -0,0 +1,163 @@ +# %% [markdown] +# # Temperature Scenario (HPC) +# + +# %% +# if running on google colab, uncomment the next line and execute this cell to install the dependencies and prevent "ModuleNotFoundError" in later cells: +# # !pip install pvdeg + +# %% +import pvdeg +import os + +# %% +# This information helps with debugging and getting support :) +import sys +import platform + +print("Working on a ", platform.system(), platform.release()) +print("Python version ", sys.version) +print("pvdeg version ", pvdeg.__version__) + +# %% [markdown] +# # Adding Modules and Pipeline Jobs (Run Functions on Scenario Object) +# +# Material: `OX003` corresponds to a set of EVA material parameters from the default file `O2Permeation.json` in the `pvdeg/data` directory. Look in these files to see available options. + +# %% +scene_temp = pvdeg.Scenario( + name="temperature and degradation", + api_key="DEMO_KEY", + email="user@mail.com", +) + +scene_temp.addLocation( + lat_long=(25.783388, -80.189029), +) + +# this module will be overwritten because another with the same name is added afterwards +scene_temp.addModule(module_name="sapm_1", temperature_model="sapm") + +scene_temp.addModule( + module_name="sapm_1", + racking="open_rack_glass_polymer", + materials="OX003", + temperature_model="sapm", + irradiance_kwarg={"azimuth": 120, "tilt": 30}, + model_kwarg={"irrad_ref": 1100}, +) + +scene_temp.addModule( + module_name="pvsyst_1", + racking="freestanding", + materials="OX003", + temperature_model="pvsyst", + irradiance_kwarg={"azimuth": 180, "tilt": 0}, + model_kwarg={"module_efficiency": 0.15}, +) +scene_temp.addModule( + module_name="sapm_2", + racking="open_rack_glass_polymer", + materials="OX003", + temperature_model="sapm", + irradiance_kwarg={"azimuth": 120, "tilt": 30}, + model_kwarg={"irrad_ref": 1000}, +) +scene_temp.addModule( + module_name="sapm_3", + racking="open_rack_glass_polymer", + materials="OX003", + temperature_model="sapm", + irradiance_kwarg={"azimuth": 180, "tilt": 0}, + model_kwarg={"irrad_ref": 1000}, +) + +scene_temp.addModule( + module_name="pvsyst_2", + racking="freestanding", + materials="OX003", + temperature_model="pvsyst", + irradiance_kwarg={"azimuth": 180, "tilt": 0}, + model_kwarg={"module_efficiency": 0.2}, +) + +scene_temp.addJob( + func=pvdeg.temperature.temperature, + func_kwarg={"cell_or_mod": "cell"}, +) + +scene_temp.addJob( + func=pvdeg.degradation.vantHoff_deg, + func_kwarg={"I_chamber": 1000, "temp_chamber": 25}, +) + +scene_temp.addJob( + func=pvdeg.degradation.vantHoff_deg, + func_kwarg={"I_chamber": 1000, "temp_chamber": 30}, +) + +scene_temp.addJob( + func=pvdeg.degradation.IwaVantHoff, +) + +# %% [markdown] +# # Run and View Scenario Results + +# %% +scene_temp.run() + +scene_temp + +# %% +scene_temp.dump() + +# %% [markdown] +# # Plotting and Extracting Results +# These methods are independent of one another (i.e. you do not need to extract before plotting but both are shown below.) + +# %% +import datetime + +t0 = datetime.datetime(1970, 1, 1, 0, 0) +tf = datetime.datetime(1970, 1, 1, 23, 59) + +# Get the first function result dynamically +function_ids = [key[1] for key in scene_temp.results.keys() if key[0] == "function"] +if function_ids: + temp_df = scene_temp.extract( + ("function", function_ids[0]), tmy=True, start_time=t0, end_time=tf + ) + display(temp_df) +else: + print("No function results found") + +# %% +# Get the first function result dynamically for plotting +function_ids = [key[1] for key in scene_temp.results.keys() if key[0] == "function"] +if function_ids: + scene_temp.plot( + ("function", function_ids[0]), + tmy=True, + start_time=t0, + end_time=tf, + title="single day cell temperature", + ) +else: + print("No function results found") + +# %% [markdown] +# # Create a Copy of a Scenario + +# %% +from pathlib import Path + +parent_dir = Path(pvdeg.PVDEG_DIR).parent +new_path = parent_dir / "tutorials" / "data" / "temperature_and_degradation.json" + +copy = pvdeg.scenario.Scenario.load_json( + file_path=str(new_path), + email="user@mail.com", + api_key="DEMO_KEY", +) + +# copy diff --git a/tutorials/04_scenario/scripts/08_module_standoff_iec63126.py b/tutorials/04_scenario/scripts/08_module_standoff_iec63126.py new file mode 100644 index 000000000..cdd201ef1 --- /dev/null +++ b/tutorials/04_scenario/scripts/08_module_standoff_iec63126.py @@ -0,0 +1,499 @@ +# %% [markdown] +# # Tools - Module Standoff for IEC TS 63126 +# +# ## Calculation of module standoff distance according to IEC TS 63126 +# +# **Requirements:** +# - Local weather data file or site longitude and latittude +# +# **Objectives:** +# 1. Import weather data. +# 2. Calculate installation standoff - Level 1 and Level 2. +# 3. Calculate $X_{eff}$ from provided module temperature data. +# 4. Calculate $T_{98}$ for a given azimuth, tilt, and $X_{eff}$. +# 5. Plot $X_{min}$ for all azimuth and tilt for a given $T_{98}$. +# 6. Plot $X_{min}$ for Level 1, Level 2, or a $T_{98}$ for a given region. +# +# **Background:** +# +# This notebook calculates the a minimum effective standoff distance ($X_{eff}$) necessary for roof-mounted PV modules to ensure that the $98^{th}$ percentile operating temperature, $T_{98}$, remains under 70Β°C for compliance to IEC 61730 and IEC 61215. For higher $T_{98}$ values above 70Β°C or 80Β°C testing must be done to the specifications for Level 1 and Level 2 of IEC TS 63126. This method is outlined in the appendix of IEC TS 63126 and is based on the model from *[King 2004] and data from **[Fuentes, 1987] to model the approximate exponential decay in temperature, $T(X)$, with increasing standoff distance, $X$, as, +# +# $$ X = -X_0 \ln\left(1-\frac{T_0-T}{\Delta T}\right), Equation 1 $$ +# +# where $T_0$ is the temperature for $X=0$ (insulated-back) and $\Delta T$ is the temperature difference between an insulated-back ($X=0$) and open-rack mounting configuration ($X=\infty)$. +# +# We used pvlib and data from the National Solar Radiation Database (NSRDB) to calculate the module temperatures for the insulated-back and open-rack mounting configurations and apply our model to obtain the minimum standoff distance for roof-mounted PV systems to achieve a temperature lower than a specified $T_{98}$. The following figure showcases this calulation for the entire world for an $X_{eff}$ that results in $T_{98}$=70Β°C. Values of $X_{eff}$ higher than this will require Level 1 or Level 2 certification. +# +# $*$ D. L. King, W. E. Boyson, and J. A. Kratochvil, "Photovoltaic array performance model," SAND2004-3535, Sandia National Laboratories, Albuquerque, NM, 2004. '\ +# $**$ M. K. Fuentes, "A simplified thermal model for Flat-Plate photovoltaic arrays," United States, 1987-05-01 1987. https://www.osti.gov/biblio/6802914 +# + +# %% [markdown] +# ![alt text](images/T98_70C_standoff_Map.png) + +# %% +# if running on google colab, uncomment the next line and execute this cell to install the dependencies +# and prevent "ModuleNotFoundError" in later cells: +# #!pip install pvdeg + +# %% +import os +import pvdeg +import pandas as pd +from pvdeg import DATA_DIR +import dask +import matplotlib.pyplot as plt +import seaborn as sns +import math +import numpy as np + +# %% +# This information helps with debugging and getting support :) +import sys +import platform + +print("Working on a ", platform.system(), platform.release()) +print("Python version ", sys.version) +print("Pandas version ", pd.__version__) +print("pvdeg version ", pvdeg.__version__) +print("dask version", dask.__version__) +print(DATA_DIR) + +# %% [markdown] +# # 1. Import Weather Data +# +# The function has these minimum requirements when using a weather data file: +# - Weather data containing (at least) DNI, DHI, GHI, Temperature, RH, and Wind-Speed data at module level. +# - Site meta-data containing (at least) latitude, longitude, and time zone +# +# Alternatively one may can get meterological data from the NSRDB or PVGIS with just the longitude and latitude. This function for the NSRDB (via NSRDB 'PSM3') works primarily for most of North America and South America. PVGIS works for most of the rest of the world (via SARAH 'PVGIS'). See the tutorial "Weather Database Access.ipynb" tutorial on PVdeg or Jensen et al. https://doi.org/10.1016/j.solener.2023.112092 for satellite coverage information. +# + +# %% +# Get data from a supplied data file (Do not use the next box of code if using your own file) +weather_file = os.path.join(DATA_DIR, "psm3_demo.csv") +WEATHER_df, META = pvdeg.weather.read(weather_file, "csv") +print(META) + +# %% +# This routine will get a meteorological dataset from anywhere in the world where it is available +# weather_id = (24.7136, 46.6753) #Riyadh, Saudi Arabia +# weather_id = (35.6754, 139.65) #Tokyo, Japan +# weather_id = (-43.52646, 172.62165) #Christchurch, New Zealand +# weather_id = (64.84031, -147.73836) #Fairbanks, Alaska +# weather_id = (65.14037, -21.91633) #Reykjavik, Iceland +# weather_id = (33.4152, -111.8315) #Mesa, Arizona +# WEATHER_df, META = pvdeg.weather.get_anywhere(id=weather_id) +# print(META) + +# %% [markdown] +# # 2. Calculate Installation Standoff Minimum - Level 1 and Level 2 +# +# According to IEC TS 63126, Level 0, Level 1 and Level 2 certification is limited to Tβ‚‰β‚ˆ<70Β°C, <80Β°C and <90Β°C, respectively. Level 0 certification is essentially compliance to IEC 61730 and IEC 61215. The default value of Tβ‚‰β‚ˆ<70Β°C represents the minimium gap to avoid higher temperature certification according to IEC TS 63126. This minimum standoff ($x_{min}$) is the distance between the bottom of the module frame and the roof and can be extimated for a given environment as, +# +# $$ X_{min} = -X_0 \ln\left(1-\frac{T_{98,0}-T}{ T_{98,0}- T_{98,inf}}\right), Equation 2 $$ +# +# where $T_{98,0}$ is the $98^{th}$ percentile temperature for an insulated back module and $T_{98,inf}$ is the $98^{th}$ percentile temperature for an open rack mounted module. +# +# Once the meterological data has been obtained, the input parameter possibilities are: +# +# - Tβ‚‰β‚ˆ : Does not necessarily need to be set at 70Β°C or 80Β°C for IEC TS 63216, you might want to use a different number to compensate for a thermal aspect of the particular system you are considering. The default is 70Β°C. +# - tilt : tilt from horizontal of PV module. The default is 0Β°. +# - azimuth : azimuth in degrees from North. The default is 180Β° for south facing. +# - sky_model : pvlib compatible model for generating sky characteristics (Options: 'isotropic', 'klucher', 'haydavies', 'reindl', 'king', 'perez'). The default is 'isotropic'. +# - temp_model : pvlib compatible module temperature model. (Options: 'sapm', 'pvsyst', 'faiman', 'sandia'). The default is 'sapm'. +# - conf_0 : Temperature model for hotest mounting configuration. Default is "insulated_back_glass_polymer". +# - conf_inf : Temperature model for open rack mounting. Default is "open_rack_glass_polymer". +# - x_0 : thermal decay constant [cm] (see documentation). The default is 6.5 cm. +# - wind_factor : Wind speed power law correction factor to account for different wind speed measurement heights between weather database (e.g. NSRDB) and the tempeature model (e.g. SAPM). The default is 0.33. + +# %% [markdown] +# The following is the minimum function call. It defaults to horizontal tilt and Tβ‚‰β‚ˆ=70Β°C. + +# %% +standoff = pvdeg.standards.standoff(weather_df=WEATHER_df, meta=META) +print(pvdeg.standards.interpret_standoff(standoff)) + +# %% [markdown] +# The following is a full function call for both Tβ‚‰β‚ˆ=70Β°C and 80Β°C separately even though the second standoff distance can be calculated using only T98_0 and T98_inf. With this function, one may also want to change the tilt, azimuth, or T98. + +# %% +standoff_1 = pvdeg.standards.standoff( + weather_df=WEATHER_df, + meta=META, + T98=70, + tilt=META["latitude"], + azimuth=None, + sky_model="isotropic", + temp_model="sapm", + conf_0="insulated_back_glass_polymer", + conf_inf="open_rack_glass_polymer", + x_0=6.5, + wind_factor=0.33, +) +print("First calculation standoff = ", "%.1f" % standoff_1["x"].iloc[0], " cm.") +standoff_2 = pvdeg.standards.standoff( + weather_df=WEATHER_df, + meta=META, + T98=80, + tilt=META["latitude"], + azimuth=None, + sky_model="isotropic", + temp_model="sapm", + conf_0="insulated_back_glass_polymer", + conf_inf="open_rack_glass_polymer", + x_0=6.5, + wind_factor=0.33, +) +print("Second calculation standoff = ", "%.1f" % standoff_2["x"].iloc[0], " cm.") +print(pvdeg.standards.interpret_standoff(standoff_1=standoff_1, standoff_2=standoff_2)) + +# %% [markdown] +# # 3. Calculate $X_{eff}$ from provided module temperature data. +# +# To do this calculation, one must use a set of data with: +# - meterological irradiance data sufficient to calculate the POA irradiance (DHI, GHI, and DNI), +# - ambient temperature data, +# - wind speed at module height, (wind_factor=0.33 will be used unless otherwise specified) +# - temperature measurements of the module in the test system. Ideally this would be measured under a worst case scenario that maximizes the module temperature for a given site, +# - geographic meta data including longitude and latitude, +# +# To create a weather file of your own, copy the format of the example file 'xeff_demo.csv'. This is formatted with the first row containing meta data variable names, the second row containing the corresponding values, the third row containing meteorological data headers, and all the remaining rows containing the meteorological data. +# +# To do this calculation, one should also filter the data to remove times when the sun is not shining or when snow is likely to be on the module. The recommendations and programmed defaults are to use poa_min=100 W/mΒ² and data when the minimum ambient temperature t_amb_min=0. + +# %% +# Read the weather file +weather_file = os.path.join(DATA_DIR, "xeff_demo.csv") +xeff_weather, xeff_meta = pvdeg.weather.read(weather_file, "csv") +# Pull measured temperature and calculate theoretical insulated back module temperature and open rack module temperature +T_0, T_inf, xeff_poa = pvdeg.standards.eff_gap_parameters( + weather_df=xeff_weather, + meta=xeff_meta, + sky_model="isotropic", + temp_model="sapm", + conf_0="insulated_back_glass_polymer", + conf_inf="open_rack_glass_polymer", + wind_factor=0.33, +) +# Now calculate X_eff. +x_eff = pvdeg.standards.eff_gap( + T_0, + T_inf, + xeff_weather["module_temperature"], + xeff_weather["temp_air"], + xeff_poa["poa_global"], + x_0=6.5, + poa_min=100, + t_amb_min=0, +) +print("The effective standoff for this system is", "%.1f" % x_eff, "cm.") + +# %% [markdown] +# # 4. Calculate $T_{98}$ for a given azimuth, tilt, and $X_{eff}$. +# +# Equation 2 can be reorganized as, +# +# $$ T_{98} = T_{98,0} -( T_{98,0}- T_{98,inf}) \left(1-e^{-\frac{x_{eff}}{x_{0}}}\right), Equation 3 $$ +# +# and used to calculate the $98^{th}$ percential temperature, $T_{98}$, for a PV system having a given effective standoff height, $X_{eff}$, for an arbitrarily oriented module. Here, $T_{98,0}$ is the $98^{th}$ percentile for an insulated-back module and $T_{98,inf}$ is the $98^{th}$ percentile for a rack-mounted module. The input parameter possibilities are the same as shown in Objective #2 above, but the example below uses the default parameters. The actual tilt [degrees], azimuth [degrees] and $X_{eff}$ [cm] can be modifed as desired. + +# %% +# This is the minimal function call using the common default settings to estimate Tβ‚‰β‚ˆ. +T_98 = pvdeg.standards.T98_estimate( + weather_df=WEATHER_df, meta=META, tilt=-META["latitude"], azimuth=None, x_eff=10 +) +print("The 98α΅—Κ° percential temperature is estimated to be", "%.1f" % T_98, "Β°C.") + +# %% +# This code will calculate the temperature for an arbitrary x_eff distance. Either set of kwargs can be modified and use. +# irradiance_kwarg ={ +# "axis_tilt": None, +# "axis_azimuth": None, +# "x_eff": 10, +# "module_mount": '1_axis'} +irradiance_kwarg = { + "tilt": META["latitude"], + "azimuth": None, + "x_eff": 10, + "module_mount": "fixed", +} + +T_xeff = pvdeg.standards.x_eff_temperature_estimate( + weather_df=WEATHER_df, meta=META, **irradiance_kwarg +) + +print( + "The 98α΅—Κ° percential temperature is estimated to be", + "%.1f" % np.percentile(T_xeff, 98), + "Β°C.", +) + +# %% [markdown] +# # 5. Plot $X_{min}$ for all azimuth and tilt for a given $T_{98}$. +# +# The temperature of a system is affected by the orientation. This section will scan all possible tilts and azimuths calculating the minimum standoff distance for a given $T_{98}$. Similar additional factors as above can also be modified but are not included here for simplicity. The tilt_step and azimuth_step are the number of degrees for each step for the 90Β° and 180Β° tilt and azimuth spans, respectively. The default for this calculation is for $T_{98}$=70Β°C, the boundary between Level 0 and Level 1 requirements. The temperature model information given below is unnecessary as these are default values that would get populated automatically. However, they were included here for clarity into a standard practice as per IEC TS 63126. +# +# $$ X_{min} = -X_0 \ln\left(1-\frac{T_{98,0}-T}{ T_{98,0}- T_{98,inf}}\right), Equation 2 $$ + +# %% +# Scans through all the azimuth and tilt running the minimum standoff calculation +# Set up keyword parameters for the calculation + +kwarg_x = dict( + sky_model="isotropic", + temp_model="sapm", + conf_0="insulated_back_glass_polymer", + conf_inf="open_rack_glass_polymer", + T98=70, + x_0=6.5, + wind_factor=0.33, +) +# Run the calculation +x_azimuth_step = 10 +x_tilt_step = 10 +standoff_series = pvdeg.utilities.tilt_azimuth_scan( + weather_df=WEATHER_df, + meta=META, + tilt_step=x_tilt_step, + azimuth_step=x_azimuth_step, + func=pvdeg.standards.standoff_x, + **kwarg_x, +) + +# %% [markdown] +# The next cell creates a plot of the calculated data. Some of the things you may want to change are: +# - cmap="Spectral_r": Change to have different colors +# - plt.title : This will change the plot title. +# - figsize=(16,4) : Change the plot dimensions and/or aspect ratio. +# - vmax=None : This can be set to a numeric value to control the depth scale maximum +# - vmin=0 : This controls the minimum of the depth scale. +# - v_ticks=37 : This changes the number of vertical tick marks +# - h_ticks=10 : This changes the number of horizontal tick marks +# - Unblock the last two lines to ouput the plot as an *.png image file + +# %% +standoff_series_df = pd.DataFrame( + { + "Tilt": standoff_series[:, 0], + "Azimuth": standoff_series[:, 1], + "Xβ‚˜α΅’β‚™": standoff_series[:, 2], + } +) +x_fig = plt.figure(figsize=(16, 4)) +plt.title( + r"Plot of $\it{Xβ‚˜α΅’β‚™}$ for all orientations for $\it{Tβ‚‰β‚ˆ}$=" + + "%.0f" % kwarg_x["T98"] + + "Β°C.", + fontsize=15, + y=1.08, +) +x_fig = sns.heatmap( + standoff_series_df.pivot(index="Tilt", columns="Azimuth", values="Xβ‚˜α΅’β‚™"), + cbar_kws={"label": "Xβ‚˜α΅’β‚™", "format": "%.0f", "pad": 0.02}, + cmap="Spectral_r", + vmin=0, + vmax=None, +) + +h_ticks = 37 +x_number = math.ceil(360 / x_azimuth_step) + 1 +x_ticks = [ + (x * (360 / (h_ticks - 1)) / x_azimuth_step + 0.5) for x in range(h_ticks - 1) +] +x_labels = [("%.0f" % (360 / (h_ticks - 1) * x)) for x in range(h_ticks)] +x_ticks.append(x_number - 0.5) +x_fig.set_xticks(x_ticks) +x_fig.set_xticklabels(x_labels, rotation=90) + +v_ticks = 10 +y_number = math.ceil(90 / x_tilt_step) + 1 +y_ticks = [(x * (90 / (v_ticks - 1)) / x_tilt_step + 0.5) for x in range(v_ticks - 1)] +y_labels = [("%.0f" % (90 / (v_ticks - 1) * x)) for x in range(v_ticks)] +y_ticks.append(y_number - 0.5) +x_fig.set_yticks(y_ticks) +x_fig.set_yticklabels(y_labels, rotation=0) + +x_fig.set_xlabel("Azimuth [Β°]", fontsize=15, labelpad=10) +x_fig.set_ylabel("Tilt [Β°]", fontsize=15) +x_fig.figure.axes[-1].set_ylabel(r"$\it{Xβ‚˜α΅’β‚™}$ [cm]", size=15) +x_fig.invert_yaxis() + +output_folder = os.path.join( + os.path.dirname(os.path.dirname(os.getcwd())), "TEMP", "results" +) +try: + os.makedirs(output_folder) +except OSError as error: + print(error) + +plt.savefig( + os.path.join(output_folder, "Standoff_Scan.png"), bbox_inches="tight" +) # Creates an image file of the standoff plot +plt.show() + +# %% [markdown] +# # 6. Plot $T_{98}$ for all azimuth and tilt for a given $X_{eff}$. +# +# The temperature of a system is affected by the orientation and the effective standoff, $X_{eff}$, of the system. This section will scan all possible tilts and azimuths calculating the $T_{98}$ for a given $X_{eff}$. As above, additional factors can be modified but are not included here for simplicity. The tilt_step and azimuth_step are the number of degrees for each step for the 90Β° and 180Β° tilt and azimuth spans, respectively. The default for this calculation is for $X_{eff}$=10 cm, a common effective standoff distance on a rooftop system. A value of $X_{eff}$=None will run the calculations for an open rack system and $X_{eff}$=0 for an insulated-back system. + +# %% +# Scans through all the azimuth and tilt running the 98α΅—Κ° percentile temperature calculation. +# Set up keyword parameters for the calculation +kwarg_T = dict( + sky_model="isotropic", + temp_model="sapm", + conf_0="insulated_back_glass_polymer", + conf_inf="open_rack_glass_polymer", + x_eff=5, + x_0=6.5, + wind_factor=0.33, +) +# Run the calculation +T_azimuth_step = 10 +T_tilt_step = 10 +T98_series = pvdeg.utilities.tilt_azimuth_scan( + weather_df=WEATHER_df, + meta=META, + tilt_step=T_tilt_step, + azimuth_step=T_azimuth_step, + func=pvdeg.standards.T98_estimate, + **kwarg_T, +) + +# %% [markdown] +# The next cell creates a plot of the calculated data. Some of the things you may want to change are: +# - cmap="Spectral_r": Change to have different colors +# - plt.title : This will change the plot title. +# - figsize=(16,4) : Change the plot dimensions and/or aspect ratio. +# - vmax=None : This can be set to a numeric value to control the depth scale maximum +# - vmin=None : This controls the minimum of the depth scale. +# - v_ticks=37 : This changes the number of vertical tick marks +# - h_ticks=10 : This changes the number of horizontal tick marks +# - Unblock the last two lines to ouput the plot as an *.png image file + +# %% +# This produces the plot of the data +T98_series_df = pd.DataFrame( + {"Tilt": T98_series[:, 0], "Azimuth": T98_series[:, 1], "Tβ‚‰β‚ˆ": T98_series[:, 2]} +) +T98_fig = plt.figure(figsize=(16, 4)) +if kwarg_T["x_eff"] == None: + plt.title( + r"Plot of $\it{Tβ‚‰β‚ˆ}$ for all orientations for an open-rack mounting.", + fontsize=15, + y=1.08, + ) +else: + plt.title( + r"Plot of $\it{Tβ‚‰β‚ˆ}$ for all orientations for $X_{eff}$=" + + "%.0f" % kwarg_T["x_eff"] + + " cm.", + fontsize=15, + y=1.08, + ) +T98_fig = sns.heatmap( + T98_series_df.pivot(index="Tilt", columns="Azimuth", values="Tβ‚‰β‚ˆ"), + cbar_kws={"label": "Xβ‚˜α΅’β‚™", "format": "%.0f", "pad": 0.02}, + cmap="Spectral_r", + vmin=None, + vmax=None, +) + +h_ticks = 37 +x_number = math.ceil(360 / T_azimuth_step) + 1 +x_ticks = [ + (x * (360 / (h_ticks - 1)) / T_azimuth_step + 0.5) for x in range(h_ticks - 1) +] +x_labels = [("%.0f" % (360 / (h_ticks - 1) * x)) for x in range(h_ticks)] +x_ticks.append(x_number - 0.5) +T98_fig.set_xticks(x_ticks) +T98_fig.set_xticklabels(x_labels, rotation=90) + +v_ticks = 10 +y_number = math.ceil(90 / T_tilt_step) + 1 +y_ticks = [(x * (90 / (v_ticks - 1)) / T_tilt_step + 0.5) for x in range(v_ticks - 1)] +y_labels = [("%.0f" % (90 / (v_ticks - 1) * x)) for x in range(v_ticks)] +y_ticks.append(y_number - 0.5) +T98_fig.set_yticks(y_ticks) +T98_fig.set_yticklabels(y_labels, rotation=0) + +T98_fig.set_xlabel("Azimuth [Β°]", fontsize=15, labelpad=10) +T98_fig.set_ylabel("Tilt [Β°]", fontsize=15) +T98_fig.figure.axes[-1].set_ylabel(r"$\it{Tβ‚‰β‚ˆ}$ [Β°C]", size=15) +T98_fig.invert_yaxis() + +plt.savefig( + os.path.join(output_folder, "T98_Scan.png"), bbox_inches="tight" +) # Creates an image file of the standoff plot +plt.show(T98_fig) + +# %% [markdown] +# # 7. Plot $X_{min}$ for a $T_{98}$, and plot $T_{98}$ for a given region. +# +# This last Objective is much more complicated and is set up to utilize acess to a lot of computational power to run many sites simultaneously to create a regional map of standoff distance. +# For more in-depth instructions on doing this, look at the tutorial "Scenario - Geospatial.ipynb" here in PVDeg. +# +# Step #1: Create an object, "geospatial_standoff_scenario" to be used to run the computations. + +# %% +geospatial_standoff_scenario = pvdeg.GeospatialScenario( + name="standoff geospatial", + geospatial=True, +) + +# %% [markdown] +# Step #2: Identifies a subset of locations from the database to run the computations. +# Specifically all are from the NSRDB. + +# %% +geospatial_standoff_scenario.addLocation( + state="Colorado", county="Summit" +) # Identifies a subset of locations from the database to run the computations. Specifically all are from the NSRDB. + +# %% [markdown] +# Step #3: indicate which function will be run. Here the default is the standoff calculation, but it could be any other function with a key word argument dictionary. +# Here the 98th percential temperature is defined as 70C, but any arbitrary value can be specified. + +# %% +geospatial_standoff_scenario.addJob( + func=pvdeg.standards.standoff, func_params={"T98": 70} +) + +# %% [markdown] +# Step #4: Run the scenario + +# %% +geospatial_standoff_scenario.run() + +# %% [markdown] +# Step #5: Create a plot of the standoff calculation. + +# %% +geospatial_standoff_scenario.plot_world("x") + +# %% +geospatial_standoff_scenario.plot_world("T98_inf") + +# %% [markdown] +# # 8. Save data outputs. +# +# This cell contains a number of pre-scripted commands for exporting and saving data. The code to save plots is located after the plot creation and is blocked by default. First check that the output folder exists, then unblock the code for data you would like to save. + +# %% +print("Your results will be stored in %s" % output_folder) +print("The folder must already exist or the file will not be created") + +pvdeg.weather.write( + data_df=WEATHER_df, + metadata=META, + savefile=os.path.join(output_folder, "WeatherFile.csv"), +) # Writes the meterological data to an *.csv file. + +pd.DataFrame(standoff_series_df).to_csv( + os.path.join(output_folder, "Standoff_Scan.csv") +) # Writes a file with the Tilt and Azimuth scan calculations of standoff. + +pd.DataFrame(T98_series_df).to_csv( + os.path.join(output_folder, "T98_Scan.csv") +) # Writes a file with the Tilt and Azimuth scan calculations of T98. diff --git a/tutorials/04_scenario/scripts/09_geospatial_world_map.py b/tutorials/04_scenario/scripts/09_geospatial_world_map.py new file mode 100644 index 000000000..c9a93287a --- /dev/null +++ b/tutorials/04_scenario/scripts/09_geospatial_world_map.py @@ -0,0 +1,670 @@ +# %% [markdown] +# # Geospatial World Map (HPC) +# M. Springer 2024-06-05 + +# %% +print("Importing libraries...") +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +import pvdeg +import xarray as xr +import os + +print("Done!") + +# %% [markdown] +# # Calculate Standoff + +# %% +work_dir = "/projects/pvsoiling/pvdeg/analysis/world_map/standoff_fine" +data_dir = "/projects/pvsoiling/pvdeg/analysis/world_map/data" + +# %% +local = { + "manager": "local", + "n_workers": 100, +} + +kestrel = { + "manager": "slurm", + "n_jobs": 8, # Max number of nodes used for parallel processing + "cores": 100, + "processes": 50, + "memory": "245GB", + "account": "pvfem", + "queue": "standard", + "walltime": "8:00:00", + # "scheduler_options": {"host": socket.gethostname()}, +} + +print("Starting Dask client...") +client = pvdeg.geospatial.start_dask(hpc=kestrel) +print("Cluster ready!") + +# %% +# Get weather data +weather_db = "NSRDB" + +# %% +weather_arg = { + "satellite": "Himawari", + "names": "tmy-2020", + "NREL_HPC": True, + "attributes": [ + "air_temperature", + "wind_speed", + "dhi", + "ghi", + "dni", + "relative_humidity", + ], +} + +weather_ds_himawari, meta_df_himawari = pvdeg.weather.get( + weather_db, geospatial=True, **weather_arg +) + +# %% +meta_df_himawari_sub, gids_meta_df_himawari = pvdeg.utilities.gid_downsampling( + meta_df_himawari, 3 +) +weather_ds_himawari_sub = weather_ds_himawari.sel(gid=meta_df_himawari_sub.index) + +# %% +geo_himawari = { + "func": pvdeg.standards.standoff, + "weather_ds": weather_ds_himawari_sub, + "meta_df": meta_df_himawari_sub, +} + +standoff_res_himawari = pvdeg.geospatial.analysis(**geo_himawari) +standoff_res_himawari.to_netcdf(os.path.join(work_dir, "standoff_himawari.nc")) +standoff_res_himawari.to_dataframe().to_csv( + os.path.join(work_dir, "standoff_himawari.csv") +) + +# %% +weather_arg = { + "satellite": "GOES", + "names": 2021, + "NREL_HPC": True, + "attributes": [ + "air_temperature", + "wind_speed", + "dhi", + "ghi", + "dni", + "relative_humidity", + ], +} + +weather_ds_goes, meta_df_goes = pvdeg.weather.get( + weather_db, geospatial=True, **weather_arg +) + +# %% +meta_df_goes_sub, gids_meta_df_goes = pvdeg.utilities.gid_downsampling(meta_df_goes, 8) +weather_ds_goes_sub = weather_ds_goes.sel(gid=meta_df_goes_sub.index) + +# %% +geo_goes = { + "func": pvdeg.standards.standoff, + "weather_ds": weather_ds_goes_sub, + "meta_df": meta_df_goes_sub, +} + +standoff_res_goes = pvdeg.geospatial.analysis(**geo_goes) +standoff_res_goes.to_netcdf(os.path.join(work_dir, "standoff_goes.nc")) +standoff_res_goes.to_dataframe().to_csv(os.path.join(work_dir, "standoff_goes.csv")) + +# %% +weather_arg = { + "satellite": "METEOSAT", + "names": 2019, + "NREL_HPC": True, + "attributes": [ + "air_temperature", + "wind_speed", + "dhi", + "ghi", + "dni", + "relative_humidity", + ], +} + +weather_ds_meteosat, meta_df_meteosat = pvdeg.weather.get( + weather_db, geospatial=True, **weather_arg +) + +# %% +meta_df_meteosat_sub, gids_meta_df_meteosat = pvdeg.utilities.gid_downsampling( + meta_df_meteosat, 4 +) +weather_ds_meteosat_sub = weather_ds_meteosat.sel(gid=meta_df_meteosat_sub.index) + +# %% +geo_meteosat = { + "func": pvdeg.standards.standoff, + "weather_ds": weather_ds_meteosat_sub, + "meta_df": meta_df_meteosat_sub, +} + +standoff_res_meteosat = pvdeg.geospatial.analysis(**geo_meteosat) +standoff_res_meteosat.to_netcdf(os.path.join(work_dir, "standoff_meteosat.nc")) +standoff_res_meteosat.to_dataframe().to_csv( + os.path.join(work_dir, "standoff_meteosat.csv") +) + +# %% +# Auxillary data +import h5py + +fp_weather_aux = "/projects/pvsoiling/pvdeg/data/world_map_aux.h5" +fp_meta_aux = "/projects/pvsoiling/pvdeg/data/meta_world_map_aux.csv" + +# weather_aux = pvdeg.weather.read(fp_weather_aux, 'h5') +meta_aux = pd.read_csv(fp_meta_aux, index_col=0) + +# xarray work around for aux data +dss = [] +drop_variables = ["meta", "time_index", "tmy_year", "tmy_year_short", "coordinates"] + +hf = h5py.File(fp_weather_aux, "r") +attr = list(hf) + +attr_to_read = [elem for elem in attr if elem not in drop_variables] + +chunks = [] +shapes = [] +for var in attr_to_read: + chunks.append(hf[var].chunks if hf[var].chunks is not None else (np.nan, np.nan)) + shapes.append(hf[var].shape if hf[var].shape is not None else (np.nan, np.nan)) +chunks = min(set(chunks)) +shapes = min(set(shapes)) + + +time_index = pd.to_datetime(hf["time_index"][...].astype(str)).values +meta_df = meta_aux +coords = {"gid": meta_df.index.values, "time": time_index} +coords_len = {"time": time_index.shape[0], "gid": meta_df.shape[0]} + +ds = xr.open_dataset( + fp_weather_aux, + engine="h5netcdf", + phony_dims="sort", + chunks={"phony_dim_0": -1, "phony_dim_1": -1}, + drop_variables=drop_variables, + mask_and_scale=False, + decode_cf=True, +) + +for var in ds.data_vars: + if hasattr(getattr(ds, var), "psm_scale_factor"): + scale_factor = 1 / ds[var].psm_scale_factor + print(scale_factor) + getattr(ds, var).attrs["scale_factor"] = scale_factor + +rename = {} +for ( + phony, + length, +) in ds.sizes.items(): + if length == coords_len["time"]: + rename[phony] = "time" + elif length == coords_len["gid"]: + rename[phony] = "gid" +ds = ds.rename(rename) +ds = ds.assign_coords(coords) + +# TODO: In case re-chunking becomes necessary +# ax0 = list(ds.sizes.keys())[list(ds.sizes.values()).index(shapes[0])] +# ax1 = list(ds.sizes.keys())[list(ds.sizes.values()).index(shapes[1])] +# ds = ds.chunk(chunks={ax0:chunks[0], ax1:chunks[1]}) +dss.append(ds) + +ds = xr.merge(dss) +ds = xr.decode_cf(ds) + +# Rechunk time axis +ds = ds.unify_chunks() +ds = ds.chunk(chunks={"time": -1, "gid": ds.chunks["gid"]}) + +weather_ds = ds + +DSET_MAP = {"air_temperature": "temp_air", "Relative Humidity": "relative_humidity"} +META_MAP = {"elevation": "altitude", "Local Time Zone": "tz", "timezone": "tz"} + +for dset in weather_ds.data_vars: + if dset in DSET_MAP.keys(): + weather_ds = weather_ds.rename({dset: DSET_MAP[dset]}) + +for mset in meta_df.columns: + if mset in META_MAP.keys(): + meta_df.rename(columns={mset: META_MAP[mset]}, inplace=True) + +weather_ds_aux = weather_ds + +geo_aux = { + "func": pvdeg.standards.standoff, + "weather_ds": weather_ds_aux, + "meta_df": meta_aux, +} + +standoff_res_aux = pvdeg.geospatial.analysis(**geo_aux) +standoff_res_aux.to_netcdf(os.path.join(work_dir, "standoff_aux.nc")) +standoff_res_aux.to_dataframe().to_csv(os.path.join(work_dir, "standoff_aux.csv")) + +# %% +weather_db = "NSRDB" +weather_arg = { + "satellite": "METEOSAT", + "names": 2019, + "NREL_HPC": True, + "attributes": [ + "air_temperature", + "wind_speed", + "dhi", + "ghi", + "dni", + "relative_humidity", + ], +} +weather_ds_meteosat, meta_df_meteosat = pvdeg.weather.get( + weather_db, geospatial=True, **weather_arg +) + +time_hourly = pd.date_range("2019-01-01", freq="h", periods=365 * 24) +weather_ds_meteosat_hourly = weather_ds_meteosat.sel(time=time_hourly) + +europe = [ + "Spain", + "Portugal", #'Ireland', 'United Kingdom', + "France", + "Belgium", + "Netherlands", + "Norway", + "Luxembourg", + "Germany", + "Switzerland", + "Italy", + "Monaco", + "Denmark", + "Liechtenstein", + "Austria", + "Sweden", + "Czech Republic", + "San Marino", + "Slovenia", + "Croatia", + "Poland", + "Malta", + "Bosnia and Herzegovina", + "Hungary", + "Slovakia", + "Montenegro", + "Serbia", + "Albania", + "Greece", + "Romania", + "Macedonia", + "Latvia", + "Lithuania", + "Finland", + "Estonia", + "Ukraine", + "Bulgaria", + "Belarus", + "Moldova", + "Turkey", + "Cyprus", + "Northern Cyprus", + "Georgia", +] + +meta_df_europe = meta_df_meteosat[meta_df_meteosat["country"].isin(europe)] + +meta_df_europe_sub, gids_sub = pvdeg.utilities.gid_downsampling(meta_df_europe, 4) +weather_europe_sub = weather_ds_meteosat_hourly.sel(gid=meta_df_europe_sub.index) + +meta_uk = pd.read_csv("../../world_map/data/meta_pvgis_uk_4300.csv") +meta_uk_sub, gids_sub = pvdeg.utilities.gid_downsampling(meta_uk, 1) + +meta_scan1 = pd.read_csv(f"{data_dir}/meta_pvgis_scan_coarse1500.csv", index_col=0) +meta_scan12 = pd.read_csv( + f"{data_dir}/meta_pvgis_scan_coarse_1500_1599.csv", index_col=0 +) +meta_scan2 = pd.read_csv(f"{data_dir}/meta_pvgis_scan_coarse2100.csv", index_col=0) +meta_scan = pd.concat([meta_scan1, meta_scan12, meta_scan2]) + +meta_pvgis = pd.concat([meta_scan, meta_uk_sub]) + +lat_NSRDB = meta_df_europe_sub["latitude"].to_numpy() +meta_pvgis["latitude_pvgis"] = meta_pvgis["latitude"] +meta_pvgis["latitude"] = meta_pvgis["latitude"].apply( + lambda x: ( + lat_NSRDB[np.argmin(np.abs(x - lat_NSRDB))] + if x < meta_df_europe_sub["latitude"].max() + else x + ) +) + +lon_NSRDB = meta_df_europe_sub["longitude"].to_numpy() +meta_pvgis["longitude_pvgis"] = meta_pvgis["longitude"] +meta_pvgis["longitude"] = meta_pvgis["longitude"].apply( + lambda x: ( + lon_NSRDB[np.argmin(np.abs(x - lon_NSRDB))] + if x > meta_df_europe_sub["longitude"].min() + else x + ) +) +meta_pvgis["tz"] = 0 +meta_pvgis = meta_pvgis.reset_index() + +meta_eu_coarse = pd.concat([meta_df_europe_sub, meta_pvgis]) +meta_eu_coarse["tz"] = 0 +meta_eu_coarse = meta_eu_coarse.reset_index() +meta_eu_coarse.to_csv("../../world_map/data/meta_eu_coarse.csv") + +weather_uk = xr.open_dataset("../../world_map/data/weather_ds_uk_4300.nc") +weather_uk = weather_uk.sel(gid=meta_uk.index) +weather_uk_sub = weather_uk.sel(gid=meta_uk_sub.index) + +weather_scan1 = xr.open_dataset("../../world_map/data/weather_ds_scan_coarse1500.nc") +weather_scan12 = xr.open_dataset( + "../../world_map/data/weather_ds_scan_coarse_1500_1599.nc" +) +weather_scan2 = xr.open_dataset("../../world_map/data/weather_ds_scan_coarse2100.nc") +weather_scan = xr.concat( + [ + weather_scan1.sel(gid=slice(0, 1500)), + weather_scan12.sel(gid=slice(1501, 1599)), + weather_scan2.sel(gid=slice(1600, None)), + ], + dim="gid", +) +weather_scan = weather_scan.sel(gid=meta_scan.index) +# weather_scan = xr.concat([weather_scan1, weather_scan2], dim='gid') + +weather_pvgis = xr.concat([weather_scan, weather_uk_sub], dim="gid") +weather_pvgis = weather_pvgis.drop_vars(["IR(h)", "wind_direction", "pressure"]) + +weather_pvgis = weather_pvgis.assign_coords({"gid": meta_pvgis.index}) +weather_pvgis = weather_pvgis.chunk(chunks={"time": -1, "gid": 100}) +weather_pvgis = weather_pvgis.unify_chunks() + +meta_df_pvgis_sub, gids_meta_df_pvgis = pvdeg.utilities.gid_downsampling(meta_pvgis, 4) +weather_ds_pvgis_sub = weather_pvgis.sel(gid=meta_df_pvgis_sub.index) + +# weather_europe_sub = weather_europe_sub.assign_coords({'time': pd.date_range("2022-01-01", freq="h", periods=365 * 24),}) +# weather_eu_coarse = xr.concat([weather_europe_sub, weather_pvgis], dim='gid') +# weather_eu_coarse = weather_eu_coarse.assign_coords({'gid': meta_eu_coarse.index}) +# weather_eu_coarse = weather_eu_coarse.chunk(chunks={"time": -1, "gid": 100}) +# weather_eu_coarse = weather_eu_coarse.unify_chunks() + +# with open('weather_eu_coarse.pickle', 'wb') as handle: +# pickle.dump(weather_eu_coarse, handle, protocol=pickle.HIGHEST_PROTOCOL) + + +geo_pvgis = { + "func": pvdeg.standards.standoff, + "weather_ds": weather_pvgis, + "meta_df": meta_pvgis, +} + +standoff_res_pvgis = pvdeg.geospatial.analysis(**geo_pvgis) +standoff_res_pvgis.to_netcdf(os.path.join(work_dir, "standoff_pvgis.nc")) +standoff_res_pvgis.to_dataframe().to_csv(os.path.join(work_dir, "standoff_pvgis.csv")) + +# %% +meta_north = pd.read_csv(f"{data_dir}/meta_pvgis_north_3300.csv", index_col=0) +weather_north = xr.open_dataset(f"{data_dir}/weather_ds_north_3300.nc") +weather_north = weather_north.sel(gid=meta_north.index) + +weather_north = weather_north.assign_coords({"gid": meta_north.index}) +weather_north = weather_north.chunk(chunks={"time": -1, "gid": 100}) +weather_north = weather_north.unify_chunks() + + +geo_pvgis = { + "func": pvdeg.standards.standoff, + "weather_ds": weather_north, + "meta_df": meta_north, +} + +standoff_res_pvgis = pvdeg.geospatial.analysis(**geo_pvgis) +standoff_res_pvgis.to_netcdf(os.path.join(work_dir, "standoff_pvgis_north.nc")) +standoff_res_pvgis.to_dataframe().to_csv( + os.path.join(work_dir, "standoff_pvgis_north.csv") +) + +# %% [markdown] +# # Post process + +# %% +import pvdeg +import os +import pandas as pd +import numpy as np +import xarray as xr +from global_land_mask import globe +import cartopy.crs as ccrs +import cartopy.io.shapereader as shpreader + +work_dir = "/projects/pvsoiling/pvdeg/analysis/world_map/standoff_fine" +data_dir = "/projects/pvsoiling/pvdeg/analysis/world_map/data" + +# %% +# Create 0cm standoff locations + +lon_north = np.arange(-179, 180, 0.25) +lat_north = np.arange(60, 90, 0.25) +lon_grid_north, lat_grid_north = np.meshgrid(lon_north, lat_north) +land_north = globe.is_land(lat_grid_north, lon_grid_north) +lon_land_north = lon_grid_north[land_north] +lat_land_north = lat_grid_north[land_north] + +lon_south = np.arange(-179, 180, 0.25) +lat_south = np.arange(-90, -60, 0.25) +lon_grid_south, lat_grid_south = np.meshgrid(lon_south, lat_south) +land_south = globe.is_land(lat_grid_south, lon_grid_south) +lon_land_south = lon_grid_south[land_south] +lat_land_south = lat_grid_south[land_south] + + +lon_asia = np.arange(80, 105, 0.25) +lat_asia = np.arange(50, 61, 0.25) + +lon = lon_asia +lat = np.full(lon_asia.size, 61) + +for i, lat_coord in enumerate(reversed(lat_asia)): + lon_sub = lon_asia[i:-i] + lat_sub = np.full(lon_sub.size, lat_coord) + lon = np.append(lon, lon_sub) + lat = np.append(lat, lat_sub) + +lon_asia = lon +lat_asia = lat + +# %% +fig, ax = plt.subplots() + +plt.scatter(lon_land_north, lat_land_north, c="r", s=1) +plt.scatter(lon_land_south, lat_land_south, c="b", s=1) +plt.scatter(lon, lat, c="g", s=1) + +ax.set_ylim(-90, 90) +ax.set_xlim(-180, 180) + +# %% +template_params = pvdeg.geospatial.template_parameters(pvdeg.standards.standoff) +standoff_zero_north = pvdeg.geospatial.zero_template( + lat_land_north, lon_land_north, **template_params +) +standoff_zero_south = pvdeg.geospatial.zero_template( + lat_land_south, lon_land_south, **template_params +) +standoff_zero_asia = pvdeg.geospatial.zero_template( + lat_asia, lon_asia, **template_params +) + +# %% +standoff_aux = xr.open_dataset(os.path.join(work_dir, "standoff_aux.nc")) +standoff_himawari = xr.open_dataset(os.path.join(work_dir, "standoff_himawari.nc")) +standoff_meteosat = xr.open_dataset(os.path.join(work_dir, "standoff_meteosat.nc")) +standoff_north = xr.open_dataset(os.path.join(work_dir, "standoff_pvgis_north.nc")) +standoff_pvgis = xr.open_dataset(os.path.join(work_dir, "standoff_pvgis.nc")) +standoff_goes = xr.open_dataset(os.path.join(work_dir, "standoff_goes.nc")) + +# %% +fig = plt.figure(figsize=(10, 5)) +ax = fig.add_axes([0, 0, 1, 1], projection=ccrs.PlateCarree(), frameon=True) +ax.patch.set_visible(True) +ax.set_extent([-180, 180, -85, 85], ccrs.PlateCarree()) + +shapename = "admin_0_countries" +states_shp = shpreader.natural_earth( + resolution="110m", category="cultural", name=shapename +) + +cmap = "Spectral_r" +size = 0.75 + + +standoff_zero_north.plot.scatter( + x="longitude", + y="latitude", + hue="x", + cmap=cmap, + s=size, + linewidths=0, + vmin=0, + vmax=14, + add_colorbar=False, + ax=ax, +) +standoff_zero_south.plot.scatter( + x="longitude", + y="latitude", + hue="x", + cmap=cmap, + s=size, + linewidths=0, + vmin=0, + vmax=14, + add_colorbar=False, + ax=ax, +) +standoff_zero_asia.plot.scatter( + x="longitude", + y="latitude", + hue="x", + cmap=cmap, + s=size, + linewidths=0, + vmin=0, + vmax=14, + add_colorbar=False, + ax=ax, +) + +cm = standoff_himawari.plot.scatter( + x="longitude", + y="latitude", + hue="x", + cmap=cmap, + s=size, + linewidths=0, + vmin=0, + vmax=14, + add_colorbar=False, + ax=ax, +) +standoff_north.plot.scatter( + x="longitude", + y="latitude", + hue="x", + cmap=cmap, + s=size, + linewidths=0, + vmin=0, + vmax=14, + add_colorbar=False, + ax=ax, +) +standoff_aux.plot.scatter( + x="longitude", + y="latitude", + hue="x", + cmap=cmap, + s=size, + linewidths=0, + vmin=0, + vmax=14, + add_colorbar=False, + ax=ax, +) +standoff_pvgis.plot.scatter( + x="longitude", + y="latitude", + hue="x", + cmap=cmap, + s=size, + linewidths=0, + vmin=0, + vmax=14, + add_colorbar=False, + ax=ax, +) +standoff_meteosat.plot.scatter( + x="longitude", + y="latitude", + hue="x", + cmap=cmap, + s=1, + linewidths=0, + vmin=0, + vmax=14, + add_colorbar=False, + ax=ax, +) +standoff_goes.plot.scatter( + x="longitude", + y="latitude", + hue="x", + cmap=cmap, + s=1, + linewidths=0, + vmin=0, + vmax=14, + add_colorbar=False, + ax=ax, +) + + +ax.add_geometries( + shpreader.Reader(states_shp).geometries(), + ccrs.PlateCarree(), + facecolor="none", + edgecolor="gray", + linewidth=0.5, +) + +cb_title = "Standoff distance [cm]" +cb = plt.colorbar(cm, shrink=0.78, aspect=30, pad=0.02) +cb.set_label(cb_title) +# ax.set_title('title') + +ax.set_xticks(np.arange(-180, 181, 20), crs=ccrs.PlateCarree()) +ax.set_yticks(np.arange(-90, 91, 10), crs=ccrs.PlateCarree()) + +ax.set_xlim(-180, 180) +ax.set_ylim(-85, 85) + +ax.set_xlabel("Longitude") +ax.set_ylabel("Latitude") + +plt.savefig(os.path.join(work_dir, "standoff_map.png"), dpi=1200, bbox_inches="tight") + +# %% diff --git a/tutorials_and_tools/tutorials_and_tools/LETID - Outdoor Geospatial Demo.py b/tutorials/04_scenario/scripts/10_letid_outdoor_geospatial_demo.py similarity index 92% rename from tutorials_and_tools/tutorials_and_tools/LETID - Outdoor Geospatial Demo.py rename to tutorials/04_scenario/scripts/10_letid_outdoor_geospatial_demo.py index 75045e8d6..ca9f17421 100644 --- a/tutorials_and_tools/tutorials_and_tools/LETID - Outdoor Geospatial Demo.py +++ b/tutorials/04_scenario/scripts/10_letid_outdoor_geospatial_demo.py @@ -1,24 +1,16 @@ -#!/usr/bin/env python -# coding: utf-8 - -# # LETID - Outdoor Geospatioal Demo -# -# ![PVDeg Logo](../PVD_logo.png) +# %% [markdown] +# # LETID Outdoor Geospatial Demo (HPC) # +# ![PVDeg Logo](../images/pvdeg_logo.svg) -# In[1]: - - +# %% import matplotlib.pyplot as plt import pandas as pd import pvdeg from pvdeg import DATA_DIR import os - -# In[2]: - - +# %% # This information helps with debugging and getting support :) import sys import platform @@ -28,19 +20,14 @@ print("Pandas version ", pd.__version__) print("pvdeg version ", pvdeg.__version__) +# %% [markdown] +# # Single location example -# ## Single location example - -# In[2]: - - +# %% weather_file = os.path.join(DATA_DIR, "psm3_demo.csv") WEATHER, META = pvdeg.weather.read(weather_file, "psm") - -# In[3]: - - +# %% kwargs = { "tau_0": 115, # us, carrier lifetime in non-degraded states, e.g. LETID/LID states A or C "tau_deg": 55, # us, carrier lifetime in fully-degraded state, e.g. LETID/LID state B @@ -50,21 +37,16 @@ "na_0": 100, "nb_0": 0, "nc_0": 0, - "mechanism_params": 'D037', + "mechanism_params": "repins", } - -# In[4]: - - +# %% pvdeg.letid.calc_letid_outdoors(weather_df=WEATHER, meta=META, **kwargs) +# %% [markdown] +# # Start distributed compute cluster - DASK -# ### Start distributed compute cluster - DASK - -# In[2]: - - +# %% local = { "manager": "local", "n_workers": 1, @@ -85,10 +67,7 @@ pvdeg.geospatial.start_dask(hpc=kestrel) - -# In[11]: - - +# %% # Get weather data weather_db = "NSRDB" @@ -114,16 +93,13 @@ weather_SW_sub = weather_ds.sel(gid=meta_SW_sub.index) - -# In[12]: - - +# %% weather_SW_sub +# %% +meta_df -# In[13]: - - +# %% # Define desired analysis geo = { "func": pvdeg.letid.calc_letid_outdoors, @@ -137,21 +113,15 @@ "na_0": 100, "nb_0": 0, "nc_0": 0, - "mechanism_params": 'D037', + "mechanism_params": "repins", } letid_res = pvdeg.geospatial.analysis(**geo) - -# In[14]: - - +# %% letid_res - -# In[18]: - - +# %% import datetime ims = [] @@ -177,10 +147,7 @@ # ims = [imageio.imread(f'./images/RH_animation_{n}.png') for n in range(1, 13)] # imageio.mimwrite(f'./images/RH_animation.gif', ims, format='GIF', duration=1000, loop=10) - -# In[34]: - - +# %% import datetime ims = [] @@ -250,13 +217,10 @@ plt.savefig(f"./images/LETID_plot_animation_{n}.png", dpi=600) - -# In[ ]: - - +# %% import imageio -ims = [imageio.imread("./images/LETID_plot_animation_{n}.png") for n in range(1, 13)] +ims = [imageio.imread(f"./images/LETID_plot_animation_{n}.png") for n in range(1, 13)] imageio.mimwrite( "./images/LETID_plot_animation.gif", ims, format="GIF", duration=1000, loop=10 ) diff --git a/tutorials/05_advanced/04_nsrdb_distributed_api.ipynb b/tutorials/05_advanced/04_nsrdb_distributed_api.ipynb new file mode 100644 index 000000000..5a8045986 --- /dev/null +++ b/tutorials/05_advanced/04_nsrdb_distributed_api.ipynb @@ -0,0 +1,1974 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# NSRDB Distributed (API Key Required)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2025-10-22T23:42:53.672945Z", + "iopub.status.busy": "2025-10-22T23:42:53.672945Z", + "iopub.status.idle": "2025-10-22T23:43:06.677094Z", + "shell.execute_reply": "2025-10-22T23:43:06.675531Z" + } + }, + "outputs": [], + "source": [ + "from dask.distributed import LocalCluster, Client\n", + "from dotenv import load_dotenv\n", + "import pvdeg\n", + "import os" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Setting Up\n", + "\n", + "We need to get ready to make our parallelized API calls. You need to import your API key and email. This cell will not work for you unless you replace the `api_key` and `email` with your personal NSRDB api keys. [REQUEST A KEY](https://developer.nrel.gov/signup/).\n", + "\n", + "We also need to initalize a dask client. `pvdeg.weather.weather_distributed` will not work without it. It will fail silently and not populate and of the results in the resulting `weather_ds` called `geo_weather` in the example below. It is hard to recognize that this has occured so be careful. Make sure to initialize a dask client first. Visiting the link takes you to a daskboard that shows what dask is doing." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2025-10-22T23:43:06.685009Z", + "iopub.status.busy": "2025-10-22T23:43:06.684009Z", + "iopub.status.idle": "2025-10-22T23:43:12.240481Z", + "shell.execute_reply": "2025-10-22T23:43:12.239969Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Daskboard link\n", + "http://127.0.0.1:8787/status\n" + ] + } + ], + "source": [ + "load_dotenv()\n", + "\n", + "### REPLACE WITH YOUR API KEY AND EMAIL ###\n", + "api_key = \"DEMO_KEY\"\n", + "email = \"user@mail.com\"\n", + "###########################################\n", + "\n", + "workers = 4\n", + "\n", + "cluster = LocalCluster(\n", + " n_workers=workers,\n", + " processes=True,\n", + ")\n", + "\n", + "client = Client(cluster)\n", + "\n", + "print(\"Daskboard link\")\n", + "print(client.dashboard_link)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Note on .env file:** Create a file named `.env` in your project root directory (`c:\\Users\\rdaxini\\Documents\\GitHub\\PVDegradationTools_NREL\\`) with the following content:\n", + "```\n", + "api_key=YOUR_NREL_API_KEY\n", + "email=YOUR_EMAIL_ADDRESS\n", + "```\n", + "Replace `YOUR_NREL_API_KEY` and `YOUR_EMAIL_ADDRESS` with your actual NREL developer credentials." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Requesting Weather\n", + "\n", + "We will create a list of tuple (latitude, longitude) pairs and call the function on all of them at once. failed will represent a list of failed gids, unique location ID's that correspond to points in space on the NSRDB. These are different than on PVGIS where they are arbitrary indexes that do NOT correspond to a spatial location on earth.\n", + "\n", + "We will request \"PSM4\" data from the Physical Solar Model that represents a typical meteorological year (TMY) from the NSRDB. We will have to supply the api key and email from above here. The only difference between other weather sources lies in the NSRDB/PSM4 data requiring API keys." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2025-10-22T23:43:12.333382Z", + "iopub.status.busy": "2025-10-22T23:43:12.332189Z", + "iopub.status.idle": "2025-10-22T23:43:28.475109Z", + "shell.execute_reply": "2025-10-22T23:43:28.470578Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Connected to a Dask scheduler | Dashboard: http://127.0.0.1:8787/status\n" + ] + } + ], + "source": [ + "coords = [\n", + " (25.783388, -80.189029),\n", + " (24.783388, -80.189029),\n", + "]\n", + "\n", + "geo_weather, geo_meta, failed = pvdeg.weather.weather_distributed(\n", + " database=\"PSM4\", coords=coords, api_key=api_key, email=email\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Viewing Results\n", + "\n", + "Same as in the other tutorial, our results are stored in an xarray dataset with a dask backend so you will have to use `.compute()` on the dataset to inspect the individual values of the dask arrays.\n", + "\n", + "Click on the `Data variables` dropdown to expand the dataset viewer." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2025-10-22T23:43:28.484432Z", + "iopub.status.busy": "2025-10-22T23:43:28.483407Z", + "iopub.status.idle": "2025-10-22T23:43:28.660971Z", + "shell.execute_reply": "2025-10-22T23:43:28.658935Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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We can index into the first entry at the 0th index by using isel (index-select). This will grab the data from the first gid. Then we pick the air temperature attribute. This can be replaced with bracket notation so `.temp_air` becomes `[\"temp_air\"].\n", + "\n", + "This selects a single array from the dataset that is labeled as \"temp_air\". This array will be a dask array so the values will be stored out of memory, we would have to load it using `.compute()` to directly inspect it but when plotting with matplotlib it will load the array for us." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "execution": { + "iopub.execute_input": "2025-10-22T23:43:32.835160Z", + "iopub.status.busy": "2025-10-22T23:43:32.835160Z", + "iopub.status.idle": "2025-10-22T23:43:33.668469Z", + "shell.execute_reply": "2025-10-22T23:43:33.666936Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "plt.plot(geo_weather.isel(gid=0).temp_air)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Next Steps\n", + "\n", + "Now we have data ready to use for geospatial calculations. You can see how to do this in [Geospatial Templates.ipynb](../04_geospatial/02_geospatial_templates.ipynb)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "pvdeg", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.9" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tutorials/05_advanced/scripts/04_nsrdb_distributed_api.py b/tutorials/05_advanced/scripts/04_nsrdb_distributed_api.py new file mode 100644 index 000000000..2d3adad67 --- /dev/null +++ b/tutorials/05_advanced/scripts/04_nsrdb_distributed_api.py @@ -0,0 +1,95 @@ +# %% [markdown] +# # NSRDB Distributed (API Key Required) +# + +# %% +from dask.distributed import LocalCluster, Client +from dotenv import load_dotenv +import pvdeg +import os + +# %% [markdown] +# # Setting Up +# +# We need to get ready to make our parallelized API calls. You need to import your API key and email. This cell will not work for you unless you replace the `api_key` and `email` with your personal NSRDB api keys. [REQUEST A KEY](https://developer.nrel.gov/signup/). +# +# We also need to initalize a dask client. `pvdeg.weather.weather_distributed` will not work without it. It will fail silently and not populate and of the results in the resulting `weather_ds` called `geo_weather` in the example below. It is hard to recognize that this has occured so be careful. Make sure to initialize a dask client first. Visiting the link takes you to a daskboard that shows what dask is doing. + +# %% +load_dotenv() + +### REPLACE WITH YOUR API KEY AND EMAIL ### +api_key = "DEMO_KEY" +email = "user@mail.com" +########################################### + +workers = 4 + +cluster = LocalCluster( + n_workers=workers, + processes=True, +) + +client = Client(cluster) + +print("Daskboard link") +print(client.dashboard_link) + +# %% [markdown] +# **Note on .env file:** Create a file named `.env` in your project root directory (`c:\Users\rdaxini\Documents\GitHub\PVDegradationTools_NREL\`) with the following content: +# ``` +# api_key=YOUR_NREL_API_KEY +# email=YOUR_EMAIL_ADDRESS +# ``` +# Replace `YOUR_NREL_API_KEY` and `YOUR_EMAIL_ADDRESS` with your actual NREL developer credentials. + +# %% [markdown] +# # Requesting Weather +# +# We will create a list of tuple (latitude, longitude) pairs and call the function on all of them at once. failed will represent a list of failed gids, unique location ID's that correspond to points in space on the NSRDB. These are different than on PVGIS where they are arbitrary indexes that do NOT correspond to a spatial location on earth. +# +# We will request "PSM4" data from the Physical Solar Model that represents a typical meteorological year (TMY) from the NSRDB. We will have to supply the api key and email from above here. The only difference between other weather sources lies in the NSRDB/PSM4 data requiring API keys. + +# %% +coords = [ + (25.783388, -80.189029), + (24.783388, -80.189029), +] + +geo_weather, geo_meta, failed = pvdeg.weather.weather_distributed( + database="PSM4", coords=coords, api_key=api_key, email=email +) + +# %% [markdown] +# # Viewing Results +# +# Same as in the other tutorial, our results are stored in an xarray dataset with a dask backend so you will have to use `.compute()` on the dataset to inspect the individual values of the dask arrays. +# +# Click on the `Data variables` dropdown to expand the dataset viewer. + +# %% +geo_weather + +# %% +geo_weather.compute() + +# %% [markdown] +# # Spot Check +# +# We can plot the entire TMY air_temperature to check that our data has loaded correctly. +# +# Explanation of steps +# +# geo_weather is our weather xarray dataset. We can index into the first entry at the 0th index by using isel (index-select). This will grab the data from the first gid. Then we pick the air temperature attribute. This can be replaced with bracket notation so `.temp_air` becomes `["temp_air"]. +# +# This selects a single array from the dataset that is labeled as "temp_air". This array will be a dask array so the values will be stored out of memory, we would have to load it using `.compute()` to directly inspect it but when plotting with matplotlib it will load the array for us. + +# %% +import matplotlib.pyplot as plt + +plt.plot(geo_weather.isel(gid=0).temp_air) + +# %% [markdown] +# # Next Steps +# +# Now we have data ready to use for geospatial calculations. You can see how to do this in [Geospatial Templates.ipynb](../04_geospatial/02_geospatial_templates.ipynb) diff --git a/tutorials_and_tools/tutorials_and_tools/HPC Connection Required - Geospatial Templates.ipynb b/tutorials/05_geospatial/01_geospatial_templates.ipynb similarity index 99% rename from tutorials_and_tools/tutorials_and_tools/HPC Connection Required - Geospatial Templates.ipynb rename to tutorials/05_geospatial/01_geospatial_templates.ipynb index 02e9c82ec..ac868970c 100644 --- a/tutorials_and_tools/tutorials_and_tools/HPC Connection Required - Geospatial Templates.ipynb +++ b/tutorials/05_geospatial/01_geospatial_templates.ipynb @@ -1,5 +1,12 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Geospatial Templates (HPC)\n" + ] + }, { "cell_type": "code", "execution_count": 1, @@ -9,7 +16,8 @@ "iopub.status.busy": "2025-10-17T21:51:06.498163Z", "iopub.status.idle": "2025-10-17T21:51:15.430153Z", "shell.execute_reply": "2025-10-17T21:51:15.429145Z" - } + }, + "lines_to_next_cell": 2 }, "outputs": [], "source": [ @@ -17,7 +25,7 @@ "from pvdeg import TEST_DATA_DIR\n", "import pandas as pd\n", "import os\n", - "import xarray as xr\n" + "import xarray as xr" ] }, { @@ -27,7 +35,7 @@ "# Geospatial Templates\n", "\n", "When running a geospatial analysis using `pvdeg.geospatial.analysis` on arbitary `pvdeg` functions you will need to specify a template for the shape of the output data. This is because the input data comes with dimensions of gid and time while the output will have data in a different shape usually corresonding to coordinates.\n", - "- gid, identification number corresponding to an NSRDB datapoint's location \n", + "- gid, identification number corresponding to an NSRDB datapoint's location\n", "- time, timeseries corresponding to the hourly time indicies of NSRDB datapoint's yearly meteorological data.\n", "\n", "Follow the steps below to see how we generate templates before running the analysis.\n", @@ -39,7 +47,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Loading Geospatial Data\n", + "# Loading Geospatial Data\n", "\n", "This step skips over making the `pvdeg.weather.get` call with `geospatial == True`. See the [Duramat Demo](./DuraMAT%20Live%20Demo.ipynb) for information on how to do this traditionally.\n", "\n", @@ -57,7 +65,8 @@ "iopub.status.busy": "2025-10-17T21:51:15.430153Z", "iopub.status.idle": "2025-10-17T21:51:15.759623Z", "shell.execute_reply": "2025-10-17T21:51:15.759623Z" - } + }, + "lines_to_next_cell": 2 }, "outputs": [ { @@ -489,7 +498,7 @@ "# Use xarray to open NetCDF file instead of pickle\n", "geo_weather = xr.open_dataset(os.path.join(TEST_DATA_DIR, \"summit-weather.nc\"))\n", "\n", - "geo_weather\n" + "geo_weather" ] }, { @@ -498,23 +507,23 @@ "source": [ "# Creating Templates Manually\n", "\n", - "`pvdeg.geospatial.ouput_template` we can produce a template for our result data. \n", + "`pvdeg.geospatial.ouput_template` we can produce a template for our result data.\n", "\n", - "We need to do this because different functions return different types of values, some return multiple values as tuples, some return only single numerics, others return timeseries results. We need to specify the shape of our data to create an output xarray dataset. " + "We need to do this because different functions return different types of values, some return multiple values as tuples, some return only single numerics, others return timeseries results. We need to specify the shape of our data to create an output xarray dataset." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Examples\n", + "# Examples\n", "\n", "## 98α΅—Κ° module percential temperature at Standoff Height\n", "\n", "Say we want to estimate the 98α΅—Κ° percential temperature for the module at the given tilt, azimuth, and x_eff. `PVDeg` has a function to do this, `pvdeg.standards.T98_estimate` BUT it doesn't have a preset geospatial template. We will need to make one.\n", "\n", - "- look at the function return values. \n", - "From the docstring we can see that `T98_estimate` only has one return value. IMPORTANT, this value is a single float, NOT a timeseries. This means our output shape will only be dependent on the input identifier and NOT time. \n", + "- look at the function return values.\n", + "From the docstring we can see that `T98_estimate` only has one return value. IMPORTANT, this value is a single float, NOT a timeseries. This means our output shape will only be dependent on the input identifier and NOT time.\n", "\n", "Therefore we will map the output variable `T98` to the location identifier `gid` using a dictionary with `str: tuple` mappings.\n", "\n", @@ -650,9 +659,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Glass Glass Estimated Module Temperature\n", + "# Glass Glass Estimated Module Temperature\n", "\n", - "Now we want to calculate geospatial timeseries temperature values for a module using `pvdeg.temperature.module`. This is not super practical because all `pvdeg` functions that need to use tempeature for their calculations preform the temperature calculation internally, this is just for show. \n", + "Now we want to calculate geospatial timeseries temperature values for a module using `pvdeg.temperature.module`. This is not super practical because all `pvdeg` functions that need to use tempeature for their calculations preform the temperature calculation internally, this is just for show.\n", "\n", "This calculation differs from the above because the temperature functions return the module temperature in a timeseries format. So we care about 2 dimensions, location identifier and TIME." ] @@ -826,7 +835,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Self Explaining Code\n", + "# Self Explaining Code\n", "\n", "If we are looking at adding templates for other functions, we can also look at the 3 presaved templates for existing `pvdeg` functions. Visit [pvdeg.geospatial.template_parameters](../../pvdeg/geospatial.py) and inspect this function to see how these different target functions utilize templates and shapes." ] @@ -844,7 +853,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Examples Below\n", + "# Examples Below\n", "Steps\n", "- Create template using autotemplating function. Pulls in information about function to determine shape of output. Not usable on functions with ambigious return types.\n", "- Call geospatial analysis function using template" @@ -854,10 +863,10 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Geospatial Cell Temperature Calculation\n", + "# Geospatial Cell Temperature Calculation\n", "As shown below, we have two options, we can choose to provide a template that is generated by a function which supports autotemplating. Or we can provide the function to `geospatial.analysis` and let it generate a template internally.\n", "\n", - "### Providing a Template with `Geospatial.auto_template`" + "## Providing a Template with `Geospatial.auto_template`" ] }, { @@ -1422,7 +1431,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Analysis Without Providing a Template\n", + "# Analysis Without Providing a Template\n", "\n", "If a function is supported by `geospatial.auto_template` we do not need to create a template outside of the function as shown in the cell above. We can simply pass the function to `geospatial.analysis` and it will create a template for us." ] @@ -1964,7 +1973,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Geospatial Module Temperature Calculation" + "# Geospatial Module Temperature Calculation" ] }, { @@ -2515,7 +2524,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Geospatial Solar Position Calculation" + "# Geospatial Solar Position Calculation" ] }, { @@ -3212,7 +3221,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Geospatial POA Irradiance Calculation" + "# Geospatial POA Irradiance Calculation" ] }, { @@ -3925,7 +3934,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Geospatial 98th Percentile Operating Temperature Calculation" + "# Geospatial 98th Percentile Operating Temperature Calculation" ] }, { @@ -4429,7 +4438,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Geospatial Module Humidity Calculation" + "# Geospatial Module Humidity Calculation" ] }, { @@ -5157,7 +5166,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Geospatial IwaVantHoff Environment Characterization Calculation" + "# Geospatial IwaVantHoff Environment Characterization Calculation" ] }, { @@ -5669,7 +5678,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Geospatial Edge Seal Width Calculation" + "# Geospatial Edge Seal Width Calculation" ] }, { diff --git a/tutorials_and_tools/tutorials_and_tools/nrel-only/HPC Connection Required - Geospatial_world_map.ipynb b/tutorials/05_geospatial/02_geospatial_world_map.ipynb similarity index 96% rename from tutorials_and_tools/tutorials_and_tools/nrel-only/HPC Connection Required - Geospatial_world_map.ipynb rename to tutorials/05_geospatial/02_geospatial_world_map.ipynb index c086a6033..632779050 100644 --- a/tutorials_and_tools/tutorials_and_tools/nrel-only/HPC Connection Required - Geospatial_world_map.ipynb +++ b/tutorials/05_geospatial/02_geospatial_world_map.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Note: This notebook is in development\n", + "# Geospatial World Map (HPC)\n", "M. Springer 2024-06-05" ] }, @@ -83,6 +83,7 @@ { "cell_type": "code", "execution_count": null, + "id": "a4643119", "metadata": {}, "outputs": [], "source": [ @@ -102,15 +103,28 @@ "\n", "weather_ds_himawari, meta_df_himawari = pvdeg.weather.get(\n", " weather_db, geospatial=True, **weather_arg\n", - ")\n", - "\n", - "# %%\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4cf9f951", + "metadata": {}, + "outputs": [], + "source": [ "meta_df_himawari_sub, gids_meta_df_himawari = pvdeg.utilities.gid_downsampling(\n", " meta_df_himawari, 3\n", ")\n", - "weather_ds_himawari_sub = weather_ds_himawari.sel(gid=meta_df_himawari_sub.index)\n", - "\n", - "# %%\n", + "weather_ds_himawari_sub = weather_ds_himawari.sel(gid=meta_df_himawari_sub.index)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ "geo_himawari = {\n", " \"func\": pvdeg.standards.standoff,\n", " \"weather_ds\": weather_ds_himawari_sub,\n", @@ -127,6 +141,7 @@ { "cell_type": "code", "execution_count": null, + "id": "acf47855", "metadata": {}, "outputs": [], "source": [ @@ -146,13 +161,26 @@ "\n", "weather_ds_goes, meta_df_goes = pvdeg.weather.get(\n", " weather_db, geospatial=True, **weather_arg\n", - ")\n", - "\n", - "# %%\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ba3b18b8", + "metadata": {}, + "outputs": [], + "source": [ "meta_df_goes_sub, gids_meta_df_goes = pvdeg.utilities.gid_downsampling(meta_df_goes, 8)\n", - "weather_ds_goes_sub = weather_ds_goes.sel(gid=meta_df_goes_sub.index)\n", - "\n", - "# %%\n", + "weather_ds_goes_sub = weather_ds_goes.sel(gid=meta_df_goes_sub.index)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ "geo_goes = {\n", " \"func\": pvdeg.standards.standoff,\n", " \"weather_ds\": weather_ds_goes_sub,\n", @@ -167,6 +195,7 @@ { "cell_type": "code", "execution_count": null, + "id": "00fa08de", "metadata": {}, "outputs": [], "source": [ @@ -186,15 +215,28 @@ "\n", "weather_ds_meteosat, meta_df_meteosat = pvdeg.weather.get(\n", " weather_db, geospatial=True, **weather_arg\n", - ")\n", - "\n", - "# %%\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dcf9d773", + "metadata": {}, + "outputs": [], + "source": [ "meta_df_meteosat_sub, gids_meta_df_meteosat = pvdeg.utilities.gid_downsampling(\n", " meta_df_meteosat, 4\n", ")\n", - "weather_ds_meteosat_sub = weather_ds_meteosat.sel(gid=meta_df_meteosat_sub.index)\n", - "\n", - "# %%\n", + "weather_ds_meteosat_sub = weather_ds_meteosat.sel(gid=meta_df_meteosat_sub.index)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ "geo_meteosat = {\n", " \"func\": pvdeg.standards.standoff,\n", " \"weather_ds\": weather_ds_meteosat_sub,\n", diff --git a/tutorials_and_tools/tutorials_and_tools/nrel-only/HPC Connection Required - LETID - Outdoor Geospatial Demo.ipynb b/tutorials/05_geospatial/03_letid_outdoor_geospatial_demo.ipynb similarity index 98% rename from tutorials_and_tools/tutorials_and_tools/nrel-only/HPC Connection Required - LETID - Outdoor Geospatial Demo.ipynb rename to tutorials/05_geospatial/03_letid_outdoor_geospatial_demo.ipynb index dca560690..9fdfd9f09 100644 --- a/tutorials_and_tools/tutorials_and_tools/nrel-only/HPC Connection Required - LETID - Outdoor Geospatial Demo.ipynb +++ b/tutorials/05_geospatial/03_letid_outdoor_geospatial_demo.ipynb @@ -4,9 +4,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# LETID - Outdoor Geospatioal Demo\n", + "# LETID Outdoor Geospatial Demo (HPC)\n", "\n", - "![PVDeg Logo](../PVD_logo.png)\n" + "![PVDeg Logo](../images/pvdeg_logo.svg)" ] }, { @@ -47,7 +47,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Single location example" + "# Single location example" ] }, { @@ -92,7 +92,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Start distributed compute cluster - DASK" + "# Start distributed compute cluster - DASK" ] }, { diff --git a/tutorials_and_tools/tutorials_and_tools/HPC Connection Required - Scenario - Geographical Features.ipynb b/tutorials/05_geospatial/04_scenario_geographical_features.ipynb similarity index 93% rename from tutorials_and_tools/tutorials_and_tools/HPC Connection Required - Scenario - Geographical Features.ipynb rename to tutorials/05_geospatial/04_scenario_geographical_features.ipynb index 80ce96707..940051d60 100644 --- a/tutorials_and_tools/tutorials_and_tools/HPC Connection Required - Scenario - Geographical Features.ipynb +++ b/tutorials/05_geospatial/04_scenario_geographical_features.ipynb @@ -1,5 +1,12 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Geographical Features (HPC)\n" + ] + }, { "cell_type": "code", "execution_count": null, @@ -14,7 +21,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Create Scenario and Populate Location\n", + "# Create Scenario and Populate Location\n", "The default geospatial datapoints for addLocation are from the Americas satellite. This is the only data we are interested in for this case so don't specify any location arguments.\n", "To speed up calculations we will downsample to get coarser data. The downsampling function is not linear. A downsample factor of 10 takes us from 2018267 entries to only 5109." ] @@ -34,7 +41,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Intro to KDTrees\n", + "# Intro to KDTrees\n", "\n", "A K dimensional tree is a datastructure for organizing n points in a k dimensional space. They are often used for nearest neighbors searches and many ML algorithms. Letting k = 2, our dimensions will be latitude and longitude. This is much faster than iterating over tabular data structures to find neighbors. The function to create a kdtree below requires scikit learn (also known as sklearn), a python machine learning library which is not included in the pvdeg dependency list so you will have to install it independently. Scipy also has a kdtree class but it is much slower. Depending on the number of datapoints the tree build time could be quite long but for our purposes, the cell below will be quick." ] @@ -52,7 +59,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Remaining Points After Downsampling\n", + "# Remaining Points After Downsampling\n", "\n", "Use `plot_coords` to see all latitude longitude coordinate pairs included in a scenario's metadata. You can provide corners of a bounding box to change the extent of the matplotlib plot." ] @@ -74,8 +81,8 @@ "\n", "Identify moutains by using the function below. There are currently 2 methods to identify mountains from our data. Both will add a column to our `GeospatialScenario.meta_data` attribute called `mountain` containing boolean values that represent if a data point is in/near mountains or not near mountains.\n", "\n", - "- `classify_mountains_weights` uses k nearest neighbors search and calculates local changes in height with different methods, the default is the `mean` absolute change compared to a points neighbors, then it assigns a weight to each point. If the remaining points are above the threshold then the percentile argument determines sensitivity to classifying points as mountains (higher weight, more mountainous). Much better than the following option. \n", - "- `classify_mountains_radii` uses 2 nearest neighbors radius searches at each point and compares the average elevation of the inner circle to the avg radius of the circle. " + "- `classify_mountains_weights` uses k nearest neighbors search and calculates local changes in height with different methods, the default is the `mean` absolute change compared to a points neighbors, then it assigns a weight to each point. If the remaining points are above the threshold then the percentile argument determines sensitivity to classifying points as mountains (higher weight, more mountainous). Much better than the following option.\n", + "- `classify_mountains_radii` uses 2 nearest neighbors radius searches at each point and compares the average elevation of the inner circle to the avg radius of the circle." ] }, { @@ -111,7 +118,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Convenience Plotting for Binary Classifications\n", + "# Convenience Plotting for Binary Classifications\n", "\n", "the `plot_meta_classification` method allows you to plot your data with respect to a boolean classifier present in the `scenario.meta_data` dataframe columns." ] @@ -129,9 +136,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Identifying mountains within a bounding box.\n", + "# Identifying mountains within a bounding box.\n", "\n", - "Sometimes we only want information from one area. To allow for more control over the selection process. To pick only one area from an existing superset of data we can use a bounding box. \n", + "Sometimes we only want information from one area. To allow for more control over the selection process. To pick only one area from an existing superset of data we can use a bounding box.\n", "Either provide the top left and bottom right (latitude-longitude) pairs or a 2d numpy array of these pairs which will use the most extreme entries to form the bounding box." ] }, @@ -170,7 +177,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Identifying Coastlines from Geospatial Data\n", + "# Identifying Coastlines from Geospatial Data\n", "\n", "To identify points within a search radius of any [natural earth features](https://www.naturalearthdata.com/features/). The following examples show the process of identifying coastline datapoints." ] @@ -202,7 +209,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Finding Rivers\n", + "# Finding Rivers\n", "\n", "Above we saw how to find points near coastlines, now lets do points near rivers. It is very similar" ] diff --git a/tutorials_and_tools/tutorials_and_tools/HPC Connection Required - Scenario - Geospatial.ipynb b/tutorials/05_geospatial/05_scenario_geospatial.ipynb similarity index 88% rename from tutorials_and_tools/tutorials_and_tools/HPC Connection Required - Scenario - Geospatial.ipynb rename to tutorials/05_geospatial/05_scenario_geospatial.ipynb index 8ab8fcd1e..b840594d8 100644 --- a/tutorials_and_tools/tutorials_and_tools/HPC Connection Required - Scenario - Geospatial.ipynb +++ b/tutorials/05_geospatial/05_scenario_geospatial.ipynb @@ -1,5 +1,12 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Scenario Geospatial (HPC)\n" + ] + }, { "cell_type": "code", "execution_count": null, @@ -13,7 +20,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Define Geospatial Scenario Object\n", + "# Define Geospatial Scenario Object\n", "\n", "To preform geospatial analysis we can create a `GeospatialScenario` object. Alternatively, to preform single location analysis use `Scenario`. Scenario and GeospatialScenario are generalized classes that can be used to replace the legacy functional pvdeg analysis approach with an object orented one." ] @@ -33,7 +40,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Add Location\n", + "# Add Location\n", "To add locations for geospatial analysis we will use the ``.addLocation`` method. We can choose downselect from the NSRDB to a country, state and county in that order. *Support for multiple of each category in list form soon.* The ``see_added`` flag allows us to see the gids we have added to the scenario." ] }, @@ -52,8 +59,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Add Functions to the Pipeline\n", - "The scenario has a queue of jobs to preform. These are stored in an attribute called ``pipeline``, you can directly update the pipeline but this will bypass the assistance given in creating the job function and parameters. The easiest way to add a job to the pipeline is the ``.updatePipeline`` method. For geospatial analysis, weather and metadata is collected and stored in the scenario at the time of the ``.addLocation`` method call so we do not need to include it below, but if we have other function kwargs to include, they should go in the ``func_params`` argument. \n", + "# Add Functions to the Pipeline\n", + "The scenario has a queue of jobs to preform. These are stored in an attribute called ``pipeline``, you can directly update the pipeline but this will bypass the assistance given in creating the job function and parameters. The easiest way to add a job to the pipeline is the ``.updatePipeline`` method. For geospatial analysis, weather and metadata is collected and stored in the scenario at the time of the ``.addLocation`` method call so we do not need to include it below, but if we have other function kwargs to include, they should go in the ``func_params`` argument.\n", "\n", "Only a few pvdeg functions are currently supported for geospatial analysis. See the docstring for ``.updatePipeline`` to view currently supported functions. ``updatePipeline`` will not let you add unsupported geospatial functions. The ``see_added`` flag allows us to see the new job added to the pipeline." ] @@ -80,7 +87,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Run the job in the pipeline\n", + "# Run the job in the pipeline\n", "\n", "Currently ``scenario`` only supports one geospatial analysis at a time. We cannot have two geospatial jobs at the same time." ] @@ -98,7 +105,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Directly Access Results Attribute\n", + "# Directly Access Results Attribute\n", "\n", "We can either view the results of the scenario pipeline using ``.viewScenario`` as shown above. The results will be displayed only if the pipeline has been run. Alternatively, we can directly view the ``results`` atribute of the scenario." ] @@ -116,7 +123,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Cleanup\n", + "# Cleanup\n", "\n", "The scenario object will store its attributes in a file the python script's current working directory. If we want to delete this file when we are done with the scenario instance we can use the ``.clean()`` method as shown below." ] @@ -134,10 +141,10 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Example Geospatial Functionality\n", + "# Example Geospatial Functionality\n", "Many functions are supported for geospatial analysis, here are a few.\n", - "- ``pvdeg.standards.standoff`` \n", - "- ``pvdeg.humidity.module`` \n", + "- ``pvdeg.standards.standoff``\n", + "- ``pvdeg.humidity.module``\n", "- ``pvdeg.letid.calc_letid_outdoors``\n", "\n", "See the Geospatial Templates tutorial for an example on this." @@ -160,12 +167,12 @@ "geospatial_humidity_scenario.addJob(\n", " func=pvdeg.humidity.module,\n", " func_params={\n", - " 'backsheet_thickness': 0.3, # mm, thickness of PET backsheet\n", - " 'back_encap_thickness': 0.46, # mm, thickness of EVA backside encapsulant\n", - " 'encapsulant': 'W001', # EVA encapsulant\n", - " 'backsheet': 'W017' # PET backsheet\n", + " \"backsheet_thickness\": 0.3, # mm, thickness of PET backsheet\n", + " \"back_encap_thickness\": 0.46, # mm, thickness of EVA backside encapsulant\n", + " \"encapsulant\": \"W001\", # EVA encapsulant\n", + " \"backsheet\": \"W017\", # PET backsheet\n", " },\n", - " see_added=True\n", + " see_added=True,\n", ")" ] }, diff --git a/tutorials/05_geospatial/README.md b/tutorials/05_geospatial/README.md new file mode 100644 index 000000000..84e2489b4 --- /dev/null +++ b/tutorials/05_geospatial/README.md @@ -0,0 +1,31 @@ +# Geospatial Analysis + +Tutorials for large-scale geospatial analysis and mapping workflows. + +## Tutorials + +⚠️ **All tutorials in this folder require access to High Performance Computing (HPC) resources.** + +1. **01_geospatial_templates.ipynb** - Templates for geospatial analysis workflows +2. **02_geospatial_world_map.ipynb** - World-scale geospatial degradation mapping +3. **03_letid_outdoor_geospatial_demo.ipynb** - LETID outdoor geospatial demonstration +4. **04_scenario_geographical_features.ipynb** - Incorporating geographical features into scenarios +5. **05_scenario_geospatial.ipynb** - Large-scale geospatial scenario analysis + +## HPC Access + +These tutorials involve: +- Large-scale geospatial computations across multiple locations +- Distributed weather data retrieval +- Regional and global degradation mapping +- Geographic feature analysis (mountains, coastlines, rivers) +- Multi-dimensional spatial data processing + +## Topics Covered + +- Geospatial degradation mapping +- Geographic feature analysis and classification +- Regional climate-based modeling +- World-scale analysis and visualization +- KDTree-based spatial data structures +- Geospatial scenario workflows diff --git a/tutorials/05_geospatial/scripts/01_geospatial_templates.py b/tutorials/05_geospatial/scripts/01_geospatial_templates.py new file mode 100644 index 000000000..56a33c6f8 --- /dev/null +++ b/tutorials/05_geospatial/scripts/01_geospatial_templates.py @@ -0,0 +1,282 @@ +# %% [markdown] +# # Geospatial Templates (HPC) +# + +# %% +import pvdeg +from pvdeg import TEST_DATA_DIR +import pandas as pd +import os +import xarray as xr + + +# %% [markdown] +# # Geospatial Templates +# +# When running a geospatial analysis using `pvdeg.geospatial.analysis` on arbitary `pvdeg` functions you will need to specify a template for the shape of the output data. This is because the input data comes with dimensions of gid and time while the output will have data in a different shape usually corresonding to coordinates. +# - gid, identification number corresponding to an NSRDB datapoint's location +# - time, timeseries corresponding to the hourly time indicies of NSRDB datapoint's yearly meteorological data. +# +# Follow the steps below to see how we generate templates before running the analysis. +# +# The only functions where this is not required are currently `pvdeg.standards.standoff`, `pvdeg.humidity.moduke` and, `letid.calc_letid_outdoors` as they are predefined within the package. + +# %% [markdown] +# # Loading Geospatial Data +# +# This step skips over making the `pvdeg.weather.get` call with `geospatial == True`. See the [Duramat Demo](./DuraMAT%20Live%20Demo.ipynb) for information on how to do this traditionally. +# +# We can also use a `GeospatialScenario` object. See the [Geospatial Scenario Tutorial](./Scenario%20-%20Geospatial.ipynb) for more information on how to use this approach. +# +# *The cell below loads a pickled xarray object, this is not the best way to do this. xarray datasets should be stored as `.nc` - netcdf files* + +# %% +geo_meta = pd.read_csv(os.path.join(TEST_DATA_DIR, "summit-meta.csv"), index_col=0) + +# Use xarray to open NetCDF file instead of pickle +geo_weather = xr.open_dataset(os.path.join(TEST_DATA_DIR, "summit-weather.nc")) + +geo_weather + + +# %% [markdown] +# # Creating Templates Manually +# +# `pvdeg.geospatial.ouput_template` we can produce a template for our result data. +# +# We need to do this because different functions return different types of values, some return multiple values as tuples, some return only single numerics, others return timeseries results. We need to specify the shape of our data to create an output xarray dataset. + +# %% [markdown] +# # Examples +# +# ## 98α΅—Κ° module percential temperature at Standoff Height +# +# Say we want to estimate the 98α΅—Κ° percential temperature for the module at the given tilt, azimuth, and x_eff. `PVDeg` has a function to do this, `pvdeg.standards.T98_estimate` BUT it doesn't have a preset geospatial template. We will need to make one. +# +# - look at the function return values. +# From the docstring we can see that `T98_estimate` only has one return value. IMPORTANT, this value is a single float, NOT a timeseries. This means our output shape will only be dependent on the input identifier and NOT time. +# +# Therefore we will map the output variable `T98` to the location identifier `gid` using a dictionary with `str: tuple` mappings. +# +# *IMPORTANT: you must use the syntax below where the variable maps to a tuple of the coordinates. in this case there needs to be a trailing comma in the tuple or python will iterate over the characters in the tuple instead of the elements. See further examples to alleviate confusion.* + +# %% +# define output shape +shapes = { + "T98": ( + "gid", + ) # one return value at each datapoint, only dependent on datapoint, not time +} + +# create xarray template for output to be populated when analysis is run +template = pvdeg.geospatial.output_template( + ds_gids=geo_weather, + shapes=shapes, +) + +# %% +geo_estimate_temp = pvdeg.geospatial.analysis( + weather_ds=geo_weather, + meta_df=geo_meta, + func=pvdeg.standards.T98_estimate, + template=template, +) + +# %% [markdown] +# # Glass Glass Estimated Module Temperature +# +# Now we want to calculate geospatial timeseries temperature values for a module using `pvdeg.temperature.module`. This is not super practical because all `pvdeg` functions that need to use tempeature for their calculations preform the temperature calculation internally, this is just for show. +# +# This calculation differs from the above because the temperature functions return the module temperature in a timeseries format. So we care about 2 dimensions, location identifier and TIME. + +# %% +# define output shape +shapes = { + "module_temperature": ( + "gid", + "time", + ) # one return value at each datapoint, only dependent on datapoint, not time +} + +# create xarray template for output to be populated when analysis is run +temperature_template = pvdeg.geospatial.output_template( + ds_gids=geo_weather, + shapes=shapes, +) + +# %% +geo_temperature_res = pvdeg.geospatial.analysis( + weather_ds=geo_weather, + meta_df=geo_meta, + func=pvdeg.temperature.module, + template=temperature_template, # use the template we created + conf="open_rack_glass_glass", # provide kwargs for function here +) + +# %% +# plot the temperature at ONE of the geospatial analysis result locations but we have calculated all of these. +import matplotlib.pyplot as plt + +module_temps = ( + geo_temperature_res["module"].sel(latitude=39.89, longitude="-106.42").values +) + +plt.plot(module_temps) + +# %% [markdown] +# # Self Explaining Code +# +# If we are looking at adding templates for other functions, we can also look at the 3 presaved templates for existing `pvdeg` functions. Visit [pvdeg.geospatial.template_parameters](../../pvdeg/geospatial.py) and inspect this function to see how these different target functions utilize templates and shapes. + +# %% [markdown] +# # Creating Templates Programatically +# +# We can use `pvdeg.geospatial.autotemplate` to generate a template for a given function. This can return a bad result which will fail or work improperly when running `pvdeg.geospatial.analysis` with the generated template. Results should be scrutinized to make sure they are the right format. + +# %% [markdown] +# # Examples Below +# Steps +# - Create template using autotemplating function. Pulls in information about function to determine shape of output. Not usable on functions with ambigious return types. +# - Call geospatial analysis function using template + +# %% [markdown] +# # Geospatial Cell Temperature Calculation +# As shown below, we have two options, we can choose to provide a template that is generated by a function which supports autotemplating. Or we can provide the function to `geospatial.analysis` and let it generate a template internally. +# +# ## Providing a Template with `Geospatial.auto_template` + +# %% +# create a template using auto_template for the desired function +cell_temp_template = pvdeg.geospatial.auto_template( + func=pvdeg.temperature.cell, ds_gids=geo_weather +) + +# run the geospatial analysis with the template +pvdeg.geospatial.analysis( + weather_ds=geo_weather, + meta_df=geo_meta, + func=pvdeg.temperature.cell, + template=cell_temp_template, +) + +# %% [markdown] +# # Analysis Without Providing a Template +# +# If a function is supported by `geospatial.auto_template` we do not need to create a template outside of the function as shown in the cell above. We can simply pass the function to `geospatial.analysis` and it will create a template for us. + +# %% +pvdeg.geospatial.analysis( + weather_ds=geo_weather, + meta_df=geo_meta, + func=pvdeg.temperature.cell, +) + +# %% [markdown] +# # Geospatial Module Temperature Calculation + +# %% +module_temp_template = pvdeg.geospatial.auto_template( + func=pvdeg.temperature.module, ds_gids=geo_weather +) + +pvdeg.geospatial.analysis( + weather_ds=geo_weather, + meta_df=geo_meta, + func=pvdeg.temperature.module, + template=module_temp_template, +) + +# %% [markdown] +# # Geospatial Solar Position Calculation + +# %% +solar_position_template = pvdeg.geospatial.auto_template( + func=pvdeg.spectral.solar_position, ds_gids=geo_weather +) + +pvdeg.geospatial.analysis( + weather_ds=geo_weather, + meta_df=geo_meta, + func=pvdeg.spectral.solar_position, + template=solar_position_template, +) + +# %% [markdown] +# # Geospatial POA Irradiance Calculation + +# %% +poa_irradiance_template = pvdeg.geospatial.auto_template( + func=pvdeg.spectral.poa_irradiance, ds_gids=geo_weather +) + +pvdeg.geospatial.analysis( + weather_ds=geo_weather, + meta_df=geo_meta, + func=pvdeg.spectral.poa_irradiance, + template=poa_irradiance_template, +) + +# %% [markdown] +# # Geospatial 98th Percentile Operating Temperature Calculation + +# %% +standoff_template = pvdeg.geospatial.auto_template( + func=pvdeg.standards.T98_estimate, ds_gids=geo_weather +) + +pvdeg.geospatial.analysis( + weather_ds=geo_weather, + meta_df=geo_meta, + func=pvdeg.standards.T98_estimate, + template=standoff_template, +) + +# %% [markdown] +# # Geospatial Module Humidity Calculation + +# %% +humidity_template = pvdeg.geospatial.auto_template( + func=pvdeg.humidity.module, ds_gids=geo_weather +) + +pvdeg.geospatial.analysis( + weather_ds=geo_weather, + meta_df=geo_meta, + func=pvdeg.humidity.module, + template=humidity_template, + backsheet_thickness=0.3, + back_encap_thickness=0.5, +) + +# %% [markdown] +# # Geospatial IwaVantHoff Environment Characterization Calculation + +# %% +iwa_vant_hoff_template = pvdeg.geospatial.auto_template( + func=pvdeg.degradation.IwaVantHoff, ds_gids=geo_weather +) + +pvdeg.geospatial.analysis( + weather_ds=geo_weather, + meta_df=geo_meta, + func=pvdeg.degradation.IwaVantHoff, + template=iwa_vant_hoff_template, +) + +# %% [markdown] +# # Geospatial Edge Seal Width Calculation + +# %% +edge_seal_template = pvdeg.geospatial.auto_template( + func=pvdeg.design.edge_seal_width, ds_gids=geo_weather +) + +pvdeg.geospatial.analysis( + weather_ds=geo_weather, + meta_df=geo_meta, + func=pvdeg.design.edge_seal_width, + template=edge_seal_template, +) + +# %% +edge_seal_template diff --git a/tutorials/05_geospatial/scripts/02_geospatial_world_map.py b/tutorials/05_geospatial/scripts/02_geospatial_world_map.py new file mode 100644 index 000000000..c9a93287a --- /dev/null +++ b/tutorials/05_geospatial/scripts/02_geospatial_world_map.py @@ -0,0 +1,670 @@ +# %% [markdown] +# # Geospatial World Map (HPC) +# M. Springer 2024-06-05 + +# %% +print("Importing libraries...") +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +import pvdeg +import xarray as xr +import os + +print("Done!") + +# %% [markdown] +# # Calculate Standoff + +# %% +work_dir = "/projects/pvsoiling/pvdeg/analysis/world_map/standoff_fine" +data_dir = "/projects/pvsoiling/pvdeg/analysis/world_map/data" + +# %% +local = { + "manager": "local", + "n_workers": 100, +} + +kestrel = { + "manager": "slurm", + "n_jobs": 8, # Max number of nodes used for parallel processing + "cores": 100, + "processes": 50, + "memory": "245GB", + "account": "pvfem", + "queue": "standard", + "walltime": "8:00:00", + # "scheduler_options": {"host": socket.gethostname()}, +} + +print("Starting Dask client...") +client = pvdeg.geospatial.start_dask(hpc=kestrel) +print("Cluster ready!") + +# %% +# Get weather data +weather_db = "NSRDB" + +# %% +weather_arg = { + "satellite": "Himawari", + "names": "tmy-2020", + "NREL_HPC": True, + "attributes": [ + "air_temperature", + "wind_speed", + "dhi", + "ghi", + "dni", + "relative_humidity", + ], +} + +weather_ds_himawari, meta_df_himawari = pvdeg.weather.get( + weather_db, geospatial=True, **weather_arg +) + +# %% +meta_df_himawari_sub, gids_meta_df_himawari = pvdeg.utilities.gid_downsampling( + meta_df_himawari, 3 +) +weather_ds_himawari_sub = weather_ds_himawari.sel(gid=meta_df_himawari_sub.index) + +# %% +geo_himawari = { + "func": pvdeg.standards.standoff, + "weather_ds": weather_ds_himawari_sub, + "meta_df": meta_df_himawari_sub, +} + +standoff_res_himawari = pvdeg.geospatial.analysis(**geo_himawari) +standoff_res_himawari.to_netcdf(os.path.join(work_dir, "standoff_himawari.nc")) +standoff_res_himawari.to_dataframe().to_csv( + os.path.join(work_dir, "standoff_himawari.csv") +) + +# %% +weather_arg = { + "satellite": "GOES", + "names": 2021, + "NREL_HPC": True, + "attributes": [ + "air_temperature", + "wind_speed", + "dhi", + "ghi", + "dni", + "relative_humidity", + ], +} + +weather_ds_goes, meta_df_goes = pvdeg.weather.get( + weather_db, geospatial=True, **weather_arg +) + +# %% +meta_df_goes_sub, gids_meta_df_goes = pvdeg.utilities.gid_downsampling(meta_df_goes, 8) +weather_ds_goes_sub = weather_ds_goes.sel(gid=meta_df_goes_sub.index) + +# %% +geo_goes = { + "func": pvdeg.standards.standoff, + "weather_ds": weather_ds_goes_sub, + "meta_df": meta_df_goes_sub, +} + +standoff_res_goes = pvdeg.geospatial.analysis(**geo_goes) +standoff_res_goes.to_netcdf(os.path.join(work_dir, "standoff_goes.nc")) +standoff_res_goes.to_dataframe().to_csv(os.path.join(work_dir, "standoff_goes.csv")) + +# %% +weather_arg = { + "satellite": "METEOSAT", + "names": 2019, + "NREL_HPC": True, + "attributes": [ + "air_temperature", + "wind_speed", + "dhi", + "ghi", + "dni", + "relative_humidity", + ], +} + +weather_ds_meteosat, meta_df_meteosat = pvdeg.weather.get( + weather_db, geospatial=True, **weather_arg +) + +# %% +meta_df_meteosat_sub, gids_meta_df_meteosat = pvdeg.utilities.gid_downsampling( + meta_df_meteosat, 4 +) +weather_ds_meteosat_sub = weather_ds_meteosat.sel(gid=meta_df_meteosat_sub.index) + +# %% +geo_meteosat = { + "func": pvdeg.standards.standoff, + "weather_ds": weather_ds_meteosat_sub, + "meta_df": meta_df_meteosat_sub, +} + +standoff_res_meteosat = pvdeg.geospatial.analysis(**geo_meteosat) +standoff_res_meteosat.to_netcdf(os.path.join(work_dir, "standoff_meteosat.nc")) +standoff_res_meteosat.to_dataframe().to_csv( + os.path.join(work_dir, "standoff_meteosat.csv") +) + +# %% +# Auxillary data +import h5py + +fp_weather_aux = "/projects/pvsoiling/pvdeg/data/world_map_aux.h5" +fp_meta_aux = "/projects/pvsoiling/pvdeg/data/meta_world_map_aux.csv" + +# weather_aux = pvdeg.weather.read(fp_weather_aux, 'h5') +meta_aux = pd.read_csv(fp_meta_aux, index_col=0) + +# xarray work around for aux data +dss = [] +drop_variables = ["meta", "time_index", "tmy_year", "tmy_year_short", "coordinates"] + +hf = h5py.File(fp_weather_aux, "r") +attr = list(hf) + +attr_to_read = [elem for elem in attr if elem not in drop_variables] + +chunks = [] +shapes = [] +for var in attr_to_read: + chunks.append(hf[var].chunks if hf[var].chunks is not None else (np.nan, np.nan)) + shapes.append(hf[var].shape if hf[var].shape is not None else (np.nan, np.nan)) +chunks = min(set(chunks)) +shapes = min(set(shapes)) + + +time_index = pd.to_datetime(hf["time_index"][...].astype(str)).values +meta_df = meta_aux +coords = {"gid": meta_df.index.values, "time": time_index} +coords_len = {"time": time_index.shape[0], "gid": meta_df.shape[0]} + +ds = xr.open_dataset( + fp_weather_aux, + engine="h5netcdf", + phony_dims="sort", + chunks={"phony_dim_0": -1, "phony_dim_1": -1}, + drop_variables=drop_variables, + mask_and_scale=False, + decode_cf=True, +) + +for var in ds.data_vars: + if hasattr(getattr(ds, var), "psm_scale_factor"): + scale_factor = 1 / ds[var].psm_scale_factor + print(scale_factor) + getattr(ds, var).attrs["scale_factor"] = scale_factor + +rename = {} +for ( + phony, + length, +) in ds.sizes.items(): + if length == coords_len["time"]: + rename[phony] = "time" + elif length == coords_len["gid"]: + rename[phony] = "gid" +ds = ds.rename(rename) +ds = ds.assign_coords(coords) + +# TODO: In case re-chunking becomes necessary +# ax0 = list(ds.sizes.keys())[list(ds.sizes.values()).index(shapes[0])] +# ax1 = list(ds.sizes.keys())[list(ds.sizes.values()).index(shapes[1])] +# ds = ds.chunk(chunks={ax0:chunks[0], ax1:chunks[1]}) +dss.append(ds) + +ds = xr.merge(dss) +ds = xr.decode_cf(ds) + +# Rechunk time axis +ds = ds.unify_chunks() +ds = ds.chunk(chunks={"time": -1, "gid": ds.chunks["gid"]}) + +weather_ds = ds + +DSET_MAP = {"air_temperature": "temp_air", "Relative Humidity": "relative_humidity"} +META_MAP = {"elevation": "altitude", "Local Time Zone": "tz", "timezone": "tz"} + +for dset in weather_ds.data_vars: + if dset in DSET_MAP.keys(): + weather_ds = weather_ds.rename({dset: DSET_MAP[dset]}) + +for mset in meta_df.columns: + if mset in META_MAP.keys(): + meta_df.rename(columns={mset: META_MAP[mset]}, inplace=True) + +weather_ds_aux = weather_ds + +geo_aux = { + "func": pvdeg.standards.standoff, + "weather_ds": weather_ds_aux, + "meta_df": meta_aux, +} + +standoff_res_aux = pvdeg.geospatial.analysis(**geo_aux) +standoff_res_aux.to_netcdf(os.path.join(work_dir, "standoff_aux.nc")) +standoff_res_aux.to_dataframe().to_csv(os.path.join(work_dir, "standoff_aux.csv")) + +# %% +weather_db = "NSRDB" +weather_arg = { + "satellite": "METEOSAT", + "names": 2019, + "NREL_HPC": True, + "attributes": [ + "air_temperature", + "wind_speed", + "dhi", + "ghi", + "dni", + "relative_humidity", + ], +} +weather_ds_meteosat, meta_df_meteosat = pvdeg.weather.get( + weather_db, geospatial=True, **weather_arg +) + +time_hourly = pd.date_range("2019-01-01", freq="h", periods=365 * 24) +weather_ds_meteosat_hourly = weather_ds_meteosat.sel(time=time_hourly) + +europe = [ + "Spain", + "Portugal", #'Ireland', 'United Kingdom', + "France", + "Belgium", + "Netherlands", + "Norway", + "Luxembourg", + "Germany", + "Switzerland", + "Italy", + "Monaco", + "Denmark", + "Liechtenstein", + "Austria", + "Sweden", + "Czech Republic", + "San Marino", + "Slovenia", + "Croatia", + "Poland", + "Malta", + "Bosnia and Herzegovina", + "Hungary", + "Slovakia", + "Montenegro", + "Serbia", + "Albania", + "Greece", + "Romania", + "Macedonia", + "Latvia", + "Lithuania", + "Finland", + "Estonia", + "Ukraine", + "Bulgaria", + "Belarus", + "Moldova", + "Turkey", + "Cyprus", + "Northern Cyprus", + "Georgia", +] + +meta_df_europe = meta_df_meteosat[meta_df_meteosat["country"].isin(europe)] + +meta_df_europe_sub, gids_sub = pvdeg.utilities.gid_downsampling(meta_df_europe, 4) +weather_europe_sub = weather_ds_meteosat_hourly.sel(gid=meta_df_europe_sub.index) + +meta_uk = pd.read_csv("../../world_map/data/meta_pvgis_uk_4300.csv") +meta_uk_sub, gids_sub = pvdeg.utilities.gid_downsampling(meta_uk, 1) + +meta_scan1 = pd.read_csv(f"{data_dir}/meta_pvgis_scan_coarse1500.csv", index_col=0) +meta_scan12 = pd.read_csv( + f"{data_dir}/meta_pvgis_scan_coarse_1500_1599.csv", index_col=0 +) +meta_scan2 = pd.read_csv(f"{data_dir}/meta_pvgis_scan_coarse2100.csv", index_col=0) +meta_scan = pd.concat([meta_scan1, meta_scan12, meta_scan2]) + +meta_pvgis = pd.concat([meta_scan, meta_uk_sub]) + +lat_NSRDB = meta_df_europe_sub["latitude"].to_numpy() +meta_pvgis["latitude_pvgis"] = meta_pvgis["latitude"] +meta_pvgis["latitude"] = meta_pvgis["latitude"].apply( + lambda x: ( + lat_NSRDB[np.argmin(np.abs(x - lat_NSRDB))] + if x < meta_df_europe_sub["latitude"].max() + else x + ) +) + +lon_NSRDB = meta_df_europe_sub["longitude"].to_numpy() +meta_pvgis["longitude_pvgis"] = meta_pvgis["longitude"] +meta_pvgis["longitude"] = meta_pvgis["longitude"].apply( + lambda x: ( + lon_NSRDB[np.argmin(np.abs(x - lon_NSRDB))] + if x > meta_df_europe_sub["longitude"].min() + else x + ) +) +meta_pvgis["tz"] = 0 +meta_pvgis = meta_pvgis.reset_index() + +meta_eu_coarse = pd.concat([meta_df_europe_sub, meta_pvgis]) +meta_eu_coarse["tz"] = 0 +meta_eu_coarse = meta_eu_coarse.reset_index() +meta_eu_coarse.to_csv("../../world_map/data/meta_eu_coarse.csv") + +weather_uk = xr.open_dataset("../../world_map/data/weather_ds_uk_4300.nc") +weather_uk = weather_uk.sel(gid=meta_uk.index) +weather_uk_sub = weather_uk.sel(gid=meta_uk_sub.index) + +weather_scan1 = xr.open_dataset("../../world_map/data/weather_ds_scan_coarse1500.nc") +weather_scan12 = xr.open_dataset( + "../../world_map/data/weather_ds_scan_coarse_1500_1599.nc" +) +weather_scan2 = xr.open_dataset("../../world_map/data/weather_ds_scan_coarse2100.nc") +weather_scan = xr.concat( + [ + weather_scan1.sel(gid=slice(0, 1500)), + weather_scan12.sel(gid=slice(1501, 1599)), + weather_scan2.sel(gid=slice(1600, None)), + ], + dim="gid", +) +weather_scan = weather_scan.sel(gid=meta_scan.index) +# weather_scan = xr.concat([weather_scan1, weather_scan2], dim='gid') + +weather_pvgis = xr.concat([weather_scan, weather_uk_sub], dim="gid") +weather_pvgis = weather_pvgis.drop_vars(["IR(h)", "wind_direction", "pressure"]) + +weather_pvgis = weather_pvgis.assign_coords({"gid": meta_pvgis.index}) +weather_pvgis = weather_pvgis.chunk(chunks={"time": -1, "gid": 100}) +weather_pvgis = weather_pvgis.unify_chunks() + +meta_df_pvgis_sub, gids_meta_df_pvgis = pvdeg.utilities.gid_downsampling(meta_pvgis, 4) +weather_ds_pvgis_sub = weather_pvgis.sel(gid=meta_df_pvgis_sub.index) + +# weather_europe_sub = weather_europe_sub.assign_coords({'time': pd.date_range("2022-01-01", freq="h", periods=365 * 24),}) +# weather_eu_coarse = xr.concat([weather_europe_sub, weather_pvgis], dim='gid') +# weather_eu_coarse = weather_eu_coarse.assign_coords({'gid': meta_eu_coarse.index}) +# weather_eu_coarse = weather_eu_coarse.chunk(chunks={"time": -1, "gid": 100}) +# weather_eu_coarse = weather_eu_coarse.unify_chunks() + +# with open('weather_eu_coarse.pickle', 'wb') as handle: +# pickle.dump(weather_eu_coarse, handle, protocol=pickle.HIGHEST_PROTOCOL) + + +geo_pvgis = { + "func": pvdeg.standards.standoff, + "weather_ds": weather_pvgis, + "meta_df": meta_pvgis, +} + +standoff_res_pvgis = pvdeg.geospatial.analysis(**geo_pvgis) +standoff_res_pvgis.to_netcdf(os.path.join(work_dir, "standoff_pvgis.nc")) +standoff_res_pvgis.to_dataframe().to_csv(os.path.join(work_dir, "standoff_pvgis.csv")) + +# %% +meta_north = pd.read_csv(f"{data_dir}/meta_pvgis_north_3300.csv", index_col=0) +weather_north = xr.open_dataset(f"{data_dir}/weather_ds_north_3300.nc") +weather_north = weather_north.sel(gid=meta_north.index) + +weather_north = weather_north.assign_coords({"gid": meta_north.index}) +weather_north = weather_north.chunk(chunks={"time": -1, "gid": 100}) +weather_north = weather_north.unify_chunks() + + +geo_pvgis = { + "func": pvdeg.standards.standoff, + "weather_ds": weather_north, + "meta_df": meta_north, +} + +standoff_res_pvgis = pvdeg.geospatial.analysis(**geo_pvgis) +standoff_res_pvgis.to_netcdf(os.path.join(work_dir, "standoff_pvgis_north.nc")) +standoff_res_pvgis.to_dataframe().to_csv( + os.path.join(work_dir, "standoff_pvgis_north.csv") +) + +# %% [markdown] +# # Post process + +# %% +import pvdeg +import os +import pandas as pd +import numpy as np +import xarray as xr +from global_land_mask import globe +import cartopy.crs as ccrs +import cartopy.io.shapereader as shpreader + +work_dir = "/projects/pvsoiling/pvdeg/analysis/world_map/standoff_fine" +data_dir = "/projects/pvsoiling/pvdeg/analysis/world_map/data" + +# %% +# Create 0cm standoff locations + +lon_north = np.arange(-179, 180, 0.25) +lat_north = np.arange(60, 90, 0.25) +lon_grid_north, lat_grid_north = np.meshgrid(lon_north, lat_north) +land_north = globe.is_land(lat_grid_north, lon_grid_north) +lon_land_north = lon_grid_north[land_north] +lat_land_north = lat_grid_north[land_north] + +lon_south = np.arange(-179, 180, 0.25) +lat_south = np.arange(-90, -60, 0.25) +lon_grid_south, lat_grid_south = np.meshgrid(lon_south, lat_south) +land_south = globe.is_land(lat_grid_south, lon_grid_south) +lon_land_south = lon_grid_south[land_south] +lat_land_south = lat_grid_south[land_south] + + +lon_asia = np.arange(80, 105, 0.25) +lat_asia = np.arange(50, 61, 0.25) + +lon = lon_asia +lat = np.full(lon_asia.size, 61) + +for i, lat_coord in enumerate(reversed(lat_asia)): + lon_sub = lon_asia[i:-i] + lat_sub = np.full(lon_sub.size, lat_coord) + lon = np.append(lon, lon_sub) + lat = np.append(lat, lat_sub) + +lon_asia = lon +lat_asia = lat + +# %% +fig, ax = plt.subplots() + +plt.scatter(lon_land_north, lat_land_north, c="r", s=1) +plt.scatter(lon_land_south, lat_land_south, c="b", s=1) +plt.scatter(lon, lat, c="g", s=1) + +ax.set_ylim(-90, 90) +ax.set_xlim(-180, 180) + +# %% +template_params = pvdeg.geospatial.template_parameters(pvdeg.standards.standoff) +standoff_zero_north = pvdeg.geospatial.zero_template( + lat_land_north, lon_land_north, **template_params +) +standoff_zero_south = pvdeg.geospatial.zero_template( + lat_land_south, lon_land_south, **template_params +) +standoff_zero_asia = pvdeg.geospatial.zero_template( + lat_asia, lon_asia, **template_params +) + +# %% +standoff_aux = xr.open_dataset(os.path.join(work_dir, "standoff_aux.nc")) +standoff_himawari = xr.open_dataset(os.path.join(work_dir, "standoff_himawari.nc")) +standoff_meteosat = xr.open_dataset(os.path.join(work_dir, "standoff_meteosat.nc")) +standoff_north = xr.open_dataset(os.path.join(work_dir, "standoff_pvgis_north.nc")) +standoff_pvgis = xr.open_dataset(os.path.join(work_dir, "standoff_pvgis.nc")) +standoff_goes = xr.open_dataset(os.path.join(work_dir, "standoff_goes.nc")) + +# %% +fig = plt.figure(figsize=(10, 5)) +ax = fig.add_axes([0, 0, 1, 1], projection=ccrs.PlateCarree(), frameon=True) +ax.patch.set_visible(True) +ax.set_extent([-180, 180, -85, 85], ccrs.PlateCarree()) + +shapename = "admin_0_countries" +states_shp = shpreader.natural_earth( + resolution="110m", category="cultural", name=shapename +) + +cmap = "Spectral_r" +size = 0.75 + + +standoff_zero_north.plot.scatter( + x="longitude", + y="latitude", + hue="x", + cmap=cmap, + s=size, + linewidths=0, + vmin=0, + vmax=14, + add_colorbar=False, + ax=ax, +) +standoff_zero_south.plot.scatter( + x="longitude", + y="latitude", + hue="x", + cmap=cmap, + s=size, + linewidths=0, + vmin=0, + vmax=14, + add_colorbar=False, + ax=ax, +) +standoff_zero_asia.plot.scatter( + x="longitude", + y="latitude", + hue="x", + cmap=cmap, + s=size, + linewidths=0, + vmin=0, + vmax=14, + add_colorbar=False, + ax=ax, +) + +cm = standoff_himawari.plot.scatter( + x="longitude", + y="latitude", + hue="x", + cmap=cmap, + s=size, + linewidths=0, + vmin=0, + vmax=14, + add_colorbar=False, + ax=ax, +) +standoff_north.plot.scatter( + x="longitude", + y="latitude", + hue="x", + cmap=cmap, + s=size, + linewidths=0, + vmin=0, + vmax=14, + add_colorbar=False, + ax=ax, +) +standoff_aux.plot.scatter( + x="longitude", + y="latitude", + hue="x", + cmap=cmap, + s=size, + linewidths=0, + vmin=0, + vmax=14, + add_colorbar=False, + ax=ax, +) +standoff_pvgis.plot.scatter( + x="longitude", + y="latitude", + hue="x", + cmap=cmap, + s=size, + linewidths=0, + vmin=0, + vmax=14, + add_colorbar=False, + ax=ax, +) +standoff_meteosat.plot.scatter( + x="longitude", + y="latitude", + hue="x", + cmap=cmap, + s=1, + linewidths=0, + vmin=0, + vmax=14, + add_colorbar=False, + ax=ax, +) +standoff_goes.plot.scatter( + x="longitude", + y="latitude", + hue="x", + cmap=cmap, + s=1, + linewidths=0, + vmin=0, + vmax=14, + add_colorbar=False, + ax=ax, +) + + +ax.add_geometries( + shpreader.Reader(states_shp).geometries(), + ccrs.PlateCarree(), + facecolor="none", + edgecolor="gray", + linewidth=0.5, +) + +cb_title = "Standoff distance [cm]" +cb = plt.colorbar(cm, shrink=0.78, aspect=30, pad=0.02) +cb.set_label(cb_title) +# ax.set_title('title') + +ax.set_xticks(np.arange(-180, 181, 20), crs=ccrs.PlateCarree()) +ax.set_yticks(np.arange(-90, 91, 10), crs=ccrs.PlateCarree()) + +ax.set_xlim(-180, 180) +ax.set_ylim(-85, 85) + +ax.set_xlabel("Longitude") +ax.set_ylabel("Latitude") + +plt.savefig(os.path.join(work_dir, "standoff_map.png"), dpi=1200, bbox_inches="tight") + +# %% diff --git a/tutorials/05_geospatial/scripts/03_letid_outdoor_geospatial_demo.py b/tutorials/05_geospatial/scripts/03_letid_outdoor_geospatial_demo.py new file mode 100644 index 000000000..ca9f17421 --- /dev/null +++ b/tutorials/05_geospatial/scripts/03_letid_outdoor_geospatial_demo.py @@ -0,0 +1,226 @@ +# %% [markdown] +# # LETID Outdoor Geospatial Demo (HPC) +# +# ![PVDeg Logo](../images/pvdeg_logo.svg) + +# %% +import matplotlib.pyplot as plt +import pandas as pd +import pvdeg +from pvdeg import DATA_DIR +import os + +# %% +# This information helps with debugging and getting support :) +import sys +import platform + +print("Working on a ", platform.system(), platform.release()) +print("Python version ", sys.version) +print("Pandas version ", pd.__version__) +print("pvdeg version ", pvdeg.__version__) + +# %% [markdown] +# # Single location example + +# %% +weather_file = os.path.join(DATA_DIR, "psm3_demo.csv") +WEATHER, META = pvdeg.weather.read(weather_file, "psm") + +# %% +kwargs = { + "tau_0": 115, # us, carrier lifetime in non-degraded states, e.g. LETID/LID states A or C + "tau_deg": 55, # us, carrier lifetime in fully-degraded state, e.g. LETID/LID state B + "wafer_thickness": 180, # um + "s_rear": 46, # cm/s + "cell_area": 243, # cm^2 + "na_0": 100, + "nb_0": 0, + "nc_0": 0, + "mechanism_params": "repins", +} + +# %% +pvdeg.letid.calc_letid_outdoors(weather_df=WEATHER, meta=META, **kwargs) + +# %% [markdown] +# # Start distributed compute cluster - DASK + +# %% +local = { + "manager": "local", + "n_workers": 1, + "threads_per_worker": 8, # Number of CPUs +} + +kestrel = { + "manager": "slurm", + "n_jobs": 1, # Number of nodes used for parallel processing + "cores": 104, + "memory": "256GB", + "account": "pvsoiling", + "queue": "debug", + "walltime": "1:00:00", + "processes": 104, + "job_extra_directives": ["-o ./logs/slurm-%j.out"], +} + +pvdeg.geospatial.start_dask(hpc=kestrel) + +# %% +# Get weather data +weather_db = "NSRDB" + +weather_arg = { + "satellite": "Americas", + "names": 2022, + "NREL_HPC": True, + "attributes": [ + "air_temperature", + "wind_speed", + "dhi", + "ghi", + "dni", + "relative_humidity", + ], +} + +weather_ds, meta_df = pvdeg.weather.get(weather_db, geospatial=True, **weather_arg) + +# Define geographical region +meta_SW = meta_df[meta_df["state"].isin(["Colorado", "New Mexico", "Utah", "Arizona"])] +meta_SW_sub, gids_SW_sub = pvdeg.utilities.gid_downsampling(meta_SW, 6) + +weather_SW_sub = weather_ds.sel(gid=meta_SW_sub.index) + +# %% +weather_SW_sub + +# %% +meta_df + +# %% +# Define desired analysis +geo = { + "func": pvdeg.letid.calc_letid_outdoors, + "weather_ds": weather_SW_sub, + "meta_df": meta_SW_sub, + "tau_0": 115, # us, carrier lifetime in non-degraded states, e.g. LETID/LID states A or C + "tau_deg": 55, # us, carrier lifetime in fully-degraded state, e.g. LETID/LID state B + "wafer_thickness": 180, # um + "s_rear": 46, # cm/s + "cell_area": 243, # cm^2 + "na_0": 100, + "nb_0": 0, + "nc_0": 0, + "mechanism_params": "repins", +} + +letid_res = pvdeg.geospatial.analysis(**geo) + +# %% +letid_res + +# %% +import datetime + +ims = [] +for n in range(1, 13): + for i, np_t in enumerate(letid_res.time): + t = pd.Timestamp(np_t.values).time() + d = pd.Timestamp(np_t.values).day + m = pd.Timestamp(np_t.values).month + if m == n: + if d == 15: + if t == datetime.time(12): + fig, ax = pvdeg.geospatial.plot_USA( + letid_res["Pmp_norm"].sel(time=np_t), + cmap="viridis", + vmin=0.95, + vmax=1, + title=f"Normalized Power - 2022-{m}-{d} 12:00", + cb_title="Normalized Power", + ) + # plt.savefig(f'./images/RH_animation_{n}.png', dpi=600) + +# import imageio +# ims = [imageio.imread(f'./images/RH_animation_{n}.png') for n in range(1, 13)] +# imageio.mimwrite(f'./images/RH_animation.gif', ims, format='GIF', duration=1000, loop=10) + +# %% +import datetime + +ims = [] +dates = [] +subarctics = [] +coldsemiarids = [] +hotdeserts = [] + +for n in range(1, 13): + for i, np_t in enumerate(letid_res.time): + t = pd.Timestamp(np_t.values).time() + d = pd.Timestamp(np_t.values).day + m = pd.Timestamp(np_t.values).month + if m == n: + if d == 15: + if t == datetime.time(12): + dates.append(np_t.values) + + # subartic: near Crested Butte CO + # cold semi-arid: near Springfield CO + # hot desert: near Yuma AZ + + subarctic = letid_res.sel( + time=np_t, latitude=39.01, longitude=-107.1 + ) + subarctics.append(subarctic["Pmp_norm"]) + + coldsemiarid = letid_res.sel( + time=np_t, latitude=37.57, longitude=-102.3 + ) + coldsemiarids.append(coldsemiarid["Pmp_norm"]) + + hotdesert = letid_res.sel( + time=np_t, latitude=32.77, longitude=-114.3 + ) + hotdeserts.append(hotdesert["Pmp_norm"]) + + fig, ax = plt.subplots() + ax.plot( + dates, + subarctics, + marker="o", + c="C0", + label="Central CO - Subarctic Dfc", + ) + ax.plot( + dates, + coldsemiarids, + marker="o", + c="C1", + label="Southeast CO - Cold Semi-Arid BSk", + ) + ax.plot( + dates, + hotdeserts, + marker="o", + c="C2", + label="Southwest AZ - Hot Desert BWh", + ) + + ax.legend(loc="upper right") + + ax.set_xlim([datetime.date(2022, 1, 1), datetime.date(2023, 1, 1)]) + + ax.set_ylim([0.945, 1.005]) + ax.set_ylabel("Normalized Power") + + plt.savefig(f"./images/LETID_plot_animation_{n}.png", dpi=600) + +# %% +import imageio + +ims = [imageio.imread(f"./images/LETID_plot_animation_{n}.png") for n in range(1, 13)] +imageio.mimwrite( + "./images/LETID_plot_animation.gif", ims, format="GIF", duration=1000, loop=10 +) diff --git a/tutorials/05_geospatial/scripts/04_scenario_geographical_features.py b/tutorials/05_geospatial/scripts/04_scenario_geographical_features.py new file mode 100644 index 000000000..17459eaea --- /dev/null +++ b/tutorials/05_geospatial/scripts/04_scenario_geographical_features.py @@ -0,0 +1,116 @@ +# %% [markdown] +# # Geographical Features (HPC) +# + +# %% +import pvdeg +import numpy as np + +# %% [markdown] +# # Create Scenario and Populate Location +# The default geospatial datapoints for addLocation are from the Americas satellite. This is the only data we are interested in for this case so don't specify any location arguments. +# To speed up calculations we will downsample to get coarser data. The downsampling function is not linear. A downsample factor of 10 takes us from 2018267 entries to only 5109. + +# %% +features = pvdeg.GeospatialScenario(name="finding-features") + +features.addLocation(downsample_factor=10) + +# %% [markdown] +# # Intro to KDTrees +# +# A K dimensional tree is a datastructure for organizing n points in a k dimensional space. They are often used for nearest neighbors searches and many ML algorithms. Letting k = 2, our dimensions will be latitude and longitude. This is much faster than iterating over tabular data structures to find neighbors. The function to create a kdtree below requires scikit learn (also known as sklearn), a python machine learning library which is not included in the pvdeg dependency list so you will have to install it independently. Scipy also has a kdtree class but it is much slower. Depending on the number of datapoints the tree build time could be quite long but for our purposes, the cell below will be quick. + +# %% +tree = pvdeg.geospatial.meta_KDtree(meta_df=features.meta_data) + +# %% [markdown] +# # Remaining Points After Downsampling +# +# Use `plot_coords` to see all latitude longitude coordinate pairs included in a scenario's metadata. You can provide corners of a bounding box to change the extent of the matplotlib plot. + +# %% +features.plot_coords() + +# %% [markdown] +# # Identifying Mountains from Geospatial Data I +# +# Identify moutains by using the function below. There are currently 2 methods to identify mountains from our data. Both will add a column to our `GeospatialScenario.meta_data` attribute called `mountain` containing boolean values that represent if a data point is in/near mountains or not near mountains. +# +# - `classify_mountains_weights` uses k nearest neighbors search and calculates local changes in height with different methods, the default is the `mean` absolute change compared to a points neighbors, then it assigns a weight to each point. If the remaining points are above the threshold then the percentile argument determines sensitivity to classifying points as mountains (higher weight, more mountainous). Much better than the following option. +# - `classify_mountains_radii` uses 2 nearest neighbors radius searches at each point and compares the average elevation of the inner circle to the avg radius of the circle. + +# %% +features.classify_mountains_weights(kdtree=tree) + +# %% [markdown] +# # Identifing Mountains from Geospatial Data II +# +# We do not need to provide a `kdtree` when we call this method. We can provide a `kdtree` if we create one first with `pvdeg.geospatial.meta_kdtree` but it will work without supplying it. +# GeospatialScenario will create a kdtree internally for us. So the method can simply be called as shown below. + +# %% +# this will work without running the above cells to create a kdtree or the cell directly above where we utilize the created kdtree. +features.classify_mountains_weights() + +# %% [markdown] +# # Convenience Plotting for Binary Classifications +# +# the `plot_meta_classification` method allows you to plot your data with respect to a boolean classifier present in the `scenario.meta_data` dataframe columns. + +# %% +features.plot_meta_classification(col_name="mountain") + +# %% [markdown] +# # Identifying mountains within a bounding box. +# +# Sometimes we only want information from one area. To allow for more control over the selection process. To pick only one area from an existing superset of data we can use a bounding box. +# Either provide the top left and bottom right (latitude-longitude) pairs or a 2d numpy array of these pairs which will use the most extreme entries to form the bounding box. + +# %% +features.classify_mountains_radii( + kdtree=tree, + elevation_floor=0, + bbox_kwarg={ + "coords": np.array( + [ + [47.960502, -115.048828], + [47.842658, -101.118164], + [36.738884, -113.686523], + [36.633162, -100.283203], + ] + ) + }, +) + +# %% +features.plot_meta_classification(col_name="mountain") + +# %% [markdown] +# # Identifying Coastlines from Geospatial Data +# +# To identify points within a search radius of any [natural earth features](https://www.naturalearthdata.com/features/). The following examples show the process of identifying coastline datapoints. + +# %% +features.classify_feature( + kdtree=tree, + feature_name="coastline", + resolution="10m", + radius=0.5, # change this to get a wider band of selected data near the coastline +) + +# %% +features.plot_meta_classification(col_name="coastline") + +# %% [markdown] +# # Finding Rivers +# +# Above we saw how to find points near coastlines, now lets do points near rivers. It is very similar + +# %% +features.classify_feature( + kdtree=tree, feature_name="rivers_lake_centerlines", resolution="10m", radius=0.2 +) + +# %% +features.plot_meta_classification(col_name="rivers_lake_centerlines") diff --git a/tutorials/05_geospatial/scripts/05_scenario_geospatial.py b/tutorials/05_geospatial/scripts/05_scenario_geospatial.py new file mode 100644 index 000000000..2438baf0c --- /dev/null +++ b/tutorials/05_geospatial/scripts/05_scenario_geospatial.py @@ -0,0 +1,96 @@ +# %% [markdown] +# # Scenario Geospatial (HPC) +# + +# %% +import pvdeg + +# %% [markdown] +# # Define Geospatial Scenario Object +# +# To preform geospatial analysis we can create a `GeospatialScenario` object. Alternatively, to preform single location analysis use `Scenario`. Scenario and GeospatialScenario are generalized classes that can be used to replace the legacy functional pvdeg analysis approach with an object orented one. + +# %% +geospatial_standoff_scenario = pvdeg.GeospatialScenario( + name="standoff geospatial", +) + +# %% [markdown] +# # Add Location +# To add locations for geospatial analysis we will use the ``.addLocation`` method. We can choose downselect from the NSRDB to a country, state and county in that order. *Support for multiple of each category in list form soon.* The ``see_added`` flag allows us to see the gids we have added to the scenario. + +# %% +geospatial_standoff_scenario.addLocation( + state="Colorado", county="Summit", see_added=True, downsample_factor=3 +) + +# %% [markdown] +# # Add Functions to the Pipeline +# The scenario has a queue of jobs to preform. These are stored in an attribute called ``pipeline``, you can directly update the pipeline but this will bypass the assistance given in creating the job function and parameters. The easiest way to add a job to the pipeline is the ``.updatePipeline`` method. For geospatial analysis, weather and metadata is collected and stored in the scenario at the time of the ``.addLocation`` method call so we do not need to include it below, but if we have other function kwargs to include, they should go in the ``func_params`` argument. +# +# Only a few pvdeg functions are currently supported for geospatial analysis. See the docstring for ``.updatePipeline`` to view currently supported functions. ``updatePipeline`` will not let you add unsupported geospatial functions. The ``see_added`` flag allows us to see the new job added to the pipeline. + +# %% +geospatial_standoff_scenario.addJob(func=pvdeg.standards.standoff, see_added=True) + +# %% +geospatial_standoff_scenario + +# %% [markdown] +# # Run the job in the pipeline +# +# Currently ``scenario`` only supports one geospatial analysis at a time. We cannot have two geospatial jobs at the same time. + +# %% +geospatial_standoff_scenario.run() + +# %% [markdown] +# # Directly Access Results Attribute +# +# We can either view the results of the scenario pipeline using ``.viewScenario`` as shown above. The results will be displayed only if the pipeline has been run. Alternatively, we can directly view the ``results`` atribute of the scenario. + +# %% +geospatial_standoff_scenario.results + +# %% [markdown] +# # Cleanup +# +# The scenario object will store its attributes in a file the python script's current working directory. If we want to delete this file when we are done with the scenario instance we can use the ``.clean()`` method as shown below. + +# %% +geospatial_standoff_scenario.clean() + +# %% [markdown] +# # Example Geospatial Functionality +# Many functions are supported for geospatial analysis, here are a few. +# - ``pvdeg.standards.standoff`` +# - ``pvdeg.humidity.module`` +# - ``pvdeg.letid.calc_letid_outdoors`` +# +# See the Geospatial Templates tutorial for an example on this. + +# %% +geospatial_humidity_scenario = pvdeg.GeospatialScenario( + name="humidity scenario", geospatial=True +) + +geospatial_humidity_scenario.addLocation( + state="Colorado", county="Jefferson", see_added=True +) + +geospatial_humidity_scenario.addJob( + func=pvdeg.humidity.module, + func_params={ + "backsheet_thickness": 0.3, # mm, thickness of PET backsheet + "back_encap_thickness": 0.46, # mm, thickness of EVA backside encapsulant + "encapsulant": "W001", # EVA encapsulant + "backsheet": "W017", # PET backsheet + }, + see_added=True, +) + +# %% +geospatial_humidity_scenario.run() + +# %% +geospatial_humidity_scenario.results diff --git a/tutorials_and_tools/tutorials_and_tools/Custom-Functions-Nopython.ipynb b/tutorials/06_advanced/01_custom_functions_nopython.ipynb similarity index 99% rename from tutorials_and_tools/tutorials_and_tools/Custom-Functions-Nopython.ipynb rename to tutorials/06_advanced/01_custom_functions_nopython.ipynb index ee4f6ac0d..c6d2d438c 100644 --- a/tutorials_and_tools/tutorials_and_tools/Custom-Functions-Nopython.ipynb +++ b/tutorials/06_advanced/01_custom_functions_nopython.ipynb @@ -1,5 +1,12 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Custom Functions with Numba\n" + ] + }, { "cell_type": "code", "execution_count": 1, @@ -23,7 +30,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Grabbing Weather Data from PVGIS\n", + "# Grabbing Weather Data from PVGIS\n", "\n", "Use pvdeg to make an API call to PVGIS to collect location and weather data for Manhattan, NYC." ] @@ -51,7 +58,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Calculating Temperature and Irradiance\n", + "# Calculating Temperature and Irradiance\n", "\n", "Use pvdeg to calculate timeseries temperature for a theoretical module from the previous metorological data.\n", "\n", @@ -115,11 +122,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Define Custom Expressions from Latex or \n", + "# Define Custom Expressions from Latex or\n", "\n", "We will use an altered arrhenius equation with an irrandiance relation (ignore the fact that this exists in pvdeg already).\n", "\n", - "$R_{D} = R_{0}I^{X} e^{\\frac{-{Ea}}{kT}}$ *ea is one variable so this may present some issues* \n", + "$R_{D} = R_{0}I^{X} e^{\\frac{-{Ea}}{kT}}$ *ea is one variable so this may present some issues*\n", "the raw latex looks like this `R_{0}I^{X} e^{\\frac{-{Ea}}{kT}}`" ] }, @@ -164,9 +171,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Calculating Degradation Expression\n", + "# Calculating Degradation Expression\n", "\n", - "Generally more processing will have to happen outside of these functions than built in pvdeg functions. \n", + "Generally more processing will have to happen outside of these functions than built in pvdeg functions.\n", "Here we are defining our arguments and correcting units. When trying to calculate using timeseries we will pass `pandas.Series` objects to the arguments.\n", "\n", "Results should be strictly scrutinized to make sure the calculation is iterating over your series correctly. It will generally be easier to write python code\n", @@ -198,7 +205,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### This is quite slow" + "# This is quite slow" ] }, { @@ -230,7 +237,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Total degradation\n", + "# Total degradation\n", "\n", "To calculate accumulated degradation, we can sum each of the substep values and convert to log scale (need to do this because prefactor was in log scale)." ] @@ -266,7 +273,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Example with Single Values" + "# Example with Single Values" ] }, { @@ -276,21 +283,21 @@ "leakage current\n", "$I_{leak} = \\frac{V_{bias}}{R_{enc}}$\n", "\n", - "$I_{leak}$, leakage current \n", - "${V_{bias}}$, potential difference between cells and frame \n", - "${R_{enc}}$, resistance of the encapsulant \n", + "$I_{leak}$, leakage current\n", + "${V_{bias}}$, potential difference between cells and frame\n", + "${R_{enc}}$, resistance of the encapsulant\n", "\n", "electric field\n", - "$E = \\frac{V_{bias}}{d}$ \n", + "$E = \\frac{V_{bias}}{d}$\n", "\n", - "$E$, electric field \n", - "$d$, thickness of encapsulant \n", + "$E$, electric field\n", + "$d$, thickness of encapsulant\n", "\n", "degradation rage\n", - "$D = k_{D} * E * I{leak}$ \n", + "$D = k_{D} * E * I{leak}$\n", "\n", - "$D$, degradation rate \n", - "$k_D$, degradation constant " + "$D$, degradation rate\n", + "$k_D$, degradation constant" ] }, { @@ -356,7 +363,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Calculate Values in a Dataframe using a symbolic expression\n", + "# Calculate Values in a Dataframe using a symbolic expression\n", "\n", "After creating the symbolic expression in using latex with `pvdeg.symbolic.symbolic_from_latex`, feed it to the function to calculate the leakage current at each row in the dataframe.\n", "This expression calculation only accesses one dataframe row at a time so we cannot do any summations or windowed timeseries averiging within the call. At this point, write your own python function. If you create functions that would be useful, please email us your source code or create an issue and copy and paste your code there. If it is relevant it may be added to the package (credit will be given)." diff --git a/tutorials_and_tools/tutorials_and_tools/API Call Required - PYSam.ipynb b/tutorials/06_advanced/03_pysam_api.ipynb similarity index 84% rename from tutorials_and_tools/tutorials_and_tools/API Call Required - PYSam.ipynb rename to tutorials/06_advanced/03_pysam_api.ipynb index de86cb32b..125b85f54 100644 --- a/tutorials_and_tools/tutorials_and_tools/API Call Required - PYSam.ipynb +++ b/tutorials/06_advanced/03_pysam_api.ipynb @@ -1,5 +1,12 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# PySAM with API (API Key Required)\n" + ] + }, { "cell_type": "code", "execution_count": null, @@ -18,7 +25,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Pysam\n", + "# Pysam\n", "\n", "rundown on pysam...\n", "\n", @@ -29,7 +36,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### PVGIS\n", + "# PVGIS\n", "\n", "Only works with PVGIS" ] @@ -72,7 +79,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Local Geospatial\n", + "# Local Geospatial\n", "\n", "Using PySAM with geospatial data requires proper formatting of the weather DataFrame to match PySAM's expectations." ] @@ -80,7 +87,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "lines_to_next_cell": 2 + }, "outputs": [], "source": [ "GEO_META = pd.read_csv(os.path.join(TEST_DATA_DIR, \"summit-meta.csv\"), index_col=0)\n", @@ -89,9 +98,11 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "lines_to_next_cell": 2 + }, "source": [ - "### Local Geospatial - PySAM Integration\n", + "# Local Geospatial - PySAM Integration\n", "\n", "The geospatial weather data is in half-hourly format (17520 timesteps) but PySAM expects hourly data (8760 timesteps). The wrapper function below handles this conversion automatically." ] @@ -103,12 +114,14 @@ "outputs": [], "source": [ "# this is just a wrapper to grab the result we want\n", - "def pysam_annual_energy(weather_df, meta, pv_model=\"pvsamv1\", pv_model_default=\"FlatPlatePVCommercial\"):\n", + "def pysam_annual_energy(\n", + " weather_df, meta, pv_model=\"pvsamv1\", pv_model_default=\"FlatPlatePVCommercial\"\n", + "):\n", " # Drop the gid column if present (added by geospatial conversion)\n", - " weather_df = weather_df.drop(columns=['gid'])\n", + " weather_df = weather_df.drop(columns=[\"gid\"])\n", "\n", " # Resample half-hourly data to hourly (PySAM expects hourly)\n", - " weather_df = weather_df.resample('h').mean()\n", + " weather_df = weather_df.resample(\"h\").mean()\n", "\n", " results = pvdeg.pysam.pysam(\n", " weather_df=weather_df,\n", @@ -160,7 +173,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## NSRDB API" + "# NSRDB API" ] }, { @@ -169,11 +182,9 @@ "metadata": {}, "outputs": [], "source": [ - "weather_db = 'PSM4'\n", + "weather_db = \"PSM4\"\n", "weather_id = (25.783388, -80.189029)\n", - "weather_arg = {'api_key': 'DEMO_KEY',\n", - " 'email': 'user@mail.com',\n", - " 'map_variables': True}\n", + "weather_arg = {\"api_key\": \"DEMO_KEY\", \"email\": \"user@mail.com\", \"map_variables\": True}\n", "\n", "weather_df, meta = pvdeg.weather.get(weather_db, weather_id, **weather_arg)" ] @@ -182,7 +193,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Geospatial Scenario" + "# Geospatial Scenario" ] }, { @@ -250,15 +261,19 @@ "outputs": [], "source": [ "# Check weather data time dimension compatibility\n", - "if 'time' in GEO_WEATHER.dims:\n", - " times = pd.to_datetime(GEO_WEATHER['time'].values)\n", + "if \"time\" in GEO_WEATHER.dims:\n", + " times = pd.to_datetime(GEO_WEATHER[\"time\"].values)\n", " years = times.year\n", " unique_years = set(years)\n", " if len(unique_years) != 1:\n", - " print(f\"Warning: Weather data contains multiple years: {unique_years}. Pysam expects a single year.\")\n", + " print(\n", + " f\"Warning: Weather data contains multiple years: {unique_years}. Pysam expects a single year.\"\n", + " )\n", " hours_per_year = times.size / len(unique_years)\n", " if hours_per_year not in [8760, 8784]:\n", - " print(f\"Warning: Unexpected number of timesteps per year: {hours_per_year}. Expected 8760 or 8784.\")\n", + " print(\n", + " f\"Warning: Unexpected number of timesteps per year: {hours_per_year}. Expected 8760 or 8784.\"\n", + " )\n", "else:\n", " print(\"Warning: No 'time' dimension found in weather data. Pysam may fail.\")" ] diff --git a/tutorials_and_tools/tutorials_and_tools/API Call Required - load_nsrdb_distributed.ipynb b/tutorials/06_advanced/04_nsrdb_distributed_api.ipynb similarity index 99% rename from tutorials_and_tools/tutorials_and_tools/API Call Required - load_nsrdb_distributed.ipynb rename to tutorials/06_advanced/04_nsrdb_distributed_api.ipynb index 86213e039..6f1248dce 100644 --- a/tutorials_and_tools/tutorials_and_tools/API Call Required - load_nsrdb_distributed.ipynb +++ b/tutorials/06_advanced/04_nsrdb_distributed_api.ipynb @@ -1,5 +1,12 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# NSRDB Distributed (API Key Required)\n" + ] + }, { "cell_type": "code", "execution_count": 1, @@ -23,7 +30,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Setting Up\n", + "# Setting Up\n", "\n", "As in [load_pvgis_distributed.ipynb](./load_pvgis_distributed.ipynb) we need to get ready to make our parallelized API calls. The notebook linked here goes through the process in more detail but we need to import our API key and email. This cell will not work for you unless you replace the `api_key` and `email` with your personal NSRDB api keys. [REQUEST A KEY](https://developer.nrel.gov/signup/).\n", "\n", @@ -88,7 +95,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Requesting Weather\n", + "# Requesting Weather\n", "\n", "As with the other script [load_pvgis_distributed.ipynb](./load_pvgis_distributed.ipynb). We will create a list of tuple (latitude, longitude) pairs and call the function on all of them at once. failed will represent a list of failed gids, unique location ID's that correspond to points in space on the NSRDB. These are different than on PVGIS where they are arbitrary indexes that do NOT correspond to a spatial location on earth.\n", "\n", @@ -130,9 +137,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Viewing Results\n", + "# Viewing Results\n", "\n", - "Same as in the other tutorial, our results are stored in an xarray dataset with a dask backend so you will have to use `.compute()` on the dataset to inspect the individual values of the dask arrays. \n", + "Same as in the other tutorial, our results are stored in an xarray dataset with a dask backend so you will have to use `.compute()` on the dataset to inspect the individual values of the dask arrays.\n", "\n", "Click on the `Data variables` dropdown to expand the dataset viewer." ] @@ -1883,13 +1890,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Spot Check\n", + "# Spot Check\n", "\n", - "We can plot the entire TMY air_temperature to check that our data has loaded correctly. \n", + "We can plot the entire TMY air_temperature to check that our data has loaded correctly.\n", "\n", "Explanation of steps\n", "\n", - "geo_weather is our weather xarray dataset. We can index into the first entry at the 0th index by using isel (index-select). This will grab the data from the first gid. Then we pick the air temperature attribute. This can be replaced with bracket notation so `.temp_air` becomes `[\"temp_air\"]. \n", + "geo_weather is our weather xarray dataset. We can index into the first entry at the 0th index by using isel (index-select). This will grab the data from the first gid. Then we pick the air temperature attribute. This can be replaced with bracket notation so `.temp_air` becomes `[\"temp_air\"].\n", "\n", "This selects a single array from the dataset that is labeled as \"temp_air\". This array will be a dask array so the values will be stored out of memory, we would have to load it using `.compute()` to directly inspect it but when plotting with matplotlib it will load the array for us." ] @@ -1937,7 +1944,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Next Steps\n", + "# Next Steps\n", "\n", "Now we have data ready to use for geospatial calculations. This is shown in the other distributed script [load_pvgis_distributed.ipynb](./load_pvgis_distributed.ipynb). You can also see how to do this in [Geospatial Templates.ipynb](../tutorials_and_tools/tutorials_and_tools/Geospatial%20Templates.ipynb)" ] diff --git a/tutorials_and_tools/tutorials_and_tools/Removed store.py Required - load_pvgis_distributed.ipynb b/tutorials/06_advanced/05_pvgis_distributed.ipynb similarity index 94% rename from tutorials_and_tools/tutorials_and_tools/Removed store.py Required - load_pvgis_distributed.ipynb rename to tutorials/06_advanced/05_pvgis_distributed.ipynb index 3c4b740c5..d04d9f985 100644 --- a/tutorials_and_tools/tutorials_and_tools/Removed store.py Required - load_pvgis_distributed.ipynb +++ b/tutorials/06_advanced/05_pvgis_distributed.ipynb @@ -1,5 +1,12 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# PVGIS Distributed\n" + ] + }, { "cell_type": "code", "execution_count": null, @@ -17,7 +24,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Creating Coordinates List\n", + "# Creating Coordinates List\n", "\n", "Lets Generate a Grid of Latitude and Longitude Coordinates over the UK." ] @@ -55,9 +62,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Dask\n", + "# Dask\n", "\n", - "We are using dask to parallelize the weather API calls. We need to start a dask client as shown below. This can also be done with `pvdeg.geospatial.start_dask`. \n", + "We are using dask to parallelize the weather API calls. We need to start a dask client as shown below. This can also be done with `pvdeg.geospatial.start_dask`.\n", "\n", "Click on the link to open a localhost dashboard for the dask client. This will allow us to see what happens when we run functions using dask." ] @@ -84,14 +91,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Requesting Weather\n", + "# Requesting Weather\n", "\n", - "Now that we have our coordinates, and dask client initialized we can make our parallel api calls using `pvdeg.weather.weather_distributed`. \n", + "Now that we have our coordinates, and dask client initialized we can make our parallel api calls using `pvdeg.weather.weather_distributed`.\n", "\n", "We want data from PVGIS so we will use that as the database. For more information about the databases look at the `pvdeg.weather.get` docstring. We will pass in the coordinates list which is a list of tuple pairs for the latitude and longiutudes to request weather at.\n", "\n", - "`weather_ds` is the collected weather dataset from the API calls to PVGIS. \n", - "`meta_df` is the collected meta dataframe from the API calls to PVGIS. \n", + "`weather_ds` is the collected weather dataset from the API calls to PVGIS.\n", + "`meta_df` is the collected meta dataframe from the API calls to PVGIS.\n", "`failed_gids` will be a list of the failed indexes from the input coordinates list that we could not get weather from using PVGIS. These may have failed randomly so it is worth trying again." ] }, @@ -110,7 +117,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Viewing Result\n", + "# Viewing Result\n", "\n", "The result is stored in an xarray dataset with a dask array backend. This allows us to parallelize the computation/api requests but makes it a little harder to view the data. We can inspect the dataset using the following but we will not be able to inspect any values.\n", "\n", @@ -134,7 +141,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Saving Geospatial Data Locally\n", + "# Saving Geospatial Data Locally\n", "\n", "The goal of `pvdeg.store` is to create a living local database of meteoroligical data that grows overtime as your geospatial data needs grow. To do this `PVDeg` will save to a folder called `PVDeg-Meteorological` your user home directory. For me this is located at `C:\\Users\\tford\\PVDeg-Meteorological`. This directory will contain a `zarr` store, this is a popular format for storing multi-dimensional array data, not dissimilar to `h5` files. It was chosen over `h5` because `zarr` stores arrays in chunked compressed files that make access very easy without opening an entire file like `h5`. This is an oversimplification of the design process but we felt `zarr` was a better fit.\n", "\n", @@ -142,10 +149,10 @@ "\n", "We can use `pvdeg.store.store` to save geospatial data to our living dataset in the common form provided by `pvdeg`. The data is stored in various groups and subfolders but they will be arranged based on the *source* and *periodicity*.\n", "\n", - "For example: \n", - " - Hourly PVGIS data will be saved to a group called \"PVGIS-1hr\" \n", - " - 30 minute PVGIS to a group called \"PVGIS-30min\" \n", - " - 15 minute PVGIS will be saved to a group called \"PVGIS-15min\" " + "For example:\n", + " - Hourly PVGIS data will be saved to a group called \"PVGIS-1hr\"\n", + " - 30 minute PVGIS to a group called \"PVGIS-30min\"\n", + " - 15 minute PVGIS will be saved to a group called \"PVGIS-15min\"" ] }, { @@ -161,21 +168,21 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Load\n", + "# Load\n", "\n", - "`PVDeg` makes use of `dask` to handle larger than memory datasets. Trandionally, this was only useful in our HPC environment but as your local database grows overtime, it will eventually surpass the limits of your computer's volatile memory. Additionally, `dask` allows us to parallelize geospatial calculations via `pvdeg.geospatial.analysis`. This ability can be utilized on local machines or HPC clusters alike. \n", + "`PVDeg` makes use of `dask` to handle larger than memory datasets. Trandionally, this was only useful in our HPC environment but as your local database grows overtime, it will eventually surpass the limits of your computer's volatile memory. Additionally, `dask` allows us to parallelize geospatial calculations via `pvdeg.geospatial.analysis`. This ability can be utilized on local machines or HPC clusters alike.\n", "\n", - "`PVDeg` implements the ability to access your local living database via `pvdeg.store.get`. This method takes a string called `group`. Groups are created automatically in your store when you save data using `pvdeg.store.store`. As described in the `pvdeg.store.store` docstring and the *Store* section above, NSRDB will follow a similar scheme but it not implemented yet. \n", - " - Hourly PVGIS data will be saved to a group called \"PVGIS-1hr\" \n", - " - 30 minute PVGIS to a group called \"PVGIS-30min\" \n", - " - 15 minute PVGIS will be saved to a group called \"PVGIS-15min\" " + "`PVDeg` implements the ability to access your local living database via `pvdeg.store.get`. This method takes a string called `group`. Groups are created automatically in your store when you save data using `pvdeg.store.store`. As described in the `pvdeg.store.store` docstring and the *Store* section above, NSRDB will follow a similar scheme but it not implemented yet.\n", + " - Hourly PVGIS data will be saved to a group called \"PVGIS-1hr\"\n", + " - 30 minute PVGIS to a group called \"PVGIS-30min\"\n", + " - 15 minute PVGIS will be saved to a group called \"PVGIS-15min\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Load PVGIS-1hr Data\n", + "# Load PVGIS-1hr Data\n", "\n", "The example below shows us loading the hourly tmy data from PVGIS that we gathered and saved to our zarr store in the above cells. This gets us the form of a weather xarray.Dataset (`geo_weather` in this example) and a metadata dataframe (`geo_meta` in this example).\n", "\n", @@ -195,7 +202,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Inspecting the Results\n", + "# Inspecting the Results\n", "\n", "explain *.compute() and dask here*" ] @@ -222,7 +229,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Geospatial Calculations from Locally Stored Data\n", + "# Geospatial Calculations from Locally Stored Data\n", "\n", "As shown above we can load from our `zarr` store and treat it like any other geospatial data in `pvdeg`.\n", "\n", @@ -277,7 +284,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Growing Our Living Store\n", + "# Growing Our Living Store\n", "\n", "What if we want to download more points from Europe? We can keep our old download in the store and shelve it to look at northern Europe.\n", "\n", diff --git a/tutorials/06_advanced/README.md b/tutorials/06_advanced/README.md new file mode 100644 index 000000000..06738abe5 --- /dev/null +++ b/tutorials/06_advanced/README.md @@ -0,0 +1,33 @@ +# Advanced Topics + +Advanced tutorials covering custom functions, API integrations, and specialized workflows. + +## Tutorials + +### General Advanced Topics +1. **01_custom_functions_nopython.ipynb** - Creating custom functions with numba/nopython mode + +### API Required Tutorials +The following tutorials require API access to external data sources: + +3. **03_pysam_api.ipynb** - PySAM integration with API calls (requires NSRDB API key) +4. **04_nsrdb_distributed_api.ipynb** - Distributed NSRDB data loading (requires NSRDB API key) + +### Special Requirements +5. **05_pvgis_distributed.ipynb** - PVGIS distributed data access (requires specific dependencies) + +## API Access + +⚠️ **Note**: Several tutorials require API keys: + +- **NSRDB API Key**: Required for `03_pysam_api.ipynb` and `04_nsrdb_distributed_api.ipynb` + - Get your free API key at: https://developer.nrel.gov/signup/ + - Rate limits apply for free tier + +## Topics Covered + +- Custom function development with performance optimization +- PySAM (Python wrapper for System Advisor Model) +- Weather data API integration +- Distributed data processing +- Performance optimization techniques diff --git a/tutorials/06_advanced/scripts/01_custom_functions_nopython.py b/tutorials/06_advanced/scripts/01_custom_functions_nopython.py new file mode 100644 index 000000000..c191930fe --- /dev/null +++ b/tutorials/06_advanced/scripts/01_custom_functions_nopython.py @@ -0,0 +1,156 @@ +# %% [markdown] +# # Custom Functions with Numba +# + +# %% +import numpy as np +import sympy as sp +import matplotlib.pyplot as plt +import pvdeg + +# %% [markdown] +# # Grabbing Weather Data from PVGIS +# +# Use pvdeg to make an API call to PVGIS to collect location and weather data for Manhattan, NYC. + +# %% +weather_df, meta_dict = pvdeg.weather.get( + database="PVGIS", + id=(40.776676, -73.971321), # manhattan (latitude, longitude) +) + +# %% [markdown] +# # Calculating Temperature and Irradiance +# +# Use pvdeg to calculate timeseries temperature for a theoretical module from the previous metorological data. +# +# *Update this call after scenario has been merged: dev_scenario_geospatial->development->dev_symbolic* + +# %% +module_temps = pvdeg.temperature.module( + weather_df=weather_df, meta=meta_dict, conf="open_rack_glass_glass" +) + +poa_irradiance = pvdeg.spectral.poa_irradiance(weather_df=weather_df, meta=meta_dict) + +plt.figure(figsize=(10, 6)) +plt.subplot(1, 2, 1) +plt.plot( + module_temps.values +) # plotting the values in order because we are using tmy data so the years are not consistent within our data +plt.title("TMY Module Temperature, Manhattan") +plt.subplot(1, 2, 2) +plt.plot(poa_irradiance.values) +plt.legend(poa_irradiance.columns) +plt.title("TML Module Irradiance, Manhattan") +plt.show() + +# %% [markdown] +# # Define Custom Expressions from Latex or +# +# We will use an altered arrhenius equation with an irrandiance relation (ignore the fact that this exists in pvdeg already). +# +# $R_{D} = R_{0}I^{X} e^{\frac{-{Ea}}{kT}}$ *ea is one variable so this may present some issues* +# the raw latex looks like this `R_{0}I^{X} e^{\frac{-{Ea}}{kT}}` + +# %% +lnR_0, I, X, Ea, k, T = sp.symbols("lnR_0 I X Ea k T") + +ln_R_D_expr = ( + lnR_0 * I**X * sp.exp((-Ea) / (k * T)) +) # python exponentiation is ** rather than ^ + +# viewing output +ln_R_D_expr + +# %% [markdown] +# # Calculating Degradation Expression +# +# Generally more processing will have to happen outside of these functions than built in pvdeg functions. +# Here we are defining our arguments and correcting units. When trying to calculate using timeseries we will pass `pandas.Series` objects to the arguments. +# +# Results should be strictly scrutinized to make sure the calculation is iterating over your series correctly. It will generally be easier to write python code +# for more complex functions. This is about as complex as we will be able to get using arbitrary symbolic expressions if looping over timeseries data is required. + +# %% +module_temps_k = module_temps + 273.15 # convert C -> K + +poa_global = poa_irradiance[ + "poa_global" +] # take only the global irradiance series from the total irradiance dataframe +poa_global_kw = poa_global / 1000 # [W/m^2] -> [kW/m^2] + +# %% [markdown] +# # This is quite slow + +# %% +values_kwarg = { + "Ea": 62.08, # activation energy, [kJ/mol] + "k": 8.31446e-3, # boltzmans constant, [kJ/(mol * K)] + "T": module_temps_k, # module temperature, [K] + "I": poa_global_kw, # module plane of array irradiance, [W/m2] + "X": 0.0341, # irradiance relation, [unitless] + "lnR_0": 13.72, # prefactor degradation [ln(%/h)] +} + +res = pvdeg.symbolic.calc_kwarg_timeseries(expr=ln_R_D_expr, kwarg=values_kwarg) + +# %% [markdown] +# # Total degradation +# +# To calculate accumulated degradation, we can sum each of the substep values and convert to log scale (need to do this because prefactor was in log scale). + +# %% +np.log10(res.sum()) + +# %% [markdown] +# # Example with Single Values + +# %% [markdown] +# leakage current +# $I_{leak} = \frac{V_{bias}}{R_{enc}}$ +# +# $I_{leak}$, leakage current +# ${V_{bias}}$, potential difference between cells and frame +# ${R_{enc}}$, resistance of the encapsulant +# +# electric field +# $E = \frac{V_{bias}}{d}$ +# +# $E$, electric field +# $d$, thickness of encapsulant +# +# degradation rage +# $D = k_{D} * E * I{leak}$ +# +# $D$, degradation rate +# $k_D$, degradation constant + +# %% +k_d, E, Vbias, Rencap, d = sp.symbols("k_d E Vbias Rencap d") + +pid = k_d * (Vbias / d) * (Vbias / Rencap) + +# %% +import pvdeg + +pid_kwarg = { + "Vbias": 1000, + "Rencap": 1e9, + "d": 0.0005, + "k_d": 1e-9, +} + +res = pvdeg.symbolic.calc_kwarg_floats(expr=pid, kwarg=pid_kwarg) + +res + +# %% [markdown] +# # Calculate Values in a Dataframe using a symbolic expression +# +# After creating the symbolic expression in using latex with `pvdeg.symbolic.symbolic_from_latex`, feed it to the function to calculate the leakage current at each row in the dataframe. +# This expression calculation only accesses one dataframe row at a time so we cannot do any summations or windowed timeseries averiging within the call. At this point, write your own python function. If you create functions that would be useful, please email us your source code or create an issue and copy and paste your code there. If it is relevant it may be added to the package (credit will be given). + +# %% +# I_leak_expr = pid_kwarg["Vbias"] / pid_kwarg["Rencap"] +# pvdeg.symbolic.calc_df_symbolic(expr=I_leak_expr, df=values_df) diff --git a/tutorials/06_advanced/scripts/02_pysam_single_location.py b/tutorials/06_advanced/scripts/02_pysam_single_location.py new file mode 100644 index 000000000..b2db3618f --- /dev/null +++ b/tutorials/06_advanced/scripts/02_pysam_single_location.py @@ -0,0 +1,43 @@ +# %% +import pvdeg +import matplotlib.pyplot as plt +import seaborn as sns +import numpy as np + +# %% +weather, meta = pvdeg.weather.get( + database="PVGIS", id=(40.633365593159226, -73.9945801019899) +) + +# %% +out_dict = pvdeg.pysam.pysam( + weather_df=weather, + meta=meta, + pv_model="pvsamv1", + pv_model_default="FlatPlatePVCommercial", +) +for key in sorted(out_dict.keys()): + print(key) + +# %% +for key, item in out_dict.items(): + if isinstance(item, tuple): + print(key) + +# %% [markdown] +# subarray1_poa_ground_front_cs +# subarray1_ground_rear_spatial + +# %% +x = out_dict["subarray1_ground_rear_spatial"][ + 0 +] # these are the distances where the calculations are done + +data = out_dict["subarray1_ground_rear_spatial"][1:] +row = data[15] + +# %% +row_arr = np.array(row) +plot = sns.heatmap([row_arr], square=True, cmap="magma") + +plt.show() diff --git a/tutorials/06_advanced/scripts/03_pysam_api.py b/tutorials/06_advanced/scripts/03_pysam_api.py new file mode 100644 index 000000000..c31029b17 --- /dev/null +++ b/tutorials/06_advanced/scripts/03_pysam_api.py @@ -0,0 +1,165 @@ +# %% [markdown] +# # PySAM with API (API Key Required) +# + +# %% +import pvdeg +from pvdeg import TEST_DATA_DIR +import pandas as pd +import os +import pickle +import xarray as xr + +# %% [markdown] +# # Pysam +# +# rundown on pysam... +# +# https://nrel-pysam.readthedocs.io/en/main/inputs-from-sam.html + +# %% [markdown] +# # PVGIS +# +# Only works with PVGIS + +# %% +weather_pvgis, meta_pvgis = pvdeg.weather.get( + database="PVGIS", id=(25.783388, -81.189029) +) + +# %% +results = pvdeg.pysam.pysam( + weather_df=weather_pvgis, + meta=meta_pvgis, + pv_model="pvsamv1", + pv_model_default="FlatPlatePVCommercial", +) + +# %% +results["annual_energy"] + +# %% [markdown] +# # Local Geospatial +# +# Using PySAM with geospatial data requires proper formatting of the weather DataFrame to match PySAM's expectations. + +# %% +GEO_META = pd.read_csv(os.path.join(TEST_DATA_DIR, "summit-meta.csv"), index_col=0) +GEO_WEATHER = xr.open_dataset(os.path.join(TEST_DATA_DIR, "summit-weather.nc")) + + +# %% [markdown] +# # Local Geospatial - PySAM Integration +# +# The geospatial weather data is in half-hourly format (17520 timesteps) but PySAM expects hourly data (8760 timesteps). The wrapper function below handles this conversion automatically. + + +# %% +# this is just a wrapper to grab the result we want +def pysam_annual_energy( + weather_df, meta, pv_model="pvsamv1", pv_model_default="FlatPlatePVCommercial" +): + # Drop the gid column if present (added by geospatial conversion) + weather_df = weather_df.drop(columns=["gid"]) + + # Resample half-hourly data to hourly (PySAM expects hourly) + weather_df = weather_df.resample("h").mean() + + results = pvdeg.pysam.pysam( + weather_df=weather_df, + meta=meta, + pv_model=pv_model, + pv_model_default=pv_model_default, + ) + + return results["annual_energy"] + + +# %% +# Select a small subset (2 gids) for demonstration to avoid timeouts +# PySAM calculations are computationally expensive +subset_gids = GEO_META.index[:2] +GEO_META_SUB = GEO_META.loc[subset_gids] +GEO_WEATHER_SUB = GEO_WEATHER.sel(gid=subset_gids) + +template = pvdeg.geospatial.output_template( + ds_gids=GEO_WEATHER_SUB, + shapes={ + "Annual Energy": ("gid",), + }, +) + +geo_res = pvdeg.geospatial.analysis( + weather_ds=GEO_WEATHER_SUB, + meta_df=GEO_META_SUB, + func=pysam_annual_energy, + template=template, +) + +# %% +geo_res + +# %% [markdown] +# # NSRDB API + +# %% +weather_db = "PSM4" +weather_id = (25.783388, -80.189029) +weather_arg = {"api_key": "DEMO_KEY", "email": "user@mail.com", "map_variables": True} + +weather_df, meta = pvdeg.weather.get(weather_db, weather_id, **weather_arg) + +# %% [markdown] +# # Geospatial Scenario + +# %% +location_grabber = pvdeg.GeospatialScenario() + +location_grabber.addLocation(country="United States", downsample_factor=80) + +# %% +location_grabber.plot_coords() + +# %% +geo_weather, geo_meta = location_grabber.geospatial_data + +# Select a small subset (2 gids) for demonstration to avoid timeouts +# PySAM calculations are computationally expensive +subset_gids = geo_meta.index[:2] +geo_meta_sub = geo_meta.loc[subset_gids] +geo_weather_sub = geo_weather.sel(gid=subset_gids) + +template = pvdeg.geospatial.output_template( + ds_gids=geo_weather_sub, + shapes={ + "pysam_annual_energy": ("gid",), + }, +) + +geo_res = pvdeg.geospatial.analysis( + weather_ds=geo_weather_sub, + meta_df=geo_meta_sub, + func=pysam_annual_energy, # using wrapper from before + template=template, +) + +# %% +pvdeg.geospatial.plot_sparse_analysis(geo_res, data_var="pysam_annual_energy") + +# %% +# Check weather data time dimension compatibility +if "time" in GEO_WEATHER.dims: + times = pd.to_datetime(GEO_WEATHER["time"].values) + years = times.year + unique_years = set(years) + if len(unique_years) != 1: + print( + f"Warning: Weather data contains multiple years: {unique_years}. Pysam expects a single year." + ) + hours_per_year = times.size / len(unique_years) + if hours_per_year not in [8760, 8784]: + print( + f"Warning: Unexpected number of timesteps per year: {hours_per_year}. Expected 8760 or 8784." + ) +else: + print("Warning: No 'time' dimension found in weather data. Pysam may fail.") diff --git a/tutorials/06_advanced/scripts/04_nsrdb_distributed_api.py b/tutorials/06_advanced/scripts/04_nsrdb_distributed_api.py new file mode 100644 index 000000000..a611a7576 --- /dev/null +++ b/tutorials/06_advanced/scripts/04_nsrdb_distributed_api.py @@ -0,0 +1,95 @@ +# %% [markdown] +# # NSRDB Distributed (API Key Required) +# + +# %% +from dask.distributed import LocalCluster, Client +from dotenv import load_dotenv +import pvdeg +import os + +# %% [markdown] +# # Setting Up +# +# As in [load_pvgis_distributed.ipynb](./load_pvgis_distributed.ipynb) we need to get ready to make our parallelized API calls. The notebook linked here goes through the process in more detail but we need to import our API key and email. This cell will not work for you unless you replace the `api_key` and `email` with your personal NSRDB api keys. [REQUEST A KEY](https://developer.nrel.gov/signup/). +# +# We also need to initalize a dask client. `pvdeg.weather.weather_distributed` will not work without it. It will fail silently and not populate and of the results in the resulting `weather_ds` called `geo_weather` in the example below. It is hard to recognize that this has occured so be careful. Make sure to initialize a dask client first. Visiting the link takes you to a daskboard that shows what dask is doing. + +# %% +load_dotenv() + +### REPLACE WITH YOUR API KEY AND EMAIL ### +api_key = "DEMO_KEY" +email = "user@mail.com" +########################################### + +workers = 4 + +cluster = LocalCluster( + n_workers=workers, + processes=True, +) + +client = Client(cluster) + +print("Daskboard link") +print(client.dashboard_link) + +# %% [markdown] +# **Note on .env file:** Create a file named `.env` in your project root directory (`c:\Users\rdaxini\Documents\GitHub\PVDegradationTools_NREL\`) with the following content: +# ``` +# api_key=YOUR_NREL_API_KEY +# email=YOUR_EMAIL_ADDRESS +# ``` +# Replace `YOUR_NREL_API_KEY` and `YOUR_EMAIL_ADDRESS` with your actual NREL developer credentials. + +# %% [markdown] +# # Requesting Weather +# +# As with the other script [load_pvgis_distributed.ipynb](./load_pvgis_distributed.ipynb). We will create a list of tuple (latitude, longitude) pairs and call the function on all of them at once. failed will represent a list of failed gids, unique location ID's that correspond to points in space on the NSRDB. These are different than on PVGIS where they are arbitrary indexes that do NOT correspond to a spatial location on earth. +# +# We will request "PSM4" data from the Physical Solar Model that represents a typical meteorological year (TMY) from the NSRDB. We will have to supply the api key and email from above here. Refer to the linked script to see this in further detail. The only difference between the scripts, lies in the NSRDB/PSM4 data requiring Api keys. + +# %% +coords = [ + (25.783388, -80.189029), + (24.783388, -80.189029), +] + +geo_weather, geo_meta, failed = pvdeg.weather.weather_distributed( + database="PSM4", coords=coords, api_key=api_key, email=email +) + +# %% [markdown] +# # Viewing Results +# +# Same as in the other tutorial, our results are stored in an xarray dataset with a dask backend so you will have to use `.compute()` on the dataset to inspect the individual values of the dask arrays. +# +# Click on the `Data variables` dropdown to expand the dataset viewer. + +# %% +geo_weather + +# %% +geo_weather.compute() + +# %% [markdown] +# # Spot Check +# +# We can plot the entire TMY air_temperature to check that our data has loaded correctly. +# +# Explanation of steps +# +# geo_weather is our weather xarray dataset. We can index into the first entry at the 0th index by using isel (index-select). This will grab the data from the first gid. Then we pick the air temperature attribute. This can be replaced with bracket notation so `.temp_air` becomes `["temp_air"]. +# +# This selects a single array from the dataset that is labeled as "temp_air". This array will be a dask array so the values will be stored out of memory, we would have to load it using `.compute()` to directly inspect it but when plotting with matplotlib it will load the array for us. + +# %% +import matplotlib.pyplot as plt + +plt.plot(geo_weather.isel(gid=0).temp_air) + +# %% [markdown] +# # Next Steps +# +# Now we have data ready to use for geospatial calculations. This is shown in the other distributed script [load_pvgis_distributed.ipynb](./load_pvgis_distributed.ipynb). You can also see how to do this in [Geospatial Templates.ipynb](../tutorials_and_tools/tutorials_and_tools/Geospatial%20Templates.ipynb) diff --git a/tutorials/06_advanced/scripts/05_pvgis_distributed.py b/tutorials/06_advanced/scripts/05_pvgis_distributed.py new file mode 100644 index 000000000..b5aa01b92 --- /dev/null +++ b/tutorials/06_advanced/scripts/05_pvgis_distributed.py @@ -0,0 +1,246 @@ +# %% [markdown] +# # PVGIS Distributed +# + +# %% +import pvdeg +from global_land_mask import globe +import matplotlib.pyplot as plt +import numpy as np +from dask.distributed import LocalCluster, Client + +# %% [markdown] +# # Creating Coordinates List +# +# Lets Generate a Grid of Latitude and Longitude Coordinates over the UK. + +# %% +# decrease the arange step size for more fine resolution +# increase the arange step size for increased granularity +lon_UK = np.arange(-10.5, 1.76, 1) +lat_UK = np.arange(49.95, 60, 2) +lon_grid_UK, lat_grid_UK = np.meshgrid(lon_UK, lat_UK) +land_UK = globe.is_land(lat_grid_UK, lon_grid_UK) + +lon_land_UK = lon_grid_UK[land_UK] +lat_land_UK = lat_grid_UK[land_UK] + +lon_Scan = np.arange(-10.5, 31.6, 0.3) +lat_Scan = np.arange(60, 71.2, 0.3) +lon_grid_Scan, lat_grid_Scan = np.meshgrid(lon_Scan, lat_Scan) +land_Scan = globe.is_land(lat_grid_Scan, lon_grid_Scan) + +lon_land_Scan = lon_grid_Scan[land_Scan] +lat_land_Scan = lat_grid_Scan[land_Scan] + +coords = list( + zip(lat_land_UK, lon_land_UK) +) # easiest way to make a list of the right shape + +# %% [markdown] +# # Dask +# +# We are using dask to parallelize the weather API calls. We need to start a dask client as shown below. This can also be done with `pvdeg.geospatial.start_dask`. +# +# Click on the link to open a localhost dashboard for the dask client. This will allow us to see what happens when we run functions using dask. + +# %% +workers = 4 + +cluster = LocalCluster( + n_workers=workers, + processes=True, +) + +client = Client(cluster) + +print(client.dashboard_link) + +# %% [markdown] +# # Requesting Weather +# +# Now that we have our coordinates, and dask client initialized we can make our parallel api calls using `pvdeg.weather.weather_distributed`. +# +# We want data from PVGIS so we will use that as the database. For more information about the databases look at the `pvdeg.weather.get` docstring. We will pass in the coordinates list which is a list of tuple pairs for the latitude and longiutudes to request weather at. +# +# `weather_ds` is the collected weather dataset from the API calls to PVGIS. +# `meta_df` is the collected meta dataframe from the API calls to PVGIS. +# `failed_gids` will be a list of the failed indexes from the input coordinates list that we could not get weather from using PVGIS. These may have failed randomly so it is worth trying again. + +# %% +weather_ds, meta_df, failed_gids = pvdeg.weather.weather_distributed( + database="PVGIS", coords=coords +) + +# %% [markdown] +# # Viewing Result +# +# The result is stored in an xarray dataset with a dask array backend. This allows us to parallelize the computation/api requests but makes it a little harder to view the data. We can inspect the dataset using the following but we will not be able to inspect any values. +# +# ``` +# weather_ds +# ``` +# +# To load the values from the dask arrays we need to use `.compute()` as follows. +# + +# %% +weather_ds.compute() + +# %% [markdown] +# # Saving Geospatial Data Locally +# +# The goal of `pvdeg.store` is to create a living local database of meteoroligical data that grows overtime as your geospatial data needs grow. To do this `PVDeg` will save to a folder called `PVDeg-Meteorological` your user home directory. For me this is located at `C:\Users\tford\PVDeg-Meteorological`. This directory will contain a `zarr` store, this is a popular format for storing multi-dimensional array data, not dissimilar to `h5` files. It was chosen over `h5` because `zarr` stores arrays in chunked compressed files that make access very easy without opening an entire file like `h5`. This is an oversimplification of the design process but we felt `zarr` was a better fit. +# +# ## Store +# +# We can use `pvdeg.store.store` to save geospatial data to our living dataset in the common form provided by `pvdeg`. The data is stored in various groups and subfolders but they will be arranged based on the *source* and *periodicity*. +# +# For example: +# - Hourly PVGIS data will be saved to a group called "PVGIS-1hr" +# - 30 minute PVGIS to a group called "PVGIS-30min" +# - 15 minute PVGIS will be saved to a group called "PVGIS-15min" + +# %% +pvdeg.store.store(weather_ds, meta_df) + +# %% [markdown] +# # Load +# +# `PVDeg` makes use of `dask` to handle larger than memory datasets. Trandionally, this was only useful in our HPC environment but as your local database grows overtime, it will eventually surpass the limits of your computer's volatile memory. Additionally, `dask` allows us to parallelize geospatial calculations via `pvdeg.geospatial.analysis`. This ability can be utilized on local machines or HPC clusters alike. +# +# `PVDeg` implements the ability to access your local living database via `pvdeg.store.get`. This method takes a string called `group`. Groups are created automatically in your store when you save data using `pvdeg.store.store`. As described in the `pvdeg.store.store` docstring and the *Store* section above, NSRDB will follow a similar scheme but it not implemented yet. +# - Hourly PVGIS data will be saved to a group called "PVGIS-1hr" +# - 30 minute PVGIS to a group called "PVGIS-30min" +# - 15 minute PVGIS will be saved to a group called "PVGIS-15min" + +# %% [markdown] +# # Load PVGIS-1hr Data +# +# The example below shows us loading the hourly tmy data from PVGIS that we gathered and saved to our zarr store in the above cells. This gets us the form of a weather xarray.Dataset (`geo_weather` in this example) and a metadata dataframe (`geo_meta` in this example). +# +# These can be treated like any other geospatial data shown in the `pvdeg` tutorials and tools or documentation. + +# %% +geo_weather, geo_meta = pvdeg.store.get(group="PVGIS-1hr") + +# %% [markdown] +# # Inspecting the Results +# +# explain *.compute() and dask here* + +# %% +plt.plot(geo_weather.sel(gid=0).dni) + +# %% +geo_meta + +# %% [markdown] +# # Geospatial Calculations from Locally Stored Data +# +# As shown above we can load from our `zarr` store and treat it like any other geospatial data in `pvdeg`. +# +# For demonstration we can run the analysis below to estimate effective standoff height and operating temperatures for the provided data. It may look like the `geo_res` contains empty results but that is because we did not have input data for all of the points in the input grid (think of this as a 2D plane formed between the latitude and longitude axes). Clicking on the stack of three circles in the bottom cell will expand the datavariable (like an attribute of the multidimensional array structure) and show the results. +# +# Additionally, we can interpolate and plot the results. + +# %% +func = pvdeg.standards.standoff + +template = pvdeg.geospatial.auto_template(func=func, ds_gids=geo_weather) + +geo_res = pvdeg.geospatial.analysis( + weather_ds=geo_weather, meta_df=geo_meta, func=func, template=template +) + +# %% +geo_res + +# %% [markdown] +# This plot lacks information on the area and does not include some political boundary lines. For more information on plotting look at the `Scenario - Non-uniform Mountain Downselect.ipynb` tutorial in the tutorials and tools folder. + +# %% +pvdeg.geospatial.plot_sparse_analysis_land( + geo_res, data_var="T98_0", method="nearest", resolution=10j +) + +# %% [markdown] +# # Growing Our Living Store +# +# What if we want to download more points from Europe? We can keep our old download in the store and shelve it to look at northern Europe. +# +# We will start by generating a range of points that cover Europe. + +# %% +lon_EU = np.arange(-25.0, 51.0, 1) # Adjusted for EU longitudes +lat_EU = np.arange(34.0, 73.0, 2) # Adjusted for EU latitudes + +# Create meshgrid for EU +lon_grid_EU, lat_grid_EU = np.meshgrid(lon_EU, lat_EU) + +# Check land coverage in the EU +land_EU = globe.is_land(lat_grid_EU, lon_grid_EU) + +# Extract land coordinates in the EU +lon_land_EU = lon_grid_EU[land_EU] +lat_land_EU = lat_grid_EU[land_EU] + +# Define the Scan grid ranges +lon_Scan = np.arange(-10.5, 31.6, 0.3) +lat_Scan = np.arange(60, 71.2, 0.3) +lon_grid_Scan, lat_grid_Scan = np.meshgrid(lon_Scan, lat_Scan) +land_Scan = globe.is_land(lat_grid_Scan, lon_grid_Scan) + +lon_land_Scan = lon_grid_Scan[land_Scan] +lat_land_Scan = lat_grid_Scan[land_Scan] + +# %% +plt.scatter(lon_land_EU, lat_land_EU, c="r", s=1) + +# %% +w, m, failed_gids = pvdeg.weather.weather_distributed( + database="PVGIS", coords=[(lat_land_EU[0], lon_land_EU[0])] +) + +# %% +pvdeg.store.store(weather_ds=w, meta_df=m) + +# %% +loaded_weather, loaded_meta = pvdeg.store.get(group="PVGIS-1hr") + +# %% +loaded_meta + +# %% +loaded_weather + +# %% +loaded_weather.sel(gid=22).compute() + +# %% +import matplotlib.pyplot as plt + +plt.plot(loaded_weather.sel(gid=0).dhi) + +# %% +wet, met = pvdeg.store.get("PVGIS-1hr") + +# %% +wet + +# %% +loaded_meta + +# %% +func = pvdeg.standards.standoff + +template = pvdeg.geospatial.auto_template(func=func, ds_gids=loaded_weather) + +loaded_geo_res = pvdeg.geospatial.analysis( + weather_ds=loaded_weather, meta_df=loaded_meta, func=func, template=template +) + +# %% +pvdeg.geospatial.plot_sparse_analysis_land(loaded_geo_res, data_var="T98_inf") + +# %% diff --git a/tutorials_and_tools/tutorials_and_tools/ASTM Live Demo.ipynb b/tutorials/10_workshop_demos/01_astm_live_demo.ipynb similarity index 99% rename from tutorials_and_tools/tutorials_and_tools/ASTM Live Demo.ipynb rename to tutorials/10_workshop_demos/01_astm_live_demo.ipynb index 00dcc5969..1a780ff23 100644 --- a/tutorials_and_tools/tutorials_and_tools/ASTM Live Demo.ipynb +++ b/tutorials/10_workshop_demos/01_astm_live_demo.ipynb @@ -4,9 +4,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# ASTM Demonstration\n", + "# ASTM Live Demo\n", "\n", - "![PVDeg Logo](../PVD_logo.png)\n", + "![PVDeg Logo](../images/pvdeg_logo.svg)\n", "\n", "\n", "**Steps:**\n", @@ -22,12 +22,12 @@ "\n", "where $T_0$ is the temperature for $X=0$ (insulated back) and $\\Delta T$ is the temperature difference between an insulated back ($X=0$) and open rack mounting configuration ($X=\\infty)$.\n", "\n", - "The following figure showcases this calulation for the entire United States. We used pvlib and data from the National Solar Radiation Database (NSRDB) to calculate the module temperatures for different mounting configuration and applied our model to obtain the standoff distance for roof-mounted PV systems.\n" + "The following figure showcases this calulation for the entire United States. We used pvlib and data from the National Solar Radiation Database (NSRDB) to calculate the module temperatures for different mounting configuration and applied our model to obtain the standoff distance for roof-mounted PV systems." ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2025-10-22T01:17:21.557565Z", @@ -39,7 +39,7 @@ "outputs": [], "source": [ "# if running on google colab, uncomment the next line and execute this cell to install the dependencies and prevent \"ModuleNotFoundError\" in later cells:\n", - "#!pip install pvdeg==0.1.0" + "#!pip install pvdeg" ] }, { @@ -102,7 +102,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 1. Import Weather Data\n", + "# 1. Import Weather Data\n", "\n", "The function has two minimum requirements:\n", "- Weather data containing (at least) DNI, DHI, GHI, Temperature, RH, Wind-Speed\n", @@ -113,7 +113,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Where to get _Free_ Solar Irradiance Data?\n", + "# Where to get _Free_ Solar Irradiance Data?\n", "\n", "There are many different sources of solar irradiance data. For your projects, these are some of the most common:\n", "\n", @@ -125,7 +125,7 @@ "\n", "- Perhaps another useful link: https://sam.nrel.gov/weather-data.html\n", "\n", - "### Where else can you get historical irradiance data?\n", + "## Where else can you get historical irradiance data?\n", "\n", "There are several commercial providers of solar irradiance data. Data is available at different spatial and time resolutions. Each provider offers data under subscription that will provide access to irradiance (and other weather variables) via API to leverage in python.\n", "\n", @@ -141,19 +141,19 @@ "metadata": {}, "source": [ "\n", - "![NSRDB Example](images/tutorial_1_NSRDB_example.PNG)\n" + "![NSRDB Example](../images/tutorial_1_NSRDB_example.PNG)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### NREL API Key\n", + "# NREL API Key\n", "At the [NREL Developer Network](https://developer.nrel.gov/), there are [APIs](https://en.wikipedia.org/wiki/API) to a lot of valuable [solar resources](https://developer.nrel.gov/docs/solar/) like [weather data from the NSRDB](https://developer.nrel.gov/docs/solar/nsrdb/), [operational data from PVDAQ](https://developer.nrel.gov/docs/solar/pvdaq-v3/), or indicative calculations using [PVWatts](https://developer.nrel.gov/docs/solar/pvwatts/). In order to use these resources from NREL, you need to [register for a free API key](https://developer.nrel.gov/signup/). You can test out the APIs using the `DEMO_KEY` but it has limited bandwidth compared to the [usage limit for registered users](https://developer.nrel.gov/docs/rate-limits/). NREL has some [API usage instructions](https://developer.nrel.gov/docs/api-key/), but pvlib has a few builtin functions, like [`pvlib.iotools.get_psm3()`](https://pvlib-python.readthedocs.io/en/stable/reference/generated/pvlib.iotools.get_psm3.html), that wrap the NREL API, and call them for you to make it much easier to use. Skip ahead to the next section to learn more. But before you do...\n", "\n", "**Please pause now to visit https://developer.nrel.gov/signup/ and get an API key.**\n", "\n", - "### Application Programming Interface (API)\n", + "## Application Programming Interface (API)\n", "What exactly is an API? Nowadays, the phrase is used interchangeably with a \"web API\" but in general an API is just a recipe for how to interface with a application programmatically, _IE_: in code. An API could be as simple as a function signature or its published documentation, _EG_: the API for the `solarposition` function is you give it an ISO8601 formatted date with a timezone, the latitude, longitude, and elevation as numbers, and it returns the zenith and azimuth as numbers.\n", "\n", "A web API is the same, except the application is a web service, that you access at its URL using web methods. We won't go into too much more detail here, but the most common web method is `GET` which is pretty self explanatory. Look over the [NREL web usage instructions](https://developer.nrel.gov/docs/api-key/) for some examples, but interacting with a web API can be as easy as entering a URL into a browser. Try the URL below to _get_ the PVWatts energy output for a fixed tilt site in [Broomfield, CO](https://goo.gl/maps/awkEcNGzSur9Has18).\n", @@ -197,13 +197,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Fetching TMYs from the NSRDB\n", + "# Fetching TMYs from the NSRDB\n", "\n", - "The NSRDB, one of many sources of weather data intended for PV modeling, is free and easy to access using pvlib. As an example, we'll fetch a TMY dataset for Phoenix, AZ at coordinates [(33.4484, -112.0740)](https://goo.gl/maps/hGV92QHCm5FHJKbf9). \n", + "The NSRDB, one of many sources of weather data intended for PV modeling, is free and easy to access using pvlib. As an example, we'll fetch a TMY dataset for Phoenix, AZ at coordinates [(33.4484, -112.0740)](https://goo.gl/maps/hGV92QHCm5FHJKbf9).\n", "\n", "This function uses [`pvdeg.weather.get()`](https://pvdegradationtools.readthedocs.io/en/latest/_autosummary/pvdeg.weather.html#pvdeg.weather.get), which returns a Python dictionary of metadata and a Pandas dataframe of the timeseries weather data.\n", "\n", - "This function internally leverages [`pvlib.iotools.get_psm3()`](https://pvlib-python.readthedocs.io/en/stable/reference/generated/pvlib.iotools.get_psm3.html). However, for some of the NSRDB data relative humidity is not a given parameter, and `pvdeg` calculates the values from the downloaded data as an internal processing step. " + "This function internally leverages [`pvlib.iotools.get_psm3()`](https://pvlib-python.readthedocs.io/en/stable/reference/generated/pvlib.iotools.get_psm3.html). However, for some of the NSRDB data relative humidity is not a given parameter, and `pvdeg` calculates the values from the downloaded data as an internal processing step." ] }, { @@ -900,7 +900,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 2. Calculate Installation Standoff - Level 1\n", + "# 2. Calculate Installation Standoff - Level 1\n", "\n", "We use [`pvlib.standards.calc_standoff()`](https://pvdegradationtools.readthedocs.io/en/latest/_autosummary/pvdeg.standards.html#pvdeg.standards.calc_standoff) which takes at minimum the weather data and metadata, and returns the minimum installation distance in centimeters.\n", "\n" @@ -960,7 +960,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 3. Calculate Installation Standoff - Level 2\n", + "# 3. Calculate Installation Standoff - Level 2\n", "\n", "Let's take a closer look at the function and some optional parameters.\n", "\n", diff --git a/tutorials_and_tools/tutorials_and_tools/nrel-only/HPC Connection Required - DuraMAT Live Demo.ipynb b/tutorials/10_workshop_demos/02_duramat_live_demo.ipynb similarity index 96% rename from tutorials_and_tools/tutorials_and_tools/nrel-only/HPC Connection Required - DuraMAT Live Demo.ipynb rename to tutorials/10_workshop_demos/02_duramat_live_demo.ipynb index 1ce2b0a59..8cf8d4ca7 100644 --- a/tutorials_and_tools/tutorials_and_tools/nrel-only/HPC Connection Required - DuraMAT Live Demo.ipynb +++ b/tutorials/10_workshop_demos/02_duramat_live_demo.ipynb @@ -4,9 +4,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# DuraMAT Workshop Live Demo - Geospatial analysis\n", + "# DuraMAT Live Demo (HPC)\n", "\n", - "![PVDeg Logo](../PVD_logo.png)\n", + "![PVDeg Logo](../images/pvdeg_logo.svg)\n", "\n", "\n", "**Steps:**\n", @@ -16,7 +16,7 @@ "\n", "**Xarray: multi-dimensional data frame**\n", "\n", - "![Xarray](./images/xarray.webp)" + "![Xarray](../images/xarray.webp)" ] }, { @@ -65,7 +65,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 1 Start distributed compute cluster - DASK" + "# 1 Start distributed compute cluster - DASK" ] }, { @@ -82,7 +82,7 @@ } ], "source": [ - "pvdeg.geospatial.start_dask();" + "pvdeg.geospatial.start_dask()" ] }, { @@ -307,14 +307,14 @@ "\n", "# import imageio\n", "# ims = [imageio.imread(f'./images/RH_animation_{n}.png') for n in range(1, 13)]\n", - "# imageio.mimwrite(f'./images/RH_animation.gif', ims, format='GIF', duration=1000, loop=10)" + "# imageio.mimwrite(f'../images/RH_animation.gif', ims, format='GIF', duration=1000, loop=10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "![PVDeg Logo](./images/RH_animation.gif)" + "" ] }, { diff --git a/tutorials/10_workshop_demos/README.md b/tutorials/10_workshop_demos/README.md new file mode 100644 index 000000000..74669f52a --- /dev/null +++ b/tutorials/10_workshop_demos/README.md @@ -0,0 +1,42 @@ +# Workshop Demonstrations + +Live demonstration materials from PVDeg workshops and conferences. + +## Tutorials + +1. **01_astm_live_demo.ipynb** - ASTM workshop live demonstration +2. **02_duramat_live_demo.ipynb** - DuraMAT workshop live demonstration ⚠️ **Requires NREL HPC** + +## Requirements + +### ASTM Live Demo (01) +- No special requirements +- Can run locally or in Google Colab + +### DuraMAT Live Demo (02) +⚠️ **NREL HPC Access Required** +- Requires access to NREL's High Performance Computing resources +- NREL network connectivity or VPN required +- Appropriate computing allocations needed + +## About These Demos + +These notebooks were created for live demonstrations at workshops and conferences: +- **ASTM**: American Society for Testing and Materials workshops +- **DuraMAT**: DuraMAT Consortium meetings and workshops + +The demos showcase practical applications of PVDeg and provide hands-on examples for workshop participants. + +## Running the Demos + +### Local Execution +Most demos can be run locally. Follow the standard installation instructions in the main tutorials README. + +### Google Colab +Demos without HPC requirements can be launched in Google Colab using the rocket icon when viewing through the Jupyter Book. + +### HPC Execution +For demos requiring HPC (marked with ⚠️), you must: +1. Have NREL HPC account and allocations +2. Be on NREL network or connected via VPN +3. Follow NREL HPC documentation for job submission diff --git a/tutorials_and_tools/tutorials_and_tools/ASTM Live Demo.py b/tutorials/10_workshop_demos/scripts/01_astm_live_demo.py similarity index 84% rename from tutorials_and_tools/tutorials_and_tools/ASTM Live Demo.py rename to tutorials/10_workshop_demos/scripts/01_astm_live_demo.py index 909ad301c..00cd630d3 100644 --- a/tutorials_and_tools/tutorials_and_tools/ASTM Live Demo.py +++ b/tutorials/10_workshop_demos/scripts/01_astm_live_demo.py @@ -1,9 +1,7 @@ -#!/usr/bin/env python -# coding: utf-8 - -# # ASTM Demonstration +# %% [markdown] +# # ASTM Live Demo # -# ![PVDeg Logo](../PVD_logo.png) +# ![PVDeg Logo](../images/pvdeg_logo.svg) # # # **Steps:** @@ -20,28 +18,19 @@ # where $T_0$ is the temperature for $X=0$ (insulated back) and $\Delta T$ is the temperature difference between an insulated back ($X=0$) and open rack mounting configuration ($X=\infty)$. # # The following figure showcases this calulation for the entire United States. We used pvlib and data from the National Solar Radiation Database (NSRDB) to calculate the module temperatures for different mounting configuration and applied our model to obtain the standoff distance for roof-mounted PV systems. -# - -# In[1]: - +# %% # if running on google colab, uncomment the next line and execute this cell to install the dependencies and prevent "ModuleNotFoundError" in later cells: -#!pip install pvdeg==0.1.0 - - -# In[2]: - +# #!pip install pvdeg +# %% import os import pvlib import pvdeg import pandas as pd import matplotlib.pyplot as plt - -# In[ ]: - - +# %% # This information helps with debugging and getting support :) import sys import platform @@ -52,15 +41,16 @@ print("pvlib version ", pvlib.__version__) print("pvdeg version ", pvdeg.__version__) - -# ## 1. Import Weather Data +# %% [markdown] +# # 1. Import Weather Data # # The function has two minimum requirements: # - Weather data containing (at least) DNI, DHI, GHI, Temperature, RH, Wind-Speed # - Site meta-data containing (at least) Latitude, Longitude, Time Zone # -# ### Where to get _Free_ Solar Irradiance Data? +# %% [markdown] +# # Where to get _Free_ Solar Irradiance Data? # # There are many different sources of solar irradiance data. For your projects, these are some of the most common: # @@ -72,7 +62,7 @@ # # - Perhaps another useful link: https://sam.nrel.gov/weather-data.html # -# ### Where else can you get historical irradiance data? +# ## Where else can you get historical irradiance data? # # There are several commercial providers of solar irradiance data. Data is available at different spatial and time resolutions. Each provider offers data under subscription that will provide access to irradiance (and other weather variables) via API to leverage in python. # @@ -82,16 +72,18 @@ # * [Meteonorm](https://meteonorm.com/en/) # * [DNV Solar Resource Compass](https://src.dnv.com/) +# %% [markdown] # -# ![NSRDB Example](images/tutorial_1_NSRDB_example.PNG) +# ![NSRDB Example](../images/tutorial_1_NSRDB_example.PNG) # -# ### NREL API Key +# %% [markdown] +# # NREL API Key # At the [NREL Developer Network](https://developer.nrel.gov/), there are [APIs](https://en.wikipedia.org/wiki/API) to a lot of valuable [solar resources](https://developer.nrel.gov/docs/solar/) like [weather data from the NSRDB](https://developer.nrel.gov/docs/solar/nsrdb/), [operational data from PVDAQ](https://developer.nrel.gov/docs/solar/pvdaq-v3/), or indicative calculations using [PVWatts](https://developer.nrel.gov/docs/solar/pvwatts/). In order to use these resources from NREL, you need to [register for a free API key](https://developer.nrel.gov/signup/). You can test out the APIs using the `DEMO_KEY` but it has limited bandwidth compared to the [usage limit for registered users](https://developer.nrel.gov/docs/rate-limits/). NREL has some [API usage instructions](https://developer.nrel.gov/docs/api-key/), but pvlib has a few builtin functions, like [`pvlib.iotools.get_psm3()`](https://pvlib-python.readthedocs.io/en/stable/reference/generated/pvlib.iotools.get_psm3.html), that wrap the NREL API, and call them for you to make it much easier to use. Skip ahead to the next section to learn more. But before you do... # # **Please pause now to visit https://developer.nrel.gov/signup/ and get an API key.** # -# ### Application Programming Interface (API) +# ## Application Programming Interface (API) # What exactly is an API? Nowadays, the phrase is used interchangeably with a "web API" but in general an API is just a recipe for how to interface with a application programmatically, _IE_: in code. An API could be as simple as a function signature or its published documentation, _EG_: the API for the `solarposition` function is you give it an ISO8601 formatted date with a timezone, the latitude, longitude, and elevation as numbers, and it returns the zenith and azimuth as numbers. # # A web API is the same, except the application is a web service, that you access at its URL using web methods. We won't go into too much more detail here, but the most common web method is `GET` which is pretty self explanatory. Look over the [NREL web usage instructions](https://developer.nrel.gov/docs/api-key/) for some examples, but interacting with a web API can be as easy as entering a URL into a browser. Try the URL below to _get_ the PVWatts energy output for a fixed tilt site in [Broomfield, CO](https://goo.gl/maps/awkEcNGzSur9Has18). @@ -102,9 +94,7 @@ # # **If you have an NREL API key please enter it in the next cell.** -# In[3]: - - +# %% NREL_API_KEY = None # <-- please set your NREL API key here # note you must use "quotes" around your key, for example: @@ -119,8 +109,8 @@ except KeyError: NREL_API_KEY = "DEMO_KEY" # OK for this demo, but better to get your own key - -# ## Fetching TMYs from the NSRDB +# %% [markdown] +# # Fetching TMYs from the NSRDB # # The NSRDB, one of many sources of weather data intended for PV modeling, is free and easy to access using pvlib. As an example, we'll fetch a TMY dataset for Phoenix, AZ at coordinates [(33.4484, -112.0740)](https://goo.gl/maps/hGV92QHCm5FHJKbf9). # @@ -128,101 +118,78 @@ # # This function internally leverages [`pvlib.iotools.get_psm3()`](https://pvlib-python.readthedocs.io/en/stable/reference/generated/pvlib.iotools.get_psm3.html). However, for some of the NSRDB data relative humidity is not a given parameter, and `pvdeg` calculates the values from the downloaded data as an internal processing step. -# In[4]: - - -# This cell does not run on a Collab instal. WE are troubleshooting it, but the next cell performs the same request directly with PVLib. -# If TMY is requested though, relative_humidity will not be included as NSRDB TMY dataset does not have it. +# %% +# This cell is for documentation only and is not meant to be executed. +# The next cell performs the same request directly with PVLib. """ -weather_db = 'PSM3' +weather_db = 'PSM4' weather_id = (33.4484, -112.0740) weather_arg = {'api_key': NREL_API_KEY, 'email': 'user@mail.com', - 'names': '2021', # tmy is another common option; right now requesting only 2021 data - 'attributes': [], # grabs all. to select, could be 'air_temperature', 'dew_point', 'dhi', - # 'dni', 'ghi', 'surface_albedo', 'surface_pressure', 'wind_direction', 'wind_speed' + 'year': '2021', 'map_variables': True, 'leap_day': False} weather_df, meta = pvdeg.weather.get(weather_db, weather_id, **weather_arg) """ - -# In[5]: - - -weather_df, meta = pvlib.iotools.get_psm3( +# %% +weather_df, meta = pvlib.iotools.get_nsrdb_psm4_tmy( latitude=33.4484, longitude=-112.0740, api_key=NREL_API_KEY, email="silvana.ovaitt@nrel.gov", # <-- any email works here fine - names="2021", + year="tmy", map_variables=True, - attributes=[], leap_day=False, ) - -# In[13]: - - +# %% weather_df - -# In[6]: - - +# %% meta - -# In[7]: - - +# %% weather_df.head() - -# In[8]: - +# %% +# Choose the date you want to plot +date = "2010-01-01" +mask = weather_df.index.date == pd.to_datetime(date).date() +day_df = weather_df.loc[mask] fig, ax1 = plt.subplots(figsize=(9, 6)) -# Instantiate a second axes that shares the same x-axis -ax1.plot( - weather_df.loc["2021-06-28 05:00:00-07:00":"2021-06-28 20:00:00-07:00"][ - "dni_clear" - ], - label="DNI", -) +ax1.plot(day_df.index, day_df["dni"], label="DNI") ax2 = ax1.twinx() -ax2.plot( - weather_df.loc["2021-06-28 05:00:00-07:00":"2021-06-28 20:00:00-07:00"]["temp_air"], - "r", - label="Temperature", -) +ax2.plot(day_df.index, day_df["temp_air"], "r", label="Temperature") ax1.set_ylim([0, 1000]) ax2.set_ylim([0, 50]) ax1.set_ylabel("DNI") ax2.set_ylabel(r"Temperature $\degree$C") +plt.title(f"Weather Data for {date}") +plt.show() +# %% +print(weather_df.columns) +print(weather_df.index.min(), weather_df.index.max()) +print(weather_df.head()) -# ## 2. Calculate Installation Standoff - Level 1 +# %% [markdown] +# # 2. Calculate Installation Standoff - Level 1 # # We use [`pvlib.standards.calc_standoff()`](https://pvdegradationtools.readthedocs.io/en/latest/_autosummary/pvdeg.standards.html#pvdeg.standards.calc_standoff) which takes at minimum the weather data and metadata, and returns the minimum installation distance in centimeters. # # -# In[9]: - - +# %% standoff = pvdeg.standards.standoff(weather_df=weather_df, meta=meta) - -# In[10]: - - +# %% print("Minimum installation distance:", standoff["x"]) - -# ## 3. Calculate Installation Standoff - Level 2 +# %% [markdown] +# # 3. Calculate Installation Standoff - Level 2 # # Let's take a closer look at the function and some optional parameters. # @@ -234,27 +201,10 @@ # - x_0 : thermal decay constant [cm] (see documentation) # - wind_speed_factor : Wind speed correction factor to account for different wind speed measurement heights between weather database (e.g. NSRDB) and the tempeature model (e.g. SAPM) -# In[11]: - - -standoff = pvdeg.standards.standoff( - weather_df=weather_df, - meta=meta, - level=2, - tilt=None, - azimuth=180, - sky_model="isotropic", - temp_model="sapm", - module_type="glass_polymer", - x_0=6.1, - wind_speed_factor=1, -) - - -# In[12]: - +# %% +standoff = pvdeg.standards.standoff(weather_df=weather_df, meta=meta, T98=70) +# %% print("Minimum installation distance:", standoff["x"]) - -# In[ ]: +# %% diff --git a/tutorials_and_tools/tutorials_and_tools/DuraMAT Live Demo.py b/tutorials/10_workshop_demos/scripts/02_duramat_live_demo.py similarity index 84% rename from tutorials_and_tools/tutorials_and_tools/DuraMAT Live Demo.py rename to tutorials/10_workshop_demos/scripts/02_duramat_live_demo.py index 3becadbc8..1918b84a5 100644 --- a/tutorials_and_tools/tutorials_and_tools/DuraMAT Live Demo.py +++ b/tutorials/10_workshop_demos/scripts/02_duramat_live_demo.py @@ -1,9 +1,7 @@ -#!/usr/bin/env python -# coding: utf-8 - -# # DuraMAT Workshop Live Demo - Geospatial analysis +# %% [markdown] +# # DuraMAT Live Demo (HPC) # -# ![PVDeg Logo](../PVD_logo.png) +# ![PVDeg Logo](../images/pvdeg_logo.svg) # # # **Steps:** @@ -13,18 +11,13 @@ # # **Xarray: multi-dimensional data frame** # -# ![Xarray](./images/xarray.webp) - -# In[2]: - +# ![Xarray](../images/xarray.webp) +# %% import pandas as pd import pvdeg - -# In[ ]: - - +# %% # This information helps with debugging and getting support :) import sys import platform @@ -34,18 +27,13 @@ print("Pandas version ", pd.__version__) print("pvdeg version ", pvdeg.__version__) +# %% [markdown] +# # 1 Start distributed compute cluster - DASK -# ## 1 Start distributed compute cluster - DASK - -# In[3]: - - +# %% pvdeg.geospatial.start_dask() - -# In[4]: - - +# %% # Get weather data weather_db = "NSRDB" @@ -65,35 +53,17 @@ weather_ds, meta_df = pvdeg.weather.get(weather_db, geospatial=True, **weather_arg) - -# In[5]: - - +# %% weather_ds - -# In[6]: - - -meta_df["state"].unique() - - -# In[7]: - - +# %% meta_NM = meta_df[meta_df["state"] == "New Mexico"] - -# In[8]: - - +# %% meta_NM_sub, gids_NM_sub = pvdeg.utilities.gid_downsampling(meta_NM, 4) weather_NM_sub = weather_ds.sel(gid=meta_NM_sub.index) - -# In[9]: - - +# %% geo = { "func": pvdeg.standards.standoff, "weather_ds": weather_NM_sub, @@ -102,16 +72,10 @@ standoff_res = pvdeg.geospatial.analysis(**geo) - -# In[10]: - - +# %% standoff_res - -# In[11]: - - +# %% fig, ax = pvdeg.geospatial.plot_USA( standoff_res["x"], cmap="viridis", @@ -121,12 +85,10 @@ cb_title="Standoff (cm)", ) - +# %% [markdown] # # Relative Humidity Example - Time dimension -# In[12]: - - +# %% # State bar of new mexico: (35.16482, -106.58979) weather_db = "NSRDB" @@ -149,28 +111,16 @@ weather_db, weather_id, geospatial=False, **weather_arg ) - -# In[13]: - - +# %% RH_module = pvdeg.humidity.module(weather_df=weather_df, meta=meta) - -# In[14]: - - +# %% RH_module - -# In[15]: - - +# %% RH_module.plot(ls="--") - -# In[16]: - - +# %% geo = { "func": pvdeg.humidity.module, "weather_ds": weather_NM_sub, @@ -179,16 +129,10 @@ RH_module = pvdeg.geospatial.analysis(**geo) - -# In[17]: - - +# %% RH_module - -# In[18]: - - +# %% # from matplotlib.animation import FuncAnimation # from matplotlib.animation import PillowWriter # import matplotlib.animation as animation @@ -210,9 +154,9 @@ # import imageio # ims = [imageio.imread(f'./images/RH_animation_{n}.png') for n in range(1, 13)] -# imageio.mimwrite(f'./images/RH_animation.gif', ims, format='GIF', duration=1000, loop=10) - +# imageio.mimwrite(f'../images/RH_animation.gif', ims, format='GIF', duration=1000, loop=10) -# ![PVDeg Logo](./images/RH_animation.gif) +# %% [markdown] +# -# In[ ]: +# %% diff --git a/tutorials/README.md b/tutorials/README.md new file mode 100644 index 000000000..96ede1aee --- /dev/null +++ b/tutorials/README.md @@ -0,0 +1,65 @@ +# PVDeg - Tutorials and Tools + +PVDeg is an open-source Python package for modeling photovoltaic (PV) degradation. These tutorials demonstrate how to use PVDeg's modular functions, materials databases, and workflows for simulating degradation mechanisms (e.g., LeTID, hydrolysis, UV exposure) using weather data from NSRDB and PVGIS. + +## Tutorial Categories + +- `01_basics/` - Introduction to PVDeg fundamentals +- `02_degradation/` - Degradation mechanism modeling (LID, LETID, Van't Hoff) +- `03_monte_carlo/` - Monte Carlo uncertainty analysis +- `04_scenario/` - Scenario-based workflows and HPC analysis +- `05_geospatial/` - Geospatial analysis and world map visualizations +- `06_advanced/` - Advanced topics and API access +- `10_workshop_demos/` - Workshop and demonstration notebooks + +For standalone analysis tools, see the [Tools](../tools/) directory. + +## Running the Tutorials + +### Jupyter Book + +For learning to use PVDeg through our tutorials, or running the available tools online, see our [jupyter-book](https://nrel.github.io/PVDegradationTools/intro.html) +Clicking on the rocket-icon on the top allows you to launch the notebooks on [Google Colaboratory](https://colab.research.google.com/) for interactive mode. +Just uncomment the first line `pip install ...` to install the environment on each notebook if you follow this mode. + +### Binder + +To run these tutorials or tools in Binder, you can click here: +[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/NREL/PVDegradationTools/main) +It takes a minute to load the environment. + +### Locally + +You can also run the tutorials and tools locally in a virtual environment, i.e., `venv` or +[miniconda](https://docs.conda.io/en/latest/miniconda.html). + +1. Create and activate a new environment, e.g., on Mac/Linux terminal with `venv`: + ``` + python -m venv pvdeg + . pvdeg/bin/activate + ``` + or with `conda`: + ``` + conda create -n pvdeg + conda activate pvdeg + ``` + +1. Install `pvdeg` into the new environment with `pip`: + ``` + python -m pip install pvdeg + ``` + +1. Start a Jupyter session: + + ``` + jupyter notebook + ``` + +1. Use the file explorer in Jupyter lab to browse to `tutorials` + and start the first Tutorial. + + +Documentation +============= + +Full API documentation is available at [ReadTheDocs](https://PVDegradationTools.readthedocs.io) where you can find detailed information on all functions, classes, and modules. diff --git a/tutorials/_config.yml b/tutorials/_config.yml new file mode 100644 index 000000000..eaee60a7a --- /dev/null +++ b/tutorials/_config.yml @@ -0,0 +1,78 @@ +# Book settings +# Learn more at https://jupyterbook.org/customize/config.html + +title: PVDEG Tutorials +author: Martin Springer, Matthew Brown, Silvana Ovaitt, Tobin Ford, Joseph Karas, Michael Kempe +logo: ../docs/source/_static/logo-vectors/PVdeg-Logo-Horiz-Color.svg + +# Auto-exclude files not in the toc +only_build_toc_files : true + +# Exclude patterns for files that shouldn't be built +exclude_patterns: + - "_build" + - ".ipynb_checkpoints" + - "*/scripts" + - "*/scripts/*" + +# Force re-execution of notebooks on each build. +# See https://jupyterbook.org/content/execute.html +execute: + # 'auto' = execute notebooks that don't have outputs + # 'cache' = use cached outputs, don't execute + execute_notebooks: auto + exclude_patterns: + # These notebooks will be included but use cached outputs (not re-executed) + # They require HPC/SLURM access, API keys, or take too long to execute + - "02_degradation/02_letid_accelerated_test.ipynb" + - "02_degradation/05_letid_passivated_wafer.ipynb" + - "03_monte_carlo/01_arrhenius.ipynb" + - "04_scenario/02_scenario_single_location.ipynb" + - "04_scenario/03_scenario_mountain_downselect.ipynb" + - "05_geospatial/01_geospatial_templates.ipynb" + - "05_geospatial/02_geospatial_world_map.ipynb" + - "05_geospatial/03_letid_outdoor_geospatial_demo.ipynb" + - "05_geospatial/04_scenario_geographical_features.ipynb" + - "05_geospatial/05_scenario_geospatial.ipynb" + - "06_advanced/03_pysam_api.ipynb" + - "06_advanced/04_nsrdb_distributed_api.ipynb" + - "06_advanced/05_pvgis_distributed.ipynb" + - "10_workshop_demos/01_astm_live_demo.ipynb" + - "10_workshop_demos/02_duramat_live_demo.ipynb" + # Tools requiring HPC access or have execution issues + - "tools/Tools - Degradation.ipynb" + - "tools/Tools - Module Standoff for IEC TS 63126.ipynb" + +# Define the name of the latex output file for PDF builds +latex: + latex_documents: + targetname: book.tex + +# Add a bibtex file so that we can create citations +bibtex_bibfiles: + - references.bib + +# Information about where the book exists on the web +repository: + url: https://github.com/NREL/PVDegradationTools + path_to_book: tutorials # Optional path to your book, relative to the repository root + branch: main + +# Add GitHub buttons to your book +# See https://jupyterbook.org/customize/config.html#add-a-link-to-your-repository +html: + use_issues_button: true + use_repository_button: true + +# Configure Sphinx to limit TOC depth +sphinx: + config: + html_theme_options: + navigation_depth: 1 + show_nav_level: 1 + +launch_buttons: + notebook_interface: "classic" + binderhub_url: "https://mybinder.org" + colab_url: "https://colab.research.google.com" + thebe: true diff --git a/tutorials/_toc.yml b/tutorials/_toc.yml new file mode 100644 index 000000000..050372ab6 --- /dev/null +++ b/tutorials/_toc.yml @@ -0,0 +1,88 @@ +# Table of contents +# Learn more at https://jupyterbook.org/customize/toc.html + +format: jb-book +root: intro +parts: +- caption: 01 - Basics + titlesonly: true + chapters: + - file: 01_basics/01_basics_humidity_design + title: Humidity Design Basics + - file: 01_basics/02_degradation + title: Degradation Overview + - file: 01_basics/03_spectral_degradation + title: Spectral Degradation + - file: 01_basics/04_weather_database_access + title: Weather Database Access +- caption: 02 - Degradation Mechanisms + titlesonly: true + chapters: + - file: 02_degradation/01_bo_lid_accelerated_test + title: B-O LID Accelerated Test + - file: 02_degradation/02_letid_accelerated_test + title: LETID Accelerated Test + - file: 02_degradation/03_letid_outdoor + title: LETID Outdoor + - file: 02_degradation/04_letid_outdoor_scenario + title: LETID Outdoor Scenario + - file: 02_degradation/05_letid_passivated_wafer + title: LETID Passivated Wafer + - file: 02_degradation/06_vant_hoff_degradation_model + title: Van't Hoff Degradation Model +- caption: 03 - Monte Carlo Simulations + titlesonly: true + chapters: + - file: 03_monte_carlo/01_arrhenius + title: Arrhenius Monte Carlo + - file: 03_monte_carlo/02_standoff + title: Standoff Monte Carlo +- caption: 04 - Scenarios + titlesonly: true + chapters: + - file: 04_scenario/01_scenario_temperature + title: Temperature Scenario + - file: 04_scenario/02_scenario_single_location + title: Single Location + - file: 04_scenario/03_scenario_mountain_downselect + title: Mountain Downselect +- caption: 05 - Geospatial Analysis + titlesonly: true + chapters: + - file: 05_geospatial/01_geospatial_templates + title: Geospatial Templates (HPC) + - file: 05_geospatial/02_geospatial_world_map + title: Geospatial World Map (HPC) + - file: 05_geospatial/03_letid_outdoor_geospatial_demo + title: LETID Outdoor Geospatial Demo (HPC) + - file: 05_geospatial/04_scenario_geographical_features + title: Geographical Features (HPC) + - file: 05_geospatial/05_scenario_geospatial + title: Scenario Geospatial (HPC) +- caption: 06 - Advanced Topics + titlesonly: true + chapters: + - file: 06_advanced/01_custom_functions_nopython + title: Custom Functions with Numba + - file: 06_advanced/03_pysam_api + title: PySAM with API (API Key Required) + - file: 06_advanced/04_nsrdb_distributed_api + title: NSRDB Distributed (API Key Required) + - file: 06_advanced/05_pvgis_distributed + title: PVGIS Distributed +- caption: 06 - Workshop Demos + titlesonly: true + chapters: + - file: 10_workshop_demos/01_astm_live_demo + title: ASTM Live Demo + - file: 10_workshop_demos/02_duramat_live_demo + title: DuraMAT Live Demo (HPC) +- caption: Tools + titlesonly: true + chapters: + - file: tools/Tools - Edge Seal Oxygen Ingress + title: Edge Seal Oxygen Ingress + - file: tools/Tools - Degradation + title: Degradation + - file: tools/Tools - Module Standoff for IEC TS 63126 + title: Module Standoff for IEC TS 63126 (HPC) diff --git a/tutorials/data/meta_golden.json b/tutorials/data/meta_golden.json new file mode 100644 index 000000000..8fda41e2c --- /dev/null +++ b/tutorials/data/meta_golden.json @@ -0,0 +1,48 @@ +{ + "Source": "NSRDB", + "Location ID": "479494", + "City": "-", + "State": "-", + "Country": "-", + "Clearsky DHI Units": "w/m2", + "Clearsky DNI Units": "w/m2", + "Clearsky GHI Units": "w/m2", + "Dew Point Units": "c", + "DHI Units": "w/m2", + "DNI Units": "w/m2", + "GHI Units": "w/m2", + "Solar Zenith Angle Units": "Degree", + "Temperature Units": "c", + "Pressure Units": "mbar", + "Relative Humidity Units": "%", + "Precipitable Water Units": "cm", + "Wind Direction Units": "Degrees", + "Wind Speed Units": "m/s", + "Cloud Type -15": "N/A", + "Cloud Type 0": "Clear", + "Cloud Type 1": "Probably Clear", + "Cloud Type 2": "Fog", + "Cloud Type 3": "Water", + "Cloud Type 4": "Super-Cooled Water", + "Cloud Type 5": "Mixed", + "Cloud Type 6": "Opaque Ice", + "Cloud Type 7": "Cirrus", + "Cloud Type 8": "Overlapping", + "Cloud Type 9": "Overshooting", + "Cloud Type 10": "Unknown", + "Cloud Type 11": "Dust", + "Cloud Type 12": "Smoke", + "Fill Flag 0": "N/A", + "Fill Flag 1": "Missing Image", + "Fill Flag 2": "Low Irradiance", + "Fill Flag 3": "Exceeds Clearsky", + "Fill Flag 4": "Missing CLoud Properties", + "Fill Flag 5": "Rayleigh Violation", + "Surface Albedo Units": "N/A", + "Version": "4.1.2.dev4+g3b38bc8.d20250228", + "latitude": 39.73, + "longitude": -105.18, + "altitude": 1876, + "tz": -7, + "wind_height": 2 +} diff --git a/tutorials/data/meta_miami.json b/tutorials/data/meta_miami.json new file mode 100644 index 000000000..8a787b2dd --- /dev/null +++ b/tutorials/data/meta_miami.json @@ -0,0 +1,48 @@ +{ + "Source": "NSRDB", + "Location ID": "1060499", + "City": "-", + "State": "-", + "Country": "-", + "Clearsky DHI Units": "w/m2", + "Clearsky DNI Units": "w/m2", + "Clearsky GHI Units": "w/m2", + "Dew Point Units": "c", + "DHI Units": "w/m2", + "DNI Units": "w/m2", + "GHI Units": "w/m2", + "Solar Zenith Angle Units": "Degree", + "Temperature Units": "c", + "Pressure Units": "mbar", + "Relative Humidity Units": "%", + "Precipitable Water Units": "cm", + "Wind Direction Units": "Degrees", + "Wind Speed Units": "m/s", + "Cloud Type -15": "N/A", + "Cloud Type 0": "Clear", + "Cloud Type 1": "Probably Clear", + "Cloud Type 2": "Fog", + "Cloud Type 3": "Water", + "Cloud Type 4": "Super-Cooled Water", + "Cloud Type 5": "Mixed", + "Cloud Type 6": "Opaque Ice", + "Cloud Type 7": "Cirrus", + "Cloud Type 8": "Overlapping", + "Cloud Type 9": "Overshooting", + "Cloud Type 10": "Unknown", + "Cloud Type 11": "Dust", + "Cloud Type 12": "Smoke", + "Fill Flag 0": "N/A", + "Fill Flag 1": "Missing Image", + "Fill Flag 2": "Low Irradiance", + "Fill Flag 3": "Exceeds Clearsky", + "Fill Flag 4": "Missing CLoud Properties", + "Fill Flag 5": "Rayleigh Violation", + "Surface Albedo Units": "N/A", + "Version": "4.1.2.dev4+g3b38bc8.d20250228", + "latitude": 25.77, + "longitude": -80.18, + "altitude": 7, + "tz": -5, + "wind_height": 2 +} diff --git a/tutorials/data/meta_nyc.json b/tutorials/data/meta_nyc.json new file mode 100644 index 000000000..f91f301d4 --- /dev/null +++ b/tutorials/data/meta_nyc.json @@ -0,0 +1,48 @@ +{ + "Source": "NSRDB", + "Location ID": "1245247", + "City": "-", + "State": "-", + "Country": "-", + "Clearsky DHI Units": "w/m2", + "Clearsky DNI Units": "w/m2", + "Clearsky GHI Units": "w/m2", + "Dew Point Units": "c", + "DHI Units": "w/m2", + "DNI Units": "w/m2", + "GHI Units": "w/m2", + "Solar Zenith Angle Units": "Degree", + "Temperature Units": "c", + "Pressure Units": "mbar", + "Relative Humidity Units": "%", + "Precipitable Water Units": "cm", + "Wind Direction Units": "Degrees", + "Wind Speed Units": "m/s", + "Cloud Type -15": "N/A", + "Cloud Type 0": "Clear", + "Cloud Type 1": "Probably Clear", + "Cloud Type 2": "Fog", + "Cloud Type 3": "Water", + "Cloud Type 4": "Super-Cooled Water", + "Cloud Type 5": "Mixed", + "Cloud Type 6": "Opaque Ice", + "Cloud Type 7": "Cirrus", + "Cloud Type 8": "Overlapping", + "Cloud Type 9": "Overshooting", + "Cloud Type 10": "Unknown", + "Cloud Type 11": "Dust", + "Cloud Type 12": "Smoke", + "Fill Flag 0": "N/A", + "Fill Flag 1": "Missing Image", + "Fill Flag 2": "Low Irradiance", + "Fill Flag 3": "Exceeds Clearsky", + "Fill Flag 4": "Missing CLoud Properties", + "Fill Flag 5": "Rayleigh Violation", + "Surface Albedo Units": "N/A", + "Version": "4.1.2.dev4+g3b38bc8.d20250228", + "latitude": 40.65, + "longitude": -73.98, + "altitude": 27, + "tz": -5, + "wind_height": 2 +} diff --git a/tutorials/data/psm4_golden.csv b/tutorials/data/psm4_golden.csv new file mode 100644 index 000000000..93fa60465 --- /dev/null +++ b/tutorials/data/psm4_golden.csv @@ -0,0 +1,8761 @@ +,Year,Month,Day,Hour,Minute,temp_air,dew_point,dhi,dni,ghi,albedo,pressure,wind_direction,wind_speed,relative_humidity +2011-01-01 00:30:00-07:00,2011,1,1,0,30,-16.0,-26.6,0.0,0.0,0.0,0.8,794.0,276.0,4.0,39.69274117017407 +2011-01-01 01:30:00-07:00,2011,1,1,1,30,-16.0,-26.5,0.0,0.0,0.0,0.8,794.0,274.0,4.1000000000000005,40.057183224157356 +2011-01-01 02:30:00-07:00,2011,1,1,2,30,-15.9,-26.200000000000003,0.0,0.0,0.0,0.8,795.0,273.0,4.1000000000000005,40.82808190935401 +2011-01-01 03:30:00-07:00,2011,1,1,3,30,-15.8,-26.0,0.0,0.0,0.0,0.8,795.0,272.0,4.1000000000000005,41.234475574344536 +2011-01-01 04:30:00-07:00,2011,1,1,4,30,-15.7,-25.700000000000003,0.0,0.0,0.0,0.8,795.0,271.0,4.0,42.02335562797371 +2011-01-01 05:30:00-07:00,2011,1,1,5,30,-15.7,-25.5,0.0,0.0,0.0,0.8,796.0,270.0,4.0,42.79175936426906 +2011-01-01 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05:30:00-07:00,2011,1,31,5,30,-5.7,-10.7,0.0,0.0,0.0,0.8,800.0,181.0,1.8,67.81393694864089 +2011-01-31 06:30:00-07:00,2011,1,31,6,30,-6.300000000000001,-11.0,0.0,0.0,0.0,0.8,800.0,22.0,2.3000000000000003,69.31583235742201 +2011-01-31 07:30:00-07:00,2011,1,31,7,30,-6.5,-11.3,13.0,0.0,13.0,0.8,801.0,34.0,2.7,68.72232478374252 +2011-01-31 08:30:00-07:00,2011,1,31,8,30,-6.2,-11.4,28.0,0.0,28.0,0.8,801.0,38.0,2.7,66.63004603204088 +2011-01-31 09:30:00-07:00,2011,1,31,9,30,-5.5,-11.5,114.0,1.0,115.0,0.8,801.0,42.0,2.7,62.669234424546396 +2011-01-31 10:30:00-07:00,2011,1,31,10,30,-4.800000000000001,-11.4,304.0,74.0,339.0,0.8,800.0,49.0,2.9000000000000004,59.91172803859806 +2011-01-31 11:30:00-07:00,2011,1,31,11,30,-4.6000000000000005,-11.4,175.0,1.0,176.0,0.8,800.0,56.0,3.3000000000000003,59.014951678870155 +2011-01-31 12:30:00-07:00,2011,1,31,12,30,-5.2,-11.8,76.0,0.0,76.0,0.8,799.0,58.0,3.9,59.80701907326927 +2011-01-31 13:30:00-07:00,2011,1,31,13,30,-6.6000000000000005,-12.6,109.0,0.0,110.0,0.8,799.0,56.0,4.4,62.393155956326886 +2011-01-31 14:30:00-07:00,2011,1,31,14,30,-8.3,-13.9,211.0,7.0,214.0,0.8,800.0,52.0,4.7,64.02021527493977 +2011-01-31 15:30:00-07:00,2011,1,31,15,30,-10.1,-15.8,168.0,16.0,172.0,0.8,801.0,44.0,4.6000000000000005,63.04839803044443 +2011-01-31 16:30:00-07:00,2011,1,31,16,30,-11.6,-17.7,47.0,0.0,47.0,0.8,802.0,34.0,4.4,60.59088579952157 +2011-01-31 17:30:00-07:00,2011,1,31,17,30,1.5,-4.0,0.0,0.0,0.0,0.14,804.0,82.0,1.4,66.82847884147819 +2011-01-31 18:30:00-07:00,2011,1,31,18,30,0.3,-4.4,0.0,0.0,0.0,0.14,804.0,111.0,1.7000000000000002,70.7020701008966 +2011-01-31 19:30:00-07:00,2011,1,31,19,30,-0.7000000000000001,-4.9,0.0,0.0,0.0,0.14,805.0,123.0,1.8,73.2168615645383 +2011-01-31 20:30:00-07:00,2011,1,31,20,30,-1.4,-5.300000000000001,0.0,0.0,0.0,0.14,805.0,118.0,1.7000000000000002,74.76621915095576 +2011-01-31 21:30:00-07:00,2011,1,31,21,30,-2.1,-5.800000000000001,0.0,0.0,0.0,0.14,805.0,113.0,1.7000000000000002,75.78455426253339 +2011-01-31 22:30:00-07:00,2011,1,31,22,30,-2.6,-6.300000000000001,0.0,0.0,0.0,0.14,806.0,122.0,1.8,75.69643546256339 +2011-01-31 23:30:00-07:00,2011,1,31,23,30,-2.9000000000000004,-6.800000000000001,0.0,0.0,0.0,0.14,805.0,133.0,1.8,74.49199838208466 +2018-02-01 00:30:00-07:00,2018,2,1,0,30,-3.0,-7.1000000000000005,0.0,0.0,0.0,0.14,805.0,138.0,1.8,73.33716360526049 +2018-02-01 01:30:00-07:00,2018,2,1,1,30,-3.2,-7.300000000000001,0.0,0.0,0.0,0.14,806.0,141.0,1.8,73.2988989668514 +2018-02-01 02:30:00-07:00,2018,2,1,2,30,-3.3000000000000003,-7.5,0.0,0.0,0.0,0.14,806.0,145.0,1.8,72.71635680368044 +2018-02-01 03:30:00-07:00,2018,2,1,3,30,-3.4000000000000004,-7.7,0.0,0.0,0.0,0.14,806.0,149.0,1.7000000000000002,72.13700114555644 +2018-02-01 04:30:00-07:00,2018,2,1,4,30,-3.4000000000000004,-7.800000000000001,0.0,0.0,0.0,0.14,806.0,154.0,1.7000000000000002,71.58099581454573 +2018-02-01 05:30:00-07:00,2018,2,1,5,30,-3.4000000000000004,-7.9,0.0,0.0,0.0,0.14,806.0,159.0,1.7000000000000002,71.02880851247222 +2018-02-01 06:30:00-07:00,2018,2,1,6,30,-2.9000000000000004,-8.0,0.0,0.0,0.0,0.14,807.0,161.0,1.6,67.9054900970484 +2018-02-01 07:30:00-07:00,2018,2,1,7,30,-1.2000000000000002,-7.7,11.0,0.0,11.0,0.14,807.0,180.0,1.3,61.31098491641164 +2018-02-01 08:30:00-07:00,2018,2,1,8,30,0.9,-7.4,64.0,6.0,65.0,0.14,808.0,273.0,1.6,53.87418033850703 +2018-02-01 09:30:00-07:00,2018,2,1,9,30,2.3000000000000003,-7.0,19.0,0.0,19.0,0.14,809.0,349.0,2.8000000000000003,50.26220827563864 +2018-02-01 10:30:00-07:00,2018,2,1,10,30,3.4000000000000004,-6.9,18.0,0.0,18.0,0.14,809.0,353.0,3.7,46.8525696738553 +2018-02-01 11:30:00-07:00,2018,2,1,11,30,4.1000000000000005,-7.0,46.0,0.0,46.0,0.14,809.0,352.0,4.2,44.260310699158524 +2018-02-01 12:30:00-07:00,2018,2,1,12,30,4.6000000000000005,-7.1000000000000005,70.0,0.0,70.0,0.14,809.0,353.0,4.1000000000000005,42.410718834123465 +2018-02-01 13:30:00-07:00,2018,2,1,13,30,4.6000000000000005,-7.2,83.0,0.0,83.0,0.14,809.0,178.0,3.8,42.0854871042337 +2018-02-01 14:30:00-07:00,2018,2,1,14,30,4.0,-7.1000000000000005,166.0,118.0,216.0,0.14,809.0,11.0,3.2,44.23042116191027 +2018-02-01 15:30:00-07:00,2018,2,1,15,30,2.2,-6.9,118.0,34.0,128.0,0.14,810.0,39.0,2.3000000000000003,51.01185566020582 +2018-02-01 16:30:00-07:00,2018,2,1,16,30,-0.2,-6.800000000000001,61.0,2.0,61.0,0.14,811.0,79.0,1.5,61.09089610859544 +2018-02-01 17:30:00-07:00,2018,2,1,17,30,-2.0,-7.800000000000001,0.0,0.0,0.0,0.14,811.0,129.0,1.2000000000000002,64.52213292171857 +2018-02-01 18:30:00-07:00,2018,2,1,18,30,-2.9000000000000004,-7.9,0.0,0.0,0.0,0.14,811.0,167.0,1.3,68.43384725934325 +2018-02-01 19:30:00-07:00,2018,2,1,19,30,-3.2,-7.9,0.0,0.0,0.0,0.14,811.0,177.0,1.6,69.97791734170768 +2018-02-01 20:30:00-07:00,2018,2,1,20,30,-3.1,-7.9,0.0,0.0,0.0,0.14,811.0,186.0,1.8,69.45896404864928 +2018-02-01 21:30:00-07:00,2018,2,1,21,30,-2.9000000000000004,-7.800000000000001,0.0,0.0,0.0,0.14,811.0,202.0,1.8,68.96586099123645 +2018-02-01 22:30:00-07:00,2018,2,1,22,30,-2.7,-7.800000000000001,0.0,0.0,0.0,0.14,811.0,216.0,1.6,67.94970316803919 +2018-02-01 23:30:00-07:00,2018,2,1,23,30,-2.5,-7.7,0.0,0.0,0.0,0.14,811.0,226.0,1.5,67.47020562018012 +2018-02-02 00:30:00-07:00,2018,2,2,0,30,-2.2,-7.5,0.0,0.0,0.0,0.14,811.0,234.0,1.5,67.01783509094244 +2018-02-02 01:30:00-07:00,2018,2,2,1,30,-2.0,-7.5,0.0,0.0,0.0,0.14,811.0,241.0,1.3,66.03606707637287 +2018-02-02 02:30:00-07:00,2018,2,2,2,30,-1.7000000000000002,-7.4,0.0,0.0,0.0,0.14,811.0,239.0,1.0,65.09372445872071 +2018-02-02 03:30:00-07:00,2018,2,2,3,30,-1.4,-7.300000000000001,0.0,0.0,0.0,0.14,811.0,231.0,0.8,64.16792869173939 +2018-02-02 04:30:00-07:00,2018,2,2,4,30,-1.3,-7.300000000000001,0.0,0.0,0.0,0.14,811.0,226.0,0.8,63.69909786305553 +2018-02-02 05:30:00-07:00,2018,2,2,5,30,-1.3,-7.2,0.0,0.0,0.0,0.14,811.0,226.0,0.9,64.1917762928283 +2018-02-02 06:30:00-07:00,2018,2,2,6,30,-0.9,-7.2,0.0,0.0,0.0,0.14,811.0,229.0,1.3,62.339992022817846 +2018-02-02 07:30:00-07:00,2018,2,2,7,30,1.0,-6.800000000000001,25.0,5.0,25.0,0.14,811.0,226.0,2.0,56.01532745122309 +2018-02-02 08:30:00-07:00,2018,2,2,8,30,4.1000000000000005,-5.7,103.0,89.0,123.0,0.14,810.0,219.0,2.5,48.885949635308506 +2018-02-02 09:30:00-07:00,2018,2,2,9,30,7.2,-4.7,60.0,808.0,361.0,0.14,810.0,228.0,2.6,42.542211999441534 +2018-02-02 10:30:00-07:00,2018,2,2,10,30,9.5,-3.9,103.0,754.0,464.0,0.14,809.0,248.0,2.6,38.65670253499847 +2018-02-02 11:30:00-07:00,2018,2,2,11,30,10.9,-3.3000000000000003,120.0,769.0,535.0,0.14,808.0,267.0,2.7,36.819827093737906 +2018-02-02 12:30:00-07:00,2018,2,2,12,30,11.4,-2.8000000000000003,127.0,746.0,539.0,0.14,808.0,286.0,2.8000000000000003,36.96883424748842 +2018-02-02 13:30:00-07:00,2018,2,2,13,30,11.0,-2.2,271.0,140.0,343.0,0.14,807.0,299.0,2.8000000000000003,39.68621764164706 +2018-02-02 14:30:00-07:00,2018,2,2,14,30,9.5,-1.0,94.0,1.0,94.0,0.14,807.0,311.0,2.1,47.91065218928527 +2018-02-02 15:30:00-07:00,2018,2,2,15,30,6.9,-0.6000000000000001,108.0,12.0,112.0,0.14,807.0,322.0,1.3,58.8495607742606 +2018-02-02 16:30:00-07:00,2018,2,2,16,30,4.3,-1.5,60.0,3.0,60.0,0.14,808.0,321.0,1.0,65.97699104644308 +2018-02-02 17:30:00-07:00,2018,2,2,17,30,3.2,-2.2,0.0,0.0,0.0,0.14,808.0,305.0,1.0,67.70271944677239 +2018-02-02 18:30:00-07:00,2018,2,2,18,30,2.9000000000000004,-2.4000000000000004,0.0,0.0,0.0,0.14,808.0,281.0,1.1,68.13996214603549 +2018-02-02 19:30:00-07:00,2018,2,2,19,30,2.7,-2.6,0.0,0.0,0.0,0.14,808.0,265.0,1.5,68.09694182465076 +2018-02-02 20:30:00-07:00,2018,2,2,20,30,2.6,-2.7,0.0,0.0,0.0,0.14,807.0,263.0,1.8,68.07540202305557 +2018-02-02 21:30:00-07:00,2018,2,2,21,30,2.5,-2.6,0.0,0.0,0.0,0.14,807.0,265.0,1.9,69.07070459848207 +2018-02-02 22:30:00-07:00,2018,2,2,22,30,2.2,-2.7,0.0,0.0,0.0,0.14,806.0,270.0,2.0,70.03947520542177 +2018-02-02 23:30:00-07:00,2018,2,2,23,30,2.0,-2.8000000000000003,0.0,0.0,0.0,0.14,805.0,276.0,2.3000000000000003,70.52001293311066 +2018-02-03 00:30:00-07:00,2018,2,3,0,30,1.7000000000000002,-3.0,0.0,0.0,0.0,0.14,805.0,283.0,2.7,70.98499800086347 +2018-02-03 01:30:00-07:00,2018,2,3,1,30,1.5,-3.4000000000000004,0.0,0.0,0.0,0.14,805.0,287.0,3.3000000000000003,69.89516276562834 +2018-02-03 02:30:00-07:00,2018,2,3,2,30,1.6,-3.9,0.0,0.0,0.0,0.14,804.0,287.0,3.8,66.85075095622915 +2018-02-03 03:30:00-07:00,2018,2,3,3,30,1.9,-4.4,0.0,0.0,0.0,0.14,804.0,284.0,4.0,63.02216904304729 +2018-02-03 04:30:00-07:00,2018,2,3,4,30,2.1,-4.5,0.0,0.0,0.0,0.14,804.0,284.0,3.9,61.66358909470621 +2018-02-03 05:30:00-07:00,2018,2,3,5,30,2.1,-4.2,0.0,0.0,0.0,0.14,804.0,290.0,3.6,63.07027071482451 +2018-02-03 06:30:00-07:00,2018,2,3,6,30,2.3000000000000003,-3.9,0.0,0.0,0.0,0.14,805.0,298.0,3.2,63.593042588974875 +2018-02-03 07:30:00-07:00,2018,2,3,7,30,3.9,-3.9,21.0,12.0,22.0,0.14,805.0,302.0,3.3000000000000003,56.79094337400782 +2018-02-03 08:30:00-07:00,2018,2,3,8,30,6.1000000000000005,-3.5,45.0,732.0,214.0,0.14,805.0,300.0,4.2,50.20481146928326 +2018-02-03 09:30:00-07:00,2018,2,3,9,30,7.7,-4.2,139.0,370.0,278.0,0.14,805.0,299.0,5.0,42.69094163790539 +2018-02-03 10:30:00-07:00,2018,2,3,10,30,8.700000000000001,-5.1000000000000005,131.0,667.0,453.0,0.14,804.0,300.0,5.4,37.273618111744334 +2018-02-03 11:30:00-07:00,2018,2,3,11,30,9.1,-5.5,71.0,954.0,590.0,0.14,803.0,300.0,5.6000000000000005,35.1975360072089 +2018-02-03 12:30:00-07:00,2018,2,3,12,30,9.1,-5.7,179.0,592.0,509.0,0.14,803.0,296.0,5.9,34.666717740638845 +2018-02-03 13:30:00-07:00,2018,2,3,13,30,9.4,-5.6000000000000005,181.0,54.0,209.0,0.14,802.0,295.0,6.300000000000001,34.233053662259486 +2018-02-03 14:30:00-07:00,2018,2,3,14,30,8.9,-5.4,195.0,75.0,228.0,0.14,802.0,299.0,6.4,35.94719111270864 +2018-02-03 15:30:00-07:00,2018,2,3,15,30,7.300000000000001,-5.1000000000000005,86.0,558.0,257.0,0.14,803.0,302.0,5.5,40.99480497958176 +2018-02-03 16:30:00-07:00,2018,2,3,16,30,4.9,-4.7,73.0,7.0,74.0,0.14,803.0,300.0,4.3,49.86352730511883 +2018-02-03 17:30:00-07:00,2018,2,3,17,30,3.2,-4.5,0.0,0.0,0.0,0.14,804.0,296.0,3.7,57.03322231215903 +2018-02-03 18:30:00-07:00,2018,2,3,18,30,2.8000000000000003,-4.3,0.0,0.0,0.0,0.14,804.0,297.0,3.5,59.559869447409184 +2018-02-03 19:30:00-07:00,2018,2,3,19,30,2.4000000000000004,-4.1000000000000005,0.0,0.0,0.0,0.14,805.0,296.0,3.1,62.20262239200909 +2018-02-03 20:30:00-07:00,2018,2,3,20,30,1.9,-4.3,0.0,0.0,0.0,0.14,805.0,289.0,2.8000000000000003,63.49779491499006 +2018-02-03 21:30:00-07:00,2018,2,3,21,30,1.7000000000000002,-4.4,0.0,0.0,0.0,0.14,805.0,284.0,2.8000000000000003,63.929305394194664 +2018-02-03 22:30:00-07:00,2018,2,3,22,30,1.7000000000000002,-4.3,0.0,0.0,0.0,0.14,805.0,285.0,2.8000000000000003,64.41177738908968 +2018-02-03 23:30:00-07:00,2018,2,3,23,30,1.3,-4.2,0.0,0.0,0.0,0.14,805.0,291.0,2.4000000000000004,66.78387392562938 +2018-02-04 00:30:00-07:00,2018,2,4,0,30,0.8,-4.1000000000000005,0.0,0.0,0.0,0.14,805.0,295.0,2.1,69.7498941148914 +2018-02-04 01:30:00-07:00,2018,2,4,1,30,0.4,-4.0,0.0,0.0,0.0,0.14,805.0,294.0,2.2,72.33300741849081 +2018-02-04 02:30:00-07:00,2018,2,4,2,30,-0.1,-4.0,0.0,0.0,0.0,0.14,805.0,291.0,2.5,75.00054235125127 +2018-02-04 03:30:00-07:00,2018,2,4,3,30,-0.3,-4.3,0.0,0.0,0.0,0.14,805.0,287.0,2.8000000000000003,74.40364261185827 +2018-02-04 04:30:00-07:00,2018,2,4,4,30,-0.5,-4.800000000000001,0.0,0.0,0.0,0.14,805.0,283.0,2.9000000000000004,72.70428343953884 +2018-02-04 05:30:00-07:00,2018,2,4,5,30,-0.8,-5.2,0.0,0.0,0.0,0.14,805.0,286.0,2.7,72.0985429477903 +2018-02-04 06:30:00-07:00,2018,2,4,6,30,-1.1,-5.5,0.0,0.0,0.0,0.14,806.0,304.0,2.5,72.03949614857301 +2018-02-04 07:30:00-07:00,2018,2,4,7,30,-0.5,-5.800000000000001,5.0,0.0,5.0,0.14,807.0,327.0,2.4000000000000004,67.39774983226636 +2018-02-04 08:30:00-07:00,2018,2,4,8,30,0.7000000000000001,-5.9,10.0,0.0,10.0,0.14,808.0,333.0,2.4000000000000004,61.31631364542489 +2018-02-04 09:30:00-07:00,2018,2,4,9,30,2.3000000000000003,-5.800000000000001,39.0,0.0,39.0,0.14,808.0,321.0,2.4000000000000004,55.09437005687346 +2018-02-04 10:30:00-07:00,2018,2,4,10,30,4.5,-5.300000000000001,98.0,0.0,98.0,0.14,807.0,307.0,2.8000000000000003,49.001391126582824 +2018-02-04 11:30:00-07:00,2018,2,4,11,30,6.6000000000000005,-4.6000000000000005,175.0,0.0,175.0,0.14,807.0,302.0,3.4000000000000004,44.664390031678636 +2018-02-04 12:30:00-07:00,2018,2,4,12,30,8.1,-3.9,243.0,6.0,246.0,0.14,806.0,303.0,3.8,42.49031314196122 +2018-02-04 13:30:00-07:00,2018,2,4,13,30,8.9,-3.3000000000000003,166.0,594.0,477.0,0.14,806.0,305.0,3.9,42.09568037938849 +2018-02-04 14:30:00-07:00,2018,2,4,14,30,8.700000000000001,-2.8000000000000003,161.0,507.0,383.0,0.14,805.0,310.0,3.4000000000000004,44.2847341143399 +2018-02-04 15:30:00-07:00,2018,2,4,15,30,6.800000000000001,-2.5,98.0,15.0,103.0,0.14,805.0,316.0,2.1,51.53605099448234 +2018-02-04 16:30:00-07:00,2018,2,4,16,30,3.5,-2.3000000000000003,39.0,2.0,39.0,0.14,805.0,314.0,1.0,65.79673270922554 +2018-02-04 17:30:00-07:00,2018,2,4,17,30,1.4,-3.0,1.0,0.0,1.0,0.14,806.0,278.0,0.8,72.52630919175688 +2018-02-04 18:30:00-07:00,2018,2,4,18,30,0.5,-3.3000000000000003,0.0,0.0,0.0,0.14,805.0,239.0,1.2000000000000002,75.66975865487531 +2018-02-04 19:30:00-07:00,2018,2,4,19,30,0.0,-3.4000000000000004,0.0,0.0,0.0,0.14,805.0,232.0,1.7000000000000002,77.87520364258431 +2018-02-04 20:30:00-07:00,2018,2,4,20,30,0.2,-3.5,0.0,0.0,0.0,0.14,804.0,233.0,1.9,76.18415388647233 +2018-02-04 21:30:00-07:00,2018,2,4,21,30,0.9,-3.7,0.0,0.0,0.0,0.14,804.0,238.0,1.9,71.3556061186342 +2018-02-04 22:30:00-07:00,2018,2,4,22,30,1.6,-3.8,0.0,0.0,0.0,0.14,803.0,245.0,2.0,67.3531560087606 +2018-02-04 23:30:00-07:00,2018,2,4,23,30,2.1,-3.8,0.0,0.0,0.0,0.14,802.0,251.0,2.1,64.99017011523023 +2018-02-05 00:30:00-07:00,2018,2,5,0,30,2.3000000000000003,-3.8,0.0,0.0,0.0,0.14,802.0,256.0,2.3000000000000003,64.0709649076556 +2018-02-05 01:30:00-07:00,2018,2,5,1,30,2.2,-3.8,0.0,0.0,0.0,0.14,801.0,261.0,2.7,64.52874338172718 +2018-02-05 02:30:00-07:00,2018,2,5,2,30,2.2,-3.7,0.0,0.0,0.0,0.14,800.0,270.0,3.3000000000000003,65.01329103423487 +2018-02-05 03:30:00-07:00,2018,2,5,3,30,2.3000000000000003,-3.5,0.0,0.0,0.0,0.14,800.0,276.0,4.1000000000000005,65.52393276670712 +2018-02-05 04:30:00-07:00,2018,2,5,4,30,2.4000000000000004,-3.3000000000000003,0.0,0.0,0.0,0.14,800.0,279.0,4.800000000000001,66.03732192517926 +2018-02-05 05:30:00-07:00,2018,2,5,5,30,2.4000000000000004,-3.2,0.0,0.0,0.0,0.14,800.0,281.0,5.0,66.53112401849545 +2018-02-05 06:30:00-07:00,2018,2,5,6,30,3.1,-3.2,0.0,0.0,0.0,0.14,801.0,285.0,5.0,63.30954355404799 +2018-02-05 07:30:00-07:00,2018,2,5,7,30,4.5,-3.2,20.0,0.0,20.0,0.14,802.0,297.0,5.1000000000000005,57.37511378832467 +2018-02-05 08:30:00-07:00,2018,2,5,8,30,5.7,-3.6,50.0,611.0,196.0,0.14,803.0,307.0,5.2,51.228257541366155 +2018-02-05 09:30:00-07:00,2018,2,5,9,30,6.5,-4.3,70.0,825.0,386.0,0.14,804.0,310.0,5.1000000000000005,45.99936538694996 +2018-02-05 10:30:00-07:00,2018,2,5,10,30,7.1000000000000005,-5.0,76.0,884.0,510.0,0.14,804.0,308.0,4.800000000000001,41.87501918009461 +2018-02-05 11:30:00-07:00,2018,2,5,11,30,7.6,-5.800000000000001,146.0,775.0,574.0,0.14,804.0,304.0,4.5,38.08595445604397 +2018-02-05 12:30:00-07:00,2018,2,5,12,30,7.800000000000001,-6.4,53.0,1017.0,628.0,0.14,803.0,301.0,4.2,35.889265320384006 +2018-02-05 13:30:00-07:00,2018,2,5,13,30,7.6,-6.7,54.0,997.0,580.0,0.14,803.0,301.0,3.9,35.55523507510878 +2018-02-05 14:30:00-07:00,2018,2,5,14,30,6.800000000000001,-6.800000000000001,53.0,949.0,473.0,0.14,803.0,307.0,3.2,37.268188674137164 +2018-02-05 15:30:00-07:00,2018,2,5,15,30,4.800000000000001,-6.5,143.0,253.0,223.0,0.14,804.0,330.0,2.1,43.793911908496696 +2018-02-05 16:30:00-07:00,2018,2,5,16,30,1.7000000000000002,-5.7,55.0,365.0,112.0,0.14,804.0,207.0,1.3,57.94296622448192 +2018-02-05 17:30:00-07:00,2018,2,5,17,30,-1.2000000000000002,-6.9,1.0,0.0,1.0,0.14,805.0,93.0,1.3,65.21066358390064 +2018-02-05 18:30:00-07:00,2018,2,5,18,30,-3.0,-7.4,0.0,0.0,0.0,0.14,806.0,132.0,1.6,71.66147948078579 +2018-02-05 19:30:00-07:00,2018,2,5,19,30,-4.0,-7.6,0.0,0.0,0.0,0.14,806.0,154.0,1.8,76.03282420008681 +2018-02-05 20:30:00-07:00,2018,2,5,20,30,-4.1000000000000005,-7.800000000000001,0.0,0.0,0.0,0.14,806.0,172.0,1.9,75.42935353744994 +2018-02-05 21:30:00-07:00,2018,2,5,21,30,-3.6,-7.6,0.0,0.0,0.0,0.14,805.0,183.0,1.9,73.79040748505199 +2018-02-05 22:30:00-07:00,2018,2,5,22,30,-3.2,-7.5,0.0,0.0,0.0,0.14,805.0,190.0,1.7000000000000002,72.17664716044636 +2018-02-05 23:30:00-07:00,2018,2,5,23,30,-2.9000000000000004,-7.4,0.0,0.0,0.0,0.14,804.0,199.0,1.5,71.13092270743351 +2018-02-06 00:30:00-07:00,2018,2,6,0,30,-2.5,-7.300000000000001,0.0,0.0,0.0,0.14,804.0,216.0,1.3,69.58648782882413 +2018-02-06 01:30:00-07:00,2018,2,6,1,30,-2.3000000000000003,-6.9,0.0,0.0,0.0,0.14,804.0,237.0,1.1,70.70777571313879 +2018-02-06 02:30:00-07:00,2018,2,6,2,30,-2.5,-6.7,0.0,0.0,0.0,0.14,803.0,254.0,0.9,72.87173571805731 +2018-02-06 03:30:00-07:00,2018,2,6,3,30,-2.8000000000000003,-7.0,0.0,0.0,0.0,0.14,803.0,270.0,0.9,72.81361330489652 +2018-02-06 04:30:00-07:00,2018,2,6,4,30,-3.3000000000000003,-7.4,0.0,0.0,0.0,0.14,803.0,284.0,1.0,73.27973791863721 +2018-02-06 05:30:00-07:00,2018,2,6,5,30,-3.8,-7.9,0.0,0.0,0.0,0.14,803.0,292.0,1.2000000000000002,73.18364446524943 +2018-02-06 06:30:00-07:00,2018,2,6,6,30,-3.8,-8.4,0.0,0.0,0.0,0.14,803.0,293.0,1.6,70.39713765719871 +2018-02-06 07:30:00-07:00,2018,2,6,7,30,-2.1,-8.700000000000001,23.0,2.0,23.0,0.14,803.0,294.0,2.2,60.60937321608431 +2018-02-06 08:30:00-07:00,2018,2,6,8,30,0.6000000000000001,-8.0,78.0,14.0,81.0,0.14,803.0,314.0,2.5,52.55541271968973 +2018-02-06 09:30:00-07:00,2018,2,6,9,30,2.6,-7.4,188.0,175.0,256.0,0.14,803.0,337.0,2.9000000000000004,47.70919168918567 +2018-02-06 10:30:00-07:00,2018,2,6,10,30,4.1000000000000005,-7.5,197.0,47.0,220.0,0.14,804.0,347.0,3.2,42.58767009879535 +2018-02-06 11:30:00-07:00,2018,2,6,11,30,5.1000000000000005,-7.9,90.0,905.0,594.0,0.14,804.0,349.0,3.3000000000000003,38.50460524836774 +2018-02-06 12:30:00-07:00,2018,2,6,12,30,5.4,-8.4,62.0,1007.0,636.0,0.14,804.0,346.0,3.3000000000000003,36.27443104855741 +2018-02-06 13:30:00-07:00,2018,2,6,13,30,5.2,-8.8,117.0,805.0,545.0,0.14,804.0,340.0,3.2,35.65286935523501 +2018-02-06 14:30:00-07:00,2018,2,6,14,30,4.7,-9.1,126.0,659.0,420.0,0.14,804.0,334.0,3.0,36.0607493033369 +2018-02-06 15:30:00-07:00,2018,2,6,15,30,3.2,-9.0,74.0,96.0,105.0,0.14,805.0,333.0,2.3000000000000003,40.38088761537843 +2018-02-06 16:30:00-07:00,2018,2,6,16,30,0.6000000000000001,-7.7,34.0,676.0,142.0,0.14,806.0,336.0,1.4,53.79068483643814 +2018-02-06 17:30:00-07:00,2018,2,6,17,30,-1.2000000000000002,-8.200000000000001,4.0,42.0,3.0,0.15,807.0,331.0,1.0,58.980429924260726 +2018-02-06 18:30:00-07:00,2018,2,6,18,30,-1.9,-8.700000000000001,0.0,0.0,0.0,0.15,808.0,312.0,1.0,59.72221617496938 +2018-02-06 19:30:00-07:00,2018,2,6,19,30,-2.6,-9.2,0.0,0.0,0.0,0.15,809.0,289.0,1.2000000000000002,60.48137226976885 +2018-02-06 20:30:00-07:00,2018,2,6,20,30,-3.4000000000000004,-9.7,0.0,0.0,0.0,0.15,809.0,277.0,1.4,61.7168682460687 +2018-02-06 21:30:00-07:00,2018,2,6,21,30,-4.1000000000000005,-10.2,0.0,0.0,0.0,0.15,809.0,271.0,1.6,62.52102795367599 +2018-02-06 22:30:00-07:00,2018,2,6,22,30,-4.4,-10.5,0.0,0.0,0.0,0.15,810.0,266.0,1.7000000000000002,62.44622821593321 +2018-02-06 23:30:00-07:00,2018,2,6,23,30,-4.5,-10.7,0.0,0.0,0.0,0.15,810.0,262.0,1.6,61.92809655562398 +2018-02-07 00:30:00-07:00,2018,2,7,0,30,-4.3,-10.7,0.0,0.0,0.0,0.15,810.0,259.0,1.5,61.003450260787446 +2018-02-07 01:30:00-07:00,2018,2,7,1,30,-4.1000000000000005,-10.6,0.0,0.0,0.0,0.15,811.0,257.0,1.4,60.572673452172864 +2018-02-07 02:30:00-07:00,2018,2,7,2,30,-4.0,-10.6,0.0,0.0,0.0,0.15,811.0,252.0,1.4,60.12009210561681 +2018-02-07 03:30:00-07:00,2018,2,7,3,30,-4.1000000000000005,-10.8,0.0,0.0,0.0,0.15,811.0,242.0,1.6,59.61894647824942 +2018-02-07 04:30:00-07:00,2018,2,7,4,30,-4.2,-10.9,0.0,0.0,0.0,0.15,811.0,237.0,1.8,59.59275365540966 +2018-02-07 05:30:00-07:00,2018,2,7,5,30,-4.2,-11.1,0.0,0.0,0.0,0.15,811.0,236.0,2.1,58.65204716445436 +2018-02-07 06:30:00-07:00,2018,2,7,6,30,-3.5,-11.3,0.0,0.0,0.0,0.15,811.0,237.0,2.7,54.777133116520986 +2018-02-07 07:30:00-07:00,2018,2,7,7,30,-1.2000000000000002,-10.9,24.0,481.0,60.0,0.15,812.0,242.0,3.6,47.70579969547706 +2018-02-07 08:30:00-07:00,2018,2,7,8,30,1.6,-10.2,58.0,714.0,234.0,0.15,812.0,259.0,4.1000000000000005,41.17379655136414 +2018-02-07 09:30:00-07:00,2018,2,7,9,30,4.0,-9.6,132.0,565.0,353.0,0.15,812.0,282.0,4.2,36.41496386074246 +2018-02-07 10:30:00-07:00,2018,2,7,10,30,5.9,-8.9,206.0,485.0,448.0,0.15,811.0,296.0,4.3,33.699982231005905 +2018-02-07 11:30:00-07:00,2018,2,7,11,30,7.1000000000000005,-8.0,201.0,526.0,496.0,0.15,811.0,300.0,4.4,33.28152530492895 +2018-02-07 12:30:00-07:00,2018,2,7,12,30,7.9,-7.0,235.0,397.0,463.0,0.15,811.0,301.0,4.7,34.042836913142914 +2018-02-07 13:30:00-07:00,2018,2,7,13,30,8.0,-6.0,61.0,970.0,581.0,0.15,811.0,301.0,5.0,36.50245323161943 +2018-02-07 14:30:00-07:00,2018,2,7,14,30,7.6,-5.300000000000001,80.0,873.0,474.0,0.15,811.0,296.0,4.800000000000001,39.56004312156 +2018-02-07 15:30:00-07:00,2018,2,7,15,30,6.1000000000000005,-4.800000000000001,71.0,755.0,315.0,0.15,811.0,290.0,3.6,45.53729272915438 +2018-02-07 16:30:00-07:00,2018,2,7,16,30,3.3000000000000003,-4.2,86.0,66.0,97.0,0.15,812.0,288.0,2.3000000000000003,57.923783202476166 +2018-02-07 17:30:00-07:00,2018,2,7,17,30,1.5,-5.0,1.0,0.0,1.0,0.15,813.0,282.0,1.9,61.98225594209661 +2018-02-07 18:30:00-07:00,2018,2,7,18,30,0.9,-5.4,0.0,0.0,0.0,0.15,813.0,271.0,2.0,62.78041693380983 +2018-02-07 19:30:00-07:00,2018,2,7,19,30,0.4,-5.9,0.0,0.0,0.0,0.15,813.0,266.0,2.2,62.65875954730196 +2018-02-07 20:30:00-07:00,2018,2,7,20,30,0.2,-6.2,0.0,0.0,0.0,0.15,814.0,265.0,2.2,62.13384767260275 +2018-02-07 21:30:00-07:00,2018,2,7,21,30,0.2,-6.300000000000001,0.0,0.0,0.0,0.15,814.0,264.0,2.4000000000000004,61.66096811893209 +2018-02-07 22:30:00-07:00,2018,2,7,22,30,0.1,-6.300000000000001,0.0,0.0,0.0,0.15,814.0,261.0,2.8000000000000003,62.109196704601175 +2018-02-07 23:30:00-07:00,2018,2,7,23,30,-0.1,-6.5,0.0,0.0,0.0,0.15,813.0,260.0,3.1,62.059831601122234 +2018-02-08 00:30:00-07:00,2018,2,8,0,30,-0.2,-6.6000000000000005,0.0,0.0,0.0,0.15,813.0,263.0,3.2,62.03511742846935 +2018-02-08 01:30:00-07:00,2018,2,8,1,30,-0.3,-6.6000000000000005,0.0,0.0,0.0,0.15,813.0,263.0,3.1,62.48755831825753 +2018-02-08 02:30:00-07:00,2018,2,8,2,30,-0.4,-6.7,0.0,0.0,0.0,0.15,812.0,262.0,3.1,62.46301677869306 +2018-02-08 03:30:00-07:00,2018,2,8,3,30,-0.5,-6.800000000000001,0.0,0.0,0.0,0.15,812.0,262.0,3.0,62.438454127155964 +2018-02-08 04:30:00-07:00,2018,2,8,4,30,-0.5,-6.9,0.0,0.0,0.0,0.15,812.0,263.0,3.0,61.96084820365465 +2018-02-08 05:30:00-07:00,2018,2,8,5,30,-0.5,-6.9,0.0,0.0,0.0,0.15,812.0,262.0,3.0,61.96084820365465 +2018-02-08 06:30:00-07:00,2018,2,8,6,30,0.3,-6.9,0.0,0.0,0.0,0.15,812.0,263.0,3.1,58.465663624883696 +2018-02-08 07:30:00-07:00,2018,2,8,7,30,2.8000000000000003,-6.300000000000001,40.0,16.0,41.0,0.15,812.0,265.0,3.7,51.18275527326995 +2018-02-08 08:30:00-07:00,2018,2,8,8,30,5.800000000000001,-5.2,127.0,108.0,154.0,0.15,812.0,271.0,4.6000000000000005,45.10625630890571 +2018-02-08 09:30:00-07:00,2018,2,8,9,30,8.200000000000001,-4.5,137.0,12.0,142.0,0.15,812.0,281.0,5.1000000000000005,40.34356909485901 +2018-02-08 10:30:00-07:00,2018,2,8,10,30,10.0,-4.4,220.0,41.0,241.0,0.15,811.0,288.0,5.1000000000000005,36.00556523724889 +2018-02-08 11:30:00-07:00,2018,2,8,11,30,11.1,-4.1000000000000005,286.0,169.0,382.0,0.15,810.0,293.0,5.1000000000000005,34.22439275170222 +2018-02-08 12:30:00-07:00,2018,2,8,12,30,11.6,-3.8,258.0,357.0,465.0,0.15,810.0,295.0,5.1000000000000005,33.86299995496486 +2018-02-08 13:30:00-07:00,2018,2,8,13,30,11.5,-3.5,256.0,254.0,393.0,0.15,809.0,294.0,5.0,34.86055504005412 +2018-02-08 14:30:00-07:00,2018,2,8,14,30,10.9,-3.3000000000000003,225.0,136.0,287.0,0.15,808.0,287.0,4.6000000000000005,36.819827093737906 +2018-02-08 15:30:00-07:00,2018,2,8,15,30,8.8,-3.0,163.0,170.0,219.0,0.15,808.0,279.0,3.3000000000000003,43.33788441951712 +2018-02-08 16:30:00-07:00,2018,2,8,16,30,5.6000000000000005,-2.2,81.0,33.0,87.0,0.15,809.0,271.0,2.3000000000000003,57.23763994915693 +2018-02-08 17:30:00-07:00,2018,2,8,17,30,3.8,-2.8000000000000003,2.0,0.0,2.0,0.15,809.0,261.0,2.4000000000000004,62.0798214253299 +2018-02-08 18:30:00-07:00,2018,2,8,18,30,3.2,-3.2,0.0,0.0,0.0,0.15,809.0,255.0,2.8000000000000003,62.86368050604908 +2018-02-08 19:30:00-07:00,2018,2,8,19,30,2.8000000000000003,-3.7,0.0,0.0,0.0,0.15,808.0,252.0,3.1,62.30002518079584 +2018-02-08 20:30:00-07:00,2018,2,8,20,30,2.3000000000000003,-4.3,0.0,0.0,0.0,0.15,808.0,251.0,3.2,61.71286695143739 +2018-02-08 21:30:00-07:00,2018,2,8,21,30,1.9,-4.9,0.0,0.0,0.0,0.15,807.0,249.0,3.0,60.691199848971976 +2018-02-08 22:30:00-07:00,2018,2,8,22,30,1.5,-5.300000000000001,0.0,0.0,0.0,0.15,807.0,247.0,2.7,60.59063261248967 +2018-02-08 23:30:00-07:00,2018,2,8,23,30,1.1,-5.6000000000000005,0.0,0.0,0.0,0.15,807.0,245.0,2.5,60.951277799880515 +2018-02-09 00:30:00-07:00,2018,2,9,0,30,0.8,-5.800000000000001,0.0,0.0,0.0,0.15,807.0,245.0,2.2,61.341254865701146 +2018-02-09 01:30:00-07:00,2018,2,9,1,30,0.5,-6.0,0.0,0.0,0.0,0.15,807.0,244.0,1.8,61.73542585676592 +2018-02-09 02:30:00-07:00,2018,2,9,2,30,-0.1,-6.0,0.0,0.0,0.0,0.15,807.0,246.0,1.5,64.47630731213972 +2018-02-09 03:30:00-07:00,2018,2,9,3,30,-0.8,-6.1000000000000005,0.0,0.0,0.0,0.15,807.0,253.0,1.2000000000000002,67.33113826267966 +2018-02-09 04:30:00-07:00,2018,2,9,4,30,-1.2000000000000002,-6.2,0.0,0.0,0.0,0.15,807.0,252.0,1.1,68.80230634371752 +2018-02-09 05:30:00-07:00,2018,2,9,5,30,-1.6,-6.300000000000001,0.0,0.0,0.0,0.15,807.0,238.0,1.3,70.31197937457982 +2018-02-09 06:30:00-07:00,2018,2,9,6,30,-1.5,-6.5,0.0,0.0,0.0,0.15,806.0,229.0,1.8,68.73771216726243 +2018-02-09 07:30:00-07:00,2018,2,9,7,30,-0.1,-6.6000000000000005,30.0,2.0,30.0,0.15,806.0,225.0,2.6,61.5863208685527 +2018-02-09 08:30:00-07:00,2018,2,9,8,30,2.4000000000000004,-6.0,64.0,34.0,73.0,0.15,805.0,224.0,3.3000000000000003,53.8767701121592 +2018-02-09 09:30:00-07:00,2018,2,9,9,30,5.800000000000001,-5.0,17.0,0.0,17.0,0.15,804.0,233.0,3.4000000000000004,45.7940035901798 +2018-02-09 10:30:00-07:00,2018,2,9,10,30,8.8,-4.4,196.0,24.0,208.0,0.15,804.0,252.0,3.5,39.03023069973065 +2018-02-09 11:30:00-07:00,2018,2,9,11,30,10.4,-4.4,276.0,243.0,415.0,0.15,802.0,270.0,3.8,35.05630920836191 +2018-02-09 12:30:00-07:00,2018,2,9,12,30,10.9,-4.3,205.0,558.0,531.0,0.15,802.0,279.0,4.1000000000000005,34.16477704482212 +2018-02-09 13:30:00-07:00,2018,2,9,13,30,10.5,-4.2,153.0,710.0,540.0,0.15,801.0,282.0,4.1000000000000005,35.35077276051516 +2018-02-09 14:30:00-07:00,2018,2,9,14,30,9.2,-3.9,71.0,891.0,481.0,0.15,801.0,284.0,3.5,39.44446778725526 +2018-02-09 15:30:00-07:00,2018,2,9,15,30,6.800000000000001,-3.4000000000000004,49.0,853.0,332.0,0.15,801.0,295.0,2.4000000000000004,48.20200316386454 +2018-02-09 16:30:00-07:00,2018,2,9,16,30,3.2,-3.0,23.0,0.0,23.0,0.15,802.0,324.0,1.6,63.80614895498 +2018-02-09 17:30:00-07:00,2018,2,9,17,30,0.0,-3.5,3.0,0.0,3.0,0.8,803.0,184.0,1.6,77.29624262485414 +2018-02-09 18:30:00-07:00,2018,2,9,18,30,-2.0,-4.1000000000000005,0.0,0.0,0.0,0.8,804.0,33.0,2.2,85.53981957393279 +2018-02-09 19:30:00-07:00,2018,2,9,19,30,-3.7,-5.5,0.0,0.0,0.0,0.8,805.0,47.0,2.6,87.31733917992827 +2018-02-09 20:30:00-07:00,2018,2,9,20,30,-5.0,-7.300000000000001,0.0,0.0,0.0,0.8,805.0,60.0,2.5,83.8978263583673 +2018-02-09 21:30:00-07:00,2018,2,9,21,30,-5.7,-8.8,0.0,0.0,0.0,0.8,805.0,77.0,2.1,78.75280948541722 +2018-02-09 22:30:00-07:00,2018,2,9,22,30,-6.0,-9.6,0.0,0.0,0.0,0.8,804.0,100.0,1.9,75.67787965909015 +2018-02-09 23:30:00-07:00,2018,2,9,23,30,-6.0,-9.9,0.0,0.0,0.0,0.8,803.0,125.0,1.9,73.91189146854006 +2018-02-10 00:30:00-07:00,2018,2,10,0,30,-5.7,-9.9,0.0,0.0,0.0,0.8,802.0,144.0,2.1,72.24272664122738 +2018-02-10 01:30:00-07:00,2018,2,10,1,30,-5.5,-9.6,0.0,0.0,0.0,0.8,802.0,150.0,2.3000000000000003,72.85330036487183 +2018-02-10 02:30:00-07:00,2018,2,10,2,30,-5.5,-9.6,0.0,0.0,0.0,0.8,801.0,152.0,2.3000000000000003,72.85330036487183 +2018-02-10 03:30:00-07:00,2018,2,10,3,30,-5.300000000000001,-9.6,0.0,0.0,0.0,0.8,800.0,154.0,2.2,71.75642559810845 +2018-02-10 04:30:00-07:00,2018,2,10,4,30,-5.1000000000000005,-9.5,0.0,0.0,0.0,0.8,800.0,151.0,2.1,71.23538857132237 +2018-02-10 05:30:00-07:00,2018,2,10,5,30,-5.2,-9.5,0.0,0.0,0.0,0.8,800.0,141.0,2.1,71.77663495946283 +2018-02-10 06:30:00-07:00,2018,2,10,6,30,-5.2,-9.7,0.0,0.0,0.0,0.8,800.0,128.0,2.1,70.65704201253844 +2018-02-10 07:30:00-07:00,2018,2,10,7,30,-4.2,-9.8,24.0,0.0,24.0,0.8,799.0,110.0,2.0,65.01119521804782 +2018-02-10 08:30:00-07:00,2018,2,10,8,30,-2.6,-9.1,35.0,0.0,35.0,0.8,799.0,85.0,2.0,60.95682428063628 +2018-02-10 09:30:00-07:00,2018,2,10,9,30,-1.7000000000000002,-8.3,33.0,0.0,33.0,0.8,799.0,68.0,2.2,60.71138703029517 +2018-02-10 10:30:00-07:00,2018,2,10,10,30,-1.9,-8.0,69.0,0.0,69.0,0.8,799.0,65.0,2.6,63.0636104146899 +2018-02-10 11:30:00-07:00,2018,2,10,11,30,-2.4000000000000004,-8.1,31.0,0.0,31.0,0.8,798.0,64.0,2.8000000000000003,64.92912497499637 +2018-02-10 12:30:00-07:00,2018,2,10,12,30,-3.0,-8.3,130.0,1.0,131.0,0.8,798.0,60.0,3.0,66.83697779008673 +2018-02-10 13:30:00-07:00,2018,2,10,13,30,-3.7,-8.8,98.0,0.0,98.0,0.8,798.0,54.0,3.2,67.72780669568682 +2018-02-10 14:30:00-07:00,2018,2,10,14,30,-4.5,-9.3,137.0,3.0,138.0,0.8,799.0,47.0,3.3000000000000003,69.15854656786233 +2018-02-10 15:30:00-07:00,2018,2,10,15,30,-5.4,-9.9,102.0,4.0,103.0,0.8,800.0,40.0,3.2,70.6153292783353 +2018-02-10 16:30:00-07:00,2018,2,10,16,30,-6.4,-10.7,52.0,0.0,52.0,0.8,801.0,34.0,2.9000000000000004,71.53279393615773 +2018-02-10 17:30:00-07:00,2018,2,10,17,30,-7.0,-11.6,3.0,0.0,3.0,0.8,802.0,21.0,2.3000000000000003,69.71912822433562 +2018-02-10 18:30:00-07:00,2018,2,10,18,30,-7.2,-12.3,0.0,0.0,0.0,0.8,802.0,181.0,1.8,66.93455431945803 +2018-02-10 19:30:00-07:00,2018,2,10,19,30,-7.4,-12.7,0.0,0.0,0.0,0.8,803.0,342.0,1.7000000000000002,65.81796396496611 +2018-02-10 20:30:00-07:00,2018,2,10,20,30,-7.800000000000001,-13.3,0.0,0.0,0.0,0.8,803.0,332.0,1.7000000000000002,64.66597279128734 +2018-02-10 21:30:00-07:00,2018,2,10,21,30,-8.5,-14.1,0.0,0.0,0.0,0.8,803.0,324.0,1.8,63.97092439748089 +2018-02-10 22:30:00-07:00,2018,2,10,22,30,-9.2,-15.0,0.0,0.0,0.0,0.8,804.0,313.0,2.0,62.756173637377415 +2018-02-10 23:30:00-07:00,2018,2,10,23,30,-9.9,-16.0,0.0,0.0,0.0,0.8,804.0,299.0,2.3000000000000003,61.03954899681855 +2018-02-11 00:30:00-07:00,2018,2,11,0,30,-10.4,-17.1,0.0,0.0,0.0,0.8,804.0,285.0,2.7,57.92538355829642 +2018-02-11 01:30:00-07:00,2018,2,11,1,30,-10.6,-18.2,0.0,0.0,0.0,0.8,804.0,272.0,3.0,53.637304121789995 +2018-02-11 02:30:00-07:00,2018,2,11,2,30,-10.7,-19.200000000000003,0.0,0.0,0.0,0.8,804.0,261.0,3.2,49.653240751698206 +2018-02-11 03:30:00-07:00,2018,2,11,3,30,-10.6,-20.0,0.0,0.0,0.0,0.8,804.0,256.0,3.4000000000000004,45.993127864059716 +2018-02-11 04:30:00-07:00,2018,2,11,4,30,-10.5,-20.4,0.0,0.0,0.0,0.8,804.0,254.0,3.8,44.082465512174714 +2018-02-11 05:30:00-07:00,2018,2,11,5,30,-10.5,-20.5,0.0,0.0,0.0,0.8,804.0,252.0,4.2,43.70298850747169 +2018-02-11 06:30:00-07:00,2018,2,11,6,30,-9.8,-20.4,0.0,0.0,0.0,0.8,804.0,252.0,4.6000000000000005,41.711525839209166 +2018-02-11 07:30:00-07:00,2018,2,11,7,30,-7.4,-19.700000000000003,26.0,572.0,77.0,0.8,804.0,253.0,4.7,36.74588964758878 +2018-02-11 08:30:00-07:00,2018,2,11,8,30,-4.0,-17.6,47.0,867.0,274.0,0.8,804.0,261.0,4.3,33.92404433710966 +2018-02-11 09:30:00-07:00,2018,2,11,9,30,-0.8,-15.9,60.0,977.0,459.0,0.8,804.0,279.0,3.6,30.86464654219559 +2018-02-11 10:30:00-07:00,2018,2,11,10,30,1.4,-14.6,69.0,1029.0,601.0,0.8,804.0,295.0,2.8000000000000003,29.30565756010897 +2018-02-11 11:30:00-07:00,2018,2,11,11,30,2.7,-13.4,76.0,1041.0,679.0,0.8,803.0,304.0,2.3000000000000003,29.460866039616736 +2018-02-11 12:30:00-07:00,2018,2,11,12,30,3.4000000000000004,-12.6,206.0,773.0,664.0,0.8,803.0,306.0,1.8,29.91343383270961 +2018-02-11 13:30:00-07:00,2018,2,11,13,30,3.8,-12.1,191.0,730.0,596.0,0.8,802.0,308.0,1.2000000000000002,30.276942094330288 +2018-02-11 14:30:00-07:00,2018,2,11,14,30,3.5,-11.7,180.0,651.0,485.0,0.8,802.0,220.0,0.7000000000000001,31.930907428271727 +2018-02-11 15:30:00-07:00,2018,2,11,15,30,2.0,-10.7,66.0,868.0,362.0,0.8,802.0,124.0,0.5,38.46037568695974 +2018-02-11 16:30:00-07:00,2018,2,11,16,30,-1.1,-9.7,40.0,739.0,172.0,0.8,803.0,126.0,0.9,52.07204264212904 +2018-02-11 17:30:00-07:00,2018,2,11,17,30,-3.6,-13.2,8.0,104.0,8.0,0.8,803.0,150.0,1.3,47.367560058685726 +2018-02-11 18:30:00-07:00,2018,2,11,18,30,-4.5,-13.5,0.0,0.0,0.0,0.8,804.0,181.0,1.4,49.45571162346483 +2018-02-11 19:30:00-07:00,2018,2,11,19,30,-4.9,-13.1,0.0,0.0,0.0,0.8,804.0,208.0,1.4,52.65198116351788 +2018-02-11 20:30:00-07:00,2018,2,11,20,30,-4.800000000000001,-13.2,0.0,0.0,0.0,0.8,804.0,227.0,1.6,51.83410790900538 +2018-02-11 21:30:00-07:00,2018,2,11,21,30,-4.7,-13.5,0.0,0.0,0.0,0.8,804.0,242.0,1.8,50.20659318668134 +2018-02-11 22:30:00-07:00,2018,2,11,22,30,-4.7,-13.5,0.0,0.0,0.0,0.8,803.0,254.0,1.9,50.20659318668134 +2018-02-11 23:30:00-07:00,2018,2,11,23,30,-4.800000000000001,-13.3,0.0,0.0,0.0,0.8,803.0,268.0,2.0,51.41531443487646 +2018-02-12 00:30:00-07:00,2018,2,12,0,30,-5.1000000000000005,-13.4,0.0,0.0,0.0,0.8,803.0,290.0,2.0,52.16889357422606 +2018-02-12 01:30:00-07:00,2018,2,12,1,30,-5.800000000000001,-13.9,0.0,0.0,0.0,0.8,804.0,316.0,2.1,52.820648151092854 +2018-02-12 02:30:00-07:00,2018,2,12,2,30,-6.6000000000000005,-14.3,0.0,0.0,0.0,0.8,804.0,335.0,2.1,54.34235930982111 +2018-02-12 03:30:00-07:00,2018,2,12,3,30,-7.300000000000001,-14.6,0.0,0.0,0.0,0.8,804.0,347.0,1.9,55.95271587340026 +2018-02-12 04:30:00-07:00,2018,2,12,4,30,-7.7,-15.0,0.0,0.0,0.0,0.8,804.0,352.0,1.5,55.84052050594438 +2018-02-12 05:30:00-07:00,2018,2,12,5,30,-7.7,-15.0,0.0,0.0,0.0,0.8,804.0,349.0,1.1,55.84052050594438 +2018-02-12 06:30:00-07:00,2018,2,12,6,30,-7.300000000000001,-14.7,0.0,0.0,0.0,0.8,804.0,278.0,1.0,55.49510967647517 +2018-02-12 07:30:00-07:00,2018,2,12,7,30,-6.1000000000000005,-14.6,26.0,0.0,26.0,0.8,804.0,203.0,1.3,51.03336937944038 +2018-02-12 08:30:00-07:00,2018,2,12,8,30,-4.5,-13.2,72.0,1.0,72.0,0.8,804.0,192.0,1.8,50.675152198934846 +2018-02-12 09:30:00-07:00,2018,2,12,9,30,-2.4000000000000004,-11.6,103.0,1.0,103.0,0.8,803.0,186.0,2.5,49.28250420978811 +2018-02-12 10:30:00-07:00,2018,2,12,10,30,0.2,-9.4,156.0,1.0,157.0,0.8,803.0,183.0,3.2,48.50052277438473 +2018-02-12 11:30:00-07:00,2018,2,12,11,30,2.4000000000000004,-7.300000000000001,178.0,0.0,178.0,0.8,802.0,180.0,3.7,48.766581375827585 +2018-02-12 12:30:00-07:00,2018,2,12,12,30,3.5,-5.9,224.0,0.0,224.0,0.8,802.0,175.0,3.9,50.2218629871937 +2018-02-12 13:30:00-07:00,2018,2,12,13,30,3.6,-5.300000000000001,108.0,0.0,108.0,0.8,802.0,169.0,3.7,52.195376067361 +2018-02-12 14:30:00-07:00,2018,2,12,14,30,2.9000000000000004,-5.0,160.0,2.0,161.0,0.8,803.0,159.0,3.4000000000000004,56.09991053434831 +2018-02-12 15:30:00-07:00,2018,2,12,15,30,1.2000000000000002,-4.9,130.0,28.0,140.0,0.8,803.0,146.0,2.9000000000000004,63.8106888206063 +2018-02-12 16:30:00-07:00,2018,2,12,16,30,-1.3,-5.2,49.0,614.0,162.0,0.8,804.0,131.0,2.1,74.7843532326645 +2018-02-12 17:30:00-07:00,2018,2,12,17,30,-3.2,-6.4,5.0,21.0,5.0,0.15,805.0,121.0,1.6,78.54357208255927 +2018-02-12 18:30:00-07:00,2018,2,12,18,30,-4.1000000000000005,-7.0,0.0,0.0,0.0,0.15,806.0,129.0,1.2000000000000002,80.23122942879147 +2018-02-12 19:30:00-07:00,2018,2,12,19,30,-4.3,-7.2,0.0,0.0,0.0,0.15,806.0,150.0,0.9,80.20143240008521 +2018-02-12 20:30:00-07:00,2018,2,12,20,30,-4.2,-7.2,0.0,0.0,0.0,0.15,806.0,161.0,0.9,79.60119024239279 +2018-02-12 21:30:00-07:00,2018,2,12,21,30,-4.3,-7.2,0.0,0.0,0.0,0.15,806.0,164.0,1.0,80.20143240008521 +2018-02-12 22:30:00-07:00,2018,2,12,22,30,-4.3,-7.4,0.0,0.0,0.0,0.15,806.0,182.0,1.0,78.9745320479875 +2018-02-12 23:30:00-07:00,2018,2,12,23,30,-4.1000000000000005,-7.2,0.0,0.0,0.0,0.15,806.0,208.0,1.0,79.00593719802896 +2018-02-13 00:30:00-07:00,2018,2,13,0,30,-3.8,-7.1000000000000005,0.0,0.0,0.0,0.15,806.0,228.0,1.1,77.84663748475423 +2018-02-13 01:30:00-07:00,2018,2,13,1,30,-3.3000000000000003,-7.0,0.0,0.0,0.0,0.15,806.0,250.0,1.1,75.57230854788659 +2018-02-13 02:30:00-07:00,2018,2,13,2,30,-2.7,-6.9,0.0,0.0,0.0,0.15,806.0,266.0,0.8,72.83300668743016 +2018-02-13 03:30:00-07:00,2018,2,13,3,30,-2.3000000000000003,-6.800000000000001,0.0,0.0,0.0,0.15,806.0,252.0,0.6000000000000001,71.2528046063392 +2018-02-13 04:30:00-07:00,2018,2,13,4,30,-2.1,-6.7,0.0,0.0,0.0,0.15,807.0,220.0,0.6000000000000001,70.7488542784598 +2018-02-13 05:30:00-07:00,2018,2,13,5,30,-1.9,-6.7,0.0,0.0,0.0,0.15,807.0,210.0,0.7000000000000001,69.71328270110364 +2018-02-13 06:30:00-07:00,2018,2,13,6,30,-1.0,-6.800000000000001,0.0,0.0,0.0,0.15,808.0,216.0,0.8,64.758545395009 +2018-02-13 07:30:00-07:00,2018,2,13,7,30,1.6,-6.2,26.0,544.0,79.0,0.15,809.0,221.0,1.1,56.17721975030319 +2018-02-13 08:30:00-07:00,2018,2,13,8,30,4.5,-4.4,42.0,835.0,268.0,0.15,809.0,225.0,1.7000000000000002,52.44688515280286 +2018-02-13 09:30:00-07:00,2018,2,13,9,30,6.6000000000000005,-4.3,49.0,946.0,444.0,0.15,809.0,231.0,2.2,45.68414661587777 +2018-02-13 10:30:00-07:00,2018,2,13,10,30,8.1,-4.4,54.0,998.0,579.0,0.15,809.0,237.0,2.6,40.925194547468514 +2018-02-13 11:30:00-07:00,2018,2,13,11,30,9.0,-4.7,58.0,1021.0,659.0,0.15,809.0,241.0,3.0,37.64671092788406 +2018-02-13 12:30:00-07:00,2018,2,13,12,30,9.5,-4.800000000000001,57.0,1026.0,675.0,0.15,809.0,245.0,3.3000000000000003,36.12742633947306 +2018-02-13 13:30:00-07:00,2018,2,13,13,30,9.4,-4.9,55.0,1014.0,627.0,0.15,809.0,250.0,3.3000000000000003,36.09741658209632 +2018-02-13 14:30:00-07:00,2018,2,13,14,30,8.8,-4.6000000000000005,51.0,980.0,519.0,0.15,809.0,257.0,2.8000000000000003,38.446986620438146 +2018-02-13 15:30:00-07:00,2018,2,13,15,30,6.800000000000001,-3.1,44.0,908.0,361.0,0.15,809.0,267.0,1.9,49.291418314930844 +2018-02-13 16:30:00-07:00,2018,2,13,16,30,3.9,-3.8,33.0,746.0,173.0,0.15,809.0,275.0,1.3,57.217745713263675 +2018-02-13 17:30:00-07:00,2018,2,13,17,30,1.8,-5.6000000000000005,11.0,162.0,12.0,0.15,810.0,271.0,1.3,57.96919842114627 +2018-02-13 18:30:00-07:00,2018,2,13,18,30,0.8,-5.9,0.0,0.0,0.0,0.15,810.0,258.0,1.4,60.87597475031497 +2018-02-13 19:30:00-07:00,2018,2,13,19,30,-0.1,-6.2,0.0,0.0,0.0,0.15,809.0,244.0,1.7000000000000002,63.49985542857561 +2018-02-13 20:30:00-07:00,2018,2,13,20,30,-0.7000000000000001,-6.300000000000001,0.0,0.0,0.0,0.15,809.0,235.0,1.9,65.82849911632094 +2018-02-13 21:30:00-07:00,2018,2,13,21,30,-1.0,-6.6000000000000005,0.0,0.0,0.0,0.15,809.0,231.0,2.2,65.75945392804633 +2018-02-13 22:30:00-07:00,2018,2,13,22,30,-1.1,-6.800000000000001,0.0,0.0,0.0,0.15,809.0,232.0,2.5,65.23398848380677 +2018-02-13 23:30:00-07:00,2018,2,13,23,30,-1.1,-7.0,0.0,0.0,0.0,0.15,808.0,236.0,2.8000000000000003,64.23940820996746 +2018-02-14 00:30:00-07:00,2018,2,14,0,30,-1.0,-7.0,0.0,0.0,0.0,0.15,808.0,240.0,3.2,63.77121389329665 +2018-02-14 01:30:00-07:00,2018,2,14,1,30,-0.6000000000000001,-6.9,0.0,0.0,0.0,0.15,807.0,244.0,3.3000000000000003,62.41387034472912 +2018-02-14 02:30:00-07:00,2018,2,14,2,30,-0.2,-6.6000000000000005,0.0,0.0,0.0,0.15,807.0,245.0,3.2,62.03511742846935 +2018-02-14 03:30:00-07:00,2018,2,14,3,30,0.0,-6.300000000000001,0.0,0.0,0.0,0.15,806.0,243.0,3.0,62.561056454164756 +2018-02-14 04:30:00-07:00,2018,2,14,4,30,0.0,-6.1000000000000005,0.0,0.0,0.0,0.15,806.0,242.0,2.9000000000000004,63.52389097854532 +2018-02-14 05:30:00-07:00,2018,2,14,5,30,0.1,-6.0,0.0,0.0,0.0,0.15,806.0,242.0,2.8000000000000003,63.54790557586437 +2018-02-14 06:30:00-07:00,2018,2,14,6,30,1.0,-5.9,0.0,0.0,0.0,0.15,806.0,244.0,2.9000000000000004,60.00582433891939 +2018-02-14 07:30:00-07:00,2018,2,14,7,30,3.4000000000000004,-5.1000000000000005,48.0,12.0,49.0,0.15,806.0,241.0,3.2,53.744091830262406 +2018-02-14 08:30:00-07:00,2018,2,14,8,30,6.2,-3.9,121.0,322.0,210.0,0.15,805.0,244.0,4.1000000000000005,48.390384195790006 +2018-02-14 09:30:00-07:00,2018,2,14,9,30,8.6,-3.6,134.0,603.0,388.0,0.15,805.0,255.0,5.4,42.00666411150512 +2018-02-14 10:30:00-07:00,2018,2,14,10,30,10.2,-3.6,230.0,377.0,430.0,0.15,805.0,260.0,6.300000000000001,37.72327086533772 +2018-02-14 11:30:00-07:00,2018,2,14,11,30,10.9,-3.9,190.0,600.0,546.0,0.15,804.0,258.0,6.6000000000000005,35.20565854384763 +2018-02-14 12:30:00-07:00,2018,2,14,12,30,10.9,-4.0,123.0,838.0,631.0,0.15,803.0,253.0,6.300000000000001,34.942831303369935 +2018-02-14 13:30:00-07:00,2018,2,14,13,30,10.4,-3.8,143.0,742.0,565.0,0.15,803.0,247.0,5.7,36.670516781526544 +2018-02-14 14:30:00-07:00,2018,2,14,14,30,9.3,-3.3000000000000003,61.0,947.0,518.0,0.15,803.0,242.0,5.0,40.97628921670222 +2018-02-14 15:30:00-07:00,2018,2,14,15,30,7.300000000000001,-2.9000000000000004,142.0,448.0,300.0,0.15,802.0,238.0,4.1000000000000005,48.34556877227834 +2018-02-14 16:30:00-07:00,2018,2,14,16,30,4.9,-3.2,99.0,50.0,109.0,0.15,802.0,233.0,3.5,55.795501993604404 +2018-02-14 17:30:00-07:00,2018,2,14,17,30,3.7,-3.9,7.0,1.0,7.0,0.15,802.0,231.0,3.5,57.59510208141318 +2018-02-14 18:30:00-07:00,2018,2,14,18,30,3.4000000000000004,-4.2,0.0,0.0,0.0,0.15,802.0,229.0,3.8,57.51650950191704 +2018-02-14 19:30:00-07:00,2018,2,14,19,30,3.2,-4.4,0.0,0.0,0.0,0.15,802.0,230.0,3.9,57.464012079295586 +2018-02-14 20:30:00-07:00,2018,2,14,20,30,2.7,-4.7,0.0,0.0,0.0,0.15,802.0,231.0,3.7,58.2043553085906 +2018-02-14 21:30:00-07:00,2018,2,14,21,30,2.0,-5.1000000000000005,0.0,0.0,0.0,0.15,801.0,231.0,3.5,59.35535312275678 +2018-02-14 22:30:00-07:00,2018,2,14,22,30,1.4,-5.5,0.0,0.0,0.0,0.15,801.0,229.0,3.4000000000000004,60.107582894735835 +2018-02-14 23:30:00-07:00,2018,2,14,23,30,1.0,-5.800000000000001,0.0,0.0,0.0,0.15,801.0,228.0,3.3000000000000003,60.46445382266512 +2018-02-15 00:30:00-07:00,2018,2,15,0,30,0.7000000000000001,-5.9,0.0,0.0,0.0,0.15,800.0,226.0,3.3000000000000003,61.31631364542489 +2018-02-15 01:30:00-07:00,2018,2,15,1,30,0.6000000000000001,-5.9,0.0,0.0,0.0,0.15,800.0,224.0,3.3000000000000003,61.76020307405696 +2018-02-15 02:30:00-07:00,2018,2,15,2,30,0.4,-5.7,0.0,0.0,0.0,0.15,800.0,227.0,3.3000000000000003,63.61982384615665 +2018-02-15 03:30:00-07:00,2018,2,15,3,30,0.1,-5.6000000000000005,0.0,0.0,0.0,0.15,800.0,234.0,2.9000000000000004,65.51226084723248 +2018-02-15 04:30:00-07:00,2018,2,15,4,30,-0.2,-5.4,0.0,0.0,0.0,0.15,800.0,244.0,2.5,67.97807040108465 +2018-02-15 05:30:00-07:00,2018,2,15,5,30,-0.4,-5.4,0.0,0.0,0.0,0.15,800.0,253.0,1.9,68.97366873594436 +2018-02-15 06:30:00-07:00,2018,2,15,6,30,0.3,-5.4,0.0,0.0,0.0,0.15,801.0,260.0,1.5,65.55834908365945 +2018-02-15 07:30:00-07:00,2018,2,15,7,30,2.3000000000000003,-4.800000000000001,40.0,257.0,67.0,0.15,802.0,277.0,1.6,59.432202210111605 +2018-02-15 08:30:00-07:00,2018,2,15,8,30,4.3,-4.5,64.0,725.0,267.0,0.15,802.0,299.0,2.0,52.787191463128984 +2018-02-15 09:30:00-07:00,2018,2,15,9,30,5.5,-4.800000000000001,200.0,299.0,328.0,0.15,802.0,307.0,2.3000000000000003,47.46717061526555 +2018-02-15 10:30:00-07:00,2018,2,15,10,30,6.6000000000000005,-5.4,220.0,463.0,468.0,0.15,802.0,300.0,2.4000000000000004,42.04323233996104 +2018-02-15 11:30:00-07:00,2018,2,15,11,30,7.300000000000001,-6.1000000000000005,162.0,773.0,625.0,0.15,802.0,287.0,2.5,37.99539789699694 +2018-02-15 12:30:00-07:00,2018,2,15,12,30,7.5,-6.800000000000001,342.0,209.0,470.0,0.15,802.0,275.0,2.9000000000000004,35.5250031650085 +2018-02-15 13:30:00-07:00,2018,2,15,13,30,7.300000000000001,-7.300000000000001,166.0,52.0,196.0,0.15,802.0,276.0,3.0,34.6548535728552 +2018-02-15 14:30:00-07:00,2018,2,15,14,30,6.4,-7.7,163.0,11.0,168.0,0.15,803.0,290.0,2.9000000000000004,35.740752121894076 +2018-02-15 15:30:00-07:00,2018,2,15,15,30,4.6000000000000005,-8.0,87.0,14.0,92.0,0.15,804.0,317.0,2.4000000000000004,39.562501163563624 +2018-02-15 16:30:00-07:00,2018,2,15,16,30,1.8,-7.300000000000001,52.0,1.0,52.0,0.15,805.0,180.0,2.2,50.89753024845036 +2018-02-15 17:30:00-07:00,2018,2,15,17,30,-1.0,-7.1000000000000005,5.0,0.0,5.0,0.8,807.0,36.0,2.8000000000000003,63.28258939860282 +2018-02-15 18:30:00-07:00,2018,2,15,18,30,-3.1,-7.4,0.0,0.0,0.0,0.8,808.0,53.0,3.2,72.19644081033783 +2018-02-15 19:30:00-07:00,2018,2,15,19,30,-4.800000000000001,-8.4,0.0,0.0,0.0,0.8,809.0,66.0,3.3000000000000003,75.89172819960544 +2018-02-15 20:30:00-07:00,2018,2,15,20,30,-5.9,-9.8,0.0,0.0,0.0,0.8,810.0,74.0,2.9000000000000004,73.93089139196022 +2018-02-15 21:30:00-07:00,2018,2,15,21,30,-6.7,-11.0,0.0,0.0,0.0,0.8,811.0,70.0,2.4000000000000004,71.47137822714114 +2018-02-15 22:30:00-07:00,2018,2,15,22,30,-7.300000000000001,-11.9,0.0,0.0,0.0,0.8,811.0,56.0,2.0,69.65447528141094 +2018-02-15 23:30:00-07:00,2018,2,15,23,30,-7.7,-12.7,0.0,0.0,0.0,0.8,811.0,45.0,1.6,67.36097666388942 +2018-02-16 00:30:00-07:00,2018,2,16,0,30,-7.9,-13.3,0.0,0.0,0.0,0.8,811.0,45.0,1.1,65.1686945713565 +2018-02-16 01:30:00-07:00,2018,2,16,1,30,-8.0,-13.5,0.0,0.0,0.0,0.8,811.0,77.0,0.8,64.61742027066839 +2018-02-16 02:30:00-07:00,2018,2,16,2,30,-8.3,-13.9,0.0,0.0,0.0,0.8,811.0,134.0,1.1,64.02021527493977 +2018-02-16 03:30:00-07:00,2018,2,16,3,30,-8.6,-14.3,0.0,0.0,0.0,0.8,811.0,171.0,1.6,63.42508411172234 +2018-02-16 04:30:00-07:00,2018,2,16,4,30,-8.700000000000001,-14.5,0.0,0.0,0.0,0.8,810.0,186.0,2.0,62.882518824612134 +2018-02-16 05:30:00-07:00,2018,2,16,5,30,-8.700000000000001,-14.7,0.0,0.0,0.0,0.8,810.0,201.0,2.3000000000000003,61.8586069781384 +2018-02-16 06:30:00-07:00,2018,2,16,6,30,-7.800000000000001,-15.1,0.0,0.0,0.0,0.8,810.0,214.0,2.7,55.81241606548761 +2018-02-16 07:30:00-07:00,2018,2,16,7,30,-5.1000000000000005,-14.9,37.0,492.0,91.0,0.8,809.0,226.0,2.9000000000000004,46.14744522033522 +2018-02-16 08:30:00-07:00,2018,2,16,8,30,-1.6,-14.1,50.0,885.0,301.0,0.8,809.0,243.0,2.5,37.957896508553986 +2018-02-16 09:30:00-07:00,2018,2,16,9,30,1.5,-14.3,63.0,986.0,488.0,0.8,808.0,270.0,1.7000000000000002,29.82084338513731 +2018-02-16 10:30:00-07:00,2018,2,16,10,30,3.8,-14.2,69.0,1038.0,630.0,0.8,807.0,299.0,1.3,25.538127070747667 +2018-02-16 11:30:00-07:00,2018,2,16,11,30,5.300000000000001,-14.1,378.0,362.0,596.0,0.8,806.0,313.0,1.6,23.18558464092291 +2018-02-16 12:30:00-07:00,2018,2,16,12,30,6.0,-13.8,347.0,497.0,653.0,0.8,805.0,314.0,2.3000000000000003,22.635821462153874 +2018-02-16 13:30:00-07:00,2018,2,16,13,30,6.0,-13.3,246.0,622.0,605.0,0.8,804.0,308.0,2.9000000000000004,23.575343434292996 +2018-02-16 14:30:00-07:00,2018,2,16,14,30,5.300000000000001,-12.3,312.0,330.0,474.0,0.8,803.0,303.0,2.8000000000000003,26.829205683534433 +2018-02-16 15:30:00-07:00,2018,2,16,15,30,3.6,-10.9,201.0,377.0,337.0,0.8,803.0,288.0,2.0,33.79594823747741 +2018-02-16 16:30:00-07:00,2018,2,16,16,30,1.0,-9.0,123.0,162.0,155.0,0.8,802.0,251.0,1.4,47.237102871834566 +2018-02-16 17:30:00-07:00,2018,2,16,17,30,-0.8,-10.6,14.0,206.0,18.0,0.15,802.0,230.0,1.6,47.447370873176936 +2018-02-16 18:30:00-07:00,2018,2,16,18,30,-1.7000000000000002,-11.1,0.0,0.0,0.0,0.15,802.0,244.0,2.4000000000000004,48.70771972879466 +2018-02-16 19:30:00-07:00,2018,2,16,19,30,-2.3000000000000003,-11.4,0.0,0.0,0.0,0.15,803.0,261.0,3.2,49.707470787173264 +2018-02-16 20:30:00-07:00,2018,2,16,20,30,-2.9000000000000004,-11.7,0.0,0.0,0.0,0.15,803.0,270.0,3.7,50.73580952077917 +2018-02-16 21:30:00-07:00,2018,2,16,21,30,-3.3000000000000003,-12.1,0.0,0.0,0.0,0.15,802.0,272.0,3.7,50.618778084692934 +2018-02-16 22:30:00-07:00,2018,2,16,22,30,-3.6,-12.6,0.0,0.0,0.0,0.15,802.0,270.0,3.8,49.72280222824088 +2018-02-16 23:30:00-07:00,2018,2,16,23,30,-3.8,-13.0,0.0,0.0,0.0,0.15,802.0,268.0,3.8,48.866728214523455 +2018-02-17 00:30:00-07:00,2018,2,17,0,30,-3.9,-13.3,0.0,0.0,0.0,0.15,803.0,269.0,3.8,48.051252775249644 +2018-02-17 01:30:00-07:00,2018,2,17,1,30,-4.0,-13.5,0.0,0.0,0.0,0.15,803.0,271.0,3.6,47.63252695333371 +2018-02-17 02:30:00-07:00,2018,2,17,2,30,-4.1000000000000005,-13.6,0.0,0.0,0.0,0.15,803.0,277.0,3.3000000000000003,47.602348248421194 +2018-02-17 03:30:00-07:00,2018,2,17,3,30,-4.4,-13.7,0.0,0.0,0.0,0.15,804.0,284.0,2.9000000000000004,48.29260584735809 +2018-02-17 04:30:00-07:00,2018,2,17,4,30,-4.7,-13.7,0.0,0.0,0.0,0.15,804.0,290.0,2.5,49.39613549549455 +2018-02-17 05:30:00-07:00,2018,2,17,5,30,-4.800000000000001,-13.7,0.0,0.0,0.0,0.15,805.0,301.0,2.1,49.770183553609996 +2018-02-17 06:30:00-07:00,2018,2,17,6,30,-4.0,-13.6,0.0,0.0,0.0,0.15,806.0,301.0,2.0,47.2466773882517 +2018-02-17 07:30:00-07:00,2018,2,17,7,30,-1.8,-12.9,28.0,627.0,99.0,0.15,807.0,292.0,2.7,42.467835113526924 +2018-02-17 08:30:00-07:00,2018,2,17,8,30,0.6000000000000001,-13.3,41.0,884.0,296.0,0.15,808.0,292.0,3.7,34.516165332387324 +2018-02-17 09:30:00-07:00,2018,2,17,9,30,2.7,-13.4,48.0,985.0,477.0,0.15,808.0,292.0,4.1000000000000005,29.460866039616736 +2018-02-17 10:30:00-07:00,2018,2,17,10,30,4.5,-13.4,51.0,1033.0,614.0,0.15,807.0,292.0,4.0,25.953587248951237 +2018-02-17 11:30:00-07:00,2018,2,17,11,30,5.9,-12.9,53.0,1044.0,688.0,0.15,807.0,290.0,3.7,24.52070319618901 +2018-02-17 12:30:00-07:00,2018,2,17,12,30,6.9,-12.1,53.0,1049.0,704.0,0.15,806.0,282.0,3.3000000000000003,24.41381757392075 +2018-02-17 13:30:00-07:00,2018,2,17,13,30,7.6,-11.2,50.0,1032.0,651.0,0.15,806.0,264.0,3.1,25.010332917419703 +2018-02-17 14:30:00-07:00,2018,2,17,14,30,7.6,-10.2,48.0,995.0,541.0,0.15,805.0,241.0,2.9000000000000004,27.075834460882312 +2018-02-17 15:30:00-07:00,2018,2,17,15,30,6.2,-8.9,44.0,924.0,382.0,0.15,805.0,219.0,2.3000000000000003,33.009176939774406 +2018-02-17 16:30:00-07:00,2018,2,17,16,30,3.0,-6.300000000000001,35.0,767.0,191.0,0.15,804.0,205.0,1.8,50.462924711000355 +2018-02-17 17:30:00-07:00,2018,2,17,17,30,0.5,-8.3,14.0,224.0,19.0,0.15,804.0,205.0,2.1,51.71747224176728 +2018-02-17 18:30:00-07:00,2018,2,17,18,30,0.0,-8.9,0.0,0.0,0.0,0.15,804.0,212.0,2.8000000000000003,51.17332743205105 +2018-02-17 19:30:00-07:00,2018,2,17,19,30,-0.3,-9.2,0.0,0.0,0.0,0.15,803.0,217.0,3.4000000000000004,51.08699166008367 +2018-02-17 20:30:00-07:00,2018,2,17,20,30,-0.7000000000000001,-9.4,0.0,0.0,0.0,0.15,803.0,222.0,3.5,51.77856783624538 +2018-02-17 21:30:00-07:00,2018,2,17,21,30,-0.9,-9.6,0.0,0.0,0.0,0.15,802.0,227.0,3.7,51.721227841205874 +2018-02-17 22:30:00-07:00,2018,2,17,22,30,-1.0,-9.8,0.0,0.0,0.0,0.15,801.0,230.0,3.8,51.28726675124154 +2018-02-17 23:30:00-07:00,2018,2,17,23,30,-1.0,-10.1,0.0,0.0,0.0,0.15,800.0,231.0,4.0,50.08841746451389 +2018-02-18 00:30:00-07:00,2018,2,18,0,30,-0.9,-10.4,0.0,0.0,0.0,0.15,799.0,230.0,4.1000000000000005,48.5583978875021 +2018-02-18 01:30:00-07:00,2018,2,18,1,30,-0.8,-10.5,0.0,0.0,0.0,0.15,798.0,229.0,4.3,47.824884406045875 +2018-02-18 02:30:00-07:00,2018,2,18,2,30,-0.6000000000000001,-10.4,0.0,0.0,0.0,0.15,798.0,230.0,4.4,47.506976360569624 +2018-02-18 03:30:00-07:00,2018,2,18,3,30,-0.3,-10.1,0.0,0.0,0.0,0.15,797.0,234.0,4.5,47.59624237159519 +2018-02-18 04:30:00-07:00,2018,2,18,4,30,-0.2,-9.6,0.0,0.0,0.0,0.15,797.0,238.0,4.5,49.14987983729057 +2018-02-18 05:30:00-07:00,2018,2,18,5,30,-0.1,-9.2,0.0,0.0,0.0,0.15,796.0,240.0,4.3,50.35018082419967 +2018-02-18 06:30:00-07:00,2018,2,18,6,30,0.9,-8.9,0.0,0.0,0.0,0.15,796.0,238.0,4.2,47.95160205653278 +2018-02-18 07:30:00-07:00,2018,2,18,7,30,3.7,-8.1,63.0,14.0,65.0,0.15,795.0,235.0,4.3,41.81347326909336 +2018-02-18 08:30:00-07:00,2018,2,18,8,30,6.800000000000001,-7.2,156.0,250.0,229.0,0.15,795.0,244.0,5.300000000000001,36.139502218013945 +2018-02-18 09:30:00-07:00,2018,2,18,9,30,9.1,-6.800000000000001,149.0,620.0,422.0,0.15,794.0,258.0,6.4,31.872455823639488 +2018-02-18 10:30:00-07:00,2018,2,18,10,30,10.5,-6.800000000000001,59.0,1013.0,616.0,0.15,794.0,263.0,6.7,29.018475483380367 +2018-02-18 11:30:00-07:00,2018,2,18,11,30,11.2,-6.7,62.0,1034.0,696.0,0.15,794.0,261.0,6.6000000000000005,27.912860747082338 +2018-02-18 12:30:00-07:00,2018,2,18,12,30,11.5,-6.300000000000001,63.0,1035.0,711.0,0.15,793.0,257.0,6.4,28.214995681861048 +2018-02-18 13:30:00-07:00,2018,2,18,13,30,11.3,-5.800000000000001,61.0,1017.0,658.0,0.15,793.0,254.0,6.2,29.70220465472148 +2018-02-18 14:30:00-07:00,2018,2,18,14,30,10.7,-5.5,55.0,986.0,548.0,0.15,793.0,250.0,5.6000000000000005,31.621895041210237 +2018-02-18 15:30:00-07:00,2018,2,18,15,30,9.0,-5.2,47.0,919.0,388.0,0.15,793.0,246.0,4.3,36.25084573295041 +2018-02-18 16:30:00-07:00,2018,2,18,16,30,5.6000000000000005,-4.2,37.0,769.0,196.0,0.15,793.0,241.0,2.7,49.31731344234001 +2018-02-18 17:30:00-07:00,2018,2,18,17,30,2.9000000000000004,-5.0,15.0,234.0,21.0,0.15,793.0,237.0,2.2,56.09991053434831 +2018-02-18 18:30:00-07:00,2018,2,18,18,30,2.2,-5.5,0.0,0.0,0.0,0.15,793.0,236.0,2.2,56.76787584439675 +2018-02-18 19:30:00-07:00,2018,2,18,19,30,1.5,-5.7,0.0,0.0,0.0,0.15,793.0,237.0,2.2,58.77836693839153 +2018-02-18 20:30:00-07:00,2018,2,18,20,30,0.4,-5.9,0.0,0.0,0.0,0.15,794.0,236.0,1.8,62.65875954730196 +2018-02-18 21:30:00-07:00,2018,2,18,21,30,-0.9,-6.2,0.0,0.0,0.0,0.15,794.0,238.0,1.4,67.30889342885368 +2018-02-18 22:30:00-07:00,2018,2,18,22,30,-2.3000000000000003,-6.4,0.0,0.0,0.0,0.15,794.0,148.0,1.7000000000000002,73.4704889400377 +2018-02-18 23:30:00-07:00,2018,2,18,23,30,-4.0,-6.7,0.0,0.0,0.0,0.15,795.0,58.0,2.2,81.48744654273536 +2018-02-19 00:30:00-07:00,2018,2,19,0,30,-5.4,-7.7,0.0,0.0,0.0,0.15,795.0,60.0,2.4000000000000004,83.84796917539464 +2018-02-19 01:30:00-07:00,2018,2,19,1,30,-6.2,-9.0,0.0,0.0,0.0,0.15,795.0,64.0,2.1,80.54281151059043 +2018-02-19 02:30:00-07:00,2018,2,19,2,30,-6.7,-10.0,0.0,0.0,0.0,0.15,794.0,83.0,2.0,77.36335219785983 +2018-02-19 03:30:00-07:00,2018,2,19,3,30,-7.1000000000000005,-10.7,0.0,0.0,0.0,0.15,794.0,111.0,2.2,75.47949719637542 +2018-02-19 04:30:00-07:00,2018,2,19,4,30,-7.2,-11.1,0.0,0.0,0.0,0.15,794.0,131.0,2.3000000000000003,73.6823745798946 +2018-02-19 05:30:00-07:00,2018,2,19,5,30,-7.0,-11.2,0.0,0.0,0.0,0.15,794.0,138.0,2.3000000000000003,71.9813993562752 +2018-02-19 06:30:00-07:00,2018,2,19,6,30,-6.2,-11.1,0.0,0.0,0.0,0.15,794.0,142.0,2.5,68.2429677496948 +2018-02-19 07:30:00-07:00,2018,2,19,7,30,-4.4,-10.6,11.0,0.0,11.0,0.15,794.0,154.0,2.9000000000000004,61.95329871864505 +2018-02-19 08:30:00-07:00,2018,2,19,8,30,-1.9,-9.3,21.0,0.0,21.0,0.15,793.0,168.0,3.4000000000000004,56.98464518878814 +2018-02-19 09:30:00-07:00,2018,2,19,9,30,0.3,-8.1,34.0,0.0,34.0,0.15,793.0,175.0,3.9,53.29199771653128 +2018-02-19 10:30:00-07:00,2018,2,19,10,30,2.1,-7.5,53.0,0.0,53.0,0.15,792.0,182.0,4.5,49.05659465235044 +2018-02-19 11:30:00-07:00,2018,2,19,11,30,3.6,-7.7,69.0,0.0,69.0,0.15,791.0,186.0,5.0,43.434192191526265 +2018-02-19 12:30:00-07:00,2018,2,19,12,30,4.4,-8.0,96.0,3.0,98.0,0.15,790.0,185.0,5.5,40.11951943850725 +2018-02-19 13:30:00-07:00,2018,2,19,13,30,4.1000000000000005,-7.9,20.0,0.0,20.0,0.15,789.0,180.0,5.7,41.2903144603619 +2018-02-19 14:30:00-07:00,2018,2,19,14,30,2.6,-7.4,71.0,2.0,72.0,0.15,789.0,171.0,5.7,47.70919168918567 +2018-02-19 15:30:00-07:00,2018,2,19,15,30,0.4,-6.7,35.0,1.0,35.0,0.15,789.0,162.0,5.4,58.94232075289549 +2018-02-19 16:30:00-07:00,2018,2,19,16,30,-1.9,-6.4,58.0,3.0,59.0,0.15,789.0,156.0,5.1000000000000005,71.33366442745883 +2018-02-19 17:30:00-07:00,2018,2,19,17,30,-3.8,-6.6000000000000005,9.0,0.0,9.0,0.8,790.0,154.0,4.7,80.89353516948601 +2018-02-19 18:30:00-07:00,2018,2,19,18,30,-5.4,-7.6,0.0,0.0,0.0,0.8,791.0,149.0,3.8,84.49870281874244 +2018-02-19 19:30:00-07:00,2018,2,19,19,30,-6.800000000000001,-9.1,0.0,0.0,0.0,0.8,792.0,106.0,2.4000000000000004,83.6716982981128 +2018-02-19 20:30:00-07:00,2018,2,19,20,30,-7.800000000000001,-10.8,0.0,0.0,0.0,0.8,793.0,49.0,1.8,79.0394504026197 +2018-02-19 21:30:00-07:00,2018,2,19,21,30,-8.5,-12.3,0.0,0.0,0.0,0.8,794.0,36.0,1.6,74.02397286961526 +2018-02-19 22:30:00-07:00,2018,2,19,22,30,-9.2,-13.3,0.0,0.0,0.0,0.8,794.0,62.0,1.3,72.11447816160089 +2018-02-19 23:30:00-07:00,2018,2,19,23,30,-9.8,-14.4,0.0,0.0,0.0,0.8,795.0,97.0,1.2000000000000002,69.10827081813947 +2018-02-20 00:30:00-07:00,2018,2,20,0,30,-10.1,-15.2,0.0,0.0,0.0,0.8,795.0,116.0,1.2000000000000002,66.25718957644186 +2018-02-20 01:30:00-07:00,2018,2,20,1,30,-10.2,-15.6,0.0,0.0,0.0,0.8,795.0,110.0,1.2000000000000002,64.61048137367773 +2018-02-20 02:30:00-07:00,2018,2,20,2,30,-10.4,-16.0,0.0,0.0,0.0,0.8,796.0,67.0,1.1,63.49816190659488 +2018-02-20 03:30:00-07:00,2018,2,20,3,30,-10.9,-16.6,0.0,0.0,0.0,0.8,796.0,189.0,1.1,62.84540281414227 +2018-02-20 04:30:00-07:00,2018,2,20,4,30,-11.6,-17.400000000000002,0.0,0.0,0.0,0.8,797.0,344.0,1.4,62.14177507562587 +2018-02-20 05:30:00-07:00,2018,2,20,5,30,-12.4,-18.4,0.0,0.0,0.0,0.8,798.0,336.0,1.3,60.89235716667902 +2018-02-20 06:30:00-07:00,2018,2,20,6,30,-12.4,-19.4,0.0,0.0,0.0,0.8,799.0,315.0,1.3,55.91556385718034 +2018-02-20 07:30:00-07:00,2018,2,20,7,30,-10.6,-19.8,46.0,0.0,46.0,0.8,800.0,292.0,1.4,46.791336945855896 +2018-02-20 08:30:00-07:00,2018,2,20,8,30,-8.0,-18.9,125.0,560.0,294.0,0.8,800.0,281.0,1.7000000000000002,41.21851561484102 +2018-02-20 09:30:00-07:00,2018,2,20,9,30,-5.9,-18.4,258.0,427.0,450.0,0.8,800.0,277.0,1.8,36.59947195460203 +2018-02-20 10:30:00-07:00,2018,2,20,10,30,-4.5,-17.7,412.0,122.0,480.0,0.8,801.0,274.0,1.9,34.92677008640216 +2018-02-20 11:30:00-07:00,2018,2,20,11,30,-3.7,-17.2,475.0,102.0,539.0,0.8,801.0,271.0,1.9,34.30586137755198 +2018-02-20 12:30:00-07:00,2018,2,20,12,30,-3.6,-17.0,446.0,49.0,477.0,0.8,801.0,272.0,1.8,34.626655521078135 +2018-02-20 13:30:00-07:00,2018,2,20,13,30,-3.7,-16.7,432.0,83.0,482.0,0.8,801.0,280.0,1.6,35.773818229610804 +2018-02-20 14:30:00-07:00,2018,2,20,14,30,-4.0,-16.5,290.0,65.0,323.0,0.8,802.0,295.0,1.4,37.202881275326625 +2018-02-20 15:30:00-07:00,2018,2,20,15,30,-4.800000000000001,-16.3,264.0,43.0,280.0,0.8,803.0,314.0,1.3,40.17477667575386 +2018-02-20 16:30:00-07:00,2018,2,20,16,30,-6.300000000000001,-16.400000000000002,103.0,3.0,104.0,0.8,803.0,166.0,1.3,44.64876989449172 +2018-02-20 17:30:00-07:00,2018,2,20,17,30,-8.0,-17.5,11.0,0.0,11.0,0.8,804.0,16.0,1.1,46.41008873728576 +2018-02-20 18:30:00-07:00,2018,2,20,18,30,-9.5,-18.1,0.0,0.0,0.0,0.8,805.0,44.0,0.8,49.596136224212806 +2018-02-20 19:30:00-07:00,2018,2,20,19,30,-10.4,-18.6,0.0,0.0,0.0,0.8,806.0,131.0,0.7000000000000001,51.03172415635038 +2018-02-20 20:30:00-07:00,2018,2,20,20,30,-11.1,-19.0,0.0,0.0,0.0,0.8,806.0,214.0,0.9,52.14053391472899 +2018-02-20 21:30:00-07:00,2018,2,20,21,30,-11.7,-19.3,0.0,0.0,0.0,0.8,807.0,224.0,1.1,53.31436041591539 +2018-02-20 22:30:00-07:00,2018,2,20,22,30,-12.4,-19.9,0.0,0.0,0.0,0.8,807.0,216.0,1.2000000000000002,53.56649220105632 +2018-02-20 23:30:00-07:00,2018,2,20,23,30,-13.2,-20.200000000000003,0.0,0.0,0.0,0.8,807.0,212.0,1.4,55.68579611900835 +2018-02-21 00:30:00-07:00,2018,2,21,0,30,-13.8,-20.5,0.0,0.0,0.0,0.8,807.0,218.0,1.7000000000000002,56.97380464130829 +2018-02-21 01:30:00-07:00,2018,2,21,1,30,-14.4,-20.9,0.0,0.0,0.0,0.8,807.0,227.0,2.0,57.79876292777591 +2018-02-21 02:30:00-07:00,2018,2,21,2,30,-15.0,-21.4,0.0,0.0,0.0,0.8,807.0,233.0,2.3000000000000003,58.13455239367389 +2018-02-21 03:30:00-07:00,2018,2,21,3,30,-15.6,-21.9,0.0,0.0,0.0,0.8,807.0,239.0,2.4000000000000004,58.47599804251305 +2018-02-21 04:30:00-07:00,2018,2,21,4,30,-15.9,-22.5,0.0,0.0,0.0,0.8,807.0,244.0,2.6,56.871396161653266 +2018-02-21 05:30:00-07:00,2018,2,21,5,30,-16.2,-23.1,0.0,0.0,0.0,0.8,808.0,246.0,2.6,55.298562284307955 +2018-02-21 06:30:00-07:00,2018,2,21,6,30,-15.2,-23.6,0.0,0.0,0.0,0.8,808.0,245.0,2.5,48.69072606107841 +2018-02-21 07:30:00-07:00,2018,2,21,7,30,-11.6,-23.0,64.0,232.0,94.0,0.8,808.0,236.0,2.2,38.3315689594998 +2018-02-21 08:30:00-07:00,2018,2,21,8,30,-7.300000000000001,-20.1,55.0,902.0,332.0,0.8,808.0,215.0,1.8,35.230174036356246 +2018-02-21 09:30:00-07:00,2018,2,21,9,30,-4.1000000000000005,-18.7,68.0,986.0,517.0,0.8,807.0,200.0,1.9,31.138869042235804 +2018-02-21 10:30:00-07:00,2018,2,21,10,30,-2.0,-17.7,79.0,1029.0,660.0,0.8,807.0,201.0,2.1,28.991522743430988 +2018-02-21 11:30:00-07:00,2018,2,21,11,30,-0.5,-16.6,88.0,1045.0,744.0,0.8,807.0,206.0,2.2,28.48678444238933 +2018-02-21 12:30:00-07:00,2018,2,21,12,30,0.5,-15.7,89.0,1046.0,759.0,0.8,806.0,212.0,2.0,28.554383122894624 +2018-02-21 13:30:00-07:00,2018,2,21,13,30,0.9,-15.0,86.0,1019.0,699.0,0.8,806.0,217.0,1.8,29.39450842520488 +2018-02-21 14:30:00-07:00,2018,2,21,14,30,0.6000000000000001,-14.6,81.0,980.0,584.0,0.8,805.0,224.0,1.5,31.041387739928172 +2018-02-21 15:30:00-07:00,2018,2,21,15,30,-0.7000000000000001,-14.0,78.0,873.0,413.0,0.8,806.0,116.0,1.2000000000000002,35.82898942794662 +2018-02-21 16:30:00-07:00,2018,2,21,16,30,-3.6,-13.4,99.0,428.0,193.0,0.8,806.0,33.0,1.1,46.6049108181517 +2018-02-21 17:30:00-07:00,2018,2,21,17,30,-6.300000000000001,-14.1,18.0,2.0,18.0,0.8,807.0,67.0,1.1,53.98496365226359 +2018-02-21 18:30:00-07:00,2018,2,21,18,30,-7.5,-15.3,0.0,0.0,0.0,0.8,807.0,94.0,1.0,53.64026859045285 +2018-02-21 19:30:00-07:00,2018,2,21,19,30,-8.1,-15.7,0.0,0.0,0.0,0.8,807.0,141.0,0.8,54.36119736915463 +2018-02-21 20:30:00-07:00,2018,2,21,20,30,-8.3,-15.6,0.0,0.0,0.0,0.8,807.0,183.0,0.9,55.67155952012537 +2018-02-21 21:30:00-07:00,2018,2,21,21,30,-8.4,-15.4,0.0,0.0,0.0,0.8,807.0,205.0,0.9,57.04232862353537 +2018-02-21 22:30:00-07:00,2018,2,21,22,30,-8.200000000000001,-15.1,0.0,0.0,0.0,0.8,806.0,224.0,0.9,57.570610362756824 +2018-02-21 23:30:00-07:00,2018,2,21,23,30,-7.7,-14.7,0.0,0.0,0.0,0.8,805.0,244.0,0.9,57.23577894219837 +2018-02-22 00:30:00-07:00,2018,2,22,0,30,-7.6,-14.1,0.0,0.0,0.0,0.8,805.0,264.0,0.8,59.65709674233661 +2018-02-22 01:30:00-07:00,2018,2,22,1,30,-7.7,-13.9,0.0,0.0,0.0,0.8,804.0,294.0,0.7000000000000001,61.109973551756376 +2018-02-22 02:30:00-07:00,2018,2,22,2,30,-8.1,-14.0,0.0,0.0,0.0,0.8,804.0,322.0,0.7000000000000001,62.521005832553605 +2018-02-22 03:30:00-07:00,2018,2,22,3,30,-8.6,-14.1,0.0,0.0,0.0,0.8,803.0,335.0,0.8,64.47122800138258 +2018-02-22 04:30:00-07:00,2018,2,22,4,30,-8.8,-14.3,0.0,0.0,0.0,0.8,803.0,339.0,0.8,64.42231841894673 +2018-02-22 05:30:00-07:00,2018,2,22,5,30,-8.700000000000001,-14.3,0.0,0.0,0.0,0.8,803.0,340.0,0.8,63.92154380836566 +2018-02-22 06:30:00-07:00,2018,2,22,6,30,-8.0,-14.1,0.0,0.0,0.0,0.8,803.0,330.0,0.8,61.533161679405886 +2018-02-22 07:30:00-07:00,2018,2,22,7,30,-6.4,-13.4,54.0,2.0,54.0,0.8,804.0,316.0,0.9,57.59213586121602 +2018-02-22 08:30:00-07:00,2018,2,22,8,30,-4.2,-11.7,172.0,131.0,213.0,0.8,803.0,301.0,1.0,55.90886165392502 +2018-02-22 09:30:00-07:00,2018,2,22,9,30,-2.1,-10.6,174.0,657.0,476.0,0.8,803.0,295.0,1.1,52.19733610571776 +2018-02-22 10:30:00-07:00,2018,2,22,10,30,-0.5,-10.0,328.0,432.0,574.0,0.8,802.0,304.0,1.2000000000000002,48.67650494104929 +2018-02-22 11:30:00-07:00,2018,2,22,11,30,0.6000000000000001,-9.7,286.0,614.0,674.0,0.8,802.0,307.0,1.2000000000000002,46.02066285258593 +2018-02-22 12:30:00-07:00,2018,2,22,12,30,1.0,-9.7,118.0,964.0,740.0,0.8,801.0,307.0,1.3,44.713386187891366 +2018-02-22 13:30:00-07:00,2018,2,22,13,30,0.7000000000000001,-9.9,375.0,396.0,615.0,0.8,801.0,314.0,1.5,44.97600075006112 +2018-02-22 14:30:00-07:00,2018,2,22,14,30,-0.1,-10.0,373.0,201.0,477.0,0.8,802.0,330.0,1.6,47.28140710535596 +2018-02-22 15:30:00-07:00,2018,2,22,15,30,-1.2000000000000002,-10.0,169.0,9.0,172.0,0.8,803.0,349.0,1.5,51.22956318670306 +2018-02-22 16:30:00-07:00,2018,2,22,16,30,-2.9000000000000004,-10.3,95.0,4.0,96.0,0.8,803.0,193.0,1.4,56.71356862450541 +2018-02-22 17:30:00-07:00,2018,2,22,17,30,-5.0,-10.9,18.0,1.0,18.0,0.8,804.0,31.0,1.0,63.29519076292651 +2018-02-22 18:30:00-07:00,2018,2,22,18,30,-6.7,-11.5,0.0,0.0,0.0,0.8,804.0,162.0,0.6000000000000001,68.67824008750398 +2018-02-22 19:30:00-07:00,2018,2,22,19,30,-7.7,-12.4,0.0,0.0,0.0,0.8,805.0,264.0,0.7000000000000001,69.01026185118698 +2018-02-22 20:30:00-07:00,2018,2,22,20,30,-8.200000000000001,-13.0,0.0,0.0,0.0,0.8,805.0,238.0,1.0,68.34488765380593 +2018-02-22 21:30:00-07:00,2018,2,22,21,30,-8.9,-13.6,0.0,0.0,0.0,0.8,805.0,234.0,1.1,68.74502367670202 +2018-02-22 22:30:00-07:00,2018,2,22,22,30,-9.7,-14.2,0.0,0.0,0.0,0.8,805.0,232.0,1.2000000000000002,69.69841770137657 +2018-02-22 23:30:00-07:00,2018,2,22,23,30,-10.4,-14.9,0.0,0.0,0.0,0.8,805.0,235.0,1.5,69.54544664774546 +2018-02-23 00:30:00-07:00,2018,2,23,0,30,-10.7,-15.7,0.0,0.0,0.0,0.8,805.0,238.0,1.7000000000000002,66.6652003364702 +2018-02-23 01:30:00-07:00,2018,2,23,1,30,-10.6,-16.2,0.0,0.0,0.0,0.8,805.0,241.0,1.8,63.44792029884798 +2018-02-23 02:30:00-07:00,2018,2,23,2,30,-10.2,-16.400000000000002,0.0,0.0,0.0,0.8,804.0,243.0,1.7000000000000002,60.45481780702191 +2018-02-23 03:30:00-07:00,2018,2,23,3,30,-10.0,-16.2,0.0,0.0,0.0,0.8,804.0,244.0,1.4,60.50775499961164 +2018-02-23 04:30:00-07:00,2018,2,23,4,30,-10.1,-16.1,0.0,0.0,0.0,0.8,804.0,244.0,1.3,61.496702584931086 +2018-02-23 05:30:00-07:00,2018,2,23,5,30,-10.2,-16.1,0.0,0.0,0.0,0.8,805.0,246.0,1.1,61.98431321052824 +2018-02-23 06:30:00-07:00,2018,2,23,6,30,-9.2,-16.1,0.0,0.0,0.0,0.8,805.0,246.0,1.1,57.294662812828676 +2018-02-23 07:30:00-07:00,2018,2,23,7,30,-6.4,-15.4,77.0,8.0,78.0,0.8,805.0,241.0,1.1,48.88645873015076 +2018-02-23 08:30:00-07:00,2018,2,23,8,30,-3.3000000000000003,-13.6,68.0,825.0,329.0,0.8,805.0,230.0,1.2000000000000002,44.83814314009102 +2018-02-23 09:30:00-07:00,2018,2,23,9,30,-1.2000000000000002,-12.9,85.0,931.0,518.0,0.8,805.0,218.0,1.4,40.637587635132604 +2018-02-23 10:30:00-07:00,2018,2,23,10,30,0.4,-12.4,99.0,980.0,662.0,0.8,804.0,206.0,1.8,37.6611716062215 +2018-02-23 11:30:00-07:00,2018,2,23,11,30,1.5,-12.1,109.0,989.0,739.0,0.8,803.0,199.0,2.4000000000000004,35.64485777760605 +2018-02-23 12:30:00-07:00,2018,2,23,12,30,2.2,-11.7,113.0,986.0,754.0,0.8,803.0,198.0,2.9000000000000004,35.011513003943755 +2018-02-23 13:30:00-07:00,2018,2,23,13,30,2.3000000000000003,-11.3,111.0,967.0,701.0,0.8,802.0,202.0,3.1,35.89213187479887 +2018-02-23 14:30:00-07:00,2018,2,23,14,30,1.8,-10.9,362.0,266.0,501.0,0.8,802.0,205.0,2.8000000000000003,38.398716942637385 +2018-02-23 15:30:00-07:00,2018,2,23,15,30,0.4,-10.5,219.0,97.0,257.0,0.8,802.0,210.0,2.2,43.83267102756319 +2018-02-23 16:30:00-07:00,2018,2,23,16,30,-1.6,-9.6,95.0,2.0,95.0,0.8,802.0,269.0,1.5,54.443195625977694 +2018-02-23 17:30:00-07:00,2018,2,23,17,30,-3.8,-9.9,14.0,0.0,14.0,0.8,802.0,330.0,1.6,62.595631871028665 +2018-02-23 18:30:00-07:00,2018,2,23,18,30,-5.300000000000001,-10.7,0.0,0.0,0.0,0.8,803.0,340.0,2.4000000000000004,65.78562225941376 +2018-02-23 19:30:00-07:00,2018,2,23,19,30,-6.1000000000000005,-11.1,0.0,0.0,0.0,0.8,803.0,341.0,2.9000000000000004,67.72403049387407 +2018-02-23 20:30:00-07:00,2018,2,23,20,30,-6.2,-11.5,0.0,0.0,0.0,0.8,802.0,339.0,2.7,66.10001136584465 +2018-02-23 21:30:00-07:00,2018,2,23,21,30,-6.0,-11.9,0.0,0.0,0.0,0.8,802.0,341.0,2.2,63.04777791857434 +2018-02-23 22:30:00-07:00,2018,2,23,22,30,-6.2,-12.3,0.0,0.0,0.0,0.8,801.0,341.0,1.7000000000000002,61.9932875101635 +2018-02-23 23:30:00-07:00,2018,2,23,23,30,-6.5,-12.5,0.0,0.0,0.0,0.8,800.0,333.0,1.5,62.418364710769374 +2018-02-24 00:30:00-07:00,2018,2,24,0,30,-6.9,-12.9,0.0,0.0,0.0,0.8,800.0,318.0,1.6,62.31739626886008 +2018-02-24 01:30:00-07:00,2018,2,24,1,30,-7.2,-13.5,0.0,0.0,0.0,0.8,799.0,307.0,1.9,60.74366582269231 +2018-02-24 02:30:00-07:00,2018,2,24,2,30,-7.4,-14.2,0.0,0.0,0.0,0.8,799.0,299.0,2.1,58.26460186461041 +2018-02-24 03:30:00-07:00,2018,2,24,3,30,-7.9,-15.1,0.0,0.0,0.0,0.8,799.0,290.0,2.3000000000000003,56.24630913077807 +2018-02-24 04:30:00-07:00,2018,2,24,4,30,-8.700000000000001,-16.2,0.0,0.0,0.0,0.8,799.0,281.0,2.7,54.641918325743106 +2018-02-24 05:30:00-07:00,2018,2,24,5,30,-9.5,-17.5,0.0,0.0,0.0,0.8,799.0,274.0,3.2,52.173423904317524 +2018-02-24 06:30:00-07:00,2018,2,24,6,30,-9.2,-18.6,0.0,0.0,0.0,0.8,799.0,274.0,4.3,46.43055150221491 +2018-02-24 07:30:00-07:00,2018,2,24,7,30,-7.9,-18.8,45.0,588.0,130.0,0.8,800.0,281.0,5.9,41.250348071957575 +2018-02-24 08:30:00-07:00,2018,2,24,8,30,-6.4,-18.9,63.0,876.0,345.0,0.8,800.0,287.0,7.300000000000001,36.4398553119269 +2018-02-24 09:30:00-07:00,2018,2,24,9,30,-5.1000000000000005,-19.5,75.0,999.0,545.0,0.8,799.0,288.0,7.800000000000001,31.35793923774421 +2018-02-24 10:30:00-07:00,2018,2,24,10,30,-3.9,-19.3,82.0,1051.0,691.0,0.8,799.0,288.0,7.9,29.14429371514109 +2018-02-24 11:30:00-07:00,2018,2,24,11,30,-3.0,-18.6,112.0,1012.0,762.0,0.8,799.0,286.0,7.9,28.92801822554468 +2018-02-24 12:30:00-07:00,2018,2,24,12,30,-2.5,-17.900000000000002,167.0,905.0,759.0,0.8,798.0,286.0,7.6,29.578774531646335 +2018-02-24 13:30:00-07:00,2018,2,24,13,30,-2.3000000000000003,-17.0,81.0,1048.0,726.0,0.8,798.0,287.0,7.0,31.438005117344197 +2018-02-24 14:30:00-07:00,2018,2,24,14,30,-2.4000000000000004,-16.5,74.0,1015.0,609.0,0.8,798.0,289.0,6.300000000000001,33.02410479135953 +2018-02-24 15:30:00-07:00,2018,2,24,15,30,-3.3000000000000003,-16.400000000000002,63.0,946.0,438.0,0.8,798.0,288.0,5.4,35.6021535119568 +2018-02-24 16:30:00-07:00,2018,2,24,16,30,-5.1000000000000005,-16.5,47.0,798.0,232.0,0.8,798.0,282.0,4.1000000000000005,40.41618411134894 +2018-02-24 17:30:00-07:00,2018,2,24,17,30,-7.0,-16.7,21.0,308.0,35.0,0.8,799.0,273.0,3.1,45.944543224805805 +2018-02-24 18:30:00-07:00,2018,2,24,18,30,-7.9,-17.0,0.0,0.0,0.0,0.8,799.0,264.0,2.8000000000000003,48.027688576620115 +2018-02-24 19:30:00-07:00,2018,2,24,19,30,-8.1,-17.2,0.0,0.0,0.0,0.8,799.0,256.0,2.5,47.96652319296911 +2018-02-24 20:30:00-07:00,2018,2,24,20,30,-8.200000000000001,-17.0,0.0,0.0,0.0,0.8,800.0,248.0,2.2,49.158485034805956 +2018-02-24 21:30:00-07:00,2018,2,24,21,30,-8.5,-17.1,0.0,0.0,0.0,0.8,800.0,241.0,1.9,49.89862049389487 +2018-02-24 22:30:00-07:00,2018,2,24,22,30,-9.1,-17.7,0.0,0.0,0.0,0.8,800.0,239.0,1.8,49.717383467849814 +2018-02-24 23:30:00-07:00,2018,2,24,23,30,-9.8,-18.1,0.0,0.0,0.0,0.8,800.0,241.0,1.8,50.78011705444002 +2018-02-25 00:30:00-07:00,2018,2,25,0,30,-10.5,-18.6,0.0,0.0,0.0,0.8,800.0,244.0,1.7000000000000002,51.437406036072815 +2018-02-25 01:30:00-07:00,2018,2,25,1,30,-11.2,-18.9,0.0,0.0,0.0,0.8,800.0,249.0,1.9,53.00779346071936 +2018-02-25 02:30:00-07:00,2018,2,25,2,30,-11.8,-19.5,0.0,0.0,0.0,0.8,800.0,254.0,2.2,52.830248536357395 +2018-02-25 03:30:00-07:00,2018,2,25,3,30,-12.1,-19.9,0.0,0.0,0.0,0.8,800.0,258.0,2.4000000000000004,52.28958574255085 +2018-02-25 04:30:00-07:00,2018,2,25,4,30,-12.1,-19.9,0.0,0.0,0.0,0.8,800.0,261.0,2.5,52.28958574255085 +2018-02-25 05:30:00-07:00,2018,2,25,5,30,-12.1,-19.8,0.0,0.0,0.0,0.8,800.0,263.0,2.5,52.74117257366927 +2018-02-25 06:30:00-07:00,2018,2,25,6,30,-10.5,-19.6,0.0,0.0,0.0,0.8,801.0,263.0,2.8000000000000003,47.226179025846 +2018-02-25 07:30:00-07:00,2018,2,25,7,30,-6.9,-18.2,37.0,672.0,138.0,0.8,802.0,270.0,3.8,40.184875134026235 +2018-02-25 08:30:00-07:00,2018,2,25,8,30,-4.0,-16.3,143.0,576.0,331.0,0.8,802.0,283.0,5.1000000000000005,37.828582776404055 +2018-02-25 09:30:00-07:00,2018,2,25,9,30,-2.5,-16.5,143.0,816.0,531.0,0.8,802.0,290.0,5.800000000000001,33.2694009601109 +2018-02-25 10:30:00-07:00,2018,2,25,10,30,-1.7000000000000002,-16.7,80.0,1035.0,685.0,0.8,803.0,293.0,5.9,30.84266104047015 +2018-02-25 11:30:00-07:00,2018,2,25,11,30,-1.2000000000000002,-16.2,85.0,1056.0,769.0,0.8,803.0,295.0,5.9,30.997858580381525 +2018-02-25 12:30:00-07:00,2018,2,25,12,30,-0.8,-15.6,87.0,1059.0,785.0,0.8,803.0,296.0,5.7,31.642041587437454 +2018-02-25 13:30:00-07:00,2018,2,25,13,30,-0.7000000000000001,-15.1,86.0,1038.0,729.0,0.8,803.0,295.0,5.5,32.736906176315586 +2018-02-25 14:30:00-07:00,2018,2,25,14,30,-0.9,-14.7,81.0,988.0,606.0,0.8,803.0,293.0,5.2,34.330121826757434 +2018-02-25 15:30:00-07:00,2018,2,25,15,30,-1.7000000000000002,-14.5,70.0,915.0,436.0,0.8,804.0,289.0,4.6000000000000005,37.006037184438746 +2018-02-25 16:30:00-07:00,2018,2,25,16,30,-3.6,-14.2,83.0,600.0,224.0,0.8,804.0,280.0,3.6,43.662754490807664 +2018-02-25 17:30:00-07:00,2018,2,25,17,30,-5.5,-14.4,23.0,294.0,38.0,0.79,804.0,267.0,3.0,49.56165066063929 +2018-02-25 18:30:00-07:00,2018,2,25,18,30,-6.4,-14.8,0.0,0.0,0.0,0.79,804.0,255.0,3.1,51.365548952380536 +2018-02-25 19:30:00-07:00,2018,2,25,19,30,-6.9,-15.2,0.0,0.0,0.0,0.79,805.0,247.0,3.2,51.64259102302175 +2018-02-25 20:30:00-07:00,2018,2,25,20,30,-7.300000000000001,-15.7,0.0,0.0,0.0,0.79,805.0,241.0,3.1,51.099608667459016 +2018-02-25 21:30:00-07:00,2018,2,25,21,30,-7.6,-16.3,0.0,0.0,0.0,0.79,804.0,238.0,3.2,49.75296284780233 +2018-02-25 22:30:00-07:00,2018,2,25,22,30,-7.7,-16.900000000000002,0.0,0.0,0.0,0.79,804.0,238.0,3.6,47.687825614795834 +2018-02-25 23:30:00-07:00,2018,2,25,23,30,-7.6,-17.2,0.0,0.0,0.0,0.79,804.0,239.0,3.9,46.14473862828338 +2018-02-26 00:30:00-07:00,2018,2,26,0,30,-7.4,-17.3,0.0,0.0,0.0,0.79,804.0,240.0,3.9,45.05745788850459 +2018-02-26 01:30:00-07:00,2018,2,26,1,30,-7.2,-17.2,0.0,0.0,0.0,0.79,804.0,242.0,3.5,44.74254551871112 +2018-02-26 02:30:00-07:00,2018,2,26,2,30,-7.300000000000001,-17.0,0.0,0.0,0.0,0.79,803.0,245.0,3.0,45.85167947540276 +2018-02-26 03:30:00-07:00,2018,2,26,3,30,-7.6,-17.0,0.0,0.0,0.0,0.79,803.0,249.0,2.6,46.92568682195256 +2018-02-26 04:30:00-07:00,2018,2,26,4,30,-7.9,-17.1,0.0,0.0,0.0,0.79,804.0,252.0,2.3000000000000003,47.62654475036855 +2018-02-26 05:30:00-07:00,2018,2,26,5,30,-8.0,-17.2,0.0,0.0,0.0,0.79,804.0,254.0,2.1,47.59587404884621 +2018-02-26 06:30:00-07:00,2018,2,26,6,30,-6.6000000000000005,-17.2,0.0,0.0,0.0,0.79,804.0,253.0,2.2,42.727103864552966 +2018-02-26 07:30:00-07:00,2018,2,26,7,30,-3.3000000000000003,-15.5,39.0,671.0,143.0,0.79,804.0,247.0,2.5,38.365694225853694 +2018-02-26 08:30:00-07:00,2018,2,26,8,30,0.6000000000000001,-13.8,59.0,883.0,352.0,0.79,804.0,246.0,2.3000000000000003,33.1406309392555 +2018-02-26 09:30:00-07:00,2018,2,26,9,30,3.7,-13.2,74.0,970.0,540.0,0.79,804.0,253.0,2.0,27.900590802650964 +2018-02-26 10:30:00-07:00,2018,2,26,10,30,5.7,-12.3,84.0,1015.0,682.0,0.79,803.0,255.0,2.2,26.095244517256205 +2018-02-26 11:30:00-07:00,2018,2,26,11,30,7.0,-11.7,89.0,1036.0,765.0,0.79,803.0,248.0,2.5,25.03729016632796 +2018-02-26 12:30:00-07:00,2018,2,26,12,30,7.800000000000001,-11.3,91.0,1039.0,781.0,0.79,802.0,241.0,2.8000000000000003,24.47591236962072 +2018-02-26 13:30:00-07:00,2018,2,26,13,30,8.1,-10.8,87.0,1026.0,728.0,0.79,802.0,237.0,2.9000000000000004,24.955297018011866 +2018-02-26 14:30:00-07:00,2018,2,26,14,30,7.7,-10.4,79.0,994.0,612.0,0.79,802.0,240.0,2.7,26.469936705167765 +2018-02-26 15:30:00-07:00,2018,2,26,15,30,6.0,-7.9,67.0,928.0,442.0,0.79,802.0,246.0,1.8,36.17587184975627 +2018-02-26 16:30:00-07:00,2018,2,26,16,30,2.8000000000000003,-6.7,50.0,788.0,238.0,0.79,802.0,258.0,1.2000000000000002,49.6391055009161 +2018-02-26 17:30:00-07:00,2018,2,26,17,30,-0.4,-9.9,22.0,349.0,41.0,0.7000000000000001,803.0,271.0,1.1,48.70598329323531 +2018-02-26 18:30:00-07:00,2018,2,26,18,30,-2.4000000000000004,-11.3,0.0,0.0,0.0,0.7000000000000001,803.0,278.0,1.2000000000000002,50.47757805117903 +2018-02-26 19:30:00-07:00,2018,2,26,19,30,-3.6,-11.6,0.0,0.0,0.0,0.7000000000000001,803.0,277.0,1.2000000000000002,53.88117729510698 +2018-02-26 20:30:00-07:00,2018,2,26,20,30,-4.1000000000000005,-11.9,0.0,0.0,0.0,0.7000000000000001,803.0,268.0,1.3,54.60880114658753 +2018-02-26 21:30:00-07:00,2018,2,26,21,30,-4.2,-12.2,0.0,0.0,0.0,0.7000000000000001,803.0,256.0,1.4,53.71092169787024 +2018-02-26 22:30:00-07:00,2018,2,26,22,30,-4.2,-12.8,0.0,0.0,0.0,0.7000000000000001,803.0,246.0,1.6,51.17538177081757 +2018-02-26 23:30:00-07:00,2018,2,26,23,30,-4.3,-13.4,0.0,0.0,0.0,0.7000000000000001,803.0,243.0,1.8,49.11480178668549 +2018-02-27 00:30:00-07:00,2018,2,27,0,30,-4.3,-14.1,0.0,0.0,0.0,0.7000000000000001,803.0,246.0,2.1,46.39196171324673 +2018-02-27 01:30:00-07:00,2018,2,27,1,30,-4.3,-14.5,0.0,0.0,0.0,0.7000000000000001,803.0,249.0,2.3000000000000003,44.89733063968101 +2018-02-27 02:30:00-07:00,2018,2,27,2,30,-4.3,-14.9,0.0,0.0,0.0,0.7000000000000001,802.0,251.0,2.7,43.44586341923997 +2018-02-27 03:30:00-07:00,2018,2,27,3,30,-4.2,-15.2,0.0,0.0,0.0,0.7000000000000001,802.0,250.0,2.8000000000000003,42.067714383187585 +2018-02-27 04:30:00-07:00,2018,2,27,4,30,-4.4,-15.2,0.0,0.0,0.0,0.7000000000000001,802.0,249.0,2.8000000000000003,42.70480837634114 +2018-02-27 05:30:00-07:00,2018,2,27,5,30,-4.6000000000000005,-15.0,0.0,0.0,0.0,0.7000000000000001,802.0,248.0,2.8000000000000003,44.07341436524505 +2018-02-27 06:30:00-07:00,2018,2,27,6,30,-3.3000000000000003,-14.8,3.0,34.0,2.0,0.7000000000000001,802.0,246.0,3.3000000000000003,40.64601664552098 +2018-02-27 07:30:00-07:00,2018,2,27,7,30,-0.2,-13.8,39.0,686.0,149.0,0.7000000000000001,802.0,243.0,3.6,35.1168117789062 +2018-02-27 08:30:00-07:00,2018,2,27,8,30,3.3000000000000003,-11.5,59.0,892.0,359.0,0.7000000000000001,802.0,248.0,3.5,32.906653903148865 +2018-02-27 09:30:00-07:00,2018,2,27,9,30,6.2,-10.5,71.0,976.0,545.0,0.7000000000000001,802.0,262.0,3.4000000000000004,29.104899431896566 +2018-02-27 10:30:00-07:00,2018,2,27,10,30,7.800000000000001,-10.7,81.0,1020.0,687.0,0.7000000000000001,802.0,272.0,3.2,25.67259420683121 +2018-02-27 11:30:00-07:00,2018,2,27,11,30,8.6,-10.6,85.0,1041.0,769.0,0.7000000000000001,802.0,277.0,3.0,24.509622236879526 +2018-02-27 12:30:00-07:00,2018,2,27,12,30,8.8,-10.5,86.0,1043.0,784.0,0.7000000000000001,801.0,280.0,3.0,24.372906935959026 +2018-02-27 13:30:00-07:00,2018,2,27,13,30,8.700000000000001,-10.2,188.0,816.0,701.0,0.7000000000000001,801.0,282.0,2.8000000000000003,25.127500326730882 +2018-02-27 14:30:00-07:00,2018,2,27,14,30,8.200000000000001,-9.9,339.0,353.0,530.0,0.7000000000000001,801.0,285.0,2.3000000000000003,26.615658714882063 +2018-02-27 15:30:00-07:00,2018,2,27,15,30,6.5,-8.6,203.0,495.0,405.0,0.7000000000000001,801.0,294.0,1.3,33.100048874813815 +2018-02-27 16:30:00-07:00,2018,2,27,16,30,3.5,-6.0,85.0,593.0,229.0,0.7000000000000001,801.0,153.0,0.5,49.84060448363233 +2018-02-27 17:30:00-07:00,2018,2,27,17,30,1.2000000000000002,-9.9,23.0,298.0,40.0,0.16,801.0,45.0,0.5,43.386630461306616 +2018-02-27 18:30:00-07:00,2018,2,27,18,30,-0.3,-11.0,0.0,0.0,0.0,0.16,801.0,119.0,0.6000000000000001,44.31967476681656 +2018-02-27 19:30:00-07:00,2018,2,27,19,30,-1.8,-11.0,0.0,0.0,0.0,0.16,802.0,176.0,0.8,49.45950135178581 +2018-02-27 20:30:00-07:00,2018,2,27,20,30,-2.5,-10.9,0.0,0.0,0.0,0.16,802.0,218.0,1.1,52.498261430919875 +2018-02-27 21:30:00-07:00,2018,2,27,21,30,-2.5,-11.0,0.0,0.0,0.0,0.16,801.0,244.0,1.3,52.08243385573545 +2018-02-27 22:30:00-07:00,2018,2,27,22,30,-2.4000000000000004,-11.0,0.0,0.0,0.0,0.16,801.0,259.0,1.4,51.698428700386366 +2018-02-27 23:30:00-07:00,2018,2,27,23,30,-2.1,-11.2,0.0,0.0,0.0,0.16,800.0,268.0,1.3,49.766297291872974 +2018-02-28 00:30:00-07:00,2018,2,28,0,30,-1.7000000000000002,-11.1,0.0,0.0,0.0,0.16,800.0,281.0,1.1,48.70771972879466 +2018-02-28 01:30:00-07:00,2018,2,28,1,30,-1.6,-10.4,0.0,0.0,0.0,0.16,800.0,295.0,1.1,51.113913296682135 +2018-02-28 02:30:00-07:00,2018,2,28,2,30,-1.8,-10.1,0.0,0.0,0.0,0.16,799.0,308.0,1.1,53.11605796530824 +2018-02-28 03:30:00-07:00,2018,2,28,3,30,-2.1,-10.1,0.0,0.0,0.0,0.16,799.0,321.0,1.3,54.30348111034615 +2018-02-28 04:30:00-07:00,2018,2,28,4,30,-2.4000000000000004,-10.2,0.0,0.0,0.0,0.16,800.0,334.0,1.4,55.08374795527205 +2018-02-28 05:30:00-07:00,2018,2,28,5,30,-2.5,-10.2,0.0,0.0,0.0,0.16,800.0,341.0,1.4,55.49289855660582 +2018-02-28 06:30:00-07:00,2018,2,28,6,30,-1.7000000000000002,-9.9,3.0,18.0,2.0,0.16,801.0,342.0,1.6,53.56506418733645 +2018-02-28 07:30:00-07:00,2018,2,28,7,30,0.2,-9.5,46.0,555.0,137.0,0.16,802.0,348.0,1.9,48.12125912026361 +2018-02-28 08:30:00-07:00,2018,2,28,8,30,2.1,-9.2,66.0,795.0,338.0,0.16,802.0,184.0,2.2,42.981172948743215 +2018-02-28 09:30:00-07:00,2018,2,28,9,30,3.5,-8.700000000000001,79.0,900.0,520.0,0.16,802.0,16.0,2.2,40.47237021434885 +2018-02-28 10:30:00-07:00,2018,2,28,10,30,4.4,-8.5,82.0,962.0,659.0,0.16,802.0,25.0,2.2,38.59067067361227 +2018-02-28 11:30:00-07:00,2018,2,28,11,30,5.0,-8.5,293.0,509.0,630.0,0.16,801.0,32.0,2.1,37.00801194957137 +2018-02-28 12:30:00-07:00,2018,2,28,12,30,5.300000000000001,-8.4,162.0,834.0,724.0,0.16,801.0,41.0,1.8,36.52715580714237 +2018-02-28 13:30:00-07:00,2018,2,28,13,30,5.2,-8.4,157.0,806.0,668.0,0.16,801.0,53.0,1.6,36.78184704965155 +2018-02-28 14:30:00-07:00,2018,2,28,14,30,4.800000000000001,-8.3,282.0,290.0,440.0,0.16,802.0,66.0,1.6,38.11589711272491 +2018-02-28 15:30:00-07:00,2018,2,28,15,30,3.8,-8.1,163.0,527.0,381.0,0.16,802.0,79.0,1.5,41.520423294948884 +2018-02-28 16:30:00-07:00,2018,2,28,16,30,1.4,-6.5,114.0,42.0,124.0,0.16,803.0,88.0,1.3,55.69548311749553 +2018-02-28 17:30:00-07:00,2018,2,28,17,30,2.9000000000000004,-3.9,22.0,111.0,29.0,0.16,802.0,241.0,1.1,60.94116416740168 +2018-02-28 18:30:00-07:00,2018,2,28,18,30,2.1,-4.2,0.0,0.0,0.0,0.16,802.0,238.0,1.4,63.07027071482451 +2018-02-28 19:30:00-07:00,2018,2,28,19,30,1.6,-4.2,0.0,0.0,0.0,0.16,802.0,238.0,1.7000000000000002,65.36345074091197 +2018-02-28 20:30:00-07:00,2018,2,28,20,30,1.3,-4.2,0.0,0.0,0.0,0.16,802.0,242.0,1.8,66.78387392562938 +2018-02-28 21:30:00-07:00,2018,2,28,21,30,1.1,-4.2,0.0,0.0,0.0,0.16,802.0,246.0,1.8,67.74991878452813 +2018-02-28 22:30:00-07:00,2018,2,28,22,30,0.9,-4.3,0.0,0.0,0.0,0.16,802.0,250.0,1.8,68.2171567737449 +2018-02-28 23:30:00-07:00,2018,2,28,23,30,0.5,-4.3,0.0,0.0,0.0,0.16,802.0,254.0,1.7000000000000002,70.21326925022404 +2019-03-01 00:30:00-07:00,2019,3,1,0,30,-0.1,-4.6000000000000005,0.0,0.0,0.0,0.16,802.0,253.0,1.8,71.6936669415051 +2019-03-01 01:30:00-07:00,2019,3,1,1,30,-0.5,-4.9,0.0,0.0,0.0,0.16,802.0,247.0,2.0,72.15741687405793 +2019-03-01 02:30:00-07:00,2019,3,1,2,30,-0.3,-5.2,0.0,0.0,0.0,0.16,801.0,238.0,2.4000000000000004,69.51966511942167 +2019-03-01 03:30:00-07:00,2019,3,1,3,30,0.2,-5.300000000000001,0.0,0.0,0.0,0.16,801.0,235.0,3.0,66.5370936464483 +2019-03-01 04:30:00-07:00,2019,3,1,4,30,0.6000000000000001,-5.0,0.0,0.0,0.0,0.16,801.0,238.0,3.0,66.12554248914988 +2019-03-01 05:30:00-07:00,2019,3,1,5,30,0.5,-4.800000000000001,0.0,0.0,0.0,0.16,801.0,246.0,2.4000000000000004,67.61846308641042 +2019-03-01 06:30:00-07:00,2019,3,1,6,30,1.8,-4.5,1.0,0.0,1.0,0.16,802.0,263.0,1.9,62.99808719771023 +2019-03-01 07:30:00-07:00,2019,3,1,7,30,4.3,-3.4000000000000004,92.0,59.0,102.0,0.16,803.0,309.0,2.3000000000000003,57.322739076455164 +2019-03-01 08:30:00-07:00,2019,3,1,8,30,5.7,-3.4000000000000004,175.0,188.0,240.0,0.16,804.0,173.0,2.9000000000000004,51.99887110139542 +2019-03-01 09:30:00-07:00,2019,3,1,9,30,6.5,-4.1000000000000005,215.0,368.0,397.0,0.16,804.0,171.0,2.8000000000000003,46.695414602591725 +2019-03-01 10:30:00-07:00,2019,3,1,10,30,7.300000000000001,-4.7,322.0,324.0,518.0,0.16,804.0,322.0,2.6,42.25230680629203 +2019-03-01 11:30:00-07:00,2019,3,1,11,30,8.1,-5.300000000000001,303.0,50.0,336.0,0.16,804.0,298.0,2.9000000000000004,38.23661708620703 +2019-03-01 12:30:00-07:00,2019,3,1,12,30,8.5,-5.7,247.0,114.0,324.0,0.16,804.0,291.0,3.1,36.1003949518036 +2019-03-01 13:30:00-07:00,2019,3,1,13,30,8.3,-6.0,320.0,95.0,381.0,0.16,804.0,291.0,2.9000000000000004,35.766533314896854 +2019-03-01 14:30:00-07:00,2019,3,1,14,30,7.7,-6.1000000000000005,241.0,432.0,478.0,0.16,804.0,290.0,2.3000000000000003,36.97226972732943 +2019-03-01 15:30:00-07:00,2019,3,1,15,30,6.5,-6.0,200.0,377.0,357.0,0.16,804.0,285.0,1.5,40.44520988168162 +2019-03-01 16:30:00-07:00,2019,3,1,16,30,4.2,-5.300000000000001,126.0,68.0,143.0,0.16,805.0,217.0,1.1,50.041167427979744 +2019-03-01 17:30:00-07:00,2019,3,1,17,30,1.7000000000000002,-5.0,26.0,3.0,26.0,0.16,805.0,155.0,1.4,61.10131923764497 +2019-03-01 18:30:00-07:00,2019,3,1,18,30,0.1,-5.6000000000000005,0.0,0.0,0.0,0.16,805.0,164.0,1.9,65.51226084723248 +2019-03-01 19:30:00-07:00,2019,3,1,19,30,-0.5,-5.7,0.0,0.0,0.0,0.16,805.0,175.0,2.5,67.91244067808942 +2019-03-01 20:30:00-07:00,2019,3,1,20,30,-0.5,-5.300000000000001,0.0,0.0,0.0,0.16,804.0,181.0,3.2,70.0063298331612 +2019-03-01 21:30:00-07:00,2019,3,1,21,30,-0.5,-4.800000000000001,0.0,0.0,0.0,0.16,804.0,183.0,3.7,72.70428343953884 +2019-03-01 22:30:00-07:00,2019,3,1,22,30,-0.5,-4.5,0.0,0.0,0.0,0.16,803.0,185.0,4.0,74.36705189276533 +2019-03-01 23:30:00-07:00,2019,3,1,23,30,-0.5,-4.2,0.0,0.0,0.0,0.16,802.0,190.0,4.1000000000000005,76.06352734247147 +2019-03-02 00:30:00-07:00,2019,3,2,0,30,-0.7000000000000001,-4.3,0.0,0.0,0.0,0.16,801.0,196.0,3.9,76.60269151892572 +2019-03-02 01:30:00-07:00,2019,3,2,1,30,-1.0,-4.3,0.0,0.0,0.0,0.16,801.0,202.0,3.6,78.2994733686652 +2019-03-02 02:30:00-07:00,2019,3,2,2,30,-1.4,-4.5,0.0,0.0,0.0,0.16,800.0,209.0,3.2,79.42343660460278 +2019-03-02 03:30:00-07:00,2019,3,2,3,30,-1.8,-4.800000000000001,0.0,0.0,0.0,0.16,800.0,218.0,2.8000000000000003,79.96380790027541 +2019-03-02 04:30:00-07:00,2019,3,2,4,30,-2.1,-5.1000000000000005,0.0,0.0,0.0,0.16,801.0,233.0,2.5,79.91898711759026 +2019-03-02 05:30:00-07:00,2019,3,2,5,30,-2.5,-5.300000000000001,0.0,0.0,0.0,0.16,802.0,261.0,2.4000000000000004,81.07973414490078 +2019-03-02 06:30:00-07:00,2019,3,2,6,30,-1.9,-5.800000000000001,1.0,0.0,1.0,0.16,803.0,309.0,2.9000000000000004,74.67527367845501 +2019-03-02 07:30:00-07:00,2019,3,2,7,30,-0.5,-5.800000000000001,24.0,0.0,24.0,0.16,804.0,346.0,3.9,67.39774983226636 +2019-03-02 08:30:00-07:00,2019,3,2,8,30,0.9,-5.7,56.0,1.0,56.0,0.16,805.0,356.0,4.1000000000000005,61.36617511723584 +2019-03-02 09:30:00-07:00,2019,3,2,9,30,2.5,-5.2,56.0,0.0,56.0,0.16,804.0,184.0,3.7,56.84769562000123 +2019-03-02 10:30:00-07:00,2019,3,2,10,30,3.7,-4.800000000000001,95.0,0.0,95.0,0.16,804.0,18.0,3.4000000000000004,53.8267020079334 +2019-03-02 11:30:00-07:00,2019,3,2,11,30,4.0,-4.6000000000000005,83.0,0.0,83.0,0.16,804.0,34.0,3.4000000000000004,53.50465464195876 +2019-03-02 12:30:00-07:00,2019,3,2,12,30,3.4000000000000004,-4.6000000000000005,185.0,4.0,188.0,0.16,803.0,50.0,3.5,55.81177690958801 +2019-03-02 13:30:00-07:00,2019,3,2,13,30,2.0,-4.7,164.0,2.0,165.0,0.16,803.0,62.0,3.8,61.1760585759008 +2019-03-02 14:30:00-07:00,2019,3,2,14,30,0.1,-5.2,55.0,0.0,55.0,0.16,803.0,65.0,4.0,67.53042169363603 +2019-03-02 15:30:00-07:00,2019,3,2,15,30,-2.1,-6.1000000000000005,23.0,0.0,23.0,0.16,803.0,68.0,4.1000000000000005,74.07167119273352 +2019-03-02 16:30:00-07:00,2019,3,2,16,30,-4.3,-7.6,49.0,1.0,49.0,0.16,803.0,71.0,4.0,77.764363749743 +2019-03-02 17:30:00-07:00,2019,3,2,17,30,-6.300000000000001,-9.3,18.0,0.0,18.0,0.8,804.0,74.0,3.6,79.27615912413168 +2019-03-02 18:30:00-07:00,2019,3,2,18,30,-7.800000000000001,-11.1,0.0,0.0,0.0,0.8,804.0,79.0,3.1,77.17612289717631 +2019-03-02 19:30:00-07:00,2019,3,2,19,30,-8.9,-12.6,0.0,0.0,0.0,0.8,803.0,87.0,2.8000000000000003,74.54642202583219 +2019-03-02 20:30:00-07:00,2019,3,2,20,30,-9.5,-13.8,0.0,0.0,0.0,0.8,803.0,101.0,2.6,70.8892303195563 +2019-03-02 21:30:00-07:00,2019,3,2,21,30,-9.8,-14.4,0.0,0.0,0.0,0.8,803.0,114.0,2.8000000000000003,69.10827081813947 +2019-03-02 22:30:00-07:00,2019,3,2,22,30,-10.0,-14.7,0.0,0.0,0.0,0.8,802.0,118.0,2.8000000000000003,68.49915871066855 +2019-03-02 23:30:00-07:00,2019,3,2,23,30,-10.2,-15.0,0.0,0.0,0.0,0.8,803.0,103.0,2.5,67.89285653606328 +2019-03-03 00:30:00-07:00,2019,3,3,0,30,-10.7,-15.2,0.0,0.0,0.0,0.8,804.0,75.0,2.5,69.4795634687258 +2019-03-03 01:30:00-07:00,2019,3,3,1,30,-11.2,-15.8,0.0,0.0,0.0,0.8,804.0,55.0,2.4000000000000004,68.79650843630144 +2019-03-03 02:30:00-07:00,2019,3,3,2,30,-11.4,-16.3,0.0,0.0,0.0,0.8,805.0,45.0,2.2,67.0566147747755 +2019-03-03 03:30:00-07:00,2019,3,3,3,30,-11.3,-16.400000000000002,0.0,0.0,0.0,0.8,805.0,44.0,1.9,65.97142880721285 +2019-03-03 04:30:00-07:00,2019,3,3,4,30,-11.1,-16.3,0.0,0.0,0.0,0.8,805.0,46.0,1.5,65.47173249533988 +2019-03-03 05:30:00-07:00,2019,3,3,5,30,-11.2,-16.2,0.0,0.0,0.0,0.8,805.0,44.0,1.4,66.54729407784558 +2019-03-03 06:30:00-07:00,2019,3,3,6,30,-10.8,-16.400000000000002,7.0,59.0,6.0,0.8,805.0,37.0,1.5,63.397587131569665 +2019-03-03 07:30:00-07:00,2019,3,3,7,30,-9.0,-15.8,62.0,372.0,128.0,0.8,806.0,29.0,1.8,57.8279646583116 +2019-03-03 08:30:00-07:00,2019,3,3,8,30,-7.0,-14.1,34.0,0.0,34.0,0.8,806.0,33.0,2.2,56.960906793532544 +2019-03-03 09:30:00-07:00,2019,3,3,9,30,-5.6000000000000005,-12.8,95.0,0.0,95.0,0.8,805.0,39.0,2.6,56.88359758631522 +2019-03-03 10:30:00-07:00,2019,3,3,10,30,-4.5,-11.7,160.0,1.0,161.0,0.8,805.0,40.0,2.9000000000000004,57.184265328036886 +2019-03-03 11:30:00-07:00,2019,3,3,11,30,-4.0,-11.0,245.0,1.0,246.0,0.8,805.0,40.0,3.2,58.24020112619783 +2019-03-03 12:30:00-07:00,2019,3,3,12,30,-4.1000000000000005,-10.6,203.0,1.0,204.0,0.8,804.0,41.0,3.4000000000000004,60.572673452172864 +2019-03-03 13:30:00-07:00,2019,3,3,13,30,-4.4,-10.6,314.0,6.0,318.0,0.8,804.0,44.0,3.4000000000000004,61.95329871864505 +2019-03-03 14:30:00-07:00,2019,3,3,14,30,-4.800000000000001,-10.7,157.0,2.0,158.0,0.8,804.0,49.0,3.3000000000000003,63.34441042943703 +2019-03-03 15:30:00-07:00,2019,3,3,15,30,-5.9,-11.1,124.0,1.0,124.0,0.8,805.0,53.0,3.1,66.69925255623708 +2019-03-03 16:30:00-07:00,2019,3,3,16,30,-7.800000000000001,-12.0,102.0,5.0,103.0,0.8,805.0,60.0,2.7,71.81888390005832 +2019-03-03 17:30:00-07:00,2019,3,3,17,30,-9.9,-13.7,33.0,1.0,33.0,0.8,806.0,65.0,2.1,73.75403297336845 +2019-03-03 18:30:00-07:00,2019,3,3,18,30,-11.0,-15.4,0.0,0.0,0.0,0.8,806.0,66.0,1.5,69.98990799351002 +2019-03-03 19:30:00-07:00,2019,3,3,19,30,-11.1,-16.0,0.0,0.0,0.0,0.8,806.0,67.0,1.0,67.12667680166034 +2019-03-03 20:30:00-07:00,2019,3,3,20,30,-10.9,-15.9,0.0,0.0,0.0,0.8,806.0,71.0,0.8,66.61810522884029 +2019-03-03 21:30:00-07:00,2019,3,3,21,30,-10.8,-15.9,0.0,0.0,0.0,0.8,806.0,72.0,1.0,66.09088998390067 +2019-03-03 22:30:00-07:00,2019,3,3,22,30,-10.9,-16.0,0.0,0.0,0.0,0.8,806.0,73.0,1.2000000000000002,66.06704292488533 +2019-03-03 23:30:00-07:00,2019,3,3,23,30,-11.1,-16.1,0.0,0.0,0.0,0.8,806.0,74.0,1.3,66.57092029202549 +2019-03-04 00:30:00-07:00,2019,3,4,0,30,-11.6,-16.400000000000002,0.0,0.0,0.0,0.8,806.0,71.0,1.4,67.57119996557508 +2019-03-04 01:30:00-07:00,2019,3,4,1,30,-12.1,-16.900000000000002,0.0,0.0,0.0,0.8,806.0,67.0,1.3,67.45526287035786 +2019-03-04 02:30:00-07:00,2019,3,4,2,30,-12.4,-17.3,0.0,0.0,0.0,0.8,805.0,70.0,1.3,66.82161102593929 +2019-03-04 03:30:00-07:00,2019,3,4,3,30,-12.3,-17.400000000000002,0.0,0.0,0.0,0.8,805.0,80.0,1.2000000000000002,65.73080403043879 +2019-03-04 04:30:00-07:00,2019,3,4,4,30,-11.9,-17.2,0.0,0.0,0.0,0.8,805.0,91.0,1.1,64.73181368406836 +2019-03-04 05:30:00-07:00,2019,3,4,5,30,-11.5,-17.0,0.0,0.0,0.0,0.8,805.0,99.0,1.0,63.75318606261131 +2019-03-04 06:30:00-07:00,2019,3,4,6,30,-10.6,-16.7,4.0,0.0,4.0,0.8,806.0,102.0,1.1,60.855613239210435 +2019-03-04 07:30:00-07:00,2019,3,4,7,30,-8.700000000000001,-15.9,81.0,24.0,85.0,0.8,806.0,84.0,1.0,56.02188294927573 +2019-03-04 08:30:00-07:00,2019,3,4,8,30,-6.300000000000001,-14.0,70.0,876.0,386.0,0.8,806.0,58.0,1.2000000000000002,54.427778103577474 +2019-03-04 09:30:00-07:00,2019,3,4,9,30,-4.2,-12.5,81.0,973.0,577.0,0.8,805.0,46.0,1.6,52.42947545293806 +2019-03-04 10:30:00-07:00,2019,3,4,10,30,-2.6,-11.6,89.0,1020.0,720.0,0.8,805.0,38.0,2.0,50.01765146279909 +2019-03-04 11:30:00-07:00,2019,3,4,11,30,-1.5,-10.9,95.0,1040.0,803.0,0.8,805.0,31.0,2.3000000000000003,48.7669282198799 +2019-03-04 12:30:00-07:00,2019,3,4,12,30,-0.9,-10.5,97.0,1041.0,817.0,0.8,804.0,26.0,2.6,48.175422468455956 +2019-03-04 13:30:00-07:00,2019,3,4,13,30,-0.8,-10.3,95.0,1024.0,762.0,0.8,804.0,24.0,2.8000000000000003,48.58795370917863 +2019-03-04 14:30:00-07:00,2019,3,4,14,30,-1.2000000000000002,-10.1,89.0,985.0,642.0,0.8,805.0,24.0,2.7,50.826901221307246 +2019-03-04 15:30:00-07:00,2019,3,4,15,30,-2.1,-10.2,77.0,906.0,465.0,0.8,805.0,28.0,2.4000000000000004,53.876293116726146 +2019-03-04 16:30:00-07:00,2019,3,4,16,30,-4.1000000000000005,-10.3,62.0,747.0,257.0,0.8,805.0,36.0,1.9,62.028774188606725 +2019-03-04 17:30:00-07:00,2019,3,4,17,30,-6.4,-12.0,35.0,194.0,49.0,0.8,806.0,45.0,1.4,64.48403757414245 +2019-03-04 18:30:00-07:00,2019,3,4,18,30,-8.0,-13.4,0.0,0.0,0.0,0.8,807.0,122.0,0.9,65.14467005884637 +2019-03-04 19:30:00-07:00,2019,3,4,19,30,-8.700000000000001,-14.0,0.0,0.0,0.0,0.8,807.0,200.0,1.0,65.50884436728232 +2019-03-04 20:30:00-07:00,2019,3,4,20,30,-9.1,-14.3,0.0,0.0,0.0,0.8,807.0,211.0,1.3,65.95094436934474 +2019-03-04 21:30:00-07:00,2019,3,4,21,30,-9.3,-14.4,0.0,0.0,0.0,0.8,807.0,218.0,1.4,66.44590417313009 +2019-03-04 22:30:00-07:00,2019,3,4,22,30,-9.5,-14.6,0.0,0.0,0.0,0.8,807.0,221.0,1.2000000000000002,66.39885912153528 +2019-03-04 23:30:00-07:00,2019,3,4,23,30,-9.7,-14.7,0.0,0.0,0.0,0.8,807.0,224.0,0.9,66.8993351162659 +2019-03-05 00:30:00-07:00,2019,3,5,0,30,-10.1,-14.8,0.0,0.0,0.0,0.8,808.0,250.0,0.7000000000000001,68.47667670642987 +2019-03-05 01:30:00-07:00,2019,3,5,1,30,-10.7,-15.2,0.0,0.0,0.0,0.8,809.0,299.0,0.5,69.4795634687258 +2019-03-05 02:30:00-07:00,2019,3,5,2,30,-11.1,-15.5,0.0,0.0,0.0,0.8,809.0,236.0,0.4,69.96816377566037 +2019-03-05 03:30:00-07:00,2019,3,5,3,30,-11.5,-15.8,0.0,0.0,0.0,0.8,809.0,163.0,0.5,70.46332914735588 +2019-03-05 04:30:00-07:00,2019,3,5,4,30,-11.8,-16.0,0.0,0.0,0.0,0.8,810.0,184.0,0.8,70.98641695766868 +2019-03-05 05:30:00-07:00,2019,3,5,5,30,-11.9,-16.3,0.0,0.0,0.0,0.8,810.0,193.0,1.0,69.79342861247288 +2019-03-05 06:30:00-07:00,2019,3,5,6,30,-10.5,-16.6,8.0,96.0,8.0,0.8,811.0,200.0,1.5,60.88195880243996 +2019-03-05 07:30:00-07:00,2019,3,5,7,30,-7.0,-15.6,43.0,736.0,182.0,0.8,811.0,214.0,1.6,50.348396015162685 +2019-03-05 08:30:00-07:00,2019,3,5,8,30,-3.3000000000000003,-13.7,61.0,908.0,394.0,0.8,811.0,258.0,1.4,44.47461415560918 +2019-03-05 09:30:00-07:00,2019,3,5,9,30,-0.4,-12.3,75.0,989.0,584.0,0.8,811.0,306.0,1.3,40.233262117609364 +2019-03-05 10:30:00-07:00,2019,3,5,10,30,1.6,-11.0,84.0,1031.0,727.0,0.8,811.0,334.0,1.5,38.64335061338511 +2019-03-05 11:30:00-07:00,2019,3,5,11,30,2.8000000000000003,-9.7,87.0,1042.0,802.0,0.8,810.0,350.0,1.6,39.320828749164825 +2019-03-05 12:30:00-07:00,2019,3,5,12,30,3.6,-8.700000000000001,87.0,1045.0,815.0,0.8,810.0,180.0,1.5,40.188260684333486 +2019-03-05 13:30:00-07:00,2019,3,5,13,30,4.0,-8.200000000000001,84.0,1023.0,755.0,0.8,809.0,11.0,1.4,40.62453893490954 +2019-03-05 14:30:00-07:00,2019,3,5,14,30,3.9,-7.800000000000001,81.0,985.0,638.0,0.8,808.0,24.0,1.4,42.19956582447034 +2019-03-05 15:30:00-07:00,2019,3,5,15,30,2.9000000000000004,-7.5,101.0,818.0,455.0,0.8,809.0,41.0,1.5,46.34598647546742 +2019-03-05 16:30:00-07:00,2019,3,5,16,30,0.8,-7.2,121.0,410.0,230.0,0.8,809.0,75.0,1.4,55.106557540102806 +2019-03-05 17:30:00-07:00,2019,3,5,17,30,-1.4,-7.5,32.0,1.0,32.0,0.67,809.0,123.0,1.2000000000000002,63.1854777285927 +2019-03-05 18:30:00-07:00,2019,3,5,18,30,-2.2,-8.3,0.0,0.0,0.0,0.67,809.0,171.0,1.3,62.990234298162626 +2019-03-05 19:30:00-07:00,2019,3,5,19,30,-2.2,-8.4,0.0,0.0,0.0,0.67,808.0,202.0,1.3,62.502252891937964 +2019-03-05 20:30:00-07:00,2019,3,5,20,30,-2.3000000000000003,-8.0,0.0,0.0,0.0,0.67,808.0,212.0,1.3,64.95270260793998 +2019-03-05 21:30:00-07:00,2019,3,5,21,30,-2.4000000000000004,-7.7,0.0,0.0,0.0,0.67,807.0,223.0,1.4,66.97274601868803 +2019-03-05 22:30:00-07:00,2019,3,5,22,30,-2.4000000000000004,-7.5,0.0,0.0,0.0,0.67,807.0,236.0,1.4,68.01586765801265 +2019-03-05 23:30:00-07:00,2019,3,5,23,30,-2.5,-7.4,0.0,0.0,0.0,0.67,806.0,244.0,1.4,69.05195287882093 +2019-03-06 00:30:00-07:00,2019,3,6,0,30,-2.7,-7.5,0.0,0.0,0.0,0.67,806.0,244.0,1.2000000000000002,69.54406113121324 +2019-03-06 01:30:00-07:00,2019,3,6,1,30,-2.9000000000000004,-7.4,0.0,0.0,0.0,0.67,805.0,235.0,1.0,71.13092270743351 +2019-03-06 02:30:00-07:00,2019,3,6,2,30,-3.1,-7.4,0.0,0.0,0.0,0.67,805.0,222.0,1.0,72.19644081033783 +2019-03-06 03:30:00-07:00,2019,3,6,3,30,-3.2,-7.4,0.0,0.0,0.0,0.67,804.0,220.0,1.3,72.73584679225515 +2019-03-06 04:30:00-07:00,2019,3,6,4,30,-3.2,-7.4,0.0,0.0,0.0,0.67,804.0,224.0,1.5,72.73584679225515 +2019-03-06 05:30:00-07:00,2019,3,6,5,30,-3.0,-7.2,0.0,0.0,0.0,0.67,803.0,230.0,1.6,72.77476869095035 +2019-03-06 06:30:00-07:00,2019,3,6,6,30,-1.2000000000000002,-6.9,6.0,0.0,6.0,0.67,803.0,234.0,1.8,65.21066358390064 +2019-03-06 07:30:00-07:00,2019,3,6,7,30,1.8,-5.2,94.0,18.0,97.0,0.67,803.0,226.0,1.8,59.75498882187395 +2019-03-06 08:30:00-07:00,2019,3,6,8,30,4.800000000000001,-3.5,168.0,432.0,329.0,0.67,803.0,236.0,1.4,54.94298928212084 +2019-03-06 09:30:00-07:00,2019,3,6,9,30,7.5,-2.1,189.0,622.0,512.0,0.67,803.0,279.0,1.4,50.59955295019269 +2019-03-06 10:30:00-07:00,2019,3,6,10,30,9.2,-1.3,191.0,711.0,638.0,0.67,803.0,312.0,1.5,47.824996886895114 +2019-03-06 11:30:00-07:00,2019,3,6,11,30,10.0,-1.0,242.0,667.0,703.0,0.67,802.0,323.0,1.5,46.33147106177615 +2019-03-06 12:30:00-07:00,2019,3,6,12,30,10.3,-1.0,343.0,20.0,357.0,0.67,802.0,308.0,1.4,45.41194181984386 +2019-03-06 13:30:00-07:00,2019,3,6,13,30,10.3,-0.9,323.0,170.0,435.0,0.67,801.0,285.0,1.4,45.74506998371117 +2019-03-06 14:30:00-07:00,2019,3,6,14,30,9.7,-0.7000000000000001,264.0,23.0,277.0,0.67,801.0,283.0,1.5,48.318960893100744 +2019-03-06 15:30:00-07:00,2019,3,6,15,30,7.5,-0.7000000000000001,175.0,15.0,182.0,0.67,802.0,301.0,1.3,56.07274949074223 +2019-03-06 16:30:00-07:00,2019,3,6,16,30,4.2,-0.4,93.0,6.0,95.0,0.67,802.0,318.0,1.2000000000000002,72.00311336575518 +2019-03-06 17:30:00-07:00,2019,3,6,17,30,1.3,-2.2,32.0,1.0,32.0,0.65,802.0,310.0,1.2000000000000002,77.5093179930462 +2019-03-06 18:30:00-07:00,2019,3,6,18,30,-0.1,-3.3000000000000003,0.0,0.0,0.0,0.65,802.0,283.0,1.3,79.02928578782097 +2019-03-06 19:30:00-07:00,2019,3,6,19,30,-0.4,-3.7,0.0,0.0,0.0,0.65,802.0,260.0,1.5,78.39479537175046 +2019-03-06 20:30:00-07:00,2019,3,6,20,30,0.0,-3.6,0.0,0.0,0.0,0.65,801.0,248.0,1.7000000000000002,76.72110766615444 +2019-03-06 21:30:00-07:00,2019,3,6,21,30,0.3,-3.2,0.0,0.0,0.0,0.65,801.0,245.0,1.8,77.34566705516279 +2019-03-06 22:30:00-07:00,2019,3,6,22,30,0.6000000000000001,-2.9000000000000004,0.0,0.0,0.0,0.65,801.0,252.0,1.8,77.39493911991383 +2019-03-06 23:30:00-07:00,2019,3,6,23,30,0.2,-2.8000000000000003,0.0,0.0,0.0,0.65,801.0,270.0,1.6,80.25898719247036 +2019-03-07 00:30:00-07:00,2019,3,7,0,30,-0.6000000000000001,-3.2,0.0,0.0,0.0,0.65,802.0,291.0,1.3,82.56884701216148 +2019-03-07 01:30:00-07:00,2019,3,7,1,30,-1.2000000000000002,-3.8,0.0,0.0,0.0,0.65,802.0,297.0,1.0,82.48988635479661 +2019-03-07 02:30:00-07:00,2019,3,7,2,30,-1.4,-4.3,0.0,0.0,0.0,0.65,802.0,273.0,0.9,80.62727976536965 +2019-03-07 03:30:00-07:00,2019,3,7,3,30,-1.4,-4.6000000000000005,0.0,0.0,0.0,0.65,801.0,243.0,1.1,78.82752633891022 +2019-03-07 04:30:00-07:00,2019,3,7,4,30,-0.9,-4.6000000000000005,0.0,0.0,0.0,0.65,801.0,238.0,1.4,75.99421061859483 +2019-03-07 05:30:00-07:00,2019,3,7,5,30,0.0,-4.2,0.0,0.0,0.0,0.65,801.0,242.0,1.6,73.34942491705195 +2019-03-07 06:30:00-07:00,2019,3,7,6,30,2.2,-3.7,6.0,0.0,6.0,0.65,801.0,245.0,1.8,65.01329103423487 +2019-03-07 07:30:00-07:00,2019,3,7,7,30,5.1000000000000005,-1.9,44.0,684.0,180.0,0.65,801.0,250.0,2.2,60.585371682896 +2019-03-07 08:30:00-07:00,2019,3,7,8,30,7.0,-1.3,62.0,865.0,388.0,0.65,801.0,260.0,2.9000000000000004,55.532450847505636 +2019-03-07 09:30:00-07:00,2019,3,7,9,30,8.3,-1.6,112.0,845.0,556.0,0.65,801.0,265.0,3.5,49.716688612893954 +2019-03-07 10:30:00-07:00,2019,3,7,10,30,9.3,-1.8,326.0,248.0,483.0,0.65,801.0,257.0,3.7,45.79120854981347 +2019-03-07 11:30:00-07:00,2019,3,7,11,30,10.0,-1.7000000000000002,406.0,103.0,478.0,0.65,800.0,253.0,3.6,44.01319244095392 +2019-03-07 12:30:00-07:00,2019,3,7,12,30,10.0,-1.5,84.0,1012.0,799.0,0.65,800.0,259.0,3.6,44.664815770821484 +2019-03-07 13:30:00-07:00,2019,3,7,13,30,9.2,-1.5,85.0,994.0,746.0,0.65,800.0,272.0,3.3000000000000003,47.1284151456447 +2019-03-07 14:30:00-07:00,2019,3,7,14,30,7.9,-0.7000000000000001,80.0,960.0,631.0,0.65,801.0,281.0,2.7,54.5652151665328 +2019-03-07 15:30:00-07:00,2019,3,7,15,30,6.6000000000000005,-1.3,90.0,835.0,458.0,0.65,801.0,288.0,2.0,57.07793440478837 +2019-03-07 16:30:00-07:00,2019,3,7,16,30,4.800000000000001,-1.9,147.0,181.0,196.0,0.65,802.0,293.0,1.2000000000000002,61.86467377950264 +2019-03-07 17:30:00-07:00,2019,3,7,17,30,2.9000000000000004,-2.9000000000000004,27.0,453.0,65.0,0.52,802.0,275.0,0.8,65.66069626046436 +2019-03-07 18:30:00-07:00,2019,3,7,18,30,1.5,-3.7,0.0,0.0,0.0,0.52,803.0,248.0,1.0,68.34654419213437 +2019-03-07 19:30:00-07:00,2019,3,7,19,30,0.2,-4.2,0.0,0.0,0.0,0.52,803.0,242.0,1.3,72.2941204592023 +2019-03-07 20:30:00-07:00,2019,3,7,20,30,-1.0,-5.0,0.0,0.0,0.0,0.52,803.0,245.0,1.5,74.27525388002506 +2019-03-07 21:30:00-07:00,2019,3,7,21,30,-1.9,-5.9,0.0,0.0,0.0,0.52,803.0,247.0,1.6,74.10885357456775 +2019-03-07 22:30:00-07:00,2019,3,7,22,30,-2.5,-6.6000000000000005,0.0,0.0,0.0,0.52,803.0,246.0,1.8,73.43249110108096 +2019-03-07 23:30:00-07:00,2019,3,7,23,30,-2.8000000000000003,-7.1000000000000005,0.0,0.0,0.0,0.52,803.0,245.0,1.9,72.25570460540919 +2019-03-08 00:30:00-07:00,2019,3,8,0,30,-3.0,-7.5,0.0,0.0,0.0,0.52,802.0,244.0,1.9,71.11053968001626 +2019-03-08 01:30:00-07:00,2019,3,8,1,30,-3.2,-7.800000000000001,0.0,0.0,0.0,0.52,802.0,241.0,1.8,70.52193487756226 +2019-03-08 02:30:00-07:00,2019,3,8,2,30,-3.2,-8.3,0.0,0.0,0.0,0.52,801.0,235.0,1.8,67.83901493270986 +2019-03-08 03:30:00-07:00,2019,3,8,3,30,-3.0,-8.4,0.0,0.0,0.0,0.52,801.0,228.0,1.7000000000000002,66.31919590257334 +2019-03-08 04:30:00-07:00,2019,3,8,4,30,-2.7,-8.200000000000001,0.0,0.0,0.0,0.52,800.0,223.0,1.6,65.87453356572995 +2019-03-08 05:30:00-07:00,2019,3,8,5,30,-2.2,-8.1,0.0,0.0,0.0,0.52,800.0,220.0,1.5,63.976385805333145 +2019-03-08 06:30:00-07:00,2019,3,8,6,30,0.1,-7.7,11.0,123.0,13.0,0.52,800.0,220.0,1.9,55.77108810006456 +2019-03-08 07:30:00-07:00,2019,3,8,7,30,3.6,-5.2,44.0,680.0,183.0,0.52,799.0,218.0,2.5,52.59229135629517 +2019-03-08 08:30:00-07:00,2019,3,8,8,30,6.6000000000000005,-3.6,74.0,813.0,384.0,0.52,799.0,219.0,3.2,48.14445015435608 +2019-03-08 09:30:00-07:00,2019,3,8,9,30,8.6,-3.0,258.0,348.0,443.0,0.52,798.0,226.0,3.9,43.92773096002441 +2019-03-08 10:30:00-07:00,2019,3,8,10,30,9.9,-3.3000000000000003,327.0,326.0,535.0,0.52,797.0,227.0,4.3,39.35905651840114 +2019-03-08 11:30:00-07:00,2019,3,8,11,30,10.8,-3.5,269.0,558.0,660.0,0.52,796.0,222.0,4.5,36.5164751015652 +2019-03-08 12:30:00-07:00,2019,3,8,12,30,11.4,-3.3000000000000003,265.0,567.0,668.0,0.52,795.0,215.0,4.7,35.619322116165726 +2019-03-08 13:30:00-07:00,2019,3,8,13,30,11.6,-2.9000000000000004,275.0,280.0,462.0,0.52,794.0,208.0,4.7,36.21333373000676 +2019-03-08 14:30:00-07:00,2019,3,8,14,30,11.3,-2.4000000000000004,281.0,309.0,460.0,0.52,792.0,205.0,4.3,38.33382979061507 +2019-03-08 15:30:00-07:00,2019,3,8,15,30,10.0,-1.9,170.0,553.0,416.0,0.52,792.0,203.0,3.4000000000000004,43.37001845102163 +2019-03-08 16:30:00-07:00,2019,3,8,16,30,6.9,-0.8,144.0,224.0,206.0,0.52,791.0,199.0,2.4000000000000004,57.997312475389776 +2019-03-08 17:30:00-07:00,2019,3,8,17,30,3.8,-1.4,33.0,3.0,33.0,0.16,791.0,203.0,2.2,68.83593114725 +2019-03-08 18:30:00-07:00,2019,3,8,18,30,2.0,-2.0,0.0,0.0,0.0,0.16,791.0,244.0,2.2,74.81922222564012 +2019-03-08 19:30:00-07:00,2019,3,8,19,30,1.0,-1.8,0.0,0.0,0.0,0.16,791.0,293.0,2.2,81.56827317877084 +2019-03-08 20:30:00-07:00,2019,3,8,20,30,0.3,-2.3000000000000003,0.0,0.0,0.0,0.16,791.0,299.0,1.9,82.68632839205507 +2019-03-08 21:30:00-07:00,2019,3,8,21,30,-0.3,-3.3000000000000003,0.0,0.0,0.0,0.16,791.0,284.0,1.5,80.18577883645473 +2019-03-08 22:30:00-07:00,2019,3,8,22,30,-1.0,-4.4,0.0,0.0,0.0,0.16,791.0,277.0,1.5,77.71297654080702 +2019-03-08 23:30:00-07:00,2019,3,8,23,30,-1.6,-5.6000000000000005,0.0,0.0,0.0,0.16,790.0,273.0,1.8,74.16448735250604 +2019-03-09 00:30:00-07:00,2019,3,9,0,30,-2.0,-6.6000000000000005,0.0,0.0,0.0,0.16,790.0,269.0,2.4000000000000004,70.76936377880763 +2019-03-09 01:30:00-07:00,2019,3,9,1,30,-2.1,-7.5,0.0,0.0,0.0,0.16,791.0,269.0,3.2,66.52493627534326 +2019-03-09 02:30:00-07:00,2019,3,9,2,30,-2.1,-8.0,0.0,0.0,0.0,0.16,792.0,273.0,3.8,64.00040294518827 +2019-03-09 03:30:00-07:00,2019,3,9,3,30,-2.0,-8.200000000000001,0.0,0.0,0.0,0.16,793.0,279.0,4.3,62.55164059176406 +2019-03-09 04:30:00-07:00,2019,3,9,4,30,-2.4000000000000004,-8.700000000000001,0.0,0.0,0.0,0.16,794.0,283.0,4.5,61.967727266026834 +2019-03-09 05:30:00-07:00,2019,3,9,5,30,-3.1,-9.8,0.0,0.0,0.0,0.16,795.0,284.0,4.6000000000000005,59.87967582515878 +2019-03-09 06:30:00-07:00,2019,3,9,6,30,-2.5,-10.9,12.0,78.0,13.0,0.16,796.0,282.0,5.300000000000001,52.498261430919875 +2019-03-09 07:30:00-07:00,2019,3,9,7,30,-0.8,-11.4,37.0,777.0,200.0,0.16,797.0,285.0,6.2,44.52162027223002 +2019-03-09 08:30:00-07:00,2019,3,9,8,30,1.0,-12.0,49.0,938.0,412.0,0.16,798.0,293.0,6.5,37.24517919206248 +2019-03-09 09:30:00-07:00,2019,3,9,9,30,2.6,-12.3,56.0,1010.0,597.0,0.16,798.0,301.0,6.300000000000001,32.43004103786056 +2019-03-09 10:30:00-07:00,2019,3,9,10,30,3.9,-12.2,63.0,1043.0,735.0,0.16,798.0,304.0,5.9,29.82430758919008 +2019-03-09 11:30:00-07:00,2019,3,9,11,30,4.7,-11.7,71.0,1045.0,808.0,0.16,798.0,303.0,5.300000000000001,29.353583767611344 +2019-03-09 12:30:00-07:00,2019,3,9,12,30,5.2,-11.0,74.0,1040.0,818.0,0.16,798.0,301.0,4.800000000000001,29.977496283912707 +2019-03-09 13:30:00-07:00,2019,3,9,13,30,5.300000000000001,-10.3,155.0,874.0,744.0,0.16,798.0,298.0,4.4,31.46957722610022 +2019-03-09 14:30:00-07:00,2019,3,9,14,30,5.0,-9.7,68.0,996.0,648.0,0.16,799.0,296.0,3.9,33.690276115988226 +2019-03-09 15:30:00-07:00,2019,3,9,15,30,4.1000000000000005,-9.3,65.0,912.0,474.0,0.16,799.0,295.0,3.1,37.022107723621104 +2019-03-09 16:30:00-07:00,2019,3,9,16,30,1.9,-8.700000000000001,123.0,390.0,232.0,0.16,800.0,299.0,1.6,45.33663005994494 +2019-03-09 17:30:00-07:00,2019,3,9,17,30,-0.9,-7.9,36.0,313.0,65.0,0.16,801.0,212.0,0.5,59.058742080762926 +2019-03-09 18:30:00-07:00,2019,3,9,18,30,-2.5,-9.4,0.0,0.0,0.0,0.16,802.0,121.0,0.5,59.10101083361234 +2019-03-09 19:30:00-07:00,2019,3,9,19,30,-3.4000000000000004,-10.3,0.0,0.0,0.0,0.16,803.0,125.0,0.8,58.864105515256774 +2019-03-09 20:30:00-07:00,2019,3,9,20,30,-4.5,-10.9,0.0,0.0,0.0,0.16,803.0,139.0,1.0,60.95219498034704 +2019-03-09 21:30:00-07:00,2019,3,9,21,30,-5.4,-11.3,0.0,0.0,0.0,0.16,804.0,163.0,0.9,63.19648902111342 +2019-03-09 22:30:00-07:00,2019,3,9,22,30,-5.9,-11.6,0.0,0.0,0.0,0.16,804.0,187.0,0.8,64.09040553728514 +2019-03-09 23:30:00-07:00,2019,3,9,23,30,-6.1000000000000005,-11.7,0.0,0.0,0.0,0.16,804.0,205.0,0.7000000000000001,64.55654379652937 +2019-03-10 00:30:00-07:00,2019,3,10,0,30,-6.300000000000001,-11.7,0.0,0.0,0.0,0.16,804.0,216.0,0.6000000000000001,65.5500895047532 +2019-03-10 01:30:00-07:00,2019,3,10,1,30,-6.5,-11.7,0.0,0.0,0.0,0.16,804.0,222.0,0.7000000000000001,66.56064526930302 +2019-03-10 02:30:00-07:00,2019,3,10,2,30,-6.7,-12.0,0.0,0.0,0.0,0.16,804.0,225.0,0.8,65.98287326338603 +2019-03-10 03:30:00-07:00,2019,3,10,3,30,-6.6000000000000005,-12.1,0.0,0.0,0.0,0.16,804.0,227.0,1.1,64.9554287878911 +2019-03-10 04:30:00-07:00,2019,3,10,4,30,-6.4,-12.2,0.0,0.0,0.0,0.16,804.0,231.0,1.5,63.45647708776751 +2019-03-10 05:30:00-07:00,2019,3,10,5,30,-5.6000000000000005,-12.2,0.0,0.0,0.0,0.16,804.0,232.0,2.1,59.7019572718456 +2019-03-10 06:30:00-07:00,2019,3,10,6,30,-3.5,-11.8,15.0,140.0,18.0,0.16,804.0,229.0,2.7,52.630972716398446 +2019-03-10 07:30:00-07:00,2019,3,10,7,30,-0.3,-10.3,51.0,653.0,191.0,0.16,804.0,222.0,2.9000000000000004,46.850021270833246 +2019-03-10 08:30:00-07:00,2019,3,10,8,30,2.6,-8.9,67.0,838.0,396.0,0.16,804.0,218.0,2.7,42.46435231021967 +2019-03-10 09:30:00-07:00,2019,3,10,9,30,4.9,-7.9,75.0,929.0,577.0,0.16,804.0,215.0,2.5,39.04453026547535 +2019-03-10 10:30:00-07:00,2019,3,10,10,30,6.6000000000000005,-7.300000000000001,78.0,980.0,714.0,0.16,804.0,214.0,2.4000000000000004,36.35812929471678 +2019-03-10 11:30:00-07:00,2019,3,10,11,30,7.9,-6.7,76.0,1007.0,792.0,0.16,804.0,216.0,2.4000000000000004,34.836146732510876 +2019-03-10 12:30:00-07:00,2019,3,10,12,30,8.5,-6.2,77.0,1006.0,802.0,0.16,803.0,221.0,2.4000000000000004,34.750763109190444 +2019-03-10 13:30:00-07:00,2019,3,10,13,30,8.6,-5.800000000000001,215.0,693.0,685.0,0.16,803.0,229.0,2.3000000000000003,35.585164587767615 +2019-03-10 14:30:00-07:00,2019,3,10,14,30,8.3,-5.5,75.0,949.0,632.0,0.16,803.0,243.0,2.0,37.15322971603806 +2019-03-10 15:30:00-07:00,2019,3,10,15,30,7.4,-5.300000000000001,232.0,218.0,331.0,0.16,804.0,274.0,1.5,40.1036771679459 +2019-03-10 16:30:00-07:00,2019,3,10,16,30,4.9,-3.9,137.0,46.0,150.0,0.16,805.0,162.0,1.1,52.95348030683324 +2019-03-10 17:30:00-07:00,2019,3,10,17,30,2.1,-5.0,42.0,12.0,43.0,0.16,806.0,37.0,1.0,59.380990251948106 +2019-03-10 18:30:00-07:00,2019,3,10,18,30,0.7000000000000001,-6.5,0.0,0.0,0.0,0.16,807.0,50.0,0.8,58.5702334870242 +2019-03-10 19:30:00-07:00,2019,3,10,19,30,0.0,-6.7,0.0,0.0,0.0,0.16,808.0,201.0,0.7000000000000001,60.67424203711984 +2019-03-10 20:30:00-07:00,2019,3,10,20,30,-1.0,-7.0,0.0,0.0,0.0,0.16,808.0,346.0,0.8,63.77121389329665 +2019-03-10 21:30:00-07:00,2019,3,10,21,30,-2.2,-7.1000000000000005,0.0,0.0,0.0,0.16,808.0,340.0,1.0,69.11630763387406 +2019-03-10 22:30:00-07:00,2019,3,10,22,30,-3.1,-7.2,0.0,0.0,0.0,0.16,809.0,337.0,1.0,73.31804085472407 +2019-03-10 23:30:00-07:00,2019,3,10,23,30,-3.5,-7.300000000000001,0.0,0.0,0.0,0.16,809.0,331.0,1.0,74.95692805432917 +2019-03-11 00:30:00-07:00,2019,3,11,0,30,-3.4000000000000004,-7.5,0.0,0.0,0.0,0.16,809.0,323.0,0.9,73.26055768704748 +2019-03-11 01:30:00-07:00,2019,3,11,1,30,-2.9000000000000004,-7.800000000000001,0.0,0.0,0.0,0.16,809.0,314.0,0.8,68.96586099123645 +2019-03-11 02:30:00-07:00,2019,3,11,2,30,-2.4000000000000004,-8.0,0.0,0.0,0.0,0.16,809.0,306.0,0.7000000000000001,65.43475545414113 +2019-03-11 03:30:00-07:00,2019,3,11,3,30,-2.2,-8.3,0.0,0.0,0.0,0.16,809.0,308.0,0.7000000000000001,62.990234298162626 +2019-03-11 04:30:00-07:00,2019,3,11,4,30,-2.2,-8.5,0.0,0.0,0.0,0.16,809.0,326.0,0.7000000000000001,62.0176405577817 +2019-03-11 05:30:00-07:00,2019,3,11,5,30,-2.1,-8.5,0.0,0.0,0.0,0.16,810.0,347.0,0.8,61.5615168776344 +2019-03-11 06:30:00-07:00,2019,3,11,6,30,-1.3,-8.4,10.0,0.0,10.0,0.16,810.0,182.0,0.9,58.49756809776883 +2019-03-11 07:30:00-07:00,2019,3,11,7,30,0.9,-8.3,63.0,3.0,64.0,0.16,810.0,47.0,0.8,50.247181900694386 +2019-03-11 08:30:00-07:00,2019,3,11,8,30,3.5,-7.7,161.0,32.0,174.0,0.16,810.0,117.0,0.9,43.74124872296113 +2019-03-11 09:30:00-07:00,2019,3,11,9,30,5.9,-6.5,132.0,29.0,148.0,0.16,810.0,157.0,1.5,40.57381590730694 +2019-03-11 10:30:00-07:00,2019,3,11,10,30,8.0,-5.300000000000001,232.0,549.0,591.0,0.16,809.0,169.0,2.3000000000000003,38.49729470076728 +2019-03-11 11:30:00-07:00,2019,3,11,11,30,9.3,-4.5,183.0,746.0,717.0,0.16,809.0,178.0,2.9000000000000004,37.45382179527541 +2019-03-11 12:30:00-07:00,2019,3,11,12,30,9.9,-3.9,208.0,722.0,731.0,0.16,808.0,189.0,3.2,37.63356902429604 +2019-03-11 13:30:00-07:00,2019,3,11,13,30,10.0,-3.5,298.0,115.0,376.0,0.16,807.0,198.0,3.3000000000000003,38.517595153138494 +2019-03-11 14:30:00-07:00,2019,3,11,14,30,10.0,-3.0,198.0,52.0,229.0,0.16,807.0,205.0,2.9000000000000004,39.97939537472537 +2019-03-11 15:30:00-07:00,2019,3,11,15,30,9.4,-2.7,215.0,360.0,379.0,0.16,807.0,208.0,2.0,42.55829878186783 +2019-03-11 16:30:00-07:00,2019,3,11,16,30,7.300000000000001,-1.4,101.0,504.0,246.0,0.16,807.0,198.0,1.1,54.00650181391389 +2019-03-11 17:30:00-07:00,2019,3,11,17,30,4.5,-2.3000000000000003,37.0,314.0,68.0,0.16,808.0,185.0,0.8,61.33682313256708 +2019-03-11 18:30:00-07:00,2019,3,11,18,30,2.6,-4.1000000000000005,0.0,0.0,0.0,0.16,808.0,208.0,0.7000000000000001,61.32497686348252 +2019-03-11 19:30:00-07:00,2019,3,11,19,30,1.3,-4.5,0.0,0.0,0.0,0.16,809.0,253.0,0.7000000000000001,65.29436631916529 +2019-03-11 20:30:00-07:00,2019,3,11,20,30,0.1,-4.800000000000001,0.0,0.0,0.0,0.16,809.0,273.0,0.8,69.6036618807797 +2019-03-11 21:30:00-07:00,2019,3,11,21,30,-0.6000000000000001,-5.2,0.0,0.0,0.0,0.16,808.0,272.0,1.0,71.05442537461049 +2019-03-11 22:30:00-07:00,2019,3,11,22,30,-0.9,-5.5,0.0,0.0,0.0,0.16,807.0,278.0,1.2000000000000002,70.99366477863046 +2019-03-11 23:30:00-07:00,2019,3,11,23,30,-0.8,-5.6000000000000005,0.0,0.0,0.0,0.16,806.0,285.0,1.2000000000000002,69.94386283754176 +2019-03-12 00:30:00-07:00,2019,3,12,0,30,-0.5,-5.7,0.0,0.0,0.0,0.16,806.0,269.0,1.1,67.91244067808942 +2019-03-12 01:30:00-07:00,2019,3,12,1,30,0.2,-5.9,0.0,0.0,0.0,0.16,805.0,242.0,1.0,63.57189923996195 +2019-03-12 02:30:00-07:00,2019,3,12,2,30,0.5,-4.5,0.0,0.0,0.0,0.16,805.0,247.0,1.1,69.16491732482187 +2019-03-12 03:30:00-07:00,2019,3,12,3,30,0.1,-3.5,0.0,0.0,0.0,0.16,804.0,265.0,1.5,76.73795504446039 +2019-03-12 04:30:00-07:00,2019,3,12,4,30,-0.4,-3.2,0.0,0.0,0.0,0.16,804.0,269.0,1.6,81.37505736783656 +2019-03-12 05:30:00-07:00,2019,3,12,5,30,-0.2,-3.7,0.0,0.0,0.0,0.16,803.0,259.0,1.8,77.26320806946292 +2019-03-12 06:30:00-07:00,2019,3,12,6,30,2.0,-4.0,15.0,68.0,17.0,0.16,803.0,249.0,2.7,64.48201853103397 +2019-03-12 07:30:00-07:00,2019,3,12,7,30,5.300000000000001,-3.4000000000000004,121.0,82.0,139.0,0.16,802.0,249.0,3.4000000000000004,53.46140394156458 +2019-03-12 08:30:00-07:00,2019,3,12,8,30,8.8,-2.0,164.0,280.0,277.0,0.16,801.0,254.0,3.6,46.6681388838292 +2019-03-12 09:30:00-07:00,2019,3,12,9,30,11.8,-1.3,256.0,164.0,346.0,0.16,800.0,250.0,3.6,40.21694017468543 +2019-03-12 10:30:00-07:00,2019,3,12,10,30,13.8,-1.6,230.0,521.0,574.0,0.16,800.0,234.0,3.3000000000000003,34.51453836075596 +2019-03-12 11:30:00-07:00,2019,3,12,11,30,15.0,-1.8,279.0,377.0,551.0,0.16,799.0,222.0,3.4000000000000004,31.47242252273948 +2019-03-12 12:30:00-07:00,2019,3,12,12,30,15.4,-1.3,167.0,775.0,733.0,0.16,798.0,219.0,3.7,31.821584969641446 +2019-03-12 13:30:00-07:00,2019,3,12,13,30,15.4,-0.6000000000000001,200.0,600.0,612.0,0.16,797.0,223.0,3.6,33.49202584140888 +2019-03-12 14:30:00-07:00,2019,3,12,14,30,15.0,-0.2,233.0,387.0,463.0,0.16,796.0,227.0,3.1,35.37848964025672 +2019-03-12 15:30:00-07:00,2019,3,12,15,30,14.0,-0.1,221.0,93.0,264.0,0.16,796.0,223.0,2.0,38.01375196137571 +2019-03-12 16:30:00-07:00,2019,3,12,16,30,11.8,1.2000000000000002,148.0,72.0,169.0,0.16,796.0,161.0,1.1,48.21475237363251 +2019-03-12 17:30:00-07:00,2019,3,12,17,30,8.4,1.0,53.0,14.0,54.0,0.68,796.0,76.0,1.2000000000000002,59.65532943563179 +2019-03-12 18:30:00-07:00,2019,3,12,18,30,5.7,-0.5,0.0,0.0,0.0,0.68,795.0,40.0,1.9,64.38948798464415 +2019-03-12 19:30:00-07:00,2019,3,12,19,30,4.3,-0.6000000000000001,0.0,0.0,0.0,0.68,795.0,192.0,2.4000000000000004,70.46674818825826 +2019-03-12 20:30:00-07:00,2019,3,12,20,30,3.1,-0.5,0.0,0.0,0.0,0.68,794.0,350.0,2.6,77.23545730467063 +2019-03-12 21:30:00-07:00,2019,3,12,21,30,2.2,-1.0,0.0,0.0,0.0,0.68,793.0,339.0,2.3000000000000003,79.37958514534105 +2019-03-12 22:30:00-07:00,2019,3,12,22,30,2.0,-0.9,0.0,0.0,0.0,0.68,791.0,329.0,2.5,81.11001952568658 +2019-03-12 23:30:00-07:00,2019,3,12,23,30,1.8,-0.8,0.0,0.0,0.0,0.68,789.0,324.0,2.4000000000000004,82.87960668656666 +2019-03-13 00:30:00-07:00,2019,3,13,0,30,1.6,-0.8,0.0,0.0,0.0,0.68,788.0,311.0,2.1,84.0735515694944 +2019-03-13 01:30:00-07:00,2019,3,13,1,30,1.6,-1.0,0.0,0.0,0.0,0.68,786.0,302.0,2.6,82.85401670658952 +2019-03-13 02:30:00-07:00,2019,3,13,2,30,1.7000000000000002,-1.1,0.0,0.0,0.0,0.68,784.0,309.0,2.9000000000000004,81.66380187232292 +2019-03-13 03:30:00-07:00,2019,3,13,3,30,1.6,-1.1,0.0,0.0,0.0,0.68,783.0,323.0,2.7,82.25015404944014 +2019-03-13 04:30:00-07:00,2019,3,13,4,30,1.2000000000000002,-1.0,0.0,0.0,0.0,0.68,782.0,327.0,2.6,85.26435518794952 +2019-03-13 05:30:00-07:00,2019,3,13,5,30,0.8,-1.5,0.0,0.0,0.0,0.68,781.0,321.0,3.5,84.59637882108623 +2019-03-13 06:30:00-07:00,2019,3,13,6,30,1.1,-1.8,6.0,0.0,6.0,0.68,781.0,318.0,5.4,80.98393007960532 +2019-03-13 07:30:00-07:00,2019,3,13,7,30,1.4,-1.9,32.0,0.0,32.0,0.68,780.0,325.0,7.5,78.67721205757736 +2019-03-13 08:30:00-07:00,2019,3,13,8,30,1.1,-2.0,15.0,0.0,15.0,0.68,780.0,333.0,9.1,79.79952044222901 +2019-03-13 09:30:00-07:00,2019,3,13,9,30,0.1,-2.9000000000000004,21.0,0.0,21.0,0.68,781.0,339.0,10.3,80.24437653621193 +2019-03-13 10:30:00-07:00,2019,3,13,10,30,-1.2000000000000002,-4.7,33.0,0.0,33.0,0.68,782.0,344.0,10.7,77.09700955676595 +2019-03-13 11:30:00-07:00,2019,3,13,11,30,-2.2,-6.2,51.0,0.0,51.0,0.68,784.0,346.0,10.5,74.05305197492787 +2019-03-13 12:30:00-07:00,2019,3,13,12,30,-2.6,-6.9,59.0,0.0,59.0,0.68,785.0,345.0,10.1,72.29511640241911 +2019-03-13 13:30:00-07:00,2019,3,13,13,30,-2.6,-6.9,77.0,0.0,77.0,0.68,787.0,345.0,9.7,72.29511640241911 +2019-03-13 14:30:00-07:00,2019,3,13,14,30,-2.6,-6.7,60.0,0.0,60.0,0.68,789.0,343.0,9.2,73.41346369314778 +2019-03-13 15:30:00-07:00,2019,3,13,15,30,-2.7,-6.6000000000000005,76.0,2.0,77.0,0.68,790.0,342.0,8.8,74.5288018918281 +2019-03-13 16:30:00-07:00,2019,3,13,16,30,-3.1,-6.9,45.0,0.0,45.0,0.68,792.0,342.0,8.4,75.02952257172109 +2019-03-13 17:30:00-07:00,2019,3,13,17,30,-3.5,-7.4,28.0,0.0,28.0,0.8,793.0,342.0,8.0,74.38113957814575 +2019-03-13 18:30:00-07:00,2019,3,13,18,30,-3.9,-8.1,0.0,0.0,0.0,0.8,794.0,340.0,7.7,72.5990088705602 +2019-03-13 19:30:00-07:00,2019,3,13,19,30,-4.3,-8.8,0.0,0.0,0.0,0.8,795.0,337.0,7.4,70.84374263625766 +2019-03-13 20:30:00-07:00,2019,3,13,20,30,-4.6000000000000005,-9.5,0.0,0.0,0.0,0.8,796.0,334.0,6.9,68.59629771586563 +2019-03-13 21:30:00-07:00,2019,3,13,21,30,-4.7,-9.9,0.0,0.0,0.0,0.8,797.0,332.0,6.5,66.97414840125529 +2019-03-13 22:30:00-07:00,2019,3,13,22,30,-4.800000000000001,-10.1,0.0,0.0,0.0,0.8,798.0,329.0,6.1000000000000005,66.42512326520321 +2019-03-13 23:30:00-07:00,2019,3,13,23,30,-4.800000000000001,-10.2,0.0,0.0,0.0,0.8,798.0,328.0,5.7,65.90257821738274 +2019-03-14 00:30:00-07:00,2019,3,14,0,30,-5.0,-10.4,0.0,0.0,0.0,0.8,799.0,328.0,5.5,65.8558603826099 +2019-03-14 01:30:00-07:00,2019,3,14,1,30,-5.2,-10.5,0.0,0.0,0.0,0.8,800.0,329.0,5.2,66.33266422690117 +2019-03-14 02:30:00-07:00,2019,3,14,2,30,-5.6000000000000005,-10.8,0.0,0.0,0.0,0.8,801.0,330.0,4.9,66.76826544971891 +2019-03-14 03:30:00-07:00,2019,3,14,3,30,-6.0,-11.1,0.0,0.0,0.0,0.8,802.0,332.0,4.6000000000000005,67.20947223518587 +2019-03-14 04:30:00-07:00,2019,3,14,4,30,-6.4,-11.5,0.0,0.0,0.0,0.8,803.0,335.0,4.4,67.11817772243097 +2019-03-14 05:30:00-07:00,2019,3,14,5,30,-6.7,-11.8,0.0,0.0,0.0,0.8,804.0,338.0,4.2,67.04948096722737 +2019-03-14 06:30:00-07:00,2019,3,14,6,30,-6.4,-12.0,12.0,0.0,12.0,0.8,805.0,339.0,4.2,64.48403757414245 +2019-03-14 07:30:00-07:00,2019,3,14,7,30,-5.4,-11.4,118.0,24.0,124.0,0.8,806.0,341.0,4.6000000000000005,62.69420005988453 +2019-03-14 08:30:00-07:00,2019,3,14,8,30,-4.4,-10.7,270.0,81.0,304.0,0.8,807.0,344.0,5.2,61.46384094016863 +2019-03-14 09:30:00-07:00,2019,3,14,9,30,-3.5,-10.1,381.0,246.0,519.0,0.8,807.0,348.0,5.4,60.24960943331882 +2019-03-14 10:30:00-07:00,2019,3,14,10,30,-2.6,-9.4,410.0,426.0,695.0,0.8,808.0,351.0,5.2,59.540367335954556 +2019-03-14 11:30:00-07:00,2019,3,14,11,30,-1.9,-8.9,382.0,546.0,781.0,0.8,808.0,355.0,4.9,58.79695439531298 +2019-03-14 12:30:00-07:00,2019,3,14,12,30,-1.3,-8.4,522.0,311.0,752.0,0.8,808.0,180.0,4.4,58.49756809776883 +2019-03-14 13:30:00-07:00,2019,3,14,13,30,-1.0,-8.1,501.0,253.0,677.0,0.8,808.0,7.0,4.0,58.57649942140826 +2019-03-14 14:30:00-07:00,2019,3,14,14,30,-1.1,-7.9,404.0,287.0,577.0,0.8,809.0,17.0,3.4000000000000004,59.92875612117064 +2019-03-14 15:30:00-07:00,2019,3,14,15,30,-1.5,-7.800000000000001,334.0,71.0,367.0,0.8,809.0,30.0,2.8000000000000003,62.19166228756419 +2019-03-14 16:30:00-07:00,2019,3,14,16,30,-3.0,-7.4,199.0,46.0,213.0,0.8,810.0,45.0,1.9,71.66147948078579 +2019-03-14 17:30:00-07:00,2019,3,14,17,30,-5.5,-8.9,57.0,5.0,58.0,0.8,811.0,59.0,1.3,76.96167815300527 +2019-03-14 18:30:00-07:00,2019,3,14,18,30,-7.300000000000001,-12.0,0.0,0.0,0.0,0.8,812.0,71.0,0.9,69.09799089437371 +2019-03-14 19:30:00-07:00,2019,3,14,19,30,-8.6,-12.8,0.0,0.0,0.0,0.8,812.0,150.0,0.6000000000000001,71.65505926872659 +2019-03-14 20:30:00-07:00,2019,3,14,20,30,-10.2,-13.8,0.0,0.0,0.0,0.8,813.0,236.0,0.9,74.90802084088149 +2019-03-14 21:30:00-07:00,2019,3,14,21,30,-11.8,-15.1,0.0,0.0,0.0,0.8,813.0,252.0,1.4,76.47649478790655 +2019-03-14 22:30:00-07:00,2019,3,14,22,30,-13.0,-16.5,0.0,0.0,0.0,0.8,813.0,258.0,1.8,74.99931078221498 +2019-03-14 23:30:00-07:00,2019,3,14,23,30,-13.8,-17.7,0.0,0.0,0.0,0.8,813.0,260.0,2.2,72.36828225162118 +2019-03-15 00:30:00-07:00,2019,3,15,0,30,-14.3,-18.8,0.0,0.0,0.0,0.8,813.0,264.0,2.5,68.67352587212953 +2019-03-15 01:30:00-07:00,2019,3,15,1,30,-14.5,-19.700000000000003,0.0,0.0,0.0,0.8,813.0,266.0,2.8000000000000003,64.63612420290497 +2019-03-15 02:30:00-07:00,2019,3,15,2,30,-14.4,-20.1,0.0,0.0,0.0,0.8,813.0,262.0,3.0,61.939837463393296 +2019-03-15 03:30:00-07:00,2019,3,15,3,30,-14.0,-20.1,0.0,0.0,0.0,0.8,813.0,259.0,2.9000000000000004,59.94600865905552 +2019-03-15 04:30:00-07:00,2019,3,15,4,30,-13.7,-19.9,0.0,0.0,0.0,0.8,813.0,258.0,2.8000000000000003,59.51338729059853 +2019-03-15 05:30:00-07:00,2019,3,15,5,30,-13.1,-19.6,0.0,0.0,0.0,0.8,813.0,260.0,2.7,58.162347463540264 +2019-03-15 06:30:00-07:00,2019,3,15,6,30,-10.6,-19.200000000000003,21.0,272.0,34.0,0.8,813.0,261.0,2.5,49.26095873580667 +2019-03-15 07:30:00-07:00,2019,3,15,7,30,-5.7,-15.4,47.0,800.0,240.0,0.8,813.0,258.0,2.2,46.3449426136989 +2019-03-15 08:30:00-07:00,2019,3,15,8,30,-1.5,-10.6,64.0,942.0,459.0,0.8,813.0,226.0,1.4,49.94229798512198 +2019-03-15 09:30:00-07:00,2019,3,15,9,30,0.7000000000000001,-9.7,76.0,1009.0,648.0,0.8,813.0,166.0,0.6000000000000001,45.68989830321409 +2019-03-15 10:30:00-07:00,2019,3,15,10,30,2.0,-8.1,86.0,1043.0,789.0,0.8,813.0,145.0,0.6000000000000001,47.165418980302185 +2019-03-15 11:30:00-07:00,2019,3,15,11,30,3.1,-7.9,91.0,1058.0,868.0,0.8,813.0,172.0,0.7000000000000001,44.302700057664126 +2019-03-15 12:30:00-07:00,2019,3,15,12,30,4.1000000000000005,-8.0,94.0,1055.0,878.0,0.8,812.0,226.0,0.6000000000000001,40.97152435499397 +2019-03-15 13:30:00-07:00,2019,3,15,13,30,4.6000000000000005,-7.800000000000001,90.0,1034.0,814.0,0.8,812.0,293.0,0.7000000000000001,40.18028515533173 +2019-03-15 14:30:00-07:00,2019,3,15,14,30,4.7,-7.6,83.0,1004.0,693.0,0.8,812.0,333.0,1.0,40.52271560705599 +2019-03-15 15:30:00-07:00,2019,3,15,15,30,3.9,-7.2,73.0,946.0,519.0,0.8,812.0,351.0,1.2000000000000002,44.20051466644065 +2019-03-15 16:30:00-07:00,2019,3,15,16,30,1.4,-5.6000000000000005,56.0,831.0,308.0,0.8,812.0,195.0,1.0,59.652807075620984 +2019-03-15 17:30:00-07:00,2019,3,15,17,30,-1.8,-6.6000000000000005,33.0,541.0,94.0,0.74,813.0,37.0,0.6000000000000001,69.73434465330685 +2019-03-15 18:30:00-07:00,2019,3,15,18,30,-3.8,-10.1,0.0,0.0,0.0,0.74,813.0,141.0,0.6000000000000001,61.61591858994211 +2019-03-15 19:30:00-07:00,2019,3,15,19,30,-4.800000000000001,-10.7,0.0,0.0,0.0,0.74,813.0,244.0,1.0,63.34441042943703 +2019-03-15 20:30:00-07:00,2019,3,15,20,30,-5.5,-11.0,0.0,0.0,0.0,0.74,813.0,251.0,1.7000000000000002,65.21798681875505 +2019-03-15 21:30:00-07:00,2019,3,15,21,30,-6.0,-11.3,0.0,0.0,0.0,0.74,813.0,256.0,2.1,66.1467146787683 +2019-03-15 22:30:00-07:00,2019,3,15,22,30,-6.300000000000001,-11.7,0.0,0.0,0.0,0.74,813.0,261.0,2.3000000000000003,65.5500895047532 +2019-03-15 23:30:00-07:00,2019,3,15,23,30,-6.4,-12.1,0.0,0.0,0.0,0.74,813.0,266.0,2.4000000000000004,63.968416543604846 +2019-03-16 00:30:00-07:00,2019,3,16,0,30,-6.5,-12.4,0.0,0.0,0.0,0.74,813.0,270.0,2.4000000000000004,62.923245550208605 +2019-03-16 01:30:00-07:00,2019,3,16,1,30,-6.6000000000000005,-12.7,0.0,0.0,0.0,0.74,813.0,272.0,2.3000000000000003,61.89166237098923 +2019-03-16 02:30:00-07:00,2019,3,16,2,30,-6.7,-13.0,0.0,0.0,0.0,0.74,813.0,274.0,2.2,60.87354512527118 +2019-03-16 03:30:00-07:00,2019,3,16,3,30,-6.800000000000001,-13.2,0.0,0.0,0.0,0.74,813.0,277.0,2.1,60.35642179060504 +2019-03-16 04:30:00-07:00,2019,3,16,4,30,-6.9,-13.4,0.0,0.0,0.0,0.74,814.0,280.0,2.0,59.842391280855466 +2019-03-16 05:30:00-07:00,2019,3,16,5,30,-6.4,-13.5,0.0,0.0,0.0,0.74,814.0,283.0,1.9,57.12601267099779 +2019-03-16 06:30:00-07:00,2019,3,16,6,30,-3.7,-12.9,23.0,343.0,41.0,0.74,814.0,288.0,1.9,48.896555965963515 +2019-03-16 07:30:00-07:00,2019,3,16,7,30,0.2,-10.9,50.0,780.0,242.0,0.74,815.0,316.0,1.2000000000000002,43.08205711259754 +2019-03-16 08:30:00-07:00,2019,3,16,8,30,3.1,-8.9,66.0,923.0,458.0,0.74,815.0,198.0,0.5,40.98643333740214 +2019-03-16 09:30:00-07:00,2019,3,16,9,30,5.0,-8.4,79.0,991.0,646.0,0.74,815.0,65.0,0.7000000000000001,37.297196428246515 +2019-03-16 10:30:00-07:00,2019,3,16,10,30,6.4,-7.7,92.0,1018.0,784.0,0.74,814.0,81.0,1.0,35.740752121894076 +2019-03-16 11:30:00-07:00,2019,3,16,11,30,7.300000000000001,-7.2,104.0,1019.0,857.0,0.74,814.0,86.0,1.2000000000000002,34.922890317723805 +2019-03-16 12:30:00-07:00,2019,3,16,12,30,7.9,-6.800000000000001,107.0,1016.0,867.0,0.74,814.0,82.0,1.3,34.56990176325289 +2019-03-16 13:30:00-07:00,2019,3,16,13,30,8.0,-6.5,132.0,960.0,808.0,0.74,813.0,73.0,1.6,35.134395796199996 +2019-03-16 14:30:00-07:00,2019,3,16,14,30,7.7,-6.4,117.0,916.0,677.0,0.74,813.0,68.0,1.7000000000000002,36.13452761253114 +2019-03-16 15:30:00-07:00,2019,3,16,15,30,6.6000000000000005,-6.300000000000001,84.0,899.0,511.0,0.74,813.0,70.0,1.7000000000000002,39.258657176428166 +2019-03-16 16:30:00-07:00,2019,3,16,16,30,3.6,-5.0,65.0,774.0,302.0,0.74,813.0,80.0,1.4,53.394180237264564 +2019-03-16 17:30:00-07:00,2019,3,16,17,30,0.1,-5.300000000000001,37.0,469.0,91.0,0.68,814.0,99.0,1.2000000000000002,67.02076797543637 +2019-03-16 18:30:00-07:00,2019,3,16,18,30,-1.9,-7.800000000000001,0.0,0.0,0.0,0.68,814.0,131.0,1.1,64.04837345624343 +2019-03-16 19:30:00-07:00,2019,3,16,19,30,-3.1,-8.200000000000001,0.0,0.0,0.0,0.68,814.0,171.0,1.1,67.86119409133961 +2019-03-16 20:30:00-07:00,2019,3,16,20,30,-3.9,-8.5,0.0,0.0,0.0,0.68,814.0,200.0,1.2000000000000002,70.37626743538678 +2019-03-16 21:30:00-07:00,2019,3,16,21,30,-4.2,-8.8,0.0,0.0,0.0,0.68,814.0,216.0,1.3,70.31353513663514 +2019-03-16 22:30:00-07:00,2019,3,16,22,30,-4.4,-9.1,0.0,0.0,0.0,0.68,814.0,225.0,1.5,69.72397292838998 +2019-03-16 23:30:00-07:00,2019,3,16,23,30,-4.4,-9.3,0.0,0.0,0.0,0.68,813.0,229.0,1.8,68.64008652489909 +2019-03-17 00:30:00-07:00,2019,3,17,0,30,-4.4,-9.6,0.0,0.0,0.0,0.68,813.0,233.0,2.0,67.04239281959612 +2019-03-17 01:30:00-07:00,2019,3,17,1,30,-4.3,-10.0,0.0,0.0,0.0,0.68,812.0,242.0,2.3000000000000003,64.47709904812665 +2019-03-17 02:30:00-07:00,2019,3,17,2,30,-4.4,-10.4,0.0,0.0,0.0,0.68,812.0,253.0,2.7,62.94265085622262 +2019-03-17 03:30:00-07:00,2019,3,17,3,30,-4.6000000000000005,-11.0,0.0,0.0,0.0,0.68,812.0,261.0,2.9000000000000004,60.92653448281121 +2019-03-17 04:30:00-07:00,2019,3,17,4,30,-4.6000000000000005,-11.5,0.0,0.0,0.0,0.68,812.0,265.0,2.8000000000000003,58.545494248229105 +2019-03-17 05:30:00-07:00,2019,3,17,5,30,-4.0,-11.7,0.0,0.0,0.0,0.68,812.0,267.0,2.7,55.076167547287916 +2019-03-17 06:30:00-07:00,2019,3,17,6,30,-1.6,-11.2,25.0,334.0,44.0,0.68,813.0,269.0,2.9000000000000004,47.967328139807904 +2019-03-17 07:30:00-07:00,2019,3,17,7,30,1.9,-9.6,53.0,772.0,247.0,0.68,813.0,288.0,2.4000000000000004,42.25157013326966 +2019-03-17 08:30:00-07:00,2019,3,17,8,30,5.0,-7.7,69.0,917.0,463.0,0.68,813.0,164.0,1.5,39.37846420014944 +2019-03-17 09:30:00-07:00,2019,3,17,9,30,7.300000000000001,-7.7,82.0,979.0,647.0,0.68,813.0,30.0,1.5,33.60092123128044 +2019-03-17 10:30:00-07:00,2019,3,17,10,30,8.6,-7.0,93.0,1008.0,783.0,0.68,813.0,40.0,1.8,32.46409664339407 +2019-03-17 11:30:00-07:00,2019,3,17,11,30,9.4,-6.4,101.0,1020.0,860.0,0.68,813.0,45.0,2.0,32.207591735706245 +2019-03-17 12:30:00-07:00,2019,3,17,12,30,9.8,-6.0,99.0,1021.0,867.0,0.68,813.0,48.0,2.1,32.32702006972219 +2019-03-17 13:30:00-07:00,2019,3,17,13,30,9.8,-5.7,96.0,1006.0,808.0,0.68,813.0,48.0,2.3000000000000003,33.07393435965734 +2019-03-17 14:30:00-07:00,2019,3,17,14,30,9.3,-5.5,89.0,974.0,688.0,0.68,813.0,48.0,2.4000000000000004,34.72677480007866 +2019-03-17 15:30:00-07:00,2019,3,17,15,30,8.200000000000001,-5.5,78.0,913.0,515.0,0.68,813.0,51.0,2.4000000000000004,37.40611695774423 +2019-03-17 16:30:00-07:00,2019,3,17,16,30,5.6000000000000005,-5.300000000000001,61.0,793.0,307.0,0.68,813.0,61.0,1.8,45.390007957231155 +2019-03-17 17:30:00-07:00,2019,3,17,17,30,2.1,-4.0,51.0,334.0,91.0,0.68,814.0,77.0,1.3,64.0238273086661 +2019-03-17 18:30:00-07:00,2019,3,17,18,30,0.2,-6.1000000000000005,0.0,0.0,0.0,0.68,814.0,101.0,1.2000000000000002,62.60994999802062 +2019-03-17 19:30:00-07:00,2019,3,17,19,30,-0.8,-7.0,0.0,0.0,0.0,0.68,814.0,134.0,1.1,62.846176028940995 +2019-03-17 20:30:00-07:00,2019,3,17,20,30,-2.0,-7.5,0.0,0.0,0.0,0.68,815.0,162.0,1.1,66.03606707637287 +2019-03-17 21:30:00-07:00,2019,3,17,21,30,-3.1,-7.9,0.0,0.0,0.0,0.68,815.0,177.0,1.2000000000000002,69.45896404864928 +2019-03-17 22:30:00-07:00,2019,3,17,22,30,-3.8,-8.3,0.0,0.0,0.0,0.68,814.0,186.0,1.3,70.9467577530942 +2019-03-17 23:30:00-07:00,2019,3,17,23,30,-4.3,-8.700000000000001,0.0,0.0,0.0,0.68,814.0,193.0,1.3,71.39874718585263 +2019-03-18 00:30:00-07:00,2019,3,18,0,30,-4.6000000000000005,-9.0,0.0,0.0,0.0,0.68,814.0,198.0,1.3,71.3376383845228 +2019-03-18 01:30:00-07:00,2019,3,18,1,30,-5.0,-9.4,0.0,0.0,0.0,0.68,813.0,206.0,1.2000000000000002,71.25587882405677 +2019-03-18 02:30:00-07:00,2019,3,18,2,30,-5.4,-9.6,0.0,0.0,0.0,0.68,813.0,219.0,1.3,72.30255220146162 +2019-03-18 03:30:00-07:00,2019,3,18,3,30,-5.300000000000001,-9.9,0.0,0.0,0.0,0.68,812.0,232.0,1.7000000000000002,70.0819468630647 +2019-03-18 04:30:00-07:00,2019,3,18,4,30,-5.0,-10.0,0.0,0.0,0.0,0.68,812.0,242.0,2.3000000000000003,67.97045632401785 +2019-03-18 05:30:00-07:00,2019,3,18,5,30,-4.4,-10.1,0.0,0.0,0.0,0.68,812.0,247.0,2.9000000000000004,64.45309354250831 +2019-03-18 06:30:00-07:00,2019,3,18,6,30,-2.5,-9.8,33.0,6.0,33.0,0.68,812.0,248.0,3.4000000000000004,57.27163980756614 +2019-03-18 07:30:00-07:00,2019,3,18,7,30,0.9,-8.700000000000001,154.0,136.0,189.0,0.68,812.0,256.0,3.4000000000000004,48.70619530226286 +2019-03-18 08:30:00-07:00,2019,3,18,8,30,4.2,-7.6,266.0,142.0,328.0,0.68,812.0,275.0,2.8000000000000003,41.96475039255256 +2019-03-18 09:30:00-07:00,2019,3,18,9,30,6.7,-6.800000000000001,202.0,5.0,205.0,0.68,811.0,298.0,2.1,37.524924640469024 +2019-03-18 10:30:00-07:00,2019,3,18,10,30,8.5,-6.1000000000000005,343.0,488.0,679.0,0.68,811.0,318.0,1.8,35.01704179216385 +2019-03-18 11:30:00-07:00,2019,3,18,11,30,9.7,-5.5,331.0,577.0,763.0,0.68,810.0,328.0,1.7000000000000002,33.80621845251322 +2019-03-18 12:30:00-07:00,2019,3,18,12,30,10.4,-5.0,456.0,341.0,714.0,0.68,810.0,333.0,1.8,33.5055531579141 +2019-03-18 13:30:00-07:00,2019,3,18,13,30,10.5,-4.7,232.0,720.0,745.0,0.68,809.0,335.0,2.1,34.04542171870721 +2019-03-18 14:30:00-07:00,2019,3,18,14,30,10.0,-4.5,267.0,555.0,611.0,0.68,809.0,339.0,2.4000000000000004,35.73564275006211 +2019-03-18 15:30:00-07:00,2019,3,18,15,30,8.8,-4.3,283.0,305.0,430.0,0.68,809.0,346.0,2.3000000000000003,39.324790341053166 +2019-03-18 16:30:00-07:00,2019,3,18,16,30,6.5,-3.7,193.0,156.0,242.0,0.68,810.0,180.0,1.7000000000000002,48.11564646625111 +2019-03-18 17:30:00-07:00,2019,3,18,17,30,3.7,-2.6,69.0,22.0,72.0,0.6900000000000001,810.0,6.0,1.1,63.45212421061126 +2019-03-18 18:30:00-07:00,2019,3,18,18,30,1.6,-3.9,0.0,0.0,0.0,0.6900000000000001,810.0,171.0,0.9,66.85075095622915 +2019-03-18 19:30:00-07:00,2019,3,18,19,30,0.3,-4.1000000000000005,0.0,0.0,0.0,0.6900000000000001,811.0,335.0,0.9,72.31357342095824 +2019-03-18 20:30:00-07:00,2019,3,18,20,30,-0.3,-4.4,0.0,0.0,0.0,0.6900000000000001,811.0,330.0,1.0,73.84632723672816 +2019-03-18 21:30:00-07:00,2019,3,18,21,30,-0.6000000000000001,-4.6000000000000005,0.0,0.0,0.0,0.6900000000000001,811.0,333.0,1.0,74.34872904501113 +2019-03-18 22:30:00-07:00,2019,3,18,22,30,-1.0,-4.7,0.0,0.0,0.0,0.6900000000000001,811.0,332.0,1.0,75.9768372093094 +2019-03-18 23:30:00-07:00,2019,3,18,23,30,-1.5,-4.9,0.0,0.0,0.0,0.6900000000000001,811.0,330.0,1.0,77.63122930085144 +2019-03-19 00:30:00-07:00,2019,3,19,0,30,-2.2,-5.4,0.0,0.0,0.0,0.6900000000000001,810.0,329.0,1.1,78.70198929389846 +2019-03-19 01:30:00-07:00,2019,3,19,1,30,-3.0,-6.1000000000000005,0.0,0.0,0.0,0.6900000000000001,810.0,329.0,1.1,79.177475521585 +2019-03-19 02:30:00-07:00,2019,3,19,2,30,-3.6,-6.800000000000001,0.0,0.0,0.0,0.6900000000000001,810.0,331.0,1.1,78.47973530143707 +2019-03-19 03:30:00-07:00,2019,3,19,3,30,-4.1000000000000005,-7.5,0.0,0.0,0.0,0.6900000000000001,810.0,332.0,1.2000000000000002,77.19921248991804 +2019-03-19 04:30:00-07:00,2019,3,19,4,30,-4.5,-8.1,0.0,0.0,0.0,0.6900000000000001,810.0,332.0,1.2000000000000002,75.94477611119521 +2019-03-19 05:30:00-07:00,2019,3,19,5,30,-4.3,-8.700000000000001,0.0,0.0,0.0,0.6900000000000001,811.0,335.0,1.3,71.39874718585263 +2019-03-19 06:30:00-07:00,2019,3,19,6,30,-2.4000000000000004,-8.8,29.0,44.0,32.0,0.6900000000000001,811.0,170.0,2.1,61.486032951822956 +2019-03-19 07:30:00-07:00,2019,3,19,7,30,0.3,-9.1,59.0,743.0,254.0,0.6900000000000001,812.0,9.0,3.0,49.29629221696046 +2019-03-19 08:30:00-07:00,2019,3,19,8,30,2.3000000000000003,-8.6,100.0,821.0,461.0,0.6900000000000001,812.0,14.0,3.0,44.40711073107489 +2019-03-19 09:30:00-07:00,2019,3,19,9,30,3.6,-8.3,115.0,911.0,650.0,0.6900000000000001,812.0,11.0,2.8000000000000003,41.45975337113658 +2019-03-19 10:30:00-07:00,2019,3,19,10,30,4.6000000000000005,-8.3,383.0,409.0,667.0,0.6900000000000001,812.0,7.0,2.7,38.65167581385158 +2019-03-19 11:30:00-07:00,2019,3,19,11,30,5.5,-8.3,487.0,362.0,760.0,0.6900000000000001,811.0,7.0,2.9000000000000004,36.30490795913397 +2019-03-19 12:30:00-07:00,2019,3,19,12,30,5.9,-8.3,451.0,485.0,820.0,0.6900000000000001,811.0,8.0,3.4000000000000004,35.31329641948487 +2019-03-19 13:30:00-07:00,2019,3,19,13,30,5.9,-8.4,404.0,474.0,744.0,0.6900000000000001,811.0,9.0,3.9,35.03972652032177 +2019-03-19 14:30:00-07:00,2019,3,19,14,30,5.4,-8.6,126.0,914.0,695.0,0.6900000000000001,811.0,12.0,4.1000000000000005,35.713865711132165 +2019-03-19 15:30:00-07:00,2019,3,19,15,30,4.4,-8.9,240.0,392.0,431.0,0.6900000000000001,811.0,15.0,4.0,37.40517771304614 +2019-03-19 16:30:00-07:00,2019,3,19,16,30,2.8000000000000003,-9.1,175.0,248.0,254.0,0.6900000000000001,812.0,19.0,3.1,41.21644830864632 +2019-03-19 17:30:00-07:00,2019,3,19,17,30,-0.1,-8.5,54.0,88.0,65.0,0.6900000000000001,812.0,23.0,1.8,53.17959360775341 +2019-03-19 18:30:00-07:00,2019,3,19,18,30,-2.5,-8.8,0.0,0.0,0.0,0.6900000000000001,813.0,14.0,1.1,61.94273839198825 +2019-03-19 19:30:00-07:00,2019,3,19,19,30,-3.6,-9.5,0.0,0.0,0.0,0.6900000000000001,813.0,169.0,1.0,63.63790189913212 +2019-03-19 20:30:00-07:00,2019,3,19,20,30,-4.2,-10.1,0.0,0.0,0.0,0.6900000000000001,813.0,315.0,1.1,63.491546580999284 +2019-03-19 21:30:00-07:00,2019,3,19,21,30,-4.9,-10.5,0.0,0.0,0.0,0.6900000000000001,813.0,288.0,1.4,64.84460285047261 +2019-03-19 22:30:00-07:00,2019,3,19,22,30,-5.800000000000001,-10.9,0.0,0.0,0.0,0.6900000000000001,813.0,275.0,1.7000000000000002,67.25499083222131 +2019-03-19 23:30:00-07:00,2019,3,19,23,30,-6.300000000000001,-11.4,0.0,0.0,0.0,0.6900000000000001,813.0,270.0,2.1,67.1410335722935 +2019-03-20 00:30:00-07:00,2019,3,20,0,30,-6.5,-12.2,0.0,0.0,0.0,0.6900000000000001,813.0,268.0,2.4000000000000004,63.943952719850635 +2019-03-20 01:30:00-07:00,2019,3,20,1,30,-6.6000000000000005,-13.1,0.0,0.0,0.0,0.6900000000000001,813.0,267.0,2.6,59.921467356324484 +2019-03-20 02:30:00-07:00,2019,3,20,2,30,-6.300000000000001,-14.2,0.0,0.0,0.0,0.6900000000000001,813.0,268.0,2.6,53.54536956619952 +2019-03-20 03:30:00-07:00,2019,3,20,3,30,-5.9,-15.0,0.0,0.0,0.0,0.6900000000000001,813.0,274.0,2.4000000000000004,48.634876346442134 +2019-03-20 04:30:00-07:00,2019,3,20,4,30,-5.4,-15.2,0.0,0.0,0.0,0.6900000000000001,813.0,282.0,2.0,46.05543607598256 +2019-03-20 05:30:00-07:00,2019,3,20,5,30,-4.1000000000000005,-14.9,0.0,0.0,0.0,0.6900000000000001,813.0,296.0,1.8,42.79825252611037 +2019-03-20 06:30:00-07:00,2019,3,20,6,30,-1.1,-13.3,25.0,469.0,60.0,0.6900000000000001,813.0,312.0,2.3000000000000003,39.05478803701881 +2019-03-20 07:30:00-07:00,2019,3,20,7,30,2.5,-14.9,48.0,827.0,270.0,0.6900000000000001,814.0,327.0,3.0,26.433083262928665 +2019-03-20 08:30:00-07:00,2019,3,20,8,30,5.0,-14.9,63.0,954.0,488.0,0.6900000000000001,814.0,340.0,3.1,22.170938503897453 +2019-03-20 09:30:00-07:00,2019,3,20,9,30,7.1000000000000005,-13.1,73.0,1004.0,668.0,0.6900000000000001,814.0,350.0,3.1,22.215265705176023 +2019-03-20 10:30:00-07:00,2019,3,20,10,30,8.8,-11.3,82.0,1034.0,805.0,0.6900000000000001,814.0,179.0,3.0,22.87125247285278 +2019-03-20 11:30:00-07:00,2019,3,20,11,30,10.1,-9.6,89.0,1041.0,878.0,0.6900000000000001,814.0,6.0,2.9000000000000004,23.97810408538041 +2019-03-20 12:30:00-07:00,2019,3,20,12,30,10.9,-8.200000000000001,90.0,1039.0,885.0,0.6900000000000001,814.0,11.0,2.7,25.361291245864358 +2019-03-20 13:30:00-07:00,2019,3,20,13,30,11.3,-7.2,87.0,1027.0,827.0,0.6900000000000001,813.0,14.0,2.5,26.68328604390069 +2019-03-20 14:30:00-07:00,2019,3,20,14,30,11.3,-6.7,80.0,999.0,706.0,0.6900000000000001,813.0,18.0,2.3000000000000003,27.728565131968796 +2019-03-20 15:30:00-07:00,2019,3,20,15,30,10.7,-6.5,70.0,945.0,533.0,0.6900000000000001,813.0,25.0,2.1,29.300741047851787 +2019-03-20 16:30:00-07:00,2019,3,20,16,30,8.4,-5.9,56.0,837.0,324.0,0.6900000000000001,813.0,39.0,1.5,35.796672189948886 +2019-03-20 17:30:00-07:00,2019,3,20,17,30,5.5,-3.7,33.0,571.0,107.0,0.17,813.0,58.0,1.1,51.55629857262565 +2019-03-20 18:30:00-07:00,2019,3,20,18,30,4.0,-7.2,0.0,0.0,0.0,0.17,814.0,84.0,1.1,43.89123482450099 +2019-03-20 19:30:00-07:00,2019,3,20,19,30,3.4000000000000004,-8.200000000000001,0.0,0.0,0.0,0.17,814.0,124.0,0.9,42.376270237841844 +2019-03-20 20:30:00-07:00,2019,3,20,20,30,2.6,-8.8,0.0,0.0,0.0,0.17,814.0,164.0,0.9,42.79731211777003 +2019-03-20 21:30:00-07:00,2019,3,20,21,30,1.6,-9.3,0.0,0.0,0.0,0.17,814.0,188.0,1.0,44.196200321445104 +2019-03-20 22:30:00-07:00,2019,3,20,22,30,0.7000000000000001,-9.7,0.0,0.0,0.0,0.17,814.0,202.0,1.2000000000000002,45.68989830321409 +2019-03-20 23:30:00-07:00,2019,3,20,23,30,-0.1,-10.0,0.0,0.0,0.0,0.17,814.0,209.0,1.3,47.28140710535596 +2019-03-21 00:30:00-07:00,2019,3,21,0,30,-0.8,-10.2,0.0,0.0,0.0,0.17,813.0,214.0,1.4,48.97354255021601 +2019-03-21 01:30:00-07:00,2019,3,21,1,30,-1.2000000000000002,-10.3,0.0,0.0,0.0,0.17,813.0,218.0,1.4,50.03002936151696 +2019-03-21 02:30:00-07:00,2019,3,21,2,30,-1.7000000000000002,-10.5,0.0,0.0,0.0,0.17,812.0,221.0,1.4,51.08495019681386 +2019-03-21 03:30:00-07:00,2019,3,21,3,30,-2.2,-10.6,0.0,0.0,0.0,0.17,812.0,225.0,1.5,52.584078379884744 +2019-03-21 04:30:00-07:00,2019,3,21,4,30,-2.5,-10.7,0.0,0.0,0.0,0.17,812.0,228.0,1.6,53.33880763350134 +2019-03-21 05:30:00-07:00,2019,3,21,5,30,-1.8,-10.7,0.0,0.0,0.0,0.17,812.0,229.0,1.6,50.65260267135705 +2019-03-21 06:30:00-07:00,2019,3,21,6,30,0.9,-9.7,37.0,2.0,37.0,0.17,811.0,226.0,2.1,45.03628305672912 +2019-03-21 07:30:00-07:00,2019,3,21,7,30,5.0,-8.9,142.0,142.0,181.0,0.17,811.0,212.0,3.0,35.8711377547219 +2019-03-21 08:30:00-07:00,2019,3,21,8,30,8.6,-8.3,220.0,288.0,350.0,0.17,811.0,194.0,3.8,29.35996778544057 +2019-03-21 09:30:00-07:00,2019,3,21,9,30,11.0,-7.5,267.0,251.0,417.0,0.17,810.0,183.0,4.5,26.596843892911533 +2019-03-21 10:30:00-07:00,2019,3,21,10,30,12.2,-6.9,343.0,296.0,551.0,0.17,810.0,176.0,4.9,25.731761501045685 +2019-03-21 11:30:00-07:00,2019,3,21,11,30,12.5,-6.6000000000000005,310.0,472.0,670.0,0.17,809.0,172.0,5.2,25.81717671680513 +2019-03-21 12:30:00-07:00,2019,3,21,12,30,12.2,-6.6000000000000005,368.0,300.0,599.0,0.17,808.0,171.0,5.4,26.330882692642955 +2019-03-21 13:30:00-07:00,2019,3,21,13,30,11.9,-6.4,322.0,30.0,344.0,0.17,808.0,171.0,5.5,27.270435946610256 +2019-03-21 14:30:00-07:00,2019,3,21,14,30,11.8,-6.2,231.0,33.0,252.0,0.17,807.0,168.0,5.300000000000001,27.873668259826836 +2019-03-21 15:30:00-07:00,2019,3,21,15,30,11.2,-5.800000000000001,186.0,42.0,207.0,0.17,807.0,163.0,4.800000000000001,29.899617901711284 +2019-03-21 16:30:00-07:00,2019,3,21,16,30,9.1,-5.300000000000001,108.0,25.0,116.0,0.17,807.0,159.0,3.6,35.73556850780731 +2019-03-21 17:30:00-07:00,2019,3,21,17,30,6.0,-4.3,40.0,213.0,68.0,0.17,807.0,156.0,2.4000000000000004,47.612329620486165 +2019-03-21 18:30:00-07:00,2019,3,21,18,30,4.0,-4.7,0.0,0.0,0.0,0.17,807.0,157.0,2.2,53.1028761663132 +2019-03-21 19:30:00-07:00,2019,3,21,19,30,3.0,-5.2,0.0,0.0,0.0,0.17,807.0,165.0,2.3000000000000003,54.86760039476738 +2019-03-21 20:30:00-07:00,2019,3,21,20,30,2.3000000000000003,-5.0,0.0,0.0,0.0,0.17,807.0,185.0,2.0,58.54111992427008 +2019-03-21 21:30:00-07:00,2019,3,21,21,30,1.7000000000000002,-4.2,0.0,0.0,0.0,0.17,807.0,205.0,1.8,64.8974819887709 +2019-03-21 22:30:00-07:00,2019,3,21,22,30,1.2000000000000002,-3.7,0.0,0.0,0.0,0.17,807.0,220.0,1.7000000000000002,69.8330222881731 +2019-03-21 23:30:00-07:00,2019,3,21,23,30,1.0,-3.7,0.0,0.0,0.0,0.17,806.0,231.0,1.7000000000000002,70.84400746470655 +2019-03-22 00:30:00-07:00,2019,3,22,0,30,0.7000000000000001,-3.7,0.0,0.0,0.0,0.17,806.0,240.0,1.8,72.3911958457564 +2019-03-22 01:30:00-07:00,2019,3,22,1,30,0.4,-3.8,0.0,0.0,0.0,0.17,806.0,247.0,1.7000000000000002,73.4247647272037 +2019-03-22 02:30:00-07:00,2019,3,22,2,30,0.0,-4.2,0.0,0.0,0.0,0.17,805.0,246.0,1.6,73.34942491705195 +2019-03-22 03:30:00-07:00,2019,3,22,3,30,0.2,-4.4,0.0,0.0,0.0,0.17,805.0,240.0,1.4,71.21559671360943 +2019-03-22 04:30:00-07:00,2019,3,22,4,30,0.8,-4.0,0.0,0.0,0.0,0.17,805.0,238.0,1.2000000000000002,70.2749681774062 +2019-03-22 05:30:00-07:00,2019,3,22,5,30,1.5,-3.5,0.0,0.0,0.0,0.17,805.0,243.0,1.0,69.37552913802425 +2019-03-22 06:30:00-07:00,2019,3,22,6,30,2.8000000000000003,-2.7,31.0,2.0,31.0,0.17,805.0,255.0,1.0,67.11644649168397 +2019-03-22 07:30:00-07:00,2019,3,22,7,30,4.5,-1.7000000000000002,55.0,5.0,56.0,0.17,805.0,241.0,1.3,64.11105710877317 +2019-03-22 08:30:00-07:00,2019,3,22,8,30,6.1000000000000005,-1.0,195.0,62.0,223.0,0.17,805.0,204.0,1.8,60.389615719856074 +2019-03-22 09:30:00-07:00,2019,3,22,9,30,7.5,-0.7000000000000001,274.0,301.0,455.0,0.17,805.0,186.0,2.2,56.07274949074223 +2019-03-22 10:30:00-07:00,2019,3,22,10,30,8.5,-0.6000000000000001,208.0,677.0,688.0,0.17,804.0,180.0,2.2,52.771428984582414 +2019-03-22 11:30:00-07:00,2019,3,22,11,30,9.3,-0.7000000000000001,96.0,971.0,841.0,0.17,804.0,178.0,2.2,49.63470480632163 +2019-03-22 12:30:00-07:00,2019,3,22,12,30,9.7,-0.8,95.0,976.0,850.0,0.17,803.0,178.0,2.1,47.96766750338197 +2019-03-22 13:30:00-07:00,2019,3,22,13,30,9.4,-1.1,224.0,662.0,706.0,0.17,803.0,178.0,2.0,47.882118070289195 +2019-03-22 14:30:00-07:00,2019,3,22,14,30,8.9,-1.3,206.0,15.0,216.0,0.17,803.0,174.0,1.7000000000000002,48.80194271879041 +2019-03-22 15:30:00-07:00,2019,3,22,15,30,8.200000000000001,-1.5,87.0,2.0,88.0,0.17,803.0,148.0,1.3,50.42411280418772 +2019-03-22 16:30:00-07:00,2019,3,22,16,30,6.6000000000000005,-1.2000000000000002,70.0,2.0,71.0,0.17,803.0,81.0,1.3,57.497684209137155 +2019-03-22 17:30:00-07:00,2019,3,22,17,30,4.5,0.1,36.0,1.0,36.0,0.17,803.0,196.0,2.0,73.1137416159454 +2019-03-22 18:30:00-07:00,2019,3,22,18,30,3.1,-0.2,0.0,0.0,0.0,0.17,804.0,346.0,3.1,78.93913602667017 +2019-03-22 19:30:00-07:00,2019,3,22,19,30,2.2,-0.7000000000000001,0.0,0.0,0.0,0.17,804.0,331.0,3.4000000000000004,81.13787635735659 +2019-03-22 20:30:00-07:00,2019,3,22,20,30,1.4,-1.8,0.0,0.0,0.0,0.17,804.0,323.0,3.0,79.25869533245036 +2019-03-22 21:30:00-07:00,2019,3,22,21,30,0.9,-2.7,0.0,0.0,0.0,0.17,804.0,321.0,2.4000000000000004,76.8721153168852 +2019-03-22 22:30:00-07:00,2019,3,22,22,30,0.4,-3.5,0.0,0.0,0.0,0.17,804.0,324.0,2.0,75.08985317031998 +2019-03-22 23:30:00-07:00,2019,3,22,23,30,-0.1,-4.0,0.0,0.0,0.0,0.17,804.0,327.0,2.0,75.00054235125127 +2019-03-23 00:30:00-07:00,2019,3,23,0,30,-0.6000000000000001,-4.4,0.0,0.0,0.0,0.17,804.0,324.0,2.3000000000000003,75.47660563119261 +2019-03-23 01:30:00-07:00,2019,3,23,1,30,-0.8,-5.0,0.0,0.0,0.0,0.17,803.0,317.0,2.5,73.19784891893063 +2019-03-23 02:30:00-07:00,2019,3,23,2,30,-0.9,-5.4,0.0,0.0,0.0,0.17,803.0,308.0,2.3000000000000003,71.53444279130206 +2019-03-23 03:30:00-07:00,2019,3,23,3,30,-0.9,-5.800000000000001,0.0,0.0,0.0,0.17,803.0,298.0,2.1,69.39307243452086 +2019-03-23 04:30:00-07:00,2019,3,23,4,30,-1.0,-6.0,0.0,0.0,0.0,0.17,803.0,290.0,1.8,68.84526791578821 +2019-03-23 05:30:00-07:00,2019,3,23,5,30,-0.8,-6.0,0.0,0.0,0.0,0.17,804.0,282.0,1.5,67.84662800107102 +2019-03-23 06:30:00-07:00,2019,3,23,6,30,0.0,-5.800000000000001,40.0,62.0,46.0,0.17,804.0,279.0,1.5,64.99286009498181 +2019-03-23 07:30:00-07:00,2019,3,23,7,30,1.5,-5.300000000000001,118.0,382.0,226.0,0.17,805.0,290.0,2.0,60.59063261248967 +2019-03-23 08:30:00-07:00,2019,3,23,8,30,3.1,-4.6000000000000005,199.0,377.0,373.0,0.17,805.0,306.0,2.9000000000000004,57.00678003860169 +2019-03-23 09:30:00-07:00,2019,3,23,9,30,4.7,-4.1000000000000005,273.0,451.0,547.0,0.17,805.0,317.0,3.8,52.89812935392421 +2019-03-23 10:30:00-07:00,2019,3,23,10,30,5.800000000000001,-3.9,369.0,360.0,626.0,0.17,805.0,323.0,4.1000000000000005,49.74588843529513 +2019-03-23 11:30:00-07:00,2019,3,23,11,30,6.6000000000000005,-3.6,246.0,687.0,776.0,0.17,805.0,326.0,4.1000000000000005,48.14445015435608 +2019-03-23 12:30:00-07:00,2019,3,23,12,30,7.4,-3.3000000000000003,86.0,1002.0,866.0,0.17,804.0,327.0,3.9,46.60837243688164 +2019-03-23 13:30:00-07:00,2019,3,23,13,30,8.0,-3.0,238.0,637.0,705.0,0.17,804.0,331.0,3.7,45.75179009409315 +2019-03-23 14:30:00-07:00,2019,3,23,14,30,8.1,-2.9000000000000004,253.0,474.0,555.0,0.17,804.0,337.0,3.4000000000000004,45.78093614297434 +2019-03-23 15:30:00-07:00,2019,3,23,15,30,7.5,-2.8000000000000003,183.0,479.0,423.0,0.17,805.0,345.0,2.9000000000000004,48.04510436444134 +2019-03-23 16:30:00-07:00,2019,3,23,16,30,5.9,-2.6,100.0,492.0,263.0,0.17,806.0,180.0,2.0,54.42703296081799 +2019-03-23 17:30:00-07:00,2019,3,23,17,30,3.4000000000000004,-1.6,41.0,490.0,109.0,0.16,806.0,7.0,1.0,69.77029494142703 +2019-03-23 18:30:00-07:00,2019,3,23,18,30,1.2000000000000002,-2.8000000000000003,0.0,0.0,0.0,0.16,807.0,154.0,0.5,74.67575341160956 +2019-03-23 19:30:00-07:00,2019,3,23,19,30,-0.4,-3.3000000000000003,0.0,0.0,0.0,0.16,808.0,278.0,0.6000000000000001,80.77108179603061 +2019-03-23 20:30:00-07:00,2019,3,23,20,30,-1.5,-3.8,0.0,0.0,0.0,0.16,808.0,250.0,1.0,84.32472345939549 +2019-03-23 21:30:00-07:00,2019,3,23,21,30,-2.0,-4.6000000000000005,0.0,0.0,0.0,0.16,807.0,250.0,1.3,82.38380089710469 +2019-03-23 22:30:00-07:00,2019,3,23,22,30,-2.0,-5.300000000000001,0.0,0.0,0.0,0.16,807.0,253.0,1.5,78.13926934456168 +2019-03-23 23:30:00-07:00,2019,3,23,23,30,-1.9,-5.9,0.0,0.0,0.0,0.16,807.0,258.0,1.7000000000000002,74.10885357456775 +2019-03-24 00:30:00-07:00,2019,3,24,0,30,-2.0,-6.6000000000000005,0.0,0.0,0.0,0.16,807.0,262.0,1.9,70.76936377880763 +2019-03-24 01:30:00-07:00,2019,3,24,1,30,-2.1,-7.1000000000000005,0.0,0.0,0.0,0.16,807.0,263.0,2.0,68.60797509634737 +2019-03-24 02:30:00-07:00,2019,3,24,2,30,-2.3000000000000003,-7.2,0.0,0.0,0.0,0.16,807.0,260.0,2.1,69.0948764005131 +2019-03-24 03:30:00-07:00,2019,3,24,3,30,-2.5,-7.300000000000001,0.0,0.0,0.0,0.16,807.0,259.0,2.0,69.58648782882413 +2019-03-24 04:30:00-07:00,2019,3,24,4,30,-2.7,-7.4,0.0,0.0,0.0,0.16,807.0,259.0,1.9,70.08286439943119 +2019-03-24 05:30:00-07:00,2019,3,24,5,30,-2.0,-7.5,0.0,0.0,0.0,0.16,807.0,256.0,1.9,66.03606707637287 +2019-03-24 06:30:00-07:00,2019,3,24,6,30,1.0,-6.7,29.0,438.0,71.0,0.16,808.0,250.0,2.0,56.44673738803349 +2019-03-24 07:30:00-07:00,2019,3,24,7,30,5.0,-5.1000000000000005,53.0,769.0,275.0,0.16,808.0,181.0,1.3,48.04369645608824 +2019-03-24 08:30:00-07:00,2019,3,24,8,30,7.800000000000001,-4.3,66.0,896.0,483.0,0.16,808.0,103.0,0.9,42.08383967969326 +2019-03-24 09:30:00-07:00,2019,3,24,9,30,9.3,-4.1000000000000005,76.0,953.0,659.0,0.16,808.0,89.0,1.4,38.596850551573354 +2019-03-24 10:30:00-07:00,2019,3,24,10,30,10.1,-3.8,120.0,908.0,772.0,0.16,808.0,93.0,1.7000000000000002,37.41245213149429 +2019-03-24 11:30:00-07:00,2019,3,24,11,30,10.4,-3.5,287.0,623.0,770.0,0.16,808.0,104.0,1.8,37.5021115972931 +2019-03-24 12:30:00-07:00,2019,3,24,12,30,10.4,-3.1,202.0,751.0,789.0,0.16,808.0,121.0,1.7000000000000002,38.63694339893324 +2019-03-24 13:30:00-07:00,2019,3,24,13,30,10.1,-2.7,245.0,41.0,275.0,0.16,808.0,149.0,1.3,40.60746228912054 +2019-03-24 14:30:00-07:00,2019,3,24,14,30,9.4,-2.2,291.0,232.0,440.0,0.16,808.0,192.0,1.0,44.16252670815772 +2019-03-24 15:30:00-07:00,2019,3,24,15,30,8.4,-1.7000000000000002,252.0,190.0,348.0,0.16,809.0,238.0,0.9,49.01915532824131 +2019-03-24 16:30:00-07:00,2019,3,24,16,30,7.0,-0.9,55.0,2.0,56.0,0.16,809.0,264.0,0.8,57.1820198579877 +2019-03-24 17:30:00-07:00,2019,3,24,17,30,5.300000000000001,0.3,32.0,0.0,32.0,0.16,810.0,272.0,0.6000000000000001,70.1581109302263 +2019-03-24 18:30:00-07:00,2019,3,24,18,30,3.9,-0.7000000000000001,0.0,0.0,0.0,0.16,811.0,268.0,0.7000000000000001,71.94509196725033 +2019-03-24 19:30:00-07:00,2019,3,24,19,30,2.9000000000000004,-1.1,0.0,0.0,0.0,0.16,811.0,253.0,0.8,74.97926454114054 +2019-03-24 20:30:00-07:00,2019,3,24,20,30,2.0,-1.6,0.0,0.0,0.0,0.16,811.0,241.0,0.8,77.05480458775617 +2019-03-24 21:30:00-07:00,2019,3,24,21,30,1.0,-2.2,0.0,0.0,0.0,0.16,811.0,238.0,1.0,79.19787087573091 +2019-03-24 22:30:00-07:00,2019,3,24,22,30,0.2,-3.0,0.0,0.0,0.0,0.16,811.0,242.0,1.2000000000000002,79.07545622738651 +2019-03-24 23:30:00-07:00,2019,3,24,23,30,-0.4,-3.7,0.0,0.0,0.0,0.16,812.0,248.0,1.4,78.39479537175046 +2019-03-25 00:30:00-07:00,2019,3,25,0,30,-0.9,-4.6000000000000005,0.0,0.0,0.0,0.16,812.0,253.0,1.5,75.99421061859483 +2019-03-25 01:30:00-07:00,2019,3,25,1,30,-1.4,-5.5,0.0,0.0,0.0,0.16,812.0,254.0,1.6,73.64054359772402 +2019-03-25 02:30:00-07:00,2019,3,25,2,30,-1.9,-6.1000000000000005,0.0,0.0,0.0,0.16,811.0,253.0,1.7000000000000002,72.98746257682352 +2019-03-25 03:30:00-07:00,2019,3,25,3,30,-2.4000000000000004,-6.5,0.0,0.0,0.0,0.16,811.0,251.0,1.8,73.45149950910914 +2019-03-25 04:30:00-07:00,2019,3,25,4,30,-2.8000000000000003,-6.800000000000001,0.0,0.0,0.0,0.16,812.0,251.0,1.9,73.94094285972851 +2019-03-25 05:30:00-07:00,2019,3,25,5,30,-2.5,-7.1000000000000005,0.0,0.0,0.0,0.16,812.0,252.0,2.2,70.66661757792247 +2019-03-25 06:30:00-07:00,2019,3,25,6,30,-0.4,-6.800000000000001,35.0,422.0,77.0,0.16,812.0,251.0,2.5,61.98562574834458 +2019-03-25 07:30:00-07:00,2019,3,25,7,30,2.8000000000000003,-6.0,60.0,770.0,287.0,0.16,813.0,268.0,2.5,52.368362042869194 +2019-03-25 08:30:00-07:00,2019,3,25,8,30,5.6000000000000005,-6.2,74.0,908.0,502.0,0.16,813.0,298.0,2.3000000000000003,42.38621926077125 +2019-03-25 09:30:00-07:00,2019,3,25,9,30,7.5,-7.2,83.0,978.0,686.0,0.16,813.0,315.0,2.7,34.44910998767506 +2019-03-25 10:30:00-07:00,2019,3,25,10,30,8.700000000000001,-7.9,88.0,1015.0,821.0,0.16,813.0,319.0,3.2,30.08156014502122 +2019-03-25 11:30:00-07:00,2019,3,25,11,30,9.5,-8.1,90.0,1032.0,895.0,0.16,813.0,319.0,3.7,28.064382902449612 +2019-03-25 12:30:00-07:00,2019,3,25,12,30,10.1,-7.9,90.0,1025.0,896.0,0.16,813.0,318.0,3.9,27.37979595364441 +2019-03-25 13:30:00-07:00,2019,3,25,13,30,10.3,-7.6,89.0,1008.0,835.0,0.16,813.0,318.0,3.9,27.650916892290134 +2019-03-25 14:30:00-07:00,2019,3,25,14,30,10.1,-7.2,86.0,973.0,714.0,0.16,813.0,318.0,3.6,28.900992489857714 +2019-03-25 15:30:00-07:00,2019,3,25,15,30,9.5,-6.800000000000001,79.0,910.0,541.0,0.16,813.0,320.0,3.1,31.026241447043176 +2019-03-25 16:30:00-07:00,2019,3,25,16,30,7.7,-6.300000000000001,110.0,603.0,314.0,0.16,813.0,329.0,1.9,36.41187934207728 +2019-03-25 17:30:00-07:00,2019,3,25,17,30,4.800000000000001,-3.6,44.0,513.0,119.0,0.17,814.0,180.0,0.8,54.53417725195561 +2019-03-25 18:30:00-07:00,2019,3,25,18,30,2.6,-5.800000000000001,0.0,0.0,0.0,0.17,814.0,67.0,0.6000000000000001,53.93199635858635 +2019-03-25 19:30:00-07:00,2019,3,25,19,30,1.1,-6.800000000000001,0.0,0.0,0.0,0.17,815.0,131.0,0.8,55.61404189290459 +2019-03-25 20:30:00-07:00,2019,3,25,20,30,-0.4,-7.5,0.0,0.0,0.0,0.17,815.0,169.0,1.0,58.7337880329942 +2019-03-25 21:30:00-07:00,2019,3,25,21,30,-1.6,-8.1,0.0,0.0,0.0,0.17,815.0,201.0,1.2000000000000002,61.21022463509909 +2019-03-25 22:30:00-07:00,2019,3,25,22,30,-2.3000000000000003,-8.6,0.0,0.0,0.0,0.17,815.0,223.0,1.4,61.99269464827052 +2019-03-25 23:30:00-07:00,2019,3,25,23,30,-2.7,-9.0,0.0,0.0,0.0,0.17,814.0,234.0,1.7000000000000002,61.892696092241046 +2019-03-26 00:30:00-07:00,2019,3,26,0,30,-2.9000000000000004,-9.4,0.0,0.0,0.0,0.17,814.0,241.0,2.0,60.88038438122523 +2019-03-26 01:30:00-07:00,2019,3,26,1,30,-2.8000000000000003,-9.9,0.0,0.0,0.0,0.17,813.0,245.0,2.4000000000000004,58.10002374152339 +2019-03-26 02:30:00-07:00,2019,3,26,2,30,-2.5,-10.4,0.0,0.0,0.0,0.17,813.0,248.0,2.8000000000000003,54.62213058293596 +2019-03-26 03:30:00-07:00,2019,3,26,3,30,-2.2,-11.2,0.0,0.0,0.0,0.17,813.0,251.0,3.1,50.135027430754924 +2019-03-26 04:30:00-07:00,2019,3,26,4,30,-1.9,-11.9,0.0,0.0,0.0,0.17,813.0,254.0,3.1,46.36928100010621 +2019-03-26 05:30:00-07:00,2019,3,26,5,30,-0.9,-12.4,0.0,0.0,0.0,0.17,813.0,256.0,3.1,41.39247758015545 +2019-03-26 06:30:00-07:00,2019,3,26,6,30,2.2,-12.0,54.0,18.0,56.0,0.17,813.0,257.0,3.4000000000000004,34.179767083928844 +2019-03-26 07:30:00-07:00,2019,3,26,7,30,6.4,-10.7,144.0,212.0,208.0,0.17,813.0,257.0,3.2,28.254976912893447 +2019-03-26 08:30:00-07:00,2019,3,26,8,30,10.3,-8.4,168.0,579.0,444.0,0.17,813.0,254.0,1.7000000000000002,25.987796473126647 +2019-03-26 09:30:00-07:00,2019,3,26,9,30,13.4,-8.1,188.0,703.0,625.0,0.17,812.0,243.0,0.7000000000000001,21.683234823578626 +2019-03-26 10:30:00-07:00,2019,3,26,10,30,15.3,-7.7,264.0,604.0,703.0,0.17,812.0,256.0,0.8,19.780104505626134 +2019-03-26 11:30:00-07:00,2019,3,26,11,30,16.3,-7.1000000000000005,333.0,479.0,709.0,0.17,811.0,279.0,1.1,19.43407095919666 +2019-03-26 12:30:00-07:00,2019,3,26,12,30,16.900000000000002,-6.6000000000000005,271.0,626.0,766.0,0.17,810.0,279.0,1.4,19.439274303840776 +2019-03-26 13:30:00-07:00,2019,3,26,13,30,17.1,-6.2,230.0,678.0,735.0,0.17,810.0,274.0,1.7000000000000002,19.79093290068609 +2019-03-26 14:30:00-07:00,2019,3,26,14,30,16.900000000000002,-6.0,107.0,928.0,709.0,0.17,809.0,268.0,1.9,20.351477507717433 +2019-03-26 15:30:00-07:00,2019,3,26,15,30,16.1,-5.7,163.0,632.0,486.0,0.17,809.0,263.0,1.8,21.90837136745173 +2019-03-26 16:30:00-07:00,2019,3,26,16,30,13.9,-3.3000000000000003,177.0,221.0,252.0,0.17,809.0,265.0,1.1,30.23748121490761 +2019-03-26 17:30:00-07:00,2019,3,26,17,30,10.7,0.4,76.0,43.0,82.0,0.15,809.0,261.0,0.7000000000000001,48.95486903685391 +2019-03-26 18:30:00-07:00,2019,3,26,18,30,8.0,-3.6,0.0,0.0,0.0,0.15,809.0,245.0,0.9,43.75095268935306 +2019-03-26 19:30:00-07:00,2019,3,26,19,30,6.0,-4.9,0.0,0.0,0.0,0.15,808.0,236.0,1.3,45.50787025188316 +2019-03-26 20:30:00-07:00,2019,3,26,20,30,4.4,-5.1000000000000005,0.0,0.0,0.0,0.15,808.0,234.0,1.6,50.098299535956784 +2019-03-26 21:30:00-07:00,2019,3,26,21,30,3.8,-5.0,0.0,0.0,0.0,0.15,808.0,234.0,1.9,52.64807579661088 +2019-03-26 22:30:00-07:00,2019,3,26,22,30,3.8,-5.1000000000000005,0.0,0.0,0.0,0.15,808.0,237.0,2.1,52.25140451963898 +2019-03-26 23:30:00-07:00,2019,3,26,23,30,3.9,-5.0,0.0,0.0,0.0,0.15,808.0,240.0,2.2,52.27938924322847 +2019-03-27 00:30:00-07:00,2019,3,27,0,30,3.7,-5.0,0.0,0.0,0.0,0.15,808.0,246.0,2.3000000000000003,53.019664427616725 +2019-03-27 01:30:00-07:00,2019,3,27,1,30,3.3000000000000003,-5.1000000000000005,0.0,0.0,0.0,0.15,808.0,252.0,2.2,54.1246530874119 +2019-03-27 02:30:00-07:00,2019,3,27,2,30,2.9000000000000004,-5.1000000000000005,0.0,0.0,0.0,0.15,808.0,255.0,2.1,55.677231778990986 +2019-03-27 03:30:00-07:00,2019,3,27,3,30,2.7,-5.1000000000000005,0.0,0.0,0.0,0.15,808.0,255.0,1.9,56.47209289133136 +2019-03-27 04:30:00-07:00,2019,3,27,4,30,2.8000000000000003,-5.2,0.0,0.0,0.0,0.15,808.0,252.0,1.7000000000000002,55.650261642975586 +2019-03-27 05:30:00-07:00,2019,3,27,5,30,3.6,-5.1000000000000005,0.0,0.0,0.0,0.15,808.0,248.0,1.6,52.99188751645544 +2019-03-27 06:30:00-07:00,2019,3,27,6,30,5.6000000000000005,-4.3,57.0,24.0,60.0,0.15,809.0,240.0,1.4,48.9482136676059 +2019-03-27 07:30:00-07:00,2019,3,27,7,30,8.8,-1.9,125.0,79.0,149.0,0.15,809.0,228.0,1.4,47.01333847812378 +2019-03-27 08:30:00-07:00,2019,3,27,8,30,11.9,0.1,230.0,311.0,380.0,0.15,809.0,198.0,1.5,44.244254495857696 +2019-03-27 09:30:00-07:00,2019,3,27,9,30,14.4,-1.8,291.0,358.0,515.0,0.15,809.0,168.0,1.6,32.71394243232691 +2019-03-27 10:30:00-07:00,2019,3,27,10,30,16.2,-2.4000000000000004,275.0,530.0,663.0,0.15,809.0,166.0,1.6,27.88242059311953 +2019-03-27 11:30:00-07:00,2019,3,27,11,30,17.400000000000002,-2.6,299.0,578.0,755.0,0.15,808.0,183.0,1.4,25.45865092888474 +2019-03-27 12:30:00-07:00,2019,3,27,12,30,18.2,-2.7,174.0,867.0,863.0,0.15,808.0,209.0,1.2000000000000002,24.029339102998527 +2019-03-27 13:30:00-07:00,2019,3,27,13,30,18.4,-2.9000000000000004,305.0,496.0,676.0,0.15,807.0,234.0,1.1,23.38016512859767 +2019-03-27 14:30:00-07:00,2019,3,27,14,30,18.3,-3.1,258.0,484.0,574.0,0.15,807.0,252.0,1.0,23.179977661813993 +2019-03-27 15:30:00-07:00,2019,3,27,15,30,17.400000000000002,-3.3000000000000003,248.0,325.0,415.0,0.15,807.0,277.0,0.9,24.168185477768613 +2019-03-27 16:30:00-07:00,2019,3,27,16,30,15.1,-1.3,171.0,80.0,199.0,0.15,807.0,149.0,0.9,32.44049058530303 +2019-03-27 17:30:00-07:00,2019,3,27,17,30,12.6,1.8,52.0,3.0,52.0,0.16,808.0,11.0,0.9,47.75303002248277 +2019-03-27 18:30:00-07:00,2019,3,27,18,30,11.0,1.1,0.0,0.0,0.0,0.16,808.0,183.0,0.8,50.471780167249634 +2019-03-27 19:30:00-07:00,2019,3,27,19,30,10.0,1.5,0.0,0.0,0.0,0.16,808.0,312.0,0.6000000000000001,55.52043441212077 +2019-03-27 20:30:00-07:00,2019,3,27,20,30,9.4,1.3,0.0,0.0,0.0,0.16,808.0,275.0,0.9,56.97705495501816 +2019-03-27 21:30:00-07:00,2019,3,27,21,30,8.6,1.2000000000000002,0.0,0.0,0.0,0.16,808.0,273.0,1.2000000000000002,59.70505827709537 +2019-03-27 22:30:00-07:00,2019,3,27,22,30,7.7,0.4,0.0,0.0,0.0,0.16,808.0,275.0,1.2000000000000002,59.91232165814117 +2019-03-27 23:30:00-07:00,2019,3,27,23,30,6.7,-0.5,0.0,0.0,0.0,0.16,807.0,276.0,1.2000000000000002,60.099061011423316 +2019-03-28 00:30:00-07:00,2019,3,28,0,30,5.800000000000001,-1.4,0.0,0.0,0.0,0.16,807.0,276.0,1.4,59.87441763813631 +2019-03-28 01:30:00-07:00,2019,3,28,1,30,4.9,-2.2,0.0,0.0,0.0,0.16,807.0,274.0,1.6,60.090455847575406 +2019-03-28 02:30:00-07:00,2019,3,28,2,30,3.9,-2.8000000000000003,0.0,0.0,0.0,0.16,807.0,271.0,1.8,61.645085776408536 +2019-03-28 03:30:00-07:00,2019,3,28,3,30,3.0,-3.3000000000000003,0.0,0.0,0.0,0.16,808.0,269.0,1.9,63.285708626244215 +2019-03-28 04:30:00-07:00,2019,3,28,4,30,2.4000000000000004,-3.7,0.0,0.0,0.0,0.16,808.0,267.0,1.9,64.09450293480226 +2019-03-28 05:30:00-07:00,2019,3,28,5,30,3.0,-4.1000000000000005,0.0,0.0,0.0,0.16,808.0,261.0,2.0,59.61079162097314 +2019-03-28 06:30:00-07:00,2019,3,28,6,30,5.800000000000001,-3.5,34.0,476.0,89.0,0.16,808.0,254.0,2.4000000000000004,51.2563342867872 +2019-03-28 07:30:00-07:00,2019,3,28,7,30,9.5,-2.8000000000000003,113.0,465.0,257.0,0.16,808.0,256.0,2.3000000000000003,41.96084097264255 +2019-03-28 08:30:00-07:00,2019,3,28,8,30,12.7,-1.6,185.0,487.0,422.0,0.16,808.0,273.0,1.8,37.08144897133494 +2019-03-28 09:30:00-07:00,2019,3,28,9,30,14.9,-1.4,279.0,427.0,549.0,0.16,808.0,290.0,1.5,32.62049565998687 +2019-03-28 10:30:00-07:00,2019,3,28,10,30,16.1,-1.7000000000000002,295.0,506.0,668.0,0.16,808.0,299.0,1.2000000000000002,29.547626010826022 +2019-03-28 11:30:00-07:00,2019,3,28,11,30,16.900000000000002,-2.2,214.0,746.0,806.0,0.16,807.0,306.0,1.0,27.066092534002202 +2019-03-28 12:30:00-07:00,2019,3,28,12,30,17.3,-2.5,279.0,622.0,776.0,0.16,807.0,311.0,1.0,25.810458827581115 +2019-03-28 13:30:00-07:00,2019,3,28,13,30,17.1,-2.7,292.0,451.0,631.0,0.16,807.0,318.0,1.2000000000000002,25.75453129175496 +2019-03-28 14:30:00-07:00,2019,3,28,14,30,16.3,-2.7,314.0,88.0,372.0,0.16,807.0,336.0,1.5,27.096525187978887 +2019-03-28 15:30:00-07:00,2019,3,28,15,30,14.8,-2.4000000000000004,229.0,68.0,264.0,0.16,807.0,188.0,1.8,30.499644557396646 +2019-03-28 16:30:00-07:00,2019,3,28,16,30,12.6,-1.8,174.0,235.0,256.0,0.16,807.0,44.0,2.3000000000000003,36.780214942077194 +2019-03-28 17:30:00-07:00,2019,3,28,17,30,9.3,0.3,82.0,66.0,92.0,0.15,808.0,68.0,2.5,53.374281930295325 +2019-03-28 18:30:00-07:00,2019,3,28,18,30,6.300000000000001,1.0,0.0,0.0,0.0,0.15,808.0,87.0,2.3000000000000003,68.85936891885763 +2019-03-28 19:30:00-07:00,2019,3,28,19,30,4.6000000000000005,1.1,0.0,0.0,0.0,0.15,809.0,113.0,1.9,78.0376203293443 +2019-03-28 20:30:00-07:00,2019,3,28,20,30,3.5,0.7000000000000001,0.0,0.0,0.0,0.15,808.0,144.0,1.5,81.90620081567965 +2019-03-28 21:30:00-07:00,2019,3,28,21,30,2.7,0.4,0.0,0.0,0.0,0.15,808.0,166.0,1.3,84.81566898624042 +2019-03-28 22:30:00-07:00,2019,3,28,22,30,1.9,-0.2,0.0,0.0,0.0,0.15,807.0,176.0,1.2000000000000002,85.96474757037537 +2019-03-28 23:30:00-07:00,2019,3,28,23,30,1.2000000000000002,-0.8,0.0,0.0,0.0,0.15,806.0,187.0,1.1,86.5193680147029 +2019-03-29 00:30:00-07:00,2019,3,29,0,30,0.4,-1.4,0.0,0.0,0.0,0.15,806.0,200.0,1.0,87.71523152534606 +2019-03-29 01:30:00-07:00,2019,3,29,1,30,-0.2,-2.1,0.0,0.0,0.0,0.15,805.0,211.0,1.0,87.01398330813788 +2019-03-29 02:30:00-07:00,2019,3,29,2,30,-0.7000000000000001,-2.8000000000000003,0.0,0.0,0.0,0.15,805.0,228.0,0.9,85.68351793124346 +2019-03-29 03:30:00-07:00,2019,3,29,3,30,-1.2000000000000002,-3.5,0.0,0.0,0.0,0.15,805.0,248.0,0.9,84.3605488888021 +2019-03-29 04:30:00-07:00,2019,3,29,4,30,-1.7000000000000002,-4.2,0.0,0.0,0.0,0.15,805.0,262.0,0.9,83.04521680585707 +2019-03-29 05:30:00-07:00,2019,3,29,5,30,-1.4,-4.9,0.0,0.0,0.0,0.15,805.0,268.0,0.9,77.0635634839004 +2019-03-29 06:30:00-07:00,2019,3,29,6,30,0.8,-4.800000000000001,37.0,433.0,89.0,0.15,805.0,276.0,1.2000000000000002,66.17093287820467 +2019-03-29 07:30:00-07:00,2019,3,29,7,30,3.9,-4.0,47.0,0.0,47.0,0.15,805.0,282.0,1.8,56.36697155957562 +2019-03-29 08:30:00-07:00,2019,3,29,8,30,6.6000000000000005,-3.9,42.0,0.0,42.0,0.15,804.0,276.0,2.3000000000000003,47.07598309556125 +2019-03-29 09:30:00-07:00,2019,3,29,9,30,8.6,-5.1000000000000005,55.0,0.0,55.0,0.15,804.0,268.0,2.8000000000000003,37.526516292688946 +2019-03-29 10:30:00-07:00,2019,3,29,10,30,9.8,-6.0,39.0,0.0,39.0,0.15,803.0,266.0,3.1,32.32702006972219 +2019-03-29 11:30:00-07:00,2019,3,29,11,30,10.3,-6.4,65.0,0.0,65.0,0.15,803.0,269.0,3.2,30.323416036420113 +2019-03-29 12:30:00-07:00,2019,3,29,12,30,10.2,-6.4,49.0,0.0,49.0,0.15,802.0,277.0,3.1,30.52655797806565 +2019-03-29 13:30:00-07:00,2019,3,29,13,30,9.3,-6.1000000000000005,73.0,0.0,73.0,0.15,802.0,297.0,2.9000000000000004,33.17664108290109 +2019-03-29 14:30:00-07:00,2019,3,29,14,30,7.7,-5.1000000000000005,162.0,4.0,165.0,0.15,803.0,329.0,2.9000000000000004,39.890909715783216 +2019-03-29 15:30:00-07:00,2019,3,29,15,30,5.5,-3.5,164.0,18.0,173.0,0.15,804.0,179.0,3.3000000000000003,52.33249956016791 +2019-03-29 16:30:00-07:00,2019,3,29,16,30,3.3000000000000003,-2.3000000000000003,73.0,6.0,75.0,0.15,805.0,12.0,3.5,66.73146152737283 +2019-03-29 17:30:00-07:00,2019,3,29,17,30,1.7000000000000002,-2.2,37.0,0.0,37.0,0.74,806.0,15.0,3.3000000000000003,75.31997281285639 +2019-03-29 18:30:00-07:00,2019,3,29,18,30,0.5,-2.5,0.0,0.0,0.0,0.74,807.0,17.0,3.1,80.30272644608333 +2019-03-29 19:30:00-07:00,2019,3,29,19,30,-0.4,-3.1,0.0,0.0,0.0,0.74,809.0,17.0,3.3000000000000003,81.98304014690993 +2019-03-29 20:30:00-07:00,2019,3,29,20,30,-1.2000000000000002,-3.8,0.0,0.0,0.0,0.74,809.0,17.0,3.6,82.48988635479661 +2019-03-29 21:30:00-07:00,2019,3,29,21,30,-2.0,-4.6000000000000005,0.0,0.0,0.0,0.74,810.0,19.0,3.7,82.38380089710469 +2019-03-29 22:30:00-07:00,2019,3,29,22,30,-2.8000000000000003,-5.5,0.0,0.0,0.0,0.74,810.0,20.0,3.6,81.65479977799492 +2019-03-29 23:30:00-07:00,2019,3,29,23,30,-3.6,-6.300000000000001,0.0,0.0,0.0,0.74,811.0,18.0,3.5,81.54347396337657 +2019-03-30 00:30:00-07:00,2019,3,30,0,30,-4.3,-7.0,0.0,0.0,0.0,0.74,811.0,14.0,3.2,81.4452654017688 +2019-03-30 01:30:00-07:00,2019,3,30,1,30,-4.9,-7.4,0.0,0.0,0.0,0.74,811.0,11.0,2.9000000000000004,82.6266188832871 +2019-03-30 02:30:00-07:00,2019,3,30,2,30,-5.300000000000001,-7.7,0.0,0.0,0.0,0.74,812.0,11.0,2.8000000000000003,83.2146359774706 +2019-03-30 03:30:00-07:00,2019,3,30,3,30,-5.5,-7.9,0.0,0.0,0.0,0.74,812.0,12.0,2.7,83.18874967916852 +2019-03-30 04:30:00-07:00,2019,3,30,4,30,-5.6000000000000005,-8.1,0.0,0.0,0.0,0.74,813.0,11.0,2.4000000000000004,82.53306510994277 +2019-03-30 05:30:00-07:00,2019,3,30,5,30,-5.2,-8.200000000000001,0.0,0.0,0.0,0.74,813.0,5.0,2.3000000000000003,79.4473283089591 +2019-03-30 06:30:00-07:00,2019,3,30,6,30,-3.9,-8.3,35.0,0.0,35.0,0.74,814.0,181.0,2.4000000000000004,71.47994562377673 +2019-03-30 07:30:00-07:00,2019,3,30,7,30,-2.2,-8.4,85.0,0.0,85.0,0.74,814.0,360.0,2.6,62.502252891937964 +2019-03-30 08:30:00-07:00,2019,3,30,8,30,-0.5,-8.200000000000001,209.0,8.0,213.0,0.74,814.0,180.0,2.6,56.041102247357514 +2019-03-30 09:30:00-07:00,2019,3,30,9,30,1.0,-7.800000000000001,376.0,8.0,381.0,0.74,814.0,2.0,2.6,51.85986911728133 +2019-03-30 10:30:00-07:00,2019,3,30,10,30,2.4000000000000004,-7.5,503.0,14.0,513.0,0.74,814.0,6.0,2.5,48.019934634708434 +2019-03-30 11:30:00-07:00,2019,3,30,11,30,3.5,-7.1000000000000005,556.0,30.0,580.0,0.74,813.0,11.0,2.5,45.8134974908356 +2019-03-30 12:30:00-07:00,2019,3,30,12,30,4.3,-6.9,452.0,2.0,454.0,0.74,813.0,17.0,2.5,43.98102485036627 +2019-03-30 13:30:00-07:00,2019,3,30,13,30,4.7,-6.800000000000001,451.0,3.0,453.0,0.74,812.0,22.0,2.5,43.09790530945904 +2019-03-30 14:30:00-07:00,2019,3,30,14,30,4.6000000000000005,-6.800000000000001,497.0,73.0,545.0,0.74,812.0,27.0,2.5,43.39987486218737 +2019-03-30 15:30:00-07:00,2019,3,30,15,30,4.0,-7.0,230.0,2.0,231.0,0.74,812.0,35.0,2.4000000000000004,44.57193795816846 +2019-03-30 16:30:00-07:00,2019,3,30,16,30,2.8000000000000003,-7.2,154.0,1.0,154.0,0.74,812.0,49.0,2.1,47.76786843244288 +2019-03-30 17:30:00-07:00,2019,3,30,17,30,0.3,-7.300000000000001,38.0,0.0,38.0,0.72,813.0,73.0,1.4,56.69352235000104 +2019-03-30 18:30:00-07:00,2019,3,30,18,30,-2.1,-7.300000000000001,0.0,0.0,0.0,0.72,814.0,109.0,0.9,67.55931142081134 +2019-03-30 19:30:00-07:00,2019,3,30,19,30,-3.2,-8.5,0.0,0.0,0.0,0.72,814.0,151.0,0.9,66.79155413164587 +2019-03-30 20:30:00-07:00,2019,3,30,20,30,-3.9,-9.1,0.0,0.0,0.0,0.72,814.0,188.0,1.0,67.15571022466419 +2019-03-30 21:30:00-07:00,2019,3,30,21,30,-4.4,-9.3,0.0,0.0,0.0,0.72,814.0,211.0,1.0,68.64008652489909 +2019-03-30 22:30:00-07:00,2019,3,30,22,30,-4.7,-9.5,0.0,0.0,0.0,0.72,814.0,223.0,1.1,69.11530003590744 +2019-03-30 23:30:00-07:00,2019,3,30,23,30,-4.9,-9.7,0.0,0.0,0.0,0.72,814.0,232.0,1.1,69.0719705184719 +2019-03-31 00:30:00-07:00,2019,3,31,0,30,-4.9,-9.9,0.0,0.0,0.0,0.72,813.0,243.0,1.1,67.99273172443145 +2019-03-31 01:30:00-07:00,2019,3,31,1,30,-4.9,-10.0,0.0,0.0,0.0,0.72,813.0,256.0,1.0,67.45876868902923 +2019-03-31 02:30:00-07:00,2019,3,31,2,30,-5.0,-10.1,0.0,0.0,0.0,0.72,813.0,268.0,1.0,67.43621172324829 +2019-03-31 03:30:00-07:00,2019,3,31,3,30,-5.1000000000000005,-10.3,0.0,0.0,0.0,0.72,813.0,280.0,1.0,66.88285829443471 +2019-03-31 04:30:00-07:00,2019,3,31,4,30,-5.2,-10.4,0.0,0.0,0.0,0.72,813.0,287.0,1.0,66.85998248540484 +2019-03-31 05:30:00-07:00,2019,3,31,5,30,-4.5,-10.6,0.0,0.0,0.0,0.72,813.0,286.0,1.1,62.421251361795214 +2019-03-31 06:30:00-07:00,2019,3,31,6,30,-2.3000000000000003,-10.2,58.0,1.0,58.0,0.72,813.0,219.0,1.0,54.67795019081223 +2019-03-31 07:30:00-07:00,2019,3,31,7,30,0.6000000000000001,-9.5,161.0,2.0,162.0,0.72,813.0,136.0,0.9,46.74988117357645 +2019-03-31 08:30:00-07:00,2019,3,31,8,30,3.0,-8.200000000000001,335.0,112.0,391.0,0.72,813.0,110.0,1.2000000000000002,43.59084262087857 +2019-03-31 09:30:00-07:00,2019,3,31,9,30,4.800000000000001,-7.0,102.0,958.0,720.0,0.72,812.0,98.0,1.6,42.14576039592198 +2019-03-31 10:30:00-07:00,2019,3,31,10,30,6.0,-6.5,133.0,957.0,850.0,0.72,812.0,91.0,1.9,40.29443968478753 +2019-03-31 11:30:00-07:00,2019,3,31,11,30,6.9,-6.1000000000000005,165.0,933.0,917.0,0.72,811.0,90.0,2.2,39.050250651443044 +2019-03-31 12:30:00-07:00,2019,3,31,12,30,7.2,-5.9,254.0,808.0,909.0,0.72,811.0,94.0,2.3000000000000003,38.8438679563415 +2019-03-31 13:30:00-07:00,2019,3,31,13,30,7.2,-5.800000000000001,289.0,707.0,828.0,0.72,811.0,101.0,2.3000000000000003,39.14075485530123 +2019-03-31 14:30:00-07:00,2019,3,31,14,30,6.800000000000001,-6.0,395.0,345.0,625.0,0.72,810.0,110.0,2.2,39.620075132275296 +2019-03-31 15:30:00-07:00,2019,3,31,15,30,6.1000000000000005,-6.2,383.0,74.0,422.0,0.72,810.0,123.0,2.0,40.94576035999116 +2019-03-31 16:30:00-07:00,2019,3,31,16,30,4.9,-6.300000000000001,129.0,4.0,130.0,0.72,810.0,139.0,1.6,44.16015116338 +2019-03-31 17:30:00-07:00,2019,3,31,17,30,6.2,-4.7,38.0,613.0,140.0,0.16,801.0,287.0,1.0,45.566697970947104 +2019-03-31 18:30:00-07:00,2019,3,31,18,30,3.7,-5.4,0.0,0.0,0.0,0.16,802.0,255.0,0.7000000000000001,51.437784178339285 +2019-03-31 19:30:00-07:00,2019,3,31,19,30,2.7,-5.9,0.0,0.0,0.0,0.16,801.0,234.0,1.1,53.14444608852395 +2019-03-31 20:30:00-07:00,2019,3,31,20,30,2.2,-5.9,0.0,0.0,0.0,0.16,801.0,233.0,1.5,55.06712933255292 +2019-03-31 21:30:00-07:00,2019,3,31,21,30,2.0,-6.0,0.0,0.0,0.0,0.16,800.0,234.0,1.7000000000000002,55.43376504456267 +2019-03-31 22:30:00-07:00,2019,3,31,22,30,2.0,-6.0,0.0,0.0,0.0,0.16,799.0,238.0,2.0,55.43376504456267 +2019-03-31 23:30:00-07:00,2019,3,31,23,30,2.0,-5.9,0.0,0.0,0.0,0.16,799.0,246.0,2.2,55.857808744004615 +2001-04-01 00:30:00-07:00,2001,4,1,0,30,2.0,-5.7,0.0,0.0,0.0,0.16,798.0,255.0,2.2,56.71455959869701 +2001-04-01 01:30:00-07:00,2001,4,1,1,30,1.8,-5.6000000000000005,0.0,0.0,0.0,0.16,798.0,265.0,2.1,57.96919842114627 +2001-04-01 02:30:00-07:00,2001,4,1,2,30,1.8,-5.800000000000001,0.0,0.0,0.0,0.16,797.0,274.0,2.1,57.09422671460819 +2001-04-01 03:30:00-07:00,2001,4,1,3,30,1.9,-5.9,0.0,0.0,0.0,0.16,797.0,278.0,2.1,56.25788682779953 +2001-04-01 04:30:00-07:00,2001,4,1,4,30,1.9,-5.9,0.0,0.0,0.0,0.16,797.0,276.0,2.1,56.25788682779953 +2001-04-01 05:30:00-07:00,2001,4,1,5,30,3.0,-5.9,0.0,0.0,0.0,0.16,797.0,272.0,2.2,52.026817984658344 +2001-04-01 06:30:00-07:00,2001,4,1,6,30,6.300000000000001,-4.6000000000000005,74.0,98.0,88.0,0.16,797.0,271.0,2.8000000000000003,45.596085978862256 +2001-04-01 07:30:00-07:00,2001,4,1,7,30,9.9,-3.3000000000000003,141.0,444.0,288.0,0.16,797.0,279.0,3.8,39.35905651840114 +2001-04-01 08:30:00-07:00,2001,4,1,8,30,12.4,-3.7,74.0,916.0,538.0,0.16,797.0,290.0,4.9,32.36636041423156 +2001-04-01 09:30:00-07:00,2001,4,1,9,30,14.3,-3.9,81.0,970.0,713.0,0.16,797.0,295.0,5.6000000000000005,28.172293424750258 +2001-04-01 10:30:00-07:00,2001,4,1,10,30,15.7,-4.2,84.0,1005.0,844.0,0.16,797.0,295.0,5.7,25.173026457811186 +2001-04-01 11:30:00-07:00,2001,4,1,11,30,16.7,-4.4,85.0,1016.0,910.0,0.16,796.0,293.0,5.5,23.266235771665738 +2001-04-01 12:30:00-07:00,2001,4,1,12,30,17.2,-4.4,86.0,1017.0,915.0,0.16,796.0,290.0,5.2,22.54058473659408 +2001-04-01 13:30:00-07:00,2001,4,1,13,30,17.2,-4.3,86.0,995.0,849.0,0.16,796.0,288.0,4.800000000000001,22.7106976576857 +2001-04-01 14:30:00-07:00,2001,4,1,14,30,16.900000000000002,-4.3,289.0,427.0,576.0,0.16,796.0,288.0,4.2,23.146252739951528 +2001-04-01 15:30:00-07:00,2001,4,1,15,30,16.1,-4.4,79.0,895.0,556.0,0.16,796.0,290.0,3.3000000000000003,24.171820241530526 +2001-04-01 16:30:00-07:00,2001,4,1,16,30,14.2,-4.2,180.0,258.0,274.0,0.16,796.0,297.0,1.9,27.72433498248259 +2001-04-01 17:30:00-07:00,2001,4,1,17,30,11.2,-0.8,87.0,223.0,124.0,0.16,797.0,178.0,1.0,43.40313748637597 +2001-04-01 18:30:00-07:00,2001,4,1,18,30,8.9,-2.1,0.0,0.0,0.0,0.16,798.0,68.0,1.5,46.013490365354095 +2001-04-01 19:30:00-07:00,2001,4,1,19,30,7.4,-2.0,0.0,0.0,0.0,0.16,799.0,86.0,1.9,51.32333269089752 +2001-04-01 20:30:00-07:00,2001,4,1,20,30,6.5,-1.7000000000000002,0.0,0.0,0.0,0.16,799.0,94.0,1.6,55.80842678993576 +2001-04-01 21:30:00-07:00,2001,4,1,21,30,5.5,-1.4,0.0,0.0,0.0,0.16,799.0,111.0,1.2000000000000002,61.131526050640666 +2001-04-01 22:30:00-07:00,2001,4,1,22,30,4.3,-1.4,0.0,0.0,0.0,0.16,799.0,134.0,1.1,66.4629909253635 +2001-04-01 23:30:00-07:00,2001,4,1,23,30,3.1,-1.7000000000000002,0.0,0.0,0.0,0.16,798.0,155.0,1.2000000000000002,70.74219978540367 +2001-04-02 00:30:00-07:00,2001,4,2,0,30,2.2,-2.0,0.0,0.0,0.0,0.16,798.0,169.0,1.3,73.76013988917104 +2001-04-02 01:30:00-07:00,2001,4,2,1,30,1.6,-2.4000000000000004,0.0,0.0,0.0,0.16,797.0,179.0,1.4,74.7476307981026 +2001-04-02 02:30:00-07:00,2001,4,2,2,30,1.4,-2.8000000000000003,0.0,0.0,0.0,0.16,796.0,185.0,1.6,73.61181835992228 +2001-04-02 03:30:00-07:00,2001,4,2,3,30,1.6,-3.2,0.0,0.0,0.0,0.16,796.0,192.0,1.8,70.43864136293438 +2001-04-02 04:30:00-07:00,2001,4,2,4,30,1.7000000000000002,-3.5,0.0,0.0,0.0,0.16,796.0,198.0,2.1,68.38951388124603 +2001-04-02 05:30:00-07:00,2001,4,2,5,30,2.9000000000000004,-3.7,0.0,0.0,0.0,0.16,796.0,201.0,2.5,61.860204283182874 +2001-04-02 06:30:00-07:00,2001,4,2,6,30,6.0,-3.1,73.0,19.0,75.0,0.16,796.0,201.0,3.3000000000000003,52.08236596275546 +2001-04-02 07:30:00-07:00,2001,4,2,7,30,10.0,-2.2,167.0,36.0,179.0,0.16,796.0,201.0,4.0,42.42089254777639 +2001-04-02 08:30:00-07:00,2001,4,2,8,30,13.1,-1.7000000000000002,164.0,610.0,476.0,0.16,795.0,205.0,4.2,35.859490946183655 +2001-04-02 09:30:00-07:00,2001,4,2,9,30,15.3,-1.8,95.0,924.0,701.0,0.16,795.0,211.0,4.0,30.87152515380083 +2001-04-02 10:30:00-07:00,2001,4,2,10,30,16.7,-2.0,217.0,736.0,776.0,0.16,794.0,215.0,3.9,27.819254532448987 +2001-04-02 11:30:00-07:00,2001,4,2,11,30,17.6,-2.2,412.0,239.0,607.0,0.16,793.0,220.0,3.9,25.894325521054956 +2001-04-02 12:30:00-07:00,2001,4,2,12,30,18.0,-2.4000000000000004,386.0,52.0,428.0,0.16,793.0,228.0,4.1000000000000005,24.87984286736887 +2001-04-02 13:30:00-07:00,2001,4,2,13,30,17.900000000000002,-2.6,339.0,390.0,639.0,0.16,793.0,237.0,4.4,24.668812374469983 +2001-04-02 14:30:00-07:00,2001,4,2,14,30,17.3,-2.8000000000000003,336.0,281.0,525.0,0.16,793.0,247.0,4.800000000000001,25.242821081601413 +2001-04-02 15:30:00-07:00,2001,4,2,15,30,16.3,-2.9000000000000004,85.0,870.0,551.0,0.16,793.0,255.0,4.800000000000001,26.697279160227197 +2001-04-02 16:30:00-07:00,2001,4,2,16,30,14.5,-2.7,67.0,779.0,351.0,0.16,794.0,262.0,3.7,30.41232202127395 +2001-04-02 17:30:00-07:00,2001,4,2,17,30,11.1,-1.5,45.0,548.0,140.0,0.16,794.0,268.0,2.2,41.50916257128065 +2001-04-02 18:30:00-07:00,2001,4,2,18,30,8.200000000000001,-1.0,0.0,0.0,0.0,0.16,795.0,266.0,1.6,52.305674676691744 +2001-04-02 19:30:00-07:00,2001,4,2,19,30,7.0,-1.2000000000000002,0.0,0.0,0.0,0.16,795.0,257.0,1.7000000000000002,55.940835201657976 +2001-04-02 20:30:00-07:00,2001,4,2,20,30,6.300000000000001,-1.4,0.0,0.0,0.0,0.16,796.0,249.0,1.8,57.842847665708724 +2001-04-02 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22:30:00-07:00,2007,6,28,22,30,16.5,10.1,0.0,0.0,0.0,0.18,812.0,207.0,2.7,65.88400848736433 +2007-06-28 23:30:00-07:00,2007,6,28,23,30,15.9,9.7,0.0,0.0,0.0,0.18,811.0,211.0,2.6,66.64350957027338 +2007-06-29 00:30:00-07:00,2007,6,29,0,30,15.3,9.3,0.0,0.0,0.0,0.18,811.0,216.0,2.6,67.4180178499058 +2007-06-29 01:30:00-07:00,2007,6,29,1,30,14.9,8.8,0.0,0.0,0.0,0.18,811.0,223.0,2.4000000000000004,66.88152198276539 +2007-06-29 02:30:00-07:00,2007,6,29,2,30,14.3,8.200000000000001,0.0,0.0,0.0,0.18,810.0,230.0,2.2,66.75451918389614 +2007-06-29 03:30:00-07:00,2007,6,29,3,30,13.8,7.6,0.0,0.0,0.0,0.18,810.0,237.0,1.9,66.19544780194684 +2007-06-29 04:30:00-07:00,2007,6,29,4,30,14.8,6.800000000000001,0.0,0.0,0.0,0.18,810.0,245.0,1.8,58.74483840201296 +2007-06-29 05:30:00-07:00,2007,6,29,5,30,17.8,6.6000000000000005,77.0,42.0,83.0,0.18,810.0,254.0,2.0,47.865374511357906 +2007-06-29 06:30:00-07:00,2007,6,29,6,30,21.8,6.2,62.0,701.0,295.0,0.18,811.0,282.0,1.7000000000000002,36.33645174861917 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00:30:00-07:00,2020,7,1,0,30,12.6,0.2,0.0,0.0,0.0,0.17,811.0,228.0,0.7000000000000001,42.56192034178436 +2020-07-01 01:30:00-07:00,2020,7,1,1,30,11.6,0.0,0.0,0.0,0.0,0.17,811.0,195.0,0.9,44.80286000369663 +2020-07-01 02:30:00-07:00,2020,7,1,2,30,10.7,-0.1,0.0,0.0,0.0,0.17,811.0,189.0,1.0,47.213697317415765 +2020-07-01 03:30:00-07:00,2020,7,1,3,30,10.0,-0.3,0.0,0.0,0.0,0.17,811.0,194.0,1.1,48.7574217764769 +2020-07-01 04:30:00-07:00,2020,7,1,4,30,11.0,-0.4,0.0,0.0,0.0,0.17,811.0,201.0,1.4,45.28371961633146 +2020-07-01 05:30:00-07:00,2020,7,1,5,30,14.4,0.2,37.0,507.0,110.0,0.17,811.0,195.0,2.5,37.85644575658867 +2020-07-01 06:30:00-07:00,2020,7,1,6,30,18.0,-0.4,56.0,750.0,302.0,0.17,811.0,184.0,3.5,28.81176502473312 +2020-07-01 07:30:00-07:00,2020,7,1,7,30,21.0,-0.2,68.0,865.0,507.0,0.17,811.0,182.0,3.8,24.261196191271228 +2020-07-01 08:30:00-07:00,2020,7,1,8,30,23.5,-0.8,77.0,928.0,698.0,0.17,811.0,183.0,3.6,19.945871943018798 +2020-07-01 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09:30:00-07:00,2020,7,2,9,30,30.0,-4.1000000000000005,92.0,944.0,849.0,0.17,812.0,210.0,1.7000000000000002,10.65693932251897 +2020-07-02 10:30:00-07:00,2020,7,2,10,30,31.5,-3.8,96.0,965.0,962.0,0.17,812.0,200.0,2.1,10.003898608802922 +2020-07-02 11:30:00-07:00,2020,7,2,11,30,32.7,-3.4000000000000004,155.0,896.0,1006.0,0.17,811.0,194.0,2.3000000000000003,9.630732275244037 +2020-07-02 12:30:00-07:00,2020,7,2,12,30,33.4,-3.1,432.0,423.0,835.0,0.17,811.0,189.0,2.5,9.4685085719342 +2020-07-02 13:30:00-07:00,2020,7,2,13,30,33.6,-2.9000000000000004,403.0,381.0,749.0,0.17,810.0,182.0,2.6,9.503283174557403 +2020-07-02 14:30:00-07:00,2020,7,2,14,30,33.300000000000004,-2.7,378.0,320.0,641.0,0.17,810.0,172.0,2.5,9.808910855597068 +2020-07-02 15:30:00-07:00,2020,7,2,15,30,32.5,-2.6,347.0,274.0,537.0,0.17,810.0,160.0,2.3000000000000003,10.336793364143002 +2020-07-02 16:30:00-07:00,2020,7,2,16,30,31.200000000000003,-2.4000000000000004,214.0,446.0,452.0,0.17,810.0,141.0,2.3000000000000003,11.293315360549263 +2020-07-02 17:30:00-07:00,2020,7,2,17,30,28.8,-1.6,163.0,79.0,191.0,0.17,811.0,127.0,2.1,13.75078775367079 +2020-07-02 18:30:00-07:00,2020,7,2,18,30,25.3,3.1,52.0,433.0,127.0,0.17,811.0,127.0,2.1,23.698294588315918 +2020-07-02 19:30:00-07:00,2020,7,2,19,30,22.8,4.3,5.0,31.0,5.0,0.17,812.0,139.0,2.5,29.96280735247091 +2020-07-02 20:30:00-07:00,2020,7,2,20,30,21.8,5.7,0.0,0.0,0.0,0.17,813.0,151.0,2.8000000000000003,35.102563617517205 +2020-07-02 21:30:00-07:00,2020,7,2,21,30,20.9,6.7,0.0,0.0,0.0,0.17,813.0,160.0,2.7,39.740954396351064 +2020-07-02 22:30:00-07:00,2020,7,2,22,30,20.0,6.9,0.0,0.0,0.0,0.17,813.0,167.0,2.5,42.59072885383463 +2020-07-02 23:30:00-07:00,2020,7,2,23,30,19.3,6.5,0.0,0.0,0.0,0.17,813.0,174.0,2.3000000000000003,43.27683225329863 +2020-07-03 00:30:00-07:00,2020,7,3,0,30,18.7,6.300000000000001,0.0,0.0,0.0,0.17,813.0,181.0,2.1,44.31302640763157 +2020-07-03 01:30:00-07:00,2020,7,3,1,30,18.0,6.0,0.0,0.0,0.0,0.17,813.0,197.0,1.8,45.352703262658444 +2020-07-03 02:30:00-07:00,2020,7,3,2,30,17.0,5.6000000000000005,0.0,0.0,0.0,0.17,813.0,223.0,1.5,46.98850817608083 +2020-07-03 03:30:00-07:00,2020,7,3,3,30,16.0,4.800000000000001,0.0,0.0,0.0,0.17,813.0,256.0,1.4,47.36504378395968 +2020-07-03 04:30:00-07:00,2020,7,3,4,30,16.7,3.8,0.0,0.0,0.0,0.17,814.0,283.0,1.7000000000000002,42.23711276710074 +2020-07-03 05:30:00-07:00,2020,7,3,5,30,19.6,3.5,41.0,422.0,101.0,0.17,814.0,301.0,2.5,34.46983610194168 +2020-07-03 06:30:00-07:00,2020,7,3,6,30,23.200000000000003,2.5,65.0,676.0,285.0,0.17,815.0,324.0,2.8000000000000003,25.759133608728703 +2020-07-03 07:30:00-07:00,2020,7,3,7,30,26.200000000000003,2.2,79.0,803.0,485.0,0.17,815.0,172.0,2.3000000000000003,21.076846723949945 +2020-07-03 08:30:00-07:00,2020,7,3,8,30,28.5,2.5,90.0,872.0,672.0,0.17,815.0,23.0,1.9,18.81691158174328 +2020-07-03 09:30:00-07:00,2020,7,3,9,30,30.3,2.7,99.0,912.0,829.0,0.17,815.0,53.0,1.9,17.20279549060341 +2020-07-03 10:30:00-07:00,2020,7,3,10,30,31.6,2.7,405.0,275.0,652.0,0.17,815.0,73.0,2.4000000000000004,15.972883604495367 +2020-07-03 11:30:00-07:00,2020,7,3,11,30,32.4,2.7,436.0,297.0,718.0,0.17,814.0,86.0,2.7,15.265443652870607 +2020-07-03 12:30:00-07:00,2020,7,3,12,30,32.7,3.0,390.0,145.0,528.0,0.17,814.0,96.0,2.8000000000000003,15.331751485194925 +2020-07-03 13:30:00-07:00,2020,7,3,13,30,32.4,3.4000000000000004,336.0,10.0,345.0,0.17,814.0,113.0,2.8000000000000003,16.040303643327647 +2020-07-03 14:30:00-07:00,2020,7,3,14,30,31.6,3.7,270.0,74.0,331.0,0.17,813.0,138.0,2.7,17.142129426225566 +2020-07-03 15:30:00-07:00,2020,7,3,15,30,30.3,4.0,199.0,18.0,211.0,0.17,814.0,153.0,2.8000000000000003,18.855431067428324 +2020-07-03 16:30:00-07:00,2020,7,3,16,30,28.5,4.6000000000000005,198.0,400.0,412.0,0.17,814.0,152.0,3.1,21.817124117966586 +2020-07-03 17:30:00-07:00,2020,7,3,17,30,26.1,5.1000000000000005,107.0,526.0,295.0,0.16,814.0,159.0,3.1,26.003241149118583 +2020-07-03 18:30:00-07:00,2020,7,3,18,30,22.700000000000003,6.300000000000001,85.0,110.0,104.0,0.16,815.0,194.0,3.4000000000000004,34.6374650067565 +2020-07-03 19:30:00-07:00,2020,7,3,19,30,19.700000000000003,7.300000000000001,3.0,0.0,3.0,0.16,816.0,231.0,4.0,44.59470638141201 +2020-07-03 20:30:00-07:00,2020,7,3,20,30,17.900000000000002,8.5,0.0,0.0,0.0,0.16,817.0,241.0,3.8,54.147353410387424 +2020-07-03 21:30:00-07:00,2020,7,3,21,30,17.0,9.4,0.0,0.0,0.0,0.16,817.0,226.0,3.1,60.900304244451746 +2020-07-03 22:30:00-07:00,2020,7,3,22,30,16.400000000000002,9.8,0.0,0.0,0.0,0.16,816.0,199.0,2.5,64.98648245834545 +2020-07-03 23:30:00-07:00,2020,7,3,23,30,16.1,10.1,0.0,0.0,0.0,0.16,816.0,177.0,2.3000000000000003,67.58402007210424 +2020-07-04 00:30:00-07:00,2020,7,4,0,30,15.8,10.4,0.0,0.0,0.0,0.16,815.0,174.0,2.3000000000000003,70.28515565132406 +2020-07-04 01:30:00-07:00,2020,7,4,1,30,15.6,10.7,0.0,0.0,0.0,0.16,815.0,191.0,2.4000000000000004,72.62729544777584 +2020-07-04 02:30:00-07:00,2020,7,4,2,30,15.3,10.9,0.0,0.0,0.0,0.16,815.0,211.0,2.5,75.02860322514574 +2020-07-04 03:30:00-07:00,2020,7,4,3,30,14.9,10.8,0.0,0.0,0.0,0.16,815.0,222.0,2.7,76.47296834899032 +2020-07-04 04:30:00-07:00,2020,7,4,4,30,15.5,10.5,0.0,0.0,0.0,0.16,814.0,233.0,3.3000000000000003,72.12710585195762 +2020-07-04 05:30:00-07:00,2020,7,4,5,30,17.900000000000002,10.3,55.0,226.0,87.0,0.16,814.0,245.0,3.7,61.11176585788054 +2020-07-04 06:30:00-07:00,2020,7,4,6,30,22.0,10.0,58.0,705.0,287.0,0.16,814.0,265.0,3.2,46.46566533002917 +2020-07-04 07:30:00-07:00,2020,7,4,7,30,26.3,7.9,71.0,823.0,486.0,0.16,814.0,310.0,2.6,31.15645427127825 +2020-07-04 08:30:00-07:00,2020,7,4,8,30,28.700000000000003,5.7,83.0,881.0,670.0,0.16,814.0,178.0,3.0,23.279653204804994 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17:30:00-07:00,2020,7,4,17,30,20.700000000000003,10.2,7.0,0.0,7.0,0.16,814.0,267.0,2.4000000000000004,50.996004955367205 +2020-07-04 18:30:00-07:00,2020,7,4,18,30,19.3,10.6,82.0,38.0,89.0,0.16,814.0,256.0,2.0,57.116792737585754 +2020-07-04 19:30:00-07:00,2020,7,4,19,30,18.2,10.8,3.0,0.0,3.0,0.16,814.0,248.0,1.6,62.00147100368876 +2020-07-04 20:30:00-07:00,2020,7,4,20,30,17.8,10.5,0.0,0.0,0.0,0.16,814.0,249.0,1.2000000000000002,62.32332703083401 +2020-07-04 21:30:00-07:00,2020,7,4,21,30,17.5,10.1,0.0,0.0,0.0,0.16,814.0,250.0,1.0,61.839878492144884 +2020-07-04 22:30:00-07:00,2020,7,4,22,30,17.2,9.7,0.0,0.0,0.0,0.16,814.0,249.0,0.9,61.357670125068694 +2020-07-04 23:30:00-07:00,2020,7,4,23,30,16.8,9.0,0.0,0.0,0.0,0.16,814.0,245.0,1.0,60.03856818525603 +2020-07-05 00:30:00-07:00,2020,7,5,0,30,16.2,8.4,0.0,0.0,0.0,0.16,814.0,242.0,1.0,59.894586009574766 +2020-07-05 01:30:00-07:00,2020,7,5,1,30,15.5,7.800000000000001,0.0,0.0,0.0,0.16,813.0,243.0,1.0,60.13391747919933 +2020-07-05 02:30:00-07:00,2020,7,5,2,30,14.7,7.300000000000001,0.0,0.0,0.0,0.16,813.0,249.0,1.1,61.1844502107116 +2020-07-05 03:30:00-07:00,2020,7,5,3,30,13.9,6.7,0.0,0.0,0.0,0.16,813.0,255.0,1.2000000000000002,61.83877431979773 +2020-07-05 04:30:00-07:00,2020,7,5,4,30,14.8,6.1000000000000005,0.0,0.0,0.0,0.16,813.0,258.0,1.5,55.9820306670338 +2020-07-05 05:30:00-07:00,2020,7,5,5,30,17.900000000000002,6.5,34.0,499.0,103.0,0.16,813.0,257.0,2.1,47.239131755362195 +2020-07-05 06:30:00-07:00,2020,7,5,6,30,22.0,5.9,53.0,738.0,291.0,0.16,813.0,222.0,1.6,35.159842898749275 +2020-07-05 07:30:00-07:00,2020,7,5,7,30,25.6,5.5,64.0,850.0,491.0,0.16,813.0,160.0,1.2000000000000002,27.5396497347643 +2020-07-05 08:30:00-07:00,2020,7,5,8,30,27.9,4.800000000000001,72.0,911.0,678.0,0.16,813.0,123.0,1.8,22.910334419568933 +2020-07-05 09:30:00-07:00,2020,7,5,9,30,29.5,4.5,80.0,943.0,834.0,0.16,813.0,111.0,2.3000000000000003,20.446781016041413 +2020-07-05 10:30:00-07:00,2020,7,5,10,30,30.6,4.4,84.0,961.0,945.0,0.16,813.0,105.0,2.6,19.061117737008825 +2020-07-05 11:30:00-07:00,2020,7,5,11,30,31.200000000000003,4.6000000000000005,90.0,965.0,1004.0,0.16,813.0,103.0,2.9000000000000004,18.67868074945221 +2020-07-05 12:30:00-07:00,2020,7,5,12,30,31.3,4.800000000000001,172.0,845.0,977.0,0.16,812.0,103.0,3.0,18.833701487784104 +2020-07-05 13:30:00-07:00,2020,7,5,13,30,30.9,5.0,306.0,591.0,843.0,0.16,812.0,103.0,2.7,19.538432400213527 +2020-07-05 14:30:00-07:00,2020,7,5,14,30,30.0,5.2,296.0,103.0,380.0,0.16,812.0,101.0,2.0,20.860134595029923 +2020-07-05 15:30:00-07:00,2020,7,5,15,30,28.700000000000003,5.5,84.0,1.0,85.0,0.16,812.0,124.0,1.1,22.959272946166987 +2020-07-05 16:30:00-07:00,2020,7,5,16,30,27.200000000000003,5.9,83.0,149.0,163.0,0.16,812.0,189.0,0.9,25.76231172272818 +2020-07-05 17:30:00-07:00,2020,7,5,17,30,25.4,6.2,131.0,334.0,250.0,0.16,812.0,225.0,1.3,29.250946076532568 +2020-07-05 18:30:00-07:00,2020,7,5,18,30,22.8,8.5,52.0,410.0,122.0,0.16,813.0,228.0,1.4,40.00993797868763 +2020-07-05 19:30:00-07:00,2020,7,5,19,30,20.5,9.1,5.0,36.0,5.0,0.16,813.0,237.0,1.7000000000000002,47.955731025797355 +2020-07-05 20:30:00-07:00,2020,7,5,20,30,19.1,8.8,0.0,0.0,0.0,0.16,813.0,243.0,2.2,51.256767461572096 +2020-07-05 21:30:00-07:00,2020,7,5,21,30,17.8,9.2,0.0,0.0,0.0,0.16,813.0,245.0,2.4000000000000004,57.126124086974 +2020-07-05 22:30:00-07:00,2020,7,5,22,30,16.900000000000002,9.1,0.0,0.0,0.0,0.16,813.0,244.0,2.3000000000000003,60.06250029628461 +2020-07-05 23:30:00-07:00,2020,7,5,23,30,16.400000000000002,8.700000000000001,0.0,0.0,0.0,0.16,813.0,241.0,2.1,60.34935992848884 +2020-07-06 00:30:00-07:00,2020,7,6,0,30,15.9,8.3,0.0,0.0,0.0,0.16,813.0,238.0,2.0,60.63981678562584 +2020-07-06 01:30:00-07:00,2020,7,6,1,30,15.5,7.9,0.0,0.0,0.0,0.16,813.0,236.0,1.9,60.54453574053974 +2020-07-06 02:30:00-07:00,2020,7,6,2,30,15.0,7.7,0.0,0.0,0.0,0.16,812.0,238.0,2.0,61.67431988126177 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20:30:00-07:00,2020,7,6,20,30,22.0,8.6,0.0,0.0,0.0,0.16,812.0,188.0,3.5,42.28921378318951 +2020-07-06 21:30:00-07:00,2020,7,6,21,30,21.3,8.4,0.0,0.0,0.0,0.16,813.0,196.0,4.0,43.54481483226543 +2020-07-06 22:30:00-07:00,2020,7,6,22,30,20.5,8.6,0.0,0.0,0.0,0.16,813.0,201.0,4.2,46.36393836197522 +2020-07-06 23:30:00-07:00,2020,7,6,23,30,19.9,9.1,0.0,0.0,0.0,0.16,812.0,207.0,4.3,49.76779821601073 +2020-07-07 00:30:00-07:00,2020,7,7,0,30,19.3,9.6,0.0,0.0,0.0,0.16,812.0,212.0,4.3,53.42348169151816 +2020-07-07 01:30:00-07:00,2020,7,7,1,30,18.8,9.8,0.0,0.0,0.0,0.16,812.0,214.0,4.2,55.85964804455805 +2020-07-07 02:30:00-07:00,2020,7,7,2,30,18.5,9.5,0.0,0.0,0.0,0.16,812.0,216.0,4.0,55.78370160011057 +2020-07-07 03:30:00-07:00,2020,7,7,3,30,18.3,9.3,0.0,0.0,0.0,0.16,811.0,218.0,3.9,55.73298026989565 +2020-07-07 04:30:00-07:00,2020,7,7,4,30,18.8,9.2,0.0,0.0,0.0,0.16,811.0,221.0,4.1000000000000005,53.65329464585366 +2020-07-07 05:30:00-07:00,2020,7,7,5,30,21.3,9.5,44.0,324.0,88.0,0.16,811.0,222.0,4.4,46.898981113090606 +2020-07-07 06:30:00-07:00,2020,7,7,6,30,25.5,9.9,159.0,63.0,179.0,0.16,811.0,224.0,4.5,37.38891784287129 +2020-07-07 07:30:00-07:00,2020,7,7,7,30,29.5,7.6,87.0,764.0,469.0,0.16,811.0,226.0,4.0,25.326582955633654 +2020-07-07 08:30:00-07:00,2020,7,7,8,30,31.8,3.8,73.0,907.0,674.0,0.16,811.0,223.0,3.1,17.06829065560241 +2020-07-07 09:30:00-07:00,2020,7,7,9,30,33.300000000000004,2.5,357.0,234.0,544.0,0.16,810.0,211.0,2.5,14.306920992470053 +2020-07-07 10:30:00-07:00,2020,7,7,10,30,34.2,2.1,94.0,949.0,942.0,0.16,810.0,197.0,2.5,13.222978182173579 +2020-07-07 11:30:00-07:00,2020,7,7,11,30,34.6,2.2,301.0,67.0,364.0,0.16,809.0,191.0,2.8000000000000003,13.024356280131732 +2020-07-07 12:30:00-07:00,2020,7,7,12,30,34.4,2.7,367.0,84.0,447.0,0.16,809.0,194.0,3.0,13.646493582238536 +2020-07-07 13:30:00-07:00,2020,7,7,13,30,33.7,3.6,390.0,293.0,656.0,0.16,808.0,196.0,3.2,15.121902841386516 +2020-07-07 14:30:00-07:00,2020,7,7,14,30,32.7,4.6000000000000005,216.0,520.0,642.0,0.16,808.0,196.0,3.4000000000000004,17.157105879667377 +2020-07-07 15:30:00-07:00,2020,7,7,15,30,31.6,5.2,147.0,748.0,664.0,0.16,808.0,196.0,3.5,19.038093240240038 +2020-07-07 16:30:00-07:00,2020,7,7,16,30,30.6,5.4,225.0,59.0,256.0,0.16,808.0,196.0,3.3000000000000003,20.436687915721176 +2020-07-07 17:30:00-07:00,2020,7,7,17,30,29.200000000000003,5.0,58.0,734.0,319.0,0.16,808.0,185.0,2.7,21.542507147258103 +2020-07-07 18:30:00-07:00,2020,7,7,18,30,25.9,6.6000000000000005,79.0,169.0,108.0,0.16,808.0,170.0,2.1,29.18874055342637 +2020-07-07 19:30:00-07:00,2020,7,7,19,30,22.5,7.300000000000001,5.0,52.0,5.0,0.16,808.0,170.0,2.0,37.5510800965465 +2020-07-07 20:30:00-07:00,2020,7,7,20,30,20.700000000000003,6.300000000000001,0.0,0.0,0.0,0.16,809.0,181.0,2.2,39.141107416677144 +2020-07-07 21:30:00-07:00,2020,7,7,21,30,19.4,4.800000000000001,0.0,0.0,0.0,0.16,809.0,197.0,2.3000000000000003,38.23113424791467 +2020-07-07 22:30:00-07:00,2020,7,7,22,30,18.3,2.8000000000000003,0.0,0.0,0.0,0.16,809.0,217.0,2.4000000000000004,35.57853019177636 +2020-07-07 23:30:00-07:00,2020,7,7,23,30,17.3,0.5,0.0,0.0,0.0,0.16,809.0,232.0,2.4000000000000004,32.14144770652428 +2020-07-08 00:30:00-07:00,2020,7,8,0,30,16.6,-2.0,0.0,0.0,0.0,0.16,809.0,242.0,2.5,27.99651910116506 +2020-07-08 01:30:00-07:00,2020,7,8,1,30,16.0,-4.2,0.0,0.0,0.0,0.16,808.0,249.0,2.5,24.69497309371529 +2020-07-08 02:30:00-07:00,2020,7,8,2,30,15.4,-6.1000000000000005,0.0,0.0,0.0,0.16,808.0,256.0,2.4000000000000004,22.22398921460866 +2020-07-08 03:30:00-07:00,2020,7,8,3,30,14.8,-6.5,0.0,0.0,0.0,0.16,809.0,262.0,2.3000000000000003,22.40244627235623 +2020-07-08 04:30:00-07:00,2020,7,8,4,30,15.8,-6.1000000000000005,0.0,0.0,0.0,0.16,809.0,267.0,2.4000000000000004,21.661974545327492 +2020-07-08 05:30:00-07:00,2020,7,8,5,30,19.0,-5.7,34.0,516.0,103.0,0.16,809.0,271.0,2.8000000000000003,18.24573361063927 +2020-07-08 06:30:00-07:00,2020,7,8,6,30,23.0,-5.5,54.0,764.0,297.0,0.16,810.0,278.0,2.7,14.488337131734793 +2020-07-08 07:30:00-07:00,2020,7,8,7,30,26.700000000000003,-5.4,66.0,877.0,503.0,0.16,810.0,287.0,2.2,11.70566344617319 +2020-07-08 08:30:00-07:00,2020,7,8,8,30,29.3,-6.0,74.0,940.0,696.0,0.16,810.0,290.0,2.0,9.610323053636396 +2020-07-08 09:30:00-07:00,2020,7,8,9,30,31.1,-6.6000000000000005,81.0,976.0,858.0,0.16,809.0,286.0,1.7000000000000002,8.278855313742001 +2020-07-08 10:30:00-07:00,2020,7,8,10,30,32.4,-7.2,85.0,998.0,976.0,0.16,809.0,277.0,1.5,7.3438647338977905 +2020-07-08 11:30:00-07:00,2020,7,8,11,30,33.300000000000004,-7.6,86.0,1009.0,1040.0,0.16,809.0,269.0,1.5,6.769026172276657 +2020-07-08 12:30:00-07:00,2020,7,8,12,30,33.9,-7.9,176.0,866.0,999.0,0.16,808.0,268.0,1.6,6.3952853155478016 +2020-07-08 13:30:00-07:00,2020,7,8,13,30,34.0,-8.1,88.0,991.0,987.0,0.16,808.0,276.0,1.7000000000000002,6.261814456198493 +2020-07-08 14:30:00-07:00,2020,7,8,14,30,33.800000000000004,-8.1,362.0,280.0,591.0,0.16,808.0,290.0,1.9,6.332153039255873 +2020-07-08 15:30:00-07:00,2020,7,8,15,30,33.0,-7.9,333.0,221.0,486.0,0.16,807.0,309.0,2.0,6.7260723157090725 +2020-07-08 16:30:00-07:00,2020,7,8,16,30,31.8,-7.7,226.0,77.0,267.0,0.16,808.0,330.0,2.1,7.309881866050215 +2020-07-08 17:30:00-07:00,2020,7,8,17,30,29.200000000000003,-7.2,147.0,387.0,284.0,0.17,808.0,177.0,1.5,8.81686143157551 +2020-07-08 18:30:00-07:00,2020,7,8,18,30,25.1,0.3,89.0,68.0,101.0,0.17,809.0,27.0,1.1,19.6300444252898 +2020-07-08 19:30:00-07:00,2020,7,8,19,30,22.5,1.8,2.0,0.0,2.0,0.17,809.0,49.0,1.1,25.567589937981268 +2020-07-08 20:30:00-07:00,2020,7,8,20,30,21.8,1.5,0.0,0.0,0.0,0.17,810.0,64.0,0.9,26.114735393010864 +2020-07-08 21:30:00-07:00,2020,7,8,21,30,21.1,1.5,0.0,0.0,0.0,0.17,810.0,83.0,0.6000000000000001,27.258439619945307 +2020-07-08 22:30:00-07:00,2020,7,8,22,30,20.3,1.4,0.0,0.0,0.0,0.17,810.0,144.0,0.5,28.430510746911732 +2020-07-08 23:30:00-07:00,2020,7,8,23,30,19.3,1.2000000000000002,0.0,0.0,0.0,0.17,810.0,235.0,0.6000000000000001,29.81816757530809 +2020-07-09 00:30:00-07:00,2020,7,9,0,30,18.1,0.7000000000000001,0.0,0.0,0.0,0.17,810.0,294.0,0.9,31.00575413765574 +2020-07-09 01:30:00-07:00,2020,7,9,1,30,16.7,0.4,0.0,0.0,0.0,0.17,810.0,321.0,1.2000000000000002,33.14624992416116 +2020-07-09 02:30:00-07:00,2020,7,9,2,30,15.5,-0.2,0.0,0.0,0.0,0.17,810.0,330.0,1.4,34.26072927677561 +2020-07-09 03:30:00-07:00,2020,7,9,3,30,14.8,-0.9,0.0,0.0,0.0,0.17,811.0,326.0,1.4,34.055333483991284 +2020-07-09 04:30:00-07:00,2020,7,9,4,30,15.5,-1.6,0.0,0.0,0.0,0.17,811.0,317.0,1.3,30.92968198743981 +2020-07-09 05:30:00-07:00,2020,7,9,5,30,18.6,0.2,35.0,477.0,98.0,0.17,812.0,318.0,1.3,28.982724934318522 +2020-07-09 06:30:00-07:00,2020,7,9,6,30,22.4,-1.2000000000000002,55.0,738.0,289.0,0.17,812.0,338.0,1.2000000000000002,20.70497269957672 +2020-07-09 07:30:00-07:00,2020,7,9,7,30,25.4,-3.1,67.0,860.0,495.0,0.17,812.0,192.0,1.0,15.025757626402049 +2020-07-09 08:30:00-07:00,2020,7,9,8,30,27.8,-2.9000000000000004,76.0,923.0,685.0,0.17,812.0,65.0,1.2000000000000002,13.239855881109863 +2020-07-09 09:30:00-07:00,2020,7,9,9,30,30.0,-2.6,82.0,960.0,845.0,0.17,812.0,105.0,1.8,11.918033238441526 +2020-07-09 10:30:00-07:00,2020,7,9,10,30,31.6,-2.5,87.0,979.0,960.0,0.17,811.0,116.0,2.4000000000000004,10.95790513444354 +2020-07-09 11:30:00-07:00,2020,7,9,11,30,32.9,-2.5,233.0,787.0,977.0,0.17,810.0,125.0,3.0,10.181584830537151 +2020-07-09 12:30:00-07:00,2020,7,9,12,30,33.6,-2.4000000000000004,263.0,686.0,915.0,0.17,810.0,133.0,3.3000000000000003,9.862115278349592 +2020-07-09 13:30:00-07:00,2020,7,9,13,30,33.800000000000004,-2.2,329.0,562.0,839.0,0.17,809.0,138.0,3.4000000000000004,9.897641074197752 +2020-07-09 14:30:00-07:00,2020,7,9,14,30,33.5,-1.9,372.0,312.0,627.0,0.17,809.0,140.0,3.5,10.290381456333652 +2020-07-09 15:30:00-07:00,2020,7,9,15,30,32.7,-1.3,114.0,849.0,700.0,0.17,809.0,139.0,3.5,11.248561730879926 +2020-07-09 16:30:00-07:00,2020,7,9,16,30,31.3,-0.6000000000000001,280.0,223.0,399.0,0.17,809.0,136.0,3.5,12.815810448189055 +2020-07-09 17:30:00-07:00,2020,7,9,17,30,29.200000000000003,0.5,171.0,166.0,230.0,0.17,810.0,131.0,3.4000000000000004,15.657149394884652 +2020-07-09 18:30:00-07:00,2020,7,9,18,30,25.8,4.0,74.0,195.0,107.0,0.17,811.0,130.0,2.8000000000000003,24.510840904114268 +2020-07-09 19:30:00-07:00,2020,7,9,19,30,22.8,5.9,5.0,45.0,5.0,0.17,812.0,137.0,2.2,33.49070007736582 +2020-07-09 20:30:00-07:00,2020,7,9,20,30,21.200000000000003,6.6000000000000005,0.0,0.0,0.0,0.17,812.0,152.0,1.9,38.748257228808384 +2020-07-09 21:30:00-07:00,2020,7,9,21,30,20.1,7.5,0.0,0.0,0.0,0.17,813.0,168.0,1.8,44.10139876388638 +2020-07-09 22:30:00-07:00,2020,7,9,22,30,19.1,7.800000000000001,0.0,0.0,0.0,0.17,813.0,177.0,2.0,47.896333826189746 +2020-07-09 23:30:00-07:00,2020,7,9,23,30,18.1,7.9,0.0,0.0,0.0,0.17,814.0,182.0,2.2,51.33737558543816 +2020-07-10 00:30:00-07:00,2020,7,10,0,30,17.2,8.200000000000001,0.0,0.0,0.0,0.17,814.0,189.0,2.4000000000000004,55.45271652570925 +2020-07-10 01:30:00-07:00,2020,7,10,1,30,16.6,8.4,0.0,0.0,0.0,0.17,814.0,196.0,2.6,58.38914128448751 +2020-07-10 02:30:00-07:00,2020,7,10,2,30,15.9,8.5,0.0,0.0,0.0,0.17,814.0,203.0,2.6,61.46713379780352 +2020-07-10 03:30:00-07:00,2020,7,10,3,30,15.2,8.200000000000001,0.0,0.0,0.0,0.17,814.0,212.0,2.5,62.992136351419326 +2020-07-10 04:30:00-07:00,2020,7,10,4,30,15.9,7.1000000000000005,0.0,0.0,0.0,0.17,814.0,222.0,2.7,55.879145991479284 +2020-07-10 05:30:00-07:00,2020,7,10,5,30,18.8,5.5,34.0,514.0,101.0,0.17,814.0,233.0,3.1,41.667876619227115 +2020-07-10 06:30:00-07:00,2020,7,10,6,30,23.4,3.2,52.0,772.0,295.0,0.17,815.0,248.0,2.6,26.743876927770064 +2020-07-10 07:30:00-07:00,2020,7,10,7,30,27.700000000000003,-0.3,63.0,892.0,505.0,0.17,815.0,287.0,1.8,16.121203975557933 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16:30:00-07:00,2020,7,10,16,30,34.1,-7.800000000000001,151.0,690.0,517.0,0.17,813.0,15.0,1.4,6.373462351502523 +2020-07-10 17:30:00-07:00,2020,7,10,17,30,31.200000000000003,-7.1000000000000005,102.0,555.0,298.0,0.16,813.0,24.0,1.2000000000000002,7.921762774573723 +2020-07-10 18:30:00-07:00,2020,7,10,18,30,26.6,1.2000000000000002,86.0,148.0,111.0,0.16,814.0,45.0,0.6000000000000001,19.16447247828803 +2020-07-10 19:30:00-07:00,2020,7,10,19,30,23.6,2.5,3.0,0.0,3.0,0.16,815.0,104.0,0.4,25.144849953347347 +2020-07-10 20:30:00-07:00,2020,7,10,20,30,22.700000000000003,2.2,0.0,0.0,0.0,0.16,815.0,182.0,0.5,25.990229075994126 +2020-07-10 21:30:00-07:00,2020,7,10,21,30,22.1,1.5,0.0,0.0,0.0,0.16,815.0,216.0,0.8,25.64116228289892 +2020-07-10 22:30:00-07:00,2020,7,10,22,30,21.3,0.8,0.0,0.0,0.0,0.16,816.0,224.0,1.0,25.605494918413026 +2020-07-10 23:30:00-07:00,2020,7,10,23,30,20.3,-0.1,0.0,0.0,0.0,0.16,816.0,259.0,1.1,25.514910216059366 +2020-07-11 00:30:00-07:00,2020,7,11,0,30,19.0,-0.8,0.0,0.0,0.0,0.16,816.0,321.0,1.3,26.285296156787503 +2020-07-11 01:30:00-07:00,2020,7,11,1,30,17.7,-0.9,0.0,0.0,0.0,0.16,816.0,353.0,1.8,28.309857806491916 +2020-07-11 02:30:00-07:00,2020,7,11,2,30,17.1,-1.8,0.0,0.0,0.0,0.16,817.0,347.0,2.0,27.525236954658492 +2020-07-11 03:30:00-07:00,2020,7,11,3,30,16.5,-1.6,0.0,0.0,0.0,0.16,817.0,344.0,1.9,29.01691561099906 +2020-07-11 04:30:00-07:00,2020,7,11,4,30,17.0,-0.7000000000000001,0.0,0.0,0.0,0.16,818.0,348.0,1.8,30.025100848533565 +2020-07-11 05:30:00-07:00,2020,7,11,5,30,18.9,2.2,34.0,475.0,95.0,0.16,818.0,181.0,2.2,32.83738394304409 +2020-07-11 06:30:00-07:00,2020,7,11,6,30,21.6,5.0,55.0,730.0,284.0,0.16,819.0,14.0,2.4000000000000004,33.84859807542786 +2020-07-11 07:30:00-07:00,2020,7,11,7,30,24.700000000000003,5.9,69.0,849.0,488.0,0.16,819.0,16.0,2.1,29.871959537058014 +2020-07-11 08:30:00-07:00,2020,7,11,8,30,27.8,4.0,77.0,915.0,679.0,0.16,819.0,19.0,1.8,21.790087008653813 +2020-07-11 09:30:00-07:00,2020,7,11,9,30,30.200000000000003,2.1,84.0,955.0,841.0,0.16,818.0,33.0,1.9,16.579095611644554 +2020-07-11 10:30:00-07:00,2020,7,11,10,30,32.1,0.9,85.0,982.0,959.0,0.16,818.0,53.0,2.1,13.65253295905497 +2020-07-11 11:30:00-07:00,2020,7,11,11,30,33.5,-0.2,91.0,979.0,1015.0,0.16,817.0,70.0,2.5,11.653021458846073 +2020-07-11 12:30:00-07:00,2020,7,11,12,30,34.4,-0.9,110.0,950.0,1011.0,0.16,816.0,83.0,3.0,10.530998997606789 +2020-07-11 13:30:00-07:00,2020,7,11,13,30,34.7,-1.5,165.0,854.0,938.0,0.16,815.0,91.0,3.5,9.911560382982982 +2020-07-11 14:30:00-07:00,2020,7,11,14,30,34.4,-1.7000000000000002,89.0,944.0,860.0,0.16,815.0,98.0,4.1000000000000005,9.931209167670588 +2020-07-11 15:30:00-07:00,2020,7,11,15,30,33.5,-1.5,293.0,312.0,508.0,0.16,815.0,103.0,4.3,10.597597335073152 +2020-07-11 16:30:00-07:00,2020,7,11,16,30,32.0,-1.0,273.0,260.0,411.0,0.16,815.0,107.0,4.2,11.962070154126435 +2020-07-11 17:30:00-07:00,2020,7,11,17,30,29.6,-0.2,177.0,216.0,253.0,0.17,815.0,111.0,3.5,14.543470522041655 +2020-07-11 18:30:00-07:00,2020,7,11,18,30,26.1,2.7,88.0,38.0,94.0,0.17,815.0,115.0,2.6,21.96885742120402 +2020-07-11 19:30:00-07:00,2020,7,11,19,30,23.4,4.6000000000000005,2.0,0.0,2.0,0.17,816.0,128.0,2.2,29.507700992995947 +2020-07-11 20:30:00-07:00,2020,7,11,20,30,22.3,6.0,0.0,0.0,0.0,0.17,816.0,147.0,2.4000000000000004,34.76255752827233 +2020-07-11 21:30:00-07:00,2020,7,11,21,30,21.4,7.5,0.0,0.0,0.0,0.17,816.0,164.0,2.6,40.709625330825624 +2020-07-11 22:30:00-07:00,2020,7,11,22,30,20.700000000000003,9.0,0.0,0.0,0.0,0.17,816.0,175.0,2.7,47.050052514041525 +2020-07-11 23:30:00-07:00,2020,7,11,23,30,20.1,10.1,0.0,0.0,0.0,0.17,816.0,183.0,3.0,52.56753508242299 +2020-07-12 00:30:00-07:00,2020,7,12,0,30,19.5,10.7,0.0,0.0,0.0,0.17,815.0,192.0,3.4000000000000004,56.78744212046322 +2020-07-12 01:30:00-07:00,2020,7,12,1,30,19.0,11.0,0.0,0.0,0.0,0.17,815.0,201.0,3.5,59.76281568293624 +2020-07-12 02:30:00-07:00,2020,7,12,2,30,18.3,11.1,0.0,0.0,0.0,0.17,815.0,208.0,3.6,62.8533433997586 +2020-07-12 03:30:00-07:00,2020,7,12,3,30,17.6,10.8,0.0,0.0,0.0,0.17,814.0,212.0,3.6,64.38654428817256 +2020-07-12 04:30:00-07:00,2020,7,12,4,30,18.0,10.2,0.0,0.0,0.0,0.17,814.0,217.0,3.6,60.324534932789675 +2020-07-12 05:30:00-07:00,2020,7,12,5,30,20.700000000000003,9.5,35.0,439.0,90.0,0.17,814.0,221.0,3.7,48.660196499111144 +2020-07-12 06:30:00-07:00,2020,7,12,6,30,25.200000000000003,8.6,55.0,708.0,276.0,0.17,814.0,226.0,3.3000000000000003,34.87343832059278 +2020-07-12 07:30:00-07:00,2020,7,12,7,30,29.5,6.2,68.0,833.0,478.0,0.17,814.0,263.0,2.3000000000000003,23.00831184478886 +2020-07-12 08:30:00-07:00,2020,7,12,8,30,32.1,3.3000000000000003,76.0,900.0,667.0,0.17,813.0,156.0,2.0,16.199905904471255 +2020-07-12 09:30:00-07:00,2020,7,12,9,30,33.7,2.0,88.0,930.0,824.0,0.17,813.0,30.0,2.4000000000000004,13.500689192232096 +2020-07-12 10:30:00-07:00,2020,7,12,10,30,34.5,1.6,115.0,919.0,932.0,0.17,813.0,53.0,2.6,12.547747839636118 +2020-07-12 11:30:00-07:00,2020,7,12,11,30,34.1,1.9,258.0,736.0,952.0,0.17,813.0,47.0,2.4000000000000004,13.108530220139777 +2020-07-12 12:30:00-07:00,2020,7,12,12,30,32.5,3.0,445.0,284.0,714.0,0.17,813.0,189.0,2.2,15.505609638359644 +2020-07-12 13:30:00-07:00,2020,7,12,13,30,30.700000000000003,4.3,167.0,1.0,168.0,0.17,813.0,340.0,2.5,18.820239159278938 +2020-07-12 14:30:00-07:00,2020,7,12,14,30,30.1,4.9,162.0,3.0,164.0,0.17,813.0,339.0,2.4000000000000004,20.312107784984537 +2020-07-12 15:30:00-07:00,2020,7,12,15,30,30.5,4.6000000000000005,327.0,172.0,445.0,0.17,812.0,319.0,1.7000000000000002,19.440423181151402 +2020-07-12 16:30:00-07:00,2020,7,12,16,30,30.6,4.1000000000000005,157.0,657.0,505.0,0.17,811.0,264.0,1.5,18.664740831796507 +2020-07-12 17:30:00-07:00,2020,7,12,17,30,29.200000000000003,3.9,101.0,506.0,279.0,0.17,810.0,207.0,1.2000000000000002,19.947418029206197 +2020-07-12 18:30:00-07:00,2020,7,12,18,30,25.9,7.0,50.0,408.0,117.0,0.17,810.0,179.0,1.0,30.001071323897637 +2020-07-12 19:30:00-07:00,2020,7,12,19,30,23.200000000000003,7.5,2.0,20.0,2.0,0.17,811.0,188.0,1.2000000000000002,36.48671478635656 +2020-07-12 20:30:00-07:00,2020,7,12,20,30,22.1,7.800000000000001,0.0,0.0,0.0,0.17,811.0,237.0,1.3,39.81112268808947 +2020-07-12 21:30:00-07:00,2020,7,12,21,30,21.1,9.0,0.0,0.0,0.0,0.17,812.0,307.0,1.2000000000000002,45.906945473619025 +2020-07-12 22:30:00-07:00,2020,7,12,22,30,20.200000000000003,9.6,0.0,0.0,0.0,0.17,812.0,341.0,1.0,50.52280574828359 +2020-07-12 23:30:00-07:00,2020,7,12,23,30,19.700000000000003,9.6,0.0,0.0,0.0,0.17,811.0,319.0,1.2000000000000002,52.111820817802695 +2020-07-13 00:30:00-07:00,2020,7,13,0,30,19.200000000000003,9.5,0.0,0.0,0.0,0.17,811.0,274.0,1.5,53.39744922887757 +2020-07-13 01:30:00-07:00,2020,7,13,1,30,18.6,9.1,0.0,0.0,0.0,0.17,810.0,246.0,1.7000000000000002,53.96377792794011 +2020-07-13 02:30:00-07:00,2020,7,13,2,30,18.2,8.5,0.0,0.0,0.0,0.17,810.0,242.0,2.0,53.13615145722056 +2020-07-13 03:30:00-07:00,2020,7,13,3,30,18.3,8.0,0.0,0.0,0.0,0.17,810.0,247.0,2.4000000000000004,51.042852879327874 +2020-07-13 04:30:00-07:00,2020,7,13,4,30,19.3,7.7,0.0,0.0,0.0,0.17,810.0,264.0,3.1,46.9822848144477 +2020-07-13 05:30:00-07:00,2020,7,13,5,30,21.8,7.800000000000001,44.0,157.0,64.0,0.17,811.0,293.0,3.2,40.54640438009845 +2020-07-13 06:30:00-07:00,2020,7,13,6,30,25.3,7.6,78.0,550.0,248.0,0.17,811.0,330.0,2.2,32.390255187162296 +2020-07-13 07:30:00-07:00,2020,7,13,7,30,28.700000000000003,6.1000000000000005,216.0,86.0,258.0,0.17,811.0,344.0,1.1,23.932290674812727 +2020-07-13 08:30:00-07:00,2020,7,13,8,30,31.1,5.2,275.0,278.0,457.0,0.17,810.0,271.0,1.5,19.587451967685887 +2020-07-13 09:30:00-07:00,2020,7,13,9,30,32.800000000000004,4.7,223.0,6.0,228.0,0.17,810.0,201.0,3.0,17.180289253392264 +2020-07-13 10:30:00-07:00,2020,7,13,10,30,33.9,4.4,471.0,204.0,652.0,0.17,810.0,197.0,3.7,15.817510600759123 +2020-07-13 11:30:00-07:00,2020,7,13,11,30,34.4,4.3,393.0,48.0,438.0,0.17,809.0,197.0,3.3000000000000003,15.275389750643836 +2020-07-13 12:30:00-07:00,2020,7,13,12,30,35.0,4.0,267.0,70.0,333.0,0.17,809.0,204.0,2.1,14.467336929172335 +2020-07-13 13:30:00-07:00,2020,7,13,13,30,35.300000000000004,3.3000000000000003,239.0,656.0,832.0,0.17,808.0,237.0,1.1,13.54497939818479 +2020-07-13 14:30:00-07:00,2020,7,13,14,30,34.800000000000004,2.8000000000000003,292.0,540.0,732.0,0.17,808.0,283.0,0.9,13.441473971335261 +2020-07-13 15:30:00-07:00,2020,7,13,15,30,33.800000000000004,3.0,190.0,676.0,655.0,0.17,808.0,310.0,1.0,14.413993961079314 +2020-07-13 16:30:00-07:00,2020,7,13,16,30,32.4,3.3000000000000003,183.0,516.0,456.0,0.17,808.0,325.0,1.0,15.927521061429045 +2020-07-13 17:30:00-07:00,2020,7,13,17,30,30.3,3.8,49.0,475.0,215.0,0.17,808.0,175.0,0.8,18.59237802059075 +2020-07-13 18:30:00-07:00,2020,7,13,18,30,26.8,6.800000000000001,51.0,5.0,52.0,0.17,808.0,32.0,0.5,28.06141304819267 +2020-07-13 19:30:00-07:00,2020,7,13,19,30,23.9,9.1,1.0,0.0,1.0,0.17,809.0,185.0,0.6000000000000001,38.98792475098051 +2020-07-13 20:30:00-07:00,2020,7,13,20,30,21.6,10.0,0.0,0.0,0.0,0.17,810.0,304.0,2.1,47.61473812224418 +2020-07-13 21:30:00-07:00,2020,7,13,21,30,18.9,10.5,0.0,0.0,0.0,0.17,811.0,282.0,4.0,58.170107747542964 +2020-07-13 22:30:00-07:00,2020,7,13,22,30,17.3,10.4,0.0,0.0,0.0,0.17,811.0,264.0,4.2,63.89297948778511 +2020-07-13 23:30:00-07:00,2020,7,13,23,30,16.8,9.9,0.0,0.0,0.0,0.17,811.0,238.0,3.7,63.78131525188849 +2020-07-14 00:30:00-07:00,2020,7,14,0,30,16.900000000000002,9.3,0.0,0.0,0.0,0.17,810.0,226.0,3.3000000000000003,60.876716281083624 +2020-07-14 01:30:00-07:00,2020,7,14,1,30,16.900000000000002,8.9,0.0,0.0,0.0,0.17,809.0,230.0,3.1,59.25790747209784 +2020-07-14 02:30:00-07:00,2020,7,14,2,30,16.7,9.1,0.0,0.0,0.0,0.17,809.0,238.0,3.0,60.829484842280756 +2020-07-14 03:30:00-07:00,2020,7,14,3,30,16.5,9.7,0.0,0.0,0.0,0.17,809.0,247.0,2.8000000000000003,64.1429650027578 +2020-07-14 04:30:00-07:00,2020,7,14,4,30,16.900000000000002,10.3,0.0,0.0,0.0,0.17,810.0,257.0,2.9000000000000004,65.09562140469036 +2020-07-14 05:30:00-07:00,2020,7,14,5,30,18.7,10.8,54.0,3.0,54.0,0.17,810.0,278.0,2.6,60.08948328168739 +2020-07-14 06:30:00-07:00,2020,7,14,6,30,21.6,11.3,134.0,45.0,148.0,0.17,811.0,326.0,2.0,51.91814222126301 +2020-07-14 07:30:00-07:00,2020,7,14,7,30,24.4,10.4,107.0,696.0,448.0,0.17,811.0,190.0,1.9,41.28065805557107 +2020-07-14 08:30:00-07:00,2020,7,14,8,30,26.6,8.8,102.0,825.0,641.0,0.17,811.0,32.0,1.8,32.53544915570278 +2020-07-14 09:30:00-07:00,2020,7,14,9,30,28.3,7.800000000000001,231.0,576.0,686.0,0.17,811.0,52.0,1.7000000000000002,27.522138175270594 +2020-07-14 10:30:00-07:00,2020,7,14,10,30,29.6,7.4,297.0,595.0,825.0,0.17,811.0,68.0,1.7000000000000002,24.839652892186876 +2020-07-14 11:30:00-07:00,2020,7,14,11,30,30.4,7.4,298.0,187.0,474.0,0.17,811.0,77.0,1.7000000000000002,23.72369599245433 +2020-07-14 12:30:00-07:00,2020,7,14,12,30,30.700000000000003,7.6,255.0,262.0,503.0,0.17,811.0,87.0,1.7000000000000002,23.64033803394195 +2020-07-14 13:30:00-07:00,2020,7,14,13,30,30.6,7.800000000000001,318.0,496.0,766.0,0.17,810.0,104.0,1.6,24.102118750597864 +2020-07-14 14:30:00-07:00,2020,7,14,14,30,30.1,7.9,193.0,159.0,323.0,0.17,810.0,126.0,1.4,24.972137546044316 +2020-07-14 15:30:00-07:00,2020,7,14,15,30,29.4,7.800000000000001,240.0,173.0,359.0,0.17,810.0,173.0,1.0,25.822605541544107 +2020-07-14 16:30:00-07:00,2020,7,14,16,30,28.5,7.7,172.0,18.0,181.0,0.17,810.0,239.0,0.6000000000000001,27.019329322087312 +2020-07-14 17:30:00-07:00,2020,7,14,17,30,26.8,7.6,68.0,233.0,149.0,0.17,810.0,178.0,0.7000000000000001,29.639865821202942 +2020-07-14 18:30:00-07:00,2020,7,14,18,30,23.9,9.1,71.0,32.0,76.0,0.17,811.0,104.0,1.1,38.98792475098051 +2020-07-14 19:30:00-07:00,2020,7,14,19,30,21.3,11.0,1.0,0.0,1.0,0.17,811.0,139.0,1.5,51.839343516413635 +2020-07-14 20:30:00-07:00,2020,7,14,20,30,19.8,11.4,0.0,0.0,0.0,0.17,812.0,165.0,1.5,58.3895350354761 +2020-07-14 21:30:00-07:00,2020,7,14,21,30,18.6,11.8,0.0,0.0,0.0,0.17,812.0,177.0,1.5,64.60607191775053 +2020-07-14 22:30:00-07:00,2020,7,14,22,30,17.5,12.0,0.0,0.0,0.0,0.17,812.0,180.0,1.5,70.14836742176203 +2020-07-14 23:30:00-07:00,2020,7,14,23,30,16.7,12.0,0.0,0.0,0.0,0.17,812.0,188.0,1.4,73.79207633387006 +2020-07-15 00:30:00-07:00,2020,7,15,0,30,16.3,12.0,0.0,0.0,0.0,0.17,812.0,218.0,1.7000000000000002,75.6931697169365 +2020-07-15 01:30:00-07:00,2020,7,15,1,30,15.9,11.4,0.0,0.0,0.0,0.17,812.0,244.0,2.4000000000000004,74.63404482062678 +2020-07-15 02:30:00-07:00,2020,7,15,2,30,15.5,10.1,0.0,0.0,0.0,0.17,812.0,247.0,2.7,70.22701716096707 +2020-07-15 03:30:00-07:00,2020,7,15,3,30,15.1,9.4,0.0,0.0,0.0,0.17,812.0,253.0,2.9000000000000004,68.75046502673337 +2020-07-15 04:30:00-07:00,2020,7,15,4,30,15.3,9.0,0.0,0.0,0.0,0.17,813.0,258.0,3.1,66.06946386633437 +2020-07-15 05:30:00-07:00,2020,7,15,5,30,17.2,8.700000000000001,51.0,153.0,69.0,0.17,813.0,262.0,3.1,57.36271440540519 +2020-07-15 06:30:00-07:00,2020,7,15,6,30,21.1,8.3,55.0,717.0,274.0,0.17,813.0,292.0,2.8000000000000003,43.78460860964497 +2020-07-15 07:30:00-07:00,2020,7,15,7,30,24.6,5.6000000000000005,67.0,849.0,481.0,0.17,814.0,339.0,2.7,29.43358504703558 +2020-07-15 08:30:00-07:00,2020,7,15,8,30,26.4,3.5,160.0,203.0,292.0,0.17,814.0,191.0,3.1,22.838964577871547 +2020-07-15 09:30:00-07:00,2020,7,15,9,30,27.6,3.1,249.0,1.0,250.0,0.17,814.0,30.0,3.2,20.69188030607816 +2020-07-15 10:30:00-07:00,2020,7,15,10,30,28.700000000000003,3.4000000000000004,443.0,51.0,488.0,0.17,814.0,44.0,3.0,19.82327552058411 +2020-07-15 11:30:00-07:00,2020,7,15,11,30,29.6,3.9,490.0,173.0,653.0,0.17,814.0,60.0,2.8000000000000003,19.49221105768168 +2020-07-15 12:30:00-07:00,2020,7,15,12,30,30.200000000000003,4.3,481.0,222.0,691.0,0.17,813.0,76.0,2.9000000000000004,19.366943211448852 +2020-07-15 13:30:00-07:00,2020,7,15,13,30,30.3,4.9,369.0,403.0,733.0,0.17,813.0,92.0,3.0,20.080360839882122 +2020-07-15 14:30:00-07:00,2020,7,15,14,30,30.0,5.300000000000001,267.0,522.0,692.0,0.17,812.0,106.0,3.1,21.00558510935324 +2020-07-15 15:30:00-07:00,2020,7,15,15,30,29.200000000000003,5.2,338.0,158.0,446.0,0.17,812.0,129.0,3.5,21.844338582115917 +2020-07-15 16:30:00-07:00,2020,7,15,16,30,27.4,5.6000000000000005,146.0,203.0,253.0,0.17,812.0,154.0,4.2,24.938893323869024 +2020-07-15 17:30:00-07:00,2020,7,15,17,30,24.8,6.800000000000001,147.0,146.0,198.0,0.17,812.0,167.0,4.5,31.592910811983323 +2020-07-15 18:30:00-07:00,2020,7,15,18,30,22.200000000000003,9.0,81.0,37.0,87.0,0.17,813.0,174.0,4.0,42.92099691407237 +2020-07-15 19:30:00-07:00,2020,7,15,19,30,20.4,9.6,1.0,0.0,1.0,0.17,813.0,182.0,3.5,49.902492737794006 +2020-07-15 20:30:00-07:00,2020,7,15,20,30,19.200000000000003,9.8,0.0,0.0,0.0,0.17,813.0,192.0,3.1,54.48300033015475 +2020-07-15 21:30:00-07:00,2020,7,15,21,30,18.4,9.5,0.0,0.0,0.0,0.17,813.0,206.0,2.7,56.13426382647935 +2020-07-15 22:30:00-07:00,2020,7,15,22,30,17.7,8.8,0.0,0.0,0.0,0.17,812.0,221.0,2.3000000000000003,55.95718899814522 +2020-07-15 23:30:00-07:00,2020,7,15,23,30,17.1,7.6,0.0,0.0,0.0,0.17,812.0,228.0,2.0,53.57289165927776 +2020-07-16 00:30:00-07:00,2020,7,16,0,30,16.8,6.4,0.0,0.0,0.0,0.17,812.0,228.0,1.9,50.291482522514045 +2020-07-16 01:30:00-07:00,2020,7,16,1,30,16.5,5.800000000000001,0.0,0.0,0.0,0.17,812.0,230.0,2.0,49.17986550966178 +2020-07-16 02:30:00-07:00,2020,7,16,2,30,16.1,5.800000000000001,0.0,0.0,0.0,0.17,812.0,237.0,2.0,50.44885844166303 +2020-07-16 03:30:00-07:00,2020,7,16,3,30,15.9,6.0,0.0,0.0,0.0,0.17,812.0,248.0,1.9,51.80838371341478 +2020-07-16 04:30:00-07:00,2020,7,16,4,30,16.900000000000002,6.2,0.0,0.0,0.0,0.17,813.0,257.0,2.1,49.28962367563846 +2020-07-16 05:30:00-07:00,2020,7,16,5,30,20.1,6.800000000000001,39.0,321.0,77.0,0.17,813.0,263.0,2.6,42.03859608974243 +2020-07-16 06:30:00-07:00,2020,7,16,6,30,24.3,6.9,61.0,661.0,262.0,0.17,813.0,275.0,2.3000000000000003,32.77624352900359 +2020-07-16 07:30:00-07:00,2020,7,16,7,30,28.0,4.7,223.0,333.0,385.0,0.17,814.0,300.0,1.5,22.618732907530696 +2020-07-16 08:30:00-07:00,2020,7,16,8,30,30.3,3.1,88.0,866.0,651.0,0.17,813.0,328.0,1.0,17.697076149624223 +2020-07-16 09:30:00-07:00,2020,7,16,9,30,31.9,2.9000000000000004,119.0,852.0,789.0,0.17,813.0,204.0,0.8,15.927530583915253 +2020-07-16 10:30:00-07:00,2020,7,16,10,30,32.9,2.7,185.0,53.0,232.0,0.17,813.0,93.0,1.1,14.8412737117316 +2020-07-16 11:30:00-07:00,2020,7,16,11,30,33.1,2.6,285.0,55.0,337.0,0.17,813.0,134.0,1.7000000000000002,14.571571710931277 +2020-07-16 12:30:00-07:00,2020,7,16,12,30,32.1,3.0,366.0,119.0,478.0,0.17,813.0,170.0,2.2,15.860042784831428 +2020-07-16 13:30:00-07:00,2020,7,16,13,30,30.1,4.3,66.0,0.0,66.0,0.17,813.0,215.0,2.7,19.47842021884447 +2020-07-16 14:30:00-07:00,2020,7,16,14,30,27.3,5.9,135.0,0.0,135.0,0.17,814.0,253.0,3.5,25.61169985995941 +2020-07-16 15:30:00-07:00,2020,7,16,15,30,24.6,7.300000000000001,262.0,15.0,272.0,0.17,814.0,276.0,3.9,33.08658582124323 +2020-07-16 16:30:00-07:00,2020,7,16,16,30,22.8,8.0,228.0,38.0,248.0,0.17,814.0,277.0,3.6,38.67564728956684 +2020-07-16 17:30:00-07:00,2020,7,16,17,30,21.6,8.3,148.0,101.0,183.0,0.17,814.0,259.0,3.1,42.46339719484868 +2020-07-16 18:30:00-07:00,2020,7,16,18,30,20.5,8.700000000000001,71.0,4.0,72.0,0.17,814.0,247.0,3.1,46.678513556634876 +2020-07-16 19:30:00-07:00,2020,7,16,19,30,19.6,9.0,1.0,0.0,1.0,0.17,814.0,249.0,3.3000000000000003,50.36195682388847 +2020-07-16 20:30:00-07:00,2020,7,16,20,30,19.1,8.9,0.0,0.0,0.0,0.17,814.0,252.0,3.3000000000000003,51.60398615870124 +2020-07-16 21:30:00-07:00,2020,7,16,21,30,18.8,9.0,0.0,0.0,0.0,0.17,814.0,252.0,3.3000000000000003,52.93512523129039 +2020-07-16 22:30:00-07:00,2020,7,16,22,30,18.6,9.1,0.0,0.0,0.0,0.17,814.0,250.0,3.2,53.96377792794011 +2020-07-16 23:30:00-07:00,2020,7,16,23,30,18.5,9.2,0.0,0.0,0.0,0.17,814.0,252.0,2.9000000000000004,54.66961731331414 +2020-07-17 00:30:00-07:00,2020,7,17,0,30,18.4,9.1,0.0,0.0,0.0,0.17,814.0,258.0,2.4000000000000004,54.643897933984 +2020-07-17 01:30:00-07:00,2020,7,17,1,30,18.1,9.0,0.0,0.0,0.0,0.17,814.0,268.0,1.9,55.308119822360034 +2020-07-17 02:30:00-07:00,2020,7,17,2,30,17.6,8.9,0.0,0.0,0.0,0.17,813.0,274.0,1.7000000000000002,56.69246655576115 +2020-07-17 03:30:00-07:00,2020,7,17,3,30,17.2,8.9,0.0,0.0,0.0,0.17,813.0,280.0,1.9,58.14282059156098 +2020-07-17 04:30:00-07:00,2020,7,17,4,30,17.8,9.1,0.0,0.0,0.0,0.17,814.0,285.0,2.6,56.74266041795205 +2020-07-17 05:30:00-07:00,2020,7,17,5,30,19.9,9.5,33.0,406.0,80.0,0.17,814.0,290.0,3.5,51.125172631271916 +2020-07-17 06:30:00-07:00,2020,7,17,6,30,23.3,9.3,56.0,683.0,262.0,0.17,814.0,304.0,3.8,40.97110413042472 +2020-07-17 07:30:00-07:00,2020,7,17,7,30,26.5,7.1000000000000005,70.0,812.0,463.0,0.17,815.0,326.0,3.7,29.155008706580535 +2020-07-17 08:30:00-07:00,2020,7,17,8,30,29.0,4.7,80.0,882.0,652.0,0.17,815.0,344.0,3.4000000000000004,21.342132891869316 +2020-07-17 09:30:00-07:00,2020,7,17,9,30,30.8,3.7,87.0,922.0,811.0,0.17,814.0,179.0,3.1,17.941287986716947 +2020-07-17 10:30:00-07:00,2020,7,17,10,30,32.0,3.3000000000000003,93.0,943.0,927.0,0.17,814.0,12.0,2.8000000000000003,16.291866024769288 +2020-07-17 11:30:00-07:00,2020,7,17,11,30,32.6,3.1,98.0,949.0,988.0,0.17,813.0,23.0,2.4000000000000004,15.52784879851574 +2020-07-17 12:30:00-07:00,2020,7,17,12,30,32.7,3.1,186.0,825.0,964.0,0.17,813.0,32.0,2.1,15.440581222427234 +2020-07-17 13:30:00-07:00,2020,7,17,13,30,32.1,3.2,402.0,455.0,812.0,0.17,813.0,35.0,1.6,16.085908938189373 +2020-07-17 14:30:00-07:00,2020,7,17,14,30,31.0,3.6,242.0,639.0,761.0,0.17,813.0,185.0,1.1,17.613214546153884 +2020-07-17 15:30:00-07:00,2020,7,17,15,30,29.5,4.4,359.0,156.0,466.0,0.17,813.0,306.0,1.1,20.30428628348022 +2020-07-17 16:30:00-07:00,2020,7,17,16,30,27.9,5.4,171.0,10.0,176.0,0.17,813.0,265.0,1.7000000000000002,23.886879194189444 +2020-07-17 17:30:00-07:00,2020,7,17,17,30,25.9,7.1000000000000005,113.0,28.0,123.0,0.17,813.0,252.0,2.2,30.20724777310559 +2020-07-17 18:30:00-07:00,2020,7,17,18,30,23.9,8.6,40.0,1.0,40.0,0.17,813.0,247.0,2.7,37.69380012252108 +2020-07-17 19:30:00-07:00,2020,7,17,19,30,22.5,8.9,1.0,0.0,1.0,0.17,813.0,244.0,3.3000000000000003,41.86162750606209 +2020-07-17 20:30:00-07:00,2020,7,17,20,30,21.8,9.3,0.0,0.0,0.0,0.17,814.0,239.0,3.3000000000000003,44.87848960500956 +2020-07-17 21:30:00-07:00,2020,7,17,21,30,21.3,9.1,0.0,0.0,0.0,0.17,813.0,232.0,2.8000000000000003,45.65381217207098 +2020-07-17 22:30:00-07:00,2020,7,17,22,30,20.9,8.8,0.0,0.0,0.0,0.17,813.0,228.0,2.3000000000000003,45.85149088287751 +2020-07-17 23:30:00-07:00,2020,7,17,23,30,20.4,8.6,0.0,0.0,0.0,0.17,813.0,235.0,1.9,46.65088385634296 +2020-07-18 00:30:00-07:00,2020,7,18,0,30,19.8,8.4,0.0,0.0,0.0,0.17,812.0,250.0,2.1,47.763883420200514 +2020-07-18 01:30:00-07:00,2020,7,18,1,30,19.0,8.4,0.0,0.0,0.0,0.17,812.0,262.0,2.7,50.20049572858295 +2020-07-18 02:30:00-07:00,2020,7,18,2,30,18.4,8.6,0.0,0.0,0.0,0.17,812.0,267.0,3.2,52.830105213210544 +2020-07-18 03:30:00-07:00,2020,7,18,3,30,17.900000000000002,9.0,0.0,0.0,0.0,0.17,812.0,269.0,3.4000000000000004,56.00787265709693 +2020-07-18 04:30:00-07:00,2020,7,18,4,30,18.2,9.2,0.0,0.0,0.0,0.17,812.0,270.0,3.6,55.70759246170453 +2020-07-18 05:30:00-07:00,2020,7,18,5,30,20.4,9.2,33.0,401.0,79.0,0.17,812.0,279.0,3.6,48.578615931444425 +2020-07-18 06:30:00-07:00,2020,7,18,6,30,24.700000000000003,9.0,57.0,679.0,261.0,0.17,812.0,315.0,3.1,36.91298850938735 +2020-07-18 07:30:00-07:00,2020,7,18,7,30,28.6,7.0,72.0,807.0,461.0,0.17,813.0,180.0,3.0,25.60834536359996 +2020-07-18 08:30:00-07:00,2020,7,18,8,30,30.700000000000003,4.5,82.0,875.0,649.0,0.17,813.0,17.0,3.2,19.085433505655185 +2020-07-18 09:30:00-07:00,2020,7,18,9,30,31.9,3.7,140.0,817.0,781.0,0.17,813.0,19.0,3.1,16.85286111175991 +2020-07-18 10:30:00-07:00,2020,7,18,10,30,32.6,3.3000000000000003,403.0,296.0,664.0,0.17,813.0,20.0,2.8000000000000003,15.748803263789906 +2020-07-18 11:30:00-07:00,2020,7,18,11,30,32.800000000000004,2.9000000000000004,80.0,0.0,80.0,0.17,812.0,18.0,2.4000000000000004,15.138105421032032 +2020-07-18 12:30:00-07:00,2020,7,18,12,30,32.800000000000004,2.6,447.0,283.0,714.0,0.17,812.0,11.0,1.9,14.819494596580085 +2020-07-18 13:30:00-07:00,2020,7,18,13,30,32.6,2.6,445.0,304.0,718.0,0.17,812.0,183.0,1.6,14.987421331761578 +2020-07-18 14:30:00-07:00,2020,7,18,14,30,32.1,2.7,272.0,590.0,750.0,0.17,812.0,180.0,1.4,15.526505964910253 +2020-07-18 15:30:00-07:00,2020,7,18,15,30,31.5,2.7,155.0,3.0,157.0,0.17,812.0,3.0,1.4,16.06388646062227 +2020-07-18 16:30:00-07:00,2020,7,18,16,30,30.5,2.8000000000000003,226.0,180.0,320.0,0.17,812.0,10.0,1.6,17.127824747890934 +2020-07-18 17:30:00-07:00,2020,7,18,17,30,28.5,3.4000000000000004,146.0,26.0,155.0,0.17,812.0,20.0,1.6,20.054774206110846 +2020-07-18 18:30:00-07:00,2020,7,18,18,30,25.5,6.800000000000001,70.0,6.0,71.0,0.17,813.0,30.0,1.7000000000000002,30.302913038080625 +2020-07-18 19:30:00-07:00,2020,7,18,19,30,23.1,7.4,1.0,0.0,1.0,0.17,814.0,43.0,2.0,36.458209263011845 +2020-07-18 20:30:00-07:00,2020,7,18,20,30,21.8,8.3,0.0,0.0,0.0,0.17,814.0,63.0,1.9,41.947502647611856 +2020-07-18 21:30:00-07:00,2020,7,18,21,30,20.700000000000003,9.4,0.0,0.0,0.0,0.17,814.0,87.0,1.5,48.334334174818245 +2020-07-18 22:30:00-07:00,2020,7,18,22,30,19.9,9.9,0.0,0.0,0.0,0.17,814.0,112.0,1.3,52.51509866581123 +2020-07-18 23:30:00-07:00,2020,7,18,23,30,19.4,10.1,0.0,0.0,0.0,0.17,814.0,139.0,1.2000000000000002,54.90028533397252 +2020-07-19 00:30:00-07:00,2020,7,19,0,30,18.7,10.3,0.0,0.0,0.0,0.17,814.0,158.0,1.1,58.12114629325172 +2020-07-19 01:30:00-07:00,2020,7,19,1,30,18.0,10.5,0.0,0.0,0.0,0.17,814.0,175.0,0.9,61.544076807039396 +2020-07-19 02:30:00-07:00,2020,7,19,2,30,17.2,10.7,0.0,0.0,0.0,0.17,814.0,205.0,0.7000000000000001,65.5960307654582 +2020-07-19 03:30:00-07:00,2020,7,19,3,30,16.400000000000002,10.9,0.0,0.0,0.0,0.17,814.0,242.0,0.8,69.93504506386923 +2020-07-19 04:30:00-07:00,2020,7,19,4,30,16.7,10.8,0.0,0.0,0.0,0.17,814.0,271.0,1.1,68.15956449874616 +2020-07-19 05:30:00-07:00,2020,7,19,5,30,18.8,10.6,49.0,4.0,49.0,0.17,814.0,284.0,1.7000000000000002,58.92575055635159 +2020-07-19 06:30:00-07:00,2020,7,19,6,30,22.0,10.3,134.0,17.0,139.0,0.17,814.0,291.0,1.9,47.406531020959186 +2020-07-19 07:30:00-07:00,2020,7,19,7,30,25.6,9.3,232.0,287.0,370.0,0.17,814.0,308.0,1.5,35.70065504585558 +2020-07-19 08:30:00-07:00,2020,7,19,8,30,28.5,7.1000000000000005,92.0,848.0,640.0,0.17,814.0,160.0,0.9,25.934508421548678 +2020-07-19 09:30:00-07:00,2020,7,19,9,30,30.3,5.9,129.0,837.0,784.0,0.17,813.0,35.0,0.8,21.523454430968467 +2020-07-19 10:30:00-07:00,2020,7,19,10,30,31.3,5.4,378.0,111.0,476.0,0.17,813.0,93.0,1.1,19.636481248124422 +2020-07-19 11:30:00-07:00,2020,7,19,11,30,31.6,5.2,207.0,37.0,242.0,0.17,813.0,136.0,1.5,19.038093240240038 +2020-07-19 12:30:00-07:00,2020,7,19,12,30,31.200000000000003,5.4,300.0,8.0,308.0,0.17,812.0,166.0,1.8,19.748602400029988 +2020-07-19 13:30:00-07:00,2020,7,19,13,30,30.4,5.9,383.0,289.0,643.0,0.17,812.0,185.0,1.9,21.400453127655066 +2020-07-19 14:30:00-07:00,2020,7,19,14,30,29.200000000000003,6.300000000000001,307.0,381.0,616.0,0.17,812.0,211.0,1.9,23.572205427102407 +2020-07-19 15:30:00-07:00,2020,7,19,15,30,27.200000000000003,6.7,184.0,24.0,200.0,0.17,812.0,239.0,2.2,27.222211948351635 +2020-07-19 16:30:00-07:00,2020,7,19,16,30,24.9,7.4,171.0,454.0,408.0,0.17,813.0,248.0,2.9000000000000004,32.72175896405002 +2020-07-19 17:30:00-07:00,2020,7,19,17,30,22.700000000000003,7.800000000000001,96.0,543.0,282.0,0.17,813.0,238.0,4.0,38.38507697190701 +2020-07-19 18:30:00-07:00,2020,7,19,18,30,20.700000000000003,8.0,68.0,125.0,87.0,0.17,812.0,227.0,4.6000000000000005,43.97015355621201 +2020-07-19 19:30:00-07:00,2020,7,19,19,30,19.5,7.5,1.0,0.0,1.0,0.17,812.0,221.0,4.1000000000000005,45.7729961319064 +2020-07-19 20:30:00-07:00,2020,7,19,20,30,19.200000000000003,6.9,0.0,0.0,0.0,0.17,812.0,220.0,3.2,44.7600487957246 +2020-07-19 21:30:00-07:00,2020,7,19,21,30,18.8,6.5,0.0,0.0,0.0,0.17,812.0,221.0,2.3000000000000003,44.64746180590139 +2020-07-19 22:30:00-07:00,2020,7,19,22,30,18.3,6.1000000000000005,0.0,0.0,0.0,0.17,812.0,217.0,1.7000000000000002,44.81471436440474 +2020-07-19 23:30:00-07:00,2020,7,19,23,30,18.3,5.7,0.0,0.0,0.0,0.17,812.0,214.0,1.6,43.59260979450305 +2020-07-20 00:30:00-07:00,2020,7,20,0,30,18.4,5.1000000000000005,0.0,0.0,0.0,0.17,812.0,211.0,1.7000000000000002,41.5533321011388 +2020-07-20 01:30:00-07:00,2020,7,20,1,30,17.5,4.5,0.0,0.0,0.0,0.17,812.0,210.0,1.8,42.17103519038902 +2020-07-20 02:30:00-07:00,2020,7,20,2,30,16.3,4.3,0.0,0.0,0.0,0.17,811.0,221.0,1.9,44.87211034614512 +2020-07-20 03:30:00-07:00,2020,7,20,3,30,15.7,4.0,0.0,0.0,0.0,0.17,811.0,239.0,2.0,45.65382760492423 +2020-07-20 04:30:00-07:00,2020,7,20,4,30,16.5,3.4000000000000004,0.0,0.0,0.0,0.17,811.0,259.0,2.5,41.58921161987218 +2020-07-20 05:30:00-07:00,2020,7,20,5,30,19.1,3.3000000000000003,31.0,410.0,76.0,0.17,812.0,275.0,3.1,35.060917223353826 +2020-07-20 06:30:00-07:00,2020,7,20,6,30,22.8,3.5,55.0,692.0,260.0,0.17,812.0,300.0,3.0,28.32541902363249 +2020-07-20 07:30:00-07:00,2020,7,20,7,30,26.200000000000003,2.7,68.0,824.0,463.0,0.17,812.0,335.0,2.9000000000000004,21.839374194188387 +2020-07-20 08:30:00-07:00,2020,7,20,8,30,28.6,1.0,77.0,897.0,655.0,0.17,812.0,180.0,2.9000000000000004,16.805714937592672 +2020-07-20 09:30:00-07:00,2020,7,20,9,30,30.4,-0.1,83.0,942.0,819.0,0.17,812.0,16.0,2.8000000000000003,13.991305215187309 +2020-07-20 10:30:00-07:00,2020,7,20,10,30,31.8,-0.9,85.0,969.0,938.0,0.17,811.0,31.0,2.7,12.18716276889928 +2020-07-20 11:30:00-07:00,2020,7,20,11,30,32.9,-1.3,84.0,984.0,1004.0,0.17,811.0,44.0,2.8000000000000003,11.122617943687285 +2020-07-20 12:30:00-07:00,2020,7,20,12,30,33.4,-1.4,87.0,982.0,1011.0,0.17,810.0,55.0,2.9000000000000004,10.735648798866286 +2020-07-20 13:30:00-07:00,2020,7,20,13,30,33.4,-1.1,90.0,964.0,955.0,0.17,810.0,63.0,3.2,10.974244070118006 +2020-07-20 14:30:00-07:00,2020,7,20,14,30,32.9,-0.5,91.0,931.0,844.0,0.17,810.0,68.0,3.6,11.792079276275203 +2020-07-20 15:30:00-07:00,2020,7,20,15,30,31.9,0.4,89.0,882.0,688.0,0.17,810.0,71.0,3.8,13.318897932648897 +2020-07-20 16:30:00-07:00,2020,7,20,16,30,30.3,1.5,81.0,804.0,499.0,0.17,810.0,74.0,3.9,15.792470157616426 +2020-07-20 17:30:00-07:00,2020,7,20,17,30,28.0,2.6,108.0,520.0,285.0,0.18,811.0,74.0,3.3000000000000003,19.510625529104765 +2020-07-20 18:30:00-07:00,2020,7,20,18,30,24.6,4.6000000000000005,53.0,328.0,103.0,0.18,811.0,76.0,2.4000000000000004,27.455526527742016 +2020-07-20 19:30:00-07:00,2020,7,20,19,30,21.700000000000003,6.300000000000001,2.0,13.0,2.0,0.18,812.0,88.0,1.6,36.81199394831598 +2020-07-20 20:30:00-07:00,2020,7,20,20,30,20.3,7.5,0.0,0.0,0.0,0.18,812.0,115.0,1.1,43.559518095883305 +2020-07-20 21:30:00-07:00,2020,7,20,21,30,19.4,8.4,0.0,0.0,0.0,0.18,813.0,159.0,0.8,48.965179447998274 +2020-07-20 22:30:00-07:00,2020,7,20,22,30,18.6,9.0,0.0,0.0,0.0,0.18,813.0,208.0,0.7000000000000001,53.60125528427864 +2020-07-20 23:30:00-07:00,2020,7,20,23,30,17.7,9.5,0.0,0.0,0.0,0.18,813.0,242.0,0.9,58.65855788267351 +2020-07-21 00:30:00-07:00,2020,7,21,0,30,16.900000000000002,9.8,0.0,0.0,0.0,0.18,813.0,262.0,1.0,62.95500625737796 +2020-07-21 01:30:00-07:00,2020,7,21,1,30,16.2,10.0,0.0,0.0,0.0,0.18,813.0,276.0,1.1,66.7068633765395 +2020-07-21 02:30:00-07:00,2020,7,21,2,30,15.4,10.3,0.0,0.0,0.0,0.18,813.0,284.0,1.2000000000000002,71.62921010503737 +2020-07-21 03:30:00-07:00,2020,7,21,3,30,14.9,10.5,0.0,0.0,0.0,0.18,813.0,284.0,1.3,74.96114555894087 +2020-07-21 04:30:00-07:00,2020,7,21,4,30,15.4,10.6,0.0,0.0,0.0,0.18,813.0,283.0,1.5,73.07613749140184 +2020-07-21 05:30:00-07:00,2020,7,21,5,30,17.3,10.9,35.0,298.0,67.0,0.18,813.0,287.0,1.8,66.05505193110021 +2020-07-21 06:30:00-07:00,2020,7,21,6,30,20.3,11.5,77.0,553.0,240.0,0.18,813.0,306.0,1.5,56.9860931453768 +2020-07-21 07:30:00-07:00,2020,7,21,7,30,23.700000000000003,10.2,76.0,782.0,449.0,0.18,813.0,183.0,1.2000000000000002,42.4819236030045 +2020-07-21 08:30:00-07:00,2020,7,21,8,30,26.4,8.8,87.0,861.0,640.0,0.18,813.0,70.0,1.4,32.92138083612619 +2020-07-21 09:30:00-07:00,2020,7,21,9,30,28.6,8.0,93.0,907.0,800.0,0.18,813.0,105.0,2.0,27.416930291309473 +2020-07-21 10:30:00-07:00,2020,7,21,10,30,30.3,7.2,98.0,932.0,917.0,0.18,812.0,120.0,2.5,23.536096831289473 +2020-07-21 11:30:00-07:00,2020,7,21,11,30,31.5,6.7,103.0,940.0,980.0,0.18,812.0,133.0,2.9000000000000004,21.23743950525861 +2020-07-21 12:30:00-07:00,2020,7,21,12,30,32.2,6.4,386.0,263.0,633.0,0.18,811.0,150.0,3.3000000000000003,19.99446271331389 +2020-07-21 13:30:00-07:00,2020,7,21,13,30,32.300000000000004,6.2,200.0,7.0,206.0,0.18,811.0,168.0,3.8,19.609672492085625 +2020-07-21 14:30:00-07:00,2020,7,21,14,30,31.9,5.6000000000000005,204.0,3.0,206.0,0.18,810.0,178.0,4.2,19.24369501459237 +2020-07-21 15:30:00-07:00,2020,7,21,15,30,31.1,4.6000000000000005,269.0,26.0,287.0,0.18,810.0,179.0,4.5,18.78541104494049 +2020-07-21 16:30:00-07:00,2020,7,21,16,30,30.0,3.7,226.0,61.0,258.0,0.18,810.0,179.0,4.4,18.782717500336172 +2020-07-21 17:30:00-07:00,2020,7,21,17,30,28.0,3.0,124.0,303.0,226.0,0.17,810.0,176.0,3.9,20.071678745219934 +2020-07-21 18:30:00-07:00,2020,7,21,18,30,24.5,4.1000000000000005,40.0,470.0,111.0,0.17,810.0,175.0,3.2,26.67039625431671 +2020-07-21 19:30:00-07:00,2020,7,21,19,30,21.700000000000003,5.2,0.0,0.0,0.0,0.17,811.0,182.0,2.8000000000000003,34.11363701952363 +2020-07-21 20:30:00-07:00,2020,7,21,20,30,20.4,5.4,0.0,0.0,0.0,0.17,811.0,194.0,2.5,37.46599662866096 +2020-07-21 21:30:00-07:00,2020,7,21,21,30,19.3,5.5,0.0,0.0,0.0,0.17,811.0,207.0,2.4000000000000004,40.38871716024597 +2020-07-21 22:30:00-07:00,2020,7,21,22,30,18.5,5.300000000000001,0.0,0.0,0.0,0.17,811.0,218.0,2.3000000000000003,41.87192610090052 +2020-07-21 23:30:00-07:00,2020,7,21,23,30,18.0,4.9,0.0,0.0,0.0,0.17,811.0,228.0,2.1,42.020574203724 +2020-07-22 00:30:00-07:00,2020,7,22,0,30,17.7,4.4,0.0,0.0,0.0,0.17,811.0,238.0,2.0,41.35234001273093 +2020-07-22 01:30:00-07:00,2020,7,22,1,30,17.3,4.0,0.0,0.0,0.0,0.17,812.0,247.0,1.8,41.2371796506903 +2020-07-22 02:30:00-07:00,2020,7,22,2,30,16.7,3.6,0.0,0.0,0.0,0.17,812.0,256.0,1.6,41.646911789093586 +2020-07-22 03:30:00-07:00,2020,7,22,3,30,16.1,3.1,0.0,0.0,0.0,0.17,812.0,263.0,1.5,41.76803393230447 +2020-07-22 04:30:00-07:00,2020,7,22,4,30,16.6,2.5,0.0,0.0,0.0,0.17,812.0,270.0,1.5,38.7748905202501 +2020-07-22 05:30:00-07:00,2020,7,22,5,30,19.5,2.4000000000000004,40.0,239.0,65.0,0.17,812.0,276.0,1.9,32.08624705881556 +2020-07-22 06:30:00-07:00,2020,7,22,6,30,23.3,1.5,93.0,462.0,228.0,0.17,812.0,293.0,1.7000000000000002,23.84103280976067 +2020-07-22 07:30:00-07:00,2020,7,22,7,30,26.700000000000003,2.2,181.0,390.0,366.0,0.17,812.0,191.0,0.9,20.46434205801144 +2020-07-22 08:30:00-07:00,2020,7,22,8,30,29.4,2.6,129.0,739.0,603.0,0.17,811.0,88.0,1.3,17.988726599100122 +2020-07-22 09:30:00-07:00,2020,7,22,9,30,31.200000000000003,2.7,230.0,594.0,692.0,0.17,811.0,103.0,2.2,16.340423909141915 +2020-07-22 10:30:00-07:00,2020,7,22,10,30,32.5,2.8000000000000003,275.0,574.0,779.0,0.17,811.0,108.0,2.8000000000000003,15.287539867697216 +2020-07-22 11:30:00-07:00,2020,7,22,11,30,33.2,3.0,480.0,215.0,680.0,0.17,811.0,112.0,3.2,14.906651787723932 +2020-07-22 12:30:00-07:00,2020,7,22,12,30,33.4,3.5,159.0,845.0,952.0,0.17,810.0,116.0,3.5,15.27011317072949 +2020-07-22 13:30:00-07:00,2020,7,22,13,30,32.9,4.3,415.0,238.0,628.0,0.17,810.0,123.0,3.6,16.612783274800496 +2020-07-22 14:30:00-07:00,2020,7,22,14,30,31.700000000000003,5.1000000000000005,253.0,593.0,731.0,0.17,810.0,134.0,3.5,18.799133733327633 +2020-07-22 15:30:00-07:00,2020,7,22,15,30,29.9,5.9,220.0,30.0,240.0,0.17,810.0,154.0,3.0,22.023494892704974 +2020-07-22 16:30:00-07:00,2020,7,22,16,30,27.8,6.9,49.0,0.0,49.0,0.17,810.0,179.0,2.3000000000000003,26.64610622397774 +2020-07-22 17:30:00-07:00,2020,7,22,17,30,25.700000000000003,8.0,104.0,85.0,133.0,0.17,811.0,219.0,1.6,32.50289261771549 +2020-07-22 18:30:00-07:00,2020,7,22,18,30,23.4,9.6,71.0,20.0,74.0,0.17,811.0,261.0,1.6,41.55377571112612 +2020-07-22 19:30:00-07:00,2020,7,22,19,30,21.700000000000003,9.9,0.0,0.0,0.0,0.17,811.0,279.0,2.1,47.008835138441846 +2020-07-22 20:30:00-07:00,2020,7,22,20,30,20.6,9.8,0.0,0.0,0.0,0.17,812.0,279.0,2.4000000000000004,49.956252964170794 +2020-07-22 21:30:00-07:00,2020,7,22,21,30,20.0,9.8,0.0,0.0,0.0,0.17,812.0,280.0,2.4000000000000004,51.8424522903257 +2020-07-22 22:30:00-07:00,2020,7,22,22,30,19.5,9.9,0.0,0.0,0.0,0.17,812.0,284.0,2.2,53.83487344020089 +2020-07-22 23:30:00-07:00,2020,7,22,23,30,19.200000000000003,9.9,0.0,0.0,0.0,0.17,812.0,289.0,2.1,54.8491510235393 +2020-07-23 00:30:00-07:00,2020,7,23,0,30,18.8,10.0,0.0,0.0,0.0,0.17,812.0,287.0,1.9,56.612676003765394 +2020-07-23 01:30:00-07:00,2020,7,23,1,30,18.3,10.5,0.0,0.0,0.0,0.17,812.0,277.0,1.7000000000000002,60.39561267366114 +2020-07-23 02:30:00-07:00,2020,7,23,2,30,17.8,11.0,0.0,0.0,0.0,0.17,812.0,267.0,1.5,64.4305946315017 +2020-07-23 03:30:00-07:00,2020,7,23,3,30,17.400000000000002,11.2,0.0,0.0,0.0,0.17,812.0,264.0,1.6,66.9586597582824 +2020-07-23 04:30:00-07:00,2020,7,23,4,30,17.5,11.2,0.0,0.0,0.0,0.17,812.0,265.0,1.9,66.53729186166542 +2020-07-23 05:30:00-07:00,2020,7,23,5,30,18.6,10.9,31.0,1.0,31.0,0.17,812.0,268.0,2.5,60.86968290268063 +2020-07-23 06:30:00-07:00,2020,7,23,6,30,21.3,10.4,126.0,45.0,139.0,0.17,813.0,284.0,2.6,49.81072272487459 +2020-07-23 07:30:00-07:00,2020,7,23,7,30,24.5,8.200000000000001,159.0,516.0,403.0,0.17,813.0,326.0,1.9,35.38917466566073 +2020-07-23 08:30:00-07:00,2020,7,23,8,30,26.8,6.5,112.0,808.0,629.0,0.17,813.0,203.0,1.5,27.488923819153094 +2020-07-23 09:30:00-07:00,2020,7,23,9,30,28.6,6.1000000000000005,401.0,310.0,642.0,0.17,812.0,66.0,1.9,24.071575753388988 +2020-07-23 10:30:00-07:00,2020,7,23,10,30,30.200000000000003,6.1000000000000005,125.0,892.0,907.0,0.17,812.0,85.0,2.4000000000000004,21.94834918100604 +2020-07-23 11:30:00-07:00,2020,7,23,11,30,31.200000000000003,6.2,109.0,927.0,972.0,0.17,811.0,96.0,2.7,20.872344967965137 +2020-07-23 12:30:00-07:00,2020,7,23,12,30,31.5,6.300000000000001,181.0,820.0,949.0,0.17,811.0,106.0,2.9000000000000004,20.66103466076685 +2020-07-23 13:30:00-07:00,2020,7,23,13,30,31.1,6.6000000000000005,266.0,421.0,642.0,0.17,811.0,112.0,2.9000000000000004,21.577713327257662 +2020-07-23 14:30:00-07:00,2020,7,23,14,30,30.200000000000003,7.0,369.0,67.0,423.0,0.17,811.0,115.0,2.6,23.349568459761407 +2020-07-23 15:30:00-07:00,2020,7,23,15,30,28.9,7.300000000000001,285.0,38.0,311.0,0.17,811.0,118.0,1.9,25.688714874239775 +2020-07-23 16:30:00-07:00,2020,7,23,16,30,27.3,7.800000000000001,236.0,221.0,350.0,0.17,811.0,151.0,0.9,29.17717304735435 +2020-07-23 17:30:00-07:00,2020,7,23,17,30,25.200000000000003,8.4,155.0,68.0,178.0,0.18,811.0,244.0,1.0,34.404429816504155 +2020-07-23 18:30:00-07:00,2020,7,23,18,30,22.9,10.5,57.0,1.0,57.0,0.18,812.0,311.0,2.3000000000000003,45.48588408531043 +2020-07-23 19:30:00-07:00,2020,7,23,19,30,21.1,10.4,0.0,0.0,0.0,0.18,812.0,319.0,3.2,50.42565849137646 +2020-07-23 20:30:00-07:00,2020,7,23,20,30,20.200000000000003,10.4,0.0,0.0,0.0,0.18,812.0,325.0,3.1,53.30047610233841 +2020-07-23 21:30:00-07:00,2020,7,23,21,30,19.5,10.5,0.0,0.0,0.0,0.18,812.0,328.0,2.4000000000000004,56.03622538300464 +2020-07-23 22:30:00-07:00,2020,7,23,22,30,19.0,10.4,0.0,0.0,0.0,0.18,812.0,320.0,1.8,57.424126914299855 +2020-07-23 23:30:00-07:00,2020,7,23,23,30,18.7,10.1,0.0,0.0,0.0,0.18,812.0,306.0,1.5,57.34987196336785 +2020-07-24 00:30:00-07:00,2020,7,24,0,30,18.5,9.8,0.0,0.0,0.0,0.18,812.0,295.0,1.5,56.91776436115663 +2020-07-24 01:30:00-07:00,2020,7,24,1,30,18.3,9.6,0.0,0.0,0.0,0.18,812.0,288.0,1.4,56.86782572787149 +2020-07-24 02:30:00-07:00,2020,7,24,2,30,17.8,9.3,0.0,0.0,0.0,0.18,811.0,283.0,1.2000000000000002,57.51187217078308 +2020-07-24 03:30:00-07:00,2020,7,24,3,30,17.0,9.1,0.0,0.0,0.0,0.18,811.0,275.0,1.0,59.683079046154425 +2020-07-24 04:30:00-07:00,2020,7,24,4,30,17.2,8.8,0.0,0.0,0.0,0.18,811.0,263.0,1.5,57.751605185233075 +2020-07-24 05:30:00-07:00,2020,7,24,5,30,19.200000000000003,9.1,41.0,5.0,42.0,0.18,811.0,251.0,2.3000000000000003,51.97974582186448 +2020-07-24 06:30:00-07:00,2020,7,24,6,30,22.5,9.1,106.0,8.0,108.0,0.18,811.0,232.0,2.7,42.43001688288942 +2020-07-24 07:30:00-07:00,2020,7,24,7,30,25.9,8.3,214.0,266.0,339.0,0.18,811.0,211.0,2.6,32.78077962820768 +2020-07-24 08:30:00-07:00,2020,7,24,8,30,27.8,8.1,332.0,228.0,477.0,0.18,811.0,203.0,2.4000000000000004,28.920019983343405 +2020-07-24 09:30:00-07:00,2020,7,24,9,30,28.9,8.5,111.0,862.0,779.0,0.18,811.0,204.0,2.0,27.87365503066756 +2020-07-24 10:30:00-07:00,2020,7,24,10,30,29.700000000000003,8.8,285.0,596.0,807.0,0.18,811.0,204.0,1.6,27.160520571166362 +2020-07-24 11:30:00-07:00,2020,7,24,11,30,29.9,9.0,316.0,598.0,872.0,0.18,811.0,194.0,1.4,27.21458607033968 +2020-07-24 12:30:00-07:00,2020,7,24,12,30,29.4,9.1,263.0,4.0,267.0,0.18,811.0,125.0,1.4,28.19928746485041 +2020-07-24 13:30:00-07:00,2020,7,24,13,30,28.200000000000003,9.6,408.0,165.0,555.0,0.18,811.0,56.0,1.8,31.264400209411267 +2020-07-24 14:30:00-07:00,2020,7,24,14,30,26.6,10.3,315.0,105.0,399.0,0.18,812.0,49.0,2.2,35.982746451603994 +2020-07-24 15:30:00-07:00,2020,7,24,15,30,25.1,11.0,314.0,117.0,393.0,0.18,812.0,48.0,2.2,41.20252877210239 +2020-07-24 16:30:00-07:00,2020,7,24,16,30,23.5,11.7,263.0,134.0,332.0,0.18,812.0,42.0,1.9,47.500050961522234 +2020-07-24 17:30:00-07:00,2020,7,24,17,30,22.0,12.4,91.0,533.0,268.0,0.17,812.0,27.0,1.5,54.476313432239984 +2020-07-24 18:30:00-07:00,2020,7,24,18,30,20.700000000000003,13.2,58.0,4.0,59.0,0.17,812.0,186.0,1.4,62.16458510592052 +2020-07-24 19:30:00-07:00,2020,7,24,19,30,19.6,13.8,0.0,0.0,0.0,0.17,813.0,340.0,1.5,69.19039294481395 +2020-07-24 20:30:00-07:00,2020,7,24,20,30,19.0,14.0,0.0,0.0,0.0,0.17,813.0,314.0,1.7000000000000002,72.76126640465226 +2020-07-24 21:30:00-07:00,2020,7,24,21,30,18.5,14.0,0.0,0.0,0.0,0.17,813.0,294.0,1.7000000000000002,75.07107121026888 +2020-07-24 22:30:00-07:00,2020,7,24,22,30,18.2,13.7,0.0,0.0,0.0,0.17,813.0,277.0,1.6,75.02118720256502 +2020-07-24 23:30:00-07:00,2020,7,24,23,30,17.900000000000002,13.2,0.0,0.0,0.0,0.17,812.0,261.0,1.6,74.0000484665684 +2020-07-25 00:30:00-07:00,2020,7,25,0,30,17.6,12.6,0.0,0.0,0.0,0.17,812.0,254.0,1.7000000000000002,72.5100329199808 +2020-07-25 01:30:00-07:00,2020,7,25,1,30,17.2,11.9,0.0,0.0,0.0,0.17,812.0,254.0,2.0,71.02113452472535 +2020-07-25 02:30:00-07:00,2020,7,25,2,30,16.6,11.2,0.0,0.0,0.0,0.17,812.0,256.0,2.2,70.43942991858856 +2020-07-25 03:30:00-07:00,2020,7,25,3,30,15.9,10.6,0.0,0.0,0.0,0.17,812.0,258.0,2.4000000000000004,70.77436534727448 +2020-07-25 04:30:00-07:00,2020,7,25,4,30,15.9,9.9,0.0,0.0,0.0,0.17,812.0,260.0,2.8000000000000003,67.54262724909056 +2020-07-25 05:30:00-07:00,2020,7,25,5,30,17.7,9.6,35.0,150.0,50.0,0.17,812.0,263.0,3.3000000000000003,59.05371034696701 +2020-07-25 06:30:00-07:00,2020,7,25,6,30,21.3,9.6,46.0,705.0,247.0,0.17,813.0,286.0,2.8000000000000003,47.21491537108514 +2020-07-25 07:30:00-07:00,2020,7,25,7,30,25.1,8.6,199.0,22.0,209.0,0.17,813.0,161.0,2.0,35.08174844476946 +2020-07-25 08:30:00-07:00,2020,7,25,8,30,27.4,7.7,113.0,2.0,114.0,0.17,813.0,34.0,1.9,28.809840490055645 +2020-07-25 09:30:00-07:00,2020,7,25,9,30,28.700000000000003,7.6,288.0,405.0,601.0,0.17,813.0,62.0,2.2,26.526016262879928 +2020-07-25 10:30:00-07:00,2020,7,25,10,30,29.0,7.9,436.0,299.0,697.0,0.17,813.0,84.0,2.5,26.60699555280209 +2020-07-25 11:30:00-07:00,2020,7,25,11,30,28.6,8.5,362.0,8.0,369.0,0.17,813.0,109.0,2.7,28.36280081645092 +2020-07-25 12:30:00-07:00,2020,7,25,12,30,27.4,9.2,368.0,426.0,766.0,0.17,813.0,128.0,2.9000000000000004,31.890535038330473 +2020-07-25 13:30:00-07:00,2020,7,25,13,30,26.0,9.9,155.0,17.0,170.0,0.17,812.0,140.0,2.6,36.296792407409285 +2020-07-25 14:30:00-07:00,2020,7,25,14,30,24.700000000000003,10.6,131.0,3.0,133.0,0.17,813.0,157.0,2.1,41.09040157532104 +2020-07-25 15:30:00-07:00,2020,7,25,15,30,23.5,11.1,269.0,260.0,444.0,0.17,813.0,178.0,1.7000000000000002,45.651251258530124 +2020-07-25 16:30:00-07:00,2020,7,25,16,30,22.3,11.5,245.0,120.0,306.0,0.17,813.0,211.0,1.5,50.410509632480974 +2020-07-25 17:30:00-07:00,2020,7,25,17,30,20.700000000000003,12.0,66.0,488.0,227.0,0.17,814.0,271.0,1.6,57.46231127798349 +2020-07-25 18:30:00-07:00,2020,7,25,18,30,19.1,12.9,32.0,1.0,32.0,0.17,815.0,316.0,2.0,67.31098253373243 +2020-07-25 19:30:00-07:00,2020,7,25,19,30,17.900000000000002,13.3,0.0,0.0,0.0,0.17,815.0,319.0,2.1,74.48421243189895 +2020-07-25 20:30:00-07:00,2020,7,25,20,30,17.6,13.5,0.0,0.0,0.0,0.17,815.0,311.0,1.6,76.90030155886183 +2020-07-25 21:30:00-07:00,2020,7,25,21,30,17.6,13.4,0.0,0.0,0.0,0.17,815.0,293.0,1.3,76.40120919146531 +2020-07-25 22:30:00-07:00,2020,7,25,22,30,17.5,13.1,0.0,0.0,0.0,0.17,814.0,272.0,1.4,75.39509273805926 +2020-07-25 23:30:00-07:00,2020,7,25,23,30,17.3,12.7,0.0,0.0,0.0,0.17,814.0,263.0,1.6,74.38211673653167 +2020-07-26 00:30:00-07:00,2020,7,26,0,30,17.2,12.5,0.0,0.0,0.0,0.17,813.0,262.0,1.5,73.87901308111256 +2020-07-26 01:30:00-07:00,2020,7,26,1,30,16.900000000000002,12.3,0.0,0.0,0.0,0.17,813.0,252.0,1.1,74.31373465932737 +2020-07-26 02:30:00-07:00,2020,7,26,2,30,16.5,12.1,0.0,0.0,0.0,0.17,813.0,242.0,0.9,75.22945062810254 +2020-07-26 03:30:00-07:00,2020,7,26,3,30,16.2,11.9,0.0,0.0,0.0,0.17,813.0,240.0,0.8,75.67676884982207 +2020-07-26 04:30:00-07:00,2020,7,26,4,30,16.6,11.6,0.0,0.0,0.0,0.17,813.0,249.0,0.9,72.3286040994422 +2020-07-26 05:30:00-07:00,2020,7,26,5,30,17.8,11.4,33.0,0.0,33.0,0.17,814.0,270.0,1.2000000000000002,66.16136881489018 +2020-07-26 06:30:00-07:00,2020,7,26,6,30,19.3,10.7,145.0,98.0,173.0,0.17,814.0,314.0,1.6,57.4982185718556 +2020-07-26 07:30:00-07:00,2020,7,26,7,30,20.8,10.2,130.0,577.0,400.0,0.17,815.0,179.0,2.2,50.68304386244287 +2020-07-26 08:30:00-07:00,2020,7,26,8,30,21.9,10.0,203.0,561.0,559.0,0.17,816.0,15.0,2.7,46.7499840231113 +2020-07-26 09:30:00-07:00,2020,7,26,9,30,22.4,10.2,95.0,875.0,771.0,0.17,816.0,16.0,2.9000000000000004,45.95800817100595 +2020-07-26 10:30:00-07:00,2020,7,26,10,30,22.700000000000003,10.6,385.0,430.0,760.0,0.17,816.0,17.0,2.9000000000000004,46.34835040728109 +2020-07-26 11:30:00-07:00,2020,7,26,11,30,23.1,10.9,408.0,363.0,744.0,0.17,816.0,20.0,2.9000000000000004,46.15061856173744 +2020-07-26 12:30:00-07:00,2020,7,26,12,30,23.6,11.0,137.0,0.0,137.0,0.17,816.0,26.0,2.7,45.076854324927275 +2020-07-26 13:30:00-07:00,2020,7,26,13,30,24.1,11.2,203.0,1.0,204.0,0.17,816.0,39.0,2.7,44.325708889613985 +2020-07-26 14:30:00-07:00,2020,7,26,14,30,24.3,11.2,243.0,1.0,244.0,0.17,816.0,58.0,2.7,43.797157252002506 +2020-07-26 15:30:00-07:00,2020,7,26,15,30,23.9,11.2,179.0,0.0,179.0,0.17,816.0,75.0,2.7,44.86144557792349 +2020-07-26 16:30:00-07:00,2020,7,26,16,30,22.9,11.4,117.0,0.0,117.0,0.17,816.0,85.0,2.7,48.2870298524128 +2020-07-26 17:30:00-07:00,2020,7,26,17,30,21.4,11.8,175.0,54.0,193.0,0.17,816.0,91.0,2.1,54.32336862127332 +2020-07-26 18:30:00-07:00,2020,7,26,18,30,19.4,12.5,60.0,3.0,60.0,0.17,817.0,88.0,1.1,64.35700773500906 +2020-07-26 19:30:00-07:00,2020,7,26,19,30,17.900000000000002,13.0,0.0,0.0,0.0,0.17,817.0,79.0,0.7000000000000001,73.04002537560396 +2020-07-26 20:30:00-07:00,2020,7,26,20,30,17.400000000000002,13.2,0.0,0.0,0.0,0.17,817.0,71.0,0.6000000000000001,76.36935957973512 +2020-07-26 21:30:00-07:00,2020,7,26,21,30,17.2,13.4,0.0,0.0,0.0,0.17,817.0,58.0,0.4,78.35576874448529 +2020-07-26 22:30:00-07:00,2020,7,26,22,30,16.900000000000002,13.5,0.0,0.0,0.0,0.17,817.0,202.0,0.3,80.38018507925324 +2020-07-26 23:30:00-07:00,2020,7,26,23,30,16.6,13.6,0.0,0.0,0.0,0.17,817.0,191.0,0.4,82.46010614674658 +2020-07-27 00:30:00-07:00,2020,7,27,0,30,16.2,13.8,0.0,0.0,0.0,0.17,817.0,36.0,0.5,85.69273059593407 +2020-07-27 01:30:00-07:00,2020,7,27,1,30,16.0,14.0,0.0,0.0,0.0,0.17,817.0,57.0,0.5,87.92661502454916 +2020-07-27 02:30:00-07:00,2020,7,27,2,30,16.0,14.3,0.0,0.0,0.0,0.17,817.0,72.0,0.3,89.65152316161864 +2020-07-27 03:30:00-07:00,2020,7,27,3,30,15.8,14.4,0.0,0.0,0.0,0.17,817.0,158.0,0.2,91.393402796861 +2020-07-27 04:30:00-07:00,2020,7,27,4,30,16.0,14.3,0.0,0.0,0.0,0.17,817.0,245.0,0.4,89.65152316161864 +2020-07-27 05:30:00-07:00,2020,7,27,5,30,16.900000000000002,14.4,38.0,1.0,38.0,0.17,817.0,256.0,0.8,85.21193729008603 +2020-07-27 06:30:00-07:00,2020,7,27,6,30,18.7,14.3,83.0,514.0,227.0,0.17,817.0,248.0,0.8,75.59181331063621 +2020-07-27 07:30:00-07:00,2020,7,27,7,30,21.0,13.7,233.0,263.0,355.0,0.17,817.0,172.0,0.7000000000000001,63.04748762842389 +2020-07-27 08:30:00-07:00,2020,7,27,8,30,23.200000000000003,11.9,322.0,167.0,428.0,0.17,817.0,106.0,1.2000000000000002,49.010346516367235 +2020-07-27 09:30:00-07:00,2020,7,27,9,30,25.0,10.5,135.0,837.0,780.0,0.17,817.0,111.0,1.9,40.093202991204244 +2020-07-27 10:30:00-07:00,2020,7,27,10,30,26.3,9.6,86.0,949.0,912.0,0.17,817.0,118.0,2.5,34.94884380719755 +2020-07-27 11:30:00-07:00,2020,7,27,11,30,27.3,9.1,87.0,961.0,977.0,0.17,816.0,123.0,3.0,31.862605376917806 +2020-07-27 12:30:00-07:00,2020,7,27,12,30,27.9,8.700000000000001,88.0,960.0,982.0,0.17,816.0,125.0,3.4000000000000004,29.94461252764678 +2020-07-27 13:30:00-07:00,2020,7,27,13,30,28.200000000000003,8.4,311.0,605.0,848.0,0.17,815.0,126.0,3.6,28.83416198590477 +2020-07-27 14:30:00-07:00,2020,7,27,14,30,28.1,8.3,363.0,400.0,682.0,0.17,815.0,128.0,3.8,28.806677866777132 +2020-07-27 15:30:00-07:00,2020,7,27,15,30,27.3,8.200000000000001,365.0,187.0,490.0,0.17,814.0,133.0,3.8,29.98133169781928 +2020-07-27 16:30:00-07:00,2020,7,27,16,30,26.1,8.3,84.0,775.0,477.0,0.17,814.0,141.0,3.6,32.39507495755972 +2020-07-27 17:30:00-07:00,2020,7,27,17,30,24.3,8.6,147.0,246.0,227.0,0.17,815.0,151.0,3.2,36.79955628099539 +2020-07-27 18:30:00-07:00,2020,7,27,18,30,22.1,9.5,63.0,12.0,65.0,0.17,815.0,164.0,2.6,44.661042733586996 +2020-07-27 19:30:00-07:00,2020,7,27,19,30,20.3,10.7,0.0,0.0,0.0,0.17,815.0,178.0,2.5,54.04133051585362 +2020-07-27 20:30:00-07:00,2020,7,27,20,30,19.0,11.3,0.0,0.0,0.0,0.17,815.0,190.0,2.6,60.963332921071576 +2020-07-27 21:30:00-07:00,2020,7,27,21,30,18.1,11.6,0.0,0.0,0.0,0.17,815.0,199.0,2.5,65.78905930930473 +2020-07-27 22:30:00-07:00,2020,7,27,22,30,17.400000000000002,11.7,0.0,0.0,0.0,0.17,815.0,206.0,2.5,69.21001221740694 +2020-07-27 23:30:00-07:00,2020,7,27,23,30,17.0,11.7,0.0,0.0,0.0,0.17,815.0,211.0,2.4000000000000004,70.98335766495877 +2020-07-28 00:30:00-07:00,2020,7,28,0,30,16.8,11.6,0.0,0.0,0.0,0.17,814.0,215.0,2.3000000000000003,71.41593198018428 +2020-07-28 01:30:00-07:00,2020,7,28,1,30,16.5,11.5,0.0,0.0,0.0,0.17,814.0,219.0,2.2,72.31036981040984 +2020-07-28 02:30:00-07:00,2020,7,28,2,30,16.2,11.4,0.0,0.0,0.0,0.17,814.0,226.0,2.0,73.21885864000632 +2020-07-28 03:30:00-07:00,2020,7,28,3,30,15.8,11.1,0.0,0.0,0.0,0.17,813.0,233.0,1.8,73.63456811074317 +2020-07-28 04:30:00-07:00,2020,7,28,4,30,16.1,10.9,0.0,0.0,0.0,0.17,813.0,240.0,2.1,71.28465671509319 +2020-07-28 05:30:00-07:00,2020,7,28,5,30,17.6,10.7,33.0,3.0,33.0,0.17,813.0,244.0,2.3000000000000003,63.9597587892249 +2020-07-28 06:30:00-07:00,2020,7,28,6,30,20.5,10.8,96.0,16.0,100.0,0.17,813.0,245.0,1.9,53.734496526023875 +2020-07-28 07:30:00-07:00,2020,7,28,7,30,23.700000000000003,9.9,207.0,33.0,222.0,0.17,813.0,232.0,1.1,41.638135639927874 +2020-07-28 08:30:00-07:00,2020,7,28,8,30,25.9,9.0,301.0,91.0,358.0,0.17,813.0,147.0,0.8,34.36973655266057 +2020-07-28 09:30:00-07:00,2020,7,28,9,30,27.3,8.9,390.0,84.0,455.0,0.17,812.0,69.0,1.2000000000000002,31.435776265247462 +2020-07-28 10:30:00-07:00,2020,7,28,10,30,27.8,9.1,440.0,115.0,540.0,0.17,812.0,64.0,1.6,30.944064314716794 +2020-07-28 11:30:00-07:00,2020,7,28,11,30,27.6,9.6,464.0,278.0,721.0,0.17,812.0,60.0,1.9,32.37840581298045 +2020-07-28 12:30:00-07:00,2020,7,28,12,30,26.6,10.3,361.0,44.0,402.0,0.17,812.0,42.0,2.1,35.982746451603994 +2020-07-28 13:30:00-07:00,2020,7,28,13,30,25.3,11.1,250.0,12.0,261.0,0.17,812.0,190.0,2.5,40.98588892380766 +2020-07-28 14:30:00-07:00,2020,7,28,14,30,24.700000000000003,11.6,108.0,1.0,109.0,0.17,812.0,346.0,3.1,43.90803372321442 +2020-07-28 15:30:00-07:00,2020,7,28,15,30,24.5,11.4,163.0,601.0,564.0,0.17,812.0,348.0,3.4000000000000004,43.852624223339696 +2020-07-28 16:30:00-07:00,2020,7,28,16,30,23.9,10.6,182.0,485.0,427.0,0.17,812.0,192.0,3.2,43.10859925999459 +2020-07-28 17:30:00-07:00,2020,7,28,17,30,22.4,10.3,162.0,170.0,217.0,0.17,812.0,44.0,2.8000000000000003,46.26588901803211 +2020-07-28 18:30:00-07:00,2020,7,28,18,30,20.0,11.2,56.0,124.0,73.0,0.17,812.0,73.0,1.7000000000000002,56.911733873290004 +2020-07-28 19:30:00-07:00,2020,7,28,19,30,18.2,12.2,0.0,0.0,0.0,0.17,813.0,114.0,0.9,68.01434932902269 +2020-07-28 20:30:00-07:00,2020,7,28,20,30,17.3,12.6,0.0,0.0,0.0,0.17,813.0,167.0,0.8,73.89635203232294 +2020-07-28 21:30:00-07:00,2020,7,28,21,30,16.5,12.6,0.0,0.0,0.0,0.17,813.0,202.0,1.1,77.74080206829677 +2020-07-28 22:30:00-07:00,2020,7,28,22,30,16.0,11.9,0.0,0.0,0.0,0.17,812.0,216.0,1.2000000000000002,76.64840537291926 +2020-07-28 23:30:00-07:00,2020,7,28,23,30,15.8,10.7,0.0,0.0,0.0,0.17,812.0,226.0,1.2000000000000002,71.70380206745523 +2020-07-29 00:30:00-07:00,2020,7,29,0,30,15.6,9.7,0.0,0.0,0.0,0.17,812.0,237.0,1.2000000000000002,67.93462324106227 +2020-07-29 01:30:00-07:00,2020,7,29,1,30,15.2,9.1,0.0,0.0,0.0,0.17,812.0,250.0,1.1,66.94477759172588 +2020-07-29 02:30:00-07:00,2020,7,29,2,30,14.8,8.700000000000001,0.0,0.0,0.0,0.17,812.0,261.0,1.1,66.86040042501897 +2020-07-29 03:30:00-07:00,2020,7,29,3,30,14.7,8.200000000000001,0.0,0.0,0.0,0.17,812.0,268.0,1.1,65.05212197462089 +2020-07-29 04:30:00-07:00,2020,7,29,4,30,15.4,7.9,0.0,0.0,0.0,0.17,812.0,268.0,1.4,60.9339279092706 +2020-07-29 05:30:00-07:00,2020,7,29,5,30,18.0,8.200000000000001,28.0,358.0,59.0,0.17,812.0,264.0,2.1,52.72480237953378 +2020-07-29 06:30:00-07:00,2020,7,29,6,30,22.200000000000003,8.1,50.0,685.0,239.0,0.17,812.0,269.0,2.7,40.384844984077276 +2020-07-29 07:30:00-07:00,2020,7,29,7,30,25.6,6.6000000000000005,63.0,827.0,444.0,0.17,812.0,287.0,3.0,29.712564720485418 +2020-07-29 08:30:00-07:00,2020,7,29,8,30,27.700000000000003,4.6000000000000005,71.0,902.0,638.0,0.17,812.0,301.0,3.3000000000000003,22.85816891631322 +2020-07-29 09:30:00-07:00,2020,7,29,9,30,29.200000000000003,3.7,76.0,946.0,802.0,0.17,812.0,306.0,3.4000000000000004,19.668906674616245 +2020-07-29 10:30:00-07:00,2020,7,29,10,30,30.6,3.1,79.0,973.0,923.0,0.17,811.0,310.0,3.6,17.39562187548233 +2020-07-29 11:30:00-07:00,2020,7,29,11,30,31.6,2.7,79.0,986.0,989.0,0.17,811.0,316.0,3.8,15.972883604495367 +2020-07-29 12:30:00-07:00,2020,7,29,12,30,32.2,2.4000000000000004,80.0,985.0,995.0,0.17,810.0,323.0,4.1000000000000005,15.113463365006131 +2020-07-29 13:30:00-07:00,2020,7,29,13,30,32.2,2.2,82.0,971.0,941.0,0.17,810.0,329.0,4.4,14.899875188495177 +2020-07-29 14:30:00-07:00,2020,7,29,14,30,31.6,2.2,124.0,866.0,813.0,0.17,811.0,335.0,4.5,15.415186189768619 +2020-07-29 15:30:00-07:00,2020,7,29,15,30,30.6,2.6,250.0,489.0,575.0,0.17,811.0,343.0,4.4,16.790188889576466 +2020-07-29 16:30:00-07:00,2020,7,29,16,30,29.0,3.2,146.0,621.0,458.0,0.17,811.0,178.0,4.2,19.208546184677658 +2020-07-29 17:30:00-07:00,2020,7,29,17,30,26.4,4.3,71.0,644.0,277.0,0.17,812.0,21.0,3.5,24.159201147412155 +2020-07-29 18:30:00-07:00,2020,7,29,18,30,22.8,6.6000000000000005,60.0,91.0,72.0,0.17,813.0,46.0,2.7,35.14624031162282 +2020-07-29 19:30:00-07:00,2020,7,29,19,30,20.1,8.200000000000001,0.0,0.0,0.0,0.17,814.0,59.0,2.1,46.25307343151861 +2020-07-29 20:30:00-07:00,2020,7,29,20,30,18.9,8.9,0.0,0.0,0.0,0.17,815.0,62.0,1.6,52.25186995785486 +2020-07-29 21:30:00-07:00,2020,7,29,21,30,17.8,9.3,0.0,0.0,0.0,0.17,815.0,70.0,1.0,57.51187217078308 +2020-07-29 22:30:00-07:00,2020,7,29,22,30,16.8,9.5,0.0,0.0,0.0,0.17,815.0,108.0,0.5,62.09320434972858 +2020-07-29 23:30:00-07:00,2020,7,29,23,30,15.7,9.7,0.0,0.0,0.0,0.17,815.0,179.0,0.6000000000000001,67.50116269819564 +2020-07-30 00:30:00-07:00,2020,7,30,0,30,14.9,9.5,0.0,0.0,0.0,0.17,815.0,231.0,0.8,70.11026998939109 +2020-07-30 01:30:00-07:00,2020,7,30,1,30,14.4,9.0,0.0,0.0,0.0,0.17,815.0,249.0,1.1,70.01249943726411 +2020-07-30 02:30:00-07:00,2020,7,30,2,30,14.1,8.200000000000001,0.0,0.0,0.0,0.17,815.0,258.0,1.3,67.62439046472642 +2020-07-30 03:30:00-07:00,2020,7,30,3,30,13.8,7.6,0.0,0.0,0.0,0.17,815.0,265.0,1.6,66.19544780194684 +2020-07-30 04:30:00-07:00,2020,7,30,4,30,14.2,7.0,0.0,0.0,0.0,0.17,815.0,271.0,2.0,61.90909633931943 +2020-07-30 05:30:00-07:00,2020,7,30,5,30,16.400000000000002,6.9,26.0,348.0,56.0,0.17,815.0,278.0,2.6,53.38909583303745 +2020-07-30 06:30:00-07:00,2020,7,30,6,30,19.8,7.2,51.0,667.0,234.0,0.17,815.0,303.0,3.1,44.01693670757932 +2020-07-30 07:30:00-07:00,2020,7,30,7,30,22.6,7.1000000000000005,66.0,808.0,437.0,0.17,815.0,329.0,3.6,36.81657831671656 +2020-07-30 08:30:00-07:00,2020,7,30,8,30,24.6,6.6000000000000005,74.0,883.0,628.0,0.17,815.0,340.0,4.1000000000000005,31.53657267585288 +2020-07-30 09:30:00-07:00,2020,7,30,9,30,26.200000000000003,6.2,80.0,927.0,790.0,0.17,815.0,349.0,4.800000000000001,27.896396972330024 +2020-07-30 10:30:00-07:00,2020,7,30,10,30,27.1,6.1000000000000005,144.0,851.0,881.0,0.17,816.0,179.0,5.5,26.2743633208308 +2020-07-30 11:30:00-07:00,2020,7,30,11,30,27.4,6.5,132.0,880.0,943.0,0.17,816.0,10.0,6.1000000000000005,26.537633047361314 +2020-07-30 12:30:00-07:00,2020,7,30,12,30,26.9,7.300000000000001,93.0,950.0,974.0,0.17,816.0,20.0,6.300000000000001,28.8687209115612 +2020-07-30 13:30:00-07:00,2020,7,30,13,30,26.0,8.4,439.0,203.0,618.0,0.17,817.0,28.0,6.1000000000000005,32.80891661005862 +2020-07-30 14:30:00-07:00,2020,7,30,14,30,24.8,8.9,390.0,117.0,483.0,0.17,817.0,34.0,5.6000000000000005,36.4464559084313 +2020-07-30 15:30:00-07:00,2020,7,30,15,30,23.3,9.3,187.0,139.0,279.0,0.17,817.0,42.0,5.0,40.97110413042472 +2020-07-30 16:30:00-07:00,2020,7,30,16,30,21.8,9.7,203.0,331.0,369.0,0.17,817.0,48.0,4.2,46.10054816009678 +2020-07-30 17:30:00-07:00,2020,7,30,17,30,20.200000000000003,10.0,147.0,102.0,179.0,0.17,818.0,53.0,3.2,51.89525449425354 +2020-07-30 18:30:00-07:00,2020,7,30,18,30,18.3,10.3,49.0,198.0,74.0,0.17,818.0,60.0,2.0,59.5954119248632 +2020-07-30 19:30:00-07:00,2020,7,30,19,30,16.7,10.5,0.0,0.0,0.0,0.17,818.0,73.0,1.0,66.8120925070903 +2020-07-30 20:30:00-07:00,2020,7,30,20,30,15.8,10.6,0.0,0.0,0.0,0.17,819.0,120.0,0.5,71.2281406782294 +2020-07-30 21:30:00-07:00,2020,7,30,21,30,15.2,10.7,0.0,0.0,0.0,0.17,819.0,193.0,0.6000000000000001,74.51454812270427 +2020-07-30 22:30:00-07:00,2020,7,30,22,30,14.5,10.7,0.0,0.0,0.0,0.17,818.0,237.0,0.9,77.9509564302241 +2020-07-30 23:30:00-07:00,2020,7,30,23,30,13.9,10.6,0.0,0.0,0.0,0.17,818.0,248.0,1.0,80.50057198125077 +2020-07-31 00:30:00-07:00,2020,7,31,0,30,13.5,10.4,0.0,0.0,0.0,0.17,818.0,255.0,1.1,81.52676330419821 +2020-07-31 01:30:00-07:00,2020,7,31,1,30,13.3,9.9,0.0,0.0,0.0,0.17,817.0,261.0,1.1,79.88182739262056 +2020-07-31 02:30:00-07:00,2020,7,31,2,30,13.2,9.5,0.0,0.0,0.0,0.17,817.0,269.0,1.1,78.27639550107247 +2020-07-31 03:30:00-07:00,2020,7,31,3,30,13.2,9.1,0.0,0.0,0.0,0.17,817.0,280.0,1.1,76.19815554404921 +2020-07-31 04:30:00-07:00,2020,7,31,4,30,13.7,8.8,0.0,0.0,0.0,0.17,817.0,290.0,1.0,72.27968653545358 +2020-07-31 05:30:00-07:00,2020,7,31,5,30,16.5,8.700000000000001,27.0,339.0,55.0,0.17,817.0,304.0,1.3,59.96666064844288 +2020-07-31 06:30:00-07:00,2020,7,31,6,30,20.3,9.0,52.0,671.0,234.0,0.17,817.0,331.0,1.7000000000000002,48.22522671599542 +2020-07-31 07:30:00-07:00,2020,7,31,7,30,23.1,7.800000000000001,66.0,817.0,439.0,0.17,817.0,178.0,2.1,37.46629598542091 +2020-07-31 08:30:00-07:00,2020,7,31,8,30,25.4,5.9,75.0,893.0,633.0,0.17,817.0,13.0,2.6,28.651340172153052 +2020-07-31 09:30:00-07:00,2020,7,31,9,30,27.1,4.7,82.0,936.0,797.0,0.17,817.0,22.0,3.0,23.841599569214864 +2020-07-31 10:30:00-07:00,2020,7,31,10,30,28.5,4.0,86.0,963.0,918.0,0.17,817.0,27.0,3.3000000000000003,20.91953665440279 +2020-07-31 11:30:00-07:00,2020,7,31,11,30,29.6,3.5,85.0,977.0,983.0,0.17,816.0,32.0,3.5,18.95126737489056 +2020-07-31 12:30:00-07:00,2020,7,31,12,30,30.3,3.3000000000000003,97.0,961.0,986.0,0.17,816.0,38.0,3.8,17.94889776691935 +2020-07-31 13:30:00-07:00,2020,7,31,13,30,30.4,3.2,148.0,859.0,905.0,0.17,816.0,43.0,4.0,17.720741553227636 +2020-07-31 14:30:00-07:00,2020,7,31,14,30,30.0,3.4000000000000004,237.0,619.0,727.0,0.17,815.0,48.0,4.2,18.38993263888852 +2020-07-31 15:30:00-07:00,2020,7,31,15,30,29.0,3.9,140.0,3.0,142.0,0.17,815.0,51.0,4.1000000000000005,20.179506309262894 +2020-07-31 16:30:00-07:00,2020,7,31,16,30,27.5,4.4,163.0,63.0,194.0,0.17,816.0,53.0,3.7,22.806001385850184 +2020-07-31 17:30:00-07:00,2020,7,31,17,30,24.5,8.200000000000001,57.0,680.0,273.0,0.16,813.0,172.0,2.5,35.38917466566073 +2020-07-31 18:30:00-07:00,2020,7,31,18,30,21.6,9.6,34.0,419.0,87.0,0.16,813.0,171.0,1.7000000000000002,46.35549412657598 +2020-07-31 19:30:00-07:00,2020,7,31,19,30,19.4,9.4,0.0,0.0,0.0,0.16,813.0,176.0,1.6,52.38370259553412 +2020-07-31 20:30:00-07:00,2020,7,31,20,30,18.7,8.700000000000001,0.0,0.0,0.0,0.16,813.0,186.0,1.9,52.19901443790413 +2020-07-31 21:30:00-07:00,2020,7,31,21,30,18.3,8.3,0.0,0.0,0.0,0.16,813.0,194.0,2.2,52.09309310556172 +2020-07-31 22:30:00-07:00,2020,7,31,22,30,17.8,8.3,0.0,0.0,0.0,0.16,813.0,201.0,2.4000000000000004,53.755806654503466 +2020-07-31 23:30:00-07:00,2020,7,31,23,30,17.3,8.3,0.0,0.0,0.0,0.16,813.0,208.0,2.4000000000000004,55.47828592570913 +2015-08-01 00:30:00-07:00,2015,8,1,0,30,16.8,8.3,0.0,0.0,0.0,0.16,813.0,213.0,2.4000000000000004,57.26290831058601 +2015-08-01 01:30:00-07:00,2015,8,1,1,30,16.400000000000002,8.200000000000001,0.0,0.0,0.0,0.16,813.0,218.0,2.2,58.33991615130316 +2015-08-01 02:30:00-07:00,2015,8,1,2,30,15.8,8.3,0.0,0.0,0.0,0.16,812.0,223.0,2.2,61.02861367268972 +2015-08-01 03:30:00-07:00,2015,8,1,3,30,15.1,8.5,0.0,0.0,0.0,0.16,812.0,230.0,2.2,64.70057930593099 +2015-08-01 04:30:00-07:00,2015,8,1,4,30,15.4,8.6,0.0,0.0,0.0,0.16,812.0,238.0,2.4000000000000004,63.89716593930256 +2015-08-01 05:30:00-07:00,2015,8,1,5,30,17.5,8.700000000000001,23.0,359.0,53.0,0.16,812.0,246.0,2.9000000000000004,56.28575269203 +2015-08-01 06:30:00-07:00,2015,8,1,6,30,21.4,9.0,48.0,682.0,233.0,0.16,812.0,256.0,2.4000000000000004,45.0700787547931 +2015-08-01 07:30:00-07:00,2015,8,1,7,30,25.200000000000003,8.3,61.0,824.0,438.0,0.16,812.0,135.0,1.2000000000000002,34.17202011984666 +2015-08-01 08:30:00-07:00,2015,8,1,8,30,27.9,6.7,70.0,897.0,630.0,0.16,812.0,30.0,0.9,26.129908852625363 +2015-08-01 09:30:00-07:00,2015,8,1,9,30,29.8,6.1000000000000005,76.0,939.0,793.0,0.16,811.0,58.0,1.6,22.458638832066825 +2015-08-01 10:30:00-07:00,2015,8,1,10,30,31.1,5.7,81.0,962.0,912.0,0.16,811.0,66.0,2.2,20.278791307517842 +2015-08-01 11:30:00-07:00,2015,8,1,11,30,32.0,5.4,85.0,972.0,978.0,0.16,810.0,73.0,2.7,18.871443440135828 +2015-08-01 12:30:00-07:00,2015,8,1,12,30,32.4,5.1000000000000005,89.0,965.0,981.0,0.16,809.0,80.0,2.9000000000000004,18.068805534271654 +2015-08-01 13:30:00-07:00,2015,8,1,13,30,32.2,4.9,413.0,441.0,801.0,0.16,809.0,87.0,3.1,18.02144742064456 +2015-08-01 14:30:00-07:00,2015,8,1,14,30,31.6,4.9,351.0,277.0,570.0,0.16,809.0,96.0,3.1,18.644717749908768 +2015-08-01 15:30:00-07:00,2015,8,1,15,30,30.4,5.1000000000000005,319.0,80.0,372.0,0.16,809.0,112.0,2.7,20.24557116922957 +2015-08-01 16:30:00-07:00,2015,8,1,16,30,28.700000000000003,5.7,124.0,0.0,124.0,0.16,809.0,140.0,2.0,23.279653204804994 +2015-08-01 17:30:00-07:00,2015,8,1,17,30,26.4,7.300000000000001,172.0,46.0,186.0,0.16,809.0,175.0,1.3,29.73147144684726 +2015-08-01 18:30:00-07:00,2015,8,1,18,30,24.200000000000003,10.9,51.0,1.0,51.0,0.16,809.0,200.0,1.1,43.19220540418239 +2015-08-01 19:30:00-07:00,2015,8,1,19,30,22.700000000000003,10.4,0.0,0.0,0.0,0.16,809.0,209.0,1.6,45.734747412177406 +2015-08-01 20:30:00-07:00,2015,8,1,20,30,21.4,10.0,0.0,0.0,0.0,0.16,810.0,209.0,2.1,48.20122281492321 +2015-08-01 21:30:00-07:00,2015,8,1,21,30,20.3,9.7,0.0,0.0,0.0,0.16,810.0,210.0,2.6,50.54955448093404 +2015-08-01 22:30:00-07:00,2015,8,1,22,30,19.4,9.4,0.0,0.0,0.0,0.16,810.0,215.0,2.7,52.38370259553412 +2015-08-01 23:30:00-07:00,2015,8,1,23,30,18.7,9.0,0.0,0.0,0.0,0.16,810.0,221.0,2.6,53.26702173599801 +2015-08-02 00:30:00-07:00,2015,8,2,0,30,18.0,8.5,0.0,0.0,0.0,0.16,810.0,226.0,2.3000000000000003,53.807906746492186 +2015-08-02 01:30:00-07:00,2015,8,2,1,30,17.6,8.1,0.0,0.0,0.0,0.16,809.0,231.0,2.0,53.70363492071477 +2015-08-02 02:30:00-07:00,2015,8,2,2,30,17.2,8.0,0.0,0.0,0.0,0.16,809.0,237.0,1.8,54.70457845281879 +2015-08-02 03:30:00-07:00,2015,8,2,3,30,16.6,8.0,0.0,0.0,0.0,0.16,809.0,243.0,1.6,56.825487706607646 +2015-08-02 04:30:00-07:00,2015,8,2,4,30,16.7,7.800000000000001,0.0,0.0,0.0,0.16,809.0,253.0,1.7000000000000002,55.70267668413533 +2015-08-02 05:30:00-07:00,2015,8,2,5,30,18.7,8.200000000000001,34.0,1.0,34.0,0.16,809.0,262.0,2.2,50.46095151790361 +2015-08-02 06:30:00-07:00,2015,8,2,6,30,22.200000000000003,8.4,133.0,203.0,188.0,0.16,810.0,258.0,2.0,41.21512173427237 +2015-08-02 07:30:00-07:00,2015,8,2,7,30,25.5,8.3,192.0,64.0,221.0,0.16,810.0,199.0,1.3,33.56779592601737 +2015-08-02 08:30:00-07:00,2015,8,2,8,30,27.8,7.4,271.0,478.0,568.0,0.16,810.0,141.0,1.7000000000000002,27.5736243735875 +2015-08-02 09:30:00-07:00,2015,8,2,9,30,29.1,6.9,101.0,887.0,777.0,0.16,810.0,130.0,2.4000000000000004,24.70711333526758 +2015-08-02 10:30:00-07:00,2015,8,2,10,30,29.9,6.800000000000001,109.0,909.0,893.0,0.16,809.0,132.0,2.6,23.431838284919166 +2015-08-02 11:30:00-07:00,2015,8,2,11,30,30.0,7.0,439.0,427.0,831.0,0.16,809.0,139.0,2.8000000000000003,23.619243737864608 +2015-08-02 12:30:00-07:00,2015,8,2,12,30,29.5,7.1000000000000005,102.0,0.0,102.0,0.16,809.0,153.0,2.8000000000000003,24.47598782723038 +2015-08-02 13:30:00-07:00,2015,8,2,13,30,28.5,7.5,87.0,0.0,87.0,0.16,809.0,170.0,2.9000000000000004,26.653353193933015 +2015-08-02 14:30:00-07:00,2015,8,2,14,30,27.3,8.0,371.0,297.0,606.0,0.16,809.0,183.0,3.0,29.57683977885807 +2015-08-02 15:30:00-07:00,2015,8,2,15,30,25.9,8.5,252.0,38.0,277.0,0.16,809.0,195.0,3.0,33.22801212488126 +2015-08-02 16:30:00-07:00,2015,8,2,16,30,24.200000000000003,9.1,178.0,13.0,185.0,0.16,809.0,210.0,2.6,38.2918797913867 +2015-08-02 17:30:00-07:00,2015,8,2,17,30,22.200000000000003,10.3,52.0,1.0,52.0,0.16,810.0,234.0,2.2,46.83230748395937 +2015-08-02 18:30:00-07:00,2015,8,2,18,30,20.3,11.8,37.0,2.0,37.0,0.16,810.0,256.0,1.9,58.12629664994522 +2015-08-02 19:30:00-07:00,2015,8,2,19,30,19.1,11.8,0.0,0.0,0.0,0.16,810.0,258.0,1.7000000000000002,62.6197509218611 +2015-08-02 20:30:00-07:00,2015,8,2,20,30,18.4,11.6,0.0,0.0,0.0,0.16,810.0,238.0,1.4,64.56231095129641 +2015-08-02 21:30:00-07:00,2015,8,2,21,30,17.7,11.4,0.0,0.0,0.0,0.16,810.0,214.0,1.3,66.57938961563633 +2015-08-02 22:30:00-07:00,2015,8,2,22,30,17.3,10.9,0.0,0.0,0.0,0.16,810.0,208.0,1.7000000000000002,66.05505193110021 +2015-08-02 23:30:00-07:00,2015,8,2,23,30,17.1,10.4,0.0,0.0,0.0,0.16,810.0,215.0,2.3000000000000003,64.70635628599527 +2015-08-03 00:30:00-07:00,2015,8,3,0,30,16.8,9.7,0.0,0.0,0.0,0.16,809.0,227.0,2.7,62.9322676140213 +2015-08-03 01:30:00-07:00,2015,8,3,1,30,16.3,8.9,0.0,0.0,0.0,0.16,809.0,236.0,2.7,61.5607644800765 +2015-08-03 02:30:00-07:00,2015,8,3,2,30,15.8,8.4,0.0,0.0,0.0,0.16,809.0,241.0,2.3000000000000003,61.443679610885745 +2015-08-03 03:30:00-07:00,2015,8,3,3,30,15.5,8.1,0.0,0.0,0.0,0.16,809.0,244.0,1.9,61.37320516955733 +2015-08-03 04:30:00-07:00,2015,8,3,4,30,15.7,7.800000000000001,0.0,0.0,0.0,0.16,809.0,245.0,1.6,59.3686962112277 +2015-08-03 05:30:00-07:00,2015,8,3,5,30,17.6,8.0,31.0,3.0,31.0,0.16,809.0,242.0,1.8,53.33999026585887 +2015-08-03 06:30:00-07:00,2015,8,3,6,30,21.1,8.1,141.0,22.0,147.0,0.16,809.0,220.0,2.2,43.19435730619634 +2015-08-03 07:30:00-07:00,2015,8,3,7,30,24.1,4.9,194.0,11.0,199.0,0.16,809.0,184.0,2.6,28.888780012408823 +2015-08-03 08:30:00-07:00,2015,8,3,8,30,26.1,2.8000000000000003,271.0,60.0,309.0,0.16,809.0,163.0,3.2,22.125181741595277 +2015-08-03 09:30:00-07:00,2015,8,3,9,30,27.5,2.4000000000000004,337.0,47.0,373.0,0.16,809.0,154.0,3.6,19.805465895610585 +2015-08-03 10:30:00-07:00,2015,8,3,10,30,28.5,2.2,211.0,2.0,212.0,0.16,809.0,148.0,3.8,18.41959469273409 +2015-08-03 11:30:00-07:00,2015,8,3,11,30,29.200000000000003,2.2,457.0,30.0,485.0,0.16,809.0,145.0,4.1000000000000005,17.68740924768249 +2015-08-03 12:30:00-07:00,2015,8,3,12,30,29.5,2.5,328.0,1.0,329.0,0.16,808.0,146.0,4.2,17.75867470995297 +2015-08-03 13:30:00-07:00,2015,8,3,13,30,29.3,3.0,41.0,0.0,41.0,0.16,808.0,148.0,4.3,18.613165545711237 +2015-08-03 14:30:00-07:00,2015,8,3,14,30,28.6,3.8,95.0,0.0,95.0,0.16,808.0,151.0,4.2,20.508241766080427 +2015-08-03 15:30:00-07:00,2015,8,3,15,30,27.3,4.6000000000000005,355.0,234.0,508.0,0.16,808.0,154.0,3.9,23.399594711427863 +2015-08-03 16:30:00-07:00,2015,8,3,16,30,25.5,5.7,185.0,3.0,186.0,0.16,808.0,160.0,3.5,28.090246561077297 +2015-08-03 17:30:00-07:00,2015,8,3,17,30,23.200000000000003,7.1000000000000005,96.0,1.0,96.0,0.16,808.0,168.0,2.5,35.50266283631445 +2015-08-03 18:30:00-07:00,2015,8,3,18,30,20.8,9.9,52.0,2.0,52.0,0.16,809.0,179.0,1.7000000000000002,49.67636293285828 +2015-08-03 19:30:00-07:00,2015,8,3,19,30,19.3,10.9,0.0,0.0,0.0,0.16,809.0,193.0,1.6,58.26781240215728 +2015-08-03 20:30:00-07:00,2015,8,3,20,30,18.3,10.9,0.0,0.0,0.0,0.16,809.0,205.0,1.6,62.02448242448172 +2015-08-03 21:30:00-07:00,2015,8,3,21,30,17.2,10.8,0.0,0.0,0.0,0.16,809.0,218.0,1.7000000000000002,66.03373464754254 +2015-08-03 22:30:00-07:00,2015,8,3,22,30,16.400000000000002,10.4,0.0,0.0,0.0,0.16,809.0,232.0,2.0,67.64597512396051 +2015-08-03 23:30:00-07:00,2015,8,3,23,30,15.7,9.7,0.0,0.0,0.0,0.16,808.0,240.0,2.3000000000000003,67.50116269819564 +2015-08-04 00:30:00-07:00,2015,8,4,0,30,15.2,9.0,0.0,0.0,0.0,0.16,808.0,245.0,2.5,66.49505003958362 +2015-08-04 01:30:00-07:00,2015,8,4,1,30,14.7,8.4,0.0,0.0,0.0,0.16,808.0,247.0,2.6,65.94034781556161 +2015-08-04 02:30:00-07:00,2015,8,4,2,30,14.4,8.0,0.0,0.0,0.0,0.16,808.0,248.0,2.5,65.42947747384513 +2015-08-04 03:30:00-07:00,2015,8,4,3,30,14.2,7.7,0.0,0.0,0.0,0.16,808.0,249.0,2.4000000000000004,64.94196173425719 +2015-08-04 04:30:00-07:00,2015,8,4,4,30,14.6,7.4,0.0,0.0,0.0,0.16,808.0,251.0,2.2,62.002595272533284 +2015-08-04 05:30:00-07:00,2015,8,4,5,30,16.6,7.2,36.0,12.0,37.0,0.16,808.0,252.0,2.5,53.80867199843785 +2015-08-04 06:30:00-07:00,2015,8,4,6,30,20.1,7.0,131.0,209.0,186.0,0.16,809.0,260.0,2.8000000000000003,42.619086420000166 +2015-08-04 07:30:00-07:00,2015,8,4,7,30,23.1,5.7,209.0,281.0,336.0,0.16,809.0,277.0,2.9000000000000004,32.435996691891475 +2015-08-04 08:30:00-07:00,2015,8,4,8,30,25.200000000000003,4.5,210.0,535.0,541.0,0.16,809.0,290.0,3.0,26.30554174599644 +2015-08-04 09:30:00-07:00,2015,8,4,9,30,26.9,4.5,86.0,926.0,788.0,0.16,808.0,298.0,3.1,23.788693961460055 +2015-08-04 10:30:00-07:00,2015,8,4,10,30,28.4,4.5,89.0,955.0,909.0,0.16,808.0,304.0,3.1,21.791478842698048 +2015-08-04 11:30:00-07:00,2015,8,4,11,30,29.5,4.1000000000000005,84.0,980.0,980.0,0.16,808.0,309.0,3.0,19.882057625610663 +2015-08-04 12:30:00-07:00,2015,8,4,12,30,30.3,3.7,84.0,979.0,984.0,0.16,807.0,311.0,3.0,18.462073229521273 +2015-08-04 13:30:00-07:00,2015,8,4,13,30,30.700000000000003,3.4000000000000004,82.0,970.0,931.0,0.16,807.0,314.0,3.1,17.666766453067687 +2015-08-04 14:30:00-07:00,2015,8,4,14,30,30.6,3.1,77.0,953.0,824.0,0.16,807.0,320.0,3.2,17.39562187548233 +2015-08-04 15:30:00-07:00,2015,8,4,15,30,29.8,2.9000000000000004,239.0,643.0,659.0,0.16,808.0,327.0,3.2,17.956747400694713 +2015-08-04 16:30:00-07:00,2015,8,4,16,30,28.5,2.9000000000000004,64.0,846.0,479.0,0.16,808.0,333.0,3.0,19.35846291437035 +2015-08-04 17:30:00-07:00,2015,8,4,17,30,25.700000000000003,3.3000000000000003,52.0,722.0,273.0,0.16,809.0,346.0,2.0,23.47115474874378 +2015-08-04 18:30:00-07:00,2015,8,4,18,30,21.9,6.6000000000000005,30.0,451.0,82.0,0.16,810.0,185.0,1.1,37.12366806215465 +2015-08-04 19:30:00-07:00,2015,8,4,19,30,19.4,7.2,0.0,0.0,0.0,0.16,810.0,21.0,0.8,45.123994330123416 +2015-08-04 20:30:00-07:00,2015,8,4,20,30,18.6,7.2,0.0,0.0,0.0,0.16,811.0,44.0,0.6000000000000001,47.43314623803401 +2015-08-04 21:30:00-07:00,2015,8,4,21,30,17.8,7.1000000000000005,0.0,0.0,0.0,0.16,811.0,105.0,0.5,49.53558119339116 +2015-08-04 22:30:00-07:00,2015,8,4,22,30,16.8,7.300000000000001,0.0,0.0,0.0,0.16,812.0,174.0,0.7000000000000001,53.4942300631454 +2015-08-04 23:30:00-07:00,2015,8,4,23,30,15.9,7.300000000000001,0.0,0.0,0.0,0.16,811.0,205.0,0.9,56.648892028377276 +2015-08-05 00:30:00-07:00,2015,8,5,0,30,15.2,7.0,0.0,0.0,0.0,0.16,811.0,216.0,1.1,58.043003497193325 +2015-08-05 01:30:00-07:00,2015,8,5,1,30,14.7,6.5,0.0,0.0,0.0,0.16,811.0,226.0,1.2000000000000002,57.9184961463952 +2015-08-05 02:30:00-07:00,2015,8,5,2,30,14.4,6.1000000000000005,0.0,0.0,0.0,0.16,811.0,239.0,1.2000000000000002,57.44591414070731 +2015-08-05 03:30:00-07:00,2015,8,5,3,30,14.1,5.7,0.0,0.0,0.0,0.16,811.0,252.0,1.2000000000000002,56.97471261213698 +2015-08-05 04:30:00-07:00,2015,8,5,4,30,14.5,5.4,0.0,0.0,0.0,0.16,811.0,262.0,1.2000000000000002,54.37688141242489 +2015-08-05 05:30:00-07:00,2015,8,5,5,30,17.400000000000002,5.9,21.0,342.0,46.0,0.16,811.0,267.0,1.7000000000000002,46.775746433233685 +2015-08-05 06:30:00-07:00,2015,8,5,6,30,21.9,5.800000000000001,48.0,676.0,225.0,0.16,811.0,271.0,1.7000000000000002,35.13120832915427 +2015-08-05 07:30:00-07:00,2015,8,5,7,30,25.6,5.1000000000000005,62.0,815.0,428.0,0.16,811.0,292.0,0.9,26.78505589789665 +2015-08-05 08:30:00-07:00,2015,8,5,8,30,28.200000000000003,4.5,73.0,888.0,621.0,0.16,811.0,333.0,0.5,22.04652877191943 +2015-08-05 09:30:00-07:00,2015,8,5,9,30,29.9,4.6000000000000005,82.0,926.0,782.0,0.16,811.0,228.0,0.6000000000000001,20.121306177891256 +2015-08-05 10:30:00-07:00,2015,8,5,10,30,31.1,4.6000000000000005,91.0,943.0,899.0,0.16,810.0,119.0,0.8,18.78541104494049 +2015-08-05 11:30:00-07:00,2015,8,5,11,30,31.9,4.6000000000000005,90.0,958.0,964.0,0.16,810.0,145.0,1.0,17.950439204759064 +2015-08-05 12:30:00-07:00,2015,8,5,12,30,32.4,4.7,453.0,393.0,814.0,0.16,809.0,161.0,1.3,17.572138820515217 +2015-08-05 13:30:00-07:00,2015,8,5,13,30,32.5,4.800000000000001,366.0,527.0,826.0,0.16,808.0,175.0,1.4,17.59556791906933 +2015-08-05 14:30:00-07:00,2015,8,5,14,30,32.300000000000004,5.0,402.0,316.0,649.0,0.16,808.0,188.0,1.5,18.045124563607523 +2015-08-05 15:30:00-07:00,2015,8,5,15,30,31.9,5.0,244.0,615.0,644.0,0.16,807.0,198.0,1.4,18.45821361389521 +2015-08-05 16:30:00-07:00,2015,8,5,16,30,30.9,4.9,230.0,340.0,396.0,0.16,807.0,203.0,1.3,19.40281460928091 +2015-08-05 17:30:00-07:00,2015,8,5,17,30,28.5,6.0,117.0,350.0,223.0,0.17,807.0,194.0,1.1,24.04519503442775 +2015-08-05 18:30:00-07:00,2015,8,5,18,30,25.5,10.4,48.0,11.0,49.0,0.17,807.0,183.0,1.3,38.65920622391273 +2015-08-05 19:30:00-07:00,2015,8,5,19,30,23.4,8.8,0.0,0.0,0.0,0.17,807.0,184.0,2.1,39.37487530236861 +2015-08-05 20:30:00-07:00,2015,8,5,20,30,22.0,8.5,0.0,0.0,0.0,0.17,807.0,192.0,2.5,42.0039930628409 +2015-08-05 21:30:00-07:00,2015,8,5,21,30,21.1,7.800000000000001,0.0,0.0,0.0,0.17,807.0,202.0,2.6,42.32214873969562 +2015-08-05 22:30:00-07:00,2015,8,5,22,30,20.4,7.5,0.0,0.0,0.0,0.17,807.0,209.0,2.5,43.291384462132754 +2015-08-05 23:30:00-07:00,2015,8,5,23,30,19.700000000000003,7.2,0.0,0.0,0.0,0.17,807.0,219.0,2.5,44.29081440308426 +2015-08-06 00:30:00-07:00,2015,8,6,0,30,19.1,6.5,0.0,0.0,0.0,0.17,806.0,231.0,2.6,43.81933570236347 +2015-08-06 01:30:00-07:00,2015,8,6,1,30,18.4,5.800000000000001,0.0,0.0,0.0,0.17,807.0,246.0,2.4000000000000004,43.62100280074058 +2015-08-06 02:30:00-07:00,2015,8,6,2,30,17.5,5.5,0.0,0.0,0.0,0.17,807.0,259.0,1.9,45.21182259293095 +2015-08-06 03:30:00-07:00,2015,8,6,3,30,16.7,5.9,0.0,0.0,0.0,0.17,807.0,266.0,1.5,48.895766368969525 +2015-08-06 04:30:00-07:00,2015,8,6,4,30,16.7,6.4,0.0,0.0,0.0,0.17,808.0,265.0,1.3,50.611692911826985 +2015-08-06 05:30:00-07:00,2015,8,6,5,30,18.7,7.1000000000000005,29.0,1.0,29.0,0.17,808.0,259.0,1.7000000000000002,46.815899250474054 +2015-08-06 06:30:00-07:00,2015,8,6,6,30,22.1,7.0,111.0,195.0,162.0,0.17,809.0,265.0,2.2,37.6942991576563 +2015-08-06 07:30:00-07:00,2015,8,6,7,30,24.9,6.5,72.0,783.0,421.0,0.17,809.0,286.0,2.2,30.764197083958994 +2015-08-06 08:30:00-07:00,2015,8,6,8,30,27.0,5.6000000000000005,88.0,856.0,615.0,0.17,809.0,297.0,2.3000000000000003,25.53093152060348 +2015-08-06 09:30:00-07:00,2015,8,6,9,30,28.6,4.6000000000000005,169.0,721.0,713.0,0.17,809.0,293.0,2.5,21.690791235468527 +2015-08-06 10:30:00-07:00,2015,8,6,10,30,29.8,3.6,370.0,406.0,718.0,0.17,809.0,287.0,2.7,18.866698920939807 +2015-08-06 11:30:00-07:00,2015,8,6,11,30,30.4,2.6,447.0,253.0,677.0,0.17,808.0,281.0,2.8000000000000003,16.98353679973318 +2015-08-06 12:30:00-07:00,2015,8,6,12,30,31.0,1.9,475.0,131.0,595.0,0.17,808.0,275.0,2.8000000000000003,15.61308056496483 +2015-08-06 13:30:00-07:00,2015,8,6,13,30,31.4,1.3,426.0,234.0,630.0,0.17,808.0,271.0,2.7,14.619879472152551 +2015-08-06 14:30:00-07:00,2015,8,6,14,30,31.4,1.0,374.0,298.0,606.0,0.17,808.0,274.0,2.5,14.308173622560439 +2015-08-06 15:30:00-07:00,2015,8,6,15,30,30.4,0.6000000000000001,270.0,24.0,285.0,0.17,808.0,283.0,2.4000000000000004,14.718352158671987 +2015-08-06 16:30:00-07:00,2015,8,6,16,30,28.5,0.6000000000000001,272.0,162.0,350.0,0.17,808.0,297.0,1.8,16.423427267260188 +2015-08-06 17:30:00-07:00,2015,8,6,17,30,25.700000000000003,3.9,154.0,20.0,160.0,0.17,808.0,316.0,1.1,24.484069947853452 +2015-08-06 18:30:00-07:00,2015,8,6,18,30,22.5,8.1,51.0,12.0,52.0,0.17,809.0,331.0,0.8,39.65467896093954 +2015-08-06 19:30:00-07:00,2015,8,6,19,30,20.8,6.7,0.0,0.0,0.0,0.17,809.0,331.0,0.7000000000000001,39.98616301377766 +2015-08-06 20:30:00-07:00,2015,8,6,20,30,20.1,6.300000000000001,0.0,0.0,0.0,0.17,810.0,312.0,0.5,40.617816405578495 +2015-08-06 21:30:00-07:00,2015,8,6,21,30,19.4,6.1000000000000005,0.0,0.0,0.0,0.17,810.0,263.0,0.5,41.839272726114416 +2015-08-06 22:30:00-07:00,2015,8,6,22,30,18.4,5.800000000000001,0.0,0.0,0.0,0.17,810.0,224.0,0.8,43.62100280074058 +2015-08-06 23:30:00-07:00,2015,8,6,23,30,17.5,5.7,0.0,0.0,0.0,0.17,809.0,214.0,1.0,45.84272129123826 +2015-08-07 00:30:00-07:00,2015,8,7,0,30,16.7,5.300000000000001,0.0,0.0,0.0,0.17,809.0,208.0,1.3,46.904672529560266 +2015-08-07 01:30:00-07:00,2015,8,7,1,30,16.1,4.7,0.0,0.0,0.0,0.17,809.0,204.0,1.5,46.73655907628154 +2015-08-07 02:30:00-07:00,2015,8,7,2,30,15.8,4.3,0.0,0.0,0.0,0.17,808.0,207.0,1.6,46.326896848377224 +2015-08-07 03:30:00-07:00,2015,8,7,3,30,15.7,4.2,0.0,0.0,0.0,0.17,808.0,213.0,1.7000000000000002,46.29870543325805 +2015-08-07 04:30:00-07:00,2015,8,7,4,30,16.0,4.5,0.0,0.0,0.0,0.17,808.0,223.0,1.7000000000000002,46.3832308273156 +2015-08-07 05:30:00-07:00,2015,8,7,5,30,18.4,5.0,21.0,319.0,42.0,0.17,808.0,233.0,1.9,41.26513882711005 +2015-08-07 06:30:00-07:00,2015,8,7,6,30,22.200000000000003,5.5,140.0,73.0,159.0,0.17,809.0,223.0,2.1,33.78525687765146 +2015-08-07 07:30:00-07:00,2015,8,7,7,30,25.4,6.1000000000000005,214.0,315.0,353.0,0.17,808.0,197.0,2.0,29.04985699124646 +2015-08-07 08:30:00-07:00,2015,8,7,8,30,27.700000000000003,6.7,292.0,259.0,451.0,0.17,808.0,175.0,1.9,26.436873093060406 +2015-08-07 09:30:00-07:00,2015,8,7,9,30,28.9,6.4,398.0,101.0,475.0,0.17,808.0,170.0,2.0,24.150708470776564 +2015-08-07 10:30:00-07:00,2015,8,7,10,30,29.1,6.7,338.0,565.0,820.0,0.17,808.0,182.0,2.0,24.37032420156323 +2015-08-07 11:30:00-07:00,2015,8,7,11,30,28.3,7.300000000000001,455.0,37.0,488.0,0.17,808.0,204.0,1.9,26.599255019551123 +2015-08-07 12:30:00-07:00,2015,8,7,12,30,27.700000000000003,8.0,460.0,394.0,820.0,0.17,807.0,228.0,1.6,28.89248331064856 +2015-08-07 13:30:00-07:00,2015,8,7,13,30,27.700000000000003,8.4,370.0,383.0,703.0,0.17,807.0,249.0,1.4,29.687510977384743 +2015-08-07 14:30:00-07:00,2015,8,7,14,30,28.0,8.5,428.0,126.0,526.0,0.17,806.0,259.0,1.3,29.370387617666808 +2015-08-07 15:30:00-07:00,2015,8,7,15,30,27.9,8.3,96.0,834.0,634.0,0.17,806.0,262.0,1.3,29.14458561567445 +2015-08-07 16:30:00-07:00,2015,8,7,16,30,27.0,8.3,68.0,0.0,68.0,0.17,806.0,272.0,1.2000000000000002,30.72146438481909 +2015-08-07 17:30:00-07:00,2015,8,7,17,30,24.9,9.3,142.0,233.0,211.0,0.16,806.0,305.0,1.1,37.219278703369476 +2015-08-07 18:30:00-07:00,2015,8,7,18,30,22.0,11.7,30.0,367.0,68.0,0.16,806.0,339.0,1.4,52.02296792971392 +2015-08-07 19:30:00-07:00,2015,8,7,19,30,19.8,11.0,0.0,0.0,0.0,0.16,807.0,347.0,1.8,56.862071175074306 +2015-08-07 20:30:00-07:00,2015,8,7,20,30,18.4,11.1,0.0,0.0,0.0,0.16,807.0,340.0,1.6,62.46052062056042 +2015-08-07 21:30:00-07:00,2015,8,7,21,30,17.2,11.0,0.0,0.0,0.0,0.16,808.0,331.0,1.3,66.9168728206601 +2015-08-07 22:30:00-07:00,2015,8,7,22,30,16.3,10.8,0.0,0.0,0.0,0.16,808.0,325.0,1.2000000000000002,69.91554838616229 +2015-08-07 23:30:00-07:00,2015,8,7,23,30,15.7,10.4,0.0,0.0,0.0,0.16,808.0,313.0,1.0,70.73614382479896 +2015-08-08 00:30:00-07:00,2015,8,8,0,30,15.1,10.0,0.0,0.0,0.0,0.16,808.0,295.0,0.9,71.57308735363999 +2015-08-08 01:30:00-07:00,2015,8,8,1,30,14.8,9.6,0.0,0.0,0.0,0.16,807.0,281.0,0.9,71.03864007652525 +2015-08-08 02:30:00-07:00,2015,8,8,2,30,14.6,9.2,0.0,0.0,0.0,0.16,807.0,280.0,0.9,70.05166032219553 +2015-08-08 03:30:00-07:00,2015,8,8,3,30,14.7,9.0,0.0,0.0,0.0,0.16,807.0,284.0,0.9,68.6695888793435 +2015-08-08 04:30:00-07:00,2015,8,8,4,30,15.0,9.1,0.0,0.0,0.0,0.16,807.0,284.0,1.1,67.8110167302805 +2015-08-08 05:30:00-07:00,2015,8,8,5,30,16.1,9.4,20.0,1.0,20.0,0.16,808.0,281.0,1.4,64.48602563959652 +2015-08-08 06:30:00-07:00,2015,8,8,6,30,17.900000000000002,9.9,122.0,175.0,167.0,0.16,808.0,288.0,1.6,59.49935000960235 +2015-08-08 07:30:00-07:00,2015,8,8,7,30,20.200000000000003,9.8,209.0,313.0,347.0,0.16,808.0,321.0,1.4,51.204974854729805 +2015-08-08 08:30:00-07:00,2015,8,8,8,30,22.700000000000003,8.700000000000001,82.0,851.0,601.0,0.16,808.0,186.0,1.2000000000000002,40.80158099219108 +2015-08-08 09:30:00-07:00,2015,8,8,9,30,24.8,7.9,416.0,138.0,520.0,0.16,808.0,43.0,1.1,34.05883444417959 +2015-08-08 10:30:00-07:00,2015,8,8,10,30,26.700000000000003,7.2,332.0,581.0,827.0,0.16,807.0,75.0,1.2000000000000002,29.01133769893126 +2015-08-08 11:30:00-07:00,2015,8,8,11,30,28.0,6.7,377.0,3.0,380.0,0.16,807.0,100.0,1.6,25.977933824213505 +2015-08-08 12:30:00-07:00,2015,8,8,12,30,28.9,6.4,263.0,633.0,840.0,0.16,807.0,114.0,2.0,24.150708470776564 +2015-08-08 13:30:00-07:00,2015,8,8,13,30,29.3,6.2,415.0,19.0,432.0,0.16,806.0,123.0,2.3000000000000003,23.275420987759937 +2015-08-08 14:30:00-07:00,2015,8,8,14,30,29.1,6.1000000000000005,377.0,373.0,666.0,0.16,806.0,129.0,2.4000000000000004,23.38416056038659 +2015-08-08 15:30:00-07:00,2015,8,8,15,30,28.4,6.1000000000000005,294.0,19.0,307.0,0.16,806.0,132.0,2.5,24.35289491211739 +2015-08-08 16:30:00-07:00,2015,8,8,16,30,27.200000000000003,6.1000000000000005,216.0,24.0,227.0,0.16,807.0,131.0,2.4000000000000004,26.120644507810706 +2015-08-08 17:30:00-07:00,2015,8,8,17,30,24.9,6.4,53.0,684.0,254.0,0.16,807.0,128.0,2.0,30.553211541627007 +2015-08-08 18:30:00-07:00,2015,8,8,18,30,21.9,9.1,42.0,1.0,42.0,0.16,808.0,131.0,1.7000000000000002,44.00875814352536 +2015-08-08 19:30:00-07:00,2015,8,8,19,30,20.200000000000003,9.6,0.0,0.0,0.0,0.16,808.0,146.0,1.5,50.52280574828359 +2015-08-08 20:30:00-07:00,2015,8,8,20,30,19.4,9.4,0.0,0.0,0.0,0.16,808.0,166.0,1.2000000000000002,52.38370259553412 +2015-08-08 21:30:00-07:00,2015,8,8,21,30,18.3,9.3,0.0,0.0,0.0,0.16,809.0,193.0,0.9,55.73298026989565 +2015-08-08 22:30:00-07:00,2015,8,8,22,30,17.400000000000002,8.8,0.0,0.0,0.0,0.16,809.0,229.0,0.9,57.02620560520095 +2015-08-08 23:30:00-07:00,2015,8,8,23,30,16.900000000000002,7.800000000000001,0.0,0.0,0.0,0.16,809.0,262.0,1.3,55.000334846157834 +2015-08-09 00:30:00-07:00,2015,8,9,0,30,16.3,6.7,0.0,0.0,0.0,0.16,810.0,279.0,1.8,52.99767274855345 +2015-08-09 01:30:00-07:00,2015,8,9,1,30,15.6,5.5,0.0,0.0,0.0,0.16,810.0,286.0,2.2,51.01589869759285 +2015-08-09 02:30:00-07:00,2015,8,9,2,30,14.7,4.7,0.0,0.0,0.0,0.16,810.0,288.0,2.5,51.12710456393297 +2015-08-09 03:30:00-07:00,2015,8,9,3,30,13.9,4.9,0.0,0.0,0.0,0.16,810.0,285.0,2.4000000000000004,54.59864068574548 +2015-08-09 04:30:00-07:00,2015,8,9,4,30,13.7,5.300000000000001,0.0,0.0,0.0,0.16,810.0,281.0,2.3000000000000003,56.87316038239244 +2015-08-09 05:30:00-07:00,2015,8,9,5,30,15.6,5.9,19.0,333.0,39.0,0.16,811.0,276.0,2.7,52.44844561287288 +2015-08-09 06:30:00-07:00,2015,8,9,6,30,19.200000000000003,6.300000000000001,44.0,694.0,219.0,0.16,811.0,300.0,2.3000000000000003,42.95164110326268 +2015-08-09 07:30:00-07:00,2015,8,9,7,30,22.6,6.1000000000000005,58.0,834.0,424.0,0.16,811.0,171.0,1.5,34.370989194113015 +2015-08-09 08:30:00-07:00,2015,8,9,8,30,25.0,5.300000000000001,68.0,906.0,620.0,0.16,811.0,32.0,1.3,28.146978883561395 +2015-08-09 09:30:00-07:00,2015,8,9,9,30,26.700000000000003,4.7,75.0,948.0,785.0,0.16,811.0,59.0,1.3,24.408862091714706 +2015-08-09 10:30:00-07:00,2015,8,9,10,30,28.1,4.3,81.0,968.0,903.0,0.16,811.0,82.0,1.5,21.86718514098927 +2015-08-09 11:30:00-07:00,2015,8,9,11,30,29.200000000000003,4.1000000000000005,459.0,49.0,504.0,0.16,810.0,97.0,1.8,20.22941272515805 +2015-08-09 12:30:00-07:00,2015,8,9,12,30,29.9,4.0,84.0,977.0,973.0,0.16,810.0,103.0,2.0,19.29348707221312 +2015-08-09 13:30:00-07:00,2015,8,9,13,30,30.1,4.0,69.0,0.0,69.0,0.16,810.0,103.0,2.1,19.07304112848793 +2015-08-09 14:30:00-07:00,2015,8,9,14,30,29.8,4.0,85.0,928.0,802.0,0.16,810.0,99.0,2.2,19.404786273802323 +2015-08-09 15:30:00-07:00,2015,8,9,15,30,29.1,4.2,249.0,596.0,630.0,0.16,810.0,95.0,2.3000000000000003,20.48985430005395 +2015-08-09 16:30:00-07:00,2015,8,9,16,30,27.700000000000003,4.6000000000000005,214.0,399.0,404.0,0.16,810.0,95.0,2.3000000000000003,22.85816891631322 +2015-08-09 17:30:00-07:00,2015,8,9,17,30,25.3,5.4,141.0,240.0,210.0,0.16,811.0,100.0,1.9,27.841180620213255 +2015-08-09 18:30:00-07:00,2015,8,9,18,30,22.5,8.4,37.0,1.0,37.0,0.16,812.0,115.0,1.6,40.46994414248722 +2015-08-09 19:30:00-07:00,2015,8,9,19,30,20.700000000000003,9.5,0.0,0.0,0.0,0.16,812.0,133.0,1.6,48.660196499111144 +2015-08-09 20:30:00-07:00,2015,8,9,20,30,19.700000000000003,10.6,0.0,0.0,0.0,0.16,813.0,148.0,1.4,55.71445316903043 +2015-08-09 21:30:00-07:00,2015,8,9,21,30,18.9,11.6,0.0,0.0,0.0,0.16,813.0,157.0,1.0,62.57434735865059 +2015-08-09 22:30:00-07:00,2015,8,9,22,30,18.3,12.2,0.0,0.0,0.0,0.16,813.0,168.0,0.7000000000000001,67.588946723231 +2015-08-09 23:30:00-07:00,2015,8,9,23,30,17.6,12.7,0.0,0.0,0.0,0.16,813.0,206.0,0.6000000000000001,72.98668452354214 +2015-08-10 00:30:00-07:00,2015,8,10,0,30,16.7,13.1,0.0,0.0,0.0,0.16,813.0,282.0,0.9,79.31133172459414 +2015-08-10 01:30:00-07:00,2015,8,10,1,30,16.2,13.4,0.0,0.0,0.0,0.16,813.0,342.0,1.3,83.49220888976578 +2015-08-10 02:30:00-07:00,2015,8,10,2,30,15.8,13.3,0.0,0.0,0.0,0.16,813.0,182.0,1.5,85.09530172182046 +2015-08-10 03:30:00-07:00,2015,8,10,3,30,15.4,13.0,0.0,0.0,0.0,0.16,814.0,8.0,1.6,85.61034442816285 +2015-08-10 04:30:00-07:00,2015,8,10,4,30,15.2,12.5,0.0,0.0,0.0,0.16,814.0,10.0,1.6,83.92369494389186 +2015-08-10 05:30:00-07:00,2015,8,10,5,30,16.0,12.3,20.0,0.0,20.0,0.16,814.0,12.0,1.5,78.6926898559811 +2015-08-10 06:30:00-07:00,2015,8,10,6,30,18.0,12.6,66.0,516.0,195.0,0.16,814.0,40.0,1.3,70.7067670476825 +2015-08-10 07:30:00-07:00,2015,8,10,7,30,20.4,12.6,222.0,253.0,332.0,0.16,814.0,84.0,1.4,60.88856324809869 +2015-08-10 08:30:00-07:00,2015,8,10,8,30,22.5,11.4,302.0,325.0,499.0,0.16,814.0,112.0,2.0,49.472971043894205 +2015-08-10 09:30:00-07:00,2015,8,10,9,30,24.200000000000003,10.6,108.0,861.0,751.0,0.16,814.0,123.0,2.5,42.338988581259656 +2015-08-10 10:30:00-07:00,2015,8,10,10,30,25.200000000000003,10.2,387.0,466.0,782.0,0.16,814.0,129.0,3.1,38.83323943927887 +2015-08-10 11:30:00-07:00,2015,8,10,11,30,25.5,10.1,214.0,0.0,214.0,0.16,813.0,133.0,3.6,37.892546193225556 +2015-08-10 12:30:00-07:00,2015,8,10,12,30,25.5,10.2,355.0,12.0,366.0,0.16,813.0,141.0,3.9,38.14659631102113 +2015-08-10 13:30:00-07:00,2015,8,10,13,30,25.0,10.4,255.0,3.0,258.0,0.16,813.0,149.0,3.9,39.82681832423341 +2015-08-10 14:30:00-07:00,2015,8,10,14,30,24.200000000000003,10.7,420.0,118.0,511.0,0.16,813.0,155.0,3.8,42.62172826722108 +2015-08-10 15:30:00-07:00,2015,8,10,15,30,23.200000000000003,11.1,294.0,276.0,469.0,0.16,813.0,155.0,3.6,46.48547656408419 +2015-08-10 16:30:00-07:00,2015,8,10,16,30,22.1,11.5,252.0,199.0,346.0,0.16,813.0,152.0,3.4000000000000004,51.02813764858807 +2015-08-10 17:30:00-07:00,2015,8,10,17,30,20.5,12.0,68.0,568.0,231.0,0.16,813.0,149.0,3.1,58.17496192311163 +2015-08-10 18:30:00-07:00,2015,8,10,18,30,18.6,12.6,31.0,261.0,56.0,0.16,813.0,153.0,2.4000000000000004,68.09543531301918 +2015-08-10 19:30:00-07:00,2015,8,10,19,30,17.2,13.1,0.0,0.0,0.0,0.16,813.0,165.0,1.8,76.83768921002151 +2015-08-10 20:30:00-07:00,2015,8,10,20,30,16.8,13.2,0.0,0.0,0.0,0.16,813.0,177.0,1.6,79.32557950867556 +2015-08-10 21:30:00-07:00,2015,8,10,21,30,16.7,13.4,0.0,0.0,0.0,0.16,814.0,181.0,1.4,80.87828292757841 +2015-08-10 22:30:00-07:00,2015,8,10,22,30,16.7,13.6,0.0,0.0,0.0,0.16,814.0,179.0,1.3,81.93799641233406 +2015-08-10 23:30:00-07:00,2015,8,10,23,30,16.7,13.9,0.0,0.0,0.0,0.16,814.0,174.0,1.3,83.55048294224859 +2015-08-11 00:30:00-07:00,2015,8,11,0,30,16.6,13.9,0.0,0.0,0.0,0.16,814.0,171.0,1.3,84.08286745699185 +2015-08-11 01:30:00-07:00,2015,8,11,1,30,16.400000000000002,13.9,0.0,0.0,0.0,0.16,814.0,176.0,1.3,85.15908612701891 +2015-08-11 02:30:00-07:00,2015,8,11,2,30,16.2,14.0,0.0,0.0,0.0,0.16,814.0,189.0,1.2000000000000002,86.81200983355399 +2015-08-11 03:30:00-07:00,2015,8,11,3,30,15.9,13.9,0.0,0.0,0.0,0.16,814.0,210.0,1.1,87.91784158224168 +2015-08-11 04:30:00-07:00,2015,8,11,4,30,15.8,13.6,0.0,0.0,0.0,0.16,814.0,233.0,1.1,86.77388006229175 +2015-08-11 05:30:00-07:00,2015,8,11,5,30,16.7,13.2,15.0,0.0,15.0,0.16,813.0,248.0,1.2000000000000002,79.83065260302263 +2015-08-11 06:30:00-07:00,2015,8,11,6,30,18.7,13.5,87.0,2.0,88.0,0.16,814.0,256.0,1.2000000000000002,71.76809123652221 +2015-08-11 07:30:00-07:00,2015,8,11,7,30,21.4,13.3,64.0,787.0,406.0,0.16,814.0,145.0,1.0,59.93816870794946 +2015-08-11 08:30:00-07:00,2015,8,11,8,30,24.4,12.0,244.0,524.0,561.0,0.16,814.0,50.0,1.4,45.89827972285331 +2015-08-11 09:30:00-07:00,2015,8,11,9,30,26.4,11.1,83.0,908.0,759.0,0.16,814.0,74.0,2.1,38.39998928079682 +2015-08-11 10:30:00-07:00,2015,8,11,10,30,27.6,11.1,89.0,931.0,876.0,0.16,814.0,83.0,2.5,35.78633494866644 +2015-08-11 11:30:00-07:00,2015,8,11,11,30,28.3,11.3,489.0,254.0,717.0,0.16,813.0,89.0,2.8000000000000003,34.81291112543775 +2015-08-11 12:30:00-07:00,2015,8,11,12,30,28.5,11.6,161.0,0.0,162.0,0.16,813.0,96.0,3.1,35.10005908229034 +2015-08-11 13:30:00-07:00,2015,8,11,13,30,28.200000000000003,11.8,92.0,0.0,92.0,0.16,813.0,106.0,3.0,36.19252838147846 +2015-08-11 14:30:00-07:00,2015,8,11,14,30,27.4,11.8,362.0,128.0,460.0,0.16,813.0,119.0,2.8000000000000003,37.923440527919375 +2015-08-11 15:30:00-07:00,2015,8,11,15,30,26.1,11.7,285.0,46.0,314.0,0.16,813.0,136.0,2.5,40.66947988943286 +2015-08-11 16:30:00-07:00,2015,8,11,16,30,24.5,12.1,13.0,0.0,13.0,0.16,813.0,153.0,2.1,45.92565164454979 +2015-08-11 17:30:00-07:00,2015,8,11,17,30,22.6,13.5,9.0,0.0,9.0,0.16,814.0,167.0,1.6,56.43927797934852 +2015-08-11 18:30:00-07:00,2015,8,11,18,30,20.8,14.8,25.0,0.0,25.0,0.16,814.0,180.0,1.3,68.53644314312825 +2015-08-11 19:30:00-07:00,2015,8,11,19,30,19.6,15.1,0.0,0.0,0.0,0.16,815.0,196.0,1.2000000000000002,75.25278621147407 +2015-08-11 20:30:00-07:00,2015,8,11,20,30,18.9,15.0,0.0,0.0,0.0,0.16,815.0,214.0,1.1,78.10137447281262 +2015-08-11 21:30:00-07:00,2015,8,11,21,30,18.3,14.8,0.0,0.0,0.0,0.16,816.0,234.0,1.0,80.05196280026125 +2015-08-11 22:30:00-07:00,2015,8,11,22,30,17.7,14.5,0.0,0.0,0.0,0.16,816.0,253.0,1.1,81.53572600010335 +2015-08-11 23:30:00-07:00,2015,8,11,23,30,17.3,14.0,0.0,0.0,0.0,0.16,816.0,266.0,1.4,80.95785433247971 +2015-08-12 00:30:00-07:00,2015,8,12,0,30,16.900000000000002,13.3,0.0,0.0,0.0,0.16,815.0,268.0,1.7000000000000002,79.33981329175067 +2015-08-12 01:30:00-07:00,2015,8,12,1,30,16.6,12.5,0.0,0.0,0.0,0.16,815.0,263.0,1.9,76.74331963343634 +2015-08-12 02:30:00-07:00,2015,8,12,2,30,16.400000000000002,11.7,0.0,0.0,0.0,0.16,814.0,255.0,2.0,73.73972004056992 +2015-08-12 03:30:00-07:00,2015,8,12,3,30,16.0,11.2,0.0,0.0,0.0,0.16,814.0,251.0,2.3000000000000003,73.1832774109244 +2015-08-12 04:30:00-07:00,2015,8,12,4,30,15.8,10.7,0.0,0.0,0.0,0.16,814.0,258.0,2.7,71.70380206745523 +2015-08-12 05:30:00-07:00,2015,8,12,5,30,16.900000000000002,10.5,17.0,274.0,32.0,0.16,815.0,270.0,3.2,65.96967468008636 +2015-08-12 06:30:00-07:00,2015,8,12,6,30,20.1,10.9,45.0,641.0,202.0,0.16,815.0,295.0,3.3000000000000003,55.445927731307705 +2015-08-12 07:30:00-07:00,2015,8,12,7,30,23.8,11.1,62.0,790.0,403.0,0.16,815.0,328.0,3.2,44.83382243842954 +2015-08-12 08:30:00-07:00,2015,8,12,8,30,26.4,10.5,73.0,868.0,595.0,0.16,815.0,172.0,3.1,36.898448894361465 +2015-08-12 09:30:00-07:00,2015,8,12,9,30,28.3,10.0,81.0,910.0,756.0,0.16,815.0,10.0,2.9000000000000004,31.927329746244425 +2015-08-12 10:30:00-07:00,2015,8,12,10,30,29.6,9.7,87.0,931.0,873.0,0.16,815.0,27.0,2.7,29.023159672090557 +2015-08-12 11:30:00-07:00,2015,8,12,11,30,30.200000000000003,9.7,393.0,6.0,398.0,0.16,814.0,44.0,2.5,28.038924584870752 +2015-08-12 12:30:00-07:00,2015,8,12,12,30,30.0,9.9,340.0,2.0,341.0,0.16,814.0,61.0,2.3000000000000003,28.745414262989726 +2015-08-12 13:30:00-07:00,2015,8,12,13,30,29.3,10.4,428.0,77.0,494.0,0.16,814.0,74.0,2.0,30.945054017620148 +2015-08-12 14:30:00-07:00,2015,8,12,14,30,28.5,10.8,329.0,468.0,687.0,0.16,814.0,75.0,1.4,33.2876487510736 +2015-08-12 15:30:00-07:00,2015,8,12,15,30,27.6,11.0,335.0,219.0,474.0,0.16,814.0,54.0,0.8,35.54968357479789 +2015-08-12 16:30:00-07:00,2015,8,12,16,30,26.5,11.3,206.0,385.0,385.0,0.16,814.0,22.0,0.6000000000000001,38.68349486309269 +2015-08-12 17:30:00-07:00,2015,8,12,17,30,25.1,12.4,8.0,0.0,8.0,0.16,814.0,6.0,0.7000000000000001,45.19176766506722 +2015-08-12 18:30:00-07:00,2015,8,12,18,30,23.6,14.3,20.0,0.0,20.0,0.16,814.0,8.0,0.6000000000000001,55.957733845079524 +2015-08-12 19:30:00-07:00,2015,8,12,19,30,22.6,13.8,0.0,0.0,0.0,0.16,815.0,12.0,0.6000000000000001,57.550840328142606 +2015-08-12 20:30:00-07:00,2015,8,12,20,30,21.8,13.4,0.0,0.0,0.0,0.16,815.0,182.0,0.5,58.87192071119251 +2015-08-12 21:30:00-07:00,2015,8,12,21,30,20.9,13.0,0.0,0.0,0.0,0.16,815.0,324.0,0.5,60.607594300787156 +2015-08-12 22:30:00-07:00,2015,8,12,22,30,20.1,12.9,0.0,0.0,0.0,0.16,815.0,286.0,0.7000000000000001,63.258158620823956 +2015-08-12 23:30:00-07:00,2015,8,12,23,30,19.3,12.8,0.0,0.0,0.0,0.16,815.0,277.0,1.0,66.0441746703036 +2015-08-13 00:30:00-07:00,2015,8,13,0,30,18.8,12.6,0.0,0.0,0.0,0.16,815.0,280.0,1.3,67.24917871524461 +2015-08-13 01:30:00-07:00,2015,8,13,1,30,18.2,12.0,0.0,0.0,0.0,0.16,815.0,281.0,1.5,67.12509557188552 +2015-08-13 02:30:00-07:00,2015,8,13,2,30,17.6,11.5,0.0,0.0,0.0,0.16,815.0,278.0,1.5,67.44498584929094 +2015-08-13 03:30:00-07:00,2015,8,13,3,30,17.1,11.0,0.0,0.0,0.0,0.16,814.0,275.0,1.7000000000000002,67.34162541131057 +2015-08-13 04:30:00-07:00,2015,8,13,4,30,17.1,10.7,0.0,0.0,0.0,0.16,814.0,274.0,1.9,66.01239935576409 +2015-08-13 05:30:00-07:00,2015,8,13,5,30,18.7,10.8,18.0,214.0,28.0,0.16,815.0,274.0,2.5,60.08948328168739 +2015-08-13 06:30:00-07:00,2015,8,13,6,30,22.0,11.0,74.0,1.0,74.0,0.16,815.0,290.0,2.5,49.66749449203244 +2015-08-13 07:30:00-07:00,2015,8,13,7,30,25.3,10.7,180.0,462.0,379.0,0.16,815.0,153.0,1.8,39.911201251720755 +2015-08-13 08:30:00-07:00,2015,8,13,8,30,27.700000000000003,10.0,325.0,187.0,437.0,0.16,815.0,18.0,1.5,33.064102629265015 +2015-08-13 09:30:00-07:00,2015,8,13,9,30,29.1,9.9,87.0,892.0,747.0,0.16,815.0,41.0,1.5,30.276200029773783 +2015-08-13 10:30:00-07:00,2015,8,13,10,30,29.8,9.9,95.0,915.0,865.0,0.16,814.0,53.0,1.5,29.07789826024266 +2015-08-13 11:30:00-07:00,2015,8,13,11,30,30.0,10.2,119.0,0.0,119.0,0.16,814.0,59.0,1.5,29.327933969407688 +2015-08-13 12:30:00-07:00,2015,8,13,12,30,29.700000000000003,10.4,96.0,0.0,96.0,0.16,814.0,59.0,1.6,30.23938936402764 +2015-08-13 13:30:00-07:00,2015,8,13,13,30,29.0,10.8,453.0,148.0,580.0,0.16,814.0,49.0,1.8,32.33635938859386 +2015-08-13 14:30:00-07:00,2015,8,13,14,30,28.200000000000003,11.2,404.0,119.0,494.0,0.16,814.0,39.0,2.0,34.784925549281375 +2015-08-13 15:30:00-07:00,2015,8,13,15,30,27.3,11.6,32.0,0.0,32.0,0.16,814.0,36.0,2.1,37.64598635602925 +2015-08-13 16:30:00-07:00,2015,8,13,16,30,26.200000000000003,12.0,134.0,7.0,137.0,0.16,814.0,39.0,1.8,41.23743190212001 +2015-08-13 17:30:00-07:00,2015,8,13,17,30,24.5,13.0,143.0,139.0,181.0,0.16,814.0,40.0,1.2000000000000002,48.717528977998064 +2015-08-13 18:30:00-07:00,2015,8,13,18,30,22.9,15.1,28.0,0.0,28.0,0.16,815.0,29.0,0.7000000000000001,61.465059582791476 +2015-08-13 19:30:00-07:00,2015,8,13,19,30,22.0,15.1,0.0,0.0,0.0,0.16,815.0,175.0,0.6000000000000001,64.92058264923521 +2015-08-13 20:30:00-07:00,2015,8,13,20,30,21.3,14.8,0.0,0.0,0.0,0.16,816.0,309.0,0.7000000000000001,66.46370949700558 +2015-08-13 21:30:00-07:00,2015,8,13,21,30,20.5,14.8,0.0,0.0,0.0,0.16,816.0,282.0,0.9,69.81488782584064 +2015-08-13 22:30:00-07:00,2015,8,13,22,30,19.8,14.4,0.0,0.0,0.0,0.16,815.0,273.0,1.1,71.04561224538564 +2015-08-13 23:30:00-07:00,2015,8,13,23,30,19.0,14.1,0.0,0.0,0.0,0.16,815.0,271.0,1.2000000000000002,73.23435758628501 +2015-08-14 00:30:00-07:00,2015,8,14,0,30,18.3,13.6,0.0,0.0,0.0,0.16,815.0,270.0,1.2000000000000002,74.06885952643967 +2015-08-14 01:30:00-07:00,2015,8,14,1,30,17.8,13.2,0.0,0.0,0.0,0.16,815.0,268.0,1.2000000000000002,74.46723613296626 +2015-08-14 02:30:00-07:00,2015,8,14,2,30,17.5,12.8,0.0,0.0,0.0,0.16,815.0,268.0,1.1,73.9309816809276 +2015-08-14 03:30:00-07:00,2015,8,14,3,30,17.3,12.4,0.0,0.0,0.0,0.16,815.0,270.0,1.1,72.93319654760462 +2015-08-14 04:30:00-07:00,2015,8,14,4,30,17.5,12.0,0.0,0.0,0.0,0.16,815.0,275.0,1.0,70.14836742176203 +2015-08-14 05:30:00-07:00,2015,8,14,5,30,19.6,12.0,17.0,224.0,27.0,0.16,816.0,281.0,1.2000000000000002,61.50714579370533 +2015-08-14 06:30:00-07:00,2015,8,14,6,30,23.1,13.0,48.0,612.0,194.0,0.16,816.0,193.0,0.9,52.99846432060274 +2015-08-14 07:30:00-07:00,2015,8,14,7,30,26.200000000000003,11.3,65.0,773.0,395.0,0.16,816.0,106.0,1.0,39.37459648950211 +2015-08-14 08:30:00-07:00,2015,8,14,8,30,28.3,9.9,255.0,458.0,529.0,0.16,816.0,111.0,1.9,31.71436373352325 +2015-08-14 09:30:00-07:00,2015,8,14,9,30,29.700000000000003,9.9,86.0,898.0,749.0,0.16,815.0,116.0,2.3000000000000003,29.24576459231279 +2015-08-14 10:30:00-07:00,2015,8,14,10,30,30.6,9.9,95.0,916.0,864.0,0.16,815.0,125.0,2.6,27.773400305498473 +2015-08-14 11:30:00-07:00,2015,8,14,11,30,30.9,10.0,90.0,939.0,930.0,0.16,814.0,135.0,2.9000000000000004,27.484663913767665 +2015-08-14 12:30:00-07:00,2015,8,14,12,30,30.700000000000003,10.2,283.0,564.0,789.0,0.16,814.0,146.0,3.1,28.17464153690514 +2015-08-14 13:30:00-07:00,2015,8,14,13,30,30.200000000000003,10.4,286.0,6.0,291.0,0.16,814.0,158.0,3.1,29.382685021233357 +2015-08-14 14:30:00-07:00,2015,8,14,14,30,29.3,10.7,354.0,101.0,431.0,0.16,813.0,169.0,3.0,31.569653758095438 +2015-08-14 15:30:00-07:00,2015,8,14,15,30,28.0,11.1,84.0,0.0,85.0,0.16,813.0,181.0,2.8000000000000003,34.96011361969048 +2015-08-14 16:30:00-07:00,2015,8,14,16,30,26.3,11.8,27.0,0.0,27.0,0.16,814.0,198.0,2.2,40.45774148614872 +2015-08-14 17:30:00-07:00,2015,8,14,17,30,24.1,13.2,30.0,0.0,30.0,0.16,814.0,223.0,1.7000000000000002,50.555462326123866 +2015-08-14 18:30:00-07:00,2015,8,14,18,30,21.8,14.5,24.0,1.0,24.0,0.16,814.0,242.0,1.8,63.22565568195238 +2015-08-14 19:30:00-07:00,2015,8,14,19,30,20.200000000000003,14.5,0.0,0.0,0.0,0.16,814.0,246.0,2.3000000000000003,69.75706728747603 +2015-08-14 20:30:00-07:00,2015,8,14,20,30,19.200000000000003,14.3,0.0,0.0,0.0,0.16,814.0,239.0,2.5,73.2694807571103 +2015-08-14 21:30:00-07:00,2015,8,14,21,30,18.7,14.1,0.0,0.0,0.0,0.16,814.0,220.0,2.4000000000000004,74.61945486107494 +2015-08-14 22:30:00-07:00,2015,8,14,22,30,18.5,14.1,0.0,0.0,0.0,0.16,814.0,206.0,2.3000000000000003,75.55918066108283 +2015-08-14 23:30:00-07:00,2015,8,14,23,30,18.4,14.0,0.0,0.0,0.0,0.16,813.0,208.0,2.4000000000000004,75.54284129910266 +2015-08-15 00:30:00-07:00,2015,8,15,0,30,18.3,13.8,0.0,0.0,0.0,0.16,813.0,220.0,2.5,75.03783077558101 +2015-08-15 01:30:00-07:00,2015,8,15,1,30,18.1,13.4,0.0,0.0,0.0,0.16,812.0,233.0,2.5,74.03448589066993 +2015-08-15 02:30:00-07:00,2015,8,15,2,30,17.900000000000002,12.7,0.0,0.0,0.0,0.16,812.0,243.0,2.5,71.62054984509581 +2015-08-15 03:30:00-07:00,2015,8,15,3,30,17.6,11.9,0.0,0.0,0.0,0.16,812.0,253.0,2.5,69.24953507294423 +2015-08-15 04:30:00-07:00,2015,8,15,4,30,17.6,10.9,0.0,0.0,0.0,0.16,812.0,264.0,2.5,64.81583810740858 +2015-08-15 05:30:00-07:00,2015,8,15,5,30,19.5,10.1,16.0,250.0,27.0,0.16,812.0,276.0,2.9000000000000004,54.560028648387494 +2015-08-15 06:30:00-07:00,2015,8,15,6,30,23.0,10.0,45.0,645.0,198.0,0.16,813.0,289.0,3.0,43.72689446658758 +2015-08-15 07:30:00-07:00,2015,8,15,7,30,26.8,9.4,62.0,805.0,403.0,0.16,813.0,316.0,2.8000000000000003,33.481163063137586 +2015-08-15 08:30:00-07:00,2015,8,15,8,30,29.8,6.300000000000001,72.0,884.0,599.0,0.16,813.0,344.0,2.5,22.770515619742724 +2015-08-15 09:30:00-07:00,2015,8,15,9,30,31.700000000000003,5.6000000000000005,79.0,931.0,764.0,0.16,813.0,185.0,2.1,19.463193062010998 +2015-08-15 10:30:00-07:00,2015,8,15,10,30,33.1,5.4,303.0,640.0,839.0,0.16,812.0,32.0,1.9,17.736230691408597 +2015-08-15 11:30:00-07:00,2015,8,15,11,30,34.0,5.300000000000001,356.0,571.0,865.0,0.16,812.0,61.0,2.1,16.747898011401006 +2015-08-15 12:30:00-07:00,2015,8,15,12,30,34.4,5.5,417.0,450.0,820.0,0.16,811.0,85.0,2.5,16.607602590149835 +2015-08-15 13:30:00-07:00,2015,8,15,13,30,34.1,5.800000000000001,473.0,221.0,660.0,0.16,811.0,98.0,2.8000000000000003,17.242067202867673 +2015-08-15 14:30:00-07:00,2015,8,15,14,30,33.2,6.4,228.0,0.0,229.0,0.16,811.0,106.0,2.7,18.899140000750137 +2015-08-15 15:30:00-07:00,2015,8,15,15,30,31.8,7.1000000000000005,273.0,435.0,544.0,0.16,811.0,115.0,2.1,21.459396980572578 +2015-08-15 16:30:00-07:00,2015,8,15,16,30,29.8,8.0,231.0,36.0,248.0,0.16,811.0,148.0,1.2000000000000002,25.579834972364964 +2015-08-15 17:30:00-07:00,2015,8,15,17,30,27.3,10.0,112.0,17.0,117.0,0.16,811.0,211.0,0.7000000000000001,33.84726938778109 +2015-08-15 18:30:00-07:00,2015,8,15,18,30,24.700000000000003,12.9,17.0,0.0,17.0,0.16,812.0,253.0,1.0,47.824691544729156 +2015-08-15 19:30:00-07:00,2015,8,15,19,30,22.5,13.0,0.0,0.0,0.0,0.16,812.0,255.0,1.6,54.96138356928234 +2015-08-15 20:30:00-07:00,2015,8,15,20,30,20.9,13.3,0.0,0.0,0.0,0.16,812.0,247.0,2.0,61.805960576705935 +2015-08-15 21:30:00-07:00,2015,8,15,21,30,20.1,13.5,0.0,0.0,0.0,0.16,812.0,240.0,2.0,65.78343638298276 +2015-08-15 22:30:00-07:00,2015,8,15,22,30,19.6,13.5,0.0,0.0,0.0,0.16,813.0,240.0,1.8,67.85401913589644 +2015-08-15 23:30:00-07:00,2015,8,15,23,30,19.1,13.7,0.0,0.0,0.0,0.16,813.0,246.0,1.7000000000000002,70.914484993554 +2015-08-16 00:30:00-07:00,2015,8,16,0,30,18.5,13.2,0.0,0.0,0.0,0.16,812.0,260.0,1.5,71.26503621305132 +2015-08-16 01:30:00-07:00,2015,8,16,1,30,17.8,13.1,0.0,0.0,0.0,0.16,812.0,281.0,1.4,73.9828057892107 +2015-08-16 02:30:00-07:00,2015,8,16,2,30,17.2,12.6,0.0,0.0,0.0,0.16,811.0,292.0,1.5,74.36504515124498 +2015-08-16 03:30:00-07:00,2015,8,16,3,30,16.7,11.8,0.0,0.0,0.0,0.16,811.0,291.0,1.8,72.82577727763841 +2015-08-16 04:30:00-07:00,2015,8,16,4,30,16.6,11.0,0.0,0.0,0.0,0.16,811.0,286.0,2.2,69.51125557277939 +2015-08-16 05:30:00-07:00,2015,8,16,5,30,17.8,10.6,14.0,0.0,14.0,0.16,811.0,280.0,2.9000000000000004,62.73985149317124 +2015-08-16 06:30:00-07:00,2015,8,16,6,30,20.9,10.7,47.0,619.0,192.0,0.16,811.0,279.0,3.2,52.07951877611904 +2015-08-16 07:30:00-07:00,2015,8,16,7,30,25.1,10.8,176.0,477.0,377.0,0.16,811.0,141.0,2.6,40.65875670903635 +2015-08-16 08:30:00-07:00,2015,8,16,8,30,28.200000000000003,8.6,309.0,254.0,459.0,0.16,811.0,21.0,2.7,29.227235414291343 +2015-08-16 09:30:00-07:00,2015,8,16,9,30,29.4,7.9,374.0,90.0,440.0,0.16,811.0,46.0,3.2,25.998932543596084 +2015-08-16 10:30:00-07:00,2015,8,16,10,30,29.700000000000003,8.0,97.0,917.0,863.0,0.16,811.0,59.0,3.4000000000000004,25.7275070301368 +2015-08-16 11:30:00-07:00,2015,8,16,11,30,29.3,8.200000000000001,337.0,593.0,864.0,0.16,811.0,65.0,3.3000000000000003,26.687941941516186 +2015-08-16 12:30:00-07:00,2015,8,16,12,30,28.9,8.6,272.0,5.0,276.0,0.16,811.0,65.0,2.8000000000000003,28.062926178167793 +2015-08-16 13:30:00-07:00,2015,8,16,13,30,28.3,8.9,244.0,4.0,247.0,0.16,811.0,57.0,2.2,29.65262524285172 +2015-08-16 14:30:00-07:00,2015,8,16,14,30,27.700000000000003,9.1,226.0,0.0,226.0,0.16,811.0,47.0,1.6,31.125360280865063 +2015-08-16 15:30:00-07:00,2015,8,16,15,30,26.8,9.5,208.0,11.0,215.0,0.16,811.0,40.0,1.2000000000000002,33.70688769143851 +2015-08-16 16:30:00-07:00,2015,8,16,16,30,25.5,9.8,8.0,0.0,8.0,0.16,810.0,59.0,0.7000000000000001,37.13932458686647 +2015-08-16 17:30:00-07:00,2015,8,16,17,30,23.6,10.3,118.0,44.0,129.0,0.16,810.0,158.0,0.4,43.02486595583609 +2015-08-16 18:30:00-07:00,2015,8,16,18,30,21.3,12.0,21.0,0.0,21.0,0.16,811.0,251.0,0.4,55.3825106602253 +2015-08-16 19:30:00-07:00,2015,8,16,19,30,19.5,11.9,0.0,0.0,0.0,0.16,811.0,268.0,0.6000000000000001,61.48403369973786 +2015-08-16 20:30:00-07:00,2015,8,16,20,30,18.3,11.8,0.0,0.0,0.0,0.16,811.0,289.0,1.1,65.83175697800208 +2015-08-16 21:30:00-07:00,2015,8,16,21,30,17.1,12.1,0.0,0.0,0.0,0.16,812.0,321.0,1.9,72.41952580041084 +2015-08-16 22:30:00-07:00,2015,8,16,22,30,16.3,12.5,0.0,0.0,0.0,0.16,812.0,340.0,2.4000000000000004,78.22201396550375 +2015-08-16 23:30:00-07:00,2015,8,16,23,30,16.0,13.1,0.0,0.0,0.0,0.16,812.0,342.0,2.0,82.92582750051314 +2015-08-17 00:30:00-07:00,2015,8,17,0,30,15.8,13.1,0.0,0.0,0.0,0.16,811.0,296.0,1.8,83.99219273092457 +2015-08-17 01:30:00-07:00,2015,8,17,1,30,15.5,12.8,0.0,0.0,0.0,0.16,810.0,241.0,2.0,83.95799677861824 +2015-08-17 02:30:00-07:00,2015,8,17,2,30,15.2,12.1,0.0,0.0,0.0,0.16,809.0,229.0,2.2,81.74688835646663 +2015-08-17 03:30:00-07:00,2015,8,17,3,30,15.1,11.5,0.0,0.0,0.0,0.16,809.0,233.0,2.2,79.08144891497521 +2015-08-17 04:30:00-07:00,2015,8,17,4,30,15.3,11.1,0.0,0.0,0.0,0.16,809.0,240.0,2.3000000000000003,76.03124409874876 +2015-08-17 05:30:00-07:00,2015,8,17,5,30,16.5,11.0,12.0,0.0,12.0,0.16,809.0,245.0,2.5,69.9545243924757 +2015-08-17 06:30:00-07:00,2015,8,17,6,30,18.8,11.5,121.0,165.0,159.0,0.16,809.0,247.0,2.4000000000000004,62.551618364750176 +2015-08-17 07:30:00-07:00,2015,8,17,7,30,22.1,11.8,241.0,96.0,282.0,0.16,809.0,270.0,1.8,52.04913168708253 +2015-08-17 08:30:00-07:00,2015,8,17,8,30,24.700000000000003,9.8,295.0,366.0,511.0,0.16,809.0,146.0,1.7000000000000002,38.952331507631136 +2015-08-17 09:30:00-07:00,2015,8,17,9,30,25.8,8.0,133.0,825.0,737.0,0.16,808.0,4.0,2.1,32.3107534664065 +2015-08-17 10:30:00-07:00,2015,8,17,10,30,26.200000000000003,7.4,150.0,845.0,854.0,0.16,808.0,8.0,2.3000000000000003,30.290923859994912 +2015-08-17 11:30:00-07:00,2015,8,17,11,30,26.200000000000003,7.0,485.0,331.0,779.0,0.16,808.0,184.0,2.0,29.473329112514342 +2015-08-17 12:30:00-07:00,2015,8,17,12,30,26.4,6.800000000000001,422.0,309.0,697.0,0.16,807.0,187.0,1.3,28.730581568587095 +2015-08-17 13:30:00-07:00,2015,8,17,13,30,27.1,6.9,73.0,0.0,73.0,0.16,807.0,55.0,0.9,27.76082856185418 +2015-08-17 14:30:00-07:00,2015,8,17,14,30,27.3,7.2,383.0,357.0,651.0,0.16,806.0,123.0,1.1,28.006631084767005 +2015-08-17 15:30:00-07:00,2015,8,17,15,30,26.6,7.5,330.0,66.0,371.0,0.16,806.0,156.0,1.5,29.787039490987183 +2015-08-17 16:30:00-07:00,2015,8,17,16,30,25.0,8.0,95.0,1.0,95.0,0.16,805.0,157.0,1.9,33.88443913631402 +2015-08-17 17:30:00-07:00,2015,8,17,17,30,22.0,9.8,86.0,8.0,88.0,0.14,804.0,151.0,2.1,45.84760683842287 +2015-08-17 18:30:00-07:00,2015,8,17,18,30,18.6,12.6,21.0,0.0,21.0,0.14,805.0,179.0,1.9,68.09543531301918 +2015-08-17 19:30:00-07:00,2015,8,17,19,30,16.5,12.8,0.0,0.0,0.0,0.14,806.0,225.0,2.0,78.76583109981571 +2015-08-17 20:30:00-07:00,2015,8,17,20,30,15.6,12.0,0.0,0.0,0.0,0.14,806.0,235.0,2.1,79.15367709041828 +2015-08-17 21:30:00-07:00,2015,8,17,21,30,15.2,11.5,0.0,0.0,0.0,0.14,805.0,230.0,1.6,78.57491552248867 +2015-08-17 22:30:00-07:00,2015,8,17,22,30,14.7,10.9,0.0,0.0,0.0,0.14,805.0,237.0,1.6,77.98130991475222 +2015-08-17 23:30:00-07:00,2015,8,17,23,30,14.3,10.4,0.0,0.0,0.0,0.14,805.0,251.0,1.8,77.40282883531218 +2015-08-18 00:30:00-07:00,2015,8,18,0,30,13.6,9.8,0.0,0.0,0.0,0.14,805.0,276.0,2.0,77.81362767222384 +2015-08-18 01:30:00-07:00,2015,8,18,1,30,12.6,8.9,0.0,0.0,0.0,0.14,806.0,313.0,2.1,78.18568585994811 +2015-08-18 02:30:00-07:00,2015,8,18,2,30,11.4,8.0,0.0,0.0,0.0,0.14,806.0,345.0,1.8,79.61147974890861 +2015-08-18 03:30:00-07:00,2015,8,18,3,30,10.2,7.4,0.0,0.0,0.0,0.14,806.0,181.0,1.0,82.76902583429198 +2015-08-18 04:30:00-07:00,2015,8,18,4,30,9.6,6.9,0.0,0.0,0.0,0.14,806.0,129.0,0.4,83.26341743548329 +2015-08-18 05:30:00-07:00,2015,8,18,5,30,10.7,6.300000000000001,14.0,145.0,20.0,0.14,807.0,248.0,0.7000000000000001,74.2371415853774 +2015-08-18 06:30:00-07:00,2015,8,18,6,30,13.7,5.7,57.0,558.0,185.0,0.14,807.0,287.0,0.8,58.47278866319227 +2015-08-18 07:30:00-07:00,2015,8,18,7,30,17.3,3.4000000000000004,81.0,744.0,392.0,0.14,806.0,340.0,0.7000000000000001,39.53253556490073 +2015-08-18 08:30:00-07:00,2015,8,18,8,30,20.1,1.1,97.0,835.0,588.0,0.14,806.0,175.0,1.1,28.170984833027404 +2015-08-18 09:30:00-07:00,2015,8,18,9,30,22.200000000000003,0.4,108.0,884.0,753.0,0.14,806.0,8.0,1.7000000000000002,23.54601675248316 +2015-08-18 10:30:00-07:00,2015,8,18,10,30,23.6,0.4,308.0,490.0,715.0,0.14,805.0,20.0,2.3000000000000003,21.631738195186 +2015-08-18 11:30:00-07:00,2015,8,18,11,30,24.6,0.7000000000000001,413.0,490.0,846.0,0.14,805.0,28.0,2.9000000000000004,20.817508192552456 +2015-08-18 12:30:00-07:00,2015,8,18,12,30,24.9,1.0,138.0,891.0,929.0,0.14,804.0,37.0,3.5,20.89434637710575 +2015-08-18 13:30:00-07:00,2015,8,18,13,30,24.3,1.3,156.0,840.0,864.0,0.14,804.0,43.0,4.0,22.129470245767504 +2015-08-18 14:30:00-07:00,2015,8,18,14,30,22.5,1.8,126.0,848.0,759.0,0.14,805.0,46.0,4.5,25.567589937981268 +2015-08-18 15:30:00-07:00,2015,8,18,15,30,20.1,2.3000000000000003,280.0,275.0,448.0,0.14,805.0,49.0,4.5,30.695345274982827 +2015-08-18 16:30:00-07:00,2015,8,18,16,30,18.2,2.8000000000000003,162.0,16.0,169.0,0.14,806.0,55.0,4.2,35.802460289633885 +2015-08-18 17:30:00-07:00,2015,8,18,17,30,16.1,3.2,118.0,22.0,124.0,0.14,806.0,66.0,3.5,42.06427530360291 +2015-08-18 18:30:00-07:00,2015,8,18,18,30,14.0,3.8,14.0,0.0,14.0,0.14,807.0,80.0,2.7,50.22598247330551 +2015-08-18 19:30:00-07:00,2015,8,18,19,30,12.5,3.9,0.0,0.0,0.0,0.14,807.0,96.0,2.2,55.778160678819326 +2015-08-18 20:30:00-07:00,2015,8,18,20,30,11.5,4.1000000000000005,0.0,0.0,0.0,0.14,807.0,112.0,1.7000000000000002,60.41731886417642 +2015-08-18 21:30:00-07:00,2015,8,18,21,30,10.7,4.6000000000000005,0.0,0.0,0.0,0.14,808.0,120.0,1.4,65.97853702811713 +2015-08-18 22:30:00-07:00,2015,8,18,22,30,10.0,5.2,0.0,0.0,0.0,0.14,809.0,104.0,1.4,72.08450816359871 +2015-08-18 23:30:00-07:00,2015,8,18,23,30,9.3,5.7,0.0,0.0,0.0,0.14,809.0,70.0,1.8,78.21690062759795 +2015-08-19 00:30:00-07:00,2015,8,19,0,30,8.5,5.800000000000001,0.0,0.0,0.0,0.14,809.0,42.0,2.2,83.12912375209122 +2015-08-19 01:30:00-07:00,2015,8,19,1,30,7.9,5.5,0.0,0.0,0.0,0.14,810.0,31.0,2.1,84.8032812196914 +2015-08-19 02:30:00-07:00,2015,8,19,2,30,7.4,5.2,0.0,0.0,0.0,0.14,810.0,31.0,1.7000000000000002,85.93454715656237 +2015-08-19 03:30:00-07:00,2015,8,19,3,30,7.1000000000000005,4.9,0.0,0.0,0.0,0.14,810.0,30.0,1.1,85.90314968569962 +2015-08-19 04:30:00-07:00,2015,8,19,4,30,6.800000000000001,4.7,0.0,0.0,0.0,0.14,810.0,180.0,0.8,86.47331791774488 +2015-08-19 05:30:00-07:00,2015,8,19,5,30,7.6,4.5,15.0,77.0,17.0,0.14,810.0,326.0,0.8,80.73248985802573 +2015-08-19 06:30:00-07:00,2015,8,19,6,30,9.6,4.5,66.0,513.0,181.0,0.14,810.0,339.0,1.1,70.51639764156724 +2015-08-19 07:30:00-07:00,2015,8,19,7,30,12.3,3.8,82.0,754.0,395.0,0.14,810.0,183.0,1.4,56.11957900082418 +2015-08-19 08:30:00-07:00,2015,8,19,8,30,15.0,1.8,307.0,52.0,337.0,0.14,809.0,15.0,1.6,40.86173884452464 +2015-08-19 09:30:00-07:00,2015,8,19,9,30,17.400000000000002,0.2,414.0,250.0,596.0,0.14,809.0,29.0,1.9,31.253630713114877 +2015-08-19 10:30:00-07:00,2015,8,19,10,30,19.3,-0.8,89.0,975.0,896.0,0.14,809.0,42.0,2.4000000000000004,25.798490139721622 +2015-08-19 11:30:00-07:00,2015,8,19,11,30,20.8,-1.3,84.0,993.0,960.0,0.14,808.0,49.0,2.7,22.667000190069515 +2015-08-19 12:30:00-07:00,2015,8,19,12,30,21.8,-1.4,83.0,995.0,965.0,0.14,808.0,51.0,2.9000000000000004,21.163412513551922 +2015-08-19 13:30:00-07:00,2015,8,19,13,30,22.3,-1.5,80.0,987.0,908.0,0.14,807.0,52.0,3.0,20.378009879110472 +2015-08-19 14:30:00-07:00,2015,8,19,14,30,22.3,-1.5,73.0,965.0,791.0,0.14,807.0,52.0,3.0,20.378009879110472 +2015-08-19 15:30:00-07:00,2015,8,19,15,30,21.700000000000003,-1.4,68.0,922.0,628.0,0.14,807.0,57.0,2.8000000000000003,21.293105582234325 +2015-08-19 16:30:00-07:00,2015,8,19,16,30,20.5,-1.3,60.0,844.0,431.0,0.14,807.0,66.0,2.6,23.08981912459633 +2015-08-19 17:30:00-07:00,2015,8,19,17,30,17.6,-0.9,46.0,688.0,220.0,0.14,807.0,82.0,1.8,28.48886285277088 +2015-08-19 18:30:00-07:00,2015,8,19,18,30,14.3,2.4000000000000004,19.0,304.0,37.0,0.14,808.0,106.0,1.2000000000000002,44.61717026286814 +2015-08-19 19:30:00-07:00,2015,8,19,19,30,12.6,2.7,0.0,0.0,0.0,0.14,808.0,138.0,1.2000000000000002,50.912979823332506 +2015-08-19 20:30:00-07:00,2015,8,19,20,30,11.8,2.5,0.0,0.0,0.0,0.14,808.0,168.0,1.3,52.90624056364426 +2015-08-19 21:30:00-07:00,2015,8,19,21,30,11.2,2.6,0.0,0.0,0.0,0.14,808.0,187.0,1.6,55.43947919693697 +2015-08-19 22:30:00-07:00,2015,8,19,22,30,10.9,2.6,0.0,0.0,0.0,0.14,808.0,199.0,1.8,56.554034644234925 +2015-08-19 23:30:00-07:00,2015,8,19,23,30,10.8,2.4000000000000004,0.0,0.0,0.0,0.14,807.0,207.0,2.0,56.127838363147404 +2015-08-20 00:30:00-07:00,2015,8,20,0,30,10.8,2.1,0.0,0.0,0.0,0.14,807.0,214.0,2.2,54.94174939141809 +2015-08-20 01:30:00-07:00,2015,8,20,1,30,10.9,1.6,0.0,0.0,0.0,0.14,806.0,222.0,2.3000000000000003,52.66307115517477 +2015-08-20 02:30:00-07:00,2015,8,20,2,30,11.0,1.3,0.0,0.0,0.0,0.14,806.0,231.0,2.4000000000000004,51.20186665196504 +2015-08-20 03:30:00-07:00,2015,8,20,3,30,11.0,1.2000000000000002,0.0,0.0,0.0,0.14,806.0,242.0,2.3000000000000003,50.83566222215841 +2015-08-20 04:30:00-07:00,2015,8,20,4,30,11.2,1.1,0.0,0.0,0.0,0.14,806.0,253.0,2.2,49.806717835077094 +2015-08-20 05:30:00-07:00,2015,8,20,5,30,13.4,1.4,12.0,79.0,14.0,0.14,806.0,261.0,2.4000000000000004,44.041815564098464 +2015-08-20 06:30:00-07:00,2015,8,20,6,30,17.5,2.3000000000000003,66.0,467.0,170.0,0.14,806.0,269.0,2.9000000000000004,36.10967147504832 +2015-08-20 07:30:00-07:00,2015,8,20,7,30,21.1,2.6,240.0,132.0,295.0,0.14,807.0,284.0,2.9000000000000004,29.482755391976212 +2015-08-20 08:30:00-07:00,2015,8,20,8,30,23.6,2.9000000000000004,117.0,773.0,569.0,0.14,807.0,294.0,2.5,25.868519559902484 +2015-08-20 09:30:00-07:00,2015,8,20,9,30,25.3,2.9000000000000004,172.0,721.0,695.0,0.14,806.0,300.0,2.0,23.365272447149543 +2015-08-20 10:30:00-07:00,2015,8,20,10,30,26.8,2.7,464.0,304.0,715.0,0.14,806.0,317.0,1.5,21.08028432485714 +2015-08-20 11:30:00-07:00,2015,8,20,11,30,28.0,2.5,119.0,921.0,930.0,0.14,806.0,168.0,1.4,19.372550402155586 +2015-08-20 12:30:00-07:00,2015,8,20,12,30,28.700000000000003,2.4000000000000004,122.0,920.0,934.0,0.14,805.0,15.0,1.6,18.46796646862308 +2015-08-20 13:30:00-07:00,2015,8,20,13,30,28.9,2.3000000000000003,121.0,900.0,873.0,0.14,805.0,30.0,2.0,18.125696796852424 +2015-08-20 14:30:00-07:00,2015,8,20,14,30,28.6,2.3000000000000003,100.0,899.0,766.0,0.14,805.0,37.0,2.3000000000000003,18.443778806291533 +2015-08-20 15:30:00-07:00,2015,8,20,15,30,27.700000000000003,2.2,93.0,841.0,602.0,0.14,805.0,43.0,2.4000000000000004,19.29852003316119 +2015-08-20 16:30:00-07:00,2015,8,20,16,30,26.1,2.2,83.0,740.0,406.0,0.14,805.0,50.0,2.1,21.20180901017961 +2015-08-20 17:30:00-07:00,2015,8,20,17,30,22.9,3.2,63.0,555.0,201.0,0.15,806.0,63.0,1.5,27.564603295051562 +2015-08-20 18:30:00-07:00,2015,8,20,18,30,19.700000000000003,6.4,20.0,0.0,20.0,0.15,806.0,86.0,1.1,41.92478130688245 +2015-08-20 19:30:00-07:00,2015,8,20,19,30,17.900000000000002,5.800000000000001,0.0,0.0,0.0,0.15,807.0,123.0,1.0,45.01222042500314 +2015-08-20 20:30:00-07:00,2015,8,20,20,30,16.8,5.5,0.0,0.0,0.0,0.15,807.0,165.0,1.1,47.259350773379104 +2015-08-20 21:30:00-07:00,2015,8,20,21,30,15.7,5.300000000000001,0.0,0.0,0.0,0.15,807.0,191.0,1.3,49.99165964833575 +2015-08-20 22:30:00-07:00,2015,8,20,22,30,14.9,4.800000000000001,0.0,0.0,0.0,0.15,807.0,206.0,1.5,50.82583385333589 +2015-08-20 23:30:00-07:00,2015,8,20,23,30,14.1,4.3,0.0,0.0,0.0,0.15,806.0,217.0,1.6,51.683167707776725 +2015-08-21 00:30:00-07:00,2015,8,21,0,30,13.6,3.4000000000000004,0.0,0.0,0.0,0.15,806.0,225.0,1.7000000000000002,50.11596799572571 +2015-08-21 01:30:00-07:00,2015,8,21,1,30,13.2,2.4000000000000004,0.0,0.0,0.0,0.15,806.0,233.0,1.8,47.92205618200463 +2015-08-21 02:30:00-07:00,2015,8,21,2,30,13.0,1.4,0.0,0.0,0.0,0.15,806.0,240.0,2.1,45.206256566609575 +2015-08-21 03:30:00-07:00,2015,8,21,3,30,12.8,0.5,0.0,0.0,0.0,0.15,806.0,246.0,2.5,42.929278738664294 +2015-08-21 04:30:00-07:00,2015,8,21,4,30,12.8,-0.1,0.0,0.0,0.0,0.15,806.0,251.0,2.9000000000000004,41.104359339702 +2015-08-21 05:30:00-07:00,2015,8,21,5,30,14.7,-0.3,12.0,120.0,15.0,0.15,806.0,255.0,3.5,35.80757572312823 +2015-08-21 06:30:00-07:00,2015,8,21,6,30,18.5,0.0,55.0,562.0,179.0,0.15,806.0,259.0,3.8,28.745118476550513 +2015-08-21 07:30:00-07:00,2015,8,21,7,30,22.5,0.2,79.0,760.0,391.0,0.15,806.0,267.0,3.6,22.78820267026876 +2015-08-21 08:30:00-07:00,2015,8,21,8,30,25.5,-2.0,95.0,854.0,592.0,0.15,806.0,276.0,3.3000000000000003,16.204606400404916 +2015-08-21 09:30:00-07:00,2015,8,21,9,30,27.4,-3.3000000000000003,109.0,895.0,756.0,0.15,806.0,281.0,2.9000000000000004,13.155797453770743 +2015-08-21 10:30:00-07:00,2015,8,21,10,30,28.9,-3.1,124.0,909.0,872.0,0.15,806.0,289.0,2.5,12.236063068487617 +2015-08-21 11:30:00-07:00,2015,8,21,11,30,30.0,-2.6,126.0,921.0,935.0,0.15,805.0,301.0,2.1,11.918033238441526 +2015-08-21 12:30:00-07:00,2015,8,21,12,30,30.700000000000003,-2.2,130.0,914.0,935.0,0.15,805.0,314.0,1.7000000000000002,11.79320636307318 +2015-08-21 13:30:00-07:00,2015,8,21,13,30,31.0,-2.0,130.0,892.0,874.0,0.15,804.0,322.0,1.4,11.76525407395044 +2015-08-21 14:30:00-07:00,2015,8,21,14,30,30.8,-1.8,356.0,16.0,368.0,0.15,804.0,319.0,1.1,12.07696816169509 +2015-08-21 15:30:00-07:00,2015,8,21,15,30,30.0,-1.6,53.0,0.0,53.0,0.15,804.0,302.0,0.9,12.830710241682276 +2015-08-21 16:30:00-07:00,2015,8,21,16,30,28.3,-0.7000000000000001,111.0,651.0,392.0,0.15,804.0,274.0,0.7000000000000001,15.120059460629987 +2015-08-21 17:30:00-07:00,2015,8,21,17,30,24.9,4.3,126.0,53.0,139.0,0.14,804.0,239.0,0.6000000000000001,26.408001510714573 +2015-08-21 18:30:00-07:00,2015,8,21,18,30,21.3,6.6000000000000005,17.0,0.0,17.0,0.14,804.0,223.0,0.9,38.51135638321268 +2015-08-21 19:30:00-07:00,2015,8,21,19,30,19.4,5.0,0.0,0.0,0.0,0.14,804.0,231.0,1.3,38.76766010284013 +2015-08-21 20:30:00-07:00,2015,8,21,20,30,18.7,4.1000000000000005,0.0,0.0,0.0,0.14,804.0,239.0,1.8,38.02896182425639 +2015-08-21 21:30:00-07:00,2015,8,21,21,30,18.2,3.4000000000000004,0.0,0.0,0.0,0.14,805.0,245.0,2.2,37.35396004458271 +2015-08-21 22:30:00-07:00,2015,8,21,22,30,17.7,3.0,0.0,0.0,0.0,0.14,805.0,249.0,2.5,37.47311638750613 +2015-08-21 23:30:00-07:00,2015,8,21,23,30,17.3,3.0,0.0,0.0,0.0,0.14,805.0,252.0,2.7,38.4310398598017 +2015-08-22 00:30:00-07:00,2015,8,22,0,30,16.900000000000002,3.0,0.0,0.0,0.0,0.14,805.0,252.0,2.9000000000000004,39.416512622604216 +2015-08-22 01:30:00-07:00,2015,8,22,1,30,16.5,2.7,0.0,0.0,0.0,0.14,805.0,252.0,3.0,39.58015887152879 +2015-08-22 02:30:00-07:00,2015,8,22,2,30,16.0,2.7,0.0,0.0,0.0,0.14,805.0,252.0,2.8000000000000003,40.861365940369375 +2015-08-22 03:30:00-07:00,2015,8,22,3,30,15.6,2.8000000000000003,0.0,0.0,0.0,0.14,805.0,251.0,2.5,42.218135507506744 +2015-08-22 04:30:00-07:00,2015,8,22,4,30,15.6,2.9000000000000004,0.0,0.0,0.0,0.14,805.0,249.0,2.4000000000000004,42.51830293290766 +2015-08-22 05:30:00-07:00,2015,8,22,5,30,17.6,3.3000000000000003,8.0,0.0,8.0,0.14,805.0,245.0,2.7,38.51814607860755 +2015-08-22 06:30:00-07:00,2015,8,22,6,30,21.5,4.2,73.0,407.0,161.0,0.14,806.0,241.0,3.2,32.2054693383781 +2015-08-22 07:30:00-07:00,2015,8,22,7,30,25.1,5.300000000000001,209.0,359.0,356.0,0.14,806.0,246.0,3.5,27.979722037858583 +2015-08-22 08:30:00-07:00,2015,8,22,8,30,27.700000000000003,6.4,122.0,750.0,557.0,0.14,806.0,255.0,3.6,25.89709875711738 +2015-08-22 09:30:00-07:00,2015,8,22,9,30,29.200000000000003,6.1000000000000005,124.0,834.0,725.0,0.14,806.0,256.0,3.4000000000000004,23.24934828017566 +2015-08-22 10:30:00-07:00,2015,8,22,10,30,30.5,5.6000000000000005,129.0,873.0,846.0,0.14,806.0,257.0,3.0,20.841026249291183 +2015-08-22 11:30:00-07:00,2015,8,22,11,30,31.200000000000003,5.1000000000000005,448.0,57.0,497.0,0.14,806.0,263.0,2.6,19.34119627806083 +2015-08-22 12:30:00-07:00,2015,8,22,12,30,31.4,4.4,224.0,0.0,224.0,0.14,806.0,283.0,2.4000000000000004,18.210866937869994 +2015-08-22 13:30:00-07:00,2015,8,22,13,30,30.8,3.9,248.0,591.0,738.0,0.14,806.0,322.0,2.5,18.195336292651394 +2015-08-22 14:30:00-07:00,2015,8,22,14,30,29.4,3.9,354.0,108.0,433.0,0.14,807.0,188.0,3.1,19.71833393426283 +2015-08-22 15:30:00-07:00,2015,8,22,15,30,27.200000000000003,4.2,350.0,176.0,455.0,0.14,808.0,41.0,3.8,22.887637929376996 +2015-08-22 16:30:00-07:00,2015,8,22,16,30,24.4,4.9,249.0,69.0,279.0,0.14,809.0,58.0,4.3,28.37379801947186 +2015-08-22 17:30:00-07:00,2015,8,22,17,30,21.0,5.800000000000001,80.0,407.0,178.0,0.14,810.0,66.0,4.0,37.121639720849515 +2015-08-22 18:30:00-07:00,2015,8,22,18,30,17.900000000000002,6.6000000000000005,16.0,82.0,20.0,0.14,811.0,73.0,3.4000000000000004,47.56507986124773 +2015-08-22 19:30:00-07:00,2015,8,22,19,30,15.7,6.800000000000001,0.0,0.0,0.0,0.14,812.0,82.0,2.7,55.446332823206625 +2015-08-22 20:30:00-07:00,2015,8,22,20,30,14.2,6.800000000000001,0.0,0.0,0.0,0.14,813.0,92.0,2.1,61.06586775797882 +2015-08-22 21:30:00-07:00,2015,8,22,21,30,13.1,6.6000000000000005,0.0,0.0,0.0,0.14,813.0,98.0,1.6,64.69797137423296 +2015-08-22 22:30:00-07:00,2015,8,22,22,30,12.2,6.4,0.0,0.0,0.0,0.14,813.0,105.0,1.2000000000000002,67.69015415366488 +2015-08-22 23:30:00-07:00,2015,8,22,23,30,11.4,6.300000000000001,0.0,0.0,0.0,0.14,814.0,119.0,0.9,70.86810548589534 +2015-08-23 00:30:00-07:00,2015,8,23,0,30,10.7,6.2,0.0,0.0,0.0,0.14,814.0,137.0,0.8,73.72719753630936 +2015-08-23 01:30:00-07:00,2015,8,23,1,30,10.3,6.2,0.0,0.0,0.0,0.14,814.0,146.0,0.9,75.71880045389193 +2015-08-23 02:30:00-07:00,2015,8,23,2,30,10.0,6.0,0.0,0.0,0.0,0.14,814.0,147.0,1.1,76.19307463946734 +2015-08-23 03:30:00-07:00,2015,8,23,3,30,9.5,5.800000000000001,0.0,0.0,0.0,0.14,814.0,150.0,1.4,77.70833681115083 +2015-08-23 04:30:00-07:00,2015,8,23,4,30,9.2,5.5,0.0,0.0,0.0,0.14,814.0,160.0,1.6,77.66135685471315 +2015-08-23 05:30:00-07:00,2015,8,23,5,30,10.2,5.2,10.0,58.0,11.0,0.14,813.0,173.0,2.2,71.12718753659692 +2015-08-23 06:30:00-07:00,2015,8,23,6,30,13.0,5.1000000000000005,120.0,117.0,145.0,0.14,813.0,185.0,2.9000000000000004,58.7056270420553 +2015-08-23 07:30:00-07:00,2015,8,23,7,30,17.1,4.7,89.0,695.0,371.0,0.14,813.0,193.0,3.0,43.8591806830038 +2015-08-23 08:30:00-07:00,2015,8,23,8,30,20.9,3.6,100.0,817.0,572.0,0.14,813.0,201.0,2.4000000000000004,32.03400640783299 +2015-08-23 09:30:00-07:00,2015,8,23,9,30,23.9,3.1,105.0,887.0,741.0,0.14,812.0,213.0,1.6,25.76776686930202 +2015-08-23 10:30:00-07:00,2015,8,23,10,30,26.200000000000003,2.8000000000000003,107.0,925.0,864.0,0.14,812.0,126.0,1.4,21.994777147706657 +2015-08-23 11:30:00-07:00,2015,8,23,11,30,27.700000000000003,2.4000000000000004,97.0,962.0,935.0,0.14,812.0,36.0,2.1,19.575162330569544 +2015-08-23 12:30:00-07:00,2015,8,23,12,30,28.5,2.1,98.0,957.0,935.0,0.14,811.0,42.0,2.6,18.288816555110373 +2015-08-23 13:30:00-07:00,2015,8,23,13,30,28.700000000000003,1.8,280.0,701.0,859.0,0.14,811.0,46.0,2.8000000000000003,17.694760143407606 +2015-08-23 14:30:00-07:00,2015,8,23,14,30,28.3,1.6,400.0,290.0,612.0,0.14,811.0,53.0,2.8000000000000003,17.853572017592505 +2015-08-23 15:30:00-07:00,2015,8,23,15,30,27.200000000000003,1.6,279.0,78.0,325.0,0.14,811.0,68.0,2.7,19.038493809753984 +2015-08-23 16:30:00-07:00,2015,8,23,16,30,25.3,1.9,227.0,221.0,321.0,0.14,811.0,85.0,2.3000000000000003,21.761512166085254 +2015-08-23 17:30:00-07:00,2015,8,23,17,30,21.9,3.4000000000000004,59.0,549.0,188.0,0.11,812.0,104.0,1.3,29.708890874782327 +2015-08-23 18:30:00-07:00,2015,8,23,18,30,18.7,6.9,15.0,152.0,22.0,0.11,812.0,132.0,0.6000000000000001,46.17875297298374 +2015-08-23 19:30:00-07:00,2015,8,23,19,30,17.0,6.300000000000001,0.0,0.0,0.0,0.11,813.0,184.0,0.5,49.31702051628331 +2015-08-23 20:30:00-07:00,2015,8,23,20,30,15.8,5.5,0.0,0.0,0.0,0.11,813.0,218.0,0.9,50.367205331718985 +2015-08-23 21:30:00-07:00,2015,8,23,21,30,14.7,4.7,0.0,0.0,0.0,0.11,813.0,221.0,1.3,51.12710456393297 +2015-08-23 22:30:00-07:00,2015,8,23,22,30,13.8,3.9,0.0,0.0,0.0,0.11,813.0,227.0,1.5,51.240843190226215 +2015-08-23 23:30:00-07:00,2015,8,23,23,30,13.3,3.0,0.0,0.0,0.0,0.11,813.0,238.0,1.7000000000000002,49.6806218558401 +2015-08-24 00:30:00-07:00,2015,8,24,0,30,13.0,1.9,0.0,0.0,0.0,0.11,813.0,253.0,1.9,46.852653013964385 +2015-08-24 01:30:00-07:00,2015,8,24,1,30,12.9,0.7000000000000001,0.0,0.0,0.0,0.11,813.0,268.0,2.0,43.26935623250215 +2015-08-24 02:30:00-07:00,2015,8,24,2,30,12.7,-0.3,0.0,0.0,0.0,0.11,813.0,280.0,2.0,40.77762221802391 +2015-08-24 03:30:00-07:00,2015,8,24,3,30,12.5,-0.8,0.0,0.0,0.0,0.11,813.0,285.0,1.9,39.83789437076175 +2015-08-24 04:30:00-07:00,2015,8,24,4,30,12.5,-0.6000000000000001,0.0,0.0,0.0,0.11,813.0,287.0,1.6,40.42329697407177 +2015-08-24 05:30:00-07:00,2015,8,24,5,30,14.7,0.0,9.0,68.0,10.0,0.11,813.0,286.0,1.6,36.59611360472462 +2015-08-24 06:30:00-07:00,2015,8,24,6,30,19.1,2.1,64.0,448.0,159.0,0.11,813.0,286.0,1.8,32.19997227074151 +2015-08-24 07:30:00-07:00,2015,8,24,7,30,23.5,0.8,231.0,123.0,281.0,0.11,813.0,314.0,1.6,22.39983834391241 +2015-08-24 08:30:00-07:00,2015,8,24,8,30,26.6,1.5,124.0,741.0,549.0,0.11,813.0,178.0,1.2000000000000002,19.581283143667516 +2015-08-24 09:30:00-07:00,2015,8,24,9,30,28.5,2.0,139.0,803.0,714.0,0.11,813.0,20.0,1.3,18.15886133032139 +2015-08-24 10:30:00-07:00,2015,8,24,10,30,29.700000000000003,2.5,161.0,817.0,827.0,0.11,813.0,30.0,1.4,17.555173016782064 +2015-08-24 11:30:00-07:00,2015,8,24,11,30,30.6,2.7,169.0,829.0,890.0,0.11,812.0,39.0,1.6,16.909760856860174 +2015-08-24 12:30:00-07:00,2015,8,24,12,30,31.1,2.8000000000000003,171.0,826.0,892.0,0.11,812.0,51.0,1.8,16.55073169944127 +2015-08-24 13:30:00-07:00,2015,8,24,13,30,31.200000000000003,2.8000000000000003,169.0,805.0,832.0,0.11,812.0,60.0,2.0,16.45669785154714 +2015-08-24 14:30:00-07:00,2015,8,24,14,30,30.9,2.8000000000000003,279.0,627.0,734.0,0.11,812.0,65.0,2.3000000000000003,16.740623312796927 +2015-08-24 15:30:00-07:00,2015,8,24,15,30,30.0,2.6,268.0,18.0,278.0,0.11,812.0,72.0,2.4000000000000004,17.377812218735478 +2015-08-24 16:30:00-07:00,2015,8,24,16,30,28.3,2.5,86.0,716.0,387.0,0.11,812.0,80.0,2.2,19.036983652856872 +2015-08-24 17:30:00-07:00,2015,8,24,17,30,24.8,4.9,62.0,525.0,183.0,0.15,812.0,90.0,1.6,27.703143753134768 +2015-08-24 18:30:00-07:00,2015,8,24,18,30,21.4,7.5,11.0,0.0,11.0,0.15,813.0,105.0,1.2000000000000002,40.709625330825624 +2015-08-24 19:30:00-07:00,2015,8,24,19,30,19.9,6.9,0.0,0.0,0.0,0.15,813.0,129.0,1.1,42.85532838576149 +2015-08-24 20:30:00-07:00,2015,8,24,20,30,19.4,6.7,0.0,0.0,0.0,0.15,814.0,159.0,1.1,43.60373074158253 +2015-08-24 21:30:00-07:00,2015,8,24,21,30,18.8,6.5,0.0,0.0,0.0,0.15,814.0,184.0,1.1,44.64746180590139 +2015-08-24 22:30:00-07:00,2015,8,24,22,30,17.7,6.7,0.0,0.0,0.0,0.15,814.0,199.0,1.2000000000000002,48.499886335293084 +2015-08-24 23:30:00-07:00,2015,8,24,23,30,16.8,6.9,0.0,0.0,0.0,0.15,814.0,207.0,1.3,52.04920640804123 +2015-08-25 00:30:00-07:00,2015,8,25,0,30,16.2,7.0,0.0,0.0,0.0,0.15,814.0,215.0,1.3,54.445418618596584 +2015-08-25 01:30:00-07:00,2015,8,25,1,30,15.8,7.1000000000000005,0.0,0.0,0.0,0.15,814.0,225.0,1.3,56.23741946862517 +2015-08-25 02:30:00-07:00,2015,8,25,2,30,15.4,7.2,0.0,0.0,0.0,0.15,814.0,236.0,1.3,58.09267539800698 +2015-08-25 03:30:00-07:00,2015,8,25,3,30,15.0,7.0,0.0,0.0,0.0,0.15,814.0,245.0,1.4,58.7940571739292 +2015-08-25 04:30:00-07:00,2015,8,25,4,30,14.9,6.800000000000001,0.0,0.0,0.0,0.15,814.0,251.0,1.5,58.36769217919589 +2015-08-25 05:30:00-07:00,2015,8,25,5,30,16.8,6.6000000000000005,0.0,0.0,0.0,0.15,814.0,254.0,1.9,50.98817639389149 +2015-08-25 06:30:00-07:00,2015,8,25,6,30,21.0,7.1000000000000005,53.0,529.0,164.0,0.15,814.0,253.0,2.5,40.59576654374449 +2015-08-25 07:30:00-07:00,2015,8,25,7,30,25.4,6.4,75.0,735.0,369.0,0.15,814.0,247.0,2.0,29.65681771634052 +2015-08-25 08:30:00-07:00,2015,8,25,8,30,28.8,4.1000000000000005,88.0,839.0,567.0,0.15,814.0,258.0,1.2000000000000002,20.703242224415053 +2015-08-25 09:30:00-07:00,2015,8,25,9,30,30.700000000000003,2.7,96.0,896.0,734.0,0.15,814.0,314.0,1.0,16.813336817966913 +2015-08-25 10:30:00-07:00,2015,8,25,10,30,31.9,2.3000000000000003,101.0,931.0,857.0,0.15,813.0,193.0,1.5,15.263340399818686 +2015-08-25 11:30:00-07:00,2015,8,25,11,30,32.7,2.2,101.0,946.0,921.0,0.15,813.0,37.0,2.2,14.485270134368776 +2015-08-25 12:30:00-07:00,2015,8,25,12,30,33.1,2.3000000000000003,106.0,939.0,922.0,0.15,812.0,51.0,2.7,14.2641426273061 +2015-08-25 13:30:00-07:00,2015,8,25,13,30,32.9,2.4000000000000004,112.0,910.0,858.0,0.15,812.0,63.0,3.0,14.528406726486276 +2015-08-25 14:30:00-07:00,2015,8,25,14,30,32.2,2.6,114.0,860.0,736.0,0.15,812.0,72.0,3.1,15.32975800091567 +2015-08-25 15:30:00-07:00,2015,8,25,15,30,31.1,2.9000000000000004,269.0,487.0,554.0,0.15,812.0,82.0,2.9000000000000004,16.668406022643893 +2015-08-25 16:30:00-07:00,2015,8,25,16,30,29.200000000000003,3.3000000000000003,240.0,81.0,273.0,0.15,812.0,96.0,2.0,19.12218582933868 +2015-08-25 17:30:00-07:00,2015,8,25,17,30,25.9,7.1000000000000005,4.0,0.0,4.0,0.16,812.0,121.0,1.2000000000000002,30.20724777310559 +2015-08-25 18:30:00-07:00,2015,8,25,18,30,22.9,9.8,12.0,0.0,12.0,0.16,812.0,156.0,1.1,43.40727964931869 +2015-08-25 19:30:00-07:00,2015,8,25,19,30,21.5,9.1,0.0,0.0,0.0,0.16,813.0,184.0,1.5,45.09790526786648 +2015-08-25 20:30:00-07:00,2015,8,25,20,30,20.5,8.9,0.0,0.0,0.0,0.16,813.0,199.0,2.0,47.31331958984896 +2015-08-25 21:30:00-07:00,2015,8,25,21,30,19.9,9.0,0.0,0.0,0.0,0.16,813.0,206.0,2.4000000000000004,49.43346369624137 +2015-08-25 22:30:00-07:00,2015,8,25,22,30,19.6,9.1,0.0,0.0,0.0,0.16,813.0,215.0,2.9000000000000004,50.702571043293055 +2015-08-25 23:30:00-07:00,2015,8,25,23,30,19.200000000000003,9.5,0.0,0.0,0.0,0.16,814.0,225.0,3.4000000000000004,53.39744922887757 +2015-08-26 00:30:00-07:00,2015,8,26,0,30,18.6,9.9,0.0,0.0,0.0,0.16,814.0,234.0,3.8,56.942706405565005 +2015-08-26 01:30:00-07:00,2015,8,26,1,30,18.1,10.2,0.0,0.0,0.0,0.16,813.0,239.0,4.0,59.94665259281852 +2015-08-26 02:30:00-07:00,2015,8,26,2,30,17.8,9.9,0.0,0.0,0.0,0.16,813.0,241.0,3.9,59.87498979713214 +2015-08-26 03:30:00-07:00,2015,8,26,3,30,17.8,9.3,0.0,0.0,0.0,0.16,813.0,243.0,3.5,57.51187217078308 +2015-08-26 04:30:00-07:00,2015,8,26,4,30,17.8,8.700000000000001,0.0,0.0,0.0,0.16,813.0,245.0,3.0,55.231418370706926 +2015-08-26 05:30:00-07:00,2015,8,26,5,30,18.9,8.4,0.0,0.0,0.0,0.16,813.0,244.0,2.9000000000000004,50.51476458311773 +2015-08-26 06:30:00-07:00,2015,8,26,6,30,21.1,8.4,113.0,87.0,131.0,0.16,813.0,241.0,3.0,44.0823951487359 +2015-08-26 07:30:00-07:00,2015,8,26,7,30,24.200000000000003,8.700000000000001,208.0,230.0,299.0,0.16,813.0,235.0,2.7,37.27204218802872 +2015-08-26 08:30:00-07:00,2015,8,26,8,30,27.5,7.7,154.0,637.0,517.0,0.16,813.0,227.0,2.1,28.64166038794824 +2015-08-26 09:30:00-07:00,2015,8,26,9,30,29.9,6.300000000000001,101.0,878.0,725.0,0.16,813.0,212.0,1.8,22.639911748486828 +2015-08-26 10:30:00-07:00,2015,8,26,10,30,31.200000000000003,5.800000000000001,104.0,914.0,845.0,0.16,812.0,196.0,2.1,20.30360345017537 +2015-08-26 11:30:00-07:00,2015,8,26,11,30,31.8,5.4,443.0,56.0,492.0,0.16,812.0,185.0,2.4000000000000004,19.0865380583073 +2015-08-26 12:30:00-07:00,2015,8,26,12,30,32.0,5.1000000000000005,318.0,1.0,319.0,0.16,811.0,177.0,2.7,18.482132772364505 +2015-08-26 13:30:00-07:00,2015,8,26,13,30,31.9,4.7,453.0,281.0,683.0,0.16,811.0,171.0,2.9000000000000004,18.07621065603998 +2015-08-26 14:30:00-07:00,2015,8,26,14,30,31.1,4.5,361.0,215.0,516.0,0.16,810.0,169.0,3.1,18.654599852721585 +2015-08-26 15:30:00-07:00,2015,8,26,15,30,29.700000000000003,4.5,290.0,116.0,357.0,0.16,810.0,171.0,3.0,20.212475549861146 +2015-08-26 16:30:00-07:00,2015,8,26,16,30,27.700000000000003,5.2,197.0,16.0,204.0,0.16,810.0,178.0,2.6,23.834095759013195 +2015-08-26 17:30:00-07:00,2015,8,26,17,30,25.1,7.9,23.0,0.0,23.0,0.16,810.0,189.0,2.1,33.45482853958453 +2015-08-26 18:30:00-07:00,2015,8,26,18,30,22.8,9.2,7.0,0.0,7.0,0.16,810.0,206.0,2.1,41.94615639653989 +2015-08-26 19:30:00-07:00,2015,8,26,19,30,21.3,9.7,0.0,0.0,0.0,0.16,811.0,223.0,2.0,47.53272537861625 +2015-08-26 20:30:00-07:00,2015,8,26,20,30,20.3,10.0,0.0,0.0,0.0,0.16,811.0,238.0,1.7000000000000002,51.575567703898805 +2015-08-26 21:30:00-07:00,2015,8,26,21,30,19.4,10.3,0.0,0.0,0.0,0.16,811.0,248.0,1.4,55.63861620258264 +2015-08-26 22:30:00-07:00,2015,8,26,22,30,18.7,10.6,0.0,0.0,0.0,0.16,810.0,254.0,1.3,59.29520752016293 +2015-08-26 23:30:00-07:00,2015,8,26,23,30,18.0,10.7,0.0,0.0,0.0,0.16,810.0,257.0,1.2000000000000002,62.369131319060585 +2015-08-27 00:30:00-07:00,2015,8,27,0,30,17.5,10.7,0.0,0.0,0.0,0.16,810.0,258.0,1.2000000000000002,64.36449187445476 +2015-08-27 01:30:00-07:00,2015,8,27,1,30,17.0,10.6,0.0,0.0,0.0,0.16,810.0,255.0,1.1,65.99104603890328 +2015-08-27 02:30:00-07:00,2015,8,27,2,30,16.8,10.5,0.0,0.0,0.0,0.16,810.0,244.0,0.9,66.38938532379613 +2015-08-27 03:30:00-07:00,2015,8,27,3,30,16.7,10.5,0.0,0.0,0.0,0.16,810.0,226.0,0.6000000000000001,66.8120925070903 +2015-08-27 04:30:00-07:00,2015,8,27,4,30,16.7,10.7,0.0,0.0,0.0,0.16,810.0,223.0,0.6000000000000001,67.70776957693054 +2015-08-27 05:30:00-07:00,2015,8,27,5,30,17.2,11.5,0.0,0.0,0.0,0.16,811.0,233.0,0.9,69.1704198154559 +2015-08-27 06:30:00-07:00,2015,8,27,6,30,18.2,12.4,72.0,6.0,73.0,0.16,811.0,245.0,1.1,68.91396341857087 +2015-08-27 07:30:00-07:00,2015,8,27,7,30,19.9,12.8,64.0,741.0,357.0,0.16,811.0,288.0,0.9,63.62868156644211 +2015-08-27 08:30:00-07:00,2015,8,27,8,30,22.200000000000003,12.3,241.0,51.0,270.0,0.16,811.0,338.0,1.0,53.464174651787275 +2015-08-27 09:30:00-07:00,2015,8,27,9,30,24.5,11.2,327.0,155.0,436.0,0.16,811.0,355.0,1.4,43.275684355244344 +2015-08-27 10:30:00-07:00,2015,8,27,10,30,26.200000000000003,10.5,431.0,131.0,537.0,0.16,810.0,183.0,1.7000000000000002,37.33678806561348 +2015-08-27 11:30:00-07:00,2015,8,27,11,30,27.0,10.1,403.0,414.0,759.0,0.16,810.0,15.0,1.9,34.679503858131525 +2015-08-27 12:30:00-07:00,2015,8,27,12,30,27.0,10.2,352.0,532.0,810.0,0.16,809.0,25.0,2.1,34.91201217244019 +2015-08-27 13:30:00-07:00,2015,8,27,13,30,26.0,10.7,351.0,481.0,742.0,0.16,809.0,27.0,2.4000000000000004,38.28748664717827 +2015-08-27 14:30:00-07:00,2015,8,27,14,30,24.4,11.2,244.0,1.0,244.0,0.16,810.0,18.0,2.7,43.53554253809232 +2015-08-27 15:30:00-07:00,2015,8,27,15,30,23.0,11.6,234.0,5.0,237.0,0.16,810.0,6.0,2.8000000000000003,48.63441282725591 +2015-08-27 16:30:00-07:00,2015,8,27,16,30,21.9,11.8,170.0,11.0,175.0,0.16,810.0,3.0,2.5,52.687804723905096 +2015-08-27 17:30:00-07:00,2015,8,27,17,30,20.6,11.5,58.0,498.0,165.0,0.16,810.0,18.0,1.6,55.94099819345638 +2015-08-27 18:30:00-07:00,2015,8,27,18,30,19.1,11.9,6.0,0.0,6.0,0.16,811.0,23.0,0.8,63.033984345801265 +2015-08-27 19:30:00-07:00,2015,8,27,19,30,18.1,11.6,0.0,0.0,0.0,0.16,812.0,157.0,0.7000000000000001,65.78905930930473 +2015-08-27 20:30:00-07:00,2015,8,27,20,30,17.2,11.0,0.0,0.0,0.0,0.16,812.0,289.0,0.8,66.9168728206601 +2015-08-27 21:30:00-07:00,2015,8,27,21,30,16.3,10.7,0.0,0.0,0.0,0.16,812.0,282.0,1.0,69.45211394450878 +2015-08-27 22:30:00-07:00,2015,8,27,22,30,15.6,10.2,0.0,0.0,0.0,0.16,812.0,286.0,1.2000000000000002,70.24641402973096 +2015-08-27 23:30:00-07:00,2015,8,27,23,30,15.0,9.7,0.0,0.0,0.0,0.16,812.0,288.0,1.4,70.60182452533536 +2015-08-28 00:30:00-07:00,2015,8,28,0,30,14.5,9.3,0.0,0.0,0.0,0.16,812.0,287.0,1.5,70.9814538433012 +2015-08-28 01:30:00-07:00,2015,8,28,1,30,14.0,9.0,0.0,0.0,0.0,0.16,812.0,285.0,1.6,71.84904007653961 +2015-08-28 02:30:00-07:00,2015,8,28,2,30,13.7,8.700000000000001,0.0,0.0,0.0,0.16,812.0,285.0,1.7000000000000002,71.79296580150461 +2015-08-28 03:30:00-07:00,2015,8,28,3,30,13.3,8.4,0.0,0.0,0.0,0.16,812.0,287.0,1.7000000000000002,72.2057250725161 +2015-08-28 04:30:00-07:00,2015,8,28,4,30,13.1,8.200000000000001,0.0,0.0,0.0,0.16,812.0,290.0,1.8,72.16864158178883 +2015-08-28 05:30:00-07:00,2015,8,28,5,30,14.4,8.1,0.0,0.0,0.0,0.16,812.0,295.0,2.2,65.87554204256342 +2015-08-28 06:30:00-07:00,2015,8,28,6,30,17.3,8.4,76.0,321.0,141.0,0.16,813.0,316.0,2.6,55.85560314482148 +2015-08-28 07:30:00-07:00,2015,8,28,7,30,20.200000000000003,8.3,129.0,524.0,335.0,0.16,813.0,167.0,2.8000000000000003,46.28081327381628 +2015-08-28 08:30:00-07:00,2015,8,28,8,30,22.6,7.5,150.0,674.0,530.0,0.16,813.0,5.0,3.0,37.837048974184675 +2015-08-28 09:30:00-07:00,2015,8,28,9,30,24.4,6.9,162.0,758.0,696.0,0.16,813.0,12.0,3.0,32.580460329547215 +2015-08-28 10:30:00-07:00,2015,8,28,10,30,25.8,6.4,367.0,6.0,372.0,0.16,813.0,20.0,2.9000000000000004,28.960984919147087 +2015-08-28 11:30:00-07:00,2015,8,28,11,30,26.9,6.0,463.0,111.0,558.0,0.16,813.0,29.0,2.8000000000000003,26.40195980448176 +2015-08-28 12:30:00-07:00,2015,8,28,12,30,27.5,5.7,174.0,821.0,879.0,0.16,812.0,38.0,2.8000000000000003,24.965627828746147 +2015-08-28 13:30:00-07:00,2015,8,28,13,30,27.6,5.7,186.0,771.0,811.0,0.16,812.0,45.0,2.8000000000000003,24.819996057657537 +2015-08-28 14:30:00-07:00,2015,8,28,14,30,27.1,5.7,153.0,774.0,704.0,0.16,812.0,49.0,2.7,25.5578571477791 +2015-08-28 15:30:00-07:00,2015,8,28,15,30,26.1,5.7,25.0,0.0,25.0,0.16,812.0,51.0,2.6,27.10888867794657 +2015-08-28 16:30:00-07:00,2015,8,28,16,30,24.700000000000003,5.6000000000000005,128.0,536.0,344.0,0.16,812.0,55.0,2.0,29.258161315433295 +2015-08-28 17:30:00-07:00,2015,8,28,17,30,22.1,7.0,82.0,309.0,147.0,0.16,813.0,65.0,1.2000000000000002,37.6942991576563 +2015-08-28 18:30:00-07:00,2015,8,28,18,30,19.9,9.0,4.0,0.0,4.0,0.16,813.0,84.0,0.9,49.43346369624137 +2015-08-28 19:30:00-07:00,2015,8,28,19,30,19.0,8.1,0.0,0.0,0.0,0.16,814.0,106.0,0.9,49.18920902851113 +2015-08-28 20:30:00-07:00,2015,8,28,20,30,18.2,7.6,0.0,0.0,0.0,0.16,814.0,128.0,0.9,49.9842596949574 +2015-08-28 21:30:00-07:00,2015,8,28,21,30,17.400000000000002,7.4,0.0,0.0,0.0,0.16,814.0,145.0,1.0,51.85374260922294 +2015-08-28 22:30:00-07:00,2015,8,28,22,30,16.6,6.9,0.0,0.0,0.0,0.16,814.0,158.0,1.0,52.71437814494864 +2015-08-28 23:30:00-07:00,2015,8,28,23,30,15.8,6.6000000000000005,0.0,0.0,0.0,0.16,814.0,169.0,1.1,54.34124480964416 +2015-08-29 00:30:00-07:00,2015,8,29,0,30,15.0,6.4,0.0,0.0,0.0,0.16,814.0,182.0,1.2000000000000002,56.42050666202941 +2015-08-29 01:30:00-07:00,2015,8,29,1,30,14.3,6.1000000000000005,0.0,0.0,0.0,0.16,814.0,195.0,1.2000000000000002,57.818552444999256 +2015-08-29 02:30:00-07:00,2015,8,29,2,30,13.7,6.0,0.0,0.0,0.0,0.16,814.0,208.0,1.3,59.698476037197786 +2015-08-29 03:30:00-07:00,2015,8,29,3,30,13.2,5.9,0.0,0.0,0.0,0.16,814.0,219.0,1.3,61.24936321404716 +2015-08-29 04:30:00-07:00,2015,8,29,4,30,12.9,5.9,0.0,0.0,0.0,0.16,814.0,228.0,1.4,62.461374181827324 +2015-08-29 05:30:00-07:00,2015,8,29,5,30,14.6,5.9,0.0,0.0,0.0,0.16,814.0,236.0,1.6,55.930729948016996 +2015-08-29 06:30:00-07:00,2015,8,29,6,30,18.6,6.800000000000001,65.0,401.0,145.0,0.16,814.0,235.0,2.1,46.15083773251781 +2015-08-29 07:30:00-07:00,2015,8,29,7,30,22.700000000000003,6.2,100.0,630.0,345.0,0.16,814.0,216.0,2.4000000000000004,34.39953600278638 +2015-08-29 08:30:00-07:00,2015,8,29,8,30,25.6,4.800000000000001,118.0,760.0,545.0,0.16,814.0,199.0,2.3000000000000003,26.23116664132292 +2015-08-29 09:30:00-07:00,2015,8,29,9,30,27.6,4.2,126.0,837.0,714.0,0.16,813.0,190.0,2.1,22.357670775609623 +2015-08-29 10:30:00-07:00,2015,8,29,10,30,29.200000000000003,3.9,128.0,883.0,837.0,0.16,813.0,178.0,2.0,19.947418029206197 +2015-08-29 11:30:00-07:00,2015,8,29,11,30,30.4,3.7,120.0,916.0,903.0,0.16,812.0,162.0,2.1,18.35656697464096 +2015-08-29 12:30:00-07:00,2015,8,29,12,30,31.1,3.4000000000000004,115.0,924.0,907.0,0.16,812.0,149.0,2.5,17.26795772156863 +2015-08-29 13:30:00-07:00,2015,8,29,13,30,31.3,3.3000000000000003,111.0,910.0,844.0,0.16,811.0,144.0,3.0,16.95232920084649 +2015-08-29 14:30:00-07:00,2015,8,29,14,30,31.0,3.5,99.0,888.0,728.0,0.16,811.0,143.0,3.4000000000000004,17.48957261265048 +2015-08-29 15:30:00-07:00,2015,8,29,15,30,30.0,3.9,91.0,828.0,561.0,0.16,810.0,147.0,3.7,19.048680432614972 +2015-08-29 16:30:00-07:00,2015,8,29,16,30,28.200000000000003,4.5,78.0,719.0,364.0,0.16,810.0,152.0,3.7,22.04652877191943 +2015-08-29 17:30:00-07:00,2015,8,29,17,30,25.200000000000003,5.800000000000001,109.0,129.0,135.0,0.16,810.0,158.0,3.2,28.79445998668277 +2015-08-29 18:30:00-07:00,2015,8,29,18,30,22.3,7.4,0.0,0.0,0.0,0.16,811.0,167.0,2.8000000000000003,38.27104917731137 +2015-08-29 19:30:00-07:00,2015,8,29,19,30,20.6,7.1000000000000005,0.0,0.0,0.0,0.16,811.0,179.0,2.9000000000000004,41.60739832178296 +2015-08-29 20:30:00-07:00,2015,8,29,20,30,19.4,6.6000000000000005,0.0,0.0,0.0,0.16,811.0,190.0,3.1,43.305166949350834 +2015-08-29 21:30:00-07:00,2015,8,29,21,30,18.5,6.0,0.0,0.0,0.0,0.16,811.0,200.0,3.1,43.95201814281124 +2015-08-29 22:30:00-07:00,2015,8,29,22,30,17.8,5.5,0.0,0.0,0.0,0.16,811.0,207.0,3.0,44.36492308445113 +2015-08-29 23:30:00-07:00,2015,8,29,23,30,17.3,5.2,0.0,0.0,0.0,0.16,810.0,215.0,2.8000000000000003,44.84268805981218 +2015-08-30 00:30:00-07:00,2015,8,30,0,30,16.900000000000002,5.2,0.0,0.0,0.0,0.16,810.0,223.0,2.6,45.99257231626234 +2015-08-30 01:30:00-07:00,2015,8,30,1,30,16.7,5.2,0.0,0.0,0.0,0.16,810.0,233.0,2.6,46.579887063786515 +2015-08-30 02:30:00-07:00,2015,8,30,2,30,16.5,5.2,0.0,0.0,0.0,0.16,809.0,243.0,2.5,47.175624246557916 +2015-08-30 03:30:00-07:00,2015,8,30,3,30,16.1,5.0,0.0,0.0,0.0,0.16,809.0,251.0,2.6,47.7242386373819 +2015-08-30 04:30:00-07:00,2015,8,30,4,30,15.9,4.800000000000001,0.0,0.0,0.0,0.16,810.0,258.0,2.6,47.66849321586855 +2015-08-30 05:30:00-07:00,2015,8,30,5,30,17.5,4.5,0.0,0.0,0.0,0.16,810.0,263.0,3.0,42.17103519038902 +2015-08-30 06:30:00-07:00,2015,8,30,6,30,21.1,4.6000000000000005,89.0,279.0,144.0,0.16,810.0,268.0,3.3000000000000003,33.941640823748095 +2015-08-30 07:30:00-07:00,2015,8,30,7,30,24.9,4.800000000000001,162.0,482.0,348.0,0.16,810.0,282.0,3.0,27.34698006756219 +2015-08-30 08:30:00-07:00,2015,8,30,8,30,27.9,4.1000000000000005,65.0,903.0,570.0,0.16,810.0,301.0,2.4000000000000004,21.81578596681551 +2015-08-30 09:30:00-07:00,2015,8,30,9,30,29.8,3.0,71.0,953.0,737.0,0.16,810.0,314.0,1.4,18.084314229936588 +2015-08-30 10:30:00-07:00,2015,8,30,10,30,31.200000000000003,2.3000000000000003,456.0,223.0,634.0,0.16,809.0,191.0,0.7000000000000001,15.88256750970435 +2015-08-30 11:30:00-07:00,2015,8,30,11,30,32.2,1.9,390.0,6.0,395.0,0.16,809.0,79.0,0.8,14.584503619114216 +2015-08-30 12:30:00-07:00,2015,8,30,12,30,32.800000000000004,1.8,309.0,1.0,310.0,0.16,808.0,100.0,1.5,13.99869959906737 +2015-08-30 13:30:00-07:00,2015,8,30,13,30,32.9,1.6,257.0,723.0,837.0,0.16,807.0,105.0,2.0,13.722457035061911 +2015-08-30 14:30:00-07:00,2015,8,30,14,30,32.5,1.6,76.0,939.0,737.0,0.16,807.0,113.0,2.3000000000000003,14.035210517879124 +2015-08-30 15:30:00-07:00,2015,8,30,15,30,31.6,1.8,72.0,879.0,569.0,0.16,807.0,123.0,2.5,14.981515380907517 +2015-08-30 16:30:00-07:00,2015,8,30,16,30,29.8,2.1,64.0,777.0,369.0,0.16,806.0,133.0,2.1,16.96455243323963 +2015-08-30 17:30:00-07:00,2015,8,30,17,30,26.6,5.2,46.0,571.0,162.0,0.16,807.0,145.0,1.4,25.423201017232852 +2015-08-30 18:30:00-07:00,2015,8,30,18,30,24.0,7.9,0.0,0.0,0.0,0.16,807.0,163.0,1.4,35.73038844761591 +2015-08-30 19:30:00-07:00,2015,8,30,19,30,22.4,7.2,0.0,0.0,0.0,0.16,807.0,180.0,1.6,37.52253122411217 +2015-08-30 20:30:00-07:00,2015,8,30,20,30,20.700000000000003,7.7,0.0,0.0,0.0,0.16,807.0,195.0,1.4,43.08157888799628 +2015-08-30 21:30:00-07:00,2015,8,30,21,30,19.4,7.6,0.0,0.0,0.0,0.16,807.0,212.0,1.3,46.37371507141216 +2015-08-30 22:30:00-07:00,2015,8,30,22,30,18.7,7.6,0.0,0.0,0.0,0.16,807.0,233.0,1.4,48.44285609141386 +2015-08-30 23:30:00-07:00,2015,8,30,23,30,18.2,7.5,0.0,0.0,0.0,0.16,807.0,256.0,1.7000000000000002,49.6444527386878 +2015-08-31 00:30:00-07:00,2015,8,31,0,30,18.0,7.4,0.0,0.0,0.0,0.16,807.0,269.0,2.1,49.93002990293718 +2015-08-31 01:30:00-07:00,2015,8,31,1,30,18.0,7.4,0.0,0.0,0.0,0.16,808.0,274.0,2.4000000000000004,49.93002990293718 +2015-08-31 02:30:00-07:00,2015,8,31,2,30,17.900000000000002,7.6,0.0,0.0,0.0,0.16,808.0,277.0,2.5,50.935479902763255 +2015-08-31 03:30:00-07:00,2015,8,31,3,30,17.5,8.0,0.0,0.0,0.0,0.16,808.0,280.0,2.2,53.67752216458875 +2015-08-31 04:30:00-07:00,2015,8,31,4,30,17.1,8.5,0.0,0.0,0.0,0.16,808.0,276.0,1.7000000000000002,56.95107424979693 +2015-08-31 05:30:00-07:00,2015,8,31,5,30,17.5,9.1,0.0,0.0,0.0,0.16,809.0,271.0,1.5,57.82584343455112 +2015-08-31 06:30:00-07:00,2015,8,31,6,30,19.3,9.8,3.0,0.0,3.0,0.16,809.0,283.0,1.6,54.14481630920192 +2015-08-31 07:30:00-07:00,2015,8,31,7,30,21.9,9.6,18.0,0.0,18.0,0.16,809.0,157.0,1.5,45.513609761689395 +2015-08-31 08:30:00-07:00,2015,8,31,8,30,24.3,9.3,259.0,64.0,294.0,0.16,810.0,52.0,1.3,38.578966509059484 +2015-08-31 09:30:00-07:00,2015,8,31,9,30,25.0,8.9,150.0,574.0,550.0,0.16,810.0,103.0,1.1,36.01411291831017 +2015-08-31 10:30:00-07:00,2015,8,31,10,30,24.4,9.1,451.0,195.0,606.0,0.16,810.0,131.0,1.1,37.83561663254857 +2015-08-31 11:30:00-07:00,2015,8,31,11,30,23.4,9.7,462.0,140.0,581.0,0.16,810.0,147.0,1.1,41.833479818777384 +2015-08-31 12:30:00-07:00,2015,8,31,12,30,22.8,10.4,383.0,470.0,781.0,0.16,810.0,158.0,1.1,45.45826985588013 +2015-08-31 13:30:00-07:00,2015,8,31,13,30,22.4,10.9,356.0,8.0,363.0,0.16,810.0,160.0,1.1,48.15165677300655 +2015-08-31 14:30:00-07:00,2015,8,31,14,30,22.0,11.0,113.0,0.0,113.0,0.16,810.0,145.0,1.1,49.66749449203244 +2015-08-31 15:30:00-07:00,2015,8,31,15,30,21.4,10.9,143.0,3.0,145.0,0.16,810.0,112.0,1.0,51.18166333331646 +2015-08-31 16:30:00-07:00,2015,8,31,16,30,20.700000000000003,10.9,175.0,37.0,189.0,0.16,810.0,79.0,0.7000000000000001,53.430125132240576 +2015-08-31 17:30:00-07:00,2015,8,31,17,30,24.700000000000003,3.1,89.0,4.0,89.0,0.17,809.0,163.0,3.0,24.5614078521327 +2015-08-31 18:30:00-07:00,2015,8,31,18,30,22.0,4.6000000000000005,0.0,0.0,0.0,0.17,810.0,161.0,2.1,32.12305619879742 +2015-08-31 19:30:00-07:00,2015,8,31,19,30,20.3,5.1000000000000005,0.0,0.0,0.0,0.17,811.0,165.0,1.9,36.92035322730375 +2015-08-31 20:30:00-07:00,2015,8,31,20,30,19.1,5.1000000000000005,0.0,0.0,0.0,0.17,811.0,173.0,1.9,39.774481711163176 +2015-08-31 21:30:00-07:00,2015,8,31,21,30,18.0,5.0,0.0,0.0,0.0,0.17,811.0,184.0,1.9,42.31428094483276 +2015-08-31 22:30:00-07:00,2015,8,31,22,30,17.1,4.7,0.0,0.0,0.0,0.17,811.0,191.0,1.9,43.8591806830038 +2015-08-31 23:30:00-07:00,2015,8,31,23,30,16.5,4.1000000000000005,0.0,0.0,0.0,0.17,811.0,195.0,2.0,43.68798670022048 +2012-09-01 00:30:00-07:00,2012,9,1,0,30,16.0,3.3000000000000003,0.0,0.0,0.0,0.17,811.0,199.0,2.1,42.63356384611911 +2012-09-01 01:30:00-07:00,2012,9,1,1,30,15.6,2.5,0.0,0.0,0.0,0.17,810.0,207.0,2.1,41.328857070588455 +2012-09-01 02:30:00-07:00,2012,9,1,2,30,15.3,2.0,0.0,0.0,0.0,0.17,810.0,215.0,2.0,40.6580297135389 +2012-09-01 03:30:00-07:00,2012,9,1,3,30,15.0,2.0,0.0,0.0,0.0,0.17,810.0,224.0,1.8,41.4494160463935 +2012-09-01 04:30:00-07:00,2012,9,1,4,30,15.0,2.3000000000000003,0.0,0.0,0.0,0.17,810.0,233.0,1.7000000000000002,42.344968807776496 +2012-09-01 05:30:00-07:00,2012,9,1,5,30,16.8,3.0,0.0,0.0,0.0,0.17,811.0,242.0,1.9,39.66728739094821 +2012-09-01 06:30:00-07:00,2012,9,1,6,30,20.9,4.7,42.0,574.0,150.0,0.17,811.0,244.0,2.2,34.602060312571325 +2012-09-01 07:30:00-07:00,2012,9,1,7,30,25.1,5.1000000000000005,61.0,773.0,354.0,0.17,811.0,228.0,1.7000000000000002,27.593424959706322 +2012-09-01 08:30:00-07:00,2012,9,1,8,30,28.1,4.4,74.0,862.0,549.0,0.17,811.0,195.0,1.0,22.020772765686537 +2012-09-01 09:30:00-07:00,2012,9,1,9,30,30.0,4.1000000000000005,82.0,910.0,712.0,0.17,811.0,151.0,0.9,19.317969763144582 +2012-09-01 10:30:00-07:00,2012,9,1,10,30,31.3,3.9,92.0,932.0,829.0,0.17,810.0,126.0,1.5,17.683919618602445 +2012-09-01 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15:30:00-07:00,2005,10,6,15,30,15.2,-4.7,50.0,869.0,377.0,0.16,809.0,51.0,3.1,25.033199269753897 +2005-10-06 16:30:00-07:00,2005,10,6,16,30,12.3,-4.5,37.0,706.0,177.0,0.16,809.0,59.0,2.5,30.68161886259162 +2005-10-06 17:30:00-07:00,2005,10,6,17,30,8.8,-3.2,0.0,0.0,0.0,0.16,810.0,77.0,1.7000000000000002,42.697748799709856 +2005-10-06 18:30:00-07:00,2005,10,6,18,30,6.7,-3.2,0.0,0.0,0.0,0.16,811.0,106.0,1.1,49.262919563629005 +2005-10-06 19:30:00-07:00,2005,10,6,19,30,5.800000000000001,-3.3000000000000003,0.0,0.0,0.0,0.16,811.0,146.0,0.7000000000000001,52.02672202625309 +2005-10-06 20:30:00-07:00,2005,10,6,20,30,4.9,-3.5,0.0,0.0,0.0,0.16,811.0,193.0,0.7000000000000001,54.56131900802677 +2005-10-06 21:30:00-07:00,2005,10,6,21,30,4.0,-3.9,0.0,0.0,0.0,0.16,810.0,220.0,0.9,56.39356578411173 +2005-10-06 22:30:00-07:00,2005,10,6,22,30,3.4000000000000004,-4.4,0.0,0.0,0.0,0.16,810.0,228.0,1.0,56.65844634453424 +2005-10-06 23:30:00-07:00,2005,10,6,23,30,3.1,-5.0,0.0,0.0,0.0,0.16,809.0,237.0,1.0,55.31156214684022 +2005-10-07 00:30:00-07:00,2005,10,7,0,30,3.1,-5.6000000000000005,0.0,0.0,0.0,0.16,809.0,247.0,1.1,52.852705007950355 +2005-10-07 01:30:00-07:00,2005,10,7,1,30,3.1,-6.300000000000001,0.0,0.0,0.0,0.16,809.0,255.0,1.1,50.10724724282998 +2005-10-07 02:30:00-07:00,2005,10,7,2,30,3.0,-6.800000000000001,0.0,0.0,0.0,0.16,809.0,259.0,1.2000000000000002,48.5669395889896 +2005-10-07 03:30:00-07:00,2005,10,7,3,30,2.8000000000000003,-7.2,0.0,0.0,0.0,0.16,808.0,261.0,1.3,47.76786843244288 +2005-10-07 04:30:00-07:00,2005,10,7,4,30,2.6,-7.6,0.0,0.0,0.0,0.16,808.0,263.0,1.4,46.97811864804325 +2005-10-07 05:30:00-07:00,2005,10,7,5,30,2.9000000000000004,-8.1,0.0,0.0,0.0,0.16,808.0,261.0,1.5,44.2426811797146 +2005-10-07 06:30:00-07:00,2005,10,7,6,30,5.7,-8.1,20.0,511.0,60.0,0.16,808.0,257.0,1.9,36.36582487835337 +2005-10-07 07:30:00-07:00,2005,10,7,7,30,10.4,-6.9,39.0,832.0,259.0,0.16,807.0,246.0,2.0,28.989114168525713 +2005-10-07 08:30:00-07:00,2005,10,7,8,30,15.0,-7.800000000000001,48.0,947.0,458.0,0.16,807.0,224.0,1.3,20.00968819403468 +2005-10-07 09:30:00-07:00,2005,10,7,9,30,18.9,-9.8,54.0,1004.0,623.0,0.16,806.0,168.0,0.6000000000000001,13.368630830758484 +2005-10-07 10:30:00-07:00,2005,10,7,10,30,21.6,-10.9,57.0,1032.0,736.0,0.16,805.0,95.0,0.8,10.371279723211233 +2005-10-07 11:30:00-07:00,2005,10,7,11,30,23.4,-11.1,58.0,1045.0,788.0,0.16,805.0,61.0,1.4,9.150215990380172 +2005-10-07 12:30:00-07:00,2005,10,7,12,30,24.3,-10.9,58.0,1042.0,775.0,0.16,804.0,57.0,1.9,8.807163904279 +2005-10-07 13:30:00-07:00,2005,10,7,13,30,24.6,-10.5,57.0,1014.0,693.0,0.16,804.0,59.0,2.2,8.92937216937581 +2005-10-07 14:30:00-07:00,2005,10,7,14,30,24.1,-10.0,56.0,970.0,558.0,0.16,804.0,67.0,2.4000000000000004,9.571837533458279 +2005-10-07 15:30:00-07:00,2005,10,7,15,30,22.5,-9.4,48.0,885.0,376.0,0.16,804.0,82.0,2.1,11.052397425966648 +2005-10-07 16:30:00-07:00,2005,10,7,16,30,18.1,-5.800000000000001,35.0,714.0,173.0,0.16,804.0,104.0,1.5,19.15689257691713 +2005-10-07 17:30:00-07:00,2005,10,7,17,30,14.1,-2.1,0.0,0.0,0.0,0.16,804.0,136.0,1.3,32.625848226452064 +2005-10-07 18:30:00-07:00,2005,10,7,18,30,12.3,-3.0,0.0,0.0,0.0,0.16,804.0,175.0,1.4,34.325185636742184 +2005-10-07 19:30:00-07:00,2005,10,7,19,30,10.6,-3.0,0.0,0.0,0.0,0.16,804.0,205.0,1.6,38.4100420581202 +2005-10-07 20:30:00-07:00,2005,10,7,20,30,9.3,-3.1,0.0,0.0,0.0,0.16,804.0,222.0,1.5,41.591132484861454 +2005-10-07 21:30:00-07:00,2005,10,7,21,30,8.6,-3.6,0.0,0.0,0.0,0.16,804.0,240.0,1.5,42.00666411150512 +2005-10-07 22:30:00-07:00,2005,10,7,22,30,8.3,-4.2,0.0,0.0,0.0,0.16,804.0,258.0,1.7000000000000002,40.98492625562175 +2005-10-07 23:30:00-07:00,2005,10,7,23,30,8.1,-4.7,0.0,0.0,0.0,0.16,804.0,269.0,2.0,40.01090914668036 +2005-10-08 00:30:00-07:00,2005,10,8,0,30,7.800000000000001,-4.7,0.0,0.0,0.0,0.16,803.0,276.0,2.3000000000000003,40.83548584588951 +2005-10-08 01:30:00-07:00,2005,10,8,1,30,7.4,-4.3,0.0,0.0,0.0,0.16,803.0,279.0,2.3000000000000003,43.247477742743484 +2005-10-08 02:30:00-07:00,2005,10,8,2,30,7.0,-3.5,0.0,0.0,0.0,0.16,803.0,279.0,2.2,47.19199562909889 +2005-10-08 03:30:00-07:00,2005,10,8,3,30,6.7,-3.1,0.0,0.0,0.0,0.16,803.0,276.0,2.0,49.63098083093081 +2005-10-08 04:30:00-07:00,2005,10,8,4,30,6.6000000000000005,-3.0,0.0,0.0,0.0,0.16,803.0,273.0,1.8,50.34621287672355 +2005-10-08 05:30:00-07:00,2005,10,8,5,30,6.9,-3.3000000000000003,0.0,0.0,0.0,0.16,802.0,268.0,1.6,48.23075249480086 +2005-10-08 06:30:00-07:00,2005,10,8,6,30,9.9,-3.1,20.0,463.0,54.0,0.16,802.0,261.0,1.6,39.94963344481481 +2005-10-08 07:30:00-07:00,2005,10,8,7,30,15.0,0.0,40.0,793.0,248.0,0.16,802.0,253.0,1.7000000000000002,35.895779599989226 +2005-10-08 08:30:00-07:00,2005,10,8,8,30,19.3,-0.3,53.0,910.0,443.0,0.16,802.0,251.0,1.3,26.75550214650311 +2005-10-08 09:30:00-07:00,2005,10,8,9,30,22.3,-1.7000000000000002,59.0,971.0,605.0,0.16,802.0,222.0,0.8,20.080711291299554 +2005-10-08 10:30:00-07:00,2005,10,8,10,30,24.1,-2.7,63.0,1000.0,716.0,0.16,801.0,178.0,1.3,16.72880991748383 +2005-10-08 11:30:00-07:00,2005,10,8,11,30,25.4,-3.6,62.0,1012.0,764.0,0.16,800.0,167.0,2.1,14.475908199113531 +2005-10-08 12:30:00-07:00,2005,10,8,12,30,26.200000000000003,-4.1000000000000005,61.0,1009.0,751.0,0.16,799.0,169.0,2.6,13.298286916831414 +2005-10-08 13:30:00-07:00,2005,10,8,13,30,26.5,-4.5,58.0,987.0,672.0,0.16,798.0,171.0,2.9000000000000004,12.677965268727023 +2005-10-08 14:30:00-07:00,2005,10,8,14,30,26.1,-4.800000000000001,52.0,945.0,537.0,0.16,798.0,174.0,3.0,12.690732333462016 +2005-10-08 15:30:00-07:00,2005,10,8,15,30,24.6,-4.800000000000001,128.0,512.0,314.0,0.16,797.0,174.0,2.7,13.874780153733301 +2005-10-08 16:30:00-07:00,2005,10,8,16,30,21.0,-3.0,34.0,683.0,162.0,0.16,797.0,171.0,2.2,19.749302071996983 +2005-10-08 17:30:00-07:00,2005,10,8,17,30,17.0,-0.1,0.0,0.0,0.0,0.16,797.0,175.0,1.9,31.36487787012558 +2005-10-08 18:30:00-07:00,2005,10,8,18,30,15.0,-0.2,0.0,0.0,0.0,0.16,798.0,192.0,1.7000000000000002,35.37848964025672 +2005-10-08 19:30:00-07:00,2005,10,8,19,30,13.9,0.2,0.0,0.0,0.0,0.16,798.0,212.0,1.8,39.10227652194943 +2005-10-08 20:30:00-07:00,2005,10,8,20,30,12.9,0.7000000000000001,0.0,0.0,0.0,0.16,797.0,223.0,2.0,43.26935623250215 +2005-10-08 21:30:00-07:00,2005,10,8,21,30,12.4,1.2000000000000002,0.0,0.0,0.0,0.16,797.0,226.0,1.9,46.348216579641175 +2005-10-08 22:30:00-07:00,2005,10,8,22,30,12.4,1.5,0.0,0.0,0.0,0.16,796.0,224.0,2.1,47.356250117407434 +2005-10-08 23:30:00-07:00,2005,10,8,23,30,12.8,1.8,0.0,0.0,0.0,0.16,796.0,220.0,2.6,47.13158997122487 +2005-10-09 00:30:00-07:00,2005,10,9,0,30,12.8,2.6,0.0,0.0,0.0,0.16,795.0,220.0,3.1,49.89508760894681 +2005-10-09 01:30:00-07:00,2005,10,9,1,30,12.2,3.5,0.0,0.0,0.0,0.16,795.0,227.0,3.1,55.30928108664103 +2005-10-09 02:30:00-07:00,2005,10,9,2,30,11.1,4.1000000000000005,0.0,0.0,0.0,0.16,796.0,244.0,2.4000000000000004,62.03899302891909 +2005-10-09 03:30:00-07:00,2005,10,9,3,30,9.9,4.0,0.0,0.0,0.0,0.16,796.0,270.0,1.6,66.73379932804121 +2005-10-09 04:30:00-07:00,2005,10,9,4,30,9.0,3.4000000000000004,0.0,0.0,0.0,0.16,797.0,299.0,1.3,67.96333953551395 +2005-10-09 05:30:00-07:00,2005,10,9,5,30,8.5,2.9000000000000004,0.0,0.0,0.0,0.16,798.0,320.0,1.4,67.85778008007635 +2005-10-09 06:30:00-07:00,2005,10,9,6,30,8.4,2.6,16.0,0.0,17.0,0.16,798.0,328.0,1.8,66.88101971886991 +2005-10-09 07:30:00-07:00,2005,10,9,7,30,9.1,2.5,51.0,1.0,51.0,0.16,799.0,340.0,2.2,63.33997570216235 +2005-10-09 08:30:00-07:00,2005,10,9,8,30,10.3,2.1,28.0,0.0,28.0,0.16,799.0,184.0,2.5,56.80241258258864 +2005-10-09 09:30:00-07:00,2005,10,9,9,30,11.4,1.9,125.0,1.0,125.0,0.16,799.0,25.0,2.8000000000000003,52.050373753666 +2005-10-09 10:30:00-07:00,2005,10,9,10,30,12.1,1.9,31.0,0.0,31.0,0.16,799.0,37.0,3.0,49.700883910588544 +2005-10-09 11:30:00-07:00,2005,10,9,11,30,12.3,2.1,45.0,0.0,45.0,0.16,799.0,44.0,3.3000000000000003,49.75645970828776 +2005-10-09 12:30:00-07:00,2005,10,9,12,30,12.0,2.4000000000000004,98.0,0.0,98.0,0.16,799.0,45.0,3.7,51.84362887119627 +2005-10-09 13:30:00-07:00,2005,10,9,13,30,11.2,2.7,110.0,1.0,111.0,0.16,799.0,42.0,4.3,55.83429355169848 +2005-10-09 14:30:00-07:00,2005,10,9,14,30,9.6,2.9000000000000004,61.0,0.0,61.0,0.16,800.0,34.0,5.1000000000000005,63.00837093390499 +2005-10-09 15:30:00-07:00,2005,10,9,15,30,7.6,2.9000000000000004,12.0,0.0,12.0,0.16,801.0,24.0,5.7,72.13673468188742 +2005-10-09 16:30:00-07:00,2005,10,9,16,30,5.6000000000000005,2.4000000000000004,48.0,2.0,48.0,0.16,801.0,15.0,6.0,79.8822977225535 +2005-10-09 17:30:00-07:00,2005,10,9,17,30,3.9,1.5,0.0,0.0,0.0,0.16,802.0,11.0,5.6000000000000005,84.34573483751142 +2005-10-09 18:30:00-07:00,2005,10,9,18,30,2.7,0.6000000000000001,0.0,0.0,0.0,0.16,803.0,5.0,4.9,86.04966215023319 +2005-10-09 19:30:00-07:00,2005,10,9,19,30,2.0,-0.2,0.0,0.0,0.0,0.16,804.0,177.0,4.800000000000001,85.35340908218896 +2005-10-09 20:30:00-07:00,2005,10,9,20,30,1.7000000000000002,-0.8,0.0,0.0,0.0,0.16,805.0,353.0,4.800000000000001,83.47420059477018 +2005-10-09 21:30:00-07:00,2005,10,9,21,30,1.3,-1.1,0.0,0.0,0.0,0.16,805.0,354.0,4.9,84.03754477673657 +2005-10-09 22:30:00-07:00,2005,10,9,22,30,0.9,-1.4,0.0,0.0,0.0,0.16,806.0,355.0,5.1000000000000005,84.608034615903 +2005-10-09 23:30:00-07:00,2005,10,9,23,30,0.5,-1.7000000000000002,0.0,0.0,0.0,0.16,805.0,356.0,5.1000000000000005,85.18578601401431 +2005-10-10 00:30:00-07:00,2005,10,10,0,30,0.2,-2.0,0.0,0.0,0.0,0.16,805.0,356.0,5.0,85.15192707145415 +2005-10-10 01:30:00-07:00,2005,10,10,1,30,0.0,-2.3000000000000003,0.0,0.0,0.0,0.16,805.0,356.0,5.0,84.50266982385106 +2005-10-10 02:30:00-07:00,2005,10,10,2,30,-0.1,-2.4000000000000004,0.0,0.0,0.0,0.16,805.0,354.0,5.0,84.49089809722106 +2005-10-10 03:30:00-07:00,2005,10,10,3,30,-0.3,-2.6,0.0,0.0,0.0,0.16,805.0,351.0,5.1000000000000005,84.46731570873949 +2005-10-10 04:30:00-07:00,2005,10,10,4,30,-0.4,-2.8000000000000003,0.0,0.0,0.0,0.16,805.0,348.0,5.2,83.8312622898736 +2005-10-10 05:30:00-07:00,2005,10,10,5,30,-0.5,-3.0,0.0,0.0,0.0,0.16,805.0,351.0,5.1000000000000005,83.19844115490034 +2005-10-10 06:30:00-07:00,2005,10,10,6,30,-0.6000000000000001,-3.1,5.0,0.0,5.0,0.16,806.0,354.0,4.800000000000001,83.18574902974679 +2005-10-10 07:30:00-07:00,2005,10,10,7,30,-0.4,-2.9000000000000004,16.0,0.0,16.0,0.16,806.0,353.0,4.7,83.2111194418596 +2005-10-10 08:30:00-07:00,2005,10,10,8,30,-0.1,-2.6,101.0,2.0,102.0,0.16,807.0,352.0,4.9,83.24907146554085 +2005-10-10 09:30:00-07:00,2005,10,10,9,30,0.2,-2.3000000000000003,39.0,0.0,39.0,0.16,807.0,354.0,5.0,83.28689963524833 +2005-10-10 10:30:00-07:00,2005,10,10,10,30,0.6000000000000001,-1.9,135.0,1.0,136.0,0.16,806.0,356.0,4.9,83.3371454220572 +2005-10-10 11:30:00-07:00,2005,10,10,11,30,0.9,-1.5,248.0,9.0,255.0,0.16,806.0,357.0,4.800000000000001,83.98935203772652 +2005-10-10 12:30:00-07:00,2005,10,10,12,30,1.0,-1.3,204.0,4.0,207.0,0.16,806.0,357.0,4.6000000000000005,84.6196776198425 +2005-10-10 13:30:00-07:00,2005,10,10,13,30,1.1,-1.1,244.0,23.0,258.0,0.16,806.0,358.0,4.4,85.25316814977491 +2005-10-10 14:30:00-07:00,2005,10,10,14,30,1.1,-1.0,151.0,5.0,154.0,0.16,806.0,358.0,4.1000000000000005,85.8790782802092 +2005-10-10 15:30:00-07:00,2005,10,10,15,30,0.9,-1.0,133.0,13.0,137.0,0.16,807.0,356.0,3.8,87.12339156171751 +2005-10-10 16:30:00-07:00,2005,10,10,16,30,0.5,-1.3,70.0,8.0,72.0,0.16,807.0,350.0,3.2,87.7247070040958 +2005-10-10 17:30:00-07:00,2005,10,10,17,30,-0.1,-1.7000000000000002,0.0,0.0,0.0,0.16,807.0,342.0,2.5,88.96779833363395 +2005-10-10 18:30:00-07:00,2005,10,10,18,30,-0.7000000000000001,-2.2,0.0,0.0,0.0,0.16,808.0,335.0,2.1,89.5754297811755 +2005-10-10 19:30:00-07:00,2005,10,10,19,30,-1.1,-2.8000000000000003,0.0,0.0,0.0,0.16,808.0,331.0,2.0,88.22444772926485 +2005-10-10 20:30:00-07:00,2005,10,10,20,30,-1.3,-3.1,0.0,0.0,0.0,0.16,808.0,329.0,1.8,87.55249805436154 +2005-10-10 21:30:00-07:00,2005,10,10,21,30,-1.7000000000000002,-3.3000000000000003,0.0,0.0,0.0,0.16,808.0,327.0,1.7000000000000002,88.82909015176138 +2005-10-10 22:30:00-07:00,2005,10,10,22,30,-2.1,-3.8,0.0,0.0,0.0,0.16,807.0,327.0,1.5,88.13222678986463 +2005-10-10 23:30:00-07:00,2005,10,10,23,30,-2.5,-4.3,0.0,0.0,0.0,0.16,807.0,327.0,1.3,87.43572274269725 +2005-10-11 00:30:00-07:00,2005,10,11,0,30,-2.6,-4.6000000000000005,0.0,0.0,0.0,0.16,807.0,325.0,1.2000000000000002,86.11947938804477 +2005-10-11 01:30:00-07:00,2005,10,11,1,30,-2.7,-4.6000000000000005,0.0,0.0,0.0,0.16,806.0,317.0,1.1,86.76022572912801 +2005-10-11 02:30:00-07:00,2005,10,11,2,30,-2.8000000000000003,-4.7,0.0,0.0,0.0,0.16,806.0,308.0,1.2000000000000002,86.74992534945682 +2005-10-11 03:30:00-07:00,2005,10,11,3,30,-3.1,-5.0,0.0,0.0,0.0,0.16,806.0,302.0,1.2000000000000002,86.71895396062615 +2005-10-11 04:30:00-07:00,2005,10,11,4,30,-3.3000000000000003,-5.4,0.0,0.0,0.0,0.16,805.0,296.0,1.2000000000000002,85.39401380078331 +2005-10-11 05:30:00-07:00,2005,10,11,5,30,-3.5,-5.9,0.0,0.0,0.0,0.16,805.0,288.0,1.2000000000000002,83.44504204206974 +2005-10-11 06:30:00-07:00,2005,10,11,6,30,-2.6,-6.300000000000001,17.0,0.0,17.0,0.16,805.0,282.0,1.1,75.69643546256339 +2005-10-11 07:30:00-07:00,2005,10,11,7,30,0.0,-4.9,23.0,0.0,23.0,0.16,805.0,278.0,0.7000000000000001,69.58269246944127 +2005-10-11 08:30:00-07:00,2005,10,11,8,30,2.3000000000000003,-0.9,212.0,80.0,245.0,0.16,805.0,272.0,0.5,79.39462560741978 +2005-10-11 09:30:00-07:00,2005,10,11,9,30,3.6,-0.3,263.0,91.0,313.0,0.16,805.0,271.0,0.6000000000000001,75.65095612292082 +2005-10-11 10:30:00-07:00,2005,10,11,10,30,5.2,0.0,320.0,202.0,449.0,0.16,805.0,283.0,0.7000000000000001,69.1287718424717 +2005-10-11 11:30:00-07:00,2005,10,11,11,30,6.800000000000001,0.4,333.0,273.0,519.0,0.16,804.0,314.0,0.7000000000000001,63.71519546654014 +2005-10-11 12:30:00-07:00,2005,10,11,12,30,8.0,0.5,219.0,554.0,589.0,0.16,804.0,172.0,0.9,59.12723970886632 +2005-10-11 13:30:00-07:00,2005,10,11,13,30,8.5,0.6000000000000001,361.0,162.0,459.0,0.16,804.0,12.0,1.3,57.569514717802164 +2005-10-11 14:30:00-07:00,2005,10,11,14,30,8.4,0.7000000000000001,275.0,240.0,394.0,0.16,804.0,21.0,1.6,58.38033968733189 +2005-10-11 15:30:00-07:00,2005,10,11,15,30,7.6,0.8,148.0,350.0,270.0,0.16,804.0,32.0,1.6,62.08864578892022 +2005-10-11 16:30:00-07:00,2005,10,11,16,30,5.9,1.2000000000000002,70.0,9.0,71.0,0.16,805.0,46.0,1.1,71.81146914361472 +2005-10-11 17:30:00-07:00,2005,10,11,17,30,3.9,1.1,0.0,0.0,0.0,0.65,806.0,64.0,0.6000000000000001,81.95944063468659 +2005-10-11 18:30:00-07:00,2005,10,11,18,30,2.6,-0.4,0.0,0.0,0.0,0.65,806.0,154.0,0.6000000000000001,80.60504997845186 +2005-10-11 19:30:00-07:00,2005,10,11,19,30,1.8,-0.9,0.0,0.0,0.0,0.65,807.0,238.0,0.9,82.27655111896388 +2005-10-11 20:30:00-07:00,2005,10,11,20,30,1.3,-1.5,0.0,0.0,0.0,0.65,807.0,243.0,1.2000000000000002,81.60930147291054 +2005-10-11 21:30:00-07:00,2005,10,11,21,30,1.0,-2.0,0.0,0.0,0.0,0.65,807.0,248.0,1.4,80.37531738171191 +2005-10-11 22:30:00-07:00,2005,10,11,22,30,0.8,-2.6,0.0,0.0,0.0,0.65,807.0,254.0,1.5,78.0037805678867 +2005-10-11 23:30:00-07:00,2005,10,11,23,30,0.7000000000000001,-3.0,0.0,0.0,0.0,0.65,807.0,261.0,1.7000000000000002,76.26979111307735 +2005-10-12 00:30:00-07:00,2005,10,12,0,30,0.6000000000000001,-3.2,0.0,0.0,0.0,0.65,807.0,269.0,1.8,75.68721167444085 +2005-10-12 01:30:00-07:00,2005,10,12,1,30,0.4,-3.4000000000000004,0.0,0.0,0.0,0.65,807.0,275.0,1.9,75.65228798392008 +2005-10-12 02:30:00-07:00,2005,10,12,2,30,0.2,-3.6,0.0,0.0,0.0,0.65,807.0,280.0,1.9,75.61729360049797 +2005-10-12 03:30:00-07:00,2005,10,12,3,30,0.0,-3.9,0.0,0.0,0.0,0.65,807.0,281.0,1.8,75.01844046375166 +2005-10-12 04:30:00-07:00,2005,10,12,4,30,-0.3,-4.2,0.0,0.0,0.0,0.65,808.0,278.0,1.7000000000000002,74.96469204900266 +2005-10-12 05:30:00-07:00,2005,10,12,5,30,-0.3,-4.6000000000000005,0.0,0.0,0.0,0.65,808.0,277.0,1.6,72.74281254142261 +2005-10-12 06:30:00-07:00,2005,10,12,6,30,1.8,-4.6000000000000005,16.0,0.0,16.0,0.65,809.0,277.0,1.7000000000000002,62.52541554706126 +2005-10-12 07:30:00-07:00,2005,10,12,7,30,5.4,-2.2,146.0,134.0,179.0,0.65,809.0,291.0,1.6,58.03699766034573 +2005-10-12 08:30:00-07:00,2005,10,12,8,30,8.200000000000001,-0.7000000000000001,249.0,174.0,321.0,0.65,810.0,327.0,1.3,53.46426737976748 +2005-10-12 09:30:00-07:00,2005,10,12,9,30,10.0,-0.2,303.0,252.0,440.0,0.65,810.0,350.0,1.4,49.113024412641224 +2005-10-12 10:30:00-07:00,2005,10,12,10,30,11.3,-0.2,83.0,972.0,700.0,0.65,809.0,348.0,1.6,45.04213162776779 +2005-10-12 11:30:00-07:00,2005,10,12,11,30,12.3,-0.3,490.0,165.0,601.0,0.65,809.0,346.0,1.7000000000000002,41.861752484244576 +2005-10-12 12:30:00-07:00,2005,10,12,12,30,13.1,-0.4,395.0,388.0,653.0,0.65,809.0,348.0,1.9,39.4369603975189 +2005-10-12 13:30:00-07:00,2005,10,12,13,30,13.5,-0.5,265.0,1.0,265.0,0.65,809.0,176.0,1.9,38.1432584887754 +2005-10-12 14:30:00-07:00,2005,10,12,14,30,13.3,-0.6000000000000001,286.0,128.0,350.0,0.65,809.0,7.0,1.9,38.36293788850761 +2005-10-12 15:30:00-07:00,2005,10,12,15,30,12.4,-0.5,63.0,805.0,340.0,0.65,809.0,24.0,1.7000000000000002,40.98693874692782 +2005-10-12 16:30:00-07:00,2005,10,12,16,30,9.9,1.2000000000000002,93.0,28.0,97.0,0.65,810.0,49.0,1.2000000000000002,54.703507366889205 +2005-10-12 17:30:00-07:00,2005,10,12,17,30,7.5,1.9,0.0,0.0,0.0,0.16,810.0,79.0,0.9,67.64523929701565 +2005-10-12 18:30:00-07:00,2005,10,12,18,30,6.6000000000000005,0.5,0.0,0.0,0.0,0.16,811.0,134.0,0.8,65.06483333381033 +2005-10-12 19:30:00-07:00,2005,10,12,19,30,5.800000000000001,0.1,0.0,0.0,0.0,0.16,812.0,196.0,0.8,66.79397992439378 +2005-10-12 20:30:00-07:00,2005,10,12,20,30,4.800000000000001,-0.3,0.0,0.0,0.0,0.16,812.0,221.0,1.0,69.54947450478448 +2005-10-12 21:30:00-07:00,2005,10,12,21,30,4.1000000000000005,-0.7000000000000001,0.0,0.0,0.0,0.16,812.0,230.0,1.1,70.94218918355749 +2005-10-12 22:30:00-07:00,2005,10,12,22,30,3.5,-1.2000000000000002,0.0,0.0,0.0,0.16,812.0,234.0,1.2000000000000002,71.34324914629211 +2005-10-12 23:30:00-07:00,2005,10,12,23,30,3.1,-1.8,0.0,0.0,0.0,0.16,812.0,238.0,1.3,70.2236264994308 +2005-10-13 00:30:00-07:00,2005,10,13,0,30,2.9000000000000004,-2.3000000000000003,0.0,0.0,0.0,0.16,811.0,242.0,1.5,68.64566955117307 +2005-10-13 01:30:00-07:00,2005,10,13,1,30,2.7,-2.6,0.0,0.0,0.0,0.16,811.0,247.0,1.6,68.09694182465076 +2005-10-13 02:30:00-07:00,2005,10,13,2,30,2.5,-2.8000000000000003,0.0,0.0,0.0,0.16,811.0,251.0,1.7000000000000002,68.05384243327516 +2005-10-13 03:30:00-07:00,2005,10,13,3,30,2.2,-3.1,0.0,0.0,0.0,0.16,811.0,257.0,1.8,67.98904472761816 +2005-10-13 04:30:00-07:00,2005,10,13,4,30,2.1,-3.3000000000000003,0.0,0.0,0.0,0.16,811.0,263.0,1.8,67.4629434265985 +2005-10-13 05:30:00-07:00,2005,10,13,5,30,2.2,-3.5,0.0,0.0,0.0,0.16,811.0,264.0,1.8,65.99209250178124 +2005-10-13 06:30:00-07:00,2005,10,13,6,30,4.5,-3.6,18.0,414.0,41.0,0.16,811.0,263.0,1.9,55.688524649730304 +2005-10-13 07:30:00-07:00,2005,10,13,7,30,8.6,-1.3,39.0,772.0,227.0,0.16,812.0,263.0,2.4000000000000004,49.80124627103107 +2005-10-13 08:30:00-07:00,2005,10,13,8,30,12.0,-0.5,49.0,904.0,419.0,0.16,812.0,273.0,2.6,42.079230925192526 +2005-10-13 09:30:00-07:00,2005,10,13,9,30,14.6,-1.2000000000000002,56.0,965.0,578.0,0.16,811.0,285.0,2.2,33.74857880136196 +2005-10-13 10:30:00-07:00,2005,10,13,10,30,16.5,-1.8,59.0,997.0,687.0,0.16,811.0,284.0,1.8,28.593234098414342 +2005-10-13 11:30:00-07:00,2005,10,13,11,30,18.0,-2.2,60.0,1010.0,737.0,0.16,810.0,293.0,1.6,25.250354588786436 +2005-10-13 12:30:00-07:00,2005,10,13,12,30,19.1,-2.6,59.0,1007.0,723.0,0.16,810.0,317.0,1.6,22.88295594452401 +2005-10-13 13:30:00-07:00,2005,10,13,13,30,19.5,-2.7,201.0,582.0,548.0,0.16,810.0,336.0,1.8,22.155443058327315 +2005-10-13 14:30:00-07:00,2005,10,13,14,30,19.3,-2.7,53.0,938.0,510.0,0.16,810.0,341.0,1.7000000000000002,22.432750269358557 +2005-10-13 15:30:00-07:00,2005,10,13,15,30,18.0,-2.4000000000000004,138.0,366.0,262.0,0.16,810.0,339.0,1.2000000000000002,24.87984286736887 +2005-10-13 16:30:00-07:00,2005,10,13,16,30,14.7,1.5,31.0,657.0,138.0,0.16,810.0,299.0,0.6000000000000001,40.77434956481083 +2005-10-13 17:30:00-07:00,2005,10,13,17,30,11.1,0.5,0.0,0.0,0.0,0.16,811.0,247.0,0.7000000000000001,48.01690466050861 +2005-10-13 18:30:00-07:00,2005,10,13,18,30,9.1,-0.7000000000000001,0.0,0.0,0.0,0.16,811.0,237.0,1.2000000000000002,50.30756007965742 +2005-10-13 19:30:00-07:00,2005,10,13,19,30,8.1,-0.8,0.0,0.0,0.0,0.16,812.0,243.0,1.8,53.437118985897854 +2005-10-13 20:30:00-07:00,2005,10,13,20,30,7.6,-1.3,0.0,0.0,0.0,0.16,812.0,248.0,2.2,53.30108822890532 +2005-10-13 21:30:00-07:00,2005,10,13,21,30,7.2,-1.8,0.0,0.0,0.0,0.16,812.0,253.0,2.5,52.8019949344808 +2005-10-13 22:30:00-07:00,2005,10,13,22,30,6.9,-2.3000000000000003,0.0,0.0,0.0,0.16,812.0,259.0,2.6,51.94660444291762 +2005-10-13 23:30:00-07:00,2005,10,13,23,30,6.6000000000000005,-2.8000000000000003,0.0,0.0,0.0,0.16,812.0,264.0,2.6,51.09975012781396 +2005-10-14 00:30:00-07:00,2005,10,14,0,30,6.300000000000001,-3.2,0.0,0.0,0.0,0.16,813.0,270.0,2.5,50.63726436080432 +2005-10-14 01:30:00-07:00,2005,10,14,1,30,5.800000000000001,-3.5,0.0,0.0,0.0,0.16,813.0,273.0,2.4000000000000004,51.2563342867872 +2005-10-14 02:30:00-07:00,2005,10,14,2,30,5.5,-3.6,0.0,0.0,0.0,0.16,813.0,275.0,2.2,51.94311129301414 +2005-10-14 03:30:00-07:00,2005,10,14,3,30,5.7,-3.9,0.0,0.0,0.0,0.16,813.0,277.0,1.9,50.091351719679146 +2005-10-14 04:30:00-07:00,2005,10,14,4,30,6.4,-4.6000000000000005,0.0,0.0,0.0,0.16,814.0,273.0,1.7000000000000002,45.283131123471904 +2005-10-14 05:30:00-07:00,2005,10,14,5,30,7.1000000000000005,-5.300000000000001,0.0,0.0,0.0,0.16,814.0,258.0,1.5,40.9348427903679 +2005-10-14 06:30:00-07:00,2005,10,14,6,30,9.2,-5.6000000000000005,16.0,421.0,39.0,0.16,814.0,237.0,1.2000000000000002,34.696751695418236 +2005-10-14 07:30:00-07:00,2005,10,14,7,30,13.1,-1.1,37.0,782.0,225.0,0.16,815.0,207.0,1.3,37.47317502224633 +2005-10-14 08:30:00-07:00,2005,10,14,8,30,16.6,-0.2,47.0,913.0,417.0,0.16,815.0,174.0,2.0,31.938294420015463 +2005-10-14 09:30:00-07:00,2005,10,14,9,30,18.9,-3.1,53.0,967.0,573.0,0.16,815.0,159.0,2.7,22.325823656415952 +2005-10-14 10:30:00-07:00,2005,10,14,10,30,20.5,-3.4000000000000004,59.0,996.0,682.0,0.16,815.0,156.0,3.1,19.768915492754438 +2005-10-14 11:30:00-07:00,2005,10,14,11,30,21.700000000000003,-3.1,62.0,1006.0,732.0,0.16,814.0,155.0,3.3000000000000003,18.77985732448482 +2005-10-14 12:30:00-07:00,2005,10,14,12,30,22.4,-2.8000000000000003,63.0,996.0,714.0,0.16,814.0,154.0,3.3000000000000003,18.40107033708058 +2005-10-14 13:30:00-07:00,2005,10,14,13,30,22.6,-2.7,61.0,974.0,637.0,0.16,813.0,152.0,3.3000000000000003,18.314217003119655 +2005-10-14 14:30:00-07:00,2005,10,14,14,30,22.1,-2.6,54.0,933.0,504.0,0.16,813.0,149.0,3.3000000000000003,19.02016488108445 +2005-10-14 15:30:00-07:00,2005,10,14,15,30,20.4,-2.2,47.0,843.0,328.0,0.16,813.0,143.0,3.0,21.74413958680117 +2005-10-14 16:30:00-07:00,2005,10,14,16,30,16.5,0.2,32.0,638.0,132.0,0.16,813.0,138.0,2.4000000000000004,33.08797824937272 +2005-10-14 17:30:00-07:00,2005,10,14,17,30,12.7,0.6000000000000001,0.0,0.0,0.0,0.16,813.0,144.0,2.2,43.5243357644061 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03:30:00-07:00,2005,10,15,3,30,6.1000000000000005,-1.6,0.0,0.0,0.0,0.16,811.0,222.0,1.9,57.79119928674135 +2005-10-15 04:30:00-07:00,2005,10,15,4,30,6.0,-1.8,0.0,0.0,0.0,0.16,811.0,224.0,1.9,57.34189811821822 +2005-10-15 05:30:00-07:00,2005,10,15,5,30,5.9,-2.0,0.0,0.0,0.0,0.16,810.0,225.0,1.8,56.8950168865822 +2005-10-15 06:30:00-07:00,2005,10,15,6,30,8.1,-2.0,16.0,389.0,35.0,0.16,810.0,226.0,2.1,48.933932204560875 +2005-10-15 07:30:00-07:00,2005,10,15,7,30,12.4,0.0,37.0,764.0,218.0,0.16,810.0,220.0,2.6,42.50355254436875 +2005-10-15 08:30:00-07:00,2005,10,15,8,30,16.3,0.3,47.0,905.0,410.0,0.16,809.0,203.0,3.2,33.755221062473865 +2005-10-15 09:30:00-07:00,2005,10,15,9,30,19.4,-0.5,54.0,965.0,568.0,0.16,809.0,187.0,3.7,26.205432741640426 +2005-10-15 10:30:00-07:00,2005,10,15,10,30,21.0,-1.3,59.0,990.0,674.0,0.16,808.0,180.0,4.2,22.389953559485175 +2005-10-15 11:30:00-07:00,2005,10,15,11,30,21.9,-1.8,61.0,1002.0,723.0,0.16,807.0,176.0,4.7,20.42532759098999 +2005-10-15 12:30:00-07:00,2005,10,15,12,30,22.200000000000003,-2.1,171.0,760.0,665.0,0.16,806.0,173.0,5.0,19.616669217220945 +2005-10-15 13:30:00-07:00,2005,10,15,13,30,21.8,-2.3000000000000003,204.0,555.0,530.0,0.16,805.0,170.0,5.1000000000000005,19.80632884820688 +2005-10-15 14:30:00-07:00,2005,10,15,14,30,20.5,-2.1,181.0,492.0,416.0,0.16,804.0,167.0,5.0,21.770509751004344 +2005-10-15 15:30:00-07:00,2005,10,15,15,30,18.3,-1.2000000000000002,145.0,464.0,298.0,0.16,804.0,165.0,4.5,26.67021866678929 +2005-10-15 16:30:00-07:00,2005,10,15,16,30,15.4,0.9,71.0,236.0,107.0,0.16,804.0,164.0,3.7,37.33579999106275 +2005-10-15 17:30:00-07:00,2005,10,15,17,30,12.9,1.7000000000000002,0.0,0.0,0.0,0.16,804.0,168.0,3.2,46.49073542849331 +2005-10-15 18:30:00-07:00,2005,10,15,18,30,11.2,1.8,0.0,0.0,0.0,0.16,804.0,177.0,3.0,52.36889896270569 +2005-10-15 19:30:00-07:00,2005,10,15,19,30,10.3,1.7000000000000002,0.0,0.0,0.0,0.16,804.0,187.0,2.9000000000000004,55.20312081536384 +2005-10-15 20:30:00-07:00,2005,10,15,20,30,9.6,1.7000000000000002,0.0,0.0,0.0,0.16,804.0,197.0,2.7,57.85086175568707 +2005-10-15 21:30:00-07:00,2005,10,15,21,30,9.3,1.8,0.0,0.0,0.0,0.16,804.0,205.0,2.4000000000000004,59.45231587386471 +2005-10-15 22:30:00-07:00,2005,10,15,22,30,9.2,1.9,0.0,0.0,0.0,0.16,804.0,212.0,2.2,60.28282208724922 +2005-10-15 23:30:00-07:00,2005,10,15,23,30,9.1,1.8,0.0,0.0,0.0,0.16,804.0,215.0,1.9,60.258260109936 +2005-10-16 00:30:00-07:00,2005,10,16,0,30,9.2,1.6,0.0,0.0,0.0,0.16,804.0,220.0,1.7000000000000002,59.00377665627379 +2005-10-16 01:30:00-07:00,2005,10,16,1,30,9.3,1.4,0.0,0.0,0.0,0.16,804.0,236.0,1.9,57.774365774937856 +2005-10-16 02:30:00-07:00,2005,10,16,2,30,9.2,1.3,0.0,0.0,0.0,0.16,804.0,257.0,2.3000000000000003,57.74882800741598 +2005-10-16 03:30:00-07:00,2005,10,16,3,30,8.8,1.3,0.0,0.0,0.0,0.16,804.0,274.0,2.7,59.32768081407423 +2005-10-16 04:30:00-07:00,2005,10,16,4,30,8.4,1.4,0.0,0.0,0.0,0.16,805.0,286.0,2.9000000000000004,61.393670278886574 +2005-10-16 05:30:00-07:00,2005,10,16,5,30,8.200000000000001,1.7000000000000002,0.0,0.0,0.0,0.16,805.0,294.0,2.9000000000000004,63.58319778443817 +2005-10-16 06:30:00-07:00,2005,10,16,6,30,9.5,2.0,16.0,330.0,31.0,0.16,806.0,297.0,3.5,59.502032440696034 +2005-10-16 07:30:00-07:00,2005,10,16,7,30,12.5,2.6,40.0,725.0,209.0,0.16,807.0,300.0,4.1000000000000005,50.88554063426602 +2005-10-16 08:30:00-07:00,2005,10,16,8,30,15.9,3.2,51.0,872.0,398.0,0.16,807.0,311.0,4.4,42.60477076501897 +2005-10-16 09:30:00-07:00,2005,10,16,9,30,18.7,2.2,57.0,940.0,554.0,0.16,807.0,322.0,4.4,33.250288586658 +2005-10-16 10:30:00-07:00,2005,10,16,10,30,20.6,0.9,61.0,973.0,661.0,0.16,807.0,328.0,3.8,26.924438882412225 +2005-10-16 11:30:00-07:00,2005,10,16,11,30,21.8,0.1,62.0,987.0,710.0,0.16,807.0,335.0,3.2,23.6092242116687 +2005-10-16 12:30:00-07:00,2005,10,16,12,30,22.4,-0.3,233.0,713.0,692.0,0.16,806.0,340.0,2.6,22.110350518327675 +2005-10-16 13:30:00-07:00,2005,10,16,13,30,22.4,-0.2,271.0,442.0,528.0,0.16,806.0,342.0,2.0,22.271607997586507 +2005-10-16 14:30:00-07:00,2005,10,16,14,30,21.9,-0.1,148.0,638.0,449.0,0.16,806.0,339.0,1.5,23.12764935133665 +2005-10-16 15:30:00-07:00,2005,10,16,15,30,20.6,0.4,119.0,503.0,282.0,0.16,807.0,329.0,1.1,25.970675985944855 +2005-10-16 16:30:00-07:00,2005,10,16,16,30,17.900000000000002,3.1,79.0,34.0,84.0,0.16,807.0,164.0,0.7000000000000001,37.26688785741181 +2005-10-16 17:30:00-07:00,2005,10,16,17,30,15.1,3.2,0.0,0.0,0.0,0.16,808.0,159.0,0.6000000000000001,44.845971812526955 +2005-10-16 18:30:00-07:00,2005,10,16,18,30,13.4,2.6,0.0,0.0,0.0,0.16,809.0,294.0,1.0,47.9782609805996 +2005-10-16 19:30:00-07:00,2005,10,16,19,30,12.0,2.6,0.0,0.0,0.0,0.16,809.0,277.0,1.5,52.58558315130446 +2005-10-16 20:30:00-07:00,2005,10,16,20,30,10.8,2.8000000000000003,0.0,0.0,0.0,0.16,809.0,276.0,2.0,57.744533653893185 +2005-10-16 21:30:00-07:00,2005,10,16,21,30,10.1,3.0,0.0,0.0,0.0,0.16,810.0,274.0,2.4000000000000004,61.366045635982935 +2005-10-16 22:30:00-07:00,2005,10,16,22,30,9.7,3.0,0.0,0.0,0.0,0.16,810.0,270.0,2.8000000000000003,63.03171472262402 +2005-10-16 23:30:00-07:00,2005,10,16,23,30,9.4,3.0,0.0,0.0,0.0,0.16,810.0,267.0,3.1,64.31415207981617 +2005-10-17 00:30:00-07:00,2005,10,17,0,30,9.1,3.0,0.0,0.0,0.0,0.16,810.0,265.0,3.2,65.625827143667 +2005-10-17 01:30:00-07:00,2005,10,17,1,30,8.700000000000001,3.0,0.0,0.0,0.0,0.16,810.0,265.0,3.0,67.42148099957721 +2005-10-17 02:30:00-07:00,2005,10,17,2,30,8.3,3.0,0.0,0.0,0.0,0.16,810.0,263.0,2.7,69.27221924495764 +2005-10-17 03:30:00-07:00,2005,10,17,3,30,8.0,2.9000000000000004,0.0,0.0,0.0,0.16,810.0,262.0,2.5,70.19883990688051 +2005-10-17 04:30:00-07:00,2005,10,17,4,30,7.7,2.7,0.0,0.0,0.0,0.16,810.0,257.0,2.4000000000000004,70.63827046846815 +2005-10-17 05:30:00-07:00,2005,10,17,5,30,7.7,2.4000000000000004,0.0,0.0,0.0,0.16,810.0,251.0,2.4000000000000004,69.14915415987642 +2005-10-17 06:30:00-07:00,2005,10,17,6,30,9.2,2.3000000000000003,15.0,365.0,31.0,0.16,810.0,243.0,2.8000000000000003,62.02638870764418 +2005-10-17 07:30:00-07:00,2005,10,17,7,30,12.7,2.9000000000000004,37.0,762.0,212.0,0.16,811.0,236.0,3.2,51.30258295401566 +2005-10-17 08:30:00-07:00,2005,10,17,8,30,16.7,3.1,47.0,901.0,402.0,0.16,811.0,221.0,3.0,40.20321661660671 +2005-10-17 09:30:00-07:00,2005,10,17,9,30,19.8,1.2000000000000002,53.0,965.0,559.0,0.16,810.0,206.0,2.5,28.906210435084077 +2005-10-17 10:30:00-07:00,2005,10,17,10,30,21.700000000000003,0.0,55.0,998.0,666.0,0.16,810.0,204.0,2.1,23.582338302395456 +2005-10-17 11:30:00-07:00,2005,10,17,11,30,22.9,-0.7000000000000001,54.0,1013.0,714.0,0.16,809.0,214.0,1.5,20.8338224902231 +2005-10-17 12:30:00-07:00,2005,10,17,12,30,23.700000000000003,-1.3,54.0,1010.0,699.0,0.16,809.0,262.0,1.1,18.999209538347746 +2005-10-17 13:30:00-07:00,2005,10,17,13,30,23.700000000000003,-1.7000000000000002,53.0,983.0,619.0,0.16,808.0,327.0,1.2000000000000002,18.44933603589632 +2005-10-17 14:30:00-07:00,2005,10,17,14,30,23.1,-2.0,48.0,941.0,487.0,0.16,808.0,354.0,1.6,18.711568033199185 +2005-10-17 15:30:00-07:00,2005,10,17,15,30,21.3,-1.8,41.0,850.0,312.0,0.16,808.0,356.0,1.3,21.188829422337744 +2005-10-17 16:30:00-07:00,2005,10,17,16,30,17.6,2.2,28.0,638.0,119.0,0.16,808.0,189.0,0.8,35.62805106250292 +2005-10-17 17:30:00-07:00,2005,10,17,17,30,14.2,2.3000000000000003,0.0,0.0,0.0,0.16,809.0,188.0,0.4,44.588498896255274 +2005-10-17 18:30:00-07:00,2005,10,17,18,30,12.5,1.3,0.0,0.0,0.0,0.16,809.0,312.0,0.5,46.37675377672148 +2005-10-17 19:30:00-07:00,2005,10,17,19,30,11.3,0.8,0.0,0.0,0.0,0.16,809.0,268.0,0.9,48.42125372190489 +2005-10-17 20:30:00-07:00,2005,10,17,20,30,10.2,0.2,0.0,0.0,0.0,0.16,809.0,267.0,1.3,49.887094696403274 +2005-10-17 21:30:00-07:00,2005,10,17,21,30,9.3,-0.5,0.0,0.0,0.0,0.16,809.0,266.0,1.7000000000000002,50.36346183156824 +2005-10-17 22:30:00-07:00,2005,10,17,22,30,8.700000000000001,-1.3,0.0,0.0,0.0,0.16,809.0,265.0,2.0,49.46562639913872 +2005-10-17 23:30:00-07:00,2005,10,17,23,30,8.3,-2.0,0.0,0.0,0.0,0.16,808.0,262.0,2.3000000000000003,48.27426392879499 +2005-10-18 00:30:00-07:00,2005,10,18,0,30,7.800000000000001,-2.5,0.0,0.0,0.0,0.16,808.0,260.0,2.4000000000000004,48.13117380356778 +2005-10-18 01:30:00-07:00,2005,10,18,1,30,7.300000000000001,-2.8000000000000003,0.0,0.0,0.0,0.16,808.0,258.0,2.4000000000000004,48.70587108413759 +2005-10-18 02:30:00-07:00,2005,10,18,2,30,7.1000000000000005,-3.0,0.0,0.0,0.0,0.16,807.0,258.0,2.5,48.64867387271988 +2005-10-18 03:30:00-07:00,2005,10,18,3,30,7.0,-3.3000000000000003,0.0,0.0,0.0,0.16,807.0,259.0,2.6,47.90129595928184 +2005-10-18 04:30:00-07:00,2005,10,18,4,30,7.0,-3.5,0.0,0.0,0.0,0.16,807.0,262.0,2.5,47.19199562909889 +2005-10-18 05:30:00-07:00,2005,10,18,5,30,6.800000000000001,-3.6,0.0,0.0,0.0,0.16,807.0,267.0,2.5,47.4876584776844 +2005-10-18 06:30:00-07:00,2005,10,18,6,30,8.4,-3.6,15.0,0.0,15.0,0.16,807.0,269.0,2.7,42.579307890279196 +2005-10-18 07:30:00-07:00,2005,10,18,7,30,12.1,-2.1,93.0,451.0,194.0,0.16,807.0,270.0,3.1,37.176932674642096 +2005-10-18 08:30:00-07:00,2005,10,18,8,30,16.2,-2.2,47.0,923.0,406.0,0.16,807.0,282.0,3.1,28.297646834953824 +2005-10-18 09:30:00-07:00,2005,10,18,9,30,19.6,-3.9,53.0,985.0,566.0,0.16,807.0,312.0,2.5,20.134513834182254 +2005-10-18 10:30:00-07:00,2005,10,18,10,30,21.700000000000003,-5.300000000000001,56.0,1010.0,670.0,0.16,807.0,168.0,2.0,15.92004998365771 +2005-10-18 11:30:00-07:00,2005,10,18,11,30,23.1,-5.6000000000000005,56.0,1024.0,718.0,0.16,806.0,15.0,1.8,14.291990414761324 +2005-10-18 12:30:00-07:00,2005,10,18,12,30,23.700000000000003,-5.6000000000000005,241.0,702.0,686.0,0.16,805.0,29.0,1.9,13.783813877087963 +2005-10-18 13:30:00-07:00,2005,10,18,13,30,23.8,-5.5,197.0,564.0,519.0,0.16,805.0,38.0,2.3000000000000003,13.805562327952522 +2005-10-18 14:30:00-07:00,2005,10,18,14,30,23.1,-5.300000000000001,150.0,530.0,395.0,0.16,805.0,48.0,2.8000000000000003,14.621082544052415 +2005-10-18 15:30:00-07:00,2005,10,18,15,30,21.0,-4.800000000000001,47.0,824.0,305.0,0.16,805.0,58.0,2.7,17.258242289687047 +2005-10-18 16:30:00-07:00,2005,10,18,16,30,17.2,-1.0,31.0,587.0,112.0,0.16,805.0,66.0,2.0,29.00491750532556 +2005-10-18 17:30:00-07:00,2005,10,18,17,30,14.1,0.7000000000000001,0.0,0.0,0.0,0.16,806.0,70.0,1.5,40.01840242339994 +2005-10-18 18:30:00-07:00,2005,10,18,18,30,12.7,1.3,0.0,0.0,0.0,0.16,806.0,70.0,1.2000000000000002,45.77275489392664 +2005-10-18 19:30:00-07:00,2005,10,18,19,30,11.8,1.7000000000000002,0.0,0.0,0.0,0.16,807.0,61.0,1.0,49.973643049340374 +2005-10-18 20:30:00-07:00,2005,10,18,20,30,11.0,2.0,0.0,0.0,0.0,0.16,807.0,46.0,1.0,53.8314049973094 +2005-10-18 21:30:00-07:00,2005,10,18,21,30,9.8,1.9,0.0,0.0,0.0,0.16,806.0,37.0,1.2000000000000002,57.901761445950534 +2005-10-18 22:30:00-07:00,2005,10,18,22,30,8.700000000000001,1.9,0.0,0.0,0.0,0.16,807.0,35.0,1.4,62.35081546802396 +2005-10-18 23:30:00-07:00,2005,10,18,23,30,7.800000000000001,2.4000000000000004,0.0,0.0,0.0,0.16,807.0,31.0,1.3,68.67980583377832 +2005-10-19 00:30:00-07:00,2005,10,19,0,30,7.300000000000001,3.1,0.0,0.0,0.0,0.16,807.0,22.0,1.4,74.67881787615387 +2005-10-19 01:30:00-07:00,2005,10,19,1,30,7.0,3.6,0.0,0.0,0.0,0.16,807.0,14.0,1.7000000000000002,78.96515464444279 +2005-10-19 02:30:00-07:00,2005,10,19,2,30,6.800000000000001,3.9,0.0,0.0,0.0,0.16,807.0,8.0,2.0,81.76262763169352 +2005-10-19 03:30:00-07:00,2005,10,19,3,30,6.5,4.1000000000000005,0.0,0.0,0.0,0.16,806.0,9.0,2.0,84.64537331691854 +2005-10-19 04:30:00-07:00,2005,10,19,4,30,6.0,3.9,0.0,0.0,0.0,0.16,806.0,12.0,2.0,86.39213964554264 +2005-10-19 05:30:00-07:00,2005,10,19,5,30,5.5,3.6,0.0,0.0,0.0,0.16,806.0,17.0,2.0,87.566627891252 +2005-10-19 06:30:00-07:00,2005,10,19,6,30,5.1000000000000005,3.1,9.0,0.0,9.0,0.16,806.0,17.0,2.0,86.91254753829988 +2005-10-19 07:30:00-07:00,2005,10,19,7,30,5.2,2.9000000000000004,9.0,0.0,9.0,0.16,806.0,12.0,1.9,85.09736769306366 +2005-10-19 08:30:00-07:00,2005,10,19,8,30,5.7,2.7,154.0,17.0,160.0,0.16,806.0,181.0,1.9,81.03931401317816 +2005-10-19 09:30:00-07:00,2005,10,19,9,30,6.9,2.8000000000000003,99.0,1.0,99.0,0.16,805.0,355.0,2.2,75.13930035387558 +2005-10-19 10:30:00-07:00,2005,10,19,10,30,8.700000000000001,2.9000000000000004,57.0,0.0,57.0,0.16,805.0,180.0,2.5,66.94588958678978 +2005-10-19 11:30:00-07:00,2005,10,19,11,30,10.4,2.5,231.0,15.0,241.0,0.16,804.0,8.0,2.9000000000000004,58.05399310630741 +2005-10-19 12:30:00-07:00,2005,10,19,12,30,11.5,1.9,223.0,17.0,234.0,0.16,803.0,15.0,3.2,51.70724755146269 +2005-10-19 13:30:00-07:00,2005,10,19,13,30,11.8,1.5,12.0,0.0,12.0,0.16,803.0,19.0,3.3000000000000003,49.26338144707706 +2005-10-19 14:30:00-07:00,2005,10,19,14,30,11.4,1.4,122.0,0.0,122.0,0.16,803.0,21.0,3.3000000000000003,50.22133004068812 +2005-10-19 15:30:00-07:00,2005,10,19,15,30,10.2,1.5,15.0,0.0,15.0,0.16,803.0,23.0,3.0,54.783093498838895 +2005-10-19 16:30:00-07:00,2005,10,19,16,30,8.200000000000001,1.7000000000000002,12.0,0.0,12.0,0.16,804.0,27.0,2.2,63.58319778443817 +2005-10-19 17:30:00-07:00,2005,10,19,17,30,6.1000000000000005,1.9,0.0,0.0,0.0,0.16,805.0,20.0,1.1,74.4667608399821 +2005-10-19 18:30:00-07:00,2005,10,19,18,30,4.9,1.7000000000000002,0.0,0.0,0.0,0.16,805.0,167.0,0.6000000000000001,79.78024102169957 +2005-10-19 19:30:00-07:00,2005,10,19,19,30,4.3,1.6,0.0,0.0,0.0,0.16,805.0,315.0,0.6000000000000001,82.60179410079438 +2005-10-19 20:30:00-07:00,2005,10,19,20,30,4.1000000000000005,1.5,0.0,0.0,0.0,0.16,805.0,305.0,0.7000000000000001,83.16996912579803 +2005-10-19 21:30:00-07:00,2005,10,19,21,30,4.1000000000000005,1.3,0.0,0.0,0.0,0.16,805.0,303.0,0.9,81.98597601264838 +2005-10-19 22:30:00-07:00,2005,10,19,22,30,4.2,1.2000000000000002,0.0,0.0,0.0,0.16,805.0,303.0,1.0,80.83094721497267 +2005-10-19 23:30:00-07:00,2005,10,19,23,30,4.3,1.2000000000000002,0.0,0.0,0.0,0.16,806.0,304.0,1.2000000000000002,80.2667235720647 +2005-10-20 00:30:00-07:00,2005,10,20,0,30,4.5,1.2000000000000002,0.0,0.0,0.0,0.16,806.0,305.0,1.4,79.15140799449875 +2005-10-20 01:30:00-07:00,2005,10,20,1,30,4.7,1.2000000000000002,0.0,0.0,0.0,0.16,806.0,303.0,1.6,78.053353245313 +2005-10-20 02:30:00-07:00,2005,10,20,2,30,5.0,1.3,0.0,0.0,0.0,0.16,806.0,298.0,1.8,76.98863159694326 +2005-10-20 03:30:00-07:00,2005,10,20,3,30,5.300000000000001,1.4,0.0,0.0,0.0,0.16,806.0,296.0,2.0,75.94182472103437 +2005-10-20 04:30:00-07:00,2005,10,20,4,30,5.5,1.6,0.0,0.0,0.0,0.16,806.0,294.0,2.1,75.97572209130537 +2005-10-20 05:30:00-07:00,2005,10,20,5,30,5.7,1.8,0.0,0.0,0.0,0.16,806.0,291.0,2.3000000000000003,76.0095521515559 +2005-10-20 06:30:00-07:00,2005,10,20,6,30,7.300000000000001,2.0,14.0,236.0,22.0,0.16,806.0,288.0,2.7,69.06673589288174 +2005-10-20 07:30:00-07:00,2005,10,20,7,30,10.4,2.7,44.0,677.0,191.0,0.16,807.0,290.0,3.5,58.8841451784041 +2005-10-20 08:30:00-07:00,2005,10,20,8,30,13.4,3.3000000000000003,58.0,832.0,375.0,0.16,807.0,310.0,4.0,50.415623635064634 +2005-10-20 09:30:00-07:00,2005,10,20,9,30,15.7,3.1,66.0,905.0,529.0,0.16,807.0,338.0,4.0,42.84915368717448 +2005-10-20 10:30:00-07:00,2005,10,20,10,30,17.3,2.8000000000000003,330.0,67.0,370.0,0.16,807.0,179.0,3.6,37.89054849931933 +2005-10-20 11:30:00-07:00,2005,10,20,11,30,18.0,2.6,53.0,0.0,53.0,0.16,807.0,14.0,3.1,35.74436717356465 +2005-10-20 12:30:00-07:00,2005,10,20,12,30,18.2,2.6,181.0,0.0,182.0,0.16,807.0,28.0,2.6,35.29812294734645 +2005-10-20 13:30:00-07:00,2005,10,20,13,30,17.8,2.8000000000000003,25.0,0.0,25.0,0.16,807.0,38.0,2.3000000000000003,36.71412995509669 +2005-10-20 14:30:00-07:00,2005,10,20,14,30,17.0,3.0,226.0,25.0,237.0,0.16,807.0,45.0,2.0,39.167514288847435 +2005-10-20 15:30:00-07:00,2005,10,20,15,30,15.6,3.4000000000000004,135.0,306.0,228.0,0.16,808.0,50.0,1.6,44.04765857280453 +2005-10-20 16:30:00-07:00,2005,10,20,16,30,13.3,4.7,59.0,10.0,60.0,0.16,808.0,59.0,1.0,55.9849891332528 +2005-10-20 17:30:00-07:00,2005,10,20,17,30,11.4,5.300000000000001,0.0,0.0,0.0,0.16,809.0,65.0,0.7000000000000001,66.13131857862486 +2005-10-20 18:30:00-07:00,2005,10,20,18,30,10.7,4.800000000000001,0.0,0.0,0.0,0.16,809.0,59.0,0.6000000000000001,66.90596882295638 +2005-10-20 19:30:00-07:00,2005,10,20,19,30,10.0,4.5,0.0,0.0,0.0,0.16,809.0,38.0,0.3,68.65148451113438 +2005-10-20 20:30:00-07:00,2005,10,20,20,30,9.1,4.3,0.0,0.0,0.0,0.16,809.0,121.0,0.2,71.91432276596126 +2005-10-20 21:30:00-07:00,2005,10,20,21,30,8.200000000000001,4.0,0.0,0.0,0.0,0.16,809.0,216.0,0.4,74.83624272229343 +2005-10-20 22:30:00-07:00,2005,10,20,22,30,7.6,3.7,0.0,0.0,0.0,0.16,809.0,219.0,0.6000000000000001,76.32761175655398 +2005-10-20 23:30:00-07:00,2005,10,20,23,30,7.1000000000000005,3.3000000000000003,0.0,0.0,0.0,0.16,809.0,228.0,0.9,76.78481795519588 +2005-10-21 00:30:00-07:00,2005,10,21,0,30,6.800000000000001,2.9000000000000004,0.0,0.0,0.0,0.16,809.0,238.0,1.1,76.19442160944809 +2005-10-21 01:30:00-07:00,2005,10,21,1,30,6.4,2.5,0.0,0.0,0.0,0.16,809.0,246.0,1.3,76.12742965538618 +2005-10-21 02:30:00-07:00,2005,10,21,2,30,5.800000000000001,2.0,0.0,0.0,0.0,0.16,809.0,253.0,1.4,76.57097665762643 +2005-10-21 03:30:00-07:00,2005,10,21,3,30,5.1000000000000005,1.7000000000000002,0.0,0.0,0.0,0.16,810.0,262.0,1.4,78.67700459637678 +2005-10-21 04:30:00-07:00,2005,10,21,4,30,4.5,1.5,0.0,0.0,0.0,0.16,810.0,270.0,1.2000000000000002,80.87288251300097 +2005-10-21 05:30:00-07:00,2005,10,21,5,30,4.0,1.4,0.0,0.0,0.0,0.16,811.0,278.0,1.1,83.15749311056743 +2005-10-21 06:30:00-07:00,2005,10,21,6,30,5.1000000000000005,1.3,14.0,186.0,20.0,0.16,811.0,287.0,1.2000000000000002,76.45467668452102 +2005-10-21 07:30:00-07:00,2005,10,21,7,30,7.7,1.9,10.0,0.0,10.0,0.16,812.0,304.0,1.4,66.72898625198707 +2005-10-21 08:30:00-07:00,2005,10,21,8,30,10.1,2.7,83.0,0.0,84.0,0.16,812.0,323.0,1.3,60.07551723134718 +2005-10-21 09:30:00-07:00,2005,10,21,9,30,12.1,2.4000000000000004,34.0,0.0,34.0,0.16,811.0,333.0,1.3,51.503466131452996 +2005-10-21 10:30:00-07:00,2005,10,21,10,30,13.9,1.3,130.0,0.0,130.0,0.16,811.0,171.0,1.4,42.328552133032154 +2005-10-21 11:30:00-07:00,2005,10,21,11,30,15.1,0.2,106.0,0.0,106.0,0.16,810.0,16.0,1.8,36.18630728190365 +2005-10-21 12:30:00-07:00,2005,10,21,12,30,15.7,-0.3,87.0,0.0,87.0,0.16,810.0,31.0,2.2,33.57984375011661 +2005-10-21 13:30:00-07:00,2005,10,21,13,30,15.8,-0.5,107.0,0.0,107.0,0.16,809.0,35.0,2.4000000000000004,32.88373966140532 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22:30:00-07:00,2005,10,21,22,30,4.3,-0.3,0.0,0.0,0.0,0.16,809.0,213.0,1.3,72.0224168504412 +2005-10-21 23:30:00-07:00,2005,10,21,23,30,4.2,-1.0,0.0,0.0,0.0,0.16,809.0,222.0,1.5,68.91998593515828 +2005-10-22 00:30:00-07:00,2005,10,22,0,30,4.1000000000000005,-1.8,0.0,0.0,0.0,0.16,809.0,232.0,1.7000000000000002,65.4487337551539 +2005-10-22 01:30:00-07:00,2005,10,22,1,30,4.0,-2.7,0.0,0.0,0.0,0.16,808.0,242.0,1.9,61.66956597024578 +2005-10-22 02:30:00-07:00,2005,10,22,2,30,3.8,-3.6,0.0,0.0,0.0,0.16,808.0,252.0,1.9,58.48950064779861 +2005-10-22 03:30:00-07:00,2005,10,22,3,30,3.8,-4.3,0.0,0.0,0.0,0.16,807.0,261.0,1.9,55.50053878519048 +2005-10-22 04:30:00-07:00,2005,10,22,4,30,3.7,-4.800000000000001,0.0,0.0,0.0,0.16,807.0,268.0,1.9,53.8267020079334 +2005-10-22 05:30:00-07:00,2005,10,22,5,30,3.7,-5.0,0.0,0.0,0.0,0.16,807.0,270.0,1.8,53.019664427616725 +2005-10-22 06:30:00-07:00,2005,10,22,6,30,5.300000000000001,-5.1000000000000005,11.0,314.0,20.0,0.16,807.0,267.0,1.9,47.05178281640333 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15:30:00-07:00,2005,10,22,15,30,14.7,-1.5,43.0,809.0,281.0,0.16,806.0,43.0,2.9000000000000004,32.80195536600063 +2005-10-22 16:30:00-07:00,2005,10,22,16,30,11.4,-1.0,27.0,562.0,94.0,0.16,806.0,52.0,2.3000000000000003,42.21080699096416 +2005-10-22 17:30:00-07:00,2005,10,22,17,30,8.1,-0.1,0.0,0.0,0.0,0.15,807.0,62.0,1.7000000000000002,56.23039662822873 +2005-10-22 18:30:00-07:00,2005,10,22,18,30,6.5,-0.3,0.0,0.0,0.0,0.15,808.0,64.0,1.4,61.82407711799138 +2005-10-22 19:30:00-07:00,2005,10,22,19,30,5.2,-0.3,0.0,0.0,0.0,0.15,808.0,62.0,1.1,67.63925150993603 +2005-10-22 20:30:00-07:00,2005,10,22,20,30,4.1000000000000005,-0.5,0.0,0.0,0.0,0.15,809.0,63.0,0.9,71.9837914043348 +2005-10-22 21:30:00-07:00,2005,10,22,21,30,3.1,-0.7000000000000001,0.0,0.0,0.0,0.15,809.0,73.0,0.8,76.11786371475516 +2005-10-22 22:30:00-07:00,2005,10,22,22,30,2.4000000000000004,-0.9,0.0,0.0,0.0,0.15,809.0,92.0,0.7000000000000001,78.83184323246314 +2005-10-22 23:30:00-07:00,2005,10,22,23,30,1.8,-1.2000000000000002,0.0,0.0,0.0,0.15,809.0,113.0,0.8,80.4906681949573 +2005-10-23 00:30:00-07:00,2005,10,23,0,30,1.5,-1.4,0.0,0.0,0.0,0.15,809.0,127.0,0.9,81.04011851950804 +2005-10-23 01:30:00-07:00,2005,10,23,1,30,1.1,-1.6,0.0,0.0,0.0,0.15,808.0,134.0,1.0,82.18391305016006 +2005-10-23 02:30:00-07:00,2005,10,23,2,30,0.8,-1.7000000000000002,0.0,0.0,0.0,0.15,808.0,140.0,0.9,83.36218646831837 +2005-10-23 03:30:00-07:00,2005,10,23,3,30,0.5,-1.9,0.0,0.0,0.0,0.15,808.0,148.0,0.8,83.94094829973909 +2005-10-23 04:30:00-07:00,2005,10,23,4,30,0.3,-2.0,0.0,0.0,0.0,0.15,808.0,166.0,0.7000000000000001,84.53790735255996 +2005-10-23 05:30:00-07:00,2005,10,23,5,30,0.1,-2.2,0.0,0.0,0.0,0.15,809.0,187.0,0.6000000000000001,84.5144285964567 +2005-10-23 06:30:00-07:00,2005,10,23,6,30,1.3,-2.4000000000000004,4.0,0.0,4.0,0.15,809.0,192.0,0.8,76.37198304059929 +2005-10-23 07:30:00-07:00,2005,10,23,7,30,4.3,-2.2,17.0,0.0,17.0,0.15,809.0,273.0,0.9,62.662361850267864 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16:30:00-07:00,2005,10,23,16,30,7.2,-5.800000000000001,27.0,590.0,95.0,0.15,809.0,72.0,1.9,39.14075485530123 +2005-10-23 17:30:00-07:00,2005,10,23,17,30,4.0,-5.0,0.0,0.0,0.0,0.16,810.0,100.0,1.1,51.913579899969314 +2005-10-23 18:30:00-07:00,2005,10,23,18,30,2.7,-5.4,0.0,0.0,0.0,0.16,810.0,142.0,1.0,55.20312897886461 +2005-10-23 19:30:00-07:00,2005,10,23,19,30,2.0,-5.6000000000000005,0.0,0.0,0.0,0.16,810.0,187.0,1.0,57.14730080298467 +2005-10-23 20:30:00-07:00,2005,10,23,20,30,1.6,-5.7,0.0,0.0,0.0,0.16,810.0,218.0,1.1,58.35900103568839 +2005-10-23 21:30:00-07:00,2005,10,23,21,30,1.7000000000000002,-5.7,0.0,0.0,0.0,0.16,810.0,235.0,1.1,57.94296622448192 +2005-10-23 22:30:00-07:00,2005,10,23,22,30,2.0,-5.6000000000000005,0.0,0.0,0.0,0.16,810.0,244.0,1.1,57.14730080298467 +2005-10-23 23:30:00-07:00,2005,10,23,23,30,2.2,-5.6000000000000005,0.0,0.0,0.0,0.16,810.0,254.0,1.1,56.338368351444366 +2005-10-24 00:30:00-07:00,2005,10,24,0,30,2.3000000000000003,-5.6000000000000005,0.0,0.0,0.0,0.16,810.0,265.0,1.1,55.93869386618374 +2005-10-24 01:30:00-07:00,2005,10,24,1,30,2.2,-5.6000000000000005,0.0,0.0,0.0,0.16,810.0,275.0,1.1,56.338368351444366 +2005-10-24 02:30:00-07:00,2005,10,24,2,30,2.1,-5.800000000000001,0.0,0.0,0.0,0.16,809.0,284.0,1.2000000000000002,55.8847909899328 +2005-10-24 03:30:00-07:00,2005,10,24,3,30,1.9,-6.1000000000000005,0.0,0.0,0.0,0.16,809.0,289.0,1.4,55.406610835824694 +2005-10-24 04:30:00-07:00,2005,10,24,4,30,1.7000000000000002,-6.4,0.0,0.0,0.0,0.16,810.0,290.0,1.6,54.930619118150766 +2005-10-24 05:30:00-07:00,2005,10,24,5,30,1.5,-6.7,0.0,0.0,0.0,0.16,810.0,286.0,1.8,54.45682097127274 +2005-10-24 06:30:00-07:00,2005,10,24,6,30,2.9000000000000004,-7.0,11.0,335.0,17.0,0.16,810.0,281.0,2.1,48.16623582141466 +2005-10-24 07:30:00-07:00,2005,10,24,7,30,7.0,-6.4,32.0,797.0,193.0,0.16,810.0,281.0,2.6,37.904722379351945 +2005-10-24 08:30:00-07:00,2005,10,24,8,30,11.8,-6.300000000000001,40.0,943.0,384.0,0.16,811.0,306.0,2.6,27.66153126365489 +2005-10-24 09:30:00-07:00,2005,10,24,9,30,15.4,-7.5,45.0,1008.0,543.0,0.16,811.0,343.0,2.4000000000000004,19.959715266027295 +2005-10-24 10:30:00-07:00,2005,10,24,10,30,17.8,-8.5,48.0,1028.0,644.0,0.16,811.0,183.0,2.8000000000000003,15.857951630494272 +2005-10-24 11:30:00-07:00,2005,10,24,11,30,19.200000000000003,-8.3,47.0,1042.0,691.0,0.16,810.0,11.0,3.1,14.754671010629016 +2005-10-24 12:30:00-07:00,2005,10,24,12,30,20.1,-7.6,47.0,1029.0,669.0,0.16,810.0,18.0,3.3000000000000003,14.73088599799118 +2005-10-24 13:30:00-07:00,2005,10,24,13,30,20.3,-6.7,44.0,1000.0,586.0,0.16,810.0,25.0,3.4000000000000004,15.593699379736572 +2005-10-24 14:30:00-07:00,2005,10,24,14,30,19.700000000000003,-5.9,41.0,946.0,450.0,0.16,810.0,33.0,3.2,17.204265463522336 +2005-10-24 15:30:00-07:00,2005,10,24,15,30,17.3,-4.9,34.0,849.0,276.0,0.16,810.0,44.0,2.3000000000000003,21.570077667832262 +2005-10-24 16:30:00-07:00,2005,10,24,16,30,13.5,-0.8,22.0,602.0,89.0,0.16,811.0,64.0,1.3,37.318026991569695 +2005-10-24 17:30:00-07:00,2005,10,24,17,30,11.4,-2.0,0.0,0.0,0.0,0.16,811.0,91.0,1.0,39.22261653027865 +2005-10-24 18:30:00-07:00,2005,10,24,18,30,11.3,-3.2,0.0,0.0,0.0,0.16,812.0,170.0,0.7000000000000001,36.12399295948583 +2005-10-24 19:30:00-07:00,2005,10,24,19,30,10.8,-3.7,0.0,0.0,0.0,0.16,812.0,251.0,0.8,35.97485900690859 +2005-10-24 20:30:00-07:00,2005,10,24,20,30,10.2,-3.9,0.0,0.0,0.0,0.16,812.0,273.0,1.0,36.88608044898894 +2005-10-24 21:30:00-07:00,2005,10,24,21,30,9.8,-4.1000000000000005,0.0,0.0,0.0,0.16,812.0,286.0,1.1,37.322679482636886 +2005-10-24 22:30:00-07:00,2005,10,24,22,30,9.4,-4.3,0.0,0.0,0.0,0.16,811.0,296.0,1.1,37.76670029254231 +2005-10-24 23:30:00-07:00,2005,10,24,23,30,9.0,-4.5,0.0,0.0,0.0,0.16,811.0,301.0,1.2000000000000002,38.2182975095657 +2005-10-25 00:30:00-07:00,2005,10,25,0,30,8.5,-4.7,0.0,0.0,0.0,0.16,811.0,301.0,1.2000000000000002,38.9402631671926 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05:30:00-07:00,2013,11,6,5,30,-3.9,-13.3,0.0,0.0,0.0,0.16,807.0,264.0,3.5,48.051252775249644 +2013-11-06 06:30:00-07:00,2013,11,6,6,30,-3.1,-12.6,0.0,0.0,0.0,0.16,808.0,261.0,3.6,47.90325641842836 +2013-11-06 07:30:00-07:00,2013,11,6,7,30,-1.0,-11.7,6.0,0.0,6.0,0.16,808.0,263.0,4.1000000000000005,44.106444925151706 +2013-11-06 08:30:00-07:00,2013,11,6,8,30,1.8,-10.8,49.0,851.0,314.0,0.16,809.0,272.0,4.7,38.70502828090521 +2013-11-06 09:30:00-07:00,2013,11,6,9,30,4.2,-10.0,58.0,935.0,467.0,0.16,809.0,285.0,4.800000000000001,34.79441272480629 +2013-11-06 10:30:00-07:00,2013,11,6,10,30,6.0,-9.5,60.0,981.0,573.0,0.16,809.0,295.0,4.6000000000000005,31.93125579176912 +2013-11-06 11:30:00-07:00,2013,11,6,11,30,7.2,-9.1,61.0,998.0,619.0,0.16,809.0,301.0,4.5,30.339926610880582 +2013-11-06 12:30:00-07:00,2013,11,6,12,30,7.7,-8.700000000000001,60.0,994.0,602.0,0.16,809.0,304.0,4.3,30.25267363981489 +2013-11-06 13:30:00-07:00,2013,11,6,13,30,7.7,-8.6,56.0,968.0,524.0,0.16,809.0,303.0,4.2,30.489476371545063 +2013-11-06 14:30:00-07:00,2013,11,6,14,30,7.1000000000000005,-8.5,49.0,914.0,393.0,0.16,809.0,301.0,4.0,32.013254409155856 +2013-11-06 15:30:00-07:00,2013,11,6,15,30,5.1000000000000005,-7.7,40.0,786.0,221.0,0.16,810.0,297.0,3.0,39.10535472973577 +2013-11-06 16:30:00-07:00,2013,11,6,16,30,1.9,-6.300000000000001,19.0,433.0,45.0,0.16,810.0,295.0,1.9,54.566810298265246 +2013-11-06 17:30:00-07:00,2013,11,6,17,30,0.0,-7.0,0.0,0.0,0.0,0.16,811.0,292.0,1.6,59.29253147193167 +2013-11-06 18:30:00-07:00,2013,11,6,18,30,-0.5,-7.1000000000000005,0.0,0.0,0.0,0.16,812.0,289.0,1.6,61.01537690679563 +2013-11-06 19:30:00-07:00,2013,11,6,19,30,-0.6000000000000001,-7.300000000000001,0.0,0.0,0.0,0.16,812.0,285.0,1.6,60.52205574953835 +2013-11-06 20:30:00-07:00,2013,11,6,20,30,-0.6000000000000001,-7.5,0.0,0.0,0.0,0.16,812.0,278.0,1.5,59.59542537241655 +2013-11-06 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05:30:00-07:00,2013,11,7,5,30,-1.8,-7.800000000000001,0.0,0.0,0.0,0.16,814.0,233.0,2.2,63.57848106410386 +2013-11-07 06:30:00-07:00,2013,11,7,6,30,-1.1,-7.7,0.0,0.0,0.0,0.16,814.0,237.0,2.5,60.863765555148696 +2013-11-07 07:30:00-07:00,2013,11,7,7,30,1.5,-7.0,30.0,659.0,128.0,0.16,814.0,235.0,2.9000000000000004,53.216697281939545 +2013-11-07 08:30:00-07:00,2013,11,7,8,30,5.300000000000001,-6.1000000000000005,43.0,849.0,304.0,0.16,814.0,222.0,2.7,43.609213673848465 +2013-11-07 09:30:00-07:00,2013,11,7,9,30,8.8,-5.2,52.0,929.0,455.0,0.16,814.0,201.0,1.8,36.74344641515938 +2013-11-07 10:30:00-07:00,2013,11,7,10,30,11.2,-5.0,56.0,968.0,558.0,0.16,813.0,178.0,1.3,31.770163003353225 +2013-11-07 11:30:00-07:00,2013,11,7,11,30,12.8,-5.0,56.0,981.0,600.0,0.16,812.0,164.0,1.2000000000000002,28.59289245434345 +2013-11-07 12:30:00-07:00,2013,11,7,12,30,13.7,-4.9,56.0,975.0,584.0,0.16,811.0,161.0,1.2000000000000002,27.167505017359527 +2013-11-07 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21:30:00-07:00,2013,11,7,21,30,2.6,-4.2,0.0,0.0,0.0,0.16,806.0,235.0,2.3000000000000003,60.86639227369974 +2013-11-07 22:30:00-07:00,2013,11,7,22,30,2.5,-4.800000000000001,0.0,0.0,0.0,0.16,806.0,235.0,2.6,58.592967219823095 +2013-11-07 23:30:00-07:00,2013,11,7,23,30,2.5,-5.5,0.0,0.0,0.0,0.16,805.0,235.0,2.8000000000000003,55.569228780122984 +2013-11-08 00:30:00-07:00,2013,11,8,0,30,2.6,-6.2,0.0,0.0,0.0,0.16,804.0,235.0,3.0,52.31218143181036 +2013-11-08 01:30:00-07:00,2013,11,8,1,30,2.6,-6.6000000000000005,0.0,0.0,0.0,0.16,804.0,235.0,3.0,50.73578151082863 +2013-11-08 02:30:00-07:00,2013,11,8,2,30,2.5,-6.9,0.0,0.0,0.0,0.16,803.0,235.0,2.7,49.93474628944365 +2013-11-08 03:30:00-07:00,2013,11,8,3,30,2.6,-7.0,0.0,0.0,0.0,0.16,803.0,235.0,2.7,49.201782884493944 +2013-11-08 04:30:00-07:00,2013,11,8,4,30,3.0,-7.2,0.0,0.0,0.0,0.16,802.0,237.0,2.9000000000000004,47.09606459131395 +2013-11-08 05:30:00-07:00,2013,11,8,5,30,3.2,-7.4,0.0,0.0,0.0,0.16,802.0,239.0,3.0,45.724430340852614 +2013-11-08 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15:30:00-07:00,2013,11,9,15,30,12.4,-1.9,37.0,747.0,202.0,0.16,809.0,96.0,1.3,36.99253190487965 +2013-11-09 16:30:00-07:00,2013,11,9,16,30,8.9,0.9,16.0,380.0,35.0,0.16,809.0,107.0,1.0,57.25860525999405 +2013-11-09 17:30:00-07:00,2013,11,9,17,30,7.1000000000000005,-0.9,0.0,0.0,0.0,0.16,810.0,138.0,0.8,56.79172988846075 +2013-11-09 18:30:00-07:00,2013,11,9,18,30,6.0,-1.4,0.0,0.0,0.0,0.16,810.0,190.0,0.6000000000000001,59.05238246787064 +2013-11-09 19:30:00-07:00,2013,11,9,19,30,5.0,-1.5,0.0,0.0,0.0,0.16,810.0,230.0,0.8,62.8298844598179 +2013-11-09 20:30:00-07:00,2013,11,9,20,30,4.4,-1.8,0.0,0.0,0.0,0.16,811.0,245.0,1.1,64.08772403401024 +2013-11-09 21:30:00-07:00,2013,11,9,21,30,3.9,-2.2,0.0,0.0,0.0,0.16,811.0,250.0,1.3,64.44512533612645 +2013-11-09 22:30:00-07:00,2013,11,9,22,30,3.4000000000000004,-2.6,0.0,0.0,0.0,0.16,811.0,251.0,1.3,64.80737843073386 +2013-11-09 23:30:00-07:00,2013,11,9,23,30,3.2,-2.9000000000000004,0.0,0.0,0.0,0.16,811.0,255.0,1.2000000000000002,64.28207114495113 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08:30:00-07:00,2013,11,10,8,30,8.8,-3.4000000000000004,43.0,849.0,294.0,0.16,811.0,266.0,2.4000000000000004,42.066023614325985 +2013-11-10 09:30:00-07:00,2013,11,10,9,30,12.4,-4.0,51.0,930.0,442.0,0.16,811.0,295.0,2.0,31.647461589829113 +2013-11-10 10:30:00-07:00,2013,11,10,10,30,14.9,-5.0,56.0,968.0,546.0,0.16,811.0,330.0,1.7000000000000002,24.9492713965941 +2013-11-10 11:30:00-07:00,2013,11,10,11,30,16.1,-5.1000000000000005,58.0,983.0,592.0,0.16,810.0,345.0,1.6,22.928488346922947 +2013-11-10 12:30:00-07:00,2013,11,10,12,30,16.6,-5.1000000000000005,61.0,966.0,572.0,0.16,810.0,355.0,1.5,22.210111626744332 +2013-11-10 13:30:00-07:00,2013,11,10,13,30,16.5,-5.0,60.0,930.0,495.0,0.16,810.0,180.0,1.5,22.521429377140112 +2013-11-10 14:30:00-07:00,2013,11,10,14,30,15.6,-4.7,50.0,878.0,367.0,0.16,810.0,6.0,1.3,24.399175800859446 +2013-11-10 15:30:00-07:00,2013,11,10,15,30,12.9,-2.4000000000000004,40.0,737.0,200.0,0.16,810.0,20.0,0.9,34.502989241990676 +2013-11-10 16:30:00-07:00,2013,11,10,16,30,9.5,0.1,17.0,0.0,17.0,0.16,810.0,34.0,0.5,51.90449088922039 +2013-11-10 17:30:00-07:00,2013,11,10,17,30,7.6,-2.4000000000000004,0.0,0.0,0.0,0.16,811.0,53.0,0.4,49.153943705624144 +2013-11-10 18:30:00-07:00,2013,11,10,18,30,6.7,-2.8000000000000003,0.0,0.0,0.0,0.16,811.0,145.0,0.4,50.74985953540869 +2013-11-10 19:30:00-07:00,2013,11,10,19,30,5.800000000000001,-3.1,0.0,0.0,0.0,0.16,811.0,232.0,0.8,52.80737543371671 +2013-11-10 20:30:00-07:00,2013,11,10,20,30,4.9,-3.4000000000000004,0.0,0.0,0.0,0.16,811.0,249.0,1.3,54.969991871142504 +2013-11-10 21:30:00-07:00,2013,11,10,21,30,4.0,-3.7,0.0,0.0,0.0,0.16,811.0,258.0,1.5,57.2440245821284 +2013-11-10 22:30:00-07:00,2013,11,10,22,30,3.3000000000000003,-4.0,0.0,0.0,0.0,0.16,811.0,263.0,1.7000000000000002,58.79953028247069 +2013-11-10 23:30:00-07:00,2013,11,10,23,30,2.9000000000000004,-4.4,0.0,0.0,0.0,0.16,811.0,268.0,1.8,58.696413725347625 +2013-11-11 00:30:00-07:00,2013,11,11,0,30,2.6,-4.800000000000001,0.0,0.0,0.0,0.16,811.0,270.0,1.9,58.178309505484954 +2013-11-11 01:30:00-07:00,2013,11,11,1,30,2.3000000000000003,-5.1000000000000005,0.0,0.0,0.0,0.16,811.0,271.0,1.9,58.10004813875156 +2013-11-11 02:30:00-07:00,2013,11,11,2,30,2.1,-5.0,0.0,0.0,0.0,0.16,811.0,273.0,1.9,59.380990251948106 +2013-11-11 03:30:00-07:00,2013,11,11,3,30,1.8,-5.0,0.0,0.0,0.0,0.16,811.0,275.0,2.0,60.666089287036996 +2013-11-11 04:30:00-07:00,2013,11,11,4,30,1.7000000000000002,-5.1000000000000005,0.0,0.0,0.0,0.16,811.0,276.0,2.0,60.64095790515683 +2013-11-11 05:30:00-07:00,2013,11,11,5,30,1.6,-5.300000000000001,0.0,0.0,0.0,0.16,811.0,274.0,2.1,60.15833674133142 +2013-11-11 06:30:00-07:00,2013,11,11,6,30,2.3000000000000003,-5.5,0.0,0.0,0.0,0.16,812.0,272.0,2.2,56.365154355980295 +2013-11-11 07:30:00-07:00,2013,11,11,7,30,5.2,-4.800000000000001,26.0,666.0,115.0,0.16,812.0,271.0,2.5,48.46621354162651 +2013-11-11 08:30:00-07:00,2013,11,11,8,30,9.3,-3.3000000000000003,39.0,859.0,290.0,0.16,813.0,278.0,2.6,40.97628921670222 +2013-11-11 09:30:00-07:00,2013,11,11,9,30,12.6,-3.7,46.0,945.0,440.0,0.16,813.0,312.0,2.2,31.944501463201053 +2013-11-11 10:30:00-07:00,2013,11,11,10,30,14.7,-4.800000000000001,50.0,984.0,543.0,0.16,813.0,176.0,2.0,25.657551978530687 +2013-11-11 11:30:00-07:00,2013,11,11,11,30,15.7,-5.1000000000000005,51.0,992.0,585.0,0.16,813.0,19.0,2.1,23.521967124050477 +2013-11-11 12:30:00-07:00,2013,11,11,12,30,16.0,-5.1000000000000005,52.0,980.0,567.0,0.16,812.0,31.0,2.2,23.07526853051223 +2013-11-11 13:30:00-07:00,2013,11,11,13,30,15.5,-4.9,51.0,950.0,491.0,0.16,812.0,36.0,2.1,24.188109966891563 +2013-11-11 14:30:00-07:00,2013,11,11,14,30,13.8,-4.7,212.0,84.0,242.0,0.16,812.0,36.0,2.1,27.402099476783157 +2013-11-11 15:30:00-07:00,2013,11,11,15,30,10.6,-3.9,121.0,77.0,138.0,0.16,813.0,43.0,2.0,35.91512424291679 +2013-11-11 16:30:00-07:00,2013,11,11,16,30,6.4,-1.5,15.0,373.0,32.0,0.16,814.0,45.0,1.8,57.0257721304701 +2013-11-11 17:30:00-07:00,2013,11,11,17,30,3.3000000000000003,-1.9,0.0,0.0,0.0,0.16,816.0,41.0,1.6,68.73042546531704 +2013-11-11 18:30:00-07:00,2013,11,11,18,30,1.6,-2.2,0.0,0.0,0.0,0.16,817.0,34.0,1.4,75.86077582504379 +2013-11-11 19:30:00-07:00,2013,11,11,19,30,0.5,-2.7,0.0,0.0,0.0,0.16,817.0,25.0,1.1,79.12148183601921 +2013-11-11 20:30:00-07:00,2013,11,11,20,30,0.0,-3.1,0.0,0.0,0.0,0.16,818.0,27.0,0.8,79.63526383037762 +2013-11-11 21:30:00-07:00,2013,11,11,21,30,-0.3,-3.4000000000000004,0.0,0.0,0.0,0.16,818.0,64.0,0.6000000000000001,79.59013538172317 +2013-11-11 22:30:00-07:00,2013,11,11,22,30,-0.6000000000000001,-3.7,0.0,0.0,0.0,0.16,818.0,130.0,0.6000000000000001,79.54486392974522 +2013-11-11 23:30:00-07:00,2013,11,11,23,30,-0.8,-4.0,0.0,0.0,0.0,0.16,818.0,173.0,0.6000000000000001,78.92098832753467 +2013-11-12 00:30:00-07:00,2013,11,12,0,30,-0.9,-4.3,0.0,0.0,0.0,0.16,819.0,191.0,0.6000000000000001,77.72927509802383 +2013-11-12 01:30:00-07:00,2013,11,12,1,30,-0.8,-4.6000000000000005,0.0,0.0,0.0,0.16,819.0,194.0,0.5,75.44125514919406 +2013-11-12 02:30:00-07:00,2013,11,12,2,30,-0.8,-4.800000000000001,0.0,0.0,0.0,0.16,819.0,182.0,0.6000000000000001,74.31202826189059 +2013-11-12 03:30:00-07:00,2013,11,12,3,30,-1.0,-5.0,0.0,0.0,0.0,0.16,819.0,187.0,0.9,74.27525388002506 +2013-11-12 04:30:00-07:00,2013,11,12,4,30,-1.0,-5.1000000000000005,0.0,0.0,0.0,0.16,819.0,204.0,1.3,73.7156349507821 +2013-11-12 05:30:00-07:00,2013,11,12,5,30,-0.7000000000000001,-5.300000000000001,0.0,0.0,0.0,0.16,819.0,216.0,1.5,71.03419138440287 +2013-11-12 06:30:00-07:00,2013,11,12,6,30,0.4,-5.6000000000000005,0.0,0.0,0.0,0.16,819.0,223.0,1.5,64.10525332639168 +2013-11-12 07:30:00-07:00,2013,11,12,7,30,3.8,-5.300000000000001,28.0,646.0,111.0,0.16,819.0,224.0,1.4,51.46602313615394 +2013-11-12 08:30:00-07:00,2013,11,12,8,30,8.200000000000001,-2.4000000000000004,40.0,871.0,291.0,0.16,819.0,208.0,1.1,47.18811971692945 +2013-11-12 09:30:00-07:00,2013,11,12,9,30,11.2,-4.4,47.0,958.0,443.0,0.16,819.0,155.0,1.1,33.24059903134418 +2013-11-12 10:30:00-07:00,2013,11,12,10,30,12.9,-5.2,51.0,999.0,548.0,0.16,819.0,108.0,1.4,27.97983653996926 +2013-11-12 11:30:00-07:00,2013,11,12,11,30,13.8,-5.6000000000000005,51.0,1015.0,594.0,0.16,819.0,101.0,1.6,25.597530437338698 +2013-11-12 12:30:00-07:00,2013,11,12,12,30,14.3,-5.9,51.0,1010.0,578.0,0.16,818.0,105.0,1.6,24.22217250460603 +2013-11-12 13:30:00-07:00,2013,11,12,13,30,14.4,-6.300000000000001,47.0,985.0,501.0,0.16,818.0,115.0,1.5,23.342650948930956 +2013-11-12 14:30:00-07:00,2013,11,12,14,30,13.8,-6.6000000000000005,40.0,930.0,370.0,0.16,817.0,125.0,1.3,23.717058570246365 +2013-11-12 15:30:00-07:00,2013,11,12,15,30,11.4,-4.5,32.0,801.0,201.0,0.16,817.0,133.0,1.1,32.557358621522106 +2013-11-12 16:30:00-07:00,2013,11,12,16,30,8.200000000000001,-1.9,14.0,427.0,32.0,0.16,817.0,153.0,1.2000000000000002,48.962358065353676 +2013-11-12 17:30:00-07:00,2013,11,12,17,30,6.1000000000000005,-4.7,0.0,0.0,0.0,0.16,817.0,190.0,1.3,45.88212002680257 +2013-11-12 18:30:00-07:00,2013,11,12,18,30,4.7,-4.800000000000001,0.0,0.0,0.0,0.16,817.0,224.0,1.4,50.183854918767516 +2013-11-12 19:30:00-07:00,2013,11,12,19,30,3.7,-5.0,0.0,0.0,0.0,0.16,817.0,244.0,1.5,53.019664427616725 +2013-11-12 20:30:00-07:00,2013,11,12,20,30,3.2,-5.7,0.0,0.0,0.0,0.16,816.0,256.0,1.6,52.08308287573237 +2013-11-12 21:30:00-07:00,2013,11,12,21,30,2.7,-6.300000000000001,0.0,0.0,0.0,0.16,816.0,262.0,1.8,51.54695762027609 +2013-11-12 22:30:00-07:00,2013,11,12,22,30,2.3000000000000003,-6.800000000000001,0.0,0.0,0.0,0.16,816.0,265.0,2.1,51.04038793612307 +2013-11-12 23:30:00-07:00,2013,11,12,23,30,2.0,-7.0,0.0,0.0,0.0,0.16,815.0,265.0,2.4000000000000004,51.348169519678216 +2013-11-13 00:30:00-07:00,2013,11,13,0,30,1.9,-7.1000000000000005,0.0,0.0,0.0,0.16,815.0,262.0,2.7,51.319692335633235 +2013-11-13 01:30:00-07:00,2013,11,13,1,30,2.0,-7.2,0.0,0.0,0.0,0.16,814.0,262.0,2.8000000000000003,50.56397970201901 +2013-11-13 02:30:00-07:00,2013,11,13,2,30,2.0,-7.4,0.0,0.0,0.0,0.16,814.0,263.0,2.7,49.790465281598216 +2013-11-13 03:30:00-07:00,2013,11,13,3,30,1.9,-7.6,0.0,0.0,0.0,0.16,813.0,267.0,2.6,49.37865621075868 +2013-11-13 04:30:00-07:00,2013,11,13,4,30,1.6,-7.800000000000001,0.0,0.0,0.0,0.16,813.0,271.0,2.6,49.67469279060821 +2013-11-13 05:30:00-07:00,2013,11,13,5,30,1.5,-7.800000000000001,0.0,0.0,0.0,0.16,813.0,274.0,2.6,50.03165353383436 +2013-11-13 06:30:00-07:00,2013,11,13,6,30,2.2,-7.9,0.0,0.0,0.0,0.16,812.0,275.0,2.6,47.224486134376384 +2013-11-13 07:30:00-07:00,2013,11,13,7,30,5.4,-7.300000000000001,25.0,683.0,111.0,0.16,812.0,274.0,2.9000000000000004,39.49990757610399 +2013-11-13 08:30:00-07:00,2013,11,13,8,30,9.8,-6.0,37.0,890.0,290.0,0.16,812.0,273.0,3.3000000000000003,32.32702006972219 +2013-11-13 09:30:00-07:00,2013,11,13,9,30,13.2,-5.7,44.0,976.0,443.0,0.16,811.0,285.0,3.2,26.415399506028493 +2013-11-13 10:30:00-07:00,2013,11,13,10,30,15.5,-6.7,48.0,1002.0,542.0,0.16,811.0,305.0,2.8000000000000003,21.091383309091917 +2013-11-13 11:30:00-07:00,2013,11,13,11,30,16.5,-7.7,241.0,546.0,530.0,0.16,810.0,316.0,2.5,18.320350413618478 +2013-11-13 12:30:00-07:00,2013,11,13,12,30,16.6,-7.800000000000001,269.0,377.0,465.0,0.16,809.0,321.0,2.4000000000000004,18.06395126790805 +2013-11-13 13:30:00-07:00,2013,11,13,13,30,16.0,-7.4,192.0,437.0,391.0,0.16,808.0,325.0,2.2,19.356777378711726 +2013-11-13 14:30:00-07:00,2013,11,13,14,30,14.3,-6.1000000000000005,8.0,771.0,278.0,0.16,808.0,328.0,1.7000000000000002,23.85565049161682 +2013-11-13 15:30:00-07:00,2013,11,13,15,30,11.6,-1.1,100.0,19.0,104.0,0.16,808.0,330.0,1.2000000000000002,41.3527313034859 +2013-11-13 16:30:00-07:00,2013,11,13,16,30,9.1,-0.8,13.0,0.0,13.0,0.16,808.0,326.0,1.2000000000000002,49.94180897528325 +2013-11-13 17:30:00-07:00,2013,11,13,17,30,7.800000000000001,-1.4,0.0,0.0,0.0,0.16,808.0,313.0,1.4,52.19553456616647 +2013-11-13 18:30:00-07:00,2013,11,13,18,30,7.2,-1.5,0.0,0.0,0.0,0.16,807.0,295.0,1.7000000000000002,53.97943247707777 +2013-11-13 19:30:00-07:00,2013,11,13,19,30,6.7,-1.7000000000000002,0.0,0.0,0.0,0.16,807.0,279.0,1.9,55.04647644169734 +2013-11-13 20:30:00-07:00,2013,11,13,20,30,6.4,-1.9,0.0,0.0,0.0,0.16,806.0,269.0,2.0,55.37264056281935 +2013-11-13 21:30:00-07:00,2013,11,13,21,30,6.1000000000000005,-2.0,0.0,0.0,0.0,0.16,806.0,268.0,2.0,56.11450973438731 +2013-11-13 22:30:00-07:00,2013,11,13,22,30,5.800000000000001,-2.0,0.0,0.0,0.0,0.16,806.0,272.0,2.0,57.28980918581254 +2013-11-13 23:30:00-07:00,2013,11,13,23,30,5.4,-2.1,0.0,0.0,0.0,0.16,805.0,280.0,1.9,58.46700735309186 +2013-11-14 00:30:00-07:00,2013,11,14,0,30,4.9,-2.1,0.0,0.0,0.0,0.16,805.0,292.0,1.7000000000000002,60.53568009241338 +2013-11-14 01:30:00-07:00,2013,11,14,1,30,4.5,-1.9,0.0,0.0,0.0,0.16,805.0,304.0,1.4,63.17418881741381 +2013-11-14 02:30:00-07:00,2013,11,14,2,30,4.1000000000000005,-1.7000000000000002,0.0,0.0,0.0,0.16,806.0,315.0,1.2000000000000002,65.9320463753936 +2013-11-14 03:30:00-07:00,2013,11,14,3,30,3.8,-1.5,0.0,0.0,0.0,0.16,806.0,328.0,1.1,68.3325795264885 +2013-11-14 04:30:00-07:00,2013,11,14,4,30,3.6,-1.0,0.0,0.0,0.0,0.16,806.0,341.0,1.1,71.88690371023279 +2013-11-14 05:30:00-07:00,2013,11,14,5,30,3.2,-0.7000000000000001,0.0,0.0,0.0,0.16,807.0,353.0,0.9,75.58179694159871 +2013-11-14 06:30:00-07:00,2013,11,14,6,30,2.8000000000000003,-0.7000000000000001,0.0,0.0,0.0,0.16,807.0,185.0,0.9,77.75166677099607 +2013-11-14 07:30:00-07:00,2013,11,14,7,30,4.0,-0.7000000000000001,38.0,450.0,93.0,0.16,807.0,14.0,1.3,71.44167776857226 +2013-11-14 08:30:00-07:00,2013,11,14,8,30,6.2,-0.5,60.0,731.0,265.0,0.16,807.0,15.0,1.7000000000000002,62.20299422510958 +2013-11-14 09:30:00-07:00,2013,11,14,9,30,8.0,-1.1,68.0,867.0,419.0,0.16,807.0,185.0,1.7000000000000002,52.63457387859167 +2013-11-14 10:30:00-07:00,2013,11,14,10,30,9.8,-2.4000000000000004,71.0,924.0,523.0,0.16,806.0,355.0,1.4,42.36190117518494 +2013-11-14 11:30:00-07:00,2013,11,14,11,30,11.2,-3.8,61.0,975.0,575.0,0.16,805.0,177.0,1.2000000000000002,34.771200167191246 +2013-11-14 12:30:00-07:00,2013,11,14,12,30,12.1,-5.0,65.0,957.0,557.0,0.16,804.0,6.0,1.0,29.93698503592031 +2013-11-14 13:30:00-07:00,2013,11,14,13,30,12.4,-5.9,67.0,909.0,479.0,0.16,803.0,12.0,0.9,27.414741302912514 +2013-11-14 14:30:00-07:00,2013,11,14,14,30,12.1,-6.6000000000000005,60.0,834.0,349.0,0.16,803.0,25.0,0.8,26.504652429651845 +2013-11-14 15:30:00-07:00,2013,11,14,15,30,10.0,-6.1000000000000005,115.0,52.0,126.0,0.16,802.0,58.0,0.7000000000000001,31.654675986500866 +2013-11-14 16:30:00-07:00,2013,11,14,16,30,7.2,-3.3000000000000003,14.0,1.0,14.0,0.16,802.0,104.0,0.8,47.249895431356656 +2013-11-14 17:30:00-07:00,2013,11,14,17,30,5.4,-4.4,0.0,0.0,0.0,0.16,802.0,148.0,1.2000000000000002,49.26004098287589 +2013-11-14 18:30:00-07:00,2013,11,14,18,30,4.5,-4.2,0.0,0.0,0.0,0.16,801.0,178.0,1.7000000000000002,53.24116637250751 +2013-11-14 19:30:00-07:00,2013,11,14,19,30,4.1000000000000005,-4.2,0.0,0.0,0.0,0.16,801.0,193.0,2.2,54.753410856983834 +2013-11-14 20:30:00-07:00,2013,11,14,20,30,3.5,-4.4,0.0,0.0,0.0,0.16,800.0,200.0,2.3000000000000003,56.26039151672631 +2013-11-14 21:30:00-07:00,2013,11,14,21,30,2.8000000000000003,-4.7,0.0,0.0,0.0,0.16,799.0,204.0,2.2,57.79311546461133 +2013-11-14 22:30:00-07:00,2013,11,14,22,30,2.0,-5.1000000000000005,0.0,0.0,0.0,0.16,799.0,209.0,2.1,59.35535312275678 +2013-11-14 23:30:00-07:00,2013,11,14,23,30,1.6,-5.7,0.0,0.0,0.0,0.16,798.0,219.0,2.1,58.35900103568839 +2013-11-15 00:30:00-07:00,2013,11,15,0,30,1.4,-6.300000000000001,0.0,0.0,0.0,0.16,798.0,232.0,2.3000000000000003,56.554114920274245 +2013-11-15 01:30:00-07:00,2013,11,15,1,30,1.0,-6.9,0.0,0.0,0.0,0.16,798.0,243.0,2.4000000000000004,55.58685348319232 +2013-11-15 02:30:00-07:00,2013,11,15,2,30,0.3,-7.300000000000001,0.0,0.0,0.0,0.16,797.0,250.0,2.3000000000000003,56.69352235000104 +2013-11-15 03:30:00-07:00,2013,11,15,3,30,-0.3,-7.800000000000001,0.0,0.0,0.0,0.16,797.0,251.0,2.1,56.97141147638255 +2013-11-15 04:30:00-07:00,2013,11,15,4,30,-0.5,-8.5,0.0,0.0,0.0,0.16,796.0,250.0,2.0,54.74872491088722 +2013-11-15 05:30:00-07:00,2013,11,15,5,30,-0.4,-8.8,0.0,0.0,0.0,0.16,796.0,249.0,2.0,53.09507546885839 +2013-11-15 06:30:00-07:00,2013,11,15,6,30,0.2,-8.8,0.0,0.0,0.0,0.16,796.0,248.0,2.0,50.83255179080254 +2013-11-15 07:30:00-07:00,2013,11,15,7,30,2.9000000000000004,-8.200000000000001,28.0,612.0,101.0,0.16,796.0,242.0,2.5,43.9005170542847 +2013-11-15 08:30:00-07:00,2013,11,15,8,30,6.9,-7.300000000000001,43.0,832.0,273.0,0.16,796.0,239.0,3.3000000000000003,35.61696398015645 +2013-11-15 09:30:00-07:00,2013,11,15,9,30,10.0,-7.300000000000001,53.0,925.0,424.0,0.16,795.0,245.0,3.7,28.871606087195797 +2013-11-15 10:30:00-07:00,2013,11,15,10,30,12.1,-8.0,60.0,962.0,527.0,0.16,794.0,248.0,3.7,23.7933867144323 +2013-11-15 11:30:00-07:00,2013,11,15,11,30,13.3,-8.700000000000001,62.0,970.0,570.0,0.16,793.0,246.0,3.6,20.829560583948055 +2013-11-15 12:30:00-07:00,2013,11,15,12,30,13.6,-8.8,65.0,956.0,553.0,0.16,792.0,243.0,3.6,20.267845209561376 +2013-11-15 13:30:00-07:00,2013,11,15,13,30,13.2,-8.200000000000001,265.0,216.0,363.0,0.16,792.0,244.0,3.8,21.79789284321428 +2013-11-15 14:30:00-07:00,2013,11,15,14,30,12.1,-7.1000000000000005,126.0,2.0,126.0,0.16,792.0,248.0,3.7,25.506340711004277 +2013-11-15 15:30:00-07:00,2013,11,15,15,30,9.7,-4.800000000000001,34.0,0.0,34.0,0.16,792.0,250.0,2.7,35.64574663168291 +2013-11-15 16:30:00-07:00,2013,11,15,16,30,6.5,-3.6,14.0,277.0,24.0,0.16,792.0,248.0,1.6,48.476644920720126 +2013-11-15 17:30:00-07:00,2013,11,15,17,30,4.7,-3.4000000000000004,0.0,0.0,0.0,0.16,792.0,242.0,1.3,55.742053531142666 +2013-11-15 18:30:00-07:00,2013,11,15,18,30,4.0,-3.1,0.0,0.0,0.0,0.16,792.0,235.0,1.2000000000000002,59.864167553889246 +2013-11-15 19:30:00-07:00,2013,11,15,19,30,3.3000000000000003,-3.0,0.0,0.0,0.0,0.16,792.0,233.0,1.1,63.35715206241226 +2013-11-15 20:30:00-07:00,2013,11,15,20,30,2.6,-3.2,0.0,0.0,0.0,0.16,791.0,233.0,1.1,65.59240566133853 +2013-11-15 21:30:00-07:00,2013,11,15,21,30,2.1,-3.4000000000000004,0.0,0.0,0.0,0.16,791.0,233.0,1.1,66.96180892030485 +2013-11-15 22:30:00-07:00,2013,11,15,22,30,1.6,-3.6,0.0,0.0,0.0,0.16,791.0,240.0,1.2000000000000002,68.36803897775457 +2013-11-15 23:30:00-07:00,2013,11,15,23,30,1.2000000000000002,-3.8,0.0,0.0,0.0,0.16,790.0,250.0,1.2000000000000002,69.31255291215837 +2013-11-16 00:30:00-07:00,2013,11,16,0,30,0.8,-4.0,0.0,0.0,0.0,0.16,790.0,254.0,1.2000000000000002,70.2749681774062 +2013-11-16 01:30:00-07:00,2013,11,16,1,30,0.4,-4.4,0.0,0.0,0.0,0.16,789.0,245.0,1.2000000000000002,70.19266380027616 +2013-11-16 02:30:00-07:00,2013,11,16,2,30,0.2,-4.800000000000001,0.0,0.0,0.0,0.16,789.0,236.0,1.2000000000000002,69.10134736734952 +2013-11-16 03:30:00-07:00,2013,11,16,3,30,0.1,-5.300000000000001,0.0,0.0,0.0,0.16,789.0,233.0,1.3,67.02076797543637 +2013-11-16 04:30:00-07:00,2013,11,16,4,30,-0.1,-5.9,0.0,0.0,0.0,0.16,789.0,230.0,1.4,64.9695224465802 +2013-11-16 05:30:00-07:00,2013,11,16,5,30,-0.3,-6.4,0.0,0.0,0.0,0.16,788.0,226.0,1.5,63.4517213929153 +2013-11-16 06:30:00-07:00,2013,11,16,6,30,-0.1,-6.800000000000001,0.0,0.0,0.0,0.16,788.0,220.0,1.7000000000000002,60.648930571133896 +2013-11-16 07:30:00-07:00,2013,11,16,7,30,1.7000000000000002,-6.300000000000001,28.0,585.0,96.0,0.16,788.0,220.0,2.0,55.352240852931 +2013-11-16 08:30:00-07:00,2013,11,16,8,30,4.5,-4.7,158.0,139.0,196.0,0.16,788.0,231.0,2.7,51.27520052326731 +2013-11-16 09:30:00-07:00,2013,11,16,9,30,7.1000000000000005,-4.800000000000001,53.0,916.0,417.0,0.16,788.0,248.0,3.5,42.512418803793764 +2013-11-16 10:30:00-07:00,2013,11,16,10,30,9.0,-6.4,253.0,340.0,417.0,0.16,787.0,259.0,4.0,33.08673250588088 +2013-11-16 11:30:00-07:00,2013,11,16,11,30,10.1,-6.9,212.0,420.0,430.0,0.16,787.0,260.0,4.2,29.57563572463084 +2013-11-16 12:30:00-07:00,2013,11,16,12,30,10.2,-6.800000000000001,65.0,951.0,547.0,0.16,787.0,261.0,4.5,29.605123122161174 +2013-11-16 13:30:00-07:00,2013,11,16,13,30,9.4,-6.9,269.0,171.0,345.0,0.16,787.0,264.0,4.6000000000000005,30.996488597854302 +2013-11-16 14:30:00-07:00,2013,11,16,14,30,8.200000000000001,-5.5,56.0,843.0,343.0,0.16,787.0,266.0,4.1000000000000005,37.40611695774423 +2013-11-16 15:30:00-07:00,2013,11,16,15,30,6.5,-4.800000000000001,113.0,26.0,118.0,0.16,787.0,265.0,3.3000000000000003,44.299409835639516 +2013-11-16 16:30:00-07:00,2013,11,16,16,30,4.2,-4.6000000000000005,13.0,1.0,13.0,0.16,788.0,265.0,2.5,52.75940776802626 +2013-11-16 17:30:00-07:00,2013,11,16,17,30,2.9000000000000004,-4.5,0.0,0.0,0.0,0.16,789.0,263.0,2.2,58.25638502763677 +2013-11-16 18:30:00-07:00,2013,11,16,18,30,2.2,-4.4,0.0,0.0,0.0,0.16,789.0,261.0,2.1,61.68823838218882 +2013-11-16 19:30:00-07:00,2013,11,16,19,30,1.5,-4.4,0.0,0.0,0.0,0.16,789.0,262.0,1.9,64.8510149794297 +2013-11-16 20:30:00-07:00,2013,11,16,20,30,1.1,-4.6000000000000005,0.0,0.0,0.0,0.16,790.0,269.0,2.1,65.74187803797031 +2013-11-16 21:30:00-07:00,2013,11,16,21,30,0.9,-5.300000000000001,0.0,0.0,0.0,0.16,790.0,278.0,2.6,63.258228580242545 +2013-11-16 22:30:00-07:00,2013,11,16,22,30,0.5,-6.2,0.0,0.0,0.0,0.16,791.0,281.0,2.7,60.80048284632583 +2013-11-16 23:30:00-07:00,2013,11,16,23,30,-0.2,-6.9,0.0,0.0,0.0,0.16,791.0,279.0,2.5,60.623597962583545 +2013-11-17 00:30:00-07:00,2013,11,17,0,30,-0.7000000000000001,-7.4,0.0,0.0,0.0,0.16,792.0,276.0,2.5,60.49661716323498 +2013-11-17 01:30:00-07:00,2013,11,17,1,30,-0.9,-7.9,0.0,0.0,0.0,0.16,792.0,275.0,2.6,59.058742080762926 +2013-11-17 02:30:00-07:00,2013,11,17,2,30,-1.0,-8.3,0.0,0.0,0.0,0.16,792.0,278.0,2.7,57.6735834085381 +2013-11-17 03:30:00-07:00,2013,11,17,3,30,-1.2000000000000002,-8.5,0.0,0.0,0.0,0.16,793.0,284.0,2.6,57.620268045342954 +2013-11-17 04:30:00-07:00,2013,11,17,4,30,-1.1,-8.6,0.0,0.0,0.0,0.16,794.0,293.0,2.7,56.756091933616894 +2013-11-17 05:30:00-07:00,2013,11,17,5,30,-1.0,-8.700000000000001,0.0,0.0,0.0,0.16,795.0,301.0,3.1,55.90484303834727 +2013-11-17 06:30:00-07:00,2013,11,17,6,30,-0.8,-9.0,0.0,0.0,0.0,0.16,796.0,305.0,3.5,53.818009791077245 +2013-11-17 07:30:00-07:00,2013,11,17,7,30,0.5,-9.1,28.0,591.0,94.0,0.16,798.0,306.0,4.2,48.58878317703918 +2013-11-17 08:30:00-07:00,2013,11,17,8,30,2.5,-8.9,44.0,854.0,273.0,0.16,799.0,306.0,5.0,42.76701100586543 +2013-11-17 09:30:00-07:00,2013,11,17,9,30,4.2,-9.1,50.0,954.0,426.0,0.16,799.0,305.0,5.2,37.344001279604456 +2013-11-17 10:30:00-07:00,2013,11,17,10,30,5.7,-8.9,54.0,999.0,532.0,0.16,800.0,307.0,4.9,34.16948055996661 +2013-11-17 11:30:00-07:00,2013,11,17,11,30,6.9,-8.200000000000001,58.0,1011.0,579.0,0.16,800.0,311.0,4.3,33.22107292665362 +2013-11-17 12:30:00-07:00,2013,11,17,12,30,7.800000000000001,-7.5,59.0,990.0,558.0,0.16,800.0,318.0,3.4000000000000004,32.97999786862502 +2013-11-17 13:30:00-07:00,2013,11,17,13,30,8.200000000000001,-6.9,57.0,959.0,481.0,0.16,800.0,330.0,2.2,33.613296440535564 +2013-11-17 14:30:00-07:00,2013,11,17,14,30,7.9,-6.300000000000001,49.0,887.0,349.0,0.16,801.0,215.0,1.1,35.919462183719666 +2013-11-17 15:30:00-07:00,2013,11,17,15,30,5.9,-5.0,38.0,733.0,183.0,0.16,802.0,113.0,0.6000000000000001,45.47843053756774 +2013-11-17 16:30:00-07:00,2013,11,17,16,30,3.1,-3.8,13.0,291.0,22.0,0.16,803.0,144.0,0.7000000000000001,60.53634030032605 +2013-11-17 17:30:00-07:00,2013,11,17,17,30,1.3,-5.5,0.0,0.0,0.0,0.16,804.0,167.0,1.0,60.54022383279245 +2013-11-17 18:30:00-07:00,2013,11,17,18,30,0.2,-5.9,0.0,0.0,0.0,0.16,805.0,190.0,1.1,63.57189923996195 +2013-11-17 19:30:00-07:00,2013,11,17,19,30,-0.7000000000000001,-6.2,0.0,0.0,0.0,0.16,805.0,211.0,1.2000000000000002,66.3333395726189 +2013-11-17 20:30:00-07:00,2013,11,17,20,30,-1.1,-6.4,0.0,0.0,0.0,0.16,805.0,225.0,1.3,67.26434216720881 +2013-11-17 21:30:00-07:00,2013,11,17,21,30,-1.3,-6.4,0.0,0.0,0.0,0.16,805.0,233.0,1.4,68.25688728098793 +2013-11-17 22:30:00-07:00,2013,11,17,22,30,-1.4,-6.5,0.0,0.0,0.0,0.16,805.0,238.0,1.5,68.23507875691725 +2013-11-17 23:30:00-07:00,2013,11,17,23,30,-1.4,-6.6000000000000005,0.0,0.0,0.0,0.16,806.0,240.0,1.5,67.71445146393981 +2013-11-18 00:30:00-07:00,2013,11,18,0,30,-1.4,-6.6000000000000005,0.0,0.0,0.0,0.16,806.0,240.0,1.6,67.71445146393981 +2013-11-18 01:30:00-07:00,2013,11,18,1,30,-1.3,-6.5,0.0,0.0,0.0,0.16,806.0,242.0,1.6,67.73653206589663 +2013-11-18 02:30:00-07:00,2013,11,18,2,30,-1.2000000000000002,-6.6000000000000005,0.0,0.0,0.0,0.16,806.0,246.0,1.6,66.72898522968644 +2013-11-18 03:30:00-07:00,2013,11,18,3,30,-1.1,-6.6000000000000005,0.0,0.0,0.0,0.16,806.0,250.0,1.6,66.2422454685681 +2013-11-18 04:30:00-07:00,2013,11,18,4,30,-1.0,-6.6000000000000005,0.0,0.0,0.0,0.16,807.0,256.0,1.7000000000000002,65.75945392804633 +2013-11-18 05:30:00-07:00,2013,11,18,5,30,-0.8,-6.7,0.0,0.0,0.0,0.16,807.0,265.0,1.9,64.31069817439823 +2013-11-18 06:30:00-07:00,2013,11,18,6,30,-0.3,-6.7,0.0,0.0,0.0,0.16,808.0,267.0,2.2,62.01038215041197 +2013-11-18 07:30:00-07:00,2013,11,18,7,30,1.6,-6.5,24.0,623.0,91.0,0.16,808.0,261.0,2.5,54.90325566873904 +2013-11-18 08:30:00-07:00,2013,11,18,8,30,4.9,-4.800000000000001,36.0,860.0,264.0,0.16,808.0,249.0,2.6,49.48877772158201 +2013-11-18 09:30:00-07:00,2013,11,18,9,30,8.3,-4.800000000000001,42.0,953.0,414.0,0.16,808.0,237.0,2.3000000000000003,39.17488183030274 +2013-11-18 10:30:00-07:00,2013,11,18,10,30,11.2,-5.300000000000001,46.0,997.0,518.0,0.16,808.0,206.0,1.4,31.05686047267115 +2013-11-18 11:30:00-07:00,2013,11,18,11,30,13.0,-6.1000000000000005,47.0,1003.0,560.0,0.16,807.0,142.0,0.8,25.959467121706535 +2013-11-18 12:30:00-07:00,2013,11,18,12,30,13.8,-6.6000000000000005,47.0,997.0,546.0,0.16,807.0,91.0,0.9,23.717058570246365 +2013-11-18 13:30:00-07:00,2013,11,18,13,30,13.8,-6.5,46.0,968.0,472.0,0.16,806.0,77.0,1.1,23.899408832765886 +2013-11-18 14:30:00-07:00,2013,11,18,14,30,12.8,-6.0,41.0,905.0,344.0,0.16,806.0,80.0,1.1,26.50257304655247 +2013-11-18 15:30:00-07:00,2013,11,18,15,30,10.0,-2.6,32.0,762.0,180.0,0.16,806.0,88.0,1.0,41.184085383378445 +2013-11-18 16:30:00-07:00,2013,11,18,16,30,6.800000000000001,-3.0,12.0,344.0,21.0,0.16,806.0,101.0,1.1,49.659384520322 +2013-11-18 17:30:00-07:00,2013,11,18,17,30,5.1000000000000005,-5.4,0.0,0.0,0.0,0.16,806.0,126.0,1.0,46.638404143020466 +2013-11-18 18:30:00-07:00,2013,11,18,18,30,4.0,-6.1000000000000005,0.0,0.0,0.0,0.16,806.0,175.0,1.0,47.75277521921155 +2013-11-18 19:30:00-07:00,2013,11,18,19,30,2.7,-6.4,0.0,0.0,0.0,0.16,806.0,222.0,1.2000000000000002,51.154320983355674 +2013-11-18 20:30:00-07:00,2013,11,18,20,30,1.7000000000000002,-6.6000000000000005,0.0,0.0,0.0,0.16,806.0,244.0,1.4,54.09593609522926 +2013-11-18 21:30:00-07:00,2013,11,18,21,30,1.1,-6.800000000000001,0.0,0.0,0.0,0.16,805.0,251.0,1.6,55.61404189290459 +2013-11-18 22:30:00-07:00,2013,11,18,22,30,0.9,-7.0,0.0,0.0,0.0,0.16,805.0,254.0,1.7000000000000002,55.55964438391723 +2013-11-18 23:30:00-07:00,2013,11,18,23,30,0.8,-7.2,0.0,0.0,0.0,0.16,804.0,256.0,1.9,55.106557540102806 +2013-11-19 00:30:00-07:00,2013,11,19,0,30,0.7000000000000001,-7.6,0.0,0.0,0.0,0.16,804.0,258.0,2.0,53.818537139087084 +2013-11-19 01:30:00-07:00,2013,11,19,1,30,0.4,-8.4,0.0,0.0,0.0,0.16,803.0,258.0,2.1,51.68893269948387 +2013-11-19 02:30:00-07:00,2013,11,19,2,30,0.3,-9.0,0.0,0.0,0.0,0.16,803.0,258.0,2.1,49.68348438635354 +2013-11-19 03:30:00-07:00,2013,11,19,3,30,0.4,-9.2,0.0,0.0,0.0,0.16,802.0,258.0,2.2,48.5593822244997 +2013-11-19 04:30:00-07:00,2013,11,19,4,30,0.5,-9.1,0.0,0.0,0.0,0.16,802.0,259.0,2.4000000000000004,48.58878317703918 +2013-11-19 05:30:00-07:00,2013,11,19,5,30,0.4,-8.8,0.0,0.0,0.0,0.16,801.0,260.0,2.6,50.10239866852177 +2013-11-19 06:30:00-07:00,2013,11,19,6,30,0.9,-8.4,0.0,0.0,0.0,0.16,801.0,262.0,2.9000000000000004,49.85792012454883 +2013-11-19 07:30:00-07:00,2013,11,19,7,30,3.0,-7.6,25.0,1.0,25.0,0.16,801.0,262.0,3.5,45.66496368531739 +2013-11-19 08:30:00-07:00,2013,11,19,8,30,6.0,-6.0,73.0,4.0,74.0,0.16,801.0,262.0,3.9,41.86341807669131 +2013-11-19 09:30:00-07:00,2013,11,19,9,30,8.4,-4.6000000000000005,85.0,2.0,86.0,0.16,800.0,264.0,3.8,39.50151697221719 +2013-11-19 10:30:00-07:00,2013,11,19,10,30,10.1,-3.3000000000000003,114.0,4.0,116.0,0.16,800.0,271.0,3.6,38.835936836636215 +2013-11-19 11:30:00-07:00,2013,11,19,11,30,11.2,-2.3000000000000003,242.0,167.0,327.0,0.16,799.0,281.0,3.3000000000000003,38.87500158810056 +2013-11-19 12:30:00-07:00,2013,11,19,12,30,11.8,-1.6,186.0,26.0,198.0,0.16,798.0,289.0,2.9000000000000004,39.34113576335397 +2013-11-19 13:30:00-07:00,2013,11,19,13,30,11.7,-1.5,250.0,215.0,344.0,0.16,798.0,294.0,2.3000000000000003,39.89354056920321 +2013-11-19 14:30:00-07:00,2013,11,19,14,30,10.3,-0.5,55.0,818.0,327.0,0.16,798.0,304.0,1.4,47.09936107365665 +2013-11-19 15:30:00-07:00,2013,11,19,15,30,8.1,0.2,82.0,340.0,147.0,0.16,798.0,316.0,0.8,57.46661747713424 +2013-11-19 16:30:00-07:00,2013,11,19,16,30,6.7,-0.3,8.0,0.0,8.0,0.16,799.0,314.0,0.7000000000000001,60.97999532247896 +2013-11-19 17:30:00-07:00,2013,11,19,17,30,6.0,-0.6000000000000001,0.0,0.0,0.0,0.16,799.0,304.0,0.6000000000000001,62.60972170140715 +2013-11-19 18:30:00-07:00,2013,11,19,18,30,5.4,-0.7000000000000001,0.0,0.0,0.0,0.16,799.0,288.0,0.5,64.79120200944027 +2013-11-19 19:30:00-07:00,2013,11,19,19,30,4.7,-0.8,0.0,0.0,0.0,0.16,799.0,263.0,0.4,67.5312679421284 +2013-11-19 20:30:00-07:00,2013,11,19,20,30,4.3,-0.8,0.0,0.0,0.0,0.16,799.0,234.0,0.5,69.44626196065894 +2013-11-19 21:30:00-07:00,2013,11,19,21,30,3.9,-0.7000000000000001,0.0,0.0,0.0,0.16,799.0,220.0,0.6000000000000001,71.94509196725033 +2013-11-19 22:30:00-07:00,2013,11,19,22,30,3.7,-0.7000000000000001,0.0,0.0,0.0,0.16,799.0,223.0,0.8,72.96383313834741 +2013-11-19 23:30:00-07:00,2013,11,19,23,30,3.5,-0.8,0.0,0.0,0.0,0.16,799.0,234.0,0.9,73.46069502945018 +2013-11-20 00:30:00-07:00,2013,11,20,0,30,3.4000000000000004,-1.1,0.0,0.0,0.0,0.16,799.0,247.0,0.9,72.3759488413564 +2013-11-20 01:30:00-07:00,2013,11,20,1,30,3.2,-1.5,0.0,0.0,0.0,0.16,799.0,260.0,1.0,71.28396668853706 +2013-11-20 02:30:00-07:00,2013,11,20,2,30,3.1,-1.8,0.0,0.0,0.0,0.16,799.0,272.0,0.9,70.2236264994308 +2013-11-20 03:30:00-07:00,2013,11,20,3,30,2.8000000000000003,-2.2,0.0,0.0,0.0,0.16,799.0,287.0,0.9,69.64638967214714 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20:30:00-07:00,2013,11,20,20,30,3.4000000000000004,-0.8,0.0,0.0,0.0,0.75,802.0,68.0,1.3,73.9804458438736 +2013-11-20 21:30:00-07:00,2013,11,20,21,30,2.7,-0.3,0.0,0.0,0.0,0.75,802.0,45.0,1.7000000000000002,80.61928007687953 +2013-11-20 22:30:00-07:00,2013,11,20,22,30,1.9,-0.1,0.0,0.0,0.0,0.75,802.0,33.0,2.4000000000000004,86.59119647070882 +2013-11-20 23:30:00-07:00,2013,11,20,23,30,1.1,-0.6000000000000001,0.0,0.0,0.0,0.75,802.0,27.0,3.0,88.42367432827241 +2013-11-21 00:30:00-07:00,2013,11,21,0,30,0.3,-1.5,0.0,0.0,0.0,0.75,803.0,25.0,3.5,87.70574534420338 +2013-11-21 01:30:00-07:00,2013,11,21,1,30,-0.9,-2.7,0.0,0.0,0.0,0.75,803.0,26.0,3.8,87.59107065471972 +2013-11-21 02:30:00-07:00,2013,11,21,2,30,-2.5,-4.3,0.0,0.0,0.0,0.75,803.0,26.0,3.8,87.43572274269725 +2013-11-21 03:30:00-07:00,2013,11,21,3,30,-4.0,-6.1000000000000005,0.0,0.0,0.0,0.75,804.0,27.0,3.4000000000000004,85.31461616172963 +2013-11-21 04:30:00-07:00,2013,11,21,4,30,-5.2,-7.800000000000001,0.0,0.0,0.0,0.75,804.0,27.0,2.9000000000000004,81.95006604033034 +2013-11-21 05:30:00-07:00,2013,11,21,5,30,-6.0,-9.1,0.0,0.0,0.0,0.75,805.0,25.0,2.4000000000000004,78.7048643509342 +2013-11-21 06:30:00-07:00,2013,11,21,6,30,-6.4,-10.0,0.0,0.0,0.0,0.75,805.0,26.0,2.0,75.6060029407153 +2013-11-21 07:30:00-07:00,2013,11,21,7,30,-6.2,-10.4,26.0,0.0,26.0,0.75,806.0,41.0,1.9,72.14261803134369 +2013-11-21 08:30:00-07:00,2013,11,21,8,30,-5.4,-10.1,26.0,0.0,26.0,0.75,806.0,58.0,2.0,69.51009599169272 +2013-11-21 09:30:00-07:00,2013,11,21,9,30,-4.2,-9.5,45.0,0.0,45.0,0.75,807.0,64.0,2.1,66.56317113287221 +2013-11-21 10:30:00-07:00,2013,11,21,10,30,-3.0,-8.9,112.0,0.0,112.0,0.75,807.0,67.0,2.4000000000000004,63.783480792726166 +2013-11-21 11:30:00-07:00,2013,11,21,11,30,-2.2,-8.5,84.0,0.0,84.0,0.75,807.0,69.0,2.9000000000000004,62.0176405577817 +2013-11-21 12:30:00-07:00,2013,11,21,12,30,-2.4000000000000004,-8.6,279.0,2.0,280.0,0.75,807.0,67.0,3.4000000000000004,62.45277950538967 +2013-11-21 13:30:00-07:00,2013,11,21,13,30,-3.4000000000000004,-9.1,40.0,0.0,40.0,0.75,808.0,60.0,3.5,64.69217945691582 +2013-11-21 14:30:00-07:00,2013,11,21,14,30,-4.6000000000000005,-9.7,44.0,0.0,44.0,0.75,808.0,54.0,3.4000000000000004,67.52631259952265 +2013-11-21 15:30:00-07:00,2013,11,21,15,30,-6.0,-10.5,10.0,0.0,10.0,0.75,809.0,50.0,3.3000000000000003,70.48969980540656 +2013-11-21 16:30:00-07:00,2013,11,21,16,30,-7.6,-11.7,6.0,0.0,6.0,0.75,810.0,46.0,3.1,72.43735600607222 +2013-11-21 17:30:00-07:00,2013,11,21,17,30,-8.8,-13.1,0.0,0.0,0.0,0.8,811.0,40.0,3.0,71.0362946914232 +2013-11-21 18:30:00-07:00,2013,11,21,18,30,-9.3,-14.1,0.0,0.0,0.0,0.8,812.0,32.0,3.0,68.09735013490399 +2013-11-21 19:30:00-07:00,2013,11,21,19,30,-9.5,-14.9,0.0,0.0,0.0,0.8,813.0,25.0,3.0,64.78162911139515 +2013-11-21 20:30:00-07:00,2013,11,21,20,30,-9.5,-15.4,0.0,0.0,0.0,0.8,813.0,20.0,2.7,62.16432496806742 +2013-11-21 21:30:00-07:00,2013,11,21,21,30,-9.4,-15.6,0.0,0.0,0.0,0.8,814.0,13.0,2.3000000000000003,60.666016333579215 +2013-11-21 22:30:00-07:00,2013,11,21,22,30,-9.1,-15.6,0.0,0.0,0.0,0.8,814.0,6.0,2.1,59.256316908519594 +2013-11-21 23:30:00-07:00,2013,11,21,23,30,-8.700000000000001,-15.4,0.0,0.0,0.0,0.8,814.0,178.0,1.9,58.39117275699753 +2013-11-22 00:30:00-07:00,2013,11,22,0,30,-8.200000000000001,-15.1,0.0,0.0,0.0,0.8,814.0,351.0,1.7000000000000002,57.570610362756824 +2013-11-22 01:30:00-07:00,2013,11,22,1,30,-7.6,-14.9,0.0,0.0,0.0,0.8,814.0,342.0,1.6,55.86860269191561 +2013-11-22 02:30:00-07:00,2013,11,22,2,30,-7.0,-14.8,0.0,0.0,0.0,0.8,814.0,330.0,1.5,53.78427287260323 +2013-11-22 03:30:00-07:00,2013,11,22,3,30,-6.4,-14.6,0.0,0.0,0.0,0.8,814.0,317.0,1.3,52.21652592288966 +2013-11-22 04:30:00-07:00,2013,11,22,4,30,-5.6000000000000005,-14.3,0.0,0.0,0.0,0.8,814.0,304.0,1.0,50.35020553062373 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23:30:00-07:00,2013,11,22,23,30,-5.1000000000000005,-13.2,0.0,0.0,0.0,0.79,816.0,334.0,1.6,53.02259260219175 +2013-11-23 00:30:00-07:00,2013,11,23,0,30,-5.2,-13.1,0.0,0.0,0.0,0.79,816.0,327.0,1.5,53.86024486038318 +2013-11-23 01:30:00-07:00,2013,11,23,1,30,-5.2,-12.7,0.0,0.0,0.0,0.79,816.0,317.0,1.3,55.6311491889026 +2013-11-23 02:30:00-07:00,2013,11,23,2,30,-5.0,-12.2,0.0,0.0,0.0,0.79,816.0,301.0,1.0,57.047926574721984 +2013-11-23 03:30:00-07:00,2013,11,23,3,30,-4.9,-11.6,0.0,0.0,0.0,0.79,816.0,276.0,0.7000000000000001,59.40879057320922 +2013-11-23 04:30:00-07:00,2013,11,23,4,30,-5.0,-11.1,0.0,0.0,0.0,0.79,815.0,236.0,0.6000000000000001,62.2960391355116 +2013-11-23 05:30:00-07:00,2013,11,23,5,30,-5.1000000000000005,-10.9,0.0,0.0,0.0,0.79,816.0,204.0,0.6000000000000001,63.77570194491155 +2013-11-23 06:30:00-07:00,2013,11,23,6,30,-5.2,-10.9,0.0,0.0,0.0,0.79,816.0,190.0,0.7000000000000001,64.26026964393161 +2013-11-23 07:30:00-07:00,2013,11,23,7,30,-4.2,-10.9,44.0,1.0,44.0,0.79,816.0,179.0,1.2000000000000002,59.59275365540966 +2013-11-23 08:30:00-07:00,2013,11,23,8,30,-2.3000000000000003,-10.5,156.0,13.0,160.0,0.79,816.0,165.0,2.0,53.39549706362002 +2013-11-23 09:30:00-07:00,2013,11,23,9,30,-0.7000000000000001,-10.5,249.0,21.0,257.0,0.79,816.0,154.0,2.7,47.477183101105204 +2013-11-23 10:30:00-07:00,2013,11,23,10,30,0.6000000000000001,-10.7,340.0,120.0,395.0,0.79,816.0,149.0,3.1,42.524414511446565 +2013-11-23 11:30:00-07:00,2013,11,23,11,30,1.7000000000000002,-10.6,316.0,187.0,409.0,0.79,815.0,146.0,3.2,39.60631353962028 +2013-11-23 12:30:00-07:00,2013,11,23,12,30,2.4000000000000004,-10.4,285.0,36.0,302.0,0.79,814.0,143.0,3.3000000000000003,38.27948009887135 +2013-11-23 13:30:00-07:00,2013,11,23,13,30,2.6,-10.2,305.0,37.0,320.0,0.79,813.0,144.0,3.3000000000000003,38.34100592740324 +2013-11-23 14:30:00-07:00,2013,11,23,14,30,2.2,-10.1,172.0,25.0,180.0,0.79,813.0,148.0,3.2,39.75997798919768 +2013-11-23 15:30:00-07:00,2013,11,23,15,30,0.2,-9.9,104.0,4.0,105.0,0.79,813.0,156.0,2.6,46.63049061353149 +2013-11-23 16:30:00-07:00,2013,11,23,16,30,-2.5,-10.2,11.0,267.0,16.0,0.79,812.0,166.0,2.0,55.49289855660582 +2013-11-23 17:30:00-07:00,2013,11,23,17,30,-3.7,-11.2,0.0,0.0,0.0,0.75,812.0,179.0,1.9,56.04690602504727 +2013-11-23 18:30:00-07:00,2013,11,23,18,30,-3.8,-10.9,0.0,0.0,0.0,0.75,812.0,193.0,1.8,57.83230139303382 +2013-11-23 19:30:00-07:00,2013,11,23,19,30,-3.6,-10.2,0.0,0.0,0.0,0.75,812.0,202.0,1.7000000000000002,60.223749528286426 +2013-11-23 20:30:00-07:00,2013,11,23,20,30,-3.3000000000000003,-9.3,0.0,0.0,0.0,0.75,811.0,200.0,1.5,63.213432164988895 +2013-11-23 21:30:00-07:00,2013,11,23,21,30,-3.3000000000000003,-8.3,0.0,0.0,0.0,0.75,811.0,190.0,1.4,68.34628940426148 +2013-11-23 22:30:00-07:00,2013,11,23,22,30,-3.7,-7.9,0.0,0.0,0.0,0.75,810.0,185.0,1.5,72.63820271690106 +2013-11-23 23:30:00-07:00,2013,11,23,23,30,-4.0,-8.200000000000001,0.0,0.0,0.0,0.75,810.0,190.0,1.7000000000000002,72.57938266558978 +2013-11-24 00:30:00-07:00,2013,11,24,0,30,-3.8,-8.200000000000001,0.0,0.0,0.0,0.75,809.0,194.0,1.9,71.50019539435472 +2013-11-24 01:30:00-07:00,2013,11,24,1,30,-3.4000000000000004,-7.9,0.0,0.0,0.0,0.75,808.0,196.0,2.1,71.02880851247222 +2013-11-24 02:30:00-07:00,2013,11,24,2,30,-3.1,-7.6,0.0,0.0,0.0,0.75,807.0,198.0,2.0,71.09013676966727 +2013-11-24 03:30:00-07:00,2013,11,24,3,30,-2.8000000000000003,-7.300000000000001,0.0,0.0,0.0,0.75,806.0,201.0,1.8,71.15128587473619 +2013-11-24 04:30:00-07:00,2013,11,24,4,30,-2.9000000000000004,-7.1000000000000005,0.0,0.0,0.0,0.75,806.0,203.0,1.6,72.79420064704179 +2013-11-24 05:30:00-07:00,2013,11,24,5,30,-3.5,-7.4,0.0,0.0,0.0,0.75,805.0,204.0,1.5,74.38113957814575 +2013-11-24 06:30:00-07:00,2013,11,24,6,30,-3.7,-7.7,0.0,0.0,0.0,0.75,805.0,212.0,1.5,73.771505144707 +2013-11-24 07:30:00-07:00,2013,11,24,7,30,-2.1,-7.5,43.0,5.0,44.0,0.75,805.0,234.0,1.6,66.52493627534326 +2013-11-24 08:30:00-07:00,2013,11,24,8,30,0.3,-6.1000000000000005,133.0,17.0,137.0,0.75,805.0,263.0,1.9,62.158477609550715 +2013-11-24 09:30:00-07:00,2013,11,24,9,30,2.3000000000000003,-4.800000000000001,60.0,0.0,60.0,0.75,804.0,304.0,2.0,59.432202210111605 +2013-11-24 10:30:00-07:00,2013,11,24,10,30,3.4000000000000004,-3.6,269.0,38.0,286.0,0.75,804.0,336.0,2.2,60.1603942098818 +2013-11-24 11:30:00-07:00,2013,11,24,11,30,3.8,-2.9000000000000004,252.0,15.0,260.0,0.75,803.0,346.0,2.4000000000000004,61.62058514269126 +2013-11-24 12:30:00-07:00,2013,11,24,12,30,3.8,-2.3000000000000003,313.0,94.0,359.0,0.75,802.0,354.0,2.4000000000000004,64.4218926414594 +2013-11-24 13:30:00-07:00,2013,11,24,13,30,3.5,-1.9,273.0,106.0,318.0,0.75,802.0,358.0,2.3000000000000003,67.7676965231433 +2013-11-24 14:30:00-07:00,2013,11,24,14,30,2.8000000000000003,-1.6,194.0,50.0,210.0,0.75,802.0,355.0,1.9,72.79379094222209 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07:30:00-07:00,2013,11,25,7,30,-2.9000000000000004,-8.4,13.0,0.0,13.0,0.73,807.0,350.0,3.4000000000000004,65.82819154647683 +2013-11-25 08:30:00-07:00,2013,11,25,8,30,-1.0,-6.1000000000000005,41.0,0.0,41.0,0.73,808.0,176.0,3.6,68.32219064293048 +2013-11-25 09:30:00-07:00,2013,11,25,9,30,0.5,-4.5,186.0,2.0,186.0,0.73,808.0,6.0,3.5,69.16491732482187 +2013-11-25 10:30:00-07:00,2013,11,25,10,30,1.5,-3.8,168.0,0.0,168.0,0.73,809.0,15.0,3.2,67.83715361955326 +2013-11-25 11:30:00-07:00,2013,11,25,11,30,2.1,-3.5,205.0,0.0,205.0,0.73,809.0,19.0,2.7,66.46398322960768 +2013-11-25 12:30:00-07:00,2013,11,25,12,30,2.5,-3.5,281.0,6.0,284.0,0.73,809.0,20.0,2.2,64.59867718077535 +2013-11-25 13:30:00-07:00,2013,11,25,13,30,2.5,-3.5,269.0,20.0,278.0,0.73,809.0,22.0,1.8,64.59867718077535 +2013-11-25 14:30:00-07:00,2013,11,25,14,30,2.3000000000000003,-3.6,188.0,8.0,190.0,0.73,809.0,28.0,1.4,65.03639154754953 +2013-11-25 15:30:00-07:00,2013,11,25,15,30,1.1,-3.4000000000000004,87.0,1.0,87.0,0.73,809.0,50.0,0.8,71.93019888131566 +2013-11-25 16:30:00-07:00,2013,11,25,16,30,-0.5,-4.2,0.0,0.0,0.0,0.73,809.0,129.0,0.5,76.06352734247147 +2013-11-25 17:30:00-07:00,2013,11,25,17,30,-1.5,-4.9,0.0,0.0,0.0,0.16,809.0,211.0,0.7000000000000001,77.63122930085144 +2013-11-25 18:30:00-07:00,2013,11,25,18,30,-1.8,-5.2,0.0,0.0,0.0,0.16,810.0,247.0,0.8,77.58197660611377 +2013-11-25 19:30:00-07:00,2013,11,25,19,30,-1.7000000000000002,-5.5,0.0,0.0,0.0,0.16,810.0,279.0,0.9,75.28128750464641 +2013-11-25 20:30:00-07:00,2013,11,25,20,30,-1.7000000000000002,-5.6000000000000005,0.0,0.0,0.0,0.16,810.0,315.0,0.8,74.71170697020864 +2013-11-25 21:30:00-07:00,2013,11,25,21,30,-2.0,-5.800000000000001,0.0,0.0,0.0,0.16,811.0,344.0,0.9,75.22763927078789 +2013-11-25 22:30:00-07:00,2013,11,25,22,30,-2.6,-6.0,0.0,0.0,0.0,0.16,811.0,355.0,0.9,77.44988163480996 +2013-11-25 23:30:00-07:00,2013,11,25,23,30,-3.2,-6.2,0.0,0.0,0.0,0.16,811.0,349.0,0.9,79.75341185365947 +2013-11-26 00:30:00-07:00,2013,11,26,0,30,-3.5,-6.6000000000000005,0.0,0.0,0.0,0.16,811.0,334.0,0.9,79.09975232338009 +2013-11-26 01:30:00-07:00,2013,11,26,1,30,-3.9,-7.1000000000000005,0.0,0.0,0.0,0.16,811.0,319.0,1.1,78.43168018712466 +2013-11-26 02:30:00-07:00,2013,11,26,2,30,-4.2,-7.800000000000001,0.0,0.0,0.0,0.16,811.0,313.0,1.4,75.99765959038646 +2013-11-26 03:30:00-07:00,2013,11,26,3,30,-4.5,-8.5,0.0,0.0,0.0,0.16,811.0,314.0,1.5,73.61959835362703 +2013-11-26 04:30:00-07:00,2013,11,26,4,30,-4.7,-9.0,0.0,0.0,0.0,0.16,811.0,320.0,1.6,71.8773817972246 +2013-11-26 05:30:00-07:00,2013,11,26,5,30,-4.800000000000001,-9.2,0.0,0.0,0.0,0.16,812.0,327.0,1.6,71.29679884760479 +2013-11-26 06:30:00-07:00,2013,11,26,6,30,-4.800000000000001,-9.4,0.0,0.0,0.0,0.16,812.0,330.0,1.6,70.18752111525585 +2013-11-26 07:30:00-07:00,2013,11,26,7,30,-2.8000000000000003,-9.3,23.0,490.0,63.0,0.16,813.0,324.0,2.0,60.90588594921953 +2013-11-26 08:30:00-07:00,2013,11,26,8,30,0.5,-7.6,41.0,786.0,228.0,0.16,813.0,328.0,2.4000000000000004,54.60090210033067 +2013-11-26 09:30:00-07:00,2013,11,26,9,30,3.1,-6.6000000000000005,220.0,111.0,260.0,0.16,813.0,168.0,2.8000000000000003,48.96998573116399 +2013-11-26 10:30:00-07:00,2013,11,26,10,30,4.6000000000000005,-6.5,225.0,22.0,235.0,0.16,813.0,3.0,3.3000000000000003,44.409504010925716 +2013-11-26 11:30:00-07:00,2013,11,26,11,30,5.4,-6.5,281.0,248.0,401.0,0.16,812.0,7.0,3.5,42.00352667287176 +2013-11-26 12:30:00-07:00,2013,11,26,12,30,5.800000000000001,-6.5,53.0,974.0,517.0,0.16,812.0,10.0,3.5,40.85535603062786 +2013-11-26 13:30:00-07:00,2013,11,26,13,30,5.800000000000001,-6.4,50.0,944.0,445.0,0.16,811.0,14.0,3.1,41.169208791118564 +2013-11-26 14:30:00-07:00,2013,11,26,14,30,4.800000000000001,-6.0,42.0,882.0,322.0,0.16,811.0,24.0,2.0,45.49915218529127 +2013-11-26 15:30:00-07:00,2013,11,26,15,30,2.9000000000000004,-4.1000000000000005,32.0,731.0,162.0,0.16,811.0,49.0,0.9,60.034273641742196 +2013-11-26 16:30:00-07:00,2013,11,26,16,30,0.9,-6.300000000000001,0.0,0.0,0.0,0.16,811.0,120.0,0.6000000000000001,58.622392442815446 +2013-11-26 17:30:00-07:00,2013,11,26,17,30,-0.9,-7.800000000000001,0.0,0.0,0.0,0.16,811.0,196.0,0.8,59.51787251158953 +2013-11-26 18:30:00-07:00,2013,11,26,18,30,-2.3000000000000003,-7.800000000000001,0.0,0.0,0.0,0.16,811.0,226.0,1.2000000000000002,65.96696456593307 +2013-11-26 19:30:00-07:00,2013,11,26,19,30,-2.7,-7.9,0.0,0.0,0.0,0.16,811.0,235.0,1.5,67.42552824085229 +2013-11-26 20:30:00-07:00,2013,11,26,20,30,-2.8000000000000003,-8.0,0.0,0.0,0.0,0.16,811.0,240.0,1.8,67.40315835499543 +2013-11-26 21:30:00-07:00,2013,11,26,21,30,-2.7,-8.1,0.0,0.0,0.0,0.16,811.0,245.0,2.2,66.38796492549615 +2013-11-26 22:30:00-07:00,2013,11,26,22,30,-2.7,-8.4,0.0,0.0,0.0,0.16,810.0,251.0,2.4000000000000004,64.85826481946359 +2013-11-26 23:30:00-07:00,2013,11,26,23,30,-2.8000000000000003,-8.8,0.0,0.0,0.0,0.16,810.0,258.0,2.5,63.335650708998095 +2013-11-27 00:30:00-07:00,2013,11,27,0,30,-2.8000000000000003,-9.3,0.0,0.0,0.0,0.16,810.0,264.0,2.6,60.90588594921953 +2013-11-27 01:30:00-07:00,2013,11,27,1,30,-2.7,-9.7,0.0,0.0,0.0,0.16,809.0,268.0,2.6,58.585981237902466 +2013-11-27 02:30:00-07:00,2013,11,27,2,30,-2.6,-9.8,0.0,0.0,0.0,0.16,809.0,269.0,2.7,57.69739677845951 +2013-11-27 03:30:00-07:00,2013,11,27,3,30,-2.4000000000000004,-9.9,0.0,0.0,0.0,0.16,809.0,269.0,2.8000000000000003,56.40330420992234 +2013-11-27 04:30:00-07:00,2013,11,27,4,30,-2.2,-9.8,0.0,0.0,0.0,0.16,809.0,267.0,2.9000000000000004,56.01519375497669 +2013-11-27 05:30:00-07:00,2013,11,27,5,30,-2.1,-9.8,0.0,0.0,0.0,0.16,808.0,266.0,2.9000000000000004,55.6032165160832 +2013-11-27 06:30:00-07:00,2013,11,27,6,30,-1.6,-9.7,0.0,0.0,0.0,0.16,808.0,265.0,3.0,54.016734449748014 +2013-11-27 07:30:00-07:00,2013,11,27,7,30,0.5,-9.3,21.0,519.0,62.0,0.16,808.0,263.0,3.3000000000000003,47.833451556710415 +2013-11-27 08:30:00-07:00,2013,11,27,8,30,4.1000000000000005,-7.6,36.0,808.0,225.0,0.16,808.0,261.0,3.6,42.259975259547204 +2013-11-27 09:30:00-07:00,2013,11,27,9,30,7.2,-5.9,44.0,917.0,373.0,0.16,808.0,260.0,3.4000000000000004,38.8438679563415 +2013-11-27 10:30:00-07:00,2013,11,27,10,30,9.7,-4.0,48.0,964.0,477.0,0.16,807.0,275.0,2.7,37.85655727133472 +2013-11-27 11:30:00-07:00,2013,11,27,11,30,11.4,-3.8,50.0,976.0,522.0,0.16,806.0,305.0,2.1,34.313738567269496 +2013-11-27 12:30:00-07:00,2013,11,27,12,30,12.0,-3.7,50.0,972.0,510.0,0.16,806.0,328.0,1.8,33.22891622835689 +2013-11-27 13:30:00-07:00,2013,11,27,13,30,11.6,-3.0,48.0,941.0,440.0,0.16,805.0,345.0,1.3,35.94522274994706 +2013-11-27 14:30:00-07:00,2013,11,27,14,30,9.9,-0.1,42.0,874.0,318.0,0.16,805.0,192.0,0.8,49.80312820246527 +2013-11-27 15:30:00-07:00,2013,11,27,15,30,7.300000000000001,-0.8,33.0,717.0,159.0,0.16,806.0,40.0,0.7000000000000001,56.43064840039554 +2013-11-27 16:30:00-07:00,2013,11,27,16,30,5.0,-4.4,0.0,0.0,0.0,0.16,806.0,51.0,0.6000000000000001,50.648938425592796 +2013-11-27 17:30:00-07:00,2013,11,27,17,30,3.7,-5.7,0.0,0.0,0.0,0.16,806.0,172.0,0.5,50.27905556693787 +2013-11-27 18:30:00-07:00,2013,11,27,18,30,2.7,-6.0,0.0,0.0,0.0,0.16,806.0,291.0,0.8,52.741000840117394 +2013-11-27 19:30:00-07:00,2013,11,27,19,30,1.6,-6.2,0.0,0.0,0.0,0.16,807.0,294.0,1.3,56.17721975030319 +2013-11-27 20:30:00-07:00,2013,11,27,20,30,0.7000000000000001,-6.5,0.0,0.0,0.0,0.16,807.0,299.0,1.4,58.5702334870242 +2013-11-27 21:30:00-07:00,2013,11,27,21,30,0.8,-7.0,0.0,0.0,0.0,0.16,807.0,305.0,1.3,55.96119757365764 +2013-11-27 22:30:00-07:00,2013,11,27,22,30,1.7000000000000002,-7.6,0.0,0.0,0.0,0.16,807.0,315.0,1.0,50.089408866526405 +2013-11-27 23:30:00-07:00,2013,11,27,23,30,2.4000000000000004,-7.9,0.0,0.0,0.0,0.16,808.0,333.0,0.7000000000000001,46.55709497217177 +2013-11-28 00:30:00-07:00,2013,11,28,0,30,2.2,-7.5,0.0,0.0,0.0,0.16,808.0,339.0,0.6000000000000001,48.70829545283946 +2013-11-28 01:30:00-07:00,2013,11,28,1,30,1.5,-7.2,0.0,0.0,0.0,0.16,808.0,297.0,0.5,52.403971287452904 +2013-11-28 02:30:00-07:00,2013,11,28,2,30,0.8,-7.0,0.0,0.0,0.0,0.16,808.0,259.0,0.7000000000000001,55.96119757365764 +2013-11-28 03:30:00-07:00,2013,11,28,3,30,0.2,-7.0,0.0,0.0,0.0,0.16,807.0,261.0,0.8,58.439468576751786 +2013-11-28 04:30:00-07:00,2013,11,28,4,30,-0.1,-7.0,0.0,0.0,0.0,0.16,807.0,264.0,0.8,59.72425569876314 +2013-11-28 05:30:00-07:00,2013,11,28,5,30,-0.1,-7.0,0.0,0.0,0.0,0.16,808.0,268.0,0.8,59.72425569876314 +2013-11-28 06:30:00-07:00,2013,11,28,6,30,0.0,-7.1000000000000005,0.0,0.0,0.0,0.16,808.0,268.0,0.7000000000000001,58.83822330589822 +2013-11-28 07:30:00-07:00,2013,11,28,7,30,1.0,-6.9,30.0,2.0,30.0,0.16,808.0,252.0,0.6000000000000001,55.58685348319232 +2013-11-28 08:30:00-07:00,2013,11,28,8,30,3.3000000000000003,-4.9,129.0,65.0,144.0,0.16,809.0,228.0,0.9,54.94920781999882 +2013-11-28 09:30:00-07:00,2013,11,28,9,30,5.5,-4.800000000000001,216.0,115.0,257.0,0.16,809.0,216.0,1.2000000000000002,47.46717061526555 +2013-11-28 10:30:00-07:00,2013,11,28,10,30,7.300000000000001,-4.6000000000000005,269.0,159.0,339.0,0.16,808.0,216.0,1.5,42.571989442086995 +2013-11-28 11:30:00-07:00,2013,11,28,11,30,8.700000000000001,-4.800000000000001,228.0,465.0,451.0,0.16,808.0,223.0,1.5,38.12825083086529 +2013-11-28 12:30:00-07:00,2013,11,28,12,30,9.5,-5.0,251.0,336.0,409.0,0.16,807.0,224.0,1.5,35.58575854916742 +2013-11-28 13:30:00-07:00,2013,11,28,13,30,9.5,-5.1000000000000005,50.0,942.0,440.0,0.16,807.0,220.0,1.3,35.317641470392424 +2013-11-28 14:30:00-07:00,2013,11,28,14,30,8.1,-4.1000000000000005,43.0,877.0,318.0,0.16,807.0,194.0,0.9,41.85799731821196 +2013-11-28 15:30:00-07:00,2013,11,28,15,30,5.300000000000001,-3.2,33.0,718.0,159.0,0.16,807.0,182.0,0.7000000000000001,54.26425889228461 +2013-11-28 16:30:00-07:00,2013,11,28,16,30,2.2,-5.1000000000000005,0.0,0.0,0.0,0.16,808.0,218.0,0.8,58.515165211184865 +2013-11-28 17:30:00-07:00,2013,11,28,17,30,0.1,-6.0,0.0,0.0,0.0,0.16,808.0,249.0,1.0,63.54790557586437 +2013-11-28 18:30:00-07:00,2013,11,28,18,30,-1.0,-6.2,0.0,0.0,0.0,0.16,808.0,253.0,1.2000000000000002,67.80265095564802 +2013-11-28 19:30:00-07:00,2013,11,28,19,30,-1.4,-6.5,0.0,0.0,0.0,0.16,808.0,252.0,1.6,68.23507875691725 +2013-11-28 20:30:00-07:00,2013,11,28,20,30,-1.4,-6.800000000000001,0.0,0.0,0.0,0.16,808.0,251.0,1.9,66.68378639250598 +2013-11-28 21:30:00-07:00,2013,11,28,21,30,-1.2000000000000002,-7.2,0.0,0.0,0.0,0.16,807.0,253.0,2.3000000000000003,63.72315767087284 +2013-11-28 22:30:00-07:00,2013,11,28,22,30,-1.1,-7.6,0.0,0.0,0.0,0.16,807.0,256.0,2.7,61.33612165747385 +2013-11-28 23:30:00-07:00,2013,11,28,23,30,-1.1,-8.1,0.0,0.0,0.0,0.16,807.0,259.0,2.8000000000000003,59.00655527961199 +2013-11-29 00:30:00-07:00,2013,11,29,0,30,-0.8,-8.5,0.0,0.0,0.0,0.16,807.0,264.0,2.9000000000000004,55.95940982299475 +2013-11-29 01:30:00-07:00,2013,11,29,1,30,-0.3,-8.5,0.0,0.0,0.0,0.16,807.0,269.0,2.9000000000000004,53.957809299866035 +2013-11-29 02:30:00-07:00,2013,11,29,2,30,0.0,-8.200000000000001,0.0,0.0,0.0,0.16,807.0,274.0,2.7,54.04144095308293 +2013-11-29 03:30:00-07:00,2013,11,29,3,30,0.1,-7.9,0.0,0.0,0.0,0.16,807.0,277.0,2.7,54.91431407300356 +2013-11-29 04:30:00-07:00,2013,11,29,4,30,0.0,-7.800000000000001,0.0,0.0,0.0,0.16,807.0,276.0,2.8000000000000003,55.74384625996724 +2013-11-29 05:30:00-07:00,2013,11,29,5,30,-0.2,-7.9,0.0,0.0,0.0,0.16,807.0,272.0,3.0,56.12260570307754 +2013-11-29 06:30:00-07:00,2013,11,29,6,30,-0.2,-8.0,0.0,0.0,0.0,0.16,808.0,271.0,3.1,55.689299935867105 +2013-11-29 07:30:00-07:00,2013,11,29,7,30,1.4,-7.800000000000001,21.0,489.0,56.0,0.16,808.0,272.0,3.5,50.39147460359773 +2013-11-29 08:30:00-07:00,2013,11,29,8,30,4.4,-6.4,38.0,796.0,220.0,0.16,808.0,271.0,3.9,45.380724601691206 +2013-11-29 09:30:00-07:00,2013,11,29,9,30,7.1000000000000005,-5.300000000000001,46.0,912.0,368.0,0.16,808.0,276.0,4.1000000000000005,40.9348427903679 +2013-11-29 10:30:00-07:00,2013,11,29,10,30,9.1,-5.0,51.0,964.0,473.0,0.16,808.0,288.0,4.1000000000000005,36.55632988110877 +2013-11-29 11:30:00-07:00,2013,11,29,11,30,10.3,-5.300000000000001,50.0,989.0,522.0,0.16,807.0,300.0,3.7,32.972534062535615 +2013-11-29 12:30:00-07:00,2013,11,29,12,30,10.9,-5.300000000000001,50.0,975.0,507.0,0.16,807.0,315.0,3.1,31.68122768385701 +2013-11-29 13:30:00-07:00,2013,11,29,13,30,10.8,-5.0,47.0,946.0,437.0,0.16,807.0,330.0,2.5,32.624954850367736 +2013-11-29 14:30:00-07:00,2013,11,29,14,30,9.4,-4.0,43.0,869.0,313.0,0.16,807.0,344.0,1.6,38.626783235725604 +2013-11-29 15:30:00-07:00,2013,11,29,15,30,6.4,-1.6,33.0,707.0,156.0,0.16,808.0,352.0,1.0,56.6084362351342 +2013-11-29 16:30:00-07:00,2013,11,29,16,30,3.6,-4.1000000000000005,0.0,0.0,0.0,0.16,808.0,349.0,0.8,57.1387867949954 +2013-11-29 17:30:00-07:00,2013,11,29,17,30,2.2,-5.4,0.0,0.0,0.0,0.16,809.0,326.0,0.7000000000000001,57.2002921618026 +2013-11-29 18:30:00-07:00,2013,11,29,18,30,1.6,-5.6000000000000005,0.0,0.0,0.0,0.16,809.0,284.0,0.9,58.8042895924171 +2013-11-29 19:30:00-07:00,2013,11,29,19,30,0.7000000000000001,-5.800000000000001,0.0,0.0,0.0,0.16,809.0,260.0,1.5,61.784959307445284 +2013-11-29 20:30:00-07:00,2013,11,29,20,30,-0.3,-5.9,0.0,0.0,0.0,0.16,809.0,260.0,2.1,65.92026874693592 +2013-11-29 21:30:00-07:00,2013,11,29,21,30,-0.7000000000000001,-6.1000000000000005,0.0,0.0,0.0,0.16,809.0,260.0,2.6,66.8416206208113 +2013-11-29 22:30:00-07:00,2013,11,29,22,30,-0.9,-6.4,0.0,0.0,0.0,0.16,809.0,259.0,3.0,66.28783396159986 +2013-11-29 23:30:00-07:00,2013,11,29,23,30,-1.0,-6.9,0.0,0.0,0.0,0.16,808.0,259.0,3.2,64.2631925662058 +2013-11-30 00:30:00-07:00,2013,11,30,0,30,-1.1,-7.4,0.0,0.0,0.0,0.16,808.0,259.0,3.4000000000000004,62.29063380658647 +2013-11-30 01:30:00-07:00,2013,11,30,1,30,-1.2000000000000002,-7.800000000000001,0.0,0.0,0.0,0.16,808.0,260.0,3.4000000000000004,60.83842251540113 +2013-11-30 02:30:00-07:00,2013,11,30,2,30,-1.3,-8.200000000000001,0.0,0.0,0.0,0.16,808.0,262.0,3.4000000000000004,59.414170636486624 +2013-11-30 03:30:00-07:00,2013,11,30,3,30,-1.5,-8.4,0.0,0.0,0.0,0.16,808.0,262.0,3.4000000000000004,59.36219165276278 +2013-11-30 04:30:00-07:00,2013,11,30,4,30,-1.6,-8.700000000000001,0.0,0.0,0.0,0.16,809.0,262.0,3.4000000000000004,58.4184448433693 +2013-11-30 05:30:00-07:00,2013,11,30,5,30,-1.7000000000000002,-8.9,0.0,0.0,0.0,0.16,809.0,262.0,3.4000000000000004,57.937742198764546 +2013-11-30 06:30:00-07:00,2013,11,30,6,30,-1.5,-9.1,0.0,0.0,0.0,0.16,809.0,262.0,3.4000000000000004,56.206457197867636 +2013-11-30 07:30:00-07:00,2013,11,30,7,30,0.3,-9.1,19.0,514.0,55.0,0.16,809.0,261.0,3.7,49.29629221696046 +2013-11-30 08:30:00-07:00,2013,11,30,8,30,3.5,-8.0,36.0,804.0,217.0,0.16,809.0,259.0,3.8,42.73675612236443 +2013-11-30 09:30:00-07:00,2013,11,30,9,30,6.6000000000000005,-7.1000000000000005,44.0,918.0,365.0,0.16,809.0,268.0,3.3000000000000003,36.92248450645547 +2013-11-30 10:30:00-07:00,2013,11,30,10,30,9.0,-7.1000000000000005,48.0,969.0,470.0,0.16,809.0,292.0,2.3000000000000003,31.356679711395895 +2013-11-30 11:30:00-07:00,2013,11,30,11,30,10.3,-7.300000000000001,48.0,990.0,519.0,0.16,809.0,325.0,1.4,28.298598465155724 +2013-11-30 12:30:00-07:00,2013,11,30,12,30,10.8,-7.2,49.0,985.0,509.0,0.16,808.0,174.0,1.1,27.5833328627194 +2013-11-30 13:30:00-07:00,2013,11,30,13,30,10.6,-7.0,47.0,955.0,439.0,0.16,808.0,182.0,1.0,28.386335697293482 +2013-11-30 14:30:00-07:00,2013,11,30,14,30,9.3,-6.5,41.0,881.0,314.0,0.16,808.0,181.0,0.9,32.177712136420624 +2013-11-30 15:30:00-07:00,2013,11,30,15,30,6.2,-2.9000000000000004,32.0,723.0,157.0,0.16,808.0,3.0,0.7000000000000001,52.13793274245574 +2013-11-30 16:30:00-07:00,2013,11,30,16,30,2.9000000000000004,-5.2,0.0,0.0,0.0,0.16,808.0,178.0,0.7000000000000001,55.25738623149414 +2013-11-30 17:30:00-07:00,2013,11,30,17,30,3.6,-0.4,0.0,0.0,0.0,0.16,812.0,89.0,1.3,75.10275585707187 +2013-11-30 18:30:00-07:00,2013,11,30,18,30,2.4000000000000004,-0.8,0.0,0.0,0.0,0.16,813.0,115.0,1.0,79.40965041226323 +2013-11-30 19:30:00-07:00,2013,11,30,19,30,1.5,-1.2000000000000002,0.0,0.0,0.0,0.16,813.0,154.0,0.8,82.23693426825612 +2013-11-30 20:30:00-07:00,2013,11,30,20,30,0.9,-1.3,0.0,0.0,0.0,0.16,814.0,180.0,0.9,85.2307570140689 +2013-11-30 21:30:00-07:00,2013,11,30,21,30,0.3,-1.4,0.0,0.0,0.0,0.16,814.0,183.0,1.1,88.35180362818762 +2013-11-30 22:30:00-07:00,2013,11,30,22,30,0.0,-1.7000000000000002,0.0,0.0,0.0,0.16,813.0,182.0,1.4,88.32468351371529 +2013-11-30 23:30:00-07:00,2013,11,30,23,30,0.0,-2.2,0.0,0.0,0.0,0.16,813.0,185.0,2.0,85.12929194305147 +2003-12-01 00:30:00-07:00,2003,12,1,0,30,0.0,-2.6,0.0,0.0,0.0,0.16,813.0,187.0,2.5,82.64729517561643 +2003-12-01 01:30:00-07:00,2003,12,1,1,30,-0.3,-3.2,0.0,0.0,0.0,0.16,812.0,186.0,2.8000000000000003,80.78537773430969 +2003-12-01 02:30:00-07:00,2003,12,1,2,30,-0.6000000000000001,-3.7,0.0,0.0,0.0,0.16,812.0,186.0,3.0,79.54486392974522 +2003-12-01 03:30:00-07:00,2003,12,1,3,30,-1.0,-4.2,0.0,0.0,0.0,0.16,812.0,187.0,3.2,78.88989977683666 +2003-12-01 04:30:00-07:00,2003,12,1,4,30,-1.5,-4.6000000000000005,0.0,0.0,0.0,0.16,812.0,189.0,3.1,79.40818586352144 +2003-12-01 05:30:00-07:00,2003,12,1,5,30,-1.9,-5.1000000000000005,0.0,0.0,0.0,0.16,812.0,191.0,2.9000000000000004,78.74918963614513 +2003-12-01 06:30:00-07:00,2003,12,1,6,30,-1.9,-6.0,0.0,0.0,0.0,0.16,812.0,195.0,2.8000000000000003,73.54625734786678 +2003-12-01 07:30:00-07:00,2003,12,1,7,30,-0.5,-6.7,34.0,7.0,35.0,0.16,813.0,199.0,3.0,62.91933267906644 +2003-12-01 08:30:00-07:00,2003,12,1,8,30,2.3000000000000003,-7.0,131.0,27.0,137.0,0.16,813.0,201.0,3.3000000000000003,50.26220827563864 +2003-12-01 09:30:00-07:00,2003,12,1,9,30,5.800000000000001,-6.9,156.0,546.0,346.0,0.16,812.0,201.0,3.1,39.62112167057397 +2003-12-01 10:30:00-07:00,2003,12,1,10,30,9.2,-7.6,231.0,353.0,384.0,0.16,812.0,207.0,2.3000000000000003,29.76684747260609 +2003-12-01 11:30:00-07:00,2003,12,1,11,30,11.5,-8.8,128.0,827.0,520.0,0.16,812.0,230.0,1.5,23.260099103684194 +2003-12-01 12:30:00-07:00,2003,12,1,12,30,12.6,-9.4,136.0,750.0,485.0,0.16,811.0,295.0,1.1,20.64275386858261 +2003-12-01 13:30:00-07:00,2003,12,1,13,30,12.5,-9.8,180.0,430.0,356.0,0.16,811.0,192.0,1.2000000000000002,20.13539270699453 +2003-12-01 14:30:00-07:00,2003,12,1,14,30,10.8,-8.8,157.0,270.0,240.0,0.16,811.0,66.0,1.0,24.364982967244924 +2003-12-01 15:30:00-07:00,2003,12,1,15,30,7.6,-5.800000000000001,93.0,89.0,109.0,0.16,811.0,120.0,0.8,38.08595445604397 +2003-12-01 16:30:00-07:00,2003,12,1,16,30,4.5,-7.4,0.0,0.0,0.0,0.16,811.0,180.0,0.7000000000000001,41.73227486195788 +2003-12-01 17:30:00-07:00,2003,12,1,17,30,2.7,-8.0,0.0,0.0,0.0,0.16,811.0,224.0,0.9,45.223755086953695 +2003-12-01 18:30:00-07:00,2003,12,1,18,30,2.1,-8.6,0.0,0.0,0.0,0.16,811.0,245.0,1.2000000000000002,45.04420504510901 +2003-12-01 19:30:00-07:00,2003,12,1,19,30,1.9,-9.4,0.0,0.0,0.0,0.16,810.0,253.0,1.4,42.92048773045408 +2003-12-01 20:30:00-07:00,2003,12,1,20,30,2.0,-10.5,0.0,0.0,0.0,0.16,810.0,257.0,1.6,39.07509457074662 +2003-12-01 21:30:00-07:00,2003,12,1,21,30,2.0,-11.5,0.0,0.0,0.0,0.16,809.0,260.0,1.9,36.086809819461145 +2003-12-01 22:30:00-07:00,2003,12,1,22,30,2.0,-12.2,0.0,0.0,0.0,0.16,809.0,260.0,2.1,34.11805710741098 +2003-12-01 23:30:00-07:00,2003,12,1,23,30,2.2,-12.4,0.0,0.0,0.0,0.16,808.0,259.0,2.2,33.09820722016782 +2003-12-02 00:30:00-07:00,2003,12,2,0,30,2.6,-12.2,0.0,0.0,0.0,0.16,808.0,259.0,2.3000000000000003,32.691900294445404 +2003-12-02 01:30:00-07:00,2003,12,2,1,30,2.9000000000000004,-11.5,0.0,0.0,0.0,0.16,807.0,258.0,2.2,33.850589184889806 +2003-12-02 02:30:00-07:00,2003,12,2,2,30,3.2,-10.6,0.0,0.0,0.0,0.16,807.0,256.0,2.0,35.60085105920427 +2003-12-02 03:30:00-07:00,2003,12,2,3,30,3.3000000000000003,-9.9,0.0,0.0,0.0,0.16,806.0,254.0,1.9,37.361467457752106 +2003-12-02 04:30:00-07:00,2003,12,2,4,30,3.1,-9.5,0.0,0.0,0.0,0.16,806.0,251.0,1.8,39.104540553507874 +2003-12-02 05:30:00-07:00,2003,12,2,5,30,2.8000000000000003,-9.4,0.0,0.0,0.0,0.16,805.0,250.0,1.7000000000000002,40.258699522831655 +2003-12-02 06:30:00-07:00,2003,12,2,6,30,2.5,-9.6,0.0,0.0,0.0,0.16,805.0,250.0,1.7000000000000002,40.4840171536258 +2003-12-02 07:30:00-07:00,2003,12,2,7,30,4.1000000000000005,-9.6,27.0,1.0,27.0,0.16,805.0,251.0,1.7000000000000002,36.16036655367631 +2003-12-02 08:30:00-07:00,2003,12,2,8,30,7.300000000000001,-8.1,38.0,778.0,210.0,0.16,805.0,253.0,1.9,32.575615359896425 +2003-12-02 09:30:00-07:00,2003,12,2,9,30,9.9,-6.800000000000001,49.0,891.0,357.0,0.16,805.0,275.0,2.2,30.20506464576596 +2003-12-02 10:30:00-07:00,2003,12,2,10,30,11.2,-7.0,56.0,939.0,461.0,0.16,804.0,313.0,2.8000000000000003,27.277212186771116 +2003-12-02 11:30:00-07:00,2003,12,2,11,30,11.4,-7.800000000000001,153.0,653.0,461.0,0.16,804.0,339.0,3.4000000000000004,25.30726877304235 +2003-12-02 12:30:00-07:00,2003,12,2,12,30,11.1,-8.0,142.0,764.0,496.0,0.16,804.0,345.0,3.8,25.41832113843659 +2003-12-02 13:30:00-07:00,2003,12,2,13,30,10.5,-8.0,179.0,418.0,350.0,0.16,805.0,338.0,4.2,26.452690790505578 +2003-12-02 14:30:00-07:00,2003,12,2,14,30,9.6,-8.8,137.0,388.0,256.0,0.16,806.0,331.0,4.5,26.398339667747553 +2003-12-02 15:30:00-07:00,2003,12,2,15,30,7.0,-9.9,35.0,706.0,156.0,0.16,807.0,329.0,3.8,28.88508694991575 +2003-12-02 16:30:00-07:00,2003,12,2,16,30,3.6,-9.8,0.0,0.0,0.0,0.16,807.0,327.0,2.7,36.86882806507524 +2003-12-02 17:30:00-07:00,2003,12,2,17,30,1.5,-9.4,0.0,0.0,0.0,0.16,808.0,320.0,2.2,44.16600118651708 +2003-12-02 18:30:00-07:00,2003,12,2,18,30,0.6000000000000001,-8.8,0.0,0.0,0.0,0.16,808.0,312.0,2.2,49.38390639427374 +2003-12-02 19:30:00-07:00,2003,12,2,19,30,-0.1,-8.6,0.0,0.0,0.0,0.16,809.0,305.0,2.1,52.766914290780946 +2003-12-02 20:30:00-07:00,2003,12,2,20,30,-0.6000000000000001,-8.9,0.0,0.0,0.0,0.16,809.0,299.0,2.0,53.45485615325888 +2003-12-02 21:30:00-07:00,2003,12,2,21,30,-1.0,-9.5,0.0,0.0,0.0,0.16,809.0,296.0,1.9,52.511618995007154 +2003-12-02 22:30:00-07:00,2003,12,2,22,30,-1.4,-9.9,0.0,0.0,0.0,0.16,809.0,291.0,1.9,52.397621976895316 +2003-12-02 23:30:00-07:00,2003,12,2,23,30,-1.6,-10.0,0.0,0.0,0.0,0.16,809.0,283.0,2.0,52.755153316608784 +2003-12-03 00:30:00-07:00,2003,12,3,0,30,-1.7000000000000002,-9.7,0.0,0.0,0.0,0.16,809.0,277.0,2.2,54.41529470183523 +2003-12-03 01:30:00-07:00,2003,12,3,1,30,-1.9,-9.1,0.0,0.0,0.0,0.16,809.0,274.0,2.3000000000000003,57.88448208082754 +2003-12-03 02:30:00-07:00,2003,12,3,2,30,-2.2,-8.5,0.0,0.0,0.0,0.16,808.0,271.0,2.3000000000000003,62.0176405577817 +2003-12-03 03:30:00-07:00,2003,12,3,3,30,-2.4000000000000004,-8.200000000000001,0.0,0.0,0.0,0.16,808.0,263.0,2.4000000000000004,64.42697599420212 +2003-12-03 04:30:00-07:00,2003,12,3,4,30,-2.5,-8.200000000000001,0.0,0.0,0.0,0.16,808.0,255.0,2.5,64.90552614644574 +2003-12-03 05:30:00-07:00,2003,12,3,5,30,-2.3000000000000003,-8.6,0.0,0.0,0.0,0.16,808.0,251.0,2.5,61.99269464827052 +2003-12-03 06:30:00-07:00,2003,12,3,6,30,-1.8,-9.0,0.0,0.0,0.0,0.16,808.0,247.0,2.4000000000000004,57.91112283118507 +2003-12-03 07:30:00-07:00,2003,12,3,7,30,0.2,-9.3,18.0,486.0,48.0,0.16,808.0,243.0,2.5,48.882447203444706 +2003-12-03 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16:30:00-07:00,2003,12,3,16,30,4.3,-7.800000000000001,0.0,0.0,0.0,0.16,805.0,160.0,0.4,41.03218745910034 +2003-12-03 17:30:00-07:00,2003,12,3,17,30,2.9000000000000004,-8.4,0.0,0.0,0.0,0.16,805.0,275.0,0.5,43.22324890508882 +2003-12-03 18:30:00-07:00,2003,12,3,18,30,1.6,-8.6,0.0,0.0,0.0,0.16,805.0,253.0,0.8,46.68197304784176 +2003-12-03 19:30:00-07:00,2003,12,3,19,30,0.6000000000000001,-8.9,0.0,0.0,0.0,0.16,805.0,259.0,1.1,48.99970338816263 +2003-12-03 20:30:00-07:00,2003,12,3,20,30,0.0,-9.0,0.0,0.0,0.0,0.16,806.0,269.0,1.3,50.77486397620595 +2003-12-03 21:30:00-07:00,2003,12,3,21,30,-0.5,-9.1,0.0,0.0,0.0,0.16,806.0,278.0,1.3,52.243314959279495 +2003-12-03 22:30:00-07:00,2003,12,3,22,30,-0.7000000000000001,-9.0,0.0,0.0,0.0,0.16,806.0,275.0,1.3,53.426736660654335 +2003-12-03 23:30:00-07:00,2003,12,3,23,30,-1.0,-9.0,0.0,0.0,0.0,0.16,806.0,262.0,1.5,54.610161358391125 +2003-12-04 00:30:00-07:00,2003,12,4,0,30,-1.4,-9.1,0.0,0.0,0.0,0.16,806.0,253.0,1.7000000000000002,55.795456564095595 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09:30:00-07:00,2003,12,4,9,30,2.5,-6.800000000000001,202.0,41.0,216.0,0.16,812.0,25.0,3.6,50.319652747437665 +2003-12-04 10:30:00-07:00,2003,12,4,10,30,3.2,-7.2,92.0,0.0,92.0,0.16,812.0,27.0,3.4000000000000004,46.43477731258994 +2003-12-04 11:30:00-07:00,2003,12,4,11,30,4.0,-7.800000000000001,73.0,0.0,73.0,0.16,812.0,30.0,2.9000000000000004,41.904287021800116 +2003-12-04 12:30:00-07:00,2003,12,4,12,30,4.6000000000000005,-8.4,101.0,0.0,101.0,0.16,811.0,34.0,2.4000000000000004,38.35224369827469 +2003-12-04 13:30:00-07:00,2003,12,4,13,30,4.800000000000001,-8.9,67.0,0.0,67.0,0.16,811.0,36.0,2.4000000000000004,36.37454402894331 +2003-12-04 14:30:00-07:00,2003,12,4,14,30,3.9,-9.1,64.0,0.0,64.0,0.16,811.0,39.0,2.5,38.13836136610182 +2003-12-04 15:30:00-07:00,2003,12,4,15,30,1.1,-9.1,19.0,0.0,19.0,0.16,811.0,48.0,2.1,46.533213344017184 +2003-12-04 16:30:00-07:00,2003,12,4,16,30,-2.1,-9.0,0.0,0.0,0.0,0.16,812.0,56.0,1.4,59.20574088529207 +2003-12-04 17:30:00-07:00,2003,12,4,17,30,-3.6,-9.1,0.0,0.0,0.0,0.16,812.0,53.0,0.9,65.66532734657595 +2003-12-04 18:30:00-07:00,2003,12,4,18,30,-4.1000000000000005,-9.1,0.0,0.0,0.0,0.16,813.0,36.0,0.6000000000000001,68.17017868829753 +2003-12-04 19:30:00-07:00,2003,12,4,19,30,-4.5,-9.1,0.0,0.0,0.0,0.16,813.0,22.0,0.5,70.25061990437985 +2003-12-04 20:30:00-07:00,2003,12,4,20,30,-4.6000000000000005,-9.2,0.0,0.0,0.0,0.16,814.0,26.0,0.6000000000000001,70.22960740072119 +2003-12-04 21:30:00-07:00,2003,12,4,21,30,-4.9,-9.4,0.0,0.0,0.0,0.16,814.0,32.0,0.7000000000000001,70.71945823654875 +2003-12-04 22:30:00-07:00,2003,12,4,22,30,-5.300000000000001,-9.6,0.0,0.0,0.0,0.16,814.0,34.0,0.7000000000000001,71.75642559810845 +2003-12-04 23:30:00-07:00,2003,12,4,23,30,-5.5,-10.0,0.0,0.0,0.0,0.16,814.0,49.0,0.6000000000000001,70.59444226554335 +2003-12-05 00:30:00-07:00,2003,12,5,0,30,-5.5,-10.4,0.0,0.0,0.0,0.16,813.0,97.0,0.6000000000000001,68.39821276858282 +2003-12-05 01:30:00-07:00,2003,12,5,1,30,-5.1000000000000005,-10.9,0.0,0.0,0.0,0.16,813.0,146.0,0.8,63.77570194491155 +2003-12-05 02:30:00-07:00,2003,12,5,2,30,-4.6000000000000005,-11.3,0.0,0.0,0.0,0.16,813.0,176.0,0.8,59.487763497944954 +2003-12-05 03:30:00-07:00,2003,12,5,3,30,-4.4,-11.7,0.0,0.0,0.0,0.16,813.0,199.0,0.8,56.75557273500064 +2003-12-05 04:30:00-07:00,2003,12,5,4,30,-4.4,-12.2,0.0,0.0,0.0,0.16,813.0,213.0,0.7000000000000001,54.524346103787806 +2003-12-05 05:30:00-07:00,2003,12,5,5,30,-4.4,-12.6,0.0,0.0,0.0,0.16,813.0,226.0,0.7000000000000001,52.7960714821036 +2003-12-05 06:30:00-07:00,2003,12,5,6,30,-4.2,-13.3,0.0,0.0,0.0,0.16,813.0,224.0,0.7000000000000001,49.144625872734494 +2003-12-05 07:30:00-07:00,2003,12,5,7,30,-2.4000000000000004,-14.0,27.0,6.0,27.0,0.16,813.0,203.0,1.1,40.594342012643494 +2003-12-05 08:30:00-07:00,2003,12,5,8,30,0.8,-14.4,117.0,15.0,120.0,0.16,813.0,183.0,1.4,31.102972563410965 +2003-12-05 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17:30:00-07:00,2003,12,5,17,30,0.0,-12.9,0.0,0.0,0.0,0.16,809.0,197.0,1.6,37.23461825354401 +2003-12-05 18:30:00-07:00,2003,12,5,18,30,0.2,-12.2,0.0,0.0,0.0,0.16,808.0,211.0,1.6,38.829838432006134 +2003-12-05 19:30:00-07:00,2003,12,5,19,30,0.8,-11.8,0.0,0.0,0.0,0.16,808.0,221.0,1.6,38.396071087754684 +2003-12-05 20:30:00-07:00,2003,12,5,20,30,1.3,-11.2,0.0,0.0,0.0,0.16,807.0,229.0,1.6,38.85931783720477 +2003-12-05 21:30:00-07:00,2003,12,5,21,30,1.4,-10.1,0.0,0.0,0.0,0.16,807.0,236.0,1.6,42.09909455533139 +2003-12-05 22:30:00-07:00,2003,12,5,22,30,1.3,-8.9,0.0,0.0,0.0,0.16,806.0,239.0,1.6,46.592772219302724 +2003-12-05 23:30:00-07:00,2003,12,5,23,30,1.1,-8.3,0.0,0.0,0.0,0.16,806.0,239.0,1.6,49.52954184242018 +2003-12-06 00:30:00-07:00,2003,12,6,0,30,0.7000000000000001,-8.3,0.0,0.0,0.0,0.16,806.0,241.0,1.8,50.97642334679305 +2003-12-06 01:30:00-07:00,2003,12,6,1,30,0.9,-8.3,0.0,0.0,0.0,0.16,805.0,245.0,1.9,50.247181900694386 +2003-12-06 02:30:00-07:00,2003,12,6,2,30,1.5,-8.3,0.0,0.0,0.0,0.16,805.0,250.0,1.9,48.12826104505883 +2003-12-06 03:30:00-07:00,2003,12,6,3,30,2.0,-8.4,0.0,0.0,0.0,0.16,804.0,253.0,1.6,46.07864148235948 +2003-12-06 04:30:00-07:00,2003,12,6,4,30,2.4000000000000004,-8.700000000000001,0.0,0.0,0.0,0.16,804.0,252.0,1.5,43.749882420642706 +2003-12-06 05:30:00-07:00,2003,12,6,5,30,2.7,-9.0,0.0,0.0,0.0,0.16,804.0,249.0,1.6,41.83576668776883 +2003-12-06 06:30:00-07:00,2003,12,6,6,30,2.8000000000000003,-9.3,0.0,0.0,0.0,0.16,804.0,244.0,2.0,40.57572251455238 +2003-12-06 07:30:00-07:00,2003,12,6,7,30,3.7,-9.5,24.0,4.0,25.0,0.16,804.0,242.0,2.6,37.484199275350946 +2003-12-06 08:30:00-07:00,2003,12,6,8,30,5.800000000000001,-8.8,107.0,66.0,120.0,0.16,804.0,241.0,2.8000000000000003,34.19989767324098 +2003-12-06 09:30:00-07:00,2003,12,6,9,30,8.3,-7.2,45.0,871.0,337.0,0.16,803.0,242.0,2.7,32.624489119441854 +2003-12-06 10:30:00-07:00,2003,12,6,10,30,10.5,-4.7,51.0,924.0,440.0,0.16,803.0,245.0,2.4000000000000004,34.04542171870721 +2003-12-06 11:30:00-07:00,2003,12,6,11,30,12.3,-2.4000000000000004,157.0,573.0,422.0,0.16,802.0,246.0,1.9,35.886956371980204 +2003-12-06 12:30:00-07:00,2003,12,6,12,30,13.5,-1.7000000000000002,178.0,470.0,393.0,0.16,802.0,234.0,1.4,34.936518881931555 +2003-12-06 13:30:00-07:00,2003,12,6,13,30,13.3,-1.1,164.0,423.0,334.0,0.16,802.0,217.0,1.1,36.98740206594861 +2003-12-06 14:30:00-07:00,2003,12,6,14,30,11.6,-0.1,122.0,404.0,244.0,0.16,802.0,204.0,0.9,44.47899694557006 +2003-12-06 15:30:00-07:00,2003,12,6,15,30,9.0,-0.8,91.0,46.0,98.0,0.16,802.0,202.0,0.9,50.27958159875883 +2003-12-06 16:30:00-07:00,2003,12,6,16,30,6.800000000000001,-2.6,0.0,0.0,0.0,0.16,802.0,212.0,1.0,51.15575943561934 +2003-12-06 17:30:00-07:00,2003,12,6,17,30,5.5,-3.2,0.0,0.0,0.0,0.16,802.0,224.0,1.2000000000000002,53.5162664067943 +2003-12-06 18:30:00-07:00,2003,12,6,18,30,4.3,-3.5,0.0,0.0,0.0,0.16,802.0,233.0,1.3,56.89657478021649 +2003-12-06 19:30:00-07:00,2003,12,6,19,30,3.2,-3.8,0.0,0.0,0.0,0.16,801.0,239.0,1.5,60.110007780996476 +2003-12-06 20:30:00-07:00,2003,12,6,20,30,2.6,-4.3,0.0,0.0,0.0,0.16,801.0,244.0,1.6,60.410857085162625 +2003-12-06 21:30:00-07:00,2003,12,6,21,30,2.1,-4.9,0.0,0.0,0.0,0.16,801.0,243.0,1.6,59.83140639585027 +2003-12-06 22:30:00-07:00,2003,12,6,22,30,1.7000000000000002,-5.6000000000000005,0.0,0.0,0.0,0.16,800.0,237.0,1.6,58.38508036874052 +2003-12-06 23:30:00-07:00,2003,12,6,23,30,1.8,-6.2,0.0,0.0,0.0,0.16,800.0,229.0,1.5,55.37943610959972 +2003-12-07 00:30:00-07:00,2003,12,7,0,30,2.0,-6.6000000000000005,0.0,0.0,0.0,0.16,799.0,222.0,1.5,52.94908755333774 +2003-12-07 01:30:00-07:00,2003,12,7,1,30,1.8,-6.7,0.0,0.0,0.0,0.16,799.0,216.0,1.4,53.30045370432625 +2003-12-07 02:30:00-07:00,2003,12,7,2,30,1.4,-6.800000000000001,0.0,0.0,0.0,0.16,799.0,212.0,1.4,54.42927255151727 +2003-12-07 03:30:00-07:00,2003,12,7,3,30,1.4,-6.800000000000001,0.0,0.0,0.0,0.16,799.0,211.0,1.4,54.42927255151727 +2003-12-07 04:30:00-07:00,2003,12,7,4,30,1.5,-6.5,0.0,0.0,0.0,0.16,798.0,211.0,1.4,55.29778869648539 +2003-12-07 05:30:00-07:00,2003,12,7,5,30,1.4,-6.2,0.0,0.0,0.0,0.16,798.0,211.0,1.4,56.98782988514726 +2003-12-07 06:30:00-07:00,2003,12,7,6,30,1.5,-6.0,0.0,0.0,0.0,0.16,798.0,213.0,1.3,57.450965071775755 +2003-12-07 07:30:00-07:00,2003,12,7,7,30,2.9000000000000004,-5.6000000000000005,24.0,4.0,24.0,0.16,798.0,217.0,1.3,53.606007629522374 +2003-12-07 08:30:00-07:00,2003,12,7,8,30,5.800000000000001,-4.4,101.0,303.0,164.0,0.16,798.0,232.0,1.6,47.91351278935641 +2003-12-07 09:30:00-07:00,2003,12,7,9,30,8.8,-3.2,48.0,870.0,337.0,0.16,798.0,253.0,2.5,42.697748799709856 +2003-12-07 10:30:00-07:00,2003,12,7,10,30,11.0,-3.7,53.0,929.0,442.0,0.16,797.0,266.0,3.0,35.50007932994027 +2003-12-07 11:30:00-07:00,2003,12,7,11,30,12.4,-4.1000000000000005,53.0,962.0,497.0,0.16,796.0,271.0,3.0,31.411000988618987 +2003-12-07 12:30:00-07:00,2003,12,7,12,30,13.0,-4.1000000000000005,54.0,956.0,489.0,0.16,795.0,271.0,2.9000000000000004,30.200576073729966 +2003-12-07 13:30:00-07:00,2003,12,7,13,30,12.8,-4.1000000000000005,51.0,926.0,422.0,0.16,795.0,268.0,2.8000000000000003,30.598150932200966 +2003-12-07 14:30:00-07:00,2003,12,7,14,30,11.7,-3.6,44.0,867.0,306.0,0.16,795.0,265.0,2.5,34.1470098616852 +2003-12-07 15:30:00-07:00,2003,12,7,15,30,8.700000000000001,-2.9000000000000004,32.0,709.0,151.0,0.16,796.0,262.0,1.8,43.95713722044787 +2003-12-07 16:30:00-07:00,2003,12,7,16,30,5.300000000000001,-3.8,0.0,0.0,0.0,0.16,796.0,260.0,1.5,51.88727414602084 +2003-12-07 17:30:00-07:00,2003,12,7,17,30,3.3000000000000003,-4.1000000000000005,0.0,0.0,0.0,0.16,796.0,258.0,1.5,58.36019734444022 +2003-12-07 18:30:00-07:00,2003,12,7,18,30,2.3000000000000003,-4.2,0.0,0.0,0.0,0.16,796.0,255.0,1.6,62.17822009884032 +2003-12-07 19:30:00-07:00,2003,12,7,19,30,1.8,-4.2,0.0,0.0,0.0,0.16,796.0,249.0,1.7000000000000002,64.43521164448099 +2003-12-07 20:30:00-07:00,2003,12,7,20,30,1.5,-4.3,0.0,0.0,0.0,0.16,796.0,244.0,1.8,65.34044308091089 +2003-12-07 21:30:00-07:00,2003,12,7,21,30,1.1,-4.4,0.0,0.0,0.0,0.16,795.0,242.0,1.8,66.73918795736027 +2003-12-07 22:30:00-07:00,2003,12,7,22,30,0.7000000000000001,-4.5,0.0,0.0,0.0,0.16,795.0,241.0,1.8,68.1738676208661 +2003-12-07 23:30:00-07:00,2003,12,7,23,30,0.6000000000000001,-4.3,0.0,0.0,0.0,0.16,795.0,243.0,1.6,69.70821212514424 +2003-12-08 00:30:00-07:00,2003,12,8,0,30,0.6000000000000001,-4.2,0.0,0.0,0.0,0.16,795.0,247.0,1.5,70.2338551152486 +2003-12-08 01:30:00-07:00,2003,12,8,1,30,0.4,-4.1000000000000005,0.0,0.0,0.0,0.16,795.0,253.0,1.3,71.7925562871113 +2003-12-08 02:30:00-07:00,2003,12,8,2,30,0.1,-3.8,0.0,0.0,0.0,0.16,795.0,262.0,1.2000000000000002,75.0363205799065 +2003-12-08 03:30:00-07:00,2003,12,8,3,30,0.1,-3.4000000000000004,0.0,0.0,0.0,0.16,794.0,281.0,1.1,77.31273440038198 +2003-12-08 04:30:00-07:00,2003,12,8,4,30,-0.4,-3.1,0.0,0.0,0.0,0.16,794.0,309.0,1.0,81.98304014690993 +2003-12-08 05:30:00-07:00,2003,12,8,5,30,-1.0,-2.9000000000000004,0.0,0.0,0.0,0.16,795.0,163.0,1.2000000000000002,86.9335590419379 +2003-12-08 06:30:00-07:00,2003,12,8,6,30,-1.1,-2.7,0.0,0.0,0.0,0.16,795.0,13.0,1.5,88.88140394999613 +2003-12-08 07:30:00-07:00,2003,12,8,7,30,-0.4,-2.5,14.0,0.0,14.0,0.16,795.0,34.0,2.0,85.71638394941509 +2003-12-08 08:30:00-07:00,2003,12,8,8,30,0.4,-2.3000000000000003,14.0,0.0,14.0,0.16,795.0,48.0,2.8000000000000003,82.09057586885491 +2003-12-08 09:30:00-07:00,2003,12,8,9,30,0.8,-2.3000000000000003,21.0,0.0,21.0,0.16,794.0,60.0,3.4000000000000004,79.75491152292383 +2003-12-08 10:30:00-07:00,2003,12,8,10,30,1.0,-2.7,19.0,0.0,19.0,0.16,794.0,69.0,3.4000000000000004,76.32096491876086 +2003-12-08 11:30:00-07:00,2003,12,8,11,30,0.9,-3.1,21.0,0.0,21.0,0.16,794.0,65.0,3.3000000000000003,74.62165687646028 +2003-12-08 12:30:00-07:00,2003,12,8,12,30,0.7000000000000001,-3.4000000000000004,25.0,0.0,25.0,0.16,794.0,45.0,3.6,74.0314594723858 +2003-12-08 13:30:00-07:00,2003,12,8,13,30,0.1,-3.8,57.0,0.0,57.0,0.16,794.0,25.0,4.1000000000000005,75.0363205799065 +2003-12-08 14:30:00-07:00,2003,12,8,14,30,-0.9,-4.4,98.0,7.0,101.0,0.16,795.0,15.0,4.4,77.14704930115212 +2003-12-08 15:30:00-07:00,2003,12,8,15,30,-2.1,-5.2,14.0,0.0,14.0,0.16,796.0,13.0,4.2,79.31634165858165 +2003-12-08 16:30:00-07:00,2003,12,8,16,30,-3.2,-5.9,0.0,0.0,0.0,0.16,797.0,12.0,3.9,81.59925792974225 +2003-12-08 17:30:00-07:00,2003,12,8,17,30,-3.9,-6.6000000000000005,0.0,0.0,0.0,0.8,797.0,10.0,4.0,81.50147629512708 +2003-12-08 18:30:00-07:00,2003,12,8,18,30,-4.5,-7.4,0.0,0.0,0.0,0.8,798.0,7.0,4.3,80.1715710372979 +2003-12-08 19:30:00-07:00,2003,12,8,19,30,-5.0,-8.1,0.0,0.0,0.0,0.8,799.0,3.0,4.5,78.8640850908775 +2003-12-08 20:30:00-07:00,2003,12,8,20,30,-5.300000000000001,-8.6,0.0,0.0,0.0,0.8,800.0,2.0,4.6000000000000005,77.5985101592232 +2003-12-08 21:30:00-07:00,2003,12,8,21,30,-5.5,-9.0,0.0,0.0,0.0,0.8,801.0,1.0,4.7,76.36241252414413 +2003-12-08 22:30:00-07:00,2003,12,8,22,30,-5.7,-9.4,0.0,0.0,0.0,0.8,801.0,180.0,4.6000000000000005,75.13989157407116 +2003-12-08 23:30:00-07:00,2003,12,8,23,30,-5.800000000000001,-9.7,0.0,0.0,0.0,0.8,801.0,359.0,4.4,73.94987196780158 +2003-12-09 00:30:00-07:00,2003,12,9,0,30,-5.800000000000001,-9.9,0.0,0.0,0.0,0.8,802.0,358.0,4.0,72.79441672245524 +2003-12-09 01:30:00-07:00,2003,12,9,1,30,-5.9,-10.2,0.0,0.0,0.0,0.8,802.0,358.0,3.6,71.63474749454429 +2003-12-09 02:30:00-07:00,2003,12,9,2,30,-6.2,-10.6,0.0,0.0,0.0,0.8,802.0,355.0,3.0,71.00865795198843 +2003-12-09 03:30:00-07:00,2003,12,9,3,30,-6.6000000000000005,-11.2,0.0,0.0,0.0,0.8,803.0,351.0,2.5,69.80503852283445 +2003-12-09 04:30:00-07:00,2003,12,9,4,30,-7.1000000000000005,-11.8,0.0,0.0,0.0,0.8,803.0,346.0,2.1,69.14172808241476 +2003-12-09 05:30:00-07:00,2003,12,9,5,30,-7.4,-12.6,0.0,0.0,0.0,0.8,804.0,340.0,1.9,66.3512714487846 +2003-12-09 06:30:00-07:00,2003,12,9,6,30,-7.4,-13.2,0.0,0.0,0.0,0.8,804.0,333.0,2.0,63.20838115466074 +2003-12-09 07:30:00-07:00,2003,12,9,7,30,-6.2,-13.8,16.0,0.0,16.0,0.8,805.0,328.0,2.3000000000000003,54.902104872342925 +2003-12-09 08:30:00-07:00,2003,12,9,8,30,-3.6,-13.1,41.0,786.0,200.0,0.8,805.0,331.0,3.1,47.75304638653057 +2003-12-09 09:30:00-07:00,2003,12,9,9,30,-1.5,-12.0,55.0,915.0,355.0,0.8,805.0,339.0,3.7,44.665357733822084 +2003-12-09 10:30:00-07:00,2003,12,9,10,30,-0.3,-11.9,61.0,975.0,466.0,0.8,805.0,343.0,3.9,41.245752939000575 +2003-12-09 11:30:00-07:00,2003,12,9,11,30,0.6000000000000001,-11.9,63.0,1001.0,520.0,0.8,804.0,343.0,3.9,38.64283513822086 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21:30:00-07:00,2003,12,9,21,30,-8.1,-15.5,0.0,0.0,0.0,0.8,805.0,234.0,1.9,55.26900443700976 +2003-12-09 22:30:00-07:00,2003,12,9,22,30,-8.1,-16.2,0.0,0.0,0.0,0.8,804.0,238.0,2.1,52.14971699381587 +2003-12-09 23:30:00-07:00,2003,12,9,23,30,-7.9,-17.0,0.0,0.0,0.0,0.8,804.0,244.0,2.2,48.027688576620115 +2003-12-10 00:30:00-07:00,2003,12,10,0,30,-7.4,-17.6,0.0,0.0,0.0,0.8,803.0,248.0,2.1,43.933930788182174 +2003-12-10 01:30:00-07:00,2003,12,10,1,30,-6.7,-18.0,0.0,0.0,0.0,0.8,803.0,248.0,1.9,40.24845969684053 +2003-12-10 02:30:00-07:00,2003,12,10,2,30,-5.9,-18.1,0.0,0.0,0.0,0.8,803.0,244.0,1.6,37.542193188228325 +2003-12-10 03:30:00-07:00,2003,12,10,3,30,-5.2,-17.6,0.0,0.0,0.0,0.8,802.0,234.0,1.4,37.134163849754415 +2003-12-10 04:30:00-07:00,2003,12,10,4,30,-4.800000000000001,-16.7,0.0,0.0,0.0,0.8,802.0,219.0,1.3,38.85560545998713 +2003-12-10 05:30:00-07:00,2003,12,10,5,30,-5.2,-16.8,0.0,0.0,0.0,0.8,802.0,210.0,1.2000000000000002,39.7148885987963 +2003-12-10 06:30:00-07:00,2003,12,10,6,30,-5.9,-17.8,0.0,0.0,0.0,0.8,801.0,212.0,1.4,38.50658809599537 +2003-12-10 07:30:00-07:00,2003,12,10,7,30,-4.6000000000000005,-18.8,15.0,0.0,15.0,0.8,801.0,214.0,1.8,32.05762185951745 +2003-12-10 08:30:00-07:00,2003,12,10,8,30,-1.5,-18.1,38.0,783.0,195.0,0.8,801.0,209.0,2.2,27.0155488502822 +2003-12-10 09:30:00-07:00,2003,12,10,9,30,1.4,-15.8,50.0,911.0,348.0,0.8,800.0,202.0,2.1,26.542804702458497 +2003-12-10 10:30:00-07:00,2003,12,10,10,30,3.6,-13.6,57.0,968.0,458.0,0.8,799.0,200.0,1.9,27.19940427507661 +2003-12-10 11:30:00-07:00,2003,12,10,11,30,4.9,-12.7,62.0,1001.0,518.0,0.8,798.0,206.0,1.7000000000000002,26.71112207268629 +2003-12-10 12:30:00-07:00,2003,12,10,12,30,5.6000000000000005,-12.5,61.0,998.0,511.0,0.8,797.0,219.0,1.6,25.85680874530017 +2003-12-10 13:30:00-07:00,2003,12,10,13,30,5.300000000000001,-12.3,58.0,968.0,443.0,0.8,796.0,242.0,1.5,26.829205683534433 +2003-12-10 14:30:00-07:00,2003,12,10,14,30,3.7,-11.2,51.0,882.0,317.0,0.8,796.0,281.0,1.5,32.76708433795868 +2003-12-10 15:30:00-07:00,2003,12,10,15,30,0.2,-9.8,33.0,0.0,33.0,0.8,797.0,314.0,1.4,46.99927178290265 +2003-12-10 16:30:00-07:00,2003,12,10,16,30,-3.1,-11.3,0.0,0.0,0.0,0.8,797.0,320.0,1.3,53.16823688419068 +2003-12-10 17:30:00-07:00,2003,12,10,17,30,-4.5,-11.8,0.0,0.0,0.0,0.8,798.0,309.0,1.3,56.72819469862731 +2003-12-10 18:30:00-07:00,2003,12,10,18,30,-5.0,-11.7,0.0,0.0,0.0,0.8,798.0,293.0,1.4,59.38242230231314 +2003-12-10 19:30:00-07:00,2003,12,10,19,30,-5.300000000000001,-11.6,0.0,0.0,0.0,0.8,798.0,275.0,1.6,61.234246640138245 +2003-12-10 20:30:00-07:00,2003,12,10,20,30,-5.2,-11.7,0.0,0.0,0.0,0.8,798.0,260.0,1.8,60.28784214505505 +2003-12-10 21:30:00-07:00,2003,12,10,21,30,-5.1000000000000005,-11.8,0.0,0.0,0.0,0.8,797.0,254.0,1.9,59.35603202048899 +2003-12-10 22:30:00-07:00,2003,12,10,22,30,-5.1000000000000005,-12.0,0.0,0.0,0.0,0.8,797.0,256.0,2.0,58.411809930933195 +2003-12-10 23:30:00-07:00,2003,12,10,23,30,-5.2,-12.2,0.0,0.0,0.0,0.8,797.0,258.0,2.1,57.91775173013713 +2003-12-11 00:30:00-07:00,2003,12,11,0,30,-5.300000000000001,-12.4,0.0,0.0,0.0,0.8,797.0,258.0,2.1,57.42663818656451 +2003-12-11 01:30:00-07:00,2003,12,11,1,30,-5.5,-12.6,0.0,0.0,0.0,0.8,797.0,256.0,2.2,57.37217739410199 +2003-12-11 02:30:00-07:00,2003,12,11,2,30,-5.5,-12.8,0.0,0.0,0.0,0.8,797.0,255.0,2.2,56.45321453910768 +2003-12-11 03:30:00-07:00,2003,12,11,3,30,-5.5,-12.9,0.0,0.0,0.0,0.8,797.0,255.0,2.1,55.99867841277901 +2003-12-11 04:30:00-07:00,2003,12,11,4,30,-5.5,-12.9,0.0,0.0,0.0,0.8,797.0,256.0,2.0,55.99867841277901 +2003-12-11 05:30:00-07:00,2003,12,11,5,30,-5.6000000000000005,-13.0,0.0,0.0,0.0,0.8,797.0,257.0,1.8,55.97088906070082 +2003-12-11 06:30:00-07:00,2003,12,11,6,30,-5.6000000000000005,-13.1,0.0,0.0,0.0,0.8,797.0,257.0,1.5,55.51945545621968 +2003-12-11 07:30:00-07:00,2003,12,11,7,30,-4.3,-13.0,15.0,442.0,34.0,0.8,797.0,255.0,1.5,50.7339649452431 +2003-12-11 08:30:00-07:00,2003,12,11,8,30,-1.7000000000000002,-12.1,37.0,786.0,192.0,0.8,798.0,258.0,1.7000000000000002,44.964200018571916 +2003-12-11 09:30:00-07:00,2003,12,11,9,30,0.6000000000000001,-11.1,197.0,249.0,277.0,0.8,798.0,282.0,1.3,41.19359382761015 +2003-12-11 10:30:00-07:00,2003,12,11,10,30,2.0,-10.6,267.0,253.0,372.0,0.8,797.0,319.0,0.9,38.766648932418526 +2003-12-11 11:30:00-07:00,2003,12,11,11,30,2.7,-10.5,292.0,257.0,409.0,0.8,797.0,347.0,0.7000000000000001,37.1769731665657 +2003-12-11 12:30:00-07:00,2003,12,11,12,30,2.7,-10.5,309.0,98.0,353.0,0.8,797.0,188.0,0.8,37.1769731665657 +2003-12-11 13:30:00-07:00,2003,12,11,13,30,2.3000000000000003,-10.6,269.0,117.0,315.0,0.8,797.0,25.0,1.0,37.946773974933215 +2003-12-11 14:30:00-07:00,2003,12,11,14,30,1.4,-10.1,84.0,1.0,84.0,0.8,797.0,39.0,1.0,42.09909455533139 +2003-12-11 15:30:00-07:00,2003,12,11,15,30,-0.3,-9.1,17.0,0.0,17.0,0.8,798.0,56.0,0.8,51.488593211146615 +2003-12-11 16:30:00-07:00,2003,12,11,16,30,-2.0,-9.2,0.0,0.0,0.0,0.8,798.0,56.0,0.6000000000000001,57.85781993182845 +2003-12-11 17:30:00-07:00,2003,12,11,17,30,-3.0,-9.7,0.0,0.0,0.0,0.8,799.0,188.0,0.5,59.90562926214705 +2003-12-11 18:30:00-07:00,2003,12,11,18,30,-3.6,-9.8,0.0,0.0,0.0,0.8,799.0,315.0,0.7000000000000001,62.1541310790752 +2003-12-11 19:30:00-07:00,2003,12,11,19,30,-4.0,-9.8,0.0,0.0,0.0,0.8,799.0,293.0,0.8,64.04293298694402 +2003-12-11 20:30:00-07:00,2003,12,11,20,30,-4.5,-10.0,0.0,0.0,0.0,0.8,799.0,282.0,0.9,65.45439640560055 +2003-12-11 21:30:00-07:00,2003,12,11,21,30,-4.9,-10.2,0.0,0.0,0.0,0.8,799.0,276.0,1.0,66.40204061733228 +2003-12-11 22:30:00-07:00,2003,12,11,22,30,-5.300000000000001,-10.6,0.0,0.0,0.0,0.8,799.0,274.0,1.1,66.30949587411553 +2003-12-11 23:30:00-07:00,2003,12,11,23,30,-5.7,-11.1,0.0,0.0,0.0,0.8,799.0,274.0,1.2000000000000002,65.69166928239619 +2003-12-12 00:30:00-07:00,2003,12,12,0,30,-6.1000000000000005,-11.5,0.0,0.0,0.0,0.8,799.0,274.0,1.2000000000000002,65.597369707092 +2003-12-12 01:30:00-07:00,2003,12,12,1,30,-6.5,-11.9,0.0,0.0,0.0,0.8,799.0,274.0,1.3,65.50272212391933 +2003-12-12 02:30:00-07:00,2003,12,12,2,30,-6.7,-12.3,0.0,0.0,0.0,0.8,799.0,275.0,1.3,64.41133360586171 +2003-12-12 03:30:00-07:00,2003,12,12,3,30,-6.800000000000001,-12.7,0.0,0.0,0.0,0.8,799.0,279.0,1.3,62.848260339846064 +2003-12-12 04:30:00-07:00,2003,12,12,4,30,-7.1000000000000005,-13.0,0.0,0.0,0.0,0.8,799.0,284.0,1.2000000000000002,62.77307510435976 +2003-12-12 05:30:00-07:00,2003,12,12,5,30,-7.5,-13.3,0.0,0.0,0.0,0.8,799.0,282.0,1.3,63.18344923731113 +2003-12-12 06:30:00-07:00,2003,12,12,6,30,-7.800000000000001,-13.6,0.0,0.0,0.0,0.8,799.0,278.0,1.4,63.10851945030536 +2003-12-12 07:30:00-07:00,2003,12,12,7,30,-6.4,-13.7,16.0,378.0,31.0,0.8,800.0,275.0,1.8,56.2038585594074 +2003-12-12 08:30:00-07:00,2003,12,12,8,30,-3.3000000000000003,-12.9,40.0,779.0,192.0,0.8,800.0,282.0,1.8,47.45801856430544 +2003-12-12 09:30:00-07:00,2003,12,12,9,30,-0.5,-11.9,52.0,913.0,346.0,0.8,800.0,320.0,1.3,41.850334746732706 +2003-12-12 10:30:00-07:00,2003,12,12,10,30,1.1,-11.9,60.0,972.0,458.0,0.8,800.0,351.0,1.0,37.27616742555776 +2003-12-12 11:30:00-07:00,2003,12,12,11,30,1.9,-11.9,64.0,995.0,514.0,0.8,800.0,356.0,1.1,35.200082540281365 +2003-12-12 12:30:00-07:00,2003,12,12,12,30,2.3000000000000003,-12.0,64.0,991.0,508.0,0.8,799.0,180.0,1.2000000000000002,33.93728968858825 +2003-12-12 13:30:00-07:00,2003,12,12,13,30,2.2,-12.0,59.0,962.0,441.0,0.8,799.0,3.0,1.3,34.179767083928844 +2003-12-12 14:30:00-07:00,2003,12,12,14,30,1.3,-11.8,53.0,886.0,319.0,0.8,799.0,6.0,1.1,37.04031525277102 +2003-12-12 15:30:00-07:00,2003,12,12,15,30,-0.9,-10.6,37.0,726.0,159.0,0.8,799.0,22.0,0.7000000000000001,47.79514190616313 +2003-12-12 16:30:00-07:00,2003,12,12,16,30,-3.2,-11.2,0.0,0.0,0.0,0.8,800.0,72.0,0.4,53.99425660853605 +2003-12-12 17:30:00-07:00,2003,12,12,17,30,-4.2,-12.0,0.0,0.0,0.0,0.77,800.0,164.0,0.6000000000000001,54.580670907981194 +2003-12-12 18:30:00-07:00,2003,12,12,18,30,-4.800000000000001,-12.0,0.0,0.0,0.0,0.77,800.0,228.0,1.1,57.10252763074337 +2003-12-12 19:30:00-07:00,2003,12,12,19,30,-5.0,-12.1,0.0,0.0,0.0,0.77,801.0,242.0,1.6,57.50816461231297 +2003-12-12 20:30:00-07:00,2003,12,12,20,30,-5.2,-12.5,0.0,0.0,0.0,0.77,801.0,253.0,1.9,56.535938066856765 +2003-12-12 21:30:00-07:00,2003,12,12,21,30,-5.4,-12.8,0.0,0.0,0.0,0.77,801.0,261.0,2.1,56.02644589485546 +2003-12-12 22:30:00-07:00,2003,12,12,22,30,-5.7,-13.1,0.0,0.0,0.0,0.77,801.0,265.0,2.2,55.943077823836056 +2003-12-12 23:30:00-07:00,2003,12,12,23,30,-5.9,-13.4,0.0,0.0,0.0,0.77,800.0,263.0,2.5,55.43545602214591 +2003-12-13 00:30:00-07:00,2003,12,13,0,30,-6.1000000000000005,-13.6,0.0,0.0,0.0,0.77,800.0,260.0,2.7,55.37934707305824 +2003-12-13 01:30:00-07:00,2003,12,13,1,30,-6.1000000000000005,-13.7,0.0,0.0,0.0,0.77,800.0,260.0,2.9000000000000004,54.930354398677494 +2003-12-13 02:30:00-07:00,2003,12,13,2,30,-5.9,-13.7,0.0,0.0,0.0,0.77,800.0,263.0,3.0,54.09916619437479 +2003-12-13 03:30:00-07:00,2003,12,13,3,30,-5.7,-13.7,0.0,0.0,0.0,0.77,800.0,267.0,3.1,53.28192442783128 +2003-12-13 04:30:00-07:00,2003,12,13,4,30,-5.4,-13.4,0.0,0.0,0.0,0.77,800.0,268.0,3.2,53.36810899051808 +2003-12-13 05:30:00-07:00,2003,12,13,5,30,-5.0,-13.1,0.0,0.0,0.0,0.77,801.0,268.0,3.3000000000000003,53.05135648925529 +2003-12-13 06:30:00-07:00,2003,12,13,6,30,-4.6000000000000005,-12.8,0.0,0.0,0.0,0.77,801.0,268.0,3.4000000000000004,52.738498841447985 +2003-12-13 07:30:00-07:00,2003,12,13,7,30,-3.3000000000000003,-12.5,20.0,2.0,20.0,0.77,801.0,267.0,3.5,49.01566759907049 +2003-12-13 08:30:00-07:00,2003,12,13,8,30,-0.8,-11.3,121.0,50.0,130.0,0.77,801.0,267.0,3.8,44.87831544303378 +2003-12-13 09:30:00-07:00,2003,12,13,9,30,1.6,-9.7,197.0,208.0,263.0,0.77,802.0,275.0,3.9,42.829335289841296 +2003-12-13 10:30:00-07:00,2003,12,13,10,30,3.4000000000000004,-8.4,210.0,409.0,377.0,0.77,801.0,286.0,3.8,41.72251716066695 +2003-12-13 11:30:00-07:00,2003,12,13,11,30,4.7,-7.300000000000001,243.0,394.0,421.0,0.77,801.0,294.0,3.8,41.47189990656402 +2003-12-13 12:30:00-07:00,2003,12,13,12,30,5.5,-7.2,211.0,460.0,417.0,0.77,800.0,301.0,4.0,39.53023261120719 +2003-12-13 13:30:00-07:00,2003,12,13,13,30,5.5,-6.800000000000001,258.0,160.0,321.0,0.77,800.0,303.0,3.8,40.764816250088366 +2003-12-13 14:30:00-07:00,2003,12,13,14,30,4.4,-5.7,187.0,169.0,238.0,0.77,800.0,299.0,3.0,47.869363845736146 +2003-12-13 15:30:00-07:00,2003,12,13,15,30,2.2,-5.6000000000000005,100.0,70.0,112.0,0.77,801.0,296.0,2.2,56.338368351444366 +2003-12-13 16:30:00-07:00,2003,12,13,16,30,0.8,-6.5,0.0,0.0,0.0,0.77,801.0,288.0,1.9,58.149615377965205 +2003-12-13 17:30:00-07:00,2003,12,13,17,30,0.3,-6.800000000000001,0.0,0.0,0.0,0.65,802.0,271.0,2.2,58.916328005346976 +2003-12-13 18:30:00-07:00,2003,12,13,18,30,-0.4,-7.0,0.0,0.0,0.0,0.65,802.0,258.0,2.5,61.04057115236211 +2003-12-13 19:30:00-07:00,2003,12,13,19,30,-0.8,-7.4,0.0,0.0,0.0,0.65,802.0,251.0,2.8000000000000003,60.93966688434751 +2003-12-13 20:30:00-07:00,2003,12,13,20,30,-0.9,-7.6,0.0,0.0,0.0,0.65,801.0,244.0,2.9000000000000004,60.44567622726565 +2003-12-13 21:30:00-07:00,2003,12,13,21,30,-0.7000000000000001,-7.6,0.0,0.0,0.0,0.65,801.0,238.0,2.9000000000000004,59.569595674870925 +2003-12-13 22:30:00-07:00,2003,12,13,22,30,-0.6000000000000001,-7.5,0.0,0.0,0.0,0.65,800.0,237.0,3.1,59.59542537241655 +2003-12-13 23:30:00-07:00,2003,12,13,23,30,-0.2,-7.300000000000001,0.0,0.0,0.0,0.65,800.0,238.0,3.3000000000000003,58.78604796279767 +2003-12-14 00:30:00-07:00,2003,12,14,0,30,0.1,-7.0,0.0,0.0,0.0,0.65,799.0,240.0,3.4000000000000004,58.864279298128835 +2003-12-14 01:30:00-07:00,2003,12,14,1,30,0.4,-6.7,0.0,0.0,0.0,0.65,799.0,241.0,3.3000000000000003,58.94232075289549 +2003-12-14 02:30:00-07:00,2003,12,14,2,30,0.7000000000000001,-6.4,0.0,0.0,0.0,0.65,799.0,244.0,3.2,59.02017276667966 +2003-12-14 03:30:00-07:00,2003,12,14,3,30,0.8,-6.2,0.0,0.0,0.0,0.65,799.0,247.0,3.1,59.49890733608269 +2003-12-14 04:30:00-07:00,2003,12,14,4,30,0.8,-6.1000000000000005,0.0,0.0,0.0,0.65,799.0,250.0,3.0,59.95481935833174 +2003-12-14 05:30:00-07:00,2003,12,14,5,30,0.8,-6.0,0.0,0.0,0.0,0.65,798.0,251.0,2.9000000000000004,60.413835720505126 +2003-12-14 06:30:00-07:00,2003,12,14,6,30,0.8,-6.0,0.0,0.0,0.0,0.65,798.0,251.0,2.8000000000000003,60.413835720505126 +2003-12-14 07:30:00-07:00,2003,12,14,7,30,1.7000000000000002,-5.9,14.0,0.0,14.0,0.65,798.0,249.0,3.0,57.067658610572884 +2003-12-14 08:30:00-07:00,2003,12,14,8,30,4.0,-5.300000000000001,36.0,763.0,182.0,0.65,798.0,244.0,3.3000000000000003,50.74801811435601 +2003-12-14 09:30:00-07:00,2003,12,14,9,30,6.7,-4.3,46.0,900.0,333.0,0.65,797.0,239.0,3.4000000000000004,45.37133778446514 +2003-12-14 10:30:00-07:00,2003,12,14,10,30,8.9,-4.1000000000000005,52.0,959.0,443.0,0.65,797.0,237.0,3.3000000000000003,39.651240156900116 +2003-12-14 11:30:00-07:00,2003,12,14,11,30,10.4,-4.4,55.0,983.0,499.0,0.65,795.0,235.0,2.9000000000000004,35.05630920836191 +2003-12-14 12:30:00-07:00,2003,12,14,12,30,10.7,-4.6000000000000005,58.0,976.0,495.0,0.65,794.0,236.0,2.5,33.84923090551839 +2003-12-14 13:30:00-07:00,2003,12,14,13,30,9.8,-3.5,202.0,394.0,358.0,0.65,794.0,240.0,2.5,39.036839851003506 +2003-12-14 14:30:00-07:00,2003,12,14,14,30,8.0,-3.6,200.0,120.0,236.0,0.65,793.0,244.0,2.7,43.75095268935306 +2003-12-14 15:30:00-07:00,2003,12,14,15,30,5.0,-4.5,30.0,756.0,157.0,0.65,793.0,247.0,2.8000000000000003,50.26923913902077 +2003-12-14 16:30:00-07:00,2003,12,14,16,30,2.3000000000000003,-5.9,0.0,0.0,0.0,0.65,792.0,246.0,3.0,54.67647324479627 +2003-12-14 17:30:00-07:00,2003,12,14,17,30,1.5,-6.5,0.0,0.0,0.0,0.76,792.0,243.0,3.5,55.29778869648539 +2003-12-14 18:30:00-07:00,2003,12,14,18,30,1.3,-7.0,0.0,0.0,0.0,0.76,792.0,239.0,3.9,53.985221438762 +2003-12-14 19:30:00-07:00,2003,12,14,19,30,0.7000000000000001,-7.5,0.0,0.0,0.0,0.76,791.0,238.0,3.8,54.23586007333038 +2003-12-14 20:30:00-07:00,2003,12,14,20,30,0.0,-7.9,0.0,0.0,0.0,0.76,791.0,235.0,3.3000000000000003,55.313829273989 +2003-12-14 21:30:00-07:00,2003,12,14,21,30,-0.6000000000000001,-8.3,0.0,0.0,0.0,0.76,790.0,233.0,2.7,56.01389245434658 +2003-12-14 22:30:00-07:00,2003,12,14,22,30,-1.0,-8.5,0.0,0.0,0.0,0.76,789.0,229.0,2.3000000000000003,56.783080827725854 +2003-12-14 23:30:00-07:00,2003,12,14,23,30,-1.1,-8.5,0.0,0.0,0.0,0.76,788.0,225.0,2.1,57.199969798525196 +2003-12-15 00:30:00-07:00,2003,12,15,0,30,-1.0,-8.200000000000001,0.0,0.0,0.0,0.76,788.0,223.0,2.0,58.123480387280175 +2003-12-15 01:30:00-07:00,2003,12,15,1,30,-0.8,-7.800000000000001,0.0,0.0,0.0,0.76,787.0,227.0,1.8,59.084803559830085 +2003-12-15 02:30:00-07:00,2003,12,15,2,30,-0.6000000000000001,-7.5,0.0,0.0,0.0,0.76,786.0,238.0,1.7000000000000002,59.59542537241655 +2003-12-15 03:30:00-07:00,2003,12,15,3,30,-0.6000000000000001,-7.300000000000001,0.0,0.0,0.0,0.76,786.0,272.0,1.9,60.52205574953835 +2003-12-15 04:30:00-07:00,2003,12,15,4,30,-0.9,-6.9,0.0,0.0,0.0,0.76,787.0,150.0,3.9,63.79521034754368 +2003-12-15 05:30:00-07:00,2003,12,15,5,30,-2.2,-5.9,0.0,0.0,0.0,0.76,789.0,3.0,6.6000000000000005,75.76696655529328 +2003-12-15 06:30:00-07:00,2003,12,15,6,30,-3.7,-7.300000000000001,0.0,0.0,0.0,0.76,791.0,181.0,7.800000000000001,76.08543501060234 +2003-12-15 07:30:00-07:00,2003,12,15,7,30,-4.4,-9.7,9.0,0.0,9.0,0.76,793.0,355.0,8.0,66.51724036720306 +2003-12-15 08:30:00-07:00,2003,12,15,8,30,-4.5,-11.4,36.0,0.0,36.0,0.76,794.0,353.0,7.9,58.57216530449219 +2003-12-15 09:30:00-07:00,2003,12,15,9,30,-4.1000000000000005,-12.4,124.0,3.0,125.0,0.76,796.0,353.0,7.800000000000001,52.458323751013594 +2003-12-15 10:30:00-07:00,2003,12,15,10,30,-3.4000000000000004,-13.0,192.0,7.0,195.0,0.76,797.0,354.0,7.7,47.42788510113836 +2003-12-15 11:30:00-07:00,2003,12,15,11,30,-2.6,-13.3,304.0,61.0,332.0,0.76,797.0,355.0,7.800000000000001,43.615825997318986 +2003-12-15 12:30:00-07:00,2003,12,15,12,30,-2.1,-13.5,311.0,146.0,376.0,0.76,797.0,355.0,7.7,41.35540934601354 +2003-12-15 13:30:00-07:00,2003,12,15,13,30,-1.9,-13.8,260.0,203.0,340.0,0.76,798.0,355.0,7.300000000000001,39.766937985956055 +2003-12-15 14:30:00-07:00,2003,12,15,14,30,-2.2,-14.3,59.0,861.0,318.0,0.76,799.0,357.0,6.6000000000000005,39.02949890585054 +2003-12-15 15:30:00-07:00,2003,12,15,15,30,-3.2,-14.6,109.0,85.0,123.0,0.76,800.0,179.0,5.5,41.01272467725308 +2003-12-15 16:30:00-07:00,2003,12,15,16,30,-4.5,-14.8,0.0,0.0,0.0,0.76,802.0,178.0,4.5,44.468704493698866 +2003-12-15 17:30:00-07:00,2003,12,15,17,30,-5.4,-15.0,0.0,0.0,0.0,0.8,803.0,352.0,4.1000000000000005,46.821142421204165 +2003-12-15 18:30:00-07:00,2003,12,15,18,30,-5.9,-15.4,0.0,0.0,0.0,0.8,804.0,343.0,4.1000000000000005,47.055784483220286 +2003-12-15 19:30:00-07:00,2003,12,15,19,30,-6.5,-16.0,0.0,0.0,0.0,0.8,805.0,335.0,4.0,46.87218469211852 +2003-12-15 20:30:00-07:00,2003,12,15,20,30,-7.0,-16.7,0.0,0.0,0.0,0.8,806.0,327.0,3.9,45.944543224805805 +2003-12-15 21:30:00-07:00,2003,12,15,21,30,-7.2,-17.2,0.0,0.0,0.0,0.8,806.0,320.0,3.9,44.74254551871112 +2003-12-15 22:30:00-07:00,2003,12,15,22,30,-7.4,-17.5,0.0,0.0,0.0,0.8,807.0,315.0,3.9,44.30562266308897 +2003-12-15 23:30:00-07:00,2003,12,15,23,30,-7.7,-17.8,0.0,0.0,0.0,0.8,807.0,310.0,3.8,44.211645710202 +2003-12-16 00:30:00-07:00,2003,12,16,0,30,-8.1,-18.1,0.0,0.0,0.0,0.8,808.0,302.0,3.7,44.46106262332401 +2003-12-16 01:30:00-07:00,2003,12,16,1,30,-8.5,-18.4,0.0,0.0,0.0,0.8,808.0,292.0,3.7,44.71358378979444 +2003-12-16 02:30:00-07:00,2003,12,16,2,30,-8.700000000000001,-18.6,0.0,0.0,0.0,0.8,808.0,284.0,3.8,44.65081415470029 +2003-12-16 03:30:00-07:00,2003,12,16,3,30,-8.9,-18.8,0.0,0.0,0.0,0.8,808.0,279.0,3.9,44.58796848367127 +2003-12-16 04:30:00-07:00,2003,12,16,4,30,-9.1,-18.9,0.0,0.0,0.0,0.8,808.0,276.0,3.9,44.90648127379745 +2003-12-16 05:30:00-07:00,2003,12,16,5,30,-9.3,-19.0,0.0,0.0,0.0,0.8,808.0,272.0,3.7,45.22813581672379 +2003-12-16 06:30:00-07:00,2003,12,16,6,30,-9.5,-19.1,0.0,0.0,0.0,0.8,808.0,269.0,3.5,45.552969652983144 +2003-12-16 07:30:00-07:00,2003,12,16,7,30,-8.3,-19.3,13.0,449.0,27.0,0.8,809.0,269.0,3.9,40.77268590625043 +2003-12-16 08:30:00-07:00,2003,12,16,8,30,-5.5,-18.9,32.0,810.0,184.0,0.8,809.0,271.0,4.7,34.024431419813375 +2003-12-16 09:30:00-07:00,2003,12,16,9,30,-2.4000000000000004,-17.900000000000002,44.0,941.0,340.0,0.8,809.0,278.0,4.9,29.36068944866956 +2003-12-16 10:30:00-07:00,2003,12,16,10,30,0.0,-17.5,50.0,998.0,454.0,0.8,809.0,286.0,4.6000000000000005,25.472997653285972 +2003-12-16 11:30:00-07:00,2003,12,16,11,30,1.6,-17.0,54.0,1021.0,512.0,0.8,809.0,293.0,4.3,23.673565343343505 +2003-12-16 12:30:00-07:00,2003,12,16,12,30,2.4000000000000004,-16.6,53.0,1004.0,501.0,0.8,808.0,298.0,4.3,23.12149066273151 +2003-12-16 13:30:00-07:00,2003,12,16,13,30,2.4000000000000004,-16.1,50.0,978.0,437.0,0.8,809.0,300.0,4.3,24.105525190513788 +2003-12-16 14:30:00-07:00,2003,12,16,14,30,1.2000000000000002,-14.9,206.0,19.0,212.0,0.8,809.0,295.0,4.0,29.005127955653162 +2003-12-16 15:30:00-07:00,2003,12,16,15,30,-1.1,-13.1,106.0,72.0,118.0,0.8,809.0,287.0,3.7,39.69332549454437 +2003-12-16 16:30:00-07:00,2003,12,16,16,30,-3.2,-13.7,0.0,0.0,0.0,0.8,809.0,277.0,3.8,44.144518160787804 +2003-12-16 17:30:00-07:00,2003,12,16,17,30,-3.7,-14.3,0.0,0.0,0.0,0.6900000000000001,810.0,272.0,4.4,43.63182328766408 +2003-12-16 18:30:00-07:00,2003,12,16,18,30,-3.3000000000000003,-14.6,0.0,0.0,0.0,0.6900000000000001,810.0,275.0,4.9,41.319402306021466 +2003-12-16 19:30:00-07:00,2003,12,16,19,30,-3.0,-14.8,0.0,0.0,0.0,0.6900000000000001,810.0,278.0,4.800000000000001,39.748418465316036 +2003-12-16 20:30:00-07:00,2003,12,16,20,30,-2.9000000000000004,-14.9,0.0,0.0,0.0,0.6900000000000001,810.0,276.0,4.6000000000000005,39.13089793243571 +2003-12-16 21:30:00-07:00,2003,12,16,21,30,-2.8000000000000003,-15.0,0.0,0.0,0.0,0.6900000000000001,810.0,271.0,4.5,38.52293140990561 +2003-12-16 22:30:00-07:00,2003,12,16,22,30,-2.7,-15.0,0.0,0.0,0.0,0.6900000000000001,810.0,268.0,4.6000000000000005,38.238193789820535 +2003-12-16 23:30:00-07:00,2003,12,16,23,30,-2.6,-15.1,0.0,0.0,0.0,0.6900000000000001,809.0,265.0,4.6000000000000005,37.64428991827151 +2003-12-17 00:30:00-07:00,2003,12,17,0,30,-2.4000000000000004,-15.0,0.0,0.0,0.0,0.6900000000000001,809.0,263.0,4.6000000000000005,37.39792997395954 +2003-12-17 01:30:00-07:00,2003,12,17,1,30,-2.1,-14.7,0.0,0.0,0.0,0.6900000000000001,809.0,260.0,4.5,37.492113969650674 +2003-12-17 02:30:00-07:00,2003,12,17,2,30,-1.9,-14.2,0.0,0.0,0.0,0.6900000000000001,808.0,259.0,4.5,38.48904790473898 +2003-12-17 03:30:00-07:00,2003,12,17,3,30,-1.6,-13.6,0.0,0.0,0.0,0.6900000000000001,808.0,261.0,4.7,39.537588125466755 +2003-12-17 04:30:00-07:00,2003,12,17,4,30,-1.1,-13.3,0.0,0.0,0.0,0.6900000000000001,808.0,260.0,5.0,39.05478803701881 +2003-12-17 05:30:00-07:00,2003,12,17,5,30,-0.6000000000000001,-13.0,0.0,0.0,0.0,0.6900000000000001,808.0,260.0,5.300000000000001,38.581264958295044 +2003-12-17 06:30:00-07:00,2003,12,17,6,30,-0.1,-12.7,0.0,0.0,0.0,0.6900000000000001,808.0,260.0,5.5,38.116797778678716 +2003-12-17 07:30:00-07:00,2003,12,17,7,30,0.9,-12.4,13.0,1.0,13.0,0.6900000000000001,808.0,261.0,5.6000000000000005,36.32707405000583 +2003-12-17 08:30:00-07:00,2003,12,17,8,30,3.2,-11.6,103.0,211.0,142.0,0.6900000000000001,809.0,264.0,5.6000000000000005,32.8760046448961 +2003-12-17 09:30:00-07:00,2003,12,17,9,30,5.7,-10.2,216.0,46.0,231.0,0.6900000000000001,809.0,277.0,5.6000000000000005,30.851577509417858 +2003-12-17 10:30:00-07:00,2003,12,17,10,30,7.300000000000001,-9.6,196.0,418.0,364.0,0.6900000000000001,809.0,296.0,5.6000000000000005,28.97425406046419 +2003-12-17 11:30:00-07:00,2003,12,17,11,30,8.0,-9.7,237.0,379.0,407.0,0.6900000000000001,809.0,310.0,5.4,27.407897754563166 +2003-12-17 12:30:00-07:00,2003,12,17,12,30,8.200000000000001,-9.2,234.0,384.0,405.0,0.6900000000000001,809.0,320.0,4.9,28.120548059123127 +2003-12-17 13:30:00-07:00,2003,12,17,13,30,7.6,-8.3,225.0,302.0,345.0,0.6900000000000001,809.0,329.0,4.0,31.4232745263623 +2003-12-17 14:30:00-07:00,2003,12,17,14,30,5.7,-7.2,181.0,191.0,238.0,0.6900000000000001,810.0,338.0,2.6,38.98620791992076 +2003-12-17 15:30:00-07:00,2003,12,17,15,30,2.4000000000000004,-6.300000000000001,94.0,20.0,98.0,0.6900000000000001,810.0,347.0,1.6,52.65701335679557 +2003-12-17 16:30:00-07:00,2003,12,17,16,30,-0.4,-7.6,0.0,0.0,0.0,0.6900000000000001,811.0,347.0,1.0,58.28185536836949 +2003-12-17 17:30:00-07:00,2003,12,17,17,30,-1.4,-8.3,0.0,0.0,0.0,0.72,812.0,339.0,0.6000000000000001,59.38819182930162 +2003-12-17 18:30:00-07:00,2003,12,17,18,30,-1.8,-8.4,0.0,0.0,0.0,0.72,813.0,272.0,0.4,60.68591574973163 +2003-12-17 19:30:00-07:00,2003,12,17,19,30,-2.1,-8.6,0.0,0.0,0.0,0.72,813.0,217.0,0.5,61.08379294231747 +2003-12-17 20:30:00-07:00,2003,12,17,20,30,-2.3000000000000003,-8.8,0.0,0.0,0.0,0.72,813.0,223.0,0.7000000000000001,61.03307004273383 +2003-12-17 21:30:00-07:00,2003,12,17,21,30,-2.5,-9.1,0.0,0.0,0.0,0.72,813.0,225.0,0.9,60.50701554904559 +2003-12-17 22:30:00-07:00,2003,12,17,22,30,-2.7,-9.4,0.0,0.0,0.0,0.72,813.0,223.0,1.1,59.98335970874126 +2003-12-17 23:30:00-07:00,2003,12,17,23,30,-3.0,-9.7,0.0,0.0,0.0,0.72,813.0,221.0,1.0,59.90562926214705 +2003-12-18 00:30:00-07:00,2003,12,18,0,30,-3.4000000000000004,-10.2,0.0,0.0,0.0,0.72,813.0,219.0,1.0,59.33124480579336 +2003-12-18 01:30:00-07:00,2003,12,18,1,30,-3.4000000000000004,-10.6,0.0,0.0,0.0,0.72,813.0,219.0,1.3,57.48229411383093 +2003-12-18 02:30:00-07:00,2003,12,18,2,30,-3.4000000000000004,-10.9,0.0,0.0,0.0,0.72,813.0,224.0,1.7000000000000002,56.12947389401892 +2003-12-18 03:30:00-07:00,2003,12,18,3,30,-3.4000000000000004,-11.2,0.0,0.0,0.0,0.72,813.0,230.0,2.0,54.805113657408036 +2003-12-18 04:30:00-07:00,2003,12,18,4,30,-3.4000000000000004,-11.4,0.0,0.0,0.0,0.72,813.0,236.0,2.1,53.93775932818649 +2003-12-18 05:30:00-07:00,2003,12,18,5,30,-3.5,-11.5,0.0,0.0,0.0,0.72,813.0,244.0,2.2,53.90947891188657 +2003-12-18 06:30:00-07:00,2003,12,18,6,30,-3.5,-11.7,0.0,0.0,0.0,0.72,813.0,255.0,2.2,53.054103418525074 +2003-12-18 07:30:00-07:00,2003,12,18,7,30,-2.3000000000000003,-11.9,12.0,383.0,23.0,0.72,814.0,267.0,2.3000000000000003,47.758288799816896 +2003-12-18 08:30:00-07:00,2003,12,18,8,30,0.3,-10.9,33.0,772.0,175.0,0.72,814.0,279.0,2.2,42.771397876718325 +2003-12-18 09:30:00-07:00,2003,12,18,9,30,3.1,-10.5,220.0,76.0,244.0,0.72,814.0,306.0,1.8,36.13861752850402 +2003-12-18 10:30:00-07:00,2003,12,18,10,30,5.300000000000001,-9.8,171.0,513.0,377.0,0.72,814.0,334.0,1.8,32.736031345827236 +2003-12-18 11:30:00-07:00,2003,12,18,11,30,6.6000000000000005,-9.8,55.0,981.0,494.0,0.72,814.0,351.0,2.2,29.9237646562385 +2003-12-18 12:30:00-07:00,2003,12,18,12,30,6.800000000000001,-9.6,55.0,980.0,491.0,0.72,814.0,186.0,2.5,29.983632779444406 +2003-12-18 13:30:00-07:00,2003,12,18,13,30,6.2,-9.4,50.0,955.0,428.0,0.72,814.0,16.0,2.3000000000000003,31.74177386203174 +2003-12-18 14:30:00-07:00,2003,12,18,14,30,4.800000000000001,-9.1,146.0,380.0,261.0,0.72,814.0,27.0,1.4,35.81004600825862 +2003-12-18 15:30:00-07:00,2003,12,18,15,30,1.9,-7.6,33.0,733.0,158.0,0.72,814.0,99.0,0.6000000000000001,49.37865621075868 +2003-12-18 16:30:00-07:00,2003,12,18,16,30,-0.9,-9.5,0.0,0.0,0.0,0.72,814.0,195.0,0.6000000000000001,52.12921496275329 +2003-12-18 17:30:00-07:00,2003,12,18,17,30,-2.0,-10.3,0.0,0.0,0.0,0.16,814.0,233.0,1.0,53.05930152485638 +2003-12-18 18:30:00-07:00,2003,12,18,18,30,-1.9,-10.3,0.0,0.0,0.0,0.16,814.0,245.0,1.5,52.66970891235861 +2003-12-18 19:30:00-07:00,2003,12,18,19,30,-1.6,-10.4,0.0,0.0,0.0,0.16,814.0,254.0,1.8,51.113913296682135 +2003-12-18 20:30:00-07:00,2003,12,18,20,30,-1.4,-10.7,0.0,0.0,0.0,0.16,814.0,262.0,2.1,49.185422508293215 +2003-12-18 21:30:00-07:00,2003,12,18,21,30,-1.4,-11.2,0.0,0.0,0.0,0.16,814.0,268.0,2.1,47.268098929929224 +2003-12-18 22:30:00-07:00,2003,12,18,22,30,-1.4,-11.6,0.0,0.0,0.0,0.16,814.0,272.0,2.1,45.78253103840252 +2003-12-18 23:30:00-07:00,2003,12,18,23,30,-1.4,-12.1,0.0,0.0,0.0,0.16,814.0,274.0,2.0,43.984212299770505 +2003-12-19 00:30:00-07:00,2003,12,19,0,30,-1.4,-12.3,0.0,0.0,0.0,0.16,814.0,270.0,2.0,43.282716348088435 +2003-12-19 01:30:00-07:00,2003,12,19,1,30,-1.4,-12.5,0.0,0.0,0.0,0.16,814.0,265.0,2.0,42.59122031917332 +2003-12-19 02:30:00-07:00,2003,12,19,2,30,-1.3,-12.7,0.0,0.0,0.0,0.16,813.0,258.0,2.0,41.603395116819485 +2003-12-19 03:30:00-07:00,2003,12,19,3,30,-1.2000000000000002,-12.9,0.0,0.0,0.0,0.16,813.0,253.0,2.0,40.637587635132604 +2003-12-19 04:30:00-07:00,2003,12,19,4,30,-1.0,-13.1,0.0,0.0,0.0,0.16,813.0,251.0,2.0,39.40402971919152 +2003-12-19 05:30:00-07:00,2003,12,19,5,30,-1.0,-13.3,0.0,0.0,0.0,0.16,813.0,251.0,2.1,38.77014609670157 +2003-12-19 06:30:00-07:00,2003,12,19,6,30,-1.0,-13.4,0.0,0.0,0.0,0.16,813.0,251.0,2.2,38.45663114503851 +2003-12-19 07:30:00-07:00,2003,12,19,7,30,0.5,-13.4,11.0,377.0,22.0,0.16,813.0,251.0,2.2,34.48510802019933 +2003-12-19 08:30:00-07:00,2003,12,19,8,30,3.9,-11.1,29.0,774.0,170.0,0.16,813.0,251.0,2.2,32.56798878276805 +2003-12-19 09:30:00-07:00,2003,12,19,9,30,7.5,-8.5,37.0,910.0,320.0,0.16,813.0,248.0,2.3000000000000003,31.149852329440424 +2003-12-19 10:30:00-07:00,2003,12,19,10,30,10.0,-7.9,41.0,970.0,430.0,0.16,812.0,242.0,2.0,27.563509039962845 +2003-12-19 11:30:00-07:00,2003,12,19,11,30,11.6,-7.5,43.0,994.0,487.0,0.16,811.0,244.0,1.4,25.560841886671334 +2003-12-19 12:30:00-07:00,2003,12,19,12,30,12.2,-7.6,43.0,994.0,485.0,0.16,810.0,259.0,0.9,24.38072883490874 +2003-12-19 13:30:00-07:00,2003,12,19,13,30,11.7,-6.1000000000000005,41.0,967.0,424.0,0.16,810.0,180.0,0.4,28.273196225683858 +2003-12-19 14:30:00-07:00,2003,12,19,14,30,10.0,-2.8000000000000003,98.0,686.0,306.0,0.16,810.0,108.0,0.4,40.577771339263016 +2003-12-19 15:30:00-07:00,2003,12,19,15,30,7.2,-5.800000000000001,31.0,733.0,157.0,0.16,810.0,147.0,0.7000000000000001,39.14075485530123 +2003-12-19 16:30:00-07:00,2003,12,19,16,30,4.3,-8.4,0.0,0.0,0.0,0.16,810.0,185.0,1.0,39.16538787171568 +2003-12-19 17:30:00-07:00,2003,12,19,17,30,2.7,-9.1,0.0,0.0,0.0,0.16,810.0,218.0,1.3,41.50973316853947 +2003-12-19 18:30:00-07:00,2003,12,19,18,30,1.9,-9.1,0.0,0.0,0.0,0.16,809.0,237.0,1.6,43.941560082458466 +2003-12-19 19:30:00-07:00,2003,12,19,19,30,1.2000000000000002,-9.0,0.0,0.0,0.0,0.16,809.0,246.0,1.9,46.56300194933096 +2003-12-19 20:30:00-07:00,2003,12,19,20,30,0.9,-9.2,0.0,0.0,0.0,0.16,809.0,252.0,2.1,46.83922986613956 +2003-12-19 21:30:00-07:00,2003,12,19,21,30,0.9,-9.4,0.0,0.0,0.0,0.16,808.0,255.0,2.3000000000000003,46.11047744624564 +2003-12-19 22:30:00-07:00,2003,12,19,22,30,1.1,-9.8,0.0,0.0,0.0,0.16,808.0,255.0,2.5,44.04503196107843 +2003-12-19 23:30:00-07:00,2003,12,19,23,30,1.2000000000000002,-10.5,0.0,0.0,0.0,0.16,807.0,255.0,2.8000000000000003,41.37778774187309 +2003-12-20 00:30:00-07:00,2003,12,20,0,30,1.3,-11.0,0.0,0.0,0.0,0.16,807.0,256.0,3.1,39.483115199315385 +2003-12-20 01:30:00-07:00,2003,12,20,1,30,1.4,-11.2,0.0,0.0,0.0,0.16,806.0,257.0,3.4000000000000004,38.581615994414065 +2003-12-20 02:30:00-07:00,2003,12,20,2,30,1.6,-11.0,0.0,0.0,0.0,0.16,806.0,258.0,3.6,38.64335061338511 +2003-12-20 03:30:00-07:00,2003,12,20,3,30,1.7000000000000002,-10.8,0.0,0.0,0.0,0.16,805.0,258.0,3.6,38.9827054436324 +2003-12-20 04:30:00-07:00,2003,12,20,4,30,1.7000000000000002,-10.8,0.0,0.0,0.0,0.16,805.0,258.0,3.6,38.9827054436324 +2003-12-20 05:30:00-07:00,2003,12,20,5,30,1.8,-11.2,0.0,0.0,0.0,0.16,805.0,257.0,3.7,37.492709272732526 +2003-12-20 06:30:00-07:00,2003,12,20,6,30,2.1,-11.7,0.0,0.0,0.0,0.16,805.0,257.0,4.0,35.26187039870722 +2003-12-20 07:30:00-07:00,2003,12,20,7,30,3.6,-12.0,8.0,0.0,8.0,0.16,805.0,256.0,4.3,30.953520614925864 +2003-12-20 08:30:00-07:00,2003,12,20,8,30,6.2,-11.4,11.0,0.0,11.0,0.16,805.0,255.0,4.5,27.09462441281994 +2003-12-20 09:30:00-07:00,2003,12,20,9,30,8.8,-10.0,26.0,0.0,26.0,0.16,805.0,253.0,4.2,25.35548401866899 +2003-12-20 10:30:00-07:00,2003,12,20,10,30,10.9,-8.1,63.0,1.0,63.0,0.16,804.0,253.0,3.9,25.558959169187077 +2003-12-20 11:30:00-07:00,2003,12,20,11,30,12.2,-7.1000000000000005,49.0,0.0,49.0,0.16,803.0,256.0,3.6,25.339116102827496 +2003-12-20 12:30:00-07:00,2003,12,20,12,30,12.8,-5.7,230.0,133.0,289.0,0.16,802.0,260.0,3.2,27.11491375985752 +2003-12-20 13:30:00-07:00,2003,12,20,13,30,12.6,-3.8,171.0,381.0,322.0,0.16,802.0,263.0,2.8000000000000003,31.706417327660645 +2003-12-20 14:30:00-07:00,2003,12,20,14,30,11.2,-4.2,123.0,443.0,257.0,0.16,802.0,265.0,2.3000000000000003,33.744010882503815 +2003-12-20 15:30:00-07:00,2003,12,20,15,30,8.200000000000001,-5.4,95.0,98.0,112.0,0.16,802.0,267.0,1.8,37.691049502827596 +2003-12-20 16:30:00-07:00,2003,12,20,16,30,5.5,-6.9,0.0,0.0,0.0,0.16,803.0,266.0,1.5,40.45299690760697 +2003-12-20 17:30:00-07:00,2003,12,20,17,30,4.4,-7.9,0.0,0.0,0.0,0.16,803.0,260.0,1.4,40.43168028754712 +2003-12-20 18:30:00-07:00,2003,12,20,18,30,3.8,-8.200000000000001,0.0,0.0,0.0,0.16,803.0,252.0,1.3,41.19931257233045 +2003-12-20 19:30:00-07:00,2003,12,20,19,30,3.4000000000000004,-8.700000000000001,0.0,0.0,0.0,0.16,803.0,241.0,1.3,40.75872126741363 +2003-12-20 20:30:00-07:00,2003,12,20,20,30,3.0,-9.0,0.0,0.0,0.0,0.16,803.0,234.0,1.4,40.95596019737601 +2003-12-20 21:30:00-07:00,2003,12,20,21,30,2.2,-9.3,0.0,0.0,0.0,0.16,803.0,234.0,1.6,42.34286020122042 +2003-12-20 22:30:00-07:00,2003,12,20,22,30,1.5,-9.7,0.0,0.0,0.0,0.16,803.0,237.0,1.8,43.13710551443811 +2003-12-20 23:30:00-07:00,2003,12,20,23,30,1.1,-10.3,0.0,0.0,0.0,0.16,803.0,239.0,1.9,42.3410681668317 +2003-12-21 00:30:00-07:00,2003,12,21,0,30,0.8,-10.7,0.0,0.0,0.0,0.16,803.0,240.0,2.0,41.91558730735726 +2003-12-21 01:30:00-07:00,2003,12,21,1,30,1.0,-10.7,0.0,0.0,0.0,0.16,803.0,240.0,2.0,41.3164533191299 +2003-12-21 02:30:00-07:00,2003,12,21,2,30,1.6,-10.0,0.0,0.0,0.0,0.16,802.0,238.0,2.0,41.829040068425705 +2003-12-21 03:30:00-07:00,2003,12,21,3,30,2.1,-9.5,0.0,0.0,0.0,0.16,802.0,236.0,1.9,41.981572229874416 +2003-12-21 04:30:00-07:00,2003,12,21,4,30,2.2,-9.2,0.0,0.0,0.0,0.16,802.0,232.0,1.8,42.67600891854135 +2003-12-21 05:30:00-07:00,2003,12,21,5,30,2.3000000000000003,-9.3,0.0,0.0,0.0,0.16,802.0,228.0,1.6,42.04247235985065 +2003-12-21 06:30:00-07:00,2003,12,21,6,30,2.2,-9.3,0.0,0.0,0.0,0.16,802.0,223.0,1.5,42.34286020122042 +2003-12-21 07:30:00-07:00,2003,12,21,7,30,2.8000000000000003,-8.9,8.0,0.0,8.0,0.16,802.0,221.0,1.5,41.86617111225618 +2003-12-21 08:30:00-07:00,2003,12,21,8,30,4.3,-7.5,35.0,692.0,160.0,0.16,802.0,218.0,1.5,41.99495832890214 +2003-12-21 09:30:00-07:00,2003,12,21,9,30,6.1000000000000005,-5.7,222.0,0.0,222.0,0.16,802.0,214.0,1.6,42.53599024438863 +2003-12-21 10:30:00-07:00,2003,12,21,10,30,8.200000000000001,-4.4,104.0,729.0,395.0,0.16,802.0,213.0,1.7000000000000002,40.64829669100829 +2003-12-21 11:30:00-07:00,2003,12,21,11,30,9.9,-4.1000000000000005,243.0,289.0,372.0,0.16,801.0,222.0,1.6,37.07352840706834 +2003-12-21 12:30:00-07:00,2003,12,21,12,30,10.7,-4.2,217.0,416.0,402.0,0.16,800.0,244.0,1.5,34.883132536053914 +2003-12-21 13:30:00-07:00,2003,12,21,13,30,10.4,-4.6000000000000005,240.0,66.0,266.0,0.16,800.0,276.0,1.4,34.532448999978214 +2003-12-21 14:30:00-07:00,2003,12,21,14,30,8.5,-4.4,181.0,50.0,196.0,0.16,800.0,316.0,1.2000000000000002,39.83008334063814 +2003-12-21 15:30:00-07:00,2003,12,21,15,30,5.1000000000000005,-4.1000000000000005,92.0,158.0,119.0,0.16,801.0,170.0,1.0,51.44409078893749 +2003-12-21 16:30:00-07:00,2003,12,21,16,30,2.2,-5.6000000000000005,10.0,281.0,15.0,0.16,801.0,179.0,0.9,56.338368351444366 +2003-12-21 17:30:00-07:00,2003,12,21,17,30,0.9,-6.0,0.0,0.0,0.0,0.16,802.0,339.0,0.7000000000000001,59.98033233798564 +2003-12-21 18:30:00-07:00,2003,12,21,18,30,0.1,-6.0,0.0,0.0,0.0,0.16,802.0,310.0,0.7000000000000001,63.54790557586437 +2003-12-21 19:30:00-07:00,2003,12,21,19,30,-0.4,-6.0,0.0,0.0,0.0,0.16,802.0,297.0,0.8,65.89735741503324 +2003-12-21 20:30:00-07:00,2003,12,21,20,30,-0.8,-6.0,0.0,0.0,0.0,0.16,802.0,301.0,0.8,67.84662800107102 +2003-12-21 21:30:00-07:00,2003,12,21,21,30,-1.1,-6.0,0.0,0.0,0.0,0.16,802.0,315.0,0.8,69.35071482827423 +2003-12-21 22:30:00-07:00,2003,12,21,22,30,-1.3,-5.800000000000001,0.0,0.0,0.0,0.16,802.0,336.0,1.0,71.45436560784863 +2003-12-21 23:30:00-07:00,2003,12,21,23,30,-1.4,-5.4,0.0,0.0,0.0,0.16,803.0,176.0,1.5,74.20148360473743 +2003-12-22 00:30:00-07:00,2003,12,22,0,30,-1.4,-4.7,0.0,0.0,0.0,0.16,804.0,6.0,2.3000000000000003,78.23559272895993 +2003-12-22 01:30:00-07:00,2003,12,22,1,30,-1.7000000000000002,-4.0,0.0,0.0,0.0,0.16,805.0,7.0,2.8000000000000003,84.3007737136478 +2003-12-22 02:30:00-07:00,2003,12,22,2,30,-2.1,-3.8,0.0,0.0,0.0,0.16,805.0,8.0,3.1,88.13222678986463 +2003-12-22 03:30:00-07:00,2003,12,22,3,30,-2.6,-4.2,0.0,0.0,0.0,0.16,805.0,8.0,3.3000000000000003,88.74994004485261 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12:30:00-07:00,2003,12,22,12,30,3.1,-9.0,216.0,12.0,221.0,0.16,808.0,9.0,4.3,40.667290598597994 +2003-12-22 13:30:00-07:00,2003,12,22,13,30,3.3000000000000003,-9.3,244.0,61.0,268.0,0.16,808.0,12.0,3.9,39.16578908815541 +2003-12-22 14:30:00-07:00,2003,12,22,14,30,2.6,-9.7,40.0,900.0,314.0,0.16,808.0,15.0,2.9000000000000004,39.88264225685402 +2003-12-22 15:30:00-07:00,2003,12,22,15,30,0.3,-9.6,30.0,758.0,162.0,0.16,808.0,28.0,1.5,47.40035956862931 +2003-12-22 16:30:00-07:00,2003,12,22,16,30,-2.0,-10.2,10.0,347.0,17.0,0.16,808.0,173.0,0.5,53.48037450734252 +2003-12-22 17:30:00-07:00,2003,12,22,17,30,-3.3000000000000003,-10.7,0.0,0.0,0.0,0.16,809.0,282.0,0.5,56.60453732763894 +2003-12-22 18:30:00-07:00,2003,12,22,18,30,-4.2,-10.7,0.0,0.0,0.0,0.16,809.0,260.0,1.1,60.546889305260436 +2003-12-22 19:30:00-07:00,2003,12,22,19,30,-4.2,-10.9,0.0,0.0,0.0,0.16,809.0,268.0,1.6,59.59275365540966 +2003-12-22 20:30:00-07:00,2003,12,22,20,30,-4.0,-11.1,0.0,0.0,0.0,0.16,809.0,272.0,2.2,57.778496665101386 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05:30:00-07:00,2003,12,23,5,30,-3.1,-15.7,0.0,0.0,0.0,0.16,808.0,268.0,3.6,37.17768248315743 +2003-12-23 06:30:00-07:00,2003,12,23,6,30,-2.9000000000000004,-16.2,0.0,0.0,0.0,0.16,808.0,268.0,3.5,35.13887963382712 +2003-12-23 07:30:00-07:00,2003,12,23,7,30,-1.2000000000000002,-16.5,11.0,343.0,18.0,0.16,808.0,267.0,3.7,30.232303602663375 +2003-12-23 08:30:00-07:00,2003,12,23,8,30,2.2,-15.9,28.0,768.0,165.0,0.16,808.0,264.0,3.9,24.86084919230439 +2003-12-23 09:30:00-07:00,2003,12,23,9,30,5.6000000000000005,-14.7,37.0,921.0,319.0,0.16,808.0,264.0,3.3000000000000003,21.618600408815414 +2003-12-23 10:30:00-07:00,2003,12,23,10,30,8.4,-13.0,41.0,983.0,433.0,0.16,807.0,283.0,2.4000000000000004,20.498245392157965 +2003-12-23 11:30:00-07:00,2003,12,23,11,30,10.2,-12.5,43.0,1007.0,492.0,0.16,806.0,307.0,1.9,18.90891908942235 +2003-12-23 12:30:00-07:00,2003,12,23,12,30,10.7,-12.0,43.0,1008.0,492.0,0.16,806.0,320.0,1.7000000000000002,19.0394477718985 +2003-12-23 13:30:00-07:00,2003,12,23,13,30,10.2,-11.2,42.0,981.0,433.0,0.16,805.0,328.0,1.3,20.985279395117157 +2003-12-23 14:30:00-07:00,2003,12,23,14,30,8.0,-7.300000000000001,97.0,685.0,306.0,0.16,805.0,342.0,0.7000000000000001,33.04021106371717 +2003-12-23 15:30:00-07:00,2003,12,23,15,30,4.5,-7.300000000000001,96.0,10.0,98.0,0.16,805.0,353.0,0.4,42.05532668782013 +2003-12-23 16:30:00-07:00,2003,12,23,16,30,1.6,-10.8,7.0,0.0,7.0,0.16,805.0,319.0,0.4,39.26260417088648 +2003-12-23 17:30:00-07:00,2003,12,23,17,30,0.3,-11.7,0.0,0.0,0.0,0.16,806.0,263.0,0.6000000000000001,40.12736482126551 +2003-12-23 18:30:00-07:00,2003,12,23,18,30,-0.1,-12.0,0.0,0.0,0.0,0.16,806.0,245.0,1.3,40.326110543508314 +2003-12-23 19:30:00-07:00,2003,12,23,19,30,-0.6000000000000001,-12.4,0.0,0.0,0.0,0.16,806.0,252.0,1.9,40.496217738929374 +2003-12-23 20:30:00-07:00,2003,12,23,20,30,-1.3,-13.0,0.0,0.0,0.0,0.16,805.0,256.0,2.1,40.60654817194733 +2003-12-23 21:30:00-07:00,2003,12,23,21,30,-1.5,-13.9,0.0,0.0,0.0,0.16,805.0,255.0,1.9,38.300514579473926 +2003-12-23 22:30:00-07:00,2003,12,23,22,30,-1.4,-14.8,0.0,0.0,0.0,0.16,805.0,247.0,1.7000000000000002,35.31857332244107 +2003-12-23 23:30:00-07:00,2003,12,23,23,30,-0.9,-15.7,0.0,0.0,0.0,0.16,804.0,232.0,1.5,31.610997817293104 +2003-12-24 00:30:00-07:00,2003,12,24,0,30,-0.5,-16.3,0.0,0.0,0.0,0.16,804.0,217.0,1.4,29.208783505541824 +2003-12-24 01:30:00-07:00,2003,12,24,1,30,-0.3,-16.7,0.0,0.0,0.0,0.16,803.0,214.0,1.4,27.8415864970991 +2003-12-24 02:30:00-07:00,2003,12,24,2,30,-0.7000000000000001,-16.900000000000002,0.0,0.0,0.0,0.16,803.0,222.0,1.3,28.188676101274474 +2003-12-24 03:30:00-07:00,2003,12,24,3,30,-1.3,-17.3,0.0,0.0,0.0,0.16,802.0,234.0,1.2000000000000002,28.480723355294028 +2003-12-24 04:30:00-07:00,2003,12,24,4,30,-1.8,-18.2,0.0,0.0,0.0,0.16,802.0,243.0,1.3,27.385044199965435 +2003-12-24 05:30:00-07:00,2003,12,24,5,30,-2.1,-19.200000000000003,0.0,0.0,0.0,0.16,802.0,250.0,1.5,25.71290820022794 +2003-12-24 06:30:00-07:00,2003,12,24,6,30,-2.1,-20.0,0.0,0.0,0.0,0.16,802.0,256.0,1.7000000000000002,24.007187536735778 +2003-12-24 07:30:00-07:00,2003,12,24,7,30,-0.4,-19.8,7.0,0.0,7.0,0.16,802.0,262.0,2.0,21.563404905573698 +2003-12-24 08:30:00-07:00,2003,12,24,8,30,3.3000000000000003,-16.6,34.0,0.0,34.0,0.16,802.0,267.0,2.3000000000000003,21.69321334188623 +2003-12-24 09:30:00-07:00,2003,12,24,9,30,6.5,-15.1,161.0,208.0,225.0,0.16,801.0,270.0,2.4000000000000004,19.65827673914816 +2003-12-24 10:30:00-07:00,2003,12,24,10,30,8.4,-14.7,154.0,512.0,358.0,0.16,801.0,269.0,1.9,17.844673679209503 +2003-12-24 11:30:00-07:00,2003,12,24,11,30,9.6,-13.1,212.0,396.0,389.0,0.16,800.0,274.0,1.4,18.75240236002402 +2003-12-24 12:30:00-07:00,2003,12,24,12,30,10.4,-12.5,185.0,469.0,394.0,0.16,799.0,297.0,1.3,18.65819228165599 +2003-12-24 13:30:00-07:00,2003,12,24,13,30,9.9,-11.6,252.0,118.0,299.0,0.16,799.0,332.0,1.1,20.737639304434325 +2003-12-24 14:30:00-07:00,2003,12,24,14,30,7.6,-7.7,155.0,88.0,182.0,0.16,799.0,175.0,0.8,32.91974587613655 +2003-12-24 15:30:00-07:00,2003,12,24,15,30,4.2,-7.5,94.0,49.0,103.0,0.16,799.0,179.0,0.8,42.29015598139917 +2003-12-24 16:30:00-07:00,2003,12,24,16,30,2.2,-10.3,8.0,0.0,8.0,0.16,799.0,342.0,0.7000000000000001,39.13661502895113 +2003-12-24 17:30:00-07:00,2003,12,24,17,30,1.7000000000000002,-10.4,0.0,0.0,0.0,0.16,799.0,311.0,0.8,40.238799489076236 +2003-12-24 18:30:00-07:00,2003,12,24,18,30,1.6,-10.2,0.0,0.0,0.0,0.16,799.0,284.0,1.3,41.17379655136414 +2003-12-24 19:30:00-07:00,2003,12,24,19,30,1.2000000000000002,-9.9,0.0,0.0,0.0,0.16,799.0,271.0,1.7000000000000002,43.386630461306616 +2003-12-24 20:30:00-07:00,2003,12,24,20,30,0.6000000000000001,-9.7,0.0,0.0,0.0,0.16,799.0,266.0,1.9,46.02066285258593 +2003-12-24 21:30:00-07:00,2003,12,24,21,30,-0.1,-10.3,0.0,0.0,0.0,0.16,798.0,263.0,2.0,46.174318861824155 +2003-12-24 22:30:00-07:00,2003,12,24,22,30,-0.7000000000000001,-11.4,0.0,0.0,0.0,0.16,798.0,263.0,2.2,44.19793469182597 +2003-12-24 23:30:00-07:00,2003,12,24,23,30,-1.0,-12.9,0.0,0.0,0.0,0.16,798.0,262.0,2.5,40.04714836650311 +2003-12-25 00:30:00-07:00,2003,12,25,0,30,-1.0,-13.9,0.0,0.0,0.0,0.16,798.0,259.0,2.6,36.92275225491804 +2003-12-25 01:30:00-07:00,2003,12,25,1,30,-0.9,-14.1,0.0,0.0,0.0,0.16,797.0,257.0,2.6,36.06013554345944 +2003-12-25 02:30:00-07:00,2003,12,25,2,30,-0.9,-13.8,0.0,0.0,0.0,0.16,797.0,258.0,2.6,36.95399925872279 +2003-12-25 03:30:00-07:00,2003,12,25,3,30,-0.9,-13.2,0.0,0.0,0.0,0.16,797.0,259.0,2.5,38.801306247873484 +2003-12-25 04:30:00-07:00,2003,12,25,4,30,-0.9,-12.5,0.0,0.0,0.0,0.16,797.0,259.0,2.6,41.06035439349524 +2003-12-25 05:30:00-07:00,2003,12,25,5,30,-0.7000000000000001,-11.6,0.0,0.0,0.0,0.16,797.0,257.0,2.5,43.49725195115308 +2003-12-25 06:30:00-07:00,2003,12,25,6,30,0.2,-10.6,0.0,0.0,0.0,0.16,797.0,252.0,2.2,44.120411366248014 +2003-12-25 07:30:00-07:00,2003,12,25,7,30,1.8,-9.7,4.0,0.0,4.0,0.16,797.0,250.0,2.2,42.221107556459124 +2003-12-25 08:30:00-07:00,2003,12,25,8,30,3.2,-7.9,71.0,7.0,72.0,0.16,797.0,252.0,2.3000000000000003,43.990694382478225 +2003-12-25 09:30:00-07:00,2003,12,25,9,30,4.4,-5.800000000000001,120.0,433.0,252.0,0.16,797.0,250.0,2.2,47.50657430201234 +2003-12-25 10:30:00-07:00,2003,12,25,10,30,5.7,-5.0,157.0,19.0,164.0,0.16,797.0,246.0,2.0,46.112022774939845 +2003-12-25 11:30:00-07:00,2003,12,25,11,30,7.4,-3.9,123.0,673.0,423.0,0.16,796.0,230.0,1.7000000000000002,44.56507742743868 +2003-12-25 12:30:00-07:00,2003,12,25,12,30,8.8,-4.4,265.0,187.0,349.0,0.16,796.0,204.0,1.3,39.03023069973065 +2003-12-25 13:30:00-07:00,2003,12,25,13,30,9.0,-4.6000000000000005,58.0,0.0,58.0,0.16,796.0,185.0,0.9,37.93154744186688 +2003-12-25 14:30:00-07:00,2003,12,25,14,30,7.7,-3.9,175.0,173.0,228.0,0.16,797.0,151.0,0.6000000000000001,43.66234455452662 +2003-12-25 15:30:00-07:00,2003,12,25,15,30,4.9,-4.1000000000000005,29.0,744.0,162.0,0.16,797.0,137.0,0.6000000000000001,52.165457789589446 +2003-12-25 16:30:00-07:00,2003,12,25,16,30,2.3000000000000003,-6.0,11.0,336.0,18.0,0.16,798.0,161.0,0.8,54.261397635700696 +2003-12-25 17:30:00-07:00,2003,12,25,17,30,0.8,-6.7,0.0,0.0,0.0,0.16,798.0,182.0,1.1,57.26527712648815 +2003-12-25 18:30:00-07:00,2003,12,25,18,30,-0.4,-6.9,0.0,0.0,0.0,0.16,798.0,199.0,1.3,61.511483612008945 +2003-12-25 19:30:00-07:00,2003,12,25,19,30,-1.0,-6.7,0.0,0.0,0.0,0.16,798.0,208.0,1.5,65.25729245671495 +2003-12-25 20:30:00-07:00,2003,12,25,20,30,-1.1,-6.5,0.0,0.0,0.0,0.16,798.0,210.0,1.7000000000000002,66.75155360285038 +2003-12-25 21:30:00-07:00,2003,12,25,21,30,-0.8,-6.2,0.0,0.0,0.0,0.16,798.0,210.0,1.8,66.8191347951652 +2003-12-25 22:30:00-07:00,2003,12,25,22,30,-0.8,-5.9,0.0,0.0,0.0,0.16,798.0,210.0,1.9,68.36562459293354 +2003-12-25 23:30:00-07:00,2003,12,25,23,30,-1.0,-5.800000000000001,0.0,0.0,0.0,0.16,797.0,211.0,2.0,69.90211886325388 +2003-12-26 00:30:00-07:00,2003,12,26,0,30,-1.3,-5.800000000000001,0.0,0.0,0.0,0.16,797.0,212.0,2.0,71.45436560784863 +2003-12-26 01:30:00-07:00,2003,12,26,1,30,-1.5,-5.800000000000001,0.0,0.0,0.0,0.16,797.0,214.0,2.0,72.5104971637526 +2003-12-26 02:30:00-07:00,2003,12,26,2,30,-1.4,-5.9,0.0,0.0,0.0,0.16,796.0,219.0,2.2,71.43429762161226 +2003-12-26 03:30:00-07:00,2003,12,26,3,30,-1.0,-5.9,0.0,0.0,0.0,0.16,796.0,225.0,2.5,69.37190365977231 +2003-12-26 04:30:00-07:00,2003,12,26,4,30,-0.6000000000000001,-5.9,0.0,0.0,0.0,0.16,795.0,229.0,2.6,67.37556644306792 +2003-12-26 05:30:00-07:00,2003,12,26,5,30,-0.9,-6.1000000000000005,0.0,0.0,0.0,0.16,795.0,231.0,2.6,67.82464968543766 +2003-12-26 06:30:00-07:00,2003,12,26,6,30,-1.4,-6.4,0.0,0.0,0.0,0.16,795.0,230.0,2.6,68.75926383106284 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15:30:00-07:00,2003,12,26,15,30,4.7,-9.1,78.0,379.0,147.0,0.16,790.0,237.0,5.5,36.0607493033369 +2003-12-26 16:30:00-07:00,2003,12,26,16,30,1.5,-12.6,11.0,1.0,11.0,0.16,790.0,244.0,5.2,34.23878822540942 +2003-12-26 17:30:00-07:00,2003,12,26,17,30,-0.5,-13.6,0.0,0.0,0.0,0.16,791.0,245.0,4.6000000000000005,36.480826663440894 +2003-12-26 18:30:00-07:00,2003,12,26,18,30,-1.5,-13.2,0.0,0.0,0.0,0.16,791.0,244.0,4.3,40.54442191316921 +2003-12-26 19:30:00-07:00,2003,12,26,19,30,-1.9,-12.7,0.0,0.0,0.0,0.16,790.0,244.0,4.4,43.47872785480508 +2003-12-26 20:30:00-07:00,2003,12,26,20,30,-2.0,-12.3,0.0,0.0,0.0,0.16,790.0,247.0,4.6000000000000005,45.23540001212314 +2003-12-26 21:30:00-07:00,2003,12,26,21,30,-2.3000000000000003,-12.3,0.0,0.0,0.0,0.16,790.0,252.0,4.6000000000000005,46.248347576263825 +2003-12-26 22:30:00-07:00,2003,12,26,22,30,-2.6,-12.5,0.0,0.0,0.0,0.16,791.0,257.0,4.5,46.53112803031324 +2003-12-26 23:30:00-07:00,2003,12,26,23,30,-3.0,-12.8,0.0,0.0,0.0,0.16,791.0,264.0,4.3,46.786694516772535 +2003-12-27 00:30:00-07:00,2003,12,27,0,30,-3.5,-13.4,0.0,0.0,0.0,0.16,792.0,270.0,4.0,46.258139583892735 +2003-12-27 01:30:00-07:00,2003,12,27,1,30,-4.0,-13.8,0.0,0.0,0.0,0.16,793.0,274.0,3.6,46.48334015760766 +2003-12-27 02:30:00-07:00,2003,12,27,2,30,-4.5,-14.2,0.0,0.0,0.0,0.16,793.0,273.0,3.3000000000000003,46.71164667348493 +2003-12-27 03:30:00-07:00,2003,12,27,3,30,-4.9,-14.5,0.0,0.0,0.0,0.16,792.0,271.0,3.2,46.97355630278009 +2003-12-27 04:30:00-07:00,2003,12,27,4,30,-5.300000000000001,-14.8,0.0,0.0,0.0,0.16,793.0,269.0,3.0,47.2386777391158 +2003-12-27 05:30:00-07:00,2003,12,27,5,30,-5.7,-15.1,0.0,0.0,0.0,0.16,793.0,268.0,2.7,47.5070609974844 +2003-12-27 06:30:00-07:00,2003,12,27,6,30,-6.0,-15.4,0.0,0.0,0.0,0.16,793.0,265.0,2.6,47.4157403497642 +2003-12-27 07:30:00-07:00,2003,12,27,7,30,-5.0,-15.5,11.0,329.0,17.0,0.16,793.0,264.0,3.0,43.58739468808424 +2003-12-27 08:30:00-07:00,2003,12,27,8,30,-3.3000000000000003,-15.8,31.0,789.0,168.0,0.16,793.0,272.0,4.0,37.423928249401236 +2003-12-27 09:30:00-07:00,2003,12,27,9,30,-1.9,-16.900000000000002,40.0,933.0,324.0,0.16,793.0,285.0,4.6000000000000005,30.780521521217988 +2003-12-27 10:30:00-07:00,2003,12,27,10,30,-1.0,-17.7,46.0,994.0,441.0,0.16,793.0,293.0,4.800000000000001,26.939152796530607 +2003-12-27 11:30:00-07:00,2003,12,27,11,30,-0.6000000000000001,-18.4,48.0,1020.0,504.0,0.16,793.0,297.0,4.9,24.6591015445849 +2003-12-27 12:30:00-07:00,2003,12,27,12,30,-0.7000000000000001,-18.8,49.0,1019.0,506.0,0.16,793.0,300.0,5.0,24.008842454572537 +2003-12-27 13:30:00-07:00,2003,12,27,13,30,-1.2000000000000002,-19.1,48.0,993.0,447.0,0.16,793.0,302.0,5.0,24.273277729572808 +2003-12-27 14:30:00-07:00,2003,12,27,14,30,-2.0,-19.3,43.0,932.0,333.0,0.16,794.0,301.0,4.9,25.306573791799565 +2003-12-27 15:30:00-07:00,2003,12,27,15,30,-3.2,-19.200000000000003,34.0,790.0,178.0,0.16,795.0,299.0,4.4,27.897381140741626 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01:30:00-07:00,2003,12,28,1,30,-8.1,-20.700000000000003,0.0,0.0,0.0,0.16,797.0,287.0,6.300000000000001,35.58507322477206 +2003-12-28 02:30:00-07:00,2003,12,28,2,30,-7.9,-20.6,0.0,0.0,0.0,0.16,797.0,289.0,6.1000000000000005,35.342263376193145 +2003-12-28 03:30:00-07:00,2003,12,28,3,30,-7.6,-20.4,0.0,0.0,0.0,0.16,798.0,291.0,5.800000000000001,35.13388437438322 +2003-12-28 04:30:00-07:00,2003,12,28,4,30,-7.300000000000001,-20.200000000000003,0.0,0.0,0.0,0.16,798.0,293.0,5.5,34.927712920798335 +2003-12-28 05:30:00-07:00,2003,12,28,5,30,-7.1000000000000005,-19.9,0.0,0.0,0.0,0.16,799.0,294.0,5.2,35.29430318250085 +2003-12-28 06:30:00-07:00,2003,12,28,6,30,-7.0,-19.6,0.0,0.0,0.0,0.16,800.0,292.0,4.800000000000001,35.93832903707971 +2003-12-28 07:30:00-07:00,2003,12,28,7,30,-6.7,-19.4,0.0,0.0,0.0,0.16,801.0,290.0,4.800000000000001,35.727226532397054 +2003-12-28 08:30:00-07:00,2003,12,28,8,30,-6.0,-19.3,79.0,16.0,82.0,0.16,801.0,289.0,5.2,34.15640569923603 +2003-12-28 09:30:00-07:00,2003,12,28,9,30,-4.9,-19.4,122.0,454.0,260.0,0.16,801.0,291.0,5.5,31.153183543359866 +2003-12-28 10:30:00-07:00,2003,12,28,10,30,-3.8,-19.3,153.0,517.0,358.0,0.16,801.0,291.0,5.5,28.926898699337478 +2003-12-28 11:30:00-07:00,2003,12,28,11,30,-3.0,-19.4,246.0,254.0,360.0,0.16,800.0,291.0,5.4,27.018934738296565 +2003-12-28 12:30:00-07:00,2003,12,28,12,30,-2.7,-19.6,257.0,188.0,341.0,0.16,800.0,289.0,5.2,25.97457833469777 +2003-12-28 13:30:00-07:00,2003,12,28,13,30,-2.9000000000000004,-19.8,199.0,347.0,338.0,0.16,800.0,290.0,5.1000000000000005,25.914090629005724 +2003-12-28 14:30:00-07:00,2003,12,28,14,30,-3.5,-20.0,162.0,259.0,243.0,0.16,801.0,293.0,4.9,26.635929098939304 +2003-12-28 15:30:00-07:00,2003,12,28,15,30,-4.6000000000000005,-20.0,36.0,781.0,180.0,0.16,801.0,294.0,4.4,28.92652025828341 +2003-12-28 16:30:00-07:00,2003,12,28,16,30,-5.9,-19.4,13.0,370.0,24.0,0.16,802.0,292.0,3.8,33.60816047266581 +2003-12-28 17:30:00-07:00,2003,12,28,17,30,-6.800000000000001,-18.8,0.0,0.0,0.0,0.16,802.0,286.0,3.5,37.89561403456726 +2003-12-28 18:30:00-07:00,2003,12,28,18,30,-7.300000000000001,-18.4,0.0,0.0,0.0,0.16,802.0,281.0,3.4000000000000004,40.74399198638092 +2003-12-28 19:30:00-07:00,2003,12,28,19,30,-7.7,-18.1,0.0,0.0,0.0,0.16,802.0,275.0,3.3000000000000003,43.10436801837882 +2003-12-28 20:30:00-07:00,2003,12,28,20,30,-8.1,-18.0,0.0,0.0,0.0,0.16,802.0,270.0,3.4000000000000004,44.83889497174535 +2003-12-28 21:30:00-07:00,2003,12,28,21,30,-8.4,-18.1,0.0,0.0,0.0,0.16,802.0,266.0,3.7,45.50968907669174 +2003-12-28 22:30:00-07:00,2003,12,28,22,30,-8.700000000000001,-18.4,0.0,0.0,0.0,0.16,802.0,264.0,4.0,45.41601236035329 +2003-12-28 23:30:00-07:00,2003,12,28,23,30,-8.9,-18.7,0.0,0.0,0.0,0.16,801.0,263.0,4.2,44.969258205559676 +2003-12-29 00:30:00-07:00,2003,12,29,0,30,-9.1,-19.0,0.0,0.0,0.0,0.16,801.0,262.0,4.3,44.52504677276849 +2003-12-29 01:30:00-07:00,2003,12,29,1,30,-9.1,-19.1,0.0,0.0,0.0,0.16,801.0,262.0,4.3,44.1465158422277 +2003-12-29 02:30:00-07:00,2003,12,29,2,30,-9.1,-19.0,0.0,0.0,0.0,0.16,801.0,260.0,4.0,44.52504677276849 +2003-12-29 03:30:00-07:00,2003,12,29,3,30,-9.2,-18.8,0.0,0.0,0.0,0.16,800.0,258.0,3.8,45.646876076923995 +2003-12-29 04:30:00-07:00,2003,12,29,4,30,-9.3,-18.7,0.0,0.0,0.0,0.16,800.0,256.0,3.6,46.399436464747346 +2003-12-29 05:30:00-07:00,2003,12,29,5,30,-9.4,-18.7,0.0,0.0,0.0,0.16,799.0,253.0,3.4000000000000004,46.76481558370939 +2003-12-29 06:30:00-07:00,2003,12,29,6,30,-9.4,-18.6,0.0,0.0,0.0,0.16,799.0,250.0,3.3000000000000003,47.16436252821641 +2003-12-29 07:30:00-07:00,2003,12,29,7,30,-8.1,-18.5,0.0,0.0,0.0,0.16,799.0,245.0,3.6,42.97806438742266 +2003-12-29 08:30:00-07:00,2003,12,29,8,30,-5.5,-18.4,32.0,1.0,33.0,0.16,799.0,239.0,3.9,35.50296405579081 +2003-12-29 09:30:00-07:00,2003,12,29,9,30,-2.5,-17.6,38.0,905.0,313.0,0.16,799.0,239.0,3.8,30.337237185669352 +2003-12-29 10:30:00-07:00,2003,12,29,10,30,-0.3,-17.1,137.0,668.0,403.0,0.16,798.0,244.0,3.6,26.924202489359665 +2003-12-29 11:30:00-07:00,2003,12,29,11,30,0.9,-16.400000000000002,182.0,2.0,183.0,0.16,797.0,250.0,3.5,26.17417710843857 +2003-12-29 12:30:00-07:00,2003,12,29,12,30,1.3,-15.3,79.0,0.0,79.0,0.16,796.0,261.0,3.3000000000000003,27.86347341145945 +2003-12-29 13:30:00-07:00,2003,12,29,13,30,1.2000000000000002,-13.7,206.0,171.0,275.0,0.16,796.0,268.0,3.1,31.999389736465606 +2003-12-29 14:30:00-07:00,2003,12,29,14,30,0.6000000000000001,-12.7,190.0,118.0,227.0,0.16,796.0,270.0,2.8000000000000003,36.2339306600194 +2003-12-29 15:30:00-07:00,2003,12,29,15,30,-0.6000000000000001,-12.7,85.0,336.0,148.0,0.16,796.0,268.0,2.3000000000000003,39.52839338546702 +2003-12-29 16:30:00-07:00,2003,12,29,16,30,-1.8,-13.3,13.0,1.0,13.0,0.16,796.0,254.0,2.1,41.11364326602718 +2003-12-29 17:30:00-07:00,2003,12,29,17,30,-2.6,-13.4,0.0,0.0,0.0,0.16,796.0,237.0,2.3000000000000003,43.263126434485564 +2003-12-29 18:30:00-07:00,2003,12,29,18,30,-3.2,-13.2,0.0,0.0,0.0,0.16,796.0,229.0,2.6,45.97515127651871 +2003-12-29 19:30:00-07:00,2003,12,29,19,30,-3.7,-13.0,0.0,0.0,0.0,0.16,796.0,231.0,2.7,48.50252178741596 +2003-12-29 20:30:00-07:00,2003,12,29,20,30,-4.0,-12.9,0.0,0.0,0.0,0.16,796.0,238.0,2.8000000000000003,50.007282540399316 +2003-12-29 21:30:00-07:00,2003,12,29,21,30,-4.1000000000000005,-12.8,0.0,0.0,0.0,0.16,795.0,246.0,3.0,50.79269525943754 +2003-12-29 22:30:00-07:00,2003,12,29,22,30,-3.9,-12.6,0.0,0.0,0.0,0.16,795.0,252.0,3.2,50.851343211527436 +2003-12-29 23:30:00-07:00,2003,12,29,23,30,-3.7,-12.3,0.0,0.0,0.0,0.16,795.0,253.0,3.3000000000000003,51.32134337084756 +2003-12-30 00:30:00-07:00,2003,12,30,0,30,-3.5,-11.8,0.0,0.0,0.0,0.16,795.0,253.0,3.3000000000000003,52.630972716398446 +2003-12-30 01:30:00-07:00,2003,12,30,1,30,-3.2,-11.5,0.0,0.0,0.0,0.16,795.0,252.0,3.4000000000000004,52.71701429991781 +2003-12-30 02:30:00-07:00,2003,12,30,2,30,-2.9000000000000004,-11.3,0.0,0.0,0.0,0.16,795.0,252.0,3.5,52.38354835572934 +2003-12-30 03:30:00-07:00,2003,12,30,3,30,-2.7,-11.3,0.0,0.0,0.0,0.16,794.0,252.0,3.6,51.61171789202686 +2003-12-30 04:30:00-07:00,2003,12,30,4,30,-2.4000000000000004,-11.2,0.0,0.0,0.0,0.16,794.0,253.0,3.8,50.88164047874361 +2003-12-30 05:30:00-07:00,2003,12,30,5,30,-2.2,-11.0,0.0,0.0,0.0,0.16,795.0,255.0,3.9,50.939830489616284 +2003-12-30 06:30:00-07:00,2003,12,30,6,30,-1.9,-10.8,0.0,0.0,0.0,0.16,795.0,255.0,3.9,50.623482371632164 +2003-12-30 07:30:00-07:00,2003,12,30,7,30,-0.7000000000000001,-10.4,0.0,0.0,0.0,0.16,796.0,255.0,3.8,47.8546077953086 +2003-12-30 08:30:00-07:00,2003,12,30,8,30,1.6,-9.5,62.0,514.0,151.0,0.16,796.0,250.0,3.8,43.50798557502386 +2003-12-30 09:30:00-07:00,2003,12,30,9,30,4.2,-8.1,39.0,886.0,308.0,0.16,796.0,248.0,4.0,40.37091516254264 +2003-12-30 10:30:00-07:00,2003,12,30,10,30,6.4,-7.5,44.0,949.0,423.0,0.16,796.0,253.0,4.4,36.297425607160484 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19:30:00-07:00,2003,12,30,19,30,-2.0,-8.4,0.0,0.0,0.0,0.16,800.0,128.0,0.8,61.5866352411354 +2003-12-30 20:30:00-07:00,2003,12,30,20,30,-2.4000000000000004,-8.700000000000001,0.0,0.0,0.0,0.16,801.0,146.0,1.0,61.967727266026834 +2003-12-30 21:30:00-07:00,2003,12,30,21,30,-2.6,-8.9,0.0,0.0,0.0,0.16,801.0,167.0,1.3,61.91772800708523 +2003-12-30 22:30:00-07:00,2003,12,30,22,30,-2.6,-9.1,0.0,0.0,0.0,0.16,801.0,182.0,1.6,60.95682428063628 +2003-12-30 23:30:00-07:00,2003,12,30,23,30,-2.7,-9.4,0.0,0.0,0.0,0.16,801.0,192.0,1.7000000000000002,59.98335970874126 +2003-12-31 00:30:00-07:00,2003,12,31,0,30,-2.7,-9.6,0.0,0.0,0.0,0.16,801.0,201.0,1.9,59.04851653782011 +2003-12-31 01:30:00-07:00,2003,12,31,1,30,-2.8000000000000003,-9.8,0.0,0.0,0.0,0.16,801.0,212.0,1.9,58.559512681356104 +2003-12-31 02:30:00-07:00,2003,12,31,2,30,-2.9000000000000004,-9.9,0.0,0.0,0.0,0.16,801.0,223.0,2.0,58.533022533444765 +2003-12-31 03:30:00-07:00,2003,12,31,3,30,-2.9000000000000004,-9.9,0.0,0.0,0.0,0.16,801.0,229.0,2.1,58.533022533444765 +2003-12-31 04:30:00-07:00,2003,12,31,4,30,-2.8000000000000003,-10.0,0.0,0.0,0.0,0.16,801.0,231.0,2.3000000000000003,57.64375048632164 +2003-12-31 05:30:00-07:00,2003,12,31,5,30,-2.8000000000000003,-10.1,0.0,0.0,0.0,0.16,802.0,233.0,2.5,57.19067330939907 +2003-12-31 06:30:00-07:00,2003,12,31,6,30,-2.8000000000000003,-10.5,0.0,0.0,0.0,0.16,802.0,235.0,2.7,55.40993676193729 +2003-12-31 07:30:00-07:00,2003,12,31,7,30,-1.9,-11.2,0.0,0.0,0.0,0.16,802.0,236.0,2.9000000000000004,49.03785350983124 +2003-12-31 08:30:00-07:00,2003,12,31,8,30,0.5,-11.2,85.0,4.0,86.0,0.16,802.0,230.0,3.0,41.16283926198923 +2003-12-31 09:30:00-07:00,2003,12,31,9,30,3.4000000000000004,-11.1,42.0,909.0,319.0,0.16,802.0,219.0,2.9000000000000004,33.734610280412255 +2003-12-31 10:30:00-07:00,2003,12,31,10,30,5.9,-11.3,47.0,974.0,436.0,0.16,801.0,213.0,2.8000000000000003,27.883269544977264 +2003-12-31 11:30:00-07:00,2003,12,31,11,30,7.4,-12.3,160.0,632.0,444.0,0.16,801.0,215.0,2.6,23.216314842280255 +2003-12-31 12:30:00-07:00,2003,12,31,12,30,7.7,-12.8,229.0,426.0,422.0,0.16,800.0,219.0,2.2,21.847252364755004 +2003-12-31 13:30:00-07:00,2003,12,31,13,30,6.9,-11.5,197.0,235.0,293.0,0.16,800.0,221.0,1.8,25.615937291373196 +2003-12-31 14:30:00-07:00,2003,12,31,14,30,5.0,-9.7,165.0,162.0,217.0,0.16,800.0,222.0,1.5,33.690276115988226 +2003-12-31 15:30:00-07:00,2003,12,31,15,30,2.5,-10.0,110.0,26.0,115.0,0.16,799.0,226.0,1.5,39.22878712859162 +2003-12-31 16:30:00-07:00,2003,12,31,16,30,0.6000000000000001,-11.6,17.0,2.0,17.0,0.16,799.0,231.0,1.8,39.58236461081395 +2003-12-31 17:30:00-07:00,2003,12,31,17,30,-15.3,-25.8,0.0,0.0,0.0,0.8,792.0,295.0,3.5,40.28820937808434 +2003-12-31 18:30:00-07:00,2003,12,31,18,30,-15.6,-26.4,0.0,0.0,0.0,0.8,793.0,288.0,3.6,39.10528547560827 +2003-12-31 19:30:00-07:00,2003,12,31,19,30,-15.7,-26.8,0.0,0.0,0.0,0.8,793.0,284.0,3.9,38.014453166642674 +2003-12-31 20:30:00-07:00,2003,12,31,20,30,-15.6,-27.0,0.0,0.0,0.0,0.8,793.0,282.0,4.0,37.01574465438979 +2003-12-31 21:30:00-07:00,2003,12,31,21,30,-15.5,-26.8,0.0,0.0,0.0,0.8,793.0,281.0,4.0,37.390056371063395 +2003-12-31 22:30:00-07:00,2003,12,31,22,30,-15.5,-26.6,0.0,0.0,0.0,0.8,794.0,280.0,3.9,38.08077365650553 +2003-12-31 23:30:00-07:00,2003,12,31,23,30,-15.8,-26.6,0.0,0.0,0.0,0.8,794.0,278.0,3.9,39.03906902164422 diff --git a/tutorials/data/psm4_miami.csv b/tutorials/data/psm4_miami.csv new file mode 100644 index 000000000..f45c82ac7 --- /dev/null +++ b/tutorials/data/psm4_miami.csv @@ -0,0 +1,8761 @@ +,Year,Month,Day,Hour,Minute,temp_air,dew_point,dhi,dni,ghi,albedo,pressure,wind_direction,wind_speed,relative_humidity +2019-01-01 00:30:00-05:00,2019,1,1,0,30,24.1,21.8,0.0,0.0,0.0,0.08,1019.0,128.0,3.6,87.00057525468326 +2019-01-01 01:30:00-05:00,2019,1,1,1,30,24.1,21.700000000000003,0.0,0.0,0.0,0.08,1019.0,126.0,3.5,86.4706679784366 +2019-01-01 02:30:00-05:00,2019,1,1,2,30,24.0,21.5,0.0,0.0,0.0,0.08,1019.0,124.0,3.4000000000000004,85.93379377341454 +2019-01-01 03:30:00-05:00,2019,1,1,3,30,24.0,21.1,0.0,0.0,0.0,0.08,1019.0,122.0,3.4000000000000004,83.85222142401177 +2019-01-01 04:30:00-05:00,2019,1,1,4,30,24.0,20.9,0.0,0.0,0.0,0.08,1019.0,121.0,3.5,82.82811193155545 +2019-01-01 05:30:00-05:00,2019,1,1,5,30,23.9,20.8,0.0,0.0,0.0,0.08,1019.0,123.0,3.8,82.81635007066792 +2019-01-01 06:30:00-05:00,2019,1,1,6,30,24.0,20.8,0.0,0.0,0.0,0.08,1020.0,126.0,4.1000000000000005,82.32018205581834 +2019-01-01 07:30:00-05:00,2019,1,1,7,30,24.5,20.8,28.0,245.0,45.0,0.08,1020.0,129.0,4.5,79.88895986167935 +2019-01-01 08:30:00-05:00,2019,1,1,8,30,25.1,21.0,74.0,341.0,164.0,0.08,1020.0,132.0,5.0,78.03119328830341 +2019-01-01 09:30:00-05:00,2019,1,1,9,30,25.4,20.9,91.0,715.0,400.0,0.08,1021.0,134.0,5.1000000000000005,76.180747279633 +2019-01-01 10:30:00-05:00,2019,1,1,10,30,25.6,20.9,134.0,693.0,521.0,0.08,1020.0,136.0,5.0,75.28106943219711 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19:30:00-05:00,2019,1,1,19,30,24.6,20.6,0.0,0.0,0.0,0.08,1020.0,94.0,3.6,78.44038822062747 +2019-01-01 20:30:00-05:00,2019,1,1,20,30,24.6,20.4,0.0,0.0,0.0,0.08,1020.0,92.0,3.9,77.4787586609194 +2019-01-01 21:30:00-05:00,2019,1,1,21,30,24.5,20.3,0.0,0.0,0.0,0.08,1020.0,93.0,4.1000000000000005,77.46386035325963 +2019-01-01 22:30:00-05:00,2019,1,1,22,30,24.5,20.3,0.0,0.0,0.0,0.08,1020.0,96.0,4.3,77.46386035325963 +2019-01-01 23:30:00-05:00,2019,1,1,23,30,24.4,20.3,0.0,0.0,0.0,0.08,1020.0,99.0,4.5,77.929008814519 +2019-01-02 00:30:00-05:00,2019,1,2,0,30,24.3,20.3,0.0,0.0,0.0,0.08,1019.0,103.0,4.7,78.39730148202109 +2019-01-02 01:30:00-05:00,2019,1,2,1,30,24.1,20.3,0.0,0.0,0.0,0.08,1019.0,107.0,4.9,79.34341361994412 +2019-01-02 02:30:00-05:00,2019,1,2,2,30,23.9,20.3,0.0,0.0,0.0,0.08,1019.0,110.0,4.9,80.30238706259739 +2019-01-02 03:30:00-05:00,2019,1,2,3,30,23.8,20.200000000000003,0.0,0.0,0.0,0.08,1019.0,113.0,4.800000000000001,80.28909195294638 +2019-01-02 04:30:00-05:00,2019,1,2,4,30,23.700000000000003,20.0,0.0,0.0,0.0,0.08,1019.0,115.0,4.6000000000000005,79.7805167416697 +2019-01-02 05:30:00-05:00,2019,1,2,5,30,23.700000000000003,20.0,0.0,0.0,0.0,0.08,1019.0,115.0,4.4,79.7805167416697 +2019-01-02 06:30:00-05:00,2019,1,2,6,30,23.9,20.0,0.0,0.0,0.0,0.08,1020.0,114.0,4.2,78.82635534844951 +2019-01-02 07:30:00-05:00,2019,1,2,7,30,24.5,20.1,27.0,261.0,45.0,0.08,1020.0,113.0,4.2,76.51204998962339 +2019-01-02 08:30:00-05:00,2019,1,2,8,30,25.1,20.4,57.0,656.0,230.0,0.08,1021.0,111.0,4.2,75.2006083160191 +2019-01-02 09:30:00-05:00,2019,1,2,9,30,25.5,20.4,71.0,806.0,419.0,0.08,1021.0,110.0,4.2,73.43228880255037 +2019-01-02 10:30:00-05:00,2019,1,2,10,30,25.8,20.4,80.0,873.0,568.0,0.08,1020.0,110.0,4.1000000000000005,72.13674292629761 +2019-01-02 11:30:00-05:00,2019,1,2,11,30,26.0,20.5,85.0,905.0,662.0,0.08,1019.0,114.0,4.1000000000000005,71.72853712115179 +2019-01-02 12:30:00-05:00,2019,1,2,12,30,26.0,20.5,88.0,910.0,689.0,0.08,1018.0,118.0,4.0,71.72853712115179 +2019-01-02 13:30:00-05:00,2019,1,2,13,30,26.0,20.5,86.0,895.0,648.0,0.08,1018.0,120.0,3.7,71.72853712115179 +2019-01-02 14:30:00-05:00,2019,1,2,14,30,26.0,20.4,89.0,824.0,534.0,0.08,1018.0,117.0,3.4000000000000004,71.28734118137315 +2019-01-02 15:30:00-05:00,2019,1,2,15,30,25.8,20.4,72.0,770.0,384.0,0.08,1018.0,110.0,3.3000000000000003,72.13674292629761 +2019-01-02 16:30:00-05:00,2019,1,2,16,30,25.4,20.5,54.0,596.0,192.0,0.08,1018.0,102.0,3.5,74.3271201935749 +2019-01-02 17:30:00-05:00,2019,1,2,17,30,24.9,20.8,17.0,129.0,21.0,0.08,1019.0,100.0,3.8,78.00206642617893 +2019-01-02 18:30:00-05:00,2019,1,2,18,30,24.700000000000003,21.0,0.0,0.0,0.0,0.08,1019.0,104.0,4.4,79.9159406716968 +2019-01-02 19:30:00-05:00,2019,1,2,19,30,24.6,21.3,0.0,0.0,0.0,0.08,1019.0,109.0,4.9,81.88902089566456 +2019-01-02 20:30:00-05:00,2019,1,2,20,30,24.5,21.6,0.0,0.0,0.0,0.08,1018.0,115.0,5.300000000000001,83.90768765749962 +2019-01-02 21:30:00-05:00,2019,1,2,21,30,24.4,21.9,0.0,0.0,0.0,0.08,1018.0,119.0,5.6000000000000005,85.97292500087283 +2019-01-02 22:30:00-05:00,2019,1,2,22,30,24.3,22.200000000000003,0.0,0.0,0.0,0.08,1017.0,123.0,6.0,88.08573681225786 +2019-01-02 23:30:00-05:00,2019,1,2,23,30,24.3,22.4,0.0,0.0,0.0,0.08,1016.0,128.0,6.300000000000001,89.16414228493397 +2019-01-03 00:30:00-05:00,2019,1,3,0,30,24.200000000000003,22.6,0.0,0.0,0.0,0.08,1016.0,133.0,6.4,90.7968605458183 +2019-01-03 01:30:00-05:00,2019,1,3,1,30,24.200000000000003,22.6,0.0,0.0,0.0,0.08,1016.0,137.0,6.2,90.7968605458183 +2019-01-03 02:30:00-05:00,2019,1,3,2,30,24.200000000000003,22.6,0.0,0.0,0.0,0.08,1016.0,141.0,5.9,90.7968605458183 +2019-01-03 03:30:00-05:00,2019,1,3,3,30,24.200000000000003,22.4,0.0,0.0,0.0,0.08,1016.0,144.0,5.4,89.70035155341863 +2019-01-03 04:30:00-05:00,2019,1,3,4,30,24.200000000000003,22.3,0.0,0.0,0.0,0.08,1017.0,147.0,5.0,89.15646046891602 +2019-01-03 05:30:00-05:00,2019,1,3,5,30,24.200000000000003,22.1,0.0,0.0,0.0,0.08,1017.0,149.0,4.5,88.07733960854357 +2019-01-03 06:30:00-05:00,2019,1,3,6,30,24.3,22.0,0.0,0.0,0.0,0.08,1018.0,151.0,4.3,87.01877615805587 +2019-01-03 07:30:00-05:00,2019,1,3,7,30,24.8,22.1,26.0,253.0,43.0,0.08,1018.0,153.0,4.4,84.97083781729285 +2019-01-03 08:30:00-05:00,2019,1,3,8,30,25.3,22.200000000000003,91.0,330.0,178.0,0.08,1019.0,156.0,4.6000000000000005,82.97996433623776 +2019-01-03 09:30:00-05:00,2019,1,3,9,30,25.6,22.200000000000003,189.0,138.0,249.0,0.08,1019.0,159.0,4.6000000000000005,81.51380892113755 +2019-01-03 10:30:00-05:00,2019,1,3,10,30,25.9,22.200000000000003,268.0,283.0,426.0,0.08,1018.0,162.0,4.4,80.07674337450413 +2019-01-03 11:30:00-05:00,2019,1,3,11,30,26.1,22.200000000000003,281.0,327.0,490.0,0.08,1017.0,166.0,4.2,79.13454571233362 +2019-01-03 12:30:00-05:00,2019,1,3,12,30,26.200000000000003,22.200000000000003,287.0,329.0,505.0,0.08,1016.0,170.0,4.0,78.66813108954312 +2019-01-03 13:30:00-05:00,2019,1,3,13,30,26.200000000000003,22.200000000000003,212.0,212.0,345.0,0.08,1016.0,172.0,3.9,78.66813108954312 +2019-01-03 14:30:00-05:00,2019,1,3,14,30,26.1,22.1,228.0,132.0,300.0,0.08,1015.0,171.0,4.1000000000000005,78.65399775022775 +2019-01-03 15:30:00-05:00,2019,1,3,15,30,25.9,22.200000000000003,184.0,86.0,219.0,0.08,1016.0,169.0,4.4,80.07674337450413 +2019-01-03 16:30:00-05:00,2019,1,3,16,30,25.5,22.4,68.0,468.0,177.0,0.08,1016.0,169.0,4.4,83.00315402939309 +2019-01-03 17:30:00-05:00,2019,1,3,17,30,25.1,22.6,18.0,100.0,22.0,0.08,1016.0,170.0,4.2,86.04102419869523 +2019-01-03 18:30:00-05:00,2019,1,3,18,30,24.9,22.8,0.0,0.0,0.0,0.08,1016.0,171.0,4.0,88.13593866576328 +2019-01-03 19:30:00-05:00,2019,1,3,19,30,24.8,22.9,0.0,0.0,0.0,0.08,1017.0,172.0,3.9,89.20243179762628 +2019-01-03 20:30:00-05:00,2019,1,3,20,30,24.6,22.8,0.0,0.0,0.0,0.08,1016.0,174.0,3.9,89.72957675197985 +2019-01-03 21:30:00-05:00,2019,1,3,21,30,24.5,22.8,0.0,0.0,0.0,0.08,1016.0,174.0,3.9,90.26797351714104 +2019-01-03 22:30:00-05:00,2019,1,3,22,30,24.4,22.6,0.0,0.0,0.0,0.08,1016.0,172.0,4.0,89.71497940989823 +2019-01-03 23:30:00-05:00,2019,1,3,23,30,24.4,22.5,0.0,0.0,0.0,0.08,1016.0,169.0,4.2,89.17181611541235 +2019-01-04 00:30:00-05:00,2019,1,4,0,30,24.4,22.3,0.0,0.0,0.0,0.08,1015.0,167.0,4.4,88.0941253601151 +2019-01-04 01:30:00-05:00,2019,1,4,1,30,24.3,22.1,0.0,0.0,0.0,0.08,1015.0,167.0,4.4,87.55083235384852 +2019-01-04 02:30:00-05:00,2019,1,4,2,30,24.3,21.9,0.0,0.0,0.0,0.08,1015.0,166.0,4.3,86.48955534166726 +2019-01-04 03:30:00-05:00,2019,1,4,3,30,24.3,21.700000000000003,0.0,0.0,0.0,0.08,1015.0,166.0,4.1000000000000005,85.43956854855088 +2019-01-04 04:30:00-05:00,2019,1,4,4,30,24.200000000000003,21.5,0.0,0.0,0.0,0.08,1015.0,166.0,4.1000000000000005,84.90833346681123 +2019-01-04 05:30:00-05:00,2019,1,4,5,30,24.1,21.4,0.0,0.0,0.0,0.08,1015.0,166.0,4.1000000000000005,84.89787939045657 +2019-01-04 06:30:00-05:00,2019,1,4,6,30,24.200000000000003,21.4,0.0,0.0,0.0,0.08,1016.0,166.0,4.3,84.38999944745179 +2019-01-04 07:30:00-05:00,2019,1,4,7,30,24.6,21.5,29.0,7.0,29.0,0.08,1016.0,167.0,4.6000000000000005,82.89843966631602 +2019-01-04 08:30:00-05:00,2019,1,4,8,30,25.1,21.8,121.0,179.0,168.0,0.08,1016.0,169.0,5.0,81.95038626994669 +2019-01-04 09:30:00-05:00,2019,1,4,9,30,25.5,21.700000000000003,165.0,366.0,323.0,0.08,1016.0,173.0,5.2,79.5359377290679 +2019-01-04 10:30:00-05:00,2019,1,4,10,30,25.9,21.6,158.0,639.0,516.0,0.08,1016.0,180.0,5.2,77.19773214985348 +2019-01-04 11:30:00-05:00,2019,1,4,11,30,26.200000000000003,21.6,317.0,148.0,412.0,0.08,1015.0,189.0,5.2,75.83976391469626 +2019-01-04 12:30:00-05:00,2019,1,4,12,30,26.4,21.700000000000003,322.0,110.0,395.0,0.08,1014.0,197.0,5.300000000000001,75.40904545009998 +2019-01-04 13:30:00-05:00,2019,1,4,13,30,26.5,21.8,350.0,134.0,435.0,0.08,1014.0,204.0,5.5,75.42497687186433 +2019-01-04 14:30:00-05:00,2019,1,4,14,30,26.5,21.8,300.0,148.0,381.0,0.08,1013.0,208.0,5.7,75.42497687186433 +2019-01-04 15:30:00-05:00,2019,1,4,15,30,26.4,21.8,129.0,536.0,349.0,0.08,1013.0,210.0,5.800000000000001,75.87116518171565 +2019-01-04 16:30:00-05:00,2019,1,4,16,30,26.0,21.9,91.0,328.0,168.0,0.08,1013.0,211.0,5.5,78.16158824199735 +2019-01-04 17:30:00-05:00,2019,1,4,17,30,25.5,22.200000000000003,19.0,98.0,23.0,0.08,1014.0,213.0,5.0,81.99926330313417 +2019-01-04 18:30:00-05:00,2019,1,4,18,30,25.1,22.5,0.0,0.0,0.0,0.08,1015.0,218.0,4.5,85.5201042088329 +2019-01-04 19:30:00-05:00,2019,1,4,19,30,24.700000000000003,22.6,0.0,0.0,0.0,0.08,1015.0,222.0,4.1000000000000005,88.11923918412832 +2019-01-04 20:30:00-05:00,2019,1,4,20,30,24.5,22.700000000000003,0.0,0.0,0.0,0.08,1015.0,221.0,3.8,89.72228189005051 +2019-01-04 21:30:00-05:00,2019,1,4,21,30,24.200000000000003,22.700000000000003,0.0,0.0,0.0,0.08,1015.0,216.0,3.9,91.34950480267452 +2019-01-04 22:30:00-05:00,2019,1,4,22,30,24.0,22.700000000000003,0.0,0.0,0.0,0.08,1015.0,210.0,4.3,92.45275683198949 +2019-01-04 23:30:00-05:00,2019,1,4,23,30,23.8,22.6,0.0,0.0,0.0,0.08,1014.0,206.0,4.7,93.0049330719794 +2019-01-05 00:30:00-05:00,2019,1,5,0,30,23.700000000000003,22.5,0.0,0.0,0.0,0.08,1014.0,205.0,5.1000000000000005,92.9998638476854 +2019-01-05 01:30:00-05:00,2019,1,5,1,30,23.700000000000003,22.4,0.0,0.0,0.0,0.08,1013.0,211.0,5.300000000000001,92.43638985600242 +2019-01-05 02:30:00-05:00,2019,1,5,2,30,23.700000000000003,22.3,0.0,0.0,0.0,0.08,1013.0,219.0,5.300000000000001,91.87590901667876 +2019-01-05 03:30:00-05:00,2019,1,5,3,30,23.700000000000003,22.3,0.0,0.0,0.0,0.08,1013.0,227.0,5.300000000000001,91.87590901667876 +2019-01-05 04:30:00-05:00,2019,1,5,4,30,23.700000000000003,22.3,0.0,0.0,0.0,0.08,1013.0,238.0,5.2,91.87590901667876 +2019-01-05 05:30:00-05:00,2019,1,5,5,30,23.6,22.4,0.0,0.0,0.0,0.08,1014.0,254.0,5.2,92.99478918273282 +2019-01-05 06:30:00-05:00,2019,1,5,6,30,23.3,22.200000000000003,0.0,0.0,0.0,0.08,1015.0,271.0,5.1000000000000005,93.54760435307956 +2019-01-05 07:30:00-05:00,2019,1,5,7,30,23.0,21.8,27.0,136.0,36.0,0.08,1016.0,289.0,4.9,92.96422650821276 +2019-01-05 08:30:00-05:00,2019,1,5,8,30,22.700000000000003,21.200000000000003,85.0,297.0,163.0,0.08,1016.0,303.0,4.800000000000001,91.2557171642218 +2019-01-05 09:30:00-05:00,2019,1,5,9,30,22.6,20.4,172.0,26.0,183.0,0.08,1017.0,311.0,4.800000000000001,87.40087535726879 +2019-01-05 10:30:00-05:00,2019,1,5,10,30,22.700000000000003,19.6,267.0,131.0,340.0,0.08,1017.0,311.0,4.7,82.67429633715562 +2019-01-05 11:30:00-05:00,2019,1,5,11,30,22.9,19.0,295.0,138.0,383.0,0.08,1016.0,304.0,4.6000000000000005,78.68412136718442 +2019-01-05 12:30:00-05:00,2019,1,5,12,30,23.3,18.7,311.0,240.0,471.0,0.08,1015.0,294.0,4.5,75.3779037554851 +2019-01-05 13:30:00-05:00,2019,1,5,13,30,23.700000000000003,18.6,222.0,464.0,516.0,0.08,1014.0,285.0,4.4,73.12286027180926 +2019-01-05 14:30:00-05:00,2019,1,5,14,30,24.0,18.6,177.0,537.0,471.0,0.08,1014.0,279.0,4.4,71.81547029707295 +2019-01-05 15:30:00-05:00,2019,1,5,15,30,24.200000000000003,18.7,126.0,535.0,346.0,0.08,1015.0,276.0,4.3,71.40372780863035 +2019-01-05 16:30:00-05:00,2019,1,5,16,30,24.0,18.6,65.0,535.0,192.0,0.08,1015.0,276.0,4.1000000000000005,71.81547029707295 +2019-01-05 17:30:00-05:00,2019,1,5,17,30,23.6,18.5,19.0,119.0,24.0,0.08,1016.0,281.0,4.1000000000000005,73.1055227428291 +2019-01-05 18:30:00-05:00,2019,1,5,18,30,23.200000000000003,18.3,0.0,0.0,0.0,0.08,1017.0,288.0,4.3,73.95863775842149 +2019-01-05 19:30:00-05:00,2019,1,5,19,30,22.700000000000003,18.2,0.0,0.0,0.0,0.08,1017.0,296.0,4.6000000000000005,75.75497591293174 +2019-01-05 20:30:00-05:00,2019,1,5,20,30,22.0,18.0,0.0,0.0,0.0,0.08,1017.0,304.0,4.9,78.06286399643153 +2019-01-05 21:30:00-05:00,2019,1,5,21,30,21.200000000000003,17.7,0.0,0.0,0.0,0.08,1017.0,311.0,5.300000000000001,80.44432006422224 +2019-01-05 22:30:00-05:00,2019,1,5,22,30,20.3,17.5,0.0,0.0,0.0,0.08,1017.0,318.0,5.7,83.96140316195327 +2019-01-05 23:30:00-05:00,2019,1,5,23,30,19.4,17.1,0.0,0.0,0.0,0.08,1017.0,324.0,5.9,86.56190404346253 +2019-01-06 00:30:00-05:00,2019,1,6,0,30,18.6,16.7,0.0,0.0,0.0,0.08,1017.0,330.0,5.9,88.71319240637641 +2019-01-06 01:30:00-05:00,2019,1,6,1,30,18.0,16.400000000000002,0.0,0.0,0.0,0.08,1017.0,335.0,5.5,90.37517682197775 +2019-01-06 02:30:00-05:00,2019,1,6,2,30,17.5,16.0,0.0,0.0,0.0,0.08,1017.0,340.0,5.1000000000000005,90.91871014201544 +2019-01-06 03:30:00-05:00,2019,1,6,3,30,17.2,15.7,0.0,0.0,0.0,0.08,1017.0,345.0,4.5,90.89868629286416 +2019-01-06 04:30:00-05:00,2019,1,6,4,30,17.1,15.6,0.0,0.0,0.0,0.08,1018.0,348.0,4.2,90.89199721505763 +2019-01-06 05:30:00-05:00,2019,1,6,5,30,17.1,15.7,0.0,0.0,0.0,0.08,1018.0,349.0,4.1000000000000005,91.47566263473676 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14:30:00-05:00,2019,1,6,14,30,23.3,16.6,100.0,833.0,557.0,0.08,1017.0,58.0,3.3000000000000003,66.03241316011697 +2019-01-06 15:30:00-05:00,2019,1,6,15,30,23.4,16.900000000000002,75.0,795.0,405.0,0.08,1017.0,65.0,3.5,66.896734143844 +2019-01-06 16:30:00-05:00,2019,1,6,16,30,23.1,17.2,60.0,609.0,207.0,0.08,1018.0,66.0,3.4000000000000004,69.42655912240535 +2019-01-06 17:30:00-05:00,2019,1,6,17,30,22.700000000000003,17.3,20.0,154.0,27.0,0.08,1018.0,63.0,3.3000000000000003,71.58023898528766 +2019-01-06 18:30:00-05:00,2019,1,6,18,30,22.6,17.2,0.0,0.0,0.0,0.08,1019.0,63.0,3.3000000000000003,71.56203181624987 +2019-01-06 19:30:00-05:00,2019,1,6,19,30,22.6,17.1,0.0,0.0,0.0,0.08,1019.0,65.0,3.2,71.11065930748565 +2019-01-06 20:30:00-05:00,2019,1,6,20,30,22.5,17.0,0.0,0.0,0.0,0.08,1020.0,65.0,3.1,71.092204961606 +2019-01-06 21:30:00-05:00,2019,1,6,21,30,22.4,16.900000000000002,0.0,0.0,0.0,0.08,1020.0,64.0,3.1,71.07373433061429 +2019-01-06 22:30:00-05:00,2019,1,6,22,30,22.3,16.900000000000002,0.0,0.0,0.0,0.08,1019.0,63.0,3.3000000000000003,71.50731334711422 +2019-01-06 23:30:00-05:00,2019,1,6,23,30,22.200000000000003,16.900000000000002,0.0,0.0,0.0,0.08,1019.0,64.0,3.5,71.94386730377681 +2019-01-07 00:30:00-05:00,2019,1,7,0,30,22.1,17.0,0.0,0.0,0.0,0.08,1019.0,67.0,3.7,72.84357945118995 +2019-01-07 01:30:00-05:00,2019,1,7,1,30,22.0,17.0,0.0,0.0,0.0,0.08,1019.0,72.0,3.8,73.28896468276716 +2019-01-07 02:30:00-05:00,2019,1,7,2,30,22.0,17.0,0.0,0.0,0.0,0.08,1019.0,76.0,3.8,73.28896468276716 +2019-01-07 03:30:00-05:00,2019,1,7,3,30,21.9,17.1,0.0,0.0,0.0,0.08,1019.0,80.0,3.8,74.2058192800884 +2019-01-07 04:30:00-05:00,2019,1,7,4,30,21.9,17.1,0.0,0.0,0.0,0.08,1019.0,84.0,3.9,74.2058192800884 +2019-01-07 05:30:00-05:00,2019,1,7,5,30,21.9,17.1,0.0,0.0,0.0,0.08,1019.0,87.0,3.9,74.2058192800884 +2019-01-07 06:30:00-05:00,2019,1,7,6,30,22.0,17.2,0.0,0.0,0.0,0.08,1020.0,88.0,3.9,74.22267717910745 +2019-01-07 07:30:00-05:00,2019,1,7,7,30,22.700000000000003,17.400000000000002,26.0,268.0,43.0,0.08,1021.0,89.0,4.0,72.03389223909772 +2019-01-07 08:30:00-05:00,2019,1,7,8,30,23.4,17.6,55.0,679.0,233.0,0.08,1022.0,90.0,4.1000000000000005,69.92393739267627 +2019-01-07 09:30:00-05:00,2019,1,7,9,30,23.9,17.6,70.0,822.0,425.0,0.08,1022.0,90.0,4.1000000000000005,67.84965926408279 +2019-01-07 10:30:00-05:00,2019,1,7,10,30,24.1,17.6,79.0,891.0,580.0,0.08,1022.0,89.0,4.0,67.03939665910431 +2019-01-07 11:30:00-05:00,2019,1,7,11,30,24.3,17.6,83.0,926.0,678.0,0.08,1021.0,87.0,3.8,66.24000091843163 +2019-01-07 12:30:00-05:00,2019,1,7,12,30,24.4,17.5,86.0,933.0,709.0,0.08,1020.0,84.0,3.7,65.4302892708724 +2019-01-07 13:30:00-05:00,2019,1,7,13,30,24.4,17.3,86.0,916.0,669.0,0.08,1019.0,81.0,3.7,64.60906720348359 +2019-01-07 14:30:00-05:00,2019,1,7,14,30,24.3,17.2,80.0,879.0,565.0,0.08,1019.0,79.0,3.7,64.58766531300392 +2019-01-07 15:30:00-05:00,2019,1,7,15,30,24.200000000000003,17.1,72.0,802.0,406.0,0.08,1019.0,78.0,3.8,64.56624616462972 +2019-01-07 16:30:00-05:00,2019,1,7,16,30,23.700000000000003,17.0,55.0,640.0,211.0,0.08,1019.0,80.0,3.7,66.11565382061102 +2019-01-07 17:30:00-05:00,2019,1,7,17,30,23.200000000000003,17.1,20.0,190.0,29.0,0.08,1020.0,84.0,3.7,68.57285160352306 +2019-01-07 18:30:00-05:00,2019,1,7,18,30,23.0,17.0,0.0,0.0,0.0,0.08,1020.0,90.0,3.7,68.96917974781988 +2019-01-07 19:30:00-05:00,2019,1,7,19,30,22.9,16.900000000000002,0.0,0.0,0.0,0.09,1020.0,98.0,3.7,68.949686815783 +2019-01-07 20:30:00-05:00,2019,1,7,20,30,22.8,16.900000000000002,0.0,0.0,0.0,0.09,1020.0,105.0,3.6,69.36872302537611 +2019-01-07 21:30:00-05:00,2019,1,7,21,30,22.8,16.900000000000002,0.0,0.0,0.0,0.09,1020.0,111.0,3.4000000000000004,69.36872302537611 +2019-01-07 22:30:00-05:00,2019,1,7,22,30,22.8,16.900000000000002,0.0,0.0,0.0,0.09,1020.0,114.0,3.0,69.36872302537611 +2019-01-07 23:30:00-05:00,2019,1,7,23,30,22.9,17.0,0.0,0.0,0.0,0.09,1020.0,112.0,2.6,69.38801836277806 +2019-01-08 00:30:00-05:00,2019,1,8,0,30,22.9,17.1,0.0,0.0,0.0,0.09,1020.0,106.0,2.2,69.82879628639056 +2019-01-08 01:30:00-05:00,2019,1,8,1,30,22.8,17.1,0.0,0.0,0.0,0.09,1020.0,98.0,2.0,70.25317521351283 +2019-01-08 02:30:00-05:00,2019,1,8,2,30,22.700000000000003,17.3,0.0,0.0,0.0,0.09,1019.0,95.0,2.1,71.58023898528766 +2019-01-08 03:30:00-05:00,2019,1,8,3,30,22.6,17.5,0.0,0.0,0.0,0.09,1019.0,99.0,2.3000000000000003,72.9312871732694 +2019-01-08 04:30:00-05:00,2019,1,8,4,30,22.4,17.900000000000002,0.0,0.0,0.0,0.09,1019.0,106.0,2.5,75.70700726538732 +2019-01-08 05:30:00-05:00,2019,1,8,5,30,22.4,18.2,0.0,0.0,0.0,0.09,1019.0,110.0,2.6,77.14774152098751 +2019-01-08 06:30:00-05:00,2019,1,8,6,30,22.5,18.4,0.0,0.0,0.0,0.09,1020.0,109.0,2.4000000000000004,77.64822512140272 +2019-01-08 07:30:00-05:00,2019,1,8,7,30,22.9,18.6,25.0,269.0,42.0,0.09,1020.0,102.0,2.0,76.74204334492148 +2019-01-08 08:30:00-05:00,2019,1,8,8,30,23.5,18.8,55.0,672.0,231.0,0.09,1021.0,86.0,1.8,74.94064497951308 +2019-01-08 09:30:00-05:00,2019,1,8,9,30,23.9,18.5,71.0,813.0,422.0,0.09,1021.0,70.0,1.7000000000000002,71.79747153177209 +2019-01-08 10:30:00-05:00,2019,1,8,10,30,24.3,18.5,80.0,881.0,576.0,0.09,1021.0,65.0,1.7000000000000002,70.09415569347212 +2019-01-08 11:30:00-05:00,2019,1,8,11,30,24.6,18.5,85.0,915.0,674.0,0.09,1020.0,68.0,1.6,68.84648264982907 +2019-01-08 12:30:00-05:00,2019,1,8,12,30,24.9,18.2,86.0,922.0,704.0,0.09,1019.0,64.0,1.4,66.36373435041884 +2019-01-08 13:30:00-05:00,2019,1,8,13,30,25.0,17.900000000000002,88.0,903.0,665.0,0.09,1018.0,44.0,1.3,64.7371169738721 +2019-01-08 14:30:00-05:00,2019,1,8,14,30,25.1,17.5,84.0,861.0,561.0,0.09,1018.0,25.0,1.5,62.750826445172635 +2019-01-08 15:30:00-05:00,2019,1,8,15,30,25.0,17.3,76.0,776.0,402.0,0.09,1018.0,22.0,1.7000000000000002,62.33363766021804 +2019-01-08 16:30:00-05:00,2019,1,8,16,30,24.700000000000003,17.400000000000002,59.0,605.0,208.0,0.09,1018.0,32.0,1.7000000000000002,63.86206915948709 +2019-01-08 17:30:00-05:00,2019,1,8,17,30,24.3,17.6,21.0,167.0,29.0,0.09,1018.0,52.0,1.6,66.24000091843163 +2019-01-08 18:30:00-05:00,2019,1,8,18,30,24.1,17.3,0.0,0.0,0.0,0.09,1019.0,76.0,1.5,65.78171621463868 +2019-01-08 19:30:00-05:00,2019,1,8,19,30,23.9,17.5,0.0,0.0,0.0,0.08,1019.0,101.0,1.5,67.42301095029961 +2019-01-08 20:30:00-05:00,2019,1,8,20,30,23.8,17.6,0.0,0.0,0.0,0.08,1018.0,124.0,1.4,68.2589166062282 +2019-01-08 21:30:00-05:00,2019,1,8,21,30,23.6,17.8,0.0,0.0,0.0,0.08,1018.0,147.0,1.3,69.96187848759678 +2019-01-08 22:30:00-05:00,2019,1,8,22,30,23.5,17.900000000000002,0.0,0.0,0.0,0.08,1018.0,188.0,0.9,70.82919308847428 +2019-01-08 23:30:00-05:00,2019,1,8,23,30,23.3,17.900000000000002,0.0,0.0,0.0,0.08,1018.0,256.0,0.8,71.68914381625987 +2019-01-09 00:30:00-05:00,2019,1,9,0,30,23.0,18.0,0.0,0.0,0.0,0.08,1018.0,301.0,1.4,73.46169674935328 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09:30:00-05:00,2019,1,9,9,30,22.200000000000003,18.2,160.0,200.0,247.0,0.08,1020.0,331.0,4.7,78.09223661941822 +2019-01-09 10:30:00-05:00,2019,1,9,10,30,22.6,17.8,211.0,463.0,472.0,0.08,1019.0,326.0,4.7,74.3235012686769 +2019-01-09 11:30:00-05:00,2019,1,9,11,30,23.0,17.2,299.0,309.0,498.0,0.08,1018.0,319.0,4.6000000000000005,69.84785753064332 +2019-01-09 12:30:00-05:00,2019,1,9,12,30,23.4,16.6,262.0,408.0,536.0,0.08,1017.0,313.0,4.4,65.63502207200915 +2019-01-09 13:30:00-05:00,2019,1,9,13,30,23.700000000000003,16.2,248.0,379.0,491.0,0.08,1016.0,307.0,4.3,62.83872292853111 +2019-01-09 14:30:00-05:00,2019,1,9,14,30,23.8,16.0,215.0,391.0,432.0,0.08,1015.0,301.0,4.2,61.66988362727558 +2019-01-09 15:30:00-05:00,2019,1,9,15,30,23.9,16.0,193.0,195.0,275.0,0.08,1015.0,293.0,4.0,61.30013189492224 +2019-01-09 16:30:00-05:00,2019,1,9,16,30,23.6,16.3,85.0,7.0,87.0,0.08,1015.0,288.0,3.9,63.62240119439135 +2019-01-09 17:30:00-05:00,2019,1,9,17,30,23.1,16.6,16.0,0.0,16.0,0.08,1015.0,287.0,4.0,66.83533751315717 +2019-01-09 18:30:00-05:00,2019,1,9,18,30,22.8,16.8,0.0,0.0,0.0,0.08,1015.0,290.0,4.5,68.93017714560473 +2019-01-09 19:30:00-05:00,2019,1,9,19,30,22.4,17.2,0.0,0.0,0.0,0.07,1016.0,294.0,4.800000000000001,72.43681558289074 +2019-01-09 20:30:00-05:00,2019,1,9,20,30,21.9,17.400000000000002,0.0,0.0,0.0,0.07,1016.0,299.0,5.0,75.62676196227409 +2019-01-09 21:30:00-05:00,2019,1,9,21,30,21.3,17.400000000000002,0.0,0.0,0.0,0.07,1015.0,303.0,5.300000000000001,78.45370175062625 +2019-01-09 22:30:00-05:00,2019,1,9,22,30,20.6,17.2,0.0,0.0,0.0,0.07,1015.0,307.0,5.7,80.87416317944127 +2019-01-09 23:30:00-05:00,2019,1,9,23,30,19.9,16.6,0.0,0.0,0.0,0.07,1015.0,312.0,6.300000000000001,81.29722837272644 +2019-01-10 00:30:00-05:00,2019,1,10,0,30,19.1,15.7,0.0,0.0,0.0,0.07,1015.0,319.0,6.800000000000001,80.67607490617536 +2019-01-10 01:30:00-05:00,2019,1,10,1,30,18.3,14.6,0.0,0.0,0.0,0.07,1016.0,326.0,7.300000000000001,79.02619788455503 +2019-01-10 02:30:00-05:00,2019,1,10,2,30,17.5,13.6,0.0,0.0,0.0,0.07,1016.0,331.0,7.5,77.89205784276353 +2019-01-10 03:30:00-05:00,2019,1,10,3,30,16.900000000000002,12.8,0.0,0.0,0.0,0.07,1016.0,335.0,7.5,76.79057226620799 +2019-01-10 04:30:00-05:00,2019,1,10,4,30,16.400000000000002,12.2,0.0,0.0,0.0,0.07,1016.0,336.0,7.2,76.20919756426511 +2019-01-10 05:30:00-05:00,2019,1,10,5,30,15.9,11.8,0.0,0.0,0.0,0.07,1016.0,337.0,6.9,76.63253310246772 +2019-01-10 06:30:00-05:00,2019,1,10,6,30,15.6,11.6,0.0,0.0,0.0,0.07,1016.0,337.0,6.800000000000001,77.09263651898362 +2019-01-10 07:30:00-05:00,2019,1,10,7,30,15.6,11.4,26.0,286.0,44.0,0.07,1017.0,337.0,6.7,76.0799626031015 +2019-01-10 08:30:00-05:00,2019,1,10,8,30,15.8,11.0,57.0,712.0,243.0,0.07,1018.0,337.0,6.4,73.14763024094954 +2019-01-10 09:30:00-05:00,2019,1,10,9,30,16.3,10.5,71.0,863.0,445.0,0.07,1018.0,336.0,5.9,68.5333616905101 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18:30:00-05:00,2019,1,10,18,30,18.1,9.2,0.0,0.0,0.0,0.07,1017.0,355.0,4.6000000000000005,56.05848359046047 +2019-01-10 19:30:00-05:00,2019,1,10,19,30,17.900000000000002,9.5,0.0,0.0,0.0,0.07,1017.0,356.0,4.6000000000000005,57.924570608676405 +2019-01-10 20:30:00-05:00,2019,1,10,20,30,17.6,9.9,0.0,0.0,0.0,0.07,1017.0,357.0,4.6000000000000005,60.634277423158075 +2019-01-10 21:30:00-05:00,2019,1,10,21,30,17.400000000000002,10.3,0.0,0.0,0.0,0.07,1017.0,359.0,4.5,63.06842384652677 +2019-01-10 22:30:00-05:00,2019,1,10,22,30,17.2,11.0,0.0,0.0,0.0,0.07,1017.0,359.0,4.6000000000000005,66.9168728206601 +2019-01-10 23:30:00-05:00,2019,1,10,23,30,17.2,11.8,0.0,0.0,0.0,0.07,1017.0,359.0,4.6000000000000005,70.55441283432896 +2019-01-11 00:30:00-05:00,2019,1,11,0,30,17.1,12.7,0.0,0.0,0.0,0.07,1017.0,181.0,4.6000000000000005,75.32902339889542 +2019-01-11 01:30:00-05:00,2019,1,11,1,30,17.2,13.5,0.0,0.0,0.0,0.07,1017.0,6.0,4.5,78.86762930972654 +2019-01-11 02:30:00-05:00,2019,1,11,2,30,17.400000000000002,14.1,0.0,0.0,0.0,0.07,1017.0,13.0,4.3,80.97107002381098 +2019-01-11 03:30:00-05:00,2019,1,11,3,30,17.6,14.5,0.0,0.0,0.0,0.07,1017.0,19.0,4.0,82.05128162407722 +2019-01-11 04:30:00-05:00,2019,1,11,4,30,17.7,14.6,0.0,0.0,0.0,0.07,1017.0,25.0,3.8,82.06380567508438 +2019-01-11 05:30:00-05:00,2019,1,11,5,30,17.8,14.7,0.0,0.0,0.0,0.07,1017.0,30.0,3.6,82.07631714611342 +2019-01-11 06:30:00-05:00,2019,1,11,6,30,18.0,14.7,0.0,0.0,0.0,0.07,1018.0,35.0,3.6,81.05008842002658 +2019-01-11 07:30:00-05:00,2019,1,11,7,30,18.6,14.7,29.0,162.0,39.0,0.07,1019.0,43.0,3.7,78.05675868900163 +2019-01-11 08:30:00-05:00,2019,1,11,8,30,19.4,14.8,72.0,575.0,223.0,0.07,1019.0,52.0,3.7,74.73696868011677 +2019-01-11 09:30:00-05:00,2019,1,11,9,30,20.3,14.9,90.0,753.0,417.0,0.07,1020.0,61.0,3.8,71.13877531411072 +2019-01-11 10:30:00-05:00,2019,1,11,10,30,21.3,14.9,101.0,837.0,575.0,0.07,1020.0,69.0,3.8,66.89316860452433 +2019-01-11 11:30:00-05:00,2019,1,11,11,30,22.0,14.9,107.0,877.0,675.0,0.07,1019.0,71.0,3.9,64.09062804138038 +2019-01-11 12:30:00-05:00,2019,1,11,12,30,22.5,15.1,103.0,896.0,708.0,0.07,1018.0,70.0,3.9,62.9746563879561 +2019-01-11 13:30:00-05:00,2019,1,11,13,30,22.8,15.2,153.0,739.0,630.0,0.07,1018.0,69.0,4.1000000000000005,62.23725131591887 +2019-01-11 14:30:00-05:00,2019,1,11,14,30,22.9,15.2,229.0,345.0,423.0,0.07,1017.0,69.0,4.4,61.86129424550579 +2019-01-11 15:30:00-05:00,2019,1,11,15,30,22.8,15.3,129.0,8.0,132.0,0.07,1017.0,69.0,4.5,62.63815230816363 +2019-01-11 16:30:00-05:00,2019,1,11,16,30,22.4,15.5,50.0,416.0,156.0,0.07,1018.0,69.0,4.6000000000000005,65.00622861137929 +2019-01-11 17:30:00-05:00,2019,1,11,17,30,22.0,15.8,24.0,175.0,34.0,0.07,1018.0,69.0,4.7,67.90034663929217 +2019-01-11 18:30:00-05:00,2019,1,11,18,30,21.9,16.0,0.0,0.0,0.0,0.07,1019.0,71.0,4.9,69.19431326392903 +2019-01-11 19:30:00-05:00,2019,1,11,19,30,21.9,16.2,0.0,0.0,0.0,0.09,1019.0,75.0,5.0,70.08271961345594 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04:30:00-05:00,2019,1,12,4,30,21.8,16.900000000000002,0.0,0.0,0.0,0.09,1019.0,90.0,5.800000000000001,73.72028641918516 +2019-01-12 05:30:00-05:00,2019,1,12,5,30,21.8,16.8,0.0,0.0,0.0,0.09,1019.0,92.0,5.800000000000001,73.25423015557377 +2019-01-12 06:30:00-05:00,2019,1,12,6,30,22.0,16.8,0.0,0.0,0.0,0.09,1020.0,95.0,5.7,72.36558787435905 +2019-01-12 07:30:00-05:00,2019,1,12,7,30,22.5,16.900000000000002,26.0,224.0,40.0,0.09,1020.0,99.0,5.7,70.64310788526079 +2019-01-12 08:30:00-05:00,2019,1,12,8,30,23.1,17.1,59.0,645.0,228.0,0.09,1021.0,102.0,5.7,68.98865595824685 +2019-01-12 09:30:00-05:00,2019,1,12,9,30,23.5,17.1,163.0,421.0,346.0,0.09,1022.0,104.0,5.6000000000000005,67.34224771796754 +2019-01-12 10:30:00-05:00,2019,1,12,10,30,23.8,17.2,89.0,859.0,576.0,0.09,1021.0,107.0,5.5,66.5562197956518 +2019-01-12 11:30:00-05:00,2019,1,12,11,30,23.9,17.400000000000002,95.0,893.0,675.0,0.09,1020.0,111.0,5.2,66.99872091208394 +2019-01-12 12:30:00-05:00,2019,1,12,12,30,24.0,17.7,117.0,844.0,689.0,0.09,1020.0,115.0,5.0,67.86960687395424 +2019-01-12 13:30:00-05:00,2019,1,12,13,30,24.0,17.900000000000002,97.0,882.0,669.0,0.09,1019.0,118.0,4.6000000000000005,68.72961200154008 +2019-01-12 14:30:00-05:00,2019,1,12,14,30,23.9,18.1,92.0,843.0,567.0,0.09,1019.0,119.0,4.3,70.01866695826021 +2019-01-12 15:30:00-05:00,2019,1,12,15,30,23.700000000000003,18.2,79.0,769.0,410.0,0.09,1019.0,120.0,3.9,71.31258711744105 +2019-01-12 16:30:00-05:00,2019,1,12,16,30,23.3,18.3,61.0,611.0,218.0,0.09,1019.0,120.0,3.6,73.51321234216394 +2019-01-12 17:30:00-05:00,2019,1,12,17,30,22.9,18.6,25.0,191.0,36.0,0.09,1020.0,118.0,3.4000000000000004,76.74204334492148 +2019-01-12 18:30:00-05:00,2019,1,12,18,30,22.700000000000003,18.7,0.0,0.0,0.0,0.09,1020.0,117.0,3.3000000000000003,78.16542406318528 +2019-01-12 19:30:00-05:00,2019,1,12,19,30,22.700000000000003,19.0,0.0,0.0,0.0,0.1,1021.0,117.0,3.3000000000000003,79.64378366680654 +2019-01-12 20:30:00-05:00,2019,1,12,20,30,22.700000000000003,19.200000000000003,0.0,0.0,0.0,0.1,1021.0,117.0,3.4000000000000004,80.64293730589348 +2019-01-12 21:30:00-05:00,2019,1,12,21,30,22.6,19.5,0.0,0.0,0.0,0.1,1020.0,118.0,3.6,82.66238212842283 +2019-01-12 22:30:00-05:00,2019,1,12,22,30,22.6,19.700000000000003,0.0,0.0,0.0,0.1,1020.0,121.0,3.7,83.69543568707813 +2019-01-12 23:30:00-05:00,2019,1,12,23,30,22.6,19.9,0.0,0.0,0.0,0.1,1020.0,124.0,3.8,84.73979851905557 +2019-01-13 00:30:00-05:00,2019,1,13,0,30,22.5,20.0,0.0,0.0,0.0,0.1,1019.0,126.0,3.8,85.78562746321194 +2019-01-13 01:30:00-05:00,2019,1,13,1,30,22.5,20.0,0.0,0.0,0.0,0.1,1019.0,128.0,3.8,85.78562746321194 +2019-01-13 02:30:00-05:00,2019,1,13,2,30,22.4,20.1,0.0,0.0,0.0,0.1,1019.0,131.0,3.8,86.84435222429794 +2019-01-13 03:30:00-05:00,2019,1,13,3,30,22.4,20.1,0.0,0.0,0.0,0.1,1019.0,134.0,3.7,86.84435222429794 +2019-01-13 04:30:00-05:00,2019,1,13,4,30,22.4,20.0,0.0,0.0,0.0,0.1,1019.0,138.0,3.6,86.30855971977265 +2019-01-13 05:30:00-05:00,2019,1,13,5,30,22.3,20.0,0.0,0.0,0.0,0.1,1019.0,143.0,3.5,86.83507743818534 +2019-01-13 06:30:00-05:00,2019,1,13,6,30,22.4,20.0,0.0,0.0,0.0,0.1,1019.0,147.0,3.3000000000000003,86.30855971977265 +2019-01-13 07:30:00-05:00,2019,1,13,7,30,22.8,20.200000000000003,25.0,230.0,40.0,0.1,1019.0,151.0,3.3000000000000003,85.28679888074355 +2019-01-13 08:30:00-05:00,2019,1,13,8,30,23.4,20.6,57.0,645.0,227.0,0.1,1020.0,154.0,3.3000000000000003,84.303446850675 +2019-01-13 09:30:00-05:00,2019,1,13,9,30,23.8,20.4,73.0,795.0,419.0,0.1,1020.0,158.0,3.2,81.28712563035198 +2019-01-13 10:30:00-05:00,2019,1,13,10,30,24.200000000000003,20.3,83.0,866.0,575.0,0.1,1020.0,165.0,2.9000000000000004,78.86876185388796 +2019-01-13 11:30:00-05:00,2019,1,13,11,30,24.5,20.1,87.0,901.0,674.0,0.1,1019.0,176.0,2.6,76.51204998962339 +2019-01-13 12:30:00-05:00,2019,1,13,12,30,24.700000000000003,19.8,89.0,908.0,706.0,0.1,1018.0,188.0,2.3000000000000003,74.21067197210797 +2019-01-13 13:30:00-05:00,2019,1,13,13,30,24.8,19.6,90.0,890.0,669.0,0.1,1018.0,200.0,2.1,72.85886757685263 +2019-01-13 14:30:00-05:00,2019,1,13,14,30,24.9,19.3,86.0,850.0,568.0,0.1,1017.0,207.0,2.0,71.08698923224469 +2019-01-13 15:30:00-05:00,2019,1,13,15,30,24.8,19.200000000000003,78.0,768.0,411.0,0.1,1017.0,208.0,2.0,71.06867975286197 +2019-01-13 16:30:00-05:00,2019,1,13,16,30,24.5,19.3,63.0,596.0,218.0,0.1,1018.0,201.0,2.1,72.80660487164259 +2019-01-13 17:30:00-05:00,2019,1,13,17,30,23.9,19.6,25.0,186.0,36.0,0.1,1018.0,198.0,2.3000000000000003,76.89536042992802 +2019-01-13 18:30:00-05:00,2019,1,13,18,30,23.6,19.6,0.0,0.0,0.0,0.1,1019.0,203.0,2.7,78.29628816720248 +2019-01-13 19:30:00-05:00,2019,1,13,19,30,23.4,19.6,0.0,0.0,0.0,0.1,1019.0,214.0,3.1,79.24618084760029 +2019-01-13 20:30:00-05:00,2019,1,13,20,30,23.1,19.700000000000003,0.0,0.0,0.0,0.1,1019.0,226.0,3.3000000000000003,81.19789176646812 +2019-01-13 21:30:00-05:00,2019,1,13,21,30,22.700000000000003,19.9,0.0,0.0,0.0,0.1,1019.0,240.0,3.4000000000000004,84.22714152020409 +2019-01-13 22:30:00-05:00,2019,1,13,22,30,22.200000000000003,20.0,0.0,0.0,0.0,0.1,1019.0,257.0,3.4000000000000004,87.36520778231849 +2019-01-13 23:30:00-05:00,2019,1,13,23,30,21.8,20.1,0.0,0.0,0.0,0.1,1019.0,274.0,3.4000000000000004,90.07815025003076 +2019-01-14 00:30:00-05:00,2019,1,14,0,30,21.5,20.0,0.0,0.0,0.0,0.1,1018.0,287.0,3.5,91.17960314176902 +2019-01-14 01:30:00-05:00,2019,1,14,1,30,21.200000000000003,19.9,0.0,0.0,0.0,0.1,1018.0,299.0,3.6,92.2979339131495 +2019-01-14 02:30:00-05:00,2019,1,14,2,30,21.0,19.8,0.0,0.0,0.0,0.1,1018.0,309.0,3.8,92.86091278956887 +2019-01-14 03:30:00-05:00,2019,1,14,3,30,20.8,19.700000000000003,0.0,0.0,0.0,0.1,1018.0,317.0,4.1000000000000005,93.42862406578288 +2019-01-14 04:30:00-05:00,2019,1,14,4,30,20.6,19.700000000000003,0.0,0.0,0.0,0.1,1018.0,323.0,4.1000000000000005,94.5864469096051 +2019-01-14 05:30:00-05:00,2019,1,14,5,30,20.4,19.6,0.0,0.0,0.0,0.1,1018.0,329.0,4.0,95.1678131907141 +2019-01-14 06:30:00-05:00,2019,1,14,6,30,20.4,19.4,0.0,0.0,0.0,0.1,1019.0,334.0,3.9,93.99226650694143 +2019-01-14 07:30:00-05:00,2019,1,14,7,30,20.6,19.200000000000003,27.0,177.0,38.0,0.1,1019.0,337.0,3.9,91.69144992281467 +2019-01-14 08:30:00-05:00,2019,1,14,8,30,21.0,19.0,69.0,567.0,218.0,0.1,1020.0,337.0,3.9,88.35366560432124 +2019-01-14 09:30:00-05:00,2019,1,14,9,30,21.5,18.7,89.0,733.0,409.0,0.1,1021.0,336.0,3.7,84.09507629370734 +2019-01-14 10:30:00-05:00,2019,1,14,10,30,22.1,18.4,100.0,818.0,566.0,0.1,1020.0,335.0,3.5,79.56110882943427 +2019-01-14 11:30:00-05:00,2019,1,14,11,30,22.700000000000003,18.1,106.0,857.0,666.0,0.1,1019.0,333.0,3.3000000000000003,75.28079703215496 +2019-01-14 12:30:00-05:00,2019,1,14,12,30,23.200000000000003,17.900000000000002,107.0,874.0,703.0,0.1,1018.0,331.0,3.1,72.12351698141116 +2019-01-14 13:30:00-05:00,2019,1,14,13,30,23.6,17.5,103.0,868.0,670.0,0.1,1018.0,329.0,2.9000000000000004,68.65136550436803 +2019-01-14 14:30:00-05:00,2019,1,14,14,30,23.8,17.2,98.0,830.0,571.0,0.1,1017.0,328.0,2.9000000000000004,66.5562197956518 +2019-01-14 15:30:00-05:00,2019,1,14,15,30,23.8,16.900000000000002,87.0,749.0,414.0,0.1,1017.0,331.0,2.9000000000000004,65.30379677434946 +2019-01-14 16:30:00-05:00,2019,1,14,16,30,23.3,16.7,67.0,584.0,221.0,0.1,1017.0,339.0,2.8000000000000003,66.45317235862305 +2019-01-14 17:30:00-05:00,2019,1,14,17,30,22.700000000000003,16.5,26.0,186.0,38.0,0.1,1017.0,348.0,2.6,68.04044205728263 +2019-01-14 18:30:00-05:00,2019,1,14,18,30,22.200000000000003,16.0,0.0,0.0,0.0,0.1,1018.0,356.0,2.6,67.94045895449041 +2019-01-14 19:30:00-05:00,2019,1,14,19,30,21.700000000000003,16.0,0.0,0.0,0.0,0.1,1018.0,179.0,2.7,70.04465832442168 +2019-01-14 20:30:00-05:00,2019,1,14,20,30,21.1,16.0,0.0,0.0,0.0,0.1,1018.0,2.0,2.7,72.66697348434249 +2019-01-14 21:30:00-05:00,2019,1,14,21,30,20.6,16.0,0.0,0.0,0.0,0.1,1018.0,4.0,2.9000000000000004,74.93665125577456 +2019-01-14 22:30:00-05:00,2019,1,14,22,30,20.1,16.1,0.0,0.0,0.0,0.1,1018.0,2.0,3.1,77.78101247356933 +2019-01-14 23:30:00-05:00,2019,1,14,23,30,19.700000000000003,16.3,0.0,0.0,0.0,0.1,1018.0,3.0,3.3000000000000003,80.75566170256006 +2019-01-15 00:30:00-05:00,2019,1,15,0,30,19.4,16.400000000000002,0.0,0.0,0.0,0.1,1018.0,5.0,3.4000000000000004,82.80027590062022 +2019-01-15 01:30:00-05:00,2019,1,15,1,30,19.200000000000003,16.5,0.0,0.0,0.0,0.1,1018.0,4.0,3.4000000000000004,84.37247492510438 +2019-01-15 02:30:00-05:00,2019,1,15,2,30,19.0,16.6,0.0,0.0,0.0,0.1,1017.0,181.0,3.7,85.97573902310485 +2019-01-15 03:30:00-05:00,2019,1,15,3,30,18.8,16.7,0.0,0.0,0.0,0.1,1017.0,355.0,4.0,87.61070845809938 +2019-01-15 04:30:00-05:00,2019,1,15,4,30,18.4,16.7,0.0,0.0,0.0,0.1,1017.0,350.0,4.3,89.83126862847055 +2019-01-15 05:30:00-05:00,2019,1,15,5,30,18.1,16.6,0.0,0.0,0.0,0.1,1018.0,347.0,4.4,90.95856352882676 +2019-01-15 06:30:00-05:00,2019,1,15,6,30,17.900000000000002,16.3,0.0,0.0,0.0,0.1,1018.0,345.0,4.7,90.368144065778 +2019-01-15 07:30:00-05:00,2019,1,15,7,30,17.900000000000002,16.2,28.0,134.0,37.0,0.1,1019.0,342.0,5.1000000000000005,89.79420217114152 +2019-01-15 08:30:00-05:00,2019,1,15,8,30,18.2,16.0,89.0,417.0,199.0,0.1,1020.0,340.0,5.4,87.0002728768003 +2019-01-15 09:30:00-05:00,2019,1,15,9,30,18.6,15.7,168.0,434.0,358.0,0.1,1020.0,338.0,5.300000000000001,83.23514898572651 +2019-01-15 10:30:00-05:00,2019,1,15,10,30,19.1,15.3,127.0,783.0,574.0,0.1,1020.0,335.0,5.0,78.63432754179922 +2019-01-15 11:30:00-05:00,2019,1,15,11,30,19.6,15.1,133.0,830.0,677.0,0.1,1019.0,330.0,4.5,75.25278621147407 +2019-01-15 12:30:00-05:00,2019,1,15,12,30,20.0,14.9,183.0,708.0,667.0,0.1,1018.0,324.0,4.1000000000000005,72.47085629140702 +2019-01-15 13:30:00-05:00,2019,1,15,13,30,20.3,14.8,342.0,174.0,456.0,0.1,1018.0,320.0,3.6,70.68205909639056 +2019-01-15 14:30:00-05:00,2019,1,15,14,30,20.4,14.5,315.0,68.0,354.0,0.1,1017.0,320.0,3.3000000000000003,68.90059829745981 +2019-01-15 15:30:00-05:00,2019,1,15,15,30,20.4,14.4,169.0,8.0,173.0,0.1,1017.0,323.0,3.5,68.45688037790066 +2019-01-15 16:30:00-05:00,2019,1,15,16,30,20.0,14.4,80.0,282.0,155.0,0.1,1018.0,328.0,3.8,70.17068309813398 +2019-01-15 17:30:00-05:00,2019,1,15,17,30,19.5,14.4,29.0,134.0,38.0,0.1,1018.0,334.0,4.0,72.3810651858662 +2019-01-15 18:30:00-05:00,2019,1,15,18,30,19.1,14.1,0.0,0.0,0.0,0.1,1019.0,340.0,4.2,72.77908924991898 +2019-01-15 19:30:00-05:00,2019,1,15,19,30,18.7,13.9,0.0,0.0,0.0,0.1,1019.0,345.0,4.4,73.65811951494364 +2019-01-15 20:30:00-05:00,2019,1,15,20,30,18.3,13.7,0.0,0.0,0.0,0.1,1019.0,348.0,4.6000000000000005,74.55195962279107 +2019-01-15 21:30:00-05:00,2019,1,15,21,30,17.900000000000002,13.7,0.0,0.0,0.0,0.1,1020.0,351.0,4.7,76.44887003144295 +2019-01-15 22:30:00-05:00,2019,1,15,22,30,17.5,13.7,0.0,0.0,0.0,0.1,1019.0,353.0,4.7,78.40009402543778 +2019-01-15 23:30:00-05:00,2019,1,15,23,30,17.3,13.8,0.0,0.0,0.0,0.1,1019.0,355.0,4.6000000000000005,79.91405353060519 +2019-01-16 00:30:00-05:00,2019,1,16,0,30,17.1,14.0,0.0,0.0,0.0,0.1,1019.0,355.0,4.7,81.98847210730904 +2019-01-16 01:30:00-05:00,2019,1,16,1,30,17.0,14.2,0.0,0.0,0.0,0.1,1019.0,353.0,4.7,83.58530465643027 +2019-01-16 02:30:00-05:00,2019,1,16,2,30,16.8,14.4,0.0,0.0,0.0,0.1,1019.0,351.0,4.6000000000000005,85.75407058428847 +2019-01-16 03:30:00-05:00,2019,1,16,3,30,16.6,14.5,0.0,0.0,0.0,0.1,1019.0,348.0,4.7,87.41291612542301 +2019-01-16 04:30:00-05:00,2019,1,16,4,30,16.400000000000002,14.6,0.0,0.0,0.0,0.1,1019.0,346.0,4.800000000000001,89.10514839058384 +2019-01-16 05:30:00-05:00,2019,1,16,5,30,16.3,14.6,0.0,0.0,0.0,0.1,1019.0,344.0,4.7,89.6742455766154 +2019-01-16 06:30:00-05:00,2019,1,16,6,30,16.3,14.6,0.0,0.0,0.0,0.1,1020.0,342.0,4.800000000000001,89.6742455766154 +2019-01-16 07:30:00-05:00,2019,1,16,7,30,16.6,14.7,28.0,175.0,39.0,0.1,1020.0,342.0,4.800000000000001,88.54842781206553 +2019-01-16 08:30:00-05:00,2019,1,16,8,30,17.1,14.6,73.0,576.0,226.0,0.1,1021.0,340.0,4.5,85.2330011964802 +2019-01-16 09:30:00-05:00,2019,1,16,9,30,17.8,14.6,90.0,763.0,425.0,0.1,1021.0,338.0,4.5,81.54856547900246 +2019-01-16 10:30:00-05:00,2019,1,16,10,30,18.7,14.6,94.0,858.0,586.0,0.1,1021.0,340.0,4.4,77.07125531808674 +2019-01-16 11:30:00-05:00,2019,1,16,11,30,19.5,14.4,288.0,346.0,515.0,0.1,1021.0,340.0,4.3,72.3810651858662 +2019-01-16 12:30:00-05:00,2019,1,16,12,30,20.3,14.3,110.0,890.0,721.0,0.1,1020.0,339.0,4.1000000000000005,68.4369468382543 +2019-01-16 13:30:00-05:00,2019,1,16,13,30,20.8,14.0,114.0,862.0,682.0,0.1,1019.0,342.0,3.9,65.0827172832376 +2019-01-16 14:30:00-05:00,2019,1,16,14,30,21.1,13.6,141.0,727.0,559.0,0.1,1019.0,349.0,3.3000000000000003,62.255393780441246 +2019-01-16 15:30:00-05:00,2019,1,16,15,30,21.1,13.3,164.0,456.0,366.0,0.1,1019.0,178.0,2.8000000000000003,61.0511079342587 +2019-01-16 16:30:00-05:00,2019,1,16,16,30,20.8,13.3,76.0,551.0,224.0,0.1,1019.0,7.0,2.5,62.18731412928012 +2019-01-16 17:30:00-05:00,2019,1,16,17,30,20.3,13.6,30.0,176.0,42.0,0.1,1019.0,13.0,2.6,65.39926471650453 +2019-01-16 18:30:00-05:00,2019,1,16,18,30,20.1,13.7,0.0,0.0,0.0,0.1,1020.0,15.0,3.0,66.64469260997923 +2019-01-16 19:30:00-05:00,2019,1,16,19,30,19.9,14.1,0.0,0.0,0.0,0.1,1020.0,15.0,3.5,69.24919007468063 +2019-01-16 20:30:00-05:00,2019,1,16,20,30,19.700000000000003,14.5,0.0,0.0,0.0,0.1,1020.0,17.0,4.0,71.95102760863305 +2019-01-16 21:30:00-05:00,2019,1,16,21,30,19.5,15.0,0.0,0.0,0.0,0.1,1021.0,19.0,4.1000000000000005,75.2363437536495 +2019-01-16 22:30:00-05:00,2019,1,16,22,30,19.3,15.4,0.0,0.0,0.0,0.1,1021.0,22.0,4.0,78.16066187721832 +2019-01-16 23:30:00-05:00,2019,1,16,23,30,19.1,15.7,0.0,0.0,0.0,0.1,1021.0,23.0,3.8,80.67607490617536 +2019-01-17 00:30:00-05:00,2019,1,17,0,30,19.0,15.9,0.0,0.0,0.0,0.1,1020.0,25.0,3.6,82.22547936455294 +2019-01-17 01:30:00-05:00,2019,1,17,1,30,19.0,16.0,0.0,0.0,0.0,0.1,1020.0,27.0,3.6,82.75226606225803 +2019-01-17 02:30:00-05:00,2019,1,17,2,30,19.0,16.1,0.0,0.0,0.0,0.1,1020.0,29.0,3.5,83.28201720072231 +2019-01-17 03:30:00-05:00,2019,1,17,3,30,19.0,16.1,0.0,0.0,0.0,0.1,1020.0,28.0,3.5,83.28201720072231 +2019-01-17 04:30:00-05:00,2019,1,17,4,30,18.9,16.1,0.0,0.0,0.0,0.1,1020.0,30.0,3.4000000000000004,83.80338544159636 +2019-01-17 05:30:00-05:00,2019,1,17,5,30,19.0,16.1,0.0,0.0,0.0,0.1,1020.0,35.0,3.4000000000000004,83.28201720072231 +2019-01-17 06:30:00-05:00,2019,1,17,6,30,19.200000000000003,16.1,0.0,0.0,0.0,0.1,1020.0,39.0,3.3000000000000003,82.25016576603355 +2019-01-17 07:30:00-05:00,2019,1,17,7,30,19.8,16.2,26.0,234.0,41.0,0.1,1021.0,41.0,3.3000000000000003,79.74657911912934 +2019-01-17 08:30:00-05:00,2019,1,17,8,30,20.6,16.2,58.0,647.0,230.0,0.1,1021.0,48.0,3.2,75.89878518915113 +2019-01-17 09:30:00-05:00,2019,1,17,9,30,21.4,16.3,77.0,790.0,425.0,0.1,1022.0,59.0,3.0,72.72012266748742 +2019-01-17 10:30:00-05:00,2019,1,17,10,30,22.3,16.400000000000002,93.0,848.0,580.0,0.1,1022.0,72.0,2.7,69.27199609705535 +2019-01-17 11:30:00-05:00,2019,1,17,11,30,22.8,16.400000000000002,99.0,884.0,682.0,0.1,1021.0,81.0,2.4000000000000004,67.20025806794618 +2019-01-17 12:30:00-05:00,2019,1,17,12,30,23.0,16.5,103.0,891.0,717.0,0.1,1020.0,86.0,2.2,66.81483777876102 +2019-01-17 13:30:00-05:00,2019,1,17,13,30,23.200000000000003,16.5,104.0,874.0,682.0,0.1,1019.0,89.0,2.0,66.0115600266525 +2019-01-17 14:30:00-05:00,2019,1,17,14,30,23.3,16.400000000000002,97.0,840.0,583.0,0.1,1019.0,89.0,2.0,65.19791247320425 +2019-01-17 15:30:00-05:00,2019,1,17,15,30,23.200000000000003,16.400000000000002,86.0,765.0,427.0,0.1,1019.0,89.0,2.0,65.59295448507187 +2019-01-17 16:30:00-05:00,2019,1,17,16,30,22.9,16.400000000000002,68.0,598.0,231.0,0.1,1019.0,92.0,2.0,66.79432092226497 +2019-01-17 17:30:00-05:00,2019,1,17,17,30,22.5,16.6,30.0,208.0,45.0,0.1,1019.0,97.0,2.0,69.31073698328056 +2019-01-17 18:30:00-05:00,2019,1,17,18,30,22.3,16.6,0.0,0.0,0.0,0.1,1020.0,102.0,2.1,70.15864301769973 +2019-01-17 19:30:00-05:00,2019,1,17,19,30,22.1,16.7,0.0,0.0,0.0,0.1,1020.0,111.0,2.0,71.4707533963589 +2019-01-17 20:30:00-05:00,2019,1,17,20,30,22.0,16.7,0.0,0.0,0.0,0.1,1020.0,120.0,1.7000000000000002,71.90774480029951 +2019-01-17 21:30:00-05:00,2019,1,17,21,30,21.700000000000003,16.5,0.0,0.0,0.0,0.1,1020.0,128.0,1.4,72.3119991825284 +2019-01-17 22:30:00-05:00,2019,1,17,22,30,21.5,16.3,0.0,0.0,0.0,0.1,1020.0,140.0,1.3,72.27619317397917 +2019-01-17 23:30:00-05:00,2019,1,17,23,30,21.200000000000003,16.2,0.0,0.0,0.0,0.1,1020.0,167.0,1.1,73.14964788312551 +2019-01-18 00:30:00-05:00,2019,1,18,0,30,20.9,15.9,0.0,0.0,0.0,0.1,1019.0,228.0,0.9,73.09714288157205 +2019-01-18 01:30:00-05:00,2019,1,18,1,30,20.5,15.8,0.0,0.0,0.0,0.1,1019.0,285.0,1.3,74.44280005962923 +2019-01-18 02:30:00-05:00,2019,1,18,2,30,20.1,15.7,0.0,0.0,0.0,0.1,1019.0,306.0,1.9,75.81853289339183 +2019-01-18 03:30:00-05:00,2019,1,18,3,30,19.700000000000003,15.8,0.0,0.0,0.0,0.1,1019.0,316.0,2.5,78.21972136085722 +2019-01-18 04:30:00-05:00,2019,1,18,4,30,19.5,15.7,0.0,0.0,0.0,0.1,1019.0,326.0,2.8000000000000003,78.6923206548702 +2019-01-18 05:30:00-05:00,2019,1,18,5,30,19.3,15.6,0.0,0.0,0.0,0.1,1019.0,333.0,2.9000000000000004,79.16888301754386 +2019-01-18 06:30:00-05:00,2019,1,18,6,30,19.4,15.4,0.0,0.0,0.0,0.1,1019.0,336.0,2.9000000000000004,77.67587569511647 +2019-01-18 07:30:00-05:00,2019,1,18,7,30,19.9,15.4,27.0,231.0,42.0,0.1,1020.0,337.0,2.7,75.30202157017906 +2019-01-18 08:30:00-05:00,2019,1,18,8,30,20.700000000000003,15.3,63.0,636.0,233.0,0.1,1020.0,340.0,2.4000000000000004,71.21300788701545 +2019-01-18 09:30:00-05:00,2019,1,18,9,30,21.5,15.2,80.0,792.0,430.0,0.1,1021.0,342.0,2.0,67.36583030106355 +2019-01-18 10:30:00-05:00,2019,1,18,10,30,22.4,15.1,90.0,869.0,591.0,0.1,1020.0,343.0,1.7000000000000002,63.35853746623127 +2019-01-18 11:30:00-05:00,2019,1,18,11,30,23.1,14.8,95.0,906.0,695.0,0.1,1019.0,346.0,1.5,59.56434388326334 +2019-01-18 12:30:00-05:00,2019,1,18,12,30,23.5,14.5,96.0,919.0,732.0,0.1,1018.0,176.0,1.2000000000000002,57.028464478848036 +2019-01-18 13:30:00-05:00,2019,1,18,13,30,23.700000000000003,14.4,95.0,910.0,699.0,0.1,1017.0,16.0,1.0,55.982533576850784 +2019-01-18 14:30:00-05:00,2019,1,18,14,30,23.8,14.0,141.0,721.0,560.0,0.1,1017.0,49.0,1.0,54.22424116304208 +2019-01-18 15:30:00-05:00,2019,1,18,15,30,23.700000000000003,14.1,175.0,396.0,352.0,0.1,1017.0,76.0,1.2000000000000002,54.906250631532686 +2019-01-18 16:30:00-05:00,2019,1,18,16,30,23.4,14.5,87.0,464.0,215.0,0.1,1017.0,84.0,1.4,57.373486792708235 +2019-01-18 17:30:00-05:00,2019,1,18,17,30,23.0,14.8,30.0,250.0,49.0,0.1,1017.0,87.0,1.6,59.92579580574036 +2019-01-18 18:30:00-05:00,2019,1,18,18,30,22.700000000000003,14.9,0.0,0.0,0.0,0.1,1018.0,89.0,1.8,61.41934913544901 +2019-01-18 19:30:00-05:00,2019,1,18,19,30,22.6,15.4,0.0,0.0,0.0,0.1,1018.0,90.0,1.9,63.810781359345505 +2019-01-18 20:30:00-05:00,2019,1,18,20,30,22.5,15.7,0.0,0.0,0.0,0.1,1018.0,93.0,2.0,65.44517289783091 +2019-01-18 21:30:00-05:00,2019,1,18,21,30,22.3,16.1,0.0,0.0,0.0,0.1,1018.0,97.0,2.1,67.96048956333121 +2019-01-18 22:30:00-05:00,2019,1,18,22,30,22.1,16.3,0.0,0.0,0.0,0.1,1018.0,102.0,2.4000000000000004,69.67570931414247 +2019-01-18 23:30:00-05:00,2019,1,18,23,30,22.0,16.6,0.0,0.0,0.0,0.1,1017.0,106.0,2.8000000000000003,71.45244907859531 +2019-01-19 00:30:00-05:00,2019,1,19,0,30,21.9,16.8,0.0,0.0,0.0,0.1,1017.0,111.0,3.0,72.80838556642742 +2019-01-19 01:30:00-05:00,2019,1,19,1,30,21.9,17.1,0.0,0.0,0.0,0.1,1017.0,115.0,3.1,74.2058192800884 +2019-01-19 02:30:00-05:00,2019,1,19,2,30,21.8,17.2,0.0,0.0,0.0,0.1,1016.0,119.0,3.2,75.13412433380783 +2019-01-19 03:30:00-05:00,2019,1,19,3,30,21.8,17.3,0.0,0.0,0.0,0.1,1016.0,125.0,3.2,75.6106681157808 +2019-01-19 04:30:00-05:00,2019,1,19,4,30,21.700000000000003,17.400000000000002,0.0,0.0,0.0,0.1,1016.0,131.0,3.4000000000000004,76.55615688567448 +2019-01-19 05:30:00-05:00,2019,1,19,5,30,21.6,17.400000000000002,0.0,0.0,0.0,0.1,1016.0,135.0,3.6,77.02566195716375 +2019-01-19 06:30:00-05:00,2019,1,19,6,30,21.8,17.400000000000002,0.0,0.0,0.0,0.1,1016.0,137.0,3.9,76.08986497373662 +2019-01-19 07:30:00-05:00,2019,1,19,7,30,22.3,17.5,26.0,239.0,42.0,0.1,1017.0,139.0,4.3,74.27315840120272 +2019-01-19 08:30:00-05:00,2019,1,19,8,30,23.0,17.6,59.0,650.0,233.0,0.1,1017.0,142.0,4.6000000000000005,71.63476376732821 +2019-01-19 09:30:00-05:00,2019,1,19,9,30,23.4,17.400000000000002,76.0,799.0,430.0,0.1,1017.0,145.0,4.800000000000001,69.0469844249597 +2019-01-19 10:30:00-05:00,2019,1,19,10,30,23.700000000000003,17.2,121.0,757.0,559.0,0.1,1017.0,148.0,4.7,66.95797725147439 +2019-01-19 11:30:00-05:00,2019,1,19,11,30,23.9,17.1,356.0,228.0,507.0,0.1,1016.0,152.0,4.800000000000001,65.7398895179365 +2019-01-19 12:30:00-05:00,2019,1,19,12,30,23.9,17.1,284.0,79.0,339.0,0.1,1015.0,157.0,4.7,65.7398895179365 +2019-01-19 13:30:00-05:00,2019,1,19,13,30,24.0,17.1,281.0,386.0,538.0,0.1,1014.0,163.0,4.6000000000000005,65.34602972514544 +2019-01-19 14:30:00-05:00,2019,1,19,14,30,23.9,17.1,246.0,404.0,482.0,0.1,1014.0,170.0,4.5,65.7398895179365 +2019-01-19 15:30:00-05:00,2019,1,19,15,30,23.700000000000003,17.3,122.0,615.0,400.0,0.1,1014.0,175.0,4.4,67.38266321135747 +2019-01-19 16:30:00-05:00,2019,1,19,16,30,23.3,17.5,64.0,631.0,239.0,0.1,1014.0,177.0,4.3,69.90494216112094 +2019-01-19 17:30:00-05:00,2019,1,19,17,30,22.8,17.8,30.0,260.0,50.0,0.1,1014.0,178.0,4.3,73.42727529120872 +2019-01-19 18:30:00-05:00,2019,1,19,18,30,22.6,18.1,0.0,0.0,0.0,0.1,1014.0,179.0,4.4,75.7390012022252 +2019-01-19 19:30:00-05:00,2019,1,19,19,30,22.5,18.5,0.0,0.0,0.0,0.1,1014.0,181.0,4.7,78.13619085138018 +2019-01-19 20:30:00-05:00,2019,1,19,20,30,22.5,18.8,0.0,0.0,0.0,0.1,1014.0,183.0,5.0,79.61627855960843 +2019-01-19 21:30:00-05:00,2019,1,19,21,30,22.6,19.200000000000003,0.0,0.0,0.0,0.1,1013.0,186.0,5.300000000000001,81.13377868400069 +2019-01-19 22:30:00-05:00,2019,1,19,22,30,22.6,19.6,0.0,0.0,0.0,0.1,1013.0,189.0,5.5,83.17750178705444 +2019-01-19 23:30:00-05:00,2019,1,19,23,30,22.700000000000003,20.1,0.0,0.0,0.0,0.1,1013.0,191.0,5.800000000000001,85.27653151241127 +2019-01-20 00:30:00-05:00,2019,1,20,0,30,22.9,20.6,0.0,0.0,0.0,0.1,1012.0,192.0,6.300000000000001,86.89058340794872 +2019-01-20 01:30:00-05:00,2019,1,20,1,30,23.1,21.0,0.0,0.0,0.0,0.1,1011.0,194.0,6.7,87.98439575392055 +2019-01-20 02:30:00-05:00,2019,1,20,2,30,23.200000000000003,21.5,0.0,0.0,0.0,0.1,1011.0,198.0,7.0,90.17725044563133 +2019-01-20 03:30:00-05:00,2019,1,20,3,30,23.200000000000003,21.8,0.0,0.0,0.0,0.1,1010.0,201.0,7.2,91.84656915277186 +2019-01-20 04:30:00-05:00,2019,1,20,4,30,23.200000000000003,22.0,0.0,0.0,0.0,0.1,1010.0,205.0,7.2,92.97443596255125 +2019-01-20 05:30:00-05:00,2019,1,20,5,30,23.1,22.200000000000003,0.0,0.0,0.0,0.1,1011.0,210.0,7.300000000000001,94.68510101735447 +2019-01-20 06:30:00-05:00,2019,1,20,6,30,23.0,22.200000000000003,0.0,0.0,0.0,0.1,1011.0,217.0,7.7,95.25967482378684 +2019-01-20 07:30:00-05:00,2019,1,20,7,30,23.1,22.200000000000003,12.0,0.0,12.0,0.1,1012.0,223.0,8.0,94.68510101735447 +2019-01-20 08:30:00-05:00,2019,1,20,8,30,23.3,22.3,36.0,1.0,36.0,0.1,1012.0,232.0,8.1,94.11871484381221 +2019-01-20 09:30:00-05:00,2019,1,20,9,30,23.3,22.200000000000003,22.0,0.0,22.0,0.1,1013.0,247.0,8.3,93.54760435307956 +2019-01-20 10:30:00-05:00,2019,1,20,10,30,22.9,21.4,112.0,3.0,114.0,0.1,1013.0,269.0,8.5,91.2683085988807 +2019-01-20 11:30:00-05:00,2019,1,20,11,30,22.0,20.0,175.0,8.0,180.0,0.1,1013.0,285.0,8.8,88.43641612798179 +2019-01-20 12:30:00-05:00,2019,1,20,12,30,21.5,18.2,345.0,135.0,439.0,0.1,1013.0,288.0,9.0,81.50177083253901 +2019-01-20 13:30:00-05:00,2019,1,20,13,30,21.3,16.900000000000002,288.0,400.0,556.0,0.1,1013.0,283.0,9.0,76.0105089646011 +2019-01-20 14:30:00-05:00,2019,1,20,14,30,21.1,16.2,252.0,381.0,476.0,0.1,1013.0,282.0,9.1,73.59996635036241 +2019-01-20 15:30:00-05:00,2019,1,20,15,30,20.4,15.4,159.0,539.0,404.0,0.1,1014.0,286.0,9.2,73.00931609495443 +2019-01-20 16:30:00-05:00,2019,1,20,16,30,19.700000000000003,14.4,93.0,517.0,238.0,0.1,1014.0,290.0,8.9,71.48766500991056 +2019-01-20 17:30:00-05:00,2019,1,20,17,30,19.1,13.8,37.0,34.0,40.0,0.1,1015.0,293.0,8.4,71.37664994196926 +2019-01-20 18:30:00-05:00,2019,1,20,18,30,18.6,13.6,0.0,0.0,0.0,0.1,1016.0,296.0,8.0,72.6898124112037 +2019-01-20 19:30:00-05:00,2019,1,20,19,30,18.1,13.5,0.0,0.0,0.0,0.1,1017.0,298.0,7.7,74.51811759261798 +2019-01-20 20:30:00-05:00,2019,1,20,20,30,17.5,13.4,0.0,0.0,0.0,0.1,1018.0,302.0,7.5,76.88467094455378 +2019-01-20 21:30:00-05:00,2019,1,20,21,30,16.8,13.3,0.0,0.0,0.0,0.1,1018.0,307.0,7.6,79.84458710289917 +2019-01-20 22:30:00-05:00,2019,1,20,22,30,15.9,13.2,0.0,0.0,0.0,0.1,1019.0,314.0,8.200000000000001,84.00356792444678 +2019-01-20 23:30:00-05:00,2019,1,20,23,30,15.0,12.8,0.0,0.0,0.0,0.1,1020.0,322.0,8.700000000000001,86.69713639932762 +2019-01-21 00:30:00-05:00,2019,1,21,0,30,14.1,12.3,0.0,0.0,0.0,0.1,1020.0,329.0,8.5,88.9199908723776 +2019-01-21 01:30:00-05:00,2019,1,21,1,30,13.5,11.8,0.0,0.0,0.0,0.1,1020.0,335.0,8.0,89.4592793670136 +2019-01-21 02:30:00-05:00,2019,1,21,2,30,13.2,11.4,0.0,0.0,0.0,0.1,1020.0,340.0,7.6,88.84627958630638 +2019-01-21 03:30:00-05:00,2019,1,21,3,30,12.9,11.2,0.0,0.0,0.0,0.1,1020.0,345.0,7.1000000000000005,89.41236048141903 +2019-01-21 04:30:00-05:00,2019,1,21,4,30,12.6,11.0,0.0,0.0,0.0,0.1,1020.0,350.0,6.2,89.98431008084961 +2019-01-21 05:30:00-05:00,2019,1,21,5,30,12.3,10.8,0.0,0.0,0.0,0.1,1021.0,352.0,5.5,90.56220498879804 +2019-01-21 06:30:00-05:00,2019,1,21,6,30,12.4,10.9,0.0,0.0,0.0,0.1,1021.0,354.0,5.300000000000001,90.56925386231364 +2019-01-21 07:30:00-05:00,2019,1,21,7,30,12.9,11.2,29.0,247.0,46.0,0.1,1022.0,356.0,5.1000000000000005,89.41236048141903 +2019-01-21 08:30:00-05:00,2019,1,21,8,30,13.7,11.4,68.0,663.0,247.0,0.1,1023.0,179.0,4.800000000000001,86.00034235635995 +2019-01-21 09:30:00-05:00,2019,1,21,9,30,14.7,11.7,90.0,806.0,450.0,0.1,1023.0,4.0,4.3,82.22372569087761 +2019-01-21 10:30:00-05:00,2019,1,21,10,30,16.0,12.0,108.0,862.0,610.0,0.1,1023.0,8.0,4.1000000000000005,77.15504026859814 +2019-01-21 11:30:00-05:00,2019,1,21,11,30,17.3,12.5,121.0,883.0,711.0,0.1,1023.0,12.0,4.2,73.41338322781999 +2019-01-21 12:30:00-05:00,2019,1,21,12,30,18.3,13.0,123.0,890.0,746.0,0.1,1022.0,16.0,4.4,71.2276979425602 +2019-01-21 13:30:00-05:00,2019,1,21,13,30,19.200000000000003,13.5,119.0,880.0,711.0,0.1,1022.0,20.0,4.800000000000001,69.56323111631204 +2019-01-21 14:30:00-05:00,2019,1,21,14,30,19.8,14.1,115.0,830.0,605.0,0.1,1022.0,26.0,5.1000000000000005,69.6797365710662 +2019-01-21 15:30:00-05:00,2019,1,21,15,30,20.1,14.5,116.0,690.0,432.0,0.1,1022.0,30.0,5.4,70.18978021468729 +2019-01-21 16:30:00-05:00,2019,1,21,16,30,20.1,14.9,97.0,441.0,222.0,0.1,1022.0,31.0,5.5,72.02374210835481 +2019-01-21 17:30:00-05:00,2019,1,21,17,30,20.1,15.1,37.0,187.0,52.0,0.1,1023.0,32.0,5.6000000000000005,72.95642818843476 +2019-01-21 18:30:00-05:00,2019,1,21,18,30,20.200000000000003,15.2,0.0,0.0,0.0,0.1,1023.0,34.0,5.7,72.97407351745628 +2019-01-21 19:30:00-05:00,2019,1,21,19,30,20.200000000000003,15.3,0.0,0.0,0.0,0.1,1024.0,36.0,5.9,73.44413570469506 +2019-01-21 20:30:00-05:00,2019,1,21,20,30,20.200000000000003,15.3,0.0,0.0,0.0,0.1,1024.0,41.0,6.0,73.44413570469506 +2019-01-21 21:30:00-05:00,2019,1,21,21,30,20.3,15.3,0.0,0.0,0.0,0.1,1024.0,50.0,6.2,72.99170281381461 +2019-01-21 22:30:00-05:00,2019,1,21,22,30,20.4,15.2,0.0,0.0,0.0,0.1,1024.0,60.0,6.300000000000001,72.0781065068981 +2019-01-21 23:30:00-05:00,2019,1,21,23,30,20.6,15.2,0.0,0.0,0.0,0.1,1024.0,67.0,6.5,71.19447449796583 +2019-01-22 00:30:00-05:00,2019,1,22,0,30,20.8,15.3,0.0,0.0,0.0,0.1,1023.0,74.0,6.6000000000000005,70.77597559795171 +2019-01-22 01:30:00-05:00,2019,1,22,1,30,20.9,15.4,0.0,0.0,0.0,0.1,1023.0,78.0,6.7,70.79470910392617 +2019-01-22 02:30:00-05:00,2019,1,22,2,30,21.0,15.5,0.0,0.0,0.0,0.1,1023.0,81.0,6.800000000000001,70.8134260519586 +2019-01-22 03:30:00-05:00,2019,1,22,3,30,21.1,15.6,0.0,0.0,0.0,0.1,1023.0,84.0,6.9,70.83212645923042 +2019-01-22 04:30:00-05:00,2019,1,22,4,30,21.200000000000003,15.6,0.0,0.0,0.0,0.1,1023.0,85.0,6.9,70.39874291029768 +2019-01-22 05:30:00-05:00,2019,1,22,5,30,21.3,15.6,0.0,0.0,0.0,0.1,1023.0,86.0,7.0,69.968335895452 +2019-01-22 06:30:00-05:00,2019,1,22,6,30,21.4,15.6,0.0,0.0,0.0,0.1,1024.0,86.0,7.2,69.54088286535064 +2019-01-22 07:30:00-05:00,2019,1,22,7,30,21.700000000000003,15.7,31.0,170.0,43.0,0.1,1024.0,87.0,7.4,68.71446131354351 +2019-01-22 08:30:00-05:00,2019,1,22,8,30,22.1,15.8,88.0,263.0,159.0,0.1,1025.0,88.0,7.7,67.48770864197544 +2019-01-22 09:30:00-05:00,2019,1,22,9,30,22.4,15.8,158.0,439.0,355.0,0.1,1025.0,89.0,7.9,66.26660576599681 +2019-01-22 10:30:00-05:00,2019,1,22,10,30,22.6,15.9,263.0,148.0,350.0,0.1,1026.0,89.0,8.0,65.88607907918012 +2019-01-22 11:30:00-05:00,2019,1,22,11,30,22.6,16.0,219.0,569.0,601.0,0.1,1025.0,88.0,8.0,66.3081855879057 +2019-01-22 12:30:00-05:00,2019,1,22,12,30,22.700000000000003,16.2,319.0,284.0,519.0,0.1,1025.0,88.0,8.1,66.75323577884919 +2019-01-22 13:30:00-05:00,2019,1,22,13,30,22.700000000000003,16.2,232.0,537.0,595.0,0.1,1024.0,88.0,8.1,66.75323577884919 +2019-01-22 14:30:00-05:00,2019,1,22,14,30,22.700000000000003,16.2,280.0,139.0,363.0,0.1,1024.0,89.0,8.200000000000001,66.75323577884919 +2019-01-22 15:30:00-05:00,2019,1,22,15,30,22.6,16.3,170.0,294.0,305.0,0.1,1024.0,90.0,8.200000000000001,67.58880290394055 +2019-01-22 16:30:00-05:00,2019,1,22,16,30,22.4,16.3,81.0,469.0,216.0,0.1,1024.0,91.0,8.200000000000001,68.41501739347423 +2019-01-22 17:30:00-05:00,2019,1,22,17,30,22.3,16.400000000000002,37.0,190.0,53.0,0.1,1024.0,93.0,8.200000000000001,69.27199609705535 +2019-01-22 18:30:00-05:00,2019,1,22,18,30,22.3,16.400000000000002,0.0,0.0,0.0,0.1,1024.0,95.0,8.3,69.27199609705535 +2019-01-22 19:30:00-05:00,2019,1,22,19,30,22.3,16.400000000000002,0.0,0.0,0.0,0.09,1024.0,98.0,8.4,69.27199609705535 +2019-01-22 20:30:00-05:00,2019,1,22,20,30,22.3,16.5,0.0,0.0,0.0,0.09,1024.0,100.0,8.4,69.71408079456931 +2019-01-22 21:30:00-05:00,2019,1,22,21,30,22.3,16.5,0.0,0.0,0.0,0.09,1024.0,102.0,8.4,69.71408079456931 +2019-01-22 22:30:00-05:00,2019,1,22,22,30,22.200000000000003,16.5,0.0,0.0,0.0,0.09,1024.0,104.0,8.5,70.13968702114128 +2019-01-22 23:30:00-05:00,2019,1,22,23,30,22.200000000000003,16.5,0.0,0.0,0.0,0.09,1023.0,106.0,8.5,70.13968702114128 +2019-01-23 00:30:00-05:00,2019,1,23,0,30,22.1,16.5,0.0,0.0,0.0,0.09,1023.0,108.0,8.4,70.56821557181696 +2019-01-23 01:30:00-05:00,2019,1,23,1,30,22.1,16.6,0.0,0.0,0.0,0.09,1023.0,109.0,8.4,71.01822455765442 +2019-01-23 02:30:00-05:00,2019,1,23,2,30,22.1,16.6,0.0,0.0,0.0,0.09,1022.0,109.0,8.4,71.01822455765442 +2019-01-23 03:30:00-05:00,2019,1,23,3,30,22.1,16.7,0.0,0.0,0.0,0.09,1022.0,110.0,8.3,71.4707533963589 +2019-01-23 04:30:00-05:00,2019,1,23,4,30,22.200000000000003,16.7,0.0,0.0,0.0,0.09,1022.0,113.0,8.200000000000001,71.03674414558691 +2019-01-23 05:30:00-05:00,2019,1,23,5,30,22.3,16.8,0.0,0.0,0.0,0.09,1022.0,115.0,8.200000000000001,71.05524739758785 +2019-01-23 06:30:00-05:00,2019,1,23,6,30,22.5,16.900000000000002,0.0,0.0,0.0,0.09,1022.0,118.0,8.3,70.64310788526079 +2019-01-23 07:30:00-05:00,2019,1,23,7,30,22.700000000000003,17.0,32.0,141.0,42.0,0.09,1022.0,121.0,8.200000000000001,70.23430175657111 +2019-01-23 08:30:00-05:00,2019,1,23,8,30,23.0,17.3,83.0,432.0,201.0,0.09,1022.0,124.0,8.0,70.29087277152748 +2019-01-23 09:30:00-05:00,2019,1,23,9,30,23.3,17.6,186.0,327.0,333.0,0.09,1022.0,126.0,8.0,70.3472959699732 +2019-01-23 10:30:00-05:00,2019,1,23,10,30,23.5,17.900000000000002,271.0,147.0,357.0,0.09,1022.0,128.0,8.0,70.82919308847428 +2019-01-23 11:30:00-05:00,2019,1,23,11,30,23.700000000000003,18.4,226.0,6.0,230.0,0.09,1021.0,129.0,7.9,72.21274364304877 +2019-01-23 12:30:00-05:00,2019,1,23,12,30,23.8,18.8,325.0,71.0,375.0,0.09,1020.0,131.0,7.6,73.5987618697542 +2019-01-23 13:30:00-05:00,2019,1,23,13,30,23.9,19.1,321.0,120.0,402.0,0.09,1019.0,132.0,7.4,74.54006608751507 +2019-01-23 14:30:00-05:00,2019,1,23,14,30,23.9,19.4,226.0,15.0,235.0,0.09,1018.0,132.0,7.2,75.9455215829456 +2019-01-23 15:30:00-05:00,2019,1,23,15,30,23.8,19.700000000000003,129.0,2.0,130.0,0.09,1018.0,131.0,7.0,77.84088394506858 +2019-01-23 16:30:00-05:00,2019,1,23,16,30,23.6,19.9,53.0,139.0,93.0,0.09,1018.0,130.0,6.7,79.76690260624395 +2019-01-23 17:30:00-05:00,2019,1,23,17,30,23.5,20.0,32.0,68.0,38.0,0.09,1018.0,130.0,6.6000000000000005,80.74768587999363 +2019-01-23 18:30:00-05:00,2019,1,23,18,30,23.5,20.200000000000003,0.0,0.0,0.0,0.09,1018.0,132.0,6.7,81.7529559318024 +2019-01-23 19:30:00-05:00,2019,1,23,19,30,23.6,20.3,0.0,0.0,0.0,0.09,1018.0,136.0,7.0,81.7653861405178 +2019-01-23 20:30:00-05:00,2019,1,23,20,30,23.6,20.4,0.0,0.0,0.0,0.09,1017.0,140.0,7.4,82.27181600810616 +2019-01-23 21:30:00-05:00,2019,1,23,21,30,23.6,20.6,0.0,0.0,0.0,0.09,1017.0,144.0,7.6,83.29293471950034 +2019-01-23 22:30:00-05:00,2019,1,23,22,30,23.5,20.8,0.0,0.0,0.0,0.09,1017.0,149.0,7.800000000000001,84.83493359135012 +2019-01-23 23:30:00-05:00,2019,1,23,23,30,23.5,21.1,0.0,0.0,0.0,0.09,1016.0,152.0,7.800000000000001,86.41377434235692 +2019-01-24 00:30:00-05:00,2019,1,24,0,30,23.4,21.3,0.0,0.0,0.0,0.09,1015.0,156.0,7.9,88.00984897364457 +2019-01-24 01:30:00-05:00,2019,1,24,1,30,23.4,21.6,0.0,0.0,0.0,0.09,1015.0,159.0,8.0,89.64153331568741 +2019-01-24 02:30:00-05:00,2019,1,24,2,30,23.5,21.8,0.0,0.0,0.0,0.09,1014.0,162.0,8.200000000000001,90.1982966625472 +2019-01-24 03:30:00-05:00,2019,1,24,3,30,23.6,22.0,0.0,0.0,0.0,0.09,1013.0,164.0,8.3,90.75725438930368 +2019-01-24 04:30:00-05:00,2019,1,24,4,30,23.6,22.1,0.0,0.0,0.0,0.09,1013.0,165.0,8.200000000000001,91.3121686462366 +2019-01-24 05:30:00-05:00,2019,1,24,5,30,23.8,22.3,0.0,0.0,0.0,0.09,1014.0,167.0,8.0,91.32464040055406 +2019-01-24 06:30:00-05:00,2019,1,24,6,30,23.9,22.5,0.0,0.0,0.0,0.09,1015.0,172.0,7.6,91.88760131439639 +2019-01-24 07:30:00-05:00,2019,1,24,7,30,24.1,22.5,23.0,2.0,23.0,0.09,1015.0,178.0,7.1000000000000005,90.790276906083 +2019-01-24 08:30:00-05:00,2019,1,24,8,30,24.5,22.6,111.0,98.0,138.0,0.09,1016.0,180.0,6.800000000000001,89.17948197114916 +2019-01-24 09:30:00-05:00,2019,1,24,9,30,24.8,22.700000000000003,160.0,439.0,358.0,0.09,1016.0,181.0,6.800000000000001,88.12759322404813 +2019-01-24 10:30:00-05:00,2019,1,24,10,30,25.1,22.6,251.0,237.0,391.0,0.09,1016.0,184.0,6.7,86.04102419869523 +2019-01-24 11:30:00-05:00,2019,1,24,11,30,25.3,22.5,217.0,572.0,604.0,0.09,1015.0,191.0,6.4,84.50788366750213 +2019-01-24 12:30:00-05:00,2019,1,24,12,30,25.3,22.4,297.0,252.0,476.0,0.09,1014.0,197.0,5.9,83.99586147124604 +2019-01-24 13:30:00-05:00,2019,1,24,13,30,25.3,22.3,220.0,20.0,234.0,0.09,1014.0,200.0,5.5,83.48655911737376 +2019-01-24 14:30:00-05:00,2019,1,24,14,30,25.200000000000003,22.3,228.0,39.0,251.0,0.09,1013.0,201.0,5.1000000000000005,83.9848780598474 +2019-01-24 15:30:00-05:00,2019,1,24,15,30,24.9,22.4,180.0,66.0,211.0,0.09,1013.0,199.0,4.7,86.0216165010677 +2019-01-24 16:30:00-05:00,2019,1,24,16,30,24.5,22.5,40.0,1.0,40.0,0.09,1014.0,198.0,4.5,88.63956075011559 +2019-01-24 17:30:00-05:00,2019,1,24,17,30,24.0,22.6,21.0,0.0,21.0,0.09,1014.0,198.0,4.3,91.89343814488669 +2019-01-24 18:30:00-05:00,2019,1,24,18,30,23.6,22.6,0.0,0.0,0.0,0.09,1015.0,201.0,4.1000000000000005,94.13156981758299 +2019-01-24 19:30:00-05:00,2019,1,24,19,30,23.4,22.6,0.0,0.0,0.0,0.09,1016.0,207.0,4.0,95.27357656218281 +2019-01-24 20:30:00-05:00,2019,1,24,20,30,23.200000000000003,22.6,0.0,0.0,0.0,0.09,1016.0,215.0,4.0,96.43118524187913 +2019-01-24 21:30:00-05:00,2019,1,24,21,30,23.0,22.5,0.0,0.0,0.0,0.09,1016.0,223.0,3.8,97.01370183280618 +2019-01-24 22:30:00-05:00,2019,1,24,22,30,22.8,22.4,0.0,0.0,0.0,0.09,1016.0,233.0,3.6,97.60106694646583 +2019-01-24 23:30:00-05:00,2019,1,24,23,30,22.5,22.1,0.0,0.0,0.0,0.09,1016.0,248.0,3.5,97.59570506724275 +2019-01-25 00:30:00-05:00,2019,1,25,0,30,22.1,21.700000000000003,0.0,0.0,0.0,0.09,1016.0,269.0,3.5,97.58852800306377 +2019-01-25 01:30:00-05:00,2019,1,25,1,30,21.4,21.1,0.0,0.0,0.0,0.09,1016.0,291.0,3.7,98.17703680741111 +2019-01-25 02:30:00-05:00,2019,1,25,2,30,20.8,20.4,0.0,0.0,0.0,0.09,1016.0,310.0,4.2,97.56498021355463 +2019-01-25 03:30:00-05:00,2019,1,25,3,30,20.1,19.6,0.0,0.0,0.0,0.09,1016.0,321.0,4.7,96.94847441716493 +2019-01-25 04:30:00-05:00,2019,1,25,4,30,19.6,18.8,0.0,0.0,0.0,0.09,1016.0,329.0,5.2,95.13901516968379 +2019-01-25 05:30:00-05:00,2019,1,25,5,30,19.1,18.1,0.0,0.0,0.0,0.09,1017.0,335.0,5.7,93.93427002875684 +2019-01-25 06:30:00-05:00,2019,1,25,6,30,18.7,17.400000000000002,0.0,0.0,0.0,0.09,1018.0,338.0,6.1000000000000005,92.15569812666644 +2019-01-25 07:30:00-05:00,2019,1,25,7,30,18.3,16.8,12.0,0.0,12.0,0.09,1019.0,338.0,6.4,90.97179071488311 +2019-01-25 08:30:00-05:00,2019,1,25,8,30,17.900000000000002,16.3,82.0,8.0,84.0,0.09,1020.0,336.0,6.6000000000000005,90.368144065778 +2019-01-25 09:30:00-05:00,2019,1,25,9,30,17.6,15.8,221.0,84.0,259.0,0.09,1021.0,334.0,6.9,89.19995445855837 +2019-01-25 10:30:00-05:00,2019,1,25,10,30,17.3,15.3,269.0,31.0,287.0,0.09,1021.0,336.0,6.9,88.03982626849958 +2019-01-25 11:30:00-05:00,2019,1,25,11,30,17.1,14.8,314.0,52.0,349.0,0.09,1020.0,341.0,6.4,86.3393308014476 +2019-01-25 12:30:00-05:00,2019,1,25,12,30,17.2,14.3,344.0,163.0,460.0,0.09,1019.0,348.0,5.800000000000001,83.06960667778529 +2019-01-25 13:30:00-05:00,2019,1,25,13,30,17.6,13.9,347.0,209.0,490.0,0.09,1018.0,356.0,5.300000000000001,78.92548771138722 +2019-01-25 14:30:00-05:00,2019,1,25,14,30,18.2,13.5,314.0,201.0,435.0,0.09,1017.0,183.0,5.1000000000000005,74.05168067317032 +2019-01-25 15:30:00-05:00,2019,1,25,15,30,18.6,13.2,182.0,365.0,354.0,0.09,1017.0,9.0,5.2,70.8203204429854 +2019-01-25 16:30:00-05:00,2019,1,25,16,30,18.7,13.1,107.0,402.0,226.0,0.09,1017.0,14.0,5.300000000000001,69.92088310366232 +2019-01-25 17:30:00-05:00,2019,1,25,17,30,18.7,13.4,39.0,2.0,39.0,0.09,1018.0,18.0,5.4,71.30230754214557 +2019-01-25 18:30:00-05:00,2019,1,25,18,30,18.8,13.6,0.0,0.0,0.0,0.09,1019.0,21.0,5.5,71.78645915321792 +2019-01-25 19:30:00-05:00,2019,1,25,19,30,18.9,14.0,0.0,0.0,0.0,0.09,1019.0,23.0,5.5,73.21677186362466 +2019-01-25 20:30:00-05:00,2019,1,25,20,30,19.0,14.4,0.0,0.0,0.0,0.09,1020.0,24.0,5.2,74.6699116293101 +2019-01-25 21:30:00-05:00,2019,1,25,21,30,19.0,14.7,0.0,0.0,0.0,0.09,1020.0,25.0,4.800000000000001,76.13016520476386 +2019-01-25 22:30:00-05:00,2019,1,25,22,30,18.9,15.0,0.0,0.0,0.0,0.09,1020.0,24.0,4.5,78.10137447281262 +2019-01-25 23:30:00-05:00,2019,1,25,23,30,18.8,15.3,0.0,0.0,0.0,0.09,1020.0,20.0,4.4,80.12040984377265 +2019-01-26 00:30:00-05:00,2019,1,26,0,30,18.6,15.6,0.0,0.0,0.0,0.09,1019.0,13.0,4.7,82.70406260968358 +2019-01-26 01:30:00-05:00,2019,1,26,1,30,18.4,15.9,0.0,0.0,0.0,0.09,1019.0,8.0,5.2,85.36885963246381 +2019-01-26 02:30:00-05:00,2019,1,26,2,30,18.3,16.1,0.0,0.0,0.0,0.09,1019.0,11.0,5.7,87.00958282281972 +2019-01-26 03:30:00-05:00,2019,1,26,3,30,18.4,16.3,0.0,0.0,0.0,0.09,1019.0,18.0,5.800000000000001,87.57508579162156 +2019-01-26 04:30:00-05:00,2019,1,26,4,30,18.6,16.5,0.0,0.0,0.0,0.09,1018.0,25.0,5.7,87.59291581941238 +2019-01-26 05:30:00-05:00,2019,1,26,5,30,18.7,16.7,0.0,0.0,0.0,0.09,1018.0,28.0,5.7,88.16001646077532 +2019-01-26 06:30:00-05:00,2019,1,26,6,30,18.9,16.900000000000002,0.0,0.0,0.0,0.09,1018.0,31.0,5.800000000000001,88.17704199639206 +2019-01-26 07:30:00-05:00,2019,1,26,7,30,19.200000000000003,17.1,5.0,0.0,5.0,0.09,1018.0,40.0,5.9,87.64618197365981 +2019-01-26 08:30:00-05:00,2019,1,26,8,30,19.700000000000003,17.3,15.0,0.0,15.0,0.09,1018.0,52.0,5.7,86.04521709465872 +2019-01-26 09:30:00-05:00,2019,1,26,9,30,20.3,17.6,42.0,0.0,42.0,0.09,1018.0,59.0,5.1000000000000005,84.49270531795345 +2019-01-26 10:30:00-05:00,2019,1,26,10,30,20.5,17.7,151.0,9.0,156.0,0.09,1018.0,56.0,4.6000000000000005,83.98379473708378 +2019-01-26 11:30:00-05:00,2019,1,26,11,30,20.6,17.6,65.0,0.0,65.0,0.09,1017.0,45.0,4.4,82.94315357773108 +2019-01-26 12:30:00-05:00,2019,1,26,12,30,20.4,17.6,164.0,0.0,164.0,0.09,1017.0,36.0,4.5,83.97260461223745 +2019-01-26 13:30:00-05:00,2019,1,26,13,30,20.4,17.6,239.0,4.0,242.0,0.09,1016.0,37.0,4.800000000000001,83.97260461223745 +2019-01-26 14:30:00-05:00,2019,1,26,14,30,20.4,17.7,99.0,1.0,100.0,0.09,1015.0,43.0,5.0,84.50356877594058 +2019-01-26 15:30:00-05:00,2019,1,26,15,30,20.3,17.8,65.0,2.0,66.0,0.09,1016.0,46.0,5.1000000000000005,85.5641754903033 +2019-01-26 16:30:00-05:00,2019,1,26,16,30,20.1,17.900000000000002,26.0,0.0,26.0,0.09,1016.0,45.0,5.1000000000000005,87.17551035426513 +2019-01-26 17:30:00-05:00,2019,1,26,17,30,19.9,17.900000000000002,13.0,0.0,13.0,0.09,1016.0,43.0,5.1000000000000005,88.26163421505619 +2019-01-26 18:30:00-05:00,2019,1,26,18,30,19.9,18.0,0.0,0.0,0.0,0.09,1016.0,44.0,5.4,88.81843188840548 +2019-01-26 19:30:00-05:00,2019,1,26,19,30,20.0,18.1,0.0,0.0,0.0,0.09,1016.0,48.0,5.7,88.82646755484863 +2019-01-26 20:30:00-05:00,2019,1,26,20,30,20.1,18.2,0.0,0.0,0.0,0.09,1016.0,53.0,5.800000000000001,88.83449475417073 +2019-01-26 21:30:00-05:00,2019,1,26,21,30,20.3,18.4,0.0,0.0,0.0,0.09,1016.0,61.0,5.7,88.85052379780615 +2019-01-26 22:30:00-05:00,2019,1,26,22,30,20.6,18.4,0.0,0.0,0.0,0.09,1015.0,69.0,5.6000000000000005,87.22105196053377 +2019-01-26 23:30:00-05:00,2019,1,26,23,30,20.8,18.5,0.0,0.0,0.0,0.09,1014.0,77.0,5.300000000000001,86.69480298335615 +2019-01-27 00:30:00-05:00,2019,1,27,0,30,21.0,18.6,0.0,0.0,0.0,0.09,1014.0,83.0,5.1000000000000005,86.17292431656145 +2019-01-27 01:30:00-05:00,2019,1,27,1,30,21.1,18.8,0.0,0.0,0.0,0.09,1013.0,88.0,5.1000000000000005,86.72303177339619 +2019-01-27 02:30:00-05:00,2019,1,27,2,30,21.3,19.0,0.0,0.0,0.0,0.09,1012.0,94.0,5.300000000000001,86.74180244692695 +2019-01-27 03:30:00-05:00,2019,1,27,3,30,21.5,19.3,0.0,0.0,0.0,0.09,1012.0,102.0,5.7,87.30243350968101 +2019-01-27 04:30:00-05:00,2019,1,27,4,30,21.700000000000003,19.6,0.0,0.0,0.0,0.09,1011.0,113.0,5.6000000000000005,87.86456213960803 +2019-01-27 05:30:00-05:00,2019,1,27,5,30,21.9,20.0,0.0,0.0,0.0,0.09,1011.0,126.0,5.5,88.97754958804926 +2019-01-27 06:30:00-05:00,2019,1,27,6,30,22.3,20.3,0.0,0.0,0.0,0.09,1011.0,142.0,5.7,88.46107330762102 +2019-01-27 07:30:00-05:00,2019,1,27,7,30,22.700000000000003,20.8,13.0,0.0,13.0,0.09,1011.0,154.0,6.1000000000000005,89.04026756651004 +2019-01-27 08:30:00-05:00,2019,1,27,8,30,23.3,21.3,100.0,157.0,144.0,0.09,1010.0,162.0,6.6000000000000005,88.54270976265246 +2019-01-27 09:30:00-05:00,2019,1,27,9,30,23.700000000000003,21.700000000000003,163.0,9.0,167.0,0.09,1010.0,168.0,6.800000000000001,88.57512745700429 +2019-01-27 10:30:00-05:00,2019,1,27,10,30,23.9,22.0,91.0,1.0,92.0,0.09,1009.0,174.0,6.7,89.13336699937618 +2019-01-27 11:30:00-05:00,2019,1,27,11,30,23.9,22.1,269.0,81.0,324.0,0.09,1008.0,181.0,6.5,89.67835237216217 +2019-01-27 12:30:00-05:00,2019,1,27,12,30,23.9,22.1,288.0,41.0,317.0,0.09,1007.0,189.0,6.300000000000001,89.67835237216217 +2019-01-27 13:30:00-05:00,2019,1,27,13,30,23.8,22.1,302.0,250.0,475.0,0.09,1007.0,198.0,6.0,90.21927659400626 +2019-01-27 14:30:00-05:00,2019,1,27,14,30,23.700000000000003,22.1,210.0,30.0,228.0,0.09,1006.0,203.0,5.300000000000001,90.76387283372435 +2019-01-27 15:30:00-05:00,2019,1,27,15,30,23.5,22.1,83.0,1.0,83.0,0.09,1006.0,195.0,4.800000000000001,91.86419181215088 +2019-01-27 16:30:00-05:00,2019,1,27,16,30,23.5,22.1,38.0,1.0,38.0,0.09,1005.0,180.0,5.1000000000000005,91.86419181215088 +2019-01-27 17:30:00-05:00,2019,1,27,17,30,23.6,22.1,24.0,0.0,24.0,0.09,1005.0,171.0,5.9,91.3121686462366 +2019-01-27 18:30:00-05:00,2019,1,27,18,30,23.700000000000003,22.1,0.0,0.0,0.0,0.09,1005.0,173.0,6.2,90.76387283372435 +2019-01-27 19:30:00-05:00,2019,1,27,19,30,23.6,22.1,0.0,0.0,0.0,0.09,1005.0,183.0,5.9,91.3121686462366 +2019-01-27 20:30:00-05:00,2019,1,27,20,30,23.1,22.1,0.0,0.0,0.0,0.09,1006.0,203.0,5.800000000000001,94.11012163349497 +2019-01-27 21:30:00-05:00,2019,1,27,21,30,22.0,21.6,0.0,0.0,0.0,0.09,1006.0,232.0,6.2,97.58672873666778 +2019-01-27 22:30:00-05:00,2019,1,27,22,30,20.200000000000003,20.200000000000003,0.0,0.0,0.0,0.09,1006.0,263.0,6.800000000000001,100.0 +2019-01-27 23:30:00-05:00,2019,1,27,23,30,18.2,18.2,0.0,0.0,0.0,0.09,1006.0,288.0,7.800000000000001,100.0 +2019-01-28 00:30:00-05:00,2019,1,28,0,30,16.5,16.5,0.0,0.0,0.0,0.09,1007.0,304.0,9.1,100.0 +2019-01-28 01:30:00-05:00,2019,1,28,1,30,15.4,15.2,0.0,0.0,0.0,0.09,1008.0,313.0,10.1,98.72453319950947 +2019-01-28 02:30:00-05:00,2019,1,28,2,30,14.9,14.4,0.0,0.0,0.0,0.09,1009.0,318.0,10.5,96.8260714568847 +2019-01-28 03:30:00-05:00,2019,1,28,3,30,14.7,14.0,0.0,0.0,0.0,0.09,1010.0,321.0,10.3,95.57481743136847 +2019-01-28 04:30:00-05:00,2019,1,28,4,30,14.5,13.7,0.0,0.0,0.0,0.09,1010.0,325.0,9.7,94.9492823686514 +2019-01-28 05:30:00-05:00,2019,1,28,5,30,14.3,13.5,0.0,0.0,0.0,0.09,1011.0,325.0,9.3,94.9416187988495 +2019-01-28 06:30:00-05:00,2019,1,28,6,30,13.9,13.2,0.0,0.0,0.0,0.09,1012.0,321.0,9.2,95.54780510150113 +2019-01-28 07:30:00-05:00,2019,1,28,7,30,13.5,12.8,32.0,243.0,50.0,0.09,1013.0,320.0,9.1,95.53420683280841 +2019-01-28 08:30:00-05:00,2019,1,28,8,30,13.7,12.6,84.0,84.0,108.0,0.09,1014.0,321.0,8.5,93.07242864482721 +2019-01-28 09:30:00-05:00,2019,1,28,9,30,14.3,12.5,94.0,786.0,456.0,0.09,1014.0,322.0,7.7,88.93627409080955 +2019-01-28 10:30:00-05:00,2019,1,28,10,30,15.0,12.6,119.0,829.0,616.0,0.09,1015.0,320.0,7.0,85.56889233057333 +2019-01-28 11:30:00-05:00,2019,1,28,11,30,15.6,12.7,105.0,931.0,745.0,0.09,1014.0,317.0,6.6000000000000005,82.87750924942381 +2019-01-28 12:30:00-05:00,2019,1,28,12,30,16.1,12.7,105.0,948.0,788.0,0.09,1014.0,317.0,6.300000000000001,80.27097855946434 +2019-01-28 13:30:00-05:00,2019,1,28,13,30,16.5,12.7,102.0,942.0,757.0,0.09,1013.0,319.0,5.9,78.25183863076656 +2019-01-28 14:30:00-05:00,2019,1,28,14,30,16.8,12.6,206.0,597.0,572.0,0.09,1013.0,325.0,5.5,76.2734457329867 +2019-01-28 15:30:00-05:00,2019,1,28,15,30,16.8,12.5,87.0,839.0,490.0,0.09,1013.0,331.0,5.1000000000000005,75.7749408151138 +2019-01-28 16:30:00-05:00,2019,1,28,16,30,16.400000000000002,12.6,85.0,586.0,264.0,0.09,1014.0,334.0,4.6000000000000005,78.236933563031 +2019-01-28 17:30:00-05:00,2019,1,28,17,30,15.9,12.7,40.0,311.0,72.0,0.09,1014.0,336.0,4.2,81.30240247635517 +2019-01-28 18:30:00-05:00,2019,1,28,18,30,15.4,12.8,0.0,0.0,0.0,0.09,1015.0,336.0,4.0,84.4979726170329 +2019-01-28 19:30:00-05:00,2019,1,28,19,30,15.1,12.8,0.0,0.0,0.0,0.07,1015.0,334.0,3.6,86.1413938927045 +2019-01-28 20:30:00-05:00,2019,1,28,20,30,14.7,12.8,0.0,0.0,0.0,0.07,1016.0,332.0,3.0,88.3886152698311 +2019-01-28 21:30:00-05:00,2019,1,28,21,30,14.5,12.7,0.0,0.0,0.0,0.07,1016.0,326.0,2.3000000000000003,88.95252222620354 +2019-01-28 22:30:00-05:00,2019,1,28,22,30,14.6,12.4,0.0,0.0,0.0,0.07,1016.0,313.0,1.6,86.65852074243855 +2019-01-28 23:30:00-05:00,2019,1,28,23,30,14.8,12.0,0.0,0.0,0.0,0.07,1015.0,292.0,1.3,83.32744452477554 +2019-01-29 00:30:00-05:00,2019,1,29,0,30,15.0,12.1,0.0,0.0,0.0,0.07,1015.0,279.0,1.5,82.80466099679545 +2019-01-29 01:30:00-05:00,2019,1,29,1,30,15.1,12.4,0.0,0.0,0.0,0.07,1015.0,281.0,2.1,83.91223739715117 +2019-01-29 02:30:00-05:00,2019,1,29,2,30,15.1,12.8,0.0,0.0,0.0,0.07,1015.0,289.0,2.7,86.1413938927045 +2019-01-29 03:30:00-05:00,2019,1,29,3,30,15.0,13.0,0.0,0.0,0.0,0.07,1015.0,298.0,3.2,87.83845905653965 +2019-01-29 04:30:00-05:00,2019,1,29,4,30,14.9,13.1,0.0,0.0,0.0,0.07,1015.0,306.0,3.7,88.98491364044341 +2019-01-29 05:30:00-05:00,2019,1,29,5,30,14.8,13.1,0.0,0.0,0.0,0.07,1015.0,310.0,3.9,89.55989481263302 +2019-01-29 06:30:00-05:00,2019,1,29,6,30,14.7,13.1,0.0,0.0,0.0,0.07,1016.0,309.0,3.8,90.13904176219975 +2019-01-29 07:30:00-05:00,2019,1,29,7,30,15.0,12.9,35.0,226.0,52.0,0.07,1016.0,302.0,3.5,87.26615484007344 +2019-01-29 08:30:00-05:00,2019,1,29,8,30,15.4,12.6,73.0,648.0,256.0,0.07,1017.0,286.0,3.0,83.3983476422492 +2019-01-29 09:30:00-05:00,2019,1,29,9,30,16.2,12.4,86.0,825.0,468.0,0.07,1017.0,267.0,3.0,78.20707982058062 +2019-01-29 10:30:00-05:00,2019,1,29,10,30,17.0,12.4,93.0,916.0,644.0,0.07,1017.0,253.0,3.6,74.3308541306892 +2019-01-29 11:30:00-05:00,2019,1,29,11,30,17.8,12.4,94.0,961.0,758.0,0.07,1016.0,247.0,4.2,70.66878053078243 +2019-01-29 12:30:00-05:00,2019,1,29,12,30,18.4,12.3,92.0,980.0,801.0,0.07,1015.0,246.0,4.6000000000000005,67.60944202765148 +2019-01-29 13:30:00-05:00,2019,1,29,13,30,18.8,12.1,88.0,977.0,770.0,0.07,1014.0,248.0,4.800000000000001,65.07675037227436 +2019-01-29 14:30:00-05:00,2019,1,29,14,30,19.200000000000003,11.8,81.0,948.0,666.0,0.07,1014.0,251.0,4.7,62.23076526106292 +2019-01-29 15:30:00-05:00,2019,1,29,15,30,19.3,11.5,73.0,885.0,501.0,0.07,1014.0,255.0,4.5,60.631350263809125 +2019-01-29 16:30:00-05:00,2019,1,29,16,30,19.1,11.4,59.0,754.0,292.0,0.07,1014.0,261.0,4.1000000000000005,60.98669987470594 +2019-01-29 17:30:00-05:00,2019,1,29,17,30,18.7,11.7,33.0,426.0,78.0,0.07,1015.0,267.0,3.8,63.78096993250251 +2019-01-29 18:30:00-05:00,2019,1,29,18,30,18.5,11.5,0.0,0.0,0.0,0.07,1015.0,275.0,3.7,63.73649672217544 +2019-01-29 19:30:00-05:00,2019,1,29,19,30,18.6,11.6,0.0,0.0,0.0,0.07,1016.0,282.0,3.5,63.758742374116 +2019-01-29 20:30:00-05:00,2019,1,29,20,30,18.6,11.7,0.0,0.0,0.0,0.07,1016.0,288.0,3.3000000000000003,64.18117514763497 +2019-01-29 21:30:00-05:00,2019,1,29,21,30,18.6,11.7,0.0,0.0,0.0,0.07,1016.0,290.0,3.1,64.18117514763497 +2019-01-29 22:30:00-05:00,2019,1,29,22,30,18.4,11.9,0.0,0.0,0.0,0.07,1016.0,290.0,3.0,65.85307897158431 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07:30:00-05:00,2019,1,30,7,30,18.5,12.6,29.0,289.0,52.0,0.07,1018.0,331.0,2.2,68.52304018353911 +2019-01-30 08:30:00-05:00,2019,1,30,8,30,19.1,12.7,91.0,133.0,129.0,0.07,1018.0,337.0,2.3000000000000003,66.43570277927194 +2019-01-30 09:30:00-05:00,2019,1,30,9,30,19.700000000000003,12.6,135.0,1.0,135.0,0.07,1019.0,345.0,2.3000000000000003,63.58427653124552 +2019-01-30 10:30:00-05:00,2019,1,30,10,30,20.3,12.7,199.0,2.0,200.0,0.07,1018.0,354.0,2.0,61.6684242758208 +2019-01-30 11:30:00-05:00,2019,1,30,11,30,20.700000000000003,13.1,187.0,0.0,187.0,0.07,1018.0,182.0,1.6,61.76018697197458 +2019-01-30 12:30:00-05:00,2019,1,30,12,30,20.9,13.4,373.0,88.0,437.0,0.07,1017.0,10.0,1.1,62.21002523973613 +2019-01-30 13:30:00-05:00,2019,1,30,13,30,21.1,13.6,311.0,33.0,334.0,0.07,1017.0,19.0,0.9,62.255393780441246 +2019-01-30 14:30:00-05:00,2019,1,30,14,30,21.200000000000003,13.9,266.0,24.0,281.0,0.07,1017.0,28.0,0.9,63.092136323045374 +2019-01-30 15:30:00-05:00,2019,1,30,15,30,21.1,14.1,214.0,183.0,303.0,0.07,1017.0,32.0,1.2000000000000002,64.30904401894777 +2019-01-30 16:30:00-05:00,2019,1,30,16,30,21.0,14.5,71.0,76.0,95.0,0.07,1017.0,29.0,1.6,66.40122606049285 +2019-01-30 17:30:00-05:00,2019,1,30,17,30,20.8,15.1,22.0,22.0,24.0,0.07,1018.0,21.0,2.2,69.87255673728518 +2019-01-30 18:30:00-05:00,2019,1,30,18,30,20.6,15.2,0.0,0.0,0.0,0.07,1019.0,13.0,2.9000000000000004,71.19447449796583 +2019-01-30 19:30:00-05:00,2019,1,30,19,30,20.3,15.6,0.0,0.0,0.0,0.07,1019.0,10.0,3.7,74.40911875165884 +2019-01-30 20:30:00-05:00,2019,1,30,20,30,19.9,16.0,0.0,0.0,0.0,0.07,1020.0,10.0,4.2,78.24916597246201 +2019-01-30 21:30:00-05:00,2019,1,30,21,30,19.5,16.2,0.0,0.0,0.0,0.07,1020.0,9.0,4.5,81.2455851831502 +2019-01-30 22:30:00-05:00,2019,1,30,22,30,19.0,16.400000000000002,0.0,0.0,0.0,0.07,1020.0,10.0,4.6000000000000005,84.88919970341291 +2019-01-30 23:30:00-05:00,2019,1,30,23,30,18.8,16.7,0.0,0.0,0.0,0.07,1020.0,16.0,4.6000000000000005,87.61070845809938 +2019-01-31 00:30:00-05:00,2019,1,31,0,30,18.8,16.900000000000002,0.0,0.0,0.0,0.07,1020.0,23.0,4.4,88.7294773924538 +2019-01-31 01:30:00-05:00,2019,1,31,1,30,18.9,17.2,0.0,0.0,0.0,0.07,1019.0,27.0,4.3,89.86813750387414 +2019-01-31 02:30:00-05:00,2019,1,31,2,30,19.0,17.400000000000002,0.0,0.0,0.0,0.07,1019.0,29.0,4.3,90.44509053017225 +2019-01-31 03:30:00-05:00,2019,1,31,3,30,19.200000000000003,17.5,0.0,0.0,0.0,0.07,1019.0,31.0,4.2,89.89016456058542 +2019-01-31 04:30:00-05:00,2019,1,31,4,30,19.4,17.6,0.0,0.0,0.0,0.07,1019.0,37.0,4.1000000000000005,89.33990819982618 +2019-01-31 05:30:00-05:00,2019,1,31,5,30,19.6,17.8,0.0,0.0,0.0,0.07,1019.0,46.0,4.0,89.35529400601024 +2019-01-31 06:30:00-05:00,2019,1,31,6,30,19.9,17.900000000000002,0.0,0.0,0.0,0.07,1020.0,55.0,3.9,88.26163421505619 +2019-01-31 07:30:00-05:00,2019,1,31,7,30,20.5,18.2,35.0,119.0,45.0,0.07,1021.0,65.0,4.0,86.66648654646751 +2019-01-31 08:30:00-05:00,2019,1,31,8,30,21.1,18.5,83.0,22.0,89.0,0.07,1021.0,72.0,4.1000000000000005,85.11082764039354 +2019-01-31 09:30:00-05:00,2019,1,31,9,30,21.6,18.7,217.0,30.0,231.0,0.07,1022.0,76.0,4.2,83.58209369896291 +2019-01-31 10:30:00-05:00,2019,1,31,10,30,21.9,18.7,223.0,138.0,307.0,0.07,1022.0,76.0,4.0,82.06411920218584 +2019-01-31 11:30:00-05:00,2019,1,31,11,30,22.1,18.7,234.0,9.0,240.0,0.07,1021.0,74.0,3.8,81.06935123833387 +2019-01-31 12:30:00-05:00,2019,1,31,12,30,22.3,18.6,358.0,105.0,435.0,0.07,1020.0,73.0,3.7,79.58872032333835 +2019-01-31 13:30:00-05:00,2019,1,31,13,30,22.3,18.6,171.0,7.0,176.0,0.07,1020.0,71.0,3.7,79.58872032333835 +2019-01-31 14:30:00-05:00,2019,1,31,14,30,22.3,18.6,167.0,5.0,170.0,0.07,1019.0,69.0,3.8,79.58872032333835 +2019-01-31 15:30:00-05:00,2019,1,31,15,30,22.200000000000003,18.7,41.0,0.0,41.0,0.07,1019.0,68.0,3.9,80.57705408572943 +2019-01-31 16:30:00-05:00,2019,1,31,16,30,21.8,18.8,26.0,70.0,48.0,0.07,1020.0,68.0,3.9,83.08432393117518 +2019-01-31 17:30:00-05:00,2019,1,31,17,30,21.5,18.9,42.0,207.0,65.0,0.07,1020.0,68.0,3.6,85.15250667753344 +2019-01-31 18:30:00-05:00,2019,1,31,18,30,21.3,18.8,0.0,0.0,0.0,0.07,1021.0,70.0,3.5,85.66545323872897 +2019-01-31 19:30:00-05:00,2019,1,31,19,30,21.8,17.900000000000002,0.0,0.0,0.0,0.08,1016.0,47.0,2.0,78.52608719814607 +2019-01-31 20:30:00-05:00,2019,1,31,20,30,21.6,18.0,0.0,0.0,0.0,0.08,1017.0,41.0,1.6,79.99331979564528 +2019-01-31 21:30:00-05:00,2019,1,31,21,30,21.4,18.1,0.0,0.0,0.0,0.08,1017.0,22.0,1.2000000000000002,81.48908134926712 +2019-01-31 22:30:00-05:00,2019,1,31,22,30,21.1,17.900000000000002,0.0,0.0,0.0,0.08,1017.0,163.0,1.3,81.96516542358266 +2019-01-31 23:30:00-05:00,2019,1,31,23,30,20.700000000000003,17.8,0.0,0.0,0.0,0.08,1017.0,309.0,1.8,83.4791088458305 +2003-02-01 00:30:00-05:00,2003,2,1,0,30,20.200000000000003,17.7,0.0,0.0,0.0,0.08,1017.0,298.0,2.1,85.5539905137873 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01:30:00-05:00,2003,2,25,1,30,22.9,20.200000000000003,0.0,0.0,0.0,0.1,1018.0,67.0,4.7,84.7716062208146 +2003-02-25 02:30:00-05:00,2003,2,25,2,30,22.9,20.3,0.0,0.0,0.0,0.1,1018.0,67.0,4.5,85.29705584575203 +2003-02-25 03:30:00-05:00,2003,2,25,3,30,22.9,20.4,0.0,0.0,0.0,0.1,1017.0,68.0,4.5,85.82535979852005 +2003-02-25 04:30:00-05:00,2003,2,25,4,30,22.9,20.5,0.0,0.0,0.0,0.1,1017.0,69.0,4.5,86.35653124699375 +2003-02-25 05:30:00-05:00,2003,2,25,5,30,23.0,20.6,0.0,0.0,0.0,0.1,1017.0,70.0,4.4,86.36609614824316 +2003-02-25 06:30:00-05:00,2003,2,25,6,30,23.3,20.700000000000003,0.0,0.0,0.0,0.1,1017.0,72.0,4.5,85.33797994293663 +2003-02-25 07:30:00-05:00,2003,2,25,7,30,23.9,21.0,54.0,281.0,95.0,0.1,1018.0,73.0,4.4,83.84109497169752 +2003-02-25 08:30:00-05:00,2003,2,25,8,30,24.3,21.0,96.0,581.0,307.0,0.1,1018.0,71.0,4.2,81.85205744824505 +2003-02-25 09:30:00-05:00,2003,2,25,9,30,24.700000000000003,21.0,120.0,720.0,515.0,0.1,1018.0,69.0,3.8,79.9159406716968 +2003-02-25 10:30:00-05:00,2003,2,25,10,30,25.0,21.1,133.0,793.0,684.0,0.1,1018.0,68.0,3.5,78.98125580461064 +2003-02-25 11:30:00-05:00,2003,2,25,11,30,25.200000000000003,21.200000000000003,428.0,133.0,532.0,0.1,1018.0,63.0,3.0,78.52619557782191 +2003-02-25 12:30:00-05:00,2003,2,25,12,30,25.4,21.1,454.0,175.0,597.0,0.1,1017.0,52.0,2.5,77.12266693241895 +2003-02-25 13:30:00-05:00,2003,2,25,13,30,25.6,21.0,360.0,133.0,465.0,0.1,1016.0,38.0,2.4000000000000004,75.74521383477543 +2003-02-25 14:30:00-05:00,2003,2,25,14,30,25.6,21.0,292.0,384.0,565.0,0.1,1016.0,27.0,2.5,75.74521383477543 +2003-02-25 15:30:00-05:00,2003,2,25,15,30,25.6,20.9,286.0,45.0,311.0,0.1,1015.0,22.0,2.8000000000000003,75.28106943219711 +2003-02-25 16:30:00-05:00,2003,2,25,16,30,25.200000000000003,21.1,174.0,44.0,191.0,0.1,1015.0,22.0,3.2,78.04573617296003 +2003-02-25 17:30:00-05:00,2003,2,25,17,30,24.700000000000003,21.5,3.0,0.0,3.0,0.1,1016.0,28.0,3.6,82.40436619184688 +2003-02-25 18:30:00-05:00,2003,2,25,18,30,24.200000000000003,21.5,0.0,0.0,0.0,0.1,1016.0,38.0,3.9,84.90833346681123 +2003-02-25 19:30:00-05:00,2003,2,25,19,30,23.9,21.5,0.0,0.0,0.0,0.11,1016.0,47.0,4.1000000000000005,86.45174209180065 +2003-02-25 20:30:00-05:00,2003,2,25,20,30,23.700000000000003,21.5,0.0,0.0,0.0,0.11,1016.0,52.0,4.0,87.49820573097291 +2003-02-25 21:30:00-05:00,2003,2,25,21,30,23.5,21.5,0.0,0.0,0.0,0.11,1016.0,52.0,3.7,88.55893543915133 +2003-02-25 22:30:00-05:00,2003,2,25,22,30,23.5,21.4,0.0,0.0,0.0,0.11,1016.0,51.0,3.4000000000000004,88.0183158429098 +2003-02-25 23:30:00-05:00,2003,2,25,23,30,23.4,21.200000000000003,0.0,0.0,0.0,0.11,1015.0,52.0,3.2,87.47177038292268 +2003-02-26 00:30:00-05:00,2003,2,26,0,30,23.3,21.1,0.0,0.0,0.0,0.11,1015.0,53.0,3.1,87.46294044034684 +2003-02-26 01:30:00-05:00,2003,2,26,1,30,23.3,20.9,0.0,0.0,0.0,0.11,1014.0,54.0,3.0,86.3947322757692 +2003-02-26 02:30:00-05:00,2003,2,26,2,30,23.3,20.8,0.0,0.0,0.0,0.11,1014.0,51.0,2.9000000000000004,85.86493068297864 +2003-02-26 03:30:00-05:00,2003,2,26,3,30,23.200000000000003,20.700000000000003,0.0,0.0,0.0,0.11,1014.0,46.0,2.9000000000000004,85.85505305166893 +2003-02-26 04:30:00-05:00,2003,2,26,4,30,23.1,20.700000000000003,0.0,0.0,0.0,0.11,1014.0,45.0,2.9000000000000004,86.37565127821391 +2003-02-26 05:30:00-05:00,2003,2,26,5,30,23.1,20.700000000000003,0.0,0.0,0.0,0.11,1014.0,47.0,2.9000000000000004,86.37565127821391 +2003-02-26 06:30:00-05:00,2003,2,26,6,30,23.3,20.6,0.0,0.0,0.0,0.11,1015.0,48.0,2.9000000000000004,84.81386701079128 +2003-02-26 07:30:00-05:00,2003,2,26,7,30,23.8,21.0,54.0,317.0,102.0,0.11,1015.0,53.0,3.0,84.34680987230043 +2003-02-26 08:30:00-05:00,2003,2,26,8,30,24.3,21.0,92.0,628.0,322.0,0.11,1016.0,58.0,3.0,81.85205744824505 +2003-02-26 09:30:00-05:00,2003,2,26,9,30,24.8,20.9,109.0,771.0,536.0,0.11,1016.0,61.0,2.8000000000000003,78.95321249408512 +2003-02-26 10:30:00-05:00,2003,2,26,10,30,25.3,20.700000000000003,120.0,841.0,707.0,0.11,1016.0,62.0,2.6,75.69774320958695 +2003-02-26 11:30:00-05:00,2003,2,26,11,30,25.700000000000003,20.5,124.0,876.0,817.0,0.11,1015.0,63.0,2.3000000000000003,73.01481908433114 +2003-02-26 12:30:00-05:00,2003,2,26,12,30,26.0,20.3,124.0,890.0,858.0,0.11,1014.0,64.0,1.7000000000000002,70.84852701016717 +2003-02-26 13:30:00-05:00,2003,2,26,13,30,26.200000000000003,19.9,134.0,858.0,819.0,0.11,1014.0,69.0,1.2000000000000002,68.30422874023395 +2003-02-26 14:30:00-05:00,2003,2,26,14,30,26.3,19.6,302.0,535.0,684.0,0.11,1013.0,80.0,1.0,66.65007810633571 +2003-02-26 15:30:00-05:00,2003,2,26,15,30,26.200000000000003,19.5,218.0,468.0,487.0,0.11,1012.0,92.0,1.1,66.62973426639375 +2003-02-26 16:30:00-05:00,2003,2,26,16,30,25.9,19.5,181.0,150.0,240.0,0.11,1012.0,104.0,1.2000000000000002,67.82278996673193 +2003-02-26 17:30:00-05:00,2003,2,26,17,30,25.4,19.9,98.0,131.0,121.0,0.11,1013.0,114.0,1.5,71.62083740281858 +2003-02-26 18:30:00-05:00,2003,2,26,18,30,24.9,20.0,0.0,0.0,0.0,0.11,1013.0,119.0,1.8,74.2440182497378 +2003-02-26 19:30:00-05:00,2003,2,26,19,30,24.700000000000003,20.200000000000003,0.0,0.0,0.0,0.12,1013.0,123.0,2.1,76.07138099010922 +2003-02-26 20:30:00-05:00,2003,2,26,20,30,24.4,20.3,0.0,0.0,0.0,0.12,1014.0,126.0,2.4000000000000004,77.929008814519 +2003-02-26 21:30:00-05:00,2003,2,26,21,30,24.200000000000003,20.5,0.0,0.0,0.0,0.12,1014.0,131.0,2.5,79.8483913649193 +2003-02-26 22:30:00-05:00,2003,2,26,22,30,24.0,20.6,0.0,0.0,0.0,0.12,1014.0,145.0,2.5,81.31250928232977 +2003-02-26 23:30:00-05:00,2003,2,26,23,30,23.8,20.700000000000003,0.0,0.0,0.0,0.12,1013.0,164.0,2.7,82.80457657896676 +2003-02-27 00:30:00-05:00,2003,2,27,0,30,23.6,20.8,0.0,0.0,0.0,0.12,1013.0,181.0,2.9000000000000004,84.32515009794076 +2003-02-27 01:30:00-05:00,2003,2,27,1,30,23.4,20.9,0.0,0.0,0.0,0.12,1012.0,191.0,3.3000000000000003,85.8747982763468 +2003-02-27 02:30:00-05:00,2003,2,27,2,30,23.3,21.1,0.0,0.0,0.0,0.12,1012.0,196.0,3.6,87.46294044034684 +2003-02-27 03:30:00-05:00,2003,2,27,3,30,23.200000000000003,21.200000000000003,0.0,0.0,0.0,0.12,1012.0,200.0,3.8,88.53458427400807 +2003-02-27 04:30:00-05:00,2003,2,27,4,30,23.200000000000003,21.3,0.0,0.0,0.0,0.12,1012.0,199.0,4.0,89.07920071572133 +2003-02-27 05:30:00-05:00,2003,2,27,5,30,23.3,21.4,0.0,0.0,0.0,0.12,1012.0,200.0,4.1000000000000005,89.0869629849579 +2003-02-27 06:30:00-05:00,2003,2,27,6,30,23.5,21.5,0.0,0.0,0.0,0.12,1013.0,203.0,4.2,88.55893543915133 +2003-02-27 07:30:00-05:00,2003,2,27,7,30,24.1,21.700000000000003,71.0,19.0,74.0,0.12,1013.0,207.0,4.4,86.4706679784366 +2003-02-27 08:30:00-05:00,2003,2,27,8,30,24.6,21.8,170.0,260.0,267.0,0.12,1014.0,212.0,4.5,84.43301646305675 +2003-02-27 09:30:00-05:00,2003,2,27,9,30,25.1,21.9,206.0,444.0,454.0,0.12,1014.0,215.0,4.4,82.45221220678982 +2003-02-27 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10:30:00-05:00,2000,3,30,10,30,26.6,22.3,212.0,707.0,791.0,0.1,1015.0,174.0,6.300000000000001,77.30223850538337 +2000-03-30 11:30:00-05:00,2000,3,30,11,30,26.9,22.200000000000003,230.0,730.0,890.0,0.1,1015.0,179.0,6.300000000000001,75.48855752594542 +2000-03-30 12:30:00-05:00,2000,3,30,12,30,27.1,22.200000000000003,212.0,770.0,928.0,0.1,1014.0,186.0,6.2,74.60687300012985 +2000-03-30 13:30:00-05:00,2000,3,30,13,30,27.200000000000003,22.0,204.0,765.0,888.0,0.1,1014.0,191.0,5.800000000000001,73.27197620568609 +2000-03-30 14:30:00-05:00,2000,3,30,14,30,27.200000000000003,21.9,199.0,718.0,773.0,0.1,1013.0,195.0,5.5,72.8263591012172 +2000-03-30 15:30:00-05:00,2000,3,30,15,30,27.1,21.700000000000003,186.0,638.0,603.0,0.1,1013.0,197.0,5.2,72.3656209344505 +2000-03-30 16:30:00-05:00,2000,3,30,16,30,27.0,21.6,125.0,603.0,405.0,0.1,1013.0,198.0,4.7,72.34810781589083 +2000-03-30 17:30:00-05:00,2000,3,30,17,30,26.5,21.6,85.0,378.0,178.0,0.1,1014.0,197.0,4.0,74.50862686540798 +2000-03-30 18:30:00-05:00,2000,3,30,18,30,26.1,21.700000000000003,8.0,12.0,8.0,0.1,1014.0,195.0,3.5,76.75727862001015 +2000-03-30 19:30:00-05:00,2000,3,30,19,30,25.8,21.700000000000003,0.0,0.0,0.0,0.12,1014.0,194.0,3.2,78.13270683678483 +2000-03-30 20:30:00-05:00,2000,3,30,20,30,25.700000000000003,21.8,0.0,0.0,0.0,0.12,1015.0,192.0,3.2,79.07898894956534 +2000-03-30 21:30:00-05:00,2000,3,30,21,30,25.5,22.0,0.0,0.0,0.0,0.12,1015.0,189.0,3.3000000000000003,81.00602659099238 +2000-03-30 22:30:00-05:00,2000,3,30,22,30,25.4,22.200000000000003,0.0,0.0,0.0,0.12,1015.0,189.0,3.5,82.48797372378452 +2000-03-30 23:30:00-05:00,2000,3,30,23,30,25.3,22.4,0.0,0.0,0.0,0.12,1014.0,192.0,3.7,83.99586147124604 +2000-03-31 00:30:00-05:00,2000,3,31,0,30,25.200000000000003,22.5,0.0,0.0,0.0,0.12,1014.0,196.0,3.8,85.01229874539113 +2000-03-31 01:30:00-05:00,2000,3,31,1,30,25.1,22.6,0.0,0.0,0.0,0.12,1013.0,199.0,3.9,86.04102419869523 +2000-03-31 02:30:00-05:00,2000,3,31,2,30,25.0,22.8,0.0,0.0,0.0,0.12,1013.0,200.0,4.0,87.61182167843045 +2000-03-31 03:30:00-05:00,2000,3,31,3,30,25.0,22.9,0.0,0.0,0.0,0.12,1013.0,199.0,4.0,88.14427552074834 +2000-03-31 04:30:00-05:00,2000,3,31,4,30,24.9,22.9,0.0,0.0,0.0,0.12,1013.0,199.0,4.1000000000000005,88.67157778717231 +2000-03-31 05:30:00-05:00,2000,3,31,5,30,24.9,22.9,0.0,0.0,0.0,0.12,1013.0,203.0,4.0,88.67157778717231 +2000-03-31 06:30:00-05:00,2000,3,31,6,30,25.3,22.9,23.0,49.0,25.0,0.12,1014.0,207.0,4.0,86.58341783376855 +2000-03-31 07:30:00-05:00,2000,3,31,7,30,25.8,23.0,95.0,428.0,217.0,0.12,1015.0,210.0,4.0,84.56104094151235 +2000-03-31 08:30:00-05:00,2000,3,31,8,30,26.3,22.700000000000003,129.0,636.0,447.0,0.12,1015.0,214.0,3.9,80.61765392538541 +2000-03-31 09:30:00-05:00,2000,3,31,9,30,26.700000000000003,22.4,151.0,738.0,655.0,0.12,1015.0,219.0,3.8,77.31711255206692 +2000-03-31 10:30:00-05:00,2000,3,31,10,30,27.1,22.200000000000003,213.0,714.0,799.0,0.12,1015.0,225.0,3.8,74.60687300012985 +2000-03-31 11:30:00-05:00,2000,3,31,11,30,27.4,22.200000000000003,497.0,128.0,612.0,0.12,1015.0,228.0,3.8,73.30600536406749 +2000-03-31 12:30:00-05:00,2000,3,31,12,30,27.700000000000003,22.3,507.0,128.0,627.0,0.12,1014.0,232.0,3.9,72.47037671119828 +2000-03-31 13:30:00-05:00,2000,3,31,13,30,27.9,22.4,235.0,720.0,880.0,0.12,1014.0,235.0,4.0,72.0658733243101 +2000-03-31 14:30:00-05:00,2000,3,31,14,30,28.0,22.3,352.0,8.0,358.0,0.12,1013.0,237.0,4.0,71.2123042612599 +2000-03-31 15:30:00-05:00,2000,3,31,15,30,27.9,22.4,246.0,1.0,247.0,0.12,1013.0,241.0,3.9,72.0658733243101 +2000-03-31 16:30:00-05:00,2000,3,31,16,30,27.8,22.4,124.0,610.0,408.0,0.12,1013.0,246.0,3.8,72.48778233514803 +2000-03-31 17:30:00-05:00,2000,3,31,17,30,27.4,22.700000000000003,84.0,388.0,180.0,0.12,1014.0,251.0,3.7,75.56770822677902 +2000-03-31 18:30:00-05:00,2000,3,31,18,30,26.9,22.8,8.0,14.0,9.0,0.12,1014.0,259.0,3.8,78.29088570094365 +2000-03-31 19:30:00-05:00,2000,3,31,19,30,24.3,19.3,0.0,0.0,0.0,0.1,1015.0,167.0,3.0,73.68392597496424 +2000-03-31 20:30:00-05:00,2000,3,31,20,30,24.1,19.5,0.0,0.0,0.0,0.1,1016.0,172.0,3.0,75.50654651068312 +2000-03-31 21:30:00-05:00,2000,3,31,21,30,24.0,19.6,0.0,0.0,0.0,0.1,1016.0,180.0,2.9000000000000004,76.43466615514889 +2000-03-31 22:30:00-05:00,2000,3,31,22,30,23.8,19.700000000000003,0.0,0.0,0.0,0.1,1016.0,192.0,3.0,77.84088394506858 +2000-03-31 23:30:00-05:00,2000,3,31,23,30,23.700000000000003,19.8,0.0,0.0,0.0,0.1,1016.0,206.0,3.0,78.79801694648188 +2019-04-01 00:30:00-05:00,2019,4,1,0,30,23.5,19.9,0.0,0.0,0.0,0.1,1015.0,221.0,3.0,80.24912944155732 +2019-04-01 01:30:00-05:00,2019,4,1,1,30,23.3,19.9,0.0,0.0,0.0,0.1,1015.0,231.0,3.0,81.22344941130798 +2019-04-01 02:30:00-05:00,2019,4,1,2,30,23.200000000000003,19.8,0.0,0.0,0.0,0.1,1015.0,237.0,2.8000000000000003,81.21067682976117 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12:30:00-05:00,2019,4,1,12,30,26.3,18.2,131.0,906.0,976.0,0.1,1016.0,190.0,3.8,61.07188431274639 +2019-04-01 13:30:00-05:00,2019,4,1,13,30,26.5,18.1,130.0,894.0,932.0,0.1,1015.0,188.0,3.9,59.97763345856531 +2019-04-01 14:30:00-05:00,2019,4,1,14,30,26.6,18.0,125.0,861.0,816.0,0.1,1015.0,186.0,3.9,59.251672332400254 +2019-04-01 15:30:00-05:00,2019,4,1,15,30,26.5,18.1,113.0,809.0,643.0,0.1,1015.0,184.0,3.9,59.97763345856531 +2019-04-01 16:30:00-05:00,2019,4,1,16,30,26.3,18.4,92.0,718.0,427.0,0.1,1015.0,183.0,3.8,61.84277564367993 +2019-04-01 17:30:00-05:00,2019,4,1,17,30,25.8,18.7,64.0,531.0,196.0,0.1,1015.0,182.0,3.6,64.90689001430802 +2019-04-01 18:30:00-05:00,2019,4,1,18,30,25.200000000000003,19.3,13.0,81.0,15.0,0.1,1016.0,183.0,3.4000000000000004,69.82725085286096 +2019-04-01 19:30:00-05:00,2019,4,1,19,30,25.0,19.6,0.0,0.0,0.0,0.09,1016.0,188.0,3.4000000000000004,71.99458544351828 +2019-04-01 20:30:00-05:00,2019,4,1,20,30,24.8,19.9,0.0,0.0,0.0,0.09,1017.0,196.0,3.2,74.2273526631688 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14:30:00-05:00,2019,4,2,14,30,27.8,20.200000000000003,212.0,9.0,219.0,0.09,1015.0,223.0,3.3000000000000003,63.34204232336772 +2019-04-02 15:30:00-05:00,2019,4,2,15,30,27.8,20.200000000000003,208.0,69.0,253.0,0.09,1015.0,230.0,3.2,63.34204232336772 +2019-04-02 16:30:00-05:00,2019,4,2,16,30,27.6,20.200000000000003,117.0,628.0,411.0,0.09,1015.0,237.0,3.2,64.08671358098603 +2019-04-02 17:30:00-05:00,2019,4,2,17,30,27.1,20.5,78.0,428.0,185.0,0.09,1016.0,246.0,3.1,67.22572717468144 +2019-04-02 18:30:00-05:00,2019,4,2,18,30,26.6,20.700000000000003,13.0,48.0,14.0,0.09,1016.0,259.0,3.2,70.09038415009992 +2019-04-02 19:30:00-05:00,2019,4,2,19,30,26.1,20.8,0.0,0.0,0.0,0.1,1017.0,276.0,3.4000000000000004,72.63555629465829 +2019-04-02 20:30:00-05:00,2019,4,2,20,30,25.6,21.200000000000003,0.0,0.0,0.0,0.1,1018.0,294.0,3.5,76.681035085095 +2019-04-02 21:30:00-05:00,2019,4,2,21,30,25.1,21.6,0.0,0.0,0.0,0.1,1018.0,314.0,3.6,80.95475802977975 +2019-04-02 22:30:00-05:00,2019,4,2,22,30,24.700000000000003,21.9,0.0,0.0,0.0,0.1,1019.0,331.0,3.9,84.4437438579537 +2019-04-02 23:30:00-05:00,2019,4,2,23,30,24.3,22.0,0.0,0.0,0.0,0.1,1019.0,346.0,4.2,87.01877615805587 +2019-04-03 00:30:00-05:00,2019,4,3,0,30,24.0,22.0,0.0,0.0,0.0,0.1,1018.0,180.0,4.4,88.59935257199315 +2019-04-03 01:30:00-05:00,2019,4,3,1,30,23.700000000000003,22.1,0.0,0.0,0.0,0.1,1018.0,12.0,4.6000000000000005,90.76387283372435 +2019-04-03 02:30:00-05:00,2019,4,3,2,30,23.6,22.1,0.0,0.0,0.0,0.1,1018.0,20.0,4.7,91.3121686462366 +2019-04-03 03:30:00-05:00,2019,4,3,3,30,23.5,22.1,0.0,0.0,0.0,0.1,1018.0,27.0,4.9,91.86419181215088 +2019-04-03 04:30:00-05:00,2019,4,3,4,30,23.5,22.0,0.0,0.0,0.0,0.1,1019.0,32.0,5.1000000000000005,91.30592284872633 +2019-04-03 05:30:00-05:00,2019,4,3,5,30,23.6,21.9,0.0,0.0,0.0,0.1,1020.0,38.0,5.4,90.20529732461392 +2019-04-03 06:30:00-05:00,2019,4,3,6,30,23.9,21.8,13.0,0.0,13.0,0.1,1020.0,45.0,5.7,88.05209594629902 +2019-04-03 07:30:00-05:00,2019,4,3,7,30,24.3,21.8,71.0,50.0,86.0,0.1,1021.0,52.0,5.9,85.96315706835442 +2019-04-03 08:30:00-05:00,2019,4,3,8,30,24.8,21.6,248.0,32.0,264.0,0.1,1022.0,59.0,6.1000000000000005,82.4163453099894 +2019-04-03 09:30:00-05:00,2019,4,3,9,30,25.200000000000003,21.5,320.0,116.0,400.0,0.1,1022.0,63.0,6.300000000000001,79.98316661484331 +2019-04-03 10:30:00-05:00,2019,4,3,10,30,25.4,21.3,438.0,161.0,571.0,0.1,1022.0,66.0,6.4,78.0747809293731 +2019-04-03 11:30:00-05:00,2019,4,3,11,30,25.5,21.200000000000003,425.0,58.0,478.0,0.1,1022.0,67.0,6.5,77.13770794814462 +2019-04-03 12:30:00-05:00,2019,4,3,12,30,25.4,21.1,459.0,129.0,580.0,0.1,1021.0,67.0,6.6000000000000005,77.12266693241895 +2019-04-03 13:30:00-05:00,2019,4,3,13,30,25.200000000000003,21.0,324.0,455.0,734.0,0.1,1021.0,67.0,6.7,77.5678558469233 +2019-04-03 14:30:00-05:00,2019,4,3,14,30,25.0,20.9,345.0,228.0,529.0,0.1,1020.0,65.0,6.800000000000001,78.01663671137742 +2019-04-03 15:30:00-05:00,2019,4,3,15,30,24.700000000000003,20.8,223.0,433.0,508.0,0.1,1020.0,63.0,6.9,78.93917083416129 +2019-04-03 16:30:00-05:00,2019,4,3,16,30,24.5,20.700000000000003,120.0,618.0,410.0,0.1,1020.0,62.0,6.9,79.39868349174054 +2019-04-03 17:30:00-05:00,2019,4,3,17,30,24.3,20.6,89.0,299.0,164.0,0.1,1021.0,61.0,6.9,79.86192718890787 +2019-04-03 18:30:00-05:00,2019,4,3,18,30,24.200000000000003,20.5,8.0,6.0,8.0,0.1,1021.0,62.0,6.9,79.8483913649193 +2019-04-03 19:30:00-05:00,2019,4,3,19,30,24.200000000000003,20.5,0.0,0.0,0.0,0.1,1022.0,64.0,7.0,79.8483913649193 +2019-04-03 20:30:00-05:00,2019,4,3,20,30,24.200000000000003,20.6,0.0,0.0,0.0,0.1,1022.0,66.0,6.9,80.34219542746608 +2019-04-03 21:30:00-05:00,2019,4,3,21,30,24.200000000000003,20.8,0.0,0.0,0.0,0.1,1022.0,70.0,6.800000000000001,81.33784349698333 +2019-04-03 22:30:00-05:00,2019,4,3,22,30,24.1,20.9,0.0,0.0,0.0,0.1,1022.0,72.0,6.6000000000000005,82.33224385432793 +2019-04-03 23:30:00-05:00,2019,4,3,23,30,24.0,21.0,0.0,0.0,0.0,0.1,1022.0,75.0,6.5,83.33878752130364 +2019-04-04 00:30:00-05:00,2019,4,4,0,30,23.9,21.200000000000003,0.0,0.0,0.0,0.1,1022.0,77.0,6.4,84.87693967200721 +2019-04-04 01:30:00-05:00,2019,4,4,1,30,23.9,21.3,0.0,0.0,0.0,0.1,1021.0,79.0,6.2,85.39905628041207 +2019-04-04 02:30:00-05:00,2019,4,4,2,30,23.8,21.4,0.0,0.0,0.0,0.1,1021.0,81.0,6.1000000000000005,86.44226467052782 +2019-04-04 03:30:00-05:00,2019,4,4,3,30,23.8,21.5,0.0,0.0,0.0,0.1,1021.0,82.0,6.0,86.9732039617066 +2019-04-04 04:30:00-05:00,2019,4,4,4,30,23.700000000000003,21.5,0.0,0.0,0.0,0.1,1021.0,83.0,5.800000000000001,87.49820573097291 +2019-04-04 05:30:00-05:00,2019,4,4,5,30,23.700000000000003,21.6,0.0,0.0,0.0,0.1,1021.0,84.0,5.6000000000000005,88.03522334593023 +2019-04-04 06:30:00-05:00,2019,4,4,6,30,23.9,21.6,5.0,0.0,5.0,0.1,1022.0,85.0,5.5,86.98233706753979 +2019-04-04 07:30:00-05:00,2019,4,4,7,30,24.200000000000003,21.700000000000003,31.0,2.0,32.0,0.1,1022.0,84.0,5.4,85.95337922823713 +2019-04-04 08:30:00-05:00,2019,4,4,8,30,24.4,21.700000000000003,48.0,0.0,48.0,0.1,1023.0,84.0,5.2,84.92921012154552 +2019-04-04 09:30:00-05:00,2019,4,4,9,30,24.5,21.700000000000003,86.0,0.0,86.0,0.1,1023.0,82.0,5.2,84.42227833831143 +2019-04-04 10:30:00-05:00,2019,4,4,10,30,24.6,21.700000000000003,92.0,0.0,92.0,0.1,1022.0,82.0,5.300000000000001,83.9187477970364 +2019-04-04 11:30:00-05:00,2019,4,4,11,30,24.6,21.8,257.0,1.0,258.0,0.1,1022.0,83.0,5.4,84.43301646305675 +2019-04-04 12:30:00-05:00,2019,4,4,12,30,24.700000000000003,21.700000000000003,208.0,0.0,208.0,0.1,1021.0,84.0,5.4,83.41859330119696 +2019-04-04 13:30:00-05:00,2019,4,4,13,30,24.700000000000003,21.6,275.0,3.0,278.0,0.1,1020.0,85.0,5.4,82.91012051931818 +2019-04-04 14:30:00-05:00,2019,4,4,14,30,24.700000000000003,21.5,178.0,7.0,184.0,0.1,1019.0,87.0,5.2,82.40436619184688 +2019-04-04 15:30:00-05:00,2019,4,4,15,30,24.6,21.5,264.0,30.0,284.0,0.1,1019.0,89.0,5.0,82.89843966631602 +2019-04-04 16:30:00-05:00,2019,4,4,16,30,24.6,21.4,200.0,54.0,225.0,0.1,1019.0,89.0,4.7,82.39237530635937 +2019-04-04 17:30:00-05:00,2019,4,4,17,30,24.5,21.5,48.0,3.0,49.0,0.1,1019.0,89.0,4.4,83.39584813929504 +2019-04-04 18:30:00-05:00,2019,4,4,18,30,24.4,21.6,7.0,0.0,7.0,0.1,1020.0,90.0,4.3,84.41152946997488 +2019-04-04 19:30:00-05:00,2019,4,4,19,30,24.5,21.700000000000003,0.0,0.0,0.0,0.1,1020.0,93.0,4.3,84.42227833831143 +2019-04-04 20:30:00-05:00,2019,4,4,20,30,24.6,21.700000000000003,0.0,0.0,0.0,0.1,1020.0,96.0,4.4,83.9187477970364 +2019-04-04 21:30:00-05:00,2019,4,4,21,30,24.6,21.8,0.0,0.0,0.0,0.1,1020.0,99.0,4.5,84.43301646305675 +2019-04-04 22:30:00-05:00,2019,4,4,22,30,24.700000000000003,21.9,0.0,0.0,0.0,0.1,1020.0,103.0,4.7,84.4437438579537 +2019-04-04 23:30:00-05:00,2019,4,4,23,30,24.6,21.8,0.0,0.0,0.0,0.1,1019.0,109.0,5.1000000000000005,84.43301646305675 +2019-04-05 00:30:00-05:00,2019,4,5,0,30,24.5,21.8,0.0,0.0,0.0,0.1,1019.0,114.0,5.6000000000000005,84.93963272696895 +2019-04-05 01:30:00-05:00,2019,4,5,1,30,24.4,21.6,0.0,0.0,0.0,0.1,1018.0,117.0,5.9,84.41152946997488 +2019-04-05 02:30:00-05:00,2019,4,5,2,30,24.4,21.4,0.0,0.0,0.0,0.1,1018.0,120.0,6.1000000000000005,83.38445864005315 +2019-04-05 03:30:00-05:00,2019,4,5,3,30,24.3,21.200000000000003,0.0,0.0,0.0,0.1,1017.0,122.0,6.2,82.86332787530509 +2019-04-05 04:30:00-05:00,2019,4,5,4,30,24.3,21.0,0.0,0.0,0.0,0.1,1018.0,126.0,6.1000000000000005,81.85205744824505 +2019-04-05 05:30:00-05:00,2019,4,5,5,30,24.3,20.8,0.0,0.0,0.0,0.1,1018.0,130.0,5.6000000000000005,80.85162348996738 +2019-04-05 06:30:00-05:00,2019,4,5,6,30,24.700000000000003,20.700000000000003,31.0,4.0,31.0,0.1,1018.0,135.0,5.4,78.45472329360548 +2019-04-05 07:30:00-05:00,2019,4,5,7,30,25.200000000000003,20.700000000000003,137.0,164.0,187.0,0.1,1019.0,139.0,5.4,76.1495718601223 +2019-04-05 08:30:00-05:00,2019,4,5,8,30,25.6,20.5,227.0,233.0,347.0,0.1,1019.0,144.0,5.300000000000001,73.44933327378524 +2019-04-05 09:30:00-05:00,2019,4,5,9,30,26.0,20.4,235.0,481.0,570.0,0.1,1019.0,148.0,5.0,71.28734118137315 +2019-04-05 10:30:00-05:00,2019,4,5,10,30,26.3,20.3,297.0,448.0,671.0,0.1,1019.0,151.0,4.7,69.60316383099017 +2019-04-05 11:30:00-05:00,2019,4,5,11,30,26.5,20.3,355.0,374.0,698.0,0.1,1019.0,154.0,4.5,68.78661571719786 +2019-04-05 12:30:00-05:00,2019,4,5,12,30,26.6,20.3,348.0,392.0,717.0,0.1,1018.0,154.0,4.4,68.38238987267924 +2019-04-05 13:30:00-05:00,2019,4,5,13,30,26.6,20.3,356.0,373.0,693.0,0.1,1017.0,154.0,4.4,68.38238987267924 +2019-04-05 14:30:00-05:00,2019,4,5,14,30,26.5,20.3,316.0,356.0,604.0,0.1,1017.0,153.0,4.5,68.78661571719786 +2019-04-05 15:30:00-05:00,2019,4,5,15,30,26.4,20.3,283.0,287.0,473.0,0.1,1016.0,151.0,4.4,69.19353375788072 +2019-04-05 16:30:00-05:00,2019,4,5,16,30,26.1,20.4,184.0,355.0,352.0,0.1,1016.0,149.0,4.2,70.8668661268887 +2019-04-05 17:30:00-05:00,2019,4,5,17,30,25.700000000000003,20.6,116.0,56.0,130.0,0.1,1017.0,146.0,3.9,73.46636248644157 +2019-04-05 18:30:00-05:00,2019,4,5,18,30,25.4,20.8,12.0,1.0,12.0,0.1,1017.0,143.0,3.6,75.71358128252226 +2019-04-05 19:30:00-05:00,2019,4,5,19,30,25.200000000000003,20.9,0.0,0.0,0.0,0.1,1018.0,139.0,3.3000000000000003,77.0925428299202 +2019-04-05 20:30:00-05:00,2019,4,5,20,30,25.1,21.0,0.0,0.0,0.0,0.1,1018.0,136.0,3.1,78.03119328830341 +2019-04-05 21:30:00-05:00,2019,4,5,21,30,25.0,21.1,0.0,0.0,0.0,0.1,1018.0,135.0,3.0,78.98125580461064 +2019-04-05 22:30:00-05:00,2019,4,5,22,30,24.9,21.3,0.0,0.0,0.0,0.1,1019.0,133.0,3.0,80.43463464682452 +2019-04-05 23:30:00-05:00,2019,4,5,23,30,24.8,21.3,0.0,0.0,0.0,0.1,1018.0,131.0,3.0,80.91617618975445 +2019-04-06 00:30:00-05:00,2019,4,6,0,30,24.700000000000003,21.3,0.0,0.0,0.0,0.1,1018.0,128.0,3.0,81.40096354214072 +2019-04-06 01:30:00-05:00,2019,4,6,1,30,24.6,21.3,0.0,0.0,0.0,0.1,1017.0,123.0,2.9000000000000004,81.88902089566456 +2019-04-06 02:30:00-05:00,2019,4,6,2,30,24.5,21.3,0.0,0.0,0.0,0.1,1017.0,116.0,2.9000000000000004,82.38037263885073 +2019-04-06 03:30:00-05:00,2019,4,6,3,30,24.4,21.3,0.0,0.0,0.0,0.1,1017.0,108.0,2.8000000000000003,82.8750433587992 +2019-04-06 04:30:00-05:00,2019,4,6,4,30,24.4,21.3,0.0,0.0,0.0,0.1,1018.0,103.0,2.8000000000000003,82.8750433587992 +2019-04-06 05:30:00-05:00,2019,4,6,5,30,24.5,21.3,0.0,0.0,0.0,0.1,1018.0,99.0,2.8000000000000003,82.38037263885073 +2019-04-06 06:30:00-05:00,2019,4,6,6,30,24.9,21.5,31.0,186.0,45.0,0.1,1019.0,97.0,3.1,81.42612568109206 +2019-04-06 07:30:00-05:00,2019,4,6,7,30,25.5,21.6,76.0,565.0,249.0,0.1,1019.0,95.0,3.6,79.0511314297047 +2019-04-06 08:30:00-05:00,2019,4,6,8,30,25.9,21.5,98.0,731.0,478.0,0.1,1020.0,94.0,3.9,76.72682357004189 +2019-04-06 09:30:00-05:00,2019,4,6,9,30,26.3,21.4,147.0,743.0,667.0,0.1,1020.0,95.0,4.3,74.47575971992512 +2019-04-06 10:30:00-05:00,2019,4,6,10,30,26.5,21.200000000000003,145.0,812.0,825.0,0.1,1020.0,96.0,4.6000000000000005,72.70515418077163 +2019-04-06 11:30:00-05:00,2019,4,6,11,30,26.700000000000003,21.0,240.0,662.0,849.0,0.1,1019.0,98.0,4.7,70.97656721824985 +2019-04-06 12:30:00-05:00,2019,4,6,12,30,26.700000000000003,20.9,255.0,643.0,862.0,0.1,1019.0,98.0,4.6000000000000005,70.54164368023682 +2019-04-06 13:30:00-05:00,2019,4,6,13,30,26.6,20.6,278.0,585.0,808.0,0.1,1018.0,98.0,4.4,69.65991606570577 +2019-04-06 14:30:00-05:00,2019,4,6,14,30,26.5,20.5,207.0,670.0,750.0,0.1,1017.0,96.0,4.2,69.64101481180234 +2019-04-06 15:30:00-05:00,2019,4,6,15,30,26.3,20.5,213.0,538.0,570.0,0.1,1017.0,93.0,4.2,70.46770527613555 +2019-04-06 16:30:00-05:00,2019,4,6,16,30,26.0,20.700000000000003,194.0,202.0,290.0,0.1,1017.0,92.0,4.4,72.6181182590597 +2019-04-06 17:30:00-05:00,2019,4,6,17,30,25.700000000000003,21.0,121.0,125.0,153.0,0.1,1018.0,93.0,4.5,75.29711759308779 +2019-04-06 18:30:00-05:00,2019,4,6,18,30,25.3,21.4,14.0,40.0,15.0,0.1,1018.0,95.0,4.4,79.02322098398218 +2019-04-06 19:30:00-05:00,2019,4,6,19,30,25.1,21.700000000000003,0.0,0.0,0.0,0.1,1019.0,97.0,4.3,81.45123892697173 +2019-04-06 20:30:00-05:00,2019,4,6,20,30,25.1,21.9,0.0,0.0,0.0,0.1,1019.0,100.0,4.5,82.45221220678982 +2019-04-06 21:30:00-05:00,2019,4,6,21,30,25.0,22.1,0.0,0.0,0.0,0.1,1019.0,102.0,4.7,83.96287846488495 +2019-04-06 22:30:00-05:00,2019,4,6,22,30,24.9,22.3,0.0,0.0,0.0,0.1,1019.0,103.0,4.800000000000001,85.5000311395926 +2019-04-06 23:30:00-05:00,2019,4,6,23,30,24.9,22.4,0.0,0.0,0.0,0.1,1019.0,104.0,4.800000000000001,86.0216165010677 +2019-04-07 00:30:00-05:00,2019,4,7,0,30,24.8,22.5,0.0,0.0,0.0,0.1,1018.0,106.0,4.800000000000001,87.06411594521352 +2019-04-07 01:30:00-05:00,2019,4,7,1,30,24.8,22.6,0.0,0.0,0.0,0.1,1018.0,107.0,4.800000000000001,87.59444081812065 +2019-04-07 02:30:00-05:00,2019,4,7,2,30,24.700000000000003,22.6,0.0,0.0,0.0,0.1,1018.0,107.0,4.9,88.11923918412832 +2019-04-07 03:30:00-05:00,2019,4,7,3,30,24.700000000000003,22.6,0.0,0.0,0.0,0.1,1018.0,107.0,5.0,88.11923918412832 +2019-04-07 04:30:00-05:00,2019,4,7,4,30,24.700000000000003,22.5,0.0,0.0,0.0,0.1,1018.0,106.0,5.0,87.58573701339093 +2019-04-07 05:30:00-05:00,2019,4,7,5,30,24.9,22.4,0.0,0.0,0.0,0.1,1019.0,105.0,5.0,86.0216165010677 +2019-04-07 06:30:00-05:00,2019,4,7,6,30,25.3,22.3,33.0,157.0,46.0,0.1,1019.0,105.0,5.1000000000000005,83.48655911737376 +2019-04-07 07:30:00-05:00,2019,4,7,7,30,25.8,22.200000000000003,77.0,566.0,253.0,0.1,1020.0,106.0,5.2,80.55257262849392 +2019-04-07 08:30:00-05:00,2019,4,7,8,30,26.1,22.0,101.0,731.0,483.0,0.1,1020.0,108.0,5.1000000000000005,78.17600861292594 +2019-04-07 09:30:00-05:00,2019,4,7,9,30,26.4,21.9,117.0,815.0,690.0,0.1,1020.0,108.0,5.2,76.33576480448347 +2019-04-07 10:30:00-05:00,2019,4,7,10,30,26.5,21.8,232.0,620.0,753.0,0.1,1020.0,107.0,5.300000000000001,75.42497687186433 +2019-04-07 11:30:00-05:00,2019,4,7,11,30,26.700000000000003,21.700000000000003,298.0,546.0,802.0,0.1,1019.0,107.0,5.5,74.08741416205547 +2019-04-07 12:30:00-05:00,2019,4,7,12,30,26.700000000000003,21.5,179.0,803.0,939.0,0.1,1019.0,108.0,5.300000000000001,73.18663819675605 +2019-04-07 13:30:00-05:00,2019,4,7,13,30,26.6,21.4,183.0,785.0,896.0,0.1,1019.0,109.0,5.1000000000000005,73.16952501754473 +2019-04-07 14:30:00-05:00,2019,4,7,14,30,26.4,21.200000000000003,247.0,552.0,696.0,0.1,1018.0,107.0,5.1000000000000005,73.13525295185259 +2019-04-07 15:30:00-05:00,2019,4,7,15,30,26.3,21.200000000000003,109.0,807.0,646.0,0.1,1018.0,105.0,4.9,73.56821825058087 +2019-04-07 16:30:00-05:00,2019,4,7,16,30,26.200000000000003,21.3,101.0,686.0,427.0,0.1,1018.0,100.0,4.7,74.45930386781104 +2019-04-07 17:30:00-05:00,2019,4,7,17,30,26.0,21.5,112.0,224.0,170.0,0.1,1018.0,96.0,4.6000000000000005,76.2739291908271 +2019-04-07 18:30:00-05:00,2019,4,7,18,30,25.6,21.8,13.0,1.0,13.0,0.1,1018.0,96.0,4.800000000000001,79.5495912631393 +2019-04-07 19:30:00-05:00,2019,4,7,19,30,25.5,22.1,0.0,0.0,0.0,0.1,1018.0,100.0,5.2,81.50131921917182 +2019-04-07 20:30:00-05:00,2019,4,7,20,30,25.4,22.3,0.0,0.0,0.0,0.1,1018.0,105.0,5.5,82.99156488978728 +2019-04-07 21:30:00-05:00,2019,4,7,21,30,25.4,22.5,0.0,0.0,0.0,0.1,1018.0,110.0,5.7,84.00683397707049 +2019-04-07 22:30:00-05:00,2019,4,7,22,30,25.3,22.6,0.0,0.0,0.0,0.1,1018.0,113.0,5.7,85.02263802041853 +2019-04-07 23:30:00-05:00,2019,4,7,23,30,25.3,22.700000000000003,0.0,0.0,0.0,0.1,1018.0,116.0,5.7,85.54013688901695 +2019-04-08 00:30:00-05:00,2019,4,8,0,30,25.3,22.700000000000003,0.0,0.0,0.0,0.1,1017.0,120.0,5.9,85.54013688901695 +2019-04-08 01:30:00-05:00,2019,4,8,1,30,25.3,22.6,0.0,0.0,0.0,0.1,1017.0,123.0,6.0,85.02263802041853 +2019-04-08 02:30:00-05:00,2019,4,8,2,30,25.200000000000003,22.5,0.0,0.0,0.0,0.1,1016.0,126.0,6.0,85.01229874539113 +2019-04-08 03:30:00-05:00,2019,4,8,3,30,25.200000000000003,22.3,0.0,0.0,0.0,0.1,1016.0,127.0,5.9,83.9848780598474 +2019-04-08 04:30:00-05:00,2019,4,8,4,30,25.200000000000003,22.200000000000003,0.0,0.0,0.0,0.1,1017.0,128.0,5.6000000000000005,83.47525948927431 +2019-04-08 05:30:00-05:00,2019,4,8,5,30,25.3,22.1,0.0,0.0,0.0,0.1,1017.0,129.0,5.5,82.47606490270397 +2019-04-08 06:30:00-05:00,2019,4,8,6,30,25.5,22.1,36.0,5.0,36.0,0.1,1017.0,130.0,5.4,81.50131921917182 +2019-04-08 07:30:00-05:00,2019,4,8,7,30,25.9,22.200000000000003,124.0,203.0,188.0,0.1,1018.0,133.0,5.4,80.07674337450413 +2019-04-08 08:30:00-05:00,2019,4,8,8,30,26.3,22.1,209.0,331.0,383.0,0.1,1018.0,137.0,5.4,77.7299066762135 +2019-04-08 09:30:00-05:00,2019,4,8,9,30,26.6,22.0,168.0,668.0,640.0,0.1,1018.0,140.0,5.4,75.90250895113047 +2019-04-08 10:30:00-05:00,2019,4,8,10,30,26.8,22.0,256.0,589.0,752.0,0.1,1018.0,143.0,5.4,75.01402814660541 +2019-04-08 11:30:00-05:00,2019,4,8,11,30,27.0,22.0,288.0,542.0,789.0,0.1,1017.0,145.0,5.4,74.13724053720263 +2019-04-08 12:30:00-05:00,2019,4,8,12,30,27.1,21.9,254.0,614.0,836.0,0.1,1016.0,147.0,5.4,73.25493893485287 +2019-04-08 13:30:00-05:00,2019,4,8,13,30,27.1,21.9,358.0,393.0,716.0,0.1,1016.0,147.0,5.300000000000001,73.25493893485287 +2019-04-08 14:30:00-05:00,2019,4,8,14,30,27.1,21.8,123.0,847.0,813.0,0.1,1015.0,148.0,5.2,72.80909003188867 +2019-04-08 15:30:00-05:00,2019,4,8,15,30,26.9,21.8,230.0,457.0,535.0,0.1,1015.0,148.0,5.0,73.66952882845527 +2019-04-08 16:30:00-05:00,2019,4,8,16,30,26.6,21.8,173.0,400.0,364.0,0.1,1015.0,148.0,4.9,74.98174057282652 +2019-04-08 17:30:00-05:00,2019,4,8,17,30,26.3,21.8,81.0,400.0,185.0,0.1,1015.0,149.0,4.6000000000000005,76.32032725297273 +2019-04-08 18:30:00-05:00,2019,4,8,18,30,25.9,21.9,15.0,14.0,15.0,0.1,1015.0,150.0,4.5,78.62569104043504 +2019-04-08 19:30:00-05:00,2019,4,8,19,30,25.8,22.0,0.0,0.0,0.0,0.1,1015.0,152.0,4.5,79.57685932121313 +2019-04-08 20:30:00-05:00,2019,4,8,20,30,25.700000000000003,22.1,0.0,0.0,0.0,0.1,1015.0,154.0,4.6000000000000005,80.53951879326632 +2019-04-08 21:30:00-05:00,2019,4,8,21,30,25.6,22.200000000000003,0.0,0.0,0.0,0.1,1015.0,156.0,4.9,81.51380892113755 +2019-04-08 22:30:00-05:00,2019,4,8,22,30,25.6,22.3,0.0,0.0,0.0,0.1,1015.0,161.0,5.1000000000000005,82.01145278638 +2019-04-08 23:30:00-05:00,2019,4,8,23,30,25.5,22.3,0.0,0.0,0.0,0.1,1015.0,166.0,5.300000000000001,82.4998708796576 +2019-04-09 00:30:00-05:00,2019,4,9,0,30,25.4,22.200000000000003,0.0,0.0,0.0,0.1,1014.0,170.0,5.5,82.48797372378452 +2019-04-09 01:30:00-05:00,2019,4,9,1,30,25.3,22.1,0.0,0.0,0.0,0.1,1013.0,173.0,5.5,82.47606490270397 +2019-04-09 02:30:00-05:00,2019,4,9,2,30,25.200000000000003,22.1,0.0,0.0,0.0,0.1,1013.0,177.0,5.5,82.96835235443523 +2019-04-09 03:30:00-05:00,2019,4,9,3,30,25.200000000000003,22.1,0.0,0.0,0.0,0.1,1012.0,177.0,5.5,82.96835235443523 +2019-04-09 04:30:00-05:00,2019,4,9,4,30,25.200000000000003,22.1,0.0,0.0,0.0,0.1,1012.0,175.0,5.5,82.96835235443523 +2019-04-09 05:30:00-05:00,2019,4,9,5,30,25.3,22.200000000000003,0.0,0.0,0.0,0.1,1012.0,174.0,5.7,82.97996433623776 +2019-04-09 06:30:00-05:00,2019,4,9,6,30,25.6,22.3,38.0,5.0,38.0,0.1,1012.0,174.0,6.0,82.01145278638 +2019-04-09 07:30:00-05:00,2019,4,9,7,30,25.9,22.4,78.0,5.0,80.0,0.1,1013.0,176.0,6.300000000000001,81.05709730482545 +2019-04-09 08:30:00-05:00,2019,4,9,8,30,26.3,22.5,192.0,42.0,214.0,0.1,1013.0,180.0,6.4,79.64480263005237 +2019-04-09 09:30:00-05:00,2019,4,9,9,30,26.6,22.5,237.0,33.0,260.0,0.1,1013.0,181.0,6.5,78.2479077819723 +2019-04-09 10:30:00-05:00,2019,4,9,10,30,26.700000000000003,22.6,159.0,2.0,161.0,0.1,1012.0,181.0,6.6000000000000005,78.26224718879386 +2019-04-09 11:30:00-05:00,2019,4,9,11,30,26.6,22.8,40.0,0.0,40.0,0.1,1012.0,184.0,6.6000000000000005,79.68541368731411 +2019-04-09 12:30:00-05:00,2019,4,9,12,30,26.5,22.9,42.0,0.0,42.0,0.1,1011.0,190.0,6.5,80.64359904940797 +2019-04-09 13:30:00-05:00,2019,4,9,13,30,26.3,22.9,80.0,0.0,80.0,0.1,1010.0,198.0,6.300000000000001,81.6008983438513 +2019-04-09 14:30:00-05:00,2019,4,9,14,30,26.0,23.0,149.0,1.0,150.0,0.1,1010.0,206.0,5.9,83.5653445347348 +2019-04-09 15:30:00-05:00,2019,4,9,15,30,25.6,23.1,82.0,0.0,82.0,0.1,1009.0,215.0,5.5,86.08937208209473 +2019-04-09 16:30:00-05:00,2019,4,9,16,30,25.3,23.200000000000003,22.0,0.0,22.0,0.1,1010.0,224.0,5.1000000000000005,88.1692346797932 +2019-04-09 17:30:00-05:00,2019,4,9,17,30,24.9,23.200000000000003,36.0,0.0,36.0,0.1,1010.0,234.0,5.0,90.29564028477958 +2019-04-09 18:30:00-05:00,2019,4,9,18,30,24.6,23.200000000000003,8.0,0.0,8.0,0.1,1010.0,241.0,4.7,91.92832921457973 +2019-04-09 19:30:00-05:00,2019,4,9,19,30,24.3,22.9,0.0,0.0,0.0,0.12,1010.0,240.0,4.3,91.91091146661829 +2019-04-09 20:30:00-05:00,2019,4,9,20,30,24.200000000000003,22.6,0.0,0.0,0.0,0.12,1010.0,235.0,3.8,90.7968605458183 +2019-04-09 21:30:00-05:00,2019,4,9,21,30,24.1,22.3,0.0,0.0,0.0,0.12,1010.0,229.0,3.8,89.69302615629533 +2019-04-09 22:30:00-05:00,2019,4,9,22,30,24.1,22.3,0.0,0.0,0.0,0.12,1010.0,223.0,4.2,89.69302615629533 +2019-04-09 23:30:00-05:00,2019,4,9,23,30,24.1,22.3,0.0,0.0,0.0,0.12,1009.0,220.0,4.6000000000000005,89.69302615629533 +2019-04-10 00:30:00-05:00,2019,4,10,0,30,24.0,22.4,0.0,0.0,0.0,0.12,1009.0,224.0,4.800000000000001,90.78368632458509 +2019-04-10 01:30:00-05:00,2019,4,10,1,30,24.0,22.4,0.0,0.0,0.0,0.12,1008.0,233.0,5.0,90.78368632458509 +2019-04-10 02:30:00-05:00,2019,4,10,2,30,23.9,22.5,0.0,0.0,0.0,0.12,1008.0,243.0,5.0,91.88760131439639 +2019-04-10 03:30:00-05:00,2019,4,10,3,30,23.8,22.5,0.0,0.0,0.0,0.12,1008.0,252.0,4.9,92.44185134155835 +2019-04-10 04:30:00-05:00,2019,4,10,4,30,23.8,22.4,0.0,0.0,0.0,0.12,1008.0,258.0,4.7,91.88175827454802 +2019-04-10 05:30:00-05:00,2019,4,10,5,30,23.9,22.3,0.0,0.0,0.0,0.12,1009.0,258.0,4.7,90.77708879175361 +2019-04-10 06:30:00-05:00,2019,4,10,6,30,24.1,22.200000000000003,21.0,0.0,21.0,0.12,1009.0,255.0,4.9,89.14877065654245 +2019-04-10 07:30:00-05:00,2019,4,10,7,30,24.4,22.200000000000003,81.0,8.0,84.0,0.12,1010.0,250.0,5.0,87.55957195860968 +2019-04-10 08:30:00-05:00,2019,4,10,8,30,24.8,21.9,223.0,39.0,244.0,0.12,1010.0,245.0,4.9,83.9408350810903 +2019-04-10 09:30:00-05:00,2019,4,10,9,30,25.4,21.4,260.0,10.0,267.0,0.12,1010.0,240.0,4.9,78.5546900174901 +2019-04-10 10:30:00-05:00,2019,4,10,10,30,25.9,21.0,364.0,192.0,527.0,0.12,1009.0,235.0,5.0,74.40984699859129 +2019-04-10 11:30:00-05:00,2019,4,10,11,30,26.3,20.700000000000003,432.0,259.0,673.0,0.12,1009.0,229.0,5.2,71.34164951034492 +2019-04-10 12:30:00-05:00,2019,4,10,12,30,26.700000000000003,20.5,224.0,728.0,917.0,0.12,1008.0,226.0,5.5,68.82522705149493 +2019-04-10 13:30:00-05:00,2019,4,10,13,30,26.9,20.3,375.0,365.0,709.0,0.12,1007.0,228.0,5.7,67.18566951898258 +2019-04-10 14:30:00-05:00,2019,4,10,14,30,27.0,20.1,324.0,430.0,676.0,0.12,1007.0,231.0,5.800000000000001,65.97133345468069 +2019-04-10 15:30:00-05:00,2019,4,10,15,30,27.0,19.9,185.0,603.0,589.0,0.12,1007.0,233.0,5.800000000000001,65.15950802191401 +2019-04-10 16:30:00-05:00,2019,4,10,16,30,26.700000000000003,19.8,207.0,266.0,335.0,0.12,1007.0,235.0,5.7,65.90948818556873 +2019-04-10 17:30:00-05:00,2019,4,10,17,30,26.3,19.9,70.0,516.0,206.0,0.12,1007.0,239.0,5.5,67.90194546200273 +2019-04-10 18:30:00-05:00,2019,4,10,18,30,25.9,20.0,16.0,61.0,18.0,0.12,1008.0,247.0,5.2,69.95921323444949 +2019-04-10 19:30:00-05:00,2019,4,10,19,30,25.5,20.200000000000003,0.0,0.0,0.0,0.12,1008.0,256.0,5.0,72.53069587912493 +2019-04-10 20:30:00-05:00,2019,4,10,20,30,25.200000000000003,20.5,0.0,0.0,0.0,0.12,1009.0,263.0,4.800000000000001,75.21672996872452 +2019-04-10 21:30:00-05:00,2019,4,10,21,30,24.8,20.6,0.0,0.0,0.0,0.12,1009.0,270.0,4.6000000000000005,77.50851340303502 +2019-04-10 22:30:00-05:00,2019,4,10,22,30,24.4,20.700000000000003,0.0,0.0,0.0,0.12,1009.0,276.0,4.4,79.87545001594654 +2019-04-10 23:30:00-05:00,2019,4,10,23,30,24.0,20.8,0.0,0.0,0.0,0.12,1009.0,280.0,4.2,82.32018205581834 +2019-04-11 00:30:00-05:00,2019,4,11,0,30,23.700000000000003,20.8,0.0,0.0,0.0,0.12,1009.0,283.0,3.7,83.81880874854961 +2019-04-11 01:30:00-05:00,2019,4,11,1,30,23.4,20.700000000000003,0.0,0.0,0.0,0.12,1009.0,286.0,3.3000000000000003,84.82440560749298 +2019-04-11 02:30:00-05:00,2019,4,11,2,30,23.3,20.5,0.0,0.0,0.0,0.12,1009.0,292.0,2.8000000000000003,84.2925788898058 +2019-04-11 03:30:00-05:00,2019,4,11,3,30,23.200000000000003,20.3,0.0,0.0,0.0,0.12,1009.0,298.0,2.3000000000000003,83.76289817082439 +2019-04-11 04:30:00-05:00,2019,4,11,4,30,23.1,20.1,0.0,0.0,0.0,0.12,1009.0,302.0,1.7000000000000002,83.23536182023041 +2019-04-11 05:30:00-05:00,2019,4,11,5,30,23.200000000000003,19.700000000000003,0.0,0.0,0.0,0.12,1010.0,299.0,1.2000000000000002,80.70850062640415 +2019-04-11 06:30:00-05:00,2019,4,11,6,30,23.6,19.5,33.0,287.0,61.0,0.12,1010.0,276.0,0.7000000000000001,77.81139794609358 +2019-04-11 07:30:00-05:00,2019,4,11,7,30,24.200000000000003,19.4,67.0,648.0,277.0,0.12,1011.0,226.0,0.7000000000000001,74.58967877163509 +2019-04-11 08:30:00-05:00,2019,4,11,8,30,24.9,19.200000000000003,84.0,794.0,510.0,0.12,1011.0,186.0,1.2000000000000002,70.64574180258202 +2019-04-11 09:30:00-05:00,2019,4,11,9,30,25.700000000000003,19.3,97.0,868.0,718.0,0.12,1012.0,175.0,1.7000000000000002,67.78311275028572 +2019-04-11 10:30:00-05:00,2019,4,11,10,30,26.3,19.4,105.0,908.0,877.0,0.12,1011.0,172.0,2.1,65.82679263129722 +2019-04-11 11:30:00-05:00,2019,4,11,11,30,26.8,19.5,109.0,926.0,972.0,0.12,1011.0,171.0,2.4000000000000004,64.31382741722626 +2019-04-11 12:30:00-05:00,2019,4,11,12,30,27.200000000000003,19.6,112.0,930.0,999.0,0.12,1010.0,168.0,2.5,63.21173775235905 +2019-04-11 13:30:00-05:00,2019,4,11,13,30,27.4,19.700000000000003,111.0,917.0,950.0,0.12,1010.0,165.0,2.6,62.8640939855227 +2019-04-11 14:30:00-05:00,2019,4,11,14,30,27.6,19.8,106.0,890.0,835.0,0.12,1010.0,161.0,2.6,62.51915000658861 +2019-04-11 15:30:00-05:00,2019,4,11,15,30,27.5,19.9,99.0,837.0,661.0,0.12,1010.0,159.0,2.7,63.27696644025663 +2019-04-11 16:30:00-05:00,2019,4,11,16,30,27.3,20.1,86.0,743.0,444.0,0.12,1010.0,157.0,2.9000000000000004,64.82019349959317 +2019-04-11 17:30:00-05:00,2019,4,11,17,30,26.8,20.200000000000003,64.0,557.0,211.0,0.12,1011.0,157.0,3.1,67.16561583301454 +2019-04-11 18:30:00-05:00,2019,4,11,18,30,26.3,20.5,18.0,116.0,22.0,0.12,1011.0,158.0,3.2,70.46770527613555 +2019-04-11 19:30:00-05:00,2019,4,11,19,30,25.9,20.700000000000003,0.0,0.0,0.0,0.12,1012.0,160.0,3.4000000000000004,73.04930540173858 +2019-04-11 20:30:00-05:00,2019,4,11,20,30,25.6,20.9,0.0,0.0,0.0,0.12,1012.0,162.0,3.6,75.28106943219711 +2019-04-11 21:30:00-05:00,2019,4,11,21,30,25.3,21.0,0.0,0.0,0.0,0.12,1012.0,164.0,3.5,77.10761189838719 +2019-04-11 22:30:00-05:00,2019,4,11,22,30,25.200000000000003,21.0,0.0,0.0,0.0,0.12,1013.0,164.0,3.4000000000000004,77.5678558469233 +2019-04-11 23:30:00-05:00,2019,4,11,23,30,25.0,20.9,0.0,0.0,0.0,0.12,1013.0,162.0,3.1,78.01663671137742 +2019-04-12 00:30:00-05:00,2019,4,12,0,30,25.0,20.700000000000003,0.0,0.0,0.0,0.12,1013.0,158.0,2.9000000000000004,77.0623625250608 +2019-04-12 01:30:00-05:00,2019,4,12,1,30,25.0,20.6,0.0,0.0,0.0,0.12,1013.0,155.0,2.7,76.58907524068796 +2019-04-12 02:30:00-05:00,2019,4,12,2,30,24.9,20.6,0.0,0.0,0.0,0.12,1013.0,150.0,2.6,77.04725125630709 +2019-04-12 03:30:00-05:00,2019,4,12,3,30,24.9,20.700000000000003,0.0,0.0,0.0,0.12,1013.0,145.0,2.7,77.52336986984166 +2019-04-12 04:30:00-05:00,2019,4,12,4,30,24.8,20.9,0.0,0.0,0.0,0.12,1013.0,141.0,3.0,78.95321249408512 +2019-04-12 05:30:00-05:00,2019,4,12,5,30,24.8,21.1,0.0,0.0,0.0,0.12,1013.0,139.0,3.3000000000000003,79.92941166716213 +2019-04-12 06:30:00-05:00,2019,4,12,6,30,25.3,21.5,36.0,292.0,65.0,0.12,1014.0,139.0,3.8,79.50859157319356 +2019-04-12 07:30:00-05:00,2019,4,12,7,30,26.0,21.8,70.0,652.0,284.0,0.12,1014.0,140.0,4.3,77.68587617563925 +2019-04-12 08:30:00-05:00,2019,4,12,8,30,26.4,21.8,87.0,803.0,520.0,0.12,1014.0,140.0,4.6000000000000005,75.87116518171565 +2019-04-12 09:30:00-05:00,2019,4,12,9,30,26.8,21.8,117.0,829.0,712.0,0.12,1014.0,141.0,4.5,74.10403787090756 +2019-04-12 10:30:00-05:00,2019,4,12,10,30,27.200000000000003,21.700000000000003,175.0,766.0,828.0,0.12,1015.0,143.0,4.3,71.94224407779089 +2019-04-12 11:30:00-05:00,2019,4,12,11,30,27.6,21.5,439.0,123.0,554.0,0.12,1015.0,144.0,4.0,69.4219684883362 +2019-04-12 12:30:00-05:00,2019,4,12,12,30,27.8,21.200000000000003,394.0,365.0,743.0,0.12,1014.0,144.0,3.7,67.36540856745334 +2019-04-12 13:30:00-05:00,2019,4,12,13,30,27.9,21.0,108.0,925.0,956.0,0.12,1014.0,141.0,3.7,66.15596642593498 +2019-04-12 14:30:00-05:00,2019,4,12,14,30,27.8,21.0,164.0,769.0,795.0,0.12,1013.0,137.0,3.9,66.5432759396931 +2019-04-12 15:30:00-05:00,2019,4,12,15,30,27.6,21.1,96.0,843.0,663.0,0.12,1013.0,133.0,4.1000000000000005,67.7403617105945 +2019-04-12 16:30:00-05:00,2019,4,12,16,30,27.3,21.3,84.0,751.0,447.0,0.12,1013.0,127.0,4.3,69.79177391983481 +2019-04-12 17:30:00-05:00,2019,4,12,17,30,26.9,21.5,62.0,571.0,214.0,0.12,1014.0,122.0,4.2,72.33057927645822 +2019-04-12 18:30:00-05:00,2019,4,12,18,30,26.5,21.700000000000003,18.0,127.0,23.0,0.12,1014.0,118.0,3.9,74.96557480540517 +2019-04-12 19:30:00-05:00,2019,4,12,19,30,26.200000000000003,22.0,0.0,0.0,0.0,0.12,1015.0,116.0,3.8,77.71524355462876 +2019-04-12 20:30:00-05:00,2019,4,12,20,30,26.1,22.200000000000003,0.0,0.0,0.0,0.12,1015.0,114.0,3.8,79.13454571233362 +2019-04-12 21:30:00-05:00,2019,4,12,21,30,25.9,22.3,0.0,0.0,0.0,0.12,1015.0,114.0,3.9,80.56561391823581 +2019-04-12 22:30:00-05:00,2019,4,12,22,30,25.8,22.5,0.0,0.0,0.0,0.12,1015.0,113.0,4.0,82.03579612571595 +2019-04-12 23:30:00-05:00,2019,4,12,23,30,25.8,22.6,0.0,0.0,0.0,0.12,1015.0,115.0,4.2,82.53549250098932 +2019-04-13 00:30:00-05:00,2019,4,13,0,30,25.700000000000003,22.6,0.0,0.0,0.0,0.12,1014.0,118.0,4.4,83.0262981239067 +2019-04-13 01:30:00-05:00,2019,4,13,1,30,25.6,22.700000000000003,0.0,0.0,0.0,0.12,1014.0,122.0,4.7,84.02874632731391 +2019-04-13 02:30:00-05:00,2019,4,13,2,30,25.5,22.700000000000003,0.0,0.0,0.0,0.12,1014.0,124.0,5.1000000000000005,84.52917838487782 +2019-04-13 03:30:00-05:00,2019,4,13,3,30,25.4,22.8,0.0,0.0,0.0,0.12,1014.0,125.0,5.300000000000001,85.5501381158545 +2019-04-13 04:30:00-05:00,2019,4,13,4,30,25.3,22.8,0.0,0.0,0.0,0.12,1014.0,127.0,5.4,86.0603926771802 +2019-04-13 05:30:00-05:00,2019,4,13,5,30,25.4,22.700000000000003,0.0,0.0,0.0,0.12,1014.0,128.0,5.4,85.03296693933089 +2019-04-13 06:30:00-05:00,2019,4,13,6,30,25.8,22.700000000000003,39.0,213.0,61.0,0.12,1015.0,129.0,5.6000000000000005,83.03785310732827 +2019-04-13 07:30:00-05:00,2019,4,13,7,30,26.3,22.700000000000003,126.0,355.0,244.0,0.12,1015.0,131.0,5.7,80.61765392538541 +2019-04-13 08:30:00-05:00,2019,4,13,8,30,26.700000000000003,22.6,129.0,663.0,488.0,0.12,1016.0,134.0,5.800000000000001,78.26224718879386 +2019-04-13 09:30:00-05:00,2019,4,13,9,30,27.0,22.6,102.0,856.0,719.0,0.12,1016.0,137.0,5.800000000000001,76.89363104546618 +2019-04-13 10:30:00-05:00,2019,4,13,10,30,27.3,22.5,105.0,904.0,878.0,0.12,1015.0,140.0,5.5,75.09449139476281 +2019-04-13 11:30:00-05:00,2019,4,13,11,30,27.5,22.4,147.0,848.0,941.0,0.12,1015.0,143.0,5.1000000000000005,73.77029772964892 +2019-04-13 12:30:00-05:00,2019,4,13,12,30,27.6,22.3,246.0,664.0,882.0,0.12,1015.0,144.0,4.6000000000000005,72.8952829067595 +2019-04-13 13:30:00-05:00,2019,4,13,13,30,27.700000000000003,22.0,116.0,904.0,946.0,0.12,1014.0,143.0,4.2,71.15813879866435 +2019-04-13 14:30:00-05:00,2019,4,13,14,30,27.6,21.8,109.0,880.0,832.0,0.12,1013.0,138.0,4.0,70.70707520994743 +2019-04-13 15:30:00-05:00,2019,4,13,15,30,27.4,21.700000000000003,118.0,784.0,646.0,0.12,1013.0,133.0,4.0,71.10383243626454 +2019-04-13 16:30:00-05:00,2019,4,13,16,30,27.1,21.700000000000003,85.0,743.0,445.0,0.12,1013.0,127.0,4.1000000000000005,72.3656209344505 +2019-04-13 17:30:00-05:00,2019,4,13,17,30,26.700000000000003,21.9,63.0,562.0,213.0,0.12,1013.0,122.0,4.1000000000000005,74.99789168117 +2019-04-13 18:30:00-05:00,2019,4,13,18,30,26.4,22.200000000000003,19.0,125.0,24.0,0.12,1014.0,118.0,4.3,77.74455610700481 +2019-04-13 19:30:00-05:00,2019,4,13,19,30,26.3,22.5,0.0,0.0,0.0,0.12,1014.0,117.0,4.800000000000001,79.64480263005237 +2019-04-13 20:30:00-05:00,2019,4,13,20,30,26.3,22.8,0.0,0.0,0.0,0.12,1014.0,119.0,5.5,81.10797113328562 +2019-04-13 21:30:00-05:00,2019,4,13,21,30,26.4,23.1,0.0,0.0,0.0,0.12,1014.0,122.0,5.9,82.1085421799944 +2019-04-13 22:30:00-05:00,2019,4,13,22,30,26.6,23.3,0.0,0.0,0.0,0.12,1014.0,124.0,6.1000000000000005,82.13269659882694 +2019-04-13 23:30:00-05:00,2019,4,13,23,30,26.6,23.3,0.0,0.0,0.0,0.12,1013.0,128.0,6.1000000000000005,82.13269659882694 +2019-04-14 00:30:00-05:00,2019,4,14,0,30,26.6,23.4,0.0,0.0,0.0,0.12,1013.0,133.0,6.300000000000001,82.62997382431229 +2019-04-14 01:30:00-05:00,2019,4,14,1,30,26.5,23.4,0.0,0.0,0.0,0.12,1013.0,137.0,6.6000000000000005,83.11842079163638 +2019-04-14 02:30:00-05:00,2019,4,14,2,30,26.3,23.3,0.0,0.0,0.0,0.12,1012.0,141.0,6.800000000000001,83.5989433521268 +2019-04-14 03:30:00-05:00,2019,4,14,3,30,26.200000000000003,23.4,0.0,0.0,0.0,0.12,1012.0,144.0,6.7,84.60337648131704 +2019-04-14 04:30:00-05:00,2019,4,14,4,30,26.200000000000003,23.5,0.0,0.0,0.0,0.12,1013.0,148.0,6.6000000000000005,85.11522706296468 +2019-04-14 05:30:00-05:00,2019,4,14,5,30,26.3,23.5,0.0,0.0,0.0,0.12,1013.0,151.0,6.4,84.61393405077794 +2019-04-14 06:30:00-05:00,2019,4,14,6,30,26.6,23.6,45.0,154.0,62.0,0.12,1013.0,155.0,6.5,83.63244289602052 +2019-04-14 07:30:00-05:00,2019,4,14,7,30,27.0,23.6,97.0,273.0,188.0,0.12,1014.0,159.0,6.5,81.68739902508572 +2019-04-14 08:30:00-05:00,2019,4,14,8,30,27.5,23.5,138.0,642.0,488.0,0.12,1014.0,163.0,6.5,78.85065779603138 +2019-04-14 09:30:00-05:00,2019,4,14,9,30,27.8,23.4,160.0,730.0,688.0,0.12,1014.0,166.0,6.4,77.0138849054383 +2019-04-14 10:30:00-05:00,2019,4,14,10,30,28.1,23.5,326.0,406.0,674.0,0.12,1014.0,167.0,6.300000000000001,76.13576744008373 +2019-04-14 11:30:00-05:00,2019,4,14,11,30,28.3,23.5,420.0,352.0,750.0,0.12,1014.0,168.0,6.2,75.25432891622025 +2019-04-14 12:30:00-05:00,2019,4,14,12,30,28.4,23.5,499.0,172.0,664.0,0.12,1014.0,167.0,6.2,74.81792523943739 +2019-04-14 13:30:00-05:00,2019,4,14,13,30,28.3,23.6,319.0,462.0,744.0,0.12,1013.0,166.0,6.1000000000000005,75.70927521212995 +2019-04-14 14:30:00-05:00,2019,4,14,14,30,28.200000000000003,23.6,260.0,525.0,692.0,0.12,1012.0,166.0,5.9,76.15120458899844 +2019-04-14 15:30:00-05:00,2019,4,14,15,30,28.1,23.5,122.0,776.0,646.0,0.12,1011.0,166.0,5.6000000000000005,76.13576744008373 +2019-04-14 16:30:00-05:00,2019,4,14,16,30,27.8,23.6,106.0,672.0,433.0,0.12,1011.0,167.0,5.300000000000001,77.94821943486646 +2019-04-14 17:30:00-05:00,2019,4,14,17,30,27.5,23.6,79.0,465.0,204.0,0.12,1012.0,169.0,5.0,79.32734551899658 +2019-04-14 18:30:00-05:00,2019,4,14,18,30,27.1,23.8,19.0,66.0,22.0,0.12,1013.0,170.0,4.800000000000001,82.19287753846746 +2019-04-14 19:30:00-05:00,2019,4,14,19,30,26.9,23.9,0.0,0.0,0.0,0.12,1013.0,173.0,4.800000000000001,83.66584354037937 +2019-04-14 20:30:00-05:00,2019,4,14,20,30,26.700000000000003,24.1,0.0,0.0,0.0,0.12,1014.0,176.0,4.7,85.67924348697159 +2019-04-14 21:30:00-05:00,2019,4,14,21,30,26.6,24.1,0.0,0.0,0.0,0.12,1014.0,181.0,4.7,86.18533883635469 +2019-04-14 22:30:00-05:00,2019,4,14,22,30,26.4,24.200000000000003,0.0,0.0,0.0,0.12,1014.0,184.0,4.9,87.73249653147649 +2019-04-14 23:30:00-05:00,2019,4,14,23,30,26.3,24.200000000000003,0.0,0.0,0.0,0.12,1014.0,185.0,5.2,88.25187843057152 +2019-04-15 00:30:00-05:00,2019,4,15,0,30,26.200000000000003,24.200000000000003,0.0,0.0,0.0,0.12,1013.0,185.0,5.7,88.7747243479242 +2019-04-15 01:30:00-05:00,2019,4,15,1,30,26.200000000000003,24.200000000000003,0.0,0.0,0.0,0.12,1013.0,185.0,6.1000000000000005,88.7747243479242 +2019-04-15 02:30:00-05:00,2019,4,15,2,30,26.1,24.3,0.0,0.0,0.0,0.12,1013.0,187.0,6.4,89.83809249503989 +2019-04-15 03:30:00-05:00,2019,4,15,3,30,26.0,24.4,0.0,0.0,0.0,0.12,1012.0,189.0,6.5,90.9141898350019 +2019-04-15 04:30:00-05:00,2019,4,15,4,30,25.9,24.5,0.0,0.0,0.0,0.12,1013.0,192.0,6.2,92.00316956053501 +2019-04-15 05:30:00-05:00,2019,4,15,5,30,25.9,24.6,0.0,0.0,0.0,0.12,1013.0,198.0,5.9,92.55520837169408 +2019-04-15 06:30:00-05:00,2019,4,15,6,30,26.1,24.700000000000003,38.0,104.0,50.0,0.12,1013.0,206.0,5.6000000000000005,92.01459240971013 +2019-04-15 07:30:00-05:00,2019,4,15,7,30,26.5,24.700000000000003,107.0,372.0,233.0,0.12,1014.0,217.0,5.300000000000001,89.86674533665325 +2019-04-15 08:30:00-05:00,2019,4,15,8,30,27.0,24.5,188.0,126.0,257.0,0.12,1014.0,232.0,5.0,86.22345560421888 +2019-04-15 09:30:00-05:00,2019,4,15,9,30,27.4,24.3,279.0,344.0,529.0,0.12,1015.0,248.0,4.7,83.22119791119955 +2019-04-15 10:30:00-05:00,2019,4,15,10,30,27.8,23.9,315.0,372.0,635.0,0.12,1015.0,264.0,4.4,79.36832881554841 +2019-04-15 11:30:00-05:00,2019,4,15,11,30,28.200000000000003,23.6,361.0,354.0,694.0,0.12,1015.0,278.0,4.1000000000000005,76.15120458899844 +2019-04-15 12:30:00-05:00,2019,4,15,12,30,28.5,23.4,415.0,144.0,553.0,0.12,1015.0,290.0,3.9,73.93705162982768 +2019-04-15 13:30:00-05:00,2019,4,15,13,30,28.700000000000003,23.1,281.0,523.0,763.0,0.12,1014.0,300.0,3.6,71.77104322070846 +2019-04-15 14:30:00-05:00,2019,4,15,14,30,28.8,22.9,358.0,395.0,684.0,0.12,1014.0,308.0,3.1,70.4975444274192 +2019-04-15 15:30:00-05:00,2019,4,15,15,30,28.8,22.700000000000003,288.0,365.0,535.0,0.12,1014.0,317.0,2.4000000000000004,69.648089109149 +2019-04-15 16:30:00-05:00,2019,4,15,16,30,28.6,22.4,136.0,513.0,386.0,0.12,1014.0,333.0,1.8,69.18880061124511 +2019-04-15 17:30:00-05:00,2019,4,15,17,30,28.3,22.200000000000003,77.0,475.0,206.0,0.12,1014.0,177.0,1.5,69.5541516661567 +2019-04-15 18:30:00-05:00,2019,4,15,18,30,27.700000000000003,22.1,20.0,96.0,24.0,0.12,1015.0,15.0,1.7000000000000002,71.59321879289612 +2019-04-15 19:30:00-05:00,2019,4,15,19,30,27.200000000000003,21.6,0.0,0.0,0.0,0.12,1015.0,22.0,2.2,71.50372465983864 +2019-04-15 20:30:00-05:00,2019,4,15,20,30,26.6,21.3,0.0,0.0,0.0,0.12,1016.0,29.0,2.9000000000000004,72.72251516973927 +2019-04-15 21:30:00-05:00,2019,4,15,21,30,26.0,20.700000000000003,0.0,0.0,0.0,0.12,1016.0,36.0,3.6,72.6181182590597 +2019-04-15 22:30:00-05:00,2019,4,15,22,30,25.5,20.3,0.0,0.0,0.0,0.12,1017.0,43.0,4.0,72.98027125754662 +2019-04-15 23:30:00-05:00,2019,4,15,23,30,25.1,20.3,0.0,0.0,0.0,0.12,1017.0,48.0,4.1000000000000005,74.73770575764732 +2019-04-16 00:30:00-05:00,2019,4,16,0,30,24.8,20.200000000000003,0.0,0.0,0.0,0.12,1017.0,51.0,4.1000000000000005,75.61833422287445 +2019-04-16 01:30:00-05:00,2019,4,16,1,30,24.6,20.200000000000003,0.0,0.0,0.0,0.12,1016.0,51.0,4.1000000000000005,76.52748366100347 +2019-04-16 02:30:00-05:00,2019,4,16,2,30,24.5,20.200000000000003,0.0,0.0,0.0,0.12,1016.0,48.0,4.3,76.98666502728693 +2019-04-16 03:30:00-05:00,2019,4,16,3,30,24.4,20.1,0.0,0.0,0.0,0.12,1017.0,45.0,4.5,76.97148310021423 +2019-04-16 04:30:00-05:00,2019,4,16,4,30,24.3,19.9,0.0,0.0,0.0,0.12,1017.0,44.0,4.6000000000000005,76.4811396005855 +2019-04-16 05:30:00-05:00,2019,4,16,5,30,24.3,19.6,0.0,0.0,0.0,0.12,1018.0,45.0,4.800000000000001,75.07110279915447 +2019-04-16 06:30:00-05:00,2019,4,16,6,30,24.700000000000003,19.3,42.0,280.0,74.0,0.12,1018.0,48.0,4.9,71.94101700371118 +2019-04-16 07:30:00-05:00,2019,4,16,7,30,25.200000000000003,18.8,82.0,631.0,297.0,0.12,1019.0,52.0,4.800000000000001,67.6836285790571 +2019-04-16 08:30:00-05:00,2019,4,16,8,30,25.6,18.3,102.0,786.0,535.0,0.12,1019.0,56.0,4.6000000000000005,64.05660503524051 +2019-04-16 09:30:00-05:00,2019,4,16,9,30,25.9,18.0,113.0,870.0,747.0,0.12,1019.0,59.0,4.4,61.75302875361797 +2019-04-16 10:30:00-05:00,2019,4,16,10,30,26.1,17.8,118.0,919.0,910.0,0.12,1019.0,59.0,4.3,60.26339659595786 +2019-04-16 11:30:00-05:00,2019,4,16,11,30,26.3,17.7,123.0,939.0,1007.0,0.12,1019.0,59.0,4.5,59.18145295504284 +2019-04-16 12:30:00-05:00,2019,4,16,12,30,26.3,17.7,123.0,945.0,1033.0,0.12,1018.0,59.0,4.800000000000001,59.18145295504284 +2019-04-16 13:30:00-05:00,2019,4,16,13,30,26.200000000000003,17.7,120.0,937.0,985.0,0.12,1018.0,58.0,5.1000000000000005,59.53207190628576 +2019-04-16 14:30:00-05:00,2019,4,16,14,30,26.0,17.6,115.0,909.0,866.0,0.12,1017.0,57.0,5.300000000000001,59.8618371488112 +2019-04-16 15:30:00-05:00,2019,4,16,15,30,25.8,17.7,106.0,859.0,688.0,0.12,1017.0,57.0,5.6000000000000005,60.95812217144736 +2019-04-16 16:30:00-05:00,2019,4,16,16,30,25.4,17.8,92.0,766.0,467.0,0.12,1017.0,57.0,5.800000000000001,62.81713542633776 +2019-04-16 17:30:00-05:00,2019,4,16,17,30,25.0,18.1,68.0,579.0,226.0,0.12,1018.0,56.0,5.800000000000001,65.55616454339095 +2019-04-16 18:30:00-05:00,2019,4,16,18,30,24.6,18.3,21.0,130.0,27.0,0.12,1018.0,55.0,5.6000000000000005,67.98894000118017 +2019-04-16 19:30:00-05:00,2019,4,16,19,30,24.4,18.5,0.0,0.0,0.0,0.12,1018.0,55.0,5.6000000000000005,69.67546042558038 +2019-04-16 20:30:00-05:00,2019,4,16,20,30,24.3,18.7,0.0,0.0,0.0,0.12,1019.0,56.0,5.5,70.9768917930263 +2019-04-16 21:30:00-05:00,2019,4,16,21,30,24.200000000000003,18.7,0.0,0.0,0.0,0.12,1019.0,60.0,5.300000000000001,71.40372780863035 +2019-04-16 22:30:00-05:00,2019,4,16,22,30,24.0,18.7,0.0,0.0,0.0,0.12,1018.0,65.0,5.0,72.26608943583008 +2019-04-16 23:30:00-05:00,2019,4,16,23,30,23.8,18.7,0.0,0.0,0.0,0.12,1018.0,70.0,4.800000000000001,73.14018222667886 +2019-04-17 00:30:00-05:00,2019,4,17,0,30,23.700000000000003,18.7,0.0,0.0,0.0,0.12,1017.0,71.0,4.6000000000000005,73.58168286508669 +2019-04-17 01:30:00-05:00,2019,4,17,1,30,23.5,18.7,0.0,0.0,0.0,0.12,1017.0,71.0,4.5,74.47370432244934 +2019-04-17 02:30:00-05:00,2019,4,17,2,30,23.4,18.7,0.0,0.0,0.0,0.12,1017.0,72.0,4.5,74.9242703691049 +2019-04-17 03:30:00-05:00,2019,4,17,3,30,23.3,18.7,0.0,0.0,0.0,0.12,1017.0,73.0,4.4,75.3779037554851 +2019-04-17 04:30:00-05:00,2019,4,17,4,30,23.200000000000003,18.7,0.0,0.0,0.0,0.12,1017.0,74.0,4.2,75.83462756182121 +2019-04-17 05:30:00-05:00,2019,4,17,5,30,23.3,18.6,0.0,0.0,0.0,0.12,1017.0,74.0,4.0,74.90788072896203 +2019-04-17 06:30:00-05:00,2019,4,17,6,30,23.9,18.6,43.0,312.0,80.0,0.12,1018.0,74.0,3.9,72.2483233161373 +2019-04-17 07:30:00-05:00,2019,4,17,7,30,24.5,18.4,79.0,651.0,303.0,0.12,1018.0,76.0,3.9,68.82704567787226 +2019-04-17 08:30:00-05:00,2019,4,17,8,30,24.9,18.3,98.0,800.0,541.0,0.12,1018.0,79.0,3.9,66.78142550986743 +2019-04-17 09:30:00-05:00,2019,4,17,9,30,25.3,18.4,109.0,876.0,750.0,0.12,1018.0,83.0,3.9,65.61887175547515 +2019-04-17 10:30:00-05:00,2019,4,17,10,30,25.6,18.5,345.0,445.0,730.0,0.12,1018.0,83.0,4.0,64.86454925005579 +2019-04-17 11:30:00-05:00,2019,4,17,11,30,25.8,18.6,502.0,214.0,704.0,0.12,1017.0,80.0,4.1000000000000005,64.5021595645772 +2019-04-17 12:30:00-05:00,2019,4,17,12,30,25.9,18.7,440.0,383.0,809.0,0.12,1017.0,78.0,4.4,64.52348081896496 +2019-04-17 13:30:00-05:00,2019,4,17,13,30,25.9,18.8,244.0,709.0,900.0,0.12,1016.0,77.0,4.7,64.92803483979355 +2019-04-17 14:30:00-05:00,2019,4,17,14,30,25.8,19.0,237.0,666.0,788.0,0.12,1015.0,78.0,4.9,66.13448809036113 +2019-04-17 15:30:00-05:00,2019,4,17,15,30,25.6,19.1,113.0,836.0,681.0,0.12,1015.0,81.0,5.0,67.34231282359893 +2019-04-17 16:30:00-05:00,2019,4,17,16,30,25.3,19.200000000000003,89.0,771.0,467.0,0.12,1015.0,85.0,4.9,68.9820789627854 +2019-04-17 17:30:00-05:00,2019,4,17,17,30,25.0,19.200000000000003,68.0,569.0,224.0,0.12,1015.0,88.0,4.6000000000000005,70.22563357066225 +2019-04-17 18:30:00-05:00,2019,4,17,18,30,24.700000000000003,19.3,21.0,140.0,28.0,0.12,1015.0,92.0,4.3,71.94101700371118 +2019-04-17 19:30:00-05:00,2019,4,17,19,30,24.5,19.5,0.0,0.0,0.0,0.11,1016.0,96.0,4.1000000000000005,73.71788416713926 +2019-04-17 20:30:00-05:00,2019,4,17,20,30,24.5,19.8,0.0,0.0,0.0,0.11,1016.0,99.0,3.9,75.10356812517155 +2019-04-17 21:30:00-05:00,2019,4,17,21,30,24.5,20.1,0.0,0.0,0.0,0.11,1016.0,103.0,3.8,76.51204998962339 +2019-04-17 22:30:00-05:00,2019,4,17,22,30,24.5,20.5,0.0,0.0,0.0,0.11,1016.0,110.0,3.9,78.42603957170671 +2019-04-17 23:30:00-05:00,2019,4,17,23,30,24.5,21.0,0.0,0.0,0.0,0.11,1015.0,118.0,4.2,80.87748210634429 +2019-04-18 00:30:00-05:00,2019,4,18,0,30,24.6,21.4,0.0,0.0,0.0,0.11,1014.0,122.0,4.4,82.39237530635937 +2019-04-18 01:30:00-05:00,2019,4,18,1,30,24.700000000000003,21.700000000000003,0.0,0.0,0.0,0.11,1014.0,124.0,4.6000000000000005,83.41859330119696 +2019-04-18 02:30:00-05:00,2019,4,18,2,30,24.8,21.9,0.0,0.0,0.0,0.11,1014.0,123.0,4.800000000000001,83.9408350810903 +2019-04-18 03:30:00-05:00,2019,4,18,3,30,24.9,22.0,0.0,0.0,0.0,0.11,1014.0,122.0,4.9,83.95186225354803 +2019-04-18 04:30:00-05:00,2019,4,18,4,30,24.9,22.0,0.0,0.0,0.0,0.11,1014.0,121.0,5.1000000000000005,83.95186225354803 +2019-04-18 05:30:00-05:00,2019,4,18,5,30,25.200000000000003,22.0,0.0,0.0,0.0,0.11,1014.0,121.0,5.4,82.46414440188862 +2019-04-18 06:30:00-05:00,2019,4,18,6,30,25.700000000000003,22.200000000000003,38.0,338.0,79.0,0.11,1014.0,122.0,5.7,81.03158661857887 +2019-04-18 07:30:00-05:00,2019,4,18,7,30,26.3,22.3,130.0,110.0,168.0,0.11,1015.0,124.0,6.0,78.68225110602832 +2019-04-18 08:30:00-05:00,2019,4,18,8,30,26.700000000000003,22.3,145.0,615.0,487.0,0.11,1015.0,126.0,6.0,76.8483062712854 +2019-04-18 09:30:00-05:00,2019,4,18,9,30,27.1,22.3,95.0,872.0,735.0,0.11,1015.0,128.0,5.9,75.06234984687318 +2019-04-18 10:30:00-05:00,2019,4,18,10,30,27.5,22.3,202.0,686.0,796.0,0.11,1015.0,131.0,5.800000000000001,73.32299728387123 +2019-04-18 11:30:00-05:00,2019,4,18,11,30,27.700000000000003,22.3,108.0,926.0,983.0,0.11,1015.0,134.0,5.800000000000001,72.47037671119828 +2019-04-18 12:30:00-05:00,2019,4,18,12,30,27.8,22.3,118.0,916.0,1003.0,0.11,1014.0,135.0,5.9,72.04825832142134 +2019-04-18 13:30:00-05:00,2019,4,18,13,30,27.8,22.4,125.0,893.0,952.0,0.11,1013.0,136.0,5.9,72.48778233514803 +2019-04-18 14:30:00-05:00,2019,4,18,14,30,27.8,22.5,390.0,294.0,634.0,0.11,1013.0,137.0,5.9,72.92965355193031 +2019-04-18 15:30:00-05:00,2019,4,18,15,30,27.6,22.6,221.0,416.0,504.0,0.11,1013.0,137.0,5.800000000000001,74.23649168800931 +2019-04-18 16:30:00-05:00,2019,4,18,16,30,27.4,22.8,80.0,758.0,453.0,0.11,1012.0,138.0,5.7,76.02731162505535 +2019-04-18 17:30:00-05:00,2019,4,18,17,30,27.0,22.9,89.0,308.0,174.0,0.11,1013.0,139.0,5.6000000000000005,78.30518483755301 +2019-04-18 18:30:00-05:00,2019,4,18,18,30,26.8,23.0,21.0,151.0,28.0,0.11,1013.0,139.0,5.6000000000000005,79.71242342812477 +2019-04-18 19:30:00-05:00,2019,4,18,19,30,26.700000000000003,23.1,0.0,0.0,0.0,0.1,1013.0,139.0,5.800000000000001,80.66949441439695 +2019-04-18 20:30:00-05:00,2019,4,18,20,30,26.700000000000003,23.0,0.0,0.0,0.0,0.1,1013.0,140.0,6.2,80.18292359044239 +2019-04-18 21:30:00-05:00,2019,4,18,21,30,26.700000000000003,22.8,0.0,0.0,0.0,0.1,1013.0,141.0,6.6000000000000005,79.21748703267285 +2019-04-18 22:30:00-05:00,2019,4,18,22,30,26.700000000000003,22.6,0.0,0.0,0.0,0.1,1013.0,143.0,7.2,78.26224718879386 +2019-04-18 23:30:00-05:00,2019,4,18,23,30,26.6,22.4,0.0,0.0,0.0,0.1,1013.0,146.0,7.6,77.7738139595237 +2019-04-19 00:30:00-05:00,2019,4,19,0,30,26.6,22.3,0.0,0.0,0.0,0.1,1012.0,149.0,7.9,77.30223850538337 +2019-04-19 01:30:00-05:00,2019,4,19,1,30,26.5,22.4,0.0,0.0,0.0,0.1,1012.0,152.0,8.0,78.23355492043127 +2019-04-19 02:30:00-05:00,2019,4,19,2,30,26.5,22.5,0.0,0.0,0.0,0.1,1012.0,154.0,8.0,78.71045123305488 +2019-04-19 03:30:00-05:00,2019,4,19,3,30,26.4,22.6,0.0,0.0,0.0,0.1,1012.0,155.0,7.800000000000001,79.6583525290496 +2019-04-19 04:30:00-05:00,2019,4,19,4,30,26.3,22.9,0.0,0.0,0.0,0.1,1012.0,157.0,7.5,81.6008983438513 +2019-04-19 05:30:00-05:00,2019,4,19,5,30,26.200000000000003,23.3,0.0,0.0,0.0,0.1,1012.0,159.0,7.4,84.09422307879058 +2019-04-19 06:30:00-05:00,2019,4,19,6,30,26.4,23.6,28.0,34.0,32.0,0.1,1012.0,162.0,7.5,84.62448112085993 +2019-04-19 07:30:00-05:00,2019,4,19,7,30,26.700000000000003,23.700000000000003,36.0,0.0,36.0,0.1,1012.0,167.0,7.7,83.64358741455791 +2019-04-19 08:30:00-05:00,2019,4,19,8,30,27.1,23.8,184.0,7.0,188.0,0.1,1013.0,172.0,7.9,82.19287753846746 +2019-04-19 09:30:00-05:00,2019,4,19,9,30,27.5,23.8,281.0,69.0,332.0,0.1,1013.0,176.0,8.0,80.28829564756369 +2019-04-19 10:30:00-05:00,2019,4,19,10,30,27.8,23.700000000000003,148.0,818.0,858.0,0.1,1012.0,177.0,8.200000000000001,78.41909711972703 +2019-04-19 11:30:00-05:00,2019,4,19,11,30,28.0,23.5,168.0,813.0,938.0,0.1,1012.0,177.0,8.3,76.58084423952792 +2019-04-19 12:30:00-05:00,2019,4,19,12,30,28.1,23.5,315.0,569.0,865.0,0.1,1011.0,177.0,8.3,76.13576744008373 +2019-04-19 13:30:00-05:00,2019,4,19,13,30,28.1,23.4,139.0,859.0,935.0,0.1,1010.0,179.0,8.0,75.67791591112561 +2019-04-19 14:30:00-05:00,2019,4,19,14,30,28.1,23.4,131.0,829.0,819.0,0.1,1010.0,182.0,7.6,75.67791591112561 +2019-04-19 15:30:00-05:00,2019,4,19,15,30,28.0,23.200000000000003,146.0,692.0,618.0,0.1,1010.0,185.0,7.1000000000000005,75.20652944472882 +2019-04-19 16:30:00-05:00,2019,4,19,16,30,27.700000000000003,23.3,191.0,349.0,363.0,0.1,1010.0,188.0,6.7,76.99890168651491 +2019-04-19 17:30:00-05:00,2019,4,19,17,30,27.4,23.4,130.0,26.0,137.0,0.1,1010.0,190.0,6.2,78.83669643935437 +2019-04-19 18:30:00-05:00,2019,4,19,18,30,27.0,23.700000000000003,17.0,0.0,17.0,0.1,1010.0,191.0,5.9,82.18086473365595 +2019-04-19 19:30:00-05:00,2019,4,19,19,30,26.8,24.0,0.0,0.0,0.0,0.1,1011.0,192.0,5.9,84.66656467455705 +2019-04-19 20:30:00-05:00,2019,4,19,20,30,26.6,24.200000000000003,0.0,0.0,0.0,0.1,1011.0,196.0,6.1000000000000005,86.70402357699523 +2019-04-19 21:30:00-05:00,2019,4,19,21,30,26.3,24.3,0.0,0.0,0.0,0.1,1012.0,203.0,6.1000000000000005,88.78260158859207 +2019-04-19 22:30:00-05:00,2019,4,19,22,30,25.8,24.3,0.0,0.0,0.0,0.1,1012.0,216.0,5.9,91.44791829371898 +2019-04-19 23:30:00-05:00,2019,4,19,23,30,25.1,24.200000000000003,0.0,0.0,0.0,0.1,1011.0,238.0,5.7,94.76211366942235 +2019-04-20 00:30:00-05:00,2019,4,20,0,30,24.3,23.6,0.0,0.0,0.0,0.1,1011.0,264.0,5.6000000000000005,95.88079404075889 +2019-04-20 01:30:00-05:00,2019,4,20,1,30,23.3,22.6,0.0,0.0,0.0,0.1,1012.0,286.0,5.5,95.85041601561413 +2019-04-20 02:30:00-05:00,2019,4,20,2,30,22.4,21.3,0.0,0.0,0.0,0.1,1012.0,297.0,5.5,93.5051419846617 +2019-04-20 03:30:00-05:00,2019,4,20,3,30,21.9,19.9,0.0,0.0,0.0,0.1,1012.0,299.0,5.5,88.42817990964961 +2019-04-20 04:30:00-05:00,2019,4,20,4,30,21.8,18.8,0.0,0.0,0.0,0.1,1012.0,298.0,5.300000000000001,83.08432393117518 +2019-04-20 05:30:00-05:00,2019,4,20,5,30,22.0,18.0,0.0,0.0,0.0,0.1,1013.0,295.0,5.1000000000000005,78.06286399643153 +2019-04-20 06:30:00-05:00,2019,4,20,6,30,22.5,17.5,47.0,116.0,62.0,0.1,1013.0,292.0,5.1000000000000005,73.37552617094617 +2019-04-20 07:30:00-05:00,2019,4,20,7,30,23.3,17.1,96.0,1.0,96.0,0.1,1014.0,293.0,5.4,68.15986277767102 +2019-04-20 08:30:00-05:00,2019,4,20,8,30,24.0,16.7,191.0,4.0,193.0,0.1,1015.0,292.0,5.7,63.70979634218643 +2019-04-20 09:30:00-05:00,2019,4,20,9,30,24.6,16.5,292.0,10.0,299.0,0.1,1015.0,288.0,5.800000000000001,60.68332422098103 +2019-04-20 10:30:00-05:00,2019,4,20,10,30,25.1,16.5,280.0,1.0,281.0,0.1,1015.0,282.0,5.6000000000000005,58.89901922703104 +2019-04-20 11:30:00-05:00,2019,4,20,11,30,25.6,16.400000000000002,312.0,9.0,321.0,0.1,1014.0,274.0,5.4,56.810970373319556 +2019-04-20 12:30:00-05:00,2019,4,20,12,30,25.9,16.3,371.0,26.0,396.0,0.1,1014.0,266.0,5.300000000000001,55.45522721974166 +2019-04-20 13:30:00-05:00,2019,4,20,13,30,26.1,16.2,226.0,1.0,227.0,0.1,1013.0,260.0,5.300000000000001,54.45467031692616 +2019-04-20 14:30:00-05:00,2019,4,20,14,30,26.1,16.2,352.0,348.0,641.0,0.1,1013.0,257.0,5.4,54.45467031692616 +2019-04-20 15:30:00-05:00,2019,4,20,15,30,25.9,16.2,127.0,792.0,668.0,0.1,1013.0,258.0,5.6000000000000005,55.103022596010234 +2019-04-20 16:30:00-05:00,2019,4,20,16,30,25.6,16.2,104.0,708.0,454.0,0.1,1013.0,262.0,5.800000000000001,56.091907157882936 +2019-04-20 17:30:00-05:00,2019,4,20,17,30,25.0,16.2,75.0,532.0,223.0,0.1,1014.0,268.0,5.800000000000001,58.13017769528709 +2019-04-20 18:30:00-05:00,2019,4,20,18,30,24.3,16.3,22.0,112.0,28.0,0.1,1014.0,278.0,5.9,61.00166345297969 +2019-04-20 19:30:00-05:00,2019,4,20,19,30,23.6,16.3,0.0,0.0,0.0,0.11,1015.0,287.0,6.1000000000000005,63.62240119439135 +2019-04-20 20:30:00-05:00,2019,4,20,20,30,22.9,16.3,0.0,0.0,0.0,0.11,1016.0,294.0,6.2,66.37042596964702 +2019-04-20 21:30:00-05:00,2019,4,20,21,30,22.3,16.3,0.0,0.0,0.0,0.11,1017.0,298.0,6.1000000000000005,68.83237714296064 +2019-04-20 22:30:00-05:00,2019,4,20,22,30,21.700000000000003,16.2,0.0,0.0,0.0,0.11,1017.0,299.0,5.9,70.9439825068657 +2019-04-20 23:30:00-05:00,2019,4,20,23,30,21.3,15.9,0.0,0.0,0.0,0.11,1017.0,299.0,5.6000000000000005,71.3238628714392 +2019-04-21 00:30:00-05:00,2019,4,21,0,30,20.9,15.6,0.0,0.0,0.0,0.11,1017.0,298.0,5.4,71.70791429727394 +2019-04-21 01:30:00-05:00,2019,4,21,1,30,20.700000000000003,15.2,0.0,0.0,0.0,0.11,1017.0,299.0,5.2,70.7572255168366 +2019-04-21 02:30:00-05:00,2019,4,21,2,30,20.5,14.8,0.0,0.0,0.0,0.11,1017.0,300.0,5.2,69.81488782584064 +2019-04-21 03:30:00-05:00,2019,4,21,3,30,20.4,14.5,0.0,0.0,0.0,0.11,1017.0,303.0,5.0,68.90059829745981 +2019-04-21 04:30:00-05:00,2019,4,21,4,30,20.4,14.0,0.0,0.0,0.0,0.11,1018.0,305.0,4.6000000000000005,66.70704707855381 +2019-04-21 05:30:00-05:00,2019,4,21,5,30,20.6,13.4,0.0,0.0,0.0,0.11,1018.0,306.0,4.0,63.369572274920664 +2019-04-21 06:30:00-05:00,2019,4,21,6,30,21.200000000000003,13.4,42.0,381.0,92.0,0.11,1018.0,306.0,3.4000000000000004,61.07425681270728 +2019-04-21 07:30:00-05:00,2019,4,21,7,30,21.8,12.9,73.0,698.0,322.0,0.11,1019.0,301.0,2.9000000000000004,56.981779167868595 +2019-04-21 08:30:00-05:00,2019,4,21,8,30,22.4,12.6,91.0,830.0,560.0,0.11,1019.0,294.0,2.5,53.86767061434076 +2019-04-21 09:30:00-05:00,2019,4,21,9,30,23.0,12.6,104.0,895.0,767.0,0.11,1019.0,288.0,2.3000000000000003,51.94239078984017 +2019-04-21 10:30:00-05:00,2019,4,21,10,30,23.5,12.7,271.0,631.0,821.0,0.11,1019.0,287.0,2.1,50.728257540809885 +2019-04-21 11:30:00-05:00,2019,4,21,11,30,24.0,12.8,216.0,778.0,956.0,0.11,1019.0,287.0,1.9,49.54785434377454 +2019-04-21 12:30:00-05:00,2019,4,21,12,30,24.4,12.7,120.0,950.0,1042.0,0.11,1018.0,288.0,1.5,48.05759178967936 +2019-04-21 13:30:00-05:00,2019,4,21,13,30,24.8,12.6,117.0,941.0,992.0,0.11,1018.0,290.0,1.1,46.615253812209104 +2019-04-21 14:30:00-05:00,2019,4,21,14,30,25.0,12.4,112.0,914.0,873.0,0.11,1017.0,197.0,0.7000000000000001,45.46191446999852 +2019-04-21 15:30:00-05:00,2019,4,21,15,30,25.1,12.3,105.0,860.0,693.0,0.11,1017.0,109.0,0.6000000000000001,44.89594385877152 +2019-04-21 16:30:00-05:00,2019,4,21,16,30,25.0,12.6,91.0,768.0,472.0,0.11,1017.0,115.0,1.0,46.06228431994735 +2019-04-21 17:30:00-05:00,2019,4,21,17,30,24.6,13.4,69.0,586.0,233.0,0.11,1017.0,114.0,1.4,49.70733850619889 +2019-04-21 18:30:00-05:00,2019,4,21,18,30,24.200000000000003,14.6,24.0,149.0,32.0,0.11,1018.0,109.0,1.7000000000000002,55.031749366021195 +2019-04-21 19:30:00-05:00,2019,4,21,19,30,23.8,14.8,0.0,0.0,0.0,0.11,1018.0,102.0,2.0,57.1017433902882 +2019-04-21 20:30:00-05:00,2019,4,21,20,30,23.5,15.4,0.0,0.0,0.0,0.11,1018.0,96.0,2.5,60.42921676196241 +2019-04-21 21:30:00-05:00,2019,4,21,21,30,23.200000000000003,16.1,0.0,0.0,0.0,0.11,1019.0,93.0,2.9000000000000004,64.35110217504297 +2019-04-21 22:30:00-05:00,2019,4,21,22,30,22.9,16.7,0.0,0.0,0.0,0.11,1019.0,90.0,3.1,68.08031656916498 +2019-04-21 23:30:00-05:00,2019,4,21,23,30,22.6,17.0,0.0,0.0,0.0,0.11,1019.0,86.0,3.2,70.66178992374721 +2019-04-22 00:30:00-05:00,2019,4,22,0,30,22.4,17.2,0.0,0.0,0.0,0.11,1019.0,79.0,3.3000000000000003,72.43681558289074 +2019-04-22 01:30:00-05:00,2019,4,22,1,30,22.200000000000003,17.3,0.0,0.0,0.0,0.11,1018.0,71.0,3.5,73.78869695026029 +2019-04-22 02:30:00-05:00,2019,4,22,2,30,22.1,17.3,0.0,0.0,0.0,0.11,1018.0,64.0,3.8,74.23951965425684 +2019-04-22 03:30:00-05:00,2019,4,22,3,30,22.0,17.3,0.0,0.0,0.0,0.11,1018.0,59.0,4.0,74.69344003958808 +2019-04-22 04:30:00-05:00,2019,4,22,4,30,22.0,16.900000000000002,0.0,0.0,0.0,0.11,1018.0,55.0,4.1000000000000005,72.82599043987832 +2019-04-22 05:30:00-05:00,2019,4,22,5,30,22.200000000000003,16.5,0.0,0.0,0.0,0.11,1019.0,52.0,4.0,70.13968702114128 +2019-04-22 06:30:00-05:00,2019,4,22,6,30,22.8,16.400000000000002,46.0,339.0,92.0,0.11,1020.0,50.0,4.1000000000000005,67.20025806794618 +2019-04-22 07:30:00-05:00,2019,4,22,7,30,23.4,15.9,85.0,635.0,313.0,0.11,1020.0,49.0,4.1000000000000005,62.77202399532299 +2019-04-22 08:30:00-05:00,2019,4,22,8,30,23.9,15.6,151.0,621.0,503.0,0.11,1020.0,49.0,4.0,59.752298549824815 +2019-04-22 09:30:00-05:00,2019,4,22,9,30,24.3,15.5,294.0,417.0,604.0,0.11,1020.0,51.0,3.9,57.96224617311969 +2019-04-22 10:30:00-05:00,2019,4,22,10,30,24.6,15.6,417.0,326.0,702.0,0.11,1020.0,54.0,3.9,57.29638520177546 +2019-04-22 11:30:00-05:00,2019,4,22,11,30,24.9,15.8,398.0,425.0,803.0,0.11,1020.0,56.0,4.1000000000000005,57.003605480300195 +2019-04-22 12:30:00-05:00,2019,4,22,12,30,25.0,15.9,249.0,734.0,962.0,0.11,1019.0,58.0,4.3,57.02793240831254 +2019-04-22 13:30:00-05:00,2019,4,22,13,30,25.0,16.1,128.0,923.0,987.0,0.11,1018.0,58.0,4.5,57.760700022117 +2019-04-22 14:30:00-05:00,2019,4,22,14,30,25.0,16.3,123.0,893.0,867.0,0.11,1018.0,56.0,4.800000000000001,58.50173113097996 +2019-04-22 15:30:00-05:00,2019,4,22,15,30,24.8,16.400000000000002,113.0,839.0,688.0,0.11,1017.0,55.0,5.0,59.582158565371024 +2019-04-22 16:30:00-05:00,2019,4,22,16,30,24.6,16.6,99.0,745.0,469.0,0.11,1017.0,53.0,5.2,61.07029788849764 +2019-04-22 17:30:00-05:00,2019,4,22,17,30,24.200000000000003,16.7,73.0,561.0,231.0,0.11,1018.0,51.0,5.1000000000000005,62.94953818969572 +2019-04-22 18:30:00-05:00,2019,4,22,18,30,23.8,16.8,24.0,140.0,32.0,0.11,1018.0,49.0,4.9,64.89094917157115 +2019-04-22 19:30:00-05:00,2019,4,22,19,30,23.5,16.7,0.0,0.0,0.0,0.11,1018.0,48.0,4.9,65.65602998962049 +2019-04-22 20:30:00-05:00,2019,4,22,20,30,23.4,16.7,0.0,0.0,0.0,0.11,1018.0,49.0,4.9,66.0532490905187 +2019-04-22 21:30:00-05:00,2019,4,22,21,30,23.3,16.7,0.0,0.0,0.0,0.11,1018.0,50.0,4.9,66.45317235862305 +2019-04-22 22:30:00-05:00,2019,4,22,22,30,23.1,16.8,0.0,0.0,0.0,0.11,1018.0,51.0,5.0,67.68947114186032 +2019-04-22 23:30:00-05:00,2019,4,22,23,30,23.0,16.900000000000002,0.0,0.0,0.0,0.11,1018.0,52.0,5.0,68.53349404923449 +2019-04-23 00:30:00-05:00,2019,4,23,0,30,22.8,17.0,0.0,0.0,0.0,0.11,1017.0,53.0,4.9,69.80971849730659 +2019-04-23 01:30:00-05:00,2019,4,23,1,30,22.6,17.0,0.0,0.0,0.0,0.11,1017.0,53.0,4.7,70.66178992374721 +2019-04-23 02:30:00-05:00,2019,4,23,2,30,22.5,16.900000000000002,0.0,0.0,0.0,0.11,1017.0,52.0,4.5,70.64310788526079 +2019-04-23 03:30:00-05:00,2019,4,23,3,30,22.5,16.900000000000002,0.0,0.0,0.0,0.11,1017.0,51.0,4.4,70.64310788526079 +2019-04-23 04:30:00-05:00,2019,4,23,4,30,22.4,16.8,0.0,0.0,0.0,0.11,1017.0,51.0,4.3,70.62440944770913 +2019-04-23 05:30:00-05:00,2019,4,23,5,30,22.700000000000003,16.6,0.0,0.0,0.0,0.11,1017.0,54.0,4.2,68.4743315934998 +2019-04-23 06:30:00-05:00,2019,4,23,6,30,23.3,16.7,45.0,360.0,95.0,0.11,1018.0,57.0,4.3,66.45317235862305 +2019-04-23 07:30:00-05:00,2019,4,23,7,30,23.9,16.5,79.0,664.0,320.0,0.11,1018.0,59.0,4.2,63.28441302324104 +2019-04-23 08:30:00-05:00,2019,4,23,8,30,24.4,16.3,98.0,796.0,552.0,0.11,1018.0,61.0,4.0,60.63728060866696 +2019-04-23 09:30:00-05:00,2019,4,23,9,30,24.8,16.3,111.0,866.0,756.0,0.11,1018.0,63.0,4.0,59.2040339593684 +2019-04-23 10:30:00-05:00,2019,4,23,10,30,25.1,16.400000000000002,282.0,570.0,781.0,0.11,1018.0,63.0,4.0,58.525517133880314 +2019-04-23 11:30:00-05:00,2019,4,23,11,30,25.4,16.6,308.0,574.0,855.0,0.11,1018.0,63.0,4.2,58.225755745828494 +2019-04-23 12:30:00-05:00,2019,4,23,12,30,25.5,16.8,120.0,937.0,1031.0,0.11,1017.0,62.0,4.4,58.62048735623125 +2019-04-23 13:30:00-05:00,2019,4,23,13,30,25.5,16.900000000000002,399.0,361.0,736.0,0.11,1017.0,62.0,4.7,58.993441180881405 +2019-04-23 14:30:00-05:00,2019,4,23,14,30,25.3,17.0,107.0,909.0,866.0,0.11,1016.0,62.0,4.800000000000001,60.07851786561616 +2019-04-23 15:30:00-05:00,2019,4,23,15,30,25.1,17.1,99.0,863.0,691.0,0.11,1016.0,61.0,4.9,61.18433958260545 +2019-04-23 16:30:00-05:00,2019,4,23,16,30,24.9,17.3,85.0,774.0,471.0,0.11,1016.0,58.0,4.9,62.70653389447148 +2019-04-23 17:30:00-05:00,2019,4,23,17,30,24.5,17.5,65.0,601.0,235.0,0.11,1016.0,55.0,4.800000000000001,65.0397440960128 +2019-04-23 18:30:00-05:00,2019,4,23,18,30,24.0,17.8,24.0,171.0,34.0,0.11,1016.0,52.0,4.800000000000001,68.29842066544569 +2019-04-23 19:30:00-05:00,2019,4,23,19,30,23.700000000000003,18.1,0.0,0.0,0.0,0.11,1016.0,49.0,4.9,70.86621481862983 +2019-04-23 20:30:00-05:00,2019,4,23,20,30,23.6,18.4,0.0,0.0,0.0,0.11,1016.0,48.0,5.0,72.64897387114887 +2019-04-23 21:30:00-05:00,2019,4,23,21,30,23.5,18.5,0.0,0.0,0.0,0.11,1016.0,49.0,5.1000000000000005,73.54747853808202 +2019-04-23 22:30:00-05:00,2019,4,23,22,30,23.3,18.6,0.0,0.0,0.0,0.11,1016.0,54.0,4.9,74.90788072896203 +2019-04-23 23:30:00-05:00,2019,4,23,23,30,23.1,18.4,0.0,0.0,0.0,0.11,1016.0,59.0,4.4,74.87505629233792 +2019-04-24 00:30:00-05:00,2019,4,24,0,30,23.0,18.1,0.0,0.0,0.0,0.11,1015.0,65.0,4.0,73.92477311250657 +2019-04-24 01:30:00-05:00,2019,4,24,1,30,22.8,17.8,0.0,0.0,0.0,0.11,1015.0,67.0,3.6,73.42727529120872 +2019-04-24 02:30:00-05:00,2019,4,24,2,30,22.8,17.5,0.0,0.0,0.0,0.11,1014.0,66.0,3.1,72.05184913524435 +2019-04-24 03:30:00-05:00,2019,4,24,3,30,22.8,17.2,0.0,0.0,0.0,0.11,1015.0,62.0,2.6,70.69910487094519 +2019-04-24 04:30:00-05:00,2019,4,24,4,30,22.8,16.900000000000002,0.0,0.0,0.0,0.11,1015.0,60.0,2.3000000000000003,69.36872302537611 +2019-04-24 05:30:00-05:00,2019,4,24,5,30,23.0,16.7,0.0,0.0,0.0,0.11,1015.0,58.0,2.1,67.66937147849153 +2019-04-24 06:30:00-05:00,2019,4,24,6,30,23.4,16.900000000000002,43.0,396.0,99.0,0.11,1016.0,59.0,2.2,66.896734143844 +2019-04-24 07:30:00-05:00,2019,4,24,7,30,24.0,16.8,76.0,663.0,318.0,0.11,1016.0,65.0,2.3000000000000003,64.1154423415989 +2019-04-24 08:30:00-05:00,2019,4,24,8,30,24.4,16.8,91.0,810.0,555.0,0.11,1017.0,73.0,2.5,62.595498717370965 +2019-04-24 09:30:00-05:00,2019,4,24,9,30,24.9,16.900000000000002,228.0,568.0,652.0,0.11,1017.0,79.0,2.6,61.138775178868705 +2019-04-24 10:30:00-05:00,2019,4,24,10,30,25.3,17.0,119.0,896.0,905.0,0.11,1017.0,84.0,2.8000000000000003,60.07851786561616 +2019-04-24 11:30:00-05:00,2019,4,24,11,30,25.6,16.900000000000002,109.0,941.0,1008.0,0.11,1016.0,88.0,3.0,58.64418652447739 +2019-04-24 12:30:00-05:00,2019,4,24,12,30,25.8,16.900000000000002,110.0,946.0,1031.0,0.11,1015.0,91.0,3.0,57.95263596162211 +2019-04-24 13:30:00-05:00,2019,4,24,13,30,25.8,16.7,108.0,934.0,980.0,0.11,1015.0,91.0,2.9000000000000004,57.221924920791295 +2019-04-24 14:30:00-05:00,2019,4,24,14,30,25.8,16.5,103.0,908.0,862.0,0.11,1014.0,90.0,2.7,56.49932232910316 +2019-04-24 15:30:00-05:00,2019,4,24,15,30,25.700000000000003,16.3,133.0,770.0,662.0,0.11,1014.0,88.0,2.6,56.11648099740539 +2019-04-24 16:30:00-05:00,2019,4,24,16,30,25.4,16.3,172.0,451.0,397.0,0.11,1014.0,88.0,2.7,57.12506694178329 +2019-04-24 17:30:00-05:00,2019,4,24,17,30,25.0,16.8,70.0,566.0,231.0,0.11,1014.0,90.0,3.0,60.390983883433435 +2019-04-24 18:30:00-05:00,2019,4,24,18,30,24.6,17.400000000000002,25.0,157.0,34.0,0.11,1014.0,92.0,2.9000000000000004,64.24496821999269 +2019-04-24 19:30:00-05:00,2019,4,24,19,30,24.3,17.6,0.0,0.0,0.0,0.11,1014.0,97.0,2.7,66.24000091843163 +2019-04-24 20:30:00-05:00,2019,4,24,20,30,24.1,17.900000000000002,0.0,0.0,0.0,0.11,1015.0,107.0,2.7,68.31814758738126 +2019-04-24 21:30:00-05:00,2019,4,24,21,30,24.0,18.2,0.0,0.0,0.0,0.11,1015.0,120.0,2.7,70.037563668915 +2019-04-24 22:30:00-05:00,2019,4,24,22,30,24.0,18.4,0.0,0.0,0.0,0.11,1015.0,130.0,2.7,70.92162597155469 +2019-04-24 23:30:00-05:00,2019,4,24,23,30,24.0,18.5,0.0,0.0,0.0,0.11,1014.0,132.0,2.8000000000000003,71.36731964883181 +2019-04-25 00:30:00-05:00,2019,4,25,0,30,23.9,18.6,0.0,0.0,0.0,0.11,1013.0,132.0,2.9000000000000004,72.2483233161373 +2019-04-25 01:30:00-05:00,2019,4,25,1,30,23.8,18.8,0.0,0.0,0.0,0.11,1013.0,134.0,3.0,73.5987618697542 +2019-04-25 02:30:00-05:00,2019,4,25,2,30,23.700000000000003,18.9,0.0,0.0,0.0,0.11,1013.0,135.0,3.0,74.50691547778867 +2019-04-25 03:30:00-05:00,2019,4,25,3,30,23.700000000000003,19.0,0.0,0.0,0.0,0.11,1013.0,136.0,3.1,74.9733491781394 +2019-04-25 04:30:00-05:00,2019,4,25,4,30,23.8,19.0,0.0,0.0,0.0,0.11,1013.0,138.0,3.2,74.52349834248494 +2019-04-25 05:30:00-05:00,2019,4,25,5,30,24.1,19.1,0.0,0.0,0.0,0.11,1014.0,142.0,3.3000000000000003,73.64990644959764 +2019-04-25 06:30:00-05:00,2019,4,25,6,30,24.8,19.3,49.0,320.0,95.0,0.11,1014.0,146.0,3.5,71.51256881779813 +2019-04-25 07:30:00-05:00,2019,4,25,7,30,25.4,19.4,136.0,314.0,252.0,0.11,1015.0,149.0,3.7,69.432031434702 +2019-04-25 08:30:00-05:00,2019,4,25,8,30,25.9,19.5,175.0,541.0,486.0,0.11,1015.0,150.0,3.8,67.82278996673193 +2019-04-25 09:30:00-05:00,2019,4,25,9,30,26.3,19.6,240.0,535.0,641.0,0.11,1015.0,152.0,3.9,66.65007810633571 +2019-04-25 10:30:00-05:00,2019,4,25,10,30,26.700000000000003,19.8,419.0,133.0,536.0,0.11,1015.0,157.0,4.2,65.90948818556873 +2019-04-25 11:30:00-05:00,2019,4,25,11,30,27.0,20.0,202.0,742.0,912.0,0.11,1015.0,164.0,4.3,65.56431854728332 +2019-04-25 12:30:00-05:00,2019,4,25,12,30,27.200000000000003,20.0,182.0,789.0,951.0,0.11,1014.0,169.0,4.3,64.79910978245636 +2019-04-25 13:30:00-05:00,2019,4,25,13,30,27.3,19.9,166.0,817.0,929.0,0.11,1014.0,170.0,4.3,64.02253368457704 +2019-04-25 14:30:00-05:00,2019,4,25,14,30,27.3,19.9,254.0,576.0,736.0,0.11,1013.0,171.0,4.3,64.02253368457704 +2019-04-25 15:30:00-05:00,2019,4,25,15,30,27.200000000000003,19.9,315.0,231.0,474.0,0.11,1013.0,170.0,4.3,64.39902384767183 +2019-04-25 16:30:00-05:00,2019,4,25,16,30,27.0,20.0,163.0,479.0,403.0,0.11,1013.0,169.0,4.2,65.56431854728332 +2019-04-25 17:30:00-05:00,2019,4,25,17,30,26.5,20.200000000000003,93.0,396.0,206.0,0.11,1013.0,167.0,4.1000000000000005,68.3628742284567 +2019-04-25 18:30:00-05:00,2019,4,25,18,30,26.0,20.6,25.0,111.0,32.0,0.11,1013.0,164.0,4.1000000000000005,72.17212580747973 +2019-04-25 19:30:00-05:00,2019,4,25,19,30,25.6,21.0,0.0,0.0,0.0,0.13,1014.0,163.0,4.1000000000000005,75.74521383477543 +2019-04-25 20:30:00-05:00,2019,4,25,20,30,25.4,21.3,0.0,0.0,0.0,0.13,1014.0,163.0,4.2,78.0747809293731 +2019-04-25 21:30:00-05:00,2019,4,25,21,30,25.200000000000003,21.6,0.0,0.0,0.0,0.13,1015.0,164.0,4.3,80.47406090248505 +2019-04-25 22:30:00-05:00,2019,4,25,22,30,25.1,21.700000000000003,0.0,0.0,0.0,0.13,1014.0,166.0,4.5,81.45123892697173 +2019-04-25 23:30:00-05:00,2019,4,25,23,30,25.0,21.8,0.0,0.0,0.0,0.13,1014.0,169.0,4.7,82.44026830283795 +2019-04-26 00:30:00-05:00,2019,4,26,0,30,24.9,21.9,0.0,0.0,0.0,0.13,1014.0,175.0,4.7,83.4412934425311 +2019-04-26 01:30:00-05:00,2019,4,26,1,30,24.9,21.9,0.0,0.0,0.0,0.13,1013.0,181.0,4.7,83.4412934425311 +2019-04-26 02:30:00-05:00,2019,4,26,2,30,24.9,22.0,0.0,0.0,0.0,0.13,1013.0,185.0,4.7,83.95186225354803 +2019-04-26 03:30:00-05:00,2019,4,26,3,30,24.9,22.1,0.0,0.0,0.0,0.13,1013.0,188.0,4.6000000000000005,84.46516651307014 +2019-04-26 04:30:00-05:00,2019,4,26,4,30,24.9,22.200000000000003,0.0,0.0,0.0,0.13,1013.0,192.0,4.2,84.98121865025058 +2019-04-26 05:30:00-05:00,2019,4,26,5,30,25.1,22.3,0.0,0.0,0.0,0.13,1014.0,195.0,3.9,84.4865464131878 +2019-04-26 06:30:00-05:00,2019,4,26,6,30,25.700000000000003,22.4,52.0,278.0,93.0,0.13,1014.0,192.0,4.1000000000000005,82.02363038901757 +2019-04-26 07:30:00-05:00,2019,4,26,7,30,26.200000000000003,22.5,91.0,587.0,309.0,0.13,1014.0,189.0,4.800000000000001,80.11665615469143 +2019-04-26 08:30:00-05:00,2019,4,26,8,30,26.700000000000003,22.6,149.0,633.0,514.0,0.13,1014.0,189.0,5.5,78.26224718879386 +2019-04-26 09:30:00-05:00,2019,4,26,9,30,27.200000000000003,22.5,175.0,717.0,713.0,0.13,1013.0,192.0,6.0,75.53609118292528 +2019-04-26 10:30:00-05:00,2019,4,26,10,30,27.700000000000003,22.3,219.0,686.0,823.0,0.13,1013.0,198.0,6.2,72.47037671119828 +2019-04-26 11:30:00-05:00,2019,4,26,11,30,28.1,21.9,354.0,487.0,820.0,0.13,1012.0,206.0,6.2,69.09368781167073 +2019-04-26 12:30:00-05:00,2019,4,26,12,30,28.5,21.4,388.0,404.0,782.0,0.13,1012.0,216.0,6.2,65.47187054003885 +2019-04-26 13:30:00-05:00,2019,4,26,13,30,28.700000000000003,20.9,366.0,393.0,734.0,0.13,1011.0,225.0,6.300000000000001,62.760370499242086 +2019-04-26 14:30:00-05:00,2019,4,26,14,30,28.8,20.6,384.0,130.0,493.0,0.13,1011.0,233.0,6.4,61.25572764126239 +2019-04-26 15:30:00-05:00,2019,4,26,15,30,28.6,20.5,309.0,74.0,360.0,0.13,1011.0,238.0,6.4,61.58966306822144 +2019-04-26 16:30:00-05:00,2019,4,26,16,30,28.1,20.700000000000003,167.0,12.0,173.0,0.13,1011.0,240.0,6.300000000000001,64.19334234775064 +2019-04-26 17:30:00-05:00,2019,4,26,17,30,27.4,21.200000000000003,78.0,4.0,79.0,0.13,1011.0,244.0,6.1000000000000005,68.95985408691384 +2019-04-26 18:30:00-05:00,2019,4,26,18,30,26.8,21.6,22.0,1.0,22.0,0.13,1012.0,252.0,5.9,73.20373616187543 +2019-04-26 19:30:00-05:00,2019,4,26,19,30,26.3,21.9,0.0,0.0,0.0,0.13,1012.0,261.0,5.7,76.7876773347373 +2019-04-26 20:30:00-05:00,2019,4,26,20,30,25.8,22.200000000000003,0.0,0.0,0.0,0.13,1012.0,268.0,5.5,80.55257262849392 +2019-04-26 21:30:00-05:00,2019,4,26,21,30,25.4,22.4,0.0,0.0,0.0,0.13,1013.0,272.0,5.2,83.49784757524912 +2019-04-26 22:30:00-05:00,2019,4,26,22,30,25.0,22.6,0.0,0.0,0.0,0.13,1013.0,273.0,5.0,86.5553591977721 +2019-04-26 23:30:00-05:00,2019,4,26,23,30,24.700000000000003,22.700000000000003,0.0,0.0,0.0,0.13,1012.0,277.0,4.6000000000000005,88.65558582828429 +2019-04-27 00:30:00-05:00,2019,4,27,0,30,24.5,22.700000000000003,0.0,0.0,0.0,0.13,1012.0,286.0,4.4,89.72228189005051 +2019-04-27 01:30:00-05:00,2019,4,27,1,30,24.200000000000003,22.700000000000003,0.0,0.0,0.0,0.13,1012.0,302.0,4.1000000000000005,91.34950480267452 +2019-04-27 02:30:00-05:00,2019,4,27,2,30,23.9,22.6,0.0,0.0,0.0,0.13,1012.0,321.0,4.1000000000000005,92.4473069974985 +2019-04-27 03:30:00-05:00,2019,4,27,3,30,23.6,22.4,0.0,0.0,0.0,0.13,1012.0,340.0,4.2,92.99478918273282 +2019-04-27 04:30:00-05:00,2019,4,27,4,30,23.3,22.1,0.0,0.0,0.0,0.13,1013.0,174.0,4.2,92.97953246706437 +2019-04-27 05:30:00-05:00,2019,4,27,5,30,23.200000000000003,21.8,0.0,0.0,0.0,0.13,1013.0,6.0,4.3,91.84656915277186 +2019-04-27 06:30:00-05:00,2019,4,27,6,30,23.5,21.6,49.0,336.0,99.0,0.13,1014.0,16.0,4.3,89.10246325089773 +2019-04-27 07:30:00-05:00,2019,4,27,7,30,24.1,21.4,86.0,624.0,319.0,0.13,1014.0,30.0,4.2,84.89787939045657 +2019-04-27 08:30:00-05:00,2019,4,27,8,30,24.9,21.3,107.0,761.0,548.0,0.13,1015.0,46.0,3.9,80.43463464682452 +2019-04-27 09:30:00-05:00,2019,4,27,9,30,25.6,21.1,120.0,835.0,749.0,0.13,1015.0,58.0,3.8,76.21186522133068 +2019-04-27 10:30:00-05:00,2019,4,27,10,30,26.200000000000003,21.1,130.0,876.0,903.0,0.13,1015.0,64.0,3.8,73.55128024517501 +2019-04-27 11:30:00-05:00,2019,4,27,11,30,26.6,21.0,136.0,895.0,994.0,0.13,1014.0,67.0,3.8,71.39581590815916 +2019-04-27 12:30:00-05:00,2019,4,27,12,30,26.9,21.0,153.0,874.0,1007.0,0.13,1014.0,68.0,3.9,70.14635934156131 +2019-04-27 13:30:00-05:00,2019,4,27,13,30,27.0,21.0,151.0,860.0,956.0,0.13,1013.0,68.0,4.0,69.73535987777385 +2019-04-27 14:30:00-05:00,2019,4,27,14,30,27.0,20.9,169.0,780.0,823.0,0.13,1013.0,68.0,4.0,69.30804209344946 +2019-04-27 15:30:00-05:00,2019,4,27,15,30,26.9,20.8,123.0,798.0,674.0,0.13,1013.0,69.0,4.1000000000000005,69.28899787597788 +2019-04-27 16:30:00-05:00,2019,4,27,16,30,26.700000000000003,20.8,107.0,702.0,460.0,0.13,1013.0,69.0,4.3,70.10905856543421 +2019-04-27 17:30:00-05:00,2019,4,27,17,30,26.3,21.0,78.0,526.0,230.0,0.13,1013.0,70.0,4.3,72.6703860563409 +2019-04-27 18:30:00-05:00,2019,4,27,18,30,25.9,21.200000000000003,26.0,136.0,35.0,0.13,1013.0,73.0,4.1000000000000005,75.32916998322509 +2019-04-27 19:30:00-05:00,2019,4,27,19,30,25.700000000000003,21.4,0.0,0.0,0.0,0.13,1014.0,76.0,4.0,77.16774798909218 +2019-04-27 20:30:00-05:00,2019,4,27,20,30,25.5,21.6,0.0,0.0,0.0,0.13,1014.0,80.0,3.9,79.0511314297047 +2019-04-27 21:30:00-05:00,2019,4,27,21,30,25.4,21.8,0.0,0.0,0.0,0.13,1015.0,83.0,3.7,80.5002818626729 +2019-04-27 22:30:00-05:00,2019,4,27,22,30,25.200000000000003,21.8,0.0,0.0,0.0,0.13,1015.0,88.0,3.4000000000000004,81.46377725252287 +2019-04-27 23:30:00-05:00,2019,4,27,23,30,25.1,21.700000000000003,0.0,0.0,0.0,0.13,1014.0,93.0,3.1,81.45123892697173 +2019-04-28 00:30:00-05:00,2019,4,28,0,30,25.0,21.6,0.0,0.0,0.0,0.13,1014.0,96.0,2.8000000000000003,81.43868840815787 +2019-04-28 01:30:00-05:00,2019,4,28,1,30,24.9,21.5,0.0,0.0,0.0,0.13,1014.0,96.0,2.6,81.42612568109206 +2019-04-28 02:30:00-05:00,2019,4,28,2,30,24.8,21.4,0.0,0.0,0.0,0.13,1014.0,92.0,2.5,81.41355073076362 +2019-04-28 03:30:00-05:00,2019,4,28,3,30,24.8,21.3,0.0,0.0,0.0,0.13,1014.0,86.0,2.4000000000000004,80.91617618975445 +2019-04-28 04:30:00-05:00,2019,4,28,4,30,24.8,21.200000000000003,0.0,0.0,0.0,0.13,1015.0,80.0,2.3000000000000003,80.42146721617327 +2019-04-28 05:30:00-05:00,2019,4,28,5,30,24.9,21.1,0.0,0.0,0.0,0.13,1015.0,76.0,2.4000000000000004,79.45374247427509 +2019-04-28 06:30:00-05:00,2019,4,28,6,30,25.4,21.3,51.0,312.0,99.0,0.13,1016.0,73.0,2.6,78.0747809293731 +2019-04-28 07:30:00-05:00,2019,4,28,7,30,26.0,21.200000000000003,95.0,584.0,314.0,0.13,1016.0,72.0,2.8000000000000003,74.8845255148512 +2019-04-28 08:30:00-05:00,2019,4,28,8,30,26.5,21.1,123.0,709.0,535.0,0.13,1016.0,74.0,2.9000000000000004,72.26031058621005 +2019-04-28 09:30:00-05:00,2019,4,28,9,30,26.9,21.1,167.0,722.0,712.0,0.13,1016.0,79.0,2.9000000000000004,70.57851728516349 +2019-04-28 10:30:00-05:00,2019,4,28,10,30,27.3,21.1,151.0,824.0,879.0,0.13,1016.0,85.0,3.0,68.94067034925362 +2019-04-28 11:30:00-05:00,2019,4,28,11,30,27.5,21.1,147.0,861.0,974.0,0.13,1016.0,88.0,3.1,68.13782949532663 +2019-04-28 12:30:00-05:00,2019,4,28,12,30,27.6,21.0,156.0,854.0,992.0,0.13,1016.0,89.0,3.1,67.32558202207487 +2019-04-28 13:30:00-05:00,2019,4,28,13,30,27.6,21.0,161.0,830.0,939.0,0.13,1015.0,88.0,3.2,67.32558202207487 +2019-04-28 14:30:00-05:00,2019,4,28,14,30,27.4,21.0,170.0,767.0,814.0,0.13,1015.0,86.0,3.2,68.11826272339277 +2019-04-28 15:30:00-05:00,2019,4,28,15,30,27.200000000000003,21.1,141.0,742.0,654.0,0.13,1015.0,83.0,3.4000000000000004,69.3460820493205 +2019-04-28 16:30:00-05:00,2019,4,28,16,30,26.9,21.3,134.0,562.0,418.0,0.13,1015.0,81.0,3.8,71.44984081253008 +2019-04-28 17:30:00-05:00,2019,4,28,17,30,26.5,21.700000000000003,96.0,400.0,212.0,0.13,1015.0,79.0,4.1000000000000005,74.96557480540517 +2019-04-28 18:30:00-05:00,2019,4,28,18,30,26.0,22.1,23.0,2.0,23.0,0.13,1015.0,80.0,4.3,79.12067626724063 +2019-04-28 19:30:00-05:00,2019,4,28,19,30,25.700000000000003,22.4,0.0,0.0,0.0,0.12,1016.0,83.0,4.3,82.02363038901757 +2019-04-28 20:30:00-05:00,2019,4,28,20,30,25.700000000000003,22.700000000000003,0.0,0.0,0.0,0.12,1016.0,87.0,4.3,83.53164606821205 +2019-04-28 21:30:00-05:00,2019,4,28,21,30,25.6,22.700000000000003,0.0,0.0,0.0,0.12,1017.0,91.0,4.0,84.02874632731391 +2019-04-28 22:30:00-05:00,2019,4,28,22,30,25.5,22.700000000000003,0.0,0.0,0.0,0.12,1017.0,92.0,3.5,84.52917838487782 +2019-04-28 23:30:00-05:00,2019,4,28,23,30,25.4,22.6,0.0,0.0,0.0,0.12,1016.0,91.0,2.8000000000000003,84.51853633651618 +2019-04-29 00:30:00-05:00,2019,4,29,0,30,25.3,22.4,0.0,0.0,0.0,0.12,1016.0,85.0,2.2,83.99586147124604 +2019-04-29 01:30:00-05:00,2019,4,29,1,30,25.200000000000003,22.200000000000003,0.0,0.0,0.0,0.12,1015.0,75.0,1.8,83.47525948927431 +2019-04-29 02:30:00-05:00,2019,4,29,2,30,25.1,22.1,0.0,0.0,0.0,0.12,1015.0,65.0,1.8,83.46394867685122 +2019-04-29 03:30:00-05:00,2019,4,29,3,30,25.0,22.0,0.0,0.0,0.0,0.12,1015.0,60.0,2.3000000000000003,83.45262666598398 +2019-04-29 04:30:00-05:00,2019,4,29,4,30,25.0,22.1,0.0,0.0,0.0,0.12,1016.0,59.0,2.7,83.96287846488495 +2019-04-29 05:30:00-05:00,2019,4,29,5,30,25.200000000000003,22.200000000000003,0.0,0.0,0.0,0.12,1016.0,60.0,3.2,83.47525948927431 +2019-04-29 06:30:00-05:00,2019,4,29,6,30,25.6,22.4,53.0,299.0,99.0,0.12,1017.0,63.0,3.7,82.51175638482951 +2019-04-29 07:30:00-05:00,2019,4,29,7,30,26.1,22.4,96.0,574.0,313.0,0.12,1018.0,67.0,4.1000000000000005,80.10336461834795 +2019-04-29 08:30:00-05:00,2019,4,29,8,30,26.6,22.3,171.0,533.0,482.0,0.12,1018.0,72.0,4.4,77.30223850538337 +2019-04-29 09:30:00-05:00,2019,4,29,9,30,27.0,22.1,150.0,739.0,709.0,0.12,1019.0,77.0,4.7,74.59053556051052 +2019-04-29 10:30:00-05:00,2019,4,29,10,30,27.200000000000003,22.0,245.0,597.0,774.0,0.12,1019.0,80.0,5.0,73.27197620568609 +2019-04-29 11:30:00-05:00,2019,4,29,11,30,27.3,22.0,233.0,665.0,873.0,0.12,1019.0,83.0,5.1000000000000005,72.8436129072422 +2019-04-29 12:30:00-05:00,2019,4,29,12,30,27.4,22.1,232.0,692.0,910.0,0.12,1018.0,85.0,5.2,72.86085146609881 +2019-04-29 13:30:00-05:00,2019,4,29,13,30,27.3,22.1,232.0,685.0,875.0,0.12,1018.0,86.0,5.2,73.2889983433787 +2019-04-29 14:30:00-05:00,2019,4,29,14,30,27.200000000000003,22.0,261.0,540.0,715.0,0.12,1017.0,84.0,5.1000000000000005,73.27197620568609 +2019-04-29 15:30:00-05:00,2019,4,29,15,30,27.1,22.0,124.0,774.0,660.0,0.12,1017.0,81.0,5.1000000000000005,73.70317847585238 +2019-04-29 16:30:00-05:00,2019,4,29,16,30,26.8,22.0,104.0,685.0,451.0,0.12,1017.0,81.0,5.2,75.01402814660541 +2019-04-29 17:30:00-05:00,2019,4,29,17,30,26.5,22.1,77.0,514.0,227.0,0.12,1017.0,84.0,5.300000000000001,76.81801984250816 +2019-04-29 18:30:00-05:00,2019,4,29,18,30,26.200000000000003,22.200000000000003,26.0,137.0,35.0,0.12,1018.0,87.0,5.300000000000001,78.66813108954312 +2019-04-29 19:30:00-05:00,2019,4,29,19,30,26.0,22.3,0.0,0.0,0.0,0.11,1018.0,91.0,5.300000000000001,80.0900603633796 +2019-04-29 20:30:00-05:00,2019,4,29,20,30,25.9,22.3,0.0,0.0,0.0,0.11,1019.0,96.0,5.2,80.56561391823581 +2019-04-29 21:30:00-05:00,2019,4,29,21,30,25.9,22.200000000000003,0.0,0.0,0.0,0.11,1019.0,99.0,5.1000000000000005,80.07674337450413 +2019-04-29 22:30:00-05:00,2019,4,29,22,30,25.8,22.200000000000003,0.0,0.0,0.0,0.11,1019.0,100.0,5.1000000000000005,80.55257262849392 +2019-04-29 23:30:00-05:00,2019,4,29,23,30,25.700000000000003,22.200000000000003,0.0,0.0,0.0,0.11,1019.0,99.0,5.2,81.03158661857887 +2019-04-30 00:30:00-05:00,2019,4,30,0,30,25.6,22.1,0.0,0.0,0.0,0.11,1019.0,98.0,5.300000000000001,81.01881277997141 +2019-04-30 01:30:00-05:00,2019,4,30,1,30,25.5,22.1,0.0,0.0,0.0,0.11,1018.0,97.0,5.4,81.50131921917182 +2019-04-30 02:30:00-05:00,2019,4,30,2,30,25.5,22.0,0.0,0.0,0.0,0.11,1018.0,97.0,5.5,81.00602659099238 +2019-04-30 03:30:00-05:00,2019,4,30,3,30,25.4,21.9,0.0,0.0,0.0,0.11,1018.0,96.0,5.5,80.9932280365787 +2019-04-30 04:30:00-05:00,2019,4,30,4,30,25.3,21.8,0.0,0.0,0.0,0.11,1019.0,96.0,5.4,80.98041710164598 +2019-04-30 05:30:00-05:00,2019,4,30,5,30,25.4,21.6,0.0,0.0,0.0,0.11,1019.0,95.0,5.4,79.52227117093723 +2019-04-30 06:30:00-05:00,2019,4,30,6,30,25.8,21.5,52.0,331.0,104.0,0.11,1019.0,93.0,5.5,77.18274704646991 +2019-04-30 07:30:00-05:00,2019,4,30,7,30,26.200000000000003,21.3,87.0,616.0,321.0,0.11,1020.0,91.0,5.800000000000001,74.45930386781104 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15:30:00-05:00,2019,4,30,15,30,27.0,20.8,112.0,809.0,674.0,0.11,1018.0,73.0,8.0,68.88302183900734 +2019-04-30 16:30:00-05:00,2019,4,30,16,30,26.9,20.6,100.0,714.0,462.0,0.11,1018.0,72.0,8.1,68.44083847061376 +2019-04-30 17:30:00-05:00,2019,4,30,17,30,26.700000000000003,20.4,78.0,530.0,233.0,0.11,1018.0,73.0,8.200000000000001,68.40188911727533 +2019-04-30 18:30:00-05:00,2019,4,30,18,30,26.4,20.4,28.0,129.0,37.0,0.11,1018.0,75.0,8.1,69.62209740556841 +2019-04-30 19:30:00-05:00,2019,4,30,19,30,23.0,16.900000000000002,0.0,0.0,0.0,0.1,1018.0,33.0,6.5,68.53349404923449 +2019-04-30 20:30:00-05:00,2019,4,30,20,30,22.9,17.3,0.0,0.0,0.0,0.1,1018.0,33.0,6.300000000000001,70.71773781332548 +2019-04-30 21:30:00-05:00,2019,4,30,21,30,22.8,17.6,0.0,0.0,0.0,0.1,1018.0,34.0,6.2,72.5077884281539 +2019-04-30 22:30:00-05:00,2019,4,30,22,30,22.700000000000003,17.5,0.0,0.0,0.0,0.1,1017.0,37.0,5.9,72.49006905075738 +2019-04-30 23:30:00-05:00,2019,4,30,23,30,22.5,17.3,0.0,0.0,0.0,0.1,1017.0,39.0,5.6000000000000005,72.45458264510621 +2006-05-01 00:30:00-05:00,2006,5,1,0,30,22.4,17.1,0.0,0.0,0.0,0.1,1016.0,39.0,5.300000000000001,71.97992543672365 +2006-05-01 01:30:00-05:00,2006,5,1,1,30,22.3,17.0,0.0,0.0,0.0,0.1,1016.0,38.0,5.1000000000000005,71.96190440805181 +2006-05-01 02:30:00-05:00,2006,5,1,2,30,22.3,16.900000000000002,0.0,0.0,0.0,0.1,1016.0,34.0,5.0,71.50731334711422 +2006-05-01 03:30:00-05:00,2006,5,1,3,30,22.200000000000003,16.8,0.0,0.0,0.0,0.1,1016.0,30.0,5.0,71.4890414801942 +2006-05-01 04:30:00-05:00,2006,5,1,4,30,22.1,16.900000000000002,0.0,0.0,0.0,0.1,1016.0,26.0,5.1000000000000005,72.38341875453249 +2006-05-01 05:30:00-05:00,2006,5,1,5,30,22.3,17.1,0.0,0.0,0.0,0.1,1017.0,24.0,5.2,72.41903259174407 +2006-05-01 06:30:00-05:00,2006,5,1,6,30,22.8,17.400000000000002,58.0,283.0,103.0,0.1,1017.0,25.0,5.5,71.59843002221132 +2006-05-01 07:30:00-05:00,2006,5,1,7,30,23.4,17.3,210.0,74.0,238.0,0.1,1018.0,26.0,5.7,68.61214204484529 +2006-05-01 08:30:00-05:00,2006,5,1,8,30,23.8,17.3,131.0,700.0,543.0,0.1,1018.0,28.0,5.6000000000000005,66.97835757893561 +2006-05-01 09:30:00-05:00,2006,5,1,9,30,24.4,17.400000000000002,145.0,784.0,741.0,0.1,1018.0,32.0,5.7,65.01853934241451 +2006-05-01 10:30:00-05:00,2006,5,1,10,30,24.8,17.400000000000002,152.0,834.0,893.0,0.1,1018.0,35.0,5.7,63.48173553592122 +2006-05-01 11:30:00-05:00,2006,5,1,11,30,25.1,17.3,155.0,860.0,983.0,0.1,1018.0,35.0,5.800000000000001,61.9632345821698 +2006-05-01 12:30:00-05:00,2006,5,1,12,30,25.3,17.3,154.0,866.0,1005.0,0.1,1018.0,33.0,5.7,61.22983441350054 +2006-05-01 13:30:00-05:00,2006,5,1,13,30,25.5,17.3,141.0,870.0,959.0,0.1,1017.0,31.0,5.800000000000001,60.50618790019567 +2006-05-01 14:30:00-05:00,2006,5,1,14,30,25.6,17.3,136.0,842.0,845.0,0.1,1017.0,29.0,5.9,60.14797743743243 +2006-05-01 15:30:00-05:00,2006,5,1,15,30,25.5,17.400000000000002,124.0,791.0,675.0,0.1,1016.0,28.0,6.1000000000000005,60.88965726835758 +2006-05-01 16:30:00-05:00,2006,5,1,16,30,25.200000000000003,17.6,105.0,708.0,465.0,0.1,1017.0,26.0,6.300000000000001,62.77294673734722 +2006-05-01 17:30:00-05:00,2006,5,1,17,30,24.700000000000003,17.8,78.0,533.0,235.0,0.1,1017.0,23.0,6.4,65.49330385296405 +2006-05-01 18:30:00-05:00,2006,5,1,18,30,24.200000000000003,18.0,27.0,149.0,38.0,0.1,1017.0,23.0,6.5,68.33785779247415 +2006-05-01 19:30:00-05:00,2006,5,1,19,30,23.700000000000003,18.4,0.0,0.0,0.0,0.1,1018.0,22.0,6.5,72.21274364304877 +2006-05-01 20:30:00-05:00,2006,5,1,20,30,23.3,18.6,0.0,0.0,0.0,0.1,1018.0,20.0,6.5,74.90788072896203 +2006-05-01 21:30:00-05:00,2006,5,1,21,30,23.0,18.9,0.0,0.0,0.0,0.1,1018.0,19.0,6.4,77.72260499738546 +2006-05-01 22:30:00-05:00,2006,5,1,22,30,22.8,18.9,0.0,0.0,0.0,0.1,1018.0,19.0,6.300000000000001,78.66982318165591 +2006-05-01 23:30:00-05:00,2006,5,1,23,30,22.6,18.7,0.0,0.0,0.0,0.1,1017.0,20.0,6.1000000000000005,78.64118580685759 +2006-05-02 00:30:00-05:00,2006,5,2,0,30,22.5,18.5,0.0,0.0,0.0,0.1,1017.0,21.0,5.800000000000001,78.13619085138018 +2006-05-02 01:30:00-05:00,2006,5,2,1,30,22.4,18.3,0.0,0.0,0.0,0.1,1017.0,21.0,5.5,77.6333068665811 +2006-05-02 02:30:00-05:00,2006,5,2,2,30,22.4,18.2,0.0,0.0,0.0,0.1,1017.0,20.0,5.2,77.14774152098751 +2006-05-02 03:30:00-05:00,2006,5,2,3,30,22.4,18.2,0.0,0.0,0.0,0.1,1017.0,20.0,5.1000000000000005,77.14774152098751 +2006-05-02 04:30:00-05:00,2006,5,2,4,30,22.3,18.2,0.0,0.0,0.0,0.1,1017.0,21.0,5.0,77.61837447996871 +2006-05-02 05:30:00-05:00,2006,5,2,5,30,22.5,18.3,0.0,0.0,0.0,0.1,1017.0,22.0,5.0,77.16293683056355 +2006-05-02 06:30:00-05:00,2006,5,2,6,30,23.0,18.5,57.0,309.0,108.0,0.1,1018.0,23.0,5.2,75.80281121607034 +2006-05-02 07:30:00-05:00,2006,5,2,7,30,23.5,18.4,97.0,599.0,328.0,0.1,1018.0,25.0,5.300000000000001,73.08816962294563 +2006-05-02 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23:30:00-05:00,2006,5,14,23,30,25.700000000000003,20.4,0.0,0.0,0.0,0.09,1016.0,148.0,3.2,72.5657113370301 +2006-05-15 00:30:00-05:00,2006,5,15,0,30,25.5,20.3,0.0,0.0,0.0,0.09,1015.0,153.0,3.3000000000000003,72.98027125754662 +2006-05-15 01:30:00-05:00,2006,5,15,1,30,25.4,20.3,0.0,0.0,0.0,0.09,1015.0,157.0,3.5,73.41522905629661 +2006-05-15 02:30:00-05:00,2006,5,15,2,30,25.4,20.4,0.0,0.0,0.0,0.09,1015.0,160.0,3.7,73.86994059726645 +2006-05-15 03:30:00-05:00,2006,5,15,3,30,25.5,20.4,0.0,0.0,0.0,0.09,1015.0,164.0,3.8,73.43228880255037 +2006-05-15 04:30:00-05:00,2006,5,15,4,30,25.5,20.700000000000003,0.0,0.0,0.0,0.09,1015.0,168.0,3.9,74.80310731087239 +2006-05-15 05:30:00-05:00,2006,5,15,5,30,25.8,21.200000000000003,0.0,0.0,0.0,0.09,1016.0,173.0,3.8,75.77678837086546 +2006-05-15 06:30:00-05:00,2006,5,15,6,30,26.3,21.700000000000003,72.0,248.0,119.0,0.09,1016.0,177.0,3.8,75.85547174347195 +2006-05-15 07:30:00-05:00,2006,5,15,7,30,26.700000000000003,22.1,146.0,423.0,318.0,0.09,1016.0,179.0,3.8,75.91815931451441 +2006-05-15 08:30:00-05:00,2006,5,15,8,30,26.9,22.4,204.0,518.0,519.0,0.09,1016.0,179.0,3.7,76.41273976595906 +2006-05-15 09:30:00-05:00,2006,5,15,9,30,27.1,22.9,244.0,591.0,703.0,0.09,1016.0,178.0,3.8,77.84672010783402 +2006-05-15 10:30:00-05:00,2006,5,15,10,30,27.3,23.1,405.0,471.0,830.0,0.09,1016.0,180.0,3.9,77.87578750434238 +2006-05-15 11:30:00-05:00,2006,5,15,11,30,27.4,23.1,544.0,234.0,772.0,0.09,1015.0,185.0,4.0,77.42084507110734 +2006-05-15 12:30:00-05:00,2006,5,15,12,30,27.5,23.200000000000003,538.0,154.0,691.0,0.09,1015.0,192.0,3.9,77.43560907640924 +2006-05-15 13:30:00-05:00,2006,5,15,13,30,27.5,23.4,448.0,113.0,555.0,0.09,1015.0,198.0,3.6,78.37647994972967 +2006-05-15 14:30:00-05:00,2006,5,15,14,30,27.4,23.6,419.0,136.0,535.0,0.09,1015.0,202.0,3.3000000000000003,79.79314536748959 +2006-05-15 15:30:00-05:00,2006,5,15,15,30,27.3,23.8,215.0,5.0,218.0,0.09,1014.0,203.0,3.0,81.2343005954092 +2006-05-15 16:30:00-05:00,2006,5,15,16,30,27.0,24.0,29.0,0.0,29.0,0.09,1014.0,198.0,2.7,83.6769551752209 +2006-05-15 17:30:00-05:00,2006,5,15,17,30,26.6,24.1,143.0,31.0,153.0,0.09,1014.0,189.0,2.6,86.18533883635469 +2006-05-15 18:30:00-05:00,2006,5,15,18,30,26.3,24.3,31.0,78.0,39.0,0.09,1014.0,180.0,2.9000000000000004,88.78260158859207 +2006-05-15 19:30:00-05:00,2006,5,15,19,30,26.1,24.4,0.0,0.0,0.0,0.1,1015.0,173.0,3.5,90.37794948305748 +2006-05-15 20:30:00-05:00,2006,5,15,20,30,26.1,24.6,0.0,0.0,0.0,0.1,1015.0,168.0,4.3,91.46618679970442 +2006-05-15 21:30:00-05:00,2006,5,15,21,30,26.1,24.700000000000003,0.0,0.0,0.0,0.1,1015.0,168.0,4.800000000000001,92.01459240971013 +2006-05-15 22:30:00-05:00,2006,5,15,22,30,26.200000000000003,24.6,0.0,0.0,0.0,0.1,1014.0,171.0,5.0,90.92709016838806 +2006-05-15 23:30:00-05:00,2006,5,15,23,30,26.1,24.4,0.0,0.0,0.0,0.1,1014.0,175.0,4.7,90.37794948305748 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06:30:00-05:00,2008,6,29,6,30,28.3,24.200000000000003,55.0,0.0,55.0,0.1,1021.0,136.0,4.2,78.48986058139029 +2008-06-29 07:30:00-05:00,2008,6,29,7,30,28.5,24.200000000000003,115.0,510.0,322.0,0.1,1021.0,136.0,4.3,77.58249907420259 +2008-06-29 08:30:00-05:00,2008,6,29,8,30,28.700000000000003,23.9,143.0,643.0,531.0,0.1,1021.0,136.0,4.4,75.31786020368929 +2008-06-29 09:30:00-05:00,2008,6,29,9,30,28.9,23.700000000000003,163.0,718.0,716.0,0.1,1021.0,136.0,4.3,73.5593075524466 +2008-06-29 10:30:00-05:00,2008,6,29,10,30,29.1,23.700000000000003,183.0,748.0,856.0,0.1,1021.0,137.0,4.1000000000000005,72.71266968240806 +2008-06-29 11:30:00-05:00,2008,6,29,11,30,29.3,23.8,190.0,772.0,944.0,0.1,1021.0,137.0,3.7,72.31087397320795 +2008-06-29 12:30:00-05:00,2008,6,29,12,30,29.4,23.8,193.0,777.0,969.0,0.1,1020.0,137.0,3.3000000000000003,71.89460395336968 +2008-06-29 13:30:00-05:00,2008,6,29,13,30,29.4,23.8,190.0,767.0,930.0,0.1,1020.0,134.0,2.9000000000000004,71.89460395336968 +2008-06-29 14:30:00-05:00,2008,6,29,14,30,29.5,23.8,331.0,544.0,809.0,0.1,1020.0,129.0,2.5,71.48103310439485 +2008-06-29 15:30:00-05:00,2008,6,29,15,30,29.4,23.700000000000003,166.0,690.0,678.0,0.1,1019.0,119.0,2.3000000000000003,71.46322603012092 +2008-06-29 16:30:00-05:00,2008,6,29,16,30,29.3,23.700000000000003,237.0,251.0,379.0,0.1,1019.0,107.0,2.2,71.87699837020585 +2008-06-29 17:30:00-05:00,2008,6,29,17,30,29.0,23.8,144.0,13.0,148.0,0.1,1020.0,96.0,2.3000000000000003,73.57607488115697 +2008-06-29 18:30:00-05:00,2008,6,29,18,30,28.700000000000003,24.200000000000003,43.0,1.0,43.0,0.1,1020.0,90.0,2.4000000000000004,76.68693942486561 +2008-06-29 19:30:00-05:00,2008,6,29,19,30,28.4,24.3,0.0,0.0,0.0,0.11,1020.0,90.0,2.6,78.50397363904351 +2008-06-29 20:30:00-05:00,2008,6,29,20,30,28.3,24.4,0.0,0.0,0.0,0.11,1020.0,93.0,2.8000000000000003,79.43637698347322 +2008-06-29 21:30:00-05:00,2008,6,29,21,30,28.1,24.6,0.0,0.0,0.0,0.11,1020.0,98.0,3.1,81.33449242804365 +2008-06-29 22:30:00-05:00,2008,6,29,22,30,28.0,24.700000000000003,0.0,0.0,0.0,0.11,1020.0,103.0,3.3000000000000003,82.30046967529572 +2008-06-29 23:30:00-05:00,2008,6,29,23,30,28.0,24.700000000000003,0.0,0.0,0.0,0.11,1020.0,111.0,3.6,82.30046967529572 +2008-06-30 00:30:00-05:00,2008,6,30,0,30,27.9,24.6,0.0,0.0,0.0,0.11,1019.0,118.0,3.6,82.28856132037376 +2008-06-30 01:30:00-05:00,2008,6,30,1,30,27.9,24.5,0.0,0.0,0.0,0.11,1019.0,124.0,3.6,81.79775717912155 +2008-06-30 02:30:00-05:00,2008,6,30,2,30,27.9,24.4,0.0,0.0,0.0,0.11,1019.0,129.0,3.4000000000000004,81.30951664501062 +2008-06-30 03:30:00-05:00,2008,6,30,3,30,27.8,24.3,0.0,0.0,0.0,0.11,1019.0,133.0,3.2,81.2970107462092 +2008-06-30 04:30:00-05:00,2008,6,30,4,30,27.8,24.200000000000003,0.0,0.0,0.0,0.11,1019.0,139.0,3.0,80.81103482853118 +2008-06-30 05:30:00-05:00,2008,6,30,5,30,27.9,24.200000000000003,0.0,0.0,0.0,0.11,1019.0,144.0,2.8000000000000003,80.34068102998818 +2008-06-30 06:30:00-05:00,2008,6,30,6,30,28.200000000000003,24.4,90.0,10.0,92.0,0.11,1019.0,148.0,2.6,79.9000621592015 +2008-06-30 07:30:00-05:00,2008,6,30,7,30,28.5,24.4,208.0,150.0,268.0,0.11,1019.0,151.0,2.5,78.51807352094492 +2008-06-30 08:30:00-05:00,2008,6,30,8,30,28.700000000000003,24.3,249.0,314.0,438.0,0.11,1019.0,152.0,2.4000000000000004,77.14811413745277 +2008-06-30 09:30:00-05:00,2008,6,30,9,30,28.8,24.200000000000003,386.0,96.0,459.0,0.11,1019.0,151.0,2.3000000000000003,76.2435322004174 +2008-06-30 10:30:00-05:00,2008,6,30,10,30,29.0,24.3,307.0,478.0,736.0,0.11,1019.0,147.0,2.2,75.81858529426795 +2008-06-30 11:30:00-05:00,2008,6,30,11,30,29.200000000000003,24.3,248.0,654.0,885.0,0.11,1019.0,143.0,2.2,74.94658155009303 +2008-06-30 12:30:00-05:00,2008,6,30,12,30,29.3,24.4,511.0,263.0,773.0,0.11,1019.0,141.0,2.2,74.9625986484097 +2008-06-30 13:30:00-05:00,2008,6,30,13,30,29.3,24.4,315.0,558.0,853.0,0.11,1018.0,139.0,2.1,74.9625986484097 +2008-06-30 14:30:00-05:00,2008,6,30,14,30,29.3,24.3,317.0,498.0,753.0,0.11,1018.0,136.0,1.9,74.51482258188268 +2008-06-30 15:30:00-05:00,2008,6,30,15,30,29.3,24.3,187.0,652.0,671.0,0.11,1018.0,130.0,1.7000000000000002,74.51482258188268 +2008-06-30 16:30:00-05:00,2008,6,30,16,30,29.1,24.3,271.0,166.0,365.0,0.11,1018.0,123.0,1.6,75.3811623147245 +2008-06-30 17:30:00-05:00,2008,6,30,17,30,28.9,24.200000000000003,64.0,0.0,64.0,0.11,1018.0,120.0,1.6,75.80301205850061 +2008-06-30 18:30:00-05:00,2008,6,30,18,30,28.6,24.4,44.0,1.0,45.0,0.11,1018.0,124.0,1.6,78.06341164605891 +2008-06-30 19:30:00-05:00,2008,6,30,19,30,28.700000000000003,24.5,0.0,0.0,0.0,0.09,1016.0,218.0,2.3000000000000003,78.07775051774519 +2008-06-30 20:30:00-05:00,2008,6,30,20,30,28.4,24.6,0.0,0.0,0.0,0.09,1017.0,223.0,2.4000000000000004,79.92666493652756 +2008-06-30 21:30:00-05:00,2008,6,30,21,30,28.3,24.700000000000003,0.0,0.0,0.0,0.09,1017.0,230.0,2.4000000000000004,80.87488034908993 +2008-06-30 22:30:00-05:00,2008,6,30,22,30,28.200000000000003,24.6,0.0,0.0,0.0,0.09,1017.0,235.0,2.4000000000000004,80.86213564882358 +2008-06-30 23:30:00-05:00,2008,6,30,23,30,28.1,24.5,0.0,0.0,0.0,0.09,1016.0,236.0,2.5,80.8493787613347 +2000-07-01 00:30:00-05:00,2000,7,1,0,30,27.9,24.5,0.0,0.0,0.0,0.09,1016.0,237.0,2.7,81.79775717912155 +2000-07-01 01:30:00-05:00,2000,7,1,1,30,27.8,24.4,0.0,0.0,0.0,0.09,1015.0,240.0,2.8000000000000003,81.78554248299675 +2000-07-01 02:30:00-05:00,2000,7,1,2,30,27.700000000000003,24.4,0.0,0.0,0.0,0.09,1015.0,246.0,2.9000000000000004,82.26470995080618 +2000-07-01 03:30:00-05:00,2000,7,1,3,30,27.6,24.3,0.0,0.0,0.0,0.09,1015.0,252.0,3.0,82.2527669077161 +2000-07-01 04:30:00-05:00,2000,7,1,4,30,27.5,24.3,0.0,0.0,0.0,0.09,1015.0,257.0,3.0,82.73538648968068 +2000-07-01 05:30:00-05:00,2000,7,1,5,30,27.5,24.4,0.0,0.0,0.0,0.09,1016.0,262.0,3.1,83.23256174476812 +2000-07-01 06:30:00-05:00,2000,7,1,6,30,27.8,24.700000000000003,86.0,31.0,92.0,0.09,1016.0,267.0,3.1,83.2665865913535 +2000-07-01 07:30:00-05:00,2000,7,1,7,30,28.0,24.6,96.0,571.0,327.0,0.09,1016.0,270.0,3.1,81.80996009312997 +2000-07-01 08:30:00-05:00,2000,7,1,8,30,28.200000000000003,24.4,117.0,697.0,536.0,0.09,1016.0,269.0,3.1,79.9000621592015 +2000-07-01 09:30:00-05:00,2000,7,1,9,30,28.5,24.200000000000003,233.0,542.0,650.0,0.09,1016.0,266.0,3.0,77.58249907420259 +2000-07-01 10:30:00-05:00,2000,7,1,10,30,28.9,24.0,342.0,507.0,797.0,0.09,1016.0,261.0,3.0,74.89844382551969 +2000-07-01 11:30:00-05:00,2000,7,1,11,30,29.3,23.700000000000003,261.0,569.0,816.0,0.09,1016.0,257.0,3.0,71.87699837020585 +2000-07-01 12:30:00-05:00,2000,7,1,12,30,29.6,23.6,505.0,229.0,733.0,0.09,1016.0,254.0,3.2,70.21952167379095 +2000-07-01 13:30:00-05:00,2000,7,1,13,30,29.8,23.4,276.0,1.0,276.0,0.09,1015.0,253.0,3.3000000000000003,68.58338730413207 +2000-07-01 14:30:00-05:00,2000,7,1,14,30,29.8,23.4,105.0,0.0,105.0,0.09,1015.0,254.0,3.5,68.58338730413207 +2000-07-01 15:30:00-05:00,2000,7,1,15,30,29.700000000000003,23.4,206.0,5.0,210.0,0.09,1014.0,259.0,3.6,68.97931831553639 +2000-07-01 16:30:00-05:00,2000,7,1,16,30,29.3,23.5,75.0,0.0,75.0,0.09,1014.0,266.0,3.7,71.01607939994165 +2000-07-01 17:30:00-05:00,2000,7,1,17,30,28.700000000000003,23.700000000000003,61.0,0.0,61.0,0.09,1015.0,274.0,3.7,74.41707142265122 +2000-07-01 18:30:00-05:00,2000,7,1,18,30,28.1,23.9,20.0,0.0,21.0,0.09,1015.0,280.0,3.7,77.99151700352022 +2000-07-01 19:30:00-05:00,2000,7,1,19,30,27.5,24.1,0.0,0.0,0.0,0.1,1015.0,281.0,3.7,81.74882755778764 +2000-07-01 20:30:00-05:00,2000,7,1,20,30,27.1,24.3,0.0,0.0,0.0,0.1,1015.0,279.0,3.6,84.69801774962015 +2000-07-01 21:30:00-05:00,2000,7,1,21,30,26.8,24.4,0.0,0.0,0.0,0.1,1015.0,275.0,3.5,86.72243782517167 +2000-07-01 22:30:00-05:00,2000,7,1,22,30,26.6,24.5,0.0,0.0,0.0,0.1,1015.0,271.0,3.6,88.27650669723143 +2000-07-01 23:30:00-05:00,2000,7,1,23,30,26.5,24.6,0.0,0.0,0.0,0.1,1015.0,270.0,3.7,89.33114086343956 +2000-07-02 00:30:00-05:00,2000,7,2,0,30,26.4,24.6,0.0,0.0,0.0,0.1,1014.0,272.0,3.7,89.85959327286703 +2000-07-02 01:30:00-05:00,2000,7,2,1,30,26.3,24.6,0.0,0.0,0.0,0.1,1014.0,277.0,3.6,90.3915677184959 +2000-07-02 02:30:00-05:00,2000,7,2,2,30,26.200000000000003,24.6,0.0,0.0,0.0,0.1,1014.0,283.0,3.3000000000000003,90.92709016838806 +2000-07-02 03:30:00-05:00,2000,7,2,3,30,26.1,24.5,0.0,0.0,0.0,0.1,1014.0,290.0,3.0,90.92064337853479 +2000-07-02 04:30:00-05:00,2000,7,2,4,30,26.0,24.4,0.0,0.0,0.0,0.1,1014.0,298.0,2.6,90.9141898350019 +2000-07-02 05:30:00-05:00,2000,7,2,5,30,26.1,24.3,0.0,0.0,0.0,0.1,1015.0,307.0,2.2,89.83809249503989 +2000-07-02 06:30:00-05:00,2000,7,2,6,30,26.200000000000003,24.3,37.0,1.0,37.0,0.1,1015.0,317.0,1.8,89.30859176124392 +2000-07-02 07:30:00-05:00,2000,7,2,7,30,26.3,24.0,185.0,55.0,207.0,0.1,1015.0,327.0,1.4,87.1987560867314 +2000-07-02 08:30:00-05:00,2000,7,2,8,30,26.4,23.8,201.0,16.0,211.0,0.1,1015.0,335.0,1.2000000000000002,85.64959932544602 +2000-07-02 09:30:00-05:00,2000,7,2,9,30,26.6,23.5,279.0,397.0,584.0,0.1,1015.0,348.0,1.0,83.12988531629443 +2000-07-02 10:30:00-05:00,2000,7,2,10,30,26.9,23.3,409.0,288.0,667.0,0.1,1015.0,214.0,0.9,80.69534014043445 +2000-07-02 11:30:00-05:00,2000,7,2,11,30,27.1,23.1,376.0,348.0,715.0,0.1,1015.0,78.0,1.1,78.79473348869897 +2000-07-02 12:30:00-05:00,2000,7,2,12,30,27.3,22.9,401.0,348.0,748.0,0.1,1015.0,97.0,1.4,76.9388303584679 +2000-07-02 13:30:00-05:00,2000,7,2,13,30,27.200000000000003,22.9,391.0,335.0,715.0,0.1,1015.0,117.0,1.9,77.39127594477806 +2000-07-02 14:30:00-05:00,2000,7,2,14,30,27.1,23.1,135.0,0.0,136.0,0.1,1015.0,134.0,2.5,78.79473348869897 +2000-07-02 15:30:00-05:00,2000,7,2,15,30,27.0,23.3,263.0,13.0,273.0,0.1,1015.0,148.0,3.0,80.2225324018835 +2000-07-02 16:30:00-05:00,2000,7,2,16,30,26.9,23.5,219.0,25.0,233.0,0.1,1015.0,158.0,3.2,81.67507763929301 +2000-07-02 17:30:00-05:00,2000,7,2,17,30,26.8,23.700000000000003,123.0,6.0,125.0,0.1,1015.0,165.0,3.2,83.15278064806495 +2000-07-02 18:30:00-05:00,2000,7,2,18,30,26.700000000000003,23.700000000000003,38.0,1.0,38.0,0.1,1015.0,171.0,2.9000000000000004,83.64358741455791 +2000-07-02 19:30:00-05:00,2000,7,2,19,30,26.6,23.6,0.0,0.0,0.0,0.1,1015.0,173.0,2.5,83.63244289602052 +2000-07-02 20:30:00-05:00,2000,7,2,20,30,26.700000000000003,23.5,0.0,0.0,0.0,0.1,1016.0,169.0,2.1,82.64173212317175 +2000-07-02 21:30:00-05:00,2000,7,2,21,30,26.700000000000003,23.4,0.0,0.0,0.0,0.1,1016.0,158.0,1.9,82.14475619870736 +2000-07-02 22:30:00-05:00,2000,7,2,22,30,26.700000000000003,23.3,0.0,0.0,0.0,0.1,1016.0,146.0,1.8,81.65039907186721 +2000-07-02 23:30:00-05:00,2000,7,2,23,30,26.6,23.3,0.0,0.0,0.0,0.1,1016.0,137.0,1.9,82.13269659882694 +2000-07-03 00:30:00-05:00,2000,7,3,0,30,26.6,23.3,0.0,0.0,0.0,0.1,1015.0,130.0,2.2,82.13269659882694 +2000-07-03 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18:30:00-05:00,2021,8,31,18,30,29.4,24.8,10.0,0.0,10.0,0.1,1011.0,178.0,1.6,76.33535606218936 +2021-08-31 19:30:00-05:00,2021,8,31,19,30,29.200000000000003,24.9,0.0,0.0,0.0,0.1,1015.0,59.0,0.5,77.6845162055163 +2021-08-31 20:30:00-05:00,2021,8,31,20,30,29.0,24.9,0.0,0.0,0.0,0.1,1015.0,71.0,0.8,78.58837582919173 +2021-08-31 21:30:00-05:00,2021,8,31,21,30,28.8,24.9,0.0,0.0,0.0,0.1,1015.0,82.0,1.0,79.50410513346897 +2021-08-31 22:30:00-05:00,2021,8,31,22,30,28.700000000000003,24.9,0.0,0.0,0.0,0.1,1015.0,98.0,1.3,79.96647477417243 +2021-08-31 23:30:00-05:00,2021,8,31,23,30,28.6,24.9,0.0,0.0,0.0,0.1,1015.0,112.0,1.6,80.43187680667027 +2020-09-01 00:30:00-05:00,2020,9,1,0,30,28.5,25.0,0.0,0.0,0.0,0.1,1014.0,122.0,2.0,81.38430034286966 +2020-09-01 01:30:00-05:00,2020,9,1,1,30,28.4,25.200000000000003,0.0,0.0,0.0,0.1,1014.0,130.0,2.5,82.83987858191361 +2020-09-01 02:30:00-05:00,2020,9,1,2,30,28.3,25.4,0.0,0.0,0.0,0.1,1014.0,136.0,2.9000000000000004,84.32035426044355 +2020-09-01 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03:30:00-05:00,2020,9,2,3,30,28.4,25.5,0.0,0.0,0.0,0.1,1013.0,108.0,3.8,84.33100593991442 +2020-09-02 04:30:00-05:00,2020,9,2,4,30,28.4,25.3,0.0,0.0,0.0,0.1,1013.0,104.0,3.8,83.3343375837698 +2020-09-02 05:30:00-05:00,2020,9,2,5,30,28.5,25.200000000000003,0.0,0.0,0.0,0.1,1013.0,101.0,3.8,82.35983871834445 +2020-09-02 06:30:00-05:00,2020,9,2,6,30,29.0,25.3,38.0,194.0,57.0,0.1,1014.0,99.0,4.0,80.48371679996085 +2020-09-02 07:30:00-05:00,2020,9,2,7,30,29.5,25.4,84.0,542.0,261.0,0.1,1014.0,97.0,4.3,78.658351031073 +2020-09-02 08:30:00-05:00,2020,9,2,8,30,29.8,25.3,117.0,670.0,477.0,0.1,1014.0,96.0,4.4,76.85229197010862 +2020-09-02 09:30:00-05:00,2020,9,2,9,30,30.1,25.4,134.0,763.0,680.0,0.1,1015.0,96.0,4.6000000000000005,75.9889623050859 +2020-09-02 10:30:00-05:00,2020,9,2,10,30,30.3,25.5,255.0,564.0,734.0,0.1,1015.0,95.0,4.6000000000000005,75.56970364160983 +2020-09-02 11:30:00-05:00,2020,9,2,11,30,30.4,25.700000000000003,188.0,759.0,893.0,0.1,1014.0,95.0,4.7,76.03513493880077 +2020-09-02 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20:30:00-05:00,2020,9,2,20,30,29.4,26.8,0.0,0.0,0.0,0.1,1014.0,82.0,5.1000000000000005,85.94207610757024 +2020-09-02 21:30:00-05:00,2020,9,2,21,30,29.4,26.9,0.0,0.0,0.0,0.1,1014.0,87.0,5.1000000000000005,86.44896966030107 +2020-09-02 22:30:00-05:00,2020,9,2,22,30,29.3,26.8,0.0,0.0,0.0,0.1,1014.0,92.0,5.0,86.43968104255873 +2020-09-02 23:30:00-05:00,2020,9,2,23,30,29.3,26.700000000000003,0.0,0.0,0.0,0.1,1014.0,97.0,4.9,85.93246726012121 +2020-09-03 00:30:00-05:00,2020,9,3,0,30,29.200000000000003,26.6,0.0,0.0,0.0,0.1,1013.0,101.0,4.7,85.9228488470392 +2020-09-03 01:30:00-05:00,2020,9,3,1,30,29.200000000000003,26.5,0.0,0.0,0.0,0.1,1013.0,104.0,4.5,85.41792160535843 +2020-09-03 02:30:00-05:00,2020,9,3,2,30,29.200000000000003,26.5,0.0,0.0,0.0,0.1,1013.0,106.0,4.4,85.41792160535843 +2020-09-03 03:30:00-05:00,2020,9,3,3,30,29.200000000000003,26.4,0.0,0.0,0.0,0.1,1013.0,108.0,4.3,84.91559008084808 +2020-09-03 04:30:00-05:00,2020,9,3,4,30,29.1,26.3,0.0,0.0,0.0,0.1,1013.0,109.0,4.2,84.9053316369707 +2020-09-03 05:30:00-05:00,2020,9,3,5,30,29.200000000000003,26.3,0.0,0.0,0.0,0.1,1013.0,109.0,4.1000000000000005,84.41584297944601 +2020-09-03 06:30:00-05:00,2020,9,3,6,30,29.5,26.3,40.0,123.0,52.0,0.1,1013.0,109.0,4.2,82.9663558420841 +2020-09-03 07:30:00-05:00,2020,9,3,7,30,29.8,26.4,96.0,468.0,248.0,0.1,1013.0,109.0,4.3,82.0276141014992 +2020-09-03 08:30:00-05:00,2020,9,3,8,30,30.0,26.3,142.0,577.0,451.0,0.1,1014.0,109.0,4.3,80.61245891628118 +2020-09-03 09:30:00-05:00,2020,9,3,9,30,30.3,26.200000000000003,144.0,729.0,664.0,0.1,1014.0,109.0,4.3,78.76963569396231 +2020-09-03 10:30:00-05:00,2020,9,3,10,30,30.5,26.1,166.0,758.0,808.0,0.1,1014.0,109.0,4.3,77.41326126912969 +2020-09-03 11:30:00-05:00,2020,9,3,11,30,30.700000000000003,26.0,205.0,730.0,882.0,0.1,1014.0,108.0,4.2,76.0811824375501 +2020-09-03 12:30:00-05:00,2020,9,3,12,30,30.8,26.0,388.0,353.0,722.0,0.1,1014.0,105.0,4.2,75.64766286638327 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21:30:00-05:00,2020,9,3,21,30,29.4,26.4,0.0,0.0,0.0,0.1,1015.0,112.0,4.0,83.94038561720416 +2020-09-03 22:30:00-05:00,2020,9,3,22,30,29.3,26.4,0.0,0.0,0.0,0.1,1015.0,114.0,4.0,84.42640075693242 +2020-09-03 23:30:00-05:00,2020,9,3,23,30,29.200000000000003,26.4,0.0,0.0,0.0,0.1,1014.0,115.0,3.9,84.91559008084808 +2020-09-04 00:30:00-05:00,2020,9,4,0,30,29.200000000000003,26.4,0.0,0.0,0.0,0.1,1014.0,114.0,3.8,84.91559008084808 +2020-09-04 01:30:00-05:00,2020,9,4,1,30,29.1,26.3,0.0,0.0,0.0,0.1,1014.0,111.0,3.7,84.9053316369707 +2020-09-04 02:30:00-05:00,2020,9,4,2,30,29.0,26.3,0.0,0.0,0.0,0.1,1014.0,108.0,3.7,85.39802161419213 +2020-09-04 03:30:00-05:00,2020,9,4,3,30,28.9,26.3,0.0,0.0,0.0,0.1,1014.0,105.0,3.6,85.89393609127025 +2020-09-04 04:30:00-05:00,2020,9,4,4,30,28.9,26.200000000000003,0.0,0.0,0.0,0.1,1014.0,101.0,3.5,85.38805680929505 +2020-09-04 05:30:00-05:00,2020,9,4,5,30,29.0,26.1,0.0,0.0,0.0,0.1,1014.0,96.0,3.4000000000000004,84.39469626812703 +2020-09-04 06:30:00-05:00,2020,9,4,6,30,29.200000000000003,26.200000000000003,42.0,3.0,42.0,0.1,1015.0,90.0,3.3000000000000003,83.9186690463712 +2020-09-04 07:30:00-05:00,2020,9,4,7,30,29.6,26.4,157.0,82.0,184.0,0.1,1015.0,83.0,3.4000000000000004,82.97778697573821 +2020-09-04 08:30:00-05:00,2020,9,4,8,30,29.8,26.3,255.0,159.0,340.0,0.1,1015.0,77.0,3.5,81.54486337995182 +2020-09-04 09:30:00-05:00,2020,9,4,9,30,30.0,26.200000000000003,334.0,285.0,537.0,0.1,1016.0,72.0,3.7,80.13768532118723 +2020-09-04 10:30:00-05:00,2020,9,4,10,30,30.3,26.3,282.0,564.0,759.0,0.1,1016.0,70.0,4.0,79.23630431525797 +2020-09-04 11:30:00-05:00,2020,9,4,11,30,30.4,26.4,343.0,508.0,813.0,0.1,1016.0,72.0,4.3,79.24989132832202 +2020-09-04 12:30:00-05:00,2020,9,4,12,30,30.5,26.4,335.0,540.0,845.0,0.1,1015.0,75.0,4.5,78.79732767669387 +2020-09-04 13:30:00-05:00,2020,9,4,13,30,30.5,26.4,254.0,657.0,848.0,0.1,1015.0,77.0,4.6000000000000005,78.79732767669387 +2020-09-04 14:30:00-05:00,2020,9,4,14,30,30.3,26.4,226.0,639.0,740.0,0.1,1014.0,80.0,4.7,79.70538822190099 +2020-09-04 15:30:00-05:00,2020,9,4,15,30,30.1,26.5,229.0,505.0,559.0,0.1,1014.0,82.0,4.7,81.10221756386521 +2020-09-04 16:30:00-05:00,2020,9,4,16,30,29.8,26.5,170.0,397.0,354.0,0.1,1014.0,85.0,4.7,82.51286135002356 +2020-09-04 17:30:00-05:00,2020,9,4,17,30,29.6,26.6,106.0,202.0,155.0,0.1,1015.0,88.0,4.6000000000000005,83.96205974886357 +2020-09-04 18:30:00-05:00,2020,9,4,18,30,29.4,26.5,8.0,5.0,8.0,0.1,1015.0,93.0,4.5,84.43694816637718 +2020-09-04 19:30:00-05:00,2020,9,4,19,30,29.3,26.4,0.0,0.0,0.0,0.1,1016.0,98.0,4.5,84.42640075693242 +2020-09-04 20:30:00-05:00,2020,9,4,20,30,29.3,26.200000000000003,0.0,0.0,0.0,0.1,1016.0,105.0,4.4,83.4352228742889 +2020-09-04 21:30:00-05:00,2020,9,4,21,30,29.3,26.0,0.0,0.0,0.0,0.1,1016.0,110.0,4.3,82.45423380492927 +2020-09-04 22:30:00-05:00,2020,9,4,22,30,29.200000000000003,25.8,0.0,0.0,0.0,0.1,1016.0,113.0,4.2,81.9554812711802 +2020-09-04 23:30:00-05:00,2020,9,4,23,30,29.1,25.6,0.0,0.0,0.0,0.1,1016.0,113.0,4.1000000000000005,81.45865482351515 +2020-09-05 00:30:00-05:00,2020,9,5,0,30,29.0,25.4,0.0,0.0,0.0,0.1,1016.0,112.0,3.8,80.96375323840603 +2020-09-05 01:30:00-05:00,2020,9,5,1,30,28.9,25.1,0.0,0.0,0.0,0.1,1015.0,109.0,3.5,79.99295195434269 +2020-09-05 02:30:00-05:00,2020,9,5,2,30,28.9,25.0,0.0,0.0,0.0,0.1,1015.0,104.0,3.2,79.5176125270568 +2020-09-05 03:30:00-05:00,2020,9,5,3,30,28.8,24.8,0.0,0.0,0.0,0.1,1015.0,99.0,3.0,79.03096698905823 +2020-09-05 04:30:00-05:00,2020,9,5,4,30,28.9,24.8,0.0,0.0,0.0,0.1,1015.0,92.0,2.9000000000000004,78.57434159685675 +2020-09-05 05:30:00-05:00,2020,9,5,5,30,28.9,24.8,0.0,0.0,0.0,0.1,1016.0,87.0,2.9000000000000004,78.57434159685675 +2020-09-05 06:30:00-05:00,2020,9,5,6,30,29.200000000000003,25.0,36.0,3.0,36.0,0.1,1016.0,82.0,2.9000000000000004,78.1492454374399 +2020-09-05 07:30:00-05:00,2020,9,5,7,30,29.5,25.3,116.0,24.0,124.0,0.1,1017.0,77.0,3.1,78.19198338910188 +2020-09-05 08:30:00-05:00,2020,9,5,8,30,29.6,25.3,242.0,69.0,279.0,0.1,1017.0,75.0,3.4000000000000004,77.74251612148691 +2020-09-05 09:30:00-05:00,2020,9,5,9,30,29.8,25.4,190.0,613.0,626.0,0.1,1018.0,75.0,3.6,77.3106691672673 +2020-09-05 10:30:00-05:00,2020,9,5,10,30,29.9,25.4,279.0,520.0,718.0,0.1,1018.0,79.0,3.8,76.86724167307942 +2020-09-05 11:30:00-05:00,2020,9,5,11,30,30.0,25.5,424.0,86.0,503.0,0.1,1017.0,82.0,4.0,76.88217771852531 +2020-09-05 12:30:00-05:00,2020,9,5,12,30,30.0,25.5,379.0,352.0,711.0,0.1,1017.0,85.0,4.1000000000000005,76.88217771852531 +2020-09-05 13:30:00-05:00,2020,9,5,13,30,30.0,25.5,394.0,374.0,731.0,0.1,1016.0,85.0,4.2,76.88217771852531 +2020-09-05 14:30:00-05:00,2020,9,5,14,30,29.9,25.4,275.0,326.0,536.0,0.1,1016.0,86.0,4.2,76.86724167307942 +2020-09-05 15:30:00-05:00,2020,9,5,15,30,29.8,25.4,155.0,664.0,587.0,0.1,1016.0,86.0,4.2,77.3106691672673 +2020-09-05 16:30:00-05:00,2020,9,5,16,30,29.5,25.3,127.0,550.0,380.0,0.1,1016.0,85.0,4.1000000000000005,78.19198338910188 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01:30:00-05:00,2020,9,6,1,30,27.9,24.200000000000003,0.0,0.0,0.0,0.09,1015.0,64.0,2.3000000000000003,80.34068102998818 +2020-09-06 02:30:00-05:00,2020,9,6,2,30,27.6,24.1,0.0,0.0,0.0,0.09,1014.0,46.0,2.3000000000000003,81.27196286112057 +2020-09-06 03:30:00-05:00,2020,9,6,3,30,27.4,24.1,0.0,0.0,0.0,0.09,1014.0,31.0,2.7,82.22884603365834 +2020-09-06 04:30:00-05:00,2020,9,6,4,30,27.4,24.200000000000003,0.0,0.0,0.0,0.09,1014.0,27.0,3.0,82.72371961951409 +2020-09-06 05:30:00-05:00,2020,9,6,5,30,27.5,24.200000000000003,0.0,0.0,0.0,0.09,1014.0,30.0,3.3000000000000003,82.2408122734246 +2020-09-06 06:30:00-05:00,2020,9,6,6,30,27.8,24.3,34.0,33.0,37.0,0.09,1015.0,36.0,3.5,81.2970107462092 +2020-09-06 07:30:00-05:00,2020,9,6,7,30,28.200000000000003,24.5,149.0,67.0,170.0,0.09,1015.0,44.0,3.8,80.37983931978185 +2020-09-06 08:30:00-05:00,2020,9,6,8,30,28.4,24.5,205.0,64.0,239.0,0.09,1015.0,51.0,3.9,79.44994815453646 +2020-09-06 09:30:00-05:00,2020,9,6,9,30,28.700000000000003,24.4,209.0,584.0,623.0,0.09,1015.0,60.0,4.0,77.61171423594976 +2020-09-06 10:30:00-05:00,2020,9,6,10,30,29.0,24.3,361.0,362.0,666.0,0.09,1015.0,67.0,4.1000000000000005,75.81858529426795 +2020-09-06 11:30:00-05:00,2020,9,6,11,30,29.1,24.3,254.0,608.0,814.0,0.09,1015.0,70.0,4.0,75.3811623147245 +2020-09-06 12:30:00-05:00,2020,9,6,12,30,29.200000000000003,24.4,205.0,739.0,900.0,0.09,1014.0,70.0,3.9,75.39695215185175 +2020-09-06 13:30:00-05:00,2020,9,6,13,30,29.1,24.5,176.0,769.0,867.0,0.09,1013.0,66.0,3.7,76.2895068887946 +2020-09-06 14:30:00-05:00,2020,9,6,14,30,29.0,24.6,261.0,557.0,706.0,0.09,1013.0,61.0,3.6,77.19261053764825 +2020-09-06 15:30:00-05:00,2020,9,6,15,30,28.700000000000003,24.6,250.0,358.0,482.0,0.09,1013.0,54.0,3.4000000000000004,78.54623381869744 +2020-09-06 16:30:00-05:00,2020,9,6,16,30,28.3,24.8,164.0,93.0,206.0,0.09,1013.0,46.0,3.2,81.3594202886324 +2020-09-06 17:30:00-05:00,2020,9,6,17,30,27.8,24.9,50.0,2.0,50.0,0.09,1013.0,41.0,2.9000000000000004,84.26693810647969 +2020-09-06 18:30:00-05:00,2020,9,6,18,30,27.4,24.9,5.0,0.0,5.0,0.09,1013.0,40.0,2.8000000000000003,86.26141932109293 +2020-09-06 19:30:00-05:00,2020,9,6,19,30,27.1,24.8,0.0,0.0,0.0,0.12,1013.0,45.0,2.8000000000000003,87.26972857023115 +2020-09-06 20:30:00-05:00,2020,9,6,20,30,27.0,24.8,0.0,0.0,0.0,0.12,1014.0,51.0,2.8000000000000003,87.7836884707405 +2020-09-06 21:30:00-05:00,2020,9,6,21,30,26.8,24.700000000000003,0.0,0.0,0.0,0.12,1013.0,55.0,3.0,88.29288355071061 +2020-09-06 22:30:00-05:00,2020,9,6,22,30,26.8,24.6,0.0,0.0,0.0,0.12,1013.0,57.0,3.2,87.7666592704661 +2020-09-06 23:30:00-05:00,2020,9,6,23,30,26.8,24.6,0.0,0.0,0.0,0.12,1012.0,60.0,3.3000000000000003,87.7666592704661 +2020-09-07 00:30:00-05:00,2020,9,7,0,30,26.9,24.6,0.0,0.0,0.0,0.12,1011.0,62.0,3.4000000000000004,87.2520394444145 +2020-09-07 01:30:00-05:00,2020,9,7,1,30,26.9,24.6,0.0,0.0,0.0,0.12,1011.0,63.0,3.3000000000000003,87.2520394444145 +2020-09-07 02:30:00-05:00,2020,9,7,2,30,26.9,24.5,0.0,0.0,0.0,0.12,1010.0,61.0,3.3000000000000003,86.73163099875832 +2020-09-07 03:30:00-05:00,2020,9,7,3,30,26.9,24.5,0.0,0.0,0.0,0.12,1010.0,57.0,3.2,86.73163099875832 +2020-09-07 04:30:00-05:00,2020,9,7,4,30,26.9,24.5,0.0,0.0,0.0,0.12,1010.0,53.0,3.2,86.73163099875832 +2020-09-07 05:30:00-05:00,2020,9,7,5,30,27.0,24.4,0.0,0.0,0.0,0.12,1011.0,52.0,3.2,85.70879863233019 +2020-09-07 06:30:00-05:00,2020,9,7,6,30,27.3,24.5,34.0,202.0,52.0,0.12,1011.0,55.0,3.2,84.71893447945013 +2020-09-07 07:30:00-05:00,2020,9,7,7,30,27.700000000000003,24.700000000000003,116.0,300.0,212.0,0.12,1011.0,62.0,3.2,83.754431242606 +2020-09-07 08:30:00-05:00,2020,9,7,8,30,27.9,24.6,108.0,674.0,465.0,0.12,1011.0,70.0,3.3000000000000003,82.28856132037376 +2020-09-07 09:30:00-05:00,2020,9,7,9,30,28.200000000000003,24.5,227.0,346.0,472.0,0.12,1012.0,80.0,3.3000000000000003,80.37983931978185 +2020-09-07 10:30:00-05:00,2020,9,7,10,30,28.200000000000003,24.4,180.0,3.0,183.0,0.12,1011.0,92.0,3.4000000000000004,79.9000621592015 +2020-09-07 11:30:00-05:00,2020,9,7,11,30,28.200000000000003,24.5,389.0,205.0,577.0,0.12,1011.0,101.0,3.6,80.37983931978185 +2020-09-07 12:30:00-05:00,2020,9,7,12,30,28.1,24.6,433.0,46.0,476.0,0.12,1010.0,106.0,3.6,81.33449242804365 +2020-09-07 13:30:00-05:00,2020,9,7,13,30,28.0,24.700000000000003,347.0,21.0,366.0,0.12,1010.0,108.0,3.6,82.30046967529572 +2020-09-07 14:30:00-05:00,2020,9,7,14,30,27.700000000000003,24.700000000000003,238.0,7.0,244.0,0.12,1009.0,108.0,3.5,83.754431242606 +2020-09-07 15:30:00-05:00,2020,9,7,15,30,27.4,24.8,128.0,2.0,129.0,0.12,1009.0,107.0,3.4000000000000004,85.74806761675889 +2020-09-07 16:30:00-05:00,2020,9,7,16,30,27.200000000000003,24.700000000000003,185.0,63.0,213.0,0.12,1009.0,103.0,3.1,86.24245654446752 +2020-09-07 17:30:00-05:00,2020,9,7,17,30,27.0,24.700000000000003,75.0,18.0,79.0,0.12,1009.0,97.0,2.8000000000000003,87.26088849314073 +2020-09-07 18:30:00-05:00,2020,9,7,18,30,26.9,24.700000000000003,4.0,0.0,4.0,0.12,1009.0,90.0,2.7,87.77517820847537 +2020-09-07 19:30:00-05:00,2020,9,7,19,30,27.0,24.700000000000003,0.0,0.0,0.0,0.12,1009.0,84.0,2.7,87.26088849314073 +2020-09-07 20:30:00-05:00,2020,9,7,20,30,27.0,24.700000000000003,0.0,0.0,0.0,0.12,1010.0,79.0,2.8000000000000003,87.26088849314073 +2020-09-07 21:30:00-05:00,2020,9,7,21,30,27.0,24.700000000000003,0.0,0.0,0.0,0.12,1010.0,80.0,2.9000000000000004,87.26088849314073 +2020-09-07 22:30:00-05:00,2020,9,7,22,30,27.0,24.700000000000003,0.0,0.0,0.0,0.12,1010.0,84.0,2.9000000000000004,87.26088849314073 +2020-09-07 23:30:00-05:00,2020,9,7,23,30,26.9,24.700000000000003,0.0,0.0,0.0,0.12,1009.0,90.0,2.8000000000000003,87.77517820847537 +2020-09-08 00:30:00-05:00,2020,9,8,0,30,26.9,24.700000000000003,0.0,0.0,0.0,0.12,1009.0,98.0,2.6,87.77517820847537 +2020-09-08 01:30:00-05:00,2020,9,8,1,30,26.9,24.700000000000003,0.0,0.0,0.0,0.12,1008.0,106.0,2.4000000000000004,87.77517820847537 +2020-09-08 02:30:00-05:00,2020,9,8,2,30,26.9,24.6,0.0,0.0,0.0,0.12,1008.0,115.0,2.2,87.2520394444145 +2020-09-08 03:30:00-05:00,2020,9,8,3,30,27.0,24.6,0.0,0.0,0.0,0.12,1008.0,121.0,2.1,86.7408148881777 +2020-09-08 04:30:00-05:00,2020,9,8,4,30,27.1,24.5,0.0,0.0,0.0,0.12,1008.0,123.0,1.9,85.71863062550217 +2020-09-08 05:30:00-05:00,2020,9,8,5,30,27.200000000000003,24.5,0.0,0.0,0.0,0.12,1009.0,120.0,1.9,85.21713165509654 +2020-09-08 06:30:00-05:00,2020,9,8,6,30,27.5,24.700000000000003,32.0,210.0,51.0,0.12,1009.0,117.0,2.1,84.73980974304553 +2020-09-08 07:30:00-05:00,2020,9,8,7,30,27.8,25.0,112.0,205.0,177.0,0.12,1010.0,119.0,2.4000000000000004,84.77104511950627 +2020-09-08 08:30:00-05:00,2020,9,8,8,30,28.0,25.0,87.0,736.0,476.0,0.12,1011.0,124.0,2.8000000000000003,83.78747242805218 +2020-09-08 09:30:00-05:00,2020,9,8,9,30,28.3,25.1,122.0,765.0,663.0,0.12,1011.0,131.0,3.2,82.82831349161563 +2020-09-08 10:30:00-05:00,2020,9,8,10,30,28.6,25.200000000000003,105.0,858.0,825.0,0.12,1011.0,140.0,3.7,81.88293095675752 +2020-09-08 11:30:00-05:00,2020,9,8,11,30,28.8,25.200000000000003,110.0,878.0,915.0,0.12,1011.0,149.0,3.9,80.93842155480759 +2020-09-08 12:30:00-05:00,2020,9,8,12,30,28.9,25.0,113.0,877.0,934.0,0.12,1010.0,157.0,4.0,79.5176125270568 +2020-09-08 13:30:00-05:00,2020,9,8,13,30,29.0,24.9,116.0,859.0,883.0,0.12,1010.0,162.0,3.9,78.58837582919173 +2020-09-08 14:30:00-05:00,2020,9,8,14,30,29.0,24.700000000000003,152.0,751.0,747.0,0.12,1009.0,165.0,3.8,77.65543578652994 +2020-09-08 15:30:00-05:00,2020,9,8,15,30,28.9,24.6,135.0,677.0,569.0,0.12,1009.0,165.0,3.7,77.64087540802214 +2020-09-08 16:30:00-05:00,2020,9,8,16,30,28.700000000000003,24.6,156.0,390.0,331.0,0.12,1009.0,164.0,3.6,78.54623381869744 +2020-09-08 17:30:00-05:00,2020,9,8,17,30,28.4,24.700000000000003,103.0,129.0,132.0,0.12,1010.0,162.0,3.5,80.40588280898817 +2020-09-08 18:30:00-05:00,2020,9,8,18,30,28.1,24.700000000000003,5.0,0.0,5.0,0.12,1010.0,159.0,3.5,81.82215123940509 +2020-09-08 19:30:00-05:00,2020,9,8,19,30,28.0,24.8,0.0,0.0,0.0,0.12,1011.0,156.0,3.6,82.79355064714575 +2020-09-08 20:30:00-05:00,2020,9,8,20,30,27.9,24.9,0.0,0.0,0.0,0.12,1011.0,154.0,3.6,83.77646951498014 +2020-09-08 21:30:00-05:00,2020,9,8,21,30,27.700000000000003,25.0,0.0,0.0,0.0,0.12,1011.0,151.0,3.6,85.26770413527203 +2020-09-08 22:30:00-05:00,2020,9,8,22,30,27.6,25.0,0.0,0.0,0.0,0.12,1011.0,150.0,3.5,85.76764324712101 +2020-09-08 23:30:00-05:00,2020,9,8,23,30,27.6,25.1,0.0,0.0,0.0,0.12,1011.0,149.0,3.4000000000000004,86.2803440328353 +2020-09-09 00:30:00-05:00,2020,9,9,0,30,27.5,25.0,0.0,0.0,0.0,0.12,1011.0,151.0,3.1,86.27088642891519 +2020-09-09 01:30:00-05:00,2020,9,9,1,30,27.5,24.9,0.0,0.0,0.0,0.12,1010.0,154.0,2.7,85.7578603265765 +2020-09-09 02:30:00-05:00,2020,9,9,2,30,27.5,24.700000000000003,0.0,0.0,0.0,0.12,1010.0,158.0,2.4000000000000004,84.73980974304553 +2020-09-09 03:30:00-05:00,2020,9,9,3,30,27.6,24.5,0.0,0.0,0.0,0.12,1010.0,160.0,2.1,83.24391446008386 +2020-09-09 04:30:00-05:00,2020,9,9,4,30,27.700000000000003,24.4,0.0,0.0,0.0,0.12,1011.0,159.0,1.9,82.26470995080618 +2020-09-09 05:30:00-05:00,2020,9,9,5,30,27.8,24.4,0.0,0.0,0.0,0.12,1011.0,154.0,1.9,81.78554248299675 +2020-09-09 06:30:00-05:00,2020,9,9,6,30,28.0,24.5,31.0,244.0,52.0,0.12,1012.0,149.0,1.9,81.32201053410122 +2020-09-09 07:30:00-05:00,2020,9,9,7,30,28.3,24.8,51.0,7.0,53.0,0.12,1013.0,146.0,2.0,81.3594202886324 +2020-09-09 08:30:00-05:00,2020,9,9,8,30,28.6,24.8,23.0,1.0,24.0,0.12,1013.0,146.0,2.0,79.95321738550082 +2020-09-09 09:30:00-05:00,2020,9,9,9,30,28.700000000000003,24.6,56.0,0.0,56.0,0.12,1014.0,145.0,2.0,78.54623381869744 +2020-09-09 10:30:00-05:00,2020,9,9,10,30,28.700000000000003,24.6,120.0,0.0,120.0,0.12,1014.0,146.0,1.9,78.54623381869744 +2020-09-09 11:30:00-05:00,2020,9,9,11,30,28.8,24.700000000000003,397.0,169.0,552.0,0.12,1014.0,150.0,1.9,78.56029426510064 +2020-09-09 12:30:00-05:00,2020,9,9,12,30,28.700000000000003,24.8,398.0,278.0,658.0,0.12,1014.0,153.0,1.8,79.49058501443984 +2020-09-09 13:30:00-05:00,2020,9,9,13,30,28.700000000000003,24.8,328.0,375.0,662.0,0.12,1014.0,153.0,1.8,79.49058501443984 +2020-09-09 14:30:00-05:00,2020,9,9,14,30,28.5,24.8,251.0,464.0,617.0,0.12,1013.0,152.0,1.8,80.41888598687427 +2020-09-09 15:30:00-05:00,2020,9,9,15,30,28.4,24.8,216.0,432.0,491.0,0.12,1013.0,147.0,1.9,80.88761287680352 +2020-09-09 16:30:00-05:00,2020,9,9,16,30,28.3,24.9,182.0,261.0,298.0,0.12,1013.0,140.0,2.0,81.84649828618528 +2020-09-09 17:30:00-05:00,2020,9,9,17,30,28.200000000000003,24.9,74.0,307.0,143.0,0.12,1013.0,133.0,2.1,82.32425179637455 +2020-09-09 18:30:00-05:00,2020,9,9,18,30,28.1,25.0,6.0,24.0,6.0,0.12,1014.0,128.0,2.3000000000000003,83.30051174706053 +2020-09-09 19:30:00-05:00,2020,9,9,19,30,28.1,25.0,0.0,0.0,0.0,0.11,1015.0,124.0,2.6,83.30051174706053 +2020-09-09 20:30:00-05:00,2020,9,9,20,30,28.200000000000003,25.0,0.0,0.0,0.0,0.11,1015.0,120.0,2.8000000000000003,82.81673714834355 +2020-09-09 21:30:00-05:00,2020,9,9,21,30,28.1,25.0,0.0,0.0,0.0,0.11,1015.0,118.0,3.0,83.30051174706053 +2020-09-09 22:30:00-05:00,2020,9,9,22,30,28.0,25.0,0.0,0.0,0.0,0.11,1015.0,116.0,3.1,83.78747242805218 +2020-09-09 23:30:00-05:00,2020,9,9,23,30,27.9,25.0,0.0,0.0,0.0,0.11,1015.0,113.0,3.1,84.27764241574158 +2020-09-10 00:30:00-05:00,2020,9,10,0,30,27.700000000000003,25.0,0.0,0.0,0.0,0.11,1014.0,110.0,3.2,85.26770413527203 +2020-09-10 01:30:00-05:00,2020,9,10,1,30,27.6,25.1,0.0,0.0,0.0,0.11,1014.0,107.0,3.3000000000000003,86.2803440328353 +2020-09-10 02:30:00-05:00,2020,9,10,2,30,27.5,25.1,0.0,0.0,0.0,0.11,1014.0,106.0,3.4000000000000004,86.78659549566581 +2020-09-10 03:30:00-05:00,2020,9,10,3,30,27.4,25.200000000000003,0.0,0.0,0.0,0.11,1014.0,106.0,3.5,87.81764299095894 +2020-09-10 04:30:00-05:00,2020,9,10,4,30,27.4,25.3,0.0,0.0,0.0,0.11,1014.0,106.0,3.5,88.34181353348015 +2020-09-10 05:30:00-05:00,2020,9,10,5,30,27.5,25.4,0.0,0.0,0.0,0.11,1014.0,105.0,3.4000000000000004,88.34993939594236 +2020-09-10 06:30:00-05:00,2020,9,10,6,30,27.8,25.6,12.0,0.0,12.0,0.11,1015.0,100.0,3.1,87.8514596114882 +2020-09-10 07:30:00-05:00,2020,9,10,7,30,28.1,25.8,70.0,3.0,71.0,0.11,1015.0,93.0,3.0,87.35763847945336 +2020-09-10 08:30:00-05:00,2020,9,10,8,30,28.3,25.700000000000003,228.0,230.0,349.0,0.11,1016.0,86.0,2.9000000000000004,85.83585064618416 +2020-09-10 09:30:00-05:00,2020,9,10,9,30,28.5,25.700000000000003,149.0,683.0,630.0,0.11,1016.0,83.0,3.0,84.84356774191912 +2020-09-10 10:30:00-05:00,2020,9,10,10,30,28.700000000000003,25.6,290.0,423.0,644.0,0.11,1016.0,85.0,2.9000000000000004,83.36806447173022 +2020-09-10 11:30:00-05:00,2020,9,10,11,30,28.9,25.5,355.0,315.0,643.0,0.11,1015.0,87.0,2.9000000000000004,81.91925849043852 +2020-09-10 12:30:00-05:00,2020,9,10,12,30,28.9,25.5,352.0,15.0,366.0,0.11,1015.0,85.0,2.8000000000000003,81.91925849043852 +2020-09-10 13:30:00-05:00,2020,9,10,13,30,29.0,25.4,369.0,165.0,515.0,0.11,1014.0,79.0,2.7,80.96375323840603 +2020-09-10 14:30:00-05:00,2020,9,10,14,30,29.0,25.200000000000003,306.0,296.0,539.0,0.11,1013.0,72.0,2.7,80.00617177556343 +2020-09-10 15:30:00-05:00,2020,9,10,15,30,29.0,25.200000000000003,245.0,302.0,436.0,0.11,1013.0,66.0,2.7,80.00617177556343 +2020-09-10 16:30:00-05:00,2020,9,10,16,30,28.9,25.3,196.0,153.0,263.0,0.11,1013.0,59.0,2.7,80.95109343908808 +2020-09-10 17:30:00-05:00,2020,9,10,17,30,28.700000000000003,25.3,90.0,201.0,134.0,0.11,1013.0,52.0,2.7,81.89505179753375 +2020-09-10 18:30:00-05:00,2020,9,10,18,30,28.6,25.3,4.0,8.0,4.0,0.11,1013.0,47.0,2.8000000000000003,82.37167807955662 +2020-09-10 19:30:00-05:00,2020,9,10,19,30,28.5,25.3,0.0,0.0,0.0,0.11,1014.0,44.0,2.9000000000000004,82.85143243314499 +2020-09-10 20:30:00-05:00,2020,9,10,20,30,28.5,25.3,0.0,0.0,0.0,0.11,1014.0,42.0,3.0,82.85143243314499 +2020-09-10 21:30:00-05:00,2020,9,10,21,30,28.4,25.3,0.0,0.0,0.0,0.11,1014.0,40.0,3.1,83.3343375837698 +2020-09-10 22:30:00-05:00,2020,9,10,22,30,28.200000000000003,25.4,0.0,0.0,0.0,0.11,1013.0,40.0,3.2,84.81254813644142 +2020-09-10 23:30:00-05:00,2020,9,10,23,30,28.1,25.5,0.0,0.0,0.0,0.11,1012.0,41.0,3.3000000000000003,85.81641145062478 +2020-09-11 00:30:00-05:00,2020,9,11,0,30,28.0,25.5,0.0,0.0,0.0,0.11,1012.0,43.0,3.4000000000000004,86.3180796551029 +2020-09-11 01:30:00-05:00,2020,9,11,1,30,27.8,25.6,0.0,0.0,0.0,0.11,1011.0,46.0,3.4000000000000004,87.8514596114882 +2020-09-11 02:30:00-05:00,2020,9,11,2,30,27.700000000000003,25.700000000000003,0.0,0.0,0.0,0.11,1011.0,50.0,3.4000000000000004,88.89203693489392 +2020-09-11 03:30:00-05:00,2020,9,11,3,30,27.6,25.700000000000003,0.0,0.0,0.0,0.11,1011.0,55.0,3.4000000000000004,89.41322612893127 +2020-09-11 04:30:00-05:00,2020,9,11,4,30,27.5,25.8,0.0,0.0,0.0,0.11,1011.0,59.0,3.3000000000000003,90.47268437969822 +2020-09-11 05:30:00-05:00,2020,9,11,5,30,27.5,25.8,0.0,0.0,0.0,0.11,1011.0,65.0,3.2,90.47268437969822 +2020-09-11 06:30:00-05:00,2020,9,11,6,30,27.700000000000003,25.8,24.0,19.0,26.0,0.11,1011.0,70.0,3.2,89.42064238981021 +2020-09-11 07:30:00-05:00,2020,9,11,7,30,28.0,26.0,111.0,301.0,205.0,0.11,1011.0,74.0,3.1,88.91528354225218 +2020-09-11 08:30:00-05:00,2020,9,11,8,30,28.200000000000003,25.9,206.0,140.0,279.0,0.11,1011.0,78.0,2.8000000000000003,87.36638064071761 +2020-09-11 09:30:00-05:00,2020,9,11,9,30,28.3,25.8,243.0,372.0,504.0,0.11,1011.0,81.0,2.5,86.34628218137539 +2020-09-11 10:30:00-05:00,2020,9,11,10,30,28.6,25.6,321.0,319.0,587.0,0.11,1011.0,82.0,2.4000000000000004,83.85326363500616 +2020-09-11 11:30:00-05:00,2020,9,11,11,30,28.8,25.6,332.0,395.0,692.0,0.11,1011.0,81.0,2.6,82.8860266912129 +2020-09-11 12:30:00-05:00,2020,9,11,12,30,28.8,25.5,329.0,424.0,723.0,0.11,1010.0,80.0,2.7,82.39532246726411 +2020-09-11 13:30:00-05:00,2020,9,11,13,30,28.8,25.5,302.0,455.0,705.0,0.11,1010.0,79.0,2.8000000000000003,82.39532246726411 +2020-09-11 14:30:00-05:00,2020,9,11,14,30,28.8,25.5,299.0,327.0,555.0,0.11,1009.0,79.0,3.0,82.39532246726411 +2020-09-11 15:30:00-05:00,2020,9,11,15,30,28.6,25.5,238.0,246.0,393.0,0.11,1008.0,80.0,3.3000000000000003,83.35683314726086 +2020-09-11 16:30:00-05:00,2020,9,11,16,30,28.3,25.6,176.0,187.0,258.0,0.11,1008.0,78.0,3.4000000000000004,85.3280598548014 +2020-09-11 17:30:00-05:00,2020,9,11,17,30,28.1,25.6,96.0,60.0,109.0,0.11,1008.0,76.0,3.3000000000000003,86.32748992355242 +2020-09-11 18:30:00-05:00,2020,9,11,18,30,27.9,25.6,3.0,0.0,3.0,0.11,1008.0,74.0,3.1,87.34012761551156 +2020-09-11 19:30:00-05:00,2020,9,11,19,30,27.8,25.5,0.0,0.0,0.0,0.11,1008.0,72.0,3.0,87.33135888966177 +2020-09-11 20:30:00-05:00,2020,9,11,20,30,27.700000000000003,25.5,0.0,0.0,0.0,0.11,1009.0,73.0,2.8000000000000003,87.84301834473253 +2020-09-11 21:30:00-05:00,2020,9,11,21,30,27.700000000000003,25.6,0.0,0.0,0.0,0.11,1009.0,82.0,2.7,88.36616624749482 +2020-09-11 22:30:00-05:00,2020,9,11,22,30,27.5,25.700000000000003,0.0,0.0,0.0,0.11,1008.0,97.0,2.8000000000000003,89.93785983353193 +2020-09-11 23:30:00-05:00,2020,9,11,23,30,27.4,25.700000000000003,0.0,0.0,0.0,0.11,1008.0,111.0,3.3000000000000003,90.4659632411294 +2020-09-12 00:30:00-05:00,2020,9,12,0,30,27.3,25.8,0.0,0.0,0.0,0.11,1007.0,117.0,4.2,91.53868791580214 +2020-09-12 01:30:00-05:00,2020,9,12,1,30,27.3,25.9,0.0,0.0,0.0,0.11,1007.0,119.0,5.2,92.08262594008558 +2020-09-12 02:30:00-05:00,2020,9,12,2,30,27.3,26.0,0.0,0.0,0.0,0.11,1006.0,121.0,5.9,92.62938817134898 +2020-09-12 03:30:00-05:00,2020,9,12,3,30,27.3,26.0,0.0,0.0,0.0,0.11,1006.0,122.0,6.5,92.62938817134898 +2020-09-12 04:30:00-05:00,2020,9,12,4,30,27.3,26.0,0.0,0.0,0.0,0.11,1006.0,123.0,7.2,92.62938817134898 +2020-09-12 05:30:00-05:00,2020,9,12,5,30,27.3,25.9,0.0,0.0,0.0,0.11,1006.0,124.0,8.0,92.08262594008558 +2020-09-12 06:30:00-05:00,2020,9,12,6,30,27.3,25.8,14.0,0.0,14.0,0.11,1007.0,126.0,8.700000000000001,91.53868791580214 +2020-09-12 07:30:00-05:00,2020,9,12,7,30,27.3,25.5,13.0,0.0,13.0,0.11,1007.0,129.0,9.3,89.92369577023814 +2020-09-12 08:30:00-05:00,2020,9,12,8,30,27.3,25.3,125.0,4.0,127.0,0.11,1008.0,132.0,9.8,88.86093005266055 +2020-09-12 09:30:00-05:00,2020,9,12,9,30,27.4,25.200000000000003,141.0,2.0,142.0,0.11,1008.0,134.0,9.9,87.81764299095894 +2020-09-12 10:30:00-05:00,2020,9,12,10,30,27.5,25.200000000000003,188.0,2.0,190.0,0.11,1008.0,134.0,10.0,87.30499939717893 +2020-09-12 11:30:00-05:00,2020,9,12,11,30,27.6,25.4,324.0,12.0,335.0,0.11,1008.0,133.0,9.9,87.83456849327322 +2020-09-12 12:30:00-05:00,2020,9,12,12,30,27.700000000000003,25.700000000000003,322.0,8.0,329.0,0.11,1007.0,131.0,9.9,88.89203693489392 +2020-09-12 13:30:00-05:00,2020,9,12,13,30,27.6,26.0,174.0,1.0,175.0,0.11,1006.0,131.0,10.2,91.01664123615788 +2020-09-12 14:30:00-05:00,2020,9,12,14,30,27.5,26.200000000000003,151.0,1.0,152.0,0.11,1006.0,132.0,10.5,92.63989595857034 +2020-09-12 15:30:00-05:00,2020,9,12,15,30,27.3,26.1,107.0,1.0,108.0,0.11,1005.0,134.0,10.5,93.17898700599295 +2020-09-12 16:30:00-05:00,2020,9,12,16,30,27.0,25.700000000000003,48.0,0.0,48.0,0.11,1006.0,137.0,10.4,92.61358483153343 +2020-09-12 17:30:00-05:00,2020,9,12,17,30,26.8,25.4,19.0,0.0,19.0,0.11,1006.0,141.0,10.4,92.05438311051279 +2020-09-12 18:30:00-05:00,2020,9,12,18,30,26.5,25.200000000000003,1.0,0.0,1.0,0.11,1007.0,145.0,10.2,92.58713432604608 +2020-09-12 19:30:00-05:00,2020,9,12,19,30,26.4,24.8,0.0,0.0,0.0,0.12,1009.0,151.0,9.5,90.93996352399876 +2020-09-12 20:30:00-05:00,2020,9,12,20,30,26.4,24.4,0.0,0.0,0.0,0.12,1010.0,158.0,8.4,88.79047072518293 +2020-09-12 21:30:00-05:00,2020,9,12,21,30,26.6,24.1,0.0,0.0,0.0,0.12,1010.0,163.0,7.2,86.18533883635469 +2020-09-12 22:30:00-05:00,2020,9,12,22,30,27.0,24.0,0.0,0.0,0.0,0.12,1010.0,161.0,6.300000000000001,83.6769551752209 +2020-09-12 23:30:00-05:00,2020,9,12,23,30,27.4,24.0,0.0,0.0,0.0,0.12,1010.0,154.0,5.800000000000001,81.73656558895809 +2020-09-13 00:30:00-05:00,2020,9,13,0,30,27.700000000000003,24.1,0.0,0.0,0.0,0.12,1009.0,143.0,5.4,80.79822904504796 +2020-09-13 01:30:00-05:00,2020,9,13,1,30,27.8,24.5,0.0,0.0,0.0,0.12,1009.0,132.0,5.2,82.27664141694791 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10:30:00-05:00,2020,9,13,10,30,28.8,26.3,369.0,114.0,464.0,0.12,1012.0,156.0,3.9,86.3930984317788 +2020-09-13 11:30:00-05:00,2020,9,13,11,30,28.8,25.9,399.0,54.0,448.0,0.12,1012.0,161.0,3.7,84.37350792778383 +2020-09-13 12:30:00-05:00,2020,9,13,12,30,28.9,25.6,427.0,136.0,553.0,0.12,1011.0,164.0,3.4000000000000004,82.40712752183873 +2020-09-13 13:30:00-05:00,2020,9,13,13,30,29.0,25.4,385.0,213.0,572.0,0.12,1011.0,166.0,3.0,80.96375323840603 +2020-09-13 14:30:00-05:00,2020,9,13,14,30,29.0,25.3,302.0,279.0,519.0,0.12,1010.0,167.0,2.7,80.48371679996085 +2020-09-13 15:30:00-05:00,2020,9,13,15,30,29.0,25.200000000000003,266.0,190.0,384.0,0.12,1010.0,167.0,2.5,80.00617177556343 +2020-09-13 16:30:00-05:00,2020,9,13,16,30,28.9,25.3,186.0,81.0,221.0,0.12,1010.0,165.0,2.4000000000000004,80.95109343908808 +2020-09-13 17:30:00-05:00,2020,9,13,17,30,28.700000000000003,25.5,70.0,7.0,71.0,0.12,1010.0,162.0,2.4000000000000004,82.87450647393744 +2020-09-13 18:30:00-05:00,2020,9,13,18,30,28.5,25.6,1.0,0.0,1.0,0.12,1011.0,161.0,2.5,84.34164713318661 +2020-09-13 19:30:00-05:00,2020,9,13,19,30,28.4,25.8,0.0,0.0,0.0,0.12,1011.0,160.0,3.0,85.84555570406624 +2020-09-13 20:30:00-05:00,2020,9,13,20,30,28.4,26.0,0.0,0.0,0.0,0.12,1012.0,161.0,3.5,86.86842124513788 +2020-09-13 21:30:00-05:00,2020,9,13,21,30,28.3,26.200000000000003,0.0,0.0,0.0,0.12,1012.0,162.0,4.0,88.4146486040039 +2020-09-13 22:30:00-05:00,2020,9,13,22,30,28.3,26.200000000000003,0.0,0.0,0.0,0.12,1012.0,160.0,4.1000000000000005,88.4146486040039 +2020-09-13 23:30:00-05:00,2020,9,13,23,30,28.200000000000003,26.200000000000003,0.0,0.0,0.0,0.12,1012.0,156.0,3.9,88.93074165144274 +2020-09-14 00:30:00-05:00,2020,9,14,0,30,28.1,26.1,0.0,0.0,0.0,0.12,1012.0,154.0,3.8,88.92301655272077 +2020-09-14 01:30:00-05:00,2020,9,14,1,30,28.0,26.0,0.0,0.0,0.0,0.12,1012.0,155.0,3.7,88.91528354225218 +2020-09-14 02:30:00-05:00,2020,9,14,2,30,27.9,25.9,0.0,0.0,0.0,0.12,1012.0,151.0,3.6,88.90754260949821 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11:30:00-05:00,2020,9,14,11,30,29.1,25.5,340.0,396.0,698.0,0.12,1014.0,136.0,3.2,80.97640096728918 +2020-09-14 12:30:00-05:00,2020,9,14,12,30,29.200000000000003,25.4,329.0,445.0,739.0,0.12,1014.0,138.0,3.1,80.03257395443063 +2020-09-14 13:30:00-05:00,2020,9,14,13,30,29.200000000000003,25.3,302.0,466.0,710.0,0.12,1014.0,141.0,2.9000000000000004,79.55805850493611 +2020-09-14 14:30:00-05:00,2020,9,14,14,30,29.200000000000003,25.200000000000003,225.0,560.0,658.0,0.12,1013.0,143.0,2.6,79.08600581526994 +2020-09-14 15:30:00-05:00,2020,9,14,15,30,29.1,25.1,245.0,288.0,423.0,0.12,1013.0,143.0,2.3000000000000003,79.07226544009794 +2020-09-14 16:30:00-05:00,2020,9,14,16,30,28.9,25.200000000000003,198.0,162.0,267.0,0.12,1013.0,141.0,2.0,80.4707752650722 +2020-09-14 17:30:00-05:00,2020,9,14,17,30,28.6,25.1,84.0,133.0,111.0,0.12,1014.0,138.0,1.8,81.39672247879624 +2020-09-14 18:30:00-05:00,2020,9,14,18,30,28.4,25.200000000000003,2.0,0.0,2.0,0.12,1014.0,135.0,2.0,82.83987858191361 +2020-09-14 19:30:00-05:00,2020,9,14,19,30,28.3,25.3,0.0,0.0,0.0,0.12,1015.0,130.0,2.4000000000000004,83.82041643729903 +2020-09-14 20:30:00-05:00,2020,9,14,20,30,28.200000000000003,25.4,0.0,0.0,0.0,0.12,1015.0,126.0,2.8000000000000003,84.81254813644142 +2020-09-14 21:30:00-05:00,2020,9,14,21,30,28.200000000000003,25.5,0.0,0.0,0.0,0.12,1015.0,124.0,3.3000000000000003,85.31802555668312 +2020-09-14 22:30:00-05:00,2020,9,14,22,30,28.1,25.5,0.0,0.0,0.0,0.12,1015.0,123.0,3.5,85.81641145062478 +2020-09-14 23:30:00-05:00,2020,9,14,23,30,28.1,25.4,0.0,0.0,0.0,0.12,1014.0,123.0,3.6,85.30798128019565 +2020-09-15 00:30:00-05:00,2020,9,15,0,30,28.1,25.4,0.0,0.0,0.0,0.12,1014.0,123.0,3.6,85.30798128019565 +2020-09-15 01:30:00-05:00,2020,9,15,1,30,28.0,25.4,0.0,0.0,0.0,0.12,1014.0,123.0,3.6,85.80667728802291 +2020-09-15 02:30:00-05:00,2020,9,15,2,30,28.0,25.4,0.0,0.0,0.0,0.12,1014.0,124.0,3.6,85.80667728802291 +2020-09-15 03:30:00-05:00,2020,9,15,3,30,28.0,25.5,0.0,0.0,0.0,0.12,1014.0,125.0,3.5,86.3180796551029 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12:30:00-05:00,2020,9,15,12,30,29.4,25.6,199.0,719.0,859.0,0.12,1014.0,136.0,3.0,80.05892628050286 +2020-09-15 13:30:00-05:00,2020,9,15,13,30,29.4,25.5,137.0,826.0,858.0,0.12,1014.0,135.0,2.8000000000000003,79.58495913719892 +2020-09-15 14:30:00-05:00,2020,9,15,14,30,29.4,25.4,130.0,792.0,739.0,0.12,1013.0,132.0,2.5,79.11344799319119 +2020-09-15 15:30:00-05:00,2020,9,15,15,30,29.3,25.3,117.0,728.0,565.0,0.12,1013.0,128.0,2.3000000000000003,79.09973332795721 +2020-09-15 16:30:00-05:00,2020,9,15,16,30,29.1,25.4,129.0,488.0,334.0,0.12,1014.0,125.0,2.2,80.49664605038467 +2020-09-15 17:30:00-05:00,2020,9,15,17,30,28.9,25.3,69.0,304.0,129.0,0.12,1014.0,124.0,2.2,80.95109343908808 +2020-09-15 18:30:00-05:00,2020,9,15,18,30,28.700000000000003,25.4,1.0,0.0,1.0,0.12,1014.0,122.0,2.4000000000000004,82.38350599117993 +2020-09-15 19:30:00-05:00,2020,9,15,19,30,28.6,25.4,0.0,0.0,0.0,0.11,1014.0,121.0,2.7,82.86297505919713 +2020-09-15 20:30:00-05:00,2020,9,15,20,30,28.5,25.5,0.0,0.0,0.0,0.11,1015.0,122.0,3.0,83.84232530349826 +2020-09-15 21:30:00-05:00,2020,9,15,21,30,28.5,25.6,0.0,0.0,0.0,0.11,1015.0,125.0,3.3000000000000003,84.34164713318661 +2020-09-15 22:30:00-05:00,2020,9,15,22,30,28.4,25.6,0.0,0.0,0.0,0.11,1014.0,127.0,3.6,84.8332381004962 +2020-09-15 23:30:00-05:00,2020,9,15,23,30,28.200000000000003,25.6,0.0,0.0,0.0,0.11,1014.0,130.0,3.8,85.82613589918793 +2020-09-16 00:30:00-05:00,2020,9,16,0,30,28.1,25.6,0.0,0.0,0.0,0.11,1013.0,132.0,4.0,86.32748992355242 +2020-09-16 01:30:00-05:00,2020,9,16,1,30,28.0,25.6,0.0,0.0,0.0,0.11,1013.0,133.0,4.2,86.83214580620918 +2020-09-16 02:30:00-05:00,2020,9,16,2,30,27.9,25.5,0.0,0.0,0.0,0.11,1012.0,133.0,4.3,86.82305409598065 +2020-09-16 03:30:00-05:00,2020,9,16,3,30,27.9,25.5,0.0,0.0,0.0,0.11,1012.0,132.0,4.3,86.82305409598065 +2020-09-16 04:30:00-05:00,2020,9,16,4,30,27.9,25.5,0.0,0.0,0.0,0.11,1012.0,132.0,4.3,86.82305409598065 +2020-09-16 05:30:00-05:00,2020,9,16,5,30,28.0,25.5,0.0,0.0,0.0,0.11,1012.0,133.0,4.2,86.3180796551029 +2020-09-16 06:30:00-05:00,2020,9,16,6,30,28.3,25.5,31.0,173.0,44.0,0.11,1013.0,135.0,4.1000000000000005,84.82289823632867 +2020-09-16 07:30:00-05:00,2020,9,16,7,30,28.700000000000003,25.700000000000003,85.0,430.0,217.0,0.11,1013.0,140.0,4.0,83.86419125002784 +2020-09-16 08:30:00-05:00,2020,9,16,8,30,29.0,25.5,149.0,475.0,394.0,0.11,1014.0,145.0,3.9,81.44629208459418 +2020-09-16 09:30:00-05:00,2020,9,16,9,30,29.200000000000003,25.4,112.0,785.0,656.0,0.11,1014.0,150.0,3.8,80.03257395443063 +2020-09-16 10:30:00-05:00,2020,9,16,10,30,29.4,25.3,119.0,832.0,805.0,0.11,1014.0,154.0,3.8,78.64438206814766 +2020-09-16 11:30:00-05:00,2020,9,16,11,30,29.6,25.3,323.0,464.0,740.0,0.11,1013.0,157.0,3.8,77.74251612148691 +2020-09-16 12:30:00-05:00,2020,9,16,12,30,29.700000000000003,25.200000000000003,378.0,392.0,737.0,0.11,1013.0,159.0,3.7,76.83732859422055 +2020-09-16 13:30:00-05:00,2020,9,16,13,30,29.700000000000003,25.1,314.0,459.0,713.0,0.11,1012.0,160.0,3.6,76.38108014109501 +2020-09-16 14:30:00-05:00,2020,9,16,14,30,29.6,25.0,110.0,822.0,740.0,0.11,1012.0,160.0,3.5,76.36585265495461 +2020-09-16 15:30:00-05:00,2020,9,16,15,30,29.4,25.0,102.0,759.0,566.0,0.11,1012.0,159.0,3.3000000000000003,77.25174837113636 +2020-09-16 16:30:00-05:00,2020,9,16,16,30,29.200000000000003,25.0,85.0,646.0,354.0,0.11,1012.0,156.0,3.1,78.1492454374399 +2020-09-16 17:30:00-05:00,2020,9,16,17,30,28.9,25.200000000000003,60.0,365.0,131.0,0.11,1013.0,155.0,3.0,80.4707752650722 +2020-09-16 18:30:00-05:00,2020,9,16,18,30,28.700000000000003,25.200000000000003,2.0,8.0,2.0,0.11,1013.0,153.0,3.0,81.40913270652204 +2020-09-16 19:30:00-05:00,2020,9,16,19,30,28.6,25.0,0.0,0.0,0.0,0.11,1013.0,151.0,3.1,80.91304147314835 +2020-09-16 20:30:00-05:00,2020,9,16,20,30,28.6,24.9,0.0,0.0,0.0,0.11,1013.0,148.0,3.2,80.43187680667027 +2020-09-16 21:30:00-05:00,2020,9,16,21,30,28.5,24.8,0.0,0.0,0.0,0.11,1013.0,146.0,3.4000000000000004,80.41888598687427 +2020-09-16 22:30:00-05:00,2020,9,16,22,30,28.4,24.8,0.0,0.0,0.0,0.11,1013.0,144.0,3.7,80.88761287680352 +2020-09-16 23:30:00-05:00,2020,9,16,23,30,28.3,24.8,0.0,0.0,0.0,0.11,1012.0,144.0,4.0,81.3594202886324 +2020-09-17 00:30:00-05:00,2020,9,17,0,30,28.200000000000003,24.700000000000003,0.0,0.0,0.0,0.11,1012.0,145.0,4.2,81.3469623413801 +2020-09-17 01:30:00-05:00,2020,9,17,1,30,28.0,24.700000000000003,0.0,0.0,0.0,0.11,1011.0,148.0,4.3,82.30046967529572 +2020-09-17 02:30:00-05:00,2020,9,17,2,30,27.9,24.700000000000003,0.0,0.0,0.0,0.11,1011.0,150.0,4.3,82.78194046130388 +2020-09-17 03:30:00-05:00,2020,9,17,3,30,27.8,24.6,0.0,0.0,0.0,0.11,1011.0,152.0,4.2,82.77031896665547 +2020-09-17 04:30:00-05:00,2020,9,17,4,30,27.8,24.5,0.0,0.0,0.0,0.11,1011.0,155.0,4.0,82.27664141694791 +2020-09-17 05:30:00-05:00,2020,9,17,5,30,27.9,24.5,0.0,0.0,0.0,0.11,1011.0,157.0,3.9,81.79775717912155 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14:30:00-05:00,2020,9,17,14,30,30.200000000000003,24.4,235.0,523.0,634.0,0.11,1010.0,196.0,4.0,71.17785036682086 +2020-09-17 15:30:00-05:00,2020,9,17,15,30,30.200000000000003,24.3,204.0,465.0,486.0,0.11,1010.0,196.0,3.9,70.75268183697054 +2020-09-17 16:30:00-05:00,2020,9,17,16,30,30.0,24.3,169.0,302.0,293.0,0.11,1010.0,195.0,3.7,71.56983823040939 +2020-09-17 17:30:00-05:00,2020,9,17,17,30,29.6,24.6,85.0,119.0,108.0,0.11,1010.0,195.0,3.5,74.56350188322439 +2020-09-17 18:30:00-05:00,2020,9,17,18,30,29.3,24.8,1.0,0.0,1.0,0.11,1011.0,196.0,3.3000000000000003,76.77733805297936 +2020-09-17 19:30:00-05:00,2020,9,17,19,30,29.1,24.9,0.0,0.0,0.0,0.11,1011.0,199.0,3.2,78.13497299426331 +2020-09-17 20:30:00-05:00,2020,9,17,20,30,29.0,25.0,0.0,0.0,0.0,0.11,1011.0,202.0,3.1,79.0585121873896 +2020-09-17 21:30:00-05:00,2020,9,17,21,30,28.9,25.1,0.0,0.0,0.0,0.11,1011.0,205.0,3.0,79.99295195434269 +2020-09-17 22:30:00-05:00,2020,9,17,22,30,28.700000000000003,25.200000000000003,0.0,0.0,0.0,0.11,1011.0,208.0,2.9000000000000004,81.40913270652204 +2020-09-17 23:30:00-05:00,2020,9,17,23,30,28.6,25.3,0.0,0.0,0.0,0.11,1011.0,210.0,2.8000000000000003,82.37167807955662 +2020-09-18 00:30:00-05:00,2020,9,18,0,30,28.4,25.4,0.0,0.0,0.0,0.11,1011.0,211.0,2.7,83.8313762420779 +2020-09-18 01:30:00-05:00,2020,9,18,1,30,28.3,25.4,0.0,0.0,0.0,0.11,1011.0,209.0,2.7,84.32035426044355 +2020-09-18 02:30:00-05:00,2020,9,18,2,30,28.200000000000003,25.5,0.0,0.0,0.0,0.11,1011.0,204.0,2.7,85.31802555668312 +2020-09-18 03:30:00-05:00,2020,9,18,3,30,28.1,25.5,0.0,0.0,0.0,0.11,1010.0,198.0,2.9000000000000004,85.81641145062478 +2020-09-18 04:30:00-05:00,2020,9,18,4,30,28.0,25.6,0.0,0.0,0.0,0.11,1011.0,192.0,3.1,86.83214580620918 +2020-09-18 05:30:00-05:00,2020,9,18,5,30,28.0,25.6,0.0,0.0,0.0,0.11,1011.0,188.0,3.3000000000000003,86.83214580620918 +2020-09-18 06:30:00-05:00,2020,9,18,6,30,28.4,25.700000000000003,29.0,13.0,30.0,0.11,1011.0,189.0,3.6,85.33808418728074 +2020-09-18 07:30:00-05:00,2020,9,18,7,30,28.8,26.0,72.0,544.0,237.0,0.11,1012.0,191.0,3.8,84.87449545917909 +2020-09-18 08:30:00-05:00,2020,9,18,8,30,29.1,25.9,92.0,727.0,465.0,0.11,1012.0,194.0,3.9,82.9205202964216 +2020-09-18 09:30:00-05:00,2020,9,18,9,30,29.4,25.9,106.0,806.0,662.0,0.11,1012.0,198.0,3.9,81.49567207971836 +2020-09-18 10:30:00-05:00,2020,9,18,10,30,29.8,25.8,117.0,846.0,810.0,0.11,1012.0,202.0,3.9,79.16817847952795 +2020-09-18 11:30:00-05:00,2020,9,18,11,30,30.200000000000003,25.700000000000003,124.0,863.0,896.0,0.11,1012.0,204.0,3.8,76.91200889831605 +2020-09-18 12:30:00-05:00,2020,9,18,12,30,30.4,25.6,130.0,858.0,910.0,0.11,1011.0,206.0,3.7,75.5853235714897 +2020-09-18 13:30:00-05:00,2020,9,18,13,30,30.6,25.5,132.0,835.0,853.0,0.11,1011.0,209.0,3.6,74.28243957799879 +2020-09-18 14:30:00-05:00,2020,9,18,14,30,30.6,25.4,190.0,648.0,682.0,0.11,1010.0,212.0,3.4000000000000004,73.84234388096228 +2020-09-18 15:30:00-05:00,2020,9,18,15,30,30.6,25.3,172.0,558.0,509.0,0.11,1010.0,218.0,3.1,73.4045304849024 +2020-09-18 16:30:00-05:00,2020,9,18,16,30,30.4,25.3,178.0,126.0,229.0,0.11,1010.0,226.0,2.9000000000000004,74.24982249791255 +2020-09-18 17:30:00-05:00,2020,9,18,17,30,30.200000000000003,25.3,81.0,12.0,83.0,0.11,1010.0,235.0,2.7,75.10610737068163 +2020-09-18 18:30:00-05:00,2020,9,18,18,30,29.9,25.4,0.0,0.0,0.0,0.11,1011.0,246.0,2.6,76.86724167307942 +2020-09-18 19:30:00-05:00,2020,9,18,19,30,29.700000000000003,25.4,0.0,0.0,0.0,0.11,1011.0,259.0,2.6,77.75698266444124 +2020-09-18 20:30:00-05:00,2020,9,18,20,30,29.6,25.4,0.0,0.0,0.0,0.11,1012.0,273.0,2.7,78.20620296447258 +2020-09-18 21:30:00-05:00,2020,9,18,21,30,29.3,25.6,0.0,0.0,0.0,0.11,1012.0,286.0,2.7,80.52246775647552 +2020-09-18 22:30:00-05:00,2020,9,18,22,30,29.0,25.700000000000003,0.0,0.0,0.0,0.11,1012.0,297.0,2.7,82.41892116891317 +2020-09-18 23:30:00-05:00,2020,9,18,23,30,28.700000000000003,25.8,0.0,0.0,0.0,0.11,1011.0,306.0,2.7,84.36289811394386 +2020-09-19 00:30:00-05:00,2020,9,19,0,30,28.5,25.9,0.0,0.0,0.0,0.11,1011.0,314.0,2.5,85.8552510852675 +2020-09-19 01:30:00-05:00,2020,9,19,1,30,28.200000000000003,25.9,0.0,0.0,0.0,0.11,1011.0,319.0,2.2,87.36638064071761 +2020-09-19 02:30:00-05:00,2020,9,19,2,30,28.1,25.8,0.0,0.0,0.0,0.11,1010.0,323.0,1.8,87.35763847945336 +2020-09-19 03:30:00-05:00,2020,9,19,3,30,28.0,25.6,0.0,0.0,0.0,0.11,1010.0,323.0,1.3,86.83214580620918 +2020-09-19 04:30:00-05:00,2020,9,19,4,30,28.0,25.4,0.0,0.0,0.0,0.11,1010.0,317.0,0.9,85.80667728802291 +2020-09-19 05:30:00-05:00,2020,9,19,5,30,28.0,25.3,0.0,0.0,0.0,0.11,1011.0,300.0,0.8,85.29792701258955 +2020-09-19 06:30:00-05:00,2020,9,19,6,30,28.200000000000003,25.3,28.0,198.0,43.0,0.11,1011.0,276.0,0.9,84.30969208154741 +2020-09-19 07:30:00-05:00,2020,9,19,7,30,28.700000000000003,25.5,69.0,566.0,240.0,0.11,1011.0,257.0,1.2000000000000002,82.87450647393744 +2020-09-19 08:30:00-05:00,2020,9,19,8,30,29.200000000000003,25.3,92.0,723.0,462.0,0.11,1012.0,252.0,1.5,79.55805850493611 +2020-09-19 09:30:00-05:00,2020,9,19,9,30,29.700000000000003,25.3,106.0,801.0,657.0,0.11,1012.0,258.0,1.8,77.29595926162824 +2020-09-19 10:30:00-05:00,2020,9,19,10,30,30.200000000000003,25.3,156.0,738.0,759.0,0.11,1012.0,263.0,2.0,75.10610737068163 +2020-09-19 11:30:00-05:00,2020,9,19,11,30,30.6,25.1,151.0,811.0,875.0,0.11,1012.0,269.0,2.1,72.53571052413479 +2020-09-19 12:30:00-05:00,2020,9,19,12,30,30.9,25.0,127.0,858.0,905.0,0.11,1011.0,277.0,2.0,70.87911071211867 +2020-09-19 13:30:00-05:00,2020,9,19,13,30,31.200000000000003,24.9,132.0,832.0,848.0,0.11,1011.0,287.0,1.8,69.2626346656465 +2020-09-19 14:30:00-05:00,2020,9,19,14,30,31.200000000000003,24.8,127.0,795.0,727.0,0.11,1010.0,304.0,1.6,68.85044469900656 +2020-09-19 15:30:00-05:00,2020,9,19,15,30,31.200000000000003,24.8,115.0,726.0,550.0,0.11,1010.0,325.0,1.4,68.85044469900656 +2020-09-19 16:30:00-05:00,2020,9,19,16,30,30.9,24.9,95.0,598.0,337.0,0.11,1010.0,344.0,1.3,70.45761470795009 +2020-09-19 17:30:00-05:00,2020,9,19,17,30,30.5,25.0,59.0,341.0,121.0,0.11,1010.0,180.0,1.3,72.51850566612495 +2020-09-19 18:30:00-05:00,2020,9,19,18,30,30.1,25.1,0.0,0.0,0.0,0.11,1011.0,17.0,1.2000000000000002,74.64434473635608 +2020-09-19 19:30:00-05:00,2020,9,19,19,30,29.8,25.3,0.0,0.0,0.0,0.11,1011.0,39.0,1.1,76.85229197010862 +2020-09-19 20:30:00-05:00,2020,9,19,20,30,29.6,25.4,0.0,0.0,0.0,0.11,1011.0,65.0,1.1,78.20620296447258 +2020-09-19 21:30:00-05:00,2020,9,19,21,30,29.4,25.5,0.0,0.0,0.0,0.11,1011.0,90.0,1.1,79.58495913719892 +2020-09-19 22:30:00-05:00,2020,9,19,22,30,29.1,25.6,0.0,0.0,0.0,0.11,1011.0,108.0,1.2000000000000002,81.45865482351515 +2020-09-19 23:30:00-05:00,2020,9,19,23,30,28.9,25.6,0.0,0.0,0.0,0.11,1011.0,124.0,1.2000000000000002,82.40712752183873 +2020-09-20 00:30:00-05:00,2020,9,20,0,30,28.8,25.6,0.0,0.0,0.0,0.11,1010.0,145.0,1.3,82.8860266912129 +2020-09-20 01:30:00-05:00,2020,9,20,1,30,28.700000000000003,25.5,0.0,0.0,0.0,0.11,1010.0,177.0,1.3,82.87450647393744 +2020-09-20 02:30:00-05:00,2020,9,20,2,30,28.5,25.5,0.0,0.0,0.0,0.11,1010.0,220.0,1.4,83.84232530349826 +2020-09-20 03:30:00-05:00,2020,9,20,3,30,28.4,25.6,0.0,0.0,0.0,0.11,1009.0,258.0,1.5,84.8332381004962 +2020-09-20 04:30:00-05:00,2020,9,20,4,30,28.3,25.6,0.0,0.0,0.0,0.11,1010.0,283.0,1.4,85.3280598548014 +2020-09-20 05:30:00-05:00,2020,9,20,5,30,28.3,25.5,0.0,0.0,0.0,0.11,1010.0,302.0,1.3,84.82289823632867 +2020-09-20 06:30:00-05:00,2020,9,20,6,30,28.4,25.6,27.0,165.0,39.0,0.11,1010.0,324.0,1.0,84.8332381004962 +2020-09-20 07:30:00-05:00,2020,9,20,7,30,28.700000000000003,25.8,88.0,442.0,221.0,0.11,1011.0,231.0,0.9,84.36289811394386 +2020-09-20 08:30:00-05:00,2020,9,20,8,30,29.1,25.6,101.0,695.0,455.0,0.11,1011.0,137.0,1.1,81.45865482351515 +2020-09-20 09:30:00-05:00,2020,9,20,9,30,29.5,25.700000000000003,116.0,777.0,649.0,0.11,1012.0,153.0,1.6,80.07208378565453 +2020-09-20 10:30:00-05:00,2020,9,20,10,30,29.9,25.700000000000003,127.0,822.0,797.0,0.11,1012.0,158.0,2.0,78.24878267772924 +2020-09-20 11:30:00-05:00,2020,9,20,11,30,30.200000000000003,25.8,164.0,772.0,851.0,0.11,1012.0,160.0,2.2,77.36937390933494 +2020-09-20 12:30:00-05:00,2020,9,20,12,30,30.3,25.700000000000003,314.0,457.0,727.0,0.11,1011.0,158.0,2.2,76.47215468970488 +2020-09-20 13:30:00-05:00,2020,9,20,13,30,30.4,25.700000000000003,156.0,778.0,823.0,0.11,1011.0,148.0,2.1,76.03513493880077 +2020-09-20 14:30:00-05:00,2020,9,20,14,30,30.3,25.5,270.0,400.0,571.0,0.11,1010.0,133.0,2.0,75.56970364160983 +2020-09-20 15:30:00-05:00,2020,9,20,15,30,30.200000000000003,25.5,106.0,746.0,550.0,0.11,1010.0,120.0,2.1,76.00436711258348 +2020-09-20 16:30:00-05:00,2020,9,20,16,30,30.1,25.5,86.0,628.0,337.0,0.11,1010.0,113.0,2.0,76.44185171209942 +2020-09-20 17:30:00-05:00,2020,9,20,17,30,29.8,25.700000000000003,53.0,378.0,120.0,0.11,1011.0,106.0,1.8,78.70017992928679 +2020-09-20 18:30:00-05:00,2020,9,20,18,30,29.6,25.700000000000003,0.0,0.0,0.0,0.11,1011.0,97.0,1.7000000000000002,79.61180922614783 +2020-09-20 19:30:00-05:00,2020,9,20,19,30,29.5,25.700000000000003,0.0,0.0,0.0,0.11,1011.0,88.0,1.7000000000000002,80.07208378565453 +2020-09-20 20:30:00-05:00,2020,9,20,20,30,29.5,25.700000000000003,0.0,0.0,0.0,0.11,1012.0,76.0,1.7000000000000002,80.07208378565453 +2020-09-20 21:30:00-05:00,2020,9,20,21,30,29.3,25.8,0.0,0.0,0.0,0.11,1012.0,61.0,2.0,81.48334480676819 +2020-09-20 22:30:00-05:00,2020,9,20,22,30,29.1,26.0,0.0,0.0,0.0,0.11,1012.0,49.0,2.7,83.41288037229837 +2020-09-20 23:30:00-05:00,2020,9,20,23,30,28.9,26.3,0.0,0.0,0.0,0.11,1012.0,45.0,3.5,85.89393609127025 +2020-09-21 00:30:00-05:00,2020,9,21,0,30,28.700000000000003,26.5,0.0,0.0,0.0,0.11,1011.0,46.0,4.1000000000000005,87.92704655901689 +2020-09-21 01:30:00-05:00,2020,9,21,1,30,28.5,26.6,0.0,0.0,0.0,0.11,1011.0,50.0,4.4,89.47969871929588 +2020-09-21 02:30:00-05:00,2020,9,21,2,30,28.4,26.6,0.0,0.0,0.0,0.11,1011.0,54.0,4.6000000000000005,90.00123716611479 +2020-09-21 03:30:00-05:00,2020,9,21,3,30,28.4,26.700000000000003,0.0,0.0,0.0,0.11,1011.0,55.0,4.800000000000001,90.53286192976508 +2020-09-21 04:30:00-05:00,2020,9,21,4,30,28.3,26.6,0.0,0.0,0.0,0.11,1011.0,55.0,5.0,90.52620321788116 +2020-09-21 05:30:00-05:00,2020,9,21,5,30,28.3,26.6,0.0,0.0,0.0,0.11,1011.0,55.0,5.300000000000001,90.52620321788116 +2020-09-21 06:30:00-05:00,2020,9,21,6,30,28.3,26.700000000000003,8.0,0.0,8.0,0.11,1012.0,55.0,5.800000000000001,91.06092888282994 +2020-09-21 07:30:00-05:00,2020,9,21,7,30,28.3,26.8,78.0,3.0,79.0,0.11,1012.0,55.0,6.300000000000001,91.598413254495 +2020-09-21 08:30:00-05:00,2020,9,21,8,30,28.4,26.8,15.0,0.0,15.0,0.11,1013.0,55.0,6.800000000000001,91.06722940225121 +2020-09-21 09:30:00-05:00,2020,9,21,9,30,28.5,26.700000000000003,135.0,4.0,138.0,0.11,1013.0,55.0,7.300000000000001,90.00824282803235 +2020-09-21 10:30:00-05:00,2020,9,21,10,30,28.6,26.6,91.0,0.0,91.0,0.11,1014.0,55.0,7.7,88.96156313904507 +2020-09-21 11:30:00-05:00,2020,9,21,11,30,28.6,26.4,356.0,45.0,396.0,0.11,1013.0,55.0,8.1,87.91868204829599 +2020-09-21 12:30:00-05:00,2020,9,21,12,30,28.6,26.1,79.0,0.0,79.0,0.11,1013.0,54.0,8.3,86.37440005285019 +2020-09-21 13:30:00-05:00,2020,9,21,13,30,28.5,25.9,109.0,0.0,109.0,0.11,1013.0,53.0,8.5,85.8552510852675 +2020-09-21 14:30:00-05:00,2020,9,21,14,30,28.4,25.6,110.0,0.0,110.0,0.11,1012.0,51.0,8.6,84.8332381004962 +2020-09-21 15:30:00-05:00,2020,9,21,15,30,28.3,25.4,34.0,0.0,34.0,0.11,1012.0,49.0,8.8,84.32035426044355 +2020-09-21 16:30:00-05:00,2020,9,21,16,30,28.200000000000003,25.0,53.0,0.0,53.0,0.11,1012.0,47.0,9.1,82.81673714834355 +2020-09-21 17:30:00-05:00,2020,9,21,17,30,28.1,24.700000000000003,33.0,0.0,33.0,0.11,1013.0,46.0,9.3,81.82215123940509 +2020-09-21 18:30:00-05:00,2020,9,21,18,30,27.9,24.3,0.0,0.0,0.0,0.11,1013.0,46.0,9.4,80.8238283659092 +2020-09-21 19:30:00-05:00,2020,9,21,19,30,27.9,24.0,0.0,0.0,0.0,0.11,1013.0,48.0,9.4,79.38196414127542 +2020-09-21 20:30:00-05:00,2020,9,21,20,30,27.8,23.700000000000003,0.0,0.0,0.0,0.11,1014.0,51.0,9.4,78.41909711972703 +2020-09-21 21:30:00-05:00,2020,9,21,21,30,27.8,23.4,0.0,0.0,0.0,0.11,1014.0,53.0,9.2,77.0138849054383 +2020-09-21 22:30:00-05:00,2020,9,21,22,30,27.700000000000003,23.200000000000003,0.0,0.0,0.0,0.11,1014.0,56.0,9.0,76.53516588939115 +2020-09-21 23:30:00-05:00,2020,9,21,23,30,27.6,22.9,0.0,0.0,0.0,0.11,1014.0,58.0,8.6,75.59926777139484 +2020-09-22 00:30:00-05:00,2020,9,22,0,30,27.5,22.6,0.0,0.0,0.0,0.11,1014.0,60.0,8.3,74.67207563164956 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09:30:00-05:00,2020,9,22,9,30,28.4,21.6,268.0,415.0,551.0,0.11,1015.0,67.0,7.1000000000000005,66.66461434155383 +2020-09-22 10:30:00-05:00,2020,9,22,10,30,28.6,21.700000000000003,279.0,490.0,676.0,0.11,1015.0,66.0,7.0,66.29863890492842 +2020-09-22 11:30:00-05:00,2020,9,22,11,30,28.700000000000003,21.8,412.0,309.0,685.0,0.11,1015.0,65.0,6.800000000000001,66.31895453125186 +2020-09-22 12:30:00-05:00,2020,9,22,12,30,28.8,21.9,356.0,455.0,764.0,0.11,1014.0,63.0,6.800000000000001,66.33925366327628 +2020-09-22 13:30:00-05:00,2020,9,22,13,30,28.700000000000003,22.0,276.0,559.0,751.0,0.11,1013.0,61.0,6.7,67.13334475143147 +2020-09-22 14:30:00-05:00,2020,9,22,14,30,28.6,22.1,212.0,620.0,673.0,0.11,1013.0,59.0,6.7,67.9369186743415 +2020-09-22 15:30:00-05:00,2020,9,22,15,30,28.5,22.1,274.0,132.0,352.0,0.11,1013.0,56.0,6.7,68.33260118638059 +2020-09-22 16:30:00-05:00,2020,9,22,16,30,28.200000000000003,22.1,200.0,91.0,236.0,0.11,1013.0,54.0,6.6000000000000005,69.53531636044691 +2020-09-22 17:30:00-05:00,2020,9,22,17,30,27.9,22.1,85.0,46.0,93.0,0.11,1013.0,53.0,6.5,70.76193447459943 +2020-09-22 18:30:00-05:00,2020,9,22,18,30,27.6,22.1,0.0,0.0,0.0,0.11,1013.0,53.0,6.5,72.01298206177631 +2020-09-22 19:30:00-05:00,2020,9,22,19,30,27.4,22.1,0.0,0.0,0.0,0.1,1013.0,54.0,6.6000000000000005,72.86085146609881 +2020-09-22 20:30:00-05:00,2020,9,22,20,30,27.3,22.0,0.0,0.0,0.0,0.1,1014.0,56.0,6.5,72.8436129072422 +2020-09-22 21:30:00-05:00,2020,9,22,21,30,27.200000000000003,22.0,0.0,0.0,0.0,0.1,1014.0,59.0,6.5,73.27197620568609 +2020-09-22 22:30:00-05:00,2020,9,22,22,30,27.1,21.9,0.0,0.0,0.0,0.1,1014.0,61.0,6.4,73.25493893485287 +2020-09-22 23:30:00-05:00,2020,9,22,23,30,27.0,21.8,0.0,0.0,0.0,0.1,1014.0,62.0,6.4,73.23788651472465 +2020-09-23 00:30:00-05:00,2020,9,23,0,30,26.9,21.700000000000003,0.0,0.0,0.0,0.1,1013.0,64.0,6.300000000000001,73.22081892912894 +2020-09-23 01:30:00-05:00,2020,9,23,1,30,26.9,21.6,0.0,0.0,0.0,0.1,1013.0,67.0,6.1000000000000005,72.77450603869325 +2020-09-23 02:30:00-05:00,2020,9,23,2,30,26.9,21.5,0.0,0.0,0.0,0.1,1013.0,71.0,5.800000000000001,72.33057927645822 +2020-09-23 03:30:00-05:00,2020,9,23,3,30,26.9,21.3,0.0,0.0,0.0,0.1,1013.0,75.0,5.5,71.44984081253008 +2020-09-23 04:30:00-05:00,2020,9,23,4,30,26.9,21.1,0.0,0.0,0.0,0.1,1013.0,77.0,5.2,70.57851728516349 +2020-09-23 05:30:00-05:00,2020,9,23,5,30,26.9,21.0,0.0,0.0,0.0,0.1,1013.0,78.0,4.9,70.14635934156131 +2020-09-23 06:30:00-05:00,2020,9,23,6,30,27.3,20.9,29.0,137.0,38.0,0.1,1014.0,79.0,4.800000000000001,68.09867959788212 +2020-09-23 07:30:00-05:00,2020,9,23,7,30,27.8,21.200000000000003,89.0,435.0,217.0,0.1,1014.0,80.0,4.9,67.36540856745334 +2020-09-23 08:30:00-05:00,2020,9,23,8,30,28.200000000000003,20.9,122.0,646.0,448.0,0.1,1014.0,82.0,5.0,64.61083295723952 +2020-09-23 09:30:00-05:00,2020,9,23,9,30,28.6,20.700000000000003,123.0,795.0,663.0,0.1,1015.0,84.0,5.0,62.35350135009038 +2020-09-23 10:30:00-05:00,2020,9,23,10,30,28.8,20.700000000000003,320.0,394.0,638.0,0.1,1014.0,84.0,4.9,61.63426148432692 +2020-09-23 11:30:00-05:00,2020,9,23,11,30,29.0,20.8,276.0,582.0,789.0,0.1,1014.0,84.0,4.800000000000001,61.30055711754129 +2020-09-23 12:30:00-05:00,2020,9,23,12,30,29.1,20.8,336.0,413.0,706.0,0.1,1013.0,83.0,4.7,60.94689353706224 +2020-09-23 13:30:00-05:00,2020,9,23,13,30,29.1,20.9,378.0,298.0,630.0,0.1,1013.0,81.0,4.6000000000000005,61.32294649622422 +2020-09-23 14:30:00-05:00,2020,9,23,14,30,29.1,21.0,263.0,459.0,603.0,0.1,1012.0,77.0,4.6000000000000005,61.70103228300315 +2020-09-23 15:30:00-05:00,2020,9,23,15,30,28.9,21.1,213.0,438.0,469.0,0.1,1012.0,73.0,4.6000000000000005,62.80400895971384 +2020-09-23 16:30:00-05:00,2020,9,23,16,30,28.700000000000003,21.4,177.0,228.0,265.0,0.1,1012.0,71.0,4.6000000000000005,64.71610775691177 +2020-09-23 17:30:00-05:00,2020,9,23,17,30,28.3,22.0,81.0,32.0,86.0,0.1,1013.0,70.0,4.6000000000000005,68.71165950057082 +2020-09-23 18:30:00-05:00,2020,9,23,18,30,27.9,22.4,0.0,0.0,0.0,0.1,1013.0,72.0,4.800000000000001,72.0658733243101 +2020-09-23 19:30:00-05:00,2020,9,23,19,30,27.700000000000003,22.8,0.0,0.0,0.0,0.09,1014.0,77.0,5.0,74.70458889628618 +2020-09-23 20:30:00-05:00,2020,9,23,20,30,27.700000000000003,23.200000000000003,0.0,0.0,0.0,0.09,1014.0,83.0,5.2,76.53516588939115 +2020-09-23 21:30:00-05:00,2020,9,23,21,30,27.700000000000003,23.4,0.0,0.0,0.0,0.09,1014.0,89.0,5.4,77.4650960498038 +2020-09-23 22:30:00-05:00,2020,9,23,22,30,27.6,23.5,0.0,0.0,0.0,0.09,1014.0,93.0,5.6000000000000005,78.39069896683198 +2020-09-23 23:30:00-05:00,2020,9,23,23,30,27.6,23.6,0.0,0.0,0.0,0.09,1014.0,96.0,5.800000000000001,78.86460603161275 +2020-09-24 00:30:00-05:00,2020,9,24,0,30,27.4,23.6,0.0,0.0,0.0,0.09,1013.0,100.0,5.9,79.79314536748959 +2020-09-24 01:30:00-05:00,2020,9,24,1,30,27.3,23.700000000000003,0.0,0.0,0.0,0.09,1013.0,103.0,6.0,80.74688315431527 +2020-09-24 02:30:00-05:00,2020,9,24,2,30,27.3,23.700000000000003,0.0,0.0,0.0,0.09,1012.0,106.0,5.9,80.74688315431527 +2020-09-24 03:30:00-05:00,2020,9,24,3,30,27.200000000000003,23.8,0.0,0.0,0.0,0.09,1012.0,108.0,5.800000000000001,81.71200607377615 +2020-09-24 04:30:00-05:00,2020,9,24,4,30,27.200000000000003,23.9,0.0,0.0,0.0,0.09,1012.0,109.0,5.7,82.20487868050458 +2020-09-24 05:30:00-05:00,2020,9,24,5,30,27.3,24.0,0.0,0.0,0.0,0.09,1013.0,111.0,5.7,82.21686817412316 +2020-09-24 06:30:00-05:00,2020,9,24,6,30,27.6,24.3,18.0,0.0,18.0,0.09,1013.0,111.0,5.800000000000001,82.2527669077161 +2020-09-24 07:30:00-05:00,2020,9,24,7,30,28.0,24.6,72.0,1.0,72.0,0.09,1013.0,112.0,5.9,81.80996009312997 +2020-09-24 08:30:00-05:00,2020,9,24,8,30,28.3,24.8,192.0,18.0,201.0,0.09,1014.0,113.0,6.0,81.3594202886324 +2020-09-24 09:30:00-05:00,2020,9,24,9,30,28.6,24.9,235.0,6.0,239.0,0.09,1014.0,114.0,6.0,80.43187680667027 +2020-09-24 10:30:00-05:00,2020,9,24,10,30,28.8,25.200000000000003,261.0,40.0,293.0,0.09,1014.0,115.0,5.7,80.93842155480759 +2020-09-24 11:30:00-05:00,2020,9,24,11,30,28.9,25.3,223.0,5.0,227.0,0.09,1014.0,117.0,5.4,80.95109343908808 +2020-09-24 12:30:00-05:00,2020,9,24,12,30,28.9,25.4,341.0,42.0,378.0,0.09,1013.0,120.0,5.1000000000000005,81.4339174950313 +2020-09-24 13:30:00-05:00,2020,9,24,13,30,28.8,25.5,102.0,0.0,102.0,0.09,1013.0,122.0,4.7,82.39532246726411 +2020-09-24 14:30:00-05:00,2020,9,24,14,30,28.5,25.5,71.0,0.0,71.0,0.09,1012.0,125.0,4.4,83.84232530349826 +2020-09-24 15:30:00-05:00,2020,9,24,15,30,28.200000000000003,25.6,194.0,24.0,208.0,0.09,1012.0,127.0,4.1000000000000005,85.82613589918793 +2020-09-24 16:30:00-05:00,2020,9,24,16,30,27.8,25.700000000000003,157.0,138.0,210.0,0.09,1012.0,127.0,3.8,88.37426725854075 +2020-09-24 17:30:00-05:00,2020,9,24,17,30,27.5,25.8,57.0,219.0,92.0,0.09,1012.0,128.0,3.7,90.47268437969822 +2020-09-24 18:30:00-05:00,2020,9,24,18,30,27.3,25.9,0.0,0.0,0.0,0.09,1012.0,129.0,3.7,92.08262594008558 +2020-09-24 19:30:00-05:00,2020,9,24,19,30,27.200000000000003,26.0,0.0,0.0,0.0,0.09,1013.0,130.0,4.0,93.17410346849445 +2020-09-24 20:30:00-05:00,2020,9,24,20,30,27.200000000000003,26.0,0.0,0.0,0.0,0.09,1013.0,130.0,4.2,93.17410346849445 +2020-09-24 21:30:00-05:00,2020,9,24,21,30,27.3,26.1,0.0,0.0,0.0,0.09,1013.0,131.0,4.5,93.17898700599295 +2020-09-24 22:30:00-05:00,2020,9,24,22,30,27.4,26.1,0.0,0.0,0.0,0.09,1013.0,132.0,4.800000000000001,92.63464483709328 +2020-09-24 23:30:00-05:00,2020,9,24,23,30,27.5,26.200000000000003,0.0,0.0,0.0,0.09,1013.0,135.0,5.0,92.63989595857034 +2020-09-25 00:30:00-05:00,2020,9,25,0,30,27.6,26.200000000000003,0.0,0.0,0.0,0.09,1013.0,139.0,5.1000000000000005,92.09950049107174 +2020-09-25 01:30:00-05:00,2020,9,25,1,30,27.6,26.200000000000003,0.0,0.0,0.0,0.09,1012.0,144.0,5.1000000000000005,92.09950049107174 +2020-09-25 02:30:00-05:00,2020,9,25,2,30,27.700000000000003,26.3,0.0,0.0,0.0,0.09,1012.0,148.0,5.1000000000000005,92.10511353258156 +2020-09-25 03:30:00-05:00,2020,9,25,3,30,27.700000000000003,26.3,0.0,0.0,0.0,0.09,1012.0,153.0,5.0,92.10511353258156 +2020-09-25 04:30:00-05:00,2020,9,25,4,30,27.700000000000003,26.3,0.0,0.0,0.0,0.09,1012.0,158.0,4.800000000000001,92.10511353258156 +2020-09-25 05:30:00-05:00,2020,9,25,5,30,27.700000000000003,26.3,0.0,0.0,0.0,0.09,1013.0,163.0,4.6000000000000005,92.10511353258156 +2020-09-25 06:30:00-05:00,2020,9,25,6,30,27.9,26.4,22.0,1.0,22.0,0.09,1013.0,167.0,4.5,91.57459801815133 +2020-09-25 07:30:00-05:00,2020,9,25,7,30,28.1,26.6,116.0,122.0,152.0,0.09,1014.0,173.0,4.5,91.58651807397624 +2020-09-25 08:30:00-05:00,2020,9,25,8,30,28.3,26.700000000000003,202.0,230.0,317.0,0.09,1015.0,181.0,4.4,91.06092888282994 +2020-09-25 09:30:00-05:00,2020,9,25,9,30,28.5,26.700000000000003,253.0,384.0,512.0,0.09,1015.0,189.0,4.2,90.00824282803235 +2020-09-25 10:30:00-05:00,2020,9,25,10,30,28.700000000000003,26.6,127.0,814.0,781.0,0.09,1015.0,193.0,4.0,88.44680588181215 +2020-09-25 11:30:00-05:00,2020,9,25,11,30,28.9,26.5,321.0,177.0,476.0,0.09,1015.0,194.0,3.9,86.9135607779953 +2020-09-25 12:30:00-05:00,2020,9,25,12,30,29.1,26.3,134.0,2.0,136.0,0.09,1015.0,191.0,4.0,84.9053316369707 +2020-09-25 13:30:00-05:00,2020,9,25,13,30,29.1,26.200000000000003,343.0,190.0,503.0,0.09,1014.0,186.0,4.0,84.40527482086745 +2020-09-25 14:30:00-05:00,2020,9,25,14,30,29.1,26.1,256.0,283.0,463.0,0.09,1013.0,180.0,4.1000000000000005,83.90779481308158 +2020-09-25 15:30:00-05:00,2020,9,25,15,30,28.9,26.200000000000003,35.0,0.0,35.0,0.09,1013.0,177.0,4.0,85.38805680929505 +2020-09-25 16:30:00-05:00,2020,9,25,16,30,28.700000000000003,26.3,100.0,8.0,103.0,0.09,1013.0,176.0,3.8,86.89553218427623 +2020-09-25 17:30:00-05:00,2020,9,25,17,30,28.4,26.4,48.0,4.0,49.0,0.09,1014.0,179.0,3.6,88.94616815571686 +2020-09-25 18:30:00-05:00,2020,9,25,18,30,28.200000000000003,26.3,0.0,0.0,0.0,0.09,1014.0,183.0,3.5,89.45760947597503 +2020-09-25 19:30:00-05:00,2020,9,25,19,30,28.1,26.3,0.0,0.0,0.0,0.1,1014.0,187.0,3.5,89.98017678080498 +2020-09-25 20:30:00-05:00,2020,9,25,20,30,28.1,26.3,0.0,0.0,0.0,0.1,1014.0,190.0,3.5,89.98017678080498 +2020-09-25 21:30:00-05:00,2020,9,25,21,30,28.0,26.3,0.0,0.0,0.0,0.1,1015.0,193.0,3.6,90.50618565208272 +2020-09-25 22:30:00-05:00,2020,9,25,22,30,28.0,26.3,0.0,0.0,0.0,0.1,1015.0,195.0,3.7,90.50618565208272 +2020-09-25 23:30:00-05:00,2020,9,25,23,30,27.9,26.200000000000003,0.0,0.0,0.0,0.1,1014.0,197.0,3.7,90.49949928891753 +2020-09-26 00:30:00-05:00,2020,9,26,0,30,27.8,26.200000000000003,0.0,0.0,0.0,0.1,1014.0,199.0,3.7,91.02932779809949 +2020-09-26 01:30:00-05:00,2020,9,26,1,30,27.700000000000003,26.1,0.0,0.0,0.0,0.1,1014.0,202.0,3.5,91.0229878209704 +2020-09-26 02:30:00-05:00,2020,9,26,2,30,27.700000000000003,25.9,0.0,0.0,0.0,0.1,1013.0,206.0,3.3000000000000003,89.9519946372491 +2020-09-26 03:30:00-05:00,2020,9,26,3,30,27.6,25.8,0.0,0.0,0.0,0.1,1013.0,210.0,3.0,89.94493088790799 +2020-09-26 04:30:00-05:00,2020,9,26,4,30,27.6,25.8,0.0,0.0,0.0,0.1,1013.0,214.0,2.8000000000000003,89.94493088790799 +2020-09-26 05:30:00-05:00,2020,9,26,5,30,27.700000000000003,25.8,0.0,0.0,0.0,0.1,1014.0,220.0,2.5,89.42064238981021 +2020-09-26 06:30:00-05:00,2020,9,26,6,30,28.0,25.8,25.0,172.0,36.0,0.1,1014.0,226.0,2.4000000000000004,87.86831643606601 +2020-09-26 07:30:00-05:00,2020,9,26,7,30,28.5,26.1,66.0,566.0,230.0,0.1,1015.0,233.0,2.3000000000000003,86.87746731371037 +2020-09-26 08:30:00-05:00,2020,9,26,8,30,28.8,26.1,86.0,727.0,449.0,0.1,1015.0,240.0,2.3000000000000003,85.37808211473457 +2020-09-26 09:30:00-05:00,2020,9,26,9,30,29.200000000000003,26.0,100.0,806.0,643.0,0.1,1015.0,244.0,2.2,82.93199586190887 +2020-09-26 10:30:00-05:00,2020,9,26,10,30,29.5,26.0,119.0,822.0,777.0,0.1,1015.0,245.0,2.0,81.50798755926293 +2020-09-26 11:30:00-05:00,2020,9,26,11,30,29.8,25.8,138.0,814.0,849.0,0.1,1015.0,241.0,1.7000000000000002,79.16817847952795 +2020-09-26 12:30:00-05:00,2020,9,26,12,30,29.9,25.6,311.0,413.0,677.0,0.1,1014.0,228.0,1.4,77.78587573405082 +2020-09-26 13:30:00-05:00,2020,9,26,13,30,30.0,25.5,161.0,123.0,264.0,0.1,1014.0,205.0,1.2000000000000002,76.88217771852531 +2020-09-26 14:30:00-05:00,2020,9,26,14,30,30.0,25.4,73.0,0.0,73.0,0.1,1013.0,179.0,1.2000000000000002,76.42667954446237 +2020-09-26 15:30:00-05:00,2020,9,26,15,30,29.8,25.4,63.0,0.0,63.0,0.1,1013.0,159.0,1.3,77.3106691672673 +2020-09-26 16:30:00-05:00,2020,9,26,16,30,29.4,25.700000000000003,119.0,27.0,129.0,0.1,1013.0,147.0,1.5,80.53536024141101 +2020-09-26 17:30:00-05:00,2020,9,26,17,30,29.0,25.8,58.0,8.0,59.0,0.1,1013.0,141.0,1.6,82.90903358865795 +2020-09-26 18:30:00-05:00,2020,9,26,18,30,28.5,26.0,0.0,0.0,0.0,0.1,1014.0,139.0,1.7000000000000002,86.36503681487521 +2020-09-26 19:30:00-05:00,2020,9,26,19,30,28.200000000000003,26.1,0.0,0.0,0.0,0.1,1014.0,139.0,1.9,88.40658879318767 +2020-09-26 20:30:00-05:00,2020,9,26,20,30,28.0,26.1,0.0,0.0,0.0,0.1,1014.0,139.0,1.9,89.44284544438268 +2020-09-26 21:30:00-05:00,2020,9,26,21,30,27.8,26.1,0.0,0.0,0.0,0.1,1014.0,142.0,2.0,90.49280598950578 +2020-09-26 22:30:00-05:00,2020,9,26,22,30,27.6,26.0,0.0,0.0,0.0,0.1,1014.0,150.0,1.9,91.01664123615788 +2020-09-26 23:30:00-05:00,2020,9,26,23,30,27.5,25.9,0.0,0.0,0.0,0.1,1013.0,160.0,1.7000000000000002,91.01028803465094 +2020-09-27 00:30:00-05:00,2020,9,27,0,30,27.5,25.8,0.0,0.0,0.0,0.1,1013.0,169.0,1.5,90.47268437969822 +2020-09-27 01:30:00-05:00,2020,9,27,1,30,27.4,25.700000000000003,0.0,0.0,0.0,0.1,1012.0,177.0,1.3,90.4659632411294 +2020-09-27 02:30:00-05:00,2020,9,27,2,30,27.3,25.6,0.0,0.0,0.0,0.1,1012.0,183.0,1.1,90.45923511915049 +2020-09-27 03:30:00-05:00,2020,9,27,3,30,27.3,25.5,0.0,0.0,0.0,0.1,1012.0,181.0,0.9,89.92369577023814 +2020-09-27 04:30:00-05:00,2020,9,27,4,30,27.3,25.4,0.0,0.0,0.0,0.1,1012.0,141.0,0.7000000000000001,89.39093147500324 +2020-09-27 05:30:00-05:00,2020,9,27,5,30,27.3,25.3,0.0,0.0,0.0,0.1,1012.0,91.0,0.7000000000000001,88.86093005266055 +2020-09-27 06:30:00-05:00,2020,9,27,6,30,27.4,25.3,25.0,149.0,34.0,0.1,1013.0,76.0,0.8,88.34181353348015 +2020-09-27 07:30:00-05:00,2020,9,27,7,30,27.700000000000003,25.5,79.0,492.0,221.0,0.1,1013.0,75.0,1.0,87.84301834473253 +2020-09-27 08:30:00-05:00,2020,9,27,8,30,27.9,25.4,91.0,716.0,447.0,0.1,1013.0,77.0,1.2000000000000002,86.30865994403459 +2020-09-27 09:30:00-05:00,2020,9,27,9,30,28.200000000000003,25.3,105.0,799.0,641.0,0.1,1013.0,82.0,1.4,84.30969208154741 +2020-09-27 10:30:00-05:00,2020,9,27,10,30,28.6,25.200000000000003,114.0,843.0,787.0,0.1,1013.0,89.0,1.6,81.88293095675752 +2020-09-27 11:30:00-05:00,2020,9,27,11,30,29.0,25.0,120.0,862.0,870.0,0.1,1013.0,94.0,1.7000000000000002,79.0585121873896 +2020-09-27 12:30:00-05:00,2020,9,27,12,30,29.200000000000003,24.8,121.0,863.0,882.0,0.1,1012.0,95.0,1.8,77.22220664571155 +2020-09-27 13:30:00-05:00,2020,9,27,13,30,29.3,24.700000000000003,120.0,846.0,824.0,0.1,1011.0,95.0,1.9,76.32008692451205 +2020-09-27 14:30:00-05:00,2020,9,27,14,30,29.3,24.6,114.0,806.0,699.0,0.1,1011.0,96.0,2.1,75.8652203351002 +2020-09-27 15:30:00-05:00,2020,9,27,15,30,29.200000000000003,24.6,104.0,734.0,521.0,0.1,1011.0,98.0,2.3000000000000003,76.30480387190511 +2020-09-27 16:30:00-05:00,2020,9,27,16,30,29.0,24.8,84.0,601.0,307.0,0.1,1011.0,99.0,2.3000000000000003,78.12068729600648 +2020-09-27 17:30:00-05:00,2020,9,27,17,30,28.6,24.9,47.0,323.0,95.0,0.1,1011.0,99.0,2.3000000000000003,80.43187680667027 +2020-09-27 18:30:00-05:00,2020,9,27,18,30,28.4,25.0,0.0,0.0,0.0,0.1,1012.0,101.0,2.4000000000000004,81.85865421535438 +2020-09-27 19:30:00-05:00,2020,9,27,19,30,28.3,25.0,0.0,0.0,0.0,0.1,1012.0,105.0,2.6,82.33612559085367 +2020-09-27 20:30:00-05:00,2020,9,27,20,30,28.200000000000003,25.0,0.0,0.0,0.0,0.1,1012.0,112.0,2.7,82.81673714834355 +2020-09-27 21:30:00-05:00,2020,9,27,21,30,28.1,25.0,0.0,0.0,0.0,0.1,1012.0,121.0,2.8000000000000003,83.30051174706053 +2020-09-27 22:30:00-05:00,2020,9,27,22,30,28.1,25.0,0.0,0.0,0.0,0.1,1012.0,129.0,2.9000000000000004,83.30051174706053 +2020-09-27 23:30:00-05:00,2020,9,27,23,30,28.0,25.0,0.0,0.0,0.0,0.1,1011.0,136.0,3.1,83.78747242805218 +2020-09-28 00:30:00-05:00,2020,9,28,0,30,27.9,25.0,0.0,0.0,0.0,0.1,1011.0,141.0,3.3000000000000003,84.27764241574158 +2020-09-28 01:30:00-05:00,2020,9,28,1,30,27.9,25.1,0.0,0.0,0.0,0.1,1010.0,146.0,3.5,84.78143629241603 +2020-09-28 02:30:00-05:00,2020,9,28,2,30,27.8,25.1,0.0,0.0,0.0,0.1,1010.0,150.0,3.6,85.27778845292592 +2020-09-28 03:30:00-05:00,2020,9,28,3,30,27.8,25.200000000000003,0.0,0.0,0.0,0.1,1010.0,155.0,3.6,85.7871797707201 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12:30:00-05:00,2020,9,28,12,30,28.9,25.5,135.0,835.0,869.0,0.1,1011.0,179.0,2.9000000000000004,81.91925849043852 +2020-09-28 13:30:00-05:00,2020,9,28,13,30,28.9,25.5,173.0,722.0,771.0,0.1,1011.0,181.0,2.8000000000000003,81.91925849043852 +2020-09-28 14:30:00-05:00,2020,9,28,14,30,28.9,25.5,298.0,134.0,395.0,0.1,1010.0,180.0,2.7,81.91925849043852 +2020-09-28 15:30:00-05:00,2020,9,28,15,30,28.8,25.5,244.0,138.0,322.0,0.1,1010.0,174.0,2.6,82.39532246726411 +2020-09-28 16:30:00-05:00,2020,9,28,16,30,28.6,25.5,108.0,9.0,111.0,0.1,1010.0,166.0,2.4000000000000004,83.35683314726086 +2020-09-28 17:30:00-05:00,2020,9,28,17,30,28.4,25.4,30.0,1.0,30.0,0.1,1011.0,154.0,2.4000000000000004,83.8313762420779 +2020-09-28 18:30:00-05:00,2020,9,28,18,30,28.3,25.4,0.0,0.0,0.0,0.1,1011.0,143.0,2.6,84.32035426044355 +2020-09-28 19:30:00-05:00,2020,9,28,19,30,28.3,25.4,0.0,0.0,0.0,0.1,1012.0,135.0,3.0,84.32035426044355 +2020-09-28 20:30:00-05:00,2020,9,28,20,30,28.3,25.5,0.0,0.0,0.0,0.1,1012.0,130.0,3.5,84.82289823632867 +2020-09-28 21:30:00-05:00,2020,9,28,21,30,28.3,25.5,0.0,0.0,0.0,0.1,1012.0,128.0,4.0,84.82289823632867 +2020-09-28 22:30:00-05:00,2020,9,28,22,30,28.200000000000003,25.6,0.0,0.0,0.0,0.1,1012.0,128.0,4.4,85.82613589918793 +2020-09-28 23:30:00-05:00,2020,9,28,23,30,28.200000000000003,25.6,0.0,0.0,0.0,0.1,1011.0,132.0,4.6000000000000005,85.82613589918793 +2020-09-29 00:30:00-05:00,2020,9,29,0,30,28.1,25.5,0.0,0.0,0.0,0.1,1011.0,137.0,4.7,85.81641145062478 +2020-09-29 01:30:00-05:00,2020,9,29,1,30,28.1,25.5,0.0,0.0,0.0,0.1,1011.0,143.0,4.6000000000000005,85.81641145062478 +2020-09-29 02:30:00-05:00,2020,9,29,2,30,28.0,25.4,0.0,0.0,0.0,0.1,1010.0,149.0,4.4,85.80667728802291 +2020-09-29 03:30:00-05:00,2020,9,29,3,30,28.0,25.3,0.0,0.0,0.0,0.1,1010.0,154.0,4.3,85.29792701258955 +2020-09-29 04:30:00-05:00,2020,9,29,4,30,28.1,25.200000000000003,0.0,0.0,0.0,0.1,1011.0,158.0,4.2,84.29901938997921 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13:30:00-05:00,2020,9,29,13,30,30.0,25.200000000000003,145.0,797.0,803.0,0.1,1010.0,178.0,4.3,75.52275934966991 +2020-09-29 14:30:00-05:00,2020,9,29,14,30,29.9,25.1,106.0,829.0,701.0,0.1,1009.0,184.0,4.1000000000000005,75.50708303321723 +2020-09-29 15:30:00-05:00,2020,9,29,15,30,29.9,25.0,96.0,760.0,521.0,0.1,1009.0,189.0,3.8,75.05839983390834 +2020-09-29 16:30:00-05:00,2020,9,29,16,30,29.700000000000003,25.0,78.0,629.0,306.0,0.1,1010.0,192.0,3.4000000000000004,75.92720341817487 +2020-09-29 17:30:00-05:00,2020,9,29,17,30,29.3,25.1,46.0,325.0,91.0,0.1,1010.0,194.0,3.0,78.16350464080747 +2020-09-29 18:30:00-05:00,2020,9,29,18,30,29.1,25.0,0.0,0.0,0.0,0.1,1011.0,194.0,2.7,78.60239697731203 +2020-09-29 19:30:00-05:00,2020,9,29,19,30,29.0,24.9,0.0,0.0,0.0,0.1,1011.0,195.0,2.6,78.58837582919173 +2020-09-29 20:30:00-05:00,2020,9,29,20,30,28.9,24.8,0.0,0.0,0.0,0.1,1012.0,196.0,2.4000000000000004,78.57434159685675 +2020-09-29 21:30:00-05:00,2020,9,29,21,30,28.9,24.8,0.0,0.0,0.0,0.1,1012.0,198.0,2.3000000000000003,78.57434159685675 +2020-09-29 22:30:00-05:00,2020,9,29,22,30,28.700000000000003,24.700000000000003,0.0,0.0,0.0,0.1,1012.0,199.0,2.2,79.01717501323223 +2020-09-29 23:30:00-05:00,2020,9,29,23,30,28.5,24.6,0.0,0.0,0.0,0.1,1011.0,200.0,2.1,79.46350654001047 +2020-09-30 00:30:00-05:00,2020,9,30,0,30,28.3,24.6,0.0,0.0,0.0,0.1,1011.0,200.0,2.0,80.39286725822609 +2020-09-30 01:30:00-05:00,2020,9,30,1,30,28.200000000000003,24.5,0.0,0.0,0.0,0.1,1011.0,201.0,1.9,80.37983931978185 +2020-09-30 02:30:00-05:00,2020,9,30,2,30,28.0,24.4,0.0,0.0,0.0,0.1,1011.0,201.0,1.8,80.83660967193336 +2020-09-30 03:30:00-05:00,2020,9,30,3,30,27.9,24.200000000000003,0.0,0.0,0.0,0.1,1011.0,201.0,1.7000000000000002,80.34068102998818 +2020-09-30 04:30:00-05:00,2020,9,30,4,30,27.9,24.1,0.0,0.0,0.0,0.1,1011.0,197.0,1.5,79.86006336561971 +2020-09-30 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03:30:00-05:00,2005,11,3,3,30,23.8,21.1,0.0,0.0,0.0,0.11,1019.0,39.0,5.6000000000000005,84.86645400274384 +2005-11-03 04:30:00-05:00,2005,11,3,4,30,23.9,21.1,0.0,0.0,0.0,0.11,1019.0,42.0,5.5,84.35762349195751 +2005-11-03 05:30:00-05:00,2005,11,3,5,30,24.0,21.1,0.0,0.0,0.0,0.11,1020.0,44.0,5.4,83.85222142401177 +2005-11-03 06:30:00-05:00,2005,11,3,6,30,24.200000000000003,21.1,0.0,0.0,0.0,0.11,1020.0,46.0,5.4,82.85160081929877 +2005-11-03 07:30:00-05:00,2005,11,3,7,30,24.6,21.4,67.0,405.0,150.0,0.11,1021.0,48.0,5.6000000000000005,82.39237530635937 +2005-11-03 08:30:00-05:00,2005,11,3,8,30,25.1,21.5,99.0,642.0,357.0,0.11,1021.0,50.0,5.7,80.46093147463182 +2005-11-03 09:30:00-05:00,2005,11,3,9,30,25.4,21.5,265.0,420.0,502.0,0.11,1022.0,53.0,5.9,79.037182830417 +2005-11-03 10:30:00-05:00,2005,11,3,10,30,25.6,21.4,323.0,35.0,347.0,0.11,1022.0,54.0,6.0,77.6269764291538 +2005-11-03 11:30:00-05:00,2005,11,3,11,30,25.8,21.4,365.0,54.0,406.0,0.11,1021.0,53.0,6.0,76.71157487916507 +2005-11-03 12:30:00-05:00,2005,11,3,12,30,25.8,21.5,122.0,855.0,763.0,0.11,1020.0,51.0,6.0,77.18274704646991 +2005-11-03 13:30:00-05:00,2005,11,3,13,30,25.8,21.5,137.0,795.0,689.0,0.11,1020.0,49.0,6.1000000000000005,77.18274704646991 +2005-11-03 14:30:00-05:00,2005,11,3,14,30,25.700000000000003,21.5,124.0,749.0,562.0,0.11,1019.0,47.0,6.2,77.64172202916798 +2005-11-03 15:30:00-05:00,2005,11,3,15,30,25.6,21.5,104.0,653.0,383.0,0.11,1019.0,45.0,6.300000000000001,78.10377110822355 +2005-11-03 16:30:00-05:00,2005,11,3,16,30,25.3,21.5,71.0,461.0,179.0,0.11,1019.0,43.0,6.4,79.50859157319356 +2005-11-03 17:30:00-05:00,2005,11,3,17,30,25.0,21.6,11.0,24.0,12.0,0.11,1020.0,43.0,6.5,81.43868840815787 +2005-11-03 18:30:00-05:00,2005,11,3,18,30,24.8,21.6,0.0,0.0,0.0,0.11,1020.0,43.0,6.6000000000000005,82.4163453099894 +2005-11-03 19:30:00-05:00,2005,11,3,19,30,24.8,21.6,0.0,0.0,0.0,0.1,1021.0,44.0,6.6000000000000005,82.4163453099894 +2005-11-03 20:30:00-05:00,2005,11,3,20,30,24.8,21.700000000000003,0.0,0.0,0.0,0.1,1021.0,45.0,6.6000000000000005,82.92178985776674 +2005-11-03 21:30:00-05:00,2005,11,3,21,30,24.8,21.6,0.0,0.0,0.0,0.1,1021.0,47.0,6.6000000000000005,82.4163453099894 +2005-11-03 22:30:00-05:00,2005,11,3,22,30,24.700000000000003,21.5,0.0,0.0,0.0,0.1,1020.0,48.0,6.5,82.40436619184688 +2005-11-03 23:30:00-05:00,2005,11,3,23,30,24.6,21.4,0.0,0.0,0.0,0.1,1020.0,50.0,6.4,82.39237530635937 +2005-11-04 00:30:00-05:00,2005,11,4,0,30,24.5,21.3,0.0,0.0,0.0,0.1,1019.0,51.0,6.2,82.38037263885073 +2005-11-04 01:30:00-05:00,2005,11,4,1,30,24.4,21.200000000000003,0.0,0.0,0.0,0.1,1018.0,53.0,6.1000000000000005,82.36835817462315 +2005-11-04 02:30:00-05:00,2005,11,4,2,30,24.3,21.1,0.0,0.0,0.0,0.1,1018.0,54.0,5.9,82.35633189895745 +2005-11-04 03:30:00-05:00,2005,11,4,3,30,24.200000000000003,21.1,0.0,0.0,0.0,0.1,1018.0,54.0,5.7,82.85160081929877 +2005-11-04 04:30:00-05:00,2005,11,4,4,30,24.200000000000003,21.1,0.0,0.0,0.0,0.1,1018.0,54.0,5.6000000000000005,82.85160081929877 +2005-11-04 05:30:00-05:00,2005,11,4,5,30,24.200000000000003,21.1,0.0,0.0,0.0,0.1,1018.0,54.0,5.5,82.85160081929877 +2005-11-04 06:30:00-05:00,2005,11,4,6,30,24.4,21.0,0.0,0.0,0.0,0.1,1019.0,53.0,5.6000000000000005,81.36312839586225 +2005-11-04 07:30:00-05:00,2005,11,4,7,30,24.9,21.200000000000003,52.0,535.0,160.0,0.1,1019.0,52.0,5.7,79.94286974368336 +2005-11-04 08:30:00-05:00,2005,11,4,8,30,25.4,21.4,73.0,749.0,372.0,0.1,1020.0,52.0,5.800000000000001,78.5546900174901 +2005-11-04 09:30:00-05:00,2005,11,4,9,30,25.8,21.3,86.0,843.0,560.0,0.1,1020.0,53.0,5.7,76.24292581518071 +2005-11-04 10:30:00-05:00,2005,11,4,10,30,26.1,21.5,373.0,188.0,501.0,0.1,1020.0,54.0,5.5,75.82404167923288 +2005-11-04 11:30:00-05:00,2005,11,4,11,30,26.3,21.700000000000003,100.0,901.0,769.0,0.1,1020.0,55.0,5.300000000000001,75.85547174347195 +2005-11-04 12:30:00-05:00,2005,11,4,12,30,26.4,21.9,389.0,280.0,598.0,0.1,1019.0,56.0,5.300000000000001,76.33576480448347 +2005-11-04 13:30:00-05:00,2005,11,4,13,30,26.5,22.1,114.0,848.0,699.0,0.1,1018.0,57.0,5.4,76.81801984250816 +2005-11-04 14:30:00-05:00,2005,11,4,14,30,26.4,22.200000000000003,291.0,300.0,466.0,0.1,1018.0,58.0,5.5,77.74455610700481 +2005-11-04 15:30:00-05:00,2005,11,4,15,30,26.3,22.4,88.0,705.0,388.0,0.1,1018.0,59.0,5.5,79.16224520471829 +2005-11-04 16:30:00-05:00,2005,11,4,16,30,26.0,22.6,92.0,2.0,92.0,0.1,1018.0,60.0,5.6000000000000005,81.56364669114777 +2005-11-04 17:30:00-05:00,2005,11,4,17,30,25.700000000000003,22.700000000000003,11.0,27.0,11.0,0.1,1018.0,62.0,5.7,83.53164606821205 +2005-11-04 18:30:00-05:00,2005,11,4,18,30,25.5,22.8,0.0,0.0,0.0,0.1,1019.0,64.0,5.9,85.04328551543426 +2005-11-04 19:30:00-05:00,2005,11,4,19,30,25.5,22.700000000000003,0.0,0.0,0.0,0.1,1019.0,66.0,6.1000000000000005,84.52917838487782 +2005-11-04 20:30:00-05:00,2005,11,4,20,30,25.3,22.5,0.0,0.0,0.0,0.1,1019.0,68.0,6.1000000000000005,84.50788366750213 +2005-11-04 21:30:00-05:00,2005,11,4,21,30,25.200000000000003,22.3,0.0,0.0,0.0,0.1,1019.0,69.0,6.1000000000000005,83.9848780598474 +2005-11-04 22:30:00-05:00,2005,11,4,22,30,25.0,22.1,0.0,0.0,0.0,0.1,1019.0,70.0,6.0,83.96287846488495 +2005-11-04 23:30:00-05:00,2005,11,4,23,30,24.9,22.0,0.0,0.0,0.0,0.1,1019.0,70.0,5.800000000000001,83.95186225354803 +2005-11-05 00:30:00-05:00,2005,11,5,0,30,24.8,21.9,0.0,0.0,0.0,0.1,1018.0,70.0,5.6000000000000005,83.9408350810903 +2005-11-05 01:30:00-05:00,2005,11,5,1,30,24.6,21.8,0.0,0.0,0.0,0.1,1018.0,69.0,5.5,84.43301646305675 +2005-11-05 02:30:00-05:00,2005,11,5,2,30,24.5,21.700000000000003,0.0,0.0,0.0,0.1,1017.0,69.0,5.4,84.42227833831143 +2005-11-05 03:30:00-05:00,2005,11,5,3,30,24.5,21.700000000000003,0.0,0.0,0.0,0.1,1017.0,69.0,5.300000000000001,84.42227833831143 +2005-11-05 04:30:00-05:00,2005,11,5,4,30,24.4,21.6,0.0,0.0,0.0,0.1,1017.0,70.0,5.300000000000001,84.41152946997488 +2005-11-05 05:30:00-05:00,2005,11,5,5,30,24.4,21.6,0.0,0.0,0.0,0.1,1018.0,70.0,5.2,84.41152946997488 +2005-11-05 06:30:00-05:00,2005,11,5,6,30,24.6,21.6,0.0,0.0,0.0,0.1,1018.0,68.0,5.300000000000001,83.40722635492729 +2005-11-05 07:30:00-05:00,2005,11,5,7,30,25.1,21.8,52.0,524.0,156.0,0.1,1018.0,65.0,5.4,81.95038626994669 +2005-11-05 08:30:00-05:00,2005,11,5,8,30,25.6,21.8,220.0,91.0,256.0,0.1,1019.0,61.0,5.5,79.5495912631393 +2005-11-05 09:30:00-05:00,2005,11,5,9,30,25.9,21.700000000000003,87.0,841.0,557.0,0.1,1019.0,56.0,5.5,77.67117188646347 +2005-11-05 10:30:00-05:00,2005,11,5,10,30,26.200000000000003,21.700000000000003,92.0,894.0,697.0,0.1,1019.0,54.0,5.5,76.30487547759346 +2005-11-05 11:30:00-05:00,2005,11,5,11,30,26.4,21.700000000000003,95.0,919.0,773.0,0.1,1018.0,53.0,5.5,75.40904545009998 +2005-11-05 12:30:00-05:00,2005,11,5,12,30,26.5,21.8,94.0,921.0,778.0,0.1,1018.0,53.0,5.4,75.42497687186433 +2005-11-05 13:30:00-05:00,2005,11,5,13,30,26.5,21.8,98.0,886.0,707.0,0.1,1017.0,53.0,5.300000000000001,75.42497687186433 +2005-11-05 14:30:00-05:00,2005,11,5,14,30,26.4,21.9,90.0,842.0,577.0,0.1,1016.0,53.0,5.300000000000001,76.33576480448347 +2005-11-05 15:30:00-05:00,2005,11,5,15,30,26.1,22.0,76.0,757.0,395.0,0.1,1016.0,49.0,5.300000000000001,78.17600861292594 +2005-11-05 16:30:00-05:00,2005,11,5,16,30,25.8,22.200000000000003,54.0,573.0,185.0,0.1,1017.0,45.0,5.300000000000001,80.55257262849392 +2005-11-05 17:30:00-05:00,2005,11,5,17,30,25.3,22.3,0.0,0.0,0.0,0.1,1017.0,42.0,5.300000000000001,83.48655911737376 +2005-11-05 18:30:00-05:00,2005,11,5,18,30,25.1,22.4,0.0,0.0,0.0,0.1,1017.0,43.0,5.4,85.00194910092202 +2005-11-05 19:30:00-05:00,2005,11,5,19,30,25.1,22.5,0.0,0.0,0.0,0.1,1018.0,48.0,5.4,85.5201042088329 +2005-11-05 20:30:00-05:00,2005,11,5,20,30,25.200000000000003,22.700000000000003,0.0,0.0,0.0,0.1,1018.0,53.0,5.2,86.05071333394693 +2005-11-05 21:30:00-05:00,2005,11,5,21,30,25.200000000000003,22.8,0.0,0.0,0.0,0.1,1018.0,58.0,4.9,86.57407445208084 +2005-11-05 22:30:00-05:00,2005,11,5,22,30,25.1,22.8,0.0,0.0,0.0,0.1,1018.0,63.0,4.800000000000001,87.09120889789614 +2005-11-05 23:30:00-05:00,2005,11,5,23,30,25.1,22.8,0.0,0.0,0.0,0.1,1017.0,67.0,4.6000000000000005,87.09120889789614 +2005-11-06 00:30:00-05:00,2005,11,6,0,30,25.1,22.700000000000003,0.0,0.0,0.0,0.1,1017.0,71.0,4.3,86.56472157756485 +2005-11-06 01:30:00-05:00,2005,11,6,1,30,25.0,22.5,0.0,0.0,0.0,0.1,1017.0,73.0,4.0,86.03132525866289 +2005-11-06 02:30:00-05:00,2005,11,6,2,30,25.0,22.3,0.0,0.0,0.0,0.1,1017.0,76.0,3.6,84.99158907366433 +2005-11-06 03:30:00-05:00,2005,11,6,3,30,25.0,22.200000000000003,0.0,0.0,0.0,0.1,1017.0,76.0,3.4000000000000004,84.4758618006713 +2005-11-06 04:30:00-05:00,2005,11,6,4,30,24.9,22.1,0.0,0.0,0.0,0.1,1017.0,75.0,3.2,84.46516651307014 +2005-11-06 05:30:00-05:00,2005,11,6,5,30,24.9,22.0,0.0,0.0,0.0,0.1,1018.0,72.0,3.1,83.95186225354803 +2005-11-06 06:30:00-05:00,2005,11,6,6,30,25.1,22.0,0.0,0.0,0.0,0.1,1018.0,67.0,3.1,82.95672893004904 +2005-11-06 07:30:00-05:00,2005,11,6,7,30,25.6,22.3,43.0,573.0,156.0,0.1,1019.0,62.0,3.1,82.01145278638 +2005-11-06 08:30:00-05:00,2005,11,6,8,30,26.1,22.5,63.0,774.0,367.0,0.1,1020.0,58.0,3.2,80.59165892191228 +2005-11-06 09:30:00-05:00,2005,11,6,9,30,26.5,22.3,302.0,99.0,357.0,0.1,1020.0,57.0,3.2,77.75919186283691 +2005-11-06 10:30:00-05:00,2005,11,6,10,30,26.8,22.3,356.0,97.0,421.0,0.1,1020.0,56.0,3.0,76.39737309305461 +2005-11-06 11:30:00-05:00,2005,11,6,11,30,27.1,22.3,325.0,442.0,650.0,0.1,1019.0,55.0,2.9000000000000004,75.06234984687318 +2005-11-06 12:30:00-05:00,2005,11,6,12,30,27.3,22.3,207.0,0.0,207.0,0.1,1018.0,53.0,2.8000000000000003,74.1869329006626 +2005-11-06 13:30:00-05:00,2005,11,6,13,30,27.3,22.3,290.0,479.0,619.0,0.1,1017.0,51.0,2.9000000000000004,74.1869329006626 +2005-11-06 14:30:00-05:00,2005,11,6,14,30,27.3,22.3,86.0,833.0,566.0,0.1,1017.0,51.0,3.1,74.1869329006626 +2005-11-06 15:30:00-05:00,2005,11,6,15,30,27.1,22.3,74.0,743.0,386.0,0.1,1017.0,53.0,3.3000000000000003,75.06234984687318 +2005-11-06 16:30:00-05:00,2005,11,6,16,30,26.6,22.5,52.0,569.0,180.0,0.1,1017.0,56.0,3.5,78.2479077819723 +2005-11-06 17:30:00-05:00,2005,11,6,17,30,26.1,22.8,0.0,0.0,0.0,0.1,1018.0,58.0,3.8,82.07222228655003 +2005-11-06 18:30:00-05:00,2005,11,6,18,30,25.8,22.700000000000003,0.0,0.0,0.0,0.1,1018.0,59.0,4.0,83.03785310732827 +2005-11-06 19:30:00-05:00,2005,11,6,19,30,25.700000000000003,22.8,0.0,0.0,0.0,0.1,1019.0,60.0,4.2,84.03968619933993 +2005-11-06 20:30:00-05:00,2005,11,6,20,30,25.6,22.700000000000003,0.0,0.0,0.0,0.1,1019.0,61.0,4.3,84.02874632731391 +2005-11-06 21:30:00-05:00,2005,11,6,21,30,25.5,22.700000000000003,0.0,0.0,0.0,0.1,1019.0,62.0,4.4,84.52917838487782 +2005-11-06 22:30:00-05:00,2005,11,6,22,30,25.4,22.700000000000003,0.0,0.0,0.0,0.1,1019.0,62.0,4.4,85.03296693933089 +2005-11-06 23:30:00-05:00,2005,11,6,23,30,25.3,22.700000000000003,0.0,0.0,0.0,0.1,1019.0,62.0,4.3,85.54013688901695 +2005-11-07 00:30:00-05:00,2005,11,7,0,30,25.200000000000003,22.6,0.0,0.0,0.0,0.1,1018.0,61.0,4.2,85.53012559102362 +2005-11-07 01:30:00-05:00,2005,11,7,1,30,25.1,22.700000000000003,0.0,0.0,0.0,0.1,1018.0,60.0,4.1000000000000005,86.56472157756485 +2005-11-07 02:30:00-05:00,2005,11,7,2,30,25.0,22.6,0.0,0.0,0.0,0.1,1018.0,58.0,4.0,86.5553591977721 +2005-11-07 03:30:00-05:00,2005,11,7,3,30,24.9,22.6,0.0,0.0,0.0,0.1,1018.0,57.0,4.0,87.07315614835665 +2005-11-07 04:30:00-05:00,2005,11,7,4,30,24.9,22.6,0.0,0.0,0.0,0.1,1018.0,57.0,4.0,87.07315614835665 +2005-11-07 05:30:00-05:00,2005,11,7,5,30,24.9,22.5,0.0,0.0,0.0,0.1,1019.0,57.0,4.0,86.54598730023442 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22:30:00-05:00,2005,11,7,22,30,25.0,22.200000000000003,0.0,0.0,0.0,0.1,1020.0,57.0,6.4,84.4758618006713 +2005-11-07 23:30:00-05:00,2005,11,7,23,30,24.9,22.1,0.0,0.0,0.0,0.1,1019.0,57.0,6.2,84.46516651307014 +2005-11-08 00:30:00-05:00,2005,11,8,0,30,24.8,21.9,0.0,0.0,0.0,0.1,1019.0,55.0,6.0,83.9408350810903 +2005-11-08 01:30:00-05:00,2005,11,8,1,30,24.6,21.700000000000003,0.0,0.0,0.0,0.1,1019.0,54.0,5.7,83.9187477970364 +2005-11-08 02:30:00-05:00,2005,11,8,2,30,24.5,21.6,0.0,0.0,0.0,0.1,1019.0,53.0,5.5,83.90768765749962 +2005-11-08 03:30:00-05:00,2005,11,8,3,30,24.4,21.5,0.0,0.0,0.0,0.1,1018.0,52.0,5.300000000000001,83.89661650096086 +2005-11-08 04:30:00-05:00,2005,11,8,4,30,24.4,21.4,0.0,0.0,0.0,0.1,1018.0,50.0,5.1000000000000005,83.38445864005315 +2005-11-08 05:30:00-05:00,2005,11,8,5,30,24.4,21.4,0.0,0.0,0.0,0.1,1018.0,50.0,5.0,83.38445864005315 +2005-11-08 06:30:00-05:00,2005,11,8,6,30,24.6,21.4,0.0,0.0,0.0,0.1,1019.0,49.0,5.0,82.39237530635937 +2005-11-08 07:30:00-05:00,2005,11,8,7,30,25.0,21.4,50.0,516.0,148.0,0.1,1019.0,46.0,5.1000000000000005,80.44778939452424 +2005-11-08 08:30:00-05:00,2005,11,8,8,30,25.6,21.5,194.0,227.0,282.0,0.1,1020.0,45.0,5.5,78.10377110822355 +2005-11-08 09:30:00-05:00,2005,11,8,9,30,25.9,21.1,85.0,834.0,543.0,0.1,1020.0,47.0,5.800000000000001,74.86827145232643 +2005-11-08 10:30:00-05:00,2005,11,8,10,30,26.200000000000003,21.0,95.0,876.0,678.0,0.1,1020.0,48.0,5.800000000000001,73.10091983463201 +2005-11-08 11:30:00-05:00,2005,11,8,11,30,26.3,21.0,311.0,493.0,670.0,0.1,1019.0,47.0,5.800000000000001,72.6703860563409 +2005-11-08 12:30:00-05:00,2005,11,8,12,30,26.4,21.1,398.0,218.0,557.0,0.1,1018.0,46.0,5.800000000000001,72.68777781506405 +2005-11-08 13:30:00-05:00,2005,11,8,13,30,26.4,21.3,92.0,888.0,695.0,0.1,1017.0,45.0,5.800000000000001,73.58514111197726 +2005-11-08 14:30:00-05:00,2005,11,8,14,30,26.4,21.5,85.0,842.0,564.0,0.1,1017.0,44.0,5.800000000000001,74.49220071370465 +2005-11-08 15:30:00-05:00,2005,11,8,15,30,26.200000000000003,21.6,73.0,756.0,385.0,0.1,1017.0,44.0,5.800000000000001,75.83976391469626 +2005-11-08 16:30:00-05:00,2005,11,8,16,30,25.9,21.700000000000003,52.0,569.0,178.0,0.1,1017.0,44.0,5.800000000000001,77.67117188646347 +2005-11-08 17:30:00-05:00,2005,11,8,17,30,25.5,21.8,0.0,0.0,0.0,0.1,1018.0,45.0,5.7,80.02334777354962 +2005-11-08 18:30:00-05:00,2005,11,8,18,30,25.3,21.8,0.0,0.0,0.0,0.1,1018.0,46.0,5.800000000000001,80.98041710164598 +2005-11-08 19:30:00-05:00,2005,11,8,19,30,25.3,21.8,0.0,0.0,0.0,0.1,1019.0,48.0,5.800000000000001,80.98041710164598 +2005-11-08 20:30:00-05:00,2005,11,8,20,30,25.200000000000003,21.8,0.0,0.0,0.0,0.1,1019.0,50.0,5.800000000000001,81.46377725252287 +2005-11-08 21:30:00-05:00,2005,11,8,21,30,25.200000000000003,21.8,0.0,0.0,0.0,0.1,1019.0,52.0,5.7,81.46377725252287 +2005-11-08 22:30:00-05:00,2005,11,8,22,30,25.1,21.700000000000003,0.0,0.0,0.0,0.1,1019.0,53.0,5.5,81.45123892697173 +2005-11-08 23:30:00-05:00,2005,11,8,23,30,25.0,21.700000000000003,0.0,0.0,0.0,0.1,1019.0,53.0,5.300000000000001,81.93813716288265 +2005-11-09 00:30:00-05:00,2005,11,9,0,30,24.8,21.700000000000003,0.0,0.0,0.0,0.1,1018.0,51.0,5.0,82.92178985776674 +2005-11-09 01:30:00-05:00,2005,11,9,1,30,24.6,21.6,0.0,0.0,0.0,0.1,1018.0,49.0,4.800000000000001,83.40722635492729 +2005-11-09 02:30:00-05:00,2005,11,9,2,30,24.4,21.6,0.0,0.0,0.0,0.1,1018.0,45.0,4.6000000000000005,84.41152946997488 +2005-11-09 03:30:00-05:00,2005,11,9,3,30,24.3,21.5,0.0,0.0,0.0,0.1,1018.0,41.0,4.6000000000000005,84.4007698442832 +2005-11-09 04:30:00-05:00,2005,11,9,4,30,24.3,21.5,0.0,0.0,0.0,0.1,1018.0,39.0,4.7,84.4007698442832 +2005-11-09 05:30:00-05:00,2005,11,9,5,30,24.200000000000003,21.4,0.0,0.0,0.0,0.1,1019.0,39.0,5.0,84.38999944745179 +2005-11-09 06:30:00-05:00,2005,11,9,6,30,24.4,21.200000000000003,0.0,0.0,0.0,0.1,1019.0,42.0,5.2,82.36835817462315 +2005-11-09 07:30:00-05:00,2005,11,9,7,30,24.9,21.3,52.0,496.0,145.0,0.1,1020.0,45.0,5.5,80.43463464682452 +2005-11-09 08:30:00-05:00,2005,11,9,8,30,25.4,21.3,76.0,730.0,357.0,0.1,1020.0,49.0,5.800000000000001,78.0747809293731 +2005-11-09 09:30:00-05:00,2005,11,9,9,30,25.700000000000003,20.9,90.0,836.0,547.0,0.1,1020.0,52.0,5.800000000000001,74.83571899254589 +2005-11-09 10:30:00-05:00,2005,11,9,10,30,26.0,20.700000000000003,92.0,902.0,689.0,0.1,1020.0,52.0,5.7,72.6181182590597 +2005-11-09 11:30:00-05:00,2005,11,9,11,30,26.1,20.6,96.0,924.0,764.0,0.1,1019.0,51.0,5.6000000000000005,71.74643201629246 +2005-11-09 12:30:00-05:00,2005,11,9,12,30,26.200000000000003,20.5,292.0,585.0,718.0,0.1,1019.0,49.0,5.6000000000000005,70.88518933045883 +2005-11-09 13:30:00-05:00,2005,11,9,13,30,26.200000000000003,20.4,106.0,876.0,697.0,0.1,1018.0,49.0,5.6000000000000005,70.44918074896195 +2005-11-09 14:30:00-05:00,2005,11,9,14,30,26.1,20.3,100.0,824.0,566.0,0.1,1017.0,50.0,5.5,70.43064021903338 +2005-11-09 15:30:00-05:00,2005,11,9,15,30,25.9,20.200000000000003,86.0,726.0,384.0,0.1,1017.0,49.0,5.300000000000001,70.8301719640559 +2005-11-09 16:30:00-05:00,2005,11,9,16,30,25.4,20.200000000000003,61.0,520.0,175.0,0.1,1017.0,47.0,5.2,72.96297423706714 +2005-11-09 17:30:00-05:00,2005,11,9,17,30,24.9,20.3,0.0,0.0,0.0,0.1,1017.0,46.0,5.2,75.63424522443381 +2005-11-09 18:30:00-05:00,2005,11,9,18,30,24.700000000000003,20.3,0.0,0.0,0.0,0.1,1018.0,47.0,5.2,76.54290300545956 +2005-11-09 19:30:00-05:00,2005,11,9,19,30,24.6,20.200000000000003,0.0,0.0,0.0,0.1,1018.0,50.0,5.1000000000000005,76.52748366100347 +2005-11-09 20:30:00-05:00,2005,11,9,20,30,24.4,20.1,0.0,0.0,0.0,0.1,1019.0,53.0,4.800000000000001,76.97148310021423 +2005-11-09 21:30:00-05:00,2005,11,9,21,30,24.3,20.0,0.0,0.0,0.0,0.1,1018.0,55.0,4.4,76.95628699261529 +2005-11-09 22:30:00-05:00,2005,11,9,22,30,24.200000000000003,19.9,0.0,0.0,0.0,0.1,1018.0,56.0,4.1000000000000005,76.94107668815428 +2005-11-09 23:30:00-05:00,2005,11,9,23,30,24.0,19.700000000000003,0.0,0.0,0.0,0.1,1018.0,56.0,3.7,76.91061342319857 +2005-11-10 00:30:00-05:00,2005,11,10,0,30,23.9,19.5,0.0,0.0,0.0,0.1,1017.0,53.0,3.3000000000000003,76.4191461777084 +2005-11-10 01:30:00-05:00,2005,11,10,1,30,23.8,19.3,0.0,0.0,0.0,0.1,1017.0,48.0,3.0,75.92972355990815 +2005-11-10 02:30:00-05:00,2005,11,10,2,30,23.8,19.0,0.0,0.0,0.0,0.1,1017.0,38.0,2.7,74.52349834248494 +2005-11-10 03:30:00-05:00,2005,11,10,3,30,23.6,18.9,0.0,0.0,0.0,0.1,1017.0,25.0,2.6,74.95700457694504 +2005-11-10 04:30:00-05:00,2005,11,10,4,30,23.4,18.8,0.0,0.0,0.0,0.1,1017.0,13.0,2.7,75.39403601799349 +2005-11-10 05:30:00-05:00,2005,11,10,5,30,23.200000000000003,18.9,0.0,0.0,0.0,0.1,1017.0,4.0,3.0,76.78818920733228 +2005-11-10 06:30:00-05:00,2005,11,10,6,30,23.200000000000003,19.1,0.0,0.0,0.0,0.1,1018.0,180.0,3.4000000000000004,77.75225860307187 +2005-11-10 07:30:00-05:00,2005,11,10,7,30,23.6,19.5,52.0,494.0,144.0,0.1,1018.0,359.0,3.8,77.81139794609358 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16:30:00-05:00,2005,11,10,16,30,25.6,19.4,62.0,511.0,172.0,0.1,1015.0,5.0,3.0,68.61205443506742 +2005-11-10 17:30:00-05:00,2005,11,10,17,30,25.1,19.5,0.0,0.0,0.0,0.1,1015.0,10.0,2.7,71.12356021033422 +2005-11-10 18:30:00-05:00,2005,11,10,18,30,24.8,19.3,0.0,0.0,0.0,0.1,1016.0,15.0,2.5,71.51256881779813 +2005-11-10 19:30:00-05:00,2005,11,10,19,30,24.700000000000003,19.4,0.0,0.0,0.0,0.1,1016.0,17.0,2.5,72.39001111749039 +2005-11-10 20:30:00-05:00,2005,11,10,20,30,24.5,19.4,0.0,0.0,0.0,0.1,1016.0,19.0,2.3000000000000003,73.26100124235181 +2005-11-10 21:30:00-05:00,2005,11,10,21,30,24.3,19.4,0.0,0.0,0.0,0.1,1017.0,17.0,2.0,74.14379783139346 +2005-11-10 22:30:00-05:00,2005,11,10,22,30,24.200000000000003,19.200000000000003,0.0,0.0,0.0,0.1,1016.0,10.0,1.8,73.66692388726497 +2005-11-10 23:30:00-05:00,2005,11,10,23,30,24.0,19.0,0.0,0.0,0.0,0.1,1016.0,178.0,1.6,73.63287364526842 +2005-11-11 00:30:00-05:00,2005,11,11,0,30,23.700000000000003,18.9,0.0,0.0,0.0,0.1,1016.0,344.0,1.6,74.50691547778867 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09:30:00-05:00,2005,11,11,9,30,24.4,19.9,118.0,754.0,525.0,0.1,1018.0,13.0,3.9,76.02429279335996 +2005-11-11 10:30:00-05:00,2005,11,11,10,30,25.1,19.8,316.0,430.0,598.0,0.1,1018.0,17.0,3.8,72.46047834811137 +2005-11-11 11:30:00-05:00,2005,11,11,11,30,25.6,19.700000000000003,138.0,836.0,737.0,0.1,1017.0,19.0,3.9,69.9027551656513 +2005-11-11 12:30:00-05:00,2005,11,11,12,30,26.0,19.8,138.0,836.0,741.0,0.1,1016.0,20.0,4.2,68.68980129070269 +2005-11-11 13:30:00-05:00,2005,11,11,13,30,26.200000000000003,19.9,130.0,818.0,677.0,0.1,1015.0,23.0,4.5,68.30422874023395 +2005-11-11 14:30:00-05:00,2005,11,11,14,30,26.3,20.1,118.0,766.0,548.0,0.1,1015.0,28.0,4.9,68.74793905424795 +2005-11-11 15:30:00-05:00,2005,11,11,15,30,26.1,20.3,97.0,668.0,369.0,0.1,1015.0,33.0,5.300000000000001,70.43064021903338 +2005-11-11 16:30:00-05:00,2005,11,11,16,30,25.6,20.6,6.0,0.0,6.0,0.1,1015.0,38.0,5.6000000000000005,73.90356382924115 +2005-11-11 17:30:00-05:00,2005,11,11,17,30,25.1,20.9,0.0,0.0,0.0,0.1,1016.0,43.0,5.9,77.55304107567287 +2005-11-11 18:30:00-05:00,2005,11,11,18,30,24.8,20.8,0.0,0.0,0.0,0.1,1017.0,49.0,6.1000000000000005,78.46904480661803 +2005-11-11 19:30:00-05:00,2005,11,11,19,30,24.6,20.700000000000003,0.0,0.0,0.0,0.1,1017.0,54.0,6.300000000000001,78.9251158166903 +2005-11-11 20:30:00-05:00,2005,11,11,20,30,24.5,20.5,0.0,0.0,0.0,0.1,1018.0,58.0,6.300000000000001,78.42603957170671 +2005-11-11 21:30:00-05:00,2005,11,11,21,30,24.3,20.4,0.0,0.0,0.0,0.1,1018.0,62.0,6.2,78.88287046032437 +2005-11-11 22:30:00-05:00,2005,11,11,22,30,24.1,20.200000000000003,0.0,0.0,0.0,0.1,1018.0,64.0,6.1000000000000005,78.85463981040904 +2005-11-11 23:30:00-05:00,2005,11,11,23,30,24.0,20.0,0.0,0.0,0.0,0.1,1018.0,66.0,5.800000000000001,78.3540921272656 +2005-11-12 00:30:00-05:00,2005,11,12,0,30,23.9,19.9,0.0,0.0,0.0,0.1,1017.0,66.0,5.6000000000000005,78.339661685973 +2005-11-12 01:30:00-05:00,2005,11,12,1,30,23.8,19.8,0.0,0.0,0.0,0.1,1017.0,65.0,5.4,78.32521755630037 +2005-11-12 02:30:00-05:00,2005,11,12,2,30,23.700000000000003,19.700000000000003,0.0,0.0,0.0,0.1,1017.0,62.0,5.300000000000001,78.31075972209996 +2005-11-12 03:30:00-05:00,2005,11,12,3,30,23.700000000000003,19.6,0.0,0.0,0.0,0.1,1017.0,60.0,5.300000000000001,77.82614790469653 +2005-11-12 04:30:00-05:00,2005,11,12,4,30,23.6,19.4,0.0,0.0,0.0,0.1,1017.0,59.0,5.4,77.32914456243964 +2005-11-12 05:30:00-05:00,2005,11,12,5,30,23.6,19.3,0.0,0.0,0.0,0.1,1018.0,60.0,5.5,76.8495158098516 +2005-11-12 06:30:00-05:00,2005,11,12,6,30,23.700000000000003,19.200000000000003,0.0,0.0,0.0,0.1,1018.0,62.0,5.6000000000000005,75.9139109045795 +2005-11-12 07:30:00-05:00,2005,11,12,7,30,24.200000000000003,19.3,55.0,432.0,133.0,0.1,1019.0,64.0,5.800000000000001,74.1270413690411 +2005-11-12 08:30:00-05:00,2005,11,12,8,30,24.6,19.4,168.0,10.0,172.0,0.1,1020.0,67.0,6.1000000000000005,72.82404132684132 +2005-11-12 09:30:00-05:00,2005,11,12,9,30,24.9,19.3,97.0,799.0,526.0,0.1,1020.0,69.0,6.1000000000000005,71.08698923224469 +2005-11-12 10:30:00-05:00,2005,11,12,10,30,25.200000000000003,19.4,128.0,807.0,654.0,0.1,1020.0,71.0,6.0,70.2630526516137 +2005-11-12 11:30:00-05:00,2005,11,12,11,30,25.4,19.5,127.0,848.0,732.0,0.1,1020.0,72.0,5.800000000000001,69.86503547584091 +2005-11-12 12:30:00-05:00,2005,11,12,12,30,25.4,19.6,356.0,373.0,625.0,0.1,1019.0,73.0,5.7,70.300407071698 +2005-11-12 13:30:00-05:00,2005,11,12,13,30,25.4,19.700000000000003,356.0,76.0,407.0,0.1,1019.0,73.0,5.6000000000000005,70.73815722318867 +2005-11-12 14:30:00-05:00,2005,11,12,14,30,25.4,19.700000000000003,277.0,346.0,470.0,0.1,1019.0,73.0,5.5,70.73815722318867 +2005-11-12 15:30:00-05:00,2005,11,12,15,30,25.200000000000003,19.700000000000003,95.0,671.0,366.0,0.1,1019.0,73.0,5.5,71.58481125711234 +2005-11-12 16:30:00-05:00,2005,11,12,16,30,24.9,19.700000000000003,64.0,462.0,163.0,0.1,1019.0,73.0,5.4,72.87625740485696 +2005-11-12 17:30:00-05:00,2005,11,12,17,30,24.6,19.700000000000003,0.0,0.0,0.0,0.1,1019.0,74.0,5.5,74.19397615999193 +2005-11-12 18:30:00-05:00,2005,11,12,18,30,24.4,19.5,0.0,0.0,0.0,0.1,1020.0,76.0,5.6000000000000005,74.16053910624595 +2005-11-12 19:30:00-05:00,2005,11,12,19,30,24.4,19.5,0.0,0.0,0.0,0.1,1020.0,79.0,5.7,74.16053910624595 +2005-11-12 20:30:00-05:00,2005,11,12,20,30,24.4,19.6,0.0,0.0,0.0,0.1,1021.0,82.0,5.7,74.62267860192362 +2005-11-12 21:30:00-05:00,2005,11,12,21,30,24.3,19.6,0.0,0.0,0.0,0.1,1021.0,85.0,5.7,75.07110279915447 +2005-11-12 22:30:00-05:00,2005,11,12,22,30,24.200000000000003,19.700000000000003,0.0,0.0,0.0,0.1,1021.0,88.0,5.7,75.99282802401139 +2005-11-12 23:30:00-05:00,2005,11,12,23,30,24.1,19.9,0.0,0.0,0.0,0.1,1020.0,90.0,5.6000000000000005,77.40412716687173 +2005-11-13 00:30:00-05:00,2005,11,13,0,30,24.1,19.9,0.0,0.0,0.0,0.1,1020.0,92.0,5.5,77.40412716687173 +2005-11-13 01:30:00-05:00,2005,11,13,1,30,24.0,20.0,0.0,0.0,0.0,0.1,1020.0,92.0,5.4,78.3540921272656 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10:30:00-05:00,2005,11,13,10,30,25.8,20.1,101.0,861.0,659.0,0.1,1022.0,82.0,6.0,70.81180097230057 +2005-11-13 11:30:00-05:00,2005,11,13,11,30,25.9,20.3,106.0,883.0,734.0,0.1,1022.0,83.0,5.9,71.26920678894551 +2005-11-13 12:30:00-05:00,2005,11,13,12,30,26.0,20.5,106.0,883.0,738.0,0.1,1021.0,82.0,5.800000000000001,71.72853712115179 +2005-11-13 13:30:00-05:00,2005,11,13,13,30,26.0,20.6,374.0,155.0,477.0,0.1,1021.0,81.0,5.800000000000001,72.17212580747973 +2005-11-13 14:30:00-05:00,2005,11,13,14,30,25.9,20.700000000000003,285.0,283.0,442.0,0.1,1020.0,78.0,5.7,73.04930540173858 +2005-11-13 15:30:00-05:00,2005,11,13,15,30,25.700000000000003,20.9,91.0,677.0,362.0,0.1,1020.0,76.0,5.6000000000000005,74.83571899254589 +2005-11-13 16:30:00-05:00,2005,11,13,16,30,25.3,21.0,60.0,479.0,161.0,0.1,1020.0,74.0,5.5,77.10761189838719 +2005-11-13 17:30:00-05:00,2005,11,13,17,30,24.9,21.0,0.0,0.0,0.0,0.1,1021.0,73.0,5.300000000000001,78.96724081229702 +2005-11-13 18:30:00-05:00,2005,11,13,18,30,24.700000000000003,20.8,0.0,0.0,0.0,0.1,1021.0,74.0,5.300000000000001,78.93917083416129 +2005-11-13 19:30:00-05:00,2005,11,13,19,30,24.700000000000003,20.700000000000003,0.0,0.0,0.0,0.1,1021.0,77.0,5.300000000000001,78.45472329360548 +2005-11-13 20:30:00-05:00,2005,11,13,20,30,24.700000000000003,20.6,0.0,0.0,0.0,0.1,1022.0,81.0,5.300000000000001,77.97288466684681 +2005-11-13 21:30:00-05:00,2005,11,13,21,30,24.8,20.4,0.0,0.0,0.0,0.1,1021.0,85.0,5.2,76.55830803934221 +2005-11-13 22:30:00-05:00,2005,11,13,22,30,24.700000000000003,20.4,0.0,0.0,0.0,0.1,1021.0,89.0,5.2,77.01698640509356 +2005-11-13 23:30:00-05:00,2005,11,13,23,30,24.700000000000003,20.4,0.0,0.0,0.0,0.1,1021.0,91.0,5.2,77.01698640509356 +2005-11-14 00:30:00-05:00,2005,11,14,0,30,24.6,20.4,0.0,0.0,0.0,0.1,1020.0,92.0,5.1000000000000005,77.4787586609194 +2005-11-14 01:30:00-05:00,2005,11,14,1,30,24.5,20.4,0.0,0.0,0.0,0.1,1020.0,91.0,4.9,77.94364788186208 +2005-11-14 02:30:00-05:00,2005,11,14,2,30,24.5,20.3,0.0,0.0,0.0,0.1,1020.0,89.0,4.7,77.46386035325963 +2005-11-14 03:30:00-05:00,2005,11,14,3,30,24.4,20.3,0.0,0.0,0.0,0.1,1020.0,85.0,4.5,77.929008814519 +2005-11-14 04:30:00-05:00,2005,11,14,4,30,24.4,20.1,0.0,0.0,0.0,0.1,1020.0,84.0,4.5,76.97148310021423 +2005-11-14 05:30:00-05:00,2005,11,14,5,30,24.4,19.8,0.0,0.0,0.0,0.1,1020.0,83.0,4.5,75.5545437025466 +2005-11-14 06:30:00-05:00,2005,11,14,6,30,24.6,19.5,0.0,0.0,0.0,0.1,1021.0,84.0,4.7,73.2781992066413 +2005-11-14 07:30:00-05:00,2005,11,14,7,30,25.200000000000003,19.6,58.0,384.0,125.0,0.1,1021.0,86.0,4.9,71.14182174194357 +2005-11-14 08:30:00-05:00,2005,11,14,8,30,25.700000000000003,19.8,91.0,642.0,327.0,0.1,1022.0,89.0,5.2,69.92159070117638 +2005-11-14 09:30:00-05:00,2005,11,14,9,30,26.0,19.4,109.0,759.0,511.0,0.1,1022.0,90.0,5.6000000000000005,67.00458770890886 +2005-11-14 10:30:00-05:00,2005,11,14,10,30,26.200000000000003,19.5,115.0,829.0,649.0,0.1,1022.0,90.0,5.800000000000001,66.62973426639375 +2005-11-14 11:30:00-05:00,2005,11,14,11,30,26.3,19.9,118.0,855.0,723.0,0.1,1021.0,89.0,5.800000000000001,67.90194546200273 +2005-11-14 12:30:00-05:00,2005,11,14,12,30,26.3,20.4,118.0,857.0,728.0,0.1,1020.0,87.0,5.9,70.03426460243496 +2005-11-14 13:30:00-05:00,2005,11,14,13,30,26.3,20.9,357.0,89.0,416.0,0.1,1019.0,85.0,5.9,72.2250832944419 +2005-11-14 14:30:00-05:00,2005,11,14,14,30,26.200000000000003,21.3,247.0,18.0,257.0,0.1,1019.0,84.0,5.9,74.45930386781104 +2005-11-14 15:30:00-05:00,2005,11,14,15,30,26.0,21.5,155.0,5.0,157.0,0.1,1019.0,85.0,6.0,76.2739291908271 +2005-11-14 16:30:00-05:00,2005,11,14,16,30,25.8,21.6,62.0,455.0,157.0,0.1,1019.0,86.0,6.0,77.65645384295199 +2005-11-14 17:30:00-05:00,2005,11,14,17,30,25.5,21.4,0.0,0.0,0.0,0.1,1019.0,87.0,6.0,78.0892828330312 +2005-11-14 18:30:00-05:00,2005,11,14,18,30,25.4,21.200000000000003,0.0,0.0,0.0,0.1,1019.0,88.0,6.300000000000001,77.59744380650204 +2005-11-14 19:30:00-05:00,2005,11,14,19,30,25.4,21.0,0.0,0.0,0.0,0.11,1020.0,90.0,6.6000000000000005,76.6504386336582 +2005-11-14 20:30:00-05:00,2005,11,14,20,30,25.3,20.700000000000003,0.0,0.0,0.0,0.11,1020.0,92.0,6.7,75.69774320958695 +2005-11-14 21:30:00-05:00,2005,11,14,21,30,25.1,20.6,0.0,0.0,0.0,0.11,1020.0,95.0,6.7,76.13396255548486 +2005-11-14 22:30:00-05:00,2005,11,14,22,30,25.0,20.5,0.0,0.0,0.0,0.11,1020.0,97.0,6.5,76.11833883251128 +2005-11-14 23:30:00-05:00,2005,11,14,23,30,24.9,20.4,0.0,0.0,0.0,0.11,1020.0,99.0,6.2,76.10270067486522 +2005-11-15 00:30:00-05:00,2005,11,15,0,30,24.700000000000003,20.3,0.0,0.0,0.0,0.11,1019.0,99.0,5.9,76.54290300545956 +2005-11-15 01:30:00-05:00,2005,11,15,1,30,24.6,20.3,0.0,0.0,0.0,0.11,1019.0,99.0,5.6000000000000005,77.00183279014863 +2005-11-15 02:30:00-05:00,2005,11,15,2,30,24.5,20.3,0.0,0.0,0.0,0.11,1019.0,98.0,5.4,77.46386035325963 +2005-11-15 03:30:00-05:00,2005,11,15,3,30,24.5,20.3,0.0,0.0,0.0,0.11,1019.0,96.0,5.300000000000001,77.46386035325963 +2005-11-15 04:30:00-05:00,2005,11,15,4,30,24.4,20.3,0.0,0.0,0.0,0.11,1019.0,94.0,5.300000000000001,77.929008814519 +2005-11-15 05:30:00-05:00,2005,11,15,5,30,24.4,20.4,0.0,0.0,0.0,0.11,1019.0,92.0,5.4,78.41167733084453 +2005-11-15 06:30:00-05:00,2005,11,15,6,30,24.6,20.5,0.0,0.0,0.0,0.11,1019.0,91.0,5.5,77.95827316060198 +2005-11-15 07:30:00-05:00,2005,11,15,7,30,25.0,20.8,55.0,400.0,124.0,0.11,1020.0,90.0,5.7,77.53821242201315 +2005-11-15 08:30:00-05:00,2005,11,15,8,30,25.5,21.200000000000003,84.0,667.0,327.0,0.11,1020.0,89.0,6.0,77.13770794814462 +2005-11-15 09:30:00-05:00,2005,11,15,9,30,25.8,21.200000000000003,98.0,788.0,513.0,0.11,1020.0,90.0,6.2,75.77678837086546 +2005-11-15 10:30:00-05:00,2005,11,15,10,30,26.1,21.3,106.0,846.0,649.0,0.11,1020.0,90.0,6.2,74.90076482087753 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19:30:00-05:00,2005,11,15,19,30,25.3,21.200000000000003,0.0,0.0,0.0,0.11,1019.0,96.0,5.800000000000001,78.06026538132947 +2005-11-15 20:30:00-05:00,2005,11,15,20,30,25.200000000000003,21.1,0.0,0.0,0.0,0.11,1020.0,99.0,5.800000000000001,78.04573617296003 +2005-11-15 21:30:00-05:00,2005,11,15,21,30,25.200000000000003,21.1,0.0,0.0,0.0,0.11,1020.0,101.0,5.800000000000001,78.04573617296003 +2005-11-15 22:30:00-05:00,2005,11,15,22,30,25.1,21.200000000000003,0.0,0.0,0.0,0.11,1019.0,102.0,5.7,78.99525748681867 +2005-11-15 23:30:00-05:00,2005,11,15,23,30,24.9,21.5,0.0,0.0,0.0,0.11,1019.0,103.0,5.5,81.42612568109206 +2005-11-16 00:30:00-05:00,2005,11,16,0,30,24.8,21.6,0.0,0.0,0.0,0.11,1019.0,103.0,5.300000000000001,82.4163453099894 +2005-11-16 01:30:00-05:00,2005,11,16,1,30,24.700000000000003,21.6,0.0,0.0,0.0,0.11,1018.0,102.0,5.0,82.91012051931818 +2005-11-16 02:30:00-05:00,2005,11,16,2,30,24.6,21.6,0.0,0.0,0.0,0.11,1018.0,100.0,4.7,83.40722635492729 +2005-11-16 03:30:00-05:00,2005,11,16,3,30,24.5,21.5,0.0,0.0,0.0,0.11,1018.0,96.0,4.3,83.39584813929504 +2005-11-16 04:30:00-05:00,2005,11,16,4,30,24.5,21.3,0.0,0.0,0.0,0.11,1018.0,90.0,3.9,82.38037263885073 +2005-11-16 05:30:00-05:00,2005,11,16,5,30,24.5,21.200000000000003,0.0,0.0,0.0,0.11,1019.0,83.0,3.7,81.8767117948703 +2005-11-16 06:30:00-05:00,2005,11,16,6,30,24.700000000000003,21.200000000000003,0.0,0.0,0.0,0.11,1019.0,77.0,3.6,80.90329065374303 +2005-11-16 07:30:00-05:00,2005,11,16,7,30,25.200000000000003,21.4,52.0,420.0,123.0,0.11,1020.0,74.0,3.6,79.49489892026216 +2005-11-16 08:30:00-05:00,2005,11,16,8,30,25.8,21.6,80.0,681.0,327.0,0.11,1020.0,76.0,3.7,77.65645384295199 +2005-11-16 09:30:00-05:00,2005,11,16,9,30,26.200000000000003,21.3,95.0,797.0,512.0,0.11,1020.0,78.0,3.7,74.45930386781104 +2005-11-16 10:30:00-05:00,2005,11,16,10,30,26.5,21.3,105.0,850.0,647.0,0.11,1020.0,77.0,3.4000000000000004,73.15239660799756 +2005-11-16 11:30:00-05:00,2005,11,16,11,30,26.700000000000003,21.4,290.0,561.0,684.0,0.11,1019.0,73.0,3.2,72.73986079822475 +2005-11-16 12:30:00-05:00,2005,11,16,12,30,26.8,21.4,275.0,618.0,712.0,0.11,1018.0,67.0,3.0,72.31303529997608 +2005-11-16 13:30:00-05:00,2005,11,16,13,30,26.8,21.3,111.0,844.0,664.0,0.11,1018.0,61.0,2.9000000000000004,71.8712579494187 +2005-11-16 14:30:00-05:00,2005,11,16,14,30,26.700000000000003,21.200000000000003,102.0,793.0,537.0,0.11,1017.0,56.0,2.8000000000000003,71.85347252374494 +2005-11-16 15:30:00-05:00,2005,11,16,15,30,26.6,21.1,86.0,697.0,361.0,0.11,1017.0,52.0,2.9000000000000004,71.83567150828277 +2005-11-16 16:30:00-05:00,2005,11,16,16,30,26.1,21.200000000000003,59.0,489.0,159.0,0.11,1017.0,51.0,3.0,74.44283314108475 +2005-11-16 17:30:00-05:00,2005,11,16,17,30,25.5,21.4,0.0,0.0,0.0,0.11,1017.0,54.0,3.1,78.0892828330312 +2005-11-16 18:30:00-05:00,2005,11,16,18,30,25.1,21.3,0.0,0.0,0.0,0.11,1018.0,56.0,3.3000000000000003,79.48119319654701 +2005-11-16 19:30:00-05:00,2005,11,16,19,30,24.9,21.4,0.0,0.0,0.0,0.1,1018.0,57.0,3.3000000000000003,80.92904925429298 +2005-11-16 20:30:00-05:00,2005,11,16,20,30,24.700000000000003,21.4,0.0,0.0,0.0,0.1,1019.0,59.0,3.1,81.90131796799164 +2005-11-16 21:30:00-05:00,2005,11,16,21,30,24.6,21.4,0.0,0.0,0.0,0.1,1019.0,60.0,2.9000000000000004,82.39237530635937 +2005-11-16 22:30:00-05:00,2005,11,16,22,30,24.4,21.4,0.0,0.0,0.0,0.1,1019.0,60.0,2.8000000000000003,83.38445864005315 +2005-11-16 23:30:00-05:00,2005,11,16,23,30,24.4,21.4,0.0,0.0,0.0,0.1,1018.0,61.0,2.8000000000000003,83.38445864005315 +2005-11-17 00:30:00-05:00,2005,11,17,0,30,24.3,21.4,0.0,0.0,0.0,0.1,1018.0,63.0,2.9000000000000004,83.8855343133972 +2005-11-17 01:30:00-05:00,2005,11,17,1,30,24.200000000000003,21.4,0.0,0.0,0.0,0.1,1017.0,66.0,2.9000000000000004,84.38999944745179 +2005-11-17 02:30:00-05:00,2005,11,17,2,30,24.200000000000003,21.4,0.0,0.0,0.0,0.1,1017.0,70.0,2.9000000000000004,84.38999944745179 +2005-11-17 03:30:00-05:00,2005,11,17,3,30,24.1,21.4,0.0,0.0,0.0,0.1,1017.0,68.0,2.7,84.89787939045657 +2005-11-17 04:30:00-05:00,2005,11,17,4,30,24.0,21.4,0.0,0.0,0.0,0.1,1017.0,56.0,2.6,85.40919969758345 +2005-11-17 05:30:00-05:00,2005,11,17,5,30,23.9,21.3,0.0,0.0,0.0,0.1,1017.0,41.0,2.6,85.39905628041207 +2005-11-17 06:30:00-05:00,2005,11,17,6,30,24.0,21.4,0.0,0.0,0.0,0.1,1018.0,28.0,2.7,85.40919969758345 +2005-11-17 07:30:00-05:00,2005,11,17,7,30,24.5,21.6,46.0,461.0,122.0,0.1,1018.0,17.0,2.9000000000000004,83.90768765749962 +2005-11-17 08:30:00-05:00,2005,11,17,8,30,24.9,21.9,71.0,704.0,324.0,0.1,1019.0,9.0,3.2,83.4412934425311 +2005-11-17 09:30:00-05:00,2005,11,17,9,30,25.3,22.0,256.0,294.0,409.0,0.1,1019.0,9.0,3.5,81.97484863603587 +2005-11-17 10:30:00-05:00,2005,11,17,10,30,25.8,22.1,173.0,0.0,173.0,0.1,1019.0,12.0,3.6,80.06341363641828 +2005-11-17 11:30:00-05:00,2005,11,17,11,30,26.200000000000003,22.1,378.0,105.0,452.0,0.1,1018.0,16.0,3.6,78.19041545046963 +2005-11-17 12:30:00-05:00,2005,11,17,12,30,26.5,22.0,389.0,184.0,519.0,0.1,1017.0,21.0,3.8,76.35118814820123 +2005-11-17 13:30:00-05:00,2005,11,17,13,30,26.6,22.0,213.0,575.0,588.0,0.1,1016.0,26.0,3.9,75.90250895113047 +2005-11-17 14:30:00-05:00,2005,11,17,14,30,26.5,22.0,183.0,490.0,450.0,0.1,1016.0,30.0,4.1000000000000005,76.35118814820123 +2005-11-17 15:30:00-05:00,2005,11,17,15,30,26.200000000000003,22.1,147.0,407.0,307.0,0.1,1015.0,32.0,4.2,78.19041545046963 +2005-11-17 16:30:00-05:00,2005,11,17,16,30,25.700000000000003,22.3,104.0,120.0,128.0,0.1,1016.0,33.0,4.4,81.52628650446705 +2005-11-17 17:30:00-05:00,2005,11,17,17,30,25.3,22.5,0.0,0.0,0.0,0.1,1016.0,32.0,4.6000000000000005,84.50788366750213 +2005-11-17 18:30:00-05:00,2005,11,17,18,30,25.0,22.6,0.0,0.0,0.0,0.1,1017.0,30.0,4.800000000000001,86.5553591977721 +2005-11-17 19:30:00-05:00,2005,11,17,19,30,24.8,22.700000000000003,0.0,0.0,0.0,0.11,1017.0,29.0,5.1000000000000005,88.12759322404813 +2005-11-17 20:30:00-05:00,2005,11,17,20,30,24.6,22.700000000000003,0.0,0.0,0.0,0.11,1017.0,28.0,5.2,89.1871398629249 +2005-11-17 21:30:00-05:00,2005,11,17,21,30,24.5,22.8,0.0,0.0,0.0,0.11,1017.0,30.0,5.2,90.26797351714104 +2005-11-17 22:30:00-05:00,2005,11,17,22,30,24.4,22.8,0.0,0.0,0.0,0.11,1017.0,32.0,5.1000000000000005,90.8100070382052 +2005-11-17 23:30:00-05:00,2005,11,17,23,30,24.3,22.8,0.0,0.0,0.0,0.11,1017.0,33.0,4.9,91.35570447589521 +2005-11-18 00:30:00-05:00,2005,11,18,0,30,24.1,22.8,0.0,0.0,0.0,0.11,1017.0,34.0,4.6000000000000005,92.45820085318411 +2005-11-18 01:30:00-05:00,2005,11,18,1,30,24.0,22.700000000000003,0.0,0.0,0.0,0.11,1016.0,33.0,4.4,92.45275683198949 +2005-11-18 02:30:00-05:00,2005,11,18,2,30,23.8,22.5,0.0,0.0,0.0,0.11,1016.0,30.0,4.4,92.44185134155835 +2005-11-18 03:30:00-05:00,2005,11,18,3,30,23.700000000000003,22.4,0.0,0.0,0.0,0.11,1015.0,29.0,4.4,92.43638985600242 +2005-11-18 04:30:00-05:00,2005,11,18,4,30,23.6,22.3,0.0,0.0,0.0,0.11,1015.0,30.0,4.6000000000000005,92.43092253264987 +2005-11-18 05:30:00-05:00,2005,11,18,5,30,23.6,22.3,0.0,0.0,0.0,0.11,1016.0,33.0,4.6000000000000005,92.43092253264987 +2005-11-18 06:30:00-05:00,2005,11,18,6,30,23.700000000000003,22.3,0.0,0.0,0.0,0.11,1016.0,37.0,4.7,91.87590901667876 +2005-11-18 07:30:00-05:00,2005,11,18,7,30,23.9,22.3,29.0,0.0,29.0,0.11,1017.0,41.0,4.7,90.77708879175361 +2005-11-18 08:30:00-05:00,2005,11,18,8,30,24.200000000000003,22.5,94.0,5.0,96.0,0.11,1018.0,44.0,4.800000000000001,90.24714719552352 +2005-11-18 09:30:00-05:00,2005,11,18,9,30,24.5,22.5,9.0,0.0,9.0,0.11,1018.0,47.0,4.800000000000001,88.63956075011559 +2005-11-18 10:30:00-05:00,2005,11,18,10,30,24.9,22.6,191.0,10.0,198.0,0.11,1018.0,49.0,4.9,87.07315614835665 +2005-11-18 11:30:00-05:00,2005,11,18,11,30,25.200000000000003,22.6,265.0,2.0,266.0,0.11,1017.0,48.0,5.0,85.53012559102362 +2005-11-18 12:30:00-05:00,2005,11,18,12,30,25.4,22.5,268.0,405.0,551.0,0.11,1016.0,46.0,5.1000000000000005,84.00683397707049 +2005-11-18 13:30:00-05:00,2005,11,18,13,30,25.5,22.5,197.0,1.0,197.0,0.11,1015.0,44.0,5.300000000000001,83.50912487697896 +2005-11-18 14:30:00-05:00,2005,11,18,14,30,25.4,22.6,169.0,1.0,170.0,0.11,1015.0,41.0,5.4,84.51853633651618 +2005-11-18 15:30:00-05:00,2005,11,18,15,30,25.3,22.700000000000003,148.0,30.0,159.0,0.11,1015.0,40.0,5.6000000000000005,85.54013688901695 +2005-11-18 16:30:00-05:00,2005,11,18,16,30,25.1,22.8,26.0,0.0,26.0,0.11,1015.0,40.0,5.7,87.09120889789614 +2005-11-18 17:30:00-05:00,2005,11,18,17,30,25.0,22.9,0.0,0.0,0.0,0.11,1015.0,40.0,5.9,88.14427552074834 +2005-11-18 18:30:00-05:00,2005,11,18,18,30,24.8,22.9,0.0,0.0,0.0,0.11,1016.0,40.0,6.2,89.20243179762628 +2005-11-18 19:30:00-05:00,2005,11,18,19,30,24.8,22.8,0.0,0.0,0.0,0.11,1016.0,39.0,6.4,88.66358594207703 +2005-11-18 20:30:00-05:00,2005,11,18,20,30,24.700000000000003,22.8,0.0,0.0,0.0,0.11,1017.0,39.0,6.7,89.1947898015023 +2005-11-18 21:30:00-05:00,2005,11,18,21,30,24.6,22.700000000000003,0.0,0.0,0.0,0.11,1017.0,40.0,6.9,89.1871398629249 +2005-11-18 22:30:00-05:00,2005,11,18,22,30,24.6,22.6,0.0,0.0,0.0,0.11,1016.0,43.0,7.1000000000000005,88.64757743467587 +2005-11-18 23:30:00-05:00,2005,11,18,23,30,24.6,22.6,0.0,0.0,0.0,0.11,1016.0,47.0,7.1000000000000005,88.64757743467587 +2005-11-19 00:30:00-05:00,2005,11,19,0,30,24.6,22.5,0.0,0.0,0.0,0.11,1015.0,51.0,7.0,88.1108765345105 +2005-11-19 01:30:00-05:00,2005,11,19,1,30,24.5,22.5,0.0,0.0,0.0,0.11,1015.0,55.0,6.7,88.63956075011559 +2005-11-19 02:30:00-05:00,2005,11,19,2,30,24.5,22.5,0.0,0.0,0.0,0.11,1014.0,59.0,6.2,88.63956075011559 +2005-11-19 03:30:00-05:00,2005,11,19,3,30,24.5,22.5,0.0,0.0,0.0,0.11,1014.0,60.0,5.7,88.63956075011559 +2005-11-19 04:30:00-05:00,2005,11,19,4,30,24.5,22.4,0.0,0.0,0.0,0.11,1014.0,60.0,5.4,88.10250526368289 +2005-11-19 05:30:00-05:00,2005,11,19,5,30,24.5,22.5,0.0,0.0,0.0,0.11,1015.0,60.0,5.300000000000001,88.63956075011559 +2005-11-19 06:30:00-05:00,2005,11,19,6,30,24.6,22.5,0.0,0.0,0.0,0.11,1016.0,60.0,5.300000000000001,88.1108765345105 +2005-11-19 07:30:00-05:00,2005,11,19,7,30,24.9,22.6,75.0,6.0,76.0,0.11,1016.0,61.0,5.4,87.07315614835665 +2005-11-19 08:30:00-05:00,2005,11,19,8,30,25.3,22.8,191.0,120.0,233.0,0.11,1017.0,63.0,5.5,86.0603926771802 +2005-11-19 09:30:00-05:00,2005,11,19,9,30,25.6,22.8,241.0,354.0,423.0,0.11,1017.0,66.0,5.6000000000000005,84.5398098261438 +2005-11-19 10:30:00-05:00,2005,11,19,10,30,25.9,22.8,350.0,145.0,441.0,0.11,1017.0,70.0,5.6000000000000005,83.04939673383412 +2005-11-19 11:30:00-05:00,2005,11,19,11,30,26.1,22.8,267.0,430.0,564.0,0.11,1016.0,74.0,5.5,82.07222228655003 +2005-11-19 12:30:00-05:00,2005,11,19,12,30,26.1,22.8,255.0,451.0,570.0,0.11,1016.0,78.0,5.300000000000001,82.07222228655003 +2005-11-19 13:30:00-05:00,2005,11,19,13,30,26.1,22.9,272.0,337.0,490.0,0.11,1015.0,82.0,5.2,82.57100965641476 +2005-11-19 14:30:00-05:00,2005,11,19,14,30,26.0,22.9,268.0,45.0,293.0,0.11,1015.0,85.0,5.0,83.06092901762867 +2005-11-19 15:30:00-05:00,2005,11,19,15,30,25.9,23.0,171.0,185.0,243.0,0.11,1015.0,87.0,4.800000000000001,84.06153340606255 +2005-11-19 16:30:00-05:00,2005,11,19,16,30,25.6,23.1,77.0,13.0,79.0,0.11,1015.0,87.0,4.4,86.08937208209473 +2005-11-19 17:30:00-05:00,2005,11,19,17,30,25.3,23.1,0.0,0.0,0.0,0.11,1016.0,88.0,4.1000000000000005,87.63782627324721 +2005-11-19 18:30:00-05:00,2005,11,19,18,30,25.200000000000003,23.1,0.0,0.0,0.0,0.11,1016.0,90.0,3.9,88.16092351633488 +2005-11-19 19:30:00-05:00,2005,11,19,19,30,25.1,23.1,0.0,0.0,0.0,0.11,1017.0,93.0,3.8,88.68753671565264 +2005-11-19 20:30:00-05:00,2005,11,19,20,30,25.1,23.1,0.0,0.0,0.0,0.11,1017.0,97.0,3.7,88.68753671565264 +2005-11-19 21:30:00-05:00,2005,11,19,21,30,25.0,23.200000000000003,0.0,0.0,0.0,0.11,1018.0,101.0,3.7,89.75868022431186 +2005-11-19 22:30:00-05:00,2005,11,19,22,30,25.0,23.200000000000003,0.0,0.0,0.0,0.11,1017.0,105.0,3.7,89.75868022431186 +2005-11-19 23:30:00-05:00,2005,11,19,23,30,24.9,23.200000000000003,0.0,0.0,0.0,0.11,1017.0,108.0,3.8,90.29564028477958 +2005-11-20 00:30:00-05:00,2005,11,20,0,30,24.8,23.200000000000003,0.0,0.0,0.0,0.11,1017.0,111.0,3.8,90.83621714117355 +2005-11-20 01:30:00-05:00,2005,11,20,1,30,24.700000000000003,23.1,0.0,0.0,0.0,0.11,1017.0,114.0,3.7,90.82967495191984 +2005-11-20 02:30:00-05:00,2005,11,20,2,30,24.6,23.0,0.0,0.0,0.0,0.11,1016.0,118.0,3.6,90.82312587799315 +2005-11-20 03:30:00-05:00,2005,11,20,3,30,24.5,22.9,0.0,0.0,0.0,0.11,1016.0,121.0,3.4000000000000004,90.81656990991853 +2005-11-20 04:30:00-05:00,2005,11,20,4,30,24.5,22.8,0.0,0.0,0.0,0.11,1016.0,123.0,3.4000000000000004,90.26797351714104 +2005-11-20 05:30:00-05:00,2005,11,20,5,30,24.5,22.700000000000003,0.0,0.0,0.0,0.11,1017.0,124.0,3.4000000000000004,89.72228189005051 +2005-11-20 06:30:00-05:00,2005,11,20,6,30,24.6,22.700000000000003,0.0,0.0,0.0,0.11,1017.0,124.0,3.4000000000000004,89.1871398629249 +2005-11-20 07:30:00-05:00,2005,11,20,7,30,25.1,22.9,40.0,465.0,113.0,0.11,1017.0,125.0,3.6,87.62049875765955 +2005-11-20 08:30:00-05:00,2005,11,20,8,30,25.5,23.200000000000003,155.0,57.0,176.0,0.11,1018.0,126.0,3.9,87.12720410976242 +2005-11-20 09:30:00-05:00,2005,11,20,9,30,25.700000000000003,23.200000000000003,171.0,474.0,413.0,0.11,1018.0,128.0,4.1000000000000005,86.09901238448035 +2005-11-20 10:30:00-05:00,2005,11,20,10,30,26.0,23.1,169.0,582.0,534.0,0.11,1018.0,132.0,4.2,84.07244076824206 +2005-11-20 11:30:00-05:00,2005,11,20,11,30,26.200000000000003,23.0,303.0,424.0,596.0,0.11,1017.0,137.0,4.3,82.58282556083141 +2005-11-20 12:30:00-05:00,2005,11,20,12,30,26.3,22.9,260.0,377.0,522.0,0.11,1016.0,141.0,4.2,81.6008983438513 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21:30:00-05:00,2005,11,20,21,30,25.3,23.1,0.0,0.0,0.0,0.11,1015.0,140.0,4.800000000000001,87.63782627324721 +2005-11-20 22:30:00-05:00,2005,11,20,22,30,25.3,23.0,0.0,0.0,0.0,0.11,1014.0,144.0,5.0,87.10922485273613 +2005-11-20 23:30:00-05:00,2005,11,20,23,30,25.200000000000003,22.9,0.0,0.0,0.0,0.11,1014.0,150.0,5.0,87.10022146859099 +2005-11-21 00:30:00-05:00,2005,11,21,0,30,25.200000000000003,22.8,0.0,0.0,0.0,0.11,1014.0,157.0,5.0,86.57407445208084 +2005-11-21 01:30:00-05:00,2005,11,21,1,30,25.200000000000003,22.8,0.0,0.0,0.0,0.11,1014.0,165.0,4.9,86.57407445208084 +2005-11-21 02:30:00-05:00,2005,11,21,2,30,25.200000000000003,22.700000000000003,0.0,0.0,0.0,0.11,1013.0,170.0,4.7,86.05071333394693 +2005-11-21 03:30:00-05:00,2005,11,21,3,30,25.1,22.8,0.0,0.0,0.0,0.11,1013.0,175.0,4.5,87.09120889789614 +2005-11-21 04:30:00-05:00,2005,11,21,4,30,25.1,22.8,0.0,0.0,0.0,0.11,1012.0,179.0,4.4,87.09120889789614 +2005-11-21 05:30:00-05:00,2005,11,21,5,30,25.0,22.9,0.0,0.0,0.0,0.11,1012.0,184.0,4.4,88.14427552074834 +2005-11-21 06:30:00-05:00,2005,11,21,6,30,25.1,23.0,0.0,0.0,0.0,0.11,1013.0,189.0,4.4,88.15260380045967 +2005-11-21 07:30:00-05:00,2005,11,21,7,30,25.3,23.3,49.0,5.0,49.0,0.11,1013.0,194.0,4.7,88.70346270230925 +2005-11-21 08:30:00-05:00,2005,11,21,8,30,25.6,23.5,123.0,3.0,125.0,0.11,1013.0,199.0,5.1000000000000005,88.19411696958406 +2005-11-21 09:30:00-05:00,2005,11,21,9,30,25.8,23.5,85.0,777.0,479.0,0.11,1013.0,204.0,5.5,87.15410439808417 +2005-11-21 10:30:00-05:00,2005,11,21,10,30,26.1,23.4,117.0,776.0,601.0,0.11,1012.0,210.0,5.800000000000001,85.10498051565492 +2005-11-21 11:30:00-05:00,2005,11,21,11,30,26.3,23.200000000000003,168.0,3.0,170.0,0.11,1011.0,214.0,6.0,83.09545795447868 +2005-11-21 12:30:00-05:00,2005,11,21,12,30,26.5,23.1,306.0,471.0,632.0,0.11,1010.0,218.0,6.300000000000001,81.62567267915173 +2005-11-21 13:30:00-05:00,2005,11,21,13,30,26.6,23.1,136.0,748.0,616.0,0.11,1009.0,221.0,6.5,81.14599787426259 +2005-11-21 14:30:00-05:00,2005,11,21,14,30,26.6,23.1,270.0,41.0,292.0,0.11,1008.0,226.0,6.7,81.14599787426259 +2005-11-21 15:30:00-05:00,2005,11,21,15,30,26.5,23.1,140.0,3.0,141.0,0.11,1008.0,230.0,6.800000000000001,81.62567267915173 +2005-11-21 16:30:00-05:00,2005,11,21,16,30,26.1,23.200000000000003,58.0,1.0,58.0,0.11,1008.0,236.0,6.800000000000001,84.08333732125473 +2005-11-21 17:30:00-05:00,2005,11,21,17,30,25.5,23.200000000000003,0.0,0.0,0.0,0.11,1008.0,243.0,6.9,87.12720410976242 +2005-11-21 18:30:00-05:00,2005,11,21,18,30,24.9,23.0,0.0,0.0,0.0,0.11,1009.0,256.0,7.2,89.21006586202446 +2005-11-21 19:30:00-05:00,2005,11,21,19,30,24.0,22.5,0.0,0.0,0.0,0.1,1009.0,274.0,7.9,91.33708576167581 +2005-11-21 20:30:00-05:00,2005,11,21,20,30,22.9,21.5,0.0,0.0,0.0,0.1,1010.0,292.0,8.6,91.82889006049871 +2005-11-21 21:30:00-05:00,2005,11,21,21,30,22.0,20.4,0.0,0.0,0.0,0.1,1010.0,300.0,8.9,90.65040206615419 +2005-11-21 22:30:00-05:00,2005,11,21,22,30,21.6,19.200000000000003,0.0,0.0,0.0,0.1,1010.0,298.0,9.0,86.23129245243052 +2005-11-21 23:30:00-05:00,2005,11,21,23,30,21.3,18.1,0.0,0.0,0.0,0.1,1010.0,296.0,9.5,81.98997741655413 +2005-11-22 00:30:00-05:00,2005,11,22,0,30,20.9,17.1,0.0,0.0,0.0,0.1,1010.0,295.0,9.8,78.89354012939926 +2005-11-22 01:30:00-05:00,2005,11,22,1,30,20.4,16.2,0.0,0.0,0.0,0.1,1011.0,296.0,9.9,76.84080486841886 +2005-11-22 02:30:00-05:00,2005,11,22,2,30,20.0,15.6,0.0,0.0,0.0,0.1,1011.0,298.0,9.6,75.80243725044953 +2005-11-22 03:30:00-05:00,2005,11,22,3,30,19.700000000000003,15.1,0.0,0.0,0.0,0.1,1011.0,301.0,9.2,74.78709810399013 +2005-11-22 04:30:00-05:00,2005,11,22,4,30,19.4,14.9,0.0,0.0,0.0,0.1,1011.0,306.0,8.8,75.21988593091311 +2005-11-22 05:30:00-05:00,2005,11,22,5,30,19.0,14.9,0.0,0.0,0.0,0.1,1012.0,311.0,8.6,77.11756813613941 +2005-11-22 06:30:00-05:00,2005,11,22,6,30,18.5,14.8,0.0,0.0,0.0,0.1,1012.0,315.0,8.4,79.05484617836439 +2005-11-22 07:30:00-05:00,2005,11,22,7,30,18.3,14.6,53.0,381.0,110.0,0.1,1013.0,315.0,8.200000000000001,79.02619788455503 +2005-11-22 08:30:00-05:00,2005,11,22,8,30,18.7,14.5,200.0,82.0,228.0,0.1,1014.0,312.0,8.0,76.57530264901484 +2005-11-22 09:30:00-05:00,2005,11,22,9,30,19.3,14.7,114.0,758.0,497.0,0.1,1014.0,307.0,7.800000000000001,74.72022779029311 +2005-11-22 10:30:00-05:00,2005,11,22,10,30,20.1,14.8,119.0,842.0,641.0,0.1,1013.0,301.0,7.800000000000001,71.56134433812984 +2005-11-22 11:30:00-05:00,2005,11,22,11,30,20.9,14.8,122.0,876.0,721.0,0.1,1013.0,297.0,7.7,68.11615459329484 +2005-11-22 12:30:00-05:00,2005,11,22,12,30,21.5,14.7,117.0,889.0,731.0,0.1,1012.0,296.0,7.5,65.23257718490284 +2005-11-22 13:30:00-05:00,2005,11,22,13,30,21.8,14.6,112.0,871.0,669.0,0.1,1011.0,295.0,7.2,63.63514714466341 +2005-11-22 14:30:00-05:00,2005,11,22,14,30,22.0,14.5,101.0,823.0,542.0,0.1,1011.0,295.0,6.9,62.458669382633104 +2005-11-22 15:30:00-05:00,2005,11,22,15,30,21.9,14.6,85.0,725.0,363.0,0.1,1011.0,293.0,6.6000000000000005,63.24784683485569 +2005-11-22 16:30:00-05:00,2005,11,22,16,30,21.4,14.7,58.0,506.0,158.0,0.1,1011.0,292.0,6.4,65.6332439007082 +2005-11-22 17:30:00-05:00,2005,11,22,17,30,20.9,15.0,0.0,0.0,0.0,0.1,1012.0,292.0,6.4,68.99892706653176 +2005-11-22 18:30:00-05:00,2005,11,22,18,30,20.5,15.3,0.0,0.0,0.0,0.1,1013.0,292.0,6.5,72.09619540390257 +2005-11-22 19:30:00-05:00,2005,11,22,19,30,20.200000000000003,15.6,0.0,0.0,0.0,0.1,1013.0,291.0,6.5,74.87033737524062 +2005-11-22 20:30:00-05:00,2005,11,22,20,30,20.0,15.8,0.0,0.0,0.0,0.1,1014.0,289.0,6.6000000000000005,76.77871810299209 +2005-11-22 21:30:00-05:00,2005,11,22,21,30,19.9,16.0,0.0,0.0,0.0,0.1,1014.0,289.0,6.6000000000000005,78.24916597246201 +2005-11-22 22:30:00-05:00,2005,11,22,22,30,19.8,16.1,0.0,0.0,0.0,0.1,1014.0,290.0,6.7,79.23970675671096 +2005-11-22 23:30:00-05:00,2005,11,22,23,30,19.8,16.2,0.0,0.0,0.0,0.1,1014.0,293.0,6.800000000000001,79.74657911912934 +2005-11-23 00:30:00-05:00,2005,11,23,0,30,19.8,16.3,0.0,0.0,0.0,0.1,1014.0,297.0,6.800000000000001,80.25629914117681 +2005-11-23 01:30:00-05:00,2005,11,23,1,30,19.700000000000003,16.1,0.0,0.0,0.0,0.1,1014.0,303.0,6.800000000000001,79.73274398061388 +2005-11-23 02:30:00-05:00,2005,11,23,2,30,19.5,15.9,0.0,0.0,0.0,0.1,1013.0,310.0,6.800000000000001,79.70503315138035 +2005-11-23 03:30:00-05:00,2005,11,23,3,30,19.3,15.8,0.0,0.0,0.0,0.1,1013.0,318.0,6.800000000000001,80.18852126943597 +2005-11-23 04:30:00-05:00,2005,11,23,4,30,18.8,15.6,0.0,0.0,0.0,0.1,1014.0,327.0,6.9,81.67625717273346 +2005-11-23 05:30:00-05:00,2005,11,23,5,30,18.2,15.3,0.0,0.0,0.0,0.1,1014.0,333.0,7.0,83.18809066437363 +2005-11-23 06:30:00-05:00,2005,11,23,6,30,17.5,14.6,0.0,0.0,0.0,0.1,1015.0,336.0,7.0,83.10527819751513 +2005-11-23 07:30:00-05:00,2005,11,23,7,30,17.2,13.7,46.0,429.0,110.0,0.1,1016.0,336.0,7.0,79.90018766462913 +2005-11-23 08:30:00-05:00,2005,11,23,8,30,17.1,12.9,73.0,716.0,318.0,0.1,1017.0,335.0,7.0,76.32147122959566 +2005-11-23 09:30:00-05:00,2005,11,23,9,30,17.400000000000002,12.2,85.0,840.0,507.0,0.1,1018.0,333.0,6.9,71.52779386197412 +2005-11-23 10:30:00-05:00,2005,11,23,10,30,17.900000000000002,11.6,92.0,906.0,651.0,0.1,1018.0,331.0,6.7,66.62141594869547 +2005-11-23 11:30:00-05:00,2005,11,23,11,30,18.4,11.1,93.0,939.0,733.0,0.1,1017.0,329.0,6.2,62.46052062056042 +2005-11-23 12:30:00-05:00,2005,11,23,12,30,18.9,10.8,93.0,945.0,742.0,0.1,1016.0,326.0,5.800000000000001,59.343287451996915 +2005-11-23 13:30:00-05:00,2005,11,23,13,30,19.200000000000003,10.5,91.0,927.0,682.0,0.1,1016.0,323.0,5.4,57.09197760510308 +2005-11-23 14:30:00-05:00,2005,11,23,14,30,19.4,10.3,84.0,879.0,553.0,0.1,1015.0,321.0,5.1000000000000005,55.63861620258264 +2005-11-23 15:30:00-05:00,2005,11,23,15,30,19.4,10.2,72.0,786.0,373.0,0.1,1015.0,319.0,4.800000000000001,55.26836363319749 +2005-11-23 16:30:00-05:00,2005,11,23,16,30,19.0,10.5,50.0,578.0,164.0,0.1,1015.0,316.0,4.6000000000000005,57.80821250204687 +2005-11-23 17:30:00-05:00,2005,11,23,17,30,18.5,11.0,0.0,0.0,0.0,0.1,1016.0,312.0,4.4,61.65998495558101 +2005-11-23 18:30:00-05:00,2005,11,23,18,30,18.4,11.0,0.0,0.0,0.0,0.1,1016.0,307.0,4.3,62.04747558427481 +2005-11-23 19:30:00-05:00,2005,11,23,19,30,18.3,11.2,0.0,0.0,0.0,0.1,1017.0,302.0,4.3,63.2714227953765 +2005-11-23 20:30:00-05:00,2005,11,23,20,30,18.3,11.5,0.0,0.0,0.0,0.1,1017.0,298.0,4.5,64.54040341955574 +2005-11-23 21:30:00-05:00,2005,11,23,21,30,18.2,11.8,0.0,0.0,0.0,0.1,1017.0,294.0,4.6000000000000005,66.24609988938025 +2005-11-23 22:30:00-05:00,2005,11,23,22,30,18.2,12.2,0.0,0.0,0.0,0.1,1017.0,291.0,4.6000000000000005,68.01434932902269 +2005-11-23 23:30:00-05:00,2005,11,23,23,30,18.2,12.4,0.0,0.0,0.0,0.1,1016.0,287.0,4.5,68.91396341857087 +2005-11-24 00:30:00-05:00,2005,11,24,0,30,18.2,12.5,0.0,0.0,0.0,0.1,1016.0,282.0,4.3,69.36768777018142 +2005-11-24 01:30:00-05:00,2005,11,24,1,30,18.3,12.8,0.0,0.0,0.0,0.1,1015.0,279.0,4.1000000000000005,70.3022060070679 +2005-11-24 02:30:00-05:00,2005,11,24,2,30,18.4,13.2,0.0,0.0,0.0,0.1,1015.0,278.0,4.1000000000000005,71.71288798768238 +2005-11-24 03:30:00-05:00,2005,11,24,3,30,18.5,13.8,0.0,0.0,0.0,0.1,1016.0,278.0,4.1000000000000005,74.10316951656723 +2005-11-24 04:30:00-05:00,2005,11,24,4,30,18.5,14.3,0.0,0.0,0.0,0.1,1016.0,274.0,4.0,76.54378458099241 +2005-11-24 05:30:00-05:00,2005,11,24,5,30,18.6,14.8,0.0,0.0,0.0,0.1,1016.0,269.0,3.9,78.56151959545844 +2005-11-24 06:30:00-05:00,2005,11,24,6,30,18.9,15.2,0.0,0.0,0.0,0.1,1017.0,263.0,3.9,79.11197559476813 +2005-11-24 07:30:00-05:00,2005,11,24,7,30,19.700000000000003,15.6,40.0,482.0,110.0,0.1,1017.0,258.0,3.9,77.22511741144793 +2005-11-24 08:30:00-05:00,2005,11,24,8,30,20.6,16.2,65.0,737.0,314.0,0.1,1017.0,254.0,3.8,75.89878518915113 +2005-11-24 09:30:00-05:00,2005,11,24,9,30,21.200000000000003,16.3,78.0,848.0,500.0,0.1,1017.0,254.0,3.8,73.617201984425 +2005-11-24 10:30:00-05:00,2005,11,24,10,30,22.0,16.400000000000002,89.0,895.0,639.0,0.1,1017.0,256.0,3.7,70.54945136907499 +2005-11-24 11:30:00-05:00,2005,11,24,11,30,22.6,16.400000000000002,94.0,913.0,714.0,0.1,1016.0,257.0,3.7,68.02047939216398 +2005-11-24 12:30:00-05:00,2005,11,24,12,30,23.0,16.400000000000002,97.0,911.0,721.0,0.1,1016.0,256.0,3.5,66.39113833365309 +2005-11-24 13:30:00-05:00,2005,11,24,13,30,23.200000000000003,16.400000000000002,99.0,877.0,656.0,0.1,1015.0,252.0,3.3000000000000003,65.59295448507187 +2005-11-24 14:30:00-05:00,2005,11,24,14,30,23.4,16.400000000000002,93.0,824.0,531.0,0.1,1015.0,246.0,3.1,64.80554351165712 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23:30:00-05:00,2005,11,24,23,30,21.5,18.0,0.0,0.0,0.0,0.1,1017.0,249.0,3.3000000000000003,80.48427639812986 +2005-11-25 00:30:00-05:00,2005,11,25,0,30,21.4,18.0,0.0,0.0,0.0,0.1,1017.0,253.0,3.0,80.97862097395435 +2005-11-25 01:30:00-05:00,2005,11,25,1,30,21.3,17.900000000000002,0.0,0.0,0.0,0.1,1016.0,256.0,2.7,80.96560858418466 +2005-11-25 02:30:00-05:00,2005,11,25,2,30,21.200000000000003,17.7,0.0,0.0,0.0,0.1,1016.0,262.0,2.5,80.44432006422224 +2005-11-25 03:30:00-05:00,2005,11,25,3,30,21.1,17.7,0.0,0.0,0.0,0.1,1017.0,270.0,2.5,80.93954545433094 +2005-11-25 04:30:00-05:00,2005,11,25,4,30,21.0,17.7,0.0,0.0,0.0,0.1,1017.0,278.0,2.6,81.43819809786041 +2005-11-25 05:30:00-05:00,2005,11,25,5,30,21.1,17.8,0.0,0.0,0.0,0.1,1018.0,285.0,2.5,81.45093773972204 +2005-11-25 06:30:00-05:00,2005,11,25,6,30,21.200000000000003,17.7,0.0,0.0,0.0,0.1,1019.0,290.0,2.1,80.44432006422224 +2005-11-25 07:30:00-05:00,2005,11,25,7,30,21.700000000000003,17.6,40.0,461.0,106.0,0.1,1020.0,294.0,1.7000000000000002,77.52848246277235 +2005-11-25 08:30:00-05:00,2005,11,25,8,30,22.3,17.7,66.0,719.0,307.0,0.1,1020.0,304.0,1.5,75.2157597464933 +2005-11-25 09:30:00-05:00,2005,11,25,9,30,22.9,17.6,80.0,828.0,491.0,0.1,1021.0,316.0,1.5,72.06979004064144 +2005-11-25 10:30:00-05:00,2005,11,25,10,30,23.4,17.6,88.0,883.0,628.0,0.1,1021.0,327.0,1.6,69.92393739267627 +2005-11-25 11:30:00-05:00,2005,11,25,11,30,23.8,17.7,381.0,145.0,479.0,0.1,1020.0,339.0,1.6,68.69052211302972 +2005-11-25 12:30:00-05:00,2005,11,25,12,30,24.200000000000003,17.6,319.0,24.0,335.0,0.1,1019.0,173.0,1.5,66.63835054112403 +2005-11-25 13:30:00-05:00,2005,11,25,13,30,24.4,17.400000000000002,99.0,872.0,651.0,0.1,1018.0,9.0,1.4,65.01853934241451 +2005-11-25 14:30:00-05:00,2005,11,25,14,30,24.5,17.3,93.0,820.0,527.0,0.1,1018.0,28.0,1.6,64.22342379995163 +2005-11-25 15:30:00-05:00,2005,11,25,15,30,24.4,17.400000000000002,80.0,716.0,353.0,0.1,1018.0,45.0,1.7000000000000002,65.01853934241451 +2005-11-25 16:30:00-05:00,2005,11,25,16,30,24.1,17.6,55.0,499.0,152.0,0.1,1018.0,52.0,1.9,67.03939665910431 +2005-11-25 17:30:00-05:00,2005,11,25,17,30,23.700000000000003,17.6,0.0,0.0,0.0,0.1,1019.0,53.0,2.0,68.67095215688182 +2005-11-25 18:30:00-05:00,2005,11,25,18,30,23.5,17.5,0.0,0.0,0.0,0.1,1020.0,53.0,2.3000000000000003,69.06639391396459 +2005-11-25 19:30:00-05:00,2005,11,25,19,30,23.3,17.6,0.0,0.0,0.0,0.1,1020.0,55.0,2.5,70.3472959699732 +2005-11-25 20:30:00-05:00,2005,11,25,20,30,23.0,17.7,0.0,0.0,0.0,0.1,1020.0,61.0,2.8000000000000003,72.08771497220543 +2005-11-25 21:30:00-05:00,2005,11,25,21,30,22.9,17.900000000000002,0.0,0.0,0.0,0.1,1020.0,65.0,3.0,73.44449380451665 +2005-11-25 22:30:00-05:00,2005,11,25,22,30,22.8,18.1,0.0,0.0,0.0,0.1,1020.0,66.0,3.2,74.82570649427612 +2005-11-25 23:30:00-05:00,2005,11,25,23,30,22.700000000000003,18.2,0.0,0.0,0.0,0.1,1019.0,65.0,3.3000000000000003,75.75497591293174 +2005-11-26 00:30:00-05:00,2005,11,26,0,30,22.6,18.3,0.0,0.0,0.0,0.1,1019.0,63.0,3.3000000000000003,76.69576763254587 +2005-11-26 01:30:00-05:00,2005,11,26,1,30,22.5,18.3,0.0,0.0,0.0,0.1,1019.0,59.0,3.3000000000000003,77.16293683056355 +2005-11-26 02:30:00-05:00,2005,11,26,2,30,22.4,18.2,0.0,0.0,0.0,0.1,1019.0,56.0,3.3000000000000003,77.14774152098751 +2005-11-26 03:30:00-05:00,2005,11,26,3,30,22.3,18.1,0.0,0.0,0.0,0.1,1019.0,56.0,3.5,77.1325318868574 +2005-11-26 04:30:00-05:00,2005,11,26,4,30,22.200000000000003,18.1,0.0,0.0,0.0,0.1,1019.0,56.0,3.7,77.60342794498723 +2005-11-26 05:30:00-05:00,2005,11,26,5,30,22.1,18.2,0.0,0.0,0.0,0.1,1019.0,58.0,3.9,78.56935242073986 +2005-11-26 06:30:00-05:00,2005,11,26,6,30,22.3,18.3,0.0,0.0,0.0,0.1,1019.0,60.0,4.1000000000000005,78.10690197391389 +2005-11-26 07:30:00-05:00,2005,11,26,7,30,22.9,18.4,68.0,6.0,69.0,0.1,1020.0,64.0,4.4,75.7868809030579 +2005-11-26 08:30:00-05:00,2005,11,26,8,30,23.5,18.8,10.0,0.0,10.0,0.1,1021.0,69.0,4.6000000000000005,74.94064497951308 +2005-11-26 09:30:00-05:00,2005,11,26,9,30,24.0,18.7,48.0,0.0,48.0,0.1,1021.0,73.0,4.7,72.26608943583008 +2005-11-26 10:30:00-05:00,2005,11,26,10,30,24.3,18.6,32.0,0.0,32.0,0.1,1021.0,76.0,4.6000000000000005,70.53431151642461 +2005-11-26 11:30:00-05:00,2005,11,26,11,30,24.5,18.6,221.0,1.0,222.0,0.1,1020.0,77.0,4.4,69.69449144464059 +2005-11-26 12:30:00-05:00,2005,11,26,12,30,24.6,18.6,106.0,884.0,708.0,0.1,1019.0,78.0,4.3,69.27880371751749 +2005-11-26 13:30:00-05:00,2005,11,26,13,30,24.700000000000003,18.6,102.0,865.0,648.0,0.1,1018.0,80.0,4.4,68.86590307188943 +2005-11-26 14:30:00-05:00,2005,11,26,14,30,24.700000000000003,18.6,91.0,825.0,527.0,0.1,1017.0,81.0,4.6000000000000005,68.86590307188943 +2005-11-26 15:30:00-05:00,2005,11,26,15,30,24.6,18.6,75.0,735.0,354.0,0.1,1017.0,82.0,4.7,69.27880371751749 +2005-11-26 16:30:00-05:00,2005,11,26,16,30,24.200000000000003,18.8,51.0,530.0,154.0,0.1,1017.0,83.0,4.7,71.85142010328785 +2005-11-26 17:30:00-05:00,2005,11,26,17,30,23.700000000000003,19.0,0.0,0.0,0.0,0.1,1017.0,86.0,4.7,74.9733491781394 +2005-11-26 18:30:00-05:00,2005,11,26,18,30,23.5,19.1,0.0,0.0,0.0,0.1,1018.0,91.0,4.800000000000001,76.3569216831363 +2005-11-26 19:30:00-05:00,2005,11,26,19,30,23.4,19.200000000000003,0.0,0.0,0.0,0.11,1018.0,96.0,4.800000000000001,77.29905275229335 +2005-11-26 20:30:00-05:00,2005,11,26,20,30,23.4,19.4,0.0,0.0,0.0,0.11,1018.0,101.0,4.800000000000001,78.26730383053193 +2005-11-26 21:30:00-05:00,2005,11,26,21,30,23.3,19.5,0.0,0.0,0.0,0.11,1018.0,104.0,4.800000000000001,79.23223715850996 +2005-11-26 22:30:00-05:00,2005,11,26,22,30,23.3,19.4,0.0,0.0,0.0,0.11,1018.0,105.0,4.800000000000001,78.74117780894481 +2005-11-26 23:30:00-05:00,2005,11,26,23,30,23.200000000000003,19.3,0.0,0.0,0.0,0.11,1018.0,106.0,4.800000000000001,78.72693411115581 +2005-11-27 00:30:00-05:00,2005,11,27,0,30,23.1,19.200000000000003,0.0,0.0,0.0,0.11,1018.0,107.0,5.0,78.71267681572247 +2005-11-27 01:30:00-05:00,2005,11,27,1,30,23.0,19.1,0.0,0.0,0.0,0.11,1017.0,110.0,5.300000000000001,78.69840590646565 +2005-11-27 02:30:00-05:00,2005,11,27,2,30,23.0,19.1,0.0,0.0,0.0,0.11,1016.0,112.0,5.800000000000001,78.69840590646565 +2005-11-27 03:30:00-05:00,2005,11,27,3,30,22.9,19.200000000000003,0.0,0.0,0.0,0.11,1016.0,115.0,6.1000000000000005,79.67123577314082 +2005-11-27 04:30:00-05:00,2005,11,27,4,30,22.9,19.3,0.0,0.0,0.0,0.11,1016.0,116.0,6.2,80.16885568774497 +2005-11-27 05:30:00-05:00,2005,11,27,5,30,23.0,19.3,0.0,0.0,0.0,0.11,1016.0,118.0,6.2,79.68494199095247 +2005-11-27 06:30:00-05:00,2005,11,27,6,30,23.1,19.4,0.0,0.0,0.0,0.11,1016.0,118.0,6.1000000000000005,79.69863500646635 +2005-11-27 07:30:00-05:00,2005,11,27,7,30,23.6,19.5,40.0,412.0,97.0,0.11,1017.0,117.0,6.0,77.81139794609358 +2005-11-27 08:30:00-05:00,2005,11,27,8,30,24.1,19.700000000000003,68.0,679.0,292.0,0.11,1017.0,115.0,5.800000000000001,76.45017170712919 +2005-11-27 09:30:00-05:00,2005,11,27,9,30,24.4,19.8,84.0,790.0,471.0,0.11,1018.0,113.0,5.6000000000000005,75.5545437025466 +2005-11-27 10:30:00-05:00,2005,11,27,10,30,24.6,19.8,280.0,392.0,517.0,0.11,1018.0,113.0,5.2,74.65561835345126 +2005-11-27 11:30:00-05:00,2005,11,27,11,30,24.8,19.8,98.0,867.0,680.0,0.11,1017.0,114.0,4.9,73.76870674689857 +2005-11-27 12:30:00-05:00,2005,11,27,12,30,24.9,19.8,325.0,355.0,566.0,0.11,1016.0,114.0,4.7,73.32970062303599 +2005-11-27 13:30:00-05:00,2005,11,27,13,30,24.9,19.8,100.0,835.0,626.0,0.11,1015.0,116.0,4.6000000000000005,73.32970062303599 +2005-11-27 14:30:00-05:00,2005,11,27,14,30,24.9,19.8,92.0,786.0,506.0,0.11,1014.0,117.0,4.6000000000000005,73.32970062303599 +2005-11-27 15:30:00-05:00,2005,11,27,15,30,24.700000000000003,19.9,76.0,693.0,338.0,0.11,1014.0,118.0,4.6000000000000005,74.672065741155 +2005-11-27 16:30:00-05:00,2005,11,27,16,30,24.5,20.200000000000003,100.0,63.0,112.0,0.11,1014.0,115.0,4.5,76.98666502728693 +2005-11-27 17:30:00-05:00,2005,11,27,17,30,24.4,20.5,0.0,0.0,0.0,0.11,1015.0,113.0,4.5,78.89696564566025 +2005-11-27 18:30:00-05:00,2005,11,27,18,30,24.4,20.8,0.0,0.0,0.0,0.11,1015.0,112.0,4.6000000000000005,80.36867035611907 +2005-11-27 19:30:00-05:00,2005,11,27,19,30,24.5,21.0,0.0,0.0,0.0,0.11,1015.0,111.0,4.9,80.87748210634429 +2005-11-27 20:30:00-05:00,2005,11,27,20,30,24.5,21.200000000000003,0.0,0.0,0.0,0.11,1016.0,111.0,5.1000000000000005,81.8767117948703 +2005-11-27 21:30:00-05:00,2005,11,27,21,30,24.5,21.3,0.0,0.0,0.0,0.11,1016.0,112.0,5.2,82.38037263885073 +2005-11-27 22:30:00-05:00,2005,11,27,22,30,24.5,21.4,0.0,0.0,0.0,0.11,1015.0,114.0,5.300000000000001,82.88674728430267 +2005-11-27 23:30:00-05:00,2005,11,27,23,30,24.4,21.4,0.0,0.0,0.0,0.11,1015.0,116.0,5.4,83.38445864005315 +2005-11-28 00:30:00-05:00,2005,11,28,0,30,24.4,21.3,0.0,0.0,0.0,0.11,1015.0,119.0,5.5,82.8750433587992 +2005-11-28 01:30:00-05:00,2005,11,28,1,30,24.3,21.3,0.0,0.0,0.0,0.11,1014.0,122.0,5.6000000000000005,83.37305784293329 +2005-11-28 02:30:00-05:00,2005,11,28,2,30,24.3,21.3,0.0,0.0,0.0,0.11,1014.0,123.0,5.800000000000001,83.37305784293329 +2005-11-28 03:30:00-05:00,2005,11,28,3,30,24.200000000000003,21.3,0.0,0.0,0.0,0.11,1014.0,124.0,5.7,83.8744410807648 +2005-11-28 04:30:00-05:00,2005,11,28,4,30,24.200000000000003,21.3,0.0,0.0,0.0,0.11,1014.0,124.0,5.5,83.8744410807648 +2005-11-28 05:30:00-05:00,2005,11,28,5,30,24.200000000000003,21.4,0.0,0.0,0.0,0.11,1014.0,123.0,5.300000000000001,84.38999944745179 +2005-11-28 06:30:00-05:00,2005,11,28,6,30,24.200000000000003,21.5,0.0,0.0,0.0,0.11,1015.0,122.0,5.1000000000000005,84.90833346681123 +2005-11-28 07:30:00-05:00,2005,11,28,7,30,24.4,21.700000000000003,49.0,6.0,50.0,0.11,1015.0,122.0,5.0,84.92921012154552 +2005-11-28 08:30:00-05:00,2005,11,28,8,30,24.700000000000003,22.0,74.0,623.0,277.0,0.11,1016.0,123.0,5.1000000000000005,84.96044656138277 +2005-11-28 09:30:00-05:00,2005,11,28,9,30,24.8,22.200000000000003,252.0,48.0,275.0,0.11,1016.0,125.0,5.1000000000000005,85.48997942635889 +2005-11-28 10:30:00-05:00,2005,11,28,10,30,25.0,22.200000000000003,289.0,323.0,484.0,0.11,1016.0,127.0,5.1000000000000005,84.4758618006713 +2005-11-28 11:30:00-05:00,2005,11,28,11,30,25.1,22.3,329.0,69.0,374.0,0.11,1016.0,129.0,5.1000000000000005,84.4865464131878 +2005-11-28 12:30:00-05:00,2005,11,28,12,30,25.1,22.3,103.0,838.0,669.0,0.11,1015.0,132.0,4.9,84.4865464131878 +2005-11-28 13:30:00-05:00,2005,11,28,13,30,25.0,22.3,325.0,74.0,372.0,0.11,1014.0,134.0,4.800000000000001,84.99158907366433 +2005-11-28 14:30:00-05:00,2005,11,28,14,30,24.9,22.3,262.0,260.0,398.0,0.11,1014.0,134.0,4.800000000000001,85.5000311395926 +2005-11-28 15:30:00-05:00,2005,11,28,15,30,24.8,22.4,133.0,0.0,134.0,0.11,1014.0,131.0,4.800000000000001,86.53660587246388 +2005-11-28 16:30:00-05:00,2005,11,28,16,30,24.700000000000003,22.4,52.0,463.0,141.0,0.11,1014.0,127.0,4.800000000000001,87.05506650692374 +2005-11-28 17:30:00-05:00,2005,11,28,17,30,24.6,22.4,0.0,0.0,0.0,0.11,1014.0,122.0,5.0,87.5770242764803 +2005-11-28 18:30:00-05:00,2005,11,28,18,30,24.700000000000003,22.5,0.0,0.0,0.0,0.11,1015.0,119.0,5.300000000000001,87.58573701339093 +2005-11-28 19:30:00-05:00,2005,11,28,19,30,24.700000000000003,22.5,0.0,0.0,0.0,0.11,1015.0,118.0,5.5,87.58573701339093 +2005-11-28 20:30:00-05:00,2005,11,28,20,30,24.8,22.5,0.0,0.0,0.0,0.11,1016.0,119.0,5.800000000000001,87.06411594521352 +2005-11-28 21:30:00-05:00,2005,11,28,21,30,24.8,22.5,0.0,0.0,0.0,0.11,1016.0,121.0,5.9,87.06411594521352 +2005-11-28 22:30:00-05:00,2005,11,28,22,30,24.8,22.5,0.0,0.0,0.0,0.11,1016.0,124.0,5.7,87.06411594521352 +2005-11-28 23:30:00-05:00,2005,11,28,23,30,24.700000000000003,22.4,0.0,0.0,0.0,0.11,1015.0,129.0,5.4,87.05506650692374 +2005-11-29 00:30:00-05:00,2005,11,29,0,30,24.700000000000003,22.3,0.0,0.0,0.0,0.11,1015.0,132.0,5.1000000000000005,86.52721490195314 +2005-11-29 01:30:00-05:00,2005,11,29,1,30,24.6,22.200000000000003,0.0,0.0,0.0,0.11,1014.0,135.0,5.0,86.51781437617487 +2005-11-29 02:30:00-05:00,2005,11,29,2,30,24.6,22.200000000000003,0.0,0.0,0.0,0.11,1014.0,138.0,4.9,86.51781437617487 +2005-11-29 03:30:00-05:00,2005,11,29,3,30,24.5,22.1,0.0,0.0,0.0,0.11,1014.0,140.0,4.9,86.50840428258228 +2005-11-29 04:30:00-05:00,2005,11,29,4,30,24.4,22.1,0.0,0.0,0.0,0.11,1014.0,141.0,4.9,87.02786265910188 +2005-11-29 05:30:00-05:00,2005,11,29,5,30,24.4,22.200000000000003,0.0,0.0,0.0,0.11,1014.0,141.0,4.9,87.55957195860968 +2005-11-29 06:30:00-05:00,2005,11,29,6,30,24.4,22.200000000000003,0.0,0.0,0.0,0.11,1015.0,142.0,4.800000000000001,87.55957195860968 +2005-11-29 07:30:00-05:00,2005,11,29,7,30,24.5,22.3,51.0,6.0,52.0,0.11,1016.0,144.0,4.800000000000001,87.56830259551693 +2005-11-29 08:30:00-05:00,2005,11,29,8,30,24.8,22.5,111.0,12.0,115.0,0.11,1016.0,147.0,4.800000000000001,87.06411594521352 +2005-11-29 09:30:00-05:00,2005,11,29,9,30,24.9,22.6,173.0,17.0,181.0,0.11,1017.0,152.0,4.800000000000001,87.07315614835665 +2005-11-29 10:30:00-05:00,2005,11,29,10,30,25.0,22.6,237.0,33.0,257.0,0.11,1017.0,158.0,4.6000000000000005,86.5553591977721 +2005-11-29 11:30:00-05:00,2005,11,29,11,30,25.0,22.6,183.0,2.0,184.0,0.11,1016.0,165.0,4.3,86.5553591977721 +2005-11-29 12:30:00-05:00,2005,11,29,12,30,24.9,22.5,213.0,7.0,218.0,0.11,1015.0,170.0,4.0,86.54598730023442 +2005-11-29 13:30:00-05:00,2005,11,29,13,30,24.8,22.5,166.0,4.0,168.0,0.11,1015.0,174.0,3.6,87.06411594521352 +2005-11-29 14:30:00-05:00,2005,11,29,14,30,24.700000000000003,22.4,160.0,7.0,164.0,0.11,1014.0,172.0,3.1,87.05506650692374 +2005-11-29 15:30:00-05:00,2005,11,29,15,30,24.5,22.4,79.0,2.0,79.0,0.11,1014.0,163.0,2.9000000000000004,88.10250526368289 +2005-11-29 16:30:00-05:00,2005,11,29,16,30,24.200000000000003,22.3,69.0,4.0,70.0,0.11,1014.0,151.0,3.1,89.15646046891602 +2005-11-29 17:30:00-05:00,2005,11,29,17,30,24.0,22.200000000000003,0.0,0.0,0.0,0.11,1015.0,144.0,3.6,89.68569309935687 +2005-11-29 18:30:00-05:00,2005,11,29,18,30,24.0,22.200000000000003,0.0,0.0,0.0,0.11,1015.0,142.0,4.0,89.68569309935687 +2005-11-29 19:30:00-05:00,2005,11,29,19,30,24.0,22.200000000000003,0.0,0.0,0.0,0.11,1015.0,143.0,4.1000000000000005,89.68569309935687 +2005-11-29 20:30:00-05:00,2005,11,29,20,30,24.0,22.1,0.0,0.0,0.0,0.11,1015.0,148.0,4.0,89.14107283697969 +2005-11-29 21:30:00-05:00,2005,11,29,21,30,24.0,22.1,0.0,0.0,0.0,0.11,1015.0,156.0,3.8,89.14107283697969 +2005-11-29 22:30:00-05:00,2005,11,29,22,30,24.0,22.0,0.0,0.0,0.0,0.11,1015.0,165.0,3.6,88.59935257199315 +2005-11-29 23:30:00-05:00,2005,11,29,23,30,24.0,21.9,0.0,0.0,0.0,0.11,1015.0,173.0,3.4000000000000004,88.06051918717874 +2005-11-30 00:30:00-05:00,2005,11,30,0,30,24.0,21.8,0.0,0.0,0.0,0.11,1015.0,181.0,3.3000000000000003,87.52455961305394 +2005-11-30 01:30:00-05:00,2005,11,30,1,30,24.0,21.700000000000003,0.0,0.0,0.0,0.11,1015.0,189.0,3.1,86.9914608277474 +2005-11-30 02:30:00-05:00,2005,11,30,2,30,23.9,21.700000000000003,0.0,0.0,0.0,0.11,1015.0,199.0,2.9000000000000004,87.51578401739368 +2005-11-30 03:30:00-05:00,2005,11,30,3,30,23.8,21.6,0.0,0.0,0.0,0.11,1015.0,213.0,2.7,87.50699939404173 +2005-11-30 04:30:00-05:00,2005,11,30,4,30,23.700000000000003,21.6,0.0,0.0,0.0,0.11,1015.0,228.0,2.6,88.03522334593023 +2005-11-30 05:30:00-05:00,2005,11,30,5,30,23.6,21.5,0.0,0.0,0.0,0.11,1015.0,246.0,2.5,88.02677396309888 +2005-11-30 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05:30:00-05:00,2009,3,9,5,30,4.9,4.800000000000001,0.0,0.0,0.0,0.13,1004.0,106.0,2.5,99.30533398513451 +2009-03-09 06:30:00-05:00,2009,3,9,6,30,5.0,4.7,15.0,71.0,18.0,0.13,1004.0,99.0,2.0,97.93044459314491 +2009-03-09 07:30:00-05:00,2009,3,9,7,30,5.300000000000001,4.800000000000001,6.0,0.0,6.0,0.13,1005.0,86.0,1.7000000000000002,96.58001380419186 +2009-03-09 08:30:00-05:00,2009,3,9,8,30,5.9,5.1000000000000005,13.0,0.0,13.0,0.13,1005.0,63.0,1.5,94.60346960161753 +2009-03-09 09:30:00-05:00,2009,3,9,9,30,6.300000000000001,5.4,22.0,0.0,22.0,0.13,1005.0,27.0,1.4,93.96618757117693 +2009-03-09 10:30:00-05:00,2009,3,9,10,30,6.9,5.6000000000000005,51.0,0.0,51.0,0.13,1006.0,170.0,1.8,91.42901413802271 +2009-03-09 11:30:00-05:00,2009,3,9,11,30,7.5,5.6000000000000005,47.0,0.0,47.0,0.13,1007.0,338.0,3.0,87.75240626809607 +2009-03-09 12:30:00-05:00,2009,3,9,12,30,7.9,5.5,78.0,0.0,78.0,0.13,1008.0,347.0,4.0,84.8032812196914 +2009-03-09 13:30:00-05:00,2009,3,9,13,30,7.9,5.300000000000001,83.0,0.0,83.0,0.13,1009.0,177.0,4.3,83.63433100472275 +2009-03-09 14:30:00-05:00,2009,3,9,14,30,7.7,5.0,79.0,0.0,79.0,0.13,1011.0,6.0,4.1000000000000005,83.03047570921892 +2009-03-09 15:30:00-05:00,2009,3,9,15,30,7.2,4.800000000000001,92.0,0.0,92.0,0.13,1012.0,15.0,3.7,84.72462337491412 +2009-03-09 16:30:00-05:00,2009,3,9,16,30,6.2,4.7,80.0,522.0,217.0,0.13,1014.0,26.0,3.1,90.11711296891802 +2009-03-09 17:30:00-05:00,2009,3,9,17,30,4.800000000000001,4.2,30.0,183.0,44.0,0.13,1015.0,36.0,2.8000000000000003,95.89236498825953 +2009-03-09 18:30:00-05:00,2009,3,9,18,30,4.0,3.5,0.0,0.0,0.0,0.13,1017.0,43.0,2.7,96.54451981265565 +2009-03-09 19:30:00-05:00,2009,3,9,19,30,3.6,3.1,0.0,0.0,0.0,0.13,1018.0,41.0,2.5,96.53348805357292 +2009-03-09 20:30:00-05:00,2009,3,9,20,30,3.2,2.7,0.0,0.0,0.0,0.13,1019.0,34.0,2.5,96.52240370482859 +2009-03-09 21:30:00-05:00,2009,3,9,21,30,2.6,2.2,0.0,0.0,0.0,0.13,1020.0,28.0,2.5,97.19576256588778 +2009-03-09 22:30:00-05:00,2009,3,9,22,30,2.1,1.7000000000000002,0.0,0.0,0.0,0.13,1021.0,22.0,2.4000000000000004,97.1844650702125 +2009-03-09 23:30:00-05:00,2009,3,9,23,30,1.8,1.3,0.0,0.0,0.0,0.13,1021.0,20.0,2.3000000000000003,96.48318951403768 +2009-03-10 00:30:00-05:00,2009,3,10,0,30,1.6,0.9,0.0,0.0,0.0,0.13,1022.0,25.0,2.4000000000000004,95.09962275503979 +2009-03-10 01:30:00-05:00,2009,3,10,1,30,1.4,0.6000000000000001,0.0,0.0,0.0,0.13,1022.0,30.0,2.4000000000000004,94.40833575366685 +2009-03-10 02:30:00-05:00,2009,3,10,2,30,1.0,0.3,0.0,0.0,0.0,0.13,1022.0,36.0,2.4000000000000004,95.07606693706266 +2009-03-10 03:30:00-05:00,2009,3,10,3,30,0.5,-0.1,0.0,0.0,0.0,0.13,1023.0,44.0,2.4000000000000004,95.74900987721772 +2009-03-10 04:30:00-05:00,2009,3,10,4,30,0.1,-0.5,0.0,0.0,0.0,0.13,1023.0,53.0,2.5,95.73529727263231 +2009-03-10 05:30:00-05:00,2009,3,10,5,30,0.2,-0.6000000000000001,0.0,0.0,0.0,0.13,1024.0,62.0,2.5,94.35453027868782 +2009-03-10 06:30:00-05:00,2009,3,10,6,30,1.5,-0.5,4.0,0.0,4.0,0.13,1024.0,74.0,2.6,86.55022018278777 +2009-03-10 07:30:00-05:00,2009,3,10,7,30,3.3000000000000003,0.2,41.0,0.0,41.0,0.13,1024.0,84.0,2.7,80.12239838392426 +2009-03-10 08:30:00-05:00,2009,3,10,8,30,4.5,0.3,77.0,770.0,386.0,0.13,1025.0,92.0,2.6,74.18012666101237 +2009-03-10 09:30:00-05:00,2009,3,10,9,30,5.2,0.5,90.0,853.0,554.0,0.13,1025.0,105.0,2.2,71.6760058265314 +2009-03-10 10:30:00-05:00,2009,3,10,10,30,5.7,0.8,376.0,106.0,445.0,0.13,1025.0,116.0,1.8,70.74694856696364 +2009-03-10 11:30:00-05:00,2009,3,10,11,30,5.7,1.1,340.0,454.0,660.0,0.13,1025.0,116.0,1.9,72.29073728434035 +2009-03-10 12:30:00-05:00,2009,3,10,12,30,5.6000000000000005,1.2000000000000002,280.0,424.0,581.0,0.13,1024.0,120.0,2.4000000000000004,73.31798049193237 +2009-03-10 13:30:00-05:00,2009,3,10,13,30,5.300000000000001,1.4,346.0,77.0,398.0,0.13,1024.0,132.0,2.6,75.94182472103437 +2009-03-10 14:30:00-05:00,2009,3,10,14,30,4.9,1.7000000000000002,213.0,22.0,226.0,0.13,1024.0,138.0,2.3000000000000003,79.78024102169957 +2009-03-10 15:30:00-05:00,2009,3,10,15,30,4.4,1.9,115.0,4.0,117.0,0.13,1023.0,136.0,2.6,83.80377295983543 +2009-03-10 16:30:00-05:00,2009,3,10,16,30,3.9,2.1,73.0,6.0,74.0,0.13,1023.0,132.0,3.1,88.04061539124189 +2009-03-10 17:30:00-05:00,2009,3,10,17,30,3.4000000000000004,2.2,9.0,0.0,9.0,0.13,1022.0,136.0,3.1,91.84643269192976 +2009-03-10 18:30:00-05:00,2009,3,10,18,30,3.3000000000000003,2.3000000000000003,0.0,0.0,0.0,0.13,1023.0,138.0,2.6,93.15767339495859 +2009-03-10 19:30:00-05:00,2009,3,10,19,30,3.3000000000000003,2.5,0.0,0.0,0.0,0.15,1023.0,135.0,2.4000000000000004,94.49198334436952 +2009-03-10 20:30:00-05:00,2009,3,10,20,30,3.3000000000000003,2.6,0.0,0.0,0.0,0.15,1022.0,137.0,2.6,95.16546165905241 +2009-03-10 21:30:00-05:00,2009,3,10,21,30,3.4000000000000004,2.8000000000000003,0.0,0.0,0.0,0.15,1021.0,141.0,2.7,95.84649190314204 +2009-03-10 22:30:00-05:00,2009,3,10,22,30,3.5,2.9000000000000004,0.0,0.0,0.0,0.15,1020.0,143.0,3.0,95.8497937880499 +2009-03-10 23:30:00-05:00,2009,3,10,23,30,3.7,3.1,0.0,0.0,0.0,0.15,1020.0,145.0,3.2,95.85638584363429 +2009-03-11 00:30:00-05:00,2009,3,11,0,30,4.0,3.3000000000000003,0.0,0.0,0.0,0.15,1019.0,148.0,3.1,95.19218999943757 +2009-03-11 01:30:00-05:00,2009,3,11,1,30,4.3,3.6,0.0,0.0,0.0,0.15,1019.0,152.0,3.2,95.20357777556202 +2009-03-11 02:30:00-05:00,2009,3,11,2,30,4.6000000000000005,3.9,0.0,0.0,0.0,0.15,1018.0,158.0,3.6,95.21492548634782 +2009-03-11 03:30:00-05:00,2009,3,11,3,30,4.800000000000001,4.2,0.0,0.0,0.0,0.15,1017.0,163.0,4.0,95.89236498825953 +2009-03-11 04:30:00-05:00,2009,3,11,4,30,4.9,4.4,0.0,0.0,0.0,0.15,1015.0,165.0,4.0,96.56915069518146 +2009-03-11 05:30:00-05:00,2009,3,11,5,30,5.1000000000000005,4.6000000000000005,0.0,0.0,0.0,0.15,1014.0,168.0,4.2,96.57458867029787 +2009-03-11 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11:30:00-05:00,2009,3,19,11,30,6.300000000000001,4.2,119.0,0.0,119.0,0.12,1014.0,8.0,3.9,86.42266452144449 +2009-03-19 12:30:00-05:00,2009,3,19,12,30,6.0,3.8,96.0,0.0,96.0,0.12,1015.0,5.0,4.0,85.78714849015273 +2009-03-19 13:30:00-05:00,2009,3,19,13,30,5.800000000000001,3.5,207.0,2.0,208.0,0.12,1015.0,2.0,3.7,85.16384528733975 +2009-03-19 14:30:00-05:00,2009,3,19,14,30,5.800000000000001,3.4000000000000004,118.0,1.0,118.0,0.12,1015.0,4.0,2.9000000000000004,84.56552531991298 +2009-03-19 15:30:00-05:00,2009,3,19,15,30,5.7,3.5,93.0,1.0,94.0,0.12,1015.0,8.0,1.9,85.75527068206367 +2009-03-19 16:30:00-05:00,2009,3,19,16,30,5.4,4.1000000000000005,120.0,13.0,124.0,0.12,1016.0,15.0,1.0,91.3295780058295 +2009-03-19 17:30:00-05:00,2009,3,19,17,30,4.7,3.6,42.0,1.0,42.0,0.12,1016.0,123.0,0.5,92.57832082384604 +2009-03-19 18:30:00-05:00,2009,3,19,18,30,4.1000000000000005,3.1,0.0,0.0,0.0,0.12,1016.0,225.0,0.5,93.20044694029636 +2009-03-19 19:30:00-05:00,2009,3,19,19,30,4.1000000000000005,3.2,0.0,0.0,0.0,0.12,1017.0,235.0,0.7000000000000001,93.86147465953185 +2009-03-19 20:30:00-05:00,2009,3,19,20,30,4.2,3.3000000000000003,0.0,0.0,0.0,0.12,1017.0,264.0,0.9,93.86629260799387 +2009-03-19 21:30:00-05:00,2009,3,19,21,30,4.2,3.2,0.0,0.0,0.0,0.12,1017.0,298.0,1.2000000000000002,93.20576577181731 +2009-03-19 22:30:00-05:00,2009,3,19,22,30,4.1000000000000005,2.9000000000000004,0.0,0.0,0.0,0.12,1017.0,321.0,1.5,91.89074035858212 +2009-03-19 23:30:00-05:00,2009,3,19,23,30,3.7,2.4000000000000004,0.0,0.0,0.0,0.12,1017.0,332.0,1.8,91.21481297706822 +2009-03-20 00:30:00-05:00,2009,3,20,0,30,3.2,1.9,0.0,0.0,0.0,0.12,1017.0,336.0,2.3000000000000003,91.18063207257242 +2009-03-20 01:30:00-05:00,2009,3,20,1,30,2.6,1.2000000000000002,0.0,0.0,0.0,0.12,1018.0,342.0,3.1,90.48751137985101 +2009-03-20 02:30:00-05:00,2009,3,20,2,30,2.0,0.7000000000000001,0.0,0.0,0.0,0.12,1018.0,174.0,3.6,91.09779343065976 +2009-03-20 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20:30:00-05:00,2009,3,20,20,30,0.4,-2.8000000000000003,0.0,0.0,0.0,0.12,1026.0,1.0,3.4000000000000004,79.10615602375697 +2009-03-20 21:30:00-05:00,2009,3,20,21,30,-0.1,-3.2,0.0,0.0,0.0,0.12,1026.0,180.0,3.5,79.62023686847691 +2009-03-20 22:30:00-05:00,2009,3,20,22,30,-0.5,-3.4000000000000004,0.0,0.0,0.0,0.12,1026.0,180.0,3.7,80.75677059863608 +2009-03-20 23:30:00-05:00,2009,3,20,23,30,-1.0,-3.5,0.0,0.0,0.0,0.12,1026.0,5.0,3.9,83.13484176183611 +2009-03-21 00:30:00-05:00,2009,3,21,0,30,-1.4,-3.4000000000000004,0.0,0.0,0.0,0.12,1027.0,11.0,4.0,86.24760679342187 +2009-03-21 01:30:00-05:00,2009,3,21,1,30,-1.7000000000000002,-3.4000000000000004,0.0,0.0,0.0,0.12,1027.0,15.0,4.0,88.16924164861757 +2009-03-21 02:30:00-05:00,2009,3,21,2,30,-1.9,-3.9,0.0,0.0,0.0,0.12,1028.0,19.0,3.9,86.19443065553102 +2009-03-21 03:30:00-05:00,2009,3,21,3,30,-2.1,-4.3,0.0,0.0,0.0,0.12,1029.0,21.0,3.7,84.88856682361201 +2009-03-21 04:30:00-05:00,2009,3,21,4,30,-2.2,-4.7,0.0,0.0,0.0,0.12,1029.0,23.0,3.5,82.98077723291355 +2009-03-21 05:30:00-05:00,2009,3,21,5,30,-1.9,-4.9,0.0,0.0,0.0,0.12,1030.0,25.0,3.4000000000000004,79.94888355191028 +2009-03-21 06:30:00-05:00,2009,3,21,6,30,-0.5,-4.6000000000000005,30.0,446.0,73.0,0.12,1030.0,29.0,3.5,73.80907944097113 +2009-03-21 07:30:00-05:00,2009,3,21,7,30,1.3,-5.5,57.0,772.0,279.0,0.12,1031.0,35.0,3.5,60.54022383279245 +2009-03-21 08:30:00-05:00,2009,3,21,8,30,2.8000000000000003,-6.800000000000001,72.0,906.0,488.0,0.12,1031.0,36.0,3.2,49.25972478985281 +2009-03-21 09:30:00-05:00,2009,3,21,9,30,4.1000000000000005,-7.2,81.0,973.0,666.0,0.12,1031.0,36.0,2.7,43.58436674046664 +2009-03-21 10:30:00-05:00,2009,3,21,10,30,5.2,-7.2,83.0,1014.0,796.0,0.12,1030.0,41.0,2.1,40.36222657157459 +2009-03-21 11:30:00-05:00,2009,3,21,11,30,6.1000000000000005,-7.300000000000001,85.0,1018.0,855.0,0.12,1030.0,54.0,1.5,37.63198441284225 +2009-03-21 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20:30:00-05:00,2009,3,21,20,30,1.0,-1.8,0.0,0.0,0.0,0.12,1028.0,189.0,2.9000000000000004,81.56827317877084 +2009-03-21 21:30:00-05:00,2009,3,21,21,30,0.5,-1.9,0.0,0.0,0.0,0.12,1028.0,203.0,3.0,83.94094829973909 +2009-03-21 22:30:00-05:00,2009,3,21,22,30,0.2,-1.8,0.0,0.0,0.0,0.12,1028.0,216.0,3.2,86.41577881524513 +2009-03-21 23:30:00-05:00,2009,3,21,23,30,-0.1,-1.5,0.0,0.0,0.0,0.12,1028.0,226.0,3.3000000000000003,90.28498279097415 +2009-03-22 00:30:00-05:00,2009,3,22,0,30,-0.3,-1.2000000000000002,0.0,0.0,0.0,0.12,1027.0,234.0,3.5,93.64380127042307 +2009-03-22 01:30:00-05:00,2009,3,22,1,30,-0.4,-1.0,0.0,0.0,0.0,0.12,1027.0,240.0,3.6,95.71806372463993 +2009-03-22 02:30:00-05:00,2009,3,22,2,30,-0.7000000000000001,-1.0,0.0,0.0,0.0,0.12,1027.0,245.0,3.7,97.83295879686143 +2009-03-22 03:30:00-05:00,2009,3,22,3,30,-0.8,-1.1,0.0,0.0,0.0,0.12,1026.0,250.0,3.7,97.83118770883961 +2009-03-22 04:30:00-05:00,2009,3,22,4,30,-0.9,-1.3,0.0,0.0,0.0,0.12,1026.0,255.0,3.7,97.11523130071518 +2009-03-22 05:30:00-05:00,2009,3,22,5,30,-0.5,-1.5,0.0,0.0,0.0,0.12,1026.0,257.0,3.8,92.94895562508698 +2009-03-22 06:30:00-05:00,2009,3,22,6,30,0.7000000000000001,-1.4,34.0,324.0,67.0,0.12,1026.0,255.0,3.9,85.8359579178865 +2009-03-22 07:30:00-05:00,2009,3,22,7,30,2.7,-0.8,164.0,41.0,176.0,0.12,1025.0,251.0,4.1000000000000005,77.73562576947056 +2009-03-22 08:30:00-05:00,2009,3,22,8,30,5.0,-0.2,217.0,20.0,226.0,0.12,1025.0,251.0,4.1000000000000005,69.0871684135396 +2009-03-22 09:30:00-05:00,2009,3,22,9,30,6.9,0.2,355.0,165.0,455.0,0.12,1024.0,255.0,3.9,62.37067854658501 +2009-03-22 10:30:00-05:00,2009,3,22,10,30,8.4,0.5,250.0,561.0,647.0,0.12,1023.0,261.0,3.3000000000000003,57.54381995157847 +2009-03-22 11:30:00-05:00,2009,3,22,11,30,9.3,1.1,374.0,428.0,700.0,0.12,1022.0,265.0,2.8000000000000003,56.54357414465039 +2009-03-22 12:30:00-05:00,2009,3,22,12,30,9.8,1.8,116.0,918.0,816.0,0.12,1021.0,270.0,2.6,57.489657786865365 +2009-03-22 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20:30:00-05:00,2009,3,26,20,30,5.5,5.4,0.0,0.0,0.0,0.12,1009.0,194.0,2.0,99.30867298554894 +2009-03-26 21:30:00-05:00,2009,3,26,21,30,5.5,5.4,0.0,0.0,0.0,0.12,1008.0,220.0,1.4,99.30867298554894 +2009-03-26 22:30:00-05:00,2009,3,26,22,30,5.5,5.5,0.0,0.0,0.0,0.12,1008.0,263.0,1.4,100.00000000000001 +2009-03-26 23:30:00-05:00,2009,3,26,23,30,5.4,5.4,0.0,0.0,0.0,0.12,1008.0,303.0,1.7000000000000002,100.0 +2009-03-27 00:30:00-05:00,2009,3,27,0,30,5.1000000000000005,5.1000000000000005,0.0,0.0,0.0,0.12,1008.0,323.0,2.1,100.0 +2009-03-27 01:30:00-05:00,2009,3,27,1,30,4.7,4.7,0.0,0.0,0.0,0.12,1008.0,328.0,2.5,100.0 +2009-03-27 02:30:00-05:00,2009,3,27,2,30,4.3,4.3,0.0,0.0,0.0,0.12,1008.0,323.0,2.5,99.99999999999999 +2009-03-27 03:30:00-05:00,2009,3,27,3,30,4.0,3.9,0.0,0.0,0.0,0.12,1009.0,316.0,2.5,99.30027999838094 +2009-03-27 04:30:00-05:00,2009,3,27,4,30,3.7,3.6,0.0,0.0,0.0,0.12,1010.0,313.0,2.5,99.29858308904899 +2009-03-27 05:30:00-05:00,2009,3,27,5,30,4.0,3.3000000000000003,0.0,0.0,0.0,0.12,1011.0,315.0,2.5,95.19218999943757 +2009-03-27 06:30:00-05:00,2009,3,27,6,30,5.2,3.7,58.0,2.0,58.0,0.12,1011.0,317.0,2.5,90.04120953664071 +2009-03-27 07:30:00-05:00,2009,3,27,7,30,7.1000000000000005,4.7,80.0,627.0,280.0,0.12,1012.0,323.0,2.3000000000000003,84.71333832884801 +2009-03-27 08:30:00-05:00,2009,3,27,8,30,9.2,5.6000000000000005,99.0,774.0,479.0,0.12,1012.0,334.0,2.0,78.20155194603664 +2009-03-27 09:30:00-05:00,2009,3,27,9,30,11.1,5.7,310.0,411.0,570.0,0.12,1012.0,339.0,1.8,69.35607150958731 +2009-03-27 10:30:00-05:00,2009,3,27,10,30,12.6,5.7,112.0,907.0,775.0,0.12,1013.0,330.0,1.5,62.82503799952369 +2009-03-27 11:30:00-05:00,2009,3,27,11,30,14.0,5.6000000000000005,214.0,743.0,796.0,0.12,1012.0,306.0,1.2000000000000002,56.9493527836096 +2009-03-27 12:30:00-05:00,2009,3,27,12,30,14.8,5.4,291.0,460.0,652.0,0.12,1012.0,265.0,1.0,53.33467936354256 +2009-03-27 13:30:00-05:00,2009,3,27,13,30,14.8,5.300000000000001,274.0,467.0,617.0,0.12,1012.0,227.0,1.2000000000000002,52.96566640130908 +2009-03-27 14:30:00-05:00,2009,3,27,14,30,14.2,5.300000000000001,278.0,312.0,476.0,0.12,1011.0,203.0,1.4,55.05835862618922 +2009-03-27 15:30:00-05:00,2009,3,27,15,30,13.2,5.800000000000001,231.0,47.0,254.0,0.12,1011.0,190.0,1.7000000000000002,60.82728505960192 +2009-03-27 16:30:00-05:00,2009,3,27,16,30,11.4,7.300000000000001,108.0,8.0,111.0,0.12,1012.0,184.0,2.1,75.90221347982079 +2009-03-27 17:30:00-05:00,2009,3,27,17,30,9.5,7.4,29.0,0.0,29.0,0.12,1012.0,182.0,2.5,86.74214152985533 +2009-03-27 18:30:00-05:00,2009,3,27,18,30,8.4,6.7,0.0,0.0,0.0,0.12,1012.0,183.0,2.9000000000000004,89.05048696470176 +2009-03-27 19:30:00-05:00,2009,3,27,19,30,7.800000000000001,6.7,0.0,0.0,0.0,0.12,1013.0,188.0,3.0,92.75424030829988 +2009-03-27 20:30:00-05:00,2009,3,27,20,30,7.300000000000001,6.6000000000000005,0.0,0.0,0.0,0.12,1013.0,192.0,2.7,95.31528229064008 +2009-03-27 21:30:00-05:00,2009,3,27,21,30,7.0,6.5,0.0,0.0,0.0,0.12,1013.0,194.0,2.1,96.62561461039537 +2009-03-27 22:30:00-05:00,2009,3,27,22,30,6.6000000000000005,6.4,0.0,0.0,0.0,0.12,1013.0,192.0,1.5,98.63361680952191 +2009-03-27 23:30:00-05:00,2009,3,27,23,30,6.4,6.2,0.0,0.0,0.0,0.12,1013.0,189.0,1.2000000000000002,98.63143900438001 +2009-03-28 00:30:00-05:00,2009,3,28,0,30,6.300000000000001,6.0,0.0,0.0,0.0,0.12,1013.0,181.0,0.9,97.95175951991436 +2009-03-28 01:30:00-05:00,2009,3,28,1,30,6.2,5.800000000000001,0.0,0.0,0.0,0.12,1012.0,151.0,0.9,97.27514316832789 +2009-03-28 02:30:00-05:00,2009,3,28,2,30,5.9,5.7,0.0,0.0,0.0,0.12,1012.0,117.0,1.1,98.6259717114027 +2009-03-28 03:30:00-05:00,2009,3,28,3,30,5.7,5.7,0.0,0.0,0.0,0.12,1012.0,99.0,1.4,100.0 +2009-03-28 04:30:00-05:00,2009,3,28,4,30,5.7,5.7,0.0,0.0,0.0,0.12,1012.0,90.0,1.7000000000000002,100.0 +2009-03-28 05:30:00-05:00,2009,3,28,5,30,6.1000000000000005,5.7,0.0,0.0,0.0,0.12,1012.0,88.0,2.2,97.2729836902132 +2009-03-28 06:30:00-05:00,2009,3,28,6,30,6.800000000000001,5.9,6.0,0.0,6.0,0.12,1013.0,88.0,2.7,93.98961628580115 +2009-03-28 07:30:00-05:00,2009,3,28,7,30,7.6,6.4,152.0,162.0,204.0,0.12,1013.0,88.0,3.3000000000000003,92.10703167035639 +2009-03-28 08:30:00-05:00,2009,3,28,8,30,8.5,6.9,244.0,36.0,262.0,0.12,1013.0,85.0,3.8,89.67174655220771 +2009-03-28 09:30:00-05:00,2009,3,28,9,30,9.0,7.2,148.0,0.0,148.0,0.12,1012.0,80.0,4.1000000000000005,88.49260336622424 +2009-03-28 10:30:00-05:00,2009,3,28,10,30,9.5,7.4,405.0,167.0,527.0,0.12,1012.0,75.0,4.2,86.74214152985533 +2009-03-28 11:30:00-05:00,2009,3,28,11,30,9.9,7.5,427.0,237.0,614.0,0.12,1012.0,74.0,4.4,85.02480493940509 +2009-03-28 12:30:00-05:00,2009,3,28,12,30,10.1,7.6,418.0,278.0,637.0,0.12,1011.0,74.0,4.5,84.46898734552828 +2009-03-28 13:30:00-05:00,2009,3,28,13,30,9.9,7.6,402.0,219.0,564.0,0.12,1011.0,73.0,4.4,85.60678376241587 +2009-03-28 14:30:00-05:00,2009,3,28,14,30,9.5,7.5,273.0,294.0,461.0,0.12,1010.0,73.0,4.3,87.33635097210484 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23:30:00-05:00,2009,3,28,23,30,6.4,6.2,0.0,0.0,0.0,0.12,1006.0,89.0,3.8,98.63143900438001 +2009-03-29 00:30:00-05:00,2009,3,29,0,30,6.2,6.0,0.0,0.0,0.0,0.12,1004.0,94.0,3.9,98.62925600061709 +2009-03-29 01:30:00-05:00,2009,3,29,1,30,6.0,5.800000000000001,0.0,0.0,0.0,0.12,1003.0,93.0,4.0,98.62706778171305 +2009-03-29 02:30:00-05:00,2009,3,29,2,30,6.0,5.7,0.0,0.0,0.0,0.12,1002.0,89.0,4.0,97.946869911316 +2009-03-29 03:30:00-05:00,2009,3,29,3,30,6.0,5.6000000000000005,0.0,0.0,0.0,0.12,1001.0,92.0,3.6,97.27082165614588 +2009-03-29 04:30:00-05:00,2009,3,29,4,30,6.1000000000000005,5.6000000000000005,0.0,0.0,0.0,0.12,1000.0,103.0,2.9000000000000004,96.60158672818162 +2009-03-29 05:30:00-05:00,2009,3,29,5,30,6.300000000000001,5.800000000000001,0.0,0.0,0.0,0.12,1000.0,130.0,2.2,96.6069482550865 +2009-03-29 06:30:00-05:00,2009,3,29,6,30,6.800000000000001,6.2,3.0,0.0,3.0,0.12,999.0,163.0,1.8,95.95660032692135 +2009-03-29 07:30:00-05:00,2009,3,29,7,30,7.5,6.7,19.0,0.0,19.0,0.12,998.0,177.0,1.7000000000000002,94.67041840957172 +2009-03-29 08:30:00-05:00,2009,3,29,8,30,8.6,7.4,73.0,0.0,73.0,0.12,997.0,177.0,2.0,92.16726406013537 +2009-03-29 09:30:00-05:00,2009,3,29,9,30,9.8,8.3,76.0,0.0,76.0,0.12,996.0,178.0,2.4000000000000004,90.3834313074393 +2009-03-29 10:30:00-05:00,2009,3,29,10,30,11.0,9.2,59.0,0.0,59.0,0.12,995.0,181.0,3.0,88.66304030514664 +2009-03-29 11:30:00-05:00,2009,3,29,11,30,12.1,9.9,102.0,0.0,102.0,0.12,994.0,183.0,3.4000000000000004,86.41342740508078 +2009-03-29 12:30:00-05:00,2009,3,29,12,30,13.0,10.9,106.0,0.0,106.0,0.12,994.0,182.0,3.8,87.07916192167241 +2009-03-29 13:30:00-05:00,2009,3,29,13,30,13.4,11.7,360.0,42.0,391.0,0.12,993.0,179.0,4.1000000000000005,89.45148078805539 +2009-03-29 14:30:00-05:00,2009,3,29,14,30,13.1,12.0,326.0,60.0,365.0,0.12,992.0,177.0,4.3,93.04102544906256 +2009-03-29 15:30:00-05:00,2009,3,29,15,30,12.3,11.9,254.0,37.0,273.0,0.12,992.0,175.0,4.5,97.40219264736893 +2009-03-29 16:30:00-05:00,2009,3,29,16,30,11.6,11.5,125.0,3.0,126.0,0.12,991.0,173.0,4.7,99.3412965787286 +2009-03-29 17:30:00-05:00,2009,3,29,17,30,10.9,10.9,17.0,0.0,17.0,0.12,991.0,177.0,4.6000000000000005,100.0 +2009-03-29 18:30:00-05:00,2009,3,29,18,30,10.4,10.4,0.0,0.0,0.0,0.12,990.0,189.0,4.3,100.0 +2009-03-29 19:30:00-05:00,2009,3,29,19,30,10.0,10.0,0.0,0.0,0.0,0.12,990.0,209.0,3.5,100.0 +2009-03-29 20:30:00-05:00,2009,3,29,20,30,9.6,9.6,0.0,0.0,0.0,0.12,991.0,240.0,2.8000000000000003,100.0 +2009-03-29 21:30:00-05:00,2009,3,29,21,30,9.2,9.2,0.0,0.0,0.0,0.12,991.0,274.0,2.7,100.0 +2009-03-29 22:30:00-05:00,2009,3,29,22,30,8.8,8.6,0.0,0.0,0.0,0.12,992.0,295.0,3.3000000000000003,98.65723421716437 +2009-03-29 23:30:00-05:00,2009,3,29,23,30,8.3,7.9,0.0,0.0,0.0,0.12,993.0,300.0,3.9,97.31990881273852 +2009-03-30 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00:30:00-05:00,2000,5,31,0,30,9.3,9.0,0.0,0.0,0.0,0.14,1021.0,69.0,2.3000000000000003,97.99971279699481 +2000-05-31 01:30:00-05:00,2000,5,31,1,30,9.1,9.0,0.0,0.0,0.0,0.14,1021.0,62.0,2.3000000000000003,99.32821114906827 +2000-05-31 02:30:00-05:00,2000,5,31,2,30,8.9,8.9,0.0,0.0,0.0,0.14,1021.0,60.0,2.4000000000000004,100.0 +2000-05-31 03:30:00-05:00,2000,5,31,3,30,8.8,8.8,0.0,0.0,0.0,0.14,1021.0,60.0,2.4000000000000004,100.0 +2000-05-31 04:30:00-05:00,2000,5,31,4,30,9.4,9.0,0.0,0.0,0.0,0.14,1021.0,58.0,2.5,97.3429205497449 +2000-05-31 05:30:00-05:00,2000,5,31,5,30,10.6,9.3,57.0,391.0,125.0,0.14,1021.0,54.0,2.5,91.66721119357896 +2000-05-31 06:30:00-05:00,2000,5,31,6,30,11.9,9.5,144.0,6.0,146.0,0.14,1021.0,54.0,2.4000000000000004,85.24166933895583 +2000-05-31 07:30:00-05:00,2000,5,31,7,30,13.3,9.6,230.0,10.0,235.0,0.14,1021.0,59.0,2.1,78.29146160659708 +2000-05-31 08:30:00-05:00,2000,5,31,8,30,14.6,9.8,386.0,110.0,462.0,0.14,1021.0,72.0,1.8,72.93235422658572 +2000-05-31 09:30:00-05:00,2000,5,31,9,30,15.7,10.1,459.0,210.0,630.0,0.14,1020.0,91.0,1.7000000000000002,69.33335831799596 +2000-05-31 10:30:00-05:00,2000,5,31,10,30,16.6,10.5,127.0,905.0,943.0,0.14,1020.0,111.0,1.7000000000000002,67.23781983021009 +2000-05-31 11:30:00-05:00,2000,5,31,11,30,17.400000000000002,10.8,126.0,916.0,990.0,0.14,1020.0,129.0,1.4,65.20430586841783 +2000-05-31 12:30:00-05:00,2000,5,31,12,30,18.0,11.2,124.0,909.0,977.0,0.14,1019.0,149.0,1.2000000000000002,64.4745724370721 +2000-05-31 13:30:00-05:00,2000,5,31,13,30,18.4,11.4,118.0,889.0,907.0,0.14,1019.0,162.0,1.2000000000000002,63.71423296095101 +2000-05-31 14:30:00-05:00,2000,5,31,14,30,18.5,11.5,111.0,859.0,790.0,0.14,1018.0,164.0,1.5,63.73649672217544 +2000-05-31 15:30:00-05:00,2000,5,31,15,30,18.3,11.7,100.0,811.0,634.0,0.14,1017.0,168.0,1.6,65.39879920668616 +2000-05-31 16:30:00-05:00,2000,5,31,16,30,17.900000000000002,11.9,90.0,727.0,451.0,0.14,1017.0,175.0,1.6,67.95335081759669 +2000-05-31 17:30:00-05:00,2000,5,31,17,30,16.900000000000002,12.4,72.0,592.0,260.0,0.14,1016.0,179.0,1.6,74.80339519338641 +2000-05-31 18:30:00-05:00,2000,5,31,18,30,15.3,13.2,42.0,330.0,87.0,0.14,1016.0,183.0,1.6,87.29386238613752 +2000-05-31 19:30:00-05:00,2000,5,31,19,30,19.200000000000003,17.3,0.0,0.0,0.0,0.13,1006.0,230.0,1.7000000000000002,88.76194416601005 +2000-05-31 20:30:00-05:00,2000,5,31,20,30,18.7,16.900000000000002,0.0,0.0,0.0,0.13,1007.0,241.0,1.6,89.28579993410109 +2000-05-31 21:30:00-05:00,2000,5,31,21,30,18.3,16.5,0.0,0.0,0.0,0.13,1006.0,254.0,1.5,89.2546996907551 +2000-05-31 22:30:00-05:00,2000,5,31,22,30,17.8,16.2,0.0,0.0,0.0,0.13,1006.0,268.0,1.4,90.36110374266342 +2000-05-31 23:30:00-05:00,2000,5,31,23,30,17.2,15.9,0.0,0.0,0.0,0.13,1006.0,289.0,1.3,92.06848576959929 +2019-06-01 00:30:00-05:00,2019,6,1,0,30,16.7,15.7,0.0,0.0,0.0,0.13,1006.0,315.0,1.3,93.82499572830595 +2019-06-01 01:30:00-05:00,2019,6,1,1,30,16.2,15.4,0.0,0.0,0.0,0.13,1007.0,340.0,1.3,95.01373159739097 +2019-06-01 02:30:00-05:00,2019,6,1,2,30,15.6,15.2,0.0,0.0,0.0,0.13,1007.0,180.0,1.4,97.46726951652359 +2019-06-01 03:30:00-05:00,2019,6,1,3,30,15.2,14.8,0.0,0.0,0.0,0.13,1007.0,14.0,1.7000000000000002,97.4595118897884 +2019-06-01 04:30:00-05:00,2019,6,1,4,30,15.4,14.6,3.0,9.0,3.0,0.13,1007.0,24.0,2.3000000000000003,94.98355576788046 +2019-06-01 05:30:00-05:00,2019,6,1,5,30,16.2,14.8,90.0,21.0,94.0,0.13,1008.0,33.0,2.6,91.41883781841517 +2019-06-01 06:30:00-05:00,2019,6,1,6,30,17.5,15.0,184.0,152.0,238.0,0.13,1008.0,46.0,2.3000000000000003,85.27499840410066 +2019-06-01 07:30:00-05:00,2019,6,1,7,30,19.200000000000003,15.9,262.0,214.0,376.0,0.13,1008.0,70.0,1.9,81.2067182717975 +2019-06-01 08:30:00-05:00,2019,6,1,8,30,20.700000000000003,16.3,320.0,294.0,522.0,0.13,1008.0,95.0,2.1,75.91479058529396 +2019-06-01 09:30:00-05:00,2019,6,1,9,30,21.5,16.3,372.0,364.0,668.0,0.13,1008.0,110.0,2.3000000000000003,72.27619317397917 +2019-06-01 10:30:00-05:00,2019,6,1,10,30,21.9,16.5,440.0,315.0,724.0,0.13,1008.0,124.0,2.0,71.43412850987252 +2019-06-01 11:30:00-05:00,2019,6,1,11,30,22.4,16.7,441.0,324.0,747.0,0.13,1007.0,138.0,1.5,70.17758247269279 +2019-06-01 12:30:00-05:00,2019,6,1,12,30,22.8,16.7,456.0,292.0,730.0,0.13,1006.0,145.0,1.2000000000000002,68.49406925058416 +2019-06-01 13:30:00-05:00,2019,6,1,13,30,23.1,16.7,453.0,189.0,621.0,0.13,1006.0,137.0,1.2000000000000002,67.26121295368861 +2019-06-01 14:30:00-05:00,2019,6,1,14,30,22.9,16.8,389.0,207.0,553.0,0.13,1006.0,132.0,1.6,68.51379006367574 +2019-06-01 15:30:00-05:00,2019,6,1,15,30,22.200000000000003,17.1,338.0,181.0,457.0,0.13,1006.0,137.0,2.2,72.8611526174558 +2019-06-01 16:30:00-05:00,2019,6,1,16,30,21.0,17.8,263.0,147.0,336.0,0.13,1006.0,146.0,2.4000000000000004,81.95274097068759 +2019-06-01 17:30:00-05:00,2019,6,1,17,30,19.700000000000003,17.900000000000002,167.0,99.0,199.0,0.13,1006.0,157.0,2.1,89.3629746825152 +2019-06-01 18:30:00-05:00,2019,6,1,18,30,18.6,17.8,51.0,25.0,54.0,0.13,1006.0,163.0,1.8,95.10265738415612 +2019-06-01 19:30:00-05:00,2019,6,1,19,30,18.0,17.5,0.0,0.0,0.0,0.13,1006.0,161.0,1.6,96.89990474983287 +2019-06-01 20:30:00-05:00,2019,6,1,20,30,17.7,17.3,0.0,0.0,0.0,0.13,1006.0,157.0,1.6,97.50742244865056 +2019-06-01 21:30:00-05:00,2019,6,1,21,30,17.3,17.1,0.0,0.0,0.0,0.13,1006.0,156.0,1.6,98.74297233703732 +2019-06-01 22:30:00-05:00,2019,6,1,22,30,17.0,16.900000000000002,0.0,0.0,0.0,0.13,1006.0,158.0,1.7000000000000002,99.36828928489736 +2019-06-01 23:30:00-05:00,2019,6,1,23,30,16.900000000000002,16.7,0.0,0.0,0.0,0.13,1005.0,162.0,1.7000000000000002,98.73912371938577 +2019-06-02 00:30:00-05:00,2019,6,2,0,30,16.7,16.6,0.0,0.0,0.0,0.13,1005.0,168.0,1.7000000000000002,99.36683354071437 +2019-06-02 01:30:00-05:00,2019,6,2,1,30,16.7,16.6,0.0,0.0,0.0,0.13,1005.0,184.0,1.5,99.36683354071437 +2019-06-02 02:30:00-05:00,2019,6,2,2,30,16.7,16.7,0.0,0.0,0.0,0.13,1005.0,207.0,1.4,100.0 +2019-06-02 03:30:00-05:00,2019,6,2,3,30,16.8,16.8,0.0,0.0,0.0,0.13,1004.0,222.0,1.3,100.0 +2019-06-02 04:30:00-05:00,2019,6,2,4,30,17.1,16.900000000000002,2.0,5.0,2.0,0.13,1004.0,225.0,1.4,98.74105022947546 +2019-06-02 05:30:00-05:00,2019,6,2,5,30,17.7,17.2,68.0,151.0,94.0,0.13,1004.0,229.0,1.5,96.89287218713241 +2019-06-02 06:30:00-05:00,2019,6,2,6,30,18.8,17.6,142.0,346.0,265.0,0.13,1004.0,236.0,1.4,92.7446534046649 +2019-06-02 07:30:00-05:00,2019,6,2,7,30,20.3,17.8,184.0,490.0,445.0,0.13,1004.0,254.0,1.0,85.5641754903033 +2019-06-02 08:30:00-05:00,2019,6,2,8,30,21.8,17.3,227.0,558.0,611.0,0.13,1004.0,268.0,0.8,75.6106681157808 +2019-06-02 09:30:00-05:00,2019,6,2,9,30,22.700000000000003,17.0,353.0,422.0,697.0,0.13,1004.0,255.0,0.9,70.23430175657111 +2019-06-02 10:30:00-05:00,2019,6,2,10,30,23.0,16.900000000000002,401.0,385.0,748.0,0.13,1004.0,232.0,1.3,68.53349404923449 +2019-06-02 11:30:00-05:00,2019,6,2,11,30,23.0,17.0,428.0,358.0,766.0,0.13,1003.0,218.0,1.8,68.96917974781988 +2019-06-02 12:30:00-05:00,2019,6,2,12,30,22.9,17.3,287.0,636.0,885.0,0.13,1003.0,212.0,2.3000000000000003,70.71773781332548 +2019-06-02 13:30:00-05:00,2019,6,2,13,30,22.6,17.8,310.0,566.0,813.0,0.13,1003.0,210.0,2.7,74.3235012686769 +2019-06-02 14:30:00-05:00,2019,6,2,14,30,22.1,18.6,290.0,521.0,703.0,0.13,1003.0,207.0,2.9000000000000004,80.56383888088084 +2019-06-02 15:30:00-05:00,2019,6,2,15,30,21.700000000000003,19.200000000000003,312.0,318.0,522.0,0.13,1002.0,205.0,3.0,85.70567504001535 +2019-06-02 16:30:00-05:00,2019,6,2,16,30,21.3,19.5,211.0,90.0,256.0,0.13,1002.0,204.0,3.1,89.48476658428439 +2019-06-02 17:30:00-05:00,2019,6,2,17,30,20.8,19.6,124.0,296.0,219.0,0.13,1002.0,205.0,3.2,92.85045810919284 +2019-06-02 18:30:00-05:00,2019,6,2,18,30,20.200000000000003,19.6,53.0,152.0,74.0,0.13,1002.0,212.0,3.2,96.35079683468209 +2019-06-02 19:30:00-05:00,2019,6,2,19,30,19.8,19.4,0.0,0.0,0.0,0.13,1002.0,221.0,3.1,97.546632032517 +2019-06-02 20:30:00-05:00,2019,6,2,20,30,19.4,19.3,0.0,0.0,0.0,0.13,1003.0,233.0,3.0,99.37975681057642 +2019-06-02 21:30:00-05:00,2019,6,2,21,30,18.7,18.7,0.0,0.0,0.0,0.13,1003.0,253.0,2.8000000000000003,99.99999999999999 +2019-06-02 22:30:00-05:00,2019,6,2,22,30,17.6,17.6,0.0,0.0,0.0,0.13,1004.0,292.0,3.2,100.0 +2019-06-02 23:30:00-05:00,2019,6,2,23,30,16.3,15.8,0.0,0.0,0.0,0.13,1005.0,329.0,4.3,96.85973678100338 +2019-06-03 00:30:00-05:00,2019,6,3,0,30,15.2,14.0,0.0,0.0,0.0,0.13,1006.0,346.0,4.6000000000000005,92.54827895925615 +2019-06-03 01:30:00-05:00,2019,6,3,1,30,14.3,13.0,0.0,0.0,0.0,0.13,1006.0,177.0,3.8,91.89576342138946 +2019-06-03 02:30:00-05:00,2019,6,3,2,30,13.6,12.2,0.0,0.0,0.0,0.13,1007.0,3.0,3.1,91.25150185295402 +2019-06-03 03:30:00-05:00,2019,6,3,3,30,12.8,11.4,0.0,0.0,0.0,0.13,1008.0,176.0,2.6,91.19904502360808 +2019-06-03 04:30:00-05:00,2019,6,3,4,30,12.9,11.0,5.0,27.0,5.0,0.13,1008.0,340.0,2.3000000000000003,88.23418145167658 +2019-06-03 05:30:00-05:00,2019,6,3,5,30,14.0,11.4,52.0,415.0,125.0,0.13,1009.0,323.0,2.2,84.34185405958227 +2019-06-03 06:30:00-05:00,2019,6,3,6,30,15.5,11.3,81.0,640.0,310.0,0.13,1010.0,311.0,2.3000000000000003,76.06373863455075 +2019-06-03 07:30:00-05:00,2019,6,3,7,30,16.900000000000002,9.5,101.0,754.0,503.0,0.13,1010.0,301.0,2.4000000000000004,61.7006539647458 +2019-06-03 08:30:00-05:00,2019,6,3,8,30,17.8,8.8,119.0,816.0,681.0,0.13,1010.0,287.0,2.6,55.60586001949962 +2019-06-03 09:30:00-05:00,2019,6,3,9,30,18.5,9.5,356.0,412.0,692.0,0.13,1010.0,275.0,2.9000000000000004,55.78370160011057 +2019-06-03 10:30:00-05:00,2019,6,3,10,30,19.0,10.1,292.0,577.0,813.0,0.13,1010.0,271.0,3.2,56.28533522138947 +2019-06-03 11:30:00-05:00,2019,6,3,11,30,19.3,10.2,282.0,592.0,842.0,0.13,1010.0,272.0,3.3000000000000003,55.61330134721722 +2019-06-03 12:30:00-05:00,2019,6,3,12,30,19.5,9.9,218.0,764.0,937.0,0.13,1010.0,276.0,3.4000000000000004,53.83487344020089 +2019-06-03 13:30:00-05:00,2019,6,3,13,30,19.5,9.4,114.0,920.0,932.0,0.13,1010.0,283.0,3.6,52.05904298920601 +2019-06-03 14:30:00-05:00,2019,6,3,14,30,19.200000000000003,8.9,105.0,904.0,822.0,0.13,1010.0,292.0,3.7,51.28342898049064 +2019-06-03 15:30:00-05:00,2019,6,3,15,30,18.9,8.700000000000001,93.0,862.0,663.0,0.13,1011.0,300.0,3.8,51.55080305780415 +2019-06-03 16:30:00-05:00,2019,6,3,16,30,18.3,8.8,80.0,791.0,476.0,0.13,1011.0,306.0,3.6,53.885922721391296 +2019-06-03 17:30:00-05:00,2019,6,3,17,30,17.3,9.6,63.0,670.0,279.0,0.13,1012.0,310.0,3.2,60.56329750493192 +2019-06-03 18:30:00-05:00,2019,6,3,18,30,15.7,10.9,39.0,429.0,99.0,0.13,1013.0,315.0,2.9000000000000004,73.1297818823137 +2019-06-03 19:30:00-05:00,2019,6,3,19,30,14.4,10.9,0.0,0.0,0.0,0.13,1014.0,321.0,3.0,79.50632158460513 +2019-06-03 20:30:00-05:00,2019,6,3,20,30,13.5,10.6,0.0,0.0,0.0,0.13,1014.0,330.0,3.1,82.62057203771371 +2019-06-03 21:30:00-05:00,2019,6,3,21,30,12.8,10.3,0.0,0.0,0.0,0.13,1014.0,341.0,3.0,84.77032985120228 +2019-06-03 22:30:00-05:00,2019,6,3,22,30,12.2,10.0,0.0,0.0,0.0,0.13,1015.0,350.0,2.9000000000000004,86.42335672108587 +2019-06-03 23:30:00-05:00,2019,6,3,23,30,11.6,9.6,0.0,0.0,0.0,0.13,1015.0,357.0,2.6,87.53158087183863 +2019-06-04 00:30:00-05:00,2019,6,4,0,30,11.2,9.4,0.0,0.0,0.0,0.13,1015.0,358.0,2.2,88.67988012453799 +2019-06-04 01:30:00-05:00,2019,6,4,1,30,10.9,9.2,0.0,0.0,0.0,0.13,1015.0,355.0,1.9,89.25372940441291 +2019-06-04 02:30:00-05:00,2019,6,4,2,30,10.7,9.2,0.0,0.0,0.0,0.13,1016.0,351.0,1.6,90.44835979796521 +2019-06-04 03:30:00-05:00,2019,6,4,3,30,10.3,9.2,0.0,0.0,0.0,0.13,1016.0,339.0,1.5,92.89165376930423 +2019-06-04 04:30:00-05:00,2019,6,4,4,30,10.7,8.9,7.0,46.0,7.0,0.13,1017.0,320.0,1.6,88.6377116314453 +2019-06-04 05:30:00-05:00,2019,6,4,5,30,12.1,9.1,49.0,454.0,129.0,0.13,1018.0,307.0,1.9,81.89275327496937 +2019-06-04 06:30:00-05:00,2019,6,4,6,30,13.7,9.0,75.0,674.0,316.0,0.13,1018.0,311.0,2.3000000000000003,73.26187114873149 +2019-06-04 07:30:00-05:00,2019,6,4,7,30,15.2,7.4,92.0,791.0,514.0,0.13,1019.0,314.0,2.4000000000000004,59.65312546903233 +2019-06-04 08:30:00-05:00,2019,6,4,8,30,16.1,6.4,103.0,858.0,695.0,0.13,1018.0,298.0,2.3000000000000003,52.58163611812481 +2019-06-04 09:30:00-05:00,2019,6,4,9,30,17.0,6.1000000000000005,108.0,902.0,844.0,0.13,1017.0,272.0,2.4000000000000004,48.64154903407281 +2019-06-04 10:30:00-05:00,2019,6,4,10,30,17.8,6.4,111.0,925.0,946.0,0.13,1017.0,255.0,2.6,47.21135007997533 +2019-06-04 11:30:00-05:00,2019,6,4,11,30,18.5,6.9,114.0,928.0,992.0,0.13,1017.0,252.0,2.8000000000000003,46.76030862307118 +2019-06-04 12:30:00-05:00,2019,6,4,12,30,19.1,7.5,180.0,783.0,917.0,0.13,1016.0,246.0,2.7,46.926887323778686 +2019-06-04 13:30:00-05:00,2019,6,4,13,30,19.5,8.3,166.0,776.0,857.0,0.13,1015.0,234.0,2.7,48.332986071919386 +2019-06-04 14:30:00-05:00,2019,6,4,14,30,19.700000000000003,9.1,97.0,889.0,803.0,0.13,1014.0,226.0,2.7,50.388807453366056 +2019-06-04 15:30:00-05:00,2019,6,4,15,30,19.6,10.1,92.0,836.0,646.0,0.13,1014.0,222.0,2.8000000000000003,54.22213747915955 +2019-06-04 16:30:00-05:00,2019,6,4,16,30,19.1,10.9,84.0,752.0,461.0,0.13,1013.0,220.0,3.1,58.998237610098045 +2019-06-04 17:30:00-05:00,2019,6,4,17,30,18.2,11.8,79.0,553.0,258.0,0.13,1013.0,222.0,3.2,66.24609988938025 +2019-06-04 18:30:00-05:00,2019,6,4,18,30,16.900000000000002,12.7,44.0,336.0,92.0,0.13,1013.0,224.0,3.3000000000000003,76.28946950010135 +2019-06-04 19:30:00-05:00,2019,6,4,19,30,16.0,12.6,0.0,0.0,0.0,0.14,1013.0,223.0,3.4000000000000004,80.25726657948107 +2019-06-04 20:30:00-05:00,2019,6,4,20,30,15.6,12.7,0.0,0.0,0.0,0.14,1013.0,219.0,3.5,82.87750924942381 +2019-06-04 21:30:00-05:00,2019,6,4,21,30,15.2,12.8,0.0,0.0,0.0,0.14,1013.0,215.0,3.6,85.58964006066603 +2019-06-04 22:30:00-05:00,2019,6,4,22,30,14.9,13.1,0.0,0.0,0.0,0.14,1013.0,213.0,3.7,88.98491364044341 +2019-06-04 23:30:00-05:00,2019,6,4,23,30,14.9,13.5,0.0,0.0,0.0,0.14,1013.0,214.0,3.8,91.33576575904104 +2019-06-05 00:30:00-05:00,2019,6,5,0,30,15.0,13.8,0.0,0.0,0.0,0.14,1013.0,216.0,4.0,92.5371395771872 +2019-06-05 01:30:00-05:00,2019,6,5,1,30,15.1,14.1,0.0,0.0,0.0,0.14,1012.0,215.0,4.0,93.75051646708793 +2019-06-05 02:30:00-05:00,2019,6,5,2,30,15.4,14.5,0.0,0.0,0.0,0.14,1012.0,212.0,4.1000000000000005,94.37233764503299 +2019-06-05 03:30:00-05:00,2019,6,5,3,30,15.8,15.0,0.0,0.0,0.0,0.14,1011.0,210.0,4.2,94.99867751301176 +2019-06-05 04:30:00-05:00,2019,6,5,4,30,16.1,15.5,2.0,0.0,2.0,0.14,1011.0,213.0,4.0,96.23649530378769 +2019-06-05 05:30:00-05:00,2019,6,5,5,30,16.900000000000002,15.8,46.0,2.0,46.0,0.14,1011.0,217.0,3.6,93.23642044435698 +2019-06-05 06:30:00-05:00,2019,6,5,6,30,18.1,16.2,41.0,0.0,41.0,0.14,1011.0,216.0,3.3000000000000003,88.67232868208957 +2019-06-05 07:30:00-05:00,2019,6,5,7,30,19.5,16.7,187.0,9.0,192.0,0.14,1011.0,212.0,3.2,83.87138208110741 +2019-06-05 08:30:00-05:00,2019,6,5,8,30,20.8,17.2,329.0,47.0,361.0,0.14,1010.0,209.0,3.3000000000000003,79.88419097239081 +2019-06-05 09:30:00-05:00,2019,6,5,9,30,21.8,17.7,340.0,35.0,369.0,0.14,1010.0,208.0,3.3000000000000003,77.54349998903822 +2019-06-05 10:30:00-05:00,2019,6,5,10,30,22.4,17.900000000000002,422.0,374.0,760.0,0.14,1009.0,206.0,3.2,75.70700726538732 +2019-06-05 11:30:00-05:00,2019,6,5,11,30,22.9,18.1,430.0,388.0,797.0,0.14,1009.0,205.0,3.1,74.37370624024206 +2019-06-05 12:30:00-05:00,2019,6,5,12,30,23.3,18.3,282.0,647.0,892.0,0.14,1008.0,203.0,3.1,73.51321234216394 +2019-06-05 13:30:00-05:00,2019,6,5,13,30,23.6,18.7,265.0,636.0,832.0,0.14,1007.0,201.0,3.3000000000000003,74.02618272315732 +2019-06-05 14:30:00-05:00,2019,6,5,14,30,23.6,19.1,393.0,207.0,558.0,0.14,1006.0,198.0,3.7,75.89808360035877 +2019-06-05 15:30:00-05:00,2019,6,5,15,30,23.0,19.700000000000003,316.0,116.0,393.0,0.14,1006.0,195.0,4.0,81.69062168115651 +2019-06-05 16:30:00-05:00,2019,6,5,16,30,22.1,20.200000000000003,231.0,90.0,276.0,0.14,1005.0,196.0,4.4,88.99327835918427 +2019-06-05 17:30:00-05:00,2019,6,5,17,30,21.4,20.4,52.0,1.0,52.0,0.14,1006.0,202.0,4.7,94.0363211678633 +2019-06-05 18:30:00-05:00,2019,6,5,18,30,20.9,20.4,40.0,0.0,40.0,0.14,1006.0,203.0,4.800000000000001,96.96667889869289 +2019-06-05 19:30:00-05:00,2019,6,5,19,30,20.3,20.1,0.0,0.0,0.0,0.14,1006.0,196.0,4.6000000000000005,98.7712846231834 +2019-06-05 20:30:00-05:00,2019,6,5,20,30,19.9,19.700000000000003,0.0,0.0,0.0,0.14,1005.0,191.0,4.6000000000000005,98.76756512261169 +2019-06-05 21:30:00-05:00,2019,6,5,21,30,19.6,19.4,0.0,0.0,0.0,0.14,1005.0,196.0,4.9,98.76476442574456 +2019-06-05 22:30:00-05:00,2019,6,5,22,30,19.4,19.200000000000003,0.0,0.0,0.0,0.14,1004.0,207.0,4.9,98.76289199850928 +2019-06-05 23:30:00-05:00,2019,6,5,23,30,19.4,19.3,0.0,0.0,0.0,0.14,1004.0,215.0,4.6000000000000005,99.37975681057642 +2019-06-06 00:30:00-05:00,2019,6,6,0,30,19.4,19.4,0.0,0.0,0.0,0.14,1003.0,216.0,4.2,100.0 +2019-06-06 01:30:00-05:00,2019,6,6,1,30,19.4,19.4,0.0,0.0,0.0,0.14,1003.0,216.0,3.7,100.0 +2019-06-06 02:30:00-05:00,2019,6,6,2,30,19.200000000000003,19.200000000000003,0.0,0.0,0.0,0.14,1003.0,217.0,3.2,100.0 +2019-06-06 03:30:00-05:00,2019,6,6,3,30,19.0,19.0,0.0,0.0,0.0,0.14,1003.0,222.0,2.9000000000000004,100.0 +2019-06-06 04:30:00-05:00,2019,6,6,4,30,19.1,19.0,6.0,24.0,6.0,0.14,1003.0,228.0,2.7,99.37834050659947 +2019-06-06 05:30:00-05:00,2019,6,6,5,30,19.700000000000003,19.3,57.0,329.0,115.0,0.14,1003.0,235.0,2.6,97.54478586534408 +2019-06-06 06:30:00-05:00,2019,6,6,6,30,20.700000000000003,19.9,86.0,563.0,288.0,0.14,1003.0,247.0,2.3000000000000003,95.17854707320242 +2019-06-06 07:30:00-05:00,2019,6,6,7,30,22.0,20.4,101.0,699.0,474.0,0.14,1003.0,264.0,2.2,90.65040206615419 +2019-06-06 08:30:00-05:00,2019,6,6,8,30,23.200000000000003,19.9,112.0,774.0,646.0,0.14,1004.0,280.0,2.0,81.71559208350563 +2019-06-06 09:30:00-05:00,2019,6,6,9,30,24.0,19.5,213.0,628.0,726.0,0.14,1004.0,291.0,1.8,75.9613049902721 +2019-06-06 10:30:00-05:00,2019,6,6,10,30,24.6,19.4,255.0,619.0,815.0,0.14,1004.0,300.0,1.5,72.82404132684132 +2019-06-06 11:30:00-05:00,2019,6,6,11,30,25.0,19.200000000000003,166.0,800.0,924.0,0.14,1004.0,312.0,1.3,70.22563357066225 +2019-06-06 12:30:00-05:00,2019,6,6,12,30,25.3,19.1,151.0,823.0,927.0,0.14,1004.0,323.0,1.2000000000000002,68.55357135658214 +2019-06-06 13:30:00-05:00,2019,6,6,13,30,25.5,19.0,232.0,671.0,830.0,0.14,1004.0,332.0,0.9,67.32223596818109 +2019-06-06 14:30:00-05:00,2019,6,6,14,30,25.4,18.9,368.0,182.0,513.0,0.14,1004.0,338.0,0.7000000000000001,67.30214238740515 +2019-06-06 15:30:00-05:00,2019,6,6,15,30,25.200000000000003,18.9,122.0,0.0,122.0,0.14,1005.0,339.0,0.5,68.10767129260715 +2019-06-06 16:30:00-05:00,2019,6,6,16,30,25.0,19.3,209.0,126.0,273.0,0.14,1005.0,333.0,0.4,70.66425703923703 +2019-06-06 17:30:00-05:00,2019,6,6,17,30,24.3,19.5,62.0,270.0,150.0,0.14,1006.0,306.0,0.2,74.60618620492927 +2019-06-06 18:30:00-05:00,2019,6,6,18,30,23.0,20.0,36.0,30.0,40.0,0.14,1006.0,235.0,0.2,83.22381284256231 +2019-06-06 19:30:00-05:00,2019,6,6,19,30,22.0,18.9,0.0,0.0,0.0,0.14,1007.0,103.0,0.2,82.59064807692023 +2019-06-06 20:30:00-05:00,2019,6,6,20,30,21.200000000000003,18.6,0.0,0.0,0.0,0.14,1007.0,12.0,0.4,85.12126331996525 +2019-06-06 21:30:00-05:00,2019,6,6,21,30,20.5,18.5,0.0,0.0,0.0,0.14,1008.0,2.0,0.7000000000000001,88.31196466858407 +2019-06-06 22:30:00-05:00,2019,6,6,22,30,19.700000000000003,18.3,0.0,0.0,0.0,0.14,1008.0,5.0,1.0,91.63673861415484 +2019-06-06 23:30:00-05:00,2019,6,6,23,30,19.1,18.2,0.0,0.0,0.0,0.14,1008.0,10.0,1.1,94.52594345391724 +2019-06-07 00:30:00-05:00,2019,6,7,0,30,18.5,18.0,0.0,0.0,0.0,0.14,1008.0,13.0,1.2000000000000002,96.91157302854653 +2019-06-07 01:30:00-05:00,2019,6,7,1,30,18.0,17.7,0.0,0.0,0.0,0.14,1008.0,14.0,1.3,98.12966234385277 +2019-06-07 02:30:00-05:00,2019,6,7,2,30,17.7,17.5,0.0,0.0,0.0,0.14,1008.0,13.0,1.3,98.74680339802785 +2019-06-07 03:30:00-05:00,2019,6,7,3,30,17.3,17.2,0.0,0.0,0.0,0.14,1009.0,13.0,1.4,99.36974001969769 +2019-06-07 04:30:00-05:00,2019,6,7,4,30,17.400000000000002,17.0,5.0,0.0,5.0,0.14,1009.0,14.0,1.6,97.50174476681984 +2019-06-07 05:30:00-05:00,2019,6,7,5,30,18.0,17.0,84.0,45.0,92.0,0.14,1009.0,16.0,1.9,93.88454500736407 +2019-06-07 06:30:00-05:00,2019,6,7,6,30,18.9,17.1,172.0,143.0,223.0,0.14,1010.0,21.0,2.1,89.30130051429217 +2019-06-07 07:30:00-05:00,2019,6,7,7,30,20.200000000000003,17.3,241.0,232.0,365.0,0.14,1010.0,29.0,2.1,83.42149095195701 +2019-06-07 08:30:00-05:00,2019,6,7,8,30,21.3,17.400000000000002,316.0,246.0,486.0,0.14,1010.0,38.0,1.9,78.45370175062625 +2019-06-07 09:30:00-05:00,2019,6,7,9,30,22.3,17.5,280.0,440.0,639.0,0.14,1010.0,51.0,1.7000000000000002,74.27315840120272 +2019-06-07 10:30:00-05:00,2019,6,7,10,30,23.1,17.3,351.0,385.0,699.0,0.14,1010.0,67.0,1.5,69.86690224674352 +2019-06-07 11:30:00-05:00,2019,6,7,11,30,23.700000000000003,17.2,381.0,322.0,686.0,0.14,1010.0,83.0,1.4,66.95797725147439 +2019-06-07 12:30:00-05:00,2019,6,7,12,30,23.9,17.1,417.0,269.0,671.0,0.14,1010.0,96.0,1.4,65.7398895179365 +2019-06-07 13:30:00-05:00,2019,6,7,13,30,23.6,17.0,401.0,273.0,645.0,0.14,1010.0,108.0,1.5,66.51505211642592 +2019-06-07 14:30:00-05:00,2019,6,7,14,30,23.3,17.1,283.0,502.0,683.0,0.14,1010.0,121.0,1.7000000000000002,68.15986277767102 +2019-06-07 15:30:00-05:00,2019,6,7,15,30,22.9,17.5,279.0,316.0,489.0,0.14,1010.0,134.0,1.9,71.6166049439283 +2019-06-07 16:30:00-05:00,2019,6,7,16,30,22.200000000000003,17.6,238.0,114.0,296.0,0.14,1010.0,145.0,1.9,75.19946283654606 +2019-06-07 17:30:00-05:00,2019,6,7,17,30,21.0,17.8,155.0,71.0,178.0,0.14,1011.0,150.0,1.6,81.95274097068759 +2019-06-07 18:30:00-05:00,2019,6,7,18,30,19.6,18.3,48.0,3.0,48.0,0.14,1012.0,151.0,1.2000000000000002,92.20734692044168 +2019-06-07 19:30:00-05:00,2019,6,7,19,30,18.6,17.6,1.0,0.0,1.0,0.14,1012.0,151.0,1.0,93.91174237662614 +2019-06-07 20:30:00-05:00,2019,6,7,20,30,18.1,17.3,0.0,0.0,0.0,0.14,1013.0,153.0,0.9,95.08432669052816 +2019-06-07 21:30:00-05:00,2019,6,7,21,30,17.7,16.900000000000002,0.0,0.0,0.0,0.14,1013.0,155.0,0.8,95.06958859170204 +2019-06-07 22:30:00-05:00,2019,6,7,22,30,17.5,16.5,0.0,0.0,0.0,0.14,1013.0,128.0,0.5,93.86174264731471 +2019-06-07 23:30:00-05:00,2019,6,7,23,30,17.3,16.2,0.0,0.0,0.0,0.14,1013.0,58.0,0.5,93.25651426306275 +2019-06-08 00:30:00-05:00,2019,6,8,0,30,16.8,16.1,0.0,0.0,0.0,0.14,1013.0,16.0,0.9,95.64457524587876 +2019-06-08 01:30:00-05:00,2019,6,8,1,30,16.3,16.0,0.0,0.0,0.0,0.14,1014.0,17.0,1.3,98.1052818499317 +2019-06-08 02:30:00-05:00,2019,6,8,2,30,16.1,15.9,0.0,0.0,0.0,0.14,1014.0,21.0,1.5,98.73137338445713 +2019-06-08 03:30:00-05:00,2019,6,8,3,30,15.8,15.6,0.0,0.0,0.0,0.14,1014.0,28.0,1.6,98.72844861614783 +2019-06-08 04:30:00-05:00,2019,6,8,4,30,16.2,15.4,5.0,0.0,5.0,0.14,1015.0,37.0,1.8,95.01373159739097 +2019-06-08 05:30:00-05:00,2019,6,8,5,30,17.3,15.7,86.0,108.0,105.0,0.14,1016.0,44.0,2.2,90.3257882505397 +2019-06-08 06:30:00-05:00,2019,6,8,6,30,18.7,16.1,130.0,432.0,285.0,0.14,1016.0,51.0,2.8000000000000003,84.85714803911081 +2019-06-08 07:30:00-05:00,2019,6,8,7,30,20.1,16.400000000000002,153.0,537.0,440.0,0.14,1017.0,61.0,3.4000000000000004,79.28203618181827 +2019-06-08 08:30:00-05:00,2019,6,8,8,30,20.9,16.0,213.0,505.0,562.0,0.14,1017.0,72.0,3.8,73.5654478742318 +2019-06-08 09:30:00-05:00,2019,6,8,9,30,21.5,15.4,242.0,516.0,664.0,0.14,1017.0,80.0,3.8,68.23615986608512 +2019-06-08 10:30:00-05:00,2019,6,8,10,30,21.9,15.0,300.0,495.0,748.0,0.14,1018.0,83.0,3.7,64.89912740664391 +2019-06-08 11:30:00-05:00,2019,6,8,11,30,22.3,14.7,267.0,608.0,843.0,0.14,1018.0,82.0,3.7,62.124375366452604 +2019-06-08 12:30:00-05:00,2019,6,8,12,30,22.5,14.6,320.0,501.0,793.0,0.14,1018.0,82.0,3.7,60.97893515325535 +2019-06-08 13:30:00-05:00,2019,6,8,13,30,22.5,14.5,222.0,697.0,845.0,0.14,1018.0,91.0,3.5,60.586536385107586 +2019-06-08 14:30:00-05:00,2019,6,8,14,30,22.200000000000003,14.4,312.0,437.0,661.0,0.14,1019.0,99.0,3.3000000000000003,61.304763090956996 +2019-06-08 15:30:00-05:00,2019,6,8,15,30,21.6,14.5,169.0,682.0,624.0,0.14,1019.0,99.0,3.1,64.00324121036402 +2019-06-08 16:30:00-05:00,2019,6,8,16,30,20.8,14.7,91.0,760.0,477.0,0.14,1019.0,99.0,3.1,68.09609375395668 +2019-06-08 17:30:00-05:00,2019,6,8,17,30,19.5,15.0,133.0,387.0,261.0,0.14,1019.0,98.0,3.0,75.2363437536495 +2019-06-08 18:30:00-05:00,2019,6,8,18,30,17.7,15.4,75.0,51.0,83.0,0.14,1020.0,97.0,2.8000000000000003,86.39790857654435 +2019-06-08 19:30:00-05:00,2019,6,8,19,30,16.3,14.9,1.0,0.0,1.0,0.14,1020.0,98.0,2.8000000000000003,91.42517924385682 +2019-06-08 20:30:00-05:00,2019,6,8,20,30,15.7,14.4,0.0,0.0,0.0,0.14,1020.0,99.0,2.9000000000000004,91.97983308093814 +2019-06-08 21:30:00-05:00,2019,6,8,21,30,15.2,14.2,0.0,0.0,0.0,0.14,1021.0,100.0,2.9000000000000004,93.75521034754517 +2019-06-08 22:30:00-05:00,2019,6,8,22,30,14.8,14.2,0.0,0.0,0.0,0.14,1021.0,102.0,2.9000000000000004,96.19913801083652 +2019-06-08 23:30:00-05:00,2019,6,8,23,30,14.5,14.2,0.0,0.0,0.0,0.14,1021.0,102.0,2.7,98.07894566941862 +2019-06-09 00:30:00-05:00,2019,6,9,0,30,14.2,14.1,0.0,0.0,0.0,0.14,1021.0,100.0,2.7,99.35450440741604 +2019-06-09 01:30:00-05:00,2019,6,9,1,30,14.0,14.0,0.0,0.0,0.0,0.14,1021.0,94.0,2.7,100.0 +2019-06-09 02:30:00-05:00,2019,6,9,2,30,13.8,13.8,0.0,0.0,0.0,0.14,1021.0,84.0,2.8000000000000003,100.00000000000001 +2019-06-09 03:30:00-05:00,2019,6,9,3,30,13.8,13.8,0.0,0.0,0.0,0.14,1022.0,75.0,3.1,100.00000000000001 +2019-06-09 04:30:00-05:00,2019,6,9,4,30,14.5,14.1,8.0,54.0,8.0,0.14,1022.0,70.0,3.3000000000000003,97.44585039786969 +2019-06-09 05:30:00-05:00,2019,6,9,5,30,15.8,14.4,49.0,479.0,134.0,0.14,1022.0,69.0,3.5,91.393402796861 +2019-06-09 06:30:00-05:00,2019,6,9,6,30,17.2,14.6,70.0,700.0,322.0,0.14,1023.0,74.0,3.6,84.69540000485469 +2019-06-09 07:30:00-05:00,2019,6,9,7,30,18.6,14.0,83.0,815.0,519.0,0.14,1023.0,81.0,3.9,74.60260460985715 +2019-06-09 08:30:00-05:00,2019,6,9,8,30,19.5,13.3,91.0,881.0,700.0,0.14,1023.0,86.0,4.1000000000000005,67.39314209175706 +2019-06-09 09:30:00-05:00,2019,6,9,9,30,20.0,12.7,231.0,636.0,751.0,0.14,1023.0,91.0,4.3,62.823171943529985 +2019-06-09 10:30:00-05:00,2019,6,9,10,30,20.4,12.4,276.0,613.0,831.0,0.14,1023.0,95.0,4.5,60.09494959822093 +2019-06-09 11:30:00-05:00,2019,6,9,11,30,20.700000000000003,12.4,347.0,556.0,874.0,0.14,1023.0,98.0,4.5,58.99366821931294 +2019-06-09 12:30:00-05:00,2019,6,9,12,30,20.700000000000003,12.5,158.0,847.0,958.0,0.14,1023.0,99.0,4.5,59.38207809351156 +2019-06-09 13:30:00-05:00,2019,6,9,13,30,20.6,12.5,372.0,502.0,821.0,0.14,1023.0,99.0,4.5,59.74903359457973 +2019-06-09 14:30:00-05:00,2019,6,9,14,30,20.3,12.4,176.0,716.0,748.0,0.14,1022.0,98.0,4.3,60.46715939021596 +2019-06-09 15:30:00-05:00,2019,6,9,15,30,19.8,12.4,90.0,873.0,673.0,0.14,1022.0,97.0,4.2,62.367470785879945 +2019-06-09 16:30:00-05:00,2019,6,9,16,30,19.0,12.5,79.0,806.0,489.0,0.14,1022.0,94.0,4.0,65.98063620571043 +2019-06-09 17:30:00-05:00,2019,6,9,17,30,18.0,12.9,64.0,685.0,291.0,0.14,1022.0,91.0,3.8,72.10923868906323 +2019-06-09 18:30:00-05:00,2019,6,9,18,30,16.400000000000002,13.6,75.0,88.0,88.0,0.14,1022.0,89.0,3.6,83.51555428322854 +2019-06-09 19:30:00-05:00,2019,6,9,19,30,15.2,13.4,1.0,0.0,1.0,0.14,1022.0,89.0,3.5,89.0091158362736 +2019-06-09 20:30:00-05:00,2019,6,9,20,30,14.9,13.1,0.0,0.0,0.0,0.14,1022.0,90.0,3.5,88.98491364044341 +2019-06-09 21:30:00-05:00,2019,6,9,21,30,14.8,13.1,0.0,0.0,0.0,0.14,1021.0,90.0,3.5,89.55989481263302 +2019-06-09 22:30:00-05:00,2019,6,9,22,30,14.6,13.1,0.0,0.0,0.0,0.14,1021.0,88.0,3.5,90.72238776322826 +2019-06-09 23:30:00-05:00,2019,6,9,23,30,14.3,13.2,0.0,0.0,0.0,0.14,1020.0,86.0,3.5,93.10362246022007 +2019-06-10 00:30:00-05:00,2019,6,10,0,30,14.1,13.4,0.0,0.0,0.0,0.14,1020.0,84.0,3.5,95.55458114093008 +2019-06-10 01:30:00-05:00,2019,6,10,1,30,14.0,13.6,0.0,0.0,0.0,0.14,1019.0,84.0,3.5,97.43602491004624 +2019-06-10 02:30:00-05:00,2019,6,10,2,30,14.1,13.8,0.0,0.0,0.0,0.14,1019.0,84.0,3.6,98.07301888165453 +2019-06-10 03:30:00-05:00,2019,6,10,3,30,14.4,14.0,0.0,0.0,0.0,0.14,1018.0,86.0,3.8,97.44388980378994 +2019-06-10 04:30:00-05:00,2019,6,10,4,30,15.1,14.3,2.0,0.0,2.0,0.14,1018.0,89.0,4.0,94.97216981482713 +2019-06-10 05:30:00-05:00,2019,6,10,5,30,15.9,14.7,16.0,0.0,16.0,0.14,1018.0,90.0,4.1000000000000005,92.5870737308018 +2019-06-10 06:30:00-05:00,2019,6,10,6,30,16.8,15.3,12.0,0.0,12.0,0.14,1017.0,92.0,4.2,90.87188645380944 +2019-06-10 07:30:00-05:00,2019,6,10,7,30,17.6,15.9,33.0,0.0,33.0,0.14,1017.0,90.0,4.4,89.77186688273885 +2019-06-10 08:30:00-05:00,2019,6,10,8,30,18.2,16.5,144.0,2.0,145.0,0.14,1016.0,87.0,4.6000000000000005,89.8164658325339 +2019-06-10 09:30:00-05:00,2019,6,10,9,30,18.8,17.2,101.0,0.0,101.0,0.14,1016.0,85.0,4.9,90.43116773273252 +2019-06-10 10:30:00-05:00,2019,6,10,10,30,19.200000000000003,17.6,173.0,1.0,174.0,0.14,1015.0,86.0,5.2,90.4589835230571 +2019-06-10 11:30:00-05:00,2019,6,10,11,30,19.5,18.0,276.0,4.0,280.0,0.14,1014.0,88.0,5.300000000000001,91.0505580556458 +2019-06-10 12:30:00-05:00,2019,6,10,12,30,19.700000000000003,18.3,155.0,0.0,155.0,0.14,1013.0,90.0,5.2,91.63673861415484 +2019-06-10 13:30:00-05:00,2019,6,10,13,30,19.700000000000003,18.5,100.0,0.0,100.0,0.14,1012.0,97.0,4.800000000000001,92.79255030269367 +2019-06-10 14:30:00-05:00,2019,6,10,14,30,19.6,18.6,90.0,0.0,90.0,0.14,1011.0,112.0,4.1000000000000005,93.95667425471606 +2019-06-10 15:30:00-05:00,2019,6,10,15,30,19.700000000000003,18.7,185.0,2.0,186.0,0.14,1011.0,133.0,3.3000000000000003,93.96114036717908 +2019-06-10 16:30:00-05:00,2019,6,10,16,30,19.8,18.9,86.0,0.0,86.0,0.14,1010.0,153.0,2.5,94.55430271600915 +2019-06-10 17:30:00-05:00,2019,6,10,17,30,19.700000000000003,19.200000000000003,66.0,2.0,67.0,0.14,1010.0,163.0,2.2,96.93931098864816 +2019-06-10 18:30:00-05:00,2019,6,10,18,30,19.3,19.3,8.0,0.0,8.0,0.14,1010.0,165.0,2.4000000000000004,100.0 +2019-06-10 19:30:00-05:00,2019,6,10,19,30,19.0,19.0,0.0,0.0,0.0,0.13,1010.0,167.0,2.8000000000000003,100.0 +2019-06-10 20:30:00-05:00,2019,6,10,20,30,18.9,18.9,0.0,0.0,0.0,0.13,1009.0,167.0,3.1,100.0 +2019-06-10 21:30:00-05:00,2019,6,10,21,30,18.8,18.8,0.0,0.0,0.0,0.13,1009.0,167.0,3.2,100.00000000000001 +2019-06-10 22:30:00-05:00,2019,6,10,22,30,18.8,18.8,0.0,0.0,0.0,0.13,1008.0,169.0,3.0,100.00000000000001 +2019-06-10 23:30:00-05:00,2019,6,10,23,30,18.7,18.7,0.0,0.0,0.0,0.13,1008.0,173.0,2.8000000000000003,99.99999999999999 +2019-06-11 00:30:00-05:00,2019,6,11,0,30,18.7,18.7,0.0,0.0,0.0,0.13,1007.0,177.0,2.6,99.99999999999999 +2019-06-11 01:30:00-05:00,2019,6,11,1,30,18.7,18.7,0.0,0.0,0.0,0.13,1007.0,179.0,2.4000000000000004,99.99999999999999 +2019-06-11 02:30:00-05:00,2019,6,11,2,30,18.6,18.6,0.0,0.0,0.0,0.13,1006.0,180.0,2.1,100.0 +2019-06-11 03:30:00-05:00,2019,6,11,3,30,18.6,18.6,0.0,0.0,0.0,0.13,1006.0,185.0,1.6,100.0 +2019-06-11 04:30:00-05:00,2019,6,11,4,30,18.4,18.4,1.0,0.0,1.0,0.13,1007.0,227.0,1.1,100.0 +2019-06-11 05:30:00-05:00,2019,6,11,5,30,17.900000000000002,17.900000000000002,14.0,0.0,14.0,0.13,1008.0,288.0,1.9,100.00000000000001 +2019-06-11 06:30:00-05:00,2019,6,11,6,30,17.3,17.1,106.0,12.0,110.0,0.13,1008.0,319.0,4.0,98.74297233703732 +2019-06-11 07:30:00-05:00,2019,6,11,7,30,17.2,15.6,87.0,2.0,88.0,0.13,1009.0,330.0,5.300000000000001,90.31870230198285 +2019-06-11 08:30:00-05:00,2019,6,11,8,30,18.2,14.8,307.0,202.0,447.0,0.13,1009.0,330.0,5.300000000000001,80.5558072189858 +2019-06-11 09:30:00-05:00,2019,6,11,9,30,19.700000000000003,14.6,272.0,480.0,664.0,0.13,1010.0,327.0,4.9,72.41703039217447 +2019-06-11 10:30:00-05:00,2019,6,11,10,30,21.1,14.5,107.0,937.0,955.0,0.13,1010.0,324.0,4.2,65.99464600737238 +2019-06-11 11:30:00-05:00,2019,6,11,11,30,22.0,14.6,191.0,755.0,908.0,0.13,1010.0,323.0,3.8,62.86319333115705 +2019-06-11 12:30:00-05:00,2019,6,11,12,30,22.5,14.6,103.0,953.0,1004.0,0.13,1010.0,323.0,3.8,60.97893515325535 +2019-06-11 13:30:00-05:00,2019,6,11,13,30,22.6,14.7,96.0,945.0,942.0,0.13,1010.0,325.0,3.8,61.00199261537489 +2019-06-11 14:30:00-05:00,2019,6,11,14,30,22.4,14.7,89.0,925.0,830.0,0.13,1011.0,326.0,3.9,61.74768906247547 +2019-06-11 15:30:00-05:00,2019,6,11,15,30,21.9,14.5,81.0,888.0,676.0,0.13,1011.0,326.0,4.1000000000000005,62.84084764532211 +2019-06-11 16:30:00-05:00,2019,6,11,16,30,21.200000000000003,14.1,71.0,826.0,493.0,0.13,1012.0,327.0,4.1000000000000005,63.91557169052001 +2019-06-11 17:30:00-05:00,2019,6,11,17,30,20.1,13.7,58.0,718.0,298.0,0.13,1013.0,328.0,3.8,66.64469260997923 +2019-06-11 18:30:00-05:00,2019,6,11,18,30,18.5,13.7,38.0,496.0,114.0,0.13,1014.0,330.0,3.6,73.62335031035798 +2019-06-11 19:30:00-05:00,2019,6,11,19,30,17.2,13.2,3.0,22.0,2.0,0.13,1014.0,334.0,3.7,77.34081298047496 +2019-06-11 20:30:00-05:00,2019,6,11,20,30,16.3,12.7,0.0,0.0,0.0,0.13,1015.0,338.0,3.8,79.25420023339639 +2019-06-11 21:30:00-05:00,2019,6,11,21,30,15.5,12.4,0.0,0.0,0.0,0.13,1015.0,340.0,3.8,81.78534196755471 +2019-06-11 22:30:00-05:00,2019,6,11,22,30,14.8,12.3,0.0,0.0,0.0,0.13,1016.0,340.0,3.5,84.98810609976982 +2019-06-11 23:30:00-05:00,2019,6,11,23,30,14.5,12.2,0.0,0.0,0.0,0.13,1016.0,338.0,3.2,86.08119786380388 +2019-06-12 00:30:00-05:00,2019,6,12,0,30,14.3,12.2,0.0,0.0,0.0,0.13,1016.0,339.0,3.0,87.20115962455715 +2019-06-12 01:30:00-05:00,2019,6,12,1,30,13.9,12.2,0.0,0.0,0.0,0.13,1017.0,344.0,2.8000000000000003,89.49038914132473 +2019-06-12 02:30:00-05:00,2019,6,12,2,30,13.4,12.1,0.0,0.0,0.0,0.13,1017.0,351.0,2.6,91.84102952649776 +2019-06-12 03:30:00-05:00,2019,6,12,3,30,13.0,12.0,0.0,0.0,0.0,0.13,1018.0,179.0,2.5,93.65072411461962 +2019-06-12 04:30:00-05:00,2019,6,12,4,30,13.5,11.9,4.0,0.0,4.0,0.13,1018.0,9.0,2.4000000000000004,90.05105788816516 +2019-06-12 05:30:00-05:00,2019,6,12,5,30,14.9,12.3,75.0,218.0,114.0,0.13,1019.0,22.0,2.4000000000000004,84.44247615045319 +2019-06-12 06:30:00-05:00,2019,6,12,6,30,16.7,12.8,97.0,583.0,307.0,0.13,1019.0,42.0,2.2,77.77117050830323 +2019-06-12 07:30:00-05:00,2019,6,12,7,30,18.6,13.6,153.0,551.0,448.0,0.13,1020.0,68.0,2.2,72.6898124112037 +2019-06-12 08:30:00-05:00,2019,6,12,8,30,19.700000000000003,13.3,150.0,735.0,658.0,0.13,1020.0,91.0,2.3000000000000003,66.5613078979887 +2019-06-12 09:30:00-05:00,2019,6,12,9,30,20.3,13.3,259.0,501.0,668.0,0.13,1020.0,108.0,2.4000000000000004,64.13416294674309 +2019-06-12 10:30:00-05:00,2019,6,12,10,30,20.6,13.4,322.0,469.0,747.0,0.13,1019.0,122.0,2.6,63.369572274920664 +2019-06-12 11:30:00-05:00,2019,6,12,11,30,20.700000000000003,13.5,301.0,531.0,805.0,0.13,1019.0,135.0,2.8000000000000003,63.3918013709581 +2019-06-12 12:30:00-05:00,2019,6,12,12,30,20.5,13.4,270.0,618.0,855.0,0.13,1019.0,143.0,3.0,63.76146686035068 +2019-06-12 13:30:00-05:00,2019,6,12,13,30,20.1,13.1,253.0,649.0,834.0,0.13,1018.0,147.0,3.1,64.0902647157352 +2019-06-12 14:30:00-05:00,2019,6,12,14,30,19.8,12.8,211.0,650.0,732.0,0.13,1018.0,151.0,3.1,64.02428339064427 +2019-06-12 15:30:00-05:00,2019,6,12,15,30,19.5,12.5,85.0,885.0,679.0,0.13,1018.0,154.0,3.0,63.95814091650578 +2019-06-12 16:30:00-05:00,2019,6,12,16,30,18.9,12.3,74.0,828.0,498.0,0.13,1018.0,153.0,2.9000000000000004,65.52765301964246 +2019-06-12 17:30:00-05:00,2019,6,12,17,30,17.900000000000002,12.6,73.0,663.0,296.0,0.13,1018.0,147.0,2.6,71.15282000979143 +2019-06-12 18:30:00-05:00,2019,6,12,18,30,16.3,13.7,68.0,183.0,96.0,0.13,1018.0,138.0,2.5,84.59714517960735 +2019-06-12 19:30:00-05:00,2019,6,12,19,30,15.4,13.7,1.0,0.0,1.0,0.13,1017.0,129.0,2.8000000000000003,89.60585734847946 +2019-06-12 20:30:00-05:00,2019,6,12,20,30,15.5,14.0,0.0,0.0,0.0,0.13,1017.0,125.0,3.1,90.78397924352043 +2019-06-12 21:30:00-05:00,2019,6,12,21,30,15.5,14.3,0.0,0.0,0.0,0.13,1016.0,126.0,3.2,92.56494197555524 +2019-06-12 22:30:00-05:00,2019,6,12,22,30,15.3,14.4,0.0,0.0,0.0,0.13,1016.0,125.0,3.2,94.36809769309413 +2019-06-12 23:30:00-05:00,2019,6,12,23,30,15.1,14.4,0.0,0.0,0.0,0.13,1015.0,123.0,3.1,95.58823222490626 +2019-06-13 00:30:00-05:00,2019,6,13,0,30,15.0,14.3,0.0,0.0,0.0,0.13,1015.0,115.0,2.8000000000000003,95.5848842059835 +2019-06-13 01:30:00-05:00,2019,6,13,1,30,15.2,14.3,0.0,0.0,0.0,0.13,1014.0,100.0,2.9000000000000004,94.36385299681037 +2019-06-13 02:30:00-05:00,2019,6,13,2,30,15.4,14.4,0.0,0.0,0.0,0.13,1013.0,87.0,3.3000000000000003,93.76458243885895 +2019-06-13 03:30:00-05:00,2019,6,13,3,30,15.4,14.4,0.0,0.0,0.0,0.13,1012.0,79.0,3.8,93.76458243885895 +2019-06-13 04:30:00-05:00,2019,6,13,4,30,15.3,14.4,2.0,0.0,2.0,0.13,1011.0,69.0,4.6000000000000005,94.36809769309413 +2019-06-13 05:30:00-05:00,2019,6,13,5,30,15.1,14.2,12.0,0.0,12.0,0.13,1010.0,59.0,5.300000000000001,94.35960354917758 +2019-06-13 06:30:00-05:00,2019,6,13,6,30,15.0,14.2,9.0,0.0,9.0,0.13,1009.0,52.0,5.800000000000001,94.96836595979867 +2019-06-13 07:30:00-05:00,2019,6,13,7,30,15.1,14.2,16.0,0.0,16.0,0.13,1008.0,49.0,6.4,94.35960354917758 +2019-06-13 08:30:00-05:00,2019,6,13,8,30,15.4,14.2,42.0,0.0,42.0,0.13,1006.0,50.0,6.7,92.55939376583218 +2019-06-13 09:30:00-05:00,2019,6,13,9,30,16.0,14.3,138.0,0.0,138.0,0.13,1005.0,52.0,6.2,89.65152316161864 +2019-06-13 10:30:00-05:00,2019,6,13,10,30,16.7,14.6,281.0,2.0,283.0,0.13,1004.0,52.0,5.2,87.42200649699032 +2019-06-13 11:30:00-05:00,2019,6,13,11,30,17.3,14.9,403.0,230.0,621.0,0.13,1003.0,47.0,4.0,85.80489532603114 +2019-06-13 12:30:00-05:00,2019,6,13,12,30,17.900000000000002,15.1,126.0,871.0,950.0,0.13,1003.0,38.0,2.9000000000000004,83.68913229409654 +2019-06-13 13:30:00-05:00,2019,6,13,13,30,18.4,15.3,156.0,804.0,877.0,0.13,1003.0,21.0,1.9,82.15112268771665 +2019-06-13 14:30:00-05:00,2019,6,13,14,30,18.6,15.5,320.0,25.0,340.0,0.13,1003.0,175.0,1.2000000000000002,82.17595795150365 +2019-06-13 15:30:00-05:00,2019,6,13,15,30,18.6,15.7,230.0,4.0,233.0,0.13,1004.0,321.0,0.8,83.23514898572651 +2019-06-13 16:30:00-05:00,2019,6,13,16,30,18.5,15.7,212.0,17.0,221.0,0.13,1004.0,271.0,0.5,83.75782359586948 +2019-06-13 17:30:00-05:00,2019,6,13,17,30,18.1,15.9,98.0,455.0,251.0,0.13,1004.0,201.0,0.5,86.99095318574707 +2019-06-13 18:30:00-05:00,2019,6,13,18,30,17.3,16.2,56.0,203.0,88.0,0.13,1005.0,151.0,0.9,93.25651426306275 +2019-06-13 19:30:00-05:00,2019,6,13,19,30,16.6,15.8,1.0,0.0,1.0,0.13,1005.0,144.0,1.3,95.02871842027984 +2019-06-13 20:30:00-05:00,2019,6,13,20,30,16.5,15.8,0.0,0.0,0.0,0.13,1005.0,162.0,1.5,95.63471046436408 +2019-06-13 21:30:00-05:00,2019,6,13,21,30,16.400000000000002,16.0,0.0,0.0,0.0,0.13,1005.0,187.0,1.5,97.4826790114397 +2019-06-13 22:30:00-05:00,2019,6,13,22,30,16.5,16.2,0.0,0.0,0.0,0.13,1005.0,199.0,1.7000000000000002,98.10817472739264 +2019-06-13 23:30:00-05:00,2019,6,13,23,30,16.7,16.6,0.0,0.0,0.0,0.13,1005.0,203.0,2.4000000000000004,99.36683354071437 +2019-06-14 00:30:00-05:00,2019,6,14,0,30,17.0,16.900000000000002,0.0,0.0,0.0,0.13,1005.0,219.0,3.5,99.36828928489736 +2019-06-14 01:30:00-05:00,2019,6,14,1,30,16.7,16.6,0.0,0.0,0.0,0.13,1006.0,242.0,4.2,99.36683354071437 +2019-06-14 02:30:00-05:00,2019,6,14,2,30,15.7,15.5,0.0,0.0,0.0,0.13,1006.0,249.0,4.1000000000000005,98.72747145030702 +2019-06-14 03:30:00-05:00,2019,6,14,3,30,14.6,14.4,0.0,0.0,0.0,0.13,1007.0,243.0,4.0,98.71664803537107 +2019-06-14 04:30:00-05:00,2019,6,14,4,30,14.4,13.7,8.0,51.0,8.0,0.13,1007.0,239.0,4.1000000000000005,95.56471646093954 +2019-06-14 05:30:00-05:00,2019,6,14,5,30,15.1,13.6,49.0,457.0,131.0,0.13,1008.0,238.0,4.4,90.75667985452658 +2019-06-14 06:30:00-05:00,2019,6,14,6,30,16.0,13.4,74.0,663.0,313.0,0.13,1008.0,244.0,5.0,84.56419016994364 +2019-06-14 07:30:00-05:00,2019,6,14,7,30,17.1,12.7,91.0,770.0,503.0,0.13,1008.0,252.0,5.2,75.32902339889542 +2019-06-14 08:30:00-05:00,2019,6,14,8,30,18.1,12.5,141.0,740.0,652.0,0.13,1008.0,256.0,5.0,69.80462114290977 +2019-06-14 09:30:00-05:00,2019,6,14,9,30,18.8,12.4,283.0,443.0,645.0,0.13,1009.0,260.0,4.800000000000001,66.37266162690366 +2019-06-14 10:30:00-05:00,2019,6,14,10,30,19.3,12.3,434.0,145.0,565.0,0.13,1009.0,264.0,4.7,63.913956198018774 +2019-06-14 11:30:00-05:00,2019,6,14,11,30,19.6,12.2,443.0,72.0,511.0,0.13,1009.0,270.0,4.6000000000000005,62.32197458496211 +2019-06-14 12:30:00-05:00,2019,6,14,12,30,20.0,12.1,234.0,511.0,718.0,0.13,1010.0,275.0,4.4,60.39669859167229 +2019-06-14 13:30:00-05:00,2019,6,14,13,30,20.3,12.3,224.0,202.0,405.0,0.13,1010.0,277.0,4.1000000000000005,60.071343378342426 +2019-06-14 14:30:00-05:00,2019,6,14,14,30,20.5,12.5,302.0,314.0,554.0,0.13,1010.0,276.0,3.9,60.118537788939875 +2019-06-14 15:30:00-05:00,2019,6,14,15,30,20.5,12.7,134.0,742.0,633.0,0.13,1011.0,274.0,3.9,60.91183785890349 +2019-06-14 16:30:00-05:00,2019,6,14,16,30,20.3,12.9,91.0,726.0,464.0,0.13,1011.0,274.0,3.9,62.48089589875886 +2019-06-14 17:30:00-05:00,2019,6,14,17,30,19.5,13.3,113.0,316.0,220.0,0.13,1012.0,274.0,3.4000000000000004,67.39314209175706 +2019-06-14 18:30:00-05:00,2019,6,14,18,30,18.0,14.0,39.0,486.0,115.0,0.13,1013.0,276.0,2.9000000000000004,77.46347403540312 +2019-06-14 19:30:00-05:00,2019,6,14,19,30,16.8,13.8,2.0,24.0,2.0,0.13,1013.0,277.0,2.7,82.48472431499 +2019-06-14 20:30:00-05:00,2019,6,14,20,30,16.2,13.6,0.0,0.0,0.0,0.13,1014.0,275.0,2.9000000000000004,84.58617152633344 +2019-06-14 21:30:00-05:00,2019,6,14,21,30,15.6,13.5,0.0,0.0,0.0,0.13,1015.0,272.0,3.0,87.32148192965687 +2019-06-14 22:30:00-05:00,2019,6,14,22,30,15.1,13.4,0.0,0.0,0.0,0.13,1015.0,267.0,3.2,89.58291332758456 +2019-06-14 23:30:00-05:00,2019,6,14,23,30,14.7,13.3,0.0,0.0,0.0,0.13,1016.0,262.0,3.4000000000000004,91.32288021391355 +2019-06-15 00:30:00-05:00,2019,6,15,0,30,14.3,13.3,0.0,0.0,0.0,0.13,1016.0,259.0,3.4000000000000004,93.71277637256303 +2019-06-15 01:30:00-05:00,2019,6,15,1,30,13.9,13.2,0.0,0.0,0.0,0.13,1016.0,256.0,3.3000000000000003,95.54780510150113 +2019-06-15 02:30:00-05:00,2019,6,15,2,30,13.6,13.1,0.0,0.0,0.0,0.13,1017.0,255.0,3.1,96.79432643858921 +2019-06-15 03:30:00-05:00,2019,6,15,3,30,13.2,12.9,0.0,0.0,0.0,0.13,1017.0,255.0,2.8000000000000003,98.05958324859783 +2019-06-15 04:30:00-05:00,2019,6,15,4,30,13.8,12.8,6.0,0.0,6.0,0.13,1017.0,251.0,2.5,93.6890169670867 +2019-06-15 05:30:00-05:00,2019,6,15,5,30,15.3,13.3,55.0,389.0,125.0,0.13,1017.0,239.0,2.3000000000000003,87.86500447911853 +2019-06-15 06:30:00-05:00,2019,6,15,6,30,17.0,13.9,71.0,681.0,316.0,0.13,1017.0,222.0,2.2,81.975872238862 +2019-06-15 07:30:00-05:00,2019,6,15,7,30,18.8,15.0,100.0,754.0,503.0,0.13,1017.0,208.0,2.4000000000000004,78.59068510019361 +2019-06-15 08:30:00-05:00,2019,6,15,8,30,20.0,15.6,102.0,842.0,684.0,0.13,1017.0,203.0,2.8000000000000003,75.80243725044953 +2019-06-15 09:30:00-05:00,2019,6,15,9,30,20.9,15.7,222.0,666.0,766.0,0.13,1016.0,202.0,3.3000000000000003,72.16838860936993 +2019-06-15 10:30:00-05:00,2019,6,15,10,30,21.8,15.9,294.0,612.0,848.0,0.13,1016.0,204.0,3.8,69.1748505703849 +2019-06-15 11:30:00-05:00,2019,6,15,11,30,22.700000000000003,16.0,337.0,432.0,747.0,0.13,1015.0,207.0,4.3,65.90703576200495 +2019-06-15 12:30:00-05:00,2019,6,15,12,30,23.200000000000003,16.0,128.0,884.0,965.0,0.13,1014.0,208.0,4.800000000000001,63.94176927480235 +2019-06-15 13:30:00-05:00,2019,6,15,13,30,23.3,16.0,250.0,664.0,846.0,0.13,1014.0,207.0,5.300000000000001,63.5566717384131 +2019-06-15 14:30:00-05:00,2019,6,15,14,30,23.0,16.1,283.0,458.0,651.0,0.13,1013.0,205.0,5.6000000000000005,65.13417424120837 +2019-06-15 15:30:00-05:00,2019,6,15,15,30,22.4,16.3,237.0,502.0,575.0,0.13,1012.0,203.0,5.6000000000000005,68.41501739347423 +2019-06-15 16:30:00-05:00,2019,6,15,16,30,21.6,16.5,188.0,47.0,212.0,0.13,1012.0,200.0,5.4,72.75547560202038 +2019-06-15 17:30:00-05:00,2019,6,15,17,30,20.6,16.8,164.0,49.0,181.0,0.13,1012.0,200.0,5.1000000000000005,78.85065029659832 +2019-06-15 18:30:00-05:00,2019,6,15,18,30,19.700000000000003,16.900000000000002,52.0,3.0,52.0,0.13,1012.0,203.0,4.9,83.89395580403942 +2019-06-15 19:30:00-05:00,2019,6,15,19,30,19.0,16.7,1.0,0.0,1.0,0.13,1012.0,205.0,4.9,86.52357729390393 +2019-06-15 20:30:00-05:00,2019,6,15,20,30,18.5,16.6,0.0,0.0,0.0,0.13,1012.0,206.0,4.800000000000001,88.70503697206566 +2019-06-15 21:30:00-05:00,2019,6,15,21,30,18.1,16.6,0.0,0.0,0.0,0.13,1012.0,209.0,4.9,90.95856352882676 +2019-06-15 22:30:00-05:00,2019,6,15,22,30,18.0,16.6,0.0,0.0,0.0,0.13,1012.0,212.0,4.9,91.53193390632146 +2019-06-15 23:30:00-05:00,2019,6,15,23,30,17.8,16.7,0.0,0.0,0.0,0.13,1012.0,213.0,4.5,93.28150742205847 +2019-06-16 00:30:00-05:00,2019,6,16,0,30,17.7,16.8,0.0,0.0,0.0,0.13,1011.0,211.0,4.2,94.46856302066479 +2019-06-16 01:30:00-05:00,2019,6,16,1,30,17.6,16.900000000000002,0.0,0.0,0.0,0.13,1010.0,212.0,4.0,95.67071969670098 +2019-06-16 02:30:00-05:00,2019,6,16,2,30,17.6,17.1,0.0,0.0,0.0,0.13,1010.0,215.0,3.8,96.89052271001879 +2019-06-16 03:30:00-05:00,2019,6,16,3,30,17.5,17.2,0.0,0.0,0.0,0.13,1010.0,214.0,3.6,98.12254053083355 +2019-06-16 04:30:00-05:00,2019,6,16,4,30,17.8,17.3,2.0,0.0,2.0,0.13,1010.0,209.0,3.7,96.895219016838 +2019-06-16 05:30:00-05:00,2019,6,16,5,30,18.5,17.7,28.0,0.0,28.0,0.13,1010.0,205.0,3.7,95.098999385029 +2019-06-16 06:30:00-05:00,2019,6,16,6,30,19.5,18.2,104.0,7.0,107.0,0.13,1009.0,202.0,3.8,92.20163302603117 +2019-06-16 07:30:00-05:00,2019,6,16,7,30,20.9,18.7,237.0,39.0,258.0,0.13,1009.0,202.0,3.8,87.24826360348135 +2019-06-16 08:30:00-05:00,2019,6,16,8,30,22.1,18.8,329.0,142.0,427.0,0.13,1009.0,202.0,3.7,81.57764576294969 +2019-06-16 09:30:00-05:00,2019,6,16,9,30,22.9,18.8,336.0,338.0,612.0,0.13,1009.0,200.0,3.7,77.70775715359615 +2019-06-16 10:30:00-05:00,2019,6,16,10,30,23.3,18.9,396.0,273.0,643.0,0.13,1008.0,199.0,3.8,76.32572245323844 +2019-06-16 11:30:00-05:00,2019,6,16,11,30,23.3,18.9,421.0,108.0,524.0,0.13,1008.0,199.0,3.6,76.32572245323844 +2019-06-16 12:30:00-05:00,2019,6,16,12,30,23.4,19.0,152.0,0.0,152.0,0.13,1007.0,196.0,3.5,76.34132933055754 +2019-06-16 13:30:00-05:00,2019,6,16,13,30,23.6,19.200000000000003,382.0,318.0,668.0,0.13,1006.0,194.0,3.6,76.37249952757337 +2019-06-16 14:30:00-05:00,2019,6,16,14,30,23.8,19.5,332.0,295.0,569.0,0.13,1006.0,198.0,3.8,76.88009317424351 +2019-06-16 15:30:00-05:00,2019,6,16,15,30,23.700000000000003,19.9,266.0,60.0,306.0,0.13,1006.0,202.0,3.8,79.28793184775152 +2019-06-16 16:30:00-05:00,2019,6,16,16,30,23.200000000000003,20.4,156.0,14.0,163.0,0.13,1006.0,204.0,3.6,84.28170001879177 +2019-06-16 17:30:00-05:00,2019,6,16,17,30,22.5,20.9,70.0,3.0,71.0,0.13,1006.0,205.0,3.5,90.68398804367082 +2019-06-16 18:30:00-05:00,2019,6,16,18,30,21.700000000000003,21.0,26.0,0.0,26.0,0.13,1006.0,209.0,3.3000000000000003,95.80111280844397 +2019-06-16 19:30:00-05:00,2019,6,16,19,30,21.1,20.9,1.0,0.0,1.0,0.13,1007.0,214.0,2.9000000000000004,98.77867339103904 +2019-06-16 20:30:00-05:00,2019,6,16,20,30,20.8,20.8,0.0,0.0,0.0,0.13,1007.0,217.0,2.4000000000000004,100.0 +2019-06-16 21:30:00-05:00,2019,6,16,21,30,20.6,20.6,0.0,0.0,0.0,0.13,1007.0,213.0,1.9,100.0 +2019-06-16 22:30:00-05:00,2019,6,16,22,30,20.4,20.4,0.0,0.0,0.0,0.13,1007.0,202.0,1.5,100.0 +2019-06-16 23:30:00-05:00,2019,6,16,23,30,20.1,20.1,0.0,0.0,0.0,0.13,1007.0,212.0,1.3,100.0 +2019-06-17 00:30:00-05:00,2019,6,17,0,30,19.8,19.8,0.0,0.0,0.0,0.13,1007.0,255.0,1.3,100.0 +2019-06-17 01:30:00-05:00,2019,6,17,1,30,19.3,19.3,0.0,0.0,0.0,0.13,1008.0,300.0,1.6,100.0 +2019-06-17 02:30:00-05:00,2019,6,17,2,30,18.8,18.8,0.0,0.0,0.0,0.13,1009.0,329.0,1.8,100.00000000000001 +2019-06-17 03:30:00-05:00,2019,6,17,3,30,18.3,18.3,0.0,0.0,0.0,0.13,1009.0,345.0,1.9,100.0 +2019-06-17 04:30:00-05:00,2019,6,17,4,30,18.3,17.900000000000002,4.0,0.0,4.0,0.13,1010.0,354.0,1.9,97.51872014868017 +2019-06-17 05:30:00-05:00,2019,6,17,5,30,19.0,18.1,48.0,4.0,49.0,0.13,1011.0,181.0,1.7000000000000002,94.52187423326806 +2019-06-17 06:30:00-05:00,2019,6,17,6,30,20.1,18.5,121.0,14.0,126.0,0.13,1011.0,13.0,1.4,90.52113538577049 +2019-06-17 07:30:00-05:00,2019,6,17,7,30,21.4,18.6,187.0,32.0,204.0,0.13,1012.0,27.0,1.2000000000000002,84.08399862234616 +2019-06-17 08:30:00-05:00,2019,6,17,8,30,22.4,18.1,128.0,3.0,130.0,0.13,1012.0,33.0,0.8,76.66484479654102 +2019-06-17 09:30:00-05:00,2019,6,17,9,30,23.200000000000003,18.0,218.0,0.0,218.0,0.13,1012.0,151.0,0.3,72.57850749689571 +2019-06-17 10:30:00-05:00,2019,6,17,10,30,23.5,18.1,393.0,43.0,432.0,0.13,1012.0,243.0,0.3,71.72531699948364 +2019-06-17 11:30:00-05:00,2019,6,17,11,30,23.6,18.3,285.0,1.0,286.0,0.13,1012.0,210.0,0.6000000000000001,72.19493005616113 +2019-06-17 12:30:00-05:00,2019,6,17,12,30,23.700000000000003,18.7,251.0,3.0,254.0,0.13,1012.0,200.0,0.8,73.58168286508669 +2019-06-17 13:30:00-05:00,2019,6,17,13,30,23.5,19.1,176.0,0.0,176.0,0.13,1012.0,190.0,0.9,76.3569216831363 +2019-06-17 14:30:00-05:00,2019,6,17,14,30,23.0,19.5,150.0,0.0,150.0,0.13,1012.0,177.0,1.0,80.6823135608427 +2019-06-17 15:30:00-05:00,2019,6,17,15,30,22.5,19.9,247.0,12.0,255.0,0.13,1012.0,165.0,1.1,85.25596551152893 +2019-06-17 16:30:00-05:00,2019,6,17,16,30,22.1,20.1,224.0,93.0,272.0,0.13,1011.0,158.0,1.3,88.44464375934447 +2019-06-17 17:30:00-05:00,2019,6,17,17,30,21.5,20.5,152.0,54.0,170.0,0.13,1012.0,154.0,1.4,94.04070025250259 +2019-06-17 18:30:00-05:00,2019,6,17,18,30,20.6,20.3,30.0,0.0,30.0,0.13,1012.0,156.0,1.7000000000000002,98.16605263804577 +2019-06-17 19:30:00-05:00,2019,6,17,19,30,20.0,19.8,1.0,0.0,1.0,0.13,1012.0,161.0,2.1,98.76849657621399 +2019-06-17 20:30:00-05:00,2019,6,17,20,30,19.6,19.5,0.0,0.0,0.0,0.13,1013.0,166.0,2.3000000000000003,99.38069832879764 +2019-06-17 21:30:00-05:00,2019,6,17,21,30,19.4,19.200000000000003,0.0,0.0,0.0,0.13,1013.0,167.0,2.5,98.76289199850928 +2019-06-17 22:30:00-05:00,2019,6,17,22,30,19.200000000000003,19.0,0.0,0.0,0.0,0.13,1012.0,170.0,2.5,98.7610153195475 +2019-06-17 23:30:00-05:00,2019,6,17,23,30,19.0,19.0,0.0,0.0,0.0,0.13,1012.0,175.0,2.3000000000000003,100.0 +2019-06-18 00:30:00-05:00,2019,6,18,0,30,18.9,18.9,0.0,0.0,0.0,0.13,1012.0,180.0,2.0,100.0 +2019-06-18 01:30:00-05:00,2019,6,18,1,30,18.8,18.8,0.0,0.0,0.0,0.13,1011.0,176.0,1.7000000000000002,100.00000000000001 +2019-06-18 02:30:00-05:00,2019,6,18,2,30,18.7,18.7,0.0,0.0,0.0,0.13,1011.0,167.0,1.3,99.99999999999999 +2019-06-18 03:30:00-05:00,2019,6,18,3,30,18.6,18.6,0.0,0.0,0.0,0.13,1011.0,162.0,1.1,100.0 +2019-06-18 04:30:00-05:00,2019,6,18,4,30,18.8,18.6,2.0,0.0,2.0,0.13,1011.0,160.0,1.1,98.7572491549771 +2019-06-18 05:30:00-05:00,2019,6,18,5,30,19.3,19.1,12.0,0.0,12.0,0.13,1011.0,163.0,1.3,98.76195419129647 +2019-06-18 06:30:00-05:00,2019,6,18,6,30,19.8,19.4,67.0,7.0,70.0,0.13,1011.0,168.0,1.4,97.546632032517 +2019-06-18 07:30:00-05:00,2019,6,18,7,30,20.5,20.0,144.0,4.0,146.0,0.13,1011.0,168.0,1.3,96.9575969733834 +2019-06-18 08:30:00-05:00,2019,6,18,8,30,21.3,20.6,228.0,13.0,237.0,0.13,1011.0,163.0,1.1,95.78865074057849 +2019-06-18 09:30:00-05:00,2019,6,18,9,30,22.0,20.9,265.0,11.0,274.0,0.13,1011.0,163.0,1.1,93.48613677989559 +2019-06-18 10:30:00-05:00,2019,6,18,10,30,22.200000000000003,20.700000000000003,409.0,135.0,531.0,0.13,1010.0,164.0,1.2000000000000002,91.22412116599256 +2019-06-18 11:30:00-05:00,2019,6,18,11,30,22.3,20.6,215.0,1.0,216.0,0.13,1010.0,165.0,1.4,90.11371133949066 +2019-06-18 12:30:00-05:00,2019,6,18,12,30,22.4,20.5,187.0,1.0,188.0,0.13,1009.0,164.0,1.6,89.01680983643384 +2019-06-18 13:30:00-05:00,2019,6,18,13,30,22.700000000000003,20.6,48.0,0.0,48.0,0.13,1009.0,162.0,1.8,87.95033492629709 +2019-06-18 14:30:00-05:00,2019,6,18,14,30,22.700000000000003,20.700000000000003,139.0,0.0,139.0,0.13,1008.0,164.0,1.8,88.49382987053212 +2019-06-18 15:30:00-05:00,2019,6,18,15,30,22.4,21.0,100.0,1.0,101.0,0.13,1008.0,167.0,1.5,91.79929885567414 +2019-06-18 16:30:00-05:00,2019,6,18,16,30,21.9,21.200000000000003,131.0,36.0,150.0,0.13,1008.0,157.0,1.2000000000000002,95.80732326344243 +2019-06-18 17:30:00-05:00,2019,6,18,17,30,21.3,21.200000000000003,16.0,0.0,16.0,0.13,1008.0,141.0,1.1,99.38861548224787 +2019-06-18 18:30:00-05:00,2019,6,18,18,30,20.8,20.8,23.0,0.0,23.0,0.13,1008.0,139.0,0.9,100.0 +2019-06-18 19:30:00-05:00,2019,6,18,19,30,20.5,20.5,1.0,0.0,1.0,0.13,1008.0,161.0,0.7000000000000001,100.0 +2019-06-18 20:30:00-05:00,2019,6,18,20,30,20.4,20.3,0.0,0.0,0.0,0.13,1009.0,199.0,0.5,99.38444306670166 +2019-06-18 21:30:00-05:00,2019,6,18,21,30,20.200000000000003,20.200000000000003,0.0,0.0,0.0,0.13,1009.0,230.0,0.4,100.0 +2019-06-18 22:30:00-05:00,2019,6,18,22,30,20.0,20.0,0.0,0.0,0.0,0.13,1008.0,258.0,0.3,100.0 +2019-06-18 23:30:00-05:00,2019,6,18,23,30,19.700000000000003,19.700000000000003,0.0,0.0,0.0,0.13,1008.0,310.0,0.3,100.0 +2019-06-19 00:30:00-05:00,2019,6,19,0,30,19.3,19.3,0.0,0.0,0.0,0.13,1008.0,191.0,0.5,100.0 +2019-06-19 01:30:00-05:00,2019,6,19,1,30,19.0,19.0,0.0,0.0,0.0,0.13,1008.0,48.0,0.7000000000000001,100.0 +2019-06-19 02:30:00-05:00,2019,6,19,2,30,18.6,18.6,0.0,0.0,0.0,0.13,1008.0,66.0,1.0,100.0 +2019-06-19 03:30:00-05:00,2019,6,19,3,30,18.4,18.4,0.0,0.0,0.0,0.13,1008.0,78.0,1.4,100.0 +2019-06-19 04:30:00-05:00,2019,6,19,4,30,18.4,18.3,2.0,0.0,2.0,0.13,1009.0,86.0,1.8,99.37501689178289 +2019-06-19 05:30:00-05:00,2019,6,19,5,30,18.9,18.5,29.0,19.0,32.0,0.13,1009.0,92.0,2.1,97.52994153839839 +2019-06-19 06:30:00-05:00,2019,6,19,6,30,19.700000000000003,19.0,40.0,0.0,40.0,0.13,1009.0,94.0,2.1,95.7382477761626 +2019-06-19 07:30:00-05:00,2019,6,19,7,30,20.4,19.5,57.0,0.0,57.0,0.13,1009.0,98.0,2.0,94.57843733317726 +2019-06-19 08:30:00-05:00,2019,6,19,8,30,20.8,19.700000000000003,177.0,0.0,177.0,0.13,1008.0,105.0,1.9,93.42862406578288 +2019-06-19 09:30:00-05:00,2019,6,19,9,30,21.200000000000003,19.6,211.0,0.0,211.0,0.13,1008.0,112.0,1.9,90.59629238174388 +2019-06-19 10:30:00-05:00,2019,6,19,10,30,21.700000000000003,19.8,192.0,0.0,192.0,0.13,1008.0,119.0,2.0,88.96178781648567 +2019-06-19 11:30:00-05:00,2019,6,19,11,30,22.1,20.0,283.0,1.0,284.0,0.13,1007.0,126.0,2.1,87.89897814059158 +2019-06-19 12:30:00-05:00,2019,6,19,12,30,22.3,20.200000000000003,246.0,0.0,246.0,0.13,1007.0,133.0,2.3000000000000003,87.91613260210468 +2019-06-19 13:30:00-05:00,2019,6,19,13,30,22.3,20.4,229.0,0.0,229.0,0.13,1006.0,140.0,2.5,89.00897421968804 +2019-06-19 14:30:00-05:00,2019,6,19,14,30,22.200000000000003,20.5,110.0,0.0,110.0,0.13,1006.0,145.0,2.6,90.10661413803582 +2019-06-19 15:30:00-05:00,2019,6,19,15,30,21.9,20.6,124.0,0.0,124.0,0.13,1005.0,147.0,2.8000000000000003,92.337076856763 +2019-06-19 16:30:00-05:00,2019,6,19,16,30,21.5,20.5,117.0,4.0,119.0,0.13,1005.0,145.0,2.8000000000000003,94.04070025250259 +2019-06-19 17:30:00-05:00,2019,6,19,17,30,20.9,20.4,37.0,5.0,39.0,0.13,1005.0,141.0,2.9000000000000004,96.96667889869289 +2019-06-19 18:30:00-05:00,2019,6,19,18,30,20.200000000000003,20.200000000000003,26.0,1.0,26.0,0.13,1005.0,135.0,2.9000000000000004,100.0 +2019-06-19 19:30:00-05:00,2019,6,19,19,30,19.700000000000003,19.700000000000003,1.0,0.0,1.0,0.13,1005.0,130.0,2.9000000000000004,100.0 +2019-06-19 20:30:00-05:00,2019,6,19,20,30,19.3,19.3,0.0,0.0,0.0,0.13,1005.0,126.0,2.7,100.0 +2019-06-19 21:30:00-05:00,2019,6,19,21,30,19.1,19.1,0.0,0.0,0.0,0.13,1004.0,126.0,2.3000000000000003,100.0 +2019-06-19 22:30:00-05:00,2019,6,19,22,30,18.9,18.9,0.0,0.0,0.0,0.13,1004.0,130.0,1.9,100.0 +2019-06-19 23:30:00-05:00,2019,6,19,23,30,18.8,18.8,0.0,0.0,0.0,0.13,1004.0,134.0,1.7000000000000002,100.00000000000001 +2019-06-20 00:30:00-05:00,2019,6,20,0,30,18.7,18.7,0.0,0.0,0.0,0.13,1003.0,141.0,1.6,99.99999999999999 +2019-06-20 01:30:00-05:00,2019,6,20,1,30,18.7,18.7,0.0,0.0,0.0,0.13,1003.0,149.0,1.5,99.99999999999999 +2019-06-20 02:30:00-05:00,2019,6,20,2,30,18.7,18.7,0.0,0.0,0.0,0.13,1002.0,154.0,1.5,99.99999999999999 +2019-06-20 03:30:00-05:00,2019,6,20,3,30,18.8,18.8,0.0,0.0,0.0,0.13,1002.0,157.0,1.5,100.00000000000001 +2019-06-20 04:30:00-05:00,2019,6,20,4,30,19.1,18.9,1.0,0.0,1.0,0.13,1001.0,159.0,1.8,98.76007538165393 +2019-06-20 05:30:00-05:00,2019,6,20,5,30,19.8,19.4,17.0,0.0,17.0,0.13,1001.0,160.0,2.1,97.546632032517 +2019-06-20 06:30:00-05:00,2019,6,20,6,30,20.5,19.9,52.0,1.0,52.0,0.13,1001.0,163.0,2.4000000000000004,96.3589564835713 +2019-06-20 07:30:00-05:00,2019,6,20,7,30,21.200000000000003,20.6,112.0,6.0,115.0,0.13,1001.0,167.0,2.6,96.37789024004225 +2019-06-20 08:30:00-05:00,2019,6,20,8,30,21.8,21.0,181.0,4.0,184.0,0.13,1000.0,170.0,2.8000000000000003,95.21760279599738 +2019-06-20 09:30:00-05:00,2019,6,20,9,30,22.200000000000003,21.3,273.0,20.0,289.0,0.13,1000.0,169.0,3.0,94.64989549968917 +2019-06-20 10:30:00-05:00,2019,6,20,10,30,22.5,21.6,357.0,33.0,387.0,0.13,999.0,169.0,3.2,94.66166906449861 +2019-06-20 11:30:00-05:00,2019,6,20,11,30,22.700000000000003,21.8,422.0,151.0,565.0,0.13,998.0,173.0,3.2,94.66949673778647 +2019-06-20 12:30:00-05:00,2019,6,20,12,30,23.1,22.0,483.0,246.0,716.0,0.13,998.0,179.0,3.2,93.53820390346667 +2019-06-20 13:30:00-05:00,2019,6,20,13,30,23.700000000000003,22.3,411.0,207.0,597.0,0.13,997.0,186.0,3.1,91.87590901667876 +2019-06-20 14:30:00-05:00,2019,6,20,14,30,24.200000000000003,22.4,275.0,82.0,341.0,0.13,996.0,194.0,3.0,89.70035155341863 +2019-06-20 15:30:00-05:00,2019,6,20,15,30,24.4,22.5,45.0,0.0,45.0,0.13,996.0,199.0,2.9000000000000004,89.17181611541235 +2019-06-20 16:30:00-05:00,2019,6,20,16,30,24.200000000000003,22.8,44.0,4.0,46.0,0.13,996.0,201.0,2.8000000000000003,91.9050932123716 +2019-06-20 17:30:00-05:00,2019,6,20,17,30,23.5,23.0,118.0,249.0,203.0,0.13,995.0,204.0,2.6,97.02473741830013 +2019-06-20 18:30:00-05:00,2019,6,20,18,30,22.700000000000003,22.700000000000003,51.0,27.0,55.0,0.13,995.0,210.0,2.5,100.0 +2019-06-20 19:30:00-05:00,2019,6,20,19,30,22.3,22.3,2.0,0.0,2.0,0.13,995.0,217.0,2.5,100.0 +2019-06-20 20:30:00-05:00,2019,6,20,20,30,22.1,22.1,0.0,0.0,0.0,0.13,996.0,223.0,2.6,100.0 +2019-06-20 21:30:00-05:00,2019,6,20,21,30,21.9,21.9,0.0,0.0,0.0,0.13,995.0,232.0,2.5,100.0 +2019-06-20 22:30:00-05:00,2019,6,20,22,30,21.8,21.8,0.0,0.0,0.0,0.13,995.0,243.0,2.4000000000000004,100.0 +2019-06-20 23:30:00-05:00,2019,6,20,23,30,21.6,21.6,0.0,0.0,0.0,0.13,995.0,254.0,2.5,100.0 +2019-06-21 00:30:00-05:00,2019,6,21,0,30,21.3,21.3,0.0,0.0,0.0,0.13,995.0,259.0,2.4000000000000004,100.0 +2019-06-21 01:30:00-05:00,2019,6,21,1,30,21.0,21.0,0.0,0.0,0.0,0.13,994.0,259.0,2.3000000000000003,100.0 +2019-06-21 02:30:00-05:00,2019,6,21,2,30,20.8,20.8,0.0,0.0,0.0,0.13,994.0,255.0,2.3000000000000003,100.0 +2019-06-21 03:30:00-05:00,2019,6,21,3,30,20.6,20.6,0.0,0.0,0.0,0.13,994.0,252.0,2.5,100.0 +2019-06-21 04:30:00-05:00,2019,6,21,4,30,20.5,20.4,2.0,0.0,2.0,0.13,994.0,251.0,2.6,99.38490877203665 +2019-06-21 05:30:00-05:00,2019,6,21,5,30,20.6,20.4,40.0,0.0,40.0,0.13,994.0,252.0,2.5,98.77406323257436 +2019-06-21 06:30:00-05:00,2019,6,21,6,30,20.700000000000003,20.5,92.0,16.0,98.0,0.13,995.0,262.0,2.2,98.77498734581032 +2019-06-21 07:30:00-05:00,2019,6,21,7,30,20.5,20.4,160.0,43.0,183.0,0.13,995.0,293.0,2.0,99.38490877203665 +2019-06-21 08:30:00-05:00,2019,6,21,8,30,19.9,19.700000000000003,283.0,222.0,436.0,0.13,996.0,329.0,2.8000000000000003,98.76756512261169 +2019-06-21 09:30:00-05:00,2019,6,21,9,30,19.4,18.7,153.0,0.0,153.0,0.13,997.0,343.0,4.5,95.72869730038803 +2019-06-21 10:30:00-05:00,2019,6,21,10,30,19.6,17.900000000000002,134.0,0.0,134.0,0.13,998.0,341.0,5.5,89.9194245998682 +2019-06-21 11:30:00-05:00,2019,6,21,11,30,20.3,17.5,232.0,3.0,235.0,0.13,999.0,336.0,5.2,83.96140316195327 +2019-06-21 12:30:00-05:00,2019,6,21,12,30,21.4,17.6,388.0,83.0,467.0,0.13,1000.0,329.0,4.4,78.9647481661836 +2019-06-21 13:30:00-05:00,2019,6,21,13,30,22.4,17.6,322.0,388.0,671.0,0.13,1001.0,322.0,4.1000000000000005,74.28995470707754 +2019-06-21 14:30:00-05:00,2019,6,21,14,30,22.9,17.400000000000002,309.0,319.0,567.0,0.13,1001.0,318.0,4.1000000000000005,71.16592480342055 +2019-06-21 15:30:00-05:00,2019,6,21,15,30,22.9,17.1,87.0,836.0,654.0,0.13,1002.0,318.0,4.1000000000000005,69.82879628639056 +2019-06-21 16:30:00-05:00,2019,6,21,16,30,22.5,16.5,73.0,791.0,484.0,0.13,1002.0,319.0,4.1000000000000005,68.87154754068656 +2019-06-21 17:30:00-05:00,2019,6,21,17,30,21.6,16.1,59.0,693.0,297.0,0.13,1003.0,319.0,4.0,70.92538098431727 +2019-06-21 18:30:00-05:00,2019,6,21,18,30,20.3,15.9,39.0,484.0,118.0,0.13,1005.0,317.0,3.9,75.85067894365798 +2019-06-21 19:30:00-05:00,2019,6,21,19,30,19.1,15.4,4.0,42.0,4.0,0.13,1006.0,315.0,3.8,79.14045698794641 +2019-06-21 20:30:00-05:00,2019,6,21,20,30,18.2,15.1,0.0,0.0,0.0,0.13,1006.0,314.0,3.8,82.12623755031768 +2019-06-21 21:30:00-05:00,2019,6,21,21,30,17.6,14.8,0.0,0.0,0.0,0.13,1007.0,312.0,3.6,83.65462891785484 +2019-06-21 22:30:00-05:00,2019,6,21,22,30,17.1,14.7,0.0,0.0,0.0,0.13,1007.0,306.0,3.6,85.78459714682093 +2019-06-21 23:30:00-05:00,2019,6,21,23,30,16.7,14.8,0.0,0.0,0.0,0.13,1007.0,301.0,3.7,88.55674952557754 +2019-06-22 00:30:00-05:00,2019,6,22,0,30,16.400000000000002,14.8,0.0,0.0,0.0,0.13,1007.0,301.0,3.7,90.26173870461338 +2019-06-22 01:30:00-05:00,2019,6,22,1,30,16.1,14.8,0.0,0.0,0.0,0.13,1007.0,301.0,3.8,92.00361638702893 +2019-06-22 02:30:00-05:00,2019,6,22,2,30,15.8,14.8,0.0,0.0,0.0,0.13,1008.0,300.0,3.8,93.78326415706877 +2019-06-22 03:30:00-05:00,2019,6,22,3,30,15.6,14.7,0.0,0.0,0.0,0.13,1008.0,301.0,3.8,94.38080334382775 +2019-06-22 04:30:00-05:00,2019,6,22,4,30,15.9,14.6,7.0,73.0,8.0,0.13,1009.0,302.0,3.9,91.99173777746742 +2019-06-22 05:30:00-05:00,2019,6,22,5,30,16.900000000000002,14.6,41.0,514.0,131.0,0.13,1009.0,303.0,4.0,86.31972315303274 +2019-06-22 06:30:00-05:00,2019,6,22,6,30,18.5,14.8,59.0,720.0,316.0,0.13,1009.0,306.0,3.9,79.05484617836439 +2019-06-22 07:30:00-05:00,2019,6,22,7,30,20.1,14.9,71.0,825.0,510.0,0.13,1009.0,310.0,3.8,72.02374210835481 +2019-06-22 08:30:00-05:00,2019,6,22,8,30,21.5,15.2,80.0,885.0,689.0,0.13,1009.0,313.0,3.5,67.36583030106355 +2019-06-22 09:30:00-05:00,2019,6,22,9,30,22.4,15.2,111.0,876.0,825.0,0.13,1009.0,315.0,3.2,63.76697843893038 +2019-06-22 10:30:00-05:00,2019,6,22,10,30,23.0,15.0,153.0,842.0,915.0,0.13,1009.0,316.0,3.0,60.70242277903299 +2019-06-22 11:30:00-05:00,2019,6,22,11,30,23.5,15.0,155.0,854.0,966.0,0.13,1009.0,314.0,2.9000000000000004,58.89636630790317 +2019-06-22 12:30:00-05:00,2019,6,22,12,30,23.8,15.2,195.0,784.0,939.0,0.13,1009.0,311.0,2.9000000000000004,58.590220988241164 +2019-06-22 13:30:00-05:00,2019,6,22,13,30,23.8,15.3,189.0,774.0,886.0,0.13,1009.0,311.0,2.9000000000000004,58.967629649989334 +2019-06-22 14:30:00-05:00,2019,6,22,14,30,23.700000000000003,15.5,197.0,703.0,765.0,0.13,1009.0,313.0,2.9000000000000004,60.089410909898746 +2019-06-22 15:30:00-05:00,2019,6,22,15,30,23.4,15.8,185.0,621.0,606.0,0.13,1009.0,318.0,2.8000000000000003,62.3721203098981 +2019-06-22 16:30:00-05:00,2019,6,22,16,30,22.9,16.2,129.0,636.0,460.0,0.13,1009.0,323.0,2.7,65.9488972503992 +2019-06-22 17:30:00-05:00,2019,6,22,17,30,21.9,16.7,111.0,445.0,264.0,0.13,1010.0,326.0,2.6,72.34774099703732 +2019-06-22 18:30:00-05:00,2019,6,22,18,30,20.5,16.900000000000002,57.0,278.0,103.0,0.13,1011.0,328.0,2.6,79.84304814024507 +2019-06-22 19:30:00-05:00,2019,6,22,19,30,19.4,16.400000000000002,3.0,9.0,3.0,0.13,1011.0,329.0,2.7,82.80027590062022 +2019-06-22 20:30:00-05:00,2019,6,22,20,30,18.7,15.9,0.0,0.0,0.0,0.13,1012.0,327.0,2.9000000000000004,83.78062767389602 +2019-06-22 21:30:00-05:00,2019,6,22,21,30,18.0,15.6,0.0,0.0,0.0,0.13,1013.0,325.0,3.0,85.87560769615662 +2019-06-22 22:30:00-05:00,2019,6,22,22,30,17.400000000000002,15.3,0.0,0.0,0.0,0.13,1013.0,324.0,3.0,87.48537124170457 +2019-06-22 23:30:00-05:00,2019,6,22,23,30,17.0,15.3,0.0,0.0,0.0,0.13,1013.0,324.0,3.0,89.72698022064127 +2019-06-23 00:30:00-05:00,2019,6,23,0,30,16.6,15.3,0.0,0.0,0.0,0.13,1013.0,321.0,3.0,92.03319927142802 +2019-06-23 01:30:00-05:00,2019,6,23,1,30,16.3,15.2,0.0,0.0,0.0,0.13,1013.0,314.0,3.1,93.20611302660107 +2019-06-23 02:30:00-05:00,2019,6,23,2,30,16.0,15.2,0.0,0.0,0.0,0.13,1013.0,312.0,3.3000000000000003,95.00621298777295 +2019-06-23 03:30:00-05:00,2019,6,23,3,30,15.8,15.1,0.0,0.0,0.0,0.13,1013.0,313.0,3.2,95.61156291898422 +2019-06-23 04:30:00-05:00,2019,6,23,4,30,16.1,14.9,6.0,35.0,6.0,0.13,1013.0,315.0,3.3000000000000003,92.59810308181461 +2019-06-23 05:30:00-05:00,2019,6,23,5,30,17.2,15.1,55.0,372.0,120.0,0.13,1014.0,318.0,3.5,87.46731472993724 +2019-06-23 06:30:00-05:00,2019,6,23,6,30,19.0,15.3,84.0,600.0,298.0,0.13,1014.0,322.0,3.5,79.12622322021699 +2019-06-23 07:30:00-05:00,2019,6,23,7,30,20.8,15.9,101.0,737.0,492.0,0.13,1014.0,323.0,3.1,73.54816499757516 +2019-06-23 08:30:00-05:00,2019,6,23,8,30,22.200000000000003,16.3,107.0,823.0,673.0,0.13,1013.0,323.0,2.7,69.25260054643806 +2019-06-23 09:30:00-05:00,2019,6,23,9,30,23.200000000000003,16.2,109.0,879.0,825.0,0.13,1013.0,320.0,2.4000000000000004,64.76273665122592 +2019-06-23 10:30:00-05:00,2019,6,23,10,30,23.9,15.8,108.0,911.0,932.0,0.13,1012.0,314.0,2.0,60.52186516384888 +2019-06-23 11:30:00-05:00,2019,6,23,11,30,24.5,15.5,108.0,925.0,987.0,0.13,1011.0,304.0,1.8,57.27211598406217 +2019-06-23 12:30:00-05:00,2019,6,23,12,30,25.0,15.5,111.0,919.0,983.0,0.13,1011.0,287.0,1.7000000000000002,55.58687336421952 +2019-06-23 13:30:00-05:00,2019,6,23,13,30,25.200000000000003,15.7,112.0,900.0,922.0,0.13,1011.0,269.0,1.8,55.63644589834177 +2019-06-23 14:30:00-05:00,2019,6,23,14,30,25.1,16.0,106.0,874.0,812.0,0.13,1010.0,257.0,2.1,57.05224200740568 +2019-06-23 15:30:00-05:00,2019,6,23,15,30,24.9,16.6,97.0,834.0,663.0,0.13,1010.0,251.0,2.3000000000000003,59.98566162724888 +2019-06-23 16:30:00-05:00,2019,6,23,16,30,24.4,17.3,84.0,766.0,483.0,0.13,1009.0,248.0,2.4000000000000004,64.60906720348359 +2019-06-23 17:30:00-05:00,2019,6,23,17,30,23.3,18.2,67.0,654.0,293.0,0.13,1009.0,246.0,2.5,73.05341654320407 +2019-06-23 18:30:00-05:00,2019,6,23,18,30,21.700000000000003,18.9,43.0,440.0,115.0,0.13,1009.0,246.0,2.6,84.1171981535736 +2019-06-23 19:30:00-05:00,2019,6,23,19,30,20.6,18.2,4.0,35.0,4.0,0.13,1009.0,249.0,2.9000000000000004,86.13381174320128 +2019-06-23 20:30:00-05:00,2019,6,23,20,30,20.0,17.900000000000002,0.0,0.0,0.0,0.13,1009.0,252.0,3.1,87.71668477749306 +2019-06-23 21:30:00-05:00,2019,6,23,21,30,19.3,17.6,0.0,0.0,0.0,0.13,1009.0,256.0,3.1,89.89749126687161 +2019-06-23 22:30:00-05:00,2019,6,23,22,30,18.7,17.400000000000002,0.0,0.0,0.0,0.13,1009.0,261.0,3.1,92.15569812666644 +2019-06-23 23:30:00-05:00,2019,6,23,23,30,18.1,17.3,0.0,0.0,0.0,0.13,1009.0,269.0,2.9000000000000004,95.08432669052816 +2019-06-24 00:30:00-05:00,2019,6,24,0,30,17.5,17.1,0.0,0.0,0.0,0.13,1009.0,279.0,2.7,97.50363947168748 +2019-06-24 01:30:00-05:00,2019,6,24,1,30,17.0,17.0,0.0,0.0,0.0,0.13,1009.0,286.0,2.6,100.0 +2019-06-24 02:30:00-05:00,2019,6,24,2,30,16.6,16.6,0.0,0.0,0.0,0.13,1009.0,289.0,2.5,100.0 +2019-06-24 03:30:00-05:00,2019,6,24,3,30,16.2,16.2,0.0,0.0,0.0,0.13,1009.0,292.0,2.3000000000000003,100.0 +2019-06-24 04:30:00-05:00,2019,6,24,4,30,16.8,16.3,7.0,52.0,7.0,0.13,1009.0,295.0,2.0,96.87163110953763 +2019-06-24 05:30:00-05:00,2019,6,24,5,30,18.2,16.7,46.0,447.0,124.0,0.13,1009.0,290.0,1.6,90.96518068983454 +2019-06-24 06:30:00-05:00,2019,6,24,6,30,19.5,16.900000000000002,70.0,658.0,304.0,0.13,1009.0,267.0,1.2000000000000002,84.9424006632547 +2019-06-24 07:30:00-05:00,2019,6,24,7,30,21.200000000000003,17.6,99.0,726.0,484.0,0.13,1009.0,247.0,1.2000000000000002,79.93886151677533 +2019-06-24 08:30:00-05:00,2019,6,24,8,30,22.8,17.8,100.0,824.0,666.0,0.13,1009.0,244.0,1.3,73.42727529120872 +2019-06-24 09:30:00-05:00,2019,6,24,9,30,23.8,17.8,113.0,852.0,807.0,0.13,1009.0,241.0,1.3,69.12452261168472 +2019-06-24 10:30:00-05:00,2019,6,24,10,30,24.4,17.900000000000002,188.0,739.0,856.0,0.13,1009.0,237.0,1.5,67.1002832198587 +2019-06-24 11:30:00-05:00,2019,6,24,11,30,24.8,18.0,234.0,672.0,872.0,0.13,1009.0,232.0,1.7000000000000002,65.92757066770312 +2019-06-24 12:30:00-05:00,2019,6,24,12,30,24.8,18.2,355.0,446.0,778.0,0.13,1009.0,230.0,1.9,66.76103701952412 +2019-06-24 13:30:00-05:00,2019,6,24,13,30,24.8,18.4,363.0,180.0,525.0,0.13,1008.0,226.0,2.0,67.60374074909846 +2019-06-24 14:30:00-05:00,2019,6,24,14,30,24.700000000000003,18.5,303.0,26.0,324.0,0.13,1008.0,222.0,2.1,68.43615863137107 +2019-06-24 15:30:00-05:00,2019,6,24,15,30,24.3,18.7,288.0,148.0,389.0,0.13,1008.0,224.0,2.2,70.9768917930263 +2019-06-24 16:30:00-05:00,2019,6,24,16,30,23.6,19.3,216.0,174.0,307.0,0.13,1008.0,232.0,2.2,76.8495158098516 +2019-06-24 17:30:00-05:00,2019,6,24,17,30,22.8,19.9,123.0,27.0,132.0,0.13,1008.0,236.0,2.0,83.71796817654175 +2019-06-24 18:30:00-05:00,2019,6,24,18,30,21.8,20.3,52.0,1.0,52.0,0.13,1008.0,233.0,1.9,91.19872298278057 +2019-06-24 19:30:00-05:00,2019,6,24,19,30,21.1,19.8,2.0,0.0,2.0,0.13,1008.0,222.0,1.8,92.29231794434115 +2019-06-24 20:30:00-05:00,2019,6,24,20,30,20.700000000000003,19.700000000000003,0.0,0.0,0.0,0.13,1008.0,206.0,2.1,94.00553346328083 +2019-06-24 21:30:00-05:00,2019,6,24,21,30,20.5,19.700000000000003,0.0,0.0,0.0,0.13,1007.0,194.0,2.6,95.17139509638034 +2019-06-24 22:30:00-05:00,2019,6,24,22,30,20.4,19.8,0.0,0.0,0.0,0.13,1007.0,189.0,2.9000000000000004,96.3562396245616 +2019-06-24 23:30:00-05:00,2019,6,24,23,30,20.3,19.9,0.0,0.0,0.0,0.13,1007.0,188.0,2.8000000000000003,97.55583183960096 +2019-06-25 00:30:00-05:00,2019,6,25,0,30,20.3,20.0,0.0,0.0,0.0,0.13,1007.0,188.0,2.6,98.16190804764337 +2019-06-25 01:30:00-05:00,2019,6,25,1,30,20.4,20.1,0.0,0.0,0.0,0.13,1007.0,181.0,2.4000000000000004,98.16329113257757 +2019-06-25 02:30:00-05:00,2019,6,25,2,30,20.4,20.200000000000003,0.0,0.0,0.0,0.13,1007.0,168.0,2.4000000000000004,98.772211872832 +2019-06-25 03:30:00-05:00,2019,6,25,3,30,20.4,20.3,0.0,0.0,0.0,0.13,1007.0,156.0,2.7,99.38444306670166 +2019-06-25 04:30:00-05:00,2019,6,25,4,30,20.5,20.3,2.0,0.0,2.0,0.13,1007.0,153.0,3.0,98.77313807543814 +2019-06-25 05:30:00-05:00,2019,6,25,5,30,20.8,20.5,13.0,0.0,13.0,0.13,1007.0,154.0,3.1,98.16880793978846 +2019-06-25 06:30:00-05:00,2019,6,25,6,30,21.200000000000003,20.8,65.0,4.0,66.0,0.13,1007.0,156.0,3.1,97.5722621370397 +2019-06-25 07:30:00-05:00,2019,6,25,7,30,22.0,21.3,148.0,32.0,165.0,0.13,1007.0,160.0,3.3000000000000003,95.81042336368797 +2019-06-25 08:30:00-05:00,2019,6,25,8,30,22.9,21.9,138.0,2.0,139.0,0.13,1007.0,167.0,3.3000000000000003,94.1015097789341 +2019-06-25 09:30:00-05:00,2019,6,25,9,30,23.700000000000003,22.4,266.0,11.0,275.0,0.13,1007.0,172.0,3.2,92.43638985600242 +2019-06-25 10:30:00-05:00,2019,6,25,10,30,24.4,22.6,307.0,15.0,321.0,0.13,1007.0,178.0,3.1,89.71497940989823 +2019-06-25 11:30:00-05:00,2019,6,25,11,30,25.0,22.700000000000003,408.0,141.0,542.0,0.13,1007.0,182.0,3.0,87.0821871285312 +2019-06-25 12:30:00-05:00,2019,6,25,12,30,25.6,22.8,405.0,136.0,534.0,0.13,1007.0,183.0,2.8000000000000003,84.5398098261438 +2019-06-25 13:30:00-05:00,2019,6,25,13,30,26.200000000000003,22.700000000000003,394.0,148.0,527.0,0.13,1007.0,185.0,2.6,81.09527108176809 +2019-06-25 14:30:00-05:00,2019,6,25,14,30,26.4,22.6,279.0,440.0,635.0,0.13,1007.0,189.0,2.2,79.6583525290496 +2019-06-25 15:30:00-05:00,2019,6,25,15,30,26.4,22.5,243.0,409.0,521.0,0.13,1007.0,193.0,1.9,79.17607528306917 +2019-06-25 16:30:00-05:00,2019,6,25,16,30,26.1,22.8,170.0,498.0,430.0,0.13,1007.0,198.0,1.7000000000000002,82.07222228655003 +2019-06-25 17:30:00-05:00,2019,6,25,17,30,25.4,22.9,136.0,66.0,159.0,0.13,1008.0,205.0,1.4,86.07006224113753 +2019-06-25 18:30:00-05:00,2019,6,25,18,30,24.4,23.0,67.0,13.0,69.0,0.13,1008.0,215.0,1.2000000000000002,91.91672354601151 +2019-06-25 19:30:00-05:00,2019,6,25,19,30,23.5,21.8,3.0,0.0,3.0,0.13,1008.0,226.0,1.1,90.1982966625472 +2019-06-25 20:30:00-05:00,2019,6,25,20,30,22.9,21.4,0.0,0.0,0.0,0.13,1009.0,238.0,1.2000000000000002,91.2683085988807 +2019-06-25 21:30:00-05:00,2019,6,25,21,30,22.4,21.200000000000003,0.0,0.0,0.0,0.13,1009.0,257.0,1.2000000000000002,92.93346602326535 +2019-06-25 22:30:00-05:00,2019,6,25,22,30,22.0,21.0,0.0,0.0,0.0,0.13,1009.0,281.0,1.2000000000000002,94.06252427588008 +2019-06-25 23:30:00-05:00,2019,6,25,23,30,21.6,20.9,0.0,0.0,0.0,0.13,1010.0,306.0,1.3,95.79800244371609 +2019-06-26 00:30:00-05:00,2019,6,26,0,30,21.200000000000003,20.700000000000003,0.0,0.0,0.0,0.13,1010.0,328.0,1.5,96.97346382285346 +2019-06-26 01:30:00-05:00,2019,6,26,1,30,20.9,20.6,0.0,0.0,0.0,0.13,1011.0,345.0,2.0,98.17018327004955 +2019-06-26 02:30:00-05:00,2019,6,26,2,30,20.6,20.4,0.0,0.0,0.0,0.13,1012.0,178.0,2.7,98.77406323257436 +2019-06-26 03:30:00-05:00,2019,6,26,3,30,20.3,20.1,0.0,0.0,0.0,0.13,1012.0,5.0,3.0,98.7712846231834 +2019-06-26 04:30:00-05:00,2019,6,26,4,30,20.4,19.700000000000003,5.0,29.0,5.0,0.13,1013.0,11.0,3.0,95.76040897182789 +2019-06-26 05:30:00-05:00,2019,6,26,5,30,21.200000000000003,19.8,59.0,287.0,108.0,0.13,1013.0,17.0,2.6,91.72763106722233 +2019-06-26 06:30:00-05:00,2019,6,26,6,30,22.5,20.1,121.0,402.0,263.0,0.13,1014.0,22.0,2.0,86.31817367114336 +2019-06-26 07:30:00-05:00,2019,6,26,7,30,23.9,19.9,89.0,750.0,485.0,0.13,1014.0,22.0,1.5,78.339661685973 +2019-06-26 08:30:00-05:00,2019,6,26,8,30,25.0,19.3,99.0,822.0,662.0,0.13,1014.0,11.0,1.1,70.66425703923703 +2019-06-26 09:30:00-05:00,2019,6,26,9,30,26.0,19.0,106.0,866.0,810.0,0.13,1014.0,165.0,0.8,65.35576219694184 +2019-06-26 10:30:00-05:00,2019,6,26,10,30,26.8,18.7,110.0,890.0,914.0,0.13,1014.0,305.0,0.7000000000000001,61.18521534996032 +2019-06-26 11:30:00-05:00,2019,6,26,11,30,27.4,18.5,114.0,896.0,965.0,0.13,1013.0,269.0,0.8,58.333196033848786 +2019-06-26 12:30:00-05:00,2019,6,26,12,30,27.700000000000003,18.5,114.0,893.0,961.0,0.13,1013.0,247.0,0.9,57.31831542599245 +2019-06-26 13:30:00-05:00,2019,6,26,13,30,27.9,18.8,111.0,878.0,902.0,0.13,1013.0,235.0,1.1,57.725920240699296 +2019-06-26 14:30:00-05:00,2019,6,26,14,30,27.9,19.200000000000003,109.0,843.0,791.0,0.13,1013.0,227.0,1.2000000000000002,59.18450834487171 +2019-06-26 15:30:00-05:00,2019,6,26,15,30,28.0,19.9,107.0,785.0,640.0,0.13,1013.0,223.0,1.3,61.45546082121615 +2019-06-26 16:30:00-05:00,2019,6,26,16,30,27.5,20.6,99.0,694.0,461.0,0.13,1013.0,225.0,1.4,66.0740741178503 +2019-06-26 17:30:00-05:00,2019,6,26,17,30,26.3,20.700000000000003,82.0,560.0,276.0,0.13,1013.0,225.0,1.4,71.34164951034492 +2019-06-26 18:30:00-05:00,2019,6,26,18,30,24.8,21.9,62.0,226.0,99.0,0.13,1013.0,221.0,1.4,83.9408350810903 +2019-06-26 19:30:00-05:00,2019,6,26,19,30,23.6,21.200000000000003,4.0,17.0,4.0,0.13,1014.0,220.0,1.6,86.42328080854224 +2019-06-26 20:30:00-05:00,2019,6,26,20,30,22.9,20.9,0.0,0.0,0.0,0.13,1014.0,224.0,1.6,88.51015706971582 +2019-06-26 21:30:00-05:00,2019,6,26,21,30,22.5,20.8,0.0,0.0,0.0,0.13,1014.0,230.0,1.6,90.1278832900525 +2019-06-26 22:30:00-05:00,2019,6,26,22,30,22.200000000000003,20.700000000000003,0.0,0.0,0.0,0.13,1014.0,238.0,1.5,91.22412116599256 +2019-06-26 23:30:00-05:00,2019,6,26,23,30,21.9,20.8,0.0,0.0,0.0,0.13,1015.0,250.0,1.4,93.48137260108707 +2019-06-27 00:30:00-05:00,2019,6,27,0,30,21.6,20.8,0.0,0.0,0.0,0.13,1015.0,266.0,1.2000000000000002,95.21053683159022 +2019-06-27 01:30:00-05:00,2019,6,27,1,30,21.3,20.9,0.0,0.0,0.0,0.13,1015.0,286.0,1.1,97.57407753541636 +2019-06-27 02:30:00-05:00,2019,6,27,2,30,20.9,20.9,0.0,0.0,0.0,0.13,1015.0,307.0,1.0,100.0 +2019-06-27 03:30:00-05:00,2019,6,27,3,30,20.6,20.6,0.0,0.0,0.0,0.13,1016.0,325.0,1.1,100.0 +2019-06-27 04:30:00-05:00,2019,6,27,4,30,20.9,20.6,5.0,36.0,5.0,0.13,1016.0,336.0,1.3,98.17018327004955 +2019-06-27 05:30:00-05:00,2019,6,27,5,30,21.9,20.9,65.0,239.0,106.0,0.13,1017.0,341.0,1.4,94.05816896843245 +2019-06-27 06:30:00-05:00,2019,6,27,6,30,23.3,21.3,132.0,398.0,272.0,0.13,1017.0,341.0,1.3,88.54270976265246 +2019-06-27 07:30:00-05:00,2019,6,27,7,30,25.0,21.9,124.0,658.0,471.0,0.13,1017.0,342.0,1.1,82.9450940487276 +2019-06-27 08:30:00-05:00,2019,6,27,8,30,26.4,21.200000000000003,103.0,818.0,663.0,0.13,1017.0,341.0,1.1,73.13525295185259 +2019-06-27 09:30:00-05:00,2019,6,27,9,30,27.3,20.5,160.0,765.0,782.0,0.13,1017.0,331.0,1.0,66.44170507958184 +2019-06-27 10:30:00-05:00,2019,6,27,10,30,28.0,20.1,109.0,893.0,915.0,0.13,1017.0,317.0,1.0,62.22113735241833 +2019-06-27 11:30:00-05:00,2019,6,27,11,30,28.6,19.8,157.0,836.0,950.0,0.13,1017.0,305.0,1.0,58.98045446782043 +2019-06-27 12:30:00-05:00,2019,6,27,12,30,28.9,19.700000000000003,116.0,893.0,963.0,0.13,1017.0,293.0,0.9,57.60485259063698 +2019-06-27 13:30:00-05:00,2019,6,27,13,30,29.0,19.6,119.0,872.0,904.0,0.13,1016.0,280.0,0.8,56.917848107124506 +2019-06-27 14:30:00-05:00,2019,6,27,14,30,28.9,19.700000000000003,116.0,838.0,794.0,0.13,1016.0,268.0,0.7000000000000001,57.60485259063698 +2019-06-27 15:30:00-05:00,2019,6,27,15,30,28.6,20.1,111.0,783.0,643.0,0.13,1016.0,261.0,0.7000000000000001,60.086565702596914 +2019-06-27 16:30:00-05:00,2019,6,27,16,30,28.1,20.4,101.0,699.0,466.0,0.13,1016.0,263.0,0.7000000000000001,63.0169550964077 +2019-06-27 17:30:00-05:00,2019,6,27,17,30,27.1,20.6,148.0,314.0,257.0,0.13,1016.0,262.0,0.7000000000000001,67.6414692656499 +2019-06-27 18:30:00-05:00,2019,6,27,18,30,25.9,21.700000000000003,71.0,113.0,90.0,0.13,1016.0,251.0,0.6000000000000001,77.67117188646347 +2019-06-27 19:30:00-05:00,2019,6,27,19,30,24.9,20.6,4.0,29.0,4.0,0.13,1016.0,246.0,0.6000000000000001,77.04725125630709 +2019-06-27 20:30:00-05:00,2019,6,27,20,30,24.200000000000003,20.200000000000003,0.0,0.0,0.0,0.13,1017.0,263.0,0.6000000000000001,78.38291200919525 +2019-06-27 21:30:00-05:00,2019,6,27,21,30,23.700000000000003,20.200000000000003,0.0,0.0,0.0,0.13,1017.0,295.0,0.6000000000000001,80.77374600049235 +2019-06-27 22:30:00-05:00,2019,6,27,22,30,23.3,20.3,0.0,0.0,0.0,0.13,1017.0,331.0,0.6000000000000001,83.25842533417551 +2019-06-27 23:30:00-05:00,2019,6,27,23,30,22.9,20.5,0.0,0.0,0.0,0.13,1017.0,179.0,0.7000000000000001,86.35653124699375 +2019-06-28 00:30:00-05:00,2019,6,28,0,30,22.5,20.5,0.0,0.0,0.0,0.13,1017.0,14.0,0.8,88.47746864720834 +2019-06-28 01:30:00-05:00,2019,6,28,1,30,21.9,20.5,0.0,0.0,0.0,0.13,1018.0,23.0,0.8,91.7695491282392 +2019-06-28 02:30:00-05:00,2019,6,28,2,30,21.3,20.3,0.0,0.0,0.0,0.13,1018.0,30.0,0.8,94.03193730727011 +2019-06-28 03:30:00-05:00,2019,6,28,3,30,20.8,19.9,0.0,0.0,0.0,0.13,1018.0,35.0,0.9,94.59443892314147 +2019-06-28 04:30:00-05:00,2019,6,28,4,30,21.200000000000003,19.8,5.0,35.0,5.0,0.13,1018.0,34.0,1.0,91.72763106722233 +2019-06-28 05:30:00-05:00,2019,6,28,5,30,22.3,20.4,61.0,255.0,104.0,0.13,1018.0,31.0,1.2000000000000002,89.00897421968804 +2019-06-28 06:30:00-05:00,2019,6,28,6,30,23.6,20.6,139.0,361.0,266.0,0.13,1018.0,31.0,1.2000000000000002,83.29293471950034 +2019-06-28 07:30:00-05:00,2019,6,28,7,30,25.200000000000003,21.3,169.0,499.0,432.0,0.13,1018.0,37.0,0.9,79.00924587469248 +2019-06-28 08:30:00-05:00,2019,6,28,8,30,26.700000000000003,20.700000000000003,162.0,683.0,629.0,0.13,1018.0,40.0,0.6000000000000001,69.67880118327776 +2019-06-28 09:30:00-05:00,2019,6,28,9,30,27.6,19.700000000000003,223.0,596.0,707.0,0.13,1018.0,40.0,0.4,62.132555157080944 +2019-06-28 10:30:00-05:00,2019,6,28,10,30,28.200000000000003,19.200000000000003,157.0,808.0,886.0,0.13,1018.0,91.0,0.2,58.15857836497426 +2019-06-28 11:30:00-05:00,2019,6,28,11,30,28.5,19.200000000000003,167.0,809.0,935.0,0.13,1018.0,164.0,0.3,57.152640542830866 +2019-06-28 12:30:00-05:00,2019,6,28,12,30,28.6,19.700000000000003,233.0,670.0,868.0,0.13,1018.0,194.0,0.7000000000000001,58.615741577186476 +2019-06-28 13:30:00-05:00,2019,6,28,13,30,28.700000000000003,20.3,189.0,741.0,856.0,0.13,1017.0,198.0,1.0,60.48203962803258 +2019-06-28 14:30:00-05:00,2019,6,28,14,30,28.8,20.8,161.0,752.0,769.0,0.13,1017.0,199.0,1.3,62.01484489774761 +2019-06-28 15:30:00-05:00,2019,6,28,15,30,28.700000000000003,21.1,155.0,684.0,620.0,0.13,1017.0,200.0,1.4,63.53635693282711 +2019-06-28 16:30:00-05:00,2019,6,28,16,30,28.3,21.4,138.0,591.0,447.0,0.13,1017.0,200.0,1.5,66.23759291094093 +2019-06-28 17:30:00-05:00,2019,6,28,17,30,26.9,21.3,155.0,237.0,237.0,0.13,1016.0,198.0,1.6,71.44984081253008 +2019-06-28 18:30:00-05:00,2019,6,28,18,30,25.0,22.3,58.0,5.0,59.0,0.13,1016.0,195.0,1.8,84.99158907366433 +2019-06-28 19:30:00-05:00,2019,6,28,19,30,23.700000000000003,21.8,2.0,0.0,2.0,0.13,1016.0,195.0,2.3000000000000003,89.11793122655128 +2019-06-28 20:30:00-05:00,2019,6,28,20,30,23.1,21.200000000000003,0.0,0.0,0.0,0.13,1016.0,200.0,2.6,89.07143034098571 +2019-06-28 21:30:00-05:00,2019,6,28,21,30,22.700000000000003,21.0,0.0,0.0,0.0,0.13,1016.0,205.0,2.7,90.14202537275521 +2019-06-28 22:30:00-05:00,2019,6,28,22,30,22.3,21.1,0.0,0.0,0.0,0.13,1016.0,210.0,2.5,92.92831992605247 +2019-06-28 23:30:00-05:00,2019,6,28,23,30,21.9,21.200000000000003,0.0,0.0,0.0,0.13,1016.0,214.0,2.2,95.80732326344243 +2019-06-29 00:30:00-05:00,2019,6,29,0,30,21.700000000000003,21.3,0.0,0.0,0.0,0.13,1015.0,216.0,2.1,97.58131888921783 +2019-06-29 01:30:00-05:00,2019,6,29,1,30,21.6,21.4,0.0,0.0,0.0,0.13,1015.0,216.0,2.0,98.78325764694613 +2019-06-29 02:30:00-05:00,2019,6,29,2,30,21.5,21.5,0.0,0.0,0.0,0.13,1015.0,216.0,1.8,100.0 +2019-06-29 03:30:00-05:00,2019,6,29,3,30,21.5,21.5,0.0,0.0,0.0,0.13,1015.0,217.0,1.7000000000000002,100.0 +2019-06-29 04:30:00-05:00,2019,6,29,4,30,21.9,21.700000000000003,3.0,9.0,3.0,0.13,1014.0,220.0,1.6,98.7859958477431 +2019-06-29 05:30:00-05:00,2019,6,29,5,30,22.700000000000003,22.200000000000003,76.0,7.0,77.0,0.13,1014.0,223.0,1.6,97.00705113892808 +2019-06-29 06:30:00-05:00,2019,6,29,6,30,23.6,22.5,155.0,249.0,242.0,0.13,1014.0,224.0,1.7000000000000002,93.56166706652016 +2019-06-29 07:30:00-05:00,2019,6,29,7,30,24.6,23.1,204.0,347.0,386.0,0.13,1014.0,225.0,1.8,91.37426421535669 +2019-06-29 08:30:00-05:00,2019,6,29,8,30,25.6,23.4,254.0,372.0,508.0,0.13,1013.0,230.0,2.0,87.66375111582929 +2019-06-29 09:30:00-05:00,2019,6,29,9,30,26.3,23.6,261.0,503.0,669.0,0.13,1013.0,234.0,2.1,85.12546337318548 +2019-06-29 10:30:00-05:00,2019,6,29,10,30,26.8,23.8,163.0,781.0,867.0,0.13,1012.0,232.0,2.1,83.65472095808121 +2019-06-29 11:30:00-05:00,2019,6,29,11,30,27.0,24.0,157.0,808.0,923.0,0.13,1012.0,229.0,2.2,83.6769551752209 +2019-06-29 12:30:00-05:00,2019,6,29,12,30,27.1,23.9,243.0,622.0,833.0,0.13,1011.0,228.0,2.4000000000000004,82.68865067822269 +2019-06-29 13:30:00-05:00,2019,6,29,13,30,27.1,23.700000000000003,322.0,436.0,715.0,0.13,1010.0,228.0,2.5,81.69970849839068 +2019-06-29 14:30:00-05:00,2019,6,29,14,30,27.0,23.5,282.0,522.0,704.0,0.13,1010.0,228.0,2.7,81.19652945190818 +2019-06-29 15:30:00-05:00,2019,6,29,15,30,26.6,23.6,286.0,61.0,327.0,0.13,1009.0,227.0,2.7,83.63244289602052 +2019-06-29 16:30:00-05:00,2019,6,29,16,30,25.8,23.6,36.0,0.0,36.0,0.13,1008.0,225.0,2.7,87.68099019376216 +2019-06-29 17:30:00-05:00,2019,6,29,17,30,24.9,23.6,126.0,128.0,170.0,0.13,1008.0,224.0,2.9000000000000004,92.50154471777661 +2019-06-29 18:30:00-05:00,2019,6,29,18,30,24.1,23.4,67.0,10.0,69.0,0.13,1008.0,227.0,3.2,95.87474498557906 +2019-06-29 19:30:00-05:00,2019,6,29,19,30,23.6,23.1,2.0,0.0,2.0,0.13,1008.0,239.0,3.7,97.02693723194324 +2019-06-29 20:30:00-05:00,2019,6,29,20,30,23.0,22.8,0.0,0.0,0.0,0.13,1008.0,250.0,3.8,98.79595740023053 +2019-06-29 21:30:00-05:00,2019,6,29,21,30,22.5,22.4,0.0,0.0,0.0,0.13,1008.0,252.0,3.4000000000000004,99.39411310041719 +2019-06-29 22:30:00-05:00,2019,6,29,22,30,22.200000000000003,22.0,0.0,0.0,0.0,0.13,1007.0,247.0,2.7,98.7887248346732 +2019-06-29 23:30:00-05:00,2019,6,29,23,30,22.200000000000003,22.0,0.0,0.0,0.0,0.13,1007.0,241.0,2.7,98.7887248346732 +2019-06-30 00:30:00-05:00,2019,6,30,0,30,22.1,22.1,0.0,0.0,0.0,0.13,1006.0,247.0,3.1,100.0 +2019-06-30 01:30:00-05:00,2019,6,30,1,30,21.9,21.9,0.0,0.0,0.0,0.13,1006.0,269.0,3.0,100.0 +2019-06-30 02:30:00-05:00,2019,6,30,2,30,21.5,21.5,0.0,0.0,0.0,0.13,1006.0,291.0,3.0,100.0 +2019-06-30 03:30:00-05:00,2019,6,30,3,30,21.200000000000003,21.200000000000003,0.0,0.0,0.0,0.13,1006.0,304.0,2.9000000000000004,100.0 +2019-06-30 04:30:00-05:00,2019,6,30,4,30,21.3,20.9,5.0,28.0,5.0,0.13,1006.0,306.0,2.8000000000000003,97.57407753541636 +2019-06-30 05:30:00-05:00,2019,6,30,5,30,21.9,21.1,47.0,412.0,116.0,0.13,1006.0,304.0,2.7,95.22112996606126 +2019-06-30 06:30:00-05:00,2019,6,30,6,30,22.8,21.3,70.0,639.0,293.0,0.13,1006.0,303.0,2.5,91.2620162268376 +2019-06-30 07:30:00-05:00,2019,6,30,7,30,23.9,20.9,83.0,764.0,484.0,0.13,1005.0,303.0,2.4000000000000004,83.32734138956313 +2019-06-30 08:30:00-05:00,2019,6,30,8,30,24.9,20.3,91.0,836.0,661.0,0.13,1005.0,297.0,2.4000000000000004,75.63424522443381 +2019-06-30 09:30:00-05:00,2019,6,30,9,30,25.5,19.6,100.0,873.0,808.0,0.13,1005.0,290.0,2.7,69.8839034292768 +2019-06-30 10:30:00-05:00,2019,6,30,10,30,25.9,19.0,240.0,659.0,834.0,0.13,1004.0,288.0,3.1,65.74382739382241 +2019-06-30 11:30:00-05:00,2019,6,30,11,30,26.1,18.8,221.0,719.0,903.0,0.13,1003.0,291.0,3.4000000000000004,64.1640796630792 +2019-06-30 12:30:00-05:00,2019,6,30,12,30,26.1,18.9,175.0,789.0,923.0,0.13,1003.0,299.0,3.8,64.56607215408452 +2019-06-30 13:30:00-05:00,2019,6,30,13,30,25.8,18.7,116.0,880.0,908.0,0.13,1003.0,311.0,4.3,64.90689001430802 +2019-06-30 14:30:00-05:00,2019,6,30,14,30,25.4,18.2,218.0,411.0,551.0,0.13,1004.0,322.0,4.6000000000000005,64.41670362294133 +2019-06-30 15:30:00-05:00,2019,6,30,15,30,24.8,17.7,85.0,853.0,665.0,0.13,1004.0,330.0,4.5,64.69450248772199 +2019-06-30 16:30:00-05:00,2019,6,30,16,30,24.1,17.400000000000002,72.0,798.0,489.0,0.13,1005.0,334.0,4.0,66.19862023766261 +2019-06-30 17:30:00-05:00,2019,6,30,17,30,23.200000000000003,17.6,96.0,488.0,265.0,0.13,1005.0,334.0,3.3000000000000003,70.77353871725207 +2019-06-30 18:30:00-05:00,2019,6,30,18,30,21.9,17.900000000000002,37.0,493.0,119.0,0.13,1006.0,334.0,2.9000000000000004,78.04815669488676 +2019-06-30 19:30:00-05:00,2019,6,30,19,30,21.0,20.200000000000003,0.0,0.0,0.0,0.14,1009.0,150.0,3.6,95.18924553815685 +2019-06-30 20:30:00-05:00,2019,6,30,20,30,20.8,20.4,0.0,0.0,0.0,0.14,1009.0,150.0,3.7,97.56498021355463 +2019-06-30 21:30:00-05:00,2019,6,30,21,30,20.8,20.4,0.0,0.0,0.0,0.14,1009.0,157.0,3.9,97.56498021355463 +2019-06-30 22:30:00-05:00,2019,6,30,22,30,20.9,20.5,0.0,0.0,0.0,0.14,1008.0,165.0,3.8,97.56680374996314 +2019-06-30 23:30:00-05:00,2019,6,30,23,30,21.0,20.700000000000003,0.0,0.0,0.0,0.14,1008.0,169.0,3.3000000000000003,98.17155705631477 +2015-07-01 00:30:00-05:00,2015,7,1,0,30,20.9,20.8,0.0,0.0,0.0,0.14,1007.0,174.0,2.9000000000000004,99.38676632378528 +2015-07-01 01:30:00-05:00,2015,7,1,1,30,20.9,20.9,0.0,0.0,0.0,0.14,1007.0,183.0,2.3000000000000003,100.0 +2015-07-01 02:30:00-05:00,2015,7,1,2,30,20.9,20.9,0.0,0.0,0.0,0.14,1006.0,194.0,1.9,100.0 +2015-07-01 03:30:00-05:00,2015,7,1,3,30,20.8,20.8,0.0,0.0,0.0,0.14,1006.0,204.0,1.7000000000000002,100.0 +2015-07-01 04:30:00-05:00,2015,7,1,4,30,20.9,20.9,0.0,0.0,0.0,0.14,1006.0,208.0,1.5,100.0 +2015-07-01 05:30:00-05:00,2015,7,1,5,30,21.1,21.1,20.0,0.0,20.0,0.14,1006.0,203.0,1.7000000000000002,100.0 +2015-07-01 06:30:00-05:00,2015,7,1,6,30,21.4,21.3,139.0,314.0,249.0,0.14,1006.0,199.0,2.4000000000000004,99.38907646633176 +2015-07-01 07:30:00-05:00,2015,7,1,7,30,21.6,21.5,206.0,407.0,419.0,0.14,1006.0,202.0,3.1,99.389996873357 +2015-07-01 08:30:00-05:00,2015,7,1,8,30,21.9,21.6,414.0,95.0,478.0,0.14,1006.0,212.0,3.6,98.1838520314598 +2015-07-01 09:30:00-05:00,2015,7,1,9,30,22.6,21.0,512.0,162.0,644.0,0.14,1006.0,223.0,3.7,90.69068390923073 +2015-07-01 10:30:00-05:00,2015,7,1,10,30,23.700000000000003,20.1,242.0,682.0,857.0,0.14,1006.0,229.0,3.5,80.27578398997194 +2015-07-01 11:30:00-05:00,2015,7,1,11,30,24.700000000000003,19.5,209.0,758.0,928.0,0.14,1006.0,230.0,3.3000000000000003,72.84146222304305 +2015-07-01 12:30:00-05:00,2015,7,1,12,30,25.3,19.200000000000003,193.0,783.0,936.0,0.14,1005.0,229.0,3.2,68.9820789627854 +2015-07-01 13:30:00-05:00,2015,7,1,13,30,25.6,19.1,426.0,25.0,448.0,0.14,1005.0,226.0,3.2,67.34231282359893 +2015-07-01 14:30:00-05:00,2015,7,1,14,30,25.6,19.1,466.0,164.0,599.0,0.14,1005.0,224.0,3.3000000000000003,67.34231282359893 +2015-07-01 15:30:00-05:00,2015,7,1,15,30,25.4,19.1,199.0,607.0,612.0,0.14,1005.0,225.0,3.3000000000000003,68.1471152460334 +2015-07-01 16:30:00-05:00,2015,7,1,16,30,24.9,19.3,166.0,526.0,440.0,0.14,1005.0,230.0,3.2,71.08698923224469 +2015-07-01 17:30:00-05:00,2015,7,1,17,30,24.1,19.700000000000003,193.0,77.0,219.0,0.14,1006.0,236.0,2.9000000000000004,76.45017170712919 +2015-07-01 18:30:00-05:00,2015,7,1,18,30,23.0,20.0,71.0,9.0,73.0,0.14,1007.0,243.0,2.6,83.22381284256231 +2015-07-01 19:30:00-05:00,2015,7,1,19,30,22.200000000000003,19.9,0.0,0.0,0.0,0.14,1007.0,253.0,2.4000000000000004,86.82579310608945 +2015-07-01 20:30:00-05:00,2015,7,1,20,30,21.6,19.700000000000003,0.0,0.0,0.0,0.14,1008.0,267.0,2.2,88.95389452740017 +2015-07-01 21:30:00-05:00,2015,7,1,21,30,21.0,19.5,0.0,0.0,0.0,0.14,1008.0,282.0,2.0,91.14760035458201 +2015-07-01 22:30:00-05:00,2015,7,1,22,30,20.4,19.200000000000003,0.0,0.0,0.0,0.14,1009.0,296.0,2.0,92.82948065720157 +2015-07-01 23:30:00-05:00,2015,7,1,23,30,19.8,19.0,0.0,0.0,0.0,0.14,1009.0,308.0,1.9,95.14623855199284 +2015-07-02 00:30:00-05:00,2015,7,2,0,30,19.3,18.8,0.0,0.0,0.0,0.14,1009.0,320.0,1.9,96.93010644465888 +2015-07-02 01:30:00-05:00,2015,7,2,1,30,18.8,18.6,0.0,0.0,0.0,0.14,1008.0,331.0,1.8,98.7572491549771 +2015-07-02 02:30:00-05:00,2015,7,2,2,30,18.4,18.4,0.0,0.0,0.0,0.14,1008.0,342.0,1.7000000000000002,100.0 +2015-07-02 03:30:00-05:00,2015,7,2,3,30,18.2,18.2,0.0,0.0,0.0,0.14,1009.0,353.0,1.5,100.0 +2015-07-02 04:30:00-05:00,2015,7,2,4,30,18.7,18.0,0.0,0.0,0.0,0.14,1009.0,186.0,1.3,95.70628857869569 +2015-07-02 05:30:00-05:00,2015,7,2,5,30,20.0,18.4,78.0,34.0,83.0,0.14,1009.0,29.0,1.1,90.51425911024683 +2015-07-02 06:30:00-05:00,2015,7,2,6,30,21.5,18.5,155.0,268.0,248.0,0.14,1009.0,56.0,0.9,83.04918998107914 +2015-07-02 07:30:00-05:00,2015,7,2,7,30,22.9,17.900000000000002,259.0,134.0,329.0,0.14,1009.0,82.0,0.8,73.44449380451665 +2015-07-02 08:30:00-05:00,2015,7,2,8,30,23.9,17.6,246.0,467.0,564.0,0.14,1009.0,114.0,0.6000000000000001,67.84965926408279 +2015-07-02 09:30:00-05:00,2015,7,2,9,30,24.6,17.5,346.0,497.0,748.0,0.14,1009.0,142.0,0.7000000000000001,64.65181927102982 +2015-07-02 10:30:00-05:00,2015,7,2,10,30,25.0,17.5,381.0,352.0,698.0,0.14,1009.0,155.0,0.9,63.125937580382015 +2015-07-02 11:30:00-05:00,2015,7,2,11,30,25.0,17.5,383.0,341.0,706.0,0.14,1009.0,159.0,1.2000000000000002,63.125937580382015 +2015-07-02 12:30:00-05:00,2015,7,2,12,30,24.9,17.5,402.0,318.0,703.0,0.14,1009.0,157.0,1.6,63.50357356138726 +2015-07-02 13:30:00-05:00,2015,7,2,13,30,24.6,17.5,462.0,67.0,523.0,0.14,1009.0,154.0,1.9,64.65181927102982 +2015-07-02 14:30:00-05:00,2015,7,2,14,30,24.3,17.900000000000002,289.0,1.0,290.0,0.14,1009.0,152.0,2.0,67.50350367777526 +2015-07-02 15:30:00-05:00,2015,7,2,15,30,23.8,18.2,349.0,161.0,458.0,0.14,1008.0,151.0,1.9,70.88470137858685 +2015-07-02 16:30:00-05:00,2015,7,2,16,30,23.0,18.7,277.0,133.0,346.0,0.14,1008.0,151.0,1.6,76.75743970465271 +2015-07-02 17:30:00-05:00,2015,7,2,17,30,22.200000000000003,19.1,169.0,10.0,173.0,0.14,1008.0,157.0,1.1,82.61460691740074 +2015-07-02 18:30:00-05:00,2015,7,2,18,30,21.4,19.200000000000003,80.0,14.0,82.0,0.14,1009.0,183.0,0.5,87.29342856926561 +2015-07-02 19:30:00-05:00,2015,7,2,19,30,20.8,18.5,0.0,0.0,0.0,0.14,1009.0,247.0,0.4,86.69480298335615 +2015-07-02 20:30:00-05:00,2015,7,2,20,30,20.3,18.1,0.0,0.0,0.0,0.14,1009.0,299.0,0.6000000000000001,87.1937554031603 +2015-07-02 21:30:00-05:00,2015,7,2,21,30,19.8,17.8,0.0,0.0,0.0,0.14,1009.0,315.0,0.8,88.25321495212934 +2015-07-02 22:30:00-05:00,2015,7,2,22,30,19.5,17.5,0.0,0.0,0.0,0.14,1009.0,326.0,1.0,88.22790403011206 +2015-07-02 23:30:00-05:00,2015,7,2,23,30,19.1,17.1,0.0,0.0,0.0,0.14,1009.0,340.0,1.2000000000000002,88.19403170471226 +2015-07-03 00:30:00-05:00,2015,7,3,0,30,18.6,16.8,0.0,0.0,0.0,0.14,1009.0,353.0,1.5,89.27803727578194 +2015-07-03 01:30:00-05:00,2015,7,3,1,30,18.0,16.5,0.0,0.0,0.0,0.14,1009.0,357.0,1.8,90.95193922178122 +2015-07-03 02:30:00-05:00,2015,7,3,2,30,17.5,16.2,0.0,0.0,0.0,0.14,1010.0,180.0,2.2,92.08604247860313 +2015-07-03 03:30:00-05:00,2015,7,3,3,30,17.0,15.9,0.0,0.0,0.0,0.14,1010.0,2.0,2.5,93.24145220532814 +2015-07-03 04:30:00-05:00,2015,7,3,4,30,17.1,15.5,0.0,0.0,0.0,0.14,1011.0,2.0,2.8000000000000003,90.3116087116889 +2015-07-03 05:30:00-05:00,2015,7,3,5,30,18.0,15.3,47.0,391.0,111.0,0.14,1011.0,2.0,2.9000000000000004,84.2397671289966 +2015-07-03 06:30:00-05:00,2015,7,3,6,30,19.5,15.2,76.0,624.0,292.0,0.14,1012.0,5.0,2.9000000000000004,76.20987250295131 +2015-07-03 07:30:00-05:00,2015,7,3,7,30,21.1,14.4,93.0,756.0,487.0,0.14,1012.0,8.0,2.7,65.56964233901508 +2015-07-03 08:30:00-05:00,2015,7,3,8,30,22.3,13.9,105.0,832.0,670.0,0.14,1012.0,4.0,2.3000000000000003,58.991398818196544 +2015-07-03 09:30:00-05:00,2015,7,3,9,30,23.200000000000003,13.8,115.0,874.0,821.0,0.14,1012.0,176.0,1.8,55.49695744508928 +2015-07-03 10:30:00-05:00,2015,7,3,10,30,24.0,14.4,121.0,898.0,929.0,0.14,1012.0,347.0,1.2000000000000002,54.98160168104349 +2015-07-03 11:30:00-05:00,2015,7,3,11,30,24.700000000000003,14.8,125.0,905.0,982.0,0.14,1012.0,330.0,0.7000000000000001,54.10209993641184 +2015-07-03 12:30:00-05:00,2015,7,3,12,30,25.3,14.1,126.0,904.0,982.0,0.14,1012.0,281.0,0.4,49.89266488161381 +2015-07-03 13:30:00-05:00,2015,7,3,13,30,25.6,14.0,307.0,645.0,887.0,0.14,1011.0,227.0,0.5,48.69451195772931 +2015-07-03 14:30:00-05:00,2015,7,3,14,30,25.6,14.8,250.0,604.0,739.0,0.14,1011.0,196.0,0.7000000000000001,51.27856963392101 +2015-07-03 15:30:00-05:00,2015,7,3,15,30,25.5,15.5,251.0,415.0,533.0,0.14,1011.0,177.0,1.1,53.95722006300255 +2015-07-03 16:30:00-05:00,2015,7,3,16,30,24.8,16.0,241.0,251.0,372.0,0.14,1010.0,171.0,1.7000000000000002,58.082284382367654 +2015-07-03 17:30:00-05:00,2015,7,3,17,30,23.3,16.5,146.0,7.0,149.0,0.14,1011.0,170.0,2.4000000000000004,65.61399690902554 +2015-07-03 18:30:00-05:00,2015,7,3,18,30,21.8,17.7,65.0,6.0,66.0,0.14,1011.0,173.0,2.9000000000000004,77.54349998903822 +2015-07-03 19:30:00-05:00,2015,7,3,19,30,20.8,17.6,0.0,0.0,0.0,0.13,1012.0,176.0,3.1,81.92785507473593 +2015-07-03 20:30:00-05:00,2015,7,3,20,30,20.3,18.0,0.0,0.0,0.0,0.13,1012.0,177.0,2.9000000000000004,86.64756005562337 +2015-07-03 21:30:00-05:00,2015,7,3,21,30,20.0,18.3,0.0,0.0,0.0,0.13,1012.0,177.0,2.5,89.9485602802799 +2015-07-03 22:30:00-05:00,2015,7,3,22,30,19.8,18.6,0.0,0.0,0.0,0.13,1012.0,173.0,2.1,92.79784327754456 +2015-07-03 23:30:00-05:00,2015,7,3,23,30,19.700000000000003,18.7,0.0,0.0,0.0,0.13,1012.0,168.0,1.9,93.96114036717908 +2015-07-04 00:30:00-05:00,2015,7,4,0,30,19.6,18.7,0.0,0.0,0.0,0.13,1012.0,158.0,1.8,94.54622237656233 +2015-07-04 01:30:00-05:00,2015,7,4,1,30,19.4,18.6,0.0,0.0,0.0,0.13,1011.0,146.0,1.8,95.13177579171627 +2015-07-04 02:30:00-05:00,2015,7,4,2,30,19.3,18.6,0.0,0.0,0.0,0.13,1011.0,136.0,1.9,95.72550672773626 +2015-07-04 03:30:00-05:00,2015,7,4,3,30,19.0,18.6,0.0,0.0,0.0,0.13,1011.0,126.0,2.0,97.53180439900943 +2015-07-04 04:30:00-05:00,2015,7,4,4,30,19.3,18.4,0.0,0.0,0.0,0.13,1011.0,114.0,2.0,94.53406843955125 +2015-07-04 05:30:00-05:00,2015,7,4,5,30,20.200000000000003,18.8,55.0,5.0,56.0,0.13,1011.0,99.0,2.1,91.66719921665938 +2015-07-04 06:30:00-05:00,2015,7,4,6,30,20.9,18.7,172.0,112.0,211.0,0.13,1011.0,87.0,2.4000000000000004,87.24826360348135 +2015-07-04 07:30:00-05:00,2015,7,4,7,30,21.6,18.3,246.0,231.0,367.0,0.13,1011.0,79.0,2.6,81.51444781518468 +2015-07-04 08:30:00-05:00,2015,7,4,8,30,22.3,17.7,278.0,62.0,320.0,0.13,1011.0,74.0,2.8000000000000003,75.2157597464933 +2015-07-04 09:30:00-05:00,2015,7,4,9,30,22.8,16.900000000000002,90.0,0.0,90.0,0.13,1011.0,69.0,2.9000000000000004,69.36872302537611 +2015-07-04 10:30:00-05:00,2015,7,4,10,30,22.9,16.5,85.0,0.0,85.0,0.13,1011.0,64.0,2.8000000000000003,67.22059342521403 +2015-07-04 11:30:00-05:00,2015,7,4,11,30,22.8,16.3,228.0,0.0,228.0,0.13,1011.0,58.0,2.6,66.77378692764103 +2015-07-04 12:30:00-05:00,2015,7,4,12,30,22.9,16.2,146.0,0.0,146.0,0.13,1011.0,53.0,2.4000000000000004,65.9488972503992 +2015-07-04 13:30:00-05:00,2015,7,4,13,30,23.3,16.2,345.0,15.0,359.0,0.13,1010.0,52.0,2.1,64.37269473313279 +2015-07-04 14:30:00-05:00,2015,7,4,14,30,23.5,16.3,424.0,211.0,594.0,0.13,1010.0,57.0,1.8,64.00702725082051 +2015-07-04 15:30:00-05:00,2015,7,4,15,30,23.5,16.3,131.0,732.0,629.0,0.13,1010.0,63.0,1.5,64.00702725082051 +2015-07-04 16:30:00-05:00,2015,7,4,16,30,23.4,16.6,120.0,639.0,454.0,0.13,1011.0,70.0,1.3,65.63502207200915 +2015-07-04 17:30:00-05:00,2015,7,4,17,30,22.9,17.400000000000002,98.0,493.0,268.0,0.13,1011.0,77.0,1.1,71.16592480342055 +2015-07-04 18:30:00-05:00,2015,7,4,18,30,21.9,18.0,68.0,10.0,69.0,0.13,1012.0,88.0,1.1,78.54052274320881 +2015-07-04 19:30:00-05:00,2015,7,4,19,30,21.200000000000003,17.1,0.0,0.0,0.0,0.13,1012.0,105.0,1.2000000000000002,77.45318077204169 +2015-07-04 20:30:00-05:00,2015,7,4,20,30,20.700000000000003,16.8,0.0,0.0,0.0,0.13,1013.0,124.0,1.4,78.36638003901605 +2015-07-04 21:30:00-05:00,2015,7,4,21,30,20.1,16.8,0.0,0.0,0.0,0.13,1013.0,144.0,1.4,81.32297338726573 +2015-07-04 22:30:00-05:00,2015,7,4,22,30,19.5,17.0,0.0,0.0,0.0,0.13,1013.0,168.0,1.3,85.48240215720892 +2015-07-04 23:30:00-05:00,2015,7,4,23,30,19.0,17.1,0.0,0.0,0.0,0.13,1013.0,199.0,1.3,88.74572794748521 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09:30:00-05:00,2015,7,5,9,30,24.5,18.2,260.0,609.0,751.0,0.13,1017.0,247.0,1.0,67.96909303434049 +2015-07-05 10:30:00-05:00,2015,7,5,10,30,25.3,18.3,352.0,495.0,796.0,0.13,1017.0,233.0,1.1,65.20876489120079 +2015-07-05 11:30:00-05:00,2015,7,5,11,30,26.0,18.4,383.0,481.0,838.0,0.13,1017.0,220.0,1.3,62.9492873515637 +2015-07-05 12:30:00-05:00,2015,7,5,12,30,26.4,18.5,351.0,519.0,842.0,0.13,1016.0,209.0,1.6,61.86516929181137 +2015-07-05 13:30:00-05:00,2015,7,5,13,30,26.6,18.6,271.0,644.0,850.0,0.13,1016.0,201.0,1.9,61.52386240772954 +2015-07-05 14:30:00-05:00,2015,7,5,14,30,26.5,18.7,258.0,604.0,746.0,0.13,1015.0,196.0,2.4000000000000004,62.275870320455944 +2015-07-05 15:30:00-05:00,2015,7,5,15,30,26.3,19.0,228.0,545.0,598.0,0.13,1015.0,195.0,2.8000000000000003,64.20694988958873 +2015-07-05 16:30:00-05:00,2015,7,5,16,30,25.8,19.4,185.0,472.0,431.0,0.13,1015.0,196.0,3.1,67.80295966071287 +2015-07-05 17:30:00-05:00,2015,7,5,17,30,24.6,19.700000000000003,196.0,50.0,213.0,0.13,1016.0,198.0,3.1,74.19397615999193 +2015-07-05 18:30:00-05:00,2015,7,5,18,30,23.1,20.4,74.0,2.0,75.0,0.13,1016.0,199.0,3.2,84.79275792395167 +2015-07-05 19:30:00-05:00,2015,7,5,19,30,22.0,19.8,0.0,0.0,0.0,0.13,1017.0,202.0,3.3000000000000003,87.34731863549206 +2015-07-05 20:30:00-05:00,2015,7,5,20,30,21.4,19.6,0.0,0.0,0.0,0.13,1017.0,208.0,3.2,89.49231046045209 +2015-07-05 21:30:00-05:00,2015,7,5,21,30,20.8,19.5,0.0,0.0,0.0,0.13,1018.0,216.0,2.9000000000000004,92.27543367040357 +2015-07-05 22:30:00-05:00,2015,7,5,22,30,20.5,19.5,0.0,0.0,0.0,0.13,1018.0,223.0,2.6,93.99669366159607 +2015-07-05 23:30:00-05:00,2015,7,5,23,30,20.4,19.4,0.0,0.0,0.0,0.13,1018.0,229.0,2.3000000000000003,93.99226650694143 +2015-07-06 00:30:00-05:00,2015,7,6,0,30,20.3,19.3,0.0,0.0,0.0,0.13,1017.0,230.0,2.0,93.98783450711277 +2015-07-06 01:30:00-05:00,2015,7,6,1,30,20.200000000000003,19.1,0.0,0.0,0.0,0.13,1017.0,226.0,1.9,93.39958514384797 +2015-07-06 02:30:00-05:00,2015,7,6,2,30,20.0,19.1,0.0,0.0,0.0,0.13,1017.0,219.0,1.8,94.562365287616 +2015-07-06 03:30:00-05:00,2015,7,6,3,30,19.8,19.1,0.0,0.0,0.0,0.13,1017.0,213.0,1.8,95.7414242046781 +2015-07-06 04:30:00-05:00,2015,7,6,4,30,20.0,19.200000000000003,0.0,0.0,0.0,0.13,1018.0,211.0,1.7000000000000002,95.15344598530199 +2015-07-06 05:30:00-05:00,2015,7,6,5,30,20.8,19.5,67.0,6.0,68.0,0.13,1018.0,212.0,1.6,92.27543367040357 +2015-07-06 06:30:00-05:00,2015,7,6,6,30,22.0,20.3,194.0,54.0,212.0,0.13,1018.0,207.0,1.5,90.09239723117317 +2015-07-06 07:30:00-05:00,2015,7,6,7,30,23.200000000000003,20.3,300.0,128.0,366.0,0.13,1018.0,196.0,1.4,83.76289817082439 +2015-07-06 08:30:00-05:00,2015,7,6,8,30,24.3,20.0,317.0,377.0,571.0,0.13,1018.0,183.0,1.4,76.95628699261529 +2015-07-06 09:30:00-05:00,2015,7,6,9,30,25.1,19.9,454.0,231.0,640.0,0.13,1018.0,173.0,1.7000000000000002,72.91099054970618 +2015-07-06 10:30:00-05:00,2015,7,6,10,30,25.5,19.9,386.0,359.0,707.0,0.13,1018.0,169.0,2.1,71.19651070409681 +2015-07-06 11:30:00-05:00,2015,7,6,11,30,25.6,19.700000000000003,515.0,254.0,755.0,0.13,1018.0,167.0,2.4000000000000004,69.9027551656513 +2015-07-06 12:30:00-05:00,2015,7,6,12,30,25.5,19.700000000000003,350.0,481.0,804.0,0.13,1017.0,163.0,2.5,70.31906007469601 +2015-07-06 13:30:00-05:00,2015,7,6,13,30,25.5,19.8,423.0,46.0,465.0,0.13,1017.0,156.0,2.5,70.75659216038555 +2015-07-06 14:30:00-05:00,2015,7,6,14,30,25.4,20.0,426.0,111.0,516.0,0.13,1017.0,147.0,2.5,72.06578963920863 +2015-07-06 15:30:00-05:00,2015,7,6,15,30,25.200000000000003,20.1,260.0,421.0,546.0,0.13,1016.0,139.0,2.6,73.38106367287794 +2015-07-06 16:30:00-05:00,2015,7,6,16,30,24.700000000000003,20.200000000000003,272.0,67.0,306.0,0.13,1016.0,135.0,2.8000000000000003,76.07138099010922 +2015-07-06 17:30:00-05:00,2015,7,6,17,30,23.8,20.5,176.0,118.0,217.0,0.13,1016.0,133.0,2.9000000000000004,81.79021003762622 +2015-07-06 18:30:00-05:00,2015,7,6,18,30,22.8,20.8,52.0,305.0,101.0,0.13,1016.0,135.0,2.8000000000000003,88.50199771739551 +2015-07-06 19:30:00-05:00,2015,7,6,19,30,22.1,21.0,0.0,0.0,0.0,0.13,1016.0,140.0,2.8000000000000003,93.49089580031506 +2015-07-06 20:30:00-05:00,2015,7,6,20,30,21.8,21.3,0.0,0.0,0.0,0.13,1016.0,149.0,2.8000000000000003,96.98696591219704 +2015-07-06 21:30:00-05:00,2015,7,6,21,30,21.8,21.6,0.0,0.0,0.0,0.13,1016.0,159.0,2.8000000000000003,98.78508413994864 +2015-07-06 22:30:00-05:00,2015,7,6,22,30,21.8,21.8,0.0,0.0,0.0,0.13,1015.0,168.0,2.7,100.0 +2015-07-06 23:30:00-05:00,2015,7,6,23,30,21.8,21.8,0.0,0.0,0.0,0.13,1015.0,176.0,2.5,100.0 +2015-07-07 00:30:00-05:00,2015,7,7,0,30,21.700000000000003,21.700000000000003,0.0,0.0,0.0,0.13,1015.0,181.0,2.2,100.0 +2015-07-07 01:30:00-05:00,2015,7,7,1,30,21.700000000000003,21.700000000000003,0.0,0.0,0.0,0.13,1014.0,183.0,2.2,100.0 +2015-07-07 02:30:00-05:00,2015,7,7,2,30,21.700000000000003,21.700000000000003,0.0,0.0,0.0,0.13,1014.0,182.0,2.2,100.0 +2015-07-07 03:30:00-05:00,2015,7,7,3,30,21.8,21.8,0.0,0.0,0.0,0.13,1014.0,180.0,2.2,100.0 +2015-07-07 04:30:00-05:00,2015,7,7,4,30,22.3,22.3,0.0,0.0,0.0,0.13,1014.0,178.0,2.3000000000000003,100.0 +2015-07-07 05:30:00-05:00,2015,7,7,5,30,23.3,22.8,51.0,293.0,97.0,0.13,1014.0,177.0,2.5,97.02033049825886 +2015-07-07 06:30:00-05:00,2015,7,7,6,30,24.200000000000003,23.3,72.0,0.0,72.0,0.13,1014.0,180.0,2.7,94.72766454245478 +2015-07-07 07:30:00-05:00,2015,7,7,7,30,25.1,23.5,106.0,662.0,448.0,0.13,1014.0,185.0,2.7,90.8558024954087 +2015-07-07 08:30:00-05:00,2015,7,7,8,30,26.1,23.200000000000003,342.0,85.0,399.0,0.13,1014.0,189.0,2.8000000000000003,84.08333732125473 +2015-07-07 09:30:00-05:00,2015,7,7,9,30,26.8,22.700000000000003,137.0,780.0,765.0,0.13,1013.0,189.0,2.9000000000000004,78.27657315656735 +2015-07-07 10:30:00-05:00,2015,7,7,10,30,27.4,22.6,461.0,277.0,709.0,0.13,1013.0,188.0,3.2,75.11054034124393 +2015-07-07 11:30:00-05:00,2015,7,7,11,30,27.8,22.5,185.0,769.0,911.0,0.13,1013.0,186.0,3.5,72.92965355193031 +2015-07-07 12:30:00-05:00,2015,7,7,12,30,28.0,22.5,179.0,779.0,914.0,0.13,1012.0,186.0,3.8,72.08347293059005 +2015-07-07 13:30:00-05:00,2015,7,7,13,30,28.0,22.5,375.0,479.0,806.0,0.13,1012.0,188.0,4.1000000000000005,72.08347293059005 +2015-07-07 14:30:00-05:00,2015,7,7,14,30,27.8,22.6,183.0,703.0,750.0,0.13,1011.0,191.0,4.3,73.37388259889411 +2015-07-07 15:30:00-05:00,2015,7,7,15,30,27.5,22.700000000000003,172.0,636.0,604.0,0.13,1011.0,195.0,4.3,75.12657475747054 +2015-07-07 16:30:00-05:00,2015,7,7,16,30,27.1,22.9,179.0,15.0,186.0,0.13,1011.0,198.0,4.1000000000000005,77.84672010783402 +2015-07-07 17:30:00-05:00,2015,7,7,17,30,26.3,23.0,185.0,108.0,222.0,0.13,1011.0,200.0,3.8,82.09644733206095 +2015-07-07 18:30:00-05:00,2015,7,7,18,30,25.200000000000003,23.200000000000003,15.0,0.0,15.0,0.13,1011.0,203.0,3.5,88.69550382118368 +2015-07-07 19:30:00-05:00,2015,7,7,19,30,24.3,23.0,0.0,0.0,0.0,0.13,1011.0,207.0,3.4000000000000004,92.46907148822473 +2015-07-07 20:30:00-05:00,2015,7,7,20,30,23.8,22.8,0.0,0.0,0.0,0.13,1012.0,213.0,3.3000000000000003,94.1401166494882 +2015-07-07 21:30:00-05:00,2015,7,7,21,30,23.3,22.6,0.0,0.0,0.0,0.13,1012.0,218.0,3.2,95.85041601561413 +2015-07-07 22:30:00-05:00,2015,7,7,22,30,22.8,22.4,0.0,0.0,0.0,0.13,1011.0,221.0,3.1,97.60106694646583 +2015-07-07 23:30:00-05:00,2015,7,7,23,30,22.5,22.3,0.0,0.0,0.0,0.13,1011.0,221.0,3.0,98.79144464894331 +2015-07-08 00:30:00-05:00,2015,7,8,0,30,22.3,22.3,0.0,0.0,0.0,0.13,1011.0,219.0,2.9000000000000004,100.0 +2015-07-08 01:30:00-05:00,2015,7,8,1,30,22.3,22.3,0.0,0.0,0.0,0.13,1010.0,216.0,2.9000000000000004,100.0 +2015-07-08 02:30:00-05:00,2015,7,8,2,30,22.4,22.4,0.0,0.0,0.0,0.13,1010.0,213.0,3.1,100.0 +2015-07-08 03:30:00-05:00,2015,7,8,3,30,22.5,22.5,0.0,0.0,0.0,0.13,1010.0,212.0,3.3000000000000003,100.0 +2015-07-08 04:30:00-05:00,2015,7,8,4,30,22.9,22.6,0.0,0.0,0.0,0.13,1010.0,213.0,3.5,98.19736867358765 +2015-07-08 05:30:00-05:00,2015,7,8,5,30,23.4,22.8,60.0,1.0,60.0,0.13,1010.0,218.0,3.5,96.43645035727725 +2015-07-08 06:30:00-05:00,2015,7,8,6,30,23.9,23.200000000000003,166.0,19.0,173.0,0.13,1011.0,226.0,3.1,95.86868269389427 +2015-07-08 07:30:00-05:00,2015,7,8,7,30,24.4,23.5,254.0,126.0,318.0,0.13,1011.0,239.0,2.7,94.73534893450257 +2015-07-08 08:30:00-05:00,2015,7,8,8,30,24.9,23.6,321.0,133.0,411.0,0.13,1011.0,248.0,2.3000000000000003,92.50154471777661 +2015-07-08 09:30:00-05:00,2015,7,8,9,30,25.6,23.3,329.0,121.0,426.0,0.13,1011.0,247.0,1.9,87.13618000678832 +2015-07-08 10:30:00-05:00,2015,7,8,10,30,26.3,22.8,374.0,6.0,379.0,0.13,1011.0,239.0,1.7000000000000002,81.10797113328562 +2015-07-08 11:30:00-05:00,2015,7,8,11,30,27.0,22.6,383.0,315.0,681.0,0.13,1011.0,228.0,1.7000000000000002,76.89363104546618 +2015-07-08 12:30:00-05:00,2015,7,8,12,30,27.4,22.6,517.0,202.0,708.0,0.13,1010.0,217.0,1.9,75.11054034124393 +2015-07-08 13:30:00-05:00,2015,7,8,13,30,27.700000000000003,22.9,491.0,203.0,673.0,0.13,1010.0,206.0,2.1,75.15860006321068 +2015-07-08 14:30:00-05:00,2015,7,8,14,30,27.6,23.3,330.0,76.0,392.0,0.13,1010.0,201.0,2.3000000000000003,77.45035939741369 +2015-07-08 15:30:00-05:00,2015,7,8,15,30,27.1,23.6,266.0,50.0,300.0,0.13,1010.0,201.0,2.3000000000000003,81.20913195511869 +2015-07-08 16:30:00-05:00,2015,7,8,16,30,26.4,23.8,199.0,32.0,215.0,0.13,1010.0,204.0,2.2,85.64959932544602 +2015-07-08 17:30:00-05:00,2015,7,8,17,30,25.5,24.0,141.0,21.0,148.0,0.13,1010.0,211.0,2.0,91.42959202232244 +2015-07-08 18:30:00-05:00,2015,7,8,18,30,24.700000000000003,24.0,33.0,0.0,33.0,0.13,1010.0,224.0,1.7000000000000002,95.89285259431945 +2015-07-08 19:30:00-05:00,2015,7,8,19,30,24.0,23.8,0.0,0.0,0.0,0.13,1011.0,247.0,1.6,98.80490755664272 +2015-07-08 20:30:00-05:00,2015,7,8,20,30,23.4,23.200000000000003,0.0,0.0,0.0,0.13,1011.0,280.0,1.7000000000000002,98.79954946442618 +2015-07-08 21:30:00-05:00,2015,7,8,21,30,22.9,22.6,0.0,0.0,0.0,0.13,1011.0,312.0,1.8,98.19736867358765 +2015-07-08 22:30:00-05:00,2015,7,8,22,30,22.3,22.0,0.0,0.0,0.0,0.13,1011.0,334.0,2.0,98.18927681614883 +2015-07-08 23:30:00-05:00,2015,7,8,23,30,21.9,21.4,0.0,0.0,0.0,0.13,1011.0,345.0,2.1,96.98920751993329 +2015-07-09 00:30:00-05:00,2015,7,9,0,30,21.4,20.9,0.0,0.0,0.0,0.13,1011.0,353.0,2.4000000000000004,96.97797453299282 +2015-07-09 01:30:00-05:00,2015,7,9,1,30,21.0,20.4,0.0,0.0,0.0,0.13,1011.0,180.0,2.7,96.37249559695174 +2015-07-09 02:30:00-05:00,2015,7,9,2,30,20.700000000000003,20.0,0.0,0.0,0.0,0.13,1011.0,9.0,2.9000000000000004,95.76985414126123 +2015-07-09 03:30:00-05:00,2015,7,9,3,30,20.4,19.6,0.0,0.0,0.0,0.13,1011.0,18.0,3.0,95.1678131907141 +2015-07-09 04:30:00-05:00,2015,7,9,4,30,20.4,19.4,0.0,0.0,0.0,0.13,1012.0,24.0,3.1,93.99226650694143 +2015-07-09 05:30:00-05:00,2015,7,9,5,30,20.700000000000003,19.3,21.0,0.0,21.0,0.13,1012.0,27.0,3.2,91.69749631833166 +2015-07-09 06:30:00-05:00,2015,7,9,6,30,21.200000000000003,19.1,140.0,7.0,142.0,0.13,1012.0,30.0,3.0,87.82134037297142 +2015-07-09 07:30:00-05:00,2015,7,9,7,30,22.0,19.0,57.0,0.0,57.0,0.13,1012.0,30.0,2.7,83.10768815769458 +2015-07-09 08:30:00-05:00,2015,7,9,8,30,23.1,19.200000000000003,185.0,0.0,185.0,0.13,1012.0,25.0,2.3000000000000003,78.71267681572247 +2015-07-09 09:30:00-05:00,2015,7,9,9,30,24.3,19.5,150.0,0.0,150.0,0.13,1012.0,18.0,1.9,74.60618620492927 +2015-07-09 10:30:00-05:00,2015,7,9,10,30,25.4,20.0,223.0,0.0,223.0,0.13,1012.0,19.0,1.4,72.06578963920863 +2015-07-09 11:30:00-05:00,2015,7,9,11,30,26.3,20.200000000000003,401.0,55.0,452.0,0.13,1011.0,41.0,0.9,69.17439221669684 +2015-07-09 12:30:00-05:00,2015,7,9,12,30,26.8,20.4,397.0,46.0,441.0,0.13,1010.0,94.0,0.8,68.00051812091587 +2015-07-09 13:30:00-05:00,2015,7,9,13,30,26.9,20.8,398.0,82.0,472.0,0.13,1010.0,142.0,1.2000000000000002,69.28899787597788 +2015-07-09 14:30:00-05:00,2015,7,9,14,30,26.6,21.1,311.0,384.0,620.0,0.13,1009.0,159.0,1.9,71.83567150828277 +2015-07-09 15:30:00-05:00,2015,7,9,15,30,26.1,21.4,237.0,531.0,597.0,0.13,1009.0,163.0,2.4000000000000004,75.36116390642819 +2015-07-09 16:30:00-05:00,2015,7,9,16,30,25.4,21.6,139.0,582.0,441.0,0.13,1008.0,161.0,2.8000000000000003,79.52227117093723 +2015-07-09 17:30:00-05:00,2015,7,9,17,30,24.3,21.9,106.0,449.0,260.0,0.13,1008.0,158.0,3.3000000000000003,86.48955534166726 +2015-07-09 18:30:00-05:00,2015,7,9,18,30,23.1,22.200000000000003,63.0,79.0,76.0,0.13,1008.0,156.0,3.6,94.68510101735447 +2015-07-09 19:30:00-05:00,2015,7,9,19,30,22.4,22.200000000000003,0.0,0.0,0.0,0.13,1008.0,159.0,3.7,98.79053906083688 +2015-07-09 20:30:00-05:00,2015,7,9,20,30,22.3,22.200000000000003,0.0,0.0,0.0,0.13,1007.0,164.0,3.8,99.39320198785077 +2015-07-09 21:30:00-05:00,2015,7,9,21,30,22.3,22.200000000000003,0.0,0.0,0.0,0.13,1007.0,172.0,3.7,99.39320198785077 +2015-07-09 22:30:00-05:00,2015,7,9,22,30,22.5,22.4,0.0,0.0,0.0,0.13,1006.0,183.0,3.4000000000000004,99.39411310041719 +2015-07-09 23:30:00-05:00,2015,7,9,23,30,22.700000000000003,22.6,0.0,0.0,0.0,0.13,1005.0,199.0,3.0,99.39502216454267 +2015-07-10 00:30:00-05:00,2015,7,10,0,30,22.8,22.8,0.0,0.0,0.0,0.13,1005.0,221.0,2.9000000000000004,100.0 +2015-07-10 01:30:00-05:00,2015,7,10,1,30,22.700000000000003,22.700000000000003,0.0,0.0,0.0,0.13,1005.0,248.0,3.1,100.0 +2015-07-10 02:30:00-05:00,2015,7,10,2,30,22.200000000000003,22.200000000000003,0.0,0.0,0.0,0.13,1005.0,277.0,3.8,100.0 +2015-07-10 03:30:00-05:00,2015,7,10,3,30,21.5,21.5,0.0,0.0,0.0,0.13,1006.0,303.0,4.800000000000001,100.0 +2015-07-10 04:30:00-05:00,2015,7,10,4,30,20.8,20.5,0.0,0.0,0.0,0.13,1007.0,319.0,5.5,98.16880793978846 +2015-07-10 05:30:00-05:00,2015,7,10,5,30,20.9,19.8,54.0,1.0,54.0,0.13,1009.0,329.0,5.6000000000000005,93.43344547560373 +2015-07-10 06:30:00-05:00,2015,7,10,6,30,21.6,19.200000000000003,159.0,215.0,231.0,0.13,1009.0,337.0,5.5,86.23129245243052 +2015-07-10 07:30:00-05:00,2015,7,10,7,30,22.6,18.5,266.0,167.0,351.0,0.13,1010.0,344.0,5.1000000000000005,77.66312926099062 +2015-07-10 08:30:00-05:00,2015,7,10,8,30,23.8,17.900000000000002,106.0,797.0,640.0,0.13,1011.0,347.0,4.6000000000000005,69.56092941247769 +2015-07-10 09:30:00-05:00,2015,7,10,9,30,24.8,17.5,313.0,535.0,742.0,0.13,1011.0,347.0,3.9,63.88375318088307 +2015-07-10 10:30:00-05:00,2015,7,10,10,30,25.6,17.400000000000002,113.0,883.0,903.0,0.13,1011.0,344.0,3.2,60.52917657928209 +2015-07-10 11:30:00-05:00,2015,7,10,11,30,26.200000000000003,17.400000000000002,117.0,894.0,959.0,0.13,1011.0,341.0,2.5,58.41608018204501 +2015-07-10 12:30:00-05:00,2015,7,10,12,30,26.6,17.6,119.0,889.0,957.0,0.13,1011.0,335.0,1.9,57.778131164496905 +2015-07-10 13:30:00-05:00,2015,7,10,13,30,26.9,17.7,120.0,870.0,899.0,0.13,1011.0,326.0,1.6,57.12593107901772 +2015-07-10 14:30:00-05:00,2015,7,10,14,30,27.0,17.8,116.0,839.0,792.0,0.13,1011.0,316.0,1.5,57.1500388456088 +2015-07-10 15:30:00-05:00,2015,7,10,15,30,26.9,17.900000000000002,110.0,787.0,643.0,0.13,1011.0,306.0,1.4,57.849798446296134 +2015-07-10 16:30:00-05:00,2015,7,10,16,30,26.6,18.1,98.0,708.0,466.0,0.13,1011.0,299.0,1.3,59.62517376441939 +2015-07-10 17:30:00-05:00,2015,7,10,17,30,25.700000000000003,18.3,80.0,578.0,278.0,0.13,1011.0,297.0,1.1,63.677656683016274 +2015-07-10 18:30:00-05:00,2015,7,10,18,30,24.3,19.200000000000003,48.0,352.0,104.0,0.13,1012.0,302.0,1.0,73.22655897581218 +2015-07-10 19:30:00-05:00,2015,7,10,19,30,23.3,18.9,0.0,0.0,0.0,0.13,1012.0,315.0,1.0,76.32572245323844 +2015-07-10 20:30:00-05:00,2015,7,10,20,30,22.700000000000003,18.9,0.0,0.0,0.0,0.13,1013.0,332.0,1.1,79.14829366758862 +2015-07-10 21:30:00-05:00,2015,7,10,21,30,22.1,19.0,0.0,0.0,0.0,0.13,1013.0,348.0,1.2000000000000002,82.60263344589478 +2015-07-10 22:30:00-05:00,2015,7,10,22,30,21.4,18.9,0.0,0.0,0.0,0.13,1013.0,179.0,1.5,85.67552411246282 +2015-07-10 23:30:00-05:00,2015,7,10,23,30,20.8,18.7,0.0,0.0,0.0,0.13,1013.0,6.0,1.8,87.78660060157432 +2015-07-11 00:30:00-05:00,2015,7,11,0,30,20.200000000000003,18.4,0.0,0.0,0.0,0.13,1013.0,11.0,2.1,89.40125624804945 +2015-07-11 01:30:00-05:00,2015,7,11,1,30,19.9,18.1,0.0,0.0,0.0,0.13,1013.0,12.0,2.4000000000000004,89.37831163853151 +2015-07-11 02:30:00-05:00,2015,7,11,2,30,19.5,18.0,0.0,0.0,0.0,0.13,1014.0,10.0,2.4000000000000004,91.0505580556458 +2015-07-11 03:30:00-05:00,2015,7,11,3,30,19.200000000000003,17.900000000000002,0.0,0.0,0.0,0.13,1014.0,8.0,2.5,92.18445414057032 +2015-07-11 04:30:00-05:00,2015,7,11,4,30,19.3,17.6,0.0,0.0,0.0,0.13,1014.0,8.0,2.5,89.89749126687161 +2015-07-11 05:30:00-05:00,2015,7,11,5,30,20.1,17.5,68.0,16.0,70.0,0.13,1015.0,9.0,2.5,85.00588352401161 +2015-07-11 06:30:00-05:00,2015,7,11,6,30,21.6,17.400000000000002,163.0,128.0,206.0,0.13,1015.0,14.0,2.6,77.02566195716375 +2015-07-11 07:30:00-05:00,2015,7,11,7,30,23.6,17.3,184.0,493.0,436.0,0.13,1015.0,24.0,2.4000000000000004,67.78971538885096 +2015-07-11 08:30:00-05:00,2015,7,11,8,30,25.1,17.2,167.0,624.0,585.0,0.13,1015.0,32.0,1.8,61.57270511209754 +2015-07-11 09:30:00-05:00,2015,7,11,9,30,26.200000000000003,16.7,210.0,651.0,731.0,0.13,1015.0,38.0,1.2000000000000002,55.88327900619366 +2015-07-11 10:30:00-05:00,2015,7,11,10,30,27.1,16.7,138.0,852.0,899.0,0.13,1015.0,45.0,0.8,52.998293488125206 +2015-07-11 11:30:00-05:00,2015,7,11,11,30,27.700000000000003,16.3,138.0,868.0,955.0,0.13,1015.0,67.0,0.5,49.88308301477632 +2015-07-11 12:30:00-05:00,2015,7,11,12,30,28.1,15.7,135.0,872.0,957.0,0.13,1015.0,108.0,0.5,46.90097831573842 +2015-07-11 13:30:00-05:00,2015,7,11,13,30,28.200000000000003,16.0,125.0,869.0,903.0,0.13,1014.0,142.0,0.6000000000000001,47.53124864026501 +2015-07-11 14:30:00-05:00,2015,7,11,14,30,28.200000000000003,16.400000000000002,120.0,841.0,797.0,0.13,1014.0,162.0,0.8,48.75866063825264 +2015-07-11 15:30:00-05:00,2015,7,11,15,30,28.200000000000003,16.6,113.0,790.0,647.0,0.13,1014.0,177.0,1.1,49.3827471197376 +2015-07-11 16:30:00-05:00,2015,7,11,16,30,28.0,16.900000000000002,100.0,711.0,469.0,0.13,1014.0,188.0,1.4,50.92200554979949 +2015-07-11 17:30:00-05:00,2015,7,11,17,30,26.8,16.8,80.0,583.0,279.0,0.13,1014.0,194.0,1.7000000000000002,54.28434245652817 +2015-07-11 18:30:00-05:00,2015,7,11,18,30,24.9,18.4,47.0,358.0,104.0,0.13,1015.0,198.0,2.0,67.20142305242662 +2015-07-11 19:30:00-05:00,2015,7,11,19,30,23.5,18.5,0.0,0.0,0.0,0.13,1015.0,203.0,2.3000000000000003,73.54747853808202 +2015-07-11 20:30:00-05:00,2015,7,11,20,30,22.6,18.8,0.0,0.0,0.0,0.13,1015.0,209.0,2.4000000000000004,79.13425604294972 +2015-07-11 21:30:00-05:00,2015,7,11,21,30,21.9,19.200000000000003,0.0,0.0,0.0,0.13,1015.0,217.0,2.4000000000000004,84.66520458630976 +2015-07-11 22:30:00-05:00,2015,7,11,22,30,21.200000000000003,19.5,0.0,0.0,0.0,0.13,1015.0,228.0,2.4000000000000004,90.03522802897636 +2015-07-11 23:30:00-05:00,2015,7,11,23,30,20.6,19.4,0.0,0.0,0.0,0.13,1015.0,242.0,2.3000000000000003,92.83998075329615 +2015-07-12 00:30:00-05:00,2015,7,12,0,30,20.200000000000003,19.200000000000003,0.0,0.0,0.0,0.13,1014.0,256.0,2.3000000000000003,93.98339765512023 +2015-07-12 01:30:00-05:00,2015,7,12,1,30,19.8,18.8,0.0,0.0,0.0,0.13,1014.0,273.0,2.2,93.96560158527643 +2015-07-12 02:30:00-05:00,2015,7,12,2,30,19.4,18.4,0.0,0.0,0.0,0.13,1014.0,288.0,2.0,93.94772731836991 +2015-07-12 03:30:00-05:00,2015,7,12,3,30,19.1,18.0,0.0,0.0,0.0,0.13,1014.0,300.0,1.8,93.34585104133329 +2015-07-12 04:30:00-05:00,2015,7,12,4,30,19.5,17.7,0.0,0.0,0.0,0.13,1014.0,313.0,1.6,89.34760518219885 +2015-07-12 05:30:00-05:00,2015,7,12,5,30,20.8,17.900000000000002,44.0,324.0,92.0,0.13,1014.0,321.0,1.2000000000000002,83.49059767012787 +2015-07-12 06:30:00-05:00,2015,7,12,6,30,22.700000000000003,18.0,81.0,546.0,262.0,0.13,1014.0,321.0,0.8,74.80922632267337 +2015-07-12 07:30:00-05:00,2015,7,12,7,30,25.1,18.4,107.0,666.0,446.0,0.13,1014.0,325.0,0.5,66.40484304013277 +2015-07-12 08:30:00-05:00,2015,7,12,8,30,27.0,17.6,127.0,736.0,620.0,0.13,1014.0,270.0,0.3,56.43438230336064 +2015-07-12 09:30:00-05:00,2015,7,12,9,30,27.700000000000003,17.6,280.0,481.0,664.0,0.13,1014.0,203.0,0.4,54.166645263041346 +2015-07-12 10:30:00-05:00,2015,7,12,10,30,28.200000000000003,17.8,258.0,605.0,797.0,0.13,1013.0,194.0,0.7000000000000001,53.27681322139631 +2015-07-12 11:30:00-05:00,2015,7,12,11,30,28.6,17.900000000000002,334.0,586.0,885.0,0.13,1013.0,190.0,1.1,52.38077030558969 +2015-07-12 12:30:00-05:00,2015,7,12,12,30,28.8,18.0,353.0,561.0,881.0,0.13,1012.0,188.0,1.5,52.103196611080485 +2015-07-12 13:30:00-05:00,2015,7,12,13,30,28.9,18.3,357.0,535.0,836.0,0.13,1012.0,189.0,1.9,52.787208197551216 +2015-07-12 14:30:00-05:00,2015,7,12,14,30,28.6,18.6,272.0,570.0,731.0,0.13,1012.0,190.0,2.3000000000000003,54.73258970143153 +2015-07-12 15:30:00-05:00,2015,7,12,15,30,27.8,19.0,328.0,143.0,425.0,0.13,1011.0,192.0,2.6,58.7934235059172 +2015-07-12 16:30:00-05:00,2015,7,12,16,30,26.9,19.3,211.0,373.0,404.0,0.13,1011.0,195.0,2.9000000000000004,63.14635587999705 +2015-07-12 17:30:00-05:00,2015,7,12,17,30,25.8,19.9,113.0,416.0,254.0,0.13,1011.0,196.0,3.2,69.94041005204856 +2015-07-12 18:30:00-05:00,2015,7,12,18,30,24.5,20.700000000000003,53.0,3.0,53.0,0.13,1011.0,199.0,3.3000000000000003,79.39868349174054 +2015-07-12 19:30:00-05:00,2015,7,12,19,30,23.6,20.4,0.0,0.0,0.0,0.13,1011.0,202.0,3.3000000000000003,82.27181600810616 +2015-07-12 20:30:00-05:00,2015,7,12,20,30,23.1,20.3,0.0,0.0,0.0,0.13,1011.0,205.0,3.2,84.27081022361591 +2015-07-12 21:30:00-05:00,2015,7,12,21,30,22.700000000000003,20.3,0.0,0.0,0.0,0.13,1011.0,210.0,3.0,86.33737207908636 +2015-07-12 22:30:00-05:00,2015,7,12,22,30,22.5,20.200000000000003,0.0,0.0,0.0,0.13,1011.0,214.0,2.8000000000000003,86.85361747709986 +2015-07-12 23:30:00-05:00,2015,7,12,23,30,22.3,20.200000000000003,0.0,0.0,0.0,0.13,1011.0,219.0,2.6,87.91613260210468 +2015-07-13 00:30:00-05:00,2015,7,13,0,30,22.1,20.200000000000003,0.0,0.0,0.0,0.13,1011.0,225.0,2.5,88.99327835918427 +2015-07-13 01:30:00-05:00,2015,7,13,1,30,21.9,20.200000000000003,0.0,0.0,0.0,0.13,1010.0,228.0,2.3000000000000003,90.08527750507132 +2015-07-13 02:30:00-05:00,2015,7,13,2,30,21.8,20.1,0.0,0.0,0.0,0.13,1010.0,227.0,2.2,90.07815025003076 +2015-07-13 03:30:00-05:00,2015,7,13,3,30,21.6,20.1,0.0,0.0,0.0,0.13,1010.0,228.0,2.2,91.18598322234278 +2015-07-13 04:30:00-05:00,2015,7,13,4,30,21.6,20.1,0.0,0.0,0.0,0.13,1010.0,233.0,2.2,91.18598322234278 +2015-07-13 05:30:00-05:00,2015,7,13,5,30,22.1,20.3,63.0,7.0,64.0,0.13,1010.0,232.0,1.9,89.54489566149161 +2015-07-13 06:30:00-05:00,2015,7,13,6,30,23.4,20.4,181.0,76.0,206.0,0.13,1010.0,216.0,1.5,83.26993989952545 +2015-07-13 07:30:00-05:00,2015,7,13,7,30,25.3,20.200000000000003,251.0,169.0,337.0,0.13,1010.0,192.0,1.3,73.39815401856484 +2015-07-13 08:30:00-05:00,2015,7,13,8,30,26.8,19.8,324.0,204.0,460.0,0.13,1010.0,170.0,1.3,65.52274218653312 +2015-07-13 09:30:00-05:00,2015,7,13,9,30,27.4,19.4,238.0,555.0,681.0,0.13,1009.0,155.0,1.7000000000000002,61.70335673214363 +2015-07-13 10:30:00-05:00,2015,7,13,10,30,27.6,19.5,270.0,627.0,829.0,0.13,1009.0,149.0,2.3000000000000003,61.36565243787712 +2015-07-13 11:30:00-05:00,2015,7,13,11,30,27.700000000000003,19.6,187.0,781.0,921.0,0.13,1008.0,149.0,2.8000000000000003,61.38813010939479 +2015-07-13 12:30:00-05:00,2015,7,13,12,30,27.6,19.5,495.0,284.0,763.0,0.13,1008.0,155.0,3.2,61.36565243787712 +2015-07-13 13:30:00-05:00,2015,7,13,13,30,27.3,19.3,386.0,482.0,817.0,0.13,1008.0,160.0,3.3000000000000003,61.680979877919036 +2015-07-13 14:30:00-05:00,2015,7,13,14,30,26.8,19.0,158.0,760.0,768.0,0.13,1008.0,162.0,3.3000000000000003,62.34242458660606 +2015-07-13 15:30:00-05:00,2015,7,13,15,30,26.1,18.8,138.0,724.0,627.0,0.13,1007.0,164.0,3.3000000000000003,64.1640796630792 +2015-07-13 16:30:00-05:00,2015,7,13,16,30,25.200000000000003,18.5,114.0,660.0,455.0,0.13,1007.0,166.0,3.2,66.4253719948443 +2015-07-13 17:30:00-05:00,2015,7,13,17,30,24.200000000000003,18.4,166.0,245.0,249.0,0.13,1007.0,166.0,2.9000000000000004,70.07530802002378 +2015-07-13 18:30:00-05:00,2015,7,13,18,30,23.1,18.9,49.0,321.0,99.0,0.13,1007.0,164.0,2.6,77.25380880338423 +2015-07-13 19:30:00-05:00,2015,7,13,19,30,22.200000000000003,19.3,0.0,0.0,0.0,0.13,1007.0,165.0,2.4000000000000004,83.65023514761644 +2015-07-13 20:30:00-05:00,2015,7,13,20,30,21.8,19.6,0.0,0.0,0.0,0.13,1007.0,168.0,2.4000000000000004,87.32939245060646 +2015-07-13 21:30:00-05:00,2015,7,13,21,30,21.6,19.9,0.0,0.0,0.0,0.13,1007.0,169.0,2.4000000000000004,90.06387311155372 +2015-07-13 22:30:00-05:00,2015,7,13,22,30,21.5,20.200000000000003,0.0,0.0,0.0,0.13,1006.0,166.0,2.3000000000000003,92.31474557186237 +2015-07-13 23:30:00-05:00,2015,7,13,23,30,21.4,20.5,0.0,0.0,0.0,0.13,1005.0,162.0,2.3000000000000003,94.61831009329433 +2015-07-14 00:30:00-05:00,2015,7,14,0,30,21.3,20.700000000000003,0.0,0.0,0.0,0.13,1005.0,161.0,2.4000000000000004,96.38058307871259 +2015-07-14 01:30:00-05:00,2015,7,14,1,30,21.3,20.9,0.0,0.0,0.0,0.13,1004.0,162.0,2.6,97.57407753541636 +2015-07-14 02:30:00-05:00,2015,7,14,2,30,21.3,21.0,0.0,0.0,0.0,0.13,1004.0,162.0,2.6,98.17566917414295 +2015-07-14 03:30:00-05:00,2015,7,14,3,30,21.3,21.1,0.0,0.0,0.0,0.13,1004.0,163.0,2.6,98.78051019084454 +2015-07-14 04:30:00-05:00,2015,7,14,4,30,21.9,21.1,0.0,0.0,0.0,0.13,1004.0,162.0,2.5,95.22112996606126 +2015-07-14 05:30:00-05:00,2015,7,14,5,30,22.8,21.5,57.0,1.0,57.0,0.13,1004.0,157.0,2.4000000000000004,92.38697279878443 +2015-07-14 06:30:00-05:00,2015,7,14,6,30,23.5,21.8,85.0,517.0,254.0,0.13,1003.0,154.0,2.4000000000000004,90.1982966625472 +2015-07-14 07:30:00-05:00,2015,7,14,7,30,24.0,21.8,196.0,374.0,385.0,0.13,1003.0,157.0,2.4000000000000004,87.52455961305394 +2015-07-14 08:30:00-05:00,2015,7,14,8,30,24.4,21.5,242.0,10.0,249.0,0.13,1002.0,160.0,2.5,83.89661650096086 +2015-07-14 09:30:00-05:00,2015,7,14,9,30,24.5,21.4,239.0,1.0,239.0,0.13,1002.0,160.0,2.7,82.88674728430267 +2015-07-14 10:30:00-05:00,2015,7,14,10,30,24.700000000000003,21.6,353.0,4.0,356.0,0.13,1001.0,159.0,3.0,82.91012051931818 +2015-07-14 11:30:00-05:00,2015,7,14,11,30,25.200000000000003,22.1,361.0,18.0,378.0,0.13,1001.0,157.0,3.2,82.96835235443523 +2015-07-14 12:30:00-05:00,2015,7,14,12,30,25.700000000000003,22.3,245.0,0.0,246.0,0.13,1000.0,157.0,3.2,81.52628650446705 +2015-07-14 13:30:00-05:00,2015,7,14,13,30,26.1,22.4,224.0,2.0,225.0,0.13,1000.0,159.0,3.1,80.10336461834795 +2015-07-14 14:30:00-05:00,2015,7,14,14,30,26.0,22.6,417.0,148.0,536.0,0.13,999.0,165.0,3.0,81.56364669114777 +2015-07-14 15:30:00-05:00,2015,7,14,15,30,25.5,22.8,295.0,154.0,399.0,0.13,999.0,170.0,2.9000000000000004,85.04328551543426 +2015-07-14 16:30:00-05:00,2015,7,14,16,30,24.9,23.200000000000003,190.0,423.0,408.0,0.13,999.0,173.0,2.7,90.29564028477958 +2015-07-14 17:30:00-05:00,2015,7,14,17,30,24.3,23.6,142.0,23.0,150.0,0.13,999.0,175.0,2.4000000000000004,95.88079404075889 +2015-07-14 18:30:00-05:00,2015,7,14,18,30,23.700000000000003,23.700000000000003,36.0,1.0,36.0,0.13,999.0,179.0,2.4000000000000004,100.0 +2015-07-14 19:30:00-05:00,2015,7,14,19,30,23.200000000000003,23.200000000000003,0.0,0.0,0.0,0.13,999.0,185.0,2.5,100.0 +2015-07-14 20:30:00-05:00,2015,7,14,20,30,23.1,23.1,0.0,0.0,0.0,0.13,998.0,190.0,2.8000000000000003,100.0 +2015-07-14 21:30:00-05:00,2015,7,14,21,30,22.9,22.9,0.0,0.0,0.0,0.13,998.0,194.0,2.9000000000000004,100.0 +2015-07-14 22:30:00-05:00,2015,7,14,22,30,22.6,22.6,0.0,0.0,0.0,0.13,998.0,200.0,2.7,100.0 +2015-07-14 23:30:00-05:00,2015,7,14,23,30,22.4,22.4,0.0,0.0,0.0,0.13,999.0,207.0,2.3000000000000003,100.0 +2015-07-15 00:30:00-05:00,2015,7,15,0,30,22.3,22.3,0.0,0.0,0.0,0.13,999.0,211.0,1.9,100.0 +2015-07-15 01:30:00-05:00,2015,7,15,1,30,22.4,22.4,0.0,0.0,0.0,0.13,998.0,211.0,1.5,100.0 +2015-07-15 02:30:00-05:00,2015,7,15,2,30,22.700000000000003,22.700000000000003,0.0,0.0,0.0,0.13,998.0,201.0,0.9,100.0 +2015-07-15 03:30:00-05:00,2015,7,15,3,30,22.700000000000003,22.700000000000003,0.0,0.0,0.0,0.13,998.0,167.0,0.6000000000000001,100.0 +2015-07-15 04:30:00-05:00,2015,7,15,4,30,22.6,22.6,0.0,0.0,0.0,0.13,999.0,116.0,0.5,100.0 +2015-07-15 05:30:00-05:00,2015,7,15,5,30,22.9,22.700000000000003,52.0,6.0,53.0,0.13,999.0,82.0,0.6000000000000001,98.7950568701797 +2015-07-15 06:30:00-05:00,2015,7,15,6,30,23.5,23.3,138.0,210.0,207.0,0.13,1000.0,67.0,0.8,98.80044497394243 +2015-07-15 07:30:00-05:00,2015,7,15,7,30,24.200000000000003,23.6,99.0,0.0,99.0,0.13,1000.0,55.0,1.1,96.45739545380259 +2015-07-15 08:30:00-05:00,2015,7,15,8,30,25.0,23.6,122.0,1.0,122.0,0.13,1001.0,47.0,1.1,91.95146683042387 +2015-07-15 09:30:00-05:00,2015,7,15,9,30,25.5,23.1,176.0,2.0,177.0,0.13,1001.0,42.0,1.0,86.60207616835604 +2015-07-15 10:30:00-05:00,2015,7,15,10,30,25.700000000000003,22.4,221.0,1.0,222.0,0.13,1001.0,38.0,0.9,82.02363038901757 +2015-07-15 11:30:00-05:00,2015,7,15,11,30,26.0,22.1,184.0,0.0,184.0,0.13,1002.0,27.0,0.7000000000000001,79.12067626724063 +2015-07-15 12:30:00-05:00,2015,7,15,12,30,26.200000000000003,22.1,106.0,0.0,106.0,0.13,1002.0,12.0,0.7000000000000001,78.19041545046963 +2015-07-15 13:30:00-05:00,2015,7,15,13,30,26.1,22.1,120.0,0.0,120.0,0.13,1002.0,179.0,0.8,78.65399775022775 +2015-07-15 14:30:00-05:00,2015,7,15,14,30,25.8,22.200000000000003,212.0,3.0,214.0,0.13,1002.0,349.0,0.8,80.55257262849392 +2015-07-15 15:30:00-05:00,2015,7,15,15,30,25.200000000000003,22.200000000000003,287.0,32.0,309.0,0.13,1002.0,344.0,0.9,83.47525948927431 +2015-07-15 16:30:00-05:00,2015,7,15,16,30,24.5,22.4,264.0,94.0,313.0,0.13,1003.0,343.0,1.1,88.10250526368289 +2015-07-15 17:30:00-05:00,2015,7,15,17,30,23.8,22.4,129.0,10.0,132.0,0.13,1004.0,347.0,1.5,91.88175827454802 +2015-07-15 18:30:00-05:00,2015,7,15,18,30,23.1,22.200000000000003,45.0,1.0,45.0,0.13,1005.0,353.0,2.2,94.68510101735447 +2015-07-15 19:30:00-05:00,2015,7,15,19,30,22.4,21.5,0.0,0.0,0.0,0.13,1006.0,358.0,3.4000000000000004,94.65774882303148 +2015-07-15 20:30:00-05:00,2015,7,15,20,30,21.8,20.6,0.0,0.0,0.0,0.13,1007.0,183.0,4.3,92.90250604151835 +2015-07-15 21:30:00-05:00,2015,7,15,21,30,21.1,19.5,0.0,0.0,0.0,0.13,1008.0,8.0,4.7,90.58949625933221 +2015-07-15 22:30:00-05:00,2015,7,15,22,30,20.4,18.1,0.0,0.0,0.0,0.13,1008.0,11.0,4.800000000000001,86.65702819637295 +2015-07-15 23:30:00-05:00,2015,7,15,23,30,19.700000000000003,16.400000000000002,0.0,0.0,0.0,0.13,1009.0,15.0,4.800000000000001,81.27143234725494 +2015-07-16 00:30:00-05:00,2015,7,16,0,30,19.1,15.0,0.0,0.0,0.0,0.13,1009.0,17.0,4.7,77.13297630345755 +2015-07-16 01:30:00-05:00,2015,7,16,1,30,18.5,14.3,0.0,0.0,0.0,0.13,1010.0,18.0,4.6000000000000005,76.54378458099241 +2015-07-16 02:30:00-05:00,2015,7,16,2,30,17.8,13.9,0.0,0.0,0.0,0.13,1010.0,21.0,4.6000000000000005,77.93714994693931 +2015-07-16 03:30:00-05:00,2015,7,16,3,30,17.2,13.6,0.0,0.0,0.0,0.13,1011.0,22.0,4.4,79.38243080680031 +2015-07-16 04:30:00-05:00,2015,7,16,4,30,16.900000000000002,13.5,0.0,0.0,0.0,0.13,1012.0,22.0,4.3,80.38018507925324 +2015-07-16 05:30:00-05:00,2015,7,16,5,30,17.5,13.5,37.0,420.0,96.0,0.13,1013.0,24.0,4.5,77.38692153514398 +2015-07-16 06:30:00-05:00,2015,7,16,6,30,18.7,13.4,171.0,106.0,206.0,0.13,1013.0,28.0,4.6000000000000005,71.30230754214557 +2015-07-16 07:30:00-05:00,2015,7,16,7,30,20.1,13.1,77.0,790.0,474.0,0.13,1014.0,31.0,4.4,64.0902647157352 +2015-07-16 08:30:00-05:00,2015,7,16,8,30,21.4,12.9,87.0,860.0,657.0,0.13,1014.0,31.0,3.9,58.39306708522056 +2015-07-16 09:30:00-05:00,2015,7,16,9,30,22.6,12.9,95.0,904.0,813.0,0.13,1014.0,32.0,3.4000000000000004,54.27270138453845 +2015-07-16 10:30:00-05:00,2015,7,16,10,30,23.6,12.7,108.0,915.0,920.0,0.13,1014.0,38.0,2.8000000000000003,50.423425235897696 +2015-07-16 11:30:00-05:00,2015,7,16,11,30,24.3,12.4,111.0,923.0,976.0,0.13,1014.0,50.0,2.1,47.404621244761834 +2015-07-16 12:30:00-05:00,2015,7,16,12,30,24.8,12.4,111.0,923.0,978.0,0.13,1013.0,71.0,1.5,46.00767662949342 +2015-07-16 13:30:00-05:00,2015,7,16,13,30,25.1,12.6,112.0,906.0,921.0,0.13,1013.0,107.0,1.1,45.78856995745426 +2015-07-16 14:30:00-05:00,2015,7,16,14,30,25.200000000000003,12.5,109.0,878.0,813.0,0.13,1013.0,146.0,1.2000000000000002,45.21919822985252 +2015-07-16 15:30:00-05:00,2015,7,16,15,30,25.3,12.7,102.0,828.0,659.0,0.13,1013.0,170.0,1.4,45.54404691103501 +2015-07-16 16:30:00-05:00,2015,7,16,16,30,25.0,13.2,90.0,756.0,478.0,0.13,1013.0,178.0,1.6,47.905497936524434 +2015-07-16 17:30:00-05:00,2015,7,16,17,30,23.9,13.4,71.0,637.0,285.0,0.13,1013.0,179.0,1.8,51.837960110048314 +2015-07-16 18:30:00-05:00,2015,7,16,18,30,22.1,14.7,42.0,406.0,104.0,0.13,1014.0,177.0,2.3000000000000003,62.88552130584522 +2015-07-16 19:30:00-05:00,2015,7,16,19,30,20.700000000000003,14.4,0.0,0.0,0.0,0.13,1014.0,178.0,2.7,67.20236085300957 +2015-07-16 20:30:00-05:00,2015,7,16,20,30,19.8,15.2,0.0,0.0,0.0,0.13,1015.0,180.0,2.8000000000000003,74.80377688849234 +2015-07-16 21:30:00-05:00,2015,7,16,21,30,19.1,15.7,0.0,0.0,0.0,0.13,1015.0,181.0,2.7,80.67607490617536 +2015-07-16 22:30:00-05:00,2015,7,16,22,30,18.5,15.9,0.0,0.0,0.0,0.13,1015.0,186.0,2.5,84.83572540293457 +2015-07-16 23:30:00-05:00,2015,7,16,23,30,18.1,16.1,0.0,0.0,0.0,0.13,1015.0,194.0,2.4000000000000004,88.10872390785735 +2015-07-17 00:30:00-05:00,2015,7,17,0,30,17.7,16.1,0.0,0.0,0.0,0.13,1015.0,203.0,2.2,90.35405584200012 +2015-07-17 01:30:00-05:00,2015,7,17,1,30,17.400000000000002,16.2,0.0,0.0,0.0,0.13,1015.0,211.0,2.2,92.66920570844516 +2015-07-17 02:30:00-05:00,2015,7,17,2,30,17.0,16.2,0.0,0.0,0.0,0.13,1015.0,221.0,2.2,95.0436383780291 +2015-07-17 03:30:00-05:00,2015,7,17,3,30,16.7,16.3,0.0,0.0,0.0,0.13,1016.0,234.0,2.2,97.4884214914294 +2015-07-17 04:30:00-05:00,2015,7,17,4,30,17.2,16.3,0.0,0.0,0.0,0.13,1016.0,246.0,2.3000000000000003,94.44785340568389 +2015-07-17 05:30:00-05:00,2015,7,17,5,30,18.7,16.400000000000002,41.0,342.0,89.0,0.13,1016.0,251.0,2.3000000000000003,86.49472753274851 +2015-07-17 06:30:00-05:00,2015,7,17,6,30,20.5,16.400000000000002,74.0,587.0,263.0,0.13,1016.0,249.0,2.3000000000000003,77.34715597969345 +2015-07-17 07:30:00-05:00,2015,7,17,7,30,22.3,16.400000000000002,245.0,269.0,379.0,0.13,1016.0,242.0,2.1,69.27199609705535 +2015-07-17 08:30:00-05:00,2015,7,17,8,30,23.5,16.0,109.0,791.0,632.0,0.13,1016.0,230.0,2.0,62.79427448818006 +2015-07-17 09:30:00-05:00,2015,7,17,9,30,24.4,15.6,120.0,837.0,783.0,0.13,1016.0,211.0,2.0,57.98628871078611 +2015-07-17 10:30:00-05:00,2015,7,17,10,30,24.9,15.5,127.0,860.0,889.0,0.13,1015.0,193.0,2.5,55.919408677888036 +2015-07-17 11:30:00-05:00,2015,7,17,11,30,25.200000000000003,15.7,133.0,868.0,945.0,0.13,1015.0,183.0,3.1,55.63644589834177 +2015-07-17 12:30:00-05:00,2015,7,17,12,30,25.3,15.8,342.0,576.0,882.0,0.13,1015.0,182.0,3.6,55.66120638090343 +2015-07-17 13:30:00-05:00,2015,7,17,13,30,25.3,15.9,308.0,614.0,855.0,0.13,1015.0,184.0,3.8,56.01808252133802 +2015-07-17 14:30:00-05:00,2015,7,17,14,30,25.1,16.0,138.0,804.0,781.0,0.13,1015.0,185.0,3.8,57.05224200740568 +2015-07-17 15:30:00-05:00,2015,7,17,15,30,24.700000000000003,16.0,124.0,761.0,634.0,0.13,1014.0,182.0,3.8,58.43026865686232 +2015-07-17 16:30:00-05:00,2015,7,17,16,30,24.0,16.0,106.0,691.0,459.0,0.13,1014.0,179.0,3.8,60.93287150821879 +2015-07-17 17:30:00-05:00,2015,7,17,17,30,23.0,16.2,162.0,16.0,167.0,0.13,1013.0,176.0,3.7,65.5508177918108 +2015-07-17 18:30:00-05:00,2015,7,17,18,30,21.8,16.8,70.0,9.0,72.0,0.13,1014.0,174.0,3.4000000000000004,73.25423015557377 +2015-07-17 19:30:00-05:00,2015,7,17,19,30,21.0,17.3,0.0,0.0,0.0,0.14,1014.0,173.0,3.1,79.40828785384966 +2015-07-17 20:30:00-05:00,2015,7,17,20,30,20.700000000000003,17.6,0.0,0.0,0.0,0.14,1014.0,173.0,3.1,82.43374874471213 +2015-07-17 21:30:00-05:00,2015,7,17,21,30,20.700000000000003,17.8,0.0,0.0,0.0,0.14,1014.0,175.0,3.2,83.4791088458305 +2015-07-17 22:30:00-05:00,2015,7,17,22,30,20.700000000000003,18.0,0.0,0.0,0.0,0.14,1013.0,180.0,3.3000000000000003,84.53609300458625 +2015-07-17 23:30:00-05:00,2015,7,17,23,30,20.700000000000003,18.2,0.0,0.0,0.0,0.14,1013.0,185.0,3.5,85.60481111933169 +2015-07-18 00:30:00-05:00,2015,7,18,0,30,20.700000000000003,18.4,0.0,0.0,0.0,0.14,1013.0,190.0,3.5,86.68537392692664 +2015-07-18 01:30:00-05:00,2015,7,18,1,30,20.700000000000003,18.7,0.0,0.0,0.0,0.14,1012.0,195.0,3.4000000000000004,88.3286712503488 +2015-07-18 02:30:00-05:00,2015,7,18,2,30,20.700000000000003,19.1,0.0,0.0,0.0,0.14,1012.0,200.0,3.4000000000000004,90.56223930847649 +2015-07-18 03:30:00-05:00,2015,7,18,3,30,20.8,19.5,0.0,0.0,0.0,0.14,1012.0,203.0,3.4000000000000004,92.27543367040357 +2015-07-18 04:30:00-05:00,2015,7,18,4,30,20.9,19.8,0.0,0.0,0.0,0.14,1012.0,202.0,3.3000000000000003,93.43344547560373 +2015-07-18 05:30:00-05:00,2015,7,18,5,30,21.5,20.200000000000003,35.0,1.0,35.0,0.14,1012.0,198.0,3.2,92.31474557186237 +2015-07-18 06:30:00-05:00,2015,7,18,6,30,22.3,20.700000000000003,56.0,0.0,56.0,0.14,1012.0,196.0,3.4000000000000004,90.67057501215672 +2015-07-18 07:30:00-05:00,2015,7,18,7,30,23.4,21.1,68.0,0.0,68.0,0.14,1012.0,197.0,3.7,86.93657783435786 +2015-07-18 08:30:00-05:00,2015,7,18,8,30,24.5,21.4,47.0,0.0,47.0,0.14,1012.0,199.0,3.8,82.88674728430267 +2015-07-18 09:30:00-05:00,2015,7,18,9,30,25.4,21.700000000000003,290.0,469.0,661.0,0.14,1011.0,199.0,3.8,80.00996688516773 +2015-07-18 10:30:00-05:00,2015,7,18,10,30,26.0,22.1,270.0,547.0,754.0,0.14,1011.0,199.0,3.7,79.12067626724063 +2015-07-18 11:30:00-05:00,2015,7,18,11,30,26.200000000000003,22.4,368.0,23.0,390.0,0.14,1010.0,198.0,3.7,79.63123983066635 +2015-07-18 12:30:00-05:00,2015,7,18,12,30,26.3,22.700000000000003,346.0,15.0,360.0,0.14,1010.0,199.0,3.6,80.61765392538541 +2015-07-18 13:30:00-05:00,2015,7,18,13,30,26.200000000000003,23.0,418.0,214.0,609.0,0.14,1009.0,200.0,3.6,82.58282556083141 +2015-07-18 14:30:00-05:00,2015,7,18,14,30,26.3,23.200000000000003,399.0,310.0,647.0,0.14,1009.0,203.0,3.6,83.09545795447868 +2015-07-18 15:30:00-05:00,2015,7,18,15,30,26.4,23.5,153.0,675.0,605.0,0.14,1009.0,205.0,3.6,84.1159622620894 +2015-07-18 16:30:00-05:00,2015,7,18,16,30,26.4,23.6,130.0,596.0,434.0,0.14,1009.0,204.0,3.6,84.62448112085993 +2015-07-18 17:30:00-05:00,2015,7,18,17,30,25.9,23.700000000000003,174.0,95.0,206.0,0.14,1008.0,202.0,3.5,87.68959652264259 +2015-07-18 18:30:00-05:00,2015,7,18,18,30,24.9,23.8,51.0,252.0,88.0,0.14,1009.0,200.0,3.4000000000000004,93.62208355224344 +2015-07-18 19:30:00-05:00,2015,7,18,19,30,24.200000000000003,23.8,0.0,0.0,0.0,0.14,1009.0,201.0,3.4000000000000004,97.62585440016154 +2015-07-18 20:30:00-05:00,2015,7,18,20,30,23.9,23.8,0.0,0.0,0.0,0.14,1009.0,203.0,3.4000000000000004,99.40043387370717 +2015-07-18 21:30:00-05:00,2015,7,18,21,30,23.700000000000003,23.700000000000003,0.0,0.0,0.0,0.14,1008.0,206.0,3.3000000000000003,100.0 +2015-07-18 22:30:00-05:00,2015,7,18,22,30,23.700000000000003,23.6,0.0,0.0,0.0,0.14,1008.0,211.0,3.2,99.39953697229943 +2015-07-18 23:30:00-05:00,2015,7,18,23,30,23.6,23.6,0.0,0.0,0.0,0.14,1008.0,217.0,3.0,100.0 +2015-07-19 00:30:00-05:00,2015,7,19,0,30,23.4,23.4,0.0,0.0,0.0,0.14,1008.0,221.0,2.8000000000000003,100.0 +2015-07-19 01:30:00-05:00,2015,7,19,1,30,23.3,23.3,0.0,0.0,0.0,0.14,1007.0,227.0,2.5,100.0 +2015-07-19 02:30:00-05:00,2015,7,19,2,30,23.1,23.1,0.0,0.0,0.0,0.14,1007.0,231.0,2.2,100.0 +2015-07-19 03:30:00-05:00,2015,7,19,3,30,22.8,22.8,0.0,0.0,0.0,0.14,1007.0,233.0,1.9,100.0 +2015-07-19 04:30:00-05:00,2015,7,19,4,30,22.9,22.9,0.0,0.0,0.0,0.14,1008.0,236.0,1.7000000000000002,100.0 +2015-07-19 05:30:00-05:00,2015,7,19,5,30,23.700000000000003,23.3,57.0,12.0,58.0,0.14,1008.0,237.0,1.6,97.61704584379915 +2015-07-19 06:30:00-05:00,2015,7,19,6,30,24.9,23.8,81.0,518.0,246.0,0.14,1008.0,236.0,1.6,93.62208355224344 +2015-07-19 07:30:00-05:00,2015,7,19,7,30,26.6,24.3,225.0,352.0,400.0,0.14,1008.0,238.0,1.4,87.22543835052973 +2015-07-19 08:30:00-05:00,2015,7,19,8,30,28.200000000000003,23.8,121.0,739.0,607.0,0.14,1008.0,238.0,1.3,77.07367980056105 +2015-07-19 09:30:00-05:00,2015,7,19,9,30,29.5,23.0,133.0,784.0,753.0,0.14,1008.0,232.0,1.3,68.11243062722684 +2015-07-19 10:30:00-05:00,2015,7,19,10,30,30.4,22.5,127.0,834.0,864.0,0.14,1007.0,222.0,1.6,62.74695361986275 +2015-07-19 11:30:00-05:00,2015,7,19,11,30,30.9,22.3,130.0,846.0,920.0,0.14,1007.0,215.0,2.0,60.2412825155115 +2015-07-19 12:30:00-05:00,2015,7,19,12,30,31.200000000000003,22.3,130.0,847.0,922.0,0.14,1006.0,210.0,2.5,59.21957420723001 +2015-07-19 13:30:00-05:00,2015,7,19,13,30,31.200000000000003,22.200000000000003,139.0,817.0,866.0,0.14,1006.0,209.0,2.9000000000000004,58.860231008137305 +2015-07-19 14:30:00-05:00,2015,7,19,14,30,31.0,22.200000000000003,135.0,784.0,761.0,0.14,1005.0,210.0,3.3000000000000003,59.5350570572345 +2015-07-19 15:30:00-05:00,2015,7,19,15,30,30.6,22.200000000000003,127.0,729.0,614.0,0.14,1005.0,212.0,3.5,60.91105321755477 +2015-07-19 16:30:00-05:00,2015,7,19,16,30,29.9,22.4,192.0,433.0,413.0,0.14,1005.0,215.0,3.5,64.18249228748623 +2015-07-19 17:30:00-05:00,2015,7,19,17,30,28.700000000000003,22.8,164.0,170.0,221.0,0.14,1005.0,214.0,3.3000000000000003,70.47920316013769 +2015-07-19 18:30:00-05:00,2015,7,19,18,30,27.200000000000003,23.4,71.0,28.0,75.0,0.14,1005.0,213.0,3.2,79.76628914637874 +2015-07-19 19:30:00-05:00,2015,7,19,19,30,26.200000000000003,23.200000000000003,0.0,0.0,0.0,0.14,1005.0,215.0,3.1,83.58775479523354 +2015-07-19 20:30:00-05:00,2015,7,19,20,30,25.6,23.200000000000003,0.0,0.0,0.0,0.14,1005.0,218.0,3.1,86.6113911460552 +2015-07-19 21:30:00-05:00,2015,7,19,21,30,25.200000000000003,23.1,0.0,0.0,0.0,0.14,1005.0,221.0,3.0,88.16092351633488 +2015-07-19 22:30:00-05:00,2015,7,19,22,30,24.9,23.1,0.0,0.0,0.0,0.14,1005.0,228.0,2.9000000000000004,89.75141573189502 +2015-07-19 23:30:00-05:00,2015,7,19,23,30,24.700000000000003,23.0,0.0,0.0,0.0,0.14,1004.0,237.0,2.7,90.28182139089647 +2015-07-20 00:30:00-05:00,2015,7,20,0,30,24.5,23.0,0.0,0.0,0.0,0.14,1004.0,243.0,2.6,91.36808417317935 +2015-07-20 01:30:00-05:00,2015,7,20,1,30,24.4,22.8,0.0,0.0,0.0,0.14,1003.0,241.0,2.6,90.8100070382052 +2015-07-20 02:30:00-05:00,2015,7,20,2,30,24.3,22.700000000000003,0.0,0.0,0.0,0.14,1002.0,235.0,2.9000000000000004,90.80343725334605 +2015-07-20 03:30:00-05:00,2015,7,20,3,30,24.1,22.700000000000003,0.0,0.0,0.0,0.14,1002.0,238.0,3.0,91.89926877466728 +2015-07-20 04:30:00-05:00,2015,7,20,4,30,24.1,22.8,0.0,0.0,0.0,0.14,1003.0,252.0,2.6,92.45820085318411 +2015-07-20 05:30:00-05:00,2015,7,20,5,30,24.9,22.9,44.0,0.0,44.0,0.14,1003.0,265.0,2.2,88.67157778717231 +2015-07-20 06:30:00-05:00,2015,7,20,6,30,26.5,23.0,74.0,546.0,247.0,0.14,1004.0,278.0,1.8,81.13333451464908 +2015-07-20 07:30:00-05:00,2015,7,20,7,30,28.3,22.700000000000003,91.0,692.0,434.0,0.14,1003.0,289.0,1.5,71.70009896140965 +2015-07-20 08:30:00-05:00,2015,7,20,8,30,29.8,22.1,103.0,772.0,610.0,0.14,1003.0,280.0,1.2000000000000002,63.38474620448234 +2015-07-20 09:30:00-05:00,2015,7,20,9,30,30.8,21.8,112.0,819.0,758.0,0.14,1003.0,254.0,1.2000000000000002,58.76754970414354 +2015-07-20 10:30:00-05:00,2015,7,20,10,30,31.3,21.8,115.0,850.0,866.0,0.14,1002.0,232.0,1.5,57.11576903198099 +2015-07-20 11:30:00-05:00,2015,7,20,11,30,31.5,21.9,120.0,859.0,921.0,0.14,1002.0,221.0,1.9,56.81556659446966 +2015-07-20 12:30:00-05:00,2015,7,20,12,30,31.6,22.1,128.0,849.0,921.0,0.14,1002.0,216.0,2.2,57.18691359342319 +2015-07-20 13:30:00-05:00,2015,7,20,13,30,31.4,22.200000000000003,446.0,289.0,702.0,0.14,1001.0,213.0,2.4000000000000004,58.19402096429611 +2015-07-20 14:30:00-05:00,2015,7,20,14,30,31.0,22.5,166.0,728.0,746.0,0.14,1001.0,212.0,2.5,60.63128269788534 +2015-07-20 15:30:00-05:00,2015,7,20,15,30,30.5,22.8,255.0,0.0,255.0,0.14,1001.0,213.0,2.5,63.53478386806787 +2015-07-20 16:30:00-05:00,2015,7,20,16,30,29.8,23.1,143.0,555.0,425.0,0.14,1001.0,216.0,2.5,67.35167812377252 +2015-07-20 17:30:00-05:00,2015,7,20,17,30,28.8,23.4,165.0,151.0,214.0,0.14,1001.0,221.0,2.5,72.66100014838472 +2015-07-20 18:30:00-05:00,2015,7,20,18,30,27.6,23.8,28.0,0.0,28.0,0.14,1001.0,228.0,2.4000000000000004,79.81995065848324 +2015-07-20 19:30:00-05:00,2015,7,20,19,30,26.8,23.4,0.0,0.0,0.0,0.14,1002.0,238.0,2.2,81.66274432642336 +2015-07-20 20:30:00-05:00,2015,7,20,20,30,26.4,23.1,0.0,0.0,0.0,0.14,1002.0,252.0,2.1,82.1085421799944 +2015-07-20 21:30:00-05:00,2015,7,20,21,30,25.8,22.6,0.0,0.0,0.0,0.14,1002.0,267.0,1.7000000000000002,82.53549250098932 +2015-07-20 22:30:00-05:00,2015,7,20,22,30,25.3,22.3,0.0,0.0,0.0,0.14,1002.0,283.0,1.4,83.48655911737376 +2015-07-20 23:30:00-05:00,2015,7,20,23,30,24.8,22.0,0.0,0.0,0.0,0.14,1002.0,299.0,1.2000000000000002,84.45446053672441 +2015-07-21 00:30:00-05:00,2015,7,21,0,30,24.5,21.8,0.0,0.0,0.0,0.14,1001.0,311.0,1.0,84.93963272696895 +2015-07-21 01:30:00-05:00,2015,7,21,1,30,24.200000000000003,21.700000000000003,0.0,0.0,0.0,0.14,1001.0,315.0,0.9,85.95337922823713 +2015-07-21 02:30:00-05:00,2015,7,21,2,30,24.0,21.5,0.0,0.0,0.0,0.14,1001.0,310.0,0.8,85.93379377341454 +2015-07-21 03:30:00-05:00,2015,7,21,3,30,23.700000000000003,21.4,0.0,0.0,0.0,0.14,1001.0,302.0,0.8,86.96406149787474 +2015-07-21 04:30:00-05:00,2015,7,21,4,30,23.700000000000003,21.4,0.0,0.0,0.0,0.14,1001.0,297.0,0.7000000000000001,86.96406149787474 +2015-07-21 05:30:00-05:00,2015,7,21,5,30,24.200000000000003,21.8,48.0,5.0,48.0,0.14,1001.0,293.0,0.7000000000000001,86.48011646915204 +2015-07-21 06:30:00-05:00,2015,7,21,6,30,25.3,22.0,79.0,285.0,169.0,0.14,1001.0,284.0,0.7000000000000001,81.97484863603587 +2015-07-21 07:30:00-05:00,2015,7,21,7,30,27.1,22.1,146.0,1.0,146.0,0.14,1001.0,272.0,0.8,74.1538195270028 +2015-07-21 08:30:00-05:00,2015,7,21,8,30,28.700000000000003,21.1,292.0,216.0,434.0,0.14,1001.0,258.0,0.9,63.53635693282711 +2015-07-21 09:30:00-05:00,2015,7,21,9,30,29.6,19.9,298.0,409.0,621.0,0.14,1000.0,244.0,1.2000000000000002,56.011937464103184 +2015-07-21 10:30:00-05:00,2015,7,21,10,30,30.0,19.3,399.0,86.0,474.0,0.14,1000.0,237.0,1.5,52.73546662209652 +2015-07-21 11:30:00-05:00,2015,7,21,11,30,30.4,19.0,246.0,0.0,246.0,0.14,1000.0,234.0,1.9,50.58447473978287 +2015-07-21 12:30:00-05:00,2015,7,21,12,30,30.700000000000003,19.0,263.0,0.0,263.0,0.14,999.0,231.0,2.2,49.723436670506715 +2015-07-21 13:30:00-05:00,2015,7,21,13,30,30.8,19.3,421.0,66.0,480.0,0.14,999.0,227.0,2.4000000000000004,50.373019440025054 +2015-07-21 14:30:00-05:00,2015,7,21,14,30,30.6,19.6,361.0,31.0,386.0,0.14,999.0,224.0,2.6,51.91146833959956 +2015-07-21 15:30:00-05:00,2015,7,21,15,30,30.0,20.1,292.0,454.0,595.0,0.14,999.0,221.0,2.6,55.41940412589565 +2015-07-21 16:30:00-05:00,2015,7,21,16,30,29.3,20.6,194.0,21.0,205.0,0.14,999.0,221.0,2.7,59.50897335528702 +2015-07-21 17:30:00-05:00,2015,7,21,17,30,28.1,21.200000000000003,102.0,445.0,247.0,0.14,999.0,221.0,2.6,66.19681283636109 +2015-07-21 18:30:00-05:00,2015,7,21,18,30,26.6,22.0,52.0,207.0,82.0,0.14,999.0,224.0,2.4000000000000004,75.90250895113047 +2015-07-21 19:30:00-05:00,2015,7,21,19,30,25.4,22.0,0.0,0.0,0.0,0.14,1000.0,239.0,2.1,81.48881738368704 +2015-07-21 20:30:00-05:00,2015,7,21,20,30,24.700000000000003,21.8,0.0,0.0,0.0,0.14,1001.0,274.0,2.1,83.92979693357307 +2015-07-21 21:30:00-05:00,2015,7,21,21,30,23.9,21.3,0.0,0.0,0.0,0.14,1001.0,315.0,2.8000000000000003,85.39905628041207 +2015-07-21 22:30:00-05:00,2015,7,21,22,30,23.1,20.5,0.0,0.0,0.0,0.14,1001.0,338.0,4.0,85.31753861991703 +2015-07-21 23:30:00-05:00,2015,7,21,23,30,22.200000000000003,19.6,0.0,0.0,0.0,0.14,1002.0,343.0,4.4,85.22503814568502 +2015-07-22 00:30:00-05:00,2015,7,22,0,30,21.4,18.5,0.0,0.0,0.0,0.14,1002.0,339.0,4.3,83.5592885796014 +2015-07-22 01:30:00-05:00,2015,7,22,1,30,20.700000000000003,17.3,0.0,0.0,0.0,0.14,1002.0,334.0,4.2,80.88726534947205 +2015-07-22 02:30:00-05:00,2015,7,22,2,30,20.0,16.2,0.0,0.0,0.0,0.14,1002.0,330.0,4.2,78.76449726692503 +2015-07-22 03:30:00-05:00,2015,7,22,3,30,19.4,15.5,0.0,0.0,0.0,0.14,1003.0,326.0,4.1000000000000005,78.1754480731195 +2015-07-22 04:30:00-05:00,2015,7,22,4,30,19.3,15.1,0.0,0.0,0.0,0.14,1003.0,322.0,4.1000000000000005,76.66949846732982 +2015-07-22 05:30:00-05:00,2015,7,22,5,30,20.1,15.0,33.0,421.0,87.0,0.14,1004.0,322.0,4.3,72.48876581849257 +2015-07-22 06:30:00-05:00,2015,7,22,6,30,21.700000000000003,14.7,58.0,666.0,266.0,0.14,1004.0,328.0,4.4,64.43946085944748 +2015-07-22 07:30:00-05:00,2015,7,22,7,30,23.4,14.5,73.0,791.0,462.0,0.14,1005.0,332.0,4.2,57.373486792708235 +2015-07-22 08:30:00-05:00,2015,7,22,8,30,24.8,14.4,82.0,864.0,646.0,0.14,1005.0,328.0,3.8,52.4094293538372 +2015-07-22 09:30:00-05:00,2015,7,22,9,30,25.9,13.9,88.0,908.0,801.0,0.14,1005.0,318.0,3.4000000000000004,47.52678261683494 +2015-07-22 10:30:00-05:00,2015,7,22,10,30,26.700000000000003,13.6,94.0,928.0,911.0,0.14,1005.0,305.0,3.2,44.458967249413504 +2015-07-22 11:30:00-05:00,2015,7,22,11,30,27.200000000000003,13.6,97.0,940.0,971.0,0.14,1005.0,294.0,3.1,43.17167645111236 +2015-07-22 12:30:00-05:00,2015,7,22,12,30,27.6,14.0,96.0,938.0,971.0,0.14,1004.0,285.0,3.0,43.281762541277146 +2015-07-22 13:30:00-05:00,2015,7,22,13,30,27.700000000000003,14.5,464.0,273.0,705.0,0.14,1004.0,279.0,2.9000000000000004,44.44442665109294 +2015-07-22 14:30:00-05:00,2015,7,22,14,30,27.700000000000003,14.9,108.0,870.0,799.0,0.14,1004.0,275.0,2.8000000000000003,45.605698058621144 +2015-07-22 15:30:00-05:00,2015,7,22,15,30,27.4,15.1,275.0,498.0,606.0,0.14,1004.0,275.0,2.8000000000000003,47.01423164113743 +2015-07-22 16:30:00-05:00,2015,7,22,16,30,26.8,15.3,90.0,742.0,464.0,0.14,1004.0,280.0,2.7,49.32920603929335 +2015-07-22 17:30:00-05:00,2015,7,22,17,30,25.8,15.5,173.0,92.0,203.0,0.14,1005.0,289.0,2.5,53.00526752159875 +2015-07-22 18:30:00-05:00,2015,7,22,18,30,24.1,15.8,39.0,0.0,39.0,0.14,1006.0,301.0,2.4000000000000004,59.79911128921336 +2015-07-22 19:30:00-05:00,2015,7,22,19,30,22.700000000000003,15.5,0.0,0.0,0.0,0.14,1007.0,314.0,2.8000000000000003,63.83265647907378 +2015-07-22 20:30:00-05:00,2015,7,22,20,30,21.8,15.1,0.0,0.0,0.0,0.14,1008.0,324.0,3.1,65.71780100063405 +2015-07-22 21:30:00-05:00,2015,7,22,21,30,21.200000000000003,14.9,0.0,0.0,0.0,0.14,1009.0,328.0,3.0,67.30465886857267 +2015-07-22 22:30:00-05:00,2015,7,22,22,30,20.9,14.9,0.0,0.0,0.0,0.14,1009.0,329.0,2.8000000000000003,68.55629107048867 +2015-07-22 23:30:00-05:00,2015,7,22,23,30,20.5,14.9,0.0,0.0,0.0,0.14,1009.0,330.0,2.6,70.26600076618229 +2015-07-23 00:30:00-05:00,2015,7,23,0,30,20.200000000000003,15.0,0.0,0.0,0.0,0.14,1009.0,331.0,2.4000000000000004,72.04187987653192 +2015-07-23 01:30:00-05:00,2015,7,23,1,30,19.700000000000003,15.1,0.0,0.0,0.0,0.14,1009.0,331.0,2.4000000000000004,74.78709810399013 +2015-07-23 02:30:00-05:00,2015,7,23,2,30,19.1,15.0,0.0,0.0,0.0,0.14,1009.0,329.0,2.3000000000000003,77.13297630345755 +2015-07-23 03:30:00-05:00,2015,7,23,3,30,18.7,14.8,0.0,0.0,0.0,0.14,1009.0,326.0,2.2,78.07164495887675 +2015-07-23 04:30:00-05:00,2015,7,23,4,30,19.0,14.5,0.0,0.0,0.0,0.14,1010.0,321.0,2.1,75.15390064047922 +2015-07-23 05:30:00-05:00,2015,7,23,5,30,19.9,14.6,36.0,355.0,81.0,0.14,1010.0,314.0,2.1,71.52453765245241 +2015-07-23 06:30:00-05:00,2015,7,23,6,30,21.4,14.5,65.0,617.0,256.0,0.14,1010.0,315.0,2.4000000000000004,64.79158790157867 +2015-07-23 07:30:00-05:00,2015,7,23,7,30,23.1,13.8,82.0,756.0,452.0,0.14,1011.0,318.0,2.4000000000000004,55.83347366163821 +2015-07-23 08:30:00-05:00,2015,7,23,8,30,24.5,12.8,91.0,835.0,636.0,0.14,1011.0,307.0,2.1,48.08452129294541 +2015-07-23 09:30:00-05:00,2015,7,23,9,30,25.5,12.3,98.0,884.0,791.0,0.14,1010.0,284.0,2.1,43.84022934556657 +2015-07-23 10:30:00-05:00,2015,7,23,10,30,26.3,12.3,98.0,919.0,906.0,0.14,1010.0,265.0,2.2,41.81155554710435 +2015-07-23 11:30:00-05:00,2015,7,23,11,30,26.9,12.8,102.0,929.0,964.0,0.14,1010.0,253.0,2.3000000000000003,41.704489575316785 +2015-07-23 12:30:00-05:00,2015,7,23,12,30,27.3,13.2,103.0,925.0,964.0,0.14,1009.0,243.0,2.4000000000000004,41.8154549141264 +2015-07-23 13:30:00-05:00,2015,7,23,13,30,27.6,13.5,103.0,910.0,908.0,0.14,1009.0,235.0,2.4000000000000004,41.898539905911534 +2015-07-23 14:30:00-05:00,2015,7,23,14,30,27.6,13.7,291.0,592.0,760.0,0.14,1008.0,230.0,2.5,42.44708799613203 +2015-07-23 15:30:00-05:00,2015,7,23,15,30,27.3,13.9,93.0,836.0,647.0,0.14,1008.0,228.0,2.5,43.763909458417736 +2015-07-23 16:30:00-05:00,2015,7,23,16,30,26.700000000000003,14.1,83.0,760.0,465.0,0.14,1008.0,231.0,2.4000000000000004,45.9255577430423 +2015-07-23 17:30:00-05:00,2015,7,23,17,30,25.5,14.5,65.0,636.0,271.0,0.14,1008.0,236.0,2.1,50.59528632847579 +2015-07-23 18:30:00-05:00,2015,7,23,18,30,23.6,16.0,37.0,402.0,93.0,0.14,1009.0,244.0,1.9,62.416936011451945 +2015-07-23 19:30:00-05:00,2015,7,23,19,30,22.1,16.0,0.0,0.0,0.0,0.15,1009.0,261.0,1.9,68.35555100357858 +2015-07-23 20:30:00-05:00,2015,7,23,20,30,21.4,15.7,0.0,0.0,0.0,0.15,1010.0,285.0,2.0,69.98744152646606 +2015-07-23 21:30:00-05:00,2015,7,23,21,30,20.8,15.5,0.0,0.0,0.0,0.15,1010.0,309.0,2.1,71.6896503386998 +2015-07-23 22:30:00-05:00,2015,7,23,22,30,20.4,15.3,0.0,0.0,0.0,0.15,1010.0,324.0,2.1,72.54239732641166 +2015-07-23 23:30:00-05:00,2015,7,23,23,30,20.1,15.0,0.0,0.0,0.0,0.15,1010.0,331.0,2.0,72.48876581849257 +2015-07-24 00:30:00-05:00,2015,7,24,0,30,19.8,14.9,0.0,0.0,0.0,0.15,1010.0,332.0,1.9,73.37446534430683 +2015-07-24 01:30:00-05:00,2015,7,24,1,30,19.5,14.9,0.0,0.0,0.0,0.15,1010.0,328.0,1.7000000000000002,74.75369401731483 +2015-07-24 02:30:00-05:00,2015,7,24,2,30,19.200000000000003,15.1,0.0,0.0,0.0,0.15,1010.0,324.0,1.7000000000000002,77.14836978029264 +2015-07-24 03:30:00-05:00,2015,7,24,3,30,19.0,15.4,0.0,0.0,0.0,0.15,1010.0,328.0,1.7000000000000002,79.63551875037689 +2015-07-24 04:30:00-05:00,2015,7,24,4,30,19.200000000000003,15.6,0.0,0.0,0.0,0.15,1011.0,340.0,1.9,79.66336527860321 +2015-07-24 05:30:00-05:00,2015,7,24,5,30,20.200000000000003,15.7,38.0,305.0,76.0,0.15,1011.0,174.0,2.1,75.35111927270782 +2015-07-24 06:30:00-05:00,2015,7,24,6,30,22.1,15.6,72.0,578.0,250.0,0.15,1011.0,6.0,2.2,66.62956774880828 +2015-07-24 07:30:00-05:00,2015,7,24,7,30,24.1,14.6,92.0,721.0,444.0,0.15,1011.0,12.0,2.2,55.362944080021315 +2015-07-24 08:30:00-05:00,2015,7,24,8,30,25.3,13.9,105.0,800.0,625.0,0.15,1011.0,11.0,1.9,49.24988904851938 +2015-07-24 09:30:00-05:00,2015,7,24,9,30,26.1,13.8,115.0,847.0,778.0,0.15,1011.0,184.0,1.3,46.66369393158761 +2015-07-24 10:30:00-05:00,2015,7,24,10,30,26.700000000000003,14.0,109.0,892.0,892.0,0.15,1010.0,336.0,0.7000000000000001,45.62888037608103 +2015-07-24 11:30:00-05:00,2015,7,24,11,30,27.3,13.5,116.0,899.0,949.0,0.15,1010.0,262.0,0.4,42.6409507541312 +2015-07-24 12:30:00-05:00,2015,7,24,12,30,27.6,13.2,488.0,260.0,729.0,0.15,1010.0,199.0,0.7000000000000001,41.08741656595519 +2015-07-24 13:30:00-05:00,2015,7,24,13,30,27.6,13.3,427.0,363.0,747.0,0.15,1009.0,187.0,1.1,41.356241342992305 +2015-07-24 14:30:00-05:00,2015,7,24,14,30,27.5,13.4,250.0,0.0,250.0,0.15,1009.0,186.0,1.3,41.8708581530958 +2015-07-24 15:30:00-05:00,2015,7,24,15,30,27.200000000000003,13.5,320.0,324.0,534.0,0.15,1009.0,187.0,1.4,42.89170463061838 +2015-07-24 16:30:00-05:00,2015,7,24,16,30,26.8,13.9,102.0,700.0,453.0,0.15,1009.0,190.0,1.6,45.06787904794942 +2015-07-24 17:30:00-05:00,2015,7,24,17,30,25.8,14.3,170.0,99.0,202.0,0.15,1010.0,195.0,1.6,49.064295125355706 +2015-07-24 18:30:00-05:00,2015,7,24,18,30,24.4,15.9,44.0,311.0,86.0,0.15,1010.0,198.0,1.7000000000000002,59.10968227987573 +2015-07-24 19:30:00-05:00,2015,7,24,19,30,23.200000000000003,16.400000000000002,0.0,0.0,0.0,0.14,1010.0,199.0,2.0,65.59295448507187 +2015-07-24 20:30:00-05:00,2015,7,24,20,30,22.4,16.2,0.0,0.0,0.0,0.14,1011.0,206.0,2.2,67.9805031616027 +2015-07-24 21:30:00-05:00,2015,7,24,21,30,21.700000000000003,16.400000000000002,0.0,0.0,0.0,0.14,1011.0,218.0,2.3000000000000003,71.85344005184947 +2015-07-24 22:30:00-05:00,2015,7,24,22,30,21.0,16.7,0.0,0.0,0.0,0.14,1011.0,232.0,2.3000000000000003,76.4467521511523 +2015-07-24 23:30:00-05:00,2015,7,24,23,30,20.4,16.900000000000002,0.0,0.0,0.0,0.14,1011.0,248.0,2.2,80.33719518059269 +2015-07-25 00:30:00-05:00,2015,7,25,0,30,19.9,16.900000000000002,0.0,0.0,0.0,0.14,1011.0,270.0,2.1,82.8600174326897 +2015-07-25 01:30:00-05:00,2015,7,25,1,30,19.4,16.7,0.0,0.0,0.0,0.14,1011.0,295.0,1.9,84.39443529770787 +2015-07-25 02:30:00-05:00,2015,7,25,2,30,19.0,16.6,0.0,0.0,0.0,0.14,1011.0,318.0,1.9,85.97573902310485 +2015-07-25 03:30:00-05:00,2015,7,25,3,30,18.7,16.5,0.0,0.0,0.0,0.14,1011.0,339.0,1.9,87.04672541951784 +2015-07-25 04:30:00-05:00,2015,7,25,4,30,18.9,16.3,0.0,0.0,0.0,0.14,1011.0,176.0,2.0,84.87852676807746 +2015-07-25 05:30:00-05:00,2015,7,25,5,30,20.1,16.3,33.0,376.0,79.0,0.14,1012.0,8.0,2.1,78.77889078701952 +2015-07-25 06:30:00-05:00,2015,7,25,6,30,22.5,16.3,60.0,639.0,255.0,0.14,1012.0,21.0,2.0,68.00049976573986 +2015-07-25 07:30:00-05:00,2015,7,25,7,30,25.1,15.6,76.0,768.0,449.0,0.14,1013.0,33.0,1.8,55.61166822947315 +2015-07-25 08:30:00-05:00,2015,7,25,8,30,26.700000000000003,15.2,88.0,837.0,630.0,0.14,1013.0,46.0,1.4,49.30278649068005 +2015-07-25 09:30:00-05:00,2015,7,25,9,30,27.700000000000003,14.7,99.0,876.0,783.0,0.14,1013.0,75.0,1.2000000000000002,45.02176885754707 +2015-07-25 10:30:00-05:00,2015,7,25,10,30,28.200000000000003,14.7,109.0,891.0,890.0,0.14,1012.0,112.0,1.3,43.72764618493572 +2015-07-25 11:30:00-05:00,2015,7,25,11,30,28.5,15.1,120.0,890.0,944.0,0.14,1012.0,138.0,1.7000000000000002,44.09233046518461 +2015-07-25 12:30:00-05:00,2015,7,25,12,30,28.5,15.5,130.0,875.0,941.0,0.14,1012.0,148.0,2.2,45.238987969947026 +2015-07-25 13:30:00-05:00,2015,7,25,13,30,28.3,15.8,128.0,858.0,884.0,0.14,1011.0,153.0,2.5,46.655454982497716 +2015-07-25 14:30:00-05:00,2015,7,25,14,30,27.9,16.2,129.0,816.0,773.0,0.14,1011.0,155.0,2.7,48.99074329366922 +2015-07-25 15:30:00-05:00,2015,7,25,15,30,27.4,16.5,121.0,758.0,621.0,0.14,1011.0,156.0,2.8000000000000003,51.416602730057974 +2015-07-25 16:30:00-05:00,2015,7,25,16,30,26.700000000000003,16.8,107.0,672.0,442.0,0.14,1011.0,157.0,2.8000000000000003,54.60475414191785 +2015-07-25 17:30:00-05:00,2015,7,25,17,30,25.6,17.1,81.0,535.0,252.0,0.14,1012.0,154.0,2.8000000000000003,59.39190072233134 +2015-07-25 18:30:00-05:00,2015,7,25,18,30,24.1,17.900000000000002,43.0,292.0,82.0,0.14,1012.0,151.0,2.6,68.31814758738126 +2015-07-25 19:30:00-05:00,2015,7,25,19,30,22.9,18.5,0.0,0.0,0.0,0.14,1012.0,150.0,2.4000000000000004,76.26314936385988 +2015-07-25 20:30:00-05:00,2015,7,25,20,30,22.3,19.1,0.0,0.0,0.0,0.14,1012.0,154.0,2.3000000000000003,82.11330312488106 +2015-07-25 21:30:00-05:00,2015,7,25,21,30,21.8,19.5,0.0,0.0,0.0,0.14,1012.0,164.0,2.1,86.78856006370899 +2015-07-25 22:30:00-05:00,2015,7,25,22,30,21.3,19.700000000000003,0.0,0.0,0.0,0.14,1012.0,179.0,2.0,90.60308127816432 +2015-07-25 23:30:00-05:00,2015,7,25,23,30,20.9,19.8,0.0,0.0,0.0,0.14,1012.0,195.0,1.9,93.43344547560373 +2015-07-26 00:30:00-05:00,2015,7,26,0,30,20.6,19.700000000000003,0.0,0.0,0.0,0.14,1012.0,208.0,2.0,94.5864469096051 +2015-07-26 01:30:00-05:00,2015,7,26,1,30,20.4,19.5,0.0,0.0,0.0,0.14,1012.0,215.0,2.3000000000000003,94.57843733317726 +2015-07-26 02:30:00-05:00,2015,7,26,2,30,20.4,19.3,0.0,0.0,0.0,0.14,1011.0,219.0,2.5,93.40928587534727 +2015-07-26 03:30:00-05:00,2015,7,26,3,30,20.3,19.200000000000003,0.0,0.0,0.0,0.14,1011.0,219.0,2.7,93.40443815225615 +2015-07-26 04:30:00-05:00,2015,7,26,4,30,20.6,19.3,0.0,0.0,0.0,0.14,1012.0,214.0,2.9000000000000004,92.26414709561166 +2015-07-26 05:30:00-05:00,2015,7,26,5,30,21.6,19.5,45.0,4.0,46.0,0.14,1012.0,211.0,3.0,87.8559357612692 +2015-07-26 06:30:00-05:00,2015,7,26,6,30,23.1,19.700000000000003,128.0,5.0,129.0,0.14,1012.0,210.0,3.3000000000000003,81.19789176646812 +2015-07-26 07:30:00-05:00,2015,7,26,7,30,24.9,19.200000000000003,264.0,87.0,306.0,0.14,1012.0,211.0,3.6,70.64574180258202 +2015-07-26 08:30:00-05:00,2015,7,26,8,30,26.4,18.7,154.0,668.0,586.0,0.14,1012.0,208.0,3.7,62.64427302305153 +2015-07-26 09:30:00-05:00,2015,7,26,9,30,27.3,18.7,174.0,716.0,732.0,0.14,1012.0,204.0,3.9,59.41491548605036 +2015-07-26 10:30:00-05:00,2015,7,26,10,30,27.9,19.0,442.0,331.0,731.0,0.14,1012.0,201.0,4.2,58.45122135327762 +2015-07-26 11:30:00-05:00,2015,7,26,11,30,28.200000000000003,19.3,198.0,754.0,895.0,0.14,1012.0,199.0,4.4,58.52183201001478 +2015-07-26 12:30:00-05:00,2015,7,26,12,30,28.4,19.700000000000003,199.0,751.0,896.0,0.14,1012.0,199.0,4.5,59.30077072848389 +2015-07-26 13:30:00-05:00,2015,7,26,13,30,28.3,19.9,385.0,491.0,818.0,0.14,1011.0,199.0,4.6000000000000005,60.39094382235289 +2015-07-26 14:30:00-05:00,2015,7,26,14,30,28.1,20.200000000000003,222.0,636.0,724.0,0.14,1011.0,199.0,4.7,62.24324040363121 +2015-07-26 15:30:00-05:00,2015,7,26,15,30,27.700000000000003,20.5,310.0,252.0,477.0,0.14,1011.0,199.0,4.7,64.9043599483896 +2015-07-26 16:30:00-05:00,2015,7,26,16,30,27.1,20.6,158.0,508.0,411.0,0.14,1011.0,199.0,4.7,67.6414692656499 +2015-07-26 17:30:00-05:00,2015,7,26,17,30,26.1,20.8,154.0,50.0,170.0,0.14,1011.0,199.0,4.4,72.63555629465829 +2015-07-26 18:30:00-05:00,2015,7,26,18,30,25.0,21.0,49.0,162.0,71.0,0.14,1012.0,198.0,4.1000000000000005,78.49764721655076 +2015-07-26 19:30:00-05:00,2015,7,26,19,30,24.1,21.200000000000003,0.0,0.0,0.0,0.14,1012.0,198.0,3.9,83.86333678899838 +2015-07-26 20:30:00-05:00,2015,7,26,20,30,23.700000000000003,21.3,0.0,0.0,0.0,0.14,1013.0,199.0,3.8,86.43277758035049 +2015-07-26 21:30:00-05:00,2015,7,26,21,30,23.3,21.3,0.0,0.0,0.0,0.14,1013.0,201.0,3.7,88.54270976265246 +2015-07-26 22:30:00-05:00,2015,7,26,22,30,23.1,21.3,0.0,0.0,0.0,0.14,1013.0,202.0,3.6,89.61934916670113 +2015-07-26 23:30:00-05:00,2015,7,26,23,30,23.0,21.3,0.0,0.0,0.0,0.14,1012.0,205.0,3.5,90.16318267405795 +2015-07-27 00:30:00-05:00,2015,7,27,0,30,22.8,21.3,0.0,0.0,0.0,0.14,1012.0,209.0,3.4000000000000004,91.2620162268376 +2015-07-27 01:30:00-05:00,2015,7,27,1,30,22.6,21.200000000000003,0.0,0.0,0.0,0.14,1012.0,211.0,3.1,91.81115429820348 +2015-07-27 02:30:00-05:00,2015,7,27,2,30,22.4,21.3,0.0,0.0,0.0,0.14,1012.0,214.0,3.0,93.5051419846617 +2015-07-27 03:30:00-05:00,2015,7,27,3,30,22.3,21.3,0.0,0.0,0.0,0.14,1012.0,216.0,3.0,94.07556181529573 +2015-07-27 04:30:00-05:00,2015,7,27,4,30,22.200000000000003,21.5,0.0,0.0,0.0,0.14,1012.0,215.0,2.7,95.81661333454173 +2015-07-27 05:30:00-05:00,2015,7,27,5,30,22.5,21.700000000000003,32.0,0.0,32.0,0.14,1013.0,211.0,2.4000000000000004,95.24221196933372 +2015-07-27 06:30:00-05:00,2015,7,27,6,30,23.200000000000003,21.9,117.0,8.0,119.0,0.14,1013.0,206.0,2.2,92.40899469716274 +2015-07-27 07:30:00-05:00,2015,7,27,7,30,24.1,22.200000000000003,165.0,9.0,170.0,0.14,1013.0,201.0,2.2,89.14877065654245 +2015-07-27 08:30:00-05:00,2015,7,27,8,30,24.8,21.700000000000003,288.0,17.0,299.0,0.14,1013.0,199.0,2.5,82.92178985776674 +2015-07-27 09:30:00-05:00,2015,7,27,9,30,25.4,21.200000000000003,354.0,48.0,392.0,0.14,1013.0,195.0,2.7,77.59744380650204 +2015-07-27 10:30:00-05:00,2015,7,27,10,30,25.8,21.0,290.0,583.0,799.0,0.14,1013.0,191.0,2.9000000000000004,74.85200261699906 +2015-07-27 11:30:00-05:00,2015,7,27,11,30,26.200000000000003,21.0,296.0,606.0,856.0,0.14,1013.0,189.0,2.9000000000000004,73.10091983463201 +2015-07-27 12:30:00-05:00,2015,7,27,12,30,26.6,21.1,284.0,625.0,861.0,0.14,1013.0,186.0,3.0,71.83567150828277 +2015-07-27 13:30:00-05:00,2015,7,27,13,30,26.9,21.200000000000003,192.0,732.0,835.0,0.14,1012.0,183.0,3.0,71.01300754784374 +2015-07-27 14:30:00-05:00,2015,7,27,14,30,27.1,21.3,179.0,705.0,733.0,0.14,1012.0,181.0,3.0,70.61532733020871 +2015-07-27 15:30:00-05:00,2015,7,27,15,30,27.0,21.5,158.0,658.0,590.0,0.14,1012.0,181.0,3.1,71.90678209614686 +2015-07-27 16:30:00-05:00,2015,7,27,16,30,26.6,21.700000000000003,139.0,555.0,414.0,0.14,1012.0,183.0,3.2,74.52503819127485 +2015-07-27 17:30:00-05:00,2015,7,27,17,30,25.8,22.0,107.0,391.0,230.0,0.14,1012.0,187.0,3.1,79.57685932121313 +2015-07-27 18:30:00-05:00,2015,7,27,18,30,24.9,22.3,49.0,153.0,69.0,0.14,1012.0,190.0,3.0,85.5000311395926 +2015-07-27 19:30:00-05:00,2015,7,27,19,30,24.200000000000003,22.5,0.0,0.0,0.0,0.14,1012.0,195.0,2.9000000000000004,90.24714719552352 +2015-07-27 20:30:00-05:00,2015,7,27,20,30,23.8,22.6,0.0,0.0,0.0,0.14,1013.0,201.0,3.0,93.0049330719794 +2015-07-27 21:30:00-05:00,2015,7,27,21,30,23.4,22.700000000000003,0.0,0.0,0.0,0.14,1013.0,207.0,3.0,95.8534688029577 +2015-07-27 22:30:00-05:00,2015,7,27,22,30,23.1,22.700000000000003,0.0,0.0,0.0,0.14,1013.0,213.0,2.9000000000000004,97.60641098320963 +2015-07-27 23:30:00-05:00,2015,7,27,23,30,22.8,22.6,0.0,0.0,0.0,0.14,1013.0,220.0,2.8000000000000003,98.79415533153052 +2015-07-28 00:30:00-05:00,2015,7,28,0,30,22.5,22.4,0.0,0.0,0.0,0.14,1013.0,228.0,2.6,99.39411310041719 +2015-07-28 01:30:00-05:00,2015,7,28,1,30,22.200000000000003,22.1,0.0,0.0,0.0,0.14,1012.0,237.0,2.3000000000000003,99.39274566148045 +2015-07-28 02:30:00-05:00,2015,7,28,2,30,21.8,21.8,0.0,0.0,0.0,0.14,1012.0,248.0,2.0,100.0 +2015-07-28 03:30:00-05:00,2015,7,28,3,30,21.4,21.200000000000003,0.0,0.0,0.0,0.14,1012.0,262.0,1.8,98.78142704047978 +2015-07-28 04:30:00-05:00,2015,7,28,4,30,21.4,20.700000000000003,0.0,0.0,0.0,0.14,1012.0,274.0,1.6,95.79177141479806 +2015-07-28 05:30:00-05:00,2015,7,28,5,30,22.1,20.4,40.0,195.0,62.0,0.14,1012.0,278.0,1.5,90.09950943870936 +2015-07-28 06:30:00-05:00,2015,7,28,6,30,23.8,20.3,86.0,475.0,228.0,0.14,1012.0,277.0,1.3,80.78675709265542 +2015-07-28 07:30:00-05:00,2015,7,28,7,30,26.4,20.4,108.0,644.0,417.0,0.14,1012.0,288.0,0.9,69.62209740556841 +2015-07-28 08:30:00-05:00,2015,7,28,8,30,28.6,19.6,123.0,739.0,598.0,0.14,1012.0,300.0,0.6000000000000001,58.253008778335165 +2015-07-28 09:30:00-05:00,2015,7,28,9,30,29.9,18.9,132.0,796.0,751.0,0.14,1012.0,275.0,0.3,51.733300440086744 +2015-07-28 10:30:00-05:00,2015,7,28,10,30,30.8,18.5,139.0,827.0,860.0,0.14,1012.0,226.0,0.4,47.91892153763767 +2015-07-28 11:30:00-05:00,2015,7,28,11,30,31.4,18.3,191.0,713.0,849.0,0.14,1011.0,199.0,0.7000000000000001,45.73104196282722 +2015-07-28 12:30:00-05:00,2015,7,28,12,30,31.8,18.3,139.0,847.0,921.0,0.14,1011.0,194.0,1.1,44.70390784820042 +2015-07-28 13:30:00-05:00,2015,7,28,13,30,31.700000000000003,18.4,138.0,828.0,865.0,0.14,1011.0,195.0,1.5,45.24097775738285 +2015-07-28 14:30:00-05:00,2015,7,28,14,30,31.5,18.6,136.0,792.0,758.0,0.14,1011.0,195.0,1.7000000000000002,46.33446035565245 +2015-07-28 15:30:00-05:00,2015,7,28,15,30,31.1,18.9,126.0,736.0,608.0,0.14,1011.0,196.0,1.9,48.29861962670455 +2015-07-28 16:30:00-05:00,2015,7,28,16,30,30.6,19.3,112.0,642.0,429.0,0.14,1010.0,198.0,2.1,50.95223924737436 +2015-07-28 17:30:00-05:00,2015,7,28,17,30,29.4,19.8,86.0,494.0,241.0,0.14,1011.0,200.0,2.1,56.311604957717336 +2015-07-28 18:30:00-05:00,2015,7,28,18,30,27.700000000000003,21.0,42.0,246.0,74.0,0.14,1011.0,204.0,2.2,66.9331415817581 +2015-07-28 19:30:00-05:00,2015,7,28,19,30,26.4,21.0,0.0,0.0,0.0,0.14,1011.0,212.0,2.4000000000000004,72.24270469942107 +2015-07-28 20:30:00-05:00,2015,7,28,20,30,25.6,21.3,0.0,0.0,0.0,0.14,1012.0,221.0,2.6,77.152734961673 +2015-07-28 21:30:00-05:00,2015,7,28,21,30,24.9,21.6,0.0,0.0,0.0,0.14,1012.0,229.0,2.7,81.9258760867509 +2015-07-28 22:30:00-05:00,2015,7,28,22,30,24.3,21.6,0.0,0.0,0.0,0.14,1012.0,238.0,2.6,84.91877703933011 +2015-07-28 23:30:00-05:00,2015,7,28,23,30,23.9,21.4,0.0,0.0,0.0,0.14,1011.0,248.0,2.5,85.92398613276295 +2015-07-29 00:30:00-05:00,2015,7,29,0,30,23.5,21.200000000000003,0.0,0.0,0.0,0.14,1011.0,255.0,2.4000000000000004,86.945748446623 +2015-07-29 01:30:00-05:00,2015,7,29,1,30,23.200000000000003,21.0,0.0,0.0,0.0,0.14,1011.0,259.0,2.3000000000000003,87.45410139763736 +2015-07-29 02:30:00-05:00,2015,7,29,2,30,22.8,20.8,0.0,0.0,0.0,0.14,1011.0,265.0,2.3000000000000003,88.50199771739551 +2015-07-29 03:30:00-05:00,2015,7,29,3,30,22.5,20.5,0.0,0.0,0.0,0.14,1011.0,275.0,2.2,88.47746864720834 +2015-07-29 04:30:00-05:00,2015,7,29,4,30,22.9,20.200000000000003,0.0,0.0,0.0,0.14,1012.0,288.0,2.1,84.7716062208146 +2015-07-29 05:30:00-05:00,2015,7,29,5,30,24.1,20.200000000000003,45.0,1.0,45.0,0.14,1012.0,301.0,1.8,78.85463981040904 +2015-07-29 06:30:00-05:00,2015,7,29,6,30,26.1,20.1,74.0,521.0,229.0,0.14,1012.0,311.0,1.4,69.56524811267175 +2015-07-29 07:30:00-05:00,2015,7,29,7,30,28.5,20.200000000000003,251.0,149.0,322.0,0.14,1012.0,313.0,0.9,60.81142198386694 +2015-07-29 08:30:00-05:00,2015,7,29,8,30,30.4,18.7,267.0,483.0,577.0,0.14,1012.0,290.0,0.8,49.64551829418569 +2015-07-29 09:30:00-05:00,2015,7,29,9,30,31.200000000000003,17.8,355.0,383.0,652.0,0.14,1012.0,249.0,0.9,44.82388081012556 +2015-07-29 10:30:00-05:00,2015,7,29,10,30,31.6,17.8,250.0,577.0,752.0,0.14,1011.0,222.0,1.2000000000000002,43.815670564173665 +2015-07-29 11:30:00-05:00,2015,7,29,11,30,31.700000000000003,18.1,214.0,629.0,792.0,0.14,1011.0,211.0,1.7000000000000002,44.397383157289354 +2015-07-29 12:30:00-05:00,2015,7,29,12,30,31.9,18.5,337.0,578.0,870.0,0.14,1011.0,207.0,2.2,45.01187037948576 +2015-07-29 13:30:00-05:00,2015,7,29,13,30,32.0,18.8,209.0,705.0,826.0,0.14,1010.0,207.0,2.6,45.60543175171839 +2015-07-29 14:30:00-05:00,2015,7,29,14,30,31.700000000000003,19.1,334.0,509.0,732.0,0.14,1010.0,208.0,3.0,47.264308482620486 +2015-07-29 15:30:00-05:00,2015,7,29,15,30,31.1,19.4,252.0,451.0,546.0,0.14,1010.0,208.0,3.3000000000000003,49.82711035958983 +2015-07-29 16:30:00-05:00,2015,7,29,16,30,30.200000000000003,19.700000000000003,141.0,550.0,411.0,0.14,1010.0,208.0,3.5,53.445556569290076 +2015-07-29 17:30:00-05:00,2015,7,29,17,30,28.8,20.4,105.0,391.0,227.0,0.14,1010.0,206.0,3.7,60.50477115395405 +2015-07-29 18:30:00-05:00,2015,7,29,18,30,27.1,21.5,44.0,161.0,64.0,0.14,1010.0,207.0,3.7,71.48577902352703 +2015-07-29 19:30:00-05:00,2015,7,29,19,30,26.0,22.200000000000003,0.0,0.0,0.0,0.14,1010.0,209.0,3.6,79.60407546916551 +2015-07-29 20:30:00-05:00,2015,7,29,20,30,25.4,22.8,0.0,0.0,0.0,0.14,1010.0,213.0,3.4000000000000004,85.5501381158545 +2015-07-29 21:30:00-05:00,2015,7,29,21,30,24.9,23.3,0.0,0.0,0.0,0.14,1010.0,216.0,3.2,90.84275245521349 +2015-07-29 22:30:00-05:00,2015,7,29,22,30,24.6,23.5,0.0,0.0,0.0,0.14,1010.0,218.0,3.1,93.60821610516692 +2015-07-29 23:30:00-05:00,2015,7,29,23,30,24.4,23.700000000000003,0.0,0.0,0.0,0.14,1010.0,220.0,3.1,95.88381361660637 +2015-07-30 00:30:00-05:00,2015,7,30,0,30,24.3,23.700000000000003,0.0,0.0,0.0,0.14,1010.0,224.0,3.2,96.46000068137023 +2015-07-30 01:30:00-05:00,2015,7,30,1,30,24.3,23.700000000000003,0.0,0.0,0.0,0.14,1010.0,229.0,3.4000000000000004,96.46000068137023 +2015-07-30 02:30:00-05:00,2015,7,30,2,30,24.3,23.5,0.0,0.0,0.0,0.14,1009.0,232.0,3.4000000000000004,95.3046346207208 +2015-07-30 03:30:00-05:00,2015,7,30,3,30,24.200000000000003,23.4,0.0,0.0,0.0,0.14,1009.0,232.0,3.4000000000000004,95.30119873956589 +2015-07-30 04:30:00-05:00,2015,7,30,4,30,24.200000000000003,23.4,0.0,0.0,0.0,0.14,1010.0,231.0,3.4000000000000004,95.30119873956589 +2015-07-30 05:30:00-05:00,2015,7,30,5,30,24.700000000000003,23.4,26.0,0.0,26.0,0.14,1010.0,227.0,3.4000000000000004,92.49074335591357 +2015-07-30 06:30:00-05:00,2015,7,30,6,30,25.700000000000003,23.5,98.0,384.0,212.0,0.14,1010.0,223.0,3.4000000000000004,87.67237506204606 +2015-07-30 07:30:00-05:00,2015,7,30,7,30,26.5,23.4,27.0,0.0,27.0,0.14,1009.0,218.0,3.4000000000000004,83.11842079163638 +2015-07-30 08:30:00-05:00,2015,7,30,8,30,27.200000000000003,23.3,72.0,0.0,72.0,0.14,1009.0,214.0,3.5,79.2862459233431 +2015-07-30 09:30:00-05:00,2015,7,30,9,30,27.9,23.3,333.0,57.0,376.0,0.14,1009.0,210.0,3.7,76.1048508172108 +2015-07-30 10:30:00-05:00,2015,7,30,10,30,28.6,23.3,358.0,231.0,558.0,0.14,1008.0,206.0,4.0,73.06653074256575 +2015-07-30 11:30:00-05:00,2015,7,30,11,30,29.1,23.4,417.0,232.0,630.0,0.14,1008.0,203.0,4.4,71.40971242678879 +2015-07-30 12:30:00-05:00,2015,7,30,12,30,29.3,23.5,453.0,110.0,554.0,0.14,1007.0,201.0,4.800000000000001,71.01607939994165 +2015-07-30 13:30:00-05:00,2015,7,30,13,30,29.0,23.700000000000003,423.0,197.0,595.0,0.14,1007.0,200.0,5.0,73.13460789145613 +2015-07-30 14:30:00-05:00,2015,7,30,14,30,28.3,24.0,352.0,52.0,393.0,0.14,1006.0,199.0,5.1000000000000005,77.55322979898504 +2015-07-30 15:30:00-05:00,2015,7,30,15,30,27.3,24.5,87.0,0.0,87.0,0.14,1006.0,198.0,4.9,84.71893447945013 +2015-07-30 16:30:00-05:00,2015,7,30,16,30,26.4,24.8,231.0,85.0,272.0,0.14,1006.0,199.0,4.7,90.93996352399876 +2015-07-30 17:30:00-05:00,2015,7,30,17,30,25.8,24.9,38.0,0.0,38.0,0.14,1006.0,203.0,4.4,94.78867673105132 +2015-07-30 18:30:00-05:00,2015,7,30,18,30,25.200000000000003,25.0,12.0,0.0,12.0,0.14,1006.0,207.0,4.0,98.8155168943313 +2015-07-30 19:30:00-05:00,2015,7,30,19,30,24.8,24.8,0.0,0.0,0.0,0.14,1007.0,214.0,3.7,100.0 +2015-07-30 20:30:00-05:00,2015,7,30,20,30,24.5,24.5,0.0,0.0,0.0,0.14,1007.0,226.0,3.4000000000000004,100.0 +2015-07-30 21:30:00-05:00,2015,7,30,21,30,24.200000000000003,24.200000000000003,0.0,0.0,0.0,0.14,1007.0,244.0,2.9000000000000004,100.0 +2015-07-30 22:30:00-05:00,2015,7,30,22,30,23.8,23.8,0.0,0.0,0.0,0.14,1007.0,262.0,2.4000000000000004,100.0 +2015-07-30 23:30:00-05:00,2015,7,30,23,30,23.3,23.3,0.0,0.0,0.0,0.14,1007.0,281.0,2.3000000000000003,100.0 +2015-07-31 00:30:00-05:00,2015,7,31,0,30,22.6,22.6,0.0,0.0,0.0,0.14,1007.0,298.0,2.3000000000000003,100.0 +2015-07-31 01:30:00-05:00,2015,7,31,1,30,21.8,21.8,0.0,0.0,0.0,0.14,1007.0,311.0,2.5,100.0 +2015-07-31 02:30:00-05:00,2015,7,31,2,30,21.1,21.1,0.0,0.0,0.0,0.14,1007.0,321.0,2.7,100.0 +2015-07-31 03:30:00-05:00,2015,7,31,3,30,20.5,20.200000000000003,0.0,0.0,0.0,0.14,1008.0,327.0,2.7,98.16467266193683 +2015-07-31 04:30:00-05:00,2015,7,31,4,30,20.3,19.5,0.0,0.0,0.0,0.14,1008.0,327.0,2.5,95.16422733254252 +2015-07-31 05:30:00-05:00,2015,7,31,5,30,21.1,19.4,31.0,332.0,66.0,0.14,1008.0,322.0,2.3000000000000003,90.0280477794471 +2015-07-31 06:30:00-05:00,2015,7,31,6,30,22.700000000000003,19.6,62.0,617.0,242.0,0.14,1008.0,309.0,1.9,82.67429633715562 +2015-07-31 07:30:00-05:00,2015,7,31,7,30,25.1,20.0,80.0,755.0,437.0,0.14,1009.0,297.0,1.8,73.36395800274002 +2015-07-31 08:30:00-05:00,2015,7,31,8,30,27.1,18.8,92.0,834.0,623.0,0.14,1009.0,291.0,1.9,60.492940213435354 +2015-07-31 09:30:00-05:00,2015,7,31,9,30,28.3,17.6,100.0,878.0,777.0,0.14,1008.0,281.0,1.7000000000000002,52.304348433466394 +2015-07-31 10:30:00-05:00,2015,7,31,10,30,29.1,17.1,100.0,912.0,891.0,0.14,1008.0,267.0,1.6,48.37984340252869 +2015-07-31 11:30:00-05:00,2015,7,31,11,30,29.700000000000003,16.8,101.0,925.0,949.0,0.14,1007.0,255.0,1.6,45.8531851794117 +2015-07-31 12:30:00-05:00,2015,7,31,12,30,30.1,16.5,100.0,924.0,949.0,0.14,1007.0,246.0,1.7000000000000002,43.964786958157696 +2015-07-31 13:30:00-05:00,2015,7,31,13,30,30.3,16.3,104.0,903.0,891.0,0.14,1006.0,241.0,1.9,42.91348215475468 +2015-07-31 14:30:00-05:00,2015,7,31,14,30,30.200000000000003,16.3,100.0,875.0,782.0,0.14,1006.0,239.0,1.9,43.160312858146256 +2015-07-31 15:30:00-05:00,2015,7,31,15,30,29.8,16.2,93.0,826.0,628.0,0.14,1006.0,239.0,1.9,43.883280652153864 +2015-07-31 16:30:00-05:00,2015,7,31,16,30,29.200000000000003,16.400000000000002,82.0,747.0,445.0,0.14,1005.0,243.0,1.8,46.01065761132634 +2015-07-31 17:30:00-05:00,2015,7,31,17,30,27.8,17.0,65.0,608.0,250.0,0.14,1006.0,248.0,1.5,51.84729878092746 +2015-07-31 18:30:00-05:00,2015,7,31,18,30,26.0,18.5,34.0,335.0,74.0,0.14,1006.0,254.0,1.3,63.34488035972378 +2015-07-31 19:30:00-05:00,2015,7,31,19,30,26.1,23.1,0.0,0.0,0.0,0.13,1009.0,201.0,3.3000000000000003,83.57655519407362 +2015-07-31 20:30:00-05:00,2015,7,31,20,30,25.700000000000003,23.5,0.0,0.0,0.0,0.13,1009.0,206.0,3.4000000000000004,87.67237506204606 +2015-07-31 21:30:00-05:00,2015,7,31,21,30,25.3,23.9,0.0,0.0,0.0,0.13,1009.0,210.0,3.3000000000000003,91.968755804557 +2015-07-31 22:30:00-05:00,2015,7,31,22,30,25.0,24.1,0.0,0.0,0.0,0.13,1009.0,213.0,3.2,94.7583025301895 +2015-07-31 23:30:00-05:00,2015,7,31,23,30,24.8,24.1,0.0,0.0,0.0,0.13,1009.0,215.0,3.1,95.89585901943668 +2006-08-01 00:30:00-05:00,2006,8,1,0,30,24.6,24.200000000000003,0.0,0.0,0.0,0.13,1009.0,220.0,3.0,97.6328662706814 +2006-08-01 01:30:00-05:00,2006,8,1,1,30,24.5,24.200000000000003,0.0,0.0,0.0,0.13,1009.0,225.0,2.9000000000000004,98.21868447328862 +2006-08-01 02:30:00-05:00,2006,8,1,2,30,24.4,24.1,0.0,0.0,0.0,0.13,1009.0,231.0,2.9000000000000004,98.21736330604561 +2006-08-01 03:30:00-05:00,2006,8,1,3,30,24.3,24.1,0.0,0.0,0.0,0.13,1009.0,236.0,2.6,98.8075731875428 +2006-08-01 04:30:00-05:00,2006,8,1,4,30,24.4,24.1,0.0,0.0,0.0,0.13,1009.0,241.0,2.4000000000000004,98.21736330604561 +2006-08-01 05:30:00-05:00,2006,8,1,5,30,25.1,24.0,38.0,90.0,47.0,0.13,1009.0,245.0,2.3000000000000003,93.6313037531972 +2006-08-01 06:30:00-05:00,2006,8,1,6,30,26.200000000000003,24.1,106.0,336.0,203.0,0.13,1009.0,247.0,2.0,88.24365216725491 +2006-08-01 07:30:00-05:00,2006,8,1,7,30,27.9,24.5,150.0,496.0,383.0,0.13,1009.0,258.0,1.8,81.79775717912155 +2006-08-01 08:30:00-05:00,2006,8,1,8,30,29.6,24.8,174.0,607.0,560.0,0.13,1010.0,276.0,1.5,75.45996920875773 +2006-08-01 09:30:00-05:00,2006,8,1,9,30,30.9,24.6,185.0,681.0,708.0,0.13,1010.0,278.0,1.3,69.20625543125448 +2006-08-01 10:30:00-05:00,2006,8,1,10,30,31.700000000000003,24.3,168.0,763.0,828.0,0.13,1010.0,262.0,1.3,64.94876178997245 +2006-08-01 11:30:00-05:00,2006,8,1,11,30,32.1,24.0,169.0,785.0,887.0,0.13,1009.0,247.0,1.6,62.36044548497231 +2006-08-01 12:30:00-05:00,2006,8,1,12,30,32.2,23.8,166.0,791.0,891.0,0.13,1009.0,240.0,1.9,61.26764304883957 +2006-08-01 13:30:00-05:00,2006,8,1,13,30,32.2,23.700000000000003,176.0,759.0,837.0,0.13,1008.0,241.0,2.1,60.90002841342275 +2006-08-01 14:30:00-05:00,2006,8,1,14,30,32.0,23.700000000000003,169.0,724.0,731.0,0.13,1008.0,245.0,2.3000000000000003,61.593145694563496 +2006-08-01 15:30:00-05:00,2006,8,1,15,30,31.6,23.8,153.0,668.0,585.0,0.13,1008.0,249.0,2.4000000000000004,63.38657962285459 +2006-08-01 16:30:00-05:00,2006,8,1,16,30,30.9,24.1,130.0,575.0,408.0,0.13,1008.0,253.0,2.4000000000000004,67.16384215929736 +2006-08-01 17:30:00-05:00,2006,8,1,17,30,30.0,24.700000000000003,154.0,99.0,184.0,0.13,1008.0,257.0,2.3000000000000003,73.3037546847247 +2006-08-01 18:30:00-05:00,2006,8,1,18,30,29.0,24.9,54.0,11.0,55.0,0.13,1009.0,261.0,2.2,78.58837582919173 +2006-08-01 19:30:00-05:00,2006,8,1,19,30,28.3,25.0,0.0,0.0,0.0,0.13,1009.0,262.0,2.2,82.33612559085367 +2006-08-01 20:30:00-05:00,2006,8,1,20,30,27.9,25.200000000000003,0.0,0.0,0.0,0.13,1009.0,261.0,2.3000000000000003,85.28786274109578 +2006-08-01 21:30:00-05:00,2006,8,1,21,30,27.6,25.200000000000003,0.0,0.0,0.0,0.13,1009.0,260.0,2.3000000000000003,86.79572393355684 +2006-08-01 22:30:00-05:00,2006,8,1,22,30,27.3,25.200000000000003,0.0,0.0,0.0,0.13,1008.0,261.0,2.4000000000000004,88.33367936522714 +2006-08-01 23:30:00-05:00,2006,8,1,23,30,27.0,25.200000000000003,0.0,0.0,0.0,0.13,1008.0,265.0,2.5,89.90239464062725 +2006-08-02 00:30:00-05:00,2006,8,2,0,30,26.6,25.1,0.0,0.0,0.0,0.13,1008.0,268.0,2.3000000000000003,91.49650658855313 +2006-08-02 01:30:00-05:00,2006,8,2,1,30,26.3,25.1,0.0,0.0,0.0,0.13,1008.0,268.0,2.1,93.12991765721713 +2006-08-02 02:30:00-05:00,2006,8,2,2,30,26.0,25.1,0.0,0.0,0.0,0.13,1008.0,267.0,2.0,94.79622941585653 +2006-08-02 03:30:00-05:00,2006,8,2,3,30,25.700000000000003,25.1,0.0,0.0,0.0,0.13,1008.0,267.0,2.0,96.49617597783751 +2006-08-02 04:30:00-05:00,2006,8,2,4,30,25.700000000000003,25.0,0.0,0.0,0.0,0.13,1008.0,267.0,1.9,95.92276999763611 +2006-08-02 05:30:00-05:00,2006,8,2,5,30,26.4,25.0,35.0,176.0,53.0,0.13,1008.0,265.0,2.0,92.03168153578636 +2006-08-02 06:30:00-05:00,2006,8,2,6,30,27.9,25.1,81.0,464.0,214.0,0.13,1008.0,267.0,2.1,84.78143629241603 +2006-08-02 07:30:00-05:00,2006,8,2,7,30,29.700000000000003,25.200000000000003,108.0,624.0,401.0,0.13,1009.0,275.0,2.1,76.83732859422055 +2006-08-02 08:30:00-05:00,2006,8,2,8,30,31.3,24.9,126.0,718.0,580.0,0.13,1008.0,277.0,1.8,68.86940145220579 +2006-08-02 09:30:00-05:00,2006,8,2,9,30,32.4,24.6,138.0,773.0,731.0,0.13,1008.0,266.0,1.7000000000000002,63.55687480524881 +2006-08-02 10:30:00-05:00,2006,8,2,10,30,33.2,24.200000000000003,170.0,766.0,832.0,0.13,1008.0,252.0,1.8,59.31965677552184 +2006-08-02 11:30:00-05:00,2006,8,2,11,30,33.6,23.8,178.0,776.0,887.0,0.13,1007.0,242.0,2.0,56.62730960741225 +2006-08-02 12:30:00-05:00,2006,8,2,12,30,33.800000000000004,23.6,179.0,775.0,888.0,0.13,1007.0,238.0,2.3000000000000003,55.32716146910152 +2006-08-02 13:30:00-05:00,2006,8,2,13,30,33.7,23.5,194.0,729.0,826.0,0.13,1006.0,237.0,2.5,55.30304210013647 +2006-08-02 14:30:00-05:00,2006,8,2,14,30,33.4,23.6,185.0,690.0,720.0,0.13,1006.0,240.0,2.7,56.579859359378176 +2006-08-02 15:30:00-05:00,2006,8,2,15,30,32.9,23.8,168.0,631.0,574.0,0.13,1006.0,245.0,2.8000000000000003,58.895913920545624 +2006-08-02 16:30:00-05:00,2006,8,2,16,30,32.0,24.200000000000003,138.0,550.0,402.0,0.13,1006.0,250.0,2.8000000000000003,63.471858574485616 +2006-08-02 17:30:00-05:00,2006,8,2,17,30,30.8,24.8,97.0,411.0,220.0,0.13,1006.0,253.0,2.7,70.43939306440934 +2006-08-02 18:30:00-05:00,2006,8,2,18,30,29.6,25.1,41.0,172.0,60.0,0.13,1006.0,253.0,2.5,76.82235153000387 +2006-08-02 19:30:00-05:00,2006,8,2,19,30,28.700000000000003,25.0,0.0,0.0,0.0,0.13,1006.0,252.0,2.4000000000000004,80.44485528314186 +2006-08-02 20:30:00-05:00,2006,8,2,20,30,28.200000000000003,24.9,0.0,0.0,0.0,0.13,1006.0,251.0,2.4000000000000004,82.32425179637455 +2006-08-02 21:30:00-05:00,2006,8,2,21,30,27.700000000000003,24.6,0.0,0.0,0.0,0.13,1006.0,249.0,2.4000000000000004,83.25525607100099 +2006-08-02 22:30:00-05:00,2006,8,2,22,30,27.1,24.4,0.0,0.0,0.0,0.13,1006.0,246.0,2.5,85.20698689047646 +2006-08-02 23:30:00-05:00,2006,8,2,23,30,26.700000000000003,24.200000000000003,0.0,0.0,0.0,0.13,1005.0,245.0,2.8000000000000003,86.19488242030222 +2006-08-03 00:30:00-05:00,2006,8,3,0,30,26.200000000000003,24.0,0.0,0.0,0.0,0.13,1005.0,246.0,3.0,87.71536280863872 +2006-08-03 01:30:00-05:00,2006,8,3,1,30,25.700000000000003,23.9,0.0,0.0,0.0,0.13,1005.0,249.0,3.0,89.80932037982814 +2006-08-03 02:30:00-05:00,2006,8,3,2,30,25.3,23.9,0.0,0.0,0.0,0.13,1005.0,251.0,2.9000000000000004,91.968755804557 +2006-08-03 03:30:00-05:00,2006,8,3,3,30,25.1,23.8,0.0,0.0,0.0,0.13,1005.0,250.0,2.8000000000000003,92.51232311742584 +2006-08-03 04:30:00-05:00,2006,8,3,4,30,25.1,23.8,0.0,0.0,0.0,0.13,1005.0,249.0,2.9000000000000004,92.51232311742584 +2006-08-03 05:30:00-05:00,2006,8,3,5,30,25.8,23.8,33.0,201.0,53.0,0.13,1005.0,249.0,3.0,88.7431341272108 +2006-08-03 06:30:00-05:00,2006,8,3,6,30,27.4,23.9,74.0,509.0,218.0,0.13,1005.0,252.0,3.0,81.24686676192344 +2006-08-03 07:30:00-05:00,2006,8,3,7,30,29.200000000000003,24.1,97.0,668.0,409.0,0.13,1005.0,260.0,3.0,74.05289841666962 +2006-08-03 08:30:00-05:00,2006,8,3,8,30,30.6,24.3,111.0,758.0,589.0,0.13,1005.0,264.0,2.7,69.14973459026399 +2006-08-03 09:30:00-05:00,2006,8,3,9,30,31.8,24.1,120.0,812.0,742.0,0.13,1005.0,261.0,2.5,63.8112717184718 +2006-08-03 10:30:00-05:00,2006,8,3,10,30,32.7,23.700000000000003,131.0,833.0,849.0,0.13,1004.0,256.0,2.4000000000000004,59.20541961588445 +2006-08-03 11:30:00-05:00,2006,8,3,11,30,33.300000000000004,23.4,136.0,843.0,905.0,0.13,1004.0,253.0,2.4000000000000004,56.21600290190254 +2006-08-03 12:30:00-05:00,2006,8,3,12,30,33.6,23.200000000000003,139.0,838.0,904.0,0.13,1004.0,252.0,2.5,54.615310668155004 +2006-08-03 13:30:00-05:00,2006,8,3,13,30,33.6,23.1,148.0,801.0,842.0,0.13,1004.0,254.0,2.5,54.286136491691046 +2006-08-03 14:30:00-05:00,2006,8,3,14,30,33.4,23.200000000000003,145.0,758.0,731.0,0.13,1004.0,259.0,2.5,55.2305871611679 +2006-08-03 15:30:00-05:00,2006,8,3,15,30,32.9,23.4,137.0,691.0,580.0,0.13,1004.0,267.0,2.4000000000000004,57.493491165604546 +2006-08-03 16:30:00-05:00,2006,8,3,16,30,32.1,23.8,258.0,116.0,313.0,0.13,1005.0,275.0,2.3000000000000003,61.61518051333747 +2006-08-03 17:30:00-05:00,2006,8,3,17,30,30.8,24.3,154.0,68.0,174.0,0.13,1005.0,278.0,2.0,68.3636475303181 +2006-08-03 18:30:00-05:00,2006,8,3,18,30,29.3,24.9,44.0,31.0,47.0,0.13,1005.0,278.0,1.7000000000000002,77.23698429853539 +2006-08-03 19:30:00-05:00,2006,8,3,19,30,28.1,24.9,0.0,0.0,0.0,0.13,1006.0,278.0,1.6,82.80514953816953 +2006-08-03 20:30:00-05:00,2006,8,3,20,30,27.4,24.9,0.0,0.0,0.0,0.13,1006.0,279.0,1.6,86.26141932109293 +2006-08-03 21:30:00-05:00,2006,8,3,21,30,26.8,24.9,0.0,0.0,0.0,0.13,1006.0,278.0,1.4,89.35362019731149 +2006-08-03 22:30:00-05:00,2006,8,3,22,30,26.3,24.8,0.0,0.0,0.0,0.13,1006.0,273.0,1.2000000000000002,91.47833382948507 +2006-08-03 23:30:00-05:00,2006,8,3,23,30,25.9,24.700000000000003,0.0,0.0,0.0,0.13,1005.0,264.0,1.2000000000000002,93.11014344969657 +2006-08-04 00:30:00-05:00,2006,8,4,0,30,25.6,24.5,0.0,0.0,0.0,0.13,1005.0,257.0,1.2000000000000002,93.65426798917832 +2006-08-04 01:30:00-05:00,2006,8,4,1,30,25.3,24.4,0.0,0.0,0.0,0.13,1005.0,251.0,1.2000000000000002,94.76972360653298 +2006-08-04 02:30:00-05:00,2006,8,4,2,30,25.0,24.200000000000003,0.0,0.0,0.0,0.13,1005.0,246.0,1.2000000000000002,95.32858149219174 +2006-08-04 03:30:00-05:00,2006,8,4,3,30,24.700000000000003,24.0,0.0,0.0,0.0,0.13,1005.0,245.0,1.3,95.89285259431945 +2006-08-04 04:30:00-05:00,2006,8,4,4,30,24.8,23.9,0.0,0.0,0.0,0.13,1005.0,248.0,1.4,94.75066788099174 +2006-08-04 05:30:00-05:00,2006,8,4,5,30,25.4,23.9,34.0,0.0,34.0,0.13,1005.0,252.0,1.6,91.42347038708563 +2006-08-04 06:30:00-05:00,2006,8,4,6,30,26.6,24.0,99.0,350.0,198.0,0.13,1005.0,262.0,1.7000000000000002,85.66937200749227 +2006-08-04 07:30:00-05:00,2006,8,4,7,30,28.1,23.6,241.0,218.0,342.0,0.13,1005.0,276.0,1.8,76.5960424286717 +2006-08-04 08:30:00-05:00,2006,8,4,8,30,29.3,22.6,310.0,334.0,520.0,0.13,1005.0,282.0,2.0,67.25267994254543 +2006-08-04 09:30:00-05:00,2006,8,4,9,30,30.1,22.200000000000003,184.0,678.0,702.0,0.13,1005.0,283.0,2.0,62.68175525919579 +2006-08-04 10:30:00-05:00,2006,8,4,10,30,30.700000000000003,22.3,165.0,762.0,820.0,0.13,1005.0,285.0,2.1,60.93346493940683 +2006-08-04 11:30:00-05:00,2006,8,4,11,30,31.1,22.3,411.0,396.0,772.0,0.13,1005.0,290.0,2.2,59.557955848771435 +2006-08-04 12:30:00-05:00,2006,8,4,12,30,31.200000000000003,22.3,419.0,374.0,760.0,0.13,1005.0,299.0,2.2,59.21957420723001 +2006-08-04 13:30:00-05:00,2006,8,4,13,30,30.9,22.0,357.0,42.0,394.0,0.13,1006.0,313.0,2.3000000000000003,59.15047964675386 +2006-08-04 14:30:00-05:00,2006,8,4,14,30,30.4,21.700000000000003,338.0,188.0,483.0,0.13,1006.0,329.0,2.6,59.761586570929204 +2006-08-04 15:30:00-05:00,2006,8,4,15,30,29.8,21.3,113.0,759.0,597.0,0.13,1007.0,343.0,2.9000000000000004,60.360135587376355 +2006-08-04 16:30:00-05:00,2006,8,4,16,30,29.1,20.8,182.0,332.0,339.0,0.13,1007.0,352.0,3.1,60.94689353706224 +2006-08-04 17:30:00-05:00,2006,8,4,17,30,28.1,20.4,120.0,295.0,207.0,0.13,1008.0,178.0,2.9000000000000004,63.0169550964077 +2006-08-04 18:30:00-05:00,2006,8,4,18,30,26.700000000000003,20.6,34.0,2.0,35.0,0.13,1009.0,1.0,2.4000000000000004,69.25086088259381 +2006-08-04 19:30:00-05:00,2006,8,4,19,30,25.5,20.5,0.0,0.0,0.0,0.13,1010.0,180.0,1.9,73.88675978058711 +2006-08-04 20:30:00-05:00,2006,8,4,20,30,24.9,20.5,0.0,0.0,0.0,0.13,1010.0,355.0,1.6,76.57369877898182 +2006-08-04 21:30:00-05:00,2006,8,4,21,30,24.3,20.5,0.0,0.0,0.0,0.13,1011.0,348.0,1.5,79.37107498006698 +2006-08-04 22:30:00-05:00,2006,8,4,22,30,23.8,20.4,0.0,0.0,0.0,0.13,1011.0,341.0,1.7000000000000002,81.28712563035198 +2006-08-04 23:30:00-05:00,2006,8,4,23,30,23.200000000000003,20.200000000000003,0.0,0.0,0.0,0.13,1011.0,334.0,1.9,83.2468993125857 +2006-08-05 00:30:00-05:00,2006,8,5,0,30,22.6,19.9,0.0,0.0,0.0,0.13,1011.0,329.0,2.0,84.73979851905557 +2006-08-05 01:30:00-05:00,2006,8,5,1,30,22.0,19.6,0.0,0.0,0.0,0.13,1011.0,325.0,2.1,86.27000529494885 +2006-08-05 02:30:00-05:00,2006,8,5,2,30,21.5,19.4,0.0,0.0,0.0,0.13,1012.0,328.0,2.2,87.84730040755132 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11:30:00-05:00,2006,8,5,11,30,30.200000000000003,17.2,107.0,915.0,938.0,0.13,1014.0,304.0,0.6000000000000001,45.697505344837055 +2006-08-05 12:30:00-05:00,2006,8,5,12,30,30.6,16.400000000000002,107.0,915.0,939.0,0.13,1014.0,278.0,0.5,42.45189969098433 +2006-08-05 13:30:00-05:00,2006,8,5,13,30,30.700000000000003,16.3,113.0,886.0,877.0,0.13,1014.0,266.0,0.5,41.94195239336621 +2006-08-05 14:30:00-05:00,2006,8,5,14,30,30.5,16.3,109.0,856.0,767.0,0.13,1014.0,267.0,0.5,42.424581205000884 +2006-08-05 15:30:00-05:00,2006,8,5,15,30,30.1,16.3,100.0,805.0,612.0,0.13,1014.0,265.0,0.4,43.4087455851468 +2006-08-05 16:30:00-05:00,2006,8,5,16,30,29.3,16.6,85.0,730.0,430.0,0.13,1014.0,225.0,0.3,46.331116052410785 +2006-08-05 17:30:00-05:00,2006,8,5,17,30,27.9,17.1,65.0,587.0,235.0,0.13,1014.0,180.0,0.4,51.87296188416213 +2006-08-05 18:30:00-05:00,2006,8,5,18,30,26.1,18.4,43.0,2.0,43.0,0.13,1015.0,167.0,0.5,62.577992748759485 +2006-08-05 19:30:00-05:00,2006,8,5,19,30,24.9,18.7,0.0,0.0,0.0,0.13,1016.0,144.0,0.5,68.47536251452439 +2006-08-05 20:30:00-05:00,2006,8,5,20,30,24.200000000000003,19.3,0.0,0.0,0.0,0.13,1016.0,102.0,0.5,74.1270413690411 +2006-08-05 21:30:00-05:00,2006,8,5,21,30,23.6,19.700000000000003,0.0,0.0,0.0,0.13,1016.0,70.0,0.7000000000000001,78.78382747791223 +2006-08-05 22:30:00-05:00,2006,8,5,22,30,23.1,19.8,0.0,0.0,0.0,0.13,1017.0,57.0,1.2000000000000002,81.7031130094773 +2006-08-05 23:30:00-05:00,2006,8,5,23,30,22.700000000000003,19.8,0.0,0.0,0.0,0.13,1017.0,49.0,1.7000000000000002,83.70670756814528 +2006-08-06 00:30:00-05:00,2006,8,6,0,30,22.3,19.6,0.0,0.0,0.0,0.13,1017.0,41.0,2.2,84.70789430836359 +2006-08-06 01:30:00-05:00,2006,8,6,1,30,21.9,19.5,0.0,0.0,0.0,0.13,1017.0,37.0,2.7,86.26034196869891 +2006-08-06 02:30:00-05:00,2006,8,6,2,30,21.4,19.4,0.0,0.0,0.0,0.13,1018.0,37.0,3.0,88.38686960542145 +2006-08-06 03:30:00-05:00,2006,8,6,3,30,21.0,19.3,0.0,0.0,0.0,0.13,1018.0,38.0,3.1,90.0208599173631 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12:30:00-05:00,2006,8,6,12,30,28.200000000000003,19.5,141.0,832.0,897.0,0.13,1019.0,115.0,2.9000000000000004,59.25431684348945 +2006-08-06 13:30:00-05:00,2006,8,6,13,30,28.1,19.5,161.0,781.0,832.0,0.13,1019.0,119.0,3.0,59.60045138513241 +2006-08-06 14:30:00-05:00,2006,8,6,14,30,27.9,19.6,370.0,343.0,632.0,0.13,1018.0,121.0,3.0,60.67533927878387 +2006-08-06 15:30:00-05:00,2006,8,6,15,30,27.4,19.8,281.0,25.0,297.0,0.13,1018.0,123.0,3.1,63.25524054133917 +2006-08-06 16:30:00-05:00,2006,8,6,16,30,26.8,20.0,239.0,227.0,346.0,0.13,1018.0,128.0,3.2,66.33971808609334 +2006-08-06 17:30:00-05:00,2006,8,6,17,30,25.9,20.200000000000003,151.0,108.0,182.0,0.13,1018.0,134.0,3.3000000000000003,70.8301719640559 +2006-08-06 18:30:00-05:00,2006,8,6,18,30,24.9,20.700000000000003,36.0,144.0,50.0,0.13,1018.0,140.0,3.2,77.52336986984166 +2006-08-06 19:30:00-05:00,2006,8,6,19,30,24.3,21.3,0.0,0.0,0.0,0.13,1018.0,150.0,3.0,83.37305784293329 +2006-08-06 20:30:00-05:00,2006,8,6,20,30,23.9,21.9,0.0,0.0,0.0,0.13,1018.0,164.0,2.9000000000000004,88.59128590684051 +2006-08-06 21:30:00-05:00,2006,8,6,21,30,23.6,22.3,0.0,0.0,0.0,0.13,1018.0,176.0,2.8000000000000003,92.43092253264987 +2006-08-06 22:30:00-05:00,2006,8,6,22,30,23.3,22.5,0.0,0.0,0.0,0.13,1018.0,185.0,2.7,95.27010681771513 +2006-08-06 23:30:00-05:00,2006,8,6,23,30,23.1,22.5,0.0,0.0,0.0,0.13,1017.0,191.0,2.8000000000000003,96.42854833484094 +2006-08-07 00:30:00-05:00,2006,8,7,0,30,23.0,22.3,0.0,0.0,0.0,0.13,1017.0,197.0,2.9000000000000004,95.84123754804762 +2006-08-07 01:30:00-05:00,2006,8,7,1,30,22.9,22.1,0.0,0.0,0.0,0.13,1017.0,201.0,2.9000000000000004,95.25618988736356 +2006-08-07 02:30:00-05:00,2006,8,7,2,30,22.9,21.9,0.0,0.0,0.0,0.13,1016.0,203.0,2.9000000000000004,94.1015097789341 +2006-08-07 03:30:00-05:00,2006,8,7,3,30,22.8,21.9,0.0,0.0,0.0,0.13,1016.0,202.0,2.9000000000000004,94.67340418187906 +2006-08-07 04:30:00-05:00,2006,8,7,4,30,22.9,21.9,0.0,0.0,0.0,0.13,1016.0,203.0,3.0,94.1015097789341 +2006-08-07 05:30:00-05:00,2006,8,7,5,30,23.1,22.0,31.0,82.0,39.0,0.13,1016.0,207.0,3.1,93.53820390346667 +2006-08-07 06:30:00-05:00,2006,8,7,6,30,23.5,22.200000000000003,40.0,0.0,40.0,0.13,1016.0,215.0,2.9000000000000004,92.42544936330575 +2006-08-07 07:30:00-05:00,2006,8,7,7,30,24.1,22.1,225.0,130.0,285.0,0.13,1015.0,220.0,2.8000000000000003,88.60741087899375 +2006-08-07 08:30:00-05:00,2006,8,7,8,30,25.6,21.700000000000003,329.0,64.0,369.0,0.13,1015.0,217.0,3.0,79.06506679753222 +2006-08-07 09:30:00-05:00,2006,8,7,9,30,27.5,21.4,192.0,667.0,697.0,0.13,1014.0,212.0,3.0,69.40302102312288 +2006-08-07 10:30:00-05:00,2006,8,7,10,30,29.0,21.6,258.0,624.0,790.0,0.13,1013.0,210.0,3.1,64.38421540041372 +2006-08-07 11:30:00-05:00,2006,8,7,11,30,29.9,21.9,252.0,663.0,852.0,0.13,1013.0,207.0,3.2,62.25726033370527 +2006-08-07 12:30:00-05:00,2006,8,7,12,30,30.4,22.200000000000003,436.0,411.0,808.0,0.13,1012.0,203.0,3.5,61.612476228487665 +2006-08-07 13:30:00-05:00,2006,8,7,13,30,30.4,22.4,491.0,290.0,740.0,0.13,1012.0,204.0,3.6,62.366778047992696 +2006-08-07 14:30:00-05:00,2006,8,7,14,30,30.3,22.5,106.0,0.0,106.0,0.13,1011.0,207.0,3.6,63.10759818323487 +2006-08-07 15:30:00-05:00,2006,8,7,15,30,29.9,22.6,311.0,30.0,330.0,0.13,1011.0,212.0,3.4000000000000004,64.96706758433933 +2006-08-07 16:30:00-05:00,2006,8,7,16,30,29.200000000000003,22.700000000000003,144.0,1.0,144.0,0.13,1011.0,215.0,3.3000000000000003,68.05407215136654 +2006-08-07 17:30:00-05:00,2006,8,7,17,30,28.1,23.1,116.0,265.0,191.0,0.13,1011.0,214.0,3.3000000000000003,74.31879400941989 +2006-08-07 18:30:00-05:00,2006,8,7,18,30,26.9,23.5,33.0,66.0,39.0,0.13,1011.0,213.0,3.3000000000000003,81.67507763929301 +2006-08-07 19:30:00-05:00,2006,8,7,19,30,26.1,23.6,0.0,0.0,0.0,0.13,1011.0,213.0,3.4000000000000004,86.13747643531566 +2006-08-07 20:30:00-05:00,2006,8,7,20,30,25.6,23.8,0.0,0.0,0.0,0.13,1011.0,214.0,3.3000000000000003,89.80210863866971 +2006-08-07 21:30:00-05:00,2006,8,7,21,30,25.200000000000003,24.0,0.0,0.0,0.0,0.13,1011.0,219.0,3.1,93.07533521182421 +2006-08-07 22:30:00-05:00,2006,8,7,22,30,24.9,24.0,0.0,0.0,0.0,0.13,1011.0,227.0,2.8000000000000003,94.75448726934127 +2006-08-07 23:30:00-05:00,2006,8,7,23,30,24.6,24.0,0.0,0.0,0.0,0.13,1010.0,237.0,2.5,96.46779925124454 +2006-08-08 00:30:00-05:00,2006,8,8,0,30,24.4,23.9,0.0,0.0,0.0,0.13,1010.0,252.0,2.3000000000000003,97.04444869586715 +2006-08-08 01:30:00-05:00,2006,8,8,1,30,24.200000000000003,23.700000000000003,0.0,0.0,0.0,0.13,1010.0,274.0,2.2,97.04008528800617 +2006-08-08 02:30:00-05:00,2006,8,8,2,30,23.8,23.3,0.0,0.0,0.0,0.13,1011.0,299.0,2.4000000000000004,97.03132958424385 +2006-08-08 03:30:00-05:00,2006,8,8,3,30,23.200000000000003,22.5,0.0,0.0,0.0,0.13,1011.0,322.0,2.8000000000000003,95.8473598805886 +2006-08-08 04:30:00-05:00,2006,8,8,4,30,22.8,21.5,0.0,0.0,0.0,0.13,1011.0,331.0,3.1,92.38697279878443 +2006-08-08 05:30:00-05:00,2006,8,8,5,30,23.0,20.6,28.0,214.0,46.0,0.13,1012.0,330.0,3.3000000000000003,86.36609614824316 +2006-08-08 06:30:00-05:00,2006,8,8,6,30,24.0,19.6,64.0,564.0,218.0,0.13,1012.0,331.0,3.6,76.43466615514889 +2006-08-08 07:30:00-05:00,2006,8,8,7,30,25.4,18.2,78.0,750.0,420.0,0.13,1012.0,335.0,3.7,64.41670362294133 +2006-08-08 08:30:00-05:00,2006,8,8,8,30,26.700000000000003,17.1,87.0,845.0,612.0,0.13,1012.0,337.0,3.5,55.652799964805475 +2006-08-08 09:30:00-05:00,2006,8,8,9,30,27.700000000000003,16.5,95.0,891.0,768.0,0.13,1012.0,340.0,3.2,50.522056972573516 +2006-08-08 10:30:00-05:00,2006,8,8,10,30,28.4,16.3,104.0,909.0,878.0,0.13,1012.0,346.0,2.8000000000000003,47.88872979650521 +2006-08-08 11:30:00-05:00,2006,8,8,11,30,28.700000000000003,16.400000000000002,322.0,608.0,870.0,0.13,1012.0,350.0,2.4000000000000004,47.3622064078446 +2006-08-08 12:30:00-05:00,2006,8,8,12,30,29.0,16.7,462.0,306.0,738.0,0.13,1012.0,350.0,2.0,47.44214495265548 +2006-08-08 13:30:00-05:00,2006,8,8,13,30,29.1,16.8,292.0,636.0,836.0,0.13,1012.0,348.0,1.9,47.46876089056087 +2006-08-08 14:30:00-05:00,2006,8,8,14,30,29.1,16.8,355.0,378.0,643.0,0.13,1012.0,345.0,1.8,47.46876089056087 +2006-08-08 15:30:00-05:00,2006,8,8,15,30,28.700000000000003,16.7,101.0,801.0,603.0,0.13,1012.0,345.0,1.8,48.27407421976461 +2006-08-08 16:30:00-05:00,2006,8,8,16,30,27.9,16.7,87.0,717.0,419.0,0.13,1012.0,348.0,1.9,50.574087686834034 +2006-08-08 17:30:00-05:00,2006,8,8,17,30,26.700000000000003,16.8,66.0,566.0,224.0,0.13,1013.0,176.0,2.1,54.60475414191785 +2006-08-08 18:30:00-05:00,2006,8,8,18,30,25.3,17.1,28.0,282.0,54.0,0.13,1014.0,7.0,2.6,60.46015845693659 +2006-08-08 19:30:00-05:00,2006,8,8,19,30,23.9,16.8,0.0,0.0,0.0,0.14,1014.0,17.0,3.2,64.50188502130852 +2006-08-08 20:30:00-05:00,2006,8,8,20,30,22.8,16.7,0.0,0.0,0.0,0.14,1015.0,22.0,3.4000000000000004,68.49406925058416 +2006-08-08 21:30:00-05:00,2006,8,8,21,30,21.9,16.8,0.0,0.0,0.0,0.14,1015.0,23.0,3.2,72.80838556642742 +2006-08-08 22:30:00-05:00,2006,8,8,22,30,21.3,16.7,0.0,0.0,0.0,0.14,1015.0,18.0,2.9000000000000004,75.05211048629229 +2006-08-08 23:30:00-05:00,2006,8,8,23,30,20.8,16.7,0.0,0.0,0.0,0.14,1015.0,15.0,2.7,77.39268154070301 +2006-08-09 00:30:00-05:00,2006,8,9,0,30,20.200000000000003,16.6,0.0,0.0,0.0,0.14,1015.0,15.0,2.6,79.8017848842684 +2006-08-09 01:30:00-05:00,2006,8,9,1,30,19.8,16.7,0.0,0.0,0.0,0.14,1015.0,17.0,2.7,82.32392925577574 +2006-08-09 02:30:00-05:00,2006,8,9,2,30,19.4,16.8,0.0,0.0,0.0,0.14,1016.0,21.0,2.8000000000000003,84.93178225244176 +2006-08-09 03:30:00-05:00,2006,8,9,3,30,19.0,16.8,0.0,0.0,0.0,0.14,1016.0,25.0,2.9000000000000004,87.07448068709037 +2006-08-09 04:30:00-05:00,2006,8,9,4,30,18.9,16.8,0.0,0.0,0.0,0.14,1017.0,31.0,3.0,87.61959078824744 +2006-08-09 05:30:00-05:00,2006,8,9,5,30,19.9,16.8,25.0,304.0,49.0,0.14,1017.0,36.0,3.1,82.33618020954235 +2006-08-09 06:30:00-05:00,2006,8,9,6,30,21.9,16.5,55.0,629.0,224.0,0.14,1017.0,44.0,3.0,71.43412850987252 +2006-08-09 07:30:00-05:00,2006,8,9,7,30,23.9,14.7,70.0,780.0,424.0,0.14,1017.0,48.0,2.5,56.394699387726995 +2006-08-09 08:30:00-05:00,2006,8,9,8,30,25.200000000000003,13.4,82.0,857.0,612.0,0.14,1017.0,45.0,1.8,47.959290352447745 +2006-08-09 09:30:00-05:00,2006,8,9,9,30,26.200000000000003,12.6,91.0,900.0,770.0,0.14,1017.0,44.0,1.2000000000000002,42.89549397809907 +2006-08-09 10:30:00-05:00,2006,8,9,10,30,26.9,12.0,104.0,915.0,881.0,0.14,1016.0,65.0,0.7000000000000001,39.570715704713486 +2006-08-09 11:30:00-05:00,2006,8,9,11,30,27.5,11.5,108.0,926.0,941.0,0.14,1016.0,115.0,0.6000000000000001,36.96249660860967 +2006-08-09 12:30:00-05:00,2006,8,9,12,30,27.9,11.4,109.0,921.0,940.0,0.14,1015.0,157.0,1.0,35.87046307888004 +2006-08-09 13:30:00-05:00,2006,8,9,13,30,28.1,11.9,113.0,899.0,880.0,0.14,1015.0,169.0,1.5,36.64476161483069 +2006-08-09 14:30:00-05:00,2006,8,9,14,30,28.0,12.4,188.0,677.0,702.0,0.14,1014.0,175.0,2.0,38.09138905181467 +2006-08-09 15:30:00-05:00,2006,8,9,15,30,27.700000000000003,12.8,191.0,566.0,545.0,0.14,1014.0,179.0,2.5,39.794118063169925 +2006-08-09 16:30:00-05:00,2006,8,9,16,30,27.0,13.2,208.0,429.0,405.0,0.14,1014.0,181.0,2.9000000000000004,42.55805437724145 +2006-08-09 17:30:00-05:00,2006,8,9,17,30,25.5,14.1,71.0,542.0,221.0,0.14,1013.0,181.0,3.2,49.30300702402033 +2006-08-09 18:30:00-05:00,2006,8,9,18,30,23.700000000000003,15.6,30.0,228.0,50.0,0.14,1013.0,183.0,3.5,60.47557614118856 +2006-08-09 19:30:00-05:00,2006,8,9,19,30,22.6,16.2,0.0,0.0,0.0,0.13,1014.0,189.0,3.7,67.15953608656889 +2006-08-09 20:30:00-05:00,2006,8,9,20,30,22.200000000000003,16.900000000000002,0.0,0.0,0.0,0.13,1014.0,198.0,3.7,71.94386730377681 +2006-08-09 21:30:00-05:00,2006,8,9,21,30,21.8,17.400000000000002,0.0,0.0,0.0,0.13,1014.0,208.0,3.7,76.08986497373662 +2006-08-09 22:30:00-05:00,2006,8,9,22,30,21.4,17.7,0.0,0.0,0.0,0.13,1013.0,217.0,3.6,79.46404733244974 +2006-08-09 23:30:00-05:00,2006,8,9,23,30,20.9,17.8,0.0,0.0,0.0,0.13,1013.0,226.0,3.6,82.45801947276513 +2006-08-10 00:30:00-05:00,2006,8,10,0,30,20.6,17.8,0.0,0.0,0.0,0.13,1013.0,233.0,3.5,83.99497355112099 +2006-08-10 01:30:00-05:00,2006,8,10,1,30,20.3,17.8,0.0,0.0,0.0,0.13,1012.0,235.0,3.4000000000000004,85.5641754903033 +2006-08-10 02:30:00-05:00,2006,8,10,2,30,20.0,17.900000000000002,0.0,0.0,0.0,0.13,1012.0,237.0,3.3000000000000003,87.71668477749306 +2006-08-10 03:30:00-05:00,2006,8,10,3,30,19.700000000000003,18.2,0.0,0.0,0.0,0.13,1012.0,238.0,3.2,91.06358739272459 +2006-08-10 04:30:00-05:00,2006,8,10,4,30,19.9,18.5,0.0,0.0,0.0,0.13,1012.0,238.0,3.1,91.64894254914518 +2006-08-10 05:30:00-05:00,2006,8,10,5,30,21.0,18.9,27.0,200.0,43.0,0.13,1012.0,236.0,3.2,87.8039885838982 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14:30:00-05:00,2006,8,10,14,30,28.1,19.3,247.0,10.0,254.0,0.13,1006.0,206.0,3.6,58.86368773594268 +2006-08-10 15:30:00-05:00,2006,8,10,15,30,27.4,20.0,284.0,41.0,310.0,0.13,1006.0,209.0,3.6,64.04394390323509 +2006-08-10 16:30:00-05:00,2006,8,10,16,30,26.700000000000003,20.700000000000003,115.0,3.0,116.0,0.13,1006.0,214.0,3.6,69.67880118327776 +2006-08-10 17:30:00-05:00,2006,8,10,17,30,25.5,21.5,116.0,242.0,182.0,0.13,1005.0,221.0,3.4000000000000004,78.56891705633684 +2006-08-10 18:30:00-05:00,2006,8,10,18,30,24.200000000000003,22.0,20.0,0.0,20.0,0.13,1006.0,231.0,3.2,87.54208376930426 +2006-08-10 19:30:00-05:00,2006,8,10,19,30,23.3,21.700000000000003,0.0,0.0,0.0,0.13,1006.0,245.0,3.2,90.7373570787389 +2006-08-10 20:30:00-05:00,2006,8,10,20,30,22.6,21.4,0.0,0.0,0.0,0.13,1006.0,264.0,3.1,92.9437416009186 +2006-08-10 21:30:00-05:00,2006,8,10,21,30,21.9,21.0,0.0,0.0,0.0,0.13,1007.0,284.0,3.0,94.63808332104115 +2006-08-10 22:30:00-05:00,2006,8,10,22,30,21.200000000000003,20.5,0.0,0.0,0.0,0.13,1007.0,307.0,3.1,95.78552661981746 +2006-08-10 23:30:00-05:00,2006,8,10,23,30,20.5,20.0,0.0,0.0,0.0,0.13,1007.0,328.0,3.4000000000000004,96.9575969733834 +2006-08-11 00:30:00-05:00,2006,8,11,0,30,20.0,19.3,0.0,0.0,0.0,0.13,1007.0,348.0,3.9,95.74776649766761 +2006-08-11 01:30:00-05:00,2006,8,11,1,30,19.6,18.6,0.0,0.0,0.0,0.13,1007.0,184.0,4.5,93.95667425471606 +2006-08-11 02:30:00-05:00,2006,8,11,2,30,19.3,18.0,0.0,0.0,0.0,0.13,1007.0,16.0,4.800000000000001,92.19018664491743 +2006-08-11 03:30:00-05:00,2006,8,11,3,30,18.9,17.3,0.0,0.0,0.0,0.13,1007.0,21.0,4.9,90.43813286223745 +2006-08-11 04:30:00-05:00,2006,8,11,4,30,18.6,16.7,0.0,0.0,0.0,0.13,1008.0,23.0,4.800000000000001,88.71319240637641 +2006-08-11 05:30:00-05:00,2006,8,11,5,30,19.0,16.3,24.0,274.0,44.0,0.13,1008.0,24.0,4.9,84.35046972174376 +2006-08-11 06:30:00-05:00,2006,8,11,6,30,20.1,15.8,55.0,620.0,219.0,0.13,1009.0,27.0,5.0,76.30502625530123 +2006-08-11 07:30:00-05:00,2006,8,11,7,30,21.5,14.3,72.0,783.0,423.0,0.13,1009.0,29.0,4.9,63.568993075376355 +2006-08-11 08:30:00-05:00,2006,8,11,8,30,22.700000000000003,12.5,295.0,347.0,508.0,0.13,1010.0,30.0,4.5,52.54947516165864 +2006-08-11 09:30:00-05:00,2006,8,11,9,30,23.700000000000003,11.1,350.0,407.0,655.0,0.13,1010.0,29.0,4.0,45.10445563383891 +2006-08-11 10:30:00-05:00,2006,8,11,10,30,24.5,10.2,93.0,945.0,893.0,0.13,1010.0,27.0,3.4000000000000004,40.490156567944325 +2006-08-11 11:30:00-05:00,2006,8,11,11,30,25.1,9.7,96.0,953.0,950.0,0.13,1009.0,25.0,2.9000000000000004,37.77957728976211 +2006-08-11 12:30:00-05:00,2006,8,11,12,30,25.6,9.4,456.0,340.0,761.0,0.13,1009.0,25.0,2.5,35.94153453995169 +2006-08-11 13:30:00-05:00,2006,8,11,13,30,25.9,9.3,98.0,932.0,889.0,0.13,1009.0,23.0,2.0,35.07126252895971 +2006-08-11 14:30:00-05:00,2006,8,11,14,30,26.0,9.3,94.0,900.0,772.0,0.13,1009.0,20.0,1.6,34.86424786404487 +2006-08-11 15:30:00-05:00,2006,8,11,15,30,25.8,9.3,87.0,850.0,613.0,0.13,1009.0,13.0,1.3,35.27966177176592 +2006-08-11 16:30:00-05:00,2006,8,11,16,30,25.200000000000003,9.6,76.0,765.0,423.0,0.13,1009.0,6.0,1.0,37.30414857506864 +2006-08-11 17:30:00-05:00,2006,8,11,17,30,24.0,10.2,58.0,613.0,223.0,0.13,1010.0,8.0,0.9,41.72237397899323 +2006-08-11 18:30:00-05:00,2006,8,11,18,30,22.200000000000003,11.4,34.0,2.0,34.0,0.13,1010.0,16.0,1.0,50.38392237338324 +2006-08-11 19:30:00-05:00,2006,8,11,19,30,21.1,11.4,0.0,0.0,0.0,0.13,1011.0,21.0,1.4,53.889055321164065 +2006-08-11 20:30:00-05:00,2006,8,11,20,30,20.3,12.0,0.0,0.0,0.0,0.13,1011.0,26.0,1.9,58.897553582513915 +2006-08-11 21:30:00-05:00,2006,8,11,21,30,19.6,12.5,0.0,0.0,0.0,0.13,1011.0,29.0,2.3000000000000003,63.562047080939905 +2006-08-11 22:30:00-05:00,2006,8,11,22,30,18.9,12.9,0.0,0.0,0.0,0.13,1012.0,29.0,2.7,68.15606637967026 +2006-08-11 23:30:00-05:00,2006,8,11,23,30,18.4,13.1,0.0,0.0,0.0,0.13,1012.0,26.0,3.0,71.24637545433755 +2006-08-12 00:30:00-05:00,2006,8,12,0,30,17.900000000000002,13.1,0.0,0.0,0.0,0.13,1012.0,24.0,3.3000000000000003,73.51865731015998 +2006-08-12 01:30:00-05:00,2006,8,12,1,30,17.3,13.2,0.0,0.0,0.0,0.13,1013.0,25.0,3.5,76.85336478781859 +2006-08-12 02:30:00-05:00,2006,8,12,2,30,16.8,13.3,0.0,0.0,0.0,0.13,1013.0,29.0,3.6,79.84458710289917 +2006-08-12 03:30:00-05:00,2006,8,12,3,30,16.400000000000002,13.3,0.0,0.0,0.0,0.13,1013.0,33.0,3.5,81.90000591300875 +2006-08-12 04:30:00-05:00,2006,8,12,4,30,16.2,13.0,0.0,0.0,0.0,0.13,1014.0,35.0,3.3000000000000003,81.3415828745766 +2006-08-12 05:30:00-05:00,2006,8,12,5,30,16.900000000000002,12.9,23.0,271.0,43.0,0.13,1014.0,31.0,3.3000000000000003,77.2945710545144 +2006-08-12 06:30:00-05:00,2006,8,12,6,30,18.7,12.5,139.0,86.0,162.0,0.13,1015.0,26.0,3.3000000000000003,67.22854227616048 +2006-08-12 07:30:00-05:00,2006,8,12,7,30,20.5,10.8,72.0,783.0,421.0,0.13,1015.0,17.0,3.0,53.734496526023875 +2006-08-12 08:30:00-05:00,2006,8,12,8,30,21.8,9.6,83.0,865.0,612.0,0.13,1015.0,181.0,2.7,45.79231387642702 +2006-08-12 09:30:00-05:00,2006,8,12,9,30,22.8,8.8,90.0,915.0,774.0,0.13,1015.0,343.0,2.4000000000000004,40.82986791456215 +2006-08-12 10:30:00-05:00,2006,8,12,10,30,23.700000000000003,8.0,93.0,944.0,890.0,0.13,1014.0,326.0,2.2,36.629079195953366 +2006-08-12 11:30:00-05:00,2006,8,12,11,30,24.4,7.4,95.0,957.0,951.0,0.13,1014.0,311.0,2.1,33.71454603889198 +2006-08-12 12:30:00-05:00,2006,8,12,12,30,25.0,6.9,95.0,954.0,949.0,0.13,1013.0,299.0,2.0,31.433027853335183 +2006-08-12 13:30:00-05:00,2006,8,12,13,30,25.3,6.800000000000001,95.0,939.0,889.0,0.13,1013.0,291.0,1.8,30.665332124855002 +2006-08-12 14:30:00-05:00,2006,8,12,14,30,25.3,7.1000000000000005,90.0,910.0,774.0,0.13,1012.0,286.0,1.6,31.302426113725815 +2006-08-12 15:30:00-05:00,2006,8,12,15,30,25.1,7.7,81.0,865.0,614.0,0.13,1012.0,284.0,1.5,33.00240114774249 +2006-08-12 16:30:00-05:00,2006,8,12,16,30,24.6,8.200000000000001,72.0,774.0,421.0,0.13,1013.0,283.0,1.6,35.17809850631726 +2006-08-12 17:30:00-05:00,2006,8,12,17,30,23.200000000000003,9.2,56.0,621.0,221.0,0.13,1013.0,289.0,1.5,40.94288335262098 +2006-08-12 18:30:00-05:00,2006,8,12,18,30,21.4,10.9,30.0,1.0,30.0,0.13,1013.0,303.0,1.6,51.18166333331646 +2006-08-12 19:30:00-05:00,2006,8,12,19,30,20.1,11.3,0.0,0.0,0.0,0.13,1014.0,321.0,2.1,56.93653825574127 +2006-08-12 20:30:00-05:00,2006,8,12,20,30,19.4,11.8,0.0,0.0,0.0,0.13,1014.0,332.0,2.6,61.4609034846294 +2006-08-12 21:30:00-05:00,2006,8,12,21,30,18.5,12.4,0.0,0.0,0.0,0.13,1014.0,343.0,2.9000000000000004,67.6299197497526 +2006-08-12 22:30:00-05:00,2006,8,12,22,30,17.900000000000002,12.7,0.0,0.0,0.0,0.13,1014.0,354.0,3.1,71.62054984509581 +2006-08-12 23:30:00-05:00,2006,8,12,23,30,17.3,12.7,0.0,0.0,0.0,0.13,1014.0,182.0,3.1,74.38211673653167 +2006-08-13 00:30:00-05:00,2006,8,13,0,30,16.8,12.7,0.0,0.0,0.0,0.13,1014.0,4.0,3.0,76.77483648891005 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09:30:00-05:00,2006,8,13,9,30,23.700000000000003,8.4,92.0,901.0,764.0,0.13,1015.0,318.0,1.8,37.63699295174745 +2006-08-13 10:30:00-05:00,2006,8,13,10,30,24.700000000000003,8.200000000000001,94.0,932.0,879.0,0.13,1015.0,296.0,1.8,34.968437559450294 +2006-08-13 11:30:00-05:00,2006,8,13,11,30,25.5,8.3,98.0,941.0,936.0,0.13,1014.0,274.0,2.0,33.56779592601737 +2006-08-13 12:30:00-05:00,2006,8,13,12,30,26.0,9.2,99.0,938.0,936.0,0.13,1014.0,261.0,2.2,34.630403680237684 +2006-08-13 13:30:00-05:00,2006,8,13,13,30,26.4,9.9,98.0,921.0,874.0,0.13,1013.0,258.0,2.2,35.44891385511018 +2006-08-13 14:30:00-05:00,2006,8,13,14,30,26.5,10.3,93.0,893.0,761.0,0.13,1013.0,257.0,2.1,36.19544969434793 +2006-08-13 15:30:00-05:00,2006,8,13,15,30,26.3,10.9,188.0,578.0,542.0,0.13,1013.0,260.0,2.1,38.117932604781785 +2006-08-13 16:30:00-05:00,2006,8,13,16,30,25.6,11.9,153.0,421.0,342.0,0.13,1013.0,268.0,1.9,42.448542923327864 +2006-08-13 17:30:00-05:00,2006,8,13,17,30,24.0,13.1,107.0,329.0,193.0,0.13,1013.0,274.0,1.6,50.529087918014824 +2006-08-13 18:30:00-05:00,2006,8,13,18,30,22.200000000000003,14.5,22.0,274.0,42.0,0.13,1013.0,283.0,1.5,61.702123031807695 +2006-08-13 19:30:00-05:00,2006,8,13,19,30,21.4,14.4,0.0,0.0,0.0,0.14,1013.0,292.0,1.7000000000000002,64.37433189366266 +2006-08-13 20:30:00-05:00,2006,8,13,20,30,21.200000000000003,14.2,0.0,0.0,0.0,0.14,1014.0,291.0,1.8,64.33082432621867 +2006-08-13 21:30:00-05:00,2006,8,13,21,30,20.8,14.2,0.0,0.0,0.0,0.14,1014.0,287.0,1.8,65.93146752697646 +2006-08-13 22:30:00-05:00,2006,8,13,22,30,20.200000000000003,14.1,0.0,0.0,0.0,0.14,1014.0,287.0,1.9,67.97536743089519 +2006-08-13 23:30:00-05:00,2006,8,13,23,30,19.6,13.8,0.0,0.0,0.0,0.14,1014.0,283.0,1.9,69.19039294481395 +2006-08-14 00:30:00-05:00,2006,8,14,0,30,19.0,13.8,0.0,0.0,0.0,0.14,1014.0,275.0,2.0,71.8231453434557 +2006-08-14 01:30:00-05:00,2006,8,14,1,30,18.3,13.8,0.0,0.0,0.0,0.14,1014.0,268.0,2.2,75.03783077558101 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10:30:00-05:00,2006,8,14,10,30,27.6,16.5,181.0,765.0,823.0,0.14,1011.0,216.0,3.4000000000000004,50.81827642104819 +2006-08-14 11:30:00-05:00,2006,8,14,11,30,28.3,16.6,498.0,215.0,689.0,0.14,1011.0,209.0,3.8,49.096163515703154 +2006-08-14 12:30:00-05:00,2006,8,14,12,30,28.5,16.7,199.0,757.0,873.0,0.14,1010.0,204.0,4.2,48.83782488323549 +2006-08-14 13:30:00-05:00,2006,8,14,13,30,28.5,17.0,412.0,416.0,762.0,0.14,1009.0,201.0,4.6000000000000005,49.775912636265915 +2006-08-14 14:30:00-05:00,2006,8,14,14,30,28.4,17.3,379.0,380.0,663.0,0.14,1008.0,200.0,4.9,51.0254769121298 +2006-08-14 15:30:00-05:00,2006,8,14,15,30,28.1,17.5,185.0,586.0,542.0,0.14,1008.0,201.0,5.1000000000000005,52.58422904958821 +2006-08-14 16:30:00-05:00,2006,8,14,16,30,27.6,17.8,155.0,468.0,362.0,0.14,1007.0,200.0,5.1000000000000005,55.17515984155884 +2006-08-14 17:30:00-05:00,2006,8,14,17,30,26.4,18.4,102.0,297.0,179.0,0.14,1007.0,200.0,5.1000000000000005,61.47881717808883 +2006-08-14 18:30:00-05:00,2006,8,14,18,30,25.0,18.7,24.0,63.0,28.0,0.14,1007.0,200.0,5.300000000000001,68.06816085251296 +2006-08-14 19:30:00-05:00,2006,8,14,19,30,24.3,18.8,0.0,0.0,0.0,0.13,1007.0,201.0,5.6000000000000005,71.42190788013649 +2006-08-14 20:30:00-05:00,2006,8,14,20,30,23.9,19.1,0.0,0.0,0.0,0.13,1007.0,203.0,5.7,74.54006608751507 +2006-08-14 21:30:00-05:00,2006,8,14,21,30,23.700000000000003,19.4,0.0,0.0,0.0,0.13,1007.0,206.0,5.7,76.86481163970507 +2006-08-14 22:30:00-05:00,2006,8,14,22,30,23.4,19.700000000000003,0.0,0.0,0.0,0.13,1007.0,208.0,5.6000000000000005,79.73963499838086 +2006-08-14 23:30:00-05:00,2006,8,14,23,30,23.1,19.8,0.0,0.0,0.0,0.13,1007.0,209.0,5.5,81.7031130094773 +2006-08-15 00:30:00-05:00,2006,8,15,0,30,22.9,20.1,0.0,0.0,0.0,0.13,1007.0,208.0,5.4,84.24899780460343 +2006-08-15 01:30:00-05:00,2006,8,15,1,30,22.8,20.5,0.0,0.0,0.0,0.13,1006.0,208.0,5.300000000000001,86.88135616206597 +2006-08-15 02:30:00-05:00,2006,8,15,2,30,22.8,20.9,0.0,0.0,0.0,0.13,1006.0,210.0,5.2,89.0480704735588 +2006-08-15 03:30:00-05:00,2006,8,15,3,30,22.6,21.1,0.0,0.0,0.0,0.13,1006.0,212.0,4.9,91.24941140171893 +2006-08-15 04:30:00-05:00,2006,8,15,4,30,22.4,21.3,0.0,0.0,0.0,0.13,1006.0,214.0,4.4,93.5051419846617 +2006-08-15 05:30:00-05:00,2006,8,15,5,30,22.5,21.4,10.0,0.0,10.0,0.13,1006.0,215.0,4.1000000000000005,93.50988044488317 +2006-08-15 06:30:00-05:00,2006,8,15,6,30,22.9,21.700000000000003,6.0,0.0,6.0,0.13,1007.0,217.0,3.8,92.9591135428814 +2006-08-15 07:30:00-05:00,2006,8,15,7,30,23.4,21.9,13.0,0.0,13.0,0.13,1007.0,221.0,3.4000000000000004,91.29967042540225 +2006-08-15 08:30:00-05:00,2006,8,15,8,30,23.9,22.1,33.0,0.0,33.0,0.13,1007.0,230.0,2.8000000000000003,89.67835237216217 +2006-08-15 09:30:00-05:00,2006,8,15,9,30,24.4,22.0,126.0,1.0,127.0,0.13,1007.0,240.0,2.2,86.4989846086086 +2006-08-15 10:30:00-05:00,2006,8,15,10,30,25.200000000000003,21.700000000000003,138.0,0.0,138.0,0.13,1008.0,245.0,1.7000000000000002,80.96759377108866 +2006-08-15 11:30:00-05:00,2006,8,15,11,30,26.200000000000003,21.200000000000003,245.0,0.0,246.0,0.13,1008.0,245.0,1.5,74.00407121193724 +2006-08-15 12:30:00-05:00,2006,8,15,12,30,27.200000000000003,20.9,365.0,531.0,836.0,0.13,1008.0,240.0,1.4,68.49913989698025 +2006-08-15 13:30:00-05:00,2006,8,15,13,30,27.700000000000003,20.5,314.0,479.0,716.0,0.13,1008.0,237.0,1.3,64.9043599483896 +2006-08-15 14:30:00-05:00,2006,8,15,14,30,27.700000000000003,20.1,262.0,15.0,273.0,0.13,1008.0,244.0,1.3,63.32036731722504 +2006-08-15 15:30:00-05:00,2006,8,15,15,30,27.3,19.700000000000003,258.0,353.0,472.0,0.13,1008.0,263.0,1.2000000000000002,63.233497650060634 +2006-08-15 16:30:00-05:00,2006,8,15,16,30,26.700000000000003,19.200000000000003,221.0,243.0,327.0,0.13,1009.0,286.0,1.2000000000000002,63.49711842728156 +2006-08-15 17:30:00-05:00,2006,8,15,17,30,25.4,19.0,117.0,173.0,161.0,0.13,1009.0,305.0,1.1,67.72347223462782 +2006-08-15 18:30:00-05:00,2006,8,15,18,30,23.700000000000003,18.7,24.0,0.0,24.0,0.13,1010.0,321.0,1.1,73.58168286508669 +2006-08-15 19:30:00-05:00,2006,8,15,19,30,22.5,17.900000000000002,0.0,0.0,0.0,0.14,1011.0,331.0,1.3,75.24830842630013 +2006-08-15 20:30:00-05:00,2006,8,15,20,30,21.8,17.2,0.0,0.0,0.0,0.14,1012.0,336.0,1.3,75.13412433380783 +2006-08-15 21:30:00-05:00,2006,8,15,21,30,21.4,16.6,0.0,0.0,0.0,0.14,1012.0,340.0,1.1,74.12129782813045 +2006-08-15 22:30:00-05:00,2006,8,15,22,30,21.1,16.2,0.0,0.0,0.0,0.14,1013.0,343.0,1.0,73.59996635036241 +2006-08-15 23:30:00-05:00,2006,8,15,23,30,20.700000000000003,16.0,0.0,0.0,0.0,0.14,1013.0,345.0,1.0,74.47641926948805 +2006-08-16 00:30:00-05:00,2006,8,16,0,30,20.1,15.8,0.0,0.0,0.0,0.14,1013.0,347.0,1.2000000000000002,76.30502625530123 +2006-08-16 01:30:00-05:00,2006,8,16,1,30,19.6,15.6,0.0,0.0,0.0,0.14,1013.0,352.0,1.8,77.70598670159683 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10:30:00-05:00,2006,8,16,10,30,28.0,14.4,106.0,898.0,855.0,0.14,1016.0,246.0,0.4,43.39162743594927 +2006-08-16 11:30:00-05:00,2006,8,16,11,30,28.5,14.7,109.0,907.0,912.0,0.14,1016.0,209.0,0.8,42.97131247789961 +2006-08-16 12:30:00-05:00,2006,8,16,12,30,28.8,15.0,111.0,905.0,911.0,0.14,1016.0,197.0,1.0,43.053596755546366 +2006-08-16 13:30:00-05:00,2006,8,16,13,30,29.0,15.0,114.0,879.0,848.0,0.14,1016.0,190.0,1.2000000000000002,42.5577048750277 +2006-08-16 14:30:00-05:00,2006,8,16,14,30,28.9,15.1,110.0,846.0,734.0,0.14,1015.0,188.0,1.3,43.080997619618586 +2006-08-16 15:30:00-05:00,2006,8,16,15,30,28.5,15.2,100.0,791.0,577.0,0.14,1015.0,190.0,1.1,44.376571785517626 +2006-08-16 16:30:00-05:00,2006,8,16,16,30,27.8,15.4,77.0,727.0,394.0,0.14,1015.0,197.0,0.8,46.820447800043176 +2006-08-16 17:30:00-05:00,2006,8,16,17,30,26.4,16.1,56.0,570.0,199.0,0.14,1016.0,196.0,0.7000000000000001,53.1581434971956 +2006-08-16 18:30:00-05:00,2006,8,16,18,30,24.700000000000003,17.1,20.0,220.0,33.0,0.14,1016.0,191.0,0.8,62.662171960573886 +2006-08-16 19:30:00-05:00,2006,8,16,19,30,23.5,17.3,0.0,0.0,0.0,0.13,1017.0,199.0,0.9,68.19953473560595 +2006-08-16 20:30:00-05:00,2006,8,16,20,30,22.700000000000003,17.7,0.0,0.0,0.0,0.13,1017.0,212.0,0.7000000000000001,73.410041192488 +2006-08-16 21:30:00-05:00,2006,8,16,21,30,22.0,18.1,0.0,0.0,0.0,0.13,1018.0,217.0,0.5,78.55494447859267 +2006-08-16 22:30:00-05:00,2006,8,16,22,30,21.4,18.5,0.0,0.0,0.0,0.13,1018.0,178.0,0.2,83.5592885796014 +2006-08-16 23:30:00-05:00,2006,8,16,23,30,21.0,18.8,0.0,0.0,0.0,0.13,1018.0,113.0,0.2,87.2573153402973 +2006-08-17 00:30:00-05:00,2006,8,17,0,30,20.700000000000003,18.9,0.0,0.0,0.0,0.13,1018.0,72.0,0.3,89.43933580836521 +2006-08-17 01:30:00-05:00,2006,8,17,1,30,20.4,18.9,0.0,0.0,0.0,0.13,1018.0,48.0,0.4,91.10897050037111 +2006-08-17 02:30:00-05:00,2006,8,17,2,30,20.1,18.8,0.0,0.0,0.0,0.13,1019.0,32.0,0.6000000000000001,92.23582378252355 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19:30:00-05:00,2006,8,17,19,30,22.3,18.3,0.0,0.0,0.0,0.13,1019.0,130.0,2.1,78.10690197391389 +2006-08-17 20:30:00-05:00,2006,8,17,20,30,21.700000000000003,19.0,0.0,0.0,0.0,0.13,1020.0,133.0,2.2,84.64379485599116 +2006-08-17 21:30:00-05:00,2006,8,17,21,30,21.1,19.700000000000003,0.0,0.0,0.0,0.13,1020.0,137.0,2.1,91.72161705089256 +2006-08-17 22:30:00-05:00,2006,8,17,22,30,20.700000000000003,20.200000000000003,0.0,0.0,0.0,0.13,1020.0,141.0,2.1,96.96214299988422 +2006-08-17 23:30:00-05:00,2006,8,17,23,30,20.4,20.4,0.0,0.0,0.0,0.13,1020.0,144.0,2.0,100.0 +2006-08-18 00:30:00-05:00,2006,8,18,0,30,20.3,20.3,0.0,0.0,0.0,0.13,1019.0,146.0,2.0,100.0 +2006-08-18 01:30:00-05:00,2006,8,18,1,30,20.200000000000003,20.200000000000003,0.0,0.0,0.0,0.13,1019.0,146.0,1.9,100.0 +2006-08-18 02:30:00-05:00,2006,8,18,2,30,20.200000000000003,20.200000000000003,0.0,0.0,0.0,0.13,1020.0,145.0,1.8,100.0 +2006-08-18 03:30:00-05:00,2006,8,18,3,30,20.3,20.3,0.0,0.0,0.0,0.13,1020.0,142.0,1.7000000000000002,100.0 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12:30:00-05:00,2006,8,18,12,30,27.0,15.7,471.0,235.0,677.0,0.13,1018.0,122.0,2.0,50.01849715554629 +2006-08-18 13:30:00-05:00,2006,8,18,13,30,27.1,15.5,271.0,669.0,825.0,0.13,1018.0,124.0,2.0,49.092873551787434 +2006-08-18 14:30:00-05:00,2006,8,18,14,30,27.0,15.5,106.0,855.0,732.0,0.13,1018.0,125.0,1.9,49.38199749911459 +2006-08-18 15:30:00-05:00,2006,8,18,15,30,26.5,15.5,98.0,800.0,574.0,0.13,1017.0,125.0,1.9,50.856683562384525 +2006-08-18 16:30:00-05:00,2006,8,18,16,30,25.700000000000003,15.4,80.0,722.0,389.0,0.13,1017.0,128.0,1.9,52.97972956660448 +2006-08-18 17:30:00-05:00,2006,8,18,17,30,24.200000000000003,15.5,58.0,553.0,192.0,0.13,1017.0,131.0,2.0,58.31081559602623 +2006-08-18 18:30:00-05:00,2006,8,18,18,30,22.6,16.0,19.0,183.0,29.0,0.13,1017.0,136.0,2.1,66.3081855879057 +2006-08-18 19:30:00-05:00,2006,8,18,19,30,21.5,16.6,0.0,0.0,0.0,0.13,1017.0,141.0,2.1,73.6688146777173 +2006-08-18 20:30:00-05:00,2006,8,18,20,30,21.0,17.5,0.0,0.0,0.0,0.13,1017.0,149.0,2.0,80.41761736659069 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06:30:00-05:00,2006,8,19,6,30,22.200000000000003,19.9,64.0,498.0,185.0,0.13,1015.0,205.0,1.1,86.82579310608945 +2006-08-19 07:30:00-05:00,2006,8,19,7,30,23.8,19.6,185.0,169.0,258.0,0.13,1015.0,202.0,1.5,77.35917986799446 +2006-08-19 08:30:00-05:00,2006,8,19,8,30,25.200000000000003,19.0,286.0,45.0,313.0,0.13,1015.0,197.0,1.6,68.53404397143255 +2006-08-19 09:30:00-05:00,2006,8,19,9,30,26.3,18.2,369.0,281.0,574.0,0.13,1015.0,190.0,1.9,61.07188431274639 +2006-08-19 10:30:00-05:00,2006,8,19,10,30,27.200000000000003,17.8,432.0,252.0,641.0,0.13,1014.0,183.0,2.3000000000000003,56.48303411492859 +2006-08-19 11:30:00-05:00,2006,8,19,11,30,27.700000000000003,17.6,457.0,148.0,587.0,0.13,1013.0,178.0,2.7,54.166645263041346 +2006-08-19 12:30:00-05:00,2006,8,19,12,30,28.0,17.7,448.0,284.0,697.0,0.13,1013.0,175.0,3.2,53.562875683091065 +2006-08-19 13:30:00-05:00,2006,8,19,13,30,28.0,17.8,303.0,448.0,672.0,0.13,1012.0,174.0,3.5,53.90129667685796 +2006-08-19 14:30:00-05:00,2006,8,19,14,30,27.6,18.0,285.0,381.0,563.0,0.13,1011.0,173.0,3.8,55.87376899917513 +2006-08-19 15:30:00-05:00,2006,8,19,15,30,26.9,18.2,248.0,200.0,367.0,0.13,1011.0,172.0,4.3,58.950702963746934 +2006-08-19 16:30:00-05:00,2006,8,19,16,30,26.1,18.7,135.0,10.0,139.0,0.13,1010.0,175.0,4.5,63.76428569912334 +2006-08-19 17:30:00-05:00,2006,8,19,17,30,25.1,19.4,76.0,4.0,77.0,0.13,1010.0,178.0,4.3,70.68275618323383 +2006-08-19 18:30:00-05:00,2006,8,19,18,30,24.0,20.200000000000003,8.0,0.0,8.0,0.13,1010.0,178.0,3.9,79.32956308217655 +2006-08-19 19:30:00-05:00,2006,8,19,19,30,23.3,20.9,0.0,0.0,0.0,0.12,1010.0,177.0,3.7,86.3947322757692 +2006-08-19 20:30:00-05:00,2006,8,19,20,30,22.9,21.5,0.0,0.0,0.0,0.12,1009.0,179.0,3.7,91.82889006049871 +2006-08-19 21:30:00-05:00,2006,8,19,21,30,22.700000000000003,21.9,0.0,0.0,0.0,0.12,1009.0,182.0,3.7,95.24920857385294 +2006-08-19 22:30:00-05:00,2006,8,19,22,30,22.700000000000003,22.200000000000003,0.0,0.0,0.0,0.12,1008.0,185.0,3.7,97.00705113892808 +2006-08-19 23:30:00-05:00,2006,8,19,23,30,22.700000000000003,22.3,0.0,0.0,0.0,0.12,1008.0,188.0,3.8,97.59928163979025 +2006-08-20 00:30:00-05:00,2006,8,20,0,30,22.700000000000003,22.4,0.0,0.0,0.0,0.12,1007.0,192.0,4.1000000000000005,98.1946774064969 +2006-08-20 01:30:00-05:00,2006,8,20,1,30,22.700000000000003,22.3,0.0,0.0,0.0,0.12,1006.0,197.0,4.5,97.59928163979025 +2006-08-20 02:30:00-05:00,2006,8,20,2,30,22.6,22.200000000000003,0.0,0.0,0.0,0.12,1006.0,202.0,4.800000000000001,97.59749434769334 +2006-08-20 03:30:00-05:00,2006,8,20,3,30,22.6,22.0,0.0,0.0,0.0,0.12,1005.0,208.0,4.9,96.41532013667852 +2006-08-20 04:30:00-05:00,2006,8,20,4,30,22.5,22.0,0.0,0.0,0.0,0.12,1005.0,214.0,4.7,97.0026050570234 +2006-08-20 05:30:00-05:00,2006,8,20,5,30,22.9,22.0,17.0,54.0,20.0,0.12,1005.0,219.0,4.3,94.67730737243399 +2006-08-20 06:30:00-05:00,2006,8,20,6,30,24.0,22.4,70.0,0.0,70.0,0.12,1005.0,227.0,4.1000000000000005,90.78368632458509 +2006-08-20 07:30:00-05:00,2006,8,20,7,30,25.5,22.700000000000003,141.0,472.0,342.0,0.12,1005.0,243.0,3.6,84.52917838487782 +2006-08-20 08:30:00-05:00,2006,8,20,8,30,27.1,22.3,165.0,605.0,524.0,0.12,1005.0,260.0,2.9000000000000004,75.06234984687318 +2006-08-20 09:30:00-05:00,2006,8,20,9,30,28.4,21.6,169.0,708.0,685.0,0.12,1005.0,269.0,2.4000000000000004,66.66461434155383 +2006-08-20 10:30:00-05:00,2006,8,20,10,30,29.4,20.9,168.0,771.0,804.0,0.12,1005.0,266.0,2.1,60.26921587989686 +2006-08-20 11:30:00-05:00,2006,8,20,11,30,30.1,20.200000000000003,171.0,788.0,860.0,0.12,1005.0,256.0,2.0,55.44380698967449 +2006-08-20 12:30:00-05:00,2006,8,20,12,30,30.5,19.5,360.0,519.0,813.0,0.12,1005.0,246.0,2.1,51.88606385276916 +2006-08-20 13:30:00-05:00,2006,8,20,13,30,30.700000000000003,18.9,346.0,512.0,767.0,0.12,1004.0,240.0,2.2,49.41409092545706 +2006-08-20 14:30:00-05:00,2006,8,20,14,30,30.5,18.5,322.0,470.0,663.0,0.12,1004.0,237.0,2.3000000000000003,48.74810016461 +2006-08-20 15:30:00-05:00,2006,8,20,15,30,30.0,18.3,147.0,652.0,530.0,0.12,1004.0,241.0,2.3000000000000003,49.54140939132912 +2006-08-20 16:30:00-05:00,2006,8,20,16,30,29.1,18.3,145.0,464.0,340.0,0.12,1005.0,249.0,2.2,52.17964878731899 +2006-08-20 17:30:00-05:00,2006,8,20,17,30,27.700000000000003,18.6,88.0,304.0,159.0,0.12,1005.0,255.0,1.9,57.67824543794533 +2006-08-20 18:30:00-05:00,2006,8,20,18,30,26.1,19.0,14.0,0.0,14.0,0.12,1006.0,262.0,1.6,64.97027345216294 +2006-08-20 19:30:00-05:00,2006,8,20,19,30,25.3,18.9,0.0,0.0,0.0,0.13,1006.0,270.0,1.6,67.70355874857643 +2006-08-20 20:30:00-05:00,2006,8,20,20,30,24.8,19.0,0.0,0.0,0.0,0.13,1007.0,277.0,1.7000000000000002,70.18814969812415 +2006-08-20 21:30:00-05:00,2006,8,20,21,30,24.200000000000003,19.0,0.0,0.0,0.0,0.13,1007.0,287.0,1.9,72.75420198574115 +2006-08-20 22:30:00-05:00,2006,8,20,22,30,23.5,19.0,0.0,0.0,0.0,0.13,1007.0,305.0,2.4000000000000004,75.88224163062468 +2006-08-20 23:30:00-05:00,2006,8,20,23,30,22.6,18.7,0.0,0.0,0.0,0.13,1008.0,321.0,3.4000000000000004,78.64118580685759 +2006-08-21 00:30:00-05:00,2006,8,21,0,30,21.5,18.0,0.0,0.0,0.0,0.13,1008.0,330.0,4.1000000000000005,80.48427639812986 +2006-08-21 01:30:00-05:00,2006,8,21,1,30,20.5,17.3,0.0,0.0,0.0,0.13,1009.0,330.0,3.9,81.89043352269586 +2006-08-21 02:30:00-05:00,2006,8,21,2,30,19.8,16.8,0.0,0.0,0.0,0.13,1009.0,326.0,3.6,82.84809311244794 +2006-08-21 03:30:00-05:00,2006,8,21,3,30,19.3,16.3,0.0,0.0,0.0,0.13,1010.0,324.0,3.5,82.78829153824725 +2006-08-21 04:30:00-05:00,2006,8,21,4,30,19.0,16.0,0.0,0.0,0.0,0.13,1011.0,321.0,3.5,82.75226606225803 +2006-08-21 05:30:00-05:00,2006,8,21,5,30,19.6,16.0,17.0,203.0,26.0,0.13,1011.0,321.0,3.7,79.71889533021026 +2006-08-21 06:30:00-05:00,2006,8,21,6,30,21.0,15.9,50.0,589.0,189.0,0.13,1012.0,327.0,4.1000000000000005,72.64922507930726 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15:30:00-05:00,2006,8,21,15,30,28.1,13.5,77.0,861.0,580.0,0.13,1012.0,324.0,2.0,40.69331607446522 +2006-08-21 16:30:00-05:00,2006,8,21,16,30,27.4,13.5,65.0,777.0,389.0,0.13,1012.0,330.0,2.0,42.39184665498537 +2006-08-21 17:30:00-05:00,2006,8,21,17,30,25.700000000000003,14.0,48.0,610.0,188.0,0.13,1012.0,335.0,1.7000000000000002,48.40644322448013 +2006-08-21 18:30:00-05:00,2006,8,21,18,30,24.0,14.9,15.0,1.0,15.0,0.13,1013.0,341.0,1.4,56.78388150398731 +2006-08-21 19:30:00-05:00,2006,8,21,19,30,23.1,15.2,0.0,0.0,0.0,0.13,1013.0,346.0,1.4,61.11701436656216 +2006-08-21 20:30:00-05:00,2006,8,21,20,30,22.5,15.5,0.0,0.0,0.0,0.13,1014.0,348.0,1.4,64.61236438831008 +2006-08-21 21:30:00-05:00,2006,8,21,21,30,22.0,15.7,0.0,0.0,0.0,0.13,1014.0,351.0,1.4,67.46743848720216 +2006-08-21 22:30:00-05:00,2006,8,21,22,30,21.6,15.9,0.0,0.0,0.0,0.13,1014.0,355.0,1.5,70.02560272397504 +2006-08-21 23:30:00-05:00,2006,8,21,23,30,21.200000000000003,16.0,0.0,0.0,0.0,0.13,1014.0,357.0,1.7000000000000002,72.22236349685366 +2006-08-22 00:30:00-05:00,2006,8,22,0,30,20.8,15.7,0.0,0.0,0.0,0.13,1014.0,180.0,2.0,72.61368014958603 +2006-08-22 01:30:00-05:00,2006,8,22,1,30,20.200000000000003,15.6,0.0,0.0,0.0,0.13,1014.0,4.0,2.2,74.87033737524062 +2006-08-22 02:30:00-05:00,2006,8,22,2,30,19.700000000000003,15.7,0.0,0.0,0.0,0.13,1015.0,8.0,2.2,77.72102059263862 +2006-08-22 03:30:00-05:00,2006,8,22,3,30,19.0,15.7,0.0,0.0,0.0,0.13,1015.0,12.0,2.2,81.18074269998286 +2006-08-22 04:30:00-05:00,2006,8,22,4,30,18.6,15.7,0.0,0.0,0.0,0.13,1015.0,15.0,2.2,83.23514898572651 +2006-08-22 05:30:00-05:00,2006,8,22,5,30,19.3,15.5,17.0,1.0,17.0,0.13,1015.0,17.0,2.2,78.66335215744836 +2006-08-22 06:30:00-05:00,2006,8,22,6,30,21.4,15.3,64.0,0.0,64.0,0.13,1015.0,18.0,1.9,68.21620173273195 +2006-08-22 07:30:00-05:00,2006,8,22,7,30,24.0,14.7,214.0,208.0,302.0,0.13,1015.0,11.0,1.5,56.05682835115787 +2006-08-22 08:30:00-05:00,2006,8,22,8,30,26.0,13.5,197.0,516.0,500.0,0.13,1015.0,168.0,1.2000000000000002,46.03393328611058 +2006-08-22 09:30:00-05:00,2006,8,22,9,30,27.200000000000003,12.6,264.0,622.0,714.0,0.13,1015.0,300.0,0.9,40.44300024466982 +2006-08-22 10:30:00-05:00,2006,8,22,10,30,28.0,12.1,99.0,912.0,847.0,0.13,1014.0,248.0,1.1,37.34766326823541 +2006-08-22 11:30:00-05:00,2006,8,22,11,30,28.700000000000003,12.2,258.0,706.0,872.0,0.13,1014.0,219.0,1.6,36.09435745214932 +2006-08-22 12:30:00-05:00,2006,8,22,12,30,29.0,12.3,270.0,682.0,862.0,0.13,1013.0,206.0,2.0,35.70624124334611 +2006-08-22 13:30:00-05:00,2006,8,22,13,30,28.8,12.6,268.0,662.0,808.0,0.13,1013.0,198.0,2.4000000000000004,36.840485852192685 +2006-08-22 14:30:00-05:00,2006,8,22,14,30,28.5,12.9,327.0,266.0,519.0,0.13,1012.0,195.0,2.7,38.23103328639364 +2006-08-22 15:30:00-05:00,2006,8,22,15,30,28.0,13.4,147.0,542.0,462.0,0.13,1012.0,197.0,2.7,40.665553795278335 +2006-08-22 16:30:00-05:00,2006,8,22,16,30,27.200000000000003,14.1,195.0,85.0,230.0,0.13,1012.0,200.0,2.7,44.59580243051316 +2006-08-22 17:30:00-05:00,2006,8,22,17,30,25.6,15.8,97.0,313.0,168.0,0.13,1012.0,204.0,2.5,54.677740314134674 +2006-08-22 18:30:00-05:00,2006,8,22,18,30,24.1,16.900000000000002,14.0,1.0,14.0,0.13,1012.0,211.0,2.5,64.13707326409121 +2006-08-22 19:30:00-05:00,2006,8,22,19,30,23.4,17.0,0.0,0.0,0.0,0.12,1013.0,222.0,2.5,67.32201452321007 +2006-08-22 20:30:00-05:00,2006,8,22,20,30,22.700000000000003,17.1,0.0,0.0,0.0,0.12,1013.0,232.0,2.6,70.6804555800278 +2006-08-22 21:30:00-05:00,2006,8,22,21,30,21.9,17.1,0.0,0.0,0.0,0.12,1012.0,240.0,2.7,74.2058192800884 +2006-08-22 22:30:00-05:00,2006,8,22,22,30,21.3,16.900000000000002,0.0,0.0,0.0,0.12,1012.0,246.0,2.8000000000000003,76.0105089646011 +2006-08-22 23:30:00-05:00,2006,8,22,23,30,20.9,16.7,0.0,0.0,0.0,0.12,1012.0,252.0,2.7,76.9180835545698 +2006-08-23 00:30:00-05:00,2006,8,23,0,30,20.5,16.6,0.0,0.0,0.0,0.12,1012.0,259.0,2.6,78.33716091002505 +2006-08-23 01:30:00-05:00,2006,8,23,1,30,20.1,16.6,0.0,0.0,0.0,0.12,1012.0,268.0,2.4000000000000004,80.29680661148132 +2006-08-23 02:30:00-05:00,2006,8,23,2,30,19.8,16.6,0.0,0.0,0.0,0.12,1012.0,277.0,2.1,81.80268174776214 +2006-08-23 03:30:00-05:00,2006,8,23,3,30,19.5,16.8,0.0,0.0,0.0,0.12,1012.0,289.0,1.8,84.40539870900028 +2006-08-23 04:30:00-05:00,2006,8,23,4,30,19.4,17.0,0.0,0.0,0.0,0.12,1012.0,306.0,1.6,86.0155022957976 +2006-08-23 05:30:00-05:00,2006,8,23,5,30,20.4,17.3,15.0,112.0,19.0,0.12,1012.0,324.0,1.6,82.39725179054236 +2006-08-23 06:30:00-05:00,2006,8,23,6,30,22.4,17.6,63.0,467.0,171.0,0.12,1012.0,337.0,1.7000000000000002,74.28995470707754 +2006-08-23 07:30:00-05:00,2006,8,23,7,30,24.8,17.400000000000002,88.0,659.0,364.0,0.12,1012.0,344.0,1.8,63.48173553592122 +2006-08-23 08:30:00-05:00,2006,8,23,8,30,26.6,16.7,105.0,763.0,551.0,0.12,1012.0,341.0,1.6,54.57978243360339 +2006-08-23 09:30:00-05:00,2006,8,23,9,30,27.700000000000003,16.400000000000002,115.0,825.0,710.0,0.12,1012.0,324.0,1.3,50.20167652690259 +2006-08-23 10:30:00-05:00,2006,8,23,10,30,28.4,15.7,141.0,826.0,816.0,0.12,1011.0,303.0,1.2000000000000002,46.08916422947838 +2006-08-23 11:30:00-05:00,2006,8,23,11,30,29.0,14.6,138.0,855.0,879.0,0.12,1011.0,282.0,1.2000000000000002,41.47487504282204 +2006-08-23 12:30:00-05:00,2006,8,23,12,30,29.200000000000003,14.1,133.0,866.0,881.0,0.12,1010.0,270.0,1.3,39.69363551618603 +2006-08-23 13:30:00-05:00,2006,8,23,13,30,29.1,13.9,315.0,594.0,798.0,0.12,1010.0,273.0,1.3,39.40945542602879 +2006-08-23 14:30:00-05:00,2006,8,23,14,30,28.8,13.7,297.0,550.0,690.0,0.12,1010.0,281.0,1.3,39.58259861551406 +2006-08-23 15:30:00-05:00,2006,8,23,15,30,28.1,13.6,270.0,385.0,492.0,0.12,1010.0,287.0,1.3,40.95893810747652 +2006-08-23 16:30:00-05:00,2006,8,23,16,30,27.200000000000003,13.8,222.0,51.0,243.0,0.12,1010.0,295.0,1.1,43.73644973810253 +2006-08-23 17:30:00-05:00,2006,8,23,17,30,25.6,14.3,112.0,139.0,143.0,0.12,1010.0,308.0,0.8,49.64978084739443 +2006-08-23 18:30:00-05:00,2006,8,23,18,30,23.8,15.3,12.0,80.0,15.0,0.12,1011.0,331.0,0.8,58.967629649989334 +2006-08-23 19:30:00-05:00,2006,8,23,19,30,22.700000000000003,15.4,0.0,0.0,0.0,0.12,1011.0,351.0,1.2000000000000002,63.42474027548933 +2006-08-23 20:30:00-05:00,2006,8,23,20,30,22.200000000000003,15.7,0.0,0.0,0.0,0.12,1012.0,179.0,1.7000000000000002,66.65022216012454 +2006-08-23 21:30:00-05:00,2006,8,23,21,30,21.6,15.5,0.0,0.0,0.0,0.12,1011.0,3.0,2.1,68.25610094016602 +2006-08-23 22:30:00-05:00,2006,8,23,22,30,21.0,15.2,0.0,0.0,0.0,0.12,1011.0,9.0,2.3000000000000003,69.46347001972654 +2006-08-23 23:30:00-05:00,2006,8,23,23,30,20.5,15.0,0.0,0.0,0.0,0.12,1011.0,16.0,2.5,70.7196755603033 +2006-08-24 00:30:00-05:00,2006,8,24,0,30,20.200000000000003,15.3,0.0,0.0,0.0,0.12,1011.0,21.0,2.5,73.44413570469506 +2006-08-24 01:30:00-05:00,2006,8,24,1,30,20.0,15.7,0.0,0.0,0.0,0.12,1011.0,28.0,2.6,76.28920465248825 +2006-08-24 02:30:00-05:00,2006,8,24,2,30,19.6,16.1,0.0,0.0,0.0,0.12,1011.0,36.0,2.5,80.22922788746642 +2006-08-24 03:30:00-05:00,2006,8,24,3,30,19.3,16.6,0.0,0.0,0.0,0.12,1012.0,44.0,2.3000000000000003,84.3834607079402 +2006-08-24 04:30:00-05:00,2006,8,24,4,30,19.1,17.0,0.0,0.0,0.0,0.12,1012.0,50.0,2.3000000000000003,87.63732753340757 +2006-08-24 05:30:00-05:00,2006,8,24,5,30,19.4,17.3,4.0,0.0,4.0,0.12,1012.0,53.0,2.3000000000000003,87.6638630524536 +2006-08-24 06:30:00-05:00,2006,8,24,6,30,20.200000000000003,17.8,16.0,0.0,16.0,0.12,1012.0,50.0,2.3000000000000003,86.09453778876379 +2006-08-24 07:30:00-05:00,2006,8,24,7,30,21.8,17.8,19.0,0.0,19.0,0.12,1012.0,57.0,2.1,78.03343537790192 +2006-08-24 08:30:00-05:00,2006,8,24,8,30,23.700000000000003,15.8,167.0,606.0,520.0,0.12,1012.0,76.0,1.9,61.254458050196995 +2006-08-24 09:30:00-05:00,2006,8,24,9,30,25.1,14.1,189.0,675.0,674.0,0.12,1012.0,96.0,1.5,50.49027043109846 +2006-08-24 10:30:00-05:00,2006,8,24,10,30,26.0,14.0,360.0,491.0,760.0,0.12,1012.0,115.0,1.2000000000000002,47.553680242907866 +2006-08-24 11:30:00-05:00,2006,8,24,11,30,26.6,14.8,187.0,764.0,847.0,0.12,1012.0,127.0,1.2000000000000002,48.33408122133133 +2006-08-24 12:30:00-05:00,2006,8,24,12,30,26.9,15.1,180.0,775.0,848.0,0.12,1011.0,130.0,1.5,48.41399435358897 +2006-08-24 13:30:00-05:00,2006,8,24,13,30,27.1,15.4,449.0,247.0,649.0,0.12,1011.0,128.0,1.7000000000000002,48.77915045601025 +2006-08-24 14:30:00-05:00,2006,8,24,14,30,26.8,15.8,207.0,630.0,655.0,0.12,1010.0,126.0,2.0,50.934785139641264 +2006-08-24 15:30:00-05:00,2006,8,24,15,30,26.200000000000003,16.1,285.0,235.0,419.0,0.12,1010.0,127.0,2.2,53.7896414941051 +2006-08-24 16:30:00-05:00,2006,8,24,16,30,25.3,16.6,216.0,168.0,284.0,0.12,1010.0,131.0,2.6,58.57303697891911 +2006-08-24 17:30:00-05:00,2006,8,24,17,30,24.0,17.3,65.0,3.0,66.0,0.12,1011.0,135.0,3.0,66.17790428882493 +2006-08-24 18:30:00-05:00,2006,8,24,18,30,22.9,17.7,6.0,0.0,6.0,0.12,1011.0,135.0,3.0,72.52549194454086 +2006-08-24 19:30:00-05:00,2006,8,24,19,30,22.1,18.3,0.0,0.0,0.0,0.12,1012.0,134.0,2.8000000000000003,79.06386533854008 +2006-08-24 20:30:00-05:00,2006,8,24,20,30,21.5,18.8,0.0,0.0,0.0,0.12,1012.0,134.0,2.5,84.62234172971134 +2006-08-24 21:30:00-05:00,2006,8,24,21,30,21.1,19.1,0.0,0.0,0.0,0.12,1012.0,137.0,2.1,88.36197963142189 +2006-08-24 22:30:00-05:00,2006,8,24,22,30,21.0,19.3,0.0,0.0,0.0,0.12,1012.0,140.0,1.9,90.0208599173631 +2006-08-24 23:30:00-05:00,2006,8,24,23,30,20.8,19.4,0.0,0.0,0.0,0.12,1012.0,141.0,1.9,91.70353621976895 +2006-08-25 00:30:00-05:00,2006,8,25,0,30,20.6,19.5,0.0,0.0,0.0,0.12,1012.0,142.0,2.0,93.41896549585323 +2006-08-25 01:30:00-05:00,2006,8,25,1,30,20.700000000000003,19.6,0.0,0.0,0.0,0.12,1011.0,144.0,2.0,93.42379740837237 +2006-08-25 02:30:00-05:00,2006,8,25,2,30,20.9,19.8,0.0,0.0,0.0,0.12,1012.0,144.0,1.9,93.43344547560373 +2006-08-25 03:30:00-05:00,2006,8,25,3,30,21.0,19.9,0.0,0.0,0.0,0.12,1012.0,140.0,1.8,93.43826164534032 +2006-08-25 04:30:00-05:00,2006,8,25,4,30,21.1,20.1,0.0,0.0,0.0,0.12,1012.0,134.0,1.9,94.02315523074246 +2006-08-25 05:30:00-05:00,2006,8,25,5,30,21.4,20.200000000000003,8.0,13.0,9.0,0.12,1012.0,129.0,2.2,92.88175438133871 +2006-08-25 06:30:00-05:00,2006,8,25,6,30,22.4,20.700000000000003,118.0,11.0,121.0,0.12,1012.0,127.0,2.5,90.12080105339504 +2006-08-25 07:30:00-05:00,2006,8,25,7,30,23.5,20.9,225.0,38.0,240.0,0.12,1012.0,131.0,2.7,85.35837992250624 +2006-08-25 08:30:00-05:00,2006,8,25,8,30,24.200000000000003,20.4,128.0,0.0,128.0,0.12,1012.0,136.0,2.6,79.35725091396382 +2006-08-25 09:30:00-05:00,2006,8,25,9,30,24.200000000000003,20.200000000000003,357.0,55.0,396.0,0.12,1012.0,139.0,2.6,78.38291200919525 +2006-08-25 10:30:00-05:00,2006,8,25,10,30,23.9,20.3,135.0,0.0,135.0,0.12,1012.0,139.0,2.4000000000000004,80.30238706259739 +2006-08-25 11:30:00-05:00,2006,8,25,11,30,23.6,20.6,33.0,0.0,33.0,0.12,1012.0,135.0,2.2,83.29293471950034 +2006-08-25 12:30:00-05:00,2006,8,25,12,30,23.5,20.8,409.0,15.0,422.0,0.12,1011.0,124.0,1.8,84.83493359135012 +2006-08-25 13:30:00-05:00,2006,8,25,13,30,23.700000000000003,20.9,485.0,131.0,591.0,0.12,1011.0,101.0,1.7000000000000002,84.33598541226517 +2006-08-25 14:30:00-05:00,2006,8,25,14,30,23.8,20.8,350.0,15.0,361.0,0.12,1012.0,73.0,1.9,83.31588388828352 +2006-08-25 15:30:00-05:00,2006,8,25,15,30,23.700000000000003,20.700000000000003,216.0,11.0,222.0,0.12,1012.0,59.0,2.5,83.30441500306857 +2006-08-25 16:30:00-05:00,2006,8,25,16,30,23.1,20.6,201.0,203.0,282.0,0.12,1012.0,56.0,3.0,85.8451653692709 +2006-08-25 17:30:00-05:00,2006,8,25,17,30,22.0,20.5,75.0,0.0,75.0,0.12,1013.0,57.0,3.4000000000000004,91.21143560546284 +2006-08-25 18:30:00-05:00,2006,8,25,18,30,21.1,20.200000000000003,4.0,4.0,4.0,0.12,1013.0,59.0,3.6,94.60639412405581 +2006-08-25 19:30:00-05:00,2006,8,25,19,30,20.6,19.8,0.0,0.0,0.0,0.12,1014.0,63.0,3.7,95.17497305529535 +2006-08-25 20:30:00-05:00,2006,8,25,20,30,20.200000000000003,19.6,0.0,0.0,0.0,0.12,1014.0,70.0,3.6,96.35079683468209 +2006-08-25 21:30:00-05:00,2006,8,25,21,30,20.0,19.3,0.0,0.0,0.0,0.12,1014.0,74.0,3.4000000000000004,95.74776649766761 +2006-08-25 22:30:00-05:00,2006,8,25,22,30,19.9,19.1,0.0,0.0,0.0,0.12,1014.0,74.0,3.4000000000000004,95.14984425936287 +2006-08-25 23:30:00-05:00,2006,8,25,23,30,20.0,19.0,0.0,0.0,0.0,0.12,1015.0,71.0,3.5,93.97450936662145 +2006-08-26 00:30:00-05:00,2006,8,26,0,30,19.9,18.9,0.0,0.0,0.0,0.12,1015.0,71.0,3.6,93.97005791607309 +2006-08-26 01:30:00-05:00,2006,8,26,1,30,19.8,18.8,0.0,0.0,0.0,0.12,1015.0,72.0,3.8,93.96560158527643 +2006-08-26 02:30:00-05:00,2006,8,26,2,30,19.700000000000003,18.7,0.0,0.0,0.0,0.12,1015.0,73.0,3.9,93.96114036717908 +2006-08-26 03:30:00-05:00,2006,8,26,3,30,19.5,18.6,0.0,0.0,0.0,0.12,1015.0,73.0,3.9,94.54217552775049 +2006-08-26 04:30:00-05:00,2006,8,26,4,30,19.4,18.5,0.0,0.0,0.0,0.12,1015.0,74.0,4.0,94.53812421757729 +2006-08-26 05:30:00-05:00,2006,8,26,5,30,19.4,18.4,3.0,0.0,3.0,0.12,1016.0,77.0,4.1000000000000005,93.94772731836991 +2006-08-26 06:30:00-05:00,2006,8,26,6,30,19.8,18.5,4.0,0.0,4.0,0.12,1017.0,79.0,4.3,92.21875616096395 +2006-08-26 07:30:00-05:00,2006,8,26,7,30,20.3,18.7,20.0,0.0,20.0,0.12,1017.0,85.0,4.4,90.53486593451623 +2006-08-26 08:30:00-05:00,2006,8,26,8,30,21.0,18.7,320.0,40.0,343.0,0.12,1018.0,93.0,4.4,86.71363189362087 +2006-08-26 09:30:00-05:00,2006,8,26,9,30,21.700000000000003,18.5,144.0,0.0,144.0,0.12,1018.0,100.0,4.3,82.03945418291906 +2006-08-26 10:30:00-05:00,2006,8,26,10,30,22.200000000000003,18.3,324.0,23.0,342.0,0.12,1018.0,103.0,4.2,78.58374658607055 +2006-08-26 11:30:00-05:00,2006,8,26,11,30,22.6,18.2,207.0,0.0,207.0,0.12,1018.0,102.0,4.2,76.216066220624 +2006-08-26 12:30:00-05:00,2006,8,26,12,30,22.700000000000003,18.1,274.0,1.0,275.0,0.12,1018.0,99.0,4.3,75.28079703215496 +2006-08-26 13:30:00-05:00,2006,8,26,13,30,22.6,18.1,219.0,0.0,219.0,0.12,1018.0,98.0,4.6000000000000005,75.7390012022252 +2006-08-26 14:30:00-05:00,2006,8,26,14,30,22.3,18.0,238.0,11.0,246.0,0.12,1018.0,98.0,4.9,76.64936162006994 +2006-08-26 15:30:00-05:00,2006,8,26,15,30,21.8,17.900000000000002,113.0,1.0,113.0,0.12,1018.0,99.0,5.0,78.52608719814607 +2006-08-26 16:30:00-05:00,2006,8,26,16,30,21.200000000000003,17.8,28.0,0.0,28.0,0.12,1018.0,98.0,4.800000000000001,80.95258341625191 +2006-08-26 17:30:00-05:00,2006,8,26,17,30,20.6,17.8,112.0,50.0,123.0,0.12,1019.0,98.0,4.7,83.99497355112099 +2006-08-26 18:30:00-05:00,2006,8,26,18,30,20.200000000000003,17.7,0.0,0.0,0.0,0.12,1019.0,99.0,4.5,85.5539905137873 +2006-08-26 19:30:00-05:00,2006,8,26,19,30,20.0,17.7,0.0,0.0,0.0,0.12,1020.0,102.0,4.5,86.6190967584921 +2006-08-26 20:30:00-05:00,2006,8,26,20,30,20.0,17.7,0.0,0.0,0.0,0.12,1020.0,106.0,4.5,86.6190967584921 +2006-08-26 21:30:00-05:00,2006,8,26,21,30,20.0,17.7,0.0,0.0,0.0,0.12,1020.0,110.0,4.5,86.6190967584921 +2006-08-26 22:30:00-05:00,2006,8,26,22,30,20.0,17.7,0.0,0.0,0.0,0.12,1020.0,114.0,4.4,86.6190967584921 +2006-08-26 23:30:00-05:00,2006,8,26,23,30,20.200000000000003,17.7,0.0,0.0,0.0,0.12,1020.0,117.0,4.4,85.5539905137873 +2006-08-27 00:30:00-05:00,2006,8,27,0,30,20.3,17.8,0.0,0.0,0.0,0.12,1019.0,118.0,4.4,85.5641754903033 +2006-08-27 01:30:00-05:00,2006,8,27,1,30,20.3,17.8,0.0,0.0,0.0,0.12,1019.0,120.0,4.4,85.5641754903033 +2006-08-27 02:30:00-05:00,2006,8,27,2,30,20.3,17.900000000000002,0.0,0.0,0.0,0.12,1019.0,122.0,4.4,86.10437145372393 +2006-08-27 03:30:00-05:00,2006,8,27,3,30,20.200000000000003,18.0,0.0,0.0,0.0,0.12,1019.0,122.0,4.4,87.18463762159959 +2006-08-27 04:30:00-05:00,2006,8,27,4,30,20.0,18.0,0.0,0.0,0.0,0.12,1019.0,119.0,4.800000000000001,88.2700446425395 +2006-08-27 05:30:00-05:00,2006,8,27,5,30,20.0,17.6,5.0,0.0,5.0,0.12,1018.0,118.0,5.5,86.07484002546536 +2006-08-27 06:30:00-05:00,2006,8,27,6,30,20.1,17.400000000000002,35.0,0.0,35.0,0.12,1018.0,120.0,6.0,84.47094524313974 +2006-08-27 07:30:00-05:00,2006,8,27,7,30,20.200000000000003,17.3,13.0,0.0,13.0,0.12,1018.0,125.0,6.1000000000000005,83.42149095195701 +2006-08-27 08:30:00-05:00,2006,8,27,8,30,20.200000000000003,17.5,38.0,0.0,38.0,0.12,1018.0,130.0,6.2,84.48183081181695 +2006-08-27 09:30:00-05:00,2006,8,27,9,30,20.3,17.900000000000002,148.0,1.0,149.0,0.12,1018.0,135.0,6.300000000000001,86.10437145372393 +2006-08-27 10:30:00-05:00,2006,8,27,10,30,20.5,18.4,197.0,2.0,198.0,0.12,1017.0,139.0,6.1000000000000005,87.7604505515571 +2006-08-27 11:30:00-05:00,2006,8,27,11,30,21.0,19.0,223.0,1.0,224.0,0.12,1017.0,143.0,5.800000000000001,88.35366560432124 +2006-08-27 12:30:00-05:00,2006,8,27,12,30,21.4,19.5,159.0,0.0,160.0,0.12,1016.0,146.0,5.6000000000000005,88.9380830861729 +2006-08-27 13:30:00-05:00,2006,8,27,13,30,21.8,20.200000000000003,125.0,0.0,125.0,0.12,1016.0,149.0,5.4,90.63691772102885 +2006-08-27 14:30:00-05:00,2006,8,27,14,30,22.1,20.700000000000003,126.0,0.0,126.0,0.12,1015.0,151.0,5.1000000000000005,91.78146811306608 +2006-08-27 15:30:00-05:00,2006,8,27,15,30,22.3,21.200000000000003,90.0,0.0,90.0,0.12,1014.0,152.0,4.9,93.50039839536869 +2006-08-27 16:30:00-05:00,2006,8,27,16,30,22.4,21.6,59.0,0.0,59.0,0.12,1014.0,156.0,5.0,95.23870791911246 +2006-08-27 17:30:00-05:00,2006,8,27,17,30,22.3,21.8,10.0,0.0,10.0,0.12,1013.0,159.0,4.9,96.9981491125962 +2006-08-27 18:30:00-05:00,2006,8,27,18,30,22.3,22.0,0.0,0.0,0.0,0.12,1013.0,163.0,4.7,98.18927681614883 +2006-08-27 19:30:00-05:00,2006,8,27,19,30,22.4,22.1,0.0,0.0,0.0,0.12,1013.0,168.0,4.5,98.19062922634309 +2006-08-27 20:30:00-05:00,2006,8,27,20,30,22.5,22.3,0.0,0.0,0.0,0.12,1013.0,173.0,4.2,98.79144464894331 +2006-08-27 21:30:00-05:00,2006,8,27,21,30,22.5,22.4,0.0,0.0,0.0,0.12,1012.0,177.0,3.9,99.39411310041719 +2006-08-27 22:30:00-05:00,2006,8,27,22,30,22.5,22.5,0.0,0.0,0.0,0.12,1012.0,184.0,3.7,100.0 +2006-08-27 23:30:00-05:00,2006,8,27,23,30,22.6,22.5,0.0,0.0,0.0,0.12,1011.0,192.0,3.8,99.3945678881514 +2006-08-28 00:30:00-05:00,2006,8,28,0,30,22.6,22.4,0.0,0.0,0.0,0.12,1011.0,197.0,4.2,98.79234922242014 +2006-08-28 01:30:00-05:00,2006,8,28,1,30,22.700000000000003,22.4,0.0,0.0,0.0,0.12,1010.0,198.0,4.5,98.1946774064969 +2006-08-28 02:30:00-05:00,2006,8,28,2,30,22.700000000000003,22.5,0.0,0.0,0.0,0.12,1010.0,199.0,4.800000000000001,98.79325278277949 +2006-08-28 03:30:00-05:00,2006,8,28,3,30,22.700000000000003,22.6,0.0,0.0,0.0,0.12,1009.0,201.0,4.800000000000001,99.39502216454267 +2006-08-28 04:30:00-05:00,2006,8,28,4,30,22.6,22.5,0.0,0.0,0.0,0.12,1009.0,202.0,4.5,99.3945678881514 +2006-08-28 05:30:00-05:00,2006,8,28,5,30,22.6,22.4,4.0,0.0,4.0,0.12,1009.0,204.0,4.2,98.79234922242014 +2006-08-28 06:30:00-05:00,2006,8,28,6,30,23.1,22.700000000000003,28.0,0.0,28.0,0.12,1009.0,210.0,3.8,97.60641098320963 +2006-08-28 07:30:00-05:00,2006,8,28,7,30,23.8,23.1,109.0,0.0,109.0,0.12,1009.0,217.0,3.5,95.86564657238323 +2006-08-28 08:30:00-05:00,2006,8,28,8,30,24.8,23.4,65.0,0.0,65.0,0.12,1009.0,225.0,2.9000000000000004,91.93991028661533 +2006-08-28 09:30:00-05:00,2006,8,28,9,30,25.8,23.0,126.0,0.0,126.0,0.12,1009.0,234.0,2.1,84.56104094151235 +2006-08-28 10:30:00-05:00,2006,8,28,10,30,26.6,22.4,184.0,0.0,184.0,0.12,1008.0,235.0,1.4,77.7738139595237 +2006-08-28 11:30:00-05:00,2006,8,28,11,30,27.0,22.1,243.0,0.0,243.0,0.12,1008.0,230.0,1.0,74.59053556051052 +2006-08-28 12:30:00-05:00,2006,8,28,12,30,27.1,21.9,396.0,81.0,465.0,0.12,1008.0,218.0,0.7000000000000001,73.25493893485287 +2006-08-28 13:30:00-05:00,2006,8,28,13,30,26.8,21.9,375.0,216.0,547.0,0.12,1007.0,201.0,0.5,74.55781643036342 +2006-08-28 14:30:00-05:00,2006,8,28,14,30,26.1,21.9,321.0,58.0,362.0,0.12,1007.0,171.0,0.4,77.70056672639573 +2006-08-28 15:30:00-05:00,2006,8,28,15,30,25.3,22.0,261.0,150.0,345.0,0.12,1007.0,119.0,0.3,81.97484863603587 +2006-08-28 16:30:00-05:00,2006,8,28,16,30,24.700000000000003,22.200000000000003,194.0,213.0,276.0,0.12,1007.0,66.0,0.4,86.00216948196001 +2006-08-28 17:30:00-05:00,2006,8,28,17,30,23.9,22.4,66.0,2.0,67.0,0.12,1007.0,27.0,0.5,91.33086637569157 +2006-08-28 18:30:00-05:00,2006,8,28,18,30,23.1,22.3,0.0,0.0,0.0,0.12,1008.0,10.0,0.9,95.26315595399393 +2006-08-28 19:30:00-05:00,2006,8,28,19,30,22.6,22.0,0.0,0.0,0.0,0.12,1008.0,10.0,1.4,96.41532013667852 +2006-08-28 20:30:00-05:00,2006,8,28,20,30,22.200000000000003,21.8,0.0,0.0,0.0,0.12,1008.0,16.0,1.9,97.59032526636244 +2006-08-28 21:30:00-05:00,2006,8,28,21,30,21.8,21.5,0.0,0.0,0.0,0.12,1008.0,22.0,2.1,98.18249203803803 +2006-08-28 22:30:00-05:00,2006,8,28,22,30,21.4,21.200000000000003,0.0,0.0,0.0,0.12,1007.0,27.0,2.2,98.78142704047978 +2006-08-28 23:30:00-05:00,2006,8,28,23,30,21.0,21.0,0.0,0.0,0.0,0.12,1007.0,30.0,2.1,100.0 +2006-08-29 00:30:00-05:00,2006,8,29,0,30,20.700000000000003,20.700000000000003,0.0,0.0,0.0,0.12,1007.0,34.0,2.1,100.0 +2006-08-29 01:30:00-05:00,2006,8,29,1,30,20.5,20.5,0.0,0.0,0.0,0.12,1007.0,43.0,2.2,100.0 +2006-08-29 02:30:00-05:00,2006,8,29,2,30,20.200000000000003,20.200000000000003,0.0,0.0,0.0,0.12,1007.0,55.0,2.3000000000000003,100.0 +2006-08-29 03:30:00-05:00,2006,8,29,3,30,20.0,20.0,0.0,0.0,0.0,0.12,1007.0,64.0,2.5,100.0 +2006-08-29 04:30:00-05:00,2006,8,29,4,30,19.9,19.8,0.0,0.0,0.0,0.12,1007.0,70.0,2.7,99.38210659572964 +2006-08-29 05:30:00-05:00,2006,8,29,5,30,20.0,19.5,3.0,0.0,3.0,0.12,1007.0,73.0,3.0,96.94618740128206 +2006-08-29 06:30:00-05:00,2006,8,29,6,30,20.200000000000003,19.1,43.0,1.0,43.0,0.12,1007.0,78.0,3.2,93.39958514384797 +2006-08-29 07:30:00-05:00,2006,8,29,7,30,20.5,18.9,121.0,1.0,121.0,0.12,1007.0,83.0,3.2,90.54856721493562 +2006-08-29 08:30:00-05:00,2006,8,29,8,30,21.1,18.8,54.0,0.0,54.0,0.12,1006.0,91.0,3.2,86.72303177339619 +2006-08-29 09:30:00-05:00,2006,8,29,9,30,21.5,19.1,73.0,0.0,73.0,0.12,1006.0,101.0,3.2,86.22158939071822 +2006-08-29 10:30:00-05:00,2006,8,29,10,30,21.8,19.3,22.0,0.0,22.0,0.12,1006.0,110.0,3.3000000000000003,85.715704829656 +2006-08-29 11:30:00-05:00,2006,8,29,11,30,22.1,19.6,187.0,1.0,188.0,0.12,1006.0,118.0,3.4000000000000004,85.74573282838074 +2006-08-29 12:30:00-05:00,2006,8,29,12,30,22.3,19.8,161.0,1.0,162.0,0.12,1005.0,123.0,3.5,85.76570048648686 +2006-08-29 13:30:00-05:00,2006,8,29,13,30,22.200000000000003,20.1,41.0,0.0,41.0,0.12,1005.0,125.0,3.7,87.9075598229277 +2006-08-29 14:30:00-05:00,2006,8,29,14,30,22.0,20.3,212.0,7.0,216.0,0.12,1005.0,125.0,3.9,90.09239723117317 +2006-08-29 15:30:00-05:00,2006,8,29,15,30,21.6,20.4,64.0,0.0,64.0,0.12,1005.0,123.0,4.0,92.89214142096014 +2006-08-29 16:30:00-05:00,2006,8,29,16,30,21.200000000000003,20.4,24.0,0.0,24.0,0.12,1005.0,119.0,4.0,95.19635824792046 +2006-08-29 17:30:00-05:00,2006,8,29,17,30,20.700000000000003,20.3,69.0,37.0,76.0,0.12,1005.0,113.0,4.0,97.56315463507379 +2006-08-29 18:30:00-05:00,2006,8,29,18,30,20.4,20.200000000000003,0.0,0.0,0.0,0.12,1005.0,107.0,3.9,98.772211872832 +2006-08-29 19:30:00-05:00,2006,8,29,19,30,20.200000000000003,20.1,0.0,0.0,0.0,0.12,1006.0,99.0,3.8,99.38351006957461 +2006-08-29 20:30:00-05:00,2006,8,29,20,30,20.0,20.0,0.0,0.0,0.0,0.12,1006.0,88.0,3.8,100.0 +2006-08-29 21:30:00-05:00,2006,8,29,21,30,19.8,19.8,0.0,0.0,0.0,0.12,1006.0,77.0,3.9,100.0 +2006-08-29 22:30:00-05:00,2006,8,29,22,30,19.6,19.5,0.0,0.0,0.0,0.12,1006.0,66.0,4.2,99.38069832879764 +2006-08-29 23:30:00-05:00,2006,8,29,23,30,19.200000000000003,19.200000000000003,0.0,0.0,0.0,0.12,1007.0,57.0,4.5,100.0 +2006-08-30 00:30:00-05:00,2006,8,30,0,30,18.8,18.7,0.0,0.0,0.0,0.12,1007.0,51.0,4.7,99.37691935105258 +2006-08-30 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02:30:00-05:00,2012,9,30,2,30,14.1,12.3,0.0,0.0,0.0,0.13,1007.0,27.0,1.4,88.9199908723776 +2012-09-30 03:30:00-05:00,2012,9,30,3,30,13.9,12.1,0.0,0.0,0.0,0.13,1007.0,14.0,1.2000000000000002,88.90367247234802 +2012-09-30 04:30:00-05:00,2012,9,30,4,30,13.8,12.1,0.0,0.0,0.0,0.13,1007.0,175.0,1.2000000000000002,89.48262435508536 +2012-09-30 05:30:00-05:00,2012,9,30,5,30,13.8,12.0,0.0,0.0,0.0,0.13,1007.0,342.0,1.4,88.89550004834233 +2012-09-30 06:30:00-05:00,2012,9,30,6,30,14.4,12.2,34.0,0.0,34.0,0.13,1007.0,339.0,1.7000000000000002,86.63915156865335 +2012-09-30 07:30:00-05:00,2012,9,30,7,30,15.6,12.3,134.0,28.0,142.0,0.13,1007.0,332.0,1.8,80.7311582049925 +2012-09-30 08:30:00-05:00,2012,9,30,8,30,16.7,12.1,160.0,525.0,403.0,0.13,1007.0,314.0,1.8,74.27944770404169 +2012-09-30 09:30:00-05:00,2012,9,30,9,30,17.7,11.7,102.0,811.0,584.0,0.13,1006.0,295.0,1.9,67.91259725441333 +2012-09-30 10:30:00-05:00,2012,9,30,10,30,18.5,11.4,100.0,869.0,692.0,0.13,1006.0,281.0,1.8,63.31633332113006 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19:30:00-05:00,2012,9,30,19,30,15.2,14.5,0.0,0.0,0.0,0.13,1021.0,50.0,6.1000000000000005,95.59157646693424 +2012-09-30 20:30:00-05:00,2012,9,30,20,30,14.8,14.2,0.0,0.0,0.0,0.13,1021.0,46.0,6.0,96.19913801083652 +2012-09-30 21:30:00-05:00,2012,9,30,21,30,14.5,13.8,0.0,0.0,0.0,0.13,1020.0,43.0,5.800000000000001,95.56808726009159 +2012-09-30 22:30:00-05:00,2012,9,30,22,30,14.2,13.5,0.0,0.0,0.0,0.13,1020.0,42.0,5.7,95.55796340708034 +2012-09-30 23:30:00-05:00,2012,9,30,23,30,14.1,13.3,0.0,0.0,0.0,0.13,1020.0,41.0,5.5,94.93393793300804 +2016-10-01 00:30:00-05:00,2016,10,1,0,30,13.9,13.1,0.0,0.0,0.0,0.13,1020.0,39.0,5.300000000000001,94.92623971954224 +2016-10-01 01:30:00-05:00,2016,10,1,1,30,13.8,13.0,0.0,0.0,0.0,0.13,1019.0,37.0,5.1000000000000005,94.92238409127798 +2016-10-01 02:30:00-05:00,2016,10,1,2,30,13.8,13.0,0.0,0.0,0.0,0.13,1019.0,36.0,4.9,94.92238409127798 +2016-10-01 03:30:00-05:00,2016,10,1,3,30,13.8,13.0,0.0,0.0,0.0,0.13,1019.0,36.0,4.7,94.92238409127798 +2016-10-01 04:30:00-05:00,2016,10,1,4,30,13.8,13.0,0.0,0.0,0.0,0.13,1019.0,35.0,4.7,94.92238409127798 +2016-10-01 05:30:00-05:00,2016,10,1,5,30,13.8,13.0,0.0,0.0,0.0,0.13,1019.0,33.0,4.5,94.92238409127798 +2016-10-01 06:30:00-05:00,2016,10,1,6,30,14.1,13.1,10.0,0.0,10.0,0.13,1019.0,34.0,4.6000000000000005,93.70328855100813 +2016-10-01 07:30:00-05:00,2016,10,1,7,30,14.7,13.4,26.0,0.0,26.0,0.13,1019.0,38.0,4.7,91.91991565315384 +2016-10-01 08:30:00-05:00,2016,10,1,8,30,15.6,13.7,133.0,1.0,133.0,0.13,1019.0,42.0,4.7,88.46472062616759 +2016-10-01 09:30:00-05:00,2016,10,1,9,30,16.7,14.0,185.0,1.0,185.0,0.13,1019.0,45.0,4.5,84.0941494565074 +2016-10-01 10:30:00-05:00,2016,10,1,10,30,17.6,14.4,163.0,3.0,165.0,0.13,1018.0,47.0,4.2,81.52287367292682 +2016-10-01 11:30:00-05:00,2016,10,1,11,30,18.2,14.7,131.0,1.0,131.0,0.13,1018.0,51.0,3.9,80.03823293476121 +2016-10-01 12:30:00-05:00,2016,10,1,12,30,18.6,15.1,150.0,2.0,151.0,0.13,1018.0,58.0,3.7,80.09307141658095 +2016-10-01 13:30:00-05:00,2016,10,1,13,30,18.7,15.4,232.0,21.0,245.0,0.13,1017.0,63.0,3.5,81.141682573384 +2016-10-01 14:30:00-05:00,2016,10,1,14,30,18.5,15.6,108.0,1.0,108.0,0.13,1017.0,62.0,3.7,83.22340226616895 +2016-10-01 15:30:00-05:00,2016,10,1,15,30,18.1,15.8,168.0,196.0,243.0,0.13,1017.0,61.0,4.0,86.43675721494013 +2016-10-01 16:30:00-05:00,2016,10,1,16,30,17.6,16.0,27.0,0.0,27.0,0.13,1016.0,61.0,4.1000000000000005,90.34700035313568 +2016-10-01 17:30:00-05:00,2016,10,1,17,30,17.1,16.1,0.0,0.0,0.0,0.13,1017.0,60.0,3.9,93.84340984841995 +2016-10-01 18:30:00-05:00,2016,10,1,18,30,16.7,16.0,0.0,0.0,0.0,0.13,1017.0,59.0,3.9,95.64129067521621 +2016-10-01 19:30:00-05:00,2016,10,1,19,30,16.400000000000002,15.9,0.0,0.0,0.0,0.13,1017.0,60.0,4.0,96.86212103758015 +2016-10-01 20:30:00-05:00,2016,10,1,20,30,16.1,15.6,0.0,0.0,0.0,0.13,1017.0,62.0,4.1000000000000005,96.85496015316411 +2016-10-01 21:30:00-05:00,2016,10,1,21,30,15.8,15.3,0.0,0.0,0.0,0.13,1016.0,63.0,4.1000000000000005,96.84777486359054 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07:30:00-05:00,2016,10,2,7,30,15.3,13.8,31.0,0.0,31.0,0.13,1016.0,43.0,2.6,90.7703444199074 +2016-10-02 08:30:00-05:00,2016,10,2,8,30,16.3,14.0,202.0,67.0,233.0,0.13,1016.0,44.0,2.4000000000000004,86.26065349093835 +2016-10-02 09:30:00-05:00,2016,10,2,9,30,17.400000000000002,14.3,234.0,89.0,286.0,0.13,1015.0,46.0,2.1,82.02619571796478 +2016-10-02 10:30:00-05:00,2016,10,2,10,30,18.5,14.6,275.0,41.0,303.0,0.13,1015.0,51.0,1.7000000000000002,78.0418580542799 +2016-10-02 11:30:00-05:00,2016,10,2,11,30,19.3,15.0,160.0,2.0,162.0,0.13,1015.0,65.0,1.2000000000000002,76.17803472320432 +2016-10-02 12:30:00-05:00,2016,10,2,12,30,19.700000000000003,15.2,168.0,3.0,170.0,0.13,1014.0,93.0,0.8,75.26921332186772 +2016-10-02 13:30:00-05:00,2016,10,2,13,30,20.0,15.3,131.0,2.0,132.0,0.13,1014.0,129.0,0.7000000000000001,74.35848005153662 +2016-10-02 14:30:00-05:00,2016,10,2,14,30,20.200000000000003,15.4,174.0,13.0,181.0,0.13,1013.0,151.0,0.7000000000000001,73.91685850768738 +2016-10-02 15:30:00-05:00,2016,10,2,15,30,19.9,15.4,148.0,24.0,157.0,0.13,1013.0,150.0,0.8,75.30202157017906 +2016-10-02 16:30:00-05:00,2016,10,2,16,30,19.200000000000003,15.5,44.0,1.0,44.0,0.13,1013.0,138.0,0.9,79.15467691478645 +2016-10-02 17:30:00-05:00,2016,10,2,17,30,18.2,15.7,0.0,0.0,0.0,0.13,1013.0,126.0,1.0,85.34807689648305 +2016-10-02 18:30:00-05:00,2016,10,2,18,30,17.5,15.7,0.0,0.0,0.0,0.13,1014.0,118.0,1.1,89.19210029971087 +2016-10-02 19:30:00-05:00,2016,10,2,19,30,16.900000000000002,15.8,0.0,0.0,0.0,0.13,1014.0,112.0,1.0,93.23642044435698 +2016-10-02 20:30:00-05:00,2016,10,2,20,30,16.5,15.8,0.0,0.0,0.0,0.13,1014.0,108.0,0.8,95.63471046436408 +2016-10-02 21:30:00-05:00,2016,10,2,21,30,16.2,15.8,0.0,0.0,0.0,0.13,1013.0,114.0,0.7000000000000001,97.47883979097416 +2016-10-02 22:30:00-05:00,2016,10,2,22,30,16.0,15.7,0.0,0.0,0.0,0.13,1013.0,134.0,0.6000000000000001,98.10093011701596 +2016-10-02 23:30:00-05:00,2016,10,2,23,30,15.9,15.7,0.0,0.0,0.0,0.13,1013.0,163.0,0.6000000000000001,98.72942465931008 +2016-10-03 00:30:00-05:00,2016,10,3,0,30,15.7,15.6,0.0,0.0,0.0,0.13,1013.0,191.0,0.7000000000000001,99.3619445840915 +2016-10-03 01:30:00-05:00,2016,10,3,1,30,15.6,15.6,0.0,0.0,0.0,0.13,1013.0,215.0,0.8,100.0 +2016-10-03 02:30:00-05:00,2016,10,3,2,30,15.4,15.4,0.0,0.0,0.0,0.13,1012.0,233.0,0.9,100.0 +2016-10-03 03:30:00-05:00,2016,10,3,3,30,15.3,15.3,0.0,0.0,0.0,0.13,1012.0,245.0,1.0,99.99999999999999 +2016-10-03 04:30:00-05:00,2016,10,3,4,30,15.1,15.1,0.0,0.0,0.0,0.13,1012.0,255.0,1.1,100.0 +2016-10-03 05:30:00-05:00,2016,10,3,5,30,15.1,15.1,0.0,0.0,0.0,0.13,1012.0,266.0,1.2000000000000002,100.0 +2016-10-03 06:30:00-05:00,2016,10,3,6,30,16.0,15.3,29.0,0.0,29.0,0.13,1013.0,278.0,1.4,95.61819510862335 +2016-10-03 07:30:00-05:00,2016,10,3,7,30,17.400000000000002,15.5,73.0,568.0,236.0,0.13,1013.0,289.0,1.4,88.61475410380147 +2016-10-03 08:30:00-05:00,2016,10,3,8,30,18.9,15.4,120.0,0.0,120.0,0.13,1013.0,297.0,1.3,80.1340589120772 +2016-10-03 09:30:00-05:00,2016,10,3,9,30,20.3,14.9,267.0,25.0,282.0,0.13,1013.0,302.0,1.0,71.13877531411072 +2016-10-03 10:30:00-05:00,2016,10,3,10,30,21.3,14.5,344.0,75.0,394.0,0.13,1013.0,300.0,0.7000000000000001,65.18984802472426 +2016-10-03 11:30:00-05:00,2016,10,3,11,30,21.9,14.3,108.0,855.0,711.0,0.13,1013.0,266.0,0.4,62.03375578233429 +2016-10-03 12:30:00-05:00,2016,10,3,12,30,22.200000000000003,13.7,107.0,850.0,695.0,0.13,1012.0,210.0,0.3,58.585723024923766 +2016-10-03 13:30:00-05:00,2016,10,3,13,30,22.200000000000003,13.4,100.0,829.0,621.0,0.13,1012.0,174.0,0.4,57.45329891260777 +2016-10-03 14:30:00-05:00,2016,10,3,14,30,22.0,13.0,95.0,767.0,493.0,0.13,1012.0,145.0,0.3,56.65969850703809 +2016-10-03 15:30:00-05:00,2016,10,3,15,30,21.6,12.8,129.0,2.0,130.0,0.13,1013.0,120.0,0.3,57.306455028555 +2016-10-03 16:30:00-05:00,2016,10,3,16,30,20.700000000000003,13.2,57.0,445.0,143.0,0.13,1013.0,124.0,0.5,62.16458510592052 +2016-10-03 17:30:00-05:00,2016,10,3,17,30,19.700000000000003,13.8,0.0,0.0,0.0,0.13,1014.0,145.0,0.8,68.76222084955101 +2016-10-03 18:30:00-05:00,2016,10,3,18,30,18.9,13.8,0.0,0.0,0.0,0.13,1014.0,156.0,1.0,72.27277790760827 +2016-10-03 19:30:00-05:00,2016,10,3,19,30,18.3,13.7,0.0,0.0,0.0,0.13,1015.0,158.0,0.9,74.55195962279107 +2016-10-03 20:30:00-05:00,2016,10,3,20,30,17.7,13.8,0.0,0.0,0.0,0.13,1015.0,147.0,0.8,77.92213377192795 +2016-10-03 21:30:00-05:00,2016,10,3,21,30,17.2,14.1,0.0,0.0,0.0,0.13,1016.0,115.0,0.8,82.00105929920943 +2016-10-03 22:30:00-05:00,2016,10,3,22,30,16.7,14.2,0.0,0.0,0.0,0.13,1016.0,85.0,1.1,85.19082969402116 +2016-10-03 23:30:00-05:00,2016,10,3,23,30,16.400000000000002,14.4,0.0,0.0,0.0,0.13,1017.0,76.0,1.8,87.96161571812776 +2016-10-04 00:30:00-05:00,2016,10,4,0,30,16.2,14.8,0.0,0.0,0.0,0.13,1017.0,75.0,2.5,91.41883781841517 +2016-10-04 01:30:00-05:00,2016,10,4,1,30,16.2,15.2,0.0,0.0,0.0,0.13,1018.0,72.0,3.2,93.80186299495907 +2016-10-04 02:30:00-05:00,2016,10,4,2,30,16.1,15.4,0.0,0.0,0.0,0.13,1018.0,66.0,3.8,95.62150561080094 +2016-10-04 03:30:00-05:00,2016,10,4,3,30,15.9,15.3,0.0,0.0,0.0,0.13,1019.0,60.0,4.0,96.23078373238158 +2016-10-04 04:30:00-05:00,2016,10,4,4,30,15.5,15.0,0.0,0.0,0.0,0.13,1019.0,54.0,4.0,96.84056505846642 +2016-10-04 05:30:00-05:00,2016,10,4,5,30,15.3,14.7,0.0,0.0,0.0,0.13,1020.0,48.0,4.1000000000000005,96.21357131236407 +2016-10-04 06:30:00-05:00,2016,10,4,6,30,15.5,14.5,30.0,341.0,64.0,0.13,1021.0,45.0,4.4,93.76926066502085 +2016-10-04 07:30:00-05:00,2016,10,4,7,30,16.400000000000002,14.4,57.0,653.0,243.0,0.13,1022.0,45.0,4.7,87.96161571812776 +2016-10-04 08:30:00-05:00,2016,10,4,8,30,17.7,13.8,112.0,0.0,112.0,0.13,1022.0,47.0,4.7,77.92213377192795 +2016-10-04 09:30:00-05:00,2016,10,4,9,30,18.9,13.2,142.0,0.0,142.0,0.13,1022.0,49.0,4.7,69.50474815590738 +2016-10-04 10:30:00-05:00,2016,10,4,10,30,19.8,13.1,221.0,1.0,221.0,0.13,1022.0,51.0,4.7,65.2922046207685 +2016-10-04 11:30:00-05:00,2016,10,4,11,30,20.5,13.0,234.0,1.0,234.0,0.13,1022.0,53.0,4.6000000000000005,62.11907326195469 +2016-10-04 12:30:00-05:00,2016,10,4,12,30,21.0,12.9,339.0,74.0,390.0,0.13,1021.0,57.0,4.4,59.84374474323976 +2016-10-04 13:30:00-05:00,2016,10,4,13,30,21.200000000000003,12.9,341.0,107.0,408.0,0.13,1021.0,65.0,4.4,59.11340707934051 +2016-10-04 14:30:00-05:00,2016,10,4,14,30,20.9,12.9,282.0,87.0,327.0,0.13,1021.0,74.0,4.3,60.21271053186896 +2016-10-04 15:30:00-05:00,2016,10,4,15,30,20.200000000000003,12.9,70.0,712.0,330.0,0.13,1021.0,80.0,4.0,62.86817844275406 +2016-10-04 16:30:00-05:00,2016,10,4,16,30,19.1,13.0,51.0,482.0,142.0,0.13,1021.0,82.0,3.4000000000000004,67.752417809403 +2016-10-04 17:30:00-05:00,2016,10,4,17,30,17.900000000000002,13.4,0.0,0.0,0.0,0.13,1022.0,81.0,3.0,74.97116284762808 +2016-10-04 18:30:00-05:00,2016,10,4,18,30,17.0,13.4,0.0,0.0,0.0,0.13,1023.0,77.0,3.0,79.35403309101766 +2016-10-04 19:30:00-05:00,2016,10,4,19,30,16.400000000000002,13.5,0.0,0.0,0.0,0.13,1023.0,71.0,3.4000000000000004,82.97394914545454 +2016-10-04 20:30:00-05:00,2016,10,4,20,30,15.8,13.6,0.0,0.0,0.0,0.13,1023.0,65.0,3.8,86.77388006229175 +2016-10-04 21:30:00-05:00,2016,10,4,21,30,15.3,13.6,0.0,0.0,0.0,0.13,1023.0,59.0,4.1000000000000005,89.5982175992295 +2016-10-04 22:30:00-05:00,2016,10,4,22,30,14.8,13.5,0.0,0.0,0.0,0.13,1022.0,55.0,4.2,91.92593709832187 +2016-10-04 23:30:00-05:00,2016,10,4,23,30,14.2,13.1,0.0,0.0,0.0,0.13,1022.0,53.0,4.2,93.09843795330515 +2016-10-05 00:30:00-05:00,2016,10,5,0,30,13.5,12.5,0.0,0.0,0.0,0.13,1023.0,52.0,4.0,93.67469737295787 +2016-10-05 01:30:00-05:00,2016,10,5,1,30,13.0,11.9,0.0,0.0,0.0,0.13,1023.0,49.0,3.9,93.03577109694396 +2016-10-05 02:30:00-05:00,2016,10,5,2,30,12.6,11.3,0.0,0.0,0.0,0.13,1023.0,45.0,3.9,91.79191760702277 +2016-10-05 03:30:00-05:00,2016,10,5,3,30,12.3,10.8,0.0,0.0,0.0,0.13,1023.0,41.0,4.0,90.56220498879804 +2016-10-05 04:30:00-05:00,2016,10,5,4,30,11.9,10.6,0.0,0.0,0.0,0.13,1024.0,38.0,4.0,91.74858571936001 +2016-10-05 05:30:00-05:00,2016,10,5,5,30,11.7,10.4,0.0,0.0,0.0,0.13,1024.0,35.0,4.0,91.73614313526444 +2016-10-05 06:30:00-05:00,2016,10,5,6,30,12.5,10.4,26.0,428.0,67.0,0.13,1024.0,36.0,4.3,87.03178722809886 +2016-10-05 07:30:00-05:00,2016,10,5,7,30,14.1,9.8,49.0,722.0,252.0,0.13,1024.0,38.0,4.5,75.32872096173011 +2016-10-05 08:30:00-05:00,2016,10,5,8,30,15.6,8.8,60.0,849.0,437.0,0.13,1024.0,42.0,4.3,63.942022763058674 +2016-10-05 09:30:00-05:00,2016,10,5,9,30,17.1,8.3,68.0,910.0,589.0,0.13,1024.0,46.0,3.9,56.184541150276765 +2016-10-05 10:30:00-05:00,2016,10,5,10,30,18.4,8.1,72.0,941.0,693.0,0.13,1023.0,53.0,3.6,51.06965278206888 +2016-10-05 11:30:00-05:00,2016,10,5,11,30,19.3,8.1,75.0,947.0,736.0,0.13,1023.0,61.0,3.3000000000000003,48.27822051284218 +2016-10-05 12:30:00-05:00,2016,10,5,12,30,20.0,8.3,76.0,942.0,719.0,0.13,1022.0,68.0,3.0,46.856987253918525 +2016-10-05 13:30:00-05:00,2016,10,5,13,30,20.3,8.6,75.0,915.0,641.0,0.13,1022.0,74.0,2.8000000000000003,46.939825204869656 +2016-10-05 14:30:00-05:00,2016,10,5,14,30,20.200000000000003,9.0,69.0,864.0,508.0,0.13,1021.0,78.0,2.7,48.524146701355086 +2016-10-05 15:30:00-05:00,2016,10,5,15,30,19.6,9.5,59.0,772.0,337.0,0.13,1021.0,82.0,2.6,52.085440593266185 +2016-10-05 16:30:00-05:00,2016,10,5,16,30,17.900000000000002,10.1,42.0,578.0,147.0,0.13,1021.0,87.0,2.4000000000000004,60.30080566067038 +2016-10-05 17:30:00-05:00,2016,10,5,17,30,15.9,11.1,0.0,0.0,0.0,0.13,1022.0,92.0,2.1,73.16546207770605 +2016-10-05 18:30:00-05:00,2016,10,5,18,30,14.9,11.3,0.0,0.0,0.0,0.13,1022.0,95.0,2.1,79.05245768830524 +2016-10-05 19:30:00-05:00,2016,10,5,19,30,14.4,11.9,0.0,0.0,0.0,0.13,1022.0,95.0,2.0,84.94491417313903 +2016-10-05 20:30:00-05:00,2016,10,5,20,30,14.0,12.3,0.0,0.0,0.0,0.13,1022.0,93.0,1.9,89.49814550919542 +2016-10-05 21:30:00-05:00,2016,10,5,21,30,14.0,12.6,0.0,0.0,0.0,0.13,1022.0,90.0,1.8,91.27755749163457 +2016-10-05 22:30:00-05:00,2016,10,5,22,30,14.1,12.7,0.0,0.0,0.0,0.13,1022.0,83.0,1.7000000000000002,91.284053515888 +2016-10-05 23:30:00-05:00,2016,10,5,23,30,14.1,12.7,0.0,0.0,0.0,0.13,1022.0,74.0,1.7000000000000002,91.284053515888 +2016-10-06 00:30:00-05:00,2016,10,6,0,30,14.0,12.6,0.0,0.0,0.0,0.13,1022.0,68.0,1.9,91.27755749163457 +2016-10-06 01:30:00-05:00,2016,10,6,1,30,13.8,12.6,0.0,0.0,0.0,0.13,1022.0,63.0,2.1,92.46978318278198 +2016-10-06 02:30:00-05:00,2016,10,6,2,30,13.4,12.6,0.0,0.0,0.0,0.13,1022.0,61.0,2.3000000000000003,94.90691794977613 +2016-10-06 03:30:00-05:00,2016,10,6,3,30,13.0,12.5,0.0,0.0,0.0,0.13,1022.0,59.0,2.4000000000000004,96.77951494128284 +2016-10-06 04:30:00-05:00,2016,10,6,4,30,12.5,12.4,0.0,0.0,0.0,0.13,1022.0,56.0,2.3000000000000003,99.3459139749421 +2016-10-06 05:30:00-05:00,2016,10,6,5,30,12.3,12.2,0.0,0.0,0.0,0.13,1023.0,51.0,2.2,99.34489208728955 +2016-10-06 06:30:00-05:00,2016,10,6,6,30,13.4,12.0,28.0,367.0,62.0,0.13,1023.0,46.0,2.2,91.23843096415187 +2016-10-06 07:30:00-05:00,2016,10,6,7,30,15.4,12.3,56.0,679.0,244.0,0.13,1023.0,43.0,2.2,81.77253705760639 +2016-10-06 08:30:00-05:00,2016,10,6,8,30,17.2,12.4,70.0,816.0,429.0,0.13,1023.0,42.0,2.0,73.39578078109831 +2016-10-06 09:30:00-05:00,2016,10,6,9,30,18.8,11.8,78.0,887.0,582.0,0.13,1023.0,43.0,1.8,63.803179413056576 +2016-10-06 10:30:00-05:00,2016,10,6,10,30,20.1,11.2,78.0,930.0,687.0,0.13,1023.0,48.0,1.5,56.56061281995897 +2016-10-06 11:30:00-05:00,2016,10,6,11,30,21.200000000000003,10.4,79.0,943.0,732.0,0.13,1022.0,60.0,1.3,50.11713110519327 +2016-10-06 12:30:00-05:00,2016,10,6,12,30,22.0,10.1,79.0,937.0,715.0,0.13,1022.0,82.0,1.1,46.777440874305384 +2016-10-06 13:30:00-05:00,2016,10,6,13,30,22.3,9.8,75.0,917.0,637.0,0.13,1022.0,107.0,1.2000000000000002,45.01743359215862 +2016-10-06 14:30:00-05:00,2016,10,6,14,30,22.200000000000003,9.7,69.0,868.0,506.0,0.13,1021.0,124.0,1.5,44.98967489901451 +2016-10-06 15:30:00-05:00,2016,10,6,15,30,21.4,9.9,59.0,776.0,334.0,0.13,1021.0,133.0,1.8,47.879704469581895 +2016-10-06 16:30:00-05:00,2016,10,6,16,30,19.200000000000003,10.9,41.0,577.0,143.0,0.13,1022.0,140.0,1.9,58.631748313912105 +2016-10-06 17:30:00-05:00,2016,10,6,17,30,16.900000000000002,11.4,0.0,0.0,0.0,0.13,1022.0,146.0,1.9,70.03226857494532 +2016-10-06 18:30:00-05:00,2016,10,6,18,30,15.8,11.5,0.0,0.0,0.0,0.13,1022.0,149.0,1.9,75.61100928658588 +2016-10-06 19:30:00-05:00,2016,10,6,19,30,15.1,11.7,0.0,0.0,0.0,0.13,1023.0,150.0,1.7000000000000002,80.13324250460462 +2016-10-06 20:30:00-05:00,2016,10,6,20,30,14.5,11.8,0.0,0.0,0.0,0.13,1023.0,151.0,1.6,83.84324320588186 +2016-10-06 21:30:00-05:00,2016,10,6,21,30,14.3,11.8,0.0,0.0,0.0,0.13,1023.0,151.0,1.4,84.93408799682787 +2016-10-06 22:30:00-05:00,2016,10,6,22,30,14.5,11.6,0.0,0.0,0.0,0.13,1023.0,142.0,1.0,82.74361193449111 +2016-10-06 23:30:00-05:00,2016,10,6,23,30,14.8,11.4,0.0,0.0,0.0,0.13,1023.0,99.0,0.7000000000000001,80.0916533039291 +2016-10-07 00:30:00-05:00,2016,10,7,0,30,14.8,11.3,0.0,0.0,0.0,0.13,1022.0,60.0,1.0,79.56325972121785 +2016-10-07 01:30:00-05:00,2016,10,7,1,30,14.5,11.4,0.0,0.0,0.0,0.13,1022.0,56.0,1.4,81.65670790194712 +2016-10-07 02:30:00-05:00,2016,10,7,2,30,14.1,11.5,0.0,0.0,0.0,0.13,1022.0,60.0,1.7000000000000002,84.35308080968018 +2016-10-07 03:30:00-05:00,2016,10,7,3,30,13.7,11.5,0.0,0.0,0.0,0.13,1022.0,63.0,1.9,86.57103548465986 +2016-10-07 04:30:00-05:00,2016,10,7,4,30,13.3,11.7,0.0,0.0,0.0,0.13,1022.0,67.0,2.0,90.0362817053928 +2016-10-07 05:30:00-05:00,2016,10,7,5,30,13.0,11.9,0.0,0.0,0.0,0.13,1022.0,70.0,2.1,93.03577109694396 +2016-10-07 06:30:00-05:00,2016,10,7,6,30,14.0,12.2,26.0,413.0,62.0,0.13,1022.0,74.0,2.2,88.91183607623557 +2016-10-07 07:30:00-05:00,2016,10,7,7,30,16.2,12.7,50.0,715.0,246.0,0.13,1022.0,79.0,2.4000000000000004,79.76077309378172 +2016-10-07 08:30:00-05:00,2016,10,7,8,30,18.2,12.7,62.0,844.0,430.0,0.13,1022.0,83.0,2.6,70.283035907132 +2016-10-07 09:30:00-05:00,2016,10,7,9,30,19.9,12.3,68.0,911.0,582.0,0.13,1022.0,87.0,2.5,61.57637349360142 +2016-10-07 10:30:00-05:00,2016,10,7,10,30,21.200000000000003,11.6,70.0,943.0,683.0,0.13,1022.0,92.0,2.4000000000000004,54.27224698576954 +2016-10-07 11:30:00-05:00,2016,10,7,11,30,22.0,11.4,71.0,951.0,725.0,0.13,1021.0,99.0,2.3000000000000003,51.00169321721958 +2016-10-07 12:30:00-05:00,2016,10,7,12,30,22.5,11.3,70.0,945.0,706.0,0.13,1021.0,106.0,2.3000000000000003,49.14657997392772 +2016-10-07 13:30:00-05:00,2016,10,7,13,30,22.6,11.3,68.0,921.0,629.0,0.13,1020.0,112.0,2.4000000000000004,48.84903079689021 +2016-10-07 14:30:00-05:00,2016,10,7,14,30,22.3,11.4,63.0,872.0,496.0,0.13,1020.0,118.0,2.4000000000000004,50.07819373397874 +2016-10-07 15:30:00-05:00,2016,10,7,15,30,21.4,11.5,54.0,779.0,325.0,0.13,1020.0,121.0,2.4000000000000004,53.25776322661076 +2016-10-07 16:30:00-05:00,2016,10,7,16,30,19.5,12.0,37.0,586.0,138.0,0.13,1020.0,121.0,2.2,61.8904342875626 +2016-10-07 17:30:00-05:00,2016,10,7,17,30,17.5,12.7,0.0,0.0,0.0,0.13,1020.0,120.0,2.1,73.4485393924021 +2016-10-07 18:30:00-05:00,2016,10,7,18,30,16.400000000000002,13.1,0.0,0.0,0.0,0.13,1019.0,121.0,2.2,80.8383182399054 +2016-10-07 19:30:00-05:00,2016,10,7,19,30,15.8,13.6,0.0,0.0,0.0,0.13,1019.0,120.0,2.2,86.77388006229175 +2016-10-07 20:30:00-05:00,2016,10,7,20,30,15.3,13.9,0.0,0.0,0.0,0.13,1019.0,119.0,2.2,91.36145230484846 +2016-10-07 21:30:00-05:00,2016,10,7,21,30,15.1,14.0,0.0,0.0,0.0,0.13,1019.0,115.0,2.2,93.14489167462848 +2016-10-07 22:30:00-05:00,2016,10,7,22,30,14.9,14.0,0.0,0.0,0.0,0.13,1018.0,111.0,2.3000000000000003,94.35109037178563 +2016-10-07 23:30:00-05:00,2016,10,7,23,30,14.7,14.1,0.0,0.0,0.0,0.13,1018.0,110.0,2.3000000000000003,96.19624151518637 +2016-10-08 00:30:00-05:00,2016,10,8,0,30,14.7,14.2,0.0,0.0,0.0,0.13,1018.0,110.0,2.3000000000000003,96.82121821142722 +2016-10-08 01:30:00-05:00,2016,10,8,1,30,14.9,14.3,0.0,0.0,0.0,0.13,1017.0,106.0,2.1,96.20203122147296 +2016-10-08 02:30:00-05:00,2016,10,8,2,30,15.0,14.5,0.0,0.0,0.0,0.13,1017.0,103.0,2.1,96.82849393578888 +2016-10-08 03:30:00-05:00,2016,10,8,3,30,15.0,14.7,0.0,0.0,0.0,0.13,1017.0,104.0,2.0,98.08631590693012 +2016-10-08 04:30:00-05:00,2016,10,8,4,30,15.0,14.8,0.0,0.0,0.0,0.13,1017.0,104.0,1.9,98.72059970963647 +2016-10-08 05:30:00-05:00,2016,10,8,5,30,15.0,15.0,0.0,0.0,0.0,0.13,1017.0,102.0,1.8,100.0 +2016-10-08 06:30:00-05:00,2016,10,8,6,30,15.8,15.2,22.0,0.0,22.0,0.13,1017.0,101.0,1.9,96.2279231021833 +2016-10-08 07:30:00-05:00,2016,10,8,7,30,17.0,15.7,97.0,16.0,101.0,0.13,1017.0,100.0,1.9,92.05674930630607 +2016-10-08 08:30:00-05:00,2016,10,8,8,30,18.0,16.2,213.0,110.0,261.0,0.13,1017.0,99.0,1.9,89.23128744965707 +2016-10-08 09:30:00-05:00,2016,10,8,9,30,19.1,16.5,266.0,186.0,370.0,0.13,1016.0,102.0,1.7000000000000002,84.89986170516788 +2016-10-08 10:30:00-05:00,2016,10,8,10,30,20.0,16.7,248.0,21.0,262.0,0.13,1016.0,107.0,1.5,81.3101072483957 +2016-10-08 11:30:00-05:00,2016,10,8,11,30,20.5,16.8,241.0,13.0,250.0,0.13,1015.0,113.0,1.4,79.33828405834775 +2016-10-08 12:30:00-05:00,2016,10,8,12,30,20.700000000000003,16.900000000000002,223.0,5.0,226.0,0.13,1015.0,117.0,1.3,78.86496069703631 +2016-10-08 13:30:00-05:00,2016,10,8,13,30,20.6,17.1,150.0,3.0,152.0,0.13,1014.0,112.0,1.3,80.3640550536373 +2016-10-08 14:30:00-05:00,2016,10,8,14,30,20.3,17.400000000000002,158.0,11.0,163.0,0.13,1013.0,103.0,1.5,83.43303774999495 +2016-10-08 15:30:00-05:00,2016,10,8,15,30,19.6,17.900000000000002,81.0,3.0,82.0,0.13,1013.0,102.0,1.6,89.9194245998682 +2016-10-08 16:30:00-05:00,2016,10,8,16,30,18.9,18.1,17.0,0.0,17.0,0.13,1013.0,104.0,1.5,95.11360705806676 +2016-10-08 17:30:00-05:00,2016,10,8,17,30,18.3,18.0,0.0,0.0,0.0,0.13,1013.0,99.0,1.5,98.13391605990117 +2016-10-08 18:30:00-05:00,2016,10,8,18,30,18.2,17.900000000000002,0.0,0.0,0.0,0.13,1013.0,86.0,1.4,98.13249976318718 +2016-10-08 19:30:00-05:00,2016,10,8,19,30,18.0,17.900000000000002,0.0,0.0,0.0,0.13,1013.0,77.0,1.4,99.37310571520914 +2016-10-08 20:30:00-05:00,2016,10,8,20,30,17.900000000000002,17.8,0.0,0.0,0.0,0.13,1013.0,73.0,1.2000000000000002,99.3726265527518 +2016-10-08 21:30:00-05:00,2016,10,8,21,30,17.8,17.8,0.0,0.0,0.0,0.13,1012.0,59.0,1.0,100.00000000000001 +2016-10-08 22:30:00-05:00,2016,10,8,22,30,17.7,17.7,0.0,0.0,0.0,0.13,1012.0,33.0,1.0,100.0 +2016-10-08 23:30:00-05:00,2016,10,8,23,30,17.6,17.6,0.0,0.0,0.0,0.13,1011.0,16.0,1.3,100.0 +2016-10-09 00:30:00-05:00,2016,10,9,0,30,17.400000000000002,17.400000000000002,0.0,0.0,0.0,0.13,1011.0,8.0,1.7000000000000002,99.99999999999999 +2016-10-09 01:30:00-05:00,2016,10,9,1,30,17.1,17.1,0.0,0.0,0.0,0.13,1011.0,4.0,2.3000000000000003,100.0 +2016-10-09 02:30:00-05:00,2016,10,9,2,30,16.6,16.6,0.0,0.0,0.0,0.13,1011.0,180.0,3.2,100.0 +2016-10-09 03:30:00-05:00,2016,10,9,3,30,15.8,15.8,0.0,0.0,0.0,0.13,1011.0,181.0,4.6000000000000005,100.0 +2016-10-09 04:30:00-05:00,2016,10,9,4,30,14.8,14.6,0.0,0.0,0.0,0.13,1011.0,4.0,5.800000000000001,98.71862615253342 +2016-10-09 05:30:00-05:00,2016,10,9,5,30,13.9,13.1,0.0,0.0,0.0,0.13,1011.0,6.0,6.4,94.92623971954224 +2016-10-09 06:30:00-05:00,2016,10,9,6,30,13.5,12.3,7.0,0.0,7.0,0.13,1012.0,6.0,6.5,92.45280361827425 +2016-10-09 07:30:00-05:00,2016,10,9,7,30,13.3,11.9,27.0,0.0,27.0,0.13,1013.0,5.0,6.5,91.23188471614242 +2016-10-09 08:30:00-05:00,2016,10,9,8,30,13.1,11.6,60.0,0.0,60.0,0.13,1013.0,2.0,6.5,90.61837958714825 +2016-10-09 09:30:00-05:00,2016,10,9,9,30,13.1,11.4,11.0,0.0,11.0,0.13,1014.0,179.0,6.7,89.42803413457298 +2016-10-09 10:30:00-05:00,2016,10,9,10,30,13.1,11.2,59.0,0.0,59.0,0.13,1014.0,357.0,6.7,88.25148885064976 +2016-10-09 11:30:00-05:00,2016,10,9,11,30,13.2,10.9,74.0,0.0,74.0,0.13,1013.0,356.0,6.9,85.94945987526894 +2016-10-09 12:30:00-05:00,2016,10,9,12,30,13.4,10.5,56.0,0.0,56.0,0.13,1013.0,356.0,7.300000000000001,82.60819850356674 +2016-10-09 13:30:00-05:00,2016,10,9,13,30,13.8,10.1,81.0,0.0,81.0,0.13,1012.0,356.0,7.7,78.36656965137772 +2016-10-09 14:30:00-05:00,2016,10,9,14,30,14.3,9.6,101.0,1.0,101.0,0.13,1012.0,358.0,7.7,73.36910233420136 +2016-10-09 15:30:00-05:00,2016,10,9,15,30,14.7,9.1,160.0,44.0,175.0,0.13,1012.0,180.0,7.300000000000001,69.13402354169713 +2016-10-09 16:30:00-05:00,2016,10,9,16,30,14.6,8.6,44.0,3.0,45.0,0.13,1012.0,2.0,6.5,67.27181923516697 +2016-10-09 17:30:00-05:00,2016,10,9,17,30,14.0,8.1,0.0,0.0,0.0,0.13,1013.0,3.0,5.800000000000001,67.60356362539346 +2016-10-09 18:30:00-05:00,2016,10,9,18,30,13.7,7.6,0.0,0.0,0.0,0.13,1014.0,2.0,5.6000000000000005,66.62685777018523 +2016-10-09 19:30:00-05:00,2016,10,9,19,30,13.4,6.9,0.0,0.0,0.0,0.13,1014.0,179.0,5.9,64.76473842825952 +2016-10-09 20:30:00-05:00,2016,10,9,20,30,12.9,6.300000000000001,0.0,0.0,0.0,0.13,1014.0,356.0,6.300000000000001,64.20960914946315 +2016-10-09 21:30:00-05:00,2016,10,9,21,30,12.4,6.2,0.0,0.0,0.0,0.13,1015.0,353.0,6.5,65.89222795063225 +2016-10-09 22:30:00-05:00,2016,10,9,22,30,11.8,6.2,0.0,0.0,0.0,0.13,1015.0,351.0,6.7,68.54584034593023 +2016-10-09 23:30:00-05:00,2016,10,9,23,30,11.2,6.2,0.0,0.0,0.0,0.13,1014.0,349.0,6.7,71.31960850793376 +2016-10-10 00:30:00-05:00,2016,10,10,0,30,10.6,5.9,0.0,0.0,0.0,0.13,1014.0,347.0,6.5,72.6981445588966 +2016-10-10 01:30:00-05:00,2016,10,10,1,30,10.1,5.5,0.0,0.0,0.0,0.13,1015.0,346.0,6.300000000000001,73.11111162463541 +2016-10-10 02:30:00-05:00,2016,10,10,2,30,9.7,5.0,0.0,0.0,0.0,0.13,1015.0,347.0,6.1000000000000005,72.53138503447732 +2016-10-10 03:30:00-05:00,2016,10,10,3,30,9.4,4.800000000000001,0.0,0.0,0.0,0.13,1016.0,349.0,5.9,72.98287893680505 +2016-10-10 04:30:00-05:00,2016,10,10,4,30,9.0,4.7,0.0,0.0,0.0,0.13,1017.0,351.0,5.9,74.45377990214315 +2016-10-10 05:30:00-05:00,2016,10,10,5,30,8.8,4.5,0.0,0.0,0.0,0.13,1018.0,351.0,5.800000000000001,74.41858670160664 +2016-10-10 06:30:00-05:00,2016,10,10,6,30,9.2,4.4,22.0,448.0,57.0,0.13,1019.0,350.0,6.0,71.93330318927399 +2016-10-10 07:30:00-05:00,2016,10,10,7,30,10.1,4.0,43.0,765.0,244.0,0.13,1020.0,350.0,6.300000000000001,65.84684300958556 +2016-10-10 08:30:00-05:00,2016,10,10,8,30,11.3,3.5,54.0,887.0,430.0,0.13,1021.0,350.0,6.2,58.69338259524387 +2016-10-10 09:30:00-05:00,2016,10,10,9,30,12.6,2.9000000000000004,60.0,948.0,584.0,0.13,1021.0,348.0,5.800000000000001,51.63982536650851 +2016-10-10 10:30:00-05:00,2016,10,10,10,30,14.0,2.2,64.0,979.0,687.0,0.13,1021.0,344.0,5.4,44.84953803236204 +2016-10-10 11:30:00-05:00,2016,10,10,11,30,15.3,1.6,63.0,993.0,731.0,0.13,1020.0,341.0,5.0,39.51236670319226 +2016-10-10 12:30:00-05:00,2016,10,10,12,30,16.3,1.1,61.0,990.0,713.0,0.13,1020.0,339.0,4.6000000000000005,35.759560145608404 +2016-10-10 13:30:00-05:00,2016,10,10,13,30,17.0,0.7000000000000001,62.0,957.0,630.0,0.13,1020.0,339.0,4.3,33.233588512857665 +2016-10-10 14:30:00-05:00,2016,10,10,14,30,17.2,0.6000000000000001,58.0,904.0,493.0,0.13,1020.0,341.0,4.1000000000000005,32.57965878814125 +2016-10-10 15:30:00-05:00,2016,10,10,15,30,16.8,0.7000000000000001,49.0,811.0,319.0,0.13,1020.0,346.0,3.6,33.657645385666186 +2016-10-10 16:30:00-05:00,2016,10,10,16,30,15.0,1.9,33.0,600.0,127.0,0.13,1021.0,351.0,3.2,41.154648434573694 +2016-10-10 17:30:00-05:00,2016,10,10,17,30,12.9,3.0,0.0,0.0,0.0,0.13,1022.0,356.0,3.0,50.9951890344336 +2016-10-10 18:30:00-05:00,2016,10,10,18,30,11.9,3.0,0.0,0.0,0.0,0.13,1022.0,180.0,3.1,54.45532777319648 +2016-10-10 19:30:00-05:00,2016,10,10,19,30,11.3,3.3000000000000003,0.0,0.0,0.0,0.13,1023.0,5.0,3.3000000000000003,57.87124570672668 +2016-10-10 20:30:00-05:00,2016,10,10,20,30,10.6,3.5,0.0,0.0,0.0,0.13,1024.0,9.0,3.3000000000000003,61.48588719000942 +2016-10-10 21:30:00-05:00,2016,10,10,21,30,10.0,3.7,0.0,0.0,0.0,0.13,1025.0,12.0,3.4000000000000004,64.9057630390428 +2016-10-10 22:30:00-05:00,2016,10,10,22,30,9.6,3.9,0.0,0.0,0.0,0.13,1025.0,16.0,3.5,67.61295461700416 +2016-10-10 23:30:00-05:00,2016,10,10,23,30,9.3,3.9,0.0,0.0,0.0,0.13,1026.0,20.0,3.5,68.98970235372767 +2016-10-11 00:30:00-05:00,2016,10,11,0,30,8.8,3.8,0.0,0.0,0.0,0.13,1026.0,24.0,3.6,70.85479024493077 +2016-10-11 01:30:00-05:00,2016,10,11,1,30,8.4,3.8,0.0,0.0,0.0,0.13,1026.0,27.0,3.6,72.79820723154313 +2016-10-11 02:30:00-05:00,2016,10,11,2,30,8.0,3.7,0.0,0.0,0.0,0.13,1026.0,29.0,3.5,74.27713247350816 +2016-10-11 03:30:00-05:00,2016,10,11,3,30,7.6,3.8,0.0,0.0,0.0,0.13,1026.0,31.0,3.4000000000000004,76.86632988312203 +2016-10-11 04:30:00-05:00,2016,10,11,4,30,7.2,3.9,0.0,0.0,0.0,0.13,1027.0,31.0,3.2,79.55225532538833 +2016-10-11 05:30:00-05:00,2016,10,11,5,30,7.0,4.0,0.0,0.0,0.0,0.13,1027.0,33.0,3.0,81.21727894075245 +2016-10-11 06:30:00-05:00,2016,10,11,6,30,8.1,4.2,22.0,416.0,53.0,0.13,1027.0,37.0,3.0,76.41032184398564 +2016-10-11 07:30:00-05:00,2016,10,11,7,30,10.3,4.4,44.0,735.0,234.0,0.13,1028.0,44.0,3.0,66.82003494340427 +2016-10-11 08:30:00-05:00,2016,10,11,8,30,12.3,4.2,56.0,861.0,418.0,0.13,1028.0,51.0,2.7,57.717509396727536 +2016-10-11 09:30:00-05:00,2016,10,11,9,30,13.9,4.2,63.0,919.0,566.0,0.13,1028.0,55.0,2.3000000000000003,51.992228967774565 +2016-10-11 10:30:00-05:00,2016,10,11,10,30,15.2,4.5,66.0,953.0,668.0,0.13,1028.0,62.0,2.0,48.821260598277924 +2016-10-11 11:30:00-05:00,2016,10,11,11,30,16.2,4.7,69.0,964.0,712.0,0.13,1027.0,71.0,1.8,46.4394996867507 +2016-10-11 12:30:00-05:00,2016,10,11,12,30,16.900000000000002,5.0,69.0,954.0,691.0,0.13,1027.0,83.0,1.6,45.35707566147305 +2016-10-11 13:30:00-05:00,2016,10,11,13,30,17.2,5.2,66.0,931.0,613.0,0.13,1026.0,97.0,1.6,45.12710615014509 +2016-10-11 14:30:00-05:00,2016,10,11,14,30,17.1,5.6000000000000005,61.0,877.0,479.0,0.13,1026.0,107.0,1.6,46.69190422228278 +2016-10-11 15:30:00-05:00,2016,10,11,15,30,16.400000000000002,6.1000000000000005,52.0,781.0,307.0,0.13,1026.0,112.0,1.6,50.53035424221457 +2016-10-11 16:30:00-05:00,2016,10,11,16,30,14.6,7.0,34.0,565.0,120.0,0.13,1026.0,113.0,1.6,60.32905779776723 +2016-10-11 17:30:00-05:00,2016,10,11,17,30,12.7,7.9,0.0,0.0,0.0,0.13,1026.0,115.0,1.6,72.58655661146759 +2016-10-11 18:30:00-05:00,2016,10,11,18,30,12.1,8.200000000000001,0.0,0.0,0.0,0.13,1026.0,117.0,1.7000000000000002,77.05753407607997 +2016-10-11 19:30:00-05:00,2016,10,11,19,30,11.9,8.700000000000001,0.0,0.0,0.0,0.13,1026.0,117.0,1.7000000000000002,80.76851318875353 +2016-10-11 20:30:00-05:00,2016,10,11,20,30,12.0,9.1,0.0,0.0,0.0,0.13,1026.0,116.0,1.8,82.4336268396346 +2016-10-11 21:30:00-05:00,2016,10,11,21,30,12.1,9.5,0.0,0.0,0.0,0.13,1026.0,117.0,1.8,84.126308547161 +2016-10-11 22:30:00-05:00,2016,10,11,22,30,12.2,9.6,0.0,0.0,0.0,0.13,1025.0,121.0,1.8,84.1377599221049 +2016-10-11 23:30:00-05:00,2016,10,11,23,30,12.4,9.7,0.0,0.0,0.0,0.13,1025.0,126.0,1.8,83.59836147539178 +2016-10-12 00:30:00-05:00,2016,10,12,0,30,12.6,9.7,0.0,0.0,0.0,0.13,1025.0,130.0,1.7000000000000002,82.50875125575241 +2016-10-12 01:30:00-05:00,2016,10,12,1,30,12.9,9.6,0.0,0.0,0.0,0.13,1025.0,135.0,1.3,80.36308192750992 +2016-10-12 02:30:00-05:00,2016,10,12,2,30,13.1,9.5,0.0,0.0,0.0,0.13,1025.0,138.0,0.9,78.78893974396819 +2016-10-12 03:30:00-05:00,2016,10,12,3,30,13.2,9.4,0.0,0.0,0.0,0.13,1025.0,125.0,0.7000000000000001,77.75220262865538 +2016-10-12 04:30:00-05:00,2016,10,12,4,30,13.0,9.4,0.0,0.0,0.0,0.13,1024.0,105.0,0.7000000000000001,78.77416160954297 +2016-10-12 05:30:00-05:00,2016,10,12,5,30,12.8,9.4,0.0,0.0,0.0,0.13,1024.0,101.0,0.9,79.8111824291666 +2016-10-12 06:30:00-05:00,2016,10,12,6,30,13.2,9.4,23.0,348.0,48.0,0.13,1024.0,109.0,1.1,77.75220262865538 +2016-10-12 07:30:00-05:00,2016,10,12,7,30,14.6,10.4,50.0,675.0,222.0,0.13,1025.0,123.0,1.4,75.91702201924505 +2016-10-12 08:30:00-05:00,2016,10,12,8,30,16.1,10.7,65.0,809.0,401.0,0.13,1025.0,137.0,1.7000000000000002,70.34313806626406 +2016-10-12 09:30:00-05:00,2016,10,12,9,30,17.2,10.4,300.0,176.0,396.0,0.13,1024.0,146.0,2.0,64.29822547097321 +2016-10-12 10:30:00-05:00,2016,10,12,10,30,18.1,10.4,303.0,36.0,326.0,0.13,1024.0,151.0,2.2,60.75220934847774 +2016-10-12 11:30:00-05:00,2016,10,12,11,30,18.7,10.4,320.0,38.0,345.0,0.13,1023.0,154.0,2.3000000000000003,58.51020187034048 +2016-10-12 12:30:00-05:00,2016,10,12,12,30,19.1,10.4,245.0,444.0,532.0,0.13,1022.0,154.0,2.3000000000000003,57.06714437783474 +2016-10-12 13:30:00-05:00,2016,10,12,13,30,19.1,10.4,329.0,112.0,394.0,0.13,1021.0,153.0,2.4000000000000004,57.06714437783474 +2016-10-12 14:30:00-05:00,2016,10,12,14,30,18.9,10.4,70.0,840.0,466.0,0.13,1021.0,152.0,2.3000000000000003,57.78361767880522 +2016-10-12 15:30:00-05:00,2016,10,12,15,30,18.2,10.5,58.0,736.0,296.0,0.13,1021.0,152.0,2.1,60.77574067173409 +2016-10-12 16:30:00-05:00,2016,10,12,16,30,16.5,10.8,37.0,502.0,111.0,0.13,1020.0,154.0,1.8,69.03129668817384 +2016-10-12 17:30:00-05:00,2016,10,12,17,30,14.8,11.2,0.0,0.0,0.0,0.13,1020.0,154.0,1.7000000000000002,79.03794058522449 +2016-10-12 18:30:00-05:00,2016,10,12,18,30,14.2,11.4,0.0,0.0,0.0,0.13,1020.0,154.0,1.8,83.25610287163597 +2016-10-12 19:30:00-05:00,2016,10,12,19,30,13.9,11.8,0.0,0.0,0.0,0.13,1020.0,153.0,2.0,87.16380170773726 +2016-10-12 20:30:00-05:00,2016,10,12,20,30,13.5,12.2,0.0,0.0,0.0,0.13,1019.0,156.0,2.2,91.84713798624828 +2016-10-12 21:30:00-05:00,2016,10,12,21,30,13.3,12.6,0.0,0.0,0.0,0.13,1019.0,166.0,2.1,95.52738451097483 +2016-10-12 22:30:00-05:00,2016,10,12,22,30,13.2,12.7,0.0,0.0,0.0,0.13,1018.0,180.0,2.1,96.78446342836168 +2016-10-12 23:30:00-05:00,2016,10,12,23,30,13.0,12.7,0.0,0.0,0.0,0.13,1018.0,193.0,2.0,98.05657853593482 +2016-10-13 00:30:00-05:00,2016,10,13,0,30,12.8,12.7,0.0,0.0,0.0,0.13,1017.0,203.0,1.9,99.34744233397626 +2016-10-13 01:30:00-05:00,2016,10,13,1,30,12.6,12.6,0.0,0.0,0.0,0.13,1017.0,209.0,1.8,100.0 +2016-10-13 02:30:00-05:00,2016,10,13,2,30,12.4,12.4,0.0,0.0,0.0,0.13,1017.0,209.0,1.7000000000000002,100.0 +2016-10-13 03:30:00-05:00,2016,10,13,3,30,12.1,12.1,0.0,0.0,0.0,0.13,1016.0,214.0,1.6,100.0 +2016-10-13 04:30:00-05:00,2016,10,13,4,30,11.8,11.8,0.0,0.0,0.0,0.13,1016.0,227.0,1.6,100.0 +2016-10-13 05:30:00-05:00,2016,10,13,5,30,11.7,11.7,0.0,0.0,0.0,0.13,1016.0,237.0,1.7000000000000002,100.0 +2016-10-13 06:30:00-05:00,2016,10,13,6,30,12.7,12.1,26.0,2.0,26.0,0.13,1016.0,238.0,1.6,96.13761407329324 +2016-10-13 07:30:00-05:00,2016,10,13,7,30,14.8,12.4,49.0,672.0,218.0,0.13,1016.0,235.0,1.8,85.54810112104354 +2016-10-13 08:30:00-05:00,2016,10,13,8,30,16.6,12.8,120.0,546.0,345.0,0.13,1015.0,233.0,2.5,78.26672918629073 +2016-10-13 09:30:00-05:00,2016,10,13,9,30,17.6,12.8,75.0,862.0,539.0,0.13,1015.0,233.0,3.0,73.46609314629643 +2016-10-13 10:30:00-05:00,2016,10,13,10,30,18.2,12.7,209.0,526.0,537.0,0.13,1014.0,234.0,3.3000000000000003,70.283035907132 +2016-10-13 11:30:00-05:00,2016,10,13,11,30,19.1,12.8,328.0,104.0,397.0,0.13,1014.0,238.0,3.6,66.87208167466966 +2016-10-13 12:30:00-05:00,2016,10,13,12,30,19.8,13.0,182.0,5.0,185.0,0.13,1013.0,244.0,3.5,64.8671297438264 +2016-10-13 13:30:00-05:00,2016,10,13,13,30,19.9,13.4,137.0,5.0,139.0,0.13,1013.0,250.0,3.1,66.1707735193476 +2016-10-13 14:30:00-05:00,2016,10,13,14,30,19.9,13.8,31.0,0.0,31.0,0.13,1013.0,257.0,2.8000000000000003,67.91477125733437 +2016-10-13 15:30:00-05:00,2016,10,13,15,30,19.4,14.3,109.0,1.0,109.0,0.13,1014.0,270.0,2.6,72.36305814801338 +2016-10-13 16:30:00-05:00,2016,10,13,16,30,18.3,14.5,18.0,0.0,18.0,0.13,1014.0,286.0,2.6,78.51766518185426 +2016-10-13 17:30:00-05:00,2016,10,13,17,30,17.0,14.2,0.0,0.0,0.0,0.13,1015.0,303.0,3.1,83.58530465643027 +2016-10-13 18:30:00-05:00,2016,10,13,18,30,15.8,13.7,0.0,0.0,0.0,0.13,1016.0,319.0,3.8,87.33984624682459 +2016-10-13 19:30:00-05:00,2016,10,13,19,30,14.6,13.2,0.0,0.0,0.0,0.13,1017.0,328.0,4.2,91.3164268378706 +2016-10-13 20:30:00-05:00,2016,10,13,20,30,13.4,12.5,0.0,0.0,0.0,0.13,1018.0,333.0,4.1000000000000005,94.2866291338788 +2016-10-13 21:30:00-05:00,2016,10,13,21,30,12.3,11.4,0.0,0.0,0.0,0.13,1019.0,338.0,4.0,94.23866112501362 +2016-10-13 22:30:00-05:00,2016,10,13,22,30,11.5,10.1,0.0,0.0,0.0,0.13,1019.0,340.0,4.0,91.11280810775169 +2016-10-13 23:30:00-05:00,2016,10,13,23,30,10.8,8.9,0.0,0.0,0.0,0.13,1019.0,338.0,3.9,88.05017753713369 +2016-10-14 00:30:00-05:00,2016,10,14,0,30,10.1,7.9,0.0,0.0,0.0,0.13,1020.0,338.0,3.9,86.21259764139756 +2016-10-14 01:30:00-05:00,2016,10,14,1,30,9.6,6.9,0.0,0.0,0.0,0.13,1020.0,341.0,3.9,83.26341743548329 +2016-10-14 02:30:00-05:00,2016,10,14,2,30,9.2,6.6000000000000005,0.0,0.0,0.0,0.13,1021.0,346.0,3.9,83.7889411829924 +2016-10-14 03:30:00-05:00,2016,10,14,3,30,8.700000000000001,6.5,0.0,0.0,0.0,0.13,1021.0,351.0,3.9,86.06943291644161 +2016-10-14 04:30:00-05:00,2016,10,14,4,30,8.3,6.4,0.0,0.0,0.0,0.13,1022.0,356.0,3.9,87.82558204997216 +2016-10-14 05:30:00-05:00,2016,10,14,5,30,8.0,6.300000000000001,0.0,0.0,0.0,0.13,1023.0,180.0,3.9,89.0174453601547 +2016-10-14 06:30:00-05:00,2016,10,14,6,30,8.6,6.0,21.0,362.0,45.0,0.13,1023.0,6.0,4.1000000000000005,83.71784646587022 +2016-10-14 07:30:00-05:00,2016,10,14,7,30,10.2,5.9,47.0,716.0,224.0,0.13,1024.0,12.0,4.3,74.66352031959198 +2016-10-14 08:30:00-05:00,2016,10,14,8,30,11.8,5.0,59.0,856.0,408.0,0.13,1024.0,15.0,4.2,63.07694469954455 +2016-10-14 09:30:00-05:00,2016,10,14,9,30,13.3,4.1000000000000005,66.0,924.0,560.0,0.13,1025.0,12.0,3.8,53.68350972705306 +2016-10-14 10:30:00-05:00,2016,10,14,10,30,14.6,3.4000000000000004,67.0,962.0,662.0,0.13,1025.0,7.0,3.4000000000000004,46.97217749143968 +2016-10-14 11:30:00-05:00,2016,10,14,11,30,15.8,2.8000000000000003,69.0,969.0,702.0,0.13,1024.0,4.0,3.0,41.68131022122446 +2016-10-14 12:30:00-05:00,2016,10,14,12,30,16.7,2.2,69.0,962.0,683.0,0.13,1024.0,3.0,2.5,37.71583754348766 +2016-10-14 13:30:00-05:00,2016,10,14,13,30,17.2,1.6,64.0,941.0,603.0,0.13,1023.0,1.0,1.9,35.00725572308765 +2016-10-14 14:30:00-05:00,2016,10,14,14,30,17.400000000000002,0.9,60.0,888.0,469.0,0.13,1023.0,0.0,1.2000000000000002,32.87360079822937 +2016-10-14 15:30:00-05:00,2016,10,14,15,30,16.900000000000002,0.3,51.0,786.0,296.0,0.13,1022.0,178.0,0.6000000000000001,32.49251017777157 +2016-10-14 16:30:00-05:00,2016,10,14,16,30,15.9,1.6,32.0,557.0,108.0,0.13,1023.0,307.0,0.3,38.023060149694494 +2016-10-14 17:30:00-05:00,2016,10,14,17,30,14.7,0.7000000000000001,0.0,0.0,0.0,0.13,1023.0,157.0,0.3,38.496199045733 +2016-10-14 18:30:00-05:00,2016,10,14,18,30,13.9,0.3,0.0,0.0,0.0,0.13,1024.0,32.0,0.4,39.38628602242121 +2016-10-14 19:30:00-05:00,2016,10,14,19,30,13.0,0.4,0.0,0.0,0.0,0.13,1024.0,18.0,0.7000000000000001,42.066444817694546 +2016-10-14 20:30:00-05:00,2016,10,14,20,30,11.9,0.9,0.0,0.0,0.0,0.13,1025.0,30.0,1.2000000000000002,46.875860759388765 +2016-10-14 21:30:00-05:00,2016,10,14,21,30,11.1,1.5,0.0,0.0,0.0,0.13,1025.0,31.0,1.5,51.597811348107996 +2016-10-14 22:30:00-05:00,2016,10,14,22,30,10.2,1.7000000000000002,0.0,0.0,0.0,0.13,1025.0,31.0,1.6,55.572936311542016 +2016-10-14 23:30:00-05:00,2016,10,14,23,30,9.4,2.1,0.0,0.0,0.0,0.13,1025.0,37.0,1.7000000000000002,60.33188714180038 +2016-10-15 00:30:00-05:00,2016,10,15,0,30,8.9,2.3000000000000003,0.0,0.0,0.0,0.13,1025.0,43.0,1.6,63.29343368119136 +2016-10-15 01:30:00-05:00,2016,10,15,1,30,8.6,2.2,0.0,0.0,0.0,0.13,1025.0,45.0,1.4,64.13126606966487 +2016-10-15 02:30:00-05:00,2016,10,15,2,30,8.3,2.2,0.0,0.0,0.0,0.13,1025.0,36.0,1.2000000000000002,65.44763065977087 +2016-10-15 03:30:00-05:00,2016,10,15,3,30,7.9,2.4000000000000004,0.0,0.0,0.0,0.13,1025.0,26.0,1.4,68.21401346926969 +2016-10-15 04:30:00-05:00,2016,10,15,4,30,7.5,3.1,0.0,0.0,0.0,0.13,1026.0,25.0,1.7000000000000002,73.66568996322573 +2016-10-15 05:30:00-05:00,2016,10,15,5,30,7.300000000000001,3.8,0.0,0.0,0.0,0.13,1026.0,32.0,1.9,78.45684791912844 +2016-10-15 06:30:00-05:00,2016,10,15,6,30,8.5,4.4,19.0,372.0,42.0,0.13,1026.0,43.0,1.7000000000000002,75.41440297418777 +2016-10-15 07:30:00-05:00,2016,10,15,7,30,10.8,5.2,44.0,717.0,219.0,0.13,1027.0,60.0,1.4,68.3394728331097 +2016-10-15 08:30:00-05:00,2016,10,15,8,30,13.4,5.300000000000001,57.0,853.0,401.0,0.13,1027.0,84.0,1.0,57.994149766459 +2016-10-15 09:30:00-05:00,2016,10,15,9,30,15.5,4.9,64.0,913.0,548.0,0.13,1027.0,104.0,0.8,49.246370419279344 +2016-10-15 10:30:00-05:00,2016,10,15,10,30,16.6,5.0,68.0,948.0,650.0,0.13,1027.0,117.0,0.9,46.22898166681303 +2016-10-15 11:30:00-05:00,2016,10,15,11,30,17.400000000000002,5.2,71.0,958.0,693.0,0.13,1026.0,132.0,1.1,44.56027889496516 +2016-10-15 12:30:00-05:00,2016,10,15,12,30,17.8,5.4,71.0,952.0,673.0,0.13,1025.0,144.0,1.4,44.058216386227876 +2016-10-15 13:30:00-05:00,2016,10,15,13,30,18.0,5.800000000000001,66.0,931.0,594.0,0.13,1024.0,154.0,1.8,44.73004138770133 +2016-10-15 14:30:00-05:00,2016,10,15,14,30,17.8,6.1000000000000005,60.0,874.0,458.0,0.13,1024.0,162.0,2.1,46.245115753983484 +2016-10-15 15:30:00-05:00,2016,10,15,15,30,17.0,6.5,50.0,771.0,287.0,0.13,1024.0,167.0,2.2,50.00076672312883 +2016-10-15 16:30:00-05:00,2016,10,15,16,30,15.1,7.4,31.0,536.0,102.0,0.13,1024.0,169.0,2.2,60.037679493880205 +2016-10-15 17:30:00-05:00,2016,10,15,17,30,13.3,7.9,0.0,0.0,0.0,0.13,1024.0,170.0,2.4000000000000004,69.79585238453926 +2016-10-15 18:30:00-05:00,2016,10,15,18,30,12.8,8.200000000000001,0.0,0.0,0.0,0.13,1024.0,174.0,2.6,73.59785168715564 +2016-10-15 19:30:00-05:00,2016,10,15,19,30,12.3,8.5,0.0,0.0,0.0,0.13,1023.0,180.0,2.7,77.61311082923069 +2016-10-15 20:30:00-05:00,2016,10,15,20,30,11.7,8.700000000000001,0.0,0.0,0.0,0.13,1023.0,188.0,2.7,81.84104834583856 +2016-10-15 21:30:00-05:00,2016,10,15,21,30,11.2,8.9,0.0,0.0,0.0,0.13,1023.0,196.0,2.7,85.74321422539523 +2016-10-15 22:30:00-05:00,2016,10,15,22,30,10.8,9.1,0.0,0.0,0.0,0.13,1023.0,205.0,2.7,89.24570643845554 +2016-10-15 23:30:00-05:00,2016,10,15,23,30,10.4,9.3,0.0,0.0,0.0,0.13,1022.0,216.0,2.8000000000000003,92.89706985737045 +2016-10-16 00:30:00-05:00,2016,10,16,0,30,10.1,9.5,0.0,0.0,0.0,0.13,1022.0,224.0,2.8000000000000003,96.05936433857 +2016-10-16 01:30:00-05:00,2016,10,16,1,30,10.0,9.6,0.0,0.0,0.0,0.13,1022.0,230.0,2.9000000000000004,97.35534826961502 +2016-10-16 02:30:00-05:00,2016,10,16,2,30,10.0,9.7,0.0,0.0,0.0,0.13,1022.0,233.0,3.0,98.01066034046349 +2016-10-16 03:30:00-05:00,2016,10,16,3,30,10.0,9.8,0.0,0.0,0.0,0.13,1021.0,234.0,3.0,98.66985980461821 +2016-10-16 04:30:00-05:00,2016,10,16,4,30,9.9,9.9,0.0,0.0,0.0,0.13,1021.0,233.0,3.0,100.0 +2016-10-16 05:30:00-05:00,2016,10,16,5,30,9.9,9.9,0.0,0.0,0.0,0.13,1021.0,232.0,3.1,100.0 +2016-10-16 06:30:00-05:00,2016,10,16,6,30,11.1,10.3,20.0,271.0,36.0,0.13,1020.0,231.0,3.2,94.81661388452422 +2016-10-16 07:30:00-05:00,2016,10,16,7,30,13.2,10.7,53.0,602.0,198.0,0.13,1020.0,229.0,3.6,84.81425037776394 +2016-10-16 08:30:00-05:00,2016,10,16,8,30,15.4,11.0,75.0,726.0,365.0,0.13,1019.0,228.0,4.0,75.04542774470646 +2016-10-16 09:30:00-05:00,2016,10,16,9,30,17.1,10.7,92.0,780.0,503.0,0.13,1019.0,230.0,4.2,66.01239935576409 +2016-10-16 10:30:00-05:00,2016,10,16,10,30,18.5,11.0,335.0,110.0,401.0,0.13,1018.0,232.0,4.0,61.65998495558101 +2016-10-16 11:30:00-05:00,2016,10,16,11,30,19.6,11.7,341.0,233.0,491.0,0.13,1017.0,231.0,3.8,60.30249766631218 +2016-10-16 12:30:00-05:00,2016,10,16,12,30,20.6,12.5,231.0,451.0,514.0,0.13,1016.0,226.0,3.6,59.74903359457973 +2016-10-16 13:30:00-05:00,2016,10,16,13,30,21.200000000000003,13.3,285.0,273.0,439.0,0.13,1015.0,221.0,3.5,60.67756915820553 +2016-10-16 14:30:00-05:00,2016,10,16,14,30,21.4,13.9,239.0,182.0,321.0,0.13,1014.0,216.0,3.4000000000000004,62.32331261022911 +2016-10-16 15:30:00-05:00,2016,10,16,15,30,20.9,14.5,74.0,586.0,251.0,0.13,1014.0,212.0,3.4000000000000004,66.81062191037611 +2016-10-16 16:30:00-05:00,2016,10,16,16,30,19.6,14.9,40.0,334.0,83.0,0.13,1013.0,210.0,3.4000000000000004,74.2907431409801 +2016-10-16 17:30:00-05:00,2016,10,16,17,30,18.4,15.0,0.0,0.0,0.0,0.13,1013.0,214.0,3.7,80.58262590477159 +2016-10-16 18:30:00-05:00,2016,10,16,18,30,17.7,15.1,0.0,0.0,0.0,0.13,1014.0,219.0,4.0,84.74959346679492 +2016-10-16 19:30:00-05:00,2016,10,16,19,30,17.3,15.2,0.0,0.0,0.0,0.13,1014.0,224.0,4.2,87.47634774291258 +2016-10-16 20:30:00-05:00,2016,10,16,20,30,17.0,15.3,0.0,0.0,0.0,0.13,1013.0,230.0,4.3,89.72698022064127 +2016-10-16 21:30:00-05:00,2016,10,16,21,30,16.7,15.4,0.0,0.0,0.0,0.13,1013.0,236.0,4.1000000000000005,92.03909644173676 +2016-10-16 22:30:00-05:00,2016,10,16,22,30,16.3,15.4,0.0,0.0,0.0,0.13,1013.0,239.0,3.8,94.41028486631951 +2016-10-16 23:30:00-05:00,2016,10,16,23,30,16.1,15.4,0.0,0.0,0.0,0.13,1012.0,241.0,3.7,95.62150561080094 +2016-10-17 00:30:00-05:00,2016,10,17,0,30,15.9,15.4,0.0,0.0,0.0,0.13,1012.0,243.0,3.7,96.8501726786009 +2016-10-17 01:30:00-05:00,2016,10,17,1,30,15.7,15.3,0.0,0.0,0.0,0.13,1012.0,247.0,3.7,97.4692033954916 +2016-10-17 02:30:00-05:00,2016,10,17,2,30,15.4,15.1,0.0,0.0,0.0,0.13,1012.0,252.0,3.6,98.09218170103145 +2016-10-17 03:30:00-05:00,2016,10,17,3,30,15.0,14.9,0.0,0.0,0.0,0.13,1012.0,260.0,3.4000000000000004,99.35848858111041 +2016-10-17 04:30:00-05:00,2016,10,17,4,30,14.6,14.6,0.0,0.0,0.0,0.13,1012.0,266.0,3.2,100.0 +2016-10-17 05:30:00-05:00,2016,10,17,5,30,14.3,14.3,0.0,0.0,0.0,0.13,1012.0,269.0,2.9000000000000004,100.00000000000001 +2016-10-17 06:30:00-05:00,2016,10,17,6,30,14.9,14.3,15.0,0.0,15.0,0.13,1012.0,267.0,2.6,96.20203122147296 +2016-10-17 07:30:00-05:00,2016,10,17,7,30,16.5,14.6,108.0,7.0,109.0,0.13,1012.0,258.0,2.3000000000000003,88.54009722553633 +2016-10-17 08:30:00-05:00,2016,10,17,8,30,18.6,15.1,75.0,712.0,356.0,0.13,1012.0,246.0,2.0,80.09307141658095 +2016-10-17 09:30:00-05:00,2016,10,17,9,30,21.200000000000003,15.7,84.0,798.0,500.0,0.13,1011.0,237.0,1.9,70.85081034290565 +2016-10-17 10:30:00-05:00,2016,10,17,10,30,23.3,15.8,88.0,843.0,598.0,0.13,1011.0,234.0,1.9,62.749755968043374 +2016-10-17 11:30:00-05:00,2016,10,17,11,30,24.4,15.6,91.0,852.0,636.0,0.13,1010.0,233.0,1.9,57.98628871078611 +2016-10-17 12:30:00-05:00,2016,10,17,12,30,24.700000000000003,15.6,91.0,842.0,615.0,0.13,1009.0,233.0,2.0,56.954899593299444 +2016-10-17 13:30:00-05:00,2016,10,17,13,30,24.6,15.6,87.0,808.0,537.0,0.13,1009.0,230.0,2.2,57.29638520177546 +2016-10-17 14:30:00-05:00,2016,10,17,14,30,24.3,15.7,223.0,235.0,327.0,0.13,1008.0,223.0,2.3000000000000003,58.70933927675248 +2016-10-17 15:30:00-05:00,2016,10,17,15,30,23.6,16.1,158.0,61.0,176.0,0.13,1008.0,215.0,2.4000000000000004,62.81650746109228 +2016-10-17 16:30:00-05:00,2016,10,17,16,30,21.9,16.7,38.0,322.0,77.0,0.13,1008.0,213.0,2.5,72.34774099703732 +2016-10-17 17:30:00-05:00,2016,10,17,17,30,20.3,16.5,0.0,0.0,0.0,0.13,1008.0,217.0,2.7,78.80763615894695 +2016-10-17 18:30:00-05:00,2016,10,17,18,30,19.8,16.400000000000002,0.0,0.0,0.0,0.13,1009.0,221.0,2.8000000000000003,80.76888045468685 +2016-10-17 19:30:00-05:00,2016,10,17,19,30,19.200000000000003,16.5,0.0,0.0,0.0,0.13,1009.0,225.0,2.7,84.37247492510438 +2016-10-17 20:30:00-05:00,2016,10,17,20,30,18.6,16.5,0.0,0.0,0.0,0.13,1009.0,227.0,2.6,87.59291581941238 +2016-10-17 21:30:00-05:00,2016,10,17,21,30,18.0,16.5,0.0,0.0,0.0,0.13,1009.0,227.0,2.7,90.95193922178122 +2016-10-17 22:30:00-05:00,2016,10,17,22,30,17.7,16.5,0.0,0.0,0.0,0.13,1009.0,228.0,2.9000000000000004,92.68547047790673 +2016-10-17 23:30:00-05:00,2016,10,17,23,30,17.400000000000002,16.6,0.0,0.0,0.0,0.13,1009.0,227.0,3.0,95.05849186410352 +2016-10-18 00:30:00-05:00,2016,10,18,0,30,17.0,16.7,0.0,0.0,0.0,0.13,1009.0,226.0,3.1,98.11537809485195 +2016-10-18 01:30:00-05:00,2016,10,18,1,30,16.6,16.6,0.0,0.0,0.0,0.13,1009.0,226.0,3.0,100.0 +2016-10-18 02:30:00-05:00,2016,10,18,2,30,16.3,16.3,0.0,0.0,0.0,0.13,1009.0,226.0,3.0,100.0 +2016-10-18 03:30:00-05:00,2016,10,18,3,30,16.0,16.0,0.0,0.0,0.0,0.13,1010.0,225.0,2.8000000000000003,100.0 +2016-10-18 04:30:00-05:00,2016,10,18,4,30,15.7,15.7,0.0,0.0,0.0,0.13,1010.0,223.0,2.7,100.0 +2016-10-18 05:30:00-05:00,2016,10,18,5,30,15.6,15.6,0.0,0.0,0.0,0.13,1010.0,220.0,2.6,100.0 +2016-10-18 06:30:00-05:00,2016,10,18,6,30,16.6,16.6,18.0,233.0,30.0,0.13,1010.0,216.0,2.7,100.0 +2016-10-18 07:30:00-05:00,2016,10,18,7,30,18.5,16.900000000000002,49.0,602.0,188.0,0.13,1011.0,210.0,2.8000000000000003,90.41022747017232 +2016-10-18 08:30:00-05:00,2016,10,18,8,30,20.6,17.400000000000002,63.0,759.0,360.0,0.13,1011.0,205.0,2.7,81.90291975518063 +2016-10-18 09:30:00-05:00,2016,10,18,9,30,22.6,17.900000000000002,72.0,836.0,504.0,0.13,1010.0,202.0,3.0,74.79273100346443 +2016-10-18 10:30:00-05:00,2016,10,18,10,30,24.1,17.5,72.0,886.0,604.0,0.13,1010.0,200.0,3.4000000000000004,66.61784339189742 +2016-10-18 11:30:00-05:00,2016,10,18,11,30,24.9,16.900000000000002,74.0,902.0,646.0,0.13,1009.0,200.0,3.8,61.138775178868705 +2016-10-18 12:30:00-05:00,2016,10,18,12,30,25.200000000000003,16.5,74.0,897.0,628.0,0.13,1008.0,200.0,4.1000000000000005,58.549285745862534 +2016-10-18 13:30:00-05:00,2016,10,18,13,30,25.200000000000003,16.2,78.0,852.0,548.0,0.13,1008.0,199.0,4.3,57.44163556119121 +2016-10-18 14:30:00-05:00,2016,10,18,14,30,24.8,16.1,72.0,789.0,420.0,0.13,1007.0,198.0,4.5,58.45410690446048 +2016-10-18 15:30:00-05:00,2016,10,18,15,30,23.9,16.3,60.0,658.0,252.0,0.13,1007.0,195.0,4.6000000000000005,62.484028116540095 +2016-10-18 16:30:00-05:00,2016,10,18,16,30,22.4,16.8,35.0,376.0,78.0,0.13,1007.0,195.0,4.5,70.62440944770913 +2016-10-18 17:30:00-05:00,2016,10,18,17,30,21.0,17.2,0.0,0.0,0.0,0.13,1007.0,198.0,4.3,78.90780919446358 +2016-10-18 18:30:00-05:00,2016,10,18,18,30,20.3,17.6,0.0,0.0,0.0,0.13,1008.0,204.0,4.1000000000000005,84.49270531795345 +2016-10-18 19:30:00-05:00,2016,10,18,19,30,19.8,17.8,0.0,0.0,0.0,0.13,1008.0,208.0,4.0,88.25321495212934 +2016-10-18 20:30:00-05:00,2016,10,18,20,30,19.3,17.900000000000002,0.0,0.0,0.0,0.13,1009.0,210.0,4.0,91.6122516337024 +2016-10-18 21:30:00-05:00,2016,10,18,21,30,19.0,18.0,0.0,0.0,0.0,0.13,1009.0,212.0,4.0,93.92977440102699 +2016-10-18 22:30:00-05:00,2016,10,18,22,30,18.8,18.1,0.0,0.0,0.0,0.13,1008.0,214.0,4.0,95.70950052417027 +2016-10-18 23:30:00-05:00,2016,10,18,23,30,18.6,18.3,0.0,0.0,0.0,0.13,1008.0,215.0,3.9,98.13815532728205 +2016-10-19 00:30:00-05:00,2016,10,19,0,30,18.5,18.4,0.0,0.0,0.0,0.13,1008.0,216.0,3.8,99.37549332177504 +2016-10-19 01:30:00-05:00,2016,10,19,1,30,18.5,18.4,0.0,0.0,0.0,0.13,1008.0,216.0,3.8,99.37549332177504 +2016-10-19 02:30:00-05:00,2016,10,19,2,30,18.5,18.5,0.0,0.0,0.0,0.13,1008.0,220.0,3.8,100.0 +2016-10-19 03:30:00-05:00,2016,10,19,3,30,18.4,18.4,0.0,0.0,0.0,0.13,1009.0,226.0,3.7,100.0 +2016-10-19 04:30:00-05:00,2016,10,19,4,30,18.1,18.1,0.0,0.0,0.0,0.13,1009.0,231.0,3.2,100.0 +2016-10-19 05:30:00-05:00,2016,10,19,5,30,18.0,18.0,0.0,0.0,0.0,0.13,1010.0,235.0,2.7,100.00000000000001 +2016-10-19 06:30:00-05:00,2016,10,19,6,30,18.6,18.1,18.0,131.0,24.0,0.13,1011.0,242.0,2.5,96.91389882070926 +2016-10-19 07:30:00-05:00,2016,10,19,7,30,20.200000000000003,18.1,61.0,486.0,172.0,0.13,1011.0,252.0,2.3000000000000003,87.73421851476081 +2016-10-19 08:30:00-05:00,2016,10,19,8,30,22.200000000000003,17.900000000000002,83.0,654.0,337.0,0.13,1012.0,268.0,1.9,76.63386391561815 +2016-10-19 09:30:00-05:00,2016,10,19,9,30,23.9,17.6,278.0,82.0,320.0,0.13,1012.0,283.0,1.7000000000000002,67.84965926408279 +2016-10-19 10:30:00-05:00,2016,10,19,10,30,25.200000000000003,17.5,85.0,834.0,581.0,0.13,1012.0,287.0,1.5,62.37822151444742 +2016-10-19 11:30:00-05:00,2016,10,19,11,30,26.1,17.3,85.0,855.0,623.0,0.13,1011.0,282.0,1.5,58.39235011863905 +2016-10-19 12:30:00-05:00,2016,10,19,12,30,26.8,17.0,82.0,858.0,608.0,0.13,1011.0,278.0,1.5,54.97700459548665 +2016-10-19 13:30:00-05:00,2016,10,19,13,30,27.1,16.400000000000002,77.0,837.0,535.0,0.13,1011.0,279.0,1.4,51.997188056284024 +2016-10-19 14:30:00-05:00,2016,10,19,14,30,27.0,15.4,196.0,351.0,349.0,0.13,1011.0,286.0,1.2000000000000002,49.06642678568424 +2016-10-19 15:30:00-05:00,2016,10,19,15,30,26.0,14.4,55.0,674.0,248.0,0.13,1012.0,302.0,1.0,48.80108987711853 +2016-10-19 16:30:00-05:00,2016,10,19,16,30,23.700000000000003,14.5,31.0,414.0,77.0,0.13,1012.0,327.0,1.0,56.34539634817843 +2016-10-19 17:30:00-05:00,2016,10,19,17,30,21.4,13.5,0.0,0.0,0.0,0.13,1014.0,351.0,1.4,60.724129464991 +2016-10-19 18:30:00-05:00,2016,10,19,18,30,20.1,12.5,0.0,0.0,0.0,0.13,1015.0,182.0,2.1,61.622434953291354 +2016-10-19 19:30:00-05:00,2016,10,19,19,30,19.1,11.8,0.0,0.0,0.0,0.13,1016.0,11.0,2.6,62.6197509218611 +2016-10-19 20:30:00-05:00,2016,10,19,20,30,18.2,11.5,0.0,0.0,0.0,0.13,1016.0,25.0,2.9000000000000004,64.94661859414566 +2016-10-19 21:30:00-05:00,2016,10,19,21,30,17.5,11.6,0.0,0.0,0.0,0.13,1016.0,42.0,3.1,68.32181132745698 +2016-10-19 22:30:00-05:00,2016,10,19,22,30,17.0,11.9,0.0,0.0,0.0,0.13,1016.0,51.0,3.0,71.92595452180186 +2016-10-19 23:30:00-05:00,2016,10,19,23,30,16.6,12.2,0.0,0.0,0.0,0.13,1016.0,49.0,2.9000000000000004,75.24608528844631 +2016-10-20 00:30:00-05:00,2016,10,20,0,30,16.1,12.4,0.0,0.0,0.0,0.13,1017.0,47.0,2.8000000000000003,78.7073467817925 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09:30:00-05:00,2016,10,20,9,30,20.1,15.0,149.0,543.0,425.0,0.13,1016.0,96.0,4.7,72.48876581849257 +2016-10-20 10:30:00-05:00,2016,10,20,10,30,20.700000000000003,15.6,244.0,38.0,267.0,0.13,1016.0,103.0,4.6000000000000005,72.59588359027659 +2016-10-20 11:30:00-05:00,2016,10,20,11,30,20.8,16.1,290.0,193.0,410.0,0.13,1015.0,107.0,4.5,74.49320563098087 +2016-10-20 12:30:00-05:00,2016,10,20,12,30,20.700000000000003,16.8,189.0,11.0,196.0,0.13,1015.0,110.0,4.3,78.36638003901605 +2016-10-20 13:30:00-05:00,2016,10,20,13,30,20.6,17.3,118.0,2.0,119.0,0.13,1014.0,111.0,4.0,81.38711358549382 +2016-10-20 14:30:00-05:00,2016,10,20,14,30,20.4,17.7,72.0,1.0,73.0,0.13,1014.0,111.0,3.8,84.50356877594058 +2016-10-20 15:30:00-05:00,2016,10,20,15,30,19.9,17.900000000000002,69.0,4.0,70.0,0.13,1014.0,111.0,3.7,88.26163421505619 +2016-10-20 16:30:00-05:00,2016,10,20,16,30,19.3,18.0,20.0,0.0,20.0,0.13,1013.0,111.0,3.6,92.19018664491743 +2016-10-20 17:30:00-05:00,2016,10,20,17,30,18.7,18.1,0.0,0.0,0.0,0.13,1013.0,111.0,3.4000000000000004,96.30958692337097 +2016-10-20 18:30:00-05:00,2016,10,20,18,30,18.4,18.0,0.0,0.0,0.0,0.13,1013.0,114.0,3.2,97.52059566109483 +2016-10-20 19:30:00-05:00,2016,10,20,19,30,18.3,18.0,0.0,0.0,0.0,0.13,1013.0,120.0,3.1,98.13391605990117 +2016-10-20 20:30:00-05:00,2016,10,20,20,30,18.3,18.0,0.0,0.0,0.0,0.13,1012.0,125.0,3.0,98.13391605990117 +2016-10-20 21:30:00-05:00,2016,10,20,21,30,18.4,18.1,0.0,0.0,0.0,0.13,1011.0,133.0,3.1,98.13533075121076 +2016-10-20 22:30:00-05:00,2016,10,20,22,30,18.5,18.4,0.0,0.0,0.0,0.13,1011.0,142.0,3.2,99.37549332177504 +2016-10-20 23:30:00-05:00,2016,10,20,23,30,18.5,18.5,0.0,0.0,0.0,0.13,1010.0,153.0,3.1,100.0 +2016-10-21 00:30:00-05:00,2016,10,21,0,30,18.3,18.3,0.0,0.0,0.0,0.13,1009.0,162.0,2.9000000000000004,100.0 +2016-10-21 01:30:00-05:00,2016,10,21,1,30,18.0,18.0,0.0,0.0,0.0,0.13,1009.0,167.0,2.6,100.00000000000001 +2016-10-21 02:30:00-05:00,2016,10,21,2,30,17.400000000000002,17.400000000000002,0.0,0.0,0.0,0.13,1008.0,166.0,2.3000000000000003,99.99999999999999 +2016-10-21 03:30:00-05:00,2016,10,21,3,30,17.0,17.0,0.0,0.0,0.0,0.13,1007.0,161.0,2.2,100.0 +2016-10-21 04:30:00-05:00,2016,10,21,4,30,16.8,16.8,0.0,0.0,0.0,0.13,1007.0,155.0,2.2,100.0 +2016-10-21 05:30:00-05:00,2016,10,21,5,30,16.8,16.8,0.0,0.0,0.0,0.13,1006.0,152.0,2.1,100.0 +2016-10-21 06:30:00-05:00,2016,10,21,6,30,17.8,17.8,7.0,0.0,7.0,0.13,1006.0,151.0,2.2,100.00000000000001 +2016-10-21 07:30:00-05:00,2016,10,21,7,30,19.200000000000003,18.4,33.0,0.0,33.0,0.13,1005.0,150.0,2.3000000000000003,95.12452037126248 +2016-10-21 08:30:00-05:00,2016,10,21,8,30,20.4,19.0,24.0,0.0,24.0,0.13,1004.0,149.0,2.4000000000000004,91.67933761291522 +2016-10-21 09:30:00-05:00,2016,10,21,9,30,21.4,19.3,119.0,0.0,119.0,0.13,1004.0,149.0,2.5,87.83865606634913 +2016-10-21 10:30:00-05:00,2016,10,21,10,30,22.3,19.5,133.0,0.0,133.0,0.13,1003.0,150.0,2.6,84.18329690327157 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20:30:00-05:00,2016,10,21,20,30,18.1,18.1,0.0,0.0,0.0,0.13,993.0,191.0,0.9,100.0 +2016-10-21 21:30:00-05:00,2016,10,21,21,30,17.8,17.8,0.0,0.0,0.0,0.13,993.0,212.0,1.0,100.00000000000001 +2016-10-21 22:30:00-05:00,2016,10,21,22,30,17.5,17.5,0.0,0.0,0.0,0.13,992.0,227.0,1.1,100.0 +2016-10-21 23:30:00-05:00,2016,10,21,23,30,16.900000000000002,16.900000000000002,0.0,0.0,0.0,0.13,992.0,248.0,1.3,100.0 +2016-10-22 00:30:00-05:00,2016,10,22,0,30,15.6,15.6,0.0,0.0,0.0,0.13,991.0,279.0,2.3000000000000003,100.0 +2016-10-22 01:30:00-05:00,2016,10,22,1,30,13.8,13.8,0.0,0.0,0.0,0.13,991.0,304.0,4.0,100.00000000000001 +2016-10-22 02:30:00-05:00,2016,10,22,2,30,11.9,11.9,0.0,0.0,0.0,0.13,991.0,316.0,5.5,100.0 +2016-10-22 03:30:00-05:00,2016,10,22,3,30,10.6,9.9,0.0,0.0,0.0,0.13,991.0,320.0,6.1000000000000005,95.43374485616863 +2016-10-22 04:30:00-05:00,2016,10,22,4,30,9.8,8.6,0.0,0.0,0.0,0.13,992.0,318.0,6.2,92.23865038850572 +2016-10-22 05:30:00-05:00,2016,10,22,5,30,9.3,7.9,0.0,0.0,0.0,0.13,992.0,315.0,6.300000000000001,90.9640045491193 +2016-10-22 06:30:00-05:00,2016,10,22,6,30,8.700000000000001,7.4,3.0,0.0,3.0,0.13,992.0,309.0,6.5,91.5461317055717 +2016-10-22 07:30:00-05:00,2016,10,22,7,30,8.3,6.800000000000001,21.0,0.0,21.0,0.13,993.0,305.0,6.800000000000001,90.27376448804542 +2016-10-22 08:30:00-05:00,2016,10,22,8,30,8.6,6.4,13.0,0.0,13.0,0.13,994.0,307.0,7.0,86.05912411787325 +2016-10-22 09:30:00-05:00,2016,10,22,9,30,9.6,6.1000000000000005,69.0,0.0,69.0,0.13,994.0,307.0,7.0,78.8050427298834 +2016-10-22 10:30:00-05:00,2016,10,22,10,30,10.8,6.2,110.0,1.0,111.0,0.13,994.0,301.0,7.2,73.23849762028783 +2016-10-22 11:30:00-05:00,2016,10,22,11,30,11.6,6.2,28.0,0.0,28.0,0.13,994.0,294.0,7.4,69.45678860895526 +2016-10-22 12:30:00-05:00,2016,10,22,12,30,11.6,6.1000000000000005,130.0,2.0,131.0,0.13,994.0,288.0,7.6,68.97929970819487 +2016-10-22 13:30:00-05:00,2016,10,22,13,30,11.3,5.9,94.0,1.0,95.0,0.13,995.0,285.0,7.800000000000001,69.39641286094958 +2016-10-22 14:30:00-05:00,2016,10,22,14,30,10.8,5.800000000000001,73.0,1.0,73.0,0.13,996.0,286.0,7.800000000000001,71.2428534144674 +2016-10-22 15:30:00-05:00,2016,10,22,15,30,10.4,5.7,78.0,6.0,79.0,0.13,997.0,286.0,7.6,72.6612088162897 +2016-10-22 16:30:00-05:00,2016,10,22,16,30,10.1,5.5,23.0,0.0,23.0,0.13,998.0,285.0,7.2,73.11111162463541 +2016-10-22 17:30:00-05:00,2016,10,22,17,30,10.0,5.1000000000000005,0.0,0.0,0.0,0.13,999.0,284.0,6.800000000000001,71.58496720630917 +2016-10-22 18:30:00-05:00,2016,10,22,18,30,10.0,4.6000000000000005,0.0,0.0,0.0,0.13,1000.0,283.0,6.800000000000001,69.13288762926071 +2016-10-22 19:30:00-05:00,2016,10,22,19,30,9.9,3.9,0.0,0.0,0.0,0.13,1001.0,282.0,6.9,66.26684958630258 +2016-10-22 20:30:00-05:00,2016,10,22,20,30,9.6,3.4000000000000004,0.0,0.0,0.0,0.13,1001.0,280.0,6.800000000000001,65.27474096284378 +2016-10-22 21:30:00-05:00,2016,10,22,21,30,9.4,3.3000000000000003,0.0,0.0,0.0,0.13,1002.0,279.0,6.6000000000000005,65.69233300021958 +2016-10-22 22:30:00-05:00,2016,10,22,22,30,9.2,3.2,0.0,0.0,0.0,0.13,1002.0,278.0,6.5,66.11362601574089 +2016-10-22 23:30:00-05:00,2016,10,22,23,30,9.0,3.2,0.0,0.0,0.0,0.13,1002.0,278.0,6.4,67.01058779457718 +2016-10-23 00:30:00-05:00,2016,10,23,0,30,8.8,3.2,0.0,0.0,0.0,0.13,1003.0,279.0,6.300000000000001,67.92117237806535 +2016-10-23 01:30:00-05:00,2016,10,23,1,30,8.4,3.1,0.0,0.0,0.0,0.13,1003.0,281.0,6.300000000000001,69.29266474175363 +2016-10-23 02:30:00-05:00,2016,10,23,2,30,8.1,2.9000000000000004,0.0,0.0,0.0,0.13,1004.0,282.0,6.2,69.72350089217676 +2016-10-23 03:30:00-05:00,2016,10,23,3,30,7.7,2.7,0.0,0.0,0.0,0.13,1004.0,283.0,6.1000000000000005,70.63827046846815 +2016-10-23 04:30:00-05:00,2016,10,23,4,30,7.5,2.6,0.0,0.0,0.0,0.13,1005.0,283.0,5.9,71.10184723587196 +2016-10-23 05:30:00-05:00,2016,10,23,5,30,7.300000000000001,2.6,0.0,0.0,0.0,0.13,1006.0,281.0,5.800000000000001,72.07971449173253 +2016-10-23 06:30:00-05:00,2016,10,23,6,30,7.800000000000001,2.9000000000000004,14.0,253.0,23.0,0.13,1006.0,279.0,5.9,71.16041835916582 +2016-10-23 07:30:00-05:00,2016,10,23,7,30,9.1,3.3000000000000003,44.0,665.0,185.0,0.13,1007.0,276.0,6.2,67.03211580534212 +2016-10-23 08:30:00-05:00,2016,10,23,8,30,10.9,3.6,58.0,818.0,361.0,0.13,1007.0,276.0,6.300000000000001,60.69738369730423 +2016-10-23 09:30:00-05:00,2016,10,23,9,30,12.8,3.9,66.0,893.0,508.0,0.13,1007.0,278.0,6.4,54.692476075640016 +2016-10-23 10:30:00-05:00,2016,10,23,10,30,14.3,4.3,67.0,932.0,605.0,0.13,1007.0,279.0,6.4,51.01835279436497 +2016-10-23 11:30:00-05:00,2016,10,23,11,30,15.5,4.9,70.0,943.0,646.0,0.13,1007.0,280.0,6.4,49.246370419279344 +2016-10-23 12:30:00-05:00,2016,10,23,12,30,16.3,5.300000000000001,73.0,931.0,625.0,0.13,1006.0,280.0,6.4,48.11307005692347 +2016-10-23 13:30:00-05:00,2016,10,23,13,30,16.7,5.5,73.0,889.0,541.0,0.13,1006.0,279.0,6.300000000000001,47.560255307323715 +2016-10-23 14:30:00-05:00,2016,10,23,14,30,16.7,5.7,65.0,831.0,410.0,0.13,1006.0,277.0,6.1000000000000005,48.223924707133435 +2016-10-23 15:30:00-05:00,2016,10,23,15,30,16.3,5.9,53.0,706.0,241.0,0.13,1007.0,275.0,5.6000000000000005,50.15546012637834 +2016-10-23 16:30:00-05:00,2016,10,23,16,30,15.2,6.2,28.0,398.0,65.0,0.13,1007.0,272.0,4.9,54.93748808771447 +2016-10-23 17:30:00-05:00,2016,10,23,17,30,14.1,6.6000000000000005,0.0,0.0,0.0,0.13,1008.0,269.0,4.3,60.62412680344615 +2016-10-23 18:30:00-05:00,2016,10,23,18,30,13.4,6.9,0.0,0.0,0.0,0.13,1009.0,265.0,4.0,64.76473842825952 +2016-10-23 19:30:00-05:00,2016,10,23,19,30,12.8,7.2,0.0,0.0,0.0,0.13,1009.0,259.0,3.8,68.75037778870544 +2016-10-23 20:30:00-05:00,2016,10,23,20,30,12.2,7.5,0.0,0.0,0.0,0.13,1009.0,253.0,3.7,72.99114558683839 +2016-10-23 21:30:00-05:00,2016,10,23,21,30,11.5,7.800000000000001,0.0,0.0,0.0,0.13,1009.0,247.0,3.6,78.01798090742398 +2016-10-23 22:30:00-05:00,2016,10,23,22,30,11.2,8.0,0.0,0.0,0.0,0.13,1008.0,238.0,3.5,80.67283873859367 +2016-10-23 23:30:00-05:00,2016,10,23,23,30,11.2,8.4,0.0,0.0,0.0,0.13,1007.0,228.0,3.4000000000000004,82.89269426508937 +2016-10-24 00:30:00-05:00,2016,10,24,0,30,11.6,9.1,0.0,0.0,0.0,0.13,1006.0,221.0,3.7,84.63745663504126 +2016-10-24 01:30:00-05:00,2016,10,24,1,30,12.1,9.9,0.0,0.0,0.0,0.13,1006.0,223.0,4.1000000000000005,86.41342740508078 +2016-10-24 02:30:00-05:00,2016,10,24,2,30,12.2,10.5,0.0,0.0,0.0,0.13,1005.0,240.0,4.3,89.35723310051605 +2016-10-24 03:30:00-05:00,2016,10,24,3,30,11.9,10.7,0.0,0.0,0.0,0.13,1006.0,264.0,4.5,92.36128260190148 +2016-10-24 04:30:00-05:00,2016,10,24,4,30,11.1,10.3,0.0,0.0,0.0,0.13,1006.0,289.0,4.3,94.81661388452422 +2016-10-24 05:30:00-05:00,2016,10,24,5,30,10.3,9.8,0.0,0.0,0.0,0.13,1007.0,305.0,4.0,96.71158351188555 +2016-10-24 06:30:00-05:00,2016,10,24,6,30,10.3,9.4,14.0,134.0,18.0,0.13,1008.0,310.0,4.0,94.14990194005323 +2016-10-24 07:30:00-05:00,2016,10,24,7,30,11.3,9.3,50.0,589.0,173.0,0.13,1009.0,309.0,4.2,87.50396137892871 +2016-10-24 08:30:00-05:00,2016,10,24,8,30,12.8,9.2,64.0,782.0,350.0,0.13,1010.0,308.0,4.5,78.74456130447597 +2016-10-24 09:30:00-05:00,2016,10,24,9,30,14.2,8.3,71.0,873.0,499.0,0.13,1010.0,306.0,4.800000000000001,67.6451991387437 +2016-10-24 10:30:00-05:00,2016,10,24,10,30,15.1,7.5,79.0,908.0,598.0,0.13,1010.0,302.0,4.9,60.448955436770476 +2016-10-24 11:30:00-05:00,2016,10,24,11,30,15.8,7.0,277.0,12.0,285.0,0.13,1010.0,298.0,5.1000000000000005,55.85357677147675 +2016-10-24 12:30:00-05:00,2016,10,24,12,30,16.2,6.5,278.0,21.0,291.0,0.13,1010.0,295.0,5.300000000000001,52.60822036742159 +2016-10-24 13:30:00-05:00,2016,10,24,13,30,16.400000000000002,6.0,301.0,110.0,358.0,0.13,1010.0,295.0,5.5,50.182699340101784 +2016-10-24 14:30:00-05:00,2016,10,24,14,30,16.0,5.4,63.0,837.0,407.0,0.13,1010.0,297.0,5.7,49.38396176044235 +2016-10-24 15:30:00-05:00,2016,10,24,15,30,15.1,4.800000000000001,50.0,718.0,239.0,0.13,1011.0,302.0,5.9,50.17606865895131 +2016-10-24 16:30:00-05:00,2016,10,24,16,30,13.7,4.1000000000000005,26.0,424.0,64.0,0.13,1012.0,310.0,5.800000000000001,52.303898552786265 +2016-10-24 17:30:00-05:00,2016,10,24,17,30,12.3,3.5,0.0,0.0,0.0,0.13,1014.0,317.0,5.5,54.946945609779185 +2016-10-24 18:30:00-05:00,2016,10,24,18,30,11.2,3.2,0.0,0.0,0.0,0.13,1015.0,321.0,5.1000000000000005,57.845941883547745 +2016-10-24 19:30:00-05:00,2016,10,24,19,30,10.3,2.9000000000000004,0.0,0.0,0.0,0.13,1016.0,322.0,4.9,60.1245790898125 +2016-10-24 20:30:00-05:00,2016,10,24,20,30,9.6,2.7,0.0,0.0,0.0,0.13,1016.0,321.0,4.7,62.12150980935534 +2016-10-24 21:30:00-05:00,2016,10,24,21,30,9.0,2.7,0.0,0.0,0.0,0.13,1017.0,318.0,4.5,64.68023007606037 +2016-10-24 22:30:00-05:00,2016,10,24,22,30,8.6,2.8000000000000003,0.0,0.0,0.0,0.13,1017.0,316.0,4.3,66.92428539605629 +2016-10-24 23:30:00-05:00,2016,10,24,23,30,8.3,2.7,0.0,0.0,0.0,0.13,1017.0,315.0,4.2,67.81542394942772 +2016-10-25 00:30:00-05:00,2016,10,25,0,30,8.0,2.6,0.0,0.0,0.0,0.13,1017.0,312.0,4.2,68.72136900571824 +2016-10-25 01:30:00-05:00,2016,10,25,1,30,7.800000000000001,2.5,0.0,0.0,0.0,0.13,1017.0,309.0,4.1000000000000005,69.16971178482831 +2016-10-25 02:30:00-05:00,2016,10,25,2,30,7.6,2.4000000000000004,0.0,0.0,0.0,0.13,1018.0,306.0,4.2,69.62208831360344 +2016-10-25 03:30:00-05:00,2016,10,25,3,30,7.4,2.5,0.0,0.0,0.0,0.13,1018.0,303.0,4.2,71.08228701692809 +2016-10-25 04:30:00-05:00,2016,10,25,4,30,7.1000000000000005,2.6,0.0,0.0,0.0,0.13,1018.0,301.0,4.2,73.07262649112512 +2016-10-25 05:30:00-05:00,2016,10,25,5,30,6.800000000000001,2.8000000000000003,0.0,0.0,0.0,0.13,1019.0,300.0,4.2,75.65651012693878 +2016-10-25 06:30:00-05:00,2016,10,25,6,30,7.2,3.1,6.0,0.0,6.0,0.13,1019.0,298.0,4.3,75.19121066028795 +2016-10-25 07:30:00-05:00,2016,10,25,7,30,8.5,3.6,113.0,48.0,123.0,0.13,1020.0,297.0,4.7,71.29644793937551 +2016-10-25 08:30:00-05:00,2016,10,25,8,30,9.7,3.6,202.0,157.0,259.0,0.13,1020.0,300.0,5.0,65.75866626856805 +2016-10-25 09:30:00-05:00,2016,10,25,9,30,10.4,3.0,284.0,123.0,343.0,0.13,1020.0,304.0,5.1000000000000005,60.14908080339068 +2016-10-25 10:30:00-05:00,2016,10,25,10,30,11.0,2.2,325.0,203.0,440.0,0.13,1020.0,306.0,5.2,54.60434130499031 +2016-10-25 11:30:00-05:00,2016,10,25,11,30,11.4,1.5,202.0,1.0,202.0,0.13,1019.0,307.0,5.300000000000001,50.58251523893385 +2016-10-25 12:30:00-05:00,2016,10,25,12,30,11.8,0.9,342.0,137.0,422.0,0.13,1019.0,307.0,5.4,47.185947401648555 +2016-10-25 13:30:00-05:00,2016,10,25,13,30,11.9,0.6000000000000001,267.0,312.0,429.0,0.13,1019.0,308.0,5.4,45.873188320863754 +2016-10-25 14:30:00-05:00,2016,10,25,14,30,11.7,0.5,189.0,394.0,349.0,0.13,1019.0,311.0,5.5,46.1479879482537 +2016-10-25 15:30:00-05:00,2016,10,25,15,30,11.1,0.4,134.0,17.0,139.0,0.13,1020.0,314.0,5.5,47.67122738300715 +2016-10-25 16:30:00-05:00,2016,10,25,16,30,10.1,0.4,26.0,419.0,61.0,0.13,1021.0,318.0,5.300000000000001,50.95345183671125 +2016-10-25 17:30:00-05:00,2016,10,25,17,30,9.1,0.6000000000000001,0.0,0.0,0.0,0.13,1022.0,322.0,5.0,55.28322113517189 +2016-10-25 18:30:00-05:00,2016,10,25,18,30,8.3,0.7000000000000001,0.0,0.0,0.0,0.13,1023.0,326.0,4.9,58.77739426560701 +2016-10-25 19:30:00-05:00,2016,10,25,19,30,7.7,0.6000000000000001,0.0,0.0,0.0,0.13,1024.0,330.0,4.800000000000001,60.78399308688479 +2016-10-25 20:30:00-05:00,2016,10,25,20,30,7.1000000000000005,0.6000000000000001,0.0,0.0,0.0,0.13,1024.0,333.0,4.6000000000000005,63.32654161130795 +2016-10-25 21:30:00-05:00,2016,10,25,21,30,6.4,0.6000000000000001,0.0,0.0,0.0,0.13,1024.0,334.0,4.4,66.44412918439565 +2016-10-25 22:30:00-05:00,2016,10,25,22,30,5.800000000000001,0.5,0.0,0.0,0.0,0.13,1025.0,333.0,4.3,68.754972859823 +2016-10-25 23:30:00-05:00,2016,10,25,23,30,5.4,0.5,0.0,0.0,0.0,0.13,1025.0,333.0,4.2,70.68721501888642 +2016-10-26 00:30:00-05:00,2016,10,26,0,30,5.0,0.6000000000000001,0.0,0.0,0.0,0.13,1025.0,333.0,4.1000000000000005,73.20684672427562 +2016-10-26 01:30:00-05:00,2016,10,26,1,30,4.7,0.7000000000000001,0.0,0.0,0.0,0.13,1025.0,332.0,4.0,75.29508931528602 +2016-10-26 02:30:00-05:00,2016,10,26,2,30,4.5,0.7000000000000001,0.0,0.0,0.0,0.13,1025.0,332.0,4.0,76.35434080129414 +2016-10-26 03:30:00-05:00,2016,10,26,3,30,4.3,0.7000000000000001,0.0,0.0,0.0,0.13,1025.0,332.0,4.0,77.43024314931532 +2016-10-26 04:30:00-05:00,2016,10,26,4,30,4.0,0.7000000000000001,0.0,0.0,0.0,0.13,1026.0,332.0,4.0,79.07594827428737 +2016-10-26 05:30:00-05:00,2016,10,26,5,30,3.8,0.7000000000000001,0.0,0.0,0.0,0.13,1026.0,332.0,4.0,80.19474916689425 +2016-10-26 06:30:00-05:00,2016,10,26,6,30,4.3,0.7000000000000001,12.0,246.0,18.0,0.13,1027.0,333.0,4.1000000000000005,77.43024314931532 +2016-10-26 07:30:00-05:00,2016,10,26,7,30,5.9,0.8,40.0,686.0,178.0,0.13,1027.0,338.0,4.3,69.77486548033417 +2016-10-26 08:30:00-05:00,2016,10,26,8,30,7.4,0.1,54.0,844.0,356.0,0.13,1027.0,341.0,4.4,59.837686704282774 +2016-10-26 09:30:00-05:00,2016,10,26,9,30,8.5,-0.8,62.0,917.0,504.0,0.13,1026.0,338.0,4.2,52.00720305002346 +2016-10-26 10:30:00-05:00,2016,10,26,10,30,9.3,-1.2000000000000002,64.0,958.0,603.0,0.13,1026.0,334.0,3.9,47.85356630425037 +2016-10-26 11:30:00-05:00,2016,10,26,11,30,10.1,-1.4,67.0,968.0,644.0,0.13,1026.0,331.0,3.7,44.69393824872697 +2016-10-26 12:30:00-05:00,2016,10,26,12,30,10.6,-1.7000000000000002,68.0,958.0,623.0,0.13,1025.0,329.0,3.4000000000000004,42.285496239343544 +2016-10-26 13:30:00-05:00,2016,10,26,13,30,10.9,-2.1,66.0,927.0,541.0,0.13,1025.0,331.0,3.1,40.246617799323104 +2016-10-26 14:30:00-05:00,2016,10,26,14,30,10.7,-2.4000000000000004,212.0,168.0,279.0,0.13,1025.0,334.0,2.8000000000000003,39.89127688838815 +2016-10-26 15:30:00-05:00,2016,10,26,15,30,10.0,-2.3000000000000003,131.0,122.0,162.0,0.13,1025.0,338.0,2.5,42.108639632476184 +2016-10-26 16:30:00-05:00,2016,10,26,16,30,8.6,-0.9,34.0,1.0,34.0,0.13,1026.0,346.0,2.4000000000000004,51.28057216568079 +2016-10-26 17:30:00-05:00,2016,10,26,17,30,7.300000000000001,-0.6000000000000001,0.0,0.0,0.0,0.13,1026.0,179.0,2.4000000000000004,57.25987517058118 +2016-10-26 18:30:00-05:00,2016,10,26,18,30,6.4,-0.7000000000000001,0.0,0.0,0.0,0.13,1027.0,8.0,2.5,60.46395185098705 +2016-10-26 19:30:00-05:00,2016,10,26,19,30,5.9,-1.0,0.0,0.0,0.0,0.13,1027.0,14.0,2.6,61.22958611629641 +2016-10-26 20:30:00-05:00,2016,10,26,20,30,5.800000000000001,-0.9,0.0,0.0,0.0,0.13,1027.0,20.0,2.6,62.10673411800971 +2016-10-26 21:30:00-05:00,2016,10,26,21,30,5.6000000000000005,-0.8,0.0,0.0,0.0,0.13,1027.0,26.0,2.6,63.43425336276305 +2016-10-26 22:30:00-05:00,2016,10,26,22,30,5.4,-0.9,0.0,0.0,0.0,0.13,1027.0,32.0,2.5,63.85213877796392 +2016-10-26 23:30:00-05:00,2016,10,26,23,30,5.4,-0.8,0.0,0.0,0.0,0.13,1027.0,37.0,2.4000000000000004,64.32015046865475 +2016-10-27 00:30:00-05:00,2016,10,27,0,30,5.5,-0.6000000000000001,0.0,0.0,0.0,0.13,1027.0,40.0,2.5,64.81411372852517 +2016-10-27 01:30:00-05:00,2016,10,27,1,30,5.7,-0.4,0.0,0.0,0.0,0.13,1027.0,42.0,2.5,64.85987748389608 +2016-10-27 02:30:00-05:00,2016,10,27,2,30,5.7,-0.2,0.0,0.0,0.0,0.13,1027.0,44.0,2.5,65.80980715446732 +2016-10-27 03:30:00-05:00,2016,10,27,3,30,5.7,0.1,0.0,0.0,0.0,0.13,1027.0,50.0,2.6,67.25783469526142 +2016-10-27 04:30:00-05:00,2016,10,27,4,30,5.7,0.5,0.0,0.0,0.0,0.13,1027.0,59.0,2.8000000000000003,69.23244586289908 +2016-10-27 05:30:00-05:00,2016,10,27,5,30,5.7,1.0,0.0,0.0,0.0,0.13,1027.0,70.0,3.0,71.77285708632378 +2016-10-27 06:30:00-05:00,2016,10,27,6,30,5.9,1.7000000000000002,3.0,0.0,3.0,0.13,1026.0,80.0,3.2,74.43117612679653 +2016-10-27 07:30:00-05:00,2016,10,27,7,30,6.300000000000001,2.5,25.0,0.0,25.0,0.13,1026.0,87.0,3.5,76.6535515941293 +2016-10-27 08:30:00-05:00,2016,10,27,8,30,6.800000000000001,3.4000000000000004,44.0,0.0,44.0,0.13,1026.0,93.0,3.8,78.93508528553522 +2016-10-27 09:30:00-05:00,2016,10,27,9,30,7.2,4.2,70.0,0.0,70.0,0.13,1025.0,100.0,4.0,81.2444450816009 +2016-10-27 10:30:00-05:00,2016,10,27,10,30,7.7,4.9,112.0,1.0,112.0,0.13,1025.0,107.0,4.1000000000000005,82.45415466845591 +2016-10-27 11:30:00-05:00,2016,10,27,11,30,8.5,5.7,90.0,0.0,90.0,0.13,1024.0,115.0,4.4,82.55581001362246 +2016-10-27 12:30:00-05:00,2016,10,27,12,30,9.2,6.5,74.0,0.0,74.0,0.13,1022.0,127.0,4.800000000000001,83.21476267320288 +2016-10-27 13:30:00-05:00,2016,10,27,13,30,9.7,7.300000000000001,80.0,0.0,80.0,0.13,1021.0,139.0,5.0,85.00286433541271 +2016-10-27 14:30:00-05:00,2016,10,27,14,30,10.2,8.1,34.0,0.0,34.0,0.13,1020.0,147.0,5.2,86.81056391783137 +2016-10-27 15:30:00-05:00,2016,10,27,15,30,10.7,9.0,30.0,0.0,31.0,0.13,1019.0,152.0,5.6000000000000005,89.23767467540056 +2016-10-27 16:30:00-05:00,2016,10,27,16,30,11.4,9.8,9.0,0.0,9.0,0.13,1018.0,155.0,5.9,89.89428379005945 +2016-10-27 17:30:00-05:00,2016,10,27,17,30,11.9,10.7,0.0,0.0,0.0,0.13,1017.0,159.0,5.9,92.36128260190148 +2016-10-27 18:30:00-05:00,2016,10,27,18,30,12.4,11.6,0.0,0.0,0.0,0.13,1015.0,163.0,5.6000000000000005,94.86794490544092 +2016-10-27 19:30:00-05:00,2016,10,27,19,30,13.1,12.5,0.0,0.0,0.0,0.13,1014.0,176.0,5.2,96.14944676326489 +2016-10-27 20:30:00-05:00,2016,10,27,20,30,13.5,13.3,0.0,0.0,0.0,0.13,1014.0,201.0,4.800000000000001,98.70568625031558 +2016-10-27 21:30:00-05:00,2016,10,27,21,30,13.5,13.5,0.0,0.0,0.0,0.13,1013.0,229.0,4.3,100.0 +2016-10-27 22:30:00-05:00,2016,10,27,22,30,13.3,13.3,0.0,0.0,0.0,0.13,1012.0,249.0,4.0,100.0 +2016-10-27 23:30:00-05:00,2016,10,27,23,30,12.8,12.8,0.0,0.0,0.0,0.13,1012.0,266.0,3.9,100.0 +2016-10-28 00:30:00-05:00,2016,10,28,0,30,11.8,11.8,0.0,0.0,0.0,0.13,1012.0,281.0,3.9,100.0 +2016-10-28 01:30:00-05:00,2016,10,28,1,30,10.9,10.9,0.0,0.0,0.0,0.13,1013.0,292.0,4.1000000000000005,100.0 +2016-10-28 02:30:00-05:00,2016,10,28,2,30,10.1,10.1,0.0,0.0,0.0,0.13,1013.0,302.0,4.800000000000001,100.0 +2016-10-28 03:30:00-05:00,2016,10,28,3,30,9.0,8.9,0.0,0.0,0.0,0.13,1014.0,310.0,5.300000000000001,99.32767965308643 +2016-10-28 04:30:00-05:00,2016,10,28,4,30,8.0,7.6,0.0,0.0,0.0,0.13,1015.0,313.0,5.4,97.31358123769705 +2016-10-28 05:30:00-05:00,2016,10,28,5,30,7.1000000000000005,6.6000000000000005,0.0,0.0,0.0,0.13,1015.0,313.0,5.4,96.62826872765939 +2016-10-28 06:30:00-05:00,2016,10,28,6,30,6.7,5.7,4.0,0.0,4.0,0.13,1016.0,312.0,5.6000000000000005,93.33675867364556 +2016-10-28 07:30:00-05:00,2016,10,28,7,30,7.0,5.0,54.0,515.0,153.0,0.13,1017.0,310.0,5.9,87.09805658818217 +2016-10-28 08:30:00-05:00,2016,10,28,8,30,7.800000000000001,4.2,198.0,60.0,219.0,0.13,1017.0,309.0,6.300000000000001,77.98504664568013 +2016-10-28 09:30:00-05:00,2016,10,28,9,30,8.8,3.1,250.0,38.0,268.0,0.13,1017.0,310.0,6.5,67.4428315270641 +2016-10-28 10:30:00-05:00,2016,10,28,10,30,9.4,2.3000000000000003,225.0,3.0,227.0,0.13,1016.0,310.0,6.7,61.19744902187316 +2016-10-28 11:30:00-05:00,2016,10,28,11,30,9.7,2.0,127.0,0.0,127.0,0.13,1016.0,310.0,6.9,58.70870381192407 +2016-10-28 12:30:00-05:00,2016,10,28,12,30,9.8,1.8,183.0,1.0,183.0,0.13,1016.0,310.0,6.9,57.489657786865365 +2016-10-28 13:30:00-05:00,2016,10,28,13,30,9.8,1.8,258.0,291.0,404.0,0.13,1016.0,311.0,6.800000000000001,57.489657786865365 +2016-10-28 14:30:00-05:00,2016,10,28,14,30,9.6,1.9,210.0,198.0,288.0,0.13,1016.0,312.0,6.7,58.68356269950796 +2016-10-28 15:30:00-05:00,2016,10,28,15,30,9.0,2.0,47.0,701.0,219.0,0.13,1017.0,314.0,6.5,61.538307312126015 +2016-10-28 16:30:00-05:00,2016,10,28,16,30,8.200000000000001,2.2,22.0,383.0,50.0,0.13,1018.0,316.0,6.2,65.89310662297115 +2016-10-28 17:30:00-05:00,2016,10,28,17,30,7.4,2.4000000000000004,0.0,0.0,0.0,0.13,1019.0,319.0,5.9,70.57883493471957 +2016-10-28 18:30:00-05:00,2016,10,28,18,30,6.800000000000001,2.6,0.0,0.0,0.0,0.13,1019.0,322.0,5.6000000000000005,74.59076204886019 +2016-10-28 19:30:00-05:00,2016,10,28,19,30,6.300000000000001,2.9000000000000004,0.0,0.0,0.0,0.14,1020.0,325.0,5.1000000000000005,78.85964332369211 +2016-10-28 20:30:00-05:00,2016,10,28,20,30,5.800000000000001,3.1,0.0,0.0,0.0,0.14,1020.0,326.0,4.6000000000000005,82.79282271689713 +2016-10-28 21:30:00-05:00,2016,10,28,21,30,5.4,3.3000000000000003,0.0,0.0,0.0,0.14,1020.0,326.0,4.3,86.33078903821269 +2016-10-28 22:30:00-05:00,2016,10,28,22,30,4.9,3.3000000000000003,0.0,0.0,0.0,0.14,1019.0,327.0,3.9,89.38533480569026 +2016-10-28 23:30:00-05:00,2016,10,28,23,30,4.5,3.3000000000000003,0.0,0.0,0.0,0.14,1019.0,329.0,3.5,91.91589953024946 +2016-10-29 00:30:00-05:00,2016,10,29,0,30,4.3,3.2,0.0,0.0,0.0,0.14,1019.0,332.0,3.1,92.55516227754006 +2016-10-29 01:30:00-05:00,2016,10,29,1,30,4.1000000000000005,3.0,0.0,0.0,0.0,0.14,1019.0,332.0,2.6,92.54354290253167 +2016-10-29 02:30:00-05:00,2016,10,29,2,30,4.0,2.9000000000000004,0.0,0.0,0.0,0.14,1019.0,327.0,2.3000000000000003,92.5377231540934 +2016-10-29 03:30:00-05:00,2016,10,29,3,30,4.1000000000000005,2.7,0.0,0.0,0.0,0.14,1019.0,319.0,2.0,90.59735149417871 +2016-10-29 04:30:00-05:00,2016,10,29,4,30,4.0,2.6,0.0,0.0,0.0,0.14,1019.0,309.0,2.0,90.5900870763271 +2016-10-29 05:30:00-05:00,2016,10,29,5,30,4.0,2.5,0.0,0.0,0.0,0.14,1019.0,294.0,2.0,89.94898831940914 +2016-10-29 06:30:00-05:00,2016,10,29,6,30,4.9,2.4000000000000004,0.0,0.0,0.0,0.14,1019.0,274.0,1.8,83.86375973160085 +2016-10-29 07:30:00-05:00,2016,10,29,7,30,6.800000000000001,2.6,95.0,75.0,109.0,0.14,1018.0,249.0,2.0,74.59076204886019 +2016-10-29 08:30:00-05:00,2016,10,29,8,30,9.0,2.7,95.0,5.0,96.0,0.14,1018.0,230.0,2.9000000000000004,64.68023007606037 +2016-10-29 09:30:00-05:00,2016,10,29,9,30,11.0,3.1,120.0,468.0,339.0,0.14,1016.0,224.0,4.0,58.20552597195882 +2016-10-29 10:30:00-05:00,2016,10,29,10,30,12.8,3.8,162.0,530.0,453.0,0.14,1015.0,223.0,4.9,54.30947289470371 +2016-10-29 11:30:00-05:00,2016,10,29,11,30,14.5,4.9,77.0,883.0,591.0,0.14,1014.0,224.0,5.5,52.51867197205057 +2016-10-29 12:30:00-05:00,2016,10,29,12,30,15.7,5.9,76.0,874.0,569.0,0.14,1012.0,226.0,5.6000000000000005,52.11379546508012 +2016-10-29 13:30:00-05:00,2016,10,29,13,30,16.6,7.0,231.0,207.0,334.0,0.14,1011.0,227.0,5.300000000000001,53.07693819782147 +2016-10-29 14:30:00-05:00,2016,10,29,14,30,17.0,8.200000000000001,142.0,433.0,309.0,0.14,1010.0,225.0,4.9,56.159192522473404 +2016-10-29 15:30:00-05:00,2016,10,29,15,30,16.7,9.5,119.0,109.0,146.0,0.14,1009.0,222.0,4.4,62.4885573626292 +2016-10-29 16:30:00-05:00,2016,10,29,16,30,15.9,10.3,19.0,0.0,19.0,0.14,1009.0,221.0,4.4,69.37301367504654 +2016-10-29 17:30:00-05:00,2016,10,29,17,30,15.4,10.7,0.0,0.0,0.0,0.14,1010.0,224.0,4.6000000000000005,73.56413979986364 +2016-10-29 18:30:00-05:00,2016,10,29,18,30,15.4,11.1,0.0,0.0,0.0,0.14,1010.0,230.0,4.9,75.54499909928607 +2016-10-29 19:30:00-05:00,2016,10,29,19,30,15.5,11.5,0.0,0.0,0.0,0.14,1010.0,238.0,5.1000000000000005,77.07699786611262 +2016-10-29 20:30:00-05:00,2016,10,29,20,30,15.5,11.8,0.0,0.0,0.0,0.14,1010.0,244.0,4.9,78.61918927173075 +2016-10-29 21:30:00-05:00,2016,10,29,21,30,15.4,12.1,0.0,0.0,0.0,0.14,1010.0,248.0,4.6000000000000005,80.70423393504585 +2016-10-29 22:30:00-05:00,2016,10,29,22,30,15.2,12.4,0.0,0.0,0.0,0.14,1009.0,251.0,4.3,83.37476178355165 +2016-10-29 23:30:00-05:00,2016,10,29,23,30,15.1,12.6,0.0,0.0,0.0,0.14,1009.0,253.0,4.0,85.02038204882976 +2016-10-30 00:30:00-05:00,2016,10,30,0,30,15.1,12.9,0.0,0.0,0.0,0.14,1009.0,256.0,3.7,86.70676483426283 +2016-10-30 01:30:00-05:00,2016,10,30,1,30,15.2,13.2,0.0,0.0,0.0,0.14,1009.0,260.0,3.5,87.85616543306737 +2016-10-30 02:30:00-05:00,2016,10,30,2,30,15.3,13.6,0.0,0.0,0.0,0.14,1008.0,267.0,3.3000000000000003,89.5982175992295 +2016-10-30 03:30:00-05:00,2016,10,30,3,30,14.9,13.9,0.0,0.0,0.0,0.14,1008.0,273.0,2.9000000000000004,93.74111299820538 +2016-10-30 04:30:00-05:00,2016,10,30,4,30,14.5,14.2,0.0,0.0,0.0,0.14,1009.0,276.0,2.4000000000000004,98.07894566941862 +2016-10-30 05:30:00-05:00,2016,10,30,5,30,14.1,14.1,0.0,0.0,0.0,0.14,1009.0,278.0,2.0,100.0 +2016-10-30 06:30:00-05:00,2016,10,30,6,30,14.5,14.3,0.0,0.0,0.0,0.14,1009.0,283.0,1.8,98.71565726238593 +2016-10-30 07:30:00-05:00,2016,10,30,7,30,15.8,14.5,81.0,3.0,81.0,0.14,1010.0,289.0,1.5,91.98578869476624 +2016-10-30 08:30:00-05:00,2016,10,30,8,30,17.5,14.9,164.0,198.0,231.0,0.14,1010.0,294.0,1.2000000000000002,84.72794955188044 +2016-10-30 09:30:00-05:00,2016,10,30,9,30,19.200000000000003,14.9,175.0,491.0,402.0,0.14,1009.0,294.0,0.9,76.16209328099512 +2016-10-30 10:30:00-05:00,2016,10,30,10,30,20.5,14.6,190.0,472.0,447.0,0.14,1009.0,273.0,0.8,68.9202981116022 +2016-10-30 11:30:00-05:00,2016,10,30,11,30,21.1,14.8,113.0,765.0,555.0,0.14,1008.0,240.0,0.9,67.28423387224595 +2016-10-30 12:30:00-05:00,2016,10,30,12,30,21.3,15.2,116.0,745.0,533.0,0.14,1007.0,219.0,1.2000000000000002,68.19622652344671 +2016-10-30 13:30:00-05:00,2016,10,30,13,30,21.4,15.6,131.0,641.0,448.0,0.14,1006.0,204.0,1.5,69.54088286535064 +2016-10-30 14:30:00-05:00,2016,10,30,14,30,21.1,16.2,112.0,562.0,327.0,0.14,1005.0,190.0,1.9,73.59996635036241 +2016-10-30 15:30:00-05:00,2016,10,30,15,30,20.0,16.5,104.0,4.0,105.0,0.14,1005.0,187.0,2.3000000000000003,80.28331735432167 +2016-10-30 16:30:00-05:00,2016,10,30,16,30,18.5,16.5,14.0,0.0,14.0,0.14,1005.0,200.0,2.5,88.14295499979576 +2016-10-30 17:30:00-05:00,2016,10,30,17,30,17.5,16.400000000000002,0.0,0.0,0.0,0.14,1006.0,224.0,2.6,93.2665280273494 +2016-10-30 18:30:00-05:00,2016,10,30,18,30,16.400000000000002,16.2,0.0,0.0,0.0,0.14,1007.0,267.0,2.9000000000000004,98.73428809486713 +2016-10-30 19:30:00-05:00,2016,10,30,19,30,14.8,14.8,0.0,0.0,0.0,0.14,1009.0,321.0,4.4,100.0 +2016-10-30 20:30:00-05:00,2016,10,30,20,30,12.9,12.9,0.0,0.0,0.0,0.14,1010.0,351.0,5.800000000000001,100.0 +2016-10-30 21:30:00-05:00,2016,10,30,21,30,11.7,10.9,0.0,0.0,0.0,0.14,1011.0,353.0,5.6000000000000005,94.84039948265676 +2016-10-30 22:30:00-05:00,2016,10,30,22,30,11.0,9.9,0.0,0.0,0.0,0.14,1011.0,347.0,5.300000000000001,92.92943847495981 +2016-10-30 23:30:00-05:00,2016,10,30,23,30,10.2,8.5,0.0,0.0,0.0,0.14,1012.0,343.0,5.4,89.19738346336534 +2016-10-31 00:30:00-05:00,2016,10,31,0,30,9.2,7.0,0.0,0.0,0.0,0.14,1013.0,342.0,5.300000000000001,86.12081072781119 +2016-10-31 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17:30:00-05:00,2016,10,31,17,30,9.5,1.7000000000000002,0.0,0.0,0.0,0.14,1020.0,11.0,2.0,58.240573187563484 +2016-10-31 18:30:00-05:00,2016,10,31,18,30,8.8,1.7000000000000002,0.0,0.0,0.0,0.14,1021.0,20.0,2.1,61.05217389594059 +2016-10-31 19:30:00-05:00,2016,10,31,19,30,13.3,12.4,0.0,0.0,0.0,0.13,1005.0,276.0,4.3,94.28229294959037 +2016-10-31 20:30:00-05:00,2016,10,31,20,30,12.6,12.0,0.0,0.0,0.0,0.13,1006.0,281.0,4.2,96.13464743680161 +2016-10-31 21:30:00-05:00,2016,10,31,21,30,12.1,11.5,0.0,0.0,0.0,0.13,1006.0,282.0,4.1000000000000005,96.11976321331497 +2016-10-31 22:30:00-05:00,2016,10,31,22,30,11.7,11.2,0.0,0.0,0.0,0.13,1007.0,280.0,4.0,96.74707114333046 +2016-10-31 23:30:00-05:00,2016,10,31,23,30,11.4,11.0,0.0,0.0,0.0,0.13,1007.0,276.0,4.0,97.38401091998122 +2021-11-01 00:30:00-05:00,2021,11,1,0,30,11.2,10.8,0.0,0.0,0.0,0.13,1008.0,272.0,3.9,97.37994473575532 +2021-11-01 01:30:00-05:00,2021,11,1,1,30,11.1,10.7,0.0,0.0,0.0,0.13,1008.0,268.0,3.9,97.37790810372628 +2021-11-01 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22:30:00-05:00,2022,12,31,22,30,-5.800000000000001,-9.2,0.0,0.0,0.0,0.13,1015.0,271.0,4.2,76.91032244620173 +2022-12-31 23:30:00-05:00,2022,12,31,23,30,-5.800000000000001,-9.1,0.0,0.0,0.0,0.13,1015.0,270.0,4.5,77.51492459213861 diff --git a/tutorials_and_tools/tutorials_and_tools/temperature and degradation.json b/tutorials/data/temperature_and_degradation.json similarity index 100% rename from tutorials_and_tools/tutorials_and_tools/temperature and degradation.json rename to tutorials/data/temperature_and_degradation.json diff --git a/tutorials/images/pvdeg_logo.svg b/tutorials/images/pvdeg_logo.svg new file mode 100644 index 000000000..0f38b7b6e --- /dev/null +++ b/tutorials/images/pvdeg_logo.svg @@ -0,0 +1,56 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/tutorials_and_tools/_build/html/_images/tutorial_1_NSRDB_example.PNG b/tutorials/images/tutorial_1_NSRDB_example.PNG similarity index 100% rename from tutorials_and_tools/_build/html/_images/tutorial_1_NSRDB_example.PNG rename to tutorials/images/tutorial_1_NSRDB_example.PNG diff --git a/tutorials_and_tools/tutorials_and_tools/images/xarray.webp b/tutorials/images/xarray.webp similarity index 100% rename from tutorials_and_tools/tutorials_and_tools/images/xarray.webp rename to tutorials/images/xarray.webp diff --git a/tutorials/intro.md b/tutorials/intro.md new file mode 100644 index 000000000..13863dfff --- /dev/null +++ b/tutorials/intro.md @@ -0,0 +1,35 @@ +# Welcome to the PVDEG Tutorials + +Here is a collection of Jupyter Notebooks that provide tutorials for using PVDeg. +These are ready to read, install and use, or run in your browser through Google Colab. +Clicking on the rocket-icon on the top allows you to launch the notebooks on +[Google Colaboratory](https://colab.research.google.com/) for interactive mode. +As per the instructions in each notebook, you should uncomment the first line +that reads `pip install ...` to install the environment if you follow this mode. + +You can also clone the repository and run them locally, following the +instructions on the [PVDeg Github page](https://github.com/NREL/PVDegradationTools) + +## Tutorial Categories + +**[01 - Basics](01_basics/01_basics_humidity_design.ipynb)** - Introduction to PVDeg fundamentals including humidity design, degradation overview, spectral analysis, and weather database access + +**[02 - Degradation Mechanisms](02_degradation/01_bo_lid_accelerated_test.ipynb)** - Detailed modeling of specific degradation mechanisms: B-O LID, LETID (accelerated tests, outdoor environments, scenarios), and Van't Hoff models + +**[03 - Monte Carlo Simulations](03_monte_carlo/01_arrhenius.ipynb)** - Uncertainty quantification through Monte Carlo methods for Arrhenius degradation and standoff calculations + +**[04 - Scenarios](04_scenario/01_scenario_temperature.ipynb)** - Scenario-based workflows allowing for easy analysis of different PV module materials or deployment locations. + +**[05 - Geospatial Analysis](05_geospatial/01_geospatial_templates.ipynb)** - Large-scale spatial analysis, geospatial templates, and world map visualizations for regional degradation assessments (HPC) + +**[06 - Advanced Topics](06_advanced/01_custom_functions_nopython.ipynb)** - Custom functions with Numba, PySAM integration, and distributed computing with NSRDB/PVGIS APIs + +**[10 - Workshop Demos](10_workshop_demos/01_astm_live_demo.ipynb)** - Interactive demonstrations from ASTM and DuraMAT workshops + +## Tools + +**[Edge Seal Oxygen Ingress Calculator](tools/Tools%20-%20Edge%20Seal%20Oxygen%20Ingress.ipynb)** - Calculation of oxygen ingress profile through an edge seal and into the encapsulant + +**[Degradation and Acceleration Factors](tools/Tools%20-%20Degradation.ipynb)** - Estimation of degradation and calculation of acceleration factors using the degradation database + +**[Module Standoff for IEC TS 63126](tools/Tools%20-%20Module%20Standoff%20for%20IEC%20TS%2063126.ipynb)** - Calculation of module standoff distance according to IEC TS 63126 diff --git a/tutorials/myst.yml b/tutorials/myst.yml new file mode 100644 index 000000000..c6c73d435 --- /dev/null +++ b/tutorials/myst.yml @@ -0,0 +1,110 @@ +version: 1 +project: + title: PVDEG Tutorials + authors: + - name: Martin Springer + - name: Matthew Brown + - name: Silvana Ovaitt + - name: Tobin Ford + - name: Joseph Karas + - name: Michael Kempe + exclude: + - _build + - .ipynb_checkpoints + - '*/scripts' + - '*/scripts/*' + github: NREL/PVDegradationTools + thebe: + binder: + repo: NREL/PVDegradationTools + provider: github + ref: main + bibliography: + - references.bib + exports: + - format: pdf + template: plain_latex_book + output: exports/book.pdf + defaults: + titlesonly: true + toc: + - file: intro.md + - title: 01 - Basics + children: + - file: 01_basics/01_basics_humidity_design.ipynb + title: Humidity Design Basics + - file: 01_basics/02_degradation.ipynb + title: Degradation Overview + - file: 01_basics/03_spectral_degradation.ipynb + title: Spectral Degradation + - file: 01_basics/04_weather_database_access.ipynb + title: Weather Database Access + - title: 02 - Degradation Mechanisms + children: + - file: 02_degradation/01_bo_lid_accelerated_test.ipynb + title: B-O LID Accelerated Test + - file: 02_degradation/02_letid_accelerated_test.ipynb + title: LETID Accelerated Test + - file: 02_degradation/03_letid_outdoor.ipynb + title: LETID Outdoor + - file: 02_degradation/04_letid_outdoor_scenario.ipynb + title: LETID Outdoor Scenario + - file: 02_degradation/05_letid_passivated_wafer.ipynb + title: LETID Passivated Wafer + - file: 02_degradation/06_vant_hoff_degradation_model.ipynb + title: Van't Hoff Degradation Model + - title: 03 - Monte Carlo Simulations + children: + - file: 03_monte_carlo/01_arrhenius.ipynb + title: Arrhenius Monte Carlo + - file: 03_monte_carlo/02_standoff.ipynb + title: Standoff Monte Carlo + - title: 04 - Scenarios + children: + - file: 04_scenario/01_scenario_temperature.ipynb + title: Temperature Scenario + - file: 04_scenario/02_scenario_single_location.ipynb + title: Single Location + - file: 04_scenario/03_scenario_mountain_downselect.ipynb + title: Mountain Downselect + - title: 05 - Geospatial Analysis + children: + - file: 05_geospatial/01_geospatial_templates.ipynb + title: Geospatial Templates (HPC) + - file: 05_geospatial/02_geospatial_world_map.ipynb + title: Geospatial World Map (HPC) + - file: 05_geospatial/03_letid_outdoor_geospatial_demo.ipynb + title: LETID Outdoor Geospatial Demo (HPC) + - file: 05_geospatial/04_scenario_geographical_features.ipynb + title: Geographical Features (HPC) + - file: 05_geospatial/05_scenario_geospatial.ipynb + title: Scenario Geospatial (HPC) + - title: 06 - Advanced Topics + children: + - file: 06_advanced/01_custom_functions_nopython.ipynb + title: Custom Functions with Numba + - file: 06_advanced/03_pysam_api.ipynb + title: PySAM with API (API Key Required) + - file: 06_advanced/04_nsrdb_distributed_api.ipynb + title: NSRDB Distributed (API Key Required) + - file: 06_advanced/05_pvgis_distributed.ipynb + title: PVGIS Distributed + - title: 06 - Workshop Demos + children: + - file: 10_workshop_demos/01_astm_live_demo.ipynb + title: ASTM Live Demo + - file: 10_workshop_demos/02_duramat_live_demo.ipynb + title: DuraMAT Live Demo (HPC) + - title: Tools + children: + - file: tools/Tools - Edge Seal Oxygen Ingress.ipynb + title: Edge Seal Oxygen Ingress + - file: tools/Tools - Degradation.ipynb + title: Degradation + - file: tools/Tools - Module Standoff for IEC TS 63126.ipynb + title: Module Standoff for IEC TS 63126 (HPC) +site: + options: + logo: ../docs/source/_static/logo-vectors/PVdeg-Logo-Horiz-Color.svg + folders: true + template: book-theme diff --git a/tutorials/references.bib b/tutorials/references.bib new file mode 100644 index 000000000..3ba347911 --- /dev/null +++ b/tutorials/references.bib @@ -0,0 +1,34 @@ +% BibTeX references for tutorials + +@article{Karas2022, +author = {Karas, Joseph and Repins, Ingrid and Berger, Karl A. and Kubicek, Bernhard and Jiang, Fangdan and Zhang, Daqi and Jaubert, Jean-Nicolas and Cueli, Ana BelΓ©n and Sample, Tony and Jaeckel, Bengt and Pander, Matthias and Fokuhl, Esther and Koentopp, Max B. and Kersten, Friederike and Choi, Jun-Hong and Bora, Birinchi and Banerjee, Chandan and Wendlandt, Stefan and Erion-Lorico, Tristan and Sauer, Kenneth J. and Tsan, Jon and Pravettoni, Mauro and Caccivio, Mauro and Bellenda, Giovanni and Monokroussos, Christos and Maaroufi, Hamza}, +title = {Results from an international interlaboratory study on light- and elevated temperature-induced degradation in solar modules}, +journal = {Progress in Photovoltaics: Research and Applications}, +volume = {30}, +number = {11}, +pages = {1255-1269}, +keywords = {degradation, light- and elevated temperature-induced degradation (LETID), photovoltaics (PV), PV modules, silicon, solar cells}, +doi = {10.1002/pip.3573}, +url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/pip.3573}, +year = {2022} +} + +@article{Repins2023, +author = {Repins, I.L. and others}, +title = {Long-term impact of light- and elevated temperature-induced degradation on photovoltaic arrays}, +journal = {MRS Bulletin}, +volume = {48}, +pages = {589--601}, +doi = {10.1557/s43577-022-00438-8}, +url = {https://doi.org/10.1557/s43577-022-00438-8}, +year = {2023} +} + +@article{Wyller2021, +author = {Wyller, Joachim and others}, +title = {Light- and elevated temperature-induced degradation in p-type multicrystalline silicon}, +journal = {IEEE Journal of Photovoltaics}, +doi = {10.1109/JPHOTOV.2021.3078367}, +url = {https://doi.org/10.1109/JPHOTOV.2021.3078367}, +year = {2021} +} diff --git a/tutorials_and_tools/requirements.txt b/tutorials/requirements.txt similarity index 100% rename from tutorials_and_tools/requirements.txt rename to tutorials/requirements.txt diff --git a/tutorials/tools/README.md b/tutorials/tools/README.md new file mode 100644 index 000000000..f3c9448b7 --- /dev/null +++ b/tutorials/tools/README.md @@ -0,0 +1,27 @@ +# PVDeg Tools + +This folder contains standalone computational tools for specific PV degradation calculations. These tools are included in the [Jupyter Book](https://nrel.github.io/PVDegradationTools/intro.html) under the Tools section. + +For tutorials and guided examples, see the [tutorials](../tutorials/) directory. + +## Tools Overview + +### Tools - Degradation +General degradation calculation tools and utilities. + +### Tools - Edge Seal Oxygen Ingress +Calculations for oxygen ingress through edge seals in PV modules. + +### Tools - Module Standoff for IEC TS 63126 +**⚠️ Requires NREL HPC Access** + +Calculation of module standoff distance according to IEC TS 63126. This tool performs geospatial analysis across multiple locations and requires access to NREL's High Performance Computing (HPC) resources for execution. + +## Requirements + +Most tools can run locally with standard dependencies. Tools marked with ⚠️ require: +- NREL HPC cluster access +- Additional authentication/credentials +- Specific environment setup + +See individual notebooks for detailed requirements. diff --git a/tutorials_and_tools/tutorials_and_tools/Tools - Degradation.ipynb b/tutorials/tools/Tools - Degradation.ipynb similarity index 97% rename from tutorials_and_tools/tutorials_and_tools/Tools - Degradation.ipynb rename to tutorials/tools/Tools - Degradation.ipynb index 8e3d190f5..ce3591b29 100644 --- a/tutorials_and_tools/tutorials_and_tools/Tools - Degradation.ipynb +++ b/tutorials/tools/Tools - Degradation.ipynb @@ -25,7 +25,7 @@ "5. Generate chamber or field data for environmental comparison\n", "6. Calculate degradation acceleration factor of field location to chamber (or another location)\n", "7. Produce a map of acceleration factors for a geographic region\n", - " Select a geographic region of interest \n", + " Select a geographic region of interest\n", " downsample to select specific site coordinates\n", " Download or access the meteorological data for the chosen site coordinates.\n", " Run the calculation" @@ -33,7 +33,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2025-10-04T11:18:49.852022Z", @@ -45,7 +45,7 @@ "outputs": [], "source": [ "# if running on google colab, uncomment the next line and execute this cell to install the dependencies and prevent \"ModuleNotFoundError\" in later cells:\n", - "# !pip install pvdeg==0.4.2" + "# !pip install pvdeg" ] }, { @@ -78,6 +78,7 @@ "from IPython.display import display, Math\n", "\n", "import pvlib\n", + "\n", "print(pvlib.__version__)\n", "from pvlib import iotools" ] @@ -109,6 +110,7 @@ "source": [ "# This information helps with debugging and getting support :)\n", "import sys, platform\n", + "\n", "print(\"Working on a \", platform.system(), platform.release())\n", "print(\"Python version \", sys.version)\n", "print(\"Pandas version \", pd.__version__)\n", @@ -152,8 +154,8 @@ ], "source": [ "# Get data from a supplied data file (Do not use the next box of code if using your own file)\n", - "weather_file = os.path.join(DATA_DIR, 'psm3_demo.csv')\n", - "weather_df, meta = pvdeg.weather.read(weather_file,'csv', find_meta=True)\n", + "weather_file = os.path.join(DATA_DIR, \"psm3_demo.csv\")\n", + "weather_df, meta = pvdeg.weather.read(weather_file, \"csv\", find_meta=True)\n", "print(weather_file)\n", "print(meta)" ] @@ -180,30 +182,30 @@ ], "source": [ "# This routine will get a meteorological dataset from anywhere in the world where it is available\n", - "#weather_id = (24.7136, 46.6753) #Riyadh, Saudi Arabia\n", - "#weather_id = (35.6754, 139.65) #Tokyo, Japan\n", - "#weather_id = (-43.52646, 172.62165) #Christchurch, New Zealand\n", - "#weather_id = (64.84031, -147.73836) #Fairbanks, Alaska\n", - "#weather_id = (65.14037, -21.91633) #Reykjavik, Iceland\n", - "weather_id = (33.4152, -111.8315) #Mesa, Arizona\n", - "#weather_id = (0,0) # Somewhere else you are interested in.\n", + "# weather_id = (24.7136, 46.6753) #Riyadh, Saudi Arabia\n", + "# weather_id = (35.6754, 139.65) #Tokyo, Japan\n", + "# weather_id = (-43.52646, 172.62165) #Christchurch, New Zealand\n", + "# weather_id = (64.84031, -147.73836) #Fairbanks, Alaska\n", + "# weather_id = (65.14037, -21.91633) #Reykjavik, Iceland\n", + "weather_id = (33.4152, -111.8315) # Mesa, Arizona\n", + "# weather_id = (0,0) # Somewhere else you are interested in.\n", "weather_df, meta = pvdeg.weather.get_anywhere(id=weather_id)\n", "print(meta)\n", - "#display(weather_df)" + "# display(weather_df)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "#### POA Irradiance\n", + "### POA Irradiance\n", "Next we need to calculate the stress parameters including temperature and humidity. We start with POA irradiance.\n", - "Irradiance_kwarg governs the array orientation for doing the POA calculations. \n", - "It is defaulted to a north-south single axis tracking. A fixed tilt set of parameters is included but is blocked out. \n", - "Look in spectral.py and/or PVLib here for 1-axis kwargs, \n", - "https://pvlib-python.readthedocs.io/en/v0.7.2/generated/pvlib.tracking.singleaxis.html#pvlib.tracking.singleaxis \n", - "and for fixed tilt, \n", - "https://pvlib-python.readthedocs.io/en/v0.7.2/generated/pvlib.irradiance.gti_dirint.html?highlight=poa . \n", + "Irradiance_kwarg governs the array orientation for doing the POA calculations.\n", + "It is defaulted to a north-south single axis tracking. A fixed tilt set of parameters is included but is blocked out.\n", + "Look in spectral.py and/or PVLib here for 1-axis kwargs,\n", + "https://pvlib-python.readthedocs.io/en/v0.7.2/generated/pvlib.tracking.singleaxis.html#pvlib.tracking.singleaxis\n", + "and for fixed tilt,\n", + "https://pvlib-python.readthedocs.io/en/v0.7.2/generated/pvlib.irradiance.gti_dirint.html?highlight=poa .\n", "Here, the POA global calculation is appended to the meteorolgical data dataframe." ] }, @@ -220,17 +222,16 @@ }, "outputs": [], "source": [ - "#irradiance_kwarg ={\n", - " #\"tilt\": None,\n", - " #\"azimuth\": None,\n", - " #\"module_mount\": 'fixed'}\n", - "irradiance_kwarg ={\n", - " \"axis_tilt\": 0,\n", - " \"axis_azimuth\": 180,\n", - " \"module_mount\": 'single_axis'}\n", - "poa_df = pvdeg.spectral.poa_irradiance(weather_df=weather_df, meta=meta, **irradiance_kwarg)\n", + "# irradiance_kwarg ={\n", + "# \"tilt\": None,\n", + "# \"azimuth\": None,\n", + "# \"module_mount\": 'fixed'}\n", + "irradiance_kwarg = {\"axis_tilt\": 0, \"axis_azimuth\": 180, \"module_mount\": \"single_axis\"}\n", + "poa_df = pvdeg.spectral.poa_irradiance(\n", + " weather_df=weather_df, meta=meta, **irradiance_kwarg\n", + ")\n", "\n", - "weather_df['poa_global'] = poa_df['poa_global']" + "weather_df[\"poa_global\"] = poa_df[\"poa_global\"]" ] }, { @@ -238,8 +239,8 @@ "metadata": {}, "source": [ "\n", - "#### Get Spectrally Resolved Irradiance Data\n", - "This first set of commands will calculate spectrally resolved irradiance data. This may or may not be needed for a given degradation model and can be skipped here. " + "### Get Spectrally Resolved Irradiance Data\n", + "This first set of commands will calculate spectrally resolved irradiance data. This may or may not be needed for a given degradation model and can be skipped here." ] }, { @@ -251,7 +252,8 @@ "iopub.status.busy": "2025-10-04T11:18:58.272896Z", "iopub.status.idle": "2025-10-04T11:18:58.290765Z", "shell.execute_reply": "2025-10-04T11:18:58.290052Z" - } + }, + "lines_to_next_cell": 2 }, "outputs": [ { @@ -266,21 +268,22 @@ "# this whole block needs to be replaced with call to calculate spectrally resolved irradiance.\n", "\n", "from pvdeg import TEST_DATA_DIR\n", + "\n", "INPUT_SPECTRA = os.path.join(TEST_DATA_DIR, r\"spectra_pytest.csv\")\n", "data = pd.read_csv(INPUT_SPECTRA)\n", - "#display(data)\n", + "# display(data)\n", "print(INPUT_SPECTRA)\n", "\n", - "#Test function\n", - "#cusotm_albedo['Summer']\n", - "#custom_albedo['Winter']\n", + "# Test function\n", + "# cusotm_albedo['Summer']\n", + "# custom_albedo['Winter']\n", "# custom_albedo['Snow']\n", - "#defaults - Grass, Dry Grass, Snow\n", - "#Flexible to add complexity later\n", - "#merge in development branch changes\n", - "#KGPCY Python package\n", - "custom_albedo_summer = 'A006'\n", - "custom_albedo_winter = { #required: startDate, wavelength (if len(albedo) > 1), albedo, isSnow defaults to False\n", + "# defaults - Grass, Dry Grass, Snow\n", + "# Flexible to add complexity later\n", + "# merge in development branch changes\n", + "# KGPCY Python package\n", + "custom_albedo_summer = \"A006\"\n", + "custom_albedo_winter = { # required: startDate, wavelength (if len(albedo) > 1), albedo, isSnow defaults to False\n", " \"data_entry_person\": \"Michael Kempe\",\n", " \"date_entered\": \"7/28/2025\",\n", " \"DOI\": \"10.3390/ijerph15071507\",\n", @@ -289,32 +292,34 @@ " \"reference\": \"Turner J, Parisi AV. Ultraviolet Radiation Albedo and Reflectance in Review: The Influence to Ultraviolet Exposure in Occupational Settings. Int J Environ Res Public Health. 2018 Jul 17;15(7):1507.\",\n", " \"keywords\": \"snow, ground\",\n", " \"months\": \"1,2,3,10,11,12\",\n", - " \"startDate\": \"January 1\", #Day of Year? 0-365\n", - " \"HourOfYear\": \"1\", #Hour of Year? 1-8760\n", + " \"startDate\": \"January 1\", # Day of Year? 0-365\n", + " \"HourOfYear\": \"1\", # Hour of Year? 1-8760\n", " \"isSnow\": \"False\",\n", " \"comments\": \"Data is emperically extrapolated from 280 nm to 297 nm. Data extracted from Turner et al. Figure 1 as a reference to Doda & Green Snow-Ground. Doda D., Green A. Surface Reflectance Measurements in the UV from an Airborne Platform. Part 1. Appl. Opt. 1980;19:2140-2145. doi: 10.1364/AO.19.002140. Doda D., Green A. Surface Reflectance Measurements in the Ultraviolet from an Airborne Platform. Part 2. Appl. Opt. 1981;20:636-642. doi: 10.1364/AO.20.000636.\",\n", " \"wavelength\": \"280, 297.32034, 300.02435, 301.8514, 305.79782, 310.10962, 313.21558, 317.4543, 322.86237, 329.9513, 331.19366, 339.89038, 343.06943, 350.34103, 360.02435, 369.96347, 380.3776, 386.77222, 390.0609, 400.14615\",\n", - " \"albedo\": \"20, 29.515152, 28.30303, 29.454546, 28.90909, 34.696968, 36.757576, 39.363636, 39.21212, 38.60606, 41.272728, 40.909092, 42.242424, 42.21212, 40.575756, 43.21212, 43.090908, 43.454544, 43.60606, 39.757576\"\n", + " \"albedo\": \"20, 29.515152, 28.30303, 29.454546, 28.90909, 34.696968, 36.757576, 39.363636, 39.21212, 38.60606, 41.272728, 40.909092, 42.242424, 42.21212, 40.575756, 43.21212, 43.090908, 43.454544, 43.60606, 39.757576\",\n", "}\n", - "#Startdate, albedo, wavelength -> then next one + boolean logic for snow ()\n", + "# Startdate, albedo, wavelength -> then next one + boolean logic for snow ()\n", "custom_albedo_snow = {}\n", "# custom_albedo_snow\n", - "spectra_folder = 'spectra' #If you have already pulled the spectra from SMARTS, pass the folder path to avoid going through the donwload process again.\n", - "wavelengths = np.arange(280, 400, 25) # Example wavelengths from 280 nm to 400 nm in steps of 25 nm\n", - "#data = pvdeg.spectral.spectrally_resolved_irradiance(weather_df=weather_df, meta=meta, wavelengths=wavelengths, frontResultsOnly=None,\n", + "spectra_folder = \"spectra\" # If you have already pulled the spectra from SMARTS, pass the folder path to avoid going through the donwload process again.\n", + "wavelengths = np.arange(\n", + " 280, 400, 25\n", + ") # Example wavelengths from 280 nm to 400 nm in steps of 25 nm\n", + "# data = pvdeg.spectral.spectrally_resolved_irradiance(weather_df=weather_df, meta=meta, wavelengths=wavelengths, frontResultsOnly=None,\n", "# spectra_folder=spectra_folder, custom_albedo_summer=custom_albedo_summer, custom_albedo_winter=custom_albedo_winter, **irradiance_kwarg)\n", - "#return front, back, or both (True, False, None)\n", - "#bool frontResultsonly = True for front only\n", - "#separate columns for front and back irradiance: spectra_front: etc. , spectra_back: etc. (see spectra_pytest.csv)\n", - "#Check albedo boolean snow, winter non-snow, summer non-snow\n" + "# return front, back, or both (True, False, None)\n", + "# bool frontResultsonly = True for front only\n", + "# separate columns for front and back irradiance: spectra_front: etc. , spectra_back: etc. (see spectra_pytest.csv)\n", + "# Check albedo boolean snow, winter non-snow, summer non-snow" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "#### Get Cell Temperature and Module Surface Temperature\n", - "The following will calculate the cell and module surface temperature using the King model as a default. Other models can be used as described at, \n", + "### Get Cell Temperature and Module Surface Temperature\n", + "The following will calculate the cell and module surface temperature using the King model as a default. Other models can be used as described at,\n", "https://pvlib-python.readthedocs.io/en/stable/reference/pv_modeling/temperature.html. The difference is less than one Β°C for ground mounted systems\n", "but can be as high as 3 Β°C for a high temperature building integrated system.\n", "\n", @@ -337,18 +342,18 @@ "temp_cell = pvdeg.temperature.cell(weather_df=weather_df, meta=meta, poa=poa_df)\n", "temp_module = pvdeg.temperature.module(weather_df=weather_df, meta=meta, poa=poa_df)\n", "\n", - "weather_df['temp_cell'] = temp_cell\n", - "weather_df['temp_module'] = temp_module\n", + "weather_df[\"temp_cell\"] = temp_cell\n", + "weather_df[\"temp_module\"] = temp_module\n", "\n", - "weather_df['temperature'] = weather_df['temp_module']\n", - "#weather_df['temperature'] = weather_df['temp_cell']" + "weather_df[\"temperature\"] = weather_df[\"temp_module\"]\n", + "# weather_df['temperature'] = weather_df['temp_cell']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "#### Humidity\n", + "### Humidity\n", "Depending on the component for which the calculation is being run on, the desired humidity may be the atmospheric humidity, the module surface humidity, the humidity in front of a cell with a permeable backsheet, the humidity in the backsheet, the humidity in the back encapsulant or another custom humidity location such as a diffusion limited location. The folowing are options for doing all of these calculations. Here all the different humidities are put in the weather_df dataframe, but to select one to be specifically used it should be named 'RH' for most degradation functions (check the documentation of a specific degradation calculation if in doubt). Here the surface humidity is selected as a default.\n", "\n", "Append the calculated values into the weather DataFrame.\n", @@ -371,13 +376,14 @@ "RH_module = pvdeg.humidity.module(\n", " weather_df=weather_df,\n", " poa=poa_df,\n", - " temp_module = temp_module,\n", - " backsheet='W017',\n", + " temp_module=temp_module,\n", + " backsheet=\"W017\",\n", " backsheet_thickness=0.30,\n", - " encapsulant='W001',\n", - " back_encap_thickness=0.50)\n", + " encapsulant=\"W001\",\n", + " back_encap_thickness=0.50,\n", + ")\n", "\n", - "weather_df=pd.concat([weather_df, RH_module], axis=1)" + "weather_df = pd.concat([weather_df, RH_module], axis=1)" ] }, { @@ -404,11 +410,11 @@ "outputs": [], "source": [ "\n", - "weather_df['RH'] = RH_module['RH_surface_outside']\n", - "#weather_df['RH'] = RH_module['RH_front_encap']\n", - "#weather_df['RH'] = RH_module['Ce_back_encap']\n", - "#weather_df['RH'] = RH_module['RH_back_encap']\n", - "#weather_df['RH'] = RH_module['RH_backsheet']" + "weather_df[\"RH\"] = RH_module[\"RH_surface_outside\"]\n", + "# weather_df['RH'] = RH_module['RH_front_encap']\n", + "# weather_df['RH'] = RH_module['Ce_back_encap']\n", + "# weather_df['RH'] = RH_module['RH_back_encap']\n", + "# weather_df['RH'] = RH_module['RH_backsheet']" ] }, { @@ -455,8 +461,8 @@ } ], "source": [ - "#kwarg_variables = pvdeg.utilities._read_material(name=None, fname=\"DegradationDatabase\", item=(\"Material\", \"Equation\", \"KeyWords\", \"EquationType\"))\n", - "#print(json.dumps(kwarg_variables, skipkeys = True, indent = 0 ).replace(\"{\" + \"\\n\", \"{\").replace('\\\"' + \"\\n\", \"\\\"\").replace(': {' , ':' + \"\\n\" + \"{\").replace('},' + \"\\n\", '},' +'\\n' +'\\n'))\n", + "# kwarg_variables = pvdeg.utilities._read_material(name=None, fname=\"DegradationDatabase\", item=(\"Material\", \"Equation\", \"KeyWords\", \"EquationType\"))\n", + "# print(json.dumps(kwarg_variables, skipkeys = True, indent = 0 ).replace(\"{\" + \"\\n\", \"{\").replace('\\\"' + \"\\n\", \"\\\"\").replace(': {' , ':' + \"\\n\" + \"{\").replace('},' + \"\\n\", '},' +'\\n' +'\\n'))\n", "pvdeg.utilities.display_json(pvdeg_file=\"DegradationDatabase\", fp=DATA_DIR)" ] }, @@ -505,7 +511,9 @@ } ], "source": [ - "deg_data = pvdeg.utilities.read_material(fp=DATA_DIR, key=\"D036\", pvdeg_file=\"DegradationDatabase\")\n", + "deg_data = pvdeg.utilities.read_material(\n", + " fp=DATA_DIR, key=\"D036\", pvdeg_file=\"DegradationDatabase\"\n", + ")\n", "display(deg_data)" ] }, @@ -560,7 +568,7 @@ "source": [ "## 3. Calculate Absolute Degradation Rate\n", "\n", - "To do this calculation, we must have degradation parameter data for a process that is complete with all the necessary variables. " + "To do this calculation, we must have degradation parameter data for a process that is complete with all the necessary variables." ] }, { @@ -588,7 +596,9 @@ "source": [ "func_call = getattr(pvdeg.degradation, deg_data[\"EquationType\"])\n", "degradation = func_call(weather_df=weather_df, parameters=deg_data)\n", - "print(\"Average degradation rate for a year\", degradation/8760, deg_data[\"R_0\"][\"units\"])" + "print(\n", + " \"Average degradation rate for a year\", degradation / 8760, deg_data[\"R_0\"][\"units\"]\n", + ")" ] }, { @@ -596,9 +606,7 @@ "execution_count": null, "metadata": {}, "outputs": [], - "source": [ - "\n" - ] + "source": [] } ], "metadata": { diff --git a/tutorials_and_tools/tutorials_and_tools/Tools - Edge Seal Oxygen Ingress.ipynb b/tutorials/tools/Tools - Edge Seal Oxygen Ingress.ipynb similarity index 99% rename from tutorials_and_tools/tutorials_and_tools/Tools - Edge Seal Oxygen Ingress.ipynb rename to tutorials/tools/Tools - Edge Seal Oxygen Ingress.ipynb index ed7f2c442..bfc4fc3ae 100644 --- a/tutorials_and_tools/tutorials_and_tools/Tools - Edge Seal Oxygen Ingress.ipynb +++ b/tutorials/tools/Tools - Edge Seal Oxygen Ingress.ipynb @@ -4,9 +4,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Tools - Edge Seal Oxygen Ingress Calculator tool\n", + "# Edge Seal Oxygen Ingress Calculator\n", "\n", - "### Calculation of oxygen ingress profile through an edge seal and into the encapsulant.\n", + "## Calculation of oxygen ingress profile through an edge seal and into the encapsulant.\n", "\n", "**Requirements:**\n", "- Local weather data file or site longitude and latittude.\n", @@ -26,7 +26,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2025-10-24T21:20:13.006290Z", @@ -38,7 +38,7 @@ "outputs": [], "source": [ "# if running on google colab, uncomment the next line and execute this cell to install the dependencies and prevent \"ModuleNotFoundError\" in later cells:\n", - "# !pip install pvdeg==0.4.2" + "# !pip install pvdeg" ] }, { @@ -89,6 +89,7 @@ "source": [ "# This information helps with debugging and getting support :)\n", "import sys, platform\n", + "\n", "print(\"Working on a \", platform.system(), platform.release())\n", "print(\"Python version \", sys.version)\n", "print(\"Pandas version \", pd.__version__)\n", @@ -244,8 +245,16 @@ "sn = 20 # This is the number of edge seal nodes to use\n", "en = 50 # This is the number of encapsulant nodes to use\n", "pressure = 0.2109 * (1 - 0.0065 * meta.get(\"altitude\") / 288.15) ** 5.25588\n", - "print(pvdeg.utilities.read_material(pvdeg_file=\"O2permeation\", key=\"OX003\", values_only=True))\n", - "print(pvdeg.utilities.read_material(pvdeg_file=\"H2Opermeation\", key=\"W003\", values_only=True))" + "print(\n", + " pvdeg.utilities.read_material(\n", + " pvdeg_file=\"O2permeation\", key=\"OX003\", values_only=True\n", + " )\n", + ")\n", + "print(\n", + " pvdeg.utilities.read_material(\n", + " pvdeg_file=\"H2Opermeation\", key=\"W003\", values_only=True\n", + " )\n", + ")" ] }, { diff --git a/tutorials_and_tools/tutorials_and_tools/HPC Connection Required - Tools - Module Standoff for IEC TS 63126.ipynb b/tutorials/tools/Tools - Module Standoff for IEC TS 63126.ipynb similarity index 92% rename from tutorials_and_tools/tutorials_and_tools/HPC Connection Required - Tools - Module Standoff for IEC TS 63126.ipynb rename to tutorials/tools/Tools - Module Standoff for IEC TS 63126.ipynb index 83b906aa8..6ab1236ea 100644 --- a/tutorials_and_tools/tutorials_and_tools/HPC Connection Required - Tools - Module Standoff for IEC TS 63126.ipynb +++ b/tutorials/tools/Tools - Module Standoff for IEC TS 63126.ipynb @@ -4,9 +4,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Tools - Module Standoff for IEC TS 63126\n", + "# Module Standoff for IEC TS 63126\n", "\n", - "### Calculation of module standoff distance according to IEC TS 63126\n", + "## Calculation of module standoff distance according to IEC TS 63126\n", "\n", "**Requirements:**\n", "- Local weather data file or site longitude and latittude\n", @@ -27,7 +27,7 @@ "\n", "where $T_0$ is the temperature for $X=0$ (insulated-back) and $\\Delta T$ is the temperature difference between an insulated-back ($X=0$) and open-rack mounting configuration ($X=\\infty)$.\n", "\n", - " We used pvlib and data from the National Solar Radiation Database (NSRDB) to calculate the module temperatures for the insulated-back and open-rack mounting configurations and apply our model to obtain the minimum standoff distance for roof-mounted PV systems to achieve a temperature lower than a specified $T_{98}$. The following figure showcases this calulation for the entire world for an $X_{eff}$ that results in $T_{98}$=70Β°C. Values of $X_{eff}$ higher than this will require Level 1 or Level 2 certification. \n", + " We used pvlib and data from the National Solar Radiation Database (NSRDB) to calculate the module temperatures for the insulated-back and open-rack mounting configurations and apply our model to obtain the minimum standoff distance for roof-mounted PV systems to achieve a temperature lower than a specified $T_{98}$. The following figure showcases this calulation for the entire world for an $X_{eff}$ that results in $T_{98}$=70Β°C. Values of $X_{eff}$ higher than this will require Level 1 or Level 2 certification.\n", "\n", "$*$ D. L. King, W. E. Boyson, and J. A. Kratochvil, \"Photovoltaic array performance model,\" SAND2004-3535, Sandia National Laboratories, Albuquerque, NM, 2004. '\\\n", "$**$ M. K. Fuentes, \"A simplified thermal model for Flat-Plate photovoltaic arrays,\" United States, 1987-05-01 1987. https://www.osti.gov/biblio/6802914\n" @@ -37,7 +37,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![alt text](T98_70C_standoff_Map.png)" + "![alt text](images/T98_70C_standoff_Map.png)" ] }, { @@ -48,7 +48,7 @@ "source": [ "# if running on google colab, uncomment the next line and execute this cell to install the dependencies\n", "# and prevent \"ModuleNotFoundError\" in later cells:\n", - "#!pip install pvdeg==0.4.2" + "#!pip install pvdeg" ] }, { @@ -118,14 +118,14 @@ "outputs": [], "source": [ "# This routine will get a meteorological dataset from anywhere in the world where it is available\n", - "#weather_id = (24.7136, 46.6753) #Riyadh, Saudi Arabia\n", - "#weather_id = (35.6754, 139.65) #Tokyo, Japan\n", - "#weather_id = (-43.52646, 172.62165) #Christchurch, New Zealand\n", - "#weather_id = (64.84031, -147.73836) #Fairbanks, Alaska\n", - "#weather_id = (65.14037, -21.91633) #Reykjavik, Iceland\n", - "#weather_id = (33.4152, -111.8315) #Mesa, Arizona\n", - "#WEATHER_df, META = pvdeg.weather.get_anywhere(id=weather_id)\n", - "#print(META)" + "# weather_id = (24.7136, 46.6753) #Riyadh, Saudi Arabia\n", + "# weather_id = (35.6754, 139.65) #Tokyo, Japan\n", + "# weather_id = (-43.52646, 172.62165) #Christchurch, New Zealand\n", + "# weather_id = (64.84031, -147.73836) #Fairbanks, Alaska\n", + "# weather_id = (65.14037, -21.91633) #Reykjavik, Iceland\n", + "# weather_id = (33.4152, -111.8315) #Mesa, Arizona\n", + "# WEATHER_df, META = pvdeg.weather.get_anywhere(id=weather_id)\n", + "# print(META)" ] }, { @@ -134,7 +134,7 @@ "source": [ "## 2. Calculate Installation Standoff Minimum - Level 1 and Level 2\n", "\n", - "According to IEC TS 63126, Level 0, Level 1 and Level 2 certification is limited to Tβ‚‰β‚ˆ<70Β°C, <80Β°C and <90Β°C, respectively. Level 0 certification is essentially compliance to IEC 61730 and IEC 61215. The default value of Tβ‚‰β‚ˆ<70Β°C represents the minimium gap to avoid higher temperature certification according to IEC TS 63126. This minimum standoff ($x_{min}$) is the distance between the bottom of the module frame and the roof and can be extimated for a given environment as, \n", + "According to IEC TS 63126, Level 0, Level 1 and Level 2 certification is limited to Tβ‚‰β‚ˆ<70Β°C, <80Β°C and <90Β°C, respectively. Level 0 certification is essentially compliance to IEC 61730 and IEC 61215. The default value of Tβ‚‰β‚ˆ<70Β°C represents the minimium gap to avoid higher temperature certification according to IEC TS 63126. This minimum standoff ($x_{min}$) is the distance between the bottom of the module frame and the roof and can be extimated for a given environment as,\n", "\n", "$$ X_{min} = -X_0 \\ln\\left(1-\\frac{T_{98,0}-T}{ T_{98,0}- T_{98,inf}}\\right), Equation 2 $$\n", "\n", @@ -174,7 +174,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The following is a full function call for both Tβ‚‰β‚ˆ=70Β°C and 80Β°C separately even though the second standoff distance can be calculated using only T98_0 and T98_inf. With this function, one may also want to change the tilt, azimuth, or T98. " + "The following is a full function call for both Tβ‚‰β‚ˆ=70Β°C and 80Β°C separately even though the second standoff distance can be calculated using only T98_0 and T98_inf. With this function, one may also want to change the tilt, azimuth, or T98." ] }, { @@ -220,7 +220,7 @@ "source": [ "## 3. Calculate $X_{eff}$ from provided module temperature data.\n", "\n", - "To do this calculation, one must use a set of data with: \n", + "To do this calculation, one must use a set of data with:\n", " - meterological irradiance data sufficient to calculate the POA irradiance (DHI, GHI, and DNI),\n", " - ambient temperature data,\n", " - wind speed at module height, (wind_factor=0.33 will be used unless otherwise specified)\n", @@ -286,12 +286,9 @@ "source": [ "# This is the minimal function call using the common default settings to estimate Tβ‚‰β‚ˆ.\n", "T_98 = pvdeg.standards.T98_estimate(\n", - " weather_df=WEATHER_df,\n", - " meta=META,\n", - " tilt=-META['latitude'],\n", - " azimuth=None,\n", - " x_eff=10)\n", - "print ('The 98α΅—Κ° percential temperature is estimated to be' , '%.1f' % T_98 , 'Β°C.')" + " weather_df=WEATHER_df, meta=META, tilt=-META[\"latitude\"], azimuth=None, x_eff=10\n", + ")\n", + "print(\"The 98α΅—Κ° percential temperature is estimated to be\", \"%.1f\" % T_98, \"Β°C.\")" ] }, { @@ -301,23 +298,27 @@ "outputs": [], "source": [ "# This code will calculate the temperature for an arbitrary x_eff distance. Either set of kwargs can be modified and use.\n", - "#irradiance_kwarg ={\n", + "# irradiance_kwarg ={\n", "# \"axis_tilt\": None,\n", "# \"axis_azimuth\": None,\n", "# \"x_eff\": 10,\n", "# \"module_mount\": '1_axis'}\n", - "irradiance_kwarg ={\n", - " \"tilt\": META['latitude'],\n", + "irradiance_kwarg = {\n", + " \"tilt\": META[\"latitude\"],\n", " \"azimuth\": None,\n", " \"x_eff\": 10,\n", - " \"module_mount\": \"fixed\"}\n", + " \"module_mount\": \"fixed\",\n", + "}\n", "\n", "T_xeff = pvdeg.standards.x_eff_temperature_estimate(\n", - " weather_df=WEATHER_df,\n", - " meta=META,\n", - " **irradiance_kwarg)\n", + " weather_df=WEATHER_df, meta=META, **irradiance_kwarg\n", + ")\n", "\n", - "print ('The 98α΅—Κ° percential temperature is estimated to be' , '%.1f' % np.percentile(T_xeff, 98) , 'Β°C.')" + "print(\n", + " \"The 98α΅—Κ° percential temperature is estimated to be\",\n", + " \"%.1f\" % np.percentile(T_xeff, 98),\n", + " \"Β°C.\",\n", + ")" ] }, { @@ -565,8 +566,8 @@ "source": [ "## 7. Plot $X_{min}$ for a $T_{98}$, and plot $T_{98}$ for a given region.\n", "\n", - "This last Objective is much more complicated and is set up to utilize acess to a lot of computational power to run many sites simultaneously to create a regional map of standoff distance. \n", - "For more in-depth instructions on doing this, look at the tutorial \"Scenario - Geospatial.ipynb\" here in PVDeg.\n", + "This last Objective is much more complicated and is set up to utilize acess to a lot of computational power to run many sites simultaneously to create a regional map of standoff distance.\n", + "For more in-depth instructions on doing this, look at the tutorial \"04_scenario_geospatial.ipynb\" here in PVDeg.\n", "\n", "Step #1: Create an object, \"geospatial_standoff_scenario\" to be used to run the computations." ] @@ -679,14 +680,22 @@ }, "outputs": [], "source": [ - "print (\"Your results will be stored in %s\" % output_folder)\n", - "print ('The folder must already exist or the file will not be created')\n", + "print(\"Your results will be stored in %s\" % output_folder)\n", + "print(\"The folder must already exist or the file will not be created\")\n", "\n", - "pvdeg.weather.write(data_df=WEATHER_df, metadata=META, savefile=os.path.join(output_folder, 'WeatherFile.csv')) #Writes the meterological data to an *.csv file.\n", + "pvdeg.weather.write(\n", + " data_df=WEATHER_df,\n", + " metadata=META,\n", + " savefile=os.path.join(output_folder, \"WeatherFile.csv\"),\n", + ") # Writes the meterological data to an *.csv file.\n", "\n", - "pd.DataFrame(standoff_series_df).to_csv(os.path.join(output_folder, 'Standoff_Scan.csv')) #Writes a file with the Tilt and Azimuth scan calculations of standoff.\n", + "pd.DataFrame(standoff_series_df).to_csv(\n", + " os.path.join(output_folder, \"Standoff_Scan.csv\")\n", + ") # Writes a file with the Tilt and Azimuth scan calculations of standoff.\n", "\n", - "pd.DataFrame(T98_series_df).to_csv(os.path.join(output_folder, 'T98_Scan.csv')) #Writes a file with the Tilt and Azimuth scan calculations of T98.\n" + "pd.DataFrame(T98_series_df).to_csv(\n", + " os.path.join(output_folder, \"T98_Scan.csv\")\n", + ") # Writes a file with the Tilt and Azimuth scan calculations of T98." ] } ], diff --git a/tutorials/tools/_toc.yml b/tutorials/tools/_toc.yml new file mode 100644 index 000000000..e3f485bd6 --- /dev/null +++ b/tutorials/tools/_toc.yml @@ -0,0 +1,8 @@ +# Table of contents for Tools +# Learn more at https://jupyterbook.org/customize/toc.html + +format: jb-book +root: intro +chapters: +- file: Tools - Edge Seal Oxygen Ingress +- file: Tools - Degradation diff --git a/tutorials_and_tools/tutorials_and_tools/T98_70C_standoff_Map.png b/tutorials/tools/images/T98_70C_standoff_Map.png similarity index 100% rename from tutorials_and_tools/tutorials_and_tools/T98_70C_standoff_Map.png rename to tutorials/tools/images/T98_70C_standoff_Map.png diff --git a/tutorials/tools/intro.md b/tutorials/tools/intro.md new file mode 100644 index 000000000..0fafd63e1 --- /dev/null +++ b/tutorials/tools/intro.md @@ -0,0 +1,9 @@ +# PVDeg - Tools + +This section contains tools for photovoltaic degradation analysis. + +## Available Tools + +- **Edge Seal Oxygen Ingress**: Analysis tools for edge seal oxygen ingress +- **Degradation Tools**: Additional degradation analysis tools +- **Module Standoff for IEC TS 63126**: Calulated standoff distance for roof-mounted PV diff --git a/tutorials/tools/scripts/Tools - Degradation.py b/tutorials/tools/scripts/Tools - Degradation.py new file mode 100644 index 000000000..3af016cf1 --- /dev/null +++ b/tutorials/tools/scripts/Tools - Degradation.py @@ -0,0 +1,256 @@ +# %% [markdown] +# # Degradation and Acceleration Factors +# This tool will provide a simple method for estimating degradation and for calculating acceleration factors. It interfaces with the degradation database to simplify acquisition of the degradation parameters. +# +# **Requirements**: +# - compatible weather file (e.g., PSM3, TMY3, EPW...) +# - Accelerated testing chamber parameters +# - chamber irradiance [W/m^2] +# - chamber temperature [C] +# - chamber humidity [%] +# - & etc. +# - Activation energies for test material [kJ/mol] +# - Other degradation parameters +# +# **Objectives**: +# 1. Read in the weather data +# 2. Gather basic degradation modeling data for a material of interest +# 3. Calculate absolute degradation rate +# 4. Run Monte Carlo simulation at a single site +# 5. Generate chamber or field data for environmental comparison +# 6. Calculate degradation acceleration factor of field location to chamber (or another location) +# 7. Produce a map of acceleration factors for a geographic region +# Select a geographic region of interest +# downsample to select specific site coordinates +# Download or access the meteorological data for the chosen site coordinates. +# Run the calculation + +# %% +# if running on google colab, uncomment the next line and execute this cell to install the dependencies and prevent "ModuleNotFoundError" in later cells: +# # !pip install pvdeg + +# %% +import os +import pvdeg +import pandas as pd +import numpy as np +from pvdeg import DATA_DIR +import json +from IPython.display import display, Math + +import pvlib + +print(pvlib.__version__) +from pvlib import iotools + +# %% +# This information helps with debugging and getting support :) +import sys, platform + +print("Working on a ", platform.system(), platform.release()) +print("Python version ", sys.version) +print("Pandas version ", pd.__version__) +print("pvdeg version ", pvdeg.__version__) +print(DATA_DIR) + +# %% [markdown] +# ## 1. Read In the Weather Data +# +# The function has these minimum requirements when using a weather data file: +# - Weather data containing (at least) DNI, DHI, GHI, Temperature, RH, and Wind-Speed data at module level. +# - Site meta-data containing (at least) latitude, longitude, and time zone +# +# Alternatively one may can get meterological data from the NSRDB or PVGIS with just the longitude and latitude. This function for the NSRDB (via NSRDB 'PSM3') works primarily for most of North America and South America. PVGIS works for most of the rest of the world (via SARAH 'PVGIS'). See the tutorial "Weather Database Access.ipynb" tutorial on PVdeg or Jensen et al. https://doi.org/10.1016/j.solener.2023.112092 for satellite coverage information. + +# %% +# Get data from a supplied data file (Do not use the next box of code if using your own file) +weather_file = os.path.join(DATA_DIR, "psm3_demo.csv") +weather_df, meta = pvdeg.weather.read(weather_file, "csv", find_meta=True) +print(weather_file) +print(meta) + +# %% +# This routine will get a meteorological dataset from anywhere in the world where it is available +# weather_id = (24.7136, 46.6753) #Riyadh, Saudi Arabia +# weather_id = (35.6754, 139.65) #Tokyo, Japan +# weather_id = (-43.52646, 172.62165) #Christchurch, New Zealand +# weather_id = (64.84031, -147.73836) #Fairbanks, Alaska +# weather_id = (65.14037, -21.91633) #Reykjavik, Iceland +weather_id = (33.4152, -111.8315) # Mesa, Arizona +# weather_id = (0,0) # Somewhere else you are interested in. +weather_df, meta = pvdeg.weather.get_anywhere(id=weather_id) +print(meta) +# display(weather_df) + +# %% [markdown] +# ### POA Irradiance +# Next we need to calculate the stress parameters including temperature and humidity. We start with POA irradiance. +# Irradiance_kwarg governs the array orientation for doing the POA calculations. +# It is defaulted to a north-south single axis tracking. A fixed tilt set of parameters is included but is blocked out. +# Look in spectral.py and/or PVLib here for 1-axis kwargs, +# https://pvlib-python.readthedocs.io/en/v0.7.2/generated/pvlib.tracking.singleaxis.html#pvlib.tracking.singleaxis +# and for fixed tilt, +# https://pvlib-python.readthedocs.io/en/v0.7.2/generated/pvlib.irradiance.gti_dirint.html?highlight=poa . +# Here, the POA global calculation is appended to the meteorolgical data dataframe. + +# %% +# irradiance_kwarg ={ +# "tilt": None, +# "azimuth": None, +# "module_mount": 'fixed'} +irradiance_kwarg = {"axis_tilt": 0, "axis_azimuth": 180, "module_mount": "single_axis"} +poa_df = pvdeg.spectral.poa_irradiance( + weather_df=weather_df, meta=meta, **irradiance_kwarg +) + +weather_df["poa_global"] = poa_df["poa_global"] + +# %% [markdown] +# +# ### Get Spectrally Resolved Irradiance Data +# This first set of commands will calculate spectrally resolved irradiance data. This may or may not be needed for a given degradation model and can be skipped here. + +# %% +# this whole block needs to be replaced with call to calculate spectrally resolved irradiance. + +from pvdeg import TEST_DATA_DIR + +INPUT_SPECTRA = os.path.join(TEST_DATA_DIR, r"spectra_pytest.csv") +data = pd.read_csv(INPUT_SPECTRA) +# display(data) +print(INPUT_SPECTRA) + +# Test function +# cusotm_albedo['Summer'] +# custom_albedo['Winter'] +# custom_albedo['Snow'] +# defaults - Grass, Dry Grass, Snow +# Flexible to add complexity later +# merge in development branch changes +# KGPCY Python package +custom_albedo_summer = "A006" +custom_albedo_winter = { # required: startDate, wavelength (if len(albedo) > 1), albedo, isSnow defaults to False + "data_entry_person": "Michael Kempe", + "date_entered": "7/28/2025", + "DOI": "10.3390/ijerph15071507", + "source_title": "Ultraviolet Radiation Albedo and Reflectance in Review: The Influence to Ultraviolet Exposure in Occupational Settings", + "authors": "Joanna Turner, Alfio V. Parisi", + "reference": "Turner J, Parisi AV. Ultraviolet Radiation Albedo and Reflectance in Review: The Influence to Ultraviolet Exposure in Occupational Settings. Int J Environ Res Public Health. 2018 Jul 17;15(7):1507.", + "keywords": "snow, ground", + "months": "1,2,3,10,11,12", + "startDate": "January 1", # Day of Year? 0-365 + "HourOfYear": "1", # Hour of Year? 1-8760 + "isSnow": "False", + "comments": "Data is emperically extrapolated from 280 nm to 297 nm. Data extracted from Turner et al. Figure 1 as a reference to Doda & Green Snow-Ground. Doda D., Green A. Surface Reflectance Measurements in the UV from an Airborne Platform. Part 1. Appl. Opt. 1980;19:2140-2145. doi: 10.1364/AO.19.002140. Doda D., Green A. Surface Reflectance Measurements in the Ultraviolet from an Airborne Platform. Part 2. Appl. Opt. 1981;20:636-642. doi: 10.1364/AO.20.000636.", + "wavelength": "280, 297.32034, 300.02435, 301.8514, 305.79782, 310.10962, 313.21558, 317.4543, 322.86237, 329.9513, 331.19366, 339.89038, 343.06943, 350.34103, 360.02435, 369.96347, 380.3776, 386.77222, 390.0609, 400.14615", + "albedo": "20, 29.515152, 28.30303, 29.454546, 28.90909, 34.696968, 36.757576, 39.363636, 39.21212, 38.60606, 41.272728, 40.909092, 42.242424, 42.21212, 40.575756, 43.21212, 43.090908, 43.454544, 43.60606, 39.757576", +} +# Startdate, albedo, wavelength -> then next one + boolean logic for snow () +custom_albedo_snow = {} +# custom_albedo_snow +spectra_folder = "spectra" # If you have already pulled the spectra from SMARTS, pass the folder path to avoid going through the donwload process again. +wavelengths = np.arange( + 280, 400, 25 +) # Example wavelengths from 280 nm to 400 nm in steps of 25 nm +# data = pvdeg.spectral.spectrally_resolved_irradiance(weather_df=weather_df, meta=meta, wavelengths=wavelengths, frontResultsOnly=None, +# spectra_folder=spectra_folder, custom_albedo_summer=custom_albedo_summer, custom_albedo_winter=custom_albedo_winter, **irradiance_kwarg) +# return front, back, or both (True, False, None) +# bool frontResultsonly = True for front only +# separate columns for front and back irradiance: spectra_front: etc. , spectra_back: etc. (see spectra_pytest.csv) +# Check albedo boolean snow, winter non-snow, summer non-snow + + +# %% [markdown] +# ### Get Cell Temperature and Module Surface Temperature +# The following will calculate the cell and module surface temperature using the King model as a default. Other models can be used as described at, +# https://pvlib-python.readthedocs.io/en/stable/reference/pv_modeling/temperature.html. The difference is less than one Β°C for ground mounted systems +# but can be as high as 3 Β°C for a high temperature building integrated system. +# +# Here the temperatures are added to the dataframe and the 'temp_module' temperature is selected as the default 'temperature' for the degradation calculations. If it is a cell degradation that is being investigated, 'temp_cell' should be used for the temperature. + +# %% +temp_cell = pvdeg.temperature.cell(weather_df=weather_df, meta=meta, poa=poa_df) +temp_module = pvdeg.temperature.module(weather_df=weather_df, meta=meta, poa=poa_df) + +weather_df["temp_cell"] = temp_cell +weather_df["temp_module"] = temp_module + +weather_df["temperature"] = weather_df["temp_module"] +# weather_df['temperature'] = weather_df['temp_cell'] + +# %% [markdown] +# ### Humidity +# Depending on the component for which the calculation is being run on, the desired humidity may be the atmospheric humidity, the module surface humidity, the humidity in front of a cell with a permeable backsheet, the humidity in the backsheet, the humidity in the back encapsulant or another custom humidity location such as a diffusion limited location. The folowing are options for doing all of these calculations. Here all the different humidities are put in the weather_df dataframe, but to select one to be specifically used it should be named 'RH' for most degradation functions (check the documentation of a specific degradation calculation if in doubt). Here the surface humidity is selected as a default. +# +# Append the calculated values into the weather DataFrame. +# Note: putting the values into the weather_df DataFrame is not strictly necessary, but may be convenient for later use in the degradation calculations. + +# %% +RH_module = pvdeg.humidity.module( + weather_df=weather_df, + poa=poa_df, + temp_module=temp_module, + backsheet="W017", + backsheet_thickness=0.30, + encapsulant="W001", + back_encap_thickness=0.50, +) + +weather_df = pd.concat([weather_df, RH_module], axis=1) + +# %% [markdown] +# Each of the necessary arrays of data can be individually sent to a function for calculation in the function call, or they can be combined into a single dataframe. The degradation functions are set up to first check for a specific data set in the function call but if not found it looks for specific data or a suitable substitute in the weather dataframe. +# +# You can select one of the RH values to be used as the relative humidity in the degradation model calculations by assigning it to to column "RH" in the dataframe. +# Alternatively, the "RH" data can be sent to the degradation function explicitly in the function call. + +# %% + +weather_df["RH"] = RH_module["RH_surface_outside"] +# weather_df['RH'] = RH_module['RH_front_encap'] +# weather_df['RH'] = RH_module['Ce_back_encap'] +# weather_df['RH'] = RH_module['RH_back_encap'] +# weather_df['RH'] = RH_module['RH_backsheet'] + +# %% [markdown] +# ## 2. Gather Basic Degradation Modeling Data for a Material of Interest +# +# First we need to gather in the parameters for the degradation process of interest. This includes things such as the activiation energy and parameters defining the sensitivity to moisture, UV light, voltage, and other stressors. +# For this tutorial we will need solar position, POA, PV cell and module temperature. Let's gernate those individually with their respective functions. +# The blocked out text will produce a list of key fields from the database for each entry. + +# %% +# kwarg_variables = pvdeg.utilities._read_material(name=None, fname="DegradationDatabase", item=("Material", "Equation", "KeyWords", "EquationType")) +# print(json.dumps(kwarg_variables, skipkeys = True, indent = 0 ).replace("{" + "\n", "{").replace('\"' + "\n", "\"").replace(': {' , ':' + "\n" + "{").replace('},' + "\n", '},' +'\n' +'\n')) +pvdeg.utilities.display_json(pvdeg_file="DegradationDatabase", fp=DATA_DIR) + +# %% [markdown] +# This next set of codes will take the data from the extracted portion of the Json library and create a list of variables from it. If more variables need to be modified or added, this is where it should be done. + +# %% +deg_data = pvdeg.utilities.read_material( + fp=DATA_DIR, key="D036", pvdeg_file="DegradationDatabase" +) +display(deg_data) + +# %% [markdown] +# Here we pull out the relevant equation code identifier needed for running the calculations. + +# %% +func = "pvdeg.degradation." + deg_data["EquationType"] +print(func) +display(Math("\\Large " + deg_data["Equation"])) + +# %% [markdown] +# ## 3. Calculate Absolute Degradation Rate +# +# To do this calculation, we must have degradation parameter data for a process that is complete with all the necessary variables. + +# %% +func_call = getattr(pvdeg.degradation, deg_data["EquationType"]) +degradation = func_call(weather_df=weather_df, parameters=deg_data) +print( + "Average degradation rate for a year", degradation / 8760, deg_data["R_0"]["units"] +) + +# %% diff --git a/tutorials/tools/scripts/Tools - Edge Seal Oxygen Ingress.py b/tutorials/tools/scripts/Tools - Edge Seal Oxygen Ingress.py new file mode 100644 index 000000000..4d91bf0cc --- /dev/null +++ b/tutorials/tools/scripts/Tools - Edge Seal Oxygen Ingress.py @@ -0,0 +1,201 @@ +# %% [markdown] +# # Edge Seal Oxygen Ingress Calculator +# +# ## Calculation of oxygen ingress profile through an edge seal and into the encapsulant. +# +# **Requirements:** +# - Local weather data file or site longitude and latittude. +# - Properties and dimensions of the edge seal. +# +# **Objectives:** +# 1. Import weather data. +# 2. Set up the calculations. +# 3. Calculate oxygen ingress into an edge seal. +# 3. Incorporate an oxygen consumption model. +# 4. Plot the data. +# +# **Background:** +# +# This performs a 1-D finite difference model for oxygen ingress through an edge seal and into an encapsulant. This is effectively an infinitely long module with a prescribed width.The output is then displayed graphically. + +# %% +# if running on google colab, uncomment the next line and execute this cell to install the dependencies and prevent "ModuleNotFoundError" in later cells: +# # !pip install pvdeg + +# %% +import os +import pvdeg +import pandas as pd +from pvdeg import DATA_DIR +import matplotlib.pyplot as plt +import numpy as np +import json + +# %% +# This information helps with debugging and getting support :) +import sys, platform + +print("Working on a ", platform.system(), platform.release()) +print("Python version ", sys.version) +print("Pandas version ", pd.__version__) +print("pvdeg version ", pvdeg.__version__) + +# %% [markdown] +# ## 1. Import Weather Data +# +# The function has these minimum requirements when using a weather data file: +# - Weather data containing (at least) DNI, DHI, GHI, Temperature, RH, and Wind-Speed data at module level. +# - Site meta-data containing (at least) latitude, longitude, and time zone +# +# Alternatively one may can get meterological data from the NSRDB or PVGIS with just the longitude and latitude. This function for the NSRDB (via NSRDB 'PSM3') works primarily for most of North America and South America. PVGIS works for most of the rest of the world (via SARAH 'PVGIS'). See the tutorial "Weather Database Access.ipynb" tutorial on PVdeg or Jensen et al. https://doi.org/10.1016/j.solener.2023.112092 for satellite coverage information. + +# %% +# Get data from a supplied data file (Do not use the next box of code if using your own file) +weather_file = os.path.join(DATA_DIR, "psm3_demo.csv") +weather_df, meta = pvdeg.weather.read(weather_file, "psm") +print(sorted(meta.keys())) + +# %% +# This routine will get a meteorological dataset from anywhere in the world where it is available +# weather_id = (24.7136, 46.6753) #Riyadh, Saudi Arabia +# weather_id = (35.6754, 139.65) #Tokyo, Japan +# weather_id = (-43.52646, 172.62165) #Christchurch, New Zealand +# weather_id = (64.84031, -147.73836) #Fairbanks, Alaska +# weather_id = (65.14037, -21.91633) #Reykjavik, Iceland +weather_id = (33.4152, -111.8315) # Mesa, Arizona +weather_df, meta = pvdeg.weather.get_anywhere(id=weather_id, database="PVGIS") +print(meta) + +# %% +# This computes a module temperature. Here the default is an open rack system, but other options include: +# 'open_rack_glass_glass', +# 'close_mount_glass_glass', +# 'insulated_back_glass_polymer' + +temperature = pvdeg.temperature.temperature( + weather_df=weather_df, + meta=meta, + cell_or_mod="module", + temp_model="sapm", + conf="open_rack_glass_polymer", +) + +temperature = pd.DataFrame(temperature, columns=["module_temperature"]) +temperature["time"] = list(range(8760)) + +# %% [markdown] +# ## 2. Set up the Calculations +# +# There is a library of some materials and the relevant oxygen ingress parameters that can be used. + +# %% +es = "OX005" # This is the number for the edge seal in the json file +enc = "OX003" # This is the number for the encapsulant in the json file +esw = 1.5 # This is the edge seal width in [cm] +encw = 10 # This is the encapsulant width in [cm] +sn = 20 # This is the number of edge seal nodes to use +en = 50 # This is the number of encapsulant nodes to use +pressure = 0.2109 * (1 - 0.0065 * meta.get("altitude") / 288.15) ** 5.25588 +print( + pvdeg.utilities.read_material( + pvdeg_file="O2permeation", key="OX003", values_only=True + ) +) +print( + pvdeg.utilities.read_material( + pvdeg_file="H2Opermeation", key="W003", values_only=True + ) +) + +# %% [markdown] +# ## 3. Run the Calculations +# +# This runs the calculations for diffusion using a simple 1-D finite difference calculation. The first set of calculations is just for diffusion, then the next two (when written) will include some consumption of oxygen. In typical PV applications, it is common for oxygen ingress distance to be limited by its consumption rate in the encapsulant. + +# %% +oxygen_profile = pvdeg.diffusion.esdiffusion( + temperature=temperature, + edge_seal=es, + encapsulant=enc, + edge_seal_width=esw, + encapsulant_width=encw, + seal_nodes=sn, + encapsulant_nodes=en, + press=pressure, + repeat=2, +) + +# %% +# This sets up an a variable with the output folder information. +output_folder = os.path.join( + os.path.dirname(os.path.dirname(os.getcwd())), "TEMP", "results" +) +try: + os.makedirs(output_folder) + print(f"Created directory: {output_folder}") +except OSError as error: + print(error) + +# %% +n_lines = 10 +times = oxygen_profile.index.tolist() +for index in range(n_lines): + plt.plot( + oxygen_profile.iloc[ + int(np.trunc((index + 1) * (len(oxygen_profile) - 1) / n_lines)) + ], + label=np.round( + times[int(np.trunc((index + 1) * ((len(oxygen_profile) - 1) / n_lines)))] + / 365.25 + / 24, + 2, + ), + ) +plt.legend(title="Time [year]") +plt.ylabel("Oxygen Concentration [g/cmΒ³]") +plt.xlabel("Distance From Edge [cm]") +plt.ticklabel_format(axis="y", style="plain") + +plt.savefig( + os.path.join(output_folder, "Edge_Seal_O2_ingress.png"), bbox_inches="tight" +) # Creates an image file of the standoff plot +plt.show() + +# %% [markdown] +# ## 5. Save data outputs. +# +# This cell contains a number of pre-scripted commands for exporting and saving data. The code to save plots is located after the plot creation. First check that the output folder exists. + +# %% +fpath = os.path.join(DATA_DIR, "O2permeation.json") +with open(fpath) as f: + data = json.load(f) +f.close() + +material_list = "" +for key in data: + if "name" in data[key].keys(): + material_list = material_list + key + "=" + data[key]["name"] + "\n" +material_list = material_list[0 : len(material_list) - 1] +print(material_list) + +# %% +print("Your results will be stored in %s" % output_folder) +print("The folder must already exist or the file will not be created") + +# Writes the meterological data to an *.csv file. +pvdeg.weather.write( + data_df=weather_df, + metadata=meta, + savefile=os.path.join(output_folder, "WeatherFile.csv"), +) + +# Writes a file with the edge seal oxygen profile calculations. +pd.DataFrame(oxygen_profile).to_csv( + os.path.join(output_folder, "ES_Oxygen_profile.csv") +) + +# Writes a file with temperature data used in the model calculations. +pd.DataFrame(temperature).to_csv( + os.path.join(output_folder, "ES_Temperature_profile.csv") +) diff --git a/tutorials_and_tools/tutorials_and_tools/Tools - Module Standoff for IEC TS 63126.py b/tutorials/tools/scripts/Tools - Module Standoff for IEC TS 63126.py similarity index 79% rename from tutorials_and_tools/tutorials_and_tools/Tools - Module Standoff for IEC TS 63126.py rename to tutorials/tools/scripts/Tools - Module Standoff for IEC TS 63126.py index 80efddf7e..8bb2d0d05 100644 --- a/tutorials_and_tools/tutorials_and_tools/Tools - Module Standoff for IEC TS 63126.py +++ b/tutorials/tools/scripts/Tools - Module Standoff for IEC TS 63126.py @@ -1,9 +1,7 @@ -#!/usr/bin/env python -# coding: utf-8 - -# # Tools - Module Standoff for IEC TS 63126 -# ## Calculation of module standoff distance according to IEC TS 63126 +# %% [markdown] +# # Module Standoff for IEC TS 63126 # +# ## Calculation of module standoff distance according to IEC TS 63126 # # **Requirements:** # - Local weather data file or site longitude and latittude @@ -30,18 +28,15 @@ # $**$ M. K. Fuentes, "A simplified thermal model for Flat-Plate photovoltaic arrays," United States, 1987-05-01 1987. https://www.osti.gov/biblio/6802914 # -# ![T98 70C standoff Map.png](attachment:62279573-41e3-45dd-bf62-4fa60c1e7e69.png) - -# In[ ]: - - -# if running on google colab, uncomment the next line and execute this cell to install the dependencies and prevent "ModuleNotFoundError" in later cells: -# !pip install pvdeg==0.3.3 - - -# In[3]: +# %% [markdown] +# ![alt text](images/T98_70C_standoff_Map.png) +# %% +# if running on google colab, uncomment the next line and execute this cell to install the dependencies +# and prevent "ModuleNotFoundError" in later cells: +# #!pip install pvdeg +# %% import os import pvdeg import pandas as pd @@ -50,11 +45,9 @@ import matplotlib.pyplot as plt import seaborn as sns import math +import numpy as np - -# In[2]: - - +# %% # This information helps with debugging and getting support :) import sys import platform @@ -66,47 +59,34 @@ print("dask version", dask.__version__) print(DATA_DIR) - +# %% [markdown] # ## 1. Import Weather Data # # The function has these minimum requirements when using a weather data file: # - Weather data containing (at least) DNI, DHI, GHI, Temperature, RH, and Wind-Speed data at module level. # - Site meta-data containing (at least) latitude, longitude, and time zone # -# Alternatively one may can get meterological data from the NSRDB with just the longitude and latitude. +# Alternatively one may can get meterological data from the NSRDB or PVGIS with just the longitude and latitude. This function for the NSRDB (via NSRDB 'PSM3') works primarily for most of North America and South America. PVGIS works for most of the rest of the world (via SARAH 'PVGIS'). See the tutorial "Weather Database Access.ipynb" tutorial on PVdeg or Jensen et al. https://doi.org/10.1016/j.solener.2023.112092 for satellite coverage information. # -# In[4]: - - +# %% # Get data from a supplied data file (Do not use the next box of code if using your own file) weather_file = os.path.join(DATA_DIR, "psm3_demo.csv") -WEATHER, META = pvdeg.weather.read(weather_file, "psm") - - -# In[5]: - - -# From Tutorial 5 EXAMPLE, this works. -# API_KEY = 'your_api_key_here' -API_KEY = "DEMO_KEY" # you can activate this line to use the demonstration API key but it has limited usage. -# The example API key here is for demonstation and is rate-limited per IP. -# To get your own API key, visit https://developer.nrel.gov/signup/ - -weather_db = "PSM3" -weather_id = (33.4152, -111.8315) -weather_arg = { - "api_key": API_KEY, - "email": "user@mail.com", - "names": "tmy", - "attributes": [], - "map_variables": True, -} - -WEATHER_df, META = pvdeg.weather.get(weather_db, weather_id, **weather_arg) +WEATHER_df, META = pvdeg.weather.read(weather_file, "csv") print(META) - +# %% +# This routine will get a meteorological dataset from anywhere in the world where it is available +# weather_id = (24.7136, 46.6753) #Riyadh, Saudi Arabia +# weather_id = (35.6754, 139.65) #Tokyo, Japan +# weather_id = (-43.52646, 172.62165) #Christchurch, New Zealand +# weather_id = (64.84031, -147.73836) #Fairbanks, Alaska +# weather_id = (65.14037, -21.91633) #Reykjavik, Iceland +# weather_id = (33.4152, -111.8315) #Mesa, Arizona +# WEATHER_df, META = pvdeg.weather.get_anywhere(id=weather_id) +# print(META) + +# %% [markdown] # ## 2. Calculate Installation Standoff Minimum - Level 1 and Level 2 # # According to IEC TS 63126, Level 0, Level 1 and Level 2 certification is limited to Tβ‚‰β‚ˆ<70Β°C, <80Β°C and <90Β°C, respectively. Level 0 certification is essentially compliance to IEC 61730 and IEC 61215. The default value of Tβ‚‰β‚ˆ<70Β°C represents the minimium gap to avoid higher temperature certification according to IEC TS 63126. This minimum standoff ($x_{min}$) is the distance between the bottom of the module frame and the roof and can be extimated for a given environment as, @@ -127,21 +107,17 @@ # - x_0 : thermal decay constant [cm] (see documentation). The default is 6.5 cm. # - wind_factor : Wind speed power law correction factor to account for different wind speed measurement heights between weather database (e.g. NSRDB) and the tempeature model (e.g. SAPM). The default is 0.33. -# The following is the minimum function call. It defaults to horizontal tilt. - -# In[6]: - +# %% [markdown] +# The following is the minimum function call. It defaults to horizontal tilt and Tβ‚‰β‚ˆ=70Β°C. +# %% standoff = pvdeg.standards.standoff(weather_df=WEATHER_df, meta=META) -output = pvdeg.standards.interpret_standoff(standoff) -print(output) - - -# The following is a full function call for both Tβ‚‰β‚ˆ=70Β°C and 80Β°C. This also includes the ability to print out a detailed interpretation of the results. With this function, one may also want to change the tilt, azimuth, or T_98 - -# In[8]: +print(pvdeg.standards.interpret_standoff(standoff)) +# %% [markdown] +# The following is a full function call for both Tβ‚‰β‚ˆ=70Β°C and 80Β°C separately even though the second standoff distance can be calculated using only T98_0 and T98_inf. With this function, one may also want to change the tilt, azimuth, or T98. +# %% standoff_1 = pvdeg.standards.standoff( weather_df=WEATHER_df, meta=META, @@ -170,10 +146,9 @@ wind_factor=0.33, ) print("Second calculation standoff = ", "%.1f" % standoff_2["x"].iloc[0], " cm.") -print("\n") -print(pvdeg.standards.interpret_standoff(standoff_1, standoff_2)) - +print(pvdeg.standards.interpret_standoff(standoff_1=standoff_1, standoff_2=standoff_2)) +# %% [markdown] # ## 3. Calculate $X_{eff}$ from provided module temperature data. # # To do this calculation, one must use a set of data with: @@ -187,37 +162,34 @@ # # To do this calculation, one should also filter the data to remove times when the sun is not shining or when snow is likely to be on the module. The recommendations and programmed defaults are to use poa_min=100 W/mΒ² and data when the minimum ambient temperature t_amb_min=0. -# In[9]: - - +# %% # Read the weather file -weather_file = os.path.join(DATA_DIR, "psm3_demo.csv") -Xeff_WEATHER, Xeff_META = pvdeg.weather.read(weather_file, "psm3") - -# Get module data from a supplied data file -measured = pd.read_csv(os.path.join(DATA_DIR, "module_temperature.csv")) - +weather_file = os.path.join(DATA_DIR, "xeff_demo.csv") +xeff_weather, xeff_meta = pvdeg.weather.read(weather_file, "csv") # Pull measured temperature and calculate theoretical insulated back module temperature and open rack module temperature -T_0, T_inf, T_measured, temp_air, POA = pvdeg.standards.eff_gap_parameters( - weather_df=Xeff_WEATHER, - meta=Xeff_META, - module_temp=measured["Module_Temperature"], +T_0, T_inf, xeff_poa = pvdeg.standards.eff_gap_parameters( + weather_df=xeff_weather, + meta=xeff_meta, sky_model="isotropic", temp_model="sapm", conf_0="insulated_back_glass_polymer", conf_inf="open_rack_glass_polymer", - tilt=39.73, - azimuth=180, wind_factor=0.33, ) - # Now calculate X_eff. x_eff = pvdeg.standards.eff_gap( - T_0, T_inf, T_measured, temp_air, POA, x_0=6.5, poa_min=100, t_amb_min=0 + T_0, + T_inf, + xeff_weather["module_temperature"], + xeff_weather["temp_air"], + xeff_poa["poa_global"], + x_0=6.5, + poa_min=100, + t_amb_min=0, ) print("The effective standoff for this system is", "%.1f" % x_eff, "cm.") - +# %% [markdown] # ## 4. Calculate $T_{98}$ for a given azimuth, tilt, and $X_{eff}$. # # Equation 2 can be reorganized as, @@ -226,29 +198,45 @@ # # and used to calculate the $98^{th}$ percential temperature, $T_{98}$, for a PV system having a given effective standoff height, $X_{eff}$, for an arbitrarily oriented module. Here, $T_{98,0}$ is the $98^{th}$ percentile for an insulated-back module and $T_{98,inf}$ is the $98^{th}$ percentile for a rack-mounted module. The input parameter possibilities are the same as shown in Objective #2 above, but the example below uses the default parameters. The actual tilt [degrees], azimuth [degrees] and $X_{eff}$ [cm] can be modifed as desired. -# In[10]: - - +# %% # This is the minimal function call using the common default settings to estimate Tβ‚‰β‚ˆ. T_98 = pvdeg.standards.T98_estimate( - weather_df=WEATHER_df, - meta=META, - tilt=META["latitude"], - azimuth=None, - x_eff=0, + weather_df=WEATHER_df, meta=META, tilt=-META["latitude"], azimuth=None, x_eff=10 ) print("The 98α΅—Κ° percential temperature is estimated to be", "%.1f" % T_98, "Β°C.") +# %% +# This code will calculate the temperature for an arbitrary x_eff distance. Either set of kwargs can be modified and use. +# irradiance_kwarg ={ +# "axis_tilt": None, +# "axis_azimuth": None, +# "x_eff": 10, +# "module_mount": '1_axis'} +irradiance_kwarg = { + "tilt": META["latitude"], + "azimuth": None, + "x_eff": 10, + "module_mount": "fixed", +} + +T_xeff = pvdeg.standards.x_eff_temperature_estimate( + weather_df=WEATHER_df, meta=META, **irradiance_kwarg +) + +print( + "The 98α΅—Κ° percential temperature is estimated to be", + "%.1f" % np.percentile(T_xeff, 98), + "Β°C.", +) +# %% [markdown] # ## 5. Plot $X_{min}$ for all azimuth and tilt for a given $T_{98}$. # # The temperature of a system is affected by the orientation. This section will scan all possible tilts and azimuths calculating the minimum standoff distance for a given $T_{98}$. Similar additional factors as above can also be modified but are not included here for simplicity. The tilt_step and azimuth_step are the number of degrees for each step for the 90Β° and 180Β° tilt and azimuth spans, respectively. The default for this calculation is for $T_{98}$=70Β°C, the boundary between Level 0 and Level 1 requirements. The temperature model information given below is unnecessary as these are default values that would get populated automatically. However, they were included here for clarity into a standard practice as per IEC TS 63126. # # $$ X_{min} = -X_0 \ln\left(1-\frac{T_{98,0}-T}{ T_{98,0}- T_{98,inf}}\right), Equation 2 $$ -# In[11]: - - +# %% # Scans through all the azimuth and tilt running the minimum standoff calculation # Set up keyword parameters for the calculation @@ -273,7 +261,7 @@ **kwarg_x, ) - +# %% [markdown] # The next cell creates a plot of the calculated data. Some of the things you may want to change are: # - cmap="Spectral_r": Change to have different colors # - plt.title : This will change the plot title. @@ -284,9 +272,7 @@ # - h_ticks=10 : This changes the number of horizontal tick marks # - Unblock the last two lines to ouput the plot as an *.png image file -# In[12]: - - +# %% standoff_series_df = pd.DataFrame( { "Tilt": standoff_series[:, 0], @@ -337,23 +323,21 @@ os.path.dirname(os.path.dirname(os.getcwd())), "TEMP", "results" ) try: - os.mkdir(output_folder) + os.makedirs(output_folder) except OSError as error: print(error) plt.savefig( - output_folder + r"\Standoff_Scan.png", bbox_inches="tight" + os.path.join(output_folder, "Standoff_Scan.png"), bbox_inches="tight" ) # Creates an image file of the standoff plot plt.show() - +# %% [markdown] # ## 6. Plot $T_{98}$ for all azimuth and tilt for a given $X_{eff}$. # # The temperature of a system is affected by the orientation and the effective standoff, $X_{eff}$, of the system. This section will scan all possible tilts and azimuths calculating the $T_{98}$ for a given $X_{eff}$. As above, additional factors can be modified but are not included here for simplicity. The tilt_step and azimuth_step are the number of degrees for each step for the 90Β° and 180Β° tilt and azimuth spans, respectively. The default for this calculation is for $X_{eff}$=10 cm, a common effective standoff distance on a rooftop system. A value of $X_{eff}$=None will run the calculations for an open rack system and $X_{eff}$=0 for an insulated-back system. -# In[13]: - - +# %% # Scans through all the azimuth and tilt running the 98α΅—Κ° percentile temperature calculation. # Set up keyword parameters for the calculation kwarg_T = dict( @@ -377,7 +361,7 @@ **kwarg_T, ) - +# %% [markdown] # The next cell creates a plot of the calculated data. Some of the things you may want to change are: # - cmap="Spectral_r": Change to have different colors # - plt.title : This will change the plot title. @@ -388,9 +372,7 @@ # - h_ticks=10 : This changes the number of horizontal tick marks # - Unblock the last two lines to ouput the plot as an *.png image file -# In[14]: - - +# %% # This produces the plot of the data T98_series_df = pd.DataFrame( {"Tilt": T98_series[:, 0], "Azimuth": T98_series[:, 1], "Tβ‚‰β‚ˆ": T98_series[:, 2]} @@ -446,18 +428,59 @@ ) # Creates an image file of the standoff plot plt.show(T98_fig) - -# ## 7. Plot $X_{min}$ for Level 1, Level 2, and $T_{98}$ for a given region. +# %% [markdown] +# ## 7. Plot $X_{min}$ for a $T_{98}$, and plot $T_{98}$ for a given region. # -# This last Objective is much more complicated and is set up to utilize acess to a lot of computational power to run many sites simultaneously to create a regional map of standoff distance. This is presented as doing the computations on Amazon Web Services (AWS) for which you will need a paid account, and will be covered on a subsequent journal. +# This last Objective is much more complicated and is set up to utilize acess to a lot of computational power to run many sites simultaneously to create a regional map of standoff distance. +# For more in-depth instructions on doing this, look at the tutorial "04_scenario_geospatial.ipynb" here in PVDeg. +# +# Step #1: Create an object, "geospatial_standoff_scenario" to be used to run the computations. + +# %% +geospatial_standoff_scenario = pvdeg.GeospatialScenario( + name="standoff geospatial", + geospatial=True, +) + +# %% [markdown] +# Step #2: Identifies a subset of locations from the database to run the computations. +# Specifically all are from the NSRDB. + +# %% +geospatial_standoff_scenario.addLocation( + state="Colorado", county="Summit" +) # Identifies a subset of locations from the database to run the computations. Specifically all are from the NSRDB. + +# %% [markdown] +# Step #3: indicate which function will be run. Here the default is the standoff calculation, but it could be any other function with a key word argument dictionary. +# Here the 98th percential temperature is defined as 70C, but any arbitrary value can be specified. +# %% +geospatial_standoff_scenario.addJob( + func=pvdeg.standards.standoff, func_params={"T98": 70} +) + +# %% [markdown] +# Step #4: Run the scenario + +# %% +geospatial_standoff_scenario.run() + +# %% [markdown] +# Step #5: Create a plot of the standoff calculation. + +# %% +geospatial_standoff_scenario.plot_world("x") + +# %% +geospatial_standoff_scenario.plot_world("T98_inf") + +# %% [markdown] # ## 8. Save data outputs. # # This cell contains a number of pre-scripted commands for exporting and saving data. The code to save plots is located after the plot creation and is blocked by default. First check that the output folder exists, then unblock the code for data you would like to save. -# In[15]: - - +# %% print("Your results will be stored in %s" % output_folder) print("The folder must already exist or the file will not be created") @@ -471,4 +494,6 @@ os.path.join(output_folder, "Standoff_Scan.csv") ) # Writes a file with the Tilt and Azimuth scan calculations of standoff. -pd.DataFrame(T98_series_df).to_csv(os.path.join(output_folder, "T98_Scan.csv")) +pd.DataFrame(T98_series_df).to_csv( + os.path.join(output_folder, "T98_Scan.csv") +) # Writes a file with the Tilt and Azimuth scan calculations of T98. diff --git a/tutorials_and_tools/README.md b/tutorials_and_tools/README.md deleted file mode 100644 index b5046f782..000000000 --- a/tutorials_and_tools/README.md +++ /dev/null @@ -1,51 +0,0 @@ - - -# PV Degradation Tools - TUTORIALS and Tools (pvdeg) - -### Jupyter Book - -For learning to use PVDeg through our tutorials, or running the available tools online, see our [jupyter-book](https://nrel.github.io/PVDegradationTools/intro.html) -Clicking on the rocket-icon on the top allows you to launch the journals on [Google Colaboratory](https://colab.research.google.com/) for interactive mode. -Just uncomment the first line `pip install ...` to install the environment on each journal if you follow this mode. - -### Binder - -To run these tutorials or tools in Binder, you can click here: -[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/NREL/PVDegradationTools/main) -It takes a minute to load the environment. - -### Locally - -You can also run the tutorials and tools locally in a virtual environment, i.e., `venv` or -[miniconda](https://docs.conda.io/en/latest/miniconda.html). - -1. Create and activate a new environment, e.g., on Mac/Linux terminal with `venv`: - ``` - python -m venv pvdeg - . pvdeg/bin/activate - ``` - or with `conda`: - ``` - conda create -n pvdeg - conda activate pvdeg - ``` - -1. Install `pvdeg` into the new environment with `pip`: - ``` - python -m pip install pvdeg - ``` - -1. Start a Jupyter session: - - ``` - jupyter notebook - ``` - -1. Use the file explorer in Jupyter lab to browse to `tutorials` - and start the first Tutorial. - - -Documentation -============= - -We also have documentation in [ReadTheDocs](https://PVDegradationTools.readthedocs.io) where you can find more details on the API functions. diff --git a/tutorials_and_tools/_build/.doctrees/README.doctree b/tutorials_and_tools/_build/.doctrees/README.doctree deleted file mode 100644 index dc723b125..000000000 Binary files a/tutorials_and_tools/_build/.doctrees/README.doctree and /dev/null differ diff --git a/tutorials_and_tools/_build/.doctrees/environment.pickle b/tutorials_and_tools/_build/.doctrees/environment.pickle deleted file mode 100644 index bdf66382f..000000000 Binary files a/tutorials_and_tools/_build/.doctrees/environment.pickle and /dev/null differ diff --git a/tutorials_and_tools/_build/.doctrees/intro.doctree b/tutorials_and_tools/_build/.doctrees/intro.doctree deleted file mode 100644 index 01ff6a6bb..000000000 Binary files a/tutorials_and_tools/_build/.doctrees/intro.doctree and /dev/null differ diff --git a/tutorials_and_tools/_build/.doctrees/tutorials/1 - 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TUTORIALS for PV Degradation Tools (pvdeg)#

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Jupyter Book#

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For in depth Tutorials you can run online, see our jupyter-book -Clicking on the rocket-icon on the top allows you to launch the journals on Google Colaboratory for interactive mode. -Just uncomment the first line pip install ... to install the environment on each journal if you follow this mode.

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To run these tutorials in Binder, you can click here: -Binder -It takes a minute to load the environment.

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You can also run the tutorial locally with -miniconda by following thes -steps:

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-# TUTORIALS for PV Degradation Tools (pvdeg) - -### Jupyter Book - -For in depth Tutorials you can run online, see our [jupyter-book](https://nrel.github.io/PVDegradationTools/intro.html) -Clicking on the rocket-icon on the top allows you to launch the journals on [Google Colaboratory](https://colab.research.google.com/) for interactive mode. -Just uncomment the first line `pip install ...` to install the environment on each journal if you follow this mode. - -### Binder - -To run these tutorials in Binder, you can click here: -[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/NREL/PVDegradationTools/main) -It takes a minute to load the environment. - -### Locally - -You can also run the tutorial locally with -[miniconda](https://docs.conda.io/en/latest/miniconda.html) by following thes -steps: - -1. Install [miniconda](https://docs.conda.io/en/latest/miniconda.html). - -1. Clone the repository: - - ``` - git clone https://github.com/NREL/PVDegradationTools.git - ``` - -1. Create the environment and install the requirements. The repository includes - a `requirements.txt` file that contains a list the packages needed to run - this tutorial. To install them using conda run: - - ``` - conda create -n pvdeg jupyter -c pvlib --file requirements.txt - conda activate pvdeg - ``` - - or you can install it with `pip install pvdeg` as explained in the installation instructions into the environment. - -1. Start a Jupyter session: - - ``` - jupyter notebook - ``` - -1. Use the file explorer in Jupyter lab to browse to `tutorials` - and start the first Tutorial. - - -Documentation -============= - -We also have documentation in [ReadTheDocs](https://PVDegradationTools.readthedocs.io) where you can find more details on the API functions. diff --git a/tutorials_and_tools/_build/html/_sources/intro.md b/tutorials_and_tools/_build/html/_sources/intro.md deleted file mode 100644 index 7035b3c3f..000000000 --- a/tutorials_and_tools/_build/html/_sources/intro.md +++ /dev/null @@ -1,14 +0,0 @@ -# Welcome to the PVDEG Tutorials and Tools - -Here is a collection of Jupyter Journals that include tutorials and tools. -These are ready to read, install and use, or run in your browser through Google Collab. -Clicking on the rocket-icon on the top allows you to launch the journals on -[Google Colaboratory](https://colab.research.google.com/) for interactive mode. -As per the instructions on each journal, you should uncomment the first line -that reads `pip install ...` to install the environment if you follow this mode. - -You can also clone the repository and run them locally, following the -instructions on the [PVDegTools Github page](https://github.com/NREL/PVDegradationTools) - -```{tableofcontents} -``` diff --git a/tutorials_and_tools/_build/html/_sources/tutorials/1 - Basics, Humidity, Design.ipynb b/tutorials_and_tools/_build/html/_sources/tutorials/1 - Basics, Humidity, Design.ipynb deleted file mode 100644 index a5389fd91..000000000 --- a/tutorials_and_tools/_build/html/_sources/tutorials/1 - Basics, Humidity, Design.ipynb +++ /dev/null @@ -1,1731 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 1 - Basics, Humidity, Design\n", - "### Module Humidity and Edge Seal Width\n", - "***\n", - "Matthew Brown\n", - "\n", - "2023.06.02\n", - "***\n", - "\n", - "**Requirements:**\n", - "- weather file (psm3 preferred) demo file is provided\n", - "\n", - "**Objectives:**\n", - "1. Read in necessary weather data\n", - "2. Generate solar position, POA, and module temperature\n", - "3. Generate module humidities\n", - "4. Calculate edge seal width" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# if running on google colab, uncomment the next line and execute this cell to install the dependencies and prevent \"ModuleNotFoundError\" in later cells:\n", - "# !pip install pvdeg==0.3.3" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import pandas as pd\n", - "\n", - "import pvdeg\n", - "from pvdeg import DATA_DIR" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1. Reading in Weather Data and Site Meta-Data\n", - "Most `pvdeg` functions have been standardized to operate from popular weather files such as TMY3, EPW, and PSM3. For high-performance computer users, NSRDB and other database fetching tools can be used. For these tutorials, we will use local weather files. Unless otherwise stated, functions require the following fields within a weather file:\n", - "\n", - "- date-time index\n", - "- DNI\n", - "- DHI\n", - "- GHI\n", - "- Temperature, Dry-Bulb\n", - "- Temperature, Dew-Point\n", - "- Wind Speed\n", - "- Relative Humidity\n", - "\n", - "And the following site-specific metadata. This should be contained in the weather file header.\n", - "\n", - "- Latitude\n", - "- Longitude\n", - "- Altitude" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "PSM_FILE = os.path.join(DATA_DIR, \"psm3_demo.csv\")\n", - "WEATHER, META = pvdeg.weather.read(PSM_FILE, \"psm\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's take a closer look at the dataframe (weather) and dictionary (meta) imported above. The structure will be used by most `pvdeg` functions." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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1999-01-01 01:30:00-07:001999111300.00.00.00.0-4.01.780.84
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1999-01-01 04:30:00-07:001999114300.00.00.00.0-4.01.385.81
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" - ], - "text/plain": [ - " Year Month Day Hour Minute dni dhi ghi \\\n", - "1999-01-01 00:30:00-07:00 1999 1 1 0 30 0.0 0.0 0.0 \n", - "1999-01-01 01:30:00-07:00 1999 1 1 1 30 0.0 0.0 0.0 \n", - "1999-01-01 02:30:00-07:00 1999 1 1 2 30 0.0 0.0 0.0 \n", - "1999-01-01 03:30:00-07:00 1999 1 1 3 30 0.0 0.0 0.0 \n", - "1999-01-01 04:30:00-07:00 1999 1 1 4 30 0.0 0.0 0.0 \n", - "\n", - " temp_air Dew Point wind_speed relative_humidity \n", - "1999-01-01 00:30:00-07:00 0.0 -5.0 1.8 79.39 \n", - "1999-01-01 01:30:00-07:00 0.0 -4.0 1.7 80.84 \n", - "1999-01-01 02:30:00-07:00 0.0 -4.0 1.5 82.98 \n", - "1999-01-01 03:30:00-07:00 0.0 -4.0 1.3 85.01 \n", - "1999-01-01 04:30:00-07:00 0.0 -4.0 1.3 85.81 " - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "WEATHER.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Meta-Data will vary greatly between weather files. However, at a minimum they should all contain the fields required by `pvdeg` listed above. Let's look at the meta-data generated by our PSM3 weather file. It provides much more information that we need, but at the very end are the three most important fields." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'Source': 'NSRDB',\n", - " 'Location ID': '145809',\n", - " 'City': '-',\n", - " 'State': '-',\n", - " 'Country': '-',\n", - " 'Time Zone': -7,\n", - " 'Local Time Zone': -7,\n", - " 'Clearsky DHI Units': 'w/m2',\n", - " 'Clearsky DNI Units': 'w/m2',\n", - " 'Clearsky GHI Units': 'w/m2',\n", - " 'Dew Point Units': 'c',\n", - " 'DHI Units': 'w/m2',\n", - " 'DNI Units': 'w/m2',\n", - " 'GHI Units': 'w/m2',\n", - " 'Solar Zenith Angle Units': 'Degree',\n", - " 'Temperature Units': 'c',\n", - " 'Pressure Units': 'mbar',\n", - " 'Relative Humidity Units': '%',\n", - " 'Precipitable Water Units': 'cm',\n", - " 'Wind Direction Units': 'Degrees',\n", - " 'Wind Speed Units': 'm/s',\n", - " 'Cloud Type -15': 'N/A',\n", - " 'Cloud Type 0': 'Clear',\n", - " 'Cloud Type 1': 'Probably Clear',\n", - " 'Cloud Type 2': 'Fog',\n", - " 'Cloud Type 3': 'Water',\n", - " 'Cloud Type 4': 'Super-Cooled Water',\n", - " 'Cloud Type 5': 'Mixed',\n", - " 'Cloud Type 6': 'Opaque Ice',\n", - " 'Cloud Type 7': 'Cirrus',\n", - " 'Cloud Type 8': 'Overlapping',\n", - " 'Cloud Type 9': 'Overshooting',\n", - " 'Cloud Type 10': 'Unknown',\n", - " 'Cloud Type 11': 'Dust',\n", - " 'Cloud Type 12': 'Smoke',\n", - " 'Fill Flag 0': 'N/A',\n", - " 'Fill Flag 1': 'Missing Image',\n", - " 'Fill Flag 2': 'Low Irradiance',\n", - " 'Fill Flag 3': 'Exceeds Clearsky',\n", - " 'Fill Flag 4': 'Missing CLoud Properties',\n", - " 'Fill Flag 5': 'Rayleigh Violation',\n", - " 'Surface Albedo Units': 'N/A',\n", - " 'Version': '3.0.6',\n", - " 'latitude': 39.73,\n", - " 'longitude': -105.18,\n", - " 'altitude': 1820}" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "META" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We'll be working with a lot of time series data, generally with a date-time index. If you're\n", - "processing an entire year of data, you don't need to sort or filter the data. However, you may want\n", - "to filter the data by a few different criteria. Below are some common examples.\n", - "- Filter by date\n", - " - month\n", - " - range of months\n", - "- Filter by sun-up hours" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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YearMonthDayHourMinutednidhighitemp_airDew Pointwind_speedrelative_humidity
1999-06-01 00:30:00-07:001999610300.00.00.06.00.01.779.15
1999-06-01 01:30:00-07:001999611300.00.00.06.00.01.775.62
1999-06-01 02:30:00-07:001999612300.00.00.06.00.01.578.51
1999-06-01 03:30:00-07:001999613300.00.00.05.00.01.376.90
1999-06-01 04:30:00-07:001999614300.00.00.06.00.01.375.75
.......................................
1999-06-30 19:30:00-07:00199963019300.00.00.021.06.00.648.54
1999-06-30 20:30:00-07:00199963020300.00.00.019.07.00.455.36
1999-06-30 21:30:00-07:00199963021300.00.00.017.07.00.566.32
1999-06-30 22:30:00-07:00199963022300.00.00.015.07.00.875.74
1999-06-30 23:30:00-07:00199963023300.00.00.013.07.00.880.16
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720 rows Γ— 12 columns

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" - ], - "text/plain": [ - " Year Month Day Hour Minute dni dhi ghi \\\n", - "1999-06-01 00:30:00-07:00 1999 6 1 0 30 0.0 0.0 0.0 \n", - "1999-06-01 01:30:00-07:00 1999 6 1 1 30 0.0 0.0 0.0 \n", - "1999-06-01 02:30:00-07:00 1999 6 1 2 30 0.0 0.0 0.0 \n", - "1999-06-01 03:30:00-07:00 1999 6 1 3 30 0.0 0.0 0.0 \n", - "1999-06-01 04:30:00-07:00 1999 6 1 4 30 0.0 0.0 0.0 \n", - "... ... ... ... ... ... ... ... ... \n", - "1999-06-30 19:30:00-07:00 1999 6 30 19 30 0.0 0.0 0.0 \n", - "1999-06-30 20:30:00-07:00 1999 6 30 20 30 0.0 0.0 0.0 \n", - "1999-06-30 21:30:00-07:00 1999 6 30 21 30 0.0 0.0 0.0 \n", - "1999-06-30 22:30:00-07:00 1999 6 30 22 30 0.0 0.0 0.0 \n", - "1999-06-30 23:30:00-07:00 1999 6 30 23 30 0.0 0.0 0.0 \n", - "\n", - " temp_air Dew Point wind_speed relative_humidity \n", - "1999-06-01 00:30:00-07:00 6.0 0.0 1.7 79.15 \n", - "1999-06-01 01:30:00-07:00 6.0 0.0 1.7 75.62 \n", - "1999-06-01 02:30:00-07:00 6.0 0.0 1.5 78.51 \n", - "1999-06-01 03:30:00-07:00 5.0 0.0 1.3 76.90 \n", - "1999-06-01 04:30:00-07:00 6.0 0.0 1.3 75.75 \n", - "... ... ... ... ... \n", - "1999-06-30 19:30:00-07:00 21.0 6.0 0.6 48.54 \n", - "1999-06-30 20:30:00-07:00 19.0 7.0 0.4 55.36 \n", - "1999-06-30 21:30:00-07:00 17.0 7.0 0.5 66.32 \n", - "1999-06-30 22:30:00-07:00 15.0 7.0 0.8 75.74 \n", - "1999-06-30 23:30:00-07:00 13.0 7.0 0.8 80.16 \n", - "\n", - "[720 rows x 12 columns]" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Let's select the month of June\n", - "june_weather = WEATHER[WEATHER.index.month == 6]\n", - "june_weather" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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YearMonthDayHourMinutednidhighitemp_airDew Pointwind_speedrelative_humidity
1999-06-01 00:30:00-07:001999610300.00.00.06.00.01.779.15
1999-06-01 01:30:00-07:001999611300.00.00.06.00.01.775.62
1999-06-01 02:30:00-07:001999612300.00.00.06.00.01.578.51
1999-06-01 03:30:00-07:001999613300.00.00.05.00.01.376.90
1999-06-01 04:30:00-07:001999614300.00.00.06.00.01.375.75
.......................................
1999-09-30 19:30:00-07:00199993019300.00.00.08.0-1.02.658.57
1999-09-30 20:30:00-07:00199993020300.00.00.07.0-2.02.758.17
1999-09-30 21:30:00-07:00199993021300.00.00.07.0-3.02.954.27
1999-09-30 22:30:00-07:00199993022300.00.00.07.0-4.03.050.29
1999-09-30 23:30:00-07:00199993023300.00.00.07.0-5.03.046.91
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2928 rows Γ— 12 columns

\n", - "
" - ], - "text/plain": [ - " Year Month Day Hour Minute dni dhi ghi \\\n", - "1999-06-01 00:30:00-07:00 1999 6 1 0 30 0.0 0.0 0.0 \n", - "1999-06-01 01:30:00-07:00 1999 6 1 1 30 0.0 0.0 0.0 \n", - "1999-06-01 02:30:00-07:00 1999 6 1 2 30 0.0 0.0 0.0 \n", - "1999-06-01 03:30:00-07:00 1999 6 1 3 30 0.0 0.0 0.0 \n", - "1999-06-01 04:30:00-07:00 1999 6 1 4 30 0.0 0.0 0.0 \n", - "... ... ... ... ... ... ... ... ... \n", - "1999-09-30 19:30:00-07:00 1999 9 30 19 30 0.0 0.0 0.0 \n", - "1999-09-30 20:30:00-07:00 1999 9 30 20 30 0.0 0.0 0.0 \n", - "1999-09-30 21:30:00-07:00 1999 9 30 21 30 0.0 0.0 0.0 \n", - "1999-09-30 22:30:00-07:00 1999 9 30 22 30 0.0 0.0 0.0 \n", - "1999-09-30 23:30:00-07:00 1999 9 30 23 30 0.0 0.0 0.0 \n", - "\n", - " temp_air Dew Point wind_speed relative_humidity \n", - "1999-06-01 00:30:00-07:00 6.0 0.0 1.7 79.15 \n", - "1999-06-01 01:30:00-07:00 6.0 0.0 1.7 75.62 \n", - "1999-06-01 02:30:00-07:00 6.0 0.0 1.5 78.51 \n", - "1999-06-01 03:30:00-07:00 5.0 0.0 1.3 76.90 \n", - "1999-06-01 04:30:00-07:00 6.0 0.0 1.3 75.75 \n", - "... ... ... ... ... \n", - "1999-09-30 19:30:00-07:00 8.0 -1.0 2.6 58.57 \n", - "1999-09-30 20:30:00-07:00 7.0 -2.0 2.7 58.17 \n", - "1999-09-30 21:30:00-07:00 7.0 -3.0 2.9 54.27 \n", - "1999-09-30 22:30:00-07:00 7.0 -4.0 3.0 50.29 \n", - "1999-09-30 23:30:00-07:00 7.0 -5.0 3.0 46.91 \n", - "\n", - "[2928 rows x 12 columns]" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Let's filter the summer months for the northern hemisphere\n", - "summer_months = [6, 7, 8, 9]\n", - "summer_weather = WEATHER[WEATHER.index.month.isin(summer_months)]\n", - "summer_weather" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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YearMonthDayHourMinutednidhighitemp_airDew Pointwind_speedrelative_humidity
1999-01-01 08:30:00-07:0019991183065.065.076.01.0-3.04.776.86
1999-01-01 09:30:00-07:00199911930503.093.0246.02.0-2.06.380.37
1999-01-01 10:30:00-07:001999111030617.0109.0355.03.0-2.07.074.87
1999-01-01 11:30:00-07:001999111130497.0161.0385.04.0-3.06.870.73
1999-01-01 12:30:00-07:0019991112300.0128.0128.04.0-4.06.561.80
.......................................
1999-12-31 12:30:00-07:001999123112300.020.020.09.0-8.01.631.45
1999-12-31 13:30:00-07:00199912311330562.0125.0354.08.0-6.00.540.25
1999-12-31 14:30:00-07:00199912311430530.094.0263.07.0-4.00.453.19
1999-12-31 15:30:00-07:00199912311530636.038.0159.05.0-5.01.057.10
1999-12-31 16:30:00-07:001999123116300.021.021.04.0-6.01.356.22
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4301 rows Γ— 12 columns

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" - ], - "text/plain": [ - " Year Month Day Hour Minute dni dhi \\\n", - "1999-01-01 08:30:00-07:00 1999 1 1 8 30 65.0 65.0 \n", - "1999-01-01 09:30:00-07:00 1999 1 1 9 30 503.0 93.0 \n", - "1999-01-01 10:30:00-07:00 1999 1 1 10 30 617.0 109.0 \n", - "1999-01-01 11:30:00-07:00 1999 1 1 11 30 497.0 161.0 \n", - "1999-01-01 12:30:00-07:00 1999 1 1 12 30 0.0 128.0 \n", - "... ... ... ... ... ... ... ... \n", - "1999-12-31 12:30:00-07:00 1999 12 31 12 30 0.0 20.0 \n", - "1999-12-31 13:30:00-07:00 1999 12 31 13 30 562.0 125.0 \n", - "1999-12-31 14:30:00-07:00 1999 12 31 14 30 530.0 94.0 \n", - "1999-12-31 15:30:00-07:00 1999 12 31 15 30 636.0 38.0 \n", - "1999-12-31 16:30:00-07:00 1999 12 31 16 30 0.0 21.0 \n", - "\n", - " ghi temp_air Dew Point wind_speed \\\n", - "1999-01-01 08:30:00-07:00 76.0 1.0 -3.0 4.7 \n", - "1999-01-01 09:30:00-07:00 246.0 2.0 -2.0 6.3 \n", - "1999-01-01 10:30:00-07:00 355.0 3.0 -2.0 7.0 \n", - "1999-01-01 11:30:00-07:00 385.0 4.0 -3.0 6.8 \n", - "1999-01-01 12:30:00-07:00 128.0 4.0 -4.0 6.5 \n", - "... ... ... ... ... \n", - "1999-12-31 12:30:00-07:00 20.0 9.0 -8.0 1.6 \n", - "1999-12-31 13:30:00-07:00 354.0 8.0 -6.0 0.5 \n", - "1999-12-31 14:30:00-07:00 263.0 7.0 -4.0 0.4 \n", - "1999-12-31 15:30:00-07:00 159.0 5.0 -5.0 1.0 \n", - "1999-12-31 16:30:00-07:00 21.0 4.0 -6.0 1.3 \n", - "\n", - " relative_humidity \n", - "1999-01-01 08:30:00-07:00 76.86 \n", - "1999-01-01 09:30:00-07:00 80.37 \n", - "1999-01-01 10:30:00-07:00 74.87 \n", - "1999-01-01 11:30:00-07:00 70.73 \n", - "1999-01-01 12:30:00-07:00 61.80 \n", - "... ... \n", - "1999-12-31 12:30:00-07:00 31.45 \n", - "1999-12-31 13:30:00-07:00 40.25 \n", - "1999-12-31 14:30:00-07:00 53.19 \n", - "1999-12-31 15:30:00-07:00 57.10 \n", - "1999-12-31 16:30:00-07:00 56.22 \n", - "\n", - "[4301 rows x 12 columns]" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# lets just select hours from the year where the sun is above the horizon\n", - "sunup_weather = WEATHER[WEATHER[\"ghi\"] > 0]\n", - "sunup_weather" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2. Solar Position, POA, and Module Temperature\n", - "\n", - "Many operations with `PVDEG` require solar position, POA irradiance, and module temperature. Usually a `pvdeg` method will calculate these data sets automatically when it is required, however it will not keep the data for external use. For now, we will calculate them directly. This is helpful when you need to use the data for an external calculation. Here, we generate the solar position (azimuth, elevation, etc), plane of array irradiance (POA), and module temperature. `PVDEG` has wrappers for quickly using `PVLIB` to generate these figures with minimal input" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "sol_pos = pvdeg.spectral.solar_position(weather_df=WEATHER, meta=META)\n", - "\n", - "poa_df = pvdeg.spectral.poa_irradiance(\n", - " weather_df=WEATHER, meta=META\n", - ") # , solar_position=sol_pos)\n", - "\n", - "temp_mod = pvdeg.temperature.module(weather_df=WEATHER, meta=META) # , poa=poa_df)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For more advanced usage of these functions, refer to the documentation for `pvdeg` and `pvlib`.\n", - "\n", - "Lets inspect the output from each of these functions\n", - "\n", - "`solar_position` returns a datetime-indexed dataframe of solar position for the length and frequency given by the weather file.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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apparent_zenithzenithapparent_elevationelevationazimuthequation_of_time
1999-01-01 00:30:00-07:00162.416069162.416069-72.416069-72.41606920.152470-3.300994
1999-01-01 01:30:00-07:00155.313029155.313029-65.313029-65.31302953.821029-3.320654
1999-01-01 02:30:00-07:00144.992553144.992553-54.992553-54.99255372.531199-3.340306
1999-01-01 03:30:00-07:00133.692678133.692678-43.692678-43.69267884.789909-3.359948
1999-01-01 04:30:00-07:00122.172658122.172658-32.172658-32.17265894.462003-3.379582
.....................
1999-12-31 19:30:00-07:00120.682889120.682889-30.682889-30.682889264.307279-3.093347
1999-12-31 20:30:00-07:00132.197914132.197914-42.197914-42.197914273.785399-3.113209
1999-12-31 21:30:00-07:00143.553813143.553813-53.553813-53.553813285.552685-3.133063
1999-12-31 22:30:00-07:00154.080531154.080531-64.080531-64.080531303.037057-3.152909
1999-12-31 23:30:00-07:00161.842482161.842482-71.842482-71.842482334.208238-3.172747
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8760 rows Γ— 6 columns

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" - ], - "text/plain": [ - " apparent_zenith zenith apparent_elevation \\\n", - "1999-01-01 00:30:00-07:00 162.416069 162.416069 -72.416069 \n", - "1999-01-01 01:30:00-07:00 155.313029 155.313029 -65.313029 \n", - "1999-01-01 02:30:00-07:00 144.992553 144.992553 -54.992553 \n", - "1999-01-01 03:30:00-07:00 133.692678 133.692678 -43.692678 \n", - "1999-01-01 04:30:00-07:00 122.172658 122.172658 -32.172658 \n", - "... ... ... ... \n", - "1999-12-31 19:30:00-07:00 120.682889 120.682889 -30.682889 \n", - "1999-12-31 20:30:00-07:00 132.197914 132.197914 -42.197914 \n", - "1999-12-31 21:30:00-07:00 143.553813 143.553813 -53.553813 \n", - "1999-12-31 22:30:00-07:00 154.080531 154.080531 -64.080531 \n", - "1999-12-31 23:30:00-07:00 161.842482 161.842482 -71.842482 \n", - "\n", - " elevation azimuth equation_of_time \n", - "1999-01-01 00:30:00-07:00 -72.416069 20.152470 -3.300994 \n", - "1999-01-01 01:30:00-07:00 -65.313029 53.821029 -3.320654 \n", - "1999-01-01 02:30:00-07:00 -54.992553 72.531199 -3.340306 \n", - "1999-01-01 03:30:00-07:00 -43.692678 84.789909 -3.359948 \n", - "1999-01-01 04:30:00-07:00 -32.172658 94.462003 -3.379582 \n", - "... ... ... ... \n", - "1999-12-31 19:30:00-07:00 -30.682889 264.307279 -3.093347 \n", - "1999-12-31 20:30:00-07:00 -42.197914 273.785399 -3.113209 \n", - "1999-12-31 21:30:00-07:00 -53.553813 285.552685 -3.133063 \n", - "1999-12-31 22:30:00-07:00 -64.080531 303.037057 -3.152909 \n", - "1999-12-31 23:30:00-07:00 -71.842482 334.208238 -3.172747 \n", - "\n", - "[8760 rows x 6 columns]" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sol_pos" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "`poa_irradiance` returns a datetime-indexed dataframe as well. Each column contains either the global plane of array irradiance or a particular contribition. Lets look at a time in the index we expect to have daylight." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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poa_globalpoa_directpoa_diffusepoa_sky_diffusepoa_ground_diffuse
1999-01-01 13:30:00-07:0090.4265380.090.42653887.5687172.857821
1999-01-01 14:30:00-07:0021.9215850.021.92158521.2287800.692805
1999-01-01 15:30:00-07:0057.5441600.057.54416055.7255471.818613
1999-01-01 16:30:00-07:007.3071950.07.3071957.0762600.230935
1999-01-01 17:30:00-07:000.0000000.00.0000000.0000000.000000
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" - ], - "text/plain": [ - " poa_global poa_direct poa_diffuse \\\n", - "1999-01-01 13:30:00-07:00 90.426538 0.0 90.426538 \n", - "1999-01-01 14:30:00-07:00 21.921585 0.0 21.921585 \n", - "1999-01-01 15:30:00-07:00 57.544160 0.0 57.544160 \n", - "1999-01-01 16:30:00-07:00 7.307195 0.0 7.307195 \n", - "1999-01-01 17:30:00-07:00 0.000000 0.0 0.000000 \n", - "\n", - " poa_sky_diffuse poa_ground_diffuse \n", - "1999-01-01 13:30:00-07:00 87.568717 2.857821 \n", - "1999-01-01 14:30:00-07:00 21.228780 0.692805 \n", - "1999-01-01 15:30:00-07:00 55.725547 1.818613 \n", - "1999-01-01 16:30:00-07:00 7.076260 0.230935 \n", - "1999-01-01 17:30:00-07:00 0.000000 0.000000 " - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "poa_df.loc[\"1999-01-01 13:30:00-07:00\":\"1999-01-01 17:30:00-07:00\"]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "`temperature.module` has several optional parameters. Below is an example which explicity uses all of the default values. This function will return a datetime-indexed series matching the index of the given weather file. For more options, see the pvlib documentation for temperature models.\n", - "\n", - "https://pvlib-python.readthedocs.io/en/stable/reference/pv_modeling/temperature.html\n", - "\n", - "The optional arguments are:\n", - "- poa : manually enter the POA Irradiance\n", - "- temp_model : choose a PVLIB compatible temperature model\n", - "- conf : choose a recognized module configuration\n", - "- wind_speed_factor : change the empirical wind speed fit paramter" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1999-01-01 13:30:00-07:00 4.639740\n", - "1999-01-01 14:30:00-07:00 2.425271\n", - "1999-01-01 15:30:00-07:00 1.249262\n", - "1999-01-01 16:30:00-07:00 -0.829008\n", - "1999-01-01 17:30:00-07:00 -2.000000\n", - "dtype: float64" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "temp_mod = pvdeg.temperature.module(\n", - " weather_df=WEATHER,\n", - " meta=META,\n", - " poa=poa_df,\n", - " temp_model=\"sapm\",\n", - " conf=\"open_rack_glass_polymer\",\n", - " wind_speed_factor=1,\n", - ")\n", - "\n", - "temp_mod.loc[\"1999-01-01 13:30:00-07:00\":\"1999-01-01 17:30:00-07:00\"]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3. Module Humidities\n", - "`PVDEG` can be used to calculate the relative humidity of several layers within a PV module: the outside surface of the module, front ecapsulant, back encapsulant, and backsheet. This can be done with 2 techniques: Automatically and Manually. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 3.a. Automatic\n", - "Use the function `humidity.module`. This method does all of the calculations behind the scenes. It requires only 2 inputs (weather data and meta data). It will return a dataframe with all the layers of the module." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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RH_surface_outsideRH_front_encapRH_back_encapRH_backsheet
1999-01-01 00:30:00-07:0079.3944.19878779.39000079.390000
1999-01-01 01:30:00-07:0080.8444.19878779.41522080.127610
1999-01-01 02:30:00-07:0082.9844.19878779.47722381.228611
1999-01-01 03:30:00-07:0085.0144.19878779.57345582.291727
1999-01-01 04:30:00-07:0085.8144.19878779.68192782.745964
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" - ], - "text/plain": [ - " RH_surface_outside RH_front_encap RH_back_encap \\\n", - "1999-01-01 00:30:00-07:00 79.39 44.198787 79.390000 \n", - "1999-01-01 01:30:00-07:00 80.84 44.198787 79.415220 \n", - "1999-01-01 02:30:00-07:00 82.98 44.198787 79.477223 \n", - "1999-01-01 03:30:00-07:00 85.01 44.198787 79.573455 \n", - "1999-01-01 04:30:00-07:00 85.81 44.198787 79.681927 \n", - "\n", - " RH_backsheet \n", - "1999-01-01 00:30:00-07:00 79.390000 \n", - "1999-01-01 01:30:00-07:00 80.127610 \n", - "1999-01-01 02:30:00-07:00 81.228611 \n", - "1999-01-01 03:30:00-07:00 82.291727 \n", - "1999-01-01 04:30:00-07:00 82.745964 " - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "rh_module = pvdeg.humidity.module(weather_df=WEATHER, meta=META)\n", - "rh_module.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 3.b. Manual\n", - "Use the individual functions to calculate the RH in each layer. These are named and require more extensive input parameters. This must be done in the correct order:\n", - "1. Surface Outside\n", - "2. Front Encapsulant\n", - "2. Back Encapsulant\n", - "3. Backsheet" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "rh_surface_outside = pvdeg.humidity.surface_relative(\n", - " rh_ambient=WEATHER[\"relative_humidity\"],\n", - " temp_ambient=WEATHER[\"temp_air\"],\n", - " temp_module=temp_mod,\n", - ")\n", - "\n", - "rh_front_encap = pvdeg.humidity.front_encapsulant(\n", - " rh_ambient=rh_surface_outside,\n", - " temp_ambient=WEATHER[\"temp_air\"],\n", - " temp_module=temp_mod,\n", - ")\n", - "\n", - "rh_back_encap = pvdeg.humidity.back_encapsulant_water_concentration(\n", - " rh_ambient=rh_surface_outside,\n", - " temp_ambient=WEATHER[\"temp_air\"],\n", - " temp_module=temp_mod,\n", - ")\n", - "\n", - "rh_backsheet = pvdeg.humidity.backsheet_from_encap(\n", - " rh_back_encap=rh_back_encap, rh_surface_outside=WEATHER[\"relative_humidity\"]\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 4. Design: Edge Seal Width\n", - "`pvdeg.design.edge_seal_width` calculates the width [cm] required for several years of water ingress. If you do not specify the number of years, it will use the default value of 25. As with most `pvdeg` functions, we can automatically generate all necessary data if we pass the two arguments (weather_df, meta)." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.7170984345065904" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "edge_seal_width = pvdeg.design.edge_seal_width(weather_df=WEATHER, meta=META)\n", - "edge_seal_width" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Lets look at the calculation step by step. To get the estimated edge seal width, we need the following:\n", - "1. Humidity Saturation Point\n", - "2. Edge seal ingress rate ( k ) or the estimated rate of water ingress through edge seal in [cm/hour^0.5]" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.7170984345065904\n" - ] - } - ], - "source": [ - "psat, psat_avg = pvdeg.humidity.psat(WEATHER[\"temp_air\"])\n", - "\n", - "k = pvdeg.design.edge_seal_ingress_rate(avg_psat=psat_avg)\n", - "\n", - "edge_seal_width = pvdeg.design.edge_seal_width(\n", - " weather_df=WEATHER, meta=META, k=k, years=25\n", - ")\n", - "\n", - "print(edge_seal_width)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Alternate Edge Seal Width\n", - "If you have the dew point, or wet-bulb temperature, you can calculate the edge seal width directly." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.4499535870958669" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "edge_seal_width = pvdeg.design.edge_seal_width(\n", - " weather_df=WEATHER, meta=META, from_dew_point=True\n", - ")\n", - "edge_seal_width" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "august2025", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.13.5" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/tutorials_and_tools/_build/html/_sources/tutorials/2 - Degradation.ipynb b/tutorials_and_tools/_build/html/_sources/tutorials/2 - Degradation.ipynb deleted file mode 100644 index 2c4b8175c..000000000 --- a/tutorials_and_tools/_build/html/_sources/tutorials/2 - Degradation.ipynb +++ /dev/null @@ -1,330 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 2 - Degradation\n", - "### Degradation and Solder Fatigue\n", - "***\n", - "Matthew Brown\n", - "\n", - "2023.06.02\n", - "***\n", - "\n", - "**Requirements**:\n", - "- compatible weather file (PSM3, TMY3, EPW)\n", - "- Accelerated testing chamber parameters\n", - " - chamber irradiance [W/m^2]\n", - " - chamber temperature [C]\n", - " - chamber humidity [%]\n", - "- Activation energies for test material [kJ/mol]\n", - "\n", - "**Objectives**:\n", - "1. Read in the weather data\n", - "2. Generate basic modeling data\n", - "3. Calculate VantHoff degradation acceleration factor\n", - "4. Calculate Arrhenius degradation acceleration factor\n", - "5. Quick Method\n", - "5. Solder Fatigue" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# if running on google colab, uncomment the next line and execute this cell to install the dependencies and prevent \"ModuleNotFoundError\" in later cells:\n", - "# !pip install pvdeg==0.3.3" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import pandas as pd\n", - "\n", - "import pvdeg\n", - "from pvdeg import DATA_DIR" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1. Read In the Weather File\n", - "\n", - "This is usually the first step. Use a PSM3, TMY3, or EPW file. For this demo, use the provided PSM3 weather file." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "PSM_FILE = os.path.join(DATA_DIR, \"psm3_demo.csv\")\n", - "WEATHER, META = pvdeg.weather.read(PSM_FILE, \"psm\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2. Generate Basic Modeling Data\n", - "\n", - "For this tutorial we will need solar position, POA, PV cell and module temperature. Let's gernate those individually with their respective functions." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "sol_pos = pvdeg.spectral.solar_position(weather_df=WEATHER, meta=META)\n", - "\n", - "poa_df = pvdeg.spectral.poa_irradiance(\n", - " weather_df=WEATHER, meta=META, sol_position=sol_pos\n", - ")\n", - "\n", - "temp_cell = pvdeg.temperature.cell(weather_df=WEATHER, meta=META, poa=poa_df)\n", - "\n", - "temp_module = pvdeg.temperature.module(weather_df=WEATHER, meta=META, poa=poa_df)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3. VantHoff Degradation\n", - "\n", - "Van 't Hoff Irradiance Degradation\n", - "\n", - "For one year of degredation the controlled environmnet lamp settings will need to be set to IWa.\n", - "\n", - "As with most `pvdeg` functions, the following functions will always require two arguments (weather_df and meta)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# chamber irradiance (W/m^2)\n", - "I_chamber = 1000\n", - "# chamber temperature (C)\n", - "temp_chamber = 60\n", - "\n", - "# calculate the VantHoff Acceleration factor\n", - "vantHoff_deg = pvdeg.degradation.vantHoff_deg(\n", - " weather_df=WEATHER,\n", - " meta=META,\n", - " I_chamber=I_chamber,\n", - " temp_chamber=temp_chamber,\n", - " poa=poa_df,\n", - " temp_cell=temp_cell,\n", - ")\n", - "\n", - "# calculate the VantHoff weighted irradiance\n", - "irr_weighted_avg_v = pvdeg.degradation.IwaVantHoff(\n", - " weather_df=WEATHER, meta=META, poa=poa_df, temp_cell=temp_cell\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 4. Arrhenius\n", - "Calculate the Acceleration Factor between the rate of degredation of a modeled environmnet versus a modeled controlled environmnet\n", - "\n", - "Example: \"If the AF=25 then 1 year of Controlled Environment exposure is equal to 25 years in the field\"\n", - "\n", - "Equation:\n", - "$$ AF = N * \\frac{ I_{chamber}^x * RH_{chamber}^n * e^{\\frac{- E_a}{k T_{chamber}}} }{ \\Sigma (I_{POA}^x * RH_{outdoor}^n * e^{\\frac{-E_a}{k T_outdoor}}) }$$\n", - "\n", - "Function to calculate IWa, the Environment Characterization (W/mΒ²). For one year of degredation the controlled environmnet lamp settings will need to be set at IWa.\n", - "\n", - "Equation:\n", - "$$ I_{WA} = [ \\frac{ \\Sigma (I_{outdoor}^x * RH_{outdoor}^n e^{\\frac{-E_a}{k T_{outdood}}}) }{ N * RH_{WA}^n * e^{- \\frac{E_a}{k T_eq}} } ]^{\\frac{1}{x}} $$" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# relative humidity within chamber (%)\n", - "rh_chamber = 15\n", - "# arrhenius activation energy (kj/mol)\n", - "Ea = 40\n", - "\n", - "rh_surface = pvdeg.humidity.surface_relative(\n", - " rh_ambient=WEATHER[\"relative_humidity\"],\n", - " temp_ambient=WEATHER[\"temp_air\"],\n", - " temp_module=temp_module,\n", - ")\n", - "\n", - "arrhenius_deg = pvdeg.degradation.arrhenius_deg(\n", - " weather_df=WEATHER,\n", - " meta=META,\n", - " rh_outdoor=rh_surface,\n", - " I_chamber=I_chamber,\n", - " rh_chamber=rh_chamber,\n", - " temp_chamber=temp_chamber,\n", - " poa=poa_df,\n", - " temp_cell=temp_cell,\n", - " Ea=Ea,\n", - ")\n", - "\n", - "irr_weighted_avg_a = pvdeg.degradation.IwaArrhenius(\n", - " weather_df=WEATHER,\n", - " meta=META,\n", - " poa=poa_df,\n", - " rh_outdoor=WEATHER[\"relative_humidity\"],\n", - " temp_cell=temp_cell,\n", - " Ea=Ea,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 5. Quick Method (Degradation)\n", - "\n", - "For quick calculations, you can omit POA and both module and cell temperature. The function will calculate these figures as needed using the available weather data with the default options for PV module configuration." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# chamber settings\n", - "I_chamber = 1000\n", - "temp_chamber = 60\n", - "rh_chamber = 15\n", - "\n", - "# activation energy\n", - "Ea = 40\n", - "\n", - "vantHoff_deg = pvdeg.degradation.vantHoff_deg(\n", - " weather_df=WEATHER, meta=META, I_chamber=I_chamber, temp_chamber=temp_chamber\n", - ")\n", - "\n", - "irr_weighted_avg_v = pvdeg.degradation.IwaVantHoff(weather_df=WEATHER, meta=META)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "rh_surface = pvdeg.humidity.surface_relative(\n", - " rh_ambient=WEATHER[\"relative_humidity\"],\n", - " temp_ambient=WEATHER[\"temp_air\"],\n", - " temp_module=temp_module,\n", - ")\n", - "\n", - "arrhenius_deg = pvdeg.degradation.arrhenius_deg(\n", - " weather_df=WEATHER,\n", - " meta=META,\n", - " rh_outdoor=rh_surface,\n", - " I_chamber=I_chamber,\n", - " rh_chamber=rh_chamber,\n", - " temp_chamber=temp_chamber,\n", - " Ea=Ea,\n", - ")\n", - "\n", - "irr_weighted_avg_a = pvdeg.degradation.IwaArrhenius(\n", - " weather_df=WEATHER, meta=META, rh_outdoor=WEATHER[\"relative_humidity\"], Ea=Ea\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 6. Solder Fatigue\n", - "\n", - "Estimate the thermomechanical fatigue of flat plate photovoltaic module solder joints over the time range given using estimated cell temperature. Like other `pvdeg` funcitons, the minimal parameters are (weather_df, meta). Running the function with only these two inputs will use default PV module configurations ( open_rack_glass_polymer ) and the 'sapm' temperature model over the entire length of the weather data. " - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "fatigue = pvdeg.fatigue.solder_fatigue(weather_df=WEATHER, meta=META)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If you wish to reduce the span of time or use a non-default temperature model, you may specify the parameters manually. Let's try an explicit example.\n", - "We want the solder fatigue estimated over the month of June for a roof mounted glass-front polymer-back module.\n", - "\n", - "1. Lets create a datetime-index for the month of June.\n", - "2. Next, generate the cell temperature. Make sure to explicity restrict the weather data to our dt-index for June. Next, declare the PV module configuration.\n", - "3. Calculate the fatigue. Explicity specify the time_range (our dt-index for June from step 1) and the cell temperature as we caculated in step 2" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# select the month of June\n", - "time_range = WEATHER.index[WEATHER.index.month == 6]\n", - "\n", - "# calculate cell temperature over our selected date-time range.\n", - "# specify the module configuration\n", - "temp_cell = pvdeg.temperature.cell(\n", - " weather_df=WEATHER.loc[time_range],\n", - " meta=META,\n", - " temp_model=\"sapm\",\n", - " conf=\"insulated_back_glass_polymer\",\n", - ")\n", - "\n", - "\n", - "fatigue = pvdeg.fatigue.solder_fatigue(\n", - " weather_df=WEATHER, meta=META, time_range=time_range, temp_cell=temp_cell\n", - ")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.13" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/tutorials_and_tools/_build/html/_sources/tutorials/3 - Spectral Degradation.ipynb b/tutorials_and_tools/_build/html/_sources/tutorials/3 - Spectral Degradation.ipynb deleted file mode 100644 index 2a85ba9b2..000000000 --- a/tutorials_and_tools/_build/html/_sources/tutorials/3 - Spectral Degradation.ipynb +++ /dev/null @@ -1,227 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 3 - Spectral Degradation\n", - "***\n", - "Matthew Brown\n", - "\n", - "2022.12.29\n", - "***\n", - "\n", - "**Requirements:**\n", - "- spectral irradiance (measured or simulated)\n", - "- wavelengths of spectral irradiance data\n", - "- module RH\n", - "- module temperature\n", - "\n", - "\n", - "**Objectives:**\n", - "1. Read in spectral irradiance\n", - "2. Calculate spectral degradation" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# if running on google colab, uncomment the next line and execute this cell to install the dependencies and prevent \"ModuleNotFoundError\" in later cells:\n", - "# !pip install pvdeg==0.3.3" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import pandas as pd\n", - "import numpy as np\n", - "import pvdeg\n", - "from pvdeg import DATA_DIR" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1. Read in spectral irradiance data\n", - "\n", - "Spectral degradation has 4 main requirements:\n", - "- Spectral Irradiance [W/m^2 nm]\n", - "- Wavelength [nm] \n", - "- Module Relative Humidity [%]\n", - "- Module Temperature [C]\n", - "\n", - "For more advanced scenarios, you may want to calculate the degradation of a particular layer within the module. Below, we are using *backside* irradiance and therefore a slightly different temperature and humidity have been calculated. To calculate degradation on the backside, we used `pvdeg.humidity.rh_backsheet`. For the the front side, you should use `pvdeg.humidity.rh_surface_outside` or `rh_front_encap`\n", - "\n", - "\n", - "For this tutorial we are using pre-generated data from a ray-tracing simulation. To calculate the degradation rate, we will need the wavelengths used in the simulation. " - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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SpectraTemperatureRH
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2021-03-09 10:00:00[0.6892146677599185, 0.40215646494410884, 0.67...4559
2021-03-09 11:00:00[0.15575709102178648, 0.5464374649246564, 0.68...4456
2021-03-09 12:00:00[0.22782105874481207, 0.9056495270031296, 0.26...5939
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2021-03-09 14:00:00[0.40321187996337626, 0.6473167864022122, 0.69...2539
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" - ], - "text/plain": [ - " Spectra \\\n", - "timestamp \n", - "2021-03-09 10:00:00 [0.6892146677599185, 0.40215646494410884, 0.67... \n", - "2021-03-09 11:00:00 [0.15575709102178648, 0.5464374649246564, 0.68... \n", - "2021-03-09 12:00:00 [0.22782105874481207, 0.9056495270031296, 0.26... \n", - "2021-03-09 13:00:00 [0.3741943134512433, 0.035830980984344674, 0.4... \n", - "2021-03-09 14:00:00 [0.40321187996337626, 0.6473167864022122, 0.69... \n", - "\n", - " Temperature RH \n", - "timestamp \n", - "2021-03-09 10:00:00 45 59 \n", - "2021-03-09 11:00:00 44 56 \n", - "2021-03-09 12:00:00 59 39 \n", - "2021-03-09 13:00:00 44 13 \n", - "2021-03-09 14:00:00 25 39 " - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "wavelengths = np.array(range(280, 420, 20))\n", - "\n", - "SPECTRA = pd.read_csv(os.path.join(DATA_DIR, \"spectra.csv\"), header=0, index_col=0)\n", - "SPECTRA.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2. Calculate Degradation\n", - "\n", - "The spectral degradation function has several optional paramters. For more information, refer to the documentation. Below is a function call with the minimum required information." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Removing brackets from spectral irradiance data\n" - ] - } - ], - "source": [ - "degradation = pvdeg.degradation.degradation(\n", - " spectra=SPECTRA[\"Spectra\"],\n", - " rh_module=SPECTRA[\"RH\"],\n", - " temp_module=SPECTRA[\"Temperature\"],\n", - " wavelengths=wavelengths,\n", - ")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.13" - }, - "vscode": { - "interpreter": { - "hash": "14c04630f1cd445b2532d35c77825134bfcafda47af70d0b9c2b5023b1f357a5" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/tutorials_and_tools/_build/html/_sources/tutorials/4 - Standards.ipynb b/tutorials_and_tools/_build/html/_sources/tutorials/4 - Standards.ipynb deleted file mode 100644 index 59278de9d..000000000 --- a/tutorials_and_tools/_build/html/_sources/tutorials/4 - Standards.ipynb +++ /dev/null @@ -1,157 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 4 - Standards\n", - "***\n", - "Matthew Brown\n", - "\n", - "2023.06.09\n", - "***\n", - "\n", - "**Requirements:**\n", - "- Local weather data (provided)\n", - "\n", - "**Objectives:**\n", - "1. Import weather data\n", - "2. Calculate installation standoff - level 1\n", - "3. Calculate installation standoff - level 2\n", - "\n", - "**Background:**\n", - "\n", - "This example demonstrates the calculation of a minimum standoff distance necessary for roof-mounted PV modules to ensure that the $T_{98}$ operational temperature remains under 70Β°C, in which case the more rigorous thermal stability testing requirements of IEC TS 63126 would not needed to be considered. We use data from [Fuentes, 1987] to model the approximate exponential decay in temperature, $T(X)$, with increasing standoff distance, $X$, as,\n", - "\n", - "$$ X = -X_0 \\ln\\left(1-\\frac{T_0-T}{\\Delta T}\\right)$$\n", - "\n", - "where $T_0$ is the temperature for $X=0$ (insulated back) and $\\Delta T$ is the temperature difference between an insulated back ($X=0$) and open rack mounting configuration ($X=\\infty)$.\n", - "\n", - "The following figure showcases this calulation for the entire United States. We used pvlib and data from the National Solar Radiation Database (NSRDB) to calculate the module temperatures for different mounting configuration and applied our model to obtain the standoff distance for roof-mounted PV systems.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# if running on google colab, uncomment the next line and execute this cell to install the dependencies and prevent \"ModuleNotFoundError\" in later cells:\n", - "# !pip install pvdeg==0.3.3" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import pvdeg\n", - "import pandas as pd\n", - "from pvdeg import DATA_DIR" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1. Import Weather Data\n", - "\n", - "The function has two minimum requirements:\n", - "- Weather data containing (at least) DNI, DHI, GHI, Temperature, RH, Wind-Speed\n", - "- Site meta-data containing (at least) Latitude, Longitude, Time Zone\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "weather_file = os.path.join(DATA_DIR, \"psm3_demo.csv\")\n", - "\n", - "WEATHER, META = pvdeg.weather.read(weather_file, \"psm\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2. Calculate Installation Standoff - Level 1\n", - "\n", - "Level - 1 : Temeprature Dependence as per IEC TS 63216\n", - "\n", - "The following is the minimum function call. See step **3** or the documentation for further details and optional parameters." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "standoff = pvdeg.standards.calc_standoff(weather_df=WEATHER, meta=META)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3. Calculate Installation Standoff - Level 2\n", - "\n", - "Let's take a closer look at the function and some optional parameters.\n", - "\n", - "- level : 1 or 2 (see IEC TS 63216)\n", - "- tilt : tilt from horizontal of PV module\n", - "- azimuth : azimuth in degrees from North\n", - "- sky_model : pvlib compatible model for generating sky characteristics (Options: 'isotropic', 'klucher', 'haydavies', 'reindl', 'king', 'perez')\n", - "- temp_model : pvlib compatible module temperature model (Options: 'sapm', 'pvsyst', 'faiman', 'sandia')\n", - "- module_type : basic module construction (Options: 'glass_polymer', 'glass_glass')\n", - "- x_0 : thermal decay constant [cm] (see documentation)\n", - "- wind_speed_factor : Wind speed correction factor to account for different wind speed measurement heights between weather database (e.g. NSRDB) and the tempeature model (e.g. SAPM)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "standoff = pvdeg.standards.calc_standoff(\n", - " weather_df=WEATHER,\n", - " meta=META,\n", - " level=2,\n", - " tilt=None,\n", - " azimuth=180,\n", - " sky_model=\"isotropic\",\n", - " temp_model=\"sapm\",\n", - " module_type=\"glass_polymer\",\n", - " x_0=6.1,\n", - " wind_speed_factor=1,\n", - ")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.13" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/tutorials_and_tools/_build/html/_sources/tutorials/5 - Weather Database Access.ipynb b/tutorials_and_tools/_build/html/_sources/tutorials/5 - Weather Database Access.ipynb deleted file mode 100644 index 742f7173b..000000000 --- a/tutorials_and_tools/_build/html/_sources/tutorials/5 - Weather Database Access.ipynb +++ /dev/null @@ -1,278 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 5 - Weather Database Access\n", - "***\n", - "2023.06.14\n", - "***\n", - "\n", - "**Requirements:**\n", - "- Internet access\n", - "- NSRDB API key. API keys are free. You can request and obtain an API key in about 5 minutes. To get your own key, visit https://developer.nrel.gov/signup/\n", - "- Step **1.** is for Eagle HPC users. You will need an account with NREL's Eagle computer for this method.\n", - "\n", - "**Objectives:**\n", - "\n", - "Using direct access to large scale weather databases, we're going to estimate the minimum standoff distance for a roof mounted PV system. We'll do this in 3 ways using both the NSRDB and PVGIS database.\n", - "1. Single Location, NSRDB via NREL's high performance computer, Eagle.\n", - "2. Single Location via NSRDB public API key.\n", - "3. Single Location via the PVGIS public database\n", - "\n", - "**Background:**\n", - "\n", - "This journal will demonstrate all existing built-in methods for directly accessing public weather databases. Some methods are restriced to certain user groups. For general users, see methods **2** and **3**. For users with an active Eagle HPC account, you may use method **1** as well as **2** and **3**.\n", - "\n", - "For all users and all steps: This journal will run significantly longer than other tutorials and have significant internet traffic as you fetch large datasets." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This example demonstrates the calculation of a minimum standoff distance necessary for roof-mounted PV modules to ensure that the $T_{98}$ operational temperature remains under 70Β°C, in which case the more rigorous thermal stability testing requirements of IEC TS 63126 would not needed to be considered. We use data from [Fuentes, 1987] to model the approximate exponential decay in temperature, $T(X)$, with increasing standoff distance, $X$, as,\n", - "\n", - "$$ X = -X_0 \\ln\\left(1-\\frac{T_0-T}{\\Delta T}\\right)$$\n", - "\n", - "where $T_0$ is the temperature for $X=0$ (insulated back) and $\\Delta T$ is the temperature difference between an insulated back ($X=0$) and open rack mounting configuration ($X=\\infty)$.\n", - "\n", - "The following figure showcases this calulation for the entire United States. We used pvlib and data from the National Solar Radiation Database (NSRDB) to calculate the module temperatures for different mounting configuration and applied our model to obtain the standoff distance for roof-mounted PV systems." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Single location example" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# if running on google colab, uncomment the next line and execute this cell to install the dependencies and prevent \"ModuleNotFoundError\" in later cells:\n", - "# !pip install pvdeg==0.3.3" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import pvdeg" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1. NSRDB - HSDS on Eagle\n", - "\n", - "This method requires a direct connection to NREL's high performance computer \"Eagle\". If you are not running this journal from Eagle, skip this section and proceed to section **2.**\n", - "\n", - "In this step:\n", - "\n", - "First we select a database. Here, we will use the NSRDB. Since we are modeling a single location, we can pass the `weather_id` as tuple (lat, long). A location gid can be used as well. 'gid' is a unique identifier to a geographic location within the NSRDB. We'll look at how to find gids later on.\n", - "\n", - "Next, we want to select a satellite, named dataset (year of data), and what weather attributes we want to fetch. For further options, see the documentation for `pvdeg.weather.get`" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'x': 3.4904744924590694, 'T98_0': 82.57377792878141, 'T98_inf': 53.716463725092396}\n" - ] - } - ], - "source": [ - "# Get weather data\n", - "weather_db = \"NSRDB\"\n", - "weather_id = (39.741931, -105.169891)\n", - "# weather_id = 1933572\n", - "weather_arg = {\n", - " \"satellite\": \"GOES\",\n", - " \"names\": 2021,\n", - " \"NREL_HPC\": True,\n", - " \"attributes\": [\n", - " \"air_temperature\",\n", - " \"wind_speed\",\n", - " \"dhi\",\n", - " \"ghi\",\n", - " \"dni\",\n", - " \"relative_humidity\",\n", - " ],\n", - "}\n", - "\n", - "weather_df, meta = pvdeg.weather.get(weather_db, weather_id, **weather_arg)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "`pvdeg.weather.get` returns the same variables as `weather.read` which we have used in each journal before this. We get a weather DataFrame and a meta-data dicitonary. Each contains a minimum of consistent fields, but may have additional fields based on the database accessed or the attributes requested.\n", - "\n", - "Lets verify the weather data we fetched by running a familiar calculation; standoff distance." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "res = pvdeg.standards.calc_standoff(\n", - " weather_df,\n", - " meta,\n", - " tilt=None,\n", - " azimuth=180,\n", - " sky_model=\"isotropic\",\n", - " temp_model=\"sapm\",\n", - " module_type=\"glass_polymer\",\n", - " level=1,\n", - " x_0=6.1,\n", - ")\n", - "print(res)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2. NSRDB - API\n", - "\n", - "To access the NREL NSRDB, you will need an API key. Key's are free, but require you to set up an account. Without an API key, you can use a demonstration API which is severely limited. To set up an account and get your API key, visit https://developer.nrel.gov/signup/\n", - "\n", - "Key Notes:\n", - "- set `attributes = []` to return all possible attributes (weather fields)\n", - "- There are 2 major methods with the API\n", - " - names = 'tmy' : generate a TMY-like weather dataframe aggregate. This will calculate the relative humidity from temperature and dew point.\n", - " - names = '2019' : collect a weather dataframe including measured relative humidity." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Column \"relative_humidity\" not found in DataFrame. Calculating...\n", - "{'x': 2.0063800910065974, 'T98_0': 77.85091991839371, 'T98_inf': 49.84149463751572}\n" - ] - } - ], - "source": [ - "API_KEY = \"your_api_key_here\"\n", - "# The example API key here is for demonstation and is rate-limited per IP.\n", - "# To get your own API key, visit https://developer.nrel.gov/signup/\n", - "\n", - "weather_db = \"PSM3\"\n", - "weather_id = (39.741931, -105.169891)\n", - "weather_arg = {\n", - " \"api_key\": \"DEMO_KEY\",\n", - " \"email\": \"user@mail.com\",\n", - " \"names\": \"tmy\",\n", - " \"attributes\": [],\n", - " \"map_variables\": True,\n", - "}\n", - "\n", - "weather_df, meta = pvdeg.weather.get(weather_db, weather_id, **weather_arg)\n", - "\n", - "# Perform calculation\n", - "res = pvdeg.standards.calc_standoff(\n", - " weather_df,\n", - " meta,\n", - " tilt=None,\n", - " azimuth=180,\n", - " sky_model=\"isotropic\",\n", - " temp_model=\"sapm\",\n", - " module_type=\"glass_polymer\",\n", - " level=1,\n", - " x_0=6.1,\n", - ")\n", - "print(res)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3. PVGIS\n", - "\n", - "This method uses the PVGIS database, a public resource. It requires no API key or user account." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'x': 2.009865008105706, 'T98_0': 77.60387767516511, 'T98_inf': 50.51554003554856}\n" - ] - } - ], - "source": [ - "weather_db = \"PVGIS\"\n", - "weather_id = (39.741931, -105.169891)\n", - "# weather_id = (48.667, 15.885)\n", - "# weather_arg = {'map_variables': True}\n", - "\n", - "# TMY\n", - "weather_df, meta = pvdeg.weather.get(weather_db, weather_id)\n", - "\n", - "# Perform calculation\n", - "res = pvdeg.standards.calc_standoff(\n", - " weather_df,\n", - " meta,\n", - " tilt=None,\n", - " azimuth=180,\n", - " sky_model=\"isotropic\",\n", - " temp_model=\"sapm\",\n", - " module_type=\"glass_polymer\",\n", - " level=1,\n", - " x_0=6.1,\n", - ")\n", - "print(res)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.13" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/tutorials_and_tools/_build/html/_sources/tutorials/ASTM Live Demo.ipynb b/tutorials_and_tools/_build/html/_sources/tutorials/ASTM Live Demo.ipynb deleted file mode 100644 index 0a7321410..000000000 --- a/tutorials_and_tools/_build/html/_sources/tutorials/ASTM Live Demo.ipynb +++ /dev/null @@ -1,337 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# ASTM Demonstration\n", - "\n", - "![PVDeg Logo](../PVD_logo.png)\n", - "\n", - "***\n", - "2023.06.28\n", - "***\n", - "\n", - "**Steps:**\n", - "1. Import weather data\n", - "2. Calculate installation standoff\n", - "3. Calculate installation standoff - with more detail\n", - "\n", - "**Background:**\n", - "\n", - "This example demonstrates the calculation of a minimum standoff distance necessary for roof-mounted PV modules to ensure that the $T_{98}$ operational temperature remains under 70Β°C, in which case the more rigorous thermal stability testing requirements of IEC TS 63126 would not needed to be considered. We use data from [Fuentes, 1987] to model the approximate exponential decay in temperature, $T(X)$, with increasing standoff distance, $X$, as,\n", - "\n", - "$$ X = -X_0 \\ln\\left(1-\\frac{T_0-T}{\\Delta T}\\right)$$\n", - "\n", - "where $T_0$ is the temperature for $X=0$ (insulated back) and $\\Delta T$ is the temperature difference between an insulated back ($X=0$) and open rack mounting configuration ($X=\\infty)$.\n", - "\n", - "The following figure showcases this calulation for the entire United States. We used pvlib and data from the National Solar Radiation Database (NSRDB) to calculate the module temperatures for different mounting configuration and applied our model to obtain the standoff distance for roof-mounted PV systems.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# if running on google colab, uncomment the next line and execute this cell to install the dependencies and prevent \"ModuleNotFoundError\" in later cells:\n", - "#!pip install pvdeg==0.1.0" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import pvlib\n", - "import pvdeg\n", - "import pandas as pd\n", - "from pvdeg import DATA_DIR\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1. Import Weather Data\n", - "\n", - "The function has two minimum requirements:\n", - "- Weather data containing (at least) DNI, DHI, GHI, Temperature, RH, Wind-Speed\n", - "- Site meta-data containing (at least) Latitude, Longitude, Time Zone\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Where to get _Free_ Solar Irradiance Data?\n", - "\n", - "There are many different sources of solar irradiance data. For your projects, these are some of the most common:\n", - "\n", - "- [NSRDB](https://maps.nrel.gov/nsrdb-viewer/) - National Solar Radiation Database. You can access data through the website for many locations accross the world, or you can use their [web API](https://developer.nrel.gov/docs/solar/nsrdb/) to download data programmatically. An \"API\" is an [\"application programming interface\"](https://en.wikipedia.org/wiki/API), and a \"web API\" is a programming interface that allows you to write code to interact with web services like the NSRDB.\n", - "\n", - "- [EPW](https://www.energy.gov/eere/buildings/downloads/energyplus-0) - Energy Plus Weather data is available for many locations accross the world. It's in its own format file ('EPW') so you can't open it easily in a spreadsheet program like Excel, but you can use [`pvlib.iotools.read_epw()`](https://pvlib-python.readthedocs.io/en/stable/reference/generated/pvlib.iotools.read_epw.html) to get it into a dataframe and use it.\n", - "\n", - "- [PVGIS](https://re.jrc.ec.europa.eu/pvg_tools/en/) - Free global weather data provided by the European Union and derived from many govermental agencies including the NSRDB. PVGIS also provides a web API. You can get PVGIS TMY data using [`pvlib.iotools.get_pvgis_tmy()`](https://pvlib-python.readthedocs.io/en/stable/reference/generated/pvlib.iotools.get_pvgis_tmy.html).\n", - "\n", - "- Perhaps another useful link: https://sam.nrel.gov/weather-data.html\n", - "\n", - "### Where else can you get historical irradiance data?\n", - "\n", - "There are several commercial providers of solar irradiance data. Data is available at different spatial and time resolutions. Each provider offers data under subscription that will provide access to irradiance (and other weather variables) via API to leverage in python.\n", - "\n", - "* [SolarAnywhere](https://www.solaranywhere.com/)\n", - "* [SolarGIS](https://solargis.com/)\n", - "* [Vaisala](https://www.vaisala.com/en)\n", - "* [Meteonorm](https://meteonorm.com/en/)\n", - "* [DNV Solar Resource Compass](https://src.dnv.com/)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "![NSRDB Example](images/tutorial_1_NSRDB_example.PNG)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### NREL API Key\n", - "At the [NREL Developer Network](https://developer.nrel.gov/), there are [APIs](https://en.wikipedia.org/wiki/API) to a lot of valuable [solar resources](https://developer.nrel.gov/docs/solar/) like [weather data from the NSRDB](https://developer.nrel.gov/docs/solar/nsrdb/), [operational data from PVDAQ](https://developer.nrel.gov/docs/solar/pvdaq-v3/), or indicative calculations using [PVWatts](https://developer.nrel.gov/docs/solar/pvwatts/). In order to use these resources from NREL, you need to [register for a free API key](https://developer.nrel.gov/signup/). You can test out the APIs using the `DEMO_KEY` but it has limited bandwidth compared to the [usage limit for registered users](https://developer.nrel.gov/docs/rate-limits/). NREL has some [API usage instructions](https://developer.nrel.gov/docs/api-key/), but pvlib has a few builtin functions, like [`pvlib.iotools.get_psm3()`](https://pvlib-python.readthedocs.io/en/stable/reference/generated/pvlib.iotools.get_psm3.html), that wrap the NREL API, and call them for you to make it much easier to use. Skip ahead to the next section to learn more. But before you do...\n", - "\n", - "**Please pause now to visit https://developer.nrel.gov/signup/ and get an API key.**\n", - "\n", - "### Application Programming Interface (API)\n", - "What exactly is an API? Nowadays, the phrase is used interchangeably with a \"web API\" but in general an API is just a recipe for how to interface with a application programmatically, _IE_: in code. An API could be as simple as a function signature or its published documentation, _EG_: the API for the `solarposition` function is you give it an ISO8601 formatted date with a timezone, the latitude, longitude, and elevation as numbers, and it returns the zenith and azimuth as numbers.\n", - "\n", - "A web API is the same, except the application is a web service, that you access at its URL using web methods. We won't go into too much more detail here, but the most common web method is `GET` which is pretty self explanatory. Look over the [NREL web usage instructions](https://developer.nrel.gov/docs/api-key/) for some examples, but interacting with a web API can be as easy as entering a URL into a browser. Try the URL below to _get_ the PVWatts energy output for a fixed tilt site in [Broomfield, CO](https://goo.gl/maps/awkEcNGzSur9Has18).\n", - "\n", - "https://developer.nrel.gov/api/pvwatts/v6.json?api_key=DEMO_KEY&lat=40&lon=-105&system_capacity=4&azimuth=180&tilt=40&array_type=1&module_type=1&losses=10\n", - "\n", - "In addition to just using your browser, you can also access web APIs programmatically. The most popular Python package to interact with web APIs is [requests](https://docs.python-requests.org/en/master/). There's also free open source command-line tools like [cURL](https://curl.se/) and [HTTPie](https://httpie.io/), and a popular nagware/freemium GUI application called [Postman](https://www.postman.com/).\n", - "\n", - "**If you have an NREL API key please enter it in the next cell.**" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "NREL_API_KEY = None # <-- please set your NREL API key here\n", - "\n", - "# note you must use \"quotes\" around your key, for example:\n", - "# NREL_API_KEY = 'DEMO_KEY' # single or double both work fine\n", - "\n", - "# during the live tutorial, we've stored a dedicated key on our server\n", - "if NREL_API_KEY is None:\n", - " try:\n", - " NREL_API_KEY = os.environ[\n", - " \"NREL_API_KEY\"\n", - " ] # get dedicated key for tutorial from servier\n", - " except KeyError:\n", - " NREL_API_KEY = \"DEMO_KEY\" # OK for this demo, but better to get your own key" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Fetching TMYs from the NSRDB\n", - "\n", - "The NSRDB, one of many sources of weather data intended for PV modeling, is free and easy to access using pvlib. As an example, we'll fetch a TMY dataset for Phoenix, AZ at coordinates [(33.4484, -112.0740)](https://goo.gl/maps/hGV92QHCm5FHJKbf9). \n", - "\n", - "This function uses [`pvdeg.weather.get()`](https://pvdegradationtools.readthedocs.io/en/latest/_autosummary/pvdeg.weather.html#pvdeg.weather.get), which returns a Python dictionary of metadata and a Pandas dataframe of the timeseries weather data.\n", - "\n", - "This function internally leverages [`pvlib.iotools.get_psm3()`](https://pvlib-python.readthedocs.io/en/stable/reference/generated/pvlib.iotools.get_psm3.html). However, for some of the NSRDB data relative humidity is not a given parameter, and `pvdeg` calculates the values from the downloaded data as an internal processing step. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# This cell does not run on a Collab instal. WE are troubleshooting it, but the next cell performs the same request directly with PVLib.\n", - "# If TMY is requested though, relative_humidity will not be included as NSRDB TMY dataset does not have it.\n", - "\"\"\"\n", - "weather_db = 'PSM3'\n", - "weather_id = (33.4484, -112.0740)\n", - "weather_arg = {'api_key': NREL_API_KEY,\n", - " 'email': 'user@mail.com',\n", - " 'names': '2021', # tmy is another common option; right now requesting only 2021 data\n", - " 'attributes': [], # grabs all. to select, could be 'air_temperature', 'dew_point', 'dhi',\n", - " # 'dni', 'ghi', 'surface_albedo', 'surface_pressure', 'wind_direction', 'wind_speed'\n", - " 'map_variables': True,\n", - " 'leap_day': False}\n", - "\n", - "weather_df, meta = pvdeg.weather.get(weather_db, weather_id, **weather_arg)\n", - "\"\"\";" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "weather_df, meta = pvlib.iotools.get_psm3(\n", - " latitude=33.4484,\n", - " longitude=-112.0740,\n", - " api_key=NREL_API_KEY,\n", - " email=\"silvana.ovaitt@nrel.gov\", # <-- any email works here fine\n", - " names=\"2020\",\n", - " map_variables=True,\n", - " attributes=[],\n", - " leap_day=False,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "meta" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "weather_df.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, ax1 = plt.subplots(figsize=(9, 6))\n", - "# Instantiate a second axes that shares the same x-axis\n", - "ax1.plot(\n", - " weather_df.loc[\"2021-06-28 05:00:00-07:00\":\"2021-06-28 20:00:00-07:00\"][\n", - " \"dni_clear\"\n", - " ],\n", - " label=\"DNI\",\n", - ")\n", - "ax2 = ax1.twinx()\n", - "ax2.plot(\n", - " weather_df.loc[\"2021-06-28 05:00:00-07:00\":\"2021-06-28 20:00:00-07:00\"][\"temp_air\"],\n", - " \"r\",\n", - " label=\"Temperature\",\n", - ")\n", - "ax1.set_ylim([0, 1000])\n", - "ax2.set_ylim([0, 50])\n", - "ax1.set_ylabel(\"DNI\")\n", - "ax2.set_ylabel(\"Temperature $\\degree$C\");" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2. Calculate Installation Standoff - Level 1\n", - "\n", - "We use [`pvlib.standards.calc_standoff()`](https://pvdegradationtools.readthedocs.io/en/latest/_autosummary/pvdeg.standards.html#pvdeg.standards.calc_standoff) which takes at minimum the weather data and metadata, and returns the minimum installation distance in centimeters.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "standoff = pvdeg.standards.calc_standoff(weather_df=weather_df, meta=meta)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(\"Minimum installation distance:\", standoff[\"x\"])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3. Calculate Installation Standoff - Level 2\n", - "\n", - "Let's take a closer look at the function and some optional parameters.\n", - "\n", - "- level : 1 or 2 (see IEC TS 63216)\n", - "- tilt and azimuth : tilt from horizontal of PV module and azimuth in degrees from North\n", - "- sky_model : pvlib compatible model for generating sky characteristics (Options: 'isotropic', 'klucher', 'haydavies', 'reindl', 'king', 'perez')\n", - "- temp_model : pvlib compatible module temperature model (Options: 'sapm', 'pvsyst', 'faiman', 'sandia')\n", - "- module_type : basic module construction (Options: 'glass_polymer', 'glass_glass')\n", - "- x_0 : thermal decay constant [cm] (see documentation)\n", - "- wind_speed_factor : Wind speed correction factor to account for different wind speed measurement heights between weather database (e.g. NSRDB) and the tempeature model (e.g. SAPM)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "standoff = pvdeg.standards.calc_standoff(\n", - " weather_df=weather_df,\n", - " meta=meta,\n", - " level=2,\n", - " tilt=None,\n", - " azimuth=180,\n", - " sky_model=\"isotropic\",\n", - " temp_model=\"sapm\",\n", - " module_type=\"glass_polymer\",\n", - " x_0=6.1,\n", - " wind_speed_factor=1,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(\"Minimum installation distance:\", standoff[\"x\"])" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.13" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/tutorials_and_tools/_build/html/_sources/tutorials/B-O LID - Accelerated Test.ipynb b/tutorials_and_tools/_build/html/_sources/tutorials/B-O LID - Accelerated Test.ipynb deleted file mode 100644 index cdb7be1b6..000000000 --- a/tutorials_and_tools/_build/html/_sources/tutorials/B-O LID - Accelerated Test.ipynb +++ /dev/null @@ -1,769 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# B-O LID - Accelerated Test\n", - "\n", - "### Example: Boron-oxygen light-induced degradation (B-O LID) progression in accelerated tests\n", - "Author: Joe Karas | joseph.karas@nrel.gov\n", - "\n", - "2023\n", - "***\n", - "\n", - "This library can also be used to model B-O LID, as the defect states and transitions can be modeled in the same way as LETID. See kinetic_parameters.json for B-O LID kinetic parameters used in this example.\n", - "\n", - "In this example, we will model B-O LID progression in a test similar to [IEC 61215 MQT 19.1](https://webstore.iec.ch/publication/61215) IEC 61215 MQT 19.1, which prescribes $\\ge $ 10 $kWh/m^2$ of 1-sun illumination with maximum power point tracking at $50\\degree C$.\n", - "\n", - "**Requirements:**\n", - "- `pandas`, `numpy`, `matplotlib`\n", - "\n", - "**Objectives:**\n", - "1. Define necessary solar cell device parameters\n", - "2. Define necessary degradation parameters: degraded lifetime and defect states, this time using B-O LID kinetics\n", - "3. Create timeseries of temperature and current injection\n", - "4. Run through timeseries, calculating defect states\n", - "5. Calculate device degradation and plot\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# if running on google colab, uncomment the next line and execute this cell to install the dependencies and prevent \"ModuleNotFoundError\" in later cells:\n", - "# !pip install pvdeg==0.3.3" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "from pvdeg import letid, collection, utilities, DATA_DIR\n", - "\n", - "import os\n", - "import pandas as pd\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Device parameters\n", - "To define a device, we need to define several important quantities about the device: wafer thickness (in $\\mu m$), rear surface recombination velocity (in cm/s), and cell area (in cm2)." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "wafer_thickness = 180 # um\n", - "s_rear = 46 # cm/s\n", - "cell_area = 243 # cm^2" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### Other device parameters\n", - "Other required device parameters: base diffusivity (in $cm^2/s$), and optical generation profile, which allow us to estimate current collection in the device." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "generation_df = pd.read_excel(\n", - " os.path.join(DATA_DIR, \"PVL_GenProfile.xlsx\"), header=0\n", - ") # this is an optical generation profile generated by PVLighthouse's OPAL2 default model for 1-sun, normal incident AM1.5 sunlight on a 180-um thick SiNx-coated, pyramid-textured wafer.\n", - "generation = generation_df[\"Generation (cm-3s-1)\"]\n", - "depth = generation_df[\"Depth (um)\"]\n", - "\n", - "d_base = 27 # cm^2/s electron diffusivity. See https://www2.pvlighthouse.com.au/calculators/mobility%20calculator/mobility%20calculator.aspx for details" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Degradation parameters\n", - "To model the device's degradation, we need to define several more important quantities about the degradation the device will experience. These include undegraded and degraded lifetime (in $\\mu s$)." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "tau_0 = 115 # us, carrier lifetime in non-degraded states, e.g. LETID/LID states A or C\n", - "tau_deg = 55 # us, carrier lifetime in fully-degraded state, e.g. LETID/LID state B" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Let's see how much maximum power degradation these parameters will result in:" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(0.03495240552145925, 5.6638736096459965, 5.4659076024193585)" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "letid.calc_pmp_loss_from_tau_loss(\n", - " tau_0, tau_deg, cell_area, wafer_thickness, s_rear\n", - ") # returns % power loss, pmp_0, pmp_deg" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### Remaining degradation parameters:\n", - "The rest of the quantities to define are: the initial percentage of defects in each state (A, B, and C), and the dictionary of mechanism parameters.\n", - "\n", - "In this example, we'll assume the device starts in the fully-undegraded state (100% state A), and we'll use the parameters for B-O LID" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# starting defect state percentages\n", - "nA_0 = 100\n", - "nB_0 = 0\n", - "nC_0 = 0" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "('Choose a set of kinetic parameters:',\n", - " ['D037',\n", - " 'D038',\n", - " 'D039',\n", - " 'D040',\n", - " 'D050',\n", - " 'D051',\n", - " 'D052',\n", - " 'D053',\n", - " 'D054',\n", - " 'D055'])" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Here's a list of the possible sets of kinetic parameters from kinetic_parameters.json:\n", - "utilities.get_kinetics()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'mechanism': 'BO-LID', 'v_ab': 4000.0, 'v_ba': 10000000000000.0, 'v_bc': 12500000000.0, 'v_cb': 532000.0, 'ea_ab': 0.475, 'ea_ba': 1.32, 'ea_bc': 0.98, 'ea_cb': 0.87, 'suns_ab': 0.1, 'suns_bc': 2.7, 'temperature_ab': 400, 'temperature_bc': 434, 'tau_ab': 140, 'tau_bc': 165, 'x_ab': 0, 'x_ba': 1, 'x_bc': 0, 'structure_ab': 'wafer', 'structure_bc': 'wafer', 'thickness_ab': 200, 'thickness_bc': 200, 'srv_ab': 0, 'srv_bc': 0, 'comments': ''}\n" - ] - } - ], - "source": [ - "mechanism_params = utilities.get_kinetics(\"bo-lid\")\n", - "print(mechanism_params)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Set up timeseries\n", - "In this example, we are going to model test with constant temperature and injection. IEC 61215 MQT 19.1 prescribes 10 $kWh/m^2$ of 1-sun illumination (i.e., 10 hours of 1-sun) with maximum power point tracking at $50\\degree C$. For most typical c-Si modules, MPP injection is roughly $I_{sc}-I_{mp}$, or roughly equal to $0.05\\times I_{sc}$. So we will set injection equal to 0.05 \"suns\" of injection. \n", - "\n", - "We will create a pandas datetime series and calculate the changes in defect states for each timestep. As B-O LID can initially proceed quickly, we will create a timeseries with 1-second intervals for the first 10 minutes, then proceed with 1-minute intervals" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "temperature = 50 # degrees celsius\n", - "suns = 0.05 # \"suns\" of injection, e.g 1-sun illumination at open circuit would be 1; dark current injection is given as a fraction of Isc, e.g., injecting Isc would be 1. For this example we assume injection is 0.05*Isc.\n", - "\n", - "timesteps_initial = pd.date_range(\n", - " start=\"2022-01-01 00:00:00\", end=\"2022-01-01 00:10:00\", freq=\"S\"\n", - ") # 10 minutes of 1-second interval timesteps. In general, we should select small timesteps unless we are sure defect reactions are proceeding very slowly\n", - "timesteps = pd.date_range(\n", - " start=\"2022-01-01 00:10:00\", end=\"2022-01-01 10:00:00\", freq=\"min\"\n", - ") # a total of 10 hours of exposure\n", - "timesteps = pd.DataFrame(timesteps, columns=[\"Datetime\"])\n", - "timesteps_initial = pd.DataFrame(timesteps_initial, columns=[\"Datetime\"])\n", - "\n", - "timesteps = pd.concat([timesteps_initial, timesteps]) # concatenate the two time series\n", - "timesteps = timesteps.sort_values(by=\"Datetime\")\n", - "timesteps.reset_index(inplace=True, drop=True)\n", - "\n", - "temps = np.full(len(timesteps), temperature)\n", - "injection = np.full(len(timesteps), suns)\n", - "\n", - "timesteps[\"Temperature\"] = temps\n", - "timesteps[\"Injection\"] = injection\n", - "\n", - "timesteps[[\"NA\", \"NB\", \"NC\", \"tau\"]] = (\n", - " np.nan\n", - ") # create columns for defect state percentages and lifetime, fill with NaNs for now, to fill iteratively below\n", - "\n", - "timesteps.loc[0, [\"NA\", \"NB\", \"NC\"]] = (\n", - " nA_0,\n", - " nB_0,\n", - " nC_0,\n", - ") # assign first timestep defect state percentages\n", - "timesteps.loc[0, \"tau\"] = letid.tau_now(\n", - " tau_0, tau_deg, nB_0\n", - ") # calculate tau for the first timestep" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "for index, timestep in timesteps.iterrows():\n", - "\n", - " # first row tau has already been assigned\n", - " if index == 0:\n", - " pass\n", - "\n", - " # loop through rows, new tau calculated based on previous NB. Reaction proceeds based on new tau.\n", - " else:\n", - " n_A = timesteps.at[index - 1, \"NA\"]\n", - " n_B = timesteps.at[index - 1, \"NB\"]\n", - " n_C = timesteps.at[index - 1, \"NC\"]\n", - "\n", - " tau = letid.tau_now(tau_0, tau_deg, n_B)\n", - " jsc = collection.calculate_jsc_from_tau_cp(\n", - " tau, wafer_thickness, d_base, s_rear, generation, depth\n", - " )\n", - "\n", - " temperature = timesteps.at[index, \"Temperature\"]\n", - " injection = timesteps.at[index, \"Injection\"]\n", - "\n", - " # calculate defect reaction kinetics: reaction constant and carrier concentration factor.\n", - " k_AB = letid.k_ij(\n", - " mechanism_params[\"v_ab\"], mechanism_params[\"ea_ab\"], temperature\n", - " )\n", - " k_BA = letid.k_ij(\n", - " mechanism_params[\"v_ba\"], mechanism_params[\"ea_ba\"], temperature\n", - " )\n", - " k_BC = letid.k_ij(\n", - " mechanism_params[\"v_bc\"], mechanism_params[\"ea_bc\"], temperature\n", - " )\n", - " k_CB = letid.k_ij(\n", - " mechanism_params[\"v_cb\"], mechanism_params[\"ea_cb\"], temperature\n", - " )\n", - "\n", - " x_ab = letid.carrier_factor(\n", - " tau,\n", - " \"ab\",\n", - " temperature,\n", - " injection,\n", - " jsc,\n", - " wafer_thickness,\n", - " s_rear,\n", - " mechanism_params,\n", - " )\n", - " x_ba = letid.carrier_factor(\n", - " tau,\n", - " \"ba\",\n", - " temperature,\n", - " injection,\n", - " jsc,\n", - " wafer_thickness,\n", - " s_rear,\n", - " mechanism_params,\n", - " )\n", - " x_bc = letid.carrier_factor(\n", - " tau,\n", - " \"bc\",\n", - " temperature,\n", - " injection,\n", - " jsc,\n", - " wafer_thickness,\n", - " s_rear,\n", - " mechanism_params,\n", - " )\n", - "\n", - " # calculate the instantaneous change in NA, NB, and NC\n", - " dN_Adt = (k_BA * n_B * x_ba) - (k_AB * n_A * x_ab)\n", - " dN_Bdt = (\n", - " (k_AB * n_A * x_ab) + (k_CB * n_C) - ((k_BA * x_ba + k_BC * x_bc) * n_B)\n", - " )\n", - " dN_Cdt = (k_BC * n_B * x_bc) - (k_CB * n_C)\n", - "\n", - " t_step = (\n", - " timesteps.at[index, \"Datetime\"] - timesteps.at[index - 1, \"Datetime\"]\n", - " ).total_seconds()\n", - "\n", - " # assign new defect state percentages\n", - " timesteps.at[index, \"NA\"] = n_A + dN_Adt * t_step\n", - " timesteps.at[index, \"NB\"] = n_B + dN_Bdt * t_step\n", - " timesteps.at[index, \"NC\"] = n_C + dN_Cdt * t_step" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Finish calculating degraded device parameters.\n", - "Now that we have calculated defect states, we can calculate all the quantities that depend on defect states." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "timesteps[\"tau\"] = letid.tau_now(tau_0, tau_deg, timesteps[\"NB\"])\n", - "\n", - "# calculate device Jsc for every timestep. Unfortunately this requires an integration so I think we have to run through a loop. Device Jsc allows calculation of device Voc.\n", - "for index, timestep in timesteps.iterrows():\n", - " jsc_now = collection.calculate_jsc_from_tau_cp(\n", - " timesteps.at[index, \"tau\"], wafer_thickness, d_base, s_rear, generation, depth\n", - " )\n", - " timesteps.at[index, \"Jsc\"] = jsc_now\n", - " timesteps.at[index, \"Voc\"] = letid.calc_voc_from_tau(\n", - " timesteps.at[index, \"tau\"], wafer_thickness, s_rear, jsc_now, temperature=25\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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DatetimeTemperatureInjectionNANBNCtauJscVocIscFFPmpPmp_normtime (days)
02022-01-01 00:00:00500.05100.0000000.0000000.000000e+00115.00000041.5938610.66632910.1073080.8409875.6638741.0000000.000000
12022-01-01 00:00:01500.0599.9843660.0156340.000000e+00114.98039041.5938000.66632610.1072930.8409865.6638330.9999930.000012
22022-01-01 00:00:02500.0599.9687350.0312651.016487e-07114.96079041.5937390.66632210.1072790.8409865.6637930.9999860.000023
32022-01-01 00:00:03500.0599.9531060.0468933.049294e-07114.94120041.5936780.66631910.1072640.8409855.6637520.9999790.000035
42022-01-01 00:00:04500.0599.9374800.0625206.098257e-07114.92162041.5936170.66631610.1072490.8409855.6637120.9999710.000046
.............................................
11872022-01-01 09:56:00500.050.36423282.3135551.732221e+0160.59117941.2833500.65310410.0318540.8386275.4945580.9701060.413889
11882022-01-01 09:57:00500.050.36082082.2848701.735431e+0160.60117041.2834550.65310810.0318790.8386285.4946060.9701150.414583
11892022-01-01 09:58:00500.050.35744082.2561641.738640e+0160.61117241.2835590.65311210.0319050.8386285.4946540.9701230.415278
11902022-01-01 09:59:00500.050.35409282.2274381.741847e+0160.62118541.2836640.65311510.0319300.8386295.4947020.9701320.415972
11912022-01-01 10:00:00500.050.35077682.1986911.745053e+0160.63120841.2837690.65311910.0319560.8386305.4947500.9701400.416667
\n", - "

1192 rows Γ— 14 columns

\n", - "
" - ], - "text/plain": [ - " Datetime Temperature Injection NA NB \n", - "0 2022-01-01 00:00:00 50 0.05 100.000000 0.000000 \\\n", - "1 2022-01-01 00:00:01 50 0.05 99.984366 0.015634 \n", - "2 2022-01-01 00:00:02 50 0.05 99.968735 0.031265 \n", - "3 2022-01-01 00:00:03 50 0.05 99.953106 0.046893 \n", - "4 2022-01-01 00:00:04 50 0.05 99.937480 0.062520 \n", - "... ... ... ... ... ... \n", - "1187 2022-01-01 09:56:00 50 0.05 0.364232 82.313555 \n", - "1188 2022-01-01 09:57:00 50 0.05 0.360820 82.284870 \n", - "1189 2022-01-01 09:58:00 50 0.05 0.357440 82.256164 \n", - "1190 2022-01-01 09:59:00 50 0.05 0.354092 82.227438 \n", - "1191 2022-01-01 10:00:00 50 0.05 0.350776 82.198691 \n", - "\n", - " NC tau Jsc Voc Isc FF \n", - "0 0.000000e+00 115.000000 41.593861 0.666329 10.107308 0.840987 \\\n", - "1 0.000000e+00 114.980390 41.593800 0.666326 10.107293 0.840986 \n", - "2 1.016487e-07 114.960790 41.593739 0.666322 10.107279 0.840986 \n", - "3 3.049294e-07 114.941200 41.593678 0.666319 10.107264 0.840985 \n", - "4 6.098257e-07 114.921620 41.593617 0.666316 10.107249 0.840985 \n", - "... ... ... ... ... ... ... \n", - "1187 1.732221e+01 60.591179 41.283350 0.653104 10.031854 0.838627 \n", - "1188 1.735431e+01 60.601170 41.283455 0.653108 10.031879 0.838628 \n", - "1189 1.738640e+01 60.611172 41.283559 0.653112 10.031905 0.838628 \n", - "1190 1.741847e+01 60.621185 41.283664 0.653115 10.031930 0.838629 \n", - "1191 1.745053e+01 60.631208 41.283769 0.653119 10.031956 0.838630 \n", - "\n", - " Pmp Pmp_norm time (days) \n", - "0 5.663874 1.000000 0.000000 \n", - "1 5.663833 0.999993 0.000012 \n", - "2 5.663793 0.999986 0.000023 \n", - "3 5.663752 0.999979 0.000035 \n", - "4 5.663712 0.999971 0.000046 \n", - "... ... ... ... \n", - "1187 5.494558 0.970106 0.413889 \n", - "1188 5.494606 0.970115 0.414583 \n", - "1189 5.494654 0.970123 0.415278 \n", - "1190 5.494702 0.970132 0.415972 \n", - "1191 5.494750 0.970140 0.416667 \n", - "\n", - "[1192 rows x 14 columns]" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# this function quickly calculates the rest of the device parameters: Isc, FF, max power, and normalized max power\n", - "timesteps = letid.calc_device_params(timesteps, cell_area=243)\n", - "timesteps[\"time (days)\"] = (\n", - " timesteps[\"Datetime\"] - timesteps.iloc[0][\"Datetime\"]\n", - ").dt.total_seconds() / 86400 # create a column for days elapsed\n", - "\n", - "timesteps" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plot the results\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from cycler import cycler\n", - "\n", - "plt.style.use(\"default\")\n", - "\n", - "fig, ax = plt.subplots()\n", - "\n", - "ax.set_prop_cycle(\n", - " cycler(\"color\", [\"tab:blue\", \"tab:orange\", \"tab:green\"])\n", - " + cycler(\"linestyle\", [\"-\", \"--\", \"-.\"])\n", - ")\n", - "\n", - "ax.plot(timesteps[\"time (days)\"], timesteps[[\"NA\", \"NB\", \"NC\"]].values)\n", - "ax.legend(labels=[\"$N_A$\", \"$N_B$\", \"$N_C$\"], loc=\"upper left\")\n", - "ax.set_ylabel(\"Defect state percentages [%]\")\n", - "ax.set_xlabel(\"Time [days]\")\n", - "\n", - "ax2 = ax.twinx()\n", - "ax2.plot(\n", - " timesteps[\"time (days)\"],\n", - " timesteps[\"Pmp_norm\"],\n", - " c=\"black\",\n", - " label=\"Normalized $P_{MP}$\",\n", - ")\n", - "ax2.legend(loc=\"upper right\")\n", - "ax2.set_ylabel(\"Normalized $P_{MP}$\")\n", - "\n", - "ax.set_title(\n", - " \"Accelerated B-O LID Test\\n\"\n", - " rf\"{temperature}$\\degree$C, {suns}$\\times I_{{SC}}$ injection\"\n", - ")\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.13" - }, - "vscode": { - "interpreter": { - "hash": "848658e0671c41dd18b216771b1713479db7d685859cbb6c795b270024b1888c" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/tutorials_and_tools/_build/html/_sources/tutorials/LETID - Accelerated Test.ipynb b/tutorials_and_tools/_build/html/_sources/tutorials/LETID - Accelerated Test.ipynb deleted file mode 100644 index bad117c48..000000000 --- a/tutorials_and_tools/_build/html/_sources/tutorials/LETID - Accelerated Test.ipynb +++ /dev/null @@ -1,1258 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# LETID - Accelerated Tests\n", - "\n", - "Author: Joe Karas | joseph.karas@nrel.gov\n", - "\n", - "2023\n", - "***\n", - "\n", - "Several standardized accelerated tests have been developed for LETID. These include [IEC TS 63342](https://webstore.iec.ch/publication/67332) for c-Si photovoltaic modules, and [IEC TS 63202-4](https://webstore.iec.ch/publication/67612) for c-Si photovoltaic cells.\n", - "Both procedures essentially prescribe exposure to constant light or current injection at constant elevated temperature for a prescribed duration of time. This notebook demonstrates how to use this library to model device behavior in such a procedure.\n", - "\n", - "**Requirements:**\n", - "- `pandas`, `numpy`, `matplotlib`\n", - "\n", - "**Objectives:**\n", - "1. Define necessary solar cell device parameters\n", - "2. Define necessary degradation parameters: degraded lifetime and defect states\n", - "3. Create timeseries of temperature and current injection\n", - "4. Run through timeseries, calculating defect states\n", - "5. Calculate device degradation and plot\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Device parameters\n", - "To define a device, we need to define several important quantities about the device: wafer thickness (in $\\mu m$), rear surface recombination velocity (in cm/s), and cell area (in cm2)." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# if running on google colab, uncomment the next line and execute this cell to install the dependencies and prevent \"ModuleNotFoundError\" in later cells:\n", - "# !pip install pvdeg==0.3.3" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "from pvdeg import letid, collection, utilities, DATA_DIR\n", - "\n", - "import os\n", - "import pandas as pd\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "wafer_thickness = 180 # um\n", - "s_rear = 46 # cm/s\n", - "cell_area = 243 # cm^2" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### Other device parameters\n", - "Other required device parameters: base diffusivity (in $cm^2/s$), and optical generation profile, which allow us to estimate current collection in the device." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "generation_df = pd.read_excel(\n", - " os.path.join(DATA_DIR, \"PVL_GenProfile.xlsx\"), header=0\n", - ") # this is an optical generation profile generated by PVLighthouse's OPAL2 default model for 1-sun, normal incident AM1.5 sunlight on a 180-um thick SiNx-coated, pyramid-textured wafer.\n", - "generation = generation_df[\"Generation (cm-3s-1)\"]\n", - "depth = generation_df[\"Depth (um)\"]\n", - "\n", - "d_base = 27 # cm^2/s electron diffusivity. See https://www2.pvlighthouse.com.au/calculators/mobility%20calculator/mobility%20calculator.aspx for details" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Degradation parameters\n", - "To model the device's degradation, we need to define several more important quantities about the degradation the device will experience. These include undegraded and degraded lifetime (in $\\mu s$)." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "tau_0 = 115 # us, carrier lifetime in non-degraded states, e.g. LETID/LID states A or C\n", - "tau_deg = 55 # us, carrier lifetime in fully-degraded state, e.g. LETID/LID state B" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Let's see how much maximum power degradation these parameters will result in:" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.03572222214916342\n" - ] - } - ], - "source": [ - "loss, pmp_0, pmp_deg = letid.calc_pmp_loss_from_tau_loss(\n", - " tau_0, tau_deg, cell_area, wafer_thickness, s_rear\n", - ") # returns % power loss, pmp_0, pmp_deg\n", - "print(loss)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### Check to see the device's current collection" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "38.681200110979546\n" - ] - } - ], - "source": [ - "jsc_0 = collection.calculate_jsc_from_tau_cp(\n", - " tau_0, wafer_thickness, d_base, s_rear, generation, depth\n", - ") # returns short-circuit current (Jsc) in mA/cm^2 given required cell parameters\n", - "print(jsc_0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### Remaining degradation parameters:\n", - "The rest of the quantities to define are: the initial percentage of defects in each state (A, B, and C), and the dictionary of mechanism parameters.\n", - "\n", - "In this example, we'll assume the device starts in the fully-undegraded state (100% state A), and we'll use the parameters for LETID degradation from Repins." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# starting defect state percentages\n", - "nA_0 = 100\n", - "nB_0 = 0\n", - "nC_0 = 0" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "('Choose a set of kinetic parameters:',\n", - " ['D037',\n", - " 'D038',\n", - " 'D039',\n", - " 'D040',\n", - " 'D050',\n", - " 'D051',\n", - " 'D052',\n", - " 'D053',\n", - " 'D054',\n", - " 'D055'])" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Here's a list of the possible sets of kinetic parameters from kinetic_parameters.json:\n", - "utilities.get_kinetics()" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'mechanism': 'LETID', 'v_ab': 46700000.0, 'v_ba': 4.7e-25, 'v_bc': 19900000.0, 'v_cb': 0.0, 'ea_ab': 0.827, 'ea_ba': -1.15, 'ea_bc': 0.871, 'ea_cb': 0.0, 'suns_ab': 1.0, 'suns_bc': 1.0, 'temperature_ab': 410, 'temperature_bc': 410, 'tau_ab': 75, 'tau_bc': 75, 'x_ab': 1, 'x_ba': 1.7, 'x_bc': 1.2, 'structure_ab': 'cell', 'structure_bc': 'cell', 'thickness_ab': 200, 'thickness_bc': 200, 'srv_ab': 90, 'srv_bc': 90, 'doi': 'doi:10.1557/s43577-022-00438-8', 'comments': ''}\n" - ] - } - ], - "source": [ - "mechanism_params = utilities.get_kinetics(\"repins\")\n", - "print(mechanism_params)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Set up timeseries\n", - "In this example, we are going to model test with constant temperature and current injection. IEC TS 63342 prescribes two to three weeks of injection equivalent to $2\\times(I_{sc}-I_{mp})$, at $75\\degree C$. For most typical c-Si modules, $2\\times(I_{sc}-I_{mp})$ is roughly equal to $0.1\\times I_{sc}$. So we will set injection equal to 0.1 \"suns\" of injection. \n", - "\n", - "We will create a pandas datetime series and calculate the changes in defect states for each timestep." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "temperature = 75 # degrees celsius\n", - "suns = 0.1 # \"suns\" of injection, e.g 1-sun illumination at open circuit would be 1; dark current injection is given as a fraction of Isc, e.g., injecting Isc would be 1. For this example we assume injection is 0.1*Isc.\n", - "\n", - "duration = \"3W\"\n", - "freq = \"min\"\n", - "start = \"2022-01-01\"\n", - "\n", - "# default is 3 weeks of 1-minute interval timesteps. In general, we should select small timesteps unless we are sure defect reactions are proceeding very slowly\n", - "timesteps = pd.date_range(\n", - " start, end=pd.to_datetime(start) + pd.to_timedelta(duration), freq=freq\n", - ")\n", - "timesteps = pd.DataFrame(timesteps, columns=[\"Datetime\"])\n", - "\n", - "\n", - "temps = np.full(len(timesteps), temperature)\n", - "injection = np.full(len(timesteps), suns)\n", - "\n", - "timesteps[\"Temperature\"] = temps\n", - "timesteps[\"Injection\"] = injection\n", - "\n", - "timesteps[[\"NA\", \"NB\", \"NC\", \"tau\"]] = (\n", - " np.nan\n", - ") # create columns for defect state percentages and lifetime, fill with NaNs for now, to fill iteratively below\n", - "\n", - "timesteps.loc[0, [\"NA\", \"NB\", \"NC\"]] = (\n", - " nA_0,\n", - " nB_0,\n", - " nC_0,\n", - ") # assign first timestep defect state percentages\n", - "timesteps.loc[0, \"tau\"] = letid.tau_now(\n", - " tau_0, tau_deg, nB_0\n", - ") # calculate tau for the first timestep" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " Datetime Temperature Injection NA NB NC tau\n", - "0 2022-01-01 00:00:00 75 0.1 100.0 0.0 0.0 115.0\n", - "1 2022-01-01 00:01:00 75 0.1 NaN NaN NaN NaN\n", - "2 2022-01-01 00:02:00 75 0.1 NaN NaN NaN NaN\n", - "3 2022-01-01 00:03:00 75 0.1 NaN NaN NaN NaN\n", - "4 2022-01-01 00:04:00 75 0.1 NaN NaN NaN NaN\n", - "... ... ... ... ... ... ... ...\n", - "30236 2022-01-21 23:56:00 75 0.1 NaN NaN NaN NaN\n", - "30237 2022-01-21 23:57:00 75 0.1 NaN NaN NaN NaN\n", - "30238 2022-01-21 23:58:00 75 0.1 NaN NaN NaN NaN\n", - "30239 2022-01-21 23:59:00 75 0.1 NaN NaN NaN NaN\n", - "30240 2022-01-22 00:00:00 75 0.1 NaN NaN NaN NaN\n", - "\n", - "[30241 rows x 7 columns]" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "timesteps" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Run through timesteps\n", - "Since each timestep depends on the preceding timestep, we need to calculate in a loop. This will take a few minutes depending on the length of the timeseries." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "for index, timestep in timesteps.iterrows():\n", - "\n", - " # first row tau has already been assigned\n", - " if index == 0:\n", - " pass\n", - "\n", - " # loop through rows, new tau calculated based on previous NB. Reaction proceeds based on new tau.\n", - " else:\n", - " n_A = timesteps.at[index - 1, \"NA\"]\n", - " n_B = timesteps.at[index - 1, \"NB\"]\n", - " n_C = timesteps.at[index - 1, \"NC\"]\n", - "\n", - " tau = letid.tau_now(tau_0, tau_deg, n_B)\n", - " jsc = collection.calculate_jsc_from_tau_cp(\n", - " tau, wafer_thickness, d_base, s_rear, generation, depth\n", - " )\n", - "\n", - " temperature = timesteps.at[index, \"Temperature\"]\n", - " injection = timesteps.at[index, \"Injection\"]\n", - "\n", - " # calculate defect reaction kinetics: reaction constant and carrier concentration factor.\n", - " k_AB = letid.k_ij(\n", - " mechanism_params[\"v_ab\"], mechanism_params[\"ea_ab\"], temperature\n", - " )\n", - " k_BA = letid.k_ij(\n", - " mechanism_params[\"v_ba\"], mechanism_params[\"ea_ba\"], temperature\n", - " )\n", - " k_BC = letid.k_ij(\n", - " mechanism_params[\"v_bc\"], mechanism_params[\"ea_bc\"], temperature\n", - " )\n", - " k_CB = letid.k_ij(\n", - " mechanism_params[\"v_cb\"], mechanism_params[\"ea_cb\"], temperature\n", - " )\n", - "\n", - " x_ab = letid.carrier_factor(\n", - " tau,\n", - " \"ab\",\n", - " temperature,\n", - " injection,\n", - " jsc,\n", - " wafer_thickness,\n", - " s_rear,\n", - " mechanism_params,\n", - " )\n", - " x_ba = letid.carrier_factor(\n", - " tau,\n", - " \"ba\",\n", - " temperature,\n", - " injection,\n", - " jsc,\n", - " wafer_thickness,\n", - " s_rear,\n", - " mechanism_params,\n", - " )\n", - " x_bc = letid.carrier_factor(\n", - " tau,\n", - " \"bc\",\n", - " temperature,\n", - " injection,\n", - " jsc,\n", - " wafer_thickness,\n", - " s_rear,\n", - " mechanism_params,\n", - " )\n", - "\n", - " # calculate the instantaneous change in NA, NB, and NC\n", - " dN_Adt = (k_BA * n_B * x_ba) - (k_AB * n_A * x_ab)\n", - " dN_Bdt = (\n", - " (k_AB * n_A * x_ab) + (k_CB * n_C) - ((k_BA * x_ba + k_BC * x_bc) * n_B)\n", - " )\n", - " dN_Cdt = (k_BC * n_B * x_bc) - (k_CB * n_C)\n", - "\n", - " t_step = (\n", - " timesteps.at[index, \"Datetime\"] - timesteps.at[index - 1, \"Datetime\"]\n", - " ).total_seconds()\n", - "\n", - " # assign new defect state percentages\n", - " timesteps.at[index, \"NA\"] = n_A + dN_Adt * t_step\n", - " timesteps.at[index, \"NB\"] = n_B + dN_Bdt * t_step\n", - " timesteps.at[index, \"NC\"] = n_C + dN_Cdt * t_step" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Finish calculating degraded device parameters.\n", - "Now that we have calculated defect states, we can calculate all the quantities that depend on defect states." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "timesteps[\"tau\"] = letid.tau_now(tau_0, tau_deg, timesteps[\"NB\"])\n", - "\n", - "# calculate device Jsc for every timestep. Unfortunately this requires an integration so I think we have to run through a loop. Device Jsc allows calculation of device Voc.\n", - "for index, timestep in timesteps.iterrows():\n", - " jsc_now = collection.calculate_jsc_from_tau_cp(\n", - " timesteps.at[index, \"tau\"], wafer_thickness, d_base, s_rear, generation, depth\n", - " )\n", - " timesteps.at[index, \"Jsc\"] = jsc_now\n", - " timesteps.at[index, \"Voc\"] = letid.calc_voc_from_tau(\n", - " timesteps.at[index, \"tau\"], wafer_thickness, s_rear, jsc_now, temperature=25\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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DatetimeTemperatureInjectionNANBNCtauJscVocIscFFPmpPmp_normtime (days)
02022-01-01 00:00:00750.1100.0000000.0000000.000000115.00000038.6812000.6644649.3995320.8406595.2504591.0000000.000000
12022-01-01 00:01:00750.199.9492430.0507570.000000114.93635838.6810020.6644539.3994840.8406575.2503350.9999760.000694
22022-01-01 00:02:00750.199.8985330.1014650.000002114.87284838.6808050.6644429.3994360.8406555.2502110.9999530.001389
32022-01-01 00:03:00750.199.8478710.1521230.000005114.80947038.6806070.6644319.3993880.8406535.2500870.9999290.002083
42022-01-01 00:04:00750.199.7972570.2027320.000011114.74622438.6804100.6644219.3993400.8406515.2499640.9999060.002778
.............................................
302362022-01-21 23:56:00750.10.00965558.34652341.64382270.27159938.4590160.6543999.3455410.8388625.1302350.97710220.997222
302372022-01-21 23:57:00750.10.00965258.34527141.64507770.27218538.4590210.6543999.3455420.8388625.1302370.97710320.997917
302382022-01-21 23:58:00750.10.00965058.34401941.64633170.27277238.4590260.6543999.3455430.8388625.1302400.97710320.998611
302392022-01-21 23:59:00750.10.00964858.34276741.64758570.27335838.4590300.6543999.3455440.8388625.1302420.97710320.999306
302402022-01-22 00:00:00750.10.00964658.34151641.64883970.27394438.4590350.6544009.3455450.8388625.1302440.97710421.000000
\n", - "

30241 rows Γ— 14 columns

\n", - "
" - ], - "text/plain": [ - " Datetime Temperature Injection NA NB \n", - "0 2022-01-01 00:00:00 75 0.1 100.000000 0.000000 \\\n", - "1 2022-01-01 00:01:00 75 0.1 99.949243 0.050757 \n", - "2 2022-01-01 00:02:00 75 0.1 99.898533 0.101465 \n", - "3 2022-01-01 00:03:00 75 0.1 99.847871 0.152123 \n", - "4 2022-01-01 00:04:00 75 0.1 99.797257 0.202732 \n", - "... ... ... ... ... ... \n", - "30236 2022-01-21 23:56:00 75 0.1 0.009655 58.346523 \n", - "30237 2022-01-21 23:57:00 75 0.1 0.009652 58.345271 \n", - "30238 2022-01-21 23:58:00 75 0.1 0.009650 58.344019 \n", - "30239 2022-01-21 23:59:00 75 0.1 0.009648 58.342767 \n", - "30240 2022-01-22 00:00:00 75 0.1 0.009646 58.341516 \n", - "\n", - " NC tau Jsc Voc Isc FF \n", - "0 0.000000 115.000000 38.681200 0.664464 9.399532 0.840659 \\\n", - "1 0.000000 114.936358 38.681002 0.664453 9.399484 0.840657 \n", - "2 0.000002 114.872848 38.680805 0.664442 9.399436 0.840655 \n", - "3 0.000005 114.809470 38.680607 0.664431 9.399388 0.840653 \n", - "4 0.000011 114.746224 38.680410 0.664421 9.399340 0.840651 \n", - "... ... ... ... ... ... ... \n", - "30236 41.643822 70.271599 38.459016 0.654399 9.345541 0.838862 \n", - "30237 41.645077 70.272185 38.459021 0.654399 9.345542 0.838862 \n", - "30238 41.646331 70.272772 38.459026 0.654399 9.345543 0.838862 \n", - "30239 41.647585 70.273358 38.459030 0.654399 9.345544 0.838862 \n", - "30240 41.648839 70.273944 38.459035 0.654400 9.345545 0.838862 \n", - "\n", - " Pmp Pmp_norm time (days) \n", - "0 5.250459 1.000000 0.000000 \n", - "1 5.250335 0.999976 0.000694 \n", - "2 5.250211 0.999953 0.001389 \n", - "3 5.250087 0.999929 0.002083 \n", - "4 5.249964 0.999906 0.002778 \n", - "... ... ... ... \n", - "30236 5.130235 0.977102 20.997222 \n", - "30237 5.130237 0.977103 20.997917 \n", - "30238 5.130240 0.977103 20.998611 \n", - "30239 5.130242 0.977103 20.999306 \n", - "30240 5.130244 0.977104 21.000000 \n", - "\n", - "[30241 rows x 14 columns]" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# this function quickly calculates the rest of the device parameters: Isc, FF, max power, and normalized max power\n", - "timesteps = letid.calc_device_params(timesteps, cell_area=243)\n", - "timesteps[\"time (days)\"] = (\n", - " timesteps[\"Datetime\"] - timesteps.iloc[0][\"Datetime\"]\n", - ").dt.total_seconds() / 86400 # create a column for days elapsed\n", - "\n", - "timesteps" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plot the results\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from cycler import cycler\n", - "\n", - "plt.style.use(\"default\")\n", - "\n", - "fig, ax = plt.subplots()\n", - "\n", - "ax.set_prop_cycle(\n", - " cycler(\"color\", [\"tab:blue\", \"tab:orange\", \"tab:green\"])\n", - " + cycler(\"linestyle\", [\"-\", \"--\", \"-.\"])\n", - ")\n", - "\n", - "ax.plot(timesteps[\"time (days)\"], timesteps[[\"NA\", \"NB\", \"NC\"]].values)\n", - "\n", - "ax.legend(labels=[\"$N_A$\", \"$N_B$\", \"$N_C$\", \"80 % regeneration\"], loc=\"upper left\")\n", - "ax.set_ylabel(\"Defect state percentages [%]\")\n", - "ax.set_xlabel(\"Time [days]\")\n", - "\n", - "ax2 = ax.twinx()\n", - "ax2.plot(\n", - " timesteps[\"time (days)\"],\n", - " timesteps[\"Pmp_norm\"],\n", - " c=\"black\",\n", - " label=\"Normalized $P_{MP}$\",\n", - ")\n", - "ax2.legend(loc=\"upper right\")\n", - "ax2.set_ylabel(\"Normalized $P_{MP}$\")\n", - "\n", - "# ax.axvline(pvdeg.Degradation.calc_regeneration_time(timesteps).total_seconds()/(60*60*24), linestyle = ':' , c = 'grey')\n", - "# ax.annotate('80% regeneration', (pvdeg.Degradation.calc_regeneration_time(timesteps).total_seconds()/(60*60*24), 80),\n", - "# xytext=(0.5, 0.8), textcoords='axes fraction',\n", - "# arrowprops=dict(facecolor='black', shrink=0.1),\n", - "# horizontalalignment='right', verticalalignment='top')\n", - "\n", - "ax.set_title(\n", - " \"Accelerated LETID Test\\n\"\n", - " rf\"{temperature}$\\degree$C, {suns}$\\times I_{{SC}}$ dark current injection\"\n", - ")\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### The function `calc_letid_lab` wraps all of the steps above into a single function:" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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DatetimeTemperatureInjectionNANBNCtauJscVocIscFFPmpPmp_norm
02023-05-24 17:20:59.177108750.1100.0000000.0000000.000000115.00000038.6812000.6644649.2448070.8406595.1640321.000000
12023-05-24 17:21:59.177108750.199.9492430.0507570.000000114.93635838.6812000.6644649.2448070.8406595.1640321.000000
22023-05-24 17:22:59.177108750.199.8985330.1014650.000002114.87284838.6810020.6644539.2447600.8406575.1639100.999976
32023-05-24 17:23:59.177108750.199.8478710.1521230.000005114.80947038.6808050.6644429.2447120.8406555.1637880.999953
42023-05-24 17:24:59.177108750.199.7972570.2027320.000011114.74622438.6806070.6644319.2446650.8406535.1636660.999929
..........................................
302362023-06-14 17:16:59.177108750.10.00965558.34652341.64382270.27159938.4590120.6543999.1917040.8388625.0457850.977102
302372023-06-14 17:17:59.177108750.10.00965258.34527141.64507770.27218538.4590160.6543999.1917050.8388625.0457870.977102
302382023-06-14 17:18:59.177108750.10.00965058.34401941.64633170.27277238.4590210.6543999.1917060.8388625.0457890.977103
302392023-06-14 17:19:59.177108750.10.00964858.34276741.64758570.27335838.4590260.6543999.1917070.8388625.0457910.977103
302402023-06-14 17:20:59.177108750.10.00964658.34151641.64883970.27394438.4590300.6543999.1917080.8388625.0457930.977103
\n", - "

30241 rows Γ— 13 columns

\n", - "
" - ], - "text/plain": [ - " Datetime Temperature Injection NA \n", - "0 2023-05-24 17:20:59.177108 75 0.1 100.000000 \\\n", - "1 2023-05-24 17:21:59.177108 75 0.1 99.949243 \n", - "2 2023-05-24 17:22:59.177108 75 0.1 99.898533 \n", - "3 2023-05-24 17:23:59.177108 75 0.1 99.847871 \n", - "4 2023-05-24 17:24:59.177108 75 0.1 99.797257 \n", - "... ... ... ... ... \n", - "30236 2023-06-14 17:16:59.177108 75 0.1 0.009655 \n", - "30237 2023-06-14 17:17:59.177108 75 0.1 0.009652 \n", - "30238 2023-06-14 17:18:59.177108 75 0.1 0.009650 \n", - "30239 2023-06-14 17:19:59.177108 75 0.1 0.009648 \n", - "30240 2023-06-14 17:20:59.177108 75 0.1 0.009646 \n", - "\n", - " NB NC tau Jsc Voc Isc \n", - "0 0.000000 0.000000 115.000000 38.681200 0.664464 9.244807 \\\n", - "1 0.050757 0.000000 114.936358 38.681200 0.664464 9.244807 \n", - "2 0.101465 0.000002 114.872848 38.681002 0.664453 9.244760 \n", - "3 0.152123 0.000005 114.809470 38.680805 0.664442 9.244712 \n", - "4 0.202732 0.000011 114.746224 38.680607 0.664431 9.244665 \n", - "... ... ... ... ... ... ... \n", - "30236 58.346523 41.643822 70.271599 38.459012 0.654399 9.191704 \n", - "30237 58.345271 41.645077 70.272185 38.459016 0.654399 9.191705 \n", - "30238 58.344019 41.646331 70.272772 38.459021 0.654399 9.191706 \n", - "30239 58.342767 41.647585 70.273358 38.459026 0.654399 9.191707 \n", - "30240 58.341516 41.648839 70.273944 38.459030 0.654399 9.191708 \n", - "\n", - " FF Pmp Pmp_norm \n", - "0 0.840659 5.164032 1.000000 \n", - "1 0.840659 5.164032 1.000000 \n", - "2 0.840657 5.163910 0.999976 \n", - "3 0.840655 5.163788 0.999953 \n", - "4 0.840653 5.163666 0.999929 \n", - "... ... ... ... \n", - "30236 0.838862 5.045785 0.977102 \n", - "30237 0.838862 5.045787 0.977102 \n", - "30238 0.838862 5.045789 0.977103 \n", - "30239 0.838862 5.045791 0.977103 \n", - "30240 0.838862 5.045793 0.977103 \n", - "\n", - "[30241 rows x 13 columns]" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "letid.calc_letid_lab(\n", - " tau_0, tau_deg, wafer_thickness, s_rear, nA_0, nB_0, nC_0, 0.1, 75, \"repins\"\n", - ")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.13" - }, - "vscode": { - "interpreter": { - "hash": "848658e0671c41dd18b216771b1713479db7d685859cbb6c795b270024b1888c" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/tutorials_and_tools/_build/html/_sources/tutorials/LETID - Outdoor LETID.ipynb b/tutorials_and_tools/_build/html/_sources/tutorials/LETID - Outdoor LETID.ipynb deleted file mode 100644 index b8f680029..000000000 --- a/tutorials_and_tools/_build/html/_sources/tutorials/LETID - Outdoor LETID.ipynb +++ /dev/null @@ -1,1776 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# LETID - Outdoor Environments \n", - "\n", - "### Example: LETID progression in outdoor environments\n", - "Author: Joe Karas | joseph.karas@nrel.gov\n", - "\n", - "2023\n", - "***\n", - "\n", - "We can use the equations in this library to model LETID progression in a simulated outdoor environment, given that we have weather and system data. This example makes use of tools from the fabulous [pvlib](https://pvlib-python.readthedocs.io/en/stable/) library to calculate system irradiance and temperature, which we use to calculate progression in LETID states.\n", - "\n", - "This will illustrate the potential of \"Temporary Recovery\", i.e., the backwards transition of the LETID defect B->A that can take place with carrier injection at lower temperatures.\n", - "\n", - "\n", - "**Requirements:**\n", - "- `pvlib`, `pandas`, `numpy`, `matplotlib`\n", - "\n", - "**Objectives:**\n", - "1. Use `pvlib` and provided weather files to set up a temperature and injection timeseries\n", - "2. Define necessary solar cell device parameters\n", - "3. Define necessary degradation parameters: degraded lifetime and defect states\n", - "4. Run through timeseries, calculating defect states\n", - "5. Calculate device degradation and plot\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# if running on google colab, uncomment the next line and execute this cell to install the dependencies and prevent \"ModuleNotFoundError\" in later cells:\n", - "# !pip install pvdeg==0.3.3" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "from pvdeg import letid, collection, utilities, DATA_DIR\n", - "\n", - "import pvlib\n", - "import os\n", - "import pandas as pd\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### First, we'll use pvlib to create and run a model system, and use the irradiance, temperature, and operating point of that model to set up our LETID model\n", - "For this example, we'll model a fixed latitude tilt system at NREL, in Golden, CO, USA, using [NSRDB](https://nsrdb.nrel.gov/) hourly PSM weather data, SAPM temperature models, and module and inverter models from the CEC database." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# load weather and location data, use pvlib read_psm3 function with map_variables = True\n", - "\n", - "sam_file = r\"example data\\psm3.csv\"\n", - "weather, meta = pvlib.iotools.read_psm3(sam_file, map_variables=True)\n", - "# weather, meta = pvlib.iotools.read_psm3(sam_file)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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YearMonthDayHourMinutednidhighitemp_airDew Pointwind_speedrelative_humiditypoa_globaltemp_celltemp_module
1999-01-01 00:30:00-07:001999110300.00.00.00.0-5.01.879.390.00.00.0
1999-01-01 01:30:00-07:001999111300.00.00.00.0-4.01.780.840.00.00.0
1999-01-01 02:30:00-07:001999112300.00.00.00.0-4.01.582.980.00.00.0
1999-01-01 03:30:00-07:001999113300.00.00.00.0-4.01.385.010.00.00.0
1999-01-01 04:30:00-07:001999114300.00.00.00.0-4.01.385.810.00.00.0
................................................
1999-12-31 19:30:00-07:001999123119300.00.00.00.0-3.00.983.630.00.00.0
1999-12-31 20:30:00-07:001999123120300.00.00.00.0-3.01.286.820.00.00.0
1999-12-31 21:30:00-07:001999123121300.00.00.00.0-4.01.683.780.00.00.0
1999-12-31 22:30:00-07:001999123122300.00.00.00.0-4.01.781.220.00.00.0
1999-12-31 23:30:00-07:001999123123300.00.00.00.0-5.01.879.430.00.00.0
\n", - "

8760 rows Γ— 15 columns

\n", - "
" - ], - "text/plain": [ - " Year Month Day Hour Minute dni dhi ghi \n", - "1999-01-01 00:30:00-07:00 1999 1 1 0 30 0.0 0.0 0.0 \\\n", - "1999-01-01 01:30:00-07:00 1999 1 1 1 30 0.0 0.0 0.0 \n", - "1999-01-01 02:30:00-07:00 1999 1 1 2 30 0.0 0.0 0.0 \n", - "1999-01-01 03:30:00-07:00 1999 1 1 3 30 0.0 0.0 0.0 \n", - "1999-01-01 04:30:00-07:00 1999 1 1 4 30 0.0 0.0 0.0 \n", - "... ... ... ... ... ... ... ... ... \n", - "1999-12-31 19:30:00-07:00 1999 12 31 19 30 0.0 0.0 0.0 \n", - "1999-12-31 20:30:00-07:00 1999 12 31 20 30 0.0 0.0 0.0 \n", - "1999-12-31 21:30:00-07:00 1999 12 31 21 30 0.0 0.0 0.0 \n", - "1999-12-31 22:30:00-07:00 1999 12 31 22 30 0.0 0.0 0.0 \n", - "1999-12-31 23:30:00-07:00 1999 12 31 23 30 0.0 0.0 0.0 \n", - "\n", - " temp_air Dew Point wind_speed relative_humidity \n", - "1999-01-01 00:30:00-07:00 0.0 -5.0 1.8 79.39 \\\n", - "1999-01-01 01:30:00-07:00 0.0 -4.0 1.7 80.84 \n", - "1999-01-01 02:30:00-07:00 0.0 -4.0 1.5 82.98 \n", - "1999-01-01 03:30:00-07:00 0.0 -4.0 1.3 85.01 \n", - "1999-01-01 04:30:00-07:00 0.0 -4.0 1.3 85.81 \n", - "... ... ... ... ... \n", - "1999-12-31 19:30:00-07:00 0.0 -3.0 0.9 83.63 \n", - "1999-12-31 20:30:00-07:00 0.0 -3.0 1.2 86.82 \n", - "1999-12-31 21:30:00-07:00 0.0 -4.0 1.6 83.78 \n", - "1999-12-31 22:30:00-07:00 0.0 -4.0 1.7 81.22 \n", - "1999-12-31 23:30:00-07:00 0.0 -5.0 1.8 79.43 \n", - "\n", - " poa_global temp_cell temp_module \n", - "1999-01-01 00:30:00-07:00 0.0 0.0 0.0 \n", - "1999-01-01 01:30:00-07:00 0.0 0.0 0.0 \n", - "1999-01-01 02:30:00-07:00 0.0 0.0 0.0 \n", - "1999-01-01 03:30:00-07:00 0.0 0.0 0.0 \n", - "1999-01-01 04:30:00-07:00 0.0 0.0 0.0 \n", - "... ... ... ... \n", - "1999-12-31 19:30:00-07:00 0.0 0.0 0.0 \n", - "1999-12-31 20:30:00-07:00 0.0 0.0 0.0 \n", - "1999-12-31 21:30:00-07:00 0.0 0.0 0.0 \n", - "1999-12-31 22:30:00-07:00 0.0 0.0 0.0 \n", - "1999-12-31 23:30:00-07:00 0.0 0.0 0.0 \n", - "\n", - "[8760 rows x 15 columns]" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "weather" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# if our weather file doesn't have precipitable water, calculate it with pvlib\n", - "if not \"precipitable_water\" in weather.columns:\n", - " weather[\"precipitable_water\"] = pvlib.atmosphere.gueymard94_pw(\n", - " weather[\"temp_air\"], weather[\"relative_humidity\"]\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# rename some columns for pvlib if they haven't been already\n", - "weather.rename(\n", - " columns={\n", - " \"GHI\": \"ghi\",\n", - " \"DNI\": \"dni\",\n", - " \"DHI\": \"dhi\",\n", - " \"Temperature\": \"temp_air\",\n", - " \"Wind Speed\": \"wind_speed\",\n", - " \"Relative Humidity\": \"relative_humidity\",\n", - " \"Precipitable Water\": \"precipitable_water\",\n", - " },\n", - " inplace=True,\n", - ")\n", - "weather = weather[\n", - " [\n", - " \"ghi\",\n", - " \"dni\",\n", - " \"dhi\",\n", - " \"temp_air\",\n", - " \"wind_speed\",\n", - " \"relative_humidity\",\n", - " \"precipitable_water\",\n", - " ]\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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ghidnidhitemp_airwind_speedrelative_humidityprecipitable_water
1999-01-01 00:30:00-07:000.00.00.00.01.879.390.891869
1999-01-01 01:30:00-07:000.00.00.00.01.780.840.908158
1999-01-01 02:30:00-07:000.00.00.00.01.582.980.932199
1999-01-01 03:30:00-07:000.00.00.00.01.385.010.955004
1999-01-01 04:30:00-07:000.00.00.00.01.385.810.963991
........................
1999-12-31 19:30:00-07:000.00.00.00.00.983.630.939501
1999-12-31 20:30:00-07:000.00.00.00.01.286.820.975338
1999-12-31 21:30:00-07:000.00.00.00.01.683.780.941186
1999-12-31 22:30:00-07:000.00.00.00.01.781.220.912427
1999-12-31 23:30:00-07:000.00.00.00.01.879.430.892318
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8760 rows Γ— 7 columns

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" - ], - "text/plain": [ - " ghi dni dhi temp_air wind_speed \n", - "1999-01-01 00:30:00-07:00 0.0 0.0 0.0 0.0 1.8 \\\n", - "1999-01-01 01:30:00-07:00 0.0 0.0 0.0 0.0 1.7 \n", - "1999-01-01 02:30:00-07:00 0.0 0.0 0.0 0.0 1.5 \n", - "1999-01-01 03:30:00-07:00 0.0 0.0 0.0 0.0 1.3 \n", - "1999-01-01 04:30:00-07:00 0.0 0.0 0.0 0.0 1.3 \n", - "... ... ... ... ... ... \n", - "1999-12-31 19:30:00-07:00 0.0 0.0 0.0 0.0 0.9 \n", - "1999-12-31 20:30:00-07:00 0.0 0.0 0.0 0.0 1.2 \n", - "1999-12-31 21:30:00-07:00 0.0 0.0 0.0 0.0 1.6 \n", - "1999-12-31 22:30:00-07:00 0.0 0.0 0.0 0.0 1.7 \n", - "1999-12-31 23:30:00-07:00 0.0 0.0 0.0 0.0 1.8 \n", - "\n", - " relative_humidity precipitable_water \n", - "1999-01-01 00:30:00-07:00 79.39 0.891869 \n", - "1999-01-01 01:30:00-07:00 80.84 0.908158 \n", - "1999-01-01 02:30:00-07:00 82.98 0.932199 \n", - "1999-01-01 03:30:00-07:00 85.01 0.955004 \n", - "1999-01-01 04:30:00-07:00 85.81 0.963991 \n", - "... ... ... \n", - "1999-12-31 19:30:00-07:00 83.63 0.939501 \n", - "1999-12-31 20:30:00-07:00 86.82 0.975338 \n", - "1999-12-31 21:30:00-07:00 83.78 0.941186 \n", - "1999-12-31 22:30:00-07:00 81.22 0.912427 \n", - "1999-12-31 23:30:00-07:00 79.43 0.892318 \n", - "\n", - "[8760 rows x 7 columns]" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "weather" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# import pvlib stuff and pick a module and inverter. Choice of these things will slightly affect the pvlib results which we later use to calculate injection.\n", - "# we'll use the SAPM temperature model open-rack glass/polymer coeffecients.\n", - "\n", - "from pvlib.temperature import TEMPERATURE_MODEL_PARAMETERS\n", - "from pvlib.location import Location\n", - "from pvlib.pvsystem import PVSystem\n", - "from pvlib.modelchain import ModelChain\n", - "\n", - "cec_modules = pvlib.pvsystem.retrieve_sam(\"CECMod\")\n", - "cec_inverters = pvlib.pvsystem.retrieve_sam(\"cecinverter\")\n", - "\n", - "cec_module = cec_modules[\"Jinko_Solar_Co___Ltd_JKM260P_60\"]\n", - "cec_inverter = cec_inverters[\"ABB__ULTRA_750_TL_OUTD_1_US_690_x_y_z__690V_\"]\n", - "\n", - "temperature_model_parameters = TEMPERATURE_MODEL_PARAMETERS[\"sapm\"][\n", - " \"open_rack_glass_polymer\"\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# set up system in pvlib\n", - "lat = meta[\"latitude\"]\n", - "lon = meta[\"longitude\"]\n", - "tz = meta[\"Local Time Zone\"]\n", - "elevation = meta[\"altitude\"]\n", - "surface_tilt = lat # fixed, latitude tilt\n", - "surface_azimuth = 180 # south-facing\n", - "\n", - "location = Location(lat, lon, tz, elevation, \"Golden, CO, USA\")\n", - "\n", - "system = PVSystem(\n", - " surface_tilt=surface_tilt,\n", - " surface_azimuth=surface_azimuth,\n", - " module_parameters=cec_module,\n", - " inverter_parameters=cec_inverter,\n", - " temperature_model_parameters=temperature_model_parameters,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "ModelChain: \n", - " name: None\n", - " clearsky_model: ineichen\n", - " transposition_model: haydavies\n", - " solar_position_method: nrel_numpy\n", - " airmass_model: kastenyoung1989\n", - " dc_model: cec\n", - " ac_model: sandia_inverter\n", - " aoi_model: physical_aoi_loss\n", - " spectral_model: first_solar_spectral_loss\n", - " temperature_model: sapm_temp\n", - " losses_model: no_extra_losses" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# create and run pvlib modelchain\n", - "mc = ModelChain(system, location, aoi_model=\"physical\")\n", - "mc.run_model(weather)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Set up timeseries\n", - "In this example, injection is a function of both the operating point of the module (which we will assume is maximum power point) and irradiance. Maximum power point injection is equivalent to $(I_{sc}-I_{mp})/I_{sc}\\times Ee$, where $Ee$ is effective irradiance, the irradiance absorbed by the module's cells. We normalize it to 1-sun irradiance, 1000 $W/m^2$.\n", - "\n", - "We will use the irradiance, DC operating point, and cell temperature from the pvlib modelchain results." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "ee = mc.results.effective_irradiance\n", - "injection = (\n", - " (mc.results.dc[\"i_sc\"] - mc.results.dc[\"i_mp\"])\n", - " / (mc.results.dc[\"i_sc\"])\n", - " * (ee / 1000)\n", - ")\n", - "temperature = mc.results.cell_temperature\n", - "\n", - "timesteps = pd.DataFrame(\n", - " {\"Temperature\": temperature, \"Injection\": injection}\n", - ") # create a DataFrame with cell temperature and injection\n", - "timesteps.reset_index(\n", - " inplace=True\n", - ") # reset the index so datetime is a column. I prefer integer indexing.\n", - "timesteps.rename(columns={\"index\": \"Datetime\"}, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "# filter out times when injection is NaN, these won't progress LETID, and it'll make the calculations below run faster\n", - "timesteps = timesteps[timesteps[\"Injection\"].notnull()]\n", - "timesteps.reset_index(inplace=True, drop=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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DatetimeTemperatureInjection
01999-01-01 08:30:00-07:003.3487740.006239
11999-01-01 09:30:00-07:0012.3784390.031607
21999-01-01 10:30:00-07:0016.5950810.044220
31999-01-01 11:30:00-07:0017.4572400.043201
41999-01-01 12:30:00-07:006.3927830.007124
............
43441999-12-31 12:30:00-07:009.5155770.001151
43451999-12-31 13:30:00-07:0028.1197960.044499
43461999-12-31 14:30:00-07:0023.3146720.035130
43471999-12-31 15:30:00-07:0017.8905280.027891
43481999-12-31 16:30:00-07:004.5523650.001188
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4349 rows Γ— 3 columns

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" - ], - "text/plain": [ - " Datetime Temperature Injection\n", - "0 1999-01-01 08:30:00-07:00 3.348774 0.006239\n", - "1 1999-01-01 09:30:00-07:00 12.378439 0.031607\n", - "2 1999-01-01 10:30:00-07:00 16.595081 0.044220\n", - "3 1999-01-01 11:30:00-07:00 17.457240 0.043201\n", - "4 1999-01-01 12:30:00-07:00 6.392783 0.007124\n", - "... ... ... ...\n", - "4344 1999-12-31 12:30:00-07:00 9.515577 0.001151\n", - "4345 1999-12-31 13:30:00-07:00 28.119796 0.044499\n", - "4346 1999-12-31 14:30:00-07:00 23.314672 0.035130\n", - "4347 1999-12-31 15:30:00-07:00 17.890528 0.027891\n", - "4348 1999-12-31 16:30:00-07:00 4.552365 0.001188\n", - "\n", - "[4349 rows x 3 columns]" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "timesteps" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Device parameters\n", - "To define a device, we need to define several important quantities about the device: wafer thickness (in $\\mu m$), rear surface recombination velocity (in cm/s), and cell area (in cm2)." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "wafer_thickness = 180 # um\n", - "s_rear = 46 # cm/s\n", - "cell_area = 243 # cm^2" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### Other device parameters\n", - "Other required device parameters: base diffusivity (in cm2/s), and optical generation profile, which allow us to estimate current collection in the device." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "generation_df = pd.read_excel(\n", - " os.path.join(DATA_DIR, \"PVL_GenProfile.xlsx\"), header=0\n", - ") # this is an optical generation profile generated by PVLighthouse's OPAL2 default model for 1-sun, normal incident AM1.5 sunlight on a 180-um thick SiNx-coated, pyramid-textured wafer.\n", - "generation = generation_df[\"Generation (cm-3s-1)\"]\n", - "depth = generation_df[\"Depth (um)\"]\n", - "\n", - "d_base = 27 # cm^2/s electron diffusivity. See https://www2.pvlighthouse.com.au/calculators/mobility%20calculator/mobility%20calculator.aspx for details" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Degradation parameters\n", - "To model the device's degradation, we need to define several more important quantities about the degradation the device will experience. These include undegraded and degraded lifetime (in $\\mu s$)." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "tau_0 = 115 # us, carrier lifetime in non-degraded states, e.g. LETID/LID states A or C\n", - "tau_deg = 55 # us, carrier lifetime in fully-degraded state, e.g. LETID/LID state B" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### Remaining degradation parameters:\n", - "The rest of the quantities to define are: the initial percentage of defects in each state (A, B, and C), and the dictionary of mechanism parameters.\n", - "\n", - "In this example, we'll assume the device starts in the fully-undegraded state (100% state A), and we'll use the kinetic parameters for LETID degradation from Repins." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "# starting defect state percentages\n", - "nA_0 = 100\n", - "nB_0 = 0\n", - "nC_0 = 0\n", - "\n", - "mechanism_params = utilities.get_kinetics(\"repins\")\n", - "\n", - "timesteps[[\"NA\", \"NB\", \"NC\", \"tau\"]] = (\n", - " np.nan\n", - ") # create columns for defect state percentages and lifetime, fill with NaNs for now, to fill iteratively below\n", - "\n", - "timesteps.loc[0, [\"NA\", \"NB\", \"NC\"]] = (\n", - " nA_0,\n", - " nB_0,\n", - " nC_0,\n", - ") # assign first timestep defect state percentages\n", - "timesteps.loc[0, \"tau\"] = letid.tau_now(\n", - " tau_0, tau_deg, nB_0\n", - ") # calculate tau for the first timestep" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Run through timesteps\n", - "Since each timestep depends on the preceding timestep, we need to calculate in a loop. This will take a few minutes depending on the length of the timeseries." - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "for index, timestep in timesteps.iterrows():\n", - "\n", - " # first row tau has already been assigned\n", - " if index == 0:\n", - " # calc device parameters for first row\n", - " tau = tau_0\n", - " jsc = collection.calculate_jsc_from_tau_cp(\n", - " tau, wafer_thickness, d_base, s_rear, generation, depth\n", - " )\n", - " voc = letid.calc_voc_from_tau(tau, wafer_thickness, s_rear, jsc, temperature=25)\n", - " timesteps.at[index, \"Jsc\"] = jsc\n", - " timesteps.at[index, \"Voc\"] = voc\n", - "\n", - " # loop through rows, new tau calculated based on previous NB. Reaction proceeds based on new tau.\n", - " else:\n", - " n_A = timesteps.at[index - 1, \"NA\"]\n", - " n_B = timesteps.at[index - 1, \"NB\"]\n", - " n_C = timesteps.at[index - 1, \"NC\"]\n", - "\n", - " tau = letid.tau_now(tau_0, tau_deg, n_B)\n", - " jsc = collection.calculate_jsc_from_tau_cp(\n", - " tau, wafer_thickness, d_base, s_rear, generation, depth\n", - " )\n", - "\n", - " temperature = timesteps.at[index, \"Temperature\"]\n", - " injection = timesteps.at[index, \"Injection\"]\n", - "\n", - " # calculate defect reaction kinetics: reaction constant and carrier concentration factor.\n", - " k_AB = letid.k_ij(\n", - " mechanism_params[\"v_ab\"], mechanism_params[\"ea_ab\"], temperature\n", - " )\n", - " k_BA = letid.k_ij(\n", - " mechanism_params[\"v_ba\"], mechanism_params[\"ea_ba\"], temperature\n", - " )\n", - " k_BC = letid.k_ij(\n", - " mechanism_params[\"v_bc\"], mechanism_params[\"ea_bc\"], temperature\n", - " )\n", - " k_CB = letid.k_ij(\n", - " mechanism_params[\"v_cb\"], mechanism_params[\"ea_cb\"], temperature\n", - " )\n", - "\n", - " x_ab = letid.carrier_factor(\n", - " tau,\n", - " \"ab\",\n", - " temperature,\n", - " injection,\n", - " jsc,\n", - " wafer_thickness,\n", - " s_rear,\n", - " mechanism_params,\n", - " )\n", - " x_ba = letid.carrier_factor(\n", - " tau,\n", - " \"ba\",\n", - " temperature,\n", - " injection,\n", - " jsc,\n", - " wafer_thickness,\n", - " s_rear,\n", - " mechanism_params,\n", - " )\n", - " x_bc = letid.carrier_factor(\n", - " tau,\n", - " \"bc\",\n", - " temperature,\n", - " injection,\n", - " jsc,\n", - " wafer_thickness,\n", - " s_rear,\n", - " mechanism_params,\n", - " )\n", - "\n", - " # calculate the instantaneous change in NA, NB, and NC\n", - " dN_Adt = (k_BA * n_B * x_ba) - (k_AB * n_A * x_ab)\n", - " dN_Bdt = (\n", - " (k_AB * n_A * x_ab) + (k_CB * n_C) - ((k_BA * x_ba + k_BC * x_bc) * n_B)\n", - " )\n", - " dN_Cdt = (k_BC * n_B * x_bc) - (k_CB * n_C)\n", - "\n", - " t_step = (\n", - " timesteps.at[index, \"Datetime\"] - timesteps.at[index - 1, \"Datetime\"]\n", - " ).total_seconds()\n", - "\n", - " # assign new defect state percentages\n", - " timesteps.at[index, \"NA\"] = n_A + dN_Adt * t_step\n", - " timesteps.at[index, \"NB\"] = n_B + dN_Bdt * t_step\n", - " timesteps.at[index, \"NC\"] = n_C + dN_Cdt * t_step\n", - "\n", - " # calculate device parameters\n", - " timesteps.at[index, \"tau\"] = tau\n", - " timesteps.at[index, \"Jsc\"] = jsc\n", - " timesteps.at[index, \"Voc\"] = letid.calc_voc_from_tau(\n", - " tau, wafer_thickness, s_rear, jsc, temperature=25\n", - " )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Finish calculating degraded device parameters.\n", - "Now that we have calculated defect states, we can calculate all the quantities that depend on defect states." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "timesteps[\"tau\"] = letid.tau_now(tau_0, tau_deg, timesteps[\"NB\"])\n", - "\n", - "# calculate device Jsc for every timestep. Unfortunately this requires an integration so I think we have to run through a loop. Device Jsc allows calculation of device Voc.\n", - "for index, timestep in timesteps.iterrows():\n", - " jsc_now = collection.calculate_jsc_from_tau_cp(\n", - " timesteps.at[index, \"tau\"], wafer_thickness, d_base, s_rear, generation, depth\n", - " )\n", - " timesteps.at[index, \"Jsc\"] = jsc_now\n", - " timesteps.at[index, \"Voc\"] = letid.calc_voc_from_tau(\n", - " timesteps.at[index, \"tau\"], wafer_thickness, s_rear, jsc_now, temperature=25\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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DatetimeTemperatureInjectionNANBNCtauJscVocIscFFPmpPmp_norm
01999-01-01 08:30:00-07:003.3487740.006239100.0000000.0000000.000000e+00115.00000038.6812000.6644649.3995320.8406595.2504591.000000
11999-01-01 09:30:00-07:0012.3784390.03160799.9977180.0022820.000000e+00114.99713838.6811910.6644639.3995290.8406595.2504540.999999
21999-01-01 10:30:00-07:0016.5950810.04422099.9925150.0074855.176918e-09114.99061138.6811710.6644629.3995250.8406595.2504410.999997
31999-01-01 11:30:00-07:0017.4572400.04320199.9869120.0130882.348994e-08114.98358338.6811490.6644619.3995190.8406585.2504270.999994
41999-01-01 12:30:00-07:006.3927830.00712499.9866650.0133352.441968e-08114.98327438.6811480.6644619.3995190.8406585.2504270.999994
..........................................
43441999-12-31 12:30:00-07:009.5155770.00115140.21339956.0067053.779897e+0071.38502138.4677290.6547329.3476580.8389225.1343800.977891
43451999-12-31 13:30:00-07:0028.1197960.04449940.21411556.0056853.780200e+0071.38551438.4677330.6547329.3476590.8389225.1343810.977892
43461999-12-31 14:30:00-07:0023.3146720.03513040.21964656.0000213.780333e+0071.38825238.4677540.6547339.3476640.8389225.1343920.977894
43471999-12-31 15:30:00-07:0017.8905280.02789140.23097855.9886363.780386e+0071.39375638.4677960.6547359.3476740.8389225.1344120.977898
43481999-12-31 16:30:00-07:004.5523650.00118840.23149255.9881223.780386e+0071.39400538.4677980.6547359.3476750.8389225.1344130.977898
\n", - "

4349 rows Γ— 13 columns

\n", - "
" - ], - "text/plain": [ - " Datetime Temperature Injection NA NB \n", - "0 1999-01-01 08:30:00-07:00 3.348774 0.006239 100.000000 0.000000 \\\n", - "1 1999-01-01 09:30:00-07:00 12.378439 0.031607 99.997718 0.002282 \n", - "2 1999-01-01 10:30:00-07:00 16.595081 0.044220 99.992515 0.007485 \n", - "3 1999-01-01 11:30:00-07:00 17.457240 0.043201 99.986912 0.013088 \n", - "4 1999-01-01 12:30:00-07:00 6.392783 0.007124 99.986665 0.013335 \n", - "... ... ... ... ... ... \n", - "4344 1999-12-31 12:30:00-07:00 9.515577 0.001151 40.213399 56.006705 \n", - "4345 1999-12-31 13:30:00-07:00 28.119796 0.044499 40.214115 56.005685 \n", - "4346 1999-12-31 14:30:00-07:00 23.314672 0.035130 40.219646 56.000021 \n", - "4347 1999-12-31 15:30:00-07:00 17.890528 0.027891 40.230978 55.988636 \n", - "4348 1999-12-31 16:30:00-07:00 4.552365 0.001188 40.231492 55.988122 \n", - "\n", - " NC tau Jsc Voc Isc FF \n", - "0 0.000000e+00 115.000000 38.681200 0.664464 9.399532 0.840659 \\\n", - "1 0.000000e+00 114.997138 38.681191 0.664463 9.399529 0.840659 \n", - "2 5.176918e-09 114.990611 38.681171 0.664462 9.399525 0.840659 \n", - "3 2.348994e-08 114.983583 38.681149 0.664461 9.399519 0.840658 \n", - "4 2.441968e-08 114.983274 38.681148 0.664461 9.399519 0.840658 \n", - "... ... ... ... ... ... ... \n", - "4344 3.779897e+00 71.385021 38.467729 0.654732 9.347658 0.838922 \n", - "4345 3.780200e+00 71.385514 38.467733 0.654732 9.347659 0.838922 \n", - "4346 3.780333e+00 71.388252 38.467754 0.654733 9.347664 0.838922 \n", - "4347 3.780386e+00 71.393756 38.467796 0.654735 9.347674 0.838922 \n", - "4348 3.780386e+00 71.394005 38.467798 0.654735 9.347675 0.838922 \n", - "\n", - " Pmp Pmp_norm \n", - "0 5.250459 1.000000 \n", - "1 5.250454 0.999999 \n", - "2 5.250441 0.999997 \n", - "3 5.250427 0.999994 \n", - "4 5.250427 0.999994 \n", - "... ... ... \n", - "4344 5.134380 0.977891 \n", - "4345 5.134381 0.977892 \n", - "4346 5.134392 0.977894 \n", - "4347 5.134412 0.977898 \n", - "4348 5.134413 0.977898 \n", - "\n", - "[4349 rows x 13 columns]" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "timesteps = letid.calc_device_params(\n", - " timesteps, cell_area=243\n", - ") # this function quickly calculates the rest of the device parameters: Isc, FF, max power, and normalized max power\n", - "\n", - "timesteps" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note of course that all these calculated device parameters are modeled STC device parameters, not the instantaneous, weather-dependent values. This isn't a robust performance model of a degraded module." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plot the results" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from cycler import cycler\n", - "\n", - "plt.style.use(\"default\")\n", - "\n", - "fig, ax = plt.subplots()\n", - "\n", - "ax.set_prop_cycle(\n", - " cycler(\"color\", [\"tab:blue\", \"tab:orange\", \"tab:green\"])\n", - " + cycler(\"linestyle\", [\"-\", \"--\", \"-.\"])\n", - ")\n", - "\n", - "ax.plot(timesteps[\"Datetime\"], timesteps[[\"NA\", \"NB\", \"NC\"]].values)\n", - "ax.legend(labels=[\"$N_A$\", \"$N_B$\", \"$N_C$\"], loc=\"upper left\")\n", - "ax.set_ylabel(\"Defect state percentages [%]\")\n", - "ax.set_xlabel(\"Datetime\")\n", - "\n", - "ax2 = ax.twinx()\n", - "ax2.plot(\n", - " timesteps[\"Datetime\"],\n", - " timesteps[\"Pmp_norm\"],\n", - " c=\"black\",\n", - " label=\"Normalized STC $P_{MP}$\",\n", - ")\n", - "ax2.legend(loc=\"upper right\")\n", - "ax2.set_ylabel(\"Normalized STC $P_{MP}$\")\n", - "\n", - "ax.set_title(\"Outdoor LETID \\n\" f\"{location.name}\")\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The example data provided for Golden, CO, shows how $N_A$ increases in cold weather, and power temporarily recovers, due to temporary recovery of LETID (B->A)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### The function `calc_letid_outdoors` wraps all of the steps above into a single function:" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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DatetimeTemperatureInjectionNANBNCtauJscVocIscFFPmpPmp_norm
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" - ], - "text/plain": [ - " Datetime Temperature Injection NA NB \n", - "0 1999-01-01 00:30:00-07:00 0.0 NaN 100.0 0.000000e+00 \\\n", - "1 1999-01-01 01:30:00-07:00 0.0 NaN 100.0 1.702422e-15 \n", - "2 1999-01-01 02:30:00-07:00 0.0 NaN 100.0 3.404843e-15 \n", - "3 1999-01-01 03:30:00-07:00 0.0 NaN 100.0 5.107265e-15 \n", - "4 1999-01-01 04:30:00-07:00 0.0 NaN 100.0 6.809686e-15 \n", - "... ... ... ... ... ... \n", - "8755 1999-12-31 19:30:00-07:00 0.0 NaN NaN NaN \n", - "8756 1999-12-31 20:30:00-07:00 0.0 NaN NaN NaN \n", - "8757 1999-12-31 21:30:00-07:00 0.0 NaN NaN NaN \n", - "8758 1999-12-31 22:30:00-07:00 0.0 NaN NaN NaN \n", - "8759 1999-12-31 23:30:00-07:00 0.0 NaN NaN NaN \n", - "\n", - " NC tau Jsc Voc Isc FF Pmp \n", - "0 0.000000e+00 115.0 38.6812 0.664464 9.244807 0.840659 5.164032 \\\n", - "1 0.000000e+00 115.0 38.6812 0.664464 9.244807 0.840659 5.164032 \n", - "2 5.403329e-36 115.0 38.6812 0.664464 9.244807 0.840659 5.164032 \n", - "3 1.620999e-35 115.0 38.6812 0.664464 9.244807 0.840659 5.164032 \n", - "4 3.241997e-35 115.0 38.6812 0.664464 9.244807 0.840659 5.164032 \n", - "... ... ... ... ... ... ... ... \n", - "8755 NaN NaN NaN 0.000000 NaN 0.328504 NaN \n", - "8756 NaN NaN NaN 0.000000 NaN 0.328504 NaN \n", - "8757 NaN NaN NaN 0.000000 NaN 0.328504 NaN \n", - "8758 NaN NaN NaN 0.000000 NaN 0.328504 NaN \n", - "8759 NaN NaN NaN 0.000000 NaN 0.328504 NaN \n", - "\n", - " Pmp_norm \n", - "0 1.0 \n", - "1 1.0 \n", - "2 1.0 \n", - "3 1.0 \n", - "4 1.0 \n", - "... ... \n", - "8755 NaN \n", - "8756 NaN \n", - "8757 NaN \n", - "8758 NaN \n", - "8759 NaN \n", - "\n", - "[8760 rows x 13 columns]" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "nA_0 = 100\n", - "nB_0 = 0\n", - "nC_0 = 0\n", - "mechanism_params = \"repins\"\n", - "\n", - "letid.calc_letid_outdoors(\n", - " tau_0,\n", - " tau_deg,\n", - " wafer_thickness,\n", - " s_rear,\n", - " nA_0,\n", - " nB_0,\n", - " nC_0,\n", - " weather,\n", - " meta,\n", - " mechanism_params,\n", - " generation_df,\n", - " module_parameters=cec_module,\n", - ")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.13" - }, - "vscode": { - "interpreter": { - "hash": "848658e0671c41dd18b216771b1713479db7d685859cbb6c795b270024b1888c" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/tutorials_and_tools/_build/html/_sources/tutorials/LETID - Outdoor Scenario Based on Accelerated Test Result.ipynb b/tutorials_and_tools/_build/html/_sources/tutorials/LETID - Outdoor Scenario Based on Accelerated Test Result.ipynb deleted file mode 100644 index 51e22b8dc..000000000 --- a/tutorials_and_tools/_build/html/_sources/tutorials/LETID - Outdoor Scenario Based on Accelerated Test Result.ipynb +++ /dev/null @@ -1,3021 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# LETID - Outdoor Scenario Based on Accelerated Test\n", - "\n", - "Example: Using a test result to model LETID progression in outdoor environments\n", - "\n", - "Author: Joe Karas | joseph.karas@nrel.gov\n", - "\n", - "2023\n", - "***\n", - "\n", - "One important use case for this library is to use data from a LETID test (e.g. [IEC TS 63342](https://webstore.iec.ch/publication/67332)) to model how a module may degrade and regenerate in the field.\n", - "\n", - "We will take some data from module testing presented in [Karas *et al.* 2022](https://onlinelibrary.wiley.com/doi/10.1002/pip.3573), and use it to estimate device parameters.\n", - "\n", - "We can use the equations in this library to model LETID progression in a simulated outdoor environment, given that we have weather and system data. This example makes use of tools from the fabulous [pvlib](https://pvlib-python.readthedocs.io/en/stable/) library to calculate system irradiance and temperature, which we use to calculate progression in LETID states.\n", - "\n", - "\n", - "\n", - "**Requirements:**\n", - "- `pvlib`, `pandas`, `numpy`, `matplotlib`, `scipy`\n", - "\n", - "**Objectives:**\n", - "1. Load data from example test results\n", - "2. Use `pvlib` and provided weather files to set up a temperature and injection timeseries\n", - "3. Define necessary solar cell device parameters\n", - "4. Define necessary degradation parameters: degraded lifetime and defect states\n", - "5. Run through timeseries, calculating defect states\n", - "6. Calculate device degradation and plot\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# if running on google colab, uncomment the next line and execute this cell to install the dependencies and prevent \"ModuleNotFoundError\" in later cells:\n", - "# !pip install pvdeg==0.3.3" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "from pvdeg import letid, collection, utilities, DATA_DIR\n", - "\n", - "import pvlib\n", - "import os\n", - "import pandas as pd\n", - "import numpy as np\n", - "\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "First, we'll load some data taken from an accelerated test. See [Karas *et al.* 2022](https://onlinelibrary.wiley.com/doi/10.1002/pip.3573) for full details. This data is the average of \"Type S\" modules from Lab 3. Type S modules were prototype modules made with 48 monocrystalline cells, and degraded about 4-5% in LETID testing. Data throughout testing is shown below:" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "cell_area = 243" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " Week Pmp Isc Voc FF cell Voc Jsc % degradation\n", - "0 0 236.7 9.91 31.86 75.01 0.663750 40.781893 0.000000\n", - "1 1 230.9 9.82 31.63 74.38 0.658958 40.411523 -2.511910\n", - "2 2 228.5 9.72 31.54 74.50 0.657083 40.000000 -3.588621\n", - "3 3 226.9 9.70 31.56 74.10 0.657500 39.917695 -4.319083\n", - "4 4 224.8 9.62 31.48 74.22 0.655833 39.588477 -5.293594\n", - "5 7 235.8 9.76 31.98 75.51 0.666250 40.164609 -0.381679" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df = pd.read_csv(\"example data/module test data.csv\")\n", - "df[\"cell Voc\"] = df[\"Voc\"] / 48\n", - "df[\"Jsc\"] = df[\"Isc\"] / cell_area * 1000\n", - "df[\"% degradation\"] = (df[\"Pmp\"] - df[\"Pmp\"].iloc[0]) / df[\"Pmp\"] * 100\n", - "df" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The module parameter that is most sensitve to our device input parameters is cell open-circuit voltage, which in this case started at about 0.664 V/cell. We will select reasonable values for solar cell input parameters, and use ```letid.calc_voc_from_tau()``` to check if those parameters match the cell Voc of the device we're trying to model. The important quantities here are bulk lifetime in the initial state (```tau_0```), wafer thickness, and rear surface recombination velocity." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.6611301062313933" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tau_0 = 120 # [us] reasonable bulk lifetime for commercial-quality p-type silicon, maybe a little on the low side for typical wafer lifetimes, which could range from 100-500 us.\n", - "\n", - "wafer_thickness = 180 # [um] a reasonable wafer thickness for typical commercial silicon solar cells. Wafer thicknesses for Si solar cells are typically 160-180 um.\n", - "\n", - "srv_rear = 100 # [cm/s] a reasonable value for rear surface recombination velocity for passivated emitter and rear cell (PERC) solar cells.\n", - "# Typical PERC cells might have a rear SRV anywhere from 50 to 100 cm/s. Higher values for early PERC, lower values for more modern, higher efficiency PERC.\n", - "# Other device structures will have different SRV values. E.g., aluminum back surface field (Al-BSF) cells could be ~500 cm/s. TopCON or other high efficiency cell structures will be lower, e.g. 10 cm/s.\n", - "# Note that all of these values are intepreted as \"lumped\" values for the entire rear surface.\n", - "\n", - "isc_0 = df.query(\"Week == 0\")[\n", - " \"Isc\"\n", - "].item() # [A] we'll use the short circuit current from the Week 0 test data, instead of trying to calculate it\n", - "cell_area = 240.8 # [cm^2] typical cell size for 6-inch pseudosquare monocrystalline silicon wafers\n", - "jsc_0 = isc_0 / cell_area * 1000 # [mA/cm^2] short circuit current density\n", - "\n", - "temperature = 25 # [C] standard measurement temperature\n", - "\n", - "voc_0 = letid.calc_voc_from_tau(tau_0, wafer_thickness, srv_rear, jsc_0, temperature)\n", - "voc_0" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "generation_df = pd.read_excel(\n", - " os.path.join(DATA_DIR, \"PVL_GenProfile.xlsx\"), header=0\n", - ") # this is an optical generation profile generated by PVLighthouse's OPAL2 default model for 1-sun, normal incident AM1.5 sunlight on a 180-um thick SiNx-coated, pyramid-textured wafer.\n", - "generation = generation_df[\"Generation (cm-3s-1)\"]\n", - "depth = generation_df[\"Depth (um)\"]\n", - "\n", - "d_base = 27 # cm^2/s electron diffusivity. See https://www2.pvlighthouse.com.au/calculators/mobility%20calculator/mobility%20calculator.aspx for details" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Pretty close!" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(235.89622533600513, 236.7)" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# check to make sure power is close to the measured Week 0 power\n", - "\n", - "ff_0 = df.query(\"Week == 0\")[\"FF\"].item() # [%] fill factor\n", - "\n", - "pmp_0 = (voc_0 * 48) * (jsc_0 * cell_area / 1000) * ff_0 / 100 # [W] maximum power\n", - "pmp_0, df.query(\"Week == 0\")[\"Pmp\"].item()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we do the same thing for the degraded state to determine ```tau_deg```, the bulk lifetime when the module is in its most degraded state. So here the cell Voc target is the roughly 0.656 V measured after 4 weeks of testing." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.6538686361228122" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tau_deg = 80 # [us] degraded bulk lifetime\n", - "\n", - "isc_deg = df.query(\"Week == 4\")[\n", - " \"Isc\"\n", - "].item() # [A] we'll use the short circuit current from the Week 4 test data, instead of trying to calculate it\n", - "jsc_deg = isc_deg / cell_area * 1000 # [mA/cm^2] short circuit current density\n", - "\n", - "voc_deg = letid.calc_voc_from_tau(\n", - " tau_deg, wafer_thickness, srv_rear, jsc_deg, temperature\n", - ")\n", - "voc_deg" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(224.09272908700694, 224.8)" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# check to make sure power is close to the measured Week 4 power\n", - "\n", - "ff_deg = df.query(\"Week == 4\")[\"FF\"].item() # [%] fill factor\n", - "\n", - "pmp_deg = (\n", - " (voc_deg * 48) * (jsc_deg * cell_area / 1000) * ff_deg / 100\n", - ") # [W] maximum power\n", - "pmp_deg, df.query(\"Week == 4\")[\"Pmp\"].item()" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.050036816961295416" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "(pmp_0 - pmp_deg) / pmp_0" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "So for modeling this module, we will use ```tau_0``` = 120 $\\mu s$, ```tau_deg``` = 80 $\\mu s$, with ```wafer_thickness``` = 180 $\\mu m$ and ```srv_rear``` = 100 cm/s.\n", - "\n", - "Great!\n", - "\n", - "The example proceeds below in similar fashion to the outdoor example, using a fixed latitude tilt system at NREL, in Golden, CO, USA, using [NSRDB](https://nsrdb.nrel.gov/) hourly PSM weather data, SAPM temperature models, and module and inverter models from the CEC database." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "# load weather and location data, use pvlib read_psm3 function\n", - "\n", - "sam_file = r\"example data\\psm3.csv\"\n", - "weather, meta = pvlib.iotools.read_psm3(sam_file, map_variables=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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8760 rows Γ— 15 columns

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" - ], - "text/plain": [ - " Year Month Day Hour Minute dni dhi ghi \n", - "1999-01-01 00:30:00-07:00 1999 1 1 0 30 0.0 0.0 0.0 \\\n", - "1999-01-01 01:30:00-07:00 1999 1 1 1 30 0.0 0.0 0.0 \n", - "1999-01-01 02:30:00-07:00 1999 1 1 2 30 0.0 0.0 0.0 \n", - "1999-01-01 03:30:00-07:00 1999 1 1 3 30 0.0 0.0 0.0 \n", - "1999-01-01 04:30:00-07:00 1999 1 1 4 30 0.0 0.0 0.0 \n", - "... ... ... ... ... ... ... ... ... \n", - "1999-12-31 19:30:00-07:00 1999 12 31 19 30 0.0 0.0 0.0 \n", - "1999-12-31 20:30:00-07:00 1999 12 31 20 30 0.0 0.0 0.0 \n", - "1999-12-31 21:30:00-07:00 1999 12 31 21 30 0.0 0.0 0.0 \n", - "1999-12-31 22:30:00-07:00 1999 12 31 22 30 0.0 0.0 0.0 \n", - "1999-12-31 23:30:00-07:00 1999 12 31 23 30 0.0 0.0 0.0 \n", - "\n", - " temp_air Dew Point wind_speed relative_humidity \n", - "1999-01-01 00:30:00-07:00 0.0 -5.0 1.8 79.39 \\\n", - "1999-01-01 01:30:00-07:00 0.0 -4.0 1.7 80.84 \n", - "1999-01-01 02:30:00-07:00 0.0 -4.0 1.5 82.98 \n", - "1999-01-01 03:30:00-07:00 0.0 -4.0 1.3 85.01 \n", - "1999-01-01 04:30:00-07:00 0.0 -4.0 1.3 85.81 \n", - "... ... ... ... ... \n", - "1999-12-31 19:30:00-07:00 0.0 -3.0 0.9 83.63 \n", - "1999-12-31 20:30:00-07:00 0.0 -3.0 1.2 86.82 \n", - "1999-12-31 21:30:00-07:00 0.0 -4.0 1.6 83.78 \n", - "1999-12-31 22:30:00-07:00 0.0 -4.0 1.7 81.22 \n", - "1999-12-31 23:30:00-07:00 0.0 -5.0 1.8 79.43 \n", - "\n", - " poa_global temp_cell temp_module \n", - "1999-01-01 00:30:00-07:00 0.0 0.0 0.0 \n", - "1999-01-01 01:30:00-07:00 0.0 0.0 0.0 \n", - "1999-01-01 02:30:00-07:00 0.0 0.0 0.0 \n", - "1999-01-01 03:30:00-07:00 0.0 0.0 0.0 \n", - "1999-01-01 04:30:00-07:00 0.0 0.0 0.0 \n", - "... ... ... ... \n", - "1999-12-31 19:30:00-07:00 0.0 0.0 0.0 \n", - "1999-12-31 20:30:00-07:00 0.0 0.0 0.0 \n", - "1999-12-31 21:30:00-07:00 0.0 0.0 0.0 \n", - "1999-12-31 22:30:00-07:00 0.0 0.0 0.0 \n", - "1999-12-31 23:30:00-07:00 0.0 0.0 0.0 \n", - "\n", - "[8760 rows x 15 columns]" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "weather" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "# if our weather file doesn't have precipitable water, calculate it with pvlib\n", - "if not \"precipitable_water\" in weather.columns:\n", - " weather[\"precipitable_water\"] = pvlib.atmosphere.gueymard94_pw(\n", - " weather[\"temp_air\"], weather[\"relative_humidity\"]\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "# drop unneeded columns\n", - "weather = weather[\n", - " [\n", - " \"ghi\",\n", - " \"dni\",\n", - " \"dhi\",\n", - " \"temp_air\",\n", - " \"wind_speed\",\n", - " \"relative_humidity\",\n", - " \"precipitable_water\",\n", - " ]\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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ghidnidhitemp_airwind_speedrelative_humidityprecipitable_water
1999-01-01 00:30:00-07:000.00.00.00.01.879.390.891869
1999-01-01 01:30:00-07:000.00.00.00.01.780.840.908158
1999-01-01 02:30:00-07:000.00.00.00.01.582.980.932199
1999-01-01 03:30:00-07:000.00.00.00.01.385.010.955004
1999-01-01 04:30:00-07:000.00.00.00.01.385.810.963991
........................
1999-12-31 19:30:00-07:000.00.00.00.00.983.630.939501
1999-12-31 20:30:00-07:000.00.00.00.01.286.820.975338
1999-12-31 21:30:00-07:000.00.00.00.01.683.780.941186
1999-12-31 22:30:00-07:000.00.00.00.01.781.220.912427
1999-12-31 23:30:00-07:000.00.00.00.01.879.430.892318
\n", - "

8760 rows Γ— 7 columns

\n", - "
" - ], - "text/plain": [ - " ghi dni dhi temp_air wind_speed \n", - "1999-01-01 00:30:00-07:00 0.0 0.0 0.0 0.0 1.8 \\\n", - "1999-01-01 01:30:00-07:00 0.0 0.0 0.0 0.0 1.7 \n", - "1999-01-01 02:30:00-07:00 0.0 0.0 0.0 0.0 1.5 \n", - "1999-01-01 03:30:00-07:00 0.0 0.0 0.0 0.0 1.3 \n", - "1999-01-01 04:30:00-07:00 0.0 0.0 0.0 0.0 1.3 \n", - "... ... ... ... ... ... \n", - "1999-12-31 19:30:00-07:00 0.0 0.0 0.0 0.0 0.9 \n", - "1999-12-31 20:30:00-07:00 0.0 0.0 0.0 0.0 1.2 \n", - "1999-12-31 21:30:00-07:00 0.0 0.0 0.0 0.0 1.6 \n", - "1999-12-31 22:30:00-07:00 0.0 0.0 0.0 0.0 1.7 \n", - "1999-12-31 23:30:00-07:00 0.0 0.0 0.0 0.0 1.8 \n", - "\n", - " relative_humidity precipitable_water \n", - "1999-01-01 00:30:00-07:00 79.39 0.891869 \n", - "1999-01-01 01:30:00-07:00 80.84 0.908158 \n", - "1999-01-01 02:30:00-07:00 82.98 0.932199 \n", - "1999-01-01 03:30:00-07:00 85.01 0.955004 \n", - "1999-01-01 04:30:00-07:00 85.81 0.963991 \n", - "... ... ... \n", - "1999-12-31 19:30:00-07:00 83.63 0.939501 \n", - "1999-12-31 20:30:00-07:00 86.82 0.975338 \n", - "1999-12-31 21:30:00-07:00 83.78 0.941186 \n", - "1999-12-31 22:30:00-07:00 81.22 0.912427 \n", - "1999-12-31 23:30:00-07:00 79.43 0.892318 \n", - "\n", - "[8760 rows x 7 columns]" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "weather" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Set up PVlib model\n", - "Note that the module we select here is NOT the same \"Type S\" module that was tested for LETID. I'm simply trying to find a module in the CEC database with I-V characteristics that are reasonably close to the tested module, so the pvlib calculated DC results are close to how our Type S module might behave in the field." - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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TechnologyBifacialSTCPTCA_cLengthWidthN_sI_sc_refV_oc_ref...a_refI_L_refI_o_refR_sR_sh_refAdjustgamma_rBIPVVersionDate
Gintech_Energy_GIN_M6_48_220Mono-c-Si0220.48194.51.3131.3240.992489.3131.3...1.5211519.32280.00.10608777.16406322.488531-0.55NSAM 2018.11.11 r21/3/2019
Gintech_Energy_GIN_P6_48_220Multi-c-Si0220.378199.21.3131.3240.992489.2831.3...1.3798159.3791740.00.254869374.7245188.824213-0.49NSAM 2018.11.11 r21/3/2019
Gintech_Energy_GIN_M6_48_225Mono-c-Si0225.844199.11.3131.3240.992489.4531.5...1.5305969.4619890.00.10090579.53174622.462965-0.55NSAM 2018.11.11 r21/3/2019
LG_Electronics_Inc__LG230N8K_G4Mono-c-Si0230.127212.91.2741.30.98489.930.0...1.11089410.3120510.00.307502314.5515142.112491-0.38NSAM 2018.11.11 r21/3/2019
LG_Electronics_Inc__LG235N8K_G4Mono-c-Si0235.216217.61.2741.30.98489.9630.2...1.12468810.3759430.00.278936250.7994693.374386-0.38NSAM 2018.11.11 r21/3/2019
LG_Electronics_Inc__LG240N8K_G4Mono-c-Si0240.1222.41.2741.30.984810.0130.5...1.13498310.3139960.00.2634533976.5139163.244429-0.38NSAM 2018.11.11 r21/3/2019
LG_Electronics_Inc__LG245N8K_G4Mono-c-Si0245.016227.11.2741.30.984810.0630.8...1.15045410.367120.00.2460411112.8308114.077489-0.38NSAM 2018.11.11 r21/3/2019
Prism_Solar_Technologies_Bi48_279BSTCMono-c-Si1220.472199.31.6681.6950.984489.1531.0...1.2663699.150290.00.2109126628.00537113.999823-0.4399NSAM 2018.11.11 r21/3/2019
Prism_Solar_Technologies_Bi48_286BSTCMono-c-Si1225.298204.01.6681.6950.984489.3531.0...1.2663479.3502750.00.2064037016.81640613.996228-0.4399NSAM 2018.11.11 r21/3/2019
\n", - "

9 rows Γ— 25 columns

\n", - "
" - ], - "text/plain": [ - " Technology Bifacial STC PTC \n", - "Gintech_Energy_GIN_M6_48_220 Mono-c-Si 0 220.48 194.5 \\\n", - "Gintech_Energy_GIN_P6_48_220 Multi-c-Si 0 220.378 199.2 \n", - "Gintech_Energy_GIN_M6_48_225 Mono-c-Si 0 225.844 199.1 \n", - "LG_Electronics_Inc__LG230N8K_G4 Mono-c-Si 0 230.127 212.9 \n", - "LG_Electronics_Inc__LG235N8K_G4 Mono-c-Si 0 235.216 217.6 \n", - "LG_Electronics_Inc__LG240N8K_G4 Mono-c-Si 0 240.1 222.4 \n", - "LG_Electronics_Inc__LG245N8K_G4 Mono-c-Si 0 245.016 227.1 \n", - "Prism_Solar_Technologies_Bi48_279BSTC Mono-c-Si 1 220.472 199.3 \n", - "Prism_Solar_Technologies_Bi48_286BSTC Mono-c-Si 1 225.298 204.0 \n", - "\n", - " A_c Length Width N_s I_sc_ref \n", - "Gintech_Energy_GIN_M6_48_220 1.313 1.324 0.992 48 9.31 \\\n", - "Gintech_Energy_GIN_P6_48_220 1.313 1.324 0.992 48 9.28 \n", - "Gintech_Energy_GIN_M6_48_225 1.313 1.324 0.992 48 9.45 \n", - "LG_Electronics_Inc__LG230N8K_G4 1.274 1.3 0.98 48 9.9 \n", - "LG_Electronics_Inc__LG235N8K_G4 1.274 1.3 0.98 48 9.96 \n", - "LG_Electronics_Inc__LG240N8K_G4 1.274 1.3 0.98 48 10.01 \n", - "LG_Electronics_Inc__LG245N8K_G4 1.274 1.3 0.98 48 10.06 \n", - "Prism_Solar_Technologies_Bi48_279BSTC 1.668 1.695 0.984 48 9.15 \n", - "Prism_Solar_Technologies_Bi48_286BSTC 1.668 1.695 0.984 48 9.35 \n", - "\n", - " V_oc_ref ... a_ref I_L_ref \n", - "Gintech_Energy_GIN_M6_48_220 31.3 ... 1.521151 9.3228 \\\n", - "Gintech_Energy_GIN_P6_48_220 31.3 ... 1.379815 9.379174 \n", - "Gintech_Energy_GIN_M6_48_225 31.5 ... 1.530596 9.461989 \n", - "LG_Electronics_Inc__LG230N8K_G4 30.0 ... 1.110894 10.312051 \n", - "LG_Electronics_Inc__LG235N8K_G4 30.2 ... 1.124688 10.375943 \n", - "LG_Electronics_Inc__LG240N8K_G4 30.5 ... 1.134983 10.313996 \n", - "LG_Electronics_Inc__LG245N8K_G4 30.8 ... 1.150454 10.36712 \n", - "Prism_Solar_Technologies_Bi48_279BSTC 31.0 ... 1.266369 9.15029 \n", - "Prism_Solar_Technologies_Bi48_286BSTC 31.0 ... 1.266347 9.350275 \n", - "\n", - " I_o_ref R_s R_sh_ref \n", - "Gintech_Energy_GIN_M6_48_220 0.0 0.106087 77.164063 \\\n", - "Gintech_Energy_GIN_P6_48_220 0.0 0.254869 374.724518 \n", - "Gintech_Energy_GIN_M6_48_225 0.0 0.100905 79.531746 \n", - "LG_Electronics_Inc__LG230N8K_G4 0.0 0.307502 314.551514 \n", - "LG_Electronics_Inc__LG235N8K_G4 0.0 0.278936 250.799469 \n", - "LG_Electronics_Inc__LG240N8K_G4 0.0 0.263453 3976.513916 \n", - "LG_Electronics_Inc__LG245N8K_G4 0.0 0.246041 1112.830811 \n", - "Prism_Solar_Technologies_Bi48_279BSTC 0.0 0.210912 6628.005371 \n", - "Prism_Solar_Technologies_Bi48_286BSTC 0.0 0.206403 7016.816406 \n", - "\n", - " Adjust gamma_r BIPV \n", - "Gintech_Energy_GIN_M6_48_220 22.488531 -0.55 N \\\n", - "Gintech_Energy_GIN_P6_48_220 8.824213 -0.49 N \n", - "Gintech_Energy_GIN_M6_48_225 22.462965 -0.55 N \n", - "LG_Electronics_Inc__LG230N8K_G4 2.112491 -0.38 N \n", - "LG_Electronics_Inc__LG235N8K_G4 3.374386 -0.38 N \n", - "LG_Electronics_Inc__LG240N8K_G4 3.244429 -0.38 N \n", - "LG_Electronics_Inc__LG245N8K_G4 4.077489 -0.38 N \n", - "Prism_Solar_Technologies_Bi48_279BSTC 13.999823 -0.4399 N \n", - "Prism_Solar_Technologies_Bi48_286BSTC 13.996228 -0.4399 N \n", - "\n", - " Version Date \n", - "Gintech_Energy_GIN_M6_48_220 SAM 2018.11.11 r2 1/3/2019 \n", - "Gintech_Energy_GIN_P6_48_220 SAM 2018.11.11 r2 1/3/2019 \n", - "Gintech_Energy_GIN_M6_48_225 SAM 2018.11.11 r2 1/3/2019 \n", - "LG_Electronics_Inc__LG230N8K_G4 SAM 2018.11.11 r2 1/3/2019 \n", - "LG_Electronics_Inc__LG235N8K_G4 SAM 2018.11.11 r2 1/3/2019 \n", - "LG_Electronics_Inc__LG240N8K_G4 SAM 2018.11.11 r2 1/3/2019 \n", - "LG_Electronics_Inc__LG245N8K_G4 SAM 2018.11.11 r2 1/3/2019 \n", - "Prism_Solar_Technologies_Bi48_279BSTC SAM 2018.11.11 r2 1/3/2019 \n", - "Prism_Solar_Technologies_Bi48_286BSTC SAM 2018.11.11 r2 1/3/2019 \n", - "\n", - "[9 rows x 25 columns]" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cec_modules = pvlib.pvsystem.retrieve_sam(\"CECMod\").T\n", - "cec_modules[cec_modules[\"STC\"].between(220, 250) & (cec_modules[\"N_s\"] == 48)]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The LG ones look close to the module we're trying to model. Pmp around 235W, Isc around 9.9A. Let's go with 'LG_Electronics_Inc__LG235N8K_G4'" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "cec_modules = cec_modules.T\n", - "cec_module = cec_modules[\"LG_Electronics_Inc__LG235N8K_G4\"]" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "# import the rest of the pvlib stuff\n", - "# we'll use the SAPM temperature model open-rack glass/polymer coeffecients.\n", - "\n", - "from pvlib.temperature import TEMPERATURE_MODEL_PARAMETERS\n", - "from pvlib.location import Location\n", - "from pvlib.pvsystem import PVSystem\n", - "from pvlib.modelchain import ModelChain\n", - "\n", - "cec_inverters = pvlib.pvsystem.retrieve_sam(\"cecinverter\")\n", - "cec_inverter = cec_inverters[\"ABB__ULTRA_750_TL_OUTD_1_US_690_x_y_z__690V_\"]\n", - "\n", - "temperature_model_parameters = TEMPERATURE_MODEL_PARAMETERS[\"sapm\"][\n", - " \"open_rack_glass_polymer\"\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "# set up system in pvlib\n", - "lat = meta[\"latitude\"]\n", - "lon = meta[\"longitude\"]\n", - "tz = meta[\"Local Time Zone\"]\n", - "elevation = meta[\"altitude\"]\n", - "surface_tilt = lat # fixed, latitude tilt\n", - "surface_azimuth = 180 # south-facing\n", - "\n", - "location = Location(lat, lon, tz, elevation, \"Golden, CO, USA\")\n", - "\n", - "system = PVSystem(\n", - " surface_tilt=surface_tilt,\n", - " surface_azimuth=surface_azimuth,\n", - " module_parameters=cec_module,\n", - " inverter_parameters=cec_inverter,\n", - " temperature_model_parameters=temperature_model_parameters,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "ModelChain: \n", - " name: None\n", - " clearsky_model: ineichen\n", - " transposition_model: haydavies\n", - " solar_position_method: nrel_numpy\n", - " airmass_model: kastenyoung1989\n", - " dc_model: cec\n", - " ac_model: sandia_inverter\n", - " aoi_model: physical_aoi_loss\n", - " spectral_model: first_solar_spectral_loss\n", - " temperature_model: sapm_temp\n", - " losses_model: no_extra_losses" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# create and run pvlib modelchain\n", - "mc = ModelChain(system, location, aoi_model=\"physical\")\n", - "mc.run_model(weather)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Set up timeseries\n", - "In this example, injection is a function of both the operating point of the module (which we will assume is maximum power point) and irradiance. Maximum power point injection is equivalent to $(I_{sc}-I_{mp})/I_{sc}\\times Ee$, where $Ee$ is effective irradiance, the irradiance absorbed by the module's cells. We normalize it to 1-sun irradiance, 1000 $W/m^2$.\n", - "\n", - "We will use the irradiance, DC operating point, and cell temperature from the pvlib modelchain results." - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [], - "source": [ - "ee = mc.results.effective_irradiance\n", - "injection = (\n", - " (mc.results.dc[\"i_sc\"] - mc.results.dc[\"i_mp\"])\n", - " / (mc.results.dc[\"i_sc\"])\n", - " * (ee / 1000)\n", - ")\n", - "temperature = mc.results.cell_temperature\n", - "\n", - "timesteps = pd.DataFrame(\n", - " {\"Temperature\": temperature, \"Injection\": injection}\n", - ") # create a DataFrame with cell temperature and injection\n", - "timesteps.reset_index(\n", - " inplace=True\n", - ") # reset the index so datetime is a column. I prefer integer indexing.\n", - "timesteps.rename(columns={\"index\": \"Datetime\"}, inplace=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "##### Remaining degradation parameters:\n", - "We've already set our important device parameters: ```tau_0```, ```tau_deg```, ```wafer_thickness```, ```srv_rear```, ```cell_area```, etc, but we need a few more: generation profile and carrier diffusivity. These are necessary for calculating current collection, and the \"default\" values provided here should be sufficient for most use cases.\n", - "\n", - "The rest of the quantities to define are: the initial percentage of defects in each state (A, B, and C), and the dictionary of mechanism parameters.\n", - "\n", - "In this example, we'll assume the device starts in the fully-undegraded state (100% state A), and we'll use the parameters for LETID degradation from Repins." - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [], - "source": [ - "generation_df = pd.read_excel(\n", - " os.path.join(DATA_DIR, \"PVL_GenProfile.xlsx\"), header=0\n", - ") # this is an optical generation profile generated by PVLighthouse's OPAL2 default model for 1-sun, normal incident AM1.5 sunlight on a 180-um thick SiNx-coated, pyramid-textured wafer.\n", - "generation = generation_df[\"Generation (cm-3s-1)\"]\n", - "depth = generation_df[\"Depth (um)\"]\n", - "\n", - "d_base = 27 # cm^2/s electron diffusivity. See https://www2.pvlighthouse.com.au/calculators/mobility%20calculator/mobility%20calculator.aspx for details" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [], - "source": [ - "# starting defect state percentages\n", - "nA_0 = 100\n", - "nB_0 = 0\n", - "nC_0 = 0\n", - "\n", - "mechanism_params = utilities.get_kinetics(\"repins\")\n", - "\n", - "timesteps[[\"NA\", \"NB\", \"NC\", \"tau\"]] = (\n", - " np.nan\n", - ") # create columns for defect state percentages and lifetime, fill with NaNs for now, to fill iteratively below\n", - "\n", - "timesteps.loc[0, [\"NA\", \"NB\", \"NC\"]] = (\n", - " nA_0,\n", - " nB_0,\n", - " nC_0,\n", - ") # assign first timestep defect state percentages\n", - "timesteps.loc[0, \"tau\"] = letid.tau_now(\n", - " tau_0, tau_deg, nB_0\n", - ") # calculate tau for the first timestep" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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DatetimeTemperatureInjectionNANBNCtau
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" - ], - "text/plain": [ - " Datetime Temperature Injection NA NB NC tau\n", - "0 1999-01-01 00:30:00-07:00 0.0 NaN 100.0 0.0 0.0 120.0\n", - "1 1999-01-01 01:30:00-07:00 0.0 NaN NaN NaN NaN NaN\n", - "2 1999-01-01 02:30:00-07:00 0.0 NaN NaN NaN NaN NaN\n", - "3 1999-01-01 03:30:00-07:00 0.0 NaN NaN NaN NaN NaN\n", - "4 1999-01-01 04:30:00-07:00 0.0 NaN NaN NaN NaN NaN\n", - "... ... ... ... ... ... ... ...\n", - "8755 1999-12-31 19:30:00-07:00 0.0 NaN NaN NaN NaN NaN\n", - "8756 1999-12-31 20:30:00-07:00 0.0 NaN NaN NaN NaN NaN\n", - "8757 1999-12-31 21:30:00-07:00 0.0 NaN NaN NaN NaN NaN\n", - "8758 1999-12-31 22:30:00-07:00 0.0 NaN NaN NaN NaN NaN\n", - "8759 1999-12-31 23:30:00-07:00 0.0 NaN NaN NaN NaN NaN\n", - "\n", - "[8760 rows x 7 columns]" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "timesteps" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Run through timesteps\n", - "Since each timestep depends on the preceding timestep, we need to calculate in a loop. This will take a few minutes depending on the length of the timeseries." - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [], - "source": [ - "for index, timestep in timesteps.iterrows():\n", - "\n", - " # first row tau has already been assigned\n", - " if index == 0:\n", - " pass\n", - "\n", - " # loop through rows, new tau calculated based on previous NB. Reaction proceeds based on new tau.\n", - " else:\n", - " n_A = timesteps.at[index - 1, \"NA\"]\n", - " n_B = timesteps.at[index - 1, \"NB\"]\n", - " n_C = timesteps.at[index - 1, \"NC\"]\n", - "\n", - " tau = letid.tau_now(tau_0, tau_deg, n_B)\n", - " jsc = collection.calculate_jsc_from_tau_cp(\n", - " tau, wafer_thickness, d_base, srv_rear, generation, depth\n", - " )\n", - "\n", - " temperature = timesteps.at[index, \"Temperature\"]\n", - " injection = timesteps.at[index, \"Injection\"]\n", - "\n", - " # calculate defect reaction kinetics: reaction constant and carrier concentration factor.\n", - " k_AB = letid.k_ij(\n", - " mechanism_params[\"v_ab\"], mechanism_params[\"ea_ab\"], temperature\n", - " )\n", - " k_BA = letid.k_ij(\n", - " mechanism_params[\"v_ba\"], mechanism_params[\"ea_ba\"], temperature\n", - " )\n", - " k_BC = letid.k_ij(\n", - " mechanism_params[\"v_bc\"], mechanism_params[\"ea_bc\"], temperature\n", - " )\n", - " k_CB = letid.k_ij(\n", - " mechanism_params[\"v_cb\"], mechanism_params[\"ea_cb\"], temperature\n", - " )\n", - "\n", - " x_ab = letid.carrier_factor(\n", - " tau,\n", - " \"ab\",\n", - " temperature,\n", - " injection,\n", - " jsc,\n", - " wafer_thickness,\n", - " srv_rear,\n", - " mechanism_params,\n", - " )\n", - " x_ba = letid.carrier_factor(\n", - " tau,\n", - " \"ba\",\n", - " temperature,\n", - " injection,\n", - " jsc,\n", - " wafer_thickness,\n", - " srv_rear,\n", - " mechanism_params,\n", - " )\n", - " x_bc = letid.carrier_factor(\n", - " tau,\n", - " \"bc\",\n", - " temperature,\n", - " injection,\n", - " jsc,\n", - " wafer_thickness,\n", - " srv_rear,\n", - " mechanism_params,\n", - " )\n", - "\n", - " # calculate the instantaneous change in NA, NB, and NC\n", - " dN_Adt = (k_BA * n_B * x_ba) - (k_AB * n_A * x_ab)\n", - " dN_Bdt = (\n", - " (k_AB * n_A * x_ab) + (k_CB * n_C) - ((k_BA * x_ba + k_BC * x_bc) * n_B)\n", - " )\n", - " dN_Cdt = (k_BC * n_B * x_bc) - (k_CB * n_C)\n", - "\n", - " t_step = (\n", - " timesteps.at[index, \"Datetime\"] - timesteps.at[index - 1, \"Datetime\"]\n", - " ).total_seconds()\n", - "\n", - " # assign new defect state percentages\n", - " timesteps.at[index, \"NA\"] = n_A + dN_Adt * t_step\n", - " timesteps.at[index, \"NB\"] = n_B + dN_Bdt * t_step\n", - " timesteps.at[index, \"NC\"] = n_C + dN_Cdt * t_step" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [], - "source": [ - "timesteps[\"tau\"] = letid.tau_now(tau_0, tau_deg, timesteps[\"NB\"])\n", - "\n", - "# calculate device Jsc for every timestep. Unfortunately this requires an integration so I think we have to run through a loop. Device Jsc allows calculation of device Voc.\n", - "for index, timestep in timesteps.iterrows():\n", - " jsc_now = collection.calculate_jsc_from_tau_cp(\n", - " timesteps.at[index, \"tau\"], wafer_thickness, d_base, srv_rear, generation, depth\n", - " )\n", - " timesteps.at[index, \"Jsc\"] = jsc_now\n", - " timesteps.at[index, \"Voc\"] = letid.calc_voc_from_tau(\n", - " timesteps.at[index, \"tau\"], wafer_thickness, srv_rear, jsc_now, temperature=25\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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DatetimeTemperatureInjectionNANBNCtauJscVocIscFFPmpPmp_norm
01999-01-01 00:30:00-07:000.0NaN100.0000000.000000e+000.000000e+00120.00000038.5415120.6594459.2807960.8397685.1395261.000000
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21999-01-01 02:30:00-07:000.0NaN100.0000003.366181e-155.269266e-36120.00000038.5415120.6594459.2807960.8397685.1395261.000000
31999-01-01 03:30:00-07:000.0NaN100.0000005.049271e-151.580780e-35120.00000038.5415120.6594459.2807960.8397685.1395261.000000
41999-01-01 04:30:00-07:000.0NaN100.0000006.732362e-153.161560e-35120.00000038.5415120.6594459.2807960.8397685.1395261.000000
..........................................
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8760 rows Γ— 13 columns

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" - ], - "text/plain": [ - " Datetime Temperature Injection NA \n", - "0 1999-01-01 00:30:00-07:00 0.0 NaN 100.000000 \\\n", - "1 1999-01-01 01:30:00-07:00 0.0 NaN 100.000000 \n", - "2 1999-01-01 02:30:00-07:00 0.0 NaN 100.000000 \n", - "3 1999-01-01 03:30:00-07:00 0.0 NaN 100.000000 \n", - "4 1999-01-01 04:30:00-07:00 0.0 NaN 100.000000 \n", - "... ... ... ... ... \n", - "8755 1999-12-31 19:30:00-07:00 0.0 NaN 31.835095 \n", - "8756 1999-12-31 20:30:00-07:00 0.0 NaN 31.835095 \n", - "8757 1999-12-31 21:30:00-07:00 0.0 NaN 31.835095 \n", - "8758 1999-12-31 22:30:00-07:00 0.0 NaN 31.835095 \n", - "8759 1999-12-31 23:30:00-07:00 0.0 NaN 31.835095 \n", - "\n", - " NB NC tau Jsc Voc Isc \n", - "0 0.000000e+00 0.000000e+00 120.000000 38.541512 0.659445 9.280796 \\\n", - "1 1.683090e-15 0.000000e+00 120.000000 38.541512 0.659445 9.280796 \n", - "2 3.366181e-15 5.269266e-36 120.000000 38.541512 0.659445 9.280796 \n", - "3 5.049271e-15 1.580780e-35 120.000000 38.541512 0.659445 9.280796 \n", - "4 6.732362e-15 3.161560e-35 120.000000 38.541512 0.659445 9.280796 \n", - "... ... ... ... ... ... ... \n", - "8755 6.509121e+01 3.073695e+00 90.534877 38.435333 0.654947 9.255228 \n", - "8756 6.509121e+01 3.073695e+00 90.534877 38.435333 0.654947 9.255228 \n", - "8757 6.509121e+01 3.073695e+00 90.534877 38.435333 0.654947 9.255228 \n", - "8758 6.509121e+01 3.073695e+00 90.534877 38.435333 0.654947 9.255228 \n", - "8759 6.509121e+01 3.073695e+00 90.534877 38.435333 0.654947 9.255228 \n", - "\n", - " FF Pmp Pmp_norm \n", - "0 0.839768 5.139526 1.000000 \n", - "1 0.839768 5.139526 1.000000 \n", - "2 0.839768 5.139526 1.000000 \n", - "3 0.839768 5.139526 1.000000 \n", - "4 0.839768 5.139526 1.000000 \n", - "... ... ... ... \n", - "8755 0.838961 5.085517 0.989491 \n", - "8756 0.838961 5.085517 0.989491 \n", - "8757 0.838961 5.085517 0.989491 \n", - "8758 0.838961 5.085517 0.989491 \n", - "8759 0.838961 5.085517 0.989491 \n", - "\n", - "[8760 rows x 13 columns]" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "timesteps = letid.calc_device_params(\n", - " timesteps, cell_area\n", - ") # this function quickly calculates the rest of the device parameters: Isc, FF, max power, and normalized max power\n", - "\n", - "timesteps" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note of course that all these calculated device parameters are modeled STC device parameters, not the instantaneous, weather-dependent values. We'll merge back in the pvlib results for convenience, but these don't reflect the device degradation. We'll calculate energy loss next" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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DatetimeTemperatureInjectionNANBNCtauJscVocIsc...p_mpi_xi_xxEffective irradianceghidhidniwind_speedtemp_airprecipitable_water
01999-01-01 00:30:00-07:000.0NaN100.0000000.000000e+000.000000e+00120.00000038.5415120.6594459.280796...0.00.00.0NaN0.00.00.01.80.00.891869
11999-01-01 01:30:00-07:000.0NaN100.0000001.683090e-150.000000e+00120.00000038.5415120.6594459.280796...0.00.00.0NaN0.00.00.01.70.00.908158
21999-01-01 02:30:00-07:000.0NaN100.0000003.366181e-155.269266e-36120.00000038.5415120.6594459.280796...0.00.00.0NaN0.00.00.01.50.00.932199
31999-01-01 03:30:00-07:000.0NaN100.0000005.049271e-151.580780e-35120.00000038.5415120.6594459.280796...0.00.00.0NaN0.00.00.01.30.00.955004
41999-01-01 04:30:00-07:000.0NaN100.0000006.732362e-153.161560e-35120.00000038.5415120.6594459.280796...0.00.00.0NaN0.00.00.01.30.00.963991
..................................................................
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8760 rows Γ— 27 columns

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" - ], - "text/plain": [ - " Datetime Temperature Injection NA \n", - "0 1999-01-01 00:30:00-07:00 0.0 NaN 100.000000 \\\n", - "1 1999-01-01 01:30:00-07:00 0.0 NaN 100.000000 \n", - "2 1999-01-01 02:30:00-07:00 0.0 NaN 100.000000 \n", - "3 1999-01-01 03:30:00-07:00 0.0 NaN 100.000000 \n", - "4 1999-01-01 04:30:00-07:00 0.0 NaN 100.000000 \n", - "... ... ... ... ... \n", - "8755 1999-12-31 19:30:00-07:00 0.0 NaN 31.835095 \n", - "8756 1999-12-31 20:30:00-07:00 0.0 NaN 31.835095 \n", - "8757 1999-12-31 21:30:00-07:00 0.0 NaN 31.835095 \n", - "8758 1999-12-31 22:30:00-07:00 0.0 NaN 31.835095 \n", - "8759 1999-12-31 23:30:00-07:00 0.0 NaN 31.835095 \n", - "\n", - " NB NC tau Jsc Voc Isc \n", - "0 0.000000e+00 0.000000e+00 120.000000 38.541512 0.659445 9.280796 \\\n", - "1 1.683090e-15 0.000000e+00 120.000000 38.541512 0.659445 9.280796 \n", - "2 3.366181e-15 5.269266e-36 120.000000 38.541512 0.659445 9.280796 \n", - "3 5.049271e-15 1.580780e-35 120.000000 38.541512 0.659445 9.280796 \n", - "4 6.732362e-15 3.161560e-35 120.000000 38.541512 0.659445 9.280796 \n", - "... ... ... ... ... ... ... \n", - "8755 6.509121e+01 3.073695e+00 90.534877 38.435333 0.654947 9.255228 \n", - "8756 6.509121e+01 3.073695e+00 90.534877 38.435333 0.654947 9.255228 \n", - "8757 6.509121e+01 3.073695e+00 90.534877 38.435333 0.654947 9.255228 \n", - "8758 6.509121e+01 3.073695e+00 90.534877 38.435333 0.654947 9.255228 \n", - "8759 6.509121e+01 3.073695e+00 90.534877 38.435333 0.654947 9.255228 \n", - "\n", - " ... p_mp i_x i_xx Effective irradiance ghi dhi dni wind_speed \n", - "0 ... 0.0 0.0 0.0 NaN 0.0 0.0 0.0 1.8 \\\n", - "1 ... 0.0 0.0 0.0 NaN 0.0 0.0 0.0 1.7 \n", - "2 ... 0.0 0.0 0.0 NaN 0.0 0.0 0.0 1.5 \n", - "3 ... 0.0 0.0 0.0 NaN 0.0 0.0 0.0 1.3 \n", - "4 ... 0.0 0.0 0.0 NaN 0.0 0.0 0.0 1.3 \n", - "... ... ... ... ... ... ... ... ... ... \n", - "8755 ... 0.0 0.0 0.0 NaN 0.0 0.0 0.0 0.9 \n", - "8756 ... 0.0 0.0 0.0 NaN 0.0 0.0 0.0 1.2 \n", - "8757 ... 0.0 0.0 0.0 NaN 0.0 0.0 0.0 1.6 \n", - "8758 ... 0.0 0.0 0.0 NaN 0.0 0.0 0.0 1.7 \n", - "8759 ... 0.0 0.0 0.0 NaN 0.0 0.0 0.0 1.8 \n", - "\n", - " temp_air precipitable_water \n", - "0 0.0 0.891869 \n", - "1 0.0 0.908158 \n", - "2 0.0 0.932199 \n", - "3 0.0 0.955004 \n", - "4 0.0 0.963991 \n", - "... ... ... \n", - "8755 0.0 0.939501 \n", - "8756 0.0 0.975338 \n", - "8757 0.0 0.941186 \n", - "8758 0.0 0.912427 \n", - "8759 0.0 0.892318 \n", - "\n", - "[8760 rows x 27 columns]" - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "timesteps = timesteps.merge(mc.results.dc, left_on=\"Datetime\", right_index=True)\n", - "timesteps = timesteps.merge(\n", - " pd.DataFrame(mc.results.effective_irradiance, columns=[\"Effective irradiance\"]),\n", - " left_on=\"Datetime\",\n", - " right_index=True,\n", - ")\n", - "timesteps = timesteps.merge(mc.results.weather, left_on=\"Datetime\", right_index=True)\n", - "\n", - "timesteps" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Plot the results" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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Z2Ult27Z963YkKWfX36ioKMnExESqXr262jVDkiT5npGSkiI5OztLjo6O0vPnzzPNI0mS1KBBA6l8+fJSQkKC2vKaNWtKpUqVktPevK+kpaVJpUqVynCfio+Pl5ydnaWGDRvKaenXg86dO+dof9evXy8B0tGjR+W0mTNnZrhGS5Ik3b17V9LW1pamTJmiln7lyhVJR0dHTk+/znp6ekqJiYlyvuXLl0tAptf6N2V1zcvLczyr/cxK+vdt6dKlaulr1qyRtLS0pGPHjqmlL126VAKkEydOSJIkSXPnzs1wrX5TZvdHScq8rPHm9ScyMlICpPHjx6t9Nifnd0HyXo+MGzZsyKlTp2jZsiWXL19mxowZ+Pr64uDgkOOq+N69e6s9lqpevTqSJNG7d285TVtbmypVqnDnzp33CVfN6zVd0dHRPHnyhLp163Lnzh2io6Oz/fymTZuoXbs2FhYWPHnyRH75+PiQmprK0aNHAdi9ezc6OjoMHDhQbX9y2gkkNjYWExOTt+ZJXx4TE5OjdebE7t27+eyzz9R+/VpbW2d4dOvv709SUhLDhg1T6zTUt29fTE1N2bVrFwDnz5/n8ePHfP3112rtzZo1a0bZsmXlfK97/ZjllX79+nHgwIEMLzc3tzzfVmbSO6TExsbmKP/gwYPl/6f/Mk9KSsLf318tX9u2bdV+YT969IhLly7Ro0cPLC0t5fQKFSrQsGFDdu/eDahqIf39/fniiy8oWrSonM/V1TXLWp66deuqHS9JktiyZQstWrRAkiS188HX15fo6Gj5ca+5uTkPHz7k3LlzWe6zubk5Z86c4b///svJIQJU31dtbW2GDh2qlv7tt98iSRJ79uxRS/fx8VFrglGhQgVMTU1zfI3p1q2b2nnZrl077O3t5eMK6teYuLg4njx5Qs2aNZEkiYsXL8p5lEolR44c4fnz55lu68CBA0RFRdG5c2e1Y6utrU316tU5fPgw8Opv3r17d8zMzOTPN2zYMEff75yey+mMjY3V2soqlUqqVauWo2OYk+vvgQMHiI2NZdSoURnaqKbfMy5evEhoaCjDhg3L0HQpPc+zZ884dOgQHTp0IDY2Vj5+T58+xdfXl5CQkAzNVtJdunSJkJAQvvzyS54+fSp/Ni4ujgYNGnD06NEMzWgGDBjw1v1NSEjgyZMnfPbZZwBqTSGy8vfff5OWlkaHDh3UvgN2dnaUKlVK/g6kX2cHDBig1oY4vcnSu8rrc/xd6Onp0bNnT7W0TZs2Ua5cOcqWLasW0+effw4gH5f078Y///yTr53RcnJ+FyTv/YysatWq/P333yQlJXH58mW2bt3K3LlzadeuHZcuXcr2QlSiRAm19+lf2jerts3MzPL0gJ44cYLx48dz6tQp4uPj1ZZFR0dne/KEhIQQGBiYZTV3eqeae/fuYW9vn6FnapkyZXIUp4mJSbaFh/Tl2RUcc+PevXtUr149Q/qbcd+7dy/TdKVSScmSJeXlWeUDKFu2LMePH1dL09HRyfFjrtwoVaoUPj4+eb7enHrx4gWQs7+VlpZWhk4u6Y+Z32x/4uzsrPb+bce7XLly7Nu3j7i4OGJiYnj58iWurq4Z8mWWltm2IiMjiYqKYvny5SxfvjzTz6SfD99//z3+/v5Uq1YNV1dXGjVqxJdffkmtWrXkvDNmzKB79+4UL14cLy8vmjZtSrdu3TIcizf3t2jRohmOa7ly5eTlr3vzugNgYWGR42tMqVKl1N4rFApcXV3V/i73799n3LhxbN++PcN60ws9enp6TJ8+nW+//RZbW1s+++wzmjdvTrdu3bCzswNU1xpAvsm9Kb3Hevo+vhkbqL4H2RU8cnoupytWrFiGNqYWFhYEBga+dTuQs+vv7du3Ad7aHCkneW7duoUkSYwdO5axY8dmmufx48c4ODhkSE8/9m9r4hMdHY2FhYX8/s3zA1SF0okTJ/LXX39l6HCZkwqIkJAQJEnK9G8LrzpZZfUd0NXVfev5k528PsffhYODQ4aOciEhIVy7di3b+3DHjh357bff6NOnD6NGjaJBgwa0adOGdu3afdDRT3JyfhckedZoSqlUUrVqVapWrUrp0qXp2bMnmzZtYvz48W/9nLa2do7Tpdcah2c10HRqamq2sd6+fZsGDRpQtmxZ5syZQ/HixVEqlezevZu5c+fm6BdEWloaDRs2ZOTIkZkuz659WE6VK1eOixcvkpiYmGVP7cDAQHR1deWLwPscm4JCT0/voxymKDtBQUFA1oWtd5WbtqN5va308+Wrr77K8saZ3p6mXLly3Lhxg507d7J37162bNnC4sWLGTdunDxsQ4cOHahduzZbt25l//79zJw5k+nTp/P3339n2zYtp7K67rx+jXkfqampNGzYkGfPnvH9999TtmxZjIyMCAsLo0ePHmrXmGHDhtGiRQu2bdvGvn37GDt2LNOmTePQoUNUqlRJzrtmzZpMbyL50fY1M+96DPPi+psb6ev77rvv8PX1zTRPVudj+mdnzpyJp6dnpnne/LGf2bnYoUMHTp48iZ+fH56enhgbG5OWlkbjxo1zfL9RKBTs2bMn0+Oe3VBY7yuvz/F3kdlxTUtLo3z58syZMyfTz6RXLBkYGHD06FEOHz7Mrl272Lt3Lxs2bODzzz9n//79aGtrf7D7Znbnd0HyQa4kVapUAVSPMD6U9F9kb/Z+e/NXbGZ27NhBYmIi27dvV6spSK9efl1WXxIXFxdevHiRbW2To6MjBw8e5MWLF2on7Y0bN7KNE6B58+acOnWKTZs2ZTr0yd27dzl27Bg+Pj7yCfP6sXn9MUpmxyar/XN0dJR/Hb/uzbgdHR3l9Nd/gSYlJREaGiofn9fzvVnTcePGDXl5YbdmzRoUCgUNGzbMNm9aWhp37txR+3GR3js5u8bIrx/vN12/fh0rKyuMjIzQ19dHX1+fW7duZciXWVpmrK2tMTExITU1NUe1r0ZGRnTs2JGOHTuSlJREmzZtmDJlCqNHj5YfDdrb2/P111/z9ddf8/jxYypXrsyUKVOyLBA6Ojri7++foYnF9evX5eV56c1zQ5Ikbt26Jd8Ur1y5ws2bN1m9erVaR7gDBw5kuj4XFxe+/fZbvv32W0JCQvD09GT27NmsXbtWfrRtY2Pz1uObvo85OW/f9vnszuX3ldPrb/p+BwUFZVlgez1PVvGl74uurm6u9yF9/aampu+8/8+fP+fgwYNMnDiRcePGyemZ/Z3edr+RJAlnZ+e3Vja8/h14/TqbnJxMaGgoFStWzDbezGLI63M8r2YOc3Fx4fLlyzRo0CDbdWppadGgQQMaNGjAnDlzmDp1Kj/88AOHDx/Gx8fnvcoU2W37bed3QfJeVTCHDx/O9NdgejuanD4WfReOjo5oa2vLbfXSLV68ONvPpv/Cej326OhoVq5cmSGvkZFRpsOndOjQgVOnTrFv374My6KiokhJSQGgadOmpKSkqA2pkJqamuOZXPr374+NjQ1+fn4Z2uYkJCTQs2dPJElSu9CkX8RePzbpQwnkdP+aNm3K6dOnOXv2rJwWGRnJn3/+qZbPx8cHpVLJL7/8onY8V6xYQXR0NM2aNQNUPxJsbGxYunSp2nAAe/bs4dq1a3K+wuznn39m//79dOzYMctHP29auHCh/H9Jkli4cCG6uro0aNDgrZ+zt7fH09OT1atXq/19g4KC2L9/vzw4t7a2Nj4+Pmzbtk2tzd6tW7cytLvLira2Nm3btmXLli1yDejrIiMj5f+/PswGqJ4suLm5IUkSycnJpKamZniEZmNjQ9GiRTMMI/G6pk2bkpqaqna8AObOnYtCocizmsV0f/zxh1pTjs2bN/Po0SN5O5ldYyRJyjAUSnx8PAkJCWppLi4umJiYyPvr6+uLqakpU6dOJTk5OUMs6cf39b/568fwwIED8jBEb5PTc/l95fT626hRI0xMTJg2bVqGY5T+2cqVK+Ps7My8efMyXMfS89jY2FCvXj2WLVuWaSXF69/PN3l5eeHi4sKsWbPk5h45/Wy6zPYXkEeieF36mIVv7kubNm3Q1tZm4sSJGdYjSZJ8XlWpUgVra2uWLl1KUlKSnGfVqlU5Huoss3tCXp7jb9vP3OrQoQNhYWH8+uuvGZa9fPmSuLg4QPXI/k3pNb7p51lm983U1NQsH5G/Ln0UjDf3Jyfnd0HyXjWEQ4YMIT4+ntatW1O2bFmSkpI4efIkGzZswMnJKUMD0LxkZmZG+/btWbBgAQqFAhcXF3bu3JmhfUZmGjVqhFKppEWLFvTv358XL17w66+/YmNjk+GC4eXlxZIlS5g8eTKurq7Y2Njw+eef4+fnx/bt22nevDk9evTAy8uLuLg4rly5wubNm7l79y5WVla0aNGCWrVqMWrUKO7evYubmxt///13jtqNABQpUoTNmzfTrFkzKleunGGmklu3bjF//ny1QakbNWpEiRIl6N27N35+fmhra/P7779jbW3N/fv3c7R/I0eOZM2aNTRu3JhvvvlGHnbG0dFRrY2QtbU1o0ePZuLEiTRu3JiWLVty48YNFi9eTNWqVeVaTV1dXaZPn07Pnj2pW7cunTt3loedcXJyYvjw4Tk6Hm/z+++/ZzqWXPrQE6BqwJ3ZrzIXFxdq1Kjx3jGAasic9G0kJCRw7949tm/fTmBgIPXr18/RBQZAX1+fvXv30r17d6pXr86ePXvYtWsXY8aMyXaIBlA95mrSpAk1atSgd+/e8rAzZmZm8hhZoBpqZP/+/dSqVYuBAwfKBSsPDw8uXbqUo1h//vlnDh8+TPXq1enbty9ubm48e/aMgIAA/P395Qtyo0aNsLOzo1atWtja2nLt2jUWLlxIs2bNMDExISoqimLFitGuXTsqVqyIsbEx/v7+nDt3jtmzZ2e5/RYtWlC/fn1++OEH7t69S8WKFdm/fz///PMPw4YNyzCG5/uytLTE29ubnj17EhERwbx583B1daVv376Aql2si4sL3333HWFhYZiamrJly5YMbQlv3rxJgwYN6NChA25ubujo6LB161YiIiLo1KkToKqdWrJkCV27dqVy5cp06tRJPpd37dpFrVq15ILwtGnTaNasGd7e3vTq1Ytnz57JY8JlVqB5XU7P5feV0+uvqakpc+fOpU+fPlStWlUe2+/y5cvEx8ezevVqtLS0WLJkCS1atMDT05OePXtib2/P9evXuXr1qvyDfdGiRXh7e1O+fHn69u1LyZIliYiI4NSpUzx8+JDLly9nGquWlha//fYbTZo0wd3dnZ49e+Lg4EBYWBiHDx/G1NSUHTt2vHV/TU1NqVOnDjNmzCA5ORkHBwf2799PaGhohrxeXl4A/PDDD3Tq1AldXV1atGiBi4sLkydPZvTo0dy9e5cvvvgCExMTQkND2bp1K/369eO7775DV1eXyZMn079/fz7//HM6duxIaGgoK1euzHEbQi8vL/z9/ZkzZw5FixbF2dmZ6tWr59k5/rb9zO0g3l27dmXjxo0MGDCAw4cPU6tWLVJTU7l+/TobN25k3759VKlShUmTJnH06FGaNWuGo6Mjjx8/ZvHixRQrVkweK9Ld3Z3PPvuM0aNH8+zZMywtLfnrr7/kyp23MTAwwM3NjQ0bNlC6dGksLS3x8PAgJSUl2/O7QHmfLsp79uyRevXqJZUtW1YyNjaWlEql5OrqKg0ZMkSKiIhQy5vVsDNvDhWS2VAekpT5cB6RkZFS27ZtJUNDQ8nCwkLq37+/FBQUlKNhZ7Zv3y5VqFBB0tfXl5ycnKTp06dLv//+e4Zu5+Hh4VKzZs0kExOTDN32Y2NjpdGjR0uurq6SUqmUrKyspJo1a0qzZs1SG3rn6dOnUteuXSVTU1PJzMxM6tq1qzz8SHbDzqQLDQ2V+vbtK5UoUULS1dWVrKyspJYtW2bobp/uwoULUvXq1SWlUimVKFFCmjNnTqbd6t+2f4GBgVLdunUlfX19ycHBQfrpp5+kFStWZNo1f+HChVLZsmUlXV1dydbWVho4cGCGYSAkSZI2bNggVapUSdLT05MsLS2lLl26SA8fPlTLk93QLW9K36+sXg8ePMh22JmshpZ5l2FnXl+voaGh5OTkJLVt21bavHmz2nAeb5N+DG7fvi01atRIMjQ0lGxtbaXx48errSN9v7Ia1sDf31+qVauWZGBgIJmamkotWrSQgoODM+Q7ePCgVKlSJUmpVEouLi7Sb7/9Jn377beSvr6+Wj4gyyFhIiIipEGDBknFixeXdHV1JTs7O6lBgwbS8uXL5TzLli2T6tSpIxUpUkTS09OTXFxcJD8/Pyk6OlqSJNXQJX5+flLFihUlExMTycjISKpYsaK0ePHiDMfnzWFUYmNjpeHDh0tFixaVdHV1pVKlSkkzZ85UGy7kbfvw5jUqM+lDUKxfv14aPXq0ZGNjIxkYGEjNmjWTh5JJFxwcLPn4+EjGxsaSlZWV1LdvX3l4m/Tz/smTJ9KgQYOksmXLSkZGRpKZmZlUvXp1aePGjZlu29fXVzIzM5P09fUlFxcXqUePHtL58+fV8m3ZskUqV66cpKenJ7m5uUl///13pscrKzk5l+vWrZvp0CI53U5Or7/peWvWrCl/h6tVqyatX79eLc/x48elhg0byt+ZChUqZBhC6Pbt21K3bt0kOzs7SVdXV3JwcJCaN28ubd68Wc6T1XBmFy9elNq0aSN/bx0dHaUOHTpIBw8elPNkde+SJEl6+PCh1Lp1a8nc3FwyMzOT2rdvL/3333+ZDlXy008/SQ4ODpKWllaG47FlyxbJ29tbMjIykoyMjKSyZctKgwYNkm7cuKG2jsWLF0vOzs6Snp6eVKVKFeno0aNS3bp1czTszPXr16U6depIBgYGGa6NeXGO52Q/35TV902SVEPtTJ8+XXJ3d5f09PQkCwsLycvLS5o4caK8zYMHD0qtWrWSihYtKimVSqlo0aJS586dMwwbd/v2bcnHx0fS09OTbG1tpTFjxkgHDhzIdtgZSZKkkydPSl5eXpJSqZT/rrk5vwsChSTlUStqQRDyTI8ePdi8eXO2tTof0hdffMHVq1czbeskCIIgFC6FrxunIAi59uZ0aiEhIezevfujnqhdEARByDnNjFcgCEKBUrJkSXmu1Xv37rFkyRKUSmWWwyoJgiAIhYsoEAqCQOPGjVm/fj3h4eHo6elRo0YNpk6dmuPe0IIgCMLHTbQhFARBEARB+MSJNoSCIAiCIAifOFEgFARBEARB+MSJAqEgCDm2atUqFAoFd+/ezTavk5MTPXr0+OAxCYIgCO9PFAgFoZAKDQ1l8ODBlC5dGkNDQwwNDXFzc2PQoEFqs818SlJTU1m5ciX16tXD0tISPT09eVal8+fPZ8h/9epVvvrqKxwcHNDT06No0aJ06dKFq1evvlccTk5ONG/ePNNl58+fR6FQsGrVKrX048eP06RJExwcHNDX16dEiRK0aNGCdevWZbmdatWqoVAo1KbOFARByIwoEApCIbRz5048PDxYs2YNPj4+zJ07l/nz59OkSRN2796Np6dnjiZtL0xevnxJ8+bN6dWrF5IkMWbMGJYsWUK3bt04deoU1apV4+HDh3L+v//+m8qVK3Pw4EF69uzJ4sWL6d27N4cPH6Zy5cps3bo132LftGkTderUISIigm+++YYFCxbw1Vdf8fz580zncQXVWJLnzp3DyckpwxzkgiAIbxLDzghCIXP79m06deqEo6MjBw8exN7eXm359OnTWbx4MVpan9bvQT8/P/bu3cvcuXMZNmyY2rLx48czd+5c+f3t27fp2rUrJUuW5OjRo2pzR3/zzTfUrl2brl27EhgYmOM5Yt/HhAkTcHNz4/Tp0yiVSrVlWc3fvnbtWmxsbJg9ezbt2rXj7t27ODk5ffBYBUH4OH1adwRB+ATMmDGDuLg4Vq5cmaEwCKCjo8PQoUMpXry4WvqhQ4eoXbs2RkZGmJub06pVK65du5bt9iRJYvLkyRQrVgxDQ0Pq16+f5SPVqKgohg0bRvHixdHT08PV1ZXp06eTlpYm57l79y4KhYJZs2axfPlyXFxc0NPTo2rVqpw7dy6XR0Pl4cOHLFu2jIYNG2YoDAJoa2vz3XffUaxYMQBmzpxJfHw8y5cvVysMAlhZWbFs2TLi4uKYMWPGO8WTW7dv36Zq1aoZCoMANjY2mX5m3bp1tGvXjubNm2NmZvbWR8uCIAiihlAQCpmdO3fi6upK9erVc/wZf39/mjRpQsmSJZkwYQIvX75kwYIF1KpVi4CAgLfWLI0bN47JkyfTtGlTmjZtSkBAAI0aNSIpKUktX3x8PHXr1iUsLIz+/ftTokQJTp48yejRo3n06BHz5s1Ty79u3TpiY2Pp378/CoWCGTNm0KZNG+7cuYOurm5uDgl79uwhJSWFrl275ij/jh07cHJyonbt2pkur1OnDk5OTuzatStXcbyr9Nrehw8fyoXWtzlz5gy3bt1i5cqVKJVK2rRpw59//smYMWPyIVpBED5KkiAIhUZ0dLQESF988UWGZc+fP5ciIyPlV3x8vLzM09NTsrGxkZ4+fSqnXb58WdLS0pK6desmp61cuVICpNDQUEmSJOnx48eSUqmUmjVrJqWlpcn5xowZIwFS9+7d5bSffvpJMjIykm7evKkW16hRoyRtbW3p/v37kiRJUmhoqARIRYoUkZ49eybn++effyRA2rFjR66Py/DhwyVAunjxYrZ5o6KiJEBq1arVW/O1bNlSAqSYmJhcx+Po6Cg1a9Ys02Xnzp2TAGnlypVy2ooVKyRAUiqVUv369aWxY8dKx44dk1JTUzNdx+DBg6XixYvLf5P9+/fneP8FQfg0iUfGglCIxMTEAGBsbJxhWb169bC2tpZfixYtAuDRo0dcunSJHj16YGlpKeevUKECDRs2ZPfu3Vluz9/fn6SkJIYMGYJCoZDTM3ssu2nTJmrXro2FhQVPnjyRXz4+PqSmpnL06FG1/B07dsTCwkJ+n15bd+fOnRwcCXXpx8XExCTbvLGxsTnKm748fd0fUq9evdi7dy/16tXj+PHj/PTTT9SuXZtSpUpx8uRJtbwpKSls2LCBjh07yn+Tzz//HBsbG9G5RBCELIlHxoJQiKQXUl68eJFh2bJly4iNjSUiIoKvvvpKTk/vbVymTJkMnylXrhz79u0jLi4OIyOjDMvTP/vmnMfW1tZqhTlQ9XoNDAzM0CYv3ZudI0qUKKH2Pn19z58/z/Tzb2Nqagq8Kuy9TfoxzC5vTguO7+r1AjaAr68vvr6+xMfHc+HCBTZs2MDSpUtp3rw5169fl9sS7t+/n8jISKpVq8atW7fkz9evX5/169czffr0T65DkSAI2RMFQkEoRMzMzLC3tycoKCjDsvQ2hTkZVPpDSEtLo2HDhowcOTLT5aVLl1Z7r62tnWk+6R2mXy9btiwAV65cwdPT8615049hdmM1BgYG4uDgIBc2c0NfX5+XL19muiw+Pl7OkxlDQ0Nq165N7dq1sbKyYuLEiezZs4fu3bsDyLWAHTp0yPTz//77L/Xr1891zIIgFG6iQCgIhUyzZs347bffOHv2LNWqVcs2v6OjIwA3btzIsOz69etYWVllWjv4+mdDQkLUhl+JjIzMUJPn4uLCixcv8PHxyfG+5JUmTZqgra3N2rVrc9SxpHnz5vz6668cP34cb2/vDMuPHTvG3bt36d+//zvF4+joSHBwcKbL0v8O6cf2bapUqQKoHvsDxMXF8c8//9CxY0fatWuXIf/QoUP5888/RYFQEIQMxHMDQShkRo4ciaGhIb169SIiIiLD8jdr2Ozt7fH09GT16tVERUXJ6UFBQezfv5+mTZtmuS0fHx90dXVZsGCB2nrf7DEMqhqrU6dOsW/fvgzLoqKiSElJycHevZvixYvTt29f9u/fz4IFCzIsT0tLY/bs2fLA1H5+fhgYGNC/f3+ePn2qlvfZs2cMGDAAQ0ND/Pz83imepk2b8vDhQ7Zt26aWnpiYyG+//YaNjQ2VK1eW0w8ePJjpetLbd6Y/7t+6dStxcXEMGjSIdu3aZXg1b96cLVu2kJiY+E5xC4JQeIkaQkEoZEqVKsW6devo3LkzZcqUoUuXLlSsWBFJkggNDWXdunVoaWmpDV8yc+ZMmjRpQo0aNejdu7c87IyZmRkTJkzIclvW1tZ89913TJs2jebNm9O0aVMuXrzInj17sLKyUsvr5+fH9u3bad68OT169MDLy4u4uDiuXLnC5s2buXv3bobPZOfu3bs4OzvTvXv3DFO9vWn27Nncvn2boUOH8vfff9O8eXMsLCy4f/8+mzZt4vr163Tq1Ek+hqtXr6ZLly6UL1+e3r174+zszN27d1mxYgVPnjxh/fr1uLi4qG1DoVBQt25djhw58tZY+vXrx++//0779u3p1asXlSpV4unTp2zYsIGgoCD++OMPtTEHW7VqhbOzMy1atMDFxYW4uDj8/f3ZsWMHVatWpUWLFoDqcXGRIkWoWbNmpttt2bIlv/76K7t27aJNmzbZHF1BED4pmu3kLAjCh3Lr1i1p4MCBkqurq6Svry8ZGBhIZcuWlQYMGCBdunQpQ35/f3+pVq1akoGBgWRqaiq1aNFCCg4OVsvz5rAzkiRJqamp0sSJEyV7e3vJwMBAqlevnhQUFCQ5OjqqDTsjSZIUGxsrjR49WnJ1dZWUSqVkZWUl1axZU5o1a5aUlJQkSdKrYWdmzpyZIUZAGj9+vPz+ypUrEiCNGjUqR8ckJSVF+u2336TatWtLZmZmkq6uruTo6Cj17Nkz0yFZAgMDpc6dO0v29vaSrq6uZGdnJ3Xu3Fm6cuVKhryxsbESIHXq1ClHsTx//lwaPny45OzsLOnq6kqmpqZS/fr1pT179mTIu379eqlTp06Si4uLZGBgIOnr60tubm7SDz/8IA97ExERIeno6Ehdu3bNcpvx8fGSoaGh1Lp16xzFKAjCp0MhSe/QQlsQBKEAWLx4MSNHjuT27dvY2tpqNJbdu3fTvHlzLl++TPny5TUaiyAIQm6JNoSCIHy0Dh8+zNChQzVeGEyPpVOnTqIwKAjCR0nUEAqCIAiCIHziRA2hIAiCIAjCJ04UCAVBEARBED5xokAoCIIgCILwiRMFQkEQBEEQhE+cGJgaSElJ4eLFi9ja2opJ3wVBEAThI5GWlkZERASVKlVCR0cUad6HOHrAxYsXczTnqyAIgiAIBc/Zs2epWrWqpsP4qIkCIchjmJ09exZ7e3sNRyMIgiAIQk48evSIatWqFYixSD92okAI8mNie3t7tfldBUEQBEEo+ERzr/cnjqAgCIIgCMInThQIBUEQBEEQPnGiQCgIgiAIgvCJE20IcygtLY2kpCRNh6Fxurq6aGtrazoMQfjkpaamkpycrOkwBOGDEvec/CMKhDmQlJREaGgoaWlpmg6lQDA3N8fOzg6FQqHpUAThkyNJEuHh4URFRWk6FEHIF+Kekz9EgTAbkiTx6NEjtLW1KV68+Cfdk0mSJOLj43n8+DGAGKJHEDQgvTBoY2ODoaGhuEkKhZa45+QvUSDMRkpKCvHx8RQtWhRDQ0NNh6NxBgYGADx+/BgbGxtRlS8I+Sg1NVUuDBYpUkTT4QjCByfuOfnn063uyqHU1FQAlEqlhiMpONILxqL9kiDkr/RzTvw4FT4l4p6TP0SBMIfEY5lXxLEQBM0S56DwKRHf9/yh0QLh0aNHadGiBUWLFkWhULBt2za15ZIkMW7cOOzt7TEwMMDHx4eQkBC1PM+ePaNLly6Ymppibm5O7969efHiRT7uhSAIgiAIH4Psyh2ZOXLkCJUrV0ZPTw9XV1dWrVqVIc+iRYtwcnJCX1+f6tWrc/bsWbXlCQkJDBo0iCJFimBsbEzbtm2JiIjIo73KGxotEMbFxVGxYkUWLVqU6fIZM2bwyy+/sHTpUs6cOYORkRG+vr4kJCTIebp06cLVq1c5cOAAO3fu5OjRo/Tr1y+/dkEQBEEQhI9EduWON4WGhtKsWTPq16/PpUuXGDZsGH369GHfvn1yng0bNjBixAjGjx9PQEAAFStWxNfXV+4MAzB8+HB27NjBpk2b+Pfff/nvv/9o06ZNnu/fe5EKCEDaunWr/D4tLU2ys7OTZs6cKadFRUVJenp60vr16yVJkqTg4GAJkM6dOyfn2bNnj6RQKKSwsLAcb/vBgwcSID148CDDspcvX0rBwcHSy5cv32GvNK9OnToSIK1bt04t/ZdffpHs7e3faZ0f+zERhI+VOPc0o27dutI333yT5fv82Oan7G3f+7fdv7PzZrkjMyNHjpTc3d3V0jp27Cj5+vrK76tVqyYNGjRIfp+amioVLVpUmjZtmiRJqrKLrq6utGnTJjnPtWvXJEA6depUruP+UApsG8LQ0FDCw8Px8fGR08zMzKhevTqnTp0C4NSpU5ibm1OlShU5j4+PD1paWpw5cybfY37T7dB73A69x8P/HhH55CkxsbEkJSXn23iGkiRx8eJF7O3t2bJli9qyCxcuULly5XyJQxAEAaBHjx4oFAp+/vlntfRt27Z9VO3E/v77b3766SeNxhAZGcnAgQMpUaIEenp62NnZ4evry4kTJwBVu7u3vSZMmACohjEaMmQIJUuWRE9Pj+LFi9OiRQsOHjz41u3XrVtXXpeuri5lypRh3bp1H3q3892pU6fUyiEAvr6+cjkkKSmJCxcuqOXR0tLCx8dHznPhwgWSk5PV8pQtW5YSJUrIeQqCAjvsTHh4OAC2trZq6ba2tvKy8PBwbGxs1Jbr6OhgaWkp58lMYmIiiYmJ8vvY2Ni8CluNr68vt0NuZEjX1tbGxNQMazs7nJxKMsJvJFZW1pR2LYmRni7aWnlzYQwJCSE2Npaff/4ZPz8/4uPj5d5aAQEBtG7dOk+2IwiCkFP6+vpMnz6d/v37Y2FhkSfrTEpKyteRICwtLfNtW1lp27YtSUlJrF69mpIlSxIREcHBgwd5+vQpAI8ePZLzbtiwgXHjxnHjxqv7kbGxMXfv3qVWrVqYm5szc+ZMypcvT3JyMvv27WPQoEFcv349022nVzZMmzaNHj16EB8fz6RJk+jevTs1atTA2dn5w+58JmJjY4mJiZHf6+npoaen997rDQ8Pz7QcEhMTw8uXL3n+/DmpqamZ5kk/fuHh4SiVSszNzTPkeVtZJb8V2BrCD2natGmYmZnJLzc3tw+yHV2lEt1MLlKpqalEPX9GyLVgDuzZSZPP61C1Qjkqe1Zk4DA/Tgfd4cGzeMKex/Nf1EvCYxKIik8iPimF5JQ0UtOkHG3/woUL6Ovr06dPH0xNTdmzZw+gatx67do1UUMoCEK+8/Hxwc7OjmnTpmWZJzExkaFDh2JjY4O+vj7e3t6cO3dOXl6vXj0GDx7MsGHDsLKywtfXV04fMmQIw4YNw8LCAltbW3799Vfi4uLo2bMnJiYmuLq6ytfCdHv37sXb2xtzc3OKFClC8+bNuX37dpbx1atXj2HDhgFw9+7dTGvg6tWrB6imPZ02bRrOzs4YGBhQsWJFNm/erLa+uLg4unXrhrGxMfb29syePfutxzAqKopjx44xffp06tevj6OjI9WqVWP06NG0bNkSADs7O/llZmaGQqFQSzM2Nubrr79GoVBw9uxZ2rZtS+nSpXF3d2fEiBGcPn06y+2nVzZ4e3tjZ2dHyZIlGT16NCkpKQQGBr419g/Fzc1N7b7+tu+XkLkCWyC0s7MDyNALJyIiQl5mZ2en1mgTVANJP3v2TM6TmdGjRxMdHS2/goODcxyXJEnEJ6Xk6HUhIICo2DheJCTxPDaOR0+eEfrwEcE3b3P8zHmG+42ipGspbGxVsd6+eZ1fF8ymRnkXmjWsR6d2rRky6GumTJ3G8j/+wv/0Ja6Fx3D1v2huhsfyX9RLklOzfvwcEBBAhQoVUCqVtG7dWr4IXb58mZSUlAwFwg4dOlCrVq0cHwtBEITc0tbWZurUqSxYsICHDx9mmmfkyJFs2bKF1atXExAQgKurK76+vjx79kzOs3r1apRKJSdOnGDp0qVq6VZWVpw9e5YhQ4YwcOBA2rdvT82aNQkICKBRo0Z07dqV+Ph4+TNxcXGMGDGC8+fPc/DgQbS0tGjdunWOmvcUL16cR48eya+LFy9SpEgR6tSpA6gqIP744w+WLl3K1atXGT58OF999RX//vuvvA4/Pz/+/fdf/vnnH/bv38+RI0cICAjIcpvGxsYYGxuzbds2taddufHs2TP27t3LoEGDMDIyyrD8zdqs1124cAGFQkGFChXktPS/5Zs1ZfklODhY7b4+evToPFmvnZ1dpuUQU1NTDAwMsLKyQltbO9uySlJSUobpJl/PUxAU2EfGzs7O2NnZcfDgQTw9PQGIiYnhzJkzDBw4EIAaNWoQFRXFhQsX8PLyAuDQoUOkpaVRvXr1LNf9ZlXy69XM2XmZnIrbuH3ZZ8wJLW9o640B4BDzmOgTfxF37ShScgJXAs5l+hEdXV0sLIpQx8eXNp274eFZGW0tBdoKBdraCrRQoFCAlkLByTPnKONegcexCTRr0YrOHduTmJhIQEAA1tbWFC9eXF5vYGAgDx484NatW3mzb4Ig5Avp/9N7acK7Tp3XunVrPD09GT9+PCtWrFBbFhcXx5IlS1i1ahVNmjQB4Ndff+XAgQOsWLECPz8/AEqVKsWMGTMyrLtixYr8+OOPgOrH/88//4yVlRV9+/YFYNy4cSxZsoTAwEA+++wzQPX49XW///471tbWBAcH4+Hh8dZ90dbWlm/qCQkJfPHFF9SoUYMJEyaQmJjI1KlT8ff3p0aNGgCULFmS48ePs2zZMurWrcuLFy9YsWIFa9eupUGDBoCqUFusWLEst6mjo8OqVavo27cvS5cupXLlytStW5dOnTqpFdLe5tatW0iSRNmyZXOU/3UBAQE4OztjamoKwPXr1xk5ciSenp5Uq1YNgDlz5vDtt98SFBSEu7s7AIMGDWLx4sU8e/aMlStXMnv2bKysrIiPj2fRokU0atQo17GkMzExkePJSzVq1GD37t1qaQcOHJD/nkqlEi8vLw4ePMgXX3wBqGqFDx48yODBgwHw8vJCV1eXgwcPyt+1GzducP/+fXk9BYFGC4QvXrxQK4CEhoZy6dIlLC0tKVGiBMOGDWPy5MmUKlUKZ2dnxo4dS9GiReWDXq5cORo3biyfFMnJyQwePJhOnTpRtGhRDe3Vu9ExtaFIk6FY+g4i4f4V0l7GkpYYR0pMJCnR4SRH3iP16X1SkpOJfBzOlnWr2bJuNV/1HkiH7r0xNDTC2lb9l8aVwEs0bNGG8OgE7Mt5oa2jw6ZtOzl15hyVKlVSyztu3DimTp1Kv379ePTokZgzUhA+EvHx8RgbG2tk2y9evMi0diknpk+fzueff853332nln779m2Sk5PVnlbo6upSrVo1rl27JqelVwK86fUCkba2NkWKFKF8+fJyWnoN1utPl0JCQhg3bhxnzpzhyZMncs3g/fv3sy0Qvq5Xr17ExsZy4MABtLS0uHXrFvHx8TRs2FAtX1JSknwNvn37NklJSWqVGJaWlpQpU+at22rbti3NmjXj2LFjnD59mj179jBjxgx+++03evTokW2skpSzpkeZCQgI4O7duxgbG5OSkoJCoaBjx45Mnz4dLS3Vg8egoCDKly/PjRs3cHd35/79+5w6dYpixYphYWFBUFAQs2fPplOnTmzbto0JEya8V4Ewp7Ird4wePZqwsDD++OMPAAYMGMDChQsZOXIkvXr14tChQ2zcuJFdu3bJ6xgxYgTdu3enSpUqVKtWjXnz5snNFEDVIbZ3796MGDECS0tLTE1NGTJkCDVq1JB/lBQEGi0Qnj9/nvr168vvR4wYAUD37t1ZtWoVI0eOJC4ujn79+hEVFYW3tzd79+5FX19f/syff/7J4MGDadCgAVpaWrRt25Zffvnlg8VsoKtN8CTfD7Z+laaZpqYlJfD8+XO2b98u//JYu2IJa1csAVRfOifnkhgZGZMmpREbHc2NwAvs1dfHvVJV6vg0YePmzYRcD8a7ng+RsYnoaisIvhxA7IsX1K9fn7JlyxIcHCwKhIIgfFB16tTB19eX0aNH56gA86asCqK6urpq79N7wb7+HlB7HNyiRQscHR359ddfKVq0KGlpaXh4eJCUlJTjeCZPnsy+ffs4e/YsJiYmAPIkCbt27cLBwUEtf150eNDX16dhw4Y0bNiQsWPH0qdPH8aPH5+j41mqVCkUCkWWHUfeJiAgAD8/P/r06YOhoSH29vYZaoqDgoLo0KGD3JHlp59+onXr1nIv6KCgIIYOHQqAg4ODPE3sh5ZduePRo0fcv39fXu7s7MyuXbsYPnw48+fPp1ixYvz2229yu1WAjh07EhkZybhx4wgPD8fT05O9e/eqPT6fO3euXEZJTEzE19eXxYsX58Me55xGC4T16tV7668UhULBpEmTmDRpUpZ5LC0t87Wru0KhwFCpocOmVLUbGTRoEC1atOD777/nyJEjxMXFERsbS3R0NJcvXVT7yF9r/+CvtapfOq07dMZ/3x4SEl7Sd8i3PIp+CcDIMT/Sf9hI7j+No0yZMgQHB8uPLgRBKNgMDQ01NjvT+86p/PPPP+Pp6alWG+bi4iK3DXR0dARUc9ieO3dO7siRl54+fcqNGzf49ddfqV27NgDHjx/P1Tq2bNnCpEmT2LNnDy4uLnK6m5sbenp63L9/n7p162b6WRcXF3R1dTlz5gwlSpQA4Pnz59y8eTPLz2TFzc0tRzNvgOre6evry6JFixg6dGiGAnZUVFSm7Qjv3LlDVFQUDRs2xNXVNdN1S5LE3bt3ad68OXPnzuXOnTvcuXOH6tWr4+HhgSRJXL9+nTJlypCamsqSJUto2jTzipC8ll25I7NZSOrVq8fFixczZn7N4MGD5YqazOjr67No0aIcD4itCQW2DaHwdiVKlGD9+vXy++joaEJCQggPD+fly5f88ccfXLx4kWbNmrF8+XIAtm5cj0KhQJIkzIwMMNXX5cSJE1w4fZJRg/sAEB8XR6PmX9DmeTx2ZvpoaxXYfkeCIKD6kfquj201rXz58nTp0kXtqY6RkREDBw7Ez89Pfow3Y8YM4uPj6d27d57HYGFhQZEiRVi+fDn29vbcv3+fUaNG5fjzQUFBdOvWje+//x53d3d5GBGlUomlpSXfffcdw4cPJy0tDW9vb6Kjozlx4gSmpqZ0794dY2NjevfujZ+fH0WKFMHGxoYffvhBfvSamadPn9K+fXt69epFhQoVMDEx4fz588yYMYNWrVrlOPZFixZRq1YtqlWrxqRJk6hQoQIpKSkcOHCAJUuWqD2iT5feoeT18X/fFBoaSvHixSlXrhy3b9/mp59+YuzYsWzdupXKlSsTGhpKYmIiNWrUQKlU4uPjk6tjLnwYokBYSJiZmamdoO3bt5f/P2LECCpUqEBSUpL8y6jPVx04efIkS2ZOwt//AMXKehKTkMLlgHPMmzKep3FJPI1LwtnKCBN93QzbEwRByAuTJk1iw4YNamk///wzaWlpdO3aldjYWKpUqcK+ffvybNzC12lpafHXX38xdOhQPDw8KFOmDL/88os8bEx2zp8/T3x8PJMnT2by5Mlyet26dTly5Ag//fQT1tbWTJs2jTt37mBubk7lypUZM2aMnHfmzJm8ePGCFi1aYGJiwrfffkt0dHSW2zQ2NqZ69erMnTtXbnNZvHhx+vbtq7be7JQsWZKAgACmTJnCt99+y6NHj7C2tsbLy4slS5Zk+pn0Xt9mZmZZrjcoKAgPDw/09PSIi4sjLCyMevXq8dNPP9GtWzeCgoJo0aJFhuF3BM1SSO/TsrSQePjwIcWLF+fBgwcZenYlJCQQGhqKs7OzWtvFj01sbCxr1qwhJCSEefPmqS3z8vKiePHi+Pn54ViyFBU8ynH40qtGtyb6upSwNJBrCwvLMRGEj40494SPwdSpU9HV1cXPz48NGzbg7u6Oh4cH9vb23Llzh7lz55KcnMz48eNztL63fe/fdv8WckfUEH4iTExM+PrrrwFVW4adO3cSEhJCYmIiFy5c4MKFC+zatYv79+/z9MkT4hJTuBMZh4REbEIyV/9LRqmthUKhQEkKSSn5M/2eIAiC8HEJCgqia9eugKrDBah6dZuYmGBgYEBQUBDt2rXTZIhCJkQDsU/QtGnTuHLlCi9fvuTYsWPyWF7JycnY29tjbW3N3xvX42ZvTFFzA3T+P5VeUmoaiSmpxCQkExWf8953giAIwqdj3bp18hiS6WxsbLh586a8vE2bNpoITXgLUUP4CVMoFHh7e+Pt7U1UVBRTp04F4MmTJ3Tr1o2HDx8yevRoihgpSUxJI02SiE1IIfxZEkmpEsdDInEvYYWWQoFSWwsDpTZ6Olof1ST1giAIgiCIAqHwf1OmTGHSpEns2LGD1q1bAzBmzBi0tLT4/vvv0dfVBlTjMD6OUhX4xm+/Slis+thRVsZKnIoY4WJtTDVnS8rYmaCno4W5oRJrk/cfd0sQBEEQhLwnCoSCTFtbmy+++IKwsDB5ENVRo0aRkpLCDz/8AKhqFR3MDYgK10KprYWOlqrmMO3/XZOevEjiyYskzt97zobzD9TW7+dbhg5ViouCoSAIgiAUMKJAKGRQtGhRHj9+TMmSJXnx4gU//vgjL168oH///piZmWFoYICViR57htWRe3ylpKYRk5DCzYhYbj1+waUHUQTcf054dALxSapaxJn7bjBz3w1+bFaOPrVLanIXBUEQBEF4jehUImTK2tqau3fvyt34f/75Z5ydnbG0tKRChQocPHhQLb+OthaWRko+K1mErz5zZFb7ihz6th7Bkxrzr189mld4NRXe5F3XmLr7GsmpoqeyIAiCIBQEokAoZKlIkSKcPHmSb775BgcHB7S1Ve0IQ0JCmD9/fo7X41jEiIVfVubSuIZYGKoGuV5+9A7lJ+xjxIZLxCQkf5D4BaGwEsPHCp8S8X3PH6JAKLxV8eLFmTdvHg8fPiQlJUWeUeDp06e5Xpe5oZKjI+vToKwNAAnJafx9MYwKE/YzeF0Ay4/e5tTt3K9XED4VurqqH1Tx8fEajkQQ8k/69z39+y98GKINoZAr6dM5PX/+nN27d+d6LCkTfV1W9KhKQnIq4/4JYuP5hwDsDHzEzsBHAHSuVoKvPitBESM9bE31xDA2gvB/2tramJub8/jxYwAMDQ3F+SEUWpIkER8fz+PHjzE3N5efUgkfhpi6jk9j6rq8IkkSNjY2PHnyBIAyZcpgY2ODvr4+BgYGFCtWjNq1a+Pj44OVlVW264uISWDzhYfcfxqfoVcygJmBLu29ijGmaTm0tMSNTxAkSSI8PJyoqChNhyII+cLc3Bw7O7tMf/yIqevyjigQUrgLhHXr1uXo0aOsW7eOzp07y+kLFixg2rRp/Pfff7le55EjR+jZsyd37959az5PT09atWqFq6srRYsW5fPPP39r/qcvEll4+Bb+1yKIeZlC9MtXbQu1FFDL1YqJLd0paW2c65gFobBJTU0lOVm0vxUKN11d3bfWDIoCYd4RBUIKb4FQkiTMzMwwNjamZs2abN68WV7Wo0cPnjx5ws6dO3O93oSEBK5du8bjx49JTk4mISGBhIQE7t27x9GjR/n3339JTEzM8LmNGzfSvn37HG/nWVwSM/fdYP3Z+2rpg+q7MKJhGbRFjaEgCMInTRQI844oEFJ4C4Q3b96kTJkyLFq0CD8/PyIjIzE0NASgQoUKtG7dmokTJ+Z6vdkdE0mS+Pvvv/nrr794+PAhp0+flpeVK1eOFStWUKNGjRxvLykljT9O3WXyrmtymq62An1dbfR1tWlRoSjf+JTCzEA0OBYEQfiUiAJh3hG9jAuxCxcuoK+vT58+fTA1NWXPnj3Aqxq+ypUrf5DtKhQK2rZty6ZNmzh16hTHjx/H0dERgGvXrslzJueUUkeLPrVLcv2nxpSzNwUgOVU1r3JkbCK/nwhl9v4beb4fgiAIgvCpEAXCd5UUl/UrOSEXeV/mLO87CAgIoEKFCiiVSlq3bi0/Mr58+TIpKSlygXDOnDkUK1YMT09PnJ2dGTRo0DttLyu1atXi7t27LF++HICdO3cyefLkXK9HX1ebPd/U5sZk1WDXO4d4y9Pgnbr9VIxVJQiCIAjvSAw7866mFs16WalG0GXTq/czXSE5i3HDHL2h565X7+eVh/hMxuKbEJ3rEAMCAuRCX5s2bWjTpg2JiYkEBARgbW1N8eLFAQgKCmL+/Pm0bduWly9fYmVlxfTp0zE2ztvOG127duXHH3/k8ePHjB07lqSkJCZOnJjrYTP0dLRxLGIEwMoeVWm+4Dghj19QbepBRjUuS1sv8dhAEARBEHJD1BAWYgEBAXh5eQGq8QN1dXXZt28fFy5cUHtcHBQUhKenJwCXLl3CxcUlzwuDAPr6+ty6dQs7OzsAfvrpJ/T09Lhx490f95a2NcHt/4+RI2MT+XbTZVouPM7Rm5G8SEzJk7gFQRAEobATNYTvasxbhmtRvNFF3u/WW/K+USYfduXdY3rNnTt3iIqKkgt+Ojo6tGzZki1btnDlyhWaNGkCqDqA3Lhxg7Zt2xIfH09UVBTHjx/PkxgyY2JiwunTp+nduzcHDx4kOTmZsmXLsmHDBho0aICFhQVaWjn/naLU0WLXUG+uhEUzassVgh/FEPgwmm6/nwWgS/USTGld/kPtjiAIgiAUCqKG8F0pjbJ+6ernIq9BzvLm0oULF1AqlXh4eMhpbdu2Zfv27Vy9elUuKIaGhlKuXDkuXbrEzZs3GTp0KHPnzs319nLD0dERf39/fv31VzmtY8eOWFlZUaFCBZKSknK1PoVCQYVi5mwfXIulX1WmmrMl6SPS/HnmPn1WnyMtTbQvFARBEISsiAJhIRUQEICHhwdKpVJOa9iwIampqSQlJckFwqCgIMqUKSPnKV++PBEREfkSY+/evTlw4AAlSpSQBx69evUqJUuW5OLFi9y/f5+0tLQcr09HW4vGHvZs7F+DW1Oa4u2qminF/9pjfj8R+kH2QRAEQRAKA1EgLKSmTZvGhQsX1NL09PSIiYlBkiScnZ0B9QJhamoq69evp0GDBvkSo0KhwMfHh3v37pGSkiL3bg4LC6Ny5co4Ojpibm7OwYMHc71uLS0Fa3pXo7qzJQCTd13jt2N38jR+QRAEQSgsRIHwExcUFMTSpUupVKkSVatWxcXFhYEDB2oklnnz5vHDDz9gY2ODgYHqUXpsbCw+Pj74+/vnen0KhYJxLdzk95N3XaPJ/GOcu/ssz2IWBEEQhMJAzFRC4Z2p5EPJr2Oyc+dOWrRoIb+3tLRk7969VK1aNVfruRP5giHrL3L1vxgArE30OD26gZj6ThAE4SMnZirJO6KGUCiwmjdvzrFjx+Shc549e0a1atU4cOBArtZT0tqYXUNrs7KHqiAZGZtIrZ8PEZOQnOcxC4IgCMLHSBQIhQLN29ubc+fO8ccff8hpjRo14vjx45w6dYrz58/neF31y9rQsYpqMO7wmAQqTNjP0xeJeR6zIAiCIHxsRIFQKPAUCgVdu3Zly5Ytclrt2rWpWbMmVatWpVevXiQkJLxlDa9Mb1eB+Z085feiPaEgCIIgiAKh8BFp06YN48ePp1KlSlhaWsrpK1euZOLEiTleTytPB9pWVrU1GbA2gCsPcz8toCAIgiAUJqJAKHxUJkyYQEBAAE+fPiU6OhpdXV1ANe5ibvi628r/b7HwOIPXBbDh3H0iY8UjZEEQBOHTIwqEwkfL1NSUHTt2AOR6MO1G7nZyJxOAnYGP+H7LFerNPMyzuNzNlCIIgiAIHztRIBQ+ajY2NgBcvnyZpUuXkptRlOqXteGoX32mty1P7VKqWU3iklK5/DDqQ4QqCIIgCAWWKBAKHzUnJyd5er6BAwdSqVKlXM2FXKKIIR2rlmBN7+o0clM9Ru658hwRMTnrpCIIgiAIhYEoEAofNQsLC86cOUPp0qUBVU2hq6srjx49yvW6qjm/6qhSf9YRbkbE5lmcgiAIglCQ6Wg6AEF4X56enty4cYNevXqxcuVKHjx4QNGiRenatStDhw6lSpUqOVpPb29nDJU6jNl6hfikVBrNPUoZWxPiklKITUghKSUNc0NdapeyYmTjslgZ633gPRMEQRCE/CFqCIVCY/ny5cyZM0d+v2bNGpo2bUpaWlqOPq9QKPiyegnmdfSU025ExPLw+UuiXybzMjmVR9EJbDz/kMbzjpGUkrP1CoIgCEJBJwqEhVzdunVRKBSsX79eLX3BggUULVpUQ1F9GDo6OgwfPpwHDx4wf/58ACIjIylfvnyuOpt8UcmBI9/VY3lXL9b2rs7fX9fEf0RdDn1blx41nQB48iIRv82Xc7VeQRAEQSioRIGwEJMkiYsXL2Jvb682ywfAhQsXqFy5soYi+7CKFSvG0KFDGTZsGADBwcHcv38/V+twsjKikbsd3qWsqFzCAlcbY0paGzOhpTs+5VSdT/659B8LDt0ShUJBEAThoycKhIVYSEgIsbGx/Pjjj+zZs4f4+Hh5WUBAAF5eXhqM7sObO3curq6uAIwaNSrPCm4TWrpR1EwfgDkHblJ27F46LDvFuH+COHnrSZ5sQxAEQRDykygQvqP45Phcv1LSUuTPp6SlEJ8cT0JKQo7W+y4uXLiAvr4+ffr0wdTUlD179gCQkJDAtWvXCm0N4evc3d0B+Ouvvzh8+HCerLOYhSF/9K5G9f/3Sk5MSeNs6DP+OHWPL387w087g0X7QkEQBOGjInoZv6Pq66rn+jOz6s7C18kXgIP3D/Ldv99RxbYKKxuvlPM03tKY54nPM3z2Svcrud5eQEAAFSpUQKlU0rp1azZv3kzbtm25fPkyKSkpagXCVatWsWDBAlJTU0lLS+Pbb7+le/fuud5mQTNz5kx27NhBWloaDRo04MSJE9SsWfO91+tqY8KG/jWIfpnMhXvPuHQ/il8O3QJgxfFQXKyN+bJ6iffejiAIgiDkB1FDWIgFBATIhb42bdqwa9cuEhMTCQgIwNramuLFiwOwbNkyfv/9d/bt28elS5c4fPhwoWkXV6pUKdatWye/r1WrFlOnTs2z9ZsZ6PJ5WVtGNCrDUb/6cvqYrVd4HJNAQnIqJ2894Z9LYaw7c58jNx5z72kcqWmF4/gKgiAIhYNCKix3/vfw8OFDihcvzoMHDyhWrJjasoSEBEJDQ3F2dkZfX19Of5fHuEptJTpaqkrZlLQUklKT0FJooa+T/XoNdQ1zvT0LCwtmzpxJnz59SElJwdbWlpUrV7J9+3YePnzI3r17ef78OaVLl+by5cs57nWc1TEpyC5cuCCPR+jh4cGVK7mvcc2JXYGPGLQuINt81iZ61Ha14vsmZbE1/TiOoSAIQkHztvu3kDvikfE7epcC2ut0tHTkwmFerjfdnTt3iIqKkmsIdXR0aNmyJVu2bOHKlSs0adIEgK1bt1K/fv1CNwTNm7y8vAgJCaFUqVIEBQWxd+9eGjdunOfbaeRuy8jGZVhxLJSncaop9IoYKSlta0JSahrP4pIIfRJHZGwif18M4++LYVgY6qKjrUVamkSaJMm1hxWKmdPWy4HWlcRFThAEQfiwRIGwkLpw4QJKpRIPDw85rW3btnTt2pX4+Hh++OEHAIKCgvD09NRQlPnLyckJY2NjXrx4QZMmTXj69CmWlpbZfzAXdLW1+LqeKz1rOvPkRSLGejqYGeiipaWQ88QnpTDfP4Rtl8KIiEnkeXxypus6fusJx289waOoGaVsTeT0tDQJhUI1kLYgCIIg5AVRICykAgIC8PDwQKlUymkNGzYkNTWVpKQkuebQyMgoxzN5fOx0dHTYs2cPtWvXBqBIkSJERUVhZmaW59syUGpT3DLz2l5DpQ6jm5ZjdNNyRMQkEBmbiJZCgbaWAq3/F/QiYhLo8tsZABrOPYq+rhZpaZCSlkaaBMZ6OvT2dqaVZ1FKWhvnefyCIAjCp0V0Kimkpk2bxoULF9TS9PT0iImJQZIknJ2dAWjSpAnr1q3j6dOnAMTExLB27dp8jze/eHt7s3jxYvn9uXPnNBgN2Jrq4+FghltRU8rYmVDK1gRXG2NquVqxvKsXdv9vX5iQnEZSqqowCPAiMYX5B0P4fPa/VJ3iz+z9N7gRHqvBPREEQRA+ZqKG8BNXs2ZNRowYQf369ZEkCYVCwfDhwzUd1gc1cOBAtmzZwsGDB3M9g0l+auRuRyN3OyJiEkhKSUNHW1WL+DIplfVnH7A/OJw7kar2iAsO3WLBoVs4FjFkXd/PcDA30HT4giAIwkdE1BAK9OnTh8DAQK5cuUJgYCA9e/bUdEgfXHoNae/evblx44aGo3k7W1N9ilsaYm9mgI2JPo5FjBjVpCyHvq3H31/XZLhPaUrbqh4b33saT5N5R3nyIlHDUQuCIBRMixYtwsnJCX19fapXr87Zs2ezzJucnMykSZNwcXFBX1+fihUrsnfvXrU8sbGxDBs2DEdHRwwMDKhZs2aGp08vXrxg8ODBFCtWDAMDA9zc3Fi6dOkH2b93JQqEwifp9cGpFyxYoMFI3k/lEhZ841OK/cPrMri+apq+mIQUms4/RuiTOA1HJwiCULBs2LCBESNGMH78eAICAqhYsSK+vr48fvw40/w//vgjy5YtY8GCBQQHBzNgwABat27NxYsX5Tx9+vThwIEDrFmzhitXrtCoUSN8fHwICwuT84wYMYK9e/eydu1arl27xrBhwxg8eDDbt2//4PucU2IcQt5tHMJPWWE5Jn379uW3334DwM7OjhEjRjBo0CAMDfNm6J/8lpiSyoTtwaw/++oxeI+aTih1tLAz1adTteIYKkUrEUEQCo/cjkNYvXp1qlatysKFCwFIS0ujePHiDBkyhFGjRmXIX7RoUX744QcGDRokp7Vt2xYDAwPWrl3Ly5cvMTEx4Z9//qFZs2ZyHi8vL5o0acLkyZMB1fi3HTt2ZOzYsVnm0TRRQyh8skaMGIGNjQ0A4eHhjBw5EicnJ2JjP87OGXo62kxt7cHY5m5y2qqTd1l+9A6TdgbTZvFJ4hJT3rIGQRCEwispKYkLFy7g4+Mjp2lpaeHj48OpU6cy/UxiYmKGig8DAwOOHz8OQEpKCqmpqW/NA6qnUtu3bycsLAxJkjh8+DA3b96kUaNGebV7700UCIVPVrly5YiIiODUqVNUr66amzoyMpLAwEANR/buFAoFvb2d+fvrmnzbsDSD67tSxdECgOvhsfhtvqzhCAVBEPJebGwsMTEx8isxMWM76idPnpCamoqtra1auq2tLeHh4Zmu19fXlzlz5hASEkJaWhoHDhzg77//5tGjRwCYmJhQo0YNfvrpJ/777z9SU1NZu3Ytp06dkvOAqmmSm5sbxYoVQ6lU0rhxYxYtWkSdOnXy8Ci8H1EgzCHxZP2VwnYsPvvsM06fPi23K3z9JP5YVS5hwZAGpfjOtwzr+n7GZyVVA3DvvhJO68Un+OdSWDZrEARB+Hi4ublhZmYmv6ZNm5Yn650/fz6lSpWibNmyKJVKBg8eTM+ePdHSelV8WrNmDZIk4eDggJ6eHr/88gudO3dWy7NgwQJOnz7N9u3buXDhArNnz2bQoEH4+/vnSZx5QTQoyoa2tjagqmo2MBBDeQDEx6vmW9bV1dVwJHnLzs4OgGHDhtG8efOPun3k65Q6WvzZ5zNaLDhO8KMYLt6P4uL9S4RFvaRt5WJiLmVBED56wcHBODg4yO/19PQy5LGyskJbW5uIiAi19IiICPn6/yZra2u2bdtGQkICT58+pWjRoowaNYqSJUvKeVxcXPj333+Ji4sjJiYGe3t7OnbsKOd5+fIlY8aMYevWrXI7wwoVKnDp0iVmzZql9ghbkwp0DWFqaipjx47F2dkZAwMDXFxc+Omnn9RqqCRJYty4cdjb22NgYICPjw8hISF5FoOOjg6GhoZERkYSHx9PQkLCJ/t6+fIlT58+5fHjx5ibm8uF5cIi/eQNCwtj9erVGo4mb2lrKdgxxJt1farLaTP23qD61INM2H5Vg5EJgiC8PxMTE0xNTeVXZgVCpVKJl5cXBw8elNPS0tI4ePAgNWrUeOv69fX1cXBwICUlhS1bttCqVasMeYyMjLC3t+f58+fs27dPzpOcnExycrJajSGoKpwK0kxhBbqGcPr06SxZsoTVq1fj7u7O+fPn6dmzJ2ZmZgwdOhSAGTNm8Msvv7B69WqcnZ0ZO3Ysvr6+BAcH50kNj0KhwN7entDQUO7du/fe6ysMzM3Ns/w19TH7/vvvWbduHf/99x8DBgygQoUK2V4kPibaWgpqulqxb1gdZuy9zpnQZ7xITGHVybu8TEplersKmg5REAThgxoxYgTdu3enSpUqVKtWjXnz5hEXFyePv9utWzccHBzkR85nzpwhLCwMT09PwsLCmDBhAmlpaYwcOVJe5759+5AkiTJlynDr1i38/PwoW7asvE5TU1Pq1q2Ln58fBgYGODo68u+///LHH38wZ86c/D8IWSjQBcKTJ0/SqlUruYrVycmJ9evXy4NISpLEvHnz+PHHH+WS+B9//IGtrS3btm2jU6dOeRKHUqmkVKlSJCUl5cn6Pma6urqFrmYwnZWVFWvWrKFBgwaAqlfY0qVLCQ4O5uHDh+jo6GBnZ0eHDh2oVauWhqN9d2XsTFjRoyqSJPHF4pNcfhDF3xcfMrVNebS1FJoOTxAE4YPp2LEjkZGRjBs3jvDwcDw9Pdm7d6/c0eT+/ftqNXkJCQn8+OOP3LlzB2NjY5o2bcqaNWswNzeX80RHRzN69GgePnyIpaUlbdu2ZcqUKWrNqv766y9Gjx5Nly5dePbsGY6OjkyZMoUBAwbk275np0CPQzh16lSWL1/O/v37KV26NJcvX6ZRo0bMmTOHLl26cOfOHVxcXLh48SKenp7y5+rWrYunpyfz58/PdL2JiYlqPZDCwsJwc3PL8ThGQuElSRK///47ffr0eWu+FStW0KtXr3yK6sNJSU3D9Yc9AKzsWZX6ZWw0HJEgCELO5XYcQiFrOaohHDFiRK5X/OOPP2JpaZnrz71u1KhRxMTEULZsWbS1tUlNTWXKlCl06dIFQO4mnpsu5ADTpk1j4sSJ7xWbUDgpFAp69erF/fv3uXjxIgqFAkdHR8qUKcPLly/x8/MDVINat2vXDlNTUw1H/H50tLUoaW3Encg4eq48R4cqxahXxoZGbrboaBfoJsaCIAhCHspRDaGWlhY1atRAqVTmaKXHjx/nxo0bar1w3sVff/2Fn58fM2fOxN3dnUuXLjFs2DDmzJlD9+7dOXnyJLVq1eK///7D3t5e/lyHDh1QKBRs2LAh0/WKGkLhXQUHB+Pu7g7AiRMn1KbA+1iduv2Ubr+fITn11aVgQF0XRjUpq8GoBEEQsidqCPNOjtsQbt26VZ7VITsmJibvHNDr/Pz8GDVqlNwWsHz58ty7d49p06bRvXt3uWNDRESEWoEwIiJC7RHym/T09NR6IMXExORJvELh5+bmRs2aNeUfIw8fPlQb6uBjVMOlCBfGNmTjuQfMOXCT+KRUlv57m7qlranhUkTT4QmCIAj5IEfPhFauXImZmVmOV7ps2bIMj3HfRXx8/Fu7aTs7O2NnZ6fWhTwmJoYzZ84Uqt6hQsHSo0cP+f/Lly8vFAN1m+rr0qd2SfYNezVqfudfTxP4MIrUtI9//wRBEIS3K9CdSnr06IG/vz/Lli3D3d2dixcv0q9fP3r16sX06dMB1dA0P//8s9qwM4GBgbkadkZUOQu5NWjQIBYvXgxAtWrV+Oabb2jUqBFWVlYajuz9HQiOoO8f5+X3Sm0t3IqaUszCgE5VS+Bd6uPfR0EQCgdx/84771UgDAoK4t9//yU1NZVatWrh5eWVl7ERGxvL2LFj2bp1K48fP6Zo0aJ07tyZcePGye0ZJUli/PjxLF++nKioKLy9vVm8eDGlS5fO8XbEF0rIrbNnz9KyZcsMI963bNmSjRs3Zjoo6sdk9cm7/H0xjMsPotTSTfR0uDC2IUod0eFEEATNE/fvvPPOBcJFixYxadIk6tatS3JyMocOHWLkyJH88MMPeR3jBye+UMK72rdvH5MnT+b48eNy2oABA1i4cGGhGK8xNU3iyI3HPHgWz4QdwXJ6GVsT1vWtThHjj7vgKwjCx03cv/NOjguEDx48oHjx4vL7cuXKcezYMfkR2alTp2jZsiWRkZEfJtIPSHyhhPf14sULPD09uX37NqAaIL1r164ajipvjf8niPXnHpCUomrDO6GFGz1qOWs4KkEQPmXi/p13cvzcx8fHh/nz58sN6IsUKcLevXtJTEwkNjYWf39/rK2tP1igglCQGRsbs3v3bvl9YGCgBqP5MCa28uDGT41pU1nVq3rCjmC6rjhDUFi0hiMTBEEQ3leOC4Tnzp3jxo0bVK9enUuXLrF8+XLmzp2LgYEB5ubmbNiwgdWrV3/IWAWhQCtdujSzZs0CYNasWYSFhSFJErdu3WLlypUsX76cGzdukJKSouFI351CoaBHTSes/v+o+FjIE5ovOM7D5/EajkwQBEF4Hzkeh9DU1JTFixdz8uRJevToweeff86xY8dITU0lNTVVbV4/QfhUOTu/eoRarFgxSpcuzc2bN9XymJiY4OXlxZQpUz7Kga0rFDPn/I8+7Az8j8HrLgLgPf0wo5qUpX+dkigUYj5kQRCEj02uuwrWrFmT8+fPY2FhQaVKlTh69KgoDArC/zVv3lxtsvKbN2+ira1NpUqVcHV1BVS9548cOUKtWrVo3Lgx165d4+bNm1y6dIkrV65w48YNQkJCCvz4hs0rFGV62/KYG6omcP95z3UuvtErWRAEQfg45LhTSUpKCsuXL+fatWtUrFiRnj17cvv2bQYMGECRIkVYuHBhngxGrQmiUaqQ1w4fPsy1a9dwcnKiatWqcvva5ORkNm7cyFdffZXtOszMzBg7dizffvvthw73vcQmJFN+wn75/bQ25WldyQF93Y+/l7UgCAWbuH/nnRwXCLt37865c+do2bIlx44dw8vLi19++QWAFStWMGXKFPz8/Bg4cOAHDfhDEF8oIb89fvyYadOmsWrVKmJiYjAxMcHAwIDExETS0tKIjlZ11NDS0iI6OhpjY2MNR/x2vx27w+Rd1+T3HasUZ3q7ChqMSBCET4G4f+edHBcIzc3NOXXqFOXKlSM+Pp7y5cvLQ2yA6gY3bNgw1q1b98GC/VDEF0rQlPTT7812d0+ePJFrFb/++mv69u1LcnIy8fHxJCQk4ODggIeHR77HmxVJkjh5+ylTd1/j6n+qucEH1HVhVJOyGo5MEITCTNy/806O2xDa2tqyf/9+kpKSOHToEEWKqE96b2Nj81EWBgVBkxQKRaadMKysrOT5uBcvXkylSpWoVq0a9erVo3HjxpQvX56WLVsSHh6e3yFnSqFQUMvVim2DamFrquqBvPTf26w5fY9bj19oODpBEAQhOzkuEC5cuJApU6ZgYGDAgAEDmDdv3gcMSxCEFStW0LdvX5ycnLCzs6NEiRKUKVNGXr5jxw7s7e3p3Lkzjx8/1mCkr+hqa3F0ZH2U2qpLy9htQfjM+ZddgY80HJkgCILwNrmauk6SJLVHWYWFqHIWPibPnj1j8uTJzJ07Vy29efPm/Prrr9jZ2WkoslduhMfy67E7bL7wUE5b2bMq9cvYaDAqQRAKG3H/zju5GnZGoVAUusKgIHxsLC0tmTNnDikpKfj5+cnpO3fuxN7enkuXLmkuuP8rY2fCrPYV8R9RR05bffKu5gISBEEQ3ipHBcLKlSvz/PnzHK/U29ubsLCwdw5KEITsaWtrM2PGDJ49e8Z3330np69YsUKDUalztTFhcZfKABy5EcnkncEajkgQBEHITI5mKrl06RKXL1/G0tIyRyu9dOkSiYmJ7xWYIAg5Y2FhwcyZM7GwsOCHH35g4cKFVK9ePUdjHeaH+mVsMDPQJfplMr8dD+X7JmXR1c71mPiCIAjCB5SjNoRaWlooFIocz5ygUCgICQmhZMmS7x1gfhBtEITC4ObNm3KnE21tba5du0apUqU0HJVKYkoqZX7cK79f0qUyDcrZotQRBUNBEN6duH/nnRzVEIaGhuZ6xeIPIwj5q3Tp0gQGBlKhQgVSU1P57LPPOHLkCObm5tjZ2aGrq6ux2PR0tOlVy5nfT6iuJQP/DADAz7cMA+u6oKUl5j8WBEHQpFz1Mi6sxC8MoTBZuHAhQ4YMUUvT19dn69atNG7cWENRqdyMiGXmvhscCI5QSzdUauNUxIgmHnZYGitRamtRsbg5pWyMMx2nURAEAcT9Oy+JAiHiCyUUPt999x0bN24kPj6ep0+fAuDu7k5AQABKpVLD0cHLpFS6/X6GgPtRpKZlfQlyMDdgxxBvLI00H7MgCAWPuH/nHVEgRHyhhMLtr7/+onPnzoBqTvJVq1ZpNqDXpKSm8Tw+mYfP4/nn0n9EvkgkOSWNR9EJXAlTzedcuYQ5a/tUx1CZoxYugiB8QsT9O++IAiHiCyUUbi9fvqRq1apcvXoVgPDwcGxtbTUcVfam7r7G8qN3ABjfwo2etZw1HJEgCAWNuH/nHdHFTxAKOQMDA44cOSK/t7OzY+7cuTkeNUBTBn/uipWxal7kSTuD2Xe1YMzbLAiCUBjlukD44MEDHj58NR3V2bNnGTZsGMuXL8/TwARByDtWVlZMnjxZfj9ixAhOnTqlwYiyZ6qvy1/9qgMgSdB/zQXCol5qOCpBEITCKdcFwi+//JLDhw8DqkdPDRs25OzZs/zwww9MmjQpzwMUBCFv/PDDD9y8eVN+X6tWLb766isCAwM1GNXbudqYsOeb2vL7Wj8fov+a8ySlpGkwKkEQhMIn1wXCoKAgqlWrBsDGjRvx8PDg5MmT/PnnnwWqsbogCBmVKlWKBQsWyO///PNPvv32Ww1GlL1y9qaMa+4mv993NYKpu6+R9pbeyYIgCELu5LpAmJycjJ6eql2Pv78/LVu2BKBs2bI8evQob6MTBCHPDR48mEuXLvH1118DqvN4xIgRGo7q7Xp5O3NjcmOaeNgBsOrkXfYHizaFgiAIeSXXBUJ3d3eWLl3KsWPHOHDggDzQ7X///UeRIkXyPEBBEPJexYoVmT9/PjY2NgAsX768wHcy0dPRZmIrd/l98H8xGoxGEAShcMl1gXD69OksW7aMevXq0blzZypWrAjA9u3b5UfJgiAUfDo6OoSEhAAQFxdHbGyshiPKno2JPiMalgbgWnjBj1cQBOFjkeuRXuvVq8eTJ0+IiYnBwsJCTu/Xrx+GhoZ5GpwgCB+WqakpJiYmxMbG8t9//2FqaqrpkLJlZ6oPwIHgCNafvU/naiU0HJEgCEL+8ff3Z/z48aSlpdG0aVNGjRqVJ3PVv9M4hJIkceHCBZYtWybXKiiVSlEgFISPkJ2dql1euXLlSEhI0HA02fMuZSX/3/+NOZEFQRAKu0GDBjFy5EgWLFjA3bt3mThxYp6sN9cFwnv37lG+fHlatWrFoEGDiIyMBFSPkr/77rs8CUoQhPzTqVMn+f+2trb8+OOPBfrxcVFzA1b2qApAeEzBL8AKQo74T4Q/O8DBSXB5A5z4BfwnwNlf4d5JTUcnFCCGhoa0atWKKlWqsHz5cvz9/fNkvbl+ZPzNN99QpUoVLl++rNaJpHXr1vTt2zdPghIEIf9MmjQJY2Njvv/+e2JiYpgyZQovXrxg3rx5mg4tS7b/f2x89b8YUtMktLUUGo5IEHIpMRZ09EH7/4/6Qg5AxBUI2Zcxb4WO4FhT9f+EaEiKBxM7UPz/ey9JkJII2krQEhOQFXaRkZFs2rSJMmXKUKZMGZKSkvJkvbkuEB47doyTJ0+iVCrV0p2cnAgLC8uToARByF8jR46kadOmNGjQgMePH8vzHhdUdmb68v97rDzLH72qoVCIQqFQgKWlQmoy3D8F/uPhWSj03AN2Hqrlnp3hiRc8va3KZ2wN+maqAqC956v1XNsJ/3wNuoZgYKHKm/QCkuNVy3vsBqdaqv/f8oend6BKL9DO9e1eKKBGjBjB3r17mTVrFtevXycxMZEOHTpQvnx5ypcvzxdffPFO6831NyQtLY3U1NQM6Q8fPsTExOSdghAEQfM8PDzYsmULtWvXxt/fnwkTJjBhwgRNh5UpSyMlbSo58PfFMI6FPKH5guP8M6gWOtqidkQoYM6tgGNzIPY/kN6YYefPdvD1KVXBrsagnK3vRQQotFQFwPRC4Ot0Xv1YIiwADk+BPX5gXQ6cvMG5DpRqCLoG775Pgkb17t0bMzMz+X1oaChBQUEEBQWxadOmdy4QKqRcDj7WsWNHzMzMWL58OSYmJgQGBmJtbU2rVq0oUaIEK1eufKdANOnhw4cUL16cBw8eUKxYMU2HIwgaExcXh6mpKWlpaZiamhIVFVWga95Gbr7MxvOqudX3DatDGTvxo1TQsOiHqgKe0kj1/sjPcGSaeh7zElBvDFTs9Oqxb24kJ6gKmPHPVAVAHT0wsFQt0zd99Rj6zHI4MBZS3mhrq9ACzy+hxYKP/hHzp3j/1tLSonjx4ri7u+Ph4UH58uXx8PDAzc1NnjjkXeS6hnD27Nn4+vri5uZGQkICX375JSEhIVhZWbF+/fp3DkQQBM0zMjLi8ePHWFlZERMTg4mJCS1atODrr7/G0dGR4sWLF6gC4ox2FTl39zmhT+J4+DxeFAiF/Bd5E57chLALcGM3RF6HSl2h8c+gZwwun4NpUXCsBUZWoKWjetz7PueRrj5YllS93qZ6P6jaB57egodnIfQoBG5Q1VRal3tVGHxwDu6fBNeGYF32oy8kFnYDBw7k9OnT1KpVCzs7Oy5cuMDq1asJDg7G1NSU69evv9N6c11DCJCSksJff/1FYGAgL168oHLlynTp0gUDg4+zCvpT/IUhCG/TsWNHNm7cmCG9X79+LFu2TAMRZa3vH+c5EBxBj5pOTGjpnv0HBCG34p7CncOqNn2uPqrC3P0zsLJxxsfAABZO0PZ3KOaV76FmKzkBrmwEt1aq/QE4Pk/VrhHAsAj08c++sFlAfKr37wcPHvDTTz8RGhrK2LFjqVOnDqDqcGJtbf1O63ynAmFh86l+oQThbWJiYli1ahVLly7l2rVrAJiZmfHs2TO0ClANwsQdV1l54i4A6/pUp6ar1ds/IAg5dXUbnF4MD86o3luVhsHnVP+/thM2dHmVt1QjsCkHbl+AQ+X8jvT9XP5L1dbx4VnV+3ItoN2qj6Ijyqd+/w4NDWXy5Mn8999/zJ49Gzc3t3deV64LhNu3b898RQoF+vr6uLq64uzs/M4BacKn/oUShOwkJydjampKQkICXl5e9OnTh/79+xeIx8dX/4um2S/HAShja8KOId4odQpOgVX4yEgSBP8D+8dC9P1X6bpGqk4ZXf5fc54UD8/uqNrrWZV+v0fABcWx2apxEAEqdIJms1WPvQuwT/H+fe3aNW7cuMGNGze4du0at2/fJi4ujtGjR9O+fft3Xm+uC4RaWlooFAre/Fh6mkKhwNvbm23btqlNbVeQfYpfKEHIrWbNmrF79275/cGDB/n88881GNErG889YOSWQACG+5TmG59SGo5I+GgFb4eNXV+91zeDTutfDeVSmCVEw+KaEKPqqEWv/VCiuur/SXGvOsoUIJ/i/VtLS4sKFSrQoUMHmjdvTrly5TQzdd2BAweoWrUqBw4cIDo6mujoaA4cOED16tXZuXMnR48e5enTp2LWEkEoZLZs2cLWrVvl90FBQRqMRl2zCvYYKbUBuBIWpdlghI/PkxAIv6L6v1tLGHoJyrWE1svB7/anURgEVeF3WCDUGAwmRcG+wqtlf7aH7UNV4ycKGjVr1iy8vLz4559/aNiwITVr1qRnz57Mnj2bffsyGdg8h3JdQ+jh4cHy5cupWbOmWvqJEyfo168fV69exd/fn169enH//v0s1lKwfIq/MAThXX333XfMnj0bgP3799OwYUMNR6TiHxxBnz/O417UlF1Da2s6HKEgSkmEk7+AnikojSEmTFUQvLYdHLzAZyI4i+8OoHp0nv4YPCEaFlZVjYEIULY5VOsHJetqLr7/E/dv9XEIr169ytq1a99pPbluMXr79m1MTU0zpJuamnLnzh0ASpUqxZMnT94pIEEQCjZvb2+5QNioUSP8/PyYMWOGhqMCG1PV+FtX/4th9v4bfNuojIYjEjTu5n6IugfV/j+takI0HJqced7wINXwMaJAqPJ6m0g9U2gwXjWmYvR9uL5T9XKspapJ/WzAq7xPb4O+OSREvRofUWmY39EXOvfu3SMwMBBbW1uqVaumtszZ2RlnZ2datGjxXtvIdQ2ht7c3JiYm/PHHH3LX5sjISLp160ZcXBxHjx7F39+fQYMGcePGjfcKLr+IXxiCkDunT5+mffv2PHyoamtUEM6dhORU6s08QniMahDeNpUdmN2+YoHo+CLko+QECFitGnPv+k5VmnNd6LoNXj6HgxMgIQbinqhm+rAuC8WqQKWvVAUYIWtpaarp8I7OgIf/721tVQYGn32VZ35FeH5X/XNG1tBrHxRxyfOQPoX79/r16+nRowfJyckoFAoqVarEnj173nl4mazkug3hihUrCA0NpVixYri6uuLq6kqxYsW4e/cuv/32GwAvXrzgxx9/zNNABUEoOD777DNu3LghDz/j6urK48ePNRqTvq42J0d9TkkrVcP3vwPCqPTTASJjEzUal5CPJAkO/QR7Rr4qDBoWAd+pqsGWjYpAywXQYTX03AX9DkPrJVC1tygM5oSWFpRuBL0PqOZhrjcaKnRQz5Nex6Slo5oRBSAuUlVrWEAsWrQIJycn9PX1qV69OmfPns0yb3JyMpMmTcLFxQV9fX0qVqzI3r171fLExsYybNgwHB0dMTAwoGbNmpw7dy7Duq5du0bLli0xMzPDyMiIqlWr5qhp3cSJE/nyyy+5fv06+/fvB2DUqFG53OvsvdM4hGlpaezfv5+bN28CUKZMGRo2bFigxibLjU/hF4YgfAgLFy5kyJAh8vuIiAhsbGw0GBHEJ6UwcXswG84/AGB+J09aeTpoNCYhn+wdrRo3EFS1Us3mqB4BG3wcI14UCmlpqqny0h8Txz9TPba3q/hBZkDJ7f17w4YNdOvWjaVLl1K9enXmzZvHpk2buHHjRqbXru+//561a9fy66+/UrZsWfbt28eIESM4efIklSpVAlQD+QcFBbFkyRKKFi3K2rVrmTt3LsHBwTg4qK49t2/fplq1avTu3ZvOnTtjamrK1atX+eyzz7K9ZiqVSm7evImTkxMA169fx8vLi7i4uFwerWxIgvTgwQMJkB48eKDpUATho/PPP/9IgARIO3bs0HQ4sp4rz0qO3++Ulv17S9OhCPklcJMkjTeVpMn2khQdpulohHyQ2/t3tWrVpEGDBsnvU1NTpaJFi0rTpk3LNL+9vb20cOFCtbQ2bdpIXbp0kSRJkuLj4yVtbW1p586dankqV64s/fDDD/L7jh07Sl999VWOYnyTQqGQIiIi1NL09fWlR48evdP6svJOxfW4uDh2797N0qVL+eWXX9RegiB8Wlq2bMkXX3wBwIABA4iNjdVsQP+X/uh49cl7JKakajgaIc8lJ6jm4L19CKLDVGl25aFydxj9QDV/sPDJiI2NJSYmRn4lJmZsKpKUlMSFCxfw8fGR07S0tPDx8eHUqVOZrjcxMRF9fX21NAMDA44fVw2Gn5KSQmpq6lvzpKWlsWvXLkqXLo2vry82NjZUr16dbdu25Xj/Vq9ezcmTJ3nx4gUAOjo6xMfH5/jzOZLbEmRAQIBkZ2cnmZqaStra2pK1tbWkUCgkIyMjydnZOU9Lq/lF1BAKwvsZM2aMXEsIZPi1rAm/HbsjOX6/U3L8fqc0ZVewpsMR8kpinCRt/VpVE5j+2v29JL2M0nRkggak37/ffI0fPz5D3rCwMAmQTp48qZbu5+cnVatWLdP1d+7cWXJzc5Nu3rwppaamSvv375cMDAwkpVIp56lRo4ZUt25dKSwsTEpJSZHWrFkjaWlpSaVLl5YkSZIePXokAZKhoaE0Z84c6eLFi9K0adMkhUIhHTlyJNt9rFOnjmRqaiopFApJW1tbcnV1lXR0dKQxY8ZIhw4dkmJiYnJxxLKW6xrC4cOH06JFC54/f46BgQGnT5/m3r17eHl5MWvWrDwrqAqC8PEYOXIkvXv3lt/PnDlTg9GotKtcDGsTVUeB5UfvMGvfxzHqgfAWseEwqxRcem2cNRs3CDsP9zKv4RE+DcHBwfJkGdHR0YwePTpP1jt//nxKlSpF2bJlUSqVDB48mJ49e6r1mVizZg2SJOHg4ICenh6//PILnTt3lvOkpaUB0KpVK4YPH46npyejRo2iefPmLF26NNsY/v33X6Kjo7lx4wZr166ldevW1K1blyVLltCgQQMsLCwoV67ce+9rrguEly5d4ttvv0VLSwttbW0SExMpXrw4M2bMYMyYMe8dkCAIHx8zMzN+++03du3aBaguYJoem9DMUJedQ7zR0VINO7Pw8C1m7ruu0ZiE9/TPYEhSPTKj0lcw7jl8fQr6+EOZxpqNTdAoExMTTE1N5ZeeXsZe41ZWVmhraxMREaGWHhERgZ2dXabrtba2Ztu2bcTFxXHv3j2uX7+OsbExJUuWlPO4uLjw77//8uLFCx48eMDZs2dJTk6W81hZWaGjo4Obm5vausuVK5erCTxKlSpFp06dmDFjBv7+/jx79ozbt2+zfv16WrduneP1ZCXXBUJdXV251GtjYyPvjJmZGQ8ePHjvgARB+Hg1bNgQMzMzQDVUQvovY02xNdXn0vhGFDFSArDo8G2evhDD0BRIkqTqoZouNQX2/wi7XpsG1b4i6BpCi/nQatEH6bUqFF5KpRIvLy8OHjwop6WlpXHw4EFq1Kjx1s/q6+vj4OBASkoKW7ZsoVWrVhnyGBkZYW9vz/Pnz9m3b5+cR6lUUrVq1QxjM9+8eRNHR8f32idnZ2fat2/P1KlT32s98A4zlVSqVIlz585RqlQp6taty7hx43jy5Alr1qzBw8PjvQMSBOHjpaury507dyhSpAjx8fF06tSJ/v37U79+fY0NS2Wsp8Ox7+tTZbI/8UmpfPnrGXYM8UapIwoTBcqi6vDkBmgrwdQBnv9/zlzb8q+mUas3GhqM1WycwkdtxIgRdO/enSpVqlCtWjXmzZtHXFwcPXv2BKBbt244ODgwbdo0AM6cOUNYWBienp6EhYUxYcIE0tLSGDlypLzOffv2IUkSZcqU4datW/j5+VG2bFl5nQB+fn507NiROnXqUL9+ffbu3cuOHTs4cuRIvu7/2+T6ijh16lTs7e0BmDJlChYWFgwcOJDIyEiWLVuW5wEKgvBxsbS0pG5d1RynmzZtwsfHhz///FOjMRkqdehWwwmAGxGx7Al6pNF4hEykDwydmvSqMAhQ1PPVNGraua7DEAQ1HTt2ZNasWYwbNw5PT08uXbrE3r17sbW1BeD+/fs8evTq+pCQkMCPP/6Im5sbrVu3xsHBgePHj2Nubi7niY6OZtCgQZQtW5Zu3brh7e3Nvn370NXVlfO0bt2apUuXMmPGDMqXL89vv/3Gli1b8Pb2zrd9z847DUxd2IiBqQUhb0VHR7NmzRp50GpbW1tu3bqFsbGxxmJKTZPo8ttpTt95BsDSr7xo7JF5uyHhA3t2B35vAs51oPa3YFNW1SlE31Q1rVzAH6pp5eqPAWsxJ7WQNXH/zju5riH8/PPPiYqKypAeExPD559/nhcxCYLwkTMzM2Pw4MH8888/gKrR9rhx4zQak7aWgnHN3eX3A9ZeYNWJ0Ld8Qvhg1raFF+FwZeOr2j/HGmDrrvq39RLV9HKiMCgI+SbXBcIjR46QlJSUIT0hIYFjx47lSVCCIBQOTZs2pXTp0gBcvXpVw9GAW1FTjo2sL7/ffSVcg9F8orYPUdUQAjSeLgp9gpCNQ4cO4ebmRkxMTIZl0dHRuLu750n5K8cFwsDAQAIDAwHVeD/p7wMDA7l48SIrVqyQ5+zLS2FhYXz11VcUKVIEAwMDypcvz/nz5+XlkiQxbtw47O3tMTAwwMfHh5CQkDyPQxCE3NPR0WHxYtXcsvv378/VyPwfSnFLQ/4ZVAuA+8/yeKR/IWuBG2Guh+pxMIBjLfhsgGZjEoSPwLx58+jbty+mpqYZlpmZmdG/f3/mzJnz3tvJcQtdT09PFAoFCoUi00fDBgYGLFiw4L0Det3z58+pVasW9evXZ8+ePVhbWxMSEoKFxauJymfMmMEvv/zC6tWrcXZ2ZuzYsfj6+hIcHJxhKhlBEPLf66MPfPPNN/I0d5pUwtIQgPCYBLquOEOl4uYM8ymN1v/HLBTekyTB2V+hTBMwL65Ki/kPov8/NJl1Oei4NuvPC4Igu3z5MtOnT89yeaNGjfJkYpAcFwhDQ0ORJImSJUty9uxZrK2t5WVKpRIbGxu0tbXfO6DXTZ8+neLFi7Ny5Uo5zdnZWf6/JEnMmzePH3/8UR7v548//sDW1pZt27bRqVOnPI1HEITcs7W15cSJE9SqVYv79+/z4sULjXYuATA31MXF2ojbkXEcC3nCsZAnHA15wh+9q2Gqr5v9CoSMUlMgaDPcPwX3z0DkNQhYDV8sAfsKULY5FHEBbT1w8galoaYjFoSPQkREhFqP5Tfp6OgQGRn53tvJ8SNjR0dHnJycSEtLo0qVKjg6Osove3v7PC8MAmzfvp0qVarQvn17bGxsqFSpEr/++qu8PDQ0lPDwcLWJqs3MzKhevXqWE1WDarLq1yfBjo2NzfPYBUF4pWbNmlhZWQHQuHHjTNsh5yeFQsH2wd6s7/sZZWxNALj0IIrN5x9qNK6P1u3DMLsMbO0PF1apCoMA8U9VA0kDWLlCuRZQupEoDApCLjg4OBAUFJTl8sDAQHk4wPfxToM6hYSEcPjwYR4/fpxhJoK87El4584dlixZwogRIxgzZgznzp1j6NChKJVKunfvTni4qkF4+vhB6WxtbeVlmZk2bRoTJ07MszgFQchevXr12Lx5MydOnEBPT49Zs2YxaNAgjTXtMNLToYZLEf7+uiatFp3g1uMXTNoZTC1XK8rYmWgkpo/G7pGq9n+W/5++6/zvEP9E9f8ipaBiRzApCuXbvRpfUBCEd9K0aVPGjh1L48aNM1wvX758yfjx42nevPl7byfX4xD++uuvDBw4ECsrK+zs7FAoXrW5USgUBAQEvHdQ6ZRKJVWqVOHkyZNy2tChQzl37hynTp3i5MmT1KpVi//++0+tdNyhQwcUCgUbNmzIdL2JiYkkJr6aviosLAw3NzcxjpEgfEDJycl89913/PLLL3La119/zaJFizQYlcqxkEi6rjgLgJ6OFvuH18GxiJGGoyqgnt+F+RWh2Ryo1BV0lHDiF3hwBrx6QimfbFchCHnlUxiHMCIigsqVK6Otrc3gwYMpU0bVM//69essWrSI1NRUAgICMlSO5Vauh52ZPHkyU6ZMITw8nEuXLnHx4kX5lZeFQQB7e/u3TgadPhl1biaqBtDT01ObBNvERNQGCMKHpqury/z58wkKCqJs2bIALF68GBsbGypXrkynTp148uSJRuY/9na1Ynrb8gAkpqTRZ/X5bD7xiUqIURUGAU7MVxUGAWoNhU5/isKgIHwAtra2nDx5Eg8PD0aPHk3r1q1p3bo1Y8aMwcPDg+PHj793YRDeoUD4/Plz2rdv/94bzolatWq9dTJoZ2dn7Ozs1CaqjomJ4cyZM9lOVC0Igma4u7tz7tw5ypdXFcAiIyO5ePEiGzZswNraGm1tbQwMDGjUqJE8sPWHplAo6Fi1BOOaq36Ahjx+weOYhHzZ9kcj6j784vnqfe1vNRaKIHxKJk2ahLW1Nbt37+bJkyecOXOG06dP8+TJE3bv3q3W2fZ95LpA2L59e/bv358nG8/O8OHDOX36NFOnTuXWrVusW7eO5cuXM2jQIEB1ER82bBiTJ09m+/btXLlyhW7dulG0aNECMbSFIAiZMzY2JjAwkMjISE6ePMmECRPUeh4nJCRw4MABvvjiCy5dupRvcfXydsbSSFXrVfPnQyQkp+bbtgu8y3+pOokAfP4jeHXXbDyC8ImYOHEiL168AMDCwoKqVatSrVo1tSH48kKuO5W4uroyduxYTp8+Tfny5TN0hR46dGieBVe1alW2bt3K6NGjmTRpEs7OzsybN48uXbrIeUaOHElcXBz9+vUjKioKb29v9u7dK8YgFISPgJWVFVZWVtSoUYMff/yR+Ph4Xr58yeXLl2nUqBEAY8eOZdu2bR9kJIPM9K9Tkml7rpOSJhES8YLyxczyZbsF3rXtqn/dW4O3qB0UhPySy64e7yzXnUreVjWpUCi4c+fOeweV3z6FRqmC8LHx8/OTB1tdu3at2g/BD6314hNcvB/FV5+V4KdWHmqd5z5Zh6fBvz9Db38oXlXT0QgC8Gncv7W0tIiIiFAb//lDyHUNYWiomAxeEIQPb/DgwXKB8MyZM/laIHQqYsTF+1GsPX2fm+Ev6FbTkYrFzLE300dHO9ctbT5uaamQkgBuLcHlc1EYFAQNKF26dLY/TJ89e/Ze23incQgBkpKSCA0NxcXFBR2dd16NIAhCphwdHVm+fDn9+vVjwYIFVK1ala5du+bLtgfUdSHwYRS3I+M4e/cZZ++qLrRORQzZO6wO+rr58/i6QFjbBip2hopi5idB0JSJEydiZvZhm6/k+pFxfHw8Q4YMYfXq1YCq12/JkiUZMmQIDg4OjBo16oME+iF9ClXOgvAxSj83ARo0aIC/v3++bTs1TWLT+QccDYnk2qNYQp/EycsaudnSv25JvBwt8y0ejTjxCxwYC6WbQNvfQE+zUw4Kwps+hfu3lpYW4eHh2NjYfNjt5PYDo0eP5vLlyxw5ckSt44aPj0+WA0ELgiC8i2LFismFwIMHDzJ79mxSUlLyZdvaWgo6VSvB4i5eHP6uHtPblkdXW/XIZn9wBG2XnGLanmv5Eku+i7wJi6qrCoMAUfdEYVAQNCS/2jDnukC4bds2Fi5ciLe3t1qQ7u7u3L59O0+DEwRBqFGjBubm5gB89913lC1blvj4+HyPo2PVEgRN9GVlz6pywXDZv3e4eP95vsfyQZ1aBIuqQuR11Xu7CtDhD83GJAifsPzqZZzrAmFkZGSm1ZZxcXGiJ54gCHnO0NCQq1evUr16dQBu377NuXPnNBKLno429cvYcGlcI3S0VNe7bivOkpqWPxfsfHFh9av/f7EE+h0Bq1IaC0cQPnVpaWn/a+++A5uq2geOf5PuXSjdlFJAKHtTiiyhspWloihLQVF4ZQgKMpQlCrJFQQRBQJYiCCLIXpa9V6GUWdqy2tI9kvv+EZsSW5C2adPxfN5ffs09OTn3yaH2Prn3nnPy/XIx5CIhbNCgAX/88Yd+OyMJ/OGHH2R1ECFEvvDy8uLQoUN06NABgJYtW5p0iis7K3MW99WNto1LSefX47dNFotRXD+Y+dyrLrjXhE8joE5PUJegATRCFELBwcFs3rzZoOynn37Cz88PNzc33n33XVJSUvK8nxwnhF988QWffvop77//Punp6cyZM4c2bdrw448/MmXKlDwHJIQQT/LWW2/pn2/atMmEkUCLyq7ULecMwMe/nmHQzyeISUw1aUy5cmUHLO0Aaf8s1dflO3j/AFjamjYuIQSgG2F8/vx5/fbZs2d55513CAoKYtSoUWzatImpU6fmeT85TgibNm3KqVOnSE9Pp2bNmvz111+4ubkRHBxM/fr18xyQEEI8yRtvvEGfProl0yIjI00cDYztWE3//I8zESwPvmHCaHJBUeDkct3zjJ/qEjbPohCF3OnTp2ndurV+e/Xq1QQEBLBo0SKGDx/O3LlzWbt2bZ73k6sJBCtWrMiiRYvyvHMhhMip557T3c929+5dE0cC9X1LcebzNry2IJhLkXGsPnqL91pUxNK8iCRVB2bBhQ2656nxJg1FCJG96Oho3N3d9dt79+6lffv2+u2GDRty69atPO8nx3+1tmzZwrZt27KUb9u2jT///DPPAQkhxNNk3Fx94cIFE0ei42htwUdtqgAQHpNEi+m72Xf5nomj+g9XdsCWj2HnBN22vTvUeMW0MQkhsuXu7q5fJS41NZUTJ07QuHFj/etxcXFYWFjkeT85TghHjRqFRqPJUq4oSpGclFoIUbRkJISHDh1i4cKFJo5Gp9lzZWjlr4srIjaZ3kuO8Mp3f5OSnvVvpcmlJcPhBXDkn75TmUH/neDsY9q4hBDZ6tChA6NGjWL//v2MHj0aW1tbmjVrpn/9zJkzVKxYMc/7yXFCeOXKFapVq5al3N/fn9DQ0DwHJIQQT9OiRQv98+3bt5swkkzWFmYs6duQBW/Vw+qfy8XHbkRz8maMaQN7nKJAUgxYWEP1rlDhBeg4E/rvkGRQiEJs0qRJmJub06JFCxYtWsSiRYuwtLTUv75kyRLatGmT5/3kOCF0cnLKdrqH0NBQ7Ozs8hyQEEI8jbOzM6tWrQLg/v37Jo7GULsanlya1I7aPs4AvP79IW49LPhJtLO4tAWmVYC9X+m2a78OvX6Dhu+Adz3TxiaEeKoyZcqwb98+oqOjiY6OpmvXrgavr1u3js8++yzP+8lxQti5c2eGDh1qsCpJaGgoH330ES+//HKeAxJCiP9SqlQpAGJiYkwbSDZUKhWv1M9cU7Xz/IPciUkyXUCKAts+haSHcPsopCXp5haUhQSEKFKcnJwwM8s6L2jp0qUNzhjmVo4TwmnTpmFnZ4e/vz9+fn74+flRtWpVXFxc+Prrr/MckBBC/JeMpewKY0II0KuxL2M6VAXgYUIqTb7cxdZzEQW2BJWBVa9DtO6GdNpMBgubgo9BCFHo5XjaGScnJ/7++2+2b9/O6dOnsbGxoVatWjRv3jw/4hNCiCwyzhDeuHGDP//802AKhsKifzM/7ieksHCv7habgStO4OFoTY+GPjjbWmBjYUbzyq54Oedjgpb8CC5v1T0v3wx8AvJvX0KIIk2l5OAra1paGjY2Npw6dYoaNWrkZ1wF6vbt2/j4+HDr1i3Kli37328QQphUXFwcjo6OgG5Zu9u3bxfatdT3Xr7H7B2XnzjA5O9RrfInKUx+BAubQfR1sHKC0TeNvw8hTEyO38aTozOEFhYWlCtXLttpZ4QQoqA4ODhw+PBhAgICuHPnDjVr1uTMmTOoC+EqGy0qu9KisisX7jxizdGbJKRqiE9OZ+t53UorTb7cRb/ny1PK1hK/MnY0rVSGUnZ5vx+I2Nvg4KlLCP075r09IUSxlqMzhACLFy9m/fr1LF++nNKlS+dXXAVKvmEIUTT179+fxYsXA7rBbcaYi6ugLD14jc83ZT+5drd63nzaoSpl7K2evcG7F+HvbyDqLLy7VzdoRKuFiFPgWVs3kESIYqa4H7+HDx/+zHVnzpyZp33lOCGsW7cuoaGhpKWl4evrm2WqmRMnTuQpIFMo7r9QQhRXiqLg6urKgwcPOHDgAM8//7ypQ8qRu3HJrDt2m5jEVCJik9lxMYrkNC0AZmoVe0a0xKe07X83FBcFs6qDNk23Pfo2WDnkY+RCFA7F/fj9wgsvGGyfOHGC9PR0qlTRrY50+fJlzMzMqF+/Prt27crTvnI8qKRLly552qEQQhiLSqWicuXKBAcHF4q1jXPKzcGaQS9U0m+nabSsPXaLMb+dQ6NV6LPkCN/3rk9FV/sn3yOZmgi/D85MBttMAbMcnFkUQhRau3fv1j+fOXMmDg4OLFu2TD+wLjo6mn79+hmsXJJbOU4IjTH5oRBCGEvGUnZFMSH8NwszNW8G+KJVYNyGc4TdTyBo5j7qlXNmxmt18Cvzr8n/H16DpZ3g0W3ddt23oMnggg9cCJHvZsyYwV9//aVPBkE348LkyZNp06YNH330UZ7az9Ud2DExMfzwww+MHj2ahw8fArrTmOHh4XkKRgghciojIRw2bBgpKSkmjsY4ejTwYWTbKng5WQNw4mYML39zgMTUdMOKu6dkJoMuz0HL0QUcqRCioDx69Ih79+5lKb937x5xcXF5bj/HCeGZM2eoXLkyX331FV9//bV+Ytj169czerT8MRJCFKwKFSoAkJSUxM8//2ziaIzD0lzNoBcq8ffo1kzqopviKy45nfqTdpCQkg6afx6txkKPldB+Ogw+Ck7F7x4qIYRO165d6devH+vXr+f27dvcvn2bX3/9lXfeeYdu3brluf0cJ4TDhw+nb9++XLlyBWtra315hw4d2LdvX54DEkKInBg8eDAeHh4AnD9/3sTRGF+vxr589GJlAJLSNHy/6yLKt43hfgiUKg9VO0HAu7IUnRDF3IIFC2jfvj09e/bE19cXX19fevbsSbt27fj222/z3H6OE8KjR4/y3nvvZSn39vYmMjIyzwEJIURO2NvbM2bMGEB3j83jN2EXF/9r/Rxtq7tjhobn/h6B6sEVOPerqcMSQhQgW1tbvv32Wx48eMDJkyc5efIkDx8+5Ntvv80y40tu5DghtLKy4tGjR1nKL1++jKura54DEkKInGrZsqX++U8//WS6QPLRsBcr00J9mk5mh3QFD6+ZNiAhhEnY2dlRq1YtatWqZZREMEOOE8KXX36ZiRMnkpamm+JApVJx8+ZNPvnkE7p37260wIQQ4lnVqFGD2bNnAxAVFWXaYPKJv4cj71TV6rfTgyaaMBohhCns37+ft956i8DAQP1A3uXLl3PgwIE8t53jhHDGjBnEx8fj5uZGUlISLVq0oFKlSjg4ODBlypQ8BySEELlRvnx5QDcvV7EUfYPnr+pWIvg+vSPTD8WbOCAhREH69ddfadu2LTY2Npw8eVI/q0JsbCxffPFFntvP8TyETk5ObN++nQMHDnDmzBni4+OpV68eQUFBeQ5GCCFyK2NurkOHDpGcnGww6K1YODBL//S81peNe8NwtbeibXWPZ1vNRAhRpE2ePJkFCxbQu3dvVq9erS9//vnnmTx5cp7bz3FCmKFp06Y0bdo0zwEIIYQxuLi46J/37dvX4A9mseBRAxy9SfFuzNYzuiX6Jv9xkRl/XebQp61xsrEwcYBCiPwUEhJC8+bNs5Q7OTnppwDMi1xNTL1z5046depExYoVqVixIp06dWLHjh15DkYIIXKratWq+uWb1qxZU2zmJOT8b7qfDfvD8AtY9VjC0rcD+bCVbsm7pDQNl6PyPimtEKJw8/DwIDQ0NEv5gQMH9POx5kWOE8Jvv/2Wdu3a4eDgwJAhQxgyZAiOjo506NCB+fPn5zkgIYTIDbVaza5du/SXjt98801WrVpl4qhyKTUBto6GKV6wri+kJRm8HFjRheFtqhBYQXdW9NUFwYTdk3sKhSjOBgwYwJAhQzh8+DAqlYo7d+6wcuVKRowYwfvvv5/n9lWKoig5eUPZsmUZNWoUgwcbrpc5f/58vvjiiyK5fN3t27fx8fHh1q1blC0rM/0LUZSdOHGCpk2bkpSkS6KOHz9OvXr1TBxVDu2bDrseuyfopblQv0+WaksOXGPi5gv67Tmv16FzHe+CiFCIQqEkHb8VReGLL75g6tSpJCYmArqpAEeMGMGkSZPy3H6OzxDGxMTQrl27LOVt2rQhNjY2zwEJIURe1KtXj0OHDum3i9TqJTE34fcPM5NB3+dhZFi2ySBAv+fLM6VrDf32ysM3CyJKIYQJqFQqxowZw8OHDzl37hyHDh3i3r17RkkGIZfzEP72229Zyjdu3EinTp2MEpQQQuRFrVq16NGjBwAPHjwwcTRPkZqQ+VyrgbW94cQy3baDJ7z2E9i5ZP9edAeINwN8WTWgMQBHrj2k27cH+e3kbVLSNfkZuRBF1vz58ylfvjzW1tYEBARw5MiRJ9ZNS0tj4sSJVKxYEWtra2rXrs3WrVsN6sTFxTF06FB8fX2xsbGhSZMmHD169IltDhw4EJVKpZ879Vm1atWKCRMmYGlpSbVq1WjUqBH29vZER0fTqlWrHLWVnRyPMq5WrRpTpkxhz549BAYGArppHg4ePMhHH33E3Llz9XU//PDDPAcohBC5kXEvYaGdl3Btb7iwET44DG7+oDYD9xqQngqNB0LdXs+8PnHdcs5UcXcgJCqOEzdjOHEzhmFrTvPRi5UZ0LwC1hZm+fxhhCga1qxZw/Dhw1mwYAEBAQHMnj2btm3bEhISgpubW5b6Y8eOZcWKFSxatAh/f3+2bdtG165d+fvvv6lbty4A/fv359y5cyxfvhwvLy9WrFhBUFAQFy5cwNvb8BaO3377jUOHDuHl5ZXj2Pfs2cPZs2c5efIkK1eu1K9Skpqayt69e3PRG4ZyfA+hn5/fszWsUhEWFparoApaSboHQYiS4tNPP2Xq1KnUqlWLY8eOYWFhwmlZEh5A8Dw4vVp3VjAlDvjnT2+FlvDWb6BWQ0o8WNnnejfHrj/kh/3X2Ho+c1352mWdWPZ2I5xtLfP2GYQohHJ6/A4ICKBhw4Z88803AGi1Wnx8fPjf//7HqFGjstT38vJizJgxDBo0SF/WvXt3bGxsWLFiBUlJSTg4OLBx40Y6duyor1O/fn3at29vMD9geHg4AQEBbNu2jY4dOzJ06FCGDh36zJ9VrVZz8uRJ3nvvPRISEti0aRPly5cnKioKLy8vNJq8XRXI8RnCa9dk/UwhROGX8W3/zJkzLF68mIEDB5omEEWBNW/CzeCsr1XpAK//nHkmMA/JIECD8qVpUL40dx8l0/XbvwmPSeL07Vg+WnuaxX0b5qltIYq61NRUjh8/zujRo/VlarWaoKAggoOz+e8TSElJyTLJvY2NjX6puPT0dDQazVPrgC7x7NWrFyNHjqR69eq5/gyenp7s3buXfv360bBhQ9atW0fVqlVz3d7jcjUPoRBCFHZvvfWW/vn7779vuisWKhX03giBg6HpMBh8HIadh4+vwRurnvmycE64OVqzZ2RLWlR2BWD/lfvk8GKQEEVKXFwcjx490j8ylnV73P3799FoNLi7uxuUu7u7ExkZmaU+QNu2bZk5cyZXrlxBq9Wyfft21q9fT0REBAAODg4EBgYyadIk7ty5g0ajYcWKFQQHB+vrAHz11VeYm5vn6VY61T9/K6ysrPj5558ZMmQI7dq149tvv811m4+ThFAIUSyVKVOGP/74Q789bNiwgtt59A1Y9hKc3wDpKWBuBW2nQNDnUKYSOJUF29L5GoKFmZpv39RNt5Oq0fLawmBJCkWxVa1aNZycnPSPqVOnGqXdOXPm8Nxzz+Hv74+lpSWDBw+mX79+qNWZ6dPy5ctRFAVvb2+srKyYO3cub7zxhr7O8ePHmTNnDkuXLtUndbnx7/9+x44dy8qVK5kxY0au23ycJIRCiGKrbdu29OrVC4Dff/+94CbPPzgHru2DdX3g7C8Fs89s2FmZ076GBwBHr0dzIeKRyWIRIj9duHCB2NhY/ePxy8IZypQpg5mZGVFRUQblUVFReHh4ZNuuq6srGzZsICEhgRs3bnDp0iXs7e0NVgapWLEie/fuJT4+nlu3bnHkyBHS0tL0dfbv38/du3cpV64c5ubmmJubc+PGDT766CPKly//zJ/x2rVrlClTxqCse/fuHDp0iCVLljxzO08iCaEQotgyMzPjm2++wcHBAYAhQ4boJ6zOF+mpcPQHOLZYt13zVaj9Rv7t7xl891Z9yrvYAtBx7gHe+P4Qtx4mmjQmIYzNwcEBR0dH/cPKyipLHUtLS+rXr8/OnTv1ZVqtlp07d+pnTXkSa2trvL29SU9P59dff6Vz585Z6tjZ2eHp6Ul0dDTbtm3T1+nVqxdnzpzh1KlT+oeXlxcjR45k27Ztz/wZfX19Dc5MZqhRowZ9+mQ/V2lO5HhQiRBCFCWOjo76b9YajYaLFy/mz8oll7fBL29D6mNLyAV9rhs9bGIftn6O4WtPAxAc9oBVR27ycTt/E0clRMEbPnw4ffr0oUGDBjRq1IjZs2eTkJBAv379AOjduzfe3t76S86HDx8mPDycOnXqEB4ezueff45Wq+Xjjz/Wt7lt2zYURaFKlSqEhoYycuRI/P399W26uLjg4mI4n6iFhQUeHh5UqVLlP+OdNGkSdnZ2DB8+/Kl1Z86cmeP+eFyuEsL9+/ezcOFCrl69yi+//IK3tzfLly/Hz8+Ppk2b5ikgIYQwNhcXF1q0aMHevXupX78+CQkJ2NraGm8H0dfhjxGZyaBTOXhtme5ewUKgW72ydKzlyZBVp9h6PpJv91zFwkzNK/XL4uFkjYWZ6ZNWIQpCjx49uHfvHuPHjycyMpI6deqwdetW/UCTmzdvGpyFS05OZuzYsYSFhWFvb0+HDh1Yvnw5zs7O+joZl6hv375N6dKl6d69O1OmTDHKVFcnT54kLS1N//xJ8nJvor6NnM5D+Ouvv9KrVy/efPNNli9fzoULF6hQoQLffPMNW7ZsYcuWLXkOqqDJPIRCFH+zZs3Sf8P+66+/ePHFF427A0WBv+dB+efBu75x2zaS0LtxBM3cZ1Dm6WTNX8Oa42BtwnkahcglOX4bT46/Fk6ePJkFCxawaNEig+z3+eef58SJE0YNTgghjGXYsGH6+4TmzJlDcnJy3htNuA8hf+qSQZUKnv+w0CaDAJXcHDg57kW61ytLBVfdKgcRsclM2nyBNI3WxNEJIUwpx5eMQ0JCaN68eZZyJycnYmJijBGTEELki7Zt2xIcHMwff/zB1KlTmTBhQt4a/P1/ELoD/DtCuy/BIfuRioVJKTtLZrxWG4Dha0+x/kQ4a4/dpnEFF7rVkzMsQhQm/3Xf4OMK/B5CDw8PQkNDswyVPnDggMEwbCGEKGyGDRvGzz//zOXLl5k4cSIuLi65nyj24TUI+ecWGW062GVdB7Ww+7itP+tPhAO6yaslIRSicHnafYOPM8Y9hDlOCAcMGMCQIUNYsmQJKpWKO3fuEBwczIgRIxg3blyeAxJCiPzi6OjIjh07KFeuHKCbmzBXCWF6KixopnuutoDuiwvFaOKc8nCyZmLn6ozfeJ7fToZToYwd/2v9nKnDEkL8Y/fu3QW2rxwnhKNGjUKr1dK6dWsSExNp3rw5VlZWjBgxgv/973/5EaMQQhiNj48PO3bsICgoiDt37uS8gYfX4KfOkBqn2+78jW4lkiLqxWruTN8WQlxyOjO2XyYkKo7/tXqOKh4Opg5NCJGNCxcucPPmTVJTU/VlKpWKl156KU/t5niUcYbU1FRCQ0OJj4+nWrVq2NvnbVF2U5JRSkKULBcuXKB69eqUKlWKhw8f5uzNl7fBz6/pntfuCV2/M36ABSw5TUOL6buJepS5/utPbzei+T9rIQtRWJWk43dYWBhdu3bl7NmzqFQq/VJ2GZeLNRpNntrP8TWOt99+m7i4OCwtLalWrRqNGjXC3t6ehIQE3n777TwFI4QQBSFjmaro6GhmzJjB8ePHs1bSauHMWt3Ake9fgPi7unK/5uATAJ3n684OFgPWFmb8NbQF7zbPvA+895IjDF97ikfJaSaMTAiRYciQIfj5+XH37l1sbW05f/48+/bto0GDBuzZsyfP7ef4DKGZmRkRERG4uRneQH3//n08PDxIT0/Pc1AFrSR9wxBC6BaJd3Nz4/79+/qyixcv4u//z+odURd0q47cu5j5pkFHwbVyAUda8I5ce8hbPxwm9Z9paByszfmhdwMCKrj8xzuFKHgl6fhdpkwZdu3aRa1atXBycuLIkSNUqVKFXbt28dFHHz3zAJQneeYzhI8ePSI2NhZFUYiLi+PRo0f6R3R0NFu2bMmSJAohRGGkUqnYtWsXEyZM0M+OULVqVa5fv66rcHB2ZjLo3QA6fF0kppQxhkZ+pTnzeRteqa87uMYlpzPylzMmjkoIodFo9OuylylTRn8PtK+vLyEhIXlu/5kTQmdnZ0qXLo1KpaJy5cqUKlVK/yhTpgxvv/02gwYNynNAT/Pll1+iUqkYOnSoviw5OZlBgwbh4uKCvb093bt3JyoqKl/jEEIUfTVr1mT8+PEGcxGuWrVK9+TBVd3P+v2g/w5oNACsHU0QpWlYW5jx9au1WdhLN8n2zYeJ9FgYjFabq1vOhRBGUKNGDU6f1q1JHhAQwLRp0zh48CATJ040yrR/zzzKePfu3SiKQqtWrfj1118pXbq0/jVLS0t8fX3x8vLKc0BPcvToURYuXEitWrUMyocNG8Yff/zBunXrcHJyYvDgwXTr1o2DBw/mWyxCiOLjrbfe4tSpU8yYMYNNmzYxatQoVOHHdC826KdbgaSEalvdgxaVXdl7+R6Hrz3kxsNE/MrYmTosIUqksWPHkpCQAMDEiRPp1KkTzZo1w8XFhTVr1uS5/WdOCFu0aAHAtWvX8PHxMVj8Ob/Fx8fz5ptvsmjRIiZPnqwvj42NZfHixfz888+0atUKgB9//JGqVaty6NAhGjduXGAxCiGKrowvmsHBwUyYMIHPK7wAcRHgVs3EkZnej30b0mDKDh4mpBIS+UgSQiFMpG3btvrnlSpV4tKlSzx8+JBSpUoZZWLqHGd1vr6+qNVqEhMTuXTpEmfOnDF45IdBgwbRsWNHgoKCDMqPHz9OWlqaQbm/vz/lypUjODg4X2IRQhQ/nTp10t+QPmHCBM5V+xgGHgQzi/94Z/GnVquo4e0EwMAVJwi7F2/iiIQQGTJu5TOGHE9Mfe/ePfr168eff/6Z7et5nQfn31avXs2JEyc4evRoltciIyOxtLTE2dnZoNzd3Z3IyMgntpmSkkJKSuZ8W3FxcUaLVwhR9JR2tOfC9PY4vrEIgFfeeptLly6ZOKrC47UGZdl3+R4AB68+oIJr0Z13VoiiLDk5mTNnznD37l20Wq3Bay+//HKe2s5xQjh06FBiYmI4fPgwLVu25LfffiMqKorJkyczY8aMPAXzb7du3WLIkCFs374da2tro7VrlEXthRDFx6VNOFxaw5KXrXn792QuX75MWloaFhZyhhCgUy0vztyO5ft9YYzbcI7KbvYyDY0QBWzr1q307t3bYLqsDCqVquAnpt61axczZ86kQYMGqNVqfH19eeutt5g2bRpTp07NUzD/dvz4ce7evUu9evUwNzfH3NycvXv3MnfuXMzNzXF3dyc1NZWYmBiD90VFReknns3O6NGjiY2N1T8uXLhg1LiFEEXIvcu6OQeBPg2csbCwQFEUNm3aZOLACpcmFTMTwD4/HuFhQupTagshjO1///sfr776KhEREWi1WoOHMa7O5jghTEhI0M83WKpUKe7d011GqFmzJidOnMhzQI9r3bo1Z8+e5dSpU/pHgwYNePPNN/XPLSws2Llzp/49ISEh3Lx5k8DAwCe2a2VlhaOjo/6RMa+PEKIECdsDW0bC/Ib6InXXb/D19QWge/fucjvJY1pWcWNJ3wYAJKdp+WjtKdMGJEQJExUVxfDhw3F3d8+X9nOcEFapUkU/AWLt2rVZuHAh4eHhLFiwAE9PT6MG5+DgQI0aNQwednZ2uLi4UKNGDZycnHjnnXcYPnw4u3fv5vjx4/Tr14/AwEAZYSyEeLqLm+HWkcztNpOhWle++SZzOborV66YILDC64UqbrxUWze92O6Qe/x5NoKkVA0amZ9QiHz3yiuvGGWJuifJ8T2EQ4YMISIiAoDPPvuMdu3asXLlSiwtLVm6dKmx4/tPs2bNQq1W0717d1JSUmjbti3ffvttgcchhChiOn4N1/bpJqH2qgNedQHd1A516tTh1KlTMsn9v6hUKma9VptdF6NISNXw/srMq0KW5mq61yvL5y9Xw8rczIRRClE8ffPNN7z66qvs37+fmjVrZrnH+cMPP8xT+zley/jfMqafKVeuHGXKlMlTMKZSktZCFKLEUxS49Af4NQNrp2yrtG/fnq1bt9KwYUP+/vtvzM1z/N25WNt7+R4TN53n6r2EbF9/qbYXXk7WvN+yIs62lgUcnShJStLxe/HixQwcOBBra2tcXFwMpptRqVSEhYXlqf0cXzKeOHEiiYmJ+m1bW1vq1auHnZ0dEydOzFMwQgiRrzRpsH4ArHkTYm49sVrGMlBHjx6lZcuW3Lr15LolUYvKruz8qCWhU9pzYWJbTo57ke71Mg/Gm07fYeG+MOpN2s7FiEcmjFSI4mPMmDFMmDCB2NhYrl+/zrVr1/SPvCaDkIszhGZmZkREROgHlmR48OABbm5uRp+HsCCUpG8YQpRox5fCpiG65y0/hZafZFstKiqKLl26cOjQIX1ZxYoVeeWVV5g6darRJoItbq7dT2Df5XscDL3PXxcyL7f3CfSleWVX1CoVVuZq6pcvJZeVhVGUpON36dKlOXr0KBUrVsyX9nN8hlBRlGz/GJ4+fdpgfWMhhChUru3LTAYdy0LAu0+s6u7uzt69exk3bpx+1PHVq1f56quvWLx4MXm806bY8itjR58m5VnYqz6fvZS57N+y4Bu8s+wY/ZYepecPh+m/7BjpGu1TWhJC/FufPn2MsmbxkzzzGcKMtfJiY2NxdHQ0SAo1Gg3x8fEMHDiQ+fPn51uw+aUkfcMQosT6viXcOal7/r8T4PLs37Jv3bpFQECAfkCdm5sby5cvp02bNvkQaPERejeeb/eEcv1+AhoFbjxIICYxDYA21dz5vncDE0coirqSdPz+8MMP+emnn6hduza1atXKMqhk5syZeWr/me+Unj17Noqi8PbbbzNhwgScnDJvxra0tKR8+fJPnftPCCFMZs+Xmclgn005SgYBfHx82LZtG6NGjWLLli3cvXuXtm3bMnnyZMaMGZMPARcPldzsmflaHf22oii8segQh8Ie8teFKK7fT6B8GTvTBShEEXL27Fnq1tXNhnDu3DmD14xxG0uO7yHcu3cvzz//fLEadVeSvmEIUeJoNXBhA5xZC1HnYehZyMMfz4sXL1Ktmu5yaJUqVWTN4xzSahVqT/yLuOR0AE6Oe5FSdjISWeSOHL+NJ8f3EDo4OHDx4kX99saNG+nSpQuffvopqamylJEQohAJPw7JsVCjOzR+H/r9madkEKBq1arcvHkT0K2M9L///c8YkZYYarWKyV1q6LfrTtrOl39e4n58ir4sNjHNYFuIki4tLY3WrVvn62T5OU4I33vvPS5fvgxAWFgYPXr0wNbWlnXr1vHxxx8bPUAhhMiV87/BolYQcVq3XaElOPsYpemyZctSr149AObPny9L3OVQ5zrefPRiZf32gr1XaTB5B2/+cIheiw9TZ9JfNJi8gw9XnSRa1kwWAgsLC86cOZOv+8hxQnj58mXq1KkDwLp162jRogU///wzS5cu5ddffzV2fEIIkTuRZ3U/T682etMqlYrg4GBAd19caGio0fdR3A1uVYn1HzShd6Cvvuxg6AP2X7lPxo1Mv5++Q4vpuzl2/aGJohSi8HjrrbdYvHhxvrWf4xsBFUVBq9VNF7Bjxw46deoE6G66vn//vnGjE0KI3Ir7Zx68MpXypXlLS0sCAwMJDg6mXr16PHz4kFKlSuXLvoojlUpFvXKlqFeuFK818GHTmTskp2qwtjCjurcTx68/ZFnwDR4lp/PKgmBqejsx7MXnqONTitJyz6EogdLT01myZAk7duygfv362NkZDsgqsFHGGRo0aMDkyZMJCgpi7969fPfddwBcu3YNd3f3PAUjhBBGEboDTq3QPbf3yLfddO3aVX+msHTp0hw+fJhGjRrl2/6KqxreTtTwNlxG8OXaXrSp7sE7y46SnKblbHgsby89BsAn7fx5v2X+TM4rRGF17tw5/a0qGbfuZTDGKOMcJ4SzZ8/mzTffZMOGDYwZM4ZKlXTfvn/55ReaNGmS54CEECJPNnwAp1ZmbpdtmG+7GjlyJBqNhtGjRwOwdu1aSQiN6PlKZTgyJoif/r7OnpB7HLsRDcBXWy8REZvExM41/qMFIYqP3bt352v7OZ525kmSk5MxMzPLMlFiUSDD1oUoJhIfwuxakBoHti7Qbyu4Vv7v9+XR9OnT+fjjj+ncuTMbNmzI9/2VVGkaLW1m7ePa/QQAStlaUNvHGQdrC1pUdqVLHS/MzXJ8a7wowkra8TsmJobFixfrZ3upXr06b7/9tsHc0LlltISwKCtpv1BCFGtpSRC6E6q0B3XBrJf7119/0bZtW0A3V2vz5s0LZL8lUUq6hiGrTrH1fGSW14KquvFDn/w7IywKn5J0/D527Bht27bFxsZGfyXi6NGjJCUl8ddff+kvJ+eWJISUrF8oIYTxRUZG4unpqd+eNm0aI0eONGFExZuiKNx6mMTZ8FjuxSUza8cVYpN0S+Ite7sRLSq7mjhCUVBK0vG7WbNmVKpUiUWLFukXB0lPT6d///6EhYWxb9++PLUvCSEl6xdKiGIpPQXWvwtedcCvBXjn7ZtybgQHBxvcR12jRg2OHTuGlZVVgcdS0iiKQsMpO7gfr5uzcFhQZT5sXckoN9qLwq0kHb9tbGw4efIk/v7+BuUXLlygQYMGJCYm5ql9udlCCFH0XdioW55ux+e6nyYQGBhIWFgYDRvqLlmeO3eOo0ePmiSWkkalUjG7R1399qwdl9l+IcqEEQlhfI6OjvpVkh5369YtHBwc8tx+jhPCiRMnZpuFJiUlMXHixDwHJIQQORJ+AtYP0D23dYFmH5ksFD8/Pw4fPkzNmjUBOHLkiMliKWmaPleGv4Zl3rv57vLjfLDyOH9flflxRfHQo0cP3nnnHdasWcOtW7e4desWq1evpn///rzxxht5bj/HCeGECROIj4/PUp6YmMiECRPyHJAQQuTI4QWZz7ssAOu8j7bLC5VKRcuWLQH46KOP+Pvvv00aT0lS2d2BnwcEYKbWXSrecjaSnosOs+FkuIkjEyLvvv76a7p160bv3r0pX7485cuXp2/fvrzyyit89dVXeW4/x/cQqtVqoqKicHU1vGl3165d9OjRg3v37uU5qIJWku5BEKLYWfYSXNsHAe9D+y9NHQ2gOzMYEBAA6O4lPHLkCDY2NiaOquSISUxl16W7DF97Wl/Wta435moVDxJSiU9J52FCKqnpWmp6O9GgfCm61SuLk03RmzatKEhO03AvLgUvZxt9sm4sJfH4nZiYSGhoKCqViooVK2Jra2uUdp95YupSpUqhUqlQqVRUrlzZ4GZdjUZDfHw8AwcONEpQQgjxzOLv6n5WaW/aOB7TqFEj1qxZQ48ePTh37hwDBgxg+fLlMsihgDjbWtKtXlkquzvQ7bu/SU3X8tsTzhLefJjIH2cjmL4thJmv1aZdDc9s6wlDp2/FcCjsAdcfJBL9T5KdmJpOZGwycSnp2FiYYaZWkZKu5WGCbrBP8OhWeDrJF6O8srW1pVatWkZv95kTwtmzZ6MoCm+//TYTJkwwmATR0tKS8uXLExgYaPQAhRDiqco31d076FS4zg507tyZgIAADh8+zMqVKwkPD2fZsmWUK1fO1KGVGDW8nfhzSDOWHLiGSgX2Vhb4utjiaG2BjaWaq3cTOHEzmj/PRZKYqmHgihOUsbdicZ8G1PZxNnX4hY6iKBy+9pA5O64QHPbgqXXjktMNtq3M1UQnpElCmAtqtfo/v0yqVCrS09OfWue/5PiS8d69e3n++ef1c+AUByXxlLMQIv8lJyfTokULg8Elw4cPp1y5cpibm/Piiy9SuXL+r6Qinu7UrRj6Lzuqn7YGoHu9skzoXB17q+JzrMstRVHYdCaC2dsvE/bPKjEAtcs60aKyK2UcrHC0tsDKXI2rgxWW5mpik9JwtrHE3EyFh6M1zrYW+XKGvCQcvzdu3PjE14KDg5k7dy5arZbk5OQ87SfHCeGWLVswMzPTz8qfYdu2bWi1Wtq3LzyXbZ5VSfiFEqJYSYqGg3N0ZwUb9jd1NP/p22+/ZdCgQVnKK1SowNWrV00Qkfi3dI2WpX9fZ/IfFw3K57xeh851vE0UlekoikJIVBy/n7rD1vORhN3LTASbV3alX5PyvODvZsIIdUrq8TskJIRRo0axadMm3nzzTSZOnIivr2+e2sxxQlirVi2+/PJLOnToYFC+detWPvnkE06fPv2EdxZeJfUXSogi684p+L4F2LvDiMumjuaZxMbGMn/+fMLCwnjw4IF+zeMXXniBHTt2oFbLtLCFQXxKOsPWnDKYx7BxhdL0CSxPZQ8H7selUMbBCm9nG6wtCmZpxIKk1SpM2xbChpPhRD7KPOOkVkHnOt4MblWJiq72JozQUEk7ft+5c4fPPvuMZcuW0bZtW6ZOnUqNGjWM0naOz4VfuXKFatWqZSn39/cnNDTUKEEJIcRT3b+i++noZdo4csDJyYlPP/1Uv92xY0e2bNnC7t27MTMzw8fHh3r16tG+fXv69u0rK5yYiL2VOYt6N+DUrRheWxBMqkbLobCHHAp7mKXu5y9Vo7S9FUFV3bC1LPqXluNT0ukwZz83H2bONVzT24nXGpSlVVV3vJ3l/j9TiY2N5YsvvmDevHnUqVOHnTt30qxZM6PuI8e/wU5OToSFhVG+fHmD8tDQUOzs7IwVlxBCZO9eCKz/5zKxY9G9lLd582aGDRvGnDlzAPQTzW7cuJFPP/2U8+fP4+HhYeIoS646Ps6cndCGvSH3+Cn4BsFhD7AwU1HG3orb0UkAfL7pgr6+T2kbmlZy5ZX6ZanvW8pUYefJz4dv6JPBl2p7MblzDZxsZSoeU5s2bRpfffUVHh4erFq1is6dO+fLfnJ8yfi9994jODiY3377jYoVKwK6ZLB79+40bNiQH374IV8CzU8l7ZSzEEXa8aWwaYju+eurwL/DU6sXdnfv3iU2NpZLly7x5ZdfGkxkfefOHTw9ZRqUwkBRFP2giL9D7zNz+2USUjVcjHiUpa6DlTk9Gvow6IVKlLKzLOhQcyQkMo5fT9zmcNgDTt+OBSCoqhvfvVUfC7PCfxtDSTh+q9VqbGxsCAoKwszsybcprF+/Pk/7yfEZwmnTptGuXTv8/f31nX/79m2aNWvG119/nadghBDiPz26o/vZ4O0inwwCuLm54ebmxnPPPcdLL73E0qVL6devHwAHDhzg1VdfNXGEAjAYIdukUhmaVCoD6C6z3o5O5Oi1hyzYG0Z4TBJxKen8cOAaPxy4xusNffiw9XN4FZLLrclpGtafCGfXpbucuR3D3bgUg9edbCwY27FakUgGS4revXsXyBymOT5DCLpvStu3b+f06dPY2NhQq1Ytmjdv/t9vLKRKwjcMIYqNY0tg8zBoNRaajzR1NPmib9++LFu2DIBx48YxaNAg3N3dTRyV+C+KonD6dizf7Aplx8Uog9dK2VpQ0dUeS3M1jfxKM6BZBewKYEqb5DQN1+4ncPxGNIfCHrD5TITB6yoVNKnoQtvqHjSp6IJfGXujryaSn+T4bTy5SggzJCcnY2VlVeRn35dfKCGKAEXRHb0ApleCHiugXGPTxpRPtm7dajCFl5ubG0ePHpVJrYuQu3HJ/HYinO/3hfEgITXbOu+3rMiQ1s/laLRyYmo6V6LieZiQyqPkNKwtzLC2MMPLyRoztYpUjZaUNC13YpLYfCaCrecj0WgND/PmahUtq7jSt4kfNbwdcbYt3Je1nyY3x+/58+czffp0IiMjqV27NvPmzaNRo0bZ1k1LS2Pq1KksW7aM8PBwqlSpwldffUW7du30deLi4hg3bhy//fYbd+/epW7dusyZM4eGDRvq2xg7dixbtmwhLCwMJycngoKC+PLLL/HyKjwD43KcEGq1WqZMmcKCBQuIiori8uXLVKhQgXHjxlG+fHneeeed/Io130hCKEQhpiiwuic8Cof39unKkmPB2unp7yvi7t27x5gxY1i0aBEApUuX5s6dOzL6uIjRahWu3I3nQXwKj5LTWHn4Jvuv3Deo06WOF2M6VsPVwYrUdC1qFZg/dsk2XaPlp+AbrDl6i8t348jNaZzqXo40ruBCgF9pmld2LTZT5uT0+L1mzRp69+7NggULCAgIYPbs2axbt46QkBDc3LLOq/jJJ5+wYsUKFi1ahL+/P9u2bWP48OH8/fff1K1bF0C/ROV3332Hl5cXK1asYNasWVy4cAFvb29iY2N55ZVXGDBgALVr1yY6OpohQ4ag0Wg4duyY0fskt3KcEE6cOJFly5YxceJEBgwYwLlz56hQoQJr1qxh9uzZBAcH51es+UYSQiEKsT1fwZ4vdM8/i8k8S1hCLF68mP79daOqR4wYwfTp000ckcirWw8Tmb4thN9P3zEod3WwIjohFY2i4OZgRSlbSxJTNQbTwACUsbfCw8kKK3MzFEUhIUXDrehEzNQqrMzVWJqpKWVnyXNu9jSv7ErXut5F/krek+T0+B0QEEDDhg355ptvAN1JLh8fH/73v/8xatSoLPW9vLwYM2aMwcTy3bt3x8bGhhUrVpCUlISDgwMbN26kY8eO+jr169enffv2TJ48Ods4jh49SqNGjbhx40ahOfOf4xsYfvrpJ77//ntat27NwIED9eW1a9fm0qVLRg1OCFHCXf4rMxn0rl/ikkGAd955h40bN7Jp0ya+/vpr+vXrl+1csKLo8Clty9w36jKpSw3m7LjCmqM3SUjVcO+xAR5Rj1KIepS5ba5W8UHLirzW0IeypWxNEXahFhcXx6NHmSO+raysspxNT01N5fjx44wePVpfplarCQoKeuLJrJSUFKytrQ3KbGxsOHDgAADp6eloNJqn1slObGwsKpUKZ2fnZ/p8BSHHCWF4eDiVKlXKUq7VaklLSzNKUEIIQeRZ+PmfEbYulaDbItPGY0IzZ85k06ZNAKxcuZIpU6aYOCJhDE42Fox/qRqfdvBnT8g9bjxMpKqnAz6lbLkdncT9+BQ8nKxxsbOktJ1lkb7XL7/9+0vSZ599xueff25Qdv/+fTQaTZYBWu7u7k88odW2bVtmzpxJ8+bNqVixIjt37mT9+vVoNBoAHBwcCAwMZNKkSVStWhV3d3dWrVpFcHBwtrkS6MZffPLJJ7zxxhs4Ojrm8hMbX44TwmrVqrF///4sa+b98ssv+uvpQgiRYxl3r2ScBdzyceZrL04El4oFH1MhUalSJQYMGMCiRYv44osvaNq0aZFcN15kz9xMTVA1wyTFp7ScBcyJjPv1MhjrXts5c+YwYMAA/P39UalUVKxYkX79+rFkyRJ9neXLl/P222/j7e2NmZkZ9erV44033uD48eNZ2ktLS+O1115DURS+++47o8RoLDlOCMePH0+fPn0IDw9Hq9Wyfv16QkJC+Omnn9i8eXN+xCiEKI7uX4HbRyH+LjwIhYu/60YO+/0zhZVXHbj5N3T4Gvw7PrWpkuCDDz7QDzA5fPiwJIRCPMbBweE/z7aVKVMGMzMzoqIMpwSKiop64qpArq6ubNiwgeTkZB48eICXlxejRo2iQoUK+joVK1Zk7969JCQk8OjRIzw9PenRo4dBHchMBm/cuMGuXbsK1dlBgBzPPNm5c2c2bdrEjh07sLOzY/z48Vy8eJFNmzbx4osv5keMQojiJCkaDsyCbxrAhvdhx2dwcrlu5HBSTGa98k2h6TDdBNSCOnXqMHbsWEA3AlkIkTOWlpbUr1+fnTt36su0Wi07d+4kMDDwqe+1trbG29ub9PR0fv3112yXj7Ozs8PT05Po6Gi2bdtmUCcjGbxy5Qo7duzAxcXFeB/MSJ7pDOHcuXN59913sba25ubNmzRt2pTt27fnd2xCiOLojxFw7pfM7Zqv6qaQcakEzz32pdK/o5wZ/BdXV1cA9uzZY7CUmhDi2QwfPpw+ffrQoEEDGjVqxOzZs0lISNCvDtS7d2+8vb2ZOnUqoDsbHx4eTp06dQgPD+fzzz9Hq9Xy8ceZt7Rs27YNRVGoUqUKoaGhjBw5En9/f32baWlpvPLKK5w4cYLNmzej0WiIjIwEdNNJWVoWjntDnykhHD58OK+//jrW1tb4+fkRERGR7Xw9Qgjxn0r5gpMPlG8GLUZC6Qr//R4BoP+7e+HCBcaPH8+kSZNMHJEQRUuPHj24d+8e48ePJzIykjp16rB161b9QJObN2+iVmdePE1OTmbs2LGEhYVhb29Phw4dWL58ucHo4NjYWEaPHs3t27cpXbo03bt3Z8qUKVhYWAC6wbi///47oDvT/7jdu3fTsmXLfP3Mz+qZ5iEsV64co0ePpkOHDvj5+XHs2DHKlCnzxLpFjcxDKEQ+u7wN7N119wVqtaCWdVJz48GDB7i6uqIoCtWqVeP8+fOmDkkIk5Ljt/E801/lsWPHMnToUCpUqIBKpaJhw4b4+fkZPMqXL4+fn19+xyuEKGoeXoOfX4Oto3X3DkoymGsuLi6cOXMG0J0lrFKlCt9++62JoxJCFAfPvFJJXFwcN27coFatWk+9IbJ27dpGDbAgyDcMIfKJJg0WtoC7/5zJ6r8LytY3bUxFXHp6Os2bNzeYSPfmzZv4+PiYMCohTEOO38bzzNPOODg4UKNGDX788Ueef/55WU9TCPFkiqJbe3jHhMxksNF7kgwagbm5OX///TcRERF4eXkBcO3aNUkIhRB5kuNrN3369CEpKYkffviB0aNH8/DhQwBOnDhBeHi40QMUQhRBW0bArOpwdq1u2+U5aDXWtDEVM56enjRr1gyAhQsXotVqTRyREKIoy/HE1GfOnCEoKAgnJyeuX7/OgAEDKF26NOvXr+fmzZv89NNP+RGnEKIoKd8U0pIgdAf4NoG2U8G6cE3CWhxUqlSJ/fv38/PPP2NhYcGiRYv0IxuFECIncnyGcNiwYfTt25crV64YLObcoUMH9u3bZ9TghBBFzN2L8OgOVO8KnefDiMvw6lJw9DR1ZMXShAkTKFWqFADLli3jiy++MHFEQoiiKscJ4bFjx3jvvfeylHt7e+snWhRClDB3TsH8xvBtY90UM5C5JrHINz4+Ppw8eVI/b9rjA02EECIncpwQWllZ8ejRoyzlly9f1s+iL4QoYfZNh3sXdc+THpo2lhLG19eXHTt2ALoVExYtWoRGozFxVEKIoibHCeHLL7/MxIkTSUtLA0ClUnHz5k0++eQTunfvbvQAhRCFWPw9+HMUXNqs235hLDT7yLQxlUA1atTQnyV89913qVChAsuXL+cZZxUTQoicJ4QzZswgPj4eNzc3kpKSaNGiBZUqVcLBwYEpU6bkR4xCiMJIq4GFzeDwd7ptp3IQ8K5pYyqhXF1dOX36NFWrVgV08xL27t2b9u3b62eCEEKIp3nmian/7cCBA5w5c4b4+Hjq1atHUFCQsWMrMDKxpRC5kPwI/hoLJ5bpzgw2HyH3DRYC169fp0mTJkRERADw0ksvsWHDBoP1WYUoLuT4bTy5TgiLE/mFEiKXjv6gSwybDTd1JOIxCQkJtGzZkmPHjgEwYMAAvv/+exNHJYTxyfHbeHI0D6FWq2Xp0qWsX7+e69evo1Kp8PPz45VXXqFXr16o5OyAEMVX6A449xvcD4FmI6BcY2jY39RRiWzY2dmxe/duqlWrxq1bt/SDToQQ4kme+RqCoii8/PLL9O/fn/DwcGrWrEn16tW5ceMGffv2pWvXrvkZpxDCVLRaSIqBFa/AqRVw+yis6gHR100dmXgKe3t7Dhw4AOiWtktOTjZxREKIwuyZzxAuXbqUffv2sXPnTl544QWD13bt2kWXLl346aef6N27t9GDFEKYyPbx8Pc3oDw2jUmX78CjFnjUMF1c4pl4enqiVqvRarXUrFmTy5cvy5UcIUS2nvkM4apVq/j000+zJIMArVq1YtSoUaxcudKowU2dOpWGDRvi4OCAm5sbXbp0ISQkxKBOcnIygwYNwsXFBXt7e7p3705UVJRR4xCiRNGkZT5PfJCZDKrNdZeK6/SUZLCIsLCwYMiQIQCEhoZy9+5dE0ckhCisnjkhPHPmDO3atXvi6+3bt+f06dNGCSrD3r17GTRoEIcOHWL79u2kpaXRpk0bEhIS9HWGDRvGpk2bWLduHXv37uXOnTt069bNqHEIUWIkx8I3DSE9Vbddvx+8sQY+jYBx96H1ONPGJ3Js5syZlCtXDoBmzZpx6dIlE0ckhCiMnvmS8cOHD3F3d3/i6+7u7kRHRxslqAxbt2412F66dClubm4cP36c5s2bExsby+LFi/n5559p1aoVAD/++CNVq1bl0KFDNG7c2KjxCFGsRV3QXSKOvgbB3+hGDpdtYOqohBE0btyYmzdvcuXKFapWrcr69evlvm8hhIFnPkOo0WgwN39y/mhmZkZ6erpRgnqS2NhYAEqXLg3A8ePHSUtLM5gD0d/fn3LlysmankI8q0cRsKQ9fBcIodt1ZQ9CTRuTMKqlS5cyevRo/fbu3btNGI0QojB65jOEiqLQt29frKyssn09JSXFaEFlR6vVMnToUJ5//nlq1NDdvxQZGYmlpSXOzs4Gdd3d3YmMjHxiWykpKQbxxsXF5UvMQhQJ6wfAzb91z91rQoUWEDDQtDEJo7KxseGLL76gXLlyvP/++8ybN4/27dvTvn17U4cmhCgknjkh7NOnz3/Wyc8RxoMGDeLcuXP6aRTyYurUqUyYMMEIUQlRxCkK3NZNXkzgYGgzWVYbKcYaNWqkf96hQwfq1KlD+/btmTJliow+FqKEe+aE8Mcff8zPOJ5q8ODBbN68mX379hnMRO7h4UFqaioxMTEGZwmjoqLw8PB4YnujR49m+PDMlRXCw8OpVq1avsQuRKGWlgjlm+rmFGw9XpLBYq5evXocPXqUli1bkpCQwKlTpzh16hSvvfYaderUMXV4QggTKtSLWyqKwuDBg/ntt9/YtWsXfn5+Bq/Xr18fCwsLdu7cqS8LCQnh5s2bBAYGPrFdKysrHB0d9Q8HB4d8+wxCFDoJ92HvdLh+ECzt4K1f4H/HwDz720FE8dKgQQNiY2M5c+aMvuz99983mL1BCFHyFOqEcNCgQaxYsYKff/4ZBwcHIiMjiYyMJCkpCQAnJyfeeecdhg8fzu7duzl+/Dj9+vUjMDBQRhgLkZ3kWFjSDnZPhr/nmToaYSJmZmbUrFmTkSNHAnDo0CFWrVpl4qiEEKZUqBPC7777jtjYWFq2bImnp6f+sWbNGn2dWbNm0alTJ7p3707z5s3x8PBg/fr1JoxaiEJIUWDDB/BlOXhwJaPQpCEJ0xs3bpx+1oarV6+aOBohhCmpFEUp8UeF27dv4+Pjw61btwzuURSiWFAU2PYpHPpWt21hq5tw+sWJYPbMtxGLYmratGl88sknWFpacvbsWSpXrmzqkIR4ZnL8Np5CfYZQCJFLj+5A9A3d87DdmclgmSow6ia0+0KSQQFkjjxOTU3l448/NnE0QghTkYRQiOLm6i6YWRUu/7PSj08AvPYTlAuEnmvAzMK08YlCpWXLlowfPx6AjRs30rRpU8LCwkwclRCioElCKERxodXCoQWw/J8lyS5v1V0utrSDap3h7a1Q2u/pbYgSafz48dSsWROAgwcPUr16de7evWviqIQQBUkSQiGKi7PrYOsnmdtNh8u8guKZmJmZcfLkSb766isAkpOTcXd358aNGyaOTAhRUCQhFKKouxcCP/eA397NLBt8HPyamS4mUeSYmZnx8ccfM3PmTH1Z+fLlZd1jIUoISQiFKOw0abpHxoQAmjQ49bNugmkAR2+Iual7rraADw5BmUqmiVUUeUOHDmXx4sX67VatWvHTTz+ZMCIhREGQaWeQYeuikLp+ELaPh4hToE3XlanMQNHonvfaABVf0D2POg8PrkKV9jJoRBjFkSNHCAgIAMDb25vbt2+bOCIhspLjt/HIGUIhTCk1UTdFTFwkxN+DhAeQEq977d4lSHyQmQxCZjIIujOFGdyrQ7WXJRkURtOoUSP98nbh4eF88cUXJo5ICJGf5Awh8g1D5AOtBlLjwdIe1GaGr2nS4cj3cHqV7sze40keQPWu8OpS3fP4u7qEsVR5XWKoTdclgtaOYO1UEJ9ElHD+/v6EhIQAcPfuXVxdXU0ckRCZ5PhtPDIzrRDGpNXAiZ/gwCyI+WeEpksl3Rm81p+BS0WIvgZ7pkLKo8z3qdSgaDOfZ7B30z2EMJE///yTChUqAHD9+nVJCIUopiQhFMKYQv6EzUMNyx6E6h4tP9Vtl3kOBuyCraPhhU/Bq27m9DBaLbLGsChM/Pz8qF+/PsePH+f69es0bNjQ1CEJIfKBJIRCGFPsLbD3gPhI+OAwWNnD7aO6S79O3pn1yjwHb/2S9f1qua1XFD7u7u4AvPbaa1y9elV/xlAIUXxIQihEXqTEw50TkBIH/h2hwTsQcRq864Obv66Ok9zXIoq2t956iy1btgCwd+9eSQiFKIbkdIQQufHgKqzoDl+Wg2Uvwc6JunJzSwgYCHV7mTY+IYzojTfe4L333gNg0aJFpKen/8c7hBBFjSSEQuRU7G3desGhOzJHCHvWznzdqw5YWJskNCHyi5+fbh3s4OBgZs2aZeJohBDGJgmhEDn15yeZI4ifHwrjH0K3700akhD5rWfPnvrnkyZNIiYmxnTBCCGMThJCIbLzMAwu/QFXd8PtYxAbritXFHh4Tfe8WmcI+jzrPINCFEM+Pj5s374dgLi4OPr27WvagIQQRiWDSoT4t9Or4bf3DMvqvAldvtVND1O/L+ybDt0WZU4XI0QJ0KJFC5o1a8b+/fs5cOCAqcMRQhiRJIRCZFAUOPoDbBmRWeZWTTeS2N49s6xqJ6jeBcytCjxEIUzJwsKCP/74A0dHRx48eED58uUZPnw4dnZ22NvbExQUhIuLi6nDFELkgiSEQmQIng9/jdE9t3SA/tvBrWrWeo5eBRuXEIWIg4MDHTp0YMuWLdy4cYMhQ4boX/P09OTo0aN4e3s/pQUhRGEkCaEQGRp/oJs/cNdkeGUxOHiYOiIhCqXNmzezY8cOVq9eTVxcHFFRUezbt4+IiAjKli1LmzZtcHNzY8aMGbi5ydKLQhQFKkVRSvw6WbI4dgl29xJc2wte9cBHluQSIrcWLFjA+++/b1BWrlw5jh8/TpkyZUwUlSju5PhtPDLKWJRc59bDtwHw58ewvj/cCDZ1REIUWQMHDuTatWssWrSIFi1aAHDz5k2aNm3KvXv3TBydEOK/SEIoSqbzv8Ev/TK3yzUBtdxBIURelC9fnv79+7Nt2zZ9UhgSEoKbmxutWrUiKCiIl156iUOHDpk4UiHEv0lCKEqmS39kPh94ELp+J5eMhTASKysrdu/ezfDhw/Vlu3fvZufOnWzevJkmTZoQFRVlwghFSTZ//nzKly+PtbU1AQEBHDly5Il109LSmDhxIhUrVsTa2pratWuzdetWgzpxcXEMHToUX19fbGxsaNKkCUePHjWooygK48ePx9PTExsbG4KCgrhy5Uq+fL7ckoRQlDwnfoKz63TPe6wEjxqmjUeIYkilUjFjxgxu3rzJ2rVrWbVqFXPnzgV0B8dXX33VxBGKkmjNmjUMHz6czz77jBMnTlC7dm3atm3L3bt3s60/duxYFi5cyLx587hw4QIDBw6ka9eunDx5Ul+nf//+bN++neXLl3P27FnatGlDUFAQ4eHh+jrTpk1j7ty5LFiwgMOHD2NnZ0fbtm1JTk7O98/8zBSh3Lp1SwGUW7dumToUURAizijK0pcUZZKboiQ8MHU0QpQoffv2VQAFUBISEkwdjijicnr8btSokTJo0CD9tkajUby8vJSpU6dmW9/T01P55ptvDMq6deumvPnmm4qiKEpiYqJiZmambN682aBOvXr1lDFjxiiKoiharVbx8PBQpk+frn89JiZGsbKyUlatWvVMcRcEOUMoSo7QnZDwADxqQs+1MDocbEubOiohSpQffvgBKyvdpO7e3t6kpqaaOCJRHMTFxfHo0SP9IyUlJUud1NRUjh8/TlBQkL5MrVYTFBREcHD2gwpTUlKwtrY2KLOxsdGv1JOeno5Go3lqnWvXrhEZGWmwXycnJwICAp64X1OQhFAUf5FnYVZNWNENIk7pyiyswUwGkQhR0MzMzBg8eDAAMTEx/P777yaOSBQH1apVw8nJSf+YOnVqljr3799Ho9Hg7u5uUO7u7k5kZGS27bZt25aZM2dy5coVtFot27dvZ/369URERAC6idoDAwOZNGkSd+7cQaPRsGLFCoKDg/V1MtrOyX5NQRJCUbzdPAQLmkLsTd127C3TxiOE4Ouvv9afLXn11Vfx8fHRn03RaDT8+uuvLF26lMTERFOGKYqQCxcuEBsbq3+MHj3aKO3OmTOH5557Dn9/fywtLRk8eDD9+vVDrc5Mn5YvX46iKHh7e2NlZcXcuXN54403DOoUBUUrWiFyQpMGm4Zmbr+yBOr3NVU0QojHfPzxx3h46FYDun37Ns2aNcPPz4/KlSvzyiuv0K9fP+rUqcP169dNG6goEhwcHHB0dNQ/Mm5LeFyZMmUwMzPLMsI9KipK/7v4b66urmzYsIGEhARu3LjBpUuXsLe3p0KFCvo6FStWZO/evcTHx3Pr1i2OHDlCWlqavk5G2znZrylIQiiKJ0WBH1rDvYu67e6LoUZ308YkhNB78cUXuXPnjsEl4+vXrxMWFqbfvnLlCn5+flmm8BAiNywtLalfvz47d+7Ul2m1Wnbu3ElgYOBT32ttbY23tzfp6en8+uuvdO7cOUsdOzs7PD09iY6OZtu2bfo6fn5+eHh4GOz30aNHHD58+D/3W5DkJipRPKUnQ5nKEHEa6r4lyaAQhZBKpeKll14iIiKCtWvXcu/ePezs7Khfvz7Hjx/XX/Zr1KgREydOZNSoUVhYWJg4alGUDR8+nD59+tCgQQMaNWrE7NmzSUhIoF8/3UIFvXv3xtvbW38P4uHDhwkPD6dOnTqEh4fz+eefo9Vq+fjjj/Vtbtu2DUVRqFKlCqGhoYwcORJ/f399myqViqFDhzJ58mSee+45/Pz8GDduHF5eXnTp0qXA++CJTD3MuTCQaWeKuLRkRTn7i24qmbB9meU7JirKzsmmi0sIkSe//fabfoqajEf9+vWVw4cPmzo0UUjk5vg9b948pVy5coqlpaXSqFEj5dChQ/rXWrRoofTp00e/vWfPHqVq1aqKlZWV4uLiovTq1UsJDw83aG/NmjVKhQoVFEtLS8XDw0MZNGiQEhMTY1BHq9Uq48aNU9zd3RUrKyuldevWSkhISO4+dD5RKYqimDIhLQxkcewiLC0J5jWAR7czywYdAdcqpotJCGE0R44coWfPnly9elVfplKpuHPnTqG6/0qYhhy/jUfuIRRF292LmcmgRy1461dJBoUoRho1akRoaCgPHz5k3LhxgG6lk3r16skchkIYkSSEouiKugCLXtA99wmAgfuhUtDT3yOEKJJKlSrFhAkTGDp0KAARERFYWVlRv3592rRpww8//IBWqzVtkEIUYTKoRBQdcZGgUoO9m2771uHM18o1Nk1MQogCo1KpmDVrFpaWlkybNg2AEydOALB9+3a2bdvGunXrTBmiEEWWnCEUhd+Dq/D7hzCjCuydplt5BMDBE7zqQbuvoOWnpo1RCFFgvvrqKxITE9m+fTsrV67EzMwMgF9++QWNRmPi6IQommRQCXJTaqGUmgBR52H3FAjbY/hal++gTk+ThCWEKHzS09Oxt7cnJSUFb29vQkNDs6wtK4onOX4bj5whFIXTb+/B4hczk0HnclC/H4y4IsmgEMKAubk5ffv2BSA8PBxPT08ePHhg2qCEKGLkHkJhOooCNw7CqZ91awx71oY2k3WvWTqA2hzcqkGDftDgbdPGKoQo1BYsWIC5uTnz588nJiaGX375hffee8/UYQlRZEhCKAqeVgt3L8CmIRB+LPs6L8+DLt+CSlWwsQkhiqzZs2dz4cIFdu/ezcCBA9myZQvDhw+nRYsWpg6tREhKSiIsLIzr169TunRp0tLSuHTpEuHh4Vy9epVFixZhY2Nj6jDFE8g9hMg9CAVuYXPdknIZLB0g4F2o2ArKNzVdXEKIIi84OJjWrVuTlJSkLzt06BABAQEmjKp4URSFv/76iz/++IOQkBDCwsKIjo7+z8v0Fy5coGrVqkaNRY7fxiNnCEXBUhSw+2famNIVodZr0HQYmFuZNi4hRLEQGBhIdHQ069ato1evXgA0bdqUq1evcuvWLa5fv05UVBRXr17FzMyMgIAAevTogbm5HA6fRUJCAgMGDGDVqlXZvu7k5ISlpSVarVa/7nT79u3x9/fH2dm5ACMVOSVnCJFvGPlGkw6JD+DueTj0Hbz5z/xgp1fr5hOs9Zpp4xNCFGurV6/mjTfeeKa677zzDvPmzZNLmk+xZMkSPvjgA1JSUgCwtrZmwoQJNGzYEGdnZ1xdXfH29kZVgLf6yPHbeOQrkcgdRYFHd3Rn9uzK6MrSkuDMWrh/GcKP6x6af5aWUplBUjTYlILar5subiFEifH6669z4cIFJk2aBIC9vT21a9fGx8cHT09PLl++zB9//AHA4sWLWbt2LYcPHzb6Zc2i7v79+3Tp0oWDBw/qy3r27MnixYtlep9iRM4QIt8wcuzsL7D9M90awv6d4PWVunKtFiaWyv495tYw+Bg4+xRcnEIIAcTFxWFlZYWlpWW2r3Xs2JH9+/fry0aOHMnkyZOzrV+S3Lhxgy+++ILvv/9eX+bl5cWGDRto2LChCSPLJMdv45EzhCJn0lNh42BIT8r6mloNfs3ByQfKPAflmoB3fUDRXSJWmxV4uEII4eDg8NTX9u3bx8KFCxk4cCAA06dP58CBA2zevJnSpUsXVJiFxvbt25k+fTrbt2/Xl1lZWbF48WJ69uxZoJeERcGRM4TIN4xnkhIPl/6AkC1wYYOubMgZKOVr0rCEEMJYbty4QWBgIBEREfqyLVu20L59+wKLIU2bhkarQatoUVDQKBoURUGraA2eaxUtWrRotBpsLWwpY1NG//7z988DUMu1FmqVbv2JsNgwHiTpRgErioL+f4rhz8O7DzOi1wh9PM7OzsyYMYM+ffqw/85+NIqG572ex9pcd6n40sNLXI+9jkbRxZyuTdfHmvFTo9XwSuVXsLWwNXp/yfHbeOQMoXiyuEjdiGC1Wnf/32/vZr7mEyDJoBCiWPH19SUsLIy3335bP4q2Q4cO9OvXj88//5xy5crp6yalJ5GYlkiaNo1UTSpp2jSS05NJTE8kMS1R9/o/zzN+1nevT7OyzQAIjw9nxJ4ROFg68H2bzEuyH+z4gEMRh3IUd/fnuvN5k88BSExLpNefutHVx986jqWZ7rL3wtML2XJty1PbSQ5PJnRMKADmpcx5+bOXWf3Bav1o4WF7hpGuTWf7K9vxMPcAYGPoRlZcXPGfMbYt3zZfEkJhPJIQiqwUBY58D39+DKNugbUjWNrp5gk0t4Gy9aGmjBAWQhR+0cnRPEh6oDvzpmiyPZOVrk3nYfJDEtISeJD0gKEzhvLOO+8QFBQEwI8//siPP/4IgI2NDWXKlCHdN51SfUqhMlOhUj/bJVSNotEnhCpUnHtwDnOVOenadMzVusNxxhm9J1Gr1LoHup/manN90gdgobbA294bFYYxudu64+fkhyrjf/9c9lWp9CVc2nFJX7/pyKY0btJYnwwC1HatTbo2HQt1ZpmPgw8NPRqiVqkxU5npH2qVGjN15nMrM5larLCTS8bIKWdAlwQm3IfLW+HQt7qVRAB6rICqL5k2NiFEiZWuTc8805aWSEJaAtEp0SSmJVLBuQKVS1UG4HbcbeaenIuDhQPjAsfp3996XWvuJt7N0T4H1RnEwNoDCQsLY+S4kaz/ef1T69tVtKNs+7K413HH2cUZW3NbbC1ssTW3xcbCRvfT3IZGHo14odwLAKRoUjh05xC2FrbUdaurTwgT0xLRKlp94memMkOlUum388Ply5f5/vvvmTFjBgDTpk1j5MiR+bIvY5Pjt/HIGUIBackwvxHE3DAsd/SGUn6miUkIUSQoikK6kk6aJk1/+TRVm6r7qUklXZuOm60brrauANxPus+um7swV5vT7blu+nY+3PUhYbFhpGnSSNemk66kk6pJJT4t/on7HlRnkD4hTExP5M9rf+Ju6844MhPCsvZlSdWkYqm2xExtlnkm67GzV2YqM0pbl8bB0gEnKydqlqkJQIUKFVi+dDkjvhrByb0nqeZdDQ8PD0aOHMnmzZv1+0i4mkDINyGEEIKvry/z5s2j44sdUaufnMBZmVnRwifrknoFdVk1OTmZLVu28M0337B7926D11q2bFkgMYjCRRLCkmj3VAjbA+9s020nRWeuFGLrohslXPt1aNgfzCye2IwQovhK06YRnRyNWqXWD1gAGLJrCGGxYdxNvEuaVpcE/pfh9YfTr0Y/QHcmb9KhSXjbexskhOHx4dx4dONJTWCuNsfOwg4bcxtKWZXC3tIeTztP/eulrUszpN4QPOw8DN63pO0SzPIww4GthS2BZQMJfDNQX/bbb79x/vx57O3tWbduHaGhoaxcuZLk5GRu3LjByy+/jJeXFytWrKBly5aFalRuVFQUH374IRs3btRPMA3g4+PDu+++y6uvvkqVKlVMGKEwFUkIS4L0VNg/A06thIR7kJ6sK9dqdFPBOHqCew1oOAAaDYBC9MdLCJE7KZoUUjQp3E+6T3hcOPeT7hOXGkdcWpzuZ2ocj1IfEZcaR3xqPHGpcfSp3oeeVXsCcOnBJXpu6Ul1l+qs7rRa3+71R9e5/uj6E/erQoWlmSWWaksszCywNLPExjxz9Q83Wzdalm2Jt4M3iqLok6XxgeNJ16ZjqbbEXG2ufzhbOWNvYY/Ff3w5LWNThv41+2cpz0sy+CTm5ubUrl0bgFGjRgHwzTffsGTJEj7//HPu3bvHnTt3aNWqFX5+fvzyyy/Uq1fP6HHkxIEDB5g+fTq///67vszOzo6OHTvSp08fWrdujZWV3OdXkhWbhHD+/PlMnz6dyMhIateuzbx582jUqJGpw8pfmjS4cRCSYyElDhIf6pK9wEG6QSAAh7+H7eOzzhvo/K8Rwq/+WDAxCyEMZEwzolE0pGnSMi+3alN125pUkjXJJKYlEp0STXh8OI08GtHQQzcx8Nl7ZxlzcAzPOT/HjJYz9O2+tuk1wmLDchRLXGqc/rmlmSVqlZrb8bdJTEvUX8oc1WgUKpUKTztPrMyssFBbYGFmgbnKXP/zaWfEvOy9mNd6Xpby2q61cxRrYWNtbc0HH3zA+++/z9q1axkzZgxXr17l2rVr1K9fn1KlSuHn54eVlRUdOnRg1KhR+b5+8qlTp3j//fc5dMhw1LKFhQVDhgzhq6++euplbVGyFIuEcM2aNQwfPpwFCxYQEBDA7Nmzadu2LSEhIbi5uZk6POML2wvHlmTOB/hvdd/KTAijr2cmg9W6QJP/6ZaPK+Wnm05GiBIuY/61jBv2Y5JjuP7oOhZmFlR3qa6vt+PGDpLSk/TzqmUkaRnTi2RMQ5KYnkh8ajwJaQm8V/s9XvR9EYDgO8EM2zOMis4VWdlhpb7dzhs6cyvuVo5itjW31SeEWrRci71GbEqsQZ2MkaB2FnZ423vjauOKo5UjjpaOOFg6GDwcLRyxt7THy95L//7KpSpzqtepLMldoFcg4slUKhU9evTgtdde488//2TQoEFcv36d6OhooqOjAQgODmbcuHEsXLiQAQMGPPMl5UePHnHs2DEOHTrErVu3uHfvHk5OTtjZ2eHu7o6lpSXJycnExsZy7Ngx9u7da/D+wMBAevXqRY8ePUrkhNvi6YpFQjhz5kwGDBhAv366e1QWLFjAH3/8wZIlS/Sn84uEhAdwdSfcOgypCZCeolsLOPGBbom4JoN19SLPZCaDZpbgVVeXANq5goUNPD68v2Z3KP88lG0E9q4F/pGEMJY0bRpq1PpLgNHJ0dyOu42thS0VnSsCoNFqWHlxpW5UanoiSWlZk7WMnxllSelJzHphFq3LtQbg4J2DjNo/ioYeDVnSdol+/xOCJxCTEpOjmDMmAgbddCEJaQkkpiUa1DFTmWXZtjSzxEJtob/0amVuha25LY6WjrjbuVOjTA19/YpOFVnSdgm2FrYGl2CXd1iubys3CtN9b0WRSqWiQ4cOhIWFcf36dcLDw4mLi2PSpEkEBwcD8N577zF27FjWrl371IEciqIwd+5cPv30UxITE59YLztqtZoxY8YwfPhwnJ2d8/CJRHFX5BPC1NRUjh8/zujRo/VlarWaoKAg/X90pnQ38gxpqXG6y7tKOoomHbRpkJYIqMC3CQoKaBWYVwcAB62Cs1YLQBoQYW6Oqowf+lWAm/yPKG0aSRZWULktqM11bWRIfwSxj3Rl9qV0D00cDonoR/pptBr9fUB+Tn76syORCZHEp+pG9Rm0+a/tx2crsre0x9veW78d8jAEgApOFfT3/UQlRPEw+eF/tvv4UxtzGyo4VzBoN02bRgWnCvrLV3cT7xKZEJl9u0+YUenfZ37OPzhPYloilUtVxsnKSR/v1ZirWWbzz2jXYIZ/FFB0B/yMKSUAjkYe5X7SfWq51tL3T2RCJEcjjxrE+3h7GeUGbQPdKnXTJ0J/3/mb67HXqetWl6ouVQG4l3iPP6/9mbXNbGJ/fJWDPtX6YG9pD8Cum7s4GnmUxp6N9aMf7yfdZ97JeWi0GhQMV0v496oJGkWjn+vt88DPKeeom8T3l8u/sPLiSlqXa83gurovNfGp8XTe0BktuvcatPdPWcZD/xoKi9osorFnYwC2Xd/GlMNTCCoXxKwXZgG6f4MZx2egVbTZ/ts/yeNJWimrUvg4+BgMWABo6NGQhLQE/Txr1ubW2JjbYGuhm1IkY2oRG3Mb7C3tsbew1yeqoFs14o+uf2RJ0FZ1XKWfUsRCbZHje97sLe31Zwsf9/h9e8J0VCoVfn5++PnpZmxo3749hw8fpmPHjjx48IB79+7xwgsv4O/vz9tvv82+ffu4ffs2gYGBlC5dmlu3brFlyxbu378P6FYOad26Nf7+/ri6upKQkMCDBw+4c+cOlpaWWFlZYWdnh6+vL40bN6ZevXolfk1m8WyKfEJ4//59NBoN7u7uBuXu7u5cunQp2/ekpKQYjK6Ki4vLtp4xDP6zFxfVTzk4HX/suY8uaXjbsizDyrUDMysitYl0vPIjdnHHefwukM+SLnPw6kG4tOCZY+laqSsTn58I6GbZ77KxCwDH3jqmnzR0zok5bA7b/KQmstWybEuDe4J6/tGTVG2qbjb7f0b8LbuwjOUXlueo3VplarGyY+altUE7BxGVGMWaTmuo5lINgN+v/s6cE3Ny1G5Z+7L82f1P/faEvydw8eFFvgv6jqbeTQE4EH6Az4M/z1G7tua2HH7zsH77h7M/8Pedv/mi6Rf6hPDig4t8euDTHLUL8HLFlzFDlyj8fvV3/gj7gxENRugTwvD4cKYfm57jdrs/112fEB6LOsaKiyuwNLPUJ4RJaUmsv/L0Odiy8/hUITEpMYTGhBqc1QK4m5SzueEAUjWp+ufO1s542Xnpk3jQHXw7V+yMWqXWzwP3758ZSZyteeY8cU6WmW008W7Clm5ZV3SY2XJmjuN9nI25jT5JflxG/4uSIyAggIiICFasWMGHH35IfHw8ly5d4uOPP9bXOXXqVJb3DR48mGnTpmFjI8m+ML4inxDmxtSpU5kwYUKB7MtSZY61NoWMSeNVj/1/VCr4Zz3Ix1+z8O8I/5xJUcXdxu76WmzNDeemsjG3wcHisQXbVY8/NbzUk3Hp5/EzBiqVCmcr5yzx2lnYUdr6yfeWPN52RrsOloYLx7vaupKqSTWo62DhgJtt5v2cT4rx8ddcbFwM6rjbuWOmMtNP4JrR7uNnJ5+l3X9PS1HWoSwpmhSD/nG2cqZyqcr6Gf0Nfj72XPd/uv/9+4xMlVJVSNOm4WKd+TlKWZci0DNQH5s+3sfaeXw/GeVqMu/3rFmmJmmaNHwdMwcGlbYuTQe/DllizPjc//4MGfOvPR5zY8/GWKotaeDRQF/mZO3Eh3U/RKXKfI/BAzVqdeaqCRZmFpirzQ3+Tdr7tadGmRoG//425jase2md7rP905Z+H6gNJuJ9/OyZnYWdvo125dvRrnw7/i3jS48QhZmFhQX9+vWjb9++rFmzhtmzZ3Pjxg2ef/55qlevzq1bt4iIiKBBgwZ4e3vj4+NDhw4d5FK+yDdFfqWS1NRUbG1t+eWXX+jSpYu+vE+fPsTExLBx48Ys7/n3GcLw8HCqVasmM50LIYQQRYisVGI8RX6YqaWlJfXr12fnzp36Mq1Wy86dOwkMzH40nJWVFY6OjvqHg4NDtvWEEEIIIUqCYnHJePjw4fTp04cGDRrQqFEjZs+eTUJCgn7UsRBCCCGEeLJikRD26NGDe/fuMX78eCIjI6lTpw5bt27NMtBECCGEEEJkVSwSQtCNvho8eLCpwxBCCCGEKHKK/D2EQgghhBAibyQhFEIIIYQo4SQhFEIIIYQo4SQhFEIIIYQo4SQhFEIIIYQo4SQhFEIIIYQo4SQhFEIIIYQo4SQhFEIIIYQo4SQhFEIIIYQo4SQhFEIIIYQo4YrN0nV5odVqAYiIiDBxJEIIIYR4VhnH7YzjuMg9SQiBqKgoABo1amTiSIQQQgiRU1FRUZQrV87UYRRpKkVRFFMHYWrp6emcPHkSd3d31GrjXUWPi4ujWrVqXLhwAQcHB6O1W5xIHz076auckf7KGemvnJH+enb52VdarZaoqCjq1q2Lubmc48oLSQjz0aNHj3ByciI2NhZHR0dTh1MoSR89O+mrnJH+yhnpr5yR/np20ldFgwwqEUIIIYQo4SQhFEIIIYQo4SQhzEdWVlZ89tlnWFlZmTqUQkv66NlJX+WM9FfOSH/ljPTXs5O+KhrkHkIhhBBCiBJOzhAKIYQQQpRwkhAKIYQQQpRwkhAKIYQQQpRwJTIh3LdvHy+99BJeXl6oVCo2bNhg8HpUVBR9+/bFy8sLW1tb2rVrx5UrVwzqXL16la5du+Lq6oqjoyOvvfaafsWTDCdOnODFF1/E2dkZFxcX3n33XeLj4/8zvjNnztCsWTOsra3x8fFh2rRpBq+fP3+e7t27U758eVQqFbNnz85VPzxNUe+j9evX06BBA5ydnbGzs6NOnTosX748d53xH4p6Xy1duhSVSmXwsLa2zl1n/Iei3lctW7bM0lcqlYqOHTvmrkOeQVHvs7S0NCZOnEjFihWxtramdu3abN26NXed8R8Kc18lJyfTt29fatasibm5OV26dMlSJyIigp49e1K5cmXUajVDhw7NTTc8k6lTp9KwYUMcHBxwc3OjS5cuhISEZIl50KBBuLi4YG9vT/fu3bP0xc2bN+nYsSO2tra4ubkxcuRI0tPTDers2bOHevXqYWVlRaVKlVi6dOl/xqcoCuPHj8fT0xMbGxuCgoKy/FtNmTKFJk2aYGtri7Ozc676QWQqkQlhQkICtWvXZv78+VleUxSFLl26EBYWxsaNGzl58iS+vr4EBQWRkJCgf3+bNm1QqVTs2rWLgwcPkpqayksvvaRfT/HOnTsEBQVRqVIlDh8+zNatWzl//jx9+/Z9amyPHj2iTZs2+Pr6cvz4caZPn87nn3/O999/r6+TmJhIhQoV+PLLL/Hw8DBexzymqPdR6dKlGTNmDMHBwZw5c4Z+/frRr18/tm3bZrxO+kdR7ysAR0dHIiIi9I8bN24Yp3P+paj31fr16w366dy5c5iZmfHqq68ar5P+paj32dixY1m4cCHz5s3jwoULDBw4kK5du3Ly5EnjddI/CnNfaTQabGxs+PDDDwkKCsq2TkpKCq6urowdO5batWvnrTP+w969exk0aBCHDh1i+/btpKWl0aZNG31fAAwbNoxNmzaxbt069u7dy507d+jWrZvBZ+rYsSOpqan8/fffLFu2jKVLlzJ+/Hh9nWvXrtGxY0deeOEFTp06xdChQ+nfv/9//i2eNm0ac+fOZcGCBRw+fBg7Ozvatm1LcnKyvk5qaiqvvvoq77//vhF7pgRTSjhA+e233/TbISEhCqCcO3dOX6bRaBRXV1dl0aJFiqIoyrZt2xS1Wq3Exsbq68TExCgqlUrZvn27oiiKsnDhQsXNzU3RaDT6OmfOnFEA5cqVK0+M59tvv1VKlSqlpKSk6Ms++eQTpUqVKtnW9/X1VWbNmpWjz5xTRb2PMtStW1cZO3bss33oXCqKffXjjz8qTk5Ouf7MuVUU++rfZs2apTg4OCjx8fHP/sHzoCj2maenp/LNN98YvK9bt27Km2++mcNPnzOFra8e16dPH6Vz585PrdOiRQtlyJAhz9SeMdy9e1cBlL179yqKovvcFhYWyrp16/R1Ll68qABKcHCwoiiKsmXLFkWtViuRkZH6Ot99953i6Oio/534+OOPlerVqxvsq0ePHkrbtm2fGItWq1U8PDyU6dOn68tiYmIUKysrZdWqVVnqm+pvWHFTIs8QPk1KSgqAwSUztVqNlZUVBw4c0NdRqVQGcypZW1ujVqsN6lhaWhqsjWxjYwOgr5Od4OBgmjdvjqWlpb6sbdu2hISEEB0dbYRPmHdFrY8URWHnzp2EhITQvHnz3HzkXCsqfRUfH4+vry8+Pj507tyZ8+fP5+Vj50pR6avHLV68mNdffx07O7ucflyjKAp9lpKSkuUWBBsbm6e2mx9M3VeFXWxsLKC7ugJw/Phx0tLSDM5m+vv7U65cOYKDgwHdv3/NmjVxd3fX12nbti2PHj3S/w0JDg7Ocka0bdu2+jayc+3aNSIjIw3e5+TkREBAwFPfJ/JGEsJ/yfiFHz16NNHR0aSmpvLVV19x+/ZtIiIiAGjcuDF2dnZ88sknJCYmkpCQwIgRI9BoNPo6rVq1IjIykunTp5Oamkp0dDSjRo0C0NfJTmRkpMF/XIB+OzIyMj8+co4VlT6KjY3F3t4eS0tLOnbsyLx583jxxReN2hf/pSj0VZUqVViyZAkbN25kxYoVaLVamjRpwu3bt43eH09TFPrqcUeOHOHcuXP079/fKJ8/N4pCn7Vt25aZM2dy5coVtFot27dv1196L0im7qvCTKvVMnToUJ5//nlq1KgB6P79LC0ts9yb5+7urv+3fZZ//yfVefToEUlJSdnGk/He7N5XWI6DxZEkhP9iYWHB+vXruXz5MqVLl8bW1pbdu3fTvn17/TdCV1dX1q1bx6ZNm7C3t8fJyYmYmBjq1aunr1O9enWWLVvGjBkzsLW1xcPDAz8/P9zd3Q3q2NvbY29vT/v27U32mXOqqPSRg4MDp06d4ujRo0yZMoXhw4ezZ88eo/bFfykKfRUYGEjv3r2pU6cOLVq0YP369bi6urJw4ULjd8hTFIW+etzixYupWbMmjRo1Mk4H5EJR6LM5c+bw3HPP4e/vj6WlJYMHD6Zfv34GZ9gKQlHoK1MZNGgQ586dY/Xq1QW+75UrV+r7yt7env379xd4DELH3NQBFEb169fn1KlTxMbGkpqaiqurKwEBATRo0EBfp02bNly9epX79+9jbm6Os7MzHh4eVKhQQV+nZ8+e9OzZk6ioKOzs7FCpVMycOVNfZ8uWLaSlpQGZlxw8PDyyjOLK2M6vASS5URT6SK1WU6lSJQDq1KnDxYsXmTp1Ki1btjR+hzxFUeirx1lYWFC3bl1CQ0ON1wnPqKj0VUJCAqtXr2bixInG74QcKux95urqyoYNG0hOTubBgwd4eXkxatQog30XFFP2VWE1ePBgNm/ezL59+yhbtqy+3MPDg9TUVGJiYgzOEkZFRen/bT08PDhy5IhBe//+93/S74ijoyM2Nja8/PLLBAQE6F/z9vbWn2mNiorC09PT4H116tTJ+4cW2TP1TYymxr9uPM7O5cuXFbVarWzbtu2JdXbu3KmoVCrl0qVLT6yzePFixdbWVomOjn5inYybtFNTU/Vlo0ePLlSDSrJTmPsoQ79+/ZQWLVo8tU5eFYe+Sk9PV6pUqaIMGzbsqZ8jr4pyX/3444+KlZWVcv/+/afGb2xFuc8ypKamKhUrVlRGjx791M+RV4Wtrx5XGAaVaLVaZdCgQYqXl5dy+fLlLK9nDCr55Zdf9GWXLl3KdlBJVFSUvs7ChQsVR0dHJTk5WVEU3aCSGjVqGLT9xhtvPNOgkq+//lpfFhsbK4NK8lmJTAjj4uKUkydPKidPnlQAZebMmcrJkyeVGzduKIqiKGvXrlV2796tXL16VdmwYYPi6+urdOvWzaCNJUuWKMHBwUpoaKiyfPlypXTp0srw4cMN6sybN085fvy4EhISonzzzTeKjY2NMmfOnKfGFhMTo7i7uyu9evVSzp07p6xevVqxtbVVFi5cqK+TkpKij9/T01MZMWKEcvLkyWce4fYsinofffHFF8pff/2lXL16Vblw4YLy9ddfK+bm5vrRhMZU1PtqwoQJyrZt25SrV68qx48fV15//XXF2tpaOX/+vJF6KFNR76sMTZs2VXr06JHH3ng2Rb3PDh06pPz666/K1atXlX379imtWrVS/Pz8njl5yonC3FeKoijnz59XTp48qbz00ktKy5Yt9bE+LqOsfv36Ss+ePZWTJ0/my3+L77//vuLk5KTs2bNHiYiI0D8SExP1dQYOHKiUK1dO2bVrl3Ls2DElMDBQCQwM1L+enp6u1KhRQ2nTpo1y6tQpZevWrYqrq6tBsh8WFqbY2toqI0eOVC5evKjMnz9fMTMzU7Zu3frU+L788kvF2dlZ2bhxo3LmzBmlc+fOip+fn5KUlKSvc+PGDeXkyZPKhAkTFHt7e33fxcXFGbGnSo4SmRDu3r1bAbI8+vTpoyiKosyZM0cpW7asYmFhoZQrV04ZO3aswbQKiqKbWsHd3V2xsLBQnnvuOWXGjBmKVqs1qNOrVy+ldOnSiqWlpVKrVi3lp59+eqb4Tp8+rTRt2lSxsrJSvL29lS+//NLg9WvXrmUbvzHPfhX1PhozZoxSqVIlxdraWilVqpQSGBiorF69Ovcd8hRFva+GDh2qlCtXTrG0tFTc3d2VDh06KCdOnMh9hzxFUe8rRck8S/LXX3/lrhNyqKj32Z49e5SqVasqVlZWiouLi9KrVy8lPDw89x3yFIW9r3x9fbON73HZve7r65vrPnmS7PYDKD/++KO+TlJSkvLBBx8opUqVUmxtbZWuXbsqERERBu1cv35dad++vWJjY6OUKVNG+eijj5S0tDSDOrt371bq1KmjWFpaKhUqVDDYx5NotVpl3Lhxiru7u2JlZaW0bt1aCQkJMajTp0+fbD/D7t27c9stJZpKURQlL5echRBCCCFE0SajjIUQQgghSjhJCIUQQgghSjhJCIUQQgghSjhJCIUQQgghSjhJCIUQQgghSjhJCIUQQgghSjhJCIUQQgghSjhJCIUQQgghSjhJCIUQJVr58uWZPXu2qcMQQgiTkoRQCFEg+vbti0qlQqVSYWFhgbu7Oy+++CJLlixBq9U+cztLly7F2dk5x/t/0vuOHj3Ku+++m+P2hBCiOJGEUAhRYNq1a0dERATXr1/nzz//5IUXXmDIkCF06tSJ9PR0k8Tk6uqKra2tSfYthBCFhSSEQogCY2VlhYeHB97e3tSrV49PP/2UjRs38ueff7J06VIAZs6cSc2aNbGzs8PHx4cPPviA+Ph4APbs2UO/fv2IjY3Vn238/PPPAUhJSWHEiBF4e3tjZ2dHQEAAe/bs+c/3/fuSsUqlYuHChXTq1AlbW1uqVq1KcHAwoaGhtGzZEjs7O5o0acLVq1cNPtvGjRupV68e1tbWVKhQgQkTJpgsyRVCiJyShFAIYVKtWrWidu3arF+/HgC1Ws3cuXM5f/48y5YtY9euXXz88ccANGnShNmzZ+Po6EhERAQRERGMGDECgMGDBxMcHMzq1as5c+YMr776Ku3atePKlStPfV92Jk2aRO/evTl16hT+/v707NmT9957j9GjR3Ps2DEURWHw4MH6+vv376d3794MGTKECxcusHDhQpYuXcqUKVPyseeEEMKIFCGEKAB9+vRROnfunO1rPXr0UKpWrZrta+vWrVNcXFz02z/++KPi5ORkUOfGjRuKmZmZEh4eblDeunVrZfTo0U98n6Ioiq+vrzJr1iz9NqCMHTtWvx0cHKwAyuLFi/Vlq1atUqytrQ3288UXXxi0u3z5csXT0zPbzySEEIWNuakTUiGEUBQFlUoFwI4dO5g6dSqXLl3i0aNHpKenk5ycTGJi4hPv9Tt79iwajYbKlSsblKekpODi4pLjeGrVqqV/7u7uDkDNmjUNypKTk3n06BGOjo6cPn2agwcPGpwR1Gg0/xm3EEIUFpIQCiFM7uLFi/j5+XH9+nU6derE+++/z5QpUyhdujQHDhzgnXfeITU19YmJVXx8PGZmZhw/fhwzMzOD1+zt7XMcj4WFhf55RqKaXVnG6Oj4+HgmTJhAt27dsrRlbW2d4/0LIURBk4RQCGFSu3bt4uzZswwbNozjx4+j1WqZMWMGarXuFue1a9ca1Le0tESj0RiU1a1bF41Gw927d2nWrFm2+8nufcZSr149QkJCqFSpUr60L4QQ+U0SQiFEgUlJSSEyMhKNRkNUVBRbt25l6tSpdOrUid69e3Pu3DnS0tKYN28eL730EgcPHmTBggUGbZQvX574+Hh27txJ7dq1sbW1pXLlyrz55pv07t2bGTNmULduXe7du8fOnTupVasWHTt2zPZ9xrqUO378eDp16kS5cuV45ZVXUKvVnD59mnPnzjF58mSj7EMIIfKTjDIWQhSYrVu34unpSfny5WnXrh27d+9m7ty5bNy4ETMzM2rXrs3MmTP56quvqFGjBitXrmTq1KkGbTRp0oSBAwfSo0cPXF1dmTZtGgA//vgjvXv35qOPPqJKlSp06dKFo0ePUq5cuae+zxjatm3L5s2b+euvv2jYsCGNGzdm1qxZ+Pr6Gm0fQgiRn1SKoiimDkIIIYQQQpiOnCEUQgghhCjhJCEUQgghhCjhJCEUQgghhCjhJCEUQgghhCjhJCEUQgghhCjhJCEUQgghhCjhJCEUQgghhCjhJCEUQgghhCjhJCEUQgghhCjhJCEUQgghhCjhJCEUQgghhCjhJCEUQgghhCjh/g9rHtDt8olRtgAAAABJRU5ErkJggg==", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from cycler import cycler\n", - "\n", - "plt.style.use(\"default\")\n", - "\n", - "fig, ax = plt.subplots()\n", - "\n", - "ax.set_prop_cycle(\n", - " cycler(\"color\", [\"tab:blue\", \"tab:orange\", \"tab:green\"])\n", - " + cycler(\"linestyle\", [\"-\", \"--\", \"-.\"])\n", - ")\n", - "\n", - "ax.plot(timesteps[\"Datetime\"], timesteps[[\"NA\", \"NB\", \"NC\"]].values)\n", - "ax.legend(labels=[\"$N_A$\", \"$N_B$\", \"$N_C$\"], loc=\"upper left\")\n", - "ax.set_ylabel(\"Defect state percentages [%]\")\n", - "ax.set_xlabel(\"Datetime\")\n", - "\n", - "ax2 = ax.twinx()\n", - "ax2.plot(\n", - " timesteps[\"Datetime\"],\n", - " timesteps[\"Pmp_norm\"],\n", - " c=\"black\",\n", - " label=\"Normalized STC $P_{MP}$\",\n", - ")\n", - "ax2.legend(loc=\"upper right\")\n", - "ax2.set_ylabel(\"Normalized STC $P_{MP}$\")\n", - "\n", - "ax.set_title(\n", - " \"Simulated Outdoor LETID progression based on accelerated test results \\n\"\n", - " f\"{location.name}\"\n", - ")\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.dates as mdates\n", - "\n", - "plt.style.use(\"default\")\n", - "\n", - "fig, ax = plt.subplots()\n", - "\n", - "ax.plot(\n", - " timesteps[\"Datetime\"],\n", - " timesteps[\"Pmp_norm\"],\n", - " c=\"black\",\n", - " label=\"Normalized STC $P_{MP}$\",\n", - ")\n", - "ax.plot(timesteps[\"Datetime\"], np.ones(len(timesteps)), c=\"black\")\n", - "ax.fill_between(\n", - " timesteps[\"Datetime\"],\n", - " np.ones(len(timesteps)),\n", - " timesteps[\"Pmp_norm\"],\n", - " color=\"grey\",\n", - " alpha=0.5,\n", - ")\n", - "\n", - "\n", - "ax.set_ylabel(\"Normalized STC $P_{MP}$\")\n", - "\n", - "ax.set_title(\"Energy Loss\")\n", - "\n", - "loss = letid.calc_energy_loss(timesteps)\n", - "\n", - "ax.text(mdates.datestr2num(\"1999-08-02\"), 0.994, s=f\"Energy loss = {loss*100:.2f}%\")\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.dates as mdates\n", - "from scipy.integrate import cumulative_trapezoid, simpson\n", - "\n", - "\n", - "fig, axs = plt.subplots(3, 1, figsize=(10, 10), sharex=True)\n", - "ax1, ax2, ax3 = axs\n", - "\n", - "ax1.plot(timesteps[\"Datetime\"], timesteps[\"p_mp\"], label=\"Base $P_{MP}$\")\n", - "ax1.plot(\n", - " timesteps[\"Datetime\"],\n", - " timesteps[\"p_mp\"] * timesteps[\"Pmp_norm\"],\n", - " label=\"Degraded $P_{MP}$\",\n", - " alpha=0.7,\n", - ")\n", - "ax1.legend(loc=\"lower left\")\n", - "\n", - "axin1 = ax1.inset_axes([0.55, 0.25, 0.43, 0.43])\n", - "axin1.plot(timesteps[\"Datetime\"], timesteps[\"p_mp\"])\n", - "axin1.plot(timesteps[\"Datetime\"], timesteps[\"p_mp\"] * timesteps[\"Pmp_norm\"], alpha=0.7)\n", - "\n", - "\n", - "axin1.set_xlim(mdates.datestr2num(\"1999-12-04\"), mdates.datestr2num(\"1999-12-11\"))\n", - "axin1.set_ylim(50, 240)\n", - "axin1.tick_params(axis=\"x\", labelrotation=15)\n", - "ax1.indicate_inset_zoom(axin1)\n", - "\n", - "start = timesteps[\"Datetime\"].iloc[0]\n", - "timesteps[\"Timedelta\"] = [\n", - " (d - start).total_seconds() / 3600 for d in timesteps[\"Datetime\"]\n", - "]\n", - "\n", - "ax2.plot(\n", - " timesteps[\"Datetime\"],\n", - " cumulative_trapezoid(timesteps[\"p_mp\"] / 1000, timesteps[\"Timedelta\"], initial=0),\n", - " label=\"Cumulative energy, base\",\n", - ")\n", - "ax2.plot(\n", - " timesteps[\"Datetime\"],\n", - " cumulative_trapezoid(\n", - " (timesteps[\"p_mp\"] * timesteps[\"Pmp_norm\"]) / 1000,\n", - " timesteps[\"Timedelta\"],\n", - " initial=0,\n", - " ),\n", - " label=\"Cumulative energy, degraded\",\n", - ")\n", - "ax2.fill_between(\n", - " timesteps[\"Datetime\"],\n", - " (cumulative_trapezoid(timesteps[\"p_mp\"], timesteps[\"Timedelta\"], initial=0) / 1000),\n", - " (\n", - " cumulative_trapezoid(\n", - " timesteps[\"p_mp\"] * timesteps[\"Pmp_norm\"], timesteps[\"Timedelta\"], initial=0\n", - " )\n", - " / 1000\n", - " ),\n", - " alpha=0.5,\n", - " color=\"C1\",\n", - " label=\"Energy loss\",\n", - ")\n", - "ax2.legend()\n", - "\n", - "axin2 = ax2.inset_axes([0.55, 0.25, 0.43, 0.43])\n", - "axin2.plot(\n", - " timesteps[\"Datetime\"],\n", - " cumulative_trapezoid(timesteps[\"p_mp\"] / 1000, timesteps[\"Timedelta\"], initial=0),\n", - ")\n", - "axin2.plot(\n", - " timesteps[\"Datetime\"],\n", - " cumulative_trapezoid(\n", - " (timesteps[\"p_mp\"] * timesteps[\"Pmp_norm\"]) / 1000,\n", - " timesteps[\"Timedelta\"],\n", - " initial=0,\n", - " ),\n", - ")\n", - "axin2.fill_between(\n", - " timesteps[\"Datetime\"],\n", - " (cumulative_trapezoid(timesteps[\"p_mp\"], timesteps[\"Timedelta\"], initial=0) / 1000),\n", - " (\n", - " cumulative_trapezoid(\n", - " timesteps[\"p_mp\"] * timesteps[\"Pmp_norm\"], timesteps[\"Timedelta\"], initial=0\n", - " )\n", - " / 1000\n", - " ),\n", - " alpha=0.5,\n", - " color=\"C1\",\n", - ")\n", - "\n", - "axin2.set_xlim(mdates.datestr2num(\"1999-12-04\"), mdates.datestr2num(\"1999-12-11\"))\n", - "axin2.set_ylim(412, 427)\n", - "axin2.tick_params(axis=\"x\", labelrotation=15)\n", - "ax2.indicate_inset_zoom(axin2)\n", - "\n", - "ax3.set_xlabel(\"Datetime\")\n", - "ax3.plot(\n", - " timesteps[\"Datetime\"],\n", - " (cumulative_trapezoid(timesteps[\"p_mp\"], timesteps[\"Timedelta\"], initial=0) / 1000)\n", - " - (\n", - " cumulative_trapezoid(\n", - " timesteps[\"p_mp\"] * timesteps[\"Pmp_norm\"], timesteps[\"Timedelta\"], initial=0\n", - " )\n", - " / 1000\n", - " ),\n", - " label=\"Cumulative energy loss\",\n", - ")\n", - "ax3.legend()\n", - "\n", - "loss = letid.calc_energy_loss(timesteps)\n", - "energy = simpson(timesteps[\"p_mp\"] / 1000, timesteps[\"Timedelta\"])\n", - "ax3.text(\n", - " mdates.datestr2num(\"1999-03-02\"),\n", - " 2,\n", - " s=f\"Energy loss = {loss*energy:.1f} kWh ({loss*100:.2f}%)\",\n", - ")\n", - "\n", - "ax1.set_ylabel(\"Module $P_{MP}$ [W]\")\n", - "ax2.set_ylabel(\"Cumulative Energy [kWh]\")\n", - "ax3.set_ylabel(\"Cumulative Energy losses [kWh]\")\n", - "\n", - "ax1.set_title(\"Module power, cumulative energy, and loss due to LETID\")\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### The function `calc_letid_outdoors` wraps all of the steps above into a single function:" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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21999-01-01 02:30:00-07:000.0NaN100.03.366181e-155.269266e-36120.038.5415120.6594459.2114210.8397685.1011071.0
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41999-01-01 04:30:00-07:000.0NaN100.06.732362e-153.161560e-35120.038.5415120.6594459.2114210.8397685.1011071.0
..........................................
87551999-12-31 19:30:00-07:000.0NaNNaNNaNNaNNaNNaN0.000000NaN0.328504NaNNaN
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8760 rows Γ— 13 columns

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" - ], - "text/plain": [ - " Datetime Temperature Injection NA NB \n", - "0 1999-01-01 00:30:00-07:00 0.0 NaN 100.0 0.000000e+00 \\\n", - "1 1999-01-01 01:30:00-07:00 0.0 NaN 100.0 1.683090e-15 \n", - "2 1999-01-01 02:30:00-07:00 0.0 NaN 100.0 3.366181e-15 \n", - "3 1999-01-01 03:30:00-07:00 0.0 NaN 100.0 5.049271e-15 \n", - "4 1999-01-01 04:30:00-07:00 0.0 NaN 100.0 6.732362e-15 \n", - "... ... ... ... ... ... \n", - "8755 1999-12-31 19:30:00-07:00 0.0 NaN NaN NaN \n", - "8756 1999-12-31 20:30:00-07:00 0.0 NaN NaN NaN \n", - "8757 1999-12-31 21:30:00-07:00 0.0 NaN NaN NaN \n", - "8758 1999-12-31 22:30:00-07:00 0.0 NaN NaN NaN \n", - "8759 1999-12-31 23:30:00-07:00 0.0 NaN NaN NaN \n", - "\n", - " NC tau Jsc Voc Isc FF Pmp \n", - "0 0.000000e+00 120.0 38.541512 0.659445 9.211421 0.839768 5.101107 \\\n", - "1 0.000000e+00 120.0 38.541512 0.659445 9.211421 0.839768 5.101107 \n", - "2 5.269266e-36 120.0 38.541512 0.659445 9.211421 0.839768 5.101107 \n", - "3 1.580780e-35 120.0 38.541512 0.659445 9.211421 0.839768 5.101107 \n", - "4 3.161560e-35 120.0 38.541512 0.659445 9.211421 0.839768 5.101107 \n", - "... ... ... ... ... ... ... ... \n", - "8755 NaN NaN NaN 0.000000 NaN 0.328504 NaN \n", - "8756 NaN NaN NaN 0.000000 NaN 0.328504 NaN \n", - "8757 NaN NaN NaN 0.000000 NaN 0.328504 NaN \n", - "8758 NaN NaN NaN 0.000000 NaN 0.328504 NaN \n", - "8759 NaN NaN NaN 0.000000 NaN 0.328504 NaN \n", - "\n", - " Pmp_norm \n", - "0 1.0 \n", - "1 1.0 \n", - "2 1.0 \n", - "3 1.0 \n", - "4 1.0 \n", - "... ... \n", - "8755 NaN \n", - "8756 NaN \n", - "8757 NaN \n", - "8758 NaN \n", - "8759 NaN \n", - "\n", - "[8760 rows x 13 columns]" - ] - }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "mechanism_params = \"repins\"\n", - "\n", - "letid.calc_letid_outdoors(\n", - " tau_0,\n", - " tau_deg,\n", - " wafer_thickness,\n", - " srv_rear,\n", - " nA_0,\n", - " nB_0,\n", - " nC_0,\n", - " weather,\n", - " meta,\n", - " mechanism_params,\n", - " generation_df,\n", - " module_parameters=cec_module,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.13" - }, - "vscode": { - "interpreter": { - "hash": "848658e0671c41dd18b216771b1713479db7d685859cbb6c795b270024b1888c" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/tutorials_and_tools/_build/html/_sources/tutorials/LETID - Passivated Wafer.ipynb b/tutorials_and_tools/_build/html/_sources/tutorials/LETID - Passivated Wafer.ipynb deleted file mode 100644 index 7d3f83249..000000000 --- a/tutorials_and_tools/_build/html/_sources/tutorials/LETID - Passivated Wafer.ipynb +++ /dev/null @@ -1,1376 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# LETID - Passivated Wafer\n", - "\n", - "Example: LETID progression in passivated wafer\n", - "\n", - "Author: Joe Karas | joseph.karas@nrel.gov\n", - "\n", - "2023\n", - "***\n", - "\n", - "This example shows how to simulate an accelerated test performed on a well-passivated Si wafer, rather than a solar cell.\n", - "In a well-passivated wafer, carrier injection ($\\Delta n$) is linearly proportional to carrier lifetime, assuming surface recombination velocity can be approximated to be zero.\n", - "\n", - "LETID and boron-oxygen LID defect transitions are known to accelerate with increased carrier injection, by term $\\Delta n^{x_{ij}}$, where $x_{ij}$ is different for each transition $i \\rightarrow j$ and is related to the stoichiometric involvement of excess carriers in the defect reaction.\n", - "\n", - "**Requirements:**\n", - "- `pandas`, `numpy`, `matplotlib`, `scipy`\n", - "\n", - "**Objectives:**\n", - "1. Define necessary wafer device parameters\n", - "2. Define necessary degradation parameters: degraded lifetime and defect states\n", - "3. Create timeseries of temperature and injection\n", - "4. Run through timeseries, calculating defect states\n", - "5. Calculate device degradation and plot\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# if running on google colab, uncomment the next line and execute this cell to install the dependencies and prevent \"ModuleNotFoundError\" in later cells:\n", - "# !pip install pvdeg==0.3.3" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "from pvdeg import letid, collection, utilities, DATA_DIR\n", - "\n", - "import os\n", - "import pandas as pd\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Device parameters\n", - "For modeling a wafer, we don't need as many device parameters. Wafer thickness, lifetime, and an optical generation profile, which allows us to calculate generation current, which we take to be device current assuming surface recombination is very low. " - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "wafer_thickness = 180 # um\n", - "tau = 350 # us, lifetime of the wafer for demonstration purposes\n", - "\n", - "generation_df = pd.read_excel(\n", - " os.path.join(DATA_DIR, \"PVL_GenProfile.xlsx\"), header=0\n", - ") # this is an optical generation profile generated by PVLighthouse's OPAL2 default model for 1-sun, normal incident AM1.5 sunlight on a 180-um thick SiNx-coated, pyramid-textured wafer.\n", - "generation = generation_df[\"Generation (cm-3s-1)\"]\n", - "depth = generation_df[\"Depth (um)\"]\n", - "\n", - "j_gen = collection.generation_current(generation, depth)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Degradation parameters\n", - "Here we'll model LETID under 0.5-sun, open-circuit injection. In a passivated wafer, recombination per volume ($\\frac{\\Delta n}{\\tau}$) must equal generation per volume ($\\frac{Jq}{W}$), which gives $\\Delta n = \\frac{Jq\\tau}{W}$\n", - "\n", - "The term $\\Delta n^{x_{ij}}$ is calculated for wafers by the function ```letid.carrier_factor_wafer```, which adjusts the rate of the transition $i \\rightarrow j$ for $\\Delta n$.\n", - "\n", - "Since kinetic parameters are taken from literature that may have used different $\\Delta n$, we adjust transitions by the ratio $\\frac{\\Delta n}{\\Delta n_{lit}}$.\n", - "\n", - "See [Repins *et al.* 2023](https://doi.org/10.1557/s43577-022-00438-8) for more details." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "mechanism_params = utilities.get_kinetics(\"repins\")\n", - "injection = 0.5 #" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Demonstrate calculation of dn^x_ij\n", - "1. Calculate $\\Delta n$ of the wafer" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "2.5706584437713456e+21" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from scipy.constants import elementary_charge as q # Elementary charge, C\n", - "\n", - "dn = (\n", - " ((j_gen * 0.001 * 10000 * injection) * (tau * 1e-6)) / (wafer_thickness * 1e-6) / q\n", - ") # calculate excess carrier density\n", - "dn" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "2. Calculate $\\Delta n_{lit}$, the excess carrier density from the literature experiments where kinetic parameters were determined.\n", - "Assuming literature experiments were carried out on cells, this requires using the `calc_dn` function" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "7.064647199192719e+20" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from scipy.constants import convert_temperature\n", - "\n", - "transition = \"bc\" # for this example we'll look at the B -> C transition\n", - "\n", - "meas_tau = mechanism_params[f\"tau_{transition}\"]\n", - "meas_temp = mechanism_params[f\"temperature_{transition}\"]\n", - "meas_temp = convert_temperature(meas_temp, \"K\", \"C\")\n", - "meas_suns = mechanism_params[f\"suns_{transition}\"]\n", - "meas_jsc = 40\n", - "meas_wafer_thickness = mechanism_params[f\"thickness_{transition}\"]\n", - "meas_srv = mechanism_params[f\"srv_{transition}\"]\n", - "meas_structure = mechanism_params[f\"structure_{transition}\"]\n", - "\n", - "dn_lit = letid.calc_dn(\n", - " meas_tau,\n", - " meas_temp,\n", - " meas_suns,\n", - " meas_jsc,\n", - " wafer_thickness=meas_wafer_thickness,\n", - " s_rear=meas_srv,\n", - ")\n", - "dn_lit" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "3. Return the exponent $x_{ij}$ of the transition in question\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1.2" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "exponent = mechanism_params[f\"x_{transition}\"]\n", - "exponent" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "4. Calculate $(\\frac{\\Delta n}{\\Delta n_{lit}})^{x_{ij}}$. This is the acceleration factor of the transition due to excess carriers.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "4.711342509616992" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "(dn / dn_lit) ** exponent" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "4.711342509616992" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# the \"carrier_factor_wafer\" function yields the same result\n", - "\n", - "letid.carrier_factor_wafer(\n", - " tau, transition, injection, j_gen, wafer_thickness, mechanism_params\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Degradation parameters\n", - "To model the device's degradation, we need to define several more important quantities about the degradation the device will experience. These include undegraded and degraded lifetime (in $\\mu s$), and starting defect state percentages" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "tau_0 = 350 # us, carrier lifetime in non-degraded states, e.g. LETID/LID states A or C\n", - "tau_deg = 41 # us, carrier lifetime in fully-degraded state, e.g. LETID/LID state B" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'mechanism': 'LETID', 'v_ab': 46700000.0, 'v_ba': 4.7e-25, 'v_bc': 19900000.0, 'v_cb': 0.0, 'ea_ab': 0.827, 'ea_ba': -1.15, 'ea_bc': 0.871, 'ea_cb': 0.0, 'suns_ab': 1.0, 'suns_bc': 1.0, 'temperature_ab': 410, 'temperature_bc': 410, 'tau_ab': 75, 'tau_bc': 75, 'x_ab': 1, 'x_ba': 1.7, 'x_bc': 1.2, 'structure_ab': 'cell', 'structure_bc': 'cell', 'thickness_ab': 200, 'thickness_bc': 200, 'srv_ab': 90, 'srv_bc': 90, 'doi': 'doi:10.1557/s43577-022-00438-8', 'comments': ''}\n" - ] - } - ], - "source": [ - "# starting defect state percentages\n", - "nA_0 = 100\n", - "nB_0 = 0\n", - "nC_0 = 0\n", - "\n", - "mechanism_params = utilities.get_kinetics(\"repins\")\n", - "print(mechanism_params)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Set up timeseries\n", - "In this example, we are going to model a wafer exposed to 0.5 suns illumination at $125\\degree C$ for 24 hours, and compare to experimental data from [Wyller *et al.* 2021](https://doi.org/10.1109/JPHOTOV.2021.3078367)." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "temperature = 125 # degrees celsius\n", - "suns = 0.5 # \"suns\" of injection, e.g 1-sun illumination at open circuit would be 1; dark current injection is given as a fraction of Isc, e.g., injecting Isc would be 1. For this example we assume injection is 0.1*Isc.\n", - "\n", - "timesteps = pd.date_range(\n", - " start=\"2022-01-01 00:00:00\", end=\"2022-01-02 00:00:00\", freq=\"S\"\n", - ") # three weeks of 1-minute interval timesteps. In general, we should select small timesteps unless we are sure defect reactions are proceeding very slowly\n", - "timesteps = pd.DataFrame(timesteps, columns=[\"Datetime\"])\n", - "\n", - "temps = np.full(len(timesteps), temperature)\n", - "injection = np.full(len(timesteps), suns)\n", - "\n", - "timesteps[\"Temperature\"] = temps\n", - "timesteps[\"Injection\"] = injection\n", - "\n", - "timesteps[[\"NA\", \"NB\", \"NC\", \"tau\"]] = (\n", - " np.nan\n", - ") # create columns for defect state percentages and lifetime, fill with NaNs for now, to fill iteratively below\n", - "\n", - "timesteps.loc[0, [\"NA\", \"NB\", \"NC\"]] = (\n", - " nA_0,\n", - " nB_0,\n", - " nC_0,\n", - ") # assign first timestep defect state percentages\n", - "timesteps.loc[0, \"tau\"] = letid.tau_now(\n", - " tau_0, tau_deg, nB_0\n", - ") # calculate tau for the first timestep" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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DatetimeTemperatureInjectionNANBNCtau
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........................
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86401 rows Γ— 7 columns

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" - ], - "text/plain": [ - " Datetime Temperature Injection NA NB NC tau\n", - "0 2022-01-01 00:00:00 125 0.5 100.0 0.0 0.0 350.0\n", - "1 2022-01-01 00:00:01 125 0.5 NaN NaN NaN NaN\n", - "2 2022-01-01 00:00:02 125 0.5 NaN NaN NaN NaN\n", - "3 2022-01-01 00:00:03 125 0.5 NaN NaN NaN NaN\n", - "4 2022-01-01 00:00:04 125 0.5 NaN NaN NaN NaN\n", - "... ... ... ... ... ... ... ...\n", - "86396 2022-01-01 23:59:56 125 0.5 NaN NaN NaN NaN\n", - "86397 2022-01-01 23:59:57 125 0.5 NaN NaN NaN NaN\n", - "86398 2022-01-01 23:59:58 125 0.5 NaN NaN NaN NaN\n", - "86399 2022-01-01 23:59:59 125 0.5 NaN NaN NaN NaN\n", - "86400 2022-01-02 00:00:00 125 0.5 NaN NaN NaN NaN\n", - "\n", - "[86401 rows x 7 columns]" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "timesteps" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Run through timesteps\n", - "Since each timestep depends on the preceding timestep, we need to calculate in a loop. This will take a few minutes depending on the length of the timeseries." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "for index, timestep in timesteps.iterrows():\n", - "\n", - " # first row tau has already been assigned\n", - " if index == 0:\n", - " pass\n", - "\n", - " # loop through rows, new tau calculated based on previous NB. Reaction proceeds based on new tau.\n", - " else:\n", - " n_A = timesteps.at[index - 1, \"NA\"]\n", - " n_B = timesteps.at[index - 1, \"NB\"]\n", - " n_C = timesteps.at[index - 1, \"NC\"]\n", - "\n", - " tau = letid.tau_now(tau_0, tau_deg, n_B)\n", - " j_gen = j_gen\n", - "\n", - " temperature = timesteps.at[index, \"Temperature\"]\n", - " injection = timesteps.at[index, \"Injection\"]\n", - "\n", - " # calculate defect reaction kinetics: reaction constant and carrier concentration factor.\n", - " k_AB = letid.k_ij(\n", - " mechanism_params[\"v_ab\"], mechanism_params[\"ea_ab\"], temperature\n", - " )\n", - " k_BA = letid.k_ij(\n", - " mechanism_params[\"v_ba\"], mechanism_params[\"ea_ba\"], temperature\n", - " )\n", - " k_BC = letid.k_ij(\n", - " mechanism_params[\"v_bc\"], mechanism_params[\"ea_bc\"], temperature\n", - " )\n", - " k_CB = letid.k_ij(\n", - " mechanism_params[\"v_cb\"], mechanism_params[\"ea_cb\"], temperature\n", - " )\n", - "\n", - " x_ab = letid.carrier_factor_wafer(\n", - " tau, \"ab\", injection, j_gen, wafer_thickness, mechanism_params\n", - " )\n", - " x_ba = letid.carrier_factor_wafer(\n", - " tau, \"ba\", injection, j_gen, wafer_thickness, mechanism_params\n", - " )\n", - " x_bc = letid.carrier_factor_wafer(\n", - " tau, \"bc\", injection, j_gen, wafer_thickness, mechanism_params\n", - " )\n", - " # x_cb there is no known excess carrier acceleration factor for the c->b transition\n", - "\n", - " # calculate the instantaneous change in NA, NB, and NC\n", - " dN_Adt = (k_BA * n_B * x_ba) - (k_AB * n_A * x_ab)\n", - " dN_Bdt = (\n", - " (k_AB * n_A * x_ab) + (k_CB * n_C) - ((k_BA * x_ba + k_BC * x_bc) * n_B)\n", - " )\n", - " dN_Cdt = (k_BC * n_B * x_bc) - (k_CB * n_C)\n", - "\n", - " t_step = (\n", - " timesteps.at[index, \"Datetime\"] - timesteps.at[index - 1, \"Datetime\"]\n", - " ).total_seconds()\n", - "\n", - " # assign new defect state percentages\n", - " timesteps.at[index, \"NA\"] = n_A + dN_Adt * t_step\n", - " timesteps.at[index, \"NB\"] = n_B + dN_Bdt * t_step\n", - " timesteps.at[index, \"NC\"] = n_C + dN_Cdt * t_step" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Finish calculating degraded device parameters.\n", - "Now that we have calculated defect states, we can calculate all the quantities that depend on defect states. Since this is a wafer, device parameters like Jsc, Voc, FF, etc., aren't really appropriate\n", - "\n", - "For wafers, results are often presented in terms of normalized defect density (NDD), where\n", - "\n", - "$NDD(t) = \\frac{1}{\\tau (t)} - \\frac{1}{\\tau _0}$" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "timesteps[\"tau\"] = letid.tau_now(tau_0, tau_deg, timesteps[\"NB\"])\n", - "\n", - "timesteps[\"NDD\"] = letid.calc_ndd(timesteps[\"tau\"].iloc[0], timesteps[\"tau\"])\n", - "\n", - "timesteps[\"time (s)\"] = (\n", - " timesteps[\"Datetime\"] - timesteps.iloc[0][\"Datetime\"]\n", - ").dt.total_seconds() # create a column for seconds elapsed" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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DatetimeTemperatureInjectionNANBNCtauNDDtime (s)
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86401 rows Γ— 9 columns

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" - ], - "text/plain": [ - " Datetime Temperature Injection NA NB \n", - "0 2022-01-01 00:00:00 125 0.5 1.000000e+02 0.000000e+00 \\\n", - "1 2022-01-01 00:00:01 125 0.5 9.942175e+01 5.782480e-01 \n", - "2 2022-01-01 00:00:02 125 0.5 9.887086e+01 1.128658e+00 \n", - "3 2022-01-01 00:00:03 125 0.5 9.834396e+01 1.654652e+00 \n", - "4 2022-01-01 00:00:04 125 0.5 9.783834e+01 2.159000e+00 \n", - "... ... ... ... ... ... \n", - "86396 2022-01-01 23:59:56 125 0.5 5.771633e-34 3.352902e-27 \n", - "86397 2022-01-01 23:59:57 125 0.5 5.766526e-34 3.349935e-27 \n", - "86398 2022-01-01 23:59:58 125 0.5 5.761423e-34 3.346971e-27 \n", - "86399 2022-01-01 23:59:59 125 0.5 5.756325e-34 3.344009e-27 \n", - "86400 2022-01-02 00:00:00 125 0.5 5.751231e-34 3.341050e-27 \n", - "\n", - " NC tau NDD time (s) \n", - "0 0.000000 350.000000 0.000000 0.0 \n", - "1 0.000000 335.383917 0.000125 1.0 \n", - "2 0.000486 322.562131 0.000243 2.0 \n", - "3 0.001392 311.192917 0.000356 3.0 \n", - "4 0.002663 301.019637 0.000465 4.0 \n", - "... ... ... ... ... \n", - "86396 100.000000 350.000000 0.000000 86396.0 \n", - "86397 100.000000 350.000000 0.000000 86397.0 \n", - "86398 100.000000 350.000000 0.000000 86398.0 \n", - "86399 100.000000 350.000000 0.000000 86399.0 \n", - "86400 100.000000 350.000000 0.000000 86400.0 \n", - "\n", - "[86401 rows x 9 columns]" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "timesteps" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plot the results" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from cycler import cycler\n", - "\n", - "plt.style.use(\"default\")\n", - "\n", - "fig, ax = plt.subplots()\n", - "\n", - "ax.set_prop_cycle(\n", - " cycler(\"color\", [\"tab:blue\", \"tab:orange\", \"tab:green\"])\n", - " + cycler(\"linestyle\", [\"-\", \"--\", \"-.\"])\n", - ")\n", - "\n", - "ax.plot(timesteps[\"time (s)\"], timesteps[[\"NA\", \"NB\", \"NC\"]].values)\n", - "ax.legend(labels=[\"$N_A$\", \"$N_B$\", \"$N_C$\"], loc=\"upper left\")\n", - "ax.set_ylabel(\"Defect state percentages [%]\")\n", - "ax.set_xlabel(\"Time [s]\")\n", - "\n", - "ax2 = ax.twinx()\n", - "c_norm = 1.35 # Normalization constants \"C_norm\". See Repins 2023 for details\n", - "ax2.plot(\n", - " timesteps[\"time (s)\"], timesteps[\"NDD\"] * c_norm, c=\"black\", label=\"NDD modeled\"\n", - ")\n", - "ax.set_ylabel(\"NDD [$\\mu s^{-1}$]\")\n", - "\n", - "data = pd.read_csv(\"example data\\wyller data.csv\", header=0)\n", - "filtered = data[data[\"Series\"] == \"0.5 sun 125C\"]\n", - "ax2.scatter(\n", - " filtered[\"X\"], filtered[\"Y\"], label=\"NDD data from Wyller\", marker=\"v\", c=\"black\"\n", - ")\n", - "\n", - "ax2.legend(loc=\"center left\")\n", - "\n", - "ax.set_xscale(\"log\")\n", - "\n", - "ax.set_title(\n", - " \"Accelerated LETID Test (wafer)\\n\"\n", - " rf\"{temperature}$\\degree$C, {suns} suns illumination\"\n", - ")\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Compare model results to experimental data\n", - "Experimental data (0.5 suns, 125°C, 150°C, 175°C) from [Wyller 2021](https://doi.org/10.1109/JPHOTOV.2021.3078367).\n", - "Comparison with experimental data requires normalization constant ($C_{norm}$), which relates NDD to $N_B$ as described in [Repins 2023](https://doi.org/10.1557/s43577-022-00438-8)." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "temperatures = [125, 150, 175] # degrees celsius" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "timesteps125 = timesteps" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "temperature = temperatures[1] # degrees celsius\n", - "suns = 0.5 # \"suns\" of injection, e.g 1-sun illumination at open circuit would be 1; dark current injection is given as a fraction of Isc, e.g., injecting Isc would be 1. For this example we assume injection is 0.1*Isc.\n", - "\n", - "timesteps = pd.date_range(\n", - " start=\"2022-01-01 00:00:00\", end=\"2022-01-02 00:00:00\", freq=\"S\"\n", - ") # three weeks of 1-minute interval timesteps. In general, we should select small timesteps unless we are sure defect reactions are proceeding very slowly\n", - "timesteps = pd.DataFrame(timesteps, columns=[\"Datetime\"])\n", - "\n", - "temps = np.full(len(timesteps), temperature)\n", - "injection = np.full(len(timesteps), suns)\n", - "\n", - "timesteps[\"Temperature\"] = temps\n", - "timesteps[\"Injection\"] = injection\n", - "\n", - "timesteps[[\"NA\", \"NB\", \"NC\", \"tau\"]] = (\n", - " np.nan\n", - ") # create columns for defect state percentages and lifetime, fill with NaNs for now, to fill iteratively below\n", - "\n", - "timesteps.loc[0, [\"NA\", \"NB\", \"NC\"]] = (\n", - " nA_0,\n", - " nB_0,\n", - " nC_0,\n", - ") # assign first timestep defect state percentages\n", - "timesteps.loc[0, \"tau\"] = letid.tau_now(\n", - " tau_0, tau_deg, nB_0\n", - ") # calculate tau for the first timestep" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "for index, timestep in timesteps.iterrows():\n", - "\n", - " # first row tau has already been assigned\n", - " if index == 0:\n", - " pass\n", - "\n", - " # loop through rows, new tau calculated based on previous NB. Reaction proceeds based on new tau.\n", - " else:\n", - " n_A = timesteps.at[index - 1, \"NA\"]\n", - " n_B = timesteps.at[index - 1, \"NB\"]\n", - " n_C = timesteps.at[index - 1, \"NC\"]\n", - "\n", - " tau = letid.tau_now(tau_0, tau_deg, n_B)\n", - " j_gen = j_gen\n", - "\n", - " temperature = timesteps.at[index, \"Temperature\"]\n", - " injection = timesteps.at[index, \"Injection\"]\n", - "\n", - " # calculate defect reaction kinetics: reaction constant and carrier concentration factor.\n", - " k_AB = letid.k_ij(\n", - " mechanism_params[\"v_ab\"], mechanism_params[\"ea_ab\"], temperature\n", - " )\n", - " k_BA = letid.k_ij(\n", - " mechanism_params[\"v_ba\"], mechanism_params[\"ea_ba\"], temperature\n", - " )\n", - " k_BC = letid.k_ij(\n", - " mechanism_params[\"v_bc\"], mechanism_params[\"ea_bc\"], temperature\n", - " )\n", - " k_CB = letid.k_ij(\n", - " mechanism_params[\"v_cb\"], mechanism_params[\"ea_cb\"], temperature\n", - " )\n", - "\n", - " x_ab = letid.carrier_factor_wafer(\n", - " tau, \"ab\", injection, j_gen, wafer_thickness, mechanism_params\n", - " )\n", - " x_ba = letid.carrier_factor_wafer(\n", - " tau, \"ba\", injection, j_gen, wafer_thickness, mechanism_params\n", - " )\n", - " x_bc = letid.carrier_factor_wafer(\n", - " tau, \"bc\", injection, j_gen, wafer_thickness, mechanism_params\n", - " )\n", - " # x_cb there is no known excess carrier acceleration factor for the c->b transition\n", - "\n", - " # calculate the instantaneous change in NA, NB, and NC\n", - " dN_Adt = (k_BA * n_B * x_ba) - (k_AB * n_A * x_ab)\n", - " dN_Bdt = (\n", - " (k_AB * n_A * x_ab) + (k_CB * n_C) - ((k_BA * x_ba + k_BC * x_bc) * n_B)\n", - " )\n", - " dN_Cdt = (k_BC * n_B * x_bc) - (k_CB * n_C)\n", - "\n", - " t_step = (\n", - " timesteps.at[index, \"Datetime\"] - timesteps.at[index - 1, \"Datetime\"]\n", - " ).total_seconds()\n", - "\n", - " # assign new defect state percentages\n", - " timesteps.at[index, \"NA\"] = n_A + dN_Adt * t_step\n", - " timesteps.at[index, \"NB\"] = n_B + dN_Bdt * t_step\n", - " timesteps.at[index, \"NC\"] = n_C + dN_Cdt * t_step" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "timesteps[\"tau\"] = letid.tau_now(tau_0, tau_deg, timesteps[\"NB\"])\n", - "timesteps[\"NDD\"] = letid.calc_ndd(timesteps[\"tau\"].iloc[0], timesteps[\"tau\"])\n", - "timesteps[\"time (s)\"] = (\n", - " timesteps[\"Datetime\"] - timesteps.iloc[0][\"Datetime\"]\n", - ").dt.total_seconds() # create a column for seconds elapsed" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "timesteps150 = timesteps" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "temperature = temperatures[2] # degrees celsius\n", - "suns = 0.5 # \"suns\" of injection, e.g 1-sun illumination at open circuit would be 1; dark current injection is given as a fraction of Isc, e.g., injecting Isc would be 1. For this example we assume injection is 0.1*Isc.\n", - "\n", - "timesteps = pd.date_range(\n", - " start=\"2022-01-01 00:00:00\", end=\"2022-01-02 00:00:00\", freq=\"S\"\n", - ") # three weeks of 1-minute interval timesteps. In general, we should select small timesteps unless we are sure defect reactions are proceeding very slowly\n", - "timesteps = pd.DataFrame(timesteps, columns=[\"Datetime\"])\n", - "\n", - "temps = np.full(len(timesteps), temperature)\n", - "injection = np.full(len(timesteps), suns)\n", - "\n", - "timesteps[\"Temperature\"] = temps\n", - "timesteps[\"Injection\"] = injection\n", - "\n", - "timesteps[[\"NA\", \"NB\", \"NC\", \"tau\"]] = (\n", - " np.nan\n", - ") # create columns for defect state percentages and lifetime, fill with NaNs for now, to fill iteratively below\n", - "\n", - "timesteps.loc[0, [\"NA\", \"NB\", \"NC\"]] = (\n", - " nA_0,\n", - " nB_0,\n", - " nC_0,\n", - ") # assign first timestep defect state percentages\n", - "timesteps.loc[0, \"tau\"] = letid.tau_now(\n", - " tau_0, tau_deg, nB_0\n", - ") # calculate tau for the first timestep" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "for index, timestep in timesteps.iterrows():\n", - "\n", - " # first row tau has already been assigned\n", - " if index == 0:\n", - " pass\n", - "\n", - " # loop through rows, new tau calculated based on previous NB. Reaction proceeds based on new tau.\n", - " else:\n", - " n_A = timesteps.at[index - 1, \"NA\"]\n", - " n_B = timesteps.at[index - 1, \"NB\"]\n", - " n_C = timesteps.at[index - 1, \"NC\"]\n", - "\n", - " tau = letid.tau_now(tau_0, tau_deg, n_B)\n", - " j_gen = j_gen\n", - "\n", - " temperature = timesteps.at[index, \"Temperature\"]\n", - " injection = timesteps.at[index, \"Injection\"]\n", - "\n", - " # calculate defect reaction kinetics: reaction constant and carrier concentration factor.\n", - " k_AB = letid.k_ij(\n", - " mechanism_params[\"v_ab\"], mechanism_params[\"ea_ab\"], temperature\n", - " )\n", - " k_BA = letid.k_ij(\n", - " mechanism_params[\"v_ba\"], mechanism_params[\"ea_ba\"], temperature\n", - " )\n", - " k_BC = letid.k_ij(\n", - " mechanism_params[\"v_bc\"], mechanism_params[\"ea_bc\"], temperature\n", - " )\n", - " k_CB = letid.k_ij(\n", - " mechanism_params[\"v_cb\"], mechanism_params[\"ea_cb\"], temperature\n", - " )\n", - "\n", - " x_ab = letid.carrier_factor_wafer(\n", - " tau, \"ab\", injection, j_gen, wafer_thickness, mechanism_params\n", - " )\n", - " x_ba = letid.carrier_factor_wafer(\n", - " tau, \"ba\", injection, j_gen, wafer_thickness, mechanism_params\n", - " )\n", - " x_bc = letid.carrier_factor_wafer(\n", - " tau, \"bc\", injection, j_gen, wafer_thickness, mechanism_params\n", - " )\n", - " # x_cb there is no known excess carrier acceleration factor for the c->b transition\n", - "\n", - " # calculate the instantaneous change in NA, NB, and NC\n", - " dN_Adt = (k_BA * n_B * x_ba) - (k_AB * n_A * x_ab)\n", - " dN_Bdt = (\n", - " (k_AB * n_A * x_ab) + (k_CB * n_C) - ((k_BA * x_ba + k_BC * x_bc) * n_B)\n", - " )\n", - " dN_Cdt = (k_BC * n_B * x_bc) - (k_CB * n_C)\n", - "\n", - " t_step = (\n", - " timesteps.at[index, \"Datetime\"] - timesteps.at[index - 1, \"Datetime\"]\n", - " ).total_seconds()\n", - "\n", - " # assign new defect state percentages\n", - " timesteps.at[index, \"NA\"] = n_A + dN_Adt * t_step\n", - " timesteps.at[index, \"NB\"] = n_B + dN_Bdt * t_step\n", - " timesteps.at[index, \"NC\"] = n_C + dN_Cdt * t_step" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "timesteps[\"tau\"] = letid.tau_now(tau_0, tau_deg, timesteps[\"NB\"])\n", - "timesteps[\"NDD\"] = letid.calc_ndd(timesteps[\"tau\"].iloc[0], timesteps[\"tau\"])\n", - "timesteps[\"time (s)\"] = (\n", - " timesteps[\"Datetime\"] - timesteps.iloc[0][\"Datetime\"]\n", - ").dt.total_seconds() # create a column for seconds elapsed" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "timesteps175 = timesteps" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "timesteps = pd.concat([timesteps125, timesteps150, timesteps175])" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [], - "source": [ - "normalization_constants = {\n", - " 125: 1.35,\n", - " 150: 0.6,\n", - " 175: 0.35,\n", - "} # Normalization constants \"C_norm\". See Repins 2023 for details\n", - "\n", - "timesteps[\"Normalization constant\"] = timesteps[\"Temperature\"].map(\n", - " normalization_constants\n", - ")\n", - "timesteps[\"NDD-normalized\"] = timesteps[\"NDD\"] * timesteps[\"Normalization constant\"]" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [], - "source": [ - "grouped_model = timesteps.groupby(\"Temperature\")" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [], - "source": [ - "data = pd.read_csv(\"example data/wyller data.csv\", header=0)\n", - "data[\"Temperature\"] = pd.to_numeric(data[\"Series\"].str[-4:-1])\n", - "# normalization_constants = {'0.5 sun 125C' : 1/1.35, '0.5 sun 150C' : 1/0.6, '0.5 sun 175C' : 1/0.35}\n", - "# data['Normalization constant'] = data['Series'].map(normalization_constants)\n", - "# data['Y-normalized'] = data['Y']*data['Normalization constant']" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [], - "source": [ - "grouped_data = data.groupby(\"Temperature\")" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import itertools\n", - "\n", - "fig, ax = plt.subplots()\n", - "\n", - "for name, group in grouped_model:\n", - " ax.plot(\n", - " group[\"time (s)\"] + 0.1,\n", - " group[\"NDD-normalized\"],\n", - " label=f\"Modeled NDD {name}$\\degree$C\",\n", - " )\n", - " ax.legend()\n", - "\n", - "\n", - "marker = itertools.cycle((\"o\", \"s\", \"v\", \"o\", \"*\"))\n", - "\n", - "for name, group in grouped_data:\n", - " ax.scatter(\n", - " group[\"X\"],\n", - " group[\"Y\"],\n", - " label=f\"Wyller NDD {name}$\\degree$C\",\n", - " marker=next(marker),\n", - " )\n", - " ax.legend()\n", - "\n", - "ax.set_xscale(\"log\")\n", - "\n", - "ax.set_xlabel(\"Time [s]\")\n", - "ax.set_ylabel(\"NDD [$\\mu s^{-1}$]\")\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import itertools\n", - "\n", - "fig, ax = plt.subplots()\n", - "\n", - "colors = itertools.cycle((\"red\", \"blue\", \"green\"))\n", - "\n", - "for name, group in grouped_model:\n", - " ax.plot(\n", - " group[\"time (s)\"] + 0.1,\n", - " group[\"NDD-normalized\"],\n", - " color=next(colors),\n", - " label=f\"Modeled {name}$\\degree$C\",\n", - " linewidth=3,\n", - " )\n", - " ax.legend(prop={\"size\": 12})\n", - "\n", - "\n", - "marker = itertools.cycle((\"o\", \"s\", \"v\", \"o\", \"*\"))\n", - "\n", - "for name, group in grouped_data:\n", - " ax.scatter(\n", - " group[\"X\"],\n", - " group[\"Y\"],\n", - " label=f\"Literature data {name}$\\degree$C\",\n", - " color=next(colors),\n", - " marker=next(marker),\n", - " s=70,\n", - " )\n", - " ax.legend(prop={\"size\": 12})\n", - "\n", - "ax.set_xscale(\"log\")\n", - "\n", - "ax.tick_params(axis=\"both\", which=\"major\", labelsize=16)\n", - "\n", - "\n", - "ax.set_xlabel(\"Time [s]\", fontsize=16)\n", - "ax.set_ylabel(\"Normalized defect \\n density [$\\mu s^{-1}$]\", fontsize=16)\n", - "\n", - "ax.set_title(\"Modeled vs. Literature LETID data\", fontsize=16)\n", - "\n", - "\n", - "plt.tight_layout()\n", - "\n", - "# fig.savefig('wafer data.png', dpi = 600)\n", - "plt.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.13" - }, - "vscode": { - "interpreter": { - "hash": "848658e0671c41dd18b216771b1713479db7d685859cbb6c795b270024b1888c" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/1 - Basics, Humidity, Design.ipynb b/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/1 - Basics, Humidity, Design.ipynb deleted file mode 100644 index f1f2e5bc7..000000000 --- a/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/1 - Basics, Humidity, Design.ipynb +++ /dev/null @@ -1,1775 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 1 - Basics, Humidity, Design\n", - "### Module Humidity and Edge Seal Width\n", - "\n", - "\n", - "**Requirements:**\n", - "- weather file (psm3 preferred) demo file is provided\n", - "\n", - "**Objectives:**\n", - "1. Read in necessary weather data\n", - "2. Generate solar position, POA, and module temperature\n", - "3. Generate module humidities\n", - "4. Calculate edge seal width" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "# if running on google colab, uncomment the next line and execute this cell to install the dependencies and prevent \"ModuleNotFoundError\" in later cells:\n", - "# !pip install pvdeg==0.3.3" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import pandas as pd\n", - "\n", - "import pvdeg\n", - "from pvdeg import DATA_DIR" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Working on a Windows 10\n", - "Python version 3.11.7 | packaged by Anaconda, Inc. | (main, Dec 15 2023, 18:05:47) [MSC v.1916 64 bit (AMD64)]\n", - "Pandas version 2.2.0\n", - "pvdeg version 0.2.4.dev83+ge2ceab9.d20240422\n" - ] - } - ], - "source": [ - "# This information helps with debugging and getting support :)\n", - "import sys, platform\n", - "\n", - "print(\"Working on a \", platform.system(), platform.release())\n", - "print(\"Python version \", sys.version)\n", - "print(\"Pandas version \", pd.__version__)\n", - "print(\"pvdeg version \", pvdeg.__version__)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1. Reading in Weather Data and Site Meta-Data\n", - "Most `pvdeg` functions have been standardized to operate from popular weather files such as TMY3, EPW, and PSM3. For high-performance computer users, NSRDB and other database fetching tools can be used. For these tutorials, we will use local weather files. Unless otherwise stated, functions require the following fields within a weather file:\n", - "\n", - "- date-time index\n", - "- DNI\n", - "- DHI\n", - "- GHI\n", - "- Temperature, Dry-Bulb\n", - "- Temperature, Dew-Point\n", - "- Wind Speed\n", - "- Relative Humidity\n", - "\n", - "And the following site-specific metadata. This should be contained in the weather file header.\n", - "\n", - "- Latitude\n", - "- Longitude\n", - "- Altitude" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "PSM_FILE = os.path.join(DATA_DIR, \"psm3_demo.csv\")\n", - "\n", - "WEATHER, META = pvdeg.weather.read(PSM_FILE, \"psm\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's take a closer look at the dataframe (weather) and dictionary (meta) imported above. The structure will be used by most `pvdeg` functions." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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YearMonthDayHourMinutednidhighitemp_airdew_pointwind_speedrelative_humidity
1999-01-01 00:30:00-07:001999110300.00.00.00.0-5.01.879.39
1999-01-01 01:30:00-07:001999111300.00.00.00.0-4.01.780.84
1999-01-01 02:30:00-07:001999112300.00.00.00.0-4.01.582.98
1999-01-01 03:30:00-07:001999113300.00.00.00.0-4.01.385.01
1999-01-01 04:30:00-07:001999114300.00.00.00.0-4.01.385.81
\n", - "
" - ], - "text/plain": [ - " Year Month Day Hour Minute dni dhi ghi \\\n", - "1999-01-01 00:30:00-07:00 1999 1 1 0 30 0.0 0.0 0.0 \n", - "1999-01-01 01:30:00-07:00 1999 1 1 1 30 0.0 0.0 0.0 \n", - "1999-01-01 02:30:00-07:00 1999 1 1 2 30 0.0 0.0 0.0 \n", - "1999-01-01 03:30:00-07:00 1999 1 1 3 30 0.0 0.0 0.0 \n", - "1999-01-01 04:30:00-07:00 1999 1 1 4 30 0.0 0.0 0.0 \n", - "\n", - " temp_air dew_point wind_speed relative_humidity \n", - "1999-01-01 00:30:00-07:00 0.0 -5.0 1.8 79.39 \n", - "1999-01-01 01:30:00-07:00 0.0 -4.0 1.7 80.84 \n", - "1999-01-01 02:30:00-07:00 0.0 -4.0 1.5 82.98 \n", - "1999-01-01 03:30:00-07:00 0.0 -4.0 1.3 85.01 \n", - "1999-01-01 04:30:00-07:00 0.0 -4.0 1.3 85.81 " - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "WEATHER.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Meta-Data will vary greatly between weather files. However, at a minimum they should all contain the fields required by `pvdeg` listed above. Let's look at the meta-data generated by our PSM3 weather file. It provides much more information that we need, but at the very end are the three most important fields." - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'Source': 'NSRDB',\n", - " 'Location ID': '145809',\n", - " 'City': '-',\n", - " 'State': '-',\n", - " 'Country': '-',\n", - " 'Clearsky DHI Units': 'w/m2',\n", - " 'Clearsky DNI Units': 'w/m2',\n", - " 'Clearsky GHI Units': 'w/m2',\n", - " 'Dew Point Units': 'c',\n", - " 'DHI Units': 'w/m2',\n", - " 'DNI Units': 'w/m2',\n", - " 'GHI Units': 'w/m2',\n", - " 'Solar Zenith Angle Units': 'Degree',\n", - " 'Temperature Units': 'c',\n", - " 'Pressure Units': 'mbar',\n", - " 'Relative Humidity Units': '%',\n", - " 'Precipitable Water Units': 'cm',\n", - " 'Wind Direction Units': 'Degrees',\n", - " 'Wind Speed Units': 'm/s',\n", - " 'Cloud Type -15': 'N/A',\n", - " 'Cloud Type 0': 'Clear',\n", - " 'Cloud Type 1': 'Probably Clear',\n", - " 'Cloud Type 2': 'Fog',\n", - " 'Cloud Type 3': 'Water',\n", - " 'Cloud Type 4': 'Super-Cooled Water',\n", - " 'Cloud Type 5': 'Mixed',\n", - " 'Cloud Type 6': 'Opaque Ice',\n", - " 'Cloud Type 7': 'Cirrus',\n", - " 'Cloud Type 8': 'Overlapping',\n", - " 'Cloud Type 9': 'Overshooting',\n", - " 'Cloud Type 10': 'Unknown',\n", - " 'Cloud Type 11': 'Dust',\n", - " 'Cloud Type 12': 'Smoke',\n", - " 'Fill Flag 0': 'N/A',\n", - " 'Fill Flag 1': 'Missing Image',\n", - " 'Fill Flag 2': 'Low Irradiance',\n", - " 'Fill Flag 3': 'Exceeds Clearsky',\n", - " 'Fill Flag 4': 'Missing CLoud Properties',\n", - " 'Fill Flag 5': 'Rayleigh Violation',\n", - " 'Surface Albedo Units': 'N/A',\n", - " 'Version': '3.0.6',\n", - " 'latitude': 39.73,\n", - " 'longitude': -105.18,\n", - " 'altitude': 1820,\n", - " 'tz': -7}" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "META" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We'll be working with a lot of time series data, generally with a date-time index. If you're\n", - "processing an entire year of data, you don't need to sort or filter the data. However, you may want\n", - "to filter the data by a few different criteria. Below are some common examples.\n", - "- Filter by date\n", - " - month\n", - " - range of months\n", - "- Filter by sun-up hours" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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YearMonthDayHourMinutednidhighitemp_airdew_pointwind_speedrelative_humidity
1999-06-01 00:30:00-07:001999610300.00.00.06.00.01.779.15
1999-06-01 01:30:00-07:001999611300.00.00.06.00.01.775.62
1999-06-01 02:30:00-07:001999612300.00.00.06.00.01.578.51
1999-06-01 03:30:00-07:001999613300.00.00.05.00.01.376.90
1999-06-01 04:30:00-07:001999614300.00.00.06.00.01.375.75
.......................................
1999-06-30 19:30:00-07:00199963019300.00.00.021.06.00.648.54
1999-06-30 20:30:00-07:00199963020300.00.00.019.07.00.455.36
1999-06-30 21:30:00-07:00199963021300.00.00.017.07.00.566.32
1999-06-30 22:30:00-07:00199963022300.00.00.015.07.00.875.74
1999-06-30 23:30:00-07:00199963023300.00.00.013.07.00.880.16
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720 rows Γ— 12 columns

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" - ], - "text/plain": [ - " Year Month Day Hour Minute dni dhi ghi \\\n", - "1999-06-01 00:30:00-07:00 1999 6 1 0 30 0.0 0.0 0.0 \n", - "1999-06-01 01:30:00-07:00 1999 6 1 1 30 0.0 0.0 0.0 \n", - "1999-06-01 02:30:00-07:00 1999 6 1 2 30 0.0 0.0 0.0 \n", - "1999-06-01 03:30:00-07:00 1999 6 1 3 30 0.0 0.0 0.0 \n", - "1999-06-01 04:30:00-07:00 1999 6 1 4 30 0.0 0.0 0.0 \n", - "... ... ... ... ... ... ... ... ... \n", - "1999-06-30 19:30:00-07:00 1999 6 30 19 30 0.0 0.0 0.0 \n", - "1999-06-30 20:30:00-07:00 1999 6 30 20 30 0.0 0.0 0.0 \n", - "1999-06-30 21:30:00-07:00 1999 6 30 21 30 0.0 0.0 0.0 \n", - "1999-06-30 22:30:00-07:00 1999 6 30 22 30 0.0 0.0 0.0 \n", - "1999-06-30 23:30:00-07:00 1999 6 30 23 30 0.0 0.0 0.0 \n", - "\n", - " temp_air dew_point wind_speed relative_humidity \n", - "1999-06-01 00:30:00-07:00 6.0 0.0 1.7 79.15 \n", - "1999-06-01 01:30:00-07:00 6.0 0.0 1.7 75.62 \n", - "1999-06-01 02:30:00-07:00 6.0 0.0 1.5 78.51 \n", - "1999-06-01 03:30:00-07:00 5.0 0.0 1.3 76.90 \n", - "1999-06-01 04:30:00-07:00 6.0 0.0 1.3 75.75 \n", - "... ... ... ... ... \n", - "1999-06-30 19:30:00-07:00 21.0 6.0 0.6 48.54 \n", - "1999-06-30 20:30:00-07:00 19.0 7.0 0.4 55.36 \n", - "1999-06-30 21:30:00-07:00 17.0 7.0 0.5 66.32 \n", - "1999-06-30 22:30:00-07:00 15.0 7.0 0.8 75.74 \n", - "1999-06-30 23:30:00-07:00 13.0 7.0 0.8 80.16 \n", - "\n", - "[720 rows x 12 columns]" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Let's select the month of June\n", - "june_weather = WEATHER[WEATHER.index.month == 6]\n", - "june_weather" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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YearMonthDayHourMinutednidhighitemp_airdew_pointwind_speedrelative_humidity
1999-06-01 00:30:00-07:001999610300.00.00.06.00.01.779.15
1999-06-01 01:30:00-07:001999611300.00.00.06.00.01.775.62
1999-06-01 02:30:00-07:001999612300.00.00.06.00.01.578.51
1999-06-01 03:30:00-07:001999613300.00.00.05.00.01.376.90
1999-06-01 04:30:00-07:001999614300.00.00.06.00.01.375.75
.......................................
1999-09-30 19:30:00-07:00199993019300.00.00.08.0-1.02.658.57
1999-09-30 20:30:00-07:00199993020300.00.00.07.0-2.02.758.17
1999-09-30 21:30:00-07:00199993021300.00.00.07.0-3.02.954.27
1999-09-30 22:30:00-07:00199993022300.00.00.07.0-4.03.050.29
1999-09-30 23:30:00-07:00199993023300.00.00.07.0-5.03.046.91
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2928 rows Γ— 12 columns

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" - ], - "text/plain": [ - " Year Month Day Hour Minute dni dhi ghi \\\n", - "1999-06-01 00:30:00-07:00 1999 6 1 0 30 0.0 0.0 0.0 \n", - "1999-06-01 01:30:00-07:00 1999 6 1 1 30 0.0 0.0 0.0 \n", - "1999-06-01 02:30:00-07:00 1999 6 1 2 30 0.0 0.0 0.0 \n", - "1999-06-01 03:30:00-07:00 1999 6 1 3 30 0.0 0.0 0.0 \n", - "1999-06-01 04:30:00-07:00 1999 6 1 4 30 0.0 0.0 0.0 \n", - "... ... ... ... ... ... ... ... ... \n", - "1999-09-30 19:30:00-07:00 1999 9 30 19 30 0.0 0.0 0.0 \n", - "1999-09-30 20:30:00-07:00 1999 9 30 20 30 0.0 0.0 0.0 \n", - "1999-09-30 21:30:00-07:00 1999 9 30 21 30 0.0 0.0 0.0 \n", - "1999-09-30 22:30:00-07:00 1999 9 30 22 30 0.0 0.0 0.0 \n", - "1999-09-30 23:30:00-07:00 1999 9 30 23 30 0.0 0.0 0.0 \n", - "\n", - " temp_air dew_point wind_speed relative_humidity \n", - "1999-06-01 00:30:00-07:00 6.0 0.0 1.7 79.15 \n", - "1999-06-01 01:30:00-07:00 6.0 0.0 1.7 75.62 \n", - "1999-06-01 02:30:00-07:00 6.0 0.0 1.5 78.51 \n", - "1999-06-01 03:30:00-07:00 5.0 0.0 1.3 76.90 \n", - "1999-06-01 04:30:00-07:00 6.0 0.0 1.3 75.75 \n", - "... ... ... ... ... \n", - "1999-09-30 19:30:00-07:00 8.0 -1.0 2.6 58.57 \n", - "1999-09-30 20:30:00-07:00 7.0 -2.0 2.7 58.17 \n", - "1999-09-30 21:30:00-07:00 7.0 -3.0 2.9 54.27 \n", - "1999-09-30 22:30:00-07:00 7.0 -4.0 3.0 50.29 \n", - "1999-09-30 23:30:00-07:00 7.0 -5.0 3.0 46.91 \n", - "\n", - "[2928 rows x 12 columns]" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Let's filter the summer months for the northern hemisphere\n", - "summer_months = [6, 7, 8, 9]\n", - "summer_weather = WEATHER[WEATHER.index.month.isin(summer_months)]\n", - "summer_weather" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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YearMonthDayHourMinutednidhighitemp_airdew_pointwind_speedrelative_humidity
1999-01-01 08:30:00-07:0019991183065.065.076.01.0-3.04.776.86
1999-01-01 09:30:00-07:00199911930503.093.0246.02.0-2.06.380.37
1999-01-01 10:30:00-07:001999111030617.0109.0355.03.0-2.07.074.87
1999-01-01 11:30:00-07:001999111130497.0161.0385.04.0-3.06.870.73
1999-01-01 12:30:00-07:0019991112300.0128.0128.04.0-4.06.561.80
.......................................
1999-12-31 12:30:00-07:001999123112300.020.020.09.0-8.01.631.45
1999-12-31 13:30:00-07:00199912311330562.0125.0354.08.0-6.00.540.25
1999-12-31 14:30:00-07:00199912311430530.094.0263.07.0-4.00.453.19
1999-12-31 15:30:00-07:00199912311530636.038.0159.05.0-5.01.057.10
1999-12-31 16:30:00-07:001999123116300.021.021.04.0-6.01.356.22
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4301 rows Γ— 12 columns

\n", - "
" - ], - "text/plain": [ - " Year Month Day Hour Minute dni dhi \\\n", - "1999-01-01 08:30:00-07:00 1999 1 1 8 30 65.0 65.0 \n", - "1999-01-01 09:30:00-07:00 1999 1 1 9 30 503.0 93.0 \n", - "1999-01-01 10:30:00-07:00 1999 1 1 10 30 617.0 109.0 \n", - "1999-01-01 11:30:00-07:00 1999 1 1 11 30 497.0 161.0 \n", - "1999-01-01 12:30:00-07:00 1999 1 1 12 30 0.0 128.0 \n", - "... ... ... ... ... ... ... ... \n", - "1999-12-31 12:30:00-07:00 1999 12 31 12 30 0.0 20.0 \n", - "1999-12-31 13:30:00-07:00 1999 12 31 13 30 562.0 125.0 \n", - "1999-12-31 14:30:00-07:00 1999 12 31 14 30 530.0 94.0 \n", - "1999-12-31 15:30:00-07:00 1999 12 31 15 30 636.0 38.0 \n", - "1999-12-31 16:30:00-07:00 1999 12 31 16 30 0.0 21.0 \n", - "\n", - " ghi temp_air dew_point wind_speed \\\n", - "1999-01-01 08:30:00-07:00 76.0 1.0 -3.0 4.7 \n", - "1999-01-01 09:30:00-07:00 246.0 2.0 -2.0 6.3 \n", - "1999-01-01 10:30:00-07:00 355.0 3.0 -2.0 7.0 \n", - "1999-01-01 11:30:00-07:00 385.0 4.0 -3.0 6.8 \n", - "1999-01-01 12:30:00-07:00 128.0 4.0 -4.0 6.5 \n", - "... ... ... ... ... \n", - "1999-12-31 12:30:00-07:00 20.0 9.0 -8.0 1.6 \n", - "1999-12-31 13:30:00-07:00 354.0 8.0 -6.0 0.5 \n", - "1999-12-31 14:30:00-07:00 263.0 7.0 -4.0 0.4 \n", - "1999-12-31 15:30:00-07:00 159.0 5.0 -5.0 1.0 \n", - "1999-12-31 16:30:00-07:00 21.0 4.0 -6.0 1.3 \n", - "\n", - " relative_humidity \n", - "1999-01-01 08:30:00-07:00 76.86 \n", - "1999-01-01 09:30:00-07:00 80.37 \n", - "1999-01-01 10:30:00-07:00 74.87 \n", - "1999-01-01 11:30:00-07:00 70.73 \n", - "1999-01-01 12:30:00-07:00 61.80 \n", - "... ... \n", - "1999-12-31 12:30:00-07:00 31.45 \n", - "1999-12-31 13:30:00-07:00 40.25 \n", - "1999-12-31 14:30:00-07:00 53.19 \n", - "1999-12-31 15:30:00-07:00 57.10 \n", - "1999-12-31 16:30:00-07:00 56.22 \n", - "\n", - "[4301 rows x 12 columns]" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# lets just select hours from the year where the sun is above the horizon\n", - "sunup_weather = WEATHER[WEATHER[\"ghi\"] > 0]\n", - "sunup_weather" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2. Solar Position, POA, and Module Temperature\n", - "\n", - "Many operations with `PVDEG` require solar position, POA irradiance, and module temperature. Usually a `pvdeg` method will calculate these data sets automatically when it is required, however it will not keep the data for external use. For now, we will calculate them directly. This is helpful when you need to use the data for an external calculation. Here, we generate the solar position (azimuth, elevation, etc), plane of array irradiance (POA), and module temperature. `PVDEG` has wrappers for quickly using `PVLIB` to generate these figures with minimal input" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n" - ] - } - ], - "source": [ - "sol_pos = pvdeg.spectral.solar_position(weather_df=WEATHER, meta=META)\n", - "\n", - "poa_df = pvdeg.spectral.poa_irradiance(\n", - " weather_df=WEATHER, meta=META\n", - ") # , solar_position=sol_pos)\n", - "\n", - "temp_mod = pvdeg.temperature.module(weather_df=WEATHER, meta=META) # , poa=poa_df)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For more advanced usage of these functions, refer to the documentation for `pvdeg` and `pvlib`.\n", - "\n", - "Lets inspect the output from each of these functions\n", - "\n", - "`solar_position` returns a datetime-indexed dataframe of solar position for the length and frequency given by the weather file.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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apparent_zenithzenithapparent_elevationelevationazimuthequation_of_time
1999-01-01 00:30:00-07:00162.416069162.416069-72.416069-72.41606920.152470-3.300994
1999-01-01 01:30:00-07:00155.313029155.313029-65.313029-65.31302953.821029-3.320654
1999-01-01 02:30:00-07:00144.992553144.992553-54.992553-54.99255372.531199-3.340306
1999-01-01 03:30:00-07:00133.692678133.692678-43.692678-43.69267884.789909-3.359948
1999-01-01 04:30:00-07:00122.172658122.172658-32.172658-32.17265894.462003-3.379582
.....................
1999-12-31 19:30:00-07:00120.682889120.682889-30.682889-30.682889264.307279-3.093347
1999-12-31 20:30:00-07:00132.197914132.197914-42.197914-42.197914273.785399-3.113209
1999-12-31 21:30:00-07:00143.553813143.553813-53.553813-53.553813285.552685-3.133063
1999-12-31 22:30:00-07:00154.080531154.080531-64.080531-64.080531303.037057-3.152909
1999-12-31 23:30:00-07:00161.842482161.842482-71.842482-71.842482334.208238-3.172747
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8760 rows Γ— 6 columns

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" - ], - "text/plain": [ - " apparent_zenith zenith apparent_elevation \\\n", - "1999-01-01 00:30:00-07:00 162.416069 162.416069 -72.416069 \n", - "1999-01-01 01:30:00-07:00 155.313029 155.313029 -65.313029 \n", - "1999-01-01 02:30:00-07:00 144.992553 144.992553 -54.992553 \n", - "1999-01-01 03:30:00-07:00 133.692678 133.692678 -43.692678 \n", - "1999-01-01 04:30:00-07:00 122.172658 122.172658 -32.172658 \n", - "... ... ... ... \n", - "1999-12-31 19:30:00-07:00 120.682889 120.682889 -30.682889 \n", - "1999-12-31 20:30:00-07:00 132.197914 132.197914 -42.197914 \n", - "1999-12-31 21:30:00-07:00 143.553813 143.553813 -53.553813 \n", - "1999-12-31 22:30:00-07:00 154.080531 154.080531 -64.080531 \n", - "1999-12-31 23:30:00-07:00 161.842482 161.842482 -71.842482 \n", - "\n", - " elevation azimuth equation_of_time \n", - "1999-01-01 00:30:00-07:00 -72.416069 20.152470 -3.300994 \n", - "1999-01-01 01:30:00-07:00 -65.313029 53.821029 -3.320654 \n", - "1999-01-01 02:30:00-07:00 -54.992553 72.531199 -3.340306 \n", - "1999-01-01 03:30:00-07:00 -43.692678 84.789909 -3.359948 \n", - "1999-01-01 04:30:00-07:00 -32.172658 94.462003 -3.379582 \n", - "... ... ... ... \n", - "1999-12-31 19:30:00-07:00 -30.682889 264.307279 -3.093347 \n", - "1999-12-31 20:30:00-07:00 -42.197914 273.785399 -3.113209 \n", - "1999-12-31 21:30:00-07:00 -53.553813 285.552685 -3.133063 \n", - "1999-12-31 22:30:00-07:00 -64.080531 303.037057 -3.152909 \n", - "1999-12-31 23:30:00-07:00 -71.842482 334.208238 -3.172747 \n", - "\n", - "[8760 rows x 6 columns]" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sol_pos" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "`poa_irradiance` returns a datetime-indexed dataframe as well. Each column contains either the global plane of array irradiance or a particular contribition. Lets look at a time in the index we expect to have daylight." - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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poa_globalpoa_directpoa_diffusepoa_sky_diffusepoa_ground_diffuse
1999-01-01 13:30:00-07:0090.4265380.090.42653887.5687172.857821
1999-01-01 14:30:00-07:0021.9215850.021.92158521.2287800.692805
1999-01-01 15:30:00-07:0057.5441600.057.54416055.7255471.818613
1999-01-01 16:30:00-07:007.3071950.07.3071957.0762600.230935
1999-01-01 17:30:00-07:000.0000000.00.0000000.0000000.000000
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" - ], - "text/plain": [ - " poa_global poa_direct poa_diffuse \\\n", - "1999-01-01 13:30:00-07:00 90.426538 0.0 90.426538 \n", - "1999-01-01 14:30:00-07:00 21.921585 0.0 21.921585 \n", - "1999-01-01 15:30:00-07:00 57.544160 0.0 57.544160 \n", - "1999-01-01 16:30:00-07:00 7.307195 0.0 7.307195 \n", - "1999-01-01 17:30:00-07:00 0.000000 0.0 0.000000 \n", - "\n", - " poa_sky_diffuse poa_ground_diffuse \n", - "1999-01-01 13:30:00-07:00 87.568717 2.857821 \n", - "1999-01-01 14:30:00-07:00 21.228780 0.692805 \n", - "1999-01-01 15:30:00-07:00 55.725547 1.818613 \n", - "1999-01-01 16:30:00-07:00 7.076260 0.230935 \n", - "1999-01-01 17:30:00-07:00 0.000000 0.000000 " - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "poa_df.loc[\"1999-01-01 13:30:00-07:00\":\"1999-01-01 17:30:00-07:00\"]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "`temperature.module` has several optional parameters. Below is an example which explicity uses all of the default values. This function will return a datetime-indexed series matching the index of the given weather file. For more options, see the pvlib documentation for temperature models.\n", - "\n", - "https://pvlib-python.readthedocs.io/en/stable/reference/pv_modeling/temperature.html\n", - "\n", - "The optional arguments are:\n", - "- poa : manually enter the POA Irradiance\n", - "- temp_model : choose a PVLIB compatible temperature model\n", - "- conf : choose a recognized module configuration\n", - "- wind_speed_factor : change the empirical wind speed fit paramter" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1999-01-01 13:30:00-07:00 4.639740\n", - "1999-01-01 14:30:00-07:00 2.425271\n", - "1999-01-01 15:30:00-07:00 1.249262\n", - "1999-01-01 16:30:00-07:00 -0.829008\n", - "1999-01-01 17:30:00-07:00 -2.000000\n", - "dtype: float64" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "temp_mod = pvdeg.temperature.module(\n", - " weather_df=WEATHER,\n", - " meta=META,\n", - " poa=poa_df,\n", - " temp_model=\"sapm\",\n", - " conf=\"open_rack_glass_polymer\",\n", - ")\n", - "\n", - "temp_mod.loc[\"1999-01-01 13:30:00-07:00\":\"1999-01-01 17:30:00-07:00\"]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3. Module Humidities\n", - "`PVDEG` can be used to calculate the relative humidity of several layers within a PV module: the outside surface of the module, front ecapsulant, back encapsulant, and backsheet. This can be done with 2 techniques: Automatically and Manually. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 3.a. Automatic\n", - "Use the function `humidity.module`. This method does all of the calculations behind the scenes. It requires only 2 inputs (weather data and meta data). It will return a dataframe with all the layers of the module." - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n" - ] - }, - { - "data": { - "text/html": [ - "
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RH_surface_outsideRH_front_encapRH_back_encapRH_backsheet
1999-01-01 00:30:00-07:0079.3944.19878779.39000079.390000
1999-01-01 01:30:00-07:0080.8444.19878779.41522080.127610
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1999-01-01 04:30:00-07:0085.8144.19878779.68192782.745964
\n", - "
" - ], - "text/plain": [ - " RH_surface_outside RH_front_encap RH_back_encap \\\n", - "1999-01-01 00:30:00-07:00 79.39 44.198787 79.390000 \n", - "1999-01-01 01:30:00-07:00 80.84 44.198787 79.415220 \n", - "1999-01-01 02:30:00-07:00 82.98 44.198787 79.477223 \n", - "1999-01-01 03:30:00-07:00 85.01 44.198787 79.573455 \n", - "1999-01-01 04:30:00-07:00 85.81 44.198787 79.681927 \n", - "\n", - " RH_backsheet \n", - "1999-01-01 00:30:00-07:00 79.390000 \n", - "1999-01-01 01:30:00-07:00 80.127610 \n", - "1999-01-01 02:30:00-07:00 81.228611 \n", - "1999-01-01 03:30:00-07:00 82.291727 \n", - "1999-01-01 04:30:00-07:00 82.745964 " - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "rh_module = pvdeg.humidity.module(weather_df=WEATHER, meta=META)\n", - "rh_module.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 3.b. Manual\n", - "Use the individual functions to calculate the RH in each layer. These are named and require more extensive input parameters. This must be done in the correct order:\n", - "1. Surface Outside\n", - "2. Front Encapsulant\n", - "2. Back Encapsulant\n", - "3. Backsheet" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "rh_surface_outside = pvdeg.humidity.surface_relative(\n", - " rh_ambient=WEATHER[\"relative_humidity\"],\n", - " temp_ambient=WEATHER[\"temp_air\"],\n", - " temp_module=temp_mod,\n", - ")\n", - "\n", - "rh_front_encap = pvdeg.humidity.front_encapsulant(\n", - " rh_ambient=rh_surface_outside,\n", - " temp_ambient=WEATHER[\"temp_air\"],\n", - " temp_module=temp_mod,\n", - ")\n", - "\n", - "rh_back_encap = pvdeg.humidity.back_encapsulant_water_concentration(\n", - " rh_ambient=rh_surface_outside,\n", - " temp_ambient=WEATHER[\"temp_air\"],\n", - " temp_module=temp_mod,\n", - ")\n", - "\n", - "rh_backsheet = pvdeg.humidity.backsheet_from_encap(\n", - " rh_back_encap=rh_back_encap, rh_surface_outside=WEATHER[\"relative_humidity\"]\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 4. Design: Edge Seal Width\n", - "`pvdeg.design.edge_seal_width` calculates the width [cm] required for several years of water ingress. If you do not specify the number of years, it will use the default value of 25. As with most `pvdeg` functions, we can automatically generate all necessary data if we pass the two arguments (weather_df, meta)." - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.7170984345065904" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "edge_seal_width = pvdeg.design.edge_seal_width(weather_df=WEATHER, meta=META)\n", - "edge_seal_width" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Lets look at the calculation step by step. To get the estimated edge seal width, we need the following:\n", - "1. Humidity Saturation Point\n", - "2. Edge seal ingress rate ( k ) or the estimated rate of water ingress through edge seal in [cm/hour^0.5]" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.7170984345065904\n" - ] - } - ], - "source": [ - "psat, psat_avg = pvdeg.humidity.psat(WEATHER[\"temp_air\"])\n", - "\n", - "k = pvdeg.design.edge_seal_ingress_rate(avg_psat=psat_avg)\n", - "\n", - "edge_seal_width = pvdeg.design.edge_seal_width(\n", - " weather_df=WEATHER, meta=META, k=k, years=25\n", - ")\n", - "\n", - "print(edge_seal_width)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Alternate Edge Seal Width\n", - "If you have the dew point, or wet-bulb temperature, you can calculate the edge seal width directly." - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.4499535870958669" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "edge_seal_width = pvdeg.design.edge_seal_width(\n", - " weather_df=WEATHER, meta=META, from_dew_point=True\n", - ")\n", - "edge_seal_width" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.7" - }, - "vscode": { - "interpreter": { - "hash": "14c04630f1cd445b2532d35c77825134bfcafda47af70d0b9c2b5023b1f357a5" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/2 - Degradation.ipynb b/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/2 - Degradation.ipynb deleted file mode 100644 index 13c50b238..000000000 --- a/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/2 - Degradation.ipynb +++ /dev/null @@ -1,400 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 2 - Degradation\n", - "Degradation and Solder Fatigue\n", - "\n", - "**Requirements**:\n", - "- compatible weather file (PSM3, TMY3, EPW)\n", - "- Accelerated testing chamber parameters\n", - " - chamber irradiance [W/m^2]\n", - " - chamber temperature [C]\n", - " - chamber humidity [%]\n", - "- Activation energies for test material [kJ/mol]\n", - "\n", - "**Objectives**:\n", - "1. Read in the weather data\n", - "2. Generate basic modeling data\n", - "3. Calculate VantHoff degradation acceleration factor\n", - "4. Calculate Arrhenius degradation acceleration factor\n", - "5. Quick Method\n", - "5. Solder Fatigue" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "# if running on google colab, uncomment the next line and execute this cell to install the dependencies and prevent \"ModuleNotFoundError\" in later cells:\n", - "# !pip install pvdeg==0.3.3" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import pandas as pd\n", - "\n", - "import pvdeg\n", - "from pvdeg import DATA_DIR" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Working on a Windows 10\n", - "Python version 3.11.7 | packaged by Anaconda, Inc. | (main, Dec 15 2023, 18:05:47) [MSC v.1916 64 bit (AMD64)]\n", - "Pandas version 2.2.0\n", - "pvdeg version 0.2.4.dev83+ge2ceab9.d20240422\n" - ] - } - ], - "source": [ - "# This information helps with debugging and getting support :)\n", - "import sys, platform\n", - "\n", - "print(\"Working on a \", platform.system(), platform.release())\n", - "print(\"Python version \", sys.version)\n", - "print(\"Pandas version \", pd.__version__)\n", - "print(\"pvdeg version \", pvdeg.__version__)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1. Read In the Weather File\n", - "\n", - "This is usually the first step. Use a PSM3, TMY3, or EPW file. For this demo, use the provided PSM3 weather file." - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "PSM_FILE = os.path.join(DATA_DIR, \"psm3_demo.csv\")\n", - "WEATHER, META = pvdeg.weather.read(PSM_FILE, \"psm\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2. Generate Basic Modeling Data\n", - "\n", - "For this tutorial we will need solar position, POA, PV cell and module temperature. Let's gernate those individually with their respective functions." - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n" - ] - } - ], - "source": [ - "sol_pos = pvdeg.spectral.solar_position(weather_df=WEATHER, meta=META)\n", - "\n", - "poa_df = pvdeg.spectral.poa_irradiance(\n", - " weather_df=WEATHER, meta=META, sol_position=sol_pos\n", - ")\n", - "\n", - "temp_cell = pvdeg.temperature.cell(weather_df=WEATHER, meta=META, poa=poa_df)\n", - "\n", - "temp_module = pvdeg.temperature.module(weather_df=WEATHER, meta=META, poa=poa_df)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3. VantHoff Degradation\n", - "\n", - "Van 't Hoff Irradiance Degradation\n", - "\n", - "For one year of degredation the controlled environmnet lamp settings will need to be set to IWa.\n", - "\n", - "As with most `pvdeg` functions, the following functions will always require two arguments (weather_df and meta)" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "# chamber irradiance (W/m^2)\n", - "I_chamber = 1000\n", - "# chamber temperature (C)\n", - "temp_chamber = 60\n", - "\n", - "# calculate the VantHoff Acceleration factor\n", - "vantHoff_deg = pvdeg.degradation.vantHoff_deg(\n", - " weather_df=WEATHER,\n", - " meta=META,\n", - " I_chamber=I_chamber,\n", - " temp_chamber=temp_chamber,\n", - " poa=poa_df,\n", - " temp=temp_cell,\n", - ")\n", - "\n", - "# calculate the VantHoff weighted irradiance\n", - "irr_weighted_avg_v = pvdeg.degradation.IwaVantHoff(\n", - " weather_df=WEATHER, meta=META, poa=poa_df, temp=temp_cell\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 4. Arrhenius\n", - "Calculate the Acceleration Factor between the rate of degredation of a modeled environmnet versus a modeled controlled environmnet\n", - "\n", - "Example: \"If the AF=25 then 1 year of Controlled Environment exposure is equal to 25 years in the field\"\n", - "\n", - "Equation:\n", - "$$ AF = N * \\frac{ I_{chamber}^x * RH_{chamber}^n * e^{\\frac{- E_a}{k T_{chamber}}} }{ \\Sigma (I_{POA}^x * RH_{outdoor}^n * e^{\\frac{-E_a}{k T_outdoor}}) }$$\n", - "\n", - "Function to calculate IWa, the Environment Characterization (W/mΒ²). For one year of degredation the controlled environmnet lamp settings will need to be set at IWa.\n", - "\n", - "Equation:\n", - "$$ I_{WA} = [ \\frac{ \\Sigma (I_{outdoor}^x * RH_{outdoor}^n e^{\\frac{-E_a}{k T_{outdood}}}) }{ N * RH_{WA}^n * e^{- \\frac{E_a}{k T_eq}} } ]^{\\frac{1}{x}} $$" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# relative humidity within chamber (%)\n", - "rh_chamber = 15\n", - "# arrhenius activation energy (kj/mol)\n", - "Ea = 40\n", - "\n", - "rh_surface = pvdeg.humidity.surface_relative(\n", - " rh_ambient=WEATHER[\"relative_humidity\"],\n", - " temp_ambient=WEATHER[\"temp_air\"],\n", - " temp_module=temp_module,\n", - ")\n", - "\n", - "arrhenius_deg = pvdeg.degradation.arrhenius_deg(\n", - " weather_df=WEATHER,\n", - " meta=META,\n", - " rh_outdoor=rh_surface,\n", - " I_chamber=I_chamber,\n", - " rh_chamber=rh_chamber,\n", - " temp_chamber=temp_chamber,\n", - " poa=poa_df,\n", - " temp=temp_cell,\n", - " Ea=Ea,\n", - ")\n", - "\n", - "irr_weighted_avg_a = pvdeg.degradation.IwaArrhenius(\n", - " weather_df=WEATHER,\n", - " meta=META,\n", - " poa=poa_df,\n", - " rh_outdoor=WEATHER[\"relative_humidity\"],\n", - " temp=temp_cell,\n", - " Ea=Ea,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 5. Quick Method (Degradation)\n", - "\n", - "For quick calculations, you can omit POA and both module and cell temperature. The function will calculate these figures as needed using the available weather data with the default options for PV module configuration." - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n" - ] - } - ], - "source": [ - "# chamber settings\n", - "I_chamber = 1000\n", - "temp_chamber = 60\n", - "rh_chamber = 15\n", - "\n", - "# activation energy\n", - "Ea = 40\n", - "\n", - "vantHoff_deg = pvdeg.degradation.vantHoff_deg(\n", - " weather_df=WEATHER, meta=META, I_chamber=I_chamber, temp_chamber=temp_chamber\n", - ")\n", - "\n", - "irr_weighted_avg_v = pvdeg.degradation.IwaVantHoff(weather_df=WEATHER, meta=META)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n" - ] - } - ], - "source": [ - "rh_surface = pvdeg.humidity.surface_relative(\n", - " rh_ambient=WEATHER[\"relative_humidity\"],\n", - " temp_ambient=WEATHER[\"temp_air\"],\n", - " temp_module=temp_module,\n", - ")\n", - "\n", - "arrhenius_deg = pvdeg.degradation.arrhenius_deg(\n", - " weather_df=WEATHER,\n", - " meta=META,\n", - " rh_outdoor=rh_surface,\n", - " I_chamber=I_chamber,\n", - " rh_chamber=rh_chamber,\n", - " temp_chamber=temp_chamber,\n", - " Ea=Ea,\n", - ")\n", - "\n", - "irr_weighted_avg_a = pvdeg.degradation.IwaArrhenius(\n", - " weather_df=WEATHER, meta=META, rh_outdoor=WEATHER[\"relative_humidity\"], Ea=Ea\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 6. Solder Fatigue\n", - "\n", - "Estimate the thermomechanical fatigue of flat plate photovoltaic module solder joints over the time range given using estimated cell temperature. Like other `pvdeg` funcitons, the minimal parameters are (weather_df, meta). Running the function with only these two inputs will use default PV module configurations ( open_rack_glass_polymer ) and the 'sapm' temperature model over the entire length of the weather data. " - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n" - ] - } - ], - "source": [ - "fatigue = pvdeg.fatigue.solder_fatigue(weather_df=WEATHER, meta=META)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If you wish to reduce the span of time or use a non-default temperature model, you may specify the parameters manually. Let's try an explicit example.\n", - "We want the solder fatigue estimated over the month of June for a roof mounted glass-front polymer-back module.\n", - "\n", - "1. Lets create a datetime-index for the month of June.\n", - "2. Next, generate the cell temperature. Make sure to explicity restrict the weather data to our dt-index for June. Next, declare the PV module configuration.\n", - "3. Calculate the fatigue. Explicity specify the time_range (our dt-index for June from step 1) and the cell temperature as we caculated in step 2" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n" - ] - } - ], - "source": [ - "# select the month of June\n", - "time_range = WEATHER.index[WEATHER.index.month == 6]\n", - "\n", - "# calculate cell temperature over our selected date-time range.\n", - "# specify the module configuration\n", - "temp_cell = pvdeg.temperature.cell(\n", - " weather_df=WEATHER.loc[time_range],\n", - " meta=META,\n", - " temp_model=\"sapm\",\n", - " conf=\"insulated_back_glass_polymer\",\n", - ")\n", - "\n", - "\n", - "fatigue = pvdeg.fatigue.solder_fatigue(\n", - " weather_df=WEATHER, meta=META, time_range=time_range, temp_cell=temp_cell\n", - ")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.7" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/3 - Spectral Degradation.ipynb b/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/3 - Spectral Degradation.ipynb deleted file mode 100644 index 9c59be5b1..000000000 --- a/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/3 - Spectral Degradation.ipynb +++ /dev/null @@ -1,263 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 3 - Spectral Degradation\n", - "\n", - "**Requirements:**\n", - "- spectral irradiance (measured or simulated)\n", - "- wavelengths of spectral irradiance data\n", - "- module RH\n", - "- module temperature\n", - "\n", - "\n", - "**Objectives:**\n", - "1. Read in spectral irradiance\n", - "2. Calculate spectral degradation" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# if running on google colab, uncomment the next line and execute this cell to install the dependencies and prevent \"ModuleNotFoundError\" in later cells:\n", - "# !pip install pvdeg==0.3.3" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\mspringe\\AppData\\Local\\Temp\\1\\ipykernel_40480\\1750438778.py:2: DeprecationWarning: \n", - "Pyarrow will become a required dependency of pandas in the next major release of pandas (pandas 3.0),\n", - "(to allow more performant data types, such as the Arrow string type, and better interoperability with other libraries)\n", - "but was not found to be installed on your system.\n", - "If this would cause problems for you,\n", - "please provide us feedback at https://github.com/pandas-dev/pandas/issues/54466\n", - " \n", - " import pandas as pd\n" - ] - } - ], - "source": [ - "import os\n", - "import pandas as pd\n", - "import numpy as np\n", - "import pvdeg\n", - "from pvdeg import DATA_DIR" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Working on a Windows 10\n", - "Python version 3.11.7 | packaged by Anaconda, Inc. | (main, Dec 15 2023, 18:05:47) [MSC v.1916 64 bit (AMD64)]\n", - "Pandas version 2.2.0\n", - "pvdeg version 0.2.4.dev83+ge2ceab9.d20240422\n" - ] - } - ], - "source": [ - "# This information helps with debugging and getting support :)\n", - "import sys, platform\n", - "\n", - "print(\"Working on a \", platform.system(), platform.release())\n", - "print(\"Python version \", sys.version)\n", - "print(\"Pandas version \", pd.__version__)\n", - "print(\"pvdeg version \", pvdeg.__version__)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1. Read in spectral irradiance data\n", - "\n", - "Spectral degradation has 4 main requirements:\n", - "- Spectral Irradiance [W/m^2 nm]\n", - "- Wavelength [nm] \n", - "- Module Relative Humidity [%]\n", - "- Module Temperature [C]\n", - "\n", - "For more advanced scenarios, you may want to calculate the degradation of a particular layer within the module. Below, we are using *backside* irradiance and therefore a slightly different temperature and humidity have been calculated. To calculate degradation on the backside, we used `pvdeg.humidity.rh_backsheet`. For the the front side, you should use `pvdeg.humidity.rh_surface_outside` or `rh_front_encap`\n", - "\n", - "\n", - "For this tutorial we are using pre-generated data from a ray-tracing simulation. To calculate the degradation rate, we will need the wavelengths used in the simulation. " - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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2021-03-09 14:00:00[0.40321187996337626, 0.6473167864022122, 0.69...2539
\n", - "
" - ], - "text/plain": [ - " Spectra \\\n", - "timestamp \n", - "2021-03-09 10:00:00 [0.6892146677599185, 0.40215646494410884, 0.67... \n", - "2021-03-09 11:00:00 [0.15575709102178648, 0.5464374649246564, 0.68... \n", - "2021-03-09 12:00:00 [0.22782105874481207, 0.9056495270031296, 0.26... \n", - "2021-03-09 13:00:00 [0.3741943134512433, 0.035830980984344674, 0.4... \n", - "2021-03-09 14:00:00 [0.40321187996337626, 0.6473167864022122, 0.69... \n", - "\n", - " Temperature RH \n", - "timestamp \n", - "2021-03-09 10:00:00 45 59 \n", - "2021-03-09 11:00:00 44 56 \n", - "2021-03-09 12:00:00 59 39 \n", - "2021-03-09 13:00:00 44 13 \n", - "2021-03-09 14:00:00 25 39 " - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "wavelengths = np.array(range(280, 420, 20))\n", - "\n", - "SPECTRA = pd.read_csv(os.path.join(DATA_DIR, \"spectra.csv\"), header=0, index_col=0)\n", - "SPECTRA.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2. Calculate Degradation\n", - "\n", - "The spectral degradation function has several optional paramters. For more information, refer to the documentation. Below is a function call with the minimum required information." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Removing brackets from spectral irradiance data\n" - ] - } - ], - "source": [ - "degradation = pvdeg.degradation.degradation(\n", - " spectra=SPECTRA[\"Spectra\"],\n", - " rh_module=SPECTRA[\"RH\"],\n", - " temp_module=SPECTRA[\"Temperature\"],\n", - " wavelengths=wavelengths,\n", - ")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.7" - }, - "vscode": { - "interpreter": { - "hash": "14c04630f1cd445b2532d35c77825134bfcafda47af70d0b9c2b5023b1f357a5" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/4 - Weather Database Access.ipynb b/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/4 - Weather Database Access.ipynb deleted file mode 100644 index ca14fcae3..000000000 --- a/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/4 - Weather Database Access.ipynb +++ /dev/null @@ -1,306 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 4 - Weather Database Access\n", - "\n", - "**Requirements:**\n", - "- Internet access\n", - "- NSRDB API key. API keys are free. You can request and obtain an API key in about 5 minutes. To get your own key, visit https://developer.nrel.gov/signup/\n", - "- Step **1.** is for Eagle HPC users. You will need an account with NREL's Eagle computer for this method.\n", - "\n", - "**Objectives:**\n", - "\n", - "Using direct access to large scale weather databases, we're going to estimate the minimum standoff distance for a roof mounted PV system. We'll do this in 3 ways using both the NSRDB and PVGIS database.\n", - "1. Single Location, NSRDB via NREL's high performance computer, Eagle.\n", - "2. Single Location via NSRDB public API key.\n", - "3. Single Location via the PVGIS public database\n", - "\n", - "**Background:**\n", - "\n", - "This journal will demonstrate all existing built-in methods for directly accessing public weather databases. Some methods are restriced to certain user groups. For general users, see methods **2** and **3**. For users with an active Eagle HPC account, you may use method **1** as well as **2** and **3**.\n", - "\n", - "For all users and all steps: This journal will run significantly longer than other tutorials and have significant internet traffic as you fetch large datasets." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This example demonstrates the calculation of a minimum standoff distance necessary for roof-mounted PV modules to ensure that the $T_{98}$ operational temperature remains under 70Β°C, in which case the more rigorous thermal stability testing requirements of IEC TS 63126 would not needed to be considered. We use data from [Fuentes, 1987] to model the approximate exponential decay in temperature, $T(X)$, with increasing standoff distance, $X$, as,\n", - "\n", - "$$ X = -X_0 \\ln\\left(1-\\frac{T_0-T}{\\Delta T}\\right)$$\n", - "\n", - "where $T_0$ is the temperature for $X=0$ (insulated back) and $\\Delta T$ is the temperature difference between an insulated back ($X=0$) and open rack mounting configuration ($X=\\infty)$.\n", - "\n", - "The following figure showcases this calulation for the entire United States. We used pvlib and data from the National Solar Radiation Database (NSRDB) to calculate the module temperatures for different mounting configuration and applied our model to obtain the standoff distance for roof-mounted PV systems." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Single location example" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "# if running on google colab, uncomment the next line and execute this cell to install the dependencies and prevent \"ModuleNotFoundError\" in later cells:\n", - "# !pip install pvdeg==0.3.3" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "import pvdeg" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Working on a Windows 10\n", - "Python version 3.11.7 | packaged by Anaconda, Inc. | (main, Dec 15 2023, 18:05:47) [MSC v.1916 64 bit (AMD64)]\n", - "pvdeg version 0.2.4.dev83+ge2ceab9.d20240422\n" - ] - } - ], - "source": [ - "# This information helps with debugging and getting support :)\n", - "import sys, platform\n", - "\n", - "print(\"Working on a \", platform.system(), platform.release())\n", - "print(\"Python version \", sys.version)\n", - "print(\"pvdeg version \", pvdeg.__version__)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1. NSRDB - HSDS on Kestrel\n", - "\n", - "This method requires a direct connection to NREL's high performance computer \"Eagle\". If you are not running this journal from Eagle, skip this section and proceed to section **2.**\n", - "\n", - "In this step:\n", - "\n", - "First we select a database. Here, we will use the NSRDB. Since we are modeling a single location, we can pass the `weather_id` as tuple (lat, long). A location gid can be used as well. 'gid' is a unique identifier to a geographic location within the NSRDB. We'll look at how to find gids later on.\n", - "\n", - "Next, we want to select a satellite, named dataset (year of data), and what weather attributes we want to fetch. For further options, see the documentation for `pvdeg.weather.get`" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "# Get weather data\n", - "weather_db = \"NSRDB\"\n", - "\n", - "# Latitude and Longitude\n", - "weather_id = (33.448376, -112.074036)\n", - "# weather_id = 1933572\n", - "weather_arg = {\n", - " \"satellite\": \"GOES\",\n", - " \"names\": 2021,\n", - " \"NREL_HPC\": True,\n", - " \"attributes\": [\n", - " \"air_temperature\",\n", - " \"wind_speed\",\n", - " \"dhi\",\n", - " \"ghi\",\n", - " \"dni\",\n", - " \"relative_humidity\",\n", - " ],\n", - "}\n", - "\n", - "# Uncomment the following when working on NREL Kestrel\n", - "\n", - "# weather_df, meta = pvdeg.weather.get(weather_db, weather_id, **weather_arg)\n", - "\n", - "# res = pvdeg.standards.standoff(weather_df=weather_df, meta=meta, tilt=None, azimuth=180, sky_model='isotropic', temp_model='sapm',\n", - "# conf_0='insulated_back_glass_polymer', conf_inf='open_rack_glass_polymer', T98=70, x_0=6.5, wind_factor=0.33)\n", - "# print(pvdeg.standards.interpret_standoff(res))\n", - "# print(meta)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "`pvdeg.weather.get` returns the same variables as `weather.read` which we have used in each journal before this. We get a weather DataFrame and a meta-data dicitonary. Each contains a minimum of consistent fields, but may have additional fields based on the database accessed or the attributes requested.\n", - "\n", - "Lets verify the weather data we fetched by running a familiar calculation; standoff distance." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2. NSRDB - API\n", - "\n", - "To access the NREL NSRDB, you will need an API key. Key's are free, but require you to set up an account. Without an API key, you can use a demonstration API which is severely limited. To set up an account and get your API key, visit https://developer.nrel.gov/signup/\n", - "\n", - "Key Notes:\n", - "- set `attributes = []` to return all possible attributes (weather fields)\n", - "- There are 2 major methods with the API\n", - " - names = 'tmy' : generate a TMY-like weather dataframe aggregate. This will calculate the relative humidity from temperature and dew point.\n", - " - names = '2019' : collect a weather dataframe including measured relative humidity." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Column \"relative_humidity\" not found in DataFrame. Calculating...\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", - "The estimated Tβ‚‰β‚ˆ of an insulated-back module is 73.5Β°C. \n", - "The estimated Tβ‚‰β‚ˆ of an open-rack module is 50.0Β°C. \n", - "Level 0 certification is valid for a standoff greather than 1.0 cm. \n", - "Level 1 certification is required for a standoff less than 1.0 cm. \n", - "Level 2 certification is never required for this temperature profile.\n", - "{'Source': 'NSRDB', 'Location ID': '145809', 'City': '-', 'State': '-', 'Country': '-', 'Dew Point Units': 'c', 'DHI Units': 'w/m2', 'DNI Units': 'w/m2', 'GHI Units': 'w/m2', 'Temperature Units': 'c', 'Pressure Units': 'mbar', 'Wind Direction Units': 'Degrees', 'Wind Speed Units': 'm/s', 'Surface Albedo Units': 'N/A', 'Version': '3.2.0', 'latitude': 39.73, 'longitude': -105.18, 'altitude': 1820, 'tz': -7, 'wind_height': 2}\n" - ] - } - ], - "source": [ - "API_KEY = \"your_api_key_here\"\n", - "# The example API key here is for demonstation and is rate-limited per IP.\n", - "# To get your own API key, visit https://developer.nrel.gov/signup/\n", - "\n", - "weather_db = \"PSM3\"\n", - "weather_id = (39.741931, -105.169891)\n", - "weather_arg = {\n", - " \"api_key\": \"DEMO_KEY\",\n", - " \"email\": \"user@mail.com\",\n", - " \"names\": \"tmy\",\n", - " \"attributes\": [],\n", - " \"map_variables\": True,\n", - "}\n", - "\n", - "weather_df, meta = pvdeg.weather.get(weather_db, weather_id, **weather_arg)\n", - "# Perform calculation and output interpretation or results\n", - "res = pvdeg.standards.standoff(\n", - " weather_df=weather_df,\n", - " meta=meta,\n", - " tilt=None,\n", - " azimuth=180,\n", - " sky_model=\"isotropic\",\n", - " temp_model=\"sapm\",\n", - " conf_0=\"insulated_back_glass_polymer\",\n", - " conf_inf=\"open_rack_glass_polymer\",\n", - " T98=70,\n", - " x_0=6.5,\n", - " wind_factor=0.33,\n", - ")\n", - "print(pvdeg.standards.interpret_standoff(res))\n", - "print(meta)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3. PVGIS\n", - "\n", - "This method uses the PVGIS database, a public resource. It requires no API key or user account." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The array tilt angle was not provided, therefore the latitude tilt of 24.7 was used.\n", - "The estimated Tβ‚‰β‚ˆ of an insulated-back module is 89.6Β°C. \n", - "The estimated Tβ‚‰β‚ˆ of an open-rack module is 63.8Β°C. \n", - "Level 0 certification is valid for a standoff greather than 9.3 cm. \n", - "Level 1 certification is required for a standoff between than 9.3 cm, and 3.0 cm. \n", - "Level 2 certification is required for a standoff less than 3.0 cm.\n", - "{'latitude': 24.7136, 'longitude': 46.6753, 'altitude': 646.0, 'wind_height': 10, 'Source': 'PVGIS'}\n" - ] - } - ], - "source": [ - "weather_db = \"PVGIS\"\n", - "# weather_id = (39.741931, -105.169891)\n", - "weather_id = (24.7136, 46.6753) # Riyadh, Saudi Arabia\n", - "# weather_arg = {'map_variables': True}\n", - "\n", - "# TMY\n", - "weather_df, meta = pvdeg.weather.get(weather_db, weather_id)\n", - "\n", - "# Perform calculation\n", - "res = pvdeg.standards.standoff(\n", - " weather_df=weather_df,\n", - " meta=meta,\n", - " tilt=None,\n", - " azimuth=180,\n", - " sky_model=\"isotropic\",\n", - " temp_model=\"sapm\",\n", - " conf_0=\"insulated_back_glass_polymer\",\n", - " conf_inf=\"open_rack_glass_polymer\",\n", - " T98=70,\n", - " x_0=6.5,\n", - " wind_factor=0.33,\n", - ")\n", - "print(pvdeg.standards.interpret_standoff(res))\n", - "print(meta)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.7" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/ASTM Live Demo.ipynb b/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/ASTM Live Demo.ipynb deleted file mode 100644 index 18bac1d6a..000000000 --- a/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/ASTM Live Demo.ipynb +++ /dev/null @@ -1,1112 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# ASTM Demonstration\n", - "\n", - "![PVDeg Logo](../PVD_logo.png)\n", - "\n", - "\n", - "**Steps:**\n", - "1. Import weather data\n", - "2. Calculate installation standoff\n", - "3. Calculate installation standoff - with more detail\n", - "\n", - "**Background:**\n", - "\n", - "This example demonstrates the calculation of a minimum standoff distance necessary for roof-mounted PV modules to ensure that the $T_{98}$ operational temperature remains under 70Β°C, in which case the more rigorous thermal stability testing requirements of IEC TS 63126 would not needed to be considered. We use data from [Fuentes, 1987] to model the approximate exponential decay in temperature, $T(X)$, with increasing standoff distance, $X$, as,\n", - "\n", - "$$ X = -X_0 \\ln\\left(1-\\frac{T_0-T}{\\Delta T}\\right)$$\n", - "\n", - "where $T_0$ is the temperature for $X=0$ (insulated back) and $\\Delta T$ is the temperature difference between an insulated back ($X=0$) and open rack mounting configuration ($X=\\infty)$.\n", - "\n", - "The following figure showcases this calulation for the entire United States. We used pvlib and data from the National Solar Radiation Database (NSRDB) to calculate the module temperatures for different mounting configuration and applied our model to obtain the standoff distance for roof-mounted PV systems.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# if running on google colab, uncomment the next line and execute this cell to install the dependencies and prevent \"ModuleNotFoundError\" in later cells:\n", - "#!pip install pvdeg==0.1.0" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import pvlib\n", - "import pvdeg\n", - "import pandas as pd\n", - "from pvdeg import DATA_DIR\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Working on a Windows 10\n", - "Python version 3.11.7 | packaged by Anaconda, Inc. | (main, Dec 15 2023, 18:05:47) [MSC v.1916 64 bit (AMD64)]\n", - "Pandas version 2.2.0\n", - "pvlib version 0.10.3\n", - "pvdeg version 0.2.4.dev83+ge2ceab9.d20240422\n" - ] - } - ], - "source": [ - "# This information helps with debugging and getting support :)\n", - "import sys, platform\n", - "\n", - "print(\"Working on a \", platform.system(), platform.release())\n", - "print(\"Python version \", sys.version)\n", - "print(\"Pandas version \", pd.__version__)\n", - "print(\"pvlib version \", pvlib.__version__)\n", - "print(\"pvdeg version \", pvdeg.__version__)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1. Import Weather Data\n", - "\n", - "The function has two minimum requirements:\n", - "- Weather data containing (at least) DNI, DHI, GHI, Temperature, RH, Wind-Speed\n", - "- Site meta-data containing (at least) Latitude, Longitude, Time Zone\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Where to get _Free_ Solar Irradiance Data?\n", - "\n", - "There are many different sources of solar irradiance data. For your projects, these are some of the most common:\n", - "\n", - "- [NSRDB](https://maps.nrel.gov/nsrdb-viewer/) - National Solar Radiation Database. You can access data through the website for many locations accross the world, or you can use their [web API](https://developer.nrel.gov/docs/solar/nsrdb/) to download data programmatically. An \"API\" is an [\"application programming interface\"](https://en.wikipedia.org/wiki/API), and a \"web API\" is a programming interface that allows you to write code to interact with web services like the NSRDB.\n", - "\n", - "- [EPW](https://www.energy.gov/eere/buildings/downloads/energyplus-0) - Energy Plus Weather data is available for many locations accross the world. It's in its own format file ('EPW') so you can't open it easily in a spreadsheet program like Excel, but you can use [`pvlib.iotools.read_epw()`](https://pvlib-python.readthedocs.io/en/stable/reference/generated/pvlib.iotools.read_epw.html) to get it into a dataframe and use it.\n", - "\n", - "- [PVGIS](https://re.jrc.ec.europa.eu/pvg_tools/en/) - Free global weather data provided by the European Union and derived from many govermental agencies including the NSRDB. PVGIS also provides a web API. You can get PVGIS TMY data using [`pvlib.iotools.get_pvgis_tmy()`](https://pvlib-python.readthedocs.io/en/stable/reference/generated/pvlib.iotools.get_pvgis_tmy.html).\n", - "\n", - "- Perhaps another useful link: https://sam.nrel.gov/weather-data.html\n", - "\n", - "### Where else can you get historical irradiance data?\n", - "\n", - "There are several commercial providers of solar irradiance data. Data is available at different spatial and time resolutions. Each provider offers data under subscription that will provide access to irradiance (and other weather variables) via API to leverage in python.\n", - "\n", - "* [SolarAnywhere](https://www.solaranywhere.com/)\n", - "* [SolarGIS](https://solargis.com/)\n", - "* [Vaisala](https://www.vaisala.com/en)\n", - "* [Meteonorm](https://meteonorm.com/en/)\n", - "* [DNV Solar Resource Compass](https://src.dnv.com/)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "![NSRDB Example](images/tutorial_1_NSRDB_example.PNG)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### NREL API Key\n", - "At the [NREL Developer Network](https://developer.nrel.gov/), there are [APIs](https://en.wikipedia.org/wiki/API) to a lot of valuable [solar resources](https://developer.nrel.gov/docs/solar/) like [weather data from the NSRDB](https://developer.nrel.gov/docs/solar/nsrdb/), [operational data from PVDAQ](https://developer.nrel.gov/docs/solar/pvdaq-v3/), or indicative calculations using [PVWatts](https://developer.nrel.gov/docs/solar/pvwatts/). In order to use these resources from NREL, you need to [register for a free API key](https://developer.nrel.gov/signup/). You can test out the APIs using the `DEMO_KEY` but it has limited bandwidth compared to the [usage limit for registered users](https://developer.nrel.gov/docs/rate-limits/). NREL has some [API usage instructions](https://developer.nrel.gov/docs/api-key/), but pvlib has a few builtin functions, like [`pvlib.iotools.get_psm3()`](https://pvlib-python.readthedocs.io/en/stable/reference/generated/pvlib.iotools.get_psm3.html), that wrap the NREL API, and call them for you to make it much easier to use. Skip ahead to the next section to learn more. But before you do...\n", - "\n", - "**Please pause now to visit https://developer.nrel.gov/signup/ and get an API key.**\n", - "\n", - "### Application Programming Interface (API)\n", - "What exactly is an API? Nowadays, the phrase is used interchangeably with a \"web API\" but in general an API is just a recipe for how to interface with a application programmatically, _IE_: in code. An API could be as simple as a function signature or its published documentation, _EG_: the API for the `solarposition` function is you give it an ISO8601 formatted date with a timezone, the latitude, longitude, and elevation as numbers, and it returns the zenith and azimuth as numbers.\n", - "\n", - "A web API is the same, except the application is a web service, that you access at its URL using web methods. We won't go into too much more detail here, but the most common web method is `GET` which is pretty self explanatory. Look over the [NREL web usage instructions](https://developer.nrel.gov/docs/api-key/) for some examples, but interacting with a web API can be as easy as entering a URL into a browser. Try the URL below to _get_ the PVWatts energy output for a fixed tilt site in [Broomfield, CO](https://goo.gl/maps/awkEcNGzSur9Has18).\n", - "\n", - "https://developer.nrel.gov/api/pvwatts/v6.json?api_key=DEMO_KEY&lat=40&lon=-105&system_capacity=4&azimuth=180&tilt=40&array_type=1&module_type=1&losses=10\n", - "\n", - "In addition to just using your browser, you can also access web APIs programmatically. The most popular Python package to interact with web APIs is [requests](https://docs.python-requests.org/en/master/). There's also free open source command-line tools like [cURL](https://curl.se/) and [HTTPie](https://httpie.io/), and a popular nagware/freemium GUI application called [Postman](https://www.postman.com/).\n", - "\n", - "**If you have an NREL API key please enter it in the next cell.**" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "NREL_API_KEY = None # <-- please set your NREL API key here\n", - "\n", - "# note you must use \"quotes\" around your key, for example:\n", - "# NREL_API_KEY = 'DEMO_KEY' # single or double both work fine\n", - "\n", - "# during the live tutorial, we've stored a dedicated key on our server\n", - "if NREL_API_KEY is None:\n", - " try:\n", - " NREL_API_KEY = os.environ[\n", - " \"NREL_API_KEY\"\n", - " ] # get dedicated key for tutorial from servier\n", - " except KeyError:\n", - " NREL_API_KEY = \"DEMO_KEY\" # OK for this demo, but better to get your own key" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Fetching TMYs from the NSRDB\n", - "\n", - "The NSRDB, one of many sources of weather data intended for PV modeling, is free and easy to access using pvlib. As an example, we'll fetch a TMY dataset for Phoenix, AZ at coordinates [(33.4484, -112.0740)](https://goo.gl/maps/hGV92QHCm5FHJKbf9). \n", - "\n", - "This function uses [`pvdeg.weather.get()`](https://pvdegradationtools.readthedocs.io/en/latest/_autosummary/pvdeg.weather.html#pvdeg.weather.get), which returns a Python dictionary of metadata and a Pandas dataframe of the timeseries weather data.\n", - "\n", - "This function internally leverages [`pvlib.iotools.get_psm3()`](https://pvlib-python.readthedocs.io/en/stable/reference/generated/pvlib.iotools.get_psm3.html). However, for some of the NSRDB data relative humidity is not a given parameter, and `pvdeg` calculates the values from the downloaded data as an internal processing step. " - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# This cell does not run on a Collab instal. WE are troubleshooting it, but the next cell performs the same request directly with PVLib.\n", - "# If TMY is requested though, relative_humidity will not be included as NSRDB TMY dataset does not have it.\n", - "\"\"\"\n", - "weather_db = 'PSM3'\n", - "weather_id = (33.4484, -112.0740)\n", - "weather_arg = {'api_key': NREL_API_KEY,\n", - " 'email': 'user@mail.com',\n", - " 'names': '2021', # tmy is another common option; right now requesting only 2021 data\n", - " 'attributes': [], # grabs all. to select, could be 'air_temperature', 'dew_point', 'dhi',\n", - " # 'dni', 'ghi', 'surface_albedo', 'surface_pressure', 'wind_direction', 'wind_speed'\n", - " 'map_variables': True,\n", - " 'leap_day': False}\n", - "\n", - "weather_df, meta = pvdeg.weather.get(weather_db, weather_id, **weather_arg)\n", - "\"\"\";" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "weather_df, meta = pvlib.iotools.get_psm3(\n", - " latitude=33.4484,\n", - " longitude=-112.0740,\n", - " api_key=NREL_API_KEY,\n", - " email=\"silvana.ovaitt@nrel.gov\", # <-- any email works here fine\n", - " names=\"2021\",\n", - " map_variables=True,\n", - " attributes=[],\n", - " leap_day=False,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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8760 rows Γ— 24 columns

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" - ], - "text/plain": [ - " Year Month Day Hour Minute temp_air \\\n", - "2021-01-01 00:30:00-07:00 2021 1 1 0 30 6.5 \n", - "2021-01-01 01:30:00-07:00 2021 1 1 1 30 6.0 \n", - "2021-01-01 02:30:00-07:00 2021 1 1 2 30 5.5 \n", - "2021-01-01 03:30:00-07:00 2021 1 1 3 30 5.1 \n", - "2021-01-01 04:30:00-07:00 2021 1 1 4 30 4.7 \n", - "... ... ... ... ... ... ... \n", - "2021-12-31 19:30:00-07:00 2021 12 31 19 30 9.1 \n", - "2021-12-31 20:30:00-07:00 2021 12 31 20 30 8.5 \n", - "2021-12-31 21:30:00-07:00 2021 12 31 21 30 7.8 \n", - "2021-12-31 22:30:00-07:00 2021 12 31 22 30 7.4 \n", - "2021-12-31 23:30:00-07:00 2021 12 31 23 30 7.0 \n", - "\n", - " dhi_clear dni_clear ghi_clear Cloud Type ... \\\n", - "2021-01-01 00:30:00-07:00 0.0 0.0 0.0 0 ... \n", - "2021-01-01 01:30:00-07:00 0.0 0.0 0.0 4 ... \n", - "2021-01-01 02:30:00-07:00 0.0 0.0 0.0 4 ... \n", - "2021-01-01 03:30:00-07:00 0.0 0.0 0.0 4 ... \n", - "2021-01-01 04:30:00-07:00 0.0 0.0 0.0 0 ... \n", - "... ... ... ... ... ... \n", - "2021-12-31 19:30:00-07:00 0.0 0.0 0.0 7 ... \n", - "2021-12-31 20:30:00-07:00 0.0 0.0 0.0 7 ... \n", - "2021-12-31 21:30:00-07:00 0.0 0.0 0.0 7 ... \n", - "2021-12-31 22:30:00-07:00 0.0 0.0 0.0 7 ... \n", - "2021-12-31 23:30:00-07:00 0.0 0.0 0.0 0 ... \n", - "\n", - " ghi relative_humidity solar_zenith albedo \\\n", - "2021-01-01 00:30:00-07:00 0.0 35.09 169.51 0.16 \n", - "2021-01-01 01:30:00-07:00 0.0 36.34 163.48 0.16 \n", - "2021-01-01 02:30:00-07:00 0.0 37.37 152.07 0.16 \n", - "2021-01-01 03:30:00-07:00 0.0 38.47 139.71 0.16 \n", - "2021-01-01 04:30:00-07:00 0.0 39.97 127.21 0.16 \n", - "... ... ... ... ... \n", - "2021-12-31 19:30:00-07:00 0.0 27.14 114.11 0.16 \n", - "2021-12-31 20:30:00-07:00 0.0 28.57 126.45 0.16 \n", - "2021-12-31 21:30:00-07:00 0.0 29.85 138.95 0.16 \n", - "2021-12-31 22:30:00-07:00 0.0 31.44 151.32 0.16 \n", - "2021-12-31 23:30:00-07:00 0.0 33.22 162.85 0.16 \n", - "\n", - " pressure precipitable_water wind_direction \\\n", - "2021-01-01 00:30:00-07:00 968.0 1.0 38.0 \n", - "2021-01-01 01:30:00-07:00 968.0 1.0 42.0 \n", - "2021-01-01 02:30:00-07:00 968.0 1.0 45.0 \n", - "2021-01-01 03:30:00-07:00 968.0 1.0 46.0 \n", - "2021-01-01 04:30:00-07:00 969.0 1.0 46.0 \n", - "... ... ... ... \n", - "2021-12-31 19:30:00-07:00 967.0 0.9 28.0 \n", - "2021-12-31 20:30:00-07:00 967.0 0.9 31.0 \n", - "2021-12-31 21:30:00-07:00 967.0 0.9 33.0 \n", - "2021-12-31 22:30:00-07:00 968.0 1.0 33.0 \n", - "2021-12-31 23:30:00-07:00 968.0 1.0 34.0 \n", - "\n", - " wind_speed \\\n", - "2021-01-01 00:30:00-07:00 1.8 \n", - "2021-01-01 01:30:00-07:00 1.8 \n", - "2021-01-01 02:30:00-07:00 1.8 \n", - "2021-01-01 03:30:00-07:00 1.7 \n", - "2021-01-01 04:30:00-07:00 1.8 \n", - "... ... \n", - "2021-12-31 19:30:00-07:00 1.2 \n", - "2021-12-31 20:30:00-07:00 1.3 \n", - "2021-12-31 21:30:00-07:00 1.4 \n", - "2021-12-31 22:30:00-07:00 1.5 \n", - "2021-12-31 23:30:00-07:00 1.6 \n", - "\n", - " Global Horizontal UV Irradiance (280-400nm) \\\n", - "2021-01-01 00:30:00-07:00 0.0 \n", - "2021-01-01 01:30:00-07:00 0.0 \n", - "2021-01-01 02:30:00-07:00 0.0 \n", - "2021-01-01 03:30:00-07:00 0.0 \n", - "2021-01-01 04:30:00-07:00 0.0 \n", - "... ... \n", - "2021-12-31 19:30:00-07:00 0.0 \n", - "2021-12-31 20:30:00-07:00 0.0 \n", - "2021-12-31 21:30:00-07:00 0.0 \n", - "2021-12-31 22:30:00-07:00 0.0 \n", - "2021-12-31 23:30:00-07:00 0.0 \n", - "\n", - " Global Horizontal UV Irradiance (295-385nm) \n", - "2021-01-01 00:30:00-07:00 0.0 \n", - "2021-01-01 01:30:00-07:00 0.0 \n", - "2021-01-01 02:30:00-07:00 0.0 \n", - "2021-01-01 03:30:00-07:00 0.0 \n", - "2021-01-01 04:30:00-07:00 0.0 \n", - "... ... \n", - "2021-12-31 19:30:00-07:00 0.0 \n", - "2021-12-31 20:30:00-07:00 0.0 \n", - "2021-12-31 21:30:00-07:00 0.0 \n", - "2021-12-31 22:30:00-07:00 0.0 \n", - "2021-12-31 23:30:00-07:00 0.0 \n", - "\n", - "[8760 rows x 24 columns]" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "weather_df" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - 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" 'Cloud Type 7': 'Cirrus',\n", - " 'Cloud Type 8': 'Overlapping',\n", - " 'Cloud Type 9': 'Overshooting',\n", - " 'Cloud Type 10': 'Unknown',\n", - " 'Cloud Type 11': 'Dust',\n", - " 'Cloud Type 12': 'Smoke',\n", - " 'Fill Flag 0': 'N/A',\n", - " 'Fill Flag 1': 'Missing Image',\n", - " 'Fill Flag 2': 'Low Irradiance',\n", - " 'Fill Flag 3': 'Exceeds Clearsky',\n", - " 'Fill Flag 4': 'Missing CLoud Properties',\n", - " 'Fill Flag 5': 'Rayleigh Violation',\n", - " 'Surface Albedo Units': 'N/A',\n", - " 'Version': 'v3.2.2',\n", - " 'latitude': 33.45,\n", - " 'longitude': -112.06,\n", - " 'altitude': 334}" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "meta" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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YearMonthDayHourMinutetemp_airdhi_cleardni_clearghi_clearCloud Type...ghirelative_humiditysolar_zenithalbedopressureprecipitable_waterwind_directionwind_speedGlobal Horizontal UV Irradiance (280-400nm)Global Horizontal UV Irradiance (295-385nm)
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5 rows Γ— 24 columns

\n", - "
" - ], - "text/plain": [ - " Year Month Day Hour Minute temp_air \\\n", - "2021-01-01 00:30:00-07:00 2021 1 1 0 30 6.5 \n", - "2021-01-01 01:30:00-07:00 2021 1 1 1 30 6.0 \n", - "2021-01-01 02:30:00-07:00 2021 1 1 2 30 5.5 \n", - "2021-01-01 03:30:00-07:00 2021 1 1 3 30 5.1 \n", - "2021-01-01 04:30:00-07:00 2021 1 1 4 30 4.7 \n", - "\n", - " dhi_clear dni_clear ghi_clear Cloud Type ... \\\n", - "2021-01-01 00:30:00-07:00 0.0 0.0 0.0 0 ... \n", - "2021-01-01 01:30:00-07:00 0.0 0.0 0.0 4 ... \n", - "2021-01-01 02:30:00-07:00 0.0 0.0 0.0 4 ... \n", - "2021-01-01 03:30:00-07:00 0.0 0.0 0.0 4 ... \n", - "2021-01-01 04:30:00-07:00 0.0 0.0 0.0 0 ... \n", - "\n", - " ghi relative_humidity solar_zenith albedo \\\n", - "2021-01-01 00:30:00-07:00 0.0 35.09 169.51 0.16 \n", - "2021-01-01 01:30:00-07:00 0.0 36.34 163.48 0.16 \n", - "2021-01-01 02:30:00-07:00 0.0 37.37 152.07 0.16 \n", - "2021-01-01 03:30:00-07:00 0.0 38.47 139.71 0.16 \n", - "2021-01-01 04:30:00-07:00 0.0 39.97 127.21 0.16 \n", - "\n", - " pressure precipitable_water wind_direction \\\n", - "2021-01-01 00:30:00-07:00 968.0 1.0 38.0 \n", - "2021-01-01 01:30:00-07:00 968.0 1.0 42.0 \n", - "2021-01-01 02:30:00-07:00 968.0 1.0 45.0 \n", - "2021-01-01 03:30:00-07:00 968.0 1.0 46.0 \n", - "2021-01-01 04:30:00-07:00 969.0 1.0 46.0 \n", - "\n", - " wind_speed \\\n", - "2021-01-01 00:30:00-07:00 1.8 \n", - "2021-01-01 01:30:00-07:00 1.8 \n", - "2021-01-01 02:30:00-07:00 1.8 \n", - "2021-01-01 03:30:00-07:00 1.7 \n", - "2021-01-01 04:30:00-07:00 1.8 \n", - "\n", - " Global Horizontal UV Irradiance (280-400nm) \\\n", - "2021-01-01 00:30:00-07:00 0.0 \n", - "2021-01-01 01:30:00-07:00 0.0 \n", - "2021-01-01 02:30:00-07:00 0.0 \n", - "2021-01-01 03:30:00-07:00 0.0 \n", - "2021-01-01 04:30:00-07:00 0.0 \n", - "\n", - " Global Horizontal UV Irradiance (295-385nm) \n", - "2021-01-01 00:30:00-07:00 0.0 \n", - "2021-01-01 01:30:00-07:00 0.0 \n", - "2021-01-01 02:30:00-07:00 0.0 \n", - "2021-01-01 03:30:00-07:00 0.0 \n", - "2021-01-01 04:30:00-07:00 0.0 \n", - "\n", - "[5 rows x 24 columns]" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "weather_df.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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Cy0OjQCIitUnhR0TqrbzCEj5emcqM5cmk5RYCEODtwS19ornt/FgiQ/0srlDc1ZWdI+kT24hHv9jIj1vS+b/5O8z/3tiFtuFBVpcnIlJvKfyISL2TnlvIez8nM3dFCnlFpQA0CfLh9vNjuaVPNCF+XhZXKC7HMCAvDw4ePPnrwAHzv5mZ5v2qqAnwNpBfVMqR/GIchoHtCRvZ/l6E+HlRZxMwAwIgJARCQ82vqv7ZS3+PRMT1KPyISL2xMz2Pd5bu4ctfD1BSZl6Etg4L5M7+Lbm6W6TaC8upHT9+6lDzx3CTn1/jD20DAn/7qnC0xh+mdvj7nxyKziY8+fmB1tiJSB1T+BERl2YYBqv2HuXtJbtZsC2j4nivFg24a0ArLmofhl2d29xTcTGkpZ052GRnV/2cISEQGXnqr/Bw8Dj3gG0YBou2ZfDez3spKC7D29PGLb2juaJzZO11HzQMM9RlZ0NOjvnf8q/ff//7Px87Zv5sQYH5dfDguT22l9fZhaXf/zk42Axf3t4KUCJyVhR+RMQllTkM5m9J4+2le1iXkg2Y10CXxIVz54BW9IhpYG2BUnvKyiAj489DzcGD5n2qys8PmjU7dagpP960qTlFrJbYgIvOh/bDjvPIfzeydEcmK1Jgtt3Gi9d3oUXj2nvss1JaCrm5fx6QzhSkHA4oKTGnCmZmnnstHh5mCPL3N//f/Nl/z+U+fn7VCrQi4nxshnEWE5Td1P79+4mKiiI1NZXmzZtbXY6IWyssKePztft5d1kyyYfNaUjennaGdW/OuP6xtGwSeIYziFNzOCA5GXbuPH2oSUszA1BVeHmdfqTm98EmONipRhAMw+DjVak8++0W8ovL8PWy88il7Rmd0ML1RzINwxw9qmp4+mOQys42R/Xqiq9v9UOUvz80bgxdu4JdHf3Eubjbda7CTxW425tCxBllFxQzO3EfM3/Zy5F888InxM+LUX1jGHNeC5oE+VhcoZy1/HzYuBHWrz/xtWHDiWlVf8Zuh4iIMwebhg1d+mJz/9EC/vafDfyy+wgAfVs25MXruxDV0N/iyixWUmK+f8qn3pX/+XT/PdvbCgpqp+7mzeGmm+Dmm6F7d6cK3OK+3O06V+GnCtztTSHiTFKzCpixPJlPVqVyvMT8tL9ZqB9j+8VyU68oAnw0e9fpGQakplYOOevXw65dp+6O5uMDbduaF4q1tL7GlTgcBnOS9vHc/7ZxvKQMf28P/nF5B0b0idamvLXF4YDCwpoNVHv2mB0Dy7VpY4agm2+G9u2te67i9tztOlfhpwrc7U0h4gw2HcjhnaV7+G7jIcoc5j9TcU2DueuCllzeqak2g3RWhYWwefPJQed0TQUiIqBLl8pf7dqBp0LtH+07ks9f/7OBlclZAPRr3ZgXru9MM+1X5RoKC+H77+Gjj+Cbb8zvy3XpYoag4cMhJsa6GsUtudt1rsJPFbjbm0LEKoZhsGznYd5Zuofluw5XHO/fpjF3DmhJv9aN9Um3szAMc+3NH0PO9u2nXo/j6QkdOpwcdMLC6r52F+ZwGMz8ZS9Tf9hGYYmDQB9PHr+yAzf2jNLfDVeSlwdff20GoR9+MJtIlDvvPDMI3XCDOcIpUsvc7TpX4acK3O1NIVLXSsocfLfhEG8v3cPWQ7kAeNhtXNm5KeP6tyS+WYjFFbq54mLYtu3koHO6Ll2NGp0ccjp0MKezSY3Yk3mMhz9bz9rfOh1e2K4Jz1/XmYgQX2sLk7N35Ah8/rkZhJYsOTEV1G6Hiy4yg9B115ltvkVqgbtd5yr8VIG7vSlE6kp+USmfrEplxvJkDmQfB8DPy4PhvaO4/fxYLeq2wuHDJ4ecLVvMBeZ/ZLeba3P+GHQiI7WQuw6UOQxmLN/Dv37cQXGpgyBfT54c2pHrujfTKJCrOngQPv3UDEIrV5447u0Nl11mTosbOrRWW66L+3G361yFnypwtzeFSG3Lyi/mveXJzF6xj5zj5kV140BvxiS0YGTfGBoEeFtcoRsoK4MdO04OOqfbsDIkBDp3rhxyOnY0W/iKpXZl5PHQp+tZvz8HgMEdwnnuunjCgjQK5NJ274ZPPjGD0KZNJ44HBMBVV5kjQkOGmMFIpBrc7TpX4acK3O1NIVJbDMPgi3UHePrbLWQXmKEntnEAd/SPZVj35vh6uUf3rjqXnW22kP59yNm0qfKC699r1erk0ZyYGI3mOLHSMgdvL93DKz/toKTMINTfi6eu6shVXSI1ClQfbNpkhqCPPza7xpULDYVhw8wgdOGFbtMBUWqWu13nKvxUgbu9KURqQ2pWAY9+uYmlO8x1Iu0jgnhgcBsujovAw9U3bXQ2BQWweLHZWWrePLOl9KkEBECnTpVDTqdOEBRUp+VKzdmWlstDn65n80Fz7dxl8RE8c008jQO13qpeMAxYtcoMQp98AocOnbgtIgJuvNEMQn366MMKqTJ3u85V+KkCd3tTiNSkst+6U/3rh+0cLynD29PO/YPacOeAlmpXXVMMA3buNMPO99+bwaeoqPJ9oqNPHs1p1cqlNwCVUyspc/Dmot28vnAnpQ6DhgHePHtNPJd3amp1aVKTyspg6VIzCP3nP3D06InbWrQw1wfdfLP5gYaCkPwJd7vOVfipAnd7U4jUlG1pufz9842sT80GoHdsQ56/rhMtmwRaW1h9UFAAixadCDy/nwoDZti57DLza8AAaNDAmjrFMpsP5vDQp+vZlmZurDm0SyRPX9VRa+rqo+JimD/fDEJffmlurFouLu5EEGrd2rISxXm523Wuwk8VuNubQqS6ikrLeGPhLt5cvJtSh0GQjyeTLu/A8F5R2DXF7dycaXTHy8sMOeWBp0MHfdorFJc6eH3hTt5cvJsyh0HjQB+euzaeSzpGWF2a1JaCAvj2W3N90HffmcGoXM+eZgi68UbQ9Yz8xt2ucxV+qsDd3hQi1bF6bxZ//3wDuzPNTx4vjgvnmavjtf/IuTib0Z2LLtJaHTmt9anZPPzZenZmHAPgum7NeGJoR0L8vSyuTGpVTg588YU5IrRgwYkNiG026N/fDELXXw+NG1tbp1jK3a5zFX6qwN3eFCLnIq+whKnztjN7xT4AGgf68MzVHbk0PkLdpqrq96M7//ufueGhRnekhhSWlPHKTzt5Z+luHAaEB/vw/HWdGdg+zOrSpC5kZJhrgz76CJYvP3Hc0xMuvticGnfNNRAcbFmJYg13u85V+KkCd3tTiJytBVvTeezLTRzKMVsn39izOY9eHqdPlauiKqM7l19+YnQnUOulpHrWphzl4c/Ws+e30dlbz2vB5CvjNCXVnaSknNhMde3aE8d9feGKK8wRocsvBz8/62qUOuNu17kKP1Xgbm8Kkao6fKyIp77ZwjfrzY0xoxv6M+W6TpzfWlMoTsswzM1Fy8OORnfEAoUlZfzrh+3M+DkZw4Brukby4g1d1IHRHW3fbq4P+ugj88/lgoLMkaDbb4cLLtC/Q/WYu13nKvxUgbu9KUTOxDAM/rv2AM98Z25WarfBuP4teWBwW/y8tcneSTS6I07qq18P8NCn6yl1GAzuEM6/b+mmzYbdlWGYGyCXb6aaknLitvbt4e67YfRodY6sh9ztOlfhpwrc7U0h8mdSswr4xxcbWbbzMAAdmgYzdVhnOjUPsbgyJ6LRHXEhC7am85c5aykqdZDQshHTx/Qk0MfT6rLESg4HJCbC7NkwZw4cMxtl4Odnrg26+27o1Uv/btUT7nadq/BTBe72phA5lTKHwfs/J/N/P+6o2Kz0gcFtGNdfm5UCJ0Z3/vc/M/AkJ1e+XaM74sQSdx/hjlmryC8uo2tUKDNv60Wov/YDEiAvzwxAb70FGzacON6tG9xzj7k+SP+euTR3u85V+KkCd3tTiPzRHzcr7RPbkOeHdSa2cYC1hVlJoztSz6xPzWbM+yvJLiihXXgQs8f2JixYLerlN4YBK1aYIejTT0/8exccDKNGmaNB8fHW1ijnxN2ucxV+qsDd3hQi5QpLynhj0S7eKt+s1NeTf1zegZt6uulmpfn5ldfu/HF0Jyam8r47+jRUXMyO9DxGvptERl4RMY38+XBsH6Ia+ltdljibI0dg5kyYNg127Tpx/PzzzdGg668HHx/LypOz427XuQo/VeBubwoRgFV7s3jkd5uVDukYztNXxxPubp8El5TAjz/CBx/AV1+dfnTn8svNRcEa3REXl3KkgBEzVpCadZyIYF8+vKM3rcO0ga6cgsMBCxeaIejLL09sotq4Mdx2G9x1F7RqZWmJcmbudp3r1BP1y8rKePzxx4mNjcXPz49WrVrxzDPP8Pu8ZhgGkydPpmnTpvj5+TF48GB27txZ6TxZWVmMGDGC4OBgQkNDGTt2LMfKF++JSCV5hSU8/uUmbpiWyO7MfJoE+fDWiO68Paqn+wQfw4B16+DBB6F5c7jyyhPTPGJizOkdX30FWVnw00/w0EOa1ib1RnQjf/5z93m0CQskLbeQG6YlsnF/jtVliTOy22HwYHPz1JQUePpp89/Mw4fhxRehdWsYMgS++AJKS62uVmrB888/j81m44EHHqg4VlhYyPjx42nUqBGBgYEMGzaM9PR064r8A6cOPy+88AJvvfUW//73v9m6dSsvvPACU6dO5fXXX6+4z9SpU3nttdeYNm0aSUlJBAQEMGTIEAoLCyvuM2LECDZv3sz8+fP59ttvWbp0KXfeeacVT0nEqf20JZ2LX1rK7BX7ALipZxQ/PXgBl3VqanFldeTgQfMXdufO0L07vPKKuSt6kybwwAOwZo051e2tt+CqqzStTeqt8GBfPr0rgc7NQzhaUMIt01ewMjnL6rLEmUVGwuOPm/9GfvWVOSJus5kj59ddZ35w9OSTcOCA1ZVKDVm1ahVvv/02nTt3rnT8wQcf5JtvvuGzzz5jyZIlHDx4kOuuu86iKk/m1NPerrzySsLDw5kxY0bFsWHDhuHn58eHH36IYRhERkby0EMP8fDDDwOQk5NDeHg4M2fOZPjw4WzdupW4uDhWrVpFz549AZg3bx6XX345+/fvJzIy8ox1uNtwoLifw8eKePLrzXy74RAAMY38mXJtJ85zh81K8/PNTyVnzzZHcRwO87iPD1x9tbmvxSWXmFPcRNxMXmEJd8xaTVJyFj6edqaN6sHAdmFWlyWuYs8emD4dZsyAzEzzmIcHDB1qrg0aPNgcPRJLnct17rFjx+jevTtvvvkmzz77LF27duWVV14hJyeHJk2aMHfuXK6//noAtm3bRocOHUhMTKRv3761+VSqxKnfceeddx4LFixgx44dAKxfv57ly5dz2WWXAZCcnExaWhqDBw+u+JmQkBD69OlDYmIiAImJiYSGhlYEH4DBgwdjt9tJSkqqw2cj4nwMw+A/a/Yz+KUlfLvhEB52G3dd0JJ59w+o38GnfJ76rbdCRITZqejHH83j/frBO+9AWhp88glccYWCj7itIF8vZt3em0HtwygqdTBu1mq+3XDQ6rLEVbRsCVOmQGqquXnqBReY64K+/NKcDtemDUydeiIYicsYP348V1xxRaVrcIA1a9ZQUlJS6Xj79u2Jjo6uuDa3mlPvYvbII4+Qm5tL+/bt8fDwoKysjH/+85+MGDECgLS0NADCw8Mr/Vx4eHjFbWlpaYSFVf6UytPTk4YNG1bc54+Kiooo+t2i5ry8vBp7TiLO4o+blcY1DWbq9Z2Jb1aPNyvdts0c4Zk92/xlXK5lS3OEZ+RILc4V+QNfLw+mjerBxE/X8836g9z70TqOFZYyvHe01aWJq/DxMTdHHT4ctmwxGyR88IE5MvT3v5vT5a6/3hwNOv98rZ+0SF5eHrm5uRXf+/j44HOKrn0ff/wxa9euZdWqVSfdlpaWhre3N6GhoZWO//7a3GpOPfLz6aefMmfOHObOncvatWuZNWsW//rXv5g1a1atPu6UKVMICQmp+IqLi6vVxxOpS2UOg3eX7eGSl5eybOdhfDzt/P3S9nw14fz6GXwOH4Y33oA+fcymBM89ZwafkBC4805Yvtxs1frEEwo+Iqfh5WHnlZu6ckufaAwDHvnvRqYv3WN1WeKK4uLgtdfMtT/vvgs9e0JxMcydC/37m2su33gDctRko67FxcVVuv6dMmXKSfdJTU3l/vvvZ86cOfj6umYTJKce+fnrX//KI488wvDhwwHo1KkT+/btY8qUKYwZM4aIiAgA0tPTadr0xILs9PR0unbtCkBERAQZGRmVzltaWkpWVlbFz//RpEmTmDhxYsX3Bw4cUACSemHroVwe+XwD63/r3NS3ZUOmXFcPNystKoL//c/8ZPG778x21WDONb/sMnOUZ+hQcNF/uEWs4GG38c9r4gny9eTtJXv45/+2kldYwoMXt8WmT+rlbAUEwNix5tfq1eZo0Ny5sGkTTJhgjgjdcovZXbN7d6urdQtbtmyhWbNmFd+fatRnzZo1ZGRk0P13/0/KyspYunQp//73v/nhhx8oLi4mOzu70uhPenr6aa+765pTj/wUFBRg/8NCOA8PDxy/LUiOjY0lIiKCBQsWVNyem5tLUlISCQkJACQkJJCdnc2aNWsq7rNw4UIcDgd9+vQ55eP6+PgQHBxc8RUUpP0NxLUVlpTxrx+2M/T15azfn0OQryfPX9eJj8b1rT/BxzAgKQnGjze7Dl13nTmvvKTkROe2gwfhm2/ghhsUfETOgc1mY9JlHfjbpe0AeG3hLp76ZgsOh9P2ThJX0LOnOQp08KA5KhQXZzajmT4devSA3r3h/fehoMDqSuu1oKCgSte/pwo/gwYNYuPGjfz6668VXz179mTEiBEVf/by8qp0bb59+3ZSUlIqrs2t5tQjP0OHDuWf//wn0dHRdOzYkXXr1vHSSy9x++23A1T0FX/22Wdp06YNsbGxPP7440RGRnLNNdcA0KFDBy699FLGjRvHtGnTKCkpYcKECQwfPrxKnd5EXN3K5Cwe+e8G9tTXzUr37YMPPzRHeX5rjgKYAWjkSLOZQXy8dfWJ1EN/ubA1QT6ePP7VZmb+spfcwhKmDuuMp4dTf6Yqzi40FO691xz5WbbMHA36z39g1Srza+JEGDPG3Dy1Qwerq3VLQUFBxP/hd2pAQACNGjWqOD527FgmTpxIw4YNCQ4O5t577yUhIcEpOr2Bk4ef119/nccff5y//OUvZGRkEBkZyV133cXkyZMr7vO3v/2N/Px87rzzTrKzs+nXrx/z5s2rNA9xzpw5TJgwgUGDBmG32xk2bBivvfaaFU9JpM7kFZbw/PfbmJOUAkCTIB+eubojl8bXgz17cnPh88/NwLN48Ynj/v7miM/o0XDRReY0NxGpFaMSWhDo68nDn23gv2sPkF9Uyms3d8PHU3/vpJpsNhgwwPx65RVz1Oftt809hF591fy68EJzSty114K3t9UVy++8/PLLFdfbRUVFDBkyhDfffNPqsio49T4/zkL7/Iir+WlLOo99uYm0XHOz3+G9oph0eQdC/Fy4ZXNZmbkPzwcfmPvyHD9uHrfZYOBAM/Bcdx1omqpInfpxcxoT5q6juMxB/zaNeXtUD/y9nfqzVXFFDoe5JcFbb8G3357Yky0szFw3dOed0KKFpSW6Kne7zlX4qQJ3e1OI68rMK+LJbzbz3e83K72uE+e1cuE9ezZuNAPPnDlw6NCJ4+3bm4FnxAiIVstdESv9vOsw4z5YTUFxGd2jQ3n/1t6E+Lvwhy3i3FJTzTVC06ef+L1gs5kNbe6+Gy6/XCP/Z8HdrnMVfqrA3d4U4nrKNyt99rut5BwvwcNuY1z/ljwwuA2+Xi74CyAtzdwQ74MP4NdfTxxv1AhuvtkMPT17ai8IESeyLuUot76/ipzjJbSPCGL22D40CTp5wbRIjSkpMZvYvPWWOTOgXGws/PWv5kbWfn6Wlecq3O06V+GnCtztTSGuJeWIuVnp8l3mZqUdI4N5YZgLblZ6/Dh8/bUZeH74wZzmBuDlZbalHjMGLr1Uc7tFnNi2tFxGzVhJZl4RsY0DmD22N80b+FtdlriDnTvNdUHvvw9ZWeaxsDB44AFz89Q/bLopJ7jbda7CTxW425tCXMemAzncPH0FeYWl+HjaefDittzRL9Z1Oi45HPDzz2bg+fRTs5FBub59zRGem26Chg2tq1FEzsrew/mMeDeJA9nHiQzxZfYdfWjVJNDqssRdFBTAe+/Biy9Citnwh+BgMwDdfz80rQdNf2qYu13nKvxUgbu9KcQ1bE/L46Z3EskuKKFrVCiv3NSVFq6yZ8/eveanc7Nnm917ysXEmIFn5Eho29ay8kSkeg7lHGfku0nszsynUYA3H4ztTcdIFxuNFtdWUgIffwwvvACbN5vHfHzMqXAPPwytW1tanjNxt+tcF/l4WER+b3fmMUa8m1QRfD68o4/zBx/DgIULzbakrVrB00+bwScoyOzUs2QJ7NljHlfwEXFpTUP8+PSuBOKbBXMkv5jh76xg9d4sq8sSd+LlZe7ztmGDOaU6IQGKisypce3awfDhsG6d1VWKBRR+RFxMalYBI6YncfhYEXFNg5l1W28CfZy4rWx+PrzzDnTqBIMGwZdfmtPdBg+GuXPN5gbvvmvu52DXP0ki9UWjQB/mjutL7xYNySssZdSMlSzdkWl1WeJu7HZz3ejPP8PSpWYnOIcDPvkEunc315IuXmx+QCduQVcaIi7kUM5xbp6+grTcQtqEBTJ7rBO3k9271+y207y5uRv35s0QEADjx8PWrTB/vtm5zV+LoUXqq2BfL2bd3psL2zXheEkZY2et4vuNh878gyI1zWaD/v3hu+/MLqK33GIGox9+MPeKO+88+OqrE/sHSb2l8CPiIjLyChkxPYn9R4/TopE/c+7oQ6NAJ2sjWz617ZprzKlt//oXZGebf375ZThwAP79b3OPHhFxC37eHrwzqidXdGpKSZnB+Llr+Wx1qtVliTvr0sXcO27nTrMRgo8PrFhh/u6Kj4dZs8w1Q1IvKfyIuICs/GJGvbuSPYfzaRbqx5xxfQkL9rW6rBPy88151OVT28o/PbvkEnMn7h07zHajIVrwLOKOvD3tvHZzN27qGYXDgL/+ZwPvLU8+8w+K1KaWLeHNN2HfPpg0yewKt3Wr2RShVSt49VXz95vUKwo/Ik4u53gJo99LYnt6HuHBPswd14dmoU6yadvvp7bdfffJU9t++AGuuEJreUQED7uN54d14o5+sQA8/e0WXv1pJ2o6K5YLD4fnnjNbY7/wAkREQGqq+aFdTAw89RQcOWJ1lVJDdEUi4sSOFZVy6/sr2XQgl0YB3sy5oy8xjSzu6vZnU9teeUVT20TktGw2G49e0YGHLjY7Or780w6e/W6rApA4h5AQ+NvfzE6kb79t/l47cgSefNIMQRMnwv79Vlcp1aTwI+KkjheXccesVaxLySbEz4sP7+hD6zALNwo83dS2IUNOTG27/35NbRORP2Wz2bh3UBueHBoHwIzlyfz98w2UORSAxEn4+sKdd8K2beZeQV27mr8DX37ZnCp3++3mbeKSFH5EnFBRaRl3fbiGFXuyCPLxZPbY3nRoGmxNMcnJJ09tCww8MbVt3jxNbRORs3br+bH83w1dsNvg09X7ue+jdRSXqtOWOBFPT7jpJli71vxdd+GFZiOE99+HuDi47jpYudLqKuUs6WpFxMmUlDmYMHcdS3dk4uflwfu39aJz89C6LeJMU9v279fUNhGptmE9mvPmiB54e9j5buMhxn2wmuPFZVaXJVKZzWbOcli0CBITzd+NhgFffAF9+pizIebP115BLkLhR8SJlDkMHvjkV+ZvScfb086MMT3p2aJh3RVwqqlthqGpbSJSay6Nj2DGrT3x8/JgyY5MRr+XRG6h2gyLk+rb1ww9mzebXeE8Pc0PCy+5BHr2hM8+gzIFeGem8CPiJBwOg7/9ZwPfbTiEl4eNt0f24LzWjevmwZOT4eGHNbVNRCzRv00TPryjN8G+nqzae5Sb31nB4WNFVpclcnpxceb0t927zQ8F/f3N6XE33mjOipg+HYr0HnZGupIRcQKGYfD4V5v4fO1+POw2Xr+5GwPbh9X2g8KCBSemtv3f/2lqm4hYpkdMQz6+M4HGgd5sPpjLjW8ncijnuNVlify56Gjzd+a+ffDEE9CwIezaZTZMiI2FF1+E3Fyrq5TfUfgRsZhhGDz73VbmJKVgs8FLN3bh0vimtfeAv5/aNnhw5alt332nqW0iYpm4yGA+vSuByBBf9mTmc/1biSQf1iaT4gIaNzZbYu/bZ3aFa94cDh0yW2fHxMBjj0FGhtVVCgo/IpZ7af4OZvy20/kL13Xm6q7NaueBTje1bcIEs2XnvHlw+eWa2iYilmrZJJDP7jmPlo0DOJB9nBumJbL1kD45FxcRGGhujrp7N7z3HrRrZ86q+Oc/zRA0YYK5QbhYRlc5IhZ6Y9EuXl+4C4CnrurIjb2iavYByqe2XX115altrVufmNr2+uvmP84iIk6iWagfn9yVQIemwRw+VsRNbyeyNuWo1WWJVJ23N9x2G2zZAv/9L/TuDYWF8MYb5u/gkSNh40arq3RLCj8iFpmxPJkXf9gOwKTL2jPmvBY1d/L8fJg2DeLjzaltX39deWrb9u2a2iYiTq1JkA8f39mXHjENyC0sZeS7SSzfedjqskTOjt0O114LK1ac6ApXVgZz5kDnznDllfDzz1ZX6VYUfkQsMCdpH898uwWABwa34a4LWtXMiX8/te2ee8xPnDS1TURcVIifF7PH9qZ/m8YUFJdx+8xV/Lg5zeqyRM6ezQYDB8IPP8CaNWZXOJvN/ECyXz/o3x+Skqyu0i3oCkikjn2+Zj+PfbkJgLsvaMX9g9pU74Sa2iYi9Zi/tyfvjunJZfERFJc5uGfOWv67dr/VZYmcu+7d4ZNPzFkY48aZU+SWL9f+QHVE4UekDn234RB//c96DANuPa8Ff7+0HTab7dxOVlICH35oDptrapuI1GM+nh68fnM3ru/RnDKHwUOfrWfepkNWlyVSPW3awDvvmLM2Xn8dzjvP6orcgsKPSB35aUs693+8DocBw3tFMfnKuHMLPgUF5v47bdrAqFGwaZOmtolIvefpYWfqsM7c3Dsaw4D7Pv6VFXuOWF2WSPVFRpq/w6VO6OpIpA4s25nJX+aspdRhcE3XSP55bSfs9rMMPkePwrPPmq0y773X3EsgPBymTIHUVE1tE5F6z2638ew18VwSF05xqYNxH6xmW5raYItI1Sn8iNSypD1HGPfBaorLHFwWH8G/buiCx9kEnwMHzCYG0dHw+ONw+DC0bAlvvWUOlT/yCISG1lr9IiLOxMNu47Wbu9GrRQPyCksZ895KDmQft7osEXERCj8itWhdylFun7mKwhIHA9s14dXh3fD0qOJfu+3b4Y47IDbWbGJw7Bh06QIffWTedvfd4OdXu09ARMQJ+Xp58O7oXrQNDyQ9t4jRM5I4ml9sdVki4gIUfkRqyaYDOYx5byX5xWWc37oRb43sgbdnFf7KrV4N118PHTrAjBlmY4MBA+D772HdOhg+HDw9a/8JiIg4sRB/L2bd3pumIb7szsxn7KxVHC9WtywR+XMKPyK1YEd6HqNmJJFbWEqvFg2YPronvl4ep/+B8nbVF18MvXrB55+bx666ytz8bMkSuPRSc08AEREBoGmIHx/c3psQPy/WpmRz70drKS1zWF2WiDgxhR+RGrYn8xi3TE/iaEEJXZqH8N6tvfD3Ps1ITVmZGXR69zbbVf/0E3h4wOjRZhe3r75S60sRkT/RJjyIGWN64uNp56etGTz25SYMw7C6LBFxUgo/IjUoNauAEe8mcfhYEe0jgph1e2+CfL1OvmNRkTmlLS7OnOK2erW5fue++2D3bpg1Czp2rPsnICLignq2aMjrN3fDboOPV6Xy8k87rS5JRJyUwo9IDUnLKeSWd1dwKKeQVk0C+PCOPoT6e1e+U14evPQStGplNjPYsQMaNIDJk83W1a++arayFhGRs3JJxwievaYTAK8t2MmHK/ZZXJGIOCOtmhapAZl5Rdzy7gpSs44T08ifueP60jjQ53d3yDT34fn3v839esDc1Oyhh2DcOAgKsqZwEZF65JY+0WTkFfLKTzt5/KtNNA705tL4plaXJSJORCM/ItV0NL+YUTOS2JOZT2SIL3Pu6EN4sK9547595lS2mBh45hkz+LRta05527MHJk5U8BERqUH3D2rDzb2jMQy47+NfWZmcZXVJIuJEFH5EqiG3sITR761kW1oeYUE+zB3Xl+YN/M1mBaNHm9PbXn8djh+Hnj3N5gZbtsDtt4OPz5kfQEREzorNZuOZqztycVw4xaUO7pi1iu1peVaXJSJOQuFH5BzlF5Vy2/ur2Hggh4YB3sy5ow8tdqw321N36gSzZ5vd3Mq7uK1cCdddZ3ZzExGRWuPpYef1m7vRM6YBuYWljHlvJQeyj1tdlog4AYUfkXNQWFLGHbNWs2bfUYJ9PPgiNoc2N1wB558P33xj7sdz/fWwahXMnw+DBmmPHhGROuTr5cG7Y3rSJiyQtNxCxry3kuyCYqvLEhGLKfyInKWi0jLumr2GlbsyuGHnMpI+e5iYUTfAsmXg5WV2cdu2DT77zJzqJiIilgj192bW7b2JCPZlV8Yxxs5aTWFJmdVliYiF1O1N5CyUlDl4aFYiUZ9+xOKV/yUqO828ITAQ7roLHnwQmjWztkgREakQGerHB2N7c/1bv7Bm31EmzF3HtJHd8fTQ578i7kh/80WqqCzrKPNG3M8T9w3l2R/fNINP48ZmF7d9++Bf/1LwERFxQm3Dg5hxay98PO38tDWdx7/ahGEYVpclIhZQ+BE5k0OHMP7+d4qbRzP0k3/TpCCb45HNzS5u+/bBY49Bw4ZWVykiIn+iV4uGvHZzN+w2+GhlKq/8tNPqkkTEAgo/IqezaxfcfTdGbCy2qVPxO36M7Y2j+fW51/HbuwcmTAB/f6urFBGRKhrSMYKnr44H4NUFO5mTtM/iikSkrmnNj8gfrVsHL7xgNixwOLABq5t14K2EG7jykbFc2yPa6gpFROQcjewbQ0ZeEa8t2MnjX26icaAPQzpGWF2WiNQRhR8RAMOAJUvg+efhhx8qDu/pfQF/b30Zq6Liee7aTgo+IiL1wIOD25CZV8hHK1O576N1fHhHH3q10PRlEXeg8CPuzeGAr782Q09SknnMbofhw/lk4HD+vsucGTr5yjhu6aPgIyJSH9hsNp65Op7MvGJ+2prO2Jmr+M8959E2PMjq0kSklmnNj7in4mKYNQvi4+Haa83g4+MD99wDO3fy/l+erQg+f7u0Hbf3i7W4YBERqUmeHnZev7kbPWIakFtYypj3VnIw+7jVZYlILVP4EfeSnw+vvgqtW8Ott8LWrRAcDJMmmZ3b3nyTjw578tQ3WwC476LW/OXC1tbWLCIitcLP24MZY3rSOiyQQzmFjHlvJdkFxVaXJSK1SOFH3MORI/DUUxATAw88AKmpEBFhNjZISYHnnoPwcL5Yt59/fLERgHH9Y3nw4rbW1i0iIrUq1N+bWbf3JiLYl50Zx7hj1moKS8qsLktEaonCj9Rv+/fDxIlm6HnySTMEtWoFb78Nycnwt79BSAgAa/Zl8dCn6zEMGNk3mn9c3gGbzWZt/SIiUuuahfox6/beBPl6snrfUe79aB2lZQ6ryxKRWqDwI/XTtm1w++3QsiW8/LI53a1bN/jkE9i+He68E3x9K+5uGAYvzNuOw4ArOjfl6aviFXxERNxIu4gg3h3dE29PO/O3pPP4V5sxDMPqskSkhin8SP2yciVcdx3ExcH770NJCQwcaLavXrMGbrwRPDxO+rFfdh9hZXIW3h52HruiA3a7go+IiLvp07IRrw3vis0GH61M4dUFO60uSURqmMKPuD7DgB9/hIsugj594IsvzGPXXAOJibBwIVxyCZxmJMcwDP7vx+0A3NInmqYhfnVYvIiIOJNL45vy9NXxALzy007mJqVYXJGI1CTt8yOuq6wM/vtfc4+etWvNY56eMHKkuZanQ4cqnWbxjkzWpmTj42nnLxe2qsWCRUTEFYzqG0NmbiGvLdzFY19upHGgN5d0jLC6LBGpARr5EddTVATTp0P79uY0trVrwd/f7OK2Z4853a2KwccwDF6evwOA0QkxhAX7nuEnRETEHTx4cVtu6hmFw4B7P1rH6r1ZVpckIjVA4UdcR24uvPgixMaaDQt27YKGDeGJJ8x21S+/DFFRZ3XKn7ZmsGF/Dv7eHtx9gUZ9RETEZLPZ+Oe18QxqH0ZRqYOxs1azIz3P6rJEpJoUfsT5ZWTAo49CdLQ5ne3QIWje3Aw7+/aZLawbNTrr0zocBi/9Nuoz5rwWNAr0qeHCRUTElXl62Pn3Ld3pHh1KzvESxry3kkM5x60uS0SqQeFHnFdyMkyYYO7R89xzkJNjTnV7/33Yvduc5hYYeM6nn7c5ja2Hcgn08eTO/i1rrm4REak3/Lw9mDGmF62aBHAop5Ax760kp6DE6rJE5Bwp/Ijz2bjRbFrQpg288QYUFkLv3mYXt82b4dZbwdu7Wg9R5jix1uf2frE0CKje+UREpP5qEODNrNt7Ex7sw470Y4z7YDWFJWVWlyUi50DhR5zH8uVw5ZXQuTPMmWN2cxsyBBYtghUrzNbV9pp5y3674SA7M44R7OvJ2H6xNXJOERGpv5o38GfW7b0J8vVk5d4s7v94HWUObYIq4moUfsRahgHffgv9+kH//vDdd2bAKe/iNm8eXHjhaffoORelZQ5e/cncuG5c/5aE+HnV2LlFRKT+ah8RzPTRPfH2tPPD5nQe/2oThqEAJOJKFH7EGqWl5uhO584wdCj8/LM5le3OO2HbNvjkE+jWrVYe+qtfD7LncD4N/L24TaM+IiJyFvq2bMSrN3XFZoO5SSm8vnCX1SWJyFlQ+JG6VVBgruNp08Zc17NpEwQFmV3c9u6Ft982b6slJWUOXl1gjvrcdUErAn20z6+IiJydyzo15emrOgLw0vwdfLwyxeKKRKSqdOUndePoUXjzTXj1VcjMNI+FhZkd2+65B0JD66SMz9fsJyWrgMaB3oxOiKmTxxQRkfpnVEIL0nOL+PeiXfzji400CvTh4rhwq8sSkTPQyI/UroMHzVGdmBh47DEz+MTGmkFo716YNKnOgk9RaVnF9IS7L2iFv7eyv4iInLuHLmnLjT2b4zBgwty1rNmXZXVJInIGCj9SO7ZsMdfvxMbCiy9CXh506mSu89mxwxzt8fOr05I+Xb2fA9nHCQvyYWRfjfqIiEj12Gw2nru2E4Pah1FU6uD2mavZlZFndVki8icUfqTmFBXBRx/BBRdAx44wfToUF5/o4rZ+PdxyC3jW/YhLYUkZb/w26jN+YGt8vTzqvAYREal/PD3s/PuW7nSLDiXneAmjZ6wkLafQ6rJE5DQUfqT6kpPhkUcgKsoMN0uXgocHXHutuXfP0qVw+eU12q76bM1NSiEtt5DIEF+G946yrA4REal//Lw9mDGmFy2bBHAwp5Ax760kp6DE6rJE5BQUfuTclJbCV1/BZZdBq1bwwgvmep5mzeCpp2DfPvjvf+H8862ulOPFZby5eDcAEy5qg4+nRn1ERKRmNQzw5oPbexMW5MP29DzGfbCawpIyq8sSkT9Q+JGzc/AgPP20uZbnmmvMTUgNA4YMgS+/NJsYTJ5shiAnMXvFXg4fKyKqoR839GxudTkiIlJPNW/gz6zbexPk48nKvVk88PGvlDm0CaqIM1H4kTNzOOCnn2DYMIiOhieegP37oXFjs5Pbrl1mCLr6akvW8/yZY0WlTFuyB4B7L2qDl4fe8iIiUns6NA3mndE98fawM29zGk98vQnDUAAScRbOdaUqzuXIEZg5E6ZNMwNOuf794e67zTDk42NZeVUx65e9ZOUXE9s4gOu6Oc9olIiI1F8JrRrx8k1dmfDRWj5ckUJ4kC/3Dqq9DbxFpOoUfqQyw4DERDPwfPqp2cENIDgYRo+Gu+6C+Hhra6yi3MIS3llqjvrcP6gNnhr1ERGROnJF56YcPtaRJ77ezP/N30GTIB+G9462uiwRt6fwI6bcXHMPnmnTYMOGE8e7dzf35Bk+HAIDravvHLy3PJmc4yW0DgtkaJdIq8sRERE3M+a8FmTkFfLGot3844uNtIsIolt0A6vLEnFrCj/u7tdfzcAzZw4cO2Ye8/ODm282p7b17Glpi+pzlV1QzIxlyQA8MLgNHnbXew4iIuL6Hr6kHcmH8/nfxjTeXZbMGyMUfkSspPDjjo4fN6e0TZsGK1acON6+vTnKM2oUNHDtf5ynL9tDXlEp7SOCuDy+qdXliIiIm7LZbNx7URv+tzGNHzankZ5bSHiwr9VlibgtLYJwJ9u3w8SJZhvqW281g4+XF9x0EyxeDFu2wH33uXzwycov5v2f9wLw4MVtsWvUR0RELNShaTC9WzSk1GEwNynF6nJE3JrCT31XUgL/+Q8MGmSO7Lz8Mhw9CjEx8NxzkJoKH38MF1zgktPbTuXtJbspKC4jvlkwl8SFW12OiIgIoxJiAJi7MoXiUofF1Yi4L4Wf+iolBR57zNyX54YbYOFCsNth6FD47jvYvRsmTYLw+hUOMvIKmZW4F4CJF7fFVk8CnYiIuLYhHSNoEuRDZl4RP2xOs7ocEae0cOFC4uLiyM3NPem2nJwcOnbsyLJly6r1GAo/9UlZGfzvf2bAiY2Ff/4T0tIgIsIMQsnJ8PXXcPnl4OFhdbW1YtriPRSWOOgaFcrAdmFWlyMiIgKAt6edW35rdT07cZ/F1Yg4p1deeYVx48YRHBx80m0hISHcddddvPTSS9V6DIWf+iA9HaZMgdat4Yor4NtvweGAiy6Czz4zR4GeecYcBarH0nIK+TDJ/IWiUR8REXE2t/SJxtNuY+XeLLYeOvmTbRF3t379ei699NLT3n7JJZewZs2aaj2G04efAwcOMHLkSBo1aoSfnx+dOnVi9erVFbcbhsHkyZNp2rQpfn5+DB48mJ07d1Y6R1ZWFiNGjCA4OJjQ0FDGjh3LsfK2zq7KMMwmBTfdBFFR8I9/wN69ZrOCBx+EbdtgwQK4/nqzqYEbeGPRLopLHfRq0YD+bRpbXY6IiEgl4cG+DOkYAcAHGv0RF/XWW2/RuXNngoODCQ4OJiEhge+//77i9sLCQsaPH0+jRo0IDAxk2LBhpKenV+nc6enpeP3JdaunpyeZmZnVqt+pw8/Ro0c5//zz8fLy4vvvv2fLli383//9Hw1+141s6tSpvPbaa0ybNo2kpCQCAgIYMmQIhYWFFfcZMWIEmzdvZv78+Xz77bcsXbqUO++804qnVH1Hj8Krr0JcHAwcaLasLimBvn1h1iw4cABeegnatbO60jq1/2gBH68yO+hMvLidRn1ERMQplTc++HLdAXKOl1hcjcjZa968Oc8//zxr1qxh9erVXHTRRVx99dVs3rwZgAcffJBvvvmGzz77jCVLlnDw4EGuu+66Kp27WbNmbNq06bS3b9iwgaZNq7mFieHE/v73vxv9+vU77e0Oh8OIiIgwXnzxxYpj2dnZho+Pj/HRRx8ZhmEYW7ZsMQBj1apVFff5/vvvDZvNZhw4cKBKdaSmphqAkZqaeo7PpJocDsNISjKM224zDD8/wzDHfQwjIMAw7rrLMNats6YuJ/LI5+uNmL9/a9z8TqLVpYiIiJyWw+EwLnlpiRHz92+NGcv2WF2OSI1c5zZo0MB49913jezsbMPLy8v47LPPKm7bunWrARiJiWe+RpswYYIRHx9vHD9+/KTbCgoKjPj4eOPee+895zoNwzCceuTn66+/pmfPntxwww2EhYXRrVs3pk+fXnF7cnIyaWlpDB48uOJYSEgIffr0ITExEYDExERCQ0Pp2bNnxX0GDx6M3W4nKSnplI9bVFREbm5uxVdeXl4tPcMqSE+Hnj2hTx94/31zg9JOneDNN+HgQXOj0q5dravPCaQcKeCz1fsBc62PiIiIs7LZbBWjPx+u2IfDYVhckYgpLy+v0vVvUVHRGX+mrKyMjz/+mPz8fBISElizZg0lJSWVrs3bt29PdHR0xbX5n3nsscfIysqibdu2TJ06la+++oqvvvqKF154gXbt2pGVlcWjjz5arefp1OFnz549vPXWW7Rp04YffviBe+65h/vuu49Zs2YBkJZmtooM/0O75vDw8Irb0tLSCAur3PXL09OThg0bVtznj6ZMmUJISEjFV1xcXE0/taoLC4Njx8DHB0aNgp9/hvXr4Z574BSdMNzRqwt2UuowGNC2CT1bNLS6HBERkT91bbdmBPl4sudwPst3Hba6HBEA4uLiKl3/Tpky5bT33bhxI4GBgfj4+HD33XfzxRdfEBcXR1paGt7e3oSGhla6/++vzf9MeHg4v/zyC/Hx8UyaNIlrr72Wa6+9ln/84x/Ex8ezfPnyk677z5ZntX66ljkcDnr27Mlzzz0HQLdu3di0aRPTpk1jzJgxtfa4kyZNYuLEiRXfHzhwwLoAZLPB3LnmpqSNtYj/j/ZkHuOLdRr1ERER1xHg48mwHs2Z+ctePkjcx4C2TawuSYQtW7bQrFmziu99fHxOe9927drx66+/kpOTw3/+8x/GjBnDkiVLaqSOmJgY/ve//3H06FF27dqFYRi0adOm0pr/6nDq8NO0adOTQkeHDh34/PPPAYiIMDumpKenV1r8lJ6eTtffpoJFRESQkZFR6RylpaVkZWVV/Pwf+fj4VPoffqqNlupUjx7WPr4Te3XBThwGDO4QRteoUKvLERERqZKRfWOY+cteFm5LZ//RApo38Le6JHFzQUFBp9xf51S8vb1p3bo1AD169GDVqlW8+uqr3HTTTRQXF5OdnV1p9Cc9Pf20192n06BBA3r16nVWP1MVTj3t7fzzz2f79u2Vju3YsYOYGHOubGxsLBERESxYsKDi9tzcXJKSkkhISAAgISGB7OzsSj3BFy5ciMPhoE+fPnXwLKS27EjP4+v1BwF4YLBGfURExHW0DgukX+vGOAyYk5RidTki1eJwOCgqKqJHjx54eXlVujbfvn07KSkpFdfmf6awsJDnn3+eRx55hEOHDtVKrU498vPggw9y3nnn8dxzz3HjjTeycuVK3nnnHd555x3AXDT4wAMP8Oyzz9KmTRtiY2N5/PHHiYyM5JprrgHMkaJLL72UcePGMW3aNEpKSpgwYQLDhw8nMjLSwmcn1fXKTzswDLi0YwTxzUKsLkdEROSsjEqIYfmuw3y8MoX7B7XB18vD6pJEzmjSpElcdtllREdHk5eXx9y5c1m8eDE//PADISEhjB07lokTJ9KwYUOCg4O59957SUhIoG/fvmc899ixY/H19aV9+/YMHjy4on12TXLq8NOrVy+++OILJk2axNNPP01sbCyvvPIKI0aMqLjP3/72N/Lz87nzzjvJzs6mX79+zJs3D19f34r7zJkzhwkTJjBo0CDsdjvDhg3jtddes+IpSQ3ZcjCX/21Mw2aDB7XWR0REXNCg9mFEhvhyMKeQ7zYcYliP5laXJHJGGRkZjB49mkOHDhESEkLnzp354YcfuPjiiwF4+eWXK663i4qKGDJkCG+++WaVzr1o0SLmz59Px44defTRR8nIyDipcVl12QzDUI/FM9i/fz9RUVGkpqbSvLn+YXIG4z5Yzfwt6VzZuSn/vqW71eWIiIickzcW7eLFH7bTJSqUr8afb3U54oac6Tr35ptvJigoiLZt2zJt2jR27dpV44/h1Gt+RE5lw/5s5m9Jx27TWh8REXFtw3tF4e1hZ31qNutTs60uR8RSM2bMoEWLFqSnp1daN1STnHram8ipvDx/BwDXdG1G67BAi6sRERE5d40Cfbiic1O+WHeADxL38X/qXCpuzN/fn3/84x+1+hga+RGXsmbfURZtz8TDbuO+QW2sLkdERKTaRiWYXWy/2XCQrPxii6sRqd8UfsSllI/6DOvejBaNAyyuRkREpPq6RYXSqVkIxaUOPl2danU5IvWawo+4jKQ9R1i+6zBeHjbuvUijPiIiUj/YbLaK0Z8PV+yjzKFeVCK1ReFHXIJhGPzfb6M+N/aMIqqhdsIWEZH646oukYT6e7H/6HEWbcuwuhyRekvhR1zCL7uPsDI5C28PO+MHtra6HBERkRrl6+XBjT2jAPhgxT6LqxGx3rJlyxg5ciQJCQkcOHAAgNmzZ7N8+fJqnVfhR5yeYRj834/bAbilTzSRoX4WVyQiIlLzRvaJwWaDpTsyST6cb3U5Ipb5/PPPGTJkCH5+fqxbt46ioiIAcnJyeO6556p1boUfcXpLdmSyNiUbH087f7mwldXliIiI1IroRv4MbGfuZv+hRn/EjT377LNMmzaN6dOn4+XlVXH8/PPPZ+3atdU6t8KPODXDMHjpt7U+oxNiCAv2tbgiERGR2lPe+ODT1akUFJdaXI2INbZv386AAQNOOh4SEkJ2dna1zq3wI07tp60ZbNifg7+3B3ddoFEfERGp3y5o04SYRv7kFZby1a8HrS5HxBIRERHs2rXrpOPLly+nZcuW1Tq3wo84LYfjxKjPmPNa0DjQx+KKREREapfdbmNUX3P054PEfRiG2l6L+xk3bhz3338/SUlJ2Gw2Dh48yJw5c3j44Ye55557qnVuzxqqUaTG/bA5ja2Hcgn08eTO/tVL+SIiIq7ihh5R/OvH7Ww9lMuafUfp2aKh1SWJ1KlHHnkEh8PBoEGDKCgoYMCAAfj4+PDwww9z7733VuvcGvkRp1TmMHj5J3PU5/bzW9AgwNviikREROpGiL8XV3dpBsCsRDU+EPdjs9l49NFHycrKYtOmTaxYsYLMzEyeeeaZap9b4Uec0rcbDrIj/RjBvp6M1aiPiIi4mfLGB/M2HSIjr9DiakTqTklJCYMGDWLnzp14e3sTFxdH7969CQwMrJHzK/yI0yktc/DqTzsBGNe/JSF+Xmf4CRERkfolvlkIPWIaUFJm8PHKVKvLEakzXl5ebNiwodbOr/AjTuerXw+y53A+of5e3NYv1upyRERELDH6t9GfuUkplJQ5LK5GpO6MHDmSGTNm1Mq51fBAnEpJmYNXF5ijPncNaEWgj96iIiLini6Nj6BxoDdpuYXM35LO5Z2aWl2SSJ0oLS3lvffe46effqJHjx4EBARUuv2ll14653PrylKcyudr9pOSVUDjQG/GnBdjdTkiIiKW8fH0YHivaP69aBcfJO5V+BG3sWnTJrp37w7Ajh07Kt1ms9mqdW6FH3EaxaUOXl9obmh19wWt8PfW21NERNzbLX2ieWvJblbsyWJHeh5tw4OsLkmk1i1atKjWzq01P+I0PlmdyoHs44QF+TCyr0Z9REREIkP9uLhDOACz1fZapNr00bo4hcKSMt74bdRn/MDW+Hp5WFyRiIiIcxidEMO8zWn8d+1+/nZpO4J81QVV6renn376T2+fPHnyOZ9b4UecwkcrU0jLLSQyxJfhvaOsLkdERMRpJLRqROuwQHZlHOO/aw8w5rwWVpckUqu++OKLSt+XlJSQnJyMp6cnrVq1UvgR13a8uIw3Fu0GYMJFbfDx1KiPiIhIOZvNxuiEGCZ/tZnZK/YxOiGm2ou+RZzZunXrTjqWm5vLrbfeyrXXXlutc2vNj1hu9oq9HD5WRPMGflzfo7nV5YiIiDida7s1I8Dbg10Zx0jcfcTqckTqXHBwME899RSPP/54tc6j8COWOlZUyrQlewC4b1AbvD31lhQREfmjIF8vrutufkA4K3GvtcWIWCQnJ4ecnJxqnUPT3sRSs37ZS1Z+MbGNA7iuWzOryxEREXFaoxJimL1iH/O3pHMw+ziRoX5WlyRSK1577bVK3xuGwaFDh5g9ezaXXXZZtc6t8COWyS0s4Z2l5qjP/YPa4OmhUR8REZHTaRseRELLRiTuOcLcpBQeHtLO6pJEasXLL79c6Xu73U6TJk0YM2YMkyZNqta5FX7EMu8tTybneAmtwwIZ2iXS6nJERESc3uiEGBL3HOHjVSncO6i1mgRJvbR48WKioqKw2yt/MG4YBqmpqQQFnftmv/qoXSyRU1DCjGXJADwwuA0ednWtEREROZOL48KJCPbl8LFivt+YZnU5IrWiZcuWHD58+KTjWVlZxMbGVuvcCj9iienL9pBXVEr7iCAuj29qdTkiIiIuwdPDzi19ogH4QI0PpJ4yDOOUx48dO4avr2+1zn1W0966detWpb7ya9euPeeCpP7Lyi/m/Z/LR33aYteoj4iISJUN7x3F6wt3sjYlm00HcohvFmJ1SSI1YuLEiYC5t9XkyZPx9/evuK2srIykpCS6du1arcc4q/BzzTXXVOvBRADeXrKb/OIy4psFM6RjuNXliIiIuJSwIF8ui2/K1+sPMjtxHy9c39nqkkRqRPnmpoZhsHHjRry9vStu8/b2pkuXLjz88MPVeoyzCj9PPPFEtR5MJDOvqGJ/gokXt9UO1SIiIudgdEIMX68/yJe/HmDS5e0J9fc+8w+JOLlFixYBcNttt/Hqq68SHBxc44+hNT9Sp95avJvCEgddo0IZ2C7M6nJERERcUo+YBnRoGkxRqYPPVu+3uhyRGvX+++/XSvCBsxz5GThw4Bk/qbfZbCxYsKBaRUn9lJZTyIdJ+wCN+oiIiFSHzWZjTEIMj/x3Ix8m7WNsv1itoZV6Z8uWLaSkpFBcXFzp+FVXXXXO5zyr8PNnC4zy8vKYO3cuRUVF51yM1G9vLNpFcamDXi0a0L9NY6vLERERcWlXd23Gc//byr4jBSzZmakZFVJv7Nmzh2uvvZaNGzdis9kqur+Vf3BeVlZ2zuc+q/Dzx91WAUpLS3njjTf45z//SbNmzXjmmWfOuRipvw5kH+fjVSkATLy4nUZ9REREqsnP24MbekYxY3kyH/yyV+FH6o3777+f2NhYFixYQGxsLCtXruTIkSM89NBD/Otf/6rWuau15mfOnDm0a9eOF154gSeffJKtW7cyfPjwahUk9dO/F+6kpMwgoWUjElo1srocERGRemFk3xgAFu/IJOVIgcXViNSMxMREnn76aRo3bozdbsdut9OvXz+mTJnCfffdV61zn1P4mTdvHl27duUvf/kLt956Kzt37uQvf/kLnp5nNZAkbiLlSEHFYsyHLmlrcTUiIiL1R2zjAC5o2wTDoGJdrYirKysrIygoCIDGjRtz8OBBAGJiYti+fXu1zn1W4WflypUMHDiQa6+9loEDB7J7924ef/xxAgICqlWE1G+vLdxJqcNgQNsm9GzR0OpyRERE6pXRCebozyerUjlefO5rIUScRXx8POvXrwegT58+TJ06lZ9//pmnn36ali1bVuvcZzVU07dvX/z8/Lj77ruJjY1l7ty5p7xfdYejpP7Yk3mM/641R30mXqxRHxERkZp2YbswmjfwY//R43yz/iA39oqyuiSRannsscfIz88H4Omnn+bKK6+kf//+NGrUiE8++aRa5z6r8BMdHY3NZuPLL7887X1sNpvCj1R4dcFOHAYMah9G16hQq8sRERGpdzzsNkb2jeH577fxwYq93NCzuRoLiUsbMmRIxZ9bt27Ntm3byMrKokGDBtV+b59V+Nm7d2+1Hkzcy470PL5eb87RfFCjPiIiIrXmpp5RvDx/B5sO5LIuNZvu0Q2sLknknJSUlHDppZcybdo02rRpU3G8YcOaWTpx1g0PHA4H7733HldeeSXx8fF06tSJq6++mg8++KCiB7cIwKs/7cQw4NKOEcQ3C7G6HBERkXqrQYA3Q7tEAjA7UY0PxHV5eXmxYcOGWjv/WYUfwzAYOnQod9xxBwcOHKBTp0507NiRvXv3cuutt3LttdfWVp3iYrYczOW7jYew2eCBi9uc+QdERESkWsobH3y34RCHj2nTeXFdI0eOZMaMGbVy7rOa9jZz5kyWLVvGggULGDhwYKXbFi5cyDXXXMMHH3zA6NGja7RIcT3v/ZwMwBWdmtI+ItjiakREROq/zs1D6RIVyvrUbD5Zlcr4ga2tLknknJSWlvLee+/x008/0aNHj5M6S7/00kvnfO6zGvn56KOP+Mc//nFS8AG46KKLeOSRR5gzZ845FyP1R+LuIwDc2FMdZ0REROrKmN9Gf+as2EdpmcPiakTOzaZNm+jevTtBQUHs2LGDdevWVXz9+uuv1Tr3WY38bNiwgalTp5729ssuu4zXXnutWgWJ69t/tIAD2cfxsNvoEaMFlyIiInXl8k5Nefa7rRzMKWTBtgyGdIywuiSRs7Zo0aJaO/dZjfxkZWURHh5+2tvDw8M5evRotYsS17ZqbxYA8c1CCPA5q3wtIiIi1eDr5cFNv+3z80HiXmuLEamGZcuWMXLkSM477zwOHDgAwOzZs1m+fHm1zntW4aesrAxPz9NfzHp4eFBaWlqtgsT1rUw2A3Cf2JppSSgiIiJVN6JPNHYb/LzrCLsyjlldjshZ+/zzzxkyZAh+fn6sXbuWoiKzgUdOTg7PPfdctc59Vh/LG4bBrbfeio+PzylvLy9M3NvKZHO9T68WCj8iIiJ1rXkDfwZ1CGf+lnQ+XLGPJ6/qaHVJImfl2WefZdq0aYwePZqPP/644vj555/Ps88+W61zn1X4GTNmzBnvo05v7u3wsSJ2Z+YD0KuF1vuIiIhYYXRCDPO3pPP5mv08PKQdgZqGLi5k+/btDBgw4KTjISEhZGdnV+vcZ/U34f3336/Wg0n9tyrZXO/TPiKIUH9vi6sRERFxT+e3akzLxgHsOZzPF+sOMKpvjNUliVRZREQEu3btokWLFpWOL1++nJYtW1br3Ge15kfkTJJ+Cz+9td5HRETEMna7jZG/BZ7ZiXsxDMPiikSqbty4cdx///0kJSVhs9k4ePAgc+bM4eGHH+aee+6p1rk1Bio1qrzTm8KPiIiItYb1aM6/ftzOjvRjJCVn0bdlI6tLEqmSRx55BIfDwaBBgygoKGDAgAH4+Pjw8MMPc++991br3Br5kRqTW1jClkO5APRWswMRERFLhfh5cU23ZgDMTtxncTUiVWez2Xj00UfJyspi06ZNrFixgszMTJ555plqn1vhR2rMmr1HMQxo0cifsGBfq8sRERFxe6MTzKlv8zankZZTaHE1ImfH29ubDh060KtXLwIDA2vknAo/UmO03kdERMS5tI8IpneLhpQ5DOauTLG6HJEqmzFjBvHx8fj6+uLr60t8fDzvvvtutc+r8CM1pnx/n96xmlMsIiLiLEafZ47+fLQyheJSh8XViJzZ5MmTuf/++xk6dCifffYZn332GUOHDuXBBx9k8uTJ1Tq3Gh5IjTheXMaG/TkA9NHIj4iIiNMY0jGCsCAfMvKK+GFzGkO7RFpdksifeuutt5g+fTo333xzxbGrrrqKzp07c++99/L000+f87k18iM1Yl3qUUodBk1DfGnewM/qckREROQ3Xh52bu4dDcAHiXutLUakCkpKSujZs+dJx3v06EFpaWm1zq3wIzVi5W/rfXq1aIjNZrO4GhEREfm9W/pE42m3sWrvUbb+1plVxFmNGjWKt95666Tj77zzDiNGjKjWuTXtTWrESjU7EBERcVrhwb4MiY/guw2H+CBxH1Ou62R1SSJ/asaMGfz444/07dsXgKSkJFJSUhg9ejQTJ06suN9LL710VudV+JFqKy51sDblKKD1PiIiIs5qdN8YvttwiC/XHeCRy9oT4udldUkip7Rp0ya6d+8OwO7duwFo3LgxjRs3ZtOmTRX3O5fZRgo/Um0bD+RQWOKgYYA3rcNqpge7iIiI1KzesQ1pFx7E9vQ8/rNmP2P7xVpdksgpLVq0qNbOrfAj1bZqb/l6nwZa7yMiIuKkbDYboxJieOzLTXy4Yh+3ndcCu12/t8U5FRYWsmHDBjIyMnA4TrRot9lsDB069JzPq/Aj1fb7ZgciIiLivK7t1owXvt9G8uF8lu86zIC2TawuSeQk8+bNY9SoURw5cuSk22w2G2VlZed8bnV7k2opcxgVIz99tLmpiIiIUwvw8WRYj+YAfJC4z+JqRE7t3nvv5cYbb+TQoUM4HI5KX9UJPqDwI9W0LS2XvMJSAn086dA0yOpyRERE5AxGJcQAsGBbOqlZBRZXI3Ky9PR0Jk6cSHh4eI2fW+FHqqV8yluPmAZ4eujtJCIi4uxaNQmkX+vGGAbMSUqxuhyRk1x//fUsXry4Vs6tNT9SLeVT3rS/j4iIiOsYnRDD8l2H+WRVCg8MboOvl4fVJYlU+Pe//80NN9zAsmXL6NSpE15elduy33fffed8boUfOWeGYVSM/Gh/HxEREdcxqEM4zUL9OJB9nO82HKpYByTiDD766CN+/PFHfH19Wbx4caVuwjabrVrhR/OU5JztOZzP4WPFeHva6dQ8xOpyREREpIo87DZu6RMNwAeJe60tRuQPHn30UZ566ilycnLYu3cvycnJFV979uyp1rkVfuSclY/6dIsKxcdTw+UiIiKuZHivKLw97Kzfn8P61GyryxGpUFxczE033YTdXvNRReFHzpmmvImIiLiuRoE+XNm5KaC21+JcxowZwyeffFIr59aaHzln5eGnt/b3ERERcUmjEmL477oDfLPhII9e0YGGAd5WlyRCWVkZU6dO5YcffqBz584nNTx46aWXzvncCj9yTvYfLeBA9nE87Ta6x4RaXY6IiIicg65RoXRqFsLGAzl8siqVey5sZXVJImzcuJFu3boBsGnTpkq3/b75wblQ+JFzUt7iumOzEPy99TYSERFxRTabjVEJMfztPxv4cMU+7hzQEg979S4uRapr0aJFtXZurfmRc6L1PiIiIvXDVV0iCfX34kD2cRZty7C6HJFapfAj5ySpfL1PC4UfERERV+br5cFNPaMA+GCFGh+Ic1i2bBkjR44kISGBAwcOADB79myWL19erfO6VPh5/vnnsdlsPPDAAxXHCgsLGT9+PI0aNSIwMJBhw4aRnp5e6edSUlK44oor8Pf3JywsjL/+9a+UlpbWcfX1R2ZeEXsy87HZoJfCj4iIiMsb2TcGmw2W7shkT+Yxq8sRJzZlyhR69epFUFAQYWFhXHPNNWzfvr3Sfapyff5nPv/8c4YMGYKfnx/r1q2jqKgIgJycHJ577rlq1e8y4WfVqlW8/fbbdO7cudLxBx98kG+++YbPPvuMJUuWcPDgQa677rqK28vKyrjiiisoLi7ml19+YdasWcycOZPJkyfX9VOoN1b/tt6nXXgQIf5eZ7i3iIiIOLuohv4MbBcGwIcrUiyuRpzZkiVLGD9+PCtWrGD+/PmUlJRwySWXkJ+fX3GfM12fn8mzzz7LtGnTmD59eqVOb+effz5r166tVv0uEX6OHTvGiBEjmD59Og0aNKg4npOTw4wZM3jppZe46KKL6NGjB++//z6//PILK1asAODHH39ky5YtfPjhh3Tt2pXLLruMZ555hjfeeIPi4mKrnpJLq5jypvU+IiIi9cbohBgAPluTSkGxZsjIqc2bN49bb72Vjh070qVLF2bOnElKSgpr1qwBqnZ9fibbt29nwIABJx0PCQkhOzu7WvW7RPgZP348V1xxBYMHD650fM2aNZSUlFQ63r59e6Kjo0lMTAQgMTGRTp06ER4eXnGfIUOGkJuby+bNm0/5eEVFReTm5lZ85eXl1cKzcl0rFX5ERETqnQFtmtCikT95haV89etBq8uROpaXl1fp+rd8qtmZ5OTkANCwoXldWJXr8zOJiIhg165dJx1fvnw5LVu2rNI5Tsfpw8/HH3/M2rVrmTJlykm3paWl4e3tTWhoaKXj4eHhpKWlVdzn98Gn/Pby205lypQphISEVHzFxcXVwDOpH3KOl7A1LRdQswMREZH6xG63MbKvOfoz65e9GIZhcUVSl+Li4ipd/57q2vuPHA4HDzzwAOeffz7x8fFA1a7Pz2TcuHHcf//9JCUlYbPZOHjwIHPmzOHhhx/mnnvuOevn9ntOvUFLamoq999/P/Pnz8fX17fOHnfSpElMnDix4vsDBw4oAP1mzb4sDANiGwcQFlx3/09ERESk9t3QI4p//bidbWl5rN53VI2N3MiWLVto1qxZxfc+Pj5n/Jnx48ezadOmandg+6NHHnkEh8PBoEGDKCgoYMCAAfj4+PDwww9z7733VuvcTj3ys2bNGjIyMujevTuenp54enqyZMkSXnvtNTw9PQkPD6e4uPikuX/p6elEREQA5rDZH7tLlH9ffp8/8vHxITg4uOIrKCio5p+ci1KLaxERkforxN+La7qaF8AfJKrttTsJCgqqdP17pvAzYcIEvv32WxYtWkTz5s0rjkdERJzx+vx0nn76aQoKCrDZbDz66KNkZWWxadMmVqxYQWZmJs8888w5P79yTh1+Bg0axMaNG/n1118rvnr27MmIESMq/uzl5cWCBQsqfmb79u2kpKSQkJAAQEJCAhs3biQj48SmXfPnzyc4OFijOedgldb7iIiI1Gujfmt88P3GQ2TkFlpcjTgbwzCYMGECX3zxBQsXLiQ2NrbS7T169Djj9fnpPPXUUxw7dqLVure3N3FxcfTu3ZvAwMAaqd+pp70FBQVVzB8sFxAQQKNGjSqOjx07lokTJ9KwYUOCg4O59957SUhIoG/fvgBccsklxMXFMWrUKKZOnUpaWhqPPfYY48ePr9JwnpxwvLiMDfvNRW0KPyIiIvVTx8gQesQ0YM2+o8xdmcIDg9taXZI4kfHjxzN37ly++uorgoKCKtbxhISE4OfnR0hIyBmvz0+nLtaZOfXIT1W8/PLLXHnllQwbNowBAwYQERHBf//734rbPTw8+Pbbb/Hw8CAhIYGRI0cyevRonn76aQurdk3rUo5S6jBoGuJL8wZ+VpcjIiIitWTMeS0A+HDFPgpLyqwtRpzKW2+9RU5ODhdeeCFNmzat+Prkk08q7nOm6/M/Y7PZaqt08/yGWnmc0f79+4mKiiI1NbXSnEZ38/L8Hby6YCdXd43k1eHdrC5HREREaklJmYMLX1zMgezjPHdtJ27pE211SVJLnOk61263ExIScsYAlJWVdc6P4dTT3sS5aH8fERER9+DlYWdsv1ie/nYL05ft4aZeUXjYa/cTeREw1/2EhITU2vkVfqRKiksdrEs9CkAfhR8REZF676ZeUby6YCfJh/OZvyWdS+P/vFOXSE0YPnw4YWFhtXZ+l1/zI3Vj44EcCkscNAzwplWTmum2ISIiIs4rwMeTkX3N6W7vLN1tcTXiDmp7vQ8o/EgVlU9569WiQZ28MUVERMR6Y85rgbennbUp2azee+7rLESqQt3exGmsTD4CQO/YRhZXIiIiInUlLMiXYd3NTU+nLdljcTVS3zkcjlqd8gYKP1IFZQ6D1Xu13kdERMQd3dG/JTYb/LQ1nV0Zx878AyJOTOFHzmhbWi55RaUE+njSoWmw1eWIiIhIHWrVJJCLO4QD8O4yjf6Ia1P4kTMqX+/Ts0UDtbkUERFxQ3dd0BKA/649QEZuocXViJw7hR85oxPNDjTlTURExB31iGlIj5gGFJc5mPnLXqvLETlnCj/ypwzDqAg/Wu8jIiLivu4cYI7+fLhiH8eKSi2uRuTcKPzIn9qdmc+R/GJ8PO10al57u+2KiIiIc7u4QzgtGweQW1jKJ6tSrS5H5Jwo/MifKh/16RYdio+nh8XViIiIiFXsdhvjfhv9mbFsDyVlDosrEjl7Cj/yp1b9tqGZ9vcRERGRa7s1o3GgDwdzCvluwyGryxE5awo/8qfKR356q9mBiIiI2/P18uC281sA8PbSPRiGYW1BImdJ4UdOa//RAg5kH8fTbqN7TKjV5YiIiIgTGNknBn9vD7YeymXZzsNWlyNyVhR+5LTKR33im4Xg7+1pcTUiIiLiDEL8vbipVxQA7yzVpqfiWhR+5LTU4lpEREROZWy/WDzsNpbvOsymAzlWlyNSZQo/clorK5odKPyIiIjICc0b+HNl56YATF+m0R9xHQo/ckqZeUXsyczHZoOeMQo/IiIiUln5pqffbjjE/qMFFlcjUjUKP3JK5S2u24UHEeLvZXE1IiIi4mw6RobQr3VjyhwGM5YnW12OSJUo/Mgpab2PiIiInEn56M8nq1LJLii2uBqRM1P4kVNKStbmpiIiIvLn+rdpTIemwRQUl/Hhin1WlyNyRgo/cpKc4yVsS8sFoFdsA4urEREREWdls9m467fRn5m/7KOwpMziikT+nMKPnGTNviwMA1o2DiAsyNfqckRERMSJXdG5KZEhvhw+VsQX6w5YXY7In1L4kZOUT3nr1ULrfUREROTPeXnYGdvfHP2ZvnQPDodhcUUip6fwIydZmaz9fURERKTqhveKItjXkz2H85m/Nd3qckROS+FHKikoLmXjfnOnZoUfERERqYoAH09G9o0B4J2l2vRUnJfCj1SyLiWbUodBZIgvzRv4WV2OiIiIuIhbz2uBt4edNfuOsmZfltXliJySwo9U8vspbzabzeJqRERExFWEBftyXfdmALy9RKM/4pwUfqSS8vDTS1PeRERE5Czd8Vvjg/lb09mdecziakROpvAjFYpLHaxNOQpAH4UfEREROUutwwIZ3CEcw4B3l2n0R5yPwo9U2Hggm6JSBw0DvGnVJNDqckRERMQF3X2BOfrz+doDZOQVWlyNSGUKP1KhfH+f3i203kdERETOTc8WDekeHUpxqYNZv+y1uhyRShR+pMIq7e8jIiIiNeDOAa0A+HBFCvlFpRZXI3KCwo8AUOYwWL3XXO+j8CMiIiLVcXFcOLGNA8g5XsInq1KtLkekgsKPALD1UC55RaUE+XjSoWmw1eWIiIiIC/Ow2xj3W+e3GcuTKSlzWFyRiEnhR4ATLa57tGiAh13rfURERKR6ruvejMaB3hzIPs7/Nh6yuhwRQOFHfrNS631ERESkBvl6eTAmoQVgbnpqGIa1BYmg8COAYRis2muGH+3vIyIiIjVlVEIMfl4ebDmUy8+7jlhdjojCj8DuzHyO5Bfj42mnU7NQq8sRERGReiLU35ubekUB8PbS3RZXI6LwI5yY8tYtOhRvT70lREREpOaM7ReLh93Gsp2H2Xwwx+pyxM3pSldYmWwOQ/eObWRxJSIiIlLfRDX05/JOTQGYvnSPxdWIu1P4cXOGYZCUrPU+IiIiUnvuGmC2vf5mwyH2Hy2wuBpxZwo/bm7/0eMcyinE026jW3So1eWIiIhIPRTfLITzWzeizGHw3vK9Vpcjbkzhx82Vd3nr1DwEf29Pi6sRERGR+urOAa0A+HhVCjkFJRZXI+5K4cfNaX8fERERqQsD2jSmfUQQBcVlfJi0z+pyxE0p/Li5ivDTQuFHREREao/NZuOuC8y1P+//vJfCkjKLKxJ3pPDjxjLyCtlzOB+bDXrGKPyIiIhI7bqycySRIb4cPlbEl+sOWF2OuCGFHze2KvkoAO0jggnx97K4GhEREanvvDzs3N4vFoB3lu3B4TAsrkjcjcKPGytvdqAW1yIiIlJXhveOJsjXkz2Z+SzYlmF1OeJmFH7cWJKaHYiIiEgdC/TxZGTfGADeXrLb4mrE3Sj8uKmcghK2peUC0EvNDkRERKQO3XZeC7w97Kzed5Q1+45aXY64EYUfN7V6XxaGAS0bB9AkyMfqckRERMSNhAX7ck23SADeWarRH6k7Cj9uSvv7iIiIiJXuHGC2vf5xSzp7Mo9ZXI24C4UfN7Vyr8KPiIiIWKd1WBCDO4RhGDB9WbLV5YibUPhxQwXFpWzcnwMo/IiIiIh17hzQCoDP1+4nM6/I4mrEHSj8uKF1KdmUOgwiQ3xp3sDf6nJERETETfVq0YBu0aEUlzr4IHGv1eWIG1D4cUNqcS0iIiLOwGazcddva38+SNxHflGpxRVJfafw44ZWJh8BoHdsI4srEREREXd3cVwELRr5k3O8hE9Xp1pdjtRzCj9upqi0jHUp2YBGfkRERMR6HnYbd/Q3R39mLE+mtMxhcUVSnyn8uJlNB3IoKnXQKMCbVk0CrC5HREREhOt7NKdRgDf7jx7nf5vSrC5H6jGFHzfz+/U+NpvN4mpEREREwNfLgzHntQDg7SW7MQzD2oKk3lL4cTPlm5v2aqEpbyIiIuI8RvWNwc/Lg80Hc/ll9xGry5F6SuHHjZQ5DFbvPQpovY+IiIg4lwYB3tzUKwqAt5fusbgaqa8UftzI1kO5HCsqJcjHkw5Ng60uR0RERKSSsf1isdtg6Y5MthzMtbocqYcUftxI+ZS3ni0a4GHXeh8RERFxLlEN/bm8U1MApi/T6I/UPIUfN7KyotmB9vcRERER53TXgFYAfLP+IAezj1tcjdQ3Cj9uwjAMVu4tDz8NLK5GRERE5NQ6NQ/hvFaNKHUYvLc82epypJ5R+HETuzOPkZVfjI+nnU7NQq0uR0REROS07hxgbnr60coUco6XWFyN1CcKP26ifH+f7tEN8PbU/3YRERFxXhe0bUL7iCDyi8uYk7TP6nKkHtFVsJtY9bvNTUVEREScmc1mY1x/c/Tn/Z/3UlRaZnFFUl8o/LgBwzAqRn76KPyIiIiICxjaJZKmIb5k5hXx5boDVpcj9YTCjxvYf/Q4h3IK8bTb6BatZgciIiLi/Lw97dx+fiwA7yzdg8NhWFyR1AcKP26gvMV1p+Yh+Hl7WFyNiIiISNUM7x1FkI8nuzPzWbgtw+pypB5Q+HEDK7XeR0RERFxQkK8XI/rGAOboj0h1Kfy4gfL9fbTeR0RERFzNbee3wMvDxsq9WaxNOWp1OeLiFH7quYy8QpIP52OzQY8YhR8RERFxLeHBvlzTtRkA7yzR6I9Uj8JPPbcq2fyEpENEMCF+XhZXIyIiInL2yjc9/WFLGsmH8y2uRlyZwk89tzL5CKD1PiIiIuK62oQHMah9GIYB05dp9EfOncJPPZekZgciIiJSD5SP/vxnzX4OHyuyuBpxVQo/9VhOQQnb0/MA6NVC4UdERERcV+/YhnSJCqW41MEHv+y1uhxxUQo/9djqfVkYBrRsEkCTIB+ryxERERE5Zzabjbt/G/35YMU+CopLLa5IXJFTh58pU6bQq1cvgoKCCAsL45prrmH79u2V7lNYWMj48eNp1KgRgYGBDBs2jPT09Er3SUlJ4YorrsDf35+wsDD++te/Ulpa///ClO/voxbXIiIiUh9c0jGCFo38yS4o4dNVqVaX45aWLl3K0KFDiYyMxGaz8eWXX1a63TAMJk+eTNOmTfHz82Pw4MHs3LnTmmJPwanDz5IlSxg/fjwrVqxg/vz5lJSUcMkll5Cff6LLx4MPPsg333zDZ599xpIlSzh48CDXXXddxe1lZWVcccUVFBcX88svvzBr1ixmzpzJ5MmTrXhKdap8vY+mvImIiEh94GG3Mba/Ofrz7vJkSsscFlfkfvLz8+nSpQtvvPHGKW+fOnUqr732GtOmTSMpKYmAgACGDBlCYWFhHVd6ajbDMAyri6iqzMxMwsLCWLJkCQMGDCAnJ4cmTZowd+5crr/+egC2bdtGhw4dSExMpG/fvnz//fdceeWVHDx4kPDwcACmTZvG3//+dzIzM/H29j7j4+7fv5+oqChSU1Np3rx5rT7HmpJfVEqXp36k1GGw/O8Dad7A3+qSRERERKqtsKSM855fSFZ+Ma/f3I2hXSKtLsmlVec612az8cUXX3DNNdcA5qhPZGQkDz30EA8//DAAOTk5hIeHM3PmTIYPH17T5Z81px75+aOcnBwAGjY0RzLWrFlDSUkJgwcPrrhP+/btiY6OJjExEYDExEQ6depUEXwAhgwZQm5uLps3bz7l4xQVFZGbm1vxlZeXV1tPqdasS8mm1GHQLNRPwUdERETqDV8vD8YktADg7aW7caHP8Z1aXl5epevfoqKz76iXnJxMWlpapWvzkJAQ+vTpU3FtbjWXCT8Oh4MHHniA888/n/j4eADS0tLw9vYmNDS00n3Dw8NJS0uruM/vg0/57eW3ncqUKVMICQmp+IqLi6vhZ1P7Vu5Vi2sRERGpn0YlxODrZWfTgVwSdx+xupx6IS4urtL175QpU876HOXX1qe69j7ddXddc5nwM378eDZt2sTHH39c6481adIkcnJyKr62bNlS649Z07S5qYiIiNRXDQO8ubFnFABvL9WmpzVhy5Ytla5/J02aZHVJtcIlws+ECRP49ttvWbRoUaW5iBERERQXF5OdnV3p/unp6URERFTc54/d38q/L7/PH/n4+BAcHFzxFRQUVIPPpvYVlZaxLiUbUPgRERGR+umOfi2x22DJjky2Hsq1uhyXFxQUVOn618fn7LdJKb+2PtW19+muu+uaU4cfwzCYMGECX3zxBQsXLiQ2NrbS7T169MDLy4sFCxZUHNu+fTspKSkkJCQAkJCQwMaNG8nIyKi4z/z58wkODnbJ6WxVsXF/DkWlDhoHetOycYDV5YiIiIjUuOhG/lzWqSkA0zX64xRiY2OJiIiodG2em5tLUlJSxbW51TytLuDPjB8/nrlz5/LVV18RFBRUMVcwJCQEPz8/QkJCGDt2LBMnTqRhw4YEBwdz7733kpCQQN++fQG45JJLiIuLY9SoUUydOpW0tDQee+wxxo8ff06J1hX8vsW1zWazuBoRERGR2nHXgJZ8t+EQX68/yMND2hEZ6md1SfXesWPH2LVrV8X3ycnJ/PrrrzRs2JDo6GgeeOABnn32Wdq0aUNsbCyPP/44kZGRFR3hrObU4eett94C4MILL6x0/P333+fWW28F4OWXX8ZutzNs2DCKiooYMmQIb775ZsV9PTw8+Pbbb7nnnntISEggICCAMWPG8PTTT9fV06hz5ZubasqbiIiI1Gedm4fSt2VDVuzJ4v2fk3n0ivo5q8eZrF69moEDB1Z8P3HiRADGjBnDzJkz+dvf/kZ+fj533nkn2dnZ9OvXj3nz5uHr62tVyZW41D4/VnGlfX7KHAZdnvqRY0WlfHdfPzpGhlhdkoiIiEitWbQ9g9veX0Wgjyc/P3IRIX5eVpfkUlzpOrcmOPWaHzl7Ww/lcqyolCBfT9pHBFtdjoiIiEiturBtE9qFB3GsqJS5SSlWlyNOTuGnnilf79MzpgEedq33ERERkfrNZrMxbkBLAN7/OZmi0jKLKxJnpvBTz5zY36eRxZWIiIiI1I2rukQSEexLRl4RX/160OpyxIkp/NQjhmGwau9RQM0ORERExH14e9q5vV8LAKYt2U1JmcPagsRpKfzUI7szj5GVX4yvl51OzdToQERERNzHzb2jaeDvxZ7MfGb+vNfqcsRJKfzUI+XrfbpHN8DbU/9rRURExH0E+XrxyGXtAXjlpx2k5RRaXJE4I10h1yPa30dERETc2Q09ougeHUp+cRnPfrfF6nLECSn81BOGYZC057fw00LhR0RERNyP3W7jmWvisdvg2w2H+HnXYatLEiej8FNP7D96nLTcQjztNrpFN7C6HBERERFLdIwMYXRCCwAe/2qTWl9LJQo/9UT5lLfOzUPw8/awuBoRERER60y8pC2NA33Yk5nPu8uSrS5HnIjCTz1xYr2P9vcRERER9xbs68WjV5jND15fuJP9RwssrkichcJPPbFyrxl++qjZgYiIiAjXdG1Gn9iGFJY4ePobNT8Qk8JPPZCRW0jy4XxsNugeo/U+IiIiIjab2fzA027jxy3pLNqWYXVJ4gQUfuqB8lGfDhHBhPh5WVyNiIiIiHNoGx7E7f1iAXji680Ulqj5gbtT+KkHtL+PiIiIyKndP6gNEcG+pGQV8Nbi3VaXIxZT+KkHysOP1vuIiIiIVBbg48njV8YB8NaS3ew7km9xRWIlhR8Xl11QzPb0PAB6KfyIiIiInOTyThH0a92Y4lIHT3y9GcMwrC5JLKLw4+JW7z2KYUCrJgE0DvSxuhwRERERp2Oz2Xjq6o54edhYvD2TH7ekW12SWEThx8WVNzvQeh8RERGR02vVJJA7B7QE4OlvtlBQXGpxRWIFhR8Xl6RmByIiIiJVMmFgG5qF+nEg+zj/XrjL6nLEAgo/Liy/qJTNB3IA6B3byOJqRERERJybn7cHTww1mx9MX7aHXRnHLK5I6prCjwtbl5JNqcOgWagfzUL9rC5HRERExOldHBfORe3DKCkzeOLrTWp+4GYUflzYyuQjgFpci4iIiFSVzWbjyaEd8fG08/OuI3y74ZDVJUkdUvhxYeXrfdTiWkRERKTqohv585cLWwPw7HdbOFak5gfuQuHHRRWVlrEuNRtQswMRERGRs3XXBS2JaeRPem4Rr8zfYXU5UkcUflzUxv05FJc6aBzoTcvGAVaXIyIiIuJSfL08eOqqjgC8/8tetqXlWlyR1AWFHxf1+xbXNpvN4mpEREREXM+F7cK4tGMEZQ6DyV9uVvMDN6Dw46JWloefFpryJiIiInKuHh8ah5+XByv3ZvHftQesLkdqmcKPCyotc7Bm31FAzQ5EREREqqNZqB/3DjKbH0z5fis5x0ssrkhqk8KPC9p6KI9jRaUE+XrSPiLY6nJEREREXNod/VrSqkkAh48V838/bre6HKlFCj8uKOm3/X16tWiIh13rfURERESqw9vTzjNXxwPw4Yp9bDqQY3FFUlsUflzQqr0nmh2IiIiISPWd17oxQ7tE4jDgsS834XCo+UF9pPDjYgzDONHsQOFHREREpMY8dkUHAn08+TU1m09Xp1pdjtQChR8XsyvjGEcLSvDz8iA+MsTqckRERETqjfBgXx4Y3AaAF+Zt42h+scUVSU1T+HEx5fv7dIsOxdtT//tEREREatKt57WgfUQQRwtKmPrDNqvLkRqmq2cXoylvIiIiIrXH08POM9eYzQ8+XpXKupSjFlckNUnhx4VovY+IiIhI7evVoiHDujfHMODxrzZRpuYH9YbCjwvZf/Q4abmFeHnY6BbVwOpyREREROqtSZe3J9jXk00HcpmTtM/qcqSGKPy4kPL1Pp2bh+Ln7WFxNSIiIiL1V+NAH/46pB0AL/6wncy8Iosrkpqg8ONCVv5uc1MRERERqV239IkhvlkweYWlTPl+q9XlSA1Q+HEh5et9+mi9j4iIiEit87DbeObqeGw2+O/aAxXXYuK6FH5cREZuIXuPFGCzQY8WWu8jIiIiUhe6RTdgeK8oAB7/chMlZQ6LK5LqUPhxESv3mp80xDUNJtjXy+JqRERERNzH34a0p4G/F9vT85j1y16ry5FqUPhxEWpxLSIiImKNBgHe/P3S9gC8PH8H6bmFFlck50rhx0VovY+IiIiIdW7sGUW36FDyi8t49js1P3BVCj8uILugmG1peQD0VKc3ERERkTpn/635gd0G36w/yM+7DltdkpwDhR8XsGrvUQBaNQmgcaCPxdWIiIiIuKf4ZiGM6hsDwOSvNlFcquYHrkbhxwWs2lu+3qeRxZWIiIiIuLeJl7SjcaAPuzPzeXf5HqvLkbOk8OMCkrTeR0RERMQphPh58Y/LzeYHry/YxYHs4xZXJGdD4cfJ5ReVsulADqBObyIiIiLO4Npuzegd25DjJWU8/c1mq8uRs6Dw4+T8vDz4/v7+vHh9ZyJD/awuR0RERMTt2Wxm8wMPu40fNqezaHuG1SVJFSn8ODm73Ubb8CBu6BlldSkiIiIi8pt2EUHcdl4LAJ78ejOFJWXWFiRVovAjIiIiInIOHri4LeHBPuw7UsC0JbutLkeqQOFHREREROQcBPp48tgVcQC8uXg3+47kW1yRnInCj4iIiIjIObqyc1P6tW5McamDJ7/ejGEYVpckf0LhR0RERETkHNlsNp66uiNeHjYWbc9k/pZ0q0uSP6HwIyIiIiJSDa2aBDKuf0sAnvpmC8eL1fzAWSn8iIiIiIhU04SLWtMs1I8D2cf596KdVpcjp6HwIyIiIiJSTf7enkweajY/eGfpHnZnHrO4IjkVhR8RERERkRpwSVw4A9s1oaTM4Imv1PzAGSn8iIiIiIjUAJvNxpNXdcTb087yXYf5buMhq0uSP1D4ERERERGpITGNAvjLha0AeObbLRwrKrW4Ivk9hR8RERERkRp09wWtiG7oT3puEa/+tMPqcuR3FH5ERERERGqQr5cHT13VEYD3ft7L9rQ8iyuScgo/IiIiIiI1bGD7MC6JC6fMYfD4V5vU/MBJKPyIiIiIiNSCyUPj8PWyszI5iy/WHbC6HEHhR0RERESkVjRv4M+9F7UB4Ln/bSXneInFFYnCj4iIiIhILRnXvyUtmwRw+FgxL/243epy3J7Cj4iIiIhILfH2tPPM1fEAzF6xj00HciyuyL0p/IiIiIiI1KLzWzfmys5NcRjw+FebcDjU/MAqCj8iIiIiIrXssSviCPD2YF1KNp+tSbW6HLel8CMiIiIiUssiQnx58OK2ADz//TaO5hdbXJF7UvgREREREakDY85rQbvwII4WlDD1BzU/sILCj4iIiIhIHfDysPPMNWbzg49XpfBrara1BbkhhR8RERERkTrSO7Yh13VrhmHAY19upEzND+qUwo+IiIiISB2adHkHgnw92XQgl7lJ+6wux60o/IiIiIiI1KEmQT48fEk7AF78YTuHjxVZXJH7UPgREREREaljI/vG0DEymNzCUqb8b5vV5bgNhR8RERERkTrmYbdVND/4fO1+Vu3Nsrgi96DwIyIiIiJige7RDRjeK4q24YF42m1Wl+MWPK0uQERERETEXT12ZRw+nna8PDQmURfc6lV+4403aNGiBb6+vvTp04eVK1daXZKIiIiIuLFAH0+XCz6ufE3tWq90NXzyySdMnDiRJ554grVr19KlSxeGDBlCRkaG1aWJiIiIiLgEV7+mdpvw89JLLzFu3Dhuu+024uLimDZtGv7+/rz33ntWlyYiIiIi4hJc/ZraLdb8FBcXs2bNGiZNmlRxzG63M3jwYBITE0+6f1FREUVFJ/qt5+TkAHDo0KHaL1ZEREREpI6UX9/m5OQQHBxccdzHxwcfH59K9z3ba2pn5Bbh5/Dhw5SVlREeHl7peHh4ONu2ndxXfcqUKTz11FMnHe/du3et1SgiIiIiYpX4+PhK3z/xxBM8+eSTlY6d7TW1M3KL8HO2Jk2axMSJEyu+Ly0tZevWrURFRWG316+Zgnl5ecTFxbFlyxaCgoKsLqfe0utcd/Ra1x291nVDr3Pd0WtdN/Q6152qvNYOh4OUlBTi4uLw9DwRDf446lNfuEX4ady4MR4eHqSnp1c6np6eTkRExEn3P9Uw3/nnn1+rNVolNzcXgGbNmlUa6pSapde57ui1rjt6reuGXue6o9e6buh1rjtVfa2jo6OrdL6zvaZ2RvVrGOM0vL296dGjBwsWLKg45nA4WLBgAQkJCRZWJiIiIiLiGurDNbVbjPwATJw4kTFjxtCzZ0969+7NK6+8Qn5+PrfddpvVpYmIiIiIuARXv6Z2m/Bz0003kZmZyeTJk0lLS6Nr167MmzfvpAVb7sbHx4cnnnii3s7rdBZ6neuOXuu6o9e6buh1rjt6reuGXue6UxuvtatfU9sMwzCsLkJERERERKS2ucWaHxEREREREYUfERERERFxCwo/IiIiIiLiFhR+RERERETELSj8OJk33niDFi1a4OvrS58+fVi5cuVJ90lMTOSiiy4iICCA4OBgBgwYwPHjx097zvXr13PzzTcTFRWFn58fHTp04NVXXz3pfnPmzKFLly74+/vTtGlTbr/9do4cOfKn9aakpHDFFVfg7+9PWFgYf/3rXyktLa10n6KiIh599FFiYmLw8fGhRYsWvPfee1V8RWpHfXydz+W8dcHVXuv77ruPHj164OPjQ9euXU95nw0bNtC/f398fX2Jiopi6tSpf/4i1IH69jovXryYq6++mqZNmxIQEEDXrl2ZM2fOmV+IOlDfXuvf27VrF0FBQYSGhv7p/epKfXytDcPgX//6F23btsXHx4dmzZrxz3/+889fiFpWH1/nH374gb59+xIUFESTJk0YNmwYe/fu/dPz1gVXeq2ret7FixfTvXt3fHx8aN26NTNnzqzai2EVQ5zGxx9/bHh7exvvvfeesXnzZmPcuHFGaGiokZ6eXnGfX375xQgODjamTJlibNq0ydi2bZvxySefGIWFhac974wZM4z77rvPWLx4sbF7925j9uzZhp+fn/H6669X3Gf58uWG3W43Xn31VWPPnj3GsmXLjI4dOxrXXnvtac9bWlpqxP9/e/cfE2UdxwH8zXHedYiQDgQ8DGXCLc2UZNCFQq6TGzhtNre21BAn4GquZmFjYzFbhb8mTsNWaWQ/pkX/hEoUA8LQiymCE45ERGdawiqjH26i8O4Px81HQY/7AdzD57XdH/e9Z+/ne+/pbt/nufvy2GO0WCxsbGxkeXk5Q0JCmJeXpzhu6dKlTExMZGVlJS9cuMDjx4+zrq7Ojabco8aeXckdDr7WNUmuX7+e7733HletWsU5c+bc83p3dzfDwsK4YsUKNjc388CBAzQYDPzggw+GXpCHqLHnd955h/n5+Tx27Bjb29u5c+dOajQaHjp0aOgFeZAau+7X09PD+Ph4pqWlMTg42OlOvEWtXa9fv54mk4nffPMNOzo6ePLkSX7//fdDK8eD1NhzR0cH9Xo98/Ly2N7ezoaGBiYnJzMuLm7oBXmQr3XtTG5HRwcDAgK4YcMG2u127t69m/7+/qyoqHCzLe+Rxc8okpCQwJdfftnxvLe3l1OmTGFhYaFjLDExkfn5+W6f66WXXuLChQsdz7dt28bo6GjFMbt27aLRaBw0o7y8nBqNhlevXnWMvf/++wwKCuKNGzdIkt9++y2Dg4P5xx9/uD1nT1Fjz67kDgdf6/pOBQUFA36o7tmzhxMnTnR0T5JvvPEGTSaTaxP3ADX2PJD09HRmZmY6PVdvUHPXGzdu5MqVK1lSUjIqFj9q7Nput1Or1fLnn392a76epMaeS0tLqdVq2dvb6xgrKyujn58fe3p6XJu8B/hy14Plbty4kbNmzVIc8/zzz9Nqtbow6+EhX3sbJXp6etDQ0ACLxeIY02g0sFgssNlsAICuri7U19dj8uTJeOqppxAWFoaUlBTU1dUN+Xzd3d2YNGmS47nZbMYvv/yC8vJykERnZye+/vprpKenD5phs9kwe/ZsxR+1slqt+Pvvv9HS0gIAKCsrQ3x8PLZu3Qqj0YjY2Fi8/vrr9719601q7dmVXG/zxa6dYbPZkJycDJ1O5xizWq04e/Ysrl275la2K9TaszPnHm5q7rq6uhqlpaUoLi52O8sT1Nr1oUOHEB0djcOHD2P69OmYNm0a1q5diz///NOtXFepted58+ZBo9GgpKQEvb296O7uxmeffQaLxYJx48a5le0qtXR9d67NZlO8J+D2Z2L/exqVRmzZJRSuXLlCADx+/LhiPDc3lwkJCSRJm81GAJw0aRI//vhjnjp1iq+++ip1Oh3b2tqcPtexY8eo1Wr53XffKca/+uorBgYGUqvVEgCXLFly3yskWVlZTE1NVYz9999/BMDy8nKSpNVqpV6v5+LFi1lfX88jR44wKiqKq1evdnq+nqTWnl3J9TZf7PpOg11RXLRoEbOzsxVjLS0tBEC73e70nD1FrT3f7csvv6ROp2Nzc7PT8/U0tXb9+++/c+rUqaytrSXJUXHnR61d5+TkUK/XMzExkUePHmVNTQ3nzp2ruJI+nNTaM0n+8MMPnDx5Mv39/QmAZrOZ165dc3q+nubrXQ+WGxMTw3fffVdx3JEjRwiA169fdzp7OMmdHx/S19cHAMjJyUFmZibi4uJQVFQEk8nk2EAgLS0NgYGBCAwMxKxZs+7JaG5uxrPPPouCggKkpqY6xu12O1555RW8+eabaGhoQEVFBS5evIh169a5PWc/Pz988cUXSEhIQHp6Onbs2IH9+/eP2N2fB/HFnr2V622+2LUv8vWea2pqkJmZiY8++mjAuY0mvth1VlYWXnjhBSQnJ7uVM9x8seu+vj7cuHEDn376KRYsWICnn34a+/btQ01NDc6ePetWtrf4Ys9Xr15FVlYWMjIycOLECdTW1kKn02H58uUg6Va2N43mrgfL9UXakZ6AuC0kJAT+/v7o7OxUjHd2diI8PBwAEBERAQCYOXOm4phHH30Uly5dAgDs3bvXsai4+9au3W7HM888g+zsbOTn5yteKywsRFJSEnJzcwEAjz/+OMaPH48FCxbg7bffdpz7TuHh4ffsUtI//zvnbDQaERwcrJgvSVy+fBkxMTEPqsaj1NqzK7ne5otdOyM8PHzA99T/2nBTa8/9amtrsWTJEhQVFeHFF190K8tdau26uroaZWVl2L59O4Dbu5H19fVBq9Xiww8/xJo1a1zKdYdau46IiIBWq0VsbKxivsDtXT1NJpNLua5Sa8/FxcUIDg5W7MT5+eefY+rUqaivr8eTTz7pUq47fLnr++UO9pkYFBQEg8EweCEjSO78jBI6nQ7z5s1DVVWVY6yvrw9VVVUwm80AgGnTpmHKlCn3XB1qa2tDVFQUAMBoNGLGjBmYMWOGYwwAWlpasHDhQmRkZAy4peb169eh0Sj/Ofj7+wPAoFdJzGYzzpw5g66uLsdYZWUlgoKCHP9xk5KS8Ouvv+Lff/9VzFej0SAyMvLBxXiYWnt2JdfbfLFrZ5jNZhw9ehQ3b950jFVWVsJkMmHixIku57pKrT0Dt7dPXbx4MbZs2YLs7Gy3sjxBrV3bbDY0NTU5Hm+99RYmTJiApqYmLFu2zOVcd6i166SkJNy6dQvnz59XzBeAYn7DRa093y+3/+7KcPPVrh+UazabFe8JuP2Z2P+eRqWR+badGMjBgwep1+v5ySef0G63Mzs7mw8//LBil6+ioiIGBQWxtLSU586dY35+Ph966CG2t7cPmnvmzBmGhoZy5cqV/O233xyPrq4uxzElJSXUarXcs2cPz58/z7q6OsbHxzu+hzqQ/i2YU1NT2dTUxIqKCoaGhiq2YP7nn38YGRnJ5cuXs6WlhbW1tYyJieHatWvdbMt1auzZldzh4Gtdk+S5c+fY2NjInJwcxsbGsrGxkY2NjY7d3f766y+GhYVx1apVbG5u5sGDBxkQEDDiW12rrefq6moGBAQwLy9Pce6R3jlSjV3fbTT85odUZ9e9vb184oknmJyczFOnTvHkyZNMTEzkokWL3GzLdWrsuaqqin5+fty0aRPb2trY0NBAq9XKqKioEf0diq917Uxu/1bXubm5bG1tZXFxsWx1LYZm9+7dfOSRR6jT6ZiQkMCffvrpnmMKCwsZGRnJgIAAms1m/vjjj/fNLCgoIIB7HlFRUYrjdu3axZkzZ9JgMDAiIoIrVqzg5cuX75t98eJFpqWl0WAwMCQkhK+99hpv3rypOKa1tZUWi4UGg4GRkZHcsGHDiP8ITo09u5I7HHyt65SUlAGzL1y44Djm9OnTnD9/PvV6PY1GIzdv3ux0H96itp4zMjIGfD0lJWUotXiF2rq+22hZ/JDq7PrKlSt87rnnGBgYyLCwMK5evXrEF/Vq7PnAgQOMi4vj+PHjGRoayqVLl7K1tdXpTrzFl7p2Nrd/4w6dTsfo6GiWlJQ4W8eI8CNH8S+/hBBCCCGEEMJD5Dc/QgghhBBCiDFBFj9CCCGEEEKIMUEWP0IIIYQQQogxQRY/QgghhBBCiDFBFj9CCCGEEEKIMUEWP0IIIYQQQogxQRY/QgghhBBCiDFBFj9CCCGEEEKIMUEWP0IIIYQQQogxQRY/QgghhBBCiDFBFj9CCCGEEEKIMUEWP0IIIYQQQogx4X/NQxiWwb0eCQAAAABJRU5ErkJggg==", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax1 = plt.subplots(figsize=(9, 6))\n", - "# Instantiate a second axes that shares the same x-axis\n", - "ax1.plot(\n", - " weather_df.loc[\"2021-06-28 05:00:00-07:00\":\"2021-06-28 20:00:00-07:00\"][\n", - " \"dni_clear\"\n", - " ],\n", - " label=\"DNI\",\n", - ")\n", - "ax2 = ax1.twinx()\n", - "ax2.plot(\n", - " weather_df.loc[\"2021-06-28 05:00:00-07:00\":\"2021-06-28 20:00:00-07:00\"][\"temp_air\"],\n", - " \"r\",\n", - " label=\"Temperature\",\n", - ")\n", - "ax1.set_ylim([0, 1000])\n", - "ax2.set_ylim([0, 50])\n", - "ax1.set_ylabel(\"DNI\")\n", - "ax2.set_ylabel(\"Temperature $\\degree$C\");" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2. Calculate Installation Standoff - Level 1\n", - "\n", - "We use [`pvlib.standards.calc_standoff()`](https://pvdegradationtools.readthedocs.io/en/latest/_autosummary/pvdeg.standards.html#pvdeg.standards.calc_standoff) which takes at minimum the weather data and metadata, and returns the minimum installation distance in centimeters.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The array tilt angle was not provided, therefore the latitude tilt of 33.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n" - ] - } - ], - "source": [ - "standoff = pvdeg.standards.standoff(weather_df=weather_df, meta=meta)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Minimum installation distance: 0 11.12151\n", - "Name: x, dtype: float64\n" - ] - } - ], - "source": [ - "print(\"Minimum installation distance:\", standoff[\"x\"])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3. Calculate Installation Standoff - Level 2\n", - "\n", - "Let's take a closer look at the function and some optional parameters.\n", - "\n", - "- level : 1 or 2 (see IEC TS 63216)\n", - "- tilt and azimuth : tilt from horizontal of PV module and azimuth in degrees from North\n", - "- sky_model : pvlib compatible model for generating sky characteristics (Options: 'isotropic', 'klucher', 'haydavies', 'reindl', 'king', 'perez')\n", - "- temp_model : pvlib compatible module temperature model (Options: 'sapm', 'pvsyst', 'faiman', 'sandia')\n", - "- module_type : basic module construction (Options: 'glass_polymer', 'glass_glass')\n", - "- x_0 : thermal decay constant [cm] (see documentation)\n", - "- wind_speed_factor : Wind speed correction factor to account for different wind speed measurement heights between weather database (e.g. NSRDB) and the tempeature model (e.g. SAPM)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The array tilt angle was not provided, therefore the latitude tilt of 33.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n" - ] - } - ], - "source": [ - "standoff = pvdeg.standards.standoff(weather_df=weather_df, meta=meta, T98=70)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Minimum installation distance: 0 11.12151\n", - "Name: x, dtype: float64\n" - ] - } - ], - "source": [ - "print(\"Minimum installation distance:\", standoff[\"x\"])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "pvdeg_311", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.7" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/B-O LID - Accelerated Test.ipynb b/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/B-O LID - Accelerated Test.ipynb deleted file mode 100644 index a16c0a73f..000000000 --- a/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/B-O LID - Accelerated Test.ipynb +++ /dev/null @@ -1,804 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# B-O LID - Accelerated Test\n", - "\n", - "## Example: Boron-oxygen light-induced degradation (B-O LID) progression in accelerated tests\n", - "\n", - "\n", - "This library can also be used to model B-O LID, as the defect states and transitions can be modeled in the same way as LETID. See kinetic_parameters.json for B-O LID kinetic parameters used in this example.\n", - "\n", - "In this example, we will model B-O LID progression in a test similar to [IEC 61215 MQT 19.1](https://webstore.iec.ch/publication/61215) IEC 61215 MQT 19.1, which prescribes $\\ge $ 10 $kWh/m^2$ of 1-sun illumination with maximum power point tracking at $50\\degree C$.\n", - "\n", - "**Objectives:**\n", - "1. Define necessary solar cell device parameters\n", - "2. Define necessary degradation parameters: degraded lifetime and defect states, this time using B-O LID kinetics\n", - "3. Create timeseries of temperature and current injection\n", - "4. Run through timeseries, calculating defect states\n", - "5. Calculate device degradation and plot\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# if running on google colab, uncomment the next line and execute this cell to install the dependencies and prevent \"ModuleNotFoundError\" in later cells:\n", - "# !pip install pvdeg==0.3.3" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "from pvdeg import letid, collection, utilities, DATA_DIR\n", - "\n", - "import os\n", - "import pandas as pd\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import pvlib" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Working on a Windows 10\n", - "Python version 3.11.7 | packaged by Anaconda, Inc. | (main, Dec 15 2023, 18:05:47) [MSC v.1916 64 bit (AMD64)]\n", - "Pandas version 2.2.0\n", - "pvlib version 0.10.3\n", - "pvdeg version 0.2.4.dev83+ge2ceab9.d20240422\n" - ] - } - ], - "source": [ - "# This information helps with debugging and getting support :)\n", - "import sys, platform\n", - "import pvdeg\n", - "\n", - "print(\"Working on a \", platform.system(), platform.release())\n", - "print(\"Python version \", sys.version)\n", - "print(\"Pandas version \", pd.__version__)\n", - "print(\"pvlib version \", pvlib.__version__)\n", - "print(\"pvdeg version \", pvdeg.__version__)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Device parameters\n", - "To define a device, we need to define several important quantities about the device: wafer thickness (in $\\mu m$), rear surface recombination velocity (in cm/s), and cell area (in cm2)." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "wafer_thickness = 180 # um\n", - "s_rear = 46 # cm/s\n", - "cell_area = 243 # cm^2" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " Other device parameters \n", - "\n", - "Other required parameters are base diffusivity (in $cm^2/s$), and optical generation profile, which allow us to estimate current collection in the device." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "generation_df = pd.read_excel(\n", - " os.path.join(DATA_DIR, \"PVL_GenProfile.xlsx\"), header=0\n", - ") # this is an optical generation profile generated by PVLighthouse's OPAL2 default model for 1-sun, normal incident AM1.5 sunlight on a 180-um thick SiNx-coated, pyramid-textured wafer.\n", - "generation = generation_df[\"Generation (cm-3s-1)\"]\n", - "depth = generation_df[\"Depth (um)\"]\n", - "\n", - "d_base = 27 # cm^2/s electron diffusivity. See https://www2.pvlighthouse.com.au/calculators/mobility%20calculator/mobility%20calculator.aspx for details" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Degradation parameters\n", - "To model the device's degradation, we need to define several more important quantities about the degradation the device will experience. These include undegraded and degraded lifetime (in $\\mu s$)." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "tau_0 = 115 # us, carrier lifetime in non-degraded states, e.g. LETID/LID states A or C\n", - "tau_deg = 55 # us, carrier lifetime in fully-degraded state, e.g. LETID/LID state B" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's see how much maximum power degradation these parameters will result in:" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(0.03495240755084558, 5.663466529792824, 5.465514739492932)" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "letid.calc_pmp_loss_from_tau_loss(\n", - " tau_0, tau_deg, cell_area, wafer_thickness, s_rear\n", - ") # returns % power loss, pmp_0, pmp_deg" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Remaining degradation parameters:\n", - "\n", - "The rest of the quantities to define are: the initial percentage of defects in each state (A, B, and C), and the dictionary of mechanism parameters.\n", - "\n", - "In this example, we'll assume the device starts in the fully-undegraded state (100% state A), and we'll use the parameters for B-O LID" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "# starting defect state percentages\n", - "nA_0 = 100\n", - "nB_0 = 0\n", - "nC_0 = 0" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "('Choose a set of kinetic parameters:',\n", - " ['D037',\n", - " 'D038',\n", - " 'D039',\n", - " 'D040',\n", - " 'D050',\n", - " 'D051',\n", - " 'D052',\n", - " 'D053',\n", - " 'D054',\n", - " 'D055'])" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Here's a list of the possible sets of kinetic parameters from kinetic_parameters.json:\n", - "utilities.get_kinetics()" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'mechanism': 'BO-LID', 'v_ab': 4000.0, 'v_ba': 10000000000000.0, 'v_bc': 12500000000.0, 'v_cb': 532000.0, 'ea_ab': 0.475, 'ea_ba': 1.32, 'ea_bc': 0.98, 'ea_cb': 0.87, 'suns_ab': 0.1, 'suns_bc': 2.7, 'temperature_ab': 400, 'temperature_bc': 434, 'tau_ab': 140, 'tau_bc': 165, 'x_ab': 0, 'x_ba': 1, 'x_bc': 0, 'structure_ab': 'wafer', 'structure_bc': 'wafer', 'thickness_ab': 200, 'thickness_bc': 200, 'srv_ab': 0, 'srv_bc': 0, 'comments': ''}\n" - ] - } - ], - "source": [ - "mechanism_params = utilities.get_kinetics(\"bo-lid\")\n", - "print(mechanism_params)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Set up timeseries\n", - "In this example, we are going to model test with constant temperature and injection. IEC 61215 MQT 19.1 prescribes 10 $kWh/m^2$ of 1-sun illumination (i.e., 10 hours of 1-sun) with maximum power point tracking at $50\\degree C$. For most typical c-Si modules, MPP injection is roughly $I_{sc}-I_{mp}$, or roughly equal to $0.05\\times I_{sc}$. So we will set injection equal to 0.05 \"suns\" of injection. \n", - "\n", - "We will create a pandas datetime series and calculate the changes in defect states for each timestep. As B-O LID can initially proceed quickly, we will create a timeseries with 1-second intervals for the first 10 minutes, then proceed with 1-minute intervals" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\mspringe\\AppData\\Local\\Temp\\1\\ipykernel_42092\\928104463.py:4: FutureWarning: 'S' is deprecated and will be removed in a future version, please use 's' instead.\n", - " timesteps_initial = pd.date_range(start = '2022-01-01 00:00:00', end = '2022-01-01 00:10:00', freq = 'S') # 10 minutes of 1-second interval timesteps. In general, we should select small timesteps unless we are sure defect reactions are proceeding very slowly\n" - ] - } - ], - "source": [ - "temperature = 50 # degrees celsius\n", - "suns = 0.05 # \"suns\" of injection, e.g 1-sun illumination at open circuit would be 1; dark current injection is given as a fraction of Isc, e.g., injecting Isc would be 1. For this example we assume injection is 0.05*Isc.\n", - "\n", - "timesteps_initial = pd.date_range(\n", - " start=\"2022-01-01 00:00:00\", end=\"2022-01-01 00:10:00\", freq=\"S\"\n", - ") # 10 minutes of 1-second interval timesteps. In general, we should select small timesteps unless we are sure defect reactions are proceeding very slowly\n", - "timesteps = pd.date_range(\n", - " start=\"2022-01-01 00:10:00\", end=\"2022-01-01 10:00:00\", freq=\"min\"\n", - ") # a total of 10 hours of exposure\n", - "timesteps = pd.DataFrame(timesteps, columns=[\"Datetime\"])\n", - "timesteps_initial = pd.DataFrame(timesteps_initial, columns=[\"Datetime\"])\n", - "\n", - "timesteps = pd.concat([timesteps_initial, timesteps]) # concatenate the two time series\n", - "timesteps = timesteps.sort_values(by=\"Datetime\")\n", - "timesteps.reset_index(inplace=True, drop=True)\n", - "\n", - "temps = np.full(len(timesteps), temperature)\n", - "injection = np.full(len(timesteps), suns)\n", - "\n", - "timesteps[\"Temperature\"] = temps\n", - "timesteps[\"Injection\"] = injection\n", - "\n", - "timesteps[[\"NA\", \"NB\", \"NC\", \"tau\"]] = (\n", - " np.nan\n", - ") # create columns for defect state percentages and lifetime, fill with NaNs for now, to fill iteratively below\n", - "\n", - "timesteps.loc[0, [\"NA\", \"NB\", \"NC\"]] = (\n", - " nA_0,\n", - " nB_0,\n", - " nC_0,\n", - ") # assign first timestep defect state percentages\n", - "timesteps.loc[0, \"tau\"] = letid.tau_now(\n", - " tau_0, tau_deg, nB_0\n", - ") # calculate tau for the first timestep" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "for index, timestep in timesteps.iterrows():\n", - "\n", - " # first row tau has already been assigned\n", - " if index == 0:\n", - " pass\n", - "\n", - " # loop through rows, new tau calculated based on previous NB. Reaction proceeds based on new tau.\n", - " else:\n", - " n_A = timesteps.at[index - 1, \"NA\"]\n", - " n_B = timesteps.at[index - 1, \"NB\"]\n", - " n_C = timesteps.at[index - 1, \"NC\"]\n", - "\n", - " tau = letid.tau_now(tau_0, tau_deg, n_B)\n", - " jsc = collection.calculate_jsc_from_tau_cp(\n", - " tau, wafer_thickness, d_base, s_rear, generation, depth\n", - " )\n", - "\n", - " temperature = timesteps.at[index, \"Temperature\"]\n", - " injection = timesteps.at[index, \"Injection\"]\n", - "\n", - " # calculate defect reaction kinetics: reaction constant and carrier concentration factor.\n", - " k_AB = letid.k_ij(\n", - " mechanism_params[\"v_ab\"], mechanism_params[\"ea_ab\"], temperature\n", - " )\n", - " k_BA = letid.k_ij(\n", - " mechanism_params[\"v_ba\"], mechanism_params[\"ea_ba\"], temperature\n", - " )\n", - " k_BC = letid.k_ij(\n", - " mechanism_params[\"v_bc\"], mechanism_params[\"ea_bc\"], temperature\n", - " )\n", - " k_CB = letid.k_ij(\n", - " mechanism_params[\"v_cb\"], mechanism_params[\"ea_cb\"], temperature\n", - " )\n", - "\n", - " x_ab = letid.carrier_factor(\n", - " tau,\n", - " \"ab\",\n", - " temperature,\n", - " injection,\n", - " jsc,\n", - " wafer_thickness,\n", - " s_rear,\n", - " mechanism_params,\n", - " )\n", - " x_ba = letid.carrier_factor(\n", - " tau,\n", - " \"ba\",\n", - " temperature,\n", - " injection,\n", - " jsc,\n", - " wafer_thickness,\n", - " s_rear,\n", - " mechanism_params,\n", - " )\n", - " x_bc = letid.carrier_factor(\n", - " tau,\n", - " \"bc\",\n", - " temperature,\n", - " injection,\n", - " jsc,\n", - " wafer_thickness,\n", - " s_rear,\n", - " mechanism_params,\n", - " )\n", - "\n", - " # calculate the instantaneous change in NA, NB, and NC\n", - " dN_Adt = (k_BA * n_B * x_ba) - (k_AB * n_A * x_ab)\n", - " dN_Bdt = (\n", - " (k_AB * n_A * x_ab) + (k_CB * n_C) - ((k_BA * x_ba + k_BC * x_bc) * n_B)\n", - " )\n", - " dN_Cdt = (k_BC * n_B * x_bc) - (k_CB * n_C)\n", - "\n", - " t_step = (\n", - " timesteps.at[index, \"Datetime\"] - timesteps.at[index - 1, \"Datetime\"]\n", - " ).total_seconds()\n", - "\n", - " # assign new defect state percentages\n", - " timesteps.at[index, \"NA\"] = n_A + dN_Adt * t_step\n", - " timesteps.at[index, \"NB\"] = n_B + dN_Bdt * t_step\n", - " timesteps.at[index, \"NC\"] = n_C + dN_Cdt * t_step" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Finish calculating degraded device parameters.\n", - "Now that we have calculated defect states, we can calculate all the quantities that depend on defect states." - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "timesteps[\"tau\"] = letid.tau_now(tau_0, tau_deg, timesteps[\"NB\"])\n", - "\n", - "# calculate device Jsc for every timestep. Unfortunately this requires an integration so I think we have to run through a loop. Device Jsc allows calculation of device Voc.\n", - "for index, timestep in timesteps.iterrows():\n", - " jsc_now = collection.calculate_jsc_from_tau_cp(\n", - " timesteps.at[index, \"tau\"], wafer_thickness, d_base, s_rear, generation, depth\n", - " )\n", - " timesteps.at[index, \"Jsc\"] = jsc_now\n", - " timesteps.at[index, \"Voc\"] = letid.calc_voc_from_tau(\n", - " timesteps.at[index, \"tau\"], wafer_thickness, s_rear, jsc_now, temperature=25\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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1192 rows Γ— 14 columns

\n", - "
" - ], - "text/plain": [ - " Datetime Temperature Injection NA NB \\\n", - "0 2022-01-01 00:00:00 50 0.05 100.000000 0.000000 \n", - "1 2022-01-01 00:00:01 50 0.05 99.984366 0.015634 \n", - "2 2022-01-01 00:00:02 50 0.05 99.968735 0.031265 \n", - "3 2022-01-01 00:00:03 50 0.05 99.953106 0.046893 \n", - "4 2022-01-01 00:00:04 50 0.05 99.937480 0.062520 \n", - "... ... ... ... ... ... \n", - "1187 2022-01-01 09:56:00 50 0.05 0.364232 82.313555 \n", - "1188 2022-01-01 09:57:00 50 0.05 0.360820 82.284870 \n", - "1189 2022-01-01 09:58:00 50 0.05 0.357440 82.256164 \n", - "1190 2022-01-01 09:59:00 50 0.05 0.354092 82.227438 \n", - "1191 2022-01-01 10:00:00 50 0.05 0.350776 82.198691 \n", - "\n", - " NC tau Jsc Voc Isc FF \\\n", - "0 0.000000e+00 115.000000 41.590997 0.666327 10.106612 0.840987 \n", - "1 0.000000e+00 114.980390 41.590936 0.666324 10.106597 0.840986 \n", - "2 1.016487e-07 114.960790 41.590875 0.666321 10.106583 0.840986 \n", - "3 3.049294e-07 114.941200 41.590814 0.666317 10.106568 0.840985 \n", - "4 6.098257e-07 114.921620 41.590753 0.666314 10.106553 0.840984 \n", - "... ... ... ... ... ... ... \n", - "1187 1.732221e+01 60.591179 41.280510 0.653103 10.031164 0.838627 \n", - "1188 1.735431e+01 60.601170 41.280615 0.653106 10.031189 0.838627 \n", - "1189 1.738640e+01 60.611172 41.280719 0.653110 10.031215 0.838628 \n", - "1190 1.741847e+01 60.621185 41.280824 0.653113 10.031240 0.838629 \n", - "1191 1.745053e+01 60.631208 41.280929 0.653117 10.031266 0.838629 \n", - "\n", - " Pmp Pmp_norm time (days) \n", - "0 5.663467 1.000000 0.000000 \n", - "1 5.663426 0.999993 0.000012 \n", - "2 5.663386 0.999986 0.000023 \n", - "3 5.663345 0.999979 0.000035 \n", - "4 5.663305 0.999971 0.000046 \n", - "... ... ... ... \n", - "1187 5.494163 0.970106 0.413889 \n", - "1188 5.494211 0.970115 0.414583 \n", - "1189 5.494259 0.970123 0.415278 \n", - "1190 5.494307 0.970132 0.415972 \n", - "1191 5.494355 0.970140 0.416667 \n", - "\n", - "[1192 rows x 14 columns]" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# this function quickly calculates the rest of the device parameters: Isc, FF, max power, and normalized max power\n", - "timesteps = letid.calc_device_params(timesteps, cell_area=243)\n", - "timesteps[\"time (days)\"] = (\n", - " timesteps[\"Datetime\"] - timesteps.iloc[0][\"Datetime\"]\n", - ").dt.total_seconds() / 86400 # create a column for days elapsed\n", - "\n", - "timesteps" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plot the results\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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HKsWLF1csLCyUEiVKKAMGDNBbYiUxMVGZOHGi4uXlpZibmyvu7u5Kjx49lGvXrunVvWzZMqVOnTqKtbW1Ym9vr1SrVk0ZP368cufOHV2Z/y4toyjaZWDmzJmjVK1aVbG0tFSKFCmi1KlTR/n888+VmJgYXTlAGTFiRKbva8WKFUqFChUUS0tLxdvbW/npp5+UKVOmKP/9J+bixYtKkyZNFGtrawXQW2YmMjJSGTFihFKyZEnd+2zRooWybNkyvTpu3rypdOrUSbGxsVGcnZ2VUaNG6ZbRycnSMlZWVkrNmjWV7777TrdcT3Zt3bpVadWqlVKkSBHF0tJSKV++vPLRRx8pUVFR2bo+q6Vu0h9hYWHPXFom/WFubq64uLgoTZo0UWbOnKncu3fPoPehKBmXlnn6Xn5+foqjo6NiZWWllCtXThk4cKBy+vRpRVEU5f79+8qIESMUb29vxdbWVnF0dFR8fX31lv9RFO2SNO3bt1fs7e0VQJaZEaIQUSnKv31NQgghhBDilSNjBoUQQgghXmGSDAohhBBCvMIkGRRCCCGEeIVJMiiEEEII8QqTZFAIIYQQ4hUmyaAQQgghxCtMkkEhhBBCiFeYJINCCCGEEK8wSQaFeAEpikJKSoqxwxDPsHLlSlQqFTdu3CjQa3PLmPcWQhiHJINC5JODBw+iUqkyffzzzz96ZZOTk/n444/x9PTE2toaX19f9uzZk2m9a9aswdnZGXt7ewYNGpRpUnjt2jXeffddypYti5WVFQ4ODjRs2JAFCxbw+PHjHL0fQ2LMzfWGfG45tWvXLlQqFWvWrMmT+l40x44dY+rUqURHRxs7FCFEISDb0QmRTw4ePEjz5s358MMPqVevnt65Nm3a4OzsrHvdp08fNm7cyOjRo6lQoQIrV67k1KlTHDhwgEaNGunK3bhxg2rVqjF16lRKly7N559/zttvv8348eN1ZbZv307Pnj2xtLSkf//++Pj4kJKSwpEjR9i0aRMDBw5k2bJlBr+f7MaY2+sN+dxyau7cuXz88ccEBQVRvXr1XNeXGbVaTWpqKpaWlqhUqgK7Nju++uorxo0bR2hoKGXKlCnQewshCiGj7owsxEvswIEDCqBs2LDhmeVOnDihAMqXX36pO/b48WOlXLlySoMGDfTKbtiwQenSpYvu9datW5UOHTroXl+/fl2xs7NTvL29lTt37mS415UrV5T58+cb/F4MiTG312f3c1MURXn48KGyadOmLM+vW7dOiY+Pz3C8b9++ioWFhZKSkvLce7yMvvzySwVQQkNDjR2KEKIQkG5iIQpAXFwcaWlpmZ7buHEjpqamvPPOO7pjVlZWDBkyhOPHjxMWFqY7XrZsWf7++2/27NnDpUuXWLZsGRUqVNCdnzt3LvHx8axYsQIPD48M9ypfvjyjRo0yOH5DYszL65/1uQEsWbKEN998ky1btmQ4t2LFCt566y1WrVqV4VxQUBDe3t6Ym5s/M+7cyGzs3dSpU1GpVFy9epWBAwfi5OSEo6MjgwYNIjEx8ZnXAoSHhzN48GDc3NywtLSkatWq/PjjjxnuHR4ezpAhQ/D09MTS0hIvLy+GDx9OSkoKU6dOZdy4cQB4eXnpuuDT75XVvQMDA2nbti0ODg7Y2dnRokWLDN322X1/QojCxczYAQjxshs0aBDx8fGYmprSuHFjvvzyS+rWras7HxgYSMWKFXFwcNC7rn79+gCcOXOGkiVLAlC7dm369u1L69atAahevTorV67UXfPHH39QtmxZXn/99Tx9D4bEmFfXP+9zA/j44485efIkffr0Yfv27bRo0QKAzZs38+6779KvXz+GDx+ud01KSgqXLl2id+/eBnwCeevNN9/Ey8uL2bNnExAQwA8//ICrqytz5szJ8prIyEhee+01VCoVI0eOxMXFhR07djBkyBBiY2MZPXo0AHfu3KF+/fpER0fzzjvv4O3tTXh4OBs3biQxMZFu3bpx+fJl1q9fzzfffKPrdndxccny3ufPn6dx48Y4ODgwfvx4zM3N+f7772nWrBmHDh3C19c31+9PCGE8kgwKkU8sLCzo3r077dq1w9nZmZCQEL766isaN27MsWPHqFWrFgB3797NtBUv/didO3f0jn/77beMHj2a6OhoqlevjpmZ9q9xbGws4eHhdO7cOc/fi6Ex5ub67H5uAGZmZvz666+0adOGLl26sHfvXuLj43nrrbdo27YtP/30U4ZxbyEhIaSmpubbWMHsqFWrFitWrNC9fvDgAStWrHhmsjRx4kTUajXnzp2jWLFiALz33nv06dOHqVOn8u6772Jtbc2ECROIiIjgxIkTesnztGnTUBSF6tWrU7t2bdavX0+XLl0yjBnMzKRJk0hNTeXIkSOULVsWgP79+1OpUiXGjx/PoUOHcv3+hBDGI93EQuST119/nY0bNzJ48GA6derEJ598wj///INKpWLChAm6co8fP8bS0jLD9VZWVrrz/1W2bFlq166tSwRBmwwC2Nvb5/VbyVGMOb0+u5/b03Vs27aNihUr0q5dO7p06YKvry+//fab3ueT7uzZswDUqFHjmTHnp/fee0/vdePGjXnw4IHuZ/hfiqKwadMmOnbsiKIo3L9/X/fw8/MjJiaGgIAANBoNW7dupWPHjhlaUYEcTQhRq9Xs3r2bLl266BJB0Cbyb731FkeOHMkQt6HvTwhhXJIMClGAypcvT+fOnTlw4ABqtRoAa2trkpOTM5RNSkrSnc+O9C7YuLi4PIr2idzGmNvrM/vcnubg4MBXX33Fw4cPiY+PZ/78+VnWGRQUBJChZXDZsmX4+Phgb2+Pq6srPXr00J27f/8+H3zwASVLlsTBwYGqVasyd+7cTGPJjlKlSum9LlKkCACPHj3KtHxUVBTR0dEsW7YMFxcXvcegQYMAuHfvHlFRUcTGxuLj45OjuLK6d2JiIpUqVcpwrnLlymg0mgxjPg19f0II45JuYiEKWMmSJUlJSSEhIQEHBwc8PDwIDw/PUO7u3bsAeHp6ZqteBwcHPD09CQ4OztN4gVzHmBfv8b+f29OuX79O37598fb25vHjx3Tv3p2jR49m2jV99uxZ3NzccHNz0x1btGgRK1asYMOGDXh7e3P79m327dsHaJOshg0b0qxZM06cOIGnpydBQUF89tlnuokYhjI1Nc30uJLFSl8ajQaAfv36MWDAgEzLVK9ePcvrC5qh708IYVySDApRwK5fv46VlRV2dnYA1KxZkwMHDhAbG6uX5Jw4cUJ3Prs6dOjAsmXLOH78OA0aNMizmHMbY168x/9+bunu3r1Lq1atMDc3Z8+ePTx+/JhGjRrRunVrDh06RNGiRfXKnz17NkMX8cqVK/nwww+pXLkyoE08Bw4cCMDIkSOpXbs2y5cv15WvUaMG27Zte27MecXFxQV7e3vUajUtW7bMspxGo8HBweG5/yEwpLvYxcUFGxsbLl26lOHcxYsXMTExeebkISFE4SfdxELkk6ioqAzHgoKC2LZtG61bt8bERPvXr0ePHqjVar2FoJOTk/npp5/w9fU16It2/Pjx2NraMnToUCIjIzOcv3btGgsWLDD4vRgSY2JiIhcvXuT+/fs5uj67nxtoux39/PyIj49n7969lChRggoVKrBr1y7CwsJo3749CQkJuvIRERHcu3cvQzJoZWXFkiVL2LZtm964tmvXrrFp0yamTZtm6EeWp0xNTenevTubNm3KNNFL/8xMTEzo0qULf/zxB6dPn85QLr1lztbWFiBbO5CYmprSunVrfv/9d73lZiIjI1m3bh2NGjXK0FIrhHixSMugEPmkV69eWFtb8/rrr+Pq6kpISAjLli3DxsaGL774QlfO19eXnj17MmHCBO7du0f58uVZtWoVN27c0JuRmR3lypVj3bp19OrVi8qVK+vtQHLs2DE2bNiga/FKp1KpaNq0KQcPHsyyXkNiPHnyJM2bN2fKlClMnTrV4Ouz+7mBdp3BsLAwDh48qLfeYs2aNfnzzz9p3bo1q1at4v333weyHi+4fv16Zs2axbBhw4iJieHtt99m8eLF7Nu3jwoVKmQ6Xq6gffHFFxw4cABfX1+GDRtGlSpVePjwIQEBAezdu5eHDx8CMGvWLHbv3k3Tpk155513qFy5Mnfv3mXDhg0cOXIEJycn6tSpA2hnKPfu3Rtzc3M6duyoSxL/a8aMGezZs4dGjRrx/vvvY2Zmxvfff09ycjJz584tsM9ACJFPjLjgtRAvtQULFij169dXihYtqpiZmSkeHh5Kv379lCtXrmQo+/jxY+V///uf4u7urlhaWir16tVTdu7cmeN7X758WRk2bJhSpkwZxcLCQrG3t1caNmyoLFy4UElKStKVi4uLUwCld+/ez60zuzGm7yAyZcqUHF1vyOeWmpqqBAcHZxlzUFCQotFodK/nzp2rAMqZM2cyLa9Wq5Xdu3crVlZWytq1a5Xp06crLVq0yLL+Z/npp58y7PIxZcoUBVCioqKeWTazaxVFUSIjI5URI0YoJUuWVMzNzRV3d3elRYsWyrJly/TK3bx5U+nfv7/i4uKiWFpaKmXLllVGjBihJCcn68pMnz5dKV68uGJiYpKtewcEBCh+fn6KnZ2dYmNjozRv3lw5duyYXpnsvj8hROEiexML8Qr766+/6NChA0FBQVSrVs3Y4RQaZcqUYdKkSVhYWDBz5sxMx8vlpxUrVjB06FDCwsIoUaJEgd5bCPHqkTGDQrzCDhw4QO/evV/pRHD27NmcOHGC1NRU4uPjmTlzJgkJCXTu3JmOHTsSExPDzJkzSUxMRKPRcOrUKcaMGZOvMd29exeVSpVh8osQQuQHGTMoxCvsyy+/NHYIRvfo0SP69u1LREQEDg4ONGnShOPHj+u2Z9u3bx//+9//+Oabb9BoNJQvXz7Dosp5JTIyko0bN7J06VIaNGiAjY1NvtxHCCGeJt3EQghRSBw8eJB27dpRv359li9frjcpRggh8oskg0IIIYQQrzAZMyiEEEII8QqTZFAIIYQQ4hUmE0iAtLQ0AgMDcXNz09vdQAghhBCFl0ajITIyklq1amFmJilNTsknBwQGBlK/fn1jhyGEEEKIHDh58iT16tUzdhgvLEkGATc3N0D7y+Th4WHkaIQQQgiRHXfv3qV+/fq673GRM5IMgq5r2MPDQ1b7F0IIIV4wMsQrd+TTE0IIIYR4hUkyKIQQQgjxCpNkUAghhBDiFSZjBrNJo9GQkpJi7DCMztzcHFNTU2OHIYQwMrVaTWpqqrHDEC85+c4pGJIMZkNKSgqhoaFoNBpjh1IoODk54e7ujkqlMnYoQogCpigKERERREdHGzsU8YqQ75z8J8ngcyiKwt27dzE1NaVkyZKv9IwlRVFITEzk3r17ALIMjxCvoPRE0NXVFRsbG/mCFvlGvnMKjiSDz5GWlkZiYiKenp7Y2NgYOxyjs7a2BuDevXu4urpK870QrxC1Wq1LBIsVK2bscMQrQL5zCsar28yVTWq1GgALCwsjR1J4pCfFMl5IiFdL+t95+Y+xKEjynZP/JBnMJukKeUI+CyFebfJvgChI8vuW/yQZFEIIIYR4hRk1Gfz777/p2LEjnp6eqFQqtm7dqndeURQmT56Mh4cH1tbWtGzZkitXruiVefjwIX379sXBwQEnJyeGDBlCfHx8Ab4LIYQQQrwInpd3ZObgwYPUrl0bS0tLypcvz8qVKzOUWbx4MWXKlMHKygpfX19Onjypdz4pKYkRI0ZQrFgx7Ozs6N69O5GRkXn0rnLPqMlgQkICNWrUYPHixZmenzt3Lt9++y1Lly7lxIkT2Nra4ufnR1JSkq5M3759OX/+PHv27OHPP//k77//5p133imot1DoNW3aFJVKxfr16/WOL1y4EE9PTyNFJYQQIjuaNWvG6NGjs3xdEPd8mTwv7/iv0NBQ2rdvT/PmzTlz5gyjR49m6NCh7Nq1S1fm119/ZezYsUyZMoWAgABq1KiBn5+fbhY0wJgxY/jjjz/YsGEDhw4d4s6dO3Tr1i3P31+OKYUEoGzZskX3WqPRKO7u7sqXX36pOxYdHa1YWloq69evVxRFUUJCQhRAOXXqlK7Mjh07FJVKpYSHh2f73mFhYQqghIWFZTj3+PFjJSQkRHn8+LHB7ykhMVE5duKUkpSUZPC1eUGj0Sj29vaKh4eH0r17d71zAwYMUNq3b5+jenPzmQghXlwv8t/9AQMGKIAye/ZsveNbtmxRCtFXYQZNmzZVRo0apXv94MEDJTY2tkDvmZkmTZoogAIoZmZmSsWKFZW1a9fmSzzP+r171vf38/w378jM+PHjlapVq+od69Wrl+Ln56d7Xb9+fWXEiBG612q1WvH09NT9rkVHRyvm5ubKhg0bdGUuXLigAMrx48cNjjs/FNoxg6GhoURERNCyZUvdMUdHR3x9fTl+/DgAx48fx8nJibp16+rKtGzZEhMTE06cOJFl3cnJycTGxuoecXFxeR6/RqOhRImSvO5bj6Bz5/O8/uy4cuUKcXFxTJo0iR07dpCYmKg7FxAQQJ06dYwSlxBCGIOVlRVz5szh0aNHeVZnQe9MVbRoUezt7Qv0nv+lKAqBgYHMnj2bu3fvcunSJRo0aMCAAQMIDQ01SkxxcXF63+vJycl5Uu/x48f18hAAPz8/XR6SkpKCv7+/XhkTExNatmypK+Pv709qaqpeGW9vb0qVKqUrY2yFNhmMiIgAwM3NTe+4m5ub7lxERASurq56583MzChatKiuTGZmz56No6Oj7lGlSpU8jl77y1C2fAUAAs+ey/P6s8Pf3x8rKyuGDh2Kg4MDO3bsALRjFy5cuEDt2rWNEpcQQhhDy5YtcXd3Z/bs2VmWSU5O5sMPP8TV1RUrKysaNWrEqVOndOebNWvGyJEjGT16NM7Ozvj5+emOf/DBB4wePZoiRYrg5ubG8uXLSUhIYNCgQdjb21O+fHndv8Ppdu7cSaNGjXBycqJYsWJ06NCBa9euZRnf0124N27cQKVSZXg0a9YM0DZKzJ49Gy8vL6ytralRowYbN27Uqy8hIYH+/ftjZ2eHh4cHX3/99XM/x/SGhkaNGuHu7k7ZsmWZMGECaWlpnD179rnX54cqVarofa8/62dsiIiIiEzzkNjYWB4/fsz9+/dRq9XPzVUsLCxwcnLKsoyxFdpkMD9NmDCBmJgY3SMkJCTb1yqKQmJKWrYeFb0rAxAYdC7b1zzroSiKQe8zICCA6tWrY2FhQdeuXXX/CAQFBZGWlpYhGXzzzTdp2LChQfcQQogXhampKbNmzWLhwoXcvn070zLjx49n06ZNrFq1ioCAAMqXL4+fnx8PHz7UlVm1ahUWFhYcPXqUpUuX6h13dnbm5MmTfPDBBwwfPpyePXvy+uuvExAQQOvWrXn77bf1emkSEhIYO3Ysp0+fZt++fZiYmNC1a9dsbX9asmRJ7t69q3sEBgZSrFgxmjRpAmgbPn7++WeWLl3K+fPnGTNmDP369ePQoUO6OsaNG8ehQ4f4/fff2b17NwcPHiQgIOCZ9/X390elUlG9enXdsfTP879JUUEJCQnR+16fMGGCUeJ4URXaHUjc3d0BiIyM1NuCJjIykpo1a+rKPD1AE7Q7hjx8+FB3fWYsLS2xtLTUvY6Njc12XI9T1VSZvOv5BYHYO9qFqn/ecZSd1tm75llCpvlhY5H9H1lAQIAu4evWrRvdunUjOTmZgIAAXFxcKFmypK7s2bNnCQsL4+rVq7mOUwjxalD+3S7MGHK6FV7Xrl2pWbMmU6ZMYcWKFXrnEhIS+O6771i5ciVt27YFYPny5ezZs4cVK1Ywbtw4ACpUqMDcuXMz1F2jRg0mTZoEaBsdvvjiC5ydnRk2bBgAkydP5rvvvuPs2bO89tprAHTv3l2vjh9//BEXFxdCQkLw8fF55nsxNTXVfdclJSXRpUsXGjRowNSpU0lOTmbWrFns3buXBg0aAFC2bFmOHDnC999/T9OmTYmPj2fFihWsWbOGFi1aANqEtkSJEs+8b0BAAF5eXjg4OABw8eJFxo8fT82aNalfvz7z5s3jo48+Ijg4mKpVqwIwYsQIlixZwsOHDylSpAjz5s3j66+/xtnZmcTERBYvXkzr1q2fed9nsbe318WTl9zd3TPM+o2MjMTBwQFra2tMTU0xNTXNtEz6z8bd3Z2UlBSio6P1WgefLmNshbZl0MvLC3d3d/bt26c7Fhsby4kTJ3S/2A0aNCA6Ohp/f39dmf3796PRaPD19S3wmP/L3LkUAKlRN41y/6fHBTZr1gxzc3N27dqFv79/hlbByZMnM2vWLJycnLh7964xwhVCvGASExOxs7MzyiM3SeicOXNYtWoVFy5c0Dt+7do1UlNT9XpIzM3NqV+/vl7ZrMZbP91SZmpqSrFixahWrZruWHqr2dONGFeuXKFPnz6ULVsWBwcHypQpA8CtW7cMek+DBw8mLi6OdevWYWJiwtWrV0lMTKRVq1Z6n9vPP/+s64a+du0aKSkpet+XRYsWpVKlSs+8V0BAADdu3MDOzg4rKytq1apFtWrV2LlzJyYmJgQHB1OtWjUuXbqkey/Hjx+nRIkSFClSBIDg4GC+/vprgoKC+PLLL5k6dapB77egNGjQQC8PAdizZ48uD7GwsKBOnTp6ZTQaDfv27dOVqVOnDubm5nplLl26xK1bt3RljM2oLYPx8fF6LVGhoaGcOXOGokWLUqpUKUaPHs2MGTOoUKECXl5efPbZZ3h6etKlSxcAKleuTJs2bRg2bBhLly4lNTWVkSNH0rt373xbNsXa3JSQaX7ZKhsZWZOyv0xEHRPB3g988XRxyvW9s+v69etER0frkj4zMzM6derEpk2bOHfunO5/vQCnTp0iPj6e5s2b4+3tTUhIiGwILoR4aTVp0gQ/Pz8mTJjAwIEDDb7e1tY20+Pm5uZ6r1Uqld6x9JbMp7uAO3bsSOnSpVm+fDmenp5oNBp8fHwMmpgyY8YMdu3axcmTJ3WTS9LX292+fTvFixfXK/90z1hOBAQEMG7cOIYOHYqNjQ0eHh56rbTBwcG8+eabumRw+vTpdO3alaNHj+qV+fDDDwEoXry4buvX/Pa8vGPChAmEh4fz888/A/Dee++xaNEixo8fz+DBg9m/fz+//fYb27dv19UxduxYBgwYQN26dalfvz7z58/XjRUF7eTXIUOGMHbsWIoWLYqDgwMffPABDRo00LUQG5tRk8HTp0/TvHlz3euxY8cCMGDAAFauXMn48eNJSEjgnXfeITo6mkaNGrFz506srKx016xdu5aRI0fSokULTExM6N69O99++22+xaxSqbLdVVumhCdFiznz8MF9Ll++RPniBTcez9/fHwsLC71uhu7du+vGq0ycOFF3fPLkyUyfPh1AlwymdxkIIURWbGxsjLbIf273R/7iiy+oWbOmXitYuXLldGMBS5cuDWj3wz116lS+rLv34MEDLl26xPLly2ncuDEAR44cMaiOTZs2MW3aNHbs2EG5cuV0x6tUqYKlpSW3bt2iadOmmV5brlw5zM3NOXHiBKVKaXuyHj16xOXLl7O8Jr2hoVWrVpQvXz7DeUVRuHHjBh06dOCbb77h+vXrXL9+HV9fX933kaIoXLx4kUqVKqFWq/nuu+9o166dQe87p56Xd9y9e1evVdbLy4vt27czZswYFixYQIkSJfjhhx90E4cAevXqRVRUFJMnTyYiIoKaNWuyc+dOvfGT33zzjS5HSU5Oxs/PjyVLlhTAO84eoyaDzZo1e+akCJVKxbRp05g2bVqWZYoWLcq6devyI7xcU6lUVKlShSOH/+Zc8HnaNS+4ZDAgIAAfHx8sLCx0x1q1aoVarSYlJUXXYnj06FEOHTpEnz59AO3/mnr27FlgcQohXlwqlSrLVrLCrlq1avTt21ev8cDW1pbhw4czbtw4XUvR3LlzSUxMZMiQIXkeQ5EiRShWrBjLli3Dw8ODW7du8cknn2T7+uDgYPr378/HH39M1apVdTNTLSwsKFq0KP/73/8YM2YMGo2GRo0aERMTw9GjR3FwcGDAgAHY2dkxZMgQxo0bR7FixXB1dWXixImYmGQ9gix98sjTS7o9LTQ0lJIlS1K5cmWuXbvG9OnT+eyzz9iyZYvueyc0NJTk5GQaNGiAhYUFLVu2NOh958bz8o7Mdhdp1qwZgYGBz6x35MiRjBw5MsvzVlZWLF68ONuLXRe0Qjtm8GVRzUc7ePbihfOkpBVMMzhoZ5E9PZYStF0DsbGxKIqCl5cXAJ999hl79+7lxo0b3Lhxg+3btxs0u1oIIV5U06ZNyzBr94svvtD1otSuXZurV6+ya9cu3Vi3vGRiYsIvv/yCv78/Pj4+jBkzhi+//DLb158+fZrExERmzJiBh4eH7pG+s0V6IjZ79mzdsKrt27fr/v0H+PLLL2ncuDEdO3akZcuWNGrU6Jlr0KbPsHZ0dMz0fHBwMD4+PlhaWpKQkEB4eDjNmjXTHU8v07FjR86cOcPJkyeZNWtWrruuRe6oFEPXK3kJ3b59m5IlSxIWFpZhFlVSUhKhoaF4eXnpdU9n15IlSxgxYgSNW7Rm6+9/UNTW4vkXFZD9+/czY8YM9u/frzv28OFDKlWqRFRUVJbX5fYzEUK8mOTvvnieWbNmYW5uzrhx4/j111+pWrUqPj4+eHh4cP36daytrZk1axapqalMmTIlW3U+6/fuWd/fIvukZTCfpf9P6NrliyQkpxk5Gn1vvPGGXiII2m73ZyWCQgghRFaebgHs1asXPj4+3Lt3D3t7e6ytrXVlnp5lLYyv0K4z+LJIX2PpTtgt7j2KpmTR3A16FkIIIQqrzMbwu7q6cvny5WeWEcYlLYP5rFixYroZRRdDLpCa9vxV5YUQQgghCookgwVAr6s4pXB1FQshhBDi1SbJYAFI7yq+dvki8YVs3KAQQgghXm2SDBaA9GTw6qULJCQX3PIyQgghhBDPI8lgAXjSTXyB5DQ1qWoZNyiEEEKIwkGSwQJQpUoVACLv3iEuNqbQLTEjhBCGkOVpRUGS37f8J8lgAXByctJtFH79yiXpKhZCvJDMzc0BSExMNHIk4lWS/vuW/vsn8p6sM1hAqlatSnh4OFcvXaC+bwNjhyOEEAYzNTXFycmJe/fuAWBjY4NKpTJyVOJlpSgKiYmJ3Lt3DycnJ0xNTY0d0ktLksEC4uPjw+7du7lyMUQ3btDcVBpmhRAvFnd3dwBdQihEfnNyctL93on8IclgAalRowYA1y6GAJCQnIaTTeHZp1gIIbJDpVLh4eGBq6srqampxg5HvOTMzc2lRbAASDJYQKpXrw7A5QvnURSF+KSCSQabNm3K33//zbp16+jTp4/u+MKFC5k9ezZ37tzJ9xiEEC8fU1NT+ZIW4iUh/ZQFpHLlypiZmREd/YjIu+HEF8BOJIqiEBgYiIeHB5s2bdI75+/vT+3atfM9BiGEEEIUbpIMFhBLS0u8vb0BuHIhhJQ0DSlp+Tur+MqVK8TFxTFp0iR27NihNwMwICCAOnXq5Ov9hRBCCFH4STKYUykJWT9SkzItW91Hu95g6MUgVKmJxMfFQurj7NWbA/7+/lhZWTF06FAcHBzYsWMHAElJSVy4cEFaBoUQQgghYwZzbJZn1ucqtIa+G568/rI8pCZS434y64Dbf8ylmuUi7bnSjWDQ9idl51eDxAcZ65waY3CIAQEBVK9eHQsLC7p27crGjRvp3r07QUFBpKWl6ZLBefPmMW/ePJydnYmJiaFdu3YsXrzY4PsJIYQQ4sUjLYMFqLqbdrD12cgn29Ep5N/K6gEBAbqEr1u3bmzfvp3k5GQCAgJwcXGhZMmSAAQHB7NgwQLOnDlDSEgIK1euJD4+Pt/iEkIIIUThIS2DOfXpM2bhqv4zw27cVQCq37kLaytw6SGc7h2IpaUVFdwcsHq67OhzeRZiQECAbgZxs2bNMDc3Z9euXRkmjwQHBzNx4kQAzpw5Q7ly5bCzs8uzOIQQQghReEkymFMWtgaX9ShdDmdnZ+7fv0/47XC8vKuRoDHXTwYNqfcZrl+/TnR0tC7pMzMzo1OnTmzatIlz587Rtm1bQDvj+NKlS3Tv3p3ExESio6M5cuRInsQghBBCiMJPuokLkEql0q03GHr5AgDxyfmzxIy/vz8WFhb4+PjojnXv3p1t27Zx/vx5XZIYGhpK5cqVOXPmDJcvX+bDDz/km2++yZeYhBBCCFH4SDJYwNJ3IrlyIRjQ7kSiKHk/bjAgIAAfHx8sLJ4sbN2qVSvUajUpKSm6ZDA4OJhKlSrpylSrVo3IyMg8j0cIIYQQhZMkgwUsvWXwwvlgTFQq0jQKSama51xluNmzZ+Pv7693zNLSktjYWBRFwcvLC9BPBtVqNevXr6dFixZ5Ho8QQgghCicZM1jA0pPBoKAgbCxMiU9OIz45DWsL42zrFBwczJEjR9iwYQMqlYq2bdsyfPhwo8QihBBCiIInyWABq1KlCqampjx8+JD4h/fAtijxyWm42FsaJZ5169YZ5b5CCCGEKBykm7iAWVlZ6bplr18KAbTjBjX5MG5QCCGEEOJ5JBk0gvRJJBfOn8PMxASNopCYT7OKhRBCCCGeRZJBI0gfN3ju3DnsrbQ99fm1xIwQQgghxLNIMmgE6S2DZ86cwc5SmwzGSTIohBBCCCOQZNAIatasCcClS5cw0aQA8DhFTZo675eYEUIIIYR4FkkGjcDDwwN3d3c0Gg0XzgdjZa5dVka6ioUQQghR0CQZNJJatWoB2p1C0ruKJRkUQgghREGTZNBI0reDCwwMxC59EklS/mxNJ4QQQgiRFVl02kiebhm0tTBDpVKRotaQkqbB0tw4u5EI8cpLioH4e9o///tISYC6g8DBU1v2wh9wZh2kJkJqkvbPtCRQp4Kihu4/Qsl62rL+K2HXJNCkac9p0kDRgMpE++i1Fiq10ZY9txH+GPXvOdWTMqYWYGoObeaAdztt2ZvH4OAX2nNmltrzppZgZgHmtuDT/UkMcRFw86j2uIUNWNg+eW5uC1YO2uuFEK8cSQaNJL1lMDg4GHVaKjYWpiQkpxGXnCbJoBB56eF1iLoMCVH/Pu7rP++9BoqU0ZY9vhgOzcm6rgqtniSDj27Cpb+yLpua8OS5Rg0pcRnLKBrt42nqFEiJz7pedfKT53EREHoo67Lu1Z4kg3fPwsbBWZf1mw0N3n9S9s/RYOUIlg7aRNHKESwdtX+WbqCtGyAtWfs52hQFc+us6xdCFFqSDBpJmTJlcHJyIjo6mpCQEDzLepOQnEZ8UhrOdnm3NV3Tpk35+++/WbduHX369NEdX7hwIbNnz+bOnTt5di8hClRyPDwKhYehEHsHYm9r/4wJh+4/gFNJbbnANXD466zriYt8kgxaF9FPenRJkANY2oFNsSfXlWsOHReAmbU2CTK3BjMrbSudiRk4l39S1qc7lG0GJqbacyZmgApQQFHA2ulJ2cododRr2uOK8m/CqNYmiepUKFr2SdkS9aDbD/+eS4a0FO3ztGRtS6W7z5OylvZQprE20UxJ1J5Pf65O1r7PdPGREO6f9WfmN/tJMng3CFa00j43twHrotrE0Kao9nn1Xk9aPZNi4fapf88X0563sNW2gAohjEaSQSNRqVTUqlWLAwcOEBgYSMUqPhD7ZGs6kzz4x1FRFAIDA/Hw8GDTpk16yaC/v7+udVKIQislAe5fhgfXoHzLJ0nTwS/g4Oysr4u+9SQZdKkMnrXB1uXfR7GnnjuDS6Un1702XPvIDreq2kd2WDvpJ3zPYmmvfWSHU8kn7/N5SjeAgX9mfk79n8lrHjWh93pIjv23mzwWkmOePH/6M0uOA5WpNmFN/TfJjL395HyJek+eR12ENd3072Vqqf1Z2LlA/Xeg5lva448fwdV92p+RrSvYuWqTRxMZ6i5EXpNk0IjSk8GAgAAGDhyIqYkKtUbhcYoaW8vc/2iuXLlCXFwcX3zxBePGjSMxMREbGxtAO1axa9euub6HEHkmOgxuHYd7F7SPqAvarlj+nVQ18C8o01D73N5d+6d1UW2rnmMJ7cOhuLYb9+lkpXpP7UNkzfQ//97YuTwZl/g85VvA5AfaxDHxASQ+gscP/33+EMo0eqqwCtx8tMcTH2hbJNXJ/7bq3tYmm+nuX4FNQ/TvpTL9Nzl00Sbttfppjz9+BJd3a38v0h+WDtLiKEQ2STKYQ4mpiQZfY2FqgZmJ9iNP06RRpVoVQDujWKVSYWdpRmR8LFEJCioT/a5iG3Mbg+/n7++PlZUVQ4cOZfr06ezYsYPu3buTlJTEhQsXmD59usF1CpFrKQkQcQ7unIFKbaFIae3xK7tg+0cZy9sUA+eK+l/sPt2hSpfst7aJ/KVS/dut7ghFn1GuZD0YflT7XFG0rYiJDyD+3zGcLhWflDUx03Zrx9/Tnnv8UNv6GB+pfSQ/Na7y/hXY8o7+vcxt/k0MPaDuYKjWQ3s8OQ7uBGqP27tnvxVWiJeYJIM55LvO1+Brvmr6FX5l/ADYd2sfX9z5AtBuS6fRaLC3MqPPrp7EpcZkuPbcgHMG3y8gIIDq1atjYWFB165d2bhxI927dycoKIi0tDS9buKVK1eycOFC1Go1Go2Gjz76iAEDBhh8TyH0aDTarsGwfyDspPZL+P7lJ5MmLGyhyNva5+41oFQDcPEG1yrg6q3t4rVzyVivfIG/+FQq7c/fwhacSmU8X7y2fre2OvXfyT//JofFnhqTaWIKXk21E2riIrRd2qmJ2slDD69r/+OQLvI8rOr45LWFHdi5PUkOa/TWThQC7SzxxPtg556x9VSIl4j8dhuRpYclppamJCQkcOXKFbzKlX/+RQYICAjQJXzdunWjW7duJCcnExAQgIuLCyVLascaff/996xdu5Zdu3bh7OzMgwcP+OOPP/I0FvGKSH2sXV7Fuoj29ZXdsL5XxnJ27uBZUzsOLF3JejB4Z4GEKV5Apubg4KF9/FfxOjBg25PXKYkQH/EkOfSo8eScJg2KVfi3dTFWO4nmYTw8vKY9X+q1J2XvBsGPrbVL+9i5PRmG4FAcHItrJwWlT6QRL4zFixfz5ZdfEhERQY0aNVi4cCH169fPtGxqaiqzZ89m1apVhIeHU6lSJebMmUObNm10ZeLi4vjss8/YsmUL9+7do1atWixYsIB69Z6Mlx04cCCrVq3Sq9vPz4+dOwvHv3mSDObQibdOGHyNhamF7nmLUi042e8kzb9vzskTJwkMDKRSpUp812QzyWo1JYvY4GiduzW/AgICdJNGmjVrhrm5Obt27dKbPPLo0SMmTZpEUFAQzs7OABQrVoyBAwfm6t7iFaFOg7tn4PoBuH4Iwk7A6x9Ci8+050vW17a8FK8NJX21X9oeNTP/Qhcir1jYaGddPz3zOl2ZRvDBae3z5HhtUhgXAXF3tX+Wfv1J2ccPwcQcNKn/nr8L4U/V1e6rJ8lg2En49W1tkpieMD6dPLpU0s6iFkb166+/MnbsWJYuXYqvry/z58/Hz8+PS5cu4erqmqH8pEmTWLNmDcuXL8fb25tdu3bRtWtXjh07plsveOjQoQQHB7N69Wo8PT1Zs2YNLVu2JCQkhOLFi+vqatOmDT/99JPutaVl3q0ckluSDOZQTsbwPc3MxAwzEzPq1K7DyRMnCQgIoHfv3rjY2XM/Ppm0NPNc3eP69etER0frkj4zMzM6derEpk2bOHfuHG3btgVgy5YtNG/eHE9Pz1y9H/EKSU2CwNVw/SCEHtZ2yT3t/qUnz22Kwie3tN14QhQ2lnbaR7FymZ+v1BYm/dstHRv+7xJG4U+euz21dE9MmLY1Mj4i82V52n0F9Ydpn0eeh7+/BMeS2i5yxxLa544lZBxsPps3bx7Dhg1j0KBBACxdupTt27fz448/8sknn2Qov3r1aiZOnEi7dtoJVcOHD2fv3r18/fXXrFmzhsePH7Np0yZ+//13mjRpAsDUqVP5448/+O6775gxY4auLktLS9zd3QvgXRpOkkEje3pbOgB7KzPuxycTl6zdmk6Vw9lw/v7+WFhY4OPz5B+r7t278/bbb5OYmMjEiRMB7aLXNWvWzN2bEC83dZp2pmf6Wnym5rB/+pOZn1aO4NVE22VWtnnG1hhJBMWLzMQE7N20j+LPWI6rQmt456B2ncv/Jo2x4frjIqMuwvktmddj6QDt5z2ZAR8Trp1ln54w2rvL36kcSklJwd/fnwkTJuiOmZiY0LJlS44fP57pNcnJyVhZWekds7a25siRIwCkpaWhVqufWSbdwYMHcXV1pUiRIrzxxhvMmDGDYsWKURhIMmhkT29LpygKtpZmmKhUpKk1JKWqsbbI2Y8oICAAHx8fLCyedE23atUKtVpNSkqKLgm1tbVFo9FkVY14VaUkwtU9cHG7dtyfpT2MOqsd9G9iCq+9r53tWa65tttXvpzEq87SHjxraR/P414d/GZpl1OKSX/c1s6sTo7Vtlamu3Vcf4kdEzNt17NjSe2j3tAnu8ykJmknZ1nkrufqRRQXF0dsbKzutaWlZYZu2Pv376NWq3Fzc9M77ubmxsWLFzOt18/Pj3nz5tGkSRPKlSvHvn372Lx5M2q1GgB7e3saNGjA9OnTqVy5Mm5ubqxfv57jx49TvvyTeQBt2rShW7dueHl5ce3aNT799FPatm3L8ePHMTU1/r+fkgwamY+PD+bm5jx8+JAbN27g5eWFnaUZsUmpxCWl5TgZnD17NrNn6y/Ka2lpqfeXBaBt27YMHTqU4cOHU6xYMWJjY9m2bRv9+vXL8XsSL6jUx3B1r7bF4tJO/e3UFEU7nip9rF+zjN0pQohscq6gffxXSoK2JdD+qa5ES3so3QhibmlbGTVp2kXVo29pz1fp/KTsxT+1iaNNsX+7oEuCU+l/H6WgRF3tOo0voSpVqui9njJlClOnTs11vQsWLGDYsGF4e3ujUqkoV64cgwYN4scff9SVWb16NYMHD6Z48eKYmppSu3Zt+vTpg7//k+ECvXv31j2vVq0a1atXp1y5chw8eJAWLVrkOs7ckmTQyCwtLalevTr+/v6cOnUKLy8v7K2eJIOuDvl7/9dff52xY8fSvHlzXbf0mDFj8vemonDaPQlO/fDktVMpqNwJKrXTTv6QpTWEyF8WtvprLQJU9NM+QLvHdVyEthUxvUXRo/qTsnF3tX8mPtA+7p7Rr6v3OvBur31+7QD4/6T9e/50wuhUUhvHC+a/kzUym5zh7OyMqakpkZGRescjIyOzHMvn4uLC1q1bSUpK4sGDB3h6evLJJ59QtuyT4TDlypXj0KFDJCQkEBsbi4eHB7169dIr819ly5bF2dmZq1evSjIotOrVq6dLBt98803srbQ/lsSUNNLUGsxM83f7paFDhzJ06NB8vYcoZO5dgDNrtfvGps+GrNwRLu2Aql2hajft+CjZwUGIwsPEVDtb2bE4kMlat69/ALXefpIsprcgRt/U/vn0eN67QRDye+b3sXGGN1c92T3m4XV4cP1JsmhunedvLbfs7e1xcHh264mFhQV16tRh3759dOnSBQCNRsO+ffsYOXLkM6+1srKiePHipKamsmnTJt58880MZWxtbbG1teXRo0fs2rWLuXPnZlnf7du3efDgAR4ehWNlBUkGC4F69eqxdOlSTp06BYCFmSlWZqYkpamJT07DycbiOTUIkQ2PH8G5jXBmHdwJ0B5LS4Z2X2qfl2kCo4Nl71chXmTp+2C7+zy7XPkWYGapnzA+uqVdHSDxPlg5PSl74Q/YM/nJa1vXfxPDUtodhOoMerKTkKIU6v9Ejh07lgEDBlC3bl3q16/P/PnzSUhI0M0u7t+/P8WLF9cNszpx4gTh4eHUrFmT8PBwpk6dikajYfz48bo6d+3ahaIoVKpUiatXrzJu3Di8vb11dcbHx/P555/TvXt33N3duXbtGuPHj6d8+fL4+fkV/IeQCUkGC4H0hSn9/f1Rq9WYmppib2VGUryauCRJBkUu3QmEkz9A8EbtgtCgHYResc2T7ieQJFCIV4l7tcwXzH4crU0Onx7TaGGnXUbn0U1Iift3F5h7EP7veo1Vuz0pe2whHF+snyymDzkpBOss9urVi6ioKCZPnkxERAQ1a9Zk586dukklt27dwuSpfwuTkpKYNGkS169fx87Ojnbt2rF69WqcnJx0ZWJiYpgwYQK3b9+maNGidO/enZkzZ2Jurl0r2NTUlLNnz7Jq1Sqio6Px9PSkdevWTJ8+vdCsNahSFEUxdhDGdvv2bUqWLElYWBglSpTQO5eUlERoaCheXl4Zpo7nlbS0NBwdHUlMTOT8+fNUqVKF+KRUrt9PwMzEhMoe9jleYiY/FMRnIvKIOhXmVdaukwbaf9Br9YNqPV/ageRCiHyiKNoeBl1r4r+PNyaB1b9dtNs/0h97nO7DwMwXAc+lZ31/i+yTlsFCwMzMjNq1a3PkyBFOnTpFlSpVsElfYkaTuyVmxCsmLhKC1ml3ATEx1a4JWP8d7X7A9YZpdwQpRP+xEEK8QFQqbeueTVHtdpKZeWMS1Oz7n4TxpnZ2syi0JMPIpvxuQK1Xr54uGRwwYAAmKlWeLDGTH6QxuRC6fxWOL4Qz60GdDK5VnnQBNx3/7GuFECKvWBeB4kWevUC3KHQKT4ZRSKUvBpmSkoK1df7NoEofN5g+iQQo0CVmDJGYmAigGw8hjCjsFBydr10cmn+T9BL1tbsYCCGEENlQqJNBtVrN1KlTWbNmDREREXh6ejJw4EAmTZqkG0OnKApTpkxh+fLlREdH07BhQ7777jsqVMhkQc8cMDMzw8bGhqioKMzNzfUGlual6tW1a0WdOXOG2NhYLCwsMEeDkpZCQloK8Qkm+b7EzPMoikJiYiL37t3DycmpUKya/sp6/Ag2DoZr+58cq9QOGo6CUq8ZLy4hhBAvnEKdDM6ZM4fvvvuOVatWUbVqVU6fPs2gQYNwdHTkww8/BGDu3Ll8++23rFq1Ci8vLz777DP8/PwICQnJk8kNKpUKDw8PQkNDuXnzZq7re9Z9HBwciI2NZffu3VStWhWAR7FJpKoVUmMssLEoHMmXk5NTod1s+5Vh5QQJ97Wzgqv3hoYfgkslY0clhBDiBVSok8Fjx47RuXNn2rfXrphepkwZ1q9fz8mTJwFtS9X8+fOZNGkSnTtrt+T5+eefcXNzY+vWrXrbv+SGhYUFFSpUICUlJU/qy0q9evXYt28fd+/epUOHDgDsPnSN306H0aqyG5+0K/+cGvKfubm5tAgaw50zcHQBdFqo3bdUpdI+t3aCImWMHJwQQogXWaFOBl9//XWWLVvG5cuXqVixIkFBQRw5coR58+YBEBoaSkREBC1bttRd4+joiK+vL8ePH88yGUxOTiY5OVn3Oi4u7rmxmJiY5PsyKr6+vuzbt48zZ87o7tWgogffHLjB1uAoJnWuYfSuYlHAom/Bvulw7jfta/dq0His9nlWs/mEEEIIAxTqZPCTTz4hNjYWb29vTE1NUavVzJw5k759+wIQEREBoFssMp2bm5vuXGZmz57N559/nn+B51D9+vUB/UkktUs54WRjTnRiKv43H+FbtpixwhMF6fEjOPw1nPge1P+2SFfrCVW7GDUsIYQQL59sJYNjx441uOJJkyZRtGjuVhv/7bffWLt2LevWraNq1aqcOXOG0aNH4+npyYABA3Jc74QJE/TeU3h4OFWqVMlVrHkhfUbx+fPnSUhIwNbWFjNTE96o5MrmwHD2XbwnyeDLTlHgxFI4+AUkRWuPeTWBVtOlJVAIIUS+yFYyOH/+fBo0aICFRfa2RTty5AgjR47MdTI4btw4PvnkE113b7Vq1bh58yazZ89mwIABukkMkZGReps9R0ZGUrNmzSzrtbS01NsCJjY2Nldx5hVPT088PT25c+cOgYGBNGqk3SS8RWU3NgeGszckkk/bVTZylCJfqVRw85g2EXStAq2mQfmWslC0EEKIfJPtbuItW7bg6uqarbL29vY5DuhpiYmJGZZyMTU1RaPRAODl5YW7uzv79u3TJX+xsbGcOHGC4cOH50kMBa1evXr8/vvvnDp1SpcMNqnojLmpiuv3E7gWFU85FzsjRynyVOxd7W4hdv/+/fKbBeXegNr9tceFEEKIfJSt2Qg//fQTjo6O2a70+++/zzCOLyc6duzIzJkz2b59Ozdu3GDLli3MmzePrl27AtrlWEaPHs2MGTPYtm0b586do3///nh6etKlS5dc398YMl982pzX/u0e3nch0ihxiXygToOj38KiurBr4pPjTiWh7iBJBIUQQhSIbLUMGjo+76233spRMP+1cOFCPvvsM95//33u3buHp6cn7777LpMnT9aVGT9+PAkJCbzzzjtER0fTqFEjdu7cme8zf/NL+iSSEydO6B1vWdmNw1fuszfkHu80KWeM0EReijgHv4+Au0Ha149uQOpjMM+/XW6EEEKIzKiUXGw0GxwczKFDh1Cr1TRs2JA6derkZWwF5vbt25QsWZKwsDBKlChh1FhiYmIoUqQIiqIQGRmp65q//SiRRnMOYKIC/0mtKGKbvfGbopBJS4a/v4Qj34AmDawcofVM7cbu+bS7jRBCvKwK0/f3iyzH3z6LFy+mRYsWHDp0iAMHDvDGG28wc+bMvIztleTo6Kib2Xz8+HHd8RJFbPB2t0ejwIFL94wVnsiNqMuwtJE2GdSkQeVOMOIU1H5bEkEhhBBGk+1voLCwML3XixYt4vz58/z2229s2bKFnTt3Mn/+/LyO75XUoEEDQD8ZBGhVRTsOc6+MG3wx2btBchzYusKbP0Ov1dpjQgghhBFlOxls2bIlCxYsIL1XuVixYuzcuZPk5GTi4uLYu3cvLi4u+RboqySrZLBFZW3i8Pfl+ySnqQs8LpED0WHatQNB2yXcZz2MPAlVOhs3LiGEEOJf2U4GT506xaVLl/D19eXMmTMsW7aMb775Bmtra5ycnPj1119ZtWpVfsb6ynj99dcB7WeempqqO169uCMu9pbEJ6dx4vpDY4UnskNR4MQy7UzhM+ueHPesBdZFjBeXEEII8R/ZXmfQwcGBJUuWcOzYMQYOHMgbb7zB4cOHUavVqNVqnJyc8jHMV0vFihUpUqQIjx49IigoiLp16wJgYqKihbcrv5wKY++FSJpUlJbYQikuEn5/H67u1b6+shtq9TVuTEIIIUQWDB61/vrrr3P69GmKFClCrVq1+PvvvyURzGMmJia89tprQMau4pb/dhXvu3CPXEwEF/kl9DB831ibCJpZQdsvoedKY0clhBBCZCnbyWBaWhpLlizhgw8+YOXKlXz66af88ccffP311/Ts2ZPISJnUkJeyGjfYsLwzVuYmhEc/5vydwrGNngA0Gjj8NfzcCeIjtVvJvXMIfN+RreSEEEIUatlOBocMGcKiRYuwtbXlp59+YsyYMVSsWJH9+/fTpk0bGjRowHfffZefsb5SskoGrS1MaVZRu/bgzuCIAo9LZOFuIOybDooGarwFQ/eBq7exoxJCCCGeK9vJ4O+//86mTZv44osv2LNnD9u3b9edGzJkCP/88w+HDx/OlyBfRfXr10elUnHjxg0iIvSTvjY+7gDsPC/JYKFRvA68MRE6LYQuS8DCxtgRCSGEENmS7WTQzc2N3bt3k5KSwv79+ylWrJjeeVdXV9atW5fF1cJQDg4O+Pj4ABlbB5t7u2JuquLqvXiu3oszRngC4NxGiL715HWTcVC7v3QLCyGEeKFkOxlctGgRM2fOxNramvfee08WmC4AWXUVO1qb83o5ZwB2nZexmgVOo4bdn8GmIbD+LUhJNHZEQgghRI5lOxls1aoVkZGRREREcPv2bd1aeCL/ZJUMwlNdxTJusGAlxcC6XnDsW+3rin7aWcNCCCHEC8qgpWVUKpXsMlKA0pPB06dPk5KSoneuVRU3VCo4Fx7D7UfSMlUgHl6HH1rC1T3aBLDHj9DiM9lXWAghxAstW99itWvX5tGjR9mutFGjRoSHh+c4KKFVsWJFihYtSlJSEkFBQXrnnO0sqVemKCBdxQXitj/80AruXwZ7Txi8E3y6GzsqIYQQIteytQPJmTNnCAoKomjRotmq9MyZMyQnJ+cqMKFtiW3QoAHbt2/n2LFj1KtXT+98Wx93ToY+ZFdwBEMaeRkpyleAosDOjyHxPrhXh74bwN7d2FEJIYQQeSLb29G1aNEi2zteqGQ2ZZ55/fXX2b59O0ePHmXUqFF65/yquvP5HyGcuvmQe3FJuNrL2LV8oVJBz1VwYBa0/QIs7Y0dkRBCCJFnspUMhoaGGlxxiRIlDL5GZNS4cWMA/v77bxRF0Uu0PZ2sqVHCkaDbMewJiaSvb2ljhfnyURS4fRpK/tsa61gcuiw2bkxCCCFEPshWMli6tCQZxlKvXj0sLS2JjIzk6tWrVKhQQe+8n487Qbdj2BkcIclgXtGoYftH4L8Sui2H6j2NHZEQQgiRb2QaZCFnZWVF/fr1AW3r4H+1qaodu3b82gNiElMLNLaXkjoVNr8D/j9pX6fKTG0hhBAvN0kGXwDpXcWZbfdX1sWOim52pGkU9l6QWcW5kvoYfu0HwRvBxAx6rIA6A4wdlRBCCJGvJBl8ATRp0gTIPBkEaOPjAcCO4LsFFtNLJzkO1vaEyzu1awj2Xi9LxwghhHglSDL4AmjQoAEmJiZcv3490/UbO1TXJoOHLkcR81i6ig2WmqRNBG8cBgt76LcJKrY2dlRCCCFEgTA4GQwLC+P27du61ydPnmT06NEsW7YsTwMTTzg4OFCrVi0g89bBim72VHC1I1WtsPu8bE9nMDNLKFEXLB2h/+9QppGxIxJCCCEKjMHJ4FtvvcWBAwcAiIiIoFWrVpw8eZKJEycybdq0PA9QaD1r3CBAh+qeAPx5VrqKDaZSQavpMPwolKhj7GiEEEKIAmVwMhgcHKyb3frbb7/h4+PDsWPHWLt2LStXrszr+MS/nl5vMDMdami7io9evc+jhJRMy4inJMfDvmnaLmLQJoROJY0bkxBCCGEEBieDqampWFpaArB37146deoEgLe3N3fvSqtUfklPBoODg3n48GGG8+Vc7Kjs4UCaRmGXdBU/W+pjWN8bDn8NW98zdjRCCCGEURmcDFatWpWlS5dy+PBh9uzZQ5s2bQC4c+cOxYoVy/MAhZaLiwve3t4AHD16NNMy6RNJpKv4GdSpsGHgk8kiDUYaOyIhhBDCqAxOBufMmcP3339Ps2bN6NOnDzVq1ABg27Ztuu5jkT+e11Xc8d9xg8eu3ed+fHKBxfXC0Ghg6/Any8e89at24ogQQgjxCjM4GWzWrBn379/n/v37/Pjjj7rj77zzDkuXLs3T4IS+5603WKqYDdVLOKJRYEewdBXrURTYMR7ObdAuKP3mz1CmobGjEkIIIYwuR+sMKoqCv78/33//PXFxcQBYWFhgY2OTp8EJfektg/7+/iQkJGRaJr2rePvZOwUW1wvh0Fw4tRxQQdfvoaKfsSMSQghhBIsXL6ZMmTJYWVnh6+vLyZMnsyybmprKtGnTKFeuHFZWVtSoUYOdO3fqlYmLi2P06NGULl0aa2trXn/9dU6dOqVXRlEUJk+ejIeHB9bW1rRs2ZIrV67ky/vLCYOTwZs3b1KtWjU6d+7MiBEjiIqKArTdx//73//yPEDxROnSpSlVqhRpaWkcP3480zLtqmmTwROhD7kXm1SQ4RVuZZuBlSO0/xqq9TB2NEIIIYzg119/ZezYsUyZMoWAgABq1KiBn58f9+7dy7T8pEmT+P7771m4cCEhISG89957dO3alcDAQF2ZoUOHsmfPHlavXs25c+do3bo1LVu21NskYu7cuXz77bcsXbqUEydOYGtri5+fH0lJheR7WjFQ586dlX79+inJycmKnZ2dcu3aNUVRFOXAgQNK+fLlDa2uUAgLC1MAJSwszNihPFf//v0VQPn000+zLNNl8RGl9Md/Kj8duV6Akb0A4u8bOwIhhBB5yNDv7/r16ysjRozQvVar1Yqnp6cye/bsTMt7eHgoixYt0jvWrVs3pW/fvoqiKEpiYqJiamqq/Pnnn3plateurUycOFFRFEXRaDSKu7u78uWXX+rOR0dHK5aWlsr69euzFXd+M7hl8PDhw0yaNAkLCwu942XKlMl0qzSRt9544w0A9u/fn2UZWYD6X7f9ISL4yWtbme0uhBAvo7i4OGJjY3WP5OSMkyhTUlLw9/enZcuWumMmJia0bNkyy9625ORkrKys9I5ZW1tz5MgRANLS0lCr1c8sExoaSkREhN59HR0d8fX1zfK+Bc3gZFCj0aBWqzMcv337Nvb29nkSlMha8+bNATh16pRuvOZ/ta/mgUoFp28+4vajxIIMr/B4eB3W9YSf2kJ4gLGjEUIIkY+qVKmCo6Oj7jF79uwMZe7fv49arcbNzU3vuJubGxERmU+69PPzY968eVy5cgWNRsOePXvYvHmzbl1le3t7GjRowPTp07lz5w5qtZo1a9Zw/PhxXZn0ug25b0EzOBls3bo18+fP171WqVTEx8czZcoU2rVrl5exiUyUKlWKcuXKoVars5xV7O5oxWte2lawbUGv4ESShAewpgckPoCiXuBc0dgRCSGEyEchISHExMToHhMmTMiTehcsWECFChXw9vbGwsKCkSNHMmjQIExMnqRPq1evRlEUihcvjqWlJd9++y19+vTRK1PYGRzp119/zdGjR6lSpQpJSUm89dZbui7iOXPm5EeM4j+y01XctVZxALYEhKMoSoHEVSikJcOvfeHhNXAsBW9tAEs7Y0clhBAiH9nb2+Pg4KB7pO+U9jRnZ2dMTU2JjIzUOx4ZGYm7u3um9bq4uLB161YSEhK4efMmFy9exM7OjrJly+rKlCtXjkOHDhEfH09YWBgnT54kNTVVVya9bkPuW9AMTgZLlChBUFAQn376KWPGjKFWrVp88cUXBAYG4urqmh8xiv/ITjLYppo7FmYmXLkXT8jd2IIKzbgUBbaPhVvHwdIR+m4Ae7fnXyeEEOKlZ2FhQZ06ddi3b5/umEajYd++fTRo0OCZ11pZWVG8eHHS0tLYtGkTnTt3zlDG1tYWDw8PHj16xK5du3RlvLy8cHd317tvbGwsJ06ceO59C4pZji4yM6Nfv355HYvIpvRxg2fOnOHBgweZbgPoYGVOq8pubD93l62B4VT1dCzoMAveie8hcA2oTKDnj+DqbeyIhBBCFCJjx45lwIAB1K1bl/r16zN//nwSEhIYNGgQAP3796d48eK6MYcnTpwgPDycmjVrEh4eztSpU9FoNIwfP15X565du1AUhUqVKnH16lXGjRuHt7e3rk6VSsXo0aOZMWMGFSpUwMvLi88++wxPT0+6dOlS4J9BZgxOBrdt25bpcZVKhZWVFeXLl8fLyyvXgYmsubm5UbVqVc6fP8+hQ4fo1q1bpuU61/Rk+7m7/H7mDp+0rYypiaqAIy1AGg1c3qF93mo6lG/57PJCCCFeOb169SIqKorJkycTERFBzZo12blzp25yx61bt/TG+iUlJTFp0iSuX7+OnZ0d7dq1Y/Xq1Tg5OenKpI9RvH37NkWLFqV79+7MnDkTc3NzXZnx48eTkJDAO++8Q3R0NI0aNWLnzp0ZZiEbi0oxcECZiYkJKpUqwzi09GMqlYpGjRqxdetWihQpkqfB5pfbt29TsmRJwsLCKFGihLHDyZYPP/yQhQsXMmLECBYtWpRpmZQ0DfVn7SU6MZU1Q3xpVMG5gKMsYOpUCN4E1XuB6iVOfIUQQgAv5vd3YWTwmME9e/ZQr1499uzZo5u1s2fPHnx9ffnzzz/5+++/efDggexGks/Su4qfNW7QwsyE9v/uSLIl8CVdA1Kd9uS5qTnU6C2JoBBCCGEAg5PBUaNGMW/ePFq0aIG9vT329va0aNGCL7/8knHjxtGwYUPmz5/Pnj178iNe8a+mTZuiUqm4cOGCbi2jzKTPKt4ZfJfHKRnXh3yhKQpsHAg7PoG0FGNHI4QQQryQDE4Gr127hoODQ4bjDg4OXL9+HYAKFSpw//793EcnslS0aFFq1aoFwIEDB7IsV6d0EUoUsSYhRc2eC5FZlnshHV8EF/6AUz/A/UvGjkYIIYTId3v37qVhw4a6xa5TU1NzXafByWCdOnUYN24cUVFRumNRUVGMHz+eevXqAXDlyhVKliyZ6+DEs6UvMfOsZFClUulaB7e+TF3FN4/Dnina521mg3s148YjhBBCFIARI0Ywfvx4Fi5cyI0bN/j8889zXafByeCKFSsIDQ2lRIkSlC9fnvLly1OiRAlu3LjBDz/8AEB8fDyTJk3KdXDi2bKz3iBA55raZPDQ5SgexGfcr/GFE38PNgwERQ3VekK9ocaOSAghhCgQNjY2dO7cmbp167Js2TL27t2b6zoNXlqmUqVKhISEsHv3bi5fvqw71qpVK9107MKybs7LrlGjRpiZmXH9+nWuX7+utyL608q72lG9hCNnb8fw59m7DHi9TMEGmpfUabBxMMRHgIs3dFwgE0aEEEK8MqKiotiwYQOVKlWiUqVKpKTkfsx8jhadNjExoU2bNrRp0ybXAYics7e35/XXX+fvv/9m9+7dvPfee1mW7VqrOGdvx7DR//aLnQwenAU3DoOFHby5GixsjR2REEIIUWA++ugjdu7cyVdffcXFixdJTk7mzTffpFq1alSrVi1HDXI5SgYTEhI4dOgQt27dypCRfvjhhzmpUuRQ69at+fvvv9m1a9czk8HONYsz668LnAuP4cLdWCp7ZJwE9EJw89FuNddxPrhUNHY0QgghRIEaM2aM3uvQ0FCCg4MJDg5m48aNOUoGDV50OjAwkHbt2pGYmEhCQgJFixbl/v372NjY4OrqqptR/CJ5kRetPH36NPXq1cPBwYH79+/rrXj+X++t9mfn+QgGN/RicscqBRhlHkt8CDZFjR2FEEIII3uRv79zKiYmBkfHvN1i1uAJJGPGjKFjx448evQIa2tr/vnnH27evEmdOnX46quv8jQ48Xy1a9emWLFiuk2vn+XNetq/KFvPhJOSpimI8PKGomgTwHSSCAohhHhFFSlShNKlS9OuXTvGjx/P6tWrCQwMJDk55xNEDU4Gz5w5w0cffYSJiQmmpqYkJydTsmRJ5s6dy6effprjQETOmJiY0KpVK0C7WfazNKnggqu9JQ8TUth/8V5BhJc3Ti6Hxb5wNfczpoQQQogX2fDhw3F2dqZhw4Z4e3vj7+/PuHHj8PLywtvbO0d1GpwMmpub62YNu7q6cuvWLQAcHR0JCwvLURAid/z8/ADYvXv3M8uZmZrQtbZ2mZmN/i/IzyoiGHZPgoR7cP+qsaMRQgghjGrx4sVs3bqVmzdvsn79erp168bevXu5c+cOhw8fzlGdBieDtWrV4tSpU4B2S7TJkyezdu1aRo8ejY+PT46CELmT3jJ46tQpHjx48MyyPetoFwM/cCmKe3FJ+R5brqQ+hk1DQJ0MFfzA911jRySEEEIYXcmSJVm2bBnLli1j1apVtG3blpCQEFxcXHJUn8HJ4KxZs/Dw8ABg5syZFClShOHDhxMVFcX333+foyBE7hQvXhwfHx8URXnu4pPlXe2oXcoJtUZhS0Ah35Fk71SIugh2btBliawnKIQQ4pV34cIFtm7dypw5c/j888+5fPkykZGRnD9/Psd1Gry0TN26dXXPXV1d2blzZ45vLvJO69atCQ4OZvfu3fTq1euZZXvWLUnArWg2+N/mnSZlURXGJCv0bzixVPu88xKwdTZuPEIIIUQhULVqVapXr86bb77J2LFjqVy58jNXEskOg1sG33jjDaKjozMcj42N1W2PJgpe+rjBXbt28bzVgjpU98DK3ISr9+IJDIsugOgMlBwHW0don9cZBBVaGjceIYQQopD46quvqFOnDr///jutWrXi9ddfZ9CgQXz99dfPnUiaFYOTwYMHD2a69UlSUlKOBy4+S3h4OP369aNYsWJYW1tTrVo1Tp8+rTuvKAqTJ0/Gw8MDa2trWrZsyZUrV/I8jsKucePGWFlZER4ezoULF55Z1t7KnLY+2q7+DadvF0R4hlEU8GoMTqWh9XRjRyOEEEIY1c2bN/njjz84efIkY8eOZcWKFZw4cYLIyEh+++03unXrRkpKCqtXr85R/dnuJj579qzueUhICBEREbrXarWanTt3Urx48RwFkZVHjx7RsGFDmjdvzo4dO3BxceHKlSsUKVJEV2bu3Ll8++23rFq1Ci8vLz777DP8/PwICQnBysoqT+MpzKytrWnSpAm7d+9m165dVKny7EWle9YtwZbAcP4MusPkDlWwtjAtoEizwcpBO0bw8SOwtDd2NEIIIYTRrF+/noEDB5KamopKpaJWrVq6nAjAy8sLLy8vOnbsmPObKNmkUqkUExMTxcTERFGpVBkeNjY2yooVK7JbXbZ8/PHHSqNGjbI8r9FoFHd3d+XLL7/UHYuOjlYsLS2V9evXZ/s+YWFhCqCEhYXlKl5j+/rrrxVA8fPze25ZtVqjNJ6zXyn98Z/Kr6duFUB02ZDyWFE0GmNHIYQQ4gXxsnx/P0ulSpWUgQMHKpcuXVL27t2r1KlTRxk8eHCe3iPb3cShoaFcu3YNRVE4efIkoaGhukd4eDixsbEMHjw451lpJrZt20bdunXp2bMnrq6u1KpVi+XLl+vFFBERQcuWT8aUOTo64uvry/Hjx7OsNzk5mdjYWN0jLi4uT+M2ljZt2gDarvyEhIRnljUxUdG7vnaZmXUnbuV7bNny52hY9ybE3jV2JEIIIUShcP36daZMmULFihVp0aIFa9as4ZdffsnTe2Q7GSxdujRlypRBo9FQt25dSpcurXt4eHhgapr33YzXr1/nu+++o0KFCuzatYvhw4fz4YcfsmrVKgBdV7Wbm5vedW5ubnrd2P81e/ZsHB0ddY/ndam+KCpXrkyZMmVITk5m3759zy3fs05JzExUnAmLJuRObAFE+AxX90LQeu2fMS/IgthCCCFEPktLS8PGxkb32tvbG41G88w8x1AGLy0DcOXKFQ4cOMC9e/fQaPT3uJ08eXKeBAboEs9Zs2YB2gWvg4ODWbp0KQMGDMhxvRMmTGDs2LG61+Hh4S9FQqhSqejQoQOLFi1i+/btdOrU6ZnlXewt8avqzvZzd1l38iYzulQroEj/IyUB/hyjfe77HpSsb5w4hBBCiEJo1apVNGzYkOrVq2NnZ4eZmRmJiYl5Vr/ByeDy5ct1++K5u7vrrVGnUqnyNBn08PDIkKRVrlyZTZs2AeDu7g5AZGSkbiHs9Nc1a9bMsl5LS0ssLS11r2Njjdwqlofat2+vSwYVRXnuGoJv+ZZi+7m7bA28w6ftKmNjkaP/H+TOgVkQfQscS0HziQV/fyGEEKKQaty4MTNmzCAuLg4TExO8vLxISkpixYoVtGzZkrp162Jvn7vJlgYvLTNjxgxmzpxJREQEZ86cITAwUPcICAjIVTD/1bBhQy5duqR37PLly5QuXRrQzqBxd3fX6xKNjY3lxIkTNGjQIE9jeVE0a9YMGxsbwsPDCQoKem75BmWLUbqYDfHJafwRdKcAIvyP8AD4Z4n2eYd5YGlX8DEIIYQQhdShQ4eIiYnh0qVLrFmzhq5du9K0aVO+++47WrRoQZEiRahcuXKu7mFwMvjo0SN69uyZq5tm15gxY/jnn3+YNWsWV69eZd26dSxbtowRI7QLEqtUKkaPHs2MGTPYtm0b586do3///nh6etKlS5cCibGwsbKy0u1V/Oeffz63vImJij71SwFGmEiiToU/PgRFAz49oEKrgr2/EEII8YKoUKECvXv3Zu7cuezdu5eHDx9y7do11q9fT9euXXNVt8HJYM+ePdm9e3eubppd9erVY8uWLaxfvx4fHx+mT5/O/Pnz6du3r67M+PHj+eCDD3jnnXeoV68e8fHx7Ny585VaY/C/2rdvD2QvGQToUacE5qYqgm7HEBwek5+h6YsNh+R4sC4Cbb4ouPsKIYQQLwEvLy969uypm1uRUypFec7eZf8xe/Zs5s2bR/v27alWrVqG/fA+/PDDXAVkDLdv36ZkyZKEhYVRokQJY4eTa+Hh4ZQoUQKVSkVERASurq7PvWbkugD+PHuXt3xLMatrAU4kSUmEqItQvHbB3VMIIcRL4WX7/jYWg5NBLy+vrCtTqbh+/XqugypoL+MvU+3atQkMDGTlypXZmnl97Np93lp+AlsLU05MbImdpREmkgghhBAGeBm/v43B4G7ipxeb/u/jRUwEX1YdOnQAYPv27dkq36BsMco625KQombbmXyeSHJlD5xYBuq0/L2PEEIIIZ7L4GQwXUpKCpcuXSItTb7QC6P0cYO7du0iNTX1ueVVKhVv+Wonkvx8/AYGNhhnX+pj2D4WdoyDE0vz5x5CCCGEyDaD+wITExP54IMPdLuAXL58mbJly/LBBx9QvHhxPvnkkzwPUhiuXr16uLi4EBUVxZEjR2jevPlzr+lRpwRf7b7ExYg4ToY+xLdssbwP7Mh87ZqCDsWhzsC8r18IIYR4STy9QcbzzJs3L8f3MTgZnDBhAkFBQRw8eFC3Fy5Ay5YtmTp1qiSDhYSJiQnt2rVj1apV/PHHH9lKBp1sLOhaqzjrT4ax6viNvE8GH4bCkW+0z1vPkDUFhRBCiGcIDAzUex0QEEBaWhqVKlUCtA1ypqam1KlTJ1f3MbibeOvWrSxatIhGjRrp7W5RtWpVrl27lqtgRN7q3LkzoP2ZZbfbd8DrZQDYdT6SO9GP8zagXZ+COhm8mkDV3K2JJIQQQrzsDhw4oHt07NiRpk2bcvv2bQICAggICCAsLIzmzZvrhobllMHJYFRUVKZLlSQkJDx36zNRsFq3bo2VlRWhoaGcPXs2W9d4uzvg61UUtUZh7YmbeRfM5d1w6S8wMYN2X4H8rgghhBDZ9vXXXzN79myKFCmiO1akSBFmzJjB119/nau6DU4G69atqzdDNT0B/OGHH17ZLeAKK1tbW/z8/ADYsmVLtq8b+G/r4PqTYSSlqnMfiEYNuyZon7/2PrhUyn2dQgghxCskNjaWqKioDMejoqKIi4vLVd0GJ4OzZs3i008/Zfjw4aSlpbFgwQJat27NTz/9xMyZM3MVjMh76VvUGJIMtqrihqejFQ8TUvjz7N3cB2FiCj1+1HYNNx2f+/qEEEKIV0zXrl0ZNGgQmzdv5vbt29y+fZtNmzYxZMgQunXrlqu6DU4GGzVqxJkzZ0hLS6NatWrs3r0bV1dXjh8/nusBjCLvdezYEVNTU86ePZvtdSDNTE3o+1ppAFYdy6NlZjxqQM+VYGmf+7qEEEKIV8zSpUtp27Ytb731FqVLl6Z06dK89dZbtGnThiVLluSqboN3IHkZvewrmLdo0YL9+/fz1Vdf8dFHH2XrmocJKbw2ex8paRo2v/86tUsVef5FmUl8CDZFc3atEEII8Qwv+/d3ZhISEnQTdsuVK4etrW2u6zS4ZfCvv/5i165dGY7v2rWLHTt25Dogkfdy0lVc1NaCTjU8AW3rYI5EXYZvqsLOCbLbiBBCCJEHbG1tqV69OtWrV8+TRBBykAx+8sknqNUZJxUoiiJrDBZS6UvMHDt2jMjIyGxflz6R5K9zd4mMTTL8xnsmQ2qidn1BU9nrWAghhMiNw4cP069fPxo0aEB4eDgAq1ev5siRI7mq1+Bk8MqVK1SpUiXDcW9vb65evZqrYET+KFmyJHXr1kVRFLZt25bt63yKO1KvTBFS1YrhrYPXD8HlHdqlZFpPN+xaIYQQQujZtGkTfn5+WFtbExgYSHJyMgAxMTHMmjUrV3UbnAw6OjpmOhHh6tWredZcKfJeTrqKAYY1LgvAmn9ukpCcza5ejRp2T9Q+rzsYnCsYdE8hhBBC6JsxYwZLly5l+fLlmJub6443bNiQgICAXNVtcDLYuXNnRo8erbfbyNWrV/noo4/o1KlTroIR+Sc9Gdy3bx+xsbHZvq5lZTe8nG2JTUpjw+mw7F0U9AtEnANLR2gqQweEEEKI3Lp06RJNmjTJcNzR0ZHo6Ohc1W1wMjh37lxsbW3x9vbGy8sLLy8vKleuTLFixfjqq69yFYzIP5UrV6ZSpUqkpKTw559/Zvs6ExMVgxt5AfDj0RuoNc+ZfJ6WDAdna583+Qhs83h/YyGEECIXFi9eTJkyZbCyssLX15eTJ09mWTY1NZVp06ZRrlw5rKysqFGjBjt37tQro1ar+eyzz/Dy8sLa2ppy5coxffp0vWXZBg4ciEql0nu0adPGoLjd3d0zHY535MgRypYta1Bd/5WjbuJjx46xfft23n//fT766CP27dvH/v37cXJyylUwIn/16NEDgA0bNhh2Xe0SFLEx59bDRHafj3h24btntcvJ2HtC/XdyGqoQQgiR53799VfGjh3LlClTCAgIoEaNGvj5+XHv3r1My0+aNInvv/+ehQsXEhISwnvvvUfXrl0JDAzUlZkzZw7fffcdixYt4sKFC8yZM4e5c+eycOFCvbratGnD3bt3dY/169cbFPuwYcMYNWoUJ06cQKVScefOHdauXcv//vc/hg8fbviH8TTFACkpKYqpqaly7tw5Qy4r9MLCwhRACQsLM3Yo+SooKEgBFEtLSyUmJsaga7/adVEp/fGfSpfFR55fOO6eotz8J4dRCiGEENlj6Pd3/fr1lREjRuheq9VqxdPTU5k9e3am5T08PJRFixbpHevWrZvSt29f3ev27dsrgwcPfmaZAQMGKJ07d85WjFnRaDTKjBkzFFtbW0WlUikqlUqxsrJSJk2alKt6FUVRDGoZNDc3p1SpUpkuLSMKv2rVquHt7U1ycrJBs4oB+jcog4WpCYG3ovG/+fDZhe1coJRvLiIVQgghsi8uLo7Y2FjdI32m7dNSUlLw9/enZcuWumMmJia0bNmS48ePZ1pvcnIyVlZWesesra31lnJ5/fXX2bdvH5cvXwYgKCiII0eO0LZtW73rDh48iKurK5UqVWL48OE8ePDAoPeoUqmYOHEiDx8+JDg4mH/++YeoqCimT8/9ih0GdxNPnDiRTz/9lIcPn5MQiEJHpVLx5ptvAvDbb78ZdK2LvSVdaxUHYPnfoRkLJDyA0L9zHaMQQghhqCpVquDo6Kh7zJ49O0OZ+/fvo1arcXNz0zvu5uZGRETmQ6D8/PyYN28eV65cQaPRsGfPHjZv3szdu3d1ZT755BN69+6Nt7c35ubm1KpVi9GjR9O3b19dmTZt2vDzzz+zb98+5syZw6FDh2jbtq1BjWtvvPEGn3/+ORYWFlSpUoX69etjZ2fHo0ePeOONN7JdT2YMXgl40aJFXL16FU9PT0qXLp1hOZncTm8W+evNN99k2rRp7Nq1i+joaIPGeQ5t7MWvp8PYFRLBzQcJlC721M/+yDw4vgh834O2c/I+cCGEECILISEhFC9eXPfa0tIyT+pdsGABw4YNw9vbG5VKRbly5Rg0aBA//vijrsxvv/3G2rVrWbduHVWrVuXMmTOMHj0aT09PBgwYAEDv3r115atVq0b16tUpV64cBw8epEWLFtmK5eDBg5w7d47AwEDWrl2ry79SUlI4dOhQrt6nwclgly5dcnVDYVxVq1alSpUqhISE8Pvvv+t+UbOjgps9zSq5cPBSFD8cDmV6Fx/tiZjbcHK59nn5VvkQtRBCCJE1e3t7HBwcnlnG2dkZU1PTDDtxRUZG4u7unuk1Li4ubN26laSkJB48eICnpyeffPKJ3uzdcePG6VoHQZvs3bx5k9mzZ2f5HVu2bFmcnZ25evVqtpNBgL179/Luu+/y2muv8ccff1CmTJlsX/ssBieDU6ZMyZMbC+Pp1asXU6ZM4bfffjMoGQR4t0k5Dl6K4tfTYXzQojyu9lZwaA6ok6F0Qyif/V9qIYQQoqBYWFhQp04d9u3bp2vY0mg07Nu3j5EjRz7zWisrK4oXL05qaiqbNm3SDbkCSExMxMREf9SdqakpGo0my/pu377NgwcP8PDwMOg9eHh4cOjQIQYNGkS9evXYsGEDlStXNqiOzBg8ZhAgOjqaH374gQkTJujGDgYEBOj2yROFW8+ePQHYvXs3jx49Muja18oWpXYpJ1LSNKw4EgoPr0PgWu3JFpNBpcrrcIUQQog8MXbsWJYvX86qVau4cOECw4cPJyEhgUGDBgHQv39/JkyYoCt/4sQJNm/ezPXr1zl8+DBt2rRBo9Ewfvx4XZmOHTsyc+ZMtm/fzo0bN9iyZQvz5s3TbfYQHx/PuHHj+Oeff7hx4wb79u2jc+fOlC9fHj8/v2zHrvr3+9XS0pJ169YxatQo2rRpw5IlS3L/wRg6/TgoKEhxcXFRypcvr5iZmSnXrl1TFEVRJk6cqLz99tu5nt5sDK/K0jJPq1atmgIoK1asMPjavSERSumP/1SqfLZDSd44XFGmOCjK6m75EKUQQgiRtZx8fy9cuFApVaqUYmFhodSvX1/5558nS6E1bdpUGTBggO71wYMHlcqVKyuWlpZKsWLFlLffflsJDw/Xqy82NlYZNWqUUqpUKcXKykopW7asMnHiRCU5OVlRFEVJTExUWrdurbi4uCjm5uZK6dKllWHDhikREREGvVeVSqVERkbqHdu4caNia2urmJiYGFRXhroVRXnOlhL6WrZsSe3atZk7dy729vYEBQVRtmxZjh07xltvvcWNGzdyn6EWsNu3b1OyZEnCwsIoUaKEscMpEDNmzOCzzz7Dz88vw2rqz6MoCm0XHCY+8hqHrD7CVFHDkL1Qsl4+RSuEEEJk9Cp9f9+8eZOSJUtm6JIODg7G39/f4GFfTzO4m/jUqVO8++67GY4XL148y6nZovBJH++wd+9eoqKiDLpWpVLxfvPyuPOQO4ozaV7NJREUQggh8lHp0qUzJIIAPj4+uUoEIQcTSCwtLYmNjc1w/PLly7i4uOQqGFFwKlasSO3atQkICOC3335jxIgRBl3fvpoH83bXpvmDL/m8uCd9n3+JEEIIIQwwduxYpk+fjq2tLWPHjn1m2Xnz5uX4Pga3DHbq1Ilp06aRmpoKaFuJbt26xccff0z37t1zHIgoeG+//TYAa9asMfhaUxMV7zUtRxpmfHsyhuQ02ZVGCCGEyEuBgYG6fCswMDDLx5kzZ3J1H4PHDMbExNCjRw9Onz5NXFwcnp6eRERE0KBBA/76668Mi1C/CF6lMQdPi4iIoHjx4mg0Gq5cuUL58uWzd2H0Lbi2n2SfXjT9+hgRsUnM7laNPvVL5W/AQgghxFNe1e/vvGZwy6CjoyN79uzhjz/+4Ntvv2XkyJH89ddfHDp06IVMBF9l7u7utGqlXSR67dq12b/w8Dz4YxSWf37IsCbahTeXHrpGmjrrNZWEEEIIUTgZPGYwXaNGjWjUqFFexiKMoF+/fuzatYs1a9YwefJk3TpGWYoOg8B/u5XrDKSPZ0kWH7jKzQeJ/H7mDt3ryP/MhBBCiLzwvHGCT8vNmMEcJYP79u3jm2++4cKFCwBUrlyZ0aNH07JlyxwHIoyjS5cu2NjYcPXqVU6ePImvr++zLzj2LWhSoUxjKNMQG2BY47LM2XmRhfuv0LmmJ2amOVrLXAghhBBPCQwMzFa55zbkPIfByeCSJUsYNWoUPXr0YNSoUQD8888/tGvXjm+++cbgWanCuOzs7OjatStr165lzZo1z04GE+5DwGrt8yb/0x3u36A0yw9f58aDRLYEhtOzbsl8jloIIYR4+R04cKBA7mPwBJISJUrwySefZNjHb/HixcyaNeuF3JLuVR+AunPnTtq2bYuzszN37tzB3Nw884L7Z8Lfc8GzFgw7oLf13PeHrjF7x0VKFbVh30dNMZfWQSGEEPnsVfz+DgkJ4datW6SkpOiOqVQqOnbsmOM6DW4ZjI6Opk2bNhmOt27dmo8//jjHgQjjadmyJW5ubkRGRrJ7927at2+fsVByHJxcpn3eaEyGPYjf/rd18NbDRDYH3KZXPZlZLIQQQuSV69ev07VrV86dO4dKpSK9LS+9i1itzvkSbzlaZ3DLli0Zjv/+++906NAhx4EI4zEzM6NPnz4ArF69OvNCiQ+heG0oVh68M/6cbSzMeLdJOQAW7r9KSprMLBZCCCHyyqhRo/Dy8uLevXvY2Nhw/vx5/v77b+rWrcvBgwdzVbfB3cQzZszgq6++omHDhjRo0ADQjhk8evQoH330EQ4ODrqyH374Ya6CKyivYjPzf/n7+1O3bl0sLS25e/cuRYoUybxgUixYOWR66nGKmsZzD3A/PlnWHRRCCJHvXqXvb2dnZ/bv30/16tVxdHTk5MmTVKpUif379/PRRx9le7JJZgzuJl6xYgVFihQhJCSEkJAQ3XEnJydWrFihe61SqV6YZFBA7dq1qV69OmfPnmX9+vW8//77mRfMIhEEsLYw5b2mZZmx/QKL9l+le+0SWJjJ2EEhhBAit9RqNfb29gC6Mf6VKlWidOnSXLp0KVd1G5wMhoaG5uqGonBSqVQMHjyY0aNH8+OPPz5JBjUaOLYAavQBe/fn1tPvtdJ8//d1wqMfs8E/jL6+pfM5ciGEEOLl5+PjQ1BQEF5eXvj6+jJ37lwsLCxYtmwZZcuWzVXd0mwjdPr27Yu5uTn+/v4EBQVpD176C/ZOhaWNQZ363DqszE15v5l27OC3+67wOEX2LBZCCCFya9KkSWg02vH406ZNIzQ0lMaNG/PXX3/x7bff5qpuSQaFjrOzM507dwbgxx9/BEWBI99oT9bqB6ZZLDnzH2/5lqK4kzWRscmsOn4jn6IVQgghXh1+fn5069YNgPLly3Px4kXu37/PvXv3eOONN3JVtySDQs/gwYMBWLNmDclXj0D4aTC1hNeGZ7sOSzNTxrSqCMCSA1eJSXx+i6IQQgghDFO0aNFc7z4CudibWLycWrduTfHixQkPD2fbkkn0dASq9wQ7V4Pq6VqrOMv+vsblyHiW/n2Nj9t450/AQgghxCsiKSmJs2fPcu/ePV2XcbpOnTrluF5JBoUeU1NTBg4cyMyZM/nxz2P07GsDr2Uxs/hZ9ZioGOfnzbCfT/PT0VAGvl4GNwerfIhYCCGEePnt3LmT/v37c//+/QznVCpVwS46DXD48GH69etHgwYNdNvPrV69miNHjuQ4EFF4DBw4EIBdV9MIc6gPblVzVE/Lyq7UKV2EpFQNC/ZdycMIhRBCiFfLBx98QM+ePbl79y4ajUbvkZtEEHKQDG7atAk/Pz+sra0JDAwkOTkZgJiYGGbNmpWrYEThUL58eZpWL40CrLzpkeN6VCqVrnv411NhXI+Kz6MIhRBCiFdLZGQkY8eOxc3NLc/rNjgZnDFjBkuXLmX58uWYmz+ZXdqwYUMCAgLyNDhhPEPHzQDgh62HcvU/jvpeRXnD2xW1RuHrPZfzKjwhhBDildKjR49cbzuXFYPHDF66dIkmTZpkOO7o6Eh0dHRexCQKgR49ejB69Ghu3brFX3/9RceOHXNc1zi/Shy4dI/tZ+8ytNEjapXKYqs7IYQQQmRq0aJF9OzZk8OHD1OtWjW9BjnI3RbABieD7u7uXL16lTJlyugdP3LkSK5XwBaFQLg/KGBVog6DBg3iq6++YunSpblKBit7ONCtVgk2BdxmxvYLbHyvQZ5MhRdCCCFeFevXr2f37t1YWVlx8OBBve/R3G4BbHA38bBhwxg1ahQnTpxApVJx584d1q5dy//+9z+GD8/+WnSikNozBX54A058zzvvvAPAjh07cr0N4Ti/Slibm+J/8xHbz93Ni0iFEEKIV8bEiRP5/PPPiYmJ4caNG4SGhuoe169fz1XdBieDn3zyCW+99RYtWrQgPj6eJk2aMHToUN59910++OCDXAUjjOzuWbhxGFSm4N2eChUq0KpVKxRFYdmyZbmq2t3RinebaluOv9hxkaRU2aZOCCGEyK6UlBR69eqFiUne7xdicI0qlYqJEyfy8OFDgoOD+eeff4iKimL69Ol5HpwoYCe/1/5ZpTM4lgDgvffeA2DFihWkpKTkqvp3mpTF3cGK248e89PRG7mqSwghhHiVDBgwgF9//TVf6jZ4zODgwYNZsGAB9vb2VKlSRXc8ISGBDz74QLunrXjxPH4E5zZpn/u+qzvcsWNHPD09uXPnDps3b6Z37945voWNhRnj/Crx0YYgFh+4Ss+6JXC2s8xt5EIIIcRLT61WM3fuXHbt2kX16tUzTCCZN29ejus2uGVw1apVPH78OMPxx48f8/PPP+c4EGFkZ9ZD2mNw84GSvrrD5ubmDB06FIDvvvsu17fpWqs41Us4Ep+cxjxZakYIIYTIlnPnzlGrVi1MTEwIDg4mMDBQ9zhz5kyu6s52MhgbG0tMTAyKohAXF0dsbKzu8ejRI/766y9cXQ3bv9ZQX3zxBSqVitGjR+uOJSUlMWLECIoVK4adnR3du3cnMjIyX+N46Wg0cOoH7fN6Q+A/M32HDRuGiYkJf//9N+fPn8/VrUxMVExqr21R/uXkLS5GxOaqPiGEEOJVcODAgSwf+/fvz1Xd2U4GnZycKFq0KCqViooVK1KkSBHdw9nZmcGDBzNixIhcBfMsp06d4vvvv6d69ep6x8eMGcMff/zBhg0bOHToEHfu3KFbt275FsdLKTYc0pLBwh6qvZnhdIkSJXQbYC9atCjXt6vvVZS2Pu5oFJj2RwiKouS6TiGEEOJllZqaSosWLbhyJX+2dlUp2fwmPnToEIqi8MYbb7Bp0yaKFi2qO2dhYUHp0qXx9PTMlyDj4+OpXbs2S5YsYcaMGdSsWZP58+cTExODi4sL69ato0ePHgBcvHiRypUrc/z4cV577bVs1X/79m1KlixJWFgYJUqUyJf3UOip0+D+pSz3IT548CDNmzfH2tqa27dv6/38cyLsYSIt5x0iOU3Dordq0aF6/vzuCCGEeHm9St/fLi4uHDt2jAoVKuR53dluGWzatCnNmjUjNDSUzp0707RpU92jQYMG+ZYIAowYMYL27dvTsmVLveP+/v6kpqbqHff29qZUqVIcP3483+J5KZmaZZkIgvbnX716dR4/fswPP/yQ69uVLGrD8GblAJi5/QIJyWm5rlMIIYR4WfXr148VK1bkS90GzyYuXbo0AImJidy6dSvDciP/7cbNrV9++YWAgABOnTqV4VxERAQWFhY4OTnpHXdzcyMiIiLLOpOTk0lOTta9jouLy7N4XzhRl6BoWTA1f2ax9LGagwcPZtGiRYwdOxYzM4N/ffS817QcmwJuE/bwMYsOXOXjNt65qk8IIYR4WaWlpfHjjz+yd+9e6tSpg62trd753MwmNvjbPCoqikGDBrFjx45Mz6vVebeYcFhYGKNGjWLPnj1YWVnlWb2zZ8/m888/z7P6XlhpKbCqI6hM4O2t4PrsZKxPnz58/PHHhIWFsWXLFnr27Jmr21uZmzK5Q1WG/XyaHw5fp0edEpRzsctVnUIIIcTLKDg4mNq1awNw+bL+ahy53eLV4KVlRo8eTXR0NCdOnMDa2pqdO3eyatUqKlSowLZt23IVzH/5+/tz7949ateujZmZGWZmZhw6dIhvv/0WMzMz3NzcSElJITo6Wu+6yMhI3N3ds6x3woQJxMTE6B4hISF5GvcL4+KfEB8JikbbOvgcVlZWukWoFyxYkCchtKzsSvNKLqSqFaZuOy+TSYQQQohM5OdsYoNbBvfv38/vv/9O3bp1MTExoXTp0rRq1QoHBwdmz55N+/btcxXQ01q0aMG5c+f0jg0aNAhvb28+/vhjSpYsibm5Ofv27aN79+4AXLp0iVu3btGgQYMs67W0tMTS8slix7Gxr+jyJqf/XSC89gAws8jWJcOHD+eLL77g6NGjnD59mrp16+YqBJVKxZSOVTl69W8OX7nPrvMRtPHxyFWdQgghxMsoOjqaFStWcOHCBQCqVq3K4MGDcXR0zFW9BrcMJiQk6NYTLFKkCFFRUQBUq1aNgICAXAXzX/b29vj4+Og9bG1tKVasGD4+Pjg6OjJkyBDGjh3LgQMH8Pf3Z9CgQTRo0CDbM4lfWfev/LsPsQnUGZDtyzw8POjVqxeQd62DZZxtdfsWT//zAokpMplECCGEeNrp06cpV64c33zzDQ8fPuThw4fMmzePcuXK5Tr/MjgZrFSpEpcuXQKgRo0afP/994SHh7N06VI8PAq+Reebb76hQ4cOdO/enSZNmuDu7s7mzZsLPI4XTsC/u8VUaK3bhzi7Ro0aBcCvv/7KnTt38iSc95uVp7iTNeHRj5m/N3/WURJCCCFeVGPGjKFTp07cuHGDzZs3s3nzZkJDQ+nQoYPeZhw5ke11BtOtWbOGtLQ0Bg4ciL+/P23atOHhw4dYWFiwcuVKXavRi+RVWqcIAHUqzKsMCVHQex14G96136RJEw4fPsy4ceOYO3dunoR14OI9Bq08hYkKto1shE/x3DV7CyGEeLm9St/f1tbWBAYG4u2tP9kzJCSEunXrkpiYmOO6DW4Z7NevHwMHDgSgTp063Lx5k1OnThEWFvZCJoKvpNC/tYmgnZu2ZTAHxo8fD8DSpUuJiYnJk7Cae7vSsYYnGgUmbD5HmlqTJ/UKIYQQLzoHBwdu3bqV4XhYWBj29va5qtvgZHDatGl62aeNjQ21a9fG1taWadOm5SoYUUDKt4B3/4YO85+7vmBW2rVrR5UqVYiLi2Pp0qV5FtrkDlVwsDLjXHgMK4/dyLN6hRBCiBdZr169GDJkCL/++ithYWGEhYXxyy+/MHToUPr06ZOrug1OBj///HPi4+MzHE9MTJS1+14kHjXAu12OLzcxMdG1Ds6fP19vEe/ccLG35NN2lQH4evdlbj/KebO3EEII8V+LFy+mTJkyWFlZ4evry8mTJ7Msm5qayrRp0yhXrhxWVlbUqFGDnTt36pVRq9V89tlneHl5YW1tTbly5Zg+fbreUmmKojB58mQ8PDywtramZcuWBu8z/NVXX9GtWzf69+9PmTJlKFOmDAMHDqRHjx7MmTPHsA/hvxQDqVQq5d69exmO79u3T3F2dja0ukIhLCxMAZSwsDBjh5L/1Oo8qyo5OVkpUaKEAijLly/Ps3rVao3Sc+kxpfTHfyqDfjqpaDSaPKtbCCHEy8PQ7+9ffvlFsbCwUH788Ufl/PnzyrBhwxQnJyclMjIy0/Ljx49XPP/f3n2HR1GtDxz/bsmmb3ohIZUISO+IKCgiiAVQuIAiTUVEQBGuCugV64WLiAjiT0UFRBRBEaWIQkCKIL33loSEVNLrJrvz+2PJQkxAArvZlPfzPPPM7uyZOe8yS/bdM3POCQpS1qxZo5w9e1b55JNPFCcnJ2Xfvn2WMu+9957i4+OjrF69Wjl//ryyfPlyxc3NTfnoo48sZaZPn654eHgoK1euVA4ePKj07t1biYiIUAoKCir9nvPy8pSDBw8qhw4dUvLy8iq9f0VuOBn09PRUvLy8FLVabXlcuuj1ekWtVivPP/+8VYKqanUmGTQaFeXjjoqyYpSiZCdZ5ZAffPCBAigNGzZUSkpKrHJMRVGU08k5ym1T1iphr65WfjmQYLXjCiGEqD0q+/3doUMHZcyYMZbnRqNRCQoKUqZNm1Zh+Xr16ikff/xxmW2PPfaYMnjwYMvzhx56SHnqqaeuWcZkMimBgYHK+++/b3k9MzNTcXR0VL777rsbitvWbvgy8ezZs5k1axaKovDWW2/x4YcfWpZPP/2Ubdu2MW/evFtrphS2FbMFUo/DibXgeGs3m5YaOXIknp6enDp1yqoz0ET5uzHm3igApv5ylLRc61yGFkIIUTcZDAb27t1L9+7dLdvUajXdu3dnx44dFe5TVFRUbjpcZ2dntm3bZnl+5513Eh0dbZki7uDBg2zbto1evXoBcP78eZKSksrU6+HhQceOHa9Z79XUajUajea6i1Zb6TlEyrjhvYcNMw9MHBERQefOnW+5YmEHpWMLtvgX6Fysckh3d3eef/55/vvf/zJ9+nT69u17y3Mklhp9TwN+PZLIiaQc3vj5CJ8MbmuV4wohhKhdcnJyyswm9veZxgDS0tIwGo0EBASU2R4QEMCJEycqPG7Pnj2ZNWsWXbp0oUGDBkRHR7NixQqMRqOlzKRJk8jOzqZx48ZoNBqMRiPvvfcegwcPBiApKclSz9/rLX3ten766adrvrZjxw7mzJmDyXRro29UugOJu7u7ZRoUgJ9//pm+ffsyZcoUDAbDLQUjbCg/HY6vMj9uM9Sqh37hhRdwcnJi165drF+/3mrH1WnVzPxXS7RqFWsPJ7H6kHUGuBZCCFG7NGnSBA8PD8sybdo0qxz3o48+4rbbbqNx48bodDrGjh3LiBEjUKuvpE/Lli1jyZIlfPvtt+zbt49FixYxc+ZMFi1aZJUY+vTpU25p3LgxCxcuZObMmfzrX/+yTAZysyqdDI4aNcrSFHru3DkGDhyIi4sLy5cvt/QuFdXQoWVgNEBgC3NPYisKCAjgueeeA8y9zZXKjWN+Xc2CPXj+8uXiN36Wy8VCCCHKO3bsGFlZWZZl8uTJ5cr4+vqi0WhITk4usz05OZnAwMAKj+vn58fKlSvJy8sjNjaWEydO4ObmRmRkpKXMyy+/zKRJkxg0aBDNmzdnyJAhvPTSS5aEtPTYlan3Wi5evMjIkSNp3rw5JSUlHDhwgEWLFhEWFlap4/xdpZPBU6dO0apVKwCWL19O165d+fbbb1m4cCE//vjjLQUjbOjgt+Z16yE2OfzLL7+Mo6Mj27dvZ9OmTVY99th7o7i9np70PAP/WXnEqsmmEEKIms/d3R29Xm9Z/n6JGECn09G2bVuio6Mt20wmE9HR0XTq1Om6x3dyciI4OJiSkhJ+/PFH+vTpY3ktPz+/TEshgEajsVy6jYiIIDAwsEy92dnZ7Ny58x/rLZWVlcWrr75KVFQUR48eJTo6mlWrVtGsWbMb2v+fVDoZVBTF8gY3bNjAgw+ax6oLCQkhLS3NKkEJK0s+BokHQe0AzfrZpIqgoCBGjhwJYPXBx82Xi1ugVav49UgSqw8lWvX4Qggh6oYJEyYwf/58Fi1axPHjxxk9ejR5eXmMGDECgKFDh5ZpVdy5cycrVqzg3LlzbN26lQceeACTyVTmSugjjzzCe++9x5o1a4iJieGnn35i1qxZPProowCoVCrGjx/Pu+++yy+//MLhw4cZOnQoQUFB9O3b9x9jnjFjBpGRkaxevZrvvvuO7du3c/fdd1v136XSvUDatWvHu+++S/fu3dm8eTP/93//B5h7y/z95khRTbj6QbfXIe8SuPrYrJpXX32Vzz//nM2bN7N582a6du1qtWM3DfJgbLcoZm84zRs/H6FjhDf+eqd/3lEIIYS4bODAgaSmpvLGG2+QlJREq1atWLdunSV/iYuLK9PKV1hYyOuvv865c+dwc3PjwQcfZPHixXh6elrKzJ07l//85z88//zzpKSkEBQUxKhRo3jjjTcsZV555RXy8vJ49tlnyczM5K677mLdunXleipXZNKkSTg7OxMVFcWiRYuueS/iihUrbvJfBVRKJa+5HTp0iMGDBxMXF8eECROYOnUqAOPGjePSpUt8++23Nx2MvdSlia5tbfTo0Xz66ad069atTJO4NRQbTfSd9ydHL2bTtaEfC0e0t1rPZSGEEDVPXfj+Hj58+A191y1YsOCm66h0MngthYWFaDQaHBxubq5be6oLH6aqEhsbS1RUFCUlJWzdupW77rrLqsc/k5LDQ3O2UVRi4s1HmjC8c4RVjy+EEKLmkO9v66j0PYPX4uTkVCMTwVpvxydwZAUUF1RJdWFhYQwfPhzA0mpsTVH+7rz2kHnu4v/+eoKTSTlWr0MIIYSoS6yWDIpqqCgXNr4DP4yApCNVVu3rr7+Og4MDGzdutPqlYoAhd4RxbyM/DCUmXly6n6IS4z/vJIQQQogKSTJYmx1fBcX54N0A6rersmrDwsIs4w6+9tprVh8KRqVSMaN/S3xcdZxIyuH9dbc22KYQQghRl0kyWJsd/M68bvk4VHFHiylTpuDi4sLOnTtZtWqV1Y/v5+7IjP4tAPhi23m2nZZhjYQQQoibUelk8O233yY/P7/c9oKCAquPLyduQVY8nN9iftxiQJVXHxgYyIsvvgiYWwevnsfRWu67PYDBHUMBGP/9AVJyCq1ehxBCCFHbVToZfOutt8jNzS23PT8/n7feessqQQkrOLQMUCCsM3jd2jQ1N+vll1/Gw8ODI0eOsHTpUpvU8fpDTWgU4E5abhEvfX8Ao0lmJxFCCCEq46ZmIKlovJuDBw/i7e1tlaDELVIUOHg5+Wo5yG5heHl5WUZpf+ONNyguLrZ6Hc46DfMGt8FFp+HPM5f4eOMZq9chhBBC1GY3nAx6eXnh7e2NSqWiYcOGeHt7WxYPDw/uv/9+Bgyo+suRogKFmeDoDlonaNLnH4vb0gsvvIC/vz/nzp1j/vz5Nqkjyt+Nd/ua52ecHX2K7Wfk/kEhhBDiRt3woNOLFi1CURSeeuopZs+ejYeHh+U1nU5HeHj4DU+4XN3U2kErbTz93I36+OOPGTduHH5+fpw5cwa9Xm+Tel754SDL9sTj5+7I2hfuxs+9/ETlQgghao9a+/1dxSo9A8nmzZvp3LkzWm2lpzWutuTDZFvFxcU0a9aMU6dOMXnyZP773//apJ4Cg5E+87ZxKjmXzlE+fP1URzRqma5OCCFqK/n+to5K3zOYl5dX4UDCv/32G7/++qtVghK3ICcZCjLsHUUZDg4OzJgxA4APP/yQuLg4m9TjrNPwyeA2ODuY7x+ctV7GHxRCCCH+SaWTwUmTJlU4TIiiKEyaNMkqQYlbsPUDeP822DHP3pGU0bt3b7p06UJhYSGvvfaazeqJ8ndner/mAMzbdJZ1R5JsVpcQQghRG1Q6GTx9+jRNmjQpt71x48acOSM9Oe3KWAJHfwJTMfjcZu9oylCpVHzwwQcAfPPNN+zdu9dmdfVpFczTd0UAMHHZAc6kyPzFQgghxLVUOhn08PDg3Llz5bafOXMGV1dXqwQlblLsNshLAWcviLzH3tGU065dOwYPHgzAxIkTrT5N3dUm92rMHZHe5BmMPLt4LzmF1h/WRgghhKgNKp0M9unTh/Hjx3P27FnLtjNnzjBx4kR69+5t1eBEJR350bxu0ge0OvvGcg3vvfcejo6ObN68mRUrVtisHq1GzcdPtKGehxPnUvOYsOwgJhmQWgghhCin0sngjBkzcHV1pXHjxkRERBAREcHtt9+Oj48PM2fOtEWM4kaUGODYL+bHzfrZN5brCAsL4+WXXwZgwoQJFU5taC2+bo58+mRbdFo1648lM1cGpBZCCCHKuanLxNu3b2fNmjU8//zzTJw4kejoaDZu3Iinp6cNQhQ35OxG82DTboHmKeiqscmTJxMaGkpcXBzTp0+3aV0tQzx5t495QOoPN5xizaFEm9YnhBBC1DSVTgbB3BmgR48ejBs3jjFjxtClSxdrxyUqq/QScdNHQa2xbyz/wMXFxdKZZMaMGRXeg2pNA9qH8FTnyx1Klh/gUHymTesTQgghapJKJ4Mmk4l33nmH4OBg3NzcOH/+PAD/+c9/+PLLL60eoLhBPd6FXjOg9ZP2juSG9OvXj/vuu4+ioiImTJhg8/pee+h27m3kR2GxiWcW7SEpq9DmdQohhBA1QaWTwXfffZeFCxcyY8YMdLornRSaNWvGF198YdXgRCW4B0DHURDYzN6R3BCVSsWcOXPQarX8/PPPrFu3zqb1adQq5jzemoYBbqTkFPHM17vJN5TYtE4hhBCiJqh0Mvj111/z+eefM3jwYDSaK5cjW7ZsyYkTJ6wanKjdmjRpwgsvvADACy+8QGGhbVvr3J0c+HJYe7xddRxJyGbC99LDWAghhKh0MpiQkEBUVFS57SaTieJiGcutyhnyYckA2PMVGGvev//UqVOpV68ep0+fttmcxVcL8XbhsyFt0WnUrDuaxHtrj9u8TiGEEKI6q3Qy2KRJE7Zu3Vpu+w8//EDr1q2tEpSohLPRcPo32PohqLX2jqbS9Ho9c+bMAWD69OkcP2775Kx9uDfv/6sFAF9uO88XW23bgUUIIYSoziqdPbzxxhsMGzaMhIQETCYTK1as4OTJk3z99desXr3aFjGK6zn2s3ndpDeoVPaN5Sb169ePhx9+mNWrVzNq1Cj++OMP1Oqb6uh+w/q0CiYpq5Bpv57g3TXH8dc70btlkE3rFEIIIaqjm5qBZNWqVWzYsAFXV1feeOMNjh8/zqpVq7j//vttEaO4lpIiOPWb+XGTPvaN5RaoVCrmzZuHq6srW7du5auvvqqSep/tEsnwO8MB8xzG28+kVUm9QgghRHVyQ8ngnDlzLDf3x8XFcdddd7F+/XpSUlLIz89n27Zt9OjRw6aBigqc+wOKssG9HgS3s3c0tyQ0NJR33nkHgJdffpnk5GSb16lSqfjPw014sHkgxUaFUYv3cuxits3rFUIIIaqTG0oGJ0yYQHa2+UsyIiKC1NRUmwYlblDpJeLbHwEbX1atCuPGjaNNmzZkZmZaehnbmkatYtaAVnSI8CanqIThC3YRn2G7KfKEEEKI6uaGMoigoCB+/PFHYmNjURSF+Ph44uLiKlxEFTEWw4k15sc1+BLx1bRaLfPnz0ej0bBs2TJ++OGHKqnXyUHD/CHtLGMQDv5iJynZMii1EEKIuuGGksHXX3+d8ePHExkZiUqlon379kRERJRZwsPDiYiIsHW8olReGgS1Nl8iDu1k72ispk2bNkyePBmA0aNHk5KSUiX1erg48PVTHQnxdib2Uj5PfrmT9DxDldQthBBC2JNKUZQbGnU3JyeH2NhYWrRowYYNG/Dx8amwXMuWLa0aYFWIj48nJCSECxcuUL9+fXuHUzklRaB1tHcUVmUwGGjfvj2HDh2iX79+LF++HFUV9ZSOu5TPvz7bTnJ2Ec2C9Xw78g70Tg5VUrcQQojKqdHf39XIDSeDpRYtWsSgQYNwdKw9CYh8mKqf/fv306FDB0pKSli6dCkDBw6ssrrPpOQy8LMdXMoz0C7Mi6+f7oCLruaN4SiEELWdfH9bR6V7HQwbNoyCggK++OILJk+eTHp6OgD79u0jISHB6gGKCmQlQHaivaOwqdatW/Paa68BMGbMmCrpXVwqyt+Nr5/ugN5Jy57YDJ79ei+FxcYqq18IIYSoSpVOBg8dOkTDhg353//+x8yZM8nMzARgxYoVlnu9hI39+RHMuh22vG/vSGxqypQptGrVikuXLvHcc89RyUbsW9I0yIOFT3XARadh25k0Rn8jCaEQQojaqdLJ4EsvvcTw4cM5ffo0Tk5Olu0PPvggW7ZssWpwogImExxfBSgQ0Nze0diUTqdj4cKFaLVaVq5cyYIFC6q0/jahXnw5rD1ODmo2nUzl2cWSEAohhKh9Kp0M7tmzh1GjRpXbHhwcTFJSklWCEteRuB9yLoLODRrca+9obK5ly5aWwajHjRvHyZMnq7T+Tg18+Gp4e5wdNGw5lcozi/ZQYJCEUAghRO1R6WTQ0dHRMgD11U6dOoWfn59VghLXcWKteR3Vvdb1Ir6Wl19+mXvvvZf8/HyeeOIJDIaqHfLlzga+LBzR3nLJeMTCXeQbSqo0BiGEEMJWKp0M9u7dm7fffpvi4mLAPKVXXFwcr776Kv369bN6gOJvTv5qXjd60L5xVCGNRsPixYvx8fFh3759lo4lValjpA9fP9UBN0ctf51LZ/hXu8ktkoRQCCFEzVfpZPCDDz4gNzcXf39/CgoK6Nq1K1FRUbi7u/Pee+/ZIkZRKiMGUo6CSgO33W/vaKpUcHAwX375JQAzZ85k/fr1VR5Du3Bvvn66A+6OWnbFpDP0y51k5RdXeRxCCCGENVU6GfTw8GD9+vWsWrWKOXPmMHbsWNauXcvmzZtxdXW1RYyiVGmrYNid4OJt31jsoE+fPowePRqAoUOH2mWO7DahXnzzTEf0Tlr2xWUy4LMdJMvUdUIIIWqwSg86XRvVmEEr89Ph9Hpw9oSGPe0djV0UFBTQrl07jh07Rs+ePVmzZg0ajabK4ziRlM3QL3eRklNEiLczi5/qSLiv/BgSQoiqVGO+v6u5SrUMmkwmvvrqKx5++GGaNWtG8+bN6d27N19//XWVjgFXZ7l4Q8uBdTYRBHB2dmbp0qU4Ozvz22+/8e6779oljsaBen547k7CfFy4kF5A/093cPRill1iEUIIIW7FDSeDiqLQu3dvnnnmGRISEmjevDlNmzYlNjaW4cOH8+ijj9oyTiEsmjdvzmeffQbAW2+9xbp16+wSR6iPC8uf68Tt9fSk5RYx6LO/2Hnukl1iEUIIIW7WDSeDCxcuZMuWLURHR7N//36+++47li5dysGDB9mwYQMbN27k66+/tmWsddsf02HbbMiRsRwBhgwZYpmVZPDgwcTGxtolDn93J74fdQcdIrzJKSph6Fe7WHu4dk8VKIQQNdm8efMIDw/HycmJjh07smvXrmuWLS4u5u2336ZBgwY4OTnRsmXLcg0Q4eHhqFSqcsuYMWMsZe65555yrz/33HM2e4+VdcPJ4HfffceUKVO4997yAx1369aNSZMmsWTJEqsGN23aNNq3b4+7uzv+/v707du33KDDhYWFjBkzBh8fH9zc3OjXr1+VzmNbJUqKYPvHsGEqZMXbO5pqY/bs2bRr14709HT69+9PYaF9OnLonRz4+qkOdL89gKISE88v2cdnm8/KrRNCCFHNfP/990yYMIGpU6eyb98+WrZsSc+ePUlJSamw/Ouvv85nn33G3LlzOXbsGM899xyPPvoo+/fvt5TZvXs3iYmJlqV0tIt//etfZY41cuTIMuVmzJhhuzdaSTecDB46dIgHHnjgmq/36tWLgwcPWiWoUps3b2bMmDH89ddfrF+/nuLiYnr06EFeXp6lzEsvvcSqVatYvnw5mzdv5uLFizz22GNWjcPuYraBIQfcAiCojb2jqTYcHR354Ycf8Pb2Zs+ePbz44ot2i8XJQcNnQ9oy/M5wAKb9eoIpPx2h2GiyW0xCCCHKmjVrFiNHjmTEiBE0adKETz/9FBcXF7766qsKyy9evJgpU6bw4IMPEhkZyejRo3nwwQf54IMPLGX8/PwIDAy0LKtXr6ZBgwZ07dq1zLFcXFzKlNPr9TZ9r5Vxw8lgeno6AQEB13w9ICCAjIwMqwRVat26dQwfPpymTZvSsmVLFi5cSFxcHHv37gUgKyuLL7/8klmzZtGtWzfatm3LggUL2L59O3/99ZdVY7Grk5dnHWn4AKgrPRpQrRYWFsaSJUtQqVR8/vnnfP7553aLRaNW8Wbvpkx9pAkqFXy3K46nFu4mp1DGIhRCCFvKyckhOzvbshQVFZUrYzAY2Lt3L927d7dsU6vVdO/enR07dlR43KKiIpycnMpsc3Z2Ztu2bRWWNxgMfPPNNzz11FOoVKoyry1ZsgRfX1+aNWvG5MmTyc/Pr+zbtJkbziyMRiNarfaar2s0GkpKbDsjQ1aWubemt7d5jL29e/dSXFxc5sQ2btyY0NDQa55YMJ/cqz80OTk5No37lijKlfEFGz9k31iqqQceeMAyf/GYMWPYtGmTXeMZ0TmCz4e0w9lBw9bTafzr0x3EZ1Sf//RCCFHbNGnSBA8PD8sybdq0cmXS0tIwGo3lGrYCAgJISqr4fvyePXsya9YsTp8+jclkYv369axYsYLExIrvDV+5ciWZmZkMHz68zPYnnniCb775hk2bNjF58mQWL17Mk08+eXNv1gaund39jaIoDB8+HEfHiufDrSgLtyaTycT48ePp3LkzzZo1AyApKQmdToenp2eZstc7sWC+F/Gtt96yZbjWk3gQshPAwQUiutg7mmprypQpHD16lO+++47+/fuzc+dOoqKi7BbP/U0CWDaqE08t2s2JpBx6f/wnnwxuwx2RPnaLSQghaqtjx44RHBxseX6tXKWyPvroI0aOHEnjxo1RqVQ0aNCAESNGXPOy8pdffkmvXr0ICgoqs/3ZZ5+1PG7evDn16tXjvvvu4+zZszRo0MAqsd6KG24ZHDZsGP7+/mUy76sXf39/hg4darNAx4wZw5EjR1i6dOktH2vy5MlkZWVZlmPHjlkhQhspbRVs0A0cnO0bSzWmUqn48ssvad++Penp6fTu3dvSkmwvzet78POYzjQL1pOeZ+DJL3ayaHuMdCwRQggrc3d3R6/XW5aKkkFfX180Gk25TqbJyckEBgZWeFw/Pz9WrlxJXl4esbGxnDhxAjc3NyIjI8uVjY2NZcOGDTzzzDP/GG/Hjh0BOHPmzI28PZu74ZbBBQsW2DKO6xo7diyrV69my5YtZUYYDwwMxGAwkJmZWaZ18HonFsy/GK7+oGRnZ9skbqsozje3Cja8ducdYebs7MzPP/9M+/btOX78OIMGDWLVqlXXvb3B1oI8nVk+6k4mrTjEzwcuMvWXoxy9mMU7fZvhqK36mVOEEKKu0ul0tG3blujoaPr27QuYrzpGR0czduzY6+7r5OREcHAwxcXF/PjjjwwYMKBcmQULFuDv789DD/3zLV0HDhwAoF69epV+H7ZQrXsjKIrC2LFj+emnn9i4cSMRERFlXm/bti0ODg5ER0dbtp08eZK4uDg6depU1eHaRo934JXz0Ly/vSOpEerVq8fPP/+Ms7Mz69atY8KECXZviXPWaZg9sBWvPXg7ahUs2xPPwM/+IilL5jQWQoiqNGHCBObPn8+iRYs4fvw4o0ePJi8vjxEjRgDmee8nT55sKb9z505WrFjBuXPn2Lp1Kw888AAmk4lXXnmlzHFNJhMLFixg2LBh5Rogzp49yzvvvMPevXuJiYnhl19+YejQoXTp0oUWLVrY/k3fCKUaGz16tOLh4aH88ccfSmJiomXJz8+3lHnuueeU0NBQZePGjcqePXuUTp06KZ06dapUPRcuXFAA5cKFC9Z+C8JOli1bpgAKoMyYMcPe4VhsPpmitHjzNyXs1dVK23d+V7adTrV3SEIIUWPdzPf33LlzldDQUEWn0ykdOnRQ/vrrL8trXbt2VYYNG2Z5/scffyi333674ujoqPj4+ChDhgxREhISyh3zt99+UwDl5MmT5V6Li4tTunTponh7eyuOjo5KVFSU8vLLLytZWVmVe7M2pFKU6nsD09+7ZZdasGCBpadOYWEhEydO5LvvvqOoqIiePXvyySefXPcy8d9V24mu89PN8xGLmzJr1iwmTpwIUK16bsVeymPU4r2cSMpBpYIXut3GC/fdhkZd8eddCCFExart93cNU62TwapSLT9MxhJ4PxLcAmHICvCoJnHVMBMmTODDDz9Eq9Wydu1a7r//fnuHBEBhsZE3fznK0t0XAOgc5cPsga3xc7dODzghhKgLquX3dw1Ure8ZrNPid0NhFuSlgHv1uMG0Jpo5cyYDBw6kpKSExx57rMwUQvbk5KBher8WfDiwJc4OGv48c4kH52xlx9lL9g5NCCFumqIopOSnsDtpNz+e+pFZe2cx4Y8JlJhsOw6xuDX262Ypru/07+Z1VHdQS6/Tm6VWq1m0aBEpKSls2rSJXr16sW3bNruOQXi1R1vXp3mwB88v2cep5Fye+OIvRndtwPjuDdFp5beaEKL6URSFS4WXiM2OJS47zrzOMa8v5FygoKSg3D6JuYmE6EPsEK24EZIMVlenzRNdE1U9LmvWZI6Ojvz000906dKFQ4cOcd9997FlyxbCwsLsHRoAUf7urBzTmTd/OcqyPfF88sdZtp5OY/agVjTwc7N3eEKIOiqrKItzWedwdXCloVdDAC7kXKD/L/3JL7n2rEpqlZog1yDC9GGE6kMJ04fhqnOtqrDFTZBksDrKvgjJhwEVRN1n72hqBQ8PD37//Xe6dOnCqVOnLAnh30eJtxcXnZYZ/VtybyN/Jq04zOGELB6es43XH76dJzqEXrMzlRBC3IrMwkxic6608A1oNAB/F38Avj3+LZ8c/IRHox7l7c5vA+Dv4k9BSQEqVAS5BRHqHmpJ+ML0YYS6hxLsFoyDxsGeb0tUkiSD1VFpq2BwW3D1tW8stUhAQADR0dF06dKFs2fP0r17dzZv3oyfn5+9Q7Po1bwerUO9mLj8AH+eucRrPx1h04lU/vtYM/zdnf75AEII8Td5xXnEZsdWuGQbyk660NKvpSUZDPcIp55rPdx0V65QOGocWfXoKuq51kOn0VXp+xC2I8lgdVR6v+BtPewbRy1Uv359oqOjufvuuzl+/Dg9evRg48aNeHl52Ts0i0APJxY/1ZGv/jzPjHUn2XA8md2z0pn6SBMebR0srYRCiHKKjEXkGHLwdTY3IBSbihn5+0his2NJK0i77r7+Lv6WVj1v5yvDmfWK6EWviF7lyofpq8ctNsJ6JBmsjloPAWdPaFT+P6G4dREREZYWwgMHDnD//ffz+++/4+1dfcZ0VKtVPHN3JJ2jfHn5h4McSchmwrKDrD6UyHuPNqOeh8xTLURdU2IqITE3kZjsGKI8o6jnZh5pYl3MOl7Z/Aod63Vkfo/5ADioHYjLjrMkgt5O3pZLuVdf0g1xD8HFwcVu70lUDzLOIDJOUV11+PBhunXrRlpaGi1atGDDhg3V6pJxqWKjic+3nOOjDacxGE24O2p5/eHbGdAuRFoJhahlTIqJlPwU4rLjiMmOsfTYjcmOIT433jJEy3/u+A8DGpnnxz2QcoAhvw6hgUcDVvZdaTnW9oTt6B31hOpD0ev09ng7Niff39YhySDyYarLjh49yn333UdycjJNmjQhOjq6UrPXVKXTyTm8/MMhDlzIBOCuKF/e7duMcF/ppSdETfbF4S84dunYdYdmKeWocSRUH8rQJkPpG9UXAIPRQI4hB28n7zr3A1G+v61DkkGq2Yfpr0+hfnsIag1qGWeuKpw6dYpu3bqRkJBAw4YN2bhxI8HBwfYOq0JGk8KX287xwe+nKCoxodOqGXNPFKO6RuLkIONRClGd5BXnUWIqwcPRA4Bzmed4/c/XAfj2oW8t5QavHcyh1EOW5xqVhvru9S2XcsP14YR5hBHmHkaAawBqlXw3lKpW3981mCSDVKMPU+YFmN0MVGp45Rw4V59ODbXduXPn6NatG7GxsURGRvL777/ToEEDe4d1TefT8vjPyiNsO2O+HyjC15W3+zTl7tuq32VuIWqzImMRF7IvEJtzpYduTFYMcTnm+/Weaf4ML7Z5EYDkvGS6/9AdjUrD7id346A2D7/y85mfySrKItwjnDB9GEFuQZbXxPVVm+/vGk46kFQn5zaZ1/XbSyJYxSIjI9myZQvdunXj7Nmz3Hnnnfz666+0adPG3qFVKMLXlcVPd2D1oUTeWX2M82l5DPlyFw+1qMcbDzchQC/D0AhhbQdSDnD00lFismIss25czL2IwrXbVFLzUy2P/V38mXXPLML0YWhUV1ry+0T1sWncQvwTSQark7MbzevIe+0bRx0VGhrKtm3b6NWrFwcOHKBr16789NNPdO/e3d6hVUilUvFIyyDuaeTHrPWnWLQ9hjWHEvnjRApjukXxVOcIuXQsxE3IL87nkwOfcCHnAh/e+6Hlsuw3x7/ht5jfypV3c3Cz9NAN14cTqg+1rN117pZyKpWK+8NkVilR/UgyWF2YjHDuD/PjBt3sGkpdFhgYyObNm+nbty+bNm3iwQcf5Ouvv2bQoEH2Du2a3J0cmPpIU/q3rc/rK4+wPy6TGetOsuSvOCb1aszDLerVuZvKhbiWbEM2MVkxxGTHWNZx2XE0823Gm3e+CYBOo2PJiSWUmEpIyksiyM08U1G7gHaYFBOh7qFlhmipix03RO0iyWB1kXgQCjLAUW+eeUTYjV6v59dff2XIkCEsX76cxx9/nIsXL/LSSy9V6z/4TYM8+PG5O1l5IIEZ606SkFnAuO/2s3B7DP95uAmtQjztHaIQVaLYWMyFnAvmhO9y0hebHUtMdgzphekV7qNVa8s8fr7l8+h1elwdrvTWH9R4EIMaV98fhkLcLEkGq4vSS8QRXUAjp8XeHB0d+e677/D392fevHlMnDiREydOMG/ePBwcqu+N3Wq1isfa1OeBZoF8vuUcn20+x97YDPrO+5O+rYKY2KMRId4ywKyoPXYn7eZ81nkeiHjAMpbenP1zWHh04TX38Xf2J9wj3NxLVx9GuEc4EfqIMmVGthhpy7CFqFYk66gu4naY1w3kfsHqQqPRMHfuXCIjI/n3v//N/PnzOXv2LMuXL69Ws5VUxEWnZXz3hgxqH8qM306wYl8CKw9cZM3hRJ7oEMqYblEy17GoEfKK88q07gE83+p5y+v/+fM/JOQm0MCzAW0DzFdVwvXhuGhdLL1zI/QRlsfh+nCZcUOIv5GhZagmXdONxRC/B3wagJu/fWIQ17Rq1SqeeOIJcnNzue2221i9ejUNGza0d1g37FC8+T7C0qFonB00jOgczqguDfBwqb4tnaJuKDGVkJCbQGx2LOezzltm3ojJiiG1ILVMWW8nbzYP3Gx5/ub2N0ktSOXZFs/S0q+l5XgalaZa39YhrKNafH/XApIMIh8mcWMOHTrEI488QlxcHF5eXixZsoRevWrW/NHbz6Qx47eTlllM9E5aRnVtwLA7w3FzlAsFwnYUReFS4SWctc6W+/C2xG9h5p6ZXMi5YJlmrSLeTt6E68Mtl3aHNR0mAy8LQL6/rUWSQeTDJG5ccnIyffv25a+//kKlUvHmm2/y+uuvo65Bs8UoisKG4ynM/O0kJ5NzAPB0ceCpzhEMuzMcD2dpKRQ3r6CkgLhs8/h794Zeue3l+Q3PszVhK/+967880uARALZf3M6o9aMAcNI4XRme5XLSVzrzRm2dV1fcOvn+tg5pCqgOvukPniHQ5RXQ17N3NOI6AgIC+OOPP3jxxRf57LPPmDp1Krt27WLx4sV4edWMgcJVKhX3NwmgW2N/Vh28yJzo05xLy2PW+lPM33KOYXeG89RdEXi76uwdqqimjCYjiXmJlh66lqFasmNIykuylNs1eBfOWmcAAlwDUKEirSDN8noz32Z8fv/nhOvDZZo1IexIWgax8y+LrHj4sKlMQVcDLViwgNGjR1NUVERkZCQ//vgjrVq1sndYlWY0Kaw9nMjHG89YWgpddBqevCOMpzpHEOghHU3quqS8JL4/+X2ZcfkMJsM1y+t1esI9wvmg6wcEugYCkFWUhZPWCUeNY1WFLeoAaRm0DkkGsfOHad9i+GWseQq6ZzZUbd3ilu3bt49+/foRExODTqfj/fffZ9y4cTXyxnWTSeH3Y8nM3XiaoxezAdCqVfRuGcQzd0fSJEgu1dVGBqMBrVpraZVbcXoFK8+spGd4TwbfPhiA81nn6b2yd5n9HNQOlsGXLZd1L689HT1r5P8BUfNIMmgdcpnY3mQKuhqtTZs27N27l2HDhrF69WpefPFF1q9fz4IFC/D19bV3eJWiVqt4oFkgPZsGsOlkCp9uPseu8+ms2J/Aiv0JdI7y4Zm7I7mnoZ980dcwiqKQnJ9cZoiW89nnicmKITEvkZV9VhLhYR5nL60gjf0p+wlxD7HsX9+9PgMbDbQMzRKuD6eeW70yAzULIWouaRnEjr8sTCZ4vwEUpMOIdRDWqerqFlalKAoff/wx//73vzEYDNSrV49vvvmGbt1q9tSCh+Izmb/1PGsPJ2I0mf9U3ObvxtN3RdCnVTDOOpn7uDopMZVwIv1Emfv4YrNjic2OpaCk4Jr7ze02l3tC7gHgbOZZTmecppF3I0uCKER1JS2D1iHJIHb8MF3cD5/fAzp3ePU8aKQXZ0138OBBBg0axIkTJ1CpVLz44ou89957uLjU7EFu4zPyWfhnDEt3XyC3yDwEiN5JS/+2IQy+I5QGfm52jrDuOZd1jq3xW/F28rb0zi0oKaDDkg4VlteoNNR3r1+ml27pY19nX2ntFTWSJIPWIckgdvwwbf0Aot+GRg/C499VXb3CpvLy8njppZeYP38+ALfddhsLFy7kzjvvtHNkty67sJjvd11g8V+xxKXnW7bfFeXLk3eE0v32ALQa6RF6q0rH5Lu6dS8mK4anmz9NK/9WAKw6u4op26bQLqAdCx5YYNl30OpBOGmdLIle6cwb9d3r46CWH5yidpFk0Drkhg97cnAB70i5X7CWcXV15fPPP+fRRx/lmWee4fTp09x1111MnDiRt99+G2dnZ3uHeNP0Tg6M7BLJ03dFsOV0Kt/8FUv0iRS2nUlj25k0AvVODGgfwr/a1pc5kG9AfnE+cTlxxGTFcD77vCXpi82OJbc4t1z5TkGdLMlgY+/G9AzvSXPf5mXKLH14aVWELoSoRaRlkGrwy8Jkgho0aLG4cRkZGbz00kssWrQIgMaNG/PFF1/QuXNnO0dmPfEZ+Xy3K46luy5wKe/KcCN3RHozoF0IvZrVq9P3FpaOyZdVlEVT36aW7QNWDeB4+vFr7qdCRZBbEOEe4UToIwjTh9GhXgciPSKrImwhagS7f3/XEpIMIh8mYXurVq3i2WefJSnJPCDv008/zf/+9z98fHzsHJn1FJUY+e1oMsv3XGDbmTRK/7K4OWp5uEU9+retT9swr1p7b1pWURbns8ytew08G9DMtxkAe5P3MnzdcILdglnXb52l/LBfh7EvZR+ejp7me/j+NvNGiD5ExuQT4h/I97d1SDKInT5M2RfB1R80cqW+rkhPT+fVV1/liy++AMDHx4f333+f4cOH17oEKSGzgBV74/lhXzyxl67cWxjq7cIjLevRu2UwjQLd7RjhzTEYDVzIuVBmxo3Sy7oZRRmWcsObDmdiu4mAeaiWHj/0INwjnOUPL0ejNreSXsi5gLuDO55OnvZ4K0LUCpIMWockg9jpw/RlT0g+Cv9aCLd1r5o6RbXw559/8txzz3HkyBEA7r77bubNm0fz5s3/Yc+aR1EUdp1PZ/neeNYeTiTfYLS81ijAnd6tgnikRRChPtXn/kJFUcok54uPLWb7xe3EZMVwMe8iJsV0zX0DXAII14fTI7wHAxoNsBzPpJgsSaAQwnokGbQOSQaxw4epKBf+FwamEnjxIHiF275OUa0UFxcze/Zs3nzzTfLz81Gr1YwcOZK33nqLgIAAe4dnE/mGEqKPp/DLwYtsPpmKwXglqWoZ4skjLerRs2lglXU8ySvOo6CkAF9n8+DgWUVZjPx9JIl5iWwasMkyoPKrW15l7fm1lv1ctC7lZtwovczr4lB9kloh6gJJBq1DkkHs8GE6vR6W9AfPUBh/2Pb1iWorLi6OCRMm8OOPPwLg7u7OlClTGD9+PE5OtXdO4KyCYn47ksQvBy+y/Wwapqv+CjWpp6dn00B6NgugUYD7LV1CLzGVcDH3YplBmGOyY4jNiiWlIIWe4T2Z2XUmACbFRMclHSk0FrLm0TWE6kMB2J6wnfjceCI8ImRMPiGqGUkGrUOSQezwYfr9ddg+F1o/CX3m2b4+Ue1t2bKFCRMmsHfvXgDCwsKYNm0aAwcORF3Le5qn5hSx9nAivx5JZNf59DKJYZiPCz2aBNCzaSCtQ73QqK+dhJ3LPMfB1INlEr+4nDhKTCXX3Kd9YHu+6vmV5fnOxJ34ufgR6h4qU60JUQNIMmgdkgxihw/TZ10g8SA8Nh9aDLB9faJGMJlMLFmyhMmTJ5OQkABAixYteOedd3jkkUfqRGtUep6BDceT+f1oEltOp2EouXIp2cvFgS4N/binkR8XTb9ysSCGl9q+ZLnM+9G+j/ji8BfljumocSRUH1pmEObSy7oejh5V9t6EENYnyaB1SDJIFX+Y8tNhRiSgwMST4B5o2/pEjZOXl8esWbOYOXMm2dnZAHTo0IF3332X7t271+qk0KSYSMlP4XzWeU6ln2NH3AmOp50lPVdFTtwTlnKuDWai1qXxgM+bPNGiGy3qe7LpQjTLTi6zDNESoY8g3COcQNdA1Kra3boqRF0lyaB1SDJIFX+Yjv0Cy4aAbyMYu8u2dYkaLT09nZkzZ/LRRx+Rn28enqVLly689tpr3H///TU6KcwszLRcxi0dmiU2O5a4nDgKSgrKldfr9Mzq+AubT6Wy6WQqZ4rWoFIZKM5uiVLsi7erjruifLmzgQ93NvAlxNu5Rv/7CCFujCSD1iHJIFX8Ybp0Fo6sACcP6PisbesStUJycjLTp0/n//7v/ygqKgKgbdu2TJo0iUcffRSNpvoOWXIm4wxnMs/Q0q8l9dzqAbDi9Aqmbp96zX20Ki313euX7a3rEU4b/zaWBC85u5DNJ1PZdDKFbafTyCkqe19gsKezOTGM8qFTpC+BHrW3M44QdZkkg9YhySDyYRI1Q3x8PB988AGff/65paWwYcOGvPrqqwwePBhHx6qfrcJgNBCfE2/uoZsdS2pBKq+0f8Xy+sjfR/JX4l+80/kd+kb1BWBX4i6e/v1pAlwCCNOHlVnC9eEEuwfjoHa44RiKjSb2xWbw59lL7Dibxv64TEpMZf+sRfq50inSh3bhXrQL86a+l7QcClEbyPe3dUgyiHyYRM2SlpbG3LlzmTt3LhkZ5lkv/P39GTVqFM899xxBQUFWrc9oMnIx7yJx2XGWpK/0cWJeYrlBmP964i9cHVwBc6eOPUl7GNJkCD3CewBQbCym2FRsszH58g0l7I7JYPvZNHacvcSRhCz+lhsSoHekXZg3bcO8aBfuRZN6erQaua9QiJpGvr+tQ5JBqvDDFL8XsuIg/G5w9bVdPaJOyMnJ4fPPP+fDDz+09D7WarX079+fF154gTvuuOOGW78URSElPwWtWouPs3m+5GOXjjF562Qu5Fyg2FR8zX1dHVwJdTf31g3zCOPJ25+sVr10s/KL2Xn+ErvOp7MnNoMjCVnlWg5ddBpahXjSNsyLFvU9aVnfA3+9XFoWorqTZNA6JBmkCj9Mv4yDfV9Dp7HQ8z3b1SPqlOLiYlauXMncuXPZunWrZXvbtm0ZNWoUAwcORK/XoygKmUWZxOXEEZsdS8/wnjhqzJeW39nxDstOLeP5ls8zutVoAC5kX+DBnx4EQKfWEaoPJUwfZhmmpfTSro+TT4265FpgMHIwPpO9sRnsiTEniDmF5cciDNQ70aK+By1DPGlR34MWwZ54uNz45WshhO3dzPf3vHnzeP/990lKSqJly5bMnTuXDh06VFi2uLiYadOmsWjRIhISEmjUqBH/+9//eOCBByxlwsPDiY2NLbfv888/z7x55rGECwsLmThxIkuXLqWoqIiePXvyySefVJsZpyQZpAqTwTltIP0sPLEMGva0XT2iztq3bx8ffPQBP3z/A4YiAwBaJy31O9fH9U5XiMSSuP3U+yeivKIA+OrIV8zZN4fHGz/Oqx1eBcyzd+xK2kW4vnYPz2IyKZxOyWVPbDr74zI5FJ/J6ZRcKvrLGO7jQvP6ntxez53b6+lpUk+Pv7tjjUqGhbAVRVG4dOkSsbGxFS6bNm3Cw8O6Vw0q+/39/fffM3ToUD799FM6duzI7NmzWb58OSdPnsTf379c+VdffZVvvvmG+fPn07hxY3777TcmTJjA9u3bad26NQCpqakYjVfmXT9y5Aj3338/mzZt4p577gFg9OjRrFmzhoULF+Lh4cHYsWNRq9X8+eef1vmHuEWSDFJFyWBOEnzQCFDBqzHg7GmbekStZ1JMFJYUWu65yyzM5O2/3iYuO84yNEtJTgkZWzPI3JpJUWKRZV9dgI7699Wnfa/2vPngmzT2bgxAfnE+DmoHHDTS8gWQV1TCkYQsDsVncSghi0PxmcReyq+wrLerzpwcBuq5vZ55ifJ3Q6etncmzqLsMBgMJCQnExcWVW0oTvtLObRU5ePAgLVq0sGpMlf3+7tixI+3bt+fjjz8GzIP9h4SEMG7cOCZNmlSufFBQEK+99hpjxoyxbOvXrx/Ozs588803FdYxfvx4Vq9ezenTp1GpVGRlZeHn58e3335L//79AThx4gS33347O3bs4I477riZt25VMt9SVYnZZl4HNpdEUPwjo8lIUn4ScdlxNPFpYrkH7/sT3/P+nvd5IPwB3r3rXQBcHFyIjou2dORQq9SE1Qvj7qfvpv6L9TGcM7Bv9T62rNlCXnIe5749x7lvz5FwVwKDBg2if//+1eZSRXXh6qilY6QPHSN9LNsy8w0cis/iyMUsjifmcDwxm3OpuaTnGfjzzCX+PHPJUtZBoyLS142oADei/NyI8nfjtgA3InxdcdRW36GARN2lKAoZGRnlEryrnycmJnIj7UeBgYGEhYVVuNhKTk6OZZB+AEdHx3IjLBgMBvbu3cvkyZMt29RqNd27d2fHjh0VHreoqKjcPPHOzs5s27atwvIGg4FvvvmGCRMmWK4Y7N27l+LiYrp3724p17hxY0JDQyUZrHNit5vX4XfZNw5RbRQbiy29dONy4ojPiScuJ4647Djic+Mtc+rOu28eXep3AUDvqKfIWMSFnAuW4+g0OqZ2moqvsy+h7qEEuwWXbeG7E3gScnNzWb58OQsXLmTr1q1s27aNbdu28cILL3DfffcxaNAg+vTpg4+PD6I8TxcdXRr60aWhn2VbYbGRU8nmxPB4Yg7HErM5nphNTmEJJ5NzOJmcU+YYahWE+bjSwM+cHJYmiuG+rng4S6ussJ2rW/X+nuSVLnl5ef94HEdHR0JDQwkLCyM0NNSyhISEEBYWRkhISLnkqSo0adKkzPOpU6fy5ptvltmWlpaG0Wgs9+M3ICCAEydOVHjcnj17MmvWLLp06UKDBg2Ijo5mxYoVZS4LX23lypVkZmYyfPhwy7akpCR0Oh2enp7l6k1KSrqxN2hjkgxWldjL9wWE3WnfOESVURSFgpICy+VcRVF4c8ebXMi5QHxOPEl5SShc+1e2g9qBEPcQjKYrf3TuCr6LtY+utQzgXOqx2x77x3jc3NwYMWIEI0aMID4+nuXLl7N06VJ27drF+vXrWb9+PSNHjuSuu+6iT58+9OnThwYNGtzku68bnBw0tKjvSYv6npZtiqIQn1HAmZRcTqfkcCYl9/LjXHIKSziflsf5tDw2HE8ucyxvVx1hPi6E+7ha1uG+roT7uODpoqvidyZqEkVRSE9PrzDBq2yrnr+/f4XJXuni5+dXLe+RPXbsGMHBwZbn1hp39aOPPmLkyJE0btwYlUpFgwYNGDFiBF999VWF5b/88kt69epl9SG+bE2SwaqQnw6pl391hEoyWJsUlhRyMfci8bnxNPBsQLCb+Y/RHxf+4JUtr9DMtxlf9TT/0VCpVGxL2EZKfoplf2etM8FuwYS6m3vqhuhDCHUPJdQ9FH8XfzTqspcU3XXuuOvcbznu+vXr89JLL/HSSy9x9uxZvv/+e5YtW8bBgwfZsmULW7ZsYeLEiTRt2pQ+ffrQq1cvOnbsiIODtF79E5VKRYi3CyHeLtzb+MoN6YqikJpTxOnLyWFpsng2NY/UnCLS8wyk5xnYH5dZ7pgezg6E+7gQ5uNKiLczwZ4u1PdyJtjLmWBPZ5wc5NJzbVV6+fbChQvEx8dblr8/v5VWvdKlfv36ODs7V8G7sj53d3f0ev11y/j6+qLRaEhOLvtDLDk5mcDAwAr38fPzY+XKlRQWFnLp0iWCgoKYNGkSkZGR5crGxsayYcMGVqxYUWZ7YGAgBoOBzMzMMq2D16u3qkkyWBVcvGH8EUg8CK5yCa4mMSkm0grSiM+JJz43nviceBJyE8zPc+JJKbiS2L3W8TUGNR4EgKejJwUlBSTkJJQ53rjW49CqtdR3q0999/rVYliWBg0aMGXKFKZMmUJMTAy//PILP//8M5s3b+bo0aMcPXqU//73v7i7u3PvvffSo0cPevToQVRUlN1jr0lUKhX+eif89U50jio7zmheUQmxl/KJuZRHzKU8YtPMj2Mv5ZOUXUhWQTEH47M4GJ9V4bF93XQEe7lQ3/NKghjs6Ux9b2fq6Z3RO2vlXFVDpb1vK0rwrn5cUFB+vu6K1NRWvaqi0+lo27Yt0dHR9O3bFzB3IImOjmbs2LHX3dfJyYng4GCKi4v58ccfGTBgQLkyCxYswN/fn4ceeqjM9rZt2+Lg4EB0dDT9+vUD4OTJk8TFxdGpUyfrvLlbJL2JkUEr67ISUwnJ+cnkGnJp5N3Isv2VLa9wNO0oiXmJ1x1wGcyDLtd3q8+TTZ60TLlWWFJIUl4SQW5B6DQ18xJfRkYGa9euZfXq1axfv55Lly6VeT0sLIz777+fLl26cPfddxMWFlanv2hsJd9QQlx6PjFp+cReyiM+o4D4jHwSMgtIyCggz1DxvUtXc3JQE3g5EQ3UOxHo4USA3okAvSOBevNjf72jdG6xIpPJRFpa2nVb8+Lj4yksLLyh4/n6+hISEkL9+vUty9+f19RWvVtxM0PLDBs2jM8++4wOHTowe/Zsli1bxokTJwgICGDo0KEEBwczbdo0AHbu3ElCQgKtWrUiISGBN998k/Pnz7Nv374yrXwmk4mIiAgef/xxpk+fXq7e0aNHs3btWhYuXIher2fcuHEAbN++3Tr/ELdIWgZFrWYwGkjMS+Ri7kXzkneR9oHtuaOeuffW4bTDDP11KMFuwazrt86y38Xci8TlxAGgUWkIdA2kvnt9S4ve1WsPR49ySZCT1olwj/Aqe5+24OXlxeDBgxk8eDAmk4n9+/ezfv16fv/9d7Zt20ZsbCxffPEFX3zxBWC+9Hz33XdbliZNmqBWy/Aqt8pFp6VxoJ7GgeUvgSmKQlZB8eUEscCSICZk5lueZ+YXU1hsIuZSPjHXGB6nlLerjgC9E75uOvzcHPFx0+Hr5ohvBY8d6uj0fYqikJWVxcWLFytcEhISuHjxIomJiRQXX/+HZCl/f/8KE7zSx8HBwXbplFEbDRw4kNTUVN544w2SkpJo1aoV69ats3QqiYuLK/N3q7CwkNdff51z587h5ubGgw8+yOLFi8t1BtmwYQNxcXE89dRTFdb74Ycfolar6devX5lBp6sLaRnExi2DBRnw02gI7wx3jAH5crSqtII0EnMTSc5PJjk/maS8JJLykriYZ07+0grSyu0zotkIJrSdAEBSXhIPrniQEPcQfurzk2Vg5b3Je1EUhSC3IPxd/NGq5XfT1fLy8tiyZQvR0dFs27aNvXv3UlJSdhYPDw8P2rVrR/v27S3rkJAQaT2sYoXFRpKzC0nKKiQ5p4jkrEKSss1LyuV1clYRBqPpnw92FU8XB3xcrySIvm46vFx1eLno8HRxwNNFh5eLg+W5m2P1v1Sdm5t7zSTv6uVGL9uCucdoRQle6RIcHGy1zg51kVzZsw5JBrHxh+nEWlj6OPjcBuP2WPfYtZjRZKTYVIyT1vxrOK0gjYVHFlJoLOT1O163lBuydggHUg9c91jOWmfqudYjyC2IINcgOgd3pltoN8D8K19BqbWza1SVvLw8du7caRmyZseOHRXe0O7v70/79u1p3bo1zZo1o3nz5jRs2BCtVpJte1IUhYz8YnPSmF3IpVwDablFXMotIu3y49J1ep4Bo6nyXxtatapMkujposPT2QEvV3Oy6OHsgLuTA3onrWWtd3bA3UmLs4PmphPJ4uJiUlNTSUpKIjk5meTkZMvj0nVpkpeTk/PPB7zMy8uLoKCg6y6BgYHodDXzNpGaQpJB65C/wLZWOqRMeGf7xlFNmBQTmUWZpOanklaQRmrB5XV+KqkFqaTmp5KUn0RqfipDmwxlQjtzC16JqYRFxxahVWmZ3GGypZdtPbd6JOYlEuAaQIBLAIGugQS4BJgTv8vJn6ej5zW/SFQqFSqqd2tFTeDq6kq3bt3o1s2cZBcXF3P06FF2797N7t272bNnD4cPHyYlJYU1a9awZs0ay746nY7bb7+d5s2b07x5c5o1a0ajRo0ICwuTJLGKqFQqvF11l2dTuX6PTJNJIbOgmEu5RaTmFlkSx7TcIjLyi8nMN5CRV0xmweXH+QYKi02UmJTLCaWh0vFp1CpLkujupMXNQYVDcS6qwiyU/EyMeZkU56ZTkHWJvMx0sjNSybqURsalVDLSL/1zBVdxdXUlODjYktBd/bh0qVevXp28P0/UXvKX1tYs4wvWjcGmC0oK2Jm4kxxDDo80eMSy/e0db7MlfguXCi5RopRc5whXJOVfGYzT19mXoU2GEuASgFExosGcDP7v7v9V+0tPdZGDgwOtWrWiVatWjBw5EoCCggIOHjzI7t27OXToEIcPH+bIkSPk5eVx8OBBDh48WO4YERERNGzYkNtuu82yjoyMJDg4WFpc7EStvpI43hZwY8McFRYbycg3kJlfXG6dnltEakYOKakppF9KIzMjnayMS+RmZpCfnUFJfhbGgmwS8rMw5mdjKsjGVJAD1xmjsxyVGo2LB2pXL3RuXjh5eOOs98HV0xu9ly+efgH4+AXg618PL089ro7m1khnBw1ODmqcHDQ4OWgodNBw0aghI6UAR60BZ515u6WcVoNaLX+PRM1Ta5LBefPm8f7775OUlETLli2ZO3cuHTp0sG9Qhdnm4WSgxg02XVBSQEZhBumF6aQXppNRmGF+XpROekE6GUUZltd7RfTixTYvApBjyGHcxnFoVBoejHjQ0oKXbcgmOf/K2E5ejl74uvji5+yHr7N57efih5+zn6V1z9f5yvAbWrWWl9u/XC5OSQRrDmdnZ+64444yUy+ZTCZiY2M5fPiwZTl69ChnzpyhsLCQU6dOcerUqXLHUqlUBAUFWYbMuHoojZCQEAIDA/Hz80Ojkd6xVUVRFAoKCsjIyLjucunSJVJTU0lLS7Osb7RH7dVUajXuHt64evnirPdG5+6Fg5sXaldP1C5eKE4eGB31FDvqKdS4oFD+VpDCy0sKwCXgUgaQcUv/Djqt2pIcOl9OIp2ueq7TqtFpNeg0ahwd1Oa1Vm3errm8/ttzR63mSpm/l7tqf0et+fgaSUhFJdWKZPD7779nwoQJfPrpp3Ts2JHZs2fTs2dPTp48ib+//z8fwFYu7ATFBF7h4BH8j8WtyWgykl+ST15xHnnFeWQbsvFy9LL0cM0szOSzQ59RZCzijU5vWPZ7ZfMrbLywkSJj0Q3XdTH3ouWxt5M3TX2a4uvsS5GxCBe1efaNUS1GMazJMPxc/PBx8ik7XZqos9RqNREREURERNC7d2/LdpPJREJCAqdOneL06dNl1rGxsRQVFZGQkEBCQsI15xRVq9X4+fkREBBAYGCgZQkICMDb2xsvL69y67p66c9kMpGbm0tOTk65JTs7u8zzrKysayZ6BkPlLwGXcnR0xM/PD19fX8v66scVbbvRZF9RFIpKTOQbjOQVlZBnKCGvyEi+ocT8vPTx5ddzi0ooLDZSWGyisNhIQbHx8tpE0dXPDUYKS0wYSq50vjFcfp51431MrE6lAge1Gq1GhVatwkFT+liNg6b0ufmxVq266vGVtba0nFqFg1aNw+VyWo3KcuzS10v316jNx9Oo1ZfXqitrjYqOET64OtaKtKPWqRUdSDp27Ej79u35+OOPAfMftpCQEMaNG8ekSZP+cX+b3YC64U2Sd8zB0KQPxu5TMWHCZDKZ14oJo2JEURSMipESUwkGowGD0UB99/o08DRPA5ZVlMXKMyvRqDQ82eRJy6HnH5rP8fTj5Jfkk19sTvryi/MtzwuN5X9pP974caZ0nAKYO2Tcu+xeVKg4MPSApQPFvzf/m99ifgPM06F5O3nj7eSNl5OXeXH0sjwvfS3QNZBA1+oxirqo/UwmE6mpqWXmV736cXx8PKmpqTc09dbfOTk54eXlhZeXF25ubri6ul537eLigk6nw8HBAZ1OV+7x1c+vHq7i6hbtih6bTCaKi4spKSm5oXVRUREFBQX/uOTn51seX5385ebm3sIZKUuj0Vj+DStafHx8KkzyXF1da2xLv9GkUFRyJTksMBgvJ5PmhPJKMmmk6HKyaFmMRsvj0teKjH8vY6KoxFjBtiuPa8I3efTErjTwc7PqMaUDiXXU+BTdYDCwd+9eJk+ebNmmVqvp3r37NVsMioqKKCq60vJVmR5klVKUy9hAf07k7oGVj/xz+cueaf6M5bJrZlEmM/fMxM3BrUwyuDtpNzsSK35/V9OoNLg6uKLX6ctMY+ah8+DpZk+jd9RjNBlRXx4z7N/t/s34NuPxdPTE1aHm/nEWtZdarSYgIICAgIBr3gpSUlJCWlpauV6jpeuMjAzS09PLrI1GI4WFhSQmJpKYmFjF76p60Gg0uLu7V7jo9XrL+lqJnre3N25ubnXu74ZGrcJFp8VFZ5+vVEVRKDYqGC4nkSVGE8Umxbw2KpSYTJQYFYqN5o48xUbz8xKTCUNJ+dev3q/YqFjKmh+XPUbx5X2NJnMZ8/ry879td5ZpE6utGp8MpqWlYTQaLQNGlgoICODEiRMV7jNt2jTeeust2wf30Eyc1iTiknkGtUp9zUWj0qBVa9FpdOjUOvyc/SyHcNe581DkQ7hoXcocekCjAdwTcg+uDq64OLjgqjWvXRxccNG6WLbr1LoK/zA7aBwY33Z8ue3SwidqA61Wa7ksfCMURSEnJ8eSHGZmZpKbm0teXl6F69LH+fn5FBcXYzAYMBgM13xsMBjMwxhd1XxT+riitVqtxsHBAQcHB7RabYXrqx/rdDqcnZ3LLC4uLuW2Xb24ubmVS/icnZ3rXCJXG6hUKnRaFTqtGmTIQnETanwyeDMmT57MhAkTLM8TEhJo0qSJTepa/NCSW9rf28mb6XeXn9qme1j3WzquEOIKlUqFXq9Hr9cTFhZm73CEEKJK1fhksPQm4uTk5DLbk5OTr9kq4OjoWGbE9+zsbJvGKIQQQghRXdX4aRd0Oh1t27YlOjrass1kMhEdHU2nTp3sGJkQQgghRPVX41sGASZMmMCwYcNo164dHTp0YPbs2eTl5TFixAh7hyaEEEIIUa3VimRw4MCBpKam8sYbb5CUlESrVq1Yt25duU4lQgghhBCirFqRDAKMHTuWsWPH2jsMIYQQQogapcbfMyiEEEIIIW6eJINCCCGEEHWYJINCCCGEEHWYJINCCCGEEHWYJINCCCGEEHWYJINCCCGEEHWYJINCCCGEEHWYJINCCCGEEHWYJINCCCGEEHVYrZmB5FaYTCYAEhMT7RyJEEIIIW5U6fd26fe4uDmSDALJyckAdOjQwc6RCCGEEKKykpOTCQ0NtXcYNZZKURTF3kHYW0lJCfv37ycgIAC12npXznNycmjSpAnHjh3D3d3dascVtiHnq+aRc1azyPmqWWrC+TKZTCQnJ9O6dWu0WmnfulmSDNpQdnY2Hh4eZGVlodfr7R2O+AdyvmoeOWc1i5yvmkXOV90hHUiEEEIIIeowSQaFEEIIIeowSQZtyNHRkalTp+Lo6GjvUMQNkPNV88g5q1nkfNUscr7qDrlnUAghhBCiDpOWQSGEEEKIOkySQSGEEEKIOkySQSGEEEKIOkySQSGEEEKIOkySwVs0b948wsPDcXJyomPHjuzateu65ZcvX07jxo1xcnKiefPmrF27tooiFVC583X06FH69etHeHg4KpWK2bNnV12gwqIy52z+/PncfffdeHl54eXlRffu3f/x/6SwrsqcrxUrVtCuXTs8PT1xdXWlVatWLF68uAqjFZX9Diu1dOlSVCoVffv2tW2AokpIMngLvv/+eyZMmMDUqVPZt28fLVu2pGfPnqSkpFRYfvv27Tz++OM8/fTT7N+/n759+9K3b1+OHDlSxZHXTZU9X/n5+URGRjJ9+nQCAwOrOFoBlT9nf/zxB48//jibNm1ix44dhISE0KNHDxISEqo48rqpsufL29ub1157jR07dnDo0CFGjBjBiBEj+O2336o48rqpsuerVExMDP/+97+5++67qyhSYXOKuGkdOnRQxowZY3luNBqVoKAgZdq0aRWWHzBggPLQQw+V2daxY0dl1KhRNo1TmFX2fF0tLCxM+fDDD20YnajIrZwzRVGUkpISxd3dXVm0aJGtQhRXudXzpSiK0rp1a+X111+3RXjib27mfJWUlCh33nmn8sUXXyjDhg1T+vTpUwWRCluTlsGbZDAY2Lt3L927d7dsU6vVdO/enR07dlS4z44dO8qUB+jZs+c1ywvruZnzJezLGucsPz+f4uJivL29bRWmuOxWz5eiKERHR3Py5Em6dOliy1AFN3++3n77bfz9/Xn66aerIkxRRbT2DqCmSktLw2g0EhAQUGZ7QEAAJ06cqHCfpKSkCssnJSXZLE5hdjPnS9iXNc7Zq6++SlBQULkfYcL6bvZ8ZWVlERwcTFFRERqNhk8++YT777/f1uHWeTdzvrZt28aXX37JgQMHqiBCUZUkGRRC1ErTp09n6dKl/PHHHzg5Odk7HHEN7u7uHDhwgNzcXKKjo5kwYQKRkZHcc8899g5NXCUnJ4chQ4Ywf/58fH197R2OsDJJBm+Sr68vGo2G5OTkMtuTk5Ov2dkgMDCwUuWF9dzM+RL2dSvnbObMmUyfPp0NGzbQokULW4YpLrvZ86VWq4mKigKgVatWHD9+nGnTpkkyaGOVPV9nz54lJiaGRx55xLLNZDIBoNVqOXnyJA0aNLBt0MJm5J7Bm6TT6Wjbti3R0dGWbSaTiejoaDp16lThPp06dSpTHmD9+vXXLC+s52bOl7Cvmz1nM2bM4J133mHdunW0a9euKkIVWO//mMlkoqioyBYhiqtU9nw1btyYw4cPc+DAAcvSu3dv7r33Xg4cOEBISEhVhi+szd49WGqypUuXKo6OjsrChQuVY8eOKc8++6zi6empJCUlKYqiKEOGDFEmTZpkKf/nn38qWq1WmTlzpnL8+HFl6tSpioODg3L48GF7vYU6pbLnq6ioSNm/f7+yf/9+pV69esq///1vZf/+/crp06ft9RbqnMqes+nTpys6nU754YcflMTERMuSk5Njr7dQp1T2fP33v/9Vfv/9d+Xs2bPKsWPHlJkzZyparVaZP3++vd5CnVLZ8/V30pu49pBk8BbNnTtXCQ0NVXQ6ndKhQwflr7/+srzWtWtXZdiwYWXKL1u2TGnYsKGi0+mUpk2bKmvWrKniiOu2ypyv8+fPK0C5pWvXrlUfeB1WmXMWFhZW4TmbOnVq1QdeR1XmfL322mtKVFSU4uTkpHh5eSmdOnVSli5daoeo667KfoddTZLB2kOlKIpir1ZJIYQQQghhX3LPoBBCCCFEHSbJoBBCCCFEHSbJoBBCCCFEHSbJoBBCCCFEHSbJoBBCCCFEHSbJoBBCCCFEHSbJoBBCCCFEHSbJoBDC5oYPH07fvn2rvN6FCxeiUqlQqVSMHz/+umXDw8OZPXt2lcRVWl9pbJmZmVVWrxBC/J3W3gEIIWo2lUp13denTp3KRx99hL3Gt9fr9Zw8eRJXV1e71H8tu3fvZuvWrfTr18/eoQgh6jhJBoUQtyQxMdHy+Pvvv+eNN97g5MmTlm1ubm64ubnZIzTAnKwGBgbarf5r8fPzw9vb295hCCGEXCYWQtyawMBAy+Lh4WFJvkoXNze3cpeJ77nnHsaNG8f48ePx8vIiICCA+fPnk5eXx4gRI3B3dycqKopff/21TF1HjhyhV69euLm5ERAQwJAhQ0hLS6t0zCkpKTzyyCM4OzsTERHBkiVLypWZNWsWzZs3x9XVlZCQEJ5//nlyc3MByMvLQ6/X88MPP5TZZ+XKlbi6upKTk4PBYGDs2LHUq1cPJycnwsLCmDZtWqVjFUIIW5NkUAhhF4sWLcLX15ddu3Yxbtw4Ro8ezb/+9S/uvPNO9u3bR48ePRgyZAj5+fkAZGZm0q1bN1q3bs2ePXtYt24dycnJDBgwoNJ1Dx8+nAsXLrBp0yZ++OEHPvnkE1JSUsqUUavVzJkzh6NHj7Jo0SI2btzIK6+8AoCrqyuDBg1iwYIFZfZZsGAB/fv3x93dnTlz5vDLL7+wbNkyTp48yZIlSwgPD7+5fywhhLAlRQghrGTBggWKh4dHue3Dhg1T+vTpY3netWtX5a677rI8LykpUVxdXZUhQ4ZYtiUmJiqAsmPHDkVRFOWdd95RevToUea4Fy5cUADl5MmTNxzPyZMnFUDZtWuXZdvx48cVQPnwww+v+d6WL1+u+Pj4WJ7v3LlT0Wg0ysWLFxVFUZTk5GRFq9Uqf/zxh6IoijJu3DilW7duislkuuYxN23apABKRkbGNcsIIYStScugEMIuWrRoYXms0Wjw8fGhefPmlm0BAQEAlha7gwcPsmnTJss9iG5ubjRu3BiAs2fP3nC9x48fR6vV0rZtW8u2xo0b4+npWabchg0buO+++wgODsbd3Z0hQ4Zw6dIlS0tlhw4daNq0KYsWLQLgm2++ISwsjC5dugDm1scDBw7QqFEjXnjhBX7//fcbjlEIIaqSJINCCLtwcHAo81ylUpXZVtpL2WQyAZCbm8sjjzzCgQMHyiynT5+2JGDWEhMTw8MPP0yLFi348ccf2bt3L/PmzQPAYDBYyj3zzDMsXLgQMF8iHjFihCXuNm3acP78ed555x0KCgoYMGAA/fv3t2qcQghhDdKbWAhRI7Rp04Yff/yR8PBwtNqb/9PVuHFjSkpK2Lt3L+3btwfg5MmTZcb627t3LyaTiQ8++AC12vybedmyZeWO9eSTT/LKK68wZ84cjh07xrBhw8q8rtfrGThwIAMHDqR///488MADpKenSy9iIUS1Ii2DQogaYcyYMaSnp/P444+ze/duzp49y2+//caIESMwGo03fJxGjRrxwAMPMGrUKHbu3MnevXt55plncHZ2tpSJioqiuLiYuXPncu7cORYvXsynn35a7lheXl489thjvPzyy/To0YP69etbXps1axbfffcdJ06c4NSpUyxfvpzAwMByl6OFEMLeJBkUQtQIQUFB/PnnnxiNRnr06EHz5s0ZP348np6elta7G7VgwQKCgoLo2rUrjz32GM8++yz+/v6W11u2bMmsWbP43//+R7NmzViyZMk1h4V5+umnMRgMPPXUU2W2u7u7M2PGDNq1a0f79u2JiYlh7dq1lY5VCCFsTaUodpoWQAghbGzhwoWMHz/eptO9LV68mJdeeomLFy+i0+kqte8ff/zBvffeS0ZGhrQYCiHsRn6iCiFqtaysLNzc3Hj11Vetetz8/HzOnj3L9OnTGTVqVKUTwaZNm9KrVy+rxiSEEDdDWgaFELVWTk4OycnJAHh6euLr62u1Y7/55pu89957dOnShZ9//rnSU+7FxsZSXFwMQGRkpFw+FkLYjSSDQgghhBB1mPwUFUIIIYSowyQZFEIIIYSowyQZFEIIIYSowyQZFEIIIYSowyQZFEIIIYSowyQZFEIIIYSowyQZFEIIIYSowyQZFEIIIYSowyQZFEIIIYSow/4fBvfmvC5EdDcAAAAASUVORK5CYII=", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from cycler import cycler\n", - "\n", - "plt.style.use(\"default\")\n", - "\n", - "fig, ax = plt.subplots()\n", - "\n", - "ax.set_prop_cycle(\n", - " cycler(\"color\", [\"tab:blue\", \"tab:orange\", \"tab:green\"])\n", - " + cycler(\"linestyle\", [\"-\", \"--\", \"-.\"])\n", - ")\n", - "\n", - "ax.plot(timesteps[\"time (days)\"], timesteps[[\"NA\", \"NB\", \"NC\"]].values)\n", - "ax.legend(labels=[\"$N_A$\", \"$N_B$\", \"$N_C$\"], loc=\"upper left\")\n", - "ax.set_ylabel(\"Defect state percentages [%]\")\n", - "ax.set_xlabel(\"Time [days]\")\n", - "\n", - "ax2 = ax.twinx()\n", - "ax2.plot(\n", - " timesteps[\"time (days)\"],\n", - " timesteps[\"Pmp_norm\"],\n", - " c=\"black\",\n", - " label=\"Normalized $P_{MP}$\",\n", - ")\n", - "ax2.legend(loc=\"upper right\")\n", - "ax2.set_ylabel(\"Normalized $P_{MP}$\")\n", - "\n", - "ax.set_title(\n", - " \"Accelerated B-O LID Test\\n\"\n", - " rf\"{temperature}$\\degree$C, {suns}$\\times I_{{SC}}$ injection\"\n", - ")\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.7" - }, - "vscode": { - "interpreter": { - "hash": "848658e0671c41dd18b216771b1713479db7d685859cbb6c795b270024b1888c" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/LETID - Accelerated Test.ipynb b/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/LETID - Accelerated Test.ipynb deleted file mode 100644 index 227fe110d..000000000 --- a/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/LETID - Accelerated Test.ipynb +++ /dev/null @@ -1,1282 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# LETID - Accelerated Tests\n", - "\n", - "\n", - "Several standardized accelerated tests have been developed for LETID. These include [IEC TS 63342](https://webstore.iec.ch/publication/67332) for c-Si photovoltaic modules, and [IEC TS 63202-4](https://webstore.iec.ch/publication/67612) for c-Si photovoltaic cells.\n", - "Both procedures essentially prescribe exposure to constant light or current injection at constant elevated temperature for a prescribed duration of time. This notebook demonstrates how to use this library to model device behavior in such a procedure.\n", - "\n", - "**Requirements:**\n", - "- `pandas`, `numpy`, `matplotlib`\n", - "\n", - "**Objectives:**\n", - "1. Define necessary solar cell device parameters\n", - "2. Define necessary degradation parameters: degraded lifetime and defect states\n", - "3. Create timeseries of temperature and current injection\n", - "4. Run through timeseries, calculating defect states\n", - "5. Calculate device degradation and plot\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# if running on google colab, uncomment the next line and execute this cell to install the dependencies and prevent \"ModuleNotFoundError\" in later cells:\n", - "# !pip install pvdeg==0.3.3" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "from pvdeg import letid, collection, utilities, DATA_DIR\n", - "\n", - "import os\n", - "import pandas as pd\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import pvdeg" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Working on a Windows 10\n", - "Python version 3.11.7 | packaged by Anaconda, Inc. | (main, Dec 15 2023, 18:05:47) [MSC v.1916 64 bit (AMD64)]\n", - "Pandas version 2.2.0\n", - "pvdeg version 0.2.4.dev83+ge2ceab9.d20240422\n" - ] - } - ], - "source": [ - "# This information helps with debugging and getting support :)\n", - "import sys, platform\n", - "\n", - "print(\"Working on a \", platform.system(), platform.release())\n", - "print(\"Python version \", sys.version)\n", - "print(\"Pandas version \", pd.__version__)\n", - "print(\"pvdeg version \", pvdeg.__version__)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Device parameters\n", - "To define a device, we need to define several important quantities about the device: wafer thickness (in $\\mu m$), rear surface recombination velocity (in cm/s), and cell area (in cm2). The values defined below are representative of a typical PERC solar cell." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "wafer_thickness = 180 # um\n", - "s_rear = 46 # cm/s\n", - "cell_area = 243 # cm^2" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### Other device parameters\n", - "Other required device parameters: base diffusivity (in $cm^2/s$), and optical generation profile, which allow us to estimate current collection in the device." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "generation_df = pd.read_excel(\n", - " os.path.join(DATA_DIR, \"PVL_GenProfile.xlsx\"), header=0\n", - ") # this is an optical generation profile generated by PVLighthouse's OPAL2 default model for 1-sun, normal incident AM1.5 sunlight on a 180-um thick SiNx-coated, pyramid-textured wafer.\n", - "generation = generation_df[\"Generation (cm-3s-1)\"]\n", - "depth = generation_df[\"Depth (um)\"]\n", - "\n", - "d_base = 27 # cm^2/s electron diffusivity. See https://www2.pvlighthouse.com.au/calculators/mobility%20calculator/mobility%20calculator.aspx for details" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Degradation parameters\n", - "To model the device's degradation, we need to define several more important quantities about the degradation the device will experience. These include undegraded and degraded lifetime (in $\\mu s$)." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "tau_0 = 115 # us, carrier lifetime in non-degraded states, e.g. LETID/LID states A or C\n", - "tau_deg = 55 # us, carrier lifetime in fully-degraded state, e.g. LETID/LID state B" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's see how much maximum power degradation these parameters will result in: " - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.03495240755084558\n" - ] - } - ], - "source": [ - "loss, pmp_0, pmp_deg = letid.calc_pmp_loss_from_tau_loss(\n", - " tau_0, tau_deg, cell_area, wafer_thickness, s_rear\n", - ") # returns % power loss, pmp_0, pmp_deg\n", - "print(loss)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " Check to see the device's current collection " - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "41.59099692285122\n" - ] - } - ], - "source": [ - "jsc_0 = collection.calculate_jsc_from_tau_cp(\n", - " tau_0, wafer_thickness, d_base, s_rear, generation, depth\n", - ") # returns short-circuit current (Jsc) in mA/cm^2 given required cell parameters\n", - "print(jsc_0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " Remaining degradation parameters:\n", - "\n", - "The rest of the quantities to define are: the initial percentage of defects in each state (A, B, and C), and the dictionary of mechanism parameters.\n", - "\n", - "In this example, we'll assume the device starts in the fully-undegraded state (100% state A), and we'll use the parameters for LETID degradation from Repins." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# starting defect state percentages\n", - "nA_0 = 100\n", - "nB_0 = 0\n", - "nC_0 = 0" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "('Choose a set of kinetic parameters:',\n", - " ['D037',\n", - " 'D038',\n", - " 'D039',\n", - " 'D040',\n", - " 'D050',\n", - " 'D051',\n", - " 'D052',\n", - " 'D053',\n", - " 'D054',\n", - " 'D055'])" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Here's a list of the possible sets of kinetic parameters from kinetic_parameters.json:\n", - "utilities.get_kinetics()" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'mechanism': 'LETID', 'v_ab': 46700000.0, 'v_ba': 4.7e-25, 'v_bc': 19900000.0, 'v_cb': 0.0, 'ea_ab': 0.827, 'ea_ba': -1.15, 'ea_bc': 0.871, 'ea_cb': 0.0, 'suns_ab': 1.0, 'suns_bc': 1.0, 'temperature_ab': 410, 'temperature_bc': 410, 'tau_ab': 75, 'tau_bc': 75, 'x_ab': 1, 'x_ba': 1.7, 'x_bc': 1.2, 'structure_ab': 'cell', 'structure_bc': 'cell', 'thickness_ab': 200, 'thickness_bc': 200, 'srv_ab': 90, 'srv_bc': 90, 'doi': 'doi:10.1557/s43577-022-00438-8', 'comments': ''}\n" - ] - } - ], - "source": [ - "mechanism_params = utilities.get_kinetics(\"repins\")\n", - "print(mechanism_params)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Set up timeseries\n", - "In this example, we are going to model test with constant temperature and current injection. IEC TS 63342 prescribes two to three weeks of injection equivalent to $2\\times(I_{sc}-I_{mp})$, at $75\\degree C$. For most typical c-Si modules, $2\\times(I_{sc}-I_{mp})$ is roughly equal to $0.1\\times I_{sc}$. So we will set injection equal to 0.1 \"suns\" of injection. \n", - "\n", - "We will create a pandas datetime series and calculate the changes in defect states for each timestep." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "temperature = 75 # degrees celsius\n", - "suns = 0.1 # \"suns\" of injection, e.g 1-sun illumination at open circuit would be 1; dark current injection is given as a fraction of Isc, e.g., injecting Isc would be 1. For this example we assume injection is 0.1*Isc.\n", - "\n", - "duration = \"3W\"\n", - "freq = \"min\"\n", - "start = \"2022-01-01\"\n", - "\n", - "# default is 3 weeks of 1-minute interval timesteps. In general, we should select small timesteps unless we are sure defect reactions are proceeding very slowly\n", - "timesteps = pd.date_range(\n", - " start, end=pd.to_datetime(start) + pd.to_timedelta(duration), freq=freq\n", - ")\n", - "timesteps = pd.DataFrame(timesteps, columns=[\"Datetime\"])\n", - "\n", - "\n", - "temps = np.full(len(timesteps), temperature)\n", - "injection = np.full(len(timesteps), suns)\n", - "\n", - "timesteps[\"Temperature\"] = temps\n", - "timesteps[\"Injection\"] = injection\n", - "\n", - "timesteps[[\"NA\", \"NB\", \"NC\", \"tau\"]] = (\n", - " np.nan\n", - ") # create columns for defect state percentages and lifetime, fill with NaNs for now, to fill iteratively below\n", - "\n", - "timesteps.loc[0, [\"NA\", \"NB\", \"NC\"]] = (\n", - " nA_0,\n", - " nB_0,\n", - " nC_0,\n", - ") # assign first timestep defect state percentages\n", - "timesteps.loc[0, \"tau\"] = letid.tau_now(\n", - " tau_0, tau_deg, nB_0\n", - ") # calculate tau for the first timestep" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " Datetime Temperature Injection NA NB NC tau\n", - "0 2022-01-01 00:00:00 75 0.1 100.0 0.0 0.0 115.0\n", - "1 2022-01-01 00:01:00 75 0.1 NaN NaN NaN NaN\n", - "2 2022-01-01 00:02:00 75 0.1 NaN NaN NaN NaN\n", - "3 2022-01-01 00:03:00 75 0.1 NaN NaN NaN NaN\n", - "4 2022-01-01 00:04:00 75 0.1 NaN NaN NaN NaN\n", - "... ... ... ... ... ... ... ...\n", - "30236 2022-01-21 23:56:00 75 0.1 NaN NaN NaN NaN\n", - "30237 2022-01-21 23:57:00 75 0.1 NaN NaN NaN NaN\n", - "30238 2022-01-21 23:58:00 75 0.1 NaN NaN NaN NaN\n", - "30239 2022-01-21 23:59:00 75 0.1 NaN NaN NaN NaN\n", - "30240 2022-01-22 00:00:00 75 0.1 NaN NaN NaN NaN\n", - "\n", - "[30241 rows x 7 columns]" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "timesteps" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Run through timesteps\n", - "Since each timestep depends on the preceding timestep, we need to calculate in a loop. This will take a few minutes depending on the length of the timeseries." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "for index, timestep in timesteps.iterrows():\n", - "\n", - " # first row tau has already been assigned\n", - " if index == 0:\n", - " pass\n", - "\n", - " # loop through rows, new tau calculated based on previous NB. Reaction proceeds based on new tau.\n", - " else:\n", - " n_A = timesteps.at[index - 1, \"NA\"]\n", - " n_B = timesteps.at[index - 1, \"NB\"]\n", - " n_C = timesteps.at[index - 1, \"NC\"]\n", - "\n", - " tau = letid.tau_now(tau_0, tau_deg, n_B)\n", - " jsc = collection.calculate_jsc_from_tau_cp(\n", - " tau, wafer_thickness, d_base, s_rear, generation, depth\n", - " )\n", - "\n", - " temperature = timesteps.at[index, \"Temperature\"]\n", - " injection = timesteps.at[index, \"Injection\"]\n", - "\n", - " # calculate defect reaction kinetics: reaction constant and carrier concentration factor.\n", - " k_AB = letid.k_ij(\n", - " mechanism_params[\"v_ab\"], mechanism_params[\"ea_ab\"], temperature\n", - " )\n", - " k_BA = letid.k_ij(\n", - " mechanism_params[\"v_ba\"], mechanism_params[\"ea_ba\"], temperature\n", - " )\n", - " k_BC = letid.k_ij(\n", - " mechanism_params[\"v_bc\"], mechanism_params[\"ea_bc\"], temperature\n", - " )\n", - " k_CB = letid.k_ij(\n", - " mechanism_params[\"v_cb\"], mechanism_params[\"ea_cb\"], temperature\n", - " )\n", - "\n", - " x_ab = letid.carrier_factor(\n", - " tau,\n", - " \"ab\",\n", - " temperature,\n", - " injection,\n", - " jsc,\n", - " wafer_thickness,\n", - " s_rear,\n", - " mechanism_params,\n", - " )\n", - " x_ba = letid.carrier_factor(\n", - " tau,\n", - " \"ba\",\n", - " temperature,\n", - " injection,\n", - " jsc,\n", - " wafer_thickness,\n", - " s_rear,\n", - " mechanism_params,\n", - " )\n", - " x_bc = letid.carrier_factor(\n", - " tau,\n", - " \"bc\",\n", - " temperature,\n", - " injection,\n", - " jsc,\n", - " wafer_thickness,\n", - " s_rear,\n", - " mechanism_params,\n", - " )\n", - "\n", - " # calculate the instantaneous change in NA, NB, and NC\n", - " dN_Adt = (k_BA * n_B * x_ba) - (k_AB * n_A * x_ab)\n", - " dN_Bdt = (\n", - " (k_AB * n_A * x_ab) + (k_CB * n_C) - ((k_BA * x_ba + k_BC * x_bc) * n_B)\n", - " )\n", - " dN_Cdt = (k_BC * n_B * x_bc) - (k_CB * n_C)\n", - "\n", - " t_step = (\n", - " timesteps.at[index, \"Datetime\"] - timesteps.at[index - 1, \"Datetime\"]\n", - " ).total_seconds()\n", - "\n", - " # assign new defect state percentages\n", - " timesteps.at[index, \"NA\"] = n_A + dN_Adt * t_step\n", - " timesteps.at[index, \"NB\"] = n_B + dN_Bdt * t_step\n", - " timesteps.at[index, \"NC\"] = n_C + dN_Cdt * t_step" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Finish calculating degraded device parameters.\n", - "Now that we have calculated defect states, we can calculate all the quantities that depend on defect states." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "timesteps[\"tau\"] = letid.tau_now(tau_0, tau_deg, timesteps[\"NB\"])\n", - "\n", - "# calculate device Jsc for every timestep. Unfortunately this requires an integration so I think we have to run through a loop. Device Jsc allows calculation of device Voc.\n", - "for index, timestep in timesteps.iterrows():\n", - " jsc_now = collection.calculate_jsc_from_tau_cp(\n", - " timesteps.at[index, \"tau\"], wafer_thickness, d_base, s_rear, generation, depth\n", - " )\n", - " timesteps.at[index, \"Jsc\"] = jsc_now\n", - " timesteps.at[index, \"Voc\"] = letid.calc_voc_from_tau(\n", - " timesteps.at[index, \"tau\"], wafer_thickness, s_rear, jsc_now, temperature=25\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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DatetimeTemperatureInjectionNANBNCtauJscVocIscFFPmpPmp_normtime (days)
02022-01-01 00:00:00750.1100.0000000.0000000.000000115.00000041.5909970.66632710.1066120.8409875.6634671.0000000.000000
12022-01-01 00:01:00750.199.9454240.0545760.000000114.93157341.5907840.66631610.1065610.8409855.6633250.9999750.000694
22022-01-01 00:02:00750.199.8909040.1090940.000002114.86330041.5905720.66630410.1065090.8409835.6631840.9999500.001389
32022-01-01 00:03:00750.199.8364390.1635550.000006114.79517841.5903590.66629210.1064570.8409815.6630430.9999250.002083
42022-01-01 00:04:00750.199.7820280.2179590.000012114.72720941.5901470.66628110.1064060.8409795.6629020.9999000.002778
.............................................
302362022-01-21 23:56:00750.10.00606654.97409345.01984171.88769841.3813920.65675410.0556780.8392865.5427350.97868220.997222
302372022-01-21 23:57:00750.10.00606554.97277345.02116271.88834541.3813970.65675410.0556790.8392865.5427380.97868320.997917
302382022-01-21 23:58:00750.10.00606454.97145445.02248271.88899241.3814020.65675410.0556810.8392865.5427400.97868320.998611
302392022-01-21 23:59:00750.10.00606354.97013545.02380271.88963841.3814070.65675510.0556820.8392865.5427430.97868420.999306
302402022-01-22 00:00:00750.10.00606154.96881645.02512371.89028541.3814110.65675510.0556830.8392865.5427450.97868421.000000
\n", - "

30241 rows Γ— 14 columns

\n", - "
" - ], - "text/plain": [ - " Datetime Temperature Injection NA NB \\\n", - "0 2022-01-01 00:00:00 75 0.1 100.000000 0.000000 \n", - "1 2022-01-01 00:01:00 75 0.1 99.945424 0.054576 \n", - "2 2022-01-01 00:02:00 75 0.1 99.890904 0.109094 \n", - "3 2022-01-01 00:03:00 75 0.1 99.836439 0.163555 \n", - "4 2022-01-01 00:04:00 75 0.1 99.782028 0.217959 \n", - "... ... ... ... ... ... \n", - "30236 2022-01-21 23:56:00 75 0.1 0.006066 54.974093 \n", - "30237 2022-01-21 23:57:00 75 0.1 0.006065 54.972773 \n", - "30238 2022-01-21 23:58:00 75 0.1 0.006064 54.971454 \n", - "30239 2022-01-21 23:59:00 75 0.1 0.006063 54.970135 \n", - "30240 2022-01-22 00:00:00 75 0.1 0.006061 54.968816 \n", - "\n", - " NC tau Jsc Voc Isc FF \\\n", - "0 0.000000 115.000000 41.590997 0.666327 10.106612 0.840987 \n", - "1 0.000000 114.931573 41.590784 0.666316 10.106561 0.840985 \n", - "2 0.000002 114.863300 41.590572 0.666304 10.106509 0.840983 \n", - "3 0.000006 114.795178 41.590359 0.666292 10.106457 0.840981 \n", - "4 0.000012 114.727209 41.590147 0.666281 10.106406 0.840979 \n", - "... ... ... ... ... ... ... \n", - "30236 45.019841 71.887698 41.381392 0.656754 10.055678 0.839286 \n", - "30237 45.021162 71.888345 41.381397 0.656754 10.055679 0.839286 \n", - "30238 45.022482 71.888992 41.381402 0.656754 10.055681 0.839286 \n", - "30239 45.023802 71.889638 41.381407 0.656755 10.055682 0.839286 \n", - "30240 45.025123 71.890285 41.381411 0.656755 10.055683 0.839286 \n", - "\n", - " Pmp Pmp_norm time (days) \n", - "0 5.663467 1.000000 0.000000 \n", - "1 5.663325 0.999975 0.000694 \n", - "2 5.663184 0.999950 0.001389 \n", - "3 5.663043 0.999925 0.002083 \n", - "4 5.662902 0.999900 0.002778 \n", - "... ... ... ... \n", - "30236 5.542735 0.978682 20.997222 \n", - "30237 5.542738 0.978683 20.997917 \n", - "30238 5.542740 0.978683 20.998611 \n", - "30239 5.542743 0.978684 20.999306 \n", - "30240 5.542745 0.978684 21.000000 \n", - "\n", - "[30241 rows x 14 columns]" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# this function quickly calculates the rest of the device parameters: Isc, FF, max power, and normalized max power\n", - "timesteps = letid.calc_device_params(timesteps, cell_area=243)\n", - "timesteps[\"time (days)\"] = (\n", - " timesteps[\"Datetime\"] - timesteps.iloc[0][\"Datetime\"]\n", - ").dt.total_seconds() / 86400 # create a column for days elapsed\n", - "\n", - "timesteps" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plot the results\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from cycler import cycler\n", - "\n", - "plt.style.use(\"default\")\n", - "\n", - "fig, ax = plt.subplots()\n", - "\n", - "ax.set_prop_cycle(\n", - " cycler(\"color\", [\"tab:blue\", \"tab:orange\", \"tab:green\"])\n", - " + cycler(\"linestyle\", [\"-\", \"--\", \"-.\"])\n", - ")\n", - "\n", - "ax.plot(timesteps[\"time (days)\"], timesteps[[\"NA\", \"NB\", \"NC\"]].values)\n", - "\n", - "ax.legend(labels=[\"$N_A$\", \"$N_B$\", \"$N_C$\", \"80 % regeneration\"], loc=\"upper left\")\n", - "ax.set_ylabel(\"Defect state percentages [%]\")\n", - "ax.set_xlabel(\"Time [days]\")\n", - "\n", - "ax2 = ax.twinx()\n", - "ax2.plot(\n", - " timesteps[\"time (days)\"],\n", - " timesteps[\"Pmp_norm\"],\n", - " c=\"black\",\n", - " label=\"Normalized $P_{MP}$\",\n", - ")\n", - "ax2.legend(loc=\"upper right\")\n", - "ax2.set_ylabel(\"Normalized $P_{MP}$\")\n", - "\n", - "# ax.axvline(pvdeg.Degradation.calc_regeneration_time(timesteps).total_seconds()/(60*60*24), linestyle = ':' , c = 'grey')\n", - "# ax.annotate('80% regeneration', (pvdeg.Degradation.calc_regeneration_time(timesteps).total_seconds()/(60*60*24), 80),\n", - "# xytext=(0.5, 0.8), textcoords='axes fraction',\n", - "# arrowprops=dict(facecolor='black', shrink=0.1),\n", - "# horizontalalignment='right', verticalalignment='top')\n", - "\n", - "ax.set_title(\n", - " \"Accelerated LETID Test\\n\"\n", - " rf\"{temperature}$\\degree$C, {suns}$\\times I_{{SC}}$ dark current injection\"\n", - ")\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The function `calc_letid_lab` wraps all of the steps above into a single function:" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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DatetimeTemperatureInjectionNANBNCtauJscVocIscFFPmpPmp_norm
02024-05-06 15:13:53.364135750.1100.0000000.0000000.000000115.00000041.5909970.6663279.9402480.8409875.5702411.000000
12024-05-06 15:14:53.364135750.199.9454240.0545760.000000114.93157341.5909970.6663279.9402480.8409875.5702411.000000
22024-05-06 15:15:53.364135750.199.8909040.1090940.000002114.86330041.5907840.6663169.9401970.8409855.5701020.999975
32024-05-06 15:16:53.364135750.199.8364390.1635550.000006114.79517841.5905720.6663049.9401470.8409835.5699630.999950
42024-05-06 15:17:53.364135750.199.7820280.2179590.000012114.72720941.5903590.6662929.9400960.8409815.5698240.999925
..........................................
302362024-05-27 15:09:53.364135750.10.00606654.97409345.01984171.88769841.3813870.6567549.8901510.8392865.4514940.978682
302372024-05-27 15:10:53.364135750.10.00606554.97277345.02116271.88834541.3813920.6567549.8901530.8392865.4514970.978682
302382024-05-27 15:11:53.364135750.10.00606454.97145445.02248271.88899241.3813970.6567549.8901540.8392865.4514990.978683
302392024-05-27 15:12:53.364135750.10.00606354.97013545.02380271.88963841.3814020.6567549.8901550.8392865.4515020.978683
302402024-05-27 15:13:53.364135750.10.00606154.96881645.02512371.89028541.3814070.6567559.8901560.8392865.4515040.978684
\n", - "

30241 rows Γ— 13 columns

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" - ], - "text/plain": [ - " Datetime Temperature Injection NA \\\n", - "0 2024-05-06 15:13:53.364135 75 0.1 100.000000 \n", - "1 2024-05-06 15:14:53.364135 75 0.1 99.945424 \n", - "2 2024-05-06 15:15:53.364135 75 0.1 99.890904 \n", - "3 2024-05-06 15:16:53.364135 75 0.1 99.836439 \n", - "4 2024-05-06 15:17:53.364135 75 0.1 99.782028 \n", - "... ... ... ... ... \n", - "30236 2024-05-27 15:09:53.364135 75 0.1 0.006066 \n", - "30237 2024-05-27 15:10:53.364135 75 0.1 0.006065 \n", - "30238 2024-05-27 15:11:53.364135 75 0.1 0.006064 \n", - "30239 2024-05-27 15:12:53.364135 75 0.1 0.006063 \n", - "30240 2024-05-27 15:13:53.364135 75 0.1 0.006061 \n", - "\n", - " NB NC tau Jsc Voc Isc \\\n", - "0 0.000000 0.000000 115.000000 41.590997 0.666327 9.940248 \n", - "1 0.054576 0.000000 114.931573 41.590997 0.666327 9.940248 \n", - "2 0.109094 0.000002 114.863300 41.590784 0.666316 9.940197 \n", - "3 0.163555 0.000006 114.795178 41.590572 0.666304 9.940147 \n", - "4 0.217959 0.000012 114.727209 41.590359 0.666292 9.940096 \n", - "... ... ... ... ... ... ... \n", - "30236 54.974093 45.019841 71.887698 41.381387 0.656754 9.890151 \n", - "30237 54.972773 45.021162 71.888345 41.381392 0.656754 9.890153 \n", - "30238 54.971454 45.022482 71.888992 41.381397 0.656754 9.890154 \n", - "30239 54.970135 45.023802 71.889638 41.381402 0.656754 9.890155 \n", - "30240 54.968816 45.025123 71.890285 41.381407 0.656755 9.890156 \n", - "\n", - " FF Pmp Pmp_norm \n", - "0 0.840987 5.570241 1.000000 \n", - "1 0.840987 5.570241 1.000000 \n", - "2 0.840985 5.570102 0.999975 \n", - "3 0.840983 5.569963 0.999950 \n", - "4 0.840981 5.569824 0.999925 \n", - "... ... ... ... \n", - "30236 0.839286 5.451494 0.978682 \n", - "30237 0.839286 5.451497 0.978682 \n", - "30238 0.839286 5.451499 0.978683 \n", - "30239 0.839286 5.451502 0.978683 \n", - "30240 0.839286 5.451504 0.978684 \n", - "\n", - "[30241 rows x 13 columns]" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "letid.calc_letid_lab(\n", - " tau_0, tau_deg, wafer_thickness, s_rear, nA_0, nB_0, nC_0, 0.1, 75, \"repins\"\n", - ")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.7" - }, - "vscode": { - "interpreter": { - "hash": "848658e0671c41dd18b216771b1713479db7d685859cbb6c795b270024b1888c" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/LETID - Outdoor LETID.ipynb b/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/LETID - Outdoor LETID.ipynb deleted file mode 100644 index 2c2601041..000000000 --- a/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/LETID - Outdoor LETID.ipynb +++ /dev/null @@ -1,1813 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# LETID - Outdoor Environments \n", - "\n", - "This is an example on how to model LETID progression in outdoor environments\n", - "\n", - "We can use the equations in this library to model LETID progression in a simulated outdoor environment, given that we have weather and system data. This example makes use of tools from the fabulous [pvlib](https://pvlib-python.readthedocs.io/en/stable/) library to calculate system irradiance and temperature, which we use to calculate progression in LETID states.\n", - "\n", - "This will illustrate the potential of \"Temporary Recovery\", i.e., the backwards transition of the LETID defect B->A that can take place with carrier injection at lower temperatures.\n", - "\n", - "\n", - "**Requirements:**\n", - "- `pvlib`, `pandas`, `numpy`, `matplotlib`\n", - "\n", - "**Objectives:**\n", - "1. Use `pvlib` and provided weather files to set up a temperature and injection timeseries\n", - "2. Define necessary solar cell device parameters\n", - "3. Define necessary degradation parameters: degraded lifetime and defect states\n", - "4. Run through timeseries, calculating defect states\n", - "5. Calculate device degradation and plot\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# if running on google colab, uncomment the next line and execute this cell to install the dependencies and prevent \"ModuleNotFoundError\" in later cells:\n", - "# !pip install pvdeg==0.3.3" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "from pvdeg import letid, collection, utilities, DATA_DIR\n", - "\n", - "import pvlib\n", - "import os\n", - "import pandas as pd\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import pvdeg" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Working on a Windows 10\n", - "Python version 3.11.7 | packaged by Anaconda, Inc. | (main, Dec 15 2023, 18:05:47) [MSC v.1916 64 bit (AMD64)]\n", - "Pandas version 2.2.0\n", - "pvlib version 0.10.3\n", - "pvdeg version 0.2.4.dev83+ge2ceab9.d20240422\n" - ] - } - ], - "source": [ - "# This information helps with debugging and getting support :)\n", - "import sys, platform\n", - "\n", - "print(\"Working on a \", platform.system(), platform.release())\n", - "print(\"Python version \", sys.version)\n", - "print(\"Pandas version \", pd.__version__)\n", - "print(\"pvlib version \", pvlib.__version__)\n", - "print(\"pvdeg version \", pvdeg.__version__)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "First, we'll use pvlib to create and run a model system, and use the irradiance, temperature, and operating point of that model to set up our LETID model\n", - "For this example, we'll model a fixed latitude tilt system at NREL, in Golden, CO, USA, using [NSRDB](https://nsrdb.nrel.gov/) hourly PSM weather data, SAPM temperature models, and module and inverter models from the CEC database." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# load weather and location data, use pvlib read_psm3 function with map_variables = True\n", - "\n", - "sam_file = \"psm3.csv\"\n", - "weather, meta = pvdeg.weather.read(\n", - " os.path.join(DATA_DIR, sam_file), file_type=\"PSM3\", map_variables=True\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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YearMonthDayHourMinutednidhighitemp_airdew_pointwind_speedrelative_humiditypoa_globaltemp_celltemp_module
1999-01-01 00:30:00-07:001999110300.00.00.00.0-5.01.879.390.00.00.0
1999-01-01 01:30:00-07:001999111300.00.00.00.0-4.01.780.840.00.00.0
1999-01-01 02:30:00-07:001999112300.00.00.00.0-4.01.582.980.00.00.0
1999-01-01 03:30:00-07:001999113300.00.00.00.0-4.01.385.010.00.00.0
1999-01-01 04:30:00-07:001999114300.00.00.00.0-4.01.385.810.00.00.0
................................................
1999-12-31 19:30:00-07:001999123119300.00.00.00.0-3.00.983.630.00.00.0
1999-12-31 20:30:00-07:001999123120300.00.00.00.0-3.01.286.820.00.00.0
1999-12-31 21:30:00-07:001999123121300.00.00.00.0-4.01.683.780.00.00.0
1999-12-31 22:30:00-07:001999123122300.00.00.00.0-4.01.781.220.00.00.0
1999-12-31 23:30:00-07:001999123123300.00.00.00.0-5.01.879.430.00.00.0
\n", - "

8760 rows Γ— 15 columns

\n", - "
" - ], - "text/plain": [ - " Year Month Day Hour Minute dni dhi ghi \\\n", - "1999-01-01 00:30:00-07:00 1999 1 1 0 30 0.0 0.0 0.0 \n", - "1999-01-01 01:30:00-07:00 1999 1 1 1 30 0.0 0.0 0.0 \n", - "1999-01-01 02:30:00-07:00 1999 1 1 2 30 0.0 0.0 0.0 \n", - "1999-01-01 03:30:00-07:00 1999 1 1 3 30 0.0 0.0 0.0 \n", - "1999-01-01 04:30:00-07:00 1999 1 1 4 30 0.0 0.0 0.0 \n", - "... ... ... ... ... ... ... ... ... \n", - "1999-12-31 19:30:00-07:00 1999 12 31 19 30 0.0 0.0 0.0 \n", - "1999-12-31 20:30:00-07:00 1999 12 31 20 30 0.0 0.0 0.0 \n", - "1999-12-31 21:30:00-07:00 1999 12 31 21 30 0.0 0.0 0.0 \n", - "1999-12-31 22:30:00-07:00 1999 12 31 22 30 0.0 0.0 0.0 \n", - "1999-12-31 23:30:00-07:00 1999 12 31 23 30 0.0 0.0 0.0 \n", - "\n", - " temp_air dew_point wind_speed relative_humidity \\\n", - "1999-01-01 00:30:00-07:00 0.0 -5.0 1.8 79.39 \n", - "1999-01-01 01:30:00-07:00 0.0 -4.0 1.7 80.84 \n", - "1999-01-01 02:30:00-07:00 0.0 -4.0 1.5 82.98 \n", - "1999-01-01 03:30:00-07:00 0.0 -4.0 1.3 85.01 \n", - "1999-01-01 04:30:00-07:00 0.0 -4.0 1.3 85.81 \n", - "... ... ... ... ... \n", - "1999-12-31 19:30:00-07:00 0.0 -3.0 0.9 83.63 \n", - "1999-12-31 20:30:00-07:00 0.0 -3.0 1.2 86.82 \n", - "1999-12-31 21:30:00-07:00 0.0 -4.0 1.6 83.78 \n", - "1999-12-31 22:30:00-07:00 0.0 -4.0 1.7 81.22 \n", - "1999-12-31 23:30:00-07:00 0.0 -5.0 1.8 79.43 \n", - "\n", - " poa_global temp_cell temp_module \n", - "1999-01-01 00:30:00-07:00 0.0 0.0 0.0 \n", - "1999-01-01 01:30:00-07:00 0.0 0.0 0.0 \n", - "1999-01-01 02:30:00-07:00 0.0 0.0 0.0 \n", - "1999-01-01 03:30:00-07:00 0.0 0.0 0.0 \n", - "1999-01-01 04:30:00-07:00 0.0 0.0 0.0 \n", - "... ... ... ... \n", - "1999-12-31 19:30:00-07:00 0.0 0.0 0.0 \n", - "1999-12-31 20:30:00-07:00 0.0 0.0 0.0 \n", - "1999-12-31 21:30:00-07:00 0.0 0.0 0.0 \n", - "1999-12-31 22:30:00-07:00 0.0 0.0 0.0 \n", - "1999-12-31 23:30:00-07:00 0.0 0.0 0.0 \n", - "\n", - "[8760 rows x 15 columns]" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "weather" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# if our weather file doesn't have precipitable water, calculate it with pvlib\n", - "if not \"precipitable_water\" in weather.columns:\n", - " weather[\"precipitable_water\"] = pvlib.atmosphere.gueymard94_pw(\n", - " weather[\"temp_air\"], weather[\"relative_humidity\"]\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# rename some columns for pvlib if they haven't been already\n", - "weather.rename(\n", - " columns={\n", - " \"GHI\": \"ghi\",\n", - " \"DNI\": \"dni\",\n", - " \"DHI\": \"dhi\",\n", - " \"Temperature\": \"temp_air\",\n", - " \"Wind Speed\": \"wind_speed\",\n", - " \"Relative Humidity\": \"relative_humidity\",\n", - " \"Precipitable Water\": \"precipitable_water\",\n", - " },\n", - " inplace=True,\n", - ")\n", - "weather = weather[\n", - " [\n", - " \"ghi\",\n", - " \"dni\",\n", - " \"dhi\",\n", - " \"temp_air\",\n", - " \"wind_speed\",\n", - " \"relative_humidity\",\n", - " \"precipitable_water\",\n", - " ]\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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ghidnidhitemp_airwind_speedrelative_humidityprecipitable_water
1999-01-01 00:30:00-07:000.00.00.00.01.879.390.891869
1999-01-01 01:30:00-07:000.00.00.00.01.780.840.908158
1999-01-01 02:30:00-07:000.00.00.00.01.582.980.932199
1999-01-01 03:30:00-07:000.00.00.00.01.385.010.955004
1999-01-01 04:30:00-07:000.00.00.00.01.385.810.963991
........................
1999-12-31 19:30:00-07:000.00.00.00.00.983.630.939501
1999-12-31 20:30:00-07:000.00.00.00.01.286.820.975338
1999-12-31 21:30:00-07:000.00.00.00.01.683.780.941186
1999-12-31 22:30:00-07:000.00.00.00.01.781.220.912427
1999-12-31 23:30:00-07:000.00.00.00.01.879.430.892318
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8760 rows Γ— 7 columns

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" - ], - "text/plain": [ - " ghi dni dhi temp_air wind_speed \\\n", - "1999-01-01 00:30:00-07:00 0.0 0.0 0.0 0.0 1.8 \n", - "1999-01-01 01:30:00-07:00 0.0 0.0 0.0 0.0 1.7 \n", - "1999-01-01 02:30:00-07:00 0.0 0.0 0.0 0.0 1.5 \n", - "1999-01-01 03:30:00-07:00 0.0 0.0 0.0 0.0 1.3 \n", - "1999-01-01 04:30:00-07:00 0.0 0.0 0.0 0.0 1.3 \n", - "... ... ... ... ... ... \n", - "1999-12-31 19:30:00-07:00 0.0 0.0 0.0 0.0 0.9 \n", - "1999-12-31 20:30:00-07:00 0.0 0.0 0.0 0.0 1.2 \n", - "1999-12-31 21:30:00-07:00 0.0 0.0 0.0 0.0 1.6 \n", - "1999-12-31 22:30:00-07:00 0.0 0.0 0.0 0.0 1.7 \n", - "1999-12-31 23:30:00-07:00 0.0 0.0 0.0 0.0 1.8 \n", - "\n", - " relative_humidity precipitable_water \n", - "1999-01-01 00:30:00-07:00 79.39 0.891869 \n", - "1999-01-01 01:30:00-07:00 80.84 0.908158 \n", - "1999-01-01 02:30:00-07:00 82.98 0.932199 \n", - "1999-01-01 03:30:00-07:00 85.01 0.955004 \n", - "1999-01-01 04:30:00-07:00 85.81 0.963991 \n", - "... ... ... \n", - "1999-12-31 19:30:00-07:00 83.63 0.939501 \n", - "1999-12-31 20:30:00-07:00 86.82 0.975338 \n", - "1999-12-31 21:30:00-07:00 83.78 0.941186 \n", - "1999-12-31 22:30:00-07:00 81.22 0.912427 \n", - "1999-12-31 23:30:00-07:00 79.43 0.892318 \n", - "\n", - "[8760 rows x 7 columns]" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "weather" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# import pvlib stuff and pick a module and inverter. Choice of these things will slightly affect the pvlib results which we later use to calculate injection.\n", - "# we'll use the SAPM temperature model open-rack glass/polymer coeffecients.\n", - "\n", - "from pvlib.temperature import TEMPERATURE_MODEL_PARAMETERS\n", - "from pvlib.location import Location\n", - "from pvlib.pvsystem import PVSystem\n", - "from pvlib.modelchain import ModelChain\n", - "\n", - "cec_modules = pvlib.pvsystem.retrieve_sam(\"CECMod\")\n", - "cec_inverters = pvlib.pvsystem.retrieve_sam(\"cecinverter\")\n", - "\n", - "cec_module = cec_modules[\"Jinko_Solar_Co___Ltd_JKM260P_60\"]\n", - "cec_inverter = cec_inverters[\"ABB__ULTRA_750_TL_OUTD_1_US_690_x_y_z__690V_\"]\n", - "\n", - "temperature_model_parameters = TEMPERATURE_MODEL_PARAMETERS[\"sapm\"][\n", - " \"open_rack_glass_polymer\"\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "# set up system in pvlib\n", - "lat = meta[\"latitude\"]\n", - "lon = meta[\"longitude\"]\n", - "tz = meta[\"tz\"]\n", - "elevation = meta[\"altitude\"]\n", - "surface_tilt = lat # fixed, latitude tilt\n", - "surface_azimuth = 180 # south-facing\n", - "\n", - "location = Location(lat, lon, tz, elevation, \"Golden, CO, USA\")\n", - "\n", - "system = PVSystem(\n", - " surface_tilt=surface_tilt,\n", - " surface_azimuth=surface_azimuth,\n", - " module_parameters=cec_module,\n", - " inverter_parameters=cec_inverter,\n", - " temperature_model_parameters=temperature_model_parameters,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "ModelChain: \n", - " name: None\n", - " clearsky_model: ineichen\n", - " transposition_model: haydavies\n", - " solar_position_method: nrel_numpy\n", - " airmass_model: kastenyoung1989\n", - " dc_model: cec\n", - " ac_model: sandia_inverter\n", - " aoi_model: physical_aoi_loss\n", - " spectral_model: first_solar_spectral_loss\n", - " temperature_model: sapm_temp\n", - " losses_model: no_extra_losses" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# create and run pvlib modelchain\n", - "mc = ModelChain(system, location, aoi_model=\"physical\")\n", - "mc.run_model(weather)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Set up timeseries\n", - "In this example, injection is a function of both the operating point of the module (which we will assume is maximum power point) and irradiance. Maximum power point injection is equivalent to $(I_{sc}-I_{mp})/I_{sc}\\times Ee$, where $Ee$ is effective irradiance, the irradiance absorbed by the module's cells. We normalize it to 1-sun irradiance, 1000 $W/m^2$.\n", - "\n", - "We will use the irradiance, DC operating point, and cell temperature from the pvlib modelchain results." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "ee = mc.results.effective_irradiance\n", - "# injection = (mc.results.dc['i_sc']-mc.results.dc['i_mp'])/(mc.results.dc['i_sc'])*(ee/1000)\n", - "injection = letid.calc_injection_outdoors(mc.results)\n", - "temperature = mc.results.cell_temperature\n", - "\n", - "timesteps = pd.DataFrame(\n", - " {\"Temperature\": temperature, \"Injection\": injection}\n", - ") # create a DataFrame with cell temperature and injection\n", - "timesteps.reset_index(\n", - " inplace=True\n", - ") # reset the index so datetime is a column. I prefer integer indexing.\n", - "timesteps.rename(columns={\"index\": \"Datetime\"}, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "# filter out times when injection is NaN, these won't progress LETID, and it'll make the calculations below run faster\n", - "timesteps = timesteps[timesteps[\"Injection\"].notnull()]\n", - "timesteps.reset_index(inplace=True, drop=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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DatetimeTemperatureInjection
01999-01-01 08:30:00-07:003.3487740.006239
11999-01-01 09:30:00-07:0012.3784390.031607
21999-01-01 10:30:00-07:0016.5950810.044220
31999-01-01 11:30:00-07:0017.4572400.043201
41999-01-01 12:30:00-07:006.3927830.007124
............
42961999-12-31 12:30:00-07:009.5155770.001151
42971999-12-31 13:30:00-07:0028.1197960.044499
42981999-12-31 14:30:00-07:0023.3146720.035130
42991999-12-31 15:30:00-07:0017.8905280.027891
43001999-12-31 16:30:00-07:004.5523650.001188
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4301 rows Γ— 3 columns

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" - ], - "text/plain": [ - " Datetime Temperature Injection\n", - "0 1999-01-01 08:30:00-07:00 3.348774 0.006239\n", - "1 1999-01-01 09:30:00-07:00 12.378439 0.031607\n", - "2 1999-01-01 10:30:00-07:00 16.595081 0.044220\n", - "3 1999-01-01 11:30:00-07:00 17.457240 0.043201\n", - "4 1999-01-01 12:30:00-07:00 6.392783 0.007124\n", - "... ... ... ...\n", - "4296 1999-12-31 12:30:00-07:00 9.515577 0.001151\n", - "4297 1999-12-31 13:30:00-07:00 28.119796 0.044499\n", - "4298 1999-12-31 14:30:00-07:00 23.314672 0.035130\n", - "4299 1999-12-31 15:30:00-07:00 17.890528 0.027891\n", - "4300 1999-12-31 16:30:00-07:00 4.552365 0.001188\n", - "\n", - "[4301 rows x 3 columns]" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "timesteps" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Device parameters\n", - "To define a device, we need to define several important quantities about the device: wafer thickness (in $\\mu m$), rear surface recombination velocity (in cm/s), and cell area (in cm2)." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "wafer_thickness = 180 # um\n", - "s_rear = 46 # cm/s\n", - "cell_area = 243 # cm^2" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " Other device parameters \n", - "Other required device parameters: base diffusivity (in cm2/s), and optical generation profile, which allow us to estimate current collection in the device." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "generation_df = pd.read_excel(\n", - " os.path.join(DATA_DIR, \"PVL_GenProfile.xlsx\"), header=0\n", - ") # this is an optical generation profile generated by PVLighthouse's OPAL2 default model for 1-sun, normal incident AM1.5 sunlight on a 180-um thick SiNx-coated, pyramid-textured wafer.\n", - "generation = generation_df[\"Generation (cm-3s-1)\"]\n", - "depth = generation_df[\"Depth (um)\"]\n", - "\n", - "d_base = 27 # cm^2/s electron diffusivity. See https://www2.pvlighthouse.com.au/calculators/mobility%20calculator/mobility%20calculator.aspx for details" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Degradation parameters\n", - "To model the device's degradation, we need to define several more important quantities about the degradation the device will experience. These include undegraded and degraded lifetime (in $\\mu s$)." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "tau_0 = 115 # us, carrier lifetime in non-degraded states, e.g. LETID/LID states A or C\n", - "tau_deg = 55 # us, carrier lifetime in fully-degraded state, e.g. LETID/LID state B" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Remaining degradation parameters: \n", - "\n", - "The rest of the quantities to define are: the initial percentage of defects in each state (A, B, and C), and the dictionary of mechanism parameters.\n", - "\n", - "In this example, we'll assume the device starts in the fully-undegraded state (100% state A), and we'll use the kinetic parameters for LETID degradation from Repins." - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "# starting defect state percentages\n", - "nA_0 = 100\n", - "nB_0 = 0\n", - "nC_0 = 0\n", - "\n", - "mechanism_params = utilities.get_kinetics(\"repins\")\n", - "\n", - "timesteps[[\"NA\", \"NB\", \"NC\", \"tau\"]] = (\n", - " np.nan\n", - ") # create columns for defect state percentages and lifetime, fill with NaNs for now, to fill iteratively below\n", - "\n", - "timesteps.loc[0, [\"NA\", \"NB\", \"NC\"]] = (\n", - " nA_0,\n", - " nB_0,\n", - " nC_0,\n", - ") # assign first timestep defect state percentages\n", - "timesteps.loc[0, \"tau\"] = letid.tau_now(\n", - " tau_0, tau_deg, nB_0\n", - ") # calculate tau for the first timestep" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Run through timesteps\n", - "Since each timestep depends on the preceding timestep, we need to calculate in a loop. This will take a few minutes depending on the length of the timeseries." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "for index, timestep in timesteps.iterrows():\n", - "\n", - " # first row tau has already been assigned\n", - " if index == 0:\n", - " # calc device parameters for first row\n", - " tau = tau_0\n", - " jsc = collection.calculate_jsc_from_tau_cp(\n", - " tau, wafer_thickness, d_base, s_rear, generation, depth\n", - " )\n", - " voc = letid.calc_voc_from_tau(tau, wafer_thickness, s_rear, jsc, temperature=25)\n", - " timesteps.at[index, \"Jsc\"] = jsc\n", - " timesteps.at[index, \"Voc\"] = voc\n", - "\n", - " # loop through rows, new tau calculated based on previous NB. Reaction proceeds based on new tau.\n", - " else:\n", - " n_A = timesteps.at[index - 1, \"NA\"]\n", - " n_B = timesteps.at[index - 1, \"NB\"]\n", - " n_C = timesteps.at[index - 1, \"NC\"]\n", - "\n", - " tau = letid.tau_now(tau_0, tau_deg, n_B)\n", - " jsc = collection.calculate_jsc_from_tau_cp(\n", - " tau, wafer_thickness, d_base, s_rear, generation, depth\n", - " )\n", - "\n", - " temperature = timesteps.at[index, \"Temperature\"]\n", - " injection = timesteps.at[index, \"Injection\"]\n", - "\n", - " # calculate defect reaction kinetics: reaction constant and carrier concentration factor.\n", - " k_AB = letid.k_ij(\n", - " mechanism_params[\"v_ab\"], mechanism_params[\"ea_ab\"], temperature\n", - " )\n", - " k_BA = letid.k_ij(\n", - " mechanism_params[\"v_ba\"], mechanism_params[\"ea_ba\"], temperature\n", - " )\n", - " k_BC = letid.k_ij(\n", - " mechanism_params[\"v_bc\"], mechanism_params[\"ea_bc\"], temperature\n", - " )\n", - " k_CB = letid.k_ij(\n", - " mechanism_params[\"v_cb\"], mechanism_params[\"ea_cb\"], temperature\n", - " )\n", - "\n", - " x_ab = letid.carrier_factor(\n", - " tau,\n", - " \"ab\",\n", - " temperature,\n", - " injection,\n", - " jsc,\n", - " wafer_thickness,\n", - " s_rear,\n", - " mechanism_params,\n", - " )\n", - " x_ba = letid.carrier_factor(\n", - " tau,\n", - " \"ba\",\n", - " temperature,\n", - " injection,\n", - " jsc,\n", - " wafer_thickness,\n", - " s_rear,\n", - " mechanism_params,\n", - " )\n", - " x_bc = letid.carrier_factor(\n", - " tau,\n", - " \"bc\",\n", - " temperature,\n", - " injection,\n", - " jsc,\n", - " wafer_thickness,\n", - " s_rear,\n", - " mechanism_params,\n", - " )\n", - "\n", - " # calculate the instantaneous change in NA, NB, and NC\n", - " dN_Adt = (k_BA * n_B * x_ba) - (k_AB * n_A * x_ab)\n", - " dN_Bdt = (\n", - " (k_AB * n_A * x_ab) + (k_CB * n_C) - ((k_BA * x_ba + k_BC * x_bc) * n_B)\n", - " )\n", - " dN_Cdt = (k_BC * n_B * x_bc) - (k_CB * n_C)\n", - "\n", - " t_step = (\n", - " timesteps.at[index, \"Datetime\"] - timesteps.at[index - 1, \"Datetime\"]\n", - " ).total_seconds()\n", - "\n", - " # assign new defect state percentages\n", - " timesteps.at[index, \"NA\"] = n_A + dN_Adt * t_step\n", - " timesteps.at[index, \"NB\"] = n_B + dN_Bdt * t_step\n", - " timesteps.at[index, \"NC\"] = n_C + dN_Cdt * t_step\n", - "\n", - " # calculate device parameters\n", - " timesteps.at[index, \"tau\"] = tau\n", - " timesteps.at[index, \"Jsc\"] = jsc\n", - " timesteps.at[index, \"Voc\"] = letid.calc_voc_from_tau(\n", - " tau, wafer_thickness, s_rear, jsc, temperature=25\n", - " )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Finish calculating degraded device parameters.\n", - "Now that we have calculated defect states, we can calculate all the quantities that depend on defect states." - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "timesteps[\"tau\"] = letid.tau_now(tau_0, tau_deg, timesteps[\"NB\"])\n", - "\n", - "# calculate device Jsc for every timestep. Unfortunately this requires an integration so I think we have to run through a loop. Device Jsc allows calculation of device Voc.\n", - "for index, timestep in timesteps.iterrows():\n", - " jsc_now = collection.calculate_jsc_from_tau_cp(\n", - " timesteps.at[index, \"tau\"], wafer_thickness, d_base, s_rear, generation, depth\n", - " )\n", - " timesteps.at[index, \"Jsc\"] = jsc_now\n", - " timesteps.at[index, \"Voc\"] = letid.calc_voc_from_tau(\n", - " timesteps.at[index, \"tau\"], wafer_thickness, s_rear, jsc_now, temperature=25\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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DatetimeTemperatureInjectionNANBNCtauJscVocIscFFPmpPmp_norm
01999-01-01 08:30:00-07:003.3487740.006239100.0000000.0000000.000000e+00115.00000041.5909970.66632710.1066120.8409875.6634671.000000
11999-01-01 09:30:00-07:0012.3784390.03160799.9975470.0024530.000000e+00114.99692241.5909870.66632710.1066100.8409875.6634600.999999
21999-01-01 10:30:00-07:0016.5950810.04422099.9919530.0080476.072523e-09114.98990541.5909660.66632610.1066050.8409865.6634460.999996
31999-01-01 11:30:00-07:0017.4572400.04320199.9859290.0140712.755257e-08114.98235041.5909420.66632410.1065990.8409865.6634300.999994
41999-01-01 12:30:00-07:006.3927830.00712499.9856640.0143362.864303e-08114.98201841.5909410.66632410.1065990.8409865.6634290.999993
..........................................
42961999-12-31 12:30:00-07:009.5155770.00115142.02957553.7666324.203792e+0072.48454641.3858980.65692910.0567730.8393175.5450200.979086
42971999-12-31 13:30:00-07:0028.1197960.04449942.03023153.7656544.204115e+0072.48503441.3859010.65692910.0567740.8393185.5450220.979086
42981999-12-31 14:30:00-07:0023.3146720.03513042.03631153.7594334.204257e+0072.48813541.3859240.65693010.0567800.8393185.5450340.979088
42991999-12-31 15:30:00-07:0017.8905280.02789142.04887353.7468134.204313e+0072.49442541.3859720.65693210.0567910.8393185.5450580.979093
43001999-12-31 16:30:00-07:004.5523650.00118842.04944553.7462424.204314e+0072.49471041.3859740.65693210.0567920.8393185.5450590.979093
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4301 rows Γ— 13 columns

\n", - "
" - ], - "text/plain": [ - " Datetime Temperature Injection NA NB \\\n", - "0 1999-01-01 08:30:00-07:00 3.348774 0.006239 100.000000 0.000000 \n", - "1 1999-01-01 09:30:00-07:00 12.378439 0.031607 99.997547 0.002453 \n", - "2 1999-01-01 10:30:00-07:00 16.595081 0.044220 99.991953 0.008047 \n", - "3 1999-01-01 11:30:00-07:00 17.457240 0.043201 99.985929 0.014071 \n", - "4 1999-01-01 12:30:00-07:00 6.392783 0.007124 99.985664 0.014336 \n", - "... ... ... ... ... ... \n", - "4296 1999-12-31 12:30:00-07:00 9.515577 0.001151 42.029575 53.766632 \n", - "4297 1999-12-31 13:30:00-07:00 28.119796 0.044499 42.030231 53.765654 \n", - "4298 1999-12-31 14:30:00-07:00 23.314672 0.035130 42.036311 53.759433 \n", - "4299 1999-12-31 15:30:00-07:00 17.890528 0.027891 42.048873 53.746813 \n", - "4300 1999-12-31 16:30:00-07:00 4.552365 0.001188 42.049445 53.746242 \n", - "\n", - " NC tau Jsc Voc Isc FF \\\n", - "0 0.000000e+00 115.000000 41.590997 0.666327 10.106612 0.840987 \n", - "1 0.000000e+00 114.996922 41.590987 0.666327 10.106610 0.840987 \n", - "2 6.072523e-09 114.989905 41.590966 0.666326 10.106605 0.840986 \n", - "3 2.755257e-08 114.982350 41.590942 0.666324 10.106599 0.840986 \n", - "4 2.864303e-08 114.982018 41.590941 0.666324 10.106599 0.840986 \n", - "... ... ... ... ... ... ... \n", - "4296 4.203792e+00 72.484546 41.385898 0.656929 10.056773 0.839317 \n", - "4297 4.204115e+00 72.485034 41.385901 0.656929 10.056774 0.839318 \n", - "4298 4.204257e+00 72.488135 41.385924 0.656930 10.056780 0.839318 \n", - "4299 4.204313e+00 72.494425 41.385972 0.656932 10.056791 0.839318 \n", - "4300 4.204314e+00 72.494710 41.385974 0.656932 10.056792 0.839318 \n", - "\n", - " Pmp Pmp_norm \n", - "0 5.663467 1.000000 \n", - "1 5.663460 0.999999 \n", - "2 5.663446 0.999996 \n", - "3 5.663430 0.999994 \n", - "4 5.663429 0.999993 \n", - "... ... ... \n", - "4296 5.545020 0.979086 \n", - "4297 5.545022 0.979086 \n", - "4298 5.545034 0.979088 \n", - "4299 5.545058 0.979093 \n", - "4300 5.545059 0.979093 \n", - "\n", - "[4301 rows x 13 columns]" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "timesteps = letid.calc_device_params(\n", - " timesteps, cell_area=243\n", - ") # this function quickly calculates the rest of the device parameters: Isc, FF, max power, and normalized max power\n", - "\n", - "timesteps" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note of course that all these calculated device parameters are modeled STC device parameters, not the instantaneous, weather-dependent values. This isn't a robust performance model of a degraded module." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plot the results" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from cycler import cycler\n", - "\n", - "plt.style.use(\"default\")\n", - "\n", - "fig, ax = plt.subplots()\n", - "\n", - "ax.set_prop_cycle(\n", - " cycler(\"color\", [\"tab:blue\", \"tab:orange\", \"tab:green\"])\n", - " + cycler(\"linestyle\", [\"-\", \"--\", \"-.\"])\n", - ")\n", - "\n", - "ax.plot(timesteps[\"Datetime\"], timesteps[[\"NA\", \"NB\", \"NC\"]].values)\n", - "ax.legend(labels=[\"$N_A$\", \"$N_B$\", \"$N_C$\"], loc=\"upper left\")\n", - "ax.set_ylabel(\"Defect state percentages [%]\")\n", - "ax.set_xlabel(\"Datetime\")\n", - "\n", - "ax2 = ax.twinx()\n", - "ax2.plot(\n", - " timesteps[\"Datetime\"],\n", - " timesteps[\"Pmp_norm\"],\n", - " c=\"black\",\n", - " label=\"Normalized STC $P_{MP}$\",\n", - ")\n", - "ax2.legend(loc=\"upper right\")\n", - "ax2.set_ylabel(\"Normalized STC $P_{MP}$\")\n", - "\n", - "ax.set_title(\"Outdoor LETID \\n\" f\"{location.name}\")\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The example data provided for Golden, CO, shows how $N_A$ increases in cold weather, and power temporarily recovers, due to temporary recovery of LETID (B->A)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### The function `calc_letid_outdoors` wraps all of the steps above into a single function:" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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1999-01-01 01:30:00-07:000.0NaN100.0000001.702422e-150.000000e+00115.00000041.5909970.66632710.1066120.8409875.6634671.000000
1999-01-01 02:30:00-07:000.0NaN100.0000003.404843e-155.403329e-36115.00000041.5909970.66632710.1066120.8409875.6634671.000000
1999-01-01 03:30:00-07:000.0NaN100.0000005.107265e-151.620999e-35115.00000041.5909970.66632710.1066120.8409875.6634671.000000
1999-01-01 04:30:00-07:000.0NaN100.0000006.809686e-153.241997e-35115.00000041.5909970.66632710.1066120.8409875.6634671.000000
.......................................
1999-12-31 19:30:00-07:000.0NaN27.8336546.778463e+014.381716e+0066.11214241.3338510.65497410.0441260.8389665.5192570.974537
1999-12-31 20:30:00-07:000.0NaN27.8336546.778463e+014.381716e+0066.11214241.3338510.65497410.0441260.8389665.5192570.974537
1999-12-31 21:30:00-07:000.0NaN27.8336546.778463e+014.381716e+0066.11214241.3338510.65497410.0441260.8389665.5192570.974537
1999-12-31 22:30:00-07:000.0NaN27.8336546.778463e+014.381716e+0066.11214241.3338510.65497410.0441260.8389665.5192570.974537
1999-12-31 23:30:00-07:000.0NaN27.8336546.778463e+014.381716e+0066.11214241.3338510.65497410.0441260.8389665.5192570.974537
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8760 rows Γ— 12 columns

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" - ], - "text/plain": [ - " Temperature Injection NA NB \\\n", - "time \n", - "1999-01-01 00:30:00-07:00 0.0 NaN 100.000000 0.000000e+00 \n", - "1999-01-01 01:30:00-07:00 0.0 NaN 100.000000 1.702422e-15 \n", - "1999-01-01 02:30:00-07:00 0.0 NaN 100.000000 3.404843e-15 \n", - "1999-01-01 03:30:00-07:00 0.0 NaN 100.000000 5.107265e-15 \n", - "1999-01-01 04:30:00-07:00 0.0 NaN 100.000000 6.809686e-15 \n", - "... ... ... ... ... \n", - "1999-12-31 19:30:00-07:00 0.0 NaN 27.833654 6.778463e+01 \n", - "1999-12-31 20:30:00-07:00 0.0 NaN 27.833654 6.778463e+01 \n", - "1999-12-31 21:30:00-07:00 0.0 NaN 27.833654 6.778463e+01 \n", - "1999-12-31 22:30:00-07:00 0.0 NaN 27.833654 6.778463e+01 \n", - "1999-12-31 23:30:00-07:00 0.0 NaN 27.833654 6.778463e+01 \n", - "\n", - " NC tau Jsc Voc \\\n", - "time \n", - "1999-01-01 00:30:00-07:00 0.000000e+00 115.000000 41.590997 0.666327 \n", - "1999-01-01 01:30:00-07:00 0.000000e+00 115.000000 41.590997 0.666327 \n", - "1999-01-01 02:30:00-07:00 5.403329e-36 115.000000 41.590997 0.666327 \n", - "1999-01-01 03:30:00-07:00 1.620999e-35 115.000000 41.590997 0.666327 \n", - "1999-01-01 04:30:00-07:00 3.241997e-35 115.000000 41.590997 0.666327 \n", - "... ... ... ... ... \n", - "1999-12-31 19:30:00-07:00 4.381716e+00 66.112142 41.333851 0.654974 \n", - "1999-12-31 20:30:00-07:00 4.381716e+00 66.112142 41.333851 0.654974 \n", - "1999-12-31 21:30:00-07:00 4.381716e+00 66.112142 41.333851 0.654974 \n", - "1999-12-31 22:30:00-07:00 4.381716e+00 66.112142 41.333851 0.654974 \n", - "1999-12-31 23:30:00-07:00 4.381716e+00 66.112142 41.333851 0.654974 \n", - "\n", - " Isc FF Pmp Pmp_norm \n", - "time \n", - "1999-01-01 00:30:00-07:00 10.106612 0.840987 5.663467 1.000000 \n", - "1999-01-01 01:30:00-07:00 10.106612 0.840987 5.663467 1.000000 \n", - "1999-01-01 02:30:00-07:00 10.106612 0.840987 5.663467 1.000000 \n", - "1999-01-01 03:30:00-07:00 10.106612 0.840987 5.663467 1.000000 \n", - "1999-01-01 04:30:00-07:00 10.106612 0.840987 5.663467 1.000000 \n", - "... ... ... ... ... \n", - "1999-12-31 19:30:00-07:00 10.044126 0.838966 5.519257 0.974537 \n", - "1999-12-31 20:30:00-07:00 10.044126 0.838966 5.519257 0.974537 \n", - "1999-12-31 21:30:00-07:00 10.044126 0.838966 5.519257 0.974537 \n", - "1999-12-31 22:30:00-07:00 10.044126 0.838966 5.519257 0.974537 \n", - "1999-12-31 23:30:00-07:00 10.044126 0.838966 5.519257 0.974537 \n", - "\n", - "[8760 rows x 12 columns]" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "nA_0 = 100\n", - "nB_0 = 0\n", - "nC_0 = 0\n", - "mechanism_params = \"repins\"\n", - "\n", - "letid.calc_letid_outdoors(\n", - " tau_0,\n", - " tau_deg,\n", - " wafer_thickness,\n", - " s_rear,\n", - " nA_0,\n", - " nB_0,\n", - " nC_0,\n", - " weather,\n", - " meta,\n", - " mechanism_params,\n", - " generation_df,\n", - " module_parameters=cec_module,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.7" - }, - "vscode": { - "interpreter": { - "hash": "848658e0671c41dd18b216771b1713479db7d685859cbb6c795b270024b1888c" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/LETID - Outdoor Scenario Based on Accelerated Test Result.ipynb b/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/LETID - Outdoor Scenario Based on Accelerated Test Result.ipynb deleted file mode 100644 index f74097e8f..000000000 --- a/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/LETID - Outdoor Scenario Based on Accelerated Test Result.ipynb +++ /dev/null @@ -1,3451 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# LETID - Outdoor Scenario Based on Accelerated Test\n", - "\n", - "This is an example for using a test result to model LETID progression in outdoor environments\n", - "\n", - "One important use case for this library is to use data from a LETID test (e.g. [IEC TS 63342](https://webstore.iec.ch/publication/67332)) to model how a module may degrade and regenerate in the field.\n", - "\n", - "We will take some data from module testing presented in [Karas *et al.* 2022](https://onlinelibrary.wiley.com/doi/10.1002/pip.3573), and use it to estimate device parameters.\n", - "\n", - "We can use the equations in this library to model LETID progression in a simulated outdoor environment, given that we have weather and system data. This example makes use of tools from the fabulous [pvlib](https://pvlib-python.readthedocs.io/en/stable/) library to calculate system irradiance and temperature, which we use to calculate progression in LETID states.\n", - "\n", - "\n", - "\n", - "**Requirements:**\n", - "- `pvlib`, `pandas`, `numpy`, `matplotlib`, `scipy`\n", - "\n", - "**Objectives:**\n", - "1. Load data from example test results\n", - "2. Use `pvlib` and provided weather files to set up a temperature and injection timeseries\n", - "3. Define necessary solar cell device parameters\n", - "4. Define necessary degradation parameters: degraded lifetime and defect states\n", - "5. Run through timeseries, calculating defect states\n", - "6. Calculate device degradation and plot\n" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [], - "source": [ - "# if running on google colab, uncomment the next line and execute this cell to install the dependencies and prevent \"ModuleNotFoundError\" in later cells:\n", - "# !pip install pvdeg==0.3.3" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [], - "source": [ - "from pvdeg import letid, collection, utilities, DATA_DIR\n", - "\n", - "import pvdeg\n", - "import pvlib\n", - "import os\n", - "import pandas as pd\n", - "import numpy as np\n", - "\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Working on a Windows 10\n", - "Python version 3.11.7 | packaged by Anaconda, Inc. | (main, Dec 15 2023, 18:05:47) [MSC v.1916 64 bit (AMD64)]\n", - "Pandas version 2.2.0\n", - "pvdeg version 0.2.4.dev83+ge2ceab9.d20240422\n" - ] - } - ], - "source": [ - "# This information helps with debugging and getting support :)\n", - "import sys, platform\n", - "\n", - "print(\"Working on a \", platform.system(), platform.release())\n", - "print(\"Python version \", sys.version)\n", - "print(\"Pandas version \", pd.__version__)\n", - "print(\"pvdeg version \", pvdeg.__version__)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "First, we'll load some data taken from an accelerated test. See [Karas *et al.* 2022](https://onlinelibrary.wiley.com/doi/10.1002/pip.3573) for full details. This data is the average of \"Type S\" modules from Lab 3. Type S modules were prototype modules made with 48 monocrystalline cells, and degraded about 4-5% in LETID testing. Data throughout testing is shown below:" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [], - "source": [ - "cell_area = 243 # cm^2" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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WeekPmpIscVocFFcell VocJsc% degradation
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" - ], - "text/plain": [ - " Week Pmp Isc Voc FF cell Voc Jsc % degradation\n", - "0 0 236.7 9.91 31.86 75.01 0.663750 40.781893 0.000000\n", - "1 1 230.9 9.82 31.63 74.38 0.658958 40.411523 -2.511910\n", - "2 2 228.5 9.72 31.54 74.50 0.657083 40.000000 -3.588621\n", - "3 3 226.9 9.70 31.56 74.10 0.657500 39.917695 -4.319083\n", - "4 4 224.8 9.62 31.48 74.22 0.655833 39.588477 -5.293594\n", - "5 7 235.8 9.76 31.98 75.51 0.666250 40.164609 -0.381679" - ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df = pd.read_csv(os.path.join(DATA_DIR, \"module test data.csv\"))\n", - "df[\"cell Voc\"] = df[\"Voc\"] / 48\n", - "df[\"Jsc\"] = df[\"Isc\"] / cell_area * 1000\n", - "df[\"% degradation\"] = (df[\"Pmp\"] - df[\"Pmp\"].iloc[0]) / df[\"Pmp\"] * 100\n", - "df" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The module parameter that is most sensitve to our device input parameters is cell open-circuit voltage, which in this case started at about 0.664 V/cell. We will select reasonable values for solar cell input parameters, and use ```letid.calc_voc_from_tau()``` to check if those parameters match the cell Voc of the device we're trying to model. The important quantities here are bulk lifetime in the initial state (```tau_0```), wafer thickness, and rear surface recombination velocity." - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.6611301062313933" - ] - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tau_0 = 120 # [us] reasonable bulk lifetime for commercial-quality p-type silicon, maybe a little on the low side for typical wafer lifetimes, which could range from 100-500 us.\n", - "\n", - "wafer_thickness = 180 # [um] a reasonable wafer thickness for typical commercial silicon solar cells. Wafer thicknesses for Si solar cells are typically 160-180 um.\n", - "\n", - "srv_rear = 100 # [cm/s] a reasonable value for rear surface recombination velocity for passivated emitter and rear cell (PERC) solar cells.\n", - "# Typical PERC cells might have a rear SRV anywhere from 50 to 100 cm/s. Higher values for early PERC, lower values for more modern, higher efficiency PERC.\n", - "# Other device structures will have different SRV values. E.g., aluminum back surface field (Al-BSF) cells could be ~500 cm/s. TopCON or other high efficiency cell structures will be lower, e.g. 10 cm/s.\n", - "# Note that all of these values are intepreted as \"lumped\" values for the entire rear surface.\n", - "\n", - "isc_0 = df.query(\"Week == 0\")[\n", - " \"Isc\"\n", - "].item() # [A] we'll use the short circuit current from the Week 0 test data, instead of trying to calculate it\n", - "cell_area = 240.8 # [cm^2] typical cell size for 6-inch pseudosquare monocrystalline silicon wafers\n", - "jsc_0 = isc_0 / cell_area * 1000 # [mA/cm^2] short circuit current density\n", - "\n", - "temperature = 25 # [C] standard measurement temperature\n", - "\n", - "voc_0 = letid.calc_voc_from_tau(tau_0, wafer_thickness, srv_rear, jsc_0, temperature)\n", - "voc_0" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [], - "source": [ - "generation_df = pd.read_excel(\n", - " os.path.join(DATA_DIR, \"PVL_GenProfile.xlsx\"), header=0\n", - ") # this is an optical generation profile generated by PVLighthouse's OPAL2 default model for 1-sun, normal incident AM1.5 sunlight on a 180-um thick SiNx-coated, pyramid-textured wafer.\n", - "generation = generation_df[\"Generation (cm-3s-1)\"]\n", - "depth = generation_df[\"Depth (um)\"]\n", - "\n", - "d_base = 27 # cm^2/s electron diffusivity. See https://www2.pvlighthouse.com.au/calculators/mobility%20calculator/mobility%20calculator.aspx for details" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Pretty close!" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(235.89622533600513, 236.7)" - ] - }, - "execution_count": 42, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# check to make sure power is close to the measured Week 0 power\n", - "\n", - "ff_0 = df.query(\"Week == 0\")[\"FF\"].item() # [%] fill factor\n", - "\n", - "pmp_0 = (voc_0 * 48) * (jsc_0 * cell_area / 1000) * ff_0 / 100 # [W] maximum power\n", - "pmp_0, df.query(\"Week == 0\")[\"Pmp\"].item()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we do the same thing for the degraded state to determine ```tau_deg```, the bulk lifetime when the module is in its most degraded state. So here the cell Voc target is the roughly 0.656 V measured after 4 weeks of testing." - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.6538686361228122" - ] - }, - "execution_count": 43, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tau_deg = 80 # [us] degraded bulk lifetime\n", - "\n", - "isc_deg = df.query(\"Week == 4\")[\n", - " \"Isc\"\n", - "].item() # [A] we'll use the short circuit current from the Week 4 test data, instead of trying to calculate it\n", - "jsc_deg = isc_deg / cell_area * 1000 # [mA/cm^2] short circuit current density\n", - "\n", - "voc_deg = letid.calc_voc_from_tau(\n", - " tau_deg, wafer_thickness, srv_rear, jsc_deg, temperature\n", - ")\n", - "voc_deg" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(224.09272908700694, 224.8)" - ] - }, - "execution_count": 44, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# check to make sure power is close to the measured Week 4 power\n", - "\n", - "ff_deg = df.query(\"Week == 4\")[\"FF\"].item() # [%] fill factor\n", - "\n", - "pmp_deg = (\n", - " (voc_deg * 48) * (jsc_deg * cell_area / 1000) * ff_deg / 100\n", - ") # [W] maximum power\n", - "pmp_deg, df.query(\"Week == 4\")[\"Pmp\"].item()" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.050036816961295416" - ] - }, - "execution_count": 45, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "(pmp_0 - pmp_deg) / pmp_0" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "So for modeling this module, we will use ```tau_0``` = 120 $\\mu s$, ```tau_deg``` = 80 $\\mu s$, with ```wafer_thickness``` = 180 $\\mu m$ and ```srv_rear``` = 100 cm/s.\n", - "\n", - "Great!\n", - "\n", - "The example proceeds below in similar fashion to the outdoor example, using a fixed latitude tilt system at NREL, in Golden, CO, USA, using [NSRDB](https://nsrdb.nrel.gov/) hourly PSM weather data, SAPM temperature models, and module and inverter models from the CEC database." - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": {}, - "outputs": [], - "source": [ - "# load weather and location data, use pvlib read_psm3 function\n", - "\n", - "sam_file = \"psm3.csv\"\n", - "weather, meta = pvdeg.weather.read(\n", - " os.path.join(DATA_DIR, sam_file), file_type=\"PSM3\", map_variables=True\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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YearMonthDayHourMinutednidhighitemp_airdew_pointwind_speedrelative_humiditypoa_globaltemp_celltemp_module
1999-01-01 00:30:00-07:001999110300.00.00.00.0-5.01.879.390.00.00.0
1999-01-01 01:30:00-07:001999111300.00.00.00.0-4.01.780.840.00.00.0
1999-01-01 02:30:00-07:001999112300.00.00.00.0-4.01.582.980.00.00.0
1999-01-01 03:30:00-07:001999113300.00.00.00.0-4.01.385.010.00.00.0
1999-01-01 04:30:00-07:001999114300.00.00.00.0-4.01.385.810.00.00.0
................................................
1999-12-31 19:30:00-07:001999123119300.00.00.00.0-3.00.983.630.00.00.0
1999-12-31 20:30:00-07:001999123120300.00.00.00.0-3.01.286.820.00.00.0
1999-12-31 21:30:00-07:001999123121300.00.00.00.0-4.01.683.780.00.00.0
1999-12-31 22:30:00-07:001999123122300.00.00.00.0-4.01.781.220.00.00.0
1999-12-31 23:30:00-07:001999123123300.00.00.00.0-5.01.879.430.00.00.0
\n", - "

8760 rows Γ— 15 columns

\n", - "
" - ], - "text/plain": [ - " Year Month Day Hour Minute dni dhi ghi \\\n", - "1999-01-01 00:30:00-07:00 1999 1 1 0 30 0.0 0.0 0.0 \n", - "1999-01-01 01:30:00-07:00 1999 1 1 1 30 0.0 0.0 0.0 \n", - "1999-01-01 02:30:00-07:00 1999 1 1 2 30 0.0 0.0 0.0 \n", - "1999-01-01 03:30:00-07:00 1999 1 1 3 30 0.0 0.0 0.0 \n", - "1999-01-01 04:30:00-07:00 1999 1 1 4 30 0.0 0.0 0.0 \n", - "... ... ... ... ... ... ... ... ... \n", - "1999-12-31 19:30:00-07:00 1999 12 31 19 30 0.0 0.0 0.0 \n", - "1999-12-31 20:30:00-07:00 1999 12 31 20 30 0.0 0.0 0.0 \n", - "1999-12-31 21:30:00-07:00 1999 12 31 21 30 0.0 0.0 0.0 \n", - "1999-12-31 22:30:00-07:00 1999 12 31 22 30 0.0 0.0 0.0 \n", - "1999-12-31 23:30:00-07:00 1999 12 31 23 30 0.0 0.0 0.0 \n", - "\n", - " temp_air dew_point wind_speed relative_humidity \\\n", - "1999-01-01 00:30:00-07:00 0.0 -5.0 1.8 79.39 \n", - "1999-01-01 01:30:00-07:00 0.0 -4.0 1.7 80.84 \n", - "1999-01-01 02:30:00-07:00 0.0 -4.0 1.5 82.98 \n", - "1999-01-01 03:30:00-07:00 0.0 -4.0 1.3 85.01 \n", - "1999-01-01 04:30:00-07:00 0.0 -4.0 1.3 85.81 \n", - "... ... ... ... ... \n", - "1999-12-31 19:30:00-07:00 0.0 -3.0 0.9 83.63 \n", - "1999-12-31 20:30:00-07:00 0.0 -3.0 1.2 86.82 \n", - "1999-12-31 21:30:00-07:00 0.0 -4.0 1.6 83.78 \n", - "1999-12-31 22:30:00-07:00 0.0 -4.0 1.7 81.22 \n", - "1999-12-31 23:30:00-07:00 0.0 -5.0 1.8 79.43 \n", - "\n", - " poa_global temp_cell temp_module \n", - "1999-01-01 00:30:00-07:00 0.0 0.0 0.0 \n", - "1999-01-01 01:30:00-07:00 0.0 0.0 0.0 \n", - "1999-01-01 02:30:00-07:00 0.0 0.0 0.0 \n", - "1999-01-01 03:30:00-07:00 0.0 0.0 0.0 \n", - "1999-01-01 04:30:00-07:00 0.0 0.0 0.0 \n", - "... ... ... ... \n", - "1999-12-31 19:30:00-07:00 0.0 0.0 0.0 \n", - "1999-12-31 20:30:00-07:00 0.0 0.0 0.0 \n", - "1999-12-31 21:30:00-07:00 0.0 0.0 0.0 \n", - "1999-12-31 22:30:00-07:00 0.0 0.0 0.0 \n", - "1999-12-31 23:30:00-07:00 0.0 0.0 0.0 \n", - "\n", - "[8760 rows x 15 columns]" - ] - }, - "execution_count": 47, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "weather" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": {}, - "outputs": [], - "source": [ - "# if our weather file doesn't have precipitable water, calculate it with pvlib\n", - "if not \"precipitable_water\" in weather.columns:\n", - " weather[\"precipitable_water\"] = pvlib.atmosphere.gueymard94_pw(\n", - " weather[\"temp_air\"], weather[\"relative_humidity\"]\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "metadata": {}, - "outputs": [], - "source": [ - "# drop unneeded columns\n", - "weather = weather[\n", - " [\n", - " \"ghi\",\n", - " \"dni\",\n", - " \"dhi\",\n", - " \"temp_air\",\n", - " \"wind_speed\",\n", - " \"relative_humidity\",\n", - " \"precipitable_water\",\n", - " ]\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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ghidnidhitemp_airwind_speedrelative_humidityprecipitable_water
1999-01-01 00:30:00-07:000.00.00.00.01.879.390.891869
1999-01-01 01:30:00-07:000.00.00.00.01.780.840.908158
1999-01-01 02:30:00-07:000.00.00.00.01.582.980.932199
1999-01-01 03:30:00-07:000.00.00.00.01.385.010.955004
1999-01-01 04:30:00-07:000.00.00.00.01.385.810.963991
........................
1999-12-31 19:30:00-07:000.00.00.00.00.983.630.939501
1999-12-31 20:30:00-07:000.00.00.00.01.286.820.975338
1999-12-31 21:30:00-07:000.00.00.00.01.683.780.941186
1999-12-31 22:30:00-07:000.00.00.00.01.781.220.912427
1999-12-31 23:30:00-07:000.00.00.00.01.879.430.892318
\n", - "

8760 rows Γ— 7 columns

\n", - "
" - ], - "text/plain": [ - " ghi dni dhi temp_air wind_speed \\\n", - "1999-01-01 00:30:00-07:00 0.0 0.0 0.0 0.0 1.8 \n", - "1999-01-01 01:30:00-07:00 0.0 0.0 0.0 0.0 1.7 \n", - "1999-01-01 02:30:00-07:00 0.0 0.0 0.0 0.0 1.5 \n", - "1999-01-01 03:30:00-07:00 0.0 0.0 0.0 0.0 1.3 \n", - "1999-01-01 04:30:00-07:00 0.0 0.0 0.0 0.0 1.3 \n", - "... ... ... ... ... ... \n", - "1999-12-31 19:30:00-07:00 0.0 0.0 0.0 0.0 0.9 \n", - "1999-12-31 20:30:00-07:00 0.0 0.0 0.0 0.0 1.2 \n", - "1999-12-31 21:30:00-07:00 0.0 0.0 0.0 0.0 1.6 \n", - "1999-12-31 22:30:00-07:00 0.0 0.0 0.0 0.0 1.7 \n", - "1999-12-31 23:30:00-07:00 0.0 0.0 0.0 0.0 1.8 \n", - "\n", - " relative_humidity precipitable_water \n", - "1999-01-01 00:30:00-07:00 79.39 0.891869 \n", - "1999-01-01 01:30:00-07:00 80.84 0.908158 \n", - "1999-01-01 02:30:00-07:00 82.98 0.932199 \n", - "1999-01-01 03:30:00-07:00 85.01 0.955004 \n", - "1999-01-01 04:30:00-07:00 85.81 0.963991 \n", - "... ... ... \n", - "1999-12-31 19:30:00-07:00 83.63 0.939501 \n", - "1999-12-31 20:30:00-07:00 86.82 0.975338 \n", - "1999-12-31 21:30:00-07:00 83.78 0.941186 \n", - "1999-12-31 22:30:00-07:00 81.22 0.912427 \n", - "1999-12-31 23:30:00-07:00 79.43 0.892318 \n", - "\n", - "[8760 rows x 7 columns]" - ] - }, - "execution_count": 50, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "weather" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Set up PVlib model\n", - "Note that the module we select here is NOT the same \"Type S\" module that was tested for LETID. I'm simply trying to find a module in the CEC database with I-V characteristics that are reasonably close to the tested module, so the pvlib calculated DC results are close to how our Type S module might behave in the field." - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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TechnologyBifacialSTCPTCA_cLengthWidthN_sI_sc_refV_oc_ref...a_refI_L_refI_o_refR_sR_sh_refAdjustgamma_rBIPVVersionDate
Gintech_Energy_GIN_M6_48_220Mono-c-Si0220.48194.51.3131.3240.992489.3131.3...1.5211519.32280.00.10608777.16406322.488531-0.55NSAM 2018.11.11 r21/3/2019
Gintech_Energy_GIN_P6_48_220Multi-c-Si0220.378199.21.3131.3240.992489.2831.3...1.3798159.3791740.00.254869374.7245188.824213-0.49NSAM 2018.11.11 r21/3/2019
Gintech_Energy_GIN_M6_48_225Mono-c-Si0225.844199.11.3131.3240.992489.4531.5...1.5305969.4619890.00.10090579.53174622.462965-0.55NSAM 2018.11.11 r21/3/2019
LG_Electronics_Inc__LG230N8K_G4Mono-c-Si0230.127212.91.2741.30.98489.930.0...1.11089410.3120510.00.307502314.5515142.112491-0.38NSAM 2018.11.11 r21/3/2019
LG_Electronics_Inc__LG235N8K_G4Mono-c-Si0235.216217.61.2741.30.98489.9630.2...1.12468810.3759430.00.278936250.7994693.374386-0.38NSAM 2018.11.11 r21/3/2019
LG_Electronics_Inc__LG240N8K_G4Mono-c-Si0240.1222.41.2741.30.984810.0130.5...1.13498310.3139960.00.2634533976.5139163.244429-0.38NSAM 2018.11.11 r21/3/2019
LG_Electronics_Inc__LG245N8K_G4Mono-c-Si0245.016227.11.2741.30.984810.0630.8...1.15045410.367120.00.2460411112.8308114.077489-0.38NSAM 2018.11.11 r21/3/2019
Prism_Solar_Technologies_Bi48_279BSTCMono-c-Si1220.472199.31.6681.6950.984489.1531.0...1.2663699.150290.00.2109126628.00537113.999823-0.4399NSAM 2018.11.11 r21/3/2019
Prism_Solar_Technologies_Bi48_286BSTCMono-c-Si1225.298204.01.6681.6950.984489.3531.0...1.2663479.3502750.00.2064037016.81640613.996228-0.4399NSAM 2018.11.11 r21/3/2019
\n", - "

9 rows Γ— 25 columns

\n", - "
" - ], - "text/plain": [ - " Technology Bifacial STC PTC \\\n", - "Gintech_Energy_GIN_M6_48_220 Mono-c-Si 0 220.48 194.5 \n", - "Gintech_Energy_GIN_P6_48_220 Multi-c-Si 0 220.378 199.2 \n", - "Gintech_Energy_GIN_M6_48_225 Mono-c-Si 0 225.844 199.1 \n", - "LG_Electronics_Inc__LG230N8K_G4 Mono-c-Si 0 230.127 212.9 \n", - "LG_Electronics_Inc__LG235N8K_G4 Mono-c-Si 0 235.216 217.6 \n", - "LG_Electronics_Inc__LG240N8K_G4 Mono-c-Si 0 240.1 222.4 \n", - "LG_Electronics_Inc__LG245N8K_G4 Mono-c-Si 0 245.016 227.1 \n", - "Prism_Solar_Technologies_Bi48_279BSTC Mono-c-Si 1 220.472 199.3 \n", - "Prism_Solar_Technologies_Bi48_286BSTC Mono-c-Si 1 225.298 204.0 \n", - "\n", - " A_c Length Width N_s I_sc_ref \\\n", - "Gintech_Energy_GIN_M6_48_220 1.313 1.324 0.992 48 9.31 \n", - "Gintech_Energy_GIN_P6_48_220 1.313 1.324 0.992 48 9.28 \n", - "Gintech_Energy_GIN_M6_48_225 1.313 1.324 0.992 48 9.45 \n", - "LG_Electronics_Inc__LG230N8K_G4 1.274 1.3 0.98 48 9.9 \n", - "LG_Electronics_Inc__LG235N8K_G4 1.274 1.3 0.98 48 9.96 \n", - "LG_Electronics_Inc__LG240N8K_G4 1.274 1.3 0.98 48 10.01 \n", - "LG_Electronics_Inc__LG245N8K_G4 1.274 1.3 0.98 48 10.06 \n", - "Prism_Solar_Technologies_Bi48_279BSTC 1.668 1.695 0.984 48 9.15 \n", - "Prism_Solar_Technologies_Bi48_286BSTC 1.668 1.695 0.984 48 9.35 \n", - "\n", - " V_oc_ref ... a_ref I_L_ref \\\n", - "Gintech_Energy_GIN_M6_48_220 31.3 ... 1.521151 9.3228 \n", - "Gintech_Energy_GIN_P6_48_220 31.3 ... 1.379815 9.379174 \n", - "Gintech_Energy_GIN_M6_48_225 31.5 ... 1.530596 9.461989 \n", - "LG_Electronics_Inc__LG230N8K_G4 30.0 ... 1.110894 10.312051 \n", - "LG_Electronics_Inc__LG235N8K_G4 30.2 ... 1.124688 10.375943 \n", - "LG_Electronics_Inc__LG240N8K_G4 30.5 ... 1.134983 10.313996 \n", - "LG_Electronics_Inc__LG245N8K_G4 30.8 ... 1.150454 10.36712 \n", - "Prism_Solar_Technologies_Bi48_279BSTC 31.0 ... 1.266369 9.15029 \n", - "Prism_Solar_Technologies_Bi48_286BSTC 31.0 ... 1.266347 9.350275 \n", - "\n", - " I_o_ref R_s R_sh_ref \\\n", - "Gintech_Energy_GIN_M6_48_220 0.0 0.106087 77.164063 \n", - "Gintech_Energy_GIN_P6_48_220 0.0 0.254869 374.724518 \n", - "Gintech_Energy_GIN_M6_48_225 0.0 0.100905 79.531746 \n", - "LG_Electronics_Inc__LG230N8K_G4 0.0 0.307502 314.551514 \n", - "LG_Electronics_Inc__LG235N8K_G4 0.0 0.278936 250.799469 \n", - "LG_Electronics_Inc__LG240N8K_G4 0.0 0.263453 3976.513916 \n", - "LG_Electronics_Inc__LG245N8K_G4 0.0 0.246041 1112.830811 \n", - "Prism_Solar_Technologies_Bi48_279BSTC 0.0 0.210912 6628.005371 \n", - "Prism_Solar_Technologies_Bi48_286BSTC 0.0 0.206403 7016.816406 \n", - "\n", - " Adjust gamma_r BIPV \\\n", - "Gintech_Energy_GIN_M6_48_220 22.488531 -0.55 N \n", - "Gintech_Energy_GIN_P6_48_220 8.824213 -0.49 N \n", - "Gintech_Energy_GIN_M6_48_225 22.462965 -0.55 N \n", - "LG_Electronics_Inc__LG230N8K_G4 2.112491 -0.38 N \n", - "LG_Electronics_Inc__LG235N8K_G4 3.374386 -0.38 N \n", - "LG_Electronics_Inc__LG240N8K_G4 3.244429 -0.38 N \n", - "LG_Electronics_Inc__LG245N8K_G4 4.077489 -0.38 N \n", - "Prism_Solar_Technologies_Bi48_279BSTC 13.999823 -0.4399 N \n", - "Prism_Solar_Technologies_Bi48_286BSTC 13.996228 -0.4399 N \n", - "\n", - " Version Date \n", - "Gintech_Energy_GIN_M6_48_220 SAM 2018.11.11 r2 1/3/2019 \n", - "Gintech_Energy_GIN_P6_48_220 SAM 2018.11.11 r2 1/3/2019 \n", - "Gintech_Energy_GIN_M6_48_225 SAM 2018.11.11 r2 1/3/2019 \n", - "LG_Electronics_Inc__LG230N8K_G4 SAM 2018.11.11 r2 1/3/2019 \n", - "LG_Electronics_Inc__LG235N8K_G4 SAM 2018.11.11 r2 1/3/2019 \n", - "LG_Electronics_Inc__LG240N8K_G4 SAM 2018.11.11 r2 1/3/2019 \n", - "LG_Electronics_Inc__LG245N8K_G4 SAM 2018.11.11 r2 1/3/2019 \n", - "Prism_Solar_Technologies_Bi48_279BSTC SAM 2018.11.11 r2 1/3/2019 \n", - "Prism_Solar_Technologies_Bi48_286BSTC SAM 2018.11.11 r2 1/3/2019 \n", - "\n", - "[9 rows x 25 columns]" - ] - }, - "execution_count": 51, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cec_modules = pvlib.pvsystem.retrieve_sam(\"CECMod\").T\n", - "cec_modules[cec_modules[\"STC\"].between(220, 250) & (cec_modules[\"N_s\"] == 48)]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The LG ones look close to the module we're trying to model. Pmp around 235W, Isc around 9.9A. Let's go with 'LG_Electronics_Inc__LG235N8K_G4'" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "metadata": {}, - "outputs": [], - "source": [ - "cec_modules = cec_modules.T\n", - "cec_module = cec_modules[\"LG_Electronics_Inc__LG235N8K_G4\"]" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "metadata": {}, - "outputs": [], - "source": [ - "# import the rest of the pvlib stuff\n", - "# we'll use the SAPM temperature model open-rack glass/polymer coeffecients.\n", - "\n", - "from pvlib.temperature import TEMPERATURE_MODEL_PARAMETERS\n", - "from pvlib.location import Location\n", - "from pvlib.pvsystem import PVSystem\n", - "from pvlib.modelchain import ModelChain\n", - "\n", - "cec_inverters = pvlib.pvsystem.retrieve_sam(\"cecinverter\")\n", - "cec_inverter = cec_inverters[\"ABB__ULTRA_750_TL_OUTD_1_US_690_x_y_z__690V_\"]\n", - "\n", - "temperature_model_parameters = TEMPERATURE_MODEL_PARAMETERS[\"sapm\"][\n", - " \"open_rack_glass_polymer\"\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "metadata": {}, - "outputs": [], - "source": [ - "# set up system in pvlib\n", - "lat = meta[\"latitude\"]\n", - "lon = meta[\"longitude\"]\n", - "tz = meta[\"tz\"]\n", - "elevation = meta[\"altitude\"]\n", - "surface_tilt = lat # fixed, latitude tilt\n", - "surface_azimuth = 180 # south-facing\n", - "\n", - "location = Location(lat, lon, tz, elevation, \"Golden, CO, USA\")\n", - "\n", - "system = PVSystem(\n", - " surface_tilt=surface_tilt,\n", - " surface_azimuth=surface_azimuth,\n", - " module_parameters=cec_module,\n", - " inverter_parameters=cec_inverter,\n", - " temperature_model_parameters=temperature_model_parameters,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "ModelChain: \n", - " name: None\n", - " clearsky_model: ineichen\n", - " transposition_model: haydavies\n", - " solar_position_method: nrel_numpy\n", - " airmass_model: kastenyoung1989\n", - " dc_model: cec\n", - " ac_model: sandia_inverter\n", - " aoi_model: physical_aoi_loss\n", - " spectral_model: first_solar_spectral_loss\n", - " temperature_model: sapm_temp\n", - " losses_model: no_extra_losses" - ] - }, - "execution_count": 57, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# create and run pvlib modelchain\n", - "mc = ModelChain(system, location, aoi_model=\"physical\")\n", - "mc.run_model(weather)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Set up timeseries\n", - "In this example, injection is a function of both the operating point of the module (which we will assume is maximum power point) and irradiance. Maximum power point injection is equivalent to $(I_{sc}-I_{mp})/I_{sc}\\times Ee$, where $Ee$ is effective irradiance, the irradiance absorbed by the module's cells. We normalize it to 1-sun irradiance, 1000 $W/m^2$.\n", - "\n", - "We will use the irradiance, DC operating point, and cell temperature from the pvlib modelchain results." - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "metadata": {}, - "outputs": [], - "source": [ - "ee = mc.results.effective_irradiance\n", - "# injection = (mc.results.dc['i_sc']-mc.results.dc['i_mp'])/(mc.results.dc['i_sc'])*(ee/1000)\n", - "injection = letid.calc_injection_outdoors(mc.results)\n", - "temperature = mc.results.cell_temperature\n", - "\n", - "timesteps = pd.DataFrame(\n", - " {\"Temperature\": temperature, \"Injection\": injection}\n", - ") # create a DataFrame with cell temperature and injection\n", - "timesteps.reset_index(\n", - " inplace=True\n", - ") # reset the index so datetime is a column. I prefer integer indexing.\n", - "timesteps.rename(columns={\"index\": \"Datetime\"}, inplace=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "##### Remaining degradation parameters:\n", - "We've already set our important device parameters: ```tau_0```, ```tau_deg```, ```wafer_thickness```, ```srv_rear```, ```cell_area```, etc, but we need a few more: generation profile and carrier diffusivity. These are necessary for calculating current collection, and the \"default\" values provided here should be sufficient for most use cases.\n", - "\n", - "The rest of the quantities to define are: the initial percentage of defects in each state (A, B, and C), and the dictionary of mechanism parameters.\n", - "\n", - "In this example, we'll assume the device starts in the fully-undegraded state (100% state A), and we'll use the parameters for LETID degradation from Repins." - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "metadata": {}, - "outputs": [], - "source": [ - "generation_df = pd.read_excel(\n", - " os.path.join(DATA_DIR, \"PVL_GenProfile.xlsx\"), header=0\n", - ") # this is an optical generation profile generated by PVLighthouse's OPAL2 default model for 1-sun, normal incident AM1.5 sunlight on a 180-um thick SiNx-coated, pyramid-textured wafer.\n", - "generation = generation_df[\"Generation (cm-3s-1)\"]\n", - "depth = generation_df[\"Depth (um)\"]\n", - "\n", - "d_base = 27 # cm^2/s electron diffusivity. See https://www2.pvlighthouse.com.au/calculators/mobility%20calculator/mobility%20calculator.aspx for details" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "metadata": {}, - "outputs": [], - "source": [ - "# starting defect state percentages\n", - "nA_0 = 100\n", - "nB_0 = 0\n", - "nC_0 = 0\n", - "\n", - "mechanism_params = utilities.get_kinetics(\"repins\")\n", - "\n", - "timesteps[[\"NA\", \"NB\", \"NC\", \"tau\"]] = (\n", - " np.nan\n", - ") # create columns for defect state percentages and lifetime, fill with NaNs for now, to fill iteratively below\n", - "\n", - "timesteps.loc[0, [\"NA\", \"NB\", \"NC\"]] = (\n", - " nA_0,\n", - " nB_0,\n", - " nC_0,\n", - ") # assign first timestep defect state percentages\n", - "timesteps.loc[0, \"tau\"] = letid.tau_now(\n", - " tau_0, tau_deg, nB_0\n", - ") # calculate tau for the first timestep" - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " Datetime Temperature Injection NA NB NC tau\n", - "0 1999-01-01 00:30:00-07:00 0.0 NaN 100.0 0.0 0.0 120.0\n", - "1 1999-01-01 01:30:00-07:00 0.0 NaN NaN NaN NaN NaN\n", - "2 1999-01-01 02:30:00-07:00 0.0 NaN NaN NaN NaN NaN\n", - "3 1999-01-01 03:30:00-07:00 0.0 NaN NaN NaN NaN NaN\n", - "4 1999-01-01 04:30:00-07:00 0.0 NaN NaN NaN NaN NaN\n", - "... ... ... ... ... ... ... ...\n", - "8755 1999-12-31 19:30:00-07:00 0.0 NaN NaN NaN NaN NaN\n", - "8756 1999-12-31 20:30:00-07:00 0.0 NaN NaN NaN NaN NaN\n", - "8757 1999-12-31 21:30:00-07:00 0.0 NaN NaN NaN NaN NaN\n", - "8758 1999-12-31 22:30:00-07:00 0.0 NaN NaN NaN NaN NaN\n", - "8759 1999-12-31 23:30:00-07:00 0.0 NaN NaN NaN NaN NaN\n", - "\n", - "[8760 rows x 7 columns]" - ] - }, - "execution_count": 61, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "timesteps" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Run through timesteps\n", - "Since each timestep depends on the preceding timestep, we need to calculate in a loop. This will take a few minutes depending on the length of the timeseries." - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "metadata": {}, - "outputs": [], - "source": [ - "for index, timestep in timesteps.iterrows():\n", - "\n", - " # first row tau has already been assigned\n", - " if index == 0:\n", - " pass\n", - "\n", - " # loop through rows, new tau calculated based on previous NB. Reaction proceeds based on new tau.\n", - " else:\n", - " n_A = timesteps.at[index - 1, \"NA\"]\n", - " n_B = timesteps.at[index - 1, \"NB\"]\n", - " n_C = timesteps.at[index - 1, \"NC\"]\n", - "\n", - " tau = letid.tau_now(tau_0, tau_deg, n_B)\n", - " jsc = collection.calculate_jsc_from_tau_cp(\n", - " tau, wafer_thickness, d_base, srv_rear, generation, depth\n", - " )\n", - "\n", - " temperature = timesteps.at[index, \"Temperature\"]\n", - " injection = timesteps.at[index, \"Injection\"]\n", - "\n", - " # calculate defect reaction kinetics: reaction constant and carrier concentration factor.\n", - " k_AB = letid.k_ij(\n", - " mechanism_params[\"v_ab\"], mechanism_params[\"ea_ab\"], temperature\n", - " )\n", - " k_BA = letid.k_ij(\n", - " mechanism_params[\"v_ba\"], mechanism_params[\"ea_ba\"], temperature\n", - " )\n", - " k_BC = letid.k_ij(\n", - " mechanism_params[\"v_bc\"], mechanism_params[\"ea_bc\"], temperature\n", - " )\n", - " k_CB = letid.k_ij(\n", - " mechanism_params[\"v_cb\"], mechanism_params[\"ea_cb\"], temperature\n", - " )\n", - "\n", - " x_ab = letid.carrier_factor(\n", - " tau,\n", - " \"ab\",\n", - " temperature,\n", - " injection,\n", - " jsc,\n", - " wafer_thickness,\n", - " srv_rear,\n", - " mechanism_params,\n", - " )\n", - " x_ba = letid.carrier_factor(\n", - " tau,\n", - " \"ba\",\n", - " temperature,\n", - " injection,\n", - " jsc,\n", - " wafer_thickness,\n", - " srv_rear,\n", - " mechanism_params,\n", - " )\n", - " x_bc = letid.carrier_factor(\n", - " tau,\n", - " \"bc\",\n", - " temperature,\n", - " injection,\n", - " jsc,\n", - " wafer_thickness,\n", - " srv_rear,\n", - " mechanism_params,\n", - " )\n", - "\n", - " # calculate the instantaneous change in NA, NB, and NC\n", - " dN_Adt = (k_BA * n_B * x_ba) - (k_AB * n_A * x_ab)\n", - " dN_Bdt = (\n", - " (k_AB * n_A * x_ab) + (k_CB * n_C) - ((k_BA * x_ba + k_BC * x_bc) * n_B)\n", - " )\n", - " dN_Cdt = (k_BC * n_B * x_bc) - (k_CB * n_C)\n", - "\n", - " t_step = (\n", - " timesteps.at[index, \"Datetime\"] - timesteps.at[index - 1, \"Datetime\"]\n", - " ).total_seconds()\n", - "\n", - " # assign new defect state percentages\n", - " timesteps.at[index, \"NA\"] = n_A + dN_Adt * t_step\n", - " timesteps.at[index, \"NB\"] = n_B + dN_Bdt * t_step\n", - " timesteps.at[index, \"NC\"] = n_C + dN_Cdt * t_step" - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "metadata": {}, - "outputs": [], - "source": [ - "timesteps[\"tau\"] = letid.tau_now(tau_0, tau_deg, timesteps[\"NB\"])\n", - "\n", - "# calculate device Jsc for every timestep. Unfortunately this requires an integration so I think we have to run through a loop. Device Jsc allows calculation of device Voc.\n", - "for index, timestep in timesteps.iterrows():\n", - " jsc_now = collection.calculate_jsc_from_tau_cp(\n", - " timesteps.at[index, \"tau\"], wafer_thickness, d_base, srv_rear, generation, depth\n", - " )\n", - " timesteps.at[index, \"Jsc\"] = jsc_now\n", - " timesteps.at[index, \"Voc\"] = letid.calc_voc_from_tau(\n", - " timesteps.at[index, \"tau\"], wafer_thickness, srv_rear, jsc_now, temperature=25\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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8760 rows Γ— 13 columns

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" - ], - "text/plain": [ - " Datetime Temperature Injection NA \\\n", - "0 1999-01-01 00:30:00-07:00 0.0 NaN 100.000000 \n", - "1 1999-01-01 01:30:00-07:00 0.0 NaN 100.000000 \n", - "2 1999-01-01 02:30:00-07:00 0.0 NaN 100.000000 \n", - "3 1999-01-01 03:30:00-07:00 0.0 NaN 100.000000 \n", - "4 1999-01-01 04:30:00-07:00 0.0 NaN 100.000000 \n", - "... ... ... ... ... \n", - "8755 1999-12-31 19:30:00-07:00 0.0 NaN 30.470917 \n", - "8756 1999-12-31 20:30:00-07:00 0.0 NaN 30.470917 \n", - "8757 1999-12-31 21:30:00-07:00 0.0 NaN 30.470917 \n", - "8758 1999-12-31 22:30:00-07:00 0.0 NaN 30.470917 \n", - "8759 1999-12-31 23:30:00-07:00 0.0 NaN 30.470917 \n", - "\n", - " NB NC tau Jsc Voc Isc \\\n", - "0 0.000000e+00 0.000000e+00 120.000000 41.451328 0.661315 9.981480 \n", - "1 1.683090e-15 0.000000e+00 120.000000 41.451328 0.661315 9.981480 \n", - "2 3.366181e-15 5.269266e-36 120.000000 41.451328 0.661315 9.981480 \n", - "3 5.049271e-15 1.580780e-35 120.000000 41.451328 0.661315 9.981480 \n", - "4 6.732362e-15 3.161560e-35 120.000000 41.451328 0.661315 9.981480 \n", - "... ... ... ... ... ... ... \n", - "8755 6.606591e+01 3.463173e+00 90.203213 41.343585 0.656761 9.955535 \n", - "8756 6.606591e+01 3.463173e+00 90.203213 41.343585 0.656761 9.955535 \n", - "8757 6.606591e+01 3.463173e+00 90.203213 41.343585 0.656761 9.955535 \n", - "8758 6.606591e+01 3.463173e+00 90.203213 41.343585 0.656761 9.955535 \n", - "8759 6.606591e+01 3.463173e+00 90.203213 41.343585 0.656761 9.955535 \n", - "\n", - " ... p_mp i_x i_xx Effective irradiance ghi dhi dni wind_speed \\\n", - "0 ... 0.0 0.0 0.0 NaN 0.0 0.0 0.0 1.8 \n", - "1 ... 0.0 0.0 0.0 NaN 0.0 0.0 0.0 1.7 \n", - "2 ... 0.0 0.0 0.0 NaN 0.0 0.0 0.0 1.5 \n", - "3 ... 0.0 0.0 0.0 NaN 0.0 0.0 0.0 1.3 \n", - "4 ... 0.0 0.0 0.0 NaN 0.0 0.0 0.0 1.3 \n", - "... ... ... ... ... ... ... ... ... ... \n", - "8755 ... 0.0 0.0 0.0 NaN 0.0 0.0 0.0 0.9 \n", - "8756 ... 0.0 0.0 0.0 NaN 0.0 0.0 0.0 1.2 \n", - "8757 ... 0.0 0.0 0.0 NaN 0.0 0.0 0.0 1.6 \n", - "8758 ... 0.0 0.0 0.0 NaN 0.0 0.0 0.0 1.7 \n", - "8759 ... 0.0 0.0 0.0 NaN 0.0 0.0 0.0 1.8 \n", - "\n", - " temp_air precipitable_water \n", - "0 0.0 0.891869 \n", - "1 0.0 0.908158 \n", - "2 0.0 0.932199 \n", - "3 0.0 0.955004 \n", - "4 0.0 0.963991 \n", - "... ... ... \n", - "8755 0.0 0.939501 \n", - "8756 0.0 0.975338 \n", - "8757 0.0 0.941186 \n", - "8758 0.0 0.912427 \n", - "8759 0.0 0.892318 \n", - "\n", - "[8760 rows x 27 columns]" - ] - }, - "execution_count": 65, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "timesteps = timesteps.merge(mc.results.dc, left_on=\"Datetime\", right_index=True)\n", - "timesteps = timesteps.merge(\n", - " pd.DataFrame(mc.results.effective_irradiance, columns=[\"Effective irradiance\"]),\n", - " left_on=\"Datetime\",\n", - " right_index=True,\n", - ")\n", - "timesteps = timesteps.merge(mc.results.weather, left_on=\"Datetime\", right_index=True)\n", - "\n", - "timesteps" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Plot the results" - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from cycler import cycler\n", - "\n", - "plt.style.use(\"default\")\n", - "\n", - "fig, ax = plt.subplots()\n", - "\n", - "ax.set_prop_cycle(\n", - " cycler(\"color\", [\"tab:blue\", \"tab:orange\", \"tab:green\"])\n", - " + cycler(\"linestyle\", [\"-\", \"--\", \"-.\"])\n", - ")\n", - "\n", - "ax.plot(timesteps[\"Datetime\"], timesteps[[\"NA\", \"NB\", \"NC\"]].values)\n", - "ax.legend(labels=[\"$N_A$\", \"$N_B$\", \"$N_C$\"], loc=\"upper left\")\n", - "ax.set_ylabel(\"Defect state percentages [%]\")\n", - "ax.set_xlabel(\"Datetime\")\n", - "\n", - "ax2 = ax.twinx()\n", - "ax2.plot(\n", - " timesteps[\"Datetime\"],\n", - " timesteps[\"Pmp_norm\"],\n", - " c=\"black\",\n", - " label=\"Normalized STC $P_{MP}$\",\n", - ")\n", - "ax2.legend(loc=\"upper right\")\n", - "ax2.set_ylabel(\"Normalized STC $P_{MP}$\")\n", - "\n", - "ax.set_title(\n", - " \"Simulated Outdoor LETID progression based on accelerated test results \\n\"\n", - " f\"{location.name}\"\n", - ")\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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XL1/G5cuXxX319PRgYGAAAwMDmJqaokqVKrCxscEXX3yB2rVrq+uSiIiINI5GJVLXr1/HF198IX7+MKB70KBB2LFjB16+fInnz5+L26tUqYLff/8dkyZNwpo1a1CxYkX8+OOPcHd3F+v4+Pjg1atXmD17NmJjY+Hi4oKAgACFAeirVq2Cjo4OvL29kZGRAXd3d/zwww/FcMXqIZFI0KZNG7x8+RJz587FlStX8P79e0RGRiI5ORnZ2dnIzs7G+/fvkZiYiBcvXgAAfvnlF0yePBmdOnVS8xUQERFpBo2dR0qbqGIeCk0gCAKSk5Px/PlzZGRkIC0tDVFRUbh27RpWrVol1qtWrRqqVKmCQYMGwd7eXo0RExFRafBhsLmHh4dKn9pTxd9vJlIqUFISqU+5fv16rkHp5cuXx4wZM+Dg4IAyZcqoKTIiIirpNDmR0qhbe6S5GjdujPj4eBw4cADHjx/HqVOnEB8fj2+++QYA0LBhQ9jY2GDo0KEoV66cmqMlIiIqHuyRUoHS0CP1TxkZGejbty9u376t8HAAkPMU5Pfffw8DAwM1RUdERCWNJvdIafX0B6QehoaGOHLkCB4/fozExETs2rULjRs3BpCz5I67uzvGjx+fa/4uIiKikoaJFBWKhYUFBgwYgMDAQIXpIu7cuYM+ffrg5cuXaoyOiIioaDGRIpWwsrJCSEgIXr16hWHDhonl/fr1w+PHj9UYGRERUdFhIkUqVa5cOWzduhX79+8Xy8aNG4eIiAg1RkVERFQ0mEhRkfDx8cGGDRsAAJmZmRgzZgwSExPVGxQREZGKcfoDKjKjR49Gamoqpk6divT0dHTv3h1mZmYwMDCAh4cH+vbtCxMTE3WHSUREpDT2SFGRkUgkmDJlCpYvXy4+VpqcnIw3b95g79692LZtm5ojJCIiKhwmUlTkvv32W7x58wanT59GQEAAHBwcAADR0dFqjoyIiKhwmEhRsdDT08OXX34Jd3d3LF26FAAQHh4OzgdLRETajGOkqNh96JH6MFXCwIEDYWBgAH19fRgaGsLZ2VmlM9cSEREVFSZSVOwaN26Mxo0b4/r16wgPD8fcuXNz1bGxsUHTpk3RtWtXODk5FX+QRERE+cC19lSgtK21pyr+/v74+eefkZGRgaysLGRmZiIqKgpZWVm56lpYWKB+/fqYN28eJBKJGqIlIiJ10eS19phIqQATKdXJzs7G1atXsX//fhw4cADx8fEK2xs1aoTBgwejTp06aoqQiIiKmyYnUhxsThpFT08PzZs3x9q1axEbG4tr167h2LFjqFixIoCcRZHHjRuHS5cuqTlSIiIiJlKkwSQSCRo3boyuXbsiNDQU3333nbht27ZtyM7OVmN0RERETKRIS1hbW2PRokXYvXs3AODp06dYs2aNmqMiIqLSjokUaZVu3bqhfv36AIDffvsNI0eORGRkpJqjIiKi0oqJFGkVU1NThIaGisnUo0eP8PXXX3OWdCIiUgsmUqR1dHV1cePGDSxYsEAs69+/P7Zv345z584hMTFRfcEREVGpwgk5SSvp6elh5syZsLOzw9ChQwEAu3btAgBUq1YNW7duVWd4RERUSrBHirTakCFD8OLFC3h7e6NRo0bQ0dHBkydP4OHhgaCgIAQHByMzM1PdYRIRUQnFCTlVgBNyao4+ffrgwIEDCmU6OjpYunQpGjdurKaoiIioMDghJ1Ex2b9/P9avX4+GDRuiatWqAAC5XI4pU6bg6tWrnHuKiIhUiokUlThjx45FaGgonj59isOHD4vl06ZNQ+/evXMtO0NERKQsJlJUovXo0QMnT55Ew4YNAQBv376Fj48PIiIi1BwZERGVBEykqMRr3749QkNDsXjxYrFs2rRpCAsLw9WrV5lUERGR0jj9AZUa06ZNw9u3b7Fs2TK8evUKvr6+4rYxY8bA29sbOjr8bkFERPnHRIpKDYlEgrlz5yI6OhqXL19GYmIi3rx5AwD44YcfYGNjg9atW6s5SiIi0ib8+k2lipGREfbs2YMnT54gNjZWYZ2+u3fvqjEyIiLSRkykqNTS09NDpUqVMHPmTADg0jJERFRgTKSo1CtfvjwA4OTJk3j+/LmaoyEiIm3CRIpKvcqVK4vvBw0ahIMHD6ovGCIi0ipMpKjU8/T0xNy5c8XPGzduxNGjR8HVk4iI6L8wkaJST09PD3PmzEFcXJw4/cHatWvh6+uLCxcucFkZIiL6KCZSRP+vfPnyCA0NRaVKlQAAN2/exKxZs7BlyxY1R0ZERJqKiRTRPzRo0AB3797FnDlzxEHohw4dwvnz59UcGRERaSImUkT/Ympqirlz5+LJkydi2aZNmzhmioiIcmEiRfQRZmZmOH36NAAgJiYGGRkZao6IiIg0DRMpok9o06YN9PRyVlIaMGAAZDKZmiMiIiJNwkSK6BN0dXUxePBgAMDr169x4MABNUdERESahIkU0X/YsmUL2rRpAwDYunUrZs+ejZSUFDVHRUREmoCJFFE+LF26VJwW4fz589i/f7+aIyIiIk3ARIooH1xdXfHkyRNxOZl/PtFHRESlFxMponzS19fHmjVrAABXrlxhrxQRETGRIiqIZs2aicvIbN68GZGRkWqOiIiI1ImJFFEBWFtbIzExUfw8btw4yOVy9QVERERqpXGJ1IYNG1C5cmVIpVK4urri6tWrH62blZWF+fPnw8nJCVKpFA0aNEBAQIBCneTkZEycOBGOjo4wMjJC8+bNce3aNYU6KSkpGDduHCpWrAgjIyPUrl0bmzZtKpLrI+1nZmaGnTt3Asj52Xnz5o2aIyIiInXRqETqwIED8PX1xZw5cxAaGooGDRrA3d0d8fHxedafOXMmNm/ejHXr1uHevXsYNWoUunfvjrCwMLHOsGHDEBQUhN27d+P27dvo0KED3NzcEB0dLdbx9fVFQEAA9uzZg/v372PixIkYN24cfv311yK/ZtJOAwcOFNfie/HihZqjISIidZEIGrSAmKurK5o0aYL169cDAORyORwcHDB+/HhMnz49V307OzvMmDEDY8eOFcu8vb1hZGSEPXv2IC0tDWZmZjh+/Di8vLzEOo0aNYKnpycWLlwIAKhbty58fHwwa9asj9b5lHfv3sHCwgJJSUkwNzdX+vpJuzRs2BA3b94EABw9ehSWlpZqjYeIqKR6//49ZDIZPDw8oK+vr7LjquLvt8b0SGVmZuLGjRtwc3MTy3R0dODm5oaQkJA898nIyIBUKlUoMzIywoULFwAA2dnZkMlkn6wDAM2bN8evv/6K6OhoCIKAM2fO4NGjR+jQocNHz/vu3TuFF5U+/fv3F98fOnSIY6WIiEohjUmkXr9+DZlMBhsbG4VyGxsbxMbG5rmPu7s7/P398fjxY8jlcgQFBeHIkSN4+fIlgJyxLM2aNcOCBQsQExMDmUyGPXv2ICQkRKwDAOvWrUPt2rVRsWJFGBgYwMPDAxs2bEDr1q3zPO/ixYthYWEhvhwcHFTUCqRNJk+ejJ49ewIA9u3bh06dOmHTpk2c9ZyIqBTRmERKGWvWrEH16tXh7OwMAwMDjBs3DoMHDxYfTweA3bt3QxAE2Nvbw9DQEGvXrkXfvn0V6qxbtw6XL1/Gr7/+ihs3bmDlypUYO3YsTp06led5/fz8kJSUJL6ioqKK/FpJM02YMEFM/tPS0nDgwAF88803ao6KiIiKi8YkUuXKlYOuri7i4uIUyuPi4mBra5vnPtbW1jh27BhSU1MRGRmJBw8ewNTUFFWrVhXrODk5ITg4GCkpKYiKisLVq1eRlZUl1klLS8N3330Hf39/dO7cGfXr18e4cePg4+ODFStW5HleQ0NDmJubK7yodGrVqhViY2Nx/fp11K5dGwAQHh6OtLQ0NUdGRETFQWMSKQMDAzRq1AinT58Wy+RyOU6fPo1mzZp9cl+pVAp7e3tkZ2fj8OHD6Nq1a646JiYmqFChAt6+fYvAwECxTlZWFrKyshR6qABAV1eXY14o3xo1aoTbt29DV1cXADglAhFRKaExiRSQMw3B1q1bsXPnTty/fx+jR49GamoqBg8eDCDnkXM/Pz+x/pUrV3DkyBGEh4fj/Pnz8PDwgFwux9SpU8U6gYGBCAgIQEREBIKCgvDFF1/A2dlZPKa5uTnatGmDKVOm4OzZs4iIiMCOHTuwa9cudO/evXgbgLSajo4OrK2tAeRM5UFERCWfnroD+CcfHx+8evUKs2fPRmxsLFxcXBAQECCOQXn+/LlCz1F6ejpmzpyJ8PBwmJqaomPHjti9e7fCY+hJSUnw8/PDixcvYGVlBW9vbyxatEjh8cn9+/fDz88PX331FRISEuDo6IhFixZh1KhRxXbtVDJUqVIFsbGx+O2339ClSxdUr15d3SEREVER0qh5pLQV55GiDx48eIBatWoByBlLd+DAAVhYWKg5KiIi7abJ80hpVI8UkbZzdnbGrFmzsGDBAmRkZGDWrFnw9vbGo0ePEB8fD0NDQ1haWqJTp04ffYiCiIi0B3ukVIA9UvRPaWlp8PT0RHBw8CfrrV+/HnXq1CmmqIiItBd7pIhKESMjI/zyyy/w9vZGTEwMdHR0UK1aNVSvXh1RUVE4cuQIAGDlypXYtm2bmqMlIqLCYCJFVATKlSv30R6p1atXY9KkSYiOjoZMJhOnTCAiIu2jUdMfEJUGY8eOhY6ODjIzMzFp0iTw7joRkfZiIkVUzPT19cUFsW/fvo2YmBg1R0RERMpiIkWkBn/88Yf41F7//v2xd+9eREdHIysrS82RERFRQTCRIlIDiUSCoUOHip9//PFH9O/fH126dMHJkyfVGBkRERUEEykiNVm4cCGuXLkCNzc3ccb+9PR0LF68GPHx8WqOjoiI8oOJFJEaNW3aFEFBQcjIyMD58+fFch8fHy58TESkBZhIEWkAPT09tGzZEmvWrBHLbt++rcaIiIgoP5hIEWmQCRMmwMvLCwBw7949sVwmk+HOnTsIDw9XV2hERJQHTshJpGHs7OwAAIcOHUJ0dDSMjIxw6dIlpKWlAchZDLlq1arw8vKCh4cHJ/QkIlIjrrWnAlxrj1TpwoULaNWqVb7rr127FvXq1SvCiIiI1Itr7RFRvrVs2RLx8fHo168fXr9+jZo1a6Jdu3bo2LEjpFIpLl++jPXr1yMgIABAzu1AHR0dSKVSpKenQ1dXFxKJBDo6OqhduzbGjh2LqlWrqvmqiIhKJvZIqQB7pEgdDh48iG+++QaxsbGfrFe7dm1s2LChmKIiIlI99kgRkcr17t0bvXr1wsWLF5GcnAxLS0uYmpoiOzsbOjo6WLJkCfbv34979+4hOTkZZmZmAABBEJCVlQV9fX1IJBI1XwURkXZjj5QKsEeKNFFiYiLKlCkjfjYzM0NmZiYyMzMhCAKkUimWLVvG8VVEpPE0uUeK0x8QlVCWlpaYMWOG2BOVnJyMjIwMfPjulJ6ejgkTJmDIkCGIiIhQZ6hERFqLPVIqwB4p0mRyuRwhISGQy+UwMTGBsbExgoODMXXqVLx7906sZ29vj0mTJqFRo0ZqjJaIKDdN7pFiIqUCTKRIG2VlZWHbtm2YMGECMjMzxfJmzZphwYIFnJ+KiDSGJidSvLVHVErp6+tj5MiRSElJwQ8//ACpVAoACAkJwf3799UcHRGRdmAiRVTK6evrY/To0Xj79i2srKwAAOPHj0dKSoqaIyMi0nxMpIgIACCVSjFu3Djx86FDh9QYDRGRdmAiRUSiOXPmwN3dHQCwa9cuHD16VM0RERFpNg42VwEONqeSJDw8HE5OTuLnOnXqwMjICJUrV8aIESNUOtCTiCg/NHmwOWc2JyIFVatWRUREBKpXr47s7GzcvXsXAHD9+nXUqFED7du3V3OERESag7f2iCiXypUr4/bt29i+fTvWrVsHY2NjAMD333+P8PBwNUdHRKQ52CNFRHlydnaGs7MzAMDOzg7e3t4AgMOHD2PKlCnqDI2ISGOwR4qI/lOPHj3E5OmPP/5AQEAAOLySiIiJFBHl05AhQ8T3S5cuha+vL5MpIir1mEgRUb44Ozvjzp07qFKlCgDg5s2buHTpksLyMkREpQ0TKSLKtzp16iA8PBzVqlUDAMycORNeXl64ceOGmiMjIlIPJlJEVGBLly5F1apVAQDZ2dlYsGAB0tLS1BwVEVHxK1QiderUKbRo0UJcLT4rK0tVcRGRBuvRoweePn2KvXv3AgCSkpKwceNGNUdFRFT8CpVIjR07FlOnTsW6devw7NkzzJs3T1VxEZEW8Pb2RoUKFQAADx48UHM0RETFr1CJlLGxMbp27YrGjRtjy5YtOHXqlKriIiItYGhoiIMHDwLIWWqBiKi0KVQi9erVKxw6dAi3bt1CdnY2n94hKoVsbGwAAHFxcUhISFBzNERExatQiZSvry8CAgIwfPhwlC9fHvfu3UPv3r2xYMECHDt2TEUhEpEms7W1Fd+PGDECKSkpaoyGiKh4FSqRGjp0KH766SdcuXIFSUlJuH//PgYMGAA9PT0cOnRIVTESkQYzMzMTZz1/8+YNOnfujGXLliE1NVXNkRERFb1CrbVXpkwZODg4oE6dOqhbty7q1auHunXrokOHDjA0NFRVjESk4ZYtWwZra2tMnToVAPDnn3/CyclJXJ+PiKikKlSP1OjRo1GuXDm0aNECNWvWxI0bNzBlyhRUqVJFXOyUiEqHKVOm4O3bt7CzswMA3L17V80REREVvUIlUhs2bMCxY8cQGRmJ/fv3o0ePHjh16hRiYmJw/vx5VcVIRFrC0tJS7JU6c+YMx0oSUYlX6JnNHRwcsGXLFmzZsgU7d+6Ep6cn7t27B2tra1XER0RapmvXruL7X375BTKZTI3REBEVrUKNkbp//z4ePnyIhw8f4v79+3j69ClSU1Nx9+5d1K5dW1UxEpEWqVy5MoKCgtC+fXtER0ejY8eO+Oyzz9C9e3eUK1cOVapUgUQiUXeYREQqIREEQVB2Zx0dHdSvXx+9e/dGp06dUKtWLejr66syPq3w7t07WFhYICkpCebm5uoOh0jt0tPT0bFjR5w5cybXtjFjxqBXr15qiIqItNX79+8hk8ng4eGh0jxDFX+/C3Vrb8WKFWjUqBGOHz+O9u3bo3nz5hg8eDBWrlyJwMDAwhyaiLSYVCrFX3/9hXfv3mH+/PmoW7euuI3jJ4moJClUj9S/RURE4M6dO7hz5w7u3r2LPXv2qOrQGo09UkT/LTQ0FI0aNQIAtG7dGm5ubmjVqpWaoyIibaDJPVIFTqQiIyNx69Yt2NjYoGnTpkqdtKRhIkX03zIzM1G5cmW8fPkSAKCrq4vDhw/DwsJCzZERkabT5ESqQLf2fv75Z9SoUQNdu3ZFs2bN0LhxY7x69UqpExNR6WJgYIA7d+5g06ZNAACZTIZnz56pNygiokIqUCI1b9489OvXDw8ePMDJkycBANOnT1dpQBs2bEDlypUhlUrh6uqKq1evfrRuVlYW5s+fDycnJ0ilUjRo0AABAQEKdZKTkzFx4kQ4OjrCyMgIzZs3x7Vr13Id6/79++jSpQssLCxgYmKCJk2a4Pnz5yq9NqLSzsrKCiNHjkSzZs0AAJMnT0ZiYqJ6gyIiKoQCJVLh4eGYM2cOatSogXbt2mHPnj3Yv3+/yoI5cOAAfH19MWfOHISGhqJBgwZwd3dHfHx8nvVnzpyJzZs3Y926dbh37x5GjRqF7t27IywsTKwzbNgwBAUFYffu3bh9+zY6dOgANzc3REdHi3WePn2Kli1bwtnZGWfPnsWtW7cwa9YsSKVSlV0bEf2Pq6srgJxeqe7du+PixYtqjoiISDkFGiOlo6OD2NhYlC9fXiwzMjJCRESEwgrwynJ1dUWTJk2wfv16AIBcLoeDgwPGjx+fZ8+XnZ0dZsyYgbFjx4pl3t7eMDIywp49e5CWlgYzMzMcP34cXl5eYp1GjRrB09MTCxcuBAD06dMH+vr62L17t1Jxc4wUUcHIZDKsWrVKXOzYzs4OO3fuhJ5eoaa2I6ISqsSMkQKAnTt34tKlS0hJSQEA6Onp4f3790qd/J8yMzNx48YNuLm5/S84HR24ubkhJCQkz30yMjJy9RoZGRnhwoULAIDs7GzIZLJP1pHL5fj9999Ro0YNuLu7o3z58nB1df3k0hYZGRl49+6dwouI8k9XVxfffvuteCs+JiYGc+fOVW9QRERKKFAi1apVKyxcuBAtW7aEpaUlqlevjvT0dPz00084c+YMkpOTlQ7k9evXkMlksLGxUSi3sbFBbGxsnvu4u7vD398fjx8/hlwuR1BQEI4cOSI+FWRmZoZmzZphwYIFiImJgUwmw549exASEiLWiY+PR0pKCpYsWQIPDw+cPHkS3bt3R48ePRAcHJzneRcvXgwLCwvx5eDgoPR1E5Vmbdu2Rbt27QAAFy9exLRp09QcERFRwRQokQoODkZSUhIePnyIPXv2oHv37mjTpg02btyIdu3aoUyZMqhVq1ZRxZrLmjVrUL16dTg7O8PAwADjxo3D4MGDoaPzv8vavXs3BEGAvb09DA0NsXbtWvTt21esI5fLAeSsDzZp0iS4uLhg+vTp6NSpk/h00b/5+fkhKSlJfEVFRRX9xRKVQIaGhjh16hT69u0LALh69Sp7eIlIqyg1s3n16tXRp08fLFu2DKdOnUJCQgKePn2Kn3/+Gd27d1cqkHLlykFXVxdxcXEK5XFxcR8df2VtbY1jx44hNTUVkZGRePDgAUxNTVG1alWxjpOTE4KDg5GSkoKoqChcvXoVWVlZYp1y5cpBT08v19qAtWrV+uhTe4aGhjA3N1d4EZHy9u3bB0NDQwDA6dOn1RwNEVH+FWqJmH+qUqUKevXqhe+//16p/Q0MDNCoUSOFX6JyuRynT58WH5X+GKlUCnt7e2RnZ+Pw4cMKq89/YGJiggoVKuDt27cIDAwU6xgYGKBJkyZ4+PChQv1Hjx7B0dFRqWshooL7sIzM2rVrMWvWLCQlJak5IiKi/6ZRj8j4+vpi0KBBaNy4MZo2bYrVq1cjNTUVgwcPBgAMHDgQ9vb2WLx4MQDgypUriI6OhouLC6KjozF37lzI5XJMnTpVPGZgYCAEQUDNmjXx5MkTTJkyBc7OzuIxAWDKlCnw8fFB69at8cUXXyAgIAAnTpzA2bNni/X6iUozf39/dO7cGe/evcOFCxdQpUoVDBkyRN1hERF9kkYlUj4+Pnj16hVmz56N2NhYuLi4ICAgQByA/vz5c4XxT+np6Zg5cybCw8NhamqKjh07Yvfu3bC0tBTrJCUlwc/PDy9evICVlRW8vb2xaNEihccnu3fvjk2bNmHx4sWYMGECatasicOHD6Nly5bFdu1EpV3r1q0RExODli1b4ubNm9i9ezfat2/PhzmISKOpdNHi0orzSBGpzoULF8TFjF1dXbFkyRI1R0RE6lai5pEiIipKLVq0wIgRIwDk3L6PiIgAv+8RkaYqUCL1119/oXbt2nk+npyUlIQ6derg/PnzKguOiEofiUSCBQsWQCKRAACGDBmCPn36cPA5EWmkAiVSq1evxvDhw/Ps/rKwsMDIkSPh7++vsuCIqHQqX748fvzxR3Feuvj4eH5JIyKNVKBE6u+//4aHh8dHt3fo0AE3btwodFBEREOGDMG9e/fEyTpXrlyZa5oSIiJ1K1AiFRcX98lBXnp6enj16lWhgyIi+mDUqFHi+4sXL6oxEiKi3AqUSNnb2+POnTsf3X7r1i1UqFCh0EEREX3QunVrTJkyBQBw6tQpZGZmqjkiIqL/KVAi1bFjR8yaNQvp6em5tqWlpWHOnDno1KmTyoIjIgKAatWqAQBevnyJbdu2qTkaIqL/KdA8UnFxcfjss8+gq6uLcePGoWbNmgCABw8eYMOGDZDJZAgNDRUn0CwtOI8UUdFKSEhArVq1EB8fj1q1auGHH35Qd0hEVIw0eR6pAs1sbmNjg0uXLmH06NHw8/MT53aRSCRwd3fHhg0bSl0SRURFz8rKCr/99huaNm2K+/fv4/Xr1yhXrpy6wyIiKtitvfnz58Pa2hp//PEHXr9+jStXruDy5ct4/fo1/vjjD1SpUqWo4iSiUq569eri3FK9evVSWOCciEhdCnRrT1dXFy9fvkT58uWLMiatw1t7RMVj3bp1mDBhgvjZ0dERK1asYO8UUQmnybf2CtQjxWUaiEidxo8fj/Pnz6NixYoAgMjISAwfPhyBgYG4evUqEhIS1BwhEZU2BRojBUDsWiciUoeWLVsiKioKvr6+WLVqFRITE8WFjfX09NCjRw+YmZlBKpWiTZs2sLa2VnPERFSSFejWno6ODiwsLP4zmSpt3wp5a4+o+GVmZmLFihU4f/48Xr58ib///jvPeu3bt8d3331XzNERkSpp8q29AvdIzZs3DxYWFkqdjIhIVQwMDBQSpKioKHz//fdITExEUlISTp06haysLAQFBWHkyJEoW7asGqMlopKqwD1SsbGxHGz+L+yRItI8mZmZsLS0RFpaGipUqIBdu3ZBT6/A3x2JSANoco9UgQabc3wUEWkLAwMD+Pj4AMiZEZ0LqhNRUeBTe0RUYm3evFmcGuH333+HTCZTc0REVNIUKJGSy+W8rUdEWsPAwABDhgwBAJw/fx5BQUFqjoiISpoCJVIhISH47bffFMp27dqFKlWqoHz58hgxYgQyMjJUGiARUWFMnDhRfH/+/Hn1BUJEJVKBEql58+bh7t274ufbt29j6NChcHNzw/Tp03HixAksXrxY5UESESmrQoUK2LRpEwDg0qVLGDNmDF68eKHmqIiopChQIvX333+jXbt24uf9+/fD1dUVW7duha+vL9auXYuDBw+qPEgiosLw9vZGhQoVAAD379/H1KlTOeaTiFSiQInU27dvYWNjI34ODg6Gp6en+LlJkyaIiopSXXRERCpQrlw5REdHY+rUqQBynuK7d++emqMiopKgQImUjY0NIiIiAOTM0RIaGorPP/9c3J6cnKzS+R2IiFRFIpFg6dKlqFy5MgBg3LhxSEpKUm9QRKT1CpRIdezYEdOnT8f58+fh5+cHY2NjtGrVStx+69YtODk5qTxIIiJVWblypfj+wYMHaoyEiEqCAiVSCxYsgJ6eHtq0aYOtW7di69atMDAwELdv27YNHTp0UHmQRESq0qNHDzRv3hxA6VsXlIhUr0DrJZQrVw7nzp1DUlISTE1Noaurq7D90KFDMDU1VWmARESq9mE+PN7aI6LCKlCP1AcWFha5kigAsLKyUuihIiLSRNbW1gCAK1euqDkSItJ2SiVSRETazM7ODgBw8+ZNnDt3Ts3REJE2YyJFRKXOyJEjxfePHj1SYyREpO2YSBFRqVOhQgVMmjQJQM60LUREymIiRUSlkqWlJQDg/fv36g2EiLRavp/a8/X1zfdB/f39lQqGiKi4fEikUlNT1RsIEWm1fCdSYWFhCp9DQ0ORnZ2NmjVrAsgZZ6Crq4tGjRqpNkIioiLwIZEKCQnB7du3Ua9ePfUGRERaKd+J1JkzZ8T3/v7+MDMzw86dO1GmTBkAOevwDR48WGGmcyIiTVW3bl3x/erVq/Hjjz9CIpGoMSIi0kZKjZFauXIlFi9eLCZRAFCmTBksXLhQYfkFIiJN9dlnn+HQoUMAgPDwcCxdulTNERGRNlIqkXr37h1evXqVq/zVq1d8AoaItEaPHj1Qq1YtAMDFixchCIKaIyIibaNUItW9e3cMHjwYR44cwYsXL/DixQscPnwYQ4cORY8ePVQdIxFRkdDR0REn5ExJSUF6erqaIyIibVOgtfY+2LRpE7799lv069cPWVlZOQfS08PQoUOxfPlylQZIRFSUrKysoKOjA7lcjsTERBgZGak7JCLSIkolUsbGxvjhhx+wfPlyPH36FADg5OQEExMTlQZHRFTUdHR0UK5cOcTHxyMxMREVKlRQd0hEpEUKNSGniYkJ6tevj/r16zOJIiKtVb58eQDAjh071BsIEWkdpROp8+fPo3///mjWrBmio6MBALt378aFCxdUFhwRUXFwcnICAFy9elXsZSciyg+lEqnDhw/D3d0dRkZGCAsLQ0ZGBgAgKSkJ33//vUoDJCIqaps2bRLfP3nyRI2REJG2USqRWrhwITZt2oStW7dCX19fLG/RogVCQ0NVFhwRUXGwtbVFv379AABLlixBfHy8miMq3SZOnIj169erOwyifFFqsPnDhw/RunXrXOUWFhZITEwsbExERMXO09MT+/btAwCcO3cOPXv2VHNE+bNkyRIEBgbmKm/SpAmWLVumhojo6NGjOHDgABISEuDk5IQJEyaI85XlJSAgINeEsPr6+jh58qT4OSEhAVu2bMH169eRkpKC+vXrY8KECahYsaJYZ8OGDQgMDIRUKsXw4cPRvn17cdvZs2dx8uRJ3jUqAkolUra2tnjy5AkqV66sUH7hwgVUrVpVFXERERWr/v374+DBgzhx4gTevHmj7nAKpGnTppg2bZpC2T/vFhSFrKysIj+HNvrrr7+wceNGTJo0CbVq1cIvv/yCqVOnYteuXQqrgfybiYkJdu3alec2QRAwa9Ys6OnpYeHChTA2NsahQ4fw7bffYvv27TAyMsKlS5dw+vRpLF++HC9evMCyZcvQtGlTWFhYICUlBT/99BNWrFhRVJddqil1a2/48OH45ptvcOXKFUgkEsTExGDv3r349ttvMXr0aFXHSERULJydnQHkTM6pTfT19WFlZaXwMjMzE7d/8cUX+P333zFr1ix4eHigf//+uHjxosIxIiIiMG3aNHh6eqJHjx74/vvvkZSUJG6fOHEi1qxZg/Xr16Nr166YOnUqgJwZ4fv3748OHTpg0qRJCAgIwBdffIGUlBSkpaXBy8sLwcHBCue6cOECPD098f79+3xdX3JyMr7//nt07twZHh4emDZtGl68eCFuj42NxXfffYfOnTvD09MTX3/9NS5fvizuu3DhQnTr1g3u7u7o378//vzzz4I1cAEcOnQIXl5e8PT0ROXKleHr6wupVJqvc/773/CDFy9e4N69e5g4cSKcnZ1RqVIlTJo0CRkZGfjrr78AAJGRkXBxcUHNmjXRrl07mJiY4OXLlwCAzZs3o0uXLrCxsSmaiy7llOqRmj59OuRyOdq1a4f379+jdevWMDQ0xLfffovx48erOkYiomLx4Y/Xo0ePIAhCiVrEeOfOnRg5ciRGjRqFI0eOYNGiRdi/fz/Mzc2RkpICX19fdOzYEWPHjkVGRga2bNmCefPmwd/fXzxGYGAgunTpgnXr1gEAXr58iblz58Lb2xsdO3bEkydPsHHjRrG+kZERvvjiC/z5559o06aNWP7nn3+idevWMDY2zlfsS5YsQXR0NBYtWgRjY2Ns2bIF06dPx44dO6Cnp4c1a9YgOzsba9asgVQqRWRkpDix6rZt2xAZGYmlS5fCwsIC0dHR4gNSedmzZw/27t37yXh27NiRZ1KSlZWFR48e4auvvhLLdHR08Nlnn+Hu3bufPGZaWhr69OkDuVyO6tWrY9iwYahSpYp4XAAwMDBQOK6+vj5u374NLy8vODk54bfffkNycjJiYmKQkZEBe3t73L59G48fP8bEiRM/eX5SnlKJlEQiwYwZMzBlyhQ8efIEKSkpqF27NkxNTVUdHxFRsflnInX06FGtWfIqJCQEnp6eCmVfffUV+vfvL3728PBAu3btAADDhg3DkSNH8ODBAzRt2hRHjx5FtWrVMHz4cLH+1KlT0bt3b0RFRcHBwQEAULFiRYwaNUqss2XLFjg4OIhllSpVQkREBPbs2SPW8fLywrhx4/DmzRuULVsWb9++xZUrV/J9m+nFixe4dOkS1q1bh7p16wIAZsyYAR8fH1y4cAFt27ZFfHw8WrduLQ4tsbOzE/ePj49HtWrVULNmTQA5Q1M+pUuXLvjiiy8+WadcuXJ5liclJUEul+e6hVemTBk8f/78o8dzcHDA1KlT4eTkhJSUFBw8eBDjx4/H9u3bYW1tjUqVKsHGxgZbt27F5MmTIZVK8csvv+DVq1fibeimTZuiffv2GDVqFAwNDTF9+nRIpVKsWrUK06ZNw6+//oqjR4/C3NwckydPFpM0Kjylbu19+eWXmDdvHgwMDFC7dm00bdoUpqamePv2Lb788stCBbRhwwZUrlwZUqkUrq6uuHr16kfrZmVlYf78+XBycoJUKkWDBg0QEBCgUCc5ORkTJ06Eo6MjjIyM0Lx5c1y7du2jxxw1ahQkEglWr15dqOsgIu3j5eUlvl+3bp3WTIXQsGFD/PjjjwqvLl26KNT55/hVIyMjmJiY4O3btwCAp0+f4ubNm/D09BRfAwcOBADExMSI+9WoUUPhmFFRUWKC8sGH26Mf1KpVC5UrVxYHxAcFBcHGxgYNGjTI17VFRkZCV1dXYbC2hYUFHBwcEBkZCSBn8endu3dj3Lhx2L59u8JcYF26dMGZM2cwbNgwbNq0CXfu3Pnk+czNzWFvb//Jl66ubr5iz686derA3d0d1apVg4uLC+bPnw8LCwucOHECQM4SbPPmzcOLFy/QpUsXeHh4ICwsDK6urtDR+d+f8a+//hp79+7Ftm3b0KpVK+zbtw+NGjWCnp4edu/ejbVr18LLywuLFy9WafylnVI9UmfPnsXt27cRFhaGvXv3irOaZ2Zm5roXXhAHDhyAr68vNm3aBFdXV6xevRru7u54+PChOPPwP82cORN79uzB1q1b4ezsjMDAQHTv3h2XLl1Cw4YNAeR887pz5w52794NOzs77NmzB25ubrh37x7s7e0Vjnf06FFcvnxZ4dsMEZUe9vb2CAsLE39/DB8+HIcOHfpoD4SmkEqluX6f/ZueXu5f94IgAMi5rdSsWTOMHDkyV51/jtWRSqVKxefl5YVjx46hX79+CAgIgKenp0pvm3p5eaFJkya4fPkyrl+/jn379mH06NHo0aMHXF1dsX//fly+fBk3btzA5MmT0a1bt4+O5y3MrT0LCwvo6OiICeoHb9++VWjH/6Knp4fq1auLk10DQM2aNfHjjz8iJSUF2dnZsLS0xOjRo3Mlsh88f/4cQUFB2Lp1K/744w/Ur18flpaWaNu2LZYtW4b379/n+9YqfZrSM5ufOnUKsbGx+Pzzz/Hs2TOVBOPv74/hw4dj8ODBqF27NjZt2gRjY2Ns27Ytz/q7d+/Gd999h44dO6Jq1aoYPXo0OnbsiJUrVwLI+eVw+PBhLFu2DK1bt0a1atUwd+5cVKtWTeE+PgBER0dj/Pjx2Lt3L59EISrFXFxc8MMPP4ifBw4ciMzMTDVGVPSqV6+OZ8+ewdbWNlfvy6cWcXZwcMCjR48Uyh4+fJirXvv27REXF4fDhw8jMjIS7u7u+Y7N0dERMpkM9+/fF8uSkpIQFRWl8OR4+fLl0aVLF8yfPx+9e/fG77//Lm6ztLSEh4cHZsyYgbFjx+K333776Pm6dOmSq3fv36+PJdb6+vqoUaOGwnyKcrkcoaGhqFOnTr6vWSaTITw8PM/ky9TUFJaWlnjx4gUePXqEFi1a5KojCAJWrlyJMWPGwMjICHK5HDKZTDz2h7hINZTqkQKAChUqIDg4GIMHD0aTJk1w6NChT86T8V8yMzNx48YN+Pn5iWU6Ojpwc3NDSEhInvtkZGTk+oZkZGQkLlOTnZ0NmUz2yTpAzg/UgAEDMGXKlHz9sGdkZCgMVnz37t1/XyARaY3Ro0dDKpViyJAhSEtLQ3R0tEaPKcnKykJCQoJCma6uLiwsLPK1f7du3fD7779jwYIF6NOnD8zMzBAdHY0zZ87g22+//eitrM6dO+PQoUPYvHmzONj838MrAMDMzAytWrXC5s2b0bhxY1hbW+f72ipWrIgWLVpgxYoVmDx5MoyMjLB161aUK1dOTCLWr1+Ppk2bwsHBAcnJyQgLC0OlSpUA5Aw2r1GjBqpUqYLMzExcvnxZ3JYXc3NzmJub5zu+f+vVqxeWLFmCGjVqiNMfpKenw8PDQ6zz/fffw9raWhyTtnPnTtSuXRv29vZISUnBgQMHEBcXp3Cr+ezZs7C0tET58uURHh6O9evXo0WLFmjSpEmuGH7//XdYWlqiefPmAIC6deti586duHfvHq5cuQJHR0eOaVYhpQebA4ChoSH27duHhQsXio+kKuv169eQyWS5ukttbGzw4MGDPPdxd3eHv78/WrduDScnJ5w+fRpHjhwRM24zMzM0a9YMCxYsQK1atWBjY4Off/4ZISEhqFatmnicpUuXQk9PDxMmTMhXrIsXL8a8efOUvFIi0gaDBw/GnDlzEBUVpfETDV+9ehXe3t4KZQ4ODh+dl+jfypUrh3Xr1mHLli2YMmUKsrKyYGNjg6ZNmyqMwfm3ChUqYO7cudi4cSMOHz6MOnXqoH///li1apXCE2YA0LFjR5w+fTrXoPj8mDZtGtatWwc/Pz9kZ2ejfv36WLJkiXi7Ui6XY82aNXj16hVMTEzQpEkTjB07FkBOL9GPP/6I2NhYGBoaol69epg9e3aBY8ivL7/8EklJSdixY4c4IefSpUsVepfi4+MV2jUlJQUrV65EQkICTE1NUaNGDaxfv16hx+3Nmzf44Ycf8PbtW5QtWxYdOnTAgAEDcp0/ISEBe/bsUZgZvlatWujVqxf8/PxgaWmJ6dOnF83Fl1IS4cNN8gLQ0dFBbGyswrilw4cPY9CgQUhLSxMTmYKIiYmBvb09Ll26hGbNmonlU6dORXBwMK5cuZJrn1evXmH48OE4ceIEJBIJnJyc4Obmhm3btiEtLQ1AziDKIUOG4Ny5c9DV1cVnn32GGjVq4MaNG7h//z5u3LgBLy8vhIaGimOjKleujIkTJ370cdG8eqQcHByQlJRUqG8yRKRZ6tevj9u3b2P+/Plo1aqVusPRCnv27MGvv/6KgwcPKpSfPHkSGzZswC+//MLhE1Rg79+/h0wmg4eHh0p/ft69ewcLC4tC/f1WaoxURERErnvE3t7euHz58kfHM/2XcuXKQVdXF3FxcQrlcXFxH31c1draGseOHUNqaioiIyPx4MEDmJqaKjyd4uTkhODgYKSkpCAqKgpXr15FVlaWWOf8+fOIj49HpUqVoKenBz09PURGRmLy5Mm5Zm7/wNDQUOz+LWw3MBFprg+PsT9+/FjNkWiuY8eO4cGDB4iJicHJkyexf/9+hTFQ6enpiI6Oxr59+9C5c2cmUVTiKHVrz9HRMc/yunXrivN8FJSBgQEaNWqE06dPo1u3bgByumtPnz6NcePGfXLfD0+sZGVl4fDhw+jdu3euOiYmJuLjvoGBgeIaVAMGDICbm5tCXXd3dwwYMACDBw9W6lqIqGQoW7YsgJwHW9q1a/fR332lWXR0NPbs2YN3797BxsYGvXv3VpiQcv/+/dizZw/q16+vUE5UUuT71p6vry8WLFgAExMT+Pr6frLuP2fCLYgDBw5g0KBB2Lx5M5o2bYrVq1fj4MGDePDgAWxsbDBw4EDY29uLc2BcuXIF0dHRcHFxQXR0NObOnYuIiAiEhobC0tISQM5MvIIgoGbNmnjy5AmmTJkCqVSK8+fPf/Sb0X/d2vs3VXQNEpHmOXv2rDg5o4uLC5YtW8YeFSI10ORbe/nukQoLCxOnqQ8LC/tovcLMDeLj44NXr15h9uzZiI2NhYuLCwICAsQB6M+fP1cYoJeeno6ZM2ciPDwcpqam6NixI3bv3i0mUUDOY7J+fn548eIFrKys4O3tjUWLFvGXIRH9p7Zt28LX1xf+/v64efMmjh07hl69eqk7LCLSIEoNNidF7JEiKrlevHiBSpUqiZNXbt26VeGpXyIqeprcI6X0hJxERKVBxYoVFeZG+jDhLxERUIBbe/81LuqflB0jRUSkidq3b49Vq1Zh0qRJiIyMhFwu/+T8SkRUehRojFR+qHL9JCIiTSCRSDBmzBhMmTIFaWlpWLduHb755ht1h0VEGiDfidSZM2eKMg4iIo1mYGCAhg0b4tq1azh+/DhGjBjxyXXoiKh0UHqtPQC4d+8enj9/rrCgp0QiQefOnQsdGBGRpgkKCoKlpSUEQcClS5fQrl07dYdERGqmVCIVHh6O7t274/bt25BIJOLTLB9u6ymzRAwRkaazsLCAq6srrly5goULF6Jq1aoavZgxERU9pUZLfvPNN6hSpQri4+NhbGyMu3fv4ty5c2jcuDHOnj2r4hCJiDTHPx+mGTJkCCIjI9UYDRGpm1KJVEhICObPn49y5cpBR0cHOjo6aNmyJRYvXowJEyaoOkYiIo3RvHlz7Ny5U/z8z6kRiKj0USqRkslkMDMzA5Cz2HBMTAyAnDX4Hj58qLroiIg00MCBAzF06FAAwOvXr9UcDRGpk1KJVN26dfH3338DAFxdXbFs2TJcvHgR8+fPR9WqVVUaIBGRJmrcuDEAIDExUb2BEJFaKZVIzZw5E3K5HAAwf/58REREoFWrVvjjjz+wdu1alQZIRKSJbG1tAQAxMTG4d++ewtPLRFR6qGytvYSEBJQpU6ZUTsjJtfaISp/Q0FA0atRI/Fy5cmX89NNPnPGcqAiUirX2rKysSmUSRUSlU8OGDTFv3jx8/vnn0NXVxbNnzxR664modFC6Ryo9PR23bt1CfHx8rl8cXbp0UUlw2oI9UkSlm5eXF/744w8AwJo1a1C/fn01R0RUsmhyj5RSE3IGBARg4MCBeT6tIpFIOCEnEZUqBw4cgKWlJWQyGZ49e8ZEiqgUUerW3vjx49GrVy+8fPkScrlc4cUkiohKG1NTUwwYMAAAsGrVKjx+/FjNERFRcVEqkYqLi4Ovry9sbGxUHQ8RkVbq3r27+H758uX8UklUSiiVSPXs2ZNLwRAR/UOXLl2wf/9+AMDjx4+xceNGNUdERMVBqTFS69evR69evXD+/HnUq1cv18AvLhNDRKVR9+7dYWdnh5iYGNy9e1fd4RBRMVAqkfr5559x8uRJSKVSnD17VmHaA4lEwkSKiEolAwMDHDp0CC1atMCDBw+QlZWl0ieMiEjzKHVrb8aMGZg3bx6SkpLw7NkzREREiK/w8HBVx0hEpDUqVqwovp81a5YaIyGi4qBUIpWZmQkfHx/O4EtE9C8ODg5o27YtAODWrVucoJOohFMqExo0aBAOHDig6liIiLSeRCLBr7/+CgBIS0tDSkqKmiMioqKk1BgpmUyGZcuWITAwEPXr1881BsDf318lwRERaSNTU1MYGxvj/fv3uH37Nlq0aKHukIioiCiVSN2+fRsNGzYEANy5c0dhG9fbI6LSTiKRwM7ODk+ePMHMmTOxYMECtGzZUt1hEVERUHqtPfofrrVHRP927NgxhUk6bWxs0LBhQ3Tq1Al16tRRY2RE2keT19or8BiprKwstGvXjksgEBF9Qrdu3XDt2jXUqlULQM6KEAEBAZg8eTLS09PVHB0RqUqBEyl9fX3cunWrKGIhIipRGjdujHv37uHx48dYvHgxACAjIwOenp5YsmQJ0tLS1BwhERWWUk/t9e/fHz/99JOqYyEiKpGqVauG6dOnY9GiRZBKpQCAwMBA9OrVC48ePVJzdERUGEoNNs/Ozsa2bdtw6tQpNGrUCCYmJgrb+dQeEVFu3333HSZPnoxRo0Zhx44dSE1NxZEjRzB9+nR1h0ZESlIqkbpz5w4+++wzAMj1bYpP7RERfZyhoSG2b98OR0dHzJs3D4GBgejQoYP4O5WItAuf2lMBPrVHRAX19OlTVKtWDQBQvXp1bNmyRc0REWkuTX5qT6keKQBITEzETz/9hPv37wMA6tSpgyFDhsDCwkLZQxIRlRpOTk7YuXMnBg0ahBcvXkAul3PZLSItpNT/tdevX4eTkxNWrVqFhIQEJCQkwN/fH05OTggNDVV1jEREJVLv3r0hkUiQlpaGNWvWqDscIlKCUonUpEmT0KVLFzx79gxHjhzBkSNHEBERgU6dOmHixIkqDpGIqGSSSqVwcXEBAPz666+YPXs2MjMz1RsUERWI0j1S06ZNg57e/+4M6unpYerUqbh+/brKgiMiKumCg4NhbW0NADh//jwuXbqk5oiIqCCUSqTMzc3x/PnzXOVRUVEwMzMrdFBERKWFmZkZnj17BgcHBwDI83crEWkupRIpHx8fDB06FAcOHEBUVBSioqKwf/9+DBs2DH379lV1jEREJZqxsTH69+8PANi+fTuSk5PVHBER5ZdST+2tWLECEokEAwcORHZ2NoCcpWNGjx6NJUuWqDRAIqLSoH79+uL7fv36YenSpahdu7YaIyKi/CjUPFLv37/HkydPIJFI4OTkBGNjY1XGpjU4jxQRFVZ2djYGDBiA/fv3i2UjR45Enz591BgVkWbQ5HmkCjVpibGxMerXr4969eqV2iSKiEgV9PT0sG/fPmzevFks27ZtG7KystQYFRH9lwIlUjo6OtDV1f3k659P8hERUf5JJBKMGDECL1++BABkZWUhKipKzVER0acUKOs5evToR7eFhIRg7dq1kMvlhQ6KiKg0s7W1Rf369XHr1i0MHz4cJ06cYK8/kYYqUCLVtWvXXGUPHz7E9OnTceLECXz11VeYP3++yoIjIiqtvvrqK9y6dQtyuRwHDx7E119/re6QiCgPSo+RiomJwfDhw1GvXj1kZ2fj5s2b2LlzJxwdHVUZHxFRqTR16lQMHDgQALBz506cO3dOzRERUV4KnEglJSVh2rRpqFatGu7evYvTp0/jxIkTqFu3blHER0RUak2fPl18P2fOHOzcuZNLyBBpmALd2lu2bBmWLl0KW1tb/Pzzz3ne6iMiItWoVasW/v77bzRo0AAAsGPHDhw7dgyDBw+GoaEh9PX14erqChMTEzVHSlR6FWgeKR0dHRgZGcHNzQ26urofrXfkyBGVBKctOI8UERWl5ORk9OzZEydPnsy1rX379vjuu+/UEBVR8dHkeaQK1CM1cOBASCQSpU5ERETKMTMzQ2BgIK5fv47FixcjPj4e9+/fx5s3bxAUFITKlSujX79+6g6TqFQq1MzmlIM9UkRU3N69ewdbW1ukpaUBACwsLGBlZYUaNWrgyy+/RJMmTfjFl0oMTe6RKtTM5kVhw4YNqFy5MqRSKVxdXXH16tWP1s3KysL8+fPh5OQEqVSKBg0aICAgQKFOcnIyJk6cCEdHRxgZGaF58+a4du2awjGmTZuGevXqwcTEBHZ2dhg4cCBiYmKK7BqJiArL3Nwc8fHxqFOnDoCcB4EiIiIQGBiIadOmYfLkyZwVnagYaFQideDAAfj6+mLOnDkIDQ1FgwYN4O7ujvj4+Dzrz5w5E5s3b8a6detw7949jBo1Ct27d0dYWJhYZ9iwYQgKCsLu3btx+/ZtdOjQAW5uboiOjgaQk+WGhoZi1qxZCA0NxZEjR/Dw4UN06dKlWK6ZiEhZpqam+Pvvv3Hz5k2cPn0a8+fPh729PQAgLCwMI0eOBG86EBUtjbq15+rqiiZNmmD9+vUAALlcDgcHB4wfP17hMeAP7OzsMGPGDIwdO1Ys8/b2hpGREfbs2YO0tDSYmZnh+PHj8PLyEus0atQInp6eWLhwYZ5xXLt2DU2bNkVkZCQqVar0n3Hz1h4RaZIxY8Zg48aNAIAff/wRTk5Oao6IqHB4ay8fMjMzcePGDbi5uYllOjo6cHNzQ0hISJ77ZGRkQCqVKpQZGRnhwoULAHJWU5fJZJ+sk5ekpCRIJBJYWlp+9Lzv3r1TeBERaYoffvgBzZo1A5DTK3/w4EFkZGSoOSqikkljEqnXr19DJpPBxsZGodzGxgaxsbF57uPu7g5/f388fvwYcrkcQUFBOHLkiLjgp5mZGZo1a4YFCxYgJiYGMpkMe/bsQUhIiFjn39LT0zFt2jT07dv3o9np4sWLYWFhIb4cHBwKceVERKrXv39/8f3GjRsxYcIEJCYmqi8gohJKYxIpZaxZswbVq1eHs7MzDAwMMG7cOAwePBg6Ov+7rN27d0MQBNjb28PQ0BBr165F3759Fep8kJWVhd69e0MQBLFbPC9+fn5ISkoSX1ydnYg0zZgxY3D79m00btwYAPDo0SPMnDlTzVERlTwak0iVK1cOurq6iIuLUyiPi4uDra1tnvtYW1vj2LFjSE1NRWRkJB48eABTU1NUrVpVrOPk5ITg4GCkpKQgKioKV69eRVZWlkId4H9JVGRkJIKCgj55r9TQ0BDm5uYKLyIiTVO3bl2EhIRg+PDhAIC7d+9+clgDERWcxiRSBgYGaNSoEU6fPi2WyeVynD59WrzX/zFSqRT29vbIzs7G4cOH81y6xsTEBBUqVMDbt28RGBioUOdDEvX48WOcOnUKZcuWVd2FERGpkZ6eHjZv3oxy5coBAObPn4/s7Gw1R0VUcmhMIgUAvr6+2Lp1K3bu3In79+9j9OjRSE1NxeDBgwHkzKzu5+cn1r9y5QqOHDmC8PBwnD9/Hh4eHpDL5Zg6dapYJzAwEAEBAYiIiEBQUBC++OILODs7i8fMyspCz549cf36dezduxcymQyxsbGIjY3l4qBEVCJIJBKcOHECQM7vvDNnzqg5IqKSo0BLxBQ1Hx8fvHr1CrNnz0ZsbCxcXFwQEBAgDkB//vy5wtim9PR0zJw5E+Hh4TA1NUXHjh2xe/duhaftkpKS4OfnhxcvXsDKygre3t5YtGiR+PhkdHQ0fv31VwCAi4uLQjxnzpxB27Zti/SaiYiKw+effw4XFxfcvHkT33//PerWrYsKFSqoOywiradR80hpK84jRUTa4NSpU2jfvj0AYM6cOfyiSFqD80gREZHaubm5oUOHDgDAqRCIVISJFBFRKWJtbQ0AOH36NGQymZqjIdJ+TKSIiEqRD+Oi7ty5g1OnTqk5GiLtx0SKiKgUGT16tPh+//79fDqZqJCYSBERlSJVq1bFrl27AADPnj1Djx49cOjQITVHRaS9mEgREZUyXbp0Qbt27QAAqamp2LlzJ/gAN5FymEgREZUyFhYWOHXqFGJiYgDkJFMpKSlqjopIOzGRIiIqpWxtbWFkZAQAWLduHXuliJTARIqIqJSSSCRo0qQJACAoKAgzZszg4HOiAmIiRURUih09ehRlypQBAISEhODkyZNqjohIuzCRIiIqxaysrHD37l1IpVIAwIMHD9QcEZF2YSJFRFTKVahQAYsXLwYA/P7775g2bRpevXql5qiItAMTKSIigoeHh/j+6tWr6N27N9avX88B6ET/gYkUERHB2dkZmZmZGD9+PExNTQEAhw8fho+PD96+favm6Ig0FxMpIiICAOjr62Pt2rV49eoVWrVqBQB49eoVRo8ejYSEBDVHR6SZmEgREZECqVSK4OBgDB06FAAQFxeHPXv2qDkqIs3ERIqIiHKRSCTYsGGDOM9UVFSUmiMi0kxMpIiIKE+GhoaYM2cOAOD69et4+PChmiMi0jxMpIiI6KNq1aolvvf391djJESaiYkUERF9VNWqVbFq1SoAwKNHjzjonOhfmEgREdEnjRgxAhKJBADg7e0Nf39/ZGdnqzkqIs3ARIqIiD7J2NgYixYtEj+fOHECV65cUWNERJqDiRQREf0nPz8/xMXFwdDQEACwf/9+9koRgYkUERHlU/ny5TFu3DgAwJ07d/Ddd9/h1KlTePPmjZojI1IfJlJERJRv48ePh52dHQDg2rVrWLRoEcaPH881+ajUYiJFRET55ujoiBcvXmDDhg3o0KEDAODly5d48uSJmiMjUg8mUkREVCASiQRjxoxBYGAgHBwcAOQ82ff69Ws1R0ZU/JhIERGR0v45See9e/fUGAmRejCRIiIipfXs2RMeHh4AgL/++gsymUzNEREVLyZSRERUKDVq1AAABAcHIyAgQM3REBUvJlJERFQo06dPF+eXWrFiBQICAiCXy9UcFVHxYCJFRESFUqFCBYWZzpcuXQo/Pz81RkRUfJhIERFRoTVo0ACXLl0Sn+K7evUqFixYwDFTVOIxkSIiIpVo1qwZnj9/jtatWwPIGXw+fPhwJCUlqTkyoqLDRIqIiFTql19+weeffw4AiIiIQK9evThhJ5VYTKSIiEilrK2tce7cOYwdOxYAkJWVhR07dqg3KKIiwkSKiIhUTl9fH+vXr8ekSZMAABcvXsTVq1fVHBWR6jGRIiKiIjNt2jTxvb+/Pxc3phKHiRQRERUZGxsbHDhwAADw+vVrZGdnqzkiItViIkVEREWqW7duAACZTIaYmBj1BkOkYkykiIioSBkYGMDKygoAMGTIEGRkZKg5IiLVYSJFRERF7rvvvgMAyOVyREREqDkaItVhIkVEREVu8uTJ+OyzzwAAW7du5Vp8VGIwkSIiomLh7OwMAAgNDcW5c+fUHA2RajCRIiKiYjF//nzx/cKFC/Hq1Ss1RkOkGkykiIioWDg5OWHdunUAcp7g2717t5ojIio8JlJERFRsRowYgVatWgEATpw4gYSEBDVHRFQ4euoOgIiISg8DAwOsWLECrq6uAABvb29Uq1YN+vr6MDU1xahRo1C1alU1R0mUf+yRIiKiYtWkSROMHz9e/PzkyRPcv38f165dw9ChQ7F79248evQIqampaoySKH80LpHasGEDKleuDKlUCldX108ucpmVlYX58+fDyckJUqkUDRo0QEBAgEKd5ORkTJw4EY6OjjAyMkLz5s1x7do1hTqCIGD27NmoUKECjIyM4ObmhsePHxfJ9RERlXYSiQRr165FUlISjhw5gj179mDOnDni9m3btmHkyJHo1asXXr58qcZIif6bRiVSBw4cgK+vL+bMmYPQ0FA0aNAA7u7uiI+Pz7P+zJkzsXnzZqxbtw737t3DqFGj0L17d4SFhYl1hg0bhqCgIOzevRu3b99Ghw4d4ObmhujoaLHOsmXLsHbtWmzatAlXrlyBiYkJ3N3dkZ6eXuTXTERUWpmbm6N79+746quvMHfuXNy6dQt16tRBmTJlAABpaWno168f7t+/r+ZIiT5OImjQUtyurq5o0qQJ1q9fDyBnBlwHBweMHz8e06dPz1Xfzs4OM2bMwNixY8Uyb29vGBkZYc+ePUhLS4OZmRmOHz8OLy8vsU6jRo3g6emJhQsXQhAE2NnZYfLkyfj2228BAElJSbCxscGOHTvQp0+f/4z73bt3sLCwQFJSEszNzQvbDEREpZpMJsOxY8fQs2dPsaxDhw5o2LAhAMDU1BTNmjWDrq6uukKkYvb+/XvIZDJ4eHhAX19fZcdVxd9vjRlsnpmZiRs3bsDPz08s09HRgZubG0JCQvLcJyMjA1KpVKHMyMgIFy5cAABkZ2dDJpN9sk5ERARiY2Ph5uYmbrewsICrqytCQkLyTKQyMjIU1op69+5dAa+WiIg+RldXF97e3vjzzz/h6ekJADh58iROnjwp1hk9ejR69+6trhCJRBpza+/169eQyWSwsbFRKLexsUFsbGye+7i7u8Pf3x+PHz+GXC5HUFAQjhw5It5TNzMzQ7NmzbBgwQLExMRAJpNhz549CAkJEet8OHZBzrt48WJYWFiILwcHh0JdOxER5ebh4YGoqCj4+PigWbNm+Pzzz+Hk5AQA2LhxI3777Tc1R0ikQYmUMtasWYPq1avD2dkZBgYGGDduHAYPHgwdnf9d1u7duyEIAuzt7WFoaIi1a9eib9++CnUKys/PD0lJSeIrKipKFZdDRET/UrFiRezfvx+XLl1CSEgITp06JW7766+/1BgZUQ6NSaTKlSsHXV1dxMXFKZTHxcXB1tY2z32sra1x7NgxpKamIjIyEg8ePICpqanCHCROTk4IDg5GSkoKoqKicPXqVWRlZYl1Phy7IOc1NDSEubm5wouIiIpe5cqVERwcDAAICwvDjh071BsQlXoak0gZGBigUaNGOH36tFgml8tx+vRpNGvW7JP7SqVS2NvbIzs7G4cPH0bXrl1z1TExMUGFChXw9u1bBAYGinWqVKkCW1tbhfO+e/cOV65c+c/zEhFR8fvss8+gp5czxHfnzp0KY1aJipvGDDYHAF9fXwwaNAiNGzdG06ZNsXr1aqSmpmLw4MEAgIEDB8Le3h6LFy8GAFy5cgXR0dFwcXFBdHQ05s6dC7lcjqlTp4rHDAwMhCAIqFmzJp48eYIpU6bA2dlZPKZEIsHEiROxcOFCVK9eHVWqVMGsWbNgZ2eHbt26FXsbEBHRp5mamuL169ewtLQEkDOWys3NDd988w1MTU2RkJCAwMBAGBkZoXPnzny6j4qURiVSPj4+ePXqFWbPno3Y2Fi4uLggICBAHAj+/PlzhbFN6enpmDlzJsLDw2FqaoqOHTti9+7d4v9cQM5UBn5+fnjx4gWsrKzg7e2NRYsWKTw+OXXqVKSmpmLEiBFITExEy5YtERAQkOtpPyIi0gwWFhbw9fWFv78/AODUqVM4deoU6tSpg7t374r1fvvtNyxevBjW1tbqCpVKOI2aR0pbcR4pIiL1SE9Px9ixY7Ft27aP1jExMcHKlStRs2bNYoyMVEmT55FiIqUCTKSIiNRHEARcvHgRO3bsgCAIMDc3R6tWrRAcHIy1a9eK9erUqYMxY8agdu3aaoyWlMFEqoRjIkVEpJkOHjwIHx8fhTILCwt8//33TKi0iCYnUhrz1B4REZGq9e7dGxEREejWrRvKli0LIGfs7NixY/HgwQM1R0clARMpIiIq0SpXroyjR4/i9evXOHbsmFg+evRo/Prrr7h58ybCw8PBGzSkDI16ao+IiKgode3aFbt27cLAgQMBAKtWrRK3lStXDsuWLUOVKlXUFR5pIY6RUgGOkSIi0i6BgYFYu3YtHj58iKdPn4rln3/+uThXIRUvmUyG169f4/bt2+Lf1fj4eNy7dw9xcXHQ19fHzZs3NW6MFHukiIio1HF3d4e7uzuAnFU0xo8fjx9++AGXL1/GhQsX0LJlSzVHWLIJgoD79+/j3LlzuH//PhISEhATEwO5XP7RfUxNTYsxwvxjj5QKsEeKiEi7paeno0yZMkhPTwcAzJo1C19++aWaoyqZBEHA6tWr8euvv+bapqOjA1tbW+jq6sLY2Bi6urqwtrZG/fr10bZtW/To0UOlsbBHioiISAWkUilCQkLQsGFDAMCCBQtQu3btjy5eT8q5d+8e5s2bh/j4eAA57T5x4kTUq1cPDg4OcHZ2hqmpqbiyiEQiUWe4+cIeKRVgjxQRUckQHByMtm3bAsiZEX3o0KFo3769xt5W0hYymQxbtmzBwYMHxbJGjRohKCgIZcqUUVtcnEeKiIhIhdq0aQNfX18AQGpqKtauXYsxY8YgOTlZzZFpr7i4OIwaNUohiVq6dCkuXryo1iRKVdgjpQLskSIiKjmysrJw7NgxfPfdd3jy5AkAoGHDhli0aBEePHiA2NhYvHz5Eo8ePYK5uTmqVauGtm3bonz58mqOXLO8fPkSK1euxI0bN8Sy9u3bY/Xq1RozqzyXiNEQTKSIiEqerKwsdOrUCSdPnsxX/ZYtW2LWrFkwMDAo4sg0W0pKChYvXoxLly6JZRUqVMDw4cMxdepUmJiYqDE6RUykNAQTKSKikqtp06a4du0aAMDIyAjOzs6oWLEizM3NcffuXdy8eVOh/tKlS9G0aVM1RKp+MpkMkyZNwu3btwEAjo6O6NOnD6ZMmQIrKyuNGzzOp/aIiIiKWEhICB49egQTExNYWFjA3NxcISFITk5G37598fvvvwMApk2bhtatW2PGjBmlrnfq6NGjYhL11VdfYcuWLTA2NlZzVEWLg82JiIg+QVdXF7Vq1UKlSpVgYWGRq1fFzMwMv/32Gw4dOgQ9vZz+iXPnzqFbt2549OiROkJWmw89d8bGxpg/f36JT6IAJlJEREQq0bNnT8TFxaF169YAgLS0NIwcORKrV69GWlqamqMrWgkJCTh48CCuXr0KANi5cyeqVq2q5qiKB8dIqQDHSBER0QcymQybNm3CuHHjxDITExM0b94cVlZWiI6OhrW1Ndzc3DTm6TVlpaWlYdOmTQqzlOvr6+PevXuoVq2aGiPLHw421xBMpIiI6N8yMjLg6emJM2fOfLROpUqVoKenB3d3d9StWxc1a9aErq5uMUapnNu3b2Pfvn24fPmyWCaVSuHh4YGvv/4anTp10orrYCKlIZhIERHRx0RFRcHX1xcPHz5EmTJlUKdOHWzcuDHPupaWlvj888/Rv39/2NvbF3Ok/y0iIgKrVq0SB5QDgLm5Obp3747p06fD2dlZjdEVHBMpDcFEioiICuLGjRvYt28fZDIZbt26hZcvX+LBgwcKdZo0aYLx48fDwcFBTVHmSE5Oxvnz5/HHH3/g7t27Yrm1tTXmzZuHnj17omzZstDR0b5h10ykNAQTKSIiKqxXr15hzJgx+OWXXxTKbWxsMGXKFLi4uBTr7bKsrCysW7cOJ06cUCi3trZG//794evri4oVKxZbPEWBiZSGYCJFRESq8v79eyxfvhwbN25EXFycwjZ9fX1IpVI0b94cI0aMgJWVVYGOLZfL8fDhQ9y9excJCQl4/fo1MjIyUKFCBRgbGyMtLQ2ZmZl48+YNwsLC8O7dO3HfOnXq4Ouvv8aIESNKzN86JlIagokUERGpmiAIWLNmDfbu3YvQ0FDI5fJcdfr27YuhQ4fmq6fq9OnT2Lx5M169epXvGCQSCVq0aIHt27fDyclJ42YmLywmUhqCiRQRERWl5ORkvH79GklJSZgzZ47CdAMA0K1bNwwePBhSqRRhYWFITU1FvXr1IJPJEB4ejiNHjoiLB+vq6uKzzz6Do6MjLC0toa+vj8TERKSlpcHExAT6+vooV64cqlatis6dO8PGxgb6+vrquOwix0RKQzCRIiKi4nT//n306dMHt27dKtB+derUwY8//ojGjRuLs7CXZqr4+619Q+yJiIhKuVq1auHvv/9GZGQkPD09c22XSqXie0dHR7i4uGDAgAG4evUqPv/8cyZRKsSWJCIi0lKVKlXCH3/8gZiYGPz8889ISEiAh4cHmjdvjrdv3yIxMRGVKlUqdYsnFyfe2lMB3tojIiLSPry1R0RERKRGTKSIiIiIlMREioiIiEhJTKSIiIiIlMREioiIiEhJTKSIiIiIlMREioiIiEhJTKSIiIiIlMREioiIiEhJTKSIiIiIlMREioiIiEhJTKSIiIiIlMREioiIiEhJeuoOoCQQBAFAzirSREREpB0+/N3+8HdcGUykVCA5ORkA4ODgoOZIiIiIqKCSk5NhYWGh1L4SoTBpGAEA5HI5YmJiYGZmBolEotJjv3v3Dg4ODoiKioK5ublKj11SsI3yj21VMGyvgmF7FQzbK/+Kqq0EQUBycjLs7Oygo6PcaCf2SKmAjo4OKlasWKTnMDc35/9o/4FtlH9sq4JhexUM26tg2F75VxRtpWxP1AccbE5ERESkJCZSREREREpiIqXhDA0NMWfOHBgaGqo7FI3FNso/tlXBsL0Khu1VMGyv/NPktuJgcyIiIiIlsUeKiIiISElMpIiIiIiUxESKiIiISElMpIiIiIiUxESKiIiISElMpJR07tw5dO7cGXZ2dpBIJDh27JjC9ri4OHz99dews7ODsbExPDw88PjxY4U6T58+Rffu3WFtbQ1zc3P07t0bcXFxCnVCQ0PRvn17WFpaomzZshgxYgRSUlL+M75bt26hVatWkEqlcHBwwLJlyxS23717F97e3qhcuTIkEglWr16tVDt8jLa3z5EjR9C4cWNYWlrCxMQELi4u2L17t3KNkQ/a3l47duyARCJReEmlUuUa4z9oe1u1bds2V1tJJBJ4eXkp1yD5oO1tlpWVhfnz58PJyQlSqRQNGjRAQECAco3xHzS5rdLT0/H111+jXr160NPTQ7du3XLVefnyJfr164caNWpAR0cHEydOVKYZ8mXx4sVo0qQJzMzMUL58eXTr1g0PHz7MFfPYsWNRtmxZmJqawtvbO1dbPH/+HF5eXjA2Nkb58uUxZcoUZGdnK9Q5e/YsPvvsMxgaGqJatWrYsWPHf8YnCAJmz56NChUqwMjICG5ubrn+rRYtWoTmzZvD2NgYlpaWSrUDEyklpaamokGDBtiwYUOubYIgoFu3bggPD8fx48cRFhYGR0dHuLm5ITU1Vdy/Q4cOkEgk+Ouvv3Dx4kVkZmaic+fOkMvlAICYmBi4ubmhWrVquHLlCgICAnD37l18/fXXn4zt3bt36NChAxwdHXHjxg0sX74cc+fOxZYtW8Q679+/R9WqVbFkyRLY2tqqrmH+n7a3j5WVFWbMmIGQkBDcunULgwcPxuDBgxEYGKi6RvoHbW8vIGfphpcvX4qvyMhI1TTOv2h7Wx05ckShne7cuQNdXV306tVLdY30L9reZjNnzsTmzZuxbt063Lt3D6NGjUL37t0RFhamukb6f5rcVjKZDEZGRpgwYQLc3NzyrJORkQFra2vMnDkTDRo0KFxj/Ifg4GCMHTsWly9fRlBQELKystChQwexLQBg0qRJOHHiBA4dOoTg4GDExMSgR48eCtfk5eWFzMxMXLp0CTt37sSOHTswe/ZssU5ERAS8vLzwxRdf4ObNm5g4cSKGDRv2n7+Ply1bhrVr12LTpk24cuUKTExM4O7ujvT0dLFOZmYmevXqhdGjRyvfEAIVGgDh6NGj4ueHDx8KAIQ7d+6IZTKZTLC2tha2bt0qCIIgBAYGCjo6OkJSUpJYJzExUZBIJEJQUJAgCIKwefNmoXz58oJMJhPr3Lp1SwAgPH78+KPx/PDDD0KZMmWEjIwMsWzatGlCzZo186zv6OgorFq1qkDXXBDa3j4fNGzYUJg5c2b+LroQtLG9tm/fLlhYWCh9zcrSxrb6t1WrVglmZmZCSkpK/i+8ELSxzSpUqCCsX79eYb8ePXoIX331VQGvvmA0ra3+adCgQULXrl0/WadNmzbCN998k6/jqUJ8fLwAQAgODhYEIee69fX1hUOHDol17t+/LwAQQkJCBEEQhD/++EPQ0dERYmNjxTobN24UzM3NxZ+JqVOnCnXq1FE4l4+Pj+Du7v7RWORyuWBrayssX75cLEtMTBQMDQ2Fn3/+OVf9wvwOY49UEcjIyAAAhVsbOjo6MDQ0xIULF8Q6EolEYZZWqVQKHR0dhToGBgYKK1IbGRkBgFgnLyEhIWjdujUMDAzEMnd3dzx8+BBv375VwRUWjra1jyAIOH36NB4+fIjWrVsrc8mFoi3tlZKSAkdHRzg4OKBr1664e/duYS5bKdrSVv/0008/oU+fPjAxMSno5aqENrRZRkZGrlvFRkZGnzxuUVB3W2m6pKQkADk9+gBw48YNZGVlKfSeOTs7o1KlSggJCQGQ8+9fr1492NjYiHXc3d3x7t078XdISEhIrh44d3d38Rh5iYiIQGxsrMJ+FhYWcHV1/eR+ymAiVQQ+/KD4+fnh7du3yMzMxNKlS/HixQu8fPkSAPD555/DxMQE06ZNw/v375Gamopvv/0WMplMrPPll18iNjYWy5cvR2ZmJt6+fYvp06cDgFgnL7GxsQo/lADEz7GxsUVxyQWiLe2TlJQEU1NTGBgYwMvLC+vWrUP79u1V2hb5oQ3tVbNmTWzbtg3Hjx/Hnj17IJfL0bx5c7x48ULl7fEp2tBW/3T16lXcuXMHw4YNU8n1K0Mb2szd3R3+/v54/Pgx5HI5goKCxFukxUndbaXJ5HI5Jk6ciBYtWqBu3boAcv79DAwMco09srGxEf9t8/Pv/7E67969Q1paWp7xfNg3r/1U/XeQiVQR0NfXx5EjR/Do0SNYWVnB2NgYZ86cgaenp/gNxNraGocOHcKJEydgamoKCwsLJCYm4rPPPhPr1KlTBzt37sTKlSthbGwMW1tbVKlSBTY2Ngp1TE1NYWpqCk9PT7Vdc0FoS/uYmZnh5s2buHbtGhYtWgRfX1+cPXtWpW2RH9rQXs2aNcPAgQPh4uKCNm3a4MiRI7C2tsbmzZtV3yCfoA1t9U8//fQT6tWrh6ZNm6qmAZSgDW22Zs0aVK9eHc7OzjAwMMC4ceMwePBghR6d4qANbaUuY8eOxZ07d7B///5iP/fevXvFtjI1NcX58+eL9fx6xXq2UqRRo0a4efMmkpKSkJmZCWtra7i6uqJx48ZinQ4dOuDp06d4/fo19PT0YGlpCVtbW1StWlWs069fP/Tr1w9xcXEwMTGBRCKBv7+/WOePP/5AVlYWgP91Ddva2uZ6KuLD56IYWK4MbWgfHR0dVKtWDQDg4uKC+/fvY/HixWjbtq3qG+Q/aEN7/ZO+vj4aNmyIJ0+eqK4R8klb2io1NRX79+/H/PnzVd8IBaTpbWZtbY1jx44hPT0db968gZ2dHaZPn65w7uKizrbSVOPGjcNvv/2Gc+fOoWLFimK5ra0tMjMzkZiYqNArFRcXJ/7b2tra4urVqwrH+/e//8d+RszNzWFkZIQuXbrA1dVV3GZvby/27MXFxaFChQoK+7m4uBT+ov9JqZFVpAD/GpCYl0ePHgk6OjpCYGDgR+ucPn1akEgkwoMHDz5a56effhKMjY2Ft2/ffrTOh8GbmZmZYpmfn5/GDDbPiya3zweDBw8W2rRp88k6qlAS2is7O1uoWbOmMGnSpE9eR2Fpc1tt375dMDQ0FF6/fv3J+FVNm9vsg8zMTMHJyUnw8/P75HUUlqa11T9pwmBzuVwujB07VrCzsxMePXqUa/uHwea//PKLWPbgwYM8B5vHxcWJdTZv3iyYm5sL6enpgiDkDDavW7euwrH79u2br8HmK1asEMuSkpKKZLA5EyklJScnC2FhYUJYWJgAQPD39xfCwsKEyMhIQRAE4eDBg8KZM2eEp0+fCseOHRMcHR2FHj16KBxj27ZtQkhIiPDkyRNh9+7dgpWVleDr66tQZ926dcKNGzeEhw8fCuvXrxeMjIyENWvWfDK2xMREwcbGRhgwYIBw584dYf/+/YKxsbGwefNmsU5GRoYYf4UKFYRvv/1WCAsLy/cTIyW9fb7//nvh5MmTwtOnT4V79+4JK1asEPT09MQnc1RN29tr3rx5QmBgoPD06VPhxo0bQp8+fQSpVCrcvXtXRS30P9reVh+0bNlS8PHxKWRr5I+2t9nly5eFw4cPC0+fPhXOnTsnfPnll0KVKlXynXQUhCa3lSAIwt27d4WwsDChc+fOQtu2bcVY/+lDWaNGjYR+/foJYWFhRfL/4ujRowULCwvh7NmzwsuXL8XX+/fvxTqjRo0SKlWqJPz111/C9evXhWbNmgnNmjUTt2dnZwt169YVOnToINy8eVMICAgQrK2tFZLk8PBwwdjYWJgyZYpw//59YcOGDYKurq4QEBDwyfiWLFkiWFpaCsePHxdu3boldO3aVahSpYqQlpYm1omMjBTCwsKEefPmCaampmLbJScn57sdmEgp6cyZMwKAXK9BgwYJgiAIa9asESpWrCjo6+sLlSpVEmbOnKnweK8g5Dzia2NjI+jr6wvVq1cXVq5cKcjlcoU6AwYMEKysrAQDAwOhfv36wq5du/IV399//y20bNlSMDQ0FOzt7YUlS5YobI+IiMgzflX1uGh7+8yYMUOoVq2aIJVKhTJlygjNmjUT9u/fr3yD/Adtb6+JEycKlSpVEgwMDAQbGxuhY8eOQmhoqPIN8gna3laC8L9v5SdPnlSuEQpI29vs7NmzQq1atQRDQ0OhbNmywoABA4To6GjlG+QTNL2tHB0d84zvn/La7ujoqHSbfExe5wEgbN++XayTlpYmjBkzRihTpoxgbGwsdO/eXXj58qXCcZ49eyZ4enoKRkZGQrly5YTJkycLWVlZCnXOnDkjuLi4CAYGBkLVqlUVzvExcrlcmDVrlmBjYyMYGhoK7dq1Ex4+fKhQZ9CgQXlew5kzZ/LdDpL/bwwiIiIiKiA+tUdERESkJCZSREREREpiIkVERESkJCZSREREREpiIkVERESkJCZSREREREpiIkVERESkJCZSREREREpiIkVERESkJCZSREREREpiIkVERESkpP8DpjPXkJIWZ/MAAAAASUVORK5CYII=", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.dates as mdates\n", - "\n", - "plt.style.use(\"default\")\n", - "\n", - "fig, ax = plt.subplots()\n", - "\n", - "ax.plot(\n", - " timesteps[\"Datetime\"],\n", - " timesteps[\"Pmp_norm\"],\n", - " c=\"black\",\n", - " label=\"Normalized STC $P_{MP}$\",\n", - ")\n", - "ax.plot(timesteps[\"Datetime\"], np.ones(len(timesteps)), c=\"black\")\n", - "ax.fill_between(\n", - " timesteps[\"Datetime\"],\n", - " np.ones(len(timesteps)),\n", - " timesteps[\"Pmp_norm\"],\n", - " color=\"grey\",\n", - " alpha=0.5,\n", - ")\n", - "\n", - "\n", - "ax.set_ylabel(\"Normalized STC $P_{MP}$\")\n", - "\n", - "ax.set_title(\"Energy Loss\")\n", - "\n", - "loss = letid.calc_energy_loss(timesteps)\n", - "\n", - "ax.text(mdates.datestr2num(\"1999-08-02\"), 0.994, s=f\"Energy loss = {loss*100:.2f}%\")\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\mspringe\\AppData\\Local\\Temp\\1\\ipykernel_15280\\3402272254.py:45: DeprecationWarning: You are passing x=0 0.0\n", - "1 1.0\n", - "2 2.0\n", - "3 3.0\n", - "4 4.0\n", - " ... \n", - "8755 8755.0\n", - "8756 8756.0\n", - "8757 8757.0\n", - "8758 8758.0\n", - "8759 8759.0\n", - "Name: Timedelta, Length: 8760, dtype: float64 as a positional argument. Please change your invocation to use keyword arguments. From SciPy 1.14, passing these as positional arguments will result in an error.\n", - " energy = simpson(timesteps[\"p_mp\"]/1000, timesteps['Timedelta'])\n" - ] - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.dates as mdates\n", - "from scipy.integrate import cumulative_trapezoid, simpson\n", - "\n", - "\n", - "fig, axs = plt.subplots(3, 1, figsize=(10, 10), sharex=True)\n", - "ax1, ax2, ax3 = axs\n", - "\n", - "ax1.plot(timesteps[\"Datetime\"], timesteps[\"p_mp\"], label=\"Base $P_{MP}$\")\n", - "ax1.plot(\n", - " timesteps[\"Datetime\"],\n", - " timesteps[\"p_mp\"] * timesteps[\"Pmp_norm\"],\n", - " label=\"Degraded $P_{MP}$\",\n", - " alpha=0.7,\n", - ")\n", - "ax1.legend(loc=\"lower left\")\n", - "\n", - "axin1 = ax1.inset_axes([0.55, 0.25, 0.43, 0.43])\n", - "axin1.plot(timesteps[\"Datetime\"], timesteps[\"p_mp\"])\n", - "axin1.plot(timesteps[\"Datetime\"], timesteps[\"p_mp\"] * timesteps[\"Pmp_norm\"], alpha=0.7)\n", - "\n", - "\n", - "axin1.set_xlim(mdates.datestr2num(\"1999-12-04\"), mdates.datestr2num(\"1999-12-11\"))\n", - "axin1.set_ylim(50, 240)\n", - "axin1.tick_params(axis=\"x\", labelrotation=15)\n", - "ax1.indicate_inset_zoom(axin1)\n", - "\n", - "start = timesteps[\"Datetime\"].iloc[0]\n", - "timesteps[\"Timedelta\"] = [\n", - " (d - start).total_seconds() / 3600 for d in timesteps[\"Datetime\"]\n", - "]\n", - "\n", - "ax2.plot(\n", - " timesteps[\"Datetime\"],\n", - " cumulative_trapezoid(timesteps[\"p_mp\"] / 1000, timesteps[\"Timedelta\"], initial=0),\n", - " label=\"Cumulative energy, base\",\n", - ")\n", - "ax2.plot(\n", - " timesteps[\"Datetime\"],\n", - " cumulative_trapezoid(\n", - " (timesteps[\"p_mp\"] * timesteps[\"Pmp_norm\"]) / 1000,\n", - " timesteps[\"Timedelta\"],\n", - " initial=0,\n", - " ),\n", - " label=\"Cumulative energy, degraded\",\n", - ")\n", - "ax2.fill_between(\n", - " timesteps[\"Datetime\"],\n", - " (cumulative_trapezoid(timesteps[\"p_mp\"], timesteps[\"Timedelta\"], initial=0) / 1000),\n", - " (\n", - " cumulative_trapezoid(\n", - " timesteps[\"p_mp\"] * timesteps[\"Pmp_norm\"], timesteps[\"Timedelta\"], initial=0\n", - " )\n", - " / 1000\n", - " ),\n", - " alpha=0.5,\n", - " color=\"C1\",\n", - " label=\"Energy loss\",\n", - ")\n", - "ax2.legend()\n", - "\n", - "axin2 = ax2.inset_axes([0.55, 0.25, 0.43, 0.43])\n", - "axin2.plot(\n", - " timesteps[\"Datetime\"],\n", - " cumulative_trapezoid(timesteps[\"p_mp\"] / 1000, timesteps[\"Timedelta\"], initial=0),\n", - ")\n", - "axin2.plot(\n", - " timesteps[\"Datetime\"],\n", - " cumulative_trapezoid(\n", - " (timesteps[\"p_mp\"] * timesteps[\"Pmp_norm\"]) / 1000,\n", - " timesteps[\"Timedelta\"],\n", - " initial=0,\n", - " ),\n", - ")\n", - "axin2.fill_between(\n", - " timesteps[\"Datetime\"],\n", - " (cumulative_trapezoid(timesteps[\"p_mp\"], timesteps[\"Timedelta\"], initial=0) / 1000),\n", - " (\n", - " cumulative_trapezoid(\n", - " timesteps[\"p_mp\"] * timesteps[\"Pmp_norm\"], timesteps[\"Timedelta\"], initial=0\n", - " )\n", - " / 1000\n", - " ),\n", - " alpha=0.5,\n", - " color=\"C1\",\n", - ")\n", - "\n", - "axin2.set_xlim(mdates.datestr2num(\"1999-12-04\"), mdates.datestr2num(\"1999-12-11\"))\n", - "axin2.set_ylim(412, 427)\n", - "axin2.tick_params(axis=\"x\", labelrotation=15)\n", - "ax2.indicate_inset_zoom(axin2)\n", - "\n", - "ax3.set_xlabel(\"Datetime\")\n", - "ax3.plot(\n", - " timesteps[\"Datetime\"],\n", - " (cumulative_trapezoid(timesteps[\"p_mp\"], timesteps[\"Timedelta\"], initial=0) / 1000)\n", - " - (\n", - " cumulative_trapezoid(\n", - " timesteps[\"p_mp\"] * timesteps[\"Pmp_norm\"], timesteps[\"Timedelta\"], initial=0\n", - " )\n", - " / 1000\n", - " ),\n", - " label=\"Cumulative energy loss\",\n", - ")\n", - "ax3.legend()\n", - "\n", - "loss = letid.calc_energy_loss(timesteps)\n", - "energy = simpson(timesteps[\"p_mp\"] / 1000, timesteps[\"Timedelta\"])\n", - "ax3.text(\n", - " mdates.datestr2num(\"1999-03-02\"),\n", - " 2,\n", - " s=f\"Energy loss = {loss*energy:.1f} kWh ({loss*100:.2f}%)\",\n", - ")\n", - "\n", - "ax1.set_ylabel(\"Module $P_{MP}$ [W]\")\n", - "ax2.set_ylabel(\"Cumulative Energy [kWh]\")\n", - "ax3.set_ylabel(\"Cumulative Energy losses [kWh]\")\n", - "\n", - "ax1.set_title(\"Module power, cumulative energy, and loss due to LETID\")\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### The function `calc_letid_outdoors` wraps all of the steps above into a single function:" - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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TemperatureInjectionNANBNCtauJscVocIscFFPmpPmp_norm
time
1999-01-01 00:30:00-07:000.0NaN100.0000000.000000e+000.000000e+00120.00000041.4513280.66131510.0726730.8401015.5960881.000000
1999-01-01 01:30:00-07:000.0NaN100.0000001.683090e-150.000000e+00120.00000041.4513280.66131510.0726730.8401015.5960881.000000
1999-01-01 02:30:00-07:000.0NaN100.0000003.366181e-155.269266e-36120.00000041.4513280.66131510.0726730.8401015.5960881.000000
1999-01-01 03:30:00-07:000.0NaN100.0000005.049271e-151.580780e-35120.00000041.4513280.66131510.0726730.8401015.5960881.000000
1999-01-01 04:30:00-07:000.0NaN100.0000006.732362e-153.161560e-35120.00000041.4513280.66131510.0726730.8401015.5960881.000000
.......................................
1999-12-31 19:30:00-07:000.0NaN29.7496626.666236e+013.587983e+0090.00145541.3426230.65672310.0462570.8392815.5372490.989486
1999-12-31 20:30:00-07:000.0NaN29.7496626.666236e+013.587983e+0090.00145541.3426230.65672310.0462570.8392815.5372490.989486
1999-12-31 21:30:00-07:000.0NaN29.7496626.666236e+013.587983e+0090.00145541.3426230.65672310.0462570.8392815.5372490.989486
1999-12-31 22:30:00-07:000.0NaN29.7496626.666236e+013.587983e+0090.00145541.3426230.65672310.0462570.8392815.5372490.989486
1999-12-31 23:30:00-07:000.0NaN29.7496626.666236e+013.587983e+0090.00145541.3426230.65672310.0462570.8392815.5372490.989486
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8760 rows Γ— 12 columns

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" - ], - "text/plain": [ - " Temperature Injection NA NB \\\n", - "time \n", - "1999-01-01 00:30:00-07:00 0.0 NaN 100.000000 0.000000e+00 \n", - "1999-01-01 01:30:00-07:00 0.0 NaN 100.000000 1.683090e-15 \n", - "1999-01-01 02:30:00-07:00 0.0 NaN 100.000000 3.366181e-15 \n", - "1999-01-01 03:30:00-07:00 0.0 NaN 100.000000 5.049271e-15 \n", - "1999-01-01 04:30:00-07:00 0.0 NaN 100.000000 6.732362e-15 \n", - "... ... ... ... ... \n", - "1999-12-31 19:30:00-07:00 0.0 NaN 29.749662 6.666236e+01 \n", - "1999-12-31 20:30:00-07:00 0.0 NaN 29.749662 6.666236e+01 \n", - "1999-12-31 21:30:00-07:00 0.0 NaN 29.749662 6.666236e+01 \n", - "1999-12-31 22:30:00-07:00 0.0 NaN 29.749662 6.666236e+01 \n", - "1999-12-31 23:30:00-07:00 0.0 NaN 29.749662 6.666236e+01 \n", - "\n", - " NC tau Jsc Voc \\\n", - "time \n", - "1999-01-01 00:30:00-07:00 0.000000e+00 120.000000 41.451328 0.661315 \n", - "1999-01-01 01:30:00-07:00 0.000000e+00 120.000000 41.451328 0.661315 \n", - "1999-01-01 02:30:00-07:00 5.269266e-36 120.000000 41.451328 0.661315 \n", - "1999-01-01 03:30:00-07:00 1.580780e-35 120.000000 41.451328 0.661315 \n", - "1999-01-01 04:30:00-07:00 3.161560e-35 120.000000 41.451328 0.661315 \n", - "... ... ... ... ... \n", - "1999-12-31 19:30:00-07:00 3.587983e+00 90.001455 41.342623 0.656723 \n", - "1999-12-31 20:30:00-07:00 3.587983e+00 90.001455 41.342623 0.656723 \n", - "1999-12-31 21:30:00-07:00 3.587983e+00 90.001455 41.342623 0.656723 \n", - "1999-12-31 22:30:00-07:00 3.587983e+00 90.001455 41.342623 0.656723 \n", - "1999-12-31 23:30:00-07:00 3.587983e+00 90.001455 41.342623 0.656723 \n", - "\n", - " Isc FF Pmp Pmp_norm \n", - "time \n", - "1999-01-01 00:30:00-07:00 10.072673 0.840101 5.596088 1.000000 \n", - "1999-01-01 01:30:00-07:00 10.072673 0.840101 5.596088 1.000000 \n", - "1999-01-01 02:30:00-07:00 10.072673 0.840101 5.596088 1.000000 \n", - "1999-01-01 03:30:00-07:00 10.072673 0.840101 5.596088 1.000000 \n", - "1999-01-01 04:30:00-07:00 10.072673 0.840101 5.596088 1.000000 \n", - "... ... ... ... ... \n", - "1999-12-31 19:30:00-07:00 10.046257 0.839281 5.537249 0.989486 \n", - "1999-12-31 20:30:00-07:00 10.046257 0.839281 5.537249 0.989486 \n", - "1999-12-31 21:30:00-07:00 10.046257 0.839281 5.537249 0.989486 \n", - "1999-12-31 22:30:00-07:00 10.046257 0.839281 5.537249 0.989486 \n", - "1999-12-31 23:30:00-07:00 10.046257 0.839281 5.537249 0.989486 \n", - "\n", - "[8760 rows x 12 columns]" - ] - }, - "execution_count": 69, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "mechanism_params = \"repins\"\n", - "\n", - "letid.calc_letid_outdoors(\n", - " tau_0,\n", - " tau_deg,\n", - " wafer_thickness,\n", - " srv_rear,\n", - " nA_0,\n", - " nB_0,\n", - " nC_0,\n", - " weather,\n", - " meta,\n", - " mechanism_params,\n", - " generation_df,\n", - " module_parameters=cec_module,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 70, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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DatetimeTemperatureInjectionNANBNCtauJscVocIsc...i_xi_xxEffective irradianceghidhidniwind_speedtemp_airprecipitable_waterTimedelta
01999-01-01 00:30:00-07:000.0NaN100.0000000.000000e+000.000000e+00120.00000041.4513280.6613159.981480...0.00.0NaN0.00.00.01.80.00.8918690.0
11999-01-01 01:30:00-07:000.0NaN100.0000001.683090e-150.000000e+00120.00000041.4513280.6613159.981480...0.00.0NaN0.00.00.01.70.00.9081581.0
21999-01-01 02:30:00-07:000.0NaN100.0000003.366181e-155.269266e-36120.00000041.4513280.6613159.981480...0.00.0NaN0.00.00.01.50.00.9321992.0
31999-01-01 03:30:00-07:000.0NaN100.0000005.049271e-151.580780e-35120.00000041.4513280.6613159.981480...0.00.0NaN0.00.00.01.30.00.9550043.0
41999-01-01 04:30:00-07:000.0NaN100.0000006.732362e-153.161560e-35120.00000041.4513280.6613159.981480...0.00.0NaN0.00.00.01.30.00.9639914.0
..................................................................
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8760 rows Γ— 28 columns

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Define necessary wafer device parameters\n", - "2. Define necessary degradation parameters: degraded lifetime and defect states\n", - "3. Create timeseries of temperature and injection\n", - "4. Run through timeseries, calculating defect states\n", - "5. Calculate device degradation and plot\n" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [], - "source": [ - "# if running on google colab, uncomment the next line and execute this cell to install the dependencies and prevent \"ModuleNotFoundError\" in later cells:\n", - "# !pip install pvdeg==0.3.3" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [], - "source": [ - "from pvdeg import letid, collection, utilities, DATA_DIR\n", - "import pvdeg\n", - "import os\n", - "import pandas as pd\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Working on a Windows 10\n", - "Python version 3.11.7 | packaged by Anaconda, Inc. | (main, Dec 15 2023, 18:05:47) [MSC v.1916 64 bit (AMD64)]\n", - "Pandas version 2.2.0\n", - "pvdeg version 0.2.4.dev83+ge2ceab9.d20240422\n" - ] - } - ], - "source": [ - "# This information helps with debugging and getting support :)\n", - "import sys, platform\n", - "\n", - "print(\"Working on a \", platform.system(), platform.release())\n", - "print(\"Python version \", sys.version)\n", - "print(\"Pandas version \", pd.__version__)\n", - "print(\"pvdeg version \", pvdeg.__version__)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Device parameters\n", - "For modeling a wafer, we don't need as many device parameters. Wafer thickness, lifetime, and an optical generation profile, which allows us to calculate generation current, which we take to be device current assuming surface recombination is very low. " - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [], - "source": [ - "wafer_thickness = 180 # um\n", - "tau = 350 # us, lifetime of the wafer for demonstration purposes\n", - "\n", - "generation_df = pd.read_excel(\n", - " os.path.join(DATA_DIR, \"PVL_GenProfile.xlsx\"), header=0\n", - ") # this is an optical generation profile generated by PVLighthouse's OPAL2 default model for 1-sun, normal incident AM1.5 sunlight on a 180-um thick SiNx-coated, pyramid-textured wafer.\n", - "generation = generation_df[\"Generation (cm-3s-1)\"]\n", - "depth = generation_df[\"Depth (um)\"]\n", - "\n", - "j_gen = collection.generation_current(generation, depth)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Degradation parameters\n", - "Here we'll model LETID under 0.5-sun, open-circuit injection. In a passivated wafer, recombination per volume ($\\frac{\\Delta n}{\\tau}$) must equal generation per volume ($\\frac{Jq}{W}$), which gives $\\Delta n = \\frac{Jq\\tau}{W}$\n", - "\n", - "The term $\\Delta n^{x_{ij}}$ is calculated for wafers by the function ```letid.carrier_factor_wafer```, which adjusts the rate of the transition $i \\rightarrow j$ for $\\Delta n$.\n", - "\n", - "Since kinetic parameters are taken from literature that may have used different $\\Delta n$, we adjust transitions by the ratio $\\frac{\\Delta n}{\\Delta n_{lit}}$.\n", - "\n", - "See [Repins *et al.* 2023](https://doi.org/10.1557/s43577-022-00438-8) for more details." - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [], - "source": [ - "mechanism_params = utilities.get_kinetics(\"repins\")\n", - "injection = 0.5 #" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Demonstrate calculation of dn^x_ij\n", - "1. Calculate $\\Delta n$ of the wafer" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "2.57051594134322e+21" - ] - }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from scipy.constants import elementary_charge as q # Elementary charge, C\n", - "\n", - "dn = (\n", - " ((j_gen * 0.001 * 10000 * injection) * (tau * 1e-6)) / (wafer_thickness * 1e-6) / q\n", - ") # calculate excess carrier density\n", - "dn" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "2. Calculate $\\Delta n_{lit}$, the excess carrier density from the literature experiments where kinetic parameters were determined.\n", - "Assuming literature experiments were carried out on cells, this requires using the `calc_dn` function" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "7.064647199192719e+20" - ] - }, - "execution_count": 42, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from scipy.constants import convert_temperature\n", - "\n", - "transition = \"bc\" # for this example we'll look at the B -> C transition\n", - "\n", - "meas_tau = mechanism_params[f\"tau_{transition}\"]\n", - "meas_temp = mechanism_params[f\"temperature_{transition}\"]\n", - "meas_temp = convert_temperature(meas_temp, \"K\", \"C\")\n", - "meas_suns = mechanism_params[f\"suns_{transition}\"]\n", - "meas_jsc = 40\n", - "meas_wafer_thickness = mechanism_params[f\"thickness_{transition}\"]\n", - "meas_srv = mechanism_params[f\"srv_{transition}\"]\n", - "meas_structure = mechanism_params[f\"structure_{transition}\"]\n", - "\n", - "dn_lit = letid.calc_dn(\n", - " meas_tau,\n", - " meas_temp,\n", - " meas_suns,\n", - " meas_jsc,\n", - " wafer_thickness=meas_wafer_thickness,\n", - " s_rear=meas_srv,\n", - ")\n", - "dn_lit" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "3. Return the exponent $x_{ij}$ of the transition in question\n" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1.2" - ] - }, - "execution_count": 43, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "exponent = mechanism_params[f\"x_{transition}\"]\n", - "exponent" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "4. Calculate $(\\frac{\\Delta n}{\\Delta n_{lit}})^{x_{ij}}$. This is the acceleration factor of the transition due to excess carriers.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "4.711029107876425" - ] - }, - "execution_count": 44, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "(dn / dn_lit) ** exponent" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "4.711029107876425" - ] - }, - "execution_count": 45, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# the \"carrier_factor_wafer\" function yields the same result\n", - "\n", - "letid.carrier_factor_wafer(\n", - " tau, transition, injection, j_gen, wafer_thickness, mechanism_params\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Degradation parameters\n", - "To model the device's degradation, we need to define several more important quantities about the degradation the device will experience. These include undegraded and degraded lifetime (in $\\mu s$), and starting defect state percentages" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": {}, - "outputs": [], - "source": [ - "tau_0 = 350 # us, carrier lifetime in non-degraded states, e.g. LETID/LID states A or C\n", - "tau_deg = 41 # us, carrier lifetime in fully-degraded state, e.g. LETID/LID state B" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'mechanism': 'LETID', 'v_ab': 46700000.0, 'v_ba': 4.7e-25, 'v_bc': 19900000.0, 'v_cb': 0.0, 'ea_ab': 0.827, 'ea_ba': -1.15, 'ea_bc': 0.871, 'ea_cb': 0.0, 'suns_ab': 1.0, 'suns_bc': 1.0, 'temperature_ab': 410, 'temperature_bc': 410, 'tau_ab': 75, 'tau_bc': 75, 'x_ab': 1, 'x_ba': 1.7, 'x_bc': 1.2, 'structure_ab': 'cell', 'structure_bc': 'cell', 'thickness_ab': 200, 'thickness_bc': 200, 'srv_ab': 90, 'srv_bc': 90, 'doi': 'doi:10.1557/s43577-022-00438-8', 'comments': ''}\n" - ] - } - ], - "source": [ - "# starting defect state percentages\n", - "nA_0 = 100\n", - "nB_0 = 0\n", - "nC_0 = 0\n", - "\n", - "mechanism_params = utilities.get_kinetics(\"repins\")\n", - "print(mechanism_params)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Set up timeseries\n", - "In this example, we are going to model a wafer exposed to 0.5 suns illumination at $125\\degree C$ for 24 hours, and compare to experimental data from [Wyller *et al.* 2021](https://doi.org/10.1109/JPHOTOV.2021.3078367)." - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\mspringe\\AppData\\Local\\Temp\\1\\ipykernel_6508\\530898593.py:4: FutureWarning: 'S' is deprecated and will be removed in a future version, please use 's' instead.\n", - " timesteps = pd.date_range(start = '2022-01-01 00:00:00', end = '2022-01-02 00:00:00', freq = 'S') # three weeks of 1-minute interval timesteps. In general, we should select small timesteps unless we are sure defect reactions are proceeding very slowly\n" - ] - } - ], - "source": [ - "temperature = 125 # degrees celsius\n", - "suns = 0.5 # \"suns\" of injection, e.g 1-sun illumination at open circuit would be 1; dark current injection is given as a fraction of Isc, e.g., injecting Isc would be 1. For this example we assume injection is 0.1*Isc.\n", - "\n", - "timesteps = pd.date_range(\n", - " start=\"2022-01-01 00:00:00\", end=\"2022-01-02 00:00:00\", freq=\"S\"\n", - ") # three weeks of 1-minute interval timesteps. In general, we should select small timesteps unless we are sure defect reactions are proceeding very slowly\n", - "timesteps = pd.DataFrame(timesteps, columns=[\"Datetime\"])\n", - "\n", - "temps = np.full(len(timesteps), temperature)\n", - "injection = np.full(len(timesteps), suns)\n", - "\n", - "timesteps[\"Temperature\"] = temps\n", - "timesteps[\"Injection\"] = injection\n", - "\n", - "timesteps[[\"NA\", \"NB\", \"NC\", \"tau\"]] = (\n", - " np.nan\n", - ") # create columns for defect state percentages and lifetime, fill with NaNs for now, to fill iteratively below\n", - "\n", - "timesteps.loc[0, [\"NA\", \"NB\", \"NC\"]] = (\n", - " nA_0,\n", - " nB_0,\n", - " nC_0,\n", - ") # assign first timestep defect state percentages\n", - "timesteps.loc[0, \"tau\"] = letid.tau_now(\n", - " tau_0, tau_deg, nB_0\n", - ") # calculate tau for the first timestep" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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86401 rows Γ— 7 columns

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" - ], - "text/plain": [ - " Datetime Temperature Injection NA NB NC tau\n", - "0 2022-01-01 00:00:00 125 0.5 100.0 0.0 0.0 350.0\n", - "1 2022-01-01 00:00:01 125 0.5 NaN NaN NaN NaN\n", - "2 2022-01-01 00:00:02 125 0.5 NaN NaN NaN NaN\n", - "3 2022-01-01 00:00:03 125 0.5 NaN NaN NaN NaN\n", - "4 2022-01-01 00:00:04 125 0.5 NaN NaN NaN NaN\n", - "... ... ... ... ... ... ... ...\n", - "86396 2022-01-01 23:59:56 125 0.5 NaN NaN NaN NaN\n", - "86397 2022-01-01 23:59:57 125 0.5 NaN NaN NaN NaN\n", - "86398 2022-01-01 23:59:58 125 0.5 NaN NaN NaN NaN\n", - "86399 2022-01-01 23:59:59 125 0.5 NaN NaN NaN NaN\n", - "86400 2022-01-02 00:00:00 125 0.5 NaN NaN NaN NaN\n", - "\n", - "[86401 rows x 7 columns]" - ] - }, - "execution_count": 49, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "timesteps" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Run through timesteps\n", - "Since each timestep depends on the preceding timestep, we need to calculate in a loop. This will take a few minutes depending on the length of the timeseries." - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": {}, - "outputs": [], - "source": [ - "for index, timestep in timesteps.iterrows():\n", - "\n", - " # first row tau has already been assigned\n", - " if index == 0:\n", - " pass\n", - "\n", - " # loop through rows, new tau calculated based on previous NB. Reaction proceeds based on new tau.\n", - " else:\n", - " n_A = timesteps.at[index - 1, \"NA\"]\n", - " n_B = timesteps.at[index - 1, \"NB\"]\n", - " n_C = timesteps.at[index - 1, \"NC\"]\n", - "\n", - " tau = letid.tau_now(tau_0, tau_deg, n_B)\n", - " j_gen = j_gen\n", - "\n", - " temperature = timesteps.at[index, \"Temperature\"]\n", - " injection = timesteps.at[index, \"Injection\"]\n", - "\n", - " # calculate defect reaction kinetics: reaction constant and carrier concentration factor.\n", - " k_AB = letid.k_ij(\n", - " mechanism_params[\"v_ab\"], mechanism_params[\"ea_ab\"], temperature\n", - " )\n", - " k_BA = letid.k_ij(\n", - " mechanism_params[\"v_ba\"], mechanism_params[\"ea_ba\"], temperature\n", - " )\n", - " k_BC = letid.k_ij(\n", - " mechanism_params[\"v_bc\"], mechanism_params[\"ea_bc\"], temperature\n", - " )\n", - " k_CB = letid.k_ij(\n", - " mechanism_params[\"v_cb\"], mechanism_params[\"ea_cb\"], temperature\n", - " )\n", - "\n", - " x_ab = letid.carrier_factor_wafer(\n", - " tau, \"ab\", injection, j_gen, wafer_thickness, mechanism_params\n", - " )\n", - " x_ba = letid.carrier_factor_wafer(\n", - " tau, \"ba\", injection, j_gen, wafer_thickness, mechanism_params\n", - " )\n", - " x_bc = letid.carrier_factor_wafer(\n", - " tau, \"bc\", injection, j_gen, wafer_thickness, mechanism_params\n", - " )\n", - " # x_cb there is no known excess carrier acceleration factor for the c->b transition\n", - "\n", - " # calculate the instantaneous change in NA, NB, and NC\n", - " dN_Adt = (k_BA * n_B * x_ba) - (k_AB * n_A * x_ab)\n", - " dN_Bdt = (\n", - " (k_AB * n_A * x_ab) + (k_CB * n_C) - ((k_BA * x_ba + k_BC * x_bc) * n_B)\n", - " )\n", - " dN_Cdt = (k_BC * n_B * x_bc) - (k_CB * n_C)\n", - "\n", - " t_step = (\n", - " timesteps.at[index, \"Datetime\"] - timesteps.at[index - 1, \"Datetime\"]\n", - " ).total_seconds()\n", - "\n", - " # assign new defect state percentages\n", - " timesteps.at[index, \"NA\"] = n_A + dN_Adt * t_step\n", - " timesteps.at[index, \"NB\"] = n_B + dN_Bdt * t_step\n", - " timesteps.at[index, \"NC\"] = n_C + dN_Cdt * t_step" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Finish calculating degraded device parameters.\n", - "Now that we have calculated defect states, we can calculate all the quantities that depend on defect states. Since this is a wafer, device parameters like Jsc, Voc, FF, etc., aren't really appropriate\n", - "\n", - "For wafers, results are often presented in terms of normalized defect density (NDD), where\n", - "\n", - "$NDD(t) = \\frac{1}{\\tau (t)} - \\frac{1}{\\tau _0}$" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": {}, - "outputs": [], - "source": [ - "timesteps[\"tau\"] = letid.tau_now(tau_0, tau_deg, timesteps[\"NB\"])\n", - "\n", - "timesteps[\"NDD\"] = letid.calc_ndd(timesteps[\"tau\"].iloc[0], timesteps[\"tau\"])\n", - "\n", - "timesteps[\"time (s)\"] = (\n", - " timesteps[\"Datetime\"] - timesteps.iloc[0][\"Datetime\"]\n", - ").dt.total_seconds() # create a column for seconds elapsed" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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DatetimeTemperatureInjectionNANBNCtauNDDtime (s)
02022-01-01 00:00:001250.51.000000e+020.000000e+000.000000350.0000000.0000000.0
12022-01-01 00:00:011250.59.942178e+015.782160e-010.000000335.3846930.0001251.0
22022-01-01 00:00:021250.59.887092e+011.128597e+000.000486322.5634990.0002432.0
32022-01-01 00:00:031250.59.834404e+011.654564e+000.001392311.1947450.0003563.0
42022-01-01 00:00:041250.59.783845e+012.158887e+000.002663301.0218240.0004654.0
..............................
863962022-01-01 23:59:561250.55.800845e-343.370010e-27100.000000350.0000000.00000086396.0
863972022-01-01 23:59:571250.55.795712e-343.367028e-27100.000000350.0000000.00000086397.0
863982022-01-01 23:59:581250.55.790584e-343.364048e-27100.000000350.0000000.00000086398.0
863992022-01-01 23:59:591250.55.785460e-343.361072e-27100.000000350.0000000.00000086399.0
864002022-01-02 00:00:001250.55.780341e-343.358098e-27100.000000350.0000000.00000086400.0
\n", - "

86401 rows Γ— 9 columns

\n", - "
" - ], - "text/plain": [ - " Datetime Temperature Injection NA NB \\\n", - "0 2022-01-01 00:00:00 125 0.5 1.000000e+02 0.000000e+00 \n", - "1 2022-01-01 00:00:01 125 0.5 9.942178e+01 5.782160e-01 \n", - "2 2022-01-01 00:00:02 125 0.5 9.887092e+01 1.128597e+00 \n", - "3 2022-01-01 00:00:03 125 0.5 9.834404e+01 1.654564e+00 \n", - "4 2022-01-01 00:00:04 125 0.5 9.783845e+01 2.158887e+00 \n", - "... ... ... ... ... ... \n", - "86396 2022-01-01 23:59:56 125 0.5 5.800845e-34 3.370010e-27 \n", - "86397 2022-01-01 23:59:57 125 0.5 5.795712e-34 3.367028e-27 \n", - "86398 2022-01-01 23:59:58 125 0.5 5.790584e-34 3.364048e-27 \n", - "86399 2022-01-01 23:59:59 125 0.5 5.785460e-34 3.361072e-27 \n", - "86400 2022-01-02 00:00:00 125 0.5 5.780341e-34 3.358098e-27 \n", - "\n", - " NC tau NDD time (s) \n", - "0 0.000000 350.000000 0.000000 0.0 \n", - "1 0.000000 335.384693 0.000125 1.0 \n", - "2 0.000486 322.563499 0.000243 2.0 \n", - "3 0.001392 311.194745 0.000356 3.0 \n", - "4 0.002663 301.021824 0.000465 4.0 \n", - "... ... ... ... ... \n", - "86396 100.000000 350.000000 0.000000 86396.0 \n", - "86397 100.000000 350.000000 0.000000 86397.0 \n", - "86398 100.000000 350.000000 0.000000 86398.0 \n", - "86399 100.000000 350.000000 0.000000 86399.0 \n", - "86400 100.000000 350.000000 0.000000 86400.0 \n", - "\n", - "[86401 rows x 9 columns]" - ] - }, - "execution_count": 52, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "timesteps" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plot the results" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from cycler import cycler\n", - "\n", - "plt.style.use(\"default\")\n", - "\n", - "fig, ax = plt.subplots()\n", - "\n", - "ax.set_prop_cycle(\n", - " cycler(\"color\", [\"tab:blue\", \"tab:orange\", \"tab:green\"])\n", - " + cycler(\"linestyle\", [\"-\", \"--\", \"-.\"])\n", - ")\n", - "\n", - "ax.plot(timesteps[\"time (s)\"], timesteps[[\"NA\", \"NB\", \"NC\"]].values)\n", - "ax.legend(labels=[\"$N_A$\", \"$N_B$\", \"$N_C$\"], loc=\"upper left\")\n", - "ax.set_ylabel(\"Defect state percentages [%]\")\n", - "ax.set_xlabel(\"Time [s]\")\n", - "\n", - "ax2 = ax.twinx()\n", - "c_norm = 1.35 # Normalization constants \"C_norm\". See Repins 2023 for details\n", - "ax2.plot(\n", - " timesteps[\"time (s)\"], timesteps[\"NDD\"] * c_norm, c=\"black\", label=\"NDD modeled\"\n", - ")\n", - "ax.set_ylabel(\"NDD [$\\mu s^{-1}$]\")\n", - "\n", - "literature_data_file = os.path.join(DATA_DIR, \"wyller data.csv\")\n", - "data = pd.read_csv(literature_data_file, header=0)\n", - "filtered = data[data[\"Series\"] == \"0.5 sun 125C\"]\n", - "ax2.scatter(\n", - " filtered[\"X\"], filtered[\"Y\"], label=\"NDD data from Wyller\", marker=\"v\", c=\"black\"\n", - ")\n", - "\n", - "ax2.legend(loc=\"center left\")\n", - "\n", - "ax.set_xscale(\"log\")\n", - "\n", - "ax.set_title(\n", - " \"Accelerated LETID Test (wafer)\\n\"\n", - " rf\"{temperature}$\\degree$C, {suns} suns illumination\"\n", - ")\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Compare model results to experimental data\n", - "Experimental data (0.5 suns, 125°C, 150°C, 175°C) from [Wyller 2021](https://doi.org/10.1109/JPHOTOV.2021.3078367).\n", - "Comparison with experimental data requires normalization constant ($C_{norm}$), which relates NDD to $N_B$ as described in [Repins 2023](https://doi.org/10.1557/s43577-022-00438-8)." - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "metadata": {}, - "outputs": [], - "source": [ - "temperatures = [125, 150, 175] # degrees celsius" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "metadata": {}, - "outputs": [], - "source": [ - "timesteps125 = timesteps # we already modeled the 125C experiment above" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\mspringe\\AppData\\Local\\Temp\\1\\ipykernel_6508\\241191725.py:4: FutureWarning: 'S' is deprecated and will be removed in a future version, please use 's' instead.\n", - " timesteps = pd.date_range(start = '2022-01-01 00:00:00', end = '2022-01-02 00:00:00', freq = 'S') # three weeks of 1-minute interval timesteps. In general, we should select small timesteps unless we are sure defect reactions are proceeding very slowly\n" - ] - } - ], - "source": [ - "temperature = temperatures[1] # degrees celsius\n", - "suns = 0.5 # \"suns\" of injection, e.g 1-sun illumination at open circuit would be 1; dark current injection is given as a fraction of Isc, e.g., injecting Isc would be 1. For this example we assume injection is 0.1*Isc.\n", - "\n", - "timesteps = pd.date_range(\n", - " start=\"2022-01-01 00:00:00\", end=\"2022-01-02 00:00:00\", freq=\"S\"\n", - ") # three weeks of 1-minute interval timesteps. In general, we should select small timesteps unless we are sure defect reactions are proceeding very slowly\n", - "timesteps = pd.DataFrame(timesteps, columns=[\"Datetime\"])\n", - "\n", - "temps = np.full(len(timesteps), temperature)\n", - "injection = np.full(len(timesteps), suns)\n", - "\n", - "timesteps[\"Temperature\"] = temps\n", - "timesteps[\"Injection\"] = injection\n", - "\n", - "timesteps[[\"NA\", \"NB\", \"NC\", \"tau\"]] = (\n", - " np.nan\n", - ") # create columns for defect state percentages and lifetime, fill with NaNs for now, to fill iteratively below\n", - "\n", - "timesteps.loc[0, [\"NA\", \"NB\", \"NC\"]] = (\n", - " nA_0,\n", - " nB_0,\n", - " nC_0,\n", - ") # assign first timestep defect state percentages\n", - "timesteps.loc[0, \"tau\"] = letid.tau_now(\n", - " tau_0, tau_deg, nB_0\n", - ") # calculate tau for the first timestep" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "metadata": {}, - "outputs": [], - "source": [ - "for index, timestep in timesteps.iterrows():\n", - "\n", - " # first row tau has already been assigned\n", - " if index == 0:\n", - " pass\n", - "\n", - " # loop through rows, new tau calculated based on previous NB. Reaction proceeds based on new tau.\n", - " else:\n", - " n_A = timesteps.at[index - 1, \"NA\"]\n", - " n_B = timesteps.at[index - 1, \"NB\"]\n", - " n_C = timesteps.at[index - 1, \"NC\"]\n", - "\n", - " tau = letid.tau_now(tau_0, tau_deg, n_B)\n", - " j_gen = j_gen\n", - "\n", - " temperature = timesteps.at[index, \"Temperature\"]\n", - " injection = timesteps.at[index, \"Injection\"]\n", - "\n", - " # calculate defect reaction kinetics: reaction constant and carrier concentration factor.\n", - " k_AB = letid.k_ij(\n", - " mechanism_params[\"v_ab\"], mechanism_params[\"ea_ab\"], temperature\n", - " )\n", - " k_BA = letid.k_ij(\n", - " mechanism_params[\"v_ba\"], mechanism_params[\"ea_ba\"], temperature\n", - " )\n", - " k_BC = letid.k_ij(\n", - " mechanism_params[\"v_bc\"], mechanism_params[\"ea_bc\"], temperature\n", - " )\n", - " k_CB = letid.k_ij(\n", - " mechanism_params[\"v_cb\"], mechanism_params[\"ea_cb\"], temperature\n", - " )\n", - "\n", - " x_ab = letid.carrier_factor_wafer(\n", - " tau, \"ab\", injection, j_gen, wafer_thickness, mechanism_params\n", - " )\n", - " x_ba = letid.carrier_factor_wafer(\n", - " tau, \"ba\", injection, j_gen, wafer_thickness, mechanism_params\n", - " )\n", - " x_bc = letid.carrier_factor_wafer(\n", - " tau, \"bc\", injection, j_gen, wafer_thickness, mechanism_params\n", - " )\n", - " # x_cb there is no known excess carrier acceleration factor for the c->b transition\n", - "\n", - " # calculate the instantaneous change in NA, NB, and NC\n", - " dN_Adt = (k_BA * n_B * x_ba) - (k_AB * n_A * x_ab)\n", - " dN_Bdt = (\n", - " (k_AB * n_A * x_ab) + (k_CB * n_C) - ((k_BA * x_ba + k_BC * x_bc) * n_B)\n", - " )\n", - " dN_Cdt = (k_BC * n_B * x_bc) - (k_CB * n_C)\n", - "\n", - " t_step = (\n", - " timesteps.at[index, \"Datetime\"] - timesteps.at[index - 1, \"Datetime\"]\n", - " ).total_seconds()\n", - "\n", - " # assign new defect state percentages\n", - " timesteps.at[index, \"NA\"] = n_A + dN_Adt * t_step\n", - " timesteps.at[index, \"NB\"] = n_B + dN_Bdt * t_step\n", - " timesteps.at[index, \"NC\"] = n_C + dN_Cdt * t_step" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "metadata": {}, - "outputs": [], - "source": [ - "timesteps[\"tau\"] = letid.tau_now(tau_0, tau_deg, timesteps[\"NB\"])\n", - "timesteps[\"NDD\"] = letid.calc_ndd(timesteps[\"tau\"].iloc[0], timesteps[\"tau\"])\n", - "timesteps[\"time (s)\"] = (\n", - " timesteps[\"Datetime\"] - timesteps.iloc[0][\"Datetime\"]\n", - ").dt.total_seconds() # create a column for seconds elapsed" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "metadata": {}, - "outputs": [], - "source": [ - "timesteps150 = timesteps" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\mspringe\\AppData\\Local\\Temp\\1\\ipykernel_6508\\950821369.py:4: FutureWarning: 'S' is deprecated and will be removed in a future version, please use 's' instead.\n", - " timesteps = pd.date_range(start = '2022-01-01 00:00:00', end = '2022-01-02 00:00:00', freq = 'S') # three weeks of 1-minute interval timesteps. In general, we should select small timesteps unless we are sure defect reactions are proceeding very slowly\n" - ] - } - ], - "source": [ - "temperature = temperatures[2] # degrees celsius\n", - "suns = 0.5 # \"suns\" of injection, e.g 1-sun illumination at open circuit would be 1; dark current injection is given as a fraction of Isc, e.g., injecting Isc would be 1. For this example we assume injection is 0.1*Isc.\n", - "\n", - "timesteps = pd.date_range(\n", - " start=\"2022-01-01 00:00:00\", end=\"2022-01-02 00:00:00\", freq=\"S\"\n", - ") # three weeks of 1-minute interval timesteps. In general, we should select small timesteps unless we are sure defect reactions are proceeding very slowly\n", - "timesteps = pd.DataFrame(timesteps, columns=[\"Datetime\"])\n", - "\n", - "temps = np.full(len(timesteps), temperature)\n", - "injection = np.full(len(timesteps), suns)\n", - "\n", - "timesteps[\"Temperature\"] = temps\n", - "timesteps[\"Injection\"] = injection\n", - "\n", - "timesteps[[\"NA\", \"NB\", \"NC\", \"tau\"]] = (\n", - " np.nan\n", - ") # create columns for defect state percentages and lifetime, fill with NaNs for now, to fill iteratively below\n", - "\n", - "timesteps.loc[0, [\"NA\", \"NB\", \"NC\"]] = (\n", - " nA_0,\n", - " nB_0,\n", - " nC_0,\n", - ") # assign first timestep defect state percentages\n", - "timesteps.loc[0, \"tau\"] = letid.tau_now(\n", - " tau_0, tau_deg, nB_0\n", - ") # calculate tau for the first timestep" - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "metadata": {}, - "outputs": [], - "source": [ - "for index, timestep in timesteps.iterrows():\n", - "\n", - " # first row tau has already been assigned\n", - " if index == 0:\n", - " pass\n", - "\n", - " # loop through rows, new tau calculated based on previous NB. Reaction proceeds based on new tau.\n", - " else:\n", - " n_A = timesteps.at[index - 1, \"NA\"]\n", - " n_B = timesteps.at[index - 1, \"NB\"]\n", - " n_C = timesteps.at[index - 1, \"NC\"]\n", - "\n", - " tau = letid.tau_now(tau_0, tau_deg, n_B)\n", - " j_gen = j_gen\n", - "\n", - " temperature = timesteps.at[index, \"Temperature\"]\n", - " injection = timesteps.at[index, \"Injection\"]\n", - "\n", - " # calculate defect reaction kinetics: reaction constant and carrier concentration factor.\n", - " k_AB = letid.k_ij(\n", - " mechanism_params[\"v_ab\"], mechanism_params[\"ea_ab\"], temperature\n", - " )\n", - " k_BA = letid.k_ij(\n", - " mechanism_params[\"v_ba\"], mechanism_params[\"ea_ba\"], temperature\n", - " )\n", - " k_BC = letid.k_ij(\n", - " mechanism_params[\"v_bc\"], mechanism_params[\"ea_bc\"], temperature\n", - " )\n", - " k_CB = letid.k_ij(\n", - " mechanism_params[\"v_cb\"], mechanism_params[\"ea_cb\"], temperature\n", - " )\n", - "\n", - " x_ab = letid.carrier_factor_wafer(\n", - " tau, \"ab\", injection, j_gen, wafer_thickness, mechanism_params\n", - " )\n", - " x_ba = letid.carrier_factor_wafer(\n", - " tau, \"ba\", injection, j_gen, wafer_thickness, mechanism_params\n", - " )\n", - " x_bc = letid.carrier_factor_wafer(\n", - " tau, \"bc\", injection, j_gen, wafer_thickness, mechanism_params\n", - " )\n", - " # x_cb there is no known excess carrier acceleration factor for the c->b transition\n", - "\n", - " # calculate the instantaneous change in NA, NB, and NC\n", - " dN_Adt = (k_BA * n_B * x_ba) - (k_AB * n_A * x_ab)\n", - " dN_Bdt = (\n", - " (k_AB * n_A * x_ab) + (k_CB * n_C) - ((k_BA * x_ba + k_BC * x_bc) * n_B)\n", - " )\n", - " dN_Cdt = (k_BC * n_B * x_bc) - (k_CB * n_C)\n", - "\n", - " t_step = (\n", - " timesteps.at[index, \"Datetime\"] - timesteps.at[index - 1, \"Datetime\"]\n", - " ).total_seconds()\n", - "\n", - " # assign new defect state percentages\n", - " timesteps.at[index, \"NA\"] = n_A + dN_Adt * t_step\n", - " timesteps.at[index, \"NB\"] = n_B + dN_Bdt * t_step\n", - " timesteps.at[index, \"NC\"] = n_C + dN_Cdt * t_step" - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "metadata": {}, - "outputs": [], - "source": [ - "timesteps[\"tau\"] = letid.tau_now(tau_0, tau_deg, timesteps[\"NB\"])\n", - "timesteps[\"NDD\"] = letid.calc_ndd(timesteps[\"tau\"].iloc[0], timesteps[\"tau\"])\n", - "timesteps[\"time (s)\"] = (\n", - " timesteps[\"Datetime\"] - timesteps.iloc[0][\"Datetime\"]\n", - ").dt.total_seconds() # create a column for seconds elapsed" - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "metadata": {}, - "outputs": [], - "source": [ - "timesteps175 = timesteps" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "metadata": {}, - "outputs": [], - "source": [ - "timesteps = pd.concat([timesteps125, timesteps150, timesteps175])" - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "metadata": {}, - "outputs": [], - "source": [ - "normalization_constants = {\n", - " 125: 1.35,\n", - " 150: 0.6,\n", - " 175: 0.35,\n", - "} # Normalization constants \"C_norm\". See Repins 2023 for details\n", - "\n", - "timesteps[\"Normalization constant\"] = timesteps[\"Temperature\"].map(\n", - " normalization_constants\n", - ")\n", - "timesteps[\"NDD-normalized\"] = timesteps[\"NDD\"] * timesteps[\"Normalization constant\"]" - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "metadata": {}, - "outputs": [], - "source": [ - "grouped_model = timesteps.groupby(\"Temperature\")" - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "metadata": {}, - "outputs": [], - "source": [ - "literature_data_file = os.path.join(DATA_DIR, \"wyller data.csv\")\n", - "data = pd.read_csv(literature_data_file, header=0)\n", - "\n", - "data[\"Temperature\"] = pd.to_numeric(data[\"Series\"].str[-4:-1])\n", - "# normalization_constants = {'0.5 sun 125C' : 1/1.35, '0.5 sun 150C' : 1/0.6, '0.5 sun 175C' : 1/0.35}\n", - "# data['Normalization constant'] = data['Series'].map(normalization_constants)\n", - "# data['Y-normalized'] = data['Y']*data['Normalization constant']" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "metadata": {}, - "outputs": [], - "source": [ - "grouped_data = data.groupby(\"Temperature\")" - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import itertools\n", - "\n", - "fig, ax = plt.subplots()\n", - "\n", - "for name, group in grouped_model:\n", - " ax.plot(\n", - " group[\"time (s)\"] + 0.1,\n", - " group[\"NDD-normalized\"],\n", - " label=f\"Modeled NDD {name}$\\degree$C\",\n", - " )\n", - " ax.legend()\n", - "\n", - "\n", - "marker = itertools.cycle((\"o\", \"s\", \"v\", \"o\", \"*\"))\n", - "\n", - "for name, group in grouped_data:\n", - " ax.scatter(\n", - " group[\"X\"],\n", - " group[\"Y\"],\n", - " label=f\"Wyller NDD {name}$\\degree$C\",\n", - " marker=next(marker),\n", - " )\n", - " ax.legend()\n", - "\n", - "ax.set_xscale(\"log\")\n", - "\n", - "ax.set_xlabel(\"Time [s]\")\n", - "ax.set_ylabel(\"NDD [$\\mu s^{-1}$]\")\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 70, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import itertools\n", - "\n", - "fig, ax = plt.subplots()\n", - "\n", - "colors = itertools.cycle((\"red\", \"blue\", \"green\"))\n", - "\n", - "for name, group in grouped_model:\n", - " ax.plot(\n", - " group[\"time (s)\"] + 0.1,\n", - " group[\"NDD-normalized\"],\n", - " color=next(colors),\n", - " label=f\"Modeled {name}$\\degree$C\",\n", - " linewidth=3,\n", - " )\n", - " ax.legend(prop={\"size\": 12})\n", - "\n", - "\n", - "marker = itertools.cycle((\"o\", \"s\", \"v\", \"o\", \"*\"))\n", - "\n", - "for name, group in grouped_data:\n", - " ax.scatter(\n", - " group[\"X\"],\n", - " group[\"Y\"],\n", - " label=f\"Literature data {name}$\\degree$C\",\n", - " color=next(colors),\n", - " marker=next(marker),\n", - " s=70,\n", - " )\n", - " ax.legend(prop={\"size\": 12})\n", - "\n", - "ax.set_xscale(\"log\")\n", - "\n", - "ax.tick_params(axis=\"both\", which=\"major\", labelsize=16)\n", - "\n", - "\n", - "ax.set_xlabel(\"Time [s]\", fontsize=16)\n", - "ax.set_ylabel(\"Normalized defect \\n density [$\\mu s^{-1}$]\", fontsize=16)\n", - "\n", - "ax.set_title(\"Modeled vs. Literature LETID data\", fontsize=16)\n", - "\n", - "\n", - "plt.tight_layout()\n", - "\n", - "# fig.savefig('wafer data.png', dpi = 600)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.7" - }, - "vscode": { - "interpreter": { - "hash": "848658e0671c41dd18b216771b1713479db7d685859cbb6c795b270024b1888c" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/Tools - Module Standoff for IEC TS 63126.ipynb b/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/Tools - Module Standoff for IEC TS 63126.ipynb deleted file mode 100644 index f1a6a3a22..000000000 --- a/tutorials_and_tools/_build/html/_sources/tutorials_and_tools/Tools - Module Standoff for IEC TS 63126.ipynb +++ /dev/null @@ -1,777 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Tools - Module Standoff for IEC TS 63126\n", - "\n", - "### Calculation of module standoff distance according to IEC TS 63126\n", - "\n", - "**Requirements:**\n", - "- Local weather data file or site longitude and latittude\n", - "\n", - "**Objectives:**\n", - "1. Import weather data.\n", - "2. Calculate installation standoff - Level 1 and Level 2.\n", - "3. Calculate $X_{eff}$ from provided module temperature data.\n", - "4. Calculate $T_{98}$ for a given azimuth, tilt, and $X_{eff}$.\n", - "5. Plot $X_{min}$ for all azimuth and tilt for a given $T_{98}$.\n", - "6. Plot $X_{min}$ for Level 1, Level 2, or a $T_{98}$ for a given region.\n", - "\n", - "**Background:**\n", - "\n", - "This notebook calculates the a minimum effective standoff distance ($X_{eff}$) necessary for roof-mounted PV modules to ensure that the $98^{th}$ percentile operating temperature, $T_{98}$, remains under 70Β°C for compliance to IEC 61730 and IEC 61215. For higher $T_{98}$ values above 70Β°C or 80Β°C testing must be done to the specifications for Level 1 and Level 2 of IEC TS 63126. This method is outlined in the appendix of IEC TS 63126 and is based on the model from *[King 2004] and data from **[Fuentes, 1987] to model the approximate exponential decay in temperature, $T(X)$, with increasing standoff distance, $X$, as,\n", - "\n", - "$$ X = -X_0 \\ln\\left(1-\\frac{T_0-T}{\\Delta T}\\right), Equation 1 $$\n", - "\n", - "where $T_0$ is the temperature for $X=0$ (insulated-back) and $\\Delta T$ is the temperature difference between an insulated-back ($X=0$) and open-rack mounting configuration ($X=\\infty)$.\n", - "\n", - " We used pvlib and data from the National Solar Radiation Database (NSRDB) to calculate the module temperatures for the insulated-back and open-rack mounting configurations and apply our model to obtain the minimum standoff distance for roof-mounted PV systems to achieve a temperature lower than a specified $T_{98}$. The following figure showcases this calulation for the entire world for an $X_{eff}$ that results in $T_{98}$=70Β°C. Values of $X_{eff}$ higher than this will require Level 1 or Level 2 certification. \n", - "\n", - "$*$ D. L. King, W. E. Boyson, and J. A. Kratochvil, \"Photovoltaic array performance model,\" SAND2004-3535, Sandia National Laboratories, Albuquerque, NM, 2004. '\\\n", - "$**$ M. K. Fuentes, \"A simplified thermal model for Flat-Plate photovoltaic arrays,\" United States, 1987-05-01 1987. https://www.osti.gov/biblio/6802914\n" - ] - }, - { - "attachments": { - "62279573-41e3-45dd-bf62-4fa60c1e7e69.png": { - "image/png": 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- } - }, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![T98 70C standoff Map.png](attachment:62279573-41e3-45dd-bf62-4fa60c1e7e69.png)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "# if running on google colab, uncomment the next line and execute this cell to install the dependencies and prevent \"ModuleNotFoundError\" in later cells:\n", - "# !pip install pvdeg==0.3.3" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "from pathlib import Path\n", - "import pvdeg\n", - "import pandas as pd\n", - "from pvdeg import DATA_DIR\n", - "import dask\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import seaborn as sns\n", - "import math" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Working on a Windows 10\n", - "Python version 3.11.7 | packaged by Anaconda, Inc. | (main, Dec 15 2023, 18:05:47) [MSC v.1916 64 bit (AMD64)]\n", - "Pandas version 2.2.0\n", - "pvdeg version 0.2.4.dev83+ge2ceab9.d20240422\n", - "dask version 2024.1.1\n", - "C:\\Users\\mspringe\\OneDrive - NREL\\msp\\projects\\2023_DegradationTools\\Github\\PVDegradationTools\\pvdeg\\data\n" - ] - } - ], - "source": [ - "# This information helps with debugging and getting support :)\n", - "import sys, platform\n", - "\n", - "print(\"Working on a \", platform.system(), platform.release())\n", - "print(\"Python version \", sys.version)\n", - "print(\"Pandas version \", pd.__version__)\n", - "print(\"pvdeg version \", pvdeg.__version__)\n", - "print(\"dask version\", dask.__version__)\n", - "print(DATA_DIR)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1. Import Weather Data\n", - "\n", - "The function has these minimum requirements when using a weather data file:\n", - "- Weather data containing (at least) DNI, DHI, GHI, Temperature, RH, and Wind-Speed data at module level.\n", - "- Site meta-data containing (at least) latitude, longitude, and time zone\n", - "\n", - "Alternatively one may can get meterological data from the NSRDB or PVGIS with just the longitude and latitude. This function for the NSRDB (via NSRDB 'PSM3') works primarily for most of North America and South America. PVGIS works for most of the rest of the world (via SARAH 'PVGIS'). See the tutorial \"Weather Database Access.ipynb\" tutorial on PVdeg or Jensen et al. https://doi.org/10.1016/j.solener.2023.112092 for satellite coverage information.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'Source': 'NSRDB', 'Location ID': 145809.0, 'City': '-', 'State': '-', 'Country': '-', 'Clearsky DHI Units': 'w/m2', 'Clearsky DNI Units': 'w/m2', 'Clearsky GHI Units': 'w/m2', 'Dew Point Units': 'c', 'DHI Units': 'w/m2', 'DNI Units': 'w/m2', 'GHI Units': 'w/m2', 'Solar Zenith Angle Units': 'Degree', 'Temperature Units': 'c', 'Pressure Units': 'mbar', 'Relative Humidity Units': '%', 'Precipitable Water Units': 'cm', 'Wind Direction Units': 'Degrees', 'Wind Speed Units': 'm/s', 'Cloud Type -15': 'N/A', 'Cloud Type 0': 'Clear', 'Cloud Type 1': 'Probably Clear', 'Cloud Type 2': 'Fog', 'Cloud Type 3': 'Water', 'Cloud Type 4': 'Super-Cooled Water', 'Cloud Type 5': 'Mixed', 'Cloud Type 6': 'Opaque Ice', 'Cloud Type 7': 'Cirrus', 'Cloud Type 8': 'Overlapping', 'Cloud Type 9': 'Overshooting', 'Cloud Type 10': 'Unknown', 'Cloud Type 11': 'Dust', 'Cloud Type 12': 'Smoke', 'Fill Flag 0': 'N/A', 'Fill Flag 1': 'Missing Image', 'Fill Flag 2': 'Low Irradiance', 'Fill Flag 3': 'Exceeds Clearsky', 'Fill Flag 4': 'Missing CLoud Properties', 'Fill Flag 5': 'Rayleigh Violation', 'Surface Albedo Units': 'N/A', 'Version': '3.0.6', 'latitude': 39.73, 'longitude': -105.18, 'tz': -7.0, 'altitude': 1820.0}\n" - ] - } - ], - "source": [ - "# Get data from a supplied data file (Do not use the next box of code if using your own file)\n", - "weather_file = os.path.join(DATA_DIR, \"psm3_demo.csv\")\n", - "WEATHER_df, META = pvdeg.weather.read(weather_file, \"csv\")\n", - "print(META)" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Column \"relative_humidity\" not found in DataFrame. Calculating...\n", - "{'Source': 'NSRDB', 'Location ID': '77855', 'City': '-', 'State': '-', 'Country': '-', 'Dew Point Units': 'c', 'DHI Units': 'w/m2', 'DNI Units': 'w/m2', 'GHI Units': 'w/m2', 'Temperature Units': 'c', 'Pressure Units': 'mbar', 'Wind Direction Units': 'Degrees', 'Wind Speed Units': 'm/s', 'Surface Albedo Units': 'N/A', 'Version': '3.2.0', 'latitude': 33.41, 'longitude': -111.82, 'altitude': 381, 'tz': -7, 'wind_height': 2}\n" - ] - } - ], - "source": [ - "# From Tutorial 5 EXAMPLE, this works.\n", - "# API_KEY = 'your_api_key_here'\n", - "API_KEY = \"DEMO_KEY\" # you can activate this line to use the demonstration API key but it has limited usage.\n", - "# The example API key here is for demonstation and is rate-limited per IP.\n", - "# To get your own API key, visit https://developer.nrel.gov/signup/\n", - "\n", - "weather_db = \"PSM3\" # This pulls data from the NSRDB just for locations in North and South America.\n", - "weather_id = (33.4152, -111.8315)\n", - "weather_arg = {\n", - " \"api_key\": API_KEY,\n", - " \"email\": \"user@mail.com\",\n", - " \"names\": \"tmy\",\n", - " \"attributes\": [],\n", - " \"map_variables\": True,\n", - "}\n", - "\n", - "WEATHER_df, META = pvdeg.weather.get(weather_db, weather_id, **weather_arg)\n", - "print(META)" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'latitude': 24.7136, 'longitude': 46.6753, 'altitude': 646.0, 'wind_height': 10, 'Source': 'PVGIS'}\n" - ] - } - ], - "source": [ - "weather_db = \"PVGIS\" # This pulls data for most of the world.\n", - "weather_id = (24.7136, 46.6753) # Riyadh, Saudi Arabia\n", - "# weather_id = (35.6754, 139.65) #Tokyo, Japan\n", - "weather_arg = {\"map_variables\": True}\n", - "WEATHER_df, META = pvdeg.weather.get(weather_db, weather_id)\n", - "print(META)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2. Calculate Installation Standoff Minimum - Level 1 and Level 2\n", - "\n", - "According to IEC TS 63126, Level 0, Level 1 and Level 2 certification is limited to Tβ‚‰β‚ˆ<70Β°C, <80Β°C and <90Β°C, respectively. Level 0 certification is essentially compliance to IEC 61730 and IEC 61215. The default value of Tβ‚‰β‚ˆ<70Β°C represents the minimium gap to avoid higher temperature certification according to IEC TS 63126. This minimum standoff ($x_{min}$) is the distance between the bottom of the module frame and the roof and can be extimated for a given environment as, \n", - "\n", - "$$ X_{min} = -X_0 \\ln\\left(1-\\frac{T_{98,0}-T}{ T_{98,0}- T_{98,inf}}\\right), Equation 2 $$\n", - "\n", - "where $T_{98,0}$ is the $98^{th}$ percentile temperature for an insulated back module and $T_{98,inf}$ is the $98^{th}$ percentile temperature for an open rack mounted module.\n", - "\n", - "Once the meterological data has been obtained, the input parameter possibilities are:\n", - "\n", - "- Tβ‚‰β‚ˆ : Does not necessarily need to be set at 70Β°C or 80Β°C for IEC TS 63216, you might want to use a different number to compensate for a thermal aspect of the particular system you are considering. The default is 70Β°C.\n", - "- tilt : tilt from horizontal of PV module. The default is 0Β°.\n", - "- azimuth : azimuth in degrees from North. The default is 180Β° for south facing.\n", - "- sky_model : pvlib compatible model for generating sky characteristics (Options: 'isotropic', 'klucher', 'haydavies', 'reindl', 'king', 'perez'). The default is 'isotropic'.\n", - "- temp_model : pvlib compatible module temperature model. (Options: 'sapm', 'pvsyst', 'faiman', 'sandia'). The default is 'sapm'.\n", - "- conf_0 : Temperature model for hotest mounting configuration. Default is \"insulated_back_glass_polymer\".\n", - "- conf_inf : Temperature model for open rack mounting. Default is \"open_rack_glass_polymer\".\n", - "- x_0 : thermal decay constant [cm] (see documentation). The default is 6.5 cm.\n", - "- wind_factor : Wind speed power law correction factor to account for different wind speed measurement heights between weather database (e.g. NSRDB) and the tempeature model (e.g. SAPM). The default is 0.33." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The following is the minimum function call. It defaults to horizontal tilt and Tβ‚‰β‚ˆ=70Β°C." - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The array tilt angle was not provided, therefore the latitude tilt of 24.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The estimated Tβ‚‰β‚ˆ of an insulated-back module is 89.6Β°C. \n", - "The estimated Tβ‚‰β‚ˆ of an open-rack module is 63.8Β°C. \n", - "Level 0 certification is valid for a standoff greather than 9.3 cm. \n", - "Level 1 certification is required for a standoff between than 9.3 cm, and 3.0 cm. \n", - "Level 2 certification is required for a standoff less than 3.0 cm.\n" - ] - } - ], - "source": [ - "standoff = pvdeg.standards.standoff(weather_df=WEATHER_df, meta=META)\n", - "print(pvdeg.standards.interpret_standoff(standoff))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The following is a full function call for both Tβ‚‰β‚ˆ=70Β°C and 80Β°C separately even though the second standoff distance can be calculated using only T98_0 and T98_inf. With this function, one may also want to change the tilt, azimuth, or T98. " - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "First calculation standoff = 9.3 cm.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "Second calculation standoff = 3.0 cm.\n", - "The estimated Tβ‚‰β‚ˆ of an insulated-back module is 89.6Β°C. \n", - "The estimated Tβ‚‰β‚ˆ of an open-rack module is 63.8Β°C. \n", - "Level 0 certification is valid for a standoff greather than 9.3 cm. \n", - "Level 1 certification is required for a standoff between than 9.3 cm, and 3.0 cm. \n", - "Level 2 certification is required for a standoff less than 3.0 cm.\n" - ] - } - ], - "source": [ - "standoff_1 = pvdeg.standards.standoff(\n", - " weather_df=WEATHER_df,\n", - " meta=META,\n", - " T98=70,\n", - " tilt=META[\"latitude\"],\n", - " azimuth=None,\n", - " sky_model=\"isotropic\",\n", - " temp_model=\"sapm\",\n", - " conf_0=\"insulated_back_glass_polymer\",\n", - " conf_inf=\"open_rack_glass_polymer\",\n", - " x_0=6.5,\n", - " wind_factor=0.33,\n", - ")\n", - "print(\"First calculation standoff = \", \"%.1f\" % standoff_1[\"x\"].iloc[0], \" cm.\")\n", - "standoff_2 = pvdeg.standards.standoff(\n", - " weather_df=WEATHER_df,\n", - " meta=META,\n", - " T98=80,\n", - " tilt=META[\"latitude\"],\n", - " azimuth=None,\n", - " sky_model=\"isotropic\",\n", - " temp_model=\"sapm\",\n", - " conf_0=\"insulated_back_glass_polymer\",\n", - " conf_inf=\"open_rack_glass_polymer\",\n", - " x_0=6.5,\n", - " wind_factor=0.33,\n", - ")\n", - "print(\"Second calculation standoff = \", \"%.1f\" % standoff_2[\"x\"].iloc[0], \" cm.\")\n", - "print(pvdeg.standards.interpret_standoff(standoff_1=standoff_1, standoff_2=standoff_2))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3. Calculate $X_{eff}$ from provided module temperature data.\n", - "\n", - "To do this calculation, one must use a set of data with: \n", - " - meterological irradiance data sufficient to calculate the POA irradiance (DHI, GHI, and DNI),\n", - " - ambient temperature data,\n", - " - wind speed at module height, (wind_factor=0.33 will be used unless otherwise specified)\n", - " - temperature measurements of the module in the test system. Ideally this would be measured under a worst case scenario that maximizes the module temperature for a given site,\n", - " - geographic meta data including longitude and latitude,\n", - "\n", - "To create a weather file of your own, copy the format of the example file 'xeff_demo.csv'. This is formatted with the first row containing meta data variable names, the second row containing the corresponding values, the third row containing meteorological data headers, and all the remaining rows containing the meteorological data.\n", - "\n", - "To do this calculation, one should also filter the data to remove times when the sun is not shining or when snow is likely to be on the module. The recommendations and programmed defaults are to use poa_min=100 W/mΒ² and data when the minimum ambient temperature t_amb_min=0." - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The effective standoff for this system is 2.0 cm.\n" - ] - } - ], - "source": [ - "# Read the weather file\n", - "weather_file = os.path.join(DATA_DIR, \"xeff_demo.csv\")\n", - "xeff_weather, xeff_meta = pvdeg.weather.read(weather_file, \"csv\")\n", - "# Pull measured temperature and calculate theoretical insulated back module temperature and open rack module temperature\n", - "T_0, T_inf, xeff_poa = pvdeg.standards.eff_gap_parameters(\n", - " weather_df=xeff_weather,\n", - " meta=xeff_meta,\n", - " sky_model=\"isotropic\",\n", - " temp_model=\"sapm\",\n", - " conf_0=\"insulated_back_glass_polymer\",\n", - " conf_inf=\"open_rack_glass_polymer\",\n", - " wind_factor=0.33,\n", - ")\n", - "\n", - "# Now calculate X_eff.\n", - "x_eff = pvdeg.standards.eff_gap(\n", - " T_0,\n", - " T_inf,\n", - " xeff_weather[\"module_temperature\"],\n", - " xeff_weather[\"temp_air\"],\n", - " xeff_poa,\n", - " x_0=6.5,\n", - " poa_min=100,\n", - " t_amb_min=0,\n", - ")\n", - "print(\"The effective standoff for this system is\", \"%.1f\" % x_eff, \"cm.\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 4. Calculate $T_{98}$ for a given azimuth, tilt, and $X_{eff}$.\n", - "\n", - "Equation 2 can be reorganized as,\n", - "\n", - "$$ T_{98} = T_{98,0} -( T_{98,0}- T_{98,inf}) \\left(1-e^{-\\frac{x_{eff}}{x_{0}}}\\right), Equation 3 $$\n", - "\n", - "and used to calculate the $98^{th}$ percential temperature, $T_{98}$, for a PV system having a given effective standoff height, $X_{eff}$, for an arbitrarily oriented module. Here, $T_{98,0}$ is the $98^{th}$ percentile for an insulated-back module and $T_{98,inf}$ is the $98^{th}$ percentile for a rack-mounted module. The input parameter possibilities are the same as shown in Objective #2 above, but the example below uses the default parameters. The actual tilt [degrees], azimuth [degrees] and $X_{eff}$ [cm] can be modifed as desired." - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The 98α΅—Κ° percential temperature is estimated to be 89.6 Β°C.\n" - ] - } - ], - "source": [ - "# This is the minimal function call using the common default settings to estimate Tβ‚‰β‚ˆ.\n", - "T_98 = pvdeg.standards.T98_estimate(\n", - " weather_df=WEATHER_df,\n", - " meta=META,\n", - " tilt=META[\"latitude\"],\n", - " azimuth=None,\n", - " x_eff=0,\n", - ")\n", - "print(\"The 98α΅—Κ° percential temperature is estimated to be\", \"%.1f\" % T_98, \"Β°C.\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 5. Plot $X_{min}$ for all azimuth and tilt for a given $T_{98}$.\n", - "\n", - "The temperature of a system is affected by the orientation. This section will scan all possible tilts and azimuths calculating the minimum standoff distance for a given $T_{98}$. Similar additional factors as above can also be modified but are not included here for simplicity. The tilt_step and azimuth_step are the number of degrees for each step for the 90Β° and 180Β° tilt and azimuth spans, respectively. The default for this calculation is for $T_{98}$=70Β°C, the boundary between Level 0 and Level 1 requirements. The temperature model information given below is unnecessary as these are default values that would get populated automatically. However, they were included here for clarity into a standard practice as per IEC TS 63126.\n", - "\n", - "$$ X_{min} = -X_0 \\ln\\left(1-\\frac{T_{98,0}-T}{ T_{98,0}- T_{98,inf}}\\right), Equation 2 $$" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " \r" - ] - } - ], - "source": [ - "# Scans through all the azimuth and tilt running the minimum standoff calculation\n", - "# Set up keyword parameters for the calculation\n", - "\n", - "kwarg_x = dict(\n", - " sky_model=\"isotropic\",\n", - " temp_model=\"sapm\",\n", - " conf_0=\"insulated_back_glass_polymer\",\n", - " conf_inf=\"open_rack_glass_polymer\",\n", - " T98=70,\n", - " x_0=6.5,\n", - " wind_factor=0.33,\n", - ")\n", - "# Run the calculation\n", - "x_azimuth_step = 10\n", - "x_tilt_step = 10\n", - "standoff_series = pvdeg.utilities.tilt_azimuth_scan(\n", - " weather_df=WEATHER_df,\n", - " meta=META,\n", - " tilt_step=x_tilt_step,\n", - " azimuth_step=x_azimuth_step,\n", - " func=pvdeg.standards.standoff_x,\n", - " **kwarg_x,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The next cell creates a plot of the calculated data. Some of the things you may want to change are:\n", - "- cmap=\"Spectral_r\": Change to have different colors\n", - "- plt.title : This will change the plot title.\n", - "- figsize=(16,4) : Change the plot dimensions and/or aspect ratio.\n", - "- vmax=None : This can be set to a numeric value to control the depth scale maximum\n", - "- vmin=0 : This controls the minimum of the depth scale.\n", - "- v_ticks=37 : This changes the number of vertical tick marks\n", - "- h_ticks=10 : This changes the number of horizontal tick marks\n", - "- Unblock the last two lines to ouput the plot as an *.png image file" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[WinError 183] Cannot create a file when that file already exists: 'c:\\\\Users\\\\mspringe\\\\OneDrive - NREL\\\\msp\\\\projects\\\\2023_DegradationTools\\\\Github\\\\PVDegradationTools\\\\TEMP\\\\results'\n" - ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "standoff_series_df = pd.DataFrame(\n", - " {\n", - " \"Tilt\": standoff_series[:, 0],\n", - " \"Azimuth\": standoff_series[:, 1],\n", - " \"Xβ‚˜α΅’β‚™\": standoff_series[:, 2],\n", - " }\n", - ")\n", - "x_fig = plt.figure(figsize=(16, 4))\n", - "plt.title(\n", - " \"Plot of $\\it{Xβ‚˜α΅’β‚™}$ for all orientations for $\\it{Tβ‚‰β‚ˆ}$=\"\n", - " + \"%.0f\" % kwarg_x[\"T98\"]\n", - " + \"Β°C.\",\n", - " fontsize=15,\n", - " y=1.08,\n", - ")\n", - "x_fig = sns.heatmap(\n", - " standoff_series_df.pivot(index=\"Tilt\", columns=\"Azimuth\", values=\"Xβ‚˜α΅’β‚™\"),\n", - " cbar_kws={\"label\": \"Xβ‚˜α΅’β‚™\", \"format\": \"%.0f\", \"pad\": 0.02},\n", - " cmap=\"Spectral_r\",\n", - " vmin=0,\n", - " vmax=None,\n", - ")\n", - "\n", - "h_ticks = 37\n", - "x_number = math.ceil(360 / x_azimuth_step) + 1\n", - "x_ticks = [\n", - " (x * (360 / (h_ticks - 1)) / x_azimuth_step + 0.5) for x in range(h_ticks - 1)\n", - "]\n", - "x_labels = [(\"%.0f\" % (360 / (h_ticks - 1) * x)) for x in range(h_ticks)]\n", - "x_ticks.append(x_number - 0.5)\n", - "x_fig.set_xticks(x_ticks)\n", - "x_fig.set_xticklabels(x_labels, rotation=90)\n", - "\n", - "v_ticks = 10\n", - "y_number = math.ceil(90 / x_tilt_step) + 1\n", - "y_ticks = [(x * (90 / (v_ticks - 1)) / x_tilt_step + 0.5) for x in range(v_ticks - 1)]\n", - "y_labels = [(\"%.0f\" % (90 / (v_ticks - 1) * x)) for x in range(v_ticks)]\n", - "y_ticks.append(y_number - 0.5)\n", - "x_fig.set_yticks(y_ticks)\n", - "x_fig.set_yticklabels(y_labels, rotation=0)\n", - "\n", - "x_fig.set_xlabel(\"Azimuth [Β°]\", fontsize=15, labelpad=10)\n", - "x_fig.set_ylabel(\"Tilt [Β°]\", fontsize=15)\n", - "x_fig.figure.axes[-1].set_ylabel(\"$\\it{Xβ‚˜α΅’β‚™}$ [cm]\", size=15)\n", - "x_fig.invert_yaxis()\n", - "\n", - "output_folder = os.path.join(\n", - " os.path.dirname(os.path.dirname(os.getcwd())), \"TEMP\", \"results\"\n", - ")\n", - "try:\n", - " os.mkdir(output_folder)\n", - "except OSError as error:\n", - " print(error)\n", - "\n", - "plt.savefig(\n", - " output_folder + \"\\Standoff_Scan.png\", bbox_inches=\"tight\"\n", - ") # Creates an image file of the standoff plot\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 6. Plot $T_{98}$ for all azimuth and tilt for a given $X_{eff}$.\n", - "\n", - "The temperature of a system is affected by the orientation and the effective standoff, $X_{eff}$, of the system. This section will scan all possible tilts and azimuths calculating the $T_{98}$ for a given $X_{eff}$. As above, additional factors can be modified but are not included here for simplicity. The tilt_step and azimuth_step are the number of degrees for each step for the 90Β° and 180Β° tilt and azimuth spans, respectively. The default for this calculation is for $X_{eff}$=10 cm, a common effective standoff distance on a rooftop system. A value of $X_{eff}$=None will run the calculations for an open rack system and $X_{eff}$=0 for an insulated-back system." - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " \r" - ] - } - ], - "source": [ - "# Scans through all the azimuth and tilt running the 98α΅—Κ° percentile temperature calculation.\n", - "# Set up keyword parameters for the calculation\n", - "kwarg_T = dict(\n", - " sky_model=\"isotropic\",\n", - " temp_model=\"sapm\",\n", - " conf_0=\"insulated_back_glass_polymer\",\n", - " conf_inf=\"open_rack_glass_polymer\",\n", - " x_eff=5,\n", - " x_0=6.5,\n", - " wind_factor=0.33,\n", - ")\n", - "# Run the calculation\n", - "T_azimuth_step = 10\n", - "T_tilt_step = 10\n", - "T98_series = pvdeg.utilities.tilt_azimuth_scan(\n", - " weather_df=WEATHER_df,\n", - " meta=META,\n", - " tilt_step=T_tilt_step,\n", - " azimuth_step=T_azimuth_step,\n", - " func=pvdeg.standards.T98_estimate,\n", - " **kwarg_T,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The next cell creates a plot of the calculated data. Some of the things you may want to change are:\n", - "- cmap=\"Spectral_r\": Change to have different colors\n", - "- plt.title : This will change the plot title.\n", - "- figsize=(16,4) : Change the plot dimensions and/or aspect ratio.\n", - "- vmax=None : This can be set to a numeric value to control the depth scale maximum\n", - "- vmin=None : This controls the minimum of the depth scale.\n", - "- v_ticks=37 : This changes the number of vertical tick marks\n", - "- h_ticks=10 : This changes the number of horizontal tick marks\n", - "- Unblock the last two lines to ouput the plot as an *.png image file" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# This produces the plot of the data\n", - "T98_series_df = pd.DataFrame(\n", - " {\"Tilt\": T98_series[:, 0], \"Azimuth\": T98_series[:, 1], \"Tβ‚‰β‚ˆ\": T98_series[:, 2]}\n", - ")\n", - "T98_fig = plt.figure(figsize=(16, 4))\n", - "if kwarg_T[\"x_eff\"] == None:\n", - " plt.title(\n", - " \"Plot of $\\it{Tβ‚‰β‚ˆ}$ for all orientations for an open-rack mounting.\",\n", - " fontsize=15,\n", - " y=1.08,\n", - " )\n", - "else:\n", - " plt.title(\n", - " \"Plot of $\\it{Tβ‚‰β‚ˆ}$ for all orientations for $X_{eff}$=\"\n", - " + \"%.0f\" % kwarg_T[\"x_eff\"]\n", - " + \" cm.\",\n", - " fontsize=15,\n", - " y=1.08,\n", - " )\n", - "T98_fig = sns.heatmap(\n", - " T98_series_df.pivot(index=\"Tilt\", columns=\"Azimuth\", values=\"Tβ‚‰β‚ˆ\"),\n", - " cbar_kws={\"label\": \"Xβ‚˜α΅’β‚™\", \"format\": \"%.0f\", \"pad\": 0.02},\n", - " cmap=\"Spectral_r\",\n", - " vmin=None,\n", - " vmax=None,\n", - ")\n", - "\n", - "h_ticks = 37\n", - "x_number = math.ceil(360 / T_azimuth_step) + 1\n", - "x_ticks = [\n", - " (x * (360 / (h_ticks - 1)) / T_azimuth_step + 0.5) for x in range(h_ticks - 1)\n", - "]\n", - "x_labels = [(\"%.0f\" % (360 / (h_ticks - 1) * x)) for x in range(h_ticks)]\n", - "x_ticks.append(x_number - 0.5)\n", - "T98_fig.set_xticks(x_ticks)\n", - "T98_fig.set_xticklabels(x_labels, rotation=90)\n", - "\n", - "v_ticks = 10\n", - "y_number = math.ceil(90 / T_tilt_step) + 1\n", - "y_ticks = [(x * (90 / (v_ticks - 1)) / T_tilt_step + 0.5) for x in range(v_ticks - 1)]\n", - "y_labels = [(\"%.0f\" % (90 / (v_ticks - 1) * x)) for x in range(v_ticks)]\n", - "y_ticks.append(y_number - 0.5)\n", - "T98_fig.set_yticks(y_ticks)\n", - "T98_fig.set_yticklabels(y_labels, rotation=0)\n", - "\n", - "T98_fig.set_xlabel(\"Azimuth [Β°]\", fontsize=15, labelpad=10)\n", - "T98_fig.set_ylabel(\"Tilt [Β°]\", fontsize=15)\n", - "T98_fig.figure.axes[-1].set_ylabel(\"$\\it{Tβ‚‰β‚ˆ}$ [Β°C]\", size=15)\n", - "T98_fig.invert_yaxis()\n", - "\n", - "plt.savefig(\n", - " os.path.join(output_folder, \"T98_Scan.png\"), bbox_inches=\"tight\"\n", - ") # Creates an image file of the standoff plot\n", - "plt.show(T98_fig)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 7. Plot $X_{min}$ for Level 1, Level 2, and $T_{98}$ for a given region.\n", - "\n", - "This last Objective is much more complicated and is set up to utilize acess to a lot of computational power to run many sites simultaneously to create a regional map of standoff distance. This is presented as doing the computations on Amazon Web Services (AWS) for which you will need a paid account, and will be covered on a subsequent journal." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 8. Save data outputs.\n", - "\n", - "This cell contains a number of pre-scripted commands for exporting and saving data. The code to save plots is located after the plot creation and is blocked by default. First check that the output folder exists, then unblock the code for data you would like to save." - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Your results will be stored in c:\\Users\\mspringe\\OneDrive - NREL\\msp\\projects\\2023_DegradationTools\\Github\\PVDegradationTools\\TEMP\\results\n", - "The folder must already exist or the file will not be created\n" - ] - } - ], - "source": [ - "print(\"Your results will be stored in %s\" % output_folder)\n", - "print(\"The folder must already exist or the file will not be created\")\n", - "\n", - "pvdeg.weather.write(\n", - " data_df=WEATHER_df,\n", - " metadata=META,\n", - " savefile=os.path.join(output_folder, \"WeatherFile.csv\"),\n", - ") # Writes the meterological data to an *.csv file.\n", - "\n", - "pd.DataFrame(standoff_series_df).to_csv(\n", - " os.path.join(output_folder, \"Standoff_Scan.csv\")\n", - ") # Writes a file with the Tilt and Azimuth scan calculations of standoff.\n", - "\n", - "pd.DataFrame(T98_series_df).to_csv(\n", - " os.path.join(output_folder, \"T98_Scan.csv\")\n", - ") # Writes a file with the Tilt Azimuth scan calculations of T98." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.7" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/tutorials_and_tools/_build/html/_sphinx_design_static/design-style.1e8bd061cd6da7fc9cf755528e8ffc24.min.css b/tutorials_and_tools/_build/html/_sphinx_design_static/design-style.1e8bd061cd6da7fc9cf755528e8ffc24.min.css deleted file mode 100644 index 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-} - -div.sphinxsidebar input { - border: 1px solid #98dbcc; - font-family: sans-serif; - font-size: 1em; -} - -div.sphinxsidebar #searchbox form.search { - overflow: hidden; -} - -div.sphinxsidebar #searchbox input[type="text"] { - float: left; - width: 80%; - padding: 0.25em; - box-sizing: border-box; -} - -div.sphinxsidebar #searchbox input[type="submit"] { - float: left; - width: 20%; - border-left: none; - padding: 0.25em; - box-sizing: border-box; -} - - -img { - border: 0; - max-width: 100%; -} - -/* -- search page ----------------------------------------------------------- */ - -ul.search { - margin: 10px 0 0 20px; - padding: 0; -} - -ul.search li { - padding: 5px 0 5px 20px; - background-image: url(file.png); - background-repeat: no-repeat; - background-position: 0 7px; -} - -ul.search li a { - font-weight: bold; -} - -ul.search li p.context { - color: #888; - margin: 2px 0 0 30px; - text-align: left; -} - -ul.keywordmatches li.goodmatch a { - font-weight: bold; -} - -/* -- index page ------------------------------------------------------------ */ - -table.contentstable { - width: 90%; - margin-left: auto; - margin-right: auto; -} - -table.contentstable p.biglink { - line-height: 150%; -} - -a.biglink { - font-size: 1.3em; -} - -span.linkdescr { - font-style: italic; - padding-top: 5px; - font-size: 90%; -} - -/* -- general index --------------------------------------------------------- */ - -table.indextable { - width: 100%; -} - -table.indextable td { - text-align: left; - vertical-align: top; -} - -table.indextable ul { - margin-top: 0; - margin-bottom: 0; - list-style-type: none; -} - -table.indextable > tbody > tr > td > ul { - padding-left: 0em; -} - -table.indextable tr.pcap { - height: 10px; -} - -table.indextable tr.cap { - margin-top: 10px; - background-color: #f2f2f2; -} - -img.toggler { - margin-right: 3px; - margin-top: 3px; - cursor: pointer; -} - -div.modindex-jumpbox { - border-top: 1px solid #ddd; - border-bottom: 1px solid #ddd; - margin: 1em 0 1em 0; - padding: 0.4em; -} - -div.genindex-jumpbox { - border-top: 1px solid #ddd; - border-bottom: 1px solid #ddd; - margin: 1em 0 1em 0; - padding: 0.4em; -} - -/* -- domain module index --------------------------------------------------- */ - -table.modindextable td { - padding: 2px; - border-collapse: collapse; -} - -/* -- general body styles --------------------------------------------------- */ - -div.body { - min-width: 360px; - max-width: 800px; -} - -div.body p, div.body dd, div.body li, div.body blockquote { - -moz-hyphens: auto; - -ms-hyphens: auto; - -webkit-hyphens: auto; - hyphens: auto; -} - -a.headerlink { - visibility: hidden; -} - -a:visited { - color: #551A8B; -} - -h1:hover > a.headerlink, -h2:hover > a.headerlink, -h3:hover > a.headerlink, -h4:hover > a.headerlink, -h5:hover > a.headerlink, -h6:hover > a.headerlink, -dt:hover > a.headerlink, -caption:hover > a.headerlink, -p.caption:hover > a.headerlink, -div.code-block-caption:hover > a.headerlink { - visibility: visible; -} - -div.body p.caption { - text-align: inherit; -} - -div.body td { - text-align: left; -} - -.first { - margin-top: 0 !important; -} - -p.rubric { - margin-top: 30px; - font-weight: bold; -} - -img.align-left, figure.align-left, .figure.align-left, object.align-left { - clear: left; - float: left; - margin-right: 1em; -} - -img.align-right, figure.align-right, .figure.align-right, object.align-right { - clear: right; - float: right; - margin-left: 1em; -} - -img.align-center, figure.align-center, .figure.align-center, object.align-center { - display: block; - margin-left: auto; - margin-right: auto; -} - -img.align-default, figure.align-default, .figure.align-default { - display: block; - margin-left: auto; - margin-right: auto; -} - -.align-left { - text-align: left; -} - -.align-center { - text-align: center; -} - -.align-default { - text-align: center; -} - -.align-right { - text-align: right; -} - -/* -- sidebars -------------------------------------------------------------- */ - -div.sidebar, -aside.sidebar { - margin: 0 0 0.5em 1em; - border: 1px solid #ddb; - padding: 7px; - background-color: #ffe; - width: 40%; - float: right; - clear: right; - overflow-x: auto; -} - -p.sidebar-title { - font-weight: bold; -} - -nav.contents, -aside.topic, -div.admonition, div.topic, blockquote { - clear: left; -} - -/* -- topics ---------------------------------------------------------------- */ - -nav.contents, -aside.topic, -div.topic { - border: 1px solid #ccc; - padding: 7px; - margin: 10px 0 10px 0; -} - -p.topic-title { - font-size: 1.1em; - font-weight: bold; - margin-top: 10px; -} - -/* -- admonitions ----------------------------------------------------------- */ - -div.admonition { - margin-top: 10px; - margin-bottom: 10px; - padding: 7px; -} - -div.admonition dt { - font-weight: bold; -} - -p.admonition-title { - margin: 0px 10px 5px 0px; - font-weight: bold; -} - -div.body p.centered { - text-align: center; - margin-top: 25px; -} - -/* -- content of sidebars/topics/admonitions -------------------------------- */ - -div.sidebar > :last-child, -aside.sidebar > :last-child, -nav.contents > :last-child, -aside.topic > :last-child, -div.topic > :last-child, -div.admonition > :last-child { - margin-bottom: 0; -} - -div.sidebar::after, -aside.sidebar::after, -nav.contents::after, -aside.topic::after, -div.topic::after, -div.admonition::after, -blockquote::after { - display: block; - content: ''; - clear: both; -} - -/* -- tables ---------------------------------------------------------------- */ - -table.docutils { - margin-top: 10px; - margin-bottom: 10px; - border: 0; - border-collapse: collapse; -} - -table.align-center { - margin-left: auto; - margin-right: auto; -} - -table.align-default { - margin-left: auto; - margin-right: auto; -} - -table caption span.caption-number { - font-style: italic; -} - -table caption span.caption-text { -} - -table.docutils td, table.docutils th { - padding: 1px 8px 1px 5px; - border-top: 0; - border-left: 0; - border-right: 0; - border-bottom: 1px solid #aaa; -} - -th { - text-align: left; - padding-right: 5px; -} - -table.citation { - border-left: solid 1px gray; - margin-left: 1px; -} - -table.citation td { - border-bottom: none; -} - -th > :first-child, -td > :first-child { - margin-top: 0px; -} - -th > :last-child, -td > :last-child { - margin-bottom: 0px; -} - -/* -- figures --------------------------------------------------------------- */ - -div.figure, figure { - margin: 0.5em; - padding: 0.5em; -} - -div.figure p.caption, figcaption { - padding: 0.3em; -} - -div.figure p.caption span.caption-number, -figcaption span.caption-number { - font-style: italic; -} - -div.figure p.caption span.caption-text, -figcaption span.caption-text { -} - -/* -- field list styles ----------------------------------------------------- */ - -table.field-list td, table.field-list th { - border: 0 !important; -} - -.field-list ul { - margin: 0; - padding-left: 1em; -} - -.field-list p { - margin: 0; -} - -.field-name { - -moz-hyphens: manual; - -ms-hyphens: manual; - -webkit-hyphens: manual; - hyphens: manual; -} - -/* -- hlist styles ---------------------------------------------------------- */ - -table.hlist { - margin: 1em 0; -} - -table.hlist td { - vertical-align: top; -} - -/* -- object description styles --------------------------------------------- */ - -.sig { - font-family: 'Consolas', 'Menlo', 'DejaVu Sans Mono', 'Bitstream Vera Sans Mono', monospace; -} - -.sig-name, code.descname { - background-color: transparent; - font-weight: bold; -} - -.sig-name { - font-size: 1.1em; -} - -code.descname { - font-size: 1.2em; -} - -.sig-prename, code.descclassname { - background-color: transparent; -} - -.optional { - font-size: 1.3em; -} - -.sig-paren { - font-size: larger; -} - -.sig-param.n { - font-style: italic; -} - -/* C++ specific styling */ - -.sig-inline.c-texpr, -.sig-inline.cpp-texpr { - font-family: unset; -} - -.sig.c .k, .sig.c .kt, -.sig.cpp .k, .sig.cpp .kt { - color: #0033B3; -} - -.sig.c .m, -.sig.cpp .m { - color: #1750EB; -} - -.sig.c .s, .sig.c .sc, -.sig.cpp .s, .sig.cpp .sc { - color: #067D17; -} - - -/* -- other body styles ----------------------------------------------------- */ - -ol.arabic { - list-style: decimal; -} - -ol.loweralpha { - list-style: lower-alpha; -} - -ol.upperalpha { - list-style: upper-alpha; -} - -ol.lowerroman { - list-style: lower-roman; -} - -ol.upperroman { - list-style: upper-roman; -} - -:not(li) > ol > li:first-child > :first-child, -:not(li) > ul > li:first-child > :first-child { - margin-top: 0px; -} - -:not(li) > ol > li:last-child > :last-child, -:not(li) > ul > li:last-child > :last-child { - margin-bottom: 0px; -} - -ol.simple ol p, -ol.simple ul p, -ul.simple ol p, -ul.simple ul p { - margin-top: 0; -} - -ol.simple > li:not(:first-child) > p, -ul.simple > li:not(:first-child) > p { - margin-top: 0; -} - -ol.simple p, -ul.simple p { - margin-bottom: 0; -} - -aside.footnote > span, -div.citation > span { - float: left; -} -aside.footnote > span:last-of-type, -div.citation > span:last-of-type { - padding-right: 0.5em; -} -aside.footnote > p { - margin-left: 2em; -} -div.citation > p { - margin-left: 4em; -} -aside.footnote > p:last-of-type, -div.citation > p:last-of-type { - margin-bottom: 0em; -} -aside.footnote > p:last-of-type:after, -div.citation > p:last-of-type:after { - content: ""; - clear: both; -} - -dl.field-list { - display: grid; - grid-template-columns: fit-content(30%) auto; -} - -dl.field-list > dt { - font-weight: bold; - word-break: break-word; - padding-left: 0.5em; - padding-right: 5px; -} - -dl.field-list > dd { - padding-left: 0.5em; - margin-top: 0em; - margin-left: 0em; - margin-bottom: 0em; -} - -dl { - margin-bottom: 15px; -} - -dd > :first-child { - margin-top: 0px; -} - -dd ul, dd table { - margin-bottom: 10px; -} - -dd { - margin-top: 3px; - margin-bottom: 10px; - margin-left: 30px; -} - -.sig dd { - margin-top: 0px; - margin-bottom: 0px; -} - -.sig dl { - margin-top: 0px; - margin-bottom: 0px; -} - -dl > dd:last-child, -dl > dd:last-child > :last-child { - margin-bottom: 0; -} - -dt:target, span.highlighted { - background-color: #fbe54e; -} - -rect.highlighted { - fill: #fbe54e; -} - -dl.glossary dt { - font-weight: bold; - font-size: 1.1em; -} - -.versionmodified { - font-style: italic; -} - -.system-message { - background-color: #fda; - padding: 5px; - border: 3px solid red; -} - -.footnote:target { - background-color: #ffa; -} - -.line-block { - display: block; - margin-top: 1em; - margin-bottom: 1em; -} - -.line-block .line-block { - margin-top: 0; - margin-bottom: 0; - margin-left: 1.5em; -} - -.guilabel, .menuselection { - font-family: sans-serif; -} - -.accelerator { - text-decoration: underline; -} - -.classifier { - font-style: oblique; -} - -.classifier:before { - font-style: normal; - margin: 0 0.5em; - content: ":"; - display: inline-block; -} - -abbr, acronym { - border-bottom: dotted 1px; - cursor: help; -} - -.translated { - background-color: rgba(207, 255, 207, 0.2) -} - -.untranslated { - background-color: rgba(255, 207, 207, 0.2) -} - -/* -- code displays --------------------------------------------------------- */ - -pre { - overflow: auto; - overflow-y: hidden; /* fixes display issues on Chrome browsers */ -} - -pre, div[class*="highlight-"] { - clear: both; -} - -span.pre { - -moz-hyphens: none; - -ms-hyphens: none; - -webkit-hyphens: none; - hyphens: none; - white-space: nowrap; -} - -div[class*="highlight-"] { - margin: 1em 0; -} - -td.linenos pre { - border: 0; - background-color: transparent; - color: #aaa; -} - -table.highlighttable { - display: block; -} - -table.highlighttable tbody { - display: block; -} - -table.highlighttable tr { - display: flex; -} - -table.highlighttable td { - margin: 0; - padding: 0; -} - -table.highlighttable td.linenos { - padding-right: 0.5em; -} - -table.highlighttable td.code { - flex: 1; - overflow: hidden; -} - -.highlight .hll { - display: block; -} - -div.highlight pre, -table.highlighttable pre { - margin: 0; -} - -div.code-block-caption + div { - margin-top: 0; -} - -div.code-block-caption { - margin-top: 1em; - padding: 2px 5px; - font-size: small; -} - -div.code-block-caption code { - background-color: transparent; -} - -table.highlighttable td.linenos, -span.linenos, -div.highlight span.gp { /* gp: Generic.Prompt */ - user-select: none; - -webkit-user-select: text; /* Safari fallback only */ - -webkit-user-select: none; /* Chrome/Safari */ - -moz-user-select: none; /* Firefox */ - -ms-user-select: none; /* IE10+ */ -} - -div.code-block-caption span.caption-number { - padding: 0.1em 0.3em; - font-style: italic; -} - -div.code-block-caption span.caption-text { -} - -div.literal-block-wrapper { - margin: 1em 0; -} - -code.xref, a code { - background-color: transparent; - font-weight: bold; -} - -h1 code, h2 code, h3 code, h4 code, h5 code, h6 code { - background-color: transparent; -} - -.viewcode-link { - float: right; -} - -.viewcode-back { - float: right; - font-family: sans-serif; -} - -div.viewcode-block:target { - margin: -1px -10px; - padding: 0 10px; -} - -/* -- math display ---------------------------------------------------------- */ - -img.math { - vertical-align: middle; -} - -div.body div.math p { - text-align: center; -} - -span.eqno { - float: right; -} - -span.eqno a.headerlink { - position: absolute; - z-index: 1; -} - -div.math:hover a.headerlink { - visibility: visible; -} - -/* -- printout stylesheet --------------------------------------------------- */ - -@media print { - div.document, - div.documentwrapper, - div.bodywrapper { - margin: 0 !important; - width: 100%; - } - - div.sphinxsidebar, - div.related, - div.footer, - #top-link { - display: none; - } -} diff --git a/tutorials_and_tools/_build/html/_static/check-solid.svg b/tutorials_and_tools/_build/html/_static/check-solid.svg deleted file mode 100644 index 92fad4b5c..000000000 --- a/tutorials_and_tools/_build/html/_static/check-solid.svg +++ /dev/null @@ -1,4 +0,0 @@ - - - - diff --git a/tutorials_and_tools/_build/html/_static/clipboard.min.js b/tutorials_and_tools/_build/html/_static/clipboard.min.js deleted file mode 100644 index 54b3c4638..000000000 --- a/tutorials_and_tools/_build/html/_static/clipboard.min.js +++ /dev/null @@ -1,7 +0,0 @@ -/*! - * clipboard.js v2.0.8 - * https://clipboardjs.com/ - * - * Licensed MIT Β© Zeno Rocha - */ -!function(t,e){"object"==typeof exports&&"object"==typeof module?module.exports=e():"function"==typeof define&&define.amd?define([],e):"object"==typeof exports?exports.ClipboardJS=e():t.ClipboardJS=e()}(this,function(){return n={686:function(t,e,n){"use strict";n.d(e,{default:function(){return o}});var e=n(279),i=n.n(e),e=n(370),u=n.n(e),e=n(817),c=n.n(e);function a(t){try{return document.execCommand(t)}catch(t){return}}var f=function(t){t=c()(t);return a("cut"),t};var l=function(t){var e,n,o,r=1 - - - - diff --git a/tutorials_and_tools/_build/html/_static/copybutton.css b/tutorials_and_tools/_build/html/_static/copybutton.css deleted file mode 100644 index f1916ec7d..000000000 --- a/tutorials_and_tools/_build/html/_static/copybutton.css +++ /dev/null @@ -1,94 +0,0 @@ -/* Copy buttons */ -button.copybtn { - position: absolute; - display: flex; - top: .3em; - right: .3em; - width: 1.7em; - height: 1.7em; - opacity: 0; - transition: opacity 0.3s, border .3s, background-color .3s; - user-select: none; - padding: 0; - border: none; - outline: none; - border-radius: 0.4em; - /* The colors that GitHub uses */ - border: #1b1f2426 1px solid; - background-color: #f6f8fa; - color: #57606a; -} - -button.copybtn.success { - border-color: #22863a; - color: #22863a; -} - -button.copybtn svg { - stroke: currentColor; - width: 1.5em; - height: 1.5em; - padding: 0.1em; -} - -div.highlight { - position: relative; -} - -/* Show the copybutton */ -.highlight:hover button.copybtn, button.copybtn.success { - opacity: 1; -} - -.highlight button.copybtn:hover { - background-color: rgb(235, 235, 235); -} - -.highlight button.copybtn:active { - background-color: rgb(187, 187, 187); -} - -/** - * A minimal CSS-only tooltip copied from: - * https://codepen.io/mildrenben/pen/rVBrpK - * - * To use, write HTML like the following: - * - *

Short

- */ - .o-tooltip--left { - position: relative; - } - - .o-tooltip--left:after { - opacity: 0; - visibility: hidden; - position: absolute; - content: attr(data-tooltip); - padding: .2em; - font-size: .8em; - left: -.2em; - background: grey; - color: white; - white-space: nowrap; - z-index: 2; - border-radius: 2px; - transform: translateX(-102%) translateY(0); - transition: opacity 0.2s cubic-bezier(0.64, 0.09, 0.08, 1), transform 0.2s cubic-bezier(0.64, 0.09, 0.08, 1); -} - -.o-tooltip--left:hover:after { - display: block; - opacity: 1; - visibility: visible; - transform: translateX(-100%) translateY(0); - transition: opacity 0.2s cubic-bezier(0.64, 0.09, 0.08, 1), transform 0.2s cubic-bezier(0.64, 0.09, 0.08, 1); - transition-delay: .5s; -} - -/* By default the copy button shouldn't show up when printing a page */ -@media print { - button.copybtn { - display: none; - } -} diff --git a/tutorials_and_tools/_build/html/_static/copybutton.js b/tutorials_and_tools/_build/html/_static/copybutton.js deleted file mode 100644 index 2ea7ff3e2..000000000 --- a/tutorials_and_tools/_build/html/_static/copybutton.js +++ /dev/null @@ -1,248 +0,0 @@ -// Localization support -const messages = { - 'en': { - 'copy': 'Copy', - 'copy_to_clipboard': 'Copy to clipboard', - 'copy_success': 'Copied!', - 'copy_failure': 'Failed to copy', - }, - 'es' : { - 'copy': 'Copiar', - 'copy_to_clipboard': 'Copiar al portapapeles', - 'copy_success': 'Β‘Copiado!', - 'copy_failure': 'Error al copiar', - }, - 'de' : { - 'copy': 'Kopieren', - 'copy_to_clipboard': 'In die Zwischenablage kopieren', - 'copy_success': 'Kopiert!', - 'copy_failure': 'Fehler beim Kopieren', - }, - 'fr' : { - 'copy': 'Copier', - 'copy_to_clipboard': 'Copier dans le presse-papier', - 'copy_success': 'CopiΓ© !', - 'copy_failure': 'Γ‰chec de la copie', - }, - 'ru': { - 'copy': 'Π‘ΠΊΠΎΠΏΠΈΡ€ΠΎΠ²Π°Ρ‚ΡŒ', - 'copy_to_clipboard': 'Π‘ΠΊΠΎΠΏΠΈΡ€ΠΎΠ²Π°Ρ‚ΡŒ Π² Π±ΡƒΡ„Π΅Ρ€', - 'copy_success': 'Π‘ΠΊΠΎΠΏΠΈΡ€ΠΎΠ²Π°Π½ΠΎ!', - 'copy_failure': 'НС ΡƒΠ΄Π°Π»ΠΎΡΡŒ ΡΠΊΠΎΠΏΠΈΡ€ΠΎΠ²Π°Ρ‚ΡŒ', - }, - 'zh-CN': { - 'copy': '倍刢', - 'copy_to_clipboard': '倍刢到ε‰ͺ贴板', - 'copy_success': '倍刢成功!', - 'copy_failure': '倍刢倱θ΄₯', - }, - 'it' : { - 'copy': 'Copiare', - 'copy_to_clipboard': 'Copiato negli appunti', - 'copy_success': 'Copiato!', - 'copy_failure': 'Errore durante la copia', - } -} - -let locale = 'en' -if( document.documentElement.lang !== undefined - && messages[document.documentElement.lang] !== undefined ) { - locale = document.documentElement.lang -} - -let doc_url_root = DOCUMENTATION_OPTIONS.URL_ROOT; -if (doc_url_root == '#') { - doc_url_root = ''; -} - -/** - * SVG files for our copy buttons - */ -let iconCheck = ` - ${messages[locale]['copy_success']} - - -` - -// If the user specified their own SVG use that, otherwise use the default -let iconCopy = ``; -if (!iconCopy) { - iconCopy = ` - ${messages[locale]['copy_to_clipboard']} - - - -` -} - -/** - * Set up copy/paste for code blocks - */ - -const runWhenDOMLoaded = cb => { - if (document.readyState != 'loading') { - cb() - } else if (document.addEventListener) { - document.addEventListener('DOMContentLoaded', cb) - } else { - document.attachEvent('onreadystatechange', function() { - if (document.readyState == 'complete') cb() - }) - } -} - -const codeCellId = index => `codecell${index}` - -// Clears selected text since ClipboardJS will select the text when copying -const clearSelection = () => { - if (window.getSelection) { - window.getSelection().removeAllRanges() - } else if (document.selection) { - document.selection.empty() - } -} - -// Changes tooltip text for a moment, then changes it back -// We want the timeout of our `success` class to be a bit shorter than the -// tooltip and icon change, so that we can hide the icon before changing back. -var timeoutIcon = 2000; -var timeoutSuccessClass = 1500; - -const temporarilyChangeTooltip = (el, oldText, newText) => { - el.setAttribute('data-tooltip', newText) - el.classList.add('success') - // Remove success a little bit sooner than we change the tooltip - // So that we can use CSS to hide the copybutton first - setTimeout(() => el.classList.remove('success'), timeoutSuccessClass) - setTimeout(() => el.setAttribute('data-tooltip', oldText), timeoutIcon) -} - -// Changes the copy button icon for two seconds, then changes it back -const temporarilyChangeIcon = (el) => { - el.innerHTML = iconCheck; - setTimeout(() => {el.innerHTML = iconCopy}, timeoutIcon) -} - -const addCopyButtonToCodeCells = () => { - // If ClipboardJS hasn't loaded, wait a bit and try again. This - // happens because we load ClipboardJS asynchronously. - if (window.ClipboardJS === undefined) { - setTimeout(addCopyButtonToCodeCells, 250) - return - } - - // Add copybuttons to all of our code cells - const COPYBUTTON_SELECTOR = 'div.highlight pre'; - const codeCells = document.querySelectorAll(COPYBUTTON_SELECTOR) - codeCells.forEach((codeCell, index) => { - const id = codeCellId(index) - codeCell.setAttribute('id', id) - - const clipboardButton = id => - `` - codeCell.insertAdjacentHTML('afterend', clipboardButton(id)) - }) - -function escapeRegExp(string) { - return string.replace(/[.*+?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string -} - -/** - * Removes excluded text from a Node. - * - * @param {Node} target Node to filter. - * @param {string} exclude CSS selector of nodes to exclude. - * @returns {DOMString} Text from `target` with text removed. - */ -function filterText(target, exclude) { - const clone = target.cloneNode(true); // clone as to not modify the live DOM - if (exclude) { - // remove excluded nodes - clone.querySelectorAll(exclude).forEach(node => node.remove()); - } - return clone.innerText; -} - -// Callback when a copy button is clicked. Will be passed the node that was clicked -// should then grab the text and replace pieces of text that shouldn't be used in output -function formatCopyText(textContent, copybuttonPromptText, isRegexp = false, onlyCopyPromptLines = true, removePrompts = true, copyEmptyLines = true, lineContinuationChar = "", hereDocDelim = "") { - var regexp; - var match; - - // Do we check for line continuation characters and "HERE-documents"? - var useLineCont = !!lineContinuationChar - var useHereDoc = !!hereDocDelim - - // create regexp to capture prompt and remaining line - if (isRegexp) { - regexp = new RegExp('^(' + copybuttonPromptText + ')(.*)') - } else { - regexp = new RegExp('^(' + escapeRegExp(copybuttonPromptText) + ')(.*)') - } - - const outputLines = []; - var promptFound = false; - var gotLineCont = false; - var gotHereDoc = false; - const lineGotPrompt = []; - for (const line of textContent.split('\n')) { - match = line.match(regexp) - if (match || gotLineCont || gotHereDoc) { - promptFound = regexp.test(line) - lineGotPrompt.push(promptFound) - if (removePrompts && promptFound) { - outputLines.push(match[2]) - } else { - outputLines.push(line) - } - gotLineCont = line.endsWith(lineContinuationChar) & useLineCont - if (line.includes(hereDocDelim) & useHereDoc) - gotHereDoc = !gotHereDoc - } else if (!onlyCopyPromptLines) { - outputLines.push(line) - } else if (copyEmptyLines && line.trim() === '') { - outputLines.push(line) - } - } - - // If no lines with the prompt were found then just use original lines - if (lineGotPrompt.some(v => v === true)) { - textContent = outputLines.join('\n'); - } - - // Remove a trailing newline to avoid auto-running when pasting - if (textContent.endsWith("\n")) { - textContent = textContent.slice(0, -1) - } - return textContent -} - - -var copyTargetText = (trigger) => { - var target = document.querySelector(trigger.attributes['data-clipboard-target'].value); - - // get filtered text - let exclude = '.linenos'; - - let text = filterText(target, exclude); - return formatCopyText(text, '', false, true, true, true, '', '') -} - - // Initialize with a callback so we can modify the text before copy - const clipboard = new ClipboardJS('.copybtn', {text: copyTargetText}) - - // Update UI with error/success messages - clipboard.on('success', event => { - clearSelection() - temporarilyChangeTooltip(event.trigger, messages[locale]['copy'], messages[locale]['copy_success']) - temporarilyChangeIcon(event.trigger) - }) - - clipboard.on('error', event => { - temporarilyChangeTooltip(event.trigger, messages[locale]['copy'], messages[locale]['copy_failure']) - }) -} - -runWhenDOMLoaded(addCopyButtonToCodeCells) \ No newline at end of file diff --git a/tutorials_and_tools/_build/html/_static/copybutton_funcs.js b/tutorials_and_tools/_build/html/_static/copybutton_funcs.js deleted file mode 100644 index dbe1aaad7..000000000 --- a/tutorials_and_tools/_build/html/_static/copybutton_funcs.js +++ /dev/null @@ -1,73 +0,0 @@ -function escapeRegExp(string) { - return string.replace(/[.*+?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string -} - -/** - * Removes excluded text from a Node. - * - * @param {Node} target Node to filter. - * @param {string} exclude CSS selector of nodes to exclude. - * @returns {DOMString} Text from `target` with text removed. - */ -export function filterText(target, exclude) { - const clone = target.cloneNode(true); // clone as to not modify the live DOM - if (exclude) { - // remove excluded nodes - clone.querySelectorAll(exclude).forEach(node => node.remove()); - } - return clone.innerText; -} - -// Callback when a copy button is clicked. Will be passed the node that was clicked -// should then grab the text and replace pieces of text that shouldn't be used in output -export function formatCopyText(textContent, copybuttonPromptText, isRegexp = false, onlyCopyPromptLines = true, removePrompts = true, copyEmptyLines = true, lineContinuationChar = "", hereDocDelim = "") { - var regexp; - var match; - - // Do we check for line continuation characters and "HERE-documents"? - var useLineCont = !!lineContinuationChar - var useHereDoc = !!hereDocDelim - - // create regexp to capture prompt and remaining line - if (isRegexp) { - regexp = new RegExp('^(' + copybuttonPromptText + ')(.*)') - } else { - regexp = new RegExp('^(' + escapeRegExp(copybuttonPromptText) + ')(.*)') - } - - const outputLines = []; - var promptFound = false; - var gotLineCont = false; - var gotHereDoc = false; - const lineGotPrompt = []; - for (const line of textContent.split('\n')) { - match = line.match(regexp) - if (match || gotLineCont || gotHereDoc) { - promptFound = regexp.test(line) - lineGotPrompt.push(promptFound) - if (removePrompts && promptFound) { - outputLines.push(match[2]) - } else { - outputLines.push(line) - } - gotLineCont = line.endsWith(lineContinuationChar) & useLineCont - if (line.includes(hereDocDelim) & useHereDoc) - gotHereDoc = !gotHereDoc - } else if (!onlyCopyPromptLines) { - outputLines.push(line) - } else if (copyEmptyLines && line.trim() === '') { - outputLines.push(line) - } - } - - // If no lines with the prompt were found then just use original lines - if (lineGotPrompt.some(v => v === true)) { - textContent = outputLines.join('\n'); - } - - // Remove a trailing newline to avoid auto-running when pasting - if (textContent.endsWith("\n")) { - textContent = textContent.slice(0, -1) - } - return textContent -} diff --git a/tutorials_and_tools/_build/html/_static/design-style.1e8bd061cd6da7fc9cf755528e8ffc24.min.css 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-/** - * Small JavaScript module for the documentation. - */ -const Documentation = { - init: () => { - Documentation.initDomainIndexTable(); - Documentation.initOnKeyListeners(); - }, - - /** - * i18n support - */ - TRANSLATIONS: {}, - PLURAL_EXPR: (n) => (n === 1 ? 0 : 1), - LOCALE: "unknown", - - // gettext and ngettext don't access this so that the functions - // can safely bound to a different name (_ = Documentation.gettext) - gettext: (string) => { - const translated = Documentation.TRANSLATIONS[string]; - switch (typeof translated) { - case "undefined": - return string; // no translation - case "string": - return translated; // translation exists - default: - return translated[0]; // (singular, plural) translation tuple exists - } - }, - - ngettext: (singular, plural, n) => { - const translated = Documentation.TRANSLATIONS[singular]; - if (typeof translated !== "undefined") - return translated[Documentation.PLURAL_EXPR(n)]; - return n === 1 ? singular : plural; - }, - - addTranslations: (catalog) => { - Object.assign(Documentation.TRANSLATIONS, catalog.messages); - Documentation.PLURAL_EXPR = new Function( - "n", - `return (${catalog.plural_expr})` - ); - Documentation.LOCALE = catalog.locale; - }, - - /** - * helper function to focus on search bar - */ - focusSearchBar: () => { - document.querySelectorAll("input[name=q]")[0]?.focus(); - }, - - /** - * Initialise the domain index toggle buttons - */ - initDomainIndexTable: () => { - const toggler = (el) => { - const idNumber = el.id.substr(7); - const toggledRows = document.querySelectorAll(`tr.cg-${idNumber}`); - if (el.src.substr(-9) === "minus.png") { - el.src = `${el.src.substr(0, el.src.length - 9)}plus.png`; - toggledRows.forEach((el) => (el.style.display = "none")); - } else { - el.src = `${el.src.substr(0, el.src.length - 8)}minus.png`; - toggledRows.forEach((el) => (el.style.display = "")); - } - }; - - const togglerElements = document.querySelectorAll("img.toggler"); - togglerElements.forEach((el) => - el.addEventListener("click", (event) => toggler(event.currentTarget)) - ); - togglerElements.forEach((el) => (el.style.display = "")); - if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) togglerElements.forEach(toggler); - }, - - initOnKeyListeners: () => { - // only install a listener if it is really needed - if ( - !DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS && - !DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS - ) - return; - - document.addEventListener("keydown", (event) => { - // bail for input elements - if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return; - // bail with special keys - if (event.altKey || event.ctrlKey || event.metaKey) return; - - if (!event.shiftKey) { - switch (event.key) { - case "ArrowLeft": - if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break; - - const prevLink = document.querySelector('link[rel="prev"]'); - if (prevLink && prevLink.href) { - window.location.href = prevLink.href; - event.preventDefault(); - } - break; - case "ArrowRight": - if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break; - - const nextLink = document.querySelector('link[rel="next"]'); - if (nextLink && nextLink.href) { - window.location.href = nextLink.href; - event.preventDefault(); - } - break; - } - } - - // some keyboard layouts may need Shift to get / - switch (event.key) { - case "/": - if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) break; - Documentation.focusSearchBar(); - event.preventDefault(); - } - }); - }, -}; - -// quick alias for translations -const _ = Documentation.gettext; - -_ready(Documentation.init); diff --git a/tutorials_and_tools/_build/html/_static/documentation_options.js b/tutorials_and_tools/_build/html/_static/documentation_options.js deleted file mode 100644 index eb83c716b..000000000 --- a/tutorials_and_tools/_build/html/_static/documentation_options.js +++ /dev/null @@ -1,13 +0,0 @@ -const DOCUMENTATION_OPTIONS = { - VERSION: '', - LANGUAGE: 'en', - COLLAPSE_INDEX: false, - BUILDER: 'html', - FILE_SUFFIX: '.html', - LINK_SUFFIX: '.html', - HAS_SOURCE: true, - SOURCELINK_SUFFIX: '', - NAVIGATION_WITH_KEYS: false, - SHOW_SEARCH_SUMMARY: true, - ENABLE_SEARCH_SHORTCUTS: true, -}; diff --git a/tutorials_and_tools/_build/html/_static/file.png b/tutorials_and_tools/_build/html/_static/file.png deleted file mode 100644 index a858a410e..000000000 Binary files a/tutorials_and_tools/_build/html/_static/file.png and /dev/null differ diff --git a/tutorials_and_tools/_build/html/_static/images/logo_binder.svg b/tutorials_and_tools/_build/html/_static/images/logo_binder.svg deleted file mode 100644 index 45fecf751..000000000 --- a/tutorials_and_tools/_build/html/_static/images/logo_binder.svg +++ /dev/null @@ -1,19 +0,0 @@ - - - - -logo - - - - - - - - diff --git a/tutorials_and_tools/_build/html/_static/images/logo_colab.png b/tutorials_and_tools/_build/html/_static/images/logo_colab.png deleted file mode 100644 index b7560ec21..000000000 Binary files a/tutorials_and_tools/_build/html/_static/images/logo_colab.png and /dev/null differ diff --git a/tutorials_and_tools/_build/html/_static/images/logo_deepnote.svg b/tutorials_and_tools/_build/html/_static/images/logo_deepnote.svg deleted file mode 100644 index fa77ebfc2..000000000 --- a/tutorials_and_tools/_build/html/_static/images/logo_deepnote.svg +++ /dev/null @@ -1 +0,0 @@ - diff --git a/tutorials_and_tools/_build/html/_static/images/logo_jupyterhub.svg b/tutorials_and_tools/_build/html/_static/images/logo_jupyterhub.svg deleted file mode 100644 index 60cfe9f22..000000000 --- a/tutorials_and_tools/_build/html/_static/images/logo_jupyterhub.svg +++ /dev/null @@ -1 +0,0 @@ -logo_jupyterhubHub diff --git a/tutorials_and_tools/_build/html/_static/jquery-3.6.0.js b/tutorials_and_tools/_build/html/_static/jquery-3.6.0.js deleted file mode 100644 index fc6c299b7..000000000 --- a/tutorials_and_tools/_build/html/_static/jquery-3.6.0.js +++ /dev/null @@ -1,10881 +0,0 @@ -/*! - * jQuery JavaScript Library v3.6.0 - * https://jquery.com/ - * - * Includes Sizzle.js - * https://sizzlejs.com/ - * - * Copyright OpenJS Foundation and other contributors - * Released under the MIT license - * https://jquery.org/license - * - * Date: 2021-03-02T17:08Z - */ -( function( global, factory ) { - - "use strict"; - - if ( typeof module === "object" && typeof module.exports === "object" ) { - - // For CommonJS and CommonJS-like environments where a proper `window` - // is present, execute the factory and get jQuery. - // For environments that do not have a `window` with a `document` - // (such as Node.js), expose a factory as module.exports. - // This accentuates the need for the creation of a real `window`. - // e.g. var jQuery = require("jquery")(window); - // See ticket #14549 for more info. - module.exports = global.document ? - factory( global, true ) : - function( w ) { - if ( !w.document ) { - throw new Error( "jQuery requires a window with a document" ); - } - return factory( w ); - }; - } else { - factory( global ); - } - -// Pass this if window is not defined yet -} )( typeof window !== "undefined" ? window : this, function( window, noGlobal ) { - -// Edge <= 12 - 13+, Firefox <=18 - 45+, IE 10 - 11, Safari 5.1 - 9+, iOS 6 - 9.1 -// throw exceptions when non-strict code (e.g., ASP.NET 4.5) accesses strict mode -// arguments.callee.caller (trac-13335). But as of jQuery 3.0 (2016), strict mode should be common -// enough that all such attempts are guarded in a try block. -"use strict"; - -var arr = []; - -var getProto = Object.getPrototypeOf; - -var slice = arr.slice; - -var flat = arr.flat ? function( array ) { - return arr.flat.call( array ); -} : function( array ) { - return arr.concat.apply( [], array ); -}; - - -var push = arr.push; - -var indexOf = arr.indexOf; - -var class2type = {}; - -var toString = class2type.toString; - -var hasOwn = class2type.hasOwnProperty; - -var fnToString = hasOwn.toString; - -var ObjectFunctionString = fnToString.call( Object ); - -var support = {}; - -var isFunction = function isFunction( obj ) { - - // Support: Chrome <=57, Firefox <=52 - // In some browsers, typeof returns "function" for HTML elements - // (i.e., `typeof document.createElement( "object" ) === "function"`). - // We don't want to classify *any* DOM node as a function. - // Support: QtWeb <=3.8.5, WebKit <=534.34, wkhtmltopdf tool <=0.12.5 - // Plus for old WebKit, typeof returns "function" for HTML collections - // (e.g., `typeof document.getElementsByTagName("div") === "function"`). (gh-4756) - return typeof obj === "function" && typeof obj.nodeType !== "number" && - typeof obj.item !== "function"; - }; - - -var isWindow = function isWindow( obj ) { - return obj != null && obj === obj.window; - }; - - -var document = window.document; - - - - var preservedScriptAttributes = { - type: true, - src: true, - nonce: true, - noModule: true - }; - - function DOMEval( code, node, doc ) { - doc = doc || document; - - var i, val, - script = doc.createElement( "script" ); - - script.text = code; - if ( node ) { - for ( i in preservedScriptAttributes ) { - - // Support: Firefox 64+, Edge 18+ - // Some browsers don't support the "nonce" property on scripts. - // On the other hand, just using `getAttribute` is not enough as - // the `nonce` attribute is reset to an empty string whenever it - // becomes browsing-context connected. - // See https://github.com/whatwg/html/issues/2369 - // See https://html.spec.whatwg.org/#nonce-attributes - // The `node.getAttribute` check was added for the sake of - // `jQuery.globalEval` so that it can fake a nonce-containing node - // via an object. - val = node[ i ] || node.getAttribute && node.getAttribute( i ); - if ( val ) { - script.setAttribute( i, val ); - } - } - } - doc.head.appendChild( script ).parentNode.removeChild( script ); - } - - -function toType( obj ) { - if ( obj == null ) { - return obj + ""; - } - - // Support: Android <=2.3 only (functionish RegExp) - return typeof obj === "object" || typeof obj === "function" ? - class2type[ toString.call( obj ) ] || "object" : - typeof obj; -} -/* global Symbol */ -// Defining this global in .eslintrc.json would create a danger of using the global -// unguarded in another place, it seems safer to define global only for this module - - - -var - version = "3.6.0", - - // Define a local copy of jQuery - jQuery = function( selector, context ) { - - // The jQuery object is actually just the init constructor 'enhanced' - // Need init if jQuery is called (just allow error to be thrown if not included) - return new jQuery.fn.init( selector, context ); - }; - -jQuery.fn = jQuery.prototype = { - - // The current version of jQuery being used - jquery: version, - - constructor: jQuery, - - // The default length of a jQuery object is 0 - length: 0, - - toArray: function() { - return slice.call( this ); - }, - - // Get the Nth element in the matched element set OR - // Get the whole matched element set as a clean array - get: function( num ) { - - // Return all the elements in a clean array - if ( num == null ) { - return slice.call( this ); - } - - // Return just the one element from the set - return num < 0 ? this[ num + this.length ] : this[ num ]; - }, - - // Take an array of elements and push it onto the stack - // (returning the new matched element set) - pushStack: function( elems ) { - - // Build a new jQuery matched element set - var ret = jQuery.merge( this.constructor(), elems ); - - // Add the old object onto the stack (as a reference) - ret.prevObject = this; - - // Return the newly-formed element set - return ret; - }, - - // Execute a callback for every element in the matched set. - each: function( callback ) { - return jQuery.each( this, callback ); - }, - - map: function( callback ) { - return this.pushStack( jQuery.map( this, function( elem, i ) { - return callback.call( elem, i, elem ); - } ) ); - }, - - slice: function() { - return this.pushStack( slice.apply( this, arguments ) ); - }, - - first: function() { - return this.eq( 0 ); - }, - - last: function() { - return this.eq( -1 ); - }, - - even: function() { - return this.pushStack( jQuery.grep( this, function( _elem, i ) { - return ( i + 1 ) % 2; - } ) ); - }, - - odd: function() { - return this.pushStack( jQuery.grep( this, function( _elem, i ) { - return i % 2; - } ) ); - }, - - eq: function( i ) { - var len = this.length, - j = +i + ( i < 0 ? len : 0 ); - return this.pushStack( j >= 0 && j < len ? [ this[ j ] ] : [] ); - }, - - end: function() { - return this.prevObject || this.constructor(); - }, - - // For internal use only. - // Behaves like an Array's method, not like a jQuery method. - push: push, - sort: arr.sort, - splice: arr.splice -}; - -jQuery.extend = jQuery.fn.extend = function() { - var options, name, src, copy, copyIsArray, clone, - target = arguments[ 0 ] || {}, - i = 1, - length = arguments.length, - deep = false; - - // Handle a deep copy situation - if ( typeof target === "boolean" ) { - deep = target; - - // Skip the boolean and the target - target = arguments[ i ] || {}; - i++; - } - - // Handle case when target is a string or something (possible in deep copy) - if ( typeof target !== "object" && !isFunction( target ) ) { - target = {}; - } - - // Extend jQuery itself if only one argument is passed - if ( i === length ) { - target = this; - i--; - } - - for ( ; i < length; i++ ) { - - // Only deal with non-null/undefined values - if ( ( options = arguments[ i ] ) != null ) { - - // Extend the base object - for ( name in options ) { - copy = options[ name ]; - - // Prevent Object.prototype pollution - // Prevent never-ending loop - if ( name === "__proto__" || target === copy ) { - continue; - } - - // Recurse if we're merging plain objects or arrays - if ( deep && copy && ( jQuery.isPlainObject( copy ) || - ( copyIsArray = Array.isArray( copy ) ) ) ) { - src = target[ name ]; - - // Ensure proper type for the source value - if ( copyIsArray && !Array.isArray( src ) ) { - clone = []; - } else if ( !copyIsArray && !jQuery.isPlainObject( src ) ) { - clone = {}; - } else { - clone = src; - } - copyIsArray = false; - - // Never move original objects, clone them - target[ name ] = jQuery.extend( deep, clone, copy ); - - // Don't bring in undefined values - } else if ( copy !== undefined ) { - target[ name ] = copy; - } - } - } - } - - // Return the modified object - return target; -}; - -jQuery.extend( { - - // Unique for each copy of jQuery on the page - expando: "jQuery" + ( version + Math.random() ).replace( /\D/g, "" ), - - // Assume jQuery is ready without the ready module - isReady: true, - - error: function( msg ) { - throw new Error( msg ); - }, - - noop: function() {}, - - isPlainObject: function( obj ) { - var proto, Ctor; - - // Detect obvious negatives - // Use toString instead of jQuery.type to catch host objects - if ( !obj || toString.call( obj ) !== "[object Object]" ) { - return false; - } - - proto = getProto( obj ); - - // Objects with no prototype (e.g., `Object.create( null )`) are plain - if ( !proto ) { - return true; - } - - // Objects with prototype are plain iff they were constructed by a global Object function - Ctor = hasOwn.call( proto, "constructor" ) && proto.constructor; - return typeof Ctor === "function" && fnToString.call( Ctor ) === ObjectFunctionString; - }, - - isEmptyObject: function( obj ) { - var name; - - for ( name in obj ) { - return false; - } - return true; - }, - - // Evaluates a script in a provided context; falls back to the global one - // if not specified. - globalEval: function( code, options, doc ) { - DOMEval( code, { nonce: options && options.nonce }, doc ); - }, - - each: function( obj, callback ) { - var length, i = 0; - - if ( isArrayLike( obj ) ) { - length = obj.length; - for ( ; i < length; i++ ) { - if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) { - break; - } - } - } else { - for ( i in obj ) { - if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) { - break; - } - } - } - - return obj; - }, - - // results is for internal usage only - makeArray: function( arr, results ) { - var ret = results || []; - - if ( arr != null ) { - if ( isArrayLike( Object( arr ) ) ) { - jQuery.merge( ret, - typeof arr === "string" ? - [ arr ] : arr - ); - } else { - push.call( ret, arr ); - } - } - - return ret; - }, - - inArray: function( elem, arr, i ) { - return arr == null ? -1 : indexOf.call( arr, elem, i ); - }, - - // Support: Android <=4.0 only, PhantomJS 1 only - // push.apply(_, arraylike) throws on ancient WebKit - merge: function( first, second ) { - var len = +second.length, - j = 0, - i = first.length; - - for ( ; j < len; j++ ) { - first[ i++ ] = second[ j ]; - } - - first.length = i; - - return first; - }, - - grep: function( elems, callback, invert ) { - var callbackInverse, - matches = [], - i = 0, - length = elems.length, - callbackExpect = !invert; - - // Go through the array, only saving the items - // that pass the validator function - for ( ; i < length; i++ ) { - callbackInverse = !callback( elems[ i ], i ); - if ( callbackInverse !== callbackExpect ) { - matches.push( elems[ i ] ); - } - } - - return matches; - }, - - // arg is for internal usage only - map: function( elems, callback, arg ) { - var length, value, - i = 0, - ret = []; - - // Go through the array, translating each of the items to their new values - if ( isArrayLike( elems ) ) { - length = elems.length; - for ( ; i < length; i++ ) { - value = callback( elems[ i ], i, arg ); - - if ( value != null ) { - ret.push( value ); - } - } - - // Go through every key on the object, - } else { - for ( i in elems ) { - value = callback( elems[ i ], i, arg ); - - if ( value != null ) { - ret.push( value ); - } - } - } - - // Flatten any nested arrays - return flat( ret ); - }, - - // A global GUID counter for objects - guid: 1, - - // jQuery.support is not used in Core but other projects attach their - // properties to it so it needs to exist. - support: support -} ); - -if ( typeof Symbol === "function" ) { - jQuery.fn[ Symbol.iterator ] = arr[ Symbol.iterator ]; -} - -// Populate the class2type map -jQuery.each( "Boolean Number String Function Array Date RegExp Object Error Symbol".split( " " ), - function( _i, name ) { - class2type[ "[object " + name + "]" ] = name.toLowerCase(); - } ); - -function isArrayLike( obj ) { - - // Support: real iOS 8.2 only (not reproducible in simulator) - // `in` check used to prevent JIT error (gh-2145) - // hasOwn isn't used here due to false negatives - // regarding Nodelist length in IE - var length = !!obj && "length" in obj && obj.length, - type = toType( obj ); - - if ( isFunction( obj ) || isWindow( obj ) ) { - return false; - } - - return type === "array" || length === 0 || - typeof length === "number" && length > 0 && ( length - 1 ) in obj; -} -var Sizzle = -/*! - * Sizzle CSS Selector Engine v2.3.6 - * https://sizzlejs.com/ - * - * Copyright JS Foundation and other contributors - * Released under the MIT license - * https://js.foundation/ - * - * Date: 2021-02-16 - */ -( function( window ) { -var i, - support, - Expr, - getText, - isXML, - tokenize, - compile, - select, - outermostContext, - sortInput, - hasDuplicate, - - // Local document vars - setDocument, - document, - docElem, - documentIsHTML, - rbuggyQSA, - rbuggyMatches, - matches, - contains, - - // Instance-specific data - expando = "sizzle" + 1 * new Date(), - preferredDoc = window.document, - dirruns = 0, - done = 0, - classCache = createCache(), - tokenCache = createCache(), - compilerCache = createCache(), - nonnativeSelectorCache = createCache(), - sortOrder = function( a, b ) { - if ( a === b ) { - hasDuplicate = true; - } - return 0; - }, - - // Instance methods - hasOwn = ( {} ).hasOwnProperty, - arr = [], - pop = arr.pop, - pushNative = arr.push, - push = arr.push, - slice = arr.slice, - - // Use a stripped-down indexOf as it's faster than native - // https://jsperf.com/thor-indexof-vs-for/5 - indexOf = function( list, elem ) { - var i = 0, - len = list.length; - for ( ; i < len; i++ ) { - if ( list[ i ] === elem ) { - return i; - } - } - return -1; - }, - - booleans = "checked|selected|async|autofocus|autoplay|controls|defer|disabled|hidden|" + - "ismap|loop|multiple|open|readonly|required|scoped", - - // Regular expressions - - // http://www.w3.org/TR/css3-selectors/#whitespace - whitespace = "[\\x20\\t\\r\\n\\f]", - - // https://www.w3.org/TR/css-syntax-3/#ident-token-diagram - identifier = "(?:\\\\[\\da-fA-F]{1,6}" + whitespace + - "?|\\\\[^\\r\\n\\f]|[\\w-]|[^\0-\\x7f])+", - - // Attribute selectors: http://www.w3.org/TR/selectors/#attribute-selectors - attributes = "\\[" + whitespace + "*(" + identifier + ")(?:" + whitespace + - - // Operator (capture 2) - "*([*^$|!~]?=)" + whitespace + - - // "Attribute values must be CSS identifiers [capture 5] - // or strings [capture 3 or capture 4]" - "*(?:'((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\"|(" + identifier + "))|)" + - whitespace + "*\\]", - - pseudos = ":(" + identifier + ")(?:\\((" + - - // To reduce the number of selectors needing tokenize in the preFilter, prefer arguments: - // 1. quoted (capture 3; capture 4 or capture 5) - "('((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\")|" + - - // 2. simple (capture 6) - "((?:\\\\.|[^\\\\()[\\]]|" + attributes + ")*)|" + - - // 3. anything else (capture 2) - ".*" + - ")\\)|)", - - // Leading and non-escaped trailing whitespace, capturing some non-whitespace characters preceding the latter - rwhitespace = new RegExp( whitespace + "+", "g" ), - rtrim = new RegExp( "^" + whitespace + "+|((?:^|[^\\\\])(?:\\\\.)*)" + - whitespace + "+$", "g" ), - - rcomma = new RegExp( "^" + whitespace + "*," + whitespace + "*" ), - rcombinators = new RegExp( "^" + whitespace + "*([>+~]|" + whitespace + ")" + whitespace + - "*" ), - rdescend = new RegExp( whitespace + "|>" ), - - rpseudo = new RegExp( pseudos ), - ridentifier = new RegExp( "^" + identifier + "$" ), - - matchExpr = { - "ID": new RegExp( "^#(" + identifier + ")" ), - "CLASS": new RegExp( "^\\.(" + identifier + ")" ), - "TAG": new RegExp( "^(" + identifier + "|[*])" ), - "ATTR": new RegExp( "^" + attributes ), - "PSEUDO": new RegExp( "^" + pseudos ), - "CHILD": new RegExp( "^:(only|first|last|nth|nth-last)-(child|of-type)(?:\\(" + - whitespace + "*(even|odd|(([+-]|)(\\d*)n|)" + whitespace + "*(?:([+-]|)" + - whitespace + "*(\\d+)|))" + whitespace + "*\\)|)", "i" ), - "bool": new RegExp( "^(?:" + booleans + ")$", "i" ), - - // For use in libraries implementing .is() - // We use this for POS matching in `select` - "needsContext": new RegExp( "^" + whitespace + - "*[>+~]|:(even|odd|eq|gt|lt|nth|first|last)(?:\\(" + whitespace + - "*((?:-\\d)?\\d*)" + whitespace + "*\\)|)(?=[^-]|$)", "i" ) - }, - - rhtml = /HTML$/i, - rinputs = /^(?:input|select|textarea|button)$/i, - rheader = /^h\d$/i, - - rnative = /^[^{]+\{\s*\[native \w/, - - // Easily-parseable/retrievable ID or TAG or CLASS selectors - rquickExpr = /^(?:#([\w-]+)|(\w+)|\.([\w-]+))$/, - - rsibling = /[+~]/, - - // CSS escapes - // http://www.w3.org/TR/CSS21/syndata.html#escaped-characters - runescape = new RegExp( "\\\\[\\da-fA-F]{1,6}" + whitespace + "?|\\\\([^\\r\\n\\f])", "g" ), - funescape = function( escape, nonHex ) { - var high = "0x" + escape.slice( 1 ) - 0x10000; - - return nonHex ? - - // Strip the backslash prefix from a non-hex escape sequence - nonHex : - - // Replace a hexadecimal escape sequence with the encoded Unicode code point - // Support: IE <=11+ - // For values outside the Basic Multilingual Plane (BMP), manually construct a - // surrogate pair - high < 0 ? - String.fromCharCode( high + 0x10000 ) : - String.fromCharCode( high >> 10 | 0xD800, high & 0x3FF | 0xDC00 ); - }, - - // CSS string/identifier serialization - // https://drafts.csswg.org/cssom/#common-serializing-idioms - rcssescape = /([\0-\x1f\x7f]|^-?\d)|^-$|[^\0-\x1f\x7f-\uFFFF\w-]/g, - fcssescape = function( ch, asCodePoint ) { - if ( asCodePoint ) { - - // U+0000 NULL becomes U+FFFD REPLACEMENT CHARACTER - if ( ch === "\0" ) { - return "\uFFFD"; - } - - // Control characters and (dependent upon position) numbers get escaped as code points - return ch.slice( 0, -1 ) + "\\" + - ch.charCodeAt( ch.length - 1 ).toString( 16 ) + " "; - } - - // Other potentially-special ASCII characters get backslash-escaped - return "\\" + ch; - }, - - // Used for iframes - // See setDocument() - // Removing the function wrapper causes a "Permission Denied" - // error in IE - unloadHandler = function() { - setDocument(); - }, - - inDisabledFieldset = addCombinator( - function( elem ) { - return elem.disabled === true && elem.nodeName.toLowerCase() === "fieldset"; - }, - { dir: "parentNode", next: "legend" } - ); - -// Optimize for push.apply( _, NodeList ) -try { - push.apply( - ( arr = slice.call( preferredDoc.childNodes ) ), - preferredDoc.childNodes - ); - - // Support: Android<4.0 - // Detect silently failing push.apply - // eslint-disable-next-line no-unused-expressions - arr[ preferredDoc.childNodes.length ].nodeType; -} catch ( e ) { - push = { apply: arr.length ? - - // Leverage slice if possible - function( target, els ) { - pushNative.apply( target, slice.call( els ) ); - } : - - // Support: IE<9 - // Otherwise append directly - function( target, els ) { - var j = target.length, - i = 0; - - // Can't trust NodeList.length - while ( ( target[ j++ ] = els[ i++ ] ) ) {} - target.length = j - 1; - } - }; -} - -function Sizzle( selector, context, results, seed ) { - var m, i, elem, nid, match, groups, newSelector, - newContext = context && context.ownerDocument, - - // nodeType defaults to 9, since context defaults to document - nodeType = context ? context.nodeType : 9; - - results = results || []; - - // Return early from calls with invalid selector or context - if ( typeof selector !== "string" || !selector || - nodeType !== 1 && nodeType !== 9 && nodeType !== 11 ) { - - return results; - } - - // Try to shortcut find operations (as opposed to filters) in HTML documents - if ( !seed ) { - setDocument( context ); - context = context || document; - - if ( documentIsHTML ) { - - // If the selector is sufficiently simple, try using a "get*By*" DOM method - // (excepting DocumentFragment context, where the methods don't exist) - if ( nodeType !== 11 && ( match = rquickExpr.exec( selector ) ) ) { - - // ID selector - if ( ( m = match[ 1 ] ) ) { - - // Document context - if ( nodeType === 9 ) { - if ( ( elem = context.getElementById( m ) ) ) { - - // Support: IE, Opera, Webkit - // TODO: identify versions - // getElementById can match elements by name instead of ID - if ( elem.id === m ) { - results.push( elem ); - return results; - } - } else { - return results; - } - - // Element context - } else { - - // Support: IE, Opera, Webkit - // TODO: identify versions - // getElementById can match elements by name instead of ID - if ( newContext && ( elem = newContext.getElementById( m ) ) && - contains( context, elem ) && - elem.id === m ) { - - results.push( elem ); - return results; - } - } - - // Type selector - } else if ( match[ 2 ] ) { - push.apply( results, context.getElementsByTagName( selector ) ); - return results; - - // Class selector - } else if ( ( m = match[ 3 ] ) && support.getElementsByClassName && - context.getElementsByClassName ) { - - push.apply( results, context.getElementsByClassName( m ) ); - return results; - } - } - - // Take advantage of querySelectorAll - if ( support.qsa && - !nonnativeSelectorCache[ selector + " " ] && - ( !rbuggyQSA || !rbuggyQSA.test( selector ) ) && - - // Support: IE 8 only - // Exclude object elements - ( nodeType !== 1 || context.nodeName.toLowerCase() !== "object" ) ) { - - newSelector = selector; - newContext = context; - - // qSA considers elements outside a scoping root when evaluating child or - // descendant combinators, which is not what we want. - // In such cases, we work around the behavior by prefixing every selector in the - // list with an ID selector referencing the scope context. - // The technique has to be used as well when a leading combinator is used - // as such selectors are not recognized by querySelectorAll. - // Thanks to Andrew Dupont for this technique. - if ( nodeType === 1 && - ( rdescend.test( selector ) || rcombinators.test( selector ) ) ) { - - // Expand context for sibling selectors - newContext = rsibling.test( selector ) && testContext( context.parentNode ) || - context; - - // We can use :scope instead of the ID hack if the browser - // supports it & if we're not changing the context. - if ( newContext !== context || !support.scope ) { - - // Capture the context ID, setting it first if necessary - if ( ( nid = context.getAttribute( "id" ) ) ) { - nid = nid.replace( rcssescape, fcssescape ); - } else { - context.setAttribute( "id", ( nid = expando ) ); - } - } - - // Prefix every selector in the list - groups = tokenize( selector ); - i = groups.length; - while ( i-- ) { - groups[ i ] = ( nid ? "#" + nid : ":scope" ) + " " + - toSelector( groups[ i ] ); - } - newSelector = groups.join( "," ); - } - - try { - push.apply( results, - newContext.querySelectorAll( newSelector ) - ); - return results; - } catch ( qsaError ) { - nonnativeSelectorCache( selector, true ); - } finally { - if ( nid === expando ) { - context.removeAttribute( "id" ); - } - } - } - } - } - - // All others - return select( selector.replace( rtrim, "$1" ), context, results, seed ); -} - -/** - * Create key-value caches of limited size - * @returns {function(string, object)} Returns the Object data after storing it on itself with - * property name the (space-suffixed) string and (if the cache is larger than Expr.cacheLength) - * deleting the oldest entry - */ -function createCache() { - var keys = []; - - function cache( key, value ) { - - // Use (key + " ") to avoid collision with native prototype properties (see Issue #157) - if ( keys.push( key + " " ) > Expr.cacheLength ) { - - // Only keep the most recent entries - delete cache[ keys.shift() ]; - } - return ( cache[ key + " " ] = value ); - } - return cache; -} - -/** - * Mark a function for special use by Sizzle - * @param {Function} fn The function to mark - */ -function markFunction( fn ) { - fn[ expando ] = true; - return fn; -} - -/** - * Support testing using an element - * @param {Function} fn Passed the created element and returns a boolean result - */ -function assert( fn ) { - var el = document.createElement( "fieldset" ); - - try { - return !!fn( el ); - } catch ( e ) { - return false; - } finally { - - // Remove from its parent by default - if ( el.parentNode ) { - el.parentNode.removeChild( el ); - } - - // release memory in IE - el = null; - } -} - -/** - * Adds the same handler for all of the specified attrs - * @param {String} attrs Pipe-separated list of attributes - * @param {Function} handler The method that will be applied - */ -function addHandle( attrs, handler ) { - var arr = attrs.split( "|" ), - i = arr.length; - - while ( i-- ) { - Expr.attrHandle[ arr[ i ] ] = handler; - } -} - -/** - * Checks document order of two siblings - * @param {Element} a - * @param {Element} b - * @returns {Number} Returns less than 0 if a precedes b, greater than 0 if a follows b - */ -function siblingCheck( a, b ) { - var cur = b && a, - diff = cur && a.nodeType === 1 && b.nodeType === 1 && - a.sourceIndex - b.sourceIndex; - - // Use IE sourceIndex if available on both nodes - if ( diff ) { - return diff; - } - - // Check if b follows a - if ( cur ) { - while ( ( cur = cur.nextSibling ) ) { - if ( cur === b ) { - return -1; - } - } - } - - return a ? 1 : -1; -} - -/** - * Returns a function to use in pseudos for input types - * @param {String} type - */ -function createInputPseudo( type ) { - return function( elem ) { - var name = elem.nodeName.toLowerCase(); - return name === "input" && elem.type === type; - }; -} - -/** - * Returns a function to use in pseudos for buttons - * @param {String} type - */ -function createButtonPseudo( type ) { - return function( elem ) { - var name = elem.nodeName.toLowerCase(); - return ( name === "input" || name === "button" ) && elem.type === type; - }; -} - -/** - * Returns a function to use in pseudos for :enabled/:disabled - * @param {Boolean} disabled true for :disabled; false for :enabled - */ -function createDisabledPseudo( disabled ) { - - // Known :disabled false positives: fieldset[disabled] > legend:nth-of-type(n+2) :can-disable - return function( elem ) { - - // Only certain elements can match :enabled or :disabled - // https://html.spec.whatwg.org/multipage/scripting.html#selector-enabled - // https://html.spec.whatwg.org/multipage/scripting.html#selector-disabled - if ( "form" in elem ) { - - // Check for inherited disabledness on relevant non-disabled elements: - // * listed form-associated elements in a disabled fieldset - // https://html.spec.whatwg.org/multipage/forms.html#category-listed - // https://html.spec.whatwg.org/multipage/forms.html#concept-fe-disabled - // * option elements in a disabled optgroup - // https://html.spec.whatwg.org/multipage/forms.html#concept-option-disabled - // All such elements have a "form" property. - if ( elem.parentNode && elem.disabled === false ) { - - // Option elements defer to a parent optgroup if present - if ( "label" in elem ) { - if ( "label" in elem.parentNode ) { - return elem.parentNode.disabled === disabled; - } else { - return elem.disabled === disabled; - } - } - - // Support: IE 6 - 11 - // Use the isDisabled shortcut property to check for disabled fieldset ancestors - return elem.isDisabled === disabled || - - // Where there is no isDisabled, check manually - /* jshint -W018 */ - elem.isDisabled !== !disabled && - inDisabledFieldset( elem ) === disabled; - } - - return elem.disabled === disabled; - - // Try to winnow out elements that can't be disabled before trusting the disabled property. - // Some victims get caught in our net (label, legend, menu, track), but it shouldn't - // even exist on them, let alone have a boolean value. - } else if ( "label" in elem ) { - return elem.disabled === disabled; - } - - // Remaining elements are neither :enabled nor :disabled - return false; - }; -} - -/** - * Returns a function to use in pseudos for positionals - * @param {Function} fn - */ -function createPositionalPseudo( fn ) { - return markFunction( function( argument ) { - argument = +argument; - return markFunction( function( seed, matches ) { - var j, - matchIndexes = fn( [], seed.length, argument ), - i = matchIndexes.length; - - // Match elements found at the specified indexes - while ( i-- ) { - if ( seed[ ( j = matchIndexes[ i ] ) ] ) { - seed[ j ] = !( matches[ j ] = seed[ j ] ); - } - } - } ); - } ); -} - -/** - * Checks a node for validity as a Sizzle context - * @param {Element|Object=} context - * @returns {Element|Object|Boolean} The input node if acceptable, otherwise a falsy value - */ -function testContext( context ) { - return context && typeof context.getElementsByTagName !== "undefined" && context; -} - -// Expose support vars for convenience -support = Sizzle.support = {}; - -/** - * Detects XML nodes - * @param {Element|Object} elem An element or a document - * @returns {Boolean} True iff elem is a non-HTML XML node - */ -isXML = Sizzle.isXML = function( elem ) { - var namespace = elem && elem.namespaceURI, - docElem = elem && ( elem.ownerDocument || elem ).documentElement; - - // Support: IE <=8 - // Assume HTML when documentElement doesn't yet exist, such as inside loading iframes - // https://bugs.jquery.com/ticket/4833 - return !rhtml.test( namespace || docElem && docElem.nodeName || "HTML" ); -}; - -/** - * Sets document-related variables once based on the current document - * @param {Element|Object} [doc] An element or document object to use to set the document - * @returns {Object} Returns the current document - */ -setDocument = Sizzle.setDocument = function( node ) { - var hasCompare, subWindow, - doc = node ? node.ownerDocument || node : preferredDoc; - - // Return early if doc is invalid or already selected - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - // eslint-disable-next-line eqeqeq - if ( doc == document || doc.nodeType !== 9 || !doc.documentElement ) { - return document; - } - - // Update global variables - document = doc; - docElem = document.documentElement; - documentIsHTML = !isXML( document ); - - // Support: IE 9 - 11+, Edge 12 - 18+ - // Accessing iframe documents after unload throws "permission denied" errors (jQuery #13936) - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - // eslint-disable-next-line eqeqeq - if ( preferredDoc != document && - ( subWindow = document.defaultView ) && subWindow.top !== subWindow ) { - - // Support: IE 11, Edge - if ( subWindow.addEventListener ) { - subWindow.addEventListener( "unload", unloadHandler, false ); - - // Support: IE 9 - 10 only - } else if ( subWindow.attachEvent ) { - subWindow.attachEvent( "onunload", unloadHandler ); - } - } - - // Support: IE 8 - 11+, Edge 12 - 18+, Chrome <=16 - 25 only, Firefox <=3.6 - 31 only, - // Safari 4 - 5 only, Opera <=11.6 - 12.x only - // IE/Edge & older browsers don't support the :scope pseudo-class. - // Support: Safari 6.0 only - // Safari 6.0 supports :scope but it's an alias of :root there. - support.scope = assert( function( el ) { - docElem.appendChild( el ).appendChild( document.createElement( "div" ) ); - return typeof el.querySelectorAll !== "undefined" && - !el.querySelectorAll( ":scope fieldset div" ).length; - } ); - - /* Attributes - ---------------------------------------------------------------------- */ - - // Support: IE<8 - // Verify that getAttribute really returns attributes and not properties - // (excepting IE8 booleans) - support.attributes = assert( function( el ) { - el.className = "i"; - return !el.getAttribute( "className" ); - } ); - - /* getElement(s)By* - ---------------------------------------------------------------------- */ - - // Check if getElementsByTagName("*") returns only elements - support.getElementsByTagName = assert( function( el ) { - el.appendChild( document.createComment( "" ) ); - return !el.getElementsByTagName( "*" ).length; - } ); - - // Support: IE<9 - support.getElementsByClassName = rnative.test( document.getElementsByClassName ); - - // Support: IE<10 - // Check if getElementById returns elements by name - // The broken getElementById methods don't pick up programmatically-set names, - // so use a roundabout getElementsByName test - support.getById = assert( function( el ) { - docElem.appendChild( el ).id = expando; - return !document.getElementsByName || !document.getElementsByName( expando ).length; - } ); - - // ID filter and find - if ( support.getById ) { - Expr.filter[ "ID" ] = function( id ) { - var attrId = id.replace( runescape, funescape ); - return function( elem ) { - return elem.getAttribute( "id" ) === attrId; - }; - }; - Expr.find[ "ID" ] = function( id, context ) { - if ( typeof context.getElementById !== "undefined" && documentIsHTML ) { - var elem = context.getElementById( id ); - return elem ? [ elem ] : []; - } - }; - } else { - Expr.filter[ "ID" ] = function( id ) { - var attrId = id.replace( runescape, funescape ); - return function( elem ) { - var node = typeof elem.getAttributeNode !== "undefined" && - elem.getAttributeNode( "id" ); - return node && node.value === attrId; - }; - }; - - // Support: IE 6 - 7 only - // getElementById is not reliable as a find shortcut - Expr.find[ "ID" ] = function( id, context ) { - if ( typeof context.getElementById !== "undefined" && documentIsHTML ) { - var node, i, elems, - elem = context.getElementById( id ); - - if ( elem ) { - - // Verify the id attribute - node = elem.getAttributeNode( "id" ); - if ( node && node.value === id ) { - return [ elem ]; - } - - // Fall back on getElementsByName - elems = context.getElementsByName( id ); - i = 0; - while ( ( elem = elems[ i++ ] ) ) { - node = elem.getAttributeNode( "id" ); - if ( node && node.value === id ) { - return [ elem ]; - } - } - } - - return []; - } - }; - } - - // Tag - Expr.find[ "TAG" ] = support.getElementsByTagName ? - function( tag, context ) { - if ( typeof context.getElementsByTagName !== "undefined" ) { - return context.getElementsByTagName( tag ); - - // DocumentFragment nodes don't have gEBTN - } else if ( support.qsa ) { - return context.querySelectorAll( tag ); - } - } : - - function( tag, context ) { - var elem, - tmp = [], - i = 0, - - // By happy coincidence, a (broken) gEBTN appears on DocumentFragment nodes too - results = context.getElementsByTagName( tag ); - - // Filter out possible comments - if ( tag === "*" ) { - while ( ( elem = results[ i++ ] ) ) { - if ( elem.nodeType === 1 ) { - tmp.push( elem ); - } - } - - return tmp; - } - return results; - }; - - // Class - Expr.find[ "CLASS" ] = support.getElementsByClassName && function( className, context ) { - if ( typeof context.getElementsByClassName !== "undefined" && documentIsHTML ) { - return context.getElementsByClassName( className ); - } - }; - - /* QSA/matchesSelector - ---------------------------------------------------------------------- */ - - // QSA and matchesSelector support - - // matchesSelector(:active) reports false when true (IE9/Opera 11.5) - rbuggyMatches = []; - - // qSa(:focus) reports false when true (Chrome 21) - // We allow this because of a bug in IE8/9 that throws an error - // whenever `document.activeElement` is accessed on an iframe - // So, we allow :focus to pass through QSA all the time to avoid the IE error - // See https://bugs.jquery.com/ticket/13378 - rbuggyQSA = []; - - if ( ( support.qsa = rnative.test( document.querySelectorAll ) ) ) { - - // Build QSA regex - // Regex strategy adopted from Diego Perini - assert( function( el ) { - - var input; - - // Select is set to empty string on purpose - // This is to test IE's treatment of not explicitly - // setting a boolean content attribute, - // since its presence should be enough - // https://bugs.jquery.com/ticket/12359 - docElem.appendChild( el ).innerHTML = "" + - ""; - - // Support: IE8, Opera 11-12.16 - // Nothing should be selected when empty strings follow ^= or $= or *= - // The test attribute must be unknown in Opera but "safe" for WinRT - // https://msdn.microsoft.com/en-us/library/ie/hh465388.aspx#attribute_section - if ( el.querySelectorAll( "[msallowcapture^='']" ).length ) { - rbuggyQSA.push( "[*^$]=" + whitespace + "*(?:''|\"\")" ); - } - - // Support: IE8 - // Boolean attributes and "value" are not treated correctly - if ( !el.querySelectorAll( "[selected]" ).length ) { - rbuggyQSA.push( "\\[" + whitespace + "*(?:value|" + booleans + ")" ); - } - - // Support: Chrome<29, Android<4.4, Safari<7.0+, iOS<7.0+, PhantomJS<1.9.8+ - if ( !el.querySelectorAll( "[id~=" + expando + "-]" ).length ) { - rbuggyQSA.push( "~=" ); - } - - // Support: IE 11+, Edge 15 - 18+ - // IE 11/Edge don't find elements on a `[name='']` query in some cases. - // Adding a temporary attribute to the document before the selection works - // around the issue. - // Interestingly, IE 10 & older don't seem to have the issue. - input = document.createElement( "input" ); - input.setAttribute( "name", "" ); - el.appendChild( input ); - if ( !el.querySelectorAll( "[name='']" ).length ) { - rbuggyQSA.push( "\\[" + whitespace + "*name" + whitespace + "*=" + - whitespace + "*(?:''|\"\")" ); - } - - // Webkit/Opera - :checked should return selected option elements - // http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked - // IE8 throws error here and will not see later tests - if ( !el.querySelectorAll( ":checked" ).length ) { - rbuggyQSA.push( ":checked" ); - } - - // Support: Safari 8+, iOS 8+ - // https://bugs.webkit.org/show_bug.cgi?id=136851 - // In-page `selector#id sibling-combinator selector` fails - if ( !el.querySelectorAll( "a#" + expando + "+*" ).length ) { - rbuggyQSA.push( ".#.+[+~]" ); - } - - // Support: Firefox <=3.6 - 5 only - // Old Firefox doesn't throw on a badly-escaped identifier. - el.querySelectorAll( "\\\f" ); - rbuggyQSA.push( "[\\r\\n\\f]" ); - } ); - - assert( function( el ) { - el.innerHTML = "" + - ""; - - // Support: Windows 8 Native Apps - // The type and name attributes are restricted during .innerHTML assignment - var input = document.createElement( "input" ); - input.setAttribute( "type", "hidden" ); - el.appendChild( input ).setAttribute( "name", "D" ); - - // Support: IE8 - // Enforce case-sensitivity of name attribute - if ( el.querySelectorAll( "[name=d]" ).length ) { - rbuggyQSA.push( "name" + whitespace + "*[*^$|!~]?=" ); - } - - // FF 3.5 - :enabled/:disabled and hidden elements (hidden elements are still enabled) - // IE8 throws error here and will not see later tests - if ( el.querySelectorAll( ":enabled" ).length !== 2 ) { - rbuggyQSA.push( ":enabled", ":disabled" ); - } - - // Support: IE9-11+ - // IE's :disabled selector does not pick up the children of disabled fieldsets - docElem.appendChild( el ).disabled = true; - if ( el.querySelectorAll( ":disabled" ).length !== 2 ) { - rbuggyQSA.push( ":enabled", ":disabled" ); - } - - // Support: Opera 10 - 11 only - // Opera 10-11 does not throw on post-comma invalid pseudos - el.querySelectorAll( "*,:x" ); - rbuggyQSA.push( ",.*:" ); - } ); - } - - if ( ( support.matchesSelector = rnative.test( ( matches = docElem.matches || - docElem.webkitMatchesSelector || - docElem.mozMatchesSelector || - docElem.oMatchesSelector || - docElem.msMatchesSelector ) ) ) ) { - - assert( function( el ) { - - // Check to see if it's possible to do matchesSelector - // on a disconnected node (IE 9) - support.disconnectedMatch = matches.call( el, "*" ); - - // This should fail with an exception - // Gecko does not error, returns false instead - matches.call( el, "[s!='']:x" ); - rbuggyMatches.push( "!=", pseudos ); - } ); - } - - rbuggyQSA = rbuggyQSA.length && new RegExp( rbuggyQSA.join( "|" ) ); - rbuggyMatches = rbuggyMatches.length && new RegExp( rbuggyMatches.join( "|" ) ); - - /* Contains - ---------------------------------------------------------------------- */ - hasCompare = rnative.test( docElem.compareDocumentPosition ); - - // Element contains another - // Purposefully self-exclusive - // As in, an element does not contain itself - contains = hasCompare || rnative.test( docElem.contains ) ? - function( a, b ) { - var adown = a.nodeType === 9 ? a.documentElement : a, - bup = b && b.parentNode; - return a === bup || !!( bup && bup.nodeType === 1 && ( - adown.contains ? - adown.contains( bup ) : - a.compareDocumentPosition && a.compareDocumentPosition( bup ) & 16 - ) ); - } : - function( a, b ) { - if ( b ) { - while ( ( b = b.parentNode ) ) { - if ( b === a ) { - return true; - } - } - } - return false; - }; - - /* Sorting - ---------------------------------------------------------------------- */ - - // Document order sorting - sortOrder = hasCompare ? - function( a, b ) { - - // Flag for duplicate removal - if ( a === b ) { - hasDuplicate = true; - return 0; - } - - // Sort on method existence if only one input has compareDocumentPosition - var compare = !a.compareDocumentPosition - !b.compareDocumentPosition; - if ( compare ) { - return compare; - } - - // Calculate position if both inputs belong to the same document - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - // eslint-disable-next-line eqeqeq - compare = ( a.ownerDocument || a ) == ( b.ownerDocument || b ) ? - a.compareDocumentPosition( b ) : - - // Otherwise we know they are disconnected - 1; - - // Disconnected nodes - if ( compare & 1 || - ( !support.sortDetached && b.compareDocumentPosition( a ) === compare ) ) { - - // Choose the first element that is related to our preferred document - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - // eslint-disable-next-line eqeqeq - if ( a == document || a.ownerDocument == preferredDoc && - contains( preferredDoc, a ) ) { - return -1; - } - - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - // eslint-disable-next-line eqeqeq - if ( b == document || b.ownerDocument == preferredDoc && - contains( preferredDoc, b ) ) { - return 1; - } - - // Maintain original order - return sortInput ? - ( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) : - 0; - } - - return compare & 4 ? -1 : 1; - } : - function( a, b ) { - - // Exit early if the nodes are identical - if ( a === b ) { - hasDuplicate = true; - return 0; - } - - var cur, - i = 0, - aup = a.parentNode, - bup = b.parentNode, - ap = [ a ], - bp = [ b ]; - - // Parentless nodes are either documents or disconnected - if ( !aup || !bup ) { - - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - /* eslint-disable eqeqeq */ - return a == document ? -1 : - b == document ? 1 : - /* eslint-enable eqeqeq */ - aup ? -1 : - bup ? 1 : - sortInput ? - ( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) : - 0; - - // If the nodes are siblings, we can do a quick check - } else if ( aup === bup ) { - return siblingCheck( a, b ); - } - - // Otherwise we need full lists of their ancestors for comparison - cur = a; - while ( ( cur = cur.parentNode ) ) { - ap.unshift( cur ); - } - cur = b; - while ( ( cur = cur.parentNode ) ) { - bp.unshift( cur ); - } - - // Walk down the tree looking for a discrepancy - while ( ap[ i ] === bp[ i ] ) { - i++; - } - - return i ? - - // Do a sibling check if the nodes have a common ancestor - siblingCheck( ap[ i ], bp[ i ] ) : - - // Otherwise nodes in our document sort first - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - /* eslint-disable eqeqeq */ - ap[ i ] == preferredDoc ? -1 : - bp[ i ] == preferredDoc ? 1 : - /* eslint-enable eqeqeq */ - 0; - }; - - return document; -}; - -Sizzle.matches = function( expr, elements ) { - return Sizzle( expr, null, null, elements ); -}; - -Sizzle.matchesSelector = function( elem, expr ) { - setDocument( elem ); - - if ( support.matchesSelector && documentIsHTML && - !nonnativeSelectorCache[ expr + " " ] && - ( !rbuggyMatches || !rbuggyMatches.test( expr ) ) && - ( !rbuggyQSA || !rbuggyQSA.test( expr ) ) ) { - - try { - var ret = matches.call( elem, expr ); - - // IE 9's matchesSelector returns false on disconnected nodes - if ( ret || support.disconnectedMatch || - - // As well, disconnected nodes are said to be in a document - // fragment in IE 9 - elem.document && elem.document.nodeType !== 11 ) { - return ret; - } - } catch ( e ) { - nonnativeSelectorCache( expr, true ); - } - } - - return Sizzle( expr, document, null, [ elem ] ).length > 0; -}; - -Sizzle.contains = function( context, elem ) { - - // Set document vars if needed - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - // eslint-disable-next-line eqeqeq - if ( ( context.ownerDocument || context ) != document ) { - setDocument( context ); - } - return contains( context, elem ); -}; - -Sizzle.attr = function( elem, name ) { - - // Set document vars if needed - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - // eslint-disable-next-line eqeqeq - if ( ( elem.ownerDocument || elem ) != document ) { - setDocument( elem ); - } - - var fn = Expr.attrHandle[ name.toLowerCase() ], - - // Don't get fooled by Object.prototype properties (jQuery #13807) - val = fn && hasOwn.call( Expr.attrHandle, name.toLowerCase() ) ? - fn( elem, name, !documentIsHTML ) : - undefined; - - return val !== undefined ? - val : - support.attributes || !documentIsHTML ? - elem.getAttribute( name ) : - ( val = elem.getAttributeNode( name ) ) && val.specified ? - val.value : - null; -}; - -Sizzle.escape = function( sel ) { - return ( sel + "" ).replace( rcssescape, fcssescape ); -}; - -Sizzle.error = function( msg ) { - throw new Error( "Syntax error, unrecognized expression: " + msg ); -}; - -/** - * Document sorting and removing duplicates - * @param {ArrayLike} results - */ -Sizzle.uniqueSort = function( results ) { - var elem, - duplicates = [], - j = 0, - i = 0; - - // Unless we *know* we can detect duplicates, assume their presence - hasDuplicate = !support.detectDuplicates; - sortInput = !support.sortStable && results.slice( 0 ); - results.sort( sortOrder ); - - if ( hasDuplicate ) { - while ( ( elem = results[ i++ ] ) ) { - if ( elem === results[ i ] ) { - j = duplicates.push( i ); - } - } - while ( j-- ) { - results.splice( duplicates[ j ], 1 ); - } - } - - // Clear input after sorting to release objects - // See https://github.com/jquery/sizzle/pull/225 - sortInput = null; - - return results; -}; - -/** - * Utility function for retrieving the text value of an array of DOM nodes - * @param {Array|Element} elem - */ -getText = Sizzle.getText = function( elem ) { - var node, - ret = "", - i = 0, - nodeType = elem.nodeType; - - if ( !nodeType ) { - - // If no nodeType, this is expected to be an array - while ( ( node = elem[ i++ ] ) ) { - - // Do not traverse comment nodes - ret += getText( node ); - } - } else if ( nodeType === 1 || nodeType === 9 || nodeType === 11 ) { - - // Use textContent for elements - // innerText usage removed for consistency of new lines (jQuery #11153) - if ( typeof elem.textContent === "string" ) { - return elem.textContent; - } else { - - // Traverse its children - for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) { - ret += getText( elem ); - } - } - } else if ( nodeType === 3 || nodeType === 4 ) { - return elem.nodeValue; - } - - // Do not include comment or processing instruction nodes - - return ret; -}; - -Expr = Sizzle.selectors = { - - // Can be adjusted by the user - cacheLength: 50, - - createPseudo: markFunction, - - match: matchExpr, - - attrHandle: {}, - - find: {}, - - relative: { - ">": { dir: "parentNode", first: true }, - " ": { dir: "parentNode" }, - "+": { dir: "previousSibling", first: true }, - "~": { dir: "previousSibling" } - }, - - preFilter: { - "ATTR": function( match ) { - match[ 1 ] = match[ 1 ].replace( runescape, funescape ); - - // Move the given value to match[3] whether quoted or unquoted - match[ 3 ] = ( match[ 3 ] || match[ 4 ] || - match[ 5 ] || "" ).replace( runescape, funescape ); - - if ( match[ 2 ] === "~=" ) { - match[ 3 ] = " " + match[ 3 ] + " "; - } - - return match.slice( 0, 4 ); - }, - - "CHILD": function( match ) { - - /* matches from matchExpr["CHILD"] - 1 type (only|nth|...) - 2 what (child|of-type) - 3 argument (even|odd|\d*|\d*n([+-]\d+)?|...) - 4 xn-component of xn+y argument ([+-]?\d*n|) - 5 sign of xn-component - 6 x of xn-component - 7 sign of y-component - 8 y of y-component - */ - match[ 1 ] = match[ 1 ].toLowerCase(); - - if ( match[ 1 ].slice( 0, 3 ) === "nth" ) { - - // nth-* requires argument - if ( !match[ 3 ] ) { - Sizzle.error( match[ 0 ] ); - } - - // numeric x and y parameters for Expr.filter.CHILD - // remember that false/true cast respectively to 0/1 - match[ 4 ] = +( match[ 4 ] ? - match[ 5 ] + ( match[ 6 ] || 1 ) : - 2 * ( match[ 3 ] === "even" || match[ 3 ] === "odd" ) ); - match[ 5 ] = +( ( match[ 7 ] + match[ 8 ] ) || match[ 3 ] === "odd" ); - - // other types prohibit arguments - } else if ( match[ 3 ] ) { - Sizzle.error( match[ 0 ] ); - } - - return match; - }, - - "PSEUDO": function( match ) { - var excess, - unquoted = !match[ 6 ] && match[ 2 ]; - - if ( matchExpr[ "CHILD" ].test( match[ 0 ] ) ) { - return null; - } - - // Accept quoted arguments as-is - if ( match[ 3 ] ) { - match[ 2 ] = match[ 4 ] || match[ 5 ] || ""; - - // Strip excess characters from unquoted arguments - } else if ( unquoted && rpseudo.test( unquoted ) && - - // Get excess from tokenize (recursively) - ( excess = tokenize( unquoted, true ) ) && - - // advance to the next closing parenthesis - ( excess = unquoted.indexOf( ")", unquoted.length - excess ) - unquoted.length ) ) { - - // excess is a negative index - match[ 0 ] = match[ 0 ].slice( 0, excess ); - match[ 2 ] = unquoted.slice( 0, excess ); - } - - // Return only captures needed by the pseudo filter method (type and argument) - return match.slice( 0, 3 ); - } - }, - - filter: { - - "TAG": function( nodeNameSelector ) { - var nodeName = nodeNameSelector.replace( runescape, funescape ).toLowerCase(); - return nodeNameSelector === "*" ? - function() { - return true; - } : - function( elem ) { - return elem.nodeName && elem.nodeName.toLowerCase() === nodeName; - }; - }, - - "CLASS": function( className ) { - var pattern = classCache[ className + " " ]; - - return pattern || - ( pattern = new RegExp( "(^|" + whitespace + - ")" + className + "(" + whitespace + "|$)" ) ) && classCache( - className, function( elem ) { - return pattern.test( - typeof elem.className === "string" && elem.className || - typeof elem.getAttribute !== "undefined" && - elem.getAttribute( "class" ) || - "" - ); - } ); - }, - - "ATTR": function( name, operator, check ) { - return function( elem ) { - var result = Sizzle.attr( elem, name ); - - if ( result == null ) { - return operator === "!="; - } - if ( !operator ) { - return true; - } - - result += ""; - - /* eslint-disable max-len */ - - return operator === "=" ? result === check : - operator === "!=" ? result !== check : - operator === "^=" ? check && result.indexOf( check ) === 0 : - operator === "*=" ? check && result.indexOf( check ) > -1 : - operator === "$=" ? check && result.slice( -check.length ) === check : - operator === "~=" ? ( " " + result.replace( rwhitespace, " " ) + " " ).indexOf( check ) > -1 : - operator === "|=" ? result === check || result.slice( 0, check.length + 1 ) === check + "-" : - false; - /* eslint-enable max-len */ - - }; - }, - - "CHILD": function( type, what, _argument, first, last ) { - var simple = type.slice( 0, 3 ) !== "nth", - forward = type.slice( -4 ) !== "last", - ofType = what === "of-type"; - - return first === 1 && last === 0 ? - - // Shortcut for :nth-*(n) - function( elem ) { - return !!elem.parentNode; - } : - - function( elem, _context, xml ) { - var cache, uniqueCache, outerCache, node, nodeIndex, start, - dir = simple !== forward ? "nextSibling" : "previousSibling", - parent = elem.parentNode, - name = ofType && elem.nodeName.toLowerCase(), - useCache = !xml && !ofType, - diff = false; - - if ( parent ) { - - // :(first|last|only)-(child|of-type) - if ( simple ) { - while ( dir ) { - node = elem; - while ( ( node = node[ dir ] ) ) { - if ( ofType ? - node.nodeName.toLowerCase() === name : - node.nodeType === 1 ) { - - return false; - } - } - - // Reverse direction for :only-* (if we haven't yet done so) - start = dir = type === "only" && !start && "nextSibling"; - } - return true; - } - - start = [ forward ? parent.firstChild : parent.lastChild ]; - - // non-xml :nth-child(...) stores cache data on `parent` - if ( forward && useCache ) { - - // Seek `elem` from a previously-cached index - - // ...in a gzip-friendly way - node = parent; - outerCache = node[ expando ] || ( node[ expando ] = {} ); - - // Support: IE <9 only - // Defend against cloned attroperties (jQuery gh-1709) - uniqueCache = outerCache[ node.uniqueID ] || - ( outerCache[ node.uniqueID ] = {} ); - - cache = uniqueCache[ type ] || []; - nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ]; - diff = nodeIndex && cache[ 2 ]; - node = nodeIndex && parent.childNodes[ nodeIndex ]; - - while ( ( node = ++nodeIndex && node && node[ dir ] || - - // Fallback to seeking `elem` from the start - ( diff = nodeIndex = 0 ) || start.pop() ) ) { - - // When found, cache indexes on `parent` and break - if ( node.nodeType === 1 && ++diff && node === elem ) { - uniqueCache[ type ] = [ dirruns, nodeIndex, diff ]; - break; - } - } - - } else { - - // Use previously-cached element index if available - if ( useCache ) { - - // ...in a gzip-friendly way - node = elem; - outerCache = node[ expando ] || ( node[ expando ] = {} ); - - // Support: IE <9 only - // Defend against cloned attroperties (jQuery gh-1709) - uniqueCache = outerCache[ node.uniqueID ] || - ( outerCache[ node.uniqueID ] = {} ); - - cache = uniqueCache[ type ] || []; - nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ]; - diff = nodeIndex; - } - - // xml :nth-child(...) - // or :nth-last-child(...) or :nth(-last)?-of-type(...) - if ( diff === false ) { - - // Use the same loop as above to seek `elem` from the start - while ( ( node = ++nodeIndex && node && node[ dir ] || - ( diff = nodeIndex = 0 ) || start.pop() ) ) { - - if ( ( ofType ? - node.nodeName.toLowerCase() === name : - node.nodeType === 1 ) && - ++diff ) { - - // Cache the index of each encountered element - if ( useCache ) { - outerCache = node[ expando ] || - ( node[ expando ] = {} ); - - // Support: IE <9 only - // Defend against cloned attroperties (jQuery gh-1709) - uniqueCache = outerCache[ node.uniqueID ] || - ( outerCache[ node.uniqueID ] = {} ); - - uniqueCache[ type ] = [ dirruns, diff ]; - } - - if ( node === elem ) { - break; - } - } - } - } - } - - // Incorporate the offset, then check against cycle size - diff -= last; - return diff === first || ( diff % first === 0 && diff / first >= 0 ); - } - }; - }, - - "PSEUDO": function( pseudo, argument ) { - - // pseudo-class names are case-insensitive - // http://www.w3.org/TR/selectors/#pseudo-classes - // Prioritize by case sensitivity in case custom pseudos are added with uppercase letters - // Remember that setFilters inherits from pseudos - var args, - fn = Expr.pseudos[ pseudo ] || Expr.setFilters[ pseudo.toLowerCase() ] || - Sizzle.error( "unsupported pseudo: " + pseudo ); - - // The user may use createPseudo to indicate that - // arguments are needed to create the filter function - // just as Sizzle does - if ( fn[ expando ] ) { - return fn( argument ); - } - - // But maintain support for old signatures - if ( fn.length > 1 ) { - args = [ pseudo, pseudo, "", argument ]; - return Expr.setFilters.hasOwnProperty( pseudo.toLowerCase() ) ? - markFunction( function( seed, matches ) { - var idx, - matched = fn( seed, argument ), - i = matched.length; - while ( i-- ) { - idx = indexOf( seed, matched[ i ] ); - seed[ idx ] = !( matches[ idx ] = matched[ i ] ); - } - } ) : - function( elem ) { - return fn( elem, 0, args ); - }; - } - - return fn; - } - }, - - pseudos: { - - // Potentially complex pseudos - "not": markFunction( function( selector ) { - - // Trim the selector passed to compile - // to avoid treating leading and trailing - // spaces as combinators - var input = [], - results = [], - matcher = compile( selector.replace( rtrim, "$1" ) ); - - return matcher[ expando ] ? - markFunction( function( seed, matches, _context, xml ) { - var elem, - unmatched = matcher( seed, null, xml, [] ), - i = seed.length; - - // Match elements unmatched by `matcher` - while ( i-- ) { - if ( ( elem = unmatched[ i ] ) ) { - seed[ i ] = !( matches[ i ] = elem ); - } - } - } ) : - function( elem, _context, xml ) { - input[ 0 ] = elem; - matcher( input, null, xml, results ); - - // Don't keep the element (issue #299) - input[ 0 ] = null; - return !results.pop(); - }; - } ), - - "has": markFunction( function( selector ) { - return function( elem ) { - return Sizzle( selector, elem ).length > 0; - }; - } ), - - "contains": markFunction( function( text ) { - text = text.replace( runescape, funescape ); - return function( elem ) { - return ( elem.textContent || getText( elem ) ).indexOf( text ) > -1; - }; - } ), - - // "Whether an element is represented by a :lang() selector - // is based solely on the element's language value - // being equal to the identifier C, - // or beginning with the identifier C immediately followed by "-". - // The matching of C against the element's language value is performed case-insensitively. - // The identifier C does not have to be a valid language name." - // http://www.w3.org/TR/selectors/#lang-pseudo - "lang": markFunction( function( lang ) { - - // lang value must be a valid identifier - if ( !ridentifier.test( lang || "" ) ) { - Sizzle.error( "unsupported lang: " + lang ); - } - lang = lang.replace( runescape, funescape ).toLowerCase(); - return function( elem ) { - var elemLang; - do { - if ( ( elemLang = documentIsHTML ? - elem.lang : - elem.getAttribute( "xml:lang" ) || elem.getAttribute( "lang" ) ) ) { - - elemLang = elemLang.toLowerCase(); - return elemLang === lang || elemLang.indexOf( lang + "-" ) === 0; - } - } while ( ( elem = elem.parentNode ) && elem.nodeType === 1 ); - return false; - }; - } ), - - // Miscellaneous - "target": function( elem ) { - var hash = window.location && window.location.hash; - return hash && hash.slice( 1 ) === elem.id; - }, - - "root": function( elem ) { - return elem === docElem; - }, - - "focus": function( elem ) { - return elem === document.activeElement && - ( !document.hasFocus || document.hasFocus() ) && - !!( elem.type || elem.href || ~elem.tabIndex ); - }, - - // Boolean properties - "enabled": createDisabledPseudo( false ), - "disabled": createDisabledPseudo( true ), - - "checked": function( elem ) { - - // In CSS3, :checked should return both checked and selected elements - // http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked - var nodeName = elem.nodeName.toLowerCase(); - return ( nodeName === "input" && !!elem.checked ) || - ( nodeName === "option" && !!elem.selected ); - }, - - "selected": function( elem ) { - - // Accessing this property makes selected-by-default - // options in Safari work properly - if ( elem.parentNode ) { - // eslint-disable-next-line no-unused-expressions - elem.parentNode.selectedIndex; - } - - return elem.selected === true; - }, - - // Contents - "empty": function( elem ) { - - // http://www.w3.org/TR/selectors/#empty-pseudo - // :empty is negated by element (1) or content nodes (text: 3; cdata: 4; entity ref: 5), - // but not by others (comment: 8; processing instruction: 7; etc.) - // nodeType < 6 works because attributes (2) do not appear as children - for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) { - if ( elem.nodeType < 6 ) { - return false; - } - } - return true; - }, - - "parent": function( elem ) { - return !Expr.pseudos[ "empty" ]( elem ); - }, - - // Element/input types - "header": function( elem ) { - return rheader.test( elem.nodeName ); - }, - - "input": function( elem ) { - return rinputs.test( elem.nodeName ); - }, - - "button": function( elem ) { - var name = elem.nodeName.toLowerCase(); - return name === "input" && elem.type === "button" || name === "button"; - }, - - "text": function( elem ) { - var attr; - return elem.nodeName.toLowerCase() === "input" && - elem.type === "text" && - - // Support: IE<8 - // New HTML5 attribute values (e.g., "search") appear with elem.type === "text" - ( ( attr = elem.getAttribute( "type" ) ) == null || - attr.toLowerCase() === "text" ); - }, - - // Position-in-collection - "first": createPositionalPseudo( function() { - return [ 0 ]; - } ), - - "last": createPositionalPseudo( function( _matchIndexes, length ) { - return [ length - 1 ]; - } ), - - "eq": createPositionalPseudo( function( _matchIndexes, length, argument ) { - return [ argument < 0 ? argument + length : argument ]; - } ), - - "even": createPositionalPseudo( function( matchIndexes, length ) { - var i = 0; - for ( ; i < length; i += 2 ) { - matchIndexes.push( i ); - } - return matchIndexes; - } ), - - "odd": createPositionalPseudo( function( matchIndexes, length ) { - var i = 1; - for ( ; i < length; i += 2 ) { - matchIndexes.push( i ); - } - return matchIndexes; - } ), - - "lt": createPositionalPseudo( function( matchIndexes, length, argument ) { - var i = argument < 0 ? - argument + length : - argument > length ? - length : - argument; - for ( ; --i >= 0; ) { - matchIndexes.push( i ); - } - return matchIndexes; - } ), - - "gt": createPositionalPseudo( function( matchIndexes, length, argument ) { - var i = argument < 0 ? argument + length : argument; - for ( ; ++i < length; ) { - matchIndexes.push( i ); - } - return matchIndexes; - } ) - } -}; - -Expr.pseudos[ "nth" ] = Expr.pseudos[ "eq" ]; - -// Add button/input type pseudos -for ( i in { radio: true, checkbox: true, file: true, password: true, image: true } ) { - Expr.pseudos[ i ] = createInputPseudo( i ); -} -for ( i in { submit: true, reset: true } ) { - Expr.pseudos[ i ] = createButtonPseudo( i ); -} - -// Easy API for creating new setFilters -function setFilters() {} -setFilters.prototype = Expr.filters = Expr.pseudos; -Expr.setFilters = new setFilters(); - -tokenize = Sizzle.tokenize = function( selector, parseOnly ) { - var matched, match, tokens, type, - soFar, groups, preFilters, - cached = tokenCache[ selector + " " ]; - - if ( cached ) { - return parseOnly ? 0 : cached.slice( 0 ); - } - - soFar = selector; - groups = []; - preFilters = Expr.preFilter; - - while ( soFar ) { - - // Comma and first run - if ( !matched || ( match = rcomma.exec( soFar ) ) ) { - if ( match ) { - - // Don't consume trailing commas as valid - soFar = soFar.slice( match[ 0 ].length ) || soFar; - } - groups.push( ( tokens = [] ) ); - } - - matched = false; - - // Combinators - if ( ( match = rcombinators.exec( soFar ) ) ) { - matched = match.shift(); - tokens.push( { - value: matched, - - // Cast descendant combinators to space - type: match[ 0 ].replace( rtrim, " " ) - } ); - soFar = soFar.slice( matched.length ); - } - - // Filters - for ( type in Expr.filter ) { - if ( ( match = matchExpr[ type ].exec( soFar ) ) && ( !preFilters[ type ] || - ( match = preFilters[ type ]( match ) ) ) ) { - matched = match.shift(); - tokens.push( { - value: matched, - type: type, - matches: match - } ); - soFar = soFar.slice( matched.length ); - } - } - - if ( !matched ) { - break; - } - } - - // Return the length of the invalid excess - // if we're just parsing - // Otherwise, throw an error or return tokens - return parseOnly ? - soFar.length : - soFar ? - Sizzle.error( selector ) : - - // Cache the tokens - tokenCache( selector, groups ).slice( 0 ); -}; - -function toSelector( tokens ) { - var i = 0, - len = tokens.length, - selector = ""; - for ( ; i < len; i++ ) { - selector += tokens[ i ].value; - } - return selector; -} - -function addCombinator( matcher, combinator, base ) { - var dir = combinator.dir, - skip = combinator.next, - key = skip || dir, - checkNonElements = base && key === "parentNode", - doneName = done++; - - return combinator.first ? - - // Check against closest ancestor/preceding element - function( elem, context, xml ) { - while ( ( elem = elem[ dir ] ) ) { - if ( elem.nodeType === 1 || checkNonElements ) { - return matcher( elem, context, xml ); - } - } - return false; - } : - - // Check against all ancestor/preceding elements - function( elem, context, xml ) { - var oldCache, uniqueCache, outerCache, - newCache = [ dirruns, doneName ]; - - // We can't set arbitrary data on XML nodes, so they don't benefit from combinator caching - if ( xml ) { - while ( ( elem = elem[ dir ] ) ) { - if ( elem.nodeType === 1 || checkNonElements ) { - if ( matcher( elem, context, xml ) ) { - return true; - } - } - } - } else { - while ( ( elem = elem[ dir ] ) ) { - if ( elem.nodeType === 1 || checkNonElements ) { - outerCache = elem[ expando ] || ( elem[ expando ] = {} ); - - // Support: IE <9 only - // Defend against cloned attroperties (jQuery gh-1709) - uniqueCache = outerCache[ elem.uniqueID ] || - ( outerCache[ elem.uniqueID ] = {} ); - - if ( skip && skip === elem.nodeName.toLowerCase() ) { - elem = elem[ dir ] || elem; - } else if ( ( oldCache = uniqueCache[ key ] ) && - oldCache[ 0 ] === dirruns && oldCache[ 1 ] === doneName ) { - - // Assign to newCache so results back-propagate to previous elements - return ( newCache[ 2 ] = oldCache[ 2 ] ); - } else { - - // Reuse newcache so results back-propagate to previous elements - uniqueCache[ key ] = newCache; - - // A match means we're done; a fail means we have to keep checking - if ( ( newCache[ 2 ] = matcher( elem, context, xml ) ) ) { - return true; - } - } - } - } - } - return false; - }; -} - -function elementMatcher( matchers ) { - return matchers.length > 1 ? - function( elem, context, xml ) { - var i = matchers.length; - while ( i-- ) { - if ( !matchers[ i ]( elem, context, xml ) ) { - return false; - } - } - return true; - } : - matchers[ 0 ]; -} - -function multipleContexts( selector, contexts, results ) { - var i = 0, - len = contexts.length; - for ( ; i < len; i++ ) { - Sizzle( selector, contexts[ i ], results ); - } - return results; -} - -function condense( unmatched, map, filter, context, xml ) { - var elem, - newUnmatched = [], - i = 0, - len = unmatched.length, - mapped = map != null; - - for ( ; i < len; i++ ) { - if ( ( elem = unmatched[ i ] ) ) { - if ( !filter || filter( elem, context, xml ) ) { - newUnmatched.push( elem ); - if ( mapped ) { - map.push( i ); - } - } - } - } - - return newUnmatched; -} - -function setMatcher( preFilter, selector, matcher, postFilter, postFinder, postSelector ) { - if ( postFilter && !postFilter[ expando ] ) { - postFilter = setMatcher( postFilter ); - } - if ( postFinder && !postFinder[ expando ] ) { - postFinder = setMatcher( postFinder, postSelector ); - } - return markFunction( function( seed, results, context, xml ) { - var temp, i, elem, - preMap = [], - postMap = [], - preexisting = results.length, - - // Get initial elements from seed or context - elems = seed || multipleContexts( - selector || "*", - context.nodeType ? [ context ] : context, - [] - ), - - // Prefilter to get matcher input, preserving a map for seed-results synchronization - matcherIn = preFilter && ( seed || !selector ) ? - condense( elems, preMap, preFilter, context, xml ) : - elems, - - matcherOut = matcher ? - - // If we have a postFinder, or filtered seed, or non-seed postFilter or preexisting results, - postFinder || ( seed ? preFilter : preexisting || postFilter ) ? - - // ...intermediate processing is necessary - [] : - - // ...otherwise use results directly - results : - matcherIn; - - // Find primary matches - if ( matcher ) { - matcher( matcherIn, matcherOut, context, xml ); - } - - // Apply postFilter - if ( postFilter ) { - temp = condense( matcherOut, postMap ); - postFilter( temp, [], context, xml ); - - // Un-match failing elements by moving them back to matcherIn - i = temp.length; - while ( i-- ) { - if ( ( elem = temp[ i ] ) ) { - matcherOut[ postMap[ i ] ] = !( matcherIn[ postMap[ i ] ] = elem ); - } - } - } - - if ( seed ) { - if ( postFinder || preFilter ) { - if ( postFinder ) { - - // Get the final matcherOut by condensing this intermediate into postFinder contexts - temp = []; - i = matcherOut.length; - while ( i-- ) { - if ( ( elem = matcherOut[ i ] ) ) { - - // Restore matcherIn since elem is not yet a final match - temp.push( ( matcherIn[ i ] = elem ) ); - } - } - postFinder( null, ( matcherOut = [] ), temp, xml ); - } - - // Move matched elements from seed to results to keep them synchronized - i = matcherOut.length; - while ( i-- ) { - if ( ( elem = matcherOut[ i ] ) && - ( temp = postFinder ? indexOf( seed, elem ) : preMap[ i ] ) > -1 ) { - - seed[ temp ] = !( results[ temp ] = elem ); - } - } - } - - // Add elements to results, through postFinder if defined - } else { - matcherOut = condense( - matcherOut === results ? - matcherOut.splice( preexisting, matcherOut.length ) : - matcherOut - ); - if ( postFinder ) { - postFinder( null, results, matcherOut, xml ); - } else { - push.apply( results, matcherOut ); - } - } - } ); -} - -function matcherFromTokens( tokens ) { - var checkContext, matcher, j, - len = tokens.length, - leadingRelative = Expr.relative[ tokens[ 0 ].type ], - implicitRelative = leadingRelative || Expr.relative[ " " ], - i = leadingRelative ? 1 : 0, - - // The foundational matcher ensures that elements are reachable from top-level context(s) - matchContext = addCombinator( function( elem ) { - return elem === checkContext; - }, implicitRelative, true ), - matchAnyContext = addCombinator( function( elem ) { - return indexOf( checkContext, elem ) > -1; - }, implicitRelative, true ), - matchers = [ function( elem, context, xml ) { - var ret = ( !leadingRelative && ( xml || context !== outermostContext ) ) || ( - ( checkContext = context ).nodeType ? - matchContext( elem, context, xml ) : - matchAnyContext( elem, context, xml ) ); - - // Avoid hanging onto element (issue #299) - checkContext = null; - return ret; - } ]; - - for ( ; i < len; i++ ) { - if ( ( matcher = Expr.relative[ tokens[ i ].type ] ) ) { - matchers = [ addCombinator( elementMatcher( matchers ), matcher ) ]; - } else { - matcher = Expr.filter[ tokens[ i ].type ].apply( null, tokens[ i ].matches ); - - // Return special upon seeing a positional matcher - if ( matcher[ expando ] ) { - - // Find the next relative operator (if any) for proper handling - j = ++i; - for ( ; j < len; j++ ) { - if ( Expr.relative[ tokens[ j ].type ] ) { - break; - } - } - return setMatcher( - i > 1 && elementMatcher( matchers ), - i > 1 && toSelector( - - // If the preceding token was a descendant combinator, insert an implicit any-element `*` - tokens - .slice( 0, i - 1 ) - .concat( { value: tokens[ i - 2 ].type === " " ? "*" : "" } ) - ).replace( rtrim, "$1" ), - matcher, - i < j && matcherFromTokens( tokens.slice( i, j ) ), - j < len && matcherFromTokens( ( tokens = tokens.slice( j ) ) ), - j < len && toSelector( tokens ) - ); - } - matchers.push( matcher ); - } - } - - return elementMatcher( matchers ); -} - -function matcherFromGroupMatchers( elementMatchers, setMatchers ) { - var bySet = setMatchers.length > 0, - byElement = elementMatchers.length > 0, - superMatcher = function( seed, context, xml, results, outermost ) { - var elem, j, matcher, - matchedCount = 0, - i = "0", - unmatched = seed && [], - setMatched = [], - contextBackup = outermostContext, - - // We must always have either seed elements or outermost context - elems = seed || byElement && Expr.find[ "TAG" ]( "*", outermost ), - - // Use integer dirruns iff this is the outermost matcher - dirrunsUnique = ( dirruns += contextBackup == null ? 1 : Math.random() || 0.1 ), - len = elems.length; - - if ( outermost ) { - - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - // eslint-disable-next-line eqeqeq - outermostContext = context == document || context || outermost; - } - - // Add elements passing elementMatchers directly to results - // Support: IE<9, Safari - // Tolerate NodeList properties (IE: "length"; Safari: ) matching elements by id - for ( ; i !== len && ( elem = elems[ i ] ) != null; i++ ) { - if ( byElement && elem ) { - j = 0; - - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - // eslint-disable-next-line eqeqeq - if ( !context && elem.ownerDocument != document ) { - setDocument( elem ); - xml = !documentIsHTML; - } - while ( ( matcher = elementMatchers[ j++ ] ) ) { - if ( matcher( elem, context || document, xml ) ) { - results.push( elem ); - break; - } - } - if ( outermost ) { - dirruns = dirrunsUnique; - } - } - - // Track unmatched elements for set filters - if ( bySet ) { - - // They will have gone through all possible matchers - if ( ( elem = !matcher && elem ) ) { - matchedCount--; - } - - // Lengthen the array for every element, matched or not - if ( seed ) { - unmatched.push( elem ); - } - } - } - - // `i` is now the count of elements visited above, and adding it to `matchedCount` - // makes the latter nonnegative. - matchedCount += i; - - // Apply set filters to unmatched elements - // NOTE: This can be skipped if there are no unmatched elements (i.e., `matchedCount` - // equals `i`), unless we didn't visit _any_ elements in the above loop because we have - // no element matchers and no seed. - // Incrementing an initially-string "0" `i` allows `i` to remain a string only in that - // case, which will result in a "00" `matchedCount` that differs from `i` but is also - // numerically zero. - if ( bySet && i !== matchedCount ) { - j = 0; - while ( ( matcher = setMatchers[ j++ ] ) ) { - matcher( unmatched, setMatched, context, xml ); - } - - if ( seed ) { - - // Reintegrate element matches to eliminate the need for sorting - if ( matchedCount > 0 ) { - while ( i-- ) { - if ( !( unmatched[ i ] || setMatched[ i ] ) ) { - setMatched[ i ] = pop.call( results ); - } - } - } - - // Discard index placeholder values to get only actual matches - setMatched = condense( setMatched ); - } - - // Add matches to results - push.apply( results, setMatched ); - - // Seedless set matches succeeding multiple successful matchers stipulate sorting - if ( outermost && !seed && setMatched.length > 0 && - ( matchedCount + setMatchers.length ) > 1 ) { - - Sizzle.uniqueSort( results ); - } - } - - // Override manipulation of globals by nested matchers - if ( outermost ) { - dirruns = dirrunsUnique; - outermostContext = contextBackup; - } - - return unmatched; - }; - - return bySet ? - markFunction( superMatcher ) : - superMatcher; -} - -compile = Sizzle.compile = function( selector, match /* Internal Use Only */ ) { - var i, - setMatchers = [], - elementMatchers = [], - cached = compilerCache[ selector + " " ]; - - if ( !cached ) { - - // Generate a function of recursive functions that can be used to check each element - if ( !match ) { - match = tokenize( selector ); - } - i = match.length; - while ( i-- ) { - cached = matcherFromTokens( match[ i ] ); - if ( cached[ expando ] ) { - setMatchers.push( cached ); - } else { - elementMatchers.push( cached ); - } - } - - // Cache the compiled function - cached = compilerCache( - selector, - matcherFromGroupMatchers( elementMatchers, setMatchers ) - ); - - // Save selector and tokenization - cached.selector = selector; - } - return cached; -}; - -/** - * A low-level selection function that works with Sizzle's compiled - * selector functions - * @param {String|Function} selector A selector or a pre-compiled - * selector function built with Sizzle.compile - * @param {Element} context - * @param {Array} [results] - * @param {Array} [seed] A set of elements to match against - */ -select = Sizzle.select = function( selector, context, results, seed ) { - var i, tokens, token, type, find, - compiled = typeof selector === "function" && selector, - match = !seed && tokenize( ( selector = compiled.selector || selector ) ); - - results = results || []; - - // Try to minimize operations if there is only one selector in the list and no seed - // (the latter of which guarantees us context) - if ( match.length === 1 ) { - - // Reduce context if the leading compound selector is an ID - tokens = match[ 0 ] = match[ 0 ].slice( 0 ); - if ( tokens.length > 2 && ( token = tokens[ 0 ] ).type === "ID" && - context.nodeType === 9 && documentIsHTML && Expr.relative[ tokens[ 1 ].type ] ) { - - context = ( Expr.find[ "ID" ]( token.matches[ 0 ] - .replace( runescape, funescape ), context ) || [] )[ 0 ]; - if ( !context ) { - return results; - - // Precompiled matchers will still verify ancestry, so step up a level - } else if ( compiled ) { - context = context.parentNode; - } - - selector = selector.slice( tokens.shift().value.length ); - } - - // Fetch a seed set for right-to-left matching - i = matchExpr[ "needsContext" ].test( selector ) ? 0 : tokens.length; - while ( i-- ) { - token = tokens[ i ]; - - // Abort if we hit a combinator - if ( Expr.relative[ ( type = token.type ) ] ) { - break; - } - if ( ( find = Expr.find[ type ] ) ) { - - // Search, expanding context for leading sibling combinators - if ( ( seed = find( - token.matches[ 0 ].replace( runescape, funescape ), - rsibling.test( tokens[ 0 ].type ) && testContext( context.parentNode ) || - context - ) ) ) { - - // If seed is empty or no tokens remain, we can return early - tokens.splice( i, 1 ); - selector = seed.length && toSelector( tokens ); - if ( !selector ) { - push.apply( results, seed ); - return results; - } - - break; - } - } - } - } - - // Compile and execute a filtering function if one is not provided - // Provide `match` to avoid retokenization if we modified the selector above - ( compiled || compile( selector, match ) )( - seed, - context, - !documentIsHTML, - results, - !context || rsibling.test( selector ) && testContext( context.parentNode ) || context - ); - return results; -}; - -// One-time assignments - -// Sort stability -support.sortStable = expando.split( "" ).sort( sortOrder ).join( "" ) === expando; - -// Support: Chrome 14-35+ -// Always assume duplicates if they aren't passed to the comparison function -support.detectDuplicates = !!hasDuplicate; - -// Initialize against the default document -setDocument(); - -// Support: Webkit<537.32 - Safari 6.0.3/Chrome 25 (fixed in Chrome 27) -// Detached nodes confoundingly follow *each other* -support.sortDetached = assert( function( el ) { - - // Should return 1, but returns 4 (following) - return el.compareDocumentPosition( document.createElement( "fieldset" ) ) & 1; -} ); - -// Support: IE<8 -// Prevent attribute/property "interpolation" -// https://msdn.microsoft.com/en-us/library/ms536429%28VS.85%29.aspx -if ( !assert( function( el ) { - el.innerHTML = ""; - return el.firstChild.getAttribute( "href" ) === "#"; -} ) ) { - addHandle( "type|href|height|width", function( elem, name, isXML ) { - if ( !isXML ) { - return elem.getAttribute( name, name.toLowerCase() === "type" ? 1 : 2 ); - } - } ); -} - -// Support: IE<9 -// Use defaultValue in place of getAttribute("value") -if ( !support.attributes || !assert( function( el ) { - el.innerHTML = ""; - el.firstChild.setAttribute( "value", "" ); - return el.firstChild.getAttribute( "value" ) === ""; -} ) ) { - addHandle( "value", function( elem, _name, isXML ) { - if ( !isXML && elem.nodeName.toLowerCase() === "input" ) { - return elem.defaultValue; - } - } ); -} - -// Support: IE<9 -// Use getAttributeNode to fetch booleans when getAttribute lies -if ( !assert( function( el ) { - return el.getAttribute( "disabled" ) == null; -} ) ) { - addHandle( booleans, function( elem, name, isXML ) { - var val; - if ( !isXML ) { - return elem[ name ] === true ? name.toLowerCase() : - ( val = elem.getAttributeNode( name ) ) && val.specified ? - val.value : - null; - } - } ); -} - -return Sizzle; - -} )( window ); - - - -jQuery.find = Sizzle; -jQuery.expr = Sizzle.selectors; - -// Deprecated -jQuery.expr[ ":" ] = jQuery.expr.pseudos; -jQuery.uniqueSort = jQuery.unique = Sizzle.uniqueSort; -jQuery.text = Sizzle.getText; -jQuery.isXMLDoc = Sizzle.isXML; -jQuery.contains = Sizzle.contains; -jQuery.escapeSelector = Sizzle.escape; - - - - -var dir = function( elem, dir, until ) { - var matched = [], - truncate = until !== undefined; - - while ( ( elem = elem[ dir ] ) && elem.nodeType !== 9 ) { - if ( elem.nodeType === 1 ) { - if ( truncate && jQuery( elem ).is( until ) ) { - break; - } - matched.push( elem ); - } - } - return matched; -}; - - -var siblings = function( n, elem ) { - var matched = []; - - for ( ; n; n = n.nextSibling ) { - if ( n.nodeType === 1 && n !== elem ) { - matched.push( n ); - } - } - - return matched; -}; - - -var rneedsContext = jQuery.expr.match.needsContext; - - - -function nodeName( elem, name ) { - - return elem.nodeName && elem.nodeName.toLowerCase() === name.toLowerCase(); - -} -var rsingleTag = ( /^<([a-z][^\/\0>:\x20\t\r\n\f]*)[\x20\t\r\n\f]*\/?>(?:<\/\1>|)$/i ); - - - -// Implement the identical functionality for filter and not -function winnow( elements, qualifier, not ) { - if ( isFunction( qualifier ) ) { - return jQuery.grep( elements, function( elem, i ) { - return !!qualifier.call( elem, i, elem ) !== not; - } ); - } - - // Single element - if ( qualifier.nodeType ) { - return jQuery.grep( elements, function( elem ) { - return ( elem === qualifier ) !== not; - } ); - } - - // Arraylike of elements (jQuery, arguments, Array) - if ( typeof qualifier !== "string" ) { - return jQuery.grep( elements, function( elem ) { - return ( indexOf.call( qualifier, elem ) > -1 ) !== not; - } ); - } - - // Filtered directly for both simple and complex selectors - return jQuery.filter( qualifier, elements, not ); -} - -jQuery.filter = function( expr, elems, not ) { - var elem = elems[ 0 ]; - - if ( not ) { - expr = ":not(" + expr + ")"; - } - - if ( elems.length === 1 && elem.nodeType === 1 ) { - return jQuery.find.matchesSelector( elem, expr ) ? [ elem ] : []; - } - - return jQuery.find.matches( expr, jQuery.grep( elems, function( elem ) { - return elem.nodeType === 1; - } ) ); -}; - -jQuery.fn.extend( { - find: function( selector ) { - var i, ret, - len = this.length, - self = this; - - if ( typeof selector !== "string" ) { - return this.pushStack( jQuery( selector ).filter( function() { - for ( i = 0; i < len; i++ ) { - if ( jQuery.contains( self[ i ], this ) ) { - return true; - } - } - } ) ); - } - - ret = this.pushStack( [] ); - - for ( i = 0; i < len; i++ ) { - jQuery.find( selector, self[ i ], ret ); - } - - return len > 1 ? jQuery.uniqueSort( ret ) : ret; - }, - filter: function( selector ) { - return this.pushStack( winnow( this, selector || [], false ) ); - }, - not: function( selector ) { - return this.pushStack( winnow( this, selector || [], true ) ); - }, - is: function( selector ) { - return !!winnow( - this, - - // If this is a positional/relative selector, check membership in the returned set - // so $("p:first").is("p:last") won't return true for a doc with two "p". - typeof selector === "string" && rneedsContext.test( selector ) ? - jQuery( selector ) : - selector || [], - false - ).length; - } -} ); - - -// Initialize a jQuery object - - -// A central reference to the root jQuery(document) -var rootjQuery, - - // A simple way to check for HTML strings - // Prioritize #id over to avoid XSS via location.hash (#9521) - // Strict HTML recognition (#11290: must start with <) - // Shortcut simple #id case for speed - rquickExpr = /^(?:\s*(<[\w\W]+>)[^>]*|#([\w-]+))$/, - - init = jQuery.fn.init = function( selector, context, root ) { - var match, elem; - - // HANDLE: $(""), $(null), $(undefined), $(false) - if ( !selector ) { - return this; - } - - // Method init() accepts an alternate rootjQuery - // so migrate can support jQuery.sub (gh-2101) - root = root || rootjQuery; - - // Handle HTML strings - if ( typeof selector === "string" ) { - if ( selector[ 0 ] === "<" && - selector[ selector.length - 1 ] === ">" && - selector.length >= 3 ) { - - // Assume that strings that start and end with <> are HTML and skip the regex check - match = [ null, selector, null ]; - - } else { - match = rquickExpr.exec( selector ); - } - - // Match html or make sure no context is specified for #id - if ( match && ( match[ 1 ] || !context ) ) { - - // HANDLE: $(html) -> $(array) - if ( match[ 1 ] ) { - context = context instanceof jQuery ? context[ 0 ] : context; - - // Option to run scripts is true for back-compat - // Intentionally let the error be thrown if parseHTML is not present - jQuery.merge( this, jQuery.parseHTML( - match[ 1 ], - context && context.nodeType ? context.ownerDocument || context : document, - true - ) ); - - // HANDLE: $(html, props) - if ( rsingleTag.test( match[ 1 ] ) && jQuery.isPlainObject( context ) ) { - for ( match in context ) { - - // Properties of context are called as methods if possible - if ( isFunction( this[ match ] ) ) { - this[ match ]( context[ match ] ); - - // ...and otherwise set as attributes - } else { - this.attr( match, context[ match ] ); - } - } - } - - return this; - - // HANDLE: $(#id) - } else { - elem = document.getElementById( match[ 2 ] ); - - if ( elem ) { - - // Inject the element directly into the jQuery object - this[ 0 ] = elem; - this.length = 1; - } - return this; - } - - // HANDLE: $(expr, $(...)) - } else if ( !context || context.jquery ) { - return ( context || root ).find( selector ); - - // HANDLE: $(expr, context) - // (which is just equivalent to: $(context).find(expr) - } else { - return this.constructor( context ).find( selector ); - } - - // HANDLE: $(DOMElement) - } else if ( selector.nodeType ) { - this[ 0 ] = selector; - this.length = 1; - return this; - - // HANDLE: $(function) - // Shortcut for document ready - } else if ( isFunction( selector ) ) { - return root.ready !== undefined ? - root.ready( selector ) : - - // Execute immediately if ready is not present - selector( jQuery ); - } - - return jQuery.makeArray( selector, this ); - }; - -// Give the init function the jQuery prototype for later instantiation -init.prototype = jQuery.fn; - -// Initialize central reference -rootjQuery = jQuery( document ); - - -var rparentsprev = /^(?:parents|prev(?:Until|All))/, - - // Methods guaranteed to produce a unique set when starting from a unique set - guaranteedUnique = { - children: true, - contents: true, - next: true, - prev: true - }; - -jQuery.fn.extend( { - has: function( target ) { - var targets = jQuery( target, this ), - l = targets.length; - - return this.filter( function() { - var i = 0; - for ( ; i < l; i++ ) { - if ( jQuery.contains( this, targets[ i ] ) ) { - return true; - } - } - } ); - }, - - closest: function( selectors, context ) { - var cur, - i = 0, - l = this.length, - matched = [], - targets = typeof selectors !== "string" && jQuery( selectors ); - - // Positional selectors never match, since there's no _selection_ context - if ( !rneedsContext.test( selectors ) ) { - for ( ; i < l; i++ ) { - for ( cur = this[ i ]; cur && cur !== context; cur = cur.parentNode ) { - - // Always skip document fragments - if ( cur.nodeType < 11 && ( targets ? - targets.index( cur ) > -1 : - - // Don't pass non-elements to Sizzle - cur.nodeType === 1 && - jQuery.find.matchesSelector( cur, selectors ) ) ) { - - matched.push( cur ); - break; - } - } - } - } - - return this.pushStack( matched.length > 1 ? jQuery.uniqueSort( matched ) : matched ); - }, - - // Determine the position of an element within the set - index: function( elem ) { - - // No argument, return index in parent - if ( !elem ) { - return ( this[ 0 ] && this[ 0 ].parentNode ) ? this.first().prevAll().length : -1; - } - - // Index in selector - if ( typeof elem === "string" ) { - return indexOf.call( jQuery( elem ), this[ 0 ] ); - } - - // Locate the position of the desired element - return indexOf.call( this, - - // If it receives a jQuery object, the first element is used - elem.jquery ? elem[ 0 ] : elem - ); - }, - - add: function( selector, context ) { - return this.pushStack( - jQuery.uniqueSort( - jQuery.merge( this.get(), jQuery( selector, context ) ) - ) - ); - }, - - addBack: function( selector ) { - return this.add( selector == null ? - this.prevObject : this.prevObject.filter( selector ) - ); - } -} ); - -function sibling( cur, dir ) { - while ( ( cur = cur[ dir ] ) && cur.nodeType !== 1 ) {} - return cur; -} - -jQuery.each( { - parent: function( elem ) { - var parent = elem.parentNode; - return parent && parent.nodeType !== 11 ? parent : null; - }, - parents: function( elem ) { - return dir( elem, "parentNode" ); - }, - parentsUntil: function( elem, _i, until ) { - return dir( elem, "parentNode", until ); - }, - next: function( elem ) { - return sibling( elem, "nextSibling" ); - }, - prev: function( elem ) { - return sibling( elem, "previousSibling" ); - }, - nextAll: function( elem ) { - return dir( elem, "nextSibling" ); - }, - prevAll: function( elem ) { - return dir( elem, "previousSibling" ); - }, - nextUntil: function( elem, _i, until ) { - return dir( elem, "nextSibling", until ); - }, - prevUntil: function( elem, _i, until ) { - return dir( elem, "previousSibling", until ); - }, - siblings: function( elem ) { - return siblings( ( elem.parentNode || {} ).firstChild, elem ); - }, - children: function( elem ) { - return siblings( elem.firstChild ); - }, - contents: function( elem ) { - if ( elem.contentDocument != null && - - // Support: IE 11+ - // elements with no `data` attribute has an object - // `contentDocument` with a `null` prototype. - getProto( elem.contentDocument ) ) { - - return elem.contentDocument; - } - - // Support: IE 9 - 11 only, iOS 7 only, Android Browser <=4.3 only - // Treat the template element as a regular one in browsers that - // don't support it. - if ( nodeName( elem, "template" ) ) { - elem = elem.content || elem; - } - - return jQuery.merge( [], elem.childNodes ); - } -}, function( name, fn ) { - jQuery.fn[ name ] = function( until, selector ) { - var matched = jQuery.map( this, fn, until ); - - if ( name.slice( -5 ) !== "Until" ) { - selector = until; - } - - if ( selector && typeof selector === "string" ) { - matched = jQuery.filter( selector, matched ); - } - - if ( this.length > 1 ) { - - // Remove duplicates - if ( !guaranteedUnique[ name ] ) { - jQuery.uniqueSort( matched ); - } - - // Reverse order for parents* and prev-derivatives - if ( rparentsprev.test( name ) ) { - matched.reverse(); - } - } - - return this.pushStack( matched ); - }; -} ); -var rnothtmlwhite = ( /[^\x20\t\r\n\f]+/g ); - - - -// Convert String-formatted options into Object-formatted ones -function createOptions( options ) { - var object = {}; - jQuery.each( options.match( rnothtmlwhite ) || [], function( _, flag ) { - object[ flag ] = true; - } ); - return object; -} - -/* - * Create a callback list using the following parameters: - * - * options: an optional list of space-separated options that will change how - * the callback list behaves or a more traditional option object - * - * By default a callback list will act like an event callback list and can be - * "fired" multiple times. - * - * Possible options: - * - * once: will ensure the callback list can only be fired once (like a Deferred) - * - * memory: will keep track of previous values and will call any callback added - * after the list has been fired right away with the latest "memorized" - * values (like a Deferred) - * - * unique: will ensure a callback can only be added once (no duplicate in the list) - * - * stopOnFalse: interrupt callings when a callback returns false - * - */ -jQuery.Callbacks = function( options ) { - - // Convert options from String-formatted to Object-formatted if needed - // (we check in cache first) - options = typeof options === "string" ? - createOptions( options ) : - jQuery.extend( {}, options ); - - var // Flag to know if list is currently firing - firing, - - // Last fire value for non-forgettable lists - memory, - - // Flag to know if list was already fired - fired, - - // Flag to prevent firing - locked, - - // Actual callback list - list = [], - - // Queue of execution data for repeatable lists - queue = [], - - // Index of currently firing callback (modified by add/remove as needed) - firingIndex = -1, - - // Fire callbacks - fire = function() { - - // Enforce single-firing - locked = locked || options.once; - - // Execute callbacks for all pending executions, - // respecting firingIndex overrides and runtime changes - fired = firing = true; - for ( ; queue.length; firingIndex = -1 ) { - memory = queue.shift(); - while ( ++firingIndex < list.length ) { - - // Run callback and check for early termination - if ( list[ firingIndex ].apply( memory[ 0 ], memory[ 1 ] ) === false && - options.stopOnFalse ) { - - // Jump to end and forget the data so .add doesn't re-fire - firingIndex = list.length; - memory = false; - } - } - } - - // Forget the data if we're done with it - if ( !options.memory ) { - memory = false; - } - - firing = false; - - // Clean up if we're done firing for good - if ( locked ) { - - // Keep an empty list if we have data for future add calls - if ( memory ) { - list = []; - - // Otherwise, this object is spent - } else { - list = ""; - } - } - }, - - // Actual Callbacks object - self = { - - // Add a callback or a collection of callbacks to the list - add: function() { - if ( list ) { - - // If we have memory from a past run, we should fire after adding - if ( memory && !firing ) { - firingIndex = list.length - 1; - queue.push( memory ); - } - - ( function add( args ) { - jQuery.each( args, function( _, arg ) { - if ( isFunction( arg ) ) { - if ( !options.unique || !self.has( arg ) ) { - list.push( arg ); - } - } else if ( arg && arg.length && toType( arg ) !== "string" ) { - - // Inspect recursively - add( arg ); - } - } ); - } )( arguments ); - - if ( memory && !firing ) { - fire(); - } - } - return this; - }, - - // Remove a callback from the list - remove: function() { - jQuery.each( arguments, function( _, arg ) { - var index; - while ( ( index = jQuery.inArray( arg, list, index ) ) > -1 ) { - list.splice( index, 1 ); - - // Handle firing indexes - if ( index <= firingIndex ) { - firingIndex--; - } - } - } ); - return this; - }, - - // Check if a given callback is in the list. - // If no argument is given, return whether or not list has callbacks attached. - has: function( fn ) { - return fn ? - jQuery.inArray( fn, list ) > -1 : - list.length > 0; - }, - - // Remove all callbacks from the list - empty: function() { - if ( list ) { - list = []; - } - return this; - }, - - // Disable .fire and .add - // Abort any current/pending executions - // Clear all callbacks and values - disable: function() { - locked = queue = []; - list = memory = ""; - return this; - }, - disabled: function() { - return !list; - }, - - // Disable .fire - // Also disable .add unless we have memory (since it would have no effect) - // Abort any pending executions - lock: function() { - locked = queue = []; - if ( !memory && !firing ) { - list = memory = ""; - } - return this; - }, - locked: function() { - return !!locked; - }, - - // Call all callbacks with the given context and arguments - fireWith: function( context, args ) { - if ( !locked ) { - args = args || []; - args = [ context, args.slice ? args.slice() : args ]; - queue.push( args ); - if ( !firing ) { - fire(); - } - } - return this; - }, - - // Call all the callbacks with the given arguments - fire: function() { - self.fireWith( this, arguments ); - return this; - }, - - // To know if the callbacks have already been called at least once - fired: function() { - return !!fired; - } - }; - - return self; -}; - - -function Identity( v ) { - return v; -} -function Thrower( ex ) { - throw ex; -} - -function adoptValue( value, resolve, reject, noValue ) { - var method; - - try { - - // Check for promise aspect first to privilege synchronous behavior - if ( value && isFunction( ( method = value.promise ) ) ) { - method.call( value ).done( resolve ).fail( reject ); - - // Other thenables - } else if ( value && isFunction( ( method = value.then ) ) ) { - method.call( value, resolve, reject ); - - // Other non-thenables - } else { - - // Control `resolve` arguments by letting Array#slice cast boolean `noValue` to integer: - // * false: [ value ].slice( 0 ) => resolve( value ) - // * true: [ value ].slice( 1 ) => resolve() - resolve.apply( undefined, [ value ].slice( noValue ) ); - } - - // For Promises/A+, convert exceptions into rejections - // Since jQuery.when doesn't unwrap thenables, we can skip the extra checks appearing in - // Deferred#then to conditionally suppress rejection. - } catch ( value ) { - - // Support: Android 4.0 only - // Strict mode functions invoked without .call/.apply get global-object context - reject.apply( undefined, [ value ] ); - } -} - -jQuery.extend( { - - Deferred: function( func ) { - var tuples = [ - - // action, add listener, callbacks, - // ... .then handlers, argument index, [final state] - [ "notify", "progress", jQuery.Callbacks( "memory" ), - jQuery.Callbacks( "memory" ), 2 ], - [ "resolve", "done", jQuery.Callbacks( "once memory" ), - jQuery.Callbacks( "once memory" ), 0, "resolved" ], - [ "reject", "fail", jQuery.Callbacks( "once memory" ), - jQuery.Callbacks( "once memory" ), 1, "rejected" ] - ], - state = "pending", - promise = { - state: function() { - return state; - }, - always: function() { - deferred.done( arguments ).fail( arguments ); - return this; - }, - "catch": function( fn ) { - return promise.then( null, fn ); - }, - - // Keep pipe for back-compat - pipe: function( /* fnDone, fnFail, fnProgress */ ) { - var fns = arguments; - - return jQuery.Deferred( function( newDefer ) { - jQuery.each( tuples, function( _i, tuple ) { - - // Map tuples (progress, done, fail) to arguments (done, fail, progress) - var fn = isFunction( fns[ tuple[ 4 ] ] ) && fns[ tuple[ 4 ] ]; - - // deferred.progress(function() { bind to newDefer or newDefer.notify }) - // deferred.done(function() { bind to newDefer or newDefer.resolve }) - // deferred.fail(function() { bind to newDefer or newDefer.reject }) - deferred[ tuple[ 1 ] ]( function() { - var returned = fn && fn.apply( this, arguments ); - if ( returned && isFunction( returned.promise ) ) { - returned.promise() - .progress( newDefer.notify ) - .done( newDefer.resolve ) - .fail( newDefer.reject ); - } else { - newDefer[ tuple[ 0 ] + "With" ]( - this, - fn ? [ returned ] : arguments - ); - } - } ); - } ); - fns = null; - } ).promise(); - }, - then: function( onFulfilled, onRejected, onProgress ) { - var maxDepth = 0; - function resolve( depth, deferred, handler, special ) { - return function() { - var that = this, - args = arguments, - mightThrow = function() { - var returned, then; - - // Support: Promises/A+ section 2.3.3.3.3 - // https://promisesaplus.com/#point-59 - // Ignore double-resolution attempts - if ( depth < maxDepth ) { - return; - } - - returned = handler.apply( that, args ); - - // Support: Promises/A+ section 2.3.1 - // https://promisesaplus.com/#point-48 - if ( returned === deferred.promise() ) { - throw new TypeError( "Thenable self-resolution" ); - } - - // Support: Promises/A+ sections 2.3.3.1, 3.5 - // https://promisesaplus.com/#point-54 - // https://promisesaplus.com/#point-75 - // Retrieve `then` only once - then = returned && - - // Support: Promises/A+ section 2.3.4 - // https://promisesaplus.com/#point-64 - // Only check objects and functions for thenability - ( typeof returned === "object" || - typeof returned === "function" ) && - returned.then; - - // Handle a returned thenable - if ( isFunction( then ) ) { - - // Special processors (notify) just wait for resolution - if ( special ) { - then.call( - returned, - resolve( maxDepth, deferred, Identity, special ), - resolve( maxDepth, deferred, Thrower, special ) - ); - - // Normal processors (resolve) also hook into progress - } else { - - // ...and disregard older resolution values - maxDepth++; - - then.call( - returned, - resolve( maxDepth, deferred, Identity, special ), - resolve( maxDepth, deferred, Thrower, special ), - resolve( maxDepth, deferred, Identity, - deferred.notifyWith ) - ); - } - - // Handle all other returned values - } else { - - // Only substitute handlers pass on context - // and multiple values (non-spec behavior) - if ( handler !== Identity ) { - that = undefined; - args = [ returned ]; - } - - // Process the value(s) - // Default process is resolve - ( special || deferred.resolveWith )( that, args ); - } - }, - - // Only normal processors (resolve) catch and reject exceptions - process = special ? - mightThrow : - function() { - try { - mightThrow(); - } catch ( e ) { - - if ( jQuery.Deferred.exceptionHook ) { - jQuery.Deferred.exceptionHook( e, - process.stackTrace ); - } - - // Support: Promises/A+ section 2.3.3.3.4.1 - // https://promisesaplus.com/#point-61 - // Ignore post-resolution exceptions - if ( depth + 1 >= maxDepth ) { - - // Only substitute handlers pass on context - // and multiple values (non-spec behavior) - if ( handler !== Thrower ) { - that = undefined; - args = [ e ]; - } - - deferred.rejectWith( that, args ); - } - } - }; - - // Support: Promises/A+ section 2.3.3.3.1 - // https://promisesaplus.com/#point-57 - // Re-resolve promises immediately to dodge false rejection from - // subsequent errors - if ( depth ) { - process(); - } else { - - // Call an optional hook to record the stack, in case of exception - // since it's otherwise lost when execution goes async - if ( jQuery.Deferred.getStackHook ) { - process.stackTrace = jQuery.Deferred.getStackHook(); - } - window.setTimeout( process ); - } - }; - } - - return jQuery.Deferred( function( newDefer ) { - - // progress_handlers.add( ... ) - tuples[ 0 ][ 3 ].add( - resolve( - 0, - newDefer, - isFunction( onProgress ) ? - onProgress : - Identity, - newDefer.notifyWith - ) - ); - - // fulfilled_handlers.add( ... ) - tuples[ 1 ][ 3 ].add( - resolve( - 0, - newDefer, - isFunction( onFulfilled ) ? - onFulfilled : - Identity - ) - ); - - // rejected_handlers.add( ... ) - tuples[ 2 ][ 3 ].add( - resolve( - 0, - newDefer, - isFunction( onRejected ) ? - onRejected : - Thrower - ) - ); - } ).promise(); - }, - - // Get a promise for this deferred - // If obj is provided, the promise aspect is added to the object - promise: function( obj ) { - return obj != null ? jQuery.extend( obj, promise ) : promise; - } - }, - deferred = {}; - - // Add list-specific methods - jQuery.each( tuples, function( i, tuple ) { - var list = tuple[ 2 ], - stateString = tuple[ 5 ]; - - // promise.progress = list.add - // promise.done = list.add - // promise.fail = list.add - promise[ tuple[ 1 ] ] = list.add; - - // Handle state - if ( stateString ) { - list.add( - function() { - - // state = "resolved" (i.e., fulfilled) - // state = "rejected" - state = stateString; - }, - - // rejected_callbacks.disable - // fulfilled_callbacks.disable - tuples[ 3 - i ][ 2 ].disable, - - // rejected_handlers.disable - // fulfilled_handlers.disable - tuples[ 3 - i ][ 3 ].disable, - - // progress_callbacks.lock - tuples[ 0 ][ 2 ].lock, - - // progress_handlers.lock - tuples[ 0 ][ 3 ].lock - ); - } - - // progress_handlers.fire - // fulfilled_handlers.fire - // rejected_handlers.fire - list.add( tuple[ 3 ].fire ); - - // deferred.notify = function() { deferred.notifyWith(...) } - // deferred.resolve = function() { deferred.resolveWith(...) } - // deferred.reject = function() { deferred.rejectWith(...) } - deferred[ tuple[ 0 ] ] = function() { - deferred[ tuple[ 0 ] + "With" ]( this === deferred ? undefined : this, arguments ); - return this; - }; - - // deferred.notifyWith = list.fireWith - // deferred.resolveWith = list.fireWith - // deferred.rejectWith = list.fireWith - deferred[ tuple[ 0 ] + "With" ] = list.fireWith; - } ); - - // Make the deferred a promise - promise.promise( deferred ); - - // Call given func if any - if ( func ) { - func.call( deferred, deferred ); - } - - // All done! - return deferred; - }, - - // Deferred helper - when: function( singleValue ) { - var - - // count of uncompleted subordinates - remaining = arguments.length, - - // count of unprocessed arguments - i = remaining, - - // subordinate fulfillment data - resolveContexts = Array( i ), - resolveValues = slice.call( arguments ), - - // the primary Deferred - primary = jQuery.Deferred(), - - // subordinate callback factory - updateFunc = function( i ) { - return function( value ) { - resolveContexts[ i ] = this; - resolveValues[ i ] = arguments.length > 1 ? slice.call( arguments ) : value; - if ( !( --remaining ) ) { - primary.resolveWith( resolveContexts, resolveValues ); - } - }; - }; - - // Single- and empty arguments are adopted like Promise.resolve - if ( remaining <= 1 ) { - adoptValue( singleValue, primary.done( updateFunc( i ) ).resolve, primary.reject, - !remaining ); - - // Use .then() to unwrap secondary thenables (cf. gh-3000) - if ( primary.state() === "pending" || - isFunction( resolveValues[ i ] && resolveValues[ i ].then ) ) { - - return primary.then(); - } - } - - // Multiple arguments are aggregated like Promise.all array elements - while ( i-- ) { - adoptValue( resolveValues[ i ], updateFunc( i ), primary.reject ); - } - - return primary.promise(); - } -} ); - - -// These usually indicate a programmer mistake during development, -// warn about them ASAP rather than swallowing them by default. -var rerrorNames = /^(Eval|Internal|Range|Reference|Syntax|Type|URI)Error$/; - -jQuery.Deferred.exceptionHook = function( error, stack ) { - - // Support: IE 8 - 9 only - // Console exists when dev tools are open, which can happen at any time - if ( window.console && window.console.warn && error && rerrorNames.test( error.name ) ) { - window.console.warn( "jQuery.Deferred exception: " + error.message, error.stack, stack ); - } -}; - - - - -jQuery.readyException = function( error ) { - window.setTimeout( function() { - throw error; - } ); -}; - - - - -// The deferred used on DOM ready -var readyList = jQuery.Deferred(); - -jQuery.fn.ready = function( fn ) { - - readyList - .then( fn ) - - // Wrap jQuery.readyException in a function so that the lookup - // happens at the time of error handling instead of callback - // registration. - .catch( function( error ) { - jQuery.readyException( error ); - } ); - - return this; -}; - -jQuery.extend( { - - // Is the DOM ready to be used? Set to true once it occurs. - isReady: false, - - // A counter to track how many items to wait for before - // the ready event fires. See #6781 - readyWait: 1, - - // Handle when the DOM is ready - ready: function( wait ) { - - // Abort if there are pending holds or we're already ready - if ( wait === true ? --jQuery.readyWait : jQuery.isReady ) { - return; - } - - // Remember that the DOM is ready - jQuery.isReady = true; - - // If a normal DOM Ready event fired, decrement, and wait if need be - if ( wait !== true && --jQuery.readyWait > 0 ) { - return; - } - - // If there are functions bound, to execute - readyList.resolveWith( document, [ jQuery ] ); - } -} ); - -jQuery.ready.then = readyList.then; - -// The ready event handler and self cleanup method -function completed() { - document.removeEventListener( "DOMContentLoaded", completed ); - window.removeEventListener( "load", completed ); - jQuery.ready(); -} - -// Catch cases where $(document).ready() is called -// after the browser event has already occurred. -// Support: IE <=9 - 10 only -// Older IE sometimes signals "interactive" too soon -if ( document.readyState === "complete" || - ( document.readyState !== "loading" && !document.documentElement.doScroll ) ) { - - // Handle it asynchronously to allow scripts the opportunity to delay ready - window.setTimeout( jQuery.ready ); - -} else { - - // Use the handy event callback - document.addEventListener( "DOMContentLoaded", completed ); - - // A fallback to window.onload, that will always work - window.addEventListener( "load", completed ); -} - - - - -// Multifunctional method to get and set values of a collection -// The value/s can optionally be executed if it's a function -var access = function( elems, fn, key, value, chainable, emptyGet, raw ) { - var i = 0, - len = elems.length, - bulk = key == null; - - // Sets many values - if ( toType( key ) === "object" ) { - chainable = true; - for ( i in key ) { - access( elems, fn, i, key[ i ], true, emptyGet, raw ); - } - - // Sets one value - } else if ( value !== undefined ) { - chainable = true; - - if ( !isFunction( value ) ) { - raw = true; - } - - if ( bulk ) { - - // Bulk operations run against the entire set - if ( raw ) { - fn.call( elems, value ); - fn = null; - - // ...except when executing function values - } else { - bulk = fn; - fn = function( elem, _key, value ) { - return bulk.call( jQuery( elem ), value ); - }; - } - } - - if ( fn ) { - for ( ; i < len; i++ ) { - fn( - elems[ i ], key, raw ? - value : - value.call( elems[ i ], i, fn( elems[ i ], key ) ) - ); - } - } - } - - if ( chainable ) { - return elems; - } - - // Gets - if ( bulk ) { - return fn.call( elems ); - } - - return len ? fn( elems[ 0 ], key ) : emptyGet; -}; - - -// Matches dashed string for camelizing -var rmsPrefix = /^-ms-/, - rdashAlpha = /-([a-z])/g; - -// Used by camelCase as callback to replace() -function fcamelCase( _all, letter ) { - return letter.toUpperCase(); -} - -// Convert dashed to camelCase; used by the css and data modules -// Support: IE <=9 - 11, Edge 12 - 15 -// Microsoft forgot to hump their vendor prefix (#9572) -function camelCase( string ) { - return string.replace( rmsPrefix, "ms-" ).replace( rdashAlpha, fcamelCase ); -} -var acceptData = function( owner ) { - - // Accepts only: - // - Node - // - Node.ELEMENT_NODE - // - Node.DOCUMENT_NODE - // - Object - // - Any - return owner.nodeType === 1 || owner.nodeType === 9 || !( +owner.nodeType ); -}; - - - - -function Data() { - this.expando = jQuery.expando + Data.uid++; -} - -Data.uid = 1; - -Data.prototype = { - - cache: function( owner ) { - - // Check if the owner object already has a cache - var value = owner[ this.expando ]; - - // If not, create one - if ( !value ) { - value = {}; - - // We can accept data for non-element nodes in modern browsers, - // but we should not, see #8335. - // Always return an empty object. - if ( acceptData( owner ) ) { - - // If it is a node unlikely to be stringify-ed or looped over - // use plain assignment - if ( owner.nodeType ) { - owner[ this.expando ] = value; - - // Otherwise secure it in a non-enumerable property - // configurable must be true to allow the property to be - // deleted when data is removed - } else { - Object.defineProperty( owner, this.expando, { - value: value, - configurable: true - } ); - } - } - } - - return value; - }, - set: function( owner, data, value ) { - var prop, - cache = this.cache( owner ); - - // Handle: [ owner, key, value ] args - // Always use camelCase key (gh-2257) - if ( typeof data === "string" ) { - cache[ camelCase( data ) ] = value; - - // Handle: [ owner, { properties } ] args - } else { - - // Copy the properties one-by-one to the cache object - for ( prop in data ) { - cache[ camelCase( prop ) ] = data[ prop ]; - } - } - return cache; - }, - get: function( owner, key ) { - return key === undefined ? - this.cache( owner ) : - - // Always use camelCase key (gh-2257) - owner[ this.expando ] && owner[ this.expando ][ camelCase( key ) ]; - }, - access: function( owner, key, value ) { - - // In cases where either: - // - // 1. No key was specified - // 2. A string key was specified, but no value provided - // - // Take the "read" path and allow the get method to determine - // which value to return, respectively either: - // - // 1. The entire cache object - // 2. The data stored at the key - // - if ( key === undefined || - ( ( key && typeof key === "string" ) && value === undefined ) ) { - - return this.get( owner, key ); - } - - // When the key is not a string, or both a key and value - // are specified, set or extend (existing objects) with either: - // - // 1. An object of properties - // 2. A key and value - // - this.set( owner, key, value ); - - // Since the "set" path can have two possible entry points - // return the expected data based on which path was taken[*] - return value !== undefined ? value : key; - }, - remove: function( owner, key ) { - var i, - cache = owner[ this.expando ]; - - if ( cache === undefined ) { - return; - } - - if ( key !== undefined ) { - - // Support array or space separated string of keys - if ( Array.isArray( key ) ) { - - // If key is an array of keys... - // We always set camelCase keys, so remove that. - key = key.map( camelCase ); - } else { - key = camelCase( key ); - - // If a key with the spaces exists, use it. - // Otherwise, create an array by matching non-whitespace - key = key in cache ? - [ key ] : - ( key.match( rnothtmlwhite ) || [] ); - } - - i = key.length; - - while ( i-- ) { - delete cache[ key[ i ] ]; - } - } - - // Remove the expando if there's no more data - if ( key === undefined || jQuery.isEmptyObject( cache ) ) { - - // Support: Chrome <=35 - 45 - // Webkit & Blink performance suffers when deleting properties - // from DOM nodes, so set to undefined instead - // https://bugs.chromium.org/p/chromium/issues/detail?id=378607 (bug restricted) - if ( owner.nodeType ) { - owner[ this.expando ] = undefined; - } else { - delete owner[ this.expando ]; - } - } - }, - hasData: function( owner ) { - var cache = owner[ this.expando ]; - return cache !== undefined && !jQuery.isEmptyObject( cache ); - } -}; -var dataPriv = new Data(); - -var dataUser = new Data(); - - - -// Implementation Summary -// -// 1. Enforce API surface and semantic compatibility with 1.9.x branch -// 2. Improve the module's maintainability by reducing the storage -// paths to a single mechanism. -// 3. Use the same single mechanism to support "private" and "user" data. -// 4. _Never_ expose "private" data to user code (TODO: Drop _data, _removeData) -// 5. Avoid exposing implementation details on user objects (eg. expando properties) -// 6. Provide a clear path for implementation upgrade to WeakMap in 2014 - -var rbrace = /^(?:\{[\w\W]*\}|\[[\w\W]*\])$/, - rmultiDash = /[A-Z]/g; - -function getData( data ) { - if ( data === "true" ) { - return true; - } - - if ( data === "false" ) { - return false; - } - - if ( data === "null" ) { - return null; - } - - // Only convert to a number if it doesn't change the string - if ( data === +data + "" ) { - return +data; - } - - if ( rbrace.test( data ) ) { - return JSON.parse( data ); - } - - return data; -} - -function dataAttr( elem, key, data ) { - var name; - - // If nothing was found internally, try to fetch any - // data from the HTML5 data-* attribute - if ( data === undefined && elem.nodeType === 1 ) { - name = "data-" + key.replace( rmultiDash, "-$&" ).toLowerCase(); - data = elem.getAttribute( name ); - - if ( typeof data === "string" ) { - try { - data = getData( data ); - } catch ( e ) {} - - // Make sure we set the data so it isn't changed later - dataUser.set( elem, key, data ); - } else { - data = undefined; - } - } - return data; -} - -jQuery.extend( { - hasData: function( elem ) { - return dataUser.hasData( elem ) || dataPriv.hasData( elem ); - }, - - data: function( elem, name, data ) { - return dataUser.access( elem, name, data ); - }, - - removeData: function( elem, name ) { - dataUser.remove( elem, name ); - }, - - // TODO: Now that all calls to _data and _removeData have been replaced - // with direct calls to dataPriv methods, these can be deprecated. - _data: function( elem, name, data ) { - return dataPriv.access( elem, name, data ); - }, - - _removeData: function( elem, name ) { - dataPriv.remove( elem, name ); - } -} ); - -jQuery.fn.extend( { - data: function( key, value ) { - var i, name, data, - elem = this[ 0 ], - attrs = elem && elem.attributes; - - // Gets all values - if ( key === undefined ) { - if ( this.length ) { - data = dataUser.get( elem ); - - if ( elem.nodeType === 1 && !dataPriv.get( elem, "hasDataAttrs" ) ) { - i = attrs.length; - while ( i-- ) { - - // Support: IE 11 only - // The attrs elements can be null (#14894) - if ( attrs[ i ] ) { - name = attrs[ i ].name; - if ( name.indexOf( "data-" ) === 0 ) { - name = camelCase( name.slice( 5 ) ); - dataAttr( elem, name, data[ name ] ); - } - } - } - dataPriv.set( elem, "hasDataAttrs", true ); - } - } - - return data; - } - - // Sets multiple values - if ( typeof key === "object" ) { - return this.each( function() { - dataUser.set( this, key ); - } ); - } - - return access( this, function( value ) { - var data; - - // The calling jQuery object (element matches) is not empty - // (and therefore has an element appears at this[ 0 ]) and the - // `value` parameter was not undefined. An empty jQuery object - // will result in `undefined` for elem = this[ 0 ] which will - // throw an exception if an attempt to read a data cache is made. - if ( elem && value === undefined ) { - - // Attempt to get data from the cache - // The key will always be camelCased in Data - data = dataUser.get( elem, key ); - if ( data !== undefined ) { - return data; - } - - // Attempt to "discover" the data in - // HTML5 custom data-* attrs - data = dataAttr( elem, key ); - if ( data !== undefined ) { - return data; - } - - // We tried really hard, but the data doesn't exist. - return; - } - - // Set the data... - this.each( function() { - - // We always store the camelCased key - dataUser.set( this, key, value ); - } ); - }, null, value, arguments.length > 1, null, true ); - }, - - removeData: function( key ) { - return this.each( function() { - dataUser.remove( this, key ); - } ); - } -} ); - - -jQuery.extend( { - queue: function( elem, type, data ) { - var queue; - - if ( elem ) { - type = ( type || "fx" ) + "queue"; - queue = dataPriv.get( elem, type ); - - // Speed up dequeue by getting out quickly if this is just a lookup - if ( data ) { - if ( !queue || Array.isArray( data ) ) { - queue = dataPriv.access( elem, type, jQuery.makeArray( data ) ); - } else { - queue.push( data ); - } - } - return queue || []; - } - }, - - dequeue: function( elem, type ) { - type = type || "fx"; - - var queue = jQuery.queue( elem, type ), - startLength = queue.length, - fn = queue.shift(), - hooks = jQuery._queueHooks( elem, type ), - next = function() { - jQuery.dequeue( elem, type ); - }; - - // If the fx queue is dequeued, always remove the progress sentinel - if ( fn === "inprogress" ) { - fn = queue.shift(); - startLength--; - } - - if ( fn ) { - - // Add a progress sentinel to prevent the fx queue from being - // automatically dequeued - if ( type === "fx" ) { - queue.unshift( "inprogress" ); - } - - // Clear up the last queue stop function - delete hooks.stop; - fn.call( elem, next, hooks ); - } - - if ( !startLength && hooks ) { - hooks.empty.fire(); - } - }, - - // Not public - generate a queueHooks object, or return the current one - _queueHooks: function( elem, type ) { - var key = type + "queueHooks"; - return dataPriv.get( elem, key ) || dataPriv.access( elem, key, { - empty: jQuery.Callbacks( "once memory" ).add( function() { - dataPriv.remove( elem, [ type + "queue", key ] ); - } ) - } ); - } -} ); - -jQuery.fn.extend( { - queue: function( type, data ) { - var setter = 2; - - if ( typeof type !== "string" ) { - data = type; - type = "fx"; - setter--; - } - - if ( arguments.length < setter ) { - return jQuery.queue( this[ 0 ], type ); - } - - return data === undefined ? - this : - this.each( function() { - var queue = jQuery.queue( this, type, data ); - - // Ensure a hooks for this queue - jQuery._queueHooks( this, type ); - - if ( type === "fx" && queue[ 0 ] !== "inprogress" ) { - jQuery.dequeue( this, type ); - } - } ); - }, - dequeue: function( type ) { - return this.each( function() { - jQuery.dequeue( this, type ); - } ); - }, - clearQueue: function( type ) { - return this.queue( type || "fx", [] ); - }, - - // Get a promise resolved when queues of a certain type - // are emptied (fx is the type by default) - promise: function( type, obj ) { - var tmp, - count = 1, - defer = jQuery.Deferred(), - elements = this, - i = this.length, - resolve = function() { - if ( !( --count ) ) { - defer.resolveWith( elements, [ elements ] ); - } - }; - - if ( typeof type !== "string" ) { - obj = type; - type = undefined; - } - type = type || "fx"; - - while ( i-- ) { - tmp = dataPriv.get( elements[ i ], type + "queueHooks" ); - if ( tmp && tmp.empty ) { - count++; - tmp.empty.add( resolve ); - } - } - resolve(); - return defer.promise( obj ); - } -} ); -var pnum = ( /[+-]?(?:\d*\.|)\d+(?:[eE][+-]?\d+|)/ ).source; - -var rcssNum = new RegExp( "^(?:([+-])=|)(" + pnum + ")([a-z%]*)$", "i" ); - - -var cssExpand = [ "Top", "Right", "Bottom", "Left" ]; - -var documentElement = document.documentElement; - - - - var isAttached = function( elem ) { - return jQuery.contains( elem.ownerDocument, elem ); - }, - composed = { composed: true }; - - // Support: IE 9 - 11+, Edge 12 - 18+, iOS 10.0 - 10.2 only - // Check attachment across shadow DOM boundaries when possible (gh-3504) - // Support: iOS 10.0-10.2 only - // Early iOS 10 versions support `attachShadow` but not `getRootNode`, - // leading to errors. We need to check for `getRootNode`. - if ( documentElement.getRootNode ) { - isAttached = function( elem ) { - return jQuery.contains( elem.ownerDocument, elem ) || - elem.getRootNode( composed ) === elem.ownerDocument; - }; - } -var isHiddenWithinTree = function( elem, el ) { - - // isHiddenWithinTree might be called from jQuery#filter function; - // in that case, element will be second argument - elem = el || elem; - - // Inline style trumps all - return elem.style.display === "none" || - elem.style.display === "" && - - // Otherwise, check computed style - // Support: Firefox <=43 - 45 - // Disconnected elements can have computed display: none, so first confirm that elem is - // in the document. - isAttached( elem ) && - - jQuery.css( elem, "display" ) === "none"; - }; - - - -function adjustCSS( elem, prop, valueParts, tween ) { - var adjusted, scale, - maxIterations = 20, - currentValue = tween ? - function() { - return tween.cur(); - } : - function() { - return jQuery.css( elem, prop, "" ); - }, - initial = currentValue(), - unit = valueParts && valueParts[ 3 ] || ( jQuery.cssNumber[ prop ] ? "" : "px" ), - - // Starting value computation is required for potential unit mismatches - initialInUnit = elem.nodeType && - ( jQuery.cssNumber[ prop ] || unit !== "px" && +initial ) && - rcssNum.exec( jQuery.css( elem, prop ) ); - - if ( initialInUnit && initialInUnit[ 3 ] !== unit ) { - - // Support: Firefox <=54 - // Halve the iteration target value to prevent interference from CSS upper bounds (gh-2144) - initial = initial / 2; - - // Trust units reported by jQuery.css - unit = unit || initialInUnit[ 3 ]; - - // Iteratively approximate from a nonzero starting point - initialInUnit = +initial || 1; - - while ( maxIterations-- ) { - - // Evaluate and update our best guess (doubling guesses that zero out). - // Finish if the scale equals or crosses 1 (making the old*new product non-positive). - jQuery.style( elem, prop, initialInUnit + unit ); - if ( ( 1 - scale ) * ( 1 - ( scale = currentValue() / initial || 0.5 ) ) <= 0 ) { - maxIterations = 0; - } - initialInUnit = initialInUnit / scale; - - } - - initialInUnit = initialInUnit * 2; - jQuery.style( elem, prop, initialInUnit + unit ); - - // Make sure we update the tween properties later on - valueParts = valueParts || []; - } - - if ( valueParts ) { - initialInUnit = +initialInUnit || +initial || 0; - - // Apply relative offset (+=/-=) if specified - adjusted = valueParts[ 1 ] ? - initialInUnit + ( valueParts[ 1 ] + 1 ) * valueParts[ 2 ] : - +valueParts[ 2 ]; - if ( tween ) { - tween.unit = unit; - tween.start = initialInUnit; - tween.end = adjusted; - } - } - return adjusted; -} - - -var defaultDisplayMap = {}; - -function getDefaultDisplay( elem ) { - var temp, - doc = elem.ownerDocument, - nodeName = elem.nodeName, - display = defaultDisplayMap[ nodeName ]; - - if ( display ) { - return display; - } - - temp = doc.body.appendChild( doc.createElement( nodeName ) ); - display = jQuery.css( temp, "display" ); - - temp.parentNode.removeChild( temp ); - - if ( display === "none" ) { - display = "block"; - } - defaultDisplayMap[ nodeName ] = display; - - return display; -} - -function showHide( elements, show ) { - var display, elem, - values = [], - index = 0, - length = elements.length; - - // Determine new display value for elements that need to change - for ( ; index < length; index++ ) { - elem = elements[ index ]; - if ( !elem.style ) { - continue; - } - - display = elem.style.display; - if ( show ) { - - // Since we force visibility upon cascade-hidden elements, an immediate (and slow) - // check is required in this first loop unless we have a nonempty display value (either - // inline or about-to-be-restored) - if ( display === "none" ) { - values[ index ] = dataPriv.get( elem, "display" ) || null; - if ( !values[ index ] ) { - elem.style.display = ""; - } - } - if ( elem.style.display === "" && isHiddenWithinTree( elem ) ) { - values[ index ] = getDefaultDisplay( elem ); - } - } else { - if ( display !== "none" ) { - values[ index ] = "none"; - - // Remember what we're overwriting - dataPriv.set( elem, "display", display ); - } - } - } - - // Set the display of the elements in a second loop to avoid constant reflow - for ( index = 0; index < length; index++ ) { - if ( values[ index ] != null ) { - elements[ index ].style.display = values[ index ]; - } - } - - return elements; -} - -jQuery.fn.extend( { - show: function() { - return showHide( this, true ); - }, - hide: function() { - return showHide( this ); - }, - toggle: function( state ) { - if ( typeof state === "boolean" ) { - return state ? this.show() : this.hide(); - } - - return this.each( function() { - if ( isHiddenWithinTree( this ) ) { - jQuery( this ).show(); - } else { - jQuery( this ).hide(); - } - } ); - } -} ); -var rcheckableType = ( /^(?:checkbox|radio)$/i ); - -var rtagName = ( /<([a-z][^\/\0>\x20\t\r\n\f]*)/i ); - -var rscriptType = ( /^$|^module$|\/(?:java|ecma)script/i ); - - - -( function() { - var fragment = document.createDocumentFragment(), - div = fragment.appendChild( document.createElement( "div" ) ), - input = document.createElement( "input" ); - - // Support: Android 4.0 - 4.3 only - // Check state lost if the name is set (#11217) - // Support: Windows Web Apps (WWA) - // `name` and `type` must use .setAttribute for WWA (#14901) - input.setAttribute( "type", "radio" ); - input.setAttribute( "checked", "checked" ); - input.setAttribute( "name", "t" ); - - div.appendChild( input ); - - // Support: Android <=4.1 only - // Older WebKit doesn't clone checked state correctly in fragments - support.checkClone = div.cloneNode( true ).cloneNode( true ).lastChild.checked; - - // Support: IE <=11 only - // Make sure textarea (and checkbox) defaultValue is properly cloned - div.innerHTML = ""; - support.noCloneChecked = !!div.cloneNode( true ).lastChild.defaultValue; - - // Support: IE <=9 only - // IE <=9 replaces "; - support.option = !!div.lastChild; -} )(); - - -// We have to close these tags to support XHTML (#13200) -var wrapMap = { - - // XHTML parsers do not magically insert elements in the - // same way that tag soup parsers do. So we cannot shorten - // this by omitting or other required elements. - thead: [ 1, "", "
" ], - col: [ 2, "", "
" ], - tr: [ 2, "", "
" ], - td: [ 3, "", "
" ], - - _default: [ 0, "", "" ] -}; - -wrapMap.tbody = wrapMap.tfoot = wrapMap.colgroup = wrapMap.caption = wrapMap.thead; -wrapMap.th = wrapMap.td; - -// Support: IE <=9 only -if ( !support.option ) { - wrapMap.optgroup = wrapMap.option = [ 1, "" ]; -} - - -function getAll( context, tag ) { - - // Support: IE <=9 - 11 only - // Use typeof to avoid zero-argument method invocation on host objects (#15151) - var ret; - - if ( typeof context.getElementsByTagName !== "undefined" ) { - ret = context.getElementsByTagName( tag || "*" ); - - } else if ( typeof context.querySelectorAll !== "undefined" ) { - ret = context.querySelectorAll( tag || "*" ); - - } else { - ret = []; - } - - if ( tag === undefined || tag && nodeName( context, tag ) ) { - return jQuery.merge( [ context ], ret ); - } - - return ret; -} - - -// Mark scripts as having already been evaluated -function setGlobalEval( elems, refElements ) { - var i = 0, - l = elems.length; - - for ( ; i < l; i++ ) { - dataPriv.set( - elems[ i ], - "globalEval", - !refElements || dataPriv.get( refElements[ i ], "globalEval" ) - ); - } -} - - -var rhtml = /<|&#?\w+;/; - -function buildFragment( elems, context, scripts, selection, ignored ) { - var elem, tmp, tag, wrap, attached, j, - fragment = context.createDocumentFragment(), - nodes = [], - i = 0, - l = elems.length; - - for ( ; i < l; i++ ) { - elem = elems[ i ]; - - if ( elem || elem === 0 ) { - - // Add nodes directly - if ( toType( elem ) === "object" ) { - - // Support: Android <=4.0 only, PhantomJS 1 only - // push.apply(_, arraylike) throws on ancient WebKit - jQuery.merge( nodes, elem.nodeType ? [ elem ] : elem ); - - // Convert non-html into a text node - } else if ( !rhtml.test( elem ) ) { - nodes.push( context.createTextNode( elem ) ); - - // Convert html into DOM nodes - } else { - tmp = tmp || fragment.appendChild( context.createElement( "div" ) ); - - // Deserialize a standard representation - tag = ( rtagName.exec( elem ) || [ "", "" ] )[ 1 ].toLowerCase(); - wrap = wrapMap[ tag ] || wrapMap._default; - tmp.innerHTML = wrap[ 1 ] + jQuery.htmlPrefilter( elem ) + wrap[ 2 ]; - - // Descend through wrappers to the right content - j = wrap[ 0 ]; - while ( j-- ) { - tmp = tmp.lastChild; - } - - // Support: Android <=4.0 only, PhantomJS 1 only - // push.apply(_, arraylike) throws on ancient WebKit - jQuery.merge( nodes, tmp.childNodes ); - - // Remember the top-level container - tmp = fragment.firstChild; - - // Ensure the created nodes are orphaned (#12392) - tmp.textContent = ""; - } - } - } - - // Remove wrapper from fragment - fragment.textContent = ""; - - i = 0; - while ( ( elem = nodes[ i++ ] ) ) { - - // Skip elements already in the context collection (trac-4087) - if ( selection && jQuery.inArray( elem, selection ) > -1 ) { - if ( ignored ) { - ignored.push( elem ); - } - continue; - } - - attached = isAttached( elem ); - - // Append to fragment - tmp = getAll( fragment.appendChild( elem ), "script" ); - - // Preserve script evaluation history - if ( attached ) { - setGlobalEval( tmp ); - } - - // Capture executables - if ( scripts ) { - j = 0; - while ( ( elem = tmp[ j++ ] ) ) { - if ( rscriptType.test( elem.type || "" ) ) { - scripts.push( elem ); - } - } - } - } - - return fragment; -} - - -var rtypenamespace = /^([^.]*)(?:\.(.+)|)/; - -function returnTrue() { - return true; -} - -function returnFalse() { - return false; -} - -// Support: IE <=9 - 11+ -// focus() and blur() are asynchronous, except when they are no-op. -// So expect focus to be synchronous when the element is already active, -// and blur to be synchronous when the element is not already active. -// (focus and blur are always synchronous in other supported browsers, -// this just defines when we can count on it). -function expectSync( elem, type ) { - return ( elem === safeActiveElement() ) === ( type === "focus" ); -} - -// Support: IE <=9 only -// Accessing document.activeElement can throw unexpectedly -// https://bugs.jquery.com/ticket/13393 -function safeActiveElement() { - try { - return document.activeElement; - } catch ( err ) { } -} - -function on( elem, types, selector, data, fn, one ) { - var origFn, type; - - // Types can be a map of types/handlers - if ( typeof types === "object" ) { - - // ( types-Object, selector, data ) - if ( typeof selector !== "string" ) { - - // ( types-Object, data ) - data = data || selector; - selector = undefined; - } - for ( type in types ) { - on( elem, type, selector, data, types[ type ], one ); - } - return elem; - } - - if ( data == null && fn == null ) { - - // ( types, fn ) - fn = selector; - data = selector = undefined; - } else if ( fn == null ) { - if ( typeof selector === "string" ) { - - // ( types, selector, fn ) - fn = data; - data = undefined; - } else { - - // ( types, data, fn ) - fn = data; - data = selector; - selector = undefined; - } - } - if ( fn === false ) { - fn = returnFalse; - } else if ( !fn ) { - return elem; - } - - if ( one === 1 ) { - origFn = fn; - fn = function( event ) { - - // Can use an empty set, since event contains the info - jQuery().off( event ); - return origFn.apply( this, arguments ); - }; - - // Use same guid so caller can remove using origFn - fn.guid = origFn.guid || ( origFn.guid = jQuery.guid++ ); - } - return elem.each( function() { - jQuery.event.add( this, types, fn, data, selector ); - } ); -} - -/* - * Helper functions for managing events -- not part of the public interface. - * Props to Dean Edwards' addEvent library for many of the ideas. - */ -jQuery.event = { - - global: {}, - - add: function( elem, types, handler, data, selector ) { - - var handleObjIn, eventHandle, tmp, - events, t, handleObj, - special, handlers, type, namespaces, origType, - elemData = dataPriv.get( elem ); - - // Only attach events to objects that accept data - if ( !acceptData( elem ) ) { - return; - } - - // Caller can pass in an object of custom data in lieu of the handler - if ( handler.handler ) { - handleObjIn = handler; - handler = handleObjIn.handler; - selector = handleObjIn.selector; - } - - // Ensure that invalid selectors throw exceptions at attach time - // Evaluate against documentElement in case elem is a non-element node (e.g., document) - if ( selector ) { - jQuery.find.matchesSelector( documentElement, selector ); - } - - // Make sure that the handler has a unique ID, used to find/remove it later - if ( !handler.guid ) { - handler.guid = jQuery.guid++; - } - - // Init the element's event structure and main handler, if this is the first - if ( !( events = elemData.events ) ) { - events = elemData.events = Object.create( null ); - } - if ( !( eventHandle = elemData.handle ) ) { - eventHandle = elemData.handle = function( e ) { - - // Discard the second event of a jQuery.event.trigger() and - // when an event is called after a page has unloaded - return typeof jQuery !== "undefined" && jQuery.event.triggered !== e.type ? - jQuery.event.dispatch.apply( elem, arguments ) : undefined; - }; - } - - // Handle multiple events separated by a space - types = ( types || "" ).match( rnothtmlwhite ) || [ "" ]; - t = types.length; - while ( t-- ) { - tmp = rtypenamespace.exec( types[ t ] ) || []; - type = origType = tmp[ 1 ]; - namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort(); - - // There *must* be a type, no attaching namespace-only handlers - if ( !type ) { - continue; - } - - // If event changes its type, use the special event handlers for the changed type - special = jQuery.event.special[ type ] || {}; - - // If selector defined, determine special event api type, otherwise given type - type = ( selector ? special.delegateType : special.bindType ) || type; - - // Update special based on newly reset type - special = jQuery.event.special[ type ] || {}; - - // handleObj is passed to all event handlers - handleObj = jQuery.extend( { - type: type, - origType: origType, - data: data, - handler: handler, - guid: handler.guid, - selector: selector, - needsContext: selector && jQuery.expr.match.needsContext.test( selector ), - namespace: namespaces.join( "." ) - }, handleObjIn ); - - // Init the event handler queue if we're the first - if ( !( handlers = events[ type ] ) ) { - handlers = events[ type ] = []; - handlers.delegateCount = 0; - - // Only use addEventListener if the special events handler returns false - if ( !special.setup || - special.setup.call( elem, data, namespaces, eventHandle ) === false ) { - - if ( elem.addEventListener ) { - elem.addEventListener( type, eventHandle ); - } - } - } - - if ( special.add ) { - special.add.call( elem, handleObj ); - - if ( !handleObj.handler.guid ) { - handleObj.handler.guid = handler.guid; - } - } - - // Add to the element's handler list, delegates in front - if ( selector ) { - handlers.splice( handlers.delegateCount++, 0, handleObj ); - } else { - handlers.push( handleObj ); - } - - // Keep track of which events have ever been used, for event optimization - jQuery.event.global[ type ] = true; - } - - }, - - // Detach an event or set of events from an element - remove: function( elem, types, handler, selector, mappedTypes ) { - - var j, origCount, tmp, - events, t, handleObj, - special, handlers, type, namespaces, origType, - elemData = dataPriv.hasData( elem ) && dataPriv.get( elem ); - - if ( !elemData || !( events = elemData.events ) ) { - return; - } - - // Once for each type.namespace in types; type may be omitted - types = ( types || "" ).match( rnothtmlwhite ) || [ "" ]; - t = types.length; - while ( t-- ) { - tmp = rtypenamespace.exec( types[ t ] ) || []; - type = origType = tmp[ 1 ]; - namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort(); - - // Unbind all events (on this namespace, if provided) for the element - if ( !type ) { - for ( type in events ) { - jQuery.event.remove( elem, type + types[ t ], handler, selector, true ); - } - continue; - } - - special = jQuery.event.special[ type ] || {}; - type = ( selector ? special.delegateType : special.bindType ) || type; - handlers = events[ type ] || []; - tmp = tmp[ 2 ] && - new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" ); - - // Remove matching events - origCount = j = handlers.length; - while ( j-- ) { - handleObj = handlers[ j ]; - - if ( ( mappedTypes || origType === handleObj.origType ) && - ( !handler || handler.guid === handleObj.guid ) && - ( !tmp || tmp.test( handleObj.namespace ) ) && - ( !selector || selector === handleObj.selector || - selector === "**" && handleObj.selector ) ) { - handlers.splice( j, 1 ); - - if ( handleObj.selector ) { - handlers.delegateCount--; - } - if ( special.remove ) { - special.remove.call( elem, handleObj ); - } - } - } - - // Remove generic event handler if we removed something and no more handlers exist - // (avoids potential for endless recursion during removal of special event handlers) - if ( origCount && !handlers.length ) { - if ( !special.teardown || - special.teardown.call( elem, namespaces, elemData.handle ) === false ) { - - jQuery.removeEvent( elem, type, elemData.handle ); - } - - delete events[ type ]; - } - } - - // Remove data and the expando if it's no longer used - if ( jQuery.isEmptyObject( events ) ) { - dataPriv.remove( elem, "handle events" ); - } - }, - - dispatch: function( nativeEvent ) { - - var i, j, ret, matched, handleObj, handlerQueue, - args = new Array( arguments.length ), - - // Make a writable jQuery.Event from the native event object - event = jQuery.event.fix( nativeEvent ), - - handlers = ( - dataPriv.get( this, "events" ) || Object.create( null ) - )[ event.type ] || [], - special = jQuery.event.special[ event.type ] || {}; - - // Use the fix-ed jQuery.Event rather than the (read-only) native event - args[ 0 ] = event; - - for ( i = 1; i < arguments.length; i++ ) { - args[ i ] = arguments[ i ]; - } - - event.delegateTarget = this; - - // Call the preDispatch hook for the mapped type, and let it bail if desired - if ( special.preDispatch && special.preDispatch.call( this, event ) === false ) { - return; - } - - // Determine handlers - handlerQueue = jQuery.event.handlers.call( this, event, handlers ); - - // Run delegates first; they may want to stop propagation beneath us - i = 0; - while ( ( matched = handlerQueue[ i++ ] ) && !event.isPropagationStopped() ) { - event.currentTarget = matched.elem; - - j = 0; - while ( ( handleObj = matched.handlers[ j++ ] ) && - !event.isImmediatePropagationStopped() ) { - - // If the event is namespaced, then each handler is only invoked if it is - // specially universal or its namespaces are a superset of the event's. - if ( !event.rnamespace || handleObj.namespace === false || - event.rnamespace.test( handleObj.namespace ) ) { - - event.handleObj = handleObj; - event.data = handleObj.data; - - ret = ( ( jQuery.event.special[ handleObj.origType ] || {} ).handle || - handleObj.handler ).apply( matched.elem, args ); - - if ( ret !== undefined ) { - if ( ( event.result = ret ) === false ) { - event.preventDefault(); - event.stopPropagation(); - } - } - } - } - } - - // Call the postDispatch hook for the mapped type - if ( special.postDispatch ) { - special.postDispatch.call( this, event ); - } - - return event.result; - }, - - handlers: function( event, handlers ) { - var i, handleObj, sel, matchedHandlers, matchedSelectors, - handlerQueue = [], - delegateCount = handlers.delegateCount, - cur = event.target; - - // Find delegate handlers - if ( delegateCount && - - // Support: IE <=9 - // Black-hole SVG instance trees (trac-13180) - cur.nodeType && - - // Support: Firefox <=42 - // Suppress spec-violating clicks indicating a non-primary pointer button (trac-3861) - // https://www.w3.org/TR/DOM-Level-3-Events/#event-type-click - // Support: IE 11 only - // ...but not arrow key "clicks" of radio inputs, which can have `button` -1 (gh-2343) - !( event.type === "click" && event.button >= 1 ) ) { - - for ( ; cur !== this; cur = cur.parentNode || this ) { - - // Don't check non-elements (#13208) - // Don't process clicks on disabled elements (#6911, #8165, #11382, #11764) - if ( cur.nodeType === 1 && !( event.type === "click" && cur.disabled === true ) ) { - matchedHandlers = []; - matchedSelectors = {}; - for ( i = 0; i < delegateCount; i++ ) { - handleObj = handlers[ i ]; - - // Don't conflict with Object.prototype properties (#13203) - sel = handleObj.selector + " "; - - if ( matchedSelectors[ sel ] === undefined ) { - matchedSelectors[ sel ] = handleObj.needsContext ? - jQuery( sel, this ).index( cur ) > -1 : - jQuery.find( sel, this, null, [ cur ] ).length; - } - if ( matchedSelectors[ sel ] ) { - matchedHandlers.push( handleObj ); - } - } - if ( matchedHandlers.length ) { - handlerQueue.push( { elem: cur, handlers: matchedHandlers } ); - } - } - } - } - - // Add the remaining (directly-bound) handlers - cur = this; - if ( delegateCount < handlers.length ) { - handlerQueue.push( { elem: cur, handlers: handlers.slice( delegateCount ) } ); - } - - return handlerQueue; - }, - - addProp: function( name, hook ) { - Object.defineProperty( jQuery.Event.prototype, name, { - enumerable: true, - configurable: true, - - get: isFunction( hook ) ? - function() { - if ( this.originalEvent ) { - return hook( this.originalEvent ); - } - } : - function() { - if ( this.originalEvent ) { - return this.originalEvent[ name ]; - } - }, - - set: function( value ) { - Object.defineProperty( this, name, { - enumerable: true, - configurable: true, - writable: true, - value: value - } ); - } - } ); - }, - - fix: function( originalEvent ) { - return originalEvent[ jQuery.expando ] ? - originalEvent : - new jQuery.Event( originalEvent ); - }, - - special: { - load: { - - // Prevent triggered image.load events from bubbling to window.load - noBubble: true - }, - click: { - - // Utilize native event to ensure correct state for checkable inputs - setup: function( data ) { - - // For mutual compressibility with _default, replace `this` access with a local var. - // `|| data` is dead code meant only to preserve the variable through minification. - var el = this || data; - - // Claim the first handler - if ( rcheckableType.test( el.type ) && - el.click && nodeName( el, "input" ) ) { - - // dataPriv.set( el, "click", ... ) - leverageNative( el, "click", returnTrue ); - } - - // Return false to allow normal processing in the caller - return false; - }, - trigger: function( data ) { - - // For mutual compressibility with _default, replace `this` access with a local var. - // `|| data` is dead code meant only to preserve the variable through minification. - var el = this || data; - - // Force setup before triggering a click - if ( rcheckableType.test( el.type ) && - el.click && nodeName( el, "input" ) ) { - - leverageNative( el, "click" ); - } - - // Return non-false to allow normal event-path propagation - return true; - }, - - // For cross-browser consistency, suppress native .click() on links - // Also prevent it if we're currently inside a leveraged native-event stack - _default: function( event ) { - var target = event.target; - return rcheckableType.test( target.type ) && - target.click && nodeName( target, "input" ) && - dataPriv.get( target, "click" ) || - nodeName( target, "a" ); - } - }, - - beforeunload: { - postDispatch: function( event ) { - - // Support: Firefox 20+ - // Firefox doesn't alert if the returnValue field is not set. - if ( event.result !== undefined && event.originalEvent ) { - event.originalEvent.returnValue = event.result; - } - } - } - } -}; - -// Ensure the presence of an event listener that handles manually-triggered -// synthetic events by interrupting progress until reinvoked in response to -// *native* events that it fires directly, ensuring that state changes have -// already occurred before other listeners are invoked. -function leverageNative( el, type, expectSync ) { - - // Missing expectSync indicates a trigger call, which must force setup through jQuery.event.add - if ( !expectSync ) { - if ( dataPriv.get( el, type ) === undefined ) { - jQuery.event.add( el, type, returnTrue ); - } - return; - } - - // Register the controller as a special universal handler for all event namespaces - dataPriv.set( el, type, false ); - jQuery.event.add( el, type, { - namespace: false, - handler: function( event ) { - var notAsync, result, - saved = dataPriv.get( this, type ); - - if ( ( event.isTrigger & 1 ) && this[ type ] ) { - - // Interrupt processing of the outer synthetic .trigger()ed event - // Saved data should be false in such cases, but might be a leftover capture object - // from an async native handler (gh-4350) - if ( !saved.length ) { - - // Store arguments for use when handling the inner native event - // There will always be at least one argument (an event object), so this array - // will not be confused with a leftover capture object. - saved = slice.call( arguments ); - dataPriv.set( this, type, saved ); - - // Trigger the native event and capture its result - // Support: IE <=9 - 11+ - // focus() and blur() are asynchronous - notAsync = expectSync( this, type ); - this[ type ](); - result = dataPriv.get( this, type ); - if ( saved !== result || notAsync ) { - dataPriv.set( this, type, false ); - } else { - result = {}; - } - if ( saved !== result ) { - - // Cancel the outer synthetic event - event.stopImmediatePropagation(); - event.preventDefault(); - - // Support: Chrome 86+ - // In Chrome, if an element having a focusout handler is blurred by - // clicking outside of it, it invokes the handler synchronously. If - // that handler calls `.remove()` on the element, the data is cleared, - // leaving `result` undefined. We need to guard against this. - return result && result.value; - } - - // If this is an inner synthetic event for an event with a bubbling surrogate - // (focus or blur), assume that the surrogate already propagated from triggering the - // native event and prevent that from happening again here. - // This technically gets the ordering wrong w.r.t. to `.trigger()` (in which the - // bubbling surrogate propagates *after* the non-bubbling base), but that seems - // less bad than duplication. - } else if ( ( jQuery.event.special[ type ] || {} ).delegateType ) { - event.stopPropagation(); - } - - // If this is a native event triggered above, everything is now in order - // Fire an inner synthetic event with the original arguments - } else if ( saved.length ) { - - // ...and capture the result - dataPriv.set( this, type, { - value: jQuery.event.trigger( - - // Support: IE <=9 - 11+ - // Extend with the prototype to reset the above stopImmediatePropagation() - jQuery.extend( saved[ 0 ], jQuery.Event.prototype ), - saved.slice( 1 ), - this - ) - } ); - - // Abort handling of the native event - event.stopImmediatePropagation(); - } - } - } ); -} - -jQuery.removeEvent = function( elem, type, handle ) { - - // This "if" is needed for plain objects - if ( elem.removeEventListener ) { - elem.removeEventListener( type, handle ); - } -}; - -jQuery.Event = function( src, props ) { - - // Allow instantiation without the 'new' keyword - if ( !( this instanceof jQuery.Event ) ) { - return new jQuery.Event( src, props ); - } - - // Event object - if ( src && src.type ) { - this.originalEvent = src; - this.type = src.type; - - // Events bubbling up the document may have been marked as prevented - // by a handler lower down the tree; reflect the correct value. - this.isDefaultPrevented = src.defaultPrevented || - src.defaultPrevented === undefined && - - // Support: Android <=2.3 only - src.returnValue === false ? - returnTrue : - returnFalse; - - // Create target properties - // Support: Safari <=6 - 7 only - // Target should not be a text node (#504, #13143) - this.target = ( src.target && src.target.nodeType === 3 ) ? - src.target.parentNode : - src.target; - - this.currentTarget = src.currentTarget; - this.relatedTarget = src.relatedTarget; - - // Event type - } else { - this.type = src; - } - - // Put explicitly provided properties onto the event object - if ( props ) { - jQuery.extend( this, props ); - } - - // Create a timestamp if incoming event doesn't have one - this.timeStamp = src && src.timeStamp || Date.now(); - - // Mark it as fixed - this[ jQuery.expando ] = true; -}; - -// jQuery.Event is based on DOM3 Events as specified by the ECMAScript Language Binding -// https://www.w3.org/TR/2003/WD-DOM-Level-3-Events-20030331/ecma-script-binding.html -jQuery.Event.prototype = { - constructor: jQuery.Event, - isDefaultPrevented: returnFalse, - isPropagationStopped: returnFalse, - isImmediatePropagationStopped: returnFalse, - isSimulated: false, - - preventDefault: function() { - var e = this.originalEvent; - - this.isDefaultPrevented = returnTrue; - - if ( e && !this.isSimulated ) { - e.preventDefault(); - } - }, - stopPropagation: function() { - var e = this.originalEvent; - - this.isPropagationStopped = returnTrue; - - if ( e && !this.isSimulated ) { - e.stopPropagation(); - } - }, - stopImmediatePropagation: function() { - var e = this.originalEvent; - - this.isImmediatePropagationStopped = returnTrue; - - if ( e && !this.isSimulated ) { - e.stopImmediatePropagation(); - } - - this.stopPropagation(); - } -}; - -// Includes all common event props including KeyEvent and MouseEvent specific props -jQuery.each( { - altKey: true, - bubbles: true, - cancelable: true, - changedTouches: true, - ctrlKey: true, - detail: true, - eventPhase: true, - metaKey: true, - pageX: true, - pageY: true, - shiftKey: true, - view: true, - "char": true, - code: true, - charCode: true, - key: true, - keyCode: true, - button: true, - buttons: true, - clientX: true, - clientY: true, - offsetX: true, - offsetY: true, - pointerId: true, - pointerType: true, - screenX: true, - screenY: true, - targetTouches: true, - toElement: true, - touches: true, - which: true -}, jQuery.event.addProp ); - -jQuery.each( { focus: "focusin", blur: "focusout" }, function( type, delegateType ) { - jQuery.event.special[ type ] = { - - // Utilize native event if possible so blur/focus sequence is correct - setup: function() { - - // Claim the first handler - // dataPriv.set( this, "focus", ... ) - // dataPriv.set( this, "blur", ... ) - leverageNative( this, type, expectSync ); - - // Return false to allow normal processing in the caller - return false; - }, - trigger: function() { - - // Force setup before trigger - leverageNative( this, type ); - - // Return non-false to allow normal event-path propagation - return true; - }, - - // Suppress native focus or blur as it's already being fired - // in leverageNative. - _default: function() { - return true; - }, - - delegateType: delegateType - }; -} ); - -// Create mouseenter/leave events using mouseover/out and event-time checks -// so that event delegation works in jQuery. -// Do the same for pointerenter/pointerleave and pointerover/pointerout -// -// Support: Safari 7 only -// Safari sends mouseenter too often; see: -// https://bugs.chromium.org/p/chromium/issues/detail?id=470258 -// for the description of the bug (it existed in older Chrome versions as well). -jQuery.each( { - mouseenter: "mouseover", - mouseleave: "mouseout", - pointerenter: "pointerover", - pointerleave: "pointerout" -}, function( orig, fix ) { - jQuery.event.special[ orig ] = { - delegateType: fix, - bindType: fix, - - handle: function( event ) { - var ret, - target = this, - related = event.relatedTarget, - handleObj = event.handleObj; - - // For mouseenter/leave call the handler if related is outside the target. - // NB: No relatedTarget if the mouse left/entered the browser window - if ( !related || ( related !== target && !jQuery.contains( target, related ) ) ) { - event.type = handleObj.origType; - ret = handleObj.handler.apply( this, arguments ); - event.type = fix; - } - return ret; - } - }; -} ); - -jQuery.fn.extend( { - - on: function( types, selector, data, fn ) { - return on( this, types, selector, data, fn ); - }, - one: function( types, selector, data, fn ) { - return on( this, types, selector, data, fn, 1 ); - }, - off: function( types, selector, fn ) { - var handleObj, type; - if ( types && types.preventDefault && types.handleObj ) { - - // ( event ) dispatched jQuery.Event - handleObj = types.handleObj; - jQuery( types.delegateTarget ).off( - handleObj.namespace ? - handleObj.origType + "." + handleObj.namespace : - handleObj.origType, - handleObj.selector, - handleObj.handler - ); - return this; - } - if ( typeof types === "object" ) { - - // ( types-object [, selector] ) - for ( type in types ) { - this.off( type, selector, types[ type ] ); - } - return this; - } - if ( selector === false || typeof selector === "function" ) { - - // ( types [, fn] ) - fn = selector; - selector = undefined; - } - if ( fn === false ) { - fn = returnFalse; - } - return this.each( function() { - jQuery.event.remove( this, types, fn, selector ); - } ); - } -} ); - - -var - - // Support: IE <=10 - 11, Edge 12 - 13 only - // In IE/Edge using regex groups here causes severe slowdowns. - // See https://connect.microsoft.com/IE/feedback/details/1736512/ - rnoInnerhtml = /\s*$/g; - -// Prefer a tbody over its parent table for containing new rows -function manipulationTarget( elem, content ) { - if ( nodeName( elem, "table" ) && - nodeName( content.nodeType !== 11 ? content : content.firstChild, "tr" ) ) { - - return jQuery( elem ).children( "tbody" )[ 0 ] || elem; - } - - return elem; -} - -// Replace/restore the type attribute of script elements for safe DOM manipulation -function disableScript( elem ) { - elem.type = ( elem.getAttribute( "type" ) !== null ) + "/" + elem.type; - return elem; -} -function restoreScript( elem ) { - if ( ( elem.type || "" ).slice( 0, 5 ) === "true/" ) { - elem.type = elem.type.slice( 5 ); - } else { - elem.removeAttribute( "type" ); - } - - return elem; -} - -function cloneCopyEvent( src, dest ) { - var i, l, type, pdataOld, udataOld, udataCur, events; - - if ( dest.nodeType !== 1 ) { - return; - } - - // 1. Copy private data: events, handlers, etc. - if ( dataPriv.hasData( src ) ) { - pdataOld = dataPriv.get( src ); - events = pdataOld.events; - - if ( events ) { - dataPriv.remove( dest, "handle events" ); - - for ( type in events ) { - for ( i = 0, l = events[ type ].length; i < l; i++ ) { - jQuery.event.add( dest, type, events[ type ][ i ] ); - } - } - } - } - - // 2. Copy user data - if ( dataUser.hasData( src ) ) { - udataOld = dataUser.access( src ); - udataCur = jQuery.extend( {}, udataOld ); - - dataUser.set( dest, udataCur ); - } -} - -// Fix IE bugs, see support tests -function fixInput( src, dest ) { - var nodeName = dest.nodeName.toLowerCase(); - - // Fails to persist the checked state of a cloned checkbox or radio button. - if ( nodeName === "input" && rcheckableType.test( src.type ) ) { - dest.checked = src.checked; - - // Fails to return the selected option to the default selected state when cloning options - } else if ( nodeName === "input" || nodeName === "textarea" ) { - dest.defaultValue = src.defaultValue; - } -} - -function domManip( collection, args, callback, ignored ) { - - // Flatten any nested arrays - args = flat( args ); - - var fragment, first, scripts, hasScripts, node, doc, - i = 0, - l = collection.length, - iNoClone = l - 1, - value = args[ 0 ], - valueIsFunction = isFunction( value ); - - // We can't cloneNode fragments that contain checked, in WebKit - if ( valueIsFunction || - ( l > 1 && typeof value === "string" && - !support.checkClone && rchecked.test( value ) ) ) { - return collection.each( function( index ) { - var self = collection.eq( index ); - if ( valueIsFunction ) { - args[ 0 ] = value.call( this, index, self.html() ); - } - domManip( self, args, callback, ignored ); - } ); - } - - if ( l ) { - fragment = buildFragment( args, collection[ 0 ].ownerDocument, false, collection, ignored ); - first = fragment.firstChild; - - if ( fragment.childNodes.length === 1 ) { - fragment = first; - } - - // Require either new content or an interest in ignored elements to invoke the callback - if ( first || ignored ) { - scripts = jQuery.map( getAll( fragment, "script" ), disableScript ); - hasScripts = scripts.length; - - // Use the original fragment for the last item - // instead of the first because it can end up - // being emptied incorrectly in certain situations (#8070). - for ( ; i < l; i++ ) { - node = fragment; - - if ( i !== iNoClone ) { - node = jQuery.clone( node, true, true ); - - // Keep references to cloned scripts for later restoration - if ( hasScripts ) { - - // Support: Android <=4.0 only, PhantomJS 1 only - // push.apply(_, arraylike) throws on ancient WebKit - jQuery.merge( scripts, getAll( node, "script" ) ); - } - } - - callback.call( collection[ i ], node, i ); - } - - if ( hasScripts ) { - doc = scripts[ scripts.length - 1 ].ownerDocument; - - // Reenable scripts - jQuery.map( scripts, restoreScript ); - - // Evaluate executable scripts on first document insertion - for ( i = 0; i < hasScripts; i++ ) { - node = scripts[ i ]; - if ( rscriptType.test( node.type || "" ) && - !dataPriv.access( node, "globalEval" ) && - jQuery.contains( doc, node ) ) { - - if ( node.src && ( node.type || "" ).toLowerCase() !== "module" ) { - - // Optional AJAX dependency, but won't run scripts if not present - if ( jQuery._evalUrl && !node.noModule ) { - jQuery._evalUrl( node.src, { - nonce: node.nonce || node.getAttribute( "nonce" ) - }, doc ); - } - } else { - DOMEval( node.textContent.replace( rcleanScript, "" ), node, doc ); - } - } - } - } - } - } - - return collection; -} - -function remove( elem, selector, keepData ) { - var node, - nodes = selector ? jQuery.filter( selector, elem ) : elem, - i = 0; - - for ( ; ( node = nodes[ i ] ) != null; i++ ) { - if ( !keepData && node.nodeType === 1 ) { - jQuery.cleanData( getAll( node ) ); - } - - if ( node.parentNode ) { - if ( keepData && isAttached( node ) ) { - setGlobalEval( getAll( node, "script" ) ); - } - node.parentNode.removeChild( node ); - } - } - - return elem; -} - -jQuery.extend( { - htmlPrefilter: function( html ) { - return html; - }, - - clone: function( elem, dataAndEvents, deepDataAndEvents ) { - var i, l, srcElements, destElements, - clone = elem.cloneNode( true ), - inPage = isAttached( elem ); - - // Fix IE cloning issues - if ( !support.noCloneChecked && ( elem.nodeType === 1 || elem.nodeType === 11 ) && - !jQuery.isXMLDoc( elem ) ) { - - // We eschew Sizzle here for performance reasons: https://jsperf.com/getall-vs-sizzle/2 - destElements = getAll( clone ); - srcElements = getAll( elem ); - - for ( i = 0, l = srcElements.length; i < l; i++ ) { - fixInput( srcElements[ i ], destElements[ i ] ); - } - } - - // Copy the events from the original to the clone - if ( dataAndEvents ) { - if ( deepDataAndEvents ) { - srcElements = srcElements || getAll( elem ); - destElements = destElements || getAll( clone ); - - for ( i = 0, l = srcElements.length; i < l; i++ ) { - cloneCopyEvent( srcElements[ i ], destElements[ i ] ); - } - } else { - cloneCopyEvent( elem, clone ); - } - } - - // Preserve script evaluation history - destElements = getAll( clone, "script" ); - if ( destElements.length > 0 ) { - setGlobalEval( destElements, !inPage && getAll( elem, "script" ) ); - } - - // Return the cloned set - return clone; - }, - - cleanData: function( elems ) { - var data, elem, type, - special = jQuery.event.special, - i = 0; - - for ( ; ( elem = elems[ i ] ) !== undefined; i++ ) { - if ( acceptData( elem ) ) { - if ( ( data = elem[ dataPriv.expando ] ) ) { - if ( data.events ) { - for ( type in data.events ) { - if ( special[ type ] ) { - jQuery.event.remove( elem, type ); - - // This is a shortcut to avoid jQuery.event.remove's overhead - } else { - jQuery.removeEvent( elem, type, data.handle ); - } - } - } - - // Support: Chrome <=35 - 45+ - // Assign undefined instead of using delete, see Data#remove - elem[ dataPriv.expando ] = undefined; - } - if ( elem[ dataUser.expando ] ) { - - // Support: Chrome <=35 - 45+ - // Assign undefined instead of using delete, see Data#remove - elem[ dataUser.expando ] = undefined; - } - } - } - } -} ); - -jQuery.fn.extend( { - detach: function( selector ) { - return remove( this, selector, true ); - }, - - remove: function( selector ) { - return remove( this, selector ); - }, - - text: function( value ) { - return access( this, function( value ) { - return value === undefined ? - jQuery.text( this ) : - this.empty().each( function() { - if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) { - this.textContent = value; - } - } ); - }, null, value, arguments.length ); - }, - - append: function() { - return domManip( this, arguments, function( elem ) { - if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) { - var target = manipulationTarget( this, elem ); - target.appendChild( elem ); - } - } ); - }, - - prepend: function() { - return domManip( this, arguments, function( elem ) { - if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) { - var target = manipulationTarget( this, elem ); - target.insertBefore( elem, target.firstChild ); - } - } ); - }, - - before: function() { - return domManip( this, arguments, function( elem ) { - if ( this.parentNode ) { - this.parentNode.insertBefore( elem, this ); - } - } ); - }, - - after: function() { - return domManip( this, arguments, function( elem ) { - if ( this.parentNode ) { - this.parentNode.insertBefore( elem, this.nextSibling ); - } - } ); - }, - - empty: function() { - var elem, - i = 0; - - for ( ; ( elem = this[ i ] ) != null; i++ ) { - if ( elem.nodeType === 1 ) { - - // Prevent memory leaks - jQuery.cleanData( getAll( elem, false ) ); - - // Remove any remaining nodes - elem.textContent = ""; - } - } - - return this; - }, - - clone: function( dataAndEvents, deepDataAndEvents ) { - dataAndEvents = dataAndEvents == null ? false : dataAndEvents; - deepDataAndEvents = deepDataAndEvents == null ? dataAndEvents : deepDataAndEvents; - - return this.map( function() { - return jQuery.clone( this, dataAndEvents, deepDataAndEvents ); - } ); - }, - - html: function( value ) { - return access( this, function( value ) { - var elem = this[ 0 ] || {}, - i = 0, - l = this.length; - - if ( value === undefined && elem.nodeType === 1 ) { - return elem.innerHTML; - } - - // See if we can take a shortcut and just use innerHTML - if ( typeof value === "string" && !rnoInnerhtml.test( value ) && - !wrapMap[ ( rtagName.exec( value ) || [ "", "" ] )[ 1 ].toLowerCase() ] ) { - - value = jQuery.htmlPrefilter( value ); - - try { - for ( ; i < l; i++ ) { - elem = this[ i ] || {}; - - // Remove element nodes and prevent memory leaks - if ( elem.nodeType === 1 ) { - jQuery.cleanData( getAll( elem, false ) ); - elem.innerHTML = value; - } - } - - elem = 0; - - // If using innerHTML throws an exception, use the fallback method - } catch ( e ) {} - } - - if ( elem ) { - this.empty().append( value ); - } - }, null, value, arguments.length ); - }, - - replaceWith: function() { - var ignored = []; - - // Make the changes, replacing each non-ignored context element with the new content - return domManip( this, arguments, function( elem ) { - var parent = this.parentNode; - - if ( jQuery.inArray( this, ignored ) < 0 ) { - jQuery.cleanData( getAll( this ) ); - if ( parent ) { - parent.replaceChild( elem, this ); - } - } - - // Force callback invocation - }, ignored ); - } -} ); - -jQuery.each( { - appendTo: "append", - prependTo: "prepend", - insertBefore: "before", - insertAfter: "after", - replaceAll: "replaceWith" -}, function( name, original ) { - jQuery.fn[ name ] = function( selector ) { - var elems, - ret = [], - insert = jQuery( selector ), - last = insert.length - 1, - i = 0; - - for ( ; i <= last; i++ ) { - elems = i === last ? this : this.clone( true ); - jQuery( insert[ i ] )[ original ]( elems ); - - // Support: Android <=4.0 only, PhantomJS 1 only - // .get() because push.apply(_, arraylike) throws on ancient WebKit - push.apply( ret, elems.get() ); - } - - return this.pushStack( ret ); - }; -} ); -var rnumnonpx = new RegExp( "^(" + pnum + ")(?!px)[a-z%]+$", "i" ); - -var getStyles = function( elem ) { - - // Support: IE <=11 only, Firefox <=30 (#15098, #14150) - // IE throws on elements created in popups - // FF meanwhile throws on frame elements through "defaultView.getComputedStyle" - var view = elem.ownerDocument.defaultView; - - if ( !view || !view.opener ) { - view = window; - } - - return view.getComputedStyle( elem ); - }; - -var swap = function( elem, options, callback ) { - var ret, name, - old = {}; - - // Remember the old values, and insert the new ones - for ( name in options ) { - old[ name ] = elem.style[ name ]; - elem.style[ name ] = options[ name ]; - } - - ret = callback.call( elem ); - - // Revert the old values - for ( name in options ) { - elem.style[ name ] = old[ name ]; - } - - return ret; -}; - - -var rboxStyle = new RegExp( cssExpand.join( "|" ), "i" ); - - - -( function() { - - // Executing both pixelPosition & boxSizingReliable tests require only one layout - // so they're executed at the same time to save the second computation. - function computeStyleTests() { - - // This is a singleton, we need to execute it only once - if ( !div ) { - return; - } - - container.style.cssText = "position:absolute;left:-11111px;width:60px;" + - "margin-top:1px;padding:0;border:0"; - div.style.cssText = - "position:relative;display:block;box-sizing:border-box;overflow:scroll;" + - "margin:auto;border:1px;padding:1px;" + - "width:60%;top:1%"; - documentElement.appendChild( container ).appendChild( div ); - - var divStyle = window.getComputedStyle( div ); - pixelPositionVal = divStyle.top !== "1%"; - - // Support: Android 4.0 - 4.3 only, Firefox <=3 - 44 - reliableMarginLeftVal = roundPixelMeasures( divStyle.marginLeft ) === 12; - - // Support: Android 4.0 - 4.3 only, Safari <=9.1 - 10.1, iOS <=7.0 - 9.3 - // Some styles come back with percentage values, even though they shouldn't - div.style.right = "60%"; - pixelBoxStylesVal = roundPixelMeasures( divStyle.right ) === 36; - - // Support: IE 9 - 11 only - // Detect misreporting of content dimensions for box-sizing:border-box elements - boxSizingReliableVal = roundPixelMeasures( divStyle.width ) === 36; - - // Support: IE 9 only - // Detect overflow:scroll screwiness (gh-3699) - // Support: Chrome <=64 - // Don't get tricked when zoom affects offsetWidth (gh-4029) - div.style.position = "absolute"; - scrollboxSizeVal = roundPixelMeasures( div.offsetWidth / 3 ) === 12; - - documentElement.removeChild( container ); - - // Nullify the div so it wouldn't be stored in the memory and - // it will also be a sign that checks already performed - div = null; - } - - function roundPixelMeasures( measure ) { - return Math.round( parseFloat( measure ) ); - } - - var pixelPositionVal, boxSizingReliableVal, scrollboxSizeVal, pixelBoxStylesVal, - reliableTrDimensionsVal, reliableMarginLeftVal, - container = document.createElement( "div" ), - div = document.createElement( "div" ); - - // Finish early in limited (non-browser) environments - if ( !div.style ) { - return; - } - - // Support: IE <=9 - 11 only - // Style of cloned element affects source element cloned (#8908) - div.style.backgroundClip = "content-box"; - div.cloneNode( true ).style.backgroundClip = ""; - support.clearCloneStyle = div.style.backgroundClip === "content-box"; - - jQuery.extend( support, { - boxSizingReliable: function() { - computeStyleTests(); - return boxSizingReliableVal; - }, - pixelBoxStyles: function() { - computeStyleTests(); - return pixelBoxStylesVal; - }, - pixelPosition: function() { - computeStyleTests(); - return pixelPositionVal; - }, - reliableMarginLeft: function() { - computeStyleTests(); - return reliableMarginLeftVal; - }, - scrollboxSize: function() { - computeStyleTests(); - return scrollboxSizeVal; - }, - - // Support: IE 9 - 11+, Edge 15 - 18+ - // IE/Edge misreport `getComputedStyle` of table rows with width/height - // set in CSS while `offset*` properties report correct values. - // Behavior in IE 9 is more subtle than in newer versions & it passes - // some versions of this test; make sure not to make it pass there! - // - // Support: Firefox 70+ - // Only Firefox includes border widths - // in computed dimensions. (gh-4529) - reliableTrDimensions: function() { - var table, tr, trChild, trStyle; - if ( reliableTrDimensionsVal == null ) { - table = document.createElement( "table" ); - tr = document.createElement( "tr" ); - trChild = document.createElement( "div" ); - - table.style.cssText = "position:absolute;left:-11111px;border-collapse:separate"; - tr.style.cssText = "border:1px solid"; - - // Support: Chrome 86+ - // Height set through cssText does not get applied. - // Computed height then comes back as 0. - tr.style.height = "1px"; - trChild.style.height = "9px"; - - // Support: Android 8 Chrome 86+ - // In our bodyBackground.html iframe, - // display for all div elements is set to "inline", - // which causes a problem only in Android 8 Chrome 86. - // Ensuring the div is display: block - // gets around this issue. - trChild.style.display = "block"; - - documentElement - .appendChild( table ) - .appendChild( tr ) - .appendChild( trChild ); - - trStyle = window.getComputedStyle( tr ); - reliableTrDimensionsVal = ( parseInt( trStyle.height, 10 ) + - parseInt( trStyle.borderTopWidth, 10 ) + - parseInt( trStyle.borderBottomWidth, 10 ) ) === tr.offsetHeight; - - documentElement.removeChild( table ); - } - return reliableTrDimensionsVal; - } - } ); -} )(); - - -function curCSS( elem, name, computed ) { - var width, minWidth, maxWidth, ret, - - // Support: Firefox 51+ - // Retrieving style before computed somehow - // fixes an issue with getting wrong values - // on detached elements - style = elem.style; - - computed = computed || getStyles( elem ); - - // getPropertyValue is needed for: - // .css('filter') (IE 9 only, #12537) - // .css('--customProperty) (#3144) - if ( computed ) { - ret = computed.getPropertyValue( name ) || computed[ name ]; - - if ( ret === "" && !isAttached( elem ) ) { - ret = jQuery.style( elem, name ); - } - - // A tribute to the "awesome hack by Dean Edwards" - // Android Browser returns percentage for some values, - // but width seems to be reliably pixels. - // This is against the CSSOM draft spec: - // https://drafts.csswg.org/cssom/#resolved-values - if ( !support.pixelBoxStyles() && rnumnonpx.test( ret ) && rboxStyle.test( name ) ) { - - // Remember the original values - width = style.width; - minWidth = style.minWidth; - maxWidth = style.maxWidth; - - // Put in the new values to get a computed value out - style.minWidth = style.maxWidth = style.width = ret; - ret = computed.width; - - // Revert the changed values - style.width = width; - style.minWidth = minWidth; - style.maxWidth = maxWidth; - } - } - - return ret !== undefined ? - - // Support: IE <=9 - 11 only - // IE returns zIndex value as an integer. - ret + "" : - ret; -} - - -function addGetHookIf( conditionFn, hookFn ) { - - // Define the hook, we'll check on the first run if it's really needed. - return { - get: function() { - if ( conditionFn() ) { - - // Hook not needed (or it's not possible to use it due - // to missing dependency), remove it. - delete this.get; - return; - } - - // Hook needed; redefine it so that the support test is not executed again. - return ( this.get = hookFn ).apply( this, arguments ); - } - }; -} - - -var cssPrefixes = [ "Webkit", "Moz", "ms" ], - emptyStyle = document.createElement( "div" ).style, - vendorProps = {}; - -// Return a vendor-prefixed property or undefined -function vendorPropName( name ) { - - // Check for vendor prefixed names - var capName = name[ 0 ].toUpperCase() + name.slice( 1 ), - i = cssPrefixes.length; - - while ( i-- ) { - name = cssPrefixes[ i ] + capName; - if ( name in emptyStyle ) { - return name; - } - } -} - -// Return a potentially-mapped jQuery.cssProps or vendor prefixed property -function finalPropName( name ) { - var final = jQuery.cssProps[ name ] || vendorProps[ name ]; - - if ( final ) { - return final; - } - if ( name in emptyStyle ) { - return name; - } - return vendorProps[ name ] = vendorPropName( name ) || name; -} - - -var - - // Swappable if display is none or starts with table - // except "table", "table-cell", or "table-caption" - // See here for display values: https://developer.mozilla.org/en-US/docs/CSS/display - rdisplayswap = /^(none|table(?!-c[ea]).+)/, - rcustomProp = /^--/, - cssShow = { position: "absolute", visibility: "hidden", display: "block" }, - cssNormalTransform = { - letterSpacing: "0", - fontWeight: "400" - }; - -function setPositiveNumber( _elem, value, subtract ) { - - // Any relative (+/-) values have already been - // normalized at this point - var matches = rcssNum.exec( value ); - return matches ? - - // Guard against undefined "subtract", e.g., when used as in cssHooks - Math.max( 0, matches[ 2 ] - ( subtract || 0 ) ) + ( matches[ 3 ] || "px" ) : - value; -} - -function boxModelAdjustment( elem, dimension, box, isBorderBox, styles, computedVal ) { - var i = dimension === "width" ? 1 : 0, - extra = 0, - delta = 0; - - // Adjustment may not be necessary - if ( box === ( isBorderBox ? "border" : "content" ) ) { - return 0; - } - - for ( ; i < 4; i += 2 ) { - - // Both box models exclude margin - if ( box === "margin" ) { - delta += jQuery.css( elem, box + cssExpand[ i ], true, styles ); - } - - // If we get here with a content-box, we're seeking "padding" or "border" or "margin" - if ( !isBorderBox ) { - - // Add padding - delta += jQuery.css( elem, "padding" + cssExpand[ i ], true, styles ); - - // For "border" or "margin", add border - if ( box !== "padding" ) { - delta += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles ); - - // But still keep track of it otherwise - } else { - extra += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles ); - } - - // If we get here with a border-box (content + padding + border), we're seeking "content" or - // "padding" or "margin" - } else { - - // For "content", subtract padding - if ( box === "content" ) { - delta -= jQuery.css( elem, "padding" + cssExpand[ i ], true, styles ); - } - - // For "content" or "padding", subtract border - if ( box !== "margin" ) { - delta -= jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles ); - } - } - } - - // Account for positive content-box scroll gutter when requested by providing computedVal - if ( !isBorderBox && computedVal >= 0 ) { - - // offsetWidth/offsetHeight is a rounded sum of content, padding, scroll gutter, and border - // Assuming integer scroll gutter, subtract the rest and round down - delta += Math.max( 0, Math.ceil( - elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] - - computedVal - - delta - - extra - - 0.5 - - // If offsetWidth/offsetHeight is unknown, then we can't determine content-box scroll gutter - // Use an explicit zero to avoid NaN (gh-3964) - ) ) || 0; - } - - return delta; -} - -function getWidthOrHeight( elem, dimension, extra ) { - - // Start with computed style - var styles = getStyles( elem ), - - // To avoid forcing a reflow, only fetch boxSizing if we need it (gh-4322). - // Fake content-box until we know it's needed to know the true value. - boxSizingNeeded = !support.boxSizingReliable() || extra, - isBorderBox = boxSizingNeeded && - jQuery.css( elem, "boxSizing", false, styles ) === "border-box", - valueIsBorderBox = isBorderBox, - - val = curCSS( elem, dimension, styles ), - offsetProp = "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ); - - // Support: Firefox <=54 - // Return a confounding non-pixel value or feign ignorance, as appropriate. - if ( rnumnonpx.test( val ) ) { - if ( !extra ) { - return val; - } - val = "auto"; - } - - - // Support: IE 9 - 11 only - // Use offsetWidth/offsetHeight for when box sizing is unreliable. - // In those cases, the computed value can be trusted to be border-box. - if ( ( !support.boxSizingReliable() && isBorderBox || - - // Support: IE 10 - 11+, Edge 15 - 18+ - // IE/Edge misreport `getComputedStyle` of table rows with width/height - // set in CSS while `offset*` properties report correct values. - // Interestingly, in some cases IE 9 doesn't suffer from this issue. - !support.reliableTrDimensions() && nodeName( elem, "tr" ) || - - // Fall back to offsetWidth/offsetHeight when value is "auto" - // This happens for inline elements with no explicit setting (gh-3571) - val === "auto" || - - // Support: Android <=4.1 - 4.3 only - // Also use offsetWidth/offsetHeight for misreported inline dimensions (gh-3602) - !parseFloat( val ) && jQuery.css( elem, "display", false, styles ) === "inline" ) && - - // Make sure the element is visible & connected - elem.getClientRects().length ) { - - isBorderBox = jQuery.css( elem, "boxSizing", false, styles ) === "border-box"; - - // Where available, offsetWidth/offsetHeight approximate border box dimensions. - // Where not available (e.g., SVG), assume unreliable box-sizing and interpret the - // retrieved value as a content box dimension. - valueIsBorderBox = offsetProp in elem; - if ( valueIsBorderBox ) { - val = elem[ offsetProp ]; - } - } - - // Normalize "" and auto - val = parseFloat( val ) || 0; - - // Adjust for the element's box model - return ( val + - boxModelAdjustment( - elem, - dimension, - extra || ( isBorderBox ? "border" : "content" ), - valueIsBorderBox, - styles, - - // Provide the current computed size to request scroll gutter calculation (gh-3589) - val - ) - ) + "px"; -} - -jQuery.extend( { - - // Add in style property hooks for overriding the default - // behavior of getting and setting a style property - cssHooks: { - opacity: { - get: function( elem, computed ) { - if ( computed ) { - - // We should always get a number back from opacity - var ret = curCSS( elem, "opacity" ); - return ret === "" ? "1" : ret; - } - } - } - }, - - // Don't automatically add "px" to these possibly-unitless properties - cssNumber: { - "animationIterationCount": true, - "columnCount": true, - "fillOpacity": true, - "flexGrow": true, - "flexShrink": true, - "fontWeight": true, - "gridArea": true, - "gridColumn": true, - "gridColumnEnd": true, - "gridColumnStart": true, - "gridRow": true, - "gridRowEnd": true, - "gridRowStart": true, - "lineHeight": true, - "opacity": true, - "order": true, - "orphans": true, - "widows": true, - "zIndex": true, - "zoom": true - }, - - // Add in properties whose names you wish to fix before - // setting or getting the value - cssProps: {}, - - // Get and set the style property on a DOM Node - style: function( elem, name, value, extra ) { - - // Don't set styles on text and comment nodes - if ( !elem || elem.nodeType === 3 || elem.nodeType === 8 || !elem.style ) { - return; - } - - // Make sure that we're working with the right name - var ret, type, hooks, - origName = camelCase( name ), - isCustomProp = rcustomProp.test( name ), - style = elem.style; - - // Make sure that we're working with the right name. We don't - // want to query the value if it is a CSS custom property - // since they are user-defined. - if ( !isCustomProp ) { - name = finalPropName( origName ); - } - - // Gets hook for the prefixed version, then unprefixed version - hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ]; - - // Check if we're setting a value - if ( value !== undefined ) { - type = typeof value; - - // Convert "+=" or "-=" to relative numbers (#7345) - if ( type === "string" && ( ret = rcssNum.exec( value ) ) && ret[ 1 ] ) { - value = adjustCSS( elem, name, ret ); - - // Fixes bug #9237 - type = "number"; - } - - // Make sure that null and NaN values aren't set (#7116) - if ( value == null || value !== value ) { - return; - } - - // If a number was passed in, add the unit (except for certain CSS properties) - // The isCustomProp check can be removed in jQuery 4.0 when we only auto-append - // "px" to a few hardcoded values. - if ( type === "number" && !isCustomProp ) { - value += ret && ret[ 3 ] || ( jQuery.cssNumber[ origName ] ? "" : "px" ); - } - - // background-* props affect original clone's values - if ( !support.clearCloneStyle && value === "" && name.indexOf( "background" ) === 0 ) { - style[ name ] = "inherit"; - } - - // If a hook was provided, use that value, otherwise just set the specified value - if ( !hooks || !( "set" in hooks ) || - ( value = hooks.set( elem, value, extra ) ) !== undefined ) { - - if ( isCustomProp ) { - style.setProperty( name, value ); - } else { - style[ name ] = value; - } - } - - } else { - - // If a hook was provided get the non-computed value from there - if ( hooks && "get" in hooks && - ( ret = hooks.get( elem, false, extra ) ) !== undefined ) { - - return ret; - } - - // Otherwise just get the value from the style object - return style[ name ]; - } - }, - - css: function( elem, name, extra, styles ) { - var val, num, hooks, - origName = camelCase( name ), - isCustomProp = rcustomProp.test( name ); - - // Make sure that we're working with the right name. We don't - // want to modify the value if it is a CSS custom property - // since they are user-defined. - if ( !isCustomProp ) { - name = finalPropName( origName ); - } - - // Try prefixed name followed by the unprefixed name - hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ]; - - // If a hook was provided get the computed value from there - if ( hooks && "get" in hooks ) { - val = hooks.get( elem, true, extra ); - } - - // Otherwise, if a way to get the computed value exists, use that - if ( val === undefined ) { - val = curCSS( elem, name, styles ); - } - - // Convert "normal" to computed value - if ( val === "normal" && name in cssNormalTransform ) { - val = cssNormalTransform[ name ]; - } - - // Make numeric if forced or a qualifier was provided and val looks numeric - if ( extra === "" || extra ) { - num = parseFloat( val ); - return extra === true || isFinite( num ) ? num || 0 : val; - } - - return val; - } -} ); - -jQuery.each( [ "height", "width" ], function( _i, dimension ) { - jQuery.cssHooks[ dimension ] = { - get: function( elem, computed, extra ) { - if ( computed ) { - - // Certain elements can have dimension info if we invisibly show them - // but it must have a current display style that would benefit - return rdisplayswap.test( jQuery.css( elem, "display" ) ) && - - // Support: Safari 8+ - // Table columns in Safari have non-zero offsetWidth & zero - // getBoundingClientRect().width unless display is changed. - // Support: IE <=11 only - // Running getBoundingClientRect on a disconnected node - // in IE throws an error. - ( !elem.getClientRects().length || !elem.getBoundingClientRect().width ) ? - swap( elem, cssShow, function() { - return getWidthOrHeight( elem, dimension, extra ); - } ) : - getWidthOrHeight( elem, dimension, extra ); - } - }, - - set: function( elem, value, extra ) { - var matches, - styles = getStyles( elem ), - - // Only read styles.position if the test has a chance to fail - // to avoid forcing a reflow. - scrollboxSizeBuggy = !support.scrollboxSize() && - styles.position === "absolute", - - // To avoid forcing a reflow, only fetch boxSizing if we need it (gh-3991) - boxSizingNeeded = scrollboxSizeBuggy || extra, - isBorderBox = boxSizingNeeded && - jQuery.css( elem, "boxSizing", false, styles ) === "border-box", - subtract = extra ? - boxModelAdjustment( - elem, - dimension, - extra, - isBorderBox, - styles - ) : - 0; - - // Account for unreliable border-box dimensions by comparing offset* to computed and - // faking a content-box to get border and padding (gh-3699) - if ( isBorderBox && scrollboxSizeBuggy ) { - subtract -= Math.ceil( - elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] - - parseFloat( styles[ dimension ] ) - - boxModelAdjustment( elem, dimension, "border", false, styles ) - - 0.5 - ); - } - - // Convert to pixels if value adjustment is needed - if ( subtract && ( matches = rcssNum.exec( value ) ) && - ( matches[ 3 ] || "px" ) !== "px" ) { - - elem.style[ dimension ] = value; - value = jQuery.css( elem, dimension ); - } - - return setPositiveNumber( elem, value, subtract ); - } - }; -} ); - -jQuery.cssHooks.marginLeft = addGetHookIf( support.reliableMarginLeft, - function( elem, computed ) { - if ( computed ) { - return ( parseFloat( curCSS( elem, "marginLeft" ) ) || - elem.getBoundingClientRect().left - - swap( elem, { marginLeft: 0 }, function() { - return elem.getBoundingClientRect().left; - } ) - ) + "px"; - } - } -); - -// These hooks are used by animate to expand properties -jQuery.each( { - margin: "", - padding: "", - border: "Width" -}, function( prefix, suffix ) { - jQuery.cssHooks[ prefix + suffix ] = { - expand: function( value ) { - var i = 0, - expanded = {}, - - // Assumes a single number if not a string - parts = typeof value === "string" ? value.split( " " ) : [ value ]; - - for ( ; i < 4; i++ ) { - expanded[ prefix + cssExpand[ i ] + suffix ] = - parts[ i ] || parts[ i - 2 ] || parts[ 0 ]; - } - - return expanded; - } - }; - - if ( prefix !== "margin" ) { - jQuery.cssHooks[ prefix + suffix ].set = setPositiveNumber; - } -} ); - -jQuery.fn.extend( { - css: function( name, value ) { - return access( this, function( elem, name, value ) { - var styles, len, - map = {}, - i = 0; - - if ( Array.isArray( name ) ) { - styles = getStyles( elem ); - len = name.length; - - for ( ; i < len; i++ ) { - map[ name[ i ] ] = jQuery.css( elem, name[ i ], false, styles ); - } - - return map; - } - - return value !== undefined ? - jQuery.style( elem, name, value ) : - jQuery.css( elem, name ); - }, name, value, arguments.length > 1 ); - } -} ); - - -function Tween( elem, options, prop, end, easing ) { - return new Tween.prototype.init( elem, options, prop, end, easing ); -} -jQuery.Tween = Tween; - -Tween.prototype = { - constructor: Tween, - init: function( elem, options, prop, end, easing, unit ) { - this.elem = elem; - this.prop = prop; - this.easing = easing || jQuery.easing._default; - this.options = options; - this.start = this.now = this.cur(); - this.end = end; - this.unit = unit || ( jQuery.cssNumber[ prop ] ? "" : "px" ); - }, - cur: function() { - var hooks = Tween.propHooks[ this.prop ]; - - return hooks && hooks.get ? - hooks.get( this ) : - Tween.propHooks._default.get( this ); - }, - run: function( percent ) { - var eased, - hooks = Tween.propHooks[ this.prop ]; - - if ( this.options.duration ) { - this.pos = eased = jQuery.easing[ this.easing ]( - percent, this.options.duration * percent, 0, 1, this.options.duration - ); - } else { - this.pos = eased = percent; - } - this.now = ( this.end - this.start ) * eased + this.start; - - if ( this.options.step ) { - this.options.step.call( this.elem, this.now, this ); - } - - if ( hooks && hooks.set ) { - hooks.set( this ); - } else { - Tween.propHooks._default.set( this ); - } - return this; - } -}; - -Tween.prototype.init.prototype = Tween.prototype; - -Tween.propHooks = { - _default: { - get: function( tween ) { - var result; - - // Use a property on the element directly when it is not a DOM element, - // or when there is no matching style property that exists. - if ( tween.elem.nodeType !== 1 || - tween.elem[ tween.prop ] != null && tween.elem.style[ tween.prop ] == null ) { - return tween.elem[ tween.prop ]; - } - - // Passing an empty string as a 3rd parameter to .css will automatically - // attempt a parseFloat and fallback to a string if the parse fails. - // Simple values such as "10px" are parsed to Float; - // complex values such as "rotate(1rad)" are returned as-is. - result = jQuery.css( tween.elem, tween.prop, "" ); - - // Empty strings, null, undefined and "auto" are converted to 0. - return !result || result === "auto" ? 0 : result; - }, - set: function( tween ) { - - // Use step hook for back compat. - // Use cssHook if its there. - // Use .style if available and use plain properties where available. - if ( jQuery.fx.step[ tween.prop ] ) { - jQuery.fx.step[ tween.prop ]( tween ); - } else if ( tween.elem.nodeType === 1 && ( - jQuery.cssHooks[ tween.prop ] || - tween.elem.style[ finalPropName( tween.prop ) ] != null ) ) { - jQuery.style( tween.elem, tween.prop, tween.now + tween.unit ); - } else { - tween.elem[ tween.prop ] = tween.now; - } - } - } -}; - -// Support: IE <=9 only -// Panic based approach to setting things on disconnected nodes -Tween.propHooks.scrollTop = Tween.propHooks.scrollLeft = { - set: function( tween ) { - if ( tween.elem.nodeType && tween.elem.parentNode ) { - tween.elem[ tween.prop ] = tween.now; - } - } -}; - -jQuery.easing = { - linear: function( p ) { - return p; - }, - swing: function( p ) { - return 0.5 - Math.cos( p * Math.PI ) / 2; - }, - _default: "swing" -}; - -jQuery.fx = Tween.prototype.init; - -// Back compat <1.8 extension point -jQuery.fx.step = {}; - - - - -var - fxNow, inProgress, - rfxtypes = /^(?:toggle|show|hide)$/, - rrun = /queueHooks$/; - -function schedule() { - if ( inProgress ) { - if ( document.hidden === false && window.requestAnimationFrame ) { - window.requestAnimationFrame( schedule ); - } else { - window.setTimeout( schedule, jQuery.fx.interval ); - } - - jQuery.fx.tick(); - } -} - -// Animations created synchronously will run synchronously -function createFxNow() { - window.setTimeout( function() { - fxNow = undefined; - } ); - return ( fxNow = Date.now() ); -} - -// Generate parameters to create a standard animation -function genFx( type, includeWidth ) { - var which, - i = 0, - attrs = { height: type }; - - // If we include width, step value is 1 to do all cssExpand values, - // otherwise step value is 2 to skip over Left and Right - includeWidth = includeWidth ? 1 : 0; - for ( ; i < 4; i += 2 - includeWidth ) { - which = cssExpand[ i ]; - attrs[ "margin" + which ] = attrs[ "padding" + which ] = type; - } - - if ( includeWidth ) { - attrs.opacity = attrs.width = type; - } - - return attrs; -} - -function createTween( value, prop, animation ) { - var tween, - collection = ( Animation.tweeners[ prop ] || [] ).concat( Animation.tweeners[ "*" ] ), - index = 0, - length = collection.length; - for ( ; index < length; index++ ) { - if ( ( tween = collection[ index ].call( animation, prop, value ) ) ) { - - // We're done with this property - return tween; - } - } -} - -function defaultPrefilter( elem, props, opts ) { - var prop, value, toggle, hooks, oldfire, propTween, restoreDisplay, display, - isBox = "width" in props || "height" in props, - anim = this, - orig = {}, - style = elem.style, - hidden = elem.nodeType && isHiddenWithinTree( elem ), - dataShow = dataPriv.get( elem, "fxshow" ); - - // Queue-skipping animations hijack the fx hooks - if ( !opts.queue ) { - hooks = jQuery._queueHooks( elem, "fx" ); - if ( hooks.unqueued == null ) { - hooks.unqueued = 0; - oldfire = hooks.empty.fire; - hooks.empty.fire = function() { - if ( !hooks.unqueued ) { - oldfire(); - } - }; - } - hooks.unqueued++; - - anim.always( function() { - - // Ensure the complete handler is called before this completes - anim.always( function() { - hooks.unqueued--; - if ( !jQuery.queue( elem, "fx" ).length ) { - hooks.empty.fire(); - } - } ); - } ); - } - - // Detect show/hide animations - for ( prop in props ) { - value = props[ prop ]; - if ( rfxtypes.test( value ) ) { - delete props[ prop ]; - toggle = toggle || value === "toggle"; - if ( value === ( hidden ? "hide" : "show" ) ) { - - // Pretend to be hidden if this is a "show" and - // there is still data from a stopped show/hide - if ( value === "show" && dataShow && dataShow[ prop ] !== undefined ) { - hidden = true; - - // Ignore all other no-op show/hide data - } else { - continue; - } - } - orig[ prop ] = dataShow && dataShow[ prop ] || jQuery.style( elem, prop ); - } - } - - // Bail out if this is a no-op like .hide().hide() - propTween = !jQuery.isEmptyObject( props ); - if ( !propTween && jQuery.isEmptyObject( orig ) ) { - return; - } - - // Restrict "overflow" and "display" styles during box animations - if ( isBox && elem.nodeType === 1 ) { - - // Support: IE <=9 - 11, Edge 12 - 15 - // Record all 3 overflow attributes because IE does not infer the shorthand - // from identically-valued overflowX and overflowY and Edge just mirrors - // the overflowX value there. - opts.overflow = [ style.overflow, style.overflowX, style.overflowY ]; - - // Identify a display type, preferring old show/hide data over the CSS cascade - restoreDisplay = dataShow && dataShow.display; - if ( restoreDisplay == null ) { - restoreDisplay = dataPriv.get( elem, "display" ); - } - display = jQuery.css( elem, "display" ); - if ( display === "none" ) { - if ( restoreDisplay ) { - display = restoreDisplay; - } else { - - // Get nonempty value(s) by temporarily forcing visibility - showHide( [ elem ], true ); - restoreDisplay = elem.style.display || restoreDisplay; - display = jQuery.css( elem, "display" ); - showHide( [ elem ] ); - } - } - - // Animate inline elements as inline-block - if ( display === "inline" || display === "inline-block" && restoreDisplay != null ) { - if ( jQuery.css( elem, "float" ) === "none" ) { - - // Restore the original display value at the end of pure show/hide animations - if ( !propTween ) { - anim.done( function() { - style.display = restoreDisplay; - } ); - if ( restoreDisplay == null ) { - display = style.display; - restoreDisplay = display === "none" ? "" : display; - } - } - style.display = "inline-block"; - } - } - } - - if ( opts.overflow ) { - style.overflow = "hidden"; - anim.always( function() { - style.overflow = opts.overflow[ 0 ]; - style.overflowX = opts.overflow[ 1 ]; - style.overflowY = opts.overflow[ 2 ]; - } ); - } - - // Implement show/hide animations - propTween = false; - for ( prop in orig ) { - - // General show/hide setup for this element animation - if ( !propTween ) { - if ( dataShow ) { - if ( "hidden" in dataShow ) { - hidden = dataShow.hidden; - } - } else { - dataShow = dataPriv.access( elem, "fxshow", { display: restoreDisplay } ); - } - - // Store hidden/visible for toggle so `.stop().toggle()` "reverses" - if ( toggle ) { - dataShow.hidden = !hidden; - } - - // Show elements before animating them - if ( hidden ) { - showHide( [ elem ], true ); - } - - /* eslint-disable no-loop-func */ - - anim.done( function() { - - /* eslint-enable no-loop-func */ - - // The final step of a "hide" animation is actually hiding the element - if ( !hidden ) { - showHide( [ elem ] ); - } - dataPriv.remove( elem, "fxshow" ); - for ( prop in orig ) { - jQuery.style( elem, prop, orig[ prop ] ); - } - } ); - } - - // Per-property setup - propTween = createTween( hidden ? dataShow[ prop ] : 0, prop, anim ); - if ( !( prop in dataShow ) ) { - dataShow[ prop ] = propTween.start; - if ( hidden ) { - propTween.end = propTween.start; - propTween.start = 0; - } - } - } -} - -function propFilter( props, specialEasing ) { - var index, name, easing, value, hooks; - - // camelCase, specialEasing and expand cssHook pass - for ( index in props ) { - name = camelCase( index ); - easing = specialEasing[ name ]; - value = props[ index ]; - if ( Array.isArray( value ) ) { - easing = value[ 1 ]; - value = props[ index ] = value[ 0 ]; - } - - if ( index !== name ) { - props[ name ] = value; - delete props[ index ]; - } - - hooks = jQuery.cssHooks[ name ]; - if ( hooks && "expand" in hooks ) { - value = hooks.expand( value ); - delete props[ name ]; - - // Not quite $.extend, this won't overwrite existing keys. - // Reusing 'index' because we have the correct "name" - for ( index in value ) { - if ( !( index in props ) ) { - props[ index ] = value[ index ]; - specialEasing[ index ] = easing; - } - } - } else { - specialEasing[ name ] = easing; - } - } -} - -function Animation( elem, properties, options ) { - var result, - stopped, - index = 0, - length = Animation.prefilters.length, - deferred = jQuery.Deferred().always( function() { - - // Don't match elem in the :animated selector - delete tick.elem; - } ), - tick = function() { - if ( stopped ) { - return false; - } - var currentTime = fxNow || createFxNow(), - remaining = Math.max( 0, animation.startTime + animation.duration - currentTime ), - - // Support: Android 2.3 only - // Archaic crash bug won't allow us to use `1 - ( 0.5 || 0 )` (#12497) - temp = remaining / animation.duration || 0, - percent = 1 - temp, - index = 0, - length = animation.tweens.length; - - for ( ; index < length; index++ ) { - animation.tweens[ index ].run( percent ); - } - - deferred.notifyWith( elem, [ animation, percent, remaining ] ); - - // If there's more to do, yield - if ( percent < 1 && length ) { - return remaining; - } - - // If this was an empty animation, synthesize a final progress notification - if ( !length ) { - deferred.notifyWith( elem, [ animation, 1, 0 ] ); - } - - // Resolve the animation and report its conclusion - deferred.resolveWith( elem, [ animation ] ); - return false; - }, - animation = deferred.promise( { - elem: elem, - props: jQuery.extend( {}, properties ), - opts: jQuery.extend( true, { - specialEasing: {}, - easing: jQuery.easing._default - }, options ), - originalProperties: properties, - originalOptions: options, - startTime: fxNow || createFxNow(), - duration: options.duration, - tweens: [], - createTween: function( prop, end ) { - var tween = jQuery.Tween( elem, animation.opts, prop, end, - animation.opts.specialEasing[ prop ] || animation.opts.easing ); - animation.tweens.push( tween ); - return tween; - }, - stop: function( gotoEnd ) { - var index = 0, - - // If we are going to the end, we want to run all the tweens - // otherwise we skip this part - length = gotoEnd ? animation.tweens.length : 0; - if ( stopped ) { - return this; - } - stopped = true; - for ( ; index < length; index++ ) { - animation.tweens[ index ].run( 1 ); - } - - // Resolve when we played the last frame; otherwise, reject - if ( gotoEnd ) { - deferred.notifyWith( elem, [ animation, 1, 0 ] ); - deferred.resolveWith( elem, [ animation, gotoEnd ] ); - } else { - deferred.rejectWith( elem, [ animation, gotoEnd ] ); - } - return this; - } - } ), - props = animation.props; - - propFilter( props, animation.opts.specialEasing ); - - for ( ; index < length; index++ ) { - result = Animation.prefilters[ index ].call( animation, elem, props, animation.opts ); - if ( result ) { - if ( isFunction( result.stop ) ) { - jQuery._queueHooks( animation.elem, animation.opts.queue ).stop = - result.stop.bind( result ); - } - return result; - } - } - - jQuery.map( props, createTween, animation ); - - if ( isFunction( animation.opts.start ) ) { - animation.opts.start.call( elem, animation ); - } - - // Attach callbacks from options - animation - .progress( animation.opts.progress ) - .done( animation.opts.done, animation.opts.complete ) - .fail( animation.opts.fail ) - .always( animation.opts.always ); - - jQuery.fx.timer( - jQuery.extend( tick, { - elem: elem, - anim: animation, - queue: animation.opts.queue - } ) - ); - - return animation; -} - -jQuery.Animation = jQuery.extend( Animation, { - - tweeners: { - "*": [ function( prop, value ) { - var tween = this.createTween( prop, value ); - adjustCSS( tween.elem, prop, rcssNum.exec( value ), tween ); - return tween; - } ] - }, - - tweener: function( props, callback ) { - if ( isFunction( props ) ) { - callback = props; - props = [ "*" ]; - } else { - props = props.match( rnothtmlwhite ); - } - - var prop, - index = 0, - length = props.length; - - for ( ; index < length; index++ ) { - prop = props[ index ]; - Animation.tweeners[ prop ] = Animation.tweeners[ prop ] || []; - Animation.tweeners[ prop ].unshift( callback ); - } - }, - - prefilters: [ defaultPrefilter ], - - prefilter: function( callback, prepend ) { - if ( prepend ) { - Animation.prefilters.unshift( callback ); - } else { - Animation.prefilters.push( callback ); - } - } -} ); - -jQuery.speed = function( speed, easing, fn ) { - var opt = speed && typeof speed === "object" ? jQuery.extend( {}, speed ) : { - complete: fn || !fn && easing || - isFunction( speed ) && speed, - duration: speed, - easing: fn && easing || easing && !isFunction( easing ) && easing - }; - - // Go to the end state if fx are off - if ( jQuery.fx.off ) { - opt.duration = 0; - - } else { - if ( typeof opt.duration !== "number" ) { - if ( opt.duration in jQuery.fx.speeds ) { - opt.duration = jQuery.fx.speeds[ opt.duration ]; - - } else { - opt.duration = jQuery.fx.speeds._default; - } - } - } - - // Normalize opt.queue - true/undefined/null -> "fx" - if ( opt.queue == null || opt.queue === true ) { - opt.queue = "fx"; - } - - // Queueing - opt.old = opt.complete; - - opt.complete = function() { - if ( isFunction( opt.old ) ) { - opt.old.call( this ); - } - - if ( opt.queue ) { - jQuery.dequeue( this, opt.queue ); - } - }; - - return opt; -}; - -jQuery.fn.extend( { - fadeTo: function( speed, to, easing, callback ) { - - // Show any hidden elements after setting opacity to 0 - return this.filter( isHiddenWithinTree ).css( "opacity", 0 ).show() - - // Animate to the value specified - .end().animate( { opacity: to }, speed, easing, callback ); - }, - animate: function( prop, speed, easing, callback ) { - var empty = jQuery.isEmptyObject( prop ), - optall = jQuery.speed( speed, easing, callback ), - doAnimation = function() { - - // Operate on a copy of prop so per-property easing won't be lost - var anim = Animation( this, jQuery.extend( {}, prop ), optall ); - - // Empty animations, or finishing resolves immediately - if ( empty || dataPriv.get( this, "finish" ) ) { - anim.stop( true ); - } - }; - - doAnimation.finish = doAnimation; - - return empty || optall.queue === false ? - this.each( doAnimation ) : - this.queue( optall.queue, doAnimation ); - }, - stop: function( type, clearQueue, gotoEnd ) { - var stopQueue = function( hooks ) { - var stop = hooks.stop; - delete hooks.stop; - stop( gotoEnd ); - }; - - if ( typeof type !== "string" ) { - gotoEnd = clearQueue; - clearQueue = type; - type = undefined; - } - if ( clearQueue ) { - this.queue( type || "fx", [] ); - } - - return this.each( function() { - var dequeue = true, - index = type != null && type + "queueHooks", - timers = jQuery.timers, - data = dataPriv.get( this ); - - if ( index ) { - if ( data[ index ] && data[ index ].stop ) { - stopQueue( data[ index ] ); - } - } else { - for ( index in data ) { - if ( data[ index ] && data[ index ].stop && rrun.test( index ) ) { - stopQueue( data[ index ] ); - } - } - } - - for ( index = timers.length; index--; ) { - if ( timers[ index ].elem === this && - ( type == null || timers[ index ].queue === type ) ) { - - timers[ index ].anim.stop( gotoEnd ); - dequeue = false; - timers.splice( index, 1 ); - } - } - - // Start the next in the queue if the last step wasn't forced. - // Timers currently will call their complete callbacks, which - // will dequeue but only if they were gotoEnd. - if ( dequeue || !gotoEnd ) { - jQuery.dequeue( this, type ); - } - } ); - }, - finish: function( type ) { - if ( type !== false ) { - type = type || "fx"; - } - return this.each( function() { - var index, - data = dataPriv.get( this ), - queue = data[ type + "queue" ], - hooks = data[ type + "queueHooks" ], - timers = jQuery.timers, - length = queue ? queue.length : 0; - - // Enable finishing flag on private data - data.finish = true; - - // Empty the queue first - jQuery.queue( this, type, [] ); - - if ( hooks && hooks.stop ) { - hooks.stop.call( this, true ); - } - - // Look for any active animations, and finish them - for ( index = timers.length; index--; ) { - if ( timers[ index ].elem === this && timers[ index ].queue === type ) { - timers[ index ].anim.stop( true ); - timers.splice( index, 1 ); - } - } - - // Look for any animations in the old queue and finish them - for ( index = 0; index < length; index++ ) { - if ( queue[ index ] && queue[ index ].finish ) { - queue[ index ].finish.call( this ); - } - } - - // Turn off finishing flag - delete data.finish; - } ); - } -} ); - -jQuery.each( [ "toggle", "show", "hide" ], function( _i, name ) { - var cssFn = jQuery.fn[ name ]; - jQuery.fn[ name ] = function( speed, easing, callback ) { - return speed == null || typeof speed === "boolean" ? - cssFn.apply( this, arguments ) : - this.animate( genFx( name, true ), speed, easing, callback ); - }; -} ); - -// Generate shortcuts for custom animations -jQuery.each( { - slideDown: genFx( "show" ), - slideUp: genFx( "hide" ), - slideToggle: genFx( "toggle" ), - fadeIn: { opacity: "show" }, - fadeOut: { opacity: "hide" }, - fadeToggle: { opacity: "toggle" } -}, function( name, props ) { - jQuery.fn[ name ] = function( speed, easing, callback ) { - return this.animate( props, speed, easing, callback ); - }; -} ); - -jQuery.timers = []; -jQuery.fx.tick = function() { - var timer, - i = 0, - timers = jQuery.timers; - - fxNow = Date.now(); - - for ( ; i < timers.length; i++ ) { - timer = timers[ i ]; - - // Run the timer and safely remove it when done (allowing for external removal) - if ( !timer() && timers[ i ] === timer ) { - timers.splice( i--, 1 ); - } - } - - if ( !timers.length ) { - jQuery.fx.stop(); - } - fxNow = undefined; -}; - -jQuery.fx.timer = function( timer ) { - jQuery.timers.push( timer ); - jQuery.fx.start(); -}; - -jQuery.fx.interval = 13; -jQuery.fx.start = function() { - if ( inProgress ) { - return; - } - - inProgress = true; - schedule(); -}; - -jQuery.fx.stop = function() { - inProgress = null; -}; - -jQuery.fx.speeds = { - slow: 600, - fast: 200, - - // Default speed - _default: 400 -}; - - -// Based off of the plugin by Clint Helfers, with permission. -// https://web.archive.org/web/20100324014747/http://blindsignals.com/index.php/2009/07/jquery-delay/ -jQuery.fn.delay = function( time, type ) { - time = jQuery.fx ? jQuery.fx.speeds[ time ] || time : time; - type = type || "fx"; - - return this.queue( type, function( next, hooks ) { - var timeout = window.setTimeout( next, time ); - hooks.stop = function() { - window.clearTimeout( timeout ); - }; - } ); -}; - - -( function() { - var input = document.createElement( "input" ), - select = document.createElement( "select" ), - opt = select.appendChild( document.createElement( "option" ) ); - - input.type = "checkbox"; - - // Support: Android <=4.3 only - // Default value for a checkbox should be "on" - support.checkOn = input.value !== ""; - - // Support: IE <=11 only - // Must access selectedIndex to make default options select - support.optSelected = opt.selected; - - // Support: IE <=11 only - // An input loses its value after becoming a radio - input = document.createElement( "input" ); - input.value = "t"; - input.type = "radio"; - support.radioValue = input.value === "t"; -} )(); - - -var boolHook, - attrHandle = jQuery.expr.attrHandle; - -jQuery.fn.extend( { - attr: function( name, value ) { - return access( this, jQuery.attr, name, value, arguments.length > 1 ); - }, - - removeAttr: function( name ) { - return this.each( function() { - jQuery.removeAttr( this, name ); - } ); - } -} ); - -jQuery.extend( { - attr: function( elem, name, value ) { - var ret, hooks, - nType = elem.nodeType; - - // Don't get/set attributes on text, comment and attribute nodes - if ( nType === 3 || nType === 8 || nType === 2 ) { - return; - } - - // Fallback to prop when attributes are not supported - if ( typeof elem.getAttribute === "undefined" ) { - return jQuery.prop( elem, name, value ); - } - - // Attribute hooks are determined by the lowercase version - // Grab necessary hook if one is defined - if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) { - hooks = jQuery.attrHooks[ name.toLowerCase() ] || - ( jQuery.expr.match.bool.test( name ) ? boolHook : undefined ); - } - - if ( value !== undefined ) { - if ( value === null ) { - jQuery.removeAttr( elem, name ); - return; - } - - if ( hooks && "set" in hooks && - ( ret = hooks.set( elem, value, name ) ) !== undefined ) { - return ret; - } - - elem.setAttribute( name, value + "" ); - return value; - } - - if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) { - return ret; - } - - ret = jQuery.find.attr( elem, name ); - - // Non-existent attributes return null, we normalize to undefined - return ret == null ? undefined : ret; - }, - - attrHooks: { - type: { - set: function( elem, value ) { - if ( !support.radioValue && value === "radio" && - nodeName( elem, "input" ) ) { - var val = elem.value; - elem.setAttribute( "type", value ); - if ( val ) { - elem.value = val; - } - return value; - } - } - } - }, - - removeAttr: function( elem, value ) { - var name, - i = 0, - - // Attribute names can contain non-HTML whitespace characters - // https://html.spec.whatwg.org/multipage/syntax.html#attributes-2 - attrNames = value && value.match( rnothtmlwhite ); - - if ( attrNames && elem.nodeType === 1 ) { - while ( ( name = attrNames[ i++ ] ) ) { - elem.removeAttribute( name ); - } - } - } -} ); - -// Hooks for boolean attributes -boolHook = { - set: function( elem, value, name ) { - if ( value === false ) { - - // Remove boolean attributes when set to false - jQuery.removeAttr( elem, name ); - } else { - elem.setAttribute( name, name ); - } - return name; - } -}; - -jQuery.each( jQuery.expr.match.bool.source.match( /\w+/g ), function( _i, name ) { - var getter = attrHandle[ name ] || jQuery.find.attr; - - attrHandle[ name ] = function( elem, name, isXML ) { - var ret, handle, - lowercaseName = name.toLowerCase(); - - if ( !isXML ) { - - // Avoid an infinite loop by temporarily removing this function from the getter - handle = attrHandle[ lowercaseName ]; - attrHandle[ lowercaseName ] = ret; - ret = getter( elem, name, isXML ) != null ? - lowercaseName : - null; - attrHandle[ lowercaseName ] = handle; - } - return ret; - }; -} ); - - - - -var rfocusable = /^(?:input|select|textarea|button)$/i, - rclickable = /^(?:a|area)$/i; - -jQuery.fn.extend( { - prop: function( name, value ) { - return access( this, jQuery.prop, name, value, arguments.length > 1 ); - }, - - removeProp: function( name ) { - return this.each( function() { - delete this[ jQuery.propFix[ name ] || name ]; - } ); - } -} ); - -jQuery.extend( { - prop: function( elem, name, value ) { - var ret, hooks, - nType = elem.nodeType; - - // Don't get/set properties on text, comment and attribute nodes - if ( nType === 3 || nType === 8 || nType === 2 ) { - return; - } - - if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) { - - // Fix name and attach hooks - name = jQuery.propFix[ name ] || name; - hooks = jQuery.propHooks[ name ]; - } - - if ( value !== undefined ) { - if ( hooks && "set" in hooks && - ( ret = hooks.set( elem, value, name ) ) !== undefined ) { - return ret; - } - - return ( elem[ name ] = value ); - } - - if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) { - return ret; - } - - return elem[ name ]; - }, - - propHooks: { - tabIndex: { - get: function( elem ) { - - // Support: IE <=9 - 11 only - // elem.tabIndex doesn't always return the - // correct value when it hasn't been explicitly set - // https://web.archive.org/web/20141116233347/http://fluidproject.org/blog/2008/01/09/getting-setting-and-removing-tabindex-values-with-javascript/ - // Use proper attribute retrieval(#12072) - var tabindex = jQuery.find.attr( elem, "tabindex" ); - - if ( tabindex ) { - return parseInt( tabindex, 10 ); - } - - if ( - rfocusable.test( elem.nodeName ) || - rclickable.test( elem.nodeName ) && - elem.href - ) { - return 0; - } - - return -1; - } - } - }, - - propFix: { - "for": "htmlFor", - "class": "className" - } -} ); - -// Support: IE <=11 only -// Accessing the selectedIndex property -// forces the browser to respect setting selected -// on the option -// The getter ensures a default option is selected -// when in an optgroup -// eslint rule "no-unused-expressions" is disabled for this code -// since it considers such accessions noop -if ( !support.optSelected ) { - jQuery.propHooks.selected = { - get: function( elem ) { - - /* eslint no-unused-expressions: "off" */ - - var parent = elem.parentNode; - if ( parent && parent.parentNode ) { - parent.parentNode.selectedIndex; - } - return null; - }, - set: function( elem ) { - - /* eslint no-unused-expressions: "off" */ - - var parent = elem.parentNode; - if ( parent ) { - parent.selectedIndex; - - if ( parent.parentNode ) { - parent.parentNode.selectedIndex; - } - } - } - }; -} - -jQuery.each( [ - "tabIndex", - "readOnly", - "maxLength", - "cellSpacing", - "cellPadding", - "rowSpan", - "colSpan", - "useMap", - "frameBorder", - "contentEditable" -], function() { - jQuery.propFix[ this.toLowerCase() ] = this; -} ); - - - - - // Strip and collapse whitespace according to HTML spec - // https://infra.spec.whatwg.org/#strip-and-collapse-ascii-whitespace - function stripAndCollapse( value ) { - var tokens = value.match( rnothtmlwhite ) || []; - return tokens.join( " " ); - } - - -function getClass( elem ) { - return elem.getAttribute && elem.getAttribute( "class" ) || ""; -} - -function classesToArray( value ) { - if ( Array.isArray( value ) ) { - return value; - } - if ( typeof value === "string" ) { - return value.match( rnothtmlwhite ) || []; - } - return []; -} - -jQuery.fn.extend( { - addClass: function( value ) { - var classes, elem, cur, curValue, clazz, j, finalValue, - i = 0; - - if ( isFunction( value ) ) { - return this.each( function( j ) { - jQuery( this ).addClass( value.call( this, j, getClass( this ) ) ); - } ); - } - - classes = classesToArray( value ); - - if ( classes.length ) { - while ( ( elem = this[ i++ ] ) ) { - curValue = getClass( elem ); - cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " ); - - if ( cur ) { - j = 0; - while ( ( clazz = classes[ j++ ] ) ) { - if ( cur.indexOf( " " + clazz + " " ) < 0 ) { - cur += clazz + " "; - } - } - - // Only assign if different to avoid unneeded rendering. - finalValue = stripAndCollapse( cur ); - if ( curValue !== finalValue ) { - elem.setAttribute( "class", finalValue ); - } - } - } - } - - return this; - }, - - removeClass: function( value ) { - var classes, elem, cur, curValue, clazz, j, finalValue, - i = 0; - - if ( isFunction( value ) ) { - return this.each( function( j ) { - jQuery( this ).removeClass( value.call( this, j, getClass( this ) ) ); - } ); - } - - if ( !arguments.length ) { - return this.attr( "class", "" ); - } - - classes = classesToArray( value ); - - if ( classes.length ) { - while ( ( elem = this[ i++ ] ) ) { - curValue = getClass( elem ); - - // This expression is here for better compressibility (see addClass) - cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " ); - - if ( cur ) { - j = 0; - while ( ( clazz = classes[ j++ ] ) ) { - - // Remove *all* instances - while ( cur.indexOf( " " + clazz + " " ) > -1 ) { - cur = cur.replace( " " + clazz + " ", " " ); - } - } - - // Only assign if different to avoid unneeded rendering. - finalValue = stripAndCollapse( cur ); - if ( curValue !== finalValue ) { - elem.setAttribute( "class", finalValue ); - } - } - } - } - - return this; - }, - - toggleClass: function( value, stateVal ) { - var type = typeof value, - isValidValue = type === "string" || Array.isArray( value ); - - if ( typeof stateVal === "boolean" && isValidValue ) { - return stateVal ? this.addClass( value ) : this.removeClass( value ); - } - - if ( isFunction( value ) ) { - return this.each( function( i ) { - jQuery( this ).toggleClass( - value.call( this, i, getClass( this ), stateVal ), - stateVal - ); - } ); - } - - return this.each( function() { - var className, i, self, classNames; - - if ( isValidValue ) { - - // Toggle individual class names - i = 0; - self = jQuery( this ); - classNames = classesToArray( value ); - - while ( ( className = classNames[ i++ ] ) ) { - - // Check each className given, space separated list - if ( self.hasClass( className ) ) { - self.removeClass( className ); - } else { - self.addClass( className ); - } - } - - // Toggle whole class name - } else if ( value === undefined || type === "boolean" ) { - className = getClass( this ); - if ( className ) { - - // Store className if set - dataPriv.set( this, "__className__", className ); - } - - // If the element has a class name or if we're passed `false`, - // then remove the whole classname (if there was one, the above saved it). - // Otherwise bring back whatever was previously saved (if anything), - // falling back to the empty string if nothing was stored. - if ( this.setAttribute ) { - this.setAttribute( "class", - className || value === false ? - "" : - dataPriv.get( this, "__className__" ) || "" - ); - } - } - } ); - }, - - hasClass: function( selector ) { - var className, elem, - i = 0; - - className = " " + selector + " "; - while ( ( elem = this[ i++ ] ) ) { - if ( elem.nodeType === 1 && - ( " " + stripAndCollapse( getClass( elem ) ) + " " ).indexOf( className ) > -1 ) { - return true; - } - } - - return false; - } -} ); - - - - -var rreturn = /\r/g; - -jQuery.fn.extend( { - val: function( value ) { - var hooks, ret, valueIsFunction, - elem = this[ 0 ]; - - if ( !arguments.length ) { - if ( elem ) { - hooks = jQuery.valHooks[ elem.type ] || - jQuery.valHooks[ elem.nodeName.toLowerCase() ]; - - if ( hooks && - "get" in hooks && - ( ret = hooks.get( elem, "value" ) ) !== undefined - ) { - return ret; - } - - ret = elem.value; - - // Handle most common string cases - if ( typeof ret === "string" ) { - return ret.replace( rreturn, "" ); - } - - // Handle cases where value is null/undef or number - return ret == null ? "" : ret; - } - - return; - } - - valueIsFunction = isFunction( value ); - - return this.each( function( i ) { - var val; - - if ( this.nodeType !== 1 ) { - return; - } - - if ( valueIsFunction ) { - val = value.call( this, i, jQuery( this ).val() ); - } else { - val = value; - } - - // Treat null/undefined as ""; convert numbers to string - if ( val == null ) { - val = ""; - - } else if ( typeof val === "number" ) { - val += ""; - - } else if ( Array.isArray( val ) ) { - val = jQuery.map( val, function( value ) { - return value == null ? "" : value + ""; - } ); - } - - hooks = jQuery.valHooks[ this.type ] || jQuery.valHooks[ this.nodeName.toLowerCase() ]; - - // If set returns undefined, fall back to normal setting - if ( !hooks || !( "set" in hooks ) || hooks.set( this, val, "value" ) === undefined ) { - this.value = val; - } - } ); - } -} ); - -jQuery.extend( { - valHooks: { - option: { - get: function( elem ) { - - var val = jQuery.find.attr( elem, "value" ); - return val != null ? - val : - - // Support: IE <=10 - 11 only - // option.text throws exceptions (#14686, #14858) - // Strip and collapse whitespace - // https://html.spec.whatwg.org/#strip-and-collapse-whitespace - stripAndCollapse( jQuery.text( elem ) ); - } - }, - select: { - get: function( elem ) { - var value, option, i, - options = elem.options, - index = elem.selectedIndex, - one = elem.type === "select-one", - values = one ? null : [], - max = one ? index + 1 : options.length; - - if ( index < 0 ) { - i = max; - - } else { - i = one ? index : 0; - } - - // Loop through all the selected options - for ( ; i < max; i++ ) { - option = options[ i ]; - - // Support: IE <=9 only - // IE8-9 doesn't update selected after form reset (#2551) - if ( ( option.selected || i === index ) && - - // Don't return options that are disabled or in a disabled optgroup - !option.disabled && - ( !option.parentNode.disabled || - !nodeName( option.parentNode, "optgroup" ) ) ) { - - // Get the specific value for the option - value = jQuery( option ).val(); - - // We don't need an array for one selects - if ( one ) { - return value; - } - - // Multi-Selects return an array - values.push( value ); - } - } - - return values; - }, - - set: function( elem, value ) { - var optionSet, option, - options = elem.options, - values = jQuery.makeArray( value ), - i = options.length; - - while ( i-- ) { - option = options[ i ]; - - /* eslint-disable no-cond-assign */ - - if ( option.selected = - jQuery.inArray( jQuery.valHooks.option.get( option ), values ) > -1 - ) { - optionSet = true; - } - - /* eslint-enable no-cond-assign */ - } - - // Force browsers to behave consistently when non-matching value is set - if ( !optionSet ) { - elem.selectedIndex = -1; - } - return values; - } - } - } -} ); - -// Radios and checkboxes getter/setter -jQuery.each( [ "radio", "checkbox" ], function() { - jQuery.valHooks[ this ] = { - set: function( elem, value ) { - if ( Array.isArray( value ) ) { - return ( elem.checked = jQuery.inArray( jQuery( elem ).val(), value ) > -1 ); - } - } - }; - if ( !support.checkOn ) { - jQuery.valHooks[ this ].get = function( elem ) { - return elem.getAttribute( "value" ) === null ? "on" : elem.value; - }; - } -} ); - - - - -// Return jQuery for attributes-only inclusion - - -support.focusin = "onfocusin" in window; - - -var rfocusMorph = /^(?:focusinfocus|focusoutblur)$/, - stopPropagationCallback = function( e ) { - e.stopPropagation(); - }; - -jQuery.extend( jQuery.event, { - - trigger: function( event, data, elem, onlyHandlers ) { - - var i, cur, tmp, bubbleType, ontype, handle, special, lastElement, - eventPath = [ elem || document ], - type = hasOwn.call( event, "type" ) ? event.type : event, - namespaces = hasOwn.call( event, "namespace" ) ? event.namespace.split( "." ) : []; - - cur = lastElement = tmp = elem = elem || document; - - // Don't do events on text and comment nodes - if ( elem.nodeType === 3 || elem.nodeType === 8 ) { - return; - } - - // focus/blur morphs to focusin/out; ensure we're not firing them right now - if ( rfocusMorph.test( type + jQuery.event.triggered ) ) { - return; - } - - if ( type.indexOf( "." ) > -1 ) { - - // Namespaced trigger; create a regexp to match event type in handle() - namespaces = type.split( "." ); - type = namespaces.shift(); - namespaces.sort(); - } - ontype = type.indexOf( ":" ) < 0 && "on" + type; - - // Caller can pass in a jQuery.Event object, Object, or just an event type string - event = event[ jQuery.expando ] ? - event : - new jQuery.Event( type, typeof event === "object" && event ); - - // Trigger bitmask: & 1 for native handlers; & 2 for jQuery (always true) - event.isTrigger = onlyHandlers ? 2 : 3; - event.namespace = namespaces.join( "." ); - event.rnamespace = event.namespace ? - new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" ) : - null; - - // Clean up the event in case it is being reused - event.result = undefined; - if ( !event.target ) { - event.target = elem; - } - - // Clone any incoming data and prepend the event, creating the handler arg list - data = data == null ? - [ event ] : - jQuery.makeArray( data, [ event ] ); - - // Allow special events to draw outside the lines - special = jQuery.event.special[ type ] || {}; - if ( !onlyHandlers && special.trigger && special.trigger.apply( elem, data ) === false ) { - return; - } - - // Determine event propagation path in advance, per W3C events spec (#9951) - // Bubble up to document, then to window; watch for a global ownerDocument var (#9724) - if ( !onlyHandlers && !special.noBubble && !isWindow( elem ) ) { - - bubbleType = special.delegateType || type; - if ( !rfocusMorph.test( bubbleType + type ) ) { - cur = cur.parentNode; - } - for ( ; cur; cur = cur.parentNode ) { - eventPath.push( cur ); - tmp = cur; - } - - // Only add window if we got to document (e.g., not plain obj or detached DOM) - if ( tmp === ( elem.ownerDocument || document ) ) { - eventPath.push( tmp.defaultView || tmp.parentWindow || window ); - } - } - - // Fire handlers on the event path - i = 0; - while ( ( cur = eventPath[ i++ ] ) && !event.isPropagationStopped() ) { - lastElement = cur; - event.type = i > 1 ? - bubbleType : - special.bindType || type; - - // jQuery handler - handle = ( dataPriv.get( cur, "events" ) || Object.create( null ) )[ event.type ] && - dataPriv.get( cur, "handle" ); - if ( handle ) { - handle.apply( cur, data ); - } - - // Native handler - handle = ontype && cur[ ontype ]; - if ( handle && handle.apply && acceptData( cur ) ) { - event.result = handle.apply( cur, data ); - if ( event.result === false ) { - event.preventDefault(); - } - } - } - event.type = type; - - // If nobody prevented the default action, do it now - if ( !onlyHandlers && !event.isDefaultPrevented() ) { - - if ( ( !special._default || - special._default.apply( eventPath.pop(), data ) === false ) && - acceptData( elem ) ) { - - // Call a native DOM method on the target with the same name as the event. - // Don't do default actions on window, that's where global variables be (#6170) - if ( ontype && isFunction( elem[ type ] ) && !isWindow( elem ) ) { - - // Don't re-trigger an onFOO event when we call its FOO() method - tmp = elem[ ontype ]; - - if ( tmp ) { - elem[ ontype ] = null; - } - - // Prevent re-triggering of the same event, since we already bubbled it above - jQuery.event.triggered = type; - - if ( event.isPropagationStopped() ) { - lastElement.addEventListener( type, stopPropagationCallback ); - } - - elem[ type ](); - - if ( event.isPropagationStopped() ) { - lastElement.removeEventListener( type, stopPropagationCallback ); - } - - jQuery.event.triggered = undefined; - - if ( tmp ) { - elem[ ontype ] = tmp; - } - } - } - } - - return event.result; - }, - - // Piggyback on a donor event to simulate a different one - // Used only for `focus(in | out)` events - simulate: function( type, elem, event ) { - var e = jQuery.extend( - new jQuery.Event(), - event, - { - type: type, - isSimulated: true - } - ); - - jQuery.event.trigger( e, null, elem ); - } - -} ); - -jQuery.fn.extend( { - - trigger: function( type, data ) { - return this.each( function() { - jQuery.event.trigger( type, data, this ); - } ); - }, - triggerHandler: function( type, data ) { - var elem = this[ 0 ]; - if ( elem ) { - return jQuery.event.trigger( type, data, elem, true ); - } - } -} ); - - -// Support: Firefox <=44 -// Firefox doesn't have focus(in | out) events -// Related ticket - https://bugzilla.mozilla.org/show_bug.cgi?id=687787 -// -// Support: Chrome <=48 - 49, Safari <=9.0 - 9.1 -// focus(in | out) events fire after focus & blur events, -// which is spec violation - http://www.w3.org/TR/DOM-Level-3-Events/#events-focusevent-event-order -// Related ticket - https://bugs.chromium.org/p/chromium/issues/detail?id=449857 -if ( !support.focusin ) { - jQuery.each( { focus: "focusin", blur: "focusout" }, function( orig, fix ) { - - // Attach a single capturing handler on the document while someone wants focusin/focusout - var handler = function( event ) { - jQuery.event.simulate( fix, event.target, jQuery.event.fix( event ) ); - }; - - jQuery.event.special[ fix ] = { - setup: function() { - - // Handle: regular nodes (via `this.ownerDocument`), window - // (via `this.document`) & document (via `this`). - var doc = this.ownerDocument || this.document || this, - attaches = dataPriv.access( doc, fix ); - - if ( !attaches ) { - doc.addEventListener( orig, handler, true ); - } - dataPriv.access( doc, fix, ( attaches || 0 ) + 1 ); - }, - teardown: function() { - var doc = this.ownerDocument || this.document || this, - attaches = dataPriv.access( doc, fix ) - 1; - - if ( !attaches ) { - doc.removeEventListener( orig, handler, true ); - dataPriv.remove( doc, fix ); - - } else { - dataPriv.access( doc, fix, attaches ); - } - } - }; - } ); -} -var location = window.location; - -var nonce = { guid: Date.now() }; - -var rquery = ( /\?/ ); - - - -// Cross-browser xml parsing -jQuery.parseXML = function( data ) { - var xml, parserErrorElem; - if ( !data || typeof data !== "string" ) { - return null; - } - - // Support: IE 9 - 11 only - // IE throws on parseFromString with invalid input. - try { - xml = ( new window.DOMParser() ).parseFromString( data, "text/xml" ); - } catch ( e ) {} - - parserErrorElem = xml && xml.getElementsByTagName( "parsererror" )[ 0 ]; - if ( !xml || parserErrorElem ) { - jQuery.error( "Invalid XML: " + ( - parserErrorElem ? - jQuery.map( parserErrorElem.childNodes, function( el ) { - return el.textContent; - } ).join( "\n" ) : - data - ) ); - } - return xml; -}; - - -var - rbracket = /\[\]$/, - rCRLF = /\r?\n/g, - rsubmitterTypes = /^(?:submit|button|image|reset|file)$/i, - rsubmittable = /^(?:input|select|textarea|keygen)/i; - -function buildParams( prefix, obj, traditional, add ) { - var name; - - if ( Array.isArray( obj ) ) { - - // Serialize array item. - jQuery.each( obj, function( i, v ) { - if ( traditional || rbracket.test( prefix ) ) { - - // Treat each array item as a scalar. - add( prefix, v ); - - } else { - - // Item is non-scalar (array or object), encode its numeric index. - buildParams( - prefix + "[" + ( typeof v === "object" && v != null ? i : "" ) + "]", - v, - traditional, - add - ); - } - } ); - - } else if ( !traditional && toType( obj ) === "object" ) { - - // Serialize object item. - for ( name in obj ) { - buildParams( prefix + "[" + name + "]", obj[ name ], traditional, add ); - } - - } else { - - // Serialize scalar item. - add( prefix, obj ); - } -} - -// Serialize an array of form elements or a set of -// key/values into a query string -jQuery.param = function( a, traditional ) { - var prefix, - s = [], - add = function( key, valueOrFunction ) { - - // If value is a function, invoke it and use its return value - var value = isFunction( valueOrFunction ) ? - valueOrFunction() : - valueOrFunction; - - s[ s.length ] = encodeURIComponent( key ) + "=" + - encodeURIComponent( value == null ? "" : value ); - }; - - if ( a == null ) { - return ""; - } - - // If an array was passed in, assume that it is an array of form elements. - if ( Array.isArray( a ) || ( a.jquery && !jQuery.isPlainObject( a ) ) ) { - - // Serialize the form elements - jQuery.each( a, function() { - add( this.name, this.value ); - } ); - - } else { - - // If traditional, encode the "old" way (the way 1.3.2 or older - // did it), otherwise encode params recursively. - for ( prefix in a ) { - buildParams( prefix, a[ prefix ], traditional, add ); - } - } - - // Return the resulting serialization - return s.join( "&" ); -}; - -jQuery.fn.extend( { - serialize: function() { - return jQuery.param( this.serializeArray() ); - }, - serializeArray: function() { - return this.map( function() { - - // Can add propHook for "elements" to filter or add form elements - var elements = jQuery.prop( this, "elements" ); - return elements ? jQuery.makeArray( elements ) : this; - } ).filter( function() { - var type = this.type; - - // Use .is( ":disabled" ) so that fieldset[disabled] works - return this.name && !jQuery( this ).is( ":disabled" ) && - rsubmittable.test( this.nodeName ) && !rsubmitterTypes.test( type ) && - ( this.checked || !rcheckableType.test( type ) ); - } ).map( function( _i, elem ) { - var val = jQuery( this ).val(); - - if ( val == null ) { - return null; - } - - if ( Array.isArray( val ) ) { - return jQuery.map( val, function( val ) { - return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) }; - } ); - } - - return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) }; - } ).get(); - } -} ); - - -var - r20 = /%20/g, - rhash = /#.*$/, - rantiCache = /([?&])_=[^&]*/, - rheaders = /^(.*?):[ \t]*([^\r\n]*)$/mg, - - // #7653, #8125, #8152: local protocol detection - rlocalProtocol = /^(?:about|app|app-storage|.+-extension|file|res|widget):$/, - rnoContent = /^(?:GET|HEAD)$/, - rprotocol = /^\/\//, - - /* Prefilters - * 1) They are useful to introduce custom dataTypes (see ajax/jsonp.js for an example) - * 2) These are called: - * - BEFORE asking for a transport - * - AFTER param serialization (s.data is a string if s.processData is true) - * 3) key is the dataType - * 4) the catchall symbol "*" can be used - * 5) execution will start with transport dataType and THEN continue down to "*" if needed - */ - prefilters = {}, - - /* Transports bindings - * 1) key is the dataType - * 2) the catchall symbol "*" can be used - * 3) selection will start with transport dataType and THEN go to "*" if needed - */ - transports = {}, - - // Avoid comment-prolog char sequence (#10098); must appease lint and evade compression - allTypes = "*/".concat( "*" ), - - // Anchor tag for parsing the document origin - originAnchor = document.createElement( "a" ); - -originAnchor.href = location.href; - -// Base "constructor" for jQuery.ajaxPrefilter and jQuery.ajaxTransport -function addToPrefiltersOrTransports( structure ) { - - // dataTypeExpression is optional and defaults to "*" - return function( dataTypeExpression, func ) { - - if ( typeof dataTypeExpression !== "string" ) { - func = dataTypeExpression; - dataTypeExpression = "*"; - } - - var dataType, - i = 0, - dataTypes = dataTypeExpression.toLowerCase().match( rnothtmlwhite ) || []; - - if ( isFunction( func ) ) { - - // For each dataType in the dataTypeExpression - while ( ( dataType = dataTypes[ i++ ] ) ) { - - // Prepend if requested - if ( dataType[ 0 ] === "+" ) { - dataType = dataType.slice( 1 ) || "*"; - ( structure[ dataType ] = structure[ dataType ] || [] ).unshift( func ); - - // Otherwise append - } else { - ( structure[ dataType ] = structure[ dataType ] || [] ).push( func ); - } - } - } - }; -} - -// Base inspection function for prefilters and transports -function inspectPrefiltersOrTransports( structure, options, originalOptions, jqXHR ) { - - var inspected = {}, - seekingTransport = ( structure === transports ); - - function inspect( dataType ) { - var selected; - inspected[ dataType ] = true; - jQuery.each( structure[ dataType ] || [], function( _, prefilterOrFactory ) { - var dataTypeOrTransport = prefilterOrFactory( options, originalOptions, jqXHR ); - if ( typeof dataTypeOrTransport === "string" && - !seekingTransport && !inspected[ dataTypeOrTransport ] ) { - - options.dataTypes.unshift( dataTypeOrTransport ); - inspect( dataTypeOrTransport ); - return false; - } else if ( seekingTransport ) { - return !( selected = dataTypeOrTransport ); - } - } ); - return selected; - } - - return inspect( options.dataTypes[ 0 ] ) || !inspected[ "*" ] && inspect( "*" ); -} - -// A special extend for ajax options -// that takes "flat" options (not to be deep extended) -// Fixes #9887 -function ajaxExtend( target, src ) { - var key, deep, - flatOptions = jQuery.ajaxSettings.flatOptions || {}; - - for ( key in src ) { - if ( src[ key ] !== undefined ) { - ( flatOptions[ key ] ? target : ( deep || ( deep = {} ) ) )[ key ] = src[ key ]; - } - } - if ( deep ) { - jQuery.extend( true, target, deep ); - } - - return target; -} - -/* Handles responses to an ajax request: - * - finds the right dataType (mediates between content-type and expected dataType) - * - returns the corresponding response - */ -function ajaxHandleResponses( s, jqXHR, responses ) { - - var ct, type, finalDataType, firstDataType, - contents = s.contents, - dataTypes = s.dataTypes; - - // Remove auto dataType and get content-type in the process - while ( dataTypes[ 0 ] === "*" ) { - dataTypes.shift(); - if ( ct === undefined ) { - ct = s.mimeType || jqXHR.getResponseHeader( "Content-Type" ); - } - } - - // Check if we're dealing with a known content-type - if ( ct ) { - for ( type in contents ) { - if ( contents[ type ] && contents[ type ].test( ct ) ) { - dataTypes.unshift( type ); - break; - } - } - } - - // Check to see if we have a response for the expected dataType - if ( dataTypes[ 0 ] in responses ) { - finalDataType = dataTypes[ 0 ]; - } else { - - // Try convertible dataTypes - for ( type in responses ) { - if ( !dataTypes[ 0 ] || s.converters[ type + " " + dataTypes[ 0 ] ] ) { - finalDataType = type; - break; - } - if ( !firstDataType ) { - firstDataType = type; - } - } - - // Or just use first one - finalDataType = finalDataType || firstDataType; - } - - // If we found a dataType - // We add the dataType to the list if needed - // and return the corresponding response - if ( finalDataType ) { - if ( finalDataType !== dataTypes[ 0 ] ) { - dataTypes.unshift( finalDataType ); - } - return responses[ finalDataType ]; - } -} - -/* Chain conversions given the request and the original response - * Also sets the responseXXX fields on the jqXHR instance - */ -function ajaxConvert( s, response, jqXHR, isSuccess ) { - var conv2, current, conv, tmp, prev, - converters = {}, - - // Work with a copy of dataTypes in case we need to modify it for conversion - dataTypes = s.dataTypes.slice(); - - // Create converters map with lowercased keys - if ( dataTypes[ 1 ] ) { - for ( conv in s.converters ) { - converters[ conv.toLowerCase() ] = s.converters[ conv ]; - } - } - - current = dataTypes.shift(); - - // Convert to each sequential dataType - while ( current ) { - - if ( s.responseFields[ current ] ) { - jqXHR[ s.responseFields[ current ] ] = response; - } - - // Apply the dataFilter if provided - if ( !prev && isSuccess && s.dataFilter ) { - response = s.dataFilter( response, s.dataType ); - } - - prev = current; - current = dataTypes.shift(); - - if ( current ) { - - // There's only work to do if current dataType is non-auto - if ( current === "*" ) { - - current = prev; - - // Convert response if prev dataType is non-auto and differs from current - } else if ( prev !== "*" && prev !== current ) { - - // Seek a direct converter - conv = converters[ prev + " " + current ] || converters[ "* " + current ]; - - // If none found, seek a pair - if ( !conv ) { - for ( conv2 in converters ) { - - // If conv2 outputs current - tmp = conv2.split( " " ); - if ( tmp[ 1 ] === current ) { - - // If prev can be converted to accepted input - conv = converters[ prev + " " + tmp[ 0 ] ] || - converters[ "* " + tmp[ 0 ] ]; - if ( conv ) { - - // Condense equivalence converters - if ( conv === true ) { - conv = converters[ conv2 ]; - - // Otherwise, insert the intermediate dataType - } else if ( converters[ conv2 ] !== true ) { - current = tmp[ 0 ]; - dataTypes.unshift( tmp[ 1 ] ); - } - break; - } - } - } - } - - // Apply converter (if not an equivalence) - if ( conv !== true ) { - - // Unless errors are allowed to bubble, catch and return them - if ( conv && s.throws ) { - response = conv( response ); - } else { - try { - response = conv( response ); - } catch ( e ) { - return { - state: "parsererror", - error: conv ? e : "No conversion from " + prev + " to " + current - }; - } - } - } - } - } - } - - return { state: "success", data: response }; -} - -jQuery.extend( { - - // Counter for holding the number of active queries - active: 0, - - // Last-Modified header cache for next request - lastModified: {}, - etag: {}, - - ajaxSettings: { - url: location.href, - type: "GET", - isLocal: rlocalProtocol.test( location.protocol ), - global: true, - processData: true, - async: true, - contentType: "application/x-www-form-urlencoded; charset=UTF-8", - - /* - timeout: 0, - data: null, - dataType: null, - username: null, - password: null, - cache: null, - throws: false, - traditional: false, - headers: {}, - */ - - accepts: { - "*": allTypes, - text: "text/plain", - html: "text/html", - xml: "application/xml, text/xml", - json: "application/json, text/javascript" - }, - - contents: { - xml: /\bxml\b/, - html: /\bhtml/, - json: /\bjson\b/ - }, - - responseFields: { - xml: "responseXML", - text: "responseText", - json: "responseJSON" - }, - - // Data converters - // Keys separate source (or catchall "*") and destination types with a single space - converters: { - - // Convert anything to text - "* text": String, - - // Text to html (true = no transformation) - "text html": true, - - // Evaluate text as a json expression - "text json": JSON.parse, - - // Parse text as xml - "text xml": jQuery.parseXML - }, - - // For options that shouldn't be deep extended: - // you can add your own custom options here if - // and when you create one that shouldn't be - // deep extended (see ajaxExtend) - flatOptions: { - url: true, - context: true - } - }, - - // Creates a full fledged settings object into target - // with both ajaxSettings and settings fields. - // If target is omitted, writes into ajaxSettings. - ajaxSetup: function( target, settings ) { - return settings ? - - // Building a settings object - ajaxExtend( ajaxExtend( target, jQuery.ajaxSettings ), settings ) : - - // Extending ajaxSettings - ajaxExtend( jQuery.ajaxSettings, target ); - }, - - ajaxPrefilter: addToPrefiltersOrTransports( prefilters ), - ajaxTransport: addToPrefiltersOrTransports( transports ), - - // Main method - ajax: function( url, options ) { - - // If url is an object, simulate pre-1.5 signature - if ( typeof url === "object" ) { - options = url; - url = undefined; - } - - // Force options to be an object - options = options || {}; - - var transport, - - // URL without anti-cache param - cacheURL, - - // Response headers - responseHeadersString, - responseHeaders, - - // timeout handle - timeoutTimer, - - // Url cleanup var - urlAnchor, - - // Request state (becomes false upon send and true upon completion) - completed, - - // To know if global events are to be dispatched - fireGlobals, - - // Loop variable - i, - - // uncached part of the url - uncached, - - // Create the final options object - s = jQuery.ajaxSetup( {}, options ), - - // Callbacks context - callbackContext = s.context || s, - - // Context for global events is callbackContext if it is a DOM node or jQuery collection - globalEventContext = s.context && - ( callbackContext.nodeType || callbackContext.jquery ) ? - jQuery( callbackContext ) : - jQuery.event, - - // Deferreds - deferred = jQuery.Deferred(), - completeDeferred = jQuery.Callbacks( "once memory" ), - - // Status-dependent callbacks - statusCode = s.statusCode || {}, - - // Headers (they are sent all at once) - requestHeaders = {}, - requestHeadersNames = {}, - - // Default abort message - strAbort = "canceled", - - // Fake xhr - jqXHR = { - readyState: 0, - - // Builds headers hashtable if needed - getResponseHeader: function( key ) { - var match; - if ( completed ) { - if ( !responseHeaders ) { - responseHeaders = {}; - while ( ( match = rheaders.exec( responseHeadersString ) ) ) { - responseHeaders[ match[ 1 ].toLowerCase() + " " ] = - ( responseHeaders[ match[ 1 ].toLowerCase() + " " ] || [] ) - .concat( match[ 2 ] ); - } - } - match = responseHeaders[ key.toLowerCase() + " " ]; - } - return match == null ? null : match.join( ", " ); - }, - - // Raw string - getAllResponseHeaders: function() { - return completed ? responseHeadersString : null; - }, - - // Caches the header - setRequestHeader: function( name, value ) { - if ( completed == null ) { - name = requestHeadersNames[ name.toLowerCase() ] = - requestHeadersNames[ name.toLowerCase() ] || name; - requestHeaders[ name ] = value; - } - return this; - }, - - // Overrides response content-type header - overrideMimeType: function( type ) { - if ( completed == null ) { - s.mimeType = type; - } - return this; - }, - - // Status-dependent callbacks - statusCode: function( map ) { - var code; - if ( map ) { - if ( completed ) { - - // Execute the appropriate callbacks - jqXHR.always( map[ jqXHR.status ] ); - } else { - - // Lazy-add the new callbacks in a way that preserves old ones - for ( code in map ) { - statusCode[ code ] = [ statusCode[ code ], map[ code ] ]; - } - } - } - return this; - }, - - // Cancel the request - abort: function( statusText ) { - var finalText = statusText || strAbort; - if ( transport ) { - transport.abort( finalText ); - } - done( 0, finalText ); - return this; - } - }; - - // Attach deferreds - deferred.promise( jqXHR ); - - // Add protocol if not provided (prefilters might expect it) - // Handle falsy url in the settings object (#10093: consistency with old signature) - // We also use the url parameter if available - s.url = ( ( url || s.url || location.href ) + "" ) - .replace( rprotocol, location.protocol + "//" ); - - // Alias method option to type as per ticket #12004 - s.type = options.method || options.type || s.method || s.type; - - // Extract dataTypes list - s.dataTypes = ( s.dataType || "*" ).toLowerCase().match( rnothtmlwhite ) || [ "" ]; - - // A cross-domain request is in order when the origin doesn't match the current origin. - if ( s.crossDomain == null ) { - urlAnchor = document.createElement( "a" ); - - // Support: IE <=8 - 11, Edge 12 - 15 - // IE throws exception on accessing the href property if url is malformed, - // e.g. http://example.com:80x/ - try { - urlAnchor.href = s.url; - - // Support: IE <=8 - 11 only - // Anchor's host property isn't correctly set when s.url is relative - urlAnchor.href = urlAnchor.href; - s.crossDomain = originAnchor.protocol + "//" + originAnchor.host !== - urlAnchor.protocol + "//" + urlAnchor.host; - } catch ( e ) { - - // If there is an error parsing the URL, assume it is crossDomain, - // it can be rejected by the transport if it is invalid - s.crossDomain = true; - } - } - - // Convert data if not already a string - if ( s.data && s.processData && typeof s.data !== "string" ) { - s.data = jQuery.param( s.data, s.traditional ); - } - - // Apply prefilters - inspectPrefiltersOrTransports( prefilters, s, options, jqXHR ); - - // If request was aborted inside a prefilter, stop there - if ( completed ) { - return jqXHR; - } - - // We can fire global events as of now if asked to - // Don't fire events if jQuery.event is undefined in an AMD-usage scenario (#15118) - fireGlobals = jQuery.event && s.global; - - // Watch for a new set of requests - if ( fireGlobals && jQuery.active++ === 0 ) { - jQuery.event.trigger( "ajaxStart" ); - } - - // Uppercase the type - s.type = s.type.toUpperCase(); - - // Determine if request has content - s.hasContent = !rnoContent.test( s.type ); - - // Save the URL in case we're toying with the If-Modified-Since - // and/or If-None-Match header later on - // Remove hash to simplify url manipulation - cacheURL = s.url.replace( rhash, "" ); - - // More options handling for requests with no content - if ( !s.hasContent ) { - - // Remember the hash so we can put it back - uncached = s.url.slice( cacheURL.length ); - - // If data is available and should be processed, append data to url - if ( s.data && ( s.processData || typeof s.data === "string" ) ) { - cacheURL += ( rquery.test( cacheURL ) ? "&" : "?" ) + s.data; - - // #9682: remove data so that it's not used in an eventual retry - delete s.data; - } - - // Add or update anti-cache param if needed - if ( s.cache === false ) { - cacheURL = cacheURL.replace( rantiCache, "$1" ); - uncached = ( rquery.test( cacheURL ) ? "&" : "?" ) + "_=" + ( nonce.guid++ ) + - uncached; - } - - // Put hash and anti-cache on the URL that will be requested (gh-1732) - s.url = cacheURL + uncached; - - // Change '%20' to '+' if this is encoded form body content (gh-2658) - } else if ( s.data && s.processData && - ( s.contentType || "" ).indexOf( "application/x-www-form-urlencoded" ) === 0 ) { - s.data = s.data.replace( r20, "+" ); - } - - // Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode. - if ( s.ifModified ) { - if ( jQuery.lastModified[ cacheURL ] ) { - jqXHR.setRequestHeader( "If-Modified-Since", jQuery.lastModified[ cacheURL ] ); - } - if ( jQuery.etag[ cacheURL ] ) { - jqXHR.setRequestHeader( "If-None-Match", jQuery.etag[ cacheURL ] ); - } - } - - // Set the correct header, if data is being sent - if ( s.data && s.hasContent && s.contentType !== false || options.contentType ) { - jqXHR.setRequestHeader( "Content-Type", s.contentType ); - } - - // Set the Accepts header for the server, depending on the dataType - jqXHR.setRequestHeader( - "Accept", - s.dataTypes[ 0 ] && s.accepts[ s.dataTypes[ 0 ] ] ? - s.accepts[ s.dataTypes[ 0 ] ] + - ( s.dataTypes[ 0 ] !== "*" ? ", " + allTypes + "; q=0.01" : "" ) : - s.accepts[ "*" ] - ); - - // Check for headers option - for ( i in s.headers ) { - jqXHR.setRequestHeader( i, s.headers[ i ] ); - } - - // Allow custom headers/mimetypes and early abort - if ( s.beforeSend && - ( s.beforeSend.call( callbackContext, jqXHR, s ) === false || completed ) ) { - - // Abort if not done already and return - return jqXHR.abort(); - } - - // Aborting is no longer a cancellation - strAbort = "abort"; - - // Install callbacks on deferreds - completeDeferred.add( s.complete ); - jqXHR.done( s.success ); - jqXHR.fail( s.error ); - - // Get transport - transport = inspectPrefiltersOrTransports( transports, s, options, jqXHR ); - - // If no transport, we auto-abort - if ( !transport ) { - done( -1, "No Transport" ); - } else { - jqXHR.readyState = 1; - - // Send global event - if ( fireGlobals ) { - globalEventContext.trigger( "ajaxSend", [ jqXHR, s ] ); - } - - // If request was aborted inside ajaxSend, stop there - if ( completed ) { - return jqXHR; - } - - // Timeout - if ( s.async && s.timeout > 0 ) { - timeoutTimer = window.setTimeout( function() { - jqXHR.abort( "timeout" ); - }, s.timeout ); - } - - try { - completed = false; - transport.send( requestHeaders, done ); - } catch ( e ) { - - // Rethrow post-completion exceptions - if ( completed ) { - throw e; - } - - // Propagate others as results - done( -1, e ); - } - } - - // Callback for when everything is done - function done( status, nativeStatusText, responses, headers ) { - var isSuccess, success, error, response, modified, - statusText = nativeStatusText; - - // Ignore repeat invocations - if ( completed ) { - return; - } - - completed = true; - - // Clear timeout if it exists - if ( timeoutTimer ) { - window.clearTimeout( timeoutTimer ); - } - - // Dereference transport for early garbage collection - // (no matter how long the jqXHR object will be used) - transport = undefined; - - // Cache response headers - responseHeadersString = headers || ""; - - // Set readyState - jqXHR.readyState = status > 0 ? 4 : 0; - - // Determine if successful - isSuccess = status >= 200 && status < 300 || status === 304; - - // Get response data - if ( responses ) { - response = ajaxHandleResponses( s, jqXHR, responses ); - } - - // Use a noop converter for missing script but not if jsonp - if ( !isSuccess && - jQuery.inArray( "script", s.dataTypes ) > -1 && - jQuery.inArray( "json", s.dataTypes ) < 0 ) { - s.converters[ "text script" ] = function() {}; - } - - // Convert no matter what (that way responseXXX fields are always set) - response = ajaxConvert( s, response, jqXHR, isSuccess ); - - // If successful, handle type chaining - if ( isSuccess ) { - - // Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode. - if ( s.ifModified ) { - modified = jqXHR.getResponseHeader( "Last-Modified" ); - if ( modified ) { - jQuery.lastModified[ cacheURL ] = modified; - } - modified = jqXHR.getResponseHeader( "etag" ); - if ( modified ) { - jQuery.etag[ cacheURL ] = modified; - } - } - - // if no content - if ( status === 204 || s.type === "HEAD" ) { - statusText = "nocontent"; - - // if not modified - } else if ( status === 304 ) { - statusText = "notmodified"; - - // If we have data, let's convert it - } else { - statusText = response.state; - success = response.data; - error = response.error; - isSuccess = !error; - } - } else { - - // Extract error from statusText and normalize for non-aborts - error = statusText; - if ( status || !statusText ) { - statusText = "error"; - if ( status < 0 ) { - status = 0; - } - } - } - - // Set data for the fake xhr object - jqXHR.status = status; - jqXHR.statusText = ( nativeStatusText || statusText ) + ""; - - // Success/Error - if ( isSuccess ) { - deferred.resolveWith( callbackContext, [ success, statusText, jqXHR ] ); - } else { - deferred.rejectWith( callbackContext, [ jqXHR, statusText, error ] ); - } - - // Status-dependent callbacks - jqXHR.statusCode( statusCode ); - statusCode = undefined; - - if ( fireGlobals ) { - globalEventContext.trigger( isSuccess ? "ajaxSuccess" : "ajaxError", - [ jqXHR, s, isSuccess ? success : error ] ); - } - - // Complete - completeDeferred.fireWith( callbackContext, [ jqXHR, statusText ] ); - - if ( fireGlobals ) { - globalEventContext.trigger( "ajaxComplete", [ jqXHR, s ] ); - - // Handle the global AJAX counter - if ( !( --jQuery.active ) ) { - jQuery.event.trigger( "ajaxStop" ); - } - } - } - - return jqXHR; - }, - - getJSON: function( url, data, callback ) { - return jQuery.get( url, data, callback, "json" ); - }, - - getScript: function( url, callback ) { - return jQuery.get( url, undefined, callback, "script" ); - } -} ); - -jQuery.each( [ "get", "post" ], function( _i, method ) { - jQuery[ method ] = function( url, data, callback, type ) { - - // Shift arguments if data argument was omitted - if ( isFunction( data ) ) { - type = type || callback; - callback = data; - data = undefined; - } - - // The url can be an options object (which then must have .url) - return jQuery.ajax( jQuery.extend( { - url: url, - type: method, - dataType: type, - data: data, - success: callback - }, jQuery.isPlainObject( url ) && url ) ); - }; -} ); - -jQuery.ajaxPrefilter( function( s ) { - var i; - for ( i in s.headers ) { - if ( i.toLowerCase() === "content-type" ) { - s.contentType = s.headers[ i ] || ""; - } - } -} ); - - -jQuery._evalUrl = function( url, options, doc ) { - return jQuery.ajax( { - url: url, - - // Make this explicit, since user can override this through ajaxSetup (#11264) - type: "GET", - dataType: "script", - cache: true, - async: false, - global: false, - - // Only evaluate the response if it is successful (gh-4126) - // dataFilter is not invoked for failure responses, so using it instead - // of the default converter is kludgy but it works. - converters: { - "text script": function() {} - }, - dataFilter: function( response ) { - jQuery.globalEval( response, options, doc ); - } - } ); -}; - - -jQuery.fn.extend( { - wrapAll: function( html ) { - var wrap; - - if ( this[ 0 ] ) { - if ( isFunction( html ) ) { - html = html.call( this[ 0 ] ); - } - - // The elements to wrap the target around - wrap = jQuery( html, this[ 0 ].ownerDocument ).eq( 0 ).clone( true ); - - if ( this[ 0 ].parentNode ) { - wrap.insertBefore( this[ 0 ] ); - } - - wrap.map( function() { - var elem = this; - - while ( elem.firstElementChild ) { - elem = elem.firstElementChild; - } - - return elem; - } ).append( this ); - } - - return this; - }, - - wrapInner: function( html ) { - if ( isFunction( html ) ) { - return this.each( function( i ) { - jQuery( this ).wrapInner( html.call( this, i ) ); - } ); - } - - return this.each( function() { - var self = jQuery( this ), - contents = self.contents(); - - if ( contents.length ) { - contents.wrapAll( html ); - - } else { - self.append( html ); - } - } ); - }, - - wrap: function( html ) { - var htmlIsFunction = isFunction( html ); - - return this.each( function( i ) { - jQuery( this ).wrapAll( htmlIsFunction ? 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t=this._mergeConfigObj(t),t=this._configAfterMerge(t),this._typeCheckConfig(t),t}_configAfterMerge(t){return t}_mergeConfigObj(t,e){const i=Ft(e)?_e.getDataAttribute(e,"config"):{};return{...this.constructor.Default,..."object"==typeof i?i:{},...Ft(e)?_e.getDataAttributes(e):{},..."object"==typeof t?t:{}}}_typeCheckConfig(t,e=this.constructor.DefaultType){for(const[n,s]of Object.entries(e)){const e=t[n],o=Ft(e)?"element":null==(i=e)?`${i}`:Object.prototype.toString.call(i).match(/\s([a-z]+)/i)[1].toLowerCase();if(!new RegExp(s).test(o))throw new TypeError(`${this.constructor.NAME.toUpperCase()}: Option "${n}" provided type "${o}" but expected type "${s}".`)}var i}}class ve extends be{constructor(t,e){super(),(t=Ht(t))&&(this._element=t,this._config=this._getConfig(e),Nt.set(this._element,this.constructor.DATA_KEY,this))}dispose(){Nt.remove(this._element,this.constructor.DATA_KEY),fe.off(this._element,this.constructor.EVENT_KEY);for(const t of 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e?we.find(e):[]}},Ee=(t,e="hide")=>{const i=`click.dismiss${t.EVENT_KEY}`,n=t.NAME;fe.on(document,i,`[data-bs-dismiss="${n}"]`,(function(i){if(["A","AREA"].includes(this.tagName)&&i.preventDefault(),Wt(this))return;const s=we.getElementFromSelector(this)||this.closest(`.${n}`);t.getOrCreateInstance(s)[e]()}))},Ae=".bs.alert",Te=`close${Ae}`,Ce=`closed${Ae}`;class Oe extends ve{static get NAME(){return"alert"}close(){if(fe.trigger(this._element,Te).defaultPrevented)return;this._element.classList.remove("show");const t=this._element.classList.contains("fade");this._queueCallback((()=>this._destroyElement()),this._element,t)}_destroyElement(){this._element.remove(),fe.trigger(this._element,Ce),this.dispose()}static jQueryInterface(t){return this.each((function(){const e=Oe.getOrCreateInstance(this);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}Ee(Oe,"close"),Qt(Oe);const xe='[data-bs-toggle="button"]';class ke extends ve{static get NAME(){return"button"}toggle(){this._element.setAttribute("aria-pressed",this._element.classList.toggle("active"))}static jQueryInterface(t){return this.each((function(){const e=ke.getOrCreateInstance(this);"toggle"===t&&e[t]()}))}}fe.on(document,"click.bs.button.data-api",xe,(t=>{t.preventDefault();const e=t.target.closest(xe);ke.getOrCreateInstance(e).toggle()})),Qt(ke);const Le=".bs.swipe",Se=`touchstart${Le}`,De=`touchmove${Le}`,$e=`touchend${Le}`,Ie=`pointerdown${Le}`,Ne=`pointerup${Le}`,Pe={endCallback:null,leftCallback:null,rightCallback:null},Me={endCallback:"(function|null)",leftCallback:"(function|null)",rightCallback:"(function|null)"};class je extends be{constructor(t,e){super(),this._element=t,t&&je.isSupported()&&(this._config=this._getConfig(e),this._deltaX=0,this._supportPointerEvents=Boolean(window.PointerEvent),this._initEvents())}static get Default(){return Pe}static get DefaultType(){return Me}static get NAME(){return"swipe"}dispose(){fe.off(this._element,Le)}_start(t){this._supportPointerEvents?this._eventIsPointerPenTouch(t)&&(this._deltaX=t.clientX):this._deltaX=t.touches[0].clientX}_end(t){this._eventIsPointerPenTouch(t)&&(this._deltaX=t.clientX-this._deltaX),this._handleSwipe(),Xt(this._config.endCallback)}_move(t){this._deltaX=t.touches&&t.touches.length>1?0:t.touches[0].clientX-this._deltaX}_handleSwipe(){const t=Math.abs(this._deltaX);if(t<=40)return;const e=t/this._deltaX;this._deltaX=0,e&&Xt(e>0?this._config.rightCallback:this._config.leftCallback)}_initEvents(){this._supportPointerEvents?(fe.on(this._element,Ie,(t=>this._start(t))),fe.on(this._element,Ne,(t=>this._end(t))),this._element.classList.add("pointer-event")):(fe.on(this._element,Se,(t=>this._start(t))),fe.on(this._element,De,(t=>this._move(t))),fe.on(this._element,$e,(t=>this._end(t))))}_eventIsPointerPenTouch(t){return this._supportPointerEvents&&("pen"===t.pointerType||"touch"===t.pointerType)}static isSupported(){return"ontouchstart"in document.documentElement||navigator.maxTouchPoints>0}}const Fe=".bs.carousel",He=".data-api",Be="next",We="prev",ze="left",Re="right",qe=`slide${Fe}`,Ve=`slid${Fe}`,Ye=`keydown${Fe}`,Ke=`mouseenter${Fe}`,Qe=`mouseleave${Fe}`,Xe=`dragstart${Fe}`,Ue=`load${Fe}${He}`,Ge=`click${Fe}${He}`,Je="carousel",Ze="active",ti=".active",ei=".carousel-item",ii=ti+ei,ni={ArrowLeft:Re,ArrowRight:ze},si={interval:5e3,keyboard:!0,pause:"hover",ride:!1,touch:!0,wrap:!0},oi={interval:"(number|boolean)",keyboard:"boolean",pause:"(string|boolean)",ride:"(boolean|string)",touch:"boolean",wrap:"boolean"};class ri extends ve{constructor(t,e){super(t,e),this._interval=null,this._activeElement=null,this._isSliding=!1,this.touchTimeout=null,this._swipeHelper=null,this._indicatorsElement=we.findOne(".carousel-indicators",this._element),this._addEventListeners(),this._config.ride===Je&&this.cycle()}static get Default(){return si}static get DefaultType(){return oi}static get 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t.defaultInterval=t.interval,t}_addEventListeners(){this._config.keyboard&&fe.on(this._element,Ye,(t=>this._keydown(t))),"hover"===this._config.pause&&(fe.on(this._element,Ke,(()=>this.pause())),fe.on(this._element,Qe,(()=>this._maybeEnableCycle()))),this._config.touch&&je.isSupported()&&this._addTouchEventListeners()}_addTouchEventListeners(){for(const t of we.find(".carousel-item img",this._element))fe.on(t,Xe,(t=>t.preventDefault()));const t={leftCallback:()=>this._slide(this._directionToOrder(ze)),rightCallback:()=>this._slide(this._directionToOrder(Re)),endCallback:()=>{"hover"===this._config.pause&&(this.pause(),this.touchTimeout&&clearTimeout(this.touchTimeout),this.touchTimeout=setTimeout((()=>this._maybeEnableCycle()),500+this._config.interval))}};this._swipeHelper=new je(this._element,t)}_keydown(t){if(/input|textarea/i.test(t.target.tagName))return;const e=ni[t.key];e&&(t.preventDefault(),this._slide(this._directionToOrder(e)))}_getItemIndex(t){return 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l=n?"carousel-item-start":"carousel-item-end",c=n?"carousel-item-next":"carousel-item-prev";s.classList.add(c),qt(s),i.classList.add(l),s.classList.add(l),this._queueCallback((()=>{s.classList.remove(l,c),s.classList.add(Ze),i.classList.remove(Ze,c,l),this._isSliding=!1,r(Ve)}),i,this._isAnimated()),a&&this.cycle()}_isAnimated(){return this._element.classList.contains("slide")}_getActive(){return we.findOne(ii,this._element)}_getItems(){return we.find(ei,this._element)}_clearInterval(){this._interval&&(clearInterval(this._interval),this._interval=null)}_directionToOrder(t){return Kt()?t===ze?We:Be:t===ze?Be:We}_orderToDirection(t){return Kt()?t===We?ze:Re:t===We?Re:ze}static jQueryInterface(t){return this.each((function(){const e=ri.getOrCreateInstance(this,t);if("number"!=typeof t){if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t]()}}else e.to(t)}))}}fe.on(document,Ge,"[data-bs-slide], 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e=we.getSelectorFromElement(t),i=we.find(e).filter((t=>t===this._element));null!==e&&i.length&&this._triggerArray.push(t)}this._initializeChildren(),this._config.parent||this._addAriaAndCollapsedClass(this._triggerArray,this._isShown()),this._config.toggle&&this.toggle()}static get Default(){return bi}static get DefaultType(){return vi}static get NAME(){return"collapse"}toggle(){this._isShown()?this.hide():this.show()}show(){if(this._isTransitioning||this._isShown())return;let t=[];if(this._config.parent&&(t=this._getFirstLevelChildren(".collapse.show, .collapse.collapsing").filter((t=>t!==this._element)).map((t=>yi.getOrCreateInstance(t,{toggle:!1})))),t.length&&t[0]._isTransitioning)return;if(fe.trigger(this._element,li).defaultPrevented)return;for(const e of t)e.hide();const e=this._getDimension();this._element.classList.remove(pi),this._element.classList.add(mi),this._element.style[e]=0,this._addAriaAndCollapsedClass(this._triggerArray,!0),this._isTransitioning=!0;const i=`scroll${e[0].toUpperCase()+e.slice(1)}`;this._queueCallback((()=>{this._isTransitioning=!1,this._element.classList.remove(mi),this._element.classList.add(pi,fi),this._element.style[e]="",fe.trigger(this._element,ci)}),this._element,!0),this._element.style[e]=`${this._element[i]}px`}hide(){if(this._isTransitioning||!this._isShown())return;if(fe.trigger(this._element,hi).defaultPrevented)return;const t=this._getDimension();this._element.style[t]=`${this._element.getBoundingClientRect()[t]}px`,qt(this._element),this._element.classList.add(mi),this._element.classList.remove(pi,fi);for(const t of this._triggerArray){const e=we.getElementFromSelector(t);e&&!this._isShown(e)&&this._addAriaAndCollapsedClass([t],!1)}this._isTransitioning=!0,this._element.style[t]="",this._queueCallback((()=>{this._isTransitioning=!1,this._element.classList.remove(mi),this._element.classList.add(pi),fe.trigger(this._element,di)}),this._element,!0)}_isShown(t=this._element){return t.classList.contains(fi)}_configAfterMerge(t){return t.toggle=Boolean(t.toggle),t.parent=Ht(t.parent),t}_getDimension(){return this._element.classList.contains("collapse-horizontal")?"width":"height"}_initializeChildren(){if(!this._config.parent)return;const t=this._getFirstLevelChildren(_i);for(const e of t){const t=we.getElementFromSelector(e);t&&this._addAriaAndCollapsedClass([e],this._isShown(t))}}_getFirstLevelChildren(t){const e=we.find(gi,this._config.parent);return we.find(t,this._config.parent).filter((t=>!e.includes(t)))}_addAriaAndCollapsedClass(t,e){if(t.length)for(const i of t)i.classList.toggle("collapsed",!e),i.setAttribute("aria-expanded",e)}static jQueryInterface(t){const e={};return"string"==typeof t&&/show|hide/.test(t)&&(e.toggle=!1),this.each((function(){const i=yi.getOrCreateInstance(this,e);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t]()}}))}}fe.on(document,ui,_i,(function(t){("A"===t.target.tagName||t.delegateTarget&&"A"===t.delegateTarget.tagName)&&t.preventDefault();for(const t of we.getMultipleElementsFromSelector(this))yi.getOrCreateInstance(t,{toggle:!1}).toggle()})),Qt(yi);const wi="dropdown",Ei=".bs.dropdown",Ai=".data-api",Ti="ArrowUp",Ci="ArrowDown",Oi=`hide${Ei}`,xi=`hidden${Ei}`,ki=`show${Ei}`,Li=`shown${Ei}`,Si=`click${Ei}${Ai}`,Di=`keydown${Ei}${Ai}`,$i=`keyup${Ei}${Ai}`,Ii="show",Ni='[data-bs-toggle="dropdown"]:not(.disabled):not(:disabled)',Pi=`${Ni}.${Ii}`,Mi=".dropdown-menu",ji=Kt()?"top-end":"top-start",Fi=Kt()?"top-start":"top-end",Hi=Kt()?"bottom-end":"bottom-start",Bi=Kt()?"bottom-start":"bottom-end",Wi=Kt()?"left-start":"right-start",zi=Kt()?"right-start":"left-start",Ri={autoClose:!0,boundary:"clippingParents",display:"dynamic",offset:[0,2],popperConfig:null,reference:"toggle"},qi={autoClose:"(boolean|string)",boundary:"(string|element)",display:"string",offset:"(array|string|function)",popperConfig:"(null|object|function)",reference:"(string|element|object)"};class Vi extends ve{constructor(t,e){super(t,e),this._popper=null,this._parent=this._element.parentNode,this._menu=we.next(this._element,Mi)[0]||we.prev(this._element,Mi)[0]||we.findOne(Mi,this._parent),this._inNavbar=this._detectNavbar()}static get Default(){return Ri}static get DefaultType(){return qi}static get NAME(){return wi}toggle(){return this._isShown()?this.hide():this.show()}show(){if(Wt(this._element)||this._isShown())return;const t={relatedTarget:this._element};if(!fe.trigger(this._element,ki,t).defaultPrevented){if(this._createPopper(),"ontouchstart"in document.documentElement&&!this._parent.closest(".navbar-nav"))for(const t of[].concat(...document.body.children))fe.on(t,"mouseover",Rt);this._element.focus(),this._element.setAttribute("aria-expanded",!0),this._menu.classList.add(Ii),this._element.classList.add(Ii),fe.trigger(this._element,Li,t)}}hide(){if(Wt(this._element)||!this._isShown())return;const t={relatedTarget:this._element};this._completeHide(t)}dispose(){this._popper&&this._popper.destroy(),super.dispose()}update(){this._inNavbar=this._detectNavbar(),this._popper&&this._popper.update()}_completeHide(t){if(!fe.trigger(this._element,Oi,t).defaultPrevented){if("ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))fe.off(t,"mouseover",Rt);this._popper&&this._popper.destroy(),this._menu.classList.remove(Ii),this._element.classList.remove(Ii),this._element.setAttribute("aria-expanded","false"),_e.removeDataAttribute(this._menu,"popper"),fe.trigger(this._element,xi,t)}}_getConfig(t){if("object"==typeof(t=super._getConfig(t)).reference&&!Ft(t.reference)&&"function"!=typeof t.reference.getBoundingClientRect)throw new TypeError(`${wi.toUpperCase()}: Option "reference" provided type "object" without a required "getBoundingClientRect" method.`);return t}_createPopper(){if(void 0===e)throw new TypeError("Bootstrap's dropdowns require Popper 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t={placement:this._getPlacement(),modifiers:[{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"offset",options:{offset:this._getOffset()}}]};return(this._inNavbar||"static"===this._config.display)&&(_e.setDataAttribute(this._menu,"popper","static"),t.modifiers=[{name:"applyStyles",enabled:!1}]),{...t,...Xt(this._config.popperConfig,[t])}}_selectMenuItem({key:t,target:e}){const i=we.find(".dropdown-menu .dropdown-item:not(.disabled):not(:disabled)",this._menu).filter((t=>Bt(t)));i.length&&Gt(i,e,t===Ci,!i.includes(e)).focus()}static jQueryInterface(t){return this.each((function(){const e=Vi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}static clearMenus(t){if(2===t.button||"keyup"===t.type&&"Tab"!==t.key)return;const e=we.find(Pi);for(const i of e){const e=Vi.getInstance(i);if(!e||!1===e._config.autoClose)continue;const n=t.composedPath(),s=n.includes(e._menu);if(n.includes(e._element)||"inside"===e._config.autoClose&&!s||"outside"===e._config.autoClose&&s)continue;if(e._menu.contains(t.target)&&("keyup"===t.type&&"Tab"===t.key||/input|select|option|textarea|form/i.test(t.target.tagName)))continue;const o={relatedTarget:e._element};"click"===t.type&&(o.clickEvent=t),e._completeHide(o)}}static dataApiKeydownHandler(t){const e=/input|textarea/i.test(t.target.tagName),i="Escape"===t.key,n=[Ti,Ci].includes(t.key);if(!n&&!i)return;if(e&&!i)return;t.preventDefault();const s=this.matches(Ni)?this:we.prev(this,Ni)[0]||we.next(this,Ni)[0]||we.findOne(Ni,t.delegateTarget.parentNode),o=Vi.getOrCreateInstance(s);if(n)return t.stopPropagation(),o.show(),void o._selectMenuItem(t);o._isShown()&&(t.stopPropagation(),o.hide(),s.focus())}}fe.on(document,Di,Ni,Vi.dataApiKeydownHandler),fe.on(document,Di,Mi,Vi.dataApiKeydownHandler),fe.on(document,Si,Vi.clearMenus),fe.on(document,$i,Vi.clearMenus),fe.on(document,Si,Ni,(function(t){t.preventDefault(),Vi.getOrCreateInstance(this).toggle()})),Qt(Vi);const Yi="backdrop",Ki="show",Qi=`mousedown.bs.${Yi}`,Xi={className:"modal-backdrop",clickCallback:null,isAnimated:!1,isVisible:!0,rootElement:"body"},Ui={className:"string",clickCallback:"(function|null)",isAnimated:"boolean",isVisible:"boolean",rootElement:"(element|string)"};class Gi extends be{constructor(t){super(),this._config=this._getConfig(t),this._isAppended=!1,this._element=null}static get Default(){return Xi}static get DefaultType(){return Ui}static get NAME(){return Yi}show(t){if(!this._config.isVisible)return void Xt(t);this._append();const e=this._getElement();this._config.isAnimated&&qt(e),e.classList.add(Ki),this._emulateAnimation((()=>{Xt(t)}))}hide(t){this._config.isVisible?(this._getElement().classList.remove(Ki),this._emulateAnimation((()=>{this.dispose(),Xt(t)}))):Xt(t)}dispose(){this._isAppended&&(fe.off(this._element,Qi),this._element.remove(),this._isAppended=!1)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_configAfterMerge(t){return t.rootElement=Ht(t.rootElement),t}_append(){if(this._isAppended)return;const t=this._getElement();this._config.rootElement.append(t),fe.on(t,Qi,(()=>{Xt(this._config.clickCallback)})),this._isAppended=!0}_emulateAnimation(t){Ut(t,this._getElement(),this._config.isAnimated)}}const Ji=".bs.focustrap",Zi=`focusin${Ji}`,tn=`keydown.tab${Ji}`,en="backward",nn={autofocus:!0,trapElement:null},sn={autofocus:"boolean",trapElement:"element"};class on extends be{constructor(t){super(),this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}static get Default(){return nn}static get DefaultType(){return sn}static get NAME(){return"focustrap"}activate(){this._isActive||(this._config.autofocus&&this._config.trapElement.focus(),fe.off(document,Ji),fe.on(document,Zi,(t=>this._handleFocusin(t))),fe.on(document,tn,(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,fe.off(document,Ji))}_handleFocusin(t){const{trapElement:e}=this._config;if(t.target===document||t.target===e||e.contains(t.target))return;const i=we.focusableChildren(e);0===i.length?e.focus():this._lastTabNavDirection===en?i[i.length-1].focus():i[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?en:"forward")}}const rn=".fixed-top, .fixed-bottom, .is-fixed, .sticky-top",an=".sticky-top",ln="padding-right",cn="margin-right";class hn{constructor(){this._element=document.body}getWidth(){const t=document.documentElement.clientWidth;return Math.abs(window.innerWidth-t)}hide(){const t=this.getWidth();this._disableOverFlow(),this._setElementAttributes(this._element,ln,(e=>e+t)),this._setElementAttributes(rn,ln,(e=>e+t)),this._setElementAttributes(an,cn,(e=>e-t))}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,ln),this._resetElementAttributes(rn,ln),this._resetElementAttributes(an,cn)}isOverflowing(){return this.getWidth()>0}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,(t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t).getPropertyValue(e);t.style.setProperty(e,`${i(Number.parseFloat(s))}px`)}))}_saveInitialAttribute(t,e){const i=t.style.getPropertyValue(e);i&&_e.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=_e.getDataAttribute(t,e);null!==i?(_e.removeDataAttribute(t,e),t.style.setProperty(e,i)):t.style.removeProperty(e)}))}_applyManipulationCallback(t,e){if(Ft(t))e(t);else for(const i of we.find(t,this._element))e(i)}}const dn=".bs.modal",un=`hide${dn}`,fn=`hidePrevented${dn}`,pn=`hidden${dn}`,mn=`show${dn}`,gn=`shown${dn}`,_n=`resize${dn}`,bn=`click.dismiss${dn}`,vn=`mousedown.dismiss${dn}`,yn=`keydown.dismiss${dn}`,wn=`click${dn}.data-api`,En="modal-open",An="show",Tn="modal-static",Cn={backdrop:!0,focus:!0,keyboard:!0},On={backdrop:"(boolean|string)",focus:"boolean",keyboard:"boolean"};class xn extends ve{constructor(t,e){super(t,e),this._dialog=we.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._isTransitioning=!1,this._scrollBar=new hn,this._addEventListeners()}static get Default(){return Cn}static get DefaultType(){return On}static get NAME(){return"modal"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||fe.trigger(this._element,mn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isTransitioning=!0,this._scrollBar.hide(),document.body.classList.add(En),this._adjustDialog(),this._backdrop.show((()=>this._showElement(t))))}hide(){this._isShown&&!this._isTransitioning&&(fe.trigger(this._element,un).defaultPrevented||(this._isShown=!1,this._isTransitioning=!0,this._focustrap.deactivate(),this._element.classList.remove(An),this._queueCallback((()=>this._hideModal()),this._element,this._isAnimated())))}dispose(){fe.off(window,dn),fe.off(this._dialog,dn),this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new Gi({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new on({trapElement:this._element})}_showElement(t){document.body.contains(this._element)||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0;const e=we.findOne(".modal-body",this._dialog);e&&(e.scrollTop=0),qt(this._element),this._element.classList.add(An),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,fe.trigger(this._element,gn,{relatedTarget:t})}),this._dialog,this._isAnimated())}_addEventListeners(){fe.on(this._element,yn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():this._triggerBackdropTransition())})),fe.on(window,_n,(()=>{this._isShown&&!this._isTransitioning&&this._adjustDialog()})),fe.on(this._element,vn,(t=>{fe.one(this._element,bn,(e=>{this._element===t.target&&this._element===e.target&&("static"!==this._config.backdrop?this._config.backdrop&&this.hide():this._triggerBackdropTransition())}))}))}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(En),this._resetAdjustments(),this._scrollBar.reset(),fe.trigger(this._element,pn)}))}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(fe.trigger(this._element,fn).defaultPrevented)return;const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._element.style.overflowY;"hidden"===e||this._element.classList.contains(Tn)||(t||(this._element.style.overflowY="hidden"),this._element.classList.add(Tn),this._queueCallback((()=>{this._element.classList.remove(Tn),this._queueCallback((()=>{this._element.style.overflowY=e}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;if(i&&!t){const t=Kt()?"paddingLeft":"paddingRight";this._element.style[t]=`${e}px`}if(!i&&t){const t=Kt()?"paddingRight":"paddingLeft";this._element.style[t]=`${e}px`}}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=xn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}fe.on(document,wn,'[data-bs-toggle="modal"]',(function(t){const e=we.getElementFromSelector(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),fe.one(e,mn,(t=>{t.defaultPrevented||fe.one(e,pn,(()=>{Bt(this)&&this.focus()}))}));const i=we.findOne(".modal.show");i&&xn.getInstance(i).hide(),xn.getOrCreateInstance(e).toggle(this)})),Ee(xn),Qt(xn);const kn=".bs.offcanvas",Ln=".data-api",Sn=`load${kn}${Ln}`,Dn="show",$n="showing",In="hiding",Nn=".offcanvas.show",Pn=`show${kn}`,Mn=`shown${kn}`,jn=`hide${kn}`,Fn=`hidePrevented${kn}`,Hn=`hidden${kn}`,Bn=`resize${kn}`,Wn=`click${kn}${Ln}`,zn=`keydown.dismiss${kn}`,Rn={backdrop:!0,keyboard:!0,scroll:!1},qn={backdrop:"(boolean|string)",keyboard:"boolean",scroll:"boolean"};class Vn extends ve{constructor(t,e){super(t,e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get Default(){return Rn}static get DefaultType(){return qn}static get NAME(){return"offcanvas"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||fe.trigger(this._element,Pn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._backdrop.show(),this._config.scroll||(new hn).hide(),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add($n),this._queueCallback((()=>{this._config.scroll&&!this._config.backdrop||this._focustrap.activate(),this._element.classList.add(Dn),this._element.classList.remove($n),fe.trigger(this._element,Mn,{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(fe.trigger(this._element,jn).defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.add(In),this._backdrop.hide(),this._queueCallback((()=>{this._element.classList.remove(Dn,In),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._config.scroll||(new hn).reset(),fe.trigger(this._element,Hn)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_initializeBackDrop(){const t=Boolean(this._config.backdrop);return new Gi({className:"offcanvas-backdrop",isVisible:t,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:t?()=>{"static"!==this._config.backdrop?this.hide():fe.trigger(this._element,Fn)}:null})}_initializeFocusTrap(){return new on({trapElement:this._element})}_addEventListeners(){fe.on(this._element,zn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():fe.trigger(this._element,Fn))}))}static jQueryInterface(t){return this.each((function(){const e=Vn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}fe.on(document,Wn,'[data-bs-toggle="offcanvas"]',(function(t){const e=we.getElementFromSelector(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),Wt(this))return;fe.one(e,Hn,(()=>{Bt(this)&&this.focus()}));const i=we.findOne(Nn);i&&i!==e&&Vn.getInstance(i).hide(),Vn.getOrCreateInstance(e).toggle(this)})),fe.on(window,Sn,(()=>{for(const t of we.find(Nn))Vn.getOrCreateInstance(t).show()})),fe.on(window,Bn,(()=>{for(const t of we.find("[aria-modal][class*=show][class*=offcanvas-]"))"fixed"!==getComputedStyle(t).position&&Vn.getOrCreateInstance(t).hide()})),Ee(Vn),Qt(Vn);const Yn={"*":["class","dir","id","lang","role",/^aria-[\w-]*$/i],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],div:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]},Kn=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Qn=/^(?!javascript:)(?:[a-z0-9+.-]+:|[^&:/?#]*(?:[/?#]|$))/i,Xn=(t,e)=>{const i=t.nodeName.toLowerCase();return e.includes(i)?!Kn.has(i)||Boolean(Qn.test(t.nodeValue)):e.filter((t=>t instanceof RegExp)).some((t=>t.test(i)))},Un={allowList:Yn,content:{},extraClass:"",html:!1,sanitize:!0,sanitizeFn:null,template:"
"},Gn={allowList:"object",content:"object",extraClass:"(string|function)",html:"boolean",sanitize:"boolean",sanitizeFn:"(null|function)",template:"string"},Jn={entry:"(string|element|function|null)",selector:"(string|element)"};class Zn extends be{constructor(t){super(),this._config=this._getConfig(t)}static get Default(){return Un}static get DefaultType(){return Gn}static get NAME(){return"TemplateFactory"}getContent(){return Object.values(this._config.content).map((t=>this._resolvePossibleFunction(t))).filter(Boolean)}hasContent(){return this.getContent().length>0}changeContent(t){return this._checkContent(t),this._config.content={...this._config.content,...t},this}toHtml(){const t=document.createElement("div");t.innerHTML=this._maybeSanitize(this._config.template);for(const[e,i]of Object.entries(this._config.content))this._setContent(t,i,e);const e=t.children[0],i=this._resolvePossibleFunction(this._config.extraClass);return i&&e.classList.add(...i.split(" ")),e}_typeCheckConfig(t){super._typeCheckConfig(t),this._checkContent(t.content)}_checkContent(t){for(const[e,i]of Object.entries(t))super._typeCheckConfig({selector:e,entry:i},Jn)}_setContent(t,e,i){const n=we.findOne(i,t);n&&((e=this._resolvePossibleFunction(e))?Ft(e)?this._putElementInTemplate(Ht(e),n):this._config.html?n.innerHTML=this._maybeSanitize(e):n.textContent=e:n.remove())}_maybeSanitize(t){return this._config.sanitize?function(t,e,i){if(!t.length)return t;if(i&&"function"==typeof i)return i(t);const n=(new window.DOMParser).parseFromString(t,"text/html"),s=[].concat(...n.body.querySelectorAll("*"));for(const t of s){const i=t.nodeName.toLowerCase();if(!Object.keys(e).includes(i)){t.remove();continue}const n=[].concat(...t.attributes),s=[].concat(e["*"]||[],e[i]||[]);for(const e of n)Xn(e,s)||t.removeAttribute(e.nodeName)}return n.body.innerHTML}(t,this._config.allowList,this._config.sanitizeFn):t}_resolvePossibleFunction(t){return Xt(t,[this])}_putElementInTemplate(t,e){if(this._config.html)return e.innerHTML="",void e.append(t);e.textContent=t.textContent}}const ts=new Set(["sanitize","allowList","sanitizeFn"]),es="fade",is="show",ns=".modal",ss="hide.bs.modal",os="hover",rs="focus",as={AUTO:"auto",TOP:"top",RIGHT:Kt()?"left":"right",BOTTOM:"bottom",LEFT:Kt()?"right":"left"},ls={allowList:Yn,animation:!0,boundary:"clippingParents",container:!1,customClass:"",delay:0,fallbackPlacements:["top","right","bottom","left"],html:!1,offset:[0,6],placement:"top",popperConfig:null,sanitize:!0,sanitizeFn:null,selector:!1,template:'',title:"",trigger:"hover focus"},cs={allowList:"object",animation:"boolean",boundary:"(string|element)",container:"(string|element|boolean)",customClass:"(string|function)",delay:"(number|object)",fallbackPlacements:"array",html:"boolean",offset:"(array|string|function)",placement:"(string|function)",popperConfig:"(null|object|function)",sanitize:"boolean",sanitizeFn:"(null|function)",selector:"(string|boolean)",template:"string",title:"(string|element|function)",trigger:"string"};class hs extends ve{constructor(t,i){if(void 0===e)throw new TypeError("Bootstrap's tooltips require Popper (https://popper.js.org)");super(t,i),this._isEnabled=!0,this._timeout=0,this._isHovered=null,this._activeTrigger={},this._popper=null,this._templateFactory=null,this._newContent=null,this.tip=null,this._setListeners(),this._config.selector||this._fixTitle()}static get Default(){return ls}static get DefaultType(){return cs}static get NAME(){return"tooltip"}enable(){this._isEnabled=!0}disable(){this._isEnabled=!1}toggleEnabled(){this._isEnabled=!this._isEnabled}toggle(){this._isEnabled&&(this._activeTrigger.click=!this._activeTrigger.click,this._isShown()?this._leave():this._enter())}dispose(){clearTimeout(this._timeout),fe.off(this._element.closest(ns),ss,this._hideModalHandler),this._element.getAttribute("data-bs-original-title")&&this._element.setAttribute("title",this._element.getAttribute("data-bs-original-title")),this._disposePopper(),super.dispose()}show(){if("none"===this._element.style.display)throw new Error("Please use show on visible elements");if(!this._isWithContent()||!this._isEnabled)return;const t=fe.trigger(this._element,this.constructor.eventName("show")),e=(zt(this._element)||this._element.ownerDocument.documentElement).contains(this._element);if(t.defaultPrevented||!e)return;this._disposePopper();const i=this._getTipElement();this._element.setAttribute("aria-describedby",i.getAttribute("id"));const{container:n}=this._config;if(this._element.ownerDocument.documentElement.contains(this.tip)||(n.append(i),fe.trigger(this._element,this.constructor.eventName("inserted"))),this._popper=this._createPopper(i),i.classList.add(is),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))fe.on(t,"mouseover",Rt);this._queueCallback((()=>{fe.trigger(this._element,this.constructor.eventName("shown")),!1===this._isHovered&&this._leave(),this._isHovered=!1}),this.tip,this._isAnimated())}hide(){if(this._isShown()&&!fe.trigger(this._element,this.constructor.eventName("hide")).defaultPrevented){if(this._getTipElement().classList.remove(is),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))fe.off(t,"mouseover",Rt);this._activeTrigger.click=!1,this._activeTrigger[rs]=!1,this._activeTrigger[os]=!1,this._isHovered=null,this._queueCallback((()=>{this._isWithActiveTrigger()||(this._isHovered||this._disposePopper(),this._element.removeAttribute("aria-describedby"),fe.trigger(this._element,this.constructor.eventName("hidden")))}),this.tip,this._isAnimated())}}update(){this._popper&&this._popper.update()}_isWithContent(){return Boolean(this._getTitle())}_getTipElement(){return this.tip||(this.tip=this._createTipElement(this._newContent||this._getContentForTemplate())),this.tip}_createTipElement(t){const e=this._getTemplateFactory(t).toHtml();if(!e)return null;e.classList.remove(es,is),e.classList.add(`bs-${this.constructor.NAME}-auto`);const i=(t=>{do{t+=Math.floor(1e6*Math.random())}while(document.getElementById(t));return t})(this.constructor.NAME).toString();return e.setAttribute("id",i),this._isAnimated()&&e.classList.add(es),e}setContent(t){this._newContent=t,this._isShown()&&(this._disposePopper(),this.show())}_getTemplateFactory(t){return this._templateFactory?this._templateFactory.changeContent(t):this._templateFactory=new Zn({...this._config,content:t,extraClass:this._resolvePossibleFunction(this._config.customClass)}),this._templateFactory}_getContentForTemplate(){return{".tooltip-inner":this._getTitle()}}_getTitle(){return this._resolvePossibleFunction(this._config.title)||this._element.getAttribute("data-bs-original-title")}_initializeOnDelegatedTarget(t){return this.constructor.getOrCreateInstance(t.delegateTarget,this._getDelegateConfig())}_isAnimated(){return this._config.animation||this.tip&&this.tip.classList.contains(es)}_isShown(){return this.tip&&this.tip.classList.contains(is)}_createPopper(t){const e=Xt(this._config.placement,[this,t,this._element]),i=as[e.toUpperCase()];return Dt(this._element,t,this._getPopperConfig(i))}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_resolvePossibleFunction(t){return Xt(t,[this._element])}_getPopperConfig(t){const e={placement:t,modifiers:[{name:"flip",options:{fallbackPlacements:this._config.fallbackPlacements}},{name:"offset",options:{offset:this._getOffset()}},{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"arrow",options:{element:`.${this.constructor.NAME}-arrow`}},{name:"preSetPlacement",enabled:!0,phase:"beforeMain",fn:t=>{this._getTipElement().setAttribute("data-popper-placement",t.state.placement)}}]};return{...e,...Xt(this._config.popperConfig,[e])}}_setListeners(){const t=this._config.trigger.split(" ");for(const e of t)if("click"===e)fe.on(this._element,this.constructor.eventName("click"),this._config.selector,(t=>{this._initializeOnDelegatedTarget(t).toggle()}));else if("manual"!==e){const t=e===os?this.constructor.eventName("mouseenter"):this.constructor.eventName("focusin"),i=e===os?this.constructor.eventName("mouseleave"):this.constructor.eventName("focusout");fe.on(this._element,t,this._config.selector,(t=>{const e=this._initializeOnDelegatedTarget(t);e._activeTrigger["focusin"===t.type?rs:os]=!0,e._enter()})),fe.on(this._element,i,this._config.selector,(t=>{const e=this._initializeOnDelegatedTarget(t);e._activeTrigger["focusout"===t.type?rs:os]=e._element.contains(t.relatedTarget),e._leave()}))}this._hideModalHandler=()=>{this._element&&this.hide()},fe.on(this._element.closest(ns),ss,this._hideModalHandler)}_fixTitle(){const t=this._element.getAttribute("title");t&&(this._element.getAttribute("aria-label")||this._element.textContent.trim()||this._element.setAttribute("aria-label",t),this._element.setAttribute("data-bs-original-title",t),this._element.removeAttribute("title"))}_enter(){this._isShown()||this._isHovered?this._isHovered=!0:(this._isHovered=!0,this._setTimeout((()=>{this._isHovered&&this.show()}),this._config.delay.show))}_leave(){this._isWithActiveTrigger()||(this._isHovered=!1,this._setTimeout((()=>{this._isHovered||this.hide()}),this._config.delay.hide))}_setTimeout(t,e){clearTimeout(this._timeout),this._timeout=setTimeout(t,e)}_isWithActiveTrigger(){return Object.values(this._activeTrigger).includes(!0)}_getConfig(t){const e=_e.getDataAttributes(this._element);for(const t of Object.keys(e))ts.has(t)&&delete e[t];return t={...e,..."object"==typeof t&&t?t:{}},t=this._mergeConfigObj(t),t=this._configAfterMerge(t),this._typeCheckConfig(t),t}_configAfterMerge(t){return t.container=!1===t.container?document.body:Ht(t.container),"number"==typeof t.delay&&(t.delay={show:t.delay,hide:t.delay}),"number"==typeof t.title&&(t.title=t.title.toString()),"number"==typeof t.content&&(t.content=t.content.toString()),t}_getDelegateConfig(){const t={};for(const[e,i]of Object.entries(this._config))this.constructor.Default[e]!==i&&(t[e]=i);return t.selector=!1,t.trigger="manual",t}_disposePopper(){this._popper&&(this._popper.destroy(),this._popper=null),this.tip&&(this.tip.remove(),this.tip=null)}static jQueryInterface(t){return this.each((function(){const e=hs.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}Qt(hs);const ds={...hs.Default,content:"",offset:[0,8],placement:"right",template:'',trigger:"click"},us={...hs.DefaultType,content:"(null|string|element|function)"};class fs extends hs{static get Default(){return ds}static get DefaultType(){return us}static get NAME(){return"popover"}_isWithContent(){return this._getTitle()||this._getContent()}_getContentForTemplate(){return{".popover-header":this._getTitle(),".popover-body":this._getContent()}}_getContent(){return this._resolvePossibleFunction(this._config.content)}static jQueryInterface(t){return this.each((function(){const e=fs.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}Qt(fs);const ps=".bs.scrollspy",ms=`activate${ps}`,gs=`click${ps}`,_s=`load${ps}.data-api`,bs="active",vs="[href]",ys=".nav-link",ws=`${ys}, .nav-item > ${ys}, .list-group-item`,Es={offset:null,rootMargin:"0px 0px -25%",smoothScroll:!1,target:null,threshold:[.1,.5,1]},As={offset:"(number|null)",rootMargin:"string",smoothScroll:"boolean",target:"element",threshold:"array"};class Ts extends ve{constructor(t,e){super(t,e),this._targetLinks=new Map,this._observableSections=new Map,this._rootElement="visible"===getComputedStyle(this._element).overflowY?null:this._element,this._activeTarget=null,this._observer=null,this._previousScrollData={visibleEntryTop:0,parentScrollTop:0},this.refresh()}static get Default(){return Es}static get DefaultType(){return As}static get NAME(){return"scrollspy"}refresh(){this._initializeTargetsAndObservables(),this._maybeEnableSmoothScroll(),this._observer?this._observer.disconnect():this._observer=this._getNewObserver();for(const t of this._observableSections.values())this._observer.observe(t)}dispose(){this._observer.disconnect(),super.dispose()}_configAfterMerge(t){return t.target=Ht(t.target)||document.body,t.rootMargin=t.offset?`${t.offset}px 0px -30%`:t.rootMargin,"string"==typeof t.threshold&&(t.threshold=t.threshold.split(",").map((t=>Number.parseFloat(t)))),t}_maybeEnableSmoothScroll(){this._config.smoothScroll&&(fe.off(this._config.target,gs),fe.on(this._config.target,gs,vs,(t=>{const e=this._observableSections.get(t.target.hash);if(e){t.preventDefault();const i=this._rootElement||window,n=e.offsetTop-this._element.offsetTop;if(i.scrollTo)return void i.scrollTo({top:n,behavior:"smooth"});i.scrollTop=n}})))}_getNewObserver(){const t={root:this._rootElement,threshold:this._config.threshold,rootMargin:this._config.rootMargin};return new IntersectionObserver((t=>this._observerCallback(t)),t)}_observerCallback(t){const e=t=>this._targetLinks.get(`#${t.target.id}`),i=t=>{this._previousScrollData.visibleEntryTop=t.target.offsetTop,this._process(e(t))},n=(this._rootElement||document.documentElement).scrollTop,s=n>=this._previousScrollData.parentScrollTop;this._previousScrollData.parentScrollTop=n;for(const o of t){if(!o.isIntersecting){this._activeTarget=null,this._clearActiveClass(e(o));continue}const t=o.target.offsetTop>=this._previousScrollData.visibleEntryTop;if(s&&t){if(i(o),!n)return}else s||t||i(o)}}_initializeTargetsAndObservables(){this._targetLinks=new Map,this._observableSections=new Map;const t=we.find(vs,this._config.target);for(const e of t){if(!e.hash||Wt(e))continue;const t=we.findOne(decodeURI(e.hash),this._element);Bt(t)&&(this._targetLinks.set(decodeURI(e.hash),e),this._observableSections.set(e.hash,t))}}_process(t){this._activeTarget!==t&&(this._clearActiveClass(this._config.target),this._activeTarget=t,t.classList.add(bs),this._activateParents(t),fe.trigger(this._element,ms,{relatedTarget:t}))}_activateParents(t){if(t.classList.contains("dropdown-item"))we.findOne(".dropdown-toggle",t.closest(".dropdown")).classList.add(bs);else for(const e of we.parents(t,".nav, .list-group"))for(const t of we.prev(e,ws))t.classList.add(bs)}_clearActiveClass(t){t.classList.remove(bs);const e=we.find(`${vs}.${bs}`,t);for(const t of e)t.classList.remove(bs)}static jQueryInterface(t){return this.each((function(){const e=Ts.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t]()}}))}}fe.on(window,_s,(()=>{for(const t of we.find('[data-bs-spy="scroll"]'))Ts.getOrCreateInstance(t)})),Qt(Ts);const Cs=".bs.tab",Os=`hide${Cs}`,xs=`hidden${Cs}`,ks=`show${Cs}`,Ls=`shown${Cs}`,Ss=`click${Cs}`,Ds=`keydown${Cs}`,$s=`load${Cs}`,Is="ArrowLeft",Ns="ArrowRight",Ps="ArrowUp",Ms="ArrowDown",js="Home",Fs="End",Hs="active",Bs="fade",Ws="show",zs=".dropdown-toggle",Rs=`:not(${zs})`,qs='[data-bs-toggle="tab"], [data-bs-toggle="pill"], [data-bs-toggle="list"]',Vs=`.nav-link${Rs}, .list-group-item${Rs}, [role="tab"]${Rs}, ${qs}`,Ys=`.${Hs}[data-bs-toggle="tab"], .${Hs}[data-bs-toggle="pill"], .${Hs}[data-bs-toggle="list"]`;class Ks extends ve{constructor(t){super(t),this._parent=this._element.closest('.list-group, .nav, [role="tablist"]'),this._parent&&(this._setInitialAttributes(this._parent,this._getChildren()),fe.on(this._element,Ds,(t=>this._keydown(t))))}static get NAME(){return"tab"}show(){const t=this._element;if(this._elemIsActive(t))return;const e=this._getActiveElem(),i=e?fe.trigger(e,Os,{relatedTarget:t}):null;fe.trigger(t,ks,{relatedTarget:e}).defaultPrevented||i&&i.defaultPrevented||(this._deactivate(e,t),this._activate(t,e))}_activate(t,e){t&&(t.classList.add(Hs),this._activate(we.getElementFromSelector(t)),this._queueCallback((()=>{"tab"===t.getAttribute("role")?(t.removeAttribute("tabindex"),t.setAttribute("aria-selected",!0),this._toggleDropDown(t,!0),fe.trigger(t,Ls,{relatedTarget:e})):t.classList.add(Ws)}),t,t.classList.contains(Bs)))}_deactivate(t,e){t&&(t.classList.remove(Hs),t.blur(),this._deactivate(we.getElementFromSelector(t)),this._queueCallback((()=>{"tab"===t.getAttribute("role")?(t.setAttribute("aria-selected",!1),t.setAttribute("tabindex","-1"),this._toggleDropDown(t,!1),fe.trigger(t,xs,{relatedTarget:e})):t.classList.remove(Ws)}),t,t.classList.contains(Bs)))}_keydown(t){if(![Is,Ns,Ps,Ms,js,Fs].includes(t.key))return;t.stopPropagation(),t.preventDefault();const e=this._getChildren().filter((t=>!Wt(t)));let i;if([js,Fs].includes(t.key))i=e[t.key===js?0:e.length-1];else{const n=[Ns,Ms].includes(t.key);i=Gt(e,t.target,n,!0)}i&&(i.focus({preventScroll:!0}),Ks.getOrCreateInstance(i).show())}_getChildren(){return we.find(Vs,this._parent)}_getActiveElem(){return this._getChildren().find((t=>this._elemIsActive(t)))||null}_setInitialAttributes(t,e){this._setAttributeIfNotExists(t,"role","tablist");for(const t of e)this._setInitialAttributesOnChild(t)}_setInitialAttributesOnChild(t){t=this._getInnerElement(t);const e=this._elemIsActive(t),i=this._getOuterElement(t);t.setAttribute("aria-selected",e),i!==t&&this._setAttributeIfNotExists(i,"role","presentation"),e||t.setAttribute("tabindex","-1"),this._setAttributeIfNotExists(t,"role","tab"),this._setInitialAttributesOnTargetPanel(t)}_setInitialAttributesOnTargetPanel(t){const e=we.getElementFromSelector(t);e&&(this._setAttributeIfNotExists(e,"role","tabpanel"),t.id&&this._setAttributeIfNotExists(e,"aria-labelledby",`${t.id}`))}_toggleDropDown(t,e){const i=this._getOuterElement(t);if(!i.classList.contains("dropdown"))return;const n=(t,n)=>{const s=we.findOne(t,i);s&&s.classList.toggle(n,e)};n(zs,Hs),n(".dropdown-menu",Ws),i.setAttribute("aria-expanded",e)}_setAttributeIfNotExists(t,e,i){t.hasAttribute(e)||t.setAttribute(e,i)}_elemIsActive(t){return t.classList.contains(Hs)}_getInnerElement(t){return t.matches(Vs)?t:we.findOne(Vs,t)}_getOuterElement(t){return t.closest(".nav-item, .list-group-item")||t}static jQueryInterface(t){return this.each((function(){const e=Ks.getOrCreateInstance(this);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t]()}}))}}fe.on(document,Ss,qs,(function(t){["A","AREA"].includes(this.tagName)&&t.preventDefault(),Wt(this)||Ks.getOrCreateInstance(this).show()})),fe.on(window,$s,(()=>{for(const t of we.find(Ys))Ks.getOrCreateInstance(t)})),Qt(Ks);const Qs=".bs.toast",Xs=`mouseover${Qs}`,Us=`mouseout${Qs}`,Gs=`focusin${Qs}`,Js=`focusout${Qs}`,Zs=`hide${Qs}`,to=`hidden${Qs}`,eo=`show${Qs}`,io=`shown${Qs}`,no="hide",so="show",oo="showing",ro={animation:"boolean",autohide:"boolean",delay:"number"},ao={animation:!0,autohide:!0,delay:5e3};class lo extends ve{constructor(t,e){super(t,e),this._timeout=null,this._hasMouseInteraction=!1,this._hasKeyboardInteraction=!1,this._setListeners()}static get Default(){return ao}static get DefaultType(){return ro}static get NAME(){return"toast"}show(){fe.trigger(this._element,eo).defaultPrevented||(this._clearTimeout(),this._config.animation&&this._element.classList.add("fade"),this._element.classList.remove(no),qt(this._element),this._element.classList.add(so,oo),this._queueCallback((()=>{this._element.classList.remove(oo),fe.trigger(this._element,io),this._maybeScheduleHide()}),this._element,this._config.animation))}hide(){this.isShown()&&(fe.trigger(this._element,Zs).defaultPrevented||(this._element.classList.add(oo),this._queueCallback((()=>{this._element.classList.add(no),this._element.classList.remove(oo,so),fe.trigger(this._element,to)}),this._element,this._config.animation)))}dispose(){this._clearTimeout(),this.isShown()&&this._element.classList.remove(so),super.dispose()}isShown(){return this._element.classList.contains(so)}_maybeScheduleHide(){this._config.autohide&&(this._hasMouseInteraction||this._hasKeyboardInteraction||(this._timeout=setTimeout((()=>{this.hide()}),this._config.delay)))}_onInteraction(t,e){switch(t.type){case"mouseover":case"mouseout":this._hasMouseInteraction=e;break;case"focusin":case"focusout":this._hasKeyboardInteraction=e}if(e)return void this._clearTimeout();const i=t.relatedTarget;this._element===i||this._element.contains(i)||this._maybeScheduleHide()}_setListeners(){fe.on(this._element,Xs,(t=>this._onInteraction(t,!0))),fe.on(this._element,Us,(t=>this._onInteraction(t,!1))),fe.on(this._element,Gs,(t=>this._onInteraction(t,!0))),fe.on(this._element,Js,(t=>this._onInteraction(t,!1)))}_clearTimeout(){clearTimeout(this._timeout),this._timeout=null}static jQueryInterface(t){return this.each((function(){const e=lo.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named 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(element.nodeName || '').toLowerCase() : null;\n}","export default function getWindow(node) {\n if (node == null) {\n return window;\n }\n\n if (node.toString() !== '[object Window]') {\n var ownerDocument = node.ownerDocument;\n return ownerDocument ? ownerDocument.defaultView || window : window;\n }\n\n return node;\n}","import getWindow from \"./getWindow.js\";\n\nfunction isElement(node) {\n var OwnElement = getWindow(node).Element;\n return node instanceof OwnElement || node instanceof Element;\n}\n\nfunction isHTMLElement(node) {\n var OwnElement = getWindow(node).HTMLElement;\n return node instanceof OwnElement || node instanceof HTMLElement;\n}\n\nfunction isShadowRoot(node) {\n // IE 11 has no ShadowRoot\n if (typeof ShadowRoot === 'undefined') {\n return false;\n }\n\n var OwnElement = getWindow(node).ShadowRoot;\n return node instanceof OwnElement || node instanceof ShadowRoot;\n}\n\nexport { isElement, isHTMLElement, isShadowRoot };","import getNodeName from \"../dom-utils/getNodeName.js\";\nimport { isHTMLElement } from \"../dom-utils/instanceOf.js\"; // This modifier takes the styles prepared by the `computeStyles` modifier\n// and applies them to the HTMLElements such as popper and arrow\n\nfunction applyStyles(_ref) {\n var state = _ref.state;\n Object.keys(state.elements).forEach(function (name) {\n var style = state.styles[name] || {};\n var attributes = state.attributes[name] || {};\n var element = state.elements[name]; // arrow is optional + virtual elements\n\n if (!isHTMLElement(element) || !getNodeName(element)) {\n return;\n } // Flow doesn't support to extend this property, but it's the most\n // effective way to apply styles to an HTMLElement\n // $FlowFixMe[cannot-write]\n\n\n Object.assign(element.style, style);\n Object.keys(attributes).forEach(function (name) {\n var value = attributes[name];\n\n if (value === false) {\n element.removeAttribute(name);\n } else {\n element.setAttribute(name, value === true ? '' : value);\n }\n });\n });\n}\n\nfunction effect(_ref2) {\n var state = _ref2.state;\n var initialStyles = {\n popper: {\n position: state.options.strategy,\n left: '0',\n top: '0',\n margin: '0'\n },\n arrow: {\n position: 'absolute'\n },\n reference: {}\n };\n Object.assign(state.elements.popper.style, initialStyles.popper);\n state.styles = initialStyles;\n\n if (state.elements.arrow) {\n Object.assign(state.elements.arrow.style, initialStyles.arrow);\n }\n\n return function () {\n Object.keys(state.elements).forEach(function (name) {\n var element = state.elements[name];\n var attributes = state.attributes[name] || {};\n var styleProperties = Object.keys(state.styles.hasOwnProperty(name) ? state.styles[name] : initialStyles[name]); // Set all values to an empty string to unset them\n\n var style = styleProperties.reduce(function (style, property) {\n style[property] = '';\n return style;\n }, {}); // arrow is optional + virtual elements\n\n if (!isHTMLElement(element) || !getNodeName(element)) {\n return;\n }\n\n Object.assign(element.style, style);\n Object.keys(attributes).forEach(function (attribute) {\n element.removeAttribute(attribute);\n });\n });\n };\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'applyStyles',\n enabled: true,\n phase: 'write',\n fn: applyStyles,\n effect: effect,\n requires: ['computeStyles']\n};","import { auto } from \"../enums.js\";\nexport default function getBasePlacement(placement) {\n return placement.split('-')[0];\n}","export var max = Math.max;\nexport var min = Math.min;\nexport var round = Math.round;","export default function getUAString() {\n var uaData = navigator.userAgentData;\n\n if (uaData != null && uaData.brands && Array.isArray(uaData.brands)) {\n return uaData.brands.map(function (item) {\n return item.brand + \"/\" + item.version;\n }).join(' ');\n }\n\n return navigator.userAgent;\n}","import getUAString from \"../utils/userAgent.js\";\nexport default function isLayoutViewport() {\n return !/^((?!chrome|android).)*safari/i.test(getUAString());\n}","import { isElement, isHTMLElement } from \"./instanceOf.js\";\nimport { round } from \"../utils/math.js\";\nimport getWindow from \"./getWindow.js\";\nimport isLayoutViewport from \"./isLayoutViewport.js\";\nexport default function getBoundingClientRect(element, includeScale, isFixedStrategy) {\n if (includeScale === void 0) {\n includeScale = false;\n }\n\n if (isFixedStrategy === void 0) {\n isFixedStrategy = false;\n }\n\n var clientRect = element.getBoundingClientRect();\n var scaleX = 1;\n var scaleY = 1;\n\n if (includeScale && isHTMLElement(element)) {\n scaleX = element.offsetWidth > 0 ? round(clientRect.width) / element.offsetWidth || 1 : 1;\n scaleY = element.offsetHeight > 0 ? round(clientRect.height) / element.offsetHeight || 1 : 1;\n }\n\n var _ref = isElement(element) ? getWindow(element) : window,\n visualViewport = _ref.visualViewport;\n\n var addVisualOffsets = !isLayoutViewport() && isFixedStrategy;\n var x = (clientRect.left + (addVisualOffsets && visualViewport ? visualViewport.offsetLeft : 0)) / scaleX;\n var y = (clientRect.top + (addVisualOffsets && visualViewport ? visualViewport.offsetTop : 0)) / scaleY;\n var width = clientRect.width / scaleX;\n var height = clientRect.height / scaleY;\n return {\n width: width,\n height: height,\n top: y,\n right: x + width,\n bottom: y + height,\n left: x,\n x: x,\n y: y\n };\n}","import getBoundingClientRect from \"./getBoundingClientRect.js\"; // Returns the layout rect of an element relative to its offsetParent. Layout\n// means it doesn't take into account transforms.\n\nexport default function getLayoutRect(element) {\n var clientRect = getBoundingClientRect(element); // Use the clientRect sizes if it's not been transformed.\n // Fixes https://github.com/popperjs/popper-core/issues/1223\n\n var width = element.offsetWidth;\n var height = element.offsetHeight;\n\n if (Math.abs(clientRect.width - width) <= 1) {\n width = clientRect.width;\n }\n\n if (Math.abs(clientRect.height - height) <= 1) {\n height = clientRect.height;\n }\n\n return {\n x: element.offsetLeft,\n y: element.offsetTop,\n width: width,\n height: height\n };\n}","import { isShadowRoot } from \"./instanceOf.js\";\nexport default function contains(parent, child) {\n var rootNode = child.getRootNode && child.getRootNode(); // First, attempt with faster native method\n\n if (parent.contains(child)) {\n return true;\n } // then fallback to custom implementation with Shadow DOM support\n else if (rootNode && isShadowRoot(rootNode)) {\n var next = child;\n\n do {\n if (next && parent.isSameNode(next)) {\n return true;\n } // $FlowFixMe[prop-missing]: need a better way to handle this...\n\n\n next = next.parentNode || next.host;\n } while (next);\n } // Give up, the result is false\n\n\n return false;\n}","import getWindow from \"./getWindow.js\";\nexport default function getComputedStyle(element) {\n return getWindow(element).getComputedStyle(element);\n}","import getNodeName from \"./getNodeName.js\";\nexport default function isTableElement(element) {\n return ['table', 'td', 'th'].indexOf(getNodeName(element)) >= 0;\n}","import { isElement } from \"./instanceOf.js\";\nexport default function getDocumentElement(element) {\n // $FlowFixMe[incompatible-return]: assume body is always available\n return ((isElement(element) ? element.ownerDocument : // $FlowFixMe[prop-missing]\n element.document) || window.document).documentElement;\n}","import getNodeName from \"./getNodeName.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport { isShadowRoot } from \"./instanceOf.js\";\nexport default function getParentNode(element) {\n if (getNodeName(element) === 'html') {\n return element;\n }\n\n return (// this is a quicker (but less type safe) way to save quite some bytes from the bundle\n // $FlowFixMe[incompatible-return]\n // $FlowFixMe[prop-missing]\n element.assignedSlot || // step into the shadow DOM of the parent of a slotted node\n element.parentNode || ( // DOM Element detected\n isShadowRoot(element) ? element.host : null) || // ShadowRoot detected\n // $FlowFixMe[incompatible-call]: HTMLElement is a Node\n getDocumentElement(element) // fallback\n\n );\n}","import getWindow from \"./getWindow.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport getComputedStyle from \"./getComputedStyle.js\";\nimport { isHTMLElement, isShadowRoot } from \"./instanceOf.js\";\nimport isTableElement from \"./isTableElement.js\";\nimport getParentNode from \"./getParentNode.js\";\nimport getUAString from \"../utils/userAgent.js\";\n\nfunction getTrueOffsetParent(element) {\n if (!isHTMLElement(element) || // https://github.com/popperjs/popper-core/issues/837\n getComputedStyle(element).position === 'fixed') {\n return null;\n }\n\n return element.offsetParent;\n} // `.offsetParent` reports `null` for fixed elements, while absolute elements\n// return the containing block\n\n\nfunction getContainingBlock(element) {\n var isFirefox = /firefox/i.test(getUAString());\n var isIE = /Trident/i.test(getUAString());\n\n if (isIE && isHTMLElement(element)) {\n // In IE 9, 10 and 11 fixed elements containing block is always established by the viewport\n var elementCss = getComputedStyle(element);\n\n if (elementCss.position === 'fixed') {\n return null;\n }\n }\n\n var currentNode = getParentNode(element);\n\n if (isShadowRoot(currentNode)) {\n currentNode = currentNode.host;\n }\n\n while (isHTMLElement(currentNode) && ['html', 'body'].indexOf(getNodeName(currentNode)) < 0) {\n var css = getComputedStyle(currentNode); // This is non-exhaustive but covers the most common CSS properties that\n // create a containing block.\n // https://developer.mozilla.org/en-US/docs/Web/CSS/Containing_block#identifying_the_containing_block\n\n if (css.transform !== 'none' || css.perspective !== 'none' || css.contain === 'paint' || ['transform', 'perspective'].indexOf(css.willChange) !== -1 || isFirefox && css.willChange === 'filter' || isFirefox && css.filter && css.filter !== 'none') {\n return currentNode;\n } else {\n currentNode = currentNode.parentNode;\n }\n }\n\n return null;\n} // Gets the closest ancestor positioned element. Handles some edge cases,\n// such as table ancestors and cross browser bugs.\n\n\nexport default function getOffsetParent(element) {\n var window = getWindow(element);\n var offsetParent = getTrueOffsetParent(element);\n\n while (offsetParent && isTableElement(offsetParent) && getComputedStyle(offsetParent).position === 'static') {\n offsetParent = getTrueOffsetParent(offsetParent);\n }\n\n if (offsetParent && (getNodeName(offsetParent) === 'html' || getNodeName(offsetParent) === 'body' && getComputedStyle(offsetParent).position === 'static')) {\n return window;\n }\n\n return offsetParent || getContainingBlock(element) || window;\n}","export default function getMainAxisFromPlacement(placement) {\n return ['top', 'bottom'].indexOf(placement) >= 0 ? 'x' : 'y';\n}","import { max as mathMax, min as mathMin } from \"./math.js\";\nexport function within(min, value, max) {\n return mathMax(min, mathMin(value, max));\n}\nexport function withinMaxClamp(min, value, max) {\n var v = within(min, value, max);\n return v > max ? max : v;\n}","import getFreshSideObject from \"./getFreshSideObject.js\";\nexport default function mergePaddingObject(paddingObject) {\n return Object.assign({}, getFreshSideObject(), paddingObject);\n}","export default function getFreshSideObject() {\n return {\n top: 0,\n right: 0,\n bottom: 0,\n left: 0\n };\n}","export default function expandToHashMap(value, keys) {\n return keys.reduce(function (hashMap, key) {\n hashMap[key] = value;\n return hashMap;\n }, {});\n}","import getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getLayoutRect from \"../dom-utils/getLayoutRect.js\";\nimport contains from \"../dom-utils/contains.js\";\nimport getOffsetParent from \"../dom-utils/getOffsetParent.js\";\nimport getMainAxisFromPlacement from \"../utils/getMainAxisFromPlacement.js\";\nimport { within } from \"../utils/within.js\";\nimport mergePaddingObject from \"../utils/mergePaddingObject.js\";\nimport expandToHashMap from \"../utils/expandToHashMap.js\";\nimport { left, right, basePlacements, top, bottom } from \"../enums.js\"; // eslint-disable-next-line import/no-unused-modules\n\nvar toPaddingObject = function toPaddingObject(padding, state) {\n padding = typeof padding === 'function' ? padding(Object.assign({}, state.rects, {\n placement: state.placement\n })) : padding;\n return mergePaddingObject(typeof padding !== 'number' ? padding : expandToHashMap(padding, basePlacements));\n};\n\nfunction arrow(_ref) {\n var _state$modifiersData$;\n\n var state = _ref.state,\n name = _ref.name,\n options = _ref.options;\n var arrowElement = state.elements.arrow;\n var popperOffsets = state.modifiersData.popperOffsets;\n var basePlacement = getBasePlacement(state.placement);\n var axis = getMainAxisFromPlacement(basePlacement);\n var isVertical = [left, right].indexOf(basePlacement) >= 0;\n var len = isVertical ? 'height' : 'width';\n\n if (!arrowElement || !popperOffsets) {\n return;\n }\n\n var paddingObject = toPaddingObject(options.padding, state);\n var arrowRect = getLayoutRect(arrowElement);\n var minProp = axis === 'y' ? top : left;\n var maxProp = axis === 'y' ? bottom : right;\n var endDiff = state.rects.reference[len] + state.rects.reference[axis] - popperOffsets[axis] - state.rects.popper[len];\n var startDiff = popperOffsets[axis] - state.rects.reference[axis];\n var arrowOffsetParent = getOffsetParent(arrowElement);\n var clientSize = arrowOffsetParent ? axis === 'y' ? arrowOffsetParent.clientHeight || 0 : arrowOffsetParent.clientWidth || 0 : 0;\n var centerToReference = endDiff / 2 - startDiff / 2; // Make sure the arrow doesn't overflow the popper if the center point is\n // outside of the popper bounds\n\n var min = paddingObject[minProp];\n var max = clientSize - arrowRect[len] - paddingObject[maxProp];\n var center = clientSize / 2 - arrowRect[len] / 2 + centerToReference;\n var offset = within(min, center, max); // Prevents breaking syntax highlighting...\n\n var axisProp = axis;\n state.modifiersData[name] = (_state$modifiersData$ = {}, _state$modifiersData$[axisProp] = offset, _state$modifiersData$.centerOffset = offset - center, _state$modifiersData$);\n}\n\nfunction effect(_ref2) {\n var state = _ref2.state,\n options = _ref2.options;\n var _options$element = options.element,\n arrowElement = _options$element === void 0 ? '[data-popper-arrow]' : _options$element;\n\n if (arrowElement == null) {\n return;\n } // CSS selector\n\n\n if (typeof arrowElement === 'string') {\n arrowElement = state.elements.popper.querySelector(arrowElement);\n\n if (!arrowElement) {\n return;\n }\n }\n\n if (!contains(state.elements.popper, arrowElement)) {\n return;\n }\n\n state.elements.arrow = arrowElement;\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'arrow',\n enabled: true,\n phase: 'main',\n fn: arrow,\n effect: effect,\n requires: ['popperOffsets'],\n requiresIfExists: ['preventOverflow']\n};","export default function getVariation(placement) {\n return placement.split('-')[1];\n}","import { top, left, right, bottom, end } from \"../enums.js\";\nimport getOffsetParent from \"../dom-utils/getOffsetParent.js\";\nimport getWindow from \"../dom-utils/getWindow.js\";\nimport getDocumentElement from \"../dom-utils/getDocumentElement.js\";\nimport getComputedStyle from \"../dom-utils/getComputedStyle.js\";\nimport getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getVariation from \"../utils/getVariation.js\";\nimport { round } from \"../utils/math.js\"; // eslint-disable-next-line import/no-unused-modules\n\nvar unsetSides = {\n top: 'auto',\n right: 'auto',\n bottom: 'auto',\n left: 'auto'\n}; // Round the offsets to the nearest suitable subpixel based on the DPR.\n// Zooming can change the DPR, but it seems to report a value that will\n// cleanly divide the values into the appropriate subpixels.\n\nfunction roundOffsetsByDPR(_ref, win) {\n var x = _ref.x,\n y = _ref.y;\n var dpr = win.devicePixelRatio || 1;\n return {\n x: round(x * dpr) / dpr || 0,\n y: round(y * dpr) / dpr || 0\n };\n}\n\nexport function mapToStyles(_ref2) {\n var _Object$assign2;\n\n var popper = _ref2.popper,\n popperRect = _ref2.popperRect,\n placement = _ref2.placement,\n variation = _ref2.variation,\n offsets = _ref2.offsets,\n position = _ref2.position,\n gpuAcceleration = _ref2.gpuAcceleration,\n adaptive = _ref2.adaptive,\n roundOffsets = _ref2.roundOffsets,\n isFixed = _ref2.isFixed;\n var _offsets$x = offsets.x,\n x = _offsets$x === void 0 ? 0 : _offsets$x,\n _offsets$y = offsets.y,\n y = _offsets$y === void 0 ? 0 : _offsets$y;\n\n var _ref3 = typeof roundOffsets === 'function' ? roundOffsets({\n x: x,\n y: y\n }) : {\n x: x,\n y: y\n };\n\n x = _ref3.x;\n y = _ref3.y;\n var hasX = offsets.hasOwnProperty('x');\n var hasY = offsets.hasOwnProperty('y');\n var sideX = left;\n var sideY = top;\n var win = window;\n\n if (adaptive) {\n var offsetParent = getOffsetParent(popper);\n var heightProp = 'clientHeight';\n var widthProp = 'clientWidth';\n\n if (offsetParent === getWindow(popper)) {\n offsetParent = getDocumentElement(popper);\n\n if (getComputedStyle(offsetParent).position !== 'static' && position === 'absolute') {\n heightProp = 'scrollHeight';\n widthProp = 'scrollWidth';\n }\n } // $FlowFixMe[incompatible-cast]: force type refinement, we compare offsetParent with window above, but Flow doesn't detect it\n\n\n offsetParent = offsetParent;\n\n if (placement === top || (placement === left || placement === right) && variation === end) {\n sideY = bottom;\n var offsetY = isFixed && offsetParent === win && win.visualViewport ? win.visualViewport.height : // $FlowFixMe[prop-missing]\n offsetParent[heightProp];\n y -= offsetY - popperRect.height;\n y *= gpuAcceleration ? 1 : -1;\n }\n\n if (placement === left || (placement === top || placement === bottom) && variation === end) {\n sideX = right;\n var offsetX = isFixed && offsetParent === win && win.visualViewport ? win.visualViewport.width : // $FlowFixMe[prop-missing]\n offsetParent[widthProp];\n x -= offsetX - popperRect.width;\n x *= gpuAcceleration ? 1 : -1;\n }\n }\n\n var commonStyles = Object.assign({\n position: position\n }, adaptive && unsetSides);\n\n var _ref4 = roundOffsets === true ? roundOffsetsByDPR({\n x: x,\n y: y\n }, getWindow(popper)) : {\n x: x,\n y: y\n };\n\n x = _ref4.x;\n y = _ref4.y;\n\n if (gpuAcceleration) {\n var _Object$assign;\n\n return Object.assign({}, commonStyles, (_Object$assign = {}, _Object$assign[sideY] = hasY ? '0' : '', _Object$assign[sideX] = hasX ? '0' : '', _Object$assign.transform = (win.devicePixelRatio || 1) <= 1 ? \"translate(\" + x + \"px, \" + y + \"px)\" : \"translate3d(\" + x + \"px, \" + y + \"px, 0)\", _Object$assign));\n }\n\n return Object.assign({}, commonStyles, (_Object$assign2 = {}, _Object$assign2[sideY] = hasY ? y + \"px\" : '', _Object$assign2[sideX] = hasX ? x + \"px\" : '', _Object$assign2.transform = '', _Object$assign2));\n}\n\nfunction computeStyles(_ref5) {\n var state = _ref5.state,\n options = _ref5.options;\n var _options$gpuAccelerat = options.gpuAcceleration,\n gpuAcceleration = _options$gpuAccelerat === void 0 ? true : _options$gpuAccelerat,\n _options$adaptive = options.adaptive,\n adaptive = _options$adaptive === void 0 ? true : _options$adaptive,\n _options$roundOffsets = options.roundOffsets,\n roundOffsets = _options$roundOffsets === void 0 ? true : _options$roundOffsets;\n var commonStyles = {\n placement: getBasePlacement(state.placement),\n variation: getVariation(state.placement),\n popper: state.elements.popper,\n popperRect: state.rects.popper,\n gpuAcceleration: gpuAcceleration,\n isFixed: state.options.strategy === 'fixed'\n };\n\n if (state.modifiersData.popperOffsets != null) {\n state.styles.popper = Object.assign({}, state.styles.popper, mapToStyles(Object.assign({}, commonStyles, {\n offsets: state.modifiersData.popperOffsets,\n position: state.options.strategy,\n adaptive: adaptive,\n roundOffsets: roundOffsets\n })));\n }\n\n if (state.modifiersData.arrow != null) {\n state.styles.arrow = Object.assign({}, state.styles.arrow, mapToStyles(Object.assign({}, commonStyles, {\n offsets: state.modifiersData.arrow,\n position: 'absolute',\n adaptive: false,\n roundOffsets: roundOffsets\n })));\n }\n\n state.attributes.popper = Object.assign({}, state.attributes.popper, {\n 'data-popper-placement': state.placement\n });\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'computeStyles',\n enabled: true,\n phase: 'beforeWrite',\n fn: computeStyles,\n data: {}\n};","import getWindow from \"../dom-utils/getWindow.js\"; // eslint-disable-next-line import/no-unused-modules\n\nvar passive = {\n passive: true\n};\n\nfunction effect(_ref) {\n var state = _ref.state,\n instance = _ref.instance,\n options = _ref.options;\n var _options$scroll = options.scroll,\n scroll = _options$scroll === void 0 ? true : _options$scroll,\n _options$resize = options.resize,\n resize = _options$resize === void 0 ? true : _options$resize;\n var window = getWindow(state.elements.popper);\n var scrollParents = [].concat(state.scrollParents.reference, state.scrollParents.popper);\n\n if (scroll) {\n scrollParents.forEach(function (scrollParent) {\n scrollParent.addEventListener('scroll', instance.update, passive);\n });\n }\n\n if (resize) {\n window.addEventListener('resize', instance.update, passive);\n }\n\n return function () {\n if (scroll) {\n scrollParents.forEach(function (scrollParent) {\n scrollParent.removeEventListener('scroll', instance.update, passive);\n });\n }\n\n if (resize) {\n window.removeEventListener('resize', instance.update, passive);\n }\n };\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'eventListeners',\n enabled: true,\n phase: 'write',\n fn: function fn() {},\n effect: effect,\n data: {}\n};","var hash = {\n left: 'right',\n right: 'left',\n bottom: 'top',\n top: 'bottom'\n};\nexport default function getOppositePlacement(placement) {\n return placement.replace(/left|right|bottom|top/g, function (matched) {\n return hash[matched];\n });\n}","var hash = {\n start: 'end',\n end: 'start'\n};\nexport default function getOppositeVariationPlacement(placement) {\n return placement.replace(/start|end/g, function (matched) {\n return hash[matched];\n });\n}","import getWindow from \"./getWindow.js\";\nexport default function getWindowScroll(node) {\n var win = getWindow(node);\n var scrollLeft = win.pageXOffset;\n var scrollTop = win.pageYOffset;\n return {\n scrollLeft: scrollLeft,\n scrollTop: scrollTop\n };\n}","import getBoundingClientRect from \"./getBoundingClientRect.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport getWindowScroll from \"./getWindowScroll.js\";\nexport default function getWindowScrollBarX(element) {\n // If has a CSS width greater than the viewport, then this will be\n // incorrect for RTL.\n // Popper 1 is broken in this case and never had a bug report so let's assume\n // it's not an issue. I don't think anyone ever specifies width on \n // anyway.\n // Browsers where the left scrollbar doesn't cause an issue report `0` for\n // this (e.g. Edge 2019, IE11, Safari)\n return getBoundingClientRect(getDocumentElement(element)).left + getWindowScroll(element).scrollLeft;\n}","import getComputedStyle from \"./getComputedStyle.js\";\nexport default function isScrollParent(element) {\n // Firefox wants us to check `-x` and `-y` variations as well\n var _getComputedStyle = getComputedStyle(element),\n overflow = _getComputedStyle.overflow,\n overflowX = _getComputedStyle.overflowX,\n overflowY = _getComputedStyle.overflowY;\n\n return /auto|scroll|overlay|hidden/.test(overflow + overflowY + overflowX);\n}","import getParentNode from \"./getParentNode.js\";\nimport isScrollParent from \"./isScrollParent.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport { isHTMLElement } from \"./instanceOf.js\";\nexport default function getScrollParent(node) {\n if (['html', 'body', '#document'].indexOf(getNodeName(node)) >= 0) {\n // $FlowFixMe[incompatible-return]: assume body is always available\n return node.ownerDocument.body;\n }\n\n if (isHTMLElement(node) && isScrollParent(node)) {\n return node;\n }\n\n return getScrollParent(getParentNode(node));\n}","import getScrollParent from \"./getScrollParent.js\";\nimport getParentNode from \"./getParentNode.js\";\nimport getWindow from \"./getWindow.js\";\nimport isScrollParent from \"./isScrollParent.js\";\n/*\ngiven a DOM element, return the list of all scroll parents, up the list of ancesors\nuntil we get to the top window object. This list is what we attach scroll listeners\nto, because if any of these parent elements scroll, we'll need to re-calculate the\nreference element's position.\n*/\n\nexport default function listScrollParents(element, list) {\n var _element$ownerDocumen;\n\n if (list === void 0) {\n list = [];\n }\n\n var scrollParent = getScrollParent(element);\n var isBody = scrollParent === ((_element$ownerDocumen = element.ownerDocument) == null ? void 0 : _element$ownerDocumen.body);\n var win = getWindow(scrollParent);\n var target = isBody ? [win].concat(win.visualViewport || [], isScrollParent(scrollParent) ? scrollParent : []) : scrollParent;\n var updatedList = list.concat(target);\n return isBody ? updatedList : // $FlowFixMe[incompatible-call]: isBody tells us target will be an HTMLElement here\n updatedList.concat(listScrollParents(getParentNode(target)));\n}","export default function rectToClientRect(rect) {\n return Object.assign({}, rect, {\n left: rect.x,\n top: rect.y,\n right: rect.x + rect.width,\n bottom: rect.y + rect.height\n });\n}","import { viewport } from \"../enums.js\";\nimport getViewportRect from \"./getViewportRect.js\";\nimport getDocumentRect from \"./getDocumentRect.js\";\nimport listScrollParents from \"./listScrollParents.js\";\nimport getOffsetParent from \"./getOffsetParent.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport getComputedStyle from \"./getComputedStyle.js\";\nimport { isElement, isHTMLElement } from \"./instanceOf.js\";\nimport getBoundingClientRect from \"./getBoundingClientRect.js\";\nimport getParentNode from \"./getParentNode.js\";\nimport contains from \"./contains.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport rectToClientRect from \"../utils/rectToClientRect.js\";\nimport { max, min } from \"../utils/math.js\";\n\nfunction getInnerBoundingClientRect(element, strategy) {\n var rect = getBoundingClientRect(element, false, strategy === 'fixed');\n rect.top = rect.top + element.clientTop;\n rect.left = rect.left + element.clientLeft;\n rect.bottom = rect.top + element.clientHeight;\n rect.right = rect.left + element.clientWidth;\n rect.width = element.clientWidth;\n rect.height = element.clientHeight;\n rect.x = rect.left;\n rect.y = rect.top;\n return rect;\n}\n\nfunction getClientRectFromMixedType(element, clippingParent, strategy) {\n return clippingParent === viewport ? rectToClientRect(getViewportRect(element, strategy)) : isElement(clippingParent) ? getInnerBoundingClientRect(clippingParent, strategy) : rectToClientRect(getDocumentRect(getDocumentElement(element)));\n} // A \"clipping parent\" is an overflowable container with the characteristic of\n// clipping (or hiding) overflowing elements with a position different from\n// `initial`\n\n\nfunction getClippingParents(element) {\n var clippingParents = listScrollParents(getParentNode(element));\n var canEscapeClipping = ['absolute', 'fixed'].indexOf(getComputedStyle(element).position) >= 0;\n var clipperElement = canEscapeClipping && isHTMLElement(element) ? getOffsetParent(element) : element;\n\n if (!isElement(clipperElement)) {\n return [];\n } // $FlowFixMe[incompatible-return]: https://github.com/facebook/flow/issues/1414\n\n\n return clippingParents.filter(function (clippingParent) {\n return isElement(clippingParent) && contains(clippingParent, clipperElement) && getNodeName(clippingParent) !== 'body';\n });\n} // Gets the maximum area that the element is visible in due to any number of\n// clipping parents\n\n\nexport default function getClippingRect(element, boundary, rootBoundary, strategy) {\n var mainClippingParents = boundary === 'clippingParents' ? getClippingParents(element) : [].concat(boundary);\n var clippingParents = [].concat(mainClippingParents, [rootBoundary]);\n var firstClippingParent = clippingParents[0];\n var clippingRect = clippingParents.reduce(function (accRect, clippingParent) {\n var rect = getClientRectFromMixedType(element, clippingParent, strategy);\n accRect.top = max(rect.top, accRect.top);\n accRect.right = min(rect.right, accRect.right);\n accRect.bottom = min(rect.bottom, accRect.bottom);\n accRect.left = max(rect.left, accRect.left);\n return accRect;\n }, getClientRectFromMixedType(element, firstClippingParent, strategy));\n clippingRect.width = clippingRect.right - clippingRect.left;\n clippingRect.height = clippingRect.bottom - clippingRect.top;\n clippingRect.x = clippingRect.left;\n clippingRect.y = clippingRect.top;\n return clippingRect;\n}","import getWindow from \"./getWindow.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport getWindowScrollBarX from \"./getWindowScrollBarX.js\";\nimport isLayoutViewport from \"./isLayoutViewport.js\";\nexport default function getViewportRect(element, strategy) {\n var win = getWindow(element);\n var html = getDocumentElement(element);\n var visualViewport = win.visualViewport;\n var width = html.clientWidth;\n var height = html.clientHeight;\n var x = 0;\n var y = 0;\n\n if (visualViewport) {\n width = visualViewport.width;\n height = visualViewport.height;\n var layoutViewport = isLayoutViewport();\n\n if (layoutViewport || !layoutViewport && strategy === 'fixed') {\n x = visualViewport.offsetLeft;\n y = visualViewport.offsetTop;\n }\n }\n\n return {\n width: width,\n height: height,\n x: x + getWindowScrollBarX(element),\n y: y\n };\n}","import getDocumentElement from \"./getDocumentElement.js\";\nimport getComputedStyle from \"./getComputedStyle.js\";\nimport getWindowScrollBarX from \"./getWindowScrollBarX.js\";\nimport getWindowScroll from \"./getWindowScroll.js\";\nimport { max } from \"../utils/math.js\"; // Gets the entire size of the scrollable document area, even extending outside\n// of the `` and `` rect bounds if horizontally scrollable\n\nexport default function getDocumentRect(element) {\n var _element$ownerDocumen;\n\n var html = getDocumentElement(element);\n var winScroll = getWindowScroll(element);\n var body = (_element$ownerDocumen = element.ownerDocument) == null ? void 0 : _element$ownerDocumen.body;\n var width = max(html.scrollWidth, html.clientWidth, body ? body.scrollWidth : 0, body ? body.clientWidth : 0);\n var height = max(html.scrollHeight, html.clientHeight, body ? body.scrollHeight : 0, body ? body.clientHeight : 0);\n var x = -winScroll.scrollLeft + getWindowScrollBarX(element);\n var y = -winScroll.scrollTop;\n\n if (getComputedStyle(body || html).direction === 'rtl') {\n x += max(html.clientWidth, body ? body.clientWidth : 0) - width;\n }\n\n return {\n width: width,\n height: height,\n x: x,\n y: y\n };\n}","import getBasePlacement from \"./getBasePlacement.js\";\nimport getVariation from \"./getVariation.js\";\nimport getMainAxisFromPlacement from \"./getMainAxisFromPlacement.js\";\nimport { top, right, bottom, left, start, end } from \"../enums.js\";\nexport default function computeOffsets(_ref) {\n var reference = _ref.reference,\n element = _ref.element,\n placement = _ref.placement;\n var basePlacement = placement ? getBasePlacement(placement) : null;\n var variation = placement ? getVariation(placement) : null;\n var commonX = reference.x + reference.width / 2 - element.width / 2;\n var commonY = reference.y + reference.height / 2 - element.height / 2;\n var offsets;\n\n switch (basePlacement) {\n case top:\n offsets = {\n x: commonX,\n y: reference.y - element.height\n };\n break;\n\n case bottom:\n offsets = {\n x: commonX,\n y: reference.y + reference.height\n };\n break;\n\n case right:\n offsets = {\n x: reference.x + reference.width,\n y: commonY\n };\n break;\n\n case left:\n offsets = {\n x: reference.x - element.width,\n y: commonY\n };\n break;\n\n default:\n offsets = {\n x: reference.x,\n y: reference.y\n };\n }\n\n var mainAxis = basePlacement ? getMainAxisFromPlacement(basePlacement) : null;\n\n if (mainAxis != null) {\n var len = mainAxis === 'y' ? 'height' : 'width';\n\n switch (variation) {\n case start:\n offsets[mainAxis] = offsets[mainAxis] - (reference[len] / 2 - element[len] / 2);\n break;\n\n case end:\n offsets[mainAxis] = offsets[mainAxis] + (reference[len] / 2 - element[len] / 2);\n break;\n\n default:\n }\n }\n\n return offsets;\n}","import getClippingRect from \"../dom-utils/getClippingRect.js\";\nimport getDocumentElement from \"../dom-utils/getDocumentElement.js\";\nimport getBoundingClientRect from \"../dom-utils/getBoundingClientRect.js\";\nimport computeOffsets from \"./computeOffsets.js\";\nimport rectToClientRect from \"./rectToClientRect.js\";\nimport { clippingParents, reference, popper, bottom, top, right, basePlacements, viewport } from \"../enums.js\";\nimport { isElement } from \"../dom-utils/instanceOf.js\";\nimport mergePaddingObject from \"./mergePaddingObject.js\";\nimport expandToHashMap from \"./expandToHashMap.js\"; // eslint-disable-next-line import/no-unused-modules\n\nexport default function detectOverflow(state, options) {\n if (options === void 0) {\n options = {};\n }\n\n var _options = options,\n _options$placement = _options.placement,\n placement = _options$placement === void 0 ? state.placement : _options$placement,\n _options$strategy = _options.strategy,\n strategy = _options$strategy === void 0 ? state.strategy : _options$strategy,\n _options$boundary = _options.boundary,\n boundary = _options$boundary === void 0 ? clippingParents : _options$boundary,\n _options$rootBoundary = _options.rootBoundary,\n rootBoundary = _options$rootBoundary === void 0 ? viewport : _options$rootBoundary,\n _options$elementConte = _options.elementContext,\n elementContext = _options$elementConte === void 0 ? popper : _options$elementConte,\n _options$altBoundary = _options.altBoundary,\n altBoundary = _options$altBoundary === void 0 ? false : _options$altBoundary,\n _options$padding = _options.padding,\n padding = _options$padding === void 0 ? 0 : _options$padding;\n var paddingObject = mergePaddingObject(typeof padding !== 'number' ? padding : expandToHashMap(padding, basePlacements));\n var altContext = elementContext === popper ? reference : popper;\n var popperRect = state.rects.popper;\n var element = state.elements[altBoundary ? altContext : elementContext];\n var clippingClientRect = getClippingRect(isElement(element) ? element : element.contextElement || getDocumentElement(state.elements.popper), boundary, rootBoundary, strategy);\n var referenceClientRect = getBoundingClientRect(state.elements.reference);\n var popperOffsets = computeOffsets({\n reference: referenceClientRect,\n element: popperRect,\n strategy: 'absolute',\n placement: placement\n });\n var popperClientRect = rectToClientRect(Object.assign({}, popperRect, popperOffsets));\n var elementClientRect = elementContext === popper ? popperClientRect : referenceClientRect; // positive = overflowing the clipping rect\n // 0 or negative = within the clipping rect\n\n var overflowOffsets = {\n top: clippingClientRect.top - elementClientRect.top + paddingObject.top,\n bottom: elementClientRect.bottom - clippingClientRect.bottom + paddingObject.bottom,\n left: clippingClientRect.left - elementClientRect.left + paddingObject.left,\n right: elementClientRect.right - clippingClientRect.right + paddingObject.right\n };\n var offsetData = state.modifiersData.offset; // Offsets can be applied only to the popper element\n\n if (elementContext === popper && offsetData) {\n var offset = offsetData[placement];\n Object.keys(overflowOffsets).forEach(function (key) {\n var multiply = [right, bottom].indexOf(key) >= 0 ? 1 : -1;\n var axis = [top, bottom].indexOf(key) >= 0 ? 'y' : 'x';\n overflowOffsets[key] += offset[axis] * multiply;\n });\n }\n\n return overflowOffsets;\n}","import getOppositePlacement from \"../utils/getOppositePlacement.js\";\nimport getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getOppositeVariationPlacement from \"../utils/getOppositeVariationPlacement.js\";\nimport detectOverflow from \"../utils/detectOverflow.js\";\nimport computeAutoPlacement from \"../utils/computeAutoPlacement.js\";\nimport { bottom, top, start, right, left, auto } from \"../enums.js\";\nimport getVariation from \"../utils/getVariation.js\"; // eslint-disable-next-line import/no-unused-modules\n\nfunction getExpandedFallbackPlacements(placement) {\n if (getBasePlacement(placement) === auto) {\n return [];\n }\n\n var oppositePlacement = getOppositePlacement(placement);\n return [getOppositeVariationPlacement(placement), oppositePlacement, getOppositeVariationPlacement(oppositePlacement)];\n}\n\nfunction flip(_ref) {\n var state = _ref.state,\n options = _ref.options,\n name = _ref.name;\n\n if (state.modifiersData[name]._skip) {\n return;\n }\n\n var _options$mainAxis = options.mainAxis,\n checkMainAxis = _options$mainAxis === void 0 ? true : _options$mainAxis,\n _options$altAxis = options.altAxis,\n checkAltAxis = _options$altAxis === void 0 ? true : _options$altAxis,\n specifiedFallbackPlacements = options.fallbackPlacements,\n padding = options.padding,\n boundary = options.boundary,\n rootBoundary = options.rootBoundary,\n altBoundary = options.altBoundary,\n _options$flipVariatio = options.flipVariations,\n flipVariations = _options$flipVariatio === void 0 ? true : _options$flipVariatio,\n allowedAutoPlacements = options.allowedAutoPlacements;\n var preferredPlacement = state.options.placement;\n var basePlacement = getBasePlacement(preferredPlacement);\n var isBasePlacement = basePlacement === preferredPlacement;\n var fallbackPlacements = specifiedFallbackPlacements || (isBasePlacement || !flipVariations ? [getOppositePlacement(preferredPlacement)] : getExpandedFallbackPlacements(preferredPlacement));\n var placements = [preferredPlacement].concat(fallbackPlacements).reduce(function (acc, placement) {\n return acc.concat(getBasePlacement(placement) === auto ? computeAutoPlacement(state, {\n placement: placement,\n boundary: boundary,\n rootBoundary: rootBoundary,\n padding: padding,\n flipVariations: flipVariations,\n allowedAutoPlacements: allowedAutoPlacements\n }) : placement);\n }, []);\n var referenceRect = state.rects.reference;\n var popperRect = state.rects.popper;\n var checksMap = new Map();\n var makeFallbackChecks = true;\n var firstFittingPlacement = placements[0];\n\n for (var i = 0; i < placements.length; i++) {\n var placement = placements[i];\n\n var _basePlacement = getBasePlacement(placement);\n\n var isStartVariation = getVariation(placement) === start;\n var isVertical = [top, bottom].indexOf(_basePlacement) >= 0;\n var len = isVertical ? 'width' : 'height';\n var overflow = detectOverflow(state, {\n placement: placement,\n boundary: boundary,\n rootBoundary: rootBoundary,\n altBoundary: altBoundary,\n padding: padding\n });\n var mainVariationSide = isVertical ? isStartVariation ? right : left : isStartVariation ? bottom : top;\n\n if (referenceRect[len] > popperRect[len]) {\n mainVariationSide = getOppositePlacement(mainVariationSide);\n }\n\n var altVariationSide = getOppositePlacement(mainVariationSide);\n var checks = [];\n\n if (checkMainAxis) {\n checks.push(overflow[_basePlacement] <= 0);\n }\n\n if (checkAltAxis) {\n checks.push(overflow[mainVariationSide] <= 0, overflow[altVariationSide] <= 0);\n }\n\n if (checks.every(function (check) {\n return check;\n })) {\n firstFittingPlacement = placement;\n makeFallbackChecks = false;\n break;\n }\n\n checksMap.set(placement, checks);\n }\n\n if (makeFallbackChecks) {\n // `2` may be desired in some cases – research later\n var numberOfChecks = flipVariations ? 3 : 1;\n\n var _loop = function _loop(_i) {\n var fittingPlacement = placements.find(function (placement) {\n var checks = checksMap.get(placement);\n\n if (checks) {\n return checks.slice(0, _i).every(function (check) {\n return check;\n });\n }\n });\n\n if (fittingPlacement) {\n firstFittingPlacement = fittingPlacement;\n return \"break\";\n }\n };\n\n for (var _i = numberOfChecks; _i > 0; _i--) {\n var _ret = _loop(_i);\n\n if (_ret === \"break\") break;\n }\n }\n\n if (state.placement !== firstFittingPlacement) {\n state.modifiersData[name]._skip = true;\n state.placement = firstFittingPlacement;\n state.reset = true;\n }\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'flip',\n enabled: true,\n phase: 'main',\n fn: flip,\n requiresIfExists: ['offset'],\n data: {\n _skip: false\n }\n};","import getVariation from \"./getVariation.js\";\nimport { variationPlacements, basePlacements, placements as allPlacements } from \"../enums.js\";\nimport detectOverflow from \"./detectOverflow.js\";\nimport getBasePlacement from \"./getBasePlacement.js\";\nexport default function computeAutoPlacement(state, options) {\n if (options === void 0) {\n options = {};\n }\n\n var _options = options,\n placement = _options.placement,\n boundary = _options.boundary,\n rootBoundary = _options.rootBoundary,\n padding = _options.padding,\n flipVariations = _options.flipVariations,\n _options$allowedAutoP = _options.allowedAutoPlacements,\n allowedAutoPlacements = _options$allowedAutoP === void 0 ? allPlacements : _options$allowedAutoP;\n var variation = getVariation(placement);\n var placements = variation ? flipVariations ? variationPlacements : variationPlacements.filter(function (placement) {\n return getVariation(placement) === variation;\n }) : basePlacements;\n var allowedPlacements = placements.filter(function (placement) {\n return allowedAutoPlacements.indexOf(placement) >= 0;\n });\n\n if (allowedPlacements.length === 0) {\n allowedPlacements = placements;\n } // $FlowFixMe[incompatible-type]: Flow seems to have problems with two array unions...\n\n\n var overflows = allowedPlacements.reduce(function (acc, placement) {\n acc[placement] = detectOverflow(state, {\n placement: placement,\n boundary: boundary,\n rootBoundary: rootBoundary,\n padding: padding\n })[getBasePlacement(placement)];\n return acc;\n }, {});\n return Object.keys(overflows).sort(function (a, b) {\n return overflows[a] - overflows[b];\n });\n}","import { top, bottom, left, right } from \"../enums.js\";\nimport detectOverflow from \"../utils/detectOverflow.js\";\n\nfunction getSideOffsets(overflow, rect, preventedOffsets) {\n if (preventedOffsets === void 0) {\n preventedOffsets = {\n x: 0,\n y: 0\n };\n }\n\n return {\n top: overflow.top - rect.height - preventedOffsets.y,\n right: overflow.right - rect.width + preventedOffsets.x,\n bottom: overflow.bottom - rect.height + preventedOffsets.y,\n left: overflow.left - rect.width - preventedOffsets.x\n };\n}\n\nfunction isAnySideFullyClipped(overflow) {\n return [top, right, bottom, left].some(function (side) {\n return overflow[side] >= 0;\n });\n}\n\nfunction hide(_ref) {\n var state = _ref.state,\n name = _ref.name;\n var referenceRect = state.rects.reference;\n var popperRect = state.rects.popper;\n var preventedOffsets = state.modifiersData.preventOverflow;\n var referenceOverflow = detectOverflow(state, {\n elementContext: 'reference'\n });\n var popperAltOverflow = detectOverflow(state, {\n altBoundary: true\n });\n var referenceClippingOffsets = getSideOffsets(referenceOverflow, referenceRect);\n var popperEscapeOffsets = getSideOffsets(popperAltOverflow, popperRect, preventedOffsets);\n var isReferenceHidden = isAnySideFullyClipped(referenceClippingOffsets);\n var hasPopperEscaped = isAnySideFullyClipped(popperEscapeOffsets);\n state.modifiersData[name] = {\n referenceClippingOffsets: referenceClippingOffsets,\n popperEscapeOffsets: popperEscapeOffsets,\n isReferenceHidden: isReferenceHidden,\n hasPopperEscaped: hasPopperEscaped\n };\n state.attributes.popper = Object.assign({}, state.attributes.popper, {\n 'data-popper-reference-hidden': isReferenceHidden,\n 'data-popper-escaped': hasPopperEscaped\n });\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'hide',\n enabled: true,\n phase: 'main',\n requiresIfExists: ['preventOverflow'],\n fn: hide\n};","import getBasePlacement from \"../utils/getBasePlacement.js\";\nimport { top, left, right, placements } from \"../enums.js\"; // eslint-disable-next-line import/no-unused-modules\n\nexport function distanceAndSkiddingToXY(placement, rects, offset) {\n var basePlacement = getBasePlacement(placement);\n var invertDistance = [left, top].indexOf(basePlacement) >= 0 ? -1 : 1;\n\n var _ref = typeof offset === 'function' ? offset(Object.assign({}, rects, {\n placement: placement\n })) : offset,\n skidding = _ref[0],\n distance = _ref[1];\n\n skidding = skidding || 0;\n distance = (distance || 0) * invertDistance;\n return [left, right].indexOf(basePlacement) >= 0 ? {\n x: distance,\n y: skidding\n } : {\n x: skidding,\n y: distance\n };\n}\n\nfunction offset(_ref2) {\n var state = _ref2.state,\n options = _ref2.options,\n name = _ref2.name;\n var _options$offset = options.offset,\n offset = _options$offset === void 0 ? [0, 0] : _options$offset;\n var data = placements.reduce(function (acc, placement) {\n acc[placement] = distanceAndSkiddingToXY(placement, state.rects, offset);\n return acc;\n }, {});\n var _data$state$placement = data[state.placement],\n x = _data$state$placement.x,\n y = _data$state$placement.y;\n\n if (state.modifiersData.popperOffsets != null) {\n state.modifiersData.popperOffsets.x += x;\n state.modifiersData.popperOffsets.y += y;\n }\n\n state.modifiersData[name] = data;\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'offset',\n enabled: true,\n phase: 'main',\n requires: ['popperOffsets'],\n fn: offset\n};","import computeOffsets from \"../utils/computeOffsets.js\";\n\nfunction popperOffsets(_ref) {\n var state = _ref.state,\n name = _ref.name;\n // Offsets are the actual position the popper needs to have to be\n // properly positioned near its reference element\n // This is the most basic placement, and will be adjusted by\n // the modifiers in the next step\n state.modifiersData[name] = computeOffsets({\n reference: state.rects.reference,\n element: state.rects.popper,\n strategy: 'absolute',\n placement: state.placement\n });\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'popperOffsets',\n enabled: true,\n phase: 'read',\n fn: popperOffsets,\n data: {}\n};","import { top, left, right, bottom, start } from \"../enums.js\";\nimport getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getMainAxisFromPlacement from \"../utils/getMainAxisFromPlacement.js\";\nimport getAltAxis from \"../utils/getAltAxis.js\";\nimport { within, withinMaxClamp } from \"../utils/within.js\";\nimport getLayoutRect from \"../dom-utils/getLayoutRect.js\";\nimport getOffsetParent from \"../dom-utils/getOffsetParent.js\";\nimport detectOverflow from \"../utils/detectOverflow.js\";\nimport getVariation from \"../utils/getVariation.js\";\nimport getFreshSideObject from \"../utils/getFreshSideObject.js\";\nimport { min as mathMin, max as mathMax } from \"../utils/math.js\";\n\nfunction preventOverflow(_ref) {\n var state = _ref.state,\n options = _ref.options,\n name = _ref.name;\n var _options$mainAxis = options.mainAxis,\n checkMainAxis = _options$mainAxis === void 0 ? true : _options$mainAxis,\n _options$altAxis = options.altAxis,\n checkAltAxis = _options$altAxis === void 0 ? false : _options$altAxis,\n boundary = options.boundary,\n rootBoundary = options.rootBoundary,\n altBoundary = options.altBoundary,\n padding = options.padding,\n _options$tether = options.tether,\n tether = _options$tether === void 0 ? true : _options$tether,\n _options$tetherOffset = options.tetherOffset,\n tetherOffset = _options$tetherOffset === void 0 ? 0 : _options$tetherOffset;\n var overflow = detectOverflow(state, {\n boundary: boundary,\n rootBoundary: rootBoundary,\n padding: padding,\n altBoundary: altBoundary\n });\n var basePlacement = getBasePlacement(state.placement);\n var variation = getVariation(state.placement);\n var isBasePlacement = !variation;\n var mainAxis = getMainAxisFromPlacement(basePlacement);\n var altAxis = getAltAxis(mainAxis);\n var popperOffsets = state.modifiersData.popperOffsets;\n var referenceRect = state.rects.reference;\n var popperRect = state.rects.popper;\n var tetherOffsetValue = typeof tetherOffset === 'function' ? tetherOffset(Object.assign({}, state.rects, {\n placement: state.placement\n })) : tetherOffset;\n var normalizedTetherOffsetValue = typeof tetherOffsetValue === 'number' ? {\n mainAxis: tetherOffsetValue,\n altAxis: tetherOffsetValue\n } : Object.assign({\n mainAxis: 0,\n altAxis: 0\n }, tetherOffsetValue);\n var offsetModifierState = state.modifiersData.offset ? state.modifiersData.offset[state.placement] : null;\n var data = {\n x: 0,\n y: 0\n };\n\n if (!popperOffsets) {\n return;\n }\n\n if (checkMainAxis) {\n var _offsetModifierState$;\n\n var mainSide = mainAxis === 'y' ? top : left;\n var altSide = mainAxis === 'y' ? bottom : right;\n var len = mainAxis === 'y' ? 'height' : 'width';\n var offset = popperOffsets[mainAxis];\n var min = offset + overflow[mainSide];\n var max = offset - overflow[altSide];\n var additive = tether ? -popperRect[len] / 2 : 0;\n var minLen = variation === start ? referenceRect[len] : popperRect[len];\n var maxLen = variation === start ? -popperRect[len] : -referenceRect[len]; // We need to include the arrow in the calculation so the arrow doesn't go\n // outside the reference bounds\n\n var arrowElement = state.elements.arrow;\n var arrowRect = tether && arrowElement ? getLayoutRect(arrowElement) : {\n width: 0,\n height: 0\n };\n var arrowPaddingObject = state.modifiersData['arrow#persistent'] ? state.modifiersData['arrow#persistent'].padding : getFreshSideObject();\n var arrowPaddingMin = arrowPaddingObject[mainSide];\n var arrowPaddingMax = arrowPaddingObject[altSide]; // If the reference length is smaller than the arrow length, we don't want\n // to include its full size in the calculation. If the reference is small\n // and near the edge of a boundary, the popper can overflow even if the\n // reference is not overflowing as well (e.g. virtual elements with no\n // width or height)\n\n var arrowLen = within(0, referenceRect[len], arrowRect[len]);\n var minOffset = isBasePlacement ? referenceRect[len] / 2 - additive - arrowLen - arrowPaddingMin - normalizedTetherOffsetValue.mainAxis : minLen - arrowLen - arrowPaddingMin - normalizedTetherOffsetValue.mainAxis;\n var maxOffset = isBasePlacement ? -referenceRect[len] / 2 + additive + arrowLen + arrowPaddingMax + normalizedTetherOffsetValue.mainAxis : maxLen + arrowLen + arrowPaddingMax + normalizedTetherOffsetValue.mainAxis;\n var arrowOffsetParent = state.elements.arrow && getOffsetParent(state.elements.arrow);\n var clientOffset = arrowOffsetParent ? mainAxis === 'y' ? arrowOffsetParent.clientTop || 0 : arrowOffsetParent.clientLeft || 0 : 0;\n var offsetModifierValue = (_offsetModifierState$ = offsetModifierState == null ? void 0 : offsetModifierState[mainAxis]) != null ? _offsetModifierState$ : 0;\n var tetherMin = offset + minOffset - offsetModifierValue - clientOffset;\n var tetherMax = offset + maxOffset - offsetModifierValue;\n var preventedOffset = within(tether ? mathMin(min, tetherMin) : min, offset, tether ? mathMax(max, tetherMax) : max);\n popperOffsets[mainAxis] = preventedOffset;\n data[mainAxis] = preventedOffset - offset;\n }\n\n if (checkAltAxis) {\n var _offsetModifierState$2;\n\n var _mainSide = mainAxis === 'x' ? top : left;\n\n var _altSide = mainAxis === 'x' ? bottom : right;\n\n var _offset = popperOffsets[altAxis];\n\n var _len = altAxis === 'y' ? 'height' : 'width';\n\n var _min = _offset + overflow[_mainSide];\n\n var _max = _offset - overflow[_altSide];\n\n var isOriginSide = [top, left].indexOf(basePlacement) !== -1;\n\n var _offsetModifierValue = (_offsetModifierState$2 = offsetModifierState == null ? void 0 : offsetModifierState[altAxis]) != null ? _offsetModifierState$2 : 0;\n\n var _tetherMin = isOriginSide ? _min : _offset - referenceRect[_len] - popperRect[_len] - _offsetModifierValue + normalizedTetherOffsetValue.altAxis;\n\n var _tetherMax = isOriginSide ? _offset + referenceRect[_len] + popperRect[_len] - _offsetModifierValue - normalizedTetherOffsetValue.altAxis : _max;\n\n var _preventedOffset = tether && isOriginSide ? withinMaxClamp(_tetherMin, _offset, _tetherMax) : within(tether ? _tetherMin : _min, _offset, tether ? _tetherMax : _max);\n\n popperOffsets[altAxis] = _preventedOffset;\n data[altAxis] = _preventedOffset - _offset;\n }\n\n state.modifiersData[name] = data;\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'preventOverflow',\n enabled: true,\n phase: 'main',\n fn: preventOverflow,\n requiresIfExists: ['offset']\n};","export default function getAltAxis(axis) {\n return axis === 'x' ? 'y' : 'x';\n}","import getBoundingClientRect from \"./getBoundingClientRect.js\";\nimport getNodeScroll from \"./getNodeScroll.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport { isHTMLElement } from \"./instanceOf.js\";\nimport getWindowScrollBarX from \"./getWindowScrollBarX.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport isScrollParent from \"./isScrollParent.js\";\nimport { round } from \"../utils/math.js\";\n\nfunction isElementScaled(element) {\n var rect = element.getBoundingClientRect();\n var scaleX = round(rect.width) / element.offsetWidth || 1;\n var scaleY = round(rect.height) / element.offsetHeight || 1;\n return scaleX !== 1 || scaleY !== 1;\n} // Returns the composite rect of an element relative to its offsetParent.\n// Composite means it takes into account transforms as well as layout.\n\n\nexport default function getCompositeRect(elementOrVirtualElement, offsetParent, isFixed) {\n if (isFixed === void 0) {\n isFixed = false;\n }\n\n var isOffsetParentAnElement = isHTMLElement(offsetParent);\n var offsetParentIsScaled = isHTMLElement(offsetParent) && isElementScaled(offsetParent);\n var documentElement = getDocumentElement(offsetParent);\n var rect = getBoundingClientRect(elementOrVirtualElement, offsetParentIsScaled, isFixed);\n var scroll = {\n scrollLeft: 0,\n scrollTop: 0\n };\n var offsets = {\n x: 0,\n y: 0\n };\n\n if (isOffsetParentAnElement || !isOffsetParentAnElement && !isFixed) {\n if (getNodeName(offsetParent) !== 'body' || // https://github.com/popperjs/popper-core/issues/1078\n isScrollParent(documentElement)) {\n scroll = getNodeScroll(offsetParent);\n }\n\n if (isHTMLElement(offsetParent)) {\n offsets = getBoundingClientRect(offsetParent, true);\n offsets.x += offsetParent.clientLeft;\n offsets.y += offsetParent.clientTop;\n } else if (documentElement) {\n offsets.x = getWindowScrollBarX(documentElement);\n }\n }\n\n return {\n x: rect.left + scroll.scrollLeft - offsets.x,\n y: rect.top + scroll.scrollTop - offsets.y,\n width: rect.width,\n height: rect.height\n };\n}","import getWindowScroll from \"./getWindowScroll.js\";\nimport getWindow from \"./getWindow.js\";\nimport { isHTMLElement } from \"./instanceOf.js\";\nimport getHTMLElementScroll from \"./getHTMLElementScroll.js\";\nexport default function getNodeScroll(node) {\n if (node === getWindow(node) || !isHTMLElement(node)) {\n return getWindowScroll(node);\n } else {\n return getHTMLElementScroll(node);\n }\n}","export default function getHTMLElementScroll(element) {\n return {\n scrollLeft: element.scrollLeft,\n scrollTop: element.scrollTop\n };\n}","import { modifierPhases } from \"../enums.js\"; // source: https://stackoverflow.com/questions/49875255\n\nfunction order(modifiers) {\n var map = new Map();\n var visited = new Set();\n var result = [];\n modifiers.forEach(function (modifier) {\n map.set(modifier.name, modifier);\n }); // On visiting object, check for its dependencies and visit them recursively\n\n function sort(modifier) {\n visited.add(modifier.name);\n var requires = [].concat(modifier.requires || [], modifier.requiresIfExists || []);\n requires.forEach(function (dep) {\n if (!visited.has(dep)) {\n var depModifier = map.get(dep);\n\n if (depModifier) {\n sort(depModifier);\n }\n }\n });\n result.push(modifier);\n }\n\n modifiers.forEach(function (modifier) {\n if (!visited.has(modifier.name)) {\n // check for visited object\n sort(modifier);\n }\n });\n return result;\n}\n\nexport default function orderModifiers(modifiers) {\n // order based on dependencies\n var orderedModifiers = order(modifiers); // order based on phase\n\n return modifierPhases.reduce(function (acc, phase) {\n return acc.concat(orderedModifiers.filter(function (modifier) {\n return modifier.phase === phase;\n }));\n }, []);\n}","import getCompositeRect from \"./dom-utils/getCompositeRect.js\";\nimport getLayoutRect from \"./dom-utils/getLayoutRect.js\";\nimport listScrollParents from \"./dom-utils/listScrollParents.js\";\nimport getOffsetParent from \"./dom-utils/getOffsetParent.js\";\nimport orderModifiers from \"./utils/orderModifiers.js\";\nimport debounce from \"./utils/debounce.js\";\nimport mergeByName from \"./utils/mergeByName.js\";\nimport detectOverflow from \"./utils/detectOverflow.js\";\nimport { isElement } from \"./dom-utils/instanceOf.js\";\nvar DEFAULT_OPTIONS = {\n placement: 'bottom',\n modifiers: [],\n strategy: 'absolute'\n};\n\nfunction areValidElements() {\n for (var _len = arguments.length, args = new Array(_len), _key = 0; _key < _len; _key++) {\n args[_key] = arguments[_key];\n }\n\n return !args.some(function (element) {\n return !(element && typeof element.getBoundingClientRect === 'function');\n });\n}\n\nexport function popperGenerator(generatorOptions) {\n if (generatorOptions === void 0) {\n generatorOptions = {};\n }\n\n var _generatorOptions = generatorOptions,\n _generatorOptions$def = _generatorOptions.defaultModifiers,\n defaultModifiers = _generatorOptions$def === void 0 ? [] : _generatorOptions$def,\n _generatorOptions$def2 = _generatorOptions.defaultOptions,\n defaultOptions = _generatorOptions$def2 === void 0 ? DEFAULT_OPTIONS : _generatorOptions$def2;\n return function createPopper(reference, popper, options) {\n if (options === void 0) {\n options = defaultOptions;\n }\n\n var state = {\n placement: 'bottom',\n orderedModifiers: [],\n options: Object.assign({}, DEFAULT_OPTIONS, defaultOptions),\n modifiersData: {},\n elements: {\n reference: reference,\n popper: popper\n },\n attributes: {},\n styles: {}\n };\n var effectCleanupFns = [];\n var isDestroyed = false;\n var instance = {\n state: state,\n setOptions: function setOptions(setOptionsAction) {\n var options = typeof setOptionsAction === 'function' ? setOptionsAction(state.options) : setOptionsAction;\n cleanupModifierEffects();\n state.options = Object.assign({}, defaultOptions, state.options, options);\n state.scrollParents = {\n reference: isElement(reference) ? listScrollParents(reference) : reference.contextElement ? listScrollParents(reference.contextElement) : [],\n popper: listScrollParents(popper)\n }; // Orders the modifiers based on their dependencies and `phase`\n // properties\n\n var orderedModifiers = orderModifiers(mergeByName([].concat(defaultModifiers, state.options.modifiers))); // Strip out disabled modifiers\n\n state.orderedModifiers = orderedModifiers.filter(function (m) {\n return m.enabled;\n });\n runModifierEffects();\n return instance.update();\n },\n // Sync update – it will always be executed, even if not necessary. This\n // is useful for low frequency updates where sync behavior simplifies the\n // logic.\n // For high frequency updates (e.g. `resize` and `scroll` events), always\n // prefer the async Popper#update method\n forceUpdate: function forceUpdate() {\n if (isDestroyed) {\n return;\n }\n\n var _state$elements = state.elements,\n reference = _state$elements.reference,\n popper = _state$elements.popper; // Don't proceed if `reference` or `popper` are not valid elements\n // anymore\n\n if (!areValidElements(reference, popper)) {\n return;\n } // Store the reference and popper rects to be read by modifiers\n\n\n state.rects = {\n reference: getCompositeRect(reference, getOffsetParent(popper), state.options.strategy === 'fixed'),\n popper: getLayoutRect(popper)\n }; // Modifiers have the ability to reset the current update cycle. The\n // most common use case for this is the `flip` modifier changing the\n // placement, which then needs to re-run all the modifiers, because the\n // logic was previously ran for the previous placement and is therefore\n // stale/incorrect\n\n state.reset = false;\n state.placement = state.options.placement; // On each update cycle, the `modifiersData` property for each modifier\n // is filled with the initial data specified by the modifier. This means\n // it doesn't persist and is fresh on each update.\n // To ensure persistent data, use `${name}#persistent`\n\n state.orderedModifiers.forEach(function (modifier) {\n return state.modifiersData[modifier.name] = Object.assign({}, modifier.data);\n });\n\n for (var index = 0; index < state.orderedModifiers.length; index++) {\n if (state.reset === true) {\n state.reset = false;\n index = -1;\n continue;\n }\n\n var _state$orderedModifie = state.orderedModifiers[index],\n fn = _state$orderedModifie.fn,\n _state$orderedModifie2 = _state$orderedModifie.options,\n _options = _state$orderedModifie2 === void 0 ? {} : _state$orderedModifie2,\n name = _state$orderedModifie.name;\n\n if (typeof fn === 'function') {\n state = fn({\n state: state,\n options: _options,\n name: name,\n instance: instance\n }) || state;\n }\n }\n },\n // Async and optimistically optimized update – it will not be executed if\n // not necessary (debounced to run at most once-per-tick)\n update: debounce(function () {\n return new Promise(function (resolve) {\n instance.forceUpdate();\n resolve(state);\n });\n }),\n destroy: function destroy() {\n cleanupModifierEffects();\n isDestroyed = true;\n }\n };\n\n if (!areValidElements(reference, popper)) {\n return instance;\n }\n\n instance.setOptions(options).then(function (state) {\n if (!isDestroyed && options.onFirstUpdate) {\n options.onFirstUpdate(state);\n }\n }); // Modifiers have the ability to execute arbitrary code before the first\n // update cycle runs. They will be executed in the same order as the update\n // cycle. This is useful when a modifier adds some persistent data that\n // other modifiers need to use, but the modifier is run after the dependent\n // one.\n\n function runModifierEffects() {\n state.orderedModifiers.forEach(function (_ref) {\n var name = _ref.name,\n _ref$options = _ref.options,\n options = _ref$options === void 0 ? {} : _ref$options,\n effect = _ref.effect;\n\n if (typeof effect === 'function') {\n var cleanupFn = effect({\n state: state,\n name: name,\n instance: instance,\n options: options\n });\n\n var noopFn = function noopFn() {};\n\n effectCleanupFns.push(cleanupFn || noopFn);\n }\n });\n }\n\n function cleanupModifierEffects() {\n effectCleanupFns.forEach(function (fn) {\n return fn();\n });\n effectCleanupFns = [];\n }\n\n return instance;\n };\n}\nexport var createPopper = /*#__PURE__*/popperGenerator(); // eslint-disable-next-line import/no-unused-modules\n\nexport { detectOverflow };","export default function debounce(fn) {\n var pending;\n return function () {\n if (!pending) {\n pending = new Promise(function (resolve) {\n Promise.resolve().then(function () {\n pending = undefined;\n resolve(fn());\n });\n });\n }\n\n return pending;\n };\n}","export default function mergeByName(modifiers) {\n var merged = modifiers.reduce(function (merged, current) {\n var existing = merged[current.name];\n merged[current.name] = existing ? Object.assign({}, existing, current, {\n options: Object.assign({}, existing.options, current.options),\n data: Object.assign({}, existing.data, current.data)\n }) : current;\n return merged;\n }, {}); // IE11 does not support Object.values\n\n return Object.keys(merged).map(function (key) {\n return merged[key];\n });\n}","import { popperGenerator, detectOverflow } from \"./createPopper.js\";\nimport eventListeners from \"./modifiers/eventListeners.js\";\nimport popperOffsets from \"./modifiers/popperOffsets.js\";\nimport computeStyles from \"./modifiers/computeStyles.js\";\nimport applyStyles from \"./modifiers/applyStyles.js\";\nimport offset from \"./modifiers/offset.js\";\nimport flip from \"./modifiers/flip.js\";\nimport preventOverflow from \"./modifiers/preventOverflow.js\";\nimport arrow from \"./modifiers/arrow.js\";\nimport hide from \"./modifiers/hide.js\";\nvar defaultModifiers = [eventListeners, popperOffsets, computeStyles, applyStyles, offset, flip, preventOverflow, arrow, hide];\nvar createPopper = /*#__PURE__*/popperGenerator({\n defaultModifiers: defaultModifiers\n}); // eslint-disable-next-line import/no-unused-modules\n\nexport { createPopper, popperGenerator, defaultModifiers, detectOverflow }; // eslint-disable-next-line import/no-unused-modules\n\nexport { createPopper as createPopperLite } from \"./popper-lite.js\"; // eslint-disable-next-line import/no-unused-modules\n\nexport * from \"./modifiers/index.js\";","import { popperGenerator, detectOverflow } from \"./createPopper.js\";\nimport eventListeners from \"./modifiers/eventListeners.js\";\nimport popperOffsets from \"./modifiers/popperOffsets.js\";\nimport computeStyles from \"./modifiers/computeStyles.js\";\nimport applyStyles from \"./modifiers/applyStyles.js\";\nvar defaultModifiers = [eventListeners, popperOffsets, computeStyles, applyStyles];\nvar createPopper = /*#__PURE__*/popperGenerator({\n defaultModifiers: defaultModifiers\n}); // eslint-disable-next-line import/no-unused-modules\n\nexport { createPopper, popperGenerator, defaultModifiers, detectOverflow };","/*!\n * Bootstrap v5.3.2 (https://getbootstrap.com/)\n * Copyright 2011-2023 The Bootstrap Authors (https://github.com/twbs/bootstrap/graphs/contributors)\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n */\nimport * as Popper from '@popperjs/core';\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap dom/data.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n/**\n * Constants\n */\n\nconst elementMap = new Map();\nconst Data = {\n set(element, key, instance) {\n if (!elementMap.has(element)) {\n elementMap.set(element, new Map());\n }\n const instanceMap = elementMap.get(element);\n\n // make it clear we only want one instance per element\n // can be removed later when multiple key/instances are fine to be used\n if (!instanceMap.has(key) && instanceMap.size !== 0) {\n // eslint-disable-next-line no-console\n console.error(`Bootstrap doesn't allow more than one instance per element. Bound instance: ${Array.from(instanceMap.keys())[0]}.`);\n return;\n }\n instanceMap.set(key, instance);\n },\n get(element, key) {\n if (elementMap.has(element)) {\n return elementMap.get(element).get(key) || null;\n }\n return null;\n },\n remove(element, key) {\n if (!elementMap.has(element)) {\n return;\n }\n const instanceMap = elementMap.get(element);\n instanceMap.delete(key);\n\n // free up element references if there are no instances left for an element\n if (instanceMap.size === 0) {\n elementMap.delete(element);\n }\n }\n};\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap util/index.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nconst MAX_UID = 1000000;\nconst MILLISECONDS_MULTIPLIER = 1000;\nconst TRANSITION_END = 'transitionend';\n\n/**\n * Properly escape IDs selectors to handle weird IDs\n * @param {string} selector\n * @returns {string}\n */\nconst parseSelector = selector => {\n if (selector && window.CSS && window.CSS.escape) {\n // document.querySelector needs escaping to handle IDs (html5+) containing for instance /\n selector = selector.replace(/#([^\\s\"#']+)/g, (match, id) => `#${CSS.escape(id)}`);\n }\n return selector;\n};\n\n// Shout-out Angus Croll (https://goo.gl/pxwQGp)\nconst toType = object => {\n if (object === null || object === undefined) {\n return `${object}`;\n }\n return Object.prototype.toString.call(object).match(/\\s([a-z]+)/i)[1].toLowerCase();\n};\n\n/**\n * Public Util API\n */\n\nconst getUID = prefix => {\n do {\n prefix += Math.floor(Math.random() * MAX_UID);\n } while (document.getElementById(prefix));\n return prefix;\n};\nconst getTransitionDurationFromElement = element => {\n if (!element) {\n return 0;\n }\n\n // Get transition-duration of the element\n let {\n transitionDuration,\n transitionDelay\n } = window.getComputedStyle(element);\n const floatTransitionDuration = Number.parseFloat(transitionDuration);\n const floatTransitionDelay = Number.parseFloat(transitionDelay);\n\n // Return 0 if element or transition duration is not found\n if (!floatTransitionDuration && !floatTransitionDelay) {\n return 0;\n }\n\n // If multiple durations are defined, take the first\n transitionDuration = transitionDuration.split(',')[0];\n transitionDelay = transitionDelay.split(',')[0];\n return (Number.parseFloat(transitionDuration) + Number.parseFloat(transitionDelay)) * MILLISECONDS_MULTIPLIER;\n};\nconst triggerTransitionEnd = element => {\n element.dispatchEvent(new Event(TRANSITION_END));\n};\nconst isElement = object => {\n if (!object || typeof object !== 'object') {\n return false;\n }\n if (typeof object.jquery !== 'undefined') {\n object = object[0];\n }\n return typeof object.nodeType !== 'undefined';\n};\nconst getElement = object => {\n // it's a jQuery object or a node element\n if (isElement(object)) {\n return object.jquery ? object[0] : object;\n }\n if (typeof object === 'string' && object.length > 0) {\n return document.querySelector(parseSelector(object));\n }\n return null;\n};\nconst isVisible = element => {\n if (!isElement(element) || element.getClientRects().length === 0) {\n return false;\n }\n const elementIsVisible = getComputedStyle(element).getPropertyValue('visibility') === 'visible';\n // Handle `details` element as its content may falsie appear visible when it is closed\n const closedDetails = element.closest('details:not([open])');\n if (!closedDetails) {\n return elementIsVisible;\n }\n if (closedDetails !== element) {\n const summary = element.closest('summary');\n if (summary && summary.parentNode !== closedDetails) {\n return false;\n }\n if (summary === null) {\n return false;\n }\n }\n return elementIsVisible;\n};\nconst isDisabled = element => {\n if (!element || element.nodeType !== Node.ELEMENT_NODE) {\n return true;\n }\n if (element.classList.contains('disabled')) {\n return true;\n }\n if (typeof element.disabled !== 'undefined') {\n return element.disabled;\n }\n return element.hasAttribute('disabled') && element.getAttribute('disabled') !== 'false';\n};\nconst findShadowRoot = element => {\n if (!document.documentElement.attachShadow) {\n return null;\n }\n\n // Can find the shadow root otherwise it'll return the document\n if (typeof element.getRootNode === 'function') {\n const root = element.getRootNode();\n return root instanceof ShadowRoot ? root : null;\n }\n if (element instanceof ShadowRoot) {\n return element;\n }\n\n // when we don't find a shadow root\n if (!element.parentNode) {\n return null;\n }\n return findShadowRoot(element.parentNode);\n};\nconst noop = () => {};\n\n/**\n * Trick to restart an element's animation\n *\n * @param {HTMLElement} element\n * @return void\n *\n * @see https://www.charistheo.io/blog/2021/02/restart-a-css-animation-with-javascript/#restarting-a-css-animation\n */\nconst reflow = element => {\n element.offsetHeight; // eslint-disable-line no-unused-expressions\n};\n\nconst getjQuery = () => {\n if (window.jQuery && !document.body.hasAttribute('data-bs-no-jquery')) {\n return window.jQuery;\n }\n return null;\n};\nconst DOMContentLoadedCallbacks = [];\nconst onDOMContentLoaded = callback => {\n if (document.readyState === 'loading') {\n // add listener on the first call when the document is in loading state\n if (!DOMContentLoadedCallbacks.length) {\n document.addEventListener('DOMContentLoaded', () => {\n for (const callback of DOMContentLoadedCallbacks) {\n callback();\n }\n });\n }\n DOMContentLoadedCallbacks.push(callback);\n } else {\n callback();\n }\n};\nconst isRTL = () => document.documentElement.dir === 'rtl';\nconst defineJQueryPlugin = plugin => {\n onDOMContentLoaded(() => {\n const $ = getjQuery();\n /* istanbul ignore if */\n if ($) {\n const name = plugin.NAME;\n const JQUERY_NO_CONFLICT = $.fn[name];\n $.fn[name] = plugin.jQueryInterface;\n $.fn[name].Constructor = plugin;\n $.fn[name].noConflict = () => {\n $.fn[name] = JQUERY_NO_CONFLICT;\n return plugin.jQueryInterface;\n };\n }\n });\n};\nconst execute = (possibleCallback, args = [], defaultValue = possibleCallback) => {\n return typeof possibleCallback === 'function' ? possibleCallback(...args) : defaultValue;\n};\nconst executeAfterTransition = (callback, transitionElement, waitForTransition = true) => {\n if (!waitForTransition) {\n execute(callback);\n return;\n }\n const durationPadding = 5;\n const emulatedDuration = getTransitionDurationFromElement(transitionElement) + durationPadding;\n let called = false;\n const handler = ({\n target\n }) => {\n if (target !== transitionElement) {\n return;\n }\n called = true;\n transitionElement.removeEventListener(TRANSITION_END, handler);\n execute(callback);\n };\n transitionElement.addEventListener(TRANSITION_END, handler);\n setTimeout(() => {\n if (!called) {\n triggerTransitionEnd(transitionElement);\n }\n }, emulatedDuration);\n};\n\n/**\n * Return the previous/next element of a list.\n *\n * @param {array} list The list of elements\n * @param activeElement The active element\n * @param shouldGetNext Choose to get next or previous element\n * @param isCycleAllowed\n * @return {Element|elem} The proper element\n */\nconst getNextActiveElement = (list, activeElement, shouldGetNext, isCycleAllowed) => {\n const listLength = list.length;\n let index = list.indexOf(activeElement);\n\n // if the element does not exist in the list return an element\n // depending on the direction and if cycle is allowed\n if (index === -1) {\n return !shouldGetNext && isCycleAllowed ? list[listLength - 1] : list[0];\n }\n index += shouldGetNext ? 1 : -1;\n if (isCycleAllowed) {\n index = (index + listLength) % listLength;\n }\n return list[Math.max(0, Math.min(index, listLength - 1))];\n};\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap dom/event-handler.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst namespaceRegex = /[^.]*(?=\\..*)\\.|.*/;\nconst stripNameRegex = /\\..*/;\nconst stripUidRegex = /::\\d+$/;\nconst eventRegistry = {}; // Events storage\nlet uidEvent = 1;\nconst customEvents = {\n mouseenter: 'mouseover',\n mouseleave: 'mouseout'\n};\nconst nativeEvents = new Set(['click', 'dblclick', 'mouseup', 'mousedown', 'contextmenu', 'mousewheel', 'DOMMouseScroll', 'mouseover', 'mouseout', 'mousemove', 'selectstart', 'selectend', 'keydown', 'keypress', 'keyup', 'orientationchange', 'touchstart', 'touchmove', 'touchend', 'touchcancel', 'pointerdown', 'pointermove', 'pointerup', 'pointerleave', 'pointercancel', 'gesturestart', 'gesturechange', 'gestureend', 'focus', 'blur', 'change', 'reset', 'select', 'submit', 'focusin', 'focusout', 'load', 'unload', 'beforeunload', 'resize', 'move', 'DOMContentLoaded', 'readystatechange', 'error', 'abort', 'scroll']);\n\n/**\n * Private methods\n */\n\nfunction makeEventUid(element, uid) {\n return uid && `${uid}::${uidEvent++}` || element.uidEvent || uidEvent++;\n}\nfunction getElementEvents(element) {\n const uid = makeEventUid(element);\n element.uidEvent = uid;\n eventRegistry[uid] = eventRegistry[uid] || {};\n return eventRegistry[uid];\n}\nfunction bootstrapHandler(element, fn) {\n return function handler(event) {\n hydrateObj(event, {\n delegateTarget: element\n });\n if (handler.oneOff) {\n EventHandler.off(element, event.type, fn);\n }\n return fn.apply(element, [event]);\n };\n}\nfunction bootstrapDelegationHandler(element, selector, fn) {\n return function handler(event) {\n const domElements = element.querySelectorAll(selector);\n for (let {\n target\n } = event; target && target !== this; target = target.parentNode) {\n for (const domElement of domElements) {\n if (domElement !== target) {\n continue;\n }\n hydrateObj(event, {\n delegateTarget: target\n });\n if (handler.oneOff) {\n EventHandler.off(element, event.type, selector, fn);\n }\n return fn.apply(target, [event]);\n }\n }\n };\n}\nfunction findHandler(events, callable, delegationSelector = null) {\n return Object.values(events).find(event => event.callable === callable && event.delegationSelector === delegationSelector);\n}\nfunction normalizeParameters(originalTypeEvent, handler, delegationFunction) {\n const isDelegated = typeof handler === 'string';\n // TODO: tooltip passes `false` instead of selector, so we need to check\n const callable = isDelegated ? delegationFunction : handler || delegationFunction;\n let typeEvent = getTypeEvent(originalTypeEvent);\n if (!nativeEvents.has(typeEvent)) {\n typeEvent = originalTypeEvent;\n }\n return [isDelegated, callable, typeEvent];\n}\nfunction addHandler(element, originalTypeEvent, handler, delegationFunction, oneOff) {\n if (typeof originalTypeEvent !== 'string' || !element) {\n return;\n }\n let [isDelegated, callable, typeEvent] = normalizeParameters(originalTypeEvent, handler, delegationFunction);\n\n // in case of mouseenter or mouseleave wrap the handler within a function that checks for its DOM position\n // this prevents the handler from being dispatched the same way as mouseover or mouseout does\n if (originalTypeEvent in customEvents) {\n const wrapFunction = fn => {\n return function (event) {\n if (!event.relatedTarget || event.relatedTarget !== event.delegateTarget && !event.delegateTarget.contains(event.relatedTarget)) {\n return fn.call(this, event);\n }\n };\n };\n callable = wrapFunction(callable);\n }\n const events = getElementEvents(element);\n const handlers = events[typeEvent] || (events[typeEvent] = {});\n const previousFunction = findHandler(handlers, callable, isDelegated ? handler : null);\n if (previousFunction) {\n previousFunction.oneOff = previousFunction.oneOff && oneOff;\n return;\n }\n const uid = makeEventUid(callable, originalTypeEvent.replace(namespaceRegex, ''));\n const fn = isDelegated ? bootstrapDelegationHandler(element, handler, callable) : bootstrapHandler(element, callable);\n fn.delegationSelector = isDelegated ? handler : null;\n fn.callable = callable;\n fn.oneOff = oneOff;\n fn.uidEvent = uid;\n handlers[uid] = fn;\n element.addEventListener(typeEvent, fn, isDelegated);\n}\nfunction removeHandler(element, events, typeEvent, handler, delegationSelector) {\n const fn = findHandler(events[typeEvent], handler, delegationSelector);\n if (!fn) {\n return;\n }\n element.removeEventListener(typeEvent, fn, Boolean(delegationSelector));\n delete events[typeEvent][fn.uidEvent];\n}\nfunction removeNamespacedHandlers(element, events, typeEvent, namespace) {\n const storeElementEvent = events[typeEvent] || {};\n for (const [handlerKey, event] of Object.entries(storeElementEvent)) {\n if (handlerKey.includes(namespace)) {\n removeHandler(element, events, typeEvent, event.callable, event.delegationSelector);\n }\n }\n}\nfunction getTypeEvent(event) {\n // allow to get the native events from namespaced events ('click.bs.button' --> 'click')\n event = event.replace(stripNameRegex, '');\n return customEvents[event] || event;\n}\nconst EventHandler = {\n on(element, event, handler, delegationFunction) {\n addHandler(element, event, handler, delegationFunction, false);\n },\n one(element, event, handler, delegationFunction) {\n addHandler(element, event, handler, delegationFunction, true);\n },\n off(element, originalTypeEvent, handler, delegationFunction) {\n if (typeof originalTypeEvent !== 'string' || !element) {\n return;\n }\n const [isDelegated, callable, typeEvent] = normalizeParameters(originalTypeEvent, handler, delegationFunction);\n const inNamespace = typeEvent !== originalTypeEvent;\n const events = getElementEvents(element);\n const storeElementEvent = events[typeEvent] || {};\n const isNamespace = originalTypeEvent.startsWith('.');\n if (typeof callable !== 'undefined') {\n // Simplest case: handler is passed, remove that listener ONLY.\n if (!Object.keys(storeElementEvent).length) {\n return;\n }\n removeHandler(element, events, typeEvent, callable, isDelegated ? handler : null);\n return;\n }\n if (isNamespace) {\n for (const elementEvent of Object.keys(events)) {\n removeNamespacedHandlers(element, events, elementEvent, originalTypeEvent.slice(1));\n }\n }\n for (const [keyHandlers, event] of Object.entries(storeElementEvent)) {\n const handlerKey = keyHandlers.replace(stripUidRegex, '');\n if (!inNamespace || originalTypeEvent.includes(handlerKey)) {\n removeHandler(element, events, typeEvent, event.callable, event.delegationSelector);\n }\n }\n },\n trigger(element, event, args) {\n if (typeof event !== 'string' || !element) {\n return null;\n }\n const $ = getjQuery();\n const typeEvent = getTypeEvent(event);\n const inNamespace = event !== typeEvent;\n let jQueryEvent = null;\n let bubbles = true;\n let nativeDispatch = true;\n let defaultPrevented = false;\n if (inNamespace && $) {\n jQueryEvent = $.Event(event, args);\n $(element).trigger(jQueryEvent);\n bubbles = !jQueryEvent.isPropagationStopped();\n nativeDispatch = !jQueryEvent.isImmediatePropagationStopped();\n defaultPrevented = jQueryEvent.isDefaultPrevented();\n }\n const evt = hydrateObj(new Event(event, {\n bubbles,\n cancelable: true\n }), args);\n if (defaultPrevented) {\n evt.preventDefault();\n }\n if (nativeDispatch) {\n element.dispatchEvent(evt);\n }\n if (evt.defaultPrevented && jQueryEvent) {\n jQueryEvent.preventDefault();\n }\n return evt;\n }\n};\nfunction hydrateObj(obj, meta = {}) {\n for (const [key, value] of Object.entries(meta)) {\n try {\n obj[key] = value;\n } catch (_unused) {\n Object.defineProperty(obj, key, {\n configurable: true,\n get() {\n return value;\n }\n });\n }\n }\n return obj;\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap dom/manipulator.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nfunction normalizeData(value) {\n if (value === 'true') {\n return true;\n }\n if (value === 'false') {\n return false;\n }\n if (value === Number(value).toString()) {\n return Number(value);\n }\n if (value === '' || value === 'null') {\n return null;\n }\n if (typeof value !== 'string') {\n return value;\n }\n try {\n return JSON.parse(decodeURIComponent(value));\n } catch (_unused) {\n return value;\n }\n}\nfunction normalizeDataKey(key) {\n return key.replace(/[A-Z]/g, chr => `-${chr.toLowerCase()}`);\n}\nconst Manipulator = {\n setDataAttribute(element, key, value) {\n element.setAttribute(`data-bs-${normalizeDataKey(key)}`, value);\n },\n removeDataAttribute(element, key) {\n element.removeAttribute(`data-bs-${normalizeDataKey(key)}`);\n },\n getDataAttributes(element) {\n if (!element) {\n return {};\n }\n const attributes = {};\n const bsKeys = Object.keys(element.dataset).filter(key => key.startsWith('bs') && !key.startsWith('bsConfig'));\n for (const key of bsKeys) {\n let pureKey = key.replace(/^bs/, '');\n pureKey = pureKey.charAt(0).toLowerCase() + pureKey.slice(1, pureKey.length);\n attributes[pureKey] = normalizeData(element.dataset[key]);\n }\n return attributes;\n },\n getDataAttribute(element, key) {\n return normalizeData(element.getAttribute(`data-bs-${normalizeDataKey(key)}`));\n }\n};\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap util/config.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Class definition\n */\n\nclass Config {\n // Getters\n static get Default() {\n return {};\n }\n static get DefaultType() {\n return {};\n }\n static get NAME() {\n throw new Error('You have to implement the static method \"NAME\", for each component!');\n }\n _getConfig(config) {\n config = this._mergeConfigObj(config);\n config = this._configAfterMerge(config);\n this._typeCheckConfig(config);\n return config;\n }\n _configAfterMerge(config) {\n return config;\n }\n _mergeConfigObj(config, element) {\n const jsonConfig = isElement(element) ? Manipulator.getDataAttribute(element, 'config') : {}; // try to parse\n\n return {\n ...this.constructor.Default,\n ...(typeof jsonConfig === 'object' ? jsonConfig : {}),\n ...(isElement(element) ? Manipulator.getDataAttributes(element) : {}),\n ...(typeof config === 'object' ? config : {})\n };\n }\n _typeCheckConfig(config, configTypes = this.constructor.DefaultType) {\n for (const [property, expectedTypes] of Object.entries(configTypes)) {\n const value = config[property];\n const valueType = isElement(value) ? 'element' : toType(value);\n if (!new RegExp(expectedTypes).test(valueType)) {\n throw new TypeError(`${this.constructor.NAME.toUpperCase()}: Option \"${property}\" provided type \"${valueType}\" but expected type \"${expectedTypes}\".`);\n }\n }\n }\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap base-component.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst VERSION = '5.3.2';\n\n/**\n * Class definition\n */\n\nclass BaseComponent extends Config {\n constructor(element, config) {\n super();\n element = getElement(element);\n if (!element) {\n return;\n }\n this._element = element;\n this._config = this._getConfig(config);\n Data.set(this._element, this.constructor.DATA_KEY, this);\n }\n\n // Public\n dispose() {\n Data.remove(this._element, this.constructor.DATA_KEY);\n EventHandler.off(this._element, this.constructor.EVENT_KEY);\n for (const propertyName of Object.getOwnPropertyNames(this)) {\n this[propertyName] = null;\n }\n }\n _queueCallback(callback, element, isAnimated = true) {\n executeAfterTransition(callback, element, isAnimated);\n }\n _getConfig(config) {\n config = this._mergeConfigObj(config, this._element);\n config = this._configAfterMerge(config);\n this._typeCheckConfig(config);\n return config;\n }\n\n // Static\n static getInstance(element) {\n return Data.get(getElement(element), this.DATA_KEY);\n }\n static getOrCreateInstance(element, config = {}) {\n return this.getInstance(element) || new this(element, typeof config === 'object' ? config : null);\n }\n static get VERSION() {\n return VERSION;\n }\n static get DATA_KEY() {\n return `bs.${this.NAME}`;\n }\n static get EVENT_KEY() {\n return `.${this.DATA_KEY}`;\n }\n static eventName(name) {\n return `${name}${this.EVENT_KEY}`;\n }\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap dom/selector-engine.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nconst getSelector = element => {\n let selector = element.getAttribute('data-bs-target');\n if (!selector || selector === '#') {\n let hrefAttribute = element.getAttribute('href');\n\n // The only valid content that could double as a selector are IDs or classes,\n // so everything starting with `#` or `.`. If a \"real\" URL is used as the selector,\n // `document.querySelector` will rightfully complain it is invalid.\n // See https://github.com/twbs/bootstrap/issues/32273\n if (!hrefAttribute || !hrefAttribute.includes('#') && !hrefAttribute.startsWith('.')) {\n return null;\n }\n\n // Just in case some CMS puts out a full URL with the anchor appended\n if (hrefAttribute.includes('#') && !hrefAttribute.startsWith('#')) {\n hrefAttribute = `#${hrefAttribute.split('#')[1]}`;\n }\n selector = hrefAttribute && hrefAttribute !== '#' ? parseSelector(hrefAttribute.trim()) : null;\n }\n return selector;\n};\nconst SelectorEngine = {\n find(selector, element = document.documentElement) {\n return [].concat(...Element.prototype.querySelectorAll.call(element, selector));\n },\n findOne(selector, element = document.documentElement) {\n return Element.prototype.querySelector.call(element, selector);\n },\n children(element, selector) {\n return [].concat(...element.children).filter(child => child.matches(selector));\n },\n parents(element, selector) {\n const parents = [];\n let ancestor = element.parentNode.closest(selector);\n while (ancestor) {\n parents.push(ancestor);\n ancestor = ancestor.parentNode.closest(selector);\n }\n return parents;\n },\n prev(element, selector) {\n let previous = element.previousElementSibling;\n while (previous) {\n if (previous.matches(selector)) {\n return [previous];\n }\n previous = previous.previousElementSibling;\n }\n return [];\n },\n // TODO: this is now unused; remove later along with prev()\n next(element, selector) {\n let next = element.nextElementSibling;\n while (next) {\n if (next.matches(selector)) {\n return [next];\n }\n next = next.nextElementSibling;\n }\n return [];\n },\n focusableChildren(element) {\n const focusables = ['a', 'button', 'input', 'textarea', 'select', 'details', '[tabindex]', '[contenteditable=\"true\"]'].map(selector => `${selector}:not([tabindex^=\"-\"])`).join(',');\n return this.find(focusables, element).filter(el => !isDisabled(el) && isVisible(el));\n },\n getSelectorFromElement(element) {\n const selector = getSelector(element);\n if (selector) {\n return SelectorEngine.findOne(selector) ? selector : null;\n }\n return null;\n },\n getElementFromSelector(element) {\n const selector = getSelector(element);\n return selector ? SelectorEngine.findOne(selector) : null;\n },\n getMultipleElementsFromSelector(element) {\n const selector = getSelector(element);\n return selector ? SelectorEngine.find(selector) : [];\n }\n};\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap util/component-functions.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nconst enableDismissTrigger = (component, method = 'hide') => {\n const clickEvent = `click.dismiss${component.EVENT_KEY}`;\n const name = component.NAME;\n EventHandler.on(document, clickEvent, `[data-bs-dismiss=\"${name}\"]`, function (event) {\n if (['A', 'AREA'].includes(this.tagName)) {\n event.preventDefault();\n }\n if (isDisabled(this)) {\n return;\n }\n const target = SelectorEngine.getElementFromSelector(this) || this.closest(`.${name}`);\n const instance = component.getOrCreateInstance(target);\n\n // Method argument is left, for Alert and only, as it doesn't implement the 'hide' method\n instance[method]();\n });\n};\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap alert.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$f = 'alert';\nconst DATA_KEY$a = 'bs.alert';\nconst EVENT_KEY$b = `.${DATA_KEY$a}`;\nconst EVENT_CLOSE = `close${EVENT_KEY$b}`;\nconst EVENT_CLOSED = `closed${EVENT_KEY$b}`;\nconst CLASS_NAME_FADE$5 = 'fade';\nconst CLASS_NAME_SHOW$8 = 'show';\n\n/**\n * Class definition\n */\n\nclass Alert extends BaseComponent {\n // Getters\n static get NAME() {\n return NAME$f;\n }\n\n // Public\n close() {\n const closeEvent = EventHandler.trigger(this._element, EVENT_CLOSE);\n if (closeEvent.defaultPrevented) {\n return;\n }\n this._element.classList.remove(CLASS_NAME_SHOW$8);\n const isAnimated = this._element.classList.contains(CLASS_NAME_FADE$5);\n this._queueCallback(() => this._destroyElement(), this._element, isAnimated);\n }\n\n // Private\n _destroyElement() {\n this._element.remove();\n EventHandler.trigger(this._element, EVENT_CLOSED);\n this.dispose();\n }\n\n // Static\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Alert.getOrCreateInstance(this);\n if (typeof config !== 'string') {\n return;\n }\n if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n throw new TypeError(`No method named \"${config}\"`);\n }\n data[config](this);\n });\n }\n}\n\n/**\n * Data API implementation\n */\n\nenableDismissTrigger(Alert, 'close');\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Alert);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap button.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$e = 'button';\nconst DATA_KEY$9 = 'bs.button';\nconst EVENT_KEY$a = `.${DATA_KEY$9}`;\nconst DATA_API_KEY$6 = '.data-api';\nconst CLASS_NAME_ACTIVE$3 = 'active';\nconst SELECTOR_DATA_TOGGLE$5 = '[data-bs-toggle=\"button\"]';\nconst EVENT_CLICK_DATA_API$6 = `click${EVENT_KEY$a}${DATA_API_KEY$6}`;\n\n/**\n * Class definition\n */\n\nclass Button extends BaseComponent {\n // Getters\n static get NAME() {\n return NAME$e;\n }\n\n // Public\n toggle() {\n // Toggle class and sync the `aria-pressed` attribute with the return value of the `.toggle()` method\n this._element.setAttribute('aria-pressed', this._element.classList.toggle(CLASS_NAME_ACTIVE$3));\n }\n\n // Static\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Button.getOrCreateInstance(this);\n if (config === 'toggle') {\n data[config]();\n }\n });\n }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API$6, SELECTOR_DATA_TOGGLE$5, event => {\n event.preventDefault();\n const button = event.target.closest(SELECTOR_DATA_TOGGLE$5);\n const data = Button.getOrCreateInstance(button);\n data.toggle();\n});\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Button);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap util/swipe.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$d = 'swipe';\nconst EVENT_KEY$9 = '.bs.swipe';\nconst EVENT_TOUCHSTART = `touchstart${EVENT_KEY$9}`;\nconst EVENT_TOUCHMOVE = `touchmove${EVENT_KEY$9}`;\nconst EVENT_TOUCHEND = `touchend${EVENT_KEY$9}`;\nconst EVENT_POINTERDOWN = `pointerdown${EVENT_KEY$9}`;\nconst EVENT_POINTERUP = `pointerup${EVENT_KEY$9}`;\nconst POINTER_TYPE_TOUCH = 'touch';\nconst POINTER_TYPE_PEN = 'pen';\nconst CLASS_NAME_POINTER_EVENT = 'pointer-event';\nconst SWIPE_THRESHOLD = 40;\nconst Default$c = {\n endCallback: null,\n leftCallback: null,\n rightCallback: null\n};\nconst DefaultType$c = {\n endCallback: '(function|null)',\n leftCallback: '(function|null)',\n rightCallback: '(function|null)'\n};\n\n/**\n * Class definition\n */\n\nclass Swipe extends Config {\n constructor(element, config) {\n super();\n this._element = element;\n if (!element || !Swipe.isSupported()) {\n return;\n }\n this._config = this._getConfig(config);\n this._deltaX = 0;\n this._supportPointerEvents = Boolean(window.PointerEvent);\n this._initEvents();\n }\n\n // Getters\n static get Default() {\n return Default$c;\n }\n static get DefaultType() {\n return DefaultType$c;\n }\n static get NAME() {\n return NAME$d;\n }\n\n // Public\n dispose() {\n EventHandler.off(this._element, EVENT_KEY$9);\n }\n\n // Private\n _start(event) {\n if (!this._supportPointerEvents) {\n this._deltaX = event.touches[0].clientX;\n return;\n }\n if (this._eventIsPointerPenTouch(event)) {\n this._deltaX = event.clientX;\n }\n }\n _end(event) {\n if (this._eventIsPointerPenTouch(event)) {\n this._deltaX = event.clientX - this._deltaX;\n }\n this._handleSwipe();\n execute(this._config.endCallback);\n }\n _move(event) {\n this._deltaX = event.touches && event.touches.length > 1 ? 0 : event.touches[0].clientX - this._deltaX;\n }\n _handleSwipe() {\n const absDeltaX = Math.abs(this._deltaX);\n if (absDeltaX <= SWIPE_THRESHOLD) {\n return;\n }\n const direction = absDeltaX / this._deltaX;\n this._deltaX = 0;\n if (!direction) {\n return;\n }\n execute(direction > 0 ? this._config.rightCallback : this._config.leftCallback);\n }\n _initEvents() {\n if (this._supportPointerEvents) {\n EventHandler.on(this._element, EVENT_POINTERDOWN, event => this._start(event));\n EventHandler.on(this._element, EVENT_POINTERUP, event => this._end(event));\n this._element.classList.add(CLASS_NAME_POINTER_EVENT);\n } else {\n EventHandler.on(this._element, EVENT_TOUCHSTART, event => this._start(event));\n EventHandler.on(this._element, EVENT_TOUCHMOVE, event => this._move(event));\n EventHandler.on(this._element, EVENT_TOUCHEND, event => this._end(event));\n }\n }\n _eventIsPointerPenTouch(event) {\n return this._supportPointerEvents && (event.pointerType === POINTER_TYPE_PEN || event.pointerType === POINTER_TYPE_TOUCH);\n }\n\n // Static\n static isSupported() {\n return 'ontouchstart' in document.documentElement || navigator.maxTouchPoints > 0;\n }\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap carousel.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$c = 'carousel';\nconst DATA_KEY$8 = 'bs.carousel';\nconst EVENT_KEY$8 = `.${DATA_KEY$8}`;\nconst DATA_API_KEY$5 = '.data-api';\nconst ARROW_LEFT_KEY$1 = 'ArrowLeft';\nconst ARROW_RIGHT_KEY$1 = 'ArrowRight';\nconst TOUCHEVENT_COMPAT_WAIT = 500; // Time for mouse compat events to fire after touch\n\nconst ORDER_NEXT = 'next';\nconst ORDER_PREV = 'prev';\nconst DIRECTION_LEFT = 'left';\nconst DIRECTION_RIGHT = 'right';\nconst EVENT_SLIDE = `slide${EVENT_KEY$8}`;\nconst EVENT_SLID = `slid${EVENT_KEY$8}`;\nconst EVENT_KEYDOWN$1 = `keydown${EVENT_KEY$8}`;\nconst EVENT_MOUSEENTER$1 = `mouseenter${EVENT_KEY$8}`;\nconst EVENT_MOUSELEAVE$1 = `mouseleave${EVENT_KEY$8}`;\nconst EVENT_DRAG_START = `dragstart${EVENT_KEY$8}`;\nconst EVENT_LOAD_DATA_API$3 = `load${EVENT_KEY$8}${DATA_API_KEY$5}`;\nconst EVENT_CLICK_DATA_API$5 = `click${EVENT_KEY$8}${DATA_API_KEY$5}`;\nconst CLASS_NAME_CAROUSEL = 'carousel';\nconst CLASS_NAME_ACTIVE$2 = 'active';\nconst CLASS_NAME_SLIDE = 'slide';\nconst CLASS_NAME_END = 'carousel-item-end';\nconst CLASS_NAME_START = 'carousel-item-start';\nconst CLASS_NAME_NEXT = 'carousel-item-next';\nconst CLASS_NAME_PREV = 'carousel-item-prev';\nconst SELECTOR_ACTIVE = '.active';\nconst SELECTOR_ITEM = '.carousel-item';\nconst SELECTOR_ACTIVE_ITEM = SELECTOR_ACTIVE + SELECTOR_ITEM;\nconst SELECTOR_ITEM_IMG = '.carousel-item img';\nconst SELECTOR_INDICATORS = '.carousel-indicators';\nconst SELECTOR_DATA_SLIDE = '[data-bs-slide], [data-bs-slide-to]';\nconst SELECTOR_DATA_RIDE = '[data-bs-ride=\"carousel\"]';\nconst KEY_TO_DIRECTION = {\n [ARROW_LEFT_KEY$1]: DIRECTION_RIGHT,\n [ARROW_RIGHT_KEY$1]: DIRECTION_LEFT\n};\nconst Default$b = {\n interval: 5000,\n keyboard: true,\n pause: 'hover',\n ride: false,\n touch: true,\n wrap: true\n};\nconst DefaultType$b = {\n interval: '(number|boolean)',\n // TODO:v6 remove boolean support\n keyboard: 'boolean',\n pause: '(string|boolean)',\n ride: '(boolean|string)',\n touch: 'boolean',\n wrap: 'boolean'\n};\n\n/**\n * Class definition\n */\n\nclass Carousel extends BaseComponent {\n constructor(element, config) {\n super(element, config);\n this._interval = null;\n this._activeElement = null;\n this._isSliding = false;\n this.touchTimeout = null;\n this._swipeHelper = null;\n this._indicatorsElement = SelectorEngine.findOne(SELECTOR_INDICATORS, this._element);\n this._addEventListeners();\n if (this._config.ride === CLASS_NAME_CAROUSEL) {\n this.cycle();\n }\n }\n\n // Getters\n static get Default() {\n return Default$b;\n }\n static get DefaultType() {\n return DefaultType$b;\n }\n static get NAME() {\n return NAME$c;\n }\n\n // Public\n next() {\n this._slide(ORDER_NEXT);\n }\n nextWhenVisible() {\n // FIXME TODO use `document.visibilityState`\n // Don't call next when the page isn't visible\n // or the carousel or its parent isn't visible\n if (!document.hidden && isVisible(this._element)) {\n this.next();\n }\n }\n prev() {\n this._slide(ORDER_PREV);\n }\n pause() {\n if (this._isSliding) {\n triggerTransitionEnd(this._element);\n }\n this._clearInterval();\n }\n cycle() {\n this._clearInterval();\n this._updateInterval();\n this._interval = setInterval(() => this.nextWhenVisible(), this._config.interval);\n }\n _maybeEnableCycle() {\n if (!this._config.ride) {\n return;\n }\n if (this._isSliding) {\n EventHandler.one(this._element, EVENT_SLID, () => this.cycle());\n return;\n }\n this.cycle();\n }\n to(index) {\n const items = this._getItems();\n if (index > items.length - 1 || index < 0) {\n return;\n }\n if (this._isSliding) {\n EventHandler.one(this._element, EVENT_SLID, () => this.to(index));\n return;\n }\n const activeIndex = this._getItemIndex(this._getActive());\n if (activeIndex === index) {\n return;\n }\n const order = index > activeIndex ? ORDER_NEXT : ORDER_PREV;\n this._slide(order, items[index]);\n }\n dispose() {\n if (this._swipeHelper) {\n this._swipeHelper.dispose();\n }\n super.dispose();\n }\n\n // Private\n _configAfterMerge(config) {\n config.defaultInterval = config.interval;\n return config;\n }\n _addEventListeners() {\n if (this._config.keyboard) {\n EventHandler.on(this._element, EVENT_KEYDOWN$1, event => this._keydown(event));\n }\n if (this._config.pause === 'hover') {\n EventHandler.on(this._element, EVENT_MOUSEENTER$1, () => this.pause());\n EventHandler.on(this._element, EVENT_MOUSELEAVE$1, () => this._maybeEnableCycle());\n }\n if (this._config.touch && Swipe.isSupported()) {\n this._addTouchEventListeners();\n }\n }\n _addTouchEventListeners() {\n for (const img of SelectorEngine.find(SELECTOR_ITEM_IMG, this._element)) {\n EventHandler.on(img, EVENT_DRAG_START, event => event.preventDefault());\n }\n const endCallBack = () => {\n if (this._config.pause !== 'hover') {\n return;\n }\n\n // If it's a touch-enabled device, mouseenter/leave are fired as\n // part of the mouse compatibility events on first tap - the carousel\n // would stop cycling until user tapped out of it;\n // here, we listen for touchend, explicitly pause the carousel\n // (as if it's the second time we tap on it, mouseenter compat event\n // is NOT fired) and after a timeout (to allow for mouse compatibility\n // events to fire) we explicitly restart cycling\n\n this.pause();\n if (this.touchTimeout) {\n clearTimeout(this.touchTimeout);\n }\n this.touchTimeout = setTimeout(() => this._maybeEnableCycle(), TOUCHEVENT_COMPAT_WAIT + this._config.interval);\n };\n const swipeConfig = {\n leftCallback: () => this._slide(this._directionToOrder(DIRECTION_LEFT)),\n rightCallback: () => this._slide(this._directionToOrder(DIRECTION_RIGHT)),\n endCallback: endCallBack\n };\n this._swipeHelper = new Swipe(this._element, swipeConfig);\n }\n _keydown(event) {\n if (/input|textarea/i.test(event.target.tagName)) {\n return;\n }\n const direction = KEY_TO_DIRECTION[event.key];\n if (direction) {\n event.preventDefault();\n this._slide(this._directionToOrder(direction));\n }\n }\n _getItemIndex(element) {\n return this._getItems().indexOf(element);\n }\n _setActiveIndicatorElement(index) {\n if (!this._indicatorsElement) {\n return;\n }\n const activeIndicator = SelectorEngine.findOne(SELECTOR_ACTIVE, this._indicatorsElement);\n activeIndicator.classList.remove(CLASS_NAME_ACTIVE$2);\n activeIndicator.removeAttribute('aria-current');\n const newActiveIndicator = SelectorEngine.findOne(`[data-bs-slide-to=\"${index}\"]`, this._indicatorsElement);\n if (newActiveIndicator) {\n newActiveIndicator.classList.add(CLASS_NAME_ACTIVE$2);\n newActiveIndicator.setAttribute('aria-current', 'true');\n }\n }\n _updateInterval() {\n const element = this._activeElement || this._getActive();\n if (!element) {\n return;\n }\n const elementInterval = Number.parseInt(element.getAttribute('data-bs-interval'), 10);\n this._config.interval = elementInterval || this._config.defaultInterval;\n }\n _slide(order, element = null) {\n if (this._isSliding) {\n return;\n }\n const activeElement = this._getActive();\n const isNext = order === ORDER_NEXT;\n const nextElement = element || getNextActiveElement(this._getItems(), activeElement, isNext, this._config.wrap);\n if (nextElement === activeElement) {\n return;\n }\n const nextElementIndex = this._getItemIndex(nextElement);\n const triggerEvent = eventName => {\n return EventHandler.trigger(this._element, eventName, {\n relatedTarget: nextElement,\n direction: this._orderToDirection(order),\n from: this._getItemIndex(activeElement),\n to: nextElementIndex\n });\n };\n const slideEvent = triggerEvent(EVENT_SLIDE);\n if (slideEvent.defaultPrevented) {\n return;\n }\n if (!activeElement || !nextElement) {\n // Some weirdness is happening, so we bail\n // TODO: change tests that use empty divs to avoid this check\n return;\n }\n const isCycling = Boolean(this._interval);\n this.pause();\n this._isSliding = true;\n this._setActiveIndicatorElement(nextElementIndex);\n this._activeElement = nextElement;\n const directionalClassName = isNext ? CLASS_NAME_START : CLASS_NAME_END;\n const orderClassName = isNext ? CLASS_NAME_NEXT : CLASS_NAME_PREV;\n nextElement.classList.add(orderClassName);\n reflow(nextElement);\n activeElement.classList.add(directionalClassName);\n nextElement.classList.add(directionalClassName);\n const completeCallBack = () => {\n nextElement.classList.remove(directionalClassName, orderClassName);\n nextElement.classList.add(CLASS_NAME_ACTIVE$2);\n activeElement.classList.remove(CLASS_NAME_ACTIVE$2, orderClassName, directionalClassName);\n this._isSliding = false;\n triggerEvent(EVENT_SLID);\n };\n this._queueCallback(completeCallBack, activeElement, this._isAnimated());\n if (isCycling) {\n this.cycle();\n }\n }\n _isAnimated() {\n return this._element.classList.contains(CLASS_NAME_SLIDE);\n }\n _getActive() {\n return SelectorEngine.findOne(SELECTOR_ACTIVE_ITEM, this._element);\n }\n _getItems() {\n return SelectorEngine.find(SELECTOR_ITEM, this._element);\n }\n _clearInterval() {\n if (this._interval) {\n clearInterval(this._interval);\n this._interval = null;\n }\n }\n _directionToOrder(direction) {\n if (isRTL()) {\n return direction === DIRECTION_LEFT ? ORDER_PREV : ORDER_NEXT;\n }\n return direction === DIRECTION_LEFT ? ORDER_NEXT : ORDER_PREV;\n }\n _orderToDirection(order) {\n if (isRTL()) {\n return order === ORDER_PREV ? DIRECTION_LEFT : DIRECTION_RIGHT;\n }\n return order === ORDER_PREV ? DIRECTION_RIGHT : DIRECTION_LEFT;\n }\n\n // Static\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Carousel.getOrCreateInstance(this, config);\n if (typeof config === 'number') {\n data.to(config);\n return;\n }\n if (typeof config === 'string') {\n if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n throw new TypeError(`No method named \"${config}\"`);\n }\n data[config]();\n }\n });\n }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API$5, SELECTOR_DATA_SLIDE, function (event) {\n const target = SelectorEngine.getElementFromSelector(this);\n if (!target || !target.classList.contains(CLASS_NAME_CAROUSEL)) {\n return;\n }\n event.preventDefault();\n const carousel = Carousel.getOrCreateInstance(target);\n const slideIndex = this.getAttribute('data-bs-slide-to');\n if (slideIndex) {\n carousel.to(slideIndex);\n carousel._maybeEnableCycle();\n return;\n }\n if (Manipulator.getDataAttribute(this, 'slide') === 'next') {\n carousel.next();\n carousel._maybeEnableCycle();\n return;\n }\n carousel.prev();\n carousel._maybeEnableCycle();\n});\nEventHandler.on(window, EVENT_LOAD_DATA_API$3, () => {\n const carousels = SelectorEngine.find(SELECTOR_DATA_RIDE);\n for (const carousel of carousels) {\n Carousel.getOrCreateInstance(carousel);\n }\n});\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Carousel);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap collapse.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$b = 'collapse';\nconst DATA_KEY$7 = 'bs.collapse';\nconst EVENT_KEY$7 = `.${DATA_KEY$7}`;\nconst DATA_API_KEY$4 = '.data-api';\nconst EVENT_SHOW$6 = `show${EVENT_KEY$7}`;\nconst EVENT_SHOWN$6 = `shown${EVENT_KEY$7}`;\nconst EVENT_HIDE$6 = `hide${EVENT_KEY$7}`;\nconst EVENT_HIDDEN$6 = `hidden${EVENT_KEY$7}`;\nconst EVENT_CLICK_DATA_API$4 = `click${EVENT_KEY$7}${DATA_API_KEY$4}`;\nconst CLASS_NAME_SHOW$7 = 'show';\nconst CLASS_NAME_COLLAPSE = 'collapse';\nconst CLASS_NAME_COLLAPSING = 'collapsing';\nconst CLASS_NAME_COLLAPSED = 'collapsed';\nconst CLASS_NAME_DEEPER_CHILDREN = `:scope .${CLASS_NAME_COLLAPSE} .${CLASS_NAME_COLLAPSE}`;\nconst CLASS_NAME_HORIZONTAL = 'collapse-horizontal';\nconst WIDTH = 'width';\nconst HEIGHT = 'height';\nconst SELECTOR_ACTIVES = '.collapse.show, .collapse.collapsing';\nconst SELECTOR_DATA_TOGGLE$4 = '[data-bs-toggle=\"collapse\"]';\nconst Default$a = {\n parent: null,\n toggle: true\n};\nconst DefaultType$a = {\n parent: '(null|element)',\n toggle: 'boolean'\n};\n\n/**\n * Class definition\n */\n\nclass Collapse extends BaseComponent {\n constructor(element, config) {\n super(element, config);\n this._isTransitioning = false;\n this._triggerArray = [];\n const toggleList = SelectorEngine.find(SELECTOR_DATA_TOGGLE$4);\n for (const elem of toggleList) {\n const selector = SelectorEngine.getSelectorFromElement(elem);\n const filterElement = SelectorEngine.find(selector).filter(foundElement => foundElement === this._element);\n if (selector !== null && filterElement.length) {\n this._triggerArray.push(elem);\n }\n }\n this._initializeChildren();\n if (!this._config.parent) {\n this._addAriaAndCollapsedClass(this._triggerArray, this._isShown());\n }\n if (this._config.toggle) {\n this.toggle();\n }\n }\n\n // Getters\n static get Default() {\n return Default$a;\n }\n static get DefaultType() {\n return DefaultType$a;\n }\n static get NAME() {\n return NAME$b;\n }\n\n // Public\n toggle() {\n if (this._isShown()) {\n this.hide();\n } else {\n this.show();\n }\n }\n show() {\n if (this._isTransitioning || this._isShown()) {\n return;\n }\n let activeChildren = [];\n\n // find active children\n if (this._config.parent) {\n activeChildren = this._getFirstLevelChildren(SELECTOR_ACTIVES).filter(element => element !== this._element).map(element => Collapse.getOrCreateInstance(element, {\n toggle: false\n }));\n }\n if (activeChildren.length && activeChildren[0]._isTransitioning) {\n return;\n }\n const startEvent = EventHandler.trigger(this._element, EVENT_SHOW$6);\n if (startEvent.defaultPrevented) {\n return;\n }\n for (const activeInstance of activeChildren) {\n activeInstance.hide();\n }\n const dimension = this._getDimension();\n this._element.classList.remove(CLASS_NAME_COLLAPSE);\n this._element.classList.add(CLASS_NAME_COLLAPSING);\n this._element.style[dimension] = 0;\n this._addAriaAndCollapsedClass(this._triggerArray, true);\n this._isTransitioning = true;\n const complete = () => {\n this._isTransitioning = false;\n this._element.classList.remove(CLASS_NAME_COLLAPSING);\n this._element.classList.add(CLASS_NAME_COLLAPSE, CLASS_NAME_SHOW$7);\n this._element.style[dimension] = '';\n EventHandler.trigger(this._element, EVENT_SHOWN$6);\n };\n const capitalizedDimension = dimension[0].toUpperCase() + dimension.slice(1);\n const scrollSize = `scroll${capitalizedDimension}`;\n this._queueCallback(complete, this._element, true);\n this._element.style[dimension] = `${this._element[scrollSize]}px`;\n }\n hide() {\n if (this._isTransitioning || !this._isShown()) {\n return;\n }\n const startEvent = EventHandler.trigger(this._element, EVENT_HIDE$6);\n if (startEvent.defaultPrevented) {\n return;\n }\n const dimension = this._getDimension();\n this._element.style[dimension] = `${this._element.getBoundingClientRect()[dimension]}px`;\n reflow(this._element);\n this._element.classList.add(CLASS_NAME_COLLAPSING);\n this._element.classList.remove(CLASS_NAME_COLLAPSE, CLASS_NAME_SHOW$7);\n for (const trigger of this._triggerArray) {\n const element = SelectorEngine.getElementFromSelector(trigger);\n if (element && !this._isShown(element)) {\n this._addAriaAndCollapsedClass([trigger], false);\n }\n }\n this._isTransitioning = true;\n const complete = () => {\n this._isTransitioning = false;\n this._element.classList.remove(CLASS_NAME_COLLAPSING);\n this._element.classList.add(CLASS_NAME_COLLAPSE);\n EventHandler.trigger(this._element, EVENT_HIDDEN$6);\n };\n this._element.style[dimension] = '';\n this._queueCallback(complete, this._element, true);\n }\n _isShown(element = this._element) {\n return element.classList.contains(CLASS_NAME_SHOW$7);\n }\n\n // Private\n _configAfterMerge(config) {\n config.toggle = Boolean(config.toggle); // Coerce string values\n config.parent = getElement(config.parent);\n return config;\n }\n _getDimension() {\n return this._element.classList.contains(CLASS_NAME_HORIZONTAL) ? WIDTH : HEIGHT;\n }\n _initializeChildren() {\n if (!this._config.parent) {\n return;\n }\n const children = this._getFirstLevelChildren(SELECTOR_DATA_TOGGLE$4);\n for (const element of children) {\n const selected = SelectorEngine.getElementFromSelector(element);\n if (selected) {\n this._addAriaAndCollapsedClass([element], this._isShown(selected));\n }\n }\n }\n _getFirstLevelChildren(selector) {\n const children = SelectorEngine.find(CLASS_NAME_DEEPER_CHILDREN, this._config.parent);\n // remove children if greater depth\n return SelectorEngine.find(selector, this._config.parent).filter(element => !children.includes(element));\n }\n _addAriaAndCollapsedClass(triggerArray, isOpen) {\n if (!triggerArray.length) {\n return;\n }\n for (const element of triggerArray) {\n element.classList.toggle(CLASS_NAME_COLLAPSED, !isOpen);\n element.setAttribute('aria-expanded', isOpen);\n }\n }\n\n // Static\n static jQueryInterface(config) {\n const _config = {};\n if (typeof config === 'string' && /show|hide/.test(config)) {\n _config.toggle = false;\n }\n return this.each(function () {\n const data = Collapse.getOrCreateInstance(this, _config);\n if (typeof config === 'string') {\n if (typeof data[config] === 'undefined') {\n throw new TypeError(`No method named \"${config}\"`);\n }\n data[config]();\n }\n });\n }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API$4, SELECTOR_DATA_TOGGLE$4, function (event) {\n // preventDefault only for elements (which change the URL) not inside the collapsible element\n if (event.target.tagName === 'A' || event.delegateTarget && event.delegateTarget.tagName === 'A') {\n event.preventDefault();\n }\n for (const element of SelectorEngine.getMultipleElementsFromSelector(this)) {\n Collapse.getOrCreateInstance(element, {\n toggle: false\n }).toggle();\n }\n});\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Collapse);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap dropdown.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$a = 'dropdown';\nconst DATA_KEY$6 = 'bs.dropdown';\nconst EVENT_KEY$6 = `.${DATA_KEY$6}`;\nconst DATA_API_KEY$3 = '.data-api';\nconst ESCAPE_KEY$2 = 'Escape';\nconst TAB_KEY$1 = 'Tab';\nconst ARROW_UP_KEY$1 = 'ArrowUp';\nconst ARROW_DOWN_KEY$1 = 'ArrowDown';\nconst RIGHT_MOUSE_BUTTON = 2; // MouseEvent.button value for the secondary button, usually the right button\n\nconst EVENT_HIDE$5 = `hide${EVENT_KEY$6}`;\nconst EVENT_HIDDEN$5 = `hidden${EVENT_KEY$6}`;\nconst EVENT_SHOW$5 = `show${EVENT_KEY$6}`;\nconst EVENT_SHOWN$5 = `shown${EVENT_KEY$6}`;\nconst EVENT_CLICK_DATA_API$3 = `click${EVENT_KEY$6}${DATA_API_KEY$3}`;\nconst EVENT_KEYDOWN_DATA_API = `keydown${EVENT_KEY$6}${DATA_API_KEY$3}`;\nconst EVENT_KEYUP_DATA_API = `keyup${EVENT_KEY$6}${DATA_API_KEY$3}`;\nconst CLASS_NAME_SHOW$6 = 'show';\nconst CLASS_NAME_DROPUP = 'dropup';\nconst CLASS_NAME_DROPEND = 'dropend';\nconst CLASS_NAME_DROPSTART = 'dropstart';\nconst CLASS_NAME_DROPUP_CENTER = 'dropup-center';\nconst CLASS_NAME_DROPDOWN_CENTER = 'dropdown-center';\nconst SELECTOR_DATA_TOGGLE$3 = '[data-bs-toggle=\"dropdown\"]:not(.disabled):not(:disabled)';\nconst SELECTOR_DATA_TOGGLE_SHOWN = `${SELECTOR_DATA_TOGGLE$3}.${CLASS_NAME_SHOW$6}`;\nconst SELECTOR_MENU = '.dropdown-menu';\nconst SELECTOR_NAVBAR = '.navbar';\nconst SELECTOR_NAVBAR_NAV = '.navbar-nav';\nconst SELECTOR_VISIBLE_ITEMS = '.dropdown-menu .dropdown-item:not(.disabled):not(:disabled)';\nconst PLACEMENT_TOP = isRTL() ? 'top-end' : 'top-start';\nconst PLACEMENT_TOPEND = isRTL() ? 'top-start' : 'top-end';\nconst PLACEMENT_BOTTOM = isRTL() ? 'bottom-end' : 'bottom-start';\nconst PLACEMENT_BOTTOMEND = isRTL() ? 'bottom-start' : 'bottom-end';\nconst PLACEMENT_RIGHT = isRTL() ? 'left-start' : 'right-start';\nconst PLACEMENT_LEFT = isRTL() ? 'right-start' : 'left-start';\nconst PLACEMENT_TOPCENTER = 'top';\nconst PLACEMENT_BOTTOMCENTER = 'bottom';\nconst Default$9 = {\n autoClose: true,\n boundary: 'clippingParents',\n display: 'dynamic',\n offset: [0, 2],\n popperConfig: null,\n reference: 'toggle'\n};\nconst DefaultType$9 = {\n autoClose: '(boolean|string)',\n boundary: '(string|element)',\n display: 'string',\n offset: '(array|string|function)',\n popperConfig: '(null|object|function)',\n reference: '(string|element|object)'\n};\n\n/**\n * Class definition\n */\n\nclass Dropdown extends BaseComponent {\n constructor(element, config) {\n super(element, config);\n this._popper = null;\n this._parent = this._element.parentNode; // dropdown wrapper\n // TODO: v6 revert #37011 & change markup https://getbootstrap.com/docs/5.3/forms/input-group/\n this._menu = SelectorEngine.next(this._element, SELECTOR_MENU)[0] || SelectorEngine.prev(this._element, SELECTOR_MENU)[0] || SelectorEngine.findOne(SELECTOR_MENU, this._parent);\n this._inNavbar = this._detectNavbar();\n }\n\n // Getters\n static get Default() {\n return Default$9;\n }\n static get DefaultType() {\n return DefaultType$9;\n }\n static get NAME() {\n return NAME$a;\n }\n\n // Public\n toggle() {\n return this._isShown() ? this.hide() : this.show();\n }\n show() {\n if (isDisabled(this._element) || this._isShown()) {\n return;\n }\n const relatedTarget = {\n relatedTarget: this._element\n };\n const showEvent = EventHandler.trigger(this._element, EVENT_SHOW$5, relatedTarget);\n if (showEvent.defaultPrevented) {\n return;\n }\n this._createPopper();\n\n // If this is a touch-enabled device we add extra\n // empty mouseover listeners to the body's immediate children;\n // only needed because of broken event delegation on iOS\n // https://www.quirksmode.org/blog/archives/2014/02/mouse_event_bub.html\n if ('ontouchstart' in document.documentElement && !this._parent.closest(SELECTOR_NAVBAR_NAV)) {\n for (const element of [].concat(...document.body.children)) {\n EventHandler.on(element, 'mouseover', noop);\n }\n }\n this._element.focus();\n this._element.setAttribute('aria-expanded', true);\n this._menu.classList.add(CLASS_NAME_SHOW$6);\n this._element.classList.add(CLASS_NAME_SHOW$6);\n EventHandler.trigger(this._element, EVENT_SHOWN$5, relatedTarget);\n }\n hide() {\n if (isDisabled(this._element) || !this._isShown()) {\n return;\n }\n const relatedTarget = {\n relatedTarget: this._element\n };\n this._completeHide(relatedTarget);\n }\n dispose() {\n if (this._popper) {\n this._popper.destroy();\n }\n super.dispose();\n }\n update() {\n this._inNavbar = this._detectNavbar();\n if (this._popper) {\n this._popper.update();\n }\n }\n\n // Private\n _completeHide(relatedTarget) {\n const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE$5, relatedTarget);\n if (hideEvent.defaultPrevented) {\n return;\n }\n\n // If this is a touch-enabled device we remove the extra\n // empty mouseover listeners we added for iOS support\n if ('ontouchstart' in document.documentElement) {\n for (const element of [].concat(...document.body.children)) {\n EventHandler.off(element, 'mouseover', noop);\n }\n }\n if (this._popper) {\n this._popper.destroy();\n }\n this._menu.classList.remove(CLASS_NAME_SHOW$6);\n this._element.classList.remove(CLASS_NAME_SHOW$6);\n this._element.setAttribute('aria-expanded', 'false');\n Manipulator.removeDataAttribute(this._menu, 'popper');\n EventHandler.trigger(this._element, EVENT_HIDDEN$5, relatedTarget);\n }\n _getConfig(config) {\n config = super._getConfig(config);\n if (typeof config.reference === 'object' && !isElement(config.reference) && typeof config.reference.getBoundingClientRect !== 'function') {\n // Popper virtual elements require a getBoundingClientRect method\n throw new TypeError(`${NAME$a.toUpperCase()}: Option \"reference\" provided type \"object\" without a required \"getBoundingClientRect\" method.`);\n }\n return config;\n }\n _createPopper() {\n if (typeof Popper === 'undefined') {\n throw new TypeError('Bootstrap\\'s dropdowns require Popper (https://popper.js.org)');\n }\n let referenceElement = this._element;\n if (this._config.reference === 'parent') {\n referenceElement = this._parent;\n } else if (isElement(this._config.reference)) {\n referenceElement = getElement(this._config.reference);\n } else if (typeof this._config.reference === 'object') {\n referenceElement = this._config.reference;\n }\n const popperConfig = this._getPopperConfig();\n this._popper = Popper.createPopper(referenceElement, this._menu, popperConfig);\n }\n _isShown() {\n return this._menu.classList.contains(CLASS_NAME_SHOW$6);\n }\n _getPlacement() {\n const parentDropdown = this._parent;\n if (parentDropdown.classList.contains(CLASS_NAME_DROPEND)) {\n return PLACEMENT_RIGHT;\n }\n if (parentDropdown.classList.contains(CLASS_NAME_DROPSTART)) {\n return PLACEMENT_LEFT;\n }\n if (parentDropdown.classList.contains(CLASS_NAME_DROPUP_CENTER)) {\n return PLACEMENT_TOPCENTER;\n }\n if (parentDropdown.classList.contains(CLASS_NAME_DROPDOWN_CENTER)) {\n return PLACEMENT_BOTTOMCENTER;\n }\n\n // We need to trim the value because custom properties can also include spaces\n const isEnd = getComputedStyle(this._menu).getPropertyValue('--bs-position').trim() === 'end';\n if (parentDropdown.classList.contains(CLASS_NAME_DROPUP)) {\n return isEnd ? PLACEMENT_TOPEND : PLACEMENT_TOP;\n }\n return isEnd ? PLACEMENT_BOTTOMEND : PLACEMENT_BOTTOM;\n }\n _detectNavbar() {\n return this._element.closest(SELECTOR_NAVBAR) !== null;\n }\n _getOffset() {\n const {\n offset\n } = this._config;\n if (typeof offset === 'string') {\n return offset.split(',').map(value => Number.parseInt(value, 10));\n }\n if (typeof offset === 'function') {\n return popperData => offset(popperData, this._element);\n }\n return offset;\n }\n _getPopperConfig() {\n const defaultBsPopperConfig = {\n placement: this._getPlacement(),\n modifiers: [{\n name: 'preventOverflow',\n options: {\n boundary: this._config.boundary\n }\n }, {\n name: 'offset',\n options: {\n offset: this._getOffset()\n }\n }]\n };\n\n // Disable Popper if we have a static display or Dropdown is in Navbar\n if (this._inNavbar || this._config.display === 'static') {\n Manipulator.setDataAttribute(this._menu, 'popper', 'static'); // TODO: v6 remove\n defaultBsPopperConfig.modifiers = [{\n name: 'applyStyles',\n enabled: false\n }];\n }\n return {\n ...defaultBsPopperConfig,\n ...execute(this._config.popperConfig, [defaultBsPopperConfig])\n };\n }\n _selectMenuItem({\n key,\n target\n }) {\n const items = SelectorEngine.find(SELECTOR_VISIBLE_ITEMS, this._menu).filter(element => isVisible(element));\n if (!items.length) {\n return;\n }\n\n // if target isn't included in items (e.g. when expanding the dropdown)\n // allow cycling to get the last item in case key equals ARROW_UP_KEY\n getNextActiveElement(items, target, key === ARROW_DOWN_KEY$1, !items.includes(target)).focus();\n }\n\n // Static\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Dropdown.getOrCreateInstance(this, config);\n if (typeof config !== 'string') {\n return;\n }\n if (typeof data[config] === 'undefined') {\n throw new TypeError(`No method named \"${config}\"`);\n }\n data[config]();\n });\n }\n static clearMenus(event) {\n if (event.button === RIGHT_MOUSE_BUTTON || event.type === 'keyup' && event.key !== TAB_KEY$1) {\n return;\n }\n const openToggles = SelectorEngine.find(SELECTOR_DATA_TOGGLE_SHOWN);\n for (const toggle of openToggles) {\n const context = Dropdown.getInstance(toggle);\n if (!context || context._config.autoClose === false) {\n continue;\n }\n const composedPath = event.composedPath();\n const isMenuTarget = composedPath.includes(context._menu);\n if (composedPath.includes(context._element) || context._config.autoClose === 'inside' && !isMenuTarget || context._config.autoClose === 'outside' && isMenuTarget) {\n continue;\n }\n\n // Tab navigation through the dropdown menu or events from contained inputs shouldn't close the menu\n if (context._menu.contains(event.target) && (event.type === 'keyup' && event.key === TAB_KEY$1 || /input|select|option|textarea|form/i.test(event.target.tagName))) {\n continue;\n }\n const relatedTarget = {\n relatedTarget: context._element\n };\n if (event.type === 'click') {\n relatedTarget.clickEvent = event;\n }\n context._completeHide(relatedTarget);\n }\n }\n static dataApiKeydownHandler(event) {\n // If not an UP | DOWN | ESCAPE key => not a dropdown command\n // If input/textarea && if key is other than ESCAPE => not a dropdown command\n\n const isInput = /input|textarea/i.test(event.target.tagName);\n const isEscapeEvent = event.key === ESCAPE_KEY$2;\n const isUpOrDownEvent = [ARROW_UP_KEY$1, ARROW_DOWN_KEY$1].includes(event.key);\n if (!isUpOrDownEvent && !isEscapeEvent) {\n return;\n }\n if (isInput && !isEscapeEvent) {\n return;\n }\n event.preventDefault();\n\n // TODO: v6 revert #37011 & change markup https://getbootstrap.com/docs/5.3/forms/input-group/\n const getToggleButton = this.matches(SELECTOR_DATA_TOGGLE$3) ? this : SelectorEngine.prev(this, SELECTOR_DATA_TOGGLE$3)[0] || SelectorEngine.next(this, SELECTOR_DATA_TOGGLE$3)[0] || SelectorEngine.findOne(SELECTOR_DATA_TOGGLE$3, event.delegateTarget.parentNode);\n const instance = Dropdown.getOrCreateInstance(getToggleButton);\n if (isUpOrDownEvent) {\n event.stopPropagation();\n instance.show();\n instance._selectMenuItem(event);\n return;\n }\n if (instance._isShown()) {\n // else is escape and we check if it is shown\n event.stopPropagation();\n instance.hide();\n getToggleButton.focus();\n }\n }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_KEYDOWN_DATA_API, SELECTOR_DATA_TOGGLE$3, Dropdown.dataApiKeydownHandler);\nEventHandler.on(document, EVENT_KEYDOWN_DATA_API, SELECTOR_MENU, Dropdown.dataApiKeydownHandler);\nEventHandler.on(document, EVENT_CLICK_DATA_API$3, Dropdown.clearMenus);\nEventHandler.on(document, EVENT_KEYUP_DATA_API, Dropdown.clearMenus);\nEventHandler.on(document, EVENT_CLICK_DATA_API$3, SELECTOR_DATA_TOGGLE$3, function (event) {\n event.preventDefault();\n Dropdown.getOrCreateInstance(this).toggle();\n});\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Dropdown);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap util/backdrop.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$9 = 'backdrop';\nconst CLASS_NAME_FADE$4 = 'fade';\nconst CLASS_NAME_SHOW$5 = 'show';\nconst EVENT_MOUSEDOWN = `mousedown.bs.${NAME$9}`;\nconst Default$8 = {\n className: 'modal-backdrop',\n clickCallback: null,\n isAnimated: false,\n isVisible: true,\n // if false, we use the backdrop helper without adding any element to the dom\n rootElement: 'body' // give the choice to place backdrop under different elements\n};\n\nconst DefaultType$8 = {\n className: 'string',\n clickCallback: '(function|null)',\n isAnimated: 'boolean',\n isVisible: 'boolean',\n rootElement: '(element|string)'\n};\n\n/**\n * Class definition\n */\n\nclass Backdrop extends Config {\n constructor(config) {\n super();\n this._config = this._getConfig(config);\n this._isAppended = false;\n this._element = null;\n }\n\n // Getters\n static get Default() {\n return Default$8;\n }\n static get DefaultType() {\n return DefaultType$8;\n }\n static get NAME() {\n return NAME$9;\n }\n\n // Public\n show(callback) {\n if (!this._config.isVisible) {\n execute(callback);\n return;\n }\n this._append();\n const element = this._getElement();\n if (this._config.isAnimated) {\n reflow(element);\n }\n element.classList.add(CLASS_NAME_SHOW$5);\n this._emulateAnimation(() => {\n execute(callback);\n });\n }\n hide(callback) {\n if (!this._config.isVisible) {\n execute(callback);\n return;\n }\n this._getElement().classList.remove(CLASS_NAME_SHOW$5);\n this._emulateAnimation(() => {\n this.dispose();\n execute(callback);\n });\n }\n dispose() {\n if (!this._isAppended) {\n return;\n }\n EventHandler.off(this._element, EVENT_MOUSEDOWN);\n this._element.remove();\n this._isAppended = false;\n }\n\n // Private\n _getElement() {\n if (!this._element) {\n const backdrop = document.createElement('div');\n backdrop.className = this._config.className;\n if (this._config.isAnimated) {\n backdrop.classList.add(CLASS_NAME_FADE$4);\n }\n this._element = backdrop;\n }\n return this._element;\n }\n _configAfterMerge(config) {\n // use getElement() with the default \"body\" to get a fresh Element on each instantiation\n config.rootElement = getElement(config.rootElement);\n return config;\n }\n _append() {\n if (this._isAppended) {\n return;\n }\n const element = this._getElement();\n this._config.rootElement.append(element);\n EventHandler.on(element, EVENT_MOUSEDOWN, () => {\n execute(this._config.clickCallback);\n });\n this._isAppended = true;\n }\n _emulateAnimation(callback) {\n executeAfterTransition(callback, this._getElement(), this._config.isAnimated);\n }\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap util/focustrap.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$8 = 'focustrap';\nconst DATA_KEY$5 = 'bs.focustrap';\nconst EVENT_KEY$5 = `.${DATA_KEY$5}`;\nconst EVENT_FOCUSIN$2 = `focusin${EVENT_KEY$5}`;\nconst EVENT_KEYDOWN_TAB = `keydown.tab${EVENT_KEY$5}`;\nconst TAB_KEY = 'Tab';\nconst TAB_NAV_FORWARD = 'forward';\nconst TAB_NAV_BACKWARD = 'backward';\nconst Default$7 = {\n autofocus: true,\n trapElement: null // The element to trap focus inside of\n};\n\nconst DefaultType$7 = {\n autofocus: 'boolean',\n trapElement: 'element'\n};\n\n/**\n * Class definition\n */\n\nclass FocusTrap extends Config {\n constructor(config) {\n super();\n this._config = this._getConfig(config);\n this._isActive = false;\n this._lastTabNavDirection = null;\n }\n\n // Getters\n static get Default() {\n return Default$7;\n }\n static get DefaultType() {\n return DefaultType$7;\n }\n static get NAME() {\n return NAME$8;\n }\n\n // Public\n activate() {\n if (this._isActive) {\n return;\n }\n if (this._config.autofocus) {\n this._config.trapElement.focus();\n }\n EventHandler.off(document, EVENT_KEY$5); // guard against infinite focus loop\n EventHandler.on(document, EVENT_FOCUSIN$2, event => this._handleFocusin(event));\n EventHandler.on(document, EVENT_KEYDOWN_TAB, event => this._handleKeydown(event));\n this._isActive = true;\n }\n deactivate() {\n if (!this._isActive) {\n return;\n }\n this._isActive = false;\n EventHandler.off(document, EVENT_KEY$5);\n }\n\n // Private\n _handleFocusin(event) {\n const {\n trapElement\n } = this._config;\n if (event.target === document || event.target === trapElement || trapElement.contains(event.target)) {\n return;\n }\n const elements = SelectorEngine.focusableChildren(trapElement);\n if (elements.length === 0) {\n trapElement.focus();\n } else if (this._lastTabNavDirection === TAB_NAV_BACKWARD) {\n elements[elements.length - 1].focus();\n } else {\n elements[0].focus();\n }\n }\n _handleKeydown(event) {\n if (event.key !== TAB_KEY) {\n return;\n }\n this._lastTabNavDirection = event.shiftKey ? TAB_NAV_BACKWARD : TAB_NAV_FORWARD;\n }\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap util/scrollBar.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst SELECTOR_FIXED_CONTENT = '.fixed-top, .fixed-bottom, .is-fixed, .sticky-top';\nconst SELECTOR_STICKY_CONTENT = '.sticky-top';\nconst PROPERTY_PADDING = 'padding-right';\nconst PROPERTY_MARGIN = 'margin-right';\n\n/**\n * Class definition\n */\n\nclass ScrollBarHelper {\n constructor() {\n this._element = document.body;\n }\n\n // Public\n getWidth() {\n // https://developer.mozilla.org/en-US/docs/Web/API/Window/innerWidth#usage_notes\n const documentWidth = document.documentElement.clientWidth;\n return Math.abs(window.innerWidth - documentWidth);\n }\n hide() {\n const width = this.getWidth();\n this._disableOverFlow();\n // give padding to element to balance the hidden scrollbar width\n this._setElementAttributes(this._element, PROPERTY_PADDING, calculatedValue => calculatedValue + width);\n // trick: We adjust positive paddingRight and negative marginRight to sticky-top elements to keep showing fullwidth\n this._setElementAttributes(SELECTOR_FIXED_CONTENT, PROPERTY_PADDING, calculatedValue => calculatedValue + width);\n this._setElementAttributes(SELECTOR_STICKY_CONTENT, PROPERTY_MARGIN, calculatedValue => calculatedValue - width);\n }\n reset() {\n this._resetElementAttributes(this._element, 'overflow');\n this._resetElementAttributes(this._element, PROPERTY_PADDING);\n this._resetElementAttributes(SELECTOR_FIXED_CONTENT, PROPERTY_PADDING);\n this._resetElementAttributes(SELECTOR_STICKY_CONTENT, PROPERTY_MARGIN);\n }\n isOverflowing() {\n return this.getWidth() > 0;\n }\n\n // Private\n _disableOverFlow() {\n this._saveInitialAttribute(this._element, 'overflow');\n this._element.style.overflow = 'hidden';\n }\n _setElementAttributes(selector, styleProperty, callback) {\n const scrollbarWidth = this.getWidth();\n const manipulationCallBack = element => {\n if (element !== this._element && window.innerWidth > element.clientWidth + scrollbarWidth) {\n return;\n }\n this._saveInitialAttribute(element, styleProperty);\n const calculatedValue = window.getComputedStyle(element).getPropertyValue(styleProperty);\n element.style.setProperty(styleProperty, `${callback(Number.parseFloat(calculatedValue))}px`);\n };\n this._applyManipulationCallback(selector, manipulationCallBack);\n }\n _saveInitialAttribute(element, styleProperty) {\n const actualValue = element.style.getPropertyValue(styleProperty);\n if (actualValue) {\n Manipulator.setDataAttribute(element, styleProperty, actualValue);\n }\n }\n _resetElementAttributes(selector, styleProperty) {\n const manipulationCallBack = element => {\n const value = Manipulator.getDataAttribute(element, styleProperty);\n // We only want to remove the property if the value is `null`; the value can also be zero\n if (value === null) {\n element.style.removeProperty(styleProperty);\n return;\n }\n Manipulator.removeDataAttribute(element, styleProperty);\n element.style.setProperty(styleProperty, value);\n };\n this._applyManipulationCallback(selector, manipulationCallBack);\n }\n _applyManipulationCallback(selector, callBack) {\n if (isElement(selector)) {\n callBack(selector);\n return;\n }\n for (const sel of SelectorEngine.find(selector, this._element)) {\n callBack(sel);\n }\n }\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap modal.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$7 = 'modal';\nconst DATA_KEY$4 = 'bs.modal';\nconst EVENT_KEY$4 = `.${DATA_KEY$4}`;\nconst DATA_API_KEY$2 = '.data-api';\nconst ESCAPE_KEY$1 = 'Escape';\nconst EVENT_HIDE$4 = `hide${EVENT_KEY$4}`;\nconst EVENT_HIDE_PREVENTED$1 = `hidePrevented${EVENT_KEY$4}`;\nconst EVENT_HIDDEN$4 = `hidden${EVENT_KEY$4}`;\nconst EVENT_SHOW$4 = `show${EVENT_KEY$4}`;\nconst EVENT_SHOWN$4 = `shown${EVENT_KEY$4}`;\nconst EVENT_RESIZE$1 = `resize${EVENT_KEY$4}`;\nconst EVENT_CLICK_DISMISS = `click.dismiss${EVENT_KEY$4}`;\nconst EVENT_MOUSEDOWN_DISMISS = `mousedown.dismiss${EVENT_KEY$4}`;\nconst EVENT_KEYDOWN_DISMISS$1 = `keydown.dismiss${EVENT_KEY$4}`;\nconst EVENT_CLICK_DATA_API$2 = `click${EVENT_KEY$4}${DATA_API_KEY$2}`;\nconst CLASS_NAME_OPEN = 'modal-open';\nconst CLASS_NAME_FADE$3 = 'fade';\nconst CLASS_NAME_SHOW$4 = 'show';\nconst CLASS_NAME_STATIC = 'modal-static';\nconst OPEN_SELECTOR$1 = '.modal.show';\nconst SELECTOR_DIALOG = '.modal-dialog';\nconst SELECTOR_MODAL_BODY = '.modal-body';\nconst SELECTOR_DATA_TOGGLE$2 = '[data-bs-toggle=\"modal\"]';\nconst Default$6 = {\n backdrop: true,\n focus: true,\n keyboard: true\n};\nconst DefaultType$6 = {\n backdrop: '(boolean|string)',\n focus: 'boolean',\n keyboard: 'boolean'\n};\n\n/**\n * Class definition\n */\n\nclass Modal extends BaseComponent {\n constructor(element, config) {\n super(element, config);\n this._dialog = SelectorEngine.findOne(SELECTOR_DIALOG, this._element);\n this._backdrop = this._initializeBackDrop();\n this._focustrap = this._initializeFocusTrap();\n this._isShown = false;\n this._isTransitioning = false;\n this._scrollBar = new ScrollBarHelper();\n this._addEventListeners();\n }\n\n // Getters\n static get Default() {\n return Default$6;\n }\n static get DefaultType() {\n return DefaultType$6;\n }\n static get NAME() {\n return NAME$7;\n }\n\n // Public\n toggle(relatedTarget) {\n return this._isShown ? this.hide() : this.show(relatedTarget);\n }\n show(relatedTarget) {\n if (this._isShown || this._isTransitioning) {\n return;\n }\n const showEvent = EventHandler.trigger(this._element, EVENT_SHOW$4, {\n relatedTarget\n });\n if (showEvent.defaultPrevented) {\n return;\n }\n this._isShown = true;\n this._isTransitioning = true;\n this._scrollBar.hide();\n document.body.classList.add(CLASS_NAME_OPEN);\n this._adjustDialog();\n this._backdrop.show(() => this._showElement(relatedTarget));\n }\n hide() {\n if (!this._isShown || this._isTransitioning) {\n return;\n }\n const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE$4);\n if (hideEvent.defaultPrevented) {\n return;\n }\n this._isShown = false;\n this._isTransitioning = true;\n this._focustrap.deactivate();\n this._element.classList.remove(CLASS_NAME_SHOW$4);\n this._queueCallback(() => this._hideModal(), this._element, this._isAnimated());\n }\n dispose() {\n EventHandler.off(window, EVENT_KEY$4);\n EventHandler.off(this._dialog, EVENT_KEY$4);\n this._backdrop.dispose();\n this._focustrap.deactivate();\n super.dispose();\n }\n handleUpdate() {\n this._adjustDialog();\n }\n\n // Private\n _initializeBackDrop() {\n return new Backdrop({\n isVisible: Boolean(this._config.backdrop),\n // 'static' option will be translated to true, and booleans will keep their value,\n isAnimated: this._isAnimated()\n });\n }\n _initializeFocusTrap() {\n return new FocusTrap({\n trapElement: this._element\n });\n }\n _showElement(relatedTarget) {\n // try to append dynamic modal\n if (!document.body.contains(this._element)) {\n document.body.append(this._element);\n }\n this._element.style.display = 'block';\n this._element.removeAttribute('aria-hidden');\n this._element.setAttribute('aria-modal', true);\n this._element.setAttribute('role', 'dialog');\n this._element.scrollTop = 0;\n const modalBody = SelectorEngine.findOne(SELECTOR_MODAL_BODY, this._dialog);\n if (modalBody) {\n modalBody.scrollTop = 0;\n }\n reflow(this._element);\n this._element.classList.add(CLASS_NAME_SHOW$4);\n const transitionComplete = () => {\n if (this._config.focus) {\n this._focustrap.activate();\n }\n this._isTransitioning = false;\n EventHandler.trigger(this._element, EVENT_SHOWN$4, {\n relatedTarget\n });\n };\n this._queueCallback(transitionComplete, this._dialog, this._isAnimated());\n }\n _addEventListeners() {\n EventHandler.on(this._element, EVENT_KEYDOWN_DISMISS$1, event => {\n if (event.key !== ESCAPE_KEY$1) {\n return;\n }\n if (this._config.keyboard) {\n this.hide();\n return;\n }\n this._triggerBackdropTransition();\n });\n EventHandler.on(window, EVENT_RESIZE$1, () => {\n if (this._isShown && !this._isTransitioning) {\n this._adjustDialog();\n }\n });\n EventHandler.on(this._element, EVENT_MOUSEDOWN_DISMISS, event => {\n // a bad trick to segregate clicks that may start inside dialog but end outside, and avoid listen to scrollbar clicks\n EventHandler.one(this._element, EVENT_CLICK_DISMISS, event2 => {\n if (this._element !== event.target || this._element !== event2.target) {\n return;\n }\n if (this._config.backdrop === 'static') {\n this._triggerBackdropTransition();\n return;\n }\n if (this._config.backdrop) {\n this.hide();\n }\n });\n });\n }\n _hideModal() {\n this._element.style.display = 'none';\n this._element.setAttribute('aria-hidden', true);\n this._element.removeAttribute('aria-modal');\n this._element.removeAttribute('role');\n this._isTransitioning = false;\n this._backdrop.hide(() => {\n document.body.classList.remove(CLASS_NAME_OPEN);\n this._resetAdjustments();\n this._scrollBar.reset();\n EventHandler.trigger(this._element, EVENT_HIDDEN$4);\n });\n }\n _isAnimated() {\n return this._element.classList.contains(CLASS_NAME_FADE$3);\n }\n _triggerBackdropTransition() {\n const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE_PREVENTED$1);\n if (hideEvent.defaultPrevented) {\n return;\n }\n const isModalOverflowing = this._element.scrollHeight > document.documentElement.clientHeight;\n const initialOverflowY = this._element.style.overflowY;\n // return if the following background transition hasn't yet completed\n if (initialOverflowY === 'hidden' || this._element.classList.contains(CLASS_NAME_STATIC)) {\n return;\n }\n if (!isModalOverflowing) {\n this._element.style.overflowY = 'hidden';\n }\n this._element.classList.add(CLASS_NAME_STATIC);\n this._queueCallback(() => {\n this._element.classList.remove(CLASS_NAME_STATIC);\n this._queueCallback(() => {\n this._element.style.overflowY = initialOverflowY;\n }, this._dialog);\n }, this._dialog);\n this._element.focus();\n }\n\n /**\n * The following methods are used to handle overflowing modals\n */\n\n _adjustDialog() {\n const isModalOverflowing = this._element.scrollHeight > document.documentElement.clientHeight;\n const scrollbarWidth = this._scrollBar.getWidth();\n const isBodyOverflowing = scrollbarWidth > 0;\n if (isBodyOverflowing && !isModalOverflowing) {\n const property = isRTL() ? 'paddingLeft' : 'paddingRight';\n this._element.style[property] = `${scrollbarWidth}px`;\n }\n if (!isBodyOverflowing && isModalOverflowing) {\n const property = isRTL() ? 'paddingRight' : 'paddingLeft';\n this._element.style[property] = `${scrollbarWidth}px`;\n }\n }\n _resetAdjustments() {\n this._element.style.paddingLeft = '';\n this._element.style.paddingRight = '';\n }\n\n // Static\n static jQueryInterface(config, relatedTarget) {\n return this.each(function () {\n const data = Modal.getOrCreateInstance(this, config);\n if (typeof config !== 'string') {\n return;\n }\n if (typeof data[config] === 'undefined') {\n throw new TypeError(`No method named \"${config}\"`);\n }\n data[config](relatedTarget);\n });\n }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API$2, SELECTOR_DATA_TOGGLE$2, function (event) {\n const target = SelectorEngine.getElementFromSelector(this);\n if (['A', 'AREA'].includes(this.tagName)) {\n event.preventDefault();\n }\n EventHandler.one(target, EVENT_SHOW$4, showEvent => {\n if (showEvent.defaultPrevented) {\n // only register focus restorer if modal will actually get shown\n return;\n }\n EventHandler.one(target, EVENT_HIDDEN$4, () => {\n if (isVisible(this)) {\n this.focus();\n }\n });\n });\n\n // avoid conflict when clicking modal toggler while another one is open\n const alreadyOpen = SelectorEngine.findOne(OPEN_SELECTOR$1);\n if (alreadyOpen) {\n Modal.getInstance(alreadyOpen).hide();\n }\n const data = Modal.getOrCreateInstance(target);\n data.toggle(this);\n});\nenableDismissTrigger(Modal);\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Modal);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap offcanvas.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$6 = 'offcanvas';\nconst DATA_KEY$3 = 'bs.offcanvas';\nconst EVENT_KEY$3 = `.${DATA_KEY$3}`;\nconst DATA_API_KEY$1 = '.data-api';\nconst EVENT_LOAD_DATA_API$2 = `load${EVENT_KEY$3}${DATA_API_KEY$1}`;\nconst ESCAPE_KEY = 'Escape';\nconst CLASS_NAME_SHOW$3 = 'show';\nconst CLASS_NAME_SHOWING$1 = 'showing';\nconst CLASS_NAME_HIDING = 'hiding';\nconst CLASS_NAME_BACKDROP = 'offcanvas-backdrop';\nconst OPEN_SELECTOR = '.offcanvas.show';\nconst EVENT_SHOW$3 = `show${EVENT_KEY$3}`;\nconst EVENT_SHOWN$3 = `shown${EVENT_KEY$3}`;\nconst EVENT_HIDE$3 = `hide${EVENT_KEY$3}`;\nconst EVENT_HIDE_PREVENTED = `hidePrevented${EVENT_KEY$3}`;\nconst EVENT_HIDDEN$3 = `hidden${EVENT_KEY$3}`;\nconst EVENT_RESIZE = `resize${EVENT_KEY$3}`;\nconst EVENT_CLICK_DATA_API$1 = `click${EVENT_KEY$3}${DATA_API_KEY$1}`;\nconst EVENT_KEYDOWN_DISMISS = `keydown.dismiss${EVENT_KEY$3}`;\nconst SELECTOR_DATA_TOGGLE$1 = '[data-bs-toggle=\"offcanvas\"]';\nconst Default$5 = {\n backdrop: true,\n keyboard: true,\n scroll: false\n};\nconst DefaultType$5 = {\n backdrop: '(boolean|string)',\n keyboard: 'boolean',\n scroll: 'boolean'\n};\n\n/**\n * Class definition\n */\n\nclass Offcanvas extends BaseComponent {\n constructor(element, config) {\n super(element, config);\n this._isShown = false;\n this._backdrop = this._initializeBackDrop();\n this._focustrap = this._initializeFocusTrap();\n this._addEventListeners();\n }\n\n // Getters\n static get Default() {\n return Default$5;\n }\n static get DefaultType() {\n return DefaultType$5;\n }\n static get NAME() {\n return NAME$6;\n }\n\n // Public\n toggle(relatedTarget) {\n return this._isShown ? this.hide() : this.show(relatedTarget);\n }\n show(relatedTarget) {\n if (this._isShown) {\n return;\n }\n const showEvent = EventHandler.trigger(this._element, EVENT_SHOW$3, {\n relatedTarget\n });\n if (showEvent.defaultPrevented) {\n return;\n }\n this._isShown = true;\n this._backdrop.show();\n if (!this._config.scroll) {\n new ScrollBarHelper().hide();\n }\n this._element.setAttribute('aria-modal', true);\n this._element.setAttribute('role', 'dialog');\n this._element.classList.add(CLASS_NAME_SHOWING$1);\n const completeCallBack = () => {\n if (!this._config.scroll || this._config.backdrop) {\n this._focustrap.activate();\n }\n this._element.classList.add(CLASS_NAME_SHOW$3);\n this._element.classList.remove(CLASS_NAME_SHOWING$1);\n EventHandler.trigger(this._element, EVENT_SHOWN$3, {\n relatedTarget\n });\n };\n this._queueCallback(completeCallBack, this._element, true);\n }\n hide() {\n if (!this._isShown) {\n return;\n }\n const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE$3);\n if (hideEvent.defaultPrevented) {\n return;\n }\n this._focustrap.deactivate();\n this._element.blur();\n this._isShown = false;\n this._element.classList.add(CLASS_NAME_HIDING);\n this._backdrop.hide();\n const completeCallback = () => {\n this._element.classList.remove(CLASS_NAME_SHOW$3, CLASS_NAME_HIDING);\n this._element.removeAttribute('aria-modal');\n this._element.removeAttribute('role');\n if (!this._config.scroll) {\n new ScrollBarHelper().reset();\n }\n EventHandler.trigger(this._element, EVENT_HIDDEN$3);\n };\n this._queueCallback(completeCallback, this._element, true);\n }\n dispose() {\n this._backdrop.dispose();\n this._focustrap.deactivate();\n super.dispose();\n }\n\n // Private\n _initializeBackDrop() {\n const clickCallback = () => {\n if (this._config.backdrop === 'static') {\n EventHandler.trigger(this._element, EVENT_HIDE_PREVENTED);\n return;\n }\n this.hide();\n };\n\n // 'static' option will be translated to true, and booleans will keep their value\n const isVisible = Boolean(this._config.backdrop);\n return new Backdrop({\n className: CLASS_NAME_BACKDROP,\n isVisible,\n isAnimated: true,\n rootElement: this._element.parentNode,\n clickCallback: isVisible ? clickCallback : null\n });\n }\n _initializeFocusTrap() {\n return new FocusTrap({\n trapElement: this._element\n });\n }\n _addEventListeners() {\n EventHandler.on(this._element, EVENT_KEYDOWN_DISMISS, event => {\n if (event.key !== ESCAPE_KEY) {\n return;\n }\n if (this._config.keyboard) {\n this.hide();\n return;\n }\n EventHandler.trigger(this._element, EVENT_HIDE_PREVENTED);\n });\n }\n\n // Static\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Offcanvas.getOrCreateInstance(this, config);\n if (typeof config !== 'string') {\n return;\n }\n if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n throw new TypeError(`No method named \"${config}\"`);\n }\n data[config](this);\n });\n }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API$1, SELECTOR_DATA_TOGGLE$1, function (event) {\n const target = SelectorEngine.getElementFromSelector(this);\n if (['A', 'AREA'].includes(this.tagName)) {\n event.preventDefault();\n }\n if (isDisabled(this)) {\n return;\n }\n EventHandler.one(target, EVENT_HIDDEN$3, () => {\n // focus on trigger when it is closed\n if (isVisible(this)) {\n this.focus();\n }\n });\n\n // avoid conflict when clicking a toggler of an offcanvas, while another is open\n const alreadyOpen = SelectorEngine.findOne(OPEN_SELECTOR);\n if (alreadyOpen && alreadyOpen !== target) {\n Offcanvas.getInstance(alreadyOpen).hide();\n }\n const data = Offcanvas.getOrCreateInstance(target);\n data.toggle(this);\n});\nEventHandler.on(window, EVENT_LOAD_DATA_API$2, () => {\n for (const selector of SelectorEngine.find(OPEN_SELECTOR)) {\n Offcanvas.getOrCreateInstance(selector).show();\n }\n});\nEventHandler.on(window, EVENT_RESIZE, () => {\n for (const element of SelectorEngine.find('[aria-modal][class*=show][class*=offcanvas-]')) {\n if (getComputedStyle(element).position !== 'fixed') {\n Offcanvas.getOrCreateInstance(element).hide();\n }\n }\n});\nenableDismissTrigger(Offcanvas);\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Offcanvas);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap util/sanitizer.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n// js-docs-start allow-list\nconst ARIA_ATTRIBUTE_PATTERN = /^aria-[\\w-]*$/i;\nconst DefaultAllowlist = {\n // Global attributes allowed on any supplied element below.\n '*': ['class', 'dir', 'id', 'lang', 'role', ARIA_ATTRIBUTE_PATTERN],\n a: ['target', 'href', 'title', 'rel'],\n area: [],\n b: [],\n br: [],\n col: [],\n code: [],\n div: [],\n em: [],\n hr: [],\n h1: [],\n h2: [],\n h3: [],\n h4: [],\n h5: [],\n h6: [],\n i: [],\n img: ['src', 'srcset', 'alt', 'title', 'width', 'height'],\n li: [],\n ol: [],\n p: [],\n pre: [],\n s: [],\n small: [],\n span: [],\n sub: [],\n sup: [],\n strong: [],\n u: [],\n ul: []\n};\n// js-docs-end allow-list\n\nconst uriAttributes = new Set(['background', 'cite', 'href', 'itemtype', 'longdesc', 'poster', 'src', 'xlink:href']);\n\n/**\n * A pattern that recognizes URLs that are safe wrt. XSS in URL navigation\n * contexts.\n *\n * Shout-out to Angular https://github.com/angular/angular/blob/15.2.8/packages/core/src/sanitization/url_sanitizer.ts#L38\n */\n// eslint-disable-next-line unicorn/better-regex\nconst SAFE_URL_PATTERN = /^(?!javascript:)(?:[a-z0-9+.-]+:|[^&:/?#]*(?:[/?#]|$))/i;\nconst allowedAttribute = (attribute, allowedAttributeList) => {\n const attributeName = attribute.nodeName.toLowerCase();\n if (allowedAttributeList.includes(attributeName)) {\n if (uriAttributes.has(attributeName)) {\n return Boolean(SAFE_URL_PATTERN.test(attribute.nodeValue));\n }\n return true;\n }\n\n // Check if a regular expression validates the attribute.\n return allowedAttributeList.filter(attributeRegex => attributeRegex instanceof RegExp).some(regex => regex.test(attributeName));\n};\nfunction sanitizeHtml(unsafeHtml, allowList, sanitizeFunction) {\n if (!unsafeHtml.length) {\n return unsafeHtml;\n }\n if (sanitizeFunction && typeof sanitizeFunction === 'function') {\n return sanitizeFunction(unsafeHtml);\n }\n const domParser = new window.DOMParser();\n const createdDocument = domParser.parseFromString(unsafeHtml, 'text/html');\n const elements = [].concat(...createdDocument.body.querySelectorAll('*'));\n for (const element of elements) {\n const elementName = element.nodeName.toLowerCase();\n if (!Object.keys(allowList).includes(elementName)) {\n element.remove();\n continue;\n }\n const attributeList = [].concat(...element.attributes);\n const allowedAttributes = [].concat(allowList['*'] || [], allowList[elementName] || []);\n for (const attribute of attributeList) {\n if (!allowedAttribute(attribute, allowedAttributes)) {\n element.removeAttribute(attribute.nodeName);\n }\n }\n }\n return createdDocument.body.innerHTML;\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap util/template-factory.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$5 = 'TemplateFactory';\nconst Default$4 = {\n allowList: DefaultAllowlist,\n content: {},\n // { selector : text , selector2 : text2 , }\n extraClass: '',\n html: false,\n sanitize: true,\n sanitizeFn: null,\n template: '
'\n};\nconst DefaultType$4 = {\n allowList: 'object',\n content: 'object',\n extraClass: '(string|function)',\n html: 'boolean',\n sanitize: 'boolean',\n sanitizeFn: '(null|function)',\n template: 'string'\n};\nconst DefaultContentType = {\n entry: '(string|element|function|null)',\n selector: '(string|element)'\n};\n\n/**\n * Class definition\n */\n\nclass TemplateFactory extends Config {\n constructor(config) {\n super();\n this._config = this._getConfig(config);\n }\n\n // Getters\n static get Default() {\n return Default$4;\n }\n static get DefaultType() {\n return DefaultType$4;\n }\n static get NAME() {\n return NAME$5;\n }\n\n // Public\n getContent() {\n return Object.values(this._config.content).map(config => this._resolvePossibleFunction(config)).filter(Boolean);\n }\n hasContent() {\n return this.getContent().length > 0;\n }\n changeContent(content) {\n this._checkContent(content);\n this._config.content = {\n ...this._config.content,\n ...content\n };\n return this;\n }\n toHtml() {\n const templateWrapper = document.createElement('div');\n templateWrapper.innerHTML = this._maybeSanitize(this._config.template);\n for (const [selector, text] of Object.entries(this._config.content)) {\n this._setContent(templateWrapper, text, selector);\n }\n const template = templateWrapper.children[0];\n const extraClass = this._resolvePossibleFunction(this._config.extraClass);\n if (extraClass) {\n template.classList.add(...extraClass.split(' '));\n }\n return template;\n }\n\n // Private\n _typeCheckConfig(config) {\n super._typeCheckConfig(config);\n this._checkContent(config.content);\n }\n _checkContent(arg) {\n for (const [selector, content] of Object.entries(arg)) {\n super._typeCheckConfig({\n selector,\n entry: content\n }, DefaultContentType);\n }\n }\n _setContent(template, content, selector) {\n const templateElement = SelectorEngine.findOne(selector, template);\n if (!templateElement) {\n return;\n }\n content = this._resolvePossibleFunction(content);\n if (!content) {\n templateElement.remove();\n return;\n }\n if (isElement(content)) {\n this._putElementInTemplate(getElement(content), templateElement);\n return;\n }\n if (this._config.html) {\n templateElement.innerHTML = this._maybeSanitize(content);\n return;\n }\n templateElement.textContent = content;\n }\n _maybeSanitize(arg) {\n return this._config.sanitize ? sanitizeHtml(arg, this._config.allowList, this._config.sanitizeFn) : arg;\n }\n _resolvePossibleFunction(arg) {\n return execute(arg, [this]);\n }\n _putElementInTemplate(element, templateElement) {\n if (this._config.html) {\n templateElement.innerHTML = '';\n templateElement.append(element);\n return;\n }\n templateElement.textContent = element.textContent;\n }\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap tooltip.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$4 = 'tooltip';\nconst DISALLOWED_ATTRIBUTES = new Set(['sanitize', 'allowList', 'sanitizeFn']);\nconst CLASS_NAME_FADE$2 = 'fade';\nconst CLASS_NAME_MODAL = 'modal';\nconst CLASS_NAME_SHOW$2 = 'show';\nconst SELECTOR_TOOLTIP_INNER = '.tooltip-inner';\nconst SELECTOR_MODAL = `.${CLASS_NAME_MODAL}`;\nconst EVENT_MODAL_HIDE = 'hide.bs.modal';\nconst TRIGGER_HOVER = 'hover';\nconst TRIGGER_FOCUS = 'focus';\nconst TRIGGER_CLICK = 'click';\nconst TRIGGER_MANUAL = 'manual';\nconst EVENT_HIDE$2 = 'hide';\nconst EVENT_HIDDEN$2 = 'hidden';\nconst EVENT_SHOW$2 = 'show';\nconst EVENT_SHOWN$2 = 'shown';\nconst EVENT_INSERTED = 'inserted';\nconst EVENT_CLICK$1 = 'click';\nconst EVENT_FOCUSIN$1 = 'focusin';\nconst EVENT_FOCUSOUT$1 = 'focusout';\nconst EVENT_MOUSEENTER = 'mouseenter';\nconst EVENT_MOUSELEAVE = 'mouseleave';\nconst AttachmentMap = {\n AUTO: 'auto',\n TOP: 'top',\n RIGHT: isRTL() ? 'left' : 'right',\n BOTTOM: 'bottom',\n LEFT: isRTL() ? 'right' : 'left'\n};\nconst Default$3 = {\n allowList: DefaultAllowlist,\n animation: true,\n boundary: 'clippingParents',\n container: false,\n customClass: '',\n delay: 0,\n fallbackPlacements: ['top', 'right', 'bottom', 'left'],\n html: false,\n offset: [0, 6],\n placement: 'top',\n popperConfig: null,\n sanitize: true,\n sanitizeFn: null,\n selector: false,\n template: '
' + '
' + '
' + '
',\n title: '',\n trigger: 'hover focus'\n};\nconst DefaultType$3 = {\n allowList: 'object',\n animation: 'boolean',\n boundary: '(string|element)',\n container: '(string|element|boolean)',\n customClass: '(string|function)',\n delay: '(number|object)',\n fallbackPlacements: 'array',\n html: 'boolean',\n offset: '(array|string|function)',\n placement: '(string|function)',\n popperConfig: '(null|object|function)',\n sanitize: 'boolean',\n sanitizeFn: '(null|function)',\n selector: '(string|boolean)',\n template: 'string',\n title: '(string|element|function)',\n trigger: 'string'\n};\n\n/**\n * Class definition\n */\n\nclass Tooltip extends BaseComponent {\n constructor(element, config) {\n if (typeof Popper === 'undefined') {\n throw new TypeError('Bootstrap\\'s tooltips require Popper (https://popper.js.org)');\n }\n super(element, config);\n\n // Private\n this._isEnabled = true;\n this._timeout = 0;\n this._isHovered = null;\n this._activeTrigger = {};\n this._popper = null;\n this._templateFactory = null;\n this._newContent = null;\n\n // Protected\n this.tip = null;\n this._setListeners();\n if (!this._config.selector) {\n this._fixTitle();\n }\n }\n\n // Getters\n static get Default() {\n return Default$3;\n }\n static get DefaultType() {\n return DefaultType$3;\n }\n static get NAME() {\n return NAME$4;\n }\n\n // Public\n enable() {\n this._isEnabled = true;\n }\n disable() {\n this._isEnabled = false;\n }\n toggleEnabled() {\n this._isEnabled = !this._isEnabled;\n }\n toggle() {\n if (!this._isEnabled) {\n return;\n }\n this._activeTrigger.click = !this._activeTrigger.click;\n if (this._isShown()) {\n this._leave();\n return;\n }\n this._enter();\n }\n dispose() {\n clearTimeout(this._timeout);\n EventHandler.off(this._element.closest(SELECTOR_MODAL), EVENT_MODAL_HIDE, this._hideModalHandler);\n if (this._element.getAttribute('data-bs-original-title')) {\n this._element.setAttribute('title', this._element.getAttribute('data-bs-original-title'));\n }\n this._disposePopper();\n super.dispose();\n }\n show() {\n if (this._element.style.display === 'none') {\n throw new Error('Please use show on visible elements');\n }\n if (!(this._isWithContent() && this._isEnabled)) {\n return;\n }\n const showEvent = EventHandler.trigger(this._element, this.constructor.eventName(EVENT_SHOW$2));\n const shadowRoot = findShadowRoot(this._element);\n const isInTheDom = (shadowRoot || this._element.ownerDocument.documentElement).contains(this._element);\n if (showEvent.defaultPrevented || !isInTheDom) {\n return;\n }\n\n // TODO: v6 remove this or make it optional\n this._disposePopper();\n const tip = this._getTipElement();\n this._element.setAttribute('aria-describedby', tip.getAttribute('id'));\n const {\n container\n } = this._config;\n if (!this._element.ownerDocument.documentElement.contains(this.tip)) {\n container.append(tip);\n EventHandler.trigger(this._element, this.constructor.eventName(EVENT_INSERTED));\n }\n this._popper = this._createPopper(tip);\n tip.classList.add(CLASS_NAME_SHOW$2);\n\n // If this is a touch-enabled device we add extra\n // empty mouseover listeners to the body's immediate children;\n // only needed because of broken event delegation on iOS\n // https://www.quirksmode.org/blog/archives/2014/02/mouse_event_bub.html\n if ('ontouchstart' in document.documentElement) {\n for (const element of [].concat(...document.body.children)) {\n EventHandler.on(element, 'mouseover', noop);\n }\n }\n const complete = () => {\n EventHandler.trigger(this._element, this.constructor.eventName(EVENT_SHOWN$2));\n if (this._isHovered === false) {\n this._leave();\n }\n this._isHovered = false;\n };\n this._queueCallback(complete, this.tip, this._isAnimated());\n }\n hide() {\n if (!this._isShown()) {\n return;\n }\n const hideEvent = EventHandler.trigger(this._element, this.constructor.eventName(EVENT_HIDE$2));\n if (hideEvent.defaultPrevented) {\n return;\n }\n const tip = this._getTipElement();\n tip.classList.remove(CLASS_NAME_SHOW$2);\n\n // If this is a touch-enabled device we remove the extra\n // empty mouseover listeners we added for iOS support\n if ('ontouchstart' in document.documentElement) {\n for (const element of [].concat(...document.body.children)) {\n EventHandler.off(element, 'mouseover', noop);\n }\n }\n this._activeTrigger[TRIGGER_CLICK] = false;\n this._activeTrigger[TRIGGER_FOCUS] = false;\n this._activeTrigger[TRIGGER_HOVER] = false;\n this._isHovered = null; // it is a trick to support manual triggering\n\n const complete = () => {\n if (this._isWithActiveTrigger()) {\n return;\n }\n if (!this._isHovered) {\n this._disposePopper();\n }\n this._element.removeAttribute('aria-describedby');\n EventHandler.trigger(this._element, this.constructor.eventName(EVENT_HIDDEN$2));\n };\n this._queueCallback(complete, this.tip, this._isAnimated());\n }\n update() {\n if (this._popper) {\n this._popper.update();\n }\n }\n\n // Protected\n _isWithContent() {\n return Boolean(this._getTitle());\n }\n _getTipElement() {\n if (!this.tip) {\n this.tip = this._createTipElement(this._newContent || this._getContentForTemplate());\n }\n return this.tip;\n }\n _createTipElement(content) {\n const tip = this._getTemplateFactory(content).toHtml();\n\n // TODO: remove this check in v6\n if (!tip) {\n return null;\n }\n tip.classList.remove(CLASS_NAME_FADE$2, CLASS_NAME_SHOW$2);\n // TODO: v6 the following can be achieved with CSS only\n tip.classList.add(`bs-${this.constructor.NAME}-auto`);\n const tipId = getUID(this.constructor.NAME).toString();\n tip.setAttribute('id', tipId);\n if (this._isAnimated()) {\n tip.classList.add(CLASS_NAME_FADE$2);\n }\n return tip;\n }\n setContent(content) {\n this._newContent = content;\n if (this._isShown()) {\n this._disposePopper();\n this.show();\n }\n }\n _getTemplateFactory(content) {\n if (this._templateFactory) {\n this._templateFactory.changeContent(content);\n } else {\n this._templateFactory = new TemplateFactory({\n ...this._config,\n // the `content` var has to be after `this._config`\n // to override config.content in case of popover\n content,\n extraClass: this._resolvePossibleFunction(this._config.customClass)\n });\n }\n return this._templateFactory;\n }\n _getContentForTemplate() {\n return {\n [SELECTOR_TOOLTIP_INNER]: this._getTitle()\n };\n }\n _getTitle() {\n return this._resolvePossibleFunction(this._config.title) || this._element.getAttribute('data-bs-original-title');\n }\n\n // Private\n _initializeOnDelegatedTarget(event) {\n return this.constructor.getOrCreateInstance(event.delegateTarget, this._getDelegateConfig());\n }\n _isAnimated() {\n return this._config.animation || this.tip && this.tip.classList.contains(CLASS_NAME_FADE$2);\n }\n _isShown() {\n return this.tip && this.tip.classList.contains(CLASS_NAME_SHOW$2);\n }\n _createPopper(tip) {\n const placement = execute(this._config.placement, [this, tip, this._element]);\n const attachment = AttachmentMap[placement.toUpperCase()];\n return Popper.createPopper(this._element, tip, this._getPopperConfig(attachment));\n }\n _getOffset() {\n const {\n offset\n } = this._config;\n if (typeof offset === 'string') {\n return offset.split(',').map(value => Number.parseInt(value, 10));\n }\n if (typeof offset === 'function') {\n return popperData => offset(popperData, this._element);\n }\n return offset;\n }\n _resolvePossibleFunction(arg) {\n return execute(arg, [this._element]);\n }\n _getPopperConfig(attachment) {\n const defaultBsPopperConfig = {\n placement: attachment,\n modifiers: [{\n name: 'flip',\n options: {\n fallbackPlacements: this._config.fallbackPlacements\n }\n }, {\n name: 'offset',\n options: {\n offset: this._getOffset()\n }\n }, {\n name: 'preventOverflow',\n options: {\n boundary: this._config.boundary\n }\n }, {\n name: 'arrow',\n options: {\n element: `.${this.constructor.NAME}-arrow`\n }\n }, {\n name: 'preSetPlacement',\n enabled: true,\n phase: 'beforeMain',\n fn: data => {\n // Pre-set Popper's placement attribute in order to read the arrow sizes properly.\n // Otherwise, Popper mixes up the width and height dimensions since the initial arrow style is for top placement\n this._getTipElement().setAttribute('data-popper-placement', data.state.placement);\n }\n }]\n };\n return {\n ...defaultBsPopperConfig,\n ...execute(this._config.popperConfig, [defaultBsPopperConfig])\n };\n }\n _setListeners() {\n const triggers = this._config.trigger.split(' ');\n for (const trigger of triggers) {\n if (trigger === 'click') {\n EventHandler.on(this._element, this.constructor.eventName(EVENT_CLICK$1), this._config.selector, event => {\n const context = this._initializeOnDelegatedTarget(event);\n context.toggle();\n });\n } else if (trigger !== TRIGGER_MANUAL) {\n const eventIn = trigger === TRIGGER_HOVER ? this.constructor.eventName(EVENT_MOUSEENTER) : this.constructor.eventName(EVENT_FOCUSIN$1);\n const eventOut = trigger === TRIGGER_HOVER ? this.constructor.eventName(EVENT_MOUSELEAVE) : this.constructor.eventName(EVENT_FOCUSOUT$1);\n EventHandler.on(this._element, eventIn, this._config.selector, event => {\n const context = this._initializeOnDelegatedTarget(event);\n context._activeTrigger[event.type === 'focusin' ? TRIGGER_FOCUS : TRIGGER_HOVER] = true;\n context._enter();\n });\n EventHandler.on(this._element, eventOut, this._config.selector, event => {\n const context = this._initializeOnDelegatedTarget(event);\n context._activeTrigger[event.type === 'focusout' ? TRIGGER_FOCUS : TRIGGER_HOVER] = context._element.contains(event.relatedTarget);\n context._leave();\n });\n }\n }\n this._hideModalHandler = () => {\n if (this._element) {\n this.hide();\n }\n };\n EventHandler.on(this._element.closest(SELECTOR_MODAL), EVENT_MODAL_HIDE, this._hideModalHandler);\n }\n _fixTitle() {\n const title = this._element.getAttribute('title');\n if (!title) {\n return;\n }\n if (!this._element.getAttribute('aria-label') && !this._element.textContent.trim()) {\n this._element.setAttribute('aria-label', title);\n }\n this._element.setAttribute('data-bs-original-title', title); // DO NOT USE IT. Is only for backwards compatibility\n this._element.removeAttribute('title');\n }\n _enter() {\n if (this._isShown() || this._isHovered) {\n this._isHovered = true;\n return;\n }\n this._isHovered = true;\n this._setTimeout(() => {\n if (this._isHovered) {\n this.show();\n }\n }, this._config.delay.show);\n }\n _leave() {\n if (this._isWithActiveTrigger()) {\n return;\n }\n this._isHovered = false;\n this._setTimeout(() => {\n if (!this._isHovered) {\n this.hide();\n }\n }, this._config.delay.hide);\n }\n _setTimeout(handler, timeout) {\n clearTimeout(this._timeout);\n this._timeout = setTimeout(handler, timeout);\n }\n _isWithActiveTrigger() {\n return Object.values(this._activeTrigger).includes(true);\n }\n _getConfig(config) {\n const dataAttributes = Manipulator.getDataAttributes(this._element);\n for (const dataAttribute of Object.keys(dataAttributes)) {\n if (DISALLOWED_ATTRIBUTES.has(dataAttribute)) {\n delete dataAttributes[dataAttribute];\n }\n }\n config = {\n ...dataAttributes,\n ...(typeof config === 'object' && config ? config : {})\n };\n config = this._mergeConfigObj(config);\n config = this._configAfterMerge(config);\n this._typeCheckConfig(config);\n return config;\n }\n _configAfterMerge(config) {\n config.container = config.container === false ? document.body : getElement(config.container);\n if (typeof config.delay === 'number') {\n config.delay = {\n show: config.delay,\n hide: config.delay\n };\n }\n if (typeof config.title === 'number') {\n config.title = config.title.toString();\n }\n if (typeof config.content === 'number') {\n config.content = config.content.toString();\n }\n return config;\n }\n _getDelegateConfig() {\n const config = {};\n for (const [key, value] of Object.entries(this._config)) {\n if (this.constructor.Default[key] !== value) {\n config[key] = value;\n }\n }\n config.selector = false;\n config.trigger = 'manual';\n\n // In the future can be replaced with:\n // const keysWithDifferentValues = Object.entries(this._config).filter(entry => this.constructor.Default[entry[0]] !== this._config[entry[0]])\n // `Object.fromEntries(keysWithDifferentValues)`\n return config;\n }\n _disposePopper() {\n if (this._popper) {\n this._popper.destroy();\n this._popper = null;\n }\n if (this.tip) {\n this.tip.remove();\n this.tip = null;\n }\n }\n\n // Static\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Tooltip.getOrCreateInstance(this, config);\n if (typeof config !== 'string') {\n return;\n }\n if (typeof data[config] === 'undefined') {\n throw new TypeError(`No method named \"${config}\"`);\n }\n data[config]();\n });\n }\n}\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Tooltip);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap popover.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$3 = 'popover';\nconst SELECTOR_TITLE = '.popover-header';\nconst SELECTOR_CONTENT = '.popover-body';\nconst Default$2 = {\n ...Tooltip.Default,\n content: '',\n offset: [0, 8],\n placement: 'right',\n template: '
' + '
' + '

' + '
' + '
',\n trigger: 'click'\n};\nconst DefaultType$2 = {\n ...Tooltip.DefaultType,\n content: '(null|string|element|function)'\n};\n\n/**\n * Class definition\n */\n\nclass Popover extends Tooltip {\n // Getters\n static get Default() {\n return Default$2;\n }\n static get DefaultType() {\n return DefaultType$2;\n }\n static get NAME() {\n return NAME$3;\n }\n\n // Overrides\n _isWithContent() {\n return this._getTitle() || this._getContent();\n }\n\n // Private\n _getContentForTemplate() {\n return {\n [SELECTOR_TITLE]: this._getTitle(),\n [SELECTOR_CONTENT]: this._getContent()\n };\n }\n _getContent() {\n return this._resolvePossibleFunction(this._config.content);\n }\n\n // Static\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Popover.getOrCreateInstance(this, config);\n if (typeof config !== 'string') {\n return;\n }\n if (typeof data[config] === 'undefined') {\n throw new TypeError(`No method named \"${config}\"`);\n }\n data[config]();\n });\n }\n}\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Popover);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap scrollspy.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$2 = 'scrollspy';\nconst DATA_KEY$2 = 'bs.scrollspy';\nconst EVENT_KEY$2 = `.${DATA_KEY$2}`;\nconst DATA_API_KEY = '.data-api';\nconst EVENT_ACTIVATE = `activate${EVENT_KEY$2}`;\nconst EVENT_CLICK = `click${EVENT_KEY$2}`;\nconst EVENT_LOAD_DATA_API$1 = `load${EVENT_KEY$2}${DATA_API_KEY}`;\nconst CLASS_NAME_DROPDOWN_ITEM = 'dropdown-item';\nconst CLASS_NAME_ACTIVE$1 = 'active';\nconst SELECTOR_DATA_SPY = '[data-bs-spy=\"scroll\"]';\nconst SELECTOR_TARGET_LINKS = '[href]';\nconst SELECTOR_NAV_LIST_GROUP = '.nav, .list-group';\nconst SELECTOR_NAV_LINKS = '.nav-link';\nconst SELECTOR_NAV_ITEMS = '.nav-item';\nconst SELECTOR_LIST_ITEMS = '.list-group-item';\nconst SELECTOR_LINK_ITEMS = `${SELECTOR_NAV_LINKS}, ${SELECTOR_NAV_ITEMS} > ${SELECTOR_NAV_LINKS}, ${SELECTOR_LIST_ITEMS}`;\nconst SELECTOR_DROPDOWN = '.dropdown';\nconst SELECTOR_DROPDOWN_TOGGLE$1 = '.dropdown-toggle';\nconst Default$1 = {\n offset: null,\n // TODO: v6 @deprecated, keep it for backwards compatibility reasons\n rootMargin: '0px 0px -25%',\n smoothScroll: false,\n target: null,\n threshold: [0.1, 0.5, 1]\n};\nconst DefaultType$1 = {\n offset: '(number|null)',\n // TODO v6 @deprecated, keep it for backwards compatibility reasons\n rootMargin: 'string',\n smoothScroll: 'boolean',\n target: 'element',\n threshold: 'array'\n};\n\n/**\n * Class definition\n */\n\nclass ScrollSpy extends BaseComponent {\n constructor(element, config) {\n super(element, config);\n\n // this._element is the observablesContainer and config.target the menu links wrapper\n this._targetLinks = new Map();\n this._observableSections = new Map();\n this._rootElement = getComputedStyle(this._element).overflowY === 'visible' ? null : this._element;\n this._activeTarget = null;\n this._observer = null;\n this._previousScrollData = {\n visibleEntryTop: 0,\n parentScrollTop: 0\n };\n this.refresh(); // initialize\n }\n\n // Getters\n static get Default() {\n return Default$1;\n }\n static get DefaultType() {\n return DefaultType$1;\n }\n static get NAME() {\n return NAME$2;\n }\n\n // Public\n refresh() {\n this._initializeTargetsAndObservables();\n this._maybeEnableSmoothScroll();\n if (this._observer) {\n this._observer.disconnect();\n } else {\n this._observer = this._getNewObserver();\n }\n for (const section of this._observableSections.values()) {\n this._observer.observe(section);\n }\n }\n dispose() {\n this._observer.disconnect();\n super.dispose();\n }\n\n // Private\n _configAfterMerge(config) {\n // TODO: on v6 target should be given explicitly & remove the {target: 'ss-target'} case\n config.target = getElement(config.target) || document.body;\n\n // TODO: v6 Only for backwards compatibility reasons. Use rootMargin only\n config.rootMargin = config.offset ? `${config.offset}px 0px -30%` : config.rootMargin;\n if (typeof config.threshold === 'string') {\n config.threshold = config.threshold.split(',').map(value => Number.parseFloat(value));\n }\n return config;\n }\n _maybeEnableSmoothScroll() {\n if (!this._config.smoothScroll) {\n return;\n }\n\n // unregister any previous listeners\n EventHandler.off(this._config.target, EVENT_CLICK);\n EventHandler.on(this._config.target, EVENT_CLICK, SELECTOR_TARGET_LINKS, event => {\n const observableSection = this._observableSections.get(event.target.hash);\n if (observableSection) {\n event.preventDefault();\n const root = this._rootElement || window;\n const height = observableSection.offsetTop - this._element.offsetTop;\n if (root.scrollTo) {\n root.scrollTo({\n top: height,\n behavior: 'smooth'\n });\n return;\n }\n\n // Chrome 60 doesn't support `scrollTo`\n root.scrollTop = height;\n }\n });\n }\n _getNewObserver() {\n const options = {\n root: this._rootElement,\n threshold: this._config.threshold,\n rootMargin: this._config.rootMargin\n };\n return new IntersectionObserver(entries => this._observerCallback(entries), options);\n }\n\n // The logic of selection\n _observerCallback(entries) {\n const targetElement = entry => this._targetLinks.get(`#${entry.target.id}`);\n const activate = entry => {\n this._previousScrollData.visibleEntryTop = entry.target.offsetTop;\n this._process(targetElement(entry));\n };\n const parentScrollTop = (this._rootElement || document.documentElement).scrollTop;\n const userScrollsDown = parentScrollTop >= this._previousScrollData.parentScrollTop;\n this._previousScrollData.parentScrollTop = parentScrollTop;\n for (const entry of entries) {\n if (!entry.isIntersecting) {\n this._activeTarget = null;\n this._clearActiveClass(targetElement(entry));\n continue;\n }\n const entryIsLowerThanPrevious = entry.target.offsetTop >= this._previousScrollData.visibleEntryTop;\n // if we are scrolling down, pick the bigger offsetTop\n if (userScrollsDown && entryIsLowerThanPrevious) {\n activate(entry);\n // if parent isn't scrolled, let's keep the first visible item, breaking the iteration\n if (!parentScrollTop) {\n return;\n }\n continue;\n }\n\n // if we are scrolling up, pick the smallest offsetTop\n if (!userScrollsDown && !entryIsLowerThanPrevious) {\n activate(entry);\n }\n }\n }\n _initializeTargetsAndObservables() {\n this._targetLinks = new Map();\n this._observableSections = new Map();\n const targetLinks = SelectorEngine.find(SELECTOR_TARGET_LINKS, this._config.target);\n for (const anchor of targetLinks) {\n // ensure that the anchor has an id and is not disabled\n if (!anchor.hash || isDisabled(anchor)) {\n continue;\n }\n const observableSection = SelectorEngine.findOne(decodeURI(anchor.hash), this._element);\n\n // ensure that the observableSection exists & is visible\n if (isVisible(observableSection)) {\n this._targetLinks.set(decodeURI(anchor.hash), anchor);\n this._observableSections.set(anchor.hash, observableSection);\n }\n }\n }\n _process(target) {\n if (this._activeTarget === target) {\n return;\n }\n this._clearActiveClass(this._config.target);\n this._activeTarget = target;\n target.classList.add(CLASS_NAME_ACTIVE$1);\n this._activateParents(target);\n EventHandler.trigger(this._element, EVENT_ACTIVATE, {\n relatedTarget: target\n });\n }\n _activateParents(target) {\n // Activate dropdown parents\n if (target.classList.contains(CLASS_NAME_DROPDOWN_ITEM)) {\n SelectorEngine.findOne(SELECTOR_DROPDOWN_TOGGLE$1, target.closest(SELECTOR_DROPDOWN)).classList.add(CLASS_NAME_ACTIVE$1);\n return;\n }\n for (const listGroup of SelectorEngine.parents(target, SELECTOR_NAV_LIST_GROUP)) {\n // Set triggered links parents as active\n // With both
    and