diff --git a/.github/workflows/publish-to-pypi.yml b/.github/workflows/publish-to-pypi.yml index be80824..b9ea96c 100644 --- a/.github/workflows/publish-to-pypi.yml +++ b/.github/workflows/publish-to-pypi.yml @@ -12,7 +12,7 @@ jobs: - name: setup-python uses: actions/setup-python@v4 with: - python-version: "3.10.4" + python-version: "3.10.20" - name: install-pypa/build run: >- python3 -m @@ -22,7 +22,7 @@ jobs: - name: build-bin-whl run: python3 -m build - name: grab-build - uses: actions/upload-artifact@v3 + uses: actions/upload-artifact@v4 with: name: pkg-dist path: dist/ @@ -42,7 +42,7 @@ jobs: steps: - name: download-dists - uses: actions/download-artifact@v3 + uses: actions/download-artifact@v4 with: name: pkg-dist path: dist/ @@ -62,7 +62,7 @@ jobs: steps: - name: download-dists - uses: actions/download-artifact@v3 + uses: actions/download-artifact@v4 with: name: pkg-dist path: dist/ @@ -104,7 +104,7 @@ jobs: steps: - name: download-dists - uses: actions/download-artifact@v3 + uses: actions/download-artifact@v4 with: name: pkg-dist path: dist/ diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 397b914..9f83f4f 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -3,13 +3,13 @@ # from manual and automatic code checks. repos: - repo: https://github.com/psf/black - rev: 23.1.0 + rev: 26.3.1 hooks: - id: black args: - --line-length=79 - repo: https://github.com/pre-commit/pre-commit-hooks - rev: v4.4.0 + rev: v6.0.0 hooks: # - id: check-added-large-files # args: ['maxkb=500'] @@ -20,13 +20,13 @@ repos: - id: check-toml - id: check-yaml - repo: https://github.com/pycqa/flake8 - rev: 6.0.0 + rev: 7.3.0 hooks: - id: flake8 args: - "--ignore=E203,W503" - repo: https://github.com/pre-commit/mirrors-mypy - rev: v1.1.1 + rev: v1.20.0 hooks: - id: mypy additional_dependencies: [pandas-stubs, types-Pillow, types-setuptools, types-PyYAML] diff --git a/README.md b/README.md index ffe2ead..6b43c66 100644 --- a/README.md +++ b/README.md @@ -111,3 +111,50 @@ cd ccpem-pipeliner pip install -e . ``` After doing so, an editable install of roodmus should be set up with all requisite packages. + +# Modern (May 2025) developer install on V100 + +``` +conda create -n roodmus python=3.10.4 +conda activate roodmus + +git clone https://gitlab.com/ccpem/ccpem-pipeliner.git +cd ccpem-pipeliner +git checkout bedbedbe183ad497dbaa82a638f210d316ba9bae +cd ../ + +git clone git@github.com:ccpem/roodmus.git +cd roodmus +export CXX=/usr/bin/g++ +export CUDACXX=/usr/local/cuda-12.3/bin/nvcc +export CMAKE_CUDA_ARCHITECTURES=70 +#USE CMAKE COMPATIBLE WITH CMAKE<3.5 +pip install --upgrade pip +pip install -e . --no-cache-dir +pre-commit install +cd ../ + +cd ccpem-pipeliner +pip install -e . +``` + +# Modern (July 2026) developer install on V100 + +``` +conda create -n roodmus python=3.10.20 +conda activate roodmus + +git clone git@github.com:ccpem/roodmus.git +cd roodmus +export CXX=/usr/bin/g++ +export CUDACXX=/usr/bin/nvcc +export CMAKE_CUDA_ARCHITECTURES=70 +#USE CMAKE COMPATIBLE WITH CMAKE<3.5???? +pip install --upgrade pip +pip install -e .[dev,gpu,docs] --no-cache-dir +pre-commit install +cd ../ + +cd ccpem-pipeliner +pip install -e . +``` \ No newline at end of file diff --git a/notebooks/make_starfile_with_GT.ipynb b/notebooks/make_starfile_with_GT.ipynb new file mode 100644 index 0000000..8498b09 --- /dev/null +++ b/notebooks/make_starfile_with_GT.ipynb @@ -0,0 +1,161 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Creating STAR file with Ground Truth Particle Positions And Defoci\n", + "\n", + "Uses roodmus' simulation/write_starfile utilities:\n", + "\n", + "* read ground truth information from YAML files\n", + "* save the dataframe of truth information to file as csv\n", + "* use CLI roodmus write_starfile with csv file as input to create a particles.star file\n", + "* user particles.star file to extract (or re-extract?) particles to get extracted particle stack\n", + "* utilise cryodrgn to train on particle stack in doppio\n", + "* interpret latent space representation in another notebook" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "# read ground truth from yaml files\n", + "# data loading for DE-Shaw covid spike partially open set\n", + "project_dir = \"/mnt/dinaMISMO/synthetic_datasets/MapReconstruction/PreP/extract_frames_6xou_unsteered_0-200ns\"\n", + "mrc_dir = os.path.join(project_dir, \"Micrographs\")\n", + "figures_dir = os.path.join(project_dir, \"figures\")\n", + "\n", + "verbose=True\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "loading micrographs: 100%|██████████| 67/67 [00:14<00:00, 4.59it/s, micrograph=000058.mrc]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loaded ground-truth particle positions from config files\n", + "Dictionaries now contain 0 particles and 20100 true particles\n", + "Added 20100 particles from /mnt/dinaMISMO/synthetic_datasets/MapReconstruction/PreP/extract_frames_6xou_unsteered_0-200ns/Micrographs\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "# save ground truth dataframe to csv format\n", + "\n", + "# load into dataframe\n", + "from roodmus.analysis.utils import load_data\n", + "\n", + "loaded_data = load_data(\n", + " meta_file=None, # no reconstruction file(s)\n", + " config_dir=mrc_dir, # path to roodmus run_parakeet output directory\n", + " particle_diameter=100, # approx\n", + " ugraph_shape=(4000, 4000), \n", + " ignore_missing_files=False,\n", + " verbose=True,\n", + " enable_tqdm=True,\n", + ")\n", + "\n", + "# taking most of this code from src/roodmus/analysis/extract_particles.py?????\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'\\npd.read_csv(\\n truth_csv_name,\\n index_col=0,\\n)\\n'" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# make dict into df and save\n", + "import pandas as pd\n", + "\n", + "# make df and save to file\n", + "truth_csv_fname = os.path.join(project_dir, \"truth.csv\") \n", + "truth_df = pd.DataFrame(\n", + " loaded_data.results_truth,\n", + " columns=loaded_data.results_truth.keys(),\n", + ")\n", + "truth_df.to_csv(truth_csv_fname)\n", + "\n", + "# can load with:\n", + "\"\"\"\n", + "pd.read_csv(\n", + " truth_csv_name,\n", + " index_col=0,\n", + ")\n", + "\"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# run starfile creation script\n", + "!bash make_starfile_with_GT.sh\n", + "\n", + "\n", + "\n", + "# After this move to doppio to extract particles and run cryodrgn\n", + "# Then use notebook to trace conformations through latent space embeddings\n", + "# And then create output maps and models to compute metrics on" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "roodmus", + "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": "3.10.18" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/make_starfile_with_GT.sh b/notebooks/make_starfile_with_GT.sh new file mode 100644 index 0000000..5081919 --- /dev/null +++ b/notebooks/make_starfile_with_GT.sh @@ -0,0 +1,33 @@ +# take imported micrgraphs and make a starfile with the ground truth coordinates as well as the other optics +# group information necessary to allow particles to be extracted into particle stacks straight from the +# micrographs + +# in this case we need no motion correction! +# and can/should use ground truth per-particle defoci? +# WILL USE PER-UGRAPH DEFOCUS, simply because already coded in. MAYBE REVISE THIS? + +INPUT_CSV="truth.csv" +TYPE="data_star" #? check +OUTPUT_DIR="GT_starfile" +UGRAPH_DIR="Micrographs" # the dir of imported micrographs +PIXEL_SIZE=1.0 + +# optics group arguments +OPTICS_GROUP_NAME="opticsGroup1" +OPTICS_GROUP="1" +MTF_FILENAME="Micrographs/relion/mtf_300kV.star" +MICROGRAPH_ORIGINAL_PIXEL_SIZE="1.0" +VOLTAGE="300" +SPHERICAL_ABERRATION="2.7" +AMPLITUDE_CONTRAST="0.1" +IMAGE_PIXEL_SIZE=1.0 +IMAGE_SIZE=256 +IMAGE_DIMENSIONALITY=2 +CTF_DATA_ARE_CTF_PREMULTIPLIED=0 + +# EXTRACT_DIR="" not used in this case + +roodmus write_starfile --tqdm --verbose --input_csv $INPUT_CSV --type $TYPE --output_dir $OUTPUT_DIR --ugraph_dir $UGRAPH_DIR --pixel_size $PIXEL_SIZE --optics_group_name $OPTICS_GROUP_NAME --optics_group $OPTICS_GROUP --mtf_filename $MTF_FILENAME --micrograph_original_pixel_size $MICROGRAPH_ORIGINAL_PIXEL_SIZE --voltage $VOLTAGE --spherical_aberration $SPHERICAL_ABERRATION --amplitude_contrast $AMPLITUDE_CONTRAST --image_pixel_size $IMAGE_PIXEL_SIZE --image_size $IMAGE_SIZE --image_dimensionality $IMAGE_DIMENSIONALITY --ctf_data_are_ctf_premultiplied $CTF_DATA_ARE_CTF_PREMULTIPLIED + +# if you want per-particle defoci instead of per ugraph defoci: +# roodmus write_starfile --pp_defoci --tqdm --verbose --input_csv $INPUT_CSV --type $TYPE --output_dir $OUTPUT_DIR --ugraph_dir $UGRAPH_DIR --pixel_size $PIXEL_SIZE --optics_group_name $OPTICS_GROUP_NAME --optics_group $OPTICS_GROUP --mtf_filename $MTF_FILENAME --micrograph_original_pixel_size $MICROGRAPH_ORIGINAL_PIXEL_SIZE --voltage $VOLTAGE --spherical_aberration $SPHERICAL_ABERRATION --amplitude_contrast $AMPLITUDE_CONTRAST --image_pixel_size $IMAGE_PIXEL_SIZE --image_size $IMAGE_SIZE --image_dimensionality $IMAGE_DIMENSIONALITY --ctf_data_are_ctf_premultiplied $CTF_DATA_ARE_CTF_PREMULTIPLIED \ No newline at end of file diff --git a/notebooks/trace_PreP_through_latent.ipynb b/notebooks/trace_PreP_through_latent.ipynb new file mode 100644 index 0000000..7c41bad --- /dev/null +++ b/notebooks/trace_PreP_through_latent.ipynb @@ -0,0 +1,1771 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/dvi41342/miniforge3/envs/roodmus/lib/python3.10/site-packages/pipeliner/__init__.py:1: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " from pkg_resources import get_distribution\n" + ] + } + ], + "source": [ + "# imports\n", + "import os\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import html\n", + "\n", + "from sklearn.decomposition import PCA\n", + "\n", + "# roodmus\n", + "from roodmus.analysis.utils import load_data\n", + "from roodmus.heterogeneity.plot_heterogeneous_reconstruction import (\n", + " plot_latent_space_scatter\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# functions\n", + "def get_latents_cryodrgn(latent_file):\n", + " latents = np.load(latent_file, allow_pickle=True)\n", + " print(\"latents: {}, latents.shape[1]: {}\".format(latents, latents.shape[1]))\n", + " ndim = latents.shape[1]\n", + " return latents, ndim" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/mnt/dinaMISMO/synthetic_datasets/MapReconstruction/PreP/extract_frames_6xou_unsteered_0-200ns/Import/job007/particles.star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not 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"text": [ + "Loaded ground-truth particle positions from config files\n", + "Dictionaries now contain 20100 particles and 20100 true particles\n", + "Added 20100 particles from /mnt/dinaMISMO/synthetic_datasets/MapReconstruction/PreP/extract_frames_6xou_unsteered_0-200ns/Micrographs\n", + "For each micrograph, for each metadata file, compute the precision, recall and multiplicity\n", + "Speed of computation depends on the number of particles in the micrograph. progressbar is not accurate\n", + "Total number of groups to loop over: 67\n", + "Number of micgrographs: 67\n", + "Number of metadata files: 1\n", + "Starting loop over groups\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "computing precision: 100%|██████████| 67/67 [00:05<00:00, 12.75it/s, precision=1, recall=1, multiplicity=1.07]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "time taken to compute precision: 5.275198459625244\n", + "latent space dimensionality: 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" + ], + "text/plain": [ + " metadata_filename ugraph_filename \\\n", + "20095 /mnt/dinaMISMO/synthetic_datasets/MapReconstru... 000058.mrc \n", + "20096 /mnt/dinaMISMO/synthetic_datasets/MapReconstru... 000058.mrc \n", + "20097 /mnt/dinaMISMO/synthetic_datasets/MapReconstru... 000058.mrc \n", + "20098 /mnt/dinaMISMO/synthetic_datasets/MapReconstru... 000058.mrc \n", + "20099 /mnt/dinaMISMO/synthetic_datasets/MapReconstru... 000058.mrc \n", + "\n", + " position_x position_y euler_phi euler_theta euler_psi \\\n", + "20095 3167.964355 1419.746216 2.342370 1.593567 -1.644509 \n", + "20096 1463.147827 3179.908447 -2.946752 0.961545 -2.343948 \n", + "20097 3613.651123 2322.348145 0.240971 0.008801 -0.354462 \n", + "20098 223.331451 1715.293823 1.181207 1.805982 -1.914031 \n", + "20099 3200.553223 2070.339355 1.607324 0.033726 -1.573615 \n", + "\n", + " ugraph_shape defocusU defocusV ... latent_6 latent_7 \\\n", + "20095 (4000, 4000) -26199.68613 -26199.68613 ... -0.828033 1.786695 \n", + "20096 (4000, 4000) -26199.68613 -26199.68613 ... -1.796655 -0.934579 \n", + "20097 (4000, 4000) -26199.68613 -26199.68613 ... -0.887409 0.101615 \n", + "20098 (4000, 4000) -26199.68613 -26199.68613 ... 1.216764 1.698741 \n", + "20099 (4000, 4000) -26199.68613 -26199.68613 ... -2.041573 -1.436153 \n", + "\n", + " PCA_0 PCA_1 PCA_2 PCA_3 PCA_4 PCA_5 PCA_6 \\\n", + "20095 0.332256 0.235065 1.201625 -3.870411 -1.259568 -0.443173 1.842189 \n", + "20096 -1.437623 2.116327 -0.455647 -0.179700 -0.230144 -0.902039 -1.555978 \n", + "20097 0.465229 1.156685 -3.490604 0.290977 -1.391266 -0.340226 -0.308574 \n", + "20098 0.124830 -2.038110 -1.329813 -0.297920 -2.432164 2.702004 -0.274995 \n", + "20099 -1.146854 1.869835 -2.405998 0.340546 1.267649 0.497503 -2.125551 \n", + "\n", + " PCA_7 \n", + "20095 -2.043319 \n", + "20096 0.190705 \n", + "20097 -0.401093 \n", + "20098 -0.628627 \n", + "20099 -0.129020 \n", + "\n", + "[5 rows x 32 columns]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# data loading for DE-Shaw covid spike partially open set\n", + "project_dir = \"/mnt/dinaMISMO/synthetic_datasets/MapReconstruction/PreP/extract_frames_6xou_unsteered_0-200ns\"\n", + "config_dir = os.path.join(project_dir, \"Micrographs\")\n", + "figures_dir = os.path.join(project_dir, \"figures\")\n", + "# meta_file = os.path.join(project_dir, \"cryoDRGN\", \"run_data.star\")\n", + "meta_file = os.path.join(project_dir, \"Import/job007/particles.star\")\n", + "jobtypes = {\n", + " # os.path.join(project_dir, \"cryoDRGN\", \"run_data.star\"): \"CryoDRGN\",\n", + " os.path.join(project_dir, \"Import/job007/particles.star\"): \"CryoDRGN\",\n", + "}\n", + "\n", + "# latent_file = os.path.join(project_dir, \"CryoDRGN\", \"train_320\", \"z.19.pkl\")\n", + "latent_file = os.path.join(project_dir, \"CryoDRGN\", \"job009\", \"train_128\", \"z.24.pkl\")\n", + "\n", + "particle_diameter = 100 # approximate particle diameter in Angstroms\n", + "ugraph_shape = (4000, 4000) # shape of the micrograph in pixels. Only needs to be given if the metadata file is a .star file\n", + "verbose = True\n", + "ignore_missing_files = True\n", + "enable_tqdm = True\n", + "\n", + "print(meta_file)\n", + "for file in meta_file:\n", + " if file.endswith(\".star\"):\n", + " print(\"is star\")\n", + " else:\n", + " print(\"not star\")\n", + "print(type(meta_file))\n", + "\n", + "analysis = load_data(meta_file, config_dir, particle_diameter, ugraph_shape=ugraph_shape, verbose=verbose, enable_tqdm=enable_tqdm, ignore_missing_files=ignore_missing_files) # creates the class\n", + "df_picked = pd.DataFrame(analysis.results_picking)\n", + "df_truth = pd.DataFrame(analysis.results_truth)\n", + "df_precision, df_picked, pdb_labels = analysis.compute_precision(df_picked, df_truth, verbose=verbose)\n", + "\n", + "# latent_space, ndim = IO.get_latents_cs(latent_file)\n", + "latent_space, ndim = get_latents_cryodrgn(latent_file)\n", + "print(f\"latent space dimensionality: {ndim}\")\n", + "print(latent_space.shape)\n", + "for i in range(ndim):\n", + " df_picked[\"latent_{}\".format(i)] = latent_space[:, i]\n", + "\n", + "# perform PCA on latent space and add PCA coordinates to the dataframe\n", + "pca = PCA(n_components=ndim)\n", + "pca.fit(latent_space)\n", + "print(pca.explained_variance_ratio_)\n", + "print(pca.singular_values_)\n", + "latent_space_pca = pca.transform(latent_space)\n", + "for i in range(ndim):\n", + " df_picked[\"PCA_{}\".format(i)] = latent_space_pca[:, i]\n", + "df_picked.tail()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## FIG 4 Panel A (FP picks), 128 pixel ds\n", + "\n", + "plotting latent space of epoch 25 of cryoDRGN with the FP particle picks plotted in red" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "number of FP: 0\n", + "number of TP: 20100\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# latent space scatter plot\n", + "dim1=0\n", + "dim2=1\n", + "\n", + "grid = plot_latent_space_scatter(\n", + " df_picked,\n", + " dim_1=dim1,\n", + " dim_2=dim2,\n", + " # color_by=\"TP\", # \"#4eb3d3\",\n", + " pca=True,\n", + " palette=\"Set1\"\n", + ")\n", + "df_FP = df_picked[df_picked[\"TP\"]==0]\n", + "print(f\"number of FP: {len(df_FP)}\")\n", + "print(f\"number of TP: {len(df_picked)-len(df_FP)}\")\n", + "# ax = grid.fig.get_axes()[0] DEPRECATED, HAVENT FOUND REPLACEMENT YET\n", + "sns.scatterplot(\n", + " data=df_FP,\n", + " x=f\"PCA_{dim1}\",\n", + " y=f\"PCA_{dim2}\",\n", + " color=\"red\",\n", + " s=10,\n", + " # ax=ax,\n", + " label=\"FP\",\n", + ")\n", + "# grid.set_axis_labels(f\"PCA{dim1}\", f\"PCA{dim2}\", fontsize=16)\n", + "grid.figure.get_axes()[0].tick_params(labelsize=14)\n", + "grid.figure.get_axes()[0].set_xlim((-12, 15))\n", + "grid.figure.get_axes()[0].set_ylim((-12, 12))\n", + "\n", + "grid.savefig(os.path.join(figures_dir, f\"{os.path.basename(latent_file)}_pca_{dim1}_{dim2}_FP.pdf\"), bbox_inches=\"tight\")\n", + "grid.savefig(os.path.join(figures_dir, f\"{os.path.basename(latent_file)}_pca_{dim1}_{dim2}_FP.png\"), bbox_inches=\"tight\", dpi=600)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## FIG 4, Panel B (track MD trajectory through latent space), 128 \n", + "\n", + "plot of the latent space with each point coloured by its corresponding frame from the MD trajectory" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.clf()\n", + "\n", + "# latent space scatter plot, coloured by ground truth frames\n", + "dim1=0\n", + "dim2=1\n", + "dt = 1.2e-3# time between frames in microseconds\n", + "\n", + "fig, ax = plot_latent_space_scatter(\n", + " df_picked,\n", + " dim_1=dim1,\n", + " dim_2=dim2,\n", + " color_by=\"closest_pdb_index\",\n", + " palette=\"RdYlBu\",\n", + " pca=True,\n", + ")\n", + "# remove legend and add colorbar for the closest_pdb_index\n", + "ax.legend_.remove()\n", + "\n", + "S_m = plt.cm.ScalarMappable(cmap=\"RdYlBu\")\n", + "S_m.set_array(df_picked[\"closest_pdb_index\"])\n", + "\"\"\"\n", + "cbar = plt.colorbar(S_m)\n", + "cbar.set_label(\"time [\\u03BCs]\", rotation=270, labelpad=15, fontsize=16) # time in ps\n", + "# change the tick labels on the colorbar to go from 0 to 10 us\n", + "cbar.set_ticks(np.linspace(1, df_picked[\"closest_pdb_index\"].max(), 10))\n", + "xticklabels = [np.round(r, 1) for r in np.linspace(1, df_picked[\"closest_pdb_index\"].max(), 10)*dt]\n", + "cbar.set_ticklabels(xticklabels, fontsize=14)\n", + "ax.set_xlabel(f\"PCA{dim1}\", fontsize=16)\n", + "ax.set_ylabel(f\"PCA{dim2}\", fontsize=16)\n", + "ax.tick_params(labelsize=14)\n", + "#ax.set_xlim((-12, 16))\n", + "#ax.set_ylim((-12, 12))\n", + "ax.set_xlim((-20, 20))\n", + "ax.set_ylim((-20, 20))\n", + "#plt.xticks([-20,-10,0,10,20])\n", + "#plt.yticks([])\n", + "\"\"\"\n", + "\n", + "# add trajectory to the plot\n", + "N_volumes = 50\n", + "pdb_indices = np.unique(df_picked[\"closest_pdb_index\"])\n", + "d_pdbs = len(pdb_indices) // N_volumes\n", + "\n", + "trajectory = np.zeros((N_volumes, ndim))\n", + "trajectory_pca = np.zeros((N_volumes, ndim))\n", + "for i in range(N_volumes):\n", + " pdb_group = pdb_indices[i*d_pdbs:(i+1)*d_pdbs]\n", + " mean_latent = df_picked[df_picked[\"closest_pdb_index\"].isin(pdb_group)].agg(\n", + " {f\"latent_{i}\": \"mean\" for i in range(ndim)}\n", + " )\n", + " trajectory[i] = mean_latent.values\n", + " mean_pca = df_picked[df_picked[\"closest_pdb_index\"].isin(pdb_group)].agg(\n", + " {f\"PCA_{i}\": \"mean\" for i in range(ndim)}\n", + " )\n", + " trajectory_pca[i] = mean_pca.values\n", + "\n", + "ax.scatter(trajectory_pca[:, 0], trajectory_pca[:, 1], s=5, c=\"black\", zorder=10)\n", + "ax.plot(trajectory_pca[:, 0], trajectory_pca[:, 1], c=\"black\", zorder=10, linewidth=0.5)\n", + "ax.set_aspect(\"equal\")\n", + "\n", + "fig.savefig(os.path.join(figures_dir, f\"{os.path.basename(latent_file)}_latent_space_scatter_colored_by_closest_pdb_index_pca.png\"), dpi=600, bbox_inches=\"tight\")\n", + "fig.savefig(os.path.join(figures_dir, f\"{os.path.basename(latent_file)}_latent_space_scatter_colored_by_closest_pdb_index_pca.pdf\"), bbox_inches=\"tight\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Track MD trajectory through latent space using CV instead of time" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/mnt/dinaMISMO/synthetic_datasets/MapReconstruction/PreP/extract_frames_6xou_unsteered_0-200ns/Refine3D/job038/run_data.star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not 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+ "name": "stdout", + "output_type": "stream", + "text": [ + "Loaded ground-truth particle positions from config files\n", + "Dictionaries now contain 9707 particles and 20100 true particles\n", + "Added 20100 particles from /mnt/dinaMISMO/synthetic_datasets/MapReconstruction/PreP/extract_frames_6xou_unsteered_0-200ns/Micrographs\n", + "For each micrograph, for each metadata file, compute the precision, recall and multiplicity\n", + "Speed of computation depends on the number of particles in the micrograph. progressbar is not accurate\n", + "Total number of groups to loop over: 67\n", + "Number of micgrographs: 67\n", + "Number of metadata files: 1\n", + "Starting loop over groups\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "computing precision: 100%|██████████| 67/67 [00:02<00:00, 25.98it/s, precision=1, recall=0.564, multiplicity=0.593] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "time taken to compute precision: 2.5940940380096436\n", + "latents: [[-2.19428778e+00 -2.72671223e-01 2.15191984e+00 ... -2.26414537e+00\n", + " -3.77899599e+00 -7.30133891e-01]\n", + " [-6.95316374e-01 -1.40665615e+00 -1.14162683e-01 ... 1.11378670e+00\n", + " -3.94871116e+00 -8.15742254e-01]\n", + " [-1.71029675e+00 -1.15372825e+00 -1.09810650e+00 ... -9.98205543e-01\n", + " -1.35369539e+00 -2.13364196e+00]\n", + " ...\n", + " [-1.59409797e+00 -1.15052891e+00 1.21966088e+00 ... -1.72624707e+00\n", + " -1.87069118e-01 1.92315280e+00]\n", + " [-1.96756780e+00 3.18276525e-01 -3.10060084e-01 ... 7.27799416e-01\n", + " -1.41837192e+00 -1.86001372e+00]\n", + " [-3.87802887e+00 1.21402726e-01 1.72536820e-03 ... 1.81111479e+00\n", + " 2.93009043e-01 -3.24379349e+00]], latents.shape[1]: 8\n", + "latent space dimensionality: 8\n", + "(9707, 8)\n", + "[0.20699814 0.12541926 0.1198101 0.11734328 0.11378741 0.108757\n", + " 0.10600974 0.10187504]\n", + "[218.62451 170.17578 166.32684 164.60565 162.09242 158.46898 156.45467\n", + 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metadata_filenameugraph_filenameposition_xposition_yeuler_phieuler_thetaeuler_psiugraph_shapedefocusUdefocusV...latent_6latent_7PCA_0PCA_1PCA_2PCA_3PCA_4PCA_5PCA_6PCA_7
9702/mnt/dinaMISMO/synthetic_datasets/MapReconstru...000066.mrc1432.01032.0-0.4430040.789031-0.852520(4000, 4000)18719.0312518697.150391...-0.3118230.6595811.1431491.133475-0.033617-0.689148-0.142706-0.476242-0.5594890.729532
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9704/mnt/dinaMISMO/synthetic_datasets/MapReconstru...000066.mrc1057.0133.0-0.2239701.6702640.152480(4000, 4000)18719.0312518697.150391...-0.1870691.9231531.5858800.376755-0.888762-2.2239592.318026-1.1066930.812318-0.320980
9705/mnt/dinaMISMO/synthetic_datasets/MapReconstru...000066.mrc1846.0482.0-0.3918911.999002-2.133000(4000, 4000)18719.0312518697.150391...-1.418372-1.860014-1.524564-1.469406-1.2982370.056593-1.186507-1.221899-0.2594410.809966
9706/mnt/dinaMISMO/synthetic_datasets/MapReconstru...000066.mrc1466.01203.0-2.9613722.8858572.588374(4000, 4000)18719.0312518697.150391...0.293009-3.243793-0.345150-3.636068-3.0334890.874840-0.4504170.0707661.6169711.772781
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5 rows × 32 columns

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" + ], + "text/plain": [ + " metadata_filename ugraph_filename \\\n", + "9702 /mnt/dinaMISMO/synthetic_datasets/MapReconstru... 000066.mrc \n", + "9703 /mnt/dinaMISMO/synthetic_datasets/MapReconstru... 000066.mrc \n", + "9704 /mnt/dinaMISMO/synthetic_datasets/MapReconstru... 000066.mrc \n", + "9705 /mnt/dinaMISMO/synthetic_datasets/MapReconstru... 000066.mrc \n", + "9706 /mnt/dinaMISMO/synthetic_datasets/MapReconstru... 000066.mrc \n", + "\n", + " position_x position_y euler_phi euler_theta euler_psi ugraph_shape \\\n", + "9702 1432.0 1032.0 -0.443004 0.789031 -0.852520 (4000, 4000) \n", + "9703 679.0 3565.0 -2.475374 0.833369 -0.516780 (4000, 4000) \n", + "9704 1057.0 133.0 -0.223970 1.670264 0.152480 (4000, 4000) \n", + "9705 1846.0 482.0 -0.391891 1.999002 -2.133000 (4000, 4000) \n", + "9706 1466.0 1203.0 -2.961372 2.885857 2.588374 (4000, 4000) \n", + "\n", + " defocusU defocusV ... latent_6 latent_7 PCA_0 PCA_1 \\\n", + "9702 18719.03125 18697.150391 ... -0.311823 0.659581 1.143149 1.133475 \n", + "9703 18719.03125 18697.150391 ... 0.265405 -0.297168 -1.221051 -0.256351 \n", + "9704 18719.03125 18697.150391 ... -0.187069 1.923153 1.585880 0.376755 \n", + "9705 18719.03125 18697.150391 ... -1.418372 -1.860014 -1.524564 -1.469406 \n", + "9706 18719.03125 18697.150391 ... 0.293009 -3.243793 -0.345150 -3.636068 \n", + "\n", + " PCA_2 PCA_3 PCA_4 PCA_5 PCA_6 PCA_7 \n", + "9702 -0.033617 -0.689148 -0.142706 -0.476242 -0.559489 0.729532 \n", + "9703 0.749162 1.400694 0.437830 1.028751 -1.562045 -1.027869 \n", + "9704 -0.888762 -2.223959 2.318026 -1.106693 0.812318 -0.320980 \n", + "9705 -1.298237 0.056593 -1.186507 -1.221899 -0.259441 0.809966 \n", + "9706 -3.033489 0.874840 -0.450417 0.070766 1.616971 1.772781 \n", + "\n", + "[5 rows x 32 columns]" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# reload the PreP trajectory and get analysis precision alg\n", + "# to use prefix (index in CV sweep) for indices instead suffix (index in MD trajectory)\n", + "project_dir = \"/mnt/dinaMISMO/synthetic_datasets/MapReconstruction/PreP/extract_frames_6xou_unsteered_0-200ns\"\n", + "config_dir = os.path.join(project_dir, \"Micrographs\")\n", + "figures_dir = os.path.join(project_dir, \"figures\")\n", + "# meta_file = os.path.join(project_dir, \"cryoDRGN\", \"run_data.star\")\n", + "\n", + "\"\"\"\n", + "# for ground truth extracted picked particles\n", + "meta_file = os.path.join(project_dir, \"Import/job007/particles.star\")\n", + "jobtypes = {\n", + " # os.path.join(project_dir, \"cryoDRGN\", \"run_data.star\"): \"CryoDRGN\",\n", + " os.path.join(project_dir, \"Import/job007/particles.star\"): \"CryoDRGN\",\n", + "}\n", + "latent_file = os.path.join(project_dir, \"CryoDRGN\", \"job009\", \"train_128\", \"z.24.pkl\")\n", + "\"\"\"\n", + "\n", + "# for autopicked (topaz) extracted picked particles\n", + "meta_file = os.path.join(project_dir, \"Refine3D/job038/run_data.star\")\n", + "jobtypes = {\n", + " os.path.join(project_dir, \"Refine3D/job038/run_data.star\"): \"CryoDRGN\",\n", + "}\n", + "latent_file = os.path.join(project_dir, \"CryoDRGN\", \"job039\", \"train_128\", \"z.24.pkl\")\n", + "\n", + "particle_diameter = 100 # approximate particle diameter in Angstroms\n", + "ugraph_shape = (4000, 4000) # shape of the micrograph in pixels. Only needs to be given if the metadata file is a .star file\n", + "verbose = True\n", + "ignore_missing_files = True\n", + "enable_tqdm = True\n", + "\n", + "print(meta_file)\n", + "for file in meta_file:\n", + " if file.endswith(\".star\"):\n", + " print(\"is star\")\n", + " else:\n", + " print(\"not star\")\n", + "print(type(meta_file))\n", + "\n", + "analysis = load_data(\n", + " meta_file,\n", + " config_dir,\n", + " particle_diameter,\n", + " ugraph_shape=ugraph_shape,\n", + " verbose=verbose,\n", + " enable_tqdm=enable_tqdm,\n", + " ignore_missing_files=ignore_missing_files\n", + ") # creates the class\n", + "df_picked = pd.DataFrame(analysis.results_picking)\n", + "df_truth = pd.DataFrame(analysis.results_truth)\n", + "df_precision, df_picked, pdb_labels = analysis.compute_precision(\n", + " df_picked,\n", + " df_truth,\n", + " verbose=verbose,\n", + " index_prefix=True, # NEW: USES PREFIX INSTEAD OF SUFFIX FOR ORDERING\n", + ")\n", + "\n", + "# latent_space, ndim = IO.get_latents_cs(latent_file)\n", + "latent_space, ndim = get_latents_cryodrgn(latent_file)\n", + "print(f\"latent space dimensionality: {ndim}\")\n", + "print(latent_space.shape)\n", + "for i in range(ndim):\n", + " df_picked[\"latent_{}\".format(i)] = latent_space[:, i]\n", + "\n", + "# perform PCA on latent space and add PCA coordinates to the dataframe\n", + "pca = PCA(n_components=ndim)\n", + "pca.fit(latent_space)\n", + "print(pca.explained_variance_ratio_)\n", + "print(pca.singular_values_)\n", + "latent_space_pca = pca.transform(latent_space)\n", + "for i in range(ndim):\n", + " df_picked[\"PCA_{}\".format(i)] = latent_space_pca[:, i]\n", + "df_picked.tail()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "
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iqCqxl57CesHVSNnpvmM0vC4ixv9gvP+gkPMr4cKvoQ90IE049s3jA23P8W7A+0koFKK1tZVLLrnkmK/Pnj2b559/ni9+8YuvPbdq1Spmz579AbXwxEQQRHRBAn8TusGNaskmqUWQBAMGyQyCRJNWzFgSxkaSFLs1FElE13W07l2w7R/pEykWyM6D5Ag46xEU51u+Z3jLZkLXfY6rnU4++61vUD7NC8CqLZ209QY5fVEZNovCrAl5OOQkyc4e5PwiZKsVLXWY2LrnME6Zi2h9XdgEQYCGj4G/E7EvhXZgK2LlZLw2M0ZFIqlqxKcuw56MYJg0GzHL99qxsybk8bPrF6CjM60+503t/U/o8WGI9YKl6G0/97tFrp4K1VPfs/P9N5xQ4nP99ddzxhlnUFJSQk9PD9/5zneQJImVK1cCcOmll1JQUMCtt94KwHXXXcfChQv52c9+xmmnncY999zD1q1b+dOf/nQ8P8aHEi0WJbJmNXoyge2kMxAMbz8K0vs3QsczIJlI1l1GVAEQsIZjyGaRIlVjX0okz6y85gIQSkXRZSNWUU4X3VMM0PUQoNPVMww5Cyn0mtEHW1A1hdAzTyM6nNjPuIDI3m1ooRBaKESuvwNB0DncF+THd+9A1+GseWXccs0c9HgM/89uJL7lFeyXfQHT7EUEbvkCeiyCuvKzqJEYUnYOlqWnIUoygsGKkFOL4aKvQzyKYHVQD/zuKwtIplIU5/XDonNAqIQ3uBAIgsC0hncvOgC6loTuxyHeB/YaKDzn/3SeDzsnlPh0dXWxcuVKhoeHyc7OZt68eWzcuJHs7GwAOjo6EN/QQebMmcPdd9/NjTfeyDe/+U2qqqp45JFHPhI+PuFEiu39Y6iazuQcB07Tf/dT6rE+iBwCcxGC+c1TkPDzTzP619+gBfykBvvRG+Yy+MoWcpbNxTWh7s0nVBOv/RV1FZBAAzm5DzE5hKR5sGGjtT/IrKL0tCuaSqC6i9AXfRWbrCDYs9BaNyHqUfoDOp0DveTnHEDb9E90m4/49n7izftQisqwz2wgccZJSBYjlnE1IFpwWGKU5Ng53DdGQXZ6+qaGxohvS6fGSOzfjWnm/NdK5yQOtxF+9lEAxJEmjHNOQSqeBIAgKyC/7ntTkmsHPQ5aF6CD3g8U/le/wevooB8xYGsp9FQSbagL0ZWNYPoPXud6ALQWwApiBQjHzhL5YSCTQP494HgkkD8ciLKpJ73EPyXHTpXnvzMg6r33QawbJCsUXo4gHj2yCT52H/7f/BA0DfPSM9jxz42ED3XgnjWJhavvRpSONpTqiSD68B4EUxa6q4p4MsqGV/axsH4Pg8I4Wg6JRGUzhXYTObs3YV+ylJTbSVRNYBINWJS0jWqofT9bGg/zzHaNT003UmtvR2t6DgSBcKqeyN59ZH3hBgylxTCwHtDBN/u1qcqAP8pwIEp1kQvpyAgr8vLTJPZux7zoVIwNk0k0bkft7USNJ/H/5gcIdjves2YjF5UjL/zU23xpOuidaeERSkD0vfW+7/b3iPZBtBsspaQ2PEdq9T8Q62ZhuOjrCMrb2BvVg8Dh9P/CVBDf3yyP/04mgfz/AFkmhSyTjKqB1/Ie3N1kN9ANStYxq23aTjoDPRIm0d1N2/o2ZIcde10JdV9awdieVzD6ahl8+DFMJUVkn34KgsGBkJdOUyEAZoONhOrk3heqmOvqo+iX3wF3FrELPk3v928m0d1J7pe/hlk++sJySwrT1t/DVEHEHp6FMOMUBAQETy7OCjNOfT56KI6g2KHgzSt2PrcZn/tow7dl4SlYFr6+MmpomAINU9A1Dbm4FGGgCbF3E0Lpm20j+7tGaW4fZUqVl2KfDYRi4NgrWZGmJoLr1mKfOQvrhAn/4Qc4GsGcC+a08VrrTNcm0w5shXgU3k58BA/oXYAdhA/WQ/vdkhGfjyh2o8ziUg/ovKUn7FsRO3SIwNq1WCdNwvbqReGZD7YaULIQjoiPruukVB1dgIAuYT//UrQ9+xj4+ZXknLaUsovqyWqIAS2MNgc4/MOfgyBgLi3BNu71QNGRp54kvHM306bPwnvxYrru+AOqpsHwINamHQTgLfMjS4Xl2M//FE19cbxV1RSGBtGbVyGecSWCmJ7a6Yb3ZnlaEEVM46cB09DVlQjS0aIejaf48b276BuJMrMmm29f+taGW13X6fzeLUR27GCkooLahx5GTybTAmd/+0wBuq4TTmooooBRFpHnn4/q8CKWT0Swud7+Q4hZoM8DxA99yeaM+HyEkQQhPax4l3T9/BcEVq3CUFBA3WOPIttsCJIJzK/fwcfCCX75zx00tQ5zyfnjiLpM5FgU5kyqZ8mmR9BSKsT2A22AgGg0Yakuwzl/OpL9dbtEdF8zvTd+HVIpoodasY+vx7zkFOIDvQiSgmnWImzTZ2Ob89ZZAp8L5PCLh3aQ5dzOrz7hw6draM07EGYvAEFBMFe+6+8glkhx38uH6B4Kc/78Mqr+LYj2jcITPdSGZLcjOt3YTAoQxfIfbGyCIKAcsTUqvhxiXd20XvtFkqMBqn/7K2yTJ77lsYPRJB2hOEZRoMZtwVA2Hqls/Dv/cB9iO88byYjP/yCSNT0clxwOEEWSO9ejRUMYJs9HOJKgqr03yOoN6dCUPc1DVM4uxB9PoWo6lsJ0oitdz4VYGYgm7FM9THoyC8k4xls5zksWK4JRwRIO0vPP+0HTcCdFQpdeQ4nJQmjdegRRxDFr5lHOiQP+dNKv4UCcMWsJuTMuRhON+BMNWE0mjJJEYmCInnsfwuD1kHfhOQjS29/1W3uC3PvSIQBy3ZY3ic+rDD/1DC3XfRlTaSk1d/6Zr100kZaeIONK/q3KqZ4AUul67Uco+n834jnzLCz19Yxt30FkX9pLemznrrcVn9iRQOC4ppPSdN6jgd2Hjoz4nCCkNJ2eUAyDJJJrfXsHyIIvfwnngvlYGhoI9nayMepAE5zM3LuT7GlpH6fSfAenzCuh+ZCfeZPzcLosZFuVoyLjBUEGc3l6IzaMaEyHsejqAPRug+x5mGvrKP7N74k078c2qwG58ZfohhqU/AKSXZ30ynZ+/cBullc7qb7ualBVam7/M66F8197n9PmlSFLIvleKzUVPgThdBr7gqxvGqLQYWJ5VTa99z/Coe//DABzcSHuubPe9jvIz7LSUOLiYHeQ+n8XkjcQad4Hmkbs0CES3T0UzCgge/AAyRdWkZo6F7m4Ii082k4gAEIDiOn0p4rPh2vpUgDs02W855+LGgrhWvD2uaB9ZgVBALMkYf63hGaRRIruQAyfzYjT/NEY4bwVGfE5QegIRtkxGAJgYaEL79vUxDLk5OA57TQAWg62c1BNd+JCq0z2kX1sFgPf+OQMVE1/3aakq6AOgWAG0Qp6ClItoI2RbBoGNYIyrhBCfRBsBHMOmLKwz52Pfe58tIOPQCKIktiCcuON7N7azxZbAYQ1eoJJao1GtEgENRI+qr15XiuXn1FPMBznwdUHsZoVjDnp0VtXMEY8pWHwpVsuWq3s6UswPhDG7Xxrg6vbbuTmy6YRS6i4rCJ67wsQH4TsuQjW15fMvWefSWpsDGNBPraJE0mOjRH42f9DD/pJtjTh+uoPgRgQOPIdBYG0+OjJCGgagtGG4jRSdvU8kM0IRW+/JG+SJYpsxx7uvHhwkC0doxS5zVwyvRjDO0yT8mEkIz4nOLquQ6wrbRsxvTlsJNcl4/Gn0BDIy8t607FH2bJTbaDuA6xohln4IwGy5BYSLTGCf78fQkHMp59JsKwAn9GFIHoIbd2G5HQgl1cw6JqJI5HCKutsO+Th901DLJylsKzWRv3eF6m49jxSxmyirW0Et2zDMf1og+7qjZ389p7dAHz7c7OozbZSawhgbXsS6/wyAn/9M/dv7OOV54e5qbSL+tpCnBbLMePLhl9YQ+8/78OzeD6uc+bC0JGyOAYPWAtfy5ljriin7KZvATASS9IfSpCd5UMP+hFdry5j29ATFSTb24h7LLQFuvEqUXIP/x2SYZj2OfTRdjjwWHp3cxbkTCCWUhmMJjFJIl6z8rbBpGON+whs3Iy/IT0iHA4n0kb7jPhk+KA4PBJiKJyk0mvF9YbRTbHDnK5pLolHj3rCLTD4BCCi516AYD66IoJ7dBcXantJKm66IitIBaIUOM1EOw/R8+iT9K7ezcQffxN7dRnor0aKRxiOqjT7YWaWk94xFwc+9XOMyQSeliYu+1Q71109geWdf2J4RyuSw07ouz+hcUzDY57PyRXZzPJEcJdkY1ckpmQrRLtHMCfbGZx4BqrXx/C6tVgb6pAsr9tQXPb051JkkTy3mbqyLLTtj0HXBhAVnBP/H2ufGubs5VWIBXnsG1OpFJPHHAV23/lP/M+/zPBzL+Bd/hyKrQxiQ+imAkJ/+THJg43YLr0uvQx/hEA8RVgyIF71LbwvPIRUc8RuI4iEHllN6ME78fzkFiaUawSDLkgEIRVDHz2MYPGiI4BkBJMLSBuW+6NpZ0KzIr1tatbWb91CYNM2qj77GQovWEmJ14ZZ+Whfvh/t1v+PMRxJ8HzLMKGEykg0yfLq153aZFGgyHGMQE7t1VSgGmhvLkY3trsPS5ZOs2UGa9uTSEIPKyd4ScojcPZcivPtDK3bir26jKBWSnswF4dRwmMyoqlRXh6owaEP4BdkMMiEJR/Qzovre1lR50YMDKIOdBFKpAAIxFRiwRChgWGy2g5iaNnH4QXLceaXIHinMFpcga6DY9lJ9PljFLxBfBZNK8LjNGE2KdQcsdMkDC4MQNKSgzvLya8/W48maq9Ogl4r5fMqupaAVIyspYvwv7CGnPPPRnb5UEfrST59O0K+TmLHerThARLb1h4lPm6TTDiZwhAJEX1pFYrn9e8/2d6CaHdiyFOAOA5HiGTZyUjRfgTfeASrD+beAJIBwZYegZqOjFqMooByZNSj6zqD0SQJVcNnMbw2rZLd6c9r3LmdGd/4PMLbZOj8qPDR/wT/Q2iaTjSZXgl5NTXqf8Rak7bViApY3py+Iryrld5VzxC+YRq4behAKJJgzUAeOjChYiYVhrRNqG1MpyWgIesp6h/5B/Ld91N5x+9psdmxqCpWXUUcHWTBjFwmLKshNRJAHU7XoSrrOQjl41EkgX3/eISeb/+E6vPmITRt5s5oNf1BJ1+5tIa4ekQtFIiM9QCve+iKosCkmtcv+EAwxk6mkl1VjF/2UBlTyfX3Y6qpJWg3oelpZ8xX0dU4dDwCoRbyzzgD7/IXUTwedElmZMOLWA83w+FmLGdfReThe1AmHm20dhkVnCKENrcydPU3aC9voDKRwmyQsZ1zKdG1q0iOOsGdJJrw4qwqfe3YZEcboUf+geT2YjvvMkSLFa9ZwaJIKKLwmsgEEyoHRtM3jKSm09k+SoHXQuX3biTn3DOwjW84IYQHMuLzkSLbZmRxZRYDY3Hq3mFJG0EygnPym57X+vah7XmKrEUlyBNuILvUh9fnxRyPEX/wAZh3JgCR9l5sK+aBFsQmp5368pJROn/5e9A0OjUjL/SkDcSnlFgpHmlkxcWn0G92ERx3Ml7/KFo4jFRcwkhShSRk5+SgxRNEIxo2j5fFoX0MR1Ks2VXI0hU5oGt4g2uR7QbgrePs2jsDrH62g+nLKrDpIvG//JGeO27HNms2FX/8I6LBQCoUove+BxBkmexzliCGDgKQGDtMOKcOj1Fh3Qvb6Y/nc5LFQbyonnjlZPK+V4BotYCugfCGFT5FQV9yBg+sayPcPML8uMBCj07kxScRFAVRyUc2ZmP4twXH2MYXiL34JADK+Cn05dbjtBrw2I9Opi+LIArpEdvwWJy9fUHuev4g379yOtmnvXXOpY8iGfH5iNGQ4+CNwdKRaAizUUEQ3355XY2nhUMypu0fWss6usxl7JHrMR9uJuv2h5j0i28T3tRI+19vY7KqkSytxNrYjHTmEtSBzXie385sowth7mKkP3yHw39+GNHlQOgbRgfsHhfeL3yFrP4ghkQKX44D75evR1dVOrc3UWyTiFsteGUN62Xn477oNEzb11LRuIOcQBJTqhyvoRLnyAsI0cOQnxbAkZEIv/rtRob8ES6+Yiozx+WmsyuWubGlYjzyxw18bmU98cZGMJoQrFbUWAzRYGD0pZcYevBB+mpn0uXo5dzJCzFpfQyaaglFEuhxFa21iX+0eVjj+QyLJldSabKTpQ9gSraAUvamMAU16CeROjIC1TQizz5M9NmHAAgrFgxnXYbXefQUWKlqAKMJOSeftbFsbvvTJoqyrdz0iSlkvUGArIrMhCwbh4bDPN3cR1TVqCxxo6onXghmRnw+wqzaeYh/vdLD8olOLpxXhSgeuyRNoLmFDZd8HU1TmXnnj4iWFGNzlbEzUUx7RESsmcCkjZtZs66F/EPdKAtOwaWnsHmsmK86BX34ZaLr95N69B4UwFDgxLnchn3J99kyamRxQw4GVcX+7EMoF5zKjIKjU0kc/uNdNH/zB8jTp7L3is/z2blGjOd+j7ZN2xj75x0AOGcvxJZtR9y2i66mERRXIbmVpQDs2tPP40+m45sKq70Ul7opMKqY9j/GF+sPMRroQNzUjPWrX2Xg0Sfp/svfED2/ovT6z2Eb20LVGYXcLy1i9+4Rnm2W+P5FsxnWHZSaFEhppOwOfjI/gt9Xj6vASiwY4srbx7jwpDLOXnq0iKQa12G654dcOO9yhitmU1eUhdSVB6KIYDDSi51927r5+JKjva5Nk2eR/fsHERUjXet7GW9PcnrnM0Tv2oF66eWvOX4C2AwSgq4TS2lYFImTan3k/peBwx9GMuLzIUYd6EEwGBFdWcd8/ZVGP9GEytM7Rjl9ahSrxQDJYZDs6XCJIwxt3oNssyDPmEyTbEUOxuiT6slx6rRHohTpcYoqbLQ1bqfvBzcDUH71RfR96TM4r/kMpvnFBC0+zIIIZjPCWAywY5C6ycLFiCLj2buVoR/+CD3bSMnyUxEMLgC0VIqRHXsASG3bQeEnU2w/oDDu0H3Y2ocIFZaQ6mrHUFPNyB9+SexgK+a5i2l74kVMBYUEiutwOoyMG+djoD9MSbU37eg4tBeh7TkUwH3yNCL9DizjxhG8OR153/ev+yi4+AyEzt0giJRXGtndG6XGnER4/BEmzcxFEECqnceUnAiWA49Q0gmhljn8ITab666ZjSDAju4gVoNMtS8dMqL1tIKmUrjmr5RXVSGZ8+Gks0lYXOxtH+Xn+y18tj49Ck0kVbYeGMRuURhfakd2mUGwsmySwEjTKsyrHiACROtqsS1aetRvOz7fSZbVgEmW8FjfYQbJjxgZ8fmQkty9kfBtNyK4stA/90Pk3EJs/1bL6eQpuSS29LCowYXN4qT9zr8xsmkHRedNJ2vpha9NxRJDfgbWbKHgJzfSIygQSlDtMTMcTTG+0ICsJ3EN72LEXktOWTnS8BDJpt2gqhDw03l/jKBmQvzkD3C2HqT/spuZdO/3cc/Kw3d4I/274oR/fxPOs07G2WAnMbgWg28+gcZOutZuYVvhVApP0XGfvJhYYSG9oSgz++9ANpcy6C1D8JYilxYQa2tPf7BEDMlmZXDdRg7e9QwPhss57eIZbN83RCoSx3voaXSDOb1knYwg59lxTJoBQMFVl9NnNuE5+SRS/gBi3gRENcxp+Sq5z6/F+MJO3JfNQV93NzrA2AAmVwlICjoCks3BsmoHqkFKl0UeGyP8+CMEZ0zDMXUm8sRFEAkiuHyIRbXE2tuJHGzBPnUqpdUyt8xJUFecXpl6fmcPv32kEVEU+OlVedQU9oMwjiJfHp6ZE+m+V0G021EKj53/O9957DS0JwoZ8fmQkupqBTWFPtxPc9N+9g2InDcuH8cbXOrn1BUwpy5d1zvc3s22z/8YNI1UNIlj/hl0hlNkWRQG1mwBXUc42AaTJ+MxyYRTGoosIgKRnhhNp12PUXeQ95ObiI2OYUyMoT/6EMN+A4N/+DMA0ne+zejeFtA0+vtV9AN+jC8/ybjzr2eX59eoRXEqDG0ARP0HiEaCeEpD3LfLQdI0i+snTiIoCGA30l/7FfILAtSdZqR3axibI0DRjV8j2jEAbh++3GL8f/4jXuCaW37Er9Z30NkfovGwn3OWbccy1oYw9yrQ2xH0ITiSiiNrxTI8y5fi7+ggtOZ5wv96HlGUKLzja0yYM56gRcVUUgwjm0A2gJZEyilELZ6DmlQx20XyXryX0ZWfw2SW8Q7uIzzUy+Af/ghXqthmzmZbxfmkUhrTInEOfOYaYgdayLn8Ekq/9f8oyn7Db3jELqTrOkntSHI1fQzIwzp7HqX3PoqgGFDyjva9OlGKAv4nMuLzIcUwfTF6YISQYmWfo4zRWIqxRAqHWUEb85PatwuxsBQ5rzS9v8dF9sLpDL64Cdfk8WzqSbG1e5gsi8LiT69EkCTyIiEmlHkwSiKH/BHGokmCI1F+cft2VqyoYpZjlL7cGrpscQCqc5sR84sRjiy1W73ZtDiKcH3lRpyTZmNqXE1yuB/ppXuwzboELdBDSrMgSSLm6EHUPDtCVOPmZWGsmhmSAwwbcpDRkYQkghxHkuPE1jyPv6wa7+mn4HJnE+3sQo1HCJhNaPEEXmOAupIcAqEE8ybmYIn2gGIGewmCqRgdiaCYixAOo4wcZkTxou9vJvXwPZhPOw8tFCM+OETOipmMHWhm68+eoeGKFYzk1fPkQSMlooklQZCtdkbXN2OvqCLPdRjJGCTlFlC9NhzldehDTWzbV8G3/pKu8fWNC8fh6hsAIDnsf9NvuGScl/rtrci9HfhiZ5AccqOORpCzhyAex1Bc+qZjQuvWMPirn2Cbt5CsT1+L+C4KFRwYGKPTH6U+107eR2DUlBGfDylSdj6WldcSiyQo6w3iMMnkHlkViT1xF5F9u+ibfTbZRjc5HieK3crMf/6GaHcfjrpKWlvS/jVjsRSeZXMoOnXBa+fuD8U5PBIiy2EGt4lvfXUBA63deId7GRLS5WbsigiyhFpTRdalKxHzi/nOEwFy8suIBURq3S40/yB6cARpyyqazNM4rcJIct0IenEBiuswNl0lnDeZyUIf0UfvR12TQFt4JYF1O4mMd6N/chKJIY3Alv1YFi4gKodR9CwM0RZyqsH1i+th+CCq4GfGs3cw7vxLWNc8zNB5V+ErKUZwpKPrR6JJDh7xjcmxFjGaEohPmEP2N7OJPvggg/c9RP/DT1H5+1+y6wf/JPei0+m0TeRfa2Ns3N+OLIlMnGfA9tQ/kQrLkCZNRDKOACA6rUQeu5+IruM+/xR8ZSOvfY+CHqXqe58jdMiPe+ZMRn58A4I3B9uFn0Sx2RHbW1HvvA0VGHI4WDf9DHKHOqk7cD+JDauwn3cp5mkzGX3kQWItrWR/4TpCTz9Esq0Vf1srjjPOwVhW8Y76Syie4v4d3SQ1nZFIggsmv1cpXd8/MuLzISWV0hBEAY/FwKIK72vPjyVSJMNhDi69kl2mAgyHApxvteAwKhjcTgzudPrQOfkSCx2tiLIRg5TPG39qaypGw2gn26RSAEwmmYl71xLr7sFZWEx+bi6FoR48tW4OPfggQ/c9hCBJNJx7A89uC/LVry2gfSjMoGM8jikygqjSHLdy4bbHUJu3oooiwhe/gOrMpf+urfQYbFTWzSXZuJf8kUMIkQH8m0YZXb0VyeZAXnEa3dmVVCkW4rFBxKZX0Ft3IMtGlItvJKlKTM+pw+x/jlOvWoDoKyQxEiccaEN1O0kJr9/lLYkRcjtXM5I9jVBeMUabFfvk8UQPdyF7spn6jx8jTh5P1OtjhXcEQ5aFSFLFHNoHkRDqgT0ED/YAFsylVqJDpNOlAhomPAc28/2rTsIz2EKRugmDsBlbjZXApjHiG9LldVLjppM9Yy5yXj6m8ROJNe0lUFLDvpjCPls5xdmd+K68EGO5jZS/leHNO2msXkpZ+xDVRXbCniysc+eh5B49HXs7FFHAZzfSHYjhfqcljo4zGfH5ENLS4eeHt2/FYpT5+ienUfAGh8L+cILeqWegy0aIgsZr18ZR2LV2iDWmN8x5HHqgkb4XNlB22Tm4ijwID99P4ae/RL+u4IsGGBo3gdTCpRRZVarEdUT+fi8xWza9cikmQCwoQHTYOeOULKrpYM9OO48+N0ZXl4PPXlDBJybnYAjOIbFvG/Lsk5FLZ6HIhwnV1tDkKsPy828T27IVR88o47++nGg4RvCAlzGliOGsHAp3vILmmUrEbcNmywKLE0wOBKsfo1lFHNGgYAlSXhJoQteziWelk5+ZW3ZTJ4yimrw4W+9GSATxJcNYfKdgrHYiqBraBSsJvPQy8ZfXMHbjN/B+/3tkzVzIUCRBe3+Itgk11FYeQiosYWjCDOK7dnOAMuKqxowrrsPYd5hY036syxuY0L+F0J9/RMhqx75yBSmjFzErCmYrQn4xCV8hLxwYRNU1Zv30d5gSUXbFTdDUT7VZx1VagLV4H+h9SAVFbJz7Sf6yMYrSHuLP57op/fRi5KWfRTS/86mTUZG4aHIhw5EE+c5ju1x82MiIz4eQ3QeGONg+CkBTq/8o8fGaJWL5biIpA7NFhWyrEecbQwh0HVWHhJxPp7Icq5QiO2pg01X/D11VSQyPMv/emzCX5eP+4pXYp8yBFafTWVaDQVdxdbWQ5UwhFpShWNy8qEyk4Iob8FSVMm5WDUo0im/XKpYf3MKUQh8/SNXhDgzA31cxuGAihq//GXduAYJ8GAjjmpKH0KajJ9MBlLoqgLcEW/YQthIXPYP5iNd9mVgigdF/BYarL2Rf2ekcLjgbMzpz5WHM+El1diHaDChF6amW4jNiTgjo27YTfeDvWKbW4pjqQPdNROh6BU3KxpYbRm3bgh6PIrZt5rmiT9Ne52FhTCSxdTv/ihThsikEwgZSLh+mudOJlUzD5nSSe9ICrNs2MoaMvncjuq5hO/MsTNMaSKxejW3JAuLdQzzdU8ML/UYuDGyn4ebfk8jKZSCi80pzLxdMkjDbR5GlAqYKbip9NiyKjCJWoXeEEKLtCI5i5EIFOICm62i5k1Cm1iE48951v3GYlaMWJD7sZMTnQ8ikWh8Tqr2YjRLjKo/28XFJXbiczeiCF+TJJDa/RCw4imHOCjSrg12DIXrDcfIsVlrGRARgaZGDogtOpuOeJ8k/bQFisg3naR52Vt/CI+uClLeoNOSA74kn6fvBTwhUlFB08myMgX4+U3KYVxyzKan2kie24Q0cINHWDG1NeGnikiVTUR58lENrNtH+jyeY8OIv2TygcHK1FcluJDfLQMGgmdDnv0L2vq0UTbYgKFGIB9BTEQKmGmSDARIJECXswV6iQi4jiXSE967eAiYLEuGn/oDodCKWX43ik4iknER1HeHpR0js2UVi724sE69D8OaTEs5HLCpAtg3D0pNJrXuFYc8E7t42iqrplMxYQbci0twxSkW2me9kd8M/nqGlowP0u6j8xXdRt96Dy56Fy5FFPD8Xwe7EPMWI1tmEvvNpAKTxJ/GPvQqxZIpW1cr4YJScKicpKUqOw0Bddg+CEAM9iiDkvSELgQiFZ0MyCMYszlrch3OoCZ8SoVyUEZxLPrjOdhz56CYDOYEpL3Ty628s4sdfXkBe9r8lxNLT8dqCPkRq/y527z3ME90yndt3EE5qHA7GiKs62pG5mCwKKIrCrL98mzO2/Iq80CYij6/jvrXlfP8fhwlGEuxuGsY95sfY1QlA9FAHY/sP0/XoK7iFBK9s7iDc2kRlz/242Il5QiWCw0Ns8ikIexuxzZgEQPaiSUQlkdyXnmDr7LPo/PUOArFCNnWM8kLCRs6ppdiz+tCD3aiCD0FPMiwouH7+R0xfu5nYnBlIqVGKrDEssoDbJNMyFCZhzCEYNNDXEmTPhhA9fg9dw0a6u6NIM+aComA75WR0dwlSvA2pwE18wxZSYxIxxUH3aV9ig9ZAntuE0yJRaNHpEF1UZ9uY5FHQ16xCECVSg4OkhoZIHd6NEBxE794HkQAGr5GhRefyyvAMBsNOyMoHUUQvn0hNrpVxPiPV1hQiKoE1a5Du/QfnFZlJ6fmkk2wXgiCix0bRE+lsj6mNzxD/189IbX0WZ0Ehp587h+nzGlBmnvG+9i1d11H3PEPy0e+gtm15X9/rP3FCjXxuvfVWHnroIfbt24fZbGbOnDn86Ec/oqam5i2PufPOO7niiiuOes5oNBKLvTn9xPuOOgqpZhAsCEodsaRE43CYlKZTn2XFbpRBKgVkED0EzCluC9cRV3WGA04WP7+GkvJK/CYreVYDPosBh1HGYZTpHgmzsQkmJIy4qqfhs5VxqdvHvx7cy23fqKVPcJA/oQz5otORnS6CD9+PkpNDaEzms92P49WXoQsKgp5AcFsZvuCbvLK5g0GPzOxUO7Me/SkWs8iOZDGe5DYSqSS6Nobb2MVnZxl4ujmFJNuIdjvp/OGDIMnkff2TVNWV4cty0bNzO968MkRfinz6OMMGz7baqdEjJB55mOyzTucPzR7u/XETJ22LIfvMrN01wMrT6jn3F39EsoPifwqAUKqHlNlI8E+r6fzdPRgnTWL6Nz7HJG2A3oeeJXX7JiZe93VWx33UVGQjr7wUYfN6nDk+NNnIcPFUckMdiGYHWC2IM09hz5ABw1gQe40VY8WFRHcMkti8m69X1dL101+Q6OwgNaOa2CtPo8YSJAcHUL75/0gXEjSgDx9A3/o7MFjRKi4kteNZGO4iNdCOPGExUnEtFNeiayqpHc+gj/YiTViOmFWAHhuCkY3pPpI1B8H4X9TiigbRNv4DNBVdUqBs+n/XZ/8LTijxefnll7nmmmuYPn06qVSKb37zmyxfvpympias1rdOqelwONi/f/9r28fNwUsbAH04/dDyGIjYafGnl5BdJpkaowySJ/0AjLlJPLZ+egMxDBYzPVd/CQGo/dvv2JGqIqHDzDwHbhR+dlcL25rGGF++nEtz6wglJdwVBn745QZqClphzEJg1mJaZ5xGTXgAi99PuKWD9j/+g8LFk4mNwVCHGWtVJX5XKY1iHncd6gHA2lDDvIEHweJm0rh6Np5/EdkN4yg6LR9R6iTXbeLykiGCHXH8W0ZJdHQBkNyxB++MM9neOUTDqdXII83oUh66x8jAiIb31lvILzIgZiv4p89CbUkwZ24xPn83WZLCLpOVg4dG2PzVG5l07/exeWoQQkH29bi5e5OFK3rSd/b47t0YEwM8aS5HWXY+tUDSZeeLsRaGHnwZ1QoWr4GBwmJ+OljBlcZy1EgtkQeexL1gFr7qMAdfClPsDWCtD4MEuiGJkX7kDS+Rd93l7LDVk6u24ZIPgVMmXjMbBIF4ew+CKCKnOkCNERbz2DRqR5v2Wabt/gdWXwEYXzcsa32tqKv+mN4QZcSFl0BgDwT2pp+TneB7Pb/1q6hjYwhGI+J/KGONyYZYvxytaRVC2cz/Y0d9bzihxOeZZ545avvOO+/E5/Oxbds2FixY8BZHpcUmN/fNKUY/cMQsUE3oggWSMZyKBZsiklT1o/LSvIrdrHDekko6hiOY7QbsF5xD4vkX0eLDJPQqAILdXcTNItFgugJEOKaiS0ZIpnDKGuPzh1CxYxRsHADiqkbA68VlVgjs2INz0jiSHYexXZCFwV5G8vF/IblqKalfSFGOle6BMLVVVhhJQmgIRYtQ6DQTKCxAJ51ATBBitJnmsNkZp3x+Jb6ubkRRwLLsXMLbnyXH6UYeeQniI4weKKJ5o0qLu5iDKy7nS8vNSMFWeh21nHKpiLyniebTvoaeSnHL9VdgCSaR/vZj1lrKWTTix3vgZSYbdrK96Hz6i0+i1GbCXu5jbTCLVftDgJmxj11LU3eILw3s5eDtj7H0D5/C5ouRlRhjhRTjwO4Osu65l0R3N8kRP1ZGEM1nE9NtRGI6ZqNKoqIO85rHIZWg1eDhlTGZ3FQIF4CawjqhgbGN6xn5219IdbXju+EGJL2MZsM81nbHAQPeuZ9lUlX+UTc70eYGdx74exE9ae91jL50ORxBAOMbXKiPEFr7ModeWsvApHlUTxtPce5bj4wEUUKcfQni1HMRTO8sLcv7xQklPv9OIJC2j3g8bz9MDYVClJSUoGkaU6ZM4Qc/+AENDQ1vuX88Hicej7+2HQwG35sGS1kgLoTAVgg/icOQy7LS09ERMcnHTrHpdZroiqewGmTyLr8Y5dSlGDY/SL3ZQcLhZdCag3n3Fj7e/ywHc2rI86UY7guixtLZ/jbqZUw0DRHYv4/WwzIWr5OcCU4Kv7qIrDl1xJp7iHT0cvhTX2PyL64l6Knm4O3PAs/y1Z//BGYvoNrQidC1EC2rlJTFjH0ojuFAI8OGYmyV2QRiFtqCGjFVp8mYjf3sC+j53l9J3fYnwi+9gOeic0jM9GKU/bQ80ErfA8/jMBmx/+Q3hDQLzmAU+xP/RMnzYYn2oafSoqYNDuHfswXL/AVogHTEniKaLEyoLkCSdQ4XFPP8+j4m5noxygN4LDKl5hTF1XZafv4cAJZyK0r0IJjhjNoaevOycX7h84SeeALXx89id8kUamUT/SMRtvZpLKjtwa8WIi66FGX/BswlNUgHw6yzTMA5x4Ar24tSUEPvzZ8ivH0H7mVLCO/cx/7v34Hp5lKMxV4QILsgD/HVUY8eAH0Ewe7B8LFb0CMBBF8ZAIKrAd3gAkQEy5tXwaJ7d7Fp9tl0J0T27x/m0z438tsUkhREEY6z8MAJLD6apvHFL36RuXPnMm7cWyekqqmp4fbbb2fChAkEAgF++tOfMmfOHBobGyksPLaX6K233srNN9/83zVQ7UlXgpAK0qLzKoICqeH0/6lRDKKGIB57+TSR0sgxySwrdWORJcxKFtRX0ZXtJOvgLnamzDQHxyCnnFwtgG/nQ1hO/S4PNfXw8Jr0lOmLn5qCVdrG1gMuHt6oIkndNBjKUA80I86ZzpaSxSixGAUxHbW8mviQjCC9gK6qWNsPUFTTizzaTMw3G+PQdsyBfpqv/iOJvkGy/vobunIauGtLBzW5Ctk2BY9Rpefme0kOB4jtOABAZGcjujwF2eLB4EvHQ5kqSpg33o1bC9Dx8AH2//JeEATm/vKTTPz6Skb3tpNq3I69oQ5Hz2HGWRXGbDU4ynS2CpO48dc78XnMTMqV+UxFOyl/KxOnN9BRWIk5FcOWSOC5agX9oxp+v0iuCTTRxsiGRlIzczk8ZwlTp3jpi8TpUWVQUzisBg75x1ggJEhqAl01S8ifPIMiIcIF9hzWbe/nymcNrDwph4vzI4R3p6P5NVXDddoZFI0liZ+ylBWSgWgwzrObOphenc3EMjdojUAYdAeCdTqCzZke6bzaLSwFx+5HehLb3GqcfhjSROxmA++ygO1x44QVn2uuuYa9e/eydu3at91v9uzZzJ49+7XtOXPmUFdXxx//+EduueWWYx5zww038OUvf/m17WAwSFHRsSOTj4keh+ROQEsnZZf+LWWGYybEcsCQf1SSMDWeoHlLMyMGE+WV+dz7Sjs720b47Mk1zKl9PYeOGA1h6DrArMgoZZYcOsctZt9XbiXPLCG77FQJfbCmh7J8O5M8Y2itwxTarYiCyE8/42VyQSNjvgq2dwlUJBuxNO3GeP7pDKS89OvtWP7fVwlrRtS2rchBgcbIeJ553kdSzaXYIzPlwouwhg5hcoFveB/5ZgsH+8dYtmkbE6vjtMysYd/zmzBcdzaSvxtx4VJCO3di27ke76XXIi5aQK5PZuT+uwnNmY7Bls5l45o6ju7tgyhmE44SL64lVTjG59JurSPZ0kkqx0nXxFMIHBxE18EfiPOpxcOod6RzBpkuuIycg89zaOrpuMsqMNWUYtCtHPCr2Gx1pHQTIxufx3TWx3FISUIjHXj0KHnOOqKighwcY+LIfvx73LjKUowEo7hM+6C3F6MhjzZzPpqeLssTbd9Kxa++g2iWSHYNY/BlUbLyZLrEFHHRyN+f2Uf/aIztrcP85jOzkTABYXTVgBZtRLKNgFAP4punWUeTYsjnZs/WMWRZZv54MyJJwIAWixLZtgUlNx9jRSVq7yG09kak8omIvmPXl/8gOSHF5/Of/zxPPPEEa9asecvRy1uhKAqTJ0+mpaXlLfcxGo0Y30XA35uR051K6wfR9aZXBUM2GN7c6brXbGG9JZdoQmCobYQ97X5Sqk5jx+hR4mNb+xja3o1ookRuRQNxey6dMR+lOQKO/k6W1eZSdsNcMFuIJZIEhVKmuPv4/mXTmFBwGLQwthyNsnghkdv+TGTzJtSXinCfcjL14VYOGktJTlpM2cfy0JMqT9wepTsQZcuO9Gjq1gt8LMjdiN56L6asEj5uKqXxyb2ozz5D4A/fp7E0C+cdM/nZbj+njZ9IqAfmNK9HR0B47lEsn7iWVOsuEnGVvn89Rul3PsWsYh/+liG67n8O59xp+K76ONmBBxDHNlCi9fOEvAhLSSXxYR2vw8G5S0pxt+9H0RKogpCO6g8HiU+cj6OgENFgIDZpLvY/3UahG8K7NwBg+NbvaJTtzCOBoprYEanCrvdTmuumy25CjAzBY8/SM/M8Hu+RuXFGB0LrWgyChMjF/PAzM3GbFcw7tqOsmItgUNEm5pPa8Tzqc3/GVzoe27k3UFfspn+0l/piF5Ikgl6HOtZL8K+3E9+8FudnrsA8zwe6HYgDjqNGQq93FjM7D7nY3pLOJrCgQafMJ4JQyOj9/2Lo1z9FdHso/Pt96Pf/FPoPo1ZMxnTl9/+L/vvecEKJj67rXHvttTz88MO89NJLlJWVvetzqKrKnj17OPXUU9+HFh5BkECZCHoUhHc+9xZFEXMyQVQ2ImoqKybl0zEUZm5dWnii7Z2EDx7CUjWOxN6NSKU16KJO+fCzbG0dT/jJv7Gn5RCV15xNWX2KbfWfBrMXmnvpq5lK84iBurEcXA6Vx1sm8vD2UZaffBm1u3YiGI04LFFa5EpuaCxBPdCOaHCztK6NuspKBnenU0ZYLTLZLiACQiqGICqosQBetx3x+uvYaKtjTNToC8QYCPbj8kbZ055khjkbUUsh109l2GLl8B+fJLBxJ87xNZSGkwjb1+ERJJS5ZSQSgwjZbvSEHWIDhOMKtHVSWGKmxZXP5n0jbDzk5+KabOLWFKYLPwEJjZQgcrhsKgY9RfGe+1G6dqB/bCZjXXH8SQmnXaTL5sOmSAxhxhWNUZsbx9d2B3rAhuI+g2BOOa6W3cR3buGV3hpGJ9pwA6poZCSocVKJnS5/CtVTjHJk/iOQQlRUGD+dEd2DGElw9Sk1nDq9iEJvelSXGI0TffQJxjp7EFw+Ytv3YZ5/MmjbgTAIdSAc+0ZaV5JLsW8AowJVBRqQtiOpgVEAtGCAYcmESzamHfuU/+bG+d5xQonPNddcw913382jjz6K3W6nr68PAKfTiflInMyll15KQUEBt956KwDf/e53mTVrFpWVlYyOjvKTn/yE9vZ2rrrqqve3sYIh/XgH6FoKbe8z5EodzDJMpjFgZGcnXL60mpVHMuwlg2PsvfJaQk37KL72Skq+ciVawABtGxGCh1jQMJPDv27D3lCDHgU1LONKjhC0ZfPC9NNI6nByfpjU2t0k5o/j+eYw8aTGmqCJ+V/8DEaXG2HVn4hUn/VaPuFwDECgyhMh29nNKcvN6Ns2of2jg/hXzkWIxxlLaFirqvCNNxNKZSMk7GRlQX62lfKRHpzbXmJZQQnJF9P2kfjE2TTFDBSa0helZDUz9q/70PdsA0Apn4R/7UZ8O15EcseI5czlVw+ZyQoM09D5DxaesxSbwUH5RI1zxEcgBFHTRAb//gCOyz+JR42hpMYwNKW9lJOdLfxiQy3Pbaniaxd4mOqEUcVAefOjGLqaUFwyoCMkxzC++DDyoy+hX/oJKl55kq9Oc7MlNItij5PWAYlRycHhxx+j/MKP0ZlaQV5/K4IUQ+9uItkdolNz890DdcSbdvCdS6YyrsyDmggy2rGWsYSBHiGbn1suwOsV+NrsLNyC+UitNBfoR1dxfSOV+U5++dk5CEQwKDoIDgBc538cyelEKS5j0GCme/k12AdacVePf1dd9f3ihBKf3//+9wAsWrToqOfvuOMOLr/8cgA6OjoQxdcdu/1+P1dffTV9fX243W6mTp3K+vXrqa+v/6Ca/R/RhzvRNv8TgKxCnaa+GSybWkjpEeEB0tUgOtIeyvGeIaSiswk5Y2jxCLpswlc/EeMvbyXx8jOEnniUxJ4SykpqiGghtuhlaDp0WfIYPvkKJqlbOW+KgeeaBJbUGsgvLkMd0dgdu4bgcJyvnOajMW5HyTWzOZCP29yLHotTfHgrA2tfJpLtZb9zFmOYKTEPoXvs7Itn4zYJqIn0KqHFIDJhrop1zgLa5Ukopdmozz1Byu0lqemEvvIFqk+agdR+iOhQD9aCYkSzlYjBgW3aVOz2AIy2Ywp2M6tgBXJZLvud9Vif+gdT+w5i/PJXoDu9KmYqNGD67R0k/34b+ZFRUskEmqcOIdZHIJXNM5vS6Uc2dhhY9vLDGOadiaZYCfmNSI8+h7RiNmpSJvDCX9HjcaQCL4YVy1mco6LlD9IdNTN9cAMTjGZGDFXE/vINcnzFqOVTib2wmmSfH8npILniE1xQkUWvP0X3SIhxZR5SI7txhjfhROClrGUM7+hmOASHDHkUBeMItjwEuiG9iM9IMIY/FKcsz4H4hmmY0SABR4+ildxcPJd8EgA5pTIsFWIqLMF0DLeN44Gg68eKic7wbggGgzidTgKBAA6H4/98Hl1LQWIIFCeC9AbHs/AI6uofw1AnwvRTeVRYjNcos6Aq+ygfkZFXNhDctovsU5ZhralkV/cw2w/6MetJTnHtx2px0fOvVYw99CByro/SK6YjRvrYt/D7eJKHcae6GXBOwiEkcEWbwCyCNkbikIlm2yx+97dmTCMDnDdeR1y6kFbNyERrgsme5wklC4geNkAoTnduJYHcAkba/MxnPQPeGQzas5EAnxxlJAL1psM4Ai/T7TqTFjU/nVExGCWncz++XBcpRaTr1IvwLFtEcngI+aSTcdpkun/zZ8wVpdTdcDYED7M/XkbEUsX/+1sLiZTGx5cVsYB9NNXN5VRpF3JshJhvMu22Ojyj+3Fvf4LQqg2IdRMxzlpIOKXwVJuZbRt7OaU4wlxpPYMrv02faMUWC1Lw5+8g109Gad1KxFVDwh8mlhLIn+PFGNgO+fXoih197ysAxKtPR1p9NxjNSOd+ncDD99NiLSH7jEVUlPWTUA083pRLXY6PKmGMtpEBzMogBS6NjoSHvz6dwKgYWFAiM3PXHzFd+wnSNh8jo9EZfPsf2+kYDHPTyTnUxQ6h27IxTZn7f+5z7zXv5lo4oUY+H0UiSZXuUAyrIpGX3AGjG8FUjJ5z5mtL7ILFhThjCUJ0gKApl5HuFCOxJOOiCbIsr8/fPfNn45n/+spdIpDkH3ft5FtL4lg70tMMFl6AIa8Q0WFCDD9HJH8q5kSMvL5HAZ18xYw0sA1SYfBNQMjJZyA3m1/c1YZil7iw8SmSLzTh6drHglu+TLhb5cA6Kz99NkJ79yDfOTnJuHITLaMyrzT5cT36PJprB54vXYni8zLY1cv4nEEciQGQrKSEdBfUAI/NxB5bCYu2rcJaX4nvq19gaF8HB84+j51tOobGQb5w49exemVGDxzGUQQ7u4wUNbgQj9hXIpKJsZmnUp30Y9j9JKCjaCJFbg0xFUBJtuA8fQqpnFoMrf8i5plC8bCX8md+jB5P4P/4MhKyCTSImh24zl+KNLoLNW8qvc0KI3f9HQBz3VfITSsmQmEpmsEKJjsJTynxs76KacMLJH5+E8rF1/KD5zS+nQwjCBpGOUaFF/JdZpoaO3hkyIwoFLNynI2eaJTTlgo4xSTGrZvRh3tI7R9GqipAkIroHYliMaa/r6L96+hfuwHZ6cbS2YPrrAve1376fpARn+NMWzDKvpEIkgBn2AbSBsF4N6vXt2KxOpg7OR/18EEi9z6MXFpEx/QFSCIUOC2s7w2yrFDCLI2A4AXJha5pJBvTYQWKUsq8UgNV0c70Ly0ItMWtbHbOoq7MxahrDokXdiC89BiOZfV45UZSogPRnAdqLL24ovZxyO+jo2+MKp8RgqMAxIaDmAMBcrQwW/1etu09DMCuHgMTBmJUirtZMnUyoUdkhv/1ONG9B2j9ya2MpEyU2m3YpUZUUzHOSJSKrDhJ1cjG3hii00SytIzg0y+jzVjEIedE9h0Y45UX2jhrmpN9tz1MtLuP3EoP7oYShElGZE3jM1dOZWRojDk5oyjxbnRrNqrVhxTuR7XnsyOSTWOHhXkVn8OhD1EQ2AWAK7CHkPkMst0ukv5RjLMXIIbbsNpycViSSMNbIBVDtIxgnbiQYF4uosuFYdIUhLxq9GCc+LYmxLiDsfFnsD87bU8pLuxFeek59J3rkaU5PL8tQV1xNtGkRCxkweroJhqPAnY0HVr8Apu6VACmOCVOynKieisZe2IrqWKd2KJabv3XNkLRJBfMLyP68D3Etm8FQcBQWfWB9df3koz4HGeUI9UwRUEgZZuKQXGwp9PEr+5u4qYlYRL+AIK9DK2lmURLM8OhfGqXnUF/UsUsgknfi5YIsWaPn2DUwwJ7N/z+2wBUffpmPpPfxZbr76T60oWwZDF/bjagakEKipzYWobo+dx3AIhFPsHQWR9jTtMhvGO7QVdRx5+DPppCyMripAUS8Ugc4403ou/bx+DEqTzx4BCL3EGKhw9x1pJSegajTMwKEFWD9OZNwNB0CJ/PgOuKc2DKRLapApIo4A8opO7aiy4fpOibOTgObqQ7dwGa7qLUqVFSY0dYuJw961SqhvtYfGEVl5xZTOfj6+hftQ4Ad7GXUOMBtqmz+ee/XuHXfzodU06U7E33ICQiNE25nqhpJlKOm/j2Hdy2D0ZCSQ5XZ3FNtZXW/GWUWL3EsmqZ48jBNvViVM2LYEhgavwDCXsx4fqPES9ehKFnM3G/FaezF+W27yDYPWQVD7Npr43mgyLzlFwKO59DHbcMACmlIZZWIOUWIk9fwHcw4hdsfOY3I/hDCUp8o0w/a4DyvQdYmjcFMa8AVRIQAEkEQySIllXIzt+/ghqOEr/jIvo2NDMcTNvLrCYF6/zFRDZvxDJ7HpZZH55p17shIz7HmXKXGZtBwqKIGI0K2ArZvraRxbVDTBx9CkZBqJJRln+Mfn+Y3px6Uof9TK7JomXtXp5rV/BmubnpT+1AO8mTs3m1AlTCJCLkZlNx/gL6NndgnGd9LdVGIJpijj3JkMdBYiSIt9CFQ40hH9iC4EmHKQw9+TzNf3wZ1x1/pKjSy3B3gKDDxtgpNQT6w+zetJUzTK8w+tJLnD1rOrYzT6MnlU17fhXrB62QbWd6SQnK7j2EH+/ntAtkCksV5NZejFdcBpIf4eAu9EMbyT24g3lzvoI1GUBS0hdZWfIw/Q8/AE2lWHduwr34Ulg+D8IhCq46H7MDPuu10xiqpzcpgpyLXHsWWTvvwtXWTEpKEU+NoQ8HKLALjITAa9IxTRtHf1ShMbsOm0HCJKhkmYZAHCUVStsplEgfjo33ExZ93Nx3Op9ONhL52304GwqwLVrKgK2Ib93WQSKlMTAtmy+sOJd2nHQeHGJ4aJBut4FxE04jNjJEQ/+jDBQsotA7CX8oQWGegwE1QVaDnZKsAga8JQjARd2NKNEwpeU6giuPSY/eg5ZI8Kd9CT6h76G4wEK3p5DZtVm45lyMbclJSE73fw4m/ZCSEZ/jjCwK5NuO9rv42Mk1tLXZUVt2I/U1IuTWYl24iNHWIZ58tBFN7yYVTHDf84Msy0syZ1oukiSgqjqCCpEFF6JPGM/T4Xym9B8mqzwX96mLCbZv42PVS+mOytR6TTj1MEt/uIRYWECcW0OLqZhAdx0WowPBZqfn/nWo0ThDhwe4fUO6ptaMC300b48ij46yvEFG3p5OqB7r7SNWXISUHODQHj+GIhspTUe2mDBOmkTuglqyPDDyyz/h/uTJGC3rUJXqI050INg8JH7zO2KTZmA35yOZFQKrtqBkeUhuXQu6TlH/LspvuQy/bmG9rQCLmmCSEECSXze6d0dy2BY+nYTiZE65wuCvbye0YSOf+mIOfdUWCrY9iumsmym0G4ilFKKaRlQTiTsXIg230KkXkWPRMWoR9AMbsGSVsMBTQ+juVRTdcBVmn0B8UCL0zF7yPV5qxQAL9r/IcHY5+vQczvCFsJkUVM1Pn+ZC1AxgdjFCLpUEaFhYQq8ock97Pud4ZUS7k2K9HUULoQU7UDeuY3DKVwkrHryuGKbBYT5WpNH+pXsQOrqYcd7JZJ1UhSDYUbKPrgz7USMjPh9CTEYZS142L3EZnqoY5UXFWBIpXFZDenVL1zEYJD7n66biwduQN5fwh1t+Q8u+LsY+/w1eHg1Q/sTtzHP04ji4gbFnXsQwawF/8p6OGouw+8AQ2Zv7+OZnJ1JTP4QUiNDWnmRvsYQwYQXzbXGa+mI4F9qwXvJJyixBvrDQTVGkh8pAK5tDcxkKJLninBI8Kz6B2ruPtlgRxb4u7MZRMHsp1OJEe3rpLG9g8ZQITrPIwQMaqyd9kvEJEwvwI+m9aE47Kd9cAmE7B6oc3LZJ48YdL+LuOoClMAepehxKWQl0tyJXVPLIPhhXa0LTISQa2LY/isutky17YCzCtd/ZxHAgyRWX5/Ps7hg1W7YCIDbtperaa/AsKMbQ+Chmo5lDJeeAwYAuCOhDrUhd68ixzCWwqhFpyjQeF1ZSl+VhYUmI3opSXBMVIImSY6T9y3/hM2UNGHSRwOoX6T3QwMxzKxGEOGBFEhUsDom/7PEyHPw4l0YOseCv36Tjsz+g31jIdOcY3kMHCU3LIXvkCQDiFaXEaz/HQUM6VEdXJazfug7TSRcQ7+sHIBkYQx1pQvZ403GAH2Ey4vMhJaFqBHSFgK5gCMZ4pXWIiT47Z80owm0zMKfGR9/2EFEg1dlOmdSB7m9n6/AIwtKlNLYPcdnyNoSrajHkuOjXCqj0ZDMUPuL7YpAISVaaKs4hcuf9xOxh9OJ0MnrJPIac5WTP/JOod4nkhDewIhHlwK+f4bGPfY2iSS6WVdUxrELuzqc5VHc2CcVN1Bjk6c2D/PWpfkrzwxhliW+dE8Edb4Q4PH5wNk/sGuKhvXH+ft0UnIEI4f1+DMERUv4eCpct5vZJnXSGpmPtr6FvJE6no5hJ1m7KJwxDcj+5ei3mgRjzd9+HXljOXQPlTBtfRmsoSWFKJxLXKSxw8MLWXgZHInzlomsZlzxM4uQzGbZ5sSWjGBzZiPufo1C2EiuagFELYxhqpid7AcleGdPBbWitO+is+TJ3re/lnzfkkTW7Hj0mIZiSqANJ3PNmYB0dI664CQC620MsZcOsxNESBqIpJ50mL6c5NmF2xugcy8aVm88/uy10Dvew02XkxwOrceXloTsVBD1J3FOMtWAKjqEAQUFB3teIVDOZ5JonqLnpS4ztbsQ6fTID2RMo1FWEjPhkeD8oOVL0TZFENralo9w3Nvazo3UYl9XArGof+R+7EL8ChiIHVHgozjeRkm9iN9l4nUFEUiDA8LRZ/OElK1lmKM42c/3ZBeTXFBJXRDoCMXKWzEb8/i+ZvnQqsY4BtJc2s+DkEsryJuEM9xH47Z8QzBbKL74AtXsP8R/dhXbFJTxXOYu80mXsIR+SoMgGyvas4qpcG3cNS/z8mgk482M8/ZKZbfsT+IrTVRVKcwyMPLuP3d/7HYLFTOCXv2Vh+SAN9sNIYwcZVWu4N1LH07u7mFqu4R3noTyRzkdkFOI4d2wi9ezjACz/+m9o14GkxqjFzGc/M5Wte4cxWGT6BsNsMpQz++r5DLcFSX1iJZ3ZXsbOPYvyuo/jiA6TNfAI6CoHSj/GPa1GJKvAuUsvwnFwJ51+lUKfmej6newunIB9i8Z0Z5LubfuxH96EbcYi+gdkSv/6W2KV1Yyt2U68cy/RbbuJfOpGCs0R7CPPApBfehLxb32PuoMKncPd1DtTiAeHoX+IREccweWgU45T7k3g27UR49o1JF54Fk49Fds5n0Aor2Po1PMZ1QXkpEBuKIHi+GhUqXgrMuLzAaDpOuGkikWWkN5hvgODJFLlSWdfTBQ42ds3Riga4wpXJ7LZioCOqbyC3C99hb7gMHtGzbgNGvVXzcYTjLN6VSPdUSsJTePXz2ssW14JXjMuNUnBaAdBqxk9FccsQ3GuhOGLp6C98Bjx3kFG20ZomTaL9QYNUfBxyqJzca6+ByGRIP7XfxLv7Yc/3471J7Nol4vJklRGUhKWrk5S//gzVYLAzV+9CcEQ5GCvxE/u7OWK3BEaerdQP3c8FTWjjD2V9sYWTWZKw/2o/j60bA+65OSn91uQi9NG771dIZbMKCNSdCoGUaehJYHicxIEDKVlFCbbGIuXU+cewCiCv9bHaNMgdl3nkxeO5567dtE1PYWnaS9j3d2kurtpqlvK9pTMtNtuY9wPL8PsjBNUDOhAStOJT5pDauFJrByS2NQ0zOb8Ym5/MR00e/0iDy/GHXzrpgZMso6lp4+91dOJaAK2IT/x+x8GUULQNRKWLHSjDSERQXbm0l5ST2lqhI9n21jiGsY55QoYHEJ95QXEwkpqZttRAxZs02eROtSK7cpP47rwYgSDkY4rV+JeeTlKw2QsHQeIx0Ioy898z/vqB0lGfD4AGgfDNA2HqXSZmJLreMdpWpOaRttoFEESObMhj+jQNtQNfwFAz9d5ZKSMpVl7CBTPBcWMPyESV3VEUUAr9HL/sIcsEU5eMIIr18pAUmNEVCC3ht4WP9FOP7MSzSi+FM6CHGLbH02HJGY30NLWCTXZaDpoFfWYoksRRrrJPv90Alv34P7c1eRlWfGm/ExSBlAtYUJrdhMEBIuFoC+fG3/ehqbprLxoHFO2PEqWMkT4/l8Tn7eI/EtORcorQzTbSLz4BJHWXbw89iVGKcVtk0lEUsyq8TIvO0HZU3cxuGwR0fpiSqe3kXhxCO8NN5LIyeHmZxKcamgkR+9E9VQwvzyHiV+cQ0gVoK2LmdfmkZ9vYKzDjXXKFDSPl0RVHVarQsnZ82i/8yXKrj2PhrweYmIBsiBRsX8X0X1RipraqSksYMNIOrGcKECXkMV5JzvpzHYiJFPEDa2YUykiokK0cjLGs65AqKzC7yzAHusjPuNyko8/RPyVTQzYpnD36oMAuBZ4OXd+Dgd3V5AnGuHgRlLP3o9ivARp2nTCNZMJKRZ0VWLV4810V32chY89S0njThLbXkT5zi+O3Wn0EKC/q4Dl40VGfD4AukLpZPQdY3Em+HQU6c3io0ZjDDz5LKaiItwzpwDQPRZnz2AYARB1MIcTWAAEAaV/Dx6XD1kN4evdjFY0n7hs49BQGCniZ5rYiZZfSKF7FEHwsKUrhcMgYZdFZD3Gr3+/iRVKF7vu+iuCJDHhO58mFa8iLzeJqIHl8UeomTodX3yEHCGCXj+J2KZ1mBrGoX7qswQRmDwySPdvHyP7kmk4HK2Y59mQHV8kbCtkW8JKSk3XMdfGxtjlqWRJpYf8+ePZu64Pc0c/ygv3osxajp6bS0flDP7fo2F0HT5zQTnTxwkUOOI0Xfotev2jmPccouuXv0OzyhSNrEU90EigeCrN7dl8fXqM1OpnwFuEfMqXCIqQ0HSUskLUPA9PjaoUldlQD/4L6fRprO6IcXp1CntNPmOjOvuu+ynFX72C+ScFSbRppKweep95gLJPzELGwKKOTVinTMPqc0DzDlzjFhFUBRAV1veZ6d/QSG6WjYoyB01Z8+jvSHKafwehwf3sy6mnoH0Q5dBL5C5diUkRiSU1XKkog9sEvrkqwsrSQk4JDqa/q9EIw489RtuuAzzsmUlxzz7WrW6jbyCMsXoyJWIfub/6B1J2LqOPPECqtwfnWeeh5BcQ696P0dZF8qXd6CkdafqppHKreHh9GwOjMc6aVUJp7odHlDLi8wEwIdtORzBGgc2AIh27WtHhX/6evn2tDH36GuS93RR57cTjKcJ7Wqk8vB21vpJfNtuYP/5y5hZFkZwpZvT/EzkVRq88CfoPskkrp9hmYM7Bf2Id3Is+NpWRKeewbtCGSYQDvUGMosCSXA2bzYAUfrWQn8rwhl0MvbgO9ZqPEzzjbEadbmSjgW1tEo902rm8YIT7rKdiDbmYS1o8VUnC2NXK1getTLlsLvq6J/E/9iTmSRNZ4q1A9NnA6cLYP4C7eQcH7FOZn7uTyKIzeKQDipdeh+3nN5NzwVlgsjC+RCDLKlBepDPOtR09lcAxaxIjT7+EZ/ki6DqAkOUiGVQxTp6J87nHuL7uTJRACBBIaR7UyBjuLA+DUQGvUWDzkaSQQ4XFDJ13DY9sCTF9qRmXMYFQUIr/a78GYPjpTSj6VOIH21EWLKbyilmkxCzk3Q/hAuqKa+nAjrTxJeQZVYh2J5KqkWWIcNnpozx0wMFtz7TyufPGMS/PgS0whHbT78l3buevVSuZUBtg8qqn+JJixTyxkIp1f0AwmPjj2VdjMUbAdz5COII8ZTna40/T6Kmh3Z+g3T/M+IYcBobaKM23Yl92OnJuAdFdOxj4ftqZFEkk64pPEdq3B0kJom5eBYAgGzk40ce9a9K5frIcxoz4/K9RYDdSYH/7HCqJwSESs2bTK1sgkkIfCDE0HKFhy1NYnr6flNnMN//2Y779cB6lE+vZEBPIyVnEhOa/EhAsHLbXk6NqOAQdUywdpU1ohKGYTkoHgyIyodCFIEBXOMr8lROgr4T6qU7s5hT7f/sYiCLDVePwu734o0kSkQi+LCvxrjG6RyIoMYWlT/4Z+6SbiWb5MIaC2K//HH7ZSWtjK1ltKRKd3RiravDv3Ej285uQbFaWvPJzDJ6zGXxuP1H7JLbsSvHAU4eRZZGbZy5kZPXL5C1NcdWhbcS7eqhb+CVQ44h6jLxPLiQ1ZxGpv/4M2x23oZx6HsyejOG0aUguOzO7e+mZcQ6DU8/A8ZtbMQQewXzK6VTkFhERReqzDBzojyEFY/zlmQE0TWekbYTl4wcQsisY//vPEz48SjJuo//W9FRGHE5yeP6p9LX2cQECiBIvUs+ufaOEJ3yMxfvGePnuF1GTKT79qRr6bdOorBKZ0JvE47GQ0nVGHVlYp0xHCoc4qUGjZG8no3fejnv5aRRl90MiCmoShzqElGxDL3XR97SG1ZFDbMmpTNi5n21JkdoiJ4unFLLyjFrqCqJoB/ehHm5E8mQhebNRhwaRZJXYz67AMP5UEooNUVZAlMGTS57HTEWunfbBENWFzve7q78rMuLzIaHg8o+jbd/LoAyaJLF2XTuJeIrx5vSKhmQxY7UluPjsKrpTBjrDMTpx4C//PCXZMgfaQ+SaREwbV9E++Sy8Yj9BSzGuQJQixYDFbKE1ko4dytKSRI0ylspsKubn4ScX39Ql+P1R9jmysSVUjLKI16LgtevcONWCnqyndIef6NPtjPn9DLhyMfoKaekNMBJV6S6qYcaTj5J7y/eIrXkBx7R6Ah391HzrKuw1OqCTe2o9wb0jKIa0EDvtBuSJkyg+dRrRti5ih9KOjP41u5C6DZgnVhJv6iNfURhR07md5UQYc70NgxRGy4nT55nMqq0BQmNxJi+6mqef62TlnV/AUJiH5+xTcZ0+iz/8rhldh3NOLWfd5j5W1AiY800Ig3sgtgdTnkTUsYLQER+q1NAw67f4eaE5iTbnfMpqi3nkxQ4kUaC+1I3DDTu2t7D8ggZ6dQcp3YZgg0XTCzlweIT6mmxsqRjuHAvmUgf5qUYO790NgDbUz2goH2ftPMTCMkSXDmERjTzkrAD9oRgDihVh+hR+vEijIy6hIWA1QWr746SeuB9kA6GVP8BzycVIsox0eA2M+TH07iGWfQGWi7+LKKhI5RPwSjLfvXQq0YSK90O2OpYRnw8JjgkNNExooEbTeezFVrbu6GHW5Hw8l12Jc0Y95hIbAUc+HSNGSl3QEQSbKPLUrmG+tsLJco+MeNOXUfc3YzjzHA6eeSWNXXEcJFkafQI9z0lnzlJSgkhp+3ZkaxlrNgcZOLmIzYk8AqJGcVkO1cExskMDpIpKiI/4qYwewp6XAgkCE6sZuO1fZKtpG5aq6+SbVUaikB0OYCwsYHhnE/eYl/PimkG++oMfIs4uQg0fQLLGie/vQdy3n/zs5Zy+uJSB0TjmOWZo2oz/hbXkXnQ2eiqJSUrQsXuIaKIM4U9/AiD/so+hdnWgTBrHur5sinq6Kcxy0hWx8dhzaSOuMDGPkCoxeOVXKFh1L676HMyGBD//go+OXTGKN69i8dAOEj/sRl/2XQQ5DL7x6IkoJmcK749/QbJ/EP/a9UwqMtAZ9zDmcjMQiBJLpIW7JMfK7LpRbr62hIMpmXA4QTYgouNLhqipCZHn8rN+t5mewrlMaChAvu93uKt9mKouxFtrRIj1oI5bRNxbTKplB2obRJufwnbSUlJKhIGYEadBwm614yJOMKniNKhwpGrHtspzUZ9YTfW6dI4n5xWfQTjwMvK0FbgmTX1T37KaFKwfkhw+byQjPh8yZFFg8YwiPHk29ofi7IkJnDR3FsbAbuIRgSU5g3j0XeBYwOPbxlhQa8K8bxuh2+5F11RUIJVbzYFoiqIykZnOAUxD2STaQ0x19yAM+zEMdlLbtROnnM+m/fPIKkuX0ZFCY3h/9D1iHZ0k//V3LLt2s7+smMFBF3XuLobiFrpFhXBEQhoI0ODqxxbeS1XMgxLTSdZWk5hUyeOf3ATA1o2dzE1uIlnmI7DtENHtO/CcPp9Fh1dTfs9DxCbPoSzrbLoe20DsUBuJvn6yzjuHgfZBeqafzljvABWvfi/jx2Fetoj9cQPewWaqwi+i9xhxFl9BjsfCcCCK121it8/M79sS/O3PP8DkUBGSUUpcZgzCAGJVNYl/PYJksxL3J8BTi2l4NUKwC0bbEXcKuKcW4509hsPTzYKPLcJp7qd3XYKBCTYEo4mz6xL4Coc4vRA6WiVe3H6Y1FA3whNPEZIUvD+5iA37NX76WLow4ne8RorOvQ73wEFSkkzshfsY60tgtg9yOGceNY4Bhh+9m1R3F5F9rZQuKMbldWGQCpAlkVKLRHLXWoTW7YjTFqGc5aOlp5Ds0XTmR4wm5HFzUE6/LF0S5yNERnyOE4mUhkE+dmdx241IFgN6KE7PWJzQcAfGgZdwSGYSZUsgpTPNuQZ/+WwUi5XGeCX5C5bgqasl7MphcE87SVFioq0dR7IJnBDPKWErudQrMXKmpxAEM6aQg/jtf8KuRfFefR3K9l2Eu7uouPWzJKVhembUs2vMBO2g+YvoNCpYEyr3PrGP1o4AF59VTa2YR/7oYUarp5Hj7MXnS/Cnc1T8wxGMviCxoTCd63dQNd+KbUkugttK8PGtqKEwyiur6C4tRfMWYapM4l44h50/vQctniDP6eEfholceMkXmTYhB/nk+YiDo0Tbxni5v5xBr4ulyacYp6/ns3Pr6YkUkGUM88BYOpe0YpVJSAJIdgz+ITyJnVizA7h+dz2JtgEMYz38/EUP18wrxxLsQrWUo0UbEePdAPhizfSnTmVtWzXFeaNc2fg9hEAYoW0emr0KRCOuPRs53WamdU0fA5t2IF9yEV0jFtDThnxRFECVMSS7kCP7sO5up/n+RgTALbhhSgoxEcFcVsJYdxfmcQ2IcgQLYyC4ADtafzP6tqfRug7QUzyDNu8cJtjjvDwwStbZX6auxIXstH/khAcy4nNc2NThZ0d3gNklbibmH9sIWO21kkipZFkNuBPN6SeteWAsI5BM4k+4qM1L8ni3ytyN69jm1zFnG/nxT7bhdho4xzTKqMuEzwa6bqPNN4lYXELKyyVCNaZ4B7rDinnDKlKA4YJLGFyyggkrKjDJUUJJAbsYBkxIAkh7d5OzfS/PMA5PbhYBl4mU28rqUAnZJVWc5eknu+sFAquKMd71J3IB58qP05U/k+G2p0kEgjR8YTFGtx331DJi3SM4T1tKS7dO713PYcrNxnmKG2txLqFD3RRPtPDziS4S3QmyO7aiP9hJv7Oc1YdsbNs/wiuSwPTLTsY1sAqHlk1OjkLR4BouX3o+/f44kbgBmyGKpItEOkbwZJnQYyJmWwrJrBKLOZhR7+THz2jMmHAFMxkmWGLkZbWBSvcotaYRDg1LjCU1GnFQNuVsjIFuhqVsXB0J1IFOlIYSRgbMyDeuxHTlFXQPR9mwdphAMMZnp9nwFDjxdWxC2dxEeOpcbAuyaJhUzeDvnyD+7AMU+OwoeWHsjhjWc5dhmDEFcAJmdM0Bagz2vYAkD6HPPYVn1SJ6W4Zwm2XMdjMmQUBZewfxFyIYLrkJqWrKB9OB3yPeU/EJh8P/sTTx/zqJlMaO7gBxVWNPX5Bx5gT62ChSUeVRzoc5NiM5lenyOXpqEbgqwezDZHBgshfgTI6gjaxlfPZEOqNO5OadxK05jI0lGBtL4Otvxr/LTHPdHMoKUuSKQdqTFvplD5vHZlJtn0GhIYR43sUIikRvUSE+2jGaTByW6kkYJOzmMGdHdxHtSTHa0s3usIPaxUXgNvPFM0Si0ij3HLRikEUUNZ3gXFJEkCTQNKINk9ieX4v9q5XMl9dhlvbBmIRzQQ3uOfno8VHy1ARD475EctRPLBZj0l++hjW0F5tzCNXYTuTgdhI7t4Io4a6YQt20q9i2f5h5k3JRtF5C3gUUFlfRH7PzYvwCJq26j/DDDxFauwTvd7/AoJiFnFtE6pk7EZIxhKpZ7Ks5DTnfi6yJLElFCVgdXPxUgAmFk9iyqQ+7WeLPE9oodfcwaC4kz2YgJRehjYSw6CmGfvEH9GAQx2e+SLyonGAgxu8e34+m64wrciHoKRYvTDE6bMPz6AMAqC4f4kQNgzmLxMF0scRESwucchnGgh5QTEjFbvSYHWIBGP4TumU6kdZhIq0mrA6BrBwTvWNJzLLE2u4E+dk6NdF0tdxE8zbM/8vi09LSwuLFi1FV9b087QmFQRaZVeJmb2+QeS6d0M++gt7Xgfnyr2Gcly7XE0mmGEtoeEwyiiQiyCZwlNIfjtI1MkqRGsbSfQhZGma6aytP3vYnEiOj2Nra+NilX4KhPiaMvIjdUgAF+ZjUPZhUmJO1kP3RPDyaxs7GIKPZOrNnGenLnct86xYMai+pVAmyUkFCBSGlIz75MurDD+KYPIOS676L2WmjUmomL7wWUvCJ+nMYTSqEzNU4xDhWV5Sy6y9As3rZVj8BRiEhK+hiOlZNx0yid4yxdc0YC5w4amL4GjwQDPFw+yTWv5zkm6flEVBdmBIS4bLxRKIK7uggiUSS+TOLmDYpnzyvhBovRehsJhyz8LVfHiCaULmoYgqTeYh4SwvCgS0EnTPoM1iYZ3EjBHpRBZlHNwdZclEJVoNEuKGe9c+3omk6FlO6JLWkSCQqZhH97W00OD0YLr6MF2wl2EtzmN72CvqR8tj6aD/u0jBBXUUSBTRVp6HQyCmTglgNIaScXIQJM9GadzKWV81AykiZvAv7175CrPkw+vwlaCXTMVUmUZseRX3iPoTyWaDHEXxWdL2NwYdeRh0ZJtrRxyl//xSTCl20dI4SjCQJ5tSiyUvQhvoYffhZfA0LUQqLQdUQLZbj0r/fDZlp13FgUr6TSflOUof3E+rrAEDrTS8zJ1WNDb1BRuMpqt1mxnvtoEfRE9vxSUHGlDqCB7oZufdf6Lu3Yrz1JrynL6bn7w+TtWIBy04roPrxB9F7ujHkTk6v0kigYSCqiyRSGtpTz1C7fzuhSXMRC4bJ6dyLYpdAAF2LoYXjKIcOs6cvzoTedPkhmnaBniAlicQ1Ezu6aqiI95OVXMPaRD0Pb/LzyfESUxwajqpZxGMC4y1+chwFmI06fUOTsAazeXJniul7n8P88uMgCMg/uR7cZeAqo3OPyNYDw7xYnE+OOYnbEeOmDSnCsSxunG/A29FEUpLI8QrIsh9NhqCngr4HVpPS0lVfpRwvRV+4HKGng9HV28g5dxzmnbvRKqagRPrZHS/jyrkxxKceYOSljRROm4IzayqfmJ/HWBymjM9BFwQO9Y5geTGdNTFn6lTya8bTbcsmEErg/fy1qP2HUaaWM0aMSSNPcvOp9YyE/EzN2oShy0ISC0KuhnL2QtSTl/HcZhOjTSqiOoNsXWXRdDshReL5Hd2cNqOQ2MAoRl8V+oFXAB30aegTphEdNwPDmqcRxk/F2LqDispayrOymVKehctmIPSvbQzddTvmmTPQ4hE6r74ELRgg9/s/w9zw4SiR81a8K/HxeDxv+3pmxPPukIqrMF/yFbS+DpS56VGPquuMJdJLqtGUhq7FQQsg6H4QQEBhW3Ylhs9/m7Ida9k4VEDOFZdhP+csBksLsBiMGD72TapGdyNu/SfGDQfpmPdpdFkhx9JP/6gN+b7fowaDFAy0k1hWC7QSGzIil/k44KtjeKSHu1cPsa8rxOU1J7GiwENiyjx6ZSsNcpInn05iPzxE1Uh6SpFfdS7DgRw2HxbJ3vMM9vIC/M+/wKSfXEpVYD/DqTqEeYt49P+zd95hdpRl///MzOl1T9veezab3U3vpJBKCD10CB2kKKCogA0LRVFUBBUREAQFFJAmLSGEJKT3bHazu9neT9lz9vRzZub3x4kgP8FXFN4Xle915cpOeWaemXnOd+65n/v+3pv0/PrJDgobXZQDmqISdo1VMXvsT4jjQ1xVuwCPqZqZ0h68ciE9VDAazEyj9xrcmBYsJhxP4zG993kaMZiQwmN89YwphAe6mR19Hq3Zg29zppqE2DAH45xZDLe2sGm8hibPOEW+N9jx2H5iR7sJH2ph7RftdDy0gfTqVbTgxmrUstuvYenkyVgri9H++TcUPu6j6NqbsbjtmMoTqFUuFEbJ6h9ASMWo8v8eccJMBLMNnPmoXYOknn2S1KzJdCXszBG62CDV4lFSzHUFcNiiZOlSOOUA/tEkW6UmZtiOYIuHSY1DdE8YQQpyj/V4Vl8xn+OHnkfp2Y5UFUJUdWTbp4AgkbXmXEzTG9FXxBhfv5PE4UOZsbN/738W+SQSCT73uc8xadIHX1R3dze33Xbbx9Kx/wYIooh+wer3rTNoJGbl2fHHU5SaouD9M4gGVFsFqHG84xpUIIFAX/UMHrlvGxUlWZy2shw1EuVoUsXj0aGxh0hUziOiczCsONGlRYrlIGU2P95Fiwn/6Tks1aWkD+1Ge/zppI62EjKVMSyo5D73CPmu46lbWYM924hdjaNvsHC60oJBGWG930EqpQeNFtIpnB4XE8tsrCwJIXaZiHUcxTlzIrrBjOVg1g7gP9JPw8RivnTrQhSjhHjCfHq7R0hoDQjRTNyQqCQ43fcrWvJXotY0UmY0cNayNKl0jFlz87DatUiMIKeMyGGRhAi5B14jPb2Ebck0xw88yl3rWtk8rNCYTnNNXRXZjji6lmdI+APM8UzBqJURon4cs5qIHe3GPq2J0OuvMH5kmOgXvsYXnruXO/amaTiuDnnN/ZgObyZ5dyZdQWxvYUfeDCaMwr3bc/GNK1wxR8vktp8DKvHhKKKhBKPLi+IxMDRkIPlKF+XznAgGC6eEt5LsH8CYlYdoE0gcjGGrs5EIDpGwL+KgrYqSqqXofvRdIm+9gPDCS9z9+MN09SYQD/Uh5i9HEBQgnkkgFcyIBgOG6kJQ92NsLMC6YiVyOIZ51pz/xZH8z+EjkU9TUxNFRUWsXbv2A7fv27fvU0E+9913Hz/4wQ8YGhqisbGRe++9lxkzZnzo/k8//TRf//rX6erqoqqqirvuuuuTLZf8/yGlKPSHEugkgXyrgVyznlyzHjXaD0o48y9dg2CazvhoLxKQSsr0DWVkJ7LjIdQrbiTVM8CEX97BiGkCnQeSOGZOIZ1Ikx/tJmzJY3QoysZQPuGTLmHS/JU8eDhO/aQTmZ6rJaehHFGTosKXIBVPcc5UA4P1xahAoNVFzuA4kijR/9JRznDk8oalhP66myhzR7G/0cmPLnGjF7zEpixh+O0xYq0tJPJK0Y81E07lIReV8NuHHubttzcxYdpMbrrmc0gJmfJJbiTN6aj9AyQO9bO9aC3feyWEZv0Bvvy5WaxeXIxh1xaUrQNYT2xEqxkGI4S9Nkzv/AnN0H5arPPYNmjjoe06/vRaZmbwTUCYv5RbK+rQ/PF2tECWOZevPWnnzJmzKVjiIm/qbOL79mEsm44nvgnLqll4+ndx9Ymn0i4ZaQkmKHGa0S9ZjBJP0p+VzXdf8vOlVR52tYfw2LQUi6OIVVNIxc0Efv0EaiSM6+ar8e0fpv/XvwPAXfUVNsWzOKqWsuTklaR6W7Hu6UcZHsDWIGHKn8RqnZf+iBalqx+NI/OFoa8sw541QpUtlwOn30V+KkmRYkIQ9ahpM2p0BDErGwQHqEVoPUnyvv1tEMz/a2P3X8FHIp9Vq1YxNjb2odudTicXXnjhv9qnfwlPPvkkN954I7/4xS+YOXMmP/7xj1m+fDmtra1kZ2f/zf5btmzhnHPO4Y477uDEE0/kiSee4JRTTmH37t3U19f/r/S5ayzO7uEMkSwuFvGYjwmC6wrBWAMomb+BUW+EHQeHcWcZmDrRxcrjSpkzsJe+/S0ASDv3IFQU0//yXvJyQth7t9JTtYY3vG5K7OX0xeJIoobHDyvsPhzi7RaYsHcb9oU1vJ6/mOGUwKTjVlGelYXn4B7IdiPnFBP74z30xYoZfewpEATOfu4n2KdqASsmzyr06iZQwOCMIA8PMr5pM7vzKsg/4fNE9SZ+/tMf8Yv7f4yqqhx4ZyMeSeHzl16Kyx5GNAPZ+Qj7O7EW5nLnDWWM+lIk4il0sSj+3e2YJ9XS1hqjbiKoqg5jiZ60v5Gxnv3c9FQ723f/hvFgy7v3VAW2vLOVLeGvsLB4Mtr+Ayh6C0e6o9x0MM3ihSY2bOxCEHK515Mmvm0LsW3gLj0Xe6AT3I0IQNiUgzvbQtxo46lWB5fNUpkafYvVDfO4oHgAe2/GKpJ1TajHygqluvzoPHkgSYhmE/GSeuqUBFO1CsNuJ0PuuRj3vYktx4Cq1SBEQkjaJHmCTOrXN6O4CtBeczXWFVWIRplnXo/x5Nuj5LtM3F0zGZtGZvjlx9kdNFFSW8XEWdNBqv3kB+rHjP+4iqUzZ85k+vTp/OxnPwNAURSKioq47rrr+OpXv/o3+5911llEIhFefPHFd9fNmjWLpqYmfvGLX/xD5/xXK5a2B6LsGhpHABaXOHCbPrwawT2P7ea59R1oJIF7b16ETYoQ+95tjA5DKg3VDXqMbiPmS89D7W5H8nWyM2cVW8dMmLQi5dok4wkZMZyib3gEQzjBokMvocnLI3DuqVQY+wknHAR++zbN33kAY0E2k1/4GWOuYqRf/YqB+x9C63ZR+7OvY5vsZFy0s7HDzGxtB3bNMKEdA8R7wgjVdRz9zev0/Wk95Z+/kOt2vMrOd97+wGvSajUY9DqKs6w4yyvwB/xMmzGZs/LqYepMqhuy8KXM3PfdH9HXvY9pNaVkBxJs8PloHxhkbFwhEtEwHmp733FvXDSf2fc9jSsV40hLL1O0XSQEDz29McrnT+bAriEmDexkJKXDLqYQHvkFdbeuxWhOM1i/kqTZjtDRAuMjGBua6NjWzSSxnazhTVDYiDI6iKbAhhAdJlWwmOjhIQQ5gmXJZGSniz/+MYQtx0lTBWT/+fsA+JZ/kf6cSeQ/dBeepSVoLYFMZ0vmkhpUSP3kOwBEzFVYzz0LQ16U+9608+e94+g0Ig/cMB+XHODhlw/wQqeISSfy/SVuHPIwhkn5SIZ8EP++b/aTxKemYumqVat48MEHycvL+yRP8y6SySS7du3i5ptvfnedKIosWbKEd9555wPbvPPOO9x4443vW7d8+XKee+65Dz1PIpEgkUi8uxw6NvX6z6LUbkQnCehE8UOJR02MQnAnJ81w4RvLZXl+EvGxn5MqLaHgi5eR3XEUfH768irQZDvIVyMkh9J0P9ZOVvULNKw8Ea3OSP5AM+E0FE23kVXXhu+NBMNvrce46HgmmDvRyMOYdP1EjqkoxgZGkYKDRN0VeOqrMc2YSWo8SuxoM+qEU2iJ2Nn2p308e3iAgjwTV5fL2OfUoa0vYeulu5j25TPRDB1kvphilyCgqioCcOPqZVx444Xcev+LvPj070ml0hwKR6EvI5R+6EALv/mQ+7X+7e3/4z11OKx86YozkZ+4C+Xii+jKzqNV9XDC6B+ZMnUi/jwzOeUybxwUebjTgNlg5p5v3EC8fhIRl5ORS6+n9/RLOMn/MKgKkj2C+rsnYOYUaJwOxiwUb4DUYJS4ay7N19xP5Teux2yKoskDrTDKScuz+NMhI2J67N1+jXuDeBJHUDa+wuiqH5Av7CYk5JM6nCb28wdwn7AGdEYMhmz0VbNA8XHKnDTZnjQVdgX7lsdQiuqQTFYgglYUCD71GLk3r0LSD4LqBXXuv4W4/CdKPhs3biQWi32Sp3gfvF4vsiyTk/P+kiI5OTm0tLR8YJuhoaEP3H9oaOhDz3PHHXd8rL4tjShQbDN+4DZVVWnxR/AkD+OKt1CuEzh9/gKSX/oWAwdbMFZVMhI6m5q1pxL0dVGT04dG8jE8ls/wc9sYemkjvLSRaXI/vvxavNUz6HPnMVHYAkD8cDsAsQ3ridx8AXaGkdPZmLMC1HzxHCzlBRjlBGXhTsZmTiYVUBDTKdLLZ3IgnoVeK9CU7mXVgV8gBysIORowCiaEvYeZedF0dG4j3lf2slZR4LTTeOeN11BsTt585W2SooVhv+9ju49/jUBgnHse/B1XxkPkTC9lramPlLmQtz1n4EgmiMs6sq1GElluIE4kLiPrJZKdXZCbQ851V7KzOYaq1SMkYyBocF5yNoJ/GGFob+aeueazSdvIrGg7jttvQ9zwBHJFEdu6JnGwV2FOboqFoe2ksuqIzb0IfxxCvgSFD3wJ23d+wLNHTfTG5tI9muSqOpHCeYuQGuciKUn0xRMQ9QaggEITnFkAySfvQt7/FrL0IqtXfhlHaxsFBglTTxtKIA42DWAB/j1SLT6L8/kncPPNN7/PWgqFQhQVFf3Lx+0LxgjEUpQ5jVh0mTdXXFZo9kdpMHswSHlsj04nqdXjtmZEoUS9geHRKNnDBzDpNGikTLiDVpSxn34yI+8cwlboRjUaeDGQi3FDCxet9iFaciFpxby6lJRqIF5Tz85oGY16B/033EblSaWkayoxh3pIP/w4Un4x6ue+w+DceQDY0wKiAIRCFHYcIFVQiykZJFUxgWBYi/rz35Lq7UZtbCLvC1czvr+VG+omgd/HPW9uAGD3n575u+Eba1edSFtgiC1bdr5vvSDAzWevRluSx7fv+lXGmhIgy6QjEEm+u987B4/whcvORyougsEWiAzzwK938IXrZqECWdteZmXKh2X2TPIrsykXWkhqjazbM8jguJPZs2U03lqwuxEtGnR6C2JZKezvR1Vk9I2VTHt1I8qbf8K5/Ayk3DwCg2F+9EKIZFphsNDAtboEgQfu5pXJp6OpqcNmDBE//5tMfuMJ5pVPZ126hPoCEePd38Xv9zJuMPKOayI2XytLciOIe3ejWbAG0ZGDkF2cuf7CWpxTprLc7UGyWlFPXUFqZABNXgmiwQmC9C+Pxf8N/EeRj9vtRpIkhoeH37d+eHiY3NzcD2yTm5v7kfYH0Ov16PV/XxzsoyLob+PPHfp343zmFGd+lAZJpNphpDfiYVRZxL7RMJBg3sUXUzC1kYjBwUF9MfP1PkzeON4xC6JFx4jk4VBpCfnP/Zb8jre45S0bHQMRLj2liVhiBN7pxye5OCqWkF1chxhNM9fRgzErjP2HV+J9ZR/9P/kp1edmUmXUWATTkTaKXRWY9x1AfuMlXKKAOH0ee36bqSRRceGJHLIVMzo0xkmz5qNFwZxjQH7td9gXrCYYCLGtf+B9191Ul0cNTby9aSszJtThqCti5+FmFswq5svXX8l3ntrEO+/s4i+uyfIcCxcurOKm69ciKzqigSi7du9lXpmTOHZ+8NQLqIAAzJ0xhfDnvkyPaKIstxF7pJemGh+9BztYpO9H48rG9vbLnFQ8QlopY6y1hYEpy7h/X8Zy0M+1MmVSPcL4fgTfYbSWcoLOFdhWXYYWP1qtQmTvZhRA2LsF+cRzSdXOpOadEQ50Bsh2WhjxGWiZtIrHRj0wOsqUajcHugN8M6eJCdEhLiwFweNkpLGa8JtedpsreeqdjDWYd6qHuh0vE8qfyGi2lvKZp6CrnIzoyEXQGzFU1bx7H3VFJR/rePwgqJEAamwMwVWCIPzr1tV/FPnodDqmTp3KunXrOOWUU4CMw3ndunVce+21H9hm9uzZrFu3juuvv/7dda+//jqzZ8/+WPumxqPEX30SNRxCv/QMpOyC97aFjyBGDqETpxCTQftXGcqCIDDJbWVrJEUkGiLLIBFPKySTMpuck6ieUsbVlTH8SRNH1BR529/E2N+Kb+3XABiSRdzlszknV4c/FKMyOoTQso/01hew6YxUNq6g7Tv3A+CuuAXDokq04U2k+oZQ43FGmkfIP+lkwjENW1Zcg3bRApzzp6LZ+BYArpIijAU5pMbGEatreLktxOK6bNLTz8A9cxqpX2UcqKp/mNCBMZoCPt6BdwliatUkbjh1Aalbb6Lvpm8ib2vm+i9fjsWRwDC+l5uuOBPDeDeb39zIvBmN3LzMg+CspHf9UXo3HmbmjjauueV88qsE4sEAY94kB4cHmBkd5XPTp9IrmECFqNbJoNXFKdeojAdSdHa2IspOJp25hnQ4TuCBJwCwTllAocdIvzdGmTmEGhtF1ZiQ8KEYLHQYSsgSoUDXQVo20n3CZeS27UCpaWSXpYEHf7GflXNK+OppNbgPvYWvZjrRqAFhYxeCIKDViJS5DIiVk4gXOjBr9iAlO8i+YinmUjfD5jgCYDZocAhp4nm13P6OyP7OTVy8soZzltUQjCSxpGXESADBbEfQ/JV/R1VA6Qb8IJSA6M6sTqdQQz4EuwdB+uiWkRoNkH75dvD3Is6/DKluyUc+xv+P/yjyAbjxxhtZu3Yt06ZNY8aMGfz4xz8mEolw8cUXA3DhhRdSUFDAHXfcAcAXvvAFFixYwA9/+ENWrVrF73//e3bu3MkDx0SsPi6kWveReCHjQhWcHowrz31vo6DBoo5ygqeFkKaCwuy/1dmtFEaw7fsxaWsug1Vn8shuC2/vHsB44BD3XlrAOsWBip5owSTsP/sV7rPDKKYseofG2SmJZGdJuGSBzWoOeZKEBhCUNDp7xsEtSBJJv0I6okGDgHtRKXLqJIw1BZhOmcbob5sxzphC+uYbSCRjGLbUg3cE58wCiseXkR6LI2pETvCkmVlqgm9/mcFQiNwzL4D+DqKSC61ujMs8LvQzZ7Hp7S1MsjnR5Z/GE74y5rjHkceCAMT7R1DiLkLjRvLcB/nmZY2olywhHDPQrBQzvqMb8/bX6P9zRjeod1svb+nnMWXaMq57+EYMkSDWdc+SWPcyBef6CBpsqNEECZ2VBAIGo4bOsRgTNaMMGMvwWJLoGyeTHvWyVc6hsNDG15clqRx9kSOJ5RxITqXMFMBe1Eg8lubJ9T2MR1KsbLLzlrkWoamWi42tHGrzkUorPL+xk6aiSbTvD/D9Z5qRZYWLLppKhTVG/dgOjIObSOrPpM0xBYeQR1ngSVIaA76JDdRZ43z/9EoMdis54WGCZ9xC8w8ykyU9I2H++HYnv3vrKKdPzuK0nd9Dajoe7erP/RWhxICMHw9VD7hRFYXUyw8gb3sJzfHnoV183kcev2o0BP5MuSMCfR+5/QfhP458zjrrLEZHR/nGN77B0NAQTU1NvPLKK+86lXt6ehD/yrKYM2cOTzzxBF/72te45ZZbqKqq4rnnnvvYY3zEnEIEVy5q0I+msPL9G01loC7ELUdxW4o+8M3kTHlRUmF0/naO7tyBSOYY2VYRbX8XusJcEjJkVeRQ/PB3eXJMwBaJcMR7rNieTsIXz9Sl2jphOY1RAaWkiPJpURp/8w2G2hJ4o0Z0N38by1e/guj0UvK1BUimAJBEK8jov/5FWtCBTseCiy4nNzWEEtHSevdjAOSfdzLTVi9CGguQHB1h3XGf4+k3BKbV13L+dAsuQYttxmRuKfDQqxoJWTx8ZUeICyfrGa4soPBrt2Ie6iW6bz9Row/vurdxzrwTjVZmDAtH96XpKrLSNmZiiZyi5OQFBDuHGJg0m4d2aXDOdJGl0YHdg33tmbwgTKV2Xx+zWh8jPuckxmuPQ68kiPuCzEptwzm8G1WjY2zZl4lefROj+9p5YrsEBDjOIeOa+0XChgLKYykI92GVIzR7ZbYeynymV+cYWJIv4U50UtD/EvPLr2A4aKckx8LWjUeYMq2B+KFM7p4uHGBGZAPq4YyjX9u9G3PdYtKYkK11BGIaRsQ8PL2HyU/1oYa8hA4exLj0VG65YA4tPX5OP87Nzb9pQ1FV3jgS4aSsXNjzOtqlF4L5L9PaBiAXGAbhmD8tlUDel7FU5UNb/inyEVzFiMddAYE+hNpFH7n9B+FjJR9FUXj55Zc58cQTAbjlllv+x3ywTwLXXnvth35mbdiw4W/WrVmzhjVr1nyifdLkFmG99eeoyQSSO+NP8o3F+MPr7eh1IicsLMeblrFFVQo/oMCAUNCIMOkkUskUe3d5WBBtYdGibAqjXThchczN1xBJJqjXbmfUVILcnETnkLDqNWhUhQkWhbBFT6s/iTMZp7NiGm9u8nG+NZ+qSi+WCTr+dKQAc3IxTvMQommcQV8CW8qFzuvFPrAb8+TjIQxaVNKt7eiG3yKh91Bx1hLikRTWC0+jVTDTkRBYds1X6DyoAQIcPOLDPN+Fb2icg0krnv2HqK6rwOT1ce0JE+gfGidnci76U1dj2L4JqaiYkXvuA2DguWbyTp6N/5nXiT36LCXnXYCw7Cz2lOYx2ZrAaLaxv80HBFGTGREvSQCTIUquU0U31oHh5CUMp7IY27WXN/sNjIzFuPWYXEnKXoCVIFnrHsRpLeScOaeQjMeZ4u6jV59HQgFBryV6MM5kd4AZvoNsLmggEEkzyR2nVu/D0PMGQ2WnUN1xkOtN42xLTWRWzj4Kgh1cftr5jASSzM2Pom47hFg9HWWkm1TlTHzxTA6fR8zC6evFsW8djHYj5zYiGIwYS/JI9xxlZt1U5pToCWsHuWipm1e2BpjFCJKpBs28UzL5ZO8OFAnEiUA1CBm/pKA3oj3papSWbUgNC/6p8SsIAtKExf9U2w/Dx0I+7e3tPPTQQzzyyCOMjo6SSmUGwV/H23wGEG2O9y1v3N3PEy9nQgAsDiPaAitpRcWq02DXv//RCAYr0pQzkYALlG34rvp85piXXMFQ3TLSKZkOf5TSvCKaBy1sOfZ2Pn9xBRFJIHR0gB0mNxYNVMV3IDbMpqMryZ2PDHP52SbmVHcwOc/MmDKHzriTcDTBjXcfQZIE7ppwFIN7Mprbv8vMBUtIHT6CMDyAUGlGVzWBxLMPk3vBGmiqoqc9E/O0x13BqceLePIc1Fa5EJ0Sb3um8MfmOFZjGYsrs2nKO8IpFYPEy6eTtms42h/jxUgu10w3UfDinbT2aOmNq5SOPkfJbJlU/3TikSB4TOhSDlS3Sl6WBsdIjC+sUJmi78IkJ9FYwdcfo4FDlKa2IG81UbDyKnb26dh8zBJ5qaiBpTNKybN70XoPIKdiWP1tnJuzE60rBfEIoXSSpMaAv38c9dB+1NlTyG2s4duR11CSSXQdYYTKKoZmXk6+rQ8l20Xw3ldxW9PIJy6Cngjn5o8iRXoQzBZi89cw/tYWAr0mlLl/5fMTbIi+HuTBzOeSqMZRMSGabYiOXMZuuxZ1zlIeLV5Ojl3DtUV9jN3yLZQf/gzttMUM+qNoJAHPsRLbCCKoUZA7M6kXYg6axoXQuPDjHM7/Mv5p8onFYjz99NM8+OCDbN68mfnz5/ONb3yDU0899ePs3380SvKs6DQiBr1ErttEUith1Ih8QE3B98FVUUq4dgKJtiNECko54ouwuMiBx1yMTnGRo+9HIwlIApi3vkWv7EbfWIasqASTEHBPJj6k0hOAidUONu5IMadGg0a2ES/MY1/ShEkHRXmDdPWH8DvKcMQjxLdvw12QS2xoAMJjSCsvxlnjZ+aC7yJGjiKO/pZwehYDZg9lWpUqtlK9ZBIxoxlddxeKxQbESSsqHaMJQqYSvPtTzAy3sCHl4c3OCGvnOrFOygiT5drM3PNoCPvEmUxJvorrpIUcdk2m9eAw67f1YtRLPHRzDga7yKZWLSee4iL5wrP0+1ykjg5StCAHQmRKAgl5lNUamNoVobUvRK1TJm/dvYiVkwgsvAxjQCDVO4DiT2G1jqMY7XSFIzgHOsnav5uaGgOqIUHcVIq2xA/7X0GpX4YUO0KWYRxRiCK6QN84CcbzCOVUMVp0LdmdL6OGhlBGtBgaC+iZvATV001ekQaXNYAkKEiylYTFga5pYSZFI28CGjWNZu5qIj/8BkrAC39+kuJrFtHih7G4jqwzz8PQMJn9nT6++7u96LUSt50/hfK8Y1aQ0g2MgtoPata7VtCnCR+ZfHbs2MGDDz7I73//eyoqKjjvvPPYsmUL999/P3V1dZ9EH/9jMWVCDg9/dxmSJNKfTjM8niCQgInu9++nDDajRnyIRVMQ9GbiWS5cD/yG+MgI2wUrukCcQ2IQrVaiPrSbWn8rty2aRuiZ19gslPJ07zgTI/0sO76cdCyJ4A2gFJTimleMgyQz9r5NYIeJ+ManEG/4KsggqAqnzMkmMTTODMMO5IZFtF59PSSCJF99GV39JEz5GX+S1hZDSCVAiVOp6aK2RAHJhfZQK27vDlSDm6PGM7AYksxuzMVu0fPalm6uWFnMqVXbEdPjnO4voM7loGg8jCp7EKQwgbCERhIIC/m0JBeSbxNpsvWx35uJdzEbJTSSis0kUFPhIjYuEcyaRmxgiPGWfuJ2F6Wz1xAurCGqc+KIBDnHPURuWQCHIKMASsch/PM1hKZdQKFtJ4ahbsZ+/wxCWT3FtR6SZdWIs2fgrO8loDppHstBU7CcCQkzyY1bSVdPRnx5G+aTq0knTfSXLkAxliDoJBIakW7yKenfi9y6E7VnhIqzzyMyOkpsKEi2K8JorICIbEY36WQsRb3Y2t5Gbt0KeVVgsmGYvxx5qA95ygJkg5WpTj05lSuxn3oKAL1HekimFZJphQF/9D3ywQqMAg4+ra7dj9SrhoYGQqEQ5557Llu2bGHixEwN6w/KmfoM/xgKczIOnvR4nN5wgnyzHqMkoia8HO4L0eNNM2XoOZxjzShTziJYuZyN/UEUVWVuQRGaPQP0huJErXr06TSNUgxhvIuiwFH89VNxt2f8Cja9lnvu346qwhkLcqnOkQCZADri3lH83iT3WxaxNhZlkhAgyz9EzG4jf/gFCCcJBYJUqt0kg2PoTzge8dqLwZlCTgxztM+OQU5j1ktYqxxoE3tB0BAvWIYm0knSNQGHPZemH97JzvI1dA0JfOv8KrJjHUjxARBEuqwlhNETazlE10/uxbRsOeklS1lY6eY7Tx3CqDfzw4V9VAy+xKn1F1OVW0OlJ02fXyC31EOp1UAw0Ifj6As4bHn41VaGp53L7twa8sJRgqkQ+niCSY6DaNODKJIHGheTsBeytTnGc6/s47S5uSyTZZorTqAo5iV2x3fQFuSTvvjzxAMDCCVO1Kx8isfaiTz8EOm+bsShMQZfWsfo48XY6msQLEa2FJ9BTpef0mILqVQx2UkJHSDEY/ie3szwg48iFm2g+Rs/pWqiBYsuCQKk3EWEdo3QOe1snKUTSe84QOAHD2G/5FJ+0u8g8nY3vlCC0XmlXLIyk0g6qzab0WAcnVakqcL13sASSwE3YPzUBh1+JPJpbW3lrLPOYtGiRZ9ZOR8ziqwGso3ajGyqHGW0fzs/eNFCPK0yUjGHc2lGVlSO9AeRjwXcjSVSCFkGyh1GQgLMc/SgT/WhTm5E3ncIQWOmOtzGF8tziNnTdDoNDPriOB0aCroPIeWUYx4ZxFVRxHjNFKa3hzA89huUqhr23/0ASixO/L5vY6wrx/LMwyh730JrNOG8/ib0BVFIp4gnXDz2yxaOc41jCw5Qff4sjBYLst5Nv9cAjtlYQknUQ7vJPekEPr9nG+PDPrS988mbnoMi1zBCLn/ozUZRYVZuBRVdRwk//SS2Ravo84dQVIjEZUZTdnJMhdyzUWV/TyvXzzcyV+qha9Ya/L4o9z3ZD+nlfKkpgLWuAUt3K50FtTjdLlRBQLYbUUN2CA+ioicwYQljxmxe/OlO/KEE/u5+UgyS57GiC2QK7KmpNBP8m1Cbm9Ht3kjl2u/jfPNnhCtLSfd1o5aVYbriKnR9XUiaBLbiAkLBOFv3D5Fl0XHXpaU837yA5TMLcWp9JDZ2AJn69et29GLxOCgtzMTpaOUk23JXoehzCPhTmMoqsV16MermzVx6zRe588lM4cFxnw+5uxmppA6XzcBFS6v/dkAJEhkx+k8vPhL5HD16lEceeYTPfe5zxGIxzjnnHM4777z3CZ9/hn8eek3mDaUio1Fj6LU24mkZvT0HccY19ATivL3hINWTKykvspNKKrSNZXLnpmdbMKdHARCkMLJnJuFtPfzBOIG9/SkuVPv5wUXljKfT5BU5Cd32G5xOPbpLz2df6njGEhqa1FaiExuJRmWSgxmHdb9fZN3LXq7UZWEEJKcbiXEIjoE8jk4q4AxtC+pAiO6iJsR3RlEf+An6onzM37iBjR0CNd/5CsmePkq/eAnjT2UkOTy1RRhDaUawobFp0IoCCVlFp9UhWm2YlyzDlCsxq8SAZUEJHmOKhqoxujtmsq8787nX4hWw3/cQrrsq6Jfyae8dA2DvnCbKPn8SrmSE2tajKJXloDcip1XG9HXYtR4UczbtliK6x+IsW1jGure6OMHQgrT+ZeyA9aqLMWSfgWwtITl8JPNDMTrQPP871Kw8zAyhu/563ixahG8ozu9aDjC10saNiVepsE7EXOvBYhQxRltYWaQS3DiMptSKc2olamEFwSDMlkcp3/cUrZHlVHiM6Pa+RcI1i8deOAyoXHLyRNw2K54zTiIrV+Tnc4L0j6vYh7Yg73Yhlfx7GwAfiXwKCgq49dZbufXWW1m/fj0PPfQQc+fOJZ1O88gjj3DZZZdRXf0BLPwZPhIEjQ1H/nS+emKEgTFoyhtHiO6lzBTnnDllXPKT7fzmlrlEOg8hacvRiFCZb0cvNaJETci42K6CodLOpj9k6rZ3OIqY86tfU3LeUvRyjJCSIOeSqYjiTqaYJ/JGsgaTx05IMjNmcZJ92QUYUwnWp+0E/GP8WJnI9RdV4xZCyL5+pNwcBCAZS0FkHP8r67BOHCHVVI9OryPe3Ud251FKLAZS/YMARAeDCFdcQz5jWLrWE+2RMJ9xObroAS7KD+JXi3EeOor2/LVoZjWgT/uYUjJO2dEuuq6+g7ZTTsT65Ru4XBrlaI+PhY4xRidMJfbQU9SfdiIzJ+WgKiq5FdmoQFgRcfUcwdS7Be/0E7CnRnC89fNMX066FUVWEQWompTHglwZ3dExECWEvCJ0uTr05VX42x0MHvHhrj4ZNT8P9aU/kjhvDanBfqSQF6dBw/6uMeJJmc3NAdbMnsAqWxfXbc8llVYocbip/dkPiBxqxZuTTfnsPIwaAweePoIh24H2humY1Dhyy2F05jhZ8jDKMctWCUeoqNAgVc4k9eIDWLa/RI3RiuDMRSh6T6kwmVbY3u0nnlaYXuzAbvz0Z7TDv+CJWrx4MYsXLyYYDPL444/z0EMPcffdd1NfX8/+/fs/zj7+V0IwFlOSF6JE/jUkPUDGKtLrdFy5pgpXZADNL7/ByTOWom+YTralDH/Uw94eBTXkxeiwMyG3nfOXutjfKdNQbkFultC47WhsKVwXnIwgZiwlkxqjWgzSqzFQ3foWmqnLMZ51InkPfYuz5p5IgXUAX9xDWNvExmGJ9sFxjt/VibvYTUw0o0YzQmhmvYijqgC3aTq489nxs9cZ73yMibd/A6HtEGlBx+aGRSwI7cPStg4BGAvJJP6wHdcqE0VZUUz1UcL+HOTePvSaLvSAbmIxydMWU3xGPYlQK4VTpzF/aBuHRtzcNlyPXitypzufc0x+EkYHOWKMlKBFHxxF8CgYixrJFiOYhg4DArLFSUTvpMiQoMCoZTAtIeflEHn9dexXfQFttpG0ZCfw7KtsmnE2wRX11BVlgUaicuJE1u/r5aGdpXyuUYvVO8Z00zgjZVaqNSFMzz6G5YpLMWhFUmkFNaFiKikgcqgVc0Ux6vgIgt2JpbqU/IX1bK87ja0jYNNWc+nYXmaU6Dlr9kQGghpsiQDKnnWgt8JfAmM1WrSnXo+UV/buWOn0RXjjyLFnqZOYU/ZXvp9PMf5lN7jdbufqq6/m6quvZu/evTz00EMfR78+A4BkAGMZxDrBsQA0RtyGYk6vNKOEAuimzUc8egDrstWoqso7XQGOhFNMzMunxB7BEu3mstn9pE9dgNYwTrT4VHxPbsKxZgHaIgeq5CadirNv9zidkV5MLhe+UfCsex3vrsNIpSZy0kdAiePS9TKQqOU3G3rQaUSK975D6zN/Yup911N+6RRsjcWEeyLYunYhR8eIjsmUHVfLvvY+NP5h4oEQktXC7I599JdUkzPndJIIHMyeSOU3J+Cq8KEoInt+3Ulnf4AqUwzvulHylhYj1eRTuqoWcd+LSDoT5cs9SJOrad4gk0p5SaUUhkbGqXrzD2iuv5F+k52hYJIGs46SZB/qO29jdBWSqF2Of9cgqmKia0QlEeqiaGKmILNPNNBw/nJsziNAmnQwwaizhMMxLTNsIRqUw6SEHISgj5P4E8qUE2kfSLMwdpjkl27murNOQN37Noa6Ohyh7dw5uYoBSz0T6cY2B3KnnMpg0Tx8/T049BKGd36HpXA+aaMeSJBUQJV0iLKGvV0JDnaNsF0vMckYxOpoRrPgTMSCKoS8svcRD0CWSYtRK5JIK7j+jhDdpw0fiXxisRivv/46ixYtwmp9fxhuKBSip6eHH/zgBx9rB/+bIYg6VM9KkCNEsTIUTaGPCeSaVYiOo62px7joBHQTp5JMK/QEM/6fwVCSpGjFKk0lRopsfRhIYqqzMuC4BKfchizp0NuHaOkr5ZbngqTlAHe63sH76GN4RZGiW29iY9pOdp+TWc5RUpKH4Y5MtnUyrZCwZSElU0THFAonx4iXziV8x/Ok3eUkB3vxvpiRF5122w103fkTVFnGPn8uY8VN2DZt4KhvlLdypuIu11JjTQIK4Ziee4466RuNc0Kxg/pf/4yofzn9Vyxh6V/uiZxE4/PRfN5N1BWUctrSS9BYTUw2jxDo60PyZDMYiKMC/VIWJYIAsSBCXxCxeDbezftwf+urPL9rmFhK4Rx3GE92mizC6McPkHZkoxHGiccMaMeSlNeIzMrqwxo7CLEDqDEbqAp1zgTlig6ryY5Xkuj5w6uU3nAVzjorgnc7ZYKfAosXVatH6vUjmXUcits4rJ3Ckp9+mcTAEN33/g7TnBMpd0lMdKgYRvIJFk2mYEjmYFeQWgdoh4chlUS0uRCnfHAyZ47VwBVzykjJKtnWD4/nUVUZov2gsSDo/+/UDv+Cj0Q+DzzwAM8//zwnnXTS32yz2Wz89Kc/pbe3l2uuueZj6+B/OwRJD5KewFiA/f0xFARmFzvI2vA8yfXPktRqkXKK0BWUMaswi+5gDItRh4qCP2bgxXYDJxmtFOcE6A95OBDQUuEcIJ6yIu/uIsckYdBrCEdTCJaMeqHWbiVrej533tjF2vkxnK80U7Isj6mRYaKVc1GshRT4BIxfvIzgCy9zeGw6fTNdVE5tou32H1N0USZVRbBYSWY5EE1G5PEwWpeDiYVh2u58EBmYfILE0f5SyNER05iJjMgExjPR8XGdEUQRc76L5YPPMNxwFgbJTsSYg6H7EHI4jNR6kPNW78ZxwjySUgHJ6dMRd26lqmk6o9Ekxre2MJ49AWutGUUVSY5Hyf/1PXglN+cUKSAKBLqHqD7yCkL6KKGSlQQ2phj//cuIBhP+9Rup6RlG/uppkGpHxYAsmPE5F+AeHGe0fDo2U4IJ3/sCIxv20f/zX2O45lxcExpRtr2I1LyTgzOuodB1HK/Eanl8UwCzUcPSq69B99OfknvZOfyuN4qqqtSYBHSuIJ7gS6yceR6z6+x4/H1og6egmX78u+NBDQ6BICHYPO8bJ45/xOIJ7IHhN0BjRS05H0H3fzsb9pHI5/HHH+frX//6h26//vrr+fa3v/0Z+fwdjEYSDIbiFGUZcRj/MRNZTY4wHhxgOJJJz/BHkxizy3iy/gaCkpmTghrcxig/+NlW+kfCrF1Tz1lTBzEZWlk7OZddD4wyePpyumSJyc4QCW0JUutu1O6tZAnb+P4lX2SwpZkarZ/4PV9ELSvHmh3kG+fkUfiHnxId6Cc952pskS6WZon8oH8Zz+TM5KaeF4kdbEENhVgwWaB9fx95P/8x6bw8tCtXc9ibwGPVUfebn7BvfTObXGVcbNvJQHUpkfYeaqsczNDsIfWOjp4Nb5EMx7nlwmvpGFeZ4d9G1g2rsZdGQZeNNt+NnH8SWWoSW1cLqa9fjhzwYVk+jW5XI/64Qu1NJeQI2+j45QP0/PCPhAJBcr+xGvOcQl4NHcfCkgRj2iwEBRQp40MpkCP07wyR31SIHLMQeeIX+NZlkj+z5s5kcOJ0fvFIgLKcGaxZmMPg0S4Mu3bi7G3GuljGVKCyZ/LxjNYvp/YKP5oHbyXtmJPREjTaaB1N86d4IyaLEQiTTCp4qtwUfmU16bJKJg2PUGsJUfL0r0iV1aCZVUaBJUiJQ2S/bSJ221SMhowDWRk4hPzynSDpkE68FdFT/tEGXyrjmyM9nqkD/388Ff+RyKetrY3GxsYP3d7Q0EBbW9uHbv9vR1pWeKV1GG8kRanDyKn1+R+6rxKPkxwcpMfsYigsU6yXyDeDgoYCm4GO7Cm0xFsZ9kXJPhJivsXKaCCKUS8x6o2glTOzXHpNmuJiMyaHl1qjmRE1nyNyEbX6jKhX2lVNVW6EWlMKpUeHXo7Rv3EXRxafQll8PfGxjMD5wDu9uK+8DoMlwfwBLcF9Mo6pxYijc8le3Eg0LeA693QGJs4EwCEmsbf5KDOPYlIiBAsr2Hs0yeiEKiZ9N5vxAYHWm+8hUFFC3pQKOLgda0MTxpF28ox63CVuhNdeQbNgLUrPQezeXtKCin5gL151IuMlcexLHehKaikSopTadKj9/SjZdvJW1xDqWoK0YC4jE8tQPS6mG7WE0wLmoW5+/Uc/q89uQK9Vkba+Q8pRgFfJRvP8k1jzHIyKIu5Fc9CuWEqPPp/ew4P0jkRYXqMw0ZVCOnU64WQTVjlAr6WYtwdSQAo8LkpKClBS0DrnBn7XbcIs6qgqs+ADjp9bwiRdiBKtF6lhKoqpjNwbryDvnKmQTqG0HYRFS9FpRwnLNeQYtVh17/1E1cAAyCmQUxkL6K/IZ1fbKNtaRple42F69futonfhaAJRCzongjHng/f5X8RHIp90Os3o6CjFxcUfuH10dJR0Ov2xdOw/EgKIx/R1xb8TG6UqCr2330EoEGTLBTegAvEsFycWDoK5FlGjQxBF9rZmCGbYH6UiT+JPX9ejyAl2t8XY2FtNiS3EtsMCpzUFMKrbIQqCNIPYkVEC29tJjuchL1pDSfzPoANxwiR8z3cQaesmpt2BT9VjvfzzBCqqsRc7cDqbEdIBFhYZaLTVYu0I4PryGtqPJgjsP8KUpm70wlQSqgazHMByyXkMV5TjV2Vy9h3iyxefy/gjPdivvwDvY79HTaYwTplGojszO5oeHMT3+NNEWjsw/ubHOK64lv5XDxL16dCHt3NUtNJgD5NufYue4hkcTNu4tCQCKgiCyPYuiZceHOeihW7KrjuDLRTgTyr0eqHapeGwP06uNodt7+xjw7qjnHV2Aysa6vB9/gv40mmyb/8O9uEW6r+yllBzJzYlwDRHjJ3ZBspcApX6Ucaf24CgKOSdOxvtyEaStlPwmLMZjaTw6GXM8wuQg2Z2+vS0DYYBWDDBhCZtwGQRaOrch1zeiG5SI/qAl4ZH7kWM+Ejt0eGzl3BkMIdaYz7F2W6y/r9xIZZOg/AoSFrEoveMAFlRefCVIwwGohzuCTAWSRKOJVncWIDd/J51Legc4Jn3r4/jjwkfiXwmTpzIG2+8wdSpUz9w+2uvvfZuysVn+FtoRJEVNR4GxxMU2j9YMB4yVk/gtVfB4SRbjjEsGfFYzIj29+57bZmTFXNK2NfmZcWUHCLNWzDbMlZn6UAHg3s9bJt5HCEpRjId5i9n06XiDJpziPeOk3Rl03NonKI6K6IyTlLj5DG/QH1PM8qzP8Tzxas4PGMFI3GZnohEcUE+nd06eoatjFqysHkuYGbzBnZsDDOmd9GwbxvldUUoWkgPhJCDIeTdezHOmQmKghIKUHLVCjS5blxnLEe1ORj3xgiGTNhrZqCdMYPIXT9FNOiJmewk2kP03/84AMnzLuUlXR7lz/8cxvyUzwyxqelCYjE9JmMKNSXxw1+3EIqkmJBrwLL1TWxnXIgfHY7EOLruXrAW45U1XHXtbA7u6WfC5BzUjp2oyYzuc8wbwGq00nX3gwgaDdlnL2NqvcpP3QmCA0GkrihGXxtKOER6aApS+SJc/W9welUu4aRIafdDiOkIQuVM6g4Mssngpq7QwDTjJszaFMO/OcL4q6+RcHso+PH9+L7zJZRgAPdt92A59RR6+6LU5GYh600MRRLkmHTvC+AVzA6kmX8lQncMkigwpdLFSzuiTK32cN8LzQCIgsjJsz95edV/Fh+JfC655BJuvPFGJk6c+K5mz1/wwgsv8L3vfY8f/ehHH2sH/9PgMutxmf9+hrFkMlH8zW8S3PAW88QQTCzHIQ6jBNtQu/pQOndgaDyZGy+Yis8Xpe87d7Nz+VymT3KiE5IE+7xIsSAFP7qLCqsB7Renk9JMoNdrYE88H6+skn/x54m09/GLp7rY11TIRQstjHTpyVd6SW3cBECquwdTLAyCEYteYDBg5KqvDhMM9bL6bA1CjRPPn3fQmFfCek0RT4knsuJQghzNCAm9iezTV2CsKsdnyEaunIjtwvkYbX0Q7SQd8LP/R0+T1ViLobKUqMFKyYmrmDC9FtFqRDBbGfrNJjTZHuTxCKOuEsZ9KoItC3XMj0mvY/73b2Xw4GKqbzibcW8vjTVZOD0Wls5zMPatFvKUX1NQ7MHQuhft9NmUzi3HpKqka2wUT86jbtP9xIqaSF/1OWRUtI4szAefpepL5xLNzsFs6Eft6OdN3yze7nZzmnOEUqMFxnzIRw+hhpOEFl1C4HA71bYQmIpQPeWIWpizQEP59hFcDUZ0SR8oRkRj5rmLZgvDBw4iejNR5MnWQxgKyqhiGL/PTK/bAQkZXTqF0275wDES278H7/0/Rl9Vg+uqL3DhkiqWTSkknpJ5eUcvybSCw/Lpnnb/SORzxRVXsHHjRk466SRqa2upqckIWLe0tHDkyBHOPPNMrrjiik+ko/9tcK5ciXPlSgBG3t7K0Z3rKFpei2bHHwEVtfVN7n4qxuYt3VzZso0jTQt52V+NQStycu4ADPYTeDWjXmefV8WEZV7e3D+FSsNR9CYzY+4cyvKykKRB2rrjKNvbyMsrRRC9iCcuIDzgxeEyYuk7grl8EqrXT1IJEYlkPqu14QgLOzvRJJOIB3dy0hwR6cyzSOkkEm1vY3L4USPjjL3xNt1nXEzu2lV4XL3Q5Udt/hMeTzVT7r6OuM9L7fXzkW0TiPvTCNMzcTfpjhG8L76G+6rL2O9p5M3WOCangfCKC8na9xYj/Umi7d0EX9+AYWYE42gb31hxOomGBhBS2H5wE33Xf49SSxzFbESqm0Bx7176rv0aos2OfdWJ9PYOYJtdyNgzL6CrrCLXLYOqkogGeGFwOmdLR9AnQ+wbA413EPGl3zIwHqTwzDPQ2QYhmUBBZuNwNtXJrQhTT0XMiqLKBhgZxu4R6N0HY7lNTKx24rwiiKaggLjZw7ebrVwybw1OIUZ2QxM4KlDevB+x0IY+p5qEDOGd27DPmoVk/tvyx+EN64jt2kFs1w6sxy/H2DSVkpwMUd11yQyiiTQTirM+6WH6L+EjBxn+9re/5eSTT+bxxx/nyJEjmWnCmhpuu+02zjzzzE+ij//ViPYP89bqa0kFx4nedCb1Jx+H2r6NVOE0hp46ygV14xinncWk0S6Olk9g4qQiptRNZc+Lb6MrKUI1mWjRV1AYFJg81EX3LbejsVuZ/Mg92NMjfN+4B6PZgqFtGOVQC/lVkxhq3UVWTg6p5csYLSwDScQ1Ekd6aQvfXltPj1embMMjpA7uIXjhlTgmV5I7tYpBaxYARnc5iUNBfK9tBmDK4gOs6ysiFClmyfg+ACRvB8nVZ1Oen0TQGlDiEZSxINK63SiiQEg2o832INRW01Qk0j+WorrUQIE6hGZaKUmxnNzyKvJyTQi+NwEQAv2AAoDsdtNx1hcodB5l8K5fozkwhG35CkzVVYzv2k1gw1Z6X9tOTUJDoqeP7JPmIhw3jz2Wcp7dnuDkWTFMg4dRfb1MnLSEYPsoSkszSSAgG7FOmEnMlcPLe2RcJUUcsF+OJipRY+1DKyVQdVnE+46iMU9E9/Tb9BtEzFaF+I4d2K+/AE2Xjlv7J3LevBwumjAFQRSJ5U3AWW3GbdhEQqggsOMNlPq6DyQf0/RZBF94BkPdJLQl7w86LMv9ACnMTyE+EvnIsszdd9/N888/TzKZ5MQTT+Rb3/oWRuOH+y8+w78GQRQQ9RnzWVUNiLPWIky7AK3ezHdW/wJdPEi88whvP3iI3ITMzNY/czSUJF5Xx+ZLv0rvcJQzc+w0//T3SJpj8h3BcayxIUJhkSM/+wPVJ8+Eo5kqoCZRh+3CC+mbt4RenY2KZIKc559G8HtxFmqYu/dHTJlzPoeaMyQSiyVp2evHvOdJrNdeSVKnx+KxQlkR9rlTSY0GsE0tY/vBMNFEmhknLsSkyWLAWs3emBmhQ6Gy6znEWBx51ET0uUcz17rqPH53+q1cUFvNDPVZ6o73kTYWIBjNxHRFHErOJNk0h1z/fgbHV5Ob6kFn9xA52sHBeC5b9waQkikqD3WhDA+jsdkJPvcsic5Ocs46m/YXtyFotRiK8sm/9EzKvjQfQQzR5CqFZAuTdM2gTSLWzGCF5jAtx9ViHF2AEg6j1tfTVtBASqNhSoOMmgjyhxYFFYWT0oWUZSsIIS/abC/Jl9YTeOp5ggY9U5+5k+wzG5AH43zdtpcOSc+kxhqEY6kTg5UzyTJtAxUM4hCu809Fm/PB1X7Ns+dR9syrCHo9ou7T/Xn1YfhI5HP77bfzrW99iyVLlmA0GvnpT3/K6OjoZykVnyCMedkseuVXhJo7yF02D1EyggRqOoS1SUDAwljdVAqdC8iryGdc1LB3NAgIXHRmKWK4gwpXGyPpekRnEbaLTsVgFChwteJdN0rN+cuIDvmx5uQix2MkfQH8RbW0iRZIK1gOH2b8l78EIHnFFfRXnEGNKGD6zu14e4fZTCELB3eixr10zT8J16JpGL60AvHA76i78XiSeeW09RmJJdNM8GgZe203Q4MBji5145TGsBzZxegr60n19WI7/7JMDpOiYNUoVJU4uf3Bg1x6/FSWlvWysSObpfVdxAQrSTXjiI1qzcS7W7DlW7CGetg/4uY32zrpGYpwyckVlE2sxzs+D3XBCoaO9GJOv4oaDVF98QpMTujTF+Mz51ApRACVREpmgj2BoX83AKqliNCjD1Fx4Vp0tUEQJPyWOBERdFoNZkOCBGOoZPw54aDMyx0qksbFQk8txvJM1LltehNSziRUbxupZ+7CKghMzK4l0nOQQ4KdAqcFo8FGTC3FIA6DZEdXUPk34yGRkjnYFSDHYaDQnXmZqIkYgv7fzwD4SOTz6KOPcv/993PllVcC8MYbb7xbj/2vK0J8hr9FIiVzsM1LrttMQbYFNT6OcnQrWFxIxVM+uJESBiSckyfinPz/zSLKMQQy0hJjcpp3sqq4fHEDsUQQowQxGXRmK/mGcVBVVF02yapK0mIW+eYjSEI+GmsEgxxiTNDTly4keN3VjCRlKnRxavRJkqJEdq6Fkfx8ZFHiwXgd+7vGmZxwkBv2o+RM4NLFBYi/2kBsLE5qLMjQs+soW1WPxWBFbd3Ki0fc2F59mRsvPhmXI4k7nCTi1MOm1xGjQVK9PSh9mZIs8fFxTKech6ikeFE7kde2DRCNp3lhV5SlmkFwTOeo10ShJ0aTPkhM0eEKjTE8aS6xEjveoVFuv7+P0nwbpy+ppGJqKcZEBznXHsevB+vxmycye+Y8SkJtjAdlNNkRXm2z8fKOPnpieUwsM/DQ7/tZXOnhpIJpiJFhEgNBpPppREQduvxaVFGHEpGIqQJyUsZtTFBg7eacSaUoaSPPbwmw8VCAefXZLDQGyFtsIXfZPYh9W6D7eVIBO8jH/Gb5uWyw1rK3LYBBO0a1x8zhYRPTixpYVpsPShroArQgZTSfX9rew6Pr2nFadNx1yQyy9j5PetMf0Sw+D+2ckz/+gfsJ4iORT09PDyeccMK7y0uWLEEQBAYGBigsLPzYO/efhGffaOfnT+0nz23i3lsW4+xeh7LzSRBEhJO/i+h+/3c78hCkdgF60M0CMeNMTMkK0ViKcNiI276Ezq5u2pO11EzPYtdgkLF4ilJdjBc29JNySsyd0ohqzkIU9hORs3Dow2iFMQBEI0iFpQz/7vdY6qs5ohjZNxxiIzrutKzDEx9i3DMPjc2KdeZ0YqmMPyUVi7Ni8330r7gIr2EC7tNPR7NhI5YhP5rGBnSVZUSrT0BVRSZ0xIm8+Cr+vApqlDfQa4fQlwiItnpCm/sZUyVSl9yEYaiHnqbjmWbo59ArRzBpBlgwrYmDbV5OaRDYGa7hnbYBcgzdGBeMUJncRVIpZdcVd5C1agXmq04lpbMwe6KLoD+FRa9BFOBopBSXLUUwkbmtMXMWCV0hpjll6A+8yuWe3USrKtiyO8SGjX6OHB1jllvDaFcfkTGZ3Bl5KMX5XLfBwXeuvIRiVwIhaee5P3VwuNPP1adWMc3lIXuom6RoZduRDLHE4ik8Ti9iegTkAKrBAr4DJPtz0M45GcluRXIl0Gs0gIJJK5JKy6Rklf5Q+lhRxU6gGxBBFkDKJxTNpJ8EoynkZJz05mchFkbe+gKa2Sf9W2lrfeQgQ4PB8L51Wq323WoVn+HDMRrImN8j/hjRWAqndOw7XZRA+gD9FWWcTF3PeKYSARZGo0m2D4UQ0govPdvMSfNLKSqYg0FW8CVk/EkZl1FL10ial9/up6bcQaNlgOpiJ4dzcoj7Igg6K0m9Bw0pzFNcaHoh79ZrSc+dxYQsPfv6oditI6XksfH2lyk6yYJm5YmEN73DhZPc9M8rIe/tl5GHhymK9qPqVZTSCrqaBNy1DcSGfHTZaiiUBAxSEkexAf0tlzOIgF9fTN74YZJSMbqCLPLWVJJcrxCcXEtQraGq2IleFMl5fTvCbx9lwn2/YPHxsxg80If+4Ba0OhOHbcXUo0cTH4OIQsMNSwmH9YyI+dgYplAN8efX+2ne1sPX9m9Bt3YVvQYLS6oSeDv6yY/KMGsS+u79iAf+jBn4fCmExiT6lRyGPSoLjHsQyyaSpY6h9+7CFDvIzSffRHVuL6IQxai10zM4zkggzp4jQ2QdfJ3m7z1BwzfP5vPHL2B3n5HpZQawFkFgBOwVKLKEOpTE0FSH5DYjFsxF0Fk5LpmmOC9AttmLoqqUu7MpcZmPkchfflcKkCI9MsjqcgmrsZIij5ncbAep489D3vYS0rzT/62IBz4i+aiqykUXXfS+OuXxeJyrrroK81955J955pmPr4f/ILq6uvjOd77D+vXrGRoaIj8/n/PPP59bb70V3d9xyC1cuJC33nrrfeuuvPJKfvGLX3ys/TtlcQVWs5ayQjsl+TZUz2KwuBBMTkTHB1iNUj6oyUzVAdHFWDxFWyBKXFZAgPIyB95Qgl7NOOVuCwKQa9RglmLEGOOaJdkEywrYgIDOP4jY00l/bQOxsIgg1FKuO4De4SN7ZROD8XwKXUaKaKXemcBpN6FJmDHedAqBLW0kPXnEs1yoT/yGJd+8gmS1k4g4l8SBPegf/Dl7qxdQ9uRPkdqasV5+Ldu64lQVS+ikAQrtIoPtQ1Rt/iMDp52B7DqHsd++TMdDj1J8ykzKz1lMZeI5cNUiuEsBldwrluN78S00+bkoBg3Fs8rQjx3mlIW19B4eQNP2JKopF2X3RqR4BLu7mH6Hi6iaRTyWSRuJxNJoykuJmq1E0ioY9ARUE7/6Uws/q85Dl5WLzp4DsRA6TyGOyADG1ADR/nF+n7OaKiXJ/Lxh8O5C1VvIy3MikHmBCCisnlvIM2/1MaPGgNNYgajX0fOn3cxbNZUZDRo2hl34DLl4yo9DVdKom15B7WtF0IqIFgn8EuQuxazTMDHPA2RSInLsoCgqL2/roWtYYcWEPMoKNaT6o/hvuww1EWfVrT/EUJOxlLWzVqOdtfofGoNqKklk6xYkhwNj/YenSf1v4SORz9q1a/9m3fnnn/+xdeZfQUtLC4qi8Mtf/pLKykoOHjzI5ZdfTiQS4e677/67bS+//HK+/e1vv7tsMpk+9v4V5Vq56OSM30YJ9KL6ehDzJyKYsj64gWgG3Xt+nsFwjISsYNdJaBQVty1OkTDIgUQJhwZlJrjNGAZHaKwcR8mOsfFIhOZmPxabDl+tg4L0biqGsugIuxky29A//xpqOkrg1c3MeuwLDKVno46M4lH0WAy7EJQY+gYHw3e+irhoAdsmfY66U0/Hu+5lnGctwz45gL+5i5ayyaihBFL7YQCkwV7eOuhjaX0WRR4AlfFdexh94jmcvX0E4hGc555O7owJyL44iZ4uqFAhMYYq6xCkBKpqpPSnd5Auy0ZNCVi1oD9xBm5HFwUGA/J2K2JsFLFqCsqBt0nUzCWtqNj8Xk6cpMOuOCgS/ZTNLOFwhxd7iQsROHB4lFA4xWuPbmH5qZPxnfBNkNNYFQVTwSiW5o3sq1/IC28PI4kCjcuSGG3TwZjFWEqLKVmIRRMgmrbiqXBxVlpg/Z/bufGyUk7Y8i006hBJr59Yp5d5hs2MSafgjXkhFCFLYwAExOx8YJiMVZt5oYeSMioqdp0GQRDoHApx3/PHopRHFS427kGtmIMaztRECz37e1RBi7Fh8kcag8EXnmPkjm8haLUUPfx7DDUTPlL7jxsfiXwefvjhT6of/zJWrFjBihUr3l0uLy+ntbWVn//85/8j+ZhMJnJzcz/pLgKgxseRX/0+hL1QtxxpzkX/UDuPSUebP4bdIDHNIZB8616E0T6qJp1AqOE0Nu0dpMhlolhbh+wf5fBIMxsOZArkza8ox5otMXDTF8k3mwl9+ZsEQxrUgSD246YiSX5SO7ayOZ1DoW6cCrcNjRJDjsgA+HRmJuXABJcFnWMB/p2HcZw6n7HcKroOjzGAntIrbsTS14G/fi43JDvRDVcxsneIVCDB6KtbEfU69Pk5aDv2IrQdRjz0DqqiECrOwj5pOnprCsYCRIYE2q76BrbjFpI12kNBcRnjZXXYlT5AxeGJoU5ZCYOtKBTSLNexaSgfzVgn+TY99XOPY+lkP9ajWwknA3iKJyBLAmpaZlmdleI8M1NL9HhyBxkS3ChYSIsyieaN2NPj5BszzqFSlwax9yijA1Fi/jasHeOMnHASD21NMalJR3mWn0MDAXyKgW1bUsypjuEN1TDgHWHEZ2fS9Aa686sRqKLR7kWTtkNyOkpIjxxyIWbNRACCyTTtwTgAFTYDDoMWp9VAeZ6Vfm+EmdmQPtyJYelajKvOItnZQaylHXHr5o9MPko0k2umplKo8dhHavtJ4NNZ0OdjQjAY/IfKNT/++OP89re/JTc3l9WrV/P1r3/971o/iUSCRCLx7nIoFPrHO6UqkMq0VdPJf7iZ26RjZYULQRAQx/0kQxlhL30qSjqaJpFIk+0yMvjLhwivOgFLXSXiwQPkuk1kG4NEt4+ihEIQCpG/dxuHf/Y4giQx9+mvEx1Nsz5Sye6xJHlGC+en6tGbYsiDPgxrziPiyGFZwTB60YcyQaL11ldQU2lCUxczRQ0wN19PlcFL0mAg3N5Mf/kk1j05QN0vvouaTlP2ubMpWTMJ2Z9g4MsvIHQXgpJxXismGwOv9eFq0KPN0dP57UdRqicSmzUH+5I6lGic3dv6mVqYJFcEVZeFMvgOI7uDaHIK+W2bFlUbJpFIMXF5Nffe+TY3NLRhGNiAQdSQOuNO/BoPtscfRHr0EVZefS2KsQzthCKi0XF2ezPi9ScbZNR9uznJcJSJa87A49YjdFXTd8/3AJCjMazZuSzLc5O3+3V8VfU89nqGNFySmfJuLx7DOlzpGHuLlxDKrqDIGCIqG4jKWZilFPJYmvjPf5rJYF8YQM6dgDLlPQJRjllDDque71w0Df+vfs3oHc/SM20ylVorltPWMnznbaiCgHnm3H98zB2DbfVpCHoDGqcbw0ckrk8C/7Hk097ezr333vs/Wj3nnnsuJSUl5Ofns3//fr7yla/Q2tr6d/1Wd9xxB7fddts/1S/BaEda9iVU71HED5ti/xBoJZFoWiYoWTBd8W18+/YSKJ7KBfOrCe57FWvP84wuWMC2qBbRoeX6S6eyucXLr3bCydXHoVkVRTQYEVQNk+64jMLTGkB08uqcLzF85z0EIkkCkSRH/tCGZDbQndKRWz+PPWMyc1Ma9HpIxyAZCKJNxJFiQcKVZVR6N2IK78ekg/Ipy4m8up1JA8NUf/k84sEEeUtKMZq6UArnYL/gYuSxICVf+wqC3ohrYQ5SugPvQQfhAzGsp53FrprpjCoSU3w6qt0qyycOIeQ4ScVFBr0SPc8mccfTBO77ERfWVtO95koO9qdoPjDMli09XFAhkg+oRgsOswmLEmXg2T+CLJN4/jkKv/FNUuZakokoBZYUpGWQIpCVgzY4Sl1BgviRdjST6zHXVRM53IYmy45ZH0d74G2MpQK58V3MqpnBjrYoU7L8OPPMiIOZl4ldJ5NfHMEi7iMhWrj9jxaWTWtkptQB6YwTObxrF21P3M3kV5+hrLgEVVFx6N+beMiy6Dn65kbi3b3E+wYovPwirJPqyL/rJyDLCJq//9NNpmVe2NrN0FiME6cXU5JjRWPPwrHmbxNT/6/wqSefr371q9x1111/d5/Dhw9TW1v77nJ/fz8rVqxgzZo1XH755X+37V/nok2aNIm8vDyOP/54Ojo6qKio+MA2N998MzfeeOO7y6FQiKKion/kcgAQc6oh55+r8tEeiOFPpLE4yshLHqRxcgWSHMd24GlIxXBojFTX1NATUukcDNPjz8QCeSvLGTl+LSdWG2m76HKmv3wrWnMUGKHgklMIGrUM2/SUOkyM7znCC6NWukaj1PnHKJ+fxUstWSyucmPYvYuqJROw9m/g8ORFNIdFHCEjbiChd4HDwfCv7qD0vOOxzGug5+4XiI+l0IsRXPMP4FzTwDvtbkweN01VTqT+X4OgxdKYz8DabxDv7KXkuusZnbOSaDIjJRt1FWCVYmjsXkrs0BGXSQ70kxgcRjKbqI53488y0R2wU1/j5PFDMNZwFjuPyly2dz3Wlj9jXXshic1bMS86Htvs2ci9h8lTNdhEHXaPA51aiqoVwZqHUO4kddTHtnaJKfd9geTBUYyRo5gCrxCoW01qqBWzJcDXFvoJuzrJ6tmOoplBS+l5aFIhlJIZGLR+kEEvRTEbrbzTGqL+hBmYzruQ2OEhBh5el5ECScs4A4dQm5+E3MmotachHJv99KxcQryzG9eKJZhrqwAyM1r/A/EAdAyGeGx9pkaY1aDlwpxPX8rFp558vvjFL3LRRRf93X3Ky98TVRoYGGDRokXMmTOHBx544COfb+bMjBhWe3v7h5KPXq9/34zfx4XQuteJNR/CvnIVhsqqD9xHPfa/IssMP/4E+twcXKtWItYtRTn4Z8JDkC238rvdCucvMHDp0n6S+gIsJW50hlJiuzfjbKwi2j6GvVGHmjIjrDmdPHTM1kl0tvrIt2qwJXUwGsOiUVhRpOdgVMOfj6SZmN3IJMMLJGvn409o0OkVOnW1WGQBndOJcdiLfUI59V9dSWBfD0Ovbcc4v4Lg/oN4X97AjA0/JH+Sm+6QldaAygTbTAQlhNztI97dD4BxeJC6LC35dhFZjYBGRElpEDWgygJqfR0mj5GsIgdS5x6M3e9w4SIHwdzViMunsf3FrdjTXhZWGDGOdLHRuoLb3zaQk3Mmn5syGe9wCPdYjO5bfoIx20H28gI6mtbwSrORsiwB/eU/pmDNYm57uxetRuKWtQUMJ8zU5RRgbemmOXcWHmcuRaUG3JIR1ZzC66jlqcEsplRkow2nmagtQxEMtPSL9HoDrJzvYighUDN9GdQEKDCVoy8oxFJXi7LvYUiMQfebULIQLDmo8Qge9QCuixvRNtYgaj9aOZxsu5HSHAs9IxEq823/c4P/A3zqycfj8eDxfIgy2/+H/v5+Fi1axNSpU3n44Yf/qajrvXv3ApCX98E5NZ8UEn199N50A6TTyAE/+d/49gfuV5llwNs/zPivHiAeDKGNjDDgH2OgcCUe/SR2zL8IRfs8k++5l9mOvWi8BzGKOoSsFKlADsmD28k39SEebCZdMJfRg6NEJjYyHk+hr3azwCrSVFGF0Bmk4qiPBmeKvrEkI8cCDIdSMsaLvk9Iq2eCKOIRQHnoXiK/fwzZYkF/ycUUza1Ga7HhnJpP1uRqdHm5RPcfxDq5gfiIjNsyxD7ViC+eJi25yH79LaSkQvbSBaSiMXZryyh4bQPGUxtRjE5kVSI+lCTWE6bvD29iG/BimleJLgti3hEib6zDOutCnGYFobSc4/wHELf8ASQt8UkXMNyiB4JYrXqMuVYiikraU8lYSx8Dr27D4l7DC94uXtjppTxPx/e/fz56jYVr8qwc7IqwfthIc28Si6GIm/ydVO69D1JJ1G98laHSaejL52IIjfGNReNotQGSaUAYZNDn4ns/bee66+aQK/qxjQ4RNpZiyZpA7pnvzTQJuVNQ/e2QOwWMGR+lmkygjvQgpBKo/sGPPJ5cNgPfvmAa0USaXMenM/XiU08+/yj6+/tZuHAhJSUl3H333YyOjr677S8zWf39/Rx//PE8+uijzJgxg46ODp544glOOOEEXC4X+/fv54YbbuC4446joaHhf7X/ksWMvqycRNsRNHnvyaumFYV9o2GCiTTldiOldiPm0gJia88iYfIiHnid3vmnEldUUu4iHLMacUyo4NSTqhG7DmYOojUyEHKS3fYsRvko8nHLiIZSJGJ24rF+/hKaZhYhdNsd7LCZGTE4mHrdKaRHw9ibW6msqUIry2RbDSgaDbmdrQiHDxCfNhe72UgEkApKiPR6UcbDtN7yCNVr5zHjnBrSkgND4QXo58xg3xXfQyNC9X13c9hahJqUab3zcSovPpXk1g2oWS5GilezbEoQc98zbDo8l6S1gmXTqzAXlaLTu5G0oFbWEt69E82hZgxTpyBVzQJnpoKnLqeQlMkJ2hy0bz5P/YwLOX2pBzGlYEjJyBoRdzxEfyxG8WVrEK0mip2ZyOQvL4xgH+9CJMqKBZcRNIcJxTPbChwGXKUTUPc+j6agAE0qhW34KL2505GtZlzSAdSUiHS0D9nqIiEn6ekLsuvFzVzKUwjJKMPT1xJpWk7OXxGCkNMI2ZMQhPdelqLNCWfejO/QXjqzJlE/nsDxdypTfBBsJi0206e3gOB/DPm8/vrrtLe3097e/jepHuqxCpCpVIrW1lai0YwfRKfT8cYbb/DjH/+YSCRCUVERp59+Ol/72tc++Q6rSVACINhANKLJclD8s1+Q7O/DVP8e8XljKbpCmVmVgXCC0mMKiIbCfBSrEWXwCPnJAL06B4UWPQUv/BxJp0OOJ9i214yrfAHNI2ZiI17OnlyDPNxOurAISyqAePD32HImkfZ2YNW6CPzkYSK+MSKBIHN/uJy88v1QDh3brQwdfzIU56M8+xveePYw+w4Oc/pxVZS3dyAsXo0lGGG8d5T+B36H1pmFsbKC/ldaEMb9RBMDeG/6GkfiAhW334Vy5eVYdu2iKrofw6r5hB/6Df5kCG33ALHcUk5sMmM39bJxoJ7vPhcAdmLTa5nblE/WnJnv3hvbggbU6Z9DEFMcaNNi/dND6HQS+rnHYWtaQfJ3mUDRnPa3yZlwNsdPK8AR8xHWmAmYXMxseYWBKIRVlTljg0wqUikeOsLYUy+gKa/EVxhlZU0ER2o/s40VJH9+PyNqjPzjVpI1qwpp1+/RmByQM4WUKuKNluDYv570K78DnR7zmi/zzC1WdGoSYacRklH8Q8O8+WY7N5w2CUVVGYokSSsquWYdOun9Q2RrLJ/vbR4CRrnePMiJc0o/yRH5v47/GPK56KKL/kffUGlp6btEBFBUVPQ30c3/a0gdBqUXBCfoZoIgocvLR5f3flF5u07CY9Tij6dwHSuDq4ZaYfBljKfOQUmdjyU3lwKdHkl8L7xeMugpzM3mqT1JIhqJtQtCYBBIVy5AFkWkjg2ZaX+dkbJQJ/GyVRzOMqHcdzu5WzeiCQ3xlyqpxmwbCAJZF6zBv34bf3whE1qwo8zJrqI8etYPsSRnCo2H/pg5uSDiXrGQ8W278L2+GcfKpfSlJUDBm53PzFu+wMF7nsZy0VmMYKPbJDFsczG3pAJTaJy8mY2koh4SwYw+j0YSsLXtZrR3Jxit+F54BdeK43HPKwBZC2ISV0sz47/+KQkgVVaBOG0h+kl7UY42s09bSaEJtO37kd/5CcOn3ouc8KOQxmnJpiMQo0VxMMUeI731VQDSR9sJ+AYp8QRQjJW4hzoY3LObtptu45Bdy2p9GrOcQgwO4hnYTy95pJP9qMFjYRepFJrxURy+VwAIH3cW3Yd6+cNoEZ58gURaYSiaYDya4khLH7OPbsCQjGA55Ty0uZkxkOc2Y9RLpGWVAs/favr8u+M/hnz+7aAeC/JSo2Ryd6QP3M2o1TAn3048pWDRH3tckU5QEqSSfjqyFiOPp6jM0mIU33+M4uULuLaiDdRhVJ8X75s9RCctZ1xJMSG3HrztCKWzEKsXY9HqKbuxBL/eiKYsm9BTT5CYciJ6vQjldvrv+hHpkhxcd9/B4oZT2D0sUFefw/ouPwBjVidKKknZV76A47ip2HL2EG2ah90lYtTHMWhDtI8r6F7fRndnH4aLz+HIwoUIoTiFWSbEgT6iu3ZT/qPvEHWZMbhqqfjd61yX76ByQg7+G7+MX1GwXHoJ8XVvYZtYQPzAEQyOMLFYHcldf1WiOxEjpjGQOOcGxrwpCnxBhq7+Iutb2pjx9ZOwJkZx7H4UNaUlPnkVo7FMxrhX46Ao3wlzFuF1VVCe7cdFB8TbaZ0wD8+3bqWoMEpZYB2x8YmEak9FkhN8+9EBDnR387MvVpI9sQk0KmgNqK5s8IsgiPQLHmzHzWLRYIj6HD1DQ0OM6qwgCrjjQdJPPUgYkFw5aNdcCEBtiYNf3rQQRVEozP70zVb9q/iMfP6voJ0A8jCILhD+/ne5RhSx6P/KeW6rhYSXEdMcDgcysSVGjUSl473AyLF4im29ATRiFhP7B/Fdl4l3yv/eVOITFuNPleCympFs2e+2SSgylrY9eEz9DJx/PqOqHhGV8rF+3KVZ7PWnOevCMzh53UYuvOB0dLOLqanxcORwL8U7NqCkZezTp2JrqIWRZkzF4+ivXIMcMzLS00vw5BtRZZmSi0/DtnA6R8nM3lWKYSwHNpLQxOm/4Dzc3/8+8sxpVH79K5Sm0gyvf4PAsaBEv6KnYHoTvXfdz1COh7ofn43sG2Tkza0UXHg5mtJSlCkzMKKQlCRKCi207ThIeN8hAIZ7FGr8R/Gv6ya6cw+2C800zV2JDx0lbZvRVJSjmZlDclBLSo6ABIo2j8NDEotWL8J5+NnM/dbp0A1uJ1EwmXPWFHGlTaQ63IuypwM1JaMcfgVjKMTO2s8j6YwYLA5KcqyU5FiJvPgkCUcuVE1Bg0ocEdXhQQiPoS17v4ZPvvs/z+L5Cz4jn/8riPbMv38CgrkUzKVY4il042OkVRXr/+cw6BuL0Xksk76wqp7sL9wIgoBt4WIcViuQeZMmxjsIRUawYib0pTtJtLVhuXApLGsAVxGSnEJ99RGm98g0zTuexLRluAtdDLkLGO0aZUIqQE3Ux69HC7CX5HJVEhwaG6p7NchhpLx8nn+5nR/ef5hLr7qGicIgpSctofcr1zP1y1/Bkq2l/MgbRAxG+jvaAUjt3k3W3DmoQE8sxdjkuWT/+Afs2XoUz3ELMb7RwziQGvES6xJRBRN5F52P8+TV+DdsonXyfAylRUx56lek6EeuKkM6+2w03lECs5cjOMuJ7s4oMSY7unAfJ5N/8A3SBRWohlFGcbLOp2VBTy/mgmpa20PMrzeik8eR9SUk7SpiMokY6MEQ6GHWmmsQglGSTz+KrKqIZY0gCCRSCu6cIvxphQQQTspYdBKC3YHuV3dScNFNxN94DVc8guXWH2Fx2dHm/fdI03xGPv+GaO3y4w/GmVKXw+JiB4oCVv37H2W2RY9VJ6EVBbIdFhyX/q2wv6yorO8ROBLIYYnBR3zvXky11agdB7H94acYFpyCzj+EGorQtfJSHt4TZM3oJsoPvMWO068GFAR0jEW07DmayRtaofVwdP8AoWiKRfW5ZIlaRr1R0mmFXx4x8NA9V2IzpmEsQNGeF3BlZyo4mKafjmPNWSQ7jyJ396Ab9qLkF2WSLgUBjl/M0tkz0fV3E62qxTYvgMZuRypuxDZrDn9Joon3Zaal1VQSYftj6PqbqT/vRup/cSIDh9OEghIhRw7OL3+T8O6dRBaewKjWiSOox5Alopt+OmFfEr+ujw5TDaYXHqdsej2dxaUMqAI5RRZydm4h7fNjqS0BnYn4qBnxmd8gukshOIhizSVZl8v+0uOo0kmQVlCAhCwjpcE4dwmSw4334V8j+XzIyQShH30P0ze/+wmNmE8nPiOffzN0DQS5/vtvEUvIXH/eZE5Z/MGBkNkWPWvq8xGETFrGByGlKHSFBEClRZfDwjvvwL9zD+np1WgH2rBLIocffY3xXfvRuuZSb3BQHuxBPnSA6nNTtCW0lFhkyorHKMgxku3UYTBr+OPLRzDa9WgkgUX2NMeLPeReWIs+L4e6ukLC+/ZhWHoyvqiIPb8ULSHSHg8Ji53o3j2QTmOZNwX3aSdQanMRTKTJElS6r70OncWARhIIbdoKgG3WDGyz5rx7TfnnnYGg02BvrIWWRxDK6tE6QoBKfrGG8H07CDXWk56zHGHOcrzxNNnPPcnQD3+UqQ3/9O+pbWrgsjmlaCURh/04ku27UNSMvJeSTqHVhkn0dRNYeB320WYMIR/JwS4UyxQScgHK9n1o8vKxNlrJNuneLRA5Ek0SkVWy2w6hO7gPy7JVhDe8QfyNjFM6vnc3+pKPWAL53xifkc+/GWJxmXgyk20ejqVQ0xEIHwbJApYaBEFAjQVR2jYhmeyIFXM+9FgGjcTSKg/dgSjlLjOtrgWEJ83DkWfFZtQxtu8goa27AMjqaGW1AaTNb+A+92ImDP+ReVojpq7DbJ76Va7/cpombTu/3TjI+mebMeg1rJyay55Lb2D8QAsla06k6Vc/JDU8hG/rDobe2kFofwsW51kULq9ml1xFyWwTxuYDCFo9gXf241pZg9g6iLB5O8rkBiKHmtEuOZ5INI39jLMxWHQ4li973zUZS4ooufFausai6ItM6HxtKGknosZHuNmHZeoUwsqxBE6dhjyznojZRAAQJAlBp8UbjKMDgikZjd+Lqe8gVUdeJequxLrvFdBb0M6Yh6ZzM2rPPtTCSnRn30j0aAeJ9b8FQKmbSr4rB60okmfWE07K9EeOFScUJHw/voeaZ57BfeV1yN4REAQMTR8t1+/fHZ+Rz78ZJpQ7ue1zsxn1R1k8swjG9zM0HiGipCkSh9Cb81COvIWy43cACGY3Ql7thx6v0m2h0m1hMJygdzjz6TQUTuI26hArKsi+8ToSh1uwn3QCHNxFcvYy2n/wCxpvvxCTbydy0Uzi/QEsNRX0mQooKR9Cp5NYMcVMuV3k8HgEgHQ0439KGA3oLzydutXL6Ljm22jduaS9UWZkH0aZNJ228iaG2vtpmFeO3D9C92+2kgyNM/rSq9Q+8DOO/vYFep76M4JWy5JdL6BzuVBVldauADazjvxsC0e8Yd7q8iNQyMS8KpqG4hhzq7DUpchyOBhLpomlFEYDMTa1+7AVTWP6fT9B63bTbcrmmz95m6p8K1PnleEpmEeN1og7rwRjMoy2woGYlYPOXoe8dTuKdgrDOU10RHRMml6BsGMrajLBQGETTa73nMXGVIwCIU4sBckXnsM6dx66/AI0djtFv8xU7BD+y3TQPyOff0McN7Xg3b99/mxeHBaQVZil1dJkBvTHpmU1BlIaI81DIURBoNZlQq/52yn9eFpBIwpUOgwMRZLYtCKDwSgeqxHj5ZcipBXcFj3rYxbynv8m6bEge7/0IJW//yXda6+m4KIUvgmVjMTTVE7O5bGLzSj3fY9kZCqN999OcH8rnsUZCQjZoIV0AsFhpfami0isexZJHeHAiERHSxZPH8yms0vHTXNKWEYWradchmTRUdO9H+eiBYxsy4hsabNsKDodqpzmje39fO/BHThteq5zDGOMBTGfdDIRSUsMEe+WbZSeswbpWICwQ6/FH40wLgkU1Lg5vLOPJScsRaMR6XzrCNGETDCaJlsjkjJk488/mRyXhf5v3k7WBB1aB2grnZhOvYFkWuYnj+ykfSDErFoPN931ayKxBI1Z78+nkjc8QdaW53B4ShCuuQWN04V0TLblv410/oLPyOffHIouF5WM01Y59jjF6uMQ7HmgM9AteDjiHQfAbtC8GyH9F6QTCbo37SGZk42jMIccMcLTu/sJJdIsn5DNrFIXXf1B/AmZ0hIPwtKlyOEwloXHMdoxgByOkNy/H9MFEFXAqlUQ+/cRVFViu3bi+Vwc51UXvHs+vagjKSiIgki8oxdVFQgWLeBbzzgpzw3S2TUGwKZmmbhRz733vYHHY+Zb3zsegNLPXUC8rARdQTaJp37OaPcR+uZ+CQB/KMHAaB/C479j4sRqElMb0W/diC6SCfxTVZVk89v8+ZCKX3BQNa0AQS/hyTIiyTHURJqpjYV8s8SJkpQZScpEkjJ6UQAXZM2bxagvSHTSQtwmPcWqiqpCMpX5DE6mFSSDAdsxnXPF14vasxeyc0CXeTbquA99lpWYqEWTltF9wMvgvwWfkc+/OTwWAyursgkn0xgFGAnGcQ1tRTn0KuLEZdiL8tGKAiJg0/3t4z76i8c59NW7MNVUUPmH+xkWEgRimR9T/1icbQcGufnHm9FpRX78lYU4LjofedVS+u59kC1DekpWn0sqt4hlNiManQZTbBeJJbWoiTj6gjw0nS+gVlQj2HNREknau4fZGRMRgOULF6HEE+iK8slxpGjpG2PtBU00N48yHkly+PAIAKOjETSpNMrQViRrGakF8zB3HkTe8joAc6VO0qvqsIQCaL/6BmKum7KicWz2XsJVRVgbTgRAHTnKxh2DfP+5BNDNhQmZ4xeXM7lGS+rJW2Dcz/hZ95LUaTEYdaQDMVBUgsfIxb10MV5fhFgiTW80RbZFDwJcvaYB0imytO+3YJTdLyMWahFChxCqyhAsVyHmlrFvMMXtj62nNNfKl8+bjNv+6Uz8/KTxGfn8B6Aoy8i2IyN858XD1ORZuEV9GqJ+lF1/wF01n+XlTkRBwPgBb9loZ6ZmVrStE8tAO7lVUaYUTiaaVGnIt3Ho4DCyohJLyDT3B/nDMwdoyDUyZdseSupmMTh/NTVlbrIsmW8aVclFX9ZCzjWLiAxLdObPR6fqyZdlYtv/TFLKA3NBRhrE6aToiotRev7Ed04L0RnIIjswyPChGM1jKuecXE1JsYPCAhtNlu2oPQdJ5J5Mzu5uNAXFaOctI93ZSsiTx3EN5dS4zIwU34nJPoRZ1wK+o1gbruPZt7vZvHeAMxYUkdbbqa1RyMq2gEGiMs+G0tGK7OsDwJQcB2xoBJEqm4FQWqbCmfHdqLEQTklhTBBxG7XoJZGuUBxvUgZECkKtqLaJCNpjvh67GyQfyCCkRtHMvBJB1LL35Rb8oQT+UIKeofBn5PMZ/r0x4I+hqtAxHCE253iMh19AnLgMQdRg/jsuhewF04hOaULSaQiv30LO9HM5OScbQcyQiXu6juB4Ar1Ow1AojgrsG4px/M038eT2CO3b27n89PfKKQmGYvCchTLUxnieg7Cgg5RKVjSA3XWYaK8G2lPMmFVG7jHCSusmYexdR2nzJtR4im9+biJq0ano7e9J4CrtB4nrmxh65CWGf/04usJ8yv/wJNsCacYVaJIzEdDZi+eh+HbCQAvY6/FFJH72+32oKhj1Gr56ZiP7kn28sWeQ/b1jLJ5ZRFXhBJSJiyDsJ7t/K7aGk0EQOBpMYNCIGDQiajxM+pUf4BjtYMrxX0CbOwNBEDBImWl0vZKCt/+MstiIVDgJAKlxJYrvIAJjYKtCEDOR7LPrczjY6afQY6Kq8J8LNP1PwGfk8x+CeROyybHJ1BeGMBsKERuWgt7yofvHk2kC4STjEyayw5v54S7IdiIY3x/e77DqWXus6sbmtlHGkzJ5bhPmUgedL60DoH8k/L42gqRHKqjHlpTxj8fRSSJCdydbNuTy5ReHkZUhHKKA9s2NZDVNILp1Cz0/+xX5551G4SklaHOrwOZ4/zGLluF7+HHkYOZccmgcqwRTCh1EU2lKjlkP/liKnaESCt0XU5plIEtrYtX8Ml7Z3MWMSR7M2dlMrIzzxp5B3HYDNpMOwWBGiPlQva3gPYy5dBohWyGpY0nIMVnBlgyBt5Oe6VcyKhRQ5h8gWxcn11yGoecAmp2voOnYAyveq/Ai/L/27jw+qupu/Pjn3tkyk8xkXwkQwhJ22SMoKBgFtIotbggIlGIX0T5AFelPK9ri8vh6lNanYn1Mte0PxWJV1CIKAetChDxoUBaRLRJCNhgyWWc/zx8TBtOsEwI3y3m/Xvf1ImfuvfO9Q+abc8499xyTBV3KhEaf/aB4I2t6H0BUlGNwREJ4v0b79AQy+XQT8ZFm4i1u8J0MPKdqmAjNLCLn9vhYt+UQuw+fZsn1g4HA7XDzsGFN7n/OyL7RGK1G9KpCv+hwfvPTyzl47AxZE/s0uX+EUceQGAsqcPwPOVR/W4ZONxKf34c1ZzN5r2zEGBNF/2uH46+tpXzLDtJ/s7XJuz+KKRJz34Gc2bGbuB/eRHhqPMaEeM5NXusuOIr9k4+otdioHXYl+90GbJZaUs2x/OKODG6/MZ7wcIHLX8P1l/clLclKjM1MUowJhAd6j4SKkyi9RoAtAatBT69wIx6/IMZkQDHF4LruQfJq4hBO8KheEur+gdLndqL7JuKr64cy+TJ0cYFaYM23R7B/9Cm2caOJHBNYI0tUnUZ4v0P4Bb4DnyFOFaLG90KXIpOP1NWpVvABWEBpvh+hxuUl78hpAA4cO8P1mX0QAvrHtvwQo9WoZ3zS+WbC1eNTuXp8y88i6eoTYFh6GpaX/sKvb0tBn3U9ti2llACKUY/xuumEnT1L8sK5Ld52jr12Kv6TR6n9eCsRV5yf88iz5yMcb/6dsx9+BEDG7/7A2WEjiDEH+rgMej3h4YHanYoOnU5lRP84Ck6Ws/mT/YwdYiJx9DT8AyejmG0o+sAik8nh9f1Yp/OgdAemuCkkK704Ve0iTldNIMsrKGYz+lGJ9dEErvfY489g37odU2oK43LeRR8Rjqjcj5oUGGhomHUz7uz/QU0b2uLn153J5NOd6HqBYgUMgYXYmxEdYeKn12VwoLCCKcMSGRjXfPOsNR6/H72iNLtUb6GjDofLS79ZMxjZNw5jah/M/QZRNyqemDFDcCT2Ys2+OqyzfsXNg/rQdB0qQNHriV+0GN8Pf4Qu9vzUur6Cb1BNgYShGAykpcYzpJcVCHwGBl0YViUZIfwY1EDNxOXy8tuXvmRm1kC+88VQecaHd8ce6j7Lo89t1xM36Xujjc/mg9+N/vTHXD5wFE6fhQi3A6JuRQnvA0IA9SuAKnGB96wf52OIiUGpf7xF+BQCyUlwmFRMi5+j/5DGtR7h8+I/8QWoOtQ+oxvMcNidyOTT3ahtmyz8quFJXDX8whZKLKlxcaTCSYLFwIAoc/AZpnPO1nmC8/2k1W4n/NgHUDkAkboKc1I86Xf9kO/Kqgg/spcal5eUmNZXilX0BvRxCQ3K9BOyMHtcJD3wAIahYzAPazwFrkFtOAWpAExGPal9o3D7ocytw/vGZkrf3EL14eNM+Wf2+Z3jJsLpXIgehckYRmlZNUXfnKJf4TYM/cegm3Azitowpn6rlhNzzVVEDB+KzhxIgmrCaOzfHeTIaR+PvHKCh5Yk09STef6C3fi3/SFwvTNXovTRfo2ti0EmH6ndzji9eIXgVI2b3lZTo1v5elXBoCr4hMBQG2jmUVEEPjcYAsmgb4KVNfPG4fH66RXbMPn4nS6Ksv+Cs7CQlEXzCc9ouNyQT3jw+33oU/tjueOXwXKny0tljZuE7yUzUWtHlO5FsaagxGUQZtLzq7vGUOUTuAwKNfZahDcwnidqzPAG76NED4fo82VvfXqcW+p2oxYdwFd0AHXQJJSYhgsOmBITSLhxZsPzhFkJSxlN8Xcn+OXcZDJHJIHfAaIMlChQ62tz/vOzbYr6eYy6I5l8pHZLtBip8/qCY17+ndWkZ0b/OKo9PnQpP0SJ6Y2SkIES1nBWvoTIsEbHAlR99TUn/vMZAAxRUYQ/uCL4ml/4qHaX4cODWReNWR/oi6p1eljz4m4+yz/FAwvHcf2UQLNGHNsGx7YidCaYuhrFEkd6ahQAjhoXFX5IevG31Kz6KbZhDe/4/btYWxhHvf2I038BgyahWFtfFfccS5iBWVPP13f8zqP4nJUYrMXgjwDVjJqeCaoCih61m9Z6QCYf6QLEmQ3EmQ2UVjv5uqyK3tYwYizGBvvEWIz1c+2kQVxaSOcP652KZehg6r49QsTIhrURIfz4CKz+6ROeYPmZCieffnkKgL3flgeTD/VjbNAZQWlYQ4sMNxFZ37lsim14ix+goLiKOreXIX0Dr90yuR8HC6OpmTqN6JgoFF37vkYeeznlr+VQvOljUpcvIuxqsJoEqk6ProXZCLoLRXx/RnWpXSorK4mMjMThcGCzdc4F2prj9vgwGtr/fJHL62Pz4dM43F56WU1cmx7XgdGB+2wFvqoqzH0arwjr8lXj87sx6sLR1/fp+P2Ct7Yf4evDp7llWjpDTcdA1UNCBsrpbyAiCSWqpW7tho6cdLDsuU9xun08vGAsU0b1av2gNhBC4Nn6HMrxndSFDeTEUQvOpUvpFW4M3mXrikL5LsiaTw+2+ZPjvPTmPm6e1p/5PxiCoij46uqCHaRt4RcCT32/hNff9r9jwu9FUVv/9TNGR+EKt/LBZwUYDTqmjEsNrtJh0kU0mndfVRVmZw1kdtZA/AU7ETsDy+cw6haUjBkoOiOhsFc6qXMF+oLKK5whHdsinwel7BsAwgxVmK69ESfgCeEz7Oq65z08qU3e/ddx7JUu1m8+RFWtm9L1r5E/ZRpF6/7U5nOYDXrGp0SRERvOZW1cYcF38EN8/1iO7+CHbdp/R14hj7+Ux+p1n7P7q+LAkj/CAcLV8oHfb16dPQqnD7Tp/b5v1KA47rtlBD++fjDTxnbc/MqK3og6eQnq0GvRT15E3PjRpFiMJJg77yJ/Ha1bJZ+0tDSU+jEn57Ynn3yyxWOcTif33HMPsbGxREREMHv2bEpLSy9RxNq6eWo6SbEWFtw4BKvFSOn/fxWv/SzFL2bjralp83n6RZuZmBpFiq3pjuPvE34fYt97UGtH7HuX6mMnWj3m+81CvUEFCoEvgK8Ciy82Q+k9DmXcPEgbA7WnIKxxf06r763XcdOV/bjzukEhrxjaGl3f0egmL0btfRnRYQZSIkyE9aApNrpVn09aWhqLFy9myZIlwTKr1Up4ePMjd3/+85/zz3/+k1deeYXIyEiWLl2Kqqp89tlnbX5frft8ap0eCour6J1sxRJmwJ6zA3dRETE3zMQYG9visX6/QK1vxpS/8SZF6/5E4rw7SV60oMF+FfsPIzweokcFRuR6fX6+OFCKJczA8IGh9fPUfbYRw6lPKCuPx342juH/7+ct7u/1+dn1dQlGvcq4YYkoHAICE8W73RP4cPtJ/H7BdVkDCDM1bsoJRyHojCgRiY1ekzpWSN8F0Y307dtXPPvss23ev6KiQhgMBrFx48Zg2cGDBwUgcnNz23weh8MhAOFwOEIJt8M89XKeuOrHG8VTL+eJqgMHxecDhojP0zNE4R/+O+Rz+f1+8a99xeLRDV+KT/aXCCGEKM/NFxvCRohXdUNE0eaPhBBCfPBZgZiy8O9i6uKNYt/h8pDeo/JwgcjJWiA2mC8ThZu2NXitxukWLo+vlSArhfAfFsJfIrbmHBZjr1gnxl6xTmzZ+m1IcUgdL5TvQrdqdgE8+eSTxMbGMnr0aJ5++mm8Xm+z++7ZswePx0NWVlawbPDgwfTp04fc3Nxmj3O5XFRWVjbYtOL1+ck/VA5A/qFyRFgYav1MeroWanzN8fkFGz49zsGTDl775Bg+v6C2qASf04Xw+3GWBUYs19TV3+b2CWqdzX/GTbEO6MvEvz3N9V9tIvWma4Ll+0+cZVn2bp76x1fYq1voz1GsoAwAJRHz9/pIzD2ov6Q76FZ3u+677z7GjBlDTEwMO3fuZNWqVRQXF/PMM880uX9JSQlGo5GoqKgG5YmJiZSUlDT7Pk888QSPPvpoR4bebnqdyrK5o9m5t5hJlyVj7ZfEkFf/iruklMgrQx8r4i4p44p4He/XKlwzMhmdqpA8YzKjn12FWnWW03/7C2F6L9f+aBaqqhBuNjBmaOjNGXNSfKOyA4UVVNZ5OXDSwcnTNcREtN7HMunyPqz7/Y0IIRj/vbmtfWfPIFx16JN6ziJ8Xc7Fr4hdmJUrVwoCj+I0ux08eLDJY7Ozs4VerxdOp7PJ19evXy+MRmOj8vHjx4sHHnig2ZicTqdwOBzBrbCwUNNmV0c6sOY58Xb8GPHBDT8RZZ9/ESz3e73i07FXi5zEQeKTEVcIn8vVpvPZq5zi7S15IueNf4qTp1punh0trhSPb9wrXtp6SFTVudt9DZ6TBaLk7pvFqVuuEHV5n7b7PFLoQml2dfqaz4oVK1i4cGGL+6SnN73QWmZmJl6vl4KCAjIyMhq9npSUhNvtpqKiokHtp7S0lKSk5h+6NJlMmExddyBYS8wpifiqqnF/+RVhMVHBckWnI3XRXAqeeZ7UxfNQjQ3Hy5x1ejhQXoPZoGNYXDgmfaBFX1RUTubHzxNWegyn8zaY+4tm3zs9ycqqWxo/FBoqb0kR/rLAKGfvd0dg3BUXfM6L6aOvTvG/h09z1Yhkxg9qXCPsrjp98omPjyc+vn3/Ifn5+aiqSkJCQpOvjx07FoPBQE5ODrNnzwbg0KFDnDhxgokTJ7Y75s7A6/Xj9fsJa2LS+Jb0nf8jIgakYYyPxTqw4XQPfX6xmF7zbkNnazyep6jKxcn6fprkCCPJ9U2mXlYdij0wP7K52t6eSwmZcdgoIubcjb+yAtOka1o/oAlltW7sLg+JZiPRYR3Tl+R49y2qtm0hctZsrNMCix3WOL38afM31Ll9lNhrZfLpinJzc9m1axdTp07FarWSm5vLsmXLmDdvHtHRgfEdRUVFXHPNNfz1r39lwoQJREZGsnjxYpYvX05MTAw2m417772XiRMncvnll2t8Re3jt5dxxq3nqfX7OFlSxa9/MoGRGW3/hVZ0OuKuHN/0a4qCPrLp26eJYQr4i3CZY7GZzo9ViU5JxvXzx/Ae3Y9p/NUhXUt7qWEWrLctbvfxHp+fwmpXYJJ73B2SfHy1NZSv/U/8lQ68pcXB5BNm1DFlRDIf7DlJZkbTfyS7q26TfEwmExs2bGD16tW4XC769evHsmXLWL58eXAfj8fDoUOHqK2tDZY9++yzqKrK7NmzcblcTJ8+neeff16LS7hgni/+hTP7dxwadRd5+wJ3vL44WBZS8mkPR7WLqKNbid77BiKmH/peD/L9Xy3TyMsxjew6yVyvKsSb9ZTXeYkxdUytRzVbiLzpR5xd/wq2GTcGy3WqwuLpg5h9RRqxtu7ZlG9OtxpkqBWtBxme43xjHZ6cjVQlDODlpAWUnHVzzx0jGZze8kDDC7H762IeWfc5f/1BIdHlu0HRofvRf6FEdOwDppeaEAKvX6BXm5+lMeRzer34ztrRxcZ121VK5YOlPZR+/DWI6gpik/rw62vGoxhCe4iyPfIPnaa2zsvfDvdhydhownsP7vKJBwJNTIOuY5JO8Jx6Pfr4ntW0aolMPt2Ivu8g9AtXXdL3nDK2FwVFDsKSrKhjh6J2gYF+QgjKnR5cXh9RJgPWEDvlpY4hP3XpggzuF8Pjv7xS6zBCUufxU14/QlvgaTL5+B12hMeNLu7C5rmWmtc9G55Sh6v55ltO/PeLOPK+0DqUJnntp6na9Bp1uz9tdV+dCqb6JpWxib4Xb/F3VK35GVUP3YVnf16HxyoFyJqP1CbHnlqL/cPtGBMTGPfRexg62YyN1e/+narXXgKdnsQ/voYxral1IQJMeh3JFiNun5/wJmZx9JeeRNjLAPAVHsUwrOmhB9KFkclHahNjbGAmZlNyIqqh8/XrqObAShVKmBnF2HpHe7hBT3gzl2HIGIXpxrsQNVUYxk7pyDCl75G32jtAZ7nVfjG5T5/B8b9fEjFsMObene9hTb+zDucXuegTkjEOGNLkPgJBYB4gJ5CMQtuni5XaRt5qlzqcMS6W+BlZre+oETXMjGXStFb2qgQOnzsCSLuoMUktkx3O0gXpWhVnA3CuSdb6lK/SxSWTj9RupZs2s+uKGRSsfaFLrKypYAFGAaMBOaWq1mTykdqt+LV/UHf8O44//Qfcpy/NE+sXSsGMgg2Fjh29LIVO9vlI7ZY4+0aq9x0kee6tGOtX+hR1x6ByN0JNwvvFQdSENPSjZ7ZyJqknkslHarfkW28m4QfTGy4yWHsYvGdROAtuO76PtqOmXYYanaJdoFKnJJtd0gVptLqpuR+oYQiRiig6ghKVDKbQJ7LvUkQd+PLB9yWItq931tPJmo/UoRTLIERYP6iuRHdlOGpif1RLpNZhXVzCDpTX/zsOlG6ebDuITD5Sh1NUA4otFnX4+XE3Pr+gzusj3KDrsPlxOg0lEoQV8Af+LbWJTD7SRSeE4NPjZ8gvruTKtBjGpkZpHVLHUiJArX/+S+k5yx1fKNnn0wMds9ewo+AMB8qrL8n7uXx+9pdVAXD49KV5z0tO0cnEEyJZ8+mBvrHXUFbjoajKRarNhK2D5iluTphex1XpsRw9U8PQhO757JsUOpl8eiCrUU9ZjYdIkx6j7sL/WheUVrFjXzFpCVauHp7UZJ/OsEQbwxJl4pHOk8mnBxqXbCPVGkasWU+Y/sJb3jlfF7NtbzFQzKAUG71i5d0eqXUy+fRAZoOeftEd91/fOy6QbAYkW7FZOt9cP1LnJJOPdMGyLkthcK9IosKNWM0Xf8UMqXvoNne7PvroIxRFaXLLy2t+Ht6rr7660f4/+9nPLmHkXZ+qKPSJj8BmkYlHartuU/OZNGkSxcXFDcoefvhhcnJyGDduXIvHLlmyhMceeyz4s8ViuSgxSpJ0XrdJPkajkaSk88uceDweNm3axL333tvqiFqLxdLgWEmSLr5u0+z6d++88w5nzpxh0aJFre67fv164uLiGD58OKtWrWqwlntTXC4XlZWVDTZJkkLTbWo+/y47O5vp06eTmtryZOd33nknffv2JSUlha+++oqVK1dy6NAh3nzzzWaPeeKJJ3j00Uc7OmRJ6llEJ7dy5UoBtLgdPHiwwTGFhYVCVVXxxhtvhPx+OTk5AhBHjhxpdh+n0ykcDkdwKywsFIBwOBwhv58kdScOh6PN34VOX/NZsWIFCxcubHGf9PT0Bj+//PLLxMbGctNNN4X8fpmZmQAcOXKE/v2bXnjOZDJhMplCPrckSed1+uQTHx9PfHx8m/cXQvDyyy9z1113YWjH4nb5+fkAJCcnh3ysJElt1+06nLdv387x48f5yU9+0ui1oqIiBg8ezO7duwE4evQov/3tb9mzZw8FBQW888473HXXXUyZMoWRI0de6tAlqUfp9DWfUGVnZzNp0iQGDx7c6DWPx8OhQ4eCd7OMRiPbtm1j7dq11NTU0Lt3b2bPns1DDz10qcOWpB5HLpfcAXrCcsmS1BahfBe6XbNLkqSuQSYfSZI0IZOPJEmakMlHkiRNyOQjSZImZPKRLjq/x4PzVAnC59M6FKkTkclHuqiEEBz93X+RO/Zqvnvuf7QOR+pEZPKRLipfbR0lG98GoPSt97QNRupUut0IZ6lz0YdbGPjYrynf/CGJt8zSOhypE5EjnDuAHOEsSQFyhLMkSZ2eTD6SJGlCJh9JkjQhk48kSZqQyUeSJE3I5CNJkiZk8pEkSRMy+UiSpAmZfCRJ0oRMPpIkaUImH0m6AG6PD59fPqHUHvLBUklqpwMnzvLcO/tJjraw9KahxFjDtA6pS5HJR5La6esCO8X2OortdRwvrZbJJ0Rdptm1Zs0aJk2ahMViISoqqsl9Tpw4wQ033IDFYiEhIYH7778fr9fb4nntdjtz587FZrMRFRXF4sWLqa6uvghXIHU3Y/rHMaiXjcnDk+ifLGczCFWXqfm43W5uvfVWJk6cSHZ2dqPXfT4fN9xwA0lJSezcuZPi4uLgeu2PP/54s+edO3cuxcXFbN26FY/Hw6JFi7j77rt59dVXL+blSN3AwF6RPPXjCSiKonUoXZPoYl5++WURGRnZqHzz5s1CVVVRUlISLFu3bp2w2WzC5XI1ea4DBw4IQOTl5QXL3n//faEoiigqKmpzTA6HQwDC4XC0/UIkqRsK5bvQZZpdrcnNzWXEiBEkJiYGy6ZPn05lZSX79+9v9pioqCjGjRsXLMvKykJVVXbt2tXse7lcLiorKxtskiSFptskn5KSkgaJBwj+XFJS0uwxCQkJDcr0ej0xMTHNHgPwxBNPEBkZGdx69+59gdFLUs+jafJ58MEHURSlxe2bb77RMsQmrVq1CofDEdwKCwu1DkmSuhxNO5xXrFjBwoULW9wnPT29TedKSkpi9+7dDcpKS0uDrzV3TFlZWYMyr9eL3W5v9hgAk8mEyWRqU1ySJDVN0+QTHx9PfHx8h5xr4sSJrFmzhrKysmBTauvWrdhsNoYOHdrsMRUVFezZs4exY8cCsH37dvx+P5mZmR0SlyRJTesyfT4nTpwgPz+fEydO4PP5yM/PJz8/Pzgm57rrrmPo0KHMnz+fvXv38sEHH/DQQw9xzz33BGspu3fvZvDgwRQVFQEwZMgQZsyYwZIlS9i9ezefffYZS5cu5Y477iAlJUWza5WkHuES3H3rEAsWLBBAo23Hjh3BfQoKCsTMmTOF2WwWcXFxYsWKFcLj8QRf37FjhwDE8ePHg2VnzpwRc+bMEREREcJms4lFixaJqqqqkGKTt9olKSCU74Jct6sDyHW7JCkglO9Clxnh3Jmdy99yvI/U0537DrSlTiOTTweoqqoCkON9JKleVVUVkZGRLe4jm10dwO/3c+rUKaxWa4c851NZWUnv3r0pLCyUzTjk5/HvOvPnIYSgqqqKlJQUVLXl+1my5tMBVFUlNTW1w89rs9k63S+XluTn0VBn/Txaq/Gc02VutUuS1L3I5CNJkiZk8umETCYTjzzyiHyEo578PBrqLp+H7HCWJEkTsuYjSZImZPKRJEkTMvlIkqQJmXwkSdKETD6dXFpaWqPZHZ988kmtw7pk/vjHP5KWlkZYWBiZmZmNJozrSVavXt3od2Hw4MFah9VucoRzF/DYY4+xZMmS4M9Wq1XDaC6d119/neXLl/PCCy+QmZnJ2rVrmT59OocOHWo093ZPMWzYMLZt2xb8Wa/vul9hWfPpAqxWK0lJScEtPDxc65AuiWeeeYYlS5awaNEihg4dygsvvIDFYuHPf/6z1qFpRq/XN/hdiIuL0zqkdpPJpwt48skniY2NZfTo0Tz99NOtrsLaHbjdbvbs2UNWVlawTFVVsrKyyM3N1TAybR0+fJiUlBTS09OZO3cuJ06c0Dqkduu6dbYe4r777mPMmDHExMSwc+dOVq1aRXFxMc8884zWoV1Up0+fxufzNbkcUmdc0eRSyMzM5JVXXiEjI4Pi4mIeffRRJk+ezL59+7pmU/ziTagoNWflypVNTgn7/e3gwYNNHpudnS30er1wOp2XOOpLq6ioSABi586dDcrvv/9+MWHCBI2i6lzOnj0rbDabeOmll7QOpV1kzUcDF7JkUGZmJl6vl4KCAjIyMi5CdJ1DXFwcOp0uuPzROaWlpS0ua9STREVFMWjQII4cOaJ1KO0ik48GLmTJoPz8fFRV7fZ3e4xGI2PHjiUnJ4ebb74ZCEzalpOTw9KlS7UNrpOorq7m6NGjzJ8/X+tQ2kUmn04sNzeXXbt2MXXqVKxWK7m5uSxbtox58+YRHR2tdXgX3fLly1mwYAHjxo1jwoQJrF27lpqaGhYtWqR1aJr41a9+xY033kjfvn05deoUjzzyCDqdjjlz5mgdWrvI5NOJmUwmNmzYwOrVq3G5XPTr149ly5axfPlyrUO7JG6//XbKy8v5zW9+Q0lJCaNGjWLLli2NOqF7ipMnTzJnzhzOnDlDfHw8V155JZ9//nmHLbx5qckpNSRJ0oQc5yNJkiZk8pEkSRMy+UiSpAmZfCRJ0oRMPpIkaUImH0mSNCGTjyRJmpDJR5IkTcjkI0mSJmTykTSzcOHC4FzERqORAQMG8NhjjwUnSxNC8OKLL5KZmUlERARRUVGMGzeOtWvXUltb2+BcJ0+exGg0Mnz48JDjsNvtzJ07F5vNRlRUFIsXL6a6urpDrlFqnkw+kqZmzJhBcXExhw8fZsWKFaxevZqnn34agPnz5/Mf//EfzJo1ix07dpCfn8/DDz/Mpk2b+PDDDxuc55VXXuG2226jsrKSXbt2hRTD3Llz2b9/P1u3buW9997j448/5u677+6wa5SaofF8QlIPtmDBAjFr1qwGZddee624/PLLxeuvvy4A8fbbbzc6zu/3i4qKigY/p6eniy1btoiVK1eKJUuWtDmGAwcOCEDk5eUFy95//32hKIooKioK/aKkNpM1H6lTMZvNuN1u1q9fT0ZGBrNmzWq0j6IoREZGBn/esWMHtbW1ZGVlMW/ePDZs2EBNTU2b3i83NzfYnDsnKysLVVVDrkFJoZHJR+oUhBBs27aNDz74gGnTpnH48OE2z9SYnZ3NHXfcgU6nY/jw4aSnp7Nx48Y2HVtSUtJoYja9Xk9MTAwlJSUhX4fUdjL5SJp67733iIiIICwsjJkzZ3L77bezevVqRBtneqmoqODNN99k3rx5wbJ58+aRnZ19sUKWOoicTEzS1NSpU1m3bh1Go5GUlJTgIniDBg1q0yoVr776Kk6nk8zMzGCZEAK/38+3337LoEGDWjw+KSmJsrKyBmVerxe73S7nir7IZM1H0lR4eDgDBgygT58+DVbfvPPOO/n222/ZtGlTo2OEEDgcDiDQ5FqxYgX5+fnBbe/evUyePLlNiwtOnDiRiooK9uzZEyzbvn07fr+/QUKTLgJt+7ulnqypu13n+P1+cfvttwuz2SzWrFkj8vLyREFBgXj33XfFtGnTxFtvvSW+/PLLZpcZev7550VSUpLweDytxjFjxgwxevRosWvXLvHpp5+KgQMHijlz5lzo5UmtkMlH0kxLyUcIIXw+n1i3bp0YP368sFgswmazibFjx4rf//73ora2VixdulQMHTq0yWOLi4uFqqpi06ZNrcZx5swZMWfOHBERESFsNptYtGiRqKqqau9lSW0k53CWJEkTss9HkiRNyOQjdWuPP/44ERERTW4zZ87UOrweTTa7pG7Nbrdjt9ubfM1sNtOrV69LHJF0jkw+kiRpQja7JEnShEw+kiRpQiYfSZI0IZOPJEmakMlHkiRNyOQjSZImZPKRJEkT/weCg1Vp6rNKWAAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.clf()\n", + "\n", + "# latent space scatter plot, coloured by ground truth frames\n", + "dim1=0\n", + "dim2=1\n", + "dt = 1.2e-3# time between frames in microseconds\n", + "\n", + "fig, ax = plot_latent_space_scatter(\n", + " df_picked,\n", + " dim_1=dim1,\n", + " dim_2=dim2,\n", + " color_by=\"closest_pdb_index\",\n", + " palette=\"RdYlBu\",\n", + " pca=True,\n", + ")\n", + "# remove legend and add colorbar for the closest_pdb_index\n", + "ax.legend_.remove()\n", + "\n", + "S_m = plt.cm.ScalarMappable(cmap=\"RdYlBu\")\n", + "S_m.set_array(df_picked[\"closest_pdb_index\"])\n", + "\"\"\"\n", + "cbar = plt.colorbar(S_m)\n", + "cbar.set_label(\"time [\\u03BCs]\", rotation=270, labelpad=15, fontsize=16) # time in ps\n", + "# change the tick labels on the colorbar to go from 0 to 10 us\n", + "cbar.set_ticks(np.linspace(1, df_picked[\"closest_pdb_index\"].max(), 10))\n", + "xticklabels = [np.round(r, 1) for r in np.linspace(1, df_picked[\"closest_pdb_index\"].max(), 10)*dt]\n", + "cbar.set_ticklabels(xticklabels, fontsize=14)\n", + "ax.set_xlabel(f\"PCA{dim1}\", fontsize=16)\n", + "ax.set_ylabel(f\"PCA{dim2}\", fontsize=16)\n", + "ax.tick_params(labelsize=14)\n", + "#ax.set_xlim((-12, 16))\n", + "#ax.set_ylim((-12, 12))\n", + "ax.set_xlim((-20, 20))\n", + "ax.set_ylim((-20, 20))\n", + "#plt.xticks([-20,-10,0,10,20])\n", + "#plt.yticks([])\n", + "\"\"\"\n", + "\n", + "# add trajectory to the plot\n", + "N_volumes = 50\n", + "pdb_indices = np.unique(df_picked[\"closest_pdb_index\"])\n", + "d_pdbs = len(pdb_indices) // N_volumes\n", + "\n", + "trajectory = np.zeros((N_volumes, ndim))\n", + "trajectory_pca = np.zeros((N_volumes, ndim))\n", + "for i in range(N_volumes):\n", + " pdb_group = pdb_indices[i*d_pdbs:(i+1)*d_pdbs]\n", + " mean_latent = df_picked[df_picked[\"closest_pdb_index\"].isin(pdb_group)].agg(\n", + " {f\"latent_{i}\": \"mean\" for i in range(ndim)}\n", + " )\n", + " trajectory[i] = mean_latent.values\n", + " mean_pca = df_picked[df_picked[\"closest_pdb_index\"].isin(pdb_group)].agg(\n", + " {f\"PCA_{i}\": \"mean\" for i in range(ndim)}\n", + " )\n", + " trajectory_pca[i] = mean_pca.values\n", + "\n", + "ax.scatter(trajectory_pca[:, 0], trajectory_pca[:, 1], s=5, c=\"black\", zorder=10)\n", + "ax.plot(trajectory_pca[:, 0], trajectory_pca[:, 1], c=\"black\", zorder=10, linewidth=0.5)\n", + "ax.set_aspect(\"equal\")\n", + "\n", + "fig.savefig(os.path.join(figures_dir, f\"{os.path.basename(latent_file)}_latent_space_scatter_colored_by_closest_pdb_index_pca.png\"), dpi=600, bbox_inches=\"tight\")\n", + "fig.savefig(os.path.join(figures_dir, f\"{os.path.basename(latent_file)}_latent_space_scatter_colored_by_closest_pdb_index_pca.pdf\"), bbox_inches=\"tight\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## FIG 2A - Micrograph, PS thumbnail and radially averaged PS " + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "# imports\n", + "import os\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import seaborn as sns\n", + "import mrcfile\n", + "\n", + "# from emmer.ndimage.filter.smoothen_mask import smoothen_mask\n", + "from pipeliner.mrc_image_tools import mrc_thumbnail\n", + "# from gemmi import cif\n", + "\n", + "# roodmus\n", + "from roodmus.analysis.utils import load_data" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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nwxtvvMGF/vjx4/D7/RgaGsL6+jrDfXfu3EG1WkVbWxt3qclkki/nbDYLjUbD8AF1dA6HA6lUCjqdDpFIBOFwGOPj45DL5Wg2m1zIy+UyRCIRd3wKhQIGgwGVSgXBYBBSqRRGoxHJZPK+A1gulzEzMwOHw4F0Oo1Wq4W1tTVUq1XEYjG4XC5Uq1VUq1WEQiFIpVLE43G0t7djY2MDQqGQp9tUKoXt7W1+R0KhECqVCkqlEpLJJNra2hjOCYfDPME6HA7IZDKewEulEv9nQqEQq6ur2L9/P5LJJL797W9Dp9NhcXGRL2ur1YorV67AaDSio6MDt27dwtLSEvbu3Quz2YxisYhgMIhWq4VGowG5XI56vQ69Xg+xWIxisYhYLMbTHgBkMhmIxWKMjY1heXkZJpMJq6urWFlZweLiIkwmE4xGI9LpNOx2Ow4ePMjvpd/vx8TEBOr1OprNJpRKJfL5PMrlMsM1YrEYOp0OAwMDCIVCGBwcRC6Xww9/+EMsLCwgn89jbm4OgUAAjz32GORyOebm5tBsNrG1tQW9Xg+j0Yienh5ks1mkUim0tbWhVCoBALa2ttBqtZDNZiEQCPjuikaj6O3tRTweh1qtRqvVQrlchtlsRqlUQrFY5EaZmk1CFei7pkkvGo3CbDYjEAigVqvBZrOhUqkgkUjAYDCg2WxiY2MDTqcTjUYDqVQK4XAYbW1tPIW3tbXB6/XCYrEw3FypVJBMJrGxscH/e6vVglQqRTAYRL1eh1AoRK1WQyQS+bnv+V9YHkvwCX2QOp0ODocD+XwegUAAsVgMoVCIJw7C9RKJBMxmMx+mYrGImZkZfuGJw7Db7XwoCZoRCAT8RweDQRiNRgCAVCqFVCqFxWLBd7/7XVy4cIE7wo6ODjSbTbhcLrRaLSQSCSQSCdTrdZ5mXC4XJBIJf2larRZCoRBCoZBHxXg8DrPZjM3NTdjtdr78ADBHY7PZIBQKIRKJUC6X0Ww2edyk/1wikTD+LhKJUKlUkMlk0NvbC5vNBolEgmg0itXVVWSzWZ6m5HI55HI5Y5rVahW1Wo2nnomJCahUKrhcLshkMly4cAGVSgVtbW2QyWSQy+U4fvw4Tp06hV27dkEul8Pv9yMQCGBjYwNut5s7KofDgUcffRTRaBS/93u/h+eee46nwcXFRSwvL6O9vR27du2CXq9HIpFAJBJBtVqFUqnkAry9vQ2lUolEIgGbzYZwOIw333wTL7/8MuPvJ06cwA9+8AN87Wtfw8zMDMbGxrC5uYm/+Iu/QDAYxMmTJ6HVarF7924+0JVKBQAYWlGpVAyftLW18YFuNBooFotwu90M+1SrVYYFCXZUKBTc3ZZKJVgsFuZUqDunzk6pVPKkRBCEyWRCqVRiGNblcjHEZDAYYDAYoFarkUwmYTAYIBQKGW4tlUqQyWTM62g0GtTrddhsNlgsFsTjcYTDYUSjUYYVFAoFAoEAtra2IJPJoNFoIJVK+fKOxWLYt28fT2IAsLCwAJ/Ph+PHj+Ppp5+GTCbD3NwcEokENyCpVArz8/NotVrQ6XQAwH+/RqNhiDCdTqPZbKJerzMP0mw2EY/HUSqVUCqVeGomXqW9vR1arZZRhLa2NgwPD/P7NDExAZlMBqPRiEQiAb/fD61Wy920wWBAV1cX9Ho9otEo1Go1ms0mFhcX8cYbb2Bqagpra2vY3NzE0tISotEoRCIROjs7kUwmsX//fjQaDWxtbSGfz0On0zH0GwgEGDqlpok670KhgH379iGbzaKnp4e7eKPRiGw2y3yeSqVCMpnE8vIyotEoIpEIfD4f/H4/ZDIZ0uk0Q2f1eh21Wg3NZhNyuRxSqRQOhwNGo5HfNYVCAZVKBalUCqvVimg0yk1zvV7notPT04NwOIx4PM4IQzweRzQaRTgcRqVSgdvt5kZSJBKh2Wz+3Pf8LzxReDwe6PV6LC8vo1arMY9AL0a5XGaMjz4gp9OJeDzO8E17ezt36jabDSqVCtlslg8cvSiVSgUSiQQCgQBmsxnBYBDBYBBdXV2QyWQ8faRSKbRaLQwODuLMmTO4ePEibt++jdXVVTSbTfT29kIul0Or1WJ7extdXV18QLLZLEwmE3/hMpkM2WwWTqcTer0e2WwWCwsLTH47HA4e7egiJ0hALBbDYrHwlJHJZJDJZCCVShlSsdlsEIlEAACZTAapVAqn04lSqcSjNhHrW1tbqNVqrDajiYB+brFYhFKpxPDwMMxmM8bGxnDu3DmMjY2hs7MTzz//PE8TRJD39fVhfn6eIS0qrN3d3ejv74dSqcSrr76KGzduIBwOY2xsDPv378fy8jLu3LmD06dPM1Gm1+sRCoVQKpWg1Wq5cNILfejQIWi1WkQiEbz22mt4/PHHeTocGRnB+fPn4fV6EQqF8N577+Gv//qvMTc3B7PZjF27dmFubg4Wi4UvaYFAAJfLhWKxiFqtxlCIQCBAo9FAPp9nItxoNMLpdHKXThdtT08PGo0G3G43/H4/gsEgxsbGEI/H0Ww2GedttVrQ6/X3CQfEYjHsdjtKpRJ6e3tRq9VYMLETMiEik6Aep9N538RitVphs9mYR6CfQaS5WCyGTCZjWE4gEGB7exulUgl2ux3lchnpdJobGhJMmM1mTE1N8TmjQnX48GEUi0WsrKxgeXkZoVAI/f39CIVCDNGp1Wqsr69DKpWiVCpBqVTC4/GwUEUoFPL7Qk1PtVqFVquF1+tFV1cXN0eJRIK7c4fDgfHxcSSTSbz55pvIZDJYW1tDq9VCf38/7ty5g87OTiiVSnR1dcHr9aJer6OrqwvZbBYSiQT1eh1KpRLJZBIKhQJmsxkOhwMqlQoejwdf+MIX4HA48Cd/8id466238OlPfxo2m40vRuJKjEYjn5lSqQSj0cjTo8Viwfr6OhP3er0eqVSKJwISUyiVSmQyGYyMjCAcDjM/5/V6eWKTy+XcpFFTp9PpeEIjJKZYLCKTyfBEYbVaWSwgEomgUChgsVhgtVoRiURYZEP3y853slAoIBKJwGw2QygUIpFIQK1Ww263QygUIpfLoVgsfnCFgsaXcrkMj8fD5CBxB/QB0AdF/guBQIBQKASLxQKj0YhUKgWlUomtrS3odDrGj6lbEQqFsNls98EEpCTy+/1wuVw4cuQIk1979uyBTqfD+vo6lpaWMDU1hUOHDmF0dJThilKphKGhITQaDSar1Go1VldXWRVBIzypuqj4JRIJ6PV6CAQCLibxeBzlchnRaJTJURrHnU4ncrkcE2vFYvE+tQHBJc1mE2azmfHNRCLBSg6CRWhCkkgkmJ6eZlXY9vY2dDodK2u+9rWvwW63M8l348YN5HI53Lp1C1qtFp/4xCcgEAj4O6pUKqjVajAajZBIJAgGg/B6vXjttddQKBRQqVQwNTWF27dv8zTX19fH+KzFYkGhUEAikWA1D3XpXq+X+adms4nPfe5ziEQi+PGPfwyfz4dz587h4MGDeOaZZ3Ds2DG8+OKL2NzcZNz1W9/6FqRSKQ4dOgSxWIx8Po9QKIRIJAKFQsF/w/DwMDY3N/mzEovFkEql0Ov13IErlUpIpVIIhUKUSiVUKhUsLCygUqlALpcjn89je3ubVT+1Wg3JZBICgQACgQAikQh6vR6FQgEejwe5XI7hMFKZpVIpFiUUCgW+pEi9tLS0xPwCqZcKhQKTjdR8VCoVCAQC+Hw+xONxWCwWNJtNOJ1OjI2NoVgswufzQa1W39csEF+QTCbx8ssvw+Px4NSpUzh58iROnTqF3//938cf//EfY9++fTh58iQGBwdZCdNoNLBv3z50dnZia2sLq6urKJfLuHXrFtra2mAymaBWq/liFIlEKBQKDAdbLBZWBxaLRXR1dTG0fPXqVQSDQZhMJoZcu7u7IRaLGeY7ffo03wl79uzB1NQUAoEADAYDRCIRZDIZT2bVahW3bt3C1tYWxsfH0Wq1YLVakU6nceHCBchkMnz+85/H9vY2QqEQN5UkGCClokajYaQjGAwim82ycq9arcLv96NerzM/ScVxdXUVHo+HVWPpdJrFFcQH0qNSqbCxscH8YHt7O4LBIOLxOLa3t2G323nyNplMSKfT3BzTvxmLxbC8vMyTkMViwebmJmw2GzeXwWCQ1VZKpRLlcpknEOKkCPr+wAqF1+tlXIzG5LW1Ncjlcj6URqORXySxWIx0Og2tVgsAiMViDO+QRJFwXbpASW1CbD398fV6HSKRiDum8+fPY3FxEbFYDAaDAWazGTdu3EAsFkNHRwdOnTqF8fFx+P1+XLt2jSs8jckkl5PJZDCbzSgUCgiFQgwBNBoNhjgcDgcAYG1tjfFKmUzGJLhMJuOOLxaLodVqwePxIJlMore3F4FAgD8DIjQXFxeh0+lY6UJdGHEn1DH7fD4mOYkcHB4exvr6OmZnZ7G6uopoNIoLFy7gV3/1V3H9+nVcvHgRHR0d2L17N4/zTqcTKysrsNvtDKdUq1VYrVZIpVI899xzWFxc5IuZOufBwUHMzc1xd+L3+9Hb24vOzk4sLy+ju7ub5X00CbW1taFer2NhYQEnT55EuVzG4uIiisUicrkczp8/j2AwiD/6oz+C3W7H8PAwnnnmGdRqNVy+fBlvv/02/u7v/g7JZJIPkMPh4AMtEolgt9sB3BNYlEolmEwmiEQi+P1+5hBIVNDR0cEqk2w2y6QmyWBJkEDQSbVaRalUQkdHB3w+Hzc5xHvRpCiVSpHNZvmQEn+TTqe5QZJKpXC5XEwa06VITQjJIHfCmiaTCclkEoVCAa1WC61WC1tbW1Cr1dywkKyyWCwygQ0Abrcbjz32GN566y2srKxAq9XiwoUL0Ov1+JVf+RXYbDb8zd/8DUZHR3HixAkEAgG43W7UajWe/OVyOXNO4XCYL9ZisYhEIsEXVzgcZpVeKpWCWq1GrVaD2+3G5OQkBAIBuru7cfHiRfh8PvT29qJarWJ2dhZCoRDHjx9nVWQqlUIul4PNZkOxWOTuntSNQ0NDaDabqFQquH37NpaXlzE8PIzf//3fx6FDh3DgwAHMzc3hxo0bLIulhpSk76RUomlhe3ubVWS9vb38HlWrVYYDSWlHBbNSqTAMSqo5IprpziJ5st1u539ra2sLAGAwGCCXy6HT6ZBKpVgNuPPnEN/h9XoxOzuL7u5ujI6OMiEei8XQ19cHr9eLfD4Po9HIcuNkMgmTyYRoNMpNkkAggM1m+7nv+V+Yo1CpVLBYLCwnrVQq/OHZbDburOPxOOva29vboVarIRAI0NnZyWqYYDDI6h5SFdRqNfYqVKtV1k1LJBLuzhUKBUZGRiCTyfDaa6/h5s2bDH3l83l4PB6ecILBIHeqNGbqdDp+Of1+PzQaDUt6iZQmwpCgBeoSrVYrUqkUVlZWUK/XWXJGEs5QKHTfSymRSBAOhwHc0/2TAsfv96PRaMDlcvEBTSaTaG9vZ2EAjbIulwtyuZzJ8I2NDYhEIuzevRvlchk//elPIRKJ8G/+zb9Bd3c3rl69ypr/48eP4w/+4A/Q09PDHV21WkU6nWbRQLPZ5E5+bW0Ne/bsgUKhQG9vL/7X//pf+Iu/+At87nOfw+rqKi5duoRSqYTFxUXcuHEDFosFyWQSTqcTrVYLmUyGL3KhUIh3330Xr732Gt566y184xvfQLPZxN///d/jK1/5ClQqFV5++WV88YtfxL/9t/8WXq8XEomECe6RkREcP34ctVoNKpWKRRQET7ZaLVbFCIVCJhFpJLfZbAzjLC4usv+FeAf6HOiSFggE0Gg0TKAPDg5Cp9NBq9VCrVazTJiKF8mkCRKRSqVoNBp8+dPvKhQKWXZJXSJJjOnzI06D5JsEa5EaxmKxMIZfKpWwsbHBSi6SyRIhqlQqMTg4iI9+9KN46aWX8OUvf5kvqdnZWfy3//bfMDs7ixs3brDoYmFhAfF4HPV6HVarlQsS/Q7JZPI+3xFxLlqtFgKBgBsDu90OgUCAS5cuYXp6Gk6nE1arFRqNhsng6elpKJVKDAwMoKenB6lUChsbG8z/eL1emEwmmM1mhmHy+TwEAgEqlQr+5//8n2g2m9izZw9WVlawtrYGpVKJf/kv/yUOHDjAzdzg4CBDhTKZjIsrSdpbrRaUSiXsdjv6+/u5eQgGgwDAJHqpVEKhUOCOnKC+3t5eaLVa/gyp+S0WiwwhVioVpNNphEIh/l4bjQYGBgbY80UiBEISqMFwOp04ePAgjh07hnA4jB/84Afs5+jt7WXUw2w2A7in9CQFVCwW4wJJghMqbj/P8wtPFNVqlUkYqtY0bhGxptFo4PF4EA6HeQIgmSCNWiQhTKfTsFqtEIvF0Ov18Pv9WF1dRVtbGwqFAjY2Nvj/Ttr0/fv3I5fLYWFhAd3d3ejt7cXv/d7v4d1334VIJMJjjz2GZrOJW7duodVqQSQS8c8j/K5er7NqikbXWCwGi8XCRpl6vY5UKgUArGpJJpPQ6XRoa2tjzJCwZ5IyEvRSLpe56vv9foyPj7PZRyAQIB6PIxKJMMbe1dXFLw9JIElfHolEuBstlUrI5XI8/TgcDrhcLoyOjrIsUK/XI5/PY2pqCtlslj9zKtJKpZLNdclkElqtlj+Dp556CiaTCRsbG7h79y42NjZQq9Vw7tw5HDlyhLmi7u5u/v0nJye5UTCZTGg0Grh+/TqmpqbgcDjg8XhgMBhgsVhgMpnQ0dGBkZERzM/P4+LFi6jX6/jYxz6Gz33uc7h69Sr//V6vF0NDQ5icnIRKpUJbWxsymQzK5TJisRhkMhnK5TK/h8FgEC6XCwKBgOELgi6azSZisRhsNhscDgfq9TrC4TBLQ/P5PE+P0WgUAPj9oXec3hESLtjtdrhcLqTTaSbMqbBUKhUsLy/D4/FAIpFwR14qleByuVCpVNh8l8/nkclkWBbe0dHB5DEJBDo7O1kt5Ha7kclkeJrI5XK4c+cOrl69is3NTWxubuLAgQPYv38/Qyherxfvvfce+3G2t7cRiUTQ1dXFk89jjz2Gv/3bv2XfkV6vZ3UOQczb29vw+XzMu1Bj1N/fz/JSj8eDQqFwX2PzqU99Cm+99RY+/vGP48yZM3jooYcwNzcHv9/PSjUiziUSCWZnZ2Gz2ZBMJhnm/PrXvw6hUIhnn30W7777Ln70ox8xREw85d27dzEzM4M9e/agp6cH29vbKJfLyOVyXIycTifK5TKGh4d5YiqXy3y+Ozs7sbq6yoQ6Sd3JNJjJZNBoNNDR0YFsNsvNJ4l3iM/p7OzE9vY2T62kpKtWq2wr2NzcRKlUgkQigcFgYB7O4/Hwv724uIiRkRGsr69jYWEBBw8exMmTJ7G0tIRQKASXy8XmSrFYzJM3FTYqaB9ooSBjiNVqZUadRq1Go8FVW6vVYmVlBZ2dnTyOW61WVlfQeEVknkajgVwuR3t7O2OApDIgPXt/fz8AYGVlBZubm7h27RoTZMvLy3jvvfdYTULEOhmJCGetVCosmSODUiaTgVAoRDgchk6nw/T0NHdApGwiroMMLQ6HA4VCgSGEYrEIvV4Pn8/Hh3N9fZ0VQZFIhJVQlUoFhw4dQn9/PxYXF1GtVllZRdCcy+XiLnvPnj180XV2dkIsFmNpaQlisRijo6PY3t7Gl770JUilUnzhC19g+aLX68XGxga8Xi8OHDjAEwTJhGkSIkPk0aNHkc1m8corr6BYLLKSxGaz4dd//dcxPDyMYrGI6elpJsfIFQqAybqNjQ3cvHkTdrsdTz/9NCQSCVZXV1mBFo/HYbfb4Xa7GesXCAR47bXXcPr0aT4oP/7xj9lV3Ww2uQDSoSeykUx7ZHgDwGR0NptFsVhkgyBJBTOZDPL5PAYHB9FoNBCPx1GtVnk6IAkz+T7W19f5vSWoTS6XY319naHIVquFWq2GYrGIRqOBzs5OZLNZNjWGQiE4HA7odDo2VdL03NXVhZmZGdhsNi5UdNh1Oh1r5lutFlKpFGq1Gk8liUQCd+/eRbVahcPhwPe//30AwIkTJzAwMIDPfvaz+OY3vwmj0Yi//du/xYEDB3D79m1uvIgnFIlE2L9/PyKRCLuuiW8Kh8OwWq3cnSeTSQBgJRR9XtQgNZtN/OxnP4PFYsHExASUSiWmpqYQCoXw+OOPY3p6Gm+//TYajQYEAgHzA1S0c7kchoeHodVq4XA4cOvWLVitVpw5c4bfl+HhYahUKty9exc2mw02mw379u3D2toay7DpcyIjKqk0gXsKumg0ysZDuVzO6i3yudBnA4DNrkajEZFIBJFIhCX1RHaT74YaRq1Wy+eOmgly8MtkMoa1yadE/paXX36Z76VYLIb29nZsbW3hRz/6EeLxOP7Lf/kvEAqFmJqaYs6M/BoEh5MZN5PJIJvN/tz3/C9cKKgKLi4uoqenB4ODg6yeILUJYXRkdiF5H3VdVquVR9qtrS1ks1mW2ZGKg15GUl2IxWKo1WpIJBJotVqYTCaMjo6y+gMAurq6MDExAYvFgpWVFZjNZo6ooN+HxmZSVBCEQeqS4eFhHkvb2tqYACU3OmmjHQ4HyygBcCEhjbTf74dAIOAqH41GodFoIBaLYbVaGb74zne+A5VKhV27dmF8fJynKPr5pFyhv5GIxFqthra2NlSrVRgMBkxPTyMUCsFkMsFqteLo0aOYnZ1FOBxmCXChUGCDnEQiYRUWEbVCoRCnTp1iHqfVauEP/uAPEAwGMT8/D4/Hg9XVVaRSKdjtdthsNhgMBoTDYSQSCWxsbLCUsVQq4eTJkxAKhXjppZewvr6OdDqNzs5OhMNhHD16FEePHkVnZyf6+/tx9+5dPPfcc8hmszh37hyi0Shu376NtbU1DA0NYXl5GWKxmLu8er0OnU4Hm82GbDaLWCzGmHAwGITb7YZSqcTQ0BB7FIxGIzcPFB0DAKlUij0ZJHkl8YXJZOJLzOPxsFNZo9Ggt7cXU1NTUKvVqNfrLNONRCJMspJDlsyoxBu53W5oNBq8+eabWF5eRkdHB9RqNYaGhuDz+fh7zufzcLlcEAqFLJQgVSH5jkgObDabMTAwgMHBQfz0pz+FXC7H8vIy3nzzTZ481tbW2N9x4sQJLC4uQq/X4+7du9jc3ITJZGKTFglMyJlOsBo1cB6PB1tbW2g2mwiFQjzREvQjEAiwsrJyn/jD7Xajvb0dPp+PCxVNUSSMob+J/D5Go5F9AyKRCF1dXfiLv/gL3LhxA2fPnmWiutlsYnBwkM9ANptFIBDgcyiXy5mIpqm31WoxMby1tcVN2OLiIgYHB1mMQ+okoVCI9vZ2rK+vQy6XsyCGoMKFhQWeOKmRqlQqMBgMmJubQ29vL7LZLFZXVxkiIq+KSCTiSBiSYi8uLmL37t3o7OzE17/+dSwuLiKTyWBubg4LCwuo1+swmUz8MzQaDXK5HL/fBKeSw/0DKxStVotJknK5jFqtBrPZDL1ej9u3b8PpdDIJRAQ3dVsDAwMAwH+ISCRCvV6H0+lkvJWctuSclUqlbHKj8SwSiaCzsxMf+9jH4PP52H8Qj8cxMjLCB9vv98NgMLBPwul0cs5KvV6/TwGk1+vZAUuxFmT4IY01QTOkkDIYDGxecjgcfJBIQ01dOqlrKJqiXC4jHo9jY2ODsWO32w2Hw8FGQYlEwqa/crkMn8/HcRcmkwk3btzA6OgoxsbGMDExgZMnT8JoNKKzsxNPPvkkLl++zFk4EomEJbekdCJFiFKpxPb2Ns6fPw+dToennnoK7733Hrq7u9He3o7x8XH2m5TLZSwsLMBsNrMjORgMIhQKwePxsFv37t27rEk3Go2YnJy8D+IjCJKUbZOTkwgGg1hfX8fp06cRjUZ5wpLJZPcRnARt0JS3vb3NbmKSWJOPgGJMKEcpkUhALpcjHA6jv7+fc4ToAJFJiYQVWq2Wu0GCD0jaSNCXw+FAR0cHrl+/zr8nvUMrKyss87VarQzL/uQnP8EzzzzDmPeVK1fg9/sxMDAAsViMo0eP4o033kBnZyd7hsiAmM1mmfMimeja2hqcTifGx8fZB6LT6SASifDkk0/iK1/5Cm7evIlPfvKTsNvteOSRR3D06FFYrVaUy2W0t7fj9u3byOfzuHTpEhdHMpSS6IGaDDpDRMSSEioQCCCbzWJ8fJxFD2KxGH19fajX61heXoZUKmUXvVKp5CaoVqvxtCgQCFj2TNOAUCjE/Pw8EokEjEYjmwgPHz6M9fV1ThQgscHKygqfHYvFwlMB+UPIzJZKpXhqIyGKw+GAQCBgh3k8HofT6YTdbud8MFKjkaGQcq3I2EmmS7vdzopBOoMikQhut5sb2WQyiVAoBJvNBrvdjmw2i09+8pMcwVMul7Fnzx5cunQJx48fx6/+6q/i3//7f4/vfe97+MhHPoJarQYAzI2R5LrRaCAQCHBx/3kynv7ZCgXhjgCYUQfuXf4k98rn84wbUtYSkbqEvcpkMiZsybhDB41cmkQwk0pBoVCgvb0d8/PzmJ+fh1gsRltbG0ZHR3HhwgWYzWaYTCb4fD5sbW2xDI9gIrFYzGa+jo4OdHV14ebNmzCbzayzp6mgVCpBLBbD6XSi2WyyM5JUGJQRo9PpOJclGo0yQR2JRBAIBFhOqdVqkc1m78O39+/fjz179iCfz+Pq1assaSQyu9FoMPGZTCbZvr+wsAC73Y7l5WWYzWZcvHgRHo8Hv/Zrv4ZgMIhLly7h9u3byOVyjHMC96A9mUyGra0thvKWlpagVCrh9Xrx0EMP4fnnn8e1a9cAgL0GBoMB6+vriEQiHFtC8llStJE0M5fLcXwLheM9+uijAO5NfNvb20ilUnjxxRdZbw/cyxDr7u7G9evXcfDgQfajkFS1t7cXq6uraG9vh1gsxvb2Nnt0aBok+AAAwuEwK2cIDiFDoEQiwdLSEpLJJF+CJDelYkC+GsoqIn/LzqgJ4kAikQgboCQSCQDAYrEwNPGhD30Ik5OTWF5extTUFILBIK5fv85NRDab5UvUZrMhl8vB5XLBZDKxOZQCDuPxOIrFInNKUqkUBoMB169fZw+DwWDA7t27uRB/6UtfwrPPPovHH38carUa0WgUe/fuxcsvvwyxWIwTJ07A7/djfn4ea2trPGnT56lQKNj0mMvl0NbWhlwux7Ar8SYUpULT/NbWFvr7+xlKJviPuCAqaKVSCTqdDnK5nKdFlUrFHhiCtUhIcuXKFUilUuzbtw8ajQZnz57l7KVMJoNwOMw4PUXt+Hw+HDx4kF3n5N0gmInMc5THRCQ7KRRDoRC8Xi/HrBSLRRgMBvT29gIAGo0Gk8uRSITvl1AoxGGozWaT+Sniiej87DTQkfN7dnYWly9fxmc+8xk8/vjjuHbtGo4dO4ZsNsvTDcXNUOgk8T0Ex9brdSQSCZ50f97nFy4U5CikgycSiTA3N4dGo4He3l54PB7Mzc1BJBLxCC2VSnl09Xq9HNhG+Bthh9QFZTIZlpJS/AVhwrdu3YJKpeLDMjo6ikwmg1u3buH48eN46KGH8PLLLzOhLpPJkEwm2fxHhjWTyYSpqSm0t7dz3s7c3ByTYgD4i1Sr1VAoFKyUAsCThk6n4wuSSHOKFyHDlcPh4OBBkUiE9vZ2JqIpKLGnpweRSIQTanea72gykclkCIfDnJtFBZWwdcIyKRvJbDbzQSQV0vb2NpuVstksrFYrczXEjSwsLOBjH/sYzp07x9EL09PT7Ap3uVycz0WxLLlcDr29vVhfX+cxWi6XY3Nzk6fLpaUlliRTEFs6nebLnxJyQ6EQk8DFYpFjEcxmM0sTK5UK/9/IAUv8mFQqZee0Wq2G2Wzm5FTKWKIJjTKkSGRAclRyygLgS0GhUCCVSnFGF3CvcdLr9Vx0xGIxyuUyAoEA53lRwZ2ZmeGk5Gg0iiNHjuDo0aO4desWrl69CrFYjPb2du6aaWIn5y7Jtim7qFKp8Put1+s5loLiXzKZDN555x0cPnwYMpkMg4ODzLORuOPNN99k6bjFYmHY0Gg08tRNOVEERRGnJJfL7zOakWS4o6OD3fnBYBDf+973MDY2hkceeYTNfD6fjwsucQN2ux2RSAQmkwkrKytoNptQqVQcrEcqqfPnz6NWqyEcDjNXQmmqarX6vuwkgvycTifDPAKBAHq9nn8GKeByuRyEQiFnfuXz+fsSZ81mM4tAyHNFwgKv18sGT4fDweZdKtaVSoXvmVAoBJVKhdnZWeh0OlitVp5qybQYDodx+fJlGI1GDA0NYXt7G5ubm5iamgIAFp2QDDifz6O7u5uTKgqFAtLpNMvRSRTxgRUKwu2J5Esmk9je3sb29jYee+wxLCwsQCwWc55LoVDA5cuXsb6+DpVKxSmoZBYaHBxEOp3m0ZVUKsA9ySlJD4kroBeRoqIpsfPMmTPo7e1FsVhEOp1GT08PcrkcE5YUhUEOzzt37nAUMRUdmmYos4miuynugeSZFPdN+nmbzYatrS309PTw32yxWGA2m1mGqlAosL29jeHhYR7rKSE1l8thY2ODcX4yjRUKBc6WISKcOBbKwKLuYm5ujrsiInXb29t5xCfnJiVhzs7O3qeTJ4dnq9XCRz7yETzxxBOw2+0IhUL46U9/ypLUQqEAn8+H9vZ2rKysMClI2UhisZiD0yg9ldznyWQSkUiEA+coWTedTqO3txc9PT3cUGxvb6Ojo4N16UNDQ1yoRCIRuru7IRQKOZSNcqD0ej02NjZw6NAhyGQy/nnk2PZ4PIjFYhgeHkYikYDP52MjXSQSgdPp5LDDXC7HBCF1+uSXAMCTYTweZwhmZ8GjWOxMJoNr165hZWUFX/nKV2A2m3H37l0MDAxg7969uHXrFsrlMjo7O+FyubC+vs4Hm1JZqVjQ5BOLxTi7SCqVYnR0lCMplpeX0dPTw1AhcQYkZ6bUAIVCwSo2gog6OjoQiUR4+qUgPbqciKsjQpgIaCpUs7OzWF9fx+HDhzlyZnJykrv7jY0NVghSsSM4cGlpifk3unypcAkEAm6S2traMDY2BoPBwHJaii6njCyXy4VgMMgSWyoAlCrr9XohlUp5QiSVEqnW6GyTks5gMCCZTGJ9fZ0hQZKqEvdXrVa5QDebTUSjUTSbTfaatFotCAQCbgLoYt/ZiHZ1daHRaODrX/86yuUy9u7di83NTbz33nsYGBhAOBzG4uIijh8/jr179zJHS2kPO+PxCaZta2tjjuoDKxTkRqZOTCAQQK1Ww2q14n//7//NccJUOFQqFQYHB9lcRzg7YYaVSgVbW1tMKpP8lqJ16SKiMVGn02Fra4vzTSKRCDweD1qtFtxuN65duwadTscvYTqdvm+EbjQa3O0KhUIsLi5yDAk5pCuVCqfMkumNICvKolKr1dzlEklGeSpkuqJdA0RqURyy3+/HzZs3ceTIERw7doyNgqTF7uvrQ3d3NyqVClZWVnjCWVlZwf79+zmAj2LSCeslg5JYLMbw8DA7ZYH/k4tFXTcdVCo0BCeSC3lmZgZXrlwBAO6e6SBotVoEg0G+JEgdQ2ohukiJWDMYDIjH4zAajdyVW61W3L59m4nSWq12X4Y/FTmn08n5TMSPUeNAsljK1pJIJCxvrlQq2N7eZnkrpQRQsaR3kKSd5OOh6IZgMMgyWaFQyFH65MUgMp+8OrVaDTqdjrN3ZDIZdu/ezRPZ6uoqjh49il/6pV/C1tYW/vIv/xJerxcrKysMmw4ODjLOT0WZpgaCmYjjabVamJub42mVmiGaRihiPRAIMCFLCkJ6bwHgySefxNraGstCJRIJu5EJNiYlH4VWUqoykaU7hSo0VVMhPHnyJDo6OmCxWLCwsICNjQ1GEA4dOsSR6l/72tdQKpUwNjaGU6dOYWZmhs1qpAAMhUKcIE1EMq0hIL8K5aRRZhJN0pRITFMATT07gyKlUincbjcKhQJP4WTOJZc4BYImk0luXBuNBotbiHft7u7mYlutVpkvrNfrHCdPzRM1WDS5yGQyliD/5m/+JqrVKgYHB5ljJdUmmXGnp6cxNDSEcDiMbDbLAhU6G5Q6sLKy8sEVCsKd6/U6stksHnnkERw6dAjf/OY3eZnN/Pw8fvzjH+PmzZv4z//5PzNpViwW2bwWj8dhtVr5y5JIJLDb7VhbW0NbW9t9HTUVDpFIxASb0+nk7oMmkmAwyMoaisKgTpJ+966uLg7ootTWra0tNjzR4TQYDMwREJlNoX40NdBITIeU5Ju3bt3iYLRkMnlfnPDs7CwrRtbX11Gr1dDV1YW1tTWsra1xkaK0Vwpmc7vdHGeg0Wiwvb3NZDxpt2u1GkwmEyYnJ2EwGDA4OMgTA+HtmUwGe/bsYRim0WjwASyVSmyopJdu9+7dUKlUHPbY3d3NuyvI+0IREvF4nPO1TCYTT3Fzc3MMi9AyqEAgALPZDLfbjbt373JMAhHk1HXRfoFcLodSqcSSbAAM7ZGKq1gsYmNjgxsDys4h7w6p20iYoFarueARJEUFk/LGJBIJq1CItxIKhSgUCgzhuN1urK2t8bInp9OJdDqNbDaLtbU1zM3NMdn/yiuv4Ic//CFWVlbwyCOPYM+ePazYIbMVGRfJN0MmU3JzU7PR1dXF4gQKRKQiRtAa8XjUtft8PtRqNfh8Pjaxdnd3Mz4PAMlkkuFil8vFCiYA7FehCZtgQRIL6PV6dHd3Y3t7G++++y48Hg/naxWLRRw8eJDjLDo6OpBIJPCzn/0MarUanZ2deOyxx9Db24tEIoFr165hdHQUuVwOsViMyW4AHMVCOVR2u52LK039lBhNwgmKOSEkwu12Q/9+pDudK5rKaJETSaEB8ERKsJfL5eJ3lDgcKrg0/dJ0SZ8nGV5JXkupzfSOikQijIyMQKfTwe/38x1FuVnUQNNdSJMoSaRNJhPsdjuHMBqNxvsMjx9YoaDDYTAY+OBRzO3U1BRqtRo6Ojpw9+5dLC0t4dKlS5iYmGDDHckpm80mLwoBwGPZ8ePHOXaciPNsNovBwUFcvXqVX1ZS85CCYScXQpvBSCYpEAjuW1Li8Xhw69YtNs2QIoAUCNQx2Ww27Nq1C36/n2El2v9APpFsNstTAGUv9fT0sFuTlAbpdJojk4VCIT7zmc/gJz/5CT772c+it7cXbrcb77zzDj71qU/BbrdjfX2dXcfkT3G5XOzcpV0Ku3bt4pcpkUjwDgVyOO9cHlMqlTh2we12o9lscqwF5fiTAoUuBMI4aZMhBZ7R30MmS8q0oQmAtm/p9Xr2StBhLBaLUKvVWFpaYjMcTZzkeyBHNR18ig+vVCro7+/H8vIy7HY7L4JqNBocvkgPSYeJP6DkXorpIJ6lXC4zREkwDynnKJSRXLMUAEnNDU0TarUabrcb29vbaDQa3P2Fw2FsbW3h8OHDaLVa+NM//VM0m03s27ePYaBWq8VaelIx6fV6bk4I2iJSlIyexJfRd0NBjZSFtb6+zhlC1MzQrgnazre1tYWtrS02jqXTaeZciDBXq9UYGRmBz+eDSqXis0qdPMmNCZufmppiN3U4HOYNi2TSJe6FOD9KctiJo+/fvx8rKyuYmppiSDIWiyEajXKDSf4UWrBEly5BZXRO6MLW6/XM8dBnQs3Szk1zpVIJHo+Hmwoyq1KE/traGjo7OxnhoE141JRqtVr4fD7O/Tp79iyEQiG/K7TfgrwpwP9JRqbzSftYDAYD1tbWIJPJcOXKlfv4DDqLwWCQzzg18DsXigUCAf63f97nn2VxEeHR7e3teOmllxAKhdggY7fb8cwzzyCfz0MsFmN5eRmbm5sYGxvDrl27UCqVeIELmZ0oyoOy4ElPTeNcNpvF1tYWx++WSiWeCvTvLwKh5SakYpBIJHxYgXv29lAoBKPRyE7MnUuRaLKg7VW0DIheFnrBqcMliST9PoFAgGEMmkRoixUZYKxWK/x+P+8PoC+ODuC+ffvwxBNPQKFQYH19HcViEYuLi6yFpjG9UChgYGCAs/Apc76zs5MjDxKJxH1GOMLZaQKhPRg0ubW1tXHkABH0Fovlvg1sxPFQB08Tz04tN8WeEBdFkwzp4wnD39jY4P8O/TyBQICuri6k02k4HA6srq7yZ0R4PMllSahADYHJZIJQKGR+iYpHKBTigEqCrQivbTabEAgE6O/vZ9KSiGGBQMBxGVRUiBSkzrVQKPDl2NHRwdMlQZx+vx/5fB4nTpxAIpHA1atXcfLkSeZ3fvSjHyGTyaBareKjH/0o7ty5A5fLhd7eXqTTaSSTSV4E5HA4sLKyglgsxjAXXdhOpxOZTIY5lVKpxIIJiqmgKHDKbKIumwIIM5kM+wGazSb7XWjFcLFYZP6C9jmUy2VsbGzAZrMhn89zVhlBMKTSI96H8smuXLmCqakpGAwGHDx4kFWIKysr+OlPf4qVlRW0t7dj7969vBCq0Wiw/4rwfII5iSMgnoKi/okkJ9EHcTFyuRzd3d1cgMkkSihCPp+Hz+dDsVhEW1sb9Ho9F5F8Ps8+E5oCFAoFbDYbnxOamoF7TS0ZTWmyIBVeq9WCSqViEYBKpWI4jR6apCUSCQsdKHWa3OZtbW0cPUN3IRH0tB+bTNIfWKFwOBz3yQ0pArvZbKKvrw9jY2NIJBI4fvw4Ojo68Jd/+ZeIx+MYHx9Hf38/wuEw9u3bh/T7abC0z5rweVJ5APcHCALgQ04jKHEY1EkS+0/b0qrVKstMCaaii4ngDyLVd+7kplA/2jBH6yFprCStM7m0KTt/bW2Nu3qS3JIb3O12s6yQ8NyOjg7s2rULUqkUAwMDmJ+fx89+9jP4fD7cvXsXIyMjHFZHBjkKDyS1UPr9ndk3btzAgQMH8PGPfxxerxczMzPo6+vDwsICj5zFYpGJPPqsAaC3txder5f5IXrZyW1PHALt61Uqlcw1UQGiVF9yxBPhSuF2NNFQoaLvjJRXdPnRdDg7O8sGRUrhpLwqUkaR/JUytYTCe/uWe3p6uNin02kmoYlLIa07aeVlMhlkMhm/g0SaUmc5OzsLg8EA4F4qMO1FIA7LarXyBU5KHIlEgr6+PigUCrz33nvcXbZaLZw5cwalUgl9fX24c+cOXxTE+VSrVZbZkgSbIIZms8kQhUAg4KU7lPVEcTmrq6uQy+UwGo28DbKrq4vz10qlEq/VpLwjgnF2797Nzv0nnngC6XQar7zyCoRCIQ4ePMgGOso0IvMYyaIJhiWlHzmfLRYLzp8/j4sXL8LlcrHng1SDf/RHfwSTyYT/+B//I6sGSRre1tbG/ieKWqd7h7gUclNXKhV0d3fjzTffvG/LIkGk5AWhHdMAWAJN0HT6/VRqABylotfrEQgEUCqVOAKe3juSpwoEAuY5dDodm4rpzBAnQnco7Z2o1+uIxWJ8N9H2PYfDwdsA6TMhRRZBnLQugc5oIpFg+S3BigQ3/rzPL1woAoEA9uzZg0AggEwmg6NHj0IikeDZZ59FuVzGn/3ZnzEM8du//dvYt28frl+/jnfffZdjogcHB9Hf3w+Px8PbqXYeeopC2BkLQfn6O/fiklmOkhnb2trQ1dWFzc1NlowShkoeDLosqTsXCoXYtWsXy/JIaUHk4ZUrV9ihSZEQGxsbcLlc6OrqQjQa5aiOnXI/Okx0CGmbnVAoRHd3Nytitre3EY/HAQAjIyO4cOECrl69it/8zd9kfXRXVxcWFxehVqvxzjvvoKOjgy/C7e1t1Ot13LlzB1tbW4ynE9n+8MMPIxQK4cqVKwyT0W4OmUzGozdlc0mlUu4I6cIDwFMIpYxSd02F4NChQ9yZVSoVvPPOO5yjQ0WCIL6pqSns3r0b8XicLxVaNdrV1XXfDmKxWMwBZyQpFgrvrfukBE6SM6ZSKYRCofuUSYRN03+PSHw6wBSLQlMCEeXETVGUBTUSHR0dUCqVHG1erVZZet3b24tKpYKuri4A96SOFGu/uLiI8fFxmM1mJJNJPPzwwxgdHcU//MM/4M6dOxAKhbBYLNwV0qYz4vZ2XioU80IdL0FnlBpArn7y/6jVauzevZtzuQhC1Ol0fHHSzyIRyN27d3lfw/r6Oi5cuACTyYTOzk5eJCYUCtHb24ulpSV0dHTwljWdTof5+XmkUikOZ6S4e5FIhGeeeQaf/vSnEY/HcfXqVZ6AKfGgt7cXrVYL+/fvx9TUFIRCIXfkJDOlokETNUnC6U547bXX+F02mUyczkCO7IWFBU7+JYELxZOoVCpOXCChhEgkwvLyMudikTzd4XCwgICUTwQP01mgHeF0p1GzRVMqmZapYNDftTOixmQyMQRGMmmakIh7oeleq9VyuCk1y1REP7BCQXIvqlC0Q/jmzZs4ePAgHn30Ubz++uvo6OjA2NgYjhw5gpdffhmTk5Mcfvb6668jHA7jwIEDPFLl83ncvn0bVquVlTJEEIlEIs5vp/FTKBRifX2dA7hIs03YJyk3ZDIZB9IRPh2NRpF+fzdtIBC4b4EMjeQkM6Ul6bTPtr29nY1PhG1qtVoO6yMMcud+Y7pwSb1TLBbR09ODH/7wh3j77bf5Av+v//W/8pISCqIjVdjObor2FhQKBRw8eJCDxyiq+OrVq9BoNOjr60M8HkdnZycXX71ez2YmcgZTqq3b7eZJgIgzMgAB/2exTjgchtPp5H0Eer0e169fxwsvvIDPfOYzmJiYYP6DXPYej4c7tfb2dnbK06Y/o9GIra2t+6JKSDxAmw+p+6WsKLH43n52SvYl6Sj5RahwkQJFp9Nhc3MTANhHQGnDsViMYxdoRzlJHOlvIJcyraSlAkq5UISJ03RLUw+R0I8++ig3GmQI6+/vZ7VOKBRi5z+pedrb2zkaZ2Njgz87Mji63W7kcjmO9aDUYFK26d9fqEWikZ0S0Xw+j/7+fsRiMQDAwYMHMTw8jI2NDTSb99aI3rp1i5um7u7u+wISSTRAYgfKLSPxRzweR09PD9LpNIfWDQ4O4tKlS/jLv/xLLCwsoK2tDZ/97GfZeHjp0iWcP38eQ0NDeO211ximpbNG7yLJWmlrXUdHB9bX1znyhBoaOkN0T5BMn6J96CKmne4ER5EfhfazU8ElWLmvr4+LC703lHgQCoXYSEuCj1QqxWIZcp2TIouysojXoVQCMuaRgKHRaDCiQGpD2i6ZzWZhNptZzVetVpnHVCgUqNfr3JB+IIWCRjzCPhuNBsLhMI9IR48eRXt7O6RSKQ4cOIBEIoGLFy9ienoaer0e8XgcFy5cwPXr11kPPzs7i7Nnz0Kj0cDr9aK7u5sz66mDoax4utTJkUrx3AAYFyesmi5+2l9MTl/C6hwOB0MtZB6jPd2pVAodHR0wGAzY3NxkbJHwx0KhALfbzSFfpASjF0Gj0UAikbCDWKvVcie4ubmJQqHA+nMKJlxcXITL5cLu3bt5UctORQ5hsju1+sPDw0y2Uz7Oiy++iHq9jsXFRWxsbOCZZ57hC7rRaMBms2F9fZ3J9XK5jHA4DJVKhc7OTgBg+aPBYGBokA4ScQhbW1t8SS4tLWFhYQHPPfcchMJ7i1JIfSEWi9lJT5sJd6bk0p4Qi8UCtVqN+fl5NhhSNDq9Y/F4nB38BBHS/xCURkqPnUWDkj6pc6bsKwC8bpI2mWk0GpaXkgiCLtOdXBgpf2hBDO1F2fl9h8NhxsMnJycZnpmbm7uvuKvValatEITaarU4q4je95WVFd6vQcSy2+1GNBrlBojEAj6fjztoAFw0aMMcxW1Tdlc+n8fPfvYzJtcvXbqEl19+GYuLi3x+CKYj0jUQCCCXy933Xe50ulMq6+bmJsOsiUQC58+fZ2n9pUuX8N577yEQCGB4eBiNRgPz8/NoNBr4yEc+wjBMIBBAW1sbR4YQ95XNZrGxscFEcj6f5/NIhk9SBWk0Gm6KRCIRbDYbF0IiiokTbDQabHjt6urCnj17UKlU8NGPfpSbvnfeeYffc71ezztSCoUCDAYDE/AUKqhUKrGxscF+inq9zk1cKpViXxBxqNQQUvHKZDJIJBKw2+0YHx/nbDWSvv70pz/llFoywRKv8YFGeBDpR/rmhYUFBINBvhCy2SwGBgaQTCYRi8XwxhtvYGNjA3fu3EFvby8GBgYwPDzMmSkkb02lUjhy5Ajvf9br9TCZTIjH41hYWGDIiA4xvQhyuZzjp2nzF6Uz2mw2TnItlUowm81oNpvweDws5STCloxp8XicoQhaxUleDNJbSyQSJtQog57IUjIMbmxs8MXe3t7OnAZdzOfOnYPZbEa5XObws9u3b6Ovrw/j4+OsWSdyHwBcLhfy+TwkEglCoRCGh4dx/fp1TE5O4rnnnoPH48Hjjz+OkZER7N+/Hy6XC9evX+fPgMLpKFaALqhWq8ULjnw+H19yJBggiabVaoXT6eSYB4FAgMXFRQwMDOAzn/kMCoUCZmZm8Oqrr+LkyZOcXUV6flKT0QEHwE5qtVrNxClFbdBnTx4bcrRScZXJZMwRxONxhq9o6RIt4CHlHHXyNKVZrVZOg6XtifQ5tbW1cfw7FVfim2hibWtr413utK6zra0N6+vr3D2SJJlC5dRqNSKRCBwOB+vxaaOa2+0GAC5WBPGRE77RaEAsFvNa03K5DL1ef99SJjLC5fN59PX1MRSTSCR4CZdUKmXvg1KphMPhwPT0NGZnZ3H16lX88i//MkZHR7F7924cOHAAMzMzyOfzOHfuHEfHNJv3tvqRI5myjcxmM0d+0FIfMnImk0l0dXXht3/7t3Ho0CEWfvzwhz8EAJw9e5b3zBCRTv8WTXQajea+FaTEf1G0PIkvKBadZNLES7VaLX6vyGOk0+mQSCQ4xRa4p0Ki8E7a70EN6uXLlzEzM8PKzbfffhsAeKtfW1sbgHswPTUT5XIZS0tLsFqtLN8vFos8AZA/RSQSMacAgJVR3d3dDDF2d3dzsOTGxgYqlQoUCgVGR0fxhS98Ae+++y4n0FJjDYCL1c/z/MKFQqvV8uGi4kDj99LSEprNJsbGxqBQKHDjxg1MT0+zkWTPnj34+Mc/DolEgkbj3u7sW7duYXV1laONZTIZZmdnce3aNTz22GPo6OhAb28v7ty5A41Gw/sGSJ5Iv0s2m2W8lb50IkLpwNIlQbIyws2p4yKpLuH3pBygfKad+mwqSIRfk1GJiPi9e/cytgyAzW0kyXv77bfhcDh4kx7J8ZaXl5FKpdiPIJFIeOMfSehsNhvv03U6nTh//jzy+TzzE3v37sXBgweRz+exsLCA+fl57N27FyqVClarlVeakpkom83yhWq1WpkgpZRQ2t1BRKlQKITb7eaNWXNzcwyFtLW14ciRIzhw4ABWVlbgcrlgtVoxMjKCXC7HnzWpUyjfKBKJMJYNgDOpNBoNSqUSd6qUAkpqkc7OTkQiETZVkZKNwtEIGjOZTFAoFJx3Q5Mk5QrR3wXcIy+tViuWl5ehVqu5UNHyInInk5elVqtBKpUynKPX61nqS82O1WplQQVFVBDZSdCbyWTiaZQ4O5vNxpcXRYrQVkgSFNDaTjKmRSIRNnXR+0gx7Tt3pxAW39vbC6vVisuXL8Nut+Po0aNM2h44cAB2ux2vvvoqXnvtNV4hvH//fk4NoOaHxA4klaXvWK/X4/Lly+x7OXToEB566CHu5Kk5dDqd2NzcxMTEBLv4qSja7XY4nU74/X4mzGmfCEmLlUoltFotcrkcJ1GT6IAUXTTtkvmt2Wwik8nw5UwNCHEAwD2uKZ1OY3JykhOM0+k0xsfHcebMGfT19fE7ubi4yDu4iT8hpRmJcGiqzeVyGBwcZNUjBSTuLOgE0QaDQb789+3bh1wuh+9+97vwer149913YbPZ8IUvfAHt7e04cOAAvF4vSqUSey127tb5eZ5/lp3ZR48eRSqVYpeoTCbD5z73OVakrK6uclx0NpuF1+vFL//yL8Pv9+PMmTPQ6XT43d/9XVa4HDp0CO+99x6effZZJomvXLmCN954A5/61KcwPj4Op9PJS0fosJB6gbBXIrQMBgNnNoXDYd7eRiYX8k+QE7u7uxvBYJChGXLjUrzz1tYWJiYmUKvVePuWSCRiLTZ1uZSWq1AoMD8/j3Q6jbGxMY49J0iEoIxGo4Hx8XHU63V2Zmu1Wu66BQIBRkdHGeIhJUksFuO4a3KQP/zww9i7dy/8fj++973vcVGRy+X4/Oc/zzJRguNyuRx/Rjsz9EmOR5cmwU7E3WSzWaysrMDpdEIul2NsbAzvvvsuZmdnEYlEcPz4cYyPjyMQCODf/bt/h09+8pM4fvw4rl27hq6uLkilUqytrTG5qlarMTc3d18eD12O5PvIZDIQCATweDyco0TvAX0mNBmRO3unAcxgMMDn87FBipJc9e+neZKklvY90yRHMCsA3qEyMDCAQqGAaDTKcQ87oyyocKTf35yXTCa5y7dardjc3GTfTTQa5awfkghTCCBFtBC2vjOGxGKx8ERHRU7//oKhqakp9obQ9y2TyVjpRhseiYMLh8MIhUL4yEc+gkajgW9961u4ePEiVldXsXv3boTDYaysrODy5ctYXl7G0aNH0dXVha2tLYZvqFOnS5CKCTVF1FiQcfL1119nKbdKpWLJuM/nw82bNzEwMMDTDhl8G40GeyxoUx2FCpISrL29neXUZFBsb29nWDWfz7Ovhjgmt9vNvgUAGB4e5l3e9M6bzWYcOHCA90WQpP/s2bMIBAJ47rnnYLFY8MUvfpF33EcikfumSoLPWq0WpxnThECKufT7gYMkvbbb7ew4p7W4e/fuhdvtxp/8yZ/g+vXrqNfrPLH/h//wH/DMM8/g6NGj2Nra4kaa/m6aUn6e559lH8Xy8jJLr0gbT6qFQCDAByaXy2F8fBxbW1sc1EW59K+88gq++MUv8sg6MTGBr3/969BqtRgeHsbNmzexuLiIN998E41GA93d3dypEaFOB5EkrWS0AcDjnslkglKpZOkiBYjJZDL+UonoU6lUzClYrVbuVjQaDVZXV5mAKhaLbOYiez91IITz0g4Ns9mMnp4edmITL0AKI4rJqNfr6OzsxObmJjo6OrC8vAy9Xo+5uTmWEA4NDXFxrtfrWFlZwcTEBJ566in84z/+I3p6evCDH/wA9Xodd+/exZe//GUcPHgQo6OjePHFF1miqdVqkUqlsL29DY/Hw74YijggJz051BUKBba2tljHvWvXLpTLZY7kOHr0KJsttVotrl+/jitXrqBUKuHdd9/Frl27eNqhYEbKtiJHLh0k2iZGsAORiAB4XSaRyWTeBMA7AlZWVpBMJnmqaLVaLBVtNBoc46HX61maS4t5CNYgiSLp3gHw70TxHNTNE9xFFw35fkge7XQ6IRQKEQqFGEIhA9qTTz6Jvr4+TE9Ps3RWoVAwzCKRSDgkslwuw+PxMLeSy+WwubnJDQ/lgwFg8YRQKORJjZQ6JBAh6JgakKWlJS44lKtWLBbxve99DzabDWfPnkVbWxuGh4e5oJNCkCYhi8XCOyBIfECGz46ODg5ejEajLBqgtOHJyUmIRCIYjUbO3zKbzTxhkfmUviP63NVqNZPGtGaWsrrIw0OpCOSJIfUkmRdlMhmbJUkSTv4ilUqFsbExJo5rtRrOnj2LgwcP4tVXX4Varcbx48dhMBjQ19eHQqGAlZUVtgJQU0KxLJR+0Gw20dbWBplMxplNEomEiXfyrjQaDY5WIQVaLBaDQqHAysoK+vr68IUvfAF37tzBwsICms0m1tbWcPDgQfT39+PmzZusUvtA5bGRSATt7e2cHKl/P4Stp6cHgUCAl7UQCXzs2DFWDu3btw/j4+Po7OzE6dOnWXpIB3dwcBCvvfYaxsbGsG/fPjgcDvT393PnAoD3RHR0dCCfz/OyD9r3S2YviUSCtbU1lokR7k17lKnLIPUASQEpIZK4AEoKJTdvIBDgi7qtrY07H6FQiOXlZd6psL6+jjfeeIM3hNElQqM4bTyjeGQyUNFuC7Vaja2tLQSDQd7nS8uPqMhQgazValhfX8e7776Lo0ePssyQpMR37txBvV5n2IVeRiJBiXgmBzVBMnL5vT3dFLtCB5y4HJvNBpVKxZf0xMQEdDodXnjhBTgcDs6seu+993D37l1Eo1EcPHiQM27I8SwQCLCxscECAKPRyK7dT37yk8xhUWHW6XTY3t5mGNFisWB5eRkOhwMKhYJNVCSMcDqd2Nra4mwnUtUVCgWEw+H7XN2JRILThUnxRM3Qzo6WJNokE6UQQeIzaEqjy4WEBiR1pH0N58+fx8bGBsbGxrBnzx6EQiGO0y6VSnA4HDxBkNuXvCF04SQSCZYJE6FMKiLiCgjWI0EA7VigDKTbt29z5H5fXx/7OvL5PCKRCCYmJnjvDEWV0wa6WCzG3bbJZGIYjwh4ysoiHor+beI0ALC3gJZIkaxdr9ezkIDMsWSyJck8deeU2Es8CRldSYZKIhDy1QDg5nVhYYHPBv1OdG/EYjGsr6/jm9/8JrRaLQ4dOoQXXngBFy9exP79+9l7QRPtmTNnMDU1xfyQyWRi0Qc1JNSYWa1Wdsm3t7cz0mA2m1EoFLC1tQW9Xs/QN0lwaUo4cuQIms0mlpeXEY/HsWfPHiwvL/PGP1pPq1Ao2Bfy8zz/LIY7OqgUeGa322EwGHg3rsFg4JdZr9djcXERYrEYFosFH/3oR1lJkEwm0dPTA71ej4sXL+L8+fOIx+P4zne+wx/G6uoqTw3/4l/8Cxw8eBDT09NMZhOHQKYyCtUi3wTFUNNLT0QnmYuo2yEnLEnMaL80FRIiUIeGhhgnJ5WKVCqF3+/H5OQkzp49iw996ENYXl5GPp+Hw+HA+Pg4r2Z0Op1YWFiAx+Phjog6RIpXDwaDDPnodDrUajVWBQkEAt6zTFJh2uOsUChw8uRJDAwMcJig1WrFysoK69spF4hMcTvTLClV1Gw2Y3NzEwqFgnX1O6MuqOO6c+cOHnvsMYZZ7HY7Tr6/1S4cDuOpp55CJpNBoVDA7t27YbPZ2OBIpj7ar3zw4EF4PB7+eRcvXuRMqjfeeAPZbJbVPsVikTOIrl+/jvHxcTZG9vX1sbKFYAYqINS17dz1TS7tjo4OJvkB4NatW2hra2NRhtlsvm9TGPk0DAYDd/N0+RQKBd5jTF4RrVYL/fvxGkajEaurq5iamsLf/d3fsURzcXERtVoNTzzxBA4ePIhAIIBXXnmF13uS5p64lp0yVXLqknpqp2CAMqeGh4dZEkxwGEX/j4+PI5vNYnJyEs8//zybO8fGxqDVanH79m2Ew2Hs37+fEYJdu3YBACvYyBVOcDA55wny2EloA+AwTYqJCYVC6O3txezsLKRSKRwOBxYWFhi6okLd2dkJn88Hn88Hi8XCS38CgQCLI6h4k1y5ra0Nfr+ffS9WqxVdXV0IBAK8npZ4TRJ05HI5+P1+PPPMM5ibm2P58bPPPstrSXc6qv/wD/8QBw4cYOMqqb9oUlcqlVhfX+cUikqlwtlkFMtBPOrGxgaLXYifq9frGB0dRavVwuTkJPbt28cKusuXL0MikeBrX/saT1Kbm5t4+OGHeVIhjuvneX7hQkHBcBTCRrjj7OwsR0YQzl6r1TjpNR6PY2trC/v372fseXZ2Fr29vTw2qtVq/NZv/Raefvpp/PEf/zFefPFFtLW1ob29nbHkra0txGIxdmdSl0PmNuItstks55+QnJX8DUqlEsPDw2i1Wkin0xzERt0Q/W1SqZRlgzQ9zMzMoL29neGper3O+6BJd97Z2cldk9VqZb1/NBplqS7BX9QxkEdBKpXyhUAELPENAoGAlVt+vx/l8r0ta3RwPR4Pbt68iRMnTrBy5NatW0xiFgoFXjYlFot5vNdoNAiFQtylk7qM5MZU5ClDhoIfBQIB1tbWGFKzWCyIRqMYGhqCy+Vi8l4sFuPUqVOQyWSIx+NYWlriyenUqVMoFouYm5vDxsYGVldX4fF44HA4EA6HWSq6trYGl8sFn8/HcdylUgmdnZ0IBALo7+/ny5J4glqthtXVVfT19UGlUsHr9aLRaGB5eZmd8jTS+3w+bijEYjFGRkZYbUcJtCSJpomODFTkS4jH41zMHQ4HK6EoBI+8OMlkkiHHgYEBZDIZuFwufOtb38LAwACT88899xxHfLS1tWH37t3w+XwMcVqtVv49aGrZSRCn02lO9B0dHUWlUkEsFuPVw7S+FbjXAI6OjiKfz+PRRx9lp7zD4YBQKMQ777zDe0Moep2Kvkgk4ilJoVDcF/q5E2qjSY5+fm9vL/x+Pzc5Go0G2WyWY3ZIol2tVrG2tobu7m4kEgncuHED+vd33kciEd6JYbFYuNEitRxJ+AOBAPTvZ1gRcT0/Pw+73c6TJuXElcvl+yKCQqEQPvnJT+LWrVvMzR44cACDg4PY2trCpUuXMDg4iMcffxxtbW0MkZKAhWI8aFImQ67RaLwvB4uaANqpQS5/aoKlUilmZ2e5kdva2oLT6cQTTzyBcrmMyclJHDt2DJcuXcLDDz/M3Gwmk0Fvby9efPHFD65QUOAYjbQAWDtMBaGrq4sJRq1WizNnzrCu+tatW6xE6OnpwfT0NGw2G44ePQq1Wg2n08mZ8w8//DB+4zd+AyMjI1z5NzY20NHRwXb3VqvFBhTqKjc3N9k1nX4/xVMmk7Hm2OFwcOxvsVjklEga5aPRKLq7u7Fr1y6Ew2Hs2bMHHo8HpVIJ3/zmN5HP59HR0cEO2MHBQcZ+L1y4gNdff5270QsXLiCZTGJwcBAul4uTNtPvh+qRW5x06JRQSuoUunBIETE3Nwez2XxfNtG+fftY407d6vr6OiqVyn15VDvXThLHQPn6tEmMcGEii0n6WalU4Pf7WUtOumzaZLh3715oNBqsrKygo6MDo6OjuH79OrxeLzcHoVAI4XAYu3btgtfrRbFYxOrqKq5evYo7d+6gWCwiGAzizJkzOHz4MLxeL3p6egCAc/p//OMfw2KxYGtri4s1/S6Tk5O4du0a9Ho9jhw5gr6+PmxsbODmzZsYHR3l/KZoNMpjOJGglANlMplY7aLT6RgaJKw5HA7zxEoJr+R1oSmDlHYU/01NQjgcRk9PD9xuN7773e/yZES+gGPHjkEmk2HPnj1IJBIYGBjA5cuX0Ww2sbm5yd8nXTYUhU3mM4I1EokExzgQeb0zNp0I+3K5jFKpBLfbzbvrz549i3PnzjGUtLq6ilAoxGKSZrPJTmNK0KWpWKlU8qQzODjI5k2JRIJsNguTycSZXxRwWS6X+V2gnRDkGieIqFqtoru7m5s3WmRGck/iVSimHgArKykChPiknXHi09PTzHXQYqb19XUmz2m3A5nXHn/8cZw8eRLVapVNvMViER/96EexuLiI6elpmM1mnD59GpOTkzzJb2xscCYVQX6NRoMz0jKZDEfv094MMsnRultKgqU7lvKvKBLmoYce4kIWCoXw5JNPsnk5Go0iEol8sD4KGrMpK4cka4QrksOwu7sbPp/vPpLG4/FgeXmZXY/z8/NceZ1OJ1588UX89V//NYexUQjZzMwMKzpoFwYFtQUCAYhEIvT19SEcDmN7exvl8r3tc0tLSxwMqFKpGOMl7wP955TPFIvFsGfPHhiNRnz4wx/mUfyb3/wmnnjiCbS1tXGuPhmDKOWVvB8HDx5kyAO4lw1ExCx1rWRQarVaHBoolUrZvW2z2RhLFgjuLainBFwSDtAFTQ7TiYkJNsNRXhAtMqJIYnI553I59h5QvAhFcVDH29/fj0qlAo1Gw4oi0t6T9p9ik7u6ulAoFHDp0iX2LPzwhz+EyWTCgQMHWN5LRGJnZyf6+vqgVCqxsrKC6elpTE5OwuPxMBdDooMLFy6g1Wrh4YcfRqVSweHDhzmDi4xFdHHRroVDhw6x4OHkyZPo7u5m7Txh5WR41Gq1PJGRtJVIR7qI0uk0ALAShTgVmlpCoRD6+voQCAQ4e2ynogwAFxy32w2FQoEPfehDyGQy8Pl8cDqdOHHiBDo7O/n7CwaDHOFiNBpht9t52uzs7GR9/86Lk4q0wWCAXC5nfJ8WMlGWGcGH1NlSjpBSqYTL5cLly5dZMk2rAVQqFRcUUtKQtHRoaIil4vv378fs7Cz7dYjTJFgulUpxAdjc3ERvby+i0Sh7ASgvyWQyYdeuXZifn4fRaERXVxcWFhZQKBQYUm61Wiw+Ib6P5NVE4NP0ToS/wWDgTt7tdvPkTxMFfb9WqxWLi4totVpYXFzE8vIyHnnkEcTjcTz33HO8dG337t28Wnh2dhZTU1PMbZBZ2Gq1chLz7t27OWOLVGkUQ0RTKX1Wdrsd8XicF8PRBEYowNjYGAKBAF599VW88847nIu3f/9+PPvss7BYLDh06BBHu3ygi4uInCIjDB2W3t5ebG5u8o5mghYouIwUGRTQRTHepCcmx+rY2Bjy+Ty/DJQ8ScWDYBNSH4nFYoRCIQwMDCAQCEAsFsPhcCAQCPDhoNGcMpyI1ALu6fltNhv8fj9PPUajEbFYDH/zN3+DN998E5FIBGazmXf/7t27F8eOHWNHdTQaxTvvvMOrTInroMNPmUWU8GkymRCJRBjbNJlMbGIkvgC4x7eQwsdoNKK9vR2bm5tcXAlPp46JnL4UCEZx4xQBQctRjEYj9Ho9u9RJWZJ+P2KaHKAUbUDb/mjHBnWi5Drt6Ojg+JT29nb09fVBLBZjZmaGCbz+/n4EAgGsrKwwZ0GelIGBASiVSuzZs4eJciqIAoEAs7OzvARqdnYWTz/9NHf/lBacTqcRDofxO7/zOxAKhXjttdc46VOhUKCjowPDw8O4c+cOrFYr75SmLrm7u5tjQIjAJ1EEuaV7enrgdDpx5coVThrd3NxkCIoydkhkQWQrEamtVgu3b9/Grl278KlPfQoajQa/8zu/A6fTiZWVFTz//PPw+XzQ6/UYGBjA5z//eezevRtmsxmHDx/GxsYGSqUSw44AWDpLbmzakUIucYvFApVKxRcoQbj1ep0TmavVKn+3kUiEL8xgMMhZSDKZjGHZ5eVlAPfQhb6+PubPyL+wb98+WK1WhmpJBk0ZRzQJ0zunUqkY/szlckyyB4NB/vyz2Sw3BcA9UQ2lTFPnTNwRNV25XA7BYJAj+kktRKID4spMJhM3nCaTiRsi2mlO0xVxC+Q9mp6eZmMfmQrffPNN7vyHh4exuLiI3t5eNl6SmIYUd+T+Jt6kXC6zGjCdTvP0RTCjUqlEsVjEY489hhMnTuCVV17Bb/3WbzEX84lPfAKDg4NYWVnBxYsXceDAAY4qp8/uAykUtFeW8O1cLgedTseSMDKiud1uPugUQ0w7pumFpCUx5PCmHQm05EYsFmNhYYHHJq1Wy6Yu2ndARYO+MFo8RP+2TCbjL5/IYYJORCIRRwbT6tTl5WWIxWLcuHGDo8TJU9FqtTA4OAiHw4H19XVeBpJKpXj5EVV5rVaLrq4u+P1+fnnpS6bcJnLgEgkpk8mwurrKu5ytVitvDaNuUqFQ8FhOpjKSJVMWkVarZbKNRncKFSOIjOSctBKy2Wyip6eHYw0oglwul0OtVvOeBnKzJpNJlraSt4CiD+igZ7NZJBIJzM/PY2trC4VCATdu3EBnZyfHZly6dAl6vR5PP/00PvGJT+DVV1/F9vY27t69i2PHjmFqagqPPPIIy67Pnj3LrvWNjQ0AwPj4OJaWlrgrvHTpEpvHvF4vw5EUHtnX14eenh6kUimGMCm1mAIRKQQy/f6WvY2NDd4HTnk7NpsNbrebOR3C4El1ROoget/tdjuUSiUuX76M4eFhRCIRyOVyPP/887DZbEgmk/D5fPzvdXR0YGpqCu+99x42Nzc52I12gNhsNo75pgILgCXNSqWSvR5kFl1YWOB3nhZoTU5OQi6Xcwz+7t27IZfLcffuXU4fVSgUbHSj9a60j4KWIF25cgUSiQRHjx7F2NgY0uk0RkdHcfv2bfaP6PV69PT0MHRFMTUUL0LngdJ8C4UC+2GMRiNf6PR30ucmEonQ1tbGaj2azE0mE8LhMMe9EMRKIpCdeWI0hbS3tyMYDPIURZHjd+7cQX9/P44cOcKf6Q9/+EPcunULp0+f5kTc+fl5jI6OciMUCoX4niLegeDCSCQCv98PnU4Hj8fDcP7OZVBkKKQ9OB6PB319fRAIBIjH43jkkUdw7tw5xGIxXL58GS+//DKbh9fW1jjahLxRP8/zCxeKnaoZ+hI6OjogkUjw9ttvcwjf9evXcfLkSXg8Hpw/fx5isZjzWIgkjcVizNxT9VxcXMT6+jrOnTvHhC4tZCe5I+G/5HTs6uriXRM0fpL8cmtri6V0u3fvRrPZxNzcHPswSJlBcRy0t1qpVOKpp57CkSNHsLm5CbFYjBdeeIF3DVO0QiqVwvXr17GwsIB9+/ZhYmICgUAAU1NTuHPnDi5fvgybzYbR0VHs2rWLHbC0KY92K5BxzGw2s2OX9j6Qs3NhYYEJfJJMkiOaIrKFQiHS6TSreijsDrgXMUAOcJLX0oRIOzfoM6RF9QA4z4gmDipICoUCoVAI8/PzPBEQ3ioSiXD8+HFMT0+z8srr9WJkZAQikQhmsxkvvvgi8xAulwvpdBo3b97E6uoqPvGJTyCRSKCjowP1eh2nT58GAM74oVQASpr1eDw4evQovvvd7yKfz+OTn/wkjh49im9/+9sMs4hEIuzevRsOhwNzc3MMGa6srLDpjiAAWmlJSajk/6CtiyQrbWtrw+bmJr+jq6urfNl1dnYyr0Cy3FQqxas88/k8lpaWUKvVkEwmcfbsWSwtLcFms+FLX/oSE82Li4uYnZ3FiRMnkE6neXLc3t5mrJ+yqUhEQst8tra2+OKy2+2o1+uYmZmBXq9HV1cXZDIZ2traMDMzw9zhnTt3OFfKYDBgZGQE5XKZIzGIXKU49Ewmg7t372Jubg5KpRIf+tCHsLCwgBdeeAEf+9jH8OlPfxpXrlzBxYsXWYTS0dGBK1euoK2tDbFYjM+r3+/nyA3KKqJUX1IbUm5XMpnkCPhmswmz2cz3gNFo5OgUEsvQThn67shHRR4qupzJ1e3z+dBoNNDb24tIJIJ4PM4WAFJEPvLII5iamkIikUAul0NHRwdv7yNfDHGftVqNzXQKhQI9PT1YWlrC5OQkdu3ahaNHj6KjowOLi4s8MZLjmyYagsMTiQS6u7s5GZvef/r+NBoNv+skoLl9+/YHVyjogmhra0MkEuGRiHYUj4yMwGw2Y3p6Gu+99x7OnTvHjlrCSHO5HMdEU5gWGYvOnDkDi8UCs9mMdDrNLuBGo8GSwVgsxlEUtALVbrdz8ajX64hGo0xE0QG/cuUKQzNkFhMIBKxK8vl8yGazjJU+++yz0Gq1GBoagkAgwNWrV3H48GHe6TA2NoZwOMwqGPJmCIVCXLt2jYlDpVLJZO3o6CiP6fTQYh2KXaBMIqFQyLlYNC53dXXxZEEjP0mJiQsilQ2RmqQqcbvdWF5e5r0RtGubEjZp8qOcJCImd66UrdfrDBGQqYlMXZR86ff7mZOhkMPJyUlOpXW5XNDpdOjt7cXQ0BA0Gg1eeuklPPvss8hms/jd3/1djk6PxWK8x4OKFYVDUkz97OwshoeH0dfXx0qPX/3VX8X29jZOnDiBQqGA8fFxnD59GjMzM5wfRtp0Iqrp83I6nVCpVOyhIE0+7QQhCIS6eIojJ4WbUqlEOp1mjNtoNPIUTP/+pUuXcOXKFYyNjeFP//RP8b3vfQ+hUAg+nw9bW1v47//9v6O9vR2HDh3CZz/7WaytrXG0TDabRVdXF0tBafKlOBuC00ieHg6HWQRBsTAkRCgWiwzpNpv3Nia+/fbbUKlU+Nf/+l/D4/HgzTffxFtvvYWBgQFsb2/DarXi5MmTfKZJ7prL5SCRSGCz2XDlyhXcvn0bu3fvRq1Ww7lz5zA0NIR4PA6v18u4vkKhwK5du7CyssJcCPE0BCFRZx4IBFi2LpFIEA6HGRKlyaCtrY33WNDGO4FAgPT7e1tsNhui0Sg3KwQDEcSm1+sxPz8Pt9vNqbi0fpTuCooWUavVGBoaQrlcxvLyMpRKJfbv349KpXJfdAbB4PR507vkdDrx9NNP4/Lly7h48SKUSiUefvhhXqZFDRzFqdB0R5AyyXgpVv3EiROMDIyPj7MRcnp6mpuTD6xQ5PN5XgBEIVy0DS4QCLBvQK1Wo6urC0NDQxCLxXjjjTfuy52hbKZMJsPdPS3poQgDm82GY8eOQSQScc4KwTCEy+v1erbtk0z3yJEjTE6bzWaOoiali8lkgsPh4LHQZDLxB9nT0wOJRILbt2/j/PnzuHLlChKJBP7H//gfvBeZLkfCPwcGBvDGG2+wMW5sbAwHDx5ErVZjjiIajaLVasHn8zFmTf4DgkxIFrhzJznpyClptlAoMORVq9XuO+REVALgjokuh0KhgMXFRcZ1M5kMm9domiA1FEEEOzeobW5u8sGSSCTcZdIhpXeDVETxeBxGoxEqlQrLy8vo6OjA2toa68rn5uZw+vRp9PX1oV6v46/+6q/w5ptv4vHHH4fdbsc3v/lNVKtVfOlLX0KlUsHc3By7jbPZLOvubTYbx4cbjUacOXOG9yovLy9jdnaWJ6y7d+/i8OHDcDgcGBkZwfLyMsLhMK8JHRgY4GmSkkdpCxyJHmibGsV7FItFyGQyOJ1OjkEn6SgR4qVSiScF/fs7mlOpFH72s5/hIx/5CHp6ethAWiqVMDs7i29+85twOBywWq04cOAA5/1sb29jcHCQeUGKQLHb7Xyh7NmzB16vlyHBhYUFeL1elMv39kBTssDS0hKGh4d5zWkwGIRGo4HNZuP00lQqhYsXLzJ0k8/nMT8/D5PJBKvVil27dnGz0dnZiSeeeAInTpzA97//fZRKJTz//PNIp9P48Ic/jMOHD8Pn82F+fp4XN21sbDCnQ653CqKk5VmpVAqpVIp/dzLEkoGRFICUpECwNsG7arWa47Z3mjQJjgbAghL62RTVQSkGBoOBJ2tSqZFqT61Ww2q1MldFhZsMeJTn5vP54HA4sHv3bgQCAXi9XqTTaZ6QarUaJicnmdMjzsnv9zMpT3lhZrOZY0Jo8i2VSrh16xZ7ZNbW1u4LT/xAOQoKnyNDT7lcRl9fH1KpFA4ePIirV6+yRjsSibDm+fDhw9je3mY9u8fjQfr9VFbCgkklQtDIpUuXIJPJ0NnZyfsEKLlVoVAgHo+zUiiZTKK3t5ejICQSCY+IBoMBLpcLXq+Xs18IHyZrfiAQQLN5bwWk1WrlwL7nn38ejzzyCDQaDWvkKY6EYjr0ej127drFkdRyuRz79u2Dx+NhmSvxKfRQJg6R4VKplKXBZrMZa2trbFw0m81MUBMpTem0Ho8HCoUCBoMBHR0dXHgrlQr0ej28Xi9LSClcj6SvNpuNw+QA8OXb0dHBDlVSVREpWq/X0Wq1EIlEuFBXKhWk02msra3h+PHjHBVAU04mk8HExAS0Wi2mpqZw8eJF5lBUKhWeeuopnDp1ik1Ht2/fZqijVCpheXmZ3ccAePqhEElykBsMBgwMDEAqlcLlcvGuhdXVVZRKJSwsLGB8fBy7d+/m6PtGo8G7nKl5ob0Z9PeaTCZsbm5Cp9PxJE0cF/EXZKTL5/OIRqMwGo1c1Gg5kv79pNdSqYSZmRns378f8/PzeO655+B2u2E2m9Hf388/nxzVADA9PY1kMsnkKk3cZEKjHRXPPPMMSqUSvF4vvvGNbyASiTDBSRfcyZMncf36dRaSbG1t4fbt2yxlplSA8+fP4+TJkzh27BinBVy9ehXXrl1jOazZbMZPf/pTXL9+HX19ffjIRz6Cn/zkJ3j99dcB3NtS+Q//8A/4yU9+gieffBK7d+9Go9FAT08Pdu3aBZ1Ox7Ef5IPRv78gjKBg/furSDc3NzmpmbgkcjnT90Wc2+LiInp6epjvoBBAkgYTz0pCEdrpQY1bKBRCPB5HMBhkuHanEk7//soEMp86nU6kUinMz8+jo6ODlwiR45wMjN3d3QwHhUIhBINBfOITn2DD5o9+9CMUCgVcuHAB+/btw4EDBzAwMMAGQwrBXF9fh9Pp5NBOEnrs3r2bfwfKwKO75gMNBSSJIzl5+/r6cOrUKaRSKdy8eRPj4+M4evQoYrEYzp8/j/n5efzWb/0W8vk8MpkMuzBbrRZnoNBFV6lUcOHCBcbS5+fnYbPZcObMGZjNZo7pJZcuYX/0ZVMnWa1WAYB/HpnuaKwlgw2ZzvTvLxah1M5oNMqRFh/60IeYcEyn0/D7/di/fz9DD/V6Hd3d3TwREUFKJDN16haLhfddUwdOoV0AWGtPOxEoxXJnFDv5PmjhCmXmU44MbflbXV2FwWDgncnVapXzjCjGIZvNcjcCgFVsHo8HHo8H0WiUC9bKygp27dqFzc1NyOVyLvBEcNbrdSSTSXR2drLblZy73d3duHbtGu8TefPNN7F//360t7fj29/+NhOiRCaTWZLczHfv3oXJZOL/nXZ/0IIaSgWlzpow+8XFRXR0dODMmTM4dOgQVldX2Zn/93//9/jqV7+K/v5+nDt3jr9D0vSTN4JgGoIsiFezWq2QSCRIJBJIpVIMKQD3TJGURkCQlcFggN1u5++DFvvIZDKYzWYEAgEMDQ0hkUjwZfjFL34RFosFJpMJk5OTfDnR2l0q8LT4i34Pn8/HnabX64VWq8XIyAisViueffZZ2O12nDt3jhWHlBTQ1dWFwcFBrK+vw2w2Y3x8HNvb22wGpGm6UCigo6MDwD2/yNzcHG7evImZmRlYrVZMTk5iaWkJ+vd3KtDCJcq8ot0X5XIZ3/nOd9DZ2YnHH3+c427oLFO0PqXRDg4OIhaL8Rkn3xAZOoH/Ey8kEAjQ2dmJcrmMUCjEcfok897e3obT6WRFH/FXxFNWKhU25pE4RigUwul0cgxPs9nke2VnhheZY4nnk0qlrEQjaOv111/Ht771LSgUCgwNDfH++k996lP48z//c/zwhz/Et7/9bYyNjbEcutFosIs7/X5sEEl3AfDCNlI4EulNAgeHw/HBxozTh0AKAqPRiAsXLuDChQt8iEZGRqBQKPCP//iPiMfjzNyTfJQI0XL53g7Y1dVVTExMIBqNYnZ2Fh6PB2NjY+jv74fVaoXRaOSNZXSBkQOV5IThcJhd3uToJM8D6cw7OztZQy8QCNj7QcuI/H4/UqkUBAIBXC4XXC4XOjs7Ua/X8eqrr3JUMMWUuFwuvPDCC6wooHGe8NdgMMhGGwqFo50SpH+32+2IxWIsB9XpdFheXmb8Fri365pyXIrFIpNvJA0mRRaR3gaDgTf+kWOWlFQUfU4Z/AS/Wa1W5PN5Xqzi8XjYC2I2mxEMBtnoRzuXOzs7IRKJoFQq2dV97do1rK+vQ6FQ4MCBA4jH46jVaohEIrBYLBgbG8PY2Bh6enp4B/lv/uZv4lvf+hbee+89zvKiBFq5XM4bFcPhMLLZLG9z2zmOk7hgY2MDOp0Od+/ehVgsZvWZUqnE5OQkIpEIrl69inA4jMHBQUSjUXi9Xjz99NP3LYSiCHpanyoQCJjfarVaCAaDrBKiSZMOcLFYhNlshsvlYijC5XJhaGgIer0eS0tLvF7XaDTiW9/6Fkt0h4aGcO7cOW6iNjc3WSFGaicAfPnR6k4iiCn4MBAIoF6v48CBA9i7dy++9KUv4cqVK/j93/99BINB9Pb2sm/k1KlTHExHF6dWq8VXv/rV+wpELBbDQw89hP7+fvYryWQynD17FqdOnYLZbObU2T//8z/H6OgoXnnlFdy5cwdHjx7loMGBgQHMzMxgbm4OlUoFLpeLG6+trS0sLy9z0i8R58RREXxNEm4A7KNwOp2cEUYN1k5jJKm1KMXYarXCZDIxP0XEt0KhYGl9R0cHv3NUmOkOI9ksfefkDFer1VhbW4P+/Rw84li7urpgMBiwvb0Nv9+Pw4cP48iRI/jBD36Au3fvQiqV4sknn0RPTw86Ozshl8uxvr4OqVQKo9HIRYmCDqk5pmgUj8fDDQP5xCiXLplMcvH9QAoFhY4Rwy8SifDCCy9ga2sLDocDMzMzcDgcaG9vZxnarVu38NBDD7G8TiKRIBAIQKVSsWNzJ9ZLJiaz2YxWq4WlpSX+QrLZLEvMCGsknLxcLjOeuHMNIMnekskktre3OS6ALpdsNstRDAqFAsViEaFQCJubm+jp6UGlUsEjjzyCyclJzscBgIsXL3KnSS7UZrOJ9vZ2yGQyJJNJNgLq30++JQKKjDUbGxuM8dKEQgoJIiVJxUKYOF1cNE0B4ImiWq1ieHgYiUQCg4ODUKlUfOlRwieprSiQj9JgKUogEonwC04XEJGLhE+Td4JUQtQNy+Vy1s3/3d/9HZRKJfbu3Yv19XUMDQ3hC1/4ArLZLO/f9ng8UCqV6O7uxsDAAEOWjzzyCOr1OoLBIP9s6urC4TATzxqNBnq9HkqlkpfkCIVCtLe3w+/34/Tp02hvb8d3vvMdXL16FZ/5zGcgkUhw6NAhHDt2DC+//DL/rR6PB06nk6XWdHGQVJuWVtHqVeJIXC4XEokE4+JyuRz79++HTCZDOp3Gnj17AAA3b97ExYsXGTM2m81wu928InhiYgKZTAYzMzMMW9D3RMookm729fXdl4arVqvR1tYGjUaDCxcuoFKp4EMf+hBqtRr++q//mr0wlK8WCAQwODiIyclJSKVShn79fj8LGMrlMtra2jAxMQGlUonZ2VlYrVY2bYbDYd5zQhPD9PQ0Xn/9dZhMJjbRXr9+HYuLizhz5gyHCO7atQs3btxAqVTC2toaFAoFrxalyd5kMqFSqXBsBnkhuru7WURCYZ60E4dc2hSX7/F4GG4lDoHOzvr6Ol+8RJhTmCHl2RFvQllnJEumSaOnp4fjb2w2G8PxDocDwWCQBRG0l1wikcBgMODRRx/FV77yFUYSZDIZ/H4/vvGNbyAUCiEQCOBnP/sZjh07hvX1dW7k6CyQ56Kzs5PfDVKDkjKM1JCBQAATExM8fXwghYJkmXS4qQvt7+9nkwlp/w8fPozh4WHGsSlPibpa0nbTxUYdBxHjy8vLiMViePjhh3H48GF2HlO13LkJjJyZtJmNMM5yuYzt7W0IhUI+HDqdDl6vlw1AlAZKihba80tmQdLF63Q6DA8PQyaTIZVKsbO3Wq3yRU/LT1qtFrRaLa+ypMu6ra2NTW5kiiJzmsvluk8VAYBDwagzIF9DJBLhXdA7SeWdYYe0mIg4I4KXaM8GpfZubm6y5nx1dRXAvS6NOAQaqal71el0iMVijMPTgpxEIoFgMIjjx49DLpfjH/7hH9Db24sDBw6gvb0dpVIJKysruHnzJtLpNNRqNV5//XXMzc1heHgYjz/+OGw2GxYWFlCpVODz+dBqtdjcRofQbrczR0OBevF4HAMDA0gkEvw7ajQabG5u8oU/MTHB5jK3281FYOe2P+o+aUEOLa4nWbhCocCePXvY2+P3+5mzCAQCcLlcePTRRwEA169fZx8GhcD5/X7eYUIREQcOHEAul8OFCxeg1WpZGeXz+fhzpWgLkjSTIo12RdMES5HhBw8eZGOc2+3GJz/5SUxNTWFtbQ2zs7MIBAIs333rrbdYKEDwaE9PDz784Q8zR7K2tsZJtn6/nxsxCpK8dOkSnE4nQqEQXn/9dQwNDUEqlWJkZOQ+yLJQKPBO7qGhIWQyGUxPT0MikeDJJ59kfo2aGcq3ogRfgh9ppwOFP+40N6bfz7iiM2a32zmTitYD7OS77HY7w8+0S54SICiwb+dSI5PJBJVKhTt37vCZJwn2zvUGBBXStEMrkNvb2wEAv/d7vwexWIzdu3fjP/2n/4Q//uM/xvnz57Fv3z588Ytf5BBRMrkSFEo+MVIrlkoldrvTgq14PM4ptKSAJD7rAykU8/PzOHv2LAqFAra3t7G4uIj5+Xlsb2/j5MmTrCiamZnBmTNneHMa/bIbGxss8aOqWKvVWH72V3/1V/jGN74Bt9uNa9euoVgsYmlpCQ6HA0888QR8Ph+Gh4d5PwKpiQhTJgiGDFo6nY4xSBr3y+UyL5Yn7oSmE4qPEAqF6O/vx8rKCkqlEseNOBwOJkbJAdtsNrFv3z60Wi14vV7W/kulUt7loFar2WQFgHeMVyoViMVijI+PY2Zmhg03tIGs0WggFAoxF6HX61mVUq1WeSVmoVDAnTt3IBQK0d3dDa1We59SjMgsSrolOfO+ffu4C9tpNCMFVSwWQ0dHB1KpFF9WFONA0STkY6COm3aGEBzndrsRi8Ugk8mwvr6Ozc1N2O12dHZ2Ip/P4zvf+Q7+7M/+DBMTEyzXLBaLnJJLK1Spg5TL5dzBkoGQDrtSqcTc3BwymQzq9Tp8Ph9sNht+5Vd+BSKRCN///vcxMzPDsNnExASsViuGhoYQi8WwvLzMeK/T6eR/k/7WbDbLgYgUsEd7BWgqbrVauHv3Lv7qr/4KsVgMP/3pT6FUKjE2NoaNjQ1EIhF8+MMfhtvt5kIQCoXQ09MDr9fL8SCZTAaBQAB9fX3Q6XSYm5tj1y6tct3Y2MDAwAAX91u3bnE8ei6Xw7Vr1/Duu+/iX/2rf4WBgQHcvXsXV65cYfhVJBLB5XLhxIkTeOmll2CxWGCxWPDQQw9h9+7dWFhY4K2WO9f5UqGnJOZcLodLly7hD//wD/Frv/ZrHL3xG7/xG/jc5z7H3ew777zDEPOxY8dYLNDd3c0pD6SoFIlEWFpa4veWYBV6RymRgbxQMzMzrAiiJAaSCNOZEolEiMVirOKihF0yOlKadDAYxODgIMLhMAqFAufKicVifrdJ1UdeLuI3FQoFGwnJ3Z3NZjmp2Wg0YnNzk2E9eufJxzI2Nsa5WYR8ECSo0WjgcDiwubmJ1dVVTE9PY//+/di1axcXV5IGU0BmKBTC0NAQ3y0fSKEgEoyIHup4acdwV1cXXnnlFVQqFf7gSOFEGf5kqafdw5SiurGxAZPJhImJCaRSKTz11FOYmZnBW2+9hZ6envsWjEgkEiwsLLDkjbojGr+USuV9cegUOgaA0z0pToMgMcI0qeugJfRS6b11nWtra/xS6t+PHbdYLKxPVqvVOH36NAKBANRqNer1OvsLtre3maxeXFyEy+XiSJBwOAy1Ws2udZL/0oUrl8t5MxtFRvf39yOTyWBqagp3797FQw89xDudybhHaZ80DZGajHKPhoeHUSqVkEqleOsZ8UcUz02/P3kuyGVuNpuZKCPtNiWzDg4OsgOX3KIXL15ET08Pzp49i9HRUayurjL5ncvl8Nxzz6HRaGB2dpZXz9K2LwCsgkq/H6ZYLBYZJiNhwt27d3m7HTUKXq+Xlx99/OMfR0dHBwKBADo6OjA9PY0zZ87g2LFjiEajqNfr6O/v58MmEonYPEjheTStkuuaNpY5HA5MTExAJpNhc3MTt27dgkqlglKp5HwfSkK+du0a1tbW2DhIRS4cDkMoFKK3t5flzvr3F4UR1wKAjVzEl1CS7MrKCsRiMT796U/jzJkzLByhPSn79u3jKUin0zGBT++hzWZDpVLBwYMHYbFYsLi4iIsXL6K3txetVoslz7TfQCqV8oV86tQp9ga1t7fj0qVLeOmll2AymdDW1oZqtYrXXnuN9zdkMhkolUrEYjGUSiXs37+fIUUSLxBRrX8/XZk4AoVCgeXlZZa9074KvV7PXguCziQSCbxe732RP0qlkiPzlUolZDIZK/+WlpZQr9fZ1U8mTBLgECegVCpZ1ECfQ7FYhN1uRzQaRSAQwMDAAIcg0t1ELvbHH3+cYTWSIZNy0mQy4dVXX2Uos9lscjQ8KcAWFhawsrLCqAVlPzmdTszNzcHv93PKBZ2fDzTriT58hUKBjY0NDA0N4aGHHkI0GuWVhmKxGIuLi6zQOX78OOvvKcCNstpLpRKGhobQaDTw/PPPIxAI4OGHH+bxqb29nTfBPfzww2g2m4zTE7xEkSE0hpGumL5kiv2gReck26Sd2EKhEFarlWEX2liWyWTYs0A4IjltSWoYj8cxODjI3R95I5aWljg3aHx8HMFgEJcuXYJWq8XKygpisRhn3cjlcg4JLBaLWF9fZwc6cI9/IAmtVCpl8v3w4cOskddqtRgYGGAslFReqVSKlVAUKkacQqFQ4ChoghJisRgT4GRuo8jpUqkEiUTCkA0tjCL1ycrKCidgzs3NYWhoiC81m80GkUjEEezkIBaJRKwiI+ybuiEArBYi1VQ2m0VPTw/u3r2LeDzO8RsSiYS7RblczqGGHR0d2NjYYA/Fpz71KTgcDmxsbPAU6XA44Pf7OcuI9p9TJhEV/a2trfs4pHq9zpxPZ2cngsEgzp8/DwA4cuQI4+6PPvoop7MqFAqMjo6ivb0dPp+Pu+NkMsnKO7qIqXvduQ6TPjtan5rL5SASiXiyoMnH7XZzVlSr1YLb7ebLjy4MIkVJmWe32zE6OgqFQsH+HQpedDqdXFhoqs5kMggGgzyBulwuRKNRLC4u4oUXXsDDDz+MGzdu4NChQ5xO4HQ6MTg4iD179rDxk6J+yCMwNTWFzs5O7Nmzh/0dKpWKodJUKsUubACsQNsZuU6/k8/n42VkpBYknslutzO6QWtpJyYmEAqFMD09zZvmQqEQ2tvbGWKixrfRaPD7Fo/HWVpOO7MJaiRzL3Eb+XweAoEAJ0+e5Nh1am4HBgbw7rvvwmq1MjdBsDNN97Sfw2KxwO12Y3Nzk1WMzWaTRQ20GM5oNKJUKiH9frjlB1IoEokEB9qR+5NUIuVyGfF4HMeOHcORI0fYbU3REKQMIEyNOvpr165xzlMoFOJl6vv374dCocCTTz7JoxOttUylUtzF0j4Dq9UKALxBbWhoiFNEy+UyfD4fZyHRh0adIx2MWq3GnerO5S6RSIRdkORLUKvVWF1dZZfk1NQU7HY7fD4fNjc3uSOnl4RgOhpbFxcXeWSlxUU0Hnu9Xu6iKNOGLqZUKsWTA6mwhoeHYTKZOAep1Wqhr68PrVYLb7/9NkcVk1LGbrcztq9WqxnjdjqdzAGQDpsEDMlkkn9+rVbjLocy7y0WC+7cuYPJyUk0m02MjIxg7969UKvV2L9/PwKBAF5//XXOzKLI7v7+fi5WlHlDLuZarcYyXqlUyk7YXbt28ZRKURJE2FNelVAoZIXNj370I9TrdSwtLeGrX/0qy5yr1Spu377NsRW0vpOaAfp3G40Ge0ponaXJZGJ57vr6Ol577TVEo1E0m02Mjo7C5XJhcXERzz33HC5cuMCFj3wCJNYg3oL2LRCmHo/HEYlEMDo6yjE0lGtEOPX6+joXYZIVb25u4pVXXoFer4fVamWsfGpqCktLS6yOISKc9oGLxWLcvHmTuQeTycTfNzmMiQ+gy5YCBmlK6e3txdWrV5lbaG9vh8lkYl6oVqsxJ2EwGLihSiQS7BcitGFqaoqbOAB8EVKTQimqiUQCPT09aDab2NjYYMJXo9HwzyQPAXmQDAYDtra2ONWYGs1sNssrgAGwYnBzc5Oz18jjtFOqq1armbugmKJisciQO5n/hEIh1tfX4fF44PV6YTAYsGvXLk6ZuHnzJisa7XY7kskk3G43R39cvXoVx44d42Iik8nw6quvspHPZDIxL0Z3LaUNk6H5AykUxEFMT09zEiSt+nQ4HGhra0NbWxvnBCmVSly6dAlerxdXrlzByMgILBYLXxzVahWzs7OYmZnB6Ogoent70d/fD5PJxIe4s7MTH/vYxzA9Pc27G3aSOkNDQ7xzgrBwWt1J2mHSylerVTbrkNKBlEOZTAZut5uNNDszYCjXqLOzkw8xaftJJw0AIyMjnIJLODNlYP36r/86lpeX8eKLL+LUqVPo6urC2toaxyzTBUnTCq1DJDKLXtyBgQFoNBocOHAAsVgM8Xgcb7zxBtxuN/r7+/Huu+9Cq9XyTgdSVhDEtr29zeQ7LWZfWlqC3W6HzWZjnF3//kY2kg6T/Lm3txehUAh2ux3hcJjxVL1ej/HxcXa2b21tcaEhsQIFFq6srECj0cDr9cLpdHKoH6Vwzs/Po16vY2hoCE8//TQikQgmJyfxs5/9DCqVCkNDQxxVTTJBOpwOhwM3btzAwMAA7xd46qmn8LGPfQwXL15kd/revXvR2dnJ6jj9+7Hc5MZWKpUMARYKBbhcLi78O3cgb2xsQKVS4WMf+xi8Xi9effVVfP3rX8cjjzwCn88Hv9+PwcFBfPnLX8azzz6Ln/zkJygWi/jKV76CYDDIP4ugBprayIRHUzzBMPTup99PCAbuTcHEx9jtdpaIhsNhdpvTPgiSahNcQus+ySxKSh2KYqcGbs+ePdjc3ITT6QQAeDweLuBGoxErKyvIZDK8T4b2JZBCiUh8+lnj4+MsJqDdKDT1E0lNuywo8Xh5eZnVeqTOo4Rggoy1Wi2vHDAajTwJUNIApTLTeoPOzk6O6SACmaI7aGqjLZ4EWe0U35CsmmB1Sgom/oPWBdDeF9p34XA4IJFImL+Ry+XM59EeGI1Gg2AwyIWG1H3Dw8O4ceMGWq0Wurq6WEFJ90Bvby/naK2vr6O3txfT09M/9z3/88cH/j881IlQwaDF3RRCR/t4Y7EYqtUqbty4gbfeegsLCwvY2NjA5cuX0Wg0oFKp+L+3uLiIYDCI7u5unDp1CsC9HQCUj3P9+nXuRElx1N3dzQdhbW2NIxMAYG5ujuWUhGsKBAJ4vV4e1emglMtlDg4k/I+yZChSgXgCoVDIy3UIx25vb2evBF0wjz/+OPsFJBIJrl+/juXlZWxubmJwcBAymQyvvfYa3njjDWi1WjgcDv43SINPuwIo3pn2fxDZJ5FIcOvWLZRKJfT09GBgYABisRixWIxhO5/Ph9OnT2NgYAC9vb0A7q3FpAuBzHZms5knClpbS+Fjq6urvKnOZDKhq6uLixPlDNF3aTKZcPr0aXz4wx/G5z//eYhEIrz66qu4e/cu7ty5g1Qqxes4SUFEZCbt7SC/B6VrGgwGhhK8Xi8UCgWi0ShfGHShUQ4TKWa6urrYEVupVLBnzx40Gg3cvHkT+XweJ06cQLlcxrvvvguPxwP9+1HPO7cPisVi9i9Q7DPllRGnQEUmGo1CKBQyxkxZPwMDA+js7MTv/u7vYu/evejr68Pp06fxa7/2a7DZbJzvFYlEuLsnLw8ZukiUodPpOApdKpVywSMFIU31RJz6/X6O8K5UKgiHw0gmkygWixAKhbyAiRRFJBCQyWQ8kZnNZja3RiIRiEQihjTcbjfDdHTZk7+CTHBdXV0MFycSCQ4NpU2BtFFw5zZAvV6PkZERDA8PQygUMixkNBrv25BH5kbKMrPb7TylUYoxhUiS/JeaO9rfTRlQ2WyWTa0U1UGwN13YBJXq38+To+lfKBRyA0HGO/r9VldX+TOliVAqlXJGFX0nZDjVarXY2NhghIH21GezWTgcDqhUKm4a7HY7HA4HvvCFL+DQoUNoNBo4ffo01Go1FhYWcOfOHVy/fp3j2sPh8M99z//CEwVFLNMFSh0gcM+1TctYEokEjh8/jrfeegvr6+v4lV/5FZhMJqyvr3MOPGW3fPrTn8ZLL73Em7xo0XmpVMLAwADu3LmD559/Hr/0S7/EHQLtNSCJHHkbqtUqR1kkEglEo1HOYAoEAhwSRrsndDodL3khiKqnpwcOhwPvvfceotEoVCoVj55ErOnfz126e/cuF6Hh4WHcunUL169f59WMCwsLbBScmZmB3W7HyMgI1tbW2DNCsNjAwADDHGS+MxqNCIVCqFarcDqdjEUS+e/3+3H06FE88sgjeOmllzAzMwOJRAK3242vfvWrGBkZgV6vh1wuZ8KfJpSOjg7cuXOHDw69rN3d3bh79y4WFxe56FAsicFgwKuvvorx8XHmmSiqJJvN4p133sE777yDEydO4JFHHsEzz/z/ePvP6Mbv+8ofv+wkSIAAQVSikCDB3obDITmj0TRpVEbNapY9cmR7XWOlOGtnj1M22dhJnOQkceIcx9mTWLZlx5GlWLKkmVGdXjnD3kACBIlCVFaAJNjJ/4OZ+w7nt0+8f+/RnLNnHVuFBL7fz+dd7n3dk3jzzTfR3t6Oq1evymEXCASgUCigUCjENMlRSWlpKaxWq+RKRyIRnDt3DisrK/jt3/5tWCwWeDwekV2HQiHJVOalwWdhamoKzc3NiEQiGBoaQiwWw2/+5m+ipqYGr732GtLT03H06FGUl5fj6tWrWFtbg9PplDArKlTopk6lUkgkEgK3XF5ehkqlQl5eHsbHx5Gfn4/PfOYzePTRR5GWlobh4WEUFBRIofD222+jtLQUer0e165dExkjCwIm1jH2lYmDCoUCkUjkrnEnL1wazyg9p1OdeP319XXU19cjFAphdXVVMBY0SvLiYcWqVqvhdrtRUFAg2eZ8VwFIMBfjTQln5DKehzBHLpR1M+Y1IyMDS0tLEvtps9nEDU3nOTNqpqam4PP5EIvFcPLkSZE1T09PC7yR7DWy5yjfNpvNssciqpvCh/T0dBQWFgq2neM0+pEoEKCsnfJwVu4rKyswGo2yFyTHiSwpEiEYHMaCmhcvw7JIbEhPT5f8cuLfJyYm7hJUEDu0s7MDp9Mpl/LOzo482wQ2zs7O4kc/+hFMJhM8Hg8OHz780edRUMpFlzB3DAcPHkQoFBLpGyWde/fulcqhsLBQuEtcTKnVajz77LMwGAyC0t3e3sb58+dRUVEBh8MhIT2sspn4BdyWtlEPnZWVBb/fL/nBNKpwKc0Pi18i6Z95eXk4evSoZDzk5+ejoqICTqcToVBIFBZDQ0MoKyuTTOri4mJsbm7iscceg0KhwPe//32ZTZrNZuzfvx+ZmZlIpVKIxWJywFVVVcnymE7Y5eVloVNyTszZ9OrqqlScdPqSAMrWcnt7G2NjY9i/fz8+9rGP4dSpUzh79iyOHj0qLtSRkRE5+BcXF2GxWCRb3Gq1SlyqwWCQxDkAkuHb0tKC8vJyEQ4sLy/j1q1b0in09/eju7sbkUgEf/7nfw61Wo3i4mL09fWJm9put4u8mk5apVKJlpYWKBQKXL58WfLK8/LyZPk+MDCAPXv24Hd+53dw6dIl4dlQdaZQKGA0GmE2m+Xfx4vvtddeQ09Pj1A429vb0d3dLTkeSqUSer0eGxsb4gdhxW8wGCQFkEhx+mccDgeWlpYkpYx7O2KeuXh1Op24cuWK7I3ef/99VFRUiHybIzymFpJawIAfRnoSz0AnM7scrVYr+e5LS0siHebim90PxzJMErTZbPLXc3RDFHpmZqZcKPPz8xJFSuYax71U2FgsFvh8PoTDYdTW1op5jRnwAFBaWipjUKb0UbVUVFQkHUFVVRWKiopEeai+E7RVWVkp5IPl5WX53NgFkCK7vr6OeDwuXcXOzo44/Y1G411dBveVNDBS0k8aABlu9FnY7XYEAgEUFxfLaJKiEH4mFosFi4uLkkPCs4eU6cLCQuj1ehF/8PPhaIpw0Vu3bsnfz3OEo2nuZ4PBIKanp+FyuTA3Nwe9Xg+NRoP29nYR6PT19Qle5Ff582tfFFQDEUTFF1qpVKK/vx/BYFAiMFdWVvD444+LBt7r9Qo2m+oRkj2tVquoG5qamrCxsYFAIIBDhw6JYYyXFHlIKpUKZ86cwcbGBnJyclBZWSlLXy5CuUxKJBIoLy8XlREVEASUVVZWYnl5GX/6p3+KYDAonpBjx47JDJ7VE13XGRkZWF5eFgVSOBzGE088ITkbAFBZWYmhoSEMDQ3hiSeeQF5eHm7duiXqjpqaGjQ1NQk2mnsELjR5MZA4y8yB+fl57N+/X3YSfPiYMpaTkyNJV4uLi/D5fILiSKVSQiudmZkRhcX29rZguldXV0VUYDKZJIjq0qVLIpW9efOmzM3z8vJQVVWFrq4u/P7v/z56e3vxi1/8Al1dXYhGo+jo6MDa2hrOnTsHu90O9R06bXp6ujBpCC6MxWJwu93wer0AgOeff14qRpfLhR/84AcYGxuDWq2W8QkVSnl5ecIEI+Z9dXUVV69exfj4uLiMWUWy2OB4hwetwWCQkCPKiSnKGBkZgdFoRH19PXJycqQ7ZIZFLBYTLpFKpcLGxoZQPEkioOyYzzYlt1xqmkwmBAIB1NfXS/aG0+mEy+WCTqeT3Qbzn/k+0URKQyWxH6z0yUyjOoiHORlO7Jb43zFTnpUy09/of6JizGQyiYFUq9UiJycHPp9PRiRTU1Py3rCLJnssOztbgqBmZmawubkJr9cLtVqN5uZm2evU1NTg+eefx759+zAxMSHyVu6LOB7Ny8uTlMtkMgmLxYKFO3k3vDhphNve3kYymbzrYqSjmgUcHfG8XMiS2z3JiMfjomZi7gjD3XgBM3ucfx+TLiORCHQ6HaLRKLRaLXw+n+yPqIZkvPHOzg56enoEyEkFVmlpKe69916cOnUKP/3pT7F37154PB5UVFRAo9GgvLxcRvMfyUVRXV0tG3/SRdm65uTk4MCBA7h+/TpeeeUVXL9+Hffcc89dKo1EIoE9e/ZgbW0NLpdLmE3EIrDSzc7OFv5MUVERRkdHBT2ck5Mj7krq4TkeomKAudPr6+tiwuEHv729LYeC3W5HWVkZzp07h2vXrkm39MMf/hClpaWor6+XsBe276Sn0mizsrKCU6dOwWg0wmq14sknn8TIyAjOnj0Lg8GA48ePw+fzob6+HsFgEE1NTVAoFDh//rzQa7mHYNZAPB7HwsICrFaroCkYukLTU3Z2Nnp6ehAMBnHPPfeItPTAgQOIRCIydllaWoJOp0MymcSxY8cwNTUlOJHs7Gz09/cLibayslKWayqVCpOTk/jyl7+M3NxcXLp0CRqNBhqNBteuXZOqz+VyIRAIoLq6Ggt3MrV52Q8PD8PpdOLhhx/G0NCQpBGyisrIyMD4+LgcxkxH++d//mdMTU2htbUVXV1dcDqdOHbsmLjkWbE1NDTgxo0b4mVQKpXy2dntdjidTrz77rtIJpMoLy9HS0sLrl27hnPnzske69///d8FPMcxGpPesrKyBNpG1ZXL5ZJ5O7MGWK2Tqjs1NSU02unpaVHB7du3D1lZWaiqqsLm5iZ6e3vlRd7c3MTMzIxcooTWUSJLdMP6+rqwp5aWlmR0F4vFsLKyIqQDSsh5ERL5vrvQ4j6BAMhEIiFdJCXtjLRtbGyUUSSLMXYX7Eq2trZElUfEPZE6TMOkYIOFDc1tDPY5d+4c8vPz0d7ejoKCArS1tWFgYEDGyUyMI2m6oqJCwtCysrKENsAxFAGdTU1NkvVC6Xs0GpVdF6NU+WxxFxUOhzE2NibdNvNgeGnTrZ2WliYy9oWFBSkcqJLjXob5NaTXOp1O8XDRd+bxeGA2m4XTRI4bZdFUA4bDYZw7dw779u3DI488gueeew7/9m//hosXLyIvLw8f//jHxbB74cKFj+6iiEajgsxmu3X9+nUkEgkcOnQIeXl5cDgcGB0dFalWMBiE0+nEkSNHxL8wNTUl7s7dDmWC6LKyslBcXIxAICCmFFbxvOV58cRiMQkp2rdvH2pqalBVVSX4ZFbknP1Sf61UKpGZmYlr167h7bffxuXLl/Gtb30Lzz33HF544QXJMwiFQkKTZOKeSqUSgB1w22Q0NzeHrKwsDA0NYWFhAU1NTVhcXER+fj6ef/55vP/++/I7P/bYY6JoYa4FA4JWVlZkvMbsg93O5mQyifr6etkHcT5sMplw4sQJ1NTUYHBwEL29vTh58iSA20tswhQZqci5bUZGhgTSDAwM4PLly5KdwP1Nf38/zp49i7/4i78QjwUPAS7GPR4PotEoamtrUV1dDbvdjueee07GJhyP9PX1wePxYG1tDTabTYxjZDldvnwZmZmZ+PKXv4xPfepT+OIXv4hgMCgBQ3a7HeXl5QAgORWcx3PPQg/I+vo6YrEYVldXcd999+ErX/kKvvOd74gBkDkNdAWXlZXJOJRLVJVKJcqt1dVVtLe3y3iIBzuXmFQb2Ww2mW13dHTA7XajsLBQlqBLS0vSdVB0QZPh1tYW4vG4sKwKCwulE+ZilWOE9PR06HQ62ZVwJg5AuFWsYFlIsSAjMj4YDEoXazab4ff7AUByODiuisfjyMzMlEp7d8wujXMUfRB9AtwWwBDJzwU0q3uOqWg21ev1sFqt6Orqgsfjgc1mw+OPP46SkhJcunRJlrKUvtOXwJEfvR8cP3FJTaOg2WwWdSSl7waDQRD3fA8p2kgmk1AoFAD+az+r1WqlG+M/N5lMYmdnB2VlZZicnIRKpZK4YqPRKP+ZF9nU1JTQn81mMxKJhHSgOTk5qK2tFQMfFYr8fZlDb7fbceHCBQwODqKnpweRSAQvvvgiHnvsMcGL8LJil/Kr/vm1Lwra0sn4CQaDOHr0KM6dO4czZ85AqVRicHAQn//853HffffJQpR6Zr7QjBTkfDgjIwPBYFBcwgwzIY6AWmoAwnO6ceMGuru7RSbo8/kkRe769etQq9V4+umncebMGTGpAZCDxGw2S3YuDVShUAgffPABwuEwTCYT3G43Ll++DKvVikcffVRSt/r7+2UUVl1dLalvGxsbIoUbHx8X3gwNZpw1Ug7KkBuarrgzofOZLzoP+YqKCszMzGBoaAjJZFKcya+//jqMRiO2t7dx5coVkQTa7XYsLCwgGo3iypUr4lxeX1+XxSwVQna7HUNDQ8jJyUFjYyPS09PR2dmJl19+GWfPnoVer4fRaMQrr7yC9957D01NTXIokjbL+WoymYTf78fq6qrIlHU6HT788EMoFArBJZNAnJ2djSNHjsDn8+Fv/uZv4Pf78Xd/93cYHx9HOBxGf3+/5Er80R/9ESoqKoQBRPieTqcTwQKNhtvb26iursbg4CAqKysxPDwsskdmUh84cACVlZUAIOMsUgeKiooES0JlGFVp6enpsNlssmBkV8G9E2mdqVQKZWVlMv6hPJPET6VSievXr6O4uFjyLtLS0sSjsHsEEo1GUVlZKZfN/Pw8YrEYKisrkUwmRSVFdzovQMpqCcgjC4s/Uzwel9FRSUmJeHHW19dRUVEh0bgbGxtYXFyU54pjqKysLIFoUoJNSB5d7/n5+eL74KHb2NiIo0eP4tatW+jv75f9mM/nQ39/P86fP49nnnkGzzzzDHp7ezExMSH0gJycHFkgBwIBAWdGo1ERCfD7SUtL+z9QGUxmJOCPbm4KdWZnZ2Gz2eRwVyqVWF5eli6S+w1m3kQiEaRSKdn7MOKZz4rb7Zazp7CwEAcPHpTu9datW/JMGgwGCdwi5ohd0dLSksh3KbwoLy9HLBbDK6+8gnA4jK9+9at46KGHkJeXh3PnzgkQkqKHj+SiICI7FouhqKgIRqMRJSUlGBsbk+jK3Nzb4eZUxCwtLeGBBx6A3+9HV1eXfFFtbW0YHx+XBdDi4iKKi4tFZgYAtbW1UCgUghigwWZgYAD5+fn4i7/4CzgcDuzs7ODb3/42Ll68iBdffFHGGy+++CLKy8sFb0GFATMXtre38Vu/9Vtoa2vD7//+7+ONN97AT37yE+Tk5ODLX/4ybt68CZ1OhyeeeAKVlZW4cuWKtIDMu3C73WI8A/5LbcNqlCOe6upq4TJtbm4KW4m6aT6QOzs7cLvdMtvkglF9B23MNK20tDTU1NRgZWVFqKRUUrW1teHAgQNwuVwCvCObJhKJSLdABlRpaSlSqRSOHz8us22OFmdnZ9HQ0ACn04nV1VUxhRkMBlEuXb58Genp6dBqtQgEArh27Zokqe3OEGZ3xIMQALKzs0WGG4/Hsb6+joceeghbW1u4cuUKDh06JKNKpVKJyspKGfOsra2JdJHjJp/PJ/+dQqGQn/PmzZsYGRnB5cuX8cwzz6CwsBB/8Ad/IKFDfD7ZfXLfwOUxCbvhcFhyBlg5r66uihyWzngWQjQW0ldDGShHiDs7OygtLRU5726TKjOdiXM/cOCAjMHoY6JijJLW7Oxs8Sro9XrBT/B/Z4U8Pj6O4uJiWfryUKPUlO8c3xlefMxlprKPSO0jR47A4XBI7gLNrvQULCwsQKVSobi4WBDnVPB98MEHeOONN7B371688MILEle8vb2Nnp4e2akRhjczMwOtVitmRaIsKGhQqVSYmpoSZRyl9FSNMbM6Ly8PCwsLYnxl4TE2NiZUX1IOKB1nd0bJOT9fwkGpJCNEkMyl4uJirK2tobm5WfYzXMJvbm6iv78fSqUS9fX1aGlpEbpFIpGASqWSnSjp2YODgygvL8fnP/95vPnmmygoKEBJSQn6+vrEk6VSqcQvxpHiR3JRsBJgEhUXyQ0NDdDr9TCbzfLFsgp+7LHHMDo6iv7+fqlA1tfXMTQ0dFdbzTktsbm7CaqsToaGhtDb24tYLCapeUx/6+7uxj333IPS0lJ4PB5xCS8sLOCee+4RGCBZMaymLl++LAEhTqcTZrNZFq70KHR0dNwVIEQcBT8HRo+yTeQ4g3poHvgLCwvQarWCpWYFy+6JDywJswsLC6L64RfNQ4oyXYvFItUzFVvMt6C+njJgRqempaUJM2o3jfe9994TNQwX4SsrK2hqapLvb3FxEd/5zndgs9lw/fp1/OQnP5Hx3v3334+FO8lrR44cwdbWloTh1NfXo6enBzk5OYhEIsjNzZV0MSZ1XbhwAaFQCHV1dejs7EQoFMLRo0dx8uRJnD17Fk6nE3V1dRIbSyfs+vq6KKQACJSNmGUeDB6PRxQjjNm9efMm4vE4JiYmpFvgDJu7EzLEysvLMTk5KVLQ4uJiUfZQIkuHMbEo6jtcMM7GSb0tLCzE1NQULBaLmDr5chuNRvnrufwFIJ4WhUIh/6zq6mrB6vD7YrgXd0FMHeTye3Z2VqS0u6kJrMi5bN5NH2ZOPWWilGu3tbUJ3oPwyKWlJVRXV6Ourg43btyQpW92djYmJiZQVlYmbLe+vj5R633pS1/C6uoqysvL0dDQgLy8PJw5cwbBYBAzMzNYWlqSfQY9BtwR+Xw+lJWVYXR0VCKNg8GghETp9XpZAu9WNlGuW1RUBJ/PJymFNPLSE7Kzs4OMjAwRnhA7wnCl3XgdvvccC2q1WtjtdlRXVwuV4mc/+xmCwSBOnjwpWd/cr3CkRgYc9yj8nkhKoAjjvvvuQ3l5Obq7u8UHRLgkow9YIHwkFwVngewAeCjr9XrU1dXJwfZXf/VX2NrawpNPPomPfexjskxjK02HIYNmUqmUKCMIH2PVwJeQy2ha1YHbaoO+vj6cPn0a4+PjYt568MEH8Tu/8zs4ffo0vF4v3n33XRw8eBA2mw0TExNS9aytrcHtdsuooKioCPX19XjwwQfx6quv4tatW8jJycHRo0cxPj4umcnxeBzJZBKVlZWYm5uTlDCqKSg9nZubw+zsrCAH6Jmw2+0SX8mFPplQZMOkpaVhfn5epJKJRAJ2ux1bW1sYHh6WPIO0tDRR6RgMBly+fBlbW1uSKcwLi9Urpc1kVHEeT0MfFVtTU1MAIJTPubk5jI2NoampCX19faK+qqysFNd2fX29uLtJ/7TZbKLnXlhYwMbGBtrb2zExMSEXIGXMVIBNTk6Kao2HUmtrK5RKJYaHhxEMBnHgwAH84he/uCvrOB6Pizt5bW0NFy5cEBIsf6ZwOIybN2+Kcon7LsZjsmtj7no8HpeKnWlmlZWVYuaj4ZAjU+7ZOI5izgc7F3YNfBeysrKEv8S/3uFwiIKJeQ1UrVAmqVarsbS0JIbCYDAoexl288FgULDhlIlz1FRYWIh4PI69e/fC7XZL9cw0tt0LXY6XmIdht9uF7+VwOFBUVIT3338fiUQC58+fl7l8XV2dSHHJr+Joymw2i++FYT1bW1t4/fXXpXN/7rnnUFtbe5e0nEwlhUKBjo4OuUAItpydncXc3Jwo6IgaZ4HH0d/8/LwY5YjP4WdaW1uLeDwu4gGO9XanyDFlEIDE4vKCACDfM1Ev4XAYvb29wmXzeDyYmJjA6OiomBx3kxFY0PB3qKyslB0UqbXLy8swGAzSmRHH73a7xWjH/dr/zZ9f+6IgjZTW8ZWVFbz11lvQ6/V4+umnsbW1JfItk8mEPXv2ALjttOaoh6RZmnt8Pp8suaanpwWHy0qAizu+sA0NDaLXfu211/C5z30Oe/fuxRtvvIHMzEz5wJhpkUwmUVZWhvz8fDGxkG4L3I4RbGxsxPPPP4+JiQlcv34db7zxBsLhsChI+DuR7smxDR9OKmM4I6bOns5YVoVcRgYCATHA7du3T/KLo9EoMjMzkZ+fLx4DWv4BCEixtbVVRhDT09M4f/489Hq9ZEgAkENtd6Qq/35WQdyT+P1+9PX1ITc3F9/85jfhcrmwsrKCBx98UGadX/ziFzE/P4+rV6/ihz/8ofhXKisr0dHRgcLCQni9Xnzyk5/ExYsXMTAwIN/f2NgYiouLUVhYiHA4jImJCQAQNRoz0T/2sY9hcXERU1NTCIfDuPfee3HkyBEMDg5iaGgIk5OTqK2txWOPPSYmOI1GIy8zq1lWyisrK7jnnnuQn58v+e179+7FF77wBaSlpaGzs1M8Cfn5+YhGo1AqlXJJs1uem5uT5Sw7M2YMUIKZlpYmCO7a2loZeRAlTq4YcDvXhXLo1dVVLCwsoKys7C63eTKZRHp6OioqKmSvwoyMmZkZkZkHg0HodDpsbGzImIwqQMo1+S6xe+Pej6ou4HYcLuOCs7KyEIvFhBzLS3Z5eRkNDQ0yHhsfH8cvf/lLzMzMyG5o91lB8yd3beRGra+vw2QyidmQyqb3339fdjfl5eWSy713716cOHECly5dQmNjIxKJhLDLFhcXoVKpYLVaZZfEERHH2Jubm6Ku4zvFfSkTJnkwU1XGzoyjIeaV0O9y69YtqNVqybagSZNjZYIEHQ4Huru74fF4cO3aNRQVFeEf//Ef8cd//Mf4p3/6J5SUlODEiRPY2NhAQ0ODFGhutxvb29sYGBiAVqu9SwkXjUYlcXFxcRE2mw2BQECCvBjnTG+JUqmE2+3+1c/5/9uL4f/7hxWb2WyWaphteH9/P65fvy4qJ5PJhJKSEln4bG9vw+FwSPvHGajZbEYgEEAqlZKwEILedpvdiNoeHR3F8PCwKDUYM/jxj38cqVQKPp8PXq8XV69eFY/Hzs6OIBq2t7exvr6OoqIiqQiDwSAaGxthMBgwOjqKvr4+tLe3o62tTdzcrLACgYAohgKBgCTzkfBJmCDnwXxwGAXLcQWNUJ2dnbDZbEKPDIfDIoljJCN3Q+TrcwdDjTxBatS6c3RAZQu7OY5rsrKyROFCDMXx48cxNDSE1157TSCH5CF1d3ejuLhY+EVbW1sYGRmREVxDQwMuXrwIn8+Hzc1NtLe3o7y8XBIAefDl5uZCo9GI6Y+hUZQcr66uoqmpScBsGRkZcLlcCAaDSEtLE0T47OwsioqKZP6bmZkpnS6TzVhFR6NR2Gw2PPPMMxgaGsKhQ4fw8MMPw+fz4Wc/+xna29vl0KDpqrCwUByxuxH0VOhtbm4KVgGA7H0oPKAfQq1Wi3JPp9NJhU7aKj9Ls9kMjUYj9GBKfQmO42HNbAwyzNLT09HW1iaLdF4cFotFxroMTsrIyBA+FrNTDAaDsJoWFhbkfeNYdmZmRrLljUYjRkdHYTabRXZKHhUX+9xjbG9vo6+vDxkZGSgvL5cUPH52lMlyP0exQ3t7O+x2u/gzZmdnUV9fj0gkIqh1hvjwMiDdd3p6Gg0NDVDfgSDm5OQgOztbcC+1tbXY2NgQgy0l+4yx5Q7N7/dLt80dAo2pXCiTfMvLn2QCThjm5uakW1xZWZEdINVihPidPn0aL7/8MgKBAJ5++mmcPXsW2dnZ6OrqQkVFhZhbGTlA2jWxK4wm7u7uhslkEuinUqmUnJ1EIiH07I/souBMfmtrC8vLy3jvvfeg1+vR2NgoGt+trS20trbC7XbjP//zP7Fnzx4UFBSgvb0dXq9X0L5ZWVnCaaJqiSMItVotFbFarcbCwgLUd5zchGkR5DUxMSFLcV4yNNnxnx0MBpFKpWShS6llWloa7HY7XC4XLl26JIl8BoNBglpycnJkgWm1WhEIBCTXebdrlF/oxsaGsItSqZS4bxnonkgkJF40EomIM5T/brfbjY2NDTEKqdVquRDoHCZahAvDyspKMQbyQmFl1N7ejmQyKZJOor/J/aFpraWlRTT2HKOMjY1JeIzBYMD29jbq6upw7733St75/fffD71ej+rqanHVr66uCk9pdHRUdjfMMeF/piS6vb0dnZ2dWFlZwfXr15GZmSnI+t7eXulE9+7di5ycHEG5KxQKAbytrq6KG5tFxtWrV3Hq1CmUlpbixz/+MVQqFb7//e/jzJkzUCgUkldx7NgxFBUVyYKcaGmOSfiZuVwuuWiLi4vhcrlEZEF+GOXNBQUFklfAZxGAiEF4SBMIyOxv7sLS09PlM6Dunl0O/Q/cYVBXT9REJBIRxHggEBA2E1lYlKgS0c1Lg5ccx5lMUqRBcTdGhgmK09PTeO6551BXV4dvf/vbcLvdOH78uHRn3DdEIhGJX+VYlMa87e1tIQ2Hw2GplKls44VFBRE7LR7qDEVbXFzEgw8+KHsBlUqFuro6ybbn4U6O1vz8vMh6+flXVVWJOIX5GGR+Ldxh0DkcDunEmV9B7PjuM4wZNjTeOp1OWK1W/OAHP0BpaSlu3bolYV5vvPEGHA4HnnnmGfzt3/4t9Ho99u3bh3vvvVfeVZvNJlJqXnjs3Pi80Sip0WgEgsg42Y/somBONtU0GRkZeOSRRzA8PIybN2+isbFR+Cnf+MY3cPPmTfznf/4nPvGJT6CqqkoOcY4+iISmu5K5tvF4XAw7ZN5PT09L2As5+Uzoisfjoi2mtpwGHOKeLRYLAMjMl/PSqakp4bMwz6GgoEDcoU6nU5gsVButrKxIJ7D74GUaGFUmdAnv5ryo1WppJSORCAYHByVH+MCBAxgbGxNDH9lIpaWlcLlcgh3nYZKfny+XV15enlTCJHtyfk6aLysojrqYGZ6ZmSmmsMrKSkxMTCAQCODWrVs4efIk6uvrRb2Unp6OsrIyceNWVVUhGAyivr5e2v9UKoX+/n4RKczOzgoEbnewDNEu165dEykl8xXa29uFkjo/P4+CggIcOHBAxgX8eznjp9afFXRpaalIhYeGhvCNb3wD3/zmN1FSUoJz587hwIEDkm9ttVqRl5eHWCwGnU4nF3hJSQnUdxIAGSfLEVd2drZ0ZQaDAYWFhVhbW5NFNgCJqyQJlTN0s9ksiW3Ly8uCtGEIGFVdxHqzmnQ6nYhGozKqKi4uRigUkoV0U1OT5JqTq8a5NiWVVDZ5vV5RxdhsNkFsM8iHccccQ3GGbzAYRHp65coV3LhxAxUVFRI9Ozc3B4/Hg+9///vY2dnBm2++CbfbLZd3d3c31tbWcPLkSTGjkVDLHPL5+XlRjBHHw3ebxdva2pr4iRioNjExgbfffltoAlRdHTt2DMlkEqlUCiMjI9jZ2YFOp5OudHZ2VoKS8vPzhQpLMQNT80iYHR8fR2lpqRCEacTlNKC0tFSgoRqNBjqdDpWVldjc3ERJSQkGBgaQSqXwuc99DqFQCCsrK/jRj36E4uJi6PV6KBQKeL1eiVro7u6GVqtFY2Mj5ufnEQwGsXDHJc5Cmqq53Rh4m80mFwQhoB/JRbG0tCQtPr0NNEgtLCygublZ3K9bW1t48MEHZXm4uLiI2tpaeL1eGRmQ2UKODee1RUVFcguaTCapVD0eD/bs2YO0tDQ0NTXJbFqhUKCoqEhyp202G6amplBeXi5YcBp8yHmnqorObaIUlpaWcP/99yOZTMqB29fXJz8zqxIGt1OKyJEQSaZ8wIgPcbvdqKysFH021U2cEU9OTqK5uRnPPfccXn/9dRlfFRYWimqFtFGyryiHtNvtMideXl6Gw+GQ7I7s7GxkZmaKKYwHBY1znLkTzaLX62EwGHDgwAEYDAbk5+fjyJEjWFxcxIULF+D3++H3+zE3NycdUDweh8ViERbX6OioGNWIW+ASnWh2BufQhzA9PS35D3THUn7L3YpOp4PD4cDAwIBc2BxlajQapKenIxKJyAH61FNPwev1yj/zww8/xOrqKu6//37U19cLf4cHd3FxsVSxNHnGYjEpctixUriQSCRgNpuhUCiQm5srnC4eSk6nUxzC3B1xf8XcBM7/SR+mgomdL9PR+I5tbGzIc85Dk4VKKBSSUeXW1hasVivS09PlIOOzwyKPhRH3L6yc6QchSp7001gsJiIDGin379+PQCCAv/zLv5RQMRro1tbWMDg4iPfeew+xWAzFxcWCWykuLkZHRwc2NzfFP8XvdHp6WkKlWOnPz88LOHNxcVH2jEyy/K3f+i1cuXIFb7zxBv7mb/4GS0tL+PrXv466ujpUV1cLqpznxeTkpASIcfxJvxd9IdyD7eZJUc1GcCC7MgAyaiLFOS0tDV1dXXC5XOjo6JBOLS8vD6+//jqmpqbwqU99ClqtFlevXkVvby9+7/d+D9/61rdE+k9/GXc2TMq0WCzir+DFwd0Xx2o0lJaWlsou5SO5KDj60Gq1IoGdnJxEaWkpRkZGMDo6iszMTExNTaGzsxNf//rXodFocPPmTVRVVaG4uFgOJurDOX/kA0kvAZc3NIiR7siHem5uThRDlALeuHEDAIRB1dXVJXNXZu8yljISiaCoqAjhcFhok8PDwxLUTkUMUcB0otN5yn83dx3M2yXPhkon9Z3cZerql5aW8Ju/+ZvYs2cPjh8/LmO0hYUFnDp1Ch0dHejo6MDbb78tuxyqKViRUC8+Pz8vi27OrWkAVKlUwvyJRCKCTaA3gpGj/Kypeurp6ZExj9frlYucy//c3FyMjIwgJydHuhMqSrKyssRDwpeSnCIuZ7mIpXKmtLRU9j40QhHNvJsjlJmZKWTiwsJCab+3t7elOib7JxAIYHFxEVlZWYKzZwXc19eHQ4cOYWhoSD7HZDKJxcVFCZ3RarVQKBTy2RJvTxEG5ZkklvLzpxdBq9UKlp1VcUZGhhghmRa4vb2N7e1tuWgUCoXszejT4V9PJzjlmoRr8nI1m83SpaWlpcmlBkBGfrsT8/i5ESFhsVgwPj4uaq/8/Hy5HOnwJXm5rKxMpLHPP/88dnZ28Od//ueorq7GxMQE/vM//1Oc/p/+9Kexf/9+fP3rX0dGRgaeeuopXLlyRbrt+vp6XLt2DYuLi8KjIheKyh0iYRhVSqpCQUGBFGlarRZHjx6Fy+XC4OCgvFfBYBA9PT2i8GO0Kf8+7l7o+6Gsury8HGlpabIP5K6Kn0M0GsX8/LxA+9gx5uXlSYE2PT0tDmmPx4Pi4mIBgtpsNpw+fRqhUAjl5eVy/m1ubkrHlp+fjxs3bkCr1aKoqAgDAwMScUCyLWXSNGuSjEGZLi0NLC4/kouCQLVwOIy8vDzMzMwgKysL9fX1GBwchN/vR3NzM2ZnZzE8PIyXX34ZVqsVNpsNFotFgkbIQKEVnmMban2p0iHkb25uTvAAuxdQfInJ3jGZTJLAxiUW8yfy8vJElsvKd7e8kfN7vnBEW2dnZ0uFx3mmXq/HwsKCjLBYJXGWS7Y8Zamzs7Mi2ySYLBQK4cknn0R+fj7OnTuHH/7wh/jRj36Ehx56CH/913+NxsZGkf6pVCpotVrRabOT2d7elktpa2tLPAC5ubmSSEZkAyWPrPboh6HJkcv+VCqF8fFxTE9Py8ycexjiGjgnnZmZQWVlpeCsWfECt0d8CoUCzc3NmJycFJQ156dUCzGwhd/J5uamYFCYKkh0Cg/i5eVl+Wfwe2O3ywz1+fl5DAwMIJlMinckFouhsbERDocD6jtueI/HIyFOrPr5zPA/E9PAMRt5Y/TlMPFPp9MhLS1Nuq2pqSnxDVHEQJgmM6ap+OKegrwoAh45ftrc3BQHMAUTPMDJCaKDmhcmCacqleouUUcqlUJVVZXIPbkv4whr96E4NTWF0tJSySBpa2tDTk4OfvSjH2FiYkKcwZmZmXjwwQfxx3/8x5ibm0MwGEReXh56enqgVqtRXV2Ne+65B1/60pfwJ3/yJxgYGMCDDz4o6iTmWvv9fjms+b3R3W2xWCQvG7gtJiFleGhoSMZVb7/9Nj73uc/h+eefx/DwMN577z2Mj4/jE5/4BGpqagThTkw5pwR0w9OjUFRUBJPJJB0NO1v1HSQ+Lxd2y+yYuTcieTaZTApGfXx8HCqVCo899pjIoisqKlBfX4/Ozk5kZmZifHwc77//PvLz8/GpT30K9fX1uHHjBpLJJAoKCkQJyUuF71tJSYkULBxLseDgX/er/Pm1LwouOj0ejygftra2EI1GUVJSIlGWVqsVXq9XugGG3VBJQRfv9vY2dnZ2YLVaBRGuUqlgMBgkiYvpZQxC564ikUjIoolKFx7SXq9XqJXp6elQq9UAIFROjp7YKdC4xVS5eDwuID3SPjmnpZmJln7Oq6lM4f4FgFRGRHYnk0kcPHgQGxsbeP/993Hq1CnprO6//354PB7pHHjRUK1AqSAxzXTB76ahEtZIhAB9GFx0sQvgDHp1dVVyRDjzJ/GTlywPjKysLBEArK2tyWeysLAgiV+kYjL2UafTyQtEmSmDZRjcwqyOjo4OOJ1OrK+v48SJE4hEIgJOW11dFSkucJsfNDo6ij179kj8JRHMjIFVq9Voa2vDk08+ieHhYQwPD2NjYwNHjx7F008/jaGhIfzwhz+UKpYYB+7RiNug85Z7IRrROC4iqyotLQ16vV5c1Xl5edBqtRgdHZWfeXdhQeYWx2vhcFiW8eQSzc7Oyr8/Ly9POtiZmRnU1dXJMptpcjTv0cnv9/vl+WS3QZ8E/RXMoaCShr8z1XMajUZm9+xEXC4XxsfHMTY2hvPnz+P8+fNoaGhAaWkpjhw5goyMDBw9ehTLy8v4u7/7O9TU1KC8vBxmsxl/8id/gu7ubpHiTk5OwuVyCViPCkIqo6jUYydANhNR5/X19QCAtbU1vPbaaxgYGIDVakUikcDXvvY1XLx4EcvLy3jzzTdx7do1oRRQPWY0GqVL9fv98m5wT8QOjmcOx3XcqVLlOTw8LCOqzMxMaDQaTE5Oin/K6XQiHo/j0qVLMBgMqK+vxyOPPILMzEyUl5fD6/ViamoK4+PjmJmZQWlpKTIzMxEIBCTDhc8BO5z19fW7QtWi0ah0rcSpE3fOJM+P5KLY3t4W3jpVNsxFIKt+dXUVLS0tiMfjGBkZQSwWE5rk0aNHpbJhiDqzijUajchuie1lODgDSLi44iXEX76mpgZTU1MIBAKorKyUDygrK0uqfzpa8/LyBOnL+S+7BVavW1tb6O3thd1uF3UOZ68bGxtIJpMYGxuD2WwWmR4VBkQ7TExMCPuGD+LIyIjEah4/fhzJZBKXLl3C/fffj2AwiGg0ipMnT0q1S+gceUOMTCXug8Ylsm0AyJiJaV70n1D1xQe/qqoKw8PDCIVCYjTiX0OFTXp6uhw0rJL5gtO0yM6KZM3Z2VmYTCYUFhaiqqoKKysrGB8fh8fjwZEjR1BTU4Pt7W0ZVc7NzeHcuXMoKiqCxWLB9773PXzyk5/EAw88gJdeekkulqysLJGpUhXFwoAjMAbjkMXV0NCAtbU1Idvu3bsXzc3NOHPmDG7cuIHp6WlMTU0JcVilUqGoqAgFBQXo7OxEaWmp+BbS09PhcDjg9Xpld1FbWyvPPtE1VqtV0h6XlpZQVlYmXgYSS+maJ6KD4w1Wp+vr63C5XHA6nSJPZTfCznB0dFRCjWjU3N3lbmxsSDHHrHev1yvdNbMmCK7kOIV7HxJPySOi2TQQCEimfFNTE/bt24fGxkY0NzdDrVbjE5/4BE6ePAmFQoHXX38dDQ0N6OzsxHPPPYdYLIZ33nkHWq0Wn/jEJ3DkyBFMTk7i4MGD8Pv9wuvKz89HeXm5hPvw+eIYUa/XIzs7GzU1NQJTNJlMmJycxNWrV2E2m/HLX/4S3/nOd/DUU08hJycHL7zwgnhPhoaGoFQqpaPg50HpNXErlFszopTnkPoOKFOlUsFsNouQhHtOghvZZefm5srOkGmYQ0NDsFgsIjRJT0/H5cuXMT09jaNHjyIWi+Hee++FxWKRkSud5GVlZfLP4Xtht9sBQFRpHFcvLy/DbDZjcHDwo7soFhcX0dfXB71eL7GMWVlZ8Pl8YipKJpO477770NTUhNHRURQXF6O0tBQPPPCAHPabm5toaWnB8vKyzH0JCmQ1p9FoBMlLtAI396WlpWJpz83NlWhN5gKQJ0XdO6stvnQGgwFzc3NSMfEwYIzl+vo6Dhw4gLm5OSFjMgeAYxwiwCcmJkQfTWYOf0cioNm9XL58WQx4Go0GBw8ehFarFcwzK9OdnR2RqHLBz5kxzU4ajUYuOHZtzPYljZdoEbqEuT8gqoHce0qWOdKj2mp1dVXaXCaLEQpJpRq18exuWN0yy3dgYAAajQYTExN4/fXXYbFYUFlZifHxccFS19XVYX19HW+99RauXLmCpaUlHDlyBI8++igGBgbgcrlk9OVyuQQRTikqTVUVFRVQqVRwuVzIysqCx+NBe3s7ioqKhNi5sLCAV199FWq1Gnv27BEmF2ffDCfiM86Dl5ctF4U8MIi/Bm6rnEgUzsjIuCskCLhNGaYabzdGhWZJALJwrq6uFjks2T51dXXQ6XRiXOOy2u/3i3QXuO0KJ7ICuN2N9vb2wmg0QqvVCq6dSYPsMqj44efBHAqOxehXICGgtrZWCqnFxUWcOXNGqt1IJIJ4PC6+gcHBQTz33HP47Gc/i2g0igMHDiAjIwO9vb0wmUyw2+2ynKd4g7tLft5KpVLe462tLbkUTpw4gfr6ehGoPPvssxgdHUVnZyd+9rOf4cUXX8STTz6JgYEBjIyMQKFQwGQyCUuJ2RH06/DsoHmTudc0VhKjkZGRIV6k3aw1YjQACMCQ/qGSkhKYzWbpHvh7zszMYM+ePbLo//SnPy07RJ/PJyMtAPD7/RJ+RIbV7oTAcDgsRRU7baVS+Suf8792ZjZdyeFwWBQ3NEuxoqbSIS8vDw0NDXj++efx7LPPymhpaWkJiUQCkUgE09PTCAaDEk9IJg5bYC6K+HAsLS0hEAhgfX1dDH+U01LixqqSKioAWF5elhEVXxAuhNjesoouKSnB0tKSLKimp6cxPT2N1dVVaVczMjLENMUFNkNK2LpyBkp1xNzcHAoKCpCfn4/+/n6MjY3JrsHj8cgBc/PmTfT19WFlZUUW8DSb0dS1WyFGKV9ubq4E4nBZyoeDucWFhYWyyxkZGcHU1BSMRiMW7uDPaZJUKBSor6+/K8CG7XROTg4qKipEgswHkngMLoRjsRjGx8cxODgItVqNEydOSEdCL4lCoUBFRQX+x//4H/jt3/5tkSDOzc3hww8/FPqnxWJBUVGRVEsMF6J6KhQKyRhwbW0NFRUVyM7OvkvVMzExIYt9s9mMkpIS6PV62O12MdPRYc/wHwLYWlpaUFhYiN7eXjENMoc6kUhIN0q2jslkktRA6vlJIiVCgr8DQZFU0OTl5YkMVqPRSNJeWVkZEokEJiYmxDi6cCf4S6lUQqvVQn0HEMg8Z6qVePGRzUaFFI1mLLCys7OlamWHODk5KRSF8vJyHD58WPYwVKptbm6iq6tLWEnj4+NYX1+HxWLBxz/+cZw4cQLj4+O4ceMGDhw4gN/7vd+764KjXJSd+8bGBsLhMMLhsBSE0WgULpdLPBa7RSSxWEyKVWZLNDY2oqqqCg8//DCam5tx+fJliQzlWURFIE2Bu025uzNy+G5S4r22tobZ2VnpEohUoXiEPozdtN7i4mIppDY2NuSdZbBWOByGzWbD/Pw8bt68iUAgIPnwmZmZ0Gq1yMrKuisRj8o7KrX47nHaQrMvC75f9c+v3VFMT09DfSfekj8kD1Oa2Tg7o3TQ5/Nha2tL3IrMJlCpVAiHw6J00ev1iMViyMvLk9mb2+2G3W4X9Y1arUZGRoaoaXgw0lZPtkkgEEAgEEB6+u2MXKKS9Xq9yDF542dnZ0vsJMmmrFDpHCZJkvAvmv0SiYTgqLnMJOqDi29W2KWlpUgmk2hpacGzzz4rC0yv14vc3Fw4HA48/vjjKCgowHvvvQer1SpVPJEo/LmpAnM6nZiamhL1TTgcFvb9bts/qxEC0BKJBGpra4VSW1tbi7GxMVG0EczH7kOv18tly/ac+6q8vDwMDw9Dr9eLSoTLZpp+zp8/j46ODhiNRpw8eRINDQ1466238MMf/hDJZBK//du/DZ1Oh89//vOSdvjSSy9hbm4OBoNBIjBdLhdsNpt0CMzKZoW5vLwMAKJ0SyQScLlcMBqNeOCBB+SiaWhoQFVVFaqqqtDX1yekWI1GI4lru9VIpILOzc3BYrGIfHdra0tGGMRsULaZm5uLrq4u5Obezman4qy6uhqTk5MCziNafmNjQ/wwAASqGQ6HAUC6dyr1uLgsKCgQuSSDc5gbzWecks68vDwRR9Aolkwm4fP5EIlERF3DAsHhcAhU0W63y3vw7rvvymiS8EruZyjhZsGVkZGBZ555BolEAmfOnMHm5iYOHDggy30eyD6fD0ajER0dHWhpacGtW7dE6MLxCWXM3MlZrVYZ+dBUq9PpUFpaCp1OhxdffBGtra1yqS4uLkqGdUlJiXwupL9aLBYR61DqWl1djeLiYkF8sCDl3pPYED4/7e3tsmulh2hhYQEzMzNyUdM5zQW+TqeDwWBAOByW7An6WWKxGNbX14WOm5mZKXHCOzs7qKmpQSQSwfz8vCR6kptHr8huavBHclHQaRqJRGQ3QW4L54ms9qn2ACC6Y2KYqXjiTRkIBFBUVITCwkL5wDc3N2W5yRk122Te1qurq2LY4Rc+NTUliIzi4mLEYjH5Gfhzb29vS9YFlQO8EDhzZmBRZWUl5ufnZXlFOSj5NXq9XhDZRG3sVuHQzKZUKiUhcHV1FW1tbejq6kJOTg66u7slsMRkMiEYDEpLv7GxIUoY7gfS0tKwvr5+l1yypqZGMiGozCFl1mw2C565oKBAqiQqe9LT0+VFB26H3gwMDEh6IUdUaWlp8Hg8wq2iDyQnJwepVEpCY2icSyaTePbZZ3H+/Hm43W5YrVapvplVMjMzg7/5m79BTk4OJicn8T//5/+Ez+fDyMgIbt68iebmZlitVqH1UhLKoBoiqfnyUWev0+lQUVEBi8UiFanP54Pf74fNZsPGxgZeeeUVTE5O4lOf+hTKysokG5wHiFarRUNDA86cOQO32w31HY5OUVERKisr4fV6UVhYiIGBAUE80BNSVVUlATT8njj/LygokAU5qQR0l9NJyw6Q3Q07oXA4LN0vixeNRgMA4jkhF4pjMspjyQ/jaIX7HwCwWCwyMuXzRagjJwUXL17E1NSUoFQCgYAc8Ha7HUVFRRK+tL6+LrP1559/Hvv378fk5CQmJibEI8ELvLe3F52dnWhoaMCjjz4qptqFO1kzkUhEQsc4AmORtrKyIs+lTqdDW1sbxsbGRKbODmz//v0YHR2VQ5xAQk4OiBcvKSmR6rykpEQ6d17IwH8RnPm+c++6s7Mj8NKpqSmRzPNnNhgMAG4XMrFYTHaYKysr8Pv9gskpLCyExWLB4OCgnGF8FqhmnJiYEGENhT2czJjNZrS0tMDv94sghdOVj+SioPOUM/fd2lzOf1kdpVIpIcyyAgZuz+x25zsbDAbZ8FPKWFBQgKmpKWlDE4mE7BeYsazVajExMYG8vDyZL3LTT3mfy+USnAYPl8nJSZnlszug0YmHNFtJorvZenOxR7AaANlR0JzHipAVF53oXPLSuVxQUIBgMIjm5mZZqDFAKB6PY2ZmRmbEWVlZIgcl6IxLdbqGJycn0dDQgOXlZVlk043LWT7zGVZWVhCNRlFRUYHp6WnZS9C3QuQD5Z/8+fi/MVKTEsLdBxOR36lUCqurq6itrcXRo0clrOfll18W5ZzdbseBAwcwOjqKhYUF+P1+fO9735MRGVVMvb29MJvNGB8fF3UOMS/p6elyeVMyWFVVJV4MGuf+/d//HQaDAV//+tcRDocRjUYxPDwszvalpSWMjo6KyZCXAonFGxsbsNlsqK+vh0ajgdvtRklJiYAib9y4ISIPrVYrAg4mz/H7BCByRXbM7MTY7XJUMD4+Lj9PUVEREokEpqenZcmsVColcTI3N1e8Hgy9MhqNYmILhUKor69Hbm4uzGazqIZCoZAYSbkHTCaTAG4HOXF0wj3We++9h6NHj6K5uRnBYFDGRslkEjU1NaL75wQgmUxKlkhFRQUAQK/Xo7u7W+TcjAfY3NzEt7/9bSEs79u3D01NTdja2oLH4xGTIpU+9PeQZDsyMoIHHngAs7OzePvttzEzMwOr1YoDBw5ge3tbnguPxyO/F13M+fn5Ak5k8cldHycVvJSotuQoWavVSlzqzs4OotEo1HeSD7lf5OicRlcWt0zTo2iE4yqPx4P5+XnZTdJISbIwQ8IoG29ra0M4HEYqlZKRWmlpKSYnJ2UE+ZFdFBqNBtXV1ejs7BTtsMlkEiQEt/0lJSWiLOIHQR4LJWQLCwuSzOV0OtHb24vKykpxKlOWZ7PZ0NXVBZPJJAHnVM60tbWhuroaQ0NDgngmUZIsGZpriEdW38Emm0wmiZv0+/1ywO72W9jtdjnUufQm1iAajQoegxLhRCKBo0ePYnZ2FhaLRRbyVGPt7OzA6/WiqKhI2FIPPfSQ8OV5eBkMBgwNDUmADJVblN8S20z/hsVigcPhgE6nk0UXF6l0vNLQFIlEkJ2djdLSUoGI8bDg/onCAHaC/D6pqCLmRKPRoKioSEaPm5ubGBwclOqJOxgauFhkDA8PY3FxEY899hhsNht8Ph8SiQSqqqqgVCrR39+P6upqyT1nQhyRBMxVYGAQKaV9fX1ygC7cSdC7fv06XC4XRkdHhSBsMBgwMTGB+vp6xONxBAIBfPDBB2KQbGhoEOnq9vY23n77bXz44Yf40pe+hK6uLjkwlUol/uAP/gAFBQXw+/2yR2Fhkp2djXg8joqKCsHD82LfTY5l58llMs2lzJ3Y3t6Wz+7w4cPCKFLf4a4RXcIui36OmZkZeXfz8/Ph8/nE4W02m0UaOzExIaE99GjQMxAKhXD+/HnJGnn66adFjBAIBFBXV4cDBw4gFovhypUrUkgQG37w4EHJvFepVPj4xz8Or9eLl19+GV1dXXj00Uexs7ODZ599FlevXsXPf/5ztLS0wGQyoaurCyMjIygpKUEgEJDvmXsLAgkzMzOxd+9ezMzMyPvpdruRnp6OxsZGaLVa+Hw+lJaWSgQv1WYsRrgPIVuqvr5e8DGcaPDfxQKTY0buITnaq62tRUZGBnw+HwAI8JLmO6oFSf5lvHJ+fj5CoZCoJJmDUlRUJPJsk8kkCrGFO5gZKtRyc3Mlr6eoqEik1+zAPrKLgiYa3qis1vghcBFIw8zW1hZcLpfIF9PT08WNm0wmMT8/L2AtzoMpHWSLTIksJWzcj5w6dQp6vV4S3wYGBtDW1iaV9+bmJlwuF6xWK3JyckTaysqXI6rdL+ZuMx6XVtzF8D9Tp89oUyIacnNzBWRGnT2X/PwSmXw2PT0Nk8kkFyeBdHTBzs3NYW1tTcxAPEjIcWF7z2qTe5HZ2VmpKrkc5QKO3RkDc7ispYuYM33Om2k6opa7uroa0WhUXKtGoxEmkwlarRadnZ0yI21tbcXo6KiQK2nC270c//znP49AIIDr16+jra0NtbW1OH/+PDY3N/HZz34Wer0er732Gpqbm1FUVIRQKCQuWOreZ2ZmJGN4ZWUFXq8XBQUFmJ+fFyOWzWZDZmYmVldX8eyzz0Kj0eDChQvo7OyE2WxGc3MzIpEIBgYGBEPCHQ9hbo2Njairq8PIyAhCoRBmZmbg9/sl1pP04OHhYWEr0ZOxvb2N/Px8jIyMSCYFKbqsTGmaIz2AXSSltpxbE7PCOTud16FQCDk5OfJcsCPW6/VQq9WYmJiQ/GUSatVqNYLBIMrLy0Uh5ff7kZeXh6ysLBljcB9GbElDQwOWlpZkTr++vo62tjbBw3AZzTzooqIiSS/kJXz16lUoFAoBWSYSCVy9ehXLy8tisjx69Cgeeugh/PSnP5U8F1b9ZKcBtxVi3IPpdDrodDqMjY1Jh5SdnY2jR49CpVLhzJkzdxGMJycnhStFRprJZEIkEpFuOjs7W/79XHDb7XaZNHAPSIny2NgY6urqkEqlZJ/LCQP3ZoFAQPxhpFCw856engYA8XAlk0mYzWaRr3OMxYkLz7a8vDzcunULvb29Qku+fPmy8Kba2toQCoU+uouCszwufEpLS8UQpFAoUFNTg1QqBYfDIbkONTU1ePzxx7G5uYmxsTGR7NFVyxAYOhJJ5+QBSlAZZYsNDQ2Ix+Po7u4WFdL8/Ly0+g6HA+fOnYNKpcKBAwfQ2dmJ4uJiCY3hEo/z/P7+fhQUFEiVxBlyTU2NjAuWlpakfSSkjHNHVsqs8rncplKCFXh+fr7kc9fU1MjvTxhfRUUF0tPTMTU1hby8PLk06L5mt8M5pNFoFO4LhQFszanM4oPHkQJnvcxg4M6HZMyFO3TMRCIh8sHs7GwAQG9vr1ScNL4RHHju3DlkZWXBarXixIkTqK2tlTCfra0t1NfXy+6AEZUjIyN488030d3djb/6q78Sp/YnP/lJ3LhxQ3ZARqNR9hqUoTLXgeqYwsJCuVB2K90WFhYwNDSE9fV1/PEf/zGWlpbw5ptvSqb6yZMnEQqFcObMGTidTiwsLMDj8eDZZ5+FUqnEL3/5S0xPT+PYsWMYGBgQkcJXv/pVnD17FiMjI6KM4hhjZ2cHwO3gmmg0CgBCRWUXQPQED3hKuJeWllBeXi56/9XVVdkvpaWlSXdL4UVNTQ1sNhu2trYkXIeueeK7+T1zp0j3N+fqdBc7nU4BbdJf0dDQINJrvid85uPxOFpbW8VFTzMgDZ7JZFLMgzk5ORgcHITVakVXV5ckMXo8HiGr3rp1C1/5ylewZ88eDA0NCTOJoxatVotHHnlEYmUp0c7IyMD4+Lgo2ZxOJxKJhHTr4XAYb731lpCqjxw5IhMDok4YQEQFZm5urqB3KDnd2dlBa2srJicnxRk+OzuL8vJySZHbs2eP5I87nU75bHNzc1FXV4ednR0p8iiG4YiWUl1KvzmmIwl7cnISmZmZcDgcWFhYwMGDBwVkycL4+vXrOH78OA4dOoRTp05BoVDIyJ05P7/Kn1/7ouALwCUc0dptbW2ieV5ZWcH7778Pj8cjW/7h4WHU1taKVp3zOSqXiOMgPjs/P1+04vRRzM3Noby8XGb/zIelyYuL81gsJslbXHqzQ1AoFMJo4Uyb+5HR0VEsLS2hrq4OGxsbsvTKzc1Ff3+/xIru7OxgbGwMAESPTj4Ql5XM3+USkdkDnGWy2jSbzbJfmZiYEA8Fw+1XVlbEZ0HNNg9figYSiYTI4rjMZCXPhSk7EgAyruI+h2EsNHUR80GZoUqlwuzsLPbv34+JiQmcP39eTJSbm5sYHh6WVDeq2kwmk7i2CY+jAieVSuEnP/mJ8HiGhoZw5swZgcrRM/H000/LBRGLxRCPx+9y+u8eBWRmZgr40Wg0IhwOw+FwICcnB4lEAuPj47h06ZLsSfR6vRBjeXFzSUsnuMvlwoULF/D222+jpaUFFosFt27dwr333ouOjg6YzWZ861vfwhtvvIHFxUXo9Xq5gKnK2Y0Np/fA7/ffJQUdHh6Ww55wR87LOS5IJBKIRqOorq7GysoKxsbGxK1+4sQJlJaWYnBwEGazGVVVVXC5XKL+02g0sNvtcLvdUCqVosgiqiIUCsk4rLS0VCJBDQYD/uEf/gFvv/02HA6HxAF7PB7Mzc3hwIEDcsGRGABAlF/Ly8twu93ye1RVVckoeWJiAr29vVAoFNDpdPjEJz6BW7duweFw4J577sEPfvADvPTSS1AqleKpmJqagsFgQE1NDXp7e6FWq5GZmQm9Xo9r165JB/vNb34TDodDMiQ4rVCr1ejp6UFTU5MsoMnwYgGcn5+PqakpKBQKic1dX19HQ0ODQEo58uWIPRQKSbdOxSTFHRwFEkRIpA67AhbIZWVlssskuoT7LJpua2trZTLicDhQVVUFt9sNr9crz7LT6URxcTEGBwfFz3bgwAF4PJ6PNriI2GW6nktKSlBWVgYAovzgwZ2Tk4Mnn3wSMzMzOHfuHHp6enDw4EHRQFssFrhcLrjdbphMJqnMy8rKRHpJlQ8VIAUFBTAYDBgbG0NOTg7q6urQ0dGBpaUlnD17VnAQvKXVd7DIXIavr6+LYoc+BwCyVIrH44hGo8J3Z8vpcDjkEKaumTPAQCCAjIwMkfVy/suHhCO6mZkZLOwKcs/KyhLHLxPt+AJXVlaKRpu+CCIYKMOk7JW7FarDGFFKeGF2drYspamgoFSOnRuZVmQMEWWhUCgEP0J2DCudiYkJlJaWoqGhQS7grq4urK+v4/jx44Lspo+BY8mpqSkkk0ns2bMHTz31FAKBAGZnZ9Hf34/p6Wm4XC4ZlbHF3k3AZTYHZ8XsODQajai0GCm6vr6O6upqrK2t4cMPP0RfX5+A2yYnJ/Enf/InCAQCcDqd6OjogE6nEzIrXfDxeBx+vx9arRb/63/9L3R1deH06dNSJDHk/uGHH5a5/9LSEmZmZmTHwrRDfg8cW6hUKsFV73ZT704vo8BjN2U2NzdX0hCvXbuGM2fOYGVlBZ/85Cdhs9nkgF24k9HOPJXFxUUhsvK9cjgc4oXg7HxjYwPRaFS4XHa7HbFYDLm5ubj33nsRDodx9OhR5Ofno7OzExkZGbBarWJOzc/PR319PWZmZuQgJWbF4XCIYGVxcRE1NTWyPxkdHRUjJACEw2FUV1fD5/Ohrq5OVHImk0nGNvQNLC8v4/z580gkEggEAvD7/TCbzXjiiSfg8XjQ2dmJy5cvC1uL3cLm5qaoNBmOxK5qfX0dRqMRgUBA3k2C+Ogby8/Px8bGhkwYuLfje720tCQ4lpmZGSnIaO7jz8FFOfMyiMTh/2POiNvtRktLCy5cuCDmVLvdjmg0itzcXPh8PtkX8rkaHBwUB/hHclEw4YrpVlziPvnkkygsLMRLL72EpqYmWW4xN1mr1d6l6Y3H49BqtWJMobuVLStb2czMzLvAZ+xi1tfXUVVVJQE4t27dwr59+5CZmSkhO0RbsOqnXpsGFCoKNjc3sXAnIIgKJ0pBufAiEjuRSGB9fV00z2VlZVCpVKIomJyclPac9MrCwkJZFnOuy1awtrZWQInT09NyWE9PT0tLzE6JYyKO49LT06WypkSX+BBKB7k8s1qtCIVC4glgOA/NYdwnEUnCF7u1tVVezmvXrsHpdKKtrU3MhWfPnoVCocBTTz0lXeW9996LgoIC2SvQqbp3716EQiEUFhYKp4mxnSSFsmqcnp4WZDoX/fSccLGXSqUwNzcnOc67pc1MnmOnuhu13NzcLEvK9957D/feey+Ki4thtVqxvb2NxsZGzM3N4Y033pCOdnt7G2q1Gh9++CHef/99mEwmbGxsYHJyEtvb22ImTKVSGBgYQFZWloyh1Gq1mNwyMzNRXV2NWCwm+ngeHtyvlJSUiA6ernoeBtPT06iursYDDzwAAHj11Vel+Pjwww9x6dIl6PV6tLa2Ijs7GyMjI/Is7Ozs3AWWZN4Cfy5GFLNrczqdMJvNUCqVIrH80z/9U8zOzuKnP/0ppqencfDgQQkeYpYMO0p21uo7IUnMWCGCJj8/H1evXpXuRKPRoK+vD52dnWIgfOaZZ5BKpfDkk09icXERN27cEDXkwh2s9vz8PMxmM3Jzc1FWVibARwZ1MS7g4sWLeOqpp2C1WtHT0yO7SKVSKbkSCoVCxk8sPGi642XEDoWHO3eDxIfzHElLS4PP55OIZLVaLfBOEiDUajXS0tIk44Q7E8ru2V0S8UKwpVKpFIFGQ0MDQqEQOjs70dzcjIaGBizcYZJNTU0J9Zkj0Y/komAlTdjY1tYWurq64Pf74XQ60dPTg/r6eqno/uM//gPp6el44okn8OCDD6Kzs1PMeMXFxTAYDCKj4/iAS1+iDIh11mg0Yqj60Y9+hPz8fGm7KVO8efOm5AfwUGW2826wGvko6+vrdwECAci/n11JWVmZZG8nEglYLBaEw2EZyWi1WulSCgsL0draioU7LKT09HSJwRwbG5NqPJVKYWVlBR6PR/wIBPkRJ8DPyWKxCFyMYe+cw1PCyweY/9zKykoJbVm4k5amUqkAQLqMoqIiwXSwA+LvwQpmYGAAo6OjmJqawujoqKjMKApobW3F4OAgfD4f2tra4Ha7ceXKFXzhC1/AxsYGBgcH0djYCK/XK8vRWCyGhoYGaLVaxGIxAbzFYjHZNen1evlZOBYjKI4z8JmZGYnG5MFELAnNiiqVCgcPHoRarUYkEkFVVRWMRiOcTicGBgbwla98Bfv27cPm5ibC4TA8Hg/8fj/S0tJQVlaGpaUliVO9ePGiqKwCgQAOHjyIxx9/HEqlEg8//DCGhobQ3d0ty3Pus7iXIlRxZWVF9gM0W1L8QEc1d2gcp9FxTXllR0cHXnnlFXg8HhlvXrt2De+99x4efvhh6QhJkmVVD0B09uw4MjMzUVJSIqPXw4cPC9p9eXkZtbW12NnZgclkEnT26dOnkUwmhQQdCASg1+vl3SAWhu5z5pIQS1JXVyfmvomJCfmZ+PeQoeRwODAxMSHAPM7ZdTqdKIqIF/H7/bBarXj66acxMTGBrq4uWK1WyZ0gKI9jW61WK5ck9x7MTSGKnaNCg8Eg3iaee9wf5uXlweFwSMY2Cxya40gpYNFDlhy/CxYxsVgMW1tb4tFgEch/Tnl5uVzKzP/+zGc+I936iy++KIICnU4n3wmT8Kgm/ZXO+V/znpD5OfX8nOsePHhQTEKdnZ2oqKhAMpmUiyCVSqG7u1uqKO4gHA4HFhcXZb7ICoAObP7C/ECzs7MxNDQEAHC5XDh06JAEGg0ODuLAgQOwWq2ora2VzGCakWhOIviOKqqlpSVotdq7nKUE+vGg5WKXfy3du7sJrPwyw+Ew5ufnRdrGbAPOl3NyclBUVCShRtRVU/XCCozLXBIts7OzJUp2amoKGo0GeXl58Pv9aG1tlYdza2sLwWBQfkZ6RNgKs02mnHlxcVEOKo7TiH+IRCJwuVyIRqP43Oc+B5VKhXPnzmFqagpOpxM2mw3PPvssiouLMTw8jK6uLgwODqKmpkaMYl6vV9ypbW1t4iLnZUfOUDKZhE6nkwUrU+v4clNJRA/F6uqqZLjzAOV4iuOumZkZ5OTkwGAwCPKDLt6ysjJMTU3h3Llz4tylSTE3NxePP/44pqamcOHCBYEDlpSUCEiwubkZx48fRzAYRGdnp3gjqLlvaGgQgCC9JXwO+f/r9XoxB/KCWFtbg8fjQVFRkSxNqT5yOp2Yn5+Hx+PBqVOnEAwGpfNrb2/HpUuXREpLNpZCoRB5OiGXhAjy+6eCbmFhASMjI/LvCQaD6O/vlyyVv/u7v8P8/Dw+85nPiGeAXoK5uTnxn3Bez30XM73ZaQ0ODgrinztEKvjYdSaTSVy4cOGufOrduG7Kf4mFYVrjjRs3RDYbCoXkUjCZTAiFQqJcnJiYgFqtFhk4i0Yi9ykiYLARfw9OJlicUJlIJRhl46Q4mEwmCRzKzMxEcXExIpGIBMDx/WdBQLEPRSSFhYUIh8NIJpMi2ed3y72Sy+XCzs4Orl27hsuXL+ORRx7B0aNH4ff7RV7Nf95HclEQp8D4Rq/Xiz/8wz9EXl4efvrTn4rWnU7UI0eOiCRseXkZXq8XpaWlACAAOlaOVBcx6H51dRXhcBihUEiCww0Gg1Rm586dQ3V1NeLxOMLhMJxOJ9rb20U1cuvWLTEwUSXBGNFkMim7A+4DMjIyRLtM93RJSYkcbMwATiaT4tlIS0sTZQcfeB4ExcXFQpv0er1i4tHpdIL+NRqN8tdTasxLkW11MpkU9r3ZbAYAGecwdSsQCECpVGJ1dVVY+JxR8t/L343SYx5WHG2QeqrVakU5U1NTg1gsBqvVKo58hUKBvXv3SmdGJVRfXx+amppQU1OD2tpaUZBlZWWhubkZXq8XXV1dUN8h4/K75uLf4XDIBclnoLi4GFlZWQLP4yiO0mkAcrnG43H53Fh06HQ6XLx4UUyfW1tbkoBIVzXHkEx3I6phYmIC0WhUDKEAZEHq8/mQTCalCwoGgzAYDCgoKEA8HkdjY6Ms84H/MqMSZ84qU6lUwmq1SqfBTAnuYQoKCmCz2URSaTKZoNfr8a//+q8IhULY2NiAUqnEsWPHcPz4cfzBH/wB2tracOrUKbS0tAhtmc8YO4jp6WlYrVbMzs7K/72ysgKHw4H5+XlhIhUXF6OkpASxWAznzp2Too5VLllWWq0WC3e4ayQyKBQKOJ1OKBS30+RoCGRntLKyItHKTMObnJyUC5uCC5rr1tbWBMHD0TQ7YQah0eC2+zKm52FpaUlMgowW4LPPfz/3HnSm80KlCIeufO5J+L4uLCxI5jj3aJmZmaKqoodpdXUVQ0NDkh+ivgMM5XKcwgCfzyfYnGg0KtJ+wjPdbjfOnTuHtbU13HfffZJvQ6PkxMQEvvrVr2JxcRHj4+MyWv5V//zaF0UsFsPc3JwsQ9mGHzt2TFQZzz33HDY3N3H27Fl4vV7BVnCOxxudDw3Ryevr6ygsLEQqlUIkEpG2zWg0yixVr9dDo9HgM5/5jADVOjs7hQVTVFQEm80mS0tKBOkq5cyZDzQlejRvsYKg7Hd3GE9zc/Ndcz+9Xi9BSkRp09dBXHpHRwdCoRCUSqVUluzGmKpGxv7a2pp4OSgJ5AFPkBg5WWxPU6kUSktLYTKZ7ooGzc3NFVQHZZpcVBcWFkKr1SKZTMqIgS8VAYs8wOnkDoVCGBwclMOV48Br166hpKQEXV1dgiUpLi4WQxgJq8Sh76bpRqNRlJaWivqNc3guIzMzM8WQSekwOT2BQECc2cS91NXVSZwp9zHAbRwJjYaUPnMvxbl2ZmamVLiRSASlpaXw+/0oKCgQfAbn2VStcZfE7HEGSTF/mnP2tLQ0yX3nIntnZwcdHR2ygGQ3SHd2eXm5qK94afD7Li4uxiOPPCJFjtvtxl/8xV8glUrBYrHA7/eL8ZUafO5uaA6lG72kpES6OYZYORwOLC0tiUqJIzjSVOfm5tDX1ycdqF6vF7IrAOFCURrKTpbiEfK2CB3kjH91dVWiaT0ej3hMSFogHmh9fR1Wq1W4cEzkS6VSsFqtkl/Dn21mZkaeRZrc9Ho9xsbGUFVVJX4oXghzc3PY3NyUnQ3VdjQvMqdlfHxc1E5UJVLMQ3kzsSD8nXcvlJuamoSswCKOSqf09HSRT1MQEwqFYLFYsHAH4cNLJS0tDZcvX0ZfXx+Wl5dRU1ODT37ykxgbG8Pk5KR4w3h5fiQXBTXfe/bswcrKCm7cuIFr167B5XLJjP7MmTM4cuSIBI2PjIzAaDRienpa8m0dDodU9OPj49jY2BCFDA8hh8OBzMxM8SMsLS3h5z//OZ566inhFo2Pj6O8vFyY7e+8846goqnasFgscLvdKCgogF6vFwQwc7ktFovwUyjtAyCeC6qJWIUPDAwIQ4gZEvPz89Dr9eK/YNu9vb2NkpISDA8Pi3uZOR5GoxHA7SpvN+iQWAMAghFeW1uDQqGQy4CU0lQqhfLycsGJU45cUFCAQCCA8vJy+fxSqZTElRLEyE6EM/FYLAaz2SyYDFbbJSUlkjewZ88eAdkx+/vChQuSgFZZWSmXgl6vx8rKiqRyUROemZkJpVIpFV8wGBQFGcUEvFzpHWH1xgosPz8fRqNRJKh80Ug7nZ2dvUtFwkOEkEUuKDkH5jPGESSzIjIzM+H1erG9vS0hRFQSUQK9O9eaeQ885E0mk+yD+vv7UVFRIUtsAPJ7sfvieJcH9s7ODjIzM4WlNTAwgAMHDgjShIiMkZERPP300/jSl76E/Px8fPDBB8IEKiwslN0IiwmqeWigjUaj0g1wie3xeOR/12g0cjE//PDDoqYjraC/vx+tra2iQvvhD3+IaDQKu92OqqoqJJNJuN1uce0TsMfngSpFALDZbPKdsCPiAjsnJ0c4TUTz02zIQ52TCu4VAcjUggIVqsg47uSFyc6E4Et6StjtkrhA+OLKyopkRfBnJryQZyY9G2VlZfJecLzL4piCHY6YiBgvKCgQEUIoFILNZgMA8RTxHKSXpLm5GSaTCePj42JG1Ov18Hg8v/I5/2tfFKzEAoEAdDodjhw5IpI2i8WC4eFhcQk6HA7U1tZK6AdvQbbaWVlZAoajSoEaebqEqXefmJhAVlYWDhw4ALfbLdp8EmGpZFCr1RgYGIBOpxOjSXZ2NpxOp1SVa2tr8hJzkUVn59LSkmA5yFdhdgDD6MvKysQ9vbS0JKHtTU1NEgDz1ltv4ezZszh58iS+/OUvyxikp6dHFtbcHRQVFYnCxWazyUghHo9LK0t9NnchCoUCkUhE5s1UOlHVBdxe+A0ODoqRjL/zzMwMioqKYLfbsbq6CpvNJhkPFotFXpJUKoWKigpYrVasra3B4XAIYI7Iar1eL67U/Px8LC4uYmhoCMXFxVJV8rDjTJj7G9JQSc5NpVLweDzi7yAgj3NrvoBcXvJ34Tx7ZGQEWVlZ0p1lZGQINTgcDkOr1aKpqUmw4NzLJBIJAcjl5OTA6/WisrJS2GJU3jEfvqamBhaLRS7L7OxsORQASGocK8GZmRnBlJB9lEwm5d+/cAd8l5eXJ8Y8BhftHp3SFEks+tTUFHw+H37jN34D9fX1GBkZQVVVlbwTy8vLgoHZHZDEn41jYiIrOKZMJpOIRqOYnp6WHOeMjAxcunRJQnPS09MFsmkwGOD3+xGJRPCLX/wCiUQCH//4xxGNRnHq1CkcPnwYWq1W3u3S0lKZyRsMBiiVSuh0OsTjcdmX8J+/cIeTxA6JBlvmyNBLpL6TLc/xVHFxMdxut0QBaDQaEZjsHrlFo1FZwNONTgEMR1MUglB0wi6Q+01K3y0WiwhiGItMpzm7ue7ubtTV1WHhDl6Gfy0Ds9hZU3XF84mLbZpOKZtnrsXBgwdx5MgRVFZWoqOjAz6fD3NzcxKjS8PvR3ZREGexvr6OmpoalJWVIRaLobq6Wj5spq+p1WpRMVHyxQ+cuQbBYFBaQsr8yDbJy8uTZS8x3lTBFBUV4WMf+5h4J2i8MZvN4mgmKpjmL45iUqmUSDBZ6ZEcysU01RzU8VMyys+AUjNWpHa7XRa3VM6EQiH87Gc/w/z8PPbv34/GxkZsb2+LympnZwdut1sqW449YrEY7Ha7VNYARF7Y1NSEmZkZmR9zVJaeni6zzYU7ebnMo/D7/aisrBRyLi9ALs34z2ILvnvhvnvRxl2H2+0W42MymURJSQnq6+vhdDrFxMTOgFDIzMxMmfPyoOnr6xOXOaXIbOuB2+x/p9OJ2dlZgZ9NTU0J4waA7G54mLGy41KZnaLX6xXpbjKZRE9Pj4wkKY3mSIs/N7tIVrmbm7czmwHA5/PJ7mpra0vEDmlpaSLhZTW6vr4uADyKH9jBcbSXkZGB/Px8xGIxeDwetLW1SbGy23sRj8fR0NCASCQic/vNzU1UV1ejvLxcQms++OADzM3NoaysTLqIvLw81NfXyw6Oz7JCoYDX60VaWhrm5+dFhMAJgPoODJPRxfPz85icnITVasW+ffuQSqXQ39+P8+fPSz70wMAA9u/fD7vdjrq6Ohw6dAiTk5MYHR2Vi4rqH/oY+BwUFBRI51dQUCDJe2TLlZaWIh6PS6QvwaTMBV9eXpb9Br9bv98vo0oSlZeWlqQjYDwAcePl5eWC1WFHwhRJ/tzszokZp3GPFwAZc3a7Hbm5udKdEgqanZ0t3iSSF4iN5yXJ/y4vL0/AgVQwGQwGNDU1SVd57Ngx5ObmIhQK4fLly2IWzMvLg8lkEhHQr/Ln174omJvLReba2hqCwSBKSkrQ2tqKzs7Ou248nU6Ha9euCcGUuwFeCtT/Ly8vy38/OzuLYDCIBx54QFRIwWAQra2t8nBEIhEJ4jh//jz27dsnuwW229xRUJO+G0+uVCrvmmNTirs7e5oHEiWqTGNjNjcAGfdQzfXKK69gZmYGR48eRXt7O65du4bXXntNzE008szOzkKj0cBoNGJ7e1swwpOTk5LAx3abhwVHZTyA6J+IRqMyulhdXZXug10SK30AMv6hkTE/P1/YVOXl5fLZ8PvjvFahUKC7uxsKhULaapJ7ebkyGWx1dVWWkXQCZ2VlwWw2o6KiQiig5eXlWF1dRSgUkjk/VUm5ubmorq6WB51501SqWSwWESXQqFZWVia7KCaNcT5LjAd5XcPDw9jZ2UFtba3wxpqbm9HS0oKRkRGRSgKQMYVOp5NMgd37FHaIVO6QJ8QxlNFoFAUO0Q0+n0/8SFxkU7FWW1uLtLQ0BINBuazz8vIQDAYls4JiEo7aLBYLlpeXRarNcRgPo66uLtTW1sq7yjETpZ880FjIkbCg0+nEIEbYYDKZhNFohFqtlg5hYGAA+/btQ0NDA/75n/8ZXV1dgrFg10JaK9WQLBgHBwexuLgoXQcvV+6SiOnY3d3Rn7E7YZEClc3NTbnElUqliArMZvNdED8AkvwIQAyJeXl5GBwchE6nk8uJHTi7rZ2dHXHTLy4uora2Fi6XC/F4HHq9Xi6Kzc1N6QYBSC4PsTvcP/Ln5B6OnRVHrgCk0FWpVKitrZVi6cEHH8SZM2cwOjoq41eVSiXcsaWlpf8raSzw/+Ci4Gx8ZmZG2m2tVou2tjaB+fEXJXJCfScukhnVhP9RosklGm9ohUIBvV4v1v2VlRUcPnwY8Xgck5OTaG9vR0ZGBrxeL44dOwatVouKigrU19fLwvj69esoKSnBxsaGxJ6y3SssLMTs7Kx4EMjv4SXErFzgdsdAqR019Dz80tLSRM6Yk5Mji+yZmRk0NjaisbERtbW1yM7Oxvj4OK5cuYLa2lqMjo4Ka58LO7JmqqurxQVKpQ0rT0oCOa5YX1+XmfHY2JhULvSBUJdPhj91/XwBqbiqrKyUBRwAubg424zFYhKOxO8oKytL+FmUcnIRHw6H0drairy8PIyNjcHlcuGhhx5CIpHAd7/7XSwuLgoldmZmBlNTU9BqtULBpAs9EAhISBJDeXgIchxgNBrhcrmgUCgwOjqK0tJSpKWlSWXLTiocDktlvry8jC996UtwuVw4e/YsLl26hPLycuzfv19GdxaLBV6vV7IeiouLxZnLi5gjEUZhMnmPnwNVQOx40tPTZXZstVrFSMoEP8ZdMleB6qJoNIr19XX5fPj/Dh06BOD2jotQytHRUflrWa2mp6ejvr4e3d3d+PDDD1FWVoann34a29vbuHHjBkpLS1FVVSXZBXxu2OWz6KAIgUv/ZDKJ9vZ25OfnS27MkSNHsLKygn/6p3+CzWZDfn4+fvrTnyKZTEKr1cJms2FoaEjS16anp9HU1AS/34+ZmRk0NDTI7J0FBiM/mfPAKQHR7PxOGd5EOnI8HpeOl1h7wkoJYWSntbm5KZ03AX3hcFiYUZxssEDkBIBjXLfbDY1GIyIIFha7jXK8HKjm416CKj92fiqVCnNzc2hvb5fMEQpOqLTkKI4UZjrxWbyQQgHcLmZ9Pp8Uxb/Kn1/7ojAajTAYDPD5fAgEAjKD48OztbUloSR0zhJklp+fD5VKhYWFBfmyqSZhJZGTkyMKJMpOGUi/uroKq9UqOAUSHw0Gg8gjNzc34fP5ZI4LQHYfXBDR8c1wIlbQhPbxP9NFTckc9x7kxjM/g4vsVCqF48ePo7KyEkVFRejt7ZWdCNn1GRkZqK2tlcuG+dxkwDBGMZFIYGpqCmazGSqVCrFYTLASVHRQ2mc0GqWCXl5ehslkwsIdzhNJqDzUtre3RZZMBRElgBw37SbWUsK6O/GPKjDScg0Gg0AiaXKLx+PIyckROu3p06eRSqUwPDyM1dVVdHR0YM+ePfj+979/V6qe3W4X+FlZWZkwsWgeMplMGB4elg6Fs2a+jLzk6YrXaDSoqqqSv8fj8WBra0sw1m63G/fffz8qKiqwb98+WZLS2cpnm/wfji/y8vKEY6TRaMT4xlRDjku5C2GFx3Q2VsGEOfJAp7qOuQSMmeXvw/Hc/Py8GAW9Xi+mp6cRDoelOuaMPZFIoL6+Hna7HS6XCx0dHbj//vtx5swZubBmZ2cxNzcHt9uNvLw8VFZWSrXOd460VqJ0CgsLkZ2dLaOPW7duYWhoSHYdv/mbv4kXX3wRPT09+NM//VNhaBUUFKCoqEiqY3aflDpzpLR73s+RKBP/eLnm5+djYGAAjY2NMireTYemU57zee7KCgsL5VzhyJLeLVb8HAWurKygs7NTigT+3DywaaZkt0KMOwO0+L0zVoHubb4/8/PzotRkdxkMBpGRkYF4PI5gMAibzQaTyQSlUompqSmZbMRiMWRnZ8vOlmN0Unu5KGfuS3d390d3UTC9i7NMZlGEw2EYjUbs2bNHugaqGKheoZpjYWEBTqcTRqNRlB6M8GOVmZ2dLS02HcWs6jmS4TIzIyMDPT09Mnryer2SI0B8AjEcfHhoNqJ3g0CweDwufga1Wg273S4yQr7I/J3OnTuHoqIiaamZvmaxWHDlyhVMT0/jzTffhNFoxCc+8QnBYQQCAXlQ6NdIJBJwOByIRCKCV+A8k7nRAOQhrampEZkm9fic587MzAj6AfgvciwPPRq8qDSjqYujOR42POh5eVgsFrl8eJGwmyJLiKlw8XgcNpsNe/fuRSwWw8WLF+FyubC+vo729nYcPHhQgnI6OjpQWFiI2tpaeDwepKWlSWXH/BCCHtva2rB37154vV7RsVOBQm26VqvF7OwsysrK0N7eDoVCgRdeeEEkhOXl5XjppZdkufeNb3xD3K2MO83JybnLmc10PUqMd5ujKDnmxcpuYWBgANXV1cjOzpZxCncS7OyIrGfWOGGOLE52dnYwOzsLvV6P2tpa8d+QRur1egFAPAmhUEhk5VSsFRcXQ6lUShfNAqG6uhrf/OY34Xa78Y//+I8oKChAWVmZ7LqysrJE2MBF68DAAIqKigQy+c4774jknDuM2dlZTE9PywVSWloq+zLuxQgQ5HtcU1MjggV2YFRfUSgxPz+P+fl5We5z4bu0tCR7Lb7LFGpwfk/kDUezlZWVwrYi52xxcVFCvEhAIIKF+S6Mb9VqtSLi4dKalwC9NZWVleLh4G6FZ49Go5FxrtfrhcVikWIuEokgLy9PVHXkthUWFiIjI0N2JCzMKba5evWqfKY0RZP0zd/xV/3z/ySPwm63o7CwUEJ5yFIKBALSBgG32yTmaLONVSgUd5lrKEdlrB8TuXhAcjHKVpx4Zgak2+12RCIRMa+QrsoxWCgUkjkxVUasyjlq2T02oauVi1keUuPj41J9a7VaGAwGoUGy2piZmYHT6cTp06eRkZGBI0eO4KGHHsKRI0fg9Xrxr//6r9DpdBKgtHfvXsmdWFtbE5wDqx3KQrknoY7c4XCIW5vguvHxcTGn8Wfe2rqdIc2Uq76+Puh0OnkJOCtPJBJ45JFHkJWVhf7+ftTW1kKlUiEUCknWdkVFhZim+NIRk0x0NgBh5kQiEVRUVKC8vBwqlQr79u3Do48+Co1Gg7feeguvvvqqAOeGhoaQmZmJlpYWyYEm3sTtduPYsWNQKBS4desWNjY2sHfvXjz22GMiYSU2geZHtVqNyspKpFIpvP3225ifn8e5c+fkmfjUpz6F2dlZfO1rX0NbW5uo5Pr6+iREiyFSAIS1EwwGpXBZXl6WS5bjAT739GHw+WBnyo5nbW1N/AxcplLNFI/HJRuFBURTUxNWVlZELXb58mX8t//23/DKK69gdHQUZWVlOHz4MF5//XVotVpMTk4Kvp+Rv6OjoxgZGREgJxPzbDYbIpEInE6nXGqM0uUehjJWssDoN+IY59q1a7jvvvuwd+9ebG1t4fz58zh16hSGh4cxMjICm82GQ4cOIRaLITPzdn797gwapjly3MWRFNV0dPBzTM3xT05ODkpKSmQXxHELUUDMrtnc3BQz6erqqqgwJycnBQtCz9Dm5ibKy8uxtrYmyY1MnkylUuL6p5+FjKbS0lLpBPhzzc7OysiNpsbd8dHk0s3OzqKnp0fc8JRcs4OnYZciBHbvHJVT/s3ilwW4y+WS3S/TET+yi4LLLS6nOaPjYpGdBBENlERSZUF99M9//nOsrKzgxIkTGBoaQiAQwNjYGA4fPiwjqueeew6BQADRaBRTU1OiNwYgvzwXcdQgJxIJ+P1+bG1tSSocoWbqOzwpyhMpd9Pr9YLsaG1thdVqRW9vL3w+H/bt24f09HRcuHBBFmI8/Niysrq87777UFhYiFOnTiGRSCAYDOLAgQOIRCIy6uHfk5ubi2QyidHRUdTW1koyGitOzqm5BKVSaGpqSua0HOGx3aaUlGhw/lzEKZCKSd8IlRHb29tYXFyUjoajq8LCQkxMTKCkpEQcuVyAs1LR6XSiDKE6JScnR14sLo9JDk0kEigpKUFvb698PlRy3LhxQ9p/v98Pn8+H1dVVHD16VJAkVC+Nj49jYGAAm5ubaGxslLHb+vo6JicnkZaWhrS0NNy8eRN+vx/r67dz3b/yla+gpaUFZ86ckdETnex8lmZmZmA2mxGJRMTpbjAYhCrLpe7k5CRKS0slUImL0czMTIRCIWxvb2N6elqkt1VVVVhbWxOj2M7ODoxGI/r7+5GVlSWjTbVajUuXLsFms0k3x2XqBx98gM3NTbz++uviU7LZbFhbW8Px48dx8+ZN6X6IpYhGoygvL4f6TlgRcztOnz6NmZkZbG9v49Of/jSMRiN6enpEKcY8E5II2EkrFAqhDhQVFeHgwYPYu3cvTCYT/uzP/gzz8/P4+Mc/LhcS423Lysqwb98+2fURgEcRxu54UYI/7XY75ufnBRZaWFiISCQidIGtrS04nU65JFiMcgFORzTl1Cxax8bGYDQaBfnCqQDFGfx9iY5ZX1/Hfffdh+7ubmRlZcFms8HtdguGh2M7pVIpl5vFYpHiIT8/XzhZDodDlI9ra2twOp0Ih8Pyz+jo6IDFYsHU1JRciGVlZUilUrJn5biOwo2FhQVYLBbEYjEZa3L8xCX5R5pHEQgEhP7I4PDZ2VmpsFQqlbzgzFqgoSg/Px86nQ7vvPMOrl+/jtraWvkQFxcXUV1djeLiYuHYf/DBBzJzrq2txeDgoIxsysrKJKqQoy7a+Bmewq6EEkb+74TzkexK3AMxHcR9/PjHP8b6+jpaWlpQUVGBV199Fa2trdL+GQwGIUuq1WqRTPLgIblTqVTizJkz0Gg0ePLJJ1FTUwO/34+5uTlUVVUhHo+LDI5dAABBG2i1WskX12q1MBqNgtXY2tpCIBCQzopuVLbYu6GAU1NTYmLkwUSn8fe+9z3Y7XZUVFSguLgYOzs7YrByuVzYv38/AAjGJBKJCPWW/g5Kf41Go/CylpeXJcP4zTffRGZmJu677z6cOHECPp9POGENDQ3Y3NwU3ABR2BcuXMD3v/998R9sbd2O/fzwww+RSCTw0EMPoa2tTUYrnC3TiW6xWDA2Nobc3Fw8+OCDGB8fx4cffihiB75M7Er5fzOwqri4WBQjPCSpriHOhSOkqakplJSUyDiQ+6VoNCoI7qys27nx1N9TAcbCi5UiDwIeCjU1NaioqMB3vvMd9Pf3o729HX/5l3+JGzdu4MaNG5iYmMDjjz+O/fv34/Lly+JPodqvsbERjz76KAoLC7GzsyOplJRqUjEF/NeIl4Y6VsBUwq2vr6O0tFS6HBYlDJHSaDR4/vnnoVAoBKjZ29srEECOCxnpSy8FacMcparVanR1dcm+ihBJBogx25rBTP39/cjOzpYLjMISdp2kLodCIflupqenkZGRAYPBIOcGD+cLFy6I16WlpQW1tbXYv38/iouLZWfKz49mWDq8uV/a2NhAaWkp5ubmRLlHN/rp06flfTGZTMjMzERHRwecTqcoEbnPyMjIEMwHn3EWiX19fbITKyoqEgEAfVa5ublSYH5kF0VNTY2Y4Wg+o/QtGo1KhcrlDudsNPeMjIzIwoy4gcbGRpw8eRI/+clPcPPmTVgsFpSVleHtt9+GVqvFf//v/x2hUAhWqxWJRALV1dWYm5uD3+/Hu+++C6vVKuMwjjy4ROdSiY7nrKwsOBwOeSF4SJBuOTw8LJU5D3H+vYWFhbh69SoeffRR2bsQMVJQUACPx4NXXnkFqVQKzzzzDOrq6mR0VFhYiOrqanEI+/1+GUtwdMW2lqoM7mwojSXCobOzU2JO2TFQGUVpHB3PhBICtzlFKysr8Pv9CIfDGBkZkUAeVmv79+/H5OQkhoeHMT4+joyMDOzfv1/kjHTFsutgJcjLjiM9yp45lvnqV7+KaDQq6WAcc7W1taGxsREjIyNCxWxpaUFdXR0KCwtx48YNBINBVFZW4sSJE+jq6kJubi6qqqowMjKCBx98EDU1NfjFL34h1RvHHWT079u3D++88w7+4z/+A6urq2LSI/pEfQf/7HQ6MTU1JRnJrCz5uTIyc3NzU+I4d3Z25JCnZJkL6Pz8fHEiq1QqaDQaTE1NoaysDNPT04jFYigrK5Od2PDwMPLz87G6uiou/0QigUOHDsFsNqOrq0t4ZPv27UN1dTUWFxfxt3/7t6irq5OZdVpamhQO7LLOnDmDBx98UIyZdrsdOp0O9fX1EiX74YcfCqKFFAOaP1mIrayswGq1So4K8zhOnz4Ng8Egn+HY2BgqKyvhdDrlZ+Y0gIA7GuE4tmTGDBPj0tPTYbVaZVcQ1iSYAgABAABJREFUDAalKs7OzoZarcb4+DgyMzNRWFgokundbv3dsEju4La2tgRVzmxsFiFdXV2orq6WZyKRSKCnp0filbu7u1FRUYFHHnkE6jv4IY7CnU6njOTm5uakMKWajGmDXLBzf8pzCIAkVFIUodfrMTQ0BPWdVEStVit4dL/fj8zMTClaKZHPzs4W8Q33cOo7GUIf2UURDocFX7GxsSHxkBqNRqSkXMTyl+YChy+S2+2WhVsgEMCePXswOTkpy6x7770XDocDP/3pT2UJdO+990rOttlsRmVlJXp6etDd3Y3c3Fx88pOfxAcffIC8vDzxUOzmKO0OEWKFQZUTqZZEDaelpcFsNuPQoUMIBoO4fPkylpaW8PDDD2NpaQlDQ0M4fPiwKEAmJiZQX1+PjY0N6Tja29vF0KVSqXDs2DGRtQKQzoEVAscEXDgVFxfDZDIJBoSHSjKZFA8GEQLJZPIuTEAoFJKULaatuVwuLC0toa2tDVevXhVz4dzcHB577DGUlZXB7/fj0qVLeOONN7Czs3MX5qS7uxs5OTmoqKiA2+2Wh55cJ37PZrMZ8XhcZtmMrH3ooYfw5S9/GdFoFBUVFRgYGJCxgV6vR09Pj2AUKJiorKzEyZMn4ff7cfjwYVRUVODKlStIpVJoaGi4/UBnZqK7uxtWqxX9/f3ScXFnYbPZsL29jSNHjkhWAD/X3d/3ysoKJiYmZCRCwjFzGKh15yiTRQKZW9zBsdplR8d/F3dRJN9OT08LD4wRstwr+f1+wZQbjUZEIhGEQiFcv35dCLZZWVn43ve+h/T0dDQ0NEiML528wWBQLgB2RUqlUpao/f396OjoQFlZmczAlUol9Hq9LOjJMCLmQ61Wo7y8XMaLNJrt3bsX586dg9frlf1DPB4XH1JlZSVycnLgcrkwNzcncnNyjri74eVMGSyVV8x3p9yTfy3HdzTaJRIJkbly9ElCMGMEdkfVrq2tSUY6d0+c82u1Wjz++ONietu7dy+GhoYEue9yufD4448LDp0eBhZzAOQdordldXUVFRUVYi247777EAgE8MYbb8Bms8mzS4EC+U4KhQILCwsoKytDIBCQ3RYAWWynpaUJcYLdE0fG7Lx4Gf0qf37ti4LKGfJI2JYSZcyFG5O0VldXsW/fPni9XjG1abVa7N27F/F4HB6PB319fXC5XLhx44a0Vfn5+Whra8Nbb70lePCqqip0d3fj3LlzyM3NxY0bN1BVVYXf/d3fhU6nQ1NTE7q7u2XDn5GRAafTKZUVTYIej0cqH7bZBQUFyMnJwb59+6Sy4fLW4/HAZrOhqakJW1tbOHbsmCgjaAabmJgQhs3BgweRTCYRj8cxNDSEoqIiNDQ0YGVlBdFoFLdu3RLVBS9YjiCoJrNYLKKAoSSSL4parcby8rLsZLa2tqTiASBID61WK92KUqlEfX29mIXYuSwuLuJf/uVfUFZWJu10aWkp9uzZg/7+fgkIys3Nxezs7P+BmuBuiv4XVjMc19Ah/+qrr8Jms+Hzn/88FhYWcO7cOemiGNyjUqkkp1ytVmNoaAjT09Oy0OZIcmhoCPfccw+ysrLw8ssv46mnnkIqlYLdbpfvfX19HVNTU9je3oZSqURLSwuUSiX2798Pt9uNRCKB/v5+OBwOIZZSncTRETERfMnYCXFUlJeXh0gkItU/gW28PDg64CiPATVUJmVnZ8syn7jx3UofFl2RSATf+c53YLfbZfzV29sLq9WKmpoaNDc3y3OVTCZx+PBhRCIRgWz6fD6Romu1WvETMOuitrb2LuPlbu/E6OgoFhYW0NHRIeKTWCwm+4T6+npsbm5KPCuNlEwxnJmZwejoKFQqFQwGA65duyY0A6LLjUbjXQVnVlaW+Is8Hg+WlpZgsVigUqlk7BePx2XhTMMuR8Ac8fA8IoCSUtji4mJUVFSI6ZKyU6VSifvvvx+RSARzc3M4efIkvF4v/vf//t94+eWXZUwdj8cxOzuL5uZm5Obmorm5GQ8//LAIe6iCovKMiZ+NjY0oKyvD/Pw8BgcHkZmZCbvdjpKSEiiVSlRXV4sDnS59om6ys7PF4Ep1HMGTRUVF8t5Ttjw0NIT6+nqsra2Jau0j7Sh8Pp9IOknWTE9Ph9/vx/79+zE2NiaL6+3tbSwtLWFwcPAuNVF7ezuOHDkCj8eDy5cvw+12o7W1Fa2trZibm8NPfvITHD58GA888ADUajUefvhhOJ1OvPrqq/jwww+xsLCA3/u93xN2Un9/PxobG2U2Sccrc4ZpAmTEJBHmZrNZNM6U15WUlKCkpERCSG7cuIH19XX09/ejuLgYRqNRcOoOhwPXrl0T5govTbfbLSA8ttWcxxcXF6O9vR0GgwETExOy9C4qKhLkSG5urrS7NTU1CIfDSE9PF2UI5Xn8eenALS4ulsOGSX0kiZK+S9OaWq3GZz/7Wezs7OCDDz5AMpmEzWZDR0cHbty4AZPJhJs3bwpOurm5GUNDQ+Io3m0KI/mUOQ80WppMJtjtdnFpu91ucXsHg0FUV1djampK5vDUlNfW1mJ5eRn/+q//ivHxcZSWliISieCdd96B1+vF3r17UV5ejpGREQC3d1xlZWW4desWAIhsm6FI5eXlWFpagkajwS9/+UuYzWZ4vV4cOHBAPv+FhQWZu/MA2tnZkfhbn88nKihW9Zz/z83NwWw2IxAIiBSZIwI6fSm9JpiOCj8Asmy1Wq1iumJlr1ar0d3djYKCAvzu7/4utFotXnnlFeTl5eGJJ54QemxGRga6u7tx48YN5Ofno7q6WsZ0zPdYXl6WpTg5Z+yO6Cyfm5sTKjNFE2azWeTt7JIcDod8bgBw+vRplJaWory8HFqtFpcvX8bIyIgUGRqNBh//+MdRUlIijCS9Xi97CgY5LSwsoKSkREaEHONRuTg/Py+Y+d0RvlxWc0xYV1cnuzQAKCsrk7CjaDQqQU5EdtB1n0qlEI1GZWz4+uuvizDgYx/7GL797W/j3Xffxfj4OM6ePYvS0lLxbOTn50tXqlAo0NTUJHkkpN8ODQ3h/fffF2Mkx1NU6Xk8HlRWVv4fUQM0KVOJVl1djby8PLz77rsSBMZUyZKSEhGZ5Ofnw2633wUa/VX+/NoXRUVFhTxozF+mAW1kZARFRUVSqfNWZUtOmdn8/Dz6+/sxMTGBuro61NXVoampCQ6HA6+++io8Hg9ee+01PPDAA/jiF7+IsrIyjI6O4ty5c+jt7RV8wIkTJ3Dp0iWJniRZk0tWHl6sphbuxF9S0z09PY2Kigp4vV5ZlCUSCTz//PO4cuUKTp8+jfr6eoyNjeHChQuCfLh+/TpaW1tRX1+P+vp6ac0BCPyL1bLRaEQ8HpdKhsjkUCgkBwEvW6baLdwhZBKXwHwE0nUzMzOxvb0tucY6nQ4ul0vIuFyyBwIBqYZXV28n9XV2duK9997Dzs4OysvL8dRTT8lYZGBgAOfPn8fw8LAs8LKysgTxzcuHi7usrCwhsbIyYzASL9zBwUEJhgEgktZHH30UkUhEECZcJHPXYTQa8eSTT4o/o6ioCDqdDnv37hW3Mw1Ri4uL4jvgRUv1Cw1WDH+hT6egoADnz5+XxT+d8WSTFRYWyp6HcZTqOzka5BOlUilx5nPEw5k1APT19QnocWJiAk6nU9AMS0tLMhZgVU0nMBHwBoMBer0e9fX10Ol0qKqqgtvtht1ux+bmJk6dOoWCggKcPn0a3/rWt1BaWopAICDdyMbGhpiwcnJy5KKiIofPEDsIpqtxp0K1F8efKpVKOnEaEFdXVxEMBjE+Po5YLIZ77rkHGxsbmJiYQFVVFXJycnDz5k0UFBRgfHxcshoo4dZoNCJ1Z9dBTElxcTFWV1eFdkzDIXB72cwDmnJ3FlqUfdMXwlEW87XZ+dJzwd+XOxmPx4OjR4/i7bffxjvvvIOMjAw0NzdLIeRwOGS0RqqAUqmEz+cTBRXPF6PRKBgZpVKJH//4x6Lg3Lt3r+z3srKy0NPTA61Wi8bGRrz99ttC4WWhS7MfRQTEvaenp+Pee++FwWDA2NiYcKTozmdRV1JS8tFdFPzB2RJGIhEUFxfLhz89PS0ae35QNpsNU1NTyM3Nle1+Z2cnhoaGsG/fPuTk5GB4eBg/+MEPhMtSVVUlAUg+nw+nTp1CY2MjrFYrXn/9dXz44Yd45pln0NraKu5FUmDp9qRTmKx7AsNYwTKmlA8x5bTvvfeemOfa29tldulwONDQ0IDXXnsNTzzxhOwUqCefnp6W+bROpxPUA1tDHuyEwFE5trq6KrNwpnGxCqTE2GAwQKPRYH5+/i4wHCtajq8Y9EIEB5dZwWAQLS0tMBqNIp9VqVR49dVXMTQ0JJJILsqotOGfW7duyUXP0RXxBMRyEw+/vb2N0tJSZGdnS2paWVkZ1tbWRM1kMpmEzLm9vY0DBw4gHo8LHTc7Oxu1tbUwGo0ShanRaGRB53A4sH//fmnhF+4kGc7OziIcDkvAFVEv29vb2N7eFs08qzwWOunp6VJJbm1tYWxsTIoE0le5OyCRkxnoVAJR5cJLi452CitIZOUFkEqlUFVVhYWFBTGiqu9kR1B+HQgE8O6776KlpQWRSAQXLlzAwMAAbt68CZ1Ohy9+8YtYWlrCn/3Zn6GiogLPPfcc9Ho9Ll26JMIGRqqyYNrY2BDOEPBfbmiauFjocKlKc2UqlUIgEMD4+DhMJhPi8Thqa2vlvYzFYlhcXMTo6ChSqRTuvfde2dVYrVYUFhZKpUwEDKF6HGVyDGWz2eTn4LkzNTUlHTpNjuSOMc41KysLyWQSVVVV8Hq9gqtXKpUyeolEImhsbMTk5CRKSkqQl5cnNOShoSEZp966dUsUlzMzM/jud7+LjY0Nwex/+ctflsU5ZbPLy8syZmaHsL29jdraWukme3t7UVdXJ9gTmhR7enqwb98+rK2tCUWBkn6q5eLxOJaWlnD9+nUpXtLT0xEMBmG1WmXsR7otu23Kan/VP/9PMrOZvMVfhEgCu90u8126FsltaW9vx8LCAiYmJsS1Go/HxQNw4cIFDA8Po6ysDAcOHMB9990HrVaL8fFxvP/++3jjjTfwwgsv4Hvf+x66u7vxs5/9DCqVCs8//zz+7d/+De+++y6OHTsmdNRUKiVdD/BfeHTgNt8+kUiIcYq4BAIDOY8+ceIEHn30UWRmZsLlcqG5uVkUCsQIDwwMQKlU3rVg4u9MXT4hgKTSrq2tSWwkUQD0nVDtQVJuSUkJ9u3bJ8AxVn10cLO6zc7Ols5BfQf5ToYVXbCTk5PIz8/HI488ghMnTiArKwunTp3C0NAQtFotvvCFL+DQoUMYHx/H9PQ0PB6PtOdkd3FsQt4SFVccV1AimpaWhrGxMek2R0dHZRSg0+nEX1BUVCSXJY2W3IuwGo9Go7h69So+/elPo6qqCrW1tVhZWZGqnMqraDQqUmzi0tlVTk9PCxZieXlZ1GaFhYXCrqI7nuMuYtQ52wYgWR1UpBHNzueafx0rX5pACffjyJCX4tzcnHy26jvsMa/XK8/t9PS00GeVSiU6OjpgtVoxNDSEqqoqfPnLX0YoFMK//du/wel0orCwUMyXNNCRk7Ubk11ZWYmZmRmUlJRApVIhEAhgfX0ddXV1IkFeWVkRGS0/b16K7DRTqRQGBwcl95qL+6NHj4rCiCyr9PR0DA8Pw+l0CkF2ZGREFEZU+FVVVYmHgZU4vS2kqqrVavEjcF9Edz7HVtzbcTRLCgRR/uw86+vrxbDHd/Gll17C97//fWRlZeEXv/gFcnNz5eeuq6uT+ASNRoPx8XHY7XZMTEzI5UfMSllZGYDbhOb29nbs2bMH3d3diEQi6OzshEajgcVigcViQXNzM/Lz83Hz5k2ZCszPzwt2KBAICJOLcESNRoNz586hv78fLS0tOHbsmJwl6enpiMViIjz5v2E9pf//dz381x+aT3JycmC1WuXLCIfDuHHjhrCLOjo6sLi4KDK42tparK+v4/Tp0+jq6sLOzg5OnjyJlZUVXLx4URZzzJTl7Hp8fBy1tbWora3F5cuX8d3vflfIruvr6+KW5RdEhvvS0hJMJhN0Oh1KS0uhVqtFmkb1Cdn2/ABZlRJzcezYMeh0Ong8HsRiMfz4xz+G1+vFww8/jPr6emRkZEClUgm6IBwOi7GuoKBA6JvMHiA7iDnQu6NEqcHOysqStpkVMqs6IhCIqE6lUnctk6ni4WeysbEhlRerGeIsjh07JvCw8vJyHD16VLoELnuZzrY7FImHxG5TDzsfOrTpzObLX1xcLPgPJskR2c5Lltp+VpGU4q6treGBBx4QlVljYyMWFhbQ19cnZrqpqSlMTk5Co9GgrKwMTU1N2NjYEGAiCbicu29ubqKsrEyqd45P1tbW7gpVAiAChczMTJhMJsFaUCqaSCQwOTkp+xqO3qiuonafFzrHPcz7oHyT4g/gtuLNZrMhHo9DpVKhoKAA8/PzGB4exi9/+UvU19fj2LFjmJycxEsvvYTBwUEYDAY0NDTgwoULeOedd6TDC4fDWFtbg8FgEEhedXU1QqGQmAtpavV4PEgkEvL30YldUFAgeBy73Q69Xi8d85UrV/CLX/wCb775Ji5dugSPxwOHw4GDBw9Cp9Phww8/hEqlwp49e6BSqSRbgZBJZjUQWMl3klnP3FtwAc6LIRaLwefzyZiPxeHGxgYASKHId4CplOvr6xgdHRXi8NLSEvr7+zE2NgYAolCqqqrCoUOHoNPpcOXKFWRmZuLP/uzP8Ed/9Ed46qmnREnY398Pn88nCkEWPLtz6XkWTU5OipryyJEj2LNnj5iDOYpieBdVo4lEQrhPBIM6HA5YLBYUFhaKg31qagr5+fnyXQKQmNqpqSnJrP9V//zaHQW/6IyMDDFx6PV6qO8QYnmo8cAkrMzj8WBhYQE+n0+YR8PDw9JC/+Ef/iHOnDmD3t5eUZVwZldcXIwXX3wR//AP/4C//uu/hsViwde+9jWsra3hpz/9qXgoOjo6RH1Eo0k4HJZxh0qlQnNzszw09EbQK0EwH0cXg4OD8Pv9MJvNePzxx6VKsdlsyMrKgt/vFxQCb/2CggL09fXJaIWBQDabTdQVTLEiZIxz2tnZWTF5ARAyZjKZFPs/KzqlUikVUFpamuRtGI1GjI+PY3JyEna7XbKnyYzRarWYnp7G66+/jry8PDz22GNoaWkRWN7Zs2elEGD8KpU+Ozs7gnSgpDgvL08cxRqNBtPT04hEIqLcoNyRkavELxQWFgrdl45TLuyp5IjFYhgbG0NZWRmOHj0q7J7Ozk7h3vCZpIqLo1HO2G/evCn7sd1jRwLc6GrmMpUdJWfXpJCS8UPpKIFrU1NT0Ov10snw8yIUkXsRq9UqIxJGAtMDs7q6KuoUrVYrVTwhlnV1dXJZKJVKvPPOOwiFQqJ+YweRk5OD7373uzL6ra2tFTml3++HzWaT7yAtLQ3Dw8MIBAJy8U9MTMBut8NqtaKhoQHhcFguQ6qcKP30+/0oLy+Xwk6tVguUkKj8xcVFnDt3Di6XC2VlZTCZTOIRonqKy2TmUJNpxHEqgZUUaLDgI/BxaWlJkPBcKAO3d016vR6VlZWyE6SJkp9bTk4OSktL4fP5cPToUdTU1MDj8eDdd9+Vfd5rr70GAGhsbITH40FPTw/uvfde1NXVob+/Hy6XC0qlErdu3UJFRYWoJFmY0WHd0NAgMvL9+/djYGBA9qsDAwOiULt27Zp0nhaLBZWVlVLcVFRUYHZ2FtXV1VhbW0NfXx8OHTokEnW1Wo36+nr09PRgcHBQ3PhHjhzB3NycpDN+JBcFx07UlisUCqG7cl7GmEgCzKxWq1QMTzzxBO655x6sr6/j1KlT0Ov1qKurE8NXS0uLzH7j8bjcnPX19fj617+Ol19+GU1NTWhsbMTFixflwdna2kJ3d/ddTm3GU5JZtLa2Jnpj0hqXl5dhsViE2piWlnYXw4VqmM985jOSBzAwMCAHCefAaWlpKCkpETckF6Ymkwlra2uIRqMSzcqXkGyX+fl56dQo7+Tym1RL8p5MJpPM4bks58KeI0COOTiPz83NFUUZA9hrampkNMOlN4F89JkwYJ5KsvX1dcRiMQCQnIz+/n55GZgPUlVVJYdqYWEhQqEQFhYWJO+bpFAu5WnQ5JjI5/PJfJ8ach7W1Manp6fLopoHMKtBdmfA7QrxnnvuEZ37bvf43r17pXLmSJWKGnY1vLSYi5KVlSUcHmK2ibDnjoZmQwYpkT1GTAojTLe3t6HT6WRkoVAo5OAjjJG54gcPHpQxX3r67Yzpxx57DPX19bh48SImJibg9/tRVVUFh8Mh8biEHW5u3s5C93q9yMjIQGtrq7wfubm5yMrKEvk69yV6vR7Xrl1DS0uLcIe6u7vl96YRkCq23UE8c3Nz6O3tRXFxMfbt2ydL/qKiIvl+KezIysoSmTEX11y+b21tCRuNY8bMzEzE43FJ8qMEmqFbHPcyc4KFLUdHfP64FzSbzTAYDOju7saPfvQjTE5OwmQy4XOf+xwWFxfxsY99DKurq3jllVdEuckONCsrSxbH0WhUdn8zMzOSy8NYYGacr62tIRKJIBqNoqOjQ2SwCoUCra2t8txxjM7ME+6AmNuzvb0tgUUcQ1+6dEmUYj6fD+np6WhtbYXZbJbYgo/koiBAb3x8XOIgqb/PysqSl5gVJ8crra2tyM7OxquvvopXX30VjzzyCFpbW+UlHx0dxdLSklQwTNbanQvb0NCAv//7v8fly5cRDAahVCrx/PPP47XXXpOlNCMfZ2ZmoNPpJMOWlQtbZ1bJNLdQdsqlvFarlWCY7u5u8YTwBWIYUlFREaanpwX7sLW1JWOw7e1tuFwulJSUQK1Ww+v1SgANqZIajUYqV6pnON+naoH8eo4tMjMzkZ+fD5vNhry8POnAmBHABR7RKgsLCyLtJSaEYTzA7c6loKBAfvdgMIjy8nIx0dEAp9PpYDabkUgkREpYUlIinzvDZiibZgyt2WzG9PQ0TCaTVMl0PC/cCYrnweh2uyVhjSooVucEqfEFpTBAp9MhOzsbfr8fOzs7SCaTuO+++6QapamturoaKysrOHDgAMLhMAYHB6FUKmEymTA6OioCA5IE+B1GIhFZuDLAh4gJlUolWHwyxBgdy/0DiwWO5a5fv46dnR1hP9XW1mJ4eFjYQ7tFAexS/X4/JiYm8JnPfAZ+vx8DAwMCb5yZmcG+fftw8OBB3H///YJ8GRwcFFXW2toazGYzLBaLZKcUFhbi+vXrguT41Kc+hcrKSigUCsHRkEhLSCQREaurq7hx44Ys5EtKSmSxm5GRIYiUxcVFHD9+HHa7HRcvXhT8DnNjSF+mAU+lUkmxSewFXcVMWGQxxvEzzwt21yy0+Nn7fD4Rsmg0GgF7bm9vIxQK4fDhw/jlL3+JN998E+FwGA899JAYB7VaLV544QUMDg6ira0Nm5ubYpZlvglZXixOuaMgsYEqJBbPVNJtbm5K4UA4pN1uF3z4yMiIFHx5eXlyGZCY3dzcLHiazc1N9Pf3Y2trC7/xG7+B1dVVvPTSS6ivr5ecE+JJPpKLYn5+Hg6HQ6q27OxscaPSAEKFBV28P/7xjwWG984778BqtSI7O1s0yGazGclkEpFIRNpKfgHV1dVIpVIIhUL44IMP0NHRIYcEKxiDwYDa2lo0NDSIsaa4uFi+6JmZGaSlpeH48eOIRCIS/NHb2yvGperqagCA0+kU7ALbbeq5ORbii8M5Iscc1JzTaJVMJiULIJFIoKysTB7Aubk5JBIJOSyuXr0qpi26hcnlz8/PlxB5p9MJn88Hv98vOxd2GeRVEbYWCoXQ0NCA8vJyoX/yQl9YWIDJZJKXanl5WdpxmqgII+QCeGtrS2idlF0yxpLjKL4M7BAYscmIS6qk6FTlgWC1WqFSqUQ2CUDkqVarVWScJAjz2Zufn5dkQo1GI4cG8ycYC6pSqXD8+HE4HA643W6Mj4/L5bG5uYmOjg4kEgkMDQ3J6IYSb6Jh2IXu7OxgbGwM6jvIbs7VH3jgAQwNDUkluLW1Jawx7ipIMmWqHbsics844iPfp7i4GNeuXcPevXtRVFQEr9cLo9GI1dVV/Mu//Asee+wxHD58WJafarUap0+fxvb2NqLRqCiNaMTiZ9/b24twOCyH0r59++B2u9HT0wOPx4POzk40NDTg8ccfR0ZGBq5du4arV6/ia1/7Gnw+H37yk5/g0qVLsNvtGB4exqFDh7CysoLm5maEQiH86Z/+Kc6fPy/hUxzvMV7VZrPJHoFScsYHBINB2YkZjUZRgAGQLpUFD9lJ5LTxn8WuRK/XS3zp+Pi4uLDNZjMmJiYAAOXl5RgeHkYqlcJDDz2Ehx56CB6PBwaDAe3t7WLYLCwsRCAQEKEC0+hoqiSmnO8t0xl5uTELZnFxUeCIS0tLcDqd2NjYgFarFWMyR3pFRUWYnZ2VGGMKgR544AFEo1G88cYbmJ6elh0jKdhU51F0YDAY4Ha7P7qLgthaSuZYFTqdTlEbsKpifu/S0hL6+vpgt9tx8OBBqTBeeeUVOUyJ3FAqlZJINz8/j5GRETnYKysr5ZClfDMcDqO8vFwQzV1dXaJUycnJEd4/rfMul0vMb0wPy87OxuHDh5GRkSHuUkY88uHd3t4W3AZwm6JbWFiIkZER2ctQm02AV0ZGhjhTq6urxW25tbWF119/HQt3KJaM8NzY2IDb7YZKpRIUO19khUKBYDAoYy/uNrKzs0VpxDEaF3slJSUyI2clzPm81WoVR+3urouyPM6jl5aWMDs7K2gCr9crkkTOxf+/O5BUKoVwOCyfw/b2toxj0tPTBRZYUVEhKYk+nw+bm5vQ6XRSgVFmvLGxIaMLjhx4edCLAkB2KsBtxH1zc7P8vZcvXxaS8eLiIh588EEAgNvtFof/7qqYBxgT9Uwmk6hYuIv48Y9/jMbGRhQUFOCJJ56A1+tFNBrFwsICHnroIXR1dQkmJi0tTUYBu01lnPlXVlaKEo6V5uDgoODgp6am5OdJJBI4evQootEo6uvrMTk5KeFRzMrmO8lLjKq/3bJUdk4MGuvs7MTW1haCwSAyMzOF13bkyBHMzs6is7MTk5OT2Lt3L0ZGRsSVbLPZkEgkEIlEcOTIEfh8PgwMDMBisWBpaUmc7yzg+Ezs5jBFIhHpjthNcXyn0Whkb2Gz2cRcx8OaIzqLxSLpmMPDwxgeHkZ6ejoeffRR4Uvl5uaiuLgYk5OT8j4kk0nBvLS0tAC47XJWKBQYHx/HT37yE7nQa2pqMDo6KkoqqsPKyspgsVgEX8TR6/DwsPCsOE632+3w+XwoLi7G2toabt68KeNjALIHnJubE0wJSQFEIfF3Yb67w+HAnj178Fd/9VfipSGKiIZVeoV+lT//TzKz+SFxdr67WrXZbBIHqNFoYDKZ8MILL4h7MBgMoq2tDYuLi3jvvfewf/9+aUfn5+cl0IWkVM6CqUtn5jJftJWVFczOzsois66uTuaHlGOePXsWDzzwAFKpFHw+n5gGu7u7RbJKmNvk5CQqKythMBhEBkt9PPcckUhE9P7EDvCBoEyVYDweoMlkEn19fQBupwSOjY2hpaVFqsSKigpRygwPDwsnhzP1nJwcYRBRgcMLiiMfjUYjLte8vDyUlJQgEAjIspS+glQqJbG0uwml29vb2NnZgc/nk45jbm5OlBVEgqSnp8tlwdhSQsiYGcxcDXaIBKFR3cR58tzcnHxOzMkgeTMUCsFoNAoKgrC8aDSKZDIJjUYjyjDuA2h8tFgsUN8JnKFvYn19HclkEocOHUJzczMmJiZw9uxZ8fvU1dUhNzcXaWlpmJycREFBgezZ+JIeOHBACpiKigrU1NQI8jwWiyEWi+H48eOYmJjAzs4OvF6vLCOJp9md0RyJRNDS0oKVlRV0dXXB4/Hg4MGD8nkbDAacP39e3OWMDL169aro55mxPTs7K5dMKBTC0tISUqmU7CsoV83MzERdXZ1EB3MXRmVNaWkpioqK0N/fL+gHynrff/998QRUVFQgmUzi3XffRW9vr3Qs586dAwDcf//9EsWbSqVEXs0CiigKo9Eo4xq+Z42NjRgfHwcAKb6olGOsr8FggMvlEu8QpeUmkwmFhYV49913kZaWhvvvv19Gzezqr1+/jnA4jOXlZSQSCYkKzcrKEi4WQ7jUarUYTPv6+gS0yO6FFIeFO2mfHH8uLy+jqqpKcCbsCDgejsViMhmgdJseK5IdaNblu8fxeTwel+KvpKQEU1NTuHjxouxLent7pRienJyU7uYjuyi4HOMMkBZ5yhq5aNXpdKioqBAWy40bNxAIBJBIJPCDH/wAs7OzaG9vF90/Ja675XD0aCSTSdEDu1wuOaDJCeJ8D7itX7fZbNJycsTw+uuvS4t28uRJbGxsCPm2paUFbrdbFnE+n08czdRDWywWoU4S5sYDeuFOuhVnloyuJOueo7aSkhJMTExgcHAQzc3NOHLkCFwuF3p6elBZWSnGPVbTnE3yICRkjFJLRkLyM+NMll0E/3q282azGTk5ObJczs/PlzEAx13MfGZ1Mzk5Kb6SzMxMhMNh4VCRqZRKpWQfxNjWpqYmwUkbDAZcvXoVJpNJ5vC8OKxWq1TRBEVubGxgenoaJSUlooOnZNXhcMgsly8gLy7iE2w2GyorK3Ht2jX8/Oc/F2z18ePHMTMzAwDo7e2F0+kU/w8dr0R38OApLi4WPDgFBR6PB2fPnpVR58jICK5cuYLGxka43W7s27dPxiq8qEgLSE9Plxd89wE8Pz+P9957T/Z/BEzqdDpoNBrx+LAjueeee+B2u5FMJpGZmSmhVzR3ms1mqZp5qPCQslqt4nzW6/VobGyU/YPRaERJSQk++OADSeqjQfPEiROYnp6Gy+VCWloaLly4ALvdDrvdjszMTIyNjaG7u1t2b5wMEJ9DHwNzyUkb4GSCBjEebqQB8IBjgcAMbUabkh+nUCgQCoWwuroKo9GIr3zlKwgEAjh16hTcbjeCwSAikQgeffRRGf+Vl5djcnIS09PTWFxclO/wxo0bsFgs+OxnPyvZ13Tts7vs6upCe3u77AzVarUs8okJWl1dlXxv/u50vM/Pz6O2tla6j+npaUnmo9+Kz4xOp5PvmFEKvAiuX78u6q35+Xm4XC5xu1dWVsqk4iOFAlLOR+gZF7BscS0Wi5AQiTsgFmJlZQVutxvp6ek4fPiwyEwZesPDj6TO3YlVnG/S4j87OyshJzSqURFA7Hl5eTlqa2tluUasxV//9V/LDoSHPWV1pFAyTIezdSoOiPDmgUj/AvEVKpUK8/PzWF1dFST7/Py8EC+PHDki/Ktbt25hbm4OBw8eFFkhDXVZWVkygonFYjKSKSgoEHQGX0TmZXPmSfUU07Nyc3PR2NgoLH5Wp0RtFxQUoL+/H4ODg3A6nXcROzMyMuRwJ42WJsWtrS1kZGSIwY4hP6TBsopbX1+H1WqFTqcT5RlNbBzpbW5uSqdhtVqxuLiInZ0dqYTZeXm9XuTn58PlcgnmgsvwyspKSRVjmJDZbEYsFsMLL7wg+zPuetLS0mAwGPDYY4+JaoVwOAYQcUdRVVUlBwDT4pqbmwXY+I1vfAOtra34+7//e7z99tswm82oqqpCTU0NVldXJe+BpFku7PkZZWZmora2VrDvPT09uHDhAvbv3y/O4O3tbdkVORwOVFZWCkOKcaG8NCmHDQaDQqcldoNeAo6LVldX4ff70d7eDofDIR1Wa2srSktLMT4+LlXvnj17sLCwINki09PTqKurwxe+8AVcvnwZ165dg9FohEajEaYT+USBQABFRUUIBAIyUuLlwAqa4g/ud4ikmZ6els+PmRqU7dJzMzs7i6WlJSQSCXi93rvGVPRQvPHGG8jOzsb//J//E+vr6xgeHobVasWhQ4dw4cIFvPnmmygqKoLVasXHPvYx2b0ODg4CgDj9KUXNyckR79Xa2prsARQKhSzck8kk/H6/FK/sVHmOsGsgNy09PR29vb2w2WwwGo0YHR2Vz5QEgd3st7KyMhw/fhzp6en493//dySTSYEojo+Py3vJIKRf5c+vbbjji8lKde/evcJhb21thcPhQHZ2Nm7evIm33noLN2/+/3j77+i27/veH38S3BsgAQLgxOLeFEkNitqWvGRJ3o5nkiap02Y0Sce9p7cnN71NGydt2tObZjVTieMR2/KQLNnam3tvEiCxCIIECRAcAOfvD+n5KvU9vz9yT3rMf+LaiUsBn8/7/RrP5+PZLDkHc3NzeOyxx9DY2Ii9e/eioKBAmEw+nw9er1fkrZwbkuMCQEJPGKpCxZDVapXQDpVKJVUVHZxpaWnYs2ePZBxcv35d9PPXr1/HO++8I9gMfglckvGHF9H09DQCgYBcUMQZs+3lQb+wsCBpWisrK+js7JRENko5rVYrNBoNvvzlL+Oll16SQ5hmKFbelBISZxAZGSkVEfX1lNA1NzdLxgJldnQwW61WuN1u9PX1iVrq/fffR29vL3JzcyXuc2NjQzwlHFXFxMTIxU/3qtvtBgBZ0PHzolySZjp+Z0R+EBVCN63P55NdRVRUFDwej0DgmIsQEREBo9EoI069Xi+MKaJYeHFERt7JhC4uLsZjjz2Ghx9+GPfffz9KS0uhvJuZPjo6ivHxcQwODqK3t1dwK2NjYzh9+rQcyOzo6I3hDobPzuHDh7Ft2zY8//zz2Lt3L/bt2yfqsKioKEFPc9ZMhpdWq5XwLe6Lpqen0d/fj9jYWOj1ety6dQs3b96UZ5VjNr/fL4cVu3F6Wli9MkSMmHW73S6QQ5fLBavVitHRUczMzMDlcsl31dfXh9///vd46623cO7cOREgcIfg8/nQ3Nws71JCQgI6OzvR2dmJqakpKJVKlJaW4pFHHpHuOCsrSxDoXq/3ntEtADnUNjY20NDQIO/W6Oio0G/D4bAwjyibpbGNTnJ2tbzcqXbbv38/XnjhBYnJpWuf/5wJgAcOHEBBQQHS0tLw3HPPoaysDMCdfBONRoP8/HxotVrZB5FrZ7PZxBjL/cvq6qqYkZktAdwxwQEQp7TL5bpnoU8BRm5urki/Od6i7J2+oJKSEuzduxf79+9HYWEhDhw4gOrqaiQnJ6Ourg7FxcUiVGHH/If+/Leonqjo0ev1aGtrE6ldIBBAU1MTnE6n6NOHh4dx+PBhvPDCCygvL5fZMlU/KysrcLvdEnrDoJ2ZmRlxH1MTv7q6KqqgmZkZSVZTq9Xw+/0iX5ycnJSx08MPP4znnnsO8/PzOHXqFJaWlvDYY49Bq9XikUcekaCjmZkZREdHw2w2i96eC1wetmwxi4uLxTfAzF0qTtbX1yX1jCTVxMREcYMODw9j//794sHw+Xzo6uqS+EqtViuLcf53OFvnA8PWNDk5GaurqzCbzZLBUVJSIos+Xh7siijTpcrI6/Wiv79fku94yfGFYvfHC4zVGmfMFRUVgn5mNjDjORsaGoSQmpaWJmRLVs8pKSmYmJiQkB2CBtkdJiYmwuVyieSXexLuTcgY46yYcmxiUVwuF7RaLfR6PXbs2IG4uDjcuHEDVqsVBQUF0k2Ul5fD6XTi/PnzmJmZkUOMkmCSN3U6HQYGBqQweumll2CxWPDuu+/i9OnT6OjowOc//3ksLS3hxRdflMOR8m8GHNHdTbUZF9tRUVECm6Nfhr4cmlVp5KMkmYTh2NhYGR96vV5sbGwgPj4eIyMjcDqdsFgsEomanZ2N9PR0mEwmea7cbrewmtxuN1paWiSzgjgXZqUvLS1J0BcAZGRkICsrC5cuXZJnlrh8+jToxCclNzU1VSprCiQ43mQwVmFhIXp7e+G/G45FB/3KyorgT+jtcLvd4iCnt8jlcomZVqvV4vDhw3j33XcxMDCA4eFh/Od//qcUmvx9jUbjPT6apqYm2TVyX0kp+NY0RfpeeHHSu0T5LxMz6dam6srtdqO0tBQul0vGkQkJCdIJJiYmQqvVCviSKZdcdqtUKlH/3b59G6mpqfjiF78o0trV1VUMDg4KQv4THT0RekeFUlpaGiwWi7RZZ86cwXvvvYf/9b/+FyorK/HNb34T/f396OzslOpwcnISt2/fxuLioihW+IGrVCqpwrxeL4qKijA6OiqLIOYv6PV6kU8CEFcwuSaFhYVITk5Gb28vdDqdSPyysrJw5MgR/P73vwcAHDhwADt37kRqaira2tqkigYgqVGUuPHF4Cw4FArJfJbsIJvNJrsJp9OJbdu2ISMjAw8++CB27NgBjUaDDz74AH6/H/Pz8+jq6oJWq8V9990n6Gdq5xMTE0UqzNEVZ7PhcFhQ6lw6RkVFyRKc3gqOxBgKRcPk0tISSkpKcOPGDVFg8EXp7+9HRUWFoOQZRxsTE4Pa2lpJ3GLlz7RDMm8Ykcs8iNLSUmxsbGB8fBwOhwPr6+soKSnBvn37RCU3Pz+PUCgkow/6LVidAUB8fLx4TtbX1wVPsrS0JLsqGtzm5uZw48YNVFRUoLy8HD/72c/Q2toKtVotUtHU1FQ8/vjjuHbtmgD/tn6/DzzwAD766COpEhMTE3H+/HmUlpZCo9Hg3Xffhc/nk47gypUrqKurQzAYhN1uh8vlEgMYUwSZzJiVlSWKOqpXLBaLiAyqq6tlJMHngZdfYmKiQO4IMaRiiN8xVX98fllkUU+fmpoqOvzy8nIBSDocDhgMBhw+fFhYZfQPcezZ2NgIh8Mh4Mrs7GyZ8TOnxuFwCDZ+dHRUAq2oavR6vZiYmEBSUhKmp6eRk5MDn88Hk8kElUqFiIgIOWQjIiIAQKi7er0egUBALit6MGjMAyAhQbGxsULxZVeyurqKkpISjI6Oyr9jdnZWRBOM0uUYiFy39fV1+P1+6TSpzCKqhT4Qfp8zMzPyn7QAMHVTpVIJJYGFHEkWoVBI/CM+n0+ksnNzc/L+j4+PC84kNjYWg4OD4lwn8TctLQ2lpaXo7OyEyWRCOBz+g8/5P3r0tLGxgaKiIqhUKtE3U29fXl4Og8Eg87O8vDyYzWYkJCTg6tWruHHjBsLhsChK6PblvG5hYUEqCADQ6/VC9OQykcoJKnViY2PF0s6DkRRHjq4mJycxODiIjo4O2O12eL1e3Lp1C+fOnZPLye/3Iy0tTdAblIdurZadTqdcSKzsIyIixHDHQ5imqbi4ONTX10tFVVFRIbLZhx9+GFqtFsAd52hSUpI4JzlWo7KMqI+lpSXYbDZZsjJEhQROsm0IJyQuggfUVn0/OT6VlZVS2XJMwFkqZ+i8nOPj4zE8PAyNRoPY2FgsLS0JeZSwNYVCgfr6elRXV0u1SJRBZGQkHA4H9u7dK6ylgoICREVFoby8HDqdDqurq5iamhIOD2XQTD+bmZmRZbtarUZeXp4A8NLS0oQXpFAoMD4+jq6uLty6dQuVlZX40pe+hJKSEqyvr+PixYsYHR2VBS4r56NHjwo7Z2xsTOiiHNWsrKwI0h24kwfy7LPP4sSJEzLTz87OlsqZhzyfidnZWSGvsvthBG9ZWRl27twpnoCxsTGkpKRAp9Nh+/btqKysvGefRs9Jb28v+vr65IAEcA8+hfRR0hGI5lhdXb1H3pqSkoKHHnoImZmZUKlUcumTL8ZCTKfTSSxAYWEhuru770FmUDXI91Cv18sOjaRmYmLIWpuZmREmExfvaWlpYozjbis/Px+JiYnIysqS/BAi0CMjIzE1NSV4E+LuKeN+6qmn8O1vfxtf//rXBSFDLD27KY424+LiMD8/L9Lv/Px8rK2tyZyfFzXHi/x7xGYQbslkTafTKcIdnnuUKlMOHxcXh4aGBuzfvx8FBQWi9lpaWpJJDrtJfs+hUAhTU1Ny/qyvr4vCkecqcGc8zKL8D/n5ozsKtn9Op1MWnUtLSzh37hyefPJJbN++Hc3NzXA4HPibv/kbybj+8MMPxWFMeFxKSgoSEhJE3cQWku5ldhf8IjjvJueHy9lwOCyLrqSkJERERGBychLT09PYtm0bsrOz8eabbwqSIiYmRrj6DJXp6+sTHX5MTIzsGfj7Li0toaGhQUxpPBzpet4aJcn2s7CwEG63G++//z7Onj2Lnp4eBAIBGAwG5Obm4mtf+xrOnDmDqqoq4Rrl5eWJfyIqKgqjo6NIT08XOBydrxx5AXcAaBaLRQKOaMgi7mJ1dRWlpaXwer3weDwoKirC9PS0jClYZdbV1SEtLQ2//e1vYTAYJE+AElrgzozVZrMhOTkZSUlJ6O3tldEO1WgpKSkYGBjA22+/je7ubuTk5GDXrl0wmUz4yU9+Igqb1NRUVFdXywvEyysvL09yKxg9S0XUxsYGnE6n7G/ov+FOKC0tTeTJlG8mJyejsLAQRUVFSElJwa9+9SvJ8IiJicG2bdswPj4On88n3o4bN27g5MmTqKqqwp49ezA0NIT5+XnExMSgurpaMkwCgYAQV6m2GR8fFzUSMSQ00tHwRrUXQXcREREYHR3FwsIC+vr6cP36dVy9ehUffvghXnnlFVgsFtTV1UnQEsUUSqUS27dvF/9GZGQkNBoNJicnRT68FQMRFRWF2tpauFwudHd3yzI6MjISc3NzyMnJwbFjxxAMBtHf3y8SZ5pQtVqt5NCsr6+jp6cHGo0GSqUSPT09SE9PFzowdftU+9BVTzk0x20ABJKYkpKCxMTEewKEuHfhCBKA4Ek48h0ZGUFqaqpkp7AYamlpkXEiQ7x0Oh0cDoco5hjaRSUa/x6faYZtMQ9Go9FIOBGX8Nwx+Hw+oQkXFhbKJIAXCp3ojCtWqVSorq5GR0cHBgcHpWCsra0VAygNpgqFQiJ0JyYmpLsmfZnZ4RSxbG5uwmAwIC4uTiTSf+jPH91RUK7KlpmwtUAggPfeew8ffPAB9u3bh0cffVRkibt27cKOHTvg8XjwzW9+EyMjI6KI4EKW8/ixsTFp7/igsDqiOWZlZQUulwuBQEBcw4wQ5O/n9XpFg0/n6QsvvICMjAwMDQ3h4Ycfxqc//Wk89NBDmJqaklm9Xq+Xdj05ORlzc3MiJXW73XC73YID4UOfkJAgFTLNeXTGssvIycmByWTC3r17RaduMplQV1cnsY1kaAWDQQQCAXkY5ufnRRpHPhIvUbbyXAzy0tVoNDK3XlxclJmxTqdDRESEKHoyMjLE/b28vIyOjg40NTWhu7sbeXl5UCgU95Bnp6amJHJy68WYm5uLiooKTE5O4sc//jHeeecd9PX1wWw2iw+loaEBX/rSl2QBymKBzm7OmQmAi4uLE+kkDxaOcqgu4+KWnxMAdHV1we/335Nd3NzcjFu3buHatWtyqDY1NaGpqUkMVG1tbfje974nuA3inNVqtew0AoEAhoaGMDExAZfLBY/Hg2vXrknCYVtbmwT9LC8vS+72xsaGdHwUa0xOTsJkMskLzfFHaWkpoqOj0dDQgPvvvx82mw2nTp1CU1MTLBaL/C7E5DPqklJpxtAy64NKIX6XjADlGI7d5tjYGJqamuSfKZVKydimG580YSrfmDdjMplw4MAByYjn7H9paUk+D7PZLDsl4E6Vu3Xxm5mZCeCOCIVGU1babrdbpOGhUEiEEJRx5+TkYHZ2Fna7HRqNBvX19cjIyJDUS4ZYuVwu4VWx66Gsm13A9PQ0Njc3odfrRfZKz8/WFEBmkpPq3NraKsbOyspKhEIh2O122O12uQSJg8nMzERkZCSKi4tFkv/mm2/i7//+7/G1r30Nr732GjIzM/HYY49hYWEBbrdbzhMyx2w2mwRo8RLweDziaGc3OD09DavVKk70P+Tnj+4oaB5i3KndbsfCwgIqKyuxuLgIm82G6OhoPProo/irv/orREREoLGxEWtraygvL5dwcwbHUL63sLAg8zWfz4fc3FyZdSYnJyMuLk5093T5cm5K4x8NTgTkAXccv+wgXn75ZXz00Uew2WyoqqqSmeDt27eRk5MjlVdeXp6EzCQmJkKtVovCxmq1SmXE35XUT3Y8rIj7+vpgMpmQkpKCEydOCDupoqICVqsVTU1N4uegF2F5eRlms1nMh5x5kra5sbEh4yPmT7AN5qXN5T7nrVqtFpubm3LBAHd2Ona7HW1tbaipqUE4HMbly5dF+8841v379yM9PV202ZyRknfEAzIcDuPSpUu4ffs2IiIiMDY2hs9+9rN4+eWXcebMGTQ2NuLFF1/EF7/4RezZswcff/yx7DFCoRCqq6vlQGFuNceSzGogLjwjI0MqN0ok+VJERkaKe5pjCyayEZPNzyAUCqGnp0cu7Y2NDVy5cgXJycnSPQ4NDQlHjD4f7k927tyJ3t5eREdHo6enRyp9OoupdoqPjxceGkd8/D5ocmNORH19PTY3N8VMyrwDUmBfeOEFmM1m6Rg3NzfFFEfeETtEGsUoreUOQq1WY3x8HNXV1YIWoSscgFTtXIpzFzIxMYHi4mJER0cjNTVVaK80Z87Pz2Pbtm0ypqSSraCgQA4w7i1orPTfDdThCEin04nHg8IHon0YRjQ1NQW73Y6MjAzk5+djaGgImZmZ0Gg00tFSKu7xeJCcnIzi4mLZQdTV1cHhcIgRlYl6XBxTdsoOjW717Oxs+SzIwmKRt7KyIqZHu90u6jfgTsiSw+G4x1QcCoWQn58vzzmFBl/72tfwr//6r/jwww8lE6e8vFx8JbQAUFHF3UVSUhJmZ2dFGMHd2erqqhQ6/H4/kYuCxFjSDjnXi4iIEMqkxWJBKBTCb37zGxw8eBD/8i//Ao/HgxdeeAE7duxAR0cHxsfHYbVapTXmF8J2kKoTjqIIDKTCYHh4WJKjWPFzPs+OIiUlRcJNrl69KhI/tVqNc+fOoaSkBFNTU8jOzhZ0RmJiIrq7u6U72cqU4U3tvxsdGRUVJe04VTt2ux2tra2wWCwoLS0VNQQXaLz5OT7Zqm+mX4AVIbX2CQkJsFqtmJ+fR2ZmprCDiLymtlqv14vxj/+upKQkuYQ5M+b4zufziRP4oYcewo0bN9De3o7i4mJMT0/j/PnzePfdd7Fz505kZ2cLsoDkXC5PNRoNdDodurq6sG/fPuj1enz3u9+Fy+XC9PQ0Xn31VXg8HnR1dcnfUygUKCwshMPhQGpqKs6cOQO/34/s7Gxotdp71Gf8nNVqNRQKhbwoa2trkhVCfw0loHTHMq/DYDDIwUqT2PLyMi5evAilUoldu3ahtrZWLprR0VHZD125cgVra2uor69HcnKyGDEjIyNRUlKCxMREgbPRl0CJ8uLiorh0eaDTH0TjZk9Pj2RMJCYmoqurC5ubm6ivr0dXV5fIq+vq6uQAuH79OtLS0mRXR8moxWKRSF/6fri/MhqNIktNS0vD5OQk9Hq9jPCI3OGfkbRaPr9U/DCsi3tKAJifn8f4+DiWlpbEu0O3MWm0VA3x2aaKkOBK5nVw3EljL7tnq9UqVAiqBMfHx4UpRuT9+Pg4xsbGMDIygurqamRmZmJwcFA8OET29/T0IDExUdRDwWBQ0Bculwsmk0mkx2trazIO5uiZkmKKXjIzM3H9+nUMDAygpaUFNTU1WFpagtPpRG5urkhcl5eXxSTscrkwODiI1tZWVFdXY8+ePZiYmMD3v/99nDt3DjqdDg0NDRLXQOKt0+lESUmJPEsKhQIGgwHx8fGSbskRf2pqqryzn9hFsby8LKa4rKwspKamyqKYULDe3l6cPHkSY2NjqK+vx7Vr1zAwMIDc3Fwxvy0sLIiRjHpoys64tWfFn5KSgqWlJWRkZMDhcIgSKCIiQi4s4E6o0tzcHILBoIyGGHjPL2hlZQUJCQkwmUyYmpoSKz25R5xzU9VAtzUX3FxQcqdCUODa2pqEkJDYySolNTVVPBU0N/GfabVaqfio16aRjTuV6elpKJVK6Ti4xKajORgMQqvVilmQJFvudwjbm5iYQHR0NDIyMqBSqVBRUYHU1FR4PB689tprsNvtuHLlCo4dO4b9+/fjjTfegNfrhcvlwuzsLLZt24ZgMCgjsGAwiLy8PPT09GBwcBAtLS14+umnUV9fj+zsbJw/f15ezOeffx4PPvgg/v3f/x0XL17Egw8+iNu3b6Onp0dYNkxDq6mpgdvthtfrlVHg4uIi8vPzERMTg46ODukkcnNzobxL5s3MzBQZa25urjj8IyIiYLFY4HA4EAwGJVu9oKBAYiwp5WSXRr1/cXExXC6XyHi3Kl54ODNGNzU1VYoWdg+c59PjwxHh9PS0GME2NzfR0dEhY8KOjg4cPHgQpaWlUKvVOH/+PMxmM9bW1nDjxg1JeuPnNTAwIEIDzsxXVlZEZsmxHTOqmctB1RwPbJpYKTXu7++X4i05ORnl5eVSoRJrsrm5KdJp7gfopJ+amsL09DQqKytFITU5OYmioiKYzWYMDAwI1oUFI5VD/rtUYRpHuYckTXlrFU9KLX93Cm60Wi0+/PBD8TsUFBRIF0piMkdQNN+Gw2GYTCY4HA4ZF/Hd5+6L3fTS0pIk1L311luCJ2lubhahSHx8vCzCFxcX5SCfnJzE9u3bUVhYiO9+97uYmZnB5cuX8cILL8Dj8eDIkSNQKBRob29HfX09TCaTGBzpi+BYltnllPZSKceEO+L9aTP4RC4K8tYByA3LPFemmRkMBpjNZkxOTsLpdIrUbG5uDs3NzaLWoHtxfn4eGo1G5pV8EVdXV5GcnCwtFsmvJpNJlpfEdVOXzoec7TvHVQsLC+JMJWeIFT3DXNRqtZjguLyikY6oEmZ+M0eC2OCIiAjRetPLAUD0zh6PR5ANq6ur0Gg0glRgVjfBYISBxcbGCoKElTJxxpTDMm+Xfw3cidmkOmZ5eVkuV1I3iQNZX1+Xnc3s7CyUd/NExsbGsHfvXuzdu1cS/vR6vRjq2FUQVnfjxg2ZI//oRz/CwMAAXnrpJXznO9/Bj3/8Y9TX10sR8M4772Dbtm04duwYfvazn2FwcBDR0dGySG9sbJQqkzGwTqcTzc3NuHr1qvwZKBmmOis/Px8jIyMIhUKYnp6WdpvBQuPj4wDuhBy1tLTAZDJBoVCgoaEBt2/fxu9+9zusr6/jK1/5inSuzIyIiIjAnj17ZCRIJQzHJ8AdlVFERIQYAanhD4VCqKqqgtfrlZD77u5uWSDThc4qsby8HB0dHfD7/ZJ9XF5eLgtdLv3prKc0maNQ5d1Y0R07dggzjEtl7oqYL+12u4XATJMa321W3+vr66KW2VpRq1Qqod9yWe3xeKBWq4UDRocwd27c4XFRbTabhbZLpRnFJdT907THij4lJQWVlZVYWVnB7OysRLQSeTE5OYmcnByUlJSgs7NTcEJdXV0oKSnBrl27EAwG4fP5ZBTD3GoAInUmjJKdOicMjAGgIZiqNo59i4qK8Hd/93f4t3/7Nzk7duzYIXsSIvrpJRsZGcHOnTuxc+dOzM7O4tatWzhy5AieeOIJnDt3Dh6PBy0tLZKeyOeRDDaO2ZmZQu5cOBxGXV2dZHVQ9POH/vzRF0V0dDR8Ph+io6NFWQBA4ksNBoOwSCIjIzEyMoLo6Gh84xvfkCSwxsZG+Hw+MVHxQ5+YmIDFYsHy8rLwVIjTIAWUs0e6gzUaDQKBgGif2UJvbm4iMTERKysrIpNjm7o1CyEuLk6013q9Xl4MVg5paWliDFxbW0MgEBC5KueBoVBIRmdcEBJ6trm5KZka3DHQWem/G03ITGvOqcmU0mq1YtyJiYmR35eXSkJCgszNlXfplMPDwzAajdIFra2ticabs2BWRKurq7h165Yk+DU3N2PPnj2ora3F9evX4XK5kJ+fD6VSifj4eMldjouLk0uUXKRAIICXX34ZN27cgMvlwtraGtra2uB0OhEMBqHT6bC5uYnMzEzpAuvr65Gbm4ujR48iOjoag4ODwseJjY3Fnj170Nvbi1dffRUDAwNQKpWor69HTk4OEhIS0N/fL+5mLrCZ0cAFMsdVvEh5uI+NjcFsNiM+Pl4ObRqUtm/fjvz8fBkvjo+PC36Cy0EupePi4lBYWChVL5+XqakplJaWIhgMyvdMUCL9QHTZms1mREZGoqKiAsXFxTCbzXJopqSkoL+/X3YjfJ75/C4uLsJkMkkHTjkkuzEq23hBEHextLQk5jYuPqenp0W5RoQN0wJtNps4molI52Wak5ODmZkZmEwmUcSR1UZFDk12DDqyWq0S+Un1DicH9GjRZEofBX1Tk5OTIvVdWVm5c7BFRSEQCEgs8Pz8PFpbW5GXlydMueHhYdhsNul+6BYnK43PCA9Uo9Eo5wsA6V6oeATu7Lnefvtt6HQ6+Hw+fOpTn0JycjK6u7vR1NSEw4cPY3V1FRaLBTdu3EBERIQ8dwkJCcjKykJlZSWMRiP6+/tRV1cHg8GAjz/+GK+++iri4uLQ0tKC/fv3IysrSwoTmksJeRwaGpK9BXd8Ho8HcXFxQsj+f/n5oy8KZguwHWXVvrCwALPZjNOnT8vBePz4cURERKClpQWHDx9GVlYWzp8/L8lUc3NzaGlpkeoGgBhsKGVjxcjqitkXVOIsLCwgPT1dWDocvdAdyep5bm5O/B2Eq0VHR0OpVMouhA7I6OhoaDQaUUywml9aWhJAHHMReEko7+Yvz87OStofF5us7CmrKysrE/Ab8x644OSSnooThUKBkZER+aKpuqIUk7PIrd6CsbExLC8vo6SkBF6vVyq5reYlvV6Pmzdvwuv1ikFLpVKhvb0dlZWVmJ6ehsfjwX333Sefx8DAANxutzhs4+PjUV5eLuMojUYDjUYj/odQKITBwUHY7XZUV1fjqaeewvXr13Hq1Cn84he/wL59+1BXVwetVisqttu3b0uI0rlz5+Qwe+ihhwQ9UlFRAY/HIxjsoaEhnD17Fmq1Wrq0zMxM6Zr4vVKzz25jZWUFExMTMBqNksnNvAK1Wo3y8nJkZGTg4MGDaGpqEs+IQqGA2WwWOTVd+ywOGL7T19cnz4ZarZbxhdFolEAg/138NufJ7FDz8vJk38HwJY62mH3N7p0XNRf67Hq9Xi/MZrNkmVDSSb+R3+8Xc6bZbJZulwtyqoHsdrsYAanTpyyTpATmYbOAJL2BFzA7+K20WJ/PJ+/f2NiYiAy4MOblQs8BWV4cbc3NzUnhw7OJn/3CwgIyMzNRU1ODXbt2AbiTvMgFv9PphN/vR0FBAfx3qa9coHM3Q8YV1WJzc3OittNoNLLwLigoQEZGBn7xi1/g1KlTchY0NDSguLgY4XAYH3zwARYWFlBUVIRgMCiO8cHBQQwNDeHkyZOYmppCY2Mjrl27hvLycjz//POIjo5GR0eHdPDcddHtTdozDc+cutBLxX0PIwQ+sYsiLi4OPp8PFRUVEqhBTtDS0hIef/xxnDt3TvhBXV1dIm984403MDY2htraWlgsFtERU+nQ09Mjqhf6JBi1ygplcnJSHg7mH6yvr0v7S6YQZaLsEmj9VyqV0gUwwJ1jLUIN/X6/yG152GRlZYkxDYDc2BkZGYiKipIXgiojUnY3NjZk3krjkcPhgFKpFLMMHbrEElD+y/hPlUolozW1Wi2ARMZzEjHMz7WoqAgOhwNTU1NwOBxCWs3LyxO3LS/6hIQETE9P48qVK3j33XeRm5uLYDCIwsJCLC4uikLN4/FgdnYWBoNBqmk+7DTX+Xw+FBQUoKSkRBDY/f394jR96623kJCQgOLiYtTV1WF+fh5vvfWWdE8MiK+pqcF7772H1tZWNDQ0oKGhAU899RTef/99/PrXv0Z1dTVqampgMpkwMTGBq1evCkDt448/lpCjxcVFVFZWwu/3i8AiPj4efX19soeanp6WaFzGtkZGRuLjjz+GRqPBxMSEKGk4Wpmfn5dsEfKrgDsVKM2VxELQBMZOhhkbFCkQ88LF8Pr6umDziWAnlJG7BBIRqPKiyooafSp9AIg2n90I9wu8DPLy8uD3+yV/gVnwm5ubcLvdYmrkmDYQCMhIJTU1FSsrK5JpwqKDlzFVRbm5uVKssdMsKSkR4UpqaqoYO5npQAEH8xuWl5dFhcjOSXmXdUVlGJMEV1dXodfrYTKZ4HQ6ce7cOcTHxyM3NxdWqxWRkZFQKBQoLi6GSqVCWVmZ5H2zC1pfX0dzc7N4Pig7DgQC4otZXl7GkSNH0N3djS9/+ctwOBz41a9+hYmJCZSXl+OJJ57A8PCwiHaKi4ulI09ISMDc3BycTqcoHSsqKmQn+OKLLwqIksKOjIwMtLS0iLCAqi6OGwOBgBjz1Gq1qAS5U2YX9IlcFOFwGGVlZXC5XCLJZMVApQWt5FarVXTK4+PjaG1txYcffojo6GiUlpbKfoISu7179wqDnjNxLg3ZnvKgJyJ4Y2MDgUBAKva8vDx5KbKzs6WSKS0tFQonwX4lJSWiDKHjmtGbSUlJIm+jmoXKGi70GJeampqKvLw8iYhk6htbZKLPebEBd0xyHC2x++F/FhYWSiDJ+vq6sGe2jpDm5uZw//33o7OzE0ajUZbXdDfTyEQQHfdC7MBY2VVVVcm8md1BTk4O/H6/GKnOnz8vKpfe3l7k5+dLTCkvTs6eFQoF+vv7YTabkZmZiZdffhl+vx/p6elob2/H6Ogonn32WRw9ehS3bt2SKFq9Xo+MjAzk5ORg3759UCgUAqCk+9jpdEoaIBVrjLisq6uTNry9vR3JycmwWCxSTVEUERMTA7VaLZccNfIMBAoGg1CpVCJrZuBNIBCQVj46Olo4WExS5AKczwirZ4PBIBgMcqxGRkbkcDKbzbIEtVqtSE9Pl0UriQFqtVpiX7lnAyAdyuzsrKAyOB4CIKo9/nVMTAzi4uKQm5srQgwAkvXg9/tl3Lq2tiYyb6Y6sttdXV2VQ4ymUyLho6OjkZycLOFHaWlp6OnpgVqtRlxcHFQqlUh3KSJhBczLjZTchIQEyYSnp4JIGfo5qEDiZciD3G63i48EuDOKI8lXoVCgrKwMkZGRaG9vx5kzZ1BUVIT6+nq0trZifX1dMkFIi1CpVGLW2xoKFAwGMTQ0BK/Xix07duC+++7DW2+9JQo+q9WK0tJSTE5OiqGRxmJOPtrb27F9+3YUFBSgrq4OMzMzCIfDsFqtSEpKkl2Q1WqV/Q+5aMnJycjKyhKhBoPLGAPMMScpw3/oz3/LjoLZxllZWTLTU6vVciPevn0b8fHxaGxsFIQ0XYU1NTXw+/346KOPsH//fqkQysvLpeXnjJovfkpKCtra2qBQKJCTkyMPJADBhKelpUlADS+quLg4DA4O4ty5c7jvvvtElUVUMmehdM3y38mFHC30wJ0WzuPxCN+FaiLKYin9CwQC8jLRAMX2NTIyEqOjo6Jo0mg0orJilcblIrsidlhcuE9NTUmOwPr6OmpqatDV1YVdu3bB4/HgypUrskDcuXMn1tbWxLQYExMjTB5KmktLS4U7QwYPPzs6xDMzM5GSkiKyQiLas7KyhGfDl5RKjNnZWZhMJng8Hmzfvl2W5dXV1RLdSjUYxySRkZESzDM8PIxAIACz2Yzz588jIiJCUOkvvPCC5IUMDg7C6/Wiu7tblpt79+7F9u3bodVqJdQ+HA7D4XCgoqJCZI+RkZGScmi1WiWfghkYVKnRNEmhApVolG3z4OPYi/p6Lt5TU1NlbMSLm5U+09mIqODOjKo27u3oUudzyd0gOyL6X0ZHR6FWq+VgZSHGyOL5+XlZ1nIJPD09LSYyovPT0tIkfGxoaEguna2GOo47KKagQolwTzqnWUxqNJp7ZPWU2XNc5fP5UFhYiI2NDczNzSE5OVnc53wPOUomCJLdPCcL7EqpCLTZbLLHc7vd4i9aWVnBnj17RJXn9/uRk5ODd999Fw6HAzdu3EBNTQ2MRqPQlzMzM+HxeLC5uSmqzYyMDFgsFuzYsQPl5eWCsl9eXsYHH3yAZ599FpubmxgcHJTCg4q30tJSJCcnw2g0yuerVCqRlZWFvr4+kaATP86zj4l6HCWy+N1KalAqlbL/3RqT+4ldFMPDw9BqtcIfASB4iatXr8riNzs7G9euXUNvby+eeOIJTE1NoaioCNHR0XA4HFhYWBAeFLn4rJi5qOIIgWwXElIppxsfH5ebOTIyUiiv4XBY1BEulwtDQ0PykDz88MN4+eWXkZ+fj8nJSTE1UapIxgsPfOBOpi53E+TIEG5IvDk7F3Ll4+LiZGHMl5oZGcyQCIVCcLvdArtjyhf/PKRPUoFF2uTa2ppkfnz7299GMBjEM888g7m5OcTExGB4eBgWiwUejwcAREIbCoXkhfL5fAgGg5idnYXNZkNmZqbgEnhQLywsSHAOndgej0fcpURY8wVmAmBkZCT0er0Yli5evCgGMVZXbW1tqK2thdvtRk9PD7Kzs4UD1NfXB4vFgmPHjglMkN+1Xq9HV1cXfvnLX6Knp0eQ7ZzR22w27NixQ5IOafIymUwoLi7GxYsXERUVBbPZLHsUhiX57xIBwuGw5L5TBZSbm4vc3Fx0dnZK6hml0Nz9kP7LEUtaWpqodzg6oquWQMzJyUmp9Am1W1hYgE6nk9n0wMAAzGazXDLMKCE8kxJghUIBk8kke43IyMh7GE+xsbGYnJwUqTa/a4ZSsdtYWloSaXVSUhJSUlIENsnnnP/JXJSxsTEsLS2hvLxcaLNer1fEEMTgMCdk6wiHOdrE8DDPhd8p4Zv0W1DsQnk8Jw8kCvMzpoiCJIK4uDjxnHDhDNyR/Ofn50On0+HIkSP4x3/8RygUCuzevVsUlowEJnuJkMeIiAhs27YN6enpeP3113HmzBmBn3Knw9+RnhJCM6n8fOSRR8TLwiTMubk5mM1mZGVlSQwrHfSpqano6upCQUGBnJ0FBQXyfdGzQvo1C/X/F4THH31RcLSTkZEhunXOrqnVDwaD2L9/P65evYpQKITe3l5s374dXq8XDz30kHz50dHRouZRKBQy742JicHExATy8vIwOzsrXCm9Xi8PF+30tLQbDAbBFXNc4ff7MTExIYdCRUUFcnNzcfPmTQwMDEikY1paGqampkSDTq8E0cvj4+P3eCv4pTNghWMGJnJ5vV5hs7CCy8rKQnFxsRiuqFzhclij0WBoaAgGgwErKyviMXG5XIiLi0NGRoYghRkKxbktX9ysrCx0dXUhNTVV8paNRqMEP9EBn5OTI8RP5ZZ8hvX1dZjNZgENcrzExRh5/5QDk2/D9jw+Pl6025SupqenSyfDQCTKngGgpKQEhYWF0Ol0aGpqQnFxsfg1jEYjOjs7sba2hoMHDwq2wOVywWazITc3Fzk5OYJBIEOpqqoKPp8PeXl5SE9Px7e+9S0oFApUVVXh0KFDgvvgaHBzcxNarRbj4+PweDyorq6WwzwiIkKCmJihMT4+jqysLFHcBINBqXo9Ho90uXTCkwRKZRQlpvzeWIVrtVpR+iwsLCAjI0M+U3Yw7O6oZElOTpbRGT0avOhLSkqQl5cnZFWi+YnoYLfDfQk7BUaSsqN1u93iA+C7zxEzRR3p6enic9BqtVhfX0dBQYGMI3lJUuBBQ19cXJxclPQ1EfPO3Sc7d47+6H9gx1FRUYGxsTFJhGSEAeXD7N6pIvN4PBgYGEBcXJyo2NRqNRoaGgD8FzWaI8C5uTkkJCSIHJY7uuXlZQwMDAiJ99FHH8Xhw4exfft2GRXZbDZMTU0JzHIrZoixA8q7ZAnuyDgS5MKaApvNzU1xw3PUyQKgr69PildSLXgucefD9/gTuSjS09NRVFR0D6yKgfcajQb79+9Hf38/1tbW8I1vfANPPPGEzNwKCgqgUqmwd+9eDA4OAoDwb9h+8g9DJQJ3B2q1WhyYFosFer0e7e3tIjPjyIIuaa1Wi/b2dqSkpOC+++7DI488ArvdLolXVqsV4+PjKCoqwu7du6V9HhgYQEFBwZ0P6+6MlWla0dHRMBqN6OnpkRHM1peYOOyt8Y9bF03hcFgq8HA4LCluDJaJioqCzWaTGFQGpGxubmJgYEDGR4wszcrKwsMPP4yhoSEEg0F5yXl5lpWVSaVGXwfVW8CdvcLExIR8hsREMw9Eo9GIa55KDVYnlF5S6UUYIz8XYqA3NjZk18HlPg1DHK9xuWc0GuW/c/78ebhcLuzcuRNVVVUoKSnBqVOnRGFSWVkpI8+enh5YLBakpKRgeHgY2dnZmJqaQnt7u3Ru/Gtq+5VKJcLhsGShUCjAw4PSa7qpqQwjO2zrM0+lDJHPVFQx3VCtVmNhYUEW1hMTE6JIIvqaRjIupzlXphG0sLAQKpVKpNd2u10ghzTXMfJUq9UiKysLExMTorRLTU0VQyGllXzmKYHlgpYXV2pqqqgaw+GwOLRjY2PFDWyz2VBUVCRFG1VBvDRTUlKwvLws3TlHIiQ8ELVCxRXPCkqGGSZGk6dSqZRMa4VCIfsWFpD0HzGjmwosTjooaU5KShKBR2NjI4aGhnD+/HkMDw+jsLBQsNw9PT3yOdDJHhsbK2bO8fFxrK6uyq5yK22hra0NZWVlsnsiJHLr98tIU+4hiDOn9JW+Mn5+hYWFIiLKycmRbBAy1WJjY4VVRakzOyCeAX/Izx99URC7zbl6V1cXgsEgWltbsWfPHqlMb9y4gdLSUgwMDGB5eRm5ubkoKSmBz+eTS4Z7BboOWQkwb9tut2NjY0NyoD0ej0Qfut1u6PV6aUNbWlpQXFwsSiEqmRiccvHiRQwODsoDkJmZiVOnTqGyslJklePj41KtswXOyMjAysqKhKEwSJ3zSl5sXCgmJSUJqpzuSapNQqGQyPA4x2WgCUdOAMRwxtHezMwMkpOTUV9fD6VSiXfeeUdkcwUFBWK6aW5uFld0b28v7HY7amtrBVfNDOvFxUW0t7dLd0iIIMdl/A7Y9TDUyWAwYG5uDm63W1LRuHDlPDoUComXhiM9mseIWVGr1UL2pOKFctaioiLk5+cjMjISx48fx9raGoaHhwUPQcKqzWYTBz8RF6Ojo/D7/Zibm4NWq8Xly5dRWVmJhoYG+Hw+vPnmm5J58JnPfEZcrixIWNkmJSXJ3oBMH+5n6IEhwoMy6oWFBaSlpUkFTHwIC6C1tTWRom5sbODQoUPw+XyyK+GYhVUrn6vs7GzodDoMDQ1hZmYG4+PjcDqdmJqaQlZWluyWKKulx4bdA8UJQ0NDotQj9ZkXQnx8vDz/Op0OCoVCuqWtEteJiQlxKisUCknq4+4tOzsbc3NzAkDkuJLqLMrmmd3CAoQzfY6YJycnRbpLKfv09LTg/YuKikSl5HA4BAuSnJyMiYkJ6UgoJV9fX5fOS6vVoqqqCqFQCKdOnYLT6URFRQVGRkawsLAgEwB2AQcOHMDVq1cFzT41NSUMJQDia2Dn7/f7JSExHA5jZGQEKpVKJg9cjG9sbEjsK3doFOiQM0dTLgO0IiMj0dLSgszMTBw8eFCkzCywtiotnU6n/Ls5eqYI4hO5KPr6+gTJy8qLig5m7TJd6tVXX0Vvby/i4uLwF3/xF0hPT8c777wDrVaLUCgk834ifd1uNwwGg0gKgTtLZHooOPemsY4HAh9An88nUtJAIACTyYT29nb09vbK/Jbu5pmZGcnstlqtmJubQ0VFhYxM2AryYpiYmEBkZCSioqKQnZ0tPJvV1VU5MJghzqqFox5KZKOjo4WPxDEM9xGMLSWgjQae6elpFBYWYnV1FZOTk7hw4QJaW1sRDofFjDQyMoKSkhIkJCRINgEPb47BoqOjhdWzuroqqXIcX7jdblGkcEZOmSH/PVTHMIuaSqu0tDSkpKRI1cYuiPNRXgSUD3POy4W8y+WSv+7u7sbKygoaGxtRVlaGmZkZjI2N4dq1a7KAD4fD2L59u8yMuTsgd4u4i/j4eIyOjuIb3/gGAOCb3/wmBgYGEBl5J5mssLAQ7e3tMurjATk9PS0BOZz3swqOiYkRoUVERIQYx8rLy0XmeP36dZnp84CiAIMI7t7eXmxsbEh1v7q6KqMbu92OhoYGOJ1OLCws4Pz581haWkJubi6uXbsmiXbsPtm9MtqTnzVl1vSoqFQqobHSqBcKhWQMx90FJemTk5PQ6XTiRia2Yqspj5Jx7r4o8eWlxRELfTzsRFdWVkQ2StlybGysyGkjIiJkOcvilAggPts6nU7yuOlYZmcbFRUl/hwKQiYnJ5GRkQGv1ytjGRZEZWVlyM/Px5kzZ9DZ2Yna2lps27YNNptNigSOEGnmpUKM3ROff/paFAqFfPc0bxLYR7UXd5E0B/MsZDdOOTWjEDj6zsnJwe3btzE0NCQUZy61KWfmGJyjRJ6Tn8hFUVRUJB92cnIyDh06hL/7u7/D0NAQSkpKJMuZDszW1lZERUVhcHAQdXV1oolm4hWVIXQtE0K2lXhKJyzn8kT88p8zsIZLWtrYufCiqYsoheHhYaSnp+P5559HTU0N4uPj0d3dLbkX8fHxwrEh8oOyUrpL3W43iouL75EIsnoh6ZVfFkdU3A/wUqSklOMgMoGKiopE604c8ejoKIaHh0XmV1tbi87OTrS3t2NhYQFzc3Ow2WyIjY3F4cOHsWfPHty6dQsjIyPy0NFrMjc3J3hkLv1CoZCMCpRKJWJjY8U7QqULfS1ETmyNoA2FQhJSs9WDwkUeFUbc8/Dlzc3NlZ0BdxfERXs8Hmzbtk0OGV5urHKpWKmvrwdwRwFElv9Pf/pTMWylpKQgIyNDTJmEHgJ3FG42mw0qlUoUJjSVctRWUFCA3t5eaLVaCbsnPHB1dRUGgwFDQ0MS5cmxCMOLtFqt/BmXl5eRl5eHpaUl2UPMz88jLy8PVqsVhw8fRiAQgN1ul0uDvgTO3YuKigQ90t3djRMnTiAtLQ1tbW1SyZLDlpKSIqyq1NRUJCUlibKNvx+XyVQWkYabn58vuAvyzHJycpCUlCS+o617i+joaDidTslN8fv9QhfmYn1paUk8JhkZGSLlpmBDrVajv79fihdSYXmZp6amIj4+XoQvFA9wJ5SZmYnU1FT09fVheXlZqNBMouNiOjU1FXv27EFSUhJu3rwJh8OB999/H/Hx8Thw4AC2bdsmeB8q00wmk+x4SIbgqJICGu66iDBnV85LnBJ/nmlLS0tCGKZqi882d08mk0lG5YR6jo6Oore3F62trXjhhRekq+XlpLwLtMzJyRHhAcOOPpGLQqFQSPYxZXpHjx6VXIJgMIiXXnoJzz33HPr7+9He3g6n04mPP/4YHo9H3NDEf1NvTVMcl0Ss7DiqmZmZkYUdGUikU3I2SZ2x8i6SmrkXZWVl0Ov1uHLlioyucnNzcfXqVSwsLMgivrOzU6rYqKgoAZRxDEAXNKWdTEWLjY0VZk90dDSysrJERsm0s7S0NDmYAGB0dFTw5Lz02EoODAyIgos5A/n5+cjIyJBOKSoqCnv27JHL5wc/+AG8Xi9qa2uxfft2JCQkICkpCdnZ2SJnJkCMM1JmgfDB5xy1uLgYQ0ND4hotKSkRTEhRURG2b9+O3t5ema/TkcwRRWRkJOx2OyIjIyWCkpchx4b8ZyTBUnbMPUJKSgqsVitWV+9EeJaWlgpC4fvf/z6SkpJQVlaGixcvwuVyoba2FjExMWhpaUFaWhrq6urQ2dmJsbExfOELXxCj39bPgP6fuLg4GY2xkyNeQ6fTobe3FwAk/yQcDsvubH5+XjoKEn55YfJCJZp9eXlZkNuUrXo8HuTl5SEpKQkWi0Uu860HNwuTUCiE559/HllZWdJlFxUVISoqCm63G0qlUkJ/JicnobwLkiTin90i3cEE3xkMBonp5GFHsQhBeNwp8OIgOj0xMVFGrJyncyzHXRGVXJT70oNEtdhWkCZ3Cm63GxaLBQsLC7Ko5niQOfCEBobDYXR3d0On04mxlru8/v5+IQ1TQcjPlGFhV69ehVqthtlshkqlwuOPP475+XncunULXq8X6enpAnRkt5CTkyPyVJfLJbkl7AZSUlKQlpYGm80mo0fgzu51q4KPQh6+G7ycuQfjaJpjXma883M6fPiwjJ44FuUFy+6EnRZHdJ/IRUGaKh+4nJwctLe3IyoqCqdOncKbb76Jw4cPo62tDXNzc3jhhRegVCpFFz40NCTSu61OQeXd6EJWQ1y2UeJFJ+ni4iImJiZEU8zwE2LFl5eXheFChrzJZBKlU3FxsZhzmpqaMDIygkcffVTiPYmj4EKS/Hh2MhwpkdZILXlERIRQW6OiouT34iyXGRJc9B09ehS9vb0SbkJOEBk8VIqwndy5cydMJhNef/11uN1u6HQ6Ydg/+uijeO6555CdnY36+npxtdNPERERAaPRKJJKOnwpydvqtA2Hw+jt7YXH48F7770niXZ6vV4YSe3t7UKhvf/++3H69GkZl7lcLlm6MweY8DX+XgUFBZifn5dcZZVKJTJMOvyJIyHKobe3VyBxXNDSFEZaJsegs7OzYnobHR2FzWbDSy+9hB07dmBhYQFDQ0MoLCyE0WiUzonVGyXFXIjOz88LZoUVm/IuPHFtbQ1GoxFra2uSSd7V1QW9Xi+KMuZMT09PY21tDePj46Io4yhucXERq6ur6OjoECWP0WhEbm4uYmJiMDY2JomNFFq89957WF5exte+9jWEQiGMjY2JvHpjY0PGWPSCUHiwuroKt9uNyclJ6ciJuScu5erVq5KWtry8LJUzlWA0b1Hiy3l9OBwWtAxZYPQM0PtAdZjf70dubi7GxsZEvGG1WpGbmyufOR3vlHwyUoC7DTLc6CDPycmRICfgTmfEgCOVSoXJyUnpbOiraWlpgdFoxEMPPSTJcAMDA4L0YQHMS43xqPRWcLfHYCPuJ0mPprqPnR7l2F6vV7q+rZJ+4mB8Pp8IBoiKZ7HBboFBUCdPnpTPc3V1FWtra/dg4Qk3/ETpscnJyTAYDALt6+npQW9vryxSnnrqKZSUlOD73/++qJC+/vWvQ6lUCuve6/UiNzcXCwsL2NzclNEFGU6seDmGAP6LMUWVAKsjLuSIo+aLolKpsH37dgDAwYMH0draipycHIk4TUxMFFkds7CzsrJE/UGlBeWeW5k2VMrQUPf/Vb9wVEMVETkrDCt65JFHMDIygoSEBFRXV2NzcxMTExPweDzo7e2VS5B/VmIzBgcHERUVBY/Hg2PHjsFqtUKhUOD27duorKxERkaGmKcIY4yNjcXc3JyktVFXvrGxIUlqTqdTcjYoDXa5XMjJyRH6L+fAExMTUgSQtVRbW4uTJ09Cq9VKDjTHQsSx0GRHPDbwX9kmHCXFxcUhPT1dLkweQGNjY9i+fbukEBYWFgrr6MSJE5ifn0dBQQE2NzdRXFwsmvfOzk789V//NWw2m6TCFRUVITk5GbW1tZicnMTU1JTkqDCrG4CMCygLpaeE/H+aQqlUKigokHEl9xKcS1utVgEo8tBl8UFuUklJCaanp6Ub899N6MvIyEBHRwdu3Lghbm0eNIcPH4bP55P5OFlAHGdQAss/CwspwiHJB9t6gLKwiY+Px+TkJJKTkwWaOTc3J0UQpdfsCOhUpns+GAxKocB9nkajEcYTx07cUXBMQsQ4jbA5OTny/5uokrm5OTgcDulC+FlzPOxwOJCTkwOPxyMLXX4v6+vrqKiowNTUlKjmjEYjjh8/jldeeQX9/f1obGzE/Py84Ps5zklISJBcE2J3WCAQQEmEy1Z4JOGCFLzExMRI9C7FPDQrUpLLbJzFxUW0tbVhbW0NpaWlGB8fl4RBlUqF7u5u2Gw2HD9+HLW1tRgYGIDNZpOzgzgW7kb/0J8/+qJYXFwU3O/t27fxm9/8BgCwf/9+PPXUU7j//vvx7rvvoru7W6q/Dz/8EMXFxVheXobBYJAsg62BRVxkLS0tSWgIW15WR9PT00KZJPGSDl3m1fLGpZwxKSkJMzMz2LFjB/R6Pa5fv463334bCQkJePDBB3H06FFBhnOZzPk7vQxckAH/VaXwZaOighr5nJwcGevEx8ffI9kjefbSpUsIhUI4dOiQZPru2LFDJG5b2+qsrCw5kFjBms1mORi5fKdqgxJHjUYjL8fQ0JBU4+Pj4yJX5YtP1YXBYMDFixexd+9eHDp0CEePHpVW+eLFi7BYLDhy5Ag6Ojrws5/9TFz58/Pz+OpXv4qf/exnshdaXl4WxyxdtPyOuRSfmZkRuefm5qaoaaKjo1FWVoaOjg5cv34deXl5gjTfirbmHmht7U7ofUpKCgoLC+H1evHhhx8K7K6+vh5/8zd/g8uXLyMQCODQoUOoqqpCWVmZqOPI0uLvRPUR/TM8IDkSoBFvbm7unqyR3NxcOfwrKipw6dIlKO+C6LhkNRgM8Hg8sqPg3Do6OhparRaPP/44gsGgXFxZWVkwGAzo7+/HpUuXcPz4cVRVVaG4uFh+Zy6EafqjgmpwcFB2a3RLGwwGaLVa+P1+ucy5UCcChHJwPuM07jFwiWKTxMREUcaRPcbxHnlMWx3l7ECItuEinGgeXmgssLaayLhTGRoaQlFRkUA6+Z4TPcJwL7LMeOlxHDM5OSlAyNHRUSQmJuLll19GZGQkioqKEBkZKVnvERER6OrqglqtxuLionilIiMjJXhIp9MJXp55PXynSkpKZNTIMXNsbKwkCzImgQpIjqMtFguys7NhNpvR39+Pzs5OmM1m2Gw2uN1u7Nu3D9XV1Xj99dfh8Xhkj5SSkoLMzEysrKxI5AJwR501MTHxyV0UNMgMDw+Lk7Wurg75+fno6OhAZ2enALhUKhWeeuopaLVajIyMiI6XKhIGanAu6nK5xGDCfQPlhMRYcCzDTsJut4tzOjY2Fnq9HlNTU/B6vSgqKhLVTHZ2NsrLy0X+Nz8/D7PZDKVSKSOuxcVFeL1eaQPZ1qakpIjMjyHuBMYxX5mmNuBOeh1ZNhxDREZG4vHHH0djYyP+8z//UyiXr7/+Ovx+P4qLiwUpXlhYCJvNJkEyrK4CgYD4ArioZtARw484S6WzXK/Xy+IvKytLqjlKB2dnZxEVFQWj0SiHrcvlEhJsdna2VGlvv/02nnzySTz11FOy9O7t7cWPfvQjfP7zn0dhYaHIMwnco6t3YmICmZmZ4n6niYljBjrjk5KSJCDI4XAItuW9997D8PAwTCYT8vPzBXVttVpldBQOh9HS0oKkpCQUFhaipaUFv/zlL/Hwww/DYrHgt7/9rYS70OXP34kgPY4XtjpltVotWltbRWHFw93j8ch4MDs7W0ZMVJN1dnYKlJLRpuyWAoEAcnNzJVfA5/MJfprYChrEdu/ejfz8fDQ1NaG0tBSf+tSnsLy8jM7OTpFcs9Km8ZQKu/+vKZQXBC8WqtBo7nK5XEITIFguISEBg4ODSExMlGU4DzpmWbOAIlSSS3K+awCg0+lkFMUFOFWOFBIQ57+0tCRYC1JiqeJi58GdBs8IjppZIC0uLmJyclJMn5zR0+tiNBqRkZGBiYkJKJVKmEwmiUien5+HSqW6x92/1YNEJRQLDCo4eVnEx8eL/JwjPCrFKJpZXFwUrhtd2kwGZMRyX18fkpOTxQM2Pz+PyspK7Nq1C83NzQgEAgIHZKdvtVoFrsg/c1RUlPz//UQuCgCSKc0t/traGgwGg7hzCwoKsH//fly4cEEWUATtzczMiMKFgepU3VBayREEx1lEY1C/zOW3QqGASqXCwsICjEYj3G437Ha7uH2bm5uxsLAAk8kkmc/0X7AV9t9lPtGUlpKScs9eRK1WywyUEYaUCObl5WF1dVWiJuk6pXyN0C4e9nSKKhQKPP300zCZTPjHf/xHFBQUoLi4GLGxsXLZEmfAfOP09PR7DtTR0VHExsbC4XBI8BM7Ko6+7Ha7ODpzc3MxNDSEtLQ00diz6t/K3iGKfXx8HKdPn8b58+exb98+UZRdvnwZb731Fg4cOIAdO3bg1VdfhdvtxsmTJ7Fr1y5MTk4iLy9PXL7Dw8P3vGhcvlJGOT8/L/4Rj8eD0tJSbGxsoKmpCR9//DEefPBBUYOsr6/LgpO/O3PBBwcHYTAY0NDQALvdjomJCdTU1GBlZQW3bt3C0aNHUVxcDLvdjqmpKfziF7+A3+/HwMAAamtr8fLLL0OtVgtuJCYmRl5cq9WK1tZWbGxswGw2Iy8vD3a7HVlZWWhraxNkBGWMW8OssrOzxUMBQA48Ug04o+d36HK5cPr0aRw5ckTCfwjHI8bi6tWrsm8gDoa+h63JbazmKZZg10JHNMdp7EgtFgu6urrE3MYDmXsiItO5ZAUgDn+6isvKypCUlIShoSEAEHAfE+yo/qJEmiif0tJSuZSMRiNaWlpQVlaGjY0N+d8wS4PcKuZOsLOgXJ+m26WlJeluGT7mv5tZzuz0nJwcmEwmJCcnw2w2Y2FhAb29vRKPSrJveXk5JicnZRxI0KZOp5OFPuONqdIjHt3pdCI7O1uKEHbCzEchFUKpVOL48eMYHh5GXFwc3n77bVy/fh0HDhyQvS6FLR9++CH+4z/+Q0aQn/rUpxAfHw+XyyVoHZ1Oh9HRUTmPPlHMOGFjsbGxaGhokOzdtbU1lJSUYHx8XDgn27dvv6clJ0SLi5aWlhYJARkYGEBeXp7IJW02G2pqatDa2iovHaF/NMi53W5JpPL5fAiFQhL8cenSJVy6dAlzc3PYu3cvtFotRkdHJQ84MTERH3/8MRISEqBUKuFyuYS5T9koqyWajMgGYr4uwYh8eAOBAPLy8iQ/oKamBsFgUILn33zzTRmF9PX1wWQyCXTsW9/6FlQqFTQajVRajHHV6/Wiv+clxdkpRxr8PcnQofJmbW3tnsqQUl0eBlQ6+Xw+wVqHw2FUVFTIbB64wzravXs3bt26haGhIbS0tODVV1+FQqGATqcTqCPliCMjI2hsbITL5YLT6URnZydGRkZw8+ZNPPnkk4iIiMD4+LhgWaxWK0KhEDo7O6FSqWC1WuF2u9HX1yfpaPfff7/gzDk31mq1wtuiTt3v94uazn+XFcbiobS0FF/4whegVquxf/9+LCws4Pe//z3W19fx3HPPwWAwYGZmRrIJMjMzsba2hmPHjuGDDz6Q4oGdF//cZJBR30/yAP++1+sVPwyFBi6XC+Pj4zLftlqtyMjIgNlslkX61atX0dzcjISEBGRkZMh7oLybd8LngcoazuO3YjhIpI2IiJARhf9u5snIyAgACLrbYDDIbpCQQAAi442NjUVxcbH4TgBI1KjRaERCQoKwk8LhsCiCuM+jxyYyMlKeN3bJoVAIiYmJaGtrg06nk9EbcxZKS0sxNzeH2NhYeTdJFaCpkEojjgv5zlKcoNfrsbi4KLsFZrkEg0H09/dLUlxVVZWMxiit5hKdS3mfzydEVkqwZ2ZmZAdIkxtHk+xkyAnLzc1FSkqKuOqLiorQ2toKh8OB/fv3C0r8448/xsbGBl566SXs3r0bbrcbo6OjQj/OzMyUaQG9WbGxsbDb7VLs0JbwiV0Um5ubuHnzpsQtmkwmyTc+f/48Ojs7UVpaCq1Wi4mJCUxOTsJkMuHQoUNITU3FxMSEmE0otQwGg+jt7cXNmzdhtVrxzDPPQKvVSrUzMTGB4eFh+WJ5uLGV4/w+Pz8fRqMRXV1daGpqQjgcxgMPPCBM+IqKCuE7+Xw+cYoTJUHjEvHZUVFRMjrgC0YiZzgcFpkhDXbx8fGySIqJicHo6CgCgQD27dsnkt+xsTG88cYbWF5eRl9fH1544QXcvHkTLpcLw8PDIjVm10XpKjk4POjJ2GIlxeQ8jnW4HCZSg9UL22q20OFwGBkZGXJ5sNV3u93Ytm0b/uRP/gR2u10yGJjrkJ6ejp6eHiwvL+Ohhx6CxWKRigqAdDs+nw89PT3Ytm0bEhIScOHCBbz55ps4ePAgkpKS8Jvf/EZGa4TdkSqs0+lgMBjEi0I5LscZXEZSzbG4uIjBwUFMTU0hPj5eMp+PHDkiHd/09DSKiorgdDrxyCOPIDk5GWfPnkVLSwv+5E/+RMYxKysrgsbfv38/UlJSMDU1hddffx179+6FQqEQlQvTxdhlb2xsoLCwUHAOHHvyZ2NjA0NDQ7h06RLKysqQmZkJl8sFlUoFvV6PqqoqZGZmoqOjA2azGdnZ2fjpT3+K5eVlPP7445LWyHERdzyrq6tiEOT3nZGRITh2p9MpXgbuPjgWyszMhFarlUqX2A0SY/nO8fAlOYCVNxEhs7OzkqrHPHPO3umn4M6NLC7+M3Y8OTk54n/hYcxOjONSjsYCgYC42rmLUavV8Pl8SEtLE14VF9kkN9OTwk4lMzNTwsRo1GShQA8EJbYE8XG8RLQLuzuOGsmdIiqcBOGZmRlUVFQgLS1NpiAkQ7MwamhoQFxcHAoKCvDZz34WCQkJ2LlzJ6Kjo9HS0gKDwYBnnnkGMzMzmJycRHd3t/xO3ONyGc5i/BOFAtK9GhMTg/b2dnR3dyM/Px+7d+/G4uIiRkZGpGqyWq1YX19HU1OTqJu47Nlq4R8cHERzczMWFxexvr6Oy5cv49SpU/B6vfgf/+N/QK/XY3h4WG7+qakpGcVQ105T06VLl/D2228jGAzib//2b5GYmIjLly9jcXERDz/8MDY2NvDxxx/L/JejM+4g2FnQ+UriJV24NK5RFseZNysY7nAYWzg6Oiq5zQcPHkQwGMSvf/1rJCYmYteuXZiZmcEzzzyD6OhoOTSzsrIwMDAAu90Og8GAtbU1kRfzkqX+mvwYQt34wzaVIw+235zbqlQqmdPSE0PwIKXCRGxzYUnPClHhDz30EILBoCxaBwcHpR0ncr2vrw+3bt1CRkYG/vzP/xxKpRL//M//DOVdnHJMTAyKiookKCYzMxNGoxEulwvPPfccqqurMTo6iomJCTnUiCJgqBDRFHzBVSqVYBrS09Nx+PBhrK+v4/3330d3dzesViu6u7vx8ssv48SJE3KgxcXFyee+b98+IcJeuHABKysrqK2tRWRkJH72s5/h2rVrOHr0KAoLC+Vzpqqpt7cXu3fvFqIppaKDg4PIzMxERkYGhoeH0dzcjOzsbPz1X/81zp49i1dffRVmsxnV1dVIT09HZGQk9u/fj2AwiOLiYkml47Pf3NwMAKKSo/OY1TbVWUz4o1KPI2JmWTQ0NCAiIgJnz56VsY/BYMCJEyfETDoxMQGn04kDBw4gFApBr9cjPz8f169fR3JysvgZ6EuhKIUiChYQFARUVFSI43urr4VdBRPstl4wbrcbiYmJot7y+XyCFVlcXERMTIyA8ZRKJWZmZgRY6vF4YDAYxAvDfRS9IWSeAXfUeJSeezwe6Zzoj2CyJncLBQUFcgkxKIxO8f7+fmRmZiI/P192LQaDAXl5eZJ/c/LkSZSWlmLfvn1obm7G7OwsTp48ifn5eRw/fhwPPfQQ7HY7+vr68MEHH8BqteLgwYNS0G4dbZIyzNE5qQ8cBf+hP3/0RWE2m9HX1yesoJ6eHqyvr6OxsRFGoxHbt2/H+Pg42tvbRUkzOjoqKV88oDc2NoQ/dPbsWXR3d+Po0aP46le/iu9///tob28XbHRJSQmqq6vR3d0t8Df/XZQu4VqMEeTLo1AoMDQ0JKMBnU4Hi8UCnU4nMk9GrlosFpGPccZHiiVjKqmmWVtbk2QvKoIoOWUm+NTUlFQUADA5OYmFhQUBJpaVleHpp59GdHQ0XnnlFURFReHP//zP8dJLL8Fmswk9kq06O4uthkP6Fjhq8Pl8oj6JjY0Vp2woFBLQWl5entAnOeaiI55JeXzpwuEwpqen0d3dLW70tLQ0lJaWYmxsTObjrPjobQkEAhgbG0N8fDx27twpxYDdbsfly5dlKWq1WpGTk4O/+Zu/EXOh3W7HsWPHZDbOwKiTJ0/iyJEjsjynSdBgMIjElktd4M6uo6GhQYil6+vryMjIkAo0KioKTz/9tKDnuaD9whe+gMbGRhQXFwsDzO/34+LFi/irv/orPPHEE/jOd76D//W//pd0QQkJCSgsLMS5c+eQmZkJADKOTEtLw5kzZzA0NISqqipcunQJqamp+PrXv44XXnhBVCn/9m//hpaWFszNzWFzcxN6vR49PT0YGRmRg4BBSlQTUcm2uroq6H/uMNjJECzHjpG7EPo7KKmlJyIjIwODg4OSmvatb30LSUlJ+Na3voVgMIiLFy/KTshms8FkMsHv96O0tFTGr3yWVldXkZmZKe/p9PS0jEfy8/MxPz+PsbExmedzjELsPsc3vNjoqudoKBAIQK/XIzs7W5z+lMxzT2g0GuH1emGz2WA0GqVD4s6U2HEqL0nRZSTr2NiYfFYRERESjUzsEEeG7CY41qYEmWdFXFycGIyjo6MlgoEy40OHDuH48eMwGo0IBAI4e/YsRkZGoNPpcOHCBeGNhUIh+awMBgNaWlpkv8c/Fw2BVD9yz8tJxCd2UczMzAgdsa6uDtu2bcPAwAB6enowNTWFBx98EABQXl4uVNC8vDwZE/GWCwQCSE9Ph8FgkA9zc3MTTU1N2L17t8AG+cKOj4/LF08Co0KhEASH3+9HUVERAoGAqEHa2towOTmJkpISpKeny0E5MDCA3t5epKenyxiHNne6aKnlDgQCyMjIkA+dhzOX4FlZWdBqteju7pbwmYiICFmAx8Xdib/032VU0efQ2dkpSGoAaG1txYEDB6QSokKB8LOlpSWoVCox5rHCYlsdGRkpWvPIyDtpacq7CHHKFVlhJiYmStVJPlJBQYGw8j0ejyyfyXxyu93IyMgQiiYJmgDkUCT2eGZmRpAKO3bsQExMDM6cOYPXX38dpaWlaGxsRGlpKQwGg8hYx8fH0d/fD4/Hg8ceewwpKSlob2+HzWaDzWaDx+PB7t27Rf/OF3JtbQ1DQ0NieuJylcqa6OhodHV1icR2x44d0Gg0ePrpp3H69Gl0d3dj7969+OijjzAyMoIXXnhBRl+/+c1vMDg4iNHRUfziF7/AysoK9Ho9nnjiCSiVStkBMaRpY2NDwoA2NzclxdFqtUrRMDg4iP/9v/83Dhw4IOA6Jr9lZ2dLKuPo6Ki41ak22tjYkGeGsuSKigrxEwEQb8FW3LZarRavEMeiCwsLsFqtqKqqErf//Pw8ioqKsGvXLkxMTAgufnh4GC6XC01NTYiOjkZhYSHKy8vl3fXfjfp1u92YnZ2F0WjE9PQ0RkZGoNwSV8q9WDAYxNzcnHif6FWiEpCAQBZfGRkZQnvl/pCXAWWwlJ5zpJSWlib7IY4JAUhHoNfrxduyNVGPXTMACf7ie0VwKUd69LNkZmaKHJ8ClM7OTuTn54uLnVMUyn6B/zrcGUJEttbhw4dRWFiIqqoqvPLKK/jtb3+LXbt24emnn0ZBQYGYL7cmFDLB0O12Iz09HTabTRb2W0Utf+jPH31RZGRkCKumvr5edgc6nU7a0urqajQ1NcHn88Fut8PhcGD79u1yiE1PTyM9PV3cli+++CI0Gg3Gx8fxgx/8AP/wD/+AV199FefPn0drayucTqdgdfPy8tDZ2SlfztjYGPR6vQTZ9/X1YWxsDPX19RJ4c+LECSQlJeH8+fP48MMPce7cORk1fPWrX0VycjJSUlIQGRkJrVYrS3JSW9Vqtfz7rVarzCqTkpIkIYwKE86H/X6/sGmo4goEAhgYGBDDjMlkwve//33RV1OeyIper9eLioI8GWbtspUuKSmRg9tut0Ov10sgDSte+hbIqKcnZHp6WmbMRHLodDqBr6WkpKC4uBipqanIyMiAz+eTzovYdKPRKIH3qampMJlMUCqVCAaD6O7uFgPfAw88IO7Zvr4+fPzxx3j33XcxNzeHffv2oaSkRMCDt2/fRmxsLBISEqBWq7Fz507s3r0b4+PjIgGdnZ0VyipZPpubmxgaGkJOTg6am5sRGRmJvLw8xMXF4caNG3j22Wfxzjvv4Ec/+hGuXbsmpqn/+T//pzj2S0pKsLq6inPnzuGXv/wlqqurZY/03e9+F0ajEYcOHYLX68Xk5KQsjOPj42G327G2tibiAJ/PJ2rABx98EN3d3UhLS0N6erp02NXV1ZiYmJCslM7OTtnD0c/An62iC3KRpqamkJSUJHnd/f39sFgsgtmm3p+XFwucrVRb4qwBYGRkBJmZmdi9ezc2NzdlpGq321FRUYHt27cL5oY7M6fTKdBAGlYZMUwZOumrpPNyuRwTEyNoExpb6fng2Dc5ORnz8/MCjnQ6nUIgDgQCoirk5cCOgZc1Y0K5+Kfqjl04kRxzc3OyhOcieWJiQlRivHRKS0sRCATQ3d0teJWoqCi5yPr7+5GUlCTyeH7+ycnJIldlfO7Bgwdx9uxZvPvuu6isrMThw4cxNDQkeTeFhYXo6+vDfffdh2PHjsHv9+PatWsIBAJiJqSKjJMM7mmmpqagUqmks/xEM7N5Q9Ks4na7ERMTg7q6Orz55ptYXl7GhQsXMDQ0JDiKvLw8WZRthVwxqpIPSGRkJDIzM3Hx4kVRwVBGx8qIH3p8fDyKiorQ3t4umHOahCYnJ9He3g6tVnsPfJD4YR5aLpdLKuhXX30VR44cEVUWVQp07EZERGB1dVXkmysrK3IY01hIa7/BYJClMoB7AmoUCgUqKipQXV2N8vJyWCwWbGxsoK+vD62trfKCc+ZKbAKXnSkpKXJBkJtltVrR3NwMo9EokLulpSVUVlbKCM7n82FhYQFarVZSzZhpwdEV87e3b98uf16PxwO3242lpSWBKZI/Qy9BdHS0uNgJi+RM3+/3o7q6GiaTCbdv30ZPTw8GBgbEBKVSqVBSUoLk5GRkZ2eLao75EL/85S9RUFCAffv24cKFCzIio6+GJkm6nbnMY/IhRwSkhxqNRnEa19XVIRgM4saNG2LO8/l8aG9vR1NTk/w5H3/8cczMzMDhcMgIi6hzzpyHhoaQkZEh/o7MzEw4HA6Mj4/L7kOtVovfhmC73bt3o7u7G9/5zncQCoUQDofR0NCAgwcPYmBgQOJkV1ZW5IBkIUFkA581FhJcppKwzAOK2d3x8fHi/dnY2JBdEQ8cdsdmsxkjIyP4t3/7N4yOjuKBBx5AWloaXn31VQkmSkpKQnd3NzIyMsTlrdfrhT/EPQMX2ESX8NleXV3F1NSUgAH5DnOcyL0MLx3yy7xeLxISEpCZmSlCDpITZmZmcP36dRiNRjQ0NEClUonLmqqxwcFBqNVqSSDkv4seqeXlZVy8eBGXL19GdXU18vLy0N/fL6M6m80mSBruXklmyMvLE0RKKBSSy4FjTiJWLl++LPDTnJwcREVFobu7G2tra1hZWZE/w/Hjx2UEarfbZWzG8DB+jtHR0UIInp2dRXZ2ttgOOKb8RC8Kcm+IroiIiBAW09WrV3Hq1ClER0fj2LFjePrpp0Wxw/kZ+UjUm5tMJvT09EiA0E9+8hPk5+dLxU7FhcfjQX19vah2gsEgrl69CgCSIbFr1y4MDw9jaGgI+fn5Ip0cGRmR0UF+fr7o44eHh8VhHB8fj5ycHImfJK2ULd7IyIhILvmgx8TECDmV/x46pDlfDYVCUjGTdhsIBNDe3g7/3fwEOtXp1uQoheycsrIyaLVaDAwMQHk3+/bixYu4ffs2Dh48iMHBQUmo45ipo6NDOhoG/DD7l99bX1+fqKXy8/PFMETzGKWi1KdTVcM5cUxMDCwWC+rq6jA3Nyed4NLSEh599FHJbz537hxCoRBsNps83Hv27BExwtmzZzE5OYnHH38cpaWlMq8OhUJobW3FBx98gJmZGeTn54sRLy0tTWb1Wys7mjgHBgagUqnuUZwNDAygvLwcu3fvxsTEhCw719fXce7cOVgsFszNzaGurg4HDhzAhQsX8Jvf/EYgd11dXfJ9VlVVYXV1FS6XSw45ems4PiHyhv/c5XKhtLQU586dQzAYRE9PD+bm5sRlTaWaVqtFW1sb7Ha7mP/42fN7YAQolTSpqamS68ERDkemmZmZslCmco9QTirrFhYWMDk5KcvS8vJyTExMoKOjAysrKzCZTPB6vbh+/TpKSkpgNBphMBhw5MgRAXWSMgtAhBDx8fFCNyVAkBfa0tLSPYwz/nCuzkVsXFycGCCXlpYQDoelKOT7vLGxAZPJJH9uVtWkPNBjQoViTk4O7rvvPoRCIfh8Ppw8eRJRUVFobGzExMQE/H6/FKfcN4TDYczNzcm+iMt6vgvEgiclJcnnTLc1ANkxaLVaFBYWiiGV5IiLFy8K8pyID7VajdXVVSH16vV6OYvJV+OY3+VywWw2Iy4uDjabTZD79Cz9oT9/9EXhdrtRX1+Po0eP4siRI+jt7ZUEsH379uGpp57C66+/jsnJSRw9elRkjXTpLi0tibKnu7tbXMtarRZ2ux1VVVU4duwYPB4Pjh49ihs3bqC7u1tQE8zdJkqY/oaKigpRwDz//PO4cuUKCgoKUFJSIj6C2dlZuN1uzM3N4Y033sDi4iKee+451NTUQKFQwO12Y3FxEQ0NDUKj5UvPvABGFTL8iPGpDHah5I2SVJquXC6X7APUarXI7To6OuTfQTAfuf2Ug66vr2NzcxN2ux2Tk5Po6OhAZWUlPvroI8ki37lzJxwOB8bGxjA7OytY7MzMTHHEb9++HS0tLXIIc8cC3Ik55aGlVCpx4MAB3LhxQ0YpRMFTrcGYTi7cm5qa0NraKqlnXOy2tbXh9OnT6O3tRUxMDO677z4cPXoUgUAAXV1d6OnpwZtvvimfWW9vL27cuIGWlhZUV1cjNTUVTqcTly9fRm1trSSiAXdmvFqtVthO9BowtlSv1wua2WQyyWHZ398Po9EIpVIp2SokjtpsNqSkpGDnzp2IiorCyMgIXnvtNaH4UrmmVCqhVqsxNDQkuASqi/R6vYAea2trkZ2dLRJO4rs3NzfR3t6O119/HXFxcfjyl7+MT33qU+jv78fCwgJmZ2clDZEEU+Vd/Dt3U9zJjI6OIiMjQ5RC6+vr0m3xf7e6uirmO452XC6XIK5jY2NhNBoRExOD27dvw+PxoKqqCidOnBDZc2NjI8bHx/HVr34VKpUKBQUFaG5uRkREBFJSUmA0GgWxT2gfLzR2UfHx8dBoNBLPysvN6XRKJZ+amorExEQEAgEZpVBJyQuDY6nMzEwxJjqdTni9XuTk5EjKW319PZaWlmR3w8kG95wpKSno6+vDr3/9a1y/fh3Z2dmoqKiQCOba2lpRBwYCARn18T1lUJXD4ZAuv729HRqNRjo4Zth0d3cLGLWhoUGeoYSEBFRVVcHv98uodH19HW63W+gLZOtx2kCpMJ9tp9OJpaUlVFdXY3x8HPHx8SgsLEQ4HJZz5RONQtVoNACAqakp3L59G+FwGE1NTbh06RKMRiP+9E//FF//+tdlAeR0OhEMBsUdmpSUBKvVKmMfAro4A+zs7JSdxO7du+H3+/Hmm28iOTkZjz/+uIynSCFlx/DBBx9gdHQUe/fuRXl5OZaWljAxMQG73S7y1rGxMeTm5gpzKjs7G8nJyejr65MktrGxMQQCAVRVVclClPwl5mjT1EJlCeXCRqNRuDtzc3OoqakRR+Xc3Jy8IFxEpaenIzo6GiaTSaz4DodDlpcxMTEA7iixiClfXFyUxd22bdvQ1taGxx57DOXl5bh+/Tq6u7tx8uRJlJSUiBKEWBJ2K3V1dbLX4Ay2rq4Oo6OjaGpqkrY5MzNTYmip+KCmnRUoL7QLFy7A7XbjhRdewJe//GWh3NLparFY8OCDD4rPoKamBunp6ZienkZfXx8A4LHHHoNSqcQ3v/lNrKyswOFwIDMzE48++qgE7mwN16Gcmo5Xhu1QVMA/I5HeFD9kZ2fD7/fLHJxufSqDwuEw2tvbYTab0djYiMnJSSQkJKC2tha1tbXyHbtcLlleEyWv0+mks/F6vcjIyBDTGXAnjzkjIwO1tbV4+OGH8Q//8A/yHRAiNzo6KlhohUIhkDiSUSMiImQBPjw8LCqi9fV1eL1eaLVa5Ofno7OzU0yYGRkZACBV+tbRDw2D6enpMJvNuHjxIr7//e9jdHQUDz/8MMLhMAYGBnDt2jXU1dUhJycH/ruhRNeuXZNCbMeOHUhPT8fQ0BAqKyuh0+lgt9uRmJgojLFgMCjeH6q+WDVTBkv2EUe6lMWya/L5fAKZ1Gg0yMvLQzgcxm9/+1sZbTJToqWlBQCg1+sleIzplZcvX8bNmzdFrVVfX4+srCwoFAqsr6/jc5/7HDIyMjA0NITOzk5RDDLJb21tDSqVSip7ImjS0tLk7yclJSExMRGjo6NiKlxdXZXPbW5uDtPT0xgeHpZxJLvAmJgYHDx4UHxSJHezKNpqJmSXSeQQO0aKj7hP+sQuCjJxlEol+vv7MTIyIg84TXcHDx6U6vv8+fMSx8eRCo0+pEkuLi7C7XYjIiJCUMLMXH7mmWfw/vvvC62SiGKmZDGjYOfOnZiYmEAwGMRbb70l0tD33ntPZHr19fVCx6T6YWhoSHT6bDNZdXFGnZaWJn/NzOi8vDy5uVnlEf2QkZEBu92Ozs5OURukp6dLoAujIXNycpCYmCgyQcrzOGaIj49HUlKSVEeUB/KlIu321KlTuHbtGj7zmc/g1q1biImJwd69e4WtwzkuaaE6nQ7t7e3Cgerv70d3dzeqq6vxwQcfiJyXyAb+GZ1OJ9LS0pCdnS05AU6nExMTE2IAY8SmQqGA0WhEeXm5dCKDg4Po6+tDTEwMjh07hs3NTVRVVUGv1yM+Ph67du1CREQEnnvuOcTFxQnhlYdCVFSUsIiYU84LbXh4WPwtvFAJIeTvxFEUZ9zd3d0oKCgQGi09MF6vF9PT0zJLPn78uCA7GMnKYomYaI7vOB4iu4ejRyIsqNFnlXvo0CEJ1+rv78eNGzdQUVEhpAFmH/MQpRqIKjiOdJRKJfx3YzZTUlKQlJQkYEhGu9I3RGozVTdkJvn9foTDYRw9ehRNTU04deqUgCopM3a5XJienpbfkdThiooKrK6u4j//8z8xOTkJr9eLp556SnJZuGsgwI8mNu483W63+KKIkeczl5KSgvHxcQFAbsV+nzhxAlarFbdu3cLMzAysVitiYmKwa9cudHV14Re/+AX27NmDp59+Gj/84Q8lend8fBxWqxVnzpzBN77xDTz66KMC13vnnXdgtVphNBphtVqxY8cOkSCzmGDBlpubK6FTPHNIJyBmhIhv7rDW1tYktIyZFCw4ecZ4PB7xS1Hxxe+VHSyVWFQB8qKiIZFFDIvP/xfWU8Tm/4tGasvP/Pw8UlNT8dJLL0GtVotj8a233oLJZILFYhGtd39/P9xuN3bs2IGkpCRcvXpVZG0EYbndbuzevRvnz5/HtWvXEA6HUVNTg9raWjz99NMYGxsT2R0AYfKsrKzg0qVLMooaHR2FxWKRGWVTUxOuXbuG5557DgCwtLSEhoYG0dg3NTUhFArhtddeQ1ZWFj796U8LlZauT6qGqE6iPp1mNzKbtrKpGCRDhzPn8MQTA4DJZILD4RBoHDXvNAt6PJ578pLD4bAwjsjayc/Px+rqKjo7O1FTU4ONjQ389Kc/xfj4OD7zmc/IA/uXf/mXuHDhgsgC2W4HAgFcvnwZIyMjSElJQXl5OQKBgPgvGhsb8fzzz8uSktVkMBgUpRXVOgsLC+jo6EB7ezsCgQBqampQUlKC7u5uXLt2DQ8++CAaGhrwzjvvCOY7EAjAYrFIJnphYaEExvPlIOXT7XYDgKhoCDbjf/IZ5Gyfc+6FhQXJKbbZbMjKysLY2JhkXvOl2Ur1pXAhMzMTgUBAPA2M9ayqqsLIyAisVqsgtAl7Y7W+9d/F54IHitFolFFJKBSSsSbhiuRGkdvkdrsluXFzc1MS2lJSUjA6OoqEhAQRSVBcwR0WF8FFRUVIS0vD6OgoCgoKEAgEYLPZUFBQIJhujkemp6dx8OBBoaWePn0aTqcT3/rWt1BeXo6ZmRnxEdE/wAV/amoqjEYjLly4gGvXruG+++4T/tLhw4ehVCqFUkCVHDtC+hOIzU5OTpaZvkqlkhEc3wFmuhQUFIiq7a233sJrr72GxsZGHDt2DLdu3cK5c+cQHR0Nh8OBw4cP47e//S3OnDkjsFGqEN944w0ZDUZHR6O2thbt7e24dOkSzGYzcnJysGvXLok+oP+E2BTKVdml2Gw24bQxWIreKKI1FAoFRkZGMD8/jwcffFBG1i6XCzExMVCr1Whvb5eIYV5a3K0wapoUW3pbAMio0ev1imKO0l2Hw4Ef/vCHCAQC95hz///9/LfkUahUKigUChgMBuEWMZOZgSMffPABUlNTceTIEVRWVsrDQY4MJbYpKSkoKipCf3+/3N5RUVEYHx/HK6+8goWFBezZswe1tbXo7+8XxY7P5xOtP1UUrO7uu+8+gfLNzc3JCOfkyZNISkpCTU0NPvWpT2F4eBhXrlzBwYMHUV9fL7Jbr9cLo9GI0dFRREREQKFQCHVzcXFR5vo7duyA3W7HyMiIZDFoNBoMDg4KoprZC0RKMM1seXkZ/f390Gg0snBeWFgQRc3WKo7jNeJMqqqq7tGMc5HPh7SmpgaJiYkYHh6GwWBAMBjE8vIydDqdVJibm5s4ePCg+DAiIiIkspQkyo6ODhmVpKamStucm5srklTmDxw/fhy7du3C+++/L10Dq5zFxUVotVocOnRIwmo4brl06RJcLhfW19cxNDQkZseYmBhoNBpZfpNTw8Ag4sunp6fh9/tRVVWFyclJoW/Gx8dLVwFAXmia+PLz88W5yyqNcbWc63JOPj8/j6amJphMJpjNZoEqEvPC8Qm7QrrVg8EgdDqddJFRUVGYmpoST9Ds7Kwk4vGvCRHcSgHOyclBVlYW/H6/cK7YnbIDSktLE0Uhu/e1tTVYrVYh3DLDg0VMUlKSBBRRzcNxJc2SZWVlcrgR7EkVYUVFBZxOpxQdMzMzqKmpwX333YehoSF4PB5otVoxuyUnJyMYDIoaKz4+XiSr27Ztky6eBypHhnFxcXIRc5wzOzsLj8eD3/3ud4Lzfv7553HixAl531dXV3H9+nX5v5OTkyUorKioCPn5+cjMzMS3v/1t2Gw2zM3N4Utf+pJIgB0OB7KyspCZmYkbN24IdjwxMRELCwsIhULo6uqCQqGAWq1GRkYGduzYIfuf+fl5uQTp6t5Kw05JScHk5CQUCoUsyRnqxVGzSqVCdXU1FhcXJR62tLRULmy9Xi+JoywwWEAwE2ZjY0POwz/054++KNbX16XNbWhoENclYyyLiorQ19eHmpoa1NfXS742VUHU9kdFRaGnpwfhcBgPPvggnn32WTQ1NWFwcBA//vGP0dXVJRps+hfoLuSyjBr6iYkJyc6trq6Gz+dDTk4OhoeHsW3bNqjVarS2tuLatWvYv38/PB4P1tbWoNPp0N3dLVjwrThlt9uNlZUV2SsQQ15XVwfgTrXf1dUFr9eL3bt3o6ysTBZgm5ubWFlZEakkq1Xa6ZlU99FHH6G2tlbwFJGRkXjkkUdk2X316lWoVCqhdTIInilr165dk10EibhFRUUoKSnB4OCgUERnZ2dRWloqksmkpCQ8+eSTePTRR3Hq1CkcPHhQ9OmXLl1CV1cXRkdH4XA4UF1djcOHD8voICEhAXa7Xarm4uJiWCwWbNu2TUixBw8exPHjx9HU1ITe3l6h/waDQbjdbql6Z2dn5cB2u90oLy9HREQERkZGhE9DcQAPZB7Qa2trEirEAz09PV2iSSk0oCySslTuabZGdlJ2yZ0KjaAdHR1IS0tDRUUFWltbBZVA2SOhdCQPUwYZDoflAOZF53Q6Rdih1+slmIu/B9lWHF9SHk1iKqtWFkXx8fHQ6/VYWVmR331tbQ1ZWVmS3UJl2MTEhMy4iWmhAXZ5eVnor8PDwxI7y/yGzc1N3L59W0KnJiYmhIR69epVxMbGory8HC6XS/48lIRyv0Dlm8/nkw6N9FXKejmT9/v98nnwd15dvZOrzYAjepfm5ubg9Xrx2c9+Fna7Ha2trejr60N/fz8cDgcKCwtx4sQJ3LhxA7/5zW/kHcjLy0NrayuMRiOSk5MFj89xjs1mQ09PDz71qU/J2cMLbms4Ffde9A+tr6/DZDIhPT1d3Okulws9PT0YGxsTEQGzy3nZ0GtERRi7A3aGVIiR2UWxw/T0NJKSklBRUSGjdBKbA4GAGE8TEhJELfeJXRTE51INREWQxWKBy+XCd7/7XZF6trW1IScnBwUFBRKaMzU1hWPHjmF2dvae3Am6Zv/iL/4CoVAIRqMRlZWVqK2tRWdnJ+bn57F7924Z83BxV1paCqfTibGxMTQ2NmJqagq5ubm4desW5ubmsHPnTni9Xty8eRP79+9HXV0dLl++DK/XiwMHDuD++++XhTZBYjQHkfJIFyh3A8vLy7h8+TKam5tRV1cn8tUrV67g448/hsFgkBEN8ww4flpcXJToSV6mhBRy4cTM25ycHFFFUH5YWFgojK2CggJkZmbioYcewtraGkKhEH7+85/D5XIJ2sDr9SIvL0/aZgCCG/7oo49w4cIFvPbaa3j++edhMBhQXl4Ou90urKGamhqUlZWJwoMGPubwxsfHo66uTnTeHR0dMBgMePzxx9HU1ISSkhKUlJSgvLwcDocDDocDABAKhRARESHpeZTj0r3L2SvHj5S9cvZLmWRqaqrgHjgK2tzclBwBVtokvnL/RDyDxWKR51KhUCArK0ue7fz8fIyMjEChUCA3NxfDw8OSvWA0GtHb24u4uDjcvn1b8q6dTidiYmJEp8/0RrVaLdU8OyECCLkT4Q6COBRygxjYQy+N1+tFbGwsUlNTcf36dXEKNzY2Ct47KytLRBNVVVWYmZkR2abdbpcsl8jISFEhhsNhaDQa2cfNzMygr69PXMVcyM/Pz+P27dvo7OzE5z//eRgMBoyPjwvSJi4uDjqdDkqlEk1NTRgYGIDFYpEccTrEvV6vcMHIpNpqhmMxQkFATEwMZmdn5bDNz8/H+vq6dMh+vx+Dg4MIh8NYWVnBe++9h5qaGlRXV4tEl2PejY0NdHd3o7CwEAcPHsTm5iba2tpw9epV/PznPxeqsc1mQ0dHB7KyshAMBtHW1oY9e/Zgc3MTcXFxEjTFz6OpqQlOpxOhUAj33XcfVldXMT8/j7y8PMnQIaONkwXmepC4sL6+LkUh908A5LnhfrSlpQUqlQpDQ0MidgiFQvjLv/xLRERE4P/+3/8reSNLS0sYGBj45C4KLnM2NjYwMzOD3t5euFwuNDQ04PLly0IjdTgcGB4eRlNTEw4fPoyCggIZD83PzyMlJQVdXV1YWFjA5cuX0d7eDuDORfTMM8+goKAAw8PDGBwcxMzMDCwWi7hgKU9MS0uTRXpBQYE4D5nHzYX78PCwjECYZcAWMicnB1qtFmfPnpWHbXZ2Fpubm8Kk4lKTYezkVVVWVsLtduP06dNQKBT49a9/DavVCoPBgMXFRSwvLyMnJweTk5MSrUpJ2759+xAfHy8vUXl5Oa5du4a3334bWq0WxcXFOHjwIPr7+4XnRB7S0tISysrKBCNAn8TKygrOnTuHubk5GfcplUosLi7KIaVUKtHY2IiNjQ188MEH6OzsBHCHa3Pw4EHxm5Cym5+fL8vJpKQkmREz7wKAVIL79+9HfHw85ubmcP36dURHRyM9PR0HDx5EU1OTUDKBO9gBVlPsUJgnnJiYKB4ShUIh6HMuQwldZOXNdp6/y9zcHLKzszE5OSn5yMzYKCoqkn9eUFCAgYEB6HQ6cd8DkHxnjggIxuPLSwLA5uamHGCsIEtLS4VsTKQJK3N2Qn6/H3FxcSIbZ2b5zMwMUlJSJHNDqVQiJiZGInXZkQOQw/HAgQPo7+8HAJGJMv0RuFOV0iXOhDoavPisM9eBqBaKBaampmAwGESFw2U/8StZWVmSqdHV1YXIyEgRE3R3d4s/YdeuXVAqlZidnUVlZSXi4+Pl3Wf1zYIsLS0Ns7OzskMiHtvhcAhLiX6j5ORkWCwWyaC3Wq1y/vzyl7/E6uoqqqqqUF9fj8TERBFC8P1PTk6W502n02HXrl1QqVTIzc2VnSp/H4Irq6urRQxy4cIFPPPMMwiFQnj//ffxr//6ryJYMJvNMBgMKCsrg0qlwuLioqjZmPvCz1ShUEi3S/MqIZ5paWky9u3q6pJYgtzcXDz00ENobm6G2+3Grl27cPHiRRmH1tXViXLz1q1bUKvVMs76Q37+W4KLSHpcXFzE008/jZGREZSVlSEQCOCHP/whvvKVr8BiseDMmTNSAZB+6vF48NZbb8nSpaSkBM888wzcbjd+//vf4zvf+Q527tyJ4eFh/J//83+QkZGBF198Udj45JZotVo4nU6ZD66traG7uxsmkwkLCwsSVKJWq5Geno709HSEQiG8/fbbiIqKwp/92Z+JW5dehuTkZBlLEfHBcHseZPn5+RgYGEBycjIOHTqEhIQE/PCHP8S5c+eEGXPx4kV88YtflLwILkWJ/k1JSUFeXh5sNhvOnz8Pj8eD4uJiadV9Ph++973vYceOHSI7pPmHI4n7779fVBs3btzAP/3TP8mylJhylUqFqakpzM7OYm1tTRyjJSUliIuLw9e//nU88sgjaG5uRmlpqVSBGo0Gu3btgsPhkPxwLsKJK9kaHXrt2jUsLi6ipqYGKSkpOHnyJD766CNYLBYEAgGcOXMG/f390qZHRUUhKysLs7OzQiNWq9VQKpVCqh0aGsL4+DhycnKQkJAgcmH6ByhPpYqHeRxMJSRLaGlpSeil6enpyMjIgEajgU6ng81mk/EXTW7MF2blFgwG5cKZnp6Ww3ZxcRFms1mcxoQjZmdnSzFDH8Ha2hoWFhaQk5MjY1jgjsCACXt+v1+YP8yEZ4Y1L2kKEhYWFoR5xqXv+vq6KMS2urHX1taQm5srKhgiwu12O6KioiTjnLiUrYBAv98Pm80my392qm63W5L+Ll26hMzMTJw4cQKrq6vo6OiQHcnU1BTy8/Ol625raxNa9JUrV1BcXIyoqCgUFhaiu7tb8sIpB46IiMDs7KzQapmcSEgm34WhoSFEREQgPj4e2dnZYobs6emRJEjusLiIphn2woULYq5cWlpCd3c3Hn30UTidTvzLv/yL0BM2Nzclp4K70PHxcUxOTuJHP/oRfvGLXyAUCqGyslLYdnv27JGO1u12Q6vVSrAXxSMsuFmMFBUVYWxsTPxBer0ebW1tiIuLk85+fX0dZWVloj6cn59HQ0MDent7MTw8jDfeeEP2dmlpadBoNFL4fGIXBV8SOkhZudhsNhw5ckRS4j73uc+hoqICZ8+eFWXG5uYmHnnkEYyMjKC9vR0rKyvIy8vD5cuXYbPZhHT6z//8zzh9+jTMZjMef/xxlJeXY2RkBIODg1hZWUFRURHm5+dFQjozM4OqqioMDg6K1JazUy5wHn30UVmiHjp0CKFQCIFAQCI6+UBWVVVJ17G5uSl5yozsLCgowPnz5/Haa68hNjYWhw4dEg3/fffdh+LiYtmRGI1GnD9/XmbT8/PzknHNDOLKykqJOD19+jSSk5Mlu6C1tRVjY2PweDzIzMwUtUZ0dDR6e3uRlpaG6OhouFwuAe4tLy8LyqKrq0uCUmJiYjA9PS07gIWFBezcuRNVVVXIycmRKpnLV+4b2trasL6+Dp1OB61WK8szjlC4dGT05ebmJurq6gTeR818ZWUlpqenRcmxvr6OkpISGWNZrVbxZrDa5iKU1SBlqKxwOc64cuWKVIdpaWnYvn27KLGon+f/Zm5uDrW1tXA4HDh58qR0v9u2bbuH6gkAt27dElIv0Q4UcjCVjB6g2NhYSRnkaFapVErHwkUquwmOkIh2YdANabjr6+soLCyE1WoVXf7U1JQIGzweDyYnJwUTQ87Q+Pi4sLroZPf5fAgEAsjPzxeXNh33vb290vEQJMnlL/dnhGL6/X7k5OQgLS0Np0+fRlNTExYWFvClL30JFosFf/d3f4e1tTW88sorknW9tLQkjm5e/qmpqRgfHxcGWWRkJDIyMtDS0oL09HQxffKwJKqC+RA06pFdZjKZMDExIbuPmzdvorq6Gg888AD27NmDs2fPCvGYEmGqh1JSUoSMQOgncCeMiWY7Ko+YUb+2tga73Y7Pfe5zqKqqws9//nPJTielen5+Hj/5yU9w7NgxGakdOXIE169fF39LfHw8srKyEAqF4HA4xFxIoQYAOBwOeUeYFKnVajE4OIiuri5cv34dfX19EuxElVdubi5cLhfUarWMiT/RjiImJka+TL/fj5WVFdhsNtFKFxcXS0LdzZs30dTUhOrqatTU1CA6OhrDw8OC5FWr1fjd734nTKekpCScPXtWDtpdu3bhiSeewOzsLG7fvo3U1FTMzc3BbrcLlI+V+uDgIOLj48Xu7vf7ZfHrdDoF67B9+3akpaWJemRjY0PmiOQYRURESGb00tISSktLRdVCs11cXBza2tpw5MgRPPbYY3C5XHjyyScxOjoq4LqRkRHMzs7ek9JVUFAgi8Ty8nK5BPPz87GysiKV1ec+9zlERETglVdewfj4OOrr65GUlHRPdq/FYoHBYBCp4erqqrTZ5OOnpKSI6iUtLU2gcBsbG3j77bdhMpmE/z8xMSGHCncUeXl5cLvdAl2j6mRhYeGevQHlgUwg3LZtm7iEl5aWYLVaRd21uLiIYDAI/10oJC/99PR0UXBxlhwOhxEIBKSCTE5OhkajgcViEVRJZmYmEhMTBSxntVqh0+ng8/kAQCruhIQE6PV6mM1mvPbaa2hvb0diYiLy8vKEatza2oqcnBw88cQTGBkZkTxsHuhpaWkio3W5XNJFcifE8JzV1VXY7XbZm2i1WukCVCqVQN04yqI/hw5vdjDckyUnJ2NmZgb+u6hs6vOzsrIEo8/Km/s7csAY6zo7Oyv4GTp/ecGurKxIqiEP0IGBAcTGxmJ4eFg6uqSkJExMTMBms0GlUqG2thYGgwFWqxVxcXHIzc2VmGGCAz/66CMJsGpsbERNTQ1+9atficcqPz8fqampSE9PFxXj9PS0QDGpeGOSo16vR1FRkcSI+nw+rK2t4dlnn8Xc3Bxu376Ns2fPIiUlBSaTCSqVCtHR0UKF5kVOnwn9CDyMORJNTEyE3W6H1WpFaWmpXBQ9PT0YHByU8XRkZCQKCwuxsLCACxcuYHBwUPAmGo0GZ8+eRUFBAQYHB1FcXIxgMChyXMIlExMTYTAYBI1DOCUji9l1csdG1Scl61qtFi6XSwrQzs5OoS8UFxejp6fn/+mc/28ZPVFDTD14fHw8gsEgTp8+LcFAxDZrtVp8+tOflvZrYmIC6enposw5duwYSkpK4Ha7kZSUhObmZqysrIgssbW1FePj4+JhsFgssiyPiYmRw4wab8Zmku3CMRkdopSC+nw+WCwWOUC2slUSExMRDodhs9mkMmIIjcFggNlsxssvv4zBwUEZwTU2NiIYDMLr9eLjjz8WHDiXk3FxcYI25iG7traGlpYWtLa24u///u+FrPrqq68iKysLDzzwADIzM9Ha2irobo5pWlpaUFxcjOrqahw5cgR1dXVITU2VReDa2ppcdFzAUmHDjOfU1FTEx8djZGREjIyUw25dLNMnMDU1JVjv4uJiQQqQCAzc2T1sbGxgeHgY6+vrgpdm17a5uYnh4WFcvXoVRUVFcLvdMJvNUKvVUCgUsNvtaGhogMfjQUZGhizyiGwIh8MSCkWpJiv8K1euQKPRIDIyUgxZXq8XGo1GUr80Gg3eeust3LhxAyqVSg47Si937twpWHby++kK5piLzzsLFwCCfGF1TIdtRESELCnn5+exsLAgY6mZmRmYzWaoVCpRxnGvkZOTA+BOp0CDFQ/txMREZGdni8qLUZcULJAfRmxDKBSSJbvFYoHX6xWvCRPi8vPzkZ+fj+HhYclqoZiCnoiEhAQ4nU5cuXIF3d3dqKyshEajQXd3N2ZmZpCbm4vHHntMZKGMHoiMjMTIyAg8Hg8WFhawtLSEL3zhC7Db7VhZWYFSqRT3O/ccarValtJEk7D6X1xcxPvvvy/v9NTUFLZv3w6n04nr16/jpz/9KQDgxRdfvMfJzrET9zyUpQ4NDclex+/3i0+KhQbT72JiYmA2m6HRaIS6QL+KxWKRGIT09HScPn1aFvSxsbESvUr3dSgUkkzvnJwcLC8vw263o7S0VPAgxLHzwqAizuv1orKyErOzs2hubsbTTz+NhoYGnDlzBm63G+Pj49KZcLdK1MgffMb/MRcEABk5EJExPT2N/v5+HDt2TLwCZNUzXyE9PR2jo6OIjIxEWlqa6NRLS0vlJd29ezfm5+dx48YNAMC2bdswMzODX//617h06RJqampQV1cn7BiFQiGVY3d3t+RYx8fHw2KxID09XaibrFq5cKRzmAtmSg6pIQcgi0c+sBxxdXd3Y9euXSguLsbq6iq+9a1vYX19HceOHcPNmzfR3NwsznKdTif8Gjos+dCSa5+YmIjy8nJZQms0GuTk5ODUqVPQ6/V46aWXsLq6ip6eHpH78uWiUXHv3r1S0Xd3d8sDwpluIBCQOFF6Jdjy0/QEQECBfKi4OGZXRlQDdf3cObC1ZgXNWNCsrCzxscTGxsJisSAjIwNWq1UWfGtraygsLMSuXbswNDQk33tiYiJ6e3uF1RUZGSmh8T6fD9HR0bhw4QIiIiKkaykuLhboIgN6gDsX3OjoKPbt24eYmBiZlWdnZ+Nv//Zv4XA48Prrr8slMTMzg5/97GdITEwUtUthYaFgxKlesdvtMJvNYhqLjIwUsmcwGAQA7NmzR9DxlCnywOAoLCcnBzdv3hSXbzgclsqWwgxCN5V3o0XZvfAy4qji9u3b0Gq1iIyMhEKhwNLSEtLT08WQSDwGw3eYvkeq8NmzZzExMSEyc7PZLBh8VvmFhYWoqKiQsejs7CwaGxuliJuZmRHfSl9fHywWC2pra1FYWIjp6Wn8+Mc/xte//nXpAB0Oh0i5FxYWRArLNLrY2FjBZXNs1NPTgw8//BD19fUiMqBJ0+12C+6eWRQU4VRXV0veis1mg8FgQE1NDfx3kwMpM56dnYVSqRSnNfdKLpcLJpMJaWlpsNvtWFhYQHV1tZh1U1NTUVtbC6vViuvXr+Ptt9/GzZs3oVarRS3FHQ5ZcNwfcAzKH+4Lg8EgxsfHZQozODiI8vJylJWVISkpCW1tbRIzXV1djc985jOy02XxykL4D/35b7kodDqd6MYrKipQVlaGqKgo5ObmoqSkRKqe/Px8dHd3o6+vD1arVRaV5PgcPnwYbrcbr7/+OtbW1vDpT38aer0eWq0W5eXl6OjowMmTJ5Gbm4vDhw9LpcQlXWZmpjxgRHlYrVaRBZLkyFxcjnwoveTFsrq6KvJVYg3o+FWpVOjt7RXXOXEbOTk5snxVqVQYHBxEd3c3Wltb8fnPfx7Hjx9Hf3+/+BiolNpKOI2NjcUTTzyBlZUV/OxnP8PU1JQslHt6evDuu+/K7iMtLU1ovZxl88Flalhqaqrwrxgks76+jvX1dXFmZmVlSQWl0+ngcDigUqmka2IHxRkwOxLGsBJTQR4UOxRWp6y+U1JSBFWxtrYmTC9mpj/44IOw2Wzo7u6WsHjOozmm4Vze6/XK4cGApoSEBMlpHhgYgMvlQn19vah5xsbGkJKSIiYnzmodDofQPvV6veQ+VFRUwOVyQalUorm5Gevr63j88cfFJJednS0yzsjISCQkJEgeABlARExzhMEqlrkP9OJwz0K4IvHbsbGx8Hg8snRk8BI9ITwki4uL0dbWBq1Wi+HhYVFE8X9D7TwX6Vz+83vkoUakPpMfr1y5gq6uLmRnZ+PIkSP44IMP8M477+DJJ5/ExsaGuPyrqqpEtQMApaWlKC8vx/z8PAYHB2XfZTAY7ikutm3bhl/96ldS9Xo8HkRGRmLHjh2CpuezC0AKy7S0NFFr8f1h4cC8D4VCgZ6eHhw6dAhDQ0MIh8OoqKjAlStXcO3aNezZswd79+6VnUBaWprEHVP2y7+/tLSE8vJyqej5u0RHRyMjI0P8VFFRUbh58ybi4+MxNTWFc+fOYXR0FJ/73Ofg8XjuMaaSZM1UTzLU6GXhODsjIwMDAwOiyqIgJy8vTwx1NCAyn6Onpwc1NTXYt2+f7Aw5jrTZbDJm/0QT7ijv423NFCqbzSZz6M3NTUxNTUmYDM0xSqVSqs+EhASMjY0hJiYGFy5cgMfjQVNTExobG1FQUCBMm4qKCpw4cQI1NTXo6OiQeTaZ/6zSmIKWl5cn/BQ6Shl4k5WVJdp/un+3vjAcCW1VU01PTyM/P1/c1qwySIj89re/LYY9UmDJklepVFLpTk5OCg6Ytz/jTEdHR7G4uIji4mKUl5ffw+Wh3p8HBCmrUVFRQj9ldcrxwOzsrBhsuA+iWoJKFB5UlLtqNBq4XC45zEiFJSaAB2IoFBLJJH0QS0tLgl3g+GJ1dRVJSUmSOUHs9bvvvouSkhKUlpYKe4nhUxz58cXNzc2F1+tFdXW1GKIqKyvR09ODK1euiHkNuLM7o2SY0krgjp9gdHQUeXl5IqdNTExEYWEhdu7ciXA4LL4EpVIJj8eDmpoaKXqIs+fIaHp6GtnZ2ZKtQlYRoYDAHVNqXFwcjEYj3G637FdIBeXoiSRldrjsUrKysmT8kZ6ejqKiIly9ehU3btxAcnKyZBK8//77KCgoEIlwTEwMamtrBXTIAoqYeGI7UlJSBIVfVlYmz2ZsbCweeOAB6fZoRtNoNBIQBNy5jDghYME0MDCA/v5+QaRERESgsLAQWVlZuHTp0j35DY888ggSExNx9uxZWK1W2O12PPvsszAYDELGJa6CRN+tYVzp6enYsWMHvvKVr8BkMuHMmTP43ve+JxEEFRUV8Pv9uHr1Kt544w0ZI4ZCIVy9ehUulwvl5eU4cOAAJicnMTIyItU29yE9PT0iwSe2h/HHzEShMpKO8rW1NTzwwAOCfv+zP/szObeUd/M/bty4IaZl8tdolCOkk+87x+jEhyQnJ8NkMkk2CR3czCzPyclBdXU1+vr6MDs7K/spEmw/0czshYUFUe1wxtbf3y8qIbp/AdxzEBE1wcxnspLy8vJQWVmJyspKPPzww5iamsLg4CBKSkpQUVGBuro6lJaWSpZvKBRCWVkZenp6sLS0JEtaAriIUCALhWanpKQkaLVazM/P34Ne4H+HhiseVqxc+QIr7ybYUa3AsQyxHREREdi7d6+Mp4hi4KKZCA1qqSsrK2WRlZSUhOLiYoTDYbz++uuoqKjAo48+irW1NTmwKf+lpC49PV1GeFvdnAT3EcbG/y0RCvxrLsmUSiWmp6cxOTkpM1cAcvkToMfluMlkksqWLzHhjsvLy3IpA3dIqcnJyUL7LSoqwtGjR6U6b2trw8bGBp566inBkttsNunqaCqjqTMyMlKc+h988IH4cnQ6HR5//HHk5eXB6XTKrJ57MS4yt+5qDhw4AI1Gg9deew23b99GSUkJnE4nDh06JNr81tZWAddxiZyYmAi1Wi1eIv9dEB8xGqwKo6OjJZ+Bh8/S0hK0Wq1wf6hE40hhfX1dnNUMwaLcl3uA9PR0BINBGTsxJyE9PV2+c3b94+PjUtCFw2FsbGzA6XTKiMNkMqG5uVlGsHz+h4eHsbi4iJaWFunKNjc3kZWVJfiPrdXp+Pg4pqenUVRUJIRUOrFnZ2fR1NQk7yTHrkThx8TESNqbVquVXSCX/sAdYyq9DMAdlEdJSQksFgvOnj2LpqYmJCYm4t1330VBQQE+97nPYX19He+99x4UCgX+5E/+BFFRUbh8+TKWlpZw9epVjI6OwmazISEhATt27AAA6SBoSM3Ly8PQ0JB87rm5uXLeOBwOmQpcvnwZwWAQBw8exGOPPSbxAcziUCqVkm7JC9tgMMhlTSglUwGpUlpYWEAgEJBinAFqVFeRqZefny/vG82jExMTcqnwuZ+amvqDz/k/+qLgQUVsOFEBRC7QIRwTEyMjBMYbUhfMPQfJoocPHwZwJ4jjjTfeAPBfxj4aUBg6v76+ju7ubsFYAHeMRlwMkqxpsVgwMzMjHg7Oj7ksY4XLOFVmAdOiz0N4Y2NDTEjR0dFi1pqbmxP2FCGJOp0O999/vxykbIE1Go0kyXGpS/UJW0yFQiFsori4ONTX1+PKlSuSiQBA2D+Ep0VHR8shCABlZWUYHR2V9DiGq5B2SrYW8emk1RYUFGB8fFzGFcwoJvef1Qh/d362rKhYqXCkk5CQIJjv+Ph4Me2RPhofHw+Hw4G5uTk899xzKC4ulqXb9PS0LJ+ZUeD1eqHX6xEbG4tTp07h1KlTKCoqwpNPPom33noLKSkpKCwslEuiqKgIUVFRmJ6elqqcBzPHFAcPHoTNZpPMjfr6elRXV8NgMEj7zuVnVVWVyCsZcs/vj2wupVIpB+HY2JiA8ygL12g08Pv9osAjBysrK0syGijB5MgtOztbsskLCwuxfft24aRFRESI4mx1dRVdXV3iJSksLBSFHjvBmJgYqFQq6Szr6+tlLEmT3y9/+UuMjY1JkBMBk+Pj4yIuSU5OxuDgoHyOVBFRwcPlvd1uR0xMDHbu3InGxkYJYaJB1O/347nnnkMwGMShQ4fg9/vl3xkZGQmdTicVPIuV4uJixMfH4+2330ZcXBw+/PBD/Md//AfUajX+9E//FJcuXcLVq1fxk5/8BCdOnEBDQwPq6upgMpmgUChw9epVuQjGxsZkhFRaWoqUlBTcunVLxlyU9DLamBBEfi4Gg0HeCXavO3fuRHFxMZxOJ3bv3o25uTm8//778vmz6yBFloq6lZUVqFQqqfxZGG2Nq6U0nPvhQCCAjY0N1NXVSR4ORQNLS0swGo0SCgXcGZ19onkU1BnTARsTEyOxj0yT6u/vl1xZHuakwfKXLSgoQG9vL9LT09He3g6bzSYv+qOPPioIECo6Dh48CLfbLWMQRh9GRUXJnJUuV41Ggw8++ABFRUUC9ONDTkMVLzQAAjJcXl6Gx+MRFPHKygp0Oh2cTidyc3NlJELN/8rKiuRFU45LnTf5QazcecDT+s9lObN8fT4fMjIysH//fqyuruKNN96QF99kMsHtdksWRGxsLHp7e5GQkIDc3Fx5Mcmk2foy0qnLEQtR5QkJCfLfXVxclGqJCW0zMzOSx0v0RzAYxObmptBLOzo6xACXmZkJm80mcZMqlQo2m02q77i4OPzud7/D0NAQTCYToqOjYTabkZKSggsXLkCn04lhirNZKj02NjZQXFyM5uZm/NM//RMmJydF+cQs8/Pnz+P111/H3r17YTQahYXEw9LlcqGurg52ux3d3d0CW2Q+x/bt2xEKhTAwMCBdF3BH8UIpL1lAdDFPT0/DbDYjNjZWihJernymtmJPIiMjpWDgyJPLVprBkpOTxXhH81taWhqOHTuG8+fPi9pQpVKhs7MTJSUlQkRWqVSoqakReBzd/HSOE3dBAyPHcHwPSCL44he/iEuXLuHAgQMwGo0oLi5GU1MT4uLiBH9OoyGz0xMTE5GamirYemawqFQq1NXVYXp6Gh0dHVCpVJIf8txzz4mvhLh0m80mFIZQKIT5+XmcPXtWLpH4+HhcvHgRQ0NDmJqawubmJjweDwKBAA4fPizomKmpKezbt09il91uN5544gksLCygtbVVfDU6nQ6vv/66RBg/9thjOHXqlAgpWGhspRTT7c/9JAGQq6ur+MlPfiIqJv7OFCNwl0RDrMlkglarxcrKClpaWkQlSOLs4uIiysrK0NvbKx0WXekOh0Nc25SbM7SMSjBOUjgm5vn1iVwUNOzcuHEDsbGxyMrKkpB6BtpQxkY3KV8ALtI2NjZgt9thMpmQnZ2N999/H8PDw4iJicGJEyewfft2dHd3o6KiAqWlpSgrK5OoT2bFTk5OivmMs9NwOIyUlBT4/X5hNiUnJ99zQ3NRzducMkGfz4epqSkUFBRgaGgISqVSRgo0vHA/QvOUwWDA1NQU1tfXZZzAh29rlcBDhaoqfhZMjqPSgkojAII66erqgsFgkJfdbDZLuh7VEl6vV9DcvOzI0uEMlH9+JgJyZLGVHsvxBZeWGxsbcrCQwcMHn3gBhUKB0dFRrK6uIisrS5a/xIbo9XrYbDZ8+OGHGBgYwP79+/HAAw+gubkZZrMZTqcTHR0d2LlzJ7Zv346RkRFBiTMsKiMjQ5bndO1OTU2htbUVGxsbOHDggHSgNHSVlJTIv4czYCqR1Go1KisrMTw8LBf+0tISlpaWMDY2JuFYXBBT/UYjYFJSEhwOB3Jzc9Hc3Cxd1VZqJztp4qOpQuNly46Mi1kWEFTeUFtPocDs7CwuXbqE2dlZ7Ny5UzLmqTQzGo0wmUyIiIiQDoAeGI5AU1NTMTY2hr1796Krq0sUNmq1GnV1dYIf//nPf474+HgcP34c9fX1GB8fBwAZWWk0GlF+cQwTFRUlzxzHSCS8Mqlubm4OarUabW1t0Gg04r+ZmppCX18f0tPTkZycLAl9CoVCikW64w8fPowTJ06gt7cXL774InQ6HX7961/jBz/4AcxmM77yla/gxIkT6OrqQjgcRkdHB/r6+pCdnQ3gTte9traGkZERTExM4Je//KVIvUtLS5GcnAydTofl5WVRz9FESdUVkTrt7e33jMzcbjeUSqXsD3Q6nSjy6HvJzc1FamoqZmZm5IIkyp9La4PBgOjoaExMTCAQCCA7O1uUW0TkFBYWyoKfz28wGBQPCPe03J9wT/aJXRSswoitpbJidXVVWkQqYziK4i3sdrslW5ZmlqysLFRXVwsxcnV1VRak9AXwtkxISJAKmFp3ate3Goeo8uFB7L8LlmO3EBERAYPBIBGHLpcLk5OT0Gg0mJqakj0FAOFHkf5qNpslSyA7O1tUNdx7KBQKyevmQ0DlytraGsxms3BrOOKiGZAkXCqIaL8nhkKr1Uq+AY1dw8PDSE9PlxHA4uKijJfMZjMUCoXMi3U6Hebn5wWYx1ks9xhc6nOxR5MVDzI6dr1eL4A78j2qydbX10WfnpaWJg/t2toaysrK8NFHH2Fzc1PwyvHx8XA6nXjyySdlTGS1WpGSkgKv1ysGR4fDIXsUhuoUFhZKJ8Ula2NjIx555BGpeJ1OJwoKCqDRaCRCkwgWr9eL3t5eLC8vQ6PRwG63o6OjA3q9XmSjKSkpchFvzUTgCJPMIIVCIUURSa2MwSR6Q6lUYmRkRBajCQkJcDgccsEQG0666MLCghxGW70ETz31FJqammA2m8VNffPmTfT396Ompgbbtm1Df3+/iBncbregX1jtDw8Pi1qNI1yKLebn51FWVoaZmRkAwOXLlyUhjuFQzACnMdTn8wl7jcTlvLw8+bNMT0/D5/MhLi4ONTU1UCqVcLlcguqmCIUJhkqlEj6fD3l5efDf5WuRx3T8+HHxShHhXltbi8uXLwuMkqom7kW7urpEjtrc3Izp6WkUFBTIQn9paQmtra1Qq9X41a9+JYvubdu2CZWBlTz3XlRoUSlJIQzzdihSoDs/HA7D4/HAaDRiY2NDBCqxsbHIycnB6uqdHG2a6K5evSowzo8++gglJSUIh8Oy6+S/Y2vx53K5BE1DUc7m5ibGx8dFqMGz7A/5+W8x3NGYYjAYYLfbZUdB1Q9fcPKGqCyoq6tDeXm5yMPoQszKypIciqmpKbjdbmzbtg0nT57E3NwczGYz8vLy4PP5MDo6KkqX/v5+FBYWYnV1Fbdu3RIWP6tLXhSUQY6Pj8sBsLCwgLy8PKG78mbmTJjdC18q6p/50vCQVt7Fk1PiplDciUocGRlBXFycmNISExMlscvj8QhOgSwoehc4R95qJktLS8PWvKmNjQ1xXfPyJECPs1O6WKenp5Geno6oqCiZnVPWyzAgtqQOh0MqvG3btsmFwUOPLziras5os7KyMDExIUtPJncFg0Hk5eXBYrHgs5/9LL73ve/B4/Hg1q1b8Pl8ko5HjwAv8piYO7Gy3Ddtbm5Kh8cxIqmjCoUC//7v/47k5GQ8+uijWF9fR1NTE2pqauDxeBAdHQ2VSoXx8XFER0fDbrdjYmICqamp6O3thUajwYkTJ+Sz2jqyY8UGQIxVs7OzUKlUsrBnp0UfD30zAASJnpiYiMzMTExNTclBumfPHlHmUWW2trYmCq1gMIjKykoZCw0ODqKhoQE7duxAZ2cnBgYGYLVaYbPZMDo6inA4jIcffhgrKyuy5CY8EYAYUOmvMJlMqKioQElJCWJjY/H+++/DZDJh9+7dcDqd+MEPfoCkpCSUlZVJhrTBYBDAJcdUrJo5QiMVwWQyIScnB4FAAFqtFvHx8YKHefjhh8W9XlVVhd7eXtlRsJubmJhAfPz/j7c/D277vu/88SdJkABPHAQBkDgIgPdNiqRuWZIl21Ic346dxnGcOFc3TTbdps00yba7bZPZNtl0m6ZOczRHE8dO4tiJfEmWLFmybvG+bxAnARAkQQK8QILk7w/q+So1v5nveDcdc2ans0ksU8Dn836/jufz8czE4cOHce3aNbng29ra8MYbb6CpqQnd3d1CbeDvkpaWJimElOqyq+vs7MSpU6ckeoCG32AwKPymsrIydHR04M0330R+fj4aGxulQ+NZxvOOoyJ2QPSkMG6WMEqtVovi4mIBabJbXVpawsTEBEZHRxEMBsXVfu7cObz00kv4+7//exw8eBATExNSxAHA+Pi4dIk8C5ibAUAk2Uw7nJiYkMCs9/rzB18U/MuzktJqtTKLY4vPWFGtViv5DBqNBnl5ebh48SIKCgrEnHLlyhWprFZXV6HRaOB0OjE6Ogpge8G9a9cuWZxx3ARAlsvr6+uC36CJiFkSQ0NDKC4uhlqtltkzZ7Tj4+OyMAIgqhaGgCiVSkEKzM7OymiH2QxcYq2urkq7GggE5HJZXV2VWTrHQ1xSzt/ONSgtLUU4HMbMzIygQ4D/iPJktoDVahWjEMmprNDJCQIgiWlpaWmYm5uTRdrm5iYcDofMNOlE5mfPF5ovOemca2tr8r2oVCrEYjHU1NRgdnYWKysrWFpaQjgcviNmkyoy0n3b2tpw48YNjI+PIxQKobKyEpubm2hubhYtvV6vl79vY2OjjCzS0tIE6NjT04P29na0tbWhtrZW8pyVSiWef/55fPvb3wawvf9aXFxEMplELBYTObZarUZnZ6f8nVtbW2E2m2E2m5GSkoIbN26IRp0jPFbjBoMBvb29AlekAGFtbU0c41SiWK1WESXMzc1J18MRFhli09PTYuhkToPD4YDP50N9fb34CfR6vYzazp07B4VCIdG7lZWVaGpqQllZmeRrcJyqVquh1+sxNTWFubk5wX8zn8LhcODgwYO4cOECXnvtNXzxi19EWloafvCDH8Bmswlvidr/sbExeSdYTdMjRFYaKcZcZnP/RtS52+0WsOfk5CSam5tx8OBBnD59Wt5Bqro4ZlxeXsatW7ewsrICrVaLhx9+WPw5wLavo7q6WkCWPT09wqei36O8vBzxeFx8OwQydnV1wWAwIB6Pw+l0IisrC1VVVTh79iz0ej0UCoVcsNPT0zI1YLxuNBqFwWBAVVWVCBzIoCM9OhqNYmVlRTrTnp4e7Nq1CwAwMzOD9vZ27N69W0QijNW9evUqvvCFL2BoaAgej0diCKhepLCIpkyOnNndM+GRZxHjDd7Lzx98URDVzYeYoySycDjHZfW2tbUlY4Ls7GxoNBoYDAY5kInjYFauy+VCdXU1srKyUFdXB4fDIe0Z55d8+ZlkRo8GZ+mhUEjyktnqMpmNXH/uVhKJBObm5mRJzoOcyymil51Op0gVFQqF6NF5oBUUFEiUKfX91Lfv/KJYvVNKzD3K6uqqLMV54RqNRuHCMFWN/zwvSb6c3BMB25crl4ZcYnH5yP8NneHp6emidmL3xcQ1xnKWlJRIZjAVb2VlZUKXpe6bSh7Khbnonp6eRl1dHTQaDcLhMHp6ekRlVl9fj1/84hdYX19HRUUFmpubkZOTg3A4jI6ODtTW1opqJR6Pi9lwcHAQPT09eOyxx/DFL34Rg4ODUKlUCAQC8Pl8sgjnhUiz3Z49e0QBtbGxgfHxcfT19clBy4sU2JYb8vvknkehUIg3JSsrC/n5+XdEptpsNilEKEAwGAxyeNAISXVNb28viouL4XQ6oVAoEIvFYLPZBM0xfzvIp7q6GmfPnkUgEIDJZMJdd92FeDyOp59+WrAWHNtyN0YndCKRELEHsC3GYCTt1NQUAoEAamtrAQCnT59GVlYWrFYrrl69Kt0nsJ1jwqjShYUFlJSUyCXMXRCTK9VqtQgb6BHy+Xy4cOECBgYGpEtmPvby8jKOHDmC9vZ2kRwzfjcQCGBhYQGf+tSnUFtbixs3bmBlZUUO0Pvuuw9ut1vAhnShZ2VlSUfP35PZG/y7U7RAmGBXVxeqq6ul6HG5XLIMZtenVCrhcrmwsbEh9F2KF4g7aW1tvaODo2qM2HrCEq9fv47CwkI8++yzWF9fx9///d8LmJRRw01NTejt7RUPGj05NNDSqDg9PS1L8omJCQBAWVkZ3G43RkZG5N1/Lz9/8EXBaNDZ2VkJVAmHw1heXoZerwcAibPU6/WoqanBtWvXRGVB5UQikZAwmdzcXPT09AhFs7u7Gw6HQy4CLsBpUqIJxWAwyBKYX8Tk5CSsVqscqKwcksntNDTOvAcHB+ULMxgMEq9qNBoFNc6qjAYmjl2ob5+cnITJZIJSqUQ4HJbwHAa+7NmzB/F4XBhJxJdQBUQFGFUyXq9XLinGZy4tLckilLkFxcXF8Hg8olbiCINyW7KycnJyYDAYxNgzOTkpcj+OhjQaDWZmZmAymbC8vCzOZS5Q6Sehyo3Vejwel8U7ZZF0odMFynGe3W7H/v378YlPfALj4+PYv38/3n77bYTDYZSUlOCjH/2oZEOz8l5ZWYHL5UJbWxtSUlLQ3NwMm82GlpYWTE9P41/+5V/EFGm1WpGZmYlz587Jxej3+2E0GuXAyMrKuoMFtrS0hPLycvT29iIrKwujo6PQ6XSYmZkRsQUPsampKVn0U1JMiTW/Sz4XRHlkZWUhMzMTdXV1yMrKEh8LO++ZmRnJE2GHoNFo5LvjboLZBsPDwzh//jweeOABnDhxAv39/XjppZdE0sngo7KyMkQiEUxPT2NyclIOavpdyKIqKioSWTSNjA0NDRJwRHZaZ2enJNoxxY1zcnZ8tbW1IommkGVwcFCq7d7eXkxPT+P48eP42Mc+hueeew4FBQUYGBgQIQN9HMzGYDiRSqUSlllhYSFef/11XL16FX19faJ04kE/NjaGsbEx6fSB7f2bQqGQBDnuj1gEnD59GhqNRqIKJicnEYlEUFNTg6qqKtmdcpS3vr4uohw+p1arVRRmBKOurKyIVJpjOqvVitnZWRw9elR2Equr22mAbW1tGBwcRHt7O1ZXVzEwMCBnwOc+9zlsbm5ifHxcPt+MjAyUl5fL2FypVApShBEO7NwyMjJE0v++XRTkA7GtplKDi0XSSgmH456CNzL3GPzy+JemOoezdS7UWN0qlUoxL1GBQDs7g2s4SmH86tramhzGSqUS8XhcnLB0RrJ6VSgUYoDR3IaU8YJgdZyamoqioiJBme+ke3L3sbS0BI1GI6Mti8UiGRkcx6yurqK6uhoAZLaalZWFzs5OhEIh0WlTzhgOh8UPkUgk5PLiiG3n6I/ZC8vLy/IQ0tTEKruwsFBcuzMzMwiHwygqKpLFN/X/yWRS0uS4rBweHkZRUZEgxQ0Gg6iSeKERPb+ysgKz2Yxr165JgI9Go8HevXtx/vx50f/zcuZeZmBgAGNjYzJ6YAdISW9dXR0OHjwoSo/+/n6hADDEiYEzoVAImtvMnGRyO0OaLww7v5GREfled45rdqI/WI1S5soRD8cRzHNgDgq73/X1dczNzQkZNz09XZzHXLJbLBaEQiFxvgPbMnSitTlOIXbG7Xajt7dX5tZWq1VUdfPz8yJhp2JHrVbDZrPJbikrKwuhUAjDw8NQq9WSxsiDvqSkBAMDAzh48CDy8/Oh1+slsIt6fbPZjM3NzTsQGn6/X0CPi4uL6O7uFm1/Tk4OXC4XFhcXUVdXh4yMDFHN5ebm4sKFC1hdXUV5ebn4s6gK49jqV7/6FV544QU5dxoaGnD06FGYTCaRm/P3j0ajkjjIUU1nZyccDoconxQKBfR6vRjTSkpK8MQTT8g+jNgOvV6P2dlZ+Z3j8ThSUlIE/Jmfnw+XyyWMNXqrNjY2ZMrAZ5Ogy8HBQSmGu7u70dnZKTum8+fPC+Hi0qVLsNlsMBqNwq9jPjfFKKFQSAyBpGB7vV6YTCZRT+4M93pfLgouP+lI5mIb2F7yEn1rtVoxNzeHcDgMpVKJaDQqX35+fj4ikQgCgYC0ZTtZTDSGce7O5SGhZLykdmYMc5lkNBoxNjYmPgguFmkQVCqV0opStcGxFasFXlYMkWdlR8c208pUKpXM50OhkChMzGazzCuJtiBFc3p6Grt27YLRaMTIyAi6u7uh1WqRkZGB4uJica3T98FQGWB7ObqznV5ZWUFmZiYyMzPlUuGogvuWnXkKxBWTPzM+Pg6n0ymoCYfDgaysLNGM85+lVyYej4uahogBKkf4HVAhQuQ7d0Iej0cOl6mpKVgsFlitVuTk5ODSpUs4f/48MjIy8NBDDwltVqfT3eEhoRado7z09HQMDAwIaNFms8mCnDLT6upqUeexi2XRwkAeh8Mhy0TGc1LdR0GB3++H1WqFSqUSIx9HLBQTABDIH4uWnUlm/I4YYsPfMRQKITU1VbAg7LRZoQLbLuGqqioYDAZxHP/d3/0d5ubm0NbWJkob+gp0Op1kJDDDgLj5aDQKh8MBv98vyHJmRNBc2tDQIKiSkZER6Q43NjbEnVxWVgaj0YjnnnsOV69exczMDA4dOiSFXFZWloT7VFVVobm5GW+88QZMJhOOHTsGh8OBiYkJnD9/XhbGHCkzK3txcRGhUAi3bt2C2+1GcXExtFotmpub8cQTTyAvLw8DAwMIhUKS2c3nMxaLYdeuXVhdXYXH45HkSVbmDQ0NaG1tRVpaGurq6pBMJtHS0iJmT55lVOoRVDgzM4P19XV5TlwuF0ZGRrC6uooDBw7AaDSioqICPp8PTz75JMLhMF577TWxCZw/f17iS3NzcxEOh5Geng6r1YrS0lK4XC4Z3zocDon2NZvNOHz4sBQmv/nNb+SzO3z4MHbv3o2hoSGhPrAQLi4uRigUen8zsylVY+Y1DyuOlug6ZB4wq1pKN7VarRxYlEJy+UtJalZWFrRarYxFiJYoKCgQFQ8r15WVFcRiMWxsbAiOnDJQLqQ58wcgrH/a4qnVD4fD4nnY2tqC1+uVhR5/f9Joqa5IT0/H3NycIJDZ1g4PD8veITMzE7Ozszh06JCoOvR6PUZHR9HV1YWrV6+isbER87eJs/fddx+MRiMGBwfvUFVQOjwzMyMKFB5+NptN0CqshHiAp6WlwWg0Qq1WS6s7MTEhmb0ApGImzI8/a2trQqH0+XxyKFLZwct1ZmZGKnJiXXihKpVKIZEGAgFZjgPbRkeOBjnCGRoaQjQahd/vlyzyqakp9PT0oKKiQpymlElvbW0JlI2GP6qNOJrkBcGDjiYkYuhNJhPMZrMUBpR8Wq1WkSqT9DkxMSEjL46qVlZW4HQ6JWFubm5OSMGEKhJPnkgkYLFYZI4+f5sqzN+ZLngaRYeGhlBWViZGRBYA0WgUIyMjSEtLw8TEhMgxMzMzoVarMT4+DqvVKql6jHUliZVycn6O3I8QfUPYILtzdmTsipeWlgQTYbPZcOrUKVE8RqNR1NbWory8HM8//zxisRgSiQRWVlbwwQ9+EDdv3sRLL72Ej370o+js7ERbWxsMBgPKy8tlR7S6uiqwRI6bjx49Knsuh8MBjUaDU6dOCWaHBkaOXZLJpBAKiouLZeym1+vhdrtlHEcxCX0w3CnRyMbnhR0vTaQcw0ciEQwPD4sJ8vDhw7h06RJ++ctfYvfu3fjKV74iRs3l5WXEYjHcunULiURCRlx2ux2xWAxutxujo6Nob2/H8ePHcfz48Tsw6x0dHXjttdfQ2NgIp9OJkpIS2Xt4PB5Eo1H8/Oc/F3n67t275XIdHx9//y4K0jO5pAEgIxAmSFHpQ+JnamqqUE85qorFYjLyocqIOnwu/SKRyB0zRr7wPDi5G2DUI/89dCgTT72+vi5c/a2tLaSkpIiTmC5ZLiCprd8pcyPGgpI6cqVmZ2dlrs7RGS8Dk8kkn49arZYQHLvdjitXrkiFXlNTg4qKCvT392NtbQ0DAwNyIPAw5YFGWS6VUYQOUnvPC4/jBC5l2bHxv6PKizufnaO7nQtzwu64m6B2m58lpaxkSjF0x263ywtBVVw8HpdRTygUkqUclVt1dXUwmUyC2SADqba2FiaTCZubm/B4PAC2/R35+fny2Xo8HqSnp8PhcMh3MTAwAIVCIXLVaDSKcDgMg8GAsbExgcxxTMcUOnZ0RGb7fD7k5eWhqKhIlFRcxFIMUFlZiWQyiY2NDVG2cExGKuzS0pKMagiEdLlcEvDEceTk5CQAyFjLYDCISqm9vR133XUXTCYTsrOzMTk5ifnbDCoGPa2srEjFn5ubKwa4neFZfFczMjKEqcVLC4DwoXZeJizKWMCxAKCS7b/+1/8qAhWv14va2lrs27cPgUAADz/8MOx2O7q7u0W6yyyF8+fPQ6FQ4Nlnn5WOm5JvFkd+v1+UV9w1MPeGHTVRPMz0IP8sGAyKl4Gij5WVFdx99904deqUJDYyFoASexITEokEvF6v7DCBbU9Vbm6uxJDW1tZKZnVHRwdqamowNjaGnp4enDhxQvh39DhtbGzgIx/5iJguP/KRj9xRkNbU1GBubg5OpxM9PT2SZ0Jxy82bN8XX8fnPfx5WqxV+vx9paWkYHBzEqVOn0N7eLuNhnpXvq4+CkjA6EskIAiCjoJWVFdhsNly+fBlbW1s4ceIENjY20NnZCY/HgyNHjggDp7S0VKpNOqqp4CGdk8tYyvC4eKIMMCcnR0xGlFsWFxcjEokIjsPn88FqtYpaa2hoSHIDiITm34vaZBpcNjY2YDQaxa7PRRU7IMZkhsNhFBQUSHtOVtTGxgZu3LghlQHR1uS35OXl4eTJkwI8jMViCAQCsqfhaIcLUiZr8cVgFcYXhvNmylnZhjP9r7CwUNDqxIIrFArhcHHhywqZDzmzGXjZxmIxRCIR2TltbGygqqpKOFIZGRlSEJATVVJSgs3NTTidTgwPD8NiseCVV16BwWBAX18fnnzySfz5n/85AoEAvv71r+Pq1au49957RUrLsR8VM5RfHjp0SFLhKEPlQU72ll6vFwMh5/t86Vml05VOzTv/HHZKJH+yILBarULvZEGxtLSEmZkZGb/Ozc3JnoXKlGQyiZycHBnHzs7Owuv1yuiPO7WdmBW9Xo9oNIqmpiYhqep0OnkvDAaDmKwsFgtcLpfo7bnYpx+AZF+LxSKKLqL0iSuhJNdut4vB0Gg0yiW6sLCAc+fOCcRzdnYWL774IpxOJ1577TW89tprUCqVePrpp5Geno53330XJSUl4o8oLCzEvffei2AwiMrKSlitVrz00ktiOKTXh10ZJd9ra2t3GBhJ5OWhmJmZKcWs0WiESqVCKBTCqVOn4HK58PDDDyM3N1fOBI6Q1tfXkUgkBPvCaQg7B/KeKEslkodFWW1tLXw+HwoLC7Fv3z4MDQ1hbW0Nv/3tbwFAwrjq6+vhcrlgs9nQ09MDq9UqBc2HPvQhJJPJOwoZKtMGBgaQmpqK/fv3w+/349VXX8Xa2hpsNhs6OjqEnfbHf/zHcr7ZbDaZevC8el8uip0KH35Y9BlwpNLd3S2ywfb2dkxNTWHXrl0YHByUkPnU1FT09vZKpCrhaFwYLS0tyfhgc3NTYv6obV5aWpKKlB+C1WrFxsaGKIJowc/JyZFENM4wjUajzJMJP+OSmJ0HD3S+pOTRqFQqrKysoLi4WCSgo6OjYnAxGo1wuVxQKpXIzc2Vg5f48pMnT2JsbAyTk5NwOBzo7+/HjRs38IEPfAAnT57E5OSkpI6RG0W5ICGAc3Nzko3MCnl1dRUlJSWCe1YoFIIn4V5pZmZGuFI8AIHtpTqVNoxTZAY02VIcIZHfxZEe3feFhYVYXFwU5MFOcxzn7aurq7ILoIqktLRULp6/+7u/g0KhgMPhwOLiIv7xH/8Rw8PDIpdtbm4WlHpKSgq8Xi+USqUsxWdmZiRzPBgMYnV1VQyRnHvTx8Kx4MLCAgCIQ7anp0f8KexkNRoNTCYTQqGQ4E4ovWX+MxV9DB/i3z0SiYhxipccVWmZmZkyh+YSl4c0AJFEMoyIyj6+bxQfUGIOANXV1SJiIIKG0my6wD0ejzy//O/p9M3PzxceUWlp6R3P7urqKrq6umRvc/DgQayuruI73/mOjKLIO5uYmEA0GkV1dTUsFgv279+P1NTtNMKuri4kEgl511555RXBui8tLYm6iljxlZUVGf+QsMrCivHKLAjoeue7yynGXXfdBbvdLoXl7OwsXnjhBcH5nzx5UvaaSqUSJSUlUpgwupRJfslk8o58dBIWmAujVqtRVlaGf/mXfxEp/JEjR3DkyBEZ28diMdjtdokojkQiWF9fh8PhkP1fKBQCADHlcWdC39jc3Byef/55eDweVFZWoqurC+Xl5VJwkGnGz/F9uyio/ggEAkhLSxM9NeVx3CNMTEzgc5/7nBzMLS0tSE9Px8jICEZGRnDw4EH54Hkz5+XlyaKIcr+VlRVkZWWJb4FZv3QlE9NNc4ndbkd2dra4RznvpVt5dHRURlKswCsqKmQEQ2cpA3Di8ThGRkaEraJSqSSXYnFxUWRxhYWFSEtLky5IoVBgaGgIFRUVMJlMUCgUqKioECfyO++8g4WFBRw4cABzc3OYnp7Gj370I6ytreHee+8VXEdVVZUc/FR4cTTAJSrpscRiOJ1OkbXSS8Ddw9rampjwqIjgiIrmJqVSiYGBATHv6fV6aDQaGdGxok4mkzIGY4VNHhMX//xMOJojaXV5eVkOrRMnTsDhcKC9vR2dnZ14/vnnAUDUaQy3WVlZQSKREHcs59F8djIzM+XiJ9aFXCs6rMmQ4pgzLS1NpLksAEg95mVK/Ax18cSt8JmgdDs3N1fm8Yy85WVIOWUsFkNGRobsRVZXt0OgOB9nGh2FHBQqsLNgSiMLIe7OioqKkJKSIgmGc3NzKCwsFEopu0mHw4G0tDRkZGRIDCoFGOyuCZDkmI0QRjqMX375ZVHVffWrX0V2drbMvx9//HFUVlbC7/eLwU+lUmHPnj0oKSnBW2+9JYRmeoboXeLhyzHrzv/caDRieHhYFFuUfrNzZzYDR8YpKSnStet0OtTU1Ijfq7a2Fpubmzhw4ABmZmYwPj6O0tJSZGdnCzmZWe2E/+2UyXInqtVqpXgYGRmB1WrFjRs3hGnW0tKCUCiE8+fPSxe+d+9eKJVKTExMIJFIiKSd8tv52+FsdJtnZ2cjPz8fjzzyiIyv8/LycOLECVy5ckW6abVajWeffRZKpRKnTp3CpUuXYLfboVKp8Nhjj6GxsREul+v9uyhYadPRXFpaKouq/fv3Q6vVwufz4eWXX0Z2djb+5//8n/j5z3+OhYUFtLS0CCCMuGWOcHw+n4wFOF7ibJjMIJJHc3NzZbdhMpnExUlFQ25urrDr/X6/zBYJHkwmk3C73YLnJcBvJ3rB5/OhuLgYc3NzqKqqEq8GdzQ+n0848sRSsyXlg8q2nprn8fFxvPvuuzh37hwA4ODBg9i1axdeeuklANuGJp1OJ10UM7yB7VFZZWUlKisrJeKQATyULJaXl+PWrVvSOjO1Ljc3V+aVvHR5wBDBwDk0FWGcpwKQrGOazJjJTdlkMplEWVmZ4C3YqTAgihcaZ76sfIlW9/v9KC4uhtlsxh/90R/h4sWLgqFvbGwURhCRHfSxsMolI+jcuXMoLS2VDIXJyUkBRTJ1jmMaHrIcgRKDz30Id2Gc25PEycN/dXU7L56XEf0x/Gf53NF1nZqaKs87ZbhLS0sibeSOZWhoSHZFnClTfbQz14LLa2akkMRMhZfFYhFlGDEWNJjxO6aPhlgXkomXlpbu6KLoROduIhQKiXDghRdewNLSEurq6nD48GFUVFQA2O5QGxoa5M+/cOGCvBcMAWPGdF1dnWDi6d9ZXFwUYYFKpYJSqURBQQG2trak8CMCSK/Xw2KxoLKyEuPj4yJlByCsr3g8jjfffBNDQ0O4cuUKCgsLUVtbiyNHjqCurg79/f0IBAIoKSmRip/+pHA4LGmC3O9RUcb90d133y3u9JSUFHg8HlRUVODLX/4yqqurYbfbceTIERQWFmJsbAxOpxNut1u6BmZKaDQajI6OorKyUvLLWYwfP34cV65cgdFoxOrqKubm5vDzn/8c999/P/7kT/5ECtfMzEz8yZ/8CTo7O3Ht2jX09fXJXvi9/vynLLNZwRYVFSEQCEibfO3aNVRUVODgwYOYnJyEz+dDb28vJicnZQywf/9+yWyYmZlBMBiE1+sVxgu/nEAgIDc7OTD877n45pKbSOJoNCryOpfLJfGjiURCIlA5q95J31QoFGKIYrVIHwgrIv7+rNCampqkVaSclooV4tD5UE1MTKCyslIOCK1Wi6qqKtTX1+P69etQKBT4sz/7M3FOU3pJXwZT7GiauXnzpshvlUqlGBx58K+vr4vShogTVsxcmFHJQimhUqmUXN61tTVYLBYZRUxNTUGtVsthRdw2JcobGxuyi6FShioeOryVSqXshzgKZKiVx+NBbm4utFotDAYDPvGJT8ismaojehBIAqa3gx0pFST85xg6RYMgDaCU7lL9xKW5SqWSpSDHhRwJMcOB2SiMZgUgn7XZbEYwGBQMSkZGhhzm3PHs/LNY3fJZCwaDmL+NqjcajaIsXF9fh9frlc+TnSKNnsw8t1qtksTIi5rScb4jFRUVmJ+fl9k7gYXciTFfm1RhYDtvvLGxEWfPnkVmZqa4uw8fPoyGhgaMjY0hkUhg9+7dcDgcACCGP6LiW1paMDc3J2NZFiYchfESpj9Ho9Hg+PHj4r6emJhAY2MjTCYT+vv7AUCerZ28s6mpKdhsNgQCARF9EKvDZ06hUNyh3OMl39fXh/X17Wz6nWOkQCAgkuqcnBxBsxQWFopSsLq6Go899hjefPNNkWj/5je/EZXnkSNH8OlPfxppaWn49re/jbGxMezatQs5OTkYHh6GTqeD3++X4iCRSOD69evIzs7G/v37RaE3OzuLU6dOYXh4GHfffbdMKaqqqpCVlYXJyUlcuHBB0PktLS2wWCzYvXu3PAvv20VBjggleenp6aIWotGmvLwcn/zkJ/H9738fQ0NDeOSRR3D9+nW8++67WFhYQGpqqiwSuRCkoiQvL08yKEjuJMKawfAMO9fr9ZiYmJAPmA+B3+9HOByW1pBVOQ1mwWAQWVlZqKyshMlkQl9fH4qKiqSSS0tLw/Lysph2FhcXBYFAmNzQ0JCMoDge42VCySD3C6wc19fX8YUvfEEe1EQiAbfbjYqKCjz11FOS79Db2yv7BWZZ0N3rcDjQ3NyMCxcu4Ny5c1haWkJpaSncbjd0Oh00Gg0cDgf6+vrkomTnlJeXJ+RbmsPS0tLEOV5WViZqJWKyU1NTYbPZ5PPLyMgQ1DR5+AsLCzIWYdwlPTHk59DJzwOMF3Fubq5gVditcgbscrlEpsjsA/paNjc34fP5sLKygsLCQgwNDUkWCQUIJAQvLCyI0YlpfeRRsXOdm5vD0tISrFYrxsfHBS7IMSXl0PF4XOTIfO4ikQgikQhisRjKy8vlEOKBvNMcuZM0kJmZKYcjYYyFhYWCPaG82GQyYXh4GJrb+BuOyggt5O9EKjCr0/HxcaHGTk1NCQQyMzNTZJ8ulwuxWAyVlZWyo6IxlriRe+65Bz6fD21tbdi7dy9MJpOE9LS2tmJoaAh2u11wNnl5eUhPTxe8Orsenhckxw4NDYms1Gg0orW1FQMDAwLm5IHs8Xjw/PPPY9++fejr6xMzKncwy8vL0nVxxKzVajE4OCihTMXFxfjKV76CyclJvPTSS9KtnzlzBna7HTabTfai+fn5snNKTU2VTsXtdsNsNsuUgR3l3NyckBOGh4eRk5MjqJnNzU2cP38eTz31FHp6evBv//ZvaGlpgdVqhc/nE4YdzY6FhYVSlLEQoD9Lq9UiOzsbOTk5EsCm1+slGZSeFa1Wi7feeguFhYXCvKIR8H27KKhwsVgscrCura3BbDYLsnhsbAwjIyOYnZ3F0NAQ/uqv/gr19fWYnp7GxMQEnn/+eYyNjSEtLQ133XUXioqKpJJNSUkRBhND6FNSUtDZ2SnYbKIl6NimnJNSPsoD6ZJlVjKVEzSyuFwuCcWhTpmeEBqP2MITpW4ymVBWVnaHbHd2dhaLi4uw2Wzy5TJz1+l0oru7W9rNjIwMtLW1obOzU4QAS0tLOHv2LAwGg9jwaZojgz4cDqOiogLp6ekoKCiA1WpFMBjEtWvXRK3j9XrxyU9+Ena7XXYk8XgcS0tLKCoqgtvthtFolA5sdnZWAplYsXO8BkAWuczi5iXPZTYlobm5uRgcHERKSorQgQl2i8fjyMrKEv4X4WnMK+DYgxXhxsaGxLIuLy9L5gcAUaIQHUHEAeGUlBdT9cYKnd6ench7fq5ra2uC+1haWkIgEEBubi78fr8gXgAIFoLQQ+ImOOu32+0YHx8Xs+JOiXN+fr7M/jlipeqK+wxiIdi5UCrMnYbFYhHMB53GRUVFklfBWM9YLAaXyyWu4c3NTVGmlZaWCisqHo9LF8jnn4UPDySKT37+85+Li5z7kjfffBODg4OChycChOmNTqcT8XgcFRUVonhjB97U1CTZKzU1NeKO7uzsxN/93d/JM76+vi5BPyQIhEIh+Hw+Qd4QoW2z2XD9+nWoVCpx8XNcvLKygrNnz+Lo0aOyGxgaGkJ6ejpyc3NRVlaGffv2CceOyjHC/OgzofKRQEZ23Hl5eXjrrbcQDAblnDx58iRKSkqQn5+P7OxskW6XlJTgkUceQWVlJex2O3w+HyYmJgSrn0wmBaNCY+n8/Lx8v/v374fdbkdqaircbjcUCgXeeecdyT1/5plnRIWZn5+P3t5ezM7OCiLpvf78wRcF5YQcCbHi5gybEC3qtu12O06fPo3S0lIYDAb4/X4MDw8jGAxCpVKhtLRU2Cs8BJRKJbKzs2UWSRUM/0y2rGzXyT9hNcFqjZUTHcZTU1OwWq2ieOKXzaUqF2vMueW4JplMor6+HgUFBSgqKoLL5ZIULwYnUaYZiUSQmZkJs9mM3NxcycFlt6NUKrGxsYGenh40NTWJOoMSPkIWSW+lxr2mpgaxWExm0fv378dDDz2EsbExXLhwAUajEVNTU+js7MTc3BzKy8vhdDrR1dUFvV4vYTPsCLmsZHVuMpkQCASEA0QqLKNaAUi1y30BU8sqKirgcDjg8XikY0tNTRUyLuf8fOgp/UxPT4fL5UJJSYnIHfldsABIT09HaWkpotGoiASosnM4HKIe4XdBumYwGITT6RSFUWpqqlwClF5vbGyISYuadhYq1NdTgpuWlibJiLzYuN/gToJjHgLlaGQk8j4/P19Glwz9oQyb47KVlRUxIlIgEg6HBXvB7opVN59xLocdDgfGxsbksKTqKTc3V8ZUHDPRlc0dTyQSgdFoRH9/PyYmJkR/T/xIU1OTEAu6u7vR29srdGilUom6ujp0d3dDrVajsrISDQ0NSEtLkyAm+lMmJyfxpS99SYy3KysrCIfDuHz5MvLz83H16lUcO3ZMAtI2Njbwla98BdFoFN/+9rcRDoeh0Wjw9NNPI5FIYHh4GH19fZK+ODw8jN27d8slY7PZ0N3djYceegilpaUYGxtDcXExNBoNXC4XIpEInnzySbn8OTmgoXJhYUFwQIuLi6iurpbvi36qzc1NLC8vIzs7G/39/djc3MRTTz2FmpoavPzyyxgeHhb/kMfjwfj4uPg7CIAkgoVwQVIiMjIypEOzWCxiMu7r6xPJs0qlQl5eHvbs2YPKykoMDQ3Jn0F11fuacEeHs1KphMFgAABZcgEQmWl2drbM/ZhBQBv61tYWGhsbYTQaodfrZZ5JPEJNTY3QIFlV86Xc3NwUpRPR1lxqkjHEF57qHo63OB4DIK0eHcEMKOGYhuEyVNyQXnr+/HksLCwIt4hVLQ9RPjCBQEC+fI7Q6Jmw2Ww4ePAgcnNzsWvXLqytrQl2mn8HVsxcsubn5yMlJQUDAwMYHR3F+vo69u3bh+zsbBw8eFDkkAAwNTUlLy4fRH6+PFyYiMfF7NbWliBNEomEXLR0n3MpT3UYsSNETxC9TDmiRqNBKBSSyp7ID61WKx0Ts0XI84nFYqKWoa8hEokgHo/LGIyufPoA6CimZ4T/PlbTXNzGYjFUV1dDp9MJLsJkMglihaO+paUlzM7Ooq6uDjMzM6LiisfjMhJITU3F9PS0mNwAoKqqSv5dHo9HkDJc3POAJS+MYMGFhQUxxWVlZWHXrl1SeScSCQmzmZiYkC6NAgxSXcmb4v6juLhYTGO8rAju5KydQg2FQiGXNWXr3C0+8MADcilz53Ls2DEJw1lbW0NZWRlWV1dx1113wWg0YteuXfB4PJiYmJAdGgB5d9llBAIBuFwudHd3CxfrnnvuEVPppz71KZSUlKCnpwcjIyPS2bW0tAAAnE4nLl++DJPJJJ9hYWEhNjY2BE3Caj41NRVjY2OIRqOSvU1Fl9FohN/vh8vlkkhnYNvMOjExIRJxurEBCMPOZDJhbGwMs7OzePjhh2EymVBRUQG32y2dVkdHBy5fvgyPx4PV1VXk5+fj0qVLqK2tRU1NjWR4z8zMiDw/EAjcIQFmAFYwGER5eTnMZrP4d4ht4bPEEDaOcjmlsVqtYuZ8Lz//KQgP6qqZJ20wGKQaYdXOTT4t6L/73e8k4W1iYgJqtRqtra24evUqQqGQtL9UbgDbQEAegHS2UucNQLTT1Ckz+IeGPSqj4vH4Hcsn8nxYyVHDPjMzI3gPwuH27NkDk8mErq4uPP/882hra0NRURFqa2vxpS99CXNzcxIzuLW1BbPZjPT0dIyOjkq1RewED/2ZmRlRJHGOS9gXsev0ZMzOzkKn02F2dhb19fUwmUz4zne+g4sXL+KVV16RZRWppTabDYWFhVhYWMDg4KBIIVnNcEnN8RHZU8zT5qydFy/lwFSoKJVKUa9sbm6ipqZGOE7s3IhgIY2WCiMupqkQ4uhuYGBA8s1bWlowODgodE+q6xgqRHQ9Z918FiiHJJSRuwxG9+bn5wucjnGXxIarVCr09vaisLBQCgiXyyUIZ3a6fHHJzCFXiVUox4g0qNG7QuVXXl6eYOUXFhYwPDwsjB+OHq5fvy67KR7ENPRRjs4USV5SfLY4ftPr9ejs7BTzJ6GV9HnQg1JcXCyRnz6fDwaDAUqlEgcOHIDX65V40uHhYRiNRhgMBiQSCZSVlSE7OxttbW2yt6EKirsPfm/Em7CwJOVUoVCgt7dXEO9paWloa2uTC/9//a//hUcffRQpKSloaWkRPtcTTzyB1tZWXLt2DS+88ALeeOMNANssLIfDgdbWVjQ3N2P37t2YnJyUg3dxcRElJSX4h3/4B6SkpOCnP/0pbDYb8vPzRSE5NjYmyYKRSEQuIZIWvF4v8vLyBBdOUxsvlcLCQtTU1ODgwYNYWlrC66+/jmQyiX379qGhoQFGoxFdXV3o7u4WtSjPPPLB6JshXddgMMgzwAlKcXExJiYmYDKZUFpaKhw6ep4mJiZkd6xSqSQJNBAIvOdz/g++KFjZU4HCSolttd/vx8bGBvLz80VjbzabceDAAZw8eRKBQEAkmpRp0slK2ibHTzzs4/E4zGYzvF4vNBqNzNd3upB54PPLZNVF+S2R4Pn5+XfQbym/0+l0d8wIaWZqa2vD+Pi4hOV87GMfwy9+8QuZXaanp4tyhHN20jXpA+FivLCwEDdv3hSXLE1nKpVKjIME6ZETFQqFUF1dDb/fj4aGBpSWlmJ1dRUHDx7EPffcg/7+fmxtbeEjH/kI3G43AoGA7DI49klLS4NarUYkEpF5P3cARUVFQtpNSUmRRS2XzjysFhYWZI5O3hYhZ1qtFrFYDKFQSGiiDP7Jzc1Ff38/amtrZYabnZ0txsnl5WUUFxfL6ISyZc7nMzMzxTWbnZ0tLwzpt4y85EFCMx8Jo+FwGLFYTNzOHHHO306ey8jIgMvlguY2BZj7AxYVPKD1er18Blx4ApDfg0mEBQUF4srmbicajQpygbsuXsKc32dnZ8sym+7y1dVVTE5OipKMLnwqq9gdcFRCeObq6iqKi4slPIfyUC4/KY6g54KZzhwhGQwG2YGlpKTg/vvvh1qtxvXr1/H9738fer0eoVAIKpUKe/fuRSQSkRREyn85UmMXQfz72tqaBAVpNBocOHBA5OBnzpyBy+XC7t27odVq8b3vfQ9FRUX48Ic/jMbGRvzTP/2TGBzfeecdnD9/XmCJ2dnZaGhowOuvv47Z2Vm89dZbsNlsyMnJQUtLCx566CHMzMzA6/XizJkzsNlsSElJgcvlEn9EIpEQoxrpsHxGu7q6ZJQci8XQ2NgolAbK1VNSUjA/P49QKCR+iYqKCvFsRaNROJ1OeL1e6XYpc+Ukhl0Yn18qCelpslgsmJycFD/HlStXZDxKHwpNy1RdejweGI3G9zePgtwTAMKLoZGKCpREIoGJiQmJFezv74dWq0UkEoHBYEBxcbEsfKm1J92U6pnJyUm0trYiFApJ1aNQKBAMBuFwOMQRzKwAjlW4WwiHw0gkEti/fz/Gx8clWIUzco6raDwjsVVzOxOAGduRSASnT5/G6uoqvvSlL2HXrl348Y9/jEgkgrGxMTG9aLVanDhxQsZI5B/xMpubm5PWjywsRh/yYiV8j4Y2ykPpqKYqZXFxEU1NTfjCF76AP//zP8eFCxfwgQ98AAaDAQUFBdJRBINBaacJbExJSZFlIdU9a2vbKXp2u10ugvLyctnXUFni8Xik5SUSm1TVUCgEp9Mplx8Pay7SY7GYIC2oZOJLSfMdseU8/JhnvhNACGyPcWgwYyW1tbUlHg12cUw+ZAUIQBz+3IeQhswCgpJiLvqnp6fF7FhcXCyXRzQaFTWYUqmUcQ/hawMDA9Dczr1wOp0i0aSiiWFSIyMjgpRWKBTCKSIqnJ1ncXExuru7YTKZZOxHCjCf7Y2NDSHQcgTKQovKLq1Wi6ysLFngczcRiUQk9yKZTKK5uVkuZmr+q6urpXDJy8sTSefdd98tRjhge2zj9XqFC6VQKER5Njk5KR1OQ0MDFhcXYTAYMDQ0JBfhX/7lX0Kr1UKr1eKNN96Q/zwrKwvnz59Hf3+/JFbW1dXhvvvug8ViQVZWFjIyMjA1NSUiCbVaLbGyfr8fbW1tGBgYQGFhocQGBwIB+XcwU4ZnAP0YjAJwu91oaGgQ4QehkJR/U9FHavbIyIiEZhmNRiiVSjQ2NorajQ544n2Yd8KkThaL/Dy5SKcfCID886Q38F2i0ZQ7Svpw3peLwul0QnM7NW5qakqqdL1ej4GBAcEL8xemQiMcDku3YLfbRbk0Pj4uY46UlBRcu3YN5eXlqKyslBGIwWCAz+cTLXB7e7scnkRXs+2izJbzvr6+PmRlZYlUja5mIqtZXanVaiiVShlhccbc0NCARCKBixcv4rnnnsPDDz8sztpQKITOzk5MTEzgYx/7mCxOWfGxGmC6HrBdTXPHQCUYU/OA7R0Ppb7M12XXMDo6Kp6Na9eu4aGHHsKlS5eQl5eHn/zkJ3jyyScBbHcLXV1dks7HPQxHazabDR6PRwBt6enpaGhokEyEwsJCRKNRgd9xMUweFjsPfn4MJ+ILNT09LVX1ysoKIpEILBaLdHJVVVWYnp6G2+2WS5LmsOnpaWFvcUc0NTUlxjruv6anp2WXwvERndU8HHhJWywW6YR3otIp46QhUKFQSOVGXwVduLzQuJein4CL/WAwKMROqtIItAuHw9I9K5VKOBwO+edJI11fX4dWq5VdA01z7AgjkQjKysrQ398vbnt2DexWx8bG5PvUarUYGxuDXq9HRUXFHaNV7lr4zmVkZMBut6OtrQ1lZWVSYJD7dePGDayvb+dbr6ysSP7DysoKsrOzsbCwIFnwRUVFAo0kPTcvL0/GOAQXTk1NiZnWZrNhaGhIGFAXL16Uz7ugoAATExPw+/1SpXPXdPbsWdy6dUvoCzdv3sThw4fxzW9+UyKL/X4/urq6MDExAYvFIh0M6QDEtahUKiwsLIi6jR0izZnMZlGr1WIwpreJhsfFxUUYjUbk5uaio6MDarVaiiNSdblY7ujoECxJZWWlsLP4DFNVyTF6OBwWwynNo7m5udKpsWtmp8JpCS97XoDv9SdliyyD/8sfIog/+9nPirTUbDbLiIeqisLCQjmEU1JSoNPp4PF4ZElMRzFnfqmpqXIw8y9OpDMRH5RmchlM9QHljpS25ufnY3NzU6iwOxfsRExzpMKLgYYZjocyMjKkTb/vvvuwsrKC7u5ujI6OIhwOIzc3F1NTU2hvb4fNZsP+/fsl47ulpUXQJly0ciTHWa5Op4PFYhH5ZUNDA959911RXwGQhDQC2lgx0iCXTCblPw8EAmhubkZLSwu0Wi3Onj0r+AyOVnig0rjDg4KfB//dO3EXNDcyS5gLPHJ4WEHtBLIxT5njoJWVFUEXkEnEeFgiVmjy4ku6sLCAqqoqbG1tiSlwYmJCoiZ37pyodNu5jOdhzXHR6uoqampqZPzCipvdCfMAWNXzgmRhwWKFl1NJSYkYTvnZslAhEoUIGVJ0A4EAdu/eLYo14sM57iSgjiolACgpKRGiwOrqqnTKlM+ur6/LQUYsSiQSkaKroKBAlvrhcFgWntwd8rMg24zeAWZWUNrL5zAWi90RisVumYIAwvtUKtUdOeA0kaampsoYjEq+xcVF6PV67Nu3T1L68vPz8Wd/9me4du0avvCFL+Cxxx4TM6nL5ZLOPT09Hb/+9a/x7rvvIjU1Fd3d3RKtS3S32+0W0cHo6KgA/DjyWV9fl8JkY2MDNpsNk5OTcnl5PB5obodeUTlGfhKNcVSf0X3PCGg+/waDQVDp9CcZjcY7Yk0TiQSKiopkDE3lHV3w/PyZYpmTkyP7FD4nBG/y7PD7/VheXkZJSYnEAFy/fh2/+MUvxOvy//WT+v9ySez8oQGMqABKSLlcXFtbk5EGZ39qtVqUSqwwiP0FIDduRkaGgP1YeRCpwH8POwXOQolz4L+bOxKqMtjR8IstKytDQUGB6NSJa6YJiBVXIpHA4OAgJiYmEAgE0NfXh/7+fvzJn/wJfvazn+GJJ57A4uIijh8/jo9//OPYv3+/uF357yWYjxh0s9kMk8kk8jbKLYuLi6FWq0Umy8vT5/PJwcFDgHr6xsZGPPbYY/jnf/5nnDhxAsvLyxgbG5N2lPsRmhVp4KF0k93C1NSU4BqYdMZ5q9VqhVKplHEBpZ6c5XOXQrS1Wq0W6N3OgCiOeqampjA+Pi5dHFEbRLDw//b09AgyIhaLwWw23zHvpzqJCHku57Ozs6HVaqFUKqXzsNlsmJ2dhd/vh1KpRCQSEakjO03+OVS+8WImjZTLeY4pdgYScTFNZRR3U+R7KRQKWW4z+pSdBaW6dInn5eXBYrEI7JHuYnY4vPg55/d6vcjOzobFYhGKKB3czNPgLoCcNKYXUnSyE4nDyNT5+XkZv8zfBuLxXV5YWIDNZkN2djZ8Pp+MrUhyJgjSaDRKccbCkWPI/Px8wfMkk0lcunQJly5dQiAQgNFoxH333YfDhw8jGAyivb0diUQCZ86cwcTEBKampvDjH/8Yp0+fxvLyshR5e/fuxXPPPYcHH3wQnZ2d+Pd//3dJkcvIyBDoKN3x5ExR6cixNplyHo8HDocDiUQCoVBICKykIUejUWRlZSESiYivhgIDZqJw1DMxMSHZISqVSi5PIm2oeKNznEo7XlAcI3GkaLFYsG/fPmxubooHQ6fTyY7U5/Pd0XESIPh/8/MHj56oFuHMmFLXyclJwVnTLMblUHZ2No4ePYquri7U1dWJSkmhUMgLxYp+cHAQZrNZ2la27QaDQcxOrORY/dMCT2QwvRWcraalpSEnJ0d+983NTUFWEwFA9zSd3DSsNTc3SweTnp6OM2fO4Mknn0ReXh5qa2uFo3TmzBnpdkgXJeo8JycHDz/8MDo6OuByuTA5OYldu3aJyokvJWNfqXVPT09HcXGxLKD592pubkZ6ejqCwSCGhoaQlpaGoaEhaUvpeOc4i5cSl3S8DF0ul7wMq6urAlvj50raam5urhi7ODpMT08XpIher4fdbpdErZWVFanq1Wo1pqenEY1GkZmZCZPJhLm5OZHzxeNxlJSUyD6gqKgIdrtdwp8Yd2oymcSsNjQ0BIPBIKo4RrE6HA5BvRB/QuwIOxRWd5QLc8HKWTrHhbwwnU6n+CgqKirEMQ1AEB1VVVXwer1obm7G6OgootGo4KlpyKTxy2KxiGwxNTUVGxsb0o1wyc7IUaPRKPnk7IJpFCS8kEFJPAwqKyuh1+sFNcK8cHpZeIGrVCr09fWJ8owUVx5YHo8H2dnZyM7OFhc9P0d2GzyIqK4hOoQYGkpQCTmkuo+FUCAQQGVlJVQqFXp6eqRwMplM2L17Nzo6OuQg1mg0IqXmd1VbW4v19XXBejDFjj6elpYW+b2Ki4sxMDAg3wOJwszsIAaH+7CdmfH8XSk0WVpakq6R3DebzSYQS+7OioqKBExKYi0vzZ0eMhrh5ubm5FljKh09M8XFxTKN6e/vF7ICs+AppWcEA2OiOdrmPup9uyj0er0s3ph+RTcspXma22RVAMKqLy0tRWpqKqqrq7G2tga3241QKISBgQGUlpaK6oKdAhU3RGuYTCZJCKOyg+qrmZkZ1NTUiEyRc+Ouri5otVp5cDQajeArlpaWkJGRITRajUYjzm+O0fgl79+/X6Sjv/rVr/Dmm2+ioKAAarUa4XAYN27cgFarBbCdTcELdGBgQIJ7rl+/LsafSCQiaPSCggKUl5dDr9cLxI5uWs6yMzIyUFdXJ9WMXq8XXMj8/DxsNpukcvH/paWlyVKcoyeDwYCenh5ZEvMlYKXICjk3NxcTExMiOaQIgKM+XprM2eZhxnS9ZDIpclHmNpeVlUmlRFrw1tYWiouLsbCwgMLCQnGlUzJot9tF2siqi9UtK2z++0pLS7GysiJ/787OTpHl0mBoMpmwtfUfQVUkcXKxyEOTfy6NmTsVMTwsKZOlv4b+iezsbAQCAVGCFRYWCk2gpKREECJUunH8uLS0JHNmdhLsDlgNzs3NCcGAF7Tb7RaTIsOQLBYLLBYLAoGA+JAUCoU4sbm32qmG4xy+oqJC/Cr893k8njvYSaOjo1IZE+BJZD9HOeymAAh/i4UHw6EoRODvEovFEI1GZfRpsVjksqKggju8paUleL1erK+v44Mf/KB0Z1RexeNxjI6O4he/+AWam5tx+PDhO9AmfHZJDrBYLAgGgxIXQBMqR6L05/AzoxhDpVLB4XDIyJJjOYpygO09FmnCjFJg7KtWq8XMzAyWl5eFchAOhzE+Pi5jJUYqcFJRXFwsBloWnSw4CE/ksj8nJ0cIGv83eRR/8OhpdnZWDhO32y3yN7aanFXywfX7/ZienkpWPSYAAQAASURBVMYbb7yBmzdv4gc/+AG6u7vR3NwMm80mLlRycVi5FBQUSCVE7DHHHlza0FNBR24ikcC7776LtrY2JBIJmM1mCenhLgP4j30L1QrMiqaBiRVlZmamBIbU1dWhvr4eWVlZSE1NhdVqlXETwW6ck/MgKSgoEOjZ1atX0dnZiWQyKfiAwcFB/O///b/x4x//WJZyNLrRPERN/dLSksxI6WrOyMiQA5gHZHZ2NoqLi5GdnS2mxfn5eYTDYYHW0Q2bmpoqfC4y+9PT09Hb2ysokq2tLXnQOJIi1ZRzUCJXlEoldDod6urqkJqaitraWqSnp6O6ulouUrpNecCTd9PZ2SnEW46BgsEgIpGILPdIv2QrTnoq5/SkBnBBTF8FUSCzs7OyZDcajSKEYC4CPQepqanweDzicdFqtXA4HJiZmZFZPlUn3d3dGB8flwMkLS0Nra2torriPJj+k4KCApk/ezwejI2NYWJiQrwbAGSMxep5Z5Sq5jZ2nZ4P+nImJycxOTkpgoxbt24B2DbITkxMSCfOWXd+fr6YVSmtNRqNQjlg1gIAGQNyBGYymWS0t7m5Ke/K4OCg0Fzn5+dRWlqK9fV1TExMiDqI9GabzSaYDn6efLej0ag8e/weKBtlt8oRZFVVlZgdp6am8M4772DXrl34yEc+gng8jvHxcQFjknfGPGqOF1UqFW7evCnjHTrkiQiZnJyUxT8FEDk5OSKVXlhYECUnLwVyqjhiYuHLDptyaHaoyWRSsiZ2suC43ObonEV2YWGhFIvxeFy6UI4Bmcy5srKCeDwuheR7/fmDOwqC2CwWi7S7TL3jtj0rKwtFRUUSJer3++F2u3HgwAFcv34dL7/8smjDDxw4IPI0plcxaY2VEh3YXJCVl5eLBppz8cHBQfT29mJ8fBx79+4Ve3t1dTVOnjwJj8cDl8sFu92Onp4eeQnY9qempkpMKA1DbCW9Xi8eeeQR2O12fO5zn8Pg4CDq6+vlZWEiF79wLnU5ux4YGJCEucLCQknCi8ViaGpqQiKRQHt7u6SH0a1OtygrNl4knCEbDAYx6ZDhxOrTaDSKLJEKFwLmqBgimmMnMoAhSBsbGzI+4OfBDiolJUWqHL1eL+oVv98PnU4n89twOIy0tDTBntOlnpmZCZfLJXkOi4uLCIfDMJvNWFxclEN9p2mScadUA/Hz4AXJ4oH7LK1Wi4KCAsHZc1cTj8cRDodRWlqK6elpecEpa967d68syysqKpBMJpGfny8BMMwUoZKLsua5uTkpViYnJ6HX60XIQIEGBR6UA5tMJvlOPR6PLN8TiYTgZDietFqtolzjUp+MtVgsJvNwdg46nU6Ae1arVQKrmEfB9DhW1dT4Dw4OikqOsm+OmQnqYyBYeno67HY7/H6//B2Z9JhMJtHW1gaj0ShLdO5l1tfXMTk5KTiMeDwu0Z8ajQYDAwOy+OaFQfYbR8dGo1EWx+xUXC4XjEYjIpGI0AmeeeYZfOxjH4Pb7caNGzfw6U9/WqTQY2NjMgY0m83y3hQUFCAvLw99fX0ynuIo1mazCXY8JSVFRqlkkvFSYvfNDpgufgp+eCkR7c5gJLfbLWMnv9+PnJwcWCwWKJVK7N27F2fOnMGNGzdw7tw56WQILc3KykJnZ6coNtkBUVX4viI8KP3jPDE1NRVlZWUyEqJWnyFER48exTvvvCPzXr/fjytXruBb3/oWjh49ivvvv19Im5y3bWxsICUlRTwNTLzT3I7nzMnJkQUrX7q+vj6EQiHU19fLXB0Adu3aJQcgq5nCwkIxxpHVQu4KDzJKOa1WKwDghz/8ofBkysvL0dXVBYfDAYfDIR4RtVotX1xRUZHMx8fGxlBbWysRlgBw9uxZVFVV4cEHH0QwGMSvfvUrUd54vV5RKvDh2sk54u+90zzHC2H+Ni6dChL+PhQcTExMiEadbTod0Dtn/uw2uJBkl0UnPEmva2trImNlxUkfAy8Jh8OBpaUl5OfnY3BwUByx/E55SM3MzIjRiF4NutJpFkpLS5NdDX83kmQ5g6Ugwuv1AgB0Op0s/agemp2dhdlsFiQJ3dqhUAiZmZkSVtPX1ydphRaLBenp6bh27RpMJpPIGjm2YeXLGTrn3bw48vLyZGZcX18vIVGZmZm4ceOGUIe3trbkv+N+hzJUdjxURHHEwPk68xO4B6DHiBcWPRXEXPDvSgf/TnAmM17IWyPjjf9//m47d300hzmdTjFc7gzl4diQhjECL4mJIQ2ZVFaaadkNrK2tYXZ2FtXV1UIRcDqdUKvVuHXrFhYWFtDT0yPZFo888gguX76MoaEhTE1N4fLlywD+Q9ZM1D27VdIQmDQ4fxsZT4gjD3eiZjQaDTIzM9He3i4xB8zN2NktsNp3Op2SWcH3jJMTdtTcjVCiSwMyFZrs7Jubm9HV1YXLly8L2oQ7o3A4LO82L1sKXd7Lzx88egoGgyKfY37w4uKiPJg0cDH/ly9nd3c3QqEQbDYbGhsbUVpaig996EOIRqMYHx+/gydE+SY5JgsLCwiFQpIO5/P5JJKS+vKjR4+ioKAANpsNVqsVIyMj8Hq9+O1vf4uUlBRMTEzAbDYjJycHDocDTU1N2L17N0wmE8rLy8XVyFEFF5KFhYU4cuQIurq68Lvf/Q43b96ERqORDsJkMuHuu++WS4YqHx5qzc3Nkol79uxZ/PrXvxbY4eTkpMjW+NlUVFTAaDQiKysLZrNZMNc6nQ46nU72KwxtYRVHE19xcTFWV1eh0+nErVpcXCxzW1aGpIgSUsguge5hv98v1S67D6JH5ufnJR+dn1NZWZlkhKyvr2N8fFxUJXT/EgUeCAREdUQvx/z8vEDcuCfid2m1WuVl4qGUkpKC6elpUdQRv80ul4tpYi/oQuczNjc3h1AoJCICFhb0Y5SWlsrogEq/2dlZvPTSS+jt7UUwGJSDlg5Y7iUo0/Z6vYJ9pjRZoVDg7rvvhk6nw969e3H//feLrPFXv/oVvF7vHctNIiJoIqOGfqc3iI5z7g2oguKS2e/3C0WZlX16eroY1Oj25qiG1IDU1FSpRFNSUhAMBrG+vo7R0VFZSsdiMVE0EX7JcSDR1nzudqb1MYOEHhmO0ubm5rCysoL528RUjnY3NjYwPT2NvLw88Z1EIhHU1tYikUjg0qVL0Gg0aGtrQ05ODk6ePImf/exnUKvV+M1vfoO+vj7odDq5NMjc4rNGg53f70csFpNdA8dxFGCwyuc7vri4KEVaQUGBGC8Zvet0OkXObLVa0dnZKSmTpFUvLy+jtrYWlZWVMm6kq52EbI6jOMbl2JdGVxY8dXV1giGnOrS4uFisCu/15z8luIj2fOr1+eByDMRqig7pW7duie6dLCaVSoW2tjZcu3YNGxsbePbZZxEKhTA6Oiq6clrdeWtT/bG4uIiysjIZe7W3t6O6uhpHjx7F8PAw6urqYDKZcOHCBVitVrzwwgsYGRm5g9jKyikjI0Mqc35pq6uraGlpESBbKBSC2+2GVqvF448/jr179+L111/Hd77zHSwtLaGmpkbGNRsbGygsLJROZWBgAPX19aivrxf8wbFjx+D3+3Hz5k1873vfg8ViwYkTJ1BRUYFEIoGxsTGx5fMwJJuKlYzVasXw8LDowXfGe3L+SjREaWkpcnJyRP20sbEhju2dCzRKQ6lv5wKOMloKB2gqSk9Pl3EKXcKc+WZkZMiLwKp5YmICGo3mjrEi8zE4+6cHg98zJYEMQOJeamFhQZRTzKXW3I5tZSXH0Knu7m7ZO7DaLyoqkuU3kwnp9eDeraysDA6HQzKYmbORlZWF+++/Xxb5xMno9XqYTCbhKPHzCgQCsvxeXFyUF7ejo0PktiTZDg4OYnNzE3fddZcgVwoLC5GdnY3CwkLxA3C5GgqF5LLggcIdIbvJjIwMlJSUYGNjA3v37sXY2Jhwm3w+H3Jzc4ULZbVaJRdheXkZLpdLZL30I7BzYwfJ9zInJ0dk0BxXUQJN0uro6KioeggMBSDMIv7edN9TrUVFJXeTZIfRq0HRw969e9He3o7x8XEpvvbt24dgMChiipycnDvwJlTwsYtjB0TyBC9BeqNIp9i59+DImdiYvLw8FBcXS+dnMpkwOjqK4uJi1NfXo7OzUzwSaWlpQutdXV2Fz+eD0+mUQC5iRthNtbe34+rVq1hYWMD+/ftl18NimLsc0gdGR0exa9cu+V3el4vCZrNJ9crKhPO7xcVFcclyDELs82c+8xkcO3YMr732GkKhECwWC/r6+kQrzAuGBFmyltg5sN0lLiI1NVXaYF5e/f39GB0dxZEjR/Dkk0+KCzo/Px8PPPAARkdHZam2d+9eqZbn5+clDIWpaHv27MFLL72EN954QxQzn/nMZ7C0tIQLFy7gxRdfhOZ2EE5VVRUAiBmGvw9xDjU1Nbh58yYWFhZw5MgRbG1t4dy5c0hLS8Pk5KTo2fv6+mT0lpaWhpKSEqyvrwsaY6chzuVyCdMqmUyir68PCwsLEhS0sLAgVQkPa7vdDq1WKwtfl8slFTsjTTnfp4eBKjdWxsxu3pkNTUQH2/TJyUk5WNk5EYEQCASEaEolDYOHqC6JRCLQarXSGfDFTE9Ph16vFxYS90hc1LLbYkVHMi5HXwAkF51hQWazWZDqJNTOzc2ht7f3jiQ5tVqNPXv2wGKx4JVXXhHvj8lkkks2Pz8fk5OTYqai7JhsK34/dXV1yMzMxJtvvomXXnoJTqcT5eXlOHr0KF599VWZf/PwYkU/MDAgHRuVSnSzE/5G451arcbExASSyaRU4SsrK+jq6sLS0hKam5tlz8RMD+Z0cJHq8/lQW1sr3VdpaSkqKyuF8VVQUICuri4AEKMdANjtdhlF85kicsdkMskYkfr/1NRUURcRsbOT3TU5OYnCwkJxTU9OTgrNlmY+LpQ/+9nP4pOf/CRGR0fxla98BU899RTuueceBAIB+Hw+ZGdny76jr68PAGSEyrEZixPKxDkpSE1NlaRAcuAYeMRdEjO/WTRzDEg6g9vtln8vP0eeOyy8WYRxd8eRF6GfH//4x5Geno7m5mYZcf/qV79CVlYWHnvsMfle/X4/nE6n7Cu4h3lfLgrK3ACIhX9ra0ts4kzFmp2dRUFBAerq6tDR0SGKqHfeeQcWiwWHDh3CH/3RH4nssqenB6FQSA4gAHdIVTMyMkTSSAlqenq6WOtTU1Oxf/9+bGxswOVyob+/H/X19aiqqkJ7e7tUm8QoDw8PSwLUzswAze3s4DfffBNtbW3Yv38/cnNz0dDQgNnZWXznO9/BwsICGhoa8Mgjj+DBBx+E3+8Xcig/F4bREN3c3t4uNM0f/vCH0Gg0+Iu/+At8/etfx9TUlKT/cX6/traGq1evIjs7G36/H62trbIzYJrZ9evXYTAYhK9VXFws7XJPTw/effddWapdu3YNyWQSe/fulRjGW7duYWpqCq2trYKHJluGM++pqSmRjPJw5S6KrnlWuTSiVVVVCeZCp9OJyZAKKC5PiengQi8YDAoagYtcGoxmZmYkyIpBUTtVQURr8IKg+IGzX6YaUrbJERoDdeg/2NzclOp4fHwcp0+fhs/nw1133SX4+5dffllIpcws5z6Bh1wwGBRZ6Pz8PKampuBwOLCxsSEKNyb51dTUiLudh1RlZSUWFxclk5mjBYVCgdLSUpHIAhCQYzAYxMTEBAwGg+yQKC2lz2VwcFCeVYVCgbvuugtTU1Po6upCdna2dAscSxGGR3gi918KhQIdHR3iUqaMnaMsFndUMdITw+eHVS9HtRSzsPjkgp6KKx6kTNebmJiQoCmVSiUYlf7+fjQ1NQm6hx4ljUaDmpoakZG+/fbbYojl2cSxJxEp3FNyt8J4gfHxceTk5MBms4mEnxJXGoQ5rlUoFHA6nSIM4RSE0nR2XjTHZmVlITc3VxRQLGbZAdJfVVNTg+bmZrz11lvy2TU2NsLn82FtbQ2JRALl5eXIzc0VxVU8Hn//LoqFhQV52WkqotOQYwpK9LKysmRx3NnZidLSUtjtdtkRcL43MjKCoaEhGI1GCdhYW1uTfGamSDFLgsstvtQcVZSVlaG4uBiXL1/GuXPncOTIEXz+85/HysqKYERyc3OxZ88eVFdX48qVKwiFQnLRaDQaSei6efOmMJi4FO7r64PT6YTD4ZAquK6uDm1tbXKBsrpVq9Xy0q2vrwtmmF8qx0OHDh1CZ2cnNjc3cfjwYXg8HjFidXR0YGtrCy0tLbh165Z4C3Q6Hcxms7TIs7OzUjXuzGzY3NyUyEWiJOLxOL773e+itbUVWq0WFosFY2NjKCoqkrAemuMAiLKD3zNR7jQxcplLLANVK+R/8eLjeI/zcjKa8vPzEQgExGEcjUZRVFQklSLNkWTVcGyZSCRgtVrl5WMiISXNVOsw5CYlJUVQ9vF4XEYOCoUC5eXlKC4uxvXr18XfEo1GcejQIayurqK9vR1DQ0M4ffo03n77bQSDQYTDYdx7771ySDIUSaPRYHR0VHIVFhYWYDabMTw8jPb2diwsLCAajeILX/iCyGGbm5sRiUQwPj6OkydPorGxEUtLS5iYmIDX65U5PRVfnH0TOZOamiqqPXZ2WVlZSEtLk78LEfPPPPMMfvSjH+G5557Do48+KpnYpaWluHjxoshyR0dHYTAYpJsBtrsGYHuMkkgkpEDZSVymg1mtVqO8vBxut1v+N3q9Hkql8o5LKRKJwGq1ytiTS2wq/eZvh6Nxj9XS0gK/34/GxkZRDvr9flRWVmJtbQ3Dw8MoKirCk08+KXuEzc1NRKNR9Pf3i7lWp9OJAo60XLfbfQe9lQBGFimMCuZugyMmii0I62MBVFJSIoFoGo0GPp9P/Bn8XogLN5vN0vEwxpeyaBY28Xgc/f39iMfjqK6ulq5nYGAA999/P3Q6HW7duiW7CRZnJBT/32Rm/8HLbKVSKYA6Lp3YWtHvoFKpJIlufHxcJGeU6504cUL0yadPn8bAwICYawDI3JGz47m5OXR1dYmMlVJFzW3sRHZ2NjweDzo6OgTd7PF40N3dje7ubjQ1NaG4uFiwIjU1NcjPz8fhw4eRkZEhS7Tu7m4YjUZZiKampuLMmTOiy1epVPjQhz6Er33tazh69Kgs8sne5+yfmAUAIrtk3OPg4CBGR0cRiUTw8ssvy2KeCidyexjRurW1hbNnz2JmZgbRaBR/+7d/i1dffRXl5eW49957odVqBWrX2dmJd999F3/1V3+FiYkJbG5uoqKiAk8++STuu+8+fOxjHxOMhc/nk0v529/+Nl577TXk5OSgsbFRnMcOhwNFRUWi/WfH4PF4xKBHLwoNSDqdDgcOHEBlZSX27t0rC8Du7m7BLhObQKUJjZLEhhAkmJKSIqylzMxMwUuwEOEBxHCdnQRa/h1sNpu03ZSDknacSCTEt0HWP/c2HB3u2bMHDzzwAB588EHRz+fn56O5uRkzMzMi26UXiKo6FhPJZFJc4JQ51tfXY9euXWLE7OrqEjk5DxqaEtmVFBUVoaamRkaKXq8Xq6ur4lwnMlyr1QruWq1WS6XLjsRms6GoqEgEIENDQ/jhD38IlUqF1tZWbG1tYWRk5I73jJc0M10ACAKGh09mZqbQZJVKpchueYFRrUfoJEe9Op1OwnmYxcL91MbGhpwv3C8EAgHcuHED4+PjuHnzJi5fvozFxUXU1dWhurpaMqS/9a1v4aMf/Sg0Gg2Gh4dFplpQUCD/HnLI+DxwZ5mbmyt5KMzcJoqInTMlyHR5RyIRTE9PY2xsTPYDlPuvrq6KYrKgoACBQAAajUa+c9K4WViwGwsGg0hLSxMzIvdmJNcyWc/j8UjG/cjICPr7+9Hf34/u7m4RRHCM+15//uCOgjro3NxczM/PC5NkpyuWc1lWg1QymEwm6HQ69Pb2Ijc3VwwtXNTxZ3h4WOBYVO9QGkfTEKtHStW4FGLFkJqaKqFIDI43Go24cOEC/vVf/xVf+tKXpPKmpJGV5+TkpKg2VCoVnnzySeh0Orz55ptYW1vDqVOnUF5ejsOHD4v6x+12C2iOvysdx2tra+jt7YXRaERFRQWOHj0qrlkAqK6uRklJCbxer4xe7rnnHuTk5CAUCuG5554TgB7DiIqKiuTFCIVCggyg7X9zcxMnTpwQb8i5c+dQW1uLffv24ebNmxgeHsaRI0dQX1+PGzdu4JVXXoHmdh4zK7q0tDRh1BQWFmJgYAB5eXkybuTFwCqru7sbPT09gjbp6urClStXRJs/NzeHRx99FIWFhVL1hkIhYWvNzs6KcolIdr481dXVMs/lvJZqrbW1NYl/tNls0qqTP0ZZtN/vF5IsKcF0MnPMoNfrpRukoEClUqGkpAQ///nPkZWVhQ984AMijQ6FQuLsz8rKkshdvpSrq6uC5+alCUA6WErDfT4f3G43pqamEIlEcPLkSWxsbAjbjPwy/jBoiEowkhEqKyvR1dUl5AKj0YiysjIUFRVJ0NDY2BiamprQ3d2N3/72t1CpVPj4xz8OrVaLAwcOSAczNTUlvhrCI2m2HRwcFA4Wn1MutllUEJtN9lVnZyfy8vKkwODZEY/HRZVFTwN3BiyQiouLUVhYiBdffBHRaFSYRg899BBMJpNQELj340LbZDLB7XajvLxc0u8Y0cszgWNLdrDBYFAUnZFIBHNzcyJZZWQAi0LmQPD8oHdhZmZGRA0ejwfl5eVYWlqSYjctLQ2Li4uoqqoSbhkT8GKxmKiW+OdREdjU1CTjy+bmZlFHHj58GKWlpTAajVLoECW009D4Xn/+4IuCOcJUBExPT8toiJhdmtjYfVCyGQwGZZ46MDCAXbt2yXKOucZ0OdNctbm5KRgMwuyYqEd3MgBh+3Oeevz4cSwtLeHMmTMYGxtDY2Mjamtr8eyzz6Knp0cObhqgcnJyMDMzA5PJhOzsbBw4cABGo1Eun4WFBayvr+PFF1+UUUNBQQFcLpe0xqWlpeKcpjHJ7XYD2B7hcH5OFzirKy6rKcsjLXJpaQkrKysYHBxEPB7HXXfdBafTid7eXuTl5aG7uxuf+MQnpHLf2trCv/3bvyEzMxMf+tCHsHv3bnzjG9/ApUuXoNPpUFZWhnA4jAceeACpqakYHBxESUkJvvvd7+JnP/sZurq6kJqaCrPZLLRVmo3YxnMMwYuactV4PI6ioiIMDAzg97//Pe6++24UFhYKcpxjKMIaiTGg9JdIB+KlmYdNHw1BlGtraxgbG0NNTY2MPLkobWxsxPDwMDIzM4VoqrmNbVlZWUFZWRlGR0dlhs7Dd3p6GmazGQsLC5Ky9ulPfxrXr1/HmTNnxOk6MzMDhUKBz372s/IdU3XGBDnmLhAZHQ6HMTMzA6fTiWQyCbfbjd27d8t3eP/998usm38/i8Ui1AMuR2n6I7uJjmhmi/DfR54WLxDmi3Axy7zsrq4ugccplUp84xvfwL333otDhw4hPz8fAwMDEsRE2imFCXl5eTh48KCg6ik55/cyNjaGyclJNDY2wuVyCcxxJ+KCjmLu8gjPDIVC8p9TJMGcCEqSOf4k72n//v3IyMiA1+vFxsYGurq6pPgbGhqC1WqVnVcikRBJLneG5C9xdMux5+rqdrIhAAn3Sk9PR319PXw+H5LJ7YTBpaUlWCwWCVSjjJcXBBWfFPfwPFGpVAgGg3LJ0rTIdEgAshvZOfZzuVy4desW7r33XjQ1NSEcDkvy4+bmJj7wgQ8A2I5EZsrkzt3ye/n5gy8KzkGJF6dckjsGVr6s3igXpcaaX1RRUZH8GdTy04+Rn58Ph8Mh4UJ0HHMkw5aRrsO1tTV5gPmAffazn8Xy8jJ6e3vldq+vr8c999wjQDwAkpnMn46ODtTV1aGmpgaTk5MSIl9QUCC7jZ/+9KeIxWKorKxEWVmZpPW1tbUB2J7lUkLML53u4q6uLty8efMOftTu3bsxOzuL2dlZGI1G6HQ6vPXWWxgdHcVDDz0ky6nq6mp89atfxQsvvHCHnd9sNqO/vx/BYBCnT58W3n9HR4dkCRPrcPz4cXzqU5/C+Pg43nzzTbjdbtTU1ODDH/4wXn31VXR3d2N6ehoNDQ3i8GTeMgULRKmsrq5K9Zefn4/q6mq5+Ox2O5qbm/GRj3wE3/3ud9He3o7Ozk6piulqJ6+mtLRU3Lp5eXkikAAgFywd5PRU8H/H4CkAsmScv022NZlMgqHPzMyEWq2Gz+eTwyYcDmPfvn1S0Fy+fFmqNq1WC5vNhosXL2JjYwNPP/00gsEgotEohoeHZdykVCqlCiRagTC62tpaANvYEb/fL3nNfr9f9PtjY2OCJW9qaoLBYMDIyIgsNikFpWGKY07SgD0ej1T2PT09El1L8CUl3Hq9Hn/zN3+Dhx9+GFVVVfD7/di1a5fsNc6dOweLxYLMzEwpeojzMJlM6Ovrw6VLl3D48GGoVNvRrjTVMS9hc3NTkt+oLqLIhd85M9D5Wc7MzMgC1+v1QqfTwWq1SlDWfffdh0AggDNnzoiBz+/3Q6PRoLa2VvJhSHElPI9KI2JjiDxZWlpCSUkJpqampMBiImFGRoZcRvO3g9C0Wq3kz3OHRQyLwWAQym5a2nbePcOIUlNTEQqF5JIhBYDjUqY2cp9Avw3HTaWlpTJSpfKKsL+CggJcuXIFKpUK4+Pj0Ol0GBwcFPNdSUmJmJ5pmH1fg4soQ3S73bBYLNDpdLKc5FiIlWh6errooInaHh4eFqQ2jXvLy8vC5mF153K5xOVLxQAJspzjx2IxicWcn5+H1WoVRQKRE3/913+NlZUVaem6urqkdeSfQcdyfn4+lEol4vG4gO2IvHj33Xdx8uRJAeUxa4DUxmg0ioqKCvkieVFwkUjERlZWFj784Q+jrq5OQuN7enpgtVqldU1PT0drayvMZjOuXr2KZHI7yctisSAej+NP//RPcevWLej1ehEQuN1uXL9+HcnkdkbvO++8A6/Xi3vuuQcnT57ESy+9hB//+Mc4ceIEUlO3Q17GxsawsbGBb33rW7BYLLh165bwmZjhy+AUjmAIruOIhxdVMBiE1+uFwWCQsc/q6ipyc3Px1a9+FZ///OcRDoeRkZGBXbt2YWhoSNALOp1OlrYFBQUif5yYmIDT6RR0Cw9GzvLp+aCyx+l0ysHJzPFIJCLZ0MzMttlswr3iGIzjkX/6p39CMBhEVVUVuru78cd//Mc4cOCAeAtoSksmk6JCogyWHTHxKhsbG5ibmxPVDkdBHo8Hr776Ku6++260t7fj2rVrOHToEJxOJ6qqqsR8SK5aMpmUzIyFhQX5zCkN3tjYEJ+N5nYgz+TkpCz0OYKj1Jbf8ZUrV4QWzEOT0wC+e4uLizh58iSMRiMWFhYksIzk2NTUVElHnJubg9vtRllZmcz0mVDJcRaBe1RBcT9FV3dhYaEENRUUFECpVKKnp0ek7WVlZXj66afx3//7f8fS0hK+/vWvY319HceOHUNra6t834ReckxJWi/JAhyjBYNB2TVyr0B5P8OD6FOiP4zPLfc/8/PzIvPnvowmxvX1dUSjUZSUlCCZTMJsNsPj8WBtbU3GXpRz06xM6Co7Asqw9Xq9ZF3U1dVhYGAAIyMjKCgoQG1trfw5qampcLvdkstD5eDY2Nj7d1HMzc2hsrISBQUFiEQi0hYx45pxqHT2AtuLru7ubhQVFUn6Gw8A5sWSVa/T6ZCVlSUJZFxqUeHBSo3adPoieDGRdfTOO++gsLAQxcXFMiP2+Xy4ePGiGFo42gC2K1zOwAnS4p6gq6sL6enpePLJJ5GZmYnTp0/j/PnzKCkpERWL0+mUxWYikcDCwgLy8/Pli5qenobBYEBlZaUcJLxY8/PzZUlrNBplr3DvvfcikUiIzPQv/uIvMDw8jPLyclRVVaGgoADNzc2YmppCZmYmDh06hMrKStx3331455138Pbbb6Ourg6Tk5M4d+4c7HY7zGYzXnjhBfzkJz/Bl7/8ZeTk5ODFF1+EXq9HfX09xsbG8M///M9YXl5Gd3e3RKiS108N+cDAgCwpqciYnJzErVu3sLGxgccffxwvvfSS4LdLS0tFTcXqaXZ2Fl6vV75XosI5PmxubhaPAl9udjBEbLjdbqSnp0sCn1KphNlsFuw9W34e4lzs0g3N7pQMJR4KVMAsLCygsrISGxsbGBsbE08NcRMkEFPiS7wMPy+NRoPx8XEsLi6isbERBoMB//AP/4C3334bPp8P+/btQ2trK5544gkUFBRgZGREjFmJREJCdeg1Iu2UBw3HIZRNck/Iy5WVfmZmJq5evYpwOIysrCz09fXh5s2b0Ov1aGlpgdlshk6nw+LiouQyHzlyBJubm/j9738Pi8UCu92Oe++9F21tbbh165YwxDQaDfx+P+ZvByVFIhEUFRXJYpbfnUKhkPhbj8cjAWGMJGVlzS6fIUoulwtzc3NQq9V44oknUFZWJu9+Tk4O/H4/vvSlL+E73/kONBqNMJdIdiWYlMt+djvcpSiVyjsiY6enp2UExBE7TX6cYigUCiHfUoVJFSiRGVarVbD8pAmEw2ER1ExNTcHr9SI9PV12QBxD8oLIz88XaGV/fz+KiopgtVqxe/duke1qbmNw7r//flEP9vT0iMybWSLcib4vFwXdi1ziWCwWubFDoZCA0DhKMhqNAvHKzs7G7OwsQqEQSkpKoFQqodVq4Xa7MTo6ijNnzuBP//RPodPppNJLSUmRS4lqCJ1OB5fLJaiEnXGWxErs2rVLmD4cCXE2zaomIyND8Ax0VzJUqa2tTarolJQUlJeXY2RkBHNzczJS46KQLS1fNMZxAhBMAdPLFhYW5AGhwTAajUolwUV4NBrFAw88gLW17TzrvXv34q233hLTIVHSbrcbAwMDKCkpgdFoFHnugQMHMDg4iN27d+PgwYP45Cc/ieXlZfz617+Gy+VCdXU1KisrcfbsWVRWVuKhhx7Cc889JxU78643NzclT9xoNAqyvKCgAKurq5iamkJ1dbUsnfV6vVS/JpMJly9fRk9PD6qrq5FMJvHd734Xx44dw7Fjx7C2tibZ0OziAoEALBYLQqEQ9Hq9LCApmKC2nAtEzpN3JrGNjIwIwoL4hLm5uTuSwkh0pXKPleb169dlHKFQKCQPJCsrC+Pj45LgSBFBbm6uHI50stvtduFVLS0tyah1aGgISqUSx44dQ3d3N1QqlYz9WDlztMbdG5ekzBzgrohYcovFglgshurqakxMTKCoqEioqzTkWa1WdHR0oLe3Fw0NDYjH43C73di3bx/27NmD5uZmAJDqlN1IWVkZ/u3f/g0/+tGPsLGxgc985jN4/PHHYTQaJQOivr5edpUajUZGhGNjY9KV0CjKeTsPLRZ3DOVi90ReUTQahd1ux40bN5CTkwO1Wo23334bsVgMu3btws9//nP5PgoKCnDjxg2cOHFCnPVFRUUIBoMiW/b7/TIemp+fF5Iy3dochdHgFo1GUVZWJpMBdnTco7A4oKKItIC6ujoMDg4iJSUFoVBIlGPMmnC5XKLsjEQicg4yjY6dkNFoxMTEhLxvRPEzUOzgwYMwGAwoKChAR0cHhoeHJZtnZGREWF3Ly8uwWq3vr+qJ23qaSbiI4oPCdryhoQEAJJwGgOQK8+Xy+/144403cOjQIWRmZmJzcxM9PT2YnZ0Vtj0Tp4qKimT0sbq6Ku7hnfRFRjqS3TQ5OQmHwwGn0ymVGdU8Pp9PZoQ6nU58GkR6c7kZDAbhdrvR1taGQCCAvLw8nDhxAp/97GcF5FZXV4ebN29iYGBAxjKM76QpbafOnc5k5nhYLBZpK1kdUEXEUZTf78eHPvQhpKamCn//8uXL0k0kEgnEYjFxjKpUKjzzzDNIJpN3VKhvvvmm7GQuXryIQCAAr9crQUrXr1/Hz372M9TU1KCpqUlm8VxYx2IxccPn5ORg9+7d0Gq1UKvVgi1hXkZra6v4C44fPw6n04lnnnkGExMTeOqpp4TESXIt5aRMX0skEpiZmRG5K0dt3OskEgk89dRTUCqVIpe8dOmSKJMMBgOSySSuXLmCgoICGaGlpaVJXjERDVyg7tmzB+np6RJVyoOEFSMXuwT06fV6MY3xUNDpdBgfH0dGRoaML3kYdnV1oaqqCp/+9Keh1+uh1WplN+N2u+WCmZubk3GW2WxGIBBALBaTgz4vL08kkqSrMuJ0ZmYG9fX1cLvdEp4UiUSEYZWSkoIPf/jDUvQQFc8xKlV14XAYZ8+elaraZDLhpZdewuXLl/GRj3wEOTk56O3tRWNjIyoqKhCNRsXBz1hjYk6I7klJSRF3fXr6drY3CQycBlASX1ZWJt/122+/jcnJSXi9Xly7dg1/+7d/i4KCApw5cwaPPvqooM5Z1fP3p8GSngKOnNlhALhDXrxTCRWNRiVylM8+L2WOFQHIM0KW0uDgoCywnU6nFB1ExRMpTtc6/V07l+MMHeIud3FxUfZuw8PDiEQiKCkpkd3eCy+8gKKiInzta1+T/Bdge79nNpuFlfW+XRQ7x0FswagFJhmVskLmBxPcR/kXR03j4+Noa2tDU1MTjh07ht/85jd49913UVNTI+oOGvtI+CSWmgs/HgbxeFzGG0ysqq2tFSQwlTbsfvLy8qRCZYdEFyfVJ8ePHxeTGm/kmpoazMzM4Pvf/z5GRkYwODgoBxo7EMopuZ+g8Y38e0pBq6qqZPZJCBs7Nu4ryOKhzJIk3e7ubjEz7UQfUDVE5DsA+d/k5OTggQcekH8OgIxnvvWtb6G+vh779++H5jYPiQFSzJJgEBDpuvQNcFlnNBpRWVkJl8uFqakpbG1t4fjx4/B6vcjMzERlZSUef/xxXL58GR0dHRLnCWxXeCTashMgn4e4DpVKhaqqKqyvr2N4eFjQDY899hj8fj8uXLgAl8uFjIwMPPzww6JKo5SWTKKUlBQolUpxEqemporRiR0MK0WSTmkGS0tLk2eSTnh2cfF4XEi+/DM54qIyZmFhAaOjo/K9EJ7odrtl/EnntMlkElJsZmamKM7oOGcELGfmfH6I9aCjnliW8vJyMbgCEIoChR3MkZibm8PevXsxMDCAqqoq7Nu3D06nEzdv3kQgEBC0TDAYxPDwsCDQqbqiItBut4vhlgq0ZDIplyw5a0TZs8ikT2F0dFSq7bvvvhuRSATvvPMOVlZWBBT5y1/+EvF4HA8//DCCwSDOnTsnoVb0G9BER3wLTaL0fNAFnZubK38PHv47XfEGg0H2EAAkFKqxsRFzc3NQKBQiamA3RQ8WM3YIsczKykJKSgocDoeMrL1er/wOdOpTsh0IBITdRs6dXq/H6OgoxsfHUV1djYyMDHzve9+TEXZpaamM3Bmv+15//uCLggoGtu1erxcFBQUYHx+XSmJpaUncjGwL+QKtr6+L5b2kpATHjh3DzMwM/H6/OFhPnjyJ0tJSvP7668jNzUVnZyfS09NFq97R0SFOT/4u999/P4aHhwUExqVlbW2tOLrJlpmYmIDdbpc2jpWHUqkUVDR9EJTJXrx4Ec3Nzfjc5z6Hz33uc5iYmAAAVFVVQalUory8XB4Q3vyUlhYUFGBmZgYFBQWSArixsSFzTh68lFwCwK1bt6BUKqU67+zshNFoFEDY1atXcezYMYGfUa3B3Q0PYGYbcNxG9hET3KampjA3NycgPaPRKBTZwcFB+Hw+ueSJZSHancqj2dlZFBUVYXJyEvF4HLt378Ybb7yB8fFxrK6uYnBwEK+99hr6+vqQl5eH++67T9pxFgMku9KbMTAwgK2tLcEy5+TkiM8jGAwiGAyira1NvC99fX148MEH5eXJz8+X56GkpERecBpDmUPAJLeCggIA2+gKSrwLCgqgUqkwNDQkBwmNYTtHYJFIBGazGXa7XS5hZmnzgGFhVFxcLNkQZDPRjEcUN3lDnO+Ti0VqKlVwNABmZmYiEonILHptbU0OSxJmmbRHSCAvi/n5eenkksntJL+6ujopmKamplBRUYHFxUVcv34dX/rSl3DkyBH80z/9k8ApaRxj7DCraEJBKfUMBoMoKyvDwsICtFqt+EBogiTskaNrupq5H9i9ezdKS0sxNDSEl19+GcB2Wp3L5cL4+LiEXxERnpubK74qFl9c6NMkymkHMS+FhYUyGuJYizJ/jsWAbWVjS0sLXC6XFCB8PmdnZ6UYyMnJgVKpxMWLF5GamooTJ06gtbUVmZmZ8Hq94vmZnZ2VIlGtVsvekrwsvi9Er2RkZMDtduPNN99EJBLBo48+CpVKhcuXLyMej2Pfvn2IRCLIzc0Vpdn7PnriC8J2LhaLycMFbCfE1dfXY2ZmRuSvlJORgkhzz7Fjx/DCCy/A6/UiJSUFhw4dEiduZWWl8OP5RW5tbcFut2N6elqUBsylYHiRy+XClStXoFarJdBjdnYWpaWl0lKur69jenpaIkKJRuZy0Gg04p133kFqaioOHjx4B49KrVbjU5/6FPbt24fGxkaMj4/j1q1bgvfduYzjQ8ZcZya7UQ0xMzMDm80GnU4nLuiBgQHo9XqYzWZxByuVSgEARiIRPPjgg1hdXcXu3btFTUSODD9nALjnnnvwxhtv3EGB5WFIpcsf/dEfobe3F263W8YzoVBIXL4+n0+qG5/PBwAyDuLeh4d5JBJBR0cH6uvrYTKZ0N/fj83NTVGJ3LhxQ15ALmj5mSwvL4tXoaCgQOirJGLG43EMDAzI3+Guu+7C4OAg/vqv/1pMX3/1V3+F9fV1yVs+fPgwNBoNBgcHpXLlfDwSiQCAZCCXlJTIIjg/P1+QLDSFsYLn/oBRtOx+qHhixcldA8coVADR0Wy32zE5OYny8nLxpfAwoGqK7nXKPjnupV/D7XbLQeJ2u5GdnY2cnBxRO3FnxouAOygekDszJ5hFsrCwgKGhISgUCoRCIfzrv/4rfD4fVldXMTQ0hPvuuw8bGxvo6emRHBH6SPjnE43OKQLzP7gYpu5/ZmZGfChc+lK5t7CwgLNnzyIQCMglf+DAAXzzm9+Ey+XCsWPH8PGPfxzl5eVyyDJbhot8uvs3NjawtLSE9PTtHHridZj7AEBUStwJsGDa6WUhFobeL6LleZAzApgAS4opwuEwFhcXYbVasW/fPuh0Orz99tvYt2+fdB30cFCKHgwGUVpaKkIBrVYLnU4n2RparRYej0c6Z9IKTp48Kd+l2+0W9SCLg/fy8wcjPBh5ye09MeDLy8sCkEsmk7h165a0xzxQ6KXgvPvFF1/E2NgYBgYGcObMGXkZX3zxRXzzm9/E6uoqqqqqcODAAQCQAycWi6GqqkpGIlQdULkRiUTgcrlw4cIF4eWsra1hYGBAqgbm7ZKASrVLamqqREWmp6fD4/FgamoKpaWlKCsrQ3V1Nf7xH/8RzzzzDI4ePQqXyyWMFpqyqHriS766uorx8XE5XDc2NiSchnuLaDQqyzaqt3hQcbyzuLgobCpKYre2thAKhYREWV9fD71ej6GhIaliyVNyuVzQarVStTH2lJV/fX39HUvYjY0NiVVlHgWx0TQo0ThFhQ4XflzacWxG3wDBjsSz7CSWKpVKYevTVMngIiI8SNcdHBxEY2Mjnn32WTz77LOydP7FL36B4eFhnD9/HpOTk4hEIpKgyCwKGq9Y9TscDvHlTE5Oyr5lcHBQCge73S4HLMeClL3S+8LxI/9d7L6YoMe5MwUV3G8w8pVBQDuNiTRmskNn4iPDsgwGA+x2O3JzcyVkiyE7O9Vh8/PziEQimJ+fh8vlEoQEcxjIQ1pYWJAAsZmZGdjtdtTX1+OLX/widDod2tvb8e///u+yr6HBLyMjA4uLi7JDnJqaEjoweWwZGRlCFnA4HNJRRqNRVFZWCq2ht7cXFy5cQF5eHqqqqmQnQJPhnj17sLCwgIWFBdTX10uxSrc9hQ90d+fm5kKv16O2tlZ2IhQepKenIzc3V+TWwHa3wCzynbk0FKnsxKfvNP7yMueZwtjg1tZWPPzww8jLy8Orr76K0dFRnD17Fm+88QaGhobEwLuysiL/3omJCfFeUbFXX18vCA/+3idOnEBLS4sk4lVVVeGuu+5CMpnE2NiYjOxZ+L3Xnz+4o8jPz4fH47ljFMGZYlFREaLRqBwIPBRLSkowNjYmckSdTofi4mL85Cc/wSuvvIKCggKp/JxOJyKRiORSMBOalQbHKDQ1EWPu8XiEoPrYY4/hF7/4BRwOB6qqqlBbW4tTp04Js4VEVrvdjlAoJHGSmZmZ8p8FAgEUFhZic3MTV69eRUlJCbq6uvDiiy+K09xqtaKnpwcA5IJQqVQoKyuTYCdWH5z7c5xA1Elqaqqk9VEhtbq6KhI7IgwqKytFH+7z+dDX1yfKLEYgjo+Po7W1Vcx487fDXw4dOgSr1YrBwUG5yFnFcHREA5bJZILP50Nvby/Ky8sxOTkpIDNGk7KLYxegUqlE5ry1tYWVlRVhfuXl5YnpjNnQxcXFMBqNYgaiCsZoNN6Rb8zRSWZmJoLBIOx2u5g6MzMz8fLLL+PgwYN4+umn8cEPfhCnT59GRUUF7r33XpSWluLKlSs4ffo0qqqq7pB/qlQq2bFQHkm3LCmpGRkZqKurw8bGhoTvsMIjVjo9PR2bm5uCKM/MzJTDORqNyuKU8kdegKS5EjHBA45mq0AgAKPRKHkMarUa8XhcRqKUb+fk5EihQG4XyaUAxBym0+mgUChQWFh4RyAOgYo6nU5GjzQaWq1W9PX14cMf/jBUKhVsNpsYQpPJJJqamkRgEo1GUVpainvuuUfUY21tbZiZmRGKLwBBw7A7IdfL6XSipKQEw8PDOH36NLq6uqBQKHD8+HF88IMfREVFBSorKwUo2dLSgg9/+MMwm81IS0sTFRKw7UZmNgR9HhzXbGxswGKxiNqIcvj19XUMDg7CZDJhcXFRUjz5jACQxE3+cwAkCyU7Oxtzc3MipvH5fNIZlJeXo6mpCUajEX6/H8XFxUhLS8OFCxfg8XhEKJCRkQGr1Ypbt27J1IZ0BOZcWK1WXL16FV6vF8eOHUMikRDfDSGG5eXlGB0dRTK5nSDIUS4LtPftoiDBcn5+XqRXO3ELO7G5ZJT4fD4xBGm1WhmNHD16VJQarILol2D1S+id1WrF0NCQoM35AtfX18tY6fLlyxJc89/+23/DysqKLMyTyaSELRHKp1Kp0NTUJDM/qlk4N2bsYSAQgN1ux6FDhyQfnIH2xcXFYpEvLCyUKmVqakrUDBxjMDgkJSVFkN4FBQWCaOYlxcU1czkYj0hHKU2LR44cgUqlwltvvYXa2lpJ2Oro6EB+fr7oxfkgcjRG1gxHYazY/X6/+ELoLtVqtfL3oUubLuCd3STwH1iP3NxcLC4uor+/H/v27YPZbBZ8Rm9vL6xWKxYXF2EymQR7TZ29yWTC7OwsgsEgdDqdmJ+YaaFSqfCpT30Kt27dwm9+8xtcu3YNVqsVZWVlmJycFOqvxWJBcXGxjLEogWSVBkB2Dvz38lCh3pzPTGFhodCHe3t7YTAYpJvKycnB9PS0ZD7Pzs5iZGREPheiqQOBwB2GPKYQ0o9BZRwr+tnZWahUKjGO8qBKS0u7I5OCiJn19XWRxPLvqVarpcv3eDxySDGNkp4kqoS4fO3q6sKuXbtQVlYmF/fw8DAee+wx8bs0NzdjeHgYv/nNb6TTaWpqQjKZFOm6VquVESgdxgBEWUWlGfdi165dw+bmJo4ePYrp6WmMj48jOzsbsVgMNTU1UKvVImDZv38//H6/pFKSy8adAim7NNRSusqUQf7duVthd8gzyGQyiWCH0nYWLPzfNDQ0CE2CuHgKDlJTU6HVatHf349Tp04hPT0dKysr2LNnDx599FHpLAHgtddew/z8vCRcMu2TEwiO7a5evYqf/vSnmJycxN69e7G+vo5//Md/lEhfdiQsfFpaWsTrRpr3+3ZRcHNO2Rn5+3SoUi1CFQZv9kQiIbhcPryHDh2C1+tFe3u7uGUBwOl0oq2tTar53bt3CxiQDkYiASKRiGTInjhxAm+88QYuXryIcDiMj3zkIxgfH8fly5dlxr5nzx5cu3YNZrMZjz/+uLh6OR/nopk46ry8PNhsNhlHTE1NITc3F3a7HRMTEwLays/Ply8+Go1KO8+ugJTKoqIieL1eyT7g5ULtPu37dLTzpU8mk6KIWFtbw5UrV1BZWYl33nkHBoNB/By/+93vAGw74UdGRgRvnZWVhZqaGsEr86XhaInVxvr6uuC+aYDiC8521+VyoaioSPApRB6srKxApVKJ6oXdSUVFhYyjqqurUVpaing8jvb2dplnu91ueL1e2U9RyshnhZcrk+6OHz+OzMxM/PrXv8brr7+O1NRU9Pb2StHgcrlEzmw2mzE2NiaLyKKiInH3c+5PsCT9GQTNEUw5PT2NlZUV1NTUSBfBw4WzeZ/Ph4WFBeTk5MBisWBubk7+tzu5ZPwc2U1xvm2320V4sbi4KF0Onc9cXlN0Qa9OMpmU6lSpVEqGhUajkV0HkRTLy8uyaCZfayfVlaZBv98vGRqRSER8LUVFRbDb7cjKyoLf78c777wjLKympiZ873vfw+XLl9HY2IjHH38cXq9XVIgA0NfXh7KyMkn9UyqVyM3NxY9+9CN0dHTgv/yX/wKn04lbt27h97//Pb7xjW/AarXi7rvvlojkrq4uuZQYQxqPxyUZkmghfl7cMdBDQ/QGlUQ6nU7c1clkEvn5+SJXZS6JwWAQcCDfxeXlZTQ1NSE9PR1Hjx5FNBrF4OCgXNBVVVWSjz49PY3nn38eGo0GnZ2daGhoQElJiWSE0G9GPA0AiX4mObitrQ3RaBTPPPMMysrK8Oqrr+LNN9/Erl27RF03Pz+PhoYGiRVg3DA7lvf68wdfFIy7XFxcRH19/R2o6M3NTUl0IxaAy9zp6WlkZ2cjHA5L5e7xeOD3+9Hf3y8HTkVFBZLJpOCo8/LyEA6HRd5FpzPZ9ztDYwoLC4VdMzw8jN/97nd4/fXXAUA6koyMDNy6dUtAg2TmM0s4Ho+L3DcWi8kIhVGDdN5SJUMlF30adrtd5sFEqWs0GpjNZmg0GumIyF1pb2+XQ3Z9fV3QIFzQj4yMCKuGJqaioiI4HA6kpqaKoS4/Px9///d/j8HBQTz66KP4/Oc/D4VCgR/96Ef48Y9/DKPRiNbWVtTV1cmfn5+fL+Mvqnpyc3PF+7CysoKZmRk4HA4Btmk0Guzfv186CwLvOEaanp5GSUmJyHS5YwG2seUrKyuIxWLit9jc3BSVCfEgfOi5/KMGndp1LixtNhsOHDiAa9euISsrC/X19cjKysKDDz6IZDKJ//N//g8qKytFgsk9xM5kRF7QZJQFg0GYTCasr6/D4XCIAIAY6kAgIHjsrKwsDA8PC5qFst7y8nIEAgHMz8+LesXhcAhWghyi7OxsOYQ2NzfR29srv9e+ffvQ29sLh8MhqXJ+vx8ZGRnig9n5nDA1joKSzc1NyVHhvoHxoxxP0UyamZkpHVJBQYFk3dOZHgqFxMw5fzt+dWRkRH7PtrY2JBIJvPLKK/B4PPB4PKiqqkJ/f7/Ev/p8PqSmpkp1T3kqPweTyYSFhQV85zvfkQKjvLwcjz76KH7605/il7/8JT772c8iFAqhurpaTHC8+GhQo8CGLnWSDwjYJBmCiiMWJcXFxdja2hKyAoUjpAn4/X4oFAqo1Wrs378flZWVSCaTAiDs7u4WSSux8H19fbDZbEJwuHLlCrRaLa5fvw6PxyOUhqamJpEmc6QJQJDkZWVlWFlZwcWLF2VEGA6H0dvbC61Wi//xP/4HJicnodPpUF5ejq6uLgwODqKyslLUVDyj3reLgooN2tupS6YDNi0tDdPT09JdcMbK8Bsm1PGlu3nzJtxuN5qamtDR0YFgMIi9e/dCp9Ohvr4eTqcTHR0dWF5elsWRUqmE2+2WBRQ/iEAggAcffBBut1us78ePH8eNGzdgNptRXl6OyspKrK+vIxwOw+fz3RHnyfaM8klKNqmo0ev1WF5ehsVikYOOByXDd27cuIGSkhKRyZI/Qznm9PQ0HA6HBImwPTYajZibm8PCwsIdXRqXyOyGGhsbkZOTgw984AN45ZVXEAwGMTMzg56eHgQCAdTX1wOAhPk4nU585jOfQUtLCxKJBIaGhiRIhWoXLscpISWWgI5i8rXoV/F6vdizZw9mZ2eFiKpWqwXhwguGzldKEwmP9Pv9oghi5CsXh8w+IMeIEbeEp3Fs1t/fLyPQ8vJyGI1G7NmzB2NjYxL/+vjjj8uCnYIFkj1ZaFC+SrQI41TT0tIwOjoqWc3EkxPZwQ6JbT/lkXl5eXC73fB4PJI2SGQ31V00WnGRzn0bPxOFQoGRkRFBnodCIWxsbOD1119HKBTCgw8+CLvdjqKiIrlks7OzZcRJZRHHn/Pz8/IeEnvNSpu/Cw9cenw4PuR4bX5+Xi6azs5OXL58GSdPnsS3vvUtPPfcc+jr64NOp0NrayvUajV6enowOTmJL37xi9i3bx/6+vrEIc2RGXciDQ0N8Pl8uPvuuyXjw2g04gc/+AESiQTOnj0Ll8uFsbExxGKxO6B7HI/S7BaJRETJxVwZmmitVquo1Fjg7Uy5IzqHSBaOtnlpGgwGGSUvLy+Lb2dsbAyBQEAO9c997nNoaGjAq6++ivX1dRiNRvzud7+DwWDA8ePH8bWvfQ2xWAx2u10EA5rboWn0VDAZkBGwLEIUCgVeeOEFtLS0wOl04oknnkBVVRVef/11KJVKOVc3NzfFqR0Oh7F3716JYH1fLgqFQiEVIoFk9A/odDqZhwP/UW2vra2JU5B4Z4PBINZ+HlqVlZWoqKgQbEE8HofH45GlMHXmWVlZKCoqEuUCWzbmQuTl5aGmpgYmkwldXV0wmUyw2+0YHR3F7t27cc8990hGdHt7O2KxGEpLS+WApPSXxiDizimHYxYAPReU72VkZECn0wkamWHpXJjG43FZIqvValGOsUq22WzCmVlaWpJIUo5x1tbWMDQ0JMas1157DQqFAg8++CCqqqrQ1NQEh8OB4eFhfO9738Orr76K9PR0PPvss6ioqMCpU6fkEk1NTRU+FbHVDJ4qLS2V5TQvY0LUeIGtrKzIYp6mNGYRcGkYj8dlVwJsIyJYoRHjTH4OdztU4igUCslTLi4uFvUJA5OA7RCtvXv3StWo0+mQm5uLnp4ewXLzoGAGNGNqKR/lXoCZ0TRR8XlihU41Ew+K8fFxcT3zM+ASEgAKCwtlp0Ij1ubmJjY3N6FSqcT7otVqxcfAMUE4HEZra6u4i7VaLX77299ia2sLu3btQigUQiQSESVeV1eXHDQE6tEYyr1RRUWFFFtU+RCRvrS0BI/Hg4MHD8rvyBCi0tJSzMzMYHFxEQ6HA1arVSjD7Og5cmS3f+DAATidTkm9pDcgLS1NKL8LCwsiFX7ttdewtbWF5uZmoftOTk7i9ddfR0NDg/y7CPwkSYEmN1b8RqNR0je5X+LvQEkx8yiYsUHW1+zsrOyDCD8kyI+XDADBk7/11ls4ffo0ent70draik9+8pNob2+Hx+ORsTwxP6Ojo/jNb34jmSxGo1H2bvQQ0WjJvBJ6ioDtcfCtW7eQlZUFm82Gzc1NfPrTn8b09DSmp6fR1tYGn88HtVotFG+eU263G4WFhZKp8Z7P+f/3K2L7hyYQLuFWV1cllIikTM47AUhur8fjERjb9PS0sFaamppkPLNr1y7k5+ejp6dHktMYfkIHNfXPtLSTx7+5uYmcnBwMDw8LhZLQvFOnTuEnP/kJmpubEQgEcPjwYbncWMXRAR0Oh8XJajQaMT09LQooXhwWiwXj4+N3HDZc5mu1WpEDut1uVFdXi/Frc3MTra2tcvgolUokEgnYbDaZk3MxSJ8IXehMx/vlL3+Je++9Vyq8iooKrKys4OrVq3jqqafwu9/9DsFgEHl5eVKhz87OoqenBwqFAmVlZaKGcLlckqaVTCbR0dGBffv2YWRkRMBjLpdLqi1Wb4S9VVVVIT8/H93d3XI59/f3S0a23W4XjwLnrlTw8OVsbm6GSqVCZ2cnLBYLbty4AZPJJDiQzMxMUY94PB5JIdvY2I6KtdlsUlk+//zz0iEBEMUKZdwGgwErKyuygyEmvKCgQMakRExw1s0ZNhHU6+vrsFqtEgNaUlKCWCyGyclJ2Z8pFApsbW1JoVNaWiru/draWkFmeDwemM1m6PV6UTRxBLi+vo7Tp09ja2tL5uL/43/8D0xPT+PVV1/F1NQU7r//fhm/rK+vi6igtLQUeXl50Ol0on7h0psjGY7FiM+gKg6A7IcyMrYzs2nq4z/jdDrxwAMPyMV89epVPProo9BoNPjlL3+Jl156CR/+8IfR2NiIeDyOtrY2OTTpjqZYJCMjA8vLy6ipqcHa2poY5/r7+3Hu3Dl89atfxTe+8Q3JmpmdnZXJQiKRwP79+7G8vCw4f5/PB51OB5/PJ3GhDPFZW1tDVVWV7ORobGPBxsX3zsu7t7cXOp1OTKwejwctLS0YHx/HQw89BIfDgdnZWRw4cECEOzS5EQrocrlkOrC4uIjS0lKoVCr09/fjnnvuwYEDBxAKhQSxzyKZy3aPx4NoNCo04ObmZuTn5+OnP/0phoeHkZGRAZvNJo5ymqHT09NRUlIivq76+nr86le/en8uiqKiIuGcU2NutVplMUe1E7f6JpNJeCk7Eb2U+JWXlwvQbmtrC+fPnwcAISdSi78zZ5k3K5dxNLcNDw8jLy8PJpNJZpClpaXy0J8/fx7Nzc24fv06mpqaMDo6KqgR5tYysJ7Y8draWmGlUJLIw4WYhUAgIGYzPlQkWw4PDws7h1DEubk5cRqTM0+ZX2lpqeAwqJLhgTY+Po6xsTGo1WpUVlbiwQcfRFZWFr75zW9ibGxMApk2NzfxoQ99CH/5l38pJM+VlRXh+fDQ52XFTqmxsVG8Hevr61KlUBFmNBoRCAQAbC/s29vb5Z8HIHPr+dshU1TzmEwmcb+TY8NuJR6Pi5xwdXUVpaWlACCVelpamsgRiS2YnZ0VHhaFArzwKeGlaoiL4PX1dbnAiGpOJpN37F7YAXKHwJEqZa58ef1+v8y9aepiJ8olJ8eulKCur6/LToaiBqqtiJLp6upCRUWFqOU6OjpQU1ODu+66C1NTUwiFQrh27RpcLhfq6upEtcVUM2Cb7dPd3Y2CggJEo1FkZ2cjOztbxjVKpRKNjY2SrbC6uora2lqoVCr4/X5R8HHJ7vP5BLKZnp6O733ve7h06RIqKirwuc99DtXV1UhPT8fo6Kj4jBj4Q7YbO1DGhwLbCX1URRUWFmJ8fFzCpurq6rCysiKE5XvuuUeCkdbX12U/yNEmXdx0ei8sLMBqtUpnkZubKx0Npf0kFhCbQUc7g8N4jlG0QXd3VlYWfv3rX0tX9uCDD+JLX/oSnn76abmwScpmp56bm4uvfe1rKCsrk4hml8uF9fV1HDlyBOvr6yLrJdg0EolIV01IIwVAjPX1er2oqKjAiRMn0N3djZs3b4qBz+12w2AwICUlRZ5bvvPv5ecPvihoeKLskmMHmlLoc+Csk8lWXNTMzc0JvZOLXYLMent7xZik1+uxtrad8cv/nU6nQygUAgC5nVlJ7dT4szKYmZmBxWJBU1OTOBf5wDLUJRQKoaKiQiSAjDisra1FZ2cncnJyUFtbi56eHok+pHcgNzdXjD6sIgmZ4zydyiHSdJnIRqYQ8QfhcFgW2ZRDMjkwLS0NOTk5cohaLBacPn0asVgMjzzyiORjUEFVWVmJxx57DFNTUyJLHhoaEmMboXjz8/Ow2+1yMRBYuLm5iWQyKQc8D/FYLCb4aI6KiDnmQ848ip6eHjmgVlZWMDc3B7PZLF0gHfETExMSXFVUVISpqSlZ8BIFzewSmu5oFmNnQY4Ss4qHh4exuroqogoeAsRBMD8jIyMDo6Ojgjznkpya/IWFBTidTvE+0ETHQ5f/l0oZIlKo1Qe2VYJ+v18WlDRb0j1NpD7z5FtbW+XZdTqdYgoEgNdff11cwFarVbpBfq95eXkSx0pZKxMEmax4/fp15ObmSnfOkcjf/M3fCCoD2AbbRaNRUQClpaUJ9I+jujfffBNXrlxBamoqTp8+jbm5OTz55JP4wAc+gOHhYVkqE2C4sbFxx+XJhES6h3t7e7Fv3z7JXOeC99133xUjns1mE/Nbf38/Ll26hIKCAuzevVt2UzxUo9GocLZ4LvG7YWFLXw1l3V6vF21tbWhpacHs7CyUSiWGhoYEe0MD5fj4OKxWK9LT09HS0iLfE4Ux/Pw5OSEenEiciYkJYctREDE3NwebzSaCIfpfGL5EUyRjnpuamlBaWory8nIpmFi08J1i0p9arZYi7738pGxtbW39v1wQnLt/5jOfEXcg9dvBYFDchDU1NUgkErBardIFGAwG+P1+CRWnESsajWJrawsmk0kcikQE85DlA8YZcHZ2trD2mdnLDoZLKR4KJEnec889UCgUGB0dlUqZVSgVNUSXFxcX48iRIzh16hR+8IMfwOl0oqKi4o6IUGY8k9HEn7W1NYmJJUhsenpaMAb8svnZpaSkiLmKShyz2YzZ2VlkZ2djaWkJi4uLUCgUkn1bVlaGkpISfPnLXxZMwOLiImpra2G323Hp0iU8+uijaGpqwsWLF7GwsCARlaRa8oFJTU0VhRHn0AAk35gB81xAcvfEi47VHUc/XBJTwWQwGODz+cRPQEhZbm6uXKqrq6tSQHDnUVhYKCq09PR0wRYw3J4VulKpFHw488JJ1fT5fCLfZmjN4uKiLCxNJpOgQObm5sSYxArTbDbLnob8LS6K+fdgl0VVFC91qpOWl5fhdDrlgKUHgtgVfk5arRYzMzNobW1FWloabty4gYmJCdx9993weDyS/BaNRnH06FFUV1fD7XaLGTE9fTsH2+FwAIA8N+Xl5YJ6IIKmo6MDzc3NWF5exp49e/CrX/0KQ0NDOHv2LGZnZzE5OSkJgRwF2Ww2LC0tYffu3ejq6oJer8dvf/tbyXWxWq04f/48dDodHn/8cTz00ENYXV2VGGKz2SxnwE6pNXdfhYWFiEQiMhGorKyU5LhgMCiGUC6fiW4PBoNob2/H3NwcHnroIZSXl8Pr9cLhcEjmOsUpJAFwN3rz5k0ZPVFFxDEuzzSlUoni4mIBVVLpRnf897//faytraGoqAg5OTloaGjA1atXUVVVJcwol8uFgoICOBwOpKenC2dOpVLJaJVFyszMDKqrq+WZZtez035AeoRWq5U9305IJ7PtWbzbbDYp/oaGhvDd735XYhX+v37+U4KLWCFzjqzVauH1esUcx0MEgPgIOOsNh8Ooq6tDLBZDIpGQkUhxcbGEBgGQTqOoqEhoj/Q6sJMhI54GKr44lOFOT0+joqICFy9eRF5enrh62VkQl74ztnJpaQmDg4NYWlqSdnJ6ehrJZBLl5eWyjyAvnwtQmnx8Pp8sxUZGRqRiZ2vJUB6OgUjEZeXh9/tlpMFlm9VqFbjd/Pw8Ll26JIC0WCyGX//61ygoKMBrr72G4eFhmT1PT0+js7MTW1tbmL8dcrK2tiYhUQT7MXyGh11GRgZaW1sxMTEhxrHs7Gw5FOkXyc3Nhc1mQ1dXF/Lz80UCPTU1JcvVeDwu6YVra2vQaDSyA5qfn8fMzIx0BLxgGQ6zU5VDPwultcTI0OPCkSENezRgJRIJBAIBSRnkEnd0dBQFBQWiPovFYlhYWEBJSQk8Ho+Y5njQuN1uzM7OivmOM2P6hGimpLk0Ho/LvopdI7/vW7duiQeDSihKcrm4NRgMmJqawuXLl5Gfn4+ioiLBlcfjcbkMuSTnHH6nQosdIgC5pA4fPozx8XEA22Pk6upqjIyM4M0330ReXt4doTt0nNMVzhGI0+kU6eXdd9+N+fl5HD16FP39/RgaGhI1mFqtxp49exAOhwXUx5wQJrhR+Ua0T01NjYyGiC0h042VucvlwqVLl2Qkxm6Vo0fuLDh6Yo6N3W6X54S4dhZHeXl5QtFlgNrMzIx0enSyq9VqOJ1OOciXlpbEpJiWlobjx4+LHJnn1/LyMsbGxkTCy/Es6RYcLdfW1mJhYQHzt0nQ9IdQiJNIJKSA4c5pY2MDoVBILhOq23gWUjHHcd17/flPQXhotVqEQiF5+RnByJ0BAMEJcCFDeBZHFcPDw3dAxJaXl6U9XFtbQyAQEFcwYyR3ZnXn5ubCYrFId0FCJ9UCfMC59eeslfjlpqYmyW/mhbd7925cuXIF7777rqiMGK7C5a7b7cbQ0BBGRkaQn58vzJvh4WHpfhgmsra2dodqSq/XywOYmpoKg8EgLwXHZpzDp6SkyCVFyBglwKurq3jggQcwNzeHU6dOoaenR3wbm5ub6Ovrwwc/+EGZqxLuxtkm3d40JpEdxPktFT70HnDMRJnvTrDh4OCgCA1ogAIgLyApqMzbpvx2J6aCKOeUlBRkZ2eLkZHjxfHxcSiVSpG+krHP5DuCGqm8YpWeTCZldEZlHSGVlBtyjEEyqOZ2VgFHRVzK84Xs7e2FRqMRYx/HffQNARAKMCF0xG5kZWVBrVbL7iYcDsNsNgtpuK2tDe+++y4UCgU+8IEPSNjQsWPH8MEPfhAulwuhUAjNzc3o6+sTSS6VdexeFxcXYTab//+kn1/+8pfhcrnw29/+Fnq9Hvfeey/sdjva29vx/PPPIzs7G1VVVdi/fz8ikYhc2jSpBQIBicdlaBdNZRqNBlVVVYjFYlAoFBIXC0BGOjv3XRQ3qFQqzMzMIJlMYteuXTLiIsqioaEBqamp8Pl8+NnPfobdu3fjox/9qMjG09LS8NBDD6GhoQH9/f0Si6zVamU3x1Hx0tIScnNzMT8/j5ycHLS1tSErK0tIu0qlUrhaHONxDxuPxxEOh6FUKoUXR7FMVVWVeBwo6SbChR05fTjhcFjen6WlJcHI05sWj8cFl0LSdjQalakDn93NzU0Eg0G5ILKyskQ0shOvzhEbO+r3+vMHQwFXVlZkXMC/JBOeFAqFmGroeI7FYvD5fCJJNJlMmJ+fR2VlpXQGhYWFMtumS9psNkugD6FyTzzxBO666y4cPnxYIH9c1rrdblGMsFLli2+1WtHY2HjHDmNkZASBQEAW1Q0NDYJLn5yclEr7jTfewOzsLA4dOiSHlFarRVVVlVRD/f39grImh4jGmPn5eSQSCSGmMh+BngmqcJj1wINmcXER+fn5kveclpYGr9eLtLTtbGfCw7hUo9Lr61//Oh5//HEkk0lxdPLipVuVC3QqRDgaMhqNItFk8I5arUZra6vsADjP50tOcyIhaBznsUJ0OBxipMvKypLLMS1tO5aSeA0ujwcHB0W2ST8L3d5TU1Pyd2ae8NjYGGZmZhCJRLCxsSFcHCqf7Ha7AP2Y48DF5+LioozceNlNTU2JY5b/OS9zOsufeuopNDU1oaSkRExYzc3NKCoqwsbGhnQ0PHxCoZCMOAcGBmQHQm4SFW7r6+tSKFG339TUhJMnT4phjxp5qn/459I8yU5xaGhIHNY+n08k5gwa0mq1OHfuHF555RXcc889eOaZZ1BbW4uioiKUl5dDrVbLJcRoVGJHqHJ86KGHEIlE7sBY5OXlSffJfA5eNouLi5LjXFhYKLBCutapOGSEcVVVFSwWC7RarWTVvPjii+jo6BDa9P79+5GWloZLly4BgBCAaQIdGBi4ww/F7/TmzZsiKNBoNKI8q6urQ3p6OqLRqBCc+WcWFxffwfrat28f1Go1Ll26JMv7gYEBKRi3trZkX0QB0NbWFgYGBjA7OyvAy/z8fDQ2NiIYDMroNhQKiQl5p4SZIg+O7LjLTCaT0nVR7MO9KbtiPnfv5ec/BeHBBW52drYEsdNR6Ha7odfrBUBHhhNVMDU1NXIZcIxDXwKxH6xmd/KF8vPz0d7eLgatpqYmmM1mLC4uigqFlfBOhzSDU6ho2KmP5/9dWVlBW1sb2tvbEQ6HUV1djUceeQQzMzOYv00J3draQldXlyxGWUEXFhbKF8lLgu1tdnY2MjIyUFRUJHPHnZGtO6WZbO/r6uowMjKClZUVUVvodDpBo5CUyYr22Wefxd13343Ozk60traioqICbrcb09PTGBsbk2Qyr9crHR1R3mq1WhZfdFIrlUo5tBigwn0LKbW8cGnUi0ajcknTeU9lFcFtzFZ3uVyy+1hcXMTU1JR8TgqFAhUVFTJWYgQuYZB0EjPDm4ckf+dIJIJgMCipbclkUlRL9AfQdZyXlycLcz6frL49Ho8czDRhkj1kNptFvtve3i6mtqeeegp1dXXwer0yT6cLPZlMyogW+A/56fLyssi9Y7GYSCx/+MMforGxEY888ghKS0uxf/9+TE1N4d1330VeXp6MIb1erwgkPB4PNLeptkx/nJ2dhcViETl6R0cHBgcHUVVVhfPnz0vn3NraCr1eLy5wi8WCkZERTE1NIRaLwWazwWAwANgePbP7TCQS8Hg8svAncWBiYkI8RZQcq1Qqcd3zs6ZyMhaLoaysDEqlUkKjyP4aGBjA4OAgWlpaoNFoMDo6ihdffFF2X52dnZifn0dbWxsqKirw5JNPCn6E4VIcvcRiMYnfJWqFozAWl6zWWSwxbZAqOho4Ozo6ZNlsNBrhdrsRi8UkZ2dqakoMuuwW9Ho9AEiHS/8PsR70SJArpVarMT09LeKDkpISCTjjOUoZNIs3r9crop/V1VURs6ytrYkY5n25KPjBApA2iC+D3+9Ha2ur0FdTU1OF+c/xw050NrDNJFpeXpZF18LCglRHFosFNpsN8Xgcr732mngzKisrcejQIZw4cQLxeBxnzpzB1tYWgsGghKxQ6silIg9aXh7k9wOQNu3KlSuw2WwShzk6Oiqz4/n5eRkjEfNBbTUvOC50yWviqIx7nZ0t7Pr6dpA6DWqlpaWCOsjKypIOhBhm6vPn5ubkkOGSX6lUYs+ePaLFJvI9GAyivLxcWEP0DpAzRMcyqZf8XWKxGJxOJ5LJJMbHx0V9wvxgyjzT0tIwMDAgZiTiWnigl5WVIRwOY3NzU0Ktenp6oFKpUFBQIN4amtRSUlJQWloKt9stf2ZDQwNGRkYk4yQYDCIWi0Fzm8pKEiox7DvJsOQc+Xw+6PV66c7IXyJenME5fK4XFxexsLAAr9eLvXv3wmw2yygCgCAsnE4nFAoFzpw5A6VSiZaWFlmoGo1GCd7hJcnLl8IFjoao2edsvKGhARkZGXj99deFF8UFpUqlkuQ3jslKS0tlDKfT6bC6uip7gYKCArnYi4uL71D41NfX4+mnn8bm5ia+973vIRaL4fHHH5fvnqE/O9VPwLY0eqfmf2d4FfloKSkp0jETiMdudXV1VeCfVJoxKpb03YqKCvT09Ah/6eDBg5LpwWr80UcfRXNzM8bGxtDb24u77roLe/bsAQBcunRJlJUcewMQ/DZVi7y0V1ZWxJ1PIzGhfNwTEM++c89FeT9lz9yjLS4uimqTxR73hpSJU33IwolofxY15HXRd8SOjuO7ixcvQqfT4f7775egsIqKCilaWeilpaXJkvu9/vzBFwUASYbiw0SVEccnNJwwBtVms8FsNsvBwGUvW2NWRSSpTk1NwWazyfKHL2hVVRVKSkrw+9//Hjdu3MDY2Bg+8YlPSM7F5uYm5ufnMT09LcohUiMJ/aJbtru7G01NTQgGgxgcHEQ8Hpc0u+zsbHznO98RLPDJkydx8+ZN1NfXo66uDl1dXbJ/UKlUyMnJkYuRM3lWTQ6HQ3DcOyMyuV/R6XSy3OXehy8bNeg0fLGVbWpqEtPjjRs3BLtO0xBbabrDzWazIMCZQsbLQqVSCY+Ii2Me+HTncn9AxpHb7YZWqxVFG9EdrK4oT/Z6vcLB54KP2QKszLh74YiO5FbiRLxeL2ZmZoSqmZ6ejuHhYdHYM/Zxbm4OFRUVmJ6elq6BVSPHizk5ObJkZjVOdRSfXSqkIpEIGhoapEtOSUkRhhX9MjSMjoyMiJx1YmJChBt0Bs/PzwsAks8Ax2v8vnd6Qz772c/C4XDgxo0b6O7ulgUqkTmE/TGU6NatW2hqapJMFf6+eXl58l3SV9Ta2orW1lZ0dHRI3vvf/M3fYHp6GrW1tTh69Kggxokc39jYkAhi8spoCqRAYmccbyAQkAOTnSMvFWCbJLtzb8d3KJlMivqNhUksFhOApEqlwiOPPAKdTofBwUFR/nBkEwqFcObMGfn9mKtBBVd2djbS0tKwuroq0wh+HxQ17JSGz8/PS4dObxPBpVT4cSFdWlqKQCCASCSCzc1NlJSUiBx47f/H238HN37fd/74kwWsIAmQRCEKARaAvZPL7dqVV1qtmiXLki1btuMSx3FsJ3fnJJdyycWXmTixz5NcMo7jyJId2ZJlyba6VtZqe+Eud0kuewEIEIWoJMACFhAgf3/sPl/hzs18TxnlJ85kJlGkXRL8fN7vV3k+H89kUoo1Gu4YD0yBCEdOVHJy17u5uSlxznq9HsvLy7Db7VJgbGxs4J133pGucm5uTqJXmZHD399/JAr1A+8odDodTCaTVM78xXIhu7i4iEgkcgdOfG1tDU6nUzgsnP/6/X4oFAo0NDRIFjeXr4FAABsbGxgcHJQK89FHHxUUQSwWkwUSfwFKpVIcoFqtVqorVtKhUEhUAJz5h8Nh2Gw2+Hw+3Lx5E2fPnsW5c+eQm5uL48eP4/d///cRjUZx48YNzM3NyYKYNzsrPrVajZqaGqE/chm6srKC+fl5SS9jJcjRBDXdkUhERjiFhYVwuVySukf0dF5enqh6uKSjhJBjPqJCqOkPhUKyxCcehC83MRfESZAKyiW20WiUipBZGpToJRIJCWth20tmUDKZFDQCpcRqtVrkkISnBQIBqeTLysruAD/yMOHvrry8HE1NTbLgs9lsKC0tRUZGhrCDKNclr4fLQWZkJBIJlJSUyIXDz5jQRWZD0CG9urqKwcFB/PjHP8bQ0BDOnDmDvr4+JBIJSV6kkmd0dFT2LoWFhcjPz0dxcTEyMzPlc2JgD//55uam5Grz8iCiJhAIQKFQ4O6778YjjzyC7u5uyWxmlUnhQ3t7u3ShGxsbd2S+EERYUFCAYDCI6elpqdxfeeUVvPLKK+jo6MDv//7v42/+5m+wtbWFV199VTpnYjYood7diXKHlJubK671UCgkiiCOB4k+5wiEHhLivqkg5DNIyXYymURNTQ3MZjNu3LgheJ7S0lLMzMwIkoPAPpfLhWAwCKVSCb1efwc8MZVK3XEpEe64sLAgn/nq6ipaWlpQUFAgJt5IJAKdToelpSXJrOZejZ3wzs6OKBWnpqYkfEypVArYlOl3LICNRqPIxBcXF+XiJJFCpVKJ3L68vBzLy8vw+XxCJeZCPZFI3BF8RWw/jY0kIfCseb9fH7ijYNWyvr4OpVIJu90Oh8MhNFI6YRcWFmRp6vP5JOCIkkdWlAy92W0wKS4uRiAQwL333itBJul0Gk8//TTW1tbw0Y9+FGtra2hoaBDjHNuysrIymX3uVljo9XqoVCpxzHJBbLfbBSMRi8WETX/gwAExC1JhEY/HJUNAoVAIl4VYk2AweIcFH4AoFgDIQbS1tSWKKX7/xcXFqK2tFZ05Mxu4vGYGxuLiIlpaWlBSUiLB9jQx8iVUKpWy3CeYbnx8HEVFRVAqlSIimJqawtramoDw2Blubm7C5/Ohvr4eSqUSNTU1MqclpgS4NSriUpJYCoYaKZVK6XAWFhZEDcPxHXldVJGw6mdLHolEZNRkMBgk/Y8vwO4RBC9lr9cLnU4nfCSq5ribKigoEJliY2PjHSPKkpISYRJRHKBUKuF0OsVJzs87Ho+jq6sLACQ9jbLtmpoa/OY3v0EoFBJFFfNG/H6/zO9zc3OFTru0tCRKlYyMDPT396OtrU2kyVzUp1IptLa2Ym5uDm63G1arVUacarUa4XAYZWVlYmwkHYHPBtU3Y2NjIvIwmUw4evSoGCOvXr0qjt9EIiG+Hh5elBuzOlXdjoaly5sj452dHWRnZ2NpaQk7OzswmUzSqWVmZqKsrAx6vR5zc3Py2fFAJw4oJ+dWLnRpaSkeeughUcvV1dWho6MDANDd3S07p5/+9KcwmUyyR+Rzxyhijrc5oqScls8Azy1e4sTVE+TJ559BWxkZGeLDoNhBr9dLCiXl3haLBQsLC0KBZZUPQLox4NYInIc/uxuaSPPy8lBSUoL29na8/vrrOH36NMxms4y59+zZI9L7WCwGjUYjcnUWGLs9X/+vr/+UZTY7Au4ZSFOdnJyERqMRMxDNUhxR7Q628Xg8d5jnuBzf3NyU8YnVakVDQ4O4o8mMam5uRk5ODt58800MDw9Do9Ggvr4e7e3tArkjJnhpaQk2m01UKKx6+MBsbm7KvNnpdCIcDqO6uhpHjhzBv/7rv+LFF19EYWEhTpw4gezsbIyPj8uloVar5cYmoptYX1JuiXX2+/2wWCzSbbCypoGIencaZRYWFiRvgFp5q9Uq8Zlk+fNi4+HKuSR/Xu6D9Hq9eEnIfVLdTt3jS8+WdXV1VV70wsJCeDweqfqpBqPihocwzUOc+YdCISSTSaysrEjmBS8I8meoA6eAgT9rWVmZSBXpcVCr1QiFQuKTYbVFiSElgrvljMCtg5ysMC4ki4qKZO9DWSwluLsvzNLSUhw+fFhUa/fddx8mJibEFd3X1ydycR5u/Pzr6+tRWlqK4eFh+Sx340HW1tYwNzcnTCKtVivpkCyo6MIdGxvDzs6OxIeGQiEsLCxIbCxHhvn5+ZidnRUfR3Z2tuxpmKsxPz8v/CKOr6amphAKhXDy5EkolUpRCdHfQ0hlUVGRzMuDwaColejb2N7eFu4aC7HV1VU0NDQgMzNTCMtWqxWrq6uYnp6WUDKOH4lSWV5exuXLlxGPx2E0GrG1tYWWlhZ84xvfQDqdFsUZu8RYLIbjx4/LCHplZUX8V+ysmI9DTwcLRzLBaLjk+80ESErVo9EobDabvOPV1dUC0uSfk06nYTQaJVueXqLs7GyRSpvNZhmzcp/G8TmFPURwcGdLkygDq3Z2dtDY2IiSkhKMjIzgV7/6FcxmMzQajYQ2bW9vY35+XoornjHv5+sDj55oMAJuVRNut1vQBrwUaEVnBcK5b2ZmpkSGUkdOvXAqlZLFMLf8ExMTSCaTuOeee/A7v/M7MJvNaGxsxJkzZzA5OYny8nIcOnQIVqsVvb292NjYEC1zOp0WdyXpndFoVMI8CgoKUFVVBaVSKYE5FRUVqK6uxsbGBl555RVRuoTDYRiNRgBAOByWm5xtNRfCCoVCjF78H6qsOKulJJLjEOKS6QGhNHZzcxMbGxsIBALQaDTw+XyYnJyUaNTdXgC+ALt167tx2HwxKMXky8VlKxENvCSIe6CjnOoro9EouBEqXOhdoByQFyM7Ol6ksVgMCwsL0u6Hw2HZVdTV1cmCnw9za2urIFLcbreMJQFIN0LTGvHfHEewmyCJlgotAPKMEMbHseXuF725uRnNzc0iRCCV9MCBA/j4xz8Ok8kEj8eD8+fP49y5c1CpVAiFQvj5z38Op9OJnp4ewbSze2DoDi99VvcKhULMoVyclpeXy+eo0+lkz7CysiLCC3aZhBgSslhWViYZ8RsbG1KE7M70YCFw3333iTHyzJkzMn7jO8tDh0ZRjpoI4CSigwUBPVEcgfJcIK7DarWKdycvLw9GoxEHDx6EQqFAMBiUToB7ESp4Ojo60NHRgdLSUjz99NO4fv26SEfJj9q/fz/uuece4T8RacGfjxcBCxwu6Sk4IcKHz4LRaERGRoakzVG1yLHu1taW7FX5THLcxQ5foVCgurpa0vn43/I9YNdAcYher5cxXV1dHQDIzk6n08HpdOKll17CzZs3cejQIdTU1ODMmTN48803cfPmTblsmX1eXFwsxRBpue/36wN3FCsrK/D5fKiqqkI0GoVerxd2Ern81CcvLy9jcHAQVqtVtPN5eXkiDdu9COdLTP9DPB7H5OQkPvaxjyErKwu//OUvZfQzMDCAzs5OfPWrX5XZHSW1FRUV8Hg8EkTDA50vqMVikW6CUs5YLIYTJ07gnXfeuUM3z4jII0eOoLCwEI2NjXA4HHA6nRLGtBveF41GUVpaKjJFznK5JKX0kgcrOzG+bKzKGdxOCmw4HJald0lJCeK3g5H4YlJmTHYMAMnP4OKWXCIuOUdGRgQOx89Pr9fLweZ2u+VzoIyVZrrq6mokEgkxz5EWzN89ibE0krGzY0VbU1MDo9Eoy17iEfLz81FfXy+H0vLyMvbu3YtkMikVWjweF7hkNBrF+vq6SGP1er38XVTicIF97tw5qfzLy8vlkGVXRwxCY2MjlpaWsL29LZju9vZ2GU/V1NSIQUyhUMg/r62tlcrY6/XKzoijimQyKVGp5eXl8Pl8ohArKSkRNzHJrvF4XGigpBiXlpZKXgFhl1xUErMdi8XQ3t4Oj8cDAOKqZxHBTq2urk4EDCUlJWhoaBAmFz87hUIhFS53Hxy7ms1miUVl9C47u9XVVfHUUJJKFDk7aOr+KW9tamqSTHW+5yx2pqenYbFYUFJSgtOnT4vx7vjx45icnBSn8tGjRzE/P4/NzU3YbDapvAOBALKzs9HS0iKmOR7AVD1ptVoEg0G0tbUhFothampKJgbcKaysrMDv94tC6vz58zKu4jmTnX3riGXHzp2dxWIRJRTRRX6/Xy76iooKpNNpGWNubGwI6odFAvehra2t0Gq1kqmu1Wpx9OhRtLW1we/3Y3R0FK2trcjMzJQxNMmzH9pFQVMTXadlZWUYHx+H0WgUXXI6nUZ/fz9aW1vR2dl5hxadKF7ifQkIZO5EcXGx8J/IJ5menkY8Hsdjjz2GpaUlWczOzs7iwoULMmf0er1YWVmB2WyWQBfOvrkX8Xq9SKVSUuGT3nnp0iU0NzcLzDArKwsajQbHjh3D0tISxsbGsL6+jmQyKd3FwMCAZFkQEc4OibPxoqIiAeqRQ+92u4X+SI5/eXm5tPDMtygrK0N5eTlCoZC0wqWlpTLioLqFaGQG0nCer1arJfyEaAbglikpEolIgBBdnzQjscLSaDQYHByEVquVAJXdka8k25aXl8t+YHp6WthUlIGm02kJq+cSlBcrZ9F1dXUoLS3FyMgIrly5ArvdLhUrpbvsnFjhra6uoqKiAslkElVVVTJ2YdwuIWpEuezs7MiSktBC/k64V5mZmRGz0uzsrFSnxLYAQEdHB+x2u8yfGxoaBF5ITwdVY1RlUYDBJT1wK/GPVR419HREs7KmxNPlcmFhYUH081ziq9VqEUkQzcKFNefX7OhpytzY2EB/fz+USqVgLRh5yuhbqr00Go2gQTiq5EiK0EGSWml2nZqaEv4Wq1zmt5jNZul+fT6fXFb0y/DzaG9vR0ZGBi5fvozr168L74tRrHV1dXA6nRgZGcH4+DjsdjuOHz8Ok8mE7OxseDweCSOrqakRYB73onNzcwiHw5IWSUwPkTdECDEGV6FQSFzp7OysCGX437Fw4uKa3Tq7HibsZWRkiKqTBkAq1cgVm52dRVtbm5ASuBynAz4UCkGr1Yoog5Jxsqwooa+oqJDilcikD+2ioNqGUr6CggJJvmLlxP0E9f6UWBJ7y0WbWq1GJBJBQUGBLM1oyqOyhuOpRCIBr9eLyspKfPzjH0dZWRn6+/tlwUT1jtVqFTWJ0WgUZywXnuRAUZNO5QhVB6lUChqNRkBtV69elTESAKmQeMByPMHqkHNaKpb456pUKrlM6Cnh7oZyVXZci4uLonSg6oGXHV+saDQqYzUqqmiAjN/OqqCSZHV1VQLjV1ZWEAgE5NLY3r4VyETEBncGJpMJ0WgUlZWVgtPg7JxqL1ZbHM8wi5m5B5QYsvLln7+zsyOywMnJSZw+fRqHDh1CRUUFTp8+jWg0irvuuguFhYWiLqKShaMPk8kk2ngKGThCdLvd4krlmI05DOSUcXzA3yc/Z17SS0tLkg3CyE4qvEZHR7G2tobjx4/LbDkcDgtzhyyjoqIiOexJ86SXJ5FICFqCBGYicQhrZCXOACEe+vQkcPzDUKfd6Ijc3FwxWPKA4jKZgVCs6rlz4FIduNV58BLhWIs7N8YEFxYWirtaq9WKqpHP8tzcnCxr+XPw0gFuFSwzMzNQ3Q4q406Kn39RURGOHTuGmpoaTExMoKWlBbW1tVhbW8PKygq+8IUv4MEHH8Szzz4rB7TBYIDT6ZQigXhyKg0ZJZtKpeB0OiWXhdJfnU4Hl8slHotUKgWz2YxwOIxkMomuri7xKpG5lUwmJbI5fpupxveeXzwziMvhGDAWi4kSant7W4gLXEizQ6Z5sKGhAS+99BLOnz8vQgY+6xkZGZiamkJvby+qq6uxvLwsOzdSk9/v13+K6oljF6aHcRZNTTIfdJrteJBznlxRUSEaa4vFIg8sq2VWRLslnxqNRi6fyclJWCwWgactLy9LpsDq6ip6e3vFKcmDsaOjQ/YCubm50kmoVCo4nU54PB4sLS1haWkJlZWV0tbl5+fLkpBSTvJayKcCILc+Hzz6P7i4NpvNcDqdgmfn4pmVKV2wlObV19dLlUXVBsdN7Nw0Go18T6TRFhYWwmKxiO8hFoshkUiIJ4B7AI1GI0yiZDIpjmuOVWw2m/xdqVQK2dnZcDqdkjnMZ8FsNssIjC+mz+fD8vIyOjo6hOs1NzeHvLw8WaAmEgn09vYinU7jvffeQywWQ0dHB/R6Pe6//34cP34cx44dw8LCAvbv34/m5map3ohip4w2NzdXVF2scBmVq1KpZC+iUNxK0KPgISMjQ0CM/Gwoo+UohyMFOnfLy8sRCARQWVkp6iiys7iEpSqGoymHwyE4lpWVFVRXVwtRgFh4jrqYFElYIsd5OTk5kp9uNBrl4AFuzceXlpZkPMHsl42NDZn3K5VKudwzMjLQ2toKh8MhbCx2ILwUKPvlHofZHcwC57/Pro/PABfnJPPq9Xpx2vNZYvG4srIiMlCn0ylZN1x0U+Kel5cnY6CioiL09fWJ65tTCIfDgatXr2J7exsPPPAAOjo6sL19K4d8YmICNTU10nmvrq4iGAyioqJCfBWUECuVStTX12N8fFwyuAOBgMQ5kzy9uroKm80m7wId2DQLUyXG3VtJSYmM9jjuo2diZGRECi12ZZxqZGZmIhwOo6KiArW1tTAajfjc5z4naXaRSARPPPEE7r77bly9ehVvv/02GhsbsbW1hba2NtTX12NoaEgK7w/toqDDluobzoYLCwtx6dIldHZ2Ih6PY3FxUUYvnNdPT0+jqanpDkUEiZ4ckfCXwYXb+vq6sGd20zhJBI1EIkgkEncEs3i9XmGlMEyFi3JK2Lgo5KzZZrNJu0jFBOWs6+vrqKysFNMSZ88WiwXj4+OCH6YDVafTQaFQiFpmZmYGTqdTXsRIJALVbTLp6OgoysvLxZ3Ji5aZ13q9HlVVVWLC2+185nKRHRf5/vRiMPaS2A4eaKzqqLLhzJyZIlxMcmywunorTdBkMsnBQPMUx3H8H2q1qZYqLi4WlzkAGcmEw2E8/PDDUKlUGB8fx+HDh9Hd3Q3glkzwO9/5jvhnjh8/joKCAomxZHdJ4QO9DzzU6ZBVKBSYmpqShfVutR0VUxzzUJlC5HV7e7sUPrxcVbdzMDY3NxGJRMRcuby8fAfbh+MgShW5J+FnxKxunU6HSCQCt9stBzFHllQzUdlUVlaGqqoqqdBDoZBIMnk5KJVKlJeXiwRcqVTC6/XCbDaLoIKS5MzMTHEZA7gjR4Uu71QqJWj6ra0tuXzJeyKiPTMzE2tra+Kh4M/G7sPpdMrPkJmZKXwxutN1Oh2ampoQDAaxsrKC2dlZ1NTU4MaNG6ioqMDk5CSys7PR1dUlOziHw4HTp09je/tWtCyDwZRKJfbu3YuSkhIpImj0tVgsAIC5uTl0dHTgypUr4hGilH52dlaW1P39/WhpaYFOp0MikYDb7RZsN3BrDE/lE/cK/DwpsaVc3OPxoLW1FalUCsFgUAB/JBYT3Le4uIjq6mp4vV5UVVUhNzcXarUaGRkZ8Hq9Mi5++OGH4XK57ihIKVw5deqUQFEZAcEx5Pv9+sAXBcOC+MCwnd3c3ER1dbWMKdjusEra2dmRJSiNeGwD3333XYRCIdxzzz04fPgwrly5IvwSjk/omeDiliYY6t9ZpVHpwUOE4UDRaBQ6nU4OuMuXLyMrKwvd3d2wWCyC9WZ+ANvIjY0N1NXVibqCi0bKVW02GzQaDTwej8wZmZTHDmllZQVGo1EW9TQYpdNpQRVvbm6KSoEz6KysW7GfExMTgqRQqVSS88FWmlwiBrUHg0FRnsVisf8r07q4uFjGI1RCsfouLy9HJBIR+Sp9GJR37s785WXPLjMrK0uMVry0VlZWsLW1JW5puk8NBgMmJiZw8+ZNBINBvPHGG9jc3MSpU6fEtNTS0oLZ2Vm88sorqKiogN1uRyAQgF6vF7MW235KdTnWiEajdyDqafjjOJSphswtoI6ehz3NgURMUDmUTqeFBQVAeE2c0atUKrm8cnJysLKygsbGRsRiMfG5cCzEMQUVPLOzs/KZ8/uZmJgQdRc9EfHblIHNzU1RzFG1VVFRgeHhYVk6M0OEOxYeahMTE6itrRUpJQuR4uJiXLt2TVQ33EsQP8IOivwq7jQoD+d+Yzd1lt0DWUrs1DheSSQSmJqaglarRX9/Py5fvozCwkI88cQTkkl99uxZ6SQ4ivuHf/gHFBQUYM+ePThy5Ij4hXZ2dvDmm28Kn43PCTtjRgaUlZUJxI+FEX1Gm5ubaGlpkWdDrVbD5XLJfpNqQZ5JGRkZctlx6sAwI460WCBSHUqFE3lqfHZycnJETRUKhaTrqq2tRSAQwPb2rUxzt9st+8bR0VF0dXVJN9fc3Izy8nI4HA75e/4jCXcfWB6r1+tFy86HkxwcmjyUSiWqqqokaYp5rpRyUvpZXFyMtrY2nD9/Hr/61a9w6tQpzM3N4caNGxgYGBA2FGWqBNvRek/+yeLiIvr7++FwODA7OyszvYyMDITDYRnh0AF9/fp1eL1exONxBAIBMZ0pFArZoXBRtLq6ioWFBWH/m0wmmdFzGUt+EWfvvEhnZ2fvUKio1WoolUqYTCaobgc4sUXlQcrlMPlPxJ0YjUbodDrMzc3JOIrsfdr1s7KyMD4+jsbGRtTX1wvKm20sYY50ybLD4CKU9EtijznH5aVYWVmJqqoqOQD9fr+8EDwMCgoKYLfb5TNnxwFAXNdMY2PUamNjo3SPzAE/duwYvvnNbyIvLw83b96UfYNGo8G1a9dkLMOLiZ8djWyk+VZVVYkIgEUMR4R02hJ17vP5xGMzOzsr3RkXpLzsm5ubRcWTTqdFHUVzWSAQuGOsxCwLv98vJF8uTBlok0gkUFZWJjGkpJmST0UyM0OAKLXMzs5GY2OjZDZQFFJSUiKZzalUCm1tbbDb7XC5XHC73Zifn8eNGzcQj8fFc0KQ49bWFi5cuIDMzEwBHNLYyN8b9yxc1HLnx/0IOwx+jo2NjXLoxuNx8VpVVVWhu7sbTqcTZ8+exRtvvCEkAxYlVqsVHR0dmJmZkc+1qqpKltXMbs/IyJDfg8VigcfjgdfrxZ/+6Z/i937v95BOp/H8889jenoaFy5ckA6dfLNYLCYodx7qRILv7OygpqYGAAQLQ1EJJwrcl/Jg5g6Ru7v19XWEQiGhTlOxxewI0pxdLpdMHihC8Pv9GB8fF8n7u+++i2PHjkm+xdjYmAhmFhYWMDc3h5KSEsGUU432fr8+cEexu9KiYWp+fl7CiHazZciHmZ+flzhE/hl8SWZmZlBfXy9LPhJinU4nJicnsW/fPsEcLy8viyaaqimz2Swuz0QigWg0Kg+pxWJBV1cXtre3YTabZSlFSaFWqxUDIFkpXISrbucX2Gw2WcKRaLm5uQmTyXRH1xCJRO5ABOt0OlG75OTkiDJnYmJCugguDLlI406B+Rts63mxcmTAKnRhYQEajUYiPTnemZ2dxdzcHLq6ukT1wFStWCwGn88nVSEvNR70vNA4jiksLITf7xdjJLXoNTU1MrpRKpVwOBwoLi6W3xH3ScyJYAdJNdbo6Kg4+z/zmc/IZer1enH27Fkx5j366KMIh8P44he/iOnpaaTTaRw8eBBzc3NCUt2dm8zqlJVfLBaD3W4XKS+7MlaF9CxQWswuNxgMiihhbm5O1HO7jYO8aGiyy8vLk/HJ5uamjLXKy8uh0WgwNTUlhx+X0C6XCysrK5K3UVBQIEq3+fl5aLVa2SswA5z7Olb48Xgc1dXVQl+12+1SsFmtVlgsFjHC1dbWYn19XdzeRUVFOHjwoGDYh4eH5QBeXFyUdyArKwsjIyNCCaZKzmq1SkjS7lwZpliStLy4uAiTySSo+JqaGhkpnz9/Hk6nU96xlpYWARNeu3YNVVVV2NjYwMMPP4yrV6+ipqYGFosFH/3oR8WnsbS0hIGBARFccMTW1tYGs9mMaDSKn/3sZ+KSfvnll6HX61FZWSm5Jfz+2aHyfAEgn086nRaWF0c+7NRzcnIkfpkdOwUU3MHybKGijCoydqvcnRGhAkAW6pFIRIgBAOByuXDw4EEEAgH09fXhrbfeEhn0+fPnRSZOii3/uw/loqC7lSgKZkLH43FZ6ASDQWktCwoKRA3R2tqKiYkJbG/fChz/0Y9+hKysLKmWr169iueffx5f+tKXUFtbi5deegmzs7PIzc2F2WwW3AGR4XQcKhQKjIyMYGdnR16KlpYW7OzsoLKyEm+++SbOnDmD7u5u3H///bJXYfWtup35TDkpIX1cTNLNCUB8BnxBuVegfJPeAPJv4rdBaXTpco+xW2/Nn4OBRk6nE/X19QD+3eKfk5MjSzCC7Nj6cinOriEvLw/19fWYn5/H+vq6eDIIbOQFkZWVJagO6u7JiuL+g6wnKpe4C1laWsLGxq2scB5oHAMR5cDDm4gVUnQpj6UEsaSkBFeuXMHIyAhu3LiBcDgsl8QnPvEJTE1NCXqdwVX8M1mBKxSKO/wmm5ubUKlUmJ+fh9/vR21tLSorK+VnogAAgFTGSqUSk5OTsFqtUN2G37GL4GdGNRuXkexCd2NDeAlVVlZiZmZGLnriRXjQ8BmgootjE3aZVOVxXMMR5cTEBJaWltDU1CSMIu5CmNpHVSEVRe+++64ktgHA/v37hYrK6t1gMOD8+fOS0RGJRHD//ffDYDDg6tWr8vsyGo1SMPL3TP8Huzoq/RQKBcbGxqDRaORZZZetVCrxzjvvyHt47NgxXLx4UTwEwC3PDtMTo9EoLBaLpP8pFAq0tLRI2h19QMyFqa+vh8/nQ19fH86ePYtTp07hU5/6FPbv349/+Zd/weDgoCgoGf5F/ElWVpbgXIqKimQ8S4oul/Jra2ti4q2srIRarYbT6ZTvmWclu+h4PC5dDLtbxgPTD0RvFEe7HENzf0iXO0UTer0eDQ0N8j0+9thjSCaTUpRyH0ug4fv5+k/ZUbDC3v0X81CYn5+XzT/nyHV1dVhZWcHg4CA2Nzfx8MMPY3x8XGznjzzyiCAC7r77bhiNRrk9BwYGEAgE8OSTT4oKhyMhmuZ6e3sxOzuLwcFBZGRk4OGHH4ZWq8X09DRWV1fR0dEhL4DD4UBLSwt6enrw7W9/G6+//jq2trZw77334r777sOFCxeQnZ2Nq1evwmw2i/lvN3KZM1m6OHNyckT2qlQqJfqUFUIsFpPKk3NcLpZYeVLtwQqOyiYu2lZWVmTuTddubm6uSJNZ1TIjgAoMqkKYybu8vIzOzk4J8eFikqhrwsw4wqOkjsofRpRSWJCXlyfmJj68arUaNptNOjQqjqxWK3w+H9LpNGw2m7CJrl+/jldffRXBYBDALdnk/v37RXK4tLQkF3FxcbGk+VF6ywV7bW2t7I/Ky8uxtLQkIDim5mVkZKC6uhqTk5Pys/GAi0QiEjrDmfTuvzsjIwMmkwkABGNCDwoVMMSFENOh2pULwYjajIwM8SJYrVa4XC7Mzc2htrZW5NUlJSXQ6XTo7e3Ft7/9bQwPD6OtrU0ky1qtFteuXZNqkyFBfPf0ej3S6TQqKytlD/HOO+9gcXERP//5z2EymXDz5k309/djcXERly9fRkVFhQAen3jiCRmnfve734Ver0dvb690P+Pj45KzQlw+cS7MTXC5XCgtLRWQ5W7s+ujoKBwOhyzdVbfDhtbW1vDxj38cPT09+NznPicOdJPJJEgbrVaLmZkZ6PV6OJ1OYYyR3dXd3Y1wOAyPxyOycq/XKz+P3+/H3NwcfvzjH6O6ulpCzTgNYQIhJwW5ubkiYyeyPhAIYHZ2Fr29vcjMzBS8PQAZAfEiYLFGPwZVWBsbG5iampIRM0U0VEDl5ORAoVDIkp457Qw7CwQCQorIzc1Fa2srDhw4IJG5lO0zypcj4Pfz9YEvCprCkskk7Ha77BAoUWMQRygUEoT1yMiIzIFJE+W8TK1W4+2338b8/Dz27t2Lz372sygqKsILL7wgM/LGxkYxKvEw42z+yJEj6O3txeLiIl566SUx3Gm1Wly5ckUYNx/96EcRDofR398vrTCZLLOzs0ilUmIsm5yclFEQkRcajUYqAq1WK4s56uApea2srBTaK2V0XAhyNs6cXGbakszKObhGo5GFNcd1PJzKy8ul6uTFwhd0Z2dHVBxc2DI7eDd5lHNXznIBiNqL1RKrd8qUOb7Lz8+HRqOR0SPNQhwxED/PERTVYXR2E/5GLhgr5oMHD8rFtX//frS0tGB4eFjctXQJ0/26trYmnalKpYLD4ZDcBCpqAMjnRdw3qzmOKJi6R+xzU1OTmACrq6vFdFdRUYGSkhI4HA50dnais7MTXq9XdhoKhUIuaJ/PJ3us/Px8UX0NDw9LJ8hlKCtE6uEVCoXMy2dmZmS0e+PGDYkfnZ6eRiQSQUVFhUAceRBxdk5D2NLSEq5cuYJkMomnnnoKIyMjuHr1quzPysvL0dfXJ89NY2MjDh48iIaGBkxMTODUqVMYGhrCgQMHsGfPHgSDQRGIUBDAA4l7FRKlqfKjhJzOc1Kmr169Kgfwb37zGyGismiJx+MyBuPvPCcnRwoKnj383OjQphyfl+fVq1dx1113oaSkBIcPH4bNZsMzzzyDkpISSdVkYhwnFoyWZbIj3c15eXlCMmhra5MxK6W+lMbTOEgnPsdQXCovLS1ha2sLjY2NiEQiUCgU0Gg0WFpaku6UCBsqxgBIEiWLFDrmAYii9PLly6LQYlJkTU0NhoeH3/c5/5+C8CgsLBQkNKs0HqisSkhoDAaDgm6glntwcBA9PT149NFHxcj19ttvY3h4GH/yJ38iuIPHH39c5spcuhkMBvlAm5ubsby8LHynnp4edHZ2Ynl5GZOTk/B6vWJAOXLkCKxWK5599lkEg0HEYjH8zu/8Dp566il897vflUUsPR1msxmBQADRaPQOFAFJsqTc+v1+mR2vrq4iEAjIuM1gMIj6h5fE6uoqNBoNJiYmxMFM2J7VasX8/LxUSHNzczAYDHA4HIIYpx+Bngl+vtybcDTGIB8e/Ky+WZEQZEZZ4G6ZI0Fv6+vrmJmZQXV1NfR6vch5CQicnp5Gfn4+KisrxQBIRAGDe/iclJeXQ6vVYnBwEPn5+bLYJtSQZjc6yInz5gFKM5jL5cK+ffsQCoXg9XqRSCRkBEgjFZlinOPv7OzIYU4lCuWhNHyyKt6dykbZ8aFDh7C+vo6pqSlEIhG89tpruHjxosSRFhQUoLu7Gw0NDaKQ2d7exvj4uLiQeYFyF0QqQF5eHg4dOoTR0VHodDq888474s5nBcqApNraWmg0Gty8eVPGpDSqjYyMQK1Wy0iRzvr8/Hz8+te/htfrxQ9/+EN88YtfxF//9V/j6aefRkNDA5566ins7Oygvr4edrsdGRkZeO211/Dzn/9cEBQtLS2oqKjAz372MywuLoq5z2q1YmxsTM6D+fl58ReRR0ZfCr0LRKhUVlbi17/+NbKysuD3+8W1fuDAATQ1NUmxMj09jeLiYly/fl06Mh6OgUAAZrMZa2trCAaDMJlMMp755S9/iWAwiEQigQMHDiAjIwPb29t466238MADD6CrqwsdHR3Ys2cPLl++LFGs7O43NjYkqGt7exvV1dWy98nJuZWDXVZWJs8syRAmk0lIDKurq2hubsbc3Jxkq3OsxvPD6/UKVp1nEAU13JuRiabT6cTPxe5Jo9FI4WWxWKDX6/8vVSRd2fR8fCgXBW+vgoICubEJJeN+ghnQWVlZmJmZgVarFc4Lt/7hcBh/+qd/ipGREfz6179Gbm4u2tvb8d5776GwsBDHjh2DxWIREiWjMUOhEObm5lBXVyfz4z/+4z+GwWDAsWPHcPjwYfzDP/wDXn31VcFhE/VAcOHW1hYOHjyI7e1t8Susra3h3XffhUKhEKQwOSwkyrJKXV9fh9/vl3hFVjGcT+/s7Mgvv7i4WFhOfHhYZVLrvbS0hLW1Nbjdbuh0OvGQcCHHjAXmGFBWSKgcMeWJRAIDAwPid2AWSEFBARwOh4w/ODKjwWxubg4Wi0XUGRw/FBQU4MEHH4TD4RA1DXX2PJDsdrtwsSh1VN1OXjMYDAiHwwBudaJjY2Myz87KykJZWZlowYPBoPCfWCmS9bO1tYXq6mqpVFn909G6exlNtQd9NdnZ2YIjJ2mWMsHV1dU7qLRUmmRkZMgyvKOjQwofzvi9Xq/krfj9fjkgKioqMDIygsrKStjtdsRiMRlB8RIqKCiQbpTZ30NDQzLSOnDggLz0S0tLGBoawtjYGPR6PWpra6FSqaDX60Wpxzl4fX29kEXpqq+trcXw8DBycnKkcyBltqamBk888QT27NmDf/7nf8bJkydx8uRJ3LhxA06nUzp5Rq5yrDgxMQGbzYaHHnrojrHMbj8GD3nuYVjRBoNBSd5Tq9X47d/+beTm5uL8+fO4cuUKqqurYTQapRseHx9HdXW1cNZ2e1doviPdgMvjmZkZGQGVlJTgv/23/4atrS0MDw9Dq9Xi1Vdfxa9+9St88YtfRHd3N371q1/JfovPK3NQ8vPzhdLLqFk+i1NTUzJu4rtUWVmJwsJCQYj7/X65LGOxmEiGqUK0WCwoKiqS4pJkB3o+gsGgjPf8fj/Ky8vR0tKCRCJxRzgWx6U5OTmC8dkNaiTMk6qt9/P1gS8K4rMdDgdaW1uRk5MDn88nI55kMinUzN0a7rKyMmxvbyMQCEh1lZubK25lq9WKxx57DO3t7Th37pz4IUKhkFTYrFgphQwGg3j77bfx3nvvobe3V9Qc0WgUn/rUp3Dp0iW43W6ZI6rVagkk/+lPf4r4bYQxZ9oWiwXFxcXo6+sTmd/W1paoo7hMpFGPoyJeIHRdU+rKg5hhK0Roc05PNDbn/gBkZMeFOdtO/h3BYFAOQS5sudxNJm/lAWdkZEjmLlthhrvTmEPOEHOjicFgyNDOzo5Ig+O7ssf5s/GSGhwchM1mk99LNBoVJRapofz/MVvd5XLJGKK4uBhjY2NQKpVSCRHiyIUzD47djCl2V9wD6fV6zM/PAwCsViuGhoZgs9nkOeFojP+tQqEQZDcAAQRqNBpp6dmBaTQaGI1GmQmr1Wrcf//9cpllZmaKRNXlcgkehEgbKv6Ki4vh8/mkIiYiY3R0FFNTUygvLxeDH9MUf/zjH4sDmh4cmvpIRVAqlXKI8pCsqKiAw+EQM2BxcTF+8IMfIJlMoq6uDsXFxWhtbUV5eTmOHTuGt99+G8899xxGR0eh1+uhVqvxyCOPwGq14vnnn5cMZpVKhdraWumyAUghB0D2UY2NjSJJJy+Jy/7Z2VmYTCZ86lOfgs/nw7lz59DU1ASz2Yz33ntPfo/l5eUysia7ie8I0/VINSgpKZGcDJVKJe81QYy89EwmkxRGZrNZOmTuOCwWC7xeL+rr64Udx4yK3eSIRCIhjmnKmbk3YdGoUCgwOjqKwsJCzMzMYM+ePSIjplWAXcpuakT8dtojY5MbGhqwvLwsbLy1tTVUVlYiGAwK/JM596QGK5VKjI2N4ejRo4IsZ/Tz+/n6T5HHsoqghrqlpQXd3d1i7qLahAutYDCIkpISmXfToel0OpGdnS34YZ/PJ3RE0iQpWbPZbLJYJH+HqhKNRoPPfe5z+Nd//VfcuHEDjz76KJ566ilkZ2fLyOCNN95AfX090ulbQetjY2NSJbS0tEi8oNPplNk+Z7/M/iXigjNRjteoUAkEAtIe0sfBFCteLJmZmbLIXV9fl7CSvLw8YWARwcF/n39eTU2NhK9w6Qnc2l0sLS2Jt4BGPXKOCHwjTI++CaPRiOXlZSEBc8dEPtTKyoqM0DIyMqSa4YVQWVmJqakpBAIBKRIYUEWVDo1OS0tLIu+12+3SspOWy2eHjlVGufK/5QVGOigVHpubmxIROTs7K58l0RV8MaPRqBCF2ZFxbFRWVoZAICAqIfKvWMVtbGzgrrvukg6hoqICzc3NUKlUgoLPzMzE8PCwdIs+n08kqFVVVdjc3JSxImfM0WgUhYWFqKioQGFhIU6dOoVgMCi7EXYqarValE61tbXo6+uTC4MiiYKCApE1E0dCr4bNZoPVahW10uTkJAoKCvDTn/5UUC4KhQLPPfccEokE7Ha7OI5zcnLQ3t4OlUqF5557Tjpidml0GlOaTogjMR18jngQU+yxurqKX/7yl7h69ao8p5cvX8bMzAwqKirw4IMPysiaZklW4/w7WEjSI0OMEA/uZDKJ69evC2amra0NlZWVogTy+/1QKpUYGRlBdXW1ZNLwQqavJScnB0tLS4IKYgxuKBTCxsaGcLNYfHFHubCwgObmZgCAxWKR5zUnJ0eKBppHGTtcX18vI2E69YmnUSgUuHnzJiorK2VyQS9abW2tJHjScNvd3S1yZj5jH9pFwWpQq9WKUoS4iGvXrmFlZQV333234DhKS0sxPT2N0dFRUUQwyMVsNmN6ehrT09PCJmEkJpOleGgS/UwZJqtps9mMBx54AMePH0c0GsWrr74Kr9eL3Nxc3H333VhdXUVdXR1cLhfOnz+Pb37zm6IW8nq9KCoqEuNNJBIRuSYvBpq2mHRmtVplfkjKKsPmLRYLlpeX5ZdKcCIPE160pKCura3dMb6iwcnj8UjFTcRBZmYmLly4IGM7ABILujsfl2Ys4N+Xh7FYTKTLJSUloufmrDwUCskIidJGulR5CNHnwiUds7FtNpv8M+5ieLkkEgl5wX0+n8iQCUHk3omjNb7wZA8RO00BBCWxdKiyu2OOhsFgQFFREbxerxQyVB5ZrVbBNNPNTyNdIpFAbW0tMjIyMDMzA51OJ54Yqph4uD700EN477338Oyzz+KjH/0olpeXJQgqkUigoaEBdrtdOuGenh7odDrcuHEDwK2cDXoNSDNobW1FdnY2/vmf/xmxWAzALcnuq6++ihMnTmBnZwdjY2Pwer04fvy4dBDz8/OwWCxieCPKg+DDdDqN1tZW2O12OfCZvgYAp06dgs1mg8FgEIl1W1sb9u7di8HBQfzd3/0dGhoahMFE30lLS4sIGNjpUtG3GzY5OTkpss21tTU5ALlz2tzcFIntzMwMFhcXceDAAdx9993o7u4WFd/8/LyoKSkX53/LkSm7nYKCAjidTiwtLaG1tRVutxuBQEAKuYaGBgDApUuXZAmtVquRSCRw/vx5EbFwJ8bnnn4iPm/ALUwHLye/3y9nIkfXLS0tEvDW1dUFt9stz0VpaakYQf1+P4xGo4yZ2E3wUqyoqJB9lc1mw+bmJsbGxtDW1iY8vHA4jL//+7+HzWbDX/3VX2FiYgIXL16UuICBgQH5vt/P1we+KHQ6HSwWCyYmJoQFdO7cObz33nti2T9z5gwA4MSJE6ipqZGqj0A45lBQslhUVCTzZkaK0lgG3DK7hMNhoXH6/X643W4UFRXBYrHgxRdfxLVr1xAMBtHS0oJwOIy//Mu/xJNPPolPfOITSCQS+L3f+z2srq5KVOr+/fuxsbEhbsfKykoxzS0sLMhhS04LLwRyc5htkEqlxGXOuSwpsUR0ZGZmQqvVSjUei8XgcDjQ3Nws7S0ZMpx5Ej3h9/ulu+GBSfUEXyJmcBChTHUQP1tG0dKsRSURHaG7Q4dY3fHypLqI2v6ioiKMjIwIloJ/B9lO0WhUEsf4Zy4tLYkbnfTZ+O2EQI64ioqK5LNhRChfOAILiQvhs8ECgo5ozsE5k+dBs7q6Co/HIyC/zMxMEWFwjEYgW3V1NaLRKJxOp4x3mIjncrlExlxTUwOXy4UrV66gqKgIU1NTwoDy+XxYWFiAxWLBvn37ZHzF39fy8rLIfMnzMRgMuPfee6FQKGCxWJBMJhEOh/GTn/wETz75JD75yU/KLoexmkxt44FCPw6BdvzfS0pKJG+8oKAA+/fvRywWw+bmJqqqqnDvvffikUceQTKZlC6J+5pUKiVYGJr13G63MJays7MlBpX5Lewe29vbRQ1E9hWlp0QA1dXVYWZmBjs7OzCbzWhvb4fVakVfXx/m5uYQv50IyfFQIBCQdDvO4gn6o/+GBjxSo3nZc2TOeT7/f8wDSSaTMgXw+/3SuZA2zFEf1YhUexJgyN8ZDXfEqq+srODs2bNoamqC3+/H6uoqsrOzodFoYDAYcPnyZbmg+H2wsNrY2BAhEOMYmM3R3t6OaDSKcDiMl19+GVeuXEFWVhbGxsZw9uxZ1NfXIysrC/39/UJu+NAuCibFcdzS3NyM69ev49KlS/jKV76CoqIiXLt2DeFwGPX19TCZTAiHw1KFZ2Vlydx7d8tYVlaGpaUlzM7OyqHKin63GoBMGro4Dx48CJVKhX/8x38UphG7BM7QGxoahPNOBDUffJq2otGotK2tra3Y2dnB2bNn5RamWokOR6qFaHJT3U7z4y+Ystnt7W2RwRoMBigUCiFlsj2PxWKIxWLC8FGpVCIlTafTcDqdkkNMPABjNan6YvuvVqtlKejz+aSaYlfBTGq6v7n0ppqFFwjNYMvLy6K64cXBF2VrawvpdBo+n08qf7b9KysryM/PlzCdrKwsTE1NwWAwIBAISPIa9xRqtRrx21GYRFxYrVZZVhLZkpeXh/n5eSQSCTHQESdBfv9utR2T/bjvmJ2dlS6IihLq/MvKyoSmSnMYSceMLjWZTFIs0O9QX1+PRx99FK+//joSiQT8fr8s59PpNFwulzizqYjioex0OqFQKDA7O4tDhw4BuDXzP3nypPgF3nnnHfz5n/+5LDg5uuro6JBdEU1iBNXRRV5SUoLFxUWcPn0a6XQaVqtVkhzX19dRVlaG+fl5qfb9fr/MuB9//HHpULa3t2E0GuF2u8Wpr9Vqsba2BrPZjGAwiLKyMhmP8rkknZVafz5/+fn5cLvdKCsrQ0FBAT772c9CrVZjamoKIyMjQh6oq6uTbiGRSKCnp0cgina7HRsbG3A6nQiFQmhsbERWVpZ0hHynDxw4gHfeeUcUlkSRMC6Ui/iSkhJ59skzY+fCfBriMBgDsLm5Kd8HCQyxWAzl5eWoq6uTzp2BZUNDQ4jH40KrNpvN2L9/v3iccnNzxWtFIyJJACTc0gjo8/kwPj6O7e1tnDlzRgq5b3/723A6nfja174GnU4nPxd9KB/KRcFN+87ODpqbm2EymbC2tobTp09jdnYWX/rSlxAIBNDf3489e/bg3XffhcvlwqFDh2TeTOMWc6M52mClSG380tKSeBqoTGFexPj4ONbX13H33XejubkZv/3bvy1jHn6QXKJevnxZKl+m8K2trcHr9UKv10OlUsHr9eKtt94S85bFYkF3d7ewhLg8ZWVLLAkxJqw2AEioDhdcKpUKarVapKBcgFNZYbPZxLlJeaFSqRRJHJVNtbW1GBgYEFcw9xjMpa6oqIBarcbw8LBI/TiaCgaDglrmot7hcMiIhfsNvgCFhYWIxWKy9PV6vWhvbxfGPf9dUmW5vN/a2pIKiD4POsDLy8slEpOXMg8iRjVyqc7Di+5kfo/s+GhqW15ehkKhgN1uF0aQSqWCUqkUNVltba3sB3ZnnDOhkTN9h8MhFydzBkiVLS0txe/+7u/C4XDgwoUL2NjYwIULF2AymYQweuzYMTidTsl0DwaD8Hq98Hg8gjQnAJCcroqKCnFSMxHy4sWLcDgcsNls6Orqwq9//Ws899xzePzxx+FwOKC6nRBJ4x7FD8FgUMCS7N5ZONFjQI7RuXPnYDQaBUBJ7wnpx3l5eaLmY6wnDYXMkUkmkygrKxMTKp/ZpaUl2T9+9KMfFdw3hS7V1dWoqKjA3Nwc5ufnYbVaUVRUBLfbLeKPdDotOzEA8Hq9Ilvlf8vwq7GxsTsELuPj48jLy8M3vvENFBYWSlwtOyp6kygnJ72af/7W1pZcTHy3dxOZCSRlyh+X7Uzi4/fPooqXTHd3N0pLS2EwGBAMBvH6668jOzsbTz31FDQaDeK3kytdLpcINLxeL1S3oZP0eHDH87Of/Uz2gXv37pXI1atXr+Lzn/889u/fL2ZTFrkf2kVBeeHuoHHOCt955x3U1dWhpaUFqVQKJ0+exOrqKtrb29He3o68vDz4/X6JTqU2n8gDVhpOpxNFRUWyTOKByl/q9vY2ampqUFhYiKmpKYyPj9+BadDr9ZidncXw8LBIMcn9KS4uhtVqFQAXxzRWqxXt7e24fPkyzp07h2PHjmF2dhZZWVno7e0Vw8vCwgJSqZQE5+zs7KCurg6JRAI5OTlQq9VwOByyAKusrJQKgolTWVlZkjEQDofFD2E2myXHl9UaAOm+aHQikZLKk8rKSqkaqqurcfnyZZHcEXpHUFhHRwfcbrdkRlMlQXMWQ352J9nt9h+Qo8PFM78PcoEKCgpERVVYWCi4AS6tOeLx+/13UDRdLhfKyspQWFgIt9sty1qqusrLywFALhiOI9bX16UL4AXEfcXCwgI2NzfhcrnkgsnLy4PL5RJjFzs/drfRaBQAREXGpTpVKR6PBw6HA4cOHcJnP/tZ2T9kZWWJl4DO2mg0iu3tbZSVlWFmZkby1RnHS7c7lTL8bA0GA27cuIGcnBx88pOfxHvvvQcAIgfnO7TbwApAHMOMUE2n0+LtoeOXS2CNRiPyXGaQj42N3ZE1w/TApaUlkWOvrf17NjwREhz9rKys4PHHH0dOTg5+9atfQa/Xw+12o6GhQX43NIr6/X4ZSTkcDjgcDvHxcIdE8x4xMrsje5nklpubC61Wi5qaGty8eROxWAx9fX34+Mc/jtraWrz99tsy2tm3b58og9LpNIaHhyUTQnWbKs3CC4CIL9bW1iTa1Ww2i5qLFOiioiLo9XpUVFTIqIxJhUxU5H4hlUqhpqYGDQ0NIvd++eWXpYBix7O0tAS9Xi9dD3duFA/YbDbprq1WK7761a/CYDDg7bfflmdlenoabrcbbrf7Dtbe+/n6wBcF3aR8CKjpXVtbQ3NzMxKJBH7xi19gamoKH/vYxwSXQUwBtfjUD7OKJrKY2uJ0Oo3Z2Vmo1eo75oRERAQCAVnmMXOW6AwujFhNMRSGrXk8HpdDvrq6Gjk5Odi7dy+6u7tRVVUl4DqlUonXXnsNqVQKe/fulXFMc3MzQqGQ2ONpuuOBxcqtqKhIqn/KFNmeplIpkbEybIgh67xcuHymy3k394eS2qWlJdTX16Ovrw+BQEDmkNwr7Nu3D6lUSmSZ58+fl7EUl6IajQbNzc3o7++XXQi12bv3IpFIRPYjjPlk4hoA6QgY8xgKhWTBy6CdUCgEv98v81p2OPzdcNHn9/tFpeT3+2VfxczvjY0N+P1+6UwprEilbsVS8r/lbHpubk7yESorK6XbJOKZL2dJSYlIFpn8Nj8/Ly7ZlZUVPProozAYDLLsJhYiFotBdRv4xoRBIlxIJCYEjruZjIwM6PV6cXp3dHRIquDKygomJydx7733wmw2i0mL3TWDkQgqBCByUgDCX+LokPsnv98PAJKyRvl2WVmZLMQ1Go0gwgOBAILBIHp7exEOh+XizMnJgcvlgtlsRmlpqUhPf/CDH4j0/OWXX8a+fftQVVWFpqYmcbPznSdehkq44uJiGQWxKKC7m6NHdoQsLD/96U/j0qVLOHz4MKanp3HfffehqakJsVgMv/zlL9HX1wer1YoTJ04Isp8xBHwn2eHzc5yenkZra6so+XYn81HVNT09LTs/0hX4TjHzhuq+119/HePj47JHtdls4hmZmZmBzWbD6Oio7JNcLpeMehWKWzGotbW1Io8vLy9HW1ubQBxLS0vR19eHyclJmM1m3LhxA7m5uRgYGIDZbBaW1Pv9+sAXBb9RKkkSiQQOHjyIdDqN9vZ2MeZwtkvK6sjICHw+n6gZFAqFSNXI67Hb7aI2SCQSspDb3t6WOaXf75fFZG5urnxfrJiZIAXcOricTqfoxfPy8mQ0QngY2e0XL17EM888g2g0imPHjuHSpUs4c+aMzMk7OzsxOTmJZDIJh8MhAUJGoxEul0te2O3tbZEtEl1OJRS9GFarFQqFAn6/H0VFRbKgZTBMMBhE/HbkKSVw1E0T4KZSqeDxeODz+WAymTA1NYULFy5Ap9Ohrq4O5eXlmJ2dxZUrV+TiUavVd9A2S0tLxUjIQB8qUBoaGiRBjDsNjkvY8SQSCQmNyszMlIhMHsDEntCRPD8/L5BE5lRoNBqsr68jGo3C5XLh8OHDsgj2+/2y2CZyWa/XIxwOS+Lf7Owsqqqq5DMkPoSZzVRZMSOECA2avnZ2dqC6HVPLDov/nMo1KrSMRiPMZjN0Oh0KCwsFa8/FOMdaNFgVFhZifn4eW1tbIgHnboIjLWJBKPA4deoUdDqdJDKWl5djz549iMfjuHz5sij/SEAOBoNoaGgQJ7/ZbJY8CH6PVOQsLy/D4XCgurpaRig0jY2Ojkp3x8tyc3MTXV1dsNls4qJnoUdpMREwpBS89NJLuHjxIj760Y+is7MTc3NzstNkEcFLmQqoPXv2yNSAM3j6SniWcNbP0e/09DSMRiOMRiPOnTuH9fV1iWZ2uVzo6OjACy+8ALVajf379wOAeIomJydlfxXfFTXA0K2ioiIYDAaEQiHpOvm1uroqYyruTykoIJE6nU5jenoaBQUF2Ldvn4BCVbd5Vi6XS6TJ3/jGN9DW1ia5LB0dHfIZcMTK4mw3CLWqqkqW/8XFxbh06RKuXbuGoqIi1NTUIDc3F1NTU6irqxNz58TExPs+5z/wRcEXR6FQoK+vD729vaivr0cqlcK5c+cQDofR3d2NSCSC4uJiOJ1O2O12pNNp2O12eDwekZBRk02LO9UrDAiiDJbMKC4+6cOg0Sc3Nxebm5uIxWJCmKSCiQc6xzWpVEoegoyMDPlQ33zzTQwODgr/f//+/TCZTJiYmEA0GsVPfvITZGVl4ciRI6KE2tjYgMfjkao4OzsbJpNJ5vtc2qtUKplbArcuW7aT8/Pz0Gg0EvLDlnv3UotgMR6UCwsLYrTx+Xzo7+9HRUUFnnjiCZjNZhw7dgzhcBhf/epXkZmZiba2Njl4uciNx+Pw+XyorKyUw7+qqgqJREIO5VgshoqKCjHAccek0WjESUo56u7dBzX8dLezWyB2mrRTXhjMIyY2mxcm88N300iJ42BVqNVq7+g06VienJyUNDoAcvFS9ZRKpURSSrIrF/BZWVnY3t6W55PYaWZzsGMkaZg7Ao6EqEriQUrYIP0/RG9wJEcOFKWblJI6HA6hE7DL4vinqKgILpcLdrtdDGFFRUXIyMiA0WjE+Pi4jAJJTWCnRxJpMpkUlzk9R8Sa8NLgRUghCnOcFxcXZVSaTCZhMBgwODiIt956S0yAv/nNb3DlyhVJggyHw2hsbER+fj60Wi2GhoawvLwMj8eD7u5u+T0QLzMyMoL29nbBX7BDXF1dhdFoFOk88fIvvfQStra2UFxcjImJCdmPJZNJiTcmEYAFIy+w7e1tKW7oy2HXp1arsbi4iKKiIoRCIRQWFt4BYGS+Nz0pHE2TFjA9PY3t7W20tbXBYDCgp6cHGRkZKCsrw+zsLKqrq7Fv3z6JWfX7/dK9RCIRUSHujnt+8cUXcfnyZdx1112w2+14+eWXJTlwbGwM4+Pj0Gg06OzshNFoFNf+h3ZREANBVdI777wDADh+/DguXLgg3BvOeOvr63Hx4kVsbW0hFArBZrPB7XaL6WlzcxN79uyByWTC5cuXMTExAaPRKPhvv9+PlZUVcRWSPOr1eqVFZcvMm5fkVErz3G63zPOp29ZoNIJTPnr0KN5++23JxY3H43jllVdQV1eHT3ziExgeHsbzzz+PpqYmdHZ2IicnB4ODgzCbzaL8oYoqEokIZI7LXbbIwWAQBQUFckFw5knNOx3gXFRypEJeUHFxscy6Gxsb5QINBAIoKytDSUkJnnvuOWxubuLAgQP4zne+g42NDbS1tYmqZ2hoSGauHGutr68L4XRubk6S3shJIqSMfCZC2QCIrC8cDmNpaQm9vb3SNufk5MBsNmNhYUF+PxytcfZMY9POzg70ej2Ki4vFCEiRA4m6zKsgL4dYmNXVVSwvL6O0tFRGU2RaEQddUVEBACKf5T/n5ceXVqPR3PGzUjDAl5UmOqq3dmcaq9Vqudj5+4vvyl33eDxYX18Xui7hjmtra2hsbJRLlFWz0WjEmTNn0NnZKRJmLsVJO2BQDgCJwgyFQhJqxN0MAX0ct4TDYahUKmETsfDg31NVVSXPBZ9t0njZ4c/OzgoLq6KiAm+99RZ6enqwsLCApqYmXLhwAarbOJdEIiFCmJycHFy5ckU6XObX0ANEKGNlZaV03DQnsrqfnZ0VAOfs7CxmZmYQjUbxu7/7uzh+/Dj6+vrQ398vz4TNZoPP55NuIBqNyt6JYy2+EwxGy8vLkx0BlYM0pxK4l5mZKYIT7lrpaaJHaWRkRGISmET55S9/GS0tLXjzzTcxPDyM9vZ2HDx4EGfOnMGzzz6L48ePy/fMQChiP4qLi/HWW28hFouJ/+PcuXN45JFH0N7ejtOnT+Oee+4R0GZGRgZGRkZQXFz84V0U6+vrYt6iDnhqagrV1dVoamrCm2++idOnT6OnpwcWiwWjo6NiaqFihsE7PAiXl5fxyiuvYHp6GouLi/JwM4ubH4jZbBbsAqtFVoYkdDLqkOx4IoCZCMYDaH19HeXl5UL8rK+vF7nj2bNnceXKFcm8fvXVV/HVr34VTU1NWF1dxYULFzA2Nob6+nqUlJTA4/FIaAkVVPx7WaXTjs8gES6RKWvUarWSe0HjEishLsaYKGg2m+F2u6XKKSwshNPpxOLiIgKBAL73ve/h5ZdfhkqlQnNzMyYmJpBKpdDb24uGhgaMjIzIspJjgIyMDCFYcuTFgCrO1fnC029RW1srLwQjZLlgnZubQ1ZWFqanp1FWViYqGsqI0+m0JLBxtLWwsCDpcPQzMFhoYGBADICVlZVisqNyipUpybjcqdBc6HK5RDzB8ZhGo5FAJZJ/uRBWq9WixafLm8a20tJS2TVRhsls9cLCQplPM46UyYKEWzIECLjVPez+vgAIujsYDEpeNcd5LNJ4eJBcQBQ1RQic6y8vL4t/gQonwi357+Tk5IgyjNJydk0qlUoUaU1NTZJxwjAs+jpCoRAOHz6MvLw8CQHbs2cPvvrVr+L06dM4ffq0yHuzsrLQ1NQkFz4FJ+w0ifrWarVCdSDcjiTZ2tpa2Gw2vPbaa/jXf/1XGXVtb2/jL/7iLzA+Po7u7m6JB6WyjPnnRqNREOXshJmKyM6fuTI0pHLHSoPkq6++iurq6jsioel5Ih6dtOPW1laR93u9Xrz55puYnp4WH9qlS5dQU1MDt9stFxbPM9Ky6eIvLy9HTU0N6urqoFarxalfV1cHvV6PRx55BLFYDIuLi9K5fejBRSsrK2L2YdWzuLiId999V5hMm5ubmJyclAUQrfxEUh86dAjx23nUOzs7GBkZwcWLF9HT04P29nZMTk7i+vXr8Hg8KC4ulpHM5uamvNysSDmOWFtbk4OIWA8iDLgUW1xcFIDc1tYW/H4/MjIy4HK5UFVVhVQqJSE+586dw/T0tCwQdTodDh8+jB/96Ee4cuWK5C9QmQBAbmxiQjgqo91+98vJl5ohPtT181Bsa2uTCot6dM51aQKjooFjgWg0in379uHUqVOYn59HdnY23nvvPezs7OALX/iCjF9YBTGVjYcf872ZqLawsCCjHP47KpUKk5OTgukmFZeX8/T0tChEWI1zXDQ/Py/LW+BWUNPCwgJ0Oh2MRqNEhZaWlkrHmUqlxIFNkmo0GhWCJ6FtHCVwOcp8CBozCU0MBAJCy2WGNQGWgUBATJ3hcFjYS9vb2+LWZVXMJSgAURARMsklO/Dv4EEi0UlT5SHP3ye/SPjkz1RYWIj8/Hz5Myky4HKfezf+PTqdThz0NG8SJ8EOnp319vY2FAqFVNRU6zCjhNiZzc1NMapyicywMvLTVldXcfXqVamgBwYG0NHRISbK4eFh/PKXv0QqlcLBgwflwKfogj/HxsYGGhsbMTw8LI5uekFoaOQo1+l0YnBwEGVlZaitrcWXv/xlfPvb38bPf/5zFBQU4OjRo6iqqsL8/LxMFSjdZkdFsyUAcfIXFhZKQUzlFbsytVoNi8WCn//856KYPHbsGKLRqOwlTCaTXNbALTc+Zd6MKaaform5GZWVlfB4PBgdHYXNZkNdXZ2oHMvLy+HxeNDS0oK1tTXMzs7KHiwUCqGurk5EPaTJslPy+/2Yn58XEQUVfe/nK2OHn8p/8Gt5eRklJSX45Cc/CavVCgAC6lteXkZ3dzdGR0clWKepqQk+n084STxEc3Nz0d3djezsbJw8eRI1NTWw2WxC5czNzUVfXx/cbjf+1//6XxLxyZk+tfnkzbD9Y04GFR+rq6vQarUAIMEd5K7Mz8/LwUfiazgcxuLiIpaXl3Hp0iVsbGzgc5/7HDY3NwWIdv/998vhQzOWQqHA2bNnpbrmyKa4uFgop6wy+CIUFBTA4/EIv5+a7N06eI4vGGZSXV0tHRFFBOvr63C5XKJS4sJfpVJhYmICTz75JAKBAE6fPo38/HwcOnRIQGXFxcXY2trC5OQkKioqZJHIhzsUCsmIpaioSCS0BQUFklbHhDf6YrRaLa5fvy7xneRO0dtCnX0oFBIGEw9H7iRI2TUYDPD5fKL2oct994tLLlVJSQm2t7cxMTGBurq6O0yEvMBYJaZSKbjdbqGtEsbI75loCbrAOUZgRnxWVpZkmYyMjMBkMgkTzOVyyXvBYKv19XWYzWaRJyaTSUG4MIuE6hiLxQK73S6X5czMDAoLCwWix4JpbW0NOp1OlEOZmZnQaDSYnZ2VeE6qsXYXWqwoKbGkdJMybBrF6E2hxFd1GzfOXQa7Sv6eOLKcnZ1FbW0twuEwRkZGMDc3hwcffBDb29syhurp6ZHxrsFgQDweF+c+n3WOPDmJ4KiQMcfNzc3w+Xzo7e3FhQsXBDl+9OhRzM7O4uWXX0ZzczM+9rGPSXd09uxZ8YOQOVVUVCSFSiqVwtjYGPbt2yem3uzsbFHcRaNR2O12+XxeffVVxGIxNDY2oqSkBAcOHBDJ9+joKPLy8uDxeER56PV6sb6+LpOD7e1tOJ1OtLe3S6YFD3eaVymV50XJyQkAXL9+XXaB3Ffp9Xp8/vOfRzqdxhtvvIGGhgaMjY3JGDI3Nxd///d/L4DK/6+vD9xRlJSUoKamBk6nUypTtVqNmZkZbGxsCICOaAyz2Qy/3y9Vk16vx9DQEK5fv47+/n50dXVJpq3D4RC88969ewVNMTc3J+OadDoNo9EoxiXmwTKukFUAddednZ3w+XxSjRE9rdPpkE6n5aWfnp6G3W7H8ePH4XA4JG/gL/7iL/CLX/wCf/M3fyPpaLFYDPfffz8yMzMxOTkpVRtRxV6vFwAEu033NgChqFqtVhkD0PG5vLwsELrd4Tuq22FDvEAYD8tRUVZWlsxT77rrLgCQvQQr3+XlZQQCAXR2dqKurg5Op1Nm0Yx3JBVz79692NzcFALnbqAfacEbGxvo7OzExMSE5GlwzEG0Bkd89MZwjEKpKYPft7e3ZelP8xY7UXJv+PmlUimZXfOl5OfExTupuSTRssssLi4WTAfxzGT3cFm8tbUlc20AgmDghbO8vCzjOKLHLRaLdLtlZWXo7++X0WZlZaU4eLks5yiPi2wqYgh76+jowNTUFIaGhpCfny9zaUptmWmx21vEbHR6E7iQpuqMGQc5OTkYHx9HVVWVHII0lZI4QEWV2WyW4sHpdEKv14sDuba2VvJIZmZmcO3aNaTTaTQ2NuLuu++GXq/H9PS0iCs+85nPYHh4WJIsid7nPiIUCqGoqEguwry8PKEX0K9RXV0trCqO9sxmMxKJBH7zm99AqVTixIkTaGxshM1mg0KhwPDwMCoqKlBUVCSXOM2I7CrpzeJ0gOgXnU4nZxOd/XNzc7hw4QKuXbuG3/u938P6+rqoITnGZZHHYonmRu5D6H2orq7GysqKPPslJSWSfzExMQHV7ZwWGkXZzTICdm5uTuCYBw8ehMlkEoQM93QWi0VEANPT0+/7nP9P8VGQ57OxsYHp6Wlpj8n72dnZEd0vQ2K2trawb98+1NfX47XXXsPIyIjcgn6/H6dPn8bo6Cj27t0LjUaDyspKDAwMwOl0yvyV6GZWYpxvq9VqmVVzDECyK1tnspEII+Q4h5hiANjY2EBNTQ2OHDmC4eFhDA4O4plnnoHf78c999yDz33uc7hx4wZ+8pOfYGxsDHfddReqq6vR0tICh8MhYelciHFGzaqVIL10Oo2FhQWRaVLWZzKZRB9PSS1powQoRiIRcWNyccqYzkAggNdeew3Hjh1Df38/Ll++jKNHj+LJJ5/EzZs38e677yIrK0us/KxQuNwmpIxVdTgcluU7PRP8zFW3M6XHx8clJ4EANXYdg4ODUN0GN3JRyw6EPoRYLIaMjAwZLfIwomZ+a2tLpNSEszEAZnt7Ww4TAPJy7M5yNhqNgqWmnLS4uFik1RsbG1hYWBAWDoOQOK7hOCs3N1fYVrm5uSguLhbDKb0rNEARLZKXl4eamhrk5+eLCsXpdIoTvaKiAqWlpbK7Yvb24uIiBgYG5CDs7e2VToy7Nh7kzDsgGSAYDMrBytQ7Gh0p4+UUgIBLLu7pXbLZbIIsZ6X7wAMPIB6PY3V1VfwQzOjm9xEKhWTsVV9fjwMHDkCpVMLpdKKgoEDGfwBkx0TkCgUHHLHyc+EZ4vf75SBlxGogEBCV5H/5L/8FBoNBsjpKS0vxxhtvyMiKzxcx3lRYEW9PAyTZbBzP5efnY2JiAp2dnZLlwrEZoxLoeQkEAqitrUVZWRmMRiMKCgowPj4uP1MqlYLL5RJi7dbWFoxGoxBfSQAmcobZLGq1WgQher0eWVlZ0Ov10Gg0GBoaQk5OjuxNuMdil0ncCnFIH9pFwRl7SUkJCgoKhOtDcxjdhqWlpRKQk0qlUFdXh2g0ioGBAVy7dg1bW1t46qmn8OSTT4pi5t577xUw2tbWFnw+HxobG0WKurGxIclOrGIpf6Qy6sSJE7IspRrK5XIJB4ZoYM5kgVs7haeeegqTk5PY2dnBZz7zGcnRffrpp7GwsIDu7m5oNBo8+uijWFhYwNDQEILBIOrq6rC9vS1zTco8TSYT5ufnBXTIVD6+qAS8Mc+Y+xaCAYn8KC8vF6kgK2dGac7NzSEjIwNWqxUWi0VURdFoFMlkEn6/H2fPnoVarcbY2BiysrLgdrslFpaqMeYoUEVCpRa1/USd0APAOXssFsO9994re46FhQW0traK676xsVEOBeJD9Ho9JicnEY/HZfS0+2Vj5gAjRUtLS6UK56yfXh673Y7p6WnxqHDJrtVqZRmfSqVkj0WlCx36HAMUFhbKgcnxHceIBQUFEn5EcKXBYJAxSXZ2Njwej6AcDAYD7Ha7IEyYZdHV1YWJiQnxAywtLUk1OT09Db/fj6NHj+LNN99Ebm6uoB6i0SheeOEFHD9+XN6jgYEBOcyIrM7JyZGZOc2w7Fw4JiL8kh0I/TAc7QGQ/53drcvlkmeabvipqSkJuaLZTKfT4cCBA8jNzZXpAg2MQ0NDUtCR5svzgWKDYDCImZkZccrzOSOMkgtzVs1UN3J0VVdXh1gshqGhISwsLIi3h5dLMpmUi6++vh4DAwOSx8JxOUef/JzJuGKxNDk5KcXexz72MaHPdnV1IS8vD7W1tdizZ490/5mZmeju7sb169dRWVkpnQpT9FiYrKysSE6Ox+MRwQdpC5SIUxBEXIndbkdxcTG2t7cxPz9/h1ObyrbZ2VkAt9BLZrP5w7soOL/jg0pzitfrlVzlCxcu4NChQ9Kmk7E0OTmJK1euiNv0Yx/7GLRaLTIyMlBVVSU3PN2nHHHE43HEYjGo1Wo5NJiBYLPZsL6+jqtXr2J4eBh33303UqkUrl+/LvC8iooKtLa2wuPxYHV1FTMzM6ipqRHW1MbGBlwuF3w+H771rW/h7rvvRm9vL/r6+rC5uYn8/HycPHkS/f39ePjhh9HU1ISGhgah2FIWp1QqpeKzWCzY2tqS3ORoNAqz2QyXywWbzYZQKCQO3fLycqkemCnNboSxntxPUJ6YTqeFacTlLHM7gFvdwl133YX19XVcuXIFAHDo0CFZuu6uFgk1XFhYELc5U8PKy8tlN5CVdSu2kqY1hrXvlpnyQGX1ykqGHhnq+JPJJLKzs0XBxDk/Z7LRaFQkw/Pz89LpkD2Vn5+P4eFh5OXlCYuHSzuOJyhL5efFl5ddFBfCvCCohmFHR2McMRzcJ1BAANxagLa3t4vLmHiL9fV1XLp0CW+99RZaWlpkBNDV1SUXYjweh06nkz0T8Q5ra2t49NFHkUql8Jvf/Ab9/f1oampCa2urKN0409/a2pLFe1FRkYwqKioqhGvELnL3vq2+vl46NHbAVNm4XC65PDmCoxoPgHzevOi5w+nr65NR38bGBkKhEKxWqzzTRMG4XC753dDkyTx2dn5klfn9ftlRcH/HDoRngN1uF4Mapw/shjlKpCudKsvS0lLxV7EDSyaT/5cEnN4opVKJ6elpXLp0SUZCeXl5+PGPfyyphSdOnMCBAwfwve99DwaDARaLBdXV1fK+7+zsiPKNRRfzIphPQR8OhQb04bCr3U2Apuua72E0GhWFHF37VLMpFAoZib+frw98UdCezsOE0sbt7W0ZkSwsLEjoOSF/7e3tyMjIwOnTp6FUKuVFoaSRFEiionlQ0YFNFUdeXp5k0hoMBnmoeKhHo1HJNfb5fBJSTvelTqdDRkaGVMlc1FIRwLn7ysoKTp48CaPRiLvvvhuDg4N4++23Rb3FA4axoHq9XkBp6+vr4rPgIlev14v3ZLfyihVpVlaWZFXk5+dLVgLZPllZWXJIMlKTzmgAmJiYEHKnQqFAQ0MD9uzZA7fbjddeew2q28lkGRkZuHjxIkKhEFQqFerr6xGNRiVTt62tDV6vVw7C2dlZqfroCGea2e60Lo6bPB4PbDabgBPJ5YnFYiKl5eVsNpsFT8JxTzqdFjczcyzoUGV12NDQgKKiIgwPD6OtrU0qdB78HFNoNBpRunFXo1Kp7hhh0Tew25jm8/lkDp9Op+V7YWJhbW0ttFototGokH+XlpZkhPTWW2/h8ccfh9VqxeDgIEZHR1FfX4/i4mL09PRgz5492N7exvnz52XG7vF4oNPpUF9fjwsXLsjPkJGRgWPHjkGr1UpBdvjwYQwNDQnXqaioSKTD4+PjAvsLhUIoLy9HLBbD/v37JWEyGo1Cq9WKIZTPFtEYpCLbbDZR/u0uMOgjIPKCkDziL9bX11FSUoKSkhIxtlJxlJ2dLb/njIwMuaDobeKzTiw5l/A0Z1L+TOYZ4Y2c43u9XmExcYxERWFtbe0drCez2Qyn0wkA8k7ygGYVz1Gy1+uVoml9fV2ewf3794v7fGFhAVNTU/B4PHj33XfR3t6OBx54QM46YmGImGFxRmnwzs6OFEos+DIyMrBnzx6Bp1ZVVWFjY0MELcxt5wXJ93B9fV1ywrOysoQU8H6/Mj/oRUFFz9TUlBw04+Pj6O/vR39/v7idy8vLhczqdDrltm9vbxfaaCAQwNmzZ0WPHgqF5LDs6+sT7ffKygqcTqd4D8h1Il+moKAAWq0Wn/70p3HfffchHo+jq6sLWq0Wer1evr/6+nrRwHPJRyw253dstYuLi/GpT30KXV1dyMzMhNfrlcq6oqICLpcLXq8XgUAA8XhcTE6lpaWylKOfgpwmzlPpVub4SalUoqmpSUCHzHzgL5fzUWrAZ2dncfXqVZlPc6lNmmYikcDVq1fF8d7V1YX6+noAkIeqsbERVqsVoVAISqVS9gDMhmA+NAGQ1ITHYjHhJVHGyiyA8vJyyRfmfJ27paysLOFo0bxGyWBhYSHsdrs87GazWUYChYWFqK2tFZMXEeTcHVy9elX2FarbxE8mvXGkmZmZKV0wswlycnLk5aMaiGoUk8kEvV6PgYGBO6CS/Ls3NjbgcDjE28J92Pj4ONxuNyYmJuBwOBC/jY3/5Cc/ia9//evIzMzE008/jXg8LmlwPPTYkdHctry8LEbVBx98EHa7XebRrOQtFoscKCsrK9KZcKTKS5EoePKmiIsh5oReIlJmyQS6evWqCEQY+pSZmSlE4qysLFRXV8vhzcuUzzxhe5WVlRKGxWKPy39GjCoUChmbmUwmGAwG8bJMTEwgnU4Lj6murk48Rjs7O5ibm8P4+DgWFxfR2toqzyZHmiwyL1++jPX1deHEraysoKamRjpaKrzo0qangdkyJpNJpKv0/FgsFvzBH/wBnnzySYyPj+P8+fP4xje+gUOHDokbncbYjY0NhMNh8c1wn5ZOpwWdYjabJW89JycHpaWlaG1tRX19vXS8qVRKaAZWqxV6vV4yxHfvUGgPoD9scXHxfZ/zH7ijAG4RZBntd+nSJfh8Pvh8PslL1ul0d0SlcsZeVVWFffv2Ye/evfLAd3V1ifvQbrcjlUrJQUJJYm5uLqxWq8z+AoGA8JtUKhW6urpkbvqDH/wACoUC//t//28oFAp8//vfl1k7teGU2XL0RFmt1WoV08/AwIDkCXs8HhQVFeGuu+6SLFqSU4muWF5eFgUNOwVyd1jdsd1m9UI5nU6nE2/A5uYmioqKYLPZRH7KDoLqBY4s2OZz90E1CMd0Fy9elJc1EomIrJgPIA9K4jUITKRpavdikbPpnJwc+bwoCWU8KDsTHji7deqcgVOtwhk1DxiylcbGxu5wXDMqkhJFSit1Oh0CgYB0avQPABDuFmfkRDCobkf0EmURjUaRm5uLjIwMOYSIUEkmk7L3KSoqEv/J+vq6+H+0Wq3M1HNzc1FdXY2enh4UFRVJBCcPBCqDDAYDZmdncfjwYVy8eFE6tLy8PJw8eRLHjh0Thtj8/DwmJyclm8LlcuHzn/88MjMzobqNGqf73+/3S3rk4uKiIHEIsgwEAlCpVNIJZWRkyM/DPRmfIVKQaYylpJcH6u6OwOv1yqiNDmbudViI5eTkSLxuU1MTdnZ2ZLxFFHxVVRWGh4exurqKAwcOYG5uDlVVVfLMcBFcUFAghFu9Xo+JiQlMTExAoVCgrq4Om5ub4sXi+Mpms4lzmwh9jUYjfiCifriLoRqPBzpjet1uN+LxOIaHh3Hz5k243W5RmkUiEXg8Hhw6dAilpaXo7u6GzWaDzWaDy+WSzo3GUmatE3bq9XoFtcGR7JkzZ9DV1YVYLIYbN25AdTuugGBDCkgYMkWWHMdwVF1yp8ER1fv5+sA+ii9+8Yuw2Wzycl2/fl28C7zBuKwiCpepdYWFhaisrER1dTUuXbqEqakpyb3Oy8vD8PAw9u3bB5vNBo/HI5iNWCwmEC8C34jhvnz5Mrq6uvBv//ZvmJubQ15eHh5++GF85CMfkTaU1EW1Wg2Xy4VUKoXZ2Vk0NDSIHI2sG6IWKisrpQJlVGJmZqYYw7KzszEzMyNtu06nE4cvQ1EyMzNhtVoxMzNzh0Ochz9xxrxM9u7dC61Wi6effhpnzpxBb28vbDYbVlZW8PDDD8Nms+Ff/uVfZAfAw8vr9Qp/hmob1W08N6mqxCmz3aaihN8n/QmFhYWSYZ2TkyOze+4AKioqZDbOFyQajaKurk4wHYSg5eXlCYGVM35eIuww6binPDIvL086IX6uu7EO2dnZ0lorFApotVoZo5HvxWqdM2qOvOjH4VJ1dXUVJpMJPp8P6XQaTU1NonxhnsjKyoqMgci7ogeFl24kEoHNZpOF48jICG7cuIGMjAz09vbi0qVLGBoagkKhwL333otPf/rTiMViGB8fl8uLYpDMzExcv34d8XgcXq8XkUgETU1N2NzcxMLCgshPGQ2cSCTEs5SbmyukV1agCoVCAHq7mWhc5PIyIL0UABobG3Hx4kXYbDZ5DjiKm5iYkLhYplXysgYgIyGOC+PxOCoqKqSr4fdKSTJHitx/xWIxtLW1iW+AghWOW+i5YXSyyWTC6dOn8dOf/hT33HOP/M5tNhvMZjPee+89GWvtxqmwKOAYmVMA+r7os3A6naiursb4+Lh0fCUlJbBarejp6RGQYFlZGS5cuIBwOIyxsTGYTCZ8+ctfFnVYIpGQ7GvK1VnocR9JfwNFB1evXpXOZWJiAlVVVZIu2t3djZ6eHhQUFMDn84ktoLCwEGq1WpShlM2T4faTn/zkw/FRcJ5Ptj7RDByrtLe3Q6FQYGBgAK+88gruuecewSIsLS1hZmZGUrWmpqbQ2tqK2tpajIyMYM+ePbBYLFIh84DhrI1t+vj4uFT1Z8+elTHE3r170djYiCNHjuC73/0uLl26hE9/+tN47LHHJGc2MzMTq6ur6OzsFIjW+Pi4vETxeBwWi0VaRe48OCclsTU/P1/krUqlUgxB4XAY6XRaUsKY30x2DIFtlH7uDk0pKChAbW0tqqur8eabb4oL9p133oHH48EPfvAD3H///XjzzTdlmc3qn5c0E/sqKyvh9/tFK0/8xOXLl9HY2IjS0lJxurJCZTfCw4HIdFZC/Ix44PIFoViBqiYixWOxGFZWVlBYWCgOb44pAEjlxLAiLv9ZkcZvJ9cRQcFRG+muzCfY2dmR7I9EIiEh9zQyUWpLh/nk5CSAW16Tra0t8SKQ28TEPVbS1PyTEURKLIFtzD2vrq7G6dOnEQwGsbS0BIvFgt/6rd/CkSNH8JWvfAUnTpxAZ2cnUqkUIpEIFAqFVM5lZWUoKyvD2NiYYNj/4A/+AL/5zW+g1Wqxb98+zM7O4mc/+xleeuklPPzwwzJOmJ2dleJHoVDILoNRwUqlUhbx0WhUuFJbW1viRmdXn0qlMDc3J+MVyoYzMzNl50E8PEceBFsyx5sXBOF/brdbxncOh0O8JcCtboEXNQ9sJgxSwkzTIqNWl5eXBebpdrvh9XrR1tYGhUKBd955B+Pj4/jGN76BZDKJvXv3YmhoSBDznNlT8bWxsYFDhw5hcHBQDIWzs7OIx+OYn59HSUkJIpEIWltb5R0fGxtDeXm5yIiZnnn33XffsV+9fv26JAGWlZVJ90GKrUKhEPMu4xQGBwcln7ugoAB79+5FYWEhJicn5TJ59NFHodFooNFo4PP55H3hzoPUBypFqQL7j3QUH/iiIAXW4XCgvb0dBQUFmJiYwMjICNra2mSpSLVLRUWFxHuGw2FcuHABZWVlAiPT6/VoampCJBIRlyszJRobG+F2u6HVaoWeGg6H0dXVhUAggKmpKRw5cgQqlQp2ux2NjY3Ys2cPzpw5g1gsBoPBgMrKSjgcDoyOjiIQCMhSEIAEzfAXvLCwIKFDhHBlZ2cjPz9fHphoNIra2loxXZG5Q4ksHdRs2bkPoYwvHA6LzJeLM4agrK+v4/r167h586Z0RmazGf/4j/+IlZUVPPvss/it3/otVFVVwefzITPz1sqJqhLOiOkoJWCOC/P29nahrnK5rFQqBV1Aw9XGxq2cXrvdLuqJ1dVVBIPBO9DhrNJZgRUXF4sMF7glu+W8nJ0IjWtcgjJgh3N3s9ksuRc2mw0TExPiWyBwkbwgHi5EmKdSKTFxESjIkRTJwhUVFYIcD4VCKCgoQGVlpSBe6GXgn5uRkQGz2Sw+mbKyMkF15+TkiKiAUmeNRoPW1lYMDw/j9OnT+Kd/+ie0tbUJFpoBWtz9mM1mzMzMIJ1Oi5KmtbUVDQ0NYhSbmZmRzAfOqGtqauR5plqGrnx6SMhMowE1OzsbjY2N2NzchMPhEDIu3dCFhYWCQtnY2EB1dbV8lnQkkwyrUqkkHIu5FwDkd8DRMSmpdBvH43EZw6yuropvIxqNyu9sZWUFSqVS9jWENJLtxvHZ9PQ0JiYm0NDQgGPHjgkgk7szmgkJCGVxxp+DFGJ2p/RcFBQUAIAgSgoLCwVy2d7eLufHr3/9a+lUKGqhPJaL8Onp6TsUWhz/ZmVlYWtrS6IWyHcjH02hUOCP/uiP5HfI3/vIyIicq8SfcKzI6cj8/LxE8bLzZzTt+/36T3FmUwrKS4Ny0Gg0iv7+fjQ0NKC8vFy0x1euXMHhw4elxXS73dDpdIIpWF1dRVdXF3Jzc3H9+nWoboPIAMi4yWQyyTKXFRypn0qlEmfOnMFbb70lBFmtVov29nZUVlZKlGVjYyMqKioQCASwtbWFF198EZmZmTh48KD8OayOidygWoEE1WQyKVU4F5/EXzBtjcowHgDk1huNRvllxuPxO7hCVVVVCAaDeOaZZxAOh5FKpfCd73wHPT09KC8vR2FhIbxeL65duyY7G+YDMDJ1bm5OMBTELTNlzmazSVvLFDaO08hiqq2tlXHgysoKEomEjBxVt/NHIpGIjJ4YSs8ZNTHoubm5WFxcRHFxseRFkKtFH8HGxgYmJydRXl4u/y79CuXl5TAajRgdHb2DjEolFkd63IFQdkkC7eLiongl5ubmZH5Lttb6+vodEagAJOchmUzeIQ0lkpwdNGWNxLNYLBbp4iKRiGjpTSYTzpw5g3fffRdarRYtLS1YWFiA2WyWToi5Ky0tLXC5XKLEOXbsGFwuF06dOoXV1VVcv34dFy9eRHZ2NlpaWtDZ2Sm7EprHqqurRfCRn5+Pzs5O8fCw+wsGg3fkm3AywEjbubk5udwp+rBareJF4mFEuSo9KgQm8lLmTolwP/6eMjMz5YAkRqO5uVkUTkxRpIqsoqJCfBoKhUJAeZS3q9Vq8WtsbW2hqqoKX//61+FyufDee+8hEomgp6cH6XQakUgEkUgEgUAAGo0GLS0t8h5Go1GkUilRXe42vZL5tLGxcQfPi11mMpmEyWTC0tKSXF4rKytYXV2Fy+WSHSu9NxsbG2KuzM/Pv0OQkkwm0dzcjL179+KZZ57BlStXYLfb8T/+x/+AxWLBJz7xCcRv89paWloQj8fF/8Euxu/3S+QB3w3uBD/Ui4JMfiI1XC6XsOKZc+ByuVBcXIzh4WGsrKxgamoKGxsbeOyxx1BbWytcexrHbty4IcE4rBTpRORtOD09LQC1+vp6lJeX4/Dhwzh58qS0ZaxGCPlrbW3FwYMH5QUxm80yOjl//rwk4bHiopOXISyUHTIXwmg0iq9hfX1dZukARN0AQBaCjY2NwvIHcIfBicRJzhenpqag0+nQ0tKC1tZWaDQavPPOO7DZbHjwwQdRU1ODvXv3ori4GNeuXRNsCblJbD25hwFwRxaDWq2Gx+MRwQEPQZqWdDqdAMdY6dJrQYx6KBSSw5W4akr+WN2z1bXb7aJAYrfCkRWNaTSsEcHMSpKLd5rkKioqUF9fL9p2VrMcI6rVavn98xDSarVierNYLCKh5aI/Go1Ktcg/i/JEwvPohZmfn4fZbMba2pp8D6wKWUmTAjA1NYW1tTU0NDTgS1/6Evr6+pCVlYW6ujqB6RHuyIvC7XbD7/eLfp7vkkajQW9vL2pqajA6Oorl5WXo9Xq55PLy8qT7GR4eFrKs0WiULIWKigp4vV5Rs5WWliISicjCnnJVIsTJfqqqqpLOp7CwUOTaVHhRvcaOj4coR1G7Y0IpkEilUjKm4n6NS1Yidfi5cBzIDoVkBXbwdrsd6+vriEQimJqawh//8R9DoVDgm9/8Jn7605/iypUraG9vR29vr1zKAGSPFAgE0NHRIX6pzMxMHDhwQEZwPp9P3pHt7W15zzkGTafTosrKzMxEMpmUqQFH2kzJpNqLC2bGKZeUlMjnQrBoTU0NWltbMTo6ijNnzsDr9cJms+HGjRsCxTx16pTse4kOoSIUgMBHOZJn4UeD7fv5+sAXhVKpxN69e0W+SYkfw3juuusuRCIRbG5uoqenB1evXkUikcDAwAC6u7thMpkwMjKCYDCIjIwM2SkQtkZdc05OjmjAiebmBp/sp5aWFqjVasTjcfT29qK8vBxjY2OSLbGzs4Pp6WmcPXsWN27ckItsbm4OQ0ND+PjHP46pqSmMjIyI3E+pVMJkMomSiS7q0dFRGAwG+f4sFgu6urowNzcnaoNAIIBUKiUPUk1NjVQlubm58s91Op0s6FOpFDQajRgOuShkUNPZs2eRnZ2Nd999Fz/+8Y/R09ODrq4ucawXFBRgZmZGiLIMqOHlsbGxIcqi3NxcUcOwYuYYyeFwyMXIJSV5WsFgEFVVVcjPz8fS0pIYpajaICKD7tKsrCxBr/N3SVqqy+USeSXzGChxVN1OmmPXykqIRiM6c5mnzd8XOUparVYqRiKs+ZKS8cNLjd3tzs6OsJ84Itz90kUiEWxsbCAQCMgYkij8ra0tObw40iNi3Wg0oqamBt/5zneEmHzo0CFJgCTULxAIoLq6GsC/u9GZkpaXlyeZ6g0NDZiYmBBvAceHTqcTHR0dsFgs8nwxa4MhQMTz8/0iYbe4uBiLi4vweDziIeCSmCZHgh0VCoXwvtgh5+bmYmFhAbW1tQAgQVqU0BIeCEBEKDQx8sLj4r6yshIqlUr4XvzdcVHOMRHNk1lZWUIt6OnpwaFDh/B3f/d3ePrpp3Ho0CHBiicSCZSXl8PhcMgEoLi4GGfPnsXzzz8v58QjjzyC9fV1TE9Py74AuFVkcarBRXQqlYLX6xU/Di87Xr6kAnC0x3OSi2v+frm/5P6VpOSCggJ89rOfxdDQELRaLQYGBjA3Nwe3242qqio89NBDMhVgdgtZZWNjY+jq6pKdX0ZGBs6fP4/6+vo7EkH//35RZGRkSMbA1tYWampqYLfbMTg4iIyMDPT09ODkyZNQKBQ4evQoNjY2cOXKFcRvQ+PYgur1ejnkh4aGsHfvXtx1112iPIjFYpicnMShQ4ewvb2N8fFxgeWxpfL5fJKLMTo6Kos6Pri1tbWoqKgQrtOLL74oTsu9e/fim9/8JnZ2dvBnf/ZnKCwsRFNTk7wsJSUluHnzJkwmk4Dnfv3rX4vh6sSJEwCAyclJVFVVYXZ2FvX19bI4YgXrcrlkIb976USTmcFgkApMp9Ph+vXrePXVV0VRQbgaK8KZmRk0Njbi2rVryM/PR21tLRobG2WExwsPuEXJNRqNktVBBzW7IkocVSqVMJEIV6QDlAdNMpmU2EeOYIgdOXz4MLxeLyYnJ4Xpw1k8E8jm5+exsLCA0tJSMXxR1caFuU6nw/DwsMRaUgTB3RQVRhsbGyIL9Hq9wl2i7p2XB/0bfJk3Nzdlrks3MvPMaVTkkpcMntLSUrnQuLthUWMwGARXzQU7hQ0OhwO5ubk4ePAgNjc35WIhimJ3bOvU1JSYqba2tuD1egXHEI1GceDAASmYWltbhaN11113oaCgAIFAQHwhHD9Q6l1QUIBgMIjKykphdimVSnHK01+i0+mkMOAojTwmdpDMN2CxwMhdj8eDuro6KQba2tqQlXUrSW5ubk7MqPTJxG/ThskK83q9OHbsGLa3tzE5OSlcr7m5OQEaEoteXV0tyjifz4eTJ0+iqKgI+/btQ2ZmJsxmMw4fPiy+EarXSC9ob29HT08PXnnlFQwMDEhQ1cDAAHw+H1wuF2pra1FXV4d4PC6jz9XVVUxNTclYjt4KuvxJwPV6vbBYLJicnERxcTECgQBWV1dhtVql6BkbG5PxJ8nQ9JKxM2lqakI0GsXExATGxsZw4sQJMdEqFAoMDQ2hqKhInh3Sg69fv45EIoETJ06IKo/ECBZuH8pFodFooFQqZRbqcrnQ2dmJ3NxcnDp1ShQmer1eAoZaWlpw6NAhNDY2IhgMQqFQ4IEHHoDP58Mrr7yCV155BQ6HAyMjI+LBYEpWKBSSjGu1Wi24ap/PB51OJ3PQubk5YTtRFjg6Oopr165h//79qK+vxz//8z+jrKxMolpfeeUVqFQqnDhxQoBvW1tbGBwcRF5eHjQajfySCwsLxYG5urqKJ554Ai6XC2NjY5iZmRGVB5EmZWVlCIfDYipihcyLgfP669evw2q1YmFhATdv3kQ6ncbRo0fR3t4Ot9uNUCiEPXv24L/+1/+KSCSCd955BwMDA7BYLLh8+TKmp6dRXV2N8vJy4XARQkjsh8lkwuTkJIqKisQdTow3pZJEQgCQ/IxkMonq6mo5VKmf57KSM0+OGI1Go5gSObYiroPVFFHW/DsKCgpEGbW6uoqenh45iKenp1FaWiqiAXZmdNeSX0PcNgBBV/D3CUCQMMxZoNCC8/WZmRmZM1utViwtLYnrnYtGjlCYOkYNO0mojC7lv8sLi0gZYuF5gLIy54J7c3MTgUDgDsT35uYmamtrBYzZ2toqu7m8vDzcuHFD4HDcFVGFQ9XM4uKiOKS53CV8kRA/Kpw49orfpsyyk2BRkJ+fL8oZVqehUAgXL14UigIVU5WVlfB6vTJyYWQtf58kAA8ODsLr9cLj8aCmpgbt7e3yGRiNRkQiEeTm5kq+yOLionTeN2/ehNPpRGNjIzY2NmC323Hu3DnMz8+jo6MDx48fR21tLfr6+uT7Z0fJAusLX/gCDAYDbt68idHRUQkHys/Px8jICCoqKsQFzd8ViyZCNOl8ZtzBzMyMPEOEbZJmzEAjmiG5Z6qoqJA8lsLCQrzyyisIhUJ4/vnnYTKZcOTIEYRCIfzZn/0ZPvvZz6K2thZerxejo6MyptRoNNi7d69MA4hrr6ioQE5OjkxDPpSLglyU3WYRjpGSySTm5+dx11134fTp05Ire99994lSYnh4GHNzcwCA8fFxTE1NYf/+/bhx4wb8fj+MRiO+//3vi64/nU4LUXR7e1sMRZyv0gyn1WphNBoxOzsruu/dWQgNDQ3Yt2+fMICefvpphMNh1NfXo6GhAVarVQxLGo0GY2NjMmJ77rnncM8998jyqL29HS6XC2fPnpWc5Fgshq6uLjlgioqK7iDX0tE7Pz8vslONRiOmwZ2dHVy+fBkrKys4cuQI1tbWJMfA6/XiN7/5Dcxms0g6KYd1u90IBAKSXc6ZaSp1K9EOABKJBNra2qQzI28GgEhK+VCtra3JgbCwsICGhgZRcBDJTv8IDXtc5HLnQHnswsKCzHHLysokdIoSRcqgS0tL4fF45Ptob2/H0tIStFqtqHgYbgNAKlumH3LkRfMWsSOUhQIQhVVeXh5ycnJEeqtWq0Vtw0hWxpwCkM96fn5euhEu7OPxuKTaKZVKKJVKLCwsSOpcKBSCRqMRzhS7H44zyG3y+/3i3mXXSfgbl8NkNC0vLyMzM1MUexw3KZVKuXy4Z2DRRlosL3qiTUh25fhYp9MJkwiAiAT4PTOWlrs+fp5arRZTU1MIBALweDyorq7G5uYmmpubsby8DKfTKfsyysdv3LghS/bMzEy8+uqr6O3tRXNzM2prayV7my5x0pj5+6JK8Pjx4/jkJz+JxsZGVFVVYWFhQXwXnZ2dMnng81BcXIzLly+LAGRhYQFtbW1oamoS7wdH5WR6USTBXWw6nRYXNHd8TN/j80lkSF9fnxh7OQ5ubW29IweDe1kKNnhRHz16VAgYBoMBr732GiYmJrC2toZwOCy+m7Nnz6KrqwuFhYUYHByE3W5HU1OThIhRjMF34f18fWCEB2mldLYGAgFht584cQI1NTWYnZ2VtnB+fh5jY2PCBRoYGIDf78cLL7yAH/3oRygrK8Mf/uEfoqWlRdhAGxsbOHDgAFKplOA+6KamQoMzfFY4uyMiacKiEmNhYQGjo6PY2trC1atX8aMf/QharRatra2w2+2ypKutrZUKkqFGwK2q6Yc//CGuX78u/J1Lly6hvb0dRqNR+EX79+8XxUswGJQKkqaeYDAoOn8+UHSCrq+vo6ysDAqFAv39/Uin02hoaMCDDz6IBx98EJubm7h06RI0Gg1KS0tRUVGB3/3d34VGo8GNGzdw8+ZNqSoLCwsRDofxxhtvwOl0Ynx8XCiS9ACUl5fDYDDIpT8/Pw+DwXCHkoh4h+HhYTnI3W43DAYDtra2JCeB+wZWuhwNMT0uGo3C7XYjGo1Kdng6nRYuTTweF2koMQexWAyRSERSEwmjDIfDkhhGxD1HZUajUUCGa2trolEvKCiAw+FAKBSCx+PB0tISVLdJvZzjcnc0Pz+Pubk5mWXz85ifn5dsbso0lUqlZG2QHGwwGOTSIlNobm5OGEkEE9K3sra2hsrKSslDp9KMY91kMonc3FwZ0wG3irXx8XF5/ygWiEQiUpDo9XqpeEl3pifC4/GIPJY4jIyMDESjUZGyrqys3CExJR6+tLQUtbW1UhDwgFbdDjciW4sgPO4oiNHo6OiA0+nE3/7t3+LNN9/Evn378M1vfhPLy8sSbkSFIgDZF4XDYWRmZmJiYkIKgU9+8pPo6ekRBWZjYyP+6q/+Cp/+9KclF+TnP/85rFarTAeoGBwZGYHVakUymcRPfvIT8XG1t7cjEolgeHhYluj88ynrTSQSUrwwQKu9vV3Ghmq1GiqVCnNzc4LoMBgMaG1tFY8J/TtbW1sIBoNYWVlBXV2ddKmHDh3C1tYWjhw5Ao/HA4/HI272WCyG0dFRnD9/HpWVlWhoaMC5c+eEfs044cbGRszPz4s46D/y9Z8ij+VDQ3UFw4Xi8Tiqq6uhup1BkJOTg/3792NmZgbZ2dkYGRlBR0eHEBLb29uxurqK5557DmfOnBGm+1/+5V9ia2tL0qN2a6j5obMlNhgMYqChc5fyVzJnLBaLXBZvvvkmvva1r6G7u1vcsjdv3kRLS4tUr/Q4rKys4PHHH0djYyOmp6dx8+ZNWK1W3Lx5E/F4HOFwGPfddx86Oztx/vx5rKyswG63w+l0StVIVVVubi7MZrNI6xoaGpCXlycqmby8PBw7dgytra2wWq1wOBwAIEszOsKvXLki4w2Og+hfSSaTaG9vx0svvYSxsTFUVFTg4sWLErCTnZ2NI0eO4L777oNWqxX4XzAYhMfjEas/Rynb29viWCVigw5QBhMtLS1JJU9II3cgNCNyfl1TU4Pr169jYGAANpsNKpUKVVVVmJiYkMq5tLQUy8vL4uom/oMvaFFRERwOB9bX12G1WuXyoQOd6huOR8PhMCorK6VqZtIcDXgAhMw5OzsrzKyFhQUsLy+LFLi+vl6WjUVFRaIo4fKfjlh2GMyG52fK8RK/IpGIhCJxEc5FLzs6fn788zhOYMdMmiuNoKyGOUJbXFwUr4LVaoXP54PdbhfBA/cZVBrRa5CXlyeSaEqw2a0Q+z0yMoJkMom6ujrcd999Mva6ePEiVlZWEAwGkZmZKfvM+G0jq1arxcGDB5FKpeDz+VBVVYX29nb84he/QDQaxcmTJyWz3G63i++IDDidTiejNY7/HA6HyJCbm5tx6NAhGUGxYGHXtr29Lc55/uw9PT0ilmBULOf+xGBQgcVOhiNmXjoLCwtIJBKyb6Soo66uDsvLy2IsZFYKl/07OzsiBqJtgLtNnh80Cra2tqK9vR2JRALPPPMM0uk0vve97+ErX/mKjKHb2trQ0NCAhoYGiamdmpoCADG6figXBResxFJwYVhZWSl5AWazGWq1GkeOHBFl0/z8PAoLC0VRsba2BovFgps3b0r1zQP06aefRnZ2NhoaGlBaWir525THksBITjzRDW63Wxa3dCnvnrVyZt/V1YVIJIKnn34a6XQa99xzDzo7OzE+Pg4AItdbXV2ViuHQoUOwWq0wm81wu92w2+3o7u5GKBTCkSNHcOjQIQwNDSEcDouEkAapUCgkdM2SkhJBIHOR6Ha7BaehUqlEgsmHxGazyaz2jTfegN1uF1qmUqmUxD/KcC9fvoxYLIYjR46IHHVhYQEDAwNiVnO5XLh+/bqob6xWK1wuF4LBoCzAOLcuKyuTioTzf+rXuYPJzc2Vhe3a2prImPlZEIdw5coVSW9rbm6WUdfCwoKY8Pj5h8Nh+P1+lJeXC4E3Ly9Pwl/obaEkdrf8kjyyzMxM0bOnUilsbW2JPJZ/B5Vfer0e8/PzUKlUkvim0+lE3ZSVlSUIiI2NDXHykr9DD8jCwgLy8vIkgpVS4oqKCszMzIgKDfj3rpIxq7m5uYhGo3KJMZAIgIynWDDRDR+/Df4Lh8PIyMgQNDsr38bGRmi1WtTV1UGtVssyneMLcrPoNM/Pz5dsco4p8/PzZc+USqVk2X/z5k2Ew2E89dRTgtshk4s+iMzMTNTV1Um0bygUwgMPPIBf/OIX+MpXvoLvfve7+PSnP41f/epXyMzMxNTUFPR6vchnt7a2ZHzLZzA3N1cUQRQbxONxXLlyRS5ktVoNh8MBo9EoRUxfXx9UKhU+85nPID8/H2+++SYUCgXa29vl8igpKcHnPvc5oUlwAU+nMztZTiyI1igtLZV3jGcUpwocRTMPh/DF2dlZ8Q9ZrVbk5uaisrJSOjN2KlR2KZVK/PCHP5QJQCAQQFZWFiYmJuB0OlFXVwelUol33nlHFGx5eXliJvzQLoqVlRVcvHhRtMN+v/+OCErihBUKBRSKW2Htly9fluUjc6wHBwdx7tw5HD16FP/4j/+Ixx9/HH/1V3+FaDQqKXNsm8PhMCoqKkQpwS9WWQUFBRgaGkJra6u8QHzQyJzPyspCY2MjGhsb8cILL+DKlSsSgPPJT34SRqMR/f39Mmek05Z7E6fTiffee09iBx999FE4HA48//zzWF5eRlNTkxiPuDzKysqSqpDu0MnJSTQ3N2NjY0M4OWtra4JEDwQCUkEQ6salq06nQ21tLTo7O2V2fPXqVVy9elWQBz09PbKcVCgU6Ovrg0ajwde+9jUMDAzgxRdfxNe//nXE43HU1dXBZDJheXkZx44dk0hWBrazoiHSIyMjQ7TgCoUC+fn54jcBIIFRzDsgmTYrKwunT59GOp3GwYMHUVRUhDNnzshyj0Yyt9stShzKX8moWltbEzUQFUb83uiOJeOqoKBA0B4AJEKzuroaHo9HqjrycHgQUupKxzPnxZzzBgIBubQ5fgNuhcIEg0ExmbJ743iUBs7s7Gx0d3fLkn5tbU0uB+4YuJiknBmAKOeYBslgLF6w5FsRdUPXLqW+VP9pNBp8+9vflsjeuro6jI2NCWeqvb1dfFDt7e1SCFKtSAw6u93S0lIkk0mMjY3hW9/6Fpqbm+FwOOT5z8/PR319vexb3G43rly5IqYzuqIJauQhSmwFYZkc7ZI4HAgEZHdXWVmJqakpqNVqychhXgWXy/Pz8yKR7ejoEFjf6dOnxdhoNBrR2dmJrKws3HXXXRKnS9rs2NgYKisrAQB333237Cgpp21vb0c6nRYcCAnKnHzQKJebmwuXy4W8vDz5Xtm5sPiivJmdeiAQwOuvv466ujrk5+ejq6sLDocDnZ2dMBgM+Nu//VsZ2zscDjgcDoyPj4tAo7OzE1qtFlevXn3f5/wHvihohkkmk5ienhZpKGe+u+Vv58+fF4Af8bekt7a0tIhtfmxsDHq9Hg899BAMBgM+8pGP4NChQ/KLbG9vF902AOkuotGo6KspqaNkkVI/LmETiQS+8IUvoLq6GvF4HCdOnEB/f79gp+fn5+HxeOQiY/cSCASQn5+PxsZGJBIJ+Hw+LC4u4urVqxgdHUU4HEZfX5+0hIlEApOTk6ioqEAoFEJJSQl8Ph+MRqN0JwRyjY2NyYiEi2RWUDSqZWVl4dSpU3jggQeE3ePz+bBv3z5Zcn3rW99COp2GxWJBOp3GI488gh/96Ed44YUX5AA9fvy4/O6ysm5lbI+NjeHGjRuy9zl+/LiIEhwOB1QqlQTu8L/hwT4yMoKGhgbZTbCFTqfTqK6uluqdlajH48H6+jr+4A/+AHv27MF7770ni2yOABhR6/P5oNFoBKCYSqWwsrIibn4aJRk9y2UjR2Rc1NPpW1BQINGurPJzc3PF/8KXlHuI6upqlJWVCaOIWvy6ujoUFxdL/gGlozyQeLnF43E0NjbKe6DRaGTZ29jYKO9IQUEBjEaj/AzU4lPVt7W1JUwlRtGWl5cjPz9fArNYVHCXQVkyPSJ2ux2bm5tYXFxEJBLBqVOnRJL59ttvY35+HnV1dfjzP/9z8Wo4nU7xUQAQ8QFVV6FQCE8//TSMRiMeeughCZSanZ2FVqtFfn4++vr6AAB2u12KkdXVVVy9ehUulwtOpxM9PT2IxWL4l3/5FzzyyCNYXl5GPB7HXXfdJfGqlH3v7OzIHpLPFbMusrOzRelYVFQkzw3zF3j5ceIxPz+PS5cuCVAxGo3i9OnT+PrXvy6I71deeUUmErygiouL5XAmk4ldAX0WBw4cEGJxS0sLrl27JgIF/g4po2VHQnUdMSHsIEneJReK519lZSV++MMf4sqVK/j+97+P/v5+yQtnMfHpT39aVGLBYFAKtPf79Z/CeuKctrm5WUYtVqsVAAR/TVMWZ81ssfkD9/T0oLe3F//0T/+EZ555BnV1deLUrqurg9vtlqUm3bd0R9OpTUMQJWgej0cemnA4jHg8jqqqKsmTfvvtt8UZXl9fD5vNBr/fL4vejY0Nac+4A6ApCgB6enrwxBNP4Ny5c3jllVewsrKCvXv34vDhwwAAn88nDycJjZSjcmFKFhFjIKlwoTOYubmkWzLgZ3NzE0ajEa+//joyMzMxNjYm3UN7e7vsDm7evClz1/3790vko8PhkL/noYceEkji5cuXoVQq0dvbi+3tbfT09MDhcIgRyO12o7KyUj5bfk9ra2uIRqMyZmKwEavn3Qz+srIydHR04O2338af/umfCl6a+RbXr1/HoUOHUFVVJeE11KsDkBeMS3fi12lGI4CRYygKHyhuIC5hcHBQclJYIFAyyc9mNx2Xy20KCAhr43uQnZ0tMmAq/Obm5oRKTHdsWVmZmAh5GJDES3Aiv4/i4mKRkBMlkZeXJxBBytKJxvZ4PLJgLioqwujoKNbX16HVarFnzx4olUo8++yzMBgMyM7Ohs1mg06nAwB4PB7U1tbi4x//OO655x4sLCxga2sLnZ2d4mHgOEqpVMLtduPChQu4fv06mpubEYvFMDMzg/r6eunqL1++jMOHD8PhcMi7xEu5oaEB1dXVMjJbXFxENBqV0UgqlcKXv/xlGfuOjIyIc5zTCe4Gd+PL2VlxfwNA9nL0CJH0QH/J5cuXUVhYiIMHD+Lo0aP43ve+h0uXLsFut8s7yBREMtmGhoZkvHry5EloNBpYrVYEg0G8++67cs499dRT6O/vlwIjEonIaJdu75aWFsGhEIsPQC55LqMJlyTLzOl0Cq3A6XTC7/cLcpxjycbGRvGzbW9v4/Tp09KBvt+vD3xRMLLS7/dDp9PJhp8vEGdxfr9fZr7k+efl5SGRSODChQuw2WyiMBgfH0dJSQmmpqZw//33Y2dnBxcvXhS3IvEImZmZiMVisoOgGYdOXT5QrGi5XORlMjw8jLW1NVRXV+NXv/oVrFYrNjc3ZXHU1NSEmZkZUTlQ9bC6uorR0VFZHpMquTucieOZoqIiibckw4YdQ3d3t8wtGSJSXl4Oq9UqDm+lUgmHwyFuVLVaLQfWxMSEcOlJ4o3H46ivr0djYyO2trYEsc3AE+CWz2FkZET4MwDwxS9+EY8++ij6+vowNTUlMlMi1Mn1KSkpwY0bN6Say8nJwcLCgmA0OF8vLi5GWVmZ4OHpHGaqHJPbhoeHBWNgMBiws7ODu+++GwcPHpRKmSFL3K3wMtDpdPB6vVAoFHdIX/mS7V5GEwVOj8b6+jrsdruo6hirS8McQ5zIJqKIAIBIF+mz4dLXZDLJMp1FzfLyshQHRN2Q3MuFO9VgmZmZ0Gg00Gq1GB4elsREmgCZeZFOp0VIQEQKlXK80J1Op1xUDIwKBALSFZP99dhjj6GwsBAXLlzAM888g7179+LkyZP4+Mc/DoVCgY997GOSxcF9FdEzgUAAoVBI6Av0dDz22GNobm7G0NAQzGYznnrqKTz33HMoLi4WNzMVXmVlZbj//vvR0tKC//N//g9qa2thsVhuHU63hSi7dxFMIORlk5+fj5qaGmxubsLn88HtdqOwsBAWi0UyvMldozGRo0p2yCsrK5ibm0NRURHq6urw3nvvSZyqVquVboiBV5OTk7BYLKisrER3dzfOnz+P5eVlLC8v4yMf+QiOHj2KRx55BBcvXgQAXLp0STqsjo4OuahoeGUMK7N1eOHx2aDXh52U2+2GWq3GysoKTCYTpqen8frrr+Ohhx7C4cOHMTg4KBaA73//+4jFYrh06RKOHz+OAwcOYHp6Gh6P5/+JFt/99Z/ioygrK8Ps7CwWFxdRVlYmKpK2tjaJ9WT4BzEcJpMJ29vbyMjIgM1mk1Qr8mVyc3Nx7733ygtP7T09BzQO0XXMZeFurwIT0Hi5MMidSg0AMsvNycnB5OSktGpEH/CFXltbkxk1nbxra2uYmZmBzWbD3NycQOu4iKXOP3472UytVmNzcxMmkwkNDQ0YGxuDz+eTCyaZTCIQCIhXhJm9BoNBHhKOEMggevjhh8WAZTKZ8NZbbwkgb3x8XA6ztbU1GWekUilUV1ejtLQU9fX1mJ+fx7/927/hqaeewkc+8hGUl5ffIShgy0qXscFgkH0RKa45OTkIBoMya6Z5j8llNLVtbm5iZGQE6XQan/3sZ1FYWAiXy4WRkRGo1Wrs27cPi4uLWFpawpUrV2TnQbYQPQQ5OTmYmpoSSTRzDOgk5iUeCARksU+9PSNKKXhg90N/A/cBRE8wSYw+A2YHxG+DHKmIoxqIIwiC/WiAo849JydH8iw4EqUU2OPxSDc0Pz8vexb6WyjUYBfBERMlr1y087/neFKlUmFiYgJDQ0NYW1tDc3MzQqEQ1tfX8eMf/xhbW1v4wz/8Q0QiETz33HNCQdBoNPJ88X1lB0lfTnZ2NtLpNEZGRuTgm5+fR1dXF+x2uxB6yUljLntOTg6mp6dlj8VCh+/L5z73OVEmkYjMsc/W1pbw1EgE0Gq1kpBH+jONmzU1NeL5ysvLg8fjwerqKtrb28VxrlAo8LOf/UygkVVVVTLiViqVWF5eRiAQgNlsxvr6uvy9dKYbDAbpYJqamtDb24sXX3wRY2NjEhiWlZUl6ZccK9bV1WF2dlbQHczyJneMhR5HsNzlMIejpaUFsVgML774Iurr66HX62Gz2aDValFaWirjyOXlZaFUkE7xoV0UKpVKcLtk7BQUFKC1tVUWMeTulJSUIBaLyUIwmUzKAodoXJJgk8mk4CP8fj/y8/ORl5eHuro6IUbuDlDneIDQMjJUOLbgojgajYqKRKvVIhwOS1ASxxytra3ixzAYDHdAvkwmk3QAJSUlkm5mMBhgMBhQVVUllS8fZrKaSktLMTIyghdffBE9PT3Iy8sTmi2rwKamJvj9fmg0GgwPD2NjYwNGo1EwyDwUqNOnR+Ohhx5CeXk5wuGw7AmYAc2lGVv/w4cPo7S0FENDQzhy5Aii0SheeOEF/M//+T/x+c9/Hmq1GhaLRYw+rIgJEmR3xQefsmN+Bh6PR14g+gPit6mcxD889NBDKCsrQzKZxLPPPiuKo1AoJNLEmzdvYmVlRUBuHF+SFcWZLsdfTqcTRUVFCAaDaGxsFBbX6uqqXGAejwetra3Y2dmBw+GQNDfmSvBzYlIck+64hKbMO5VKwe12Y2VlBSUlJVLlUxO/G7LIvQgLD7rP2alGo1EolUoZofh8PhQXF6O4uFhyxBcWFgThwTzv7OxsrKys3CHfLCgogMfjAXCrAu7s7MTy8jJmZmZw9epV1NfX4+tf/zrcbjeGhoYEN63VanHx4kV4PB7U19fj8ccfx4EDB4SEsLCwgGQyKbnyi4uL6O/vR25uLpqbmyVl0mg04r777pM887m5OczOzsquYGlpSTrORCIBrVYLp9OJrKwsHDhwADqdDhMTE2hvb8f+/fuxsLAAv98vZwnjAkg9oKeGo7+dnR1hKWm1WlFlrqysYGVlRYy5AEQCnZWVhXvvvVdyWZ555hkJnnK5XMJCM5vNQltm58yYg0984hMyOXj99ddRWlqKJ598UkB9VVVVMtlglg07wBs3bsgZcODAAQwNDYmUlZf+8vKyBCyRNcVoVxr/+POur6/j9ddfh9lsxkc+8hHk5OSgubkZwWAQFy9eFAOi2+3+8C6KoqIieL1eAJCMV3JyYrEYSkpK0NPTg+rqagwMDEgVRvUBF7Xkjxw5ckT8GNFoVII/2MLTGUuFCaWgvMlJIWWrSQVWIBAQ/ThwqxNiFU70LhOumNVN5YvqNrGWJputrS3k5uZibW1N8h6ys7NhsVgkwY8VGLEmxDT84Ac/kLaRLvLa2lpMTk7CaDTC6/VCr9fj0qVLksexO10tmUxCrVZjdHQULpcLR44cwT333IO8vDxcv35d9h/T09Py+fP7I8aYozliTQKBgITevPvuuzh+/LiYfnbHonIBSA05Z+zFxcXIz8+XVt9kMskSORgMStfEyrqiogI3btxAKpXC6dOnsbKygvHxccEjnDhxAjdu3ABwa0bb29uLubk5OWhYtTP7IJFIwGAwCFSOCWLT09OS+EUZdU1NjTCw+AIuLS0JEZhVHUcsLDAoA2UcK2WSa2trSCaTKCsruyNfvL6+XmTTRM4QLEl6J2fyTU1NQoO1Wq1i/Nrc3BQarM/nQ1FREUpKSjA5OSlZ7GVlZfLncoZfUVEhhQRRIF6vF4cPH0Zrayuef/55/PSnP4Xdbkd+fj6eeuopdHZ2YmdnBz/60Y8QjUZRVFSEhoYGwU9QaOD1epGVlQW9Xo+srCzBXDzxxBOiHqJslBcs/QhEYNMlvrq6KogKHuwf+chHcOrUKTidTrz66qtiAKXIg76Z/Px8LC8vQ6lUiiyUzwEluQwIYuHJg5WoGe5V8vLy8Pjjjws09KGHHhLZMHBLsGM2m7GwsHCHu53GS6VSKal38XgcWq1WlvWPPvoo4vE4bty4IZ8LADHr7aYcEwvEHdz09LScJxkZGTCZTILYoY+FRN7s7GwMDAxAqVSitrYWLpcLOp0OQ0NDMlLf2tqSUfDi4uIditH/19cHvihoDkulboXBA5AqNzs7G9/5znfwta99DWVlZdi7d6/sLhwOh7RZfCk5T92/fz9GRkYEHFZSUoJkMnnHsocZwrm5uTAajfJAARAZJWfPlHACkGxnVsGEp9HUpNVqBaWdlZUl+bX0QbBboVGLtzOrWippuExmdciLU6PRoKOjA/fccw/6+/tF5gtAKheiKlKplJjp+DNwKUujUV5enoy6FAoFmpqa5PDiDoczT0ZtMjeA/zchavfffz9isRj8fj8ikYjkWFdXV2NwcBBra2sAIJcgRzWpVOoOphAfRADC3yKhlRdKNBoVT8ny8rJknq+vryMYDGJubk66KB7cXBTvhhDyM6CRkdVjKBSSqo1+B8IBiYUnlI07M6pICAHc3NwU7f7i4qKMDjkO4+XA54QuW46jWBAwW6O0tBQlJSXSNfDSJXeJeAgqArnwpgPdYDAI2p2FA7MX6I9hHOpuii3R4sR7JBIJWCwWgTyqVCq4XC5cuXIFS0tLiEQiOHv2LHp7e6HRaHDt2jV0dHTIs8mOiM5sVuULCwvSZTscDjFl5ubmwmKxCPiSHgjyxJi/zh0Qu/HNzU3581gpq27n0gMQmi5wy1/CVL3dBtndpsDZ2Vmsra1JxgT3T1lZWWKaJTWBdGciOtbW1uS54+icfgWCSsPhMBoaGtDa2irUXwCybwwGg9DpdNINcixM+CNHsyww+bw4HA5RMJJtVlhYiPn5ebkw6d+g7Jg4EIpUSKfOzs6WHcd/JI/iAyM8WF2TKvqRj3wEr7/+OhwOB9xuN1wuF/76r/8aIyMj8Hg8sNvtMBgMsNvtUN2mbHKpTaAb21UufJgZYDKZUFVVBaVSKYdQbW2t5FLTR8GqlZcAF0XBYFBMc/Q0jIyMQHUbZ8x5MBU4drsdNTU1ki9M1k48HpcQIjL3d2cBTE5OSr5Gb28vGhoaoNPp4PF4sGfPHuTk5AiCgBgI7kK6u7sRDocRDAYRv40tJiyORhxmK7jdbglgCQaDiEQiWF9fF9kucGs0GAqFEAqFUF1djUgkIpUsEQORSATALX8BL00eblywUZXFapwyWwCSAEb2Pl24bNXVarVkC3DMSHfyl770JZhMJlno9fX14a233sLk5CQuX74sB1pmZqZ8JiwAysrK7sgEoapEq9XKvJoLy91JfGVlZdDpdEKR5a6LXeny8jLOnz8vy1OOTmKxmIzOOEpi90qzZ0lJiUigeSCQMcZKdG5uDoWFhcIkI7qBnyOfBR6WiUQClZWVguLm/Nxut8tSncl7BCyura1Jmh0ZaCMjI/j1r38Nm82Gl19+Gf/9v/93NDQ0yM+u1WrxR3/0R/joRz8qlzafNa/XK3h3IkUY1JOZmYlr167B4XBgeHhYMtJpLOTYh6YxIlwCgQCUSiUmJiYk2GhwcFAKzlQqJTBQCkMoeaX6UK/Xy16ERlbG3VqtVmxvb2Nubg7BYFC60N1JjPy++D6yEGX8LXcylCbTPEz1XkNDA3w+H6anp7G9vS35NY2NjTCbzTh9+jScTqdg4ncHK7FT3b1no7R7fn4ei4uLMlJn1gZ3LAsLC/I9Z2RkCDanoqJCDMmhUEiiiYkVp2KNTvH3+/WBO4pkMgmv1yuyWLbj3/rWt1BfX499+/YhGAxicnIS9913HxQKhWRJc7TD6pAVWSKRgM1mE9ktH1JK0agwiUQiSKfTMJlMCAQCyM3NvWMkQR052ffsfHZ2duT2p6x294IpmUzi5s2bop6w2Wyoq6vD6uoqvF4vpqam0NjYKKE+BoMBxcXFwrgimpxLXe47VCoVBgYGsLi4iMzMTEQiEUxPT4uUbWZmBn6/H0qlEhaLRRaq1HqzVS0oKJDEsdzcXKHfRiIRzMzMSNIaEe1U/lABtry8LC+SyWRCIpFAKBTCs88+i/r6ern8jUajpH1xdEVnMvMnAoEAdDqdVMB8sIF/D+8hmoA7FpJ9CTgjb6itrQ07OzsihKBmfmxsTDAS/Gw5IhoeHoZWqxUxA7lSNLhxeV9aWirwQD4DkUhEOonCwkKEQiFYLBY5wClTJPuIkaNc7NPLkUgkBPK2s7Mj83O/3y84b0IWObZRKBSYnp6W54IXYWNjI1wuF+bn52Gz2QS2yTzliYkJNDY2oq+vT5673t5egcPxkiAmIxgMorOzE8ePH8fy8jL27NmDoqIivPHGG7hy5QpSqRTa2trw/e9/H3Nzc/iTP/kTQbQnEglcvXoVSqVSHPj0q3BMS1qzVqsVXAifj7W1NVitVvj9fhlvZmdny04mMzMTN2/ehFqthkajEaIxn28Sc8fGxqRTI0Jjc3NTcrPpKqd8WKVS4caNG/JMcLSTl3cro359fV1GrWq1WnYknPNTCku1EcmsVE2q1WphdU1NTcHv94vp0OFwwGAwCEo+mUxK6BdH7bxAeWkwSZFqS7LFCN/U6/Worq7GxsYGpqamxMNRVlb2/+Ptv4Mbv+8zD/wh2AAWgABBFKIS7J3bq3a1Wq1WkrVa70q2pMhOYsclcXwumYmvTJw7n5NJT3yJ7TjFcVEkW1axmtVW0vbG3WVvYAHRCRBEIUEQrODvj93nHe7vN/MbZZzRztzcXezsksD3+/m8y/O8HuniGVsMAF1dXUK9JUFBpVJhfHxcuF0ccX5kF4VWq0UsFkN9fb0EnLPSPHnyJBYWFjA5OYk333wT7e3tqKmpEcQwzTp0kHJcUVpaCpPJJGRJ0jw51yNlMplMipKKYfaUC/LWpBwyEAggmUxi165d0rJx9jc5OYnV1VXBctjtdnzpS1/C66+/jieeeALt7e1YWlqCTqcTgB8NVlQzsBoAbpuK6AanN4DRoHq9HouLi3j44Yfx3e9+FxqNBjt27JDPiZX72toa6urqkM1mxcTFNn5tbU3ygdVqNebm5tDQ0CD6fKaDEQNgMBiE3USExvLyMurq6kSptjXRjaoyKkmYuWEymTA0NIR0Oo3KykqRguZyOVRVVWFkZEQgfDQM8t+bn58XIx33CkqlUlr++fl5tLS04P7775fQq6qqKgEG8lJhEZBOp6UzZCXu9XrF/JbNZoWfRRktxQX8+anJByAu4FgsJoljOp1O/AYE+iUSCTFe5eXlSbFCzwY9O5TKhkIhNDU1IRgMSiQsmU7saKiQIvY8k8nA6XTKv8t9D0dhjOEsKrqd0e31ehGPxyWoijr66upq6TQbGxuRTqdRXV2Na9eu4ec//znq6upQWlqK8+fPi/JudHRUFqtzc3PSWQIQ8QQ7Ab5DACSzgrseGgP5+1KdRNUg8fMcG7OY2tzcRF5eHvx+v4zvGKpUV1cnZk+TyYRAICCjsOnpafn38/Ly0N7eLv4Ti8Ui2Sl0dVOUsLi4CIvFIp3ZxsaGZGzPz8/DYrFIB6zVakUOrtPp4Ha7MTU1hXvuuQc2mw1TU1PYsWMHVCoV3G43vF4vTCaTEJoByPNLUyvNjxybc39IPDv3FYODgzCZTOIloliA4h0KDIhPorR76+6E5zJwm2LBFL0P8+fXvijoSaBLN51Ow+v1orW1FQcOHMDzzz+P2dlZtLW1YXp6Gr/4xS/w8MMPIxAIIJ1OY/v27RgfHxfoGEdCk5OT0r4uLy+Lh4GURa/Xi4qKCszNzQmszuPxSEtI3TwVN3Txer1e2SEQJ1FWViYjpomJCTG10Q0aiUQwNTUFu90ulfZWxVE4HL6ra6ipqZGqqaWlBT/96U8xOzuLpaUlXLx4ESdOnIDZbIbL5RIkNfcV/LKJv2DbHolEsHPnTlFqMK2OP9vi4iLuvfdeZLNZScViSw1A9iA0knG+SjghlRmUjlJaqlKpYLfbMTQ0hMnJSUFfcw80Pz8vSGWOUXhoFBYWit+CXgSn0ykIjpKSEnR1daGkpESURLOzs3KIECXNTAmO0Fjt8+JcX1+XeXk8HpeZL0ddi4uLokknnoLzYsqoebh6vV7JSyf2mwUHvQ7ErFBowH+X2RI0y3Efs7i4CL/fD6fTKSY5pVIpRirOo2nKq6ioEJEHmVNutxvr6+vYvn27GEKj0SgsFouMZKj62Zo4p1KpBAjJw4HFHCWWyWRSVGIjIyNiAFQoFPD7/WLuzM/Pl8uMOdBarRY2m018LaTzkhW1ubmJ9vZ2jIyMCBiRS2l2yPxd2FnRpMuf1W63i4SVqqVwOIxYLCY7BT5vfG+SySScTif8fr8AH7lroCzaZrPB5/PaedrhAADUJElEQVRJ7Cu7Sn7v7MjpDaMnKRgMoq2tDS0tLVLAlpSUYOfOnbLDSiQSgrThfiyTuZ2Hnk6nxcTKZ5XfRyAQkItqYmICAFBTUyNjdBaelLtTAUYpdDweh9VqlT0PmWIDAwOiPOP+lUXSR3JRbG5uwuFwIC8vD8PDwwiHw+ju7sb/+T//B3v37sU//uM/orq6GocOHRJy5dTUFO69915JiANu63pp3CJNcXFxUVzQxcXFiEajEsjD4HYCs3gobbXEU8LJl543NKs2jh2odc/lcnjnnXcwMDCAI0eOQKFQyEV35MgRzM/PyyIsHA6js7NT3ODhcBhDQ0N47LHHMDIyAqVSicuXL4uj2mazoba2VlhRqVQK3d3dWFxcRDAYhM1mQ0dHh/gR6uvrRRHCYJNsNoupqSmsr6/DYDDg4Ycfxvz8vKS9lZaW4urVq7h69SqOHTsmy/GOjg4ZBXBUoFarxRXM3QplpFQ4cW7KS5A7GerRfT4f7HY7VlZWBJdCDLRWqxVn+uLiIqqrq2G1WmVWz++MGdZs/ScmJgSLwYMkmUzKvoBeGXKF1tbW4PP5ZNGs0+nE9V5aWgqLxYLCwkI4nU4MDAzIwa5SqWCz2TA0NCQEYsag8lBlh0BlCw/4RCKBeDwOu90uhxoXpn6/X0KqWOHz8OSBRnctvRU8BKmd555ufn5eXuyamhoEg0FkMhk0NDTA6/VKFxiJROB0OrG0tCSd1tZixWQyAbhtjuWl9Md//Me4cuUKvF4vHA4HSkpKZM5OknMqlRLsDUOw2AktLS2hsbERTqcTbrcb9fX1WFhYQCgUwqFDh9DX14fR0VE4nU54vV7BVmg0GtmJUdrJETTd7xxhc3RMiSiLJiq7iORmJgsAAUSycyVMMRaL3dUhZ7NZTE5OCmNteXlZ9gjs5GdnZ0XYwk7GbDYjHA4L6I+jVXbWlFTT78DMHCJQKPM2m80oLS29KyCKhACSBRhhzEKX0viKigqEw2ExxAK3pyv8e2dnZ2WxHQgEJD5ApVKJOCA/P/8/pXr6L1lmW61WQSPEYjGBvr300ksyApiensa7774Lr9eL2tpa4TtRZsh5e+pO+AvDjKjKoCqAyALqiako4ljKYDDIrJbJYMvLy3IRMOWMjtzCwkI4HA6srKwgFotJG8+gnbW1Nfze7/0e/uf//J8IhUKyNOVuhWhtqqreeecdQUW/+eabeOaZZ/Dv//7vSKVSePjhh/G7v/u74pXY2NhANpsVd3QoFEIkEpGfJRwOIxwOywHChDhK+srLy1FxB0X95ptvSitbWFgo6WCsopmgRwwBK27Oo6m+IVCO89BMJiNjIIfDITpwjq542drtdlRVVUnuAatcau/pO6DDnBjr/Px86bbcbreMYzi/5UW8uroKu90uBslQKCQGwrKyMjidThkNLCwsQK/XSwra2NgYFhYW0NnZiVOnTqGxsRHbtm0TWCWX6bwE/X6/dAT8jrkI5HiClxR3E1sr3o2NDej1elFYJZNJ2Gw28ZXodDrEYjFBTHCZTEMnPzMqpABI9kR5eTmCwaAsthmnSvECER/s2LbKnBnWFIlE0NPTA7Vajb1796Kurk4OUWJjEomEXPLr6+tihKXKT6fT4eDBg/D7/XjttdfwV3/1V/jjP/5jYaS9//77eOedd+DxeETFlZeXJ1gLkpk57uR8nUv9lZUV+e8uLy/D5/Nhc3NTxAEs/Jg2RwZYfX29dGsej0fOBQCC/OB7wsuICH0q5FKpFCYnJ5G6A+7jPiWVSgmkkWKMqqoqWaBXVFRArVbLpU2+GYsd/k4KhUI8ZRsbG0KJpYqS5xwLGHaZRJNQqgzcRuIHg0FUV1fLpINj961ofvrBeClx9Pth//zaHQUAmdVT/11XV4doNIpnn30Wp0+fxkMPPYQ/+ZM/ETieyWSCz+eDwWDA2bNnYTabpaIsLS2VuSgTxJjdXFpaKlt8v98vcrr29naRxrHapdqD/3sejwcrKytwOp0yJzUajfB4PPD5fOjo6MCBAweE137mzBno9Xq0t7fj937v96BSqaDVagX1QcdpaWmpsPwPHz6M8+fP4+LFi/LF1dbW4uDBgxgaGsLTTz+NEydOoL6+Xm596p6ZckWZX2FhoexS6BxfXV3Fo48+isnJSQQCAVy5cgVdXV1wuVwIBAL4x3/8RxgMBvz+7/8+Pv3pT+P8+fOyzGT+MNEegUBAcBLs4o4dO4aCggIcOXIEPT096Onpgdvtxo4dO5CXl4empiaMj49LFcNxD2fpVJswIpY7I7bAHC1SsMALmbkJDI3iMp0vHheLqVQKFXfyxRlsxc+LF6/JZEJrayt6enrw2muvyb8bCoWwe/duOTzW19fFtzEyMiIFD3PIadxiV7C6uopIJCLdhsFgEOUIVU7V1dXSgfl8PvEVcTzHzzmXyyEajUokKnM1OHZld8G9DRlFHMGYTCZEIhFU3ElCbGxsxODgoLj1mczHS4a+I+7xmM43NjYmrKP5+XmptNklptNpGX8FAgGZ0y8uLsJmsyGVSuHChQsYGBjAnj17hItVUFCAbDYrCBOXy4WCggIJdNJoNLDb7SJ9ppyaB+XW+F7i1tm5AXd3ZTThBQIBGI1GUdt5vV5Bw2+F+ZGxxAKPlzkT4NgdMJ6A2dI0WzKfhay62dlZYZjxcuZekdTXiooKNDc3w+v1ivGUopD19XV5n1g4AZAdWDAYRFFR0V2ZOlyocxHvcDiEoMvnnXsLKuKqqqrQ0tKCyspK/Ou//qvQND6yi6KwsBDLy8uYm5tDTU0Ndu7cic985jPo7u5Gb28vYrGYVD5arRbHjx8X3X8mk0FHRwfm5uZQXFyMyspKJJNJmWUymWtpaUkUFJzP8cVmy0qqIlU35KmUlJTIIpMmGXoJGKAEQCJM9+zZIxK1UCiEnp4e/PVf/zUqKytx4MAB7NmzB3a7XS42atdpbMpkMqJa6ezsxCOPPILh4WH09fXB7/dj7969yMvLw7vvvotDhw6hpKREvBtETaRSKWg0Gtkr8KAiS6m2thYTExNobW3Frl270N7eDoPBIMHr4XAY3/jGN5BIJARhTl7/2toaqqqq5KDi2Kq2thZ+vx9jY2Ooq6sTHsyuXbtgtVoxOTkJj8cjM+Kt7nQuFn0+H8bHx8XseODAAdmzjI+Pw2AwIJ1Ow2QywWAwyH6B7uZ4PI5IJILa2lrs2LFDXkKOWIhc56XB6omfmV6vR35+Pq5fv47h4WEEAgF5iQsLCzE9PS0XoEqlwuc//3lx51OcwESxrX8f/TZGo1GMbxwLEaeg0WjkAC4oKBDFF/dqdPCTQ8XKnGo8qnq2usO5q6ITOxgMikfGaDTKGI4HOllRJSUlGBoaks+NQT+FhYWC4+DIi5WsyWSS2T4FDdzjsQunWIPvzNzcHAKBAFQqFT7+8Y9jcHAQV69ehdPpxMGDB+VCUKvVmJ6eliwKZnaT6Moxp9VqFXw7Hf9b/z12ERzJ8h3hiHBlZQVjY2Pi95icnERpaSn0er2oBvv7+6XK1ul02NjYEIVlUVGRjE9JF6Bkl/4DhUIhnxfVVpTh8uIPh8MyatsqE2Z2OC+U+fl5Uc0xQ4U7MYYhUaKbTCaxuno7sz6bzYrbnyBJysS5y+MYtKWlBaWlpbh586YEeVEU8p/582tfFDwoiXouLi4W5kkul8MPf/hDrK2tweVyweVy4WMf+xiA216DTCYj1Ey/34+ZmRlUVVWJjJV00Pz8fOTn54sCKhKJyIyXtzIdlxsbG6isrIRer8fU1BQmJiZkYUx4HF82LmAr7vCfxsbGYDQa0dnZKQ+a0WjEpUuXoNfr8aMf/UjMT2TmMCs4m83ixo0bSKVSCIfD+NM//VOUlJTgO9/5joyS7r//fpSXl+PcuXNwu934zd/8TTQ3N0vOLeePNLZx9FBaWoq5uTnZX+Tn52Pv3r3Y2NjAq6++img0ii984Qt48skn0dPTg5s3b2JiYgKzs7Nwu914/PHHZXdEYi9RGKxc2Yrmcjlcu3YNZWVlgh24ePEinn/+eXR0dODgwYOicKJ6jUvb999/X9DLgUAA8/Pz2LFjB0ZGRkQquLGxIZklpaWlKC4uRmtrq4DgfvrTn+LixYtwuVzQaDTitHY6nSgtLUUsFhMNOg9ePvTEYGSzWUlZ3L59O1QqlbxcFRUVIjdsbGyU74/7B5/PJ3JUjhs4umQnQjRKNBpFTU2NhBFxlFBdXQ0A4sKlMoXwOZPJJAgbjiA4MuVBpVKp4PP5YDaboVQq5Xlk4BM9Gwys4SFMggGfHbPZjOLiYjnkKNOl9HxhYQGVlZUIBoMCVlxaWpJDm0INjUYjFTg5Z3q9HjMzM5iZmcHLL78sGPD3338ff/AHf4COjg5UVlaKTwT4j1gC7h24WOX4enFxEc3NzaJco1mTPK6tSqhYLIYbN26grq4OTU1NsqtJpVJwu91Qq9US3sXLiZ1bIpFAIpGQDtZgMEgEQn9/v1wS2WxWRnZcAFutVnmWjUajZGhQbkpPEMGkHOcx25rmYC7BOfEAbi/1ed7Rb+VyuUTCDUBUcJlMRkZQm5ubSCQSUkx2dXVhYGAAXq8XIyMjuHDhAnQ6HR5//HE5A/l8fpg/v/ZFwUUQ/9Fr167htddeQ0dHB/7wD/8Qv/Ebv4ErV67g4MGDUCqVEt7OSpOz8MbGRuj1ekxMTMiIpL6+HoWFhZJrzCUYcPuiqa+vF0MUfRYNDQ2ipPD7/Xjuuefwla98BW1tbchms9KS5ufn3zUKqaqqQn5+PmpqaqDX69Hf34+ysjKcOHECN2/elIXQD3/4QyQSCWmJE4kEtm/fjng8juPHjyMajaK6uhqnT5/G1NSUzO9/53d+BydPnsTc3Bx+8YtfiGEuFAphbW1NFC/Mr1YoFJiYmEBjY6P8e0R1U6lFN/DZs2eRn5+PK1euSNjQZz7zGZw5cwaRSAR9fX2SH+D1euHxeGCz2QQqVlBQgNbWVty4cQMWiwV6vR4tLS04evQoNjY2EIvFEAqF0NjYKNLAuro6MQqOjIygo6MDk5OT4tD1eDyypCSfiDgQsrM4UpuamsLrr7+O9vZ2kf3+8Ic/RENDA95//320trZKBUYHPL0gdAhzrLiysoKDBw/C5XLhwoULOHDggLzA3/nOd5DNZtHS0iJ+iIaGBiF1bm5uiryT5k0q4ZgoSH0/xy/hcBjNzc2i9qPyjzJtggJnZmYktIfyXxrweABQNkzabS6XQygUkowXGiCbmpqQyWQwMTEhjmeCBGOxmOj96R9gx8P9E+XiwG01HI1ZvFD0ej2Wl29nl1NNBUAotzzkS0tL8cgjj+CVV16RA7e+vh4dHR1YW1vDT37yE4nrJFqbplW9Xi+dGmfvJpMJpaWl6Ovrw86dO0VCT9XazMwMGhoaUFxcjDNnzmBgYEDUTM888wy6u7vxm7/5m/i///f/ymfHhMDi4mIx95LKQAUbpbiLi4sYHR0VJAgT+fiul5eXI5PJSEGQl5cnHhlGKFCCq9FoJN6UyBvGE3Ofyd0Hx9exWEzwKTxXjUaj7Nwo4hgaGoLVapULE4BAU1nscYd66dIl7N+/X6IK+LN3dnbCarV+6HP+v0Qem5+fL67h1B3SaCAQQHl5OZ5++mksLCwIqoMxlLSv5+ffjt3k3Jz8+/3792N6ehp+vx/FxcWora1FeXk5zpw5I1JCjiqMRiNaWlqg1WrhdDrx4osvoru7G8PDw1CpVAgEAjhy5IgslIj25oyebHs6WFdWVuDz+VBbW4u5uTkcP34clZWVkt9L6V55eTnMZjOA2xK25eVlNDQ04JVXXsF3v/tdtLS0QK/X4/Tp02hsbBSn9b59+wQISKMRMym0Wq1UlEVFRZIH7XK5ANye2RJFnEqlcPToUVy8eBFvvfUWZmZmsLy8LMtL4HYlNTg4KKoKqqPo9FxfX8f9998Pi8WCmzdvYnBwELW1tbh165aEHnV2dmJmZkbGQjwIiXQnApvKIPpigsEgHA6H5EdTfsrDixLba9eu4d1338XFixdx7733ig+FhE+ydHbt2oUrV66gsrJS3Kfc5zD/gW7slpYW3HffffiLv/gLFBYW4qGHHsL+/fvx7rvvyu6np6cHTU1NsFqtwvOiT4cHOF3U9FMwZUyn0wk11O/3Q6/Xw2azyS5Ao9HAYrGIAmzrspSwNz7/xMJwKU6pMJPOeBnSgd7S0iJIFMpy2fHQZV5WViaFBX8HPvfl5eWora2V0C2OlKgAIqyTI7CtuRiJRAKrq6uidHrqqacE1dHQ0CAwuzNnzkh2CXdZRHFTYstxT1lZ2V1ik7q6Oly/fl3eec75+X8KCwsxMTGB1dVV/NZv/RZcLhdu3LiBbDaLs2fPSgQo35GtnhtKvznGpgkukUjAYrEIW4lpjiUlJUK8JU2Z8QULCwvibeECnQa/2dlZtLS0yAKdMQf0N9FFnZeXh6mpKbm0KOohCUKtViObzcpSnZcTVWAcSzIznsZHKv/I0GptbcUPf/hDkRDHYjFxwH8kFwW5LyqVSlK4Tpw4AYVCgVAohK6uLokvpAP55s2bohrhAszj8chNf99990GlUmF0dBTT09NwOBxiSiMmlx2MxWKBzWYDcFsaNzY2hvfffx+3bt3CyZMn0dnZiWQyiXg8jm3btgm6l/Z3ZikXFBSIryAYDMLn80GhUGDHjh0oLS3F2NiYVFvA7WXTVjKox+PBgw8+iPz828lvHFsxdvLWrVuwWq2IRqOyXKaTmfp0fumczfKh3L9/vywb6QCl/r6trQ2HDx+GTqcThv758+cRj8dhNptRW1srLT35P7xkWVGsrq7iH/7hH/BP//RPyOVyePDBB/Hss89ieHgYV69exdGjR+XSWVxclF2Dz+eTRR0zy9nlHDp0SJRNmUwGo6OjMJvNYnh0u91izGOc5szMDHbs2IHjx4/jjTfewOXLl/H000/LDiIej6O2thaTk5MoLCwUyCDd136/H8lkEmNjY/jMZz6D8+fP4+c//7mYkOhxofmPCrcDBw6IRJfmQO7KAMiLT5wFF+o0TREhzyQ1VqsECFIWmkqlEAqFxEtC1Pj6+rrMqnlY87NkfofBYBDF1+rqqlSevIgsFot8FvQFbKWcUn9PSnNeXp6oxLhv4fvM8VpJye0cd46OtFqtdDEbGxt4//33RXSyvr6O3t5eMUn29PTgyJEj2LNnDywWC/r7+4XNRdgiHdDr6+t38bd0Op1cZAaDAUeOHMGNGzewsrKCQCAgvDZeMJFIBJ/85Cdx8OBBjIyM4PLly4L2sVgs0Gq1grEnXpuXG6Wo09PTsNvtUvzk5eVhbW0NqTvBSDQYrq6uymfFEVI6nYbFYkEsFkNlZSVCoRDy8vIEiNnS0iL7MkrUdTqd0IvZec/OzmJhYUEw/qTgcqzk9Xrv6rTS6TRqamqwsLAAi8UiuB56yn71q1/B6XQiFovhwoULiMViqKmpweHDh0WG+2H//JcsswnMq6ysFH18LpfDxYsXYTKZUFNTg4GBAczOzkpHAUBmoJw5T0xMSD7tF7/4RQwNDcFms+H111/H6OgoLBaLGIzKy8tF3lVYWIiZmRn09fVhbW0NJ0+exIEDB+QmvXTpEr75zW9i+/btaGxshNlslqyMgoLbAeeswI1GIwwGg1RCAESm6nQ6sbCwgFwuJ8gQqi5isZiA7Nrb26HRaKDT6WTeyGCh8vJy6PV65HI5uN3uuxL4crkcKioqpKrhfHVkZOSu5Z5GoxFZ5ODgID73uc/B7/dL18IFIgF7zE8gzG9paQk1NTUoLCxER0cHhoeHxdzDqpBqn8OHD4sceGJiAnl5eYLa4MvOpazf78fw8DCOHz+O06dPQ6lU4vvf/76IB5i2ZjQa0dbWJhXlAw88gB07duDv//7v4ff7ce3aNVy4cEFMT2azGS+//DLKysqwY8cOUd6wmvP7/eJGLywsxLZt26Si5k7sxo0bMBqNkj/Q39+Pe++9V7wxRMiwOyJmg5JMjhrIyOJ/l2ao0tJSVFRUYGxsTDoFVtFcfnPEQMOfTqeD2WyWoml9fR35+fmCva+trZVqMS8vT7oKeoOoysrLy0N/fz8SiYTA4bbuu6i4YmfOv0ulUmFqagotLS0YHx9HU1OT7AkpFiB6Y3l5GbW1tZJ3zeq3srISR48ele+suLgY4XAYwG01JMemHC+xGyfkkownzv+piCQhtbi4GO+++65MIQoKCmCz2fD4449jcHAQzz77LCoqKnDPPffg0UcfFVwQqQ+Dg4M4efIkmpubMTU1JRcEl/sk4xJQmUql0NzcjGg0CqVSKWICdicA7vK/0JdB6gDHfKlUCpubm6irq7trR8GuhKZDmlfZIdtsNslUYeYJ0ShE4lPWvba2hs7OTuTn5yMSieA73/kOfD4f1Go1Ojo6JPWxpaUFBoNBEv1CoZB87h/2z699UTCvmcz8zc1NCdjgjJOHXOpOZvDa2pq8LGq1WuSiRUVFOHbsGC5fvoybN29icXERx48fF62yw+FATU2N3L5erxdWqxWjo6O49957xaD1iU98Aj6fDy+99JIcYidOnBD/hdFolNAWmn7W1tZQUVGBWCwm3HgaUqhu6unpERwBg3t4EdhsNolxZPhMR0cHTCYTrl27JkAvfukVFRVI3bHVbzUaUtLI0QdHdNyr0F25sLAgucdzc3OS+MdFOCWFlHWyAmOMI5dtfr8fJpMJp06dwt69exEOh3H+/HkMDg5i27ZtslMggI9IATrieYnY7Xbs2rULuVwOo6OjOHfunKSG3Xvvvdjc3MQvf/lLVFdXy6U8MTEhB/fw8DCGhoag1+uxtLQEj8cDh8OBsrIyPPfcc4jFYvjSl74kGSUKhQJOp1OQ26xWqehgdUbezyuvvAKn0wm9Xo8rV67A7XbjK1/5ClpaWlBcXCy5DIRDplIpaLVaeVmpjsrPzxc3MP0dRGmMj4/fRTMmqnpychKdnZ2iyJuZmRFXOyXhFBlsRc9QPUYBB3dXPETJROPegfsbLvx1Oh1mZmZk5MGFOy8ejmI4GqI4g50VIXl8/qlUomchkUiIws9qteLUqVNwOBzo6enB9773Pfk86cfgJeFyueB2u1FSUiI+AyJfiOjPZrMyvtyK7JidncWePXtQXV0Ns9mMv/iLvwBw2yPxxhtvALjtLRgYGIDf70dRURGcTqcEdfEA3tjYEEw+UysBSGdJkGNZWRnUajWqqqoQCASg0WhQX18vBF4e/lTjkQ9WsSXQiKMj/q7FxcUwmUxyCdBQmZeXB4fDgZGRESleae6lX4qqJV4yoVAIw8PDki1x3333YW1tDdXV1fKzdXZ2wuFw4NKlSxLfq1KpZFfyYf782hcF56g0oxF/EQ6HxYVL2BW39VxQLS8v46mnnoLFYsFf/MVfYHBwECMjIxgfH0d+fj4efvhhfPOb34Rarcbk5CRGRkbwwgsviDKqpaUFwWAQBQUFGBoawtzcHFKpFA4cOID33nsP3//+92G32/HQQw/h4MGD2Lt3L65du3YX4pdMn0wmI+gD7lny829HUaZSKRQUFMBisYiCaqtcFbgtryXE7d5770VPTw96e3uxd+9ebG5uirGoqalJ8OFzc3Mi9ePPQukeqzBeBkqlUpardPvSf+L1emE2mzE+Po6ysjJEIhEZBW5ubkKv16OyshLXrl0TtyullDdv3sSxY8dgMBjQ3NyMN998E3/zN3+DqqoqNDQ0CNqCwU6kdqrVapmt05m8Z88e8cZcu3YN3d3d6OzsxIkTJzA/Py9z+u3bt0uyXFlZGcbHxwWEduzYMVRVVYnO/OLFi8jlcjh69KhITIl/icfjmJmZgcFgkKqqtbUVSqUS169fx8TEBIqKinD16lVUVlbCbDbj1KlTcDqd6O3tRWtrK4DbJi9KGvnZV1VVibGOI1EamIgZJ9/JZrMJnRT4D0wKn3GXyyVLUB6wiUQCTU1NYroEIKFGPJhpdiSKm5wwavvtdruoAjlepAqRM3lmbQO3cddU6XB/QQYSLzVC41ZWVuSwCQQCaGpqkpzorq4ueT/W1tYwPT0tC2NmHlgsFlE9sSujN4G7hoaGBrz77ruS0ra2tobr168LLVqpVMohTsLB5uYm3nnnHbS0tKC7uxsWi0W8LRcuXEBXV5egO/r6+rC+vi7FBAUvwG15MpV/VLyp1Wo5kAmPJBCRfqP29nYZRc7MzKCtrU2wOhxXLSwsSIGcSCRQVVV1V1wzI4KJFolEIqKq4j6GxfXKyooU0nSDs6htbm7GL37xC8zPz4sI5+GHH4ZCoRCf1C9/+UucP38eLpcLfX192LVrF8rKysRc/JFdFMRs0OjB5TY38uQAra+vSzVCIiLZJNT0sxWtrq6WOfjIyIjkRPMXpAqBOnNW1+Pj4xLgwezloaEh9PX1obm5GS+//LJUbcBtGdv4+DhKS0uRl5cnkjGOeqiYIESPOHFmTGxVJDEwZXV1FXv37oVCoUBnZ6fI9MgDGhsbEzQwteCRSASZTEYCf0ghpY59586dWF9fx+TkJNxutzigA4GAwNA4hpicnBRCJyWNhYWFiEQiuO+++3Dz5s27iJoAcP36dRQVFaG1tRVWqxUPPfQQ8vPzYbFYYDKZEI1GZUxB74TRaJSLYH5+HoFAAKFQCEtLS3jsscfucpVevHgRRUVF4jzfu3evHFBLS0vQarWS0bGwsID3338f58+fx+OPP449e/ZgampKApba2tqwd+9eRKNRWfrT6U6wJDMSPve5zyGTyeDs2bOwWq144IEH0NTUhIaGBjgcDoRCIYyPj0s4FWfQPEgpqWUGNpe6er0eRqMRPp8P1dXVwrAKBALimuWIbWVlRToHAHchTQCIa5vdOEcQDodD/m1GuIbDYSHDcozjdDrlEMlmsyKVpbjEYrGIYIPKs7y8PGg0GoyPj0tlywAq7iAYLETCQSQSEXf+0tKSjNMoO2fcMH+uvXv3yn7M6/UKoptO9OXlZVy/fh0ej0dy4aurq8VHQoHM/Pz8XQcux7hTU1PYvn07GhoaUFJSAqPRCIvFAqvVioMHDyJ1h5ybTCaxfft2IRZQaEF4ZHl5uVTgAOTQJh2Xzz/jW69fv45PfvKTeOONNxAMBtHV1QW1Wg2v14tcLidCBY4I6bKmcZQcrYo7XCv+ZywSiCziZKO4uFjwHB0dHSLu4GfQ2dkJrVaLX/3qV1hdXcXY2Bh8Pp+o56anp7GysiIGzcXFRQwODmLnzp0ixPlILgoC4PihLCwsSEXA25vtJyVrDP2Yn58X9+rXvvY1eZAuXryIM2fOwO1244/+6I+Ql5eHrq4utLS04LOf/awkwHFWqlarxVCi0Wjw3HPPYdu2bTh+/Di6urrgdrsxPT2NVCqFGzduYN++fdi2bZsoPEwmEzKZjCz3KGGbnZ2VRSDnfTxEOAqqrq6GVqsVowsvGyaPkZDJZDYuLPnvUVWxsrIiTKdMJoOamhoJHaJKiA8UDWFbLy8milGBwdS2mZkZ7N69W0Zi5OlQ2VNZWSkXgMvlgkqlwsMPPyzudb5cZHEx2Wx6ehq7du0S1MPS0pJ0Gr29vbjnnnvwwAMPoLu7G263W0ieR44cQSaTkZnw1NQUlpeX0dnZCZfLhX/7t3/DL3/5S8GSz8/PY3h4WBzmXMgRklZVVQW32y151IwbnZmZwfvvv4/m5mZ0dnYim83CbDbjZz/7mYD7mIBH9zuT1yj1bGxsRCgUEmx0KBSCzWZDc3OzgN+ICqcYgRczEdU8CKnI4iGeSqUEXc+FP7sUdoQbGxuyDAUg2cdkdc3MzCCdTovpimMtHrT8XZgHvhXBTg0/K1deDBxplJeXy/iD+Qj83KiGmpubk70kU9bYsRYWFgoqoqysDCMjIxgaGsL8/Dwef/xxaDQaDA0N4ezZs2hvbxc/QMUdjhG/R46dvF6vRNbSp8CF//j4uBywf/d3f4fe3l780R/9EU6cOIF4PC5FAf87q6urcDqdIrLhZ02QIt/Pqqoq6PV6jIyMyJitpqYGpaWlCAaDUoAYjUYMDAzAbreL5yWZTMLtdovju7KyUorDUCiE5eVl7Ny5EwqFAmNjY1Cr1bBYLEIOWFtbw+DgIBobG4VUwUmHSqUStRz9Xx6PR6S6s7Oz0tl87GMfQygUgtFoxOHDhyVDnju+j+yi4OI1GAyisbERRqMRMzMzMrcjeG1lZQUOhwPRaBTT09NoaGiQA2p0dFR2GkyVunLlChobG3H06FFks1kMDg5iYmICjzzyCGw2GxwOh/BYnE4n6uvrYTQa8eCDD2JwcBCRSESwxDSFtbS04JVXXsHLL78MlUqFnTt3ouIO1RTAXUs1Shw3NzeFgUM53ebmJlpbW2Upx5FDWVkZ5ubmxDB07do1qb5cLpd0XyqVCiaTSfhAmUwGJpNJlrRqtRrhcBizs7My3qDJCYD4RAj54mFACCKNQlShcZRXVFQEq9WKpaUlgZmVlJTAZrMhEAjg1q1bKCkpQX19vbTgW1PUHA4HioqKhD/FEQrHaJlMBpcvX0ZhYSHee+89UblZLBZMTU3BYrFgbW0Nvb29UiVnMhkUFxdjdHRUeEfME6cUNxQKoaGhAbt370Z7e7t0hHv27BFZJNMFn3jiCZhMJvzsZz/Ds88+iy996Uvo7OxEU1MTVlZW8Mwzz8DlcqGrq0vGhsFgEGaz+a5sCu7BEomE5Dhks1m8++670rWFw2F4vV7odDqYTCaUlJQIfI5jGBJ1aUblM7axsYH+/n4JkmH2yNTUFHbt2iXocl7QAOQ5Bm6nwBGrTfUKF+jcQZGjxQyEiooK6YJWV1cFJZ5KpVBbWytegmg0iuvXr6OlpUVm+sRwMNM6dQdUt76+Lrh67iH4e87OzsruJZvN4mc/+xlyuZyYIHU6nXRnNODabDaBe3Kmn81mheTMn0ehUGBkZEQ8LaFQSJzRlHKzS2QxQpQJ8x+y2axMMIDbCI28vDyJKZifn5cc+O7ubly8eFGKCcalVlRUwO12S2dWW1srY3YuuZnQScWaw+GQz5/ECJ1Oh927d2NtbQ0tLS1YWFhAIBDA3NycRO/mcjm0tLTIDhSAjNwJUi0sLMQHH3yA1dVVVFdXo6urC52dnfJcs9gLh8Mf7UUBQCpoztj0er3Mc6empsR2fvPmTbhcLmnPS0pKEA6HcezYMbz66qs4c+YMqqqqsGvXLhw7dgxPPvkkOjs7cf78eZw7dw5TU1PCzWlubpa0q4qKCgm1KS0txRe/+EWUlpbixRdfxMjICILBIOx2O/bs2YP29nbMzs6KI7a2tlZ47Zx3cxk1MzMj7CjOBWk22r9/P86dOweNRoORkRFMTEygqqoKp06dQjKZxI9+9CPpLmjk4QPCmb/b7ZYuKi8vDxMTE6JY2dzcRFtbG/x+P2w2myBMmBPMeTfHCgSVUR5HCufWw4X6eOC2cqq0tFQuo9raWuHJBAIBIWkSy80RAC8rzvGp2Y/FYlhZWcGuXbsEJf3666/j5MmTKCkpweHDh2W2zhEIAMkhr6iowMTEBPbv34/du3djY2MDHR0duHnzJn7rt35LFEzpdBovvviioEO4YH7ooYcwMTEBp9MJj8eDxsZGMdANDQ3B6XSiqqoK27dvh9VqhcPhwI0bN8S9y4qY3KPCwkJEo1H4fD7EYjEUFRXh4x//ON555x28+OKLIretq6uTKpImJ4/HI/Tc9fV1NDU1SZQvkQxzc3OwWq1QKBTiN5mZmYHdbpdiSaVSSSdC1Hx5eTkaGhpkNMPMD+42mP3O6rikpEQO3unpaZhMJjidTiQSCcnk3rdvH0ZGRkSyu7Kygq6uLsFl8LLkbot+IaqpmN/BDsbj8WBzc1N2iTt37kQkEkF1dTWefPJJ9Pb24sqVK7DZbDh9+jQ8Hg+Gh4fR0dEhF7DVahXX+draGkKhkFw6XOZXVVXh+vXrIhtub2/HCy+8gNnZWQwMDMhORq/XC1WWEwIu91lIUS7Mrp74by6SeVBTCs80uyeeeALf+973JJeGXSC7carPeJGQgcZdZDQaxeuvv46CggJMTEwIL4wKucOHD+O1115DLpdDTU0NNBqNBCdxx6fX69HR0QGXy4XV1VV85StfgVqtluKaHK6rV69KLgbHjR/ZRUGN+LVr12QeR7UAzTput1sOYBp5qqqq0NTUJIqW+++/H+FwGD6fT4Jl1tbW8Nxzz2FhYQE2mw3RaBTbtm2TdDQ6vJm7QMv72NgYTCYTLl68iGw2i3vuuQdTU1P4m7/5GxgMBpw+fRoWiwUXL16E0WgUXTVfhPn5eVk+cQHLRLmFhQU4nU5ks1lEo1G43W4UFxejsbERBQUFiMVieOuttzA5OYnPf/7zWFxcxOXLlyVgpb6+Hul0GrFYDFVVVTLqoqZ+cXFRPifqxQsLCxEIBLCwsCDVFdVL5LoolUoJ0aFR0OFwiDmOuRwcLZCTxNQ6wtiIW+aFxc+C2dissug4zsvLk9+d5jvOUvV6PbZt2yYI+oKCAhmf0Y2+FQ/P8JXKykqsrKxgaGgIBQUFeOihh+Rn6e3txXvvvYfR0VG0tbVh9+7dcDgcyGazWFpawo9//GN4vV4MDg7i4YcfxsjICAYGBoQyytwNmpKIuwAgy2wycZjb0dvbi4WFBXzmM5/ByZMn8ZOf/ASPPfYYzGYz3nvvPajVaiwvL0u2ejweh8vlwuDgoBgTFQqFBEhRbULkvV6vl7ElvwvygSi15f9fr9cLHpuHCj9P5mVoNBo0NTVhdHQU0WhUqKGUZi8sLAhfSavVyrO1urqK2tpauXyYzZLL5VBXVyf7QPKl8vPzpRsnfC+dTgsMkiq0aDQKnU6HL3zhC/jEJz6BP/zDP5TLqLGxEWfPnhV5fU1NDSYmJkThw2eCc3+6xund4L6GRklykehlYTgZTXZer1cUT+vr61hdXRWRAGWs9FqxE3O5XGhvb8fAwIDguZ9//nkAwOnTp9Hc3IyCgtvRyUQGhUIhaLVacVwTxU+EjUKhkJFXY2Mj+vr60N/fD5/Ph9///d/H+fPn8cEHH+Cxxx4DAIyPj2N2dhalpaVoa2vD2tqa5IjwWaIkn3+uXLmC/v5+nD59GhqNBlNTU/I98Xf+yC4Kcm9qamrkIQcgS2e2YOTZcO7o9/thNpslTOjw4cN49NFHsbi4iK6uLgwNDeGtt96CWq3Gww8/LJXBF7/4RTgcDhkB8ABjhUxAYTqdluwImvK6u7uh1WpRWFiIUCgEh8MhCWoGg0F02pR+kkljsVgkEIUSxatXr4rh7sCBA/jMZz4jfP+dO3eK0/TWrVviPmd1ywq/qakJU1NTIr/cmu3AuEl6RGw2m1QPvb290vZSMcXqLp1OSyYCIYiMkEylUmKw2tjYwMLCgqSQUUZcWVmJyclJ+P1+YecwApTOXCqpaN566623ZIxATIvD4RDa6erqKtrb23Hu3DkBOLLyraiokPkuQ1v6+/slnSsej2N1dRWdnZ3o7u7GD3/4QwwODqKyshLbt2+HQqHA5cuXcfXqVbm8dTodLBaLzOBdLhcGBgZkRBiJRDA7O4uGhgb4fD7MzMzA6XRKR8nOj0Y9o9GIkZER/Mmf/IlIjnfv3g2n04mXX34ZNTU1sNvtcvhysb0V663T6TA1NYXW1lZEIhHYbDaMjY2JnJcjvEAgIHu/mpoaWaibzWZBQlDIwL1DQ0ODHHr0YVD2Wl1dLWhymvaSyaQY1aampiRPnNnk3A/YbDZZpLLLpbGMEm/yjCjj5d6CyPXm5maMjo4KgPMHP/gBAoEAGhoa8MQTT2BoaAj5+fl49NFH4XK5MDExIZ0Tc7tpZDSZTDJ+6e7uRmNjI5RKJXbt2oXq6mpEo1FMTEzA5/MJxdVms2FhYUHYXHz3bTabvFscUU1PT4usnKIRFoeLi4toa2uDQqHA66+/jomJCRw7dkxkuI888oj8DEz9I7JjqxmTY66+vj4olUoolUq0trYK8+rGjRvweDxwu904d+4c5ubm8NBDD0Gn06G3txcnT55EKpVCNBoVXBEvqb/6q78SKvUbb7yBeDwunyGpFV6vV7rcj/SiACDyPfJhKM/T6XQoLS2FzWaTwBzgP5QfwG3dMvMLnnrqKayvr+Ptt9/G2NiYmHdOnTqFe++9F+Pj4+jr68O5c+cQjUZRUlIiVXMkEkFZWZk8BB6PR5bm5eXlsFgsMgLgQVtWVgabzYbp6WnZqeTn5wvCmr8XQV1c5E1NTSGXy8FoNKK2thazs7N48803MTMzg+rqauzevRvBYBCxWAzNzc1oa2sTJPDS0pKMipLJJMxms3gHqFRSq9XweDwwGo0AIAcCtfpKpVJwzuvr66JKohFPo9FIsl1nZyfGxsbEH1BbWytLsebmZpSXl4tRkbnQfKkoO+VYTq1WC4yPYfdLS0uw2+1SUdPzQXQ3cz+i0agsoAsLC8XENTg4KKylrTG6VM1RORMMBvHjH/8Yk5OTaG9vx6c+9Sns2bMHhYWFePPNNwUkOTg4KN4Jn8+HT37yk7IIPXToEHK5HAYHB8Xro1KpxCHO53dxcRE7duzAxsYGBgYGsGvXLvF0JBIJBINBfOELX8Djjz+OoqIivPfee3jqqaegUqlE/k1THknD7MDi8bgUALxI6VNhJwFARqPcRQSDQfE6UHpJzwOBkVxoLy0t4aWXXsLm5iZqampw7NgxKBQKnD9/XkZUzIiniRWAID7onA4EAggEAiILJz6G+x4WJC6XC/Pz81JE0JhmNBpx+fJlhMNhvP3229i+fTsef/xxoQPQgFtYWAij0YhkMin7S6bxcURNJRnfW3boOp0Ozc3NGBkZQV5enhgm2SnSwU7/klarlXx7kow5WlSr1YjH44J3J023q6sLv/mbv4nBwUHMzs4iGo3iyJEjMBqN6Ovrw+DgIFpaWuB2u5FKpeD1emGxWESBR/pzRUUFkskkqqur0dLSgmw2iytXrmByclKKY040amtrsWfPHuzdu1fECocOHZKOgZ8hf6+5uTn598rLy9HW1obJyUmcOHECly5dEgYdR9YsqD/sn1/7oigrK4PFYhH3MqMzGezBg4LLJC4sPR4P/H6/JGklEgnMzc1hZGQEr732GmKxGHbv3o14PI733nsPx44dw549exCPxzEyMiKxpOTskGXDyFO2qslkEtFoFOFwGHq9Hul0GsFgUDAHlPOy0rZarVJ589CjMY9JeA0NDfjud78rLznHX263GwcPHsTq6iq6u7tx3333weVyYWZmRi5NkkQByPyTJjoyhHK5HBobG+H3+0Vjz3EP1Q4MI+KYjpp/ohrI1WL+x8LCArZt2watVityVurITSaTJO2RwcPFGH0U3HcYDAbE43F4vV64XC7kcjkJq+Lcl2E/NPTFYjGBEDIEh5BHjp04ZkmlUlLFMiM4kUggEongnnvuQVtbG+rr69HV1QWPx4PW1lbo9XrZFxAHcv36dZSXlyOXy92FskjdCaWhMc1isSAQCIi2nzP4kZERxONx8VrQb9PW1oZEIgGPx4OBgQG50OliP3/+PGZmZu4Cu5HSOj8/L/LQRCIhYyd2gwUFBYL4oOHM5XJhZWUFwWBQQIMUXPAzN5vN6O3txejoqJgkNzc34Xa7pWKvrKyUqFIKUChJZwWs0Whkf0h5M5lWzMRobW2F1+uF0WgU+TMPaM7NCfebmZlBIpFAKpXCtm3b8OSTT4ph8syZMxgZGRESAIkB3B+srq4K8oJ7rfn5efj9foEJzs3NCdEgHo8LYLK4uBhlZWUoLS0Vai//roKCAqhUKglkIkmZz4hSqUQ0GhUfB1WYhA+urKzg4x//OFZXV+H1enHp0iWRvjscDhkdUyXG75zL642NDQQCAbhcLtTU1CCRSKC3t1d+l+bmZmG7Pfnkk7LUX15exvbt22E2m2UCMTMzA7fbLTLbY8eOobe3F7/61a/Q0NCAgwcPYnl5Gc888wxqa2vxd3/3d4jFYkgkEh89PTY/P19mrzS0kH/E+T7VMxaLRZgwDQ0NUCqVAtkbGxuDVqtFIpEAAHzqU5+Cy+USRY3b7UYulxMOjEqlums2XV1dLQ5Vhsjz4Ozu7kZra6toqSkPpZ6ZLWM6nZb2lMvG5eVlOBwOwS2zkpqZmcHw8DAOHz6M//2//zeCwSCGhobQ09ODtbU1HDp0CHq9XtynbrdbgHAMj1cqlTI6m52dFZdtWVkZAoGAyOIY+Uj5XEtLiziXeRDygJiampKKluh1Zimvrq7ihRdegN/vR21tLXp6erC8vIzDhw/D5XJJ10MMNZflxE9zyVpUVISNjQ2Mjo4KjhyAUGIB3MW/YRobxxXDw8NYWFhAY2OjRNIyW6G8vBwdHR3w+XxoaGjA4uIi/umf/glKpRKPP/64RG7+/d//PVZWVnDs2DEkk0kMDw9jZGQEjz/+OJqamiR7u6+vD8PDw6ioqMD09DSuXLkiBNL6+noMDw9LCBFJonV1dSJRbmhoELWI3++HWq2GSqXCU089JV0jd1uxWAxqtfou5HgwGIRer4fVahVicFVVFTQajSTmVVZWYnp6Wjprdq0Vd9AnXEgTB0J0xtzcHFQqFSKRCCYmJiRAiaNBl8uF7u5uDAwMoLGxUfYc3MVsBTharVYUFhaKkgiAAPSIkedBSUksx8zslsjeIliSSjB+Tj//+c/x+OOP46233pLlcDweRzqdxttvv436+nqYTCb09PSgsbFR6Kn0flDNlc1mhbzK3V5BQYEEiI2MjMDpdIoQg6Npyna5s1lYWJAxFQ9Nfh5VVVWiAGMQlcfjkTCi6upqPPPMM8JOAiC0YAokuBthsFd5eTm8Xi80Gg36+vpgNBrhcDjw8MMPIxQKYXBwUPatjF7weDzyPG1Nd3Q4HNI5FxQUYHJyEvfffz80Gg3efvttBINB3Hfffbh06RLW19dFfJGfny/qNFKBP8yfXzsKlQ8Pq8OtsslYLIbp6WlZuFLCyIORihymzU1NTUGv1+Oxxx4TNgmNRGyro9Go6LjphlUqlVhbW8P+/fsxOjoqXyjHYRxfJJNJcYYyT5l/dyqVgsvlkmqGVSLzJ5RKpYzQiKrW6/V49NFH0dTUJJdlxR1yKaWcdrsdwG2dvdVqlYOROwfygBhQr9VqsbCwIP8uAHEVM5eBMj7ih5uamgDcRq9z0VdQUCDz0LKyMnR2diIWi2F2dhbXr1+HWq2WbPH+/n5Rt+zevfuuUYzBYEBVVZV0CTSlcaxks9nQ39+PmZkZCZgKh8MC3eO/T3UauyIiwYkA2dzclDkx5+6MedTr9XjggQdkBzE4OIhUKoWamho4HA7cunULBQUFcLvd+PGPfywFA1EeOp0OHR0d0Gq1aGpqwo4dO9Da2ioH59zcHGKxmKjfthpFaTTbv38/XnnlFXz729/GxsYGDh06hM7OTjG2vf7661hfX4fJZBITWi6XQ3V1tRgo+Z1Qocb58tramgAG6emw2WwoLy/H4uLiXTJTyk+bm5tRUlICt9uN69evQ6vV4qGHHkJ9fT0GBgag0WjQ0dEhaJuamhrZw1GBRWoql/w+n0+WolzAJ5NJEYpszVSIRCKCtOA+j4BDyqDpamccAAOkvvGNb+Dxxx/HAw88IPvDijtxxwyGIv5jc3MTuVwO8/PzEiy1sbGBSCQiYVMcx8zOzmJ4eFj8WwwIKiwsxPDwsHiQeDlx/K3RaKRQ4uiVBkI+7w8++CAeeeQR1NbWYnx8HJcuXZJ9UDKZFDAmMzT4/rBY5mLeaDSKOiwQCKCurg7Ly8sytuvu7sbQ0BCuX7+OSCQCq9UqQg6j0Sgu78rKSlRUVODYsWPYtWsX4vE43nzzTRw9ehSf+MQn8Kd/+qfYt28fSkpK0NXVJSNYcrSoAPuwf37tjoLJW1arVeaq1dXVaGhokJhJykz5AHIEAfxH5jZDRACII1Or1cLv99+FNqBUMxgMYmZmBocPH8by8jL6+/vFpMKDYtu2baKOYmVNdU9lZSVmZ2ehVqvldyEhkxULF8Yej0eMUwUFBaioqMDJkyfl39PpdBgeHpaLTq/Xw2QyYWpqSirzwsJCwY0wKKiqqkpc7QBEE8+Xjxcb1V2BQABarVZgcnNzc4JvAP4DE11UVCSXJ12zXCDb7XZ8+ctfRl5eHqanp5HL5aDX68WsF4vFkJeXJ6iHsrIyBINBlJSUyM9TWVkp7uytvgEi0rlUBSCdpdvtFnUWD2jKDQHAYDCgt7dXwqoo9bVarWhubkZTUxO0Wi3Onj2Lmzdv4siRIzh48KB4RCiNbmhowOrqqizrNjc3cerUKbS1tUl2dyaTkReVmRhU16yursqorKysDDdu3MAjjzwioEQ+g//8z/8MhUIhWIobN24IkoaGKeZ9MI+c9GNq9cmYYjgV0+5YjNBzQeMbR1EcAzHK99atW/jkJz8p3Q8jPT0eD9ra2rBt2zaYTCYUFxeLwzmdTkOn0wn+mlUm8wyY10LBBncfAKRQYJekVCrF5Q1AOn9GeJaUlOD06dMAgG3btiEUCsnO8JVXXgEA8UgAkNwO+oU49i0rKxMVH8cxTqdTOg7G6fJSobKnrKxMKulAICCCGovFgtQd9D89UgUFBairq5MDng7rubk5wZkMDg7ik5/8JJxOp+TG8Pkj34x7UmbAU5ZbVFQkxsexsTGUlJTIu8NzIZvNSjATPyd2NMSNUJxBldrm5ib6+vrw3e9+FwaDATdv3oRarcYDDzyAhx9+GAaDAX19faioqBC0+0d6UaysrMBoNGJ8fByxWAyBQAB79+7F9PQ0fD4fbDabKGuIyg4GgygsLER9fb2kMtGqzgAawsboXeBLWlxcjIcffhjvvfceent75QGdn5/HSy+9hObmZszMzGDv3r1YWlqCw+GA3++XboEvaSQSkVaVFRPhfnQ67t+/X5Do1Jd7vV4cOXIEra2teOONN/Dmm2+iuLgYbrf79gdaUCAuZPKDuKDaynQymUxQKpUIBAICHgMgy/j19XVRWQAQdMPWQ4Pzfe5XiG3mnJcGrJmZGdGUE8J369YtnDhxAq+99prA1ubm5rBnzx4YjUYYjUbE43EkEglBJrPaDIVCEhMai8VgtVql/U+lUjIjp+KKxqZoNIrS0lJhLTG+klTMhoYGhMNhGT9WV1fj8uXL8Pl8QvKMRqOor68XNLTX68Xp06elu1xfX0d/f79EzPp8PjGEzc7OyviIsbtc7FGRwhCamZkZkbt2d3fDYDDgm9/8JioqKtDd3Y1XX31V3LbLy8uw2+0ykiH5NxaLCTySeQPcWRC1wb+DlTFNp+zeQqEQamtr5T+PxWKIx+N4//33kcvlUFtbi3A4jKtXr8JisSCdTqOzsxMtLS3YuXMnzp07h/z8fJEAp9NpWWJvBQ4yk4OXJxH07L55YXCvYrfbsb6+jmAwKOY2VvvEVczOzsJutyOXy0kn/NZbbyGdTqOrq0s8QrzcKbkNhUIIBoOCaGfRQ4ZURUUFdDqdjIWIvufy2mAwCCyT+dVMh6SwJZPJwGw2w2KxIBgMigMdgBBY2SlpNBpMTEygtrYWra2tEj5lMpng8Xju8huR30QyNc+O6elpGAwGpFIpNDQ0YGVlBWVlZZLkuX//fqhUKvzoRz/C/v37BS3OnWVfX5+IN/hdLC0tCY37yJEjiEajGB4eRkNDA1566SXo9Xp85jOfwQMPPACPx4OpqSkYDAYJfKqoqPjQ5/yvPXpiFgBbZmrC33jjDXzta1/Dj3/8YwwPD0vled9998Fut8NutwsQjcle1AYzc4F4A4/HI8uqcDgskZCHDx9GNptFe3s7/uAP/gBNTU3Suo6NjQmrxmQyibyV83WLxYK8vDyZtbIlpdopLy9P9iapVEraxcLCQvT39+Ptt9/GxMQEdu/ejUcffRQOhwMajUb48HRP0msQCoVkUavX64VBT/miRqMRqSQvlGAwiOnpaUQiEcGRV1dXS04zq06ODfhwcsdA8xeVPvzfuXXrFm7duoXV1VU8/fTT6OzsRCaTwf79+9HQ0IBoNIpnn30Wk5OTIqtTq9XyYvG7Jv6AiXPT09OSGU5ZKo1CrJyIiyaB02q1yviFiq22tjZoNBrcuHED165dQ2trK5LJJHp6elBWVobW1lbpLAmfo6mT6WJUDCmVSly5ckWyGDibZvXKkKDNzU14PB4hB/CyYiIjDYdVVVXo7OzEH/3RH+HBBx+E1WoVGGFra6vM6fPz87G+vi4yVX5OeXl5wuiiK5sjnMnJSVHtsPpXqVQiXZ2ZmZHCLJvNiuHxy1/+MvR6Pc6dOycHfUNDA/bv34+DBw+KJ4Iqwa07qMXFRbkU6LlgRgRHsgAEPkhnPDMZKEHlwUrxiM/nQ15eHsLhMDKZDLxeLyKRiHQwFy5cwE9/+lNcvnwZTU1N8jsXFBSgqakJTzzxBHbv3i1gQ51OJ3JjdhQOh0P8FzwvWBRR5stCpaCgAAcOHBC+ERV4dPWzCKUMl8tn0nNXV1eRuhMUtn//fqTTaVy5ckU+MyJTuK+z2WxSuFCxxpEkP3+O795++2188MEHKCgowJe+9CUcP35cFF1Etu/btw8Oh0NGplRopu5EL+dyORw+fBiHDh2S6N4dO3agq6sLgUAAZ8+elc61ra0NJSUlmJqa+tDn/H9JR0GODts/lUolzHWv14v77rsP//RP/wS73Y7Pfvaz6OzsFMs9q18GGzFTgPNIq9UqlwnjSH/1q19Bp9OJfp1Grl27dsnCmpUGQ42MRqPkB9vtdqytrQkBkmoEojvYsnI5RYMa8Rr19fUIhUJob28X7LjRaERDQ4NkTdy6dQuBQEBSz9RqNebn52UmGQ6HYTAYRHLIkRilr2QdGY1GGVEQ5pZIJMStTQlvS0uLOIo51mDryl0ALxs6yelRGB0dxdjY2F2yYHYA5eXl2LVrFyKRiFwa0WhUHkZWjhQGUIE2MzMjzwOx6FRFEcNgtVrl9+ZnRDJvUVGRXJQVFRUi652bm0NHRwdqa2sxNDSEeDyOd955R+bNxGjwd+DYjURXo9EoMtXl5WXx/0xPTwvbJ5PJyAXc2toq445UKgWfz4dsNouTJ0/KHoHdFdVN3I/o9XopQjiKGxkZkUJl62KRijKKKVZWVuQ5nJiYkJhLKqaIhSgoKEBzczO6urrwwgsvSCzt1NQURkZGMDk5CaVSKc9BeXm5jMT4TFB4cOPGDam42d0zU6K8vFzypBsaGkTqSWnv5uYm0um08NGoJmTRRfAiMyc2NjYwNDQkRFqO07q7u2Xs29bWhvvuuw83btxAQUEB2tvbEYvFRMVIIgPD00pKSqSQoqubPw9BhMzBYK4Dx59ms/mu2AF2SQaDQRSbZHAtLy+jqKgIDQ0NaG1txc6dOxEIBO7Ku6ZLntC/VColcaVc5DNcivL4np4eeVYII+UFSQpFX1+fpBtyTE62VDabhcPhQCwWg16vFzns5OQkSktLBbpKVSfVlx/JRUHlxsLCAiYmJnDu3Dk4nU5UVlYKqZPqnZWVFYyNjSEUCokShl0D56JUjiSTSTgcDoyOjkr7y2D2paUl9PX1obOzE3a7HT/84Q8RDoeluvnbv/1bdHR04JVXXsHY2JiMgNgmt7a2Yvfu3di3bx/+9V//VW5u2uxJr+XcWK/Xy8I9EomI6oARn7du3ZJZtdfrlVk+58mU683Pz8vPSYAi4WodHR0oKyuTMKOamhoxnnE0wo6KygsugVnxbmxsiIqDunxWQ3a7Xea1H//4x0WHv7q6CuD2fuPatWtwOp1y0N26dUsIs5xnut1uAeZdu3YNVVVVdxn/eAmXlZXJ4pQXhsVikR0Gx4C8/KLRKABIqldJSQn27NmDkpIS+P1++Hw+tLe3izy3tbVVRhBciHOBzIqYi1AeBuQeFRUVicKL4ojKykr5fJVKJZaWlkS8wAwTpi5arVb84he/QHFxMaxWq0hvt5Jn+cxxXm4ymZBMJlFXVwedTifmPOZXcJRgt9vFiT81NXVXN8E8EYVCIdGk2WwWb775JvLy8rBjxw7k5eXh9OnTmJ6exr/8y7+gsbERp06dEmMsZZhms1nAl/SO8LvkfJ5sJ+74aPbjiEilUiGXy8Hn86GxsVHMZeRNcYRGLwGDfRjDS4jeM888g507d8JgMODWrVu4efMmPve5z8khyo6bXgEaTwOBAGKxmMTUApAxr1arxfj4uBzCGxsbmJqaEq8TBQULCwtQqVQCluQ5xbFMMpmUiysSicg4sa2tDU6nE8FgEGNjYzKRYGFCXhQLEwZMkXRAHh6jSvncspNtamoSAKLFYsHCwoKwwWpqahCJROR3JeCRUQa8TN966y0hHnNEyonF9PS0eHY+kouCPJPt27dDp9Ph9ddfR3d3Nx5++GHJy56ZmcGePXtQX1+PWCyGvr4+UaK0tbXB6/XeNZYhlpujHrbxHLmUl5ejublZqtfOzk6ZjedyOVy9ehUajQZVVVUYHx/H888/j3feeQe7du1CMBjET3/6U3R0dOCb3/wmTp8+LdkFS0tLgg7f2NjAvn37EI1GcfHiRYkmJGSMoxe/338XtoQtLHcrJOdyHEFjIE003NVQoUEOz9LSklTszNnV6/XiLK+qqsLU1BSMRqNEL3KJyOU4/Q/pdBpOpxMARGK5Y8cORKNROBwOFBYW4rOf/Sx2794NnU6H999/HyMjIzh69KgswWnw4aFHgyLwH1V8JpORSo/eilgsBrPZLFp5dh58sTc2NmR/w0N2ZWUFk5OT8nxwTLRjxw5ks1l4PB709/dLpzA3NydAP7rs2QXOzs6KtLOoqAgOhwNut1sOm7W1NYHjWa1WOJ1OGWmR+7S5uYnR0VHodDpJnGPHS3R+SUkJVlZWkMvlJDuEuwmSXvndceTDXQ+d21tHHBsbG6KS48VPiCIpqgqFAl1dXZifn4darUZTUxNu3ryJGzduiFns1VdfhU6nE7UMo03n5uZEjbi6uir4EMIwGdM7Nzcnju1kMonl5WX5+wAI/vz8+fMiaOEYidJQsr/I+ioqKhJPh06ng8vlgl6vxw9+8AMMDAzgwIEDImk2Go2w2+0ySia5mc8RuwC6rMnSIsqGtNytwVDMBGE3xfEn/17ie4i0KS0tlREyD2YGfxH/Q7EOfVcsHoLBIGpqaiRDhswqjiw5sSBAlTHNfObJZ+P7vZVivbCwIONH5ohXVFTA6XRCq9XK90bmGS8wghbJfftILgq2lrFYDIWFhfjc5z4Hj8cjH3p3dzdKSkpw8uRJYeNQQdTT0yMHKbktfDkpyayqqpIKOhgMAgBcLhfq6uowMzMj7X0oFBKFSHd3N7xeL37v934PTz/9NK5du4Z0Oo1PfepT8Hg8+PrXv45YLIYTJ07g0KFDUKlU+OpXv4rx8XFYrVbMzs7i6tWr6OnpESPa2NgYHnzwQRkT8FLgPDyRSAjyPJvNSrtORdBW9Drn5FyCccxBcx3TxHhwsKqn1I+LYpoMiWegzJLjQEqKeaAzga2jowMVFRXo6enBwMAANjc3YbVaEYvF0NbWJuj1xx57DIuLi3jvvfeEz79VhcVQF8ZJknNErpDH40FVVZVwq6j64OiDwSyVlZUIh8PCKuKoanR0FJWVlaLxV6vVyM/PR11dnYxoqMgpLCxEa2ur/DtqtRojIyNycLMz40XNRSGLAo5F+Dty11JeXi6Lbs69eUAyFIhwPh4SBBZOTk5KvjS7yPn5eRgMBokfXVxcFNQ3gXi1tbUwGo0YGhoSOJ5Wq5VxE7PX19fXpTMpKSmRy/G9996D2WxGQ0MDDAYD6urqoFAoMDAwIL9fSUkJJicnRcnETo1IdT6bHINYLBY59MPhsCC6uVdwOBx3/e+xyMvlclhZWRHX89TUFBobG6FQKNDT0yNS4x//+Me4ePEiOjs78dhjj+HmzZtYX1/Hjh07hMSbSCSg1WqFxcWsDjLPstksTCYTzGazhC/Nz89LWh9/rt7eXqnU19fXZfxJbh33VuRIBYNBVFRUCIqD3QUFJysrK5iZmRE0SllZmVBqKd6pqqqShEh6qux2u8QB0G+1uroqdFtCA7n7ZWfCPByKR3Q63V0u6+LiYsGhh8NhCaSyWq1SGBmNRtkzfSQXBaVZ7777LvR6Pdra2nDy5ElcunRJLonKykrs3r0b58+fFzf2gQMHJLKRKiDOlTc3NzE9PY2CggJBYAMQSiZHGrFYDBMTE9i3bx/m5uYQCARw5MgRpFIpTExMIBaLoaGhAUeOHMG//Mu/YGRkBM3Nzdi3bx/a2tqwa9cuvP7666irqxMOyvvvv48nn3wSwWAQL774IoLBIPbu3SuEUUrLuA/geGJpaUlMYrlcTqpYPqgMp+F8keM2ZhCTKMplJXHOZD4tLS1BrVYLriEWi8lSuaamRrg8AKTaZHdGDhAA8XCworl16xauX7+OdDqN7u5u9PX1wePx4NKlS+jt7UV9fb1cBhx7cF5fUlIiy+NQKCSdINPdCFBj7OLExIQc7gUFBeLTSKVSsFqtskBkfgP9DO3t7dK9UW5JOjGzRIhfXl9fR3NzM0wmEwYGBtDS0iK4bVZ+8XhclCwMhyGbKBgMSu7C3r17YTQaMT8/jxs3bsgoi0Yzn88nxka6qjlG4vdMMjD/94LBoKSpNTc3ywjQ4/HIYR2JRKQCZsgRZdDMGGlraxPRAA9lIuPD4bAE6Lz99ts4ePAgamtr4XK5BErIXBH+/ezcWaSQDVZbW4uxsTGh2YZCITkEyUWj4okH99jYmOwelUqljPmKiopE7UXz6fvvvy85EXv27MHOnTvxzjvv4LXXXoPBYMCJEyeg0Whk9EYpKSN86TtyuVwym2f4E7tUwhepsKuvr0c8Hhc1H53kVJxx1MWCp6ioSMZFlJ/zveQYlSMnJmWS51ZXVyfhWF6vFwaDQYrRlZUVOQsoS6cnhDgY7hX9fj8MBgOmp6fFMc4CMp1Oy2SBe95kMinnC3e28XgclZWVsvNiYfqRXBSUedbV1d1lVWcLyHbT7XZjY2MDRqMRBw8exP333y8fYCqVEgorADGWceFCdDEfRkpaCQlj1ahQKPDggw9ieHgYCoUCHo8HS0tLGB8fx9raGt5++22Z8c/OzuKtt97C0tISfvjDH0qqWllZGaqrq9HT0wODwYDCwkJZRN1///3Y3NwULg0PGILvAEj7Pjs7KyOVSCQiRhsCC7mEUygUUuVu374dbrdbWloexMQJc2TCQ4CVMgOhksmkHBbA7fkq1T7EMRQVFYnb89ChQ7BarUgmk7h586bslR588EHMzc3JCKGzsxOjo6N3eRuIavB6vZiYmBB/AKNkudhmpZPJZCSSlY5iGql4qLDFJoeH89ienh5ks1lYLBbU19djcnJSOhjOXkdGRjA2NiYjiIMHDwpqnF6MmzdvYnx8HE1NTTKmoKKNme5kM1EV9tnPflbMbQT0RSIRyXAnX4wjKlaF/P5oYmOgj9PpFKloNBoVHHdpaSkSiQTUajUCgYAIHXgQ0E1M/wAzMbZ2Yw6HA1qtFqOjo5I/vbS0hO7ubqmoqVTLZDLiVB4dHZV0OeZ5swskD4lcMabdAZDREcUSFXcggVqt9i5JLsd1DocD09PTmJqaQktLC+677z40NDRImJbVakVvby+ef/55bGxsIJPJoL+/Hw8++CDsdrsseEnMBf4DPjo+Pi5dGJ3WW/ciRJ2QRZXL5eTy4d9FnAf/+xQcsEhjF1FRUYFQKCTIcwoZaASm2k2r1cJqtWJ4eBgXLlzA7t27UXEH2UPZLg3LvPCZXULfTjQahV6vR01NDfLz82UBv7VT4sie/znVdeXl5djc3BTuHjtvpk3+Z5zZ/yWsJ6fTiRs3bkChUGB0dBQqlUqs/EROjI6OoqurCydPnhR1w+LiovgNqArKy8sTF7LX64XJZJIH3WazSVJcc3OzYMyvXLmCmzdv4tChQ+ju7sb27dsRjUZRV1cn2Ii//uu/RnV1Nd577z1pMycmJiTX4sUXX0Q2m0VDQwNmZ2exbds2PP/88/jGN74Bg8GAn/zkJ5iZmYHZbJZlqVarRU9PD+x2u4wI/H4/otEoKisrJdyIDy7HT1T4lJaWioyW88uCggIZ1xBBnJ+fj4GBAZSVlWH//v0iBmBIUjweFwTx1vCWzc1Nse7ncjl5GQKBAIqKimCz2dDU1IQnn3wS9913n+Q72+12fOtb3xKFz8WLF6FUKmG324XltLy8jNXVVVFT8HtRKpWoqalBNBrFysqKtNFU8mxdZPOCZ+fFn6+hoQHDw8Py3wVuq4UKCwuxa9cuNDY2Ynp6Wp414DZfn/gGt9uNwcFBGaNRkVZTU4P19XWJnuXPzkuei/bx8XEMDg5Co9Hg+eefF6VcNBrF9u3bcfToUbzyyivIZDIIBAJobW1FNpsV+SG9MOz6WB1yDMiqnLJgxqwyVIo8KeLLedgywlOn08lBuL6+LqPOtbU1ebb1ej12794tHh12rRxXkjXF3QoPx7m5OQFd8hDcWlE3NTVJx0hMCUm9PCQJGfT5fEJY0Ol08i6k02nR9CeTSQwMDGBychKf/vSnoVQq8d/+23+THPKqqipJE+RzRBUTF83sXBQKheyR+GzRv0VJKM8Yq9WKeDwuzyz9H8PDw2hraxPEOi9Xi8UCp9OJSCQiAEYaiJmLQmUYEzhZPDDTm+NNPg82m00+e/5nJSUlGB0dlXETvzsqpojl5+gVgAAc6+vrMTU1BYVCAYVCId1eUVERzGbzXe52fkcf9s9/CWac2AUawchxb21thdlsvstxajQaEQgEZGlkMplkiXj9+nUJFhofH5c5d3V1tThYi4uLEY/HcePGDdTV1aGxsVEq/1deeQV1dXXY2NiQ5XV1dTWamprkAUmn07j//vtRXl6O+++/Hy6XCyUlJfjiF7+IYDCI8fFxHD16FBcuXBAvA/N4X3rpJRw5ckSMOeQysSLlIpmZ1NlsFsPDw6ipqcHS0hIaGhpk3La4uCjZHFTZLCwsiBlnfX0dNTU1ol8vLS1Fa2srpqamEAgEsHv3biSTSbzwwgs4ffo06urqBP/s9/uhUChkTMcxz/LyshituDTleGH//v1YXFzE8vKyqMNSqZRA/HgYcaFG3DbjXLcujCcnJ4W3xQViKBSSQ5PwRipxeOEoFAqUlpbKmIvz/127dmFkZAQ+nw9vv/02HnnkEbhcLphMJkxMTCAYDOK3fuu3kMvl8K1vfQsVFRU4fPgwfvazn2FkZAQA8NnPfhb33nuvjFoKCgrg9XpFruj1euUZveeee/Dggw/ijTfegNvtxgMPPAC3243e3l7Mz8/j85//PGpra2V3xouPhy07TipYuPivqanB4uIiBgYGpBugKTIWiwkckME8LCoymYyMJuiipsSYwD+fzycjUo411Wo1jh8/LimKNPgxH4G+gY6ODgknMhqN0hEyuEin02Hnzp3S6VVVVWF6eloOnWw2i9nZWRgMBiiVSlEH0UzIkc38/DyKi4thMBjgdDpht9vxy1/+EvF4HE1NTRJTfM899yAajQrGm2MrjninpqYkKS6RSIhsmKY3kgV6enpgs9mg1+sFzcGfk+h2YoEo0GA8LWnMhYWFWFtbk31Ne3u7AEU5ESDRl+/Irl278KUvfQnvvPMOnnvuOdhsNlRVVWFyclKQRvX19YJ34XidoM5EIiFATnYz7CDo8SB7jSFrY2NjUCqVkoTJHAxerlym05tD+fKH/fNrG+54e7NNZbWzsLAAnU6HpqYm7Ny5U5Lk/H6/PMhcEIZCIYyNjSGdTqOnpwdTU1OorKxEaWmpVDnMgebcemVlBd3d3YhEItjc3ERtbS1OnDiB5eVljI2Noa6uThDRs7Oz+Ju/+RucO3dOzFY0O/30pz/F3NwcVldXoVKp8MEHH+B//I//gWeffRZFRUUioWOOBNEhdGzSOOfz+QSPYDabRe3FyogsJeqhGYzCapVuZoY/cdTAZVtXVxf0ej2GhoZw48YN0bm73W7xNXi9XoyMjIiCg+McLruIFl5aWhK2DtEAH3zwAfr7+3Hx4kX09fXJLoZtOTMa8vLyBFLINpahSOyyKHlVKpVCnaXreW5uTub+vPg5Y2e84+LiIsxmM/Lz83HkyBFp4QGgqqoKV69exXPPPYeCgtsRnYyqXVlZwdTUFPx+P/r7+3H16lVBt0SjURlFcp68uroq3w0liiqVCu3t7WhtbYXRaERJSQlaW1uRn5+PwcFBcdCzUt5adZPno1QqUVlZCYPBIIawiooKBINB+Hw+ABDsCi9rAJKnwtl3SUmJLOH5uc/Pz4vMUa1WizuZ/++enh4AEHrw888/j6mpKRkXMeSIDCE+v4WFhWhpaRHdPseU9AqwsKGPiOgeuo8pJ8/Ly0NlZaXkezOZkc/06uoq/H4/5ubmZMeyd+9etLa2Co8qGAzK70KCMsc+CoUCtbW1qK6uljk9D2vuS+nKJjiPe71oNAqj0YhgMChKRBZrfBdJGKYYhwUw/SsMV+NuaWtHsLCwgPz8fHi9XqjVatn57dy5E0eOHEFjY6MsxOvr66XYiMfjAjXke1NcXCzSbcqoKf4gp4qjZ4VCgZ07dwqCnWY+jqhWV1eF5BuPx+WC+UhVT5FIRBa9VLP4/X4Z4XBWp1arEYvFxC1JrDjbc8pB4/G4aK3z8vIksKagoEDospRabmxsYHh4GHv37sWJEydEd24ymWQ+x0tmz549aG5uloCZo0ePQq1WY2hoCBcvXsTPf/5zVFRUiBnH4XDgqaeeQm1tLV588UUsLy/LrJqHHQ2Dfr8fGxsbcLlckqHMyk6r1YqxJR6PS1QsPxPyZBhWMzU1BbvdjqKiIknpojRvbm4O58+fh8fjQX5+PpqamhCLxXDhwgXYbDbU1dVhcnJS6KKzs7PQ6/WYnZ0VOSilnV6vF9XV1SgtLYXb7cb6+jpcLhecTqfouSmBJLocgGQF0KjIuM+GhgYEg0FxexI5AkCWmVqtFlqtFqFQSP5upVIJv9+P5eVl1NfXy6HJUddbb72Fc+fO4fr169i2bRtqamrwve99Ty5Ss9ksyX5vvfUWioqKMDo6isOHD+Mv//IvodVqMTk5iVdffRVXrlxBRUUFPv7xj2NlZeUutj/loel0Grdu3UIymYTb7RbaLH9GChzuu+8+2Gw2GVEUFRWJao7zZsZjMruc1Szdxfn5+SJUqKysxOjoqMhpKysr4fF4BDVSXV0Nj8eDRCKBjo4OhEIhAWIqlUqkUikZ1REboVQqsXfvXsmeIF1geXkZmUwGOp1OwJMAROvPRSclu0NDQ/jJT36CUCiEtrY2dHZ2iu+DyGsmxaXTaWxsbMDpdMqzmMvlBOC3trYGi8UChUIh700kEsHhw4eRuhPIU1ZWJtRpehhIHKB0lQefRqO5C73vdrvFh7U1FZCFDWOUi4uL0dfXJ9QG+nHGx8dRXV2NyspKMegReUIBjcPhkDhlLstXVlbwyCOPyMiVeS7RaBTxeBxGoxEPPfQQ+vr60NfXh6WlJdTV1WFzcxM9PT0CS+VOjSFavNwY9qZSqSSHnXifjY0NbG5uoqGhATdu3BDxCD93CkRY+JLoQMXbh/nzX7KjqKurk3mi3+8X/XxbWxvi8Timp6dF6UBNL2MzCaTjw8YXiVpkPrCsSLj440E8Ojoqs3RGNjLwJhaL4dVXX4XH48H8/LyQWo1GIxKJBC5evIj6+nq0t7ejt7cXyWQSDzzwAIaHh+F0OnHo0CF8+9vfFmWE3W4XByerDX5BqVRKkNOUntpsNlnKUmnDBTGR3CqVCj09PbJspG+B6gSbzYaamhoUFRXJZ2u1WjExMYHZ2VkcPXoUTU1NIs1j9cdFXDKZFDJtKpWSC4p52SRJ8uUk9ZKyXEIMad5jHjI7SHL6CVuj8Y76bh7GJSUlmJiYgMPhEGQ2ZYSMqwwEAlKR09l88+ZNuN1uhMNhPPXUU9jY2MDg4KD89xOJBFwulzhbiWPfv38/zp49i2w2i0cffRRTU1MoLy+HyWSCXq+XMCPyslZXb8eALiws4I033oBOp5PuzGKx4LHHHkMqlcL09DTOnTuH2dlZ7Ny5E1arFXNzc7h586Z0Tfw8OF/mDqewsFCqPp/PJ4jxTCaDsbExAdytrKxIdU4CMk1/FXdQ0S6XC5FIREY6zHa32Wzwer3Iz8/H9PS0dOV8pggWZGobAMnY5uc+ODgoe7CKigo88MADePbZZ7GwsICBgQEhzn7pS18SGfbY2BhaWlpk9k0vAEOQXC4XAMDr9aK4uFguLoPBIAq/pqYmJBIJKfi4fKUSiZ8T+UeUS1PtxIqe8miKTRialZeXh+LiYoH18WfgZ1FUVCR4HXqcuPtYW1uTSp7yfa1Wi9nZWbS1taGpqQkFBQW4dOkSzp07h0OHDqGjo0P2fRy7HzhwAGtraxgeHkZ1dbX4OLg7Yi4L0S1ms1moCFs9ZZQyE5+Un5+Pc+fOYWhoCC0tLeLapnF0ZGQE+/btk7hauuY/souCGnAaubio5TjBZrMJ9ZNMlOXlZZnvMviIIywugintSqfT0uIzFUyhUNzVrVBWSwWLUqmE2+0WSRjlqayi5+fncf36dahUKjQ2NqK+vl7mmx0dHTh//jxSqRRu3ryJsrIykYKqVCoxK22drc/OzgoDicAwJnLpdDpZUnOhNTc3Jw9eaWmpPAxUi62vryMQCIhJLpFISIDOn/3Zn2F8fBxvv/02zGYzWltboVAoZLmVn58vyhm2xUwLNJlMopDhmEutViORSAh2gaYuZklwyVdeXi6L+76+PrlQlpaWYLVaBeXAsdZW2i0NSpyRssokOZbiBSq8GMpE+Fk8Hkd5eTlOnTqFWCwGp9MJs9ksCruRkRHce++9OHXqFFpaWpDL5fDCCy/gJz/5CQoKbqftPfbYY9Dr9RLcwl0Indr8zNRqNTo6OkQpotFo0NzcjM3NTRw4cABPP/00JicnRSLKro+dFufDVVVVgt+nXJmHN4UZxEtkMhn5nFKplCA0OGooLCxEMpmUcUhJSYl01VzuqtVqhMNh6fy4DOfvRVEDSayLi4uoqamRcRyDkNbW1jA3N4eCggJMT09j//79WFlZERrvl770Jdy6dQvBYBBOpxMqlQrd3d1yIUUiEeTl5d0l51WpVCLvLC4uhkqlkudg37590uXwe/b7/dBoNFJQ8b0uKyu7yxxHaT3PAIPBgHQ6Ldw4xvhS4EGl3srKilwKVM1V3CEb88ygyo3/ObsEOtVjsZgIDdbX12E0GvGzn/0MP/rRjxAMBnHhwgWRth4+fBjJZBK9vb0iHnnmmWfw3HPPQaPRoKWlBZWVleL94FgrEAgItp5Gvfn5eYGsOp1OVFRUwGw2Q6lUYmxsTMi4AwMDuHXrFiwWi7DVKIagEkyr1X50FwUfung8Lnpqjpmy2SycTifGx8clbUqj0UhYjMlkQlNTEwYHB+V202g0UCgUCAaDaGxsFJZJQUEBDAYDxsfHkUgkUFlZiVQqJdXXwMAAqqqqpBpmoFJ9fT1sNhuKiopQXFwMnU6HW7duSZodYXM0vaRSKRw+fFhGBVVVVWhqaoJarUY6nUZxcbEYnRoaGjAzMyO3PA9gAJK7nc1mRVrHXO/CwkLU1NRIBOvWbolzQ1ZdzAw/e/Ysrl27hs9+9rPy8E9PT8sBwZyN4uJicWqz4ieQj3A6Kjko2eT3uDUZkFkSuVxOlFc6nQ7Xrl2TRTaDhjgr5byYz4Pf70d1dbX8e11dXXLpO51Oib6srKyE0+nEuXPnAECWfXTtHjx4EGVlZbh8+bKo577whS/gzJkzMnuvr68XnTkv5gcffFC+Qy5UKQPmcp8mSFb4BQUF2LZtm3hxbDYbXnjhBbzzzjsoLS3Ff//v/x1PP/00Ll26hNraWkxPT4s/hJ6OaDQqYxEatfj5MKCIeQderxd6vR4FBQUSxENCay6XQyQSQXV1NVQqlSTnBYNBGcNy7EtTXDwel0p7enoaNTU1qKurE99MIpGQvRNd1yzcent7YbPZUFtbKwfryy+/DLfbjT179mB0dFTGupRvUla81ViYy+WwvLwsRRkVaQBEpUUptN/vh9PplB1XYWEhamtrUVRUJGiRjY0NtLS0CM+IsD6ytVhdM1KUvoytaBu1Wi3cMZJZGZxWXFwsYzdKU7lfY6Qtu23Kii0Wi0jcdTodPvjgA/z1X/81TCYT/vzP/xzPPPMMLl68iNLSUvzZn/0ZEokEPv/5z2Nqagr19fUyGr5y5YrYAEi54OKa8nJ2hPF4XACddXV1OHDgACYnJ5FIJHD+/HnMzs6isbFRTMErKyswm80wGAyYmJiQc5Gd/H/mz39JR8FKkPiKiYkJCdmYnp5GMBiU8QvVHlarFaFQCPF4HCsrK4JIpnrGbrcjlUoJCwW4jZ/gcpZocPJKSGHlSGdpaQmLi4tiFNLpdGKQ2hrNSvcjfQrhcFjMRlxwd3R0SIVL4JjFYhGFF186BgUtLi6KiYxLU41GA4vFIrNSn88nudeMCSWojqRS0iK9Xi96enrg8/nwwgsvIJ1OY3p6Gk1NTYIwqaqqQm1tLT744ANRndG7QIPYVkc5TTkGg0EO85KSEsTjcSGKUgHF8aFCoRDlDkdkGo0Gi4uLIlXm0tlgMIhbF4AgTfLy8gAAfr9flqOsYik9rq6uxq5du1BVVYVf/OIXCIfD2L9/P1588UX09PTgySefxPDwMC5duiS8rcHBQTgcDhgMBpw7dw6PPPII9u3bJ36GkpISjIyMSCWYTqdFLcLvyGAwYHJyEi0tLdLOM0aW/o5r165Bq9WiubkZs7OzGBoags1mg8/nE/EC4y8rKipEIUODWE1NDebn56Uw4gvNy6y6ulr2ShxxUOrMfRjVKiyGmG/A6ruyshJra2vyPBJXMTMzA4fDAQC4ceOGyFr539m61Kciim7xW7duobW1FZ/61KdkfPvpT39aTGt79uyRIoc7x62IcKJOiK7hDoc7QZ1OJ5Tk9fV1OcBLS0vF9EbVTzgcFky3SqWScbDJZJL3O51Oo7GxEYWFhaioqLhrArFV/UN23NLSEsxms7y77FY4vmK2CQm5fHcZcevz+ZBMJmG327F9+3Zcv34dv/rVr1BWVoZvfetbOHHiBPLy8jA0NITjx4/jM5/5DC5cuCAmZVb7ZrMZBQUFKCsrE68W5cAWi0XICAqFAiMjI/B4PBgfH0c8HpepBiNVv/rVr8q0h7uUjY0NLC0tieHyw/75tVVPbIU4v+eDR9NZeXk5rFaruBz54tLcxKVUNpuVTAi229Q5U2e+tLQkrkTqn/mF8jZm+I5CoZAxislkEos/dfmsKikh48MbjUZx7tw5pNNpuN1umeOGQiFkMhnRotOFzJY1nU7LrF2lUsFqtQocjZcpfQWFhYXIy8tDRUWF4NZJPGUVypGc3+/HP//zPyOVSskCcGVlBXV1dfjyl78shzHBe3TxEjXNWTc7jmAwKGh4VkixWEzGGvz9FhYWsLq6KjROVjrZbBZ+vx+5XA4Vd/ILOKLictPtdqO/v1+quVQqJZUy1Ukkk2o0GlnYskqm2YoyUOY3bN++HZ2dnaipqUFjYyMef/xx7N27V7T9lJKyaNnc3JQKjOFT27dvlx1KIpGQAoP7M3o6hoaGRObb2dmJT3/60/jzP/9z/PZv/zbW1tbwwQcfCGaCuHjOlbmnSKVSMBqNYqxjpgeZQRsbG6ipqZF/k7sMqpi0Wq2MEOlG58XA9y4UCiGXux1lWlxcDIfDISFDTHXkwQ3c7hzD4bCYMqkYyuVyomwjQ+3GjRsIBAKYmZmBUqmE2WzGkSNHJEaWhjrmPzCEi10AZeOcudOpzyU/Ozp+3/xZ+D+vqKiQMRbd6uwquP8hWK+oqAgGg0GKD4VCIaZKSkMpWuDzylk9M+f5OXNnx5AhpVKJpqYmgZiSOMwxHnceO3bswOjoKP7qr/4KV65cwZe//GV8+9vfhtvtxle+8hUoFAqcPn0abW1tso/kJUEmW2lpKWKxGKampqBSqeQcKisrQ1NTE+6991643W4Eg0Ekk0lcvHgRg4ODqKysRFdXFyorK1FeXo6JiQlcu3ZNQse43OYFy9yUD/vn1+4omGpGBy4/wEwmg/n5eVEl3H///RJByK6DgTS1tbUoLy8XxQvjUZVKJWZnZ4X8SoqqXq/H9PS0fJFbRzbUHfMDX11dFSUCjW9UXnFW5/F44HK5BBFMPbhOp5Olj9lshtfrFSTx5uamoNKpRGCmbUFBgagYrFYrpqamRP+cyWQEaxCPxxEIBAR9UFFRIXNa/mxEFI+OjqKzsxOnT5+GUqnEW2+9hTfffBPV1dW4du2aBLZwyc/lGP0KDITZ6mo1Go3CayJeZG1tDfn5+aitrRVibk1NDWZnZ+H1ekVSmUql0NLSgrGxMXFXb3XEKpVKAchxrOFyuaTr4NiMkk/gtntbrVajuroaY2NjuHHjBtxuNz7xiU/AaDSioKAAbW1tIqMEbqvulpeX0dvbi7GxMSFlMn94bW0NFRUVGB8fh8PhEEBiLBbDtm3bsLCwIDsUuq1DoRAAwOl0CjDw1KlTEgvq8XjE5MXxkNVqRUlJieQHsEKkt2VrUcPvlbP5goICWVLTzMX9FnMMEomESIYjkQhmZmZQVVUlOwqOzZivTMHIVqex3W4XoBz9AmNjY5IrncvdTiJcWFiQDPbf+Z3fgdlsxpkzZ6BUKrFz505JCyROn9JcQgGXl5fl9yOFlnRgsriUSiXKyspw69YtGf1xxJxKpYSEylAsjurm5uZkn8ODb3FxEU6nUxR9NGGSNEvD31YpMz0VDJiqrKxEIBCAw+FAT0+PVOfsRpl053A4BLfS29uLtrY2HDt2DBqNBk8++SS+9a1v4dVXX4VWq8UXv/hFJJNJHD9+HGtra2hvbxckziuvvCImu3A4LN0ciyoypoiRj8ViSCaT8Pl8MqW5fPkyysrKpEiPxWLYvXs3Ll26hGvXrgEAHnnkEdTV1aGpqQlnz56VSU1eXt5/ynT3a18UAIQFTxNTLpeD3W5HfX09tFotlpeXEY1GEQgEJCWNfJSKO0x5VgJEB/DiKS0tFeUMU6i49OWskrsKjlWIjOa4g3NM4hloTtsK6uO/RTMcjSrpdFqqU5pzFAqF4A+4tKU6iSMrl8uFVColJio+kAxOYXa2Wq2WJTjVGjy4qdBpbm5GMpmUSqmlpQW/+tWv8O6770pKH6v+pqYmqFQqsf8Hg0HpRLZv3454PI54PC5Ez62HFefVTqcTGo0Gly5dElbT1atXBczHuMbx8XH5XYgzJgKZVTR9DAynSt1h4fPz4f+fTnNWhXq9XtQgiUQCQ0ND8vMvLCzgV7/6lfghePAyhpZKlZqaGpSVleHMmTMyOqJDnagVgv9o/iRFlvs0klr7+vrEdV9TUyOVanl5+V2Z2EqlEgsLC6LiI3JmZmZGnm1Wouvr6+KPmJmZkZe4uroaa2trcoBEIhHZ+21ubkoH1tjYiLGxMdkBMec9lUrB6XQK4p5eJF4m9fX1iEQiwlsiY4jeG75TR44cgd/vRzKZhMfjwcWLF3H8+HH8wR/8AVQqFX75y1/KAcqOl94EijUYpWs0GgVvwucO+A/QJVVyVVVVsh8sLy+X74sHPNMSOfZkIFkoFIJGoxEpM0nVlNMCkA7HZrNhfHxclvscRVPeS3gfpxj8PHjx8T/r6upCKpWS6NKlpSV84xvfwL59+zA2NoaLFy+ioqICn/rUp6RwPXv2rOS3sKPmuDUcDqO8vBxTU1OS10Kx0IMPPogzZ86gr68PVqsVbW1tmJmZEYAnd55erxeTk5Pw+XxyDq6vryMajaK1tVVgoxzXftg//yX02K0uV5vNJotq8tqJFablPJfLCXSLNz6VPQsLCzCbzdJBlJaWipyS7kqicokXpguZ8zuGwPT19YkTmBW10+lEKBQSYisfVALhaGbiOKqtrQ0ej0c49FTnUNHAjICtUkXOmKkiofO2pKRECKCs6rmQY64v9ybUOadSKeTn5+Pw4cNobm6GUqlEb28v2tvbEQgEcPjwYWg0Grz66qvQaDQiE2Qrz3EUDxCqqrgsZ3KgQqGAwWBAOBxGLBaD2+3Gs88+K87lVCqFpqYmPP744zh+/Di8Xi+CwaD4Wnj5k8lFbT87C7vdLngJhUIhRUNpaansXHjB8efo6OjA+Pg4WlpaJFXN7/eLg5pcqVwuh7a2Nvn8KHONRqO4deuWoMM5smEnQGkzOw8KJij13r59u+zCyNSivHlhYQHhcFguFF54vAhIqs3Ly0Nzc7MYwDiu44hvdnYW1dXVkq7IA4vdQWlpqYglCGWkKIIXvNfrFRc7ZeWkhFLcwY6XZkfiq4nAjkajAjEknj0/Px+7d+/GjRs38IlPfAI/+9nPkEwmhUJMmWxra6vEuPL5YldGIx5zV0gc4GiMFyWd1+FwWAoS/r6MBs7Pz5edG/Pr1Wo1xsfH0dnZKQKStbU1Odjpv6CklO8Vn30yydjpMU98fn5eyAaxWExGiE1NTeJporQ/mUyiv78fBoMBTz31lPC8+LvRbDs2NgaTyYSRkRFZgk9PT4ukP5PJCJGY0mJ23TTYbm5uYvfu3dDr9TIt6OzshMViwZUrV9DX14ehoSEBjLrdbmzfvh2JREL++/wsafr8MH/yNinT+U/+Ibf+t3/7t4Who9VqMTAwgJMnTyKVSuGnP/2pfKm3bt3C0aNHceLECWSzWXE4kgy6Fe4Xi8XEJcuUJrKCmEscjUaxY8cOeL1eZLNZGI1GABBzi1qtxsTEhCyUGSpCTlEymYTZbJZlj8lkgkajEfxFeXm5hLlQi80vjaoSjoUYzcgXs6amRhaIyWRSYjrn5uaQn58vFSXRJACE0+R2u4WHs337dnGS79mzB+vr6xgYGEBDQwNKSkokgY4LNbbcMzMzUqnwYs3lcmI0osmKl5DdbkdeXh7279+PZDKJYDCI/Px8/OAHP0Amk8G9996LV199FXNzc/jbv/1bWCwWTE9PI3UHccxZMSGI7La2AtMo5fT5fEK0pGmRBQdzPDgWoGO9oKBAvj8+A3wmeBC6XC4JjycOnN/Ptm3bcP36daGMcl5PAUNlZeX/T5dLyer8/DyCwSD0ej3q6urkM/P7/bL3IcaCrnuSZOmbASDLUDrwCXykAqi8vFwOJ/KpOHf3+/2ilqmurhagXOoOrLLiThLbpUuXUFZWho6ODgCQbm5ubg51dXVy4GQyGbhcLokCpevX5XLJSLe/v18c5V/+8peRn5+PV199FZcvX8aePXswMzMjRQYRFJy1syteWVmRMRifDXYBW6moLS0tUCgUkt9AvwNVWSw2aOD0+/2CfzcYDBgcHBQnOSkF5Butrq5ienpakjBJJV5bW5OCIxwOQ6FQoL29XXAhfL9jsRgASEY4zymOS7mrXFtbw8DAgJxblNMzk4ZTB3KZKGllkcLulSIYXi58j3lRXb9+XZIgl5eXJW2wpKQEly9fxtmzZyXxkjBHdj7MAiE89L333sNrr70meSb///782h2FSqWSH1SpVGL79u2Yn5+H2+1GxZ2UKDLz33nnHXR1dWH37t0y8qAsLBqNiuGEdnilUilfDheVnLXSKg9AFsaksHI3QW7OVqMYF1LcY4TDYahUKsRiMfn3qPwgAoNyXP77hHRxxr+xsSEqI87IM5kMKisrUVdXJ6EmRUVFmJmZEX08WfOkmbIibmxslMuHS9dgMIjJyUk5kCjF5E6FS2umbbFCVigUOH78OEZHRyW7NxaLwWQyYXPzdgrf4uIi+vv7MT4+LtTP7du3S47GE088AaPRiO9973tygA4ODopaiVUk3a0lJSUiR6XXgItqvlwcVej1evl8Od4rLS0Vlz8NWvQnMIeB2AXKsVdXV9HS0oJwOCyVPWWP5D3x0Nw6sqBnhzsskm0pZV1YWBClEBfQCwsLsNvt8Pl8dwUL+Xw+udjI51ldXRUkOKtcSq2Zt0KVHqvZsrIyEXJwPEo0it/vFxk2F9CxWAyBQECCnTgK2Yr+CAQCyM/Pl7AbsqM4RuVy02azYWBgABMTE2J+pQFRrVbjy1/+skjMh4eHxXmcl5eHwsJCuFwucUCTumCxWKSjITWAOzFGAXM0xZGeSqWS4ooXMFPj+J6q1WoRulC26nK55Hvh/9xkMkGlUokKcnV1VRRT6XT6rvEhn6W8vDwBSFI9RWc0JxfsNDmiYrdCqT0LhEAgAJ1OJymLNMrNzs5Kd8usj0QigXQ6LYv21dVVzM/Po6amBmazGWazGZWVlYIi4YJ+cHAQDQ0NItI5ffo0SkpK8Oabbwpo0Gq1wuPxiEmQY70P8+fXvigYJdjT0yNuSILGyGH65Cc/iaamJoRCIcRiMVH+8IujqUWn06G0tFR2BJxz6vV6kbgtLS3J30uNM4mmrB5Y0XC3wc0/1TOsABYXFwXCtxUZkJ+fLyYnh8Mhoy7+e/x7t85muXzkYcLLc21tTWS9fLCYNUDEANVHdA5zQb+8vIzBwUGZe6tUKlGVcFdCGSUPEo5o2GkwDCoajUpQCxUoLpcLGo0GgUAAt27dQlNTkyRlmUwmzM/PY3p6Gs8++6z8nhzTlZTczj4eHByUSoWmSSKdZ2dn5SUYGBiAw+EQwxOd4vzMOd9nBjcNm0tLS1L93bhxA3a7XcQTer1eXmqO8+LxuCh6+HNysU9kAiWO/My5xKcsmGDHubk52V9QJMFx5/j4uCyByUEiyZd/F8F+brdbFG0sAFwul1RyZWVlYrisra0VgxdBcxaLBeXl5aLW4bKb+eMUFVD6S+8RLySDwQCVSiXdB7tY/u6pO4l6hYWF0Ol0smgOBoM4cuQIfvCDH8hFpFAoJNDq3nvvxfvvvy9iCR6AuVwOpaWlIusMh8PCNNsabMW92/r6uhRe9FMQq0FlV35+vjyDKysraGhokM+HiG0qqpiCx/CfrR0Au+6tHWpFRYUsiZlFTZghfyb+7/Hd5ntI8QoFPDwv+HsolUo4nU7pIPkOb8WE8/NjfC4ZbzabDcFgUApTjUaD+vp6OJ1OVFdXI5fLSaSrXq8X8c3ExARCoZAUT263W0QPKysrUhTQLf9h/vzao6evf/3rsNvtAtzjTPyrX/0qIpEInn76aTQ1NeEHP/gBJiYm8JOf/AQnTpyAwWDA2NgYUqmUhJFQ/bCwsIDa2lpZvgUCAVEDVFZWyk6C5qa8vDxUVVWJGoqtK79QcqI4y+X4gtpyKqP4QvHgobyWPguz2SxmHs6Oy8rKxHW6trYmIwGFQiFKHdJgmeNLzhE9IFz0UpFFR3s8HhflBg9hAAJDo6JsdXUVHo8HjY2NmJ2dlTHcwsKCZHjQ+7B13MDUsq2z/tXVVfT29kKv18syf3l5WTInPv/5z0Or1QrgLnXHtV5dXY1oNCpVInPCOTKkRNPv999FTqW7myM5Vop0vVL6y10QAFitVsF98EKnP4O/KxU07EQpWY1EIpKFQZMh58ytra0oKSlBT08PSkpKZLnM5Du6dsnOUqvVaGhogNvtFlYXcTOsSPm519bWyvKexRT3aUqlUtLYKJ2mU56H3/j4OOrr67GysiI0U7q/iVihqdRut4sajBU1P4eqqirBT5OrBkA+X4IBv//978PpdOLEiRP4l3/5F1y+fBkdHR2oqqpCNptFc3MzWlpa5EBiToXFYpHZOz0I7IyTySRMJpNcaJQDEzFTXFyM6elpAJCumc8P+WC8aElCDgQCcLlcyGQyUlQxBI1qMu4jtwIQibbg90svB8dW9Hfx+1hYWJCMnPHxcezcuROpO9geyqszmQyA2/nziUQCNptN3mt+7nzut5ri+Iy43W7U19fLs8o4WRYslN7TS8NiLRqNorq6Gl6vFxsbG9JpdHZ2IpPJyM+lVqvh8/mgUCjEH/Ttb3/7oxk9UWrKW1OhUOD999/H4OAgOjo6cP/996O0tBRf+MIXBHf8+c9//i4NNl9EvrwOh0MkdlVVVdizZ4+oU3gQ02lMPX8sFpN5MZdV9C1woU5EMvEIPAj1ej1qa2tlPEWTGHcnnG9yYchlNrEDhYWFgoWgBpvsnKWlJeE8sZoHIAcPIWysrEh5pUqHCozUHZcqD0FW1RxvGAwGwRSXlJTA6/UKT6usrEyWv1xgMXQ+kUigpqZGwIRmsxmTk5P44IMP8Ld/+7d46qmnMDQ0hO9///uYmJiQhXlPT49wt7jg54HPiogjQZvNJgvSXC4HvV4v3yFJtE6nU/TzdCBbrVYMDQ2hs7MTPp9PXnrgdmDW8PCwJOpRlkvMNKF5LAYqKipkpFVRUSGtP8dYfNlJVqW5kkiF/Px8TE5OStYz90isoGnsJF6BLl/i0ycnJyVmlDj1rQYzyn2JFeEim39qa2uxtLSEcDgs1STfBY4LqQjibooybD5L6+vrkuFAFREvypKSEjHd7d+/H5cvX8aFCxdQWloKu92OY8eOoaWlBf/wD/+Ac+fOIZlMorm5GTqdDpOTkyJOWVtbw+joqKCtCSfkZwVACjcizFl9c6pQVVWF2dlZwagw64M+KUIL6TbmeG7btm0S+sU9IQ2xpBTT45G64xYvKCiAz+eTbJjl5WWYTCYBcbJbVqvVQrvlaJW7uImJCUGd19TUAICg7Pn8rK6uSsodkeaU3vPZJLmA4/DOzk5MTk6ipKRElGT890l9IAamv78fy8vLcDgcqKqqku6ZxSvfSaVSKQVGPB7/0Of8f8noicqezs5OXLp0CalUCgMDA9BoNKitrcWhQ4fw3nvv4fr16xK+wQ+IFZVarRZ0bk1NjSwLl5eXMTQ0JB+uy+WSgHhq4FN3+PmLi4vSjiuVSqkQGY/IF58/M+mb6XRaWjMuMDnbZVvOERLDVzjvPXDggBjRjEajjE0sFoto6lllkqHEZKvy8nJp9b1eL3bs2AGz2SxdEBeC0WhUwp04G+XoamVlRT4Tr9eLUCgkTBxyrjj+ofxva5gOD3qq0paWlsTE5/V68bWvfQ0lJSW4desWbDYbNBoNxsbGkMlkhMkUj8clLYy+DEqDWQF2dXUhEAhIhcRZNDOkt0a81tfXY3R0VOboVIzxouWIifgRquHoei4pKZELNh6P4+DBg9KSs1BgVcqdE+GAHCnMz8+L2IAqFIfDgaWlJVgsFunquDMixygejwt3i5j21B3jHaF1HP1w1p5KpYRlplKpJNaW3x8FCBUVFWhoaJAKmUl5FGgwD4VFEN3HHOMwS9rv96OyshI6nQ4ejwdOp1PePY/Hg8nJSbhcLrz44ot466230NXVBeB2eFRvby9MJhNaWlokXGd6elrEFFvxLHS0s1vhWJkdbzKZlPEQycbEhNNwmUgkYLfbYbfbRVTAGFGr1SrPAndSvBSYpLi8vIzFxUXU1dUhHA7L98RdBUeK3DNQ0cSdH4C7PEosZqjYWl1dRX19PdLpNOLxuKif6GPZanDjMp5gT3qmFhYW5H2oqqqS3RGpAQCk2FOr1bJr5Pu1srIiKisy8iYmJiRvg39XdXU1tFotJiYmoNfrpdP4MH/+SzoKuj/5wRCA98Ybb4ii42Mf+xg+8YlPSM7B9evX5VamrM1kMsmmvqKiAtPT03jhhRcQi8Xw6U9/GsvLy/j6178Oi8WCw4cP49ChQ9JOhUIhpO4wZ2huCwaDWFlZQX19vSzB+PByUcp4TqVSKYeP2WwWuSKXk6yUmRTFee9WExVwe28RDoeRyWRE7sgWcXV1VS4asqb8fj8GBwelDaY5Kp1Ow+PxSL748PCwKJiKiopEekkpJoOgdu3aJU5R5mVMT09L0te2bduwsrKCiYkJGYGMj4/DZrMhGo2ivb0dn/70pzE7O4vr16/j2WefhUqlwm/8xm9Ao9EgEonIjoiSRq1Wi2QyKVXf2tqajO1WV1cxPDwsIz4ioRkxW1NTg/LycnEgs4WnCo17KTKlAoGAPB8NDQ3IZrOYmZkRZQnVUHSo0lQ0NjaG1tZWxONx4XWl7mQks9vayliidJIETspZ+WxQpsqxF/MMGDrkcDiEqEvaAEOjKioq7lKFtbW1iXN/Y2MDU1NTsvjfs2ePPBMUSyiVSgwNDcnfu7q6Kgwkh8Mh6IetvqRkMilVdmVlJaqqqiTvYH19XZbxRqMRKyu3M7xbW1tx5coVwUk899xzaGtrw+nTp/HQQw8JgM/hcGBkZAR2ux3z8/OiImpsbEQ8HpfLKplMiphiY2NDDjWtVovh4WE0NjbC5/NJN8lumBcId4MsLADIWDOXy8Hv98PhcKC0tBQej0c6Eu4hKaUnxI9dbiaTQX19PYqLi+Hz+WA2m2VHRYHG7OysUFi1Wq3M+mnyo+x3ZmZGLm9SpjmmZswpI1u37gjY5RQVFWF2dhY6nU46x7y8PCmYqJLjZTM0NASNRiOdJkdyAOTyJto9k8lgx44dIhZgh/eRXBS8Mfml33PPPWhtbYXBYEAmk0FfXx9efvllJJNJfOUrX4HX68WFCxfkQiGTf3FxEZFIBO3t7RgfH5d5stPpxO7du2G320XKlclkcPToUcF0sHtgVUbJqlarhUqlwq1bt0TXT2t+ZWWldAaxWEyopNxdRCIR2Gw26ZYoaaNUbXZ2VkLRFQqFaOHZUlIlQnQDPSPcB7BiodeBeOvCwkKsr69L9et0OkUdxMOBVdDKyorAy7gMpjuc4SqcPdbV1SEajWJiYkJmxWxF2erq9XrZB3V1dWF9fR0dHR2iaCotLYXP55O5bDQalcM7l8tJi61UKqWqZiobU/9Y8ZLyS08CzXPk0vClYi5GMpmUMQC7JIL2+PKZzWa88sor6OnpkRe6s7MTm5ubeOedd3DixAm0trZibGxM/A0KhQJut1t2TEQnUCBRW1sr3WRBQYGo5ih93qqwYjej0WhEYsxLn/LHXC6HcDgs+ysqXbYiVKqrq4Ufxb0NPyvmI9tsNiwtLQmR2Ol0iqKFFxIvbsp9Q6GQhPAMDw8L5puyWarA+vv7hbAwNzeH1tZWaDQavPfeewiHwzKq5NiQqGz6IQi5YzdOWCAhfRzjmEwmCZJqaGgAAFEVcQG+vLwsCBF2+xwP84Kmb2prKmNJSYmY7jgC5XgxmUwiPz8f8XgcDQ0NgrJgTIFSqRQTLOXK/J14iRQUFIhRjhMJMr6Y4kkgJJ8R/r1kiHH0U1dXJymhFAKRTpzNZpG6g7fns7Vz506Ew2EpAFlIsfBZW1uD3W7H2tqadEqccvD3JCjxw/75tVlPLpcL6+vriMVi8Hq90Gq16OjokMrps5/9LH7nd34HCoVCcnKJK6A6gCajsrIy8QYkEglcvXpVJIVmsxnHjx/HqVOn8Nu//dsoLi7GhQsX8POf/xwFBQU4fPiwQNg4qyWMjNXB1vxeJtERCUIvQCqVEswyW2m+wNRI+3w+qeZ54W1l7xcWFmJmZgZLS0uy/C4rK5MKnlhnzg/5b7GCHR8fh8/nQ11dHcbGxhAKhRAMBsXwRSwFZ+darVaUPgyb5zx86+KSmuyamhrpwKg2SafTmJubg8fjwfT0tLTQjzzyCJqamqQz46w5mUyKDj2ZTIrIoLy8XCR8er1eWl/upIj3SCaTmJubk/S1ZDIpmPOtpjitVouamhrU1NTIKGDrKJBVKqu37du341Of+hTUarUYtV599VWEQiFcunRJMDJTU1NiZKP6Jp1OIy8vDyUlJdi/fz+MRqNU8ZQAEzszOzuLVCqFqakpSR2jymdmZkYUXRzPcESiVqtRW1srh1VeXh4SiQR8Pp8EzlB1ZbfbkUwmMTU1JQtPKoS2zvQ5ZyeXTKVSyViFhjGOa4nt3yq/JBiPIwuODY8ePSrdnsPhQEdHB06ePImmpiaEw2GMjo5K7kRhYSHm5uZkz8HKnWOxjY0NaLVaYbFtbGzIWIn0YY/HAwBSQBIUyKKLnRjxOplMBmVlZYKt2bVrl+wJjEYj9Ho9XC4X1tZuh6lVVVXBarXCbDbD5XLBaDQKTLC0tBTbtm2TRDlCHOlr4J6J7zxHxzSqcq/EZMuysjLZH9AA6fV6MTc3J3y06upqSbLr7OwUttX6+rpAHrkv3RqdSiPiwsICbDab+GB4nmxubsLn8wkmiGo9n8+H8fFxrKysCI3iw/75tTuKaDQqy5Hl5WX4fD7Mzc3h4sWLsNlsePTRRyURrbCwUMJZ0uk0+vv75balFJPO7l27diESiYj8sr+/HydOnMBf/uVfIplM4k/+5E8kCS6TyeDMmTNyGFILTZs+Nc0cJTGSkUyjqakpdHZ2CnupqqpKlDfV1dVy+NI0ZTQaBQEej8fFNWm1WsUez8qZyBHuRdgBscMg6rujowMFBQWIxWLQaDSoqanBgQMH8JOf/ARLS0u45557pDpgtUNVRDKZlPk/H8p0Og273S6jh5WVFXmoxsbG0NzcLMvHraTOpaUlkZ5ev34dWq0WdrsdTqcTCwsLCIVC2LFjh1RMfJEKCgqg0Wik62Lm8Pr6uhikOG/lfJ8BOgTiUa5ISSQBh4ODg/K9ctSTyWRQUFAgSh+lUinjKDKnOEYifNLv9+Pll1+GUqlEd3c3bDYbqqurRYGXy92ObE2lUrKg5cgikUgIc6fiThKi0+lENBqFz+cTp/P6+rpo9Dk+uHjxohjKtrr1+QJrtVoZTTKFjEqtzc1NtLa2Yn5+HouLi7J4/v+mqdIPQZ8H91vcjxGtzRRBjsrm5uaQTqfFXLe2tobOzk54PB6cP38eRqMRAwMDsNls+I3f+A10dXVhfn4ew8PDACB7Qb/fD7VaLclsJBXQZ5DNZpHJZKQgyuVyEonMAoaIjurqatkvEDHCDBeOyhYWFlBaWipxyrx4stks3G43jh8/js3NTdy8eRMTExMYHR2FzWbD/fffD6fTiba2Nmg0GvzoRz+Cx+MRjhbFL1S81dTUCGbcarUKw458M7/fL7uSdDoNm82GcDgskt35+XmZuFRUVMh5xU6GRV80GpULnuMnolVYpMbjcfkunU4nCgsLMTs7i7W1NWzbtk1G4OR58T3nKIvCDRYZH+noibdtPB4XcqXNZsM999yDf//3f8fIyAgaGxtRVVWFM2fOYHR0FGazWard3bt3IxgMyiKHALF7770XX/va1/Duu+9ifHwcsVhM/j62luT+9/T0YGNjA83NzcJG8fv90rrqdDoYDAbh2AC4S4JbXV0Nn88nVZVWq0U4HBY5GhUT6+vr0hLzIqBWniMQHvZswTnO4UyWwUHU5ysUCly8eBGrq6s4evQo/H4/AoEA4vE4rly5ItjuyclJOWhoTiOkbnV1VcKfWHnOzs7KUpMvFaNot9JVaSqqq6uTSpaXDXk07e3tSCaTGBkZQVVVFTKZjBxCNAc5HA5ks1l4vV5ZTvIzLC4uRjabhd1ul5Akm82Gubk5ESxwb0HXtMvlEu8HJZLRaBRms1nwFawkgdtyZT5rXq9XRBWPPvooTp06hX379mFmZgY7d+7E9773PYm7ZJ4F9fJbTV2Ub/p8PlitVqRSKfEA3bp1C4uLi6itrcXKygrKy8vR1NSEoaEheVE5mqRMmJJNVoPRaFTInhw/8vevqqoSVRAX/Rybcs5P5Rv9BCwIRkZGpEqemZmREahOp5NdEYsZqsY4V8/Ly0M4HEZZWRleeeUVqFQqfPnLX4bdbsfk5CRGR0dx8+ZNScXj4pg7FJpGWQwwXMpkMsnPolAoJHKX/iLm0AC32WFbs53p0WEByMs1nU4LqJJS8Wg0Crfbjby8POmO2amNjo5ibm4OarUaBw8eRHt7O3bt2oVQKCQKOhJYSXNmh0tDLQkE+fn5qLhDeaYRl9hy4t35ec/NzYkykAZPdmFM8ayvr8f8/Dyi0Sj8fr8UYbQFzM7Oyve1sbGB6elprK+vi7GXFzFHcQDEaGk2mxEIBMRAyQ7kI70oyAniAVlYWAir1YonnnhCHuampiaMjIxI9vQ3vvENNDc346WXXkJzc7McEJTSTU5Oii+CH96BAweg0WjwxhtvIBwO44knnkBZWRl6e3uRSCTw8MMPo6OjA3q9Ht3d3SK3ZBVOnTZzpJVKJfr7+6HVasVDwcohkUhIWhYlqewCGMZiMBikPdzY2IDVapXKk/gGyjxpyqExjtr3bdu2wel04rnnnhN0B1Ph6B/5zne+g5WVFTzzzDOorKxEMBgUA1BjYyNyuZy4YwlV4+gmGAwKWJAPOReuRF+YzWZs375dQITT09NiCGTF4vf7YbFYBD/NcVdvb6+AGjk62hruFIlExCB069YtDA0NQa/XY2JiQuSfVHexI2JXODMzI5eHy+VCf38/MpkMIpEIKu4gqimFJIN/dXUVExMTGBoaQigUQigUQnd3tyxG77//fnR1daG3t1dyLBhhSngcWU50OBPeyAW7VqvFhQsX0NfXJ3nhPMCpYuvq6pJlKqWzhCty5MDD3efzobi4WLAeS0tLosaj0YoUVapxRkZGUF5efhdqBICMOpm2R0IoCx4u4Tm+IFqFI1bicxgdeujQIRnvscuiamhtbQ2VlZUi9/Z4PGLoKiwslNEqBRBM/2MxQYOYQqFAJBJBLBaTeFK1Wi1jP7LGmANjNpuhVqsl8S0SiUiQEpEqlZWVOHv2rIQ67dq1C0888QT++I//GO+++y7W1tbwzjvv4FOf+pTM9s+fP4+KOxj+hoYGyQFZWVkRLPvGxoY8I2VlZVAoFOJz4r999epV2Y3xu+UoTa/XQ6FQwG63y5hVqVTC5/PB6/VKR8XCgX4L7mMYcmaxWO7CktP7wxRLeqe4s1EqlZJlQZUjAMGTfCQXBU1CpLx6vV45pDweD6LRKBYXFzExMSEqiFu3bknSFw9T7hG4VPX5fLL83bFjB44ePSryQ7fbjW3btmHbtm3IZDJ4/fXXRU7IBz6dTiMUCsHhcCB1B1/OBSIlkJS5NTY2CruelSc5+GQZcXZdWFh4V+uYyWRgNpvlwqisrBTXbVFREaanp6VyMJlMAsPbsWOHJE+dOnUKH/vYxzA+Po5//dd/BQBp24eHh+XBYOoVW1p2TXx4uWNZXV2F1WpFNpuViog0WovFImoXLqCXl5clI5npboyNZaYGnelsvYk3pnlKqbwde0kBAqunqqoqTE5OyueXl5cn4wQu2ZgYlroTusOXTqVSIRQKYWpqCrlcTjDlPMwJ1ePvzghSu90uM+qHHnoIly5dwi9+8QvU1dXha1/7Gg4cOCBUYF5WjKzlvsJoNMpojxfZgQMH8L3vfQ8vvfQSlEol9uzZA6/Xi4o7dOCzZ88K8bO+vl4gdDwoUqmUjJHo12DGxMjIiCjIWGnTB8TnmgtzunlLS0vhdDrlQCoqKhJVC9VwW6nEdC9T8cIqPZFI3AU8NJlMUgBQVMK5OZVh7OSy2Sx6enrQ2dkp3TZT+Li3UKlUAgSlD4XdEg9Io9EoZwBl0tyHkd3GYCdKPbkY5++2traGL3zhC/jggw8wOTkpUcEtLS1Qq9X40Y9+hP/3//4fzp07J+9RKpXCzp07kc1m8d5776G8vFzMq3NzczCZTDICtVgsWFhYEIQHzXlzc3OyT9Jqtaivr5eLgNJ9okq2ytZXV1cl/pSXL88YXn4cI7Mj5HtF+GNlZSXC4TBMJpMIHvLz85FIJGSPmclkkEgkZDdB5DsNvx/JRcFfNh6Po7GxUX6JkZERyZzYsWMHTp06hd27d8Pr9cLr9eLGjRtiMlpZWZHZ/vr6OoLBIAwGA2ZnZ7Fv3z7Rd1MVMzk5iR/84Af4zne+g69+9atobm6WFo8tIhUdrJw446UKg3Z6Gtai0ajkB3BmSsUD/2/ODyl95BiE3H+/3y/aaH7pRUVFMi/eGrxkNBpx5swZ/Nu//ZvgswOBgLSi/f39WF9fR3NzM3bu3CkmO5ryqBxaW1uTC7e0tBR6vV6ycoHbOyQqosiTorKqsLBQKrmGhgYx//F3ByBd0p49ezA2Nib/M6rLtFqt6OOZX8FOhqoYuvhpyquurhaNP6uizc1NkRBSzUZyKxe4lMxS5cFZP/AfbthwOIxjx45Bp9Ph3LlzuHHjxl1/DzHzPT09GBoakrCheDwusbF0SnOMRypyc3Mzjh49itHRUamIeXmGw2EEg0FcunQJH//4x9Hc3AyVSiVU1tLSUnHqAhDfD/lNXLwTBMfPORaLQavVIhKJoLm5GeFwGA0NDXJ5cCSZyWSE0FtfX4++vj5RAXJ3sb6+jqamJqTTaUSjUfELEf9OekAymZTxCEdkHFvweyJIU6FQiDKM0meOk9jxkLLKAnJ1dVUEK0yWi0QiqKmpke8/Pz9fiqNoNAqdTicFJMeVlO0CtwGI9fX1CIfDQhOurq7GzZs38Q//8A84cuQIvva1r+F//a//hfvvv1867KGhISFFTE5OYnl5WS56ngeUcEejUSksODqqrKxEKBSSA5qFJAAhAHNnRLSIzWbD9PQ0IpGI5MWvr6/Le0onODtz0gpYoIVCIaTTaYEX8swD/oNMYLPZxBXPv4MoGiLe2Vl8mD+/9kVBMxfDwTl73Or+zM/PR3d3N86dO4cHHngAHR0dkjNcXV19V+706uoqHA4HampqRKZaWVmJlZUVXL58GUqlEg6HAz6fDz//+c9x6tQpAEBraysA4ObNmxIkRGAbYYF8IfnSFBUVSXVHjwVnjJTlsZUdGxuDVqsVhg8NdEajET6fD3q9HsXFxdDr9RK6QvWV1+sVHTNRwl6vF6Ojo6L0YFbCjh070NjYiO9973tQq9VobW2Vg5iaefoIrFYr0um08P4nJydRVFSE7du3I3Un/IX7I+Y8MDiey0O66efn5yVPOZfLobKyUgBjpaWl6OvrEyMgK3h+frwAWdkQq8GqnDNodlm5XE4kxvn5+TI/JYaAGQxmsxnl5eWiFuNLxx0VCw0iGOLxuGRZs/Um9+h3f/d3odfrcfDgQfT19WFkZOQu1MjMzIyMRyruoMBpICQW5cqVKygsLMTOnTthNpslS3x1dRWDg4NYXFyEyWQSxzXHA9yB0cPBpSU7kaGhIVitVphMJkxMTKCmpgalpaWiErTb7SKj5qiClwcjhHU6neSz9Pf3C9LDZDKJi5w7E8I6yWPi+GpmZkYKPYLyuOfb2NhAbW2tdJArKysIBAJinqPJkpkwvHiIzqBShwXclStXJL+EJlHKTsmc4nNCKgOX71arVS4ZOtDpsqZjmzLf/v5+pNNpqNVqfP/730coFEJLSwtu3rwJt9sNo9EIk8mE3bt34+tf/zri8bjki9MLws6eQVZbvTGZTAa1tbVCAqitrRVMDAD5zDluisVikrnB0RufMb6LJSUlmJqaQkVFBWw2mwAXeZYZDAZRVZaVlUGr1Yr5GLgtt2WXaTAY5PPl/oJFHflXH+bPr816evLJJ1FVVSWVRTKZRFdXF9RqNX7+85+LG/SVV17B1NQUWltb8dWvfhV2ux1+v///0963BTden2c/kiXLlg+SLcuyJFuSZVteW7bXYU+w7JqFXbIhhZBQSJjQTJIhCdNhpnSapjedTHrRTKfT9KLTTnOTEtIJKSTAQgsMLJQsu8t6YfHa+CzLknU+2LIOPh8k67vwPi/yN/MxtEmZ6Xz63YQh7MHS//973/d5nwMUCgVGRkZkPOM4TA8WAJiYmMDW1hZSqRR6e3tx/PhxVFVVYWFhQTjg7ITX1tag1+tl9Pf7/aiurkZHR4fYfFOVTSotXxqK1si64H/DnQFVkaSbkv7Jy5VFbnFxURxao9HoAR41v/BisYhr165BoVBgaGhILDq+/e1vw2azYWZmRmi5W1tbmJ2dlcudzBi+lJyUuPSlRQU/E4PBgA8++OAArZYGjlx02+12xGIxBAIBycVuamoSOwa9Xi8WKOl0Gh0dHcKF5wTIghcMBuFwOKRAsRCTohwKhWT6ofaEBYhL5VL/mcrKSsRiMfT29grtE4As3Le2tuB0OoUKyOCfra0tXLlyBSdOnMDJkydl5zQyMiKLf4rykskk7Ha70CK51M7eslIvFosIhUIYHR2F2+2WXIR8Po/Ozk68++67cDgcGBwcFPEXu3UucUnlrampEXsOGhNSG8Mc82w2i3A4DGDf4ppWGzS5o2kfc+JbWloE+mOMLVl3XIjSjXZzc1PYh5yaiM8zKY6Qz9LSEubn5wVS4+W3s7MDAKIToEki92TE5kmvJXWTzzSwbyPDpTqXtXzPONXqdLoDzCn9LVNDMqCY372ysoJTp06hra0N//qv/4pnnnkGjz/+uKTFfe9738Nf/uVf4pVXXsGxY8eEDLOzs4POzk4cOXIEVVVVOHLkCFQqFcbGxsQsVK1Wiy06oXVOFnq9XhbsFDrS24mRtvSfK1Vm850ljEWhpl6vlz0cD9ld3Eckk0kpxNSYcGeyt7cnDtWEb+lwTSNEOgjPz8/j5z//+Wfj9UT3S3boHP3Onj2Lhx56CG+//Tb0ej0effRRocxxC88Pl6pDu92OhoYG+P1+oZLSQZLCqyNHjoh1NwVJNICjgI4v+NWrVzE/P49jx47hwQcfxNLSEq5fvy6jF6eIzs5OhEKhAzGupXS8TCYD/S0fKS4VealxWtnY2BDGicFggMViQaFQgNvtRiwWw+rqKpxOp4ieNjY2cO7cOVmcNjY2QqfT4d133xV5fiqVEm+m5uZmgTFoNMZcCKVSKdYfNJqjlToT7YiFMxmMUBVprXNzc6itrcXOzo6Yi/n9fvHbp8Ees0L4wjCFjVoCtVotFzq9eWjJQu0D9S1TU1M4duyYqHnpw89Oh0K6uro6yQ7mzog/C43c2LiYzWZhf3BZF4vFcPPmTYGfSLVdWlqSEZ/phNQacBpg1jBtZhoaGuB0OgVWYnRoNBqFy+WCzWZDdXU1rl+/LvGodLBNJpNCg+SUxemXWhKFQiHKdxZfPoOEHviO1dbWCuVZoVAgEolI2h8vbRIz+Hna7XZhV5HlRKEW93qkMtMvzG63I5fLwWw2iw4J+Fj5u7q6Ksy4Ur0InwFeTpz0yBDk8puTNz2NCAfSYTaXy8FqtYq9PUV5dXV1sNlsolfhFD07O4uVlRW4XC4kk0m88847ot85fvw4nnzySQwODuKVV16Bz+dDsVjEjRs3UCgU0NnZCZ/PJ7AdoVRO32q1WnaG6+vrBwoDsX9O1NTU0NJfrVZjdnZWKMrLy8ui7aArARfRRqNRolbPnDkjNPGOjg6JZHC73dDr9ZidnZWGlbsNPkcU7VVUVEho1fr6usQbf9rze/F6amtrQyKRkMxhvjgWiwWRSAQzMzMYHBzEyZMnkc1msbi4iLm5OUQiEUkv4w6A9MnGxkYxrOvq6kIwGMTg4CAWFxcxPDwsHb3RaEQwGBRM0WKxoLu7GxaLBdFoFJcuXcKxY8fQ2dkpcMfCwoI4yPr9fnz44Ycy3lOjQCptTU2NsFmIw/JFSqVSYizHPQWDa9ghUP7PxSCtOwqFAoB9GqTBYEAgEBAPfy4CuXxkB8bFJLFfFhIusKlYpSKaCz2O/5lMBkajEYlEQpZ5TBLjJOJ2u8W+nEyN7e1tuXw4tdCqg/sK5hiT5cLOkFg0iQj19fVwOByyW6FNwunTp3Hjxg2xWWB6G91SW1tbhWrKSZPc8FgsJmIlRtyyU7RarbIsrKysFJ3MysoKLBaLpOHp9Xp4vV7o9Xq0tLRAo9EgFovBZrOhpqZGCktLSwvef/99HD58GNvb2zhx4gTcbjfa2trg9Xrx7//+7+js7JQQHGo4FAoFbDYbdDodamtrodfrEQgERHBH3J4eXFSmk3ZJ5lwoFEJTU5NAln6/H5lMRorO9va2OJXy13KhzkLp8/nQ09MjEzFzIxoaGqDVahGNRsWEMJFI4PTp05idnZWGiBAGISgWctpWtLa2YnFxEWq1Wmw0WAwBCFqQTqeFSUcdyt7enuxlOLGSOpzL5UTMm8vlZFdHKvb09DTcbjeOHj0Kn8+HK1eu4I033kA0GoVarcaTTz6JaDSKUCiEq1ev4rXXXkNjYyOcTifS6bQ4IzMugIxCACJcJJHEZDJBf8udOpPJHCBsMOWSDrHcV5IAwHA1mnYySjqdTqO+vh6pVAoDAwOwWCwYGhqCwWDA3/3d32Fqagrf+c538PTTT0vBYpYNiR0NDQ3C3KQFCPVMgUBAYHOlUon/Cpj0OxcKjsWkkW1vb4sgLRqNwmazwePxYHBwULpXviiU8HPjzzERgPwQN27ckBF1bm5OFj5kUdDemR/CoUOHEAqFcOjQIaHHXb9+Ha+99hra29sFlioWi/B6vdjb20NXVxeAjzFbukiSeUSGBUdFahE4CpO2trq6Kg8p7Qb4+1KQSChOpVLB6/WKhQQ55IQliNEC+4s6crGp4rXb7TJyskjwMqGtM7n7tCmvqqqC1+vF4OCgsKEohqP7KncOxErr6+slLIcWFfRdYseu1WoxNTUlYzGVyNyPGAwG8R6iEp0LUbPZjJWVFUxNTUlHzUUxAMkt2draQiQSgdvtFuYYjSRLDf34ElBs19fXJ00Ji8zu7n72Of2XWLQJlTC1jQJL+vWcOnUKHo8HL730EmZmZg6I87LZLD744AP09PTA5XKJSppYMRP+2DFfunQJer1ejAf39vbEhYDGjbyMV1dXpXFh5gCFZqS30r2UzCG+HyQNkMLOKS+Xy4mNzdbWlrguA/uQnsfjQVtbm+zlqF0h/MkESBILyFbiMrajo0NILnwu+N4QaqOwlb5J/O/6+vowMzMDALJAJuff4/EI7EoWXTQaRT6fl8bla1/7GlwuFxYWFsSj6fz58/jiF7+IdDqNF154ARcuXEAkEpHd0GOPPSbml+FwWKCeUCgkMC73ErSu4U7AarWKVT1T69jo8U4jQmIymaBQKITJRKt2Tui0ngmFQvjKV76CkZER/OY3v8Hbb78tCMDNmzdx9913w2AwIJvNyuRFUgIbHz6/zc3NwpZjQaax4qc9vxdTQIpr2tvbpVvl+EVONi+YRCKBmzdvCuxDyID8fEIKdHJlnjLHfloQkIWhUqnk5Tt//rxY7j7//PNobGxEV1cXVCoVXnzxRdx+++3CWe/o6BAYjGM6L5BSLr1Op0MgEBCqLF9c+qUw7pJWIFzy0seH4jU+QJWVlWhoaJAM7kAgICpLfunpdFrM/yoqKrC4uIju7m4xr+PlSfiBVtaMa6XylpGhvLQIqZAhRB43w1ioGaH3PruQ/v5++b0paiKLg5kXTU1N4mNTiqNTNa1SqdDV1YXR0VGZNNhFkRRAbcfi4iJaW1tFL8EpiUZuzDQIh8OwWq1ykXCZyQZheXkZY2NjyN7KDWhra8OHH36Izs5O4eyz8/X5fAA+5paX5i+vr6/jjTfewP333w+dTieqX7vdjp///OfC9hscHMQf/uEfwm63Y2xsTP5OtDHhJQxAdmhc/NJ6nawbfiec4AhfMWyI9vg0jKOpITMnCIlw4lMqlchmszAajaIw9/l8B6A1wmmMGy4tWEqlUi5vjUaDzs5OedYoKCQjimJDwkG0xi/92blrogCOC1k2ELSk4EVcUbEf2Uv7lsbGRsRiMaytrcmzEY1GMTY2JrkVg4ODGBwcFAuT2dlZKJVKPPbYY7Db7bh+/brkNuzt7WF6elpS5zKZjFjrEIrlu26xWODz+dDd3Y1CoSC7Dk7vpT8DG65EIiGfkUajkaU2VfW07jCbzdJkFQoFDA8PY3x8HHV1dThy5AgmJiZQKBQwMDAgTKfSX0/vOe5NmedO7RUlAjSK/MwKBbCPVVLtR/EOnTx7e3vR29srrIpQKASdTgetVitdgdVqRSAQkFFVr9cLRa+xsVEk/3SMpAS9qalJnE9pwf3iiy/io48+Ej+nRx99FPPz86iurobf78eNGzfQ0dEBi8UCu92OmzdvSpdL4VJ1dTXa29tFWVpRUSHK49raWqFm0omWX8Lu7q6oWukFxakqlUrBbDZjbW1NtCPRaFR47EajURbTHPG1Wq1w0DOZjBSovr4+wfopnqO3E0VKhL3YbRNmojEaxT304mEeBtMBFxYWYDKZhFFEC4GTJ08iEomI8IsUSYq9aIVAL57Z2VkxV9TpdBK9yoLLpoBUUWL6DocDgUBAXnrulVhAeYFRlMbPnFGTHo8HPT09YuldU1Mj4Tf8/LnToraGiWP8Z61Wi0gkIrDV5OSk7CgIiZDBFIlEEIlE8P7778Pv98NoNIq54NraGg4dOoSZmRlZWFNgyoLOosLp02AwCIZM6DGdTotvVmlAEt1AtVqtTPNc5jc2Nkr0KFMLueTe2Ng4wIRqaWkRWI423RsbGzLFFAoFXLt2DQ8++CB6enpw8eJFVFVVCSRJlhMvWMJnbIKo0YnFYtIF85KjzYTD4UAikUBNTQ2Wl5dluU6XAIvFgoGBAbz//vtYW1uDTqeTz4Uivbfeekt2JR0dHcjn85iamoLP54NOp4PdbodWq0VfXx+i0ahM0oSIifdTuNrf349UKiXoAu8BZmnQISAYDIpr8c7OjuxgOOmwKdJqtUJDttvt0kjzz21sbMSRI0eEMfeDH/wABoMBw8PDOHr0KC5cuIArV65ArVbD5XJJjAKfX7VaLToVsjP5/VAEvbq6Kvfdpzm/sykgH0Z6I9EOgKMoQ+vX1tYE4+WymbbbPp9PkrJMJpOkSxWLRVFSk4fP3NhEIoFAIIDDhw/LEi+VSuH111/Hb3/7W+j1erz44oti+/HEE0/gxIkTIkThwpcXMf3veVHRRM3lcqGlpUWEXaTv0UitWCxKWEkqlYLVahWrDkJDtDnY3t4WaImGZiyYVJ3S454qcV4eBoMBbrcbZrMZCwsLYm7HLO5wOCxTAheVfMGJQxP6YVAOKbDcBRC2UCr3g+ZZHPh78+Xmi9/a2oq1tTXpvonBZ2/lkZSK5NilcQHN5SVt3smyaW5uluCqvb09eDwesd2mUyrdennZ6fV6iQqlmIy26fTkcTqdsFgssgdgMaAhHTnwnI4Jq+TzeWxsbIjdSy6Xw9zcHMLhMN58801MT0/jxo0bYtTn9Xqlq6bFCUWiZJ1Rp8H9wc7Ojmg1CPGkUinJ0+aSk98njevYpRIe4oTBySKbzYq6njstCuJos1GanLi6uoqVlRWxMqdNOWGu0kvpF7/4BXw+n9jV8Hvt7OwUujQtt0nrHh0dRSaTQSAQkMAvpVIpRAZ6vnGaIvuJcFcul8Po6Ch+8YtfYGFh4UAqHa1RCMN1d3djdXVVtFycKEnwIGmFYtlkMimEFEJMnFC5/DcajULjJxqQvZUlwiRCvm+c9EjbZe45zQjZDLE5otcWIe9gMIj5+XmEQiFsbm7iueeew9jYGIaGhvBP//RP0Gg0+OCDD+SeJUOM5IatrS2ZGAhB006HjRrzOz6TQkEBSKFQEConmSScLEKhEGZmZkRRyi5ia2tLrDC2trYQDocFXmEoC2EM/qBUlDLGk5GBVG6qVCr88R//Mb773e/i0UcfxeXLlzEyMiLS/b6+PqEMKpVKiW5sa2sTbxb60dDxlrYZ29vbWFhYQDQahUKhgNvtlgUydSP06Kdykw8wMUHSQFUqlTjp8t+R5gjsL645aVGAxBGS+DOnEV7cHDltNhsAiJkapw/ma09MTMDv98vnyr1PdXW1LMAIN1RXVwssSMuCnZ0dzM/PCxZK76vNzU3JMbBarcLAoUaCL8LGxn4utsPhkLCmUCgEpVKJnp4eJJNJTE9PY2trSwKV6NJJRhhxbgbN19bWwmg0wul0iveQWq0WXyDqUEKhkEwgoVAIsVhMdiOcVChMoxUzlfZknKXTaQQCATQ3N+PEiRPiRvrlL39ZbKcZTkUqJLNJ2JyQ5dTY2CguqclkEtFoVC6e0nQ9NlUmkwmxWEyIDYuLiwiHw4jH44jFYqisrJS9RE9Pjyy0yYih0pfTJFlq/P4Yv6pQKESQt7q6ivvuu0+ek97eXtFNZbNZvPTSS/D5fJKkx066qalJWDiFQgEmk0mKOA0P+dylUimZbhk5S9gNAI4ePYpcLofLly/jjTfewMTEhEBCvFgXFxeRSCTEnr66ulq8mQDI/mlrawvDw8NSEDmt6HQ6JJNJoZcydEuj0aC/v1/eS7VaLcw0HhasdDotRAIWkmAwKBqKlZUVNDc349SpU+jq6oJer0c0GpUCyUl8dHQUPp9PDD/n5uYkYc/lcuHuu++GxWLBzs6OKP5pZZPJZCRelil/LPqEwUlc+LTnd4aeFhcXBQsnVY4B5aWh6nwg6KlCTJyOh3QzpcBOrVbL78NdBjHtQ4cOiSCKitTp6Wk8/PDDOHfuHLxeL1599VVYLBY8/vjjmJubQyqVEvrr+Pg4EokEjh49Kt00Dci6urqwsLAAq9UKnU4nzpLMC7DZbAIFMUd7enpa2FDhcBjV1dVyWVFMQ28j4q7soKkYp+0CL/61tTX4/X4RvFGHQP46efGLi4s4deoU2tvbxdFze3tbol3psZ/P5wWCo1Np9lZQEvcoLPa00CauznQyYuiFQkHiKvn5cXnMiSeXy6Gvr09EfFT3EmpxOp0wm824fv063n33XSElMOELAAwGA9bX1+X7pu3H9vY2RkZGRHhFiw9at5AMsLCwAJ1OJ2E0GxsbOHz4sFya3d3d8Pv94sq6uroqtEEWQXaEZMzxxU4mk+jt7cW9996LmzdvQqfTSRY6RZzUWFBBT0ZRVVUVTp48KRAUbR6Y/2Cz2aRQsFhz70W33IaGBrFAn5ubg9VqFe+x5uZmWK1WTE1NidqcXSUNAevr65FIJGSqWF9fh06nEwEfLd7Z0L388st44YUXhDnlcDgETp6ZmYHdbhdRKQB5dqgHoBCVrMh4PC552VQO050V2NcaMEzM6XRCq9WKqeUDDzyA2dlZiVodGhrC8PCwGO4xjIrP7ebmpnisra+vy+ezu7srHTkn2O7ubmxtbQlSwmeFPmkMSiOrib+OjrPULLS3t8u+ra6uDvX19Th37hx6e3uh1+vxyiuv4Nq1a2hra8P29jZ8Ph/OnDmDYDCI/v5+eDweTE1N4a677sIdd9wBk8mECxcu4De/+Y003HfddReUSiUCgYA4OCuVSnkPSNIgDM2sE7Ic+f9/mvM7FwqOV4FAQGAmANKBq1QqMeNTq9Xo7OwUOh2N94D9JR8ffo5SxE4VCoVcIGRVbG5uioCH+witVouBgQE8/fTTeP311+F2u/EHf/AHqKiowOjoKDo7O/FHf/RHeOeddw4EGfEyJROGsZuMTmQHQVyflXlkZASHDh2SbpfiNb1ej3g8LrgrdQj0nmLYUCQSQaFQEMpjZWWlLBztdruwbZh2R1YKvZSY+TEzMyNL8lLMnjgvDfsYqM5LlVoLMmdaWlrEc5/0ZKpk6apLWnJzczOKxaJ0hWTPcJTm3mJ1dVUEStvb23C73aitrYXVakVtbS1+9atfyf7GYDCIuRkNE9955x2Ew2GhXXKfo1Ao4PP5ZDdDmIAFB4DsZ+hjZLfb0dTUhLGxMehv5RSfOXMG0WgUfr9fcoR5oXNKJIzCF+zw4cMIBALy59GgLxKJIBQKQavVyvTk9XqlQ6eGhe8H9260geHnwGwDmghy8uPnbzQaD5gIcs9SU1ODVCqFnp4e6cgJDVIJzkyIcDgsHm2EIglXcfq12+0SgLO1tYWzZ89iYmICb775Ji5evIiHH34YR44ckWKSSCREAMYJhRAy3zN+ZqXMN1qskIHGvQu/N7rkVlVVYWhoCB0dHaJj4Ht56NAhWURXVFSIlxxhYUJgjY2NsrAnzZtdPoWxdXV1cLlc4kb93HPPwefz4ctf/rIw/6gpGRsbE4NJ6rlu3LiBUCiEw4cP49y5cyJ+SyaTGB8fRyAQwIsvviiQVikxxGg0wuv1oqGhQdieu7u7mJqagtlsRm9vL1555RXs7e1heHhYluNut1voxw6HQ+A8Ejk4iXKypdXKZ1YoWFXpb05+vl6vF+yaZlUcgVkQiDmvr68jEAggk8mIx79CoYBerz9ghMULGIBYQlBUxJdqZ2cHAwMDuHnzJvL5PN577z10dXWhUCjAbDZDq9Xi61//OgKBwIEXdXp6WhTKa2trBzQGXDBzp8DxkqM02SFMQGNBo/Ol/pYlBLFrdihM2+K0QdyT1uJLS0swm80YGBgQv//SAhyJRGA0GhGJRGCxWCTLeWtrS3j2xOMZU0torLGxUWiOq6urMrITD6aqlBgsaauxWAwbGxu4ceOGNAG0LKG1B6Ey/qy09rBYLKitrcV7770ni2H6WdntdgwPD4unEyeYpaUlsUggkYCwHpW5xIQJrZAuTG8sLp9poDcyMoLXX38dDodDlrg1NTVwOp2y2CRzjN89T2VlJW7cuCEYfj6fR0tLC0wmk1BmSQ0nVuxyubCysiKKW2LZhHlaWlqg1+uxsbEBv98vezyq+Pf29mR3ReoxmVJ+vx/t7e3yDO7u7grZgGSQ0gChvb092cPt7OwgGAyit7cX8XhcLPFZ8HO5HLxeL8LhML7+9a/jK1/5Cn7605/io48+QjweR19fn4j0+vv7EYlEZCLY3NxENBrF7u4u7Ha72IEQTmOAEPNsSCNl0BgRA0KG3D0MDg5Co9FgamoKdrsdly9fxuLiIu677z5YrVa8+eab6OrqkmYlnU5jZWUFc3NzUmCrqqrg8Xgki0Sn00k4l9PpRGtrK6LRKC5fvoyJiQncuHED/f39mJ+fx/Xr1wUCpW/T+++/D7Vajba2NoGlp6amkEwmsbKygvvuuw8/+9nP8MILLwgsdfvttyMWi2FychJ9fX2Se0EnajaJFCu63W7k83kcPXoUn/vc5/DOO+/gpZdegslkwj333COwM22IgP3dBC3muXsiYYj3wWdWKOh9QhEZbZlVKpVoGogR0kWVak2aX+l0OqnoZL/wIiCe2tTUJJf+5OSkOKlyQcjkN7PZjMcffxw//OEPRVnc3NwsqXTRaBQAZLnOrqqyslIiGDOZjDAzurq6JMWL/PSOjg7pzhnawuCcRCIhWQnMGqisrIRarcbhw4cxPj6OXC4nfybjGlmc2Nnx1wDA6OioUBbJhCJjAoB0NKVjN5eeLEjb29uYn58Xr6J0On3AvI+USk5WAETvQpvpYrEIp9Mp2DrhMrItyM8nps6psaWlRYRZr732Gn75y18imUzi3Llz+O53v4tr167hjTfeEIFVd3e3QAZVVVWYmZlBT08PgI9NDrkMJyNpd3cXuVwOFosFk5OTsoAlJBKLxXDx4kVRnrvdbmg0Gvzyl7/El770JZw8eRKpVEqM6KiRyGazsNlsgmfzmSEssrW1BWDfZqa/vx9GoxEfffQRent7sbS0JJ8XACnChCNZ+Ci2pLcRmygmqAUCAdx2222ora3FxMSE0MSDwaAs2SsqKhAIBKQ5ofsxA6Q4XZKWyuV9c3Mz5ubmZPrQarWw2+0yVdJZ+Ne//jUuXrwo5nIUtFIV/vLLL8NoNMLlcolXGu1CiA7w4mpoaMDi4iKSyST0t0KgyAKjuSiLfClsxu/eZDLhS1/6EnQ6nfi/aTQamM1mnD17Vqbt7e1txONxKYxGo1Fyp8lANJvNOHbsGFQqFZ599llcunQJf/M3f4P3338f3//+96FQKHDu3DlcvnwZs7OzWFpawt13343p6WnEYjEcOnQI9913nxjx0ZbG5XKhsbER//AP/4C5uTnZ12q1Wnzuc5/DAw88AK/Xi5deeglqtRr3338/AoEARkZGpNDl83lZ2nMnOz4+ji984Qt48MEHRayp1+uFKKPT6YS4wrjempoaNDc3i2UNPde4D/1MCkUsFoPRaBR7AGoFSFuj2ycZTD09PZicnITL5ZKYUXLEaW1hMpkOiKJ2dnbE8oNdNJdeer0ewWBQMh70er24VXZ3d8slzKCf6upqTE1NicqaPHmymbgIMxgMqK+vFyHZ9va2UGb9fj9cLpe8RPSaYTFhpCsZYC6XC8ViEVeuXBFfFYbYUyBWX1+PZDIpBm+kHQL70xM7fBY5AFJw2WGT/hkIBAQC49KWjJ/S6EtOSVzeUS/Cl5v5xAyV4mXHvzNzGMjSYkNA6IFsmKqqKtxxxx1QKpVwOp04f/48Ll68CLVaLcr6fD6PoaEh2Gw23Hvvvchms/B4PLJUJ5xGtf/a2hqy2Sz6+vrE0oS5EdwLeDweZLNZgSXm5ubQ2dmJjo4OHD16FIVCAQ899BDOnz+P9vZ2vPzyy8K8aWhokOxjBmuVii0J17FotbS0CBuKl10gEEAul4PT6cTi4iI6OzuxuLgoEAn3GFarVaxgOHFTGQ8AbW1t8Hg8YoFBvUF7e7voCchA44VBNfzU1JT8PSmCozKaXHp+l6Viymg0is7OTlRUVCAWi+GZZ54RJp3D4YDH40FXVxdOnTqFhx9+GL/61a9kSgiHwwLz8Bnjs0Koh0LM9fV1sb3n4p1BQPl8HtXV1XC73VhYWBALnoGBAWGTPfLII/D7/QLfcl9JyAYAMpkM+vv74ff7BfLa3d1Ff3+/7CGnpqbw/PPPQ61W40/+5E9w+fJl0eSQVVddXY1vfetbkhUzPT2NVCqF22+/HUqlUqIRJicncf/99+ORRx5BRUUFfv3rXwv13O12Ix6PC23+a1/7Go4fPy4qbafTKcxDrVYrljXz8/NCaCE0WSwW0dbWJlMdGxg2WCQDUKy4tLQksH1tba1ohz6TQqG/ZTLHi4FLPS5LrFYrcrkcIpGIcJqJ/5PvTw8otVot/76UqsqHiMwbwj319fWSZgVAhHf5fB4TExOoqalBLBYTzj2tEDhycURj90ZclcEzzE7I5XLo6OjA6OioqDBp/EfjQFp9m81mmTbIMKDFB2EO0kSJ62u1WvGy54tOuIg/dzKZFLiC9ukADlh2cE9Dbx7uYADIZU/ePGEJ2p9TqEWDxXQ6jeyt3GeKwQhvcUqkQJCeMrQ5YdIfiQ1ms1kozI2NjZK/sbi4iH/8x3/E7u4uvvGNb+Duu++G2WzGzMwMpqenRahmsVhQX18vHRTZGlarVRbq5LTTYZTeRX6/X1hRf/VXf4WbN2/i2WefxfT0NIaGhsTgkDb3wMd6BS6guRsipMaLlFMF4yo5DXJy5X6Gv44+TyQE0NKE3R0vMK1WK/ROWneT4VKavVBRUYHx8XEYDAbJU6GoksKtjo4OmWAVCoVcoFS108uIKXTBYFA8xRKJBC5dugSPx4M77rgD4XAYsVgMPp9PDBSfe+45DA0N4ciRI2J1Mj4+Lt8PPysA6O3tBQDp6LPZ7AG6LymeLJJ2ux2RSEQKq9vtFuba1atXsbe3B7fbjWg0imvXrsHlcuHy5cuiJeHOjxCfSqVCe3u7CO+YlaNQKBAMBqWgkDjA+FLGM9fV1eHzn/88/vqv/xr19fXo7OyUewcA+vv7hTiRSCTw4YcfwuFwSPH46le/ijNnzuAHP/gBfvazn+HOO++UDBm6H1utVlFvlzIZKTQkpD07Oyuxqtlb4UTcOxLm510CQCj6/O4ZEPZpz+9cKBgJqr/l6ki6GTFFcr0ZfcoAH9oP8ANg4hezas1mMyorK+HxeOB0OkXFSfy/sbER4XBYlOF7e3tyEbKr556D6VhMzCNbgv5DFRUVYgJGlgJ3H8RyycVnUUmlUtKdsNukRxRT3khtpPqUohd2F4lEQvYAdXV1iMfjErjDbppcey45VSqVUHjT6bQs96lzYDY5rRFK7RUoUgQ+Dpza3NxEW1ubYJj0t6HPVXNzsxRX0jsJ9/C/t1qtiEQi4jfEn293dz8Wl/bZL7zwAj744AN885vfxJ//+Z/j3LlzKBQK8Hg8mJ+fRyqVwuc//3lMTEwgkUjAZDLJn8NdRT6fF+YR80l2dnYwPT2Nrq4usZvgFFhZWQmv1wuTyYRMJoOLFy+isrISg4ODmJ2dlcVnT08P6uvr4fP5YDab4fV6UV1dLRnI3EcQJqF9BNXtbFaouJ2enkZbW5sQPajXUKvVuO222+QlTqVS8mu4SPX7/bKQZgfIBW99fT2mp6eF+XL8+HGxiKBWo729/UCanUqlkoU2NUIARNFPhhK7Vu5p+vr6sLe3h6amJjzxxBOIRqN4++23sby8jGPHjgkkubOzI35NpA1zgqdZJ6nT3FNQiEa4mSpyalfYTCoUCtTU1KCnpwetra3427/9W6ytreGJJ57As88+K1kVtEsfHx9He3s7nE4nampqJPeGez1avXOXGAqFYLfbcfToURiNRpw/f17EcT09PTh9+jSeeOIJBINBRCIRPP/88/D5fOjt7RVBJ+nj4XAY//Iv/4JYLIY777xT4Lu/+Iu/wIcffojFxUX88z//M3K5HH70ox/BbDbjmWeekUatpmY/rjgYDEqDxGZJqVSirq5OGlI+34Tld3d3xWByZWVF7EmqqqokQoAT6s7ODtrb25FOpz+7QsEfYHR0FDqdTsyy2IlFo1GhbRUKBTQ1NWF6elqWTWTjkHJJKlssFjsgbiO332q1CmuC3jfFYlE8Tmw2m2DH9fX14ofCF437D0ag0mm0t7dXrIQJDxEGW1hYEEEOs68BCAxRWVmJRCKBQ4cOCSWYwjPiy6WUO17irPD0X1EoFHC5XDIa0uvHbrejuroa165dw+nTp4VHXygUJAyHDxvxWi5SGRTEnAd2qjRjY6HN3vKt4SVPqwwWeE5jxKVLfYZoqUDs02AwSHdDaienOgASQnPhwgVx1h0bG0NVVZVYQ1A4yCaD+Qf8c/P5/QAn2lxTxMmlb0dHB0wmE+LxOKanpwEAw8PDiEQiePLJJ9Ha2oof/vCHkqP893//96irqxMBIpPl+GKxyPN75FSXz+fFYttut8skY7FY0NDQgOrqasmPaG5uFuIEc+MpMJ2enobdbhcK5szMDFpbW+XP4/RNBwMutQknUBG8sLAgTECq34nHcyqj8p5GgKRyMo+CSu/d3V3ce++9iEQiuHbtGsbHx5HP5/HII49ge3sb/f39wlqMRqOoqKjA/Py8UH3tdrtkqdBHi5BYU1MTNjY2EAgEpOEgtFcoFMR1VafTiX7jrbfewtTUFA4fPoyjR48CgCy96Q4NQHIh5ufnZZIHIGw0suEI/V26dEmsYIaHhzExMQGtViu7i+eff150O9RUnD59Gvpbxo6c+tPpNOx2O1wuF5566ilcvXoVRqMRDz30EBoaGvDUU08hHo/jvvvuw5kzZ2SXQZU3/aI2NzflnuOer1SETPYdaen19fXY3NxEsVjE8vIyYrGYUOrz+Ty2t7fR2toqsDGtgbgD/TTn91IoaK4GAAsLC+jq6oJCocDCwgLsdrss8chuolaCTqi0wFheXhbXWQBiAc7O0Wq1QqPRIJ1Oi3c96Y4tLS1iSlZRUYG9vT2Mj48L7Y50TwpoGPVJnnQ2mxXIh9YMJpMJHo9H/KAIVVHYxwueLymxQEIstDIo9dohfZLjH0dktVotBoqlqlR2juvr6zh8+LBcllyERiIRgSJ46FjLpazH45FcX/KrdTqdpI2trKygtrYWHo/ngKEdGRpknWxvbwsGzQwS2hwkEgl0dnYKLZbxmhTXGY1GdHV1IZ/Pw+124+mnn8ZPfvITfOELX8CpU6dkB0KxIympSqVShHScWGkw197eLt2TzWaTFDK6ZZpMJqRSKayurmJiYgLLy8tii8EgJpvNhra2Nly+fFmcZjc2NsQnh//Mn4GUZMJbzc3NMi0sLS0JpVun02F+fh4mkwn9/f0YHx+Hy+WS0Jvd3V3xrtrY2BBiAgs6HYVJkqAPk16vh8lkEpYTRZ4AJPmNl0TpUrmiokLoxGSFcb80OTkJh8Mh9jukTs7MzIgSfnh4GIFAAN/73veExUfiB/dQ3EtwMe31emXxzb/X+vq6ZJ8w+4NqZ3qmkaHIzJRMJoPR0VGhiI+MjCCfz+Oee+7Bj370I3Fs3tnZEY1EMpnE5OSkTOKJREK+v/+7YXv11Vdx5coVOBwOtLe3w+PxYGhoCBMTEwiFQlhZWcHJkyfR39+PV199FW63G93d3QfgRbIanU4nqqqq8J//+Z+YmZmRfR33AX/6p38KjUaDixcvSuNJ1EKn02Fvb0/ozul0Wu41IinM86BoLhqNyrNotVrFhbtYLGJhYQFGo1E+f1rnA/vN2n/FwuN3LhS8xNn502agdPyvrq6WCYFqWgZ7aLVamQ4osydtkylhtE5gzB/9TLgcu+2224RCS8yeTrDpdFpUlIQRAIiKkfJ6Mo3467nYttlsiEaj0j3z70C20NLSkvi9eL1ebGxs4LbbbhMaLyl6XELRrpu+MNlsFiaTCcViETMzMwJnkMLKYB6TySSeN9ybsDtaXl6G3W6XYCZSlklFpHMlvYHo6mqz2cTLh9NL6QNLnycqVJn5zUuK0xRT5Kg452KW+ylaZ5OmOjg4iB//+MdQqVRwOBwCv1Fte+zYMSEl0N6ZORuJRAJ2ux2Tk5OSYcKFK5Wuer0esVgMX/ziF6FWq/H222/j9OnTUCqVuHHjBvR6PSorK/Gd73xHFOO0jAcgLCX+/NxFkMWkVCphMBjQ2tqKSCQiynx+T9RucLJMJBKw2WwiGqVDMR1yu7q6RD1OPUixWITNZpPvv7q6GpFIRFhUhAPVarUQRBwOhxTu6upq+Hw+YQe2tLRgZGREPk8KCZlrQoiHzQ6xbeLfJ06cwMMPP4zjx4+LKp+upOyIY7GYcPhJbrBYLEK2ILOrWCyipqYG7e3tUCr3w5Q6OjoELmN0a6lrbk1NjZBTYrEY/u3f/g133XUX5ufn4fP58K1vfUs0S3RU0Ol0Qj8mjZk6Av2tcK+WlhYxBWRA0Z/92Z9J7jqjV7u6urC7u4tz584hkUjg6tWrYv1CU9GlpSU0NTXhww8/xFtvvYXbb78dOzs7UjCeeuopfOMb30AwGEQwGBSm39TUFNxuN86cOYONjQ0RzxUKBcRiMTidTkEYSK2nYr6mpka8nUiyoDkh/egoiuaOinfSwsLCZ1soCOPwggYgDKBcLicvkNFolPhEeitR1JbJZEQgksvlDmTkMmaUnSP3HqSC0VyPylTuB1ipSZ/kUpnjWKltMJPyWKw0Gg28Xi/a2tqko6Slhlarld0JYaqJiQkJV+ELVltbK1YJa2trwuOORCLCb+cFw0ucHSAvxkKhIEt5MkG4HOUisK2tDcDHHSXJBCqVCsFgEC0tLUKT3dzcFF0Eu36z2YxgMIhsNitK0VI2Epet/HVc1NvtdoH0ePlWVVVJEAsfVKrJrVarROUaDAacO3cOp0+flkuhq6sLTU1NcDgcWFpakmUuizovmFwuJ6aBpZRTmrNRTHbp0iVUVlbiscceg81mQy6Xw3vvvYf/+I//QGdnp4j6dnZ2cPnyZWH6sFPb2tpCb2+vkB/47HDSUigUshQmVEMWESEU7syam5tlSqyqqoLT6RRYL5/Pi3Mv2VBa7X60KJfl+XxeXFfJtee0GgqF5BnndMPd397enoj+OOE6HA5RzxNepIXF+vq62FGT+tze3o7h4WHU1dXh3XffRSAQEFovaad8hqnqZqYMpw++DxTZMf8kFosJW43TO/c1/P1K42PvuecerK6uIhAIYGJiApubm/jmN7+Jvr4+NDY24vr16zCZTNIoUnyp1WolbnRvbw+zs7M4evQoMpkMhoaGROtCERv3Sf39/QLxVFVVwe/3y3fHxpK249wrsdH56le/ioqKCvz4xz8WK53f/va38hkRqq6srBT/J61WK3sbh8OB9fV18abiNEqyCTMnKGjkc88JktMJJ2PeEalUCoODg9I0fyaFggpgk8kkfyny8Kng5WKSDqIWiwWJRAIajQZ9fX2igWByF5kgNMQiDbStrQ2bm5vyIJDFEI/HD1g582HUarUingE+tnhWq9WCl/ILqK2tlZxu2j9TAcoXnx8+Lyd+0GRIMDmNYr1S+xFaKXD05sKY3aHJZILBYIDdbsfVq1ehVquluNEymL5YnBBCoZC4tZJKqFQqEY1Gob+VdUDKXlNTkyTRra2tSVGmWpYX29bWllh7EKJi+h67QZfLJV0jaXyELDQajYiO2N2SW76xsYH33nsPlZWVuPvuu+UiSqVSeOCBBw48yPF4XAgRZGjwO2VHpb8VNkQHzlAodEDpGolEkM/nhar97LPPYmlpCSdOnBBPqldeeUUCr8haYqIZKdwU7RFfLxaLSCaTSKfTOHHihFjRcCextbWF9fV1SezjBU3RG/dytK1m9jXJB7zQksmk2K1cv35dRKebm5tYXl4W3yk+n1VVVdjY2BBYhBdOX1+fLH3poOByuYQmy8+HPwMtswOBgHSte3v7gUK0bGHqHJXOCoVCJkfubqjJId2aFxqn5kKhIHoMlUqFEydOIBQKyXTd1taGzs5OsW/p6uoSyOTChQtwOp3o7++XNMmJiQlZ0FKDsbi4KCgAnyEKY0dHR4XSm8/nhcnmcrkkAvadd97B2bNnMTk5iWg0invvvVfILlVV+/nlnK64wG9tbYXVahU49ezZs6isrEQ0GkVLSwump6dlCrvzzjvFjcHn88mujw0moSmaRHInRWhcoVBAp9NJHkVnZ6fouCgxIAGHtH8SXD5Teiy586ULMADiNglALgkubDluko5JjDMSiaC7u1vwucbGRnR3d2NlZUVeUuoGDAYD5ufnxRqZLpikEPb19UkXyv1JIpGQrALy7ulaST8U7kv4QBEWI8uIi6bsrZAZTjJHjhzB3NwcLBYL3G430um05D8DQGtrK5LJJNrb2wV3JfSytLQkCvHJyUkcOnQIAASjJFPD7/cL5kmHTUJGzP4mS4W5GOxGJyYm0NnZiWKxiNbWVsH7uXQ2m83y++lvOZxGo1ERS5FxxqJTW1srkBHJAQDkhaS4hxenSqWSUCR2iWT4kKYL7NN9aTnAxC8Ags9ycuVnTzX9wsKCqKWp26ms3M+CPn78OD766CPodDoYDAacPn1asr9rampEwMidF9lunFSoiWHGBYADkwYbBPp2cXrgC9/a2ip2FlSjE97kkp9TJEObuPfjvoh22xRUEsYlEYQapNbWVinMgUBAXAlKl5+l7CJmixB+JOOJzzmtRSj+6+rqQiwWE00CNVBK5X4wEr3bCBdzaUqaMC9PEi30tzI3AIgOSKlUikaEBZaRpZlMBvX19Xj44YelAc1ms5ibm5N9IXVRpJfabDaJLeUOanNzE06nUyxLwuEwVCqV6Bzq6+uFLr+ysoKHHnoIV69excbGBrq6uuD1epHNZtHQ0ACj0YiFhQVMT0+jsrIS8Xgcer0ew8PD0Ov1OHToEJLJJO666y6MjY3J/UO7dzIzNzc3RUfC5ouTJZmiJIsQBbFYLGKxQwTGZDKJpsblcklyKL+vvb09DAwMCMnj05zfSx7F9vY2bt68CZvNJpSu1dVVrK6uSqwnu/fm5mbpIMjSACA5CBybw+GwqDPJNmCYPHNx+dJyZGe3tra2hpmZGfn7LC8vI5VKiRCFCmZamJNtQSohMWpSZ8kgKaXB8s/iCM4wGK1Wi8nJSWxubkqnRdZRTU2NMGBo6cBlFeGwUntruowC+5GpRqNRWFrr6+uSO6DT6SQlj5c4mVgajQYzMzOiA3E4HMI+4sPJrqXULIzFoa6uDrW1teIXtbOzI5Q7LvW5R6KHDpka9E7ii8C8DYvFgng8LpYSU1NTgv8DHzt0Ep+ntUFNTQ3i8TiKxSLW1tZEIZ9KpYRJR0YNCxgA0RKw2wqHwxJ6tLi4KHAWLVV4+FLncjlMTU1JoY1EIsIC45SQyWREREYYiZoF7o4ASGYxSQkMI1pdXZWpiM8WGxy+5Lu7u5JYRs8kwhhk97C4MqeEQVxKpRKhUEimZI1Gg1wuJyFVdBam2WQikUA+n8fx48ele15bW0M4HEZTU5O4IYTDYaH3cpnMhoY2MqRssnj09PQgHo9jbW1NRJDsiLmb02g0Yg+u1Wrx0UcfwW63I5vNSsbJysoKrl+/fgCOKbUMYn4Np2oWN7439KVjJCv913K5HO688044HA44HA4AwOzsLK5cuYKzZ89ibGxMdiO9vb1YW1uTKZAQL51lOfHRc0ulUgm8zf0FM7Xj8bg8Y6VedIVCAVNTUxILQDsUrVYLn88nTsRscAkFUujIn5+fz8rKiqR7ftqjKP5XglNLDq03vv3tb8NkMkGj0QgkxI6MKmq+6IFAQBwp2aGVjqsOhwOTk5Oor68XQz12+5lMRjxy2PnwS6elNPUYpFA2NzdjcXFRKIi0Kc9kMrBYLAcCPUqDVkh/vH79OhwOh/z+dAHV6/VicsixUKfTiW0CKa38OWmZ3dzcjNraWsHuOcZzwsjn80Kx5MViMBjEk5+dl8lkgs/nk2UxLRnom7W0tASbzYZisSixirxQqqqqYLVaxc6hNKGP9E/6xbS1tR2weqZ1BzMEAIiGgJ95V1cX5ufnha7LoJ5S/xmOygxQcjqd8Pv9ouUgfOfz+YQ7TvpzVVWVwDPFYvFAoaeWgZ0YL4Pl5WX09/djaWlJ8H8uIImR0wqbS3ySG+hwmslk5LNmhgEX7pyICV1wYuKeRqPRwGg0yt+H+w1OE9RHcJoNBoPixMqfg5AlLyRG1FJlz/0QmUtkSJHwoVarYbfb4fF4YDQahR22sbEBm80myYb8/eibRZoyfbyomSlV8HPq5lShUqmEOUgWHH9eqrA5ydExl4JHqr9Z8Ag9kpIN7DeVnKwAyBKdJAAu5A0GA1ZWVmQHRAIAm6mlpSVx4o3H49KQNjY2YmBgABUVFZicnEQ8HhdCBe28Q6GQaIyyt6z/q6urEY/H0draCr/fD51OJzsn2qvob7lmc6nMOyydTstUywRO2vrw52eB4w6Qy38ScliMDQaDmDSazWZhRxJ+5i5yenoaP/nJT2TP+j9aKB599FFZFGq1WoGJCGsYDAapinw4yfFnMh67eD5Au7u74vVTWVl5QADGBVIkEkFzc7Mswmpra7G8vIyhoSGZUiji4QXY0dGBhYUFoU7SWJDhNnzo+Xvxg66qqhKrEnbZLFys4rR/JgTkdDrFSZOsIWCfOsv9CgsVmS5UNlN8w9D1UqEMxXvFYhF1dXXSjQL7xAJ6CHFZzw6/rq4OXq9XKMncvZApxn0DsL+Ib25ultGdZnZ8KOmuqtfrsbOzg7q6OjFPZC6IyWQSR0wAQl/kn8NFJXOTFxcX5cLa3d2F1+sVaJA7pFIXXwqd6OJJTQqXvxUVFfD7/TAYDAIhhUIhGcVDoZBMR3xWBgYGkEgkhCVHOjKdR1mIqEUgFZt7lvX1dTQ0NCASiYh9Cg0R29raxL6eAijCNqSL89mnmwBtGGgNTjtrlUoFm80Gr9crnSMznVlslUql7GjIJCS8ymmTOzzupQhncXom0YLPiEKhQGNjI4LBIKqqquSSI7yUyWRkquE7wzx6Fr3sLTNDMoSYh8JLUKFQyLNJaiuhE1qKEDpi40A9E/9blepjoIREGmp8SOWmKSYndr1ej/Hxcdx2221SvNVqtZAvGIpFM0SK3jhd0xWAzwQ1HSrVflQzUQmDwYBoNCo7DTpMcCIoDZQKBoMyPVIrUbqTyOVysrek3xvNM9VqtRQrs9kstG3ucEjWefHFF/9nCwXpmd///veFRcFAkvHxcQlNaWxshN/vl06EWgUmeVE1yqUsH2Yu0WjQ53a7kc1m4XA4oNPpMDY2JhYYLB7E2LO30tco2uHlCuxrIGh3QQ+ZWCwmAUQajQZOp1M68K2tLRm7uYdhAaupqZEvm6IedkLLy8tS7BwOB4rFIiYmJtDV1YWNjQ1RUvIBZJdE5TGpcSsrK3C5XDLtsJMD9pf2pTGZFotFkuB4sZD1UV1djYWFBRF5lVqLcz/CPQL3R/zZOAqT467T6bCwsCC27rOzs/D7/WhoaJACZrFY5NKx2WxIJpMyRWg0GtGtsBCSDcTF6bVr18TVlYpo6iToD6TX62XJvLi4KHRUFudSKI0iQoo/o9GowGpkmFHYSZPH0smHKnzuLph7zARDfjfhcBiNjY0C89Cuw2w2y6+h0px28TychmdnZ7G+vi7W82azWdThCoUC4XBY9kgAxGvN6/WKZTkJG4lEAhaLBQaDARcvXsThw4fle+bEwJApn88nwjGVaj/jnCIwo9EoGiNOMBR2kmmVSqWQTqfR29t7IDelUCigWCzC6/XCarWKOyq7bU4kpHKyYVpaWsLp06fh8XjkntBoNMKu5GVOhwFi9vPz86ipqZH9V0tLi0yJLN4shPF4XOxnpqenBdpxu90YGRmB2+2WSY3hUPF4HAMDA8LeogiOUcONjY0SUkQkgwI6Ek9o9cL/5S4SgFDZ4/E4uru7sb6+jra2Nskx5352dXUVnZ2d2N7eFvYT7Y3IYOP9yN0Zs7z5e/30pz8Vev//SKGIRCJCyyyf8imf8imf/50nHA5LtMH/6/y3C8Xe3p5YcbOrLZ/yKZ/yKZ//HYeIAS35P+n8twtF+ZRP+ZRP+fz/cT65jJRP+ZRP+ZTP//enXCjKp3zKp3zK5xNPuVCUT/mUT/mUzyeecqEon/Ipn/Ipn0885UJRPuVTPuVTPp94yoWifMqnfMqnfD7xlAtF+ZRP+ZRP+XziKReK8imf8imf8vnEUy4U5VM+5VM+5fOJp1woyqd8yqd8yucTT7lQlE/5lE/5lM8nnnKhKJ/yKZ/yKZ9PPP8Hu1xwyPLSkT0AAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.clf()\n", + "from pipeliner.mrc_image_tools import mrc_thumbnail\n", + "# load the input micrograph and plot a thumbnail\n", + "project_dir = \"/home/dvi41342/MapReconstruction/DESRES-Trajectory_sarscov2-11021571-all-glueCA_fractionated\"\n", + "figures_dir = os.path.join(project_dir, \"figures\")\n", + "input_mrc_filename = os.path.join(project_dir, \"MotionCorr\", \"job007\", \"Movies\", \"000000.mrc\")\n", + "input_mrc_thumbnail = mrc_thumbnail(\n", + " input_mrc_filename,\n", + " 500,\n", + " os.path.join(figures_dir, \"motioncorr_ugraph_000000.png\"),\n", + ")\n", + "\n", + "fig, ax = plt.subplots()\n", + "ax.imshow(input_mrc_thumbnail, cmap='gray')\n", + "ax.set_xticks([])\n", + "ax.set_yticks([])\n", + "fig.savefig(os.path.join(figures_dir, \"motioncorr_ugraph_000000.png\"), dpi=600, bbox_inches=\"tight\")" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.clf()\n", + "# plot the power specrtrum\n", + "from pipeliner.mrc_image_tools import mrc_thumbnail\n", + "diagnostic_filename = os.path.join(project_dir, \"CtfFind\", \"job008\", \"Movies\", \"000000_PS.mrc\")\n", + "mrc_PS = mrc_thumbnail(\n", + " diagnostic_filename,\n", + " 500,\n", + " os.path.join(figures_dir, \"PS_ugraph_000000.png\"),\n", + ")\n", + "\n", + "fig, ax = plt.subplots()\n", + "ax.imshow(mrc_PS, cmap='gray')\n", + "ax.set_xticks([])\n", + "ax.set_yticks([])\n", + "fig.savefig(os.path.join(figures_dir, \"PS_ugraph_000000.png\"), dpi=600, bbox_inches=\"tight\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## FIG 1A - LocalRes and FSC" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [], + "source": [ + "# imports\n", + "import os\n", + "import mrcfile\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import numpy as np\n", + "import seaborn as sns\n", + "\n", + "from gemmi import cif\n", + "\n", + "# roodmus\n", + "from roodmus.analysis.utils import load_data" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "project_dir = \"/home/dvi41342/MapReconstruction/DESRES-Trajectory_sarscov2-11021571-all-glueCA_fractionated\"\n", + "figures_dir = os.path.join(project_dir, \"figures\")\n", + "job = \"job029\"\n", + "fsc_postprocess_filename = os.path.join(project_dir, \"PostProcess\", job, \"postprocess.star\")\n", + "resolution = 3.44\n", + "\n", + "\n", + "fsc_cif = cif.read(fsc_postprocess_filename).find_block(\"fsc\")\n", + "rlnResolution = fsc_cif.find_loop(\"_rlnResolution\")\n", + "rlnFourierShellCorrelationCorrected = fsc_cif.find_loop(\"_rlnFourierShellCorrelationCorrected\")\n", + "rlnFourierShellCorrelationParticleMaskFraction = fsc_cif.find_loop(\"_rlnFourierShellCorrelationParticleMaskFraction\")\n", + "rlnFourierShellCorrelationUnmaskedMaps = fsc_cif.find_loop(\"_rlnFourierShellCorrelationUnmaskedMaps\")\n", + "rlnFourierShellCorrelationMaskedMaps = fsc_cif.find_loop(\"_rlnFourierShellCorrelationMaskedMaps\")\n", + "rlnCorrectedFourierShellCorrelationPhaseRandomizedMaskedMaps = fsc_cif.find_loop(\"_rlnCorrectedFourierShellCorrelationPhaseRandomizedMaskedMaps\")\n", + "\n", + "df = pd.DataFrame({\n", + " \"resolution\": rlnResolution,\n", + " \"fsc\": rlnFourierShellCorrelationCorrected,\n", + " \"fsc_masked\": rlnFourierShellCorrelationMaskedMaps,\n", + " \"fsc_unmasked\": rlnFourierShellCorrelationUnmaskedMaps,\n", + " \"fsc_masked_random\": rlnCorrectedFourierShellCorrelationPhaseRandomizedMaskedMaps,\n", + " \"fsc_masked_fraction\": rlnFourierShellCorrelationParticleMaskFraction,\n", + "})\n", + "\n", + "# convert all columns to float\n", + "for column in df.columns:\n", + " df[column] = df[column].astype(float)\n", + "\n", + "fig, ax = plt.subplots(figsize=(3.5, 3.5))\n", + "sns.lineplot(x=\"resolution\", y=\"fsc\", data=df, ax=ax, legend=True, label=\"corrected\", linewidth=3)\n", + "sns.lineplot(x=\"resolution\", y=\"fsc_masked\", data=df, ax=ax, legend=True, label=\"masked\", linewidth=3)\n", + "sns.lineplot(x=\"resolution\", y=\"fsc_unmasked\", data=df, ax=ax, legend=True, label=\"unmasked\", linewidth=3)\n", + "ax.axhline(0.143, color=\"black\", linestyle=\"--\")\n", + "ax.axhline(0.5, color=\"black\", linestyle=\"-.\")\n", + "ax.axvline(1/resolution, color=\"black\", linestyle=\"dotted\")\n", + "ax.set_xlabel(\"Spatial Frequency ($\\AA^{-1}$)\", fontsize=21)\n", + "ax.set_ylabel(\"FSC\", fontsize=21)\n", + "# ax.set_ylim((-0.1, 1.25))\n", + "# change the fontsize of the ticks and the legend\n", + "ax.tick_params(axis='both', which='major', labelsize=20)\n", + "ax.legend(fontsize=18, bbox_to_anchor=(0.65, 1.1), loc=2, borderaxespad=0.)\n", + "fig.savefig(os.path.join(figures_dir, f\"{job}_FSC.pdf\"), bbox_inches=\"tight\")\n", + "fig.savefig(os.path.join(figures_dir, f\"{job}_FSC.png\"), bbox_inches=\"tight\", dpi=600)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## FIG 1F - Precision and Recall" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loading metadata from /home/dvi41342/MapReconstruction/DESRES-Trajectory_sarscov2-11021571-all-glueCA_fractionated/Extract/job013/particles.star...\n", + "loaded metadata from /home/dvi41342/MapReconstruction/DESRES-Trajectory_sarscov2-11021571-all-glueCA_fractionated/Extract/job013/particles.star. determined file type: star\n", + "\n", + "\n", + "Dictionaries now contain 17501 reconstructed particles\n", + "added 17501 particles from /home/dvi41342/MapReconstruction/DESRES-Trajectory_sarscov2-11021571-all-glueCA_fractionated/Extract/job013/particles.star\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "loading truth data: 100%|██████████| 67/67 [00:19<00:00, 3.49it/s, micrograph=000066.mrc]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loaded ground-truth particle positions from config files\n", + "Dictionaries now contain 17501 particles and 20100 true particles\n", + "Added 20100 particles from /home/dvi41342/MapReconstruction/DESRES-Trajectory_sarscov2-11021571-all-glueCA_fractionated/Movies\n", + "loading metadata from /home/dvi41342/MapReconstruction/DESRES-Trajectory_sarscov2-11021571-all-glueCA_fractionated/Class2D/job014/run_it200_data.star...\n", + "loaded metadata from /home/dvi41342/MapReconstruction/DESRES-Trajectory_sarscov2-11021571-all-glueCA_fractionated/Class2D/job014/run_it200_data.star. determined file type: star\n", + "checking if ugraphs exist...\n", + "\n", + "\n", + "Dictionaries now contain 35002 reconstructed particles\n", + "added 17501 particles from /home/dvi41342/MapReconstruction/DESRES-Trajectory_sarscov2-11021571-all-glueCA_fractionated/Class2D/job014/run_it200_data.star\n", + "loading metadata from /home/dvi41342/MapReconstruction/DESRES-Trajectory_sarscov2-11021571-all-glueCA_fractionated/Select/job020/particles.star...\n", + "loaded metadata from /home/dvi41342/MapReconstruction/DESRES-Trajectory_sarscov2-11021571-all-glueCA_fractionated/Select/job020/particles.star. determined file type: star\n", + "checking if ugraphs exist...\n", + "\n", + "\n", + "Dictionaries now contain 50848 reconstructed particles\n", + "added 15846 particles from /home/dvi41342/MapReconstruction/DESRES-Trajectory_sarscov2-11021571-all-glueCA_fractionated/Select/job020/particles.star\n", + "loading metadata from /home/dvi41342/MapReconstruction/DESRES-Trajectory_sarscov2-11021571-all-glueCA_fractionated/Select/job041/particles.star...\n", + "loaded metadata from /home/dvi41342/MapReconstruction/DESRES-Trajectory_sarscov2-11021571-all-glueCA_fractionated/Select/job041/particles.star. determined file type: star\n", + "checking if ugraphs exist...\n", + "\n", + "\n", + "Dictionaries now contain 53534 reconstructed particles\n", + "added 2686 particles from /home/dvi41342/MapReconstruction/DESRES-Trajectory_sarscov2-11021571-all-glueCA_fractionated/Select/job041/particles.star\n", + "loading metadata from /home/dvi41342/MapReconstruction/DESRES-Trajectory_sarscov2-11021571-all-glueCA_fractionated/Select/job042/particles.star...\n", + "loaded metadata from /home/dvi41342/MapReconstruction/DESRES-Trajectory_sarscov2-11021571-all-glueCA_fractionated/Select/job042/particles.star. determined file type: star\n", + "checking if ugraphs exist...\n", + "\n", + "\n", + "Dictionaries now contain 57056 reconstructed particles\n", + "added 3522 particles from /home/dvi41342/MapReconstruction/DESRES-Trajectory_sarscov2-11021571-all-glueCA_fractionated/Select/job042/particles.star\n", + "loading metadata from /home/dvi41342/MapReconstruction/DESRES-Trajectory_sarscov2-11021571-all-glueCA_fractionated/Select/job043/particles.star...\n", + "loaded metadata from /home/dvi41342/MapReconstruction/DESRES-Trajectory_sarscov2-11021571-all-glueCA_fractionated/Select/job043/particles.star. determined file type: star\n", + "checking if ugraphs exist...\n", + "\n", + "\n", + "Dictionaries now contain 61929 reconstructed particles\n", + "added 4873 particles from /home/dvi41342/MapReconstruction/DESRES-Trajectory_sarscov2-11021571-all-glueCA_fractionated/Select/job043/particles.star\n", + "loading metadata from /home/dvi41342/MapReconstruction/DESRES-Trajectory_sarscov2-11021571-all-glueCA_fractionated/Select/job044/particles.star...\n", + "loaded metadata from /home/dvi41342/MapReconstruction/DESRES-Trajectory_sarscov2-11021571-all-glueCA_fractionated/Select/job044/particles.star. determined file type: star\n", + "checking if ugraphs exist...\n", + "\n", + "\n", + "Dictionaries now contain 66694 reconstructed particles\n", + "added 4765 particles from /home/dvi41342/MapReconstruction/DESRES-Trajectory_sarscov2-11021571-all-glueCA_fractionated/Select/job044/particles.star\n", + "For each micrograph, for each metadata file, compute the precision, recall and multiplicity\n", + "Speed of computation depends on the number of particles in the micrograph. progressbar is not accurate\n", + "Total number of groups to loop over: 469\n", + "Number of micgrographs: 67\n", + "Number of metadata files: 7\n", + "Starting loop over groups\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "computing precision: 100%|██████████| 469/469 [00:17<00:00, 26.24it/s, precision=1, recall=0.22, multiplicity=0.23] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "time taken to compute precision: 18.138648748397827\n", + "mean precision: 0.9952617416568891\n", + "mean recall: 0.5052637739298599\n" + ] + } + ], + "source": [ + "project_dir = \"/home/dvi41342/MapReconstruction/DESRES-Trajectory_sarscov2-11021571-all-glueCA_fractionated\"\n", + "config_dir = os.path.join(project_dir, \"Movies\")\n", + "figures_dir = os.path.join(project_dir, \"figures\")\n", + "meta_files = [\n", + " os.path.join(project_dir, \"Extract\", \"job013\", \"particles.star\"),\n", + " os.path.join(project_dir, \"Class2D\", \"job014\", \"run_it200_data.star\"),\n", + " os.path.join(project_dir, \"Select\", \"job020\", \"particles.star\"),\n", + " os.path.join(project_dir, \"Select\", \"job041\", \"particles.star\"),\n", + " os.path.join(project_dir, \"Select\", \"job042\", \"particles.star\"),\n", + " os.path.join(project_dir, \"Select\", \"job043\", \"particles.star\"),\n", + " os.path.join(project_dir, \"Select\", \"job044\", \"particles.star\"),\n", + " # os.path.join(project_dir, \"Refine3D\", \"job014\", \"run_it015_data.star\"),\n", + " # os.path.join(project_dir, \"Refine3D\", \"job020\", \"run_it017_data.star\"),\n", + " # os.path.join(project_dir, \"Refine3D\", \"job008\", \"run_it011_data.star\"),\n", + "]\n", + "\n", + "jobtypes = {\n", + " os.path.join(project_dir, \"Extract\", \"job013\", \"particles.star\"): \"topaz picker\",\n", + " os.path.join(project_dir, \"Class2D\", \"job014\", \"run_it200_data.star\"): \"2D classification\",\n", + " os.path.join(project_dir, \"Select\", \"job020\", \"particles.star\"): \"2D class selection\",\n", + " os.path.join(project_dir, \"Select\", \"job041\", \"particles.star\"): \"3D class 0\",\n", + " os.path.join(project_dir, \"Select\", \"job042\", \"particles.star\"): \"3D class 1\",\n", + " os.path.join(project_dir, \"Select\", \"job043\", \"particles.star\"): \"3D class 2\",\n", + " os.path.join(project_dir, \"Select\", \"job044\", \"particles.star\"): \"3D class 3\",\n", + " # os.path.join(project_dir, \"Refine3D\", \"job014\", \"run_it015_data.star\"): \"Homogeneous refinement 1\",\n", + " # os.path.join(project_dir, \"Refine3D\", \"job020\", \"run_it017_data.star\"): \"Homogeneous refinement 2\",\n", + " # os.path.join(project_dir, \"Refine3D\", \"job008\", \"run_it011_data.star\"): \"Homogeneous refinement 3\",\n", + "}\n", + "\n", + "particle_diameter = 100 # approximate particle diameter in Angstroms\n", + "ugraph_shape = (4000, 4000) # shape of the micrograph in pixels. Only needs to be given if the metadata file is a .star file\n", + "verbose = True # prints out progress statements\n", + "ignore_missing_files = True # if .mrc files are missing, the analysis will still be performed\n", + "enable_tqdm = True # enables tqdm progress bars\n", + "\n", + "for i, meta_file in enumerate(meta_files):\n", + " if i == 0:\n", + " analysis = load_data(meta_file, config_dir, particle_diameter, ugraph_shape=ugraph_shape, verbose=verbose, enable_tqdm=enable_tqdm, ignore_missing_files=ignore_missing_files) # creates the class\n", + " else:\n", + " analysis.add_data(meta_file, config_dir, verbose=verbose) # updates the class with the next metadata file\n", + " \n", + "df_picked = pd.DataFrame(analysis.results_picking)\n", + "df_truth = pd.DataFrame(analysis.results_truth)\n", + "df_precision, df_picked = analysis.compute_precision(df_picked, df_truth, verbose=verbose)\n", + "print(f\"mean precision: {df_precision['precision'].mean()}\")\n", + "print(f\"mean recall: {df_precision['recall'].mean()}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_153676/1497854792.py:20: UserWarning: FixedFormatter should only be used together with FixedLocator\n", + " ax.set_yticklabels(ax.get_yticklabels(), fontsize=18)\n", + "/tmp/ipykernel_153676/1497854792.py:32: UserWarning: FixedFormatter should only be used together with FixedLocator\n", + " ax.set_yticklabels(ax.get_yticklabels(), fontsize=18)\n" + ] + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from importlib import reload\n", + "import roodmus.analysis.plot_picking\n", + "reload(roodmus.analysis.plot_picking)\n", + "from roodmus.analysis.plot_picking import (\n", + " plot_precision, plot_recall,\n", + ")\n", + "\n", + "order = []\n", + "for r in meta_files:\n", + " if type(r) == str:\n", + " order.append(r)\n", + " else:\n", + " order.append(r[0]) \n", + "fig, ax = plot_precision(df_precision, jobtypes, order)\n", + "xticklabels = ax.get_xticklabels()\n", + "ax.set_xticklabels(xticklabels, fontsize=12)\n", + "ax.set_title(\"\")\n", + "fig.set_size_inches(7, 7)\n", + "ax.set_xticklabels(ax.get_xticklabels(), fontsize=18)\n", + "ax.set_yticklabels(ax.get_yticklabels(), fontsize=18)\n", + "ax.set_ylabel(\"Precision\", fontsize=21)\n", + "\n", + "fig.savefig(os.path.join(figures_dir, \"precision.pdf\"), bbox_inches=\"tight\")\n", + "fig.savefig(os.path.join(figures_dir, \"precision.png\"), bbox_inches=\"tight\", dpi=600)\n", + "\n", + "fig, ax = plot_recall(df_precision, jobtypes, order)\n", + "xticklabels = ax.get_xticklabels()\n", + "ax.set_xticklabels(xticklabels, fontsize=12)\n", + "ax.set_title(\"\")\n", + "fig.set_size_inches(7, 7)\n", + "ax.set_xticklabels(ax.get_xticklabels(), fontsize=18)\n", + "ax.set_yticklabels(ax.get_yticklabels(), fontsize=18)\n", + "ax.set_ylabel(\"Recall\", fontsize=21)\n", + "\n", + "fig.savefig(os.path.join(figures_dir, \"recall.pdf\"), bbox_inches=\"tight\")\n", + "fig.savefig(os.path.join(figures_dir, \"recall.png\"), bbox_inches=\"tight\", dpi=600)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Get LocalRes Spread" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mean rad dmg dataset: 3.33144211769104\n", + "variance rad dmg dataset: 1.5853596925735474\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# rad dmg dataset\n", + "locres_sc_file = \"/home/dvi41342/MapReconstruction/DESRES-Trajectory_sarscov2-11021571-all-glueCA_fractionated/LocalRes/job039/relion_locres.mrc\"\n", + "mask_sc_file = \"/home/dvi41342/MapReconstruction/DESRES-Trajectory_sarscov2-11021571-all-glueCA_fractionated/MaskCreate/job034/mask.mrc\"\n", + "\n", + "# load the mrc files\n", + "locres_sc = mrcfile.open(locres_sc_file, mode='r')\n", + "mask_sc = mrcfile.open(mask_sc_file, mode='r')\n", + "locres_sc_data = locres_sc.data * mask_sc.data\n", + "locres_sc_data[locres_sc_data == 0] = np.nan\n", + "\n", + "# plot the local resolution\n", + "fig, ax = plt.subplots(1, 1, figsize=(4.5, 4.5))\n", + "ax.hist(locres_sc_data.flatten(), bins=100, range=(0, 10), density=True, color=\"blue\", alpha=0.5);\n", + "ax.set_xlabel(\"Local resolution ($\\AA$)\", fontsize=21)\n", + "ax.set_ylabel(\"Density\", fontsize=21)\n", + "ax.set_title(\"Rad dmg dataset\", fontsize=21)\n", + "ax.tick_params(axis='both', which='major', labelsize=18)\n", + "\n", + "fig.tight_layout()\n", + "\n", + "# print the mean and variance of the local resolution\n", + "print(f\"mean rad dmg dataset: {np.nanmean(locres_sc_data)}\")\n", + "print(f\"variance rad dmg dataset: {np.nanvar(locres_sc_data)}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "roodmus", + "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": "3.10.18" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/paper_fig_ipynbs/figure_2.ipynb b/paper_fig_ipynbs/figure_2.ipynb new file mode 100644 index 0000000..90d39d7 --- /dev/null +++ b/paper_fig_ipynbs/figure_2.ipynb @@ -0,0 +1,878 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# plotting functions of figure 2 in the manuscript\n", + "Overview plot of the first section. In the plot we show the result of reconstructing synthetic data simulated with Roodmus into a consensus (homogeneous) density map. \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# imports\n", + "import os\n", + "import re\n", + "import mrcfile\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import numpy as np\n", + "import seaborn as sns\n", + "from gemmi import cif\n", + "\n", + "# roodmus\n", + "from roodmus.analysis.utils import load_data\n", + "from roodmus.analysis.plot_picking import plot_recall, plot_precision\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# functions\n", + "def get_resolution_from_logs(project_dir, data, verbose=False):\n", + " results ={\n", + " \"Nparticles\": [],\n", + " \"res_unmasked\": [],\n", + " \"res\": [],\n", + " }\n", + " \n", + " # load the logfiles and retrieve the resolutions\n", + " for _, item in data.items():\n", + " if verbose:\n", + " print(f\"Processing {item['PostProcess']}\")\n", + " Refine3D_log = os.path.join(project_dir, \"Refine3D\", item[\"Refine3D\"], \"run.out\")\n", + " pattern = r\"Auto-refine: \\+ Final resolution \\(without masking\\) is: (\\d+(\\.\\d+)?)\"\n", + " with open(Refine3D_log, \"r\") as f:\n", + " for line in f.readlines():\n", + " if re.search(pattern, line):\n", + " res_unmasked = float(re.search(pattern, line).group(1))\n", + " break\n", + "\n", + " PostProcess_log = os.path.join(project_dir, \"PostProcess\", item[\"PostProcess\"], \"run.out\")\n", + " pattern = r\"\\+\\s*FINAL\\s+RESOLUTION:\\s+(\\d+(\\.\\d+)?)\"\n", + " with open(PostProcess_log, \"r\") as f:\n", + " for line in f.readlines():\n", + " if re.search(pattern, line):\n", + " res = float(re.search(pattern, line).group(1))\n", + " break\n", + " \n", + " if verbose:\n", + " print(f\"Nparticles: {item['Nparticles']}\")\n", + " print(f\"res_unmasked: {res_unmasked}\")\n", + " print(f\"res: {res}\")\n", + " results[\"Nparticles\"].append(item[\"Nparticles\"])\n", + " results[\"res_unmasked\"].append(res_unmasked)\n", + " results[\"res\"].append(res)\n", + "\n", + " return results\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## panel A\n", + "Results of processing the SARS-CoV-2 spike trimer dataset with a single conformation. Plots include\n", + "- the atomic model of the spike trimer (not shown here)\n", + "- selected 2D classes with relion class ranker score\n", + "- the consensus density map (not shown here)\n", + "- the FSC curve belonging to the consensus density map\n", + "- the ResLog plot of the consensus density map with B-factor" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# plotting selected 2D classes\n", + "project_dir = \"/home/mjoosten1/projects/roodmus/data/DE-Shaw_covid_spike_protein/20231116_DESRES-Trajectory_sarscov2-11021571-all-glueCA_single_conformation\"\n", + "figures_dir = os.path.join(project_dir, \"figures\")\n", + "class_averages = os.path.join(project_dir, \"Class2D\", \"job042\", \"run_it025_classes.mrcs\")\n", + "ranker_output_file = os.path.join(project_dir, \"Select\", \"job043\", \"class_averages.star\")\n", + "\n", + "num_rows = 3\n", + "num_columns = 2\n", + "\n", + "\n", + "class_averages_mrc = mrcfile.open(class_averages, mode='r')\n", + "class_ranker_cif = cif.read_file(ranker_output_file)\n", + "class_ranker_results = class_ranker_cif.sole_block()\n", + "rlnReferenceImage = class_ranker_results.find_loop('_rlnReferenceImage')\n", + "rlnPredictedClassScore = class_ranker_results.find_loop('_rlnPredictedClassScore')\n", + "rlnClassDistribution = class_ranker_results.find_loop('_rlnClassDistribution')\n", + "df = pd.DataFrame(data=[rlnReferenceImage, rlnPredictedClassScore, rlnClassDistribution], index=['rlnReferenceImage', 'rlnPredictedClassScore', 'rlnClassDistribution']).T\n", + "df[\"rlnPredictedClassScore\"] = pd.to_numeric(df[\"rlnPredictedClassScore\"])\n", + "df[\"rlnClassDistribution\"] = pd.to_numeric(df[\"rlnClassDistribution\"])\n", + "df[\"class_nr\"] = df[\"rlnReferenceImage\"].apply(lambda x: int(x.split('@')[0]))\n", + "\n", + "fig, ax = plt.subplots(num_rows, num_columns, figsize=(3.5*num_columns, 3.5*num_rows))\n", + "for i, row in df.iterrows():\n", + " if i >= num_rows * num_columns:\n", + " break\n", + " i_row = i // num_columns\n", + " i_col = i % num_columns\n", + "\n", + " class_nr = row['class_nr']\n", + " ax[i_row, i_col].imshow(class_averages_mrc.data[class_nr-1], cmap='gray', origin='upper')\n", + " ax[i_row, i_col].set_xticks([])\n", + " ax[i_row, i_col].set_yticks([])\n", + " # ax[i_row, i_col].set_title('Class {}'.format(class_nr))\n", + " # plot the predicted score as yellow text in the top left corner of the image\n", + " ax[i_row, i_col].text(0.05, 0.95, '{:.2f}'.format(df['rlnPredictedClassScore'][i]), color='yellow', transform=ax[i_row, i_col].transAxes, fontsize=38, verticalalignment='top')\n", + " # plot the fraction of particles in the class as white text in the bottom right corner of the image\n", + " ax[i_row, i_col].text(0.95, 0.05, '{:.2f}%'.format(df['rlnClassDistribution'][i]*100), color='white', transform=ax[i_row, i_col].transAxes, fontsize=38, verticalalignment='bottom', horizontalalignment='right')\n", + "# remove spacing between subplots\n", + "plt.subplots_adjust(wspace=0.02, hspace=0.02)\n", + "\n", + "# fig.savefig(os.path.join(figures_dir, \"class_averages.pdf\"), bbox_inches='tight')\n", + "print(f\"saved figure to: {os.path.join(figures_dir, 'class_averages.pdf')}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# plotting the FSC curve of the consensus map\n", + "project_dir = \"/home/mjoosten1/projects/roodmus/data/DE-Shaw_covid_spike_protein/20231116_DESRES-Trajectory_sarscov2-11021571-all-glueCA_single_conformation\"\n", + "figures_dir = os.path.join(project_dir, \"figures\")\n", + "job = \"job040\"\n", + "fsc_postprocess_filename = os.path.join(project_dir, \"PostProcess\", job, \"postprocess.star\")\n", + "resolution = 2.29\n", + "\n", + "\n", + "fsc_cif = cif.read(fsc_postprocess_filename).find_block(\"fsc\")\n", + "rlnResolution = fsc_cif.find_loop(\"_rlnResolution\")\n", + "rlnFourierShellCorrelationCorrected = fsc_cif.find_loop(\"_rlnFourierShellCorrelationCorrected\")\n", + "rlnFourierShellCorrelationParticleMaskFraction = fsc_cif.find_loop(\"_rlnFourierShellCorrelationParticleMaskFraction\")\n", + "rlnFourierShellCorrelationUnmaskedMaps = fsc_cif.find_loop(\"_rlnFourierShellCorrelationUnmaskedMaps\")\n", + "rlnFourierShellCorrelationMaskedMaps = fsc_cif.find_loop(\"_rlnFourierShellCorrelationMaskedMaps\")\n", + "rlnCorrectedFourierShellCorrelationPhaseRandomizedMaskedMaps = fsc_cif.find_loop(\"_rlnCorrectedFourierShellCorrelationPhaseRandomizedMaskedMaps\")\n", + "\n", + "df = pd.DataFrame({\n", + " \"resolution\": rlnResolution,\n", + " \"fsc\": rlnFourierShellCorrelationCorrected,\n", + " \"fsc_masked\": rlnFourierShellCorrelationMaskedMaps,\n", + " \"fsc_unmasked\": rlnFourierShellCorrelationUnmaskedMaps,\n", + " \"fsc_masked_random\": rlnCorrectedFourierShellCorrelationPhaseRandomizedMaskedMaps,\n", + " \"fsc_masked_fraction\": rlnFourierShellCorrelationParticleMaskFraction,\n", + "})\n", + "\n", + "# convert all columns to float\n", + "for column in df.columns:\n", + " df[column] = df[column].astype(float)\n", + "\n", + "fig, ax = plt.subplots(figsize=(3.5, 3.5))\n", + "sns.lineplot(x=\"resolution\", y=\"fsc\", data=df, ax=ax, legend=True, label=\"corrected\", linewidth=3)\n", + "sns.lineplot(x=\"resolution\", y=\"fsc_masked\", data=df, ax=ax, legend=True, label=\"masked\", linewidth=3)\n", + "sns.lineplot(x=\"resolution\", y=\"fsc_unmasked\", data=df, ax=ax, legend=True, label=\"unmasked\", linewidth=3)\n", + "ax.axhline(0.143, color=\"black\", linestyle=\"--\")\n", + "ax.axhline(0.5, color=\"black\", linestyle=\"-.\")\n", + "ax.axvline(1/resolution, color=\"black\", linestyle=\"dotted\")\n", + "ax.set_xlabel(\"Spatial Frequency ($\\AA^{-1}$)\", fontsize=21)\n", + "ax.set_ylabel(\"FSC\", fontsize=21)\n", + "# ax.set_ylim((-0.1, 1.25))\n", + "# change the fontsize of the ticks and the legend\n", + "ax.tick_params(axis='both', which='major', labelsize=20)\n", + "ax.legend(fontsize=18, bbox_to_anchor=(0.65, 1.1), loc=2, borderaxespad=0.)\n", + "\n", + "# fig.savefig(os.path.join(figures_dir, f\"{job}_FSC.pdf\"), bbox_inches=\"tight\")\n", + "print(f\"saved figure to: {os.path.join(figures_dir, f'{job}_FSC.pdf')}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# ResLog plot\n", + "project_dir = \"/home/mjoosten1/projects/roodmus/data/DE-Shaw_covid_spike_protein/20231116_DESRES-Trajectory_sarscov2-11021571-all-glueCA_single_conformation\"\n", + "data = {\n", + " # 0: {\n", + " # \"Nparticles\": 250,\n", + " # \"Select\": \"job033\",\n", + " # \"Refine3D\": \"job034\",\n", + " # \"MaskCreate\": \"job035\",\n", + " # \"PostProcess\": \"job036\",\n", + " # },\n", + " # 1: {\n", + " # \"Nparticles\": 500,\n", + " # \"Select\": \"job029\",\n", + " # \"Refine3D\": \"job030\",\n", + " # \"MaskCreate\": \"job031\",\n", + " # \"PostProcess\": \"job032\",\n", + " # },\n", + " 2: {\n", + " \"Nparticles\": 1000,\n", + " \"Select\": \"job025\",\n", + " \"Refine3D\": \"job026\",\n", + " \"MaskCreate\": \"job027\",\n", + " \"PostProcess\": \"job028\",\n", + " },\n", + " 3: {\n", + " \"Nparticles\": 2000,\n", + " \"Select\": \"job021\",\n", + " \"Refine3D\": \"job022\",\n", + " \"MaskCreate\": \"job023\",\n", + " \"PostProcess\": \"job024\",\n", + " },\n", + " 4: {\n", + " \"Nparticles\": 4000,\n", + " \"Select\": \"job017\",\n", + " \"Refine3D\": \"job018\",\n", + " \"MaskCreate\": \"job019\",\n", + " \"PostProcess\": \"job020\",\n", + " },\n", + " 5: {\n", + " \"Nparticles\": 8000,\n", + " \"Select\": \"job037\",\n", + " \"Refine3D\": \"job038\",\n", + " \"MaskCreate\": \"job039\",\n", + " \"PostProcess\": \"job040\",\n", + " },\n", + "}\n", + "# load the logfiles and retrieve the resolutions\n", + "results = get_resolution_from_logs(project_dir, data)\n", + "results_df = pd.DataFrame(results)\n", + "# add column for 1/res^2\n", + "results_df[\"1/res2\"] = 1/results_df[\"res\"]**2\n", + "results_df[\"1/res_unmasked2\"] = 1/results_df[\"res_unmasked\"]**2\n", + " \n", + "# plot the results\n", + "fig, ax = plt.subplots(figsize=(3.5, 3.5))\n", + "sns.scatterplot(\n", + " data=results_df, x=\"Nparticles\", y=\"res\", ax=ax, s=100,\n", + ")\n", + "p = np.polyfit(np.log10(results_df[\"Nparticles\"]), results_df[\"res\"], 1)\n", + "y = np.polyval(p, np.log10(results_df[\"Nparticles\"]))\n", + "ax.plot(results_df[\"Nparticles\"], y, color=\"black\", linestyle=\"--\", linewidth=3)\n", + "# compute B-factor as 2X slope\n", + "p = np.polyfit(np.log10(results_df[\"Nparticles\"]), results_df[\"1/res2\"], 1)\n", + "B_factor = 2/p[0]\n", + "print(f\"B-factor masked: {B_factor}\")\n", + "\n", + "ax.set_xlabel(\"Number of particles\", fontsize=21)\n", + "ax.set_ylabel(\"Resolution ($\\AA$)\", fontsize=21)\n", + "ax.grid(axis=\"both\", which=\"both\", alpha=0.5, linestyle=\"--\")\n", + "# invert x-axis\n", + "ax.invert_yaxis()\n", + "\n", + "ax.set_xscale(\"log\")\n", + "ax.set_xticks([1000, 2000, 3000, 4000, 6000, 8000])\n", + "ax.set_xticklabels([\"1k\", \"2k\", \"\", \"4k\", \"\", \"8k\"], fontsize=18)\n", + "ax.set_yticklabels(ax.get_yticklabels(), fontsize=18)\n", + "\n", + "# fig.savefig(os.path.join(project_dir, \"figures\", \"ResLog.pdf\"), bbox_inches=\"tight\")\n", + "print(f\"saved figure to: {os.path.join(project_dir, 'figures', 'ResLog.pdf')}\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## panel B\n", + "Results of processing the SARS-CoV-2 spike trimer dataset with 8334 conformations. Plots include\n", + "- the atomic model of the spike trimer (not shown here)\n", + "- selected 2D classes with relion class ranker score\n", + "- the consensus density map (not shown here)\n", + "- the FSC curve belonging to the consensus density map\n", + "- the ResLog plot of the consensus density map with B-factor" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# plotting selected 2D classes\n", + "project_dir = \"/home/mjoosten1/projects/roodmus/data/DE-Shaw_covid_spike_protein/20231116_DESRES-Trajectory_sarscov2-11021571-all-glueCA\"\n", + "figures_dir = os.path.join(project_dir, \"figures\")\n", + "class_averages = os.path.join(project_dir, \"Class2D\", \"job058\", \"run_it025_classes.mrcs\")\n", + "ranker_output_file = os.path.join(project_dir, \"Select\", \"job060\", \"class_averages.star\")\n", + "\n", + "num_rows = 3\n", + "num_columns = 2\n", + "\n", + "\n", + "class_averages_mrc = mrcfile.open(class_averages, mode='r')\n", + "class_ranker_cif = cif.read_file(ranker_output_file)\n", + "class_ranker_results = class_ranker_cif.sole_block()\n", + "rlnReferenceImage = class_ranker_results.find_loop('_rlnReferenceImage')\n", + "rlnPredictedClassScore = class_ranker_results.find_loop('_rlnPredictedClassScore')\n", + "rlnClassDistribution = class_ranker_results.find_loop('_rlnClassDistribution')\n", + "df = pd.DataFrame(data=[rlnReferenceImage, rlnPredictedClassScore, rlnClassDistribution], index=['rlnReferenceImage', 'rlnPredictedClassScore', 'rlnClassDistribution']).T\n", + "df[\"rlnPredictedClassScore\"] = pd.to_numeric(df[\"rlnPredictedClassScore\"])\n", + "df[\"rlnClassDistribution\"] = pd.to_numeric(df[\"rlnClassDistribution\"])\n", + "df[\"class_nr\"] = df[\"rlnReferenceImage\"].apply(lambda x: int(x.split('@')[0]))\n", + "\n", + "fig, ax = plt.subplots(num_rows, num_columns, figsize=(3.5*num_columns, 3.5*num_rows))\n", + "for i, row in df.iterrows():\n", + " if i >= num_rows * num_columns:\n", + " break\n", + " i_row = i // num_columns\n", + " i_col = i % num_columns\n", + "\n", + " class_nr = row['class_nr']\n", + " ax[i_row, i_col].imshow(class_averages_mrc.data[class_nr-1], cmap='gray', origin='upper')\n", + " ax[i_row, i_col].set_xticks([])\n", + " ax[i_row, i_col].set_yticks([])\n", + " # ax[i_row, i_col].set_title('Class {}'.format(class_nr))\n", + " # plot the predicted score as yellow text in the top left corner of the image\n", + " ax[i_row, i_col].text(0.05, 0.95, '{:.2f}'.format(df['rlnPredictedClassScore'][i]), color='yellow', transform=ax[i_row, i_col].transAxes, fontsize=38, verticalalignment='top')\n", + " # plot the fraction of particles in the class as white text in the bottom right corner of the image\n", + " ax[i_row, i_col].text(0.95, 0.05, '{:.2f}%'.format(df['rlnClassDistribution'][i]*100), color='white', transform=ax[i_row, i_col].transAxes, fontsize=38, verticalalignment='bottom', horizontalalignment='right')\n", + "# remove spacing between subplots\n", + "plt.subplots_adjust(wspace=0.02, hspace=0.02)\n", + "\n", + "# fig.savefig(os.path.join(figures_dir, \"class_averages.pdf\"), bbox_inches='tight')\n", + "print(f\"saved figure to: {os.path.join(figures_dir, 'class_averages.pdf')}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# plotting the FSC curve of the consensus map\n", + "project_dir = \"/home/mjoosten1/projects/roodmus/data/DE-Shaw_covid_spike_protein/20231116_DESRES-Trajectory_sarscov2-11021571-all-glueCA\"\n", + "figures_dir = os.path.join(project_dir, \"figures\")\n", + "job = \"job016\"\n", + "fsc_postprocess_filename = os.path.join(project_dir, \"PostProcess\", job, \"postprocess.star\")\n", + "figures_dir = os.path.join(project_dir, \"figures\")\n", + "resolution = 3.17\n", + "\n", + "fsc_cif = cif.read(fsc_postprocess_filename).find_block(\"fsc\")\n", + "rlnResolution = fsc_cif.find_loop(\"_rlnResolution\")\n", + "rlnFourierShellCorrelationCorrected = fsc_cif.find_loop(\"_rlnFourierShellCorrelationCorrected\")\n", + "rlnFourierShellCorrelationParticleMaskFraction = fsc_cif.find_loop(\"_rlnFourierShellCorrelationParticleMaskFraction\")\n", + "rlnFourierShellCorrelationUnmaskedMaps = fsc_cif.find_loop(\"_rlnFourierShellCorrelationUnmaskedMaps\")\n", + "rlnFourierShellCorrelationMaskedMaps = fsc_cif.find_loop(\"_rlnFourierShellCorrelationMaskedMaps\")\n", + "rlnCorrectedFourierShellCorrelationPhaseRandomizedMaskedMaps = fsc_cif.find_loop(\"_rlnCorrectedFourierShellCorrelationPhaseRandomizedMaskedMaps\")\n", + "\n", + "df = pd.DataFrame({\n", + " \"resolution\": rlnResolution,\n", + " \"fsc\": rlnFourierShellCorrelationCorrected,\n", + " \"fsc_masked\": rlnFourierShellCorrelationMaskedMaps,\n", + " \"fsc_unmasked\": rlnFourierShellCorrelationUnmaskedMaps,\n", + " \"fsc_masked_random\": rlnCorrectedFourierShellCorrelationPhaseRandomizedMaskedMaps,\n", + " \"fsc_masked_fraction\": rlnFourierShellCorrelationParticleMaskFraction,\n", + "})\n", + "\n", + "# convert all columns to float\n", + "for column in df.columns:\n", + " df[column] = df[column].astype(float)\n", + "\n", + "fig, ax = plt.subplots(figsize=(3.5, 3.5))\n", + "sns.lineplot(x=\"resolution\", y=\"fsc\", data=df, ax=ax, legend=True, label=\"corrected\", linewidth=3)\n", + "sns.lineplot(x=\"resolution\", y=\"fsc_masked\", data=df, ax=ax, legend=True, label=\"masked\", linewidth=3)\n", + "sns.lineplot(x=\"resolution\", y=\"fsc_unmasked\", data=df, ax=ax, legend=True, label=\"unmasked\", linewidth=3)\n", + "ax.axhline(0.143, color=\"black\", linestyle=\"--\")\n", + "ax.axhline(0.5, color=\"black\", linestyle=\"-.\")\n", + "ax.axvline(1/resolution, color=\"black\", linestyle=\"dotted\")\n", + "ax.set_xlabel(\"Spatial Frequency ($\\AA^{-1}$)\", fontsize=21)\n", + "ax.set_ylabel(\"FSC\", fontsize=21)\n", + "# ax.set_ylim((-0.1, 1.25))\n", + "# change the fontsize of the ticks and the legend\n", + "ax.tick_params(axis='both', which='major', labelsize=20)\n", + "# ax.legend(fontsize=18, bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n", + "ax.legend().remove()\n", + "\n", + "# fig.savefig(os.path.join(figures_dir, f\"{job}_FSC.pdf\"), bbox_inches=\"tight\")\n", + "print(f\"saved figure to: {os.path.join(figures_dir, f'{job}_FSC.pdf')}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# ResLog plot\n", + "project_dir = \"/home/mjoosten1/projects/roodmus/data/DE-Shaw_covid_spike_protein/20231116_DESRES-Trajectory_sarscov2-11021571-all-glueCA\"\n", + "figures_dir = os.path.join(project_dir, \"figures\")\n", + "data = {\n", + " # 0: {\n", + " # \"Nparticles\": 250,\n", + " # \"Select\": \"job033\",\n", + " # \"Refine3D\": \"job034\",\n", + " # \"MaskCreate\": \"job035\",\n", + " # \"PostProcess\": \"job036\",\n", + " # },\n", + " # 1: {\n", + " # \"Nparticles\": 500,\n", + " # \"Select\": \"job029\",\n", + " # \"Refine3D\": \"job030\",\n", + " # \"MaskCreate\": \"job031\",\n", + " # \"PostProcess\": \"job032\",\n", + " # },\n", + " 2: {\n", + " \"Nparticles\": 1000,\n", + " \"Select\": \"job025\",\n", + " \"Refine3D\": \"job026\",\n", + " \"MaskCreate\": \"job027\",\n", + " \"PostProcess\": \"job028\",\n", + " },\n", + " 3: {\n", + " \"Nparticles\": 2000,\n", + " \"Select\": \"job021\",\n", + " \"Refine3D\": \"job022\",\n", + " \"MaskCreate\": \"job023\",\n", + " \"PostProcess\": \"job024\",\n", + " },\n", + " 4: {\n", + " \"Nparticles\": 4000,\n", + " \"Select\": \"job017\",\n", + " \"Refine3D\": \"job018\",\n", + " \"MaskCreate\": \"job019\",\n", + " \"PostProcess\": \"job020\",\n", + " },\n", + " 5: {\n", + " \"Nparticles\": 8000,\n", + " \"Select\": \"job006\",\n", + " \"Refine3D\": \"job014\",\n", + " \"MaskCreate\": \"job015\",\n", + " \"PostProcess\": \"job016\",\n", + " },\n", + " 6: {\n", + " \"Nparticles\": 16000,\n", + " \"Select\": \"job050\",\n", + " \"Refine3D\": \"job051\",\n", + " \"MaskCreate\": \"job054\",\n", + " \"PostProcess\": \"job055\",\n", + " },\n", + " 7: {\n", + " \"Nparticles\": 32000,\n", + " \"Select\": \"job052\",\n", + " \"Refine3D\": \"job053\",\n", + " \"MaskCreate\": \"job056\",\n", + " \"PostProcess\": \"job057\",\n", + " },\n", + "}\n", + "# load the logfiles and retrieve the resolutions\n", + "results = get_resolution_from_logs(project_dir, data, False)\n", + "results_df = pd.DataFrame(results)\n", + "# add column for 1/res^2\n", + "results_df[\"1/res2\"] = 1/results_df[\"res\"]**2\n", + "results_df[\"1/res_unmasked2\"] = 1/results_df[\"res_unmasked\"]**2\n", + " \n", + "# plot the results\n", + "fig, ax = plt.subplots(figsize=(3.5, 3.5))\n", + "sns.scatterplot(\n", + " data=results_df, x=\"Nparticles\", y=\"res\", ax=ax, s=100,\n", + ")\n", + "p = np.polyfit(np.log10(results_df[\"Nparticles\"]), results_df[\"res\"], 1)\n", + "y = np.polyval(p, np.log10(results_df[\"Nparticles\"]))\n", + "ax.plot(results_df[\"Nparticles\"], y, color=\"black\", linestyle=\"--\", linewidth=3)\n", + "# compute B-factor as 2X slope\n", + "p = np.polyfit(np.log10(results_df[\"Nparticles\"]), results_df[\"1/res2\"], 1)\n", + "B_factor = 2/p[0]\n", + "print(f\"B-factor masked: {B_factor}\")\n", + "\n", + "ax.set_xlabel(\"Number of particles\", fontsize=21)\n", + "ax.set_ylabel(\"Resolution ($\\AA$)\", fontsize=21)\n", + "ax.grid(axis=\"both\", which=\"both\", alpha=0.5, linestyle=\"--\")\n", + "# invert x-axis\n", + "ax.invert_yaxis()\n", + "ax.legend().remove()\n", + "\n", + "ax.set_xscale(\"log\")\n", + "ax.set_xticks([1000, 2000, 4000, 8000, 16000, 32000])\n", + "ax.set_xticklabels([\"1k\", \"2k\", \"4k\", \"8k\", \"16k\", \"32k\"], fontsize=18)\n", + "ax.set_yticklabels(ax.get_yticklabels(), fontsize=18)\n", + "\n", + "# fig.savefig(os.path.join(figures_dir, \"ResLog.pdf\"), bbox_inches=\"tight\")\n", + "print(f\"saved figure to: {os.path.join(figures_dir, 'ResLog.pdf')}\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## panel C\n", + "plot of the resolution dependence on the number of frames of the MD trajectory that are included in the particles. The total number of particles remains constant at 4000" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# define jobs to use\n", + "project_dir = \"/home/mjoosten1/projects/roodmus/data/20240102_limited_K_dataset\"\n", + "figures_dir = os.path.join(project_dir, \"figures\")\n", + "\n", + "data = {\n", + " 0: {\n", + " \"K\": 125,\n", + " \"Nparticles\": 4000,\n", + " \"Import\": \"job007\",\n", + " \"Refine3D\": \"job009\",\n", + " \"MaskCreate\": \"job010\",\n", + " \"PostProcess\": \"job011\",\n", + " },\n", + " 1: {\n", + " \"K\": 8000,\n", + " \"Nparticles\": 4000,\n", + " \"Import\": \"job012\",\n", + " \"Refine3D\": \"job014\",\n", + " \"MaskCreate\": \"job015\",\n", + " \"PostProcess\": \"job016\",\n", + " },\n", + " 2: {\n", + " \"K\": 250,\n", + " \"Nparticles\": 4000,\n", + " \"Import\": \"job017\",\n", + " \"Refine3D\": \"job019\",\n", + " \"MaskCreate\": \"job020\",\n", + " \"PostProcess\": \"job021\",\n", + " },\n", + " 3: {\n", + " \"K\": 500,\n", + " \"Nparticles\": 4000,\n", + " \"Import\": \"job022\",\n", + " \"Refine3D\": \"job024\",\n", + " \"MaskCreate\": \"job025\",\n", + " \"PostProcess\": \"job026\",\n", + " },\n", + " 4: {\n", + " \"K\": 1000,\n", + " \"Nparticles\": 4000,\n", + " \"Import\": \"job027\",\n", + " \"Refine3D\": \"job029\",\n", + " \"MaskCreate\": \"job030\",\n", + " \"PostProcess\": \"job031\",\n", + " },\n", + " 5: {\n", + " \"K\": 2000,\n", + " \"Nparticles\": 4000,\n", + " \"Import\": \"job032\",\n", + " \"Refine3D\": \"job034\",\n", + " \"MaskCreate\": \"job035\",\n", + " \"PostProcess\": \"job036\",\n", + " },\n", + " 6: {\n", + " \"K\": 4000,\n", + " \"Nparticles\": 4000,\n", + " \"Import\": \"job037\",\n", + " \"Refine3D\": \"job039\",\n", + " \"MaskCreate\": \"job040\",\n", + " \"PostProcess\": \"job041\",\n", + " },\n", + "}\n", + "\n", + "results = {\n", + " \"K\": [],\n", + " \"Nparticles\": [],\n", + " \"res_unmasked\": [],\n", + " \"res\": [],\n", + " \"B_fac\": [],\n", + "}\n", + "\n", + "# loop over the jobs and get the resolution and B-factor from the log files\n", + "for _, item in data.items():\n", + " Refine3D_log = os.path.join(project_dir, \"Refine3D\", item[\"Refine3D\"], \"run.out\")\n", + " pattern = r\"Auto-refine: \\+ Final resolution \\(without masking\\) is: (\\d+\\.\\d+)\"\n", + " with open(Refine3D_log, \"r\") as f:\n", + " for line in f.readlines():\n", + " if re.search(pattern, line):\n", + " res_unmasked = float(re.search(pattern, line).group(1))\n", + " break\n", + "\n", + " PostProcess_log = os.path.join(project_dir, \"PostProcess\", item[\"PostProcess\"], \"run.out\")\n", + " pattern = r\"\\+\\s*FINAL\\s+RESOLUTION:\\s+(\\d+(\\.\\d+)?)\"\n", + " with open(PostProcess_log, \"r\") as f:\n", + " for line in f.readlines():\n", + " if re.search(pattern, line):\n", + " res = float(re.search(pattern, line).group(1))\n", + " break\n", + " \n", + " pattern = r\"\\+\\s*apply\\s+b-factor\\s+of:\\s+(-?\\d+(\\.\\d+)?)\"\n", + " with open(PostProcess_log, \"r\") as f:\n", + " for line in f.readlines():\n", + " if re.search(pattern, line):\n", + " B_fac = float(re.search(pattern, line).group(1))\n", + " break\n", + "\n", + " results[\"K\"].append(item[\"K\"])\n", + " results[\"Nparticles\"].append(item[\"Nparticles\"])\n", + " results[\"res_unmasked\"].append(res_unmasked)\n", + " results[\"res\"].append(res)\n", + " results[\"B_fac\"].append(-B_fac)\n", + " # results[\"localres_std\"].append(localres_std)\n", + "\n", + "results_df = pd.DataFrame(results)\n", + " \n", + "# plot the results\n", + "fig, ax_res = plt.subplots(figsize=(3.5, 3.5))\n", + "ax_Bfac = ax_res.twinx()\n", + "sns.scatterplot(\n", + " data=results_df, x=\"K\", y=\"res\", ax=ax_res, label=\"Resolution\", s=100,\n", + ")\n", + "sns.scatterplot(\n", + " data=results_df, x=\"K\", y=\"B_fac\", ax=ax_Bfac, label=\"B-factor\", color=\"orange\", s=100,\n", + ")\n", + "ax_res.set_xlabel(\"First k timepoints\", fontsize=21)\n", + "ax_res.set_ylabel(\"Resolution ($\\AA$)\", fontsize=21)\n", + "ax_res.set_xscale(\"log\")\n", + "ax_res.grid(axis=\"both\", which=\"both\", alpha=0.5, linestyle=\"--\")\n", + "# ax_res.set_ylim((3.0, 4.1))\n", + "ax_res.invert_yaxis()\n", + "ax_Bfac.set_ylabel(\"B-factor ($\\AA^2$)\", fontsize=21, rotation=270, labelpad=30)\n", + "# ax_Bfac.set_ylim((40, 65))\n", + "# remove legends from the axis and add a single legend\n", + "ax_res.get_legend().remove()\n", + "ax_Bfac.get_legend().remove()\n", + "fig.legend(loc=\"upper left\", bbox_to_anchor=(1.15, 0.40), ncol=1, fontsize=18)\n", + "\n", + "# ax_Bfac.set_yticklabels(ax_Bfac.get_yticklabels(), fontsize=18)\n", + "# ax_res.set_yticklabels(ax_res.get_yticklabels(), fontsize=18)\n", + "ax_Bfac.tick_params(axis='y', labelsize=18)\n", + "ax_res.tick_params(axis='y', labelsize=18)\n", + "ax_res.set_xticks([250, 1000, 2000, 4000, 8000])\n", + "ax_res.set_xticklabels([\"250\", \"1k\", \"2k\", \"4k\", \"8k\"], fontsize=18)\n", + "\n", + "# fig.savefig(os.path.join(figures_dir, \"resolution_Bfactor_vs_K.pdf\"), bbox_inches=\"tight\")\n", + "print(f\"saved figure to: {os.path.join(figures_dir, 'resolution_Bfactor_vs_K.pdf')}\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## panel D\n", + "plot of the particle picking precision and recall throughout the processing of the heterogeneous data set" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "project_dir = \"/home/mjoosten1/projects/roodmus/data/DE-Shaw_covid_spike_protein/DESRES-Trajectory_sarscov2-11021571-all-glueCA\"\n", + "# project_dir = \"/mnt/ceph/JGreer/MapReconstruction/DESRES-Trajectory_sarscov2-11021571-all-glueCA\"\n", + "# project_dir = \"/home/dvi41342/MapReconstruction/DESRES-Trajectory_sarscov2-11021571-all-glueCA_subset\"\n", + "config_dir = os.path.join(project_dir, \"Micrographs\")\n", + "figures_dir = os.path.join(project_dir, \"figures\")\n", + "meta_files = [\n", + " os.path.join(project_dir, \"Extract\", \"job007\", \"particles.star\"),\n", + " os.path.join(project_dir, \"Class2D\", \"job008\", \"run_it200_data.star\"),\n", + " os.path.join(project_dir, \"Select\", \"job009\", \"particles.star\"),\n", + " os.path.join(project_dir, \"Select\", \"job036\", \"particles.star\"),\n", + " os.path.join(project_dir, \"Select\", \"job037\", \"particles.star\"),\n", + " os.path.join(project_dir, \"Select\", \"job038\", \"particles.star\"),\n", + " os.path.join(project_dir, \"Select\", \"job039\", \"particles.star\"),\n", + " # os.path.join(project_dir, \"Refine3D\", \"job014\", \"run_it015_data.star\"),\n", + " # os.path.join(project_dir, \"Refine3D\", \"job020\", \"run_it017_data.star\"),\n", + " # os.path.join(project_dir, \"Refine3D\", \"job008\", \"run_it011_data.star\"),\n", + "]\n", + "\n", + "jobtypes = {\n", + " os.path.join(project_dir, \"Extract\", \"job007\", \"particles.star\"): \"topaz picker\",\n", + " os.path.join(project_dir, \"Class2D\", \"job008\", \"run_it200_data.star\"): \"2D classification\",\n", + " os.path.join(project_dir, \"Select\", \"job009\", \"particles.star\"): \"2D class selection\",\n", + " os.path.join(project_dir, \"Select\", \"job036\", \"particles.star\"): \"3D class 0\",\n", + " os.path.join(project_dir, \"Select\", \"job037\", \"particles.star\"): \"3D class 1\",\n", + " os.path.join(project_dir, \"Select\", \"job038\", \"particles.star\"): \"3D class 2\",\n", + " os.path.join(project_dir, \"Select\", \"job039\", \"particles.star\"): \"3D class 3\",\n", + " os.path.join(project_dir, \"Refine3D\", \"job014\", \"run_it015_data.star\"): \"Homogeneous refinement 1\",\n", + " os.path.join(project_dir, \"Refine3D\", \"job020\", \"run_it017_data.star\"): \"Homogeneous refinement 2\",\n", + " os.path.join(project_dir, \"Refine3D\", \"job008\", \"run_it011_data.star\"): \"Homogeneous refinement 3\",\n", + "}\n", + "\n", + "particle_diameter = 100 # approximate particle diameter in Angstroms\n", + "ugraph_shape = (4000, 4000) # shape of the micrograph in pixels. Only needs to be given if the metadata file is a .star file\n", + "verbose = True # prints out progress statements\n", + "ignore_missing_files = True # if .mrc files are missing, the analysis will still be performed\n", + "enable_tqdm = True # enables tqdm progress bars\n", + "\n", + "for i, meta_file in enumerate(meta_files):\n", + " if i == 0:\n", + " analysis = load_data(meta_file, config_dir, particle_diameter, ugraph_shape=ugraph_shape, verbose=verbose, enable_tqdm=enable_tqdm, ignore_missing_files=ignore_missing_files) # creates the class\n", + " else:\n", + " analysis.add_data(meta_file, config_dir, verbose=verbose) # updates the class with the next metadata file\n", + " \n", + "df_picked = pd.DataFrame(analysis.results_picking)\n", + "df_truth = pd.DataFrame(analysis.results_truth)\n", + "# df_precision, df_picked = analysis.compute_precision(df_picked, df_truth, verbose=verbose)\n", + "# print(f\"mean precision: {df_precision['precision'].mean()}\")\n", + "# print(f\"mean recall: {df_precision['recall'].mean()}\")\n", + "\n", + "p_match, _, p_unmatched, t_unmatched, _ = analysis._match_particles(\n", + " meta_files,\n", + " df_picked,\n", + " df_truth,\n", + " verbose=False,\n", + " enable_tqdm=True,\n", + ")\n", + "df_precision = analysis.compute_1to1_match_precision(\n", + " p_match,\n", + " p_unmatched,\n", + " t_unmatched,\n", + " df_truth,\n", + " verbose=False,\n", + ")\n", + "df_precision.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "order = []\n", + "for r in meta_files:\n", + " if type(r) == str:\n", + " order.append(r)\n", + " else:\n", + " order.append(r[0]) \n", + "fig, ax = plot_precision(df_precision, jobtypes, order)\n", + "ax.set_title(\"\")\n", + "fig.set_size_inches((7, 7))\n", + "ax.set_xticklabels(ax.get_xticklabels(), fontsize=18)\n", + "ax.set_yticklabels(ax.get_yticklabels(), fontsize=18)\n", + "ax.set_ylabel(\"Precision\", fontsize=21)\n", + "\n", + "fig.savefig(os.path.join(figures_dir, \"precision.pdf\"), bbox_inches=\"tight\")\n", + "print(f\"saved figure to: {os.path.join(figures_dir, 'precision.pdf')}\")\n", + "\n", + "fig, ax = plot_recall(df_precision, jobtypes, order)\n", + "ax.set_ylim((0,1))\n", + "xticklabels = ax.get_xticklabels()\n", + "ax.set_xticklabels(xticklabels, fontsize=12)\n", + "ax.set_title(\"\")\n", + "fig.set_size_inches(7, 7)\n", + "ax.set_xticklabels(ax.get_xticklabels(), fontsize=18)\n", + "ax.set_yticklabels(ax.get_yticklabels(), fontsize=18)\n", + "ax.set_ylabel(\"Recall\", fontsize=21)\n", + "\n", + "fig.savefig(os.path.join(figures_dir, \"recall.pdf\"), bbox_inches=\"tight\")\n", + "print(f\"saved figure to: {os.path.join(figures_dir, 'recall.pdf')}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# print the precision and recall for the topaz picking job\n", + "df_precision[\"jobtype\"] = df_precision[\"metadata_filename\"].apply(lambda x: jobtypes[x])\n", + "df_picked[\"jobtype\"] = df_picked[\"metadata_filename\"].apply(lambda x: jobtypes[x])\n", + "p_match[\"jobtype\"] = p_match[\"metadata_filename\"].apply(lambda x: jobtypes[x])\n", + "print(f\"unique jobs: {df_precision['jobtype'].unique()}\")\n", + "\n", + "print(f\"mean precision for topaz picking: {df_precision[df_precision['jobtype'] == 'topaz picker']['precision'].mean()}\")\n", + "print(f\"mean recall for topaz picking: {df_precision[df_precision['jobtype'] == 'topaz picker']['recall'].mean()}\")\n", + "print(f\"min recall for topaz picking: {df_precision[df_precision['jobtype'] == 'topaz picker']['recall'].min()}\")\n", + "print(f\"max recall for topaz picking: {df_precision[df_precision['jobtype'] == 'topaz picker']['recall'].max()}\")\n", + "\n", + "print(f\"total number of particles for topaz picking job: {len(df_picked[df_picked['jobtype'] == 'topaz picker'])}\")\n", + "print(f\"total number of TP particles picked during topaz picking job: {len(p_match[p_match['jobtype'] == 'topaz picker'])}\")\n", + "print(f\"total number of GT particles in the dataset: {len(df_truth)}\")\n", + "print(f\"fraction of GT particles picked during topaz picking job: {len(p_match[p_match['jobtype'] == 'topaz picker'])/len(df_truth)}\")\n", + "print(f\"fraction of picked particles that is matched with a GT: {len(p_match[p_match['jobtype'] == 'topaz picker'])/len(df_picked[df_picked['jobtype'] == 'topaz picker'])}\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# in text\n", + "Compare the local resolution maps for the single-conformation dataset and the conformationally heterogeneous data by masking the LocRes maps and computing the variance in resolution over the map." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# single-conformation dataset\n", + "locres_sc_file = \"/home/mjoosten1/projects/roodmus/data/DE-Shaw_covid_spike_protein/20231116_DESRES-Trajectory_sarscov2-11021571-all-glueCA_single_conformation/LocalRes/job041/relion_locres.mrc\"\n", + "mask_sc_file = \"/home/mjoosten1/projects/roodmus/data/DE-Shaw_covid_spike_protein/20231116_DESRES-Trajectory_sarscov2-11021571-all-glueCA_single_conformation/MaskCreate/job039/mask.mrc\"\n", + "\n", + "# conformationally heterogeneous dataset\n", + "locres_ht_file = \"/home/mjoosten1/projects/roodmus/data/DE-Shaw_covid_spike_protein/20231116_DESRES-Trajectory_sarscov2-11021571-all-glueCA/LocalRes/job037/relion_locres.mrc\"\n", + "mask_ht_file = \"/home/mjoosten1/projects/roodmus/data/DE-Shaw_covid_spike_protein/20231116_DESRES-Trajectory_sarscov2-11021571-all-glueCA/MaskCreate/job015/mask.mrc\"\n", + "\n", + "# load the mrc files\n", + "locres_sc = mrcfile.open(locres_sc_file, mode='r')\n", + "mask_sc = mrcfile.open(mask_sc_file, mode='r')\n", + "locres_sc_data = locres_sc.data * mask_sc.data\n", + "locres_sc_data[locres_sc_data == 0] = np.nan\n", + "locres_ht = mrcfile.open(locres_ht_file, mode='r')\n", + "mask_ht = mrcfile.open(mask_ht_file, mode='r')\n", + "locres_ht_data = locres_ht.data * mask_ht.data\n", + "locres_ht_data[locres_ht_data == 0] = np.nan\n", + "\n", + "# plot the local resolution\n", + "fig, ax = plt.subplots(1, 2, figsize=(4.5*2, 4.5))\n", + "ax[0].hist(locres_sc_data.flatten(), bins=100, range=(0, 10), density=True, color=\"blue\", alpha=0.5);\n", + "ax[0].set_xlabel(\"Local resolution ($\\AA$)\", fontsize=21)\n", + "ax[0].set_ylabel(\"Density\", fontsize=21)\n", + "ax[0].set_title(\"Single conformation\", fontsize=21)\n", + "ax[0].tick_params(axis='both', which='major', labelsize=18)\n", + "ax[1].hist(locres_ht_data.flatten(), bins=100, range=(0, 10), density=True, color=\"orange\", alpha=0.5);\n", + "ax[1].set_xlabel(\"Local resolution ($\\AA$)\", fontsize=21)\n", + "ax[1].set_ylabel(\"Density\", fontsize=21)\n", + "ax[1].set_title(\"Conformationally heterogeneous\", fontsize=21)\n", + "ax[1].tick_params(axis='both', which='major', labelsize=18)\n", + "fig.tight_layout()\n", + "\n", + "# print the mean and variance of the local resolution\n", + "print(f\"mean single conformation: {np.nanmean(locres_sc_data)}\")\n", + "print(f\"mean conformationally heterogeneous: {np.nanmean(locres_ht_data)}\")\n", + "print(f\"variance single conformation: {np.nanvar(locres_sc_data)}\")\n", + "print(f\"variance conformationally heterogeneous: {np.nanvar(locres_ht_data)}\")\n", + "print(f\"min single conformation: {np.nanmin(locres_sc_data)}\")\n", + "print(f\"min conformationally heterogeneous: {np.nanmin(locres_ht_data)}\")\n", + "print(f\"max single conformation: {np.nanmax(locres_sc_data)}\")\n", + "print(f\"max conformationally heterogeneous: {np.nanmax(locres_ht_data)}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "roodmus", + "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": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/paper_fig_ipynbs/figure_3.ipynb b/paper_fig_ipynbs/figure_3.ipynb new file mode 100644 index 0000000..0da4e08 --- /dev/null +++ b/paper_fig_ipynbs/figure_3.ipynb @@ -0,0 +1,833 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# plotting functions of figure 3 in the manuscript\n", + "In this figure, we analyse the effect of enabling radiation damage and reducing the SNR by lowering the exposure. The plots shown are:\n", + "- The output of ctffind4 for a micrograph simulated with and without radiation damage\n", + "- The B-factor of a reconstruction made from a dataset simulated with radiation damage by ResLog analysis\n", + "- The resolution of reconstructions as a function of electron dose for a dataset simulated without radiation damage\n", + "- Example 2D classes of datasets simulated with varying electron dose\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# imports\n", + "import os\n", + "import re\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import seaborn as sns\n", + "import mrcfile\n", + "from gemmi import cif\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# functions\n", + "def get_resolution_from_logs(data, verbose=False):\n", + " results ={\n", + " \"Nparticles\": [],\n", + " \"res_unmasked\": [],\n", + " \"res\": [],\n", + " \"radiation\": [],\n", + " }\n", + " \n", + " # load the logfiles and retrieve the resolutions\n", + " for _, item in data.items():\n", + " if verbose:\n", + " print(f\"Processing {item['PostProcess']}\")\n", + " Refine3D_log = os.path.join(item[\"project_dir\"], \"Refine3D\", item[\"Refine3D\"], \"run.out\")\n", + " pattern = r\"Auto-refine: \\+ Final resolution \\(without masking\\) is: (\\d+(\\.\\d+)?)\"\n", + " with open(Refine3D_log, \"r\") as f:\n", + " for line in f.readlines():\n", + " if re.search(pattern, line):\n", + " res_unmasked = float(re.search(pattern, line).group(1))\n", + " break\n", + "\n", + " PostProcess_log = os.path.join(item[\"project_dir\"], \"PostProcess\", item[\"PostProcess\"], \"run.out\")\n", + " pattern = r\"\\+\\s*FINAL\\s+RESOLUTION:\\s+(\\d+(\\.\\d+)?)\"\n", + " with open(PostProcess_log, \"r\") as f:\n", + " for line in f.readlines():\n", + " if re.search(pattern, line):\n", + " res = float(re.search(pattern, line).group(1))\n", + " break\n", + " \n", + " if verbose:\n", + " print(f\"Nparticles: {item['Nparticles']}\")\n", + " print(f\"res_unmasked: {res_unmasked}\")\n", + " print(f\"res: {res}\")\n", + " results[\"Nparticles\"].append(item[\"Nparticles\"])\n", + " results[\"res_unmasked\"].append(res_unmasked)\n", + " results[\"res\"].append(res)\n", + " results[\"radiation\"].append(item[\"radiation\"])\n", + " results[\"d^-2\"] = [1/(res**2) for res in results[\"res\"]]\n", + "\n", + " return results\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## panel A\n", + "plotting the ctffind4 output for an example micrograph with and without radiation damage" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# load the input micrograph and plot a thumbnail\n", + "project_dir = \"/home/mjoosten1/projects/roodmus/data/DESRES-Trajectory_sarscov2-11021571-all-glueCA_fractionated\"\n", + "figures_dir = os.path.join(project_dir, \"figures\")\n", + "\n", + "frac_mrc_filename = os.path.join(project_dir, \"plot_ctf\", \"fractionated\", \"diagnostic_movie.mrc\")\n", + "still_mrc_filename = os.path.join(project_dir, \"plot_ctf\", \"still\", \"diagnostic_output.mrc\")\n", + "\n", + "frac_mrc = mrcfile.open(frac_mrc_filename, mode='r').data.squeeze()\n", + "factor = 8\n", + "frac_mrc = frac_mrc[frac_mrc.shape[0]//factor:-frac_mrc.shape[0]//factor, frac_mrc.shape[1]//factor:-frac_mrc.shape[1]//factor]\n", + "\n", + "fig, ax = plt.subplots()\n", + "ax.imshow(frac_mrc, cmap='gray', vmin=np.percentile(frac_mrc, 30), vmax=np.percentile(frac_mrc, 99.3))\n", + "ax.axis('off')\n", + "\n", + "fig.savefig(os.path.join(figures_dir, \"fractionated_diagnostic.pdf\"), bbox_inches='tight')\n", + "print(f\"saved figure to: {os.path.join(figures_dir, 'fractionated_diagnostic.pdf')}\")\n", + "\n", + "still_mrc = mrcfile.open(still_mrc_filename, mode='r').data.squeeze()\n", + "factor = 8\n", + "still_mrc = still_mrc[still_mrc.shape[0]//factor:-still_mrc.shape[0]//factor, still_mrc.shape[1]//factor:-still_mrc.shape[1]//factor]\n", + "\n", + "fig, ax = plt.subplots()\n", + "ax.imshow(still_mrc, cmap='gray', vmin=np.percentile(still_mrc, 30), vmax=np.percentile(still_mrc, 99.3))\n", + "ax.axis('off')\n", + "\n", + "# fig.savefig(os.path.join(figures_dir, \"still_diagnostic.pdf\"), bbox_inches='tight')\n", + "print(f\"saved figure to: {os.path.join(figures_dir, 'still_diagnostic.pdf')}\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## panel B\n", + "plot the FSC curve of both reconstructions without and with radiation damge" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# load the fsc data from the reconstruction with radiation damage\n", + "project_dir = \"/home/mjoosten1/projects/roodmus/data/DESRES-Trajectory_sarscov2-11021571-all-glueCA_fractionated\"\n", + "figures_dir = os.path.join(project_dir, \"figures\")\n", + "fsc_postprocess_filename = os.path.join(project_dir, \"PostProcess\", \"job050\", \"postprocess.star\")\n", + "resolution = 3.6\n", + "\n", + "fsc_cif = cif.read(fsc_postprocess_filename).find_block(\"fsc\")\n", + "rlnResolution = fsc_cif.find_loop(\"_rlnResolution\")\n", + "rlnFourierShellCorrelationCorrected = fsc_cif.find_loop(\"_rlnFourierShellCorrelationCorrected\")\n", + "rlnFourierShellCorrelationParticleMaskFraction = fsc_cif.find_loop(\"_rlnFourierShellCorrelationParticleMaskFraction\")\n", + "rlnFourierShellCorrelationUnmaskedMaps = fsc_cif.find_loop(\"_rlnFourierShellCorrelationUnmaskedMaps\")\n", + "rlnFourierShellCorrelationMaskedMaps = fsc_cif.find_loop(\"_rlnFourierShellCorrelationMaskedMaps\")\n", + "rlnCorrectedFourierShellCorrelationPhaseRandomizedMaskedMaps = fsc_cif.find_loop(\"_rlnCorrectedFourierShellCorrelationPhaseRandomizedMaskedMaps\")\n", + "\n", + "df = pd.DataFrame({\n", + " \"freq\": rlnResolution,\n", + " \"fsc\": rlnFourierShellCorrelationCorrected,\n", + " \"fsc_masked\": rlnFourierShellCorrelationMaskedMaps,\n", + " \"fsc_unmasked\": rlnFourierShellCorrelationUnmaskedMaps,\n", + " \"fsc_masked_random\": rlnCorrectedFourierShellCorrelationPhaseRandomizedMaskedMaps,\n", + " \"fsc_masked_fraction\": rlnFourierShellCorrelationParticleMaskFraction,\n", + " \"radiation\": True,\n", + " \"resolution\": resolution,\n", + "})\n", + "df_all = df\n", + "\n", + "# load the fsc data from the reconstruction with radiation damage\n", + "project_dir = \"/tudelft/mjoosten1/staff-umbrella/ajlab/MJ/projects/Roodmus/data/DE-Shaw_covid_spike_protein/20231116_DESRES-Trajectory_sarscov2-11021571-all-glueCA\"\n", + "figures_dir = os.path.join(project_dir, \"figures\")\n", + "job = \"job016\"\n", + "fsc_postprocess_filename = os.path.join(project_dir, \"PostProcess\", job, \"postprocess.star\")\n", + "figures_dir = os.path.join(project_dir, \"figures\")\n", + "resolution = 3.17\n", + "\n", + "fsc_cif = cif.read(fsc_postprocess_filename).find_block(\"fsc\")\n", + "rlnResolution = fsc_cif.find_loop(\"_rlnResolution\")\n", + "rlnFourierShellCorrelationCorrected = fsc_cif.find_loop(\"_rlnFourierShellCorrelationCorrected\")\n", + "rlnFourierShellCorrelationParticleMaskFraction = fsc_cif.find_loop(\"_rlnFourierShellCorrelationParticleMaskFraction\")\n", + "rlnFourierShellCorrelationUnmaskedMaps = fsc_cif.find_loop(\"_rlnFourierShellCorrelationUnmaskedMaps\")\n", + "rlnFourierShellCorrelationMaskedMaps = fsc_cif.find_loop(\"_rlnFourierShellCorrelationMaskedMaps\")\n", + "rlnCorrectedFourierShellCorrelationPhaseRandomizedMaskedMaps = fsc_cif.find_loop(\"_rlnCorrectedFourierShellCorrelationPhaseRandomizedMaskedMaps\")\n", + "\n", + "df = pd.DataFrame({\n", + " \"freq\": rlnResolution,\n", + " \"fsc\": rlnFourierShellCorrelationCorrected,\n", + " \"fsc_masked\": rlnFourierShellCorrelationMaskedMaps,\n", + " \"fsc_unmasked\": rlnFourierShellCorrelationUnmaskedMaps,\n", + " \"fsc_masked_random\": rlnCorrectedFourierShellCorrelationPhaseRandomizedMaskedMaps,\n", + " \"fsc_masked_fraction\": rlnFourierShellCorrelationParticleMaskFraction,\n", + " \"radiation\": False,\n", + " \"resolution\": resolution,\n", + "})\n", + "df_all = pd.concat([df_all, df])\n", + "\n", + "# convert all columns to float except the radiation column\n", + "df_all = df_all.apply(pd.to_numeric, errors=\"ignore\")\n", + "\n", + "fig, ax = plt.subplots(figsize=(3.5, 3.5))\n", + "sns.lineplot(x=\"freq\", y=\"fsc\", data=df_all, ax=ax, legend=True, linewidth=3, hue=\"radiation\", palette=\"tab10\")\n", + "# sns.lineplot(x=\"resolution\", y=\"fsc_masked\", data=df, ax=ax, legend=True, label=\"masked\", linewidth=3)\n", + "# sns.lineplot(x=\"resolution\", y=\"fsc_unmasked\", data=df, ax=ax, legend=True, label=\"unmasked\", linewidth=3)\n", + "ax.axhline(0.143, color=\"black\", linestyle=\"--\")\n", + "ax.axhline(0.5, color=\"black\", linestyle=\"-.\")\n", + "ax.axvline(1/3.17, color=\"black\", linestyle=\"dotted\")\n", + "ax.axvline(1/3.6, color=\"red\", linestyle=\"dotted\")\n", + "ax.set_xlabel(\"Spatial Frequency ($\\AA^{-1}$)\", fontsize=18)\n", + "ax.set_ylabel(\"FSC\", fontsize=18)\n", + "ax.tick_params(axis='both', which='major', labelsize=16)\n", + "\n", + "# handles, _ = ax.get_legend_handles_labels()\n", + "# labels = [\"-RD\", \"+RD\"]\n", + "# ax.legend(handles, labels, fontsize=18, loc=\"upper right\")\n", + "ax.legend().remove()\n", + "\n", + "# add in text for the resolution\n", + "# ax.text(1/3.17, 1.05, \"3.17 $\\AA$\", fontsize=14, rotation=45, va=\"bottom\")\n", + "# ax.text(1/3.6*0.9, 1.05, \"3.6 $\\AA$\", fontsize=14, rotation=45, va=\"bottom\")\n", + "\n", + "# fig.savefig(os.path.join(figures_dir, f\"FSC_rad_norad.pdf\"), bbox_inches=\"tight\")\n", + "print(f\"saved figure to: {os.path.join(figures_dir, f'FSC_rad_norad.pdf')}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## panel C\n", + "plot the ResLog analysis and estimate the B-factor of the dataset simulated with radiation damage" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# plot the FSC curve and the B-factor of the reconstructed map\n", + "project_dir = \"/home/mjoosten1/projects/roodmus/data/DESRES-Trajectory_sarscov2-11021571-all-glueCA_fractionated\"\n", + "project_dir_norad = \"/tudelft/mjoosten1/staff-umbrella/ajlab/MJ/projects/Roodmus/data/DE-Shaw_covid_spike_protein/20231116_DESRES-Trajectory_sarscov2-11021571-all-glueCA\"\n", + "figures_dir = os.path.join(project_dir, \"figures\")\n", + "data = {\n", + " 0: {\n", + " \"Nparticles\": 1000,\n", + " \"Select\": \"job058\",\n", + " \"Refine3D\": \"job059\",\n", + " \"MaskCreate\": \"job053\",\n", + " \"PostProcess\": \"job060\",\n", + " \"radiation\": True,\n", + " \"project_dir\": project_dir,\n", + " },\n", + " 1: {\n", + " \"Nparticles\": 2000,\n", + " \"Select\": \"job055\",\n", + " \"Refine3D\": \"job056\",\n", + " \"MaskCreate\": \"job053\",\n", + " \"PostProcess\": \"job057\",\n", + " \"radiation\": True,\n", + " \"project_dir\": project_dir,\n", + " },\n", + " 2: {\n", + " \"Nparticles\": 4000,\n", + " \"Select\": \"job051\",\n", + " \"Refine3D\": \"job052\",\n", + " \"MaskCreate\": \"job053\",\n", + " \"PostProcess\": \"job054\",\n", + " \"radiation\": True,\n", + " \"project_dir\": project_dir,\n", + " },\n", + " 3: {\n", + " \"Nparticles\": 8000,\n", + " \"Select\": \"job045\",\n", + " \"Refine3D\": \"job048\",\n", + " \"MaskCreate\": \"job049\",\n", + " \"PostProcess\": \"job050\",\n", + " \"radiation\": True,\n", + " \"project_dir\": project_dir,\n", + " },\n", + " 4: {\n", + " \"Nparticles\": 1000,\n", + " \"Select\": \"job025\",\n", + " \"Refine3D\": \"job026\",\n", + " \"MaskCreate\": \"job027\",\n", + " \"PostProcess\": \"job028\",\n", + " \"radiation\": False,\n", + " \"project_dir\": project_dir_norad,\n", + " },\n", + " 5: {\n", + " \"Nparticles\": 2000,\n", + " \"Select\": \"job021\",\n", + " \"Refine3D\": \"job022\",\n", + " \"MaskCreate\": \"job023\",\n", + " \"PostProcess\": \"job024\",\n", + " \"radiation\": False,\n", + " \"project_dir\": project_dir_norad,\n", + " },\n", + " 6: {\n", + " \"Nparticles\": 4000,\n", + " \"Select\": \"job017\",\n", + " \"Refine3D\": \"job018\",\n", + " \"MaskCreate\": \"job019\",\n", + " \"PostProcess\": \"job020\",\n", + " \"radiation\": False,\n", + " \"project_dir\": project_dir_norad,\n", + " },\n", + " 7: {\n", + " \"Nparticles\": 8000,\n", + " \"Select\": \"job006\",\n", + " \"Refine3D\": \"job014\",\n", + " \"MaskCreate\": \"job015\",\n", + " \"PostProcess\": \"job016\",\n", + " \"radiation\": False,\n", + " \"project_dir\": project_dir_norad,\n", + " },\n", + "}\n", + "\n", + "# load the logfiles and retrieve the resolutions\n", + "results = get_resolution_from_logs(data, False)\n", + "results_df = pd.DataFrame(results)\n", + "# add column for 1/res^2\n", + "results_df[\"1/res2\"] = 1/results_df[\"res\"]**2\n", + "results_df[\"1/res_unmasked2\"] = 1/results_df[\"res_unmasked\"]**2\n", + " \n", + "# plot the results\n", + "fig, ax = plt.subplots(figsize=(3.5, 3.5))\n", + "sns.scatterplot(\n", + " data=results_df, x=\"Nparticles\", y=\"res\", ax=ax, s=100, hue=\"radiation\", palette=\"tab10\", legend=True\n", + ")\n", + "p = np.polyfit(np.log10(results_df[results_df[\"radiation\"]==False][\"Nparticles\"]), results_df[results_df[\"radiation\"]==False][\"res\"], 1)\n", + "y = np.polyval(p, np.log10(results_df[results_df[\"radiation\"]==False][\"Nparticles\"]))\n", + "ax.plot(results_df[results_df[\"radiation\"]==False][\"Nparticles\"], y, color=\"black\", linestyle=\"--\", linewidth=3)\n", + "# compute B-factor as 2X slope\n", + "p = np.polyfit(np.log10(results_df[results_df[\"radiation\"]==False][\"Nparticles\"]), results_df[results_df[\"radiation\"]==False][\"1/res2\"], 1)\n", + "B_factor_norad = 2/p[0]\n", + "print(f\"B-factor without radiation damage: {B_factor_norad}\")\n", + "\n", + "p = np.polyfit(np.log10(results_df[results_df[\"radiation\"]==True][\"Nparticles\"]), results_df[results_df[\"radiation\"]==True][\"res\"], 1)\n", + "y = np.polyval(p, np.log10(results_df[results_df[\"radiation\"]==True][\"Nparticles\"]))\n", + "ax.plot(results_df[results_df[\"radiation\"]==True][\"Nparticles\"], y, color=\"black\", linestyle=\"--\", linewidth=3)\n", + "# compute B-factor as 2X slope\n", + "p = np.polyfit(np.log10(results_df[results_df[\"radiation\"]==True][\"Nparticles\"]), results_df[results_df[\"radiation\"]==True][\"1/res2\"], 1)\n", + "B_factor_rad = 2/p[0]\n", + "print(f\"B-factor with radiation damage: {B_factor_rad}\")\n", + "\n", + "ax.set_xlabel(\"Number of particles\", fontsize=18)\n", + "ax.set_ylabel(\"Resolution ($\\AA$)\", fontsize=18)\n", + "ax.grid(axis=\"both\", which=\"both\", alpha=0.5, linestyle=\"--\")\n", + "# invert x-axis\n", + "ax.set_ylim((ax.get_ylim()[0], 5.2))\n", + "ax.invert_yaxis()\n", + "# set the legend fontsize to 14 and move it to the bottom right\n", + "handles, _ = ax.get_legend_handles_labels()\n", + "labels = [\"-RD\", \"+RD\"]\n", + "ax.legend(handles=handles, labels=labels, fontsize=18, loc=\"lower right\")\n", + "\n", + "\n", + "ax.set_xscale(\"log\")\n", + "ax.set_xticks([1000, 2000, 3000, 4000, 6000, 8000])\n", + "ax.set_xticklabels([\"1k\", \"2k\", \"\", \"4k\", \"\", \"8k\"], fontsize=18)\n", + "# change y-ticklabel fontsize to 18\n", + "yticklabels = [f\"{i:.1f}\" for i in ax.get_yticks()]\n", + "ax.set_yticklabels(yticklabels, fontsize=14)\n", + "\n", + "ax_d2 = ax.twinx()\n", + "# set the yticks for the secondary axis to 1/res^2\n", + "yticks = [1/(i**2) for i in ax.get_yticks()]\n", + "yticklabels = [f\"{i:.2f}\" for i in yticks]\n", + "ax_d2.set_yticks(ax.get_yticks())\n", + "ax_d2.set_yticklabels(yticklabels, fontsize=14)\n", + "ax_d2.set_ylim(ax.get_ylim())\n", + "ax_d2.set_ylabel(\"1/d$^2$ ($\\AA^{-2}$)\", fontsize=18, rotation=270, labelpad=20)\n", + "\n", + "# fig.savefig(os.path.join(figures_dir, \"ResLog.pdf\"), bbox_inches=\"tight\")\n", + "print(f\"saved figure to: {os.path.join(figures_dir, 'ResLog.pdf')}\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## panel D\n", + "2D classes of datasets simulated with varying electron dose. Each class is ranked by RELION's automatic class ranker and the 4 highest scoring classes are shown with their resolution and the fraction of particles in the class." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# plotting 2D classes\n", + "project_dir = \"/home/mjoosten1/projects/roodmus/data/20231017_EMPIAR_SNR_comparison\"\n", + "figures_dir = os.path.join(project_dir, \"figures\")\n", + "\n", + "data = {\n", + " 0: {\n", + " \"exposure\": 45,\n", + " \"LoG\": \"job004\",\n", + " \"Class2D\": \"job005\",\n", + " \"topaz\": \"job010\",\n", + " \"homogeneous\": \"job016\",\n", + " \"Selection\": \"job100\",\n", + " },\n", + " 1: {\n", + " \"exposure\": 35,\n", + " \"LoG\": \"job037\",\n", + " \"Class2D\": \"job038\",\n", + " \"topaz\": \"job042\",\n", + " \"homogeneous\": \"job048\",\n", + " \"Selection\": \"job102\",\n", + " },\n", + " 2: {\n", + " \"exposure\": 25,\n", + " \"LoG\": \"job054\",\n", + " \"Class2D\": \"job055\",\n", + " \"topaz\": \"job059\",\n", + " \"homogeneous\": \"job065\",\n", + " \"Selection\": \"job103\",\n", + " },\n", + " 3: {\n", + " \"exposure\": 15,\n", + " \"LoG\": \"job071\",\n", + " \"Class2D\": \"job072\",\n", + " \"topaz\": \"job076\",\n", + " \"homogeneous\": \"job082\",\n", + " \"Selection\": \"job106\",\n", + " },\n", + " 4: {\n", + " \"exposure\": 5,\n", + " \"LoG\": \"job088\",\n", + " \"Class2D\": \"job089\",\n", + " \"topaz\": None,\n", + " \"homogeneous\": None,\n", + " \"Selection\": \"job107\",\n", + " },\n", + " 5: {\n", + " \"exposure\": 10,\n", + " \"LoG\": \"job093\",\n", + " \"Class2D\": \"job094\",\n", + " \"topaz\": None,\n", + " \"homogeneous\": None,\n", + " \"Selection\": \"job108\",\n", + " },\n", + " 6: {\n", + " \"exposure\": 12,\n", + " \"LoG\": \"job098\",\n", + " \"Class2D\": \"job099\",\n", + " \"topaz\": None,\n", + " \"homogeneous\": None,\n", + " \"Selection\": \"job109\",\n", + " },\n", + " 7: {\n", + " \"exposure\": 8,\n", + " \"LoG\": \"job114\",\n", + " \"Class2D\": \"job115\",\n", + " \"topaz\": None,\n", + " \"homogeneous\": None,\n", + " \"Selection\": \"job116\",\n", + " }\n", + "}\n", + "N_classes = 4 # number of classes to select\n", + "\n", + "for key, value in data.items():\n", + " print(f\"exposure: {value['exposure']}\")\n", + " rank_starfile = os.path.join(project_dir, \"Select\", value[\"Selection\"], \"rank_model.star\")\n", + " rank_cif = cif.read(rank_starfile)\n", + " data_model_classes = rank_cif.find_block(\"model_classes\")\n", + " rlnClassScore = np.array(data_model_classes.find_loop(\"_rlnClassScore\"), dtype=float)\n", + " rlnClassDistribution = np.array(data_model_classes.find_loop(\"_rlnClassDistribution\"), dtype=float)\n", + " rlnEstimatedResolution = np.array(data_model_classes.find_loop(\"_rlnEstimatedResolution\"), dtype=float)\n", + "\n", + " # get the class averages\n", + " class_average_file = os.path.join(project_dir, \"Class2D\", value[\"Class2D\"], \"run_it025_classes.mrcs\")\n", + " class_average = mrcfile.open(class_average_file, mode=\"r\")\n", + "\n", + " # select the N best classee by class score and plot them with their class score and little space between them\n", + " idx_best_classes = np.argsort(rlnClassScore)[::-1][:N_classes]\n", + " fig, axes = plt.subplots(1, N_classes, figsize=(N_classes * 3, 3))\n", + " for i, idx in enumerate(idx_best_classes):\n", + " axes[i].imshow(class_average.data[idx, :, :], cmap=\"gray\", origin=\"lower\")\n", + " axes[i].text(0.2, 0.9, f\"{rlnClassScore[idx]:.2f}\", horizontalalignment=\"center\", verticalalignment=\"center\", transform=axes[i].transAxes, color=\"yellow\", fontsize=28)\n", + " axes[i].text(0.7, 0.1, f\"{rlnClassDistribution[idx]*100:.2f}%\", horizontalalignment=\"center\", verticalalignment=\"center\", transform=axes[i].transAxes, color=\"white\", fontsize=28)\n", + " # axes[i].text(0.2, 0.1, f\"{rlnEstimatedResolution[idx]:.2f} $\\AA$\", horizontalalignment=\"center\", verticalalignment=\"center\", transform=axes[i].transAxes, color=\"white\", fontsize=16)\n", + " axes[i].axis(\"off\")\n", + "\n", + " # reduce the space between subplots\n", + " fig.subplots_adjust(wspace=0.01)\n", + " plt.show()\n", + "\n", + " outfilename = os.path.join(figures_dir, f\"best_classes_{value['exposure']}.pdf\")\n", + " # fig.savefig(outfilename), bbox_inches=\"tight\")\n", + " print(f\"saved figure to: {outfilename}\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## panel E\n", + "plotting the number of particles selected by the class ranker for a threshold value of 0.5 and 0.3" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# plotting 2D classes\n", + "project_dir = \"/home/mjoosten1/projects/roodmus/data/20231017_EMPIAR_SNR_comparison\"\n", + "figures_dir = os.path.join(project_dir, \"figures\")\n", + "\n", + "data = {\n", + " 0: {\n", + " \"exposure\": 45,\n", + " \"LoG\": \"job004\",\n", + " \"Class2D\": \"job005\",\n", + " \"topaz\": \"job010\",\n", + " \"homogeneous\": \"job016\",\n", + " \"Selection\": \"job100\",\n", + " },\n", + " 1: {\n", + " \"exposure\": 35,\n", + " \"LoG\": \"job037\",\n", + " \"Class2D\": \"job038\",\n", + " \"topaz\": \"job042\",\n", + " \"homogeneous\": \"job048\",\n", + " \"Selection\": \"job102\",\n", + " },\n", + " 2: {\n", + " \"exposure\": 25,\n", + " \"LoG\": \"job054\",\n", + " \"Class2D\": \"job055\",\n", + " \"topaz\": \"job059\",\n", + " \"homogeneous\": \"job065\",\n", + " \"Selection\": \"job103\",\n", + " },\n", + " 3: {\n", + " \"exposure\": 15,\n", + " \"LoG\": \"job071\",\n", + " \"Class2D\": \"job072\",\n", + " \"topaz\": \"job076\",\n", + " \"homogeneous\": \"job082\",\n", + " \"Selection\": \"job106\",\n", + " },\n", + " 4: {\n", + " \"exposure\": 5,\n", + " \"LoG\": \"job088\",\n", + " \"Class2D\": \"job089\",\n", + " \"topaz\": None,\n", + " \"homogeneous\": None,\n", + " \"Selection\": \"job107\",\n", + " },\n", + " 5: {\n", + " \"exposure\": 10,\n", + " \"LoG\": \"job093\",\n", + " \"Class2D\": \"job094\",\n", + " \"topaz\": None,\n", + " \"homogeneous\": None,\n", + " \"Selection\": \"job108\",\n", + " },\n", + " 6: {\n", + " \"exposure\": 12,\n", + " \"LoG\": \"job098\",\n", + " \"Class2D\": \"job099\",\n", + " \"topaz\": None,\n", + " \"homogeneous\": None,\n", + " \"Selection\": \"job109\",\n", + " },\n", + " 7: {\n", + " \"exposure\": 8,\n", + " \"LoG\": \"job114\",\n", + " \"Class2D\": \"job115\",\n", + " \"topaz\": None,\n", + " \"homogeneous\": None,\n", + " \"Selection\": \"job116\",\n", + " }\n", + "}\n", + "\n", + "for key, value in data.items():\n", + " print(f\"exposure: {value['exposure']}\")\n", + " print(f\"selection: {value['Selection']}\")\n", + " \n", + " result = {\n", + " \"exposure\": [],\n", + " \"class\": [],\n", + " \"score\": [],\n", + " \"n_particles\": [],\n", + " }\n", + "\n", + " # read the rank_model.star file to get the score per class\n", + " rank_starfile = os.path.join(project_dir, \"Select\", value[\"Selection\"], \"rank_model.star\")\n", + " rank_cif = cif.read(rank_starfile)\n", + " data_model_classes = rank_cif.find_block(\"model_classes\")\n", + " rlnClassScore = np.array(data_model_classes.find_loop(\"_rlnClassScore\"), dtype=float)\n", + " rlnReferenceImage = data_model_classes.find_loop(\"_rlnReferenceImage\")\n", + "\n", + " result[\"class\"] = [int(i.split(\"@\")[0]) for i in rlnReferenceImage]\n", + " result[\"score\"] = rlnClassScore\n", + "\n", + " # read the rank_data.star file to obtain the number of particles per class\n", + " data_starfile = os.path.join(project_dir, \"Select\", value[\"Selection\"], \"rank_data.star\")\n", + " data_cif = cif.read(data_starfile)\n", + " data_classes = data_cif.find_block(\"particles\")\n", + " rlnClassNumber = np.array(data_classes.find_loop(\"_rlnClassNumber\"), dtype=int)\n", + "\n", + " result[\"n_particles\"] = [np.sum(rlnClassNumber == i) for i in range(1, len(result[\"class\"])+1)]\n", + " result[\"exposure\"] = [value[\"exposure\"]]*len(result[\"class\"])\n", + "\n", + " if key == 0:\n", + " df_total = pd.DataFrame(result)\n", + " else:\n", + " df = pd.DataFrame(result)\n", + " print(f\"number of unique classes: {df['class'].nunique()}\")\n", + " print(f\"total number of particles: {df['n_particles'].sum()}\")\n", + " df_total = pd.concat([df_total, df])\n", + "\n", + "df_total[\"class\"] = df_total[\"class\"].astype(int)\n", + "df_total.head()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# plot the number of selected classes and particles with a threshold of 0.3 and 0.5\n", + "\n", + "# create an aggregate dataframe with the number of selected particles and classes for a score threhsold of 0.3\n", + "fig, ax = plt.subplots(figsize=(3.5, 3.5))\n", + "ax_cl = ax.twinx()\n", + "colours = [\"black\", \"red\"]\n", + "labels = [\"0.3 ptcl\", \"0.5 ptcl\", \"0.3 class\", \"0.5 class\"]\n", + "for i, threshold in enumerate([0.3, 0.5]):\n", + " df_plot = df_total[df_total[\"score\"] > threshold].groupby(\"exposure\").agg({\"n_particles\": \"sum\", \"class\": \"nunique\"}).reset_index()\n", + " sns.lineplot(x=\"exposure\", y=\"n_particles\", data=df_plot, ax=ax, marker=\"s\", color=colours[i], legend=False, label=labels[i])\n", + " sns.lineplot(x=\"exposure\", y=\"class\", data=df_plot, ax=ax_cl, marker=\"o\", color=colours[i], legend=False, linestyle=\"--\", label=labels[i+2])\n", + "\n", + "# create one legend with 4 rows: threshold 0.3, class, threshold 0.5, class to the right of the figure\n", + "handles, _ = ax.get_legend_handles_labels()\n", + "handles_cl, _ = ax_cl.get_legend_handles_labels()\n", + "handles.extend(handles_cl)\n", + "fig.legend(handles, labels, loc=\"center\", fontsize=14, ncol=2, bbox_to_anchor=(0.5, -0.17))\n", + "ax.set_ylabel(\"#Particles\", fontsize=16)\n", + "ax_cl.set_ylabel(\"#Classes\", fontsize=16, rotation=270, labelpad=20)\n", + "ax.set_xlabel(\"Exposure ($e^-$/Ų)\", fontsize=16)\n", + "ax.tick_params(labelsize=14)\n", + "ax.set_xticks([5, 15, 25, 35, 45])\n", + "ax_cl.tick_params(labelsize=14)\n", + "ax_cl.set_yticks([5, 10, 15, 20])\n", + "ax.set_yticklabels([f\"{i:.0f}k\" for i in ax.get_yticks()/1000])\n", + "\n", + "fig.savefig(os.path.join(figures_dir, \"selected_classes_particles.pdf\"), bbox_inches=\"tight\")\n", + "print(f\"saved image to {os.path.join(figures_dir, 'selected_classes_particles.pdf')}\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## panel F\n", + "plot the resolution of the reconstruction as a function of electron dose for a dataset simulated without radiation damage" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# make a plot of the number of particles selected by automatic class selection as function of the exposure\n", + "project_dir = \"/home/mjoosten1/projects/roodmus/data/20240207_low_exposure_data_reconstruction\"\n", + "figures_dir = os.path.join(project_dir, \"figures\")\n", + "\n", + "data = {\n", + " 0: {\n", + " \"exposure\": 45,\n", + " \"Nparticles\": 30000,\n", + " \"postprocess\": \"job008\"\n", + " },\n", + " 1: {\n", + " \"exposure\": 35,\n", + " \"Nparticles\": 30000,\n", + " \"postprocess\": \"job016\",\n", + " },\n", + " 2: {\n", + " \"exposure\": 25,\n", + " \"Nparticles\": 30000,\n", + " \"postprocess\": \"job023\",\n", + " },\n", + " 3: {\n", + " \"exposure\": 15,\n", + " \"Nparticles\": 30000,\n", + " \"postprocess\": \"job030\",\n", + " },\n", + " 4: {\n", + " \"exposure\": 12,\n", + " \"Nparticles\": 30000,\n", + " \"postprocess\": \"job037\",\n", + " },\n", + " 5: {\n", + " \"exposure\": 10,\n", + " \"Nparticles\": 30000,\n", + " \"postprocess\": \"job045\",\n", + " },\n", + " 6: {\n", + " \"exposure\": 8,\n", + " \"Nparticles\": 30000,\n", + " \"postprocess\": \"job055\",\n", + " },\n", + " 7: {\n", + " \"exposure\": 5,\n", + " \"Nparticles\": 30000,\n", + " \"postprocess\": \"job062\",\n", + " },\n", + " 8: {\n", + " \"exposure\": 45,\n", + " \"Nparticles\": 8000,\n", + " \"postprocess\": \"job066\",\n", + " },\n", + " 9: {\n", + " \"exposure\": 35,\n", + " \"Nparticles\": 8000,\n", + " \"postprocess\": \"job070\",\n", + " },\n", + " 10: {\n", + " \"exposure\": 25,\n", + " \"Nparticles\": 8000,\n", + " \"postprocess\": \"job074\",\n", + " },\n", + " 11: {\n", + " \"exposure\": 15,\n", + " \"Nparticles\": 8000,\n", + " \"postprocess\": \"job079\",\n", + " },\n", + " 12: {\n", + " \"exposure\": 12,\n", + " \"Nparticles\": 8000,\n", + " \"postprocess\": \"job084\",\n", + " },\n", + " 13: {\n", + " \"exposure\": 10,\n", + " \"Nparticles\": 8000,\n", + " \"postprocess\": \"job088\",\n", + " },\n", + " 14: {\n", + " \"exposure\": 8,\n", + " \"Nparticles\": 8000,\n", + " \"postprocess\": \"job098\",\n", + " },\n", + "}\n", + "\n", + "result = {\n", + " \"exposure\": [],\n", + " \"postprocess\": [],\n", + " \"Nparticles\": [],\n", + "}\n", + "\n", + "for key, item in data.items():\n", + " result[\"exposure\"].append(item[\"exposure\"])\n", + " print(item[\"exposure\"])\n", + "\n", + " if item[\"postprocess\"]:\n", + " postprocess_star = os.path.join(project_dir, \"PostProcess\", item[\"postprocess\"], \"postprocess.star\")\n", + " postprocess_cif = cif.read_file(postprocess_star)\n", + " general = postprocess_cif.find_block(\"general\")\n", + " rlnFinalResolution = general.find_value(\"_rlnFinalResolution\")\n", + " result[\"postprocess\"].append(float(rlnFinalResolution))\n", + " else:\n", + " result[\"postprocess\"].append(np.nan)\n", + "\n", + " if item[\"Nparticles\"]:\n", + " result[\"Nparticles\"].append(item[\"Nparticles\"])\n", + " else:\n", + " result[\"Nparticles\"].append(np.nan)\n", + "\n", + "\n", + "df = pd.DataFrame(result)\n", + "# df.tail()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(3.5, 3.5))\n", + "# create a pallete for the 2 lines to be blue and red\n", + "sns.lineplot(x=\"exposure\", y=\"postprocess\", data=df, ax=ax, marker=\"s\", color=\"k\", hue=\"Nparticles\", palette=\"coolwarm\", legend=\"full\")\n", + "\n", + "# invert the y-axis\n", + "ax.invert_yaxis()\n", + "ax.set_ylabel(\"Resolution (Å)\", fontsize=16)\n", + "ax.tick_params(labelsize=14)\n", + "ax.set_xticks([5, 15, 25, 35, 45])\n", + "ax.set_xlabel(\"Fluence ($e^-$/Ų)\", fontsize=16)\n", + "\n", + "# change the legend title to #Particles\n", + "ax.legend(title=\"#Particles\", fontsize=14, title_fontsize=14)\n", + "\n", + "\n", + "fig.savefig(os.path.join(figures_dir, \"resolution_vs_exposure.pdf\"), bbox_inches=\"tight\")\n", + "print(os.path.join(figures_dir, \"resolution_vs_exposure.pdf\"))\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "roodmus", + "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": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/paper_fig_ipynbs/figure_4.ipynb b/paper_fig_ipynbs/figure_4.ipynb new file mode 100644 index 0000000..1167330 --- /dev/null +++ b/paper_fig_ipynbs/figure_4.ipynb @@ -0,0 +1,631 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# plotting functions of figure 4 in the manuscript\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# imports\n", + "import os\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import mrcfile\n", + "\n", + "from emmer.pdb.convert.convert_pdb_to_map import convert_pdb_to_map\n", + "from emmer.ndimage.filter.low_pass_filter import low_pass_filter\n", + "from emmer.ndimage.filter.smoothen_mask import smoothen_mask\n", + "\n", + "# roodmus\n", + "from roodmus.analysis.utils import load_data\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# functions\n", + "def get_precision_per_class(df_truth, df_picked, classification_job):\n", + " result = {\n", + " \"class\": [],\n", + " \"precision\": [],\n", + " \"number_of_particles\": [],\n", + " \"picked_fraction\": [],\n", + " \"fraction_of_true_positives\": [],\n", + " }\n", + "\n", + "\n", + " num_gt_particles = len(df_truth)\n", + " df_picked_grouped = df_picked.groupby(\"jobtype\").get_group(classification_job).groupby(\"class2D\")\n", + " num_gt_particles_in_picked = len(df_picked.groupby(\"jobtype\").get_group(classification_job).query(\"TP == True\"))\n", + " print(f\"number of particles in ground truth: {num_gt_particles}\")\n", + " print(f\"number of particles in picked: {num_gt_particles_in_picked}\")\n", + " num_picked_particles = len(df_picked.groupby(\"jobtype\").get_group(classification_job))\n", + " for class2d in df_picked_grouped.groups:\n", + " particles_in_class = len(df_picked_grouped.get_group(class2d))\n", + " print(f\"number of picked particles in class {class2d}: {particles_in_class}. Fraction of picked particles: {np.round(particles_in_class / num_picked_particles, 2)}\")\n", + " TP_in_class = len(df_picked_grouped.get_group(class2d).query(\"TP == True\"))\n", + " precision_in_class = TP_in_class / particles_in_class\n", + " print(f\"precision in class {class2d}: {np.round(precision_in_class, 2)}\")\n", + " print(f\"TP fraction in class: {np.round(TP_in_class / num_gt_particles_in_picked, 2)}\")\n", + " print(f\"test: {np.round((TP_in_class/num_gt_particles_in_picked) / (particles_in_class/num_picked_particles), 2)}\")\n", + "\n", + " result[\"class\"].append(int(class2d))\n", + " result[\"precision\"].append(precision_in_class)\n", + " result[\"number_of_particles\"].append(particles_in_class)\n", + " result[\"picked_fraction\"].append(particles_in_class / num_picked_particles)\n", + " result[\"fraction_of_true_positives\"].append(TP_in_class / num_gt_particles_in_picked)\n", + "\n", + " df_result = pd.DataFrame(result)\n", + " return df_result\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "### data loading\n", + "# project_dir = \"/home/mjoosten1/projects/roodmus/data/DE-Shaw_covid_spike_protein/DESRES-Trajectory_sarscov2-11021566-11021571-mixed\"\n", + "project_dir = \"/tudelft/mjoosten1/staff-umbrella/ajlab/MJ/projects/Roodmus/data/DE-Shaw_covid_spike_protein/DESRES-Trajectory_sarscov2-11021566-11021571-mixed\"\n", + "config_dir = os.path.join(project_dir, \"Micrographs\")\n", + "meta_files = [\n", + " # os.path.join(project_dir, \"Extract\", \"job004\", \"particles.star\"),\n", + " # os.path.join(project_dir, \"Extract\", \"job009\", \"particles.star\"),\n", + " # os.path.join(project_dir, \"Select\", \"job012\", \"particles.star\"),\n", + " os.path.join(project_dir, \"Class3D\", \"job014\", \"run_it025_data.star\"),\n", + " os.path.join(project_dir, \"Class3D\", \"job037\", \"run_it025_data.star\"),\n", + " os.path.join(project_dir, \"Class3D\", \"job038\", \"run_it025_data.star\"),\n", + "]\n", + "\n", + "jobtypes = {\n", + " os.path.join(project_dir, \"Extract\", \"job004\", \"particles.star\"): \"LoG\",\n", + " os.path.join(project_dir, \"Extract\", \"job009\", \"particles.star\"): \"topaz\",\n", + " os.path.join(project_dir, \"Select\", \"job012\", \"particles.star\"): \"class selection\",\n", + " os.path.join(project_dir, \"Class3D\", \"job014\", \"run_it025_data.star\"): \"3_classes\",\n", + " os.path.join(project_dir, \"Class3D\", \"job037\", \"run_it025_data.star\"): \"2_classes\",\n", + " os.path.join(project_dir, \"Class3D\", \"job038\", \"run_it025_data.star\"): \"10_classes\",\n", + "}\n", + "\n", + "particle_diameter = 100 # approximate particle diameter in Angstroms\n", + "ugraph_shape = (4000, 4000) # shape of the micrograph in pixels. Only needs to be given if the metadata file is a .star file\n", + "verbose = True # prints out progress statements\n", + "ignore_missing_files = True # if .mrc files are missing, the analysis will still be performed\n", + "enable_tqdm = True # enables tqdm progress bars\n", + "\n", + "for i, meta_file in enumerate(meta_files):\n", + " if i == 0:\n", + " analysis = load_data(meta_file, config_dir, particle_diameter, ugraph_shape=ugraph_shape, verbose=verbose, enable_tqdm=enable_tqdm, ignore_missing_files=ignore_missing_files) # creates the class\n", + " else:\n", + " analysis.add_data(meta_file, config_dir, verbose=verbose) # updates the class with the next metadata file\n", + "\n", + "df_picked = pd.DataFrame(analysis.results_picking)\n", + "df_truth = pd.DataFrame(analysis.results_truth)\n", + "# df_precision, df_picked = analysis.compute_precision(df_picked, df_truth, verbose=verbose)\n", + "df_picked[\"jobtype\"] = df_picked[\"metadata_filename\"].map(jobtypes)\n", + "df_picked_grouped = df_picked.groupby(\"jobtype\")\n", + "for group in df_picked_grouped.groups:\n", + " print(f\"jobtype: {group}, number of particles: {len(df_picked_grouped.get_group(group))}\")\n", + "\n", + "p_match, _, p_unmatched, t_unmatched, closest_truth_index = analysis._match_particles(\n", + " meta_files,\n", + " df_picked,\n", + " df_truth,\n", + " verbose=False,\n", + " enable_tqdm=True,\n", + ")\n", + "df_precision = analysis.compute_1to1_match_precision(\n", + " p_match,\n", + " p_unmatched,\n", + " t_unmatched,\n", + " df_truth,\n", + " verbose=False,\n", + ")\n", + "\n", + "df_precision[\"jobtype\"] = df_precision[\"metadata_filename\"].map(jobtypes)\n", + "df_truth[\"pdb_index\"] = df_truth[\"pdb_filename\"].apply(lambda x: int(x.strip(\".pdb\").split(\"_\")[-1]))\n", + "df_picked[\"closest_truth_index\"] = closest_truth_index\n", + "df_picked[\"TP\"] = df_picked[\"closest_truth_index\"].apply(lambda x: np.isnan(x) == False)\n", + "df_picked[\"closest_pdb_index\"] = df_picked[\"closest_truth_index\"].apply(lambda x: df_truth.loc[x, \"pdb_index\"] if np.isnan(x) == False else np.nan)\n", + "df_precision.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## panel A\n", + "distributions of the particles in each class over the MD trajectories of the open and closed states\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# add a column to the df_picked data frame that indicates if the particles originates from the open or closed state of the spike protein\n", + "for classification_job in [\"2_classes\", \"3_classes\", \"10_classes\"]:\n", + " df_truth[\"pdb_index\"] = df_truth[\"pdb_filename\"].apply(lambda x: int(x.strip(\".pdb\").split(\"_\")[-1]))\n", + " df_picked_grouped = df_picked.query(\"TP == True\").groupby(\"jobtype\").get_group(classification_job)\n", + "\n", + " # if the closest_pdb_index < 8334, the particle originates from the closed state, otherwise it originates from the open state\n", + " df_picked_grouped[\"state\"] = df_picked_grouped[\"closest_pdb_index\"] <= 8334\n", + " df_picked_grouped[\"state\"] = df_picked_grouped[\"state\"].map({True: \"closed\", False: \"open\"})\n", + "\n", + " # df_picked_grouped[\"class2D\"] = df_picked_grouped[\"class2D\"].astype(int)\n", + " df_picked_grouped.sort_values(by=\"class2D\", inplace=True)\n", + " num_classes = len(df_picked_grouped.groupby(\"class2D\").groups)\n", + " print(f\"found {num_classes} classes\")\n", + " colors = sns.color_palette(\"RdYlBu\", n_colors=num_classes)\n", + "\n", + " fig, ax = plt.subplots(figsize=(7, 3.5))\n", + " # make kde plot out of the data\n", + " kde_picked = sns.kdeplot(\n", + " data=df_picked_grouped,\n", + " x=\"closest_pdb_index\",\n", + " ax=ax,\n", + " hue=\"class2D\",\n", + " fill=False,\n", + " label=\"class2D\",\n", + " linewidth=5,\n", + " alpha=1,\n", + " palette=colors,\n", + " legend=True,\n", + " )\n", + " # add an extra black line over the second class\n", + " kde_extra = sns.kdeplot(\n", + " data=df_picked_grouped,\n", + " x=\"closest_pdb_index\",\n", + " ax=ax,\n", + " hue=\"class2D\",\n", + " palette={r+1:\"black\" for r in range(num_classes)},\n", + " label=\"picked_particles\",\n", + " linewidth=1,\n", + " alpha=1,\n", + " fill=False,\n", + " legend=False,\n", + " )\n", + " # add kdeplot of the True particles\n", + " ax_truth = ax.twinx()\n", + " kde_truth = sns.kdeplot(\n", + " data=df_truth,\n", + " x=\"pdb_index\",\n", + " ax=ax_truth,\n", + " color=\"black\",\n", + " linestyle=\"--\",\n", + " label=\"GT\",\n", + " linewidth=2,\n", + " fill=False,\n", + " alpha=1,\n", + " legend=True,\n", + " )\n", + "\n", + " # get the legend handles from kde_picked\n", + " handles, labels = ax.get_legend_handles_labels()\n", + " h = [handles[r] for r in range(len(handles)) if labels[r] == \"class2D\"]\n", + " h = h[::-1]\n", + " h.extend([handles[r] for r in range(len(handles)) if labels[r] == \"GT\"])\n", + " l = [f\"class {r+1}\" for r in range(num_classes)] + [\"GT\"]\n", + " # add the legend\n", + " ax.legend(\n", + " handles=h,\n", + " labels=l,\n", + " loc='upper right',\n", + " bbox_to_anchor=(1.40, 1.0),\n", + " ncol=1,\n", + " fontsize=14,\n", + " frameon=True,\n", + " )\n", + " ax_truth.set_ylabel(\"GT\", fontsize=16, rotation=270, labelpad=20)\n", + " if num_classes == 10:\n", + " ylim = ax.get_ylim()\n", + " ax.set_ylim(ylim[0], ylim[1]*1.1)\n", + " else:\n", + " ylim = ax_truth.get_ylim()\n", + " ax.set_ylim(ylim[0], ylim[1])\n", + "\n", + " # change the xticks to the time in us\n", + " ax.set_xticks([0, 8334-1000, 8334+1000, 16668])\n", + " ax.set_xticklabels([\"0 \\u03BCs\", \"10 \\u03BCs\", \"0 \\u03BCs\", \"10 \\u03BCs\"], fontsize=14)\n", + " ax.axvline(x=8334, color=\"red\", linestyle=\"-.\", linewidth=2)\n", + " ax.set_xlabel(\"\")\n", + " ax.set_ylabel(\"Density\", fontsize=16)\n", + " # label the right side of the plot with 'open state' and the left side with 'closed state' undeneath the x-axis\n", + " ax.text(0.25, -0.2, \"Closed state\", ha='center', va='bottom', fontsize=16, fontweight='bold', transform=ax.transAxes)\n", + " ax.text(0.75, -0.2, \"Open state\", ha='center', va='bottom', fontsize=16, fontweight='bold', transform=ax.transAxes)\n", + " ax.tick_params(axis='both', which='major', labelsize=14)\n", + " ax_truth.tick_params(axis='both', which='major', labelsize=14)\n", + "\n", + " outfilename = os.path.join(project_dir, \"figures\", f\"frame_distribution_{classification_job}.pdf\")\n", + " # fig.savefig(outfilename, bbox_inches=\"tight\")\n", + " print(f\"saved figure to: {outfilename}\")\n", + "\n", + " # break\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# for the 10-class case plot all distributions separately\n", + "df_picked_grouped = df_picked.query(\"TP == True\").groupby(\"jobtype\").get_group(\"10_classes\")\n", + "df_picked_grouped[\"state\"] = df_picked_grouped[\"closest_pdb_index\"] <= 8334\n", + "df_picked_grouped[\"state\"] = df_picked_grouped[\"state\"].map({True: \"closed\", False: \"open\"})\n", + "df_picked_grouped.sort_values(by=\"class2D\", inplace=True)\n", + "num_classes = len(df_picked_grouped.groupby(\"class2D\").groups)\n", + "print(f\"found {num_classes} classes\")\n", + "colors = sns.color_palette(\"RdYlBu\", n_colors=num_classes)\n", + "\n", + "for class2d in df_picked_grouped.groupby(\"class2D\").groups:\n", + " fig, ax = plt.subplots(figsize=(7, 3.5))\n", + " # make kde plot out of the data\n", + " kde_picked = sns.kdeplot(\n", + " data=df_picked_grouped.groupby(\"class2D\").get_group(class2d),\n", + " x=\"closest_pdb_index\",\n", + " ax=ax,\n", + " fill=False,\n", + " label=\"class2D\",\n", + " linewidth=5,\n", + " alpha=1,\n", + " legend=True,\n", + " )\n", + " # get the legend handles from kde_picked\n", + " handles, labels = ax.get_legend_handles_labels()\n", + " h = [handles[r] for r in range(len(handles)) if labels[r] == \"state\"]\n", + " h = h[::-1]\n", + " h.extend([handles[r] for r in range(len(handles)) if labels[r] == \"GT\"])\n", + " l = [\"closed\", \"open\", \"GT\"]\n", + " # add the legend\n", + " ax.legend(\n", + " handles=h,\n", + " labels=l,\n", + " loc='upper right',\n", + " bbox_to_anchor=(1.40, 1.0),\n", + " ncol=1,\n", + " fontsize=14,\n", + " frameon=True,\n", + " )\n", + " ax_truth.set_ylabel(\"GT\", fontsize=16, rotation=270, labelpad=20)\n", + " if num_classes == 10:\n", + " ylim = ax.get_ylim()\n", + " ax.set_ylim(ylim[0], ylim[1]*1.1)\n", + " else:\n", + " ylim = ax_truth.get_ylim()\n", + "\n", + " # change the xticks to the time in us\n", + " ax.set_xticks([0, 8334-1000, 8334+1000, 16668])\n", + " ax.set_xticklabels([\"0 \\u03BCs\", \"10 \\u03BCs\", \"0 \\u03BCs\", \"10 \\u03BCs\"], fontsize=14)\n", + " ax.axvline(x=8334, color=\"red\", linestyle=\"-.\", linewidth=2)\n", + " ax.set_xlabel(\"\")\n", + " ax.set_ylabel(\"Density\", fontsize=16)\n", + " # label the right side of the plot with 'open state' and the left side with 'closed state' undeneath the x-axis\n", + " ax.text(0.25, -0.2, \"closed state\", ha='center', va='bottom', fontsize=16, fontweight='bold', transform=ax.transAxes)\n", + " ax.text(0.75, -0.2, \"open state\", ha='center', va='bottom', fontsize=16, fontweight='bold', transform=ax.transAxes)\n", + " ax.tick_params(axis='both', which='major', labelsize=14)\n", + " ax_truth.tick_params(axis='both', which='major', labelsize=14)\n", + " ax.set_title(f\"class {class2d}\")\n", + "\n", + " outfilename = os.path.join(project_dir, \"figures\", f\"frame_distribution_{classification_job}_class{class2d}.png\")\n", + " # fig.savefig(outfilename, dpi=300, bbox_inches=\"tight\")\n", + " print(f\"saved figure to: {outfilename}\")\n", + "\n", + " # close the plot\n", + " plt.close(fig)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# for each class print if there are more particles from the open or closed state\n", + "df_picked[\"state\"] = df_picked[\"closest_pdb_index\"] <= 8334\n", + "df_picked[\"state\"] = df_picked[\"state\"].map({True: \"closed\", False: \"open\"})\n", + "df_picked[\"class2D\"] = df_picked[\"class2D\"].astype(int)\n", + "\n", + "for classification_job in [\"2_classes\", \"3_classes\", \"10_classes\"]:\n", + " print(f\"jobtype: {classification_job}\")\n", + " df_picked_grouped = df_picked.groupby([\"jobtype\", \"TP\"]).get_group((classification_job,True))\n", + " # df_picked_grouped[\"state\"] = df_picked_grouped[\"closest_pdb_index\"] <= 8334\n", + " # df_picked_grouped[\"state\"] = df_picked_grouped[\"state\"].map({True: \"closed\", False: \"open\"})\n", + " # df_picked_grouped[\"class2D\"] = df_picked_grouped[\"class2D\"].astype(int)\n", + " # df_picked_grouped.sort_values(by=\"class2D\", inplace=True)\n", + " num_classes = len(df_picked_grouped.groupby(\"class2D\").groups)\n", + "\n", + " for class2D in range(1, num_classes+1):\n", + " df_class = df_picked_grouped.groupby(\"class2D\").get_group(class2D)\n", + " num_open = len(df_class.query(\"state == 'open'\"))\n", + " num_closed = len(df_class.query(\"state == 'closed'\"))\n", + " percentage_open = num_open / (num_open + num_closed)*100\n", + " print(f\"class {class2D}: {num_open} ({percentage_open:.1f}%) open, {num_closed} ({100-percentage_open:.1f}%) closed\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## panel B\n", + "plotting the precision of each 3D class" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# plot the preicison per 3D class\n", + "for classification_job in [\"2_classes\", \"3_classes\", \"10_classes\"]:\n", + " df_result = get_precision_per_class(df_truth, df_picked, classification_job)\n", + " # setup colors\n", + " num_classes = len(df_result[\"class\"].unique())\n", + " colors = sns.color_palette(\"RdYlBu\", n_colors=num_classes)\n", + "\n", + " fig, ax = plt.subplots(figsize=(3.5, 3.5))\n", + " sns.barplot(\n", + " data=df_result,\n", + " x=\"class\",\n", + " y=\"precision\",\n", + " hue=\"class\",\n", + " palette=colors,\n", + " edgecolor='black',\n", + " ax=ax,\n", + " dodge=0,\n", + " linewidth=1,\n", + " )\n", + " # remove legend\n", + " ax.legend().remove()\n", + " ax.set_ylim((0, 1))\n", + " ax.set_ylabel(\"Precision\", fontsize=16)\n", + " ax.set_xlabel(\"Class\", fontsize=16)\n", + " ax.tick_params(axis='both', which='major', labelsize=14)\n", + "\n", + " outfilename = os.path.join(project_dir, \"figures\", f\"precision_{classification_job}.pdf\")\n", + " # fig.savefig(outfilename, bbox_inches=\"tight\")\n", + " print(f\"saved figure to: {outfilename}\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## panel C\n", + "plotting the fraction of particles in each class" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for classification_job in [\"2_classes\", \"3_classes\", \"10_classes\"]:\n", + " df_result = get_precision_per_class(df_truth, df_picked, classification_job)\n", + " # setup colors\n", + " num_classes = len(df_result[\"class\"].unique())\n", + " colors = sns.color_palette(\"RdYlBu\", n_colors=num_classes)\n", + "\n", + " fig, ax = plt.subplots(figsize=(3.5, 3.5))\n", + " sns.barplot(\n", + " data=df_result,\n", + " x=\"class\",\n", + " y=\"picked_fraction\",\n", + " hue=\"class\",\n", + " palette=colors,\n", + " edgecolor='black',\n", + " ax=ax,\n", + " dodge=0,\n", + " linewidth=1,\n", + " )\n", + " # remove legend\n", + " ax.legend().remove()\n", + " ax.set_ylabel(\"particle fraction\")\n", + " ax.set_ylim((0, 1))\n", + " ax.set_ylabel(\"Picked fraction\", fontsize=16)\n", + " ax.set_xlabel(\"Class\", fontsize=16)\n", + " ax.tick_params(axis='both', which='major', labelsize=14)\n", + "\n", + " outfilename = os.path.join(project_dir, \"figures\", f\"picked_fraction_{classification_job}.pdf\")\n", + " # fig.savefig(outfilename, bbox_inches=\"tight\")\n", + " print(f\"saved figure to: {outfilename}\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## panel D\n", + "plotting the correlation matrix between each class and a sampling of the frames in the MD trajectory" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "project_dir = \"/tudelft/mjoosten1/staff-umbrella/ajlab/MJ/projects/Roodmus/data/DE-Shaw_covid_spike_protein/DESRES-Trajectory_sarscov2-11021566-11021571-mixed\"\n", + "for classification_job in [\"2_classes\", \"3_classes\", \"10_classes\"]:\n", + " print(f\"loading data for {classification_job}\")\n", + " correlation_matrix_filename = os.path.join(project_dir, \"aligned_ensembles\", f\"correlation_matrix_{classification_job}.npy\")\n", + " if os.path.exists(correlation_matrix_filename):\n", + " correlation_matrix = np.load(correlation_matrix_filename)\n", + " else:\n", + " print(f\"correlation matrix for {classification_job} does not exist yet. Compute it first.\")\n", + " correlation_matrix_normalised = (correlation_matrix - correlation_matrix.min(axis=0)) / (correlation_matrix.max(axis=0) - correlation_matrix.min(axis=0))\n", + " n_classes = correlation_matrix.shape[1]\n", + " n_frames = correlation_matrix.shape[0]\n", + " print(f\"number of classes: {n_classes}\")\n", + " print(f\"number of frames: {n_frames}\")\n", + "\n", + " fig, ax = plt.subplots(figsize=(3.5, 3.5))\n", + " # show the heatmap as a square (~50x3)\n", + " ax.imshow(correlation_matrix_normalised, cmap=\"coolwarm\", aspect=\"auto\", origin=\"lower\")\n", + " ticklabels = [f\"class {i+1}\" for i in range(n_classes)]\n", + " ax.set_xticks(range(n_classes))\n", + " ax.set_xticklabels(ticklabels, fontsize=12, rotation=45)\n", + " ax.set_yticks([0, n_frames//2-2, n_frames//2+2, n_frames-1])\n", + " ax.set_yticklabels([\"0 \\u03BCs\", \"10 \\u03BCs\", \"0 \\u03BCs\", \"10 \\u03BCs\"], fontsize=12)\n", + " ax.axhline(y=n_frames//2, color=\"black\", linestyle=\"solid\", linewidth=2)\n", + " # add colorbar\n", + " cbar = ax.figure.colorbar(ax.get_images()[0], ax=ax, orientation=\"vertical\")\n", + " ax.text(\n", + " -0.65,\n", + " 3*n_frames//4,\n", + " \"Closed state\",\n", + " ha='right',\n", + " va='bottom',\n", + " fontsize=12,\n", + " fontweight='bold',\n", + " )\n", + " ax.text(\n", + " -0.65,\n", + " n_frames//4,\n", + " \"Open state\",\n", + " ha='right',\n", + " va='bottom',\n", + " fontsize=12,\n", + " fontweight='bold',\n", + " )\n", + "\n", + " # save the figure\n", + " outfilename = os.path.join(project_dir, \"figures\", f\"correlation_matrix_{n_classes}classes.pdf\")\n", + " # fig.savefig(outfilename, bbox_inches=\"tight\")\n", + " print(f\"saved figure to: {outfilename}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# print the average correlation with the open and closed states for each class\n", + "for classification_job in [\"2_classes\", \"3_classes\", \"10_classes\"]:\n", + " print(f\"loading data for {classification_job}\")\n", + " correlation_matrix_filename = os.path.join(project_dir, \"aligned_ensembles\", f\"correlation_matrix_{classification_job}.npy\")\n", + " if os.path.exists(correlation_matrix_filename):\n", + " correlation_matrix = np.load(correlation_matrix_filename)\n", + " else:\n", + " print(f\"correlation matrix for {classification_job} does not exist yet. Compute it first.\")\n", + " n_classes = correlation_matrix.shape[1]\n", + " n_frames = correlation_matrix.shape[0]\n", + " print(f\"number of classes: {n_classes}\")\n", + " print(f\"number of frames: {n_frames}\")\n", + "\n", + " # compute the average correlation with the open and closed states for each class\n", + " avg_correlation_open = np.zeros(n_classes)\n", + " avg_correlation_closed = np.zeros(n_classes)\n", + " for i in range(n_classes):\n", + " avg_correlation_open[i] = np.mean(correlation_matrix[:n_frames//2, i])\n", + " avg_correlation_closed[i] = np.mean(correlation_matrix[n_frames//2:, i])\n", + "\n", + " # plot the average correlation with the open and closed states for each class\n", + " fig, ax = plt.subplots(figsize=(3.5, 3.5))\n", + " ax.plot(range(1, n_classes+1), avg_correlation_open, label=\"open state\", color=\"blue\")\n", + " ax.plot(range(1, n_classes+1), avg_correlation_closed, label=\"closed state\", color=\"red\")\n", + " ax.set_xlabel(\"class\", fontsize=16)\n", + " ax.set_ylabel(\"average correlation\", fontsize=16)\n", + " ax.legend()\n", + " ax.tick_params(axis='both', which='major', labelsize=14)\n", + "\n", + " outfilename = os.path.join(project_dir, \"figures\", f\"average_correlation_{classification_job}.pdf\")\n", + " fig.savefig(outfilename, bbox_inches=\"tight\")\n", + " print(f\"saved figure to: {outfilename}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## in text\n", + "to report the resolution in text of the refined maps for the 3class dataset I need to load the generated confidence masks and filter them" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# project_dir = \"/home/mjoosten1/projects/roodmus/data/DE-Shaw_covid_spike_protein/DESRES-Trajectory_sarscov2-11021566-11021571-mixed\"\n", + "# project_dir = \"/tudelft/mjoosten1/staff-umbrella/ajlab/MJ/projects/Roodmus/data/DE-Shaw_covid_spike_protein/DESRES-Trajectory_sarscov2-11021566-11021571-mixed\"\n", + "# data = {\n", + "# 0:{\n", + "# \"class\": 1,\n", + "# \"ConfidenceMap\": \"job103\",\n", + "# },\n", + "# 1: {\n", + "# \"class\": 2,\n", + "# \"ConfidenceMap\": \"job104\",\n", + "# },\n", + "# 2: {\n", + "# \"class\": 3,\n", + "# \"ConfidenceMap\": \"job105\",\n", + "# },\n", + "# }\n", + "\n", + "# for key, item in data.items():\n", + "# confidence_map_file = os.path.join(project_dir, \"ConfidenceMap\", item[\"ConfidenceMap\"], \"run_class001_confidenceMap.mrc\")\n", + "# confidence_map = mrcfile.open(confidence_map_file)\n", + "# confidence_mask = confidence_map.data > 0.99\n", + "\n", + "# confidence_mask_smooth = smoothen_mask(\n", + "# confidence_mask,\n", + "# cosine_falloff_length=5,\n", + "# )\n", + "\n", + "# with mrcfile.new(os.path.join(project_dir, \"ConfidenceMap\", item[\"ConfidenceMap\"], \"run_class001_confidenceMap_filtered.mrc\"), overwrite=True) as mrc:\n", + "# mrc.set_data(confidence_mask_smooth.astype(np.float32))\n", + "# mrc.voxel_size = confidence_map.voxel_size\n", + "# mrc.header.origin.x = confidence_map.header.origin.x\n", + "# mrc.header.origin.y = confidence_map.header.origin.y\n", + "# mrc.header.origin.z = confidence_map.header.origin.z" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "roodmus", + "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": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/paper_fig_ipynbs/figure_5.ipynb b/paper_fig_ipynbs/figure_5.ipynb new file mode 100644 index 0000000..8764774 --- /dev/null +++ b/paper_fig_ipynbs/figure_5.ipynb @@ -0,0 +1,456 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# plotting functions of figure 5 in the manuscript\n", + "This figure shows the results of applying cryoDRGN to the covid spike trimer dataset (DESRES-Trajectory_sarscov2-11021571)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# imports\n", + "import os\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "# roodmus\n", + "from roodmus.analysis.utils import load_data\n", + "from roodmus.heterogeneity.hetRec import HetRec\n", + "from roodmus.heterogeneity.plot_heterogeneous_reconstruction import plot_latent_space_scatter" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# data loading for DE-Shaw covid spike partially open set\n", + "project_dir = \"/home/mjoosten1/projects/roodmus/data/DE-Shaw_covid_spike_protein/20231116_DESRES-Trajectory_sarscov2-11021571-all-glueCA\"\n", + "config_dir = \"/home/mjoosten1/projects/roodmus/data/DE-Shaw_covid_spike_protein/DESRES-Trajectory_sarscov2-11021571-all-glueCA/Micrographs\"\n", + "figures_dir = os.path.join(project_dir, \"figures\")\n", + "meta_file = os.path.join(project_dir, \"cryoDRGN\", \"run_data.star\")\n", + "jobtypes = {\n", + " os.path.join(project_dir, \"cryoDRGN\", \"run_data.star\"): \"cryoDRGN\",\n", + "}\n", + "latent_file = os.path.join(project_dir, \"cryoDRGN\", \"train_320\", \"z.19.pkl\")\n", + "\n", + "particle_diameter = 100 # approximate particle diameter in Angstroms\n", + "ugraph_shape = (4000, 4000) # shape of the micrograph in pixels. Only needs to be given if the metadata file is a .star file\n", + "verbose = True\n", + "ignore_missing_files = True\n", + "enable_tqdm = True\n", + "\n", + "analysis = load_data(meta_file, config_dir, particle_diameter, ugraph_shape=ugraph_shape, verbose=verbose, enable_tqdm=enable_tqdm, ignore_missing_files=ignore_missing_files) # creates the class\n", + "df_picked = pd.DataFrame(analysis.results_picking)\n", + "df_truth = pd.DataFrame(analysis.results_truth)\n", + "# df_precision, df_picked = analysis.compute_precision(df_picked, df_truth, verbose=verbose)\n", + "p_match, _, p_unmatched, t_unmatched, closest_truth_index = analysis._match_particles(\n", + " meta_file,\n", + " df_picked,\n", + " df_truth,\n", + " verbose=False,\n", + " enable_tqdm=True,\n", + ")\n", + "df_precision = analysis.compute_1to1_match_precision(\n", + " p_match,\n", + " p_unmatched,\n", + " t_unmatched,\n", + " df_truth,\n", + " verbose=False,\n", + ")\n", + "\n", + "df_picked, ndim = HetRec.add_latent_space_coordinates(\n", + " latent_file=latent_file,\n", + " df_picked=df_picked,\n", + ")\n", + "df_picked, pca = HetRec.compute_PCA(\n", + " df_picked=df_picked,\n", + " ndim=ndim,\n", + ")\n", + "df_truth[\"pdb_index\"] = df_truth[\"pdb_filename\"].apply(lambda x: int(x.strip(\".pdb\").split(\"_\")[-1]))\n", + "df_picked[\"closest_truth_index\"] = closest_truth_index\n", + "df_picked[\"TP\"] = df_picked[\"closest_truth_index\"].apply(lambda x: np.isnan(x) == False)\n", + "df_picked[\"closest_pdb_index\"] = df_picked[\"closest_truth_index\"].apply(lambda x: df_truth.loc[x, \"pdb_index\"] if np.isnan(x) == False else np.nan)\n", + "df_picked.tail()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## panel A\n", + "plotting latent space of epoch 20 of cryoDRGN with the FP particle picks plotted in red" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# latent space scatter plot\n", + "dim1=0\n", + "dim2=1\n", + "\n", + "grid = plot_latent_space_scatter(\n", + " df_picked,\n", + " dim_1=dim1,\n", + " dim_2=dim2,\n", + " # color=\"#4eb3d3\",\n", + " pca=True\n", + ")\n", + "df_FP = df_picked[df_picked[\"TP\"]==0]\n", + "print(f\"number of FP: {len(df_FP)}\")\n", + "print(f\"number of TP: {len(df_picked)-len(df_FP)}\")\n", + "ax = grid.figure.get_axes()[0]\n", + "sns.scatterplot(\n", + " data=df_FP,\n", + " x=f\"PCA_{dim1}\",\n", + " y=f\"PCA_{dim2}\",\n", + " color=\"red\",\n", + " s=10,\n", + " ax=ax,\n", + " label=\"FP\",\n", + ")\n", + "grid.set_axis_labels(f\"PCA{dim1}\", f\"PCA{dim2}\", fontsize=14)\n", + "grid.figure.get_axes()[0].tick_params(labelsize=12)\n", + "grid.figure.get_axes()[0].set_xlim((-12, 12))\n", + "grid.figure.get_axes()[0].set_ylim((-12, 12))\n", + "\n", + "outfilename = os.path.join(figures_dir, f\"{os.path.basename(latent_file)}_pca_{dim1}_{dim2}_FP\")\n", + "# grid.savefig(outfilename+\".pdf\", bbox_inches=\"tight\")\n", + "grid.savefig(outfilename+\".png\", bbox_inches=\"tight\", dpi=600)\n", + "print(f\"saved figure to: {outfilename}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## panel B\n", + "plot of the latent space with each point coloured by its corresponding frame from the MD trajecory" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# latent space scatter plot, coloured by ground truth frames\n", + "dim1=0\n", + "dim2=1\n", + "dt = 1.2e-3# time between frames in microseconds\n", + "\n", + "fig, ax = plot_latent_space_scatter(\n", + " df_picked,\n", + " dim_1=dim1,\n", + " dim_2=dim2,\n", + " color_by=\"closest_pdb_index\",\n", + " palette=\"RdYlBu\",\n", + " pca=True,\n", + ")\n", + "# remove legend and add colorbar for the closest_pdb_index\n", + "ax.legend_.remove()\n", + "S_m = plt.cm.ScalarMappable(cmap=\"RdYlBu\")\n", + "S_m.set_array(df_picked[\"closest_pdb_index\"])\n", + "cbar = plt.colorbar(S_m)\n", + "cbar.set_label(\"Time (\\u03BCs)\", rotation=270, labelpad=15, fontsize=16) # time in ps\n", + "# change the tick labels on the colorbar to go from 0 to 10 us\n", + "cbar.set_ticks(np.linspace(1, df_picked[\"closest_pdb_index\"].max(), 10))\n", + "xticklabels = [np.round(r, 1) for r in np.linspace(1, df_picked[\"closest_pdb_index\"].max(), 10)*dt]\n", + "cbar.set_ticklabels(xticklabels, fontsize=12)\n", + "ax.set_xlabel(f\"PCA{dim1}\", fontsize=14)\n", + "ax.set_ylabel(f\"PCA{dim2}\", fontsize=14)\n", + "ax.tick_params(labelsize=12)\n", + "ax.set_xlim((-12, 12))\n", + "ax.set_ylim((-12, 12))\n", + "\n", + "# add trajectory to the plot\n", + "N_volumes = 50\n", + "pdb_indices = np.unique(df_picked[\"closest_pdb_index\"])\n", + "d_pdbs = len(pdb_indices) // N_volumes\n", + "\n", + "trajectory = np.zeros((N_volumes, ndim))\n", + "trajectory_pca = np.zeros((N_volumes, ndim))\n", + "for i in range(N_volumes):\n", + " pdb_group = pdb_indices[i*d_pdbs:(i+1)*d_pdbs]\n", + " mean_latent = df_picked[df_picked[\"closest_pdb_index\"].isin(pdb_group)].agg(\n", + " {f\"latent_{i}\": \"mean\" for i in range(ndim)}\n", + " )\n", + " trajectory[i] = mean_latent.values\n", + " mean_pca = df_picked[df_picked[\"closest_pdb_index\"].isin(pdb_group)].agg(\n", + " {f\"PCA_{i}\": \"mean\" for i in range(ndim)}\n", + " )\n", + " trajectory_pca[i] = mean_pca.values\n", + "\n", + "ax.scatter(trajectory_pca[:, 0], trajectory_pca[:, 1], s=5, c=\"black\", zorder=10)\n", + "ax.plot(trajectory_pca[:, 0], trajectory_pca[:, 1], c=\"black\", zorder=10, linewidth=0.5)\n", + "ax.set_aspect(\"equal\")\n", + "\n", + "outfilename = os.path.join(figures_dir, f\"{os.path.basename(latent_file)}_latent_space_scatter_colored_by_closest_pdb_index_pca\")\n", + "fig.savefig(outfilename+\".png\", dpi=600, bbox_inches=\"tight\")\n", + "fig.savefig(outfilename+\".pdf\", bbox_inches=\"tight\")\n", + "print(f\"saved figure to: {outfilename}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# for the figure, repeat previous plot with only the Nth point in the trajectory marked\n", + "N = 31\n", + "fig, ax = plot_latent_space_scatter(\n", + " df_picked,\n", + " dim_1=dim1,\n", + " dim_2=dim2,\n", + " color_by=\"closest_pdb_index\",\n", + " palette=\"RdYlBu\",\n", + " pca=True,\n", + ")\n", + "# remove legend and add colorbar for the closest_pdb_index\n", + "ax.legend_.remove()\n", + "S_m = plt.cm.ScalarMappable(cmap=\"RdYlBu\")\n", + "S_m.set_array(df_picked[\"closest_pdb_index\"])\n", + "cbar = plt.colorbar(S_m)\n", + "cbar.set_label(\"time (\\u03BCs)\", rotation=270, labelpad=15, fontsize=16) # time in ps\n", + "# change the tick labels on the colorbar to go from 0 to 10 us\n", + "cbar.set_ticks(np.linspace(1, df_picked[\"closest_pdb_index\"].max(), 10))\n", + "xticklabels = [np.round(r, 1) for r in np.linspace(1, df_picked[\"closest_pdb_index\"].max(), 10)*dt]\n", + "cbar.set_ticklabels(xticklabels, fontsize=12)\n", + "ax.set_xlabel(f\"PCA{dim1}\", fontsize=14)\n", + "ax.set_ylabel(f\"PCA{dim2}\", fontsize=14)\n", + "ax.tick_params(labelsize=12)\n", + "ax.set_xlim((-12, 12))\n", + "ax.set_ylim((-12, 12))\n", + "\n", + "# add trajectory to the plot\n", + "ax.scatter(trajectory_pca[N, 0], trajectory_pca[N, 1], s=5, c=\"black\", zorder=10)\n", + "ax.set_aspect(\"equal\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## panel C\n", + "correlation matrix between the MD trajectory and the sampled volumes from the latent space" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# plot the correlation matrix\n", + "project_dir = \"/home/mjoosten1/projects/roodmus/data/DE-Shaw_covid_spike_protein/20231116_DESRES-Trajectory_sarscov2-11021571-all-glueCA\"\n", + "figures_dir = os.path.join(project_dir, \"figures\")\n", + "correlation_matrix_file = os.path.join(project_dir, \"cryoDRGN\", \"analyze_320\", \"correlation_matrix.npy\")\n", + "correlation_matrix = np.load(correlation_matrix_file)\n", + "\n", + "frames = correlation_matrix.shape[0]\n", + "\n", + "fig, ax = plt.subplots(figsize=(3.5, 3.5))\n", + "ax.imshow(correlation_matrix, cmap=\"coolwarm\")\n", + "yticks = np.linspace(0, frames, 10)\n", + "yticklabels = np.round(np.linspace(0, 10, 10, dtype=float), 1)\n", + "ax.set_yticks(yticks)\n", + "ax.set_yticklabels(yticklabels, fontsize=12)\n", + "ax.set_ylabel(\"Time (\\u03BCs)\", fontsize=16)\n", + "ax.set_xlabel(\"Sampled volume\", fontsize=16)\n", + "cbar = ax.figure.colorbar(ax.get_images()[0], ax=ax, orientation=\"vertical\", pad=0.01, shrink=0.9)\n", + "cbar.ax.tick_params(labelsize=14)\n", + "cbar.ax.set_ylabel(\"Correlation\", fontsize=16, rotation=270, labelpad=20)\n", + "ax.tick_params(axis=\"both\", which=\"major\", labelsize=14)\n", + "\n", + "outfilename = os.path.join(figures_dir, f\"{os.path.basename(latent_file)}_correlation_matrix.pdf\")\n", + "fig.savefig(outfilename, bbox_inches=\"tight\")\n", + "print(f\"saved figure to: {outfilename}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# print the best and worst fir for every sampled volume to the MD trahjectory\n", + "pdb_dir = \"/home/mjoosten1/projects/roodmus/data/DE-Shaw_covid_spike_protein/mnt/parakeet_storage4/ConformationSampling/DESRES-Trajectory_sarscov2-11021571-all-glueCA/even_sampling_8334\"\n", + "pdb_files = [r for r in os.listdir(pdb_dir) if r.endswith(\".pdb\")]\n", + "pdb_files.sort()\n", + "pdb_files = pdb_files[::int(len(pdb_files)/(frames+1))]\n", + "results = {\n", + " \"sampled_vol\": [],\n", + " \"best_fit\": [],\n", + " \"best_fit_index\": [],\n", + " \"worst_fit\": [],\n", + " \"worst_fit_index\": [],\n", + "}\n", + "for i in range(frames):\n", + " results[\"sampled_vol\"].append(i)\n", + " results[\"best_fit_index\"].append(correlation_matrix[i].argmax())\n", + " results[\"worst_fit_index\"].append(correlation_matrix[i].argmin())\n", + " results[\"best_fit\"].append(pdb_files[correlation_matrix[i].argmax()])\n", + " results[\"worst_fit\"].append(pdb_files[correlation_matrix[i].argmin()])\n", + "\n", + "df_results = pd.DataFrame(results)\n", + "df_results.head(n=len(df_results))\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## panel D\n", + "For this panel I want to write the real-space croscc-correlation between each volume and the best matching frame from the MD trajectory" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# print for each sampled volume (column) the correlation with the best matching frame of the trajectory (row), and also the conformation index\n", + "\n", + "pdb_dir = \"/home/mjoosten1/projects/roodmus/data/DE-Shaw_covid_spike_protein/mnt/parakeet_storage4/ConformationSampling/DESRES-Trajectory_sarscov2-11021571-all-glueCA/even_sampling_8334\"\n", + "pdb_files = [os.path.join(pdb_dir, f) for f in os.listdir(pdb_dir) if f.endswith(\".pdb\")]\n", + "pdb_files.sort()\n", + "N = 50\n", + "pdb_files = pdb_files[::len(pdb_files)//N]\n", + "\n", + "best_matching_frame = np.argmax(correlation_matrix, axis=0)\n", + "best_matching_frame_correlation = np.max(correlation_matrix, axis=0)\n", + "best_matching_conformation = [os.path.basename(pdb_files[r]) for r in best_matching_frame]\n", + "\n", + "for i in range(N):\n", + " print(f\"{i} {best_matching_frame[i]} {best_matching_frame_correlation[i]} {best_matching_conformation[i]}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# for the first sampled volume, print the correlation with all frames of the trajectory\n", + "correlations = correlation_matrix[:, 0]\n", + "for i in range(frames):\n", + " print(f\"{i} {correlations[i]} {os.path.basename(pdb_files[i])}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## panel E\n", + "zoom in of a selected atomic model and map from D. For this A attribute file needs to be made with the Q-scores from ChimeraX" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "project_dir = \"/home/mjoosten1/projects/roodmus/data/DE-Shaw_covid_spike_protein/20231116_DESRES-Trajectory_sarscov2-11021571-all-glueCA\"\n", + "conformation = \"007968\"\n", + "# conformation = \"008150\"\n", + "conformation = \"000163\"\n", + "qscores_file = os.path.join(project_dir, f\"chimeraX_qscores_table_conformation_{conformation}.txt\")\n", + "df_qscores = pd.read_csv(qscores_file, sep=\"\\t\" , header=0, skiprows=[1])\n", + "df_qscores.tail()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# create an attribute file for ChimeraX\n", + "attribute_file = os.path.join(project_dir, f\"chimeraX_attributes_conformation_{conformation}.defattr\")\n", + "\n", + "recipient = \"residues\"\n", + "matchmode = \"1-to-1\"\n", + "modelnr = \"4\"\n", + "\n", + "# write comments to the file explaining the attributes\n", + "with open(attribute_file, \"w\") as f:\n", + " f.write(\n", + " \"# ChimeraX attributes file\\n\"\n", + " )\n", + " f.write(\n", + " \"# This file contains attributes for the residues of the SARS-CoV-2 spike protein\\n\"\n", + " )\n", + " f.write(\n", + " \"# The attributes are the q-scores of the residues\\n\"\n", + " )\n", + "\n", + "# write the recipient and match modelines\n", + "with open(attribute_file, \"a\") as f:\n", + " f.write(\n", + " f\"recipient: {recipient}\\n\"\n", + " )\n", + " f.write(\n", + " f\"match mode: {matchmode}\\n\"\n", + " )\n", + " f.write(\n", + " f\"attribute: qscore\\n\"\n", + " )\n", + "\n", + "# add a line for each residue with the qscore as attribute\n", + "with open(attribute_file, \"a\") as f:\n", + " for i, row in df_qscores.iterrows():\n", + " atom_spec = f\"#{modelnr}/{row['Chain']}:{row['Number']}\"\n", + " attribute = row[\"Qbb\"]\n", + " f.write(\n", + " f\"\\t{atom_spec}\\t{attribute}\\n\"\n", + " )\n", + " \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "roodmus", + "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": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/paper_fig_ipynbs/figure_6.ipynb b/paper_fig_ipynbs/figure_6.ipynb new file mode 100644 index 0000000..16a383b --- /dev/null +++ b/paper_fig_ipynbs/figure_6.ipynb @@ -0,0 +1,349 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# plotting functions of figure 6 in the manuscript\n", + "This figure shows the results of training cryoDRGN on the RTC model. For now it is the same layout as figure 5, subject to change" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# imports\n", + "import os\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "from sklearn.decomposition import PCA\n", + "\n", + "# roodmus\n", + "from roodmus.analysis.utils import load_data\n", + "from roodmus.heterogeneity.hetRec import HetRec\n", + "from roodmus.heterogeneity.plot_heterogeneous_reconstruction import plot_latent_space_scatter" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "### data loading covid RTC DE-Shaw data set\n", + "project_dir = \"/home/mjoosten1/projects/roodmus/data/DE-Shaw_covid_RTC/20240124_DESRES-Trajectory_sarscov2-13795965-no-water-movies\"\n", + "config_dir = \"/home/mjoosten1/projects/roodmus/data/DE-Shaw_covid_RTC/DESRES-Trajectory_sarscov2-13795965-no-water-movies/Movies\"\n", + "# config_dir = os.path.join(project_dir, \"Movies\")\n", + "figures_dir = os.path.join(project_dir, \"figures\")\n", + "meta_file = os.path.join(project_dir, \"cryoDRGN\", \"run_data.star\")\n", + "jobtypes = {\n", + " os.path.join(project_dir, \"cryoDRGN\", \"run_data.star\"): \"cryoDRGN\",\n", + "}\n", + "latent_file = os.path.join(project_dir, \"cryoDRGN\", \"train_320\", \"z.24.pkl\")\n", + "\n", + "particle_diameter = 100 # approximate particle diameter in Angstroms\n", + "ugraph_shape = (4000, 4000) # shape of the micrograph in pixels. Only needs to be given if the metadata file is a .star file\n", + "verbose = True\n", + "ignore_missing_files = True\n", + "enable_tqdm = True\n", + "\n", + "analysis = load_data(meta_file, config_dir, particle_diameter, ugraph_shape=ugraph_shape, verbose=verbose, enable_tqdm=enable_tqdm, ignore_missing_files=ignore_missing_files) # creates the class\n", + "df_picked = pd.DataFrame(analysis.results_picking)\n", + "df_truth = pd.DataFrame(analysis.results_truth)\n", + "p_match, _, p_unmatched, t_unmatched, closest_truth_index = analysis._match_particles(\n", + " meta_file,\n", + " df_picked,\n", + " df_truth,\n", + " verbose=False,\n", + " enable_tqdm=True,\n", + ")\n", + "df_truth[\"pdb_index\"] = df_truth[\"pdb_filename\"].apply(lambda x: int(x.strip(\".pdb\").split(\"_\")[-1]))\n", + "df_picked[\"closest_truth_index\"] = closest_truth_index\n", + "df_picked[\"TP\"] = df_picked[\"closest_truth_index\"].apply(lambda x: np.isnan(x) == False)\n", + "df_picked[\"closest_pdb_index\"] = df_picked[\"closest_truth_index\"].apply(lambda x: df_truth.loc[x, \"pdb_index\"] if np.isnan(x) == False else np.nan)\n", + "\n", + "\n", + "# latent_space, ndim = IO.get_latents_cs(latent_file)\n", + "df_picked, ndim = HetRec.add_latent_space_coordinates(\n", + " latent_file=latent_file,\n", + " df_picked=df_picked,\n", + ")\n", + "df_picked, pca = HetRec.compute_PCA(\n", + " df_picked=df_picked,\n", + " ndim=ndim,\n", + ")\n", + "print(f\"latent space dimensionality: {ndim}\")\n", + "df_picked.tail()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## panel B\n", + "plotting the latent space of the RTC model\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# latent space scatter plot\n", + "dim1=0\n", + "dim2=1\n", + "\n", + "grid = plot_latent_space_scatter(\n", + " df_picked,\n", + " dim_1=dim1,\n", + " dim_2=dim2,\n", + " pca=True\n", + ")\n", + "df_FP = df_picked[df_picked[\"TP\"]==0]\n", + "print(f\"number of FP: {len(df_FP)}\")\n", + "print(f\"number of TP: {len(df_picked)-len(df_FP)}\")\n", + "ax = grid.figure.get_axes()[0]\n", + "sns.scatterplot(\n", + " data=df_FP,\n", + " x=f\"PCA_{dim1}\",\n", + " y=f\"PCA_{dim2}\",\n", + " color=\"red\",\n", + " s=10,\n", + " ax=ax,\n", + " label=\"FP\",\n", + ")\n", + "grid.set_axis_labels(f\"PCA{dim1}\", f\"PCA{dim2}\", fontsize=16)\n", + "grid.figure.get_axes()[0].tick_params(labelsize=14)\n", + "grid.figure.get_axes()[0].set_xlim((-12, 12))\n", + "grid.figure.get_axes()[0].set_ylim((-12, 12))\n", + "\n", + "grid.savefig(os.path.join(figures_dir, f\"{os.path.basename(latent_file)}_pca_{dim1}_{dim2}_FP.pdf\"), bbox_inches=\"tight\")\n", + "grid.savefig(os.path.join(figures_dir, f\"{os.path.basename(latent_file)}_pca_{dim1}_{dim2}_FP.png\"), bbox_inches=\"tight\", dpi=600)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## panel C\n", + "plot of the latent space with each point coloured by its corresponding frame from the MD trajecory" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# latent space scatter plot, coloured by ground truth frames\n", + "dim1=0\n", + "dim2=1\n", + "dt = 48e-3# time between frames in microseconds\n", + "save_trajectory = True\n", + "\n", + "fig, ax = plot_latent_space_scatter(\n", + " df_picked,\n", + " dim_1=dim1,\n", + " dim_2=dim2,\n", + " color_by=\"closest_pdb_index\",\n", + " palette=\"RdYlBu\",\n", + " pca=True,\n", + ")\n", + "# remove legend and add colorbar for the closest_pdb_index\n", + "ax.legend_.remove()\n", + "S_m = plt.cm.ScalarMappable(cmap=\"RdYlBu\")\n", + "S_m.set_array(df_picked[\"closest_pdb_index\"])\n", + "cbar = plt.colorbar(S_m)\n", + "cbar.set_label(\"Time [\\u03BCs]\", rotation=270, labelpad=15, fontsize=16) # time in ps\n", + "# change the tick labels on the colorbar to go from 0 to 10 us\n", + "cbar.set_ticks(np.linspace(1, df_picked[\"closest_pdb_index\"].max(), 10))\n", + "xticklabels = [int(r) for r in np.linspace(1, df_picked[\"closest_pdb_index\"].max(), 10)*dt]\n", + "cbar.set_ticklabels(xticklabels, fontsize=14)\n", + "ax.set_xlabel(f\"PCA{dim1}\", fontsize=16)\n", + "ax.set_ylabel(f\"PCA{dim2}\", fontsize=16)\n", + "ax.tick_params(labelsize=14)\n", + "ax.set_xlim((-12, 12))\n", + "ax.set_ylim((-12, 12))\n", + "\n", + "# add trajectory to the plot\n", + "N_volumes = 50\n", + "pdb_indices = np.unique(df_picked[\"closest_pdb_index\"])\n", + "d_pdbs = len(pdb_indices) // N_volumes\n", + "\n", + "trajectory = np.zeros((N_volumes, ndim))\n", + "trajectory_pca = np.zeros((N_volumes, ndim))\n", + "for i in range(N_volumes):\n", + " pdb_group = pdb_indices[i*d_pdbs:(i+1)*d_pdbs]\n", + " mean_latent = df_picked[df_picked[\"closest_pdb_index\"].isin(pdb_group)].agg(\n", + " {f\"latent_{i}\": \"mean\" for i in range(ndim)}\n", + " )\n", + " trajectory[i] = mean_latent.values\n", + " mean_pca = df_picked[df_picked[\"closest_pdb_index\"].isin(pdb_group)].agg(\n", + " {f\"PCA_{i}\": \"mean\" for i in range(ndim)}\n", + " )\n", + " trajectory_pca[i] = mean_pca.values\n", + "\n", + "if save_trajectory:\n", + " # save trajectory to a .txt file in the cryoDRGN directory\n", + " np.savetxt(\n", + " os.path.join(\n", + " os.path.dirname(os.path.dirname(latent_file)),\n", + " \"trajectory.txt\"\n", + " ),\n", + " trajectory\n", + " )\n", + "\n", + "ax.scatter(trajectory_pca[:, 0], trajectory_pca[:, 1], s=5, c=\"black\", zorder=10)\n", + "ax.plot(trajectory_pca[:, 0], trajectory_pca[:, 1], c=\"black\", zorder=10, linewidth=0.5)\n", + "ax.set_aspect(\"equal\")\n", + "\n", + "\n", + "fig.savefig(os.path.join(figures_dir, f\"{os.path.basename(latent_file)}_latent_space_scatter_colored_by_closest_pdb_index_pca.png\"), dpi=600, bbox_inches=\"tight\")\n", + "fig.savefig(os.path.join(figures_dir, f\"{os.path.basename(latent_file)}_latent_space_scatter_colored_by_closest_pdb_index_pca.pdf\"), bbox_inches=\"tight\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## panel D\n", + "plotting the correlation matrix between the sampled volumes and frames from the MD trajectory" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# plot the correlation matrix\n", + "project_dir = \"/home/mjoosten1/projects/roodmus/data/DE-Shaw_covid_RTC/20240124_DESRES-Trajectory_sarscov2-13795965-no-water-movies\"\n", + "figures_dir = os.path.join(project_dir, \"figures\")\n", + "correlation_matrix_file = os.path.join(project_dir, \"cryoDRGN\", \"analyze_320\", \"correlation_matrix.npy\")\n", + "correlation_matrix = np.load(correlation_matrix_file)\n", + "\n", + "frames = correlation_matrix.shape[0]\n", + "\n", + "fig, ax = plt.subplots(figsize=(3.5, 3.5))\n", + "ax.imshow(correlation_matrix, cmap=\"coolwarm\")\n", + "yticks = np.linspace(0, frames, 10)\n", + "yticklabels = np.linspace(0, 500, 10, dtype=int)\n", + "ax.set_yticks(yticks)\n", + "ax.set_yticklabels(yticklabels)\n", + "ax.set_ylabel(\"Time (\\u03BCs)\", fontsize=16)\n", + "ax.set_xlabel(\"Sampled volume\", fontsize=16)\n", + "cbar = ax.figure.colorbar(ax.get_images()[0], ax=ax, orientation=\"vertical\", pad=0.01, shrink=0.9)\n", + "cbar.ax.tick_params(labelsize=14)\n", + "cbar.ax.set_ylabel(\"Correlation\", fontsize=16, rotation=270, labelpad=20)\n", + "ax.tick_params(axis=\"both\", which=\"major\", labelsize=14)\n", + "\n", + "fig.savefig(os.path.join(figures_dir, f\"{os.path.basename(latent_file)}_correlation_matrix.pdf\"), bbox_inches=\"tight\")\n", + "print(f\"saved figure to {os.path.join(figures_dir, f'{os.path.basename(latent_file)}_correlation_matrix.pdf')}\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## panel E\n", + "plotting a zoomed-in region of the map with the atomic model, coloured by Q-scores\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "project_dir = \"/home/mjoosten1/projects/roodmus/data/DE-Shaw_covid_RTC/20240124_DESRES-Trajectory_sarscov2-13795965-no-water-movies\"\n", + "conformation = \"007600\" # best fit\n", + "conformation = \"001000\" # worst fit\n", + "qscores_file = os.path.join(project_dir, f\"chimeraX_qscores_table_conformation_{conformation}.txt\")\n", + "df_qscores = pd.read_csv(qscores_file, sep=\"\\t\" , header=0, skiprows=[1])\n", + "df_qscores.tail()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# create an attribute file for ChimeraX\n", + "attribute_file = os.path.join(project_dir, f\"chimeraX_attributes_conformation_{conformation}.defattr\")\n", + "\n", + "recipient = \"residues\"\n", + "matchmode = \"1-to-1\"\n", + "modelnr = \"2\"\n", + "\n", + "# write comments to the file explaining the attributes\n", + "with open(attribute_file, \"w\") as f:\n", + " f.write(\n", + " \"# ChimeraX attributes file\\n\"\n", + " )\n", + " f.write(\n", + " \"# This file contains attributes for the residues of the SARS-CoV-2 spike protein\\n\"\n", + " )\n", + " f.write(\n", + " \"# The attributes are the q-scores of the residues\\n\"\n", + " )\n", + "\n", + "# write the recipient and match modelines\n", + "with open(attribute_file, \"a\") as f:\n", + " f.write(\n", + " f\"recipient: {recipient}\\n\"\n", + " )\n", + " f.write(\n", + " f\"match mode: {matchmode}\\n\"\n", + " )\n", + " f.write(\n", + " f\"attribute: qscore\\n\"\n", + " )\n", + "\n", + "# add a line for each residue with the qscore as attribute\n", + "with open(attribute_file, \"a\") as f:\n", + " for i, row in df_qscores.iterrows():\n", + " atom_spec = f\"#{modelnr}/{row['Chain']}:{row['Number']}\"\n", + " attribute = row[\"Qbb\"]\n", + " f.write(\n", + " f\"\\t{atom_spec}\\t{attribute}\\n\"\n", + " )\n", + " \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "roodmus", + "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": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/paper_fig_ipynbs/plot_correlation_matrix_6xm5.ipynb b/paper_fig_ipynbs/plot_correlation_matrix_6xm5.ipynb new file mode 100644 index 0000000..9f23779 --- /dev/null +++ b/paper_fig_ipynbs/plot_correlation_matrix_6xm5.ipynb @@ -0,0 +1,372 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# plotting correlation between heterogeneous reconstruction and MD trajectory\n", + "This notebook computes the correlation between frames of the MD trajectory and volumes reconstructed by cryoDRGN / cryoSPARC" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# imports\n", + "import os\n", + "import numpy as np\n", + "import gemmi\n", + "from tqdm import tqdm\n", + "import itertools\n", + "from joblib import Parallel, delayed\n", + "import warnings\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from emmer.pdb.convert.convert_pdb_to_map import convert_pdb_to_map\n", + "from emmer.ndimage.filter.low_pass_filter import low_pass_filter" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# inputs and parameters\n", + "n_frames = 50\n", + "resolution = 3\n", + "n_jobs = 12\n", + "\n", + "## 1-dimensional latent space, plotted in figure S5E\n", + "\n", + "vseries_dir = \"../data/6xm5_steered_Roodmus_2/cryosparc_P51_J527_series_000\"\n", + "pdb_dir = \"../data/6xm5_steered_Roodmus_1/pdb\"\n", + "ensemble_filename = \"../data/6xm5_steered_Roodmus_2/cryosparc_P51_J527_series_000/ensemble_fit_to_vseries.pdb\"\n", + "mask_filename = \"../data/6xm5_steered_Roodmus_2/cryosparc_P51_J527_series_000/ensemble_mask.mrc\"\n", + "figures_dir = \"../data/6xm5_steered_Roodmus_2/figures/\"\n", + "latent_coordinates = np.linspace(-1.0827, 0.5707, 41)\n", + "inverted_maps = True\n", + "\n", + "## 2-dimensional latent space, plotted in figure S5F\n", + "\n", + "# vseries_dir = \"../data/6xm5_steered_Roodmus_2/cryosparc_P51_J620_series_000\"\n", + "# pdb_dir = \"../data/6xm5_steered_Roodmus_1/pdb\"\n", + "# ensemble_filename = \"../data/6xm5_steered_Roodmus_2/cryosparc_P51_J620_series_000/ensemble_fit_to_vseries.pdb\"\n", + "# mask_filename = \"../data/6xm5_steered_Roodmus_2/cryosparc_P51_J620_series_000/ensemble_mask.mrc\"\n", + "# figures_dir = \"../data/6xm5_steered_Roodmus_2/figures/\"\n", + "# latent_coordinates = np.linspace(-0.56, 0.69866663, 41)\n", + "# inverted_maps = True\n", + "\n", + "# vseries_dir = \"../data/6xm5_steered_Roodmus_2/cryosparc_P51_J620_series_001\"\n", + "# pdb_dir = \"../data/6xm5_steered_Roodmus_1/pdb\"\n", + "# ensemble_filename = \"../data/6xm5_steered_Roodmus_2/cryosparc_P51_J620_series_000/ensemble_fit_to_vseries.pdb\"\n", + "# mask_filename = \"../data/6xm5_steered_Roodmus_2/cryosparc_P51_J620_series_000/ensemble_mask.mrc\"\n", + "# figures_dir = \"../data/6xm5_steered_Roodmus_2/figures/\"\n", + "# latent_coordinates = np.linspace(-0.80533332, 0.87999994, 41)\n", + "# inverted_maps = True\n", + "\n", + "# vseries_dir = \"../data/6xm5_steered_Roodmus_2/cryosparc_P51_J641_series_000\"\n", + "# pdb_dir = \"../data/6xm5_steered_Roodmus_1/pdb\"\n", + "# ensemble_filename = \"../data/6xm5_steered_Roodmus_2/cryosparc_P51_J620_series_000/ensemble_fit_to_vseries.pdb\"\n", + "# mask_filename = \"../data/6xm5_steered_Roodmus_2/cryosparc_P51_J620_series_000/ensemble_mask.mrc\"\n", + "# figures_dir = \"../data/6xm5_steered_Roodmus_2/figures/\"\n", + "# latent_coordinates = np.linspace(0, 40, 41)\n", + "# inverted_maps = True\n", + "\n", + "frames = [r for r in os.listdir(pdb_dir) if r.endswith(\".pdb\")]\n", + "frames.sort()\n", + "frames = frames[::int(len(frames)/n_frames)]\n", + "\n", + "if inverted_maps:\n", + " volumes = [r for r in os.listdir(vseries_dir) if r.endswith(\"inverted.mrc\")]\n", + "else:\n", + " volumes = [r for r in os.listdir(vseries_dir) if r.endswith(\".mrc\") and not r.endswith(\"inverted.mrc\")]\n", + "volumes.sort()" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [], + "source": [ + "# step 0: create an ensemble.pdb file from the frames\n", + "ensemble_gemmi = gemmi.Structure()\n", + "for frame in frames:\n", + " frame_gemmi = gemmi.read_structure(os.path.join(pdb_dir, frame))\n", + " ensemble_gemmi.add_model(frame_gemmi[0])\n", + "\n", + "ensemble_gemmi.write_pdb(os.path.join(vseries_dir, \"ensemble.pdb\"))\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, load in the ensemble into ChimeraX and align it to the first volume in the series using fit in volume and manual placement.\n", + "Also create a mask from the atomic model using the molmap command. In the case of this protein, the mask will be limited to the moving residues.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 50/50 [01:07<00:00, 1.35s/it]\n" + ] + } + ], + "source": [ + "# step 1: load each frame and compute the modelmap\n", + "vseries_vol0 = gemmi.read_ccp4_map(os.path.join(vseries_dir, volumes[0]))\n", + "unitcell = vseries_vol0.grid.unit_cell\n", + "size = vseries_vol0.grid.shape\n", + "vsize = vseries_vol0.grid.spacing[0]\n", + "\n", + "# create list of frames\n", + "frames_fit = []\n", + "ensemble_fit_gemmi = gemmi.read_structure(ensemble_filename)\n", + "for mdl in ensemble_fit_gemmi:\n", + " tmp_struc = gemmi.Structure()\n", + " tmp_struc.add_model(mdl)\n", + " frames_fit.append(tmp_struc)\n", + "\n", + "modelmaps = []\n", + "for frame_gemmi in tqdm(frames_fit):\n", + " map_from_model_unfiltered = convert_pdb_to_map(\n", + " input_pdb=frame_gemmi,\n", + " unitcell=unitcell,\n", + " size=size,\n", + " return_grid=False,\n", + " )\n", + " map_from_model_zyx = low_pass_filter(\n", + " map_from_model_unfiltered, resolution, vsize\n", + " )\n", + " map_from_model = np.rot90(\n", + " np.flip(map_from_model_zyx, axis=0), axes=(2, 0)\n", + " )\n", + " modelmaps.append(map_from_model)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", + "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 6.5s\n", + "[Parallel(n_jobs=12)]: Done 8 tasks | elapsed: 6.5s\n", + "[Parallel(n_jobs=12)]: Done 17 tasks | elapsed: 6.6s\n", + "[Parallel(n_jobs=12)]: Done 26 tasks | elapsed: 6.7s\n", + "[Parallel(n_jobs=12)]: Done 37 tasks | elapsed: 6.9s\n", + "[Parallel(n_jobs=12)]: Done 48 tasks | elapsed: 7.0s\n", + "[Parallel(n_jobs=12)]: Done 61 tasks | elapsed: 7.2s\n", + "[Parallel(n_jobs=12)]: Done 74 tasks | elapsed: 7.3s\n", + "[Parallel(n_jobs=12)]: Batch computation too fast (0.1992s.) Setting batch_size=2.\n", + "[Parallel(n_jobs=12)]: Done 89 tasks | elapsed: 7.4s\n", + "[Parallel(n_jobs=12)]: Done 104 tasks | elapsed: 7.5s\n", + "[Parallel(n_jobs=12)]: Done 122 tasks | elapsed: 7.7s\n", + "[Parallel(n_jobs=12)]: Done 156 tasks | elapsed: 8.0s\n", + "[Parallel(n_jobs=12)]: Done 194 tasks | elapsed: 8.3s\n", + "[Parallel(n_jobs=12)]: Done 232 tasks | elapsed: 8.5s\n", + "[Parallel(n_jobs=12)]: Done 274 tasks | elapsed: 8.9s\n", + "[Parallel(n_jobs=12)]: Done 316 tasks | elapsed: 9.2s\n", + "[Parallel(n_jobs=12)]: Done 362 tasks | elapsed: 9.6s\n", + "[Parallel(n_jobs=12)]: Done 408 tasks | elapsed: 9.9s\n", + "[Parallel(n_jobs=12)]: Done 458 tasks | elapsed: 10.3s\n", + "[Parallel(n_jobs=12)]: Done 508 tasks | elapsed: 10.6s\n", + "[Parallel(n_jobs=12)]: Done 562 tasks | elapsed: 11.0s\n", + "[Parallel(n_jobs=12)]: Done 616 tasks | elapsed: 11.4s\n", + "[Parallel(n_jobs=12)]: Done 674 tasks | elapsed: 11.8s\n", + "[Parallel(n_jobs=12)]: Done 732 tasks | elapsed: 12.2s\n", + "[Parallel(n_jobs=12)]: Done 794 tasks | elapsed: 12.7s\n", + "[Parallel(n_jobs=12)]: Done 856 tasks | elapsed: 13.1s\n", + "[Parallel(n_jobs=12)]: Done 922 tasks | elapsed: 13.6s\n", + "[Parallel(n_jobs=12)]: Done 988 tasks | elapsed: 14.1s\n", + "[Parallel(n_jobs=12)]: Done 1058 tasks | elapsed: 14.6s\n", + "[Parallel(n_jobs=12)]: Done 1128 tasks | elapsed: 15.1s\n", + "[Parallel(n_jobs=12)]: Done 1202 tasks | elapsed: 15.7s\n", + "[Parallel(n_jobs=12)]: Done 1276 tasks | elapsed: 16.2s\n", + "[Parallel(n_jobs=12)]: Done 1354 tasks | elapsed: 16.8s\n", + "[Parallel(n_jobs=12)]: Done 1432 tasks | elapsed: 17.3s\n", + "[Parallel(n_jobs=12)]: Done 1514 tasks | elapsed: 17.9s\n", + "[Parallel(n_jobs=12)]: Done 1596 tasks | elapsed: 18.6s\n", + "[Parallel(n_jobs=12)]: Done 1682 tasks | elapsed: 19.2s\n", + "[Parallel(n_jobs=12)]: Done 1768 tasks | elapsed: 19.8s\n", + "[Parallel(n_jobs=12)]: Done 1858 tasks | elapsed: 20.5s\n", + "[Parallel(n_jobs=12)]: Done 1948 tasks | elapsed: 21.1s\n", + "[Parallel(n_jobs=12)]: Done 2050 out of 2050 | elapsed: 21.9s finished\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "correlation average: 0.45082173752186905\n", + "correlation std: 0.11617664042948961\n", + "correlation min: 0.2416038278133017\n", + "correlation max: 0.6391473465229378\n" + ] + } + ], + "source": [ + "# step 2: correlate each modelmap with the vseries'\n", + "def compute_correlation(modelmap, targetmap, mask, i, j):\n", + " \"\"\"Compute the correlation between two maps\"\"\"\n", + " \n", + " warnings.filterwarnings(\"ignore\", category=RuntimeWarning, message=\"invalid value encountered in scalar divide\")\n", + " # apply mask to both maps\n", + " modelmap_masked = modelmap * mask\n", + " targetmap_masked = targetmap * mask\n", + " # compute the mean of the two maps\n", + " mean_modelmap = np.mean(modelmap_masked)\n", + " mean_targetmap = np.mean(targetmap_masked)\n", + " # compute the standard deviation of the two maps\n", + " std_modelmap = np.std(modelmap_masked)\n", + " std_targetmap = np.std(targetmap_masked)\n", + " # compute the correlation\n", + " correlation = np.mean((modelmap_masked - mean_modelmap) * (targetmap_masked - mean_targetmap)) / (std_modelmap * std_targetmap)\n", + " return correlation, i, j\n", + "\n", + "\n", + "# load vseries\n", + "vseries_stack = []\n", + "for volume in volumes:\n", + " vseries_vol = gemmi.read_ccp4_map(os.path.join(vseries_dir, volume))\n", + " vseries_stack.append(np.array(vseries_vol.grid))\n", + "\n", + "\n", + "indecies = itertools.product(range(n_frames), range(len(volumes)))\n", + "mask = gemmi.read_ccp4_map(mask_filename)\n", + "mask_data = np.array(mask.grid, copy=False)\n", + "# threshold = mask_data[mask_data > 0].mean() - 2 * mask_data[mask_data > 0].std()\n", + "threshold = 0.5\n", + "mask_data[mask_data < threshold] = 0\n", + "mask_data[mask_data >= threshold] = 1\n", + "# mask_data = np.rot90(np.flip(mask_data, axis=0), axes=(2, 0))\n", + "\n", + "correlations = Parallel(n_jobs=n_jobs, verbose=10)(\n", + " delayed(compute_correlation)(modelmaps[i], vseries_stack[j], mask_data, i, j)\n", + " for i, j in indecies\n", + ")\n", + "correlation_matrix = np.zeros((n_frames, len(volumes)))\n", + "for correlation, i, j in correlations:\n", + " correlation_matrix[i, j] = correlation\n", + "\n", + "print(f\"correlation average: {np.mean(correlation_matrix)}\")\n", + "print(f\"correlation std: {np.std(correlation_matrix)}\")\n", + "print(f\"correlation min: {np.min(correlation_matrix)}\")\n", + "print(f\"correlation max: {np.max(correlation_matrix)}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "threshold: 0.5\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# create projections of the mask\n", + "print(f\"threshold: {threshold}\")\n", + "fig, ax = plt.subplots(1,2, figsize=(7, 3.5))\n", + "ax[0].imshow(mask_data.sum(axis=0))\n", + "ax[1].imshow(vseries_stack[0].sum(axis=0))\n", + "ax[0].axis(\"off\")\n", + "ax[1].axis(\"off\")\n", + "plt.tight_layout()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# step 3: plot the correlation matrix\n", + "fig, ax = plt.subplots(figsize=(7, 7))\n", + "ax.imshow(correlation_matrix, cmap=\"coolwarm\")\n", + "# change the xtick labels for latents\n", + "ax.set_xticks(np.arange(len(volumes)))\n", + "ax.set_xticklabels(\n", + " [np.round(r, 2) for r in latent_coordinates], rotation=90, fontsize=8)\n", + "# set the y-ticklabels to the frames in the pdb_dir\n", + "conformation_names = [float(r.split(\"_\")[1].strip(\".pdb\")) for r in frames]\n", + "ax.set_yticks(np.arange(len(frames)))\n", + "# ax.set_yticklabels(conformation_names)\n", + "ax.set_yticklabels([np.round(r*0.1, 1) for r in conformation_names])\n", + "fig.tight_layout()\n", + "\n", + "fig.savefig(os.path.join(figures_dir, f\"{os.path.basename(vseries_dir)}_correlation_matrix.png\"), dpi=300, bbox_inches=\"tight\")\n", + "fig.savefig(os.path.join(figures_dir, f\"{os.path.basename(vseries_dir)}_correlation_matrix.pdf\"), bbox_inches=\"tight\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "ensemble_refinement", + "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": "3.11.3" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/paper_fig_ipynbs/plot_correlation_matrix_RTC.ipynb b/paper_fig_ipynbs/plot_correlation_matrix_RTC.ipynb new file mode 100644 index 0000000..c2c26f5 --- /dev/null +++ b/paper_fig_ipynbs/plot_correlation_matrix_RTC.ipynb @@ -0,0 +1,1175 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# plotting correlation between heterogeneous reconstruction and MD trajectory\n", + "This notebook computes the correlation between frames of the MD trajectory and volumes reconstructed by cryoDRGN / cryoSPARC" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "# imports\n", + "import os\n", + "import numpy as np\n", + "import gemmi\n", + "from tqdm import tqdm\n", + "import itertools\n", + "from joblib import Parallel, delayed\n", + "import warnings\n", + "import matplotlib.pyplot as plt\n", + "import re\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "\n", + "from emmer.pdb.convert.convert_pdb_to_map import convert_pdb_to_map\n", + "from emmer.ndimage.filter.low_pass_filter import low_pass_filter" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "# functions\n", + "def filter_gemmi_model(model_gemmi, remove_sidechains=False):\n", + " # remove ligand residues and all sidechain atoms\n", + " list_of_residue_names = [\n", + " \"ZN\", \"A\", \"C\", \"G\", \"U\", \"ADP\", \"ATP\", \"ACE\", \"MG\",\n", + " ]\n", + " new_model_gemmi = gemmi.Model(model_gemmi.name)\n", + " for chn in model_gemmi:\n", + " new_chain_gemmi = gemmi.Chain(chn.name)\n", + " for res in chn:\n", + " if res.name not in list_of_residue_names:\n", + " new_res_gemmi = gemmi.Residue()\n", + " new_res_gemmi.name = res.name\n", + " new_res_gemmi.seqid.num = res.seqid.num\n", + " \n", + " if remove_sidechains:\n", + " for atm in res:\n", + " if atm.name == \"CA\":\n", + " new_atm_gemmi = gemmi.Atom()\n", + " new_atm_gemmi.name = atm.name\n", + " new_atm_gemmi.pos = atm.pos\n", + " new_atm_gemmi.element = gemmi.Element(atm.element.name)\n", + " new_res_gemmi.add_atom(new_atm_gemmi)\n", + "\n", + " else:\n", + " for atm in res:\n", + " new_atm_gemmi = gemmi.Atom()\n", + " new_atm_gemmi.name = atm.name\n", + " new_atm_gemmi.pos = atm.pos\n", + " new_atm_gemmi.element = gemmi.Element(atm.element.name)\n", + " new_res_gemmi.add_atom(new_atm_gemmi) \n", + "\n", + " new_chain_gemmi.add_residue(new_res_gemmi)\n", + " new_model_gemmi.add_chain(new_chain_gemmi)\n", + " return new_model_gemmi\n", + "\n", + "# functions\n", + "def compute_correlation(modelmap, targetmap, mask, i, j):\n", + " \"\"\"Compute the correlation between two maps\"\"\"\n", + " \n", + " warnings.filterwarnings(\"ignore\", category=RuntimeWarning, message=\"invalid value encountered in scalar divide\")\n", + " # apply mask to both maps\n", + " modelmap_masked = modelmap * mask\n", + " targetmap_masked = targetmap * mask\n", + " # compute the mean of the two maps\n", + " mean_modelmap = np.mean(modelmap_masked)\n", + " mean_targetmap = np.mean(targetmap_masked)\n", + " # compute the standard deviation of the two maps\n", + " std_modelmap = np.std(modelmap_masked)\n", + " std_targetmap = np.std(targetmap_masked)\n", + " # compute the correlation\n", + " correlation = np.mean((modelmap_masked - mean_modelmap) * (targetmap_masked - mean_targetmap)) / (std_modelmap * std_targetmap)\n", + " return correlation, i, j\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# create an ensemble structure\n", + "project_dir = \"/home/mjoosten1/projects/roodmus/data/DE-Shaw_covid_RTC/20240124_DESRES-Trajectory_sarscov2-13795965-no-water-movies\"\n", + "pdb_dir = \"/home/mjoosten1/projects/roodmus/data/DE-Shaw_covid_RTC/mnt/ceph/JGreer/ConformationSampling/DESRES-Trajectory_sarscov2-13795965-no-water/even_sampling/even_sampling_10000\"\n", + "output_dir = os.path.join(project_dir, \"cryoDRGN\", \"analyze_320\", \"aligned_ensemble\")\n", + "if not os.path.exists(output_dir):\n", + " os.makedirs(output_dir)\n", + "\n", + "pdb_files = [os.path.join(pdb_dir, f) for f in os.listdir(pdb_dir) if f.endswith(\".pdb\")]\n", + "pdb_files.sort()\n", + "N = 50\n", + "# sample N files from the total number of files, evenly spaced\n", + "pdb_files = pdb_files[::len(pdb_files)//N]\n", + "\n", + "ensemble_gemmi = gemmi.Structure()\n", + "ensemble_CA_gemmi = gemmi.Structure()\n", + "for filename in pdb_files:\n", + " pdb_path = os.path.join(pdb_dir, filename)\n", + " frame_gemmi = gemmi.read_structure(pdb_path)\n", + " frame_gemmi_filtered = filter_gemmi_model(frame_gemmi[0], remove_sidechains=False)\n", + " ensemble_gemmi.add_model(frame_gemmi_filtered)\n", + " frame_gemmi_CA = filter_gemmi_model(frame_gemmi[0], remove_sidechains=True)\n", + " ensemble_CA_gemmi.add_model(frame_gemmi_CA)\n", + "\n", + "# write the ensemble to a pdb file\n", + "ensemble_gemmi.write_pdb(os.path.join(output_dir, \"ensemble.pdb\"))\n", + "ensemble_CA_gemmi.write_pdb(os.path.join(output_dir, \"ensemble_CA.pdb\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ALA\n", + "ARG\n", + "ASN\n", + "ASP\n", + "CYM\n", + "CYS\n", + "GLN\n", + "GLU\n", + "GLY\n", + "HIS\n", + "ILE\n", + "LEU\n", + "LYS\n", + "MET\n", + "NME\n", + "PHE\n", + "PRO\n", + "SER\n", + "THR\n", + "TRP\n", + "TYR\n", + "VAL\n" + ] + } + ], + "source": [ + "unique_res = []\n", + "for chn in ensemble_gemmi[0]:\n", + " for res in chn:\n", + " unique_res.append(res.name)\n", + "unique_res = np.unique(unique_res)\n", + "for res in unique_res:\n", + " print(res)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/home/mjoosten1/projects/roodmus/data/DE-Shaw_covid_RTC/20240124_DESRES-Trajectory_sarscov2-13795965-no-water-movies\n" + ] + } + ], + "source": [ + "# save a specific frame without the ligand residues\n", + "pdb_dir = \"/home/mjoosten1/projects/roodmus/data/DE-Shaw_covid_RTC/mnt/ceph/JGreer/ConformationSampling/DESRES-Trajectory_sarscov2-13795965-no-water/even_sampling/even_sampling_10000\"\n", + "print(project_dir)\n", + "output_dir = os.path.join(project_dir, \"cryoDRGN\", \"analyze_320\", \"aligned_ensemble\")\n", + "\n", + "conformation = 1000\n", + "\n", + "conformation_filename = f\"conformation_{conformation:06d}.pdb\"\n", + "conformation_path = os.path.join(pdb_dir, conformation_filename)\n", + "conformation_gemmi = gemmi.read_structure(conformation_path)\n", + "conformation_gemmi_filtered = filter_gemmi_model(conformation_gemmi[0], remove_sidechains=False)\n", + "conformation_struct_gemmi = gemmi.Structure()\n", + "conformation_struct_gemmi.add_model(conformation_gemmi_filtered)\n", + "conformation_struct_gemmi.write_pdb(os.path.join(output_dir, conformation_filename))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## computing correlation matrix" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "# inputs and parameters\n", + "n_frames = 50\n", + "resolution = 3\n", + "n_jobs = 12\n", + "\n", + "vseries_dir = os.path.join(project_dir, \"cryoDRGN\", \"analyze_320\")\n", + "pdb_dir = \"/home/mjoosten1/projects/roodmus/data/DE-Shaw_covid_RTC/mnt/ceph/JGreer/ConformationSampling/DESRES-Trajectory_sarscov2-13795965-no-water/even_sampling/even_sampling_10000\"\n", + "ensemble_filename = os.path.join(project_dir, \"cryoDRGN\", \"analyze_320\", \"aligned_ensemble\", \"ensemble_aligned.pdb\")\n", + "mask_filename = os.path.join(project_dir, \"cryoDRGN\", \"analyze_320\", \"aligned_ensemble\", \"ensemble_map.mrc\")\n", + "figures_dir = \"/home/mjoosten1/projects/roodmus/data/DE-Shaw_covid_RTC/DESRES-Trajectory_sarscov2-13795965-no-water-movies/figures\"\n", + "latent_coordinates = np.arange(0, 50)\n", + "inverted_maps = False\n", + "\n", + "frames = [r for r in os.listdir(pdb_dir) if r.endswith(\".pdb\")]\n", + "frames.sort()\n", + "frames = frames[::int(len(frames)/n_frames)]\n", + "\n", + "if inverted_maps:\n", + " volumes = [r for r in os.listdir(vseries_dir) if r.endswith(\"inverted.mrc\") and \"mask\" not in r]\n", + "else:\n", + " volumes = [r for r in os.listdir(vseries_dir) if r.endswith(\".mrc\") and not r.endswith(\"inverted.mrc\") and \"mask\" not in r]\n", + "volumes.sort()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 50/50 [21:39<00:00, 25.99s/it]\n" + ] + } + ], + "source": [ + "# step 1: load each frame and compute the modelmap\n", + "vseries_vol0 = gemmi.read_ccp4_map(os.path.join(vseries_dir, volumes[0]))\n", + "unitcell = vseries_vol0.grid.unit_cell\n", + "size = vseries_vol0.grid.shape\n", + "vsize = vseries_vol0.grid.spacing[0]\n", + "\n", + "# create list of frames\n", + "frames_fit = []\n", + "ensemble_fit_gemmi = gemmi.read_structure(ensemble_filename)\n", + "for mdl in ensemble_fit_gemmi:\n", + " tmp_struc = gemmi.Structure()\n", + " tmp_struc.add_model(mdl)\n", + " frames_fit.append(tmp_struc)\n", + "\n", + "modelmaps = []\n", + "for frame_gemmi in tqdm(frames_fit):\n", + " map_from_model_unfiltered = convert_pdb_to_map(\n", + " input_pdb=frame_gemmi,\n", + " unitcell=unitcell,\n", + " size=size,\n", + " return_grid=False,\n", + " )\n", + " map_from_model_zyx = low_pass_filter(\n", + " map_from_model_unfiltered, resolution, vsize\n", + " )\n", + " map_from_model = np.rot90(\n", + " np.flip(map_from_model_zyx, axis=0), axes=(2, 0)\n", + " )\n", + " modelmaps.append(map_from_model)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", + "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 8.1s\n", + "[Parallel(n_jobs=12)]: Done 8 tasks | elapsed: 9.4s\n", + "[Parallel(n_jobs=12)]: Done 17 tasks | elapsed: 11.0s\n", + "[Parallel(n_jobs=12)]: Done 26 tasks | elapsed: 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1658 tasks | elapsed: 3.7min\n", + "[Parallel(n_jobs=12)]: Done 1717 tasks | elapsed: 3.8min\n", + "[Parallel(n_jobs=12)]: Done 1776 tasks | elapsed: 4.0min\n", + "[Parallel(n_jobs=12)]: Done 1837 tasks | elapsed: 4.1min\n", + "[Parallel(n_jobs=12)]: Done 1898 tasks | elapsed: 4.2min\n", + "[Parallel(n_jobs=12)]: Done 1961 tasks | elapsed: 4.4min\n", + "[Parallel(n_jobs=12)]: Done 2024 tasks | elapsed: 4.5min\n", + "[Parallel(n_jobs=12)]: Done 2089 tasks | elapsed: 4.6min\n", + "[Parallel(n_jobs=12)]: Done 2154 tasks | elapsed: 4.8min\n", + "[Parallel(n_jobs=12)]: Done 2221 tasks | elapsed: 4.9min\n", + "[Parallel(n_jobs=12)]: Done 2288 tasks | elapsed: 5.1min\n", + "[Parallel(n_jobs=12)]: Done 2357 tasks | elapsed: 5.2min\n", + "[Parallel(n_jobs=12)]: Done 2426 tasks | elapsed: 5.3min\n", + "[Parallel(n_jobs=12)]: Done 2500 out of 2500 | elapsed: 5.5min finished\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "correlation average: 0.6015237797739142\n", + "correlation std: 0.07300115615303975\n", + "correlation min: 0.41483429870060023\n", + "correlation max: 0.7188379431001728\n" + ] + } + ], + "source": [ + "# step 2: correlate each modelmap with the vseries'\n", + "\n", + "# load vseries\n", + "vseries_stack = []\n", + "for volume in volumes:\n", + " vseries_vol = gemmi.read_ccp4_map(os.path.join(vseries_dir, volume))\n", + " vseries_stack.append(np.array(vseries_vol.grid))\n", + "\n", + "indecies = itertools.product(range(n_frames), range(len(volumes)))\n", + "mask = gemmi.read_ccp4_map(mask_filename)\n", + "mask_data = np.array(mask.grid, copy=False)\n", + "# threshold = mask_data[mask_data > 0].mean() - 2 * mask_data[mask_data > 0].std()\n", + "threshold = 0.1\n", + "mask_data[mask_data < threshold] = 0\n", + "mask_data[mask_data >= threshold] = 1\n", + "# mask_data = np.rot90(np.flip(mask_data, axis=0), axes=(2, 0))\n", + "\n", + "correlations = Parallel(n_jobs=n_jobs, verbose=10)(\n", + " delayed(compute_correlation)(modelmaps[i], vseries_stack[j], mask_data, i, j)\n", + " for i, j in indecies\n", + ")\n", + "correlation_matrix = np.zeros((n_frames, len(volumes)))\n", + "for correlation, i, j in correlations:\n", + " correlation_matrix[i, j] = correlation\n", + "\n", + "print(f\"correlation average: {np.mean(correlation_matrix)}\")\n", + "print(f\"correlation std: {np.std(correlation_matrix)}\")\n", + "print(f\"correlation min: {np.min(correlation_matrix)}\")\n", + "print(f\"correlation max: {np.max(correlation_matrix)}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "# save the correlation matrix\n", + "np.save(os.path.join(project_dir, \"cryoDRGN\", \"analyze_320\", \"correlation_matrix.npy\"), correlation_matrix)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "threshold: 0.1\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# create projections of the mask\n", + "print(f\"threshold: {threshold}\")\n", + "fig, ax = plt.subplots(1,2, figsize=(7, 3.5))\n", + "ax[0].imshow(mask_data.sum(axis=0))\n", + "ax[1].imshow(vseries_stack[0].sum(axis=0))\n", + "ax[0].axis(\"off\")\n", + "ax[1].axis(\"off\")\n", + "plt.tight_layout()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# step 3: plot the correlation matrix\n", + "fig, ax = plt.subplots(figsize=(7, 7))\n", + "ax.imshow(correlation_matrix, cmap=\"coolwarm\")\n", + "# change the xtick labels for latents\n", + "# ax.set_xticks(np.arange(len(volumes)))\n", + "# ax.set_xticklabels(\n", + "# [np.round(r, 2) for r in latent_coordinates], rotation=90, fontsize=8)\n", + "# set the y-ticklabels to the frames in the pdb_dir\n", + "conformation_names = [float(r.split(\"_\")[1].strip(\".pdb\")) for r in frames]\n", + "yticks = np.linspace(0, len(frames), 10)\n", + "yticklabels = np.round(np.linspace(0, 500, 10, dtype=float), 1)\n", + "ax.set_yticks(yticks)\n", + "ax.set_yticklabels(yticklabels)\n", + "ax.set_ylabel(\"time (\\u03BCs)\", fontsize=16)\n", + "ax.set_xlabel(\"sampled volume\", fontsize=16)\n", + "# ax.set_yticklabels(np.round(np.linspace(0, 10, len(frames)), 2))\n", + "# ax.set_yticklabels(conformation_names)\n", + "# ax.set_yticklabels([np.round(r*1.2/1000, 1) for r in conformation_names])\n", + "# make a colorbar with the same size as the image\n", + "cbar = ax.figure.colorbar(ax.get_images()[0], ax=ax, orientation=\"vertical\", pad=0.01, shrink=0.8)\n", + "cbar.ax.tick_params(labelsize=14)\n", + "cbar.ax.set_ylabel(\"Correlation\", fontsize=16, rotation=270, labelpad=20)\n", + "ax.tick_params(axis=\"both\", which=\"major\", labelsize=14)\n", + "fig.tight_layout()\n", + "\n", + "# fig.savefig(os.path.join(figures_dir, f\"{os.path.basename(vseries_dir)}_correlation_matrix.png\"), dpi=300, bbox_inches=\"tight\")\n", + "# fig.savefig(os.path.join(figures_dir, f\"{os.path.basename(vseries_dir)}_correlation_matrix.pdf\"), bbox_inches=\"tight\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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volumebest_framebest_correlationworst_frameworst_correlation
0vol_000.mrcconformation_000200.pdb0.627356conformation_000000.pdb0.500968
1vol_001.mrcconformation_000400.pdb0.648770conformation_000000.pdb0.497100
2vol_002.mrcconformation_000400.pdb0.648869conformation_000000.pdb0.470880
3vol_003.mrcconformation_000400.pdb0.646540conformation_000000.pdb0.466356
4vol_004.mrcconformation_000400.pdb0.641638conformation_000000.pdb0.458567
5vol_005.mrcconformation_000400.pdb0.639067conformation_000000.pdb0.455865
6vol_006.mrcconformation_000400.pdb0.645420conformation_000000.pdb0.467018
7vol_007.mrcconformation_001600.pdb0.663088conformation_000000.pdb0.480602
8vol_008.mrcconformation_002000.pdb0.660753conformation_000000.pdb0.473428
9vol_009.mrcconformation_003600.pdb0.659719conformation_000000.pdb0.464484
10vol_010.mrcconformation_002400.pdb0.659785conformation_000000.pdb0.463538
11vol_011.mrcconformation_002400.pdb0.659505conformation_000000.pdb0.463600
12vol_012.mrcconformation_002400.pdb0.658926conformation_000000.pdb0.463887
13vol_013.mrcconformation_003600.pdb0.666065conformation_000000.pdb0.464616
14vol_014.mrcconformation_002800.pdb0.671710conformation_000000.pdb0.464195
15vol_015.mrcconformation_002800.pdb0.670483conformation_000000.pdb0.463132
16vol_016.mrcconformation_003600.pdb0.676708conformation_000000.pdb0.464797
17vol_017.mrcconformation_003600.pdb0.684720conformation_000000.pdb0.466755
18vol_018.mrcconformation_006000.pdb0.696543conformation_000000.pdb0.461165
19vol_019.mrcconformation_006000.pdb0.706509conformation_000000.pdb0.450308
20vol_020.mrcconformation_006000.pdb0.706921conformation_000000.pdb0.442383
21vol_021.mrcconformation_006000.pdb0.706149conformation_000000.pdb0.434162
22vol_022.mrcconformation_006000.pdb0.707058conformation_000000.pdb0.432441
23vol_023.mrcconformation_005200.pdb0.711741conformation_000000.pdb0.434521
24vol_024.mrcconformation_005200.pdb0.714090conformation_000000.pdb0.434719
25vol_025.mrcconformation_005200.pdb0.715746conformation_000000.pdb0.433974
26vol_026.mrcconformation_005200.pdb0.713249conformation_000000.pdb0.431757
27vol_027.mrcconformation_005200.pdb0.713081conformation_000000.pdb0.432559
28vol_028.mrcconformation_005200.pdb0.713506conformation_000000.pdb0.432427
29vol_029.mrcconformation_005200.pdb0.711981conformation_000000.pdb0.429652
30vol_030.mrcconformation_005200.pdb0.712126conformation_000000.pdb0.432120
31vol_031.mrcconformation_006000.pdb0.709216conformation_001000.pdb0.430627
32vol_032.mrcconformation_007200.pdb0.711415conformation_001000.pdb0.430847
33vol_033.mrcconformation_007600.pdb0.711925conformation_001000.pdb0.425568
34vol_034.mrcconformation_007600.pdb0.714928conformation_001000.pdb0.431058
35vol_035.mrcconformation_007600.pdb0.715022conformation_001000.pdb0.419995
36vol_036.mrcconformation_007800.pdb0.717654conformation_001000.pdb0.419677
37vol_037.mrcconformation_007600.pdb0.718495conformation_001000.pdb0.419696
38vol_038.mrcconformation_007600.pdb0.718660conformation_001000.pdb0.417342
39vol_039.mrcconformation_007600.pdb0.718095conformation_001000.pdb0.414834
40vol_040.mrcconformation_007600.pdb0.718298conformation_001000.pdb0.415836
41vol_041.mrcconformation_007600.pdb0.718191conformation_001000.pdb0.418859
42vol_042.mrcconformation_007600.pdb0.718838conformation_001000.pdb0.422090
43vol_043.mrcconformation_007600.pdb0.716274conformation_001000.pdb0.431882
44vol_044.mrcconformation_007600.pdb0.715900conformation_001000.pdb0.423408
45vol_045.mrcconformation_007600.pdb0.712411conformation_001000.pdb0.425782
46vol_046.mrcconformation_007600.pdb0.707082conformation_001000.pdb0.430436
47vol_047.mrcconformation_007600.pdb0.706737conformation_001000.pdb0.429008
48vol_048.mrcconformation_007600.pdb0.706315conformation_001000.pdb0.431358
49vol_049.mrcconformation_007600.pdb0.708058conformation_001000.pdb0.431484
\n", + "
" + ], + "text/plain": [ + " volume best_frame best_correlation \\\n", + "0 vol_000.mrc conformation_000200.pdb 0.627356 \n", + "1 vol_001.mrc conformation_000400.pdb 0.648770 \n", + "2 vol_002.mrc conformation_000400.pdb 0.648869 \n", + "3 vol_003.mrc conformation_000400.pdb 0.646540 \n", + "4 vol_004.mrc conformation_000400.pdb 0.641638 \n", + "5 vol_005.mrc conformation_000400.pdb 0.639067 \n", + "6 vol_006.mrc conformation_000400.pdb 0.645420 \n", + "7 vol_007.mrc conformation_001600.pdb 0.663088 \n", + "8 vol_008.mrc conformation_002000.pdb 0.660753 \n", + "9 vol_009.mrc conformation_003600.pdb 0.659719 \n", + "10 vol_010.mrc conformation_002400.pdb 0.659785 \n", + "11 vol_011.mrc conformation_002400.pdb 0.659505 \n", + "12 vol_012.mrc conformation_002400.pdb 0.658926 \n", + "13 vol_013.mrc conformation_003600.pdb 0.666065 \n", + "14 vol_014.mrc conformation_002800.pdb 0.671710 \n", + "15 vol_015.mrc conformation_002800.pdb 0.670483 \n", + "16 vol_016.mrc conformation_003600.pdb 0.676708 \n", + "17 vol_017.mrc conformation_003600.pdb 0.684720 \n", + "18 vol_018.mrc conformation_006000.pdb 0.696543 \n", + "19 vol_019.mrc conformation_006000.pdb 0.706509 \n", + "20 vol_020.mrc conformation_006000.pdb 0.706921 \n", + "21 vol_021.mrc conformation_006000.pdb 0.706149 \n", + "22 vol_022.mrc conformation_006000.pdb 0.707058 \n", + "23 vol_023.mrc conformation_005200.pdb 0.711741 \n", + "24 vol_024.mrc conformation_005200.pdb 0.714090 \n", + "25 vol_025.mrc conformation_005200.pdb 0.715746 \n", + "26 vol_026.mrc conformation_005200.pdb 0.713249 \n", + "27 vol_027.mrc conformation_005200.pdb 0.713081 \n", + "28 vol_028.mrc conformation_005200.pdb 0.713506 \n", + "29 vol_029.mrc conformation_005200.pdb 0.711981 \n", + "30 vol_030.mrc conformation_005200.pdb 0.712126 \n", + "31 vol_031.mrc conformation_006000.pdb 0.709216 \n", + "32 vol_032.mrc conformation_007200.pdb 0.711415 \n", + "33 vol_033.mrc conformation_007600.pdb 0.711925 \n", + "34 vol_034.mrc conformation_007600.pdb 0.714928 \n", + "35 vol_035.mrc conformation_007600.pdb 0.715022 \n", + "36 vol_036.mrc conformation_007800.pdb 0.717654 \n", + "37 vol_037.mrc conformation_007600.pdb 0.718495 \n", + "38 vol_038.mrc conformation_007600.pdb 0.718660 \n", + "39 vol_039.mrc conformation_007600.pdb 0.718095 \n", + "40 vol_040.mrc conformation_007600.pdb 0.718298 \n", + "41 vol_041.mrc conformation_007600.pdb 0.718191 \n", + "42 vol_042.mrc conformation_007600.pdb 0.718838 \n", + "43 vol_043.mrc conformation_007600.pdb 0.716274 \n", + "44 vol_044.mrc conformation_007600.pdb 0.715900 \n", + "45 vol_045.mrc conformation_007600.pdb 0.712411 \n", + "46 vol_046.mrc conformation_007600.pdb 0.707082 \n", + "47 vol_047.mrc conformation_007600.pdb 0.706737 \n", + "48 vol_048.mrc conformation_007600.pdb 0.706315 \n", + "49 vol_049.mrc conformation_007600.pdb 0.708058 \n", + "\n", + " worst_frame worst_correlation \n", + "0 conformation_000000.pdb 0.500968 \n", + "1 conformation_000000.pdb 0.497100 \n", + "2 conformation_000000.pdb 0.470880 \n", + "3 conformation_000000.pdb 0.466356 \n", + "4 conformation_000000.pdb 0.458567 \n", + "5 conformation_000000.pdb 0.455865 \n", + "6 conformation_000000.pdb 0.467018 \n", + "7 conformation_000000.pdb 0.480602 \n", + "8 conformation_000000.pdb 0.473428 \n", + "9 conformation_000000.pdb 0.464484 \n", + "10 conformation_000000.pdb 0.463538 \n", + "11 conformation_000000.pdb 0.463600 \n", + "12 conformation_000000.pdb 0.463887 \n", + "13 conformation_000000.pdb 0.464616 \n", + "14 conformation_000000.pdb 0.464195 \n", + "15 conformation_000000.pdb 0.463132 \n", + "16 conformation_000000.pdb 0.464797 \n", + "17 conformation_000000.pdb 0.466755 \n", + "18 conformation_000000.pdb 0.461165 \n", + "19 conformation_000000.pdb 0.450308 \n", + "20 conformation_000000.pdb 0.442383 \n", + "21 conformation_000000.pdb 0.434162 \n", + "22 conformation_000000.pdb 0.432441 \n", + "23 conformation_000000.pdb 0.434521 \n", + "24 conformation_000000.pdb 0.434719 \n", + "25 conformation_000000.pdb 0.433974 \n", + "26 conformation_000000.pdb 0.431757 \n", + "27 conformation_000000.pdb 0.432559 \n", + "28 conformation_000000.pdb 0.432427 \n", + "29 conformation_000000.pdb 0.429652 \n", + "30 conformation_000000.pdb 0.432120 \n", + "31 conformation_001000.pdb 0.430627 \n", + "32 conformation_001000.pdb 0.430847 \n", + "33 conformation_001000.pdb 0.425568 \n", + "34 conformation_001000.pdb 0.431058 \n", + "35 conformation_001000.pdb 0.419995 \n", + "36 conformation_001000.pdb 0.419677 \n", + "37 conformation_001000.pdb 0.419696 \n", + "38 conformation_001000.pdb 0.417342 \n", + "39 conformation_001000.pdb 0.414834 \n", + "40 conformation_001000.pdb 0.415836 \n", + "41 conformation_001000.pdb 0.418859 \n", + "42 conformation_001000.pdb 0.422090 \n", + "43 conformation_001000.pdb 0.431882 \n", + "44 conformation_001000.pdb 0.423408 \n", + "45 conformation_001000.pdb 0.425782 \n", + "46 conformation_001000.pdb 0.430436 \n", + "47 conformation_001000.pdb 0.429008 \n", + "48 conformation_001000.pdb 0.431358 \n", + "49 conformation_001000.pdb 0.431484 " + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# for each volume, print the frame with the highest and lowest correlation\n", + "results = {\n", + " \"volume\": [],\n", + " \"best_frame\": [],\n", + " \"best_correlation\": [],\n", + " \"worst_frame\": [],\n", + " \"worst_correlation\": [],\n", + "}\n", + "for i, volume in enumerate(volumes):\n", + " max_correlation = np.max(correlation_matrix[:, i])\n", + " max_correlation_index = np.argmax(correlation_matrix[:, i])\n", + " min_correlation = np.min(correlation_matrix[:, i])\n", + " min_correlation_index = np.argmin(correlation_matrix[:, i])\n", + " results[\"volume\"].append(volume)\n", + " results[\"best_frame\"].append(frames[max_correlation_index])\n", + " results[\"best_correlation\"].append(max_correlation)\n", + " results[\"worst_frame\"].append(frames[min_correlation_index])\n", + " results[\"worst_correlation\"].append(min_correlation)\n", + "\n", + "results_df = pd.DataFrame(results)\n", + "results_df.head(n=len(volumes))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# misc: plot loss function" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "<>:9: DeprecationWarning: invalid escape sequence '\\d'\n", + "<>:9: DeprecationWarning: invalid escape sequence '\\d'\n", + "/tmp/ipykernel_30441/4129352480.py:9: DeprecationWarning: invalid escape sequence '\\d'\n", + " pattern = \"Epoch: (\\d+) Average gen loss = ([0-9.]+), KLD = ([0-9.]+), total loss = ([0-9.]+)\"\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot the loss as a function of epoch\n", + "logfile = os.path.join(project_dir, \"cryoDRGN\", \"train_320\", \"run.log\")\n", + "losses = {\n", + " \"epoch\": [],\n", + " \"gen\": [],\n", + " \"KLD\": [],\n", + " \"total\": [],\n", + "}\n", + "pattern = \"Epoch: (\\d+) Average gen loss = ([0-9.]+), KLD = ([0-9.]+), total loss = ([0-9.]+)\"\n", + "with open(logfile, \"r\") as f:\n", + " lines = f.readlines()\n", + " for line in lines:\n", + " match = re.search(pattern, line)\n", + " if match:\n", + " losses[\"epoch\"].append(int(match.group(1)))\n", + " losses[\"gen\"].append(float(match.group(2)))\n", + " losses[\"KLD\"].append(float(match.group(3)))\n", + " losses[\"total\"].append(float(match.group(4)))\n", + "\n", + "df = pd.DataFrame(losses)\n", + "\n", + "fig, ax = plt.subplots(figsize=(3.5, 3.5))\n", + "sns.lineplot(data=df, x=\"epoch\", y=\"total\", ax=ax, label=\"total\", legend=False)\n", + "sns.lineplot(data=df, x=\"epoch\", y=\"gen\", ax=ax, label=\"gen\", legend=False)\n", + "# plot the KLD on a secondary y-axis\n", + "ax2 = ax.twinx()\n", + "sns.lineplot(data=df, x=\"epoch\", y=\"KLD\", ax=ax2, color=\"green\", linestyle=\"--\", label=\"KLD\", legend=False)\n", + "ax.set_xlabel(\"Epoch\")\n", + "ax.set_ylabel(\"Loss\")\n", + "ax2.set_ylabel(\"KLD\")\n", + "fig.legend(loc=\"upper right\", bbox_to_anchor=(1.3, 0.9))\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "ensemble_refinement", + "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": "3.10.13" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/paper_fig_ipynbs/plot_correlation_matrix_c3c3b.ipynb b/paper_fig_ipynbs/plot_correlation_matrix_c3c3b.ipynb new file mode 100644 index 0000000..f49ffa3 --- /dev/null +++ b/paper_fig_ipynbs/plot_correlation_matrix_c3c3b.ipynb @@ -0,0 +1,472 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# plotting correlation between heterogeneous reconstruction and MD trajectory\n", + "This notebook computes the correlation between frames of the MD trajectory and volumes reconstructed by cryoDRGN / cryoSPARC" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# imports\n", + "import os\n", + "import numpy as np\n", + "import gemmi\n", + "from tqdm import tqdm\n", + "import itertools\n", + "from joblib import Parallel, delayed\n", + "import warnings\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from emmer.pdb.convert.convert_pdb_to_map import convert_pdb_to_map\n", + "from emmer.ndimage.filter.low_pass_filter import low_pass_filter" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# functions\n", + "def filter_gemmi_model(model_gemmi, remove_sidechains=False):\n", + " # remove ligand residues and all sidechain atoms\n", + " list_of_residue_names = [\n", + " \"F2A\", \"H2A\", \"NLM\", \"NM5\", \"NM7\", \"NME\"\n", + " ]\n", + " new_model_gemmi = gemmi.Model(model_gemmi.name)\n", + " for chn in model_gemmi:\n", + " new_chain_gemmi = gemmi.Chain(chn.name)\n", + " for res in chn:\n", + " if res.name not in list_of_residue_names:\n", + " new_res_gemmi = gemmi.Residue()\n", + " new_res_gemmi.name = res.name\n", + " new_res_gemmi.seqid.num = res.seqid.num\n", + " \n", + " if remove_sidechains:\n", + " for atm in res:\n", + " if atm.name == \"CA\":\n", + " new_atm_gemmi = gemmi.Atom()\n", + " new_atm_gemmi.name = atm.name\n", + " new_atm_gemmi.pos = atm.pos\n", + " new_atm_gemmi.element = gemmi.Element(atm.element.name)\n", + " new_res_gemmi.add_atom(new_atm_gemmi)\n", + "\n", + " else:\n", + " for atm in res:\n", + " new_atm_gemmi = gemmi.Atom()\n", + " new_atm_gemmi.name = atm.name\n", + " new_atm_gemmi.pos = atm.pos\n", + " new_atm_gemmi.element = gemmi.Element(atm.element.name)\n", + " new_res_gemmi.add_atom(new_atm_gemmi) \n", + "\n", + " new_chain_gemmi.add_residue(new_res_gemmi)\n", + " new_model_gemmi.add_chain(new_chain_gemmi)\n", + " return new_model_gemmi\n", + "\n", + "# functions\n", + "def compute_correlation(modelmap, targetmap, mask, i, j):\n", + " \"\"\"Compute the correlation between two maps\"\"\"\n", + " \n", + " warnings.filterwarnings(\"ignore\", category=RuntimeWarning, message=\"invalid value encountered in scalar divide\")\n", + " # apply mask to both maps\n", + " modelmap_masked = modelmap * mask\n", + " targetmap_masked = targetmap * mask\n", + " # compute the mean of the two maps\n", + " mean_modelmap = np.mean(modelmap_masked)\n", + " mean_targetmap = np.mean(targetmap_masked)\n", + " # compute the standard deviation of the two maps\n", + " std_modelmap = np.std(modelmap_masked)\n", + " std_targetmap = np.std(targetmap_masked)\n", + " # compute the correlation\n", + " correlation = np.mean((modelmap_masked - mean_modelmap) * (targetmap_masked - mean_targetmap)) / (std_modelmap * std_targetmap)\n", + " return correlation, i, j\n", + "\n", + "\n", + "def compute_correlation(modelmap, targetmap, mask, i, j):\n", + " \"\"\"Compute the correlation between two maps\"\"\"\n", + " \n", + " warnings.filterwarnings(\"ignore\", category=RuntimeWarning, message=\"invalid value encountered in scalar divide\")\n", + " # apply mask to both maps\n", + " modelmap_masked = modelmap * mask\n", + " targetmap_masked = targetmap * mask\n", + " # compute the mean of the two maps\n", + " mean_modelmap = np.mean(modelmap_masked)\n", + " mean_targetmap = np.mean(targetmap_masked)\n", + " # compute the standard deviation of the two maps\n", + " std_modelmap = np.std(modelmap_masked)\n", + " std_targetmap = np.std(targetmap_masked)\n", + " # compute the correlation\n", + " correlation = np.mean((modelmap_masked - mean_modelmap) * (targetmap_masked - mean_targetmap)) / (std_modelmap * std_targetmap)\n", + " return correlation, i, j\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## create an ensemble structure to fit to the sampled volumes" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# create an ensemble structure\n", + "project_dir = \"/home/mjoosten1/projects/roodmus/data/c3c3b\"\n", + "pdb_dir = os.path.join(project_dir, \"pdb\")\n", + "output_dir = os.path.join(project_dir, \"cryosparc_P51_J696_series_000\", \"aligned_ensemble\")\n", + "if not os.path.exists(output_dir):\n", + " os.makedirs(output_dir)\n", + "\n", + "pdb_files = [os.path.join(pdb_dir, f) for f in os.listdir(pdb_dir) if f.endswith(\".pdb\")]\n", + "pdb_files.sort()\n", + "N = 50\n", + "# sample N files from the total number of files, evenly spaced\n", + "pdb_files = pdb_files[::len(pdb_files)//N]\n", + "\n", + "ensemble_gemmi = gemmi.Structure()\n", + "ensemble_CA_gemmi = gemmi.Structure()\n", + "for filename in pdb_files:\n", + " pdb_path = os.path.join(pdb_dir, filename)\n", + " frame_gemmi = gemmi.read_structure(pdb_path)\n", + " frame_gemmi_filtered = filter_gemmi_model(frame_gemmi[0], remove_sidechains=False)\n", + " ensemble_gemmi.add_model(frame_gemmi_filtered)\n", + " frame_gemmi_CA = filter_gemmi_model(frame_gemmi[0], remove_sidechains=True)\n", + " ensemble_CA_gemmi.add_model(frame_gemmi_CA)\n", + "\n", + "# write the ensemble to a pdb file\n", + "ensemble_gemmi.write_pdb(os.path.join(output_dir, \"ensemble.pdb\"))\n", + "ensemble_CA_gemmi.write_pdb(os.path.join(output_dir, \"ensemble_CA.pdb\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# computing correlation matrix" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# inputs and parameters\n", + "n_frames = 50\n", + "resolution = 3\n", + "n_jobs = 12\n", + "\n", + "project_dir = \"/home/mjoosten1/projects/roodmus/data/c3c3b\"\n", + "\n", + "# vseries_dir = \"../data/c3c3b/cryosparc_P51_J689_series_000\"\n", + "# pdb_dir = \"../data/c3c3b/pdb\"\n", + "# ensemble_filename = \"../data/c3c3b/cryosparc_P51_J689_series_000/ensemble_fit_to_vseries.pdb\"\n", + "# mask_filename = \"../data/c3c3b/cryosparc_P51_J689_series_000/ensemble_mask.mrc\"\n", + "# figures_dir = \"../data/c3c3b/figures/\"\n", + "# latent_coordinates = np.linspace(-0.56, 0.70933336, 41)\n", + "# inverted_maps = False\n", + "\n", + "# vseries_dir = \"../data/c3c3b/cryosparc_P51_J689_series_001\"\n", + "# pdb_dir = \"../data/c3c3b/pdb\"\n", + "# ensemble_filename = \"../data/c3c3b/cryosparc_P51_J689_series_000/ensemble_fit_to_vseries.pdb\"\n", + "# mask_filename = \"../data/c3c3b/cryosparc_P51_J689_series_000/ensemble_mask.mrc\"\n", + "# figures_dir = \"../data/c3c3b/figures/\"\n", + "# latent_coordinates = np.linspace(-0.65600002, 0.86933339, 41)\n", + "# inverted_maps = False\n", + "\n", + "vseries_dir = os.path.join(project_dir, \"cryosparc_P51_J696_series_000\")\n", + "pdb_dir = os.path.join(project_dir, \"pdb\")\n", + "ensemble_filename = os.path.join(project_dir, \"cryosparc_P51_J696_series_000\", \"aligned_ensemble\", \"ensemble_aligned.pdb\")\n", + "mask_filename = os.path.join(project_dir, \"cryosparc_P51_J696_series_000\", \"aligned_ensemble\", \"ensemble_map.mrc\")\n", + "figures_dir = os.path.join(project_dir, \"figures\")\n", + "latent_coordinates = np.arange(0, 41)\n", + "\n", + "frames = [r for r in os.listdir(pdb_dir) if r.endswith(\".pdb\")]\n", + "frames.sort()\n", + "frames = frames[::int(len(frames)/n_frames)]\n", + "\n", + "volumes = [r for r in os.listdir(vseries_dir) if r.endswith(\".mrc\") and not r.endswith(\"inverted.mrc\") and \"mask\" not in r]\n", + "volumes.sort()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 50/50 [01:12<00:00, 1.45s/it]\n" + ] + } + ], + "source": [ + "# step 1: load each frame and compute the modelmap\n", + "vseries_vol0 = gemmi.read_ccp4_map(os.path.join(vseries_dir, volumes[0]))\n", + "unitcell = vseries_vol0.grid.unit_cell\n", + "size = vseries_vol0.grid.shape\n", + "vsize = vseries_vol0.grid.spacing[0]\n", + "\n", + "# create list of frames\n", + "frames_fit = []\n", + "ensemble_fit_gemmi = gemmi.read_structure(ensemble_filename)\n", + "for mdl in ensemble_fit_gemmi:\n", + " tmp_struc = gemmi.Structure()\n", + " tmp_struc.add_model(mdl)\n", + " frames_fit.append(tmp_struc)\n", + "\n", + "modelmaps = []\n", + "for frame_gemmi in tqdm(frames_fit):\n", + " map_from_model_unfiltered = convert_pdb_to_map(\n", + " input_pdb=frame_gemmi,\n", + " unitcell=unitcell,\n", + " size=size,\n", + " return_grid=False,\n", + " )\n", + " map_from_model_zyx = low_pass_filter(\n", + " map_from_model_unfiltered, resolution, vsize\n", + " )\n", + " map_from_model = np.rot90(\n", + " np.flip(map_from_model_zyx, axis=0), axes=(2, 0)\n", + " )\n", + " modelmaps.append(map_from_model)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", + "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.9s\n", + "[Parallel(n_jobs=12)]: Done 8 tasks | elapsed: 1.0s\n", + "[Parallel(n_jobs=12)]: Done 17 tasks | elapsed: 1.2s\n", + "[Parallel(n_jobs=12)]: Done 26 tasks | elapsed: 1.3s\n", + "[Parallel(n_jobs=12)]: Done 37 tasks | elapsed: 1.5s\n", + "[Parallel(n_jobs=12)]: Done 48 tasks | elapsed: 1.6s\n", + "[Parallel(n_jobs=12)]: Done 61 tasks | 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Setting batch_size=2.\n", + "[Parallel(n_jobs=12)]: Done 661 tasks | elapsed: 9.2s\n", + "[Parallel(n_jobs=12)]: Done 724 tasks | elapsed: 9.8s\n", + "[Parallel(n_jobs=12)]: Done 802 tasks | elapsed: 10.5s\n", + "[Parallel(n_jobs=12)]: Done 880 tasks | elapsed: 11.1s\n", + "[Parallel(n_jobs=12)]: Done 962 tasks | elapsed: 11.9s\n", + "[Parallel(n_jobs=12)]: Done 1044 tasks | elapsed: 12.8s\n", + "[Parallel(n_jobs=12)]: Done 1130 tasks | elapsed: 13.6s\n", + "[Parallel(n_jobs=12)]: Done 1216 tasks | elapsed: 14.4s\n", + "[Parallel(n_jobs=12)]: Done 1306 tasks | elapsed: 15.1s\n", + "[Parallel(n_jobs=12)]: Done 1396 tasks | elapsed: 15.9s\n", + "[Parallel(n_jobs=12)]: Done 1490 tasks | elapsed: 16.7s\n", + "[Parallel(n_jobs=12)]: Done 1584 tasks | elapsed: 17.4s\n", + "[Parallel(n_jobs=12)]: Done 1682 tasks | elapsed: 18.3s\n", + "[Parallel(n_jobs=12)]: Done 1780 tasks | elapsed: 19.1s\n", + "[Parallel(n_jobs=12)]: Done 1882 tasks | elapsed: 20.0s\n", + "[Parallel(n_jobs=12)]: Done 2000 out of 2000 | elapsed: 20.9s finished\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "correlation average: 0.5458546919279752\n", + "correlation std: 0.05900050110800744\n", + "correlation min: 0.42985446276089295\n", + "correlation max: 0.7504688294040888\n" + ] + } + ], + "source": [ + "# step 2: correlate each modelmap with the vseries'\n", + "\n", + "# load vseries\n", + "vseries_stack = []\n", + "for volume in volumes:\n", + " vseries_vol = gemmi.read_ccp4_map(os.path.join(vseries_dir, volume))\n", + " vseries_stack.append(np.array(vseries_vol.grid))\n", + "\n", + "indecies = itertools.product(range(n_frames), range(len(volumes)))\n", + "mask = gemmi.read_ccp4_map(mask_filename)\n", + "mask_data = np.array(mask.grid, copy=False)\n", + "# threshold = mask_data[mask_data > 0].mean() - 2 * mask_data[mask_data > 0].std()\n", + "threshold = 0.1\n", + "mask_data[mask_data < threshold] = 0\n", + "mask_data[mask_data >= threshold] = 1\n", + "# mask_data = np.rot90(np.flip(mask_data, axis=0), axes=(2, 0))\n", + "\n", + "correlations = Parallel(n_jobs=n_jobs, verbose=10)(\n", + " delayed(compute_correlation)(modelmaps[i], vseries_stack[j], mask_data, i, j)\n", + " for i, j in indecies\n", + ")\n", + "correlation_matrix = np.zeros((n_frames, len(volumes)))\n", + "for correlation, i, j in correlations:\n", + " correlation_matrix[i, j] = correlation\n", + "\n", + "print(f\"correlation average: {np.mean(correlation_matrix)}\")\n", + "print(f\"correlation std: {np.std(correlation_matrix)}\")\n", + "print(f\"correlation min: {np.min(correlation_matrix)}\")\n", + "print(f\"correlation max: {np.max(correlation_matrix)}\")\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "threshold: 0.1\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# create projections of the mask\n", + "print(f\"threshold: {threshold}\")\n", + "fig, ax = plt.subplots(1,2, figsize=(7, 3.5))\n", + "ax[0].imshow(mask_data.sum(axis=0))\n", + "ax[1].imshow(vseries_stack[0].sum(axis=0))\n", + "ax[0].axis(\"off\")\n", + "ax[1].axis(\"off\")\n", + "plt.tight_layout()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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4/vnnMWvWLHEZnTp1wpYtW9C1a1eEhYWhY8eOOHTokA6tJSIiorrCZFbEi7l4XbZwcSUA6N+/P9LS0pCYmIjVq1ejqKgIcXFxmDdvHiZMmCAqy917W9XNZf3798eBAwewe/dubNmyBYWFhWjSpAnuuecezJgxAz169BAfh78ZMsG99dZbq11GkyZNysxTR0RERORvPXr0wKefflplXFJSUrk9t26vv/46Xn/99SrLiY+PF4/trWsMmeASERER+UIJUMRLTXtdtg89uOQbJrg6cjgccDgcnr/tdnsttoaIiIjo8sAEV0cpKSlITk4us/1CoQWq2btTr2qyOlVVFu8S/jfpEpZf7JTFA0BuvuygL1yQNepCbrEoPie7QBRfmF8kilelJ5WqpPeMJ9LeHbNZdue0ySJbRSkoOEAUHxgoK9+X3izpNTCbZfHS8k3COYOk16yoSPa5ZRHOhyo9P2bhQlz+mCVI7yq8fZkWFlR+rUyWujUGl3xjyGnC6opZs2YhJyfH88jKyqrtJhEREREZHntwdWS1Wv2+vjQRERH5jmNwjYEJLhEREVEJk1mBSTgkxOuyXUxw/YVDFIiIiIjIUC77HlybzQabzYbo6GhER0fXdnOIiIioFilmBYpOPbgK2IPrL4ZMcJcsWYK0tDQAwI8//ujZlpqaCgAYOXIkRo4cCaDyZe0uXd/5l19+KbPtH//4B5NiIiIiojrGkAluWloa3n333VLbtm3bhm3btgEAYmNjPQluZf5Yxh+3JSUlMcElIiIyEF3H4LIH128MmeAuX74cy5cv9yq2smXtNE04CS0RERER1TpDJrhEREREvlBMOk4TprEH1184iwIRERERGQp7cImIiIhKKGYTFOFSzV6XDQ599BcmuLXAZNJgMnn3IleE7wWTcLFvRViBdL1yX4Yxm4U/Dem95ro0XrqGvabyA6+2SX+O1OvnSzdp8Sad2yN9DwDyY5DWYRLmH+LyhTcZmYXxFou+5euUn5WiCOuQfj9JeVu89NxT/cQEl4iIiKgEZ1EwBo7BJSIiIiJDYQ8uERERUQlF0XEWBZU9uP7CHlwiIiIiMhT24BIRERGVUMzymwy9Lpv3FPsNe3CJiIiIyFDYg0tERERUQjErUHTrweUYXH9hDy4RERERGQp7cImIiIhKKCYTFOlKIoKyyT94pomIiIjIUNiDS0RERFRCMek4D67Oy2rT75jg1oKoEAdCQx1exWrCAemacAoSpyrrxFeF7XE45T8SmE1mXePlQkTRhYXFonhnsSqK15uJH8BVUrxd9L6EWXjDisUie98EBcs+yqXlS9sPAMJTBOkvt9LXqd7lBwbI4q2BonCYhe2Xnn9pPADUtY8Kb48hsIo4XZfq5U1mfsMhCkRERERkKOzBJSIiIirBIQrGwB5cIiIiIjIU9uASERERlVAUHacJU9iv6C8800RERERkKOzBJSIiIirBMbjGwB5cIiIiIjIU9uASERERldB1HlyVPbj+wh5cIiIiIjIU9uASERERleAYXGNgDy4RERERGQp7cGtBeGAhGgQGeBWrCdetViGMF5bvUs2ieEeALP4i4SLtkNWhasJ4VRQOa5CwfJcmq4BqnLdr2Hvihb0w0k4bi0XW92C1yiqwCMcX+jJ1p0l4UqXXwCxsk/QYpOUHCL9NA4XxZpPsc0I6jas/OhYVRd/POm9fQ6YqPtQVk47z4OpULpXFM01EREREhsIeXCIiIqISHINrDOzBJSIiIiJDYQ8uERERUQn24BoDe3CJiIiIyFDYg0tERERUgj24xsAeXCIiIiIyFPbgEhEREZW42IOr1zy47MH1Fya4RERERCUUkwKTcDEUr8t2McH1Fw5RICIiIiJDYQ8uERERUQneZGYMTHBrQYiSjxCT2atYTdjJrkH25lGl8ZqsPUGa/CUm/VnBYgoQxavCNmmad9fKrdAhO6dOp97rs/MDtSrSUyQdnmcSfqkFWITxsrcAAoVvS19eQuJzKow3C6+BtD1mk+x9GWDRN96sCOOF7feF3h8tCvQ5BqXYqUu5VLcwwSUiIiIqoZhMOt5kxpGh/sIzTURERESGwh5cIiIiohIcg2sM7MElIiIiIkNhDy4RERFRCfbgGgN7cImIiIjIUNiDS0RERFSCsygYA880ERERERkKe3CJiIiISnAMrjGwB5eIiIiIDIUJLhEREVEJ9xhcvR6+2LVrF26//XZERUUhNDQUPXr0wKpVq7zev1+/flAUpdLHihUrarze2sQhCrUgsvhXhBXl1XYzAACaYhbGy96cTlOAKB4AQoPCRPENAhqI4oMCQmTlB8uOobBIdo4ux2XR9V7DXkr6q6G0/WaTJoq3mGXxgQGqrHxhe6TtBwAFsn3E51SRHbO8fFn7A0wuUbzFJHvjS9ujSOOF18sX/qjDGxfU3NpugkhqaioGDRqEwMBAjB07FhEREVi7di0mTJiAzMxMzJ49u8oyEhIS0K9fvzLbi4uLkZKSApPJhAEDBtR4vbVJ0TStbrziasjJkyfxwQcfYMOGDTh48CBOnz6Nhg0b4qabbsKTTz6Jnj17elVOWloa1q1bh9TUVGRmZiIvLw+xsbG44447MGvWLERGRorbZrfbERERgfRvNiOsgSwp00tdTHDzTbIE94JLdi7PO2QJbk4BE9yaxgS3chbZ25IJri7lM8GtaXUmwb2Qi77Xt0VOTg7Cw8M9293f0fsfGImwQPl3lzdyi4px9b/+U6buijidTnTs2BEnTpzAjh070LVr14vl5OaiV69eOHToEPbv34/27dv71J41a9Zg9OjRGD58OD7++GO/1esPhhui8Nprr2HGjBk4evQoBg4ciL/+9a/o06cPPvroI/Tu3RurV6/2qpzRo0fjlVdeQVhYGP785z/jL3/5C0JCQrBgwQJ0794dZ86c0flIiIiI6HK2efNmHDlyBOPHj/ckmQAQFhaGOXPmwOl0YtmyZT6Xv2TJEgDA5MmT/VqvPxhuiEKPHj2wdetW9O3bt9T2r7/+GgMGDMDDDz+MO+64A1artdJyZsyYgT//+c9o1qyZZ5umaXjkkUfwxhtvIDk5Gf/85z91OQYiIiKqHYqi4ywKwp8SUlNTAQC33XZbmefc27Zs2eJTW06cOIFNmzahadOmGDp0qN/q9RfD9eDeddddZZJbAOjbty/69++Pc+fO4ccff6yynL/97W+lklvg4gtzzpw5AOr+hSUiIqK6yW63l3o4HI5y49LT0wGg3KEAUVFRiI6O9sRILVu2DKqqIiEhARZL6f5OPev1F8MluJUJCLg4puaPF9LfZRAREVHd5I9ZFGJiYhAREeF5pKSklNuWnJwcAEBERES5z4eHh3tiJDRN8wwx+OPwBD3r9afLJks7fvw4vvjiCzRt2hTXXnutz+W88847AMrvticiIqL6zR8LPWRlZZW6yayqYZM1bfPmzcjIyEB8fDzatWvn17r95bJIcIuLi3HvvffC4XBgwYIFMJuFtyiX2LNnD5KTk9G4cWM8+eSTVcY7HI5SPzvY7Xaf6iUiIiLjCA8P92oWBXcPakW9pe6ZH6TcN5dNmTLFr/X6k+GHKKiqikmTJmHr1q24//77ce+99/pUTkZGBoYNGwaXy4X33nsP0dHRVe6TkpJS6ieImJgYn+omIiIi/6hLCz24x8CWN971/PnzsNls4qm6zp8/j3Xr1iEyMhKjRo3yW73+ZugEV9M03H///Vi5ciX+9Kc/4c033/SpnGPHjqF///44e/YsPvzwQ/Tv39+r/WbNmoWcnBzPIysry6f6iYiI6PITHx8PANi0aVOZ59zb3DHeWrlyJRwOByZMmIDg4GC/1etvhk1wVVXF5MmT8c4772DcuHFYvnw5TD4skZeZmYl+/frh1KlTWL16NYYNG+b1vlar1fMzhLc/RxAREVHtUUy/j8Ot+YesLQMGDECbNm2watUq7Nmzx7M9NzcX8+bNg8ViQUJCgme7zWbDwYMHYbPZKixz6dKlAMq/uczXeusiQya4qqpiypQpWLZsGe655x6sWLHCp3G37uT25MmTeP/993HHHXfo0FoiIiKisiwWC5YsWQJVVdG3b1888MADmDlzJjp37oyffvoJSUlJ6NChgyd+8eLF6NSpExYvXlxued9//z327t2Lbt26lVrAobr11kWGu8nM3XO7fPlyjBkzBitXrqw0ubXZbLDZbIiOji41rvaPye2dd95ZY20MvvAbQlBQY+VVh6bzmqmqWb7codWaJ4yPFMUrQVeI4k2KbGnfggDZ26rYVcfWrTUA6cta+p++dBlU6VK30vggi2zZV+mysibhsriAfFlW6VK00jZJ2yMtP0ARXgOlSBQvXvoYwvOj1Y1ldC+l19K+ZuVC5fX6YRYFif79+yMtLQ2JiYlYvXo1ioqKEBcXh3nz5mHChAmisty9txXdXKZXvbVB0bQ6+KquhqSkJCQnJ6NBgwaYNm1aufPVjhw5El26dCkVn5iYiKSkJE9MbGwsjh07hhtvvBGDBg2qsC4J912HJ778AOENZEmTXupigltklQ3lyBcmuOdUWYKb7RAmuMVMcGsbE9zKMcGt+fKZ4NY8vRLc3NwLuK7b9cjJySk1dND9HX14xj0IswbqU7ejCO1eer9M3VTzDNeDm5mZCQC4cOECnn322XJjYmNjPQluRY4dOwYA2LlzJ3bu3FlujDTBJSIiojrOZLr40Kts8gvDJbjLly/H8uXLvY5PSkoqN1E1WMc2ERER0WXDcAkuERERka8URYGi0/A9vcqlsthXTkRERESGwh5cIiIiohK+rDgmKZv8g2eaiIiIiAyFPbhEREREJeraPLjkG/bgEhEREZGhsAeXiIiIyE3RcR5chf2K/sIzTURERESGwh7cWuAIiUJhSGhtNwOAfClEDbLxQ5oP/60WW4Jk8YpsSUWHS7Z8sHTp3YIi2TE7ivX9P/NyXLNEvFSvdJlY4SWTLr0bYBG+L4XX2GmWHYB0aWJAvvSu3kv7ipdXVmTLGbtMZlG8U5HFm3VemvhyUljVMsY6jsEFx+D6DXtwiYiIiMhQ2INLREREVEJRTFB0GiurV7lUFhNcIiIiIjeTot9QAg5R8Bv+K0FEREREhsIeXCIiIqISXKrXGHimiYiIiMhQ2INLREREVIJL9RoDe3CJiIiIyFDYg0tERETkpij6LakrXYWGfMYeXCIiIiIyFPbgEhEREZXgGFxjYIJbC/KtETBbG9R2M3yiCX+20SB/MzsQJIrPc4aK4nOLrKL4fIdszfh8h+yYi52i8DpHrYNL3ku/QxThz4Zm4W9fZmGDnC7ZSdWE16DYJTsAs0l+kaW/xCqQ1SFtk7R8k0n29Wg1uUTxFpPsc8Ws1ME3mpBSR46hQCuu7SaQHzDBJSIiInIzmS4+9Cqb/IJnmoiIiIgMhT24RERERCUURREPW5KUTf7BHlwiIiIiMhT24BIRERG5KTqOwdVrfl0qg2eaiIiIiAyFPbhEREREJTgPrjGwB5eIiIiIDIU9uERERERuikm/sbIcg+s3PNNEREREZCjswSUiIiJyMyny9b4lZZNfMMGtBWdcjZHnCtelbE3T982jQVa+L+1xqLI12guLZS/j33IDRPE5F0ThcBTJ1lsvKq4b67Mbid6rYZqEX1Jm2UsaFrOsfGugrAKLsD1mH86n9HtcOv+9NN5skr3PLGZZfFCAKooPMMviLSZZvPx81v/PIW+/b/IczkqfVxQTFJ2GEuhVLpXFM01EREREhsIeXCIiIiI3DlEwBPbgEhEREZGhsAeXiIiIqIRiMkHRaSC/XuVSWTzTRERERGQo7MElIiIiclMU+TQUkrLJL9iDS0RERESGwh5cIiIiIjeTot9k2pxFwW/Yg0tEREREhsIeXCIiIiI3jsE1BPbgEhEREZGhsAeXiIiIqATnwTUGJri14HRuA4RoDWq7GX7hUuU/xxQ7ZfvkO2Tx2XZNFJ9jd4riHQ6XKN5ZrIriqWqK8EYORfizofQ7ymyW7WAyy9oTHCQrPyBAFi9sPgBAEe5j0vkaSMu3WGTxARbhNTCLwuskVfZRqjvNy/YU5BXp2xCqE5jgEhEREbkpJvl/aJKyyS94pomIiIjIUNiDS0REROSmKPrNV8tZFPyGPbhEREREZCjswSUiIiIqoSgmKDqNldWrXCqLZ5qIiIiIDIU9uERERERuJh3H4OpVLpVhuB7c7OxsPPbYY+jVqxeaNm0Kq9WKFi1a4JZbbsGaNWugeTlRXmpqKsaPH49OnTohMjISISEhuOqqqzBp0iQcOnRI56MgIiKiWuGeJkyvB/mF4c60zWbDO++8g9DQUIwcORJ//etfMWTIEPz0008YPXo0HnzwQa/K+eKLL5CWloZrrrkGCQkJePTRR9GhQwf83//9Hzp37oyvvvpK5yMhIiIiIl8YbohC69atkZ2dDYul9KHl5ubixhtvxNtvv41p06YhLi6u0nKefvppzJ8/v8z2L7/8ErfeeiuefPJJ7Nq1q0bbTkRERLVMUfSbzovThPmN4XpwzWZzmeQWAMLCwjBo0CAAwOHDh6ssJygoqNztAwYMQFRUlFdlEBEREZH/Ga4HtyKFhYXYvHkzFEXB1Vdf7XM5O3bswPnz59GnTx+fyygoMkFx6DUFib7x3q717Ws8ADhdskapqryOukThTQf0B3q/JPzRiWQSVlLXOrakn11Ol77l17XzA/j2+V4Xyq/yWplMFx960KtcKsOwCW52djZefvllqKqKM2fOYMOGDcjKykJiYiLat2/vdTmpqalITU2Fw+FAeno6PvnkE0RHR+Oll16qcl+HwwGHw+H52263+3QsREREROQ9w/4rkZ2djeTkZMybNw9vvfUWTp8+jYULFyIxMVFUTmpqKpKTk/H8889jzZo1iImJwcaNG9G9e/cq901JSUFERITnERMT4+vhEBERkT/UwVkUdu3ahdtvvx1RUVEIDQ1Fjx49sGrVKnE5ubm5SExMxDXXXIOQkBBERkaiW7duSE5OLhMbGxsLRVHKfTz00EM+HYc/KZq382bVUy6XC1lZWXjvvfeQmJiIoUOHYvXq1eWO061MXl4e9u/fj2eeeQaff/453nnnHYwfP77SfcrrwY2JicGSjecREhru0/FU5XIcouAolpVvvyBrVI7dKYp3OGS/Vbpchn4L1guK8I0g/ZXRbJbtYLHI2mO1ysoPDJTF+zJkwmzWd4iCNF56DGazsHzpa0IYzyEKNacgz47HRkYiJycH4eG/fxfb7XZERETg1xXPIzyk/PtwqsueX4gm9/69TN2VSU1NxaBBgxAYGIixY8ciIiICa9euRUZGBp599lnMnj3bq3KOHz+OW265BUePHsWtt96Krl27wuFw4PDhwzh+/Dh++OGHUvGxsbHIzs7G9OnTy5TVvXt3DBs2zKt6a4vhE9xLLVy4EE8++SRef/11PPzwwz6V4XQ60b17dxw+fBgZGRlo1KiR1/u63zxMcCvHBJf8jQlu5ZjgelE+E9x6U36VCe7/e0HfBHfC37xOcJ1OJzp27IgTJ05gx44d6Nq1K4CLPbG9evXCoUOHsH///iqHXrpcLvTq1Qv79u3D+vXr0b9//zL1/LHjLzY2FgCQmZnp/QHWIYYdolCe2267DcDF/4Z8ZbFY0L9/f+Tl5eG7776roZYRERERlbZ582YcOXIE48eP9yS3wMWZoebMmQOn04lly5ZVWc6HH36IXbt2YebMmWWSWwDiX7XrA+MdUSVOnToFoPoXsqbKISIiojpGUfRbcUzYFe/ukHN30F3KvW3Lli1VlvP+++8DAMaMGYOsrCysX78e2dnZaNu2LYYMGYIGDRqUu5/D4cC7776LkydPIioqCr1790bnzp1Fx1BbDJeh7dmzB61bt0ZERESp7efOnfOMUxkyZIhnu81mg81mQ3R0NKKjoz3bt27dir59+5b56XLTpk1Yt24dIiIi0Lt3bx2PhIiIiIzoj7MqWa1WWK3WMnHp6ekAUO4QhKioKERHR3tiKuP+xTktLQ0zZswodX9Qo0aNsHr1avTr16/MfqdPn0ZCQkKpbYMHD8aKFStK5Ux1keGGKCxfvhwtWrTA8OHD8eijj+Jvf/sbxo4di1atWmHPnj0YNWpUqZvDFi9ejE6dOmHx4sWlyhkxYgTat2+PcePG4cknn8TUqVMRHx/vWSxiyZIlCA0N9euxERERkc7cK5np9QAQExNTapallJSUcpuSk5MDAGU67dzCw8M9MZU5c+YMAGDq1KmYPn06srKycPbsWbz66qvIycnByJEj8csvv5TaZ9KkSUhNTcXZs2dht9uxc+dODBkyBBs3bsSIESNQ12/hMlwP7ujRo5GTk4OdO3di69atyM/PR8OGDdGnTx/8+c9/xtixY726oSQ5ORkbN25EWloazp49C0VREBMTgylTpmD69OlVLvVLREREVJ6srKxSN5mV13tbk9SSFZGGDRuG559/3rN96tSpOHnyJF544QUsXboUTz/9tOe5uXPnliqjZ8+e+OSTTxAfH4+0tDRs2LABQ4cO1bXd1WG4BLdPnz6iVcaSkpKQlJRUZvu0adMwbdq0GmwZERER1Xl+WMksPDzcq1kU3D23FfXSumd+8KYcm82GESNGlHlu+PDheOGFF7y6cd5kMmHixIlIS0vDtm3b6nSCa7ghCkRERERG4B57W9442/Pnz8Nms3m1OutVV10FAIiMjCzznHtbQUGBV21yj73Nz8/3Kr62GK4Htz6Q/HMonbtRUWRjYvSeW9GXITryNsl2kM/PKS1f+n+jKoynmia9xvLydS1e//azK6TG6T2Prz/U10+uKs/9JWNl/V95afHx8UhJScGmTZswduzYUs9t2rTJE1OVW265Bdu2bcP+/ftx1113lXpu//79AH6f97Yq33zzjSi+tvBji4iIiKgOGjBgANq0aYNVq1Zhz549nu25ubmYN28eLBZLqVkObDYbDh48CJvNVqqciRMnwmq14rXXXsPJkydLlfPcc88BAO6++27P9v379yM7O7tMe9LS0rBo0SJYrdYyiXJdwwSXiIiIyE0x6fsQsFgsWLJkCVRVRd++ffHAAw9g5syZ6Ny5M3766SckJSWhQ4cOnviKZoZq3bo1Fi5ciDNnzqBz5864//778eijj+K6667Dnj178MADD2DAgAGe+NWrV6N58+YYPnw4pk6dipkzZ2Lw4MG4+eabUVxcjMWLF6Nly5bVO8864xAFIiIiIjdFx5vMfBjv079/f6SlpSExMRGrV69GUVER4uLiMG/ePEyYMMHrcqZOnYrY2FgsXLgQ7733HpxOJ+Li4jB79mzcf//9Zeo8cOAAdu/ejS1btqCwsBBNmjTBPffcgxkzZqBHjx7i4/A3RavrE5kZiPtux3c2nUdIaNV3TwKX5xhcp0vWqGKnLP6cveqYS53Pdorii4tlI9Ncrvo6ks049B7DKh33LY0PCjKL4gMCpO0RhQMATMJzKr0Eeo9hlR6zNB+ySMuvi2Nwdc4e9MpOCvLsmHpHJHJyckrNZOD+jv71w1cRHhqsS932vAI0Gf1Ymbqp5rEHl4iIiMitDt1kRr7jGFwiIiIiMhT24BIRERG5+XAzmKhs8gueaSIiIiIyFPbgEhEREblxDK4hsAeXiIiIiAyFPbhEREREbiYd58HVq1wqgwluLQgJUBES6N3cpyaTbCJAk3AeXGm8P35dcamyShxO2QdGsUs2AaVLGO8okrXH5dJ3Mkl+nlZN/3lwZfEm4aSnwVZZfECAKNynOVj1ntfWLHxdS49BPq+t7H1sFn6210WaJjupdWXeXEXnz1yqG5jgEhEREZXQFAWaTv/06lUulcW+HSIiIiIyFPbgEhEREbkpio7z4LIH11/Yg0tEREREhsIeXCIiIiI3rmRmCDzTRERERGQo7MElIiIiKsFZFIyBPbhEREREZCjswSUiIiJy4xhcQ+CZJiIiIiJDYQ8uERERkZui6DdfLcfg+g0T3FoQYHEh0OLyKla6XrlJkcUrwnizMN4fAoWL0jtdsg+YwiJZ+dJfoFzC9lyOTHXsFEmvsUn4pWYxy8oPDpLFBwo/+aWfQ4D8e1z4NhZ/dknbE2iWlW8xq6L4upbnaD58tKua7CCkdWjC8r29AlpxFZEm08WHHvQql8rgmSYiIiIiQ2EPLhEREVEJThNmDExwiYiIiKjW5OTkYMOGDdi+fTtOnjyJ3NxchIWFoUWLFujduzeGDh2K8PBwUZlMcImIiIjcOE2Y3xQXFyMpKQmvvfYaLly4AABQLunl1jQN//znPxEWFoapU6ciKSkJFot3qSsTXCIiIiLyq/z8fAwcOBA7duyAoiho0qQJevfujdatWyM0NBR5eXnIyMjAtm3bcPbsWTz33HNITU3F559/juDg4CrLZ4JLREREVEJTTNB06mnVq9z6KCEhATt37kTTpk3x8ssvY/To0TCVM8uEqqpYs2YNpk2bhu3btyMhIQHvv/9+leXzTBMRERGR32zbtg1r1qxBkyZNsHPnTtx9993lJrcAYDKZMGbMGHzzzTdo3rw5PvjgA2zfvr3KOpjgEhEREbm5F3rQ60F49913oWkaFixYgJYtW3q1T0xMDF544QUoioLly5dXGc8El4iIiIj85uuvv0ZwcDDGjBkj2m/06NEICgrC119/XWUsx+ASERERldCg4xhc9isCAE6dOoW2bdvCarWK9rNarWjbti2OHz9eZSzPNBERERH5TVFRkVczIZQnODgYhYWFVcaxB7cWRAXno0Gwd6ferMjWNzd5vRp3Sby0fGG8WdgeAFCE+6gwi+JDAhqI4i3mEFF8XqGsPar8FIlwyFfVpOfIpGjC8mXxFrMsPjhQ9iIKMAvfx8L3PQCYTLJjMAvPkfizSxQNmE0uUbxF589SKQ36v/E1TVaHKmyTJntJeH3MebhQeYCeY2X5gQwAiI6OxqlTp3za9+TJk4iOjq4yjj24REREROQ3Xbt2xcmTJ5Geni7a7+DBg/jll1/QtWvXKmOZ4BIRERG5Kcrvq5nV+IM9uAAwatQoKIqCl19+WbTfq6++Ck3TMHLkyCpjmeASERERkd+MHTsWMTExePvtt72a0xYAtm/fjqVLl6JZs2YYN25clfHVGoPrdDqxe/duHDt2DGfPnkVBQQGio6PRqFEjdOvWDU2bNq1O8URERER+pSkKNJ16WvUqt76xWq3YsGEDDh8+XOECD39kMpnwwQcfoG3btggJqfreGHGCe+HCBfz73//G+++/j507d6KgoKDC2DZt2mDQoEGYOHEirr/+emlVRERERGRAV199Na6++moAQF5eHg4fPowLFy6gQYMGaNeuHUJDQ0vF33jjjaLyvU5wT58+jXnz5mHFihXIy8uDpmlQFAXt27dHs2bN0LBhQwQHB+PcuXM4d+4cDh06hCNHjuD111/HG2+8gc6dO+Ppp5/GXXfdJWogERERkd+4x8vqVTZ5rF+/Hi+88AK2b98O9ZIphUwmE2688Ub8/e9/x7Bhw3wq26sEd+7cuXjppZeQl5eH5s2bY/LkyRg2bBhuuOEGhIeHV7jfwYMHsW3bNrz33ntITU3FmDFj0LNnT0/CS0RERESXn6eeegopKSlQSoZtBAUFoVGjRjh79iwcDgd27NiBESNG4G9/+xtSUlLE5Xv1r8T8+fNx/fXXY+PGjcjKysJLL72EAQMGVJrcAkDHjh0xefJkfP755zh58iSefvppHDx4EB999JG4oURERER606Do+iBgw4YNeP755xEYGIhp06bh8OHDyM/Px7Fjx5Cfn4/Dhw9j+vTpCAgIwAsvvID169eL6/CqB3fLli3o27evuPBLNW7cGMnJyfjrX/+KY8eOVassIiIiIj1oio5L9XKIAoDfp/tatmxZuTMitG7dGi+++CJuuOEGTJgwAa+99hqGDh0qqsOrM13d5PZS4eHhuPbaa2usPCIiIiKqP7777jvExMRUOd3X2LFjceWVV+L7778X18F/JYiIiIjcdFvkQceb1+qZvLw8NGnSxKvYJk2aIDc3V1xHtebBrcjx48fx73//G6dOnUK3bt1w7733ej3P2eUg0mxHA4t3i2ybIFwzXnPK4tViUbxJla3PblZl7QEAs6tIFG/SZG0Kt0aI4oMiZPM5F4RaRfFOrW69NxQIF4A3AEWRHbP0ipkU4fvYJIsPMjlE8YGK8D0mbD+g/2eX9H2vaDq3R/jZKCX9aVsVxvvy07lqMsvqEI4/1euYgyzyZIlqVrNmzXDgwAHPtGAVuXDhAg4cOIDmzZuL6/D5m/WNN95Aw4YN8eqrr5bavnPnTlx77bWYPXs2XnvtNUyaNAmDBg0qNf0DERERUV3kXuhBrwcBt99+O/Ly8jBx4kQUFhaWG1NQUICEhATk5eVhyJAh4jp87sH9+OOPYbfby8xr+/jjjyM3Nxc33XQTbrjhBqxevRqbN2/Ge++9h/Hjx/taHREREREZwKxZs/D+++9jzZo12Lp1K6ZMmYK4uDg0adIEv/76K3766Se8/fbbsNlsiIqKwqxZs8R1+JzgHjx4EI0aNcKVV17p2ZaRkYGdO3eiU6dO2Lp1KxRFwaRJk3DddddhyZIltZbgLliwAH/7298AADt27BCvhuFWXFyMG264AXv37sVVV12FgwcP1mQziYiIqJZxFgX9tWjRAp999hnuvvtuZGRklDvPraIoaN26NT744INSuaa3fE5wz549i06dOpXa9tVXXwG4eNebe+Lea665Bu3atcPhw4d9rapaDhw4gLlz5yI0NBR5eXnVKmvevHm1dhxERERERtGtWzf89NNPeO+99/DZZ5/h559/Rm5uLsLCwtChQwfcdtttGDduHKxW2X0tbj4nuC6Xq8y4ia+//hqKoiA+Pr7U9oYNG2Lv3r2+VuUzl8uF++67D507d0aHDh2wcuVKn8vavXs3UlJSsGjRIjz22GM12EoiIiKqMxTl4kOvssnDarXivvvuw3333VfjZfvcVx4bG4vDhw8jOzsbwMVkcuPGjQgKCkKvXr1KxZ47dw4NGzasVkN98cILL2Dv3r145513YDbL7va8VFFRERISEnDjjTfi0UcfrcEWEhEREVFN8znBHTp0KBwOB8aPH49PPvkEDzzwAH799VcMHToUAQEBnricnBwcPXoUrVq1qpEGe2vfvn1ITk7G008/jbi4uGqVlZSUhPT0dCxdutQz9IKIiIgMqGQMrh4PzoPrPz4PUZg9ezb+85//YOPGjfjss8+gaRoiIiIwb968UnFr1qyBqqro379/tRvrLafTiYSEBHTq1Al///vfq1XWrl27sGDBAjz33HPo0KGDaF+HwwGH4/f5Ke12e7XaQkRERFTftW7dWhSvaRoyMzNF+/ic4DZs2BC7d+/GkiVLkJ6ejpiYGEycOBHNmjUrFXf06FHccccdGDVqlK9ViT333HPYu3cvvvnmm1K9yVIOhwMJCQno2rUr/vrXv4r3T0lJQXJyss/1ExERkX9pUMSLUkjKJiArKwuaVvUCO5qmQVEUr2L/qFormYWHh+Pxxx+vNGb+/PnVqUJs7969mD9/PmbOnIlu3bpVq6w5c+YgPT0d33//vU9jeGfNmlXq/NjtdsTExFSrTURERET12dGjRyt8Li8vDwcOHMCKFSuwfv16PPPMMz5NM6vLUr2XcrlcmDt3Lq677jrcc889eleH++67D23btkVSUlK1ytm9ezcWLVqEOXPm4Nprr/WpDKvV6vP0FkREROR/nAdXfy1btqz0+U6dOuGuu+7Cs88+i6effho9e/ascp8/UjRf+n0FfvvtN3Tv3h0ulwvHjx/XsyoA8PomsHXr1mHkyJEVPr98+XJMnDixynIiIiI8M0lUxW63IyIiAhnbP0NYg1Cv9lFc0vXZhfFO2Zr04vYUl78EX6X7FMjWCVeKZMegBQWL4guiY0XxhcGRoninKVAUrzdFu/yW1VYg+xg0aS5ZvCqLt7gcVQddIsAhe8+YimXlm1zFongAgPCYFWEdilPYJulXnUvWfpiEPz2bZL8KahbhcDtp+cJ4AOIbpqR1iNvkZXvsF/Jx5YAxyMnJQXh4+O/bS76jj+z80uvvaKncC3loe+OAMnVT+VRVRePGjXHDDTfg008/Fe3rcw+u5Cd7TdPQuHFjX6sSmTx5crnbt27divT0dIwYMQKNGjVCbGxspeV06NChwrKWLl2KiIgIjB49GiEhIdVtMhEREdUVCnScB1efYo3KZDKhXbt2+O6778T7+pzgSjp+r776aixcuNDXqkSWLFlS7vaEhASkp6dj1qxZZZbqtdlssNlsiI6ORnR0NACgd+/e6N27d7llLV26FE2bNq2wLiIiIiKqvtOnT+O3334T7+fzYBBVVSt8FBUV4ejRo3j99dfRpEkTFBcXo3Pnzr5WpbvFixejU6dOWLx4cW03hYiIiGqRBpOuD/JecXEx/u///g+pqanifXU50xaLBbGxsXjooYewZs0apKenVznbAhERERGRW0BAAG6++WbcfPPN4n11v8kMuLisr91ux7lz5/Suqk7jTWZe7sObzGoVbzKrGm8y8wJvMqsinjeZ1XR8Td1klv5tKsIaNJDV7aXcCxfQvke/y/4mM5NJ3r+qqrLvJr/0lUdERMDplCVGRERERP6m1zK91Zl+bNeuXbj99tsRFRWF0NBQ9OjRA6tWrRKXk5ubi8TERFxzzTUICQlBZGQkunXrVuGiVDVV7x+ZTCYoiiJ6iOuodiur8NtvvyE9Pb3CG7aIiIiIqHypqano06cPvv76a4wePRoPP/wwbDYbJkyYgOeee87rco4fP46uXbti3rx5aN68OaZOnYqEhAQ0b94ca9as0a3e8jidTrhcrnIfFy5cwK5du/DYY48hMDAQKSkpcEl/UYHOQxR+/vln/OUvf8GhQ4fwxRdf4KqrrtKrqnqBQxS83IdDFGoVhyhUjUMUvMAhClXEc4hCTcfX1BCFQ7u+1nWIwlU39PV6iILT6UTHjh1x4sQJ7NixA127dr1YTm4uevXqhUOHDmH//v1o3759peW4XC706tUL+/btw/r169G/f/8y9VgsllJ/10S91fXOO+9gypQp+OCDDzBq1CjRvj734LZp06bSR2RkJDp16oSvvvoKRUVFGDJkSJmYtm3b+lo9ERERkaFt3rwZR44cwfjx4z1JJgCEhYVhzpw5cDqdWLZsWZXlfPjhh9i1axdmzpxZJrkFUCq5rcl6q2vSpElo2rQpXnnlFfG+Ps+Dm5mZ6XXs2bNncfbs2TLbfRlTQURERKSXurRUr3t6rNtuu63Mc+5tW7ZsqbKc999/HwAwZswYZGVlYf369cjOzkbbtm0xZMgQNPhDj3VN1VsTWrZsiR9//FG8n88JbkZGhq+7EhEREV227HZ7qb+tViusVmuZuPT0dAAodyhAVFQUoqOjPTGVca8ElpaWhhkzZsDh+H2YUqNGjbB69Wr069evxuutLlVVcezYMeTk5Ij39TnBbdWqla+7XvasOb8gyOndEr+KdBybdFxasXAMrnDsnuaQj8FVz8umk3PmXhDFa07ZOQ1ubZPFR14hileD9Fnz3Eh0HxcsHJ+pSMdnSseXFuSJ4rVce9VBl1DzZeWrDtnnBABoxbJjVotl4/tVYfmaKhyDK52SyCobS28ODpKVHygrXwmQfb0rZvkYXCVAOC5YWoc03ssxu5b8yr+XNEWBptMvzO5yY2JiSm1PTExEUlJSmXh3YhcREVFueeHh4Thx4kSV9Z45cwYAMHXqVMycOROPPvoogoKC8O9//xszZ87EyJEjceDAATRr1qxG660J33zzjU/7+ZzgEhEREZFcVlZWqZvMyuu9rUnuOWSHDRuG559/3rN96tSpOHnyJF544QUsXboUTz/9tK7t+KO8vDy89dZbWLt2Lfbv348LFy4gIiIC1157Le68805MnjwZLVu29KlsrwaD5Ofn+1S4v8ojIiIiqgkaFF0fwMUe0EsfFSW47h7Uin6id8/8UBV3zIgRI8o8N3z4cAC/D2OoyXorc+jQIXTu3BkzZ87Ejh07kJOTA5fLhXPnzmHLli2YNm0arrvuOvz0008+le9VghsbG4sXXngBFy7Ifgr+o+3bt2Pw4MF48cUXq1UOERERkdG5x8CWN971/PnzsNlsXk3V5Z6mNTIyssxz7m0FBQU1Xm9F8vLyMHToUBw9ehS33HIL1q1bh8OHDwMAunXrhvXr12PUqFHIyMjAoEGDcP78eXEdXiW4bdq0waxZsxATE4PJkyfj888/93rS3VOnTuGll15C9+7d0bdvX6SlpeGaa64RN5SIiIhIb3VpJbP4+HgAwKZNm8o8597mjqnMLbfcAgDYv39/mefc22JjY2u83oq8/vrryMjIwLhx4/DFF19gxIgRaN26NQAgKCgIgwcPxgcffIBp06bh1KlT+Mc//iGuw+uFHj744AM89dRTOHz4MBRFQVBQELp27Yrrr78ezZo1Q8OGDWG1WpGdnY1z587hwIED+O6773Ds2DFomgaLxYKJEyciOTkZTZs2FTfUCNxd+qc+XY7wUN5kVpG6dpOZtXWsKB68yazG8SazyvEms6rxJrMq4i+jm8zs+YVoet9TFS708NPub3Rd6CGuW0/RQg9XXXUVTp48iZ07d6JLly4Xy7lkwYWffvoJHTp0AADYbDbYbDZER0cjOjraU05GRgY6deqEiIgI7N69Gy1atPCUc/PNN2PPnj344osvMGDAAJ/qlerduze+/fZbnDhxolROaDab0bt3b3z99dcAgAsXLqBRo0Zo166deKowr98BY8aMwejRo7Fx40b861//woYNG7B9+3Zs37693Pls3Xlz69atMWnSJEyaNMlzdx4RERFRXXTpWFk9ypawWCxYsmQJBg0ahL59+2LcuHEIDw/H2rVrkZGRgfnz55dKMhcvXozk5OQyszK0bt0aCxcuxGOPPYbOnTvjzjvvhNVqxfr165GZmYkHHnjAk9z6Uq/UgQMHcNVVV1XZ4dmgQQO0a9fOp6lpRf/iKYqCIUOGYMiQIcjPz8eOHTuwfft2HDt2DDabDYWFhWjYsCEaN26MLl26oE+fPmjXrp24UUREREQE9O/fH2lpaUhMTMTq1atRVFSEuLg4zJs3DxMmTPC6nKlTpyI2NhYLFy7Ee++9B6fTibi4OMyePRv333+/bvWWx+FweNWD7Y41meQLb3g9RIGqj0MUvMMhCvRHHKJQOQ5RqBqHKFQRzyEKnu/oH3d/h7AwnYYo5F7Atd26ez1Ewajatm2LgIAAHDx4sNT2Pw5ROHjwIK655hrccMMN2LFjh6gOfdaiIyIiIiIqR5s2bXDkyBHk5VX8z/aPP/6IMWPGQFVVTJ48WVwHE1wiIiKiEv6YB/dyN3ToULhcLqxfv77Mcz/88APatm2LLl26YP/+/RgzZgwTXCIiIiKq2+666y7ceOONZZbhVRQFFy5cQGZmJtq0aYPFixfj/fffL3cyg6pwqd5aYCrMh8ns5Xgw4Vg/8Rhc4dhArUg2Fk8rkK9aJx1T67DJJoAuPC8rv4FwrF9g9G+ieHODMFE8TMI3unDeRUOQjtkVvs80abzwfeO6IBsj6/gtWxafI3sPOAuFnysAXEWyMbXSeFU4ll46BtdkkY3/tIYHC+O9uw/DzWyVjXc1C8cEK8LjBeTjdqXnVDrG19v2OAsqv5dEUxTxfLXe0nxI1IyoZcuW2L59e5ntmzdvRnBwMFq0aIHmzZtXqw4muERERERU626++eYaK4sJLhEREVGJujQPLvmOCS4RERFRiYtDFHRKcDlEAQCQnJxcI+UkJiZW+BwTXCIiIiLym2eeeQaapkFRFPiyHIN7P78kuKqq4rfffkNBQQFatmxZU8USERER+Y2mKdA0nXpwdSq3vklKSvIpsZWodoK7YcMGvPTSS9i+fTsKCwuhKAqczt/vhn322Wfx008/4ZVXXkGjRo2qWx0RERER1WNz5szRvY5qzYPx5JNPYvjw4fjyyy/hcrkQEBBQJiNv1qwZ3n//faxbt65aDSUiIiLSnwmaTg8uP+A/Pp/pNWvW4B//+AeaN2+OTz75BHl5ebjhhhvKxN15550AgI8//tj3VhIRERERecnnBPef//wnFEXBBx98gNtvvx3mCiZYjoqKQuvWrZGenu5zI4mIiIj8gUv1+sfu3bsxfPhwREVFISgoCB07dsQzzzyD4ksWV7Lb7Th58iRUVbh4D6qR4P7vf/9DTEwMbrzxxipjGzVqhJMnT/paFREREREZxHfffYebbroJ69evh91uR3FxMdLT05GUlIS7777bE/fRRx8hJiYG//3vf8V1+JzgOhwOREZGehWbn59fYQ8vERERUV3BHlz9Pf300ygqKsIdd9yB9PR02O12fPbZZ2jWrBk++ugjfPXVVwCA4cOHw2w246OPPhLX4XOCGxMTg8OHD5fqSi5PTk4ODh48iLZt2/paFREREREZxI4dOxAWFoaVK1eiTZs2CA0Nxa233oqFCxdCURRPj21kZCSaNGmCb7/9VlyHz9OEDRo0CP/85z/x0ksv4cknn6ww7plnnoHT6cSwYcN8rcpwsmOug6tBA69iTapLVLZJdVYddAmzWvk/KGXKdxWJ4i1FBaJ4AAjMOSOMPyeKDyuWHYMSESWKd4U1FMU7raGieCndV85R6uBdwZpsvJYifJ8pLtn7zOR0iOIDHLL3TYDwPRCad0EUr7lk58cfFOHrWgkIkFVgDZKVHyx7H2shYbJ4i+zrWjPLjlczyX9lFe8j/KyQlu/yMt51IQ/AqxXXy6V6dadpGjp06IDQ0NLvm759+wIAfv75Z8+2Zs2a+XQfl8/fTH/7298QFhaG2bNn44knnsDBgwc9z6mqih9++AGTJk3CSy+9hOjoaEybNs3XqoiIiIjIINq3b49ffvmlzPamTZsCAHJzcz3b8vPzqxwtUB6fE9wWLVrgo48+QkREBBYtWoS4uDhs374dABAQEICuXbti+fLlaNiwIdatW4crrrjC16qIiIiI/IJjcPU3efJk/PLLL/i///u/UtsDSn5pcc+aYLfbkZmZiVatWonrqNZvi/Hx8di3bx+mT5+OVq1aQdM0z6NZs2Z49NFHsXfvXvTu3bs61RARERGRQfzlL3/Bvffei/vvvx+zZ8/GuXPlD7OaM2cOCgoKMHz4cHEd1V6qt1mzZnjxxRfx4osvIi8vDzk5OWjQoAHCw8OrWzQRERGRX2maAk3TaQyuTuXWN/379wcAWCwWPP/881i4cCFatGiBmJgYAMCPP/6IVq1a4cSJE4iNja30Xq+KVDvBvVRoaGiZAcNERERERG5ff/01NE0DcPGGUVVVkZWVhaysLAAXx+C6XC4kJCTg+eef92mYa40muERERET1GWdR0N/mzZsrfC4gIAANGzZE+/btYTL5PpK22gnuZ599ho0bN+Lo0aO4cOGCJyP/I0VR8OWXX1a3OiIiIiKqx26++Wbd6/A5wbXb7Rg5ciS2bNlSYVJ7KemchURERET+xh7cuuXXX39FYWGheCYFnxPcv/3tb0hNTUXDhg3xwAMPoGvXrmjUqBETWSIiIiKqEWPGjEFaWppn6jBv+Zzgrl27FgEBAdiyZQvi4uJ8LYaIiIiozmAPrn8cPXoU69atw9GjR1FQUFDhaAD3KmYTJ070bBs5ciTuuOOOSsv3OcHNy8vDVVddxeSWiIiIDEODjtOEMcEFAHzxxRcYNmwYioqKvP7l370ohKZpiI2N1S/B7dixI3Jycnzd/bL2+clrEBzq3TzB0hEfZuENhxbhUuJmU9XjrS8VYJHFA0BItGzd+9DmsiX8ogJzqw66tHzTBVG8CtlJLdYCRfH8gKyaAtnrToHspy+zInuNmuEUxUtJX0MOzSqKd2nCDwoAqib7MHIJEwpp+U5hfJFL9vXocMrOUWGxLN4le4nCpcrOpyYsHwA02dsAqs5zwHpxOxAAoCDPrms7qGrPPfccioqKMG7cONx8880IDg6uNPbnn3/GsmXLPNu6dOlSZR0+J7iPPPIIJk+ejNTUVPTr18/XYoiIiIjqDBUKVJ06EvQqt7753//+h1atWuH//b//V2XskiVL8PPPP+PPf/6zqA6fJxibOHEipk6dirvuuguvvfYaLlyQ9XIRERER0eUnNzcXLVu21LWOas2Du2DBAmRlZWH69OmYPn06GjVqhJCQkHJjFUXBkSNHqlMdERERka54k5n+NE2r1iIO3vA5wf31119x6623Yv/+/Z47386cOVNhPKcPIyIiIqLExETExsZ6FTtp0iTceuut4jqqNQ/uTz/9hHbt2uGJJ55Aly5dOA8uERER1WuapuMsCjrfaFdfzJ071+vYhIQEn+rwOcHduHEjgoKCkJqaiubNm/taDBERERFRjarWPLgdO3ZkcktERESGoUG/sbLyiTONqXXr1qJ4TdOQmZkJALj11ltx5MiRUtvK43OCe+211+LkyZO+7k5EREREl6GsrKwKVy4rz6Wxv/zyC44fP17l/j4nuE888QRGjRqF1atX4+677/a1GF3Exsbi2LFj5T734IMP4s033/S6rNzcXPzjH//AmjVrcPToUQQGBqJNmza44447kJiYWFNNJiIiojqAY3D1d/ToUZ/3/eKLL1BcXPUCTz4nuHfeeSdeffVVTJkyBd988w0mTZqEtm3bIigoyNcia1RERASmT59eZnv37t29LuP48eO45ZZbcPToUdx6660YOnQoHA4HDh8+jDVr1jDBJSIiIhKqzhy4zZo18yrO5wTXbP59mcGXX34ZL7/8cqXxiqLA6dR3ucpLRUZGIikpyef9XS4XRo8ejVOnTuHLL79E//79Sz3vz2MhIiIi/+A8uMbgc4IrGTvhS3xt+/DDD7Fr1y7MmTOnTHILABaL72tk5DsAb5d2NwuXgDcL502WxlvMsjendD10ADCbZK+VAOFJKlIDRfFBJtm1NkP2z49FqfqnlupQeFtDlaTnyKS5dC3fqQSI4l3Cj3KnJosvVoUfRABUTfbh4hL+dCst36nK4h1O2THnFwk/h4r1/SyVfuWqPnxM6F2HtHxv4wuLZOWSvnJzc5GWloaff/4ZFy5cQIMGDdChQwf06dMHYWFhPpfrc5amqqrPlfqDw+HAu+++i5MnTyIqKgq9e/dG586dvd7//fffBwCMGTMGWVlZWL9+PbKzs9G2bVsMGTIEDRo00KvpREREVEs4Btc/CgsLMWfOHLzxxhvIz88v83xISAgeeughzJ8/36fhr9VaqrcuO336dJnJgQcPHowVK1YgOjq6yv2/++47AEBaWhpmzJgBh8Phea5Ro0ZYvXo1+vXrV2kZDoej1H52u937AyAiIiIyIJfLhWHDhmHz5s1QFAUtW7ZEp06d0LhxY5w5cwYHDx7E8ePHsWjRIvzvf//DZ599Jv7lXN+FgGvJpEmTkJqairNnz8Jut2Pnzp0YMmQINm7ciBEjRng1XMK97PDUqVMxffp0ZGVl4ezZs3j11VeRk5ODkSNH4pdffqm0jJSUFERERHgeMTExNXJ8REREpA8NgKrTgwPGLnrjjTfw1Vdf4corr8S6deuQmZmJTz/9FO+++y4+/fRTZGRk4D//+Q9atGiBr776Cm+88Ya4DkMmuHPnzkV8fDyio6MRFhaGnj174pNPPkGfPn2wY8cObNiwocoy3EMwhg0bhueffx5XXnkloqOjMXXqVMyYMQM5OTlYunRppWXMmjULOTk5nkdWVlaNHB8RERFRfbVixQpomoaPP/4YI0aMKDdm+PDh+OSTT6AoClasWCGuw6v+3jZt2gAA2rVrh02bNpXa5i1FUXDkyBFh82qOyWTCxIkTkZaWhm3btmHo0KGVxkdERMBms5V74ocPH44XXnjBM4yhIlarFVartVrtJiIiIv/hGFz9HThwAFdffTW6dOlSaVznzp3RqVMnHDp0SFyHVwmueym0Swf5VrY8WnkUpfYvqnvsbXmDmf/oqquugs1mQ2RkZJnn3NsKCgpqsnlEREREhqeqKgICvJstJiAgAC6XbOYawMsENyMjw1PJH7fVJ9988w2AiyudVeWWW27Btm3bsH//ftx1112lntu/f7/X5RAREVH9wXlw9deuXTvs27cPR44cQdu2bSuMO3r0KPbt24e4uDhxHV6NwW3VqhVatWqF5s2bl9kmefjD/v37kZ2dXWZ7WloaFi1aBKvVWiphtdlsOHjwIGw2W6n4iRMnwmq14rXXXsPJkyc923Nzc/Hcc88BQJ1bopiIiIiqxz1EQa8HAWPHjoWqqhgxYkSFwz2/++47DBs2DC6Xy6d8y+dpwrZu3YqIiAiv5pb94YcfkJ2djZtvvtnX6ry2evVqLFiwAAMGDEBsbCysViv27duHTZs2wWQy4c033yy1RNzixYuRnJyMxMTEUiuftW7dGgsXLsRjjz2Gzp07484774TVasX69euRmZmJBx54AAMGDND9eIiIiIiMZPr06fjggw+wZ88e9OjRA7169UJcXByaNGmCX3/9FT/99BN27NgBRVHQpUsXzJgxQ1yHzwluv3790LdvX2zZsqXK2GnTpuHrr7/2y/K2/fv3x4EDB7B7925s2bIFhYWFaNKkCe655x7MmDEDPXr08LqsqVOnIjY2FgsXLsR7770Hp9OJuLg4zJ49G/fff7+OR0FERES1gUMU9BcUFIQvvvgCjz76KN5//33s3LkTO3fuhKZpnnu2TCYTxowZg9dffx3BwcHiOqq10INk+V1/LdUbHx+P+Ph4r+OTkpJK9dz+0fDhwzF8+PAaaBkRERERAUBUVBT+3//7f5g/fz4+//xz/Pzzz8jNzUVYWBg6dOiAgQMHonXr1j6X75eVzH777Tefsm+jahKlISTUu4TfZJL9Y2AWxluk8WZZfIBJfudjaIBsofBQi2w2iyvUX0Xx4WcyRfHmfNmKdZpZtob95UiRLmIvpQmXHhfHy9qvBoWK4p0hEaL4YqtsfXenWT7docsk+3pRFdn7wGXy7g5sT7wia0+RJjvmQlUY7woUxTtV2bT10p5Clw9jQ6XjSaX9XNJjUL1sT15w5d8xqnbxoQe9P8rqo9atW+OBBx6o8XK9fsfb7fYyN285HA5kZWVV2DtbUFCALVu2YN++fV6N1SUiIiIiqi6vE9yXXnoJzzzzTKlt3333nddTZU2ePFnUMCIiIiJ/4xhc/R07dky8j3Q2Lq8T3MjIyFKzDxw/fhyBgYFo2rRpufGKoiA4OBht2rTBPffcgz/96U+ihhERERGR8bRp00Z8b5aqyoaGeZ3gTps2DdOmTfP8bTKZcMMNN2Dr1q2iComIiIjqKi7Vq7/Y2NgKE9y8vDzP2gRmsxlXXnmlT3X4fJPZsmXL0KRJE193JyIiIqLL0JEjRyp9Pjc3F++88w6SkpIwduxYzwJbEj4nuPfdd5+vuxIRERHVSZomn/FBUjZVLSwsDNOmTUOnTp0wePBgXH311eKhrrJ5R4iIiIiI/OC2225DbGwsXn75ZfG+fpkHl4iIiKg+UKFA1Wm2A73KNbLIyEgcOHBAvB97cImIiIjqsF27duH2229HVFQUQkND0aNHD6xatcrr/VNTU6EoSoWPnTt3ltknNja2wviHHnqoJg+vQmfOnMHBgwcRFBQk3pc9uEREREQl6tosCqmpqRg0aBACAwMxduxYREREYO3atZgwYQIyMzMxe/Zsr8uKj49Hv379ymyvaKaCiIgITJ8+vcz27t27e12nlKZpOHPmDHbt2oWnn34ahYWFGDZsmLgcJrhEREREdZDT6cSUKVOgKAq2bt2Krl27AgASExPRq1cvJCYmYsyYMWjfvr1X5fXr1w9JSUle1x8ZGSmK95bJ5N0AAkVR0Lx5c7zwwgviOpjg1oImYfkIbeDdqTcrslsuTYpsImSzSThxsuISxQeYikXxABCKC7J4x3lRfIPfMkTx2qEfRfEXjp0UxTsdla+LXobOi5lrwsm0qWpma6AoPqhxQ1F8YONGonhreKQoXguU/zyIANkxa5YAWbxZ9vWlWqyieJc1RBRfFBgmincEhIrincLzqSpmWbwPIxalq3KpmqwOTdgmb9tzQc2tvJw6NIvC5s2bceTIEUycONGT3AIXZxmYM2cOxo4di2XLlvk0jVZtMplMlS700KBBA7Rp0wZDhgzBzJkz0bCh7DMRYIJLREREVCelpqYCuDibwB+5t23ZssXr8tLT0/Hqq68iPz8frVq1wsCBAxEdHV1hvMPhwLvvvouTJ08iKioKvXv3RufOnWUHUQ6n01ntMqrCBJeIiIiohAZF3DstKVsiPT0dAModghAVFYXo6GhPjDdWrVpV6ua04OBgJCcn44knnig3/vTp00hISCi1bfDgwVixYkWliXFdwFkUiIiIiEqomr4PALDb7aUeDoej3Lbk5OQAuHizV3nCw8M9MZVp1KgRFi5ciAMHDiAvLw8nT57EypUr0bBhQzz55JN46623yuwzadIkpKam4uzZs7Db7di5cyeGDBmCjRs3YsSIEZUOMagLmOASERER+VFMTAwiIiI8j5SUFF3ri4uLw8yZM9GxY0eEhISgefPmmDBhAjZu3IjAwEAkJiZC/cP9F3PnzkV8fDyio6MRFhaGnj174pNPPkGfPn2wY8cObNiwQdc2VxcTXCIiIiK3kmnC9HigZJqwrKws5OTkeB6zZs0qtynuntuKemntdnuFvbveuOaaa9CzZ0/8+uuvOHz4cJXxJpMJEydOBABs27bN53r9gQkuERERkR+Fh4eXelit5c/y4R57W9442/Pnz8Nms3k9RVhF3GNp8/PzdYmvLUxwiYiIiEq4pwnT6yERHx8PANi0aVOZ59zb3DG+cDqd2L17NxRFQcuWLb3a55tvvgFwcaWzuowJLhEREVEdNGDAALRp0warVq3Cnj17PNtzc3Mxb948WCyWUrMc2Gw2HDx4EDabrVQ5O3bsKHNTmNPpxBNPPIFjx45h0KBBpeaa3b9/P7Kzs8u0Jy0tDYsWLYLVasVdd91VI8eoF04TRkRERFRChQJVp2nCpOVaLBYsWbIEgwYNQt++fTFu3DiEh4dj7dq1yMjIwPz589GhQwdP/OLFi5GcnIzExMRSK5CNGzcOiqKgd+/eaNGiBbKzs7F161YcOnQILVu2xJtvvlmq3tWrV2PBggUYMGAAYmNjYbVasW/fPmzatAkmkwlvvvmm1z2+tYUJLhEREVEd1b9/f6SlpSExMRGrV69GUVER4uLiMG/ePEyYMMGrMh5++GFs3LgRqampsNlssFgsaNeuHZ566in89a9/RVRUVJk6Dxw4gN27d2PLli0oLCxEkyZNcM8992DGjBno0aOHHodaoxStrk9kZiDuux0/3XkSoQ3CvdqHS/V6EV/HlurN51K99Ad1balehUv1VqnOLdVr4lK9Vcd7uVRvbi56duuInJwchIf//l3s/o5+L/U3hHj5HS2Vf8GOsf2uKFM31Tz24NaCJtbf0CDIu6TGBGHCqsmWvzOrsgRUWr6lgsmrKxNUIEtYLRd+k1Vw/IgoPGd/1VOnXOrk98dE8ecP2UXxrgLZa0Ir1jchVnUu3x/MIbIv0sCGso/OBs2CRfFXtJMlrBGtqp7o/VKBDWXTCpmDZMkhACiBsoRMCZAluCZhQmwKkF2zgGBZAmoNlZ3TkCBZ+ZpF+A+DSZjgCuN9qUOcsErbpHiX4IYU5snKpXqJCS4RERFRCc+ctTqVTf7BWRSIiIiIyFDYg0tERERUQtX0u9VB51so6BLswSUiIiIiQ2EPLhEREVEJX1Yck5RN/sEeXCIiIiIyFPbgEhEREZXQoIjn+JWUTf7BHlwiIiIiMhT24BIRERGVUKHjLAr6FEvlYA8uERERERkKe3CJiIiISnAWBWNgglsLnFoAnJp366ib4BKVrXm5Frcn3iSM12Sd/opZ/m52WoJE8abAEFG8JUy2ZnxoTFNRfLS9QBRvDpCdU1ex7EcuTfhbm+bS/0c01aXvp7w5ULaGvcUqi7eGyV6jIVc0EMU3aHGFKD64WRNRvDk8XBSPwEBZPADF4t1nnIdF+HVkll0zTdgeNVB2jV1BYbL4wGBRvGqWXQPNJDw/ivwHXVWR1iH8vhG2SfOyPUUBlX/+MME1Bg5RICIiIiJDYQ8uERERUQlVU6Bq+kznpVe5VBZ7cImIiIjIUNiDS0RERFSCY3CNgT24RERERGQo7MElIiIiKsEeXGNgDy4RERERGQp7cImIiIhKaJp+S/WyB9d/2INLRERERIbCHlwiIiKiEpqmQNNpvlq9yqWy2INLRERERIbCHtxacDwvGqGKd2vBmxTZgB2LSRXFm4Xlm00uUXyAMB4AQkILRfGhYRdE8WHhTUXxwU1bieKbte8gim9yziaKhyq7xppLfg1kFdTBQWXCNe+VwEBZfFCQLD4kTBSvhkWK4osbNBTFF1pl7XGZ5F8VqilAFK8Jr5mqmHWNdwrbX6RZhfGy15xLEx6vJuu/UiHvWZS+9TVhHdLeTm/j8zR7FeVwFgUjYA8uERERERkKe3CJiIiISqg6zqKgV7lUFntwiYiIiMhQDJ3grlu3DgMHDsQVV1yB4OBgtG7dGuPGjUNWVlaV+/br1w+KolT6WLFihR+OgoiIiPzFPQZXrwf5hyGHKGiahoceegj/+te/0LZtW4wdOxZhYWE4deoUtmzZgmPHjiEmJqbSMhISEtCvX78y24uLi5GSkgKTyYQBAwbodARERERE5CtDJrivvfYa/vWvf+GRRx7BK6+8ArO59N2nTqezyjISEhLK3b5mzRpomobbb78dzZs3r4nmEhERUR3BWRSMwXBDFAoKCpCcnIw2bdrg5ZdfLpPcAoDF4ntev2TJEgDA5MmTfS6DiIiIiPRjuB7czz//HOfOnUNCQgJcLhc+/vhj/Pzzz4iMjMStt96Kdu3a+Vz2iRMnsGnTJjRt2hRDhw6twVYTERFRXcBZFIzBcAnud999B+BiL23nzp1x6NAhz3MmkwkzZszAP/7xD5/KXrZsGVRVRUJCgle9wA6HAw6Hw/O33V755NJERERUuzhEwRgMN0ThzJkzAIAXX3wR4eHh+Pbbb5Gbm4utW7eiQ4cOePHFF/HGG2+Iy9U0DcuWLQPg/fCElJQUREREeB5V3dhGRERERNVnuARXLVnGNDAwEP/5z39www03oEGDBujbty8+/PBDmEwmvPjii+JyN2/ejIyMDMTHx3s9zGHWrFnIycnxPLyZnoyIiIhqj6rq+yD/MNwQhYiICABA9+7dy8xyEBcXhzZt2uDw4cPIzs5GZGSk1+W6by6bMmWK1/tYrVZYrWXXJz9+JgDBed6tc24S/gti1jneIlsOHQEW+e8xodZQUXxwQIQoPtLaUBQf1riZKD60YWtRfJBDNnRF0YSfkMLfxBTo/wksPgYhVZF9tGlmWbzTHCiML/s5UJlCSwNRfJ4qe88UumTtVzV5X4iqyvaRviKkbdI0RRTvVGXxRS7Zh6PDKWu/S9ge6U/h0vJ9IW2TNF718hrn53GcwOXAcD24V111FQBUmLy6txcUFHhd5vnz57Fu3TpERkZi1KhR1W0iERER1VFc6MEYDJfg9u/fHwBw4MCBMs8VFxfj8OHDCA0NRaNGjbwuc+XKlXA4HJgwYQKCg4NrrK1EREREVPMMl+C2bdsWt912Gw4fPuwZVuD2/PPPIzs7G3feeadnFgSbzYaDBw/CZrNVWObSpUsBcO5bIiIio2MPrjEYLsEFgNdffx2NGzfG/fffj2HDhmHmzJkYMGAA5s6di1atWmHhwoWe2MWLF6NTp05YvHhxuWV9//332Lt3L7p164auXbv66xCIiIiIyEeGTHDbtm2L7777DgkJCfj+++/x6quvIj09HY888gi+/fZbNG3a1Ouy3L23kpvLiIiIqH5S8ftiDzX+qO2Du4womsYOc3+x2+2IiIjAojXZCA4N92qfy3MWBZcoPjjAKYqPtHp/gyEAhFlyRfGhzhxRPGdRqHmcRaGK8v0xi4JwH86iUDnOolA172dRsOOBIZHIyclBePjv38Xu7+iFH+YgOMS772ipgnw7nhgdUaZuqnmGmyaMiIiIyFeapkGvvj/2KfqPIYcoEBEREdHliz24RERERCX0nO2AHbj+wx5cIiIiIjIU9uASERERldBUQNXpPlid76+lSzDBrQW/nXchyOHdTAFms+zOVrNwlgOTSd/yLcL2A0BIkOxlGWKVNaowRFZ+vlV2x3logOwO+KCQaFG8gro1K4K0Pf6gQfa6UyF7DRVrwlkXVFl8gSNAFJ9XJHuNFjplx+t0yd/H8jvmhddM5zvypcdcJJvMBcXCeJfwbeySTUbjE71/bter/MJ8fcqluoUJLhEREVEJjsE1Bo7BJSIiIiJDYQ8uERERUQn3qmN6lU3+wR5cIiIiIjIU9uASERERleAYXGNggktERERUQlM1aDqNJdCrXCqLQxSIiIiIyFDYg0tERERUgjeZGQN7cImIiIjIUNiDS0RERFSCN5kZA3twiYiIiMhQ2INLREREVEJVNag6DZbVq1wqiwluLcjPd8KlOb2KNZkUUdkmsyzeLCzfLCzfYpHFA4Cqyn5YcLpkdSiKWRQPWEXRTk3W/iKz7G2oKLIPSAX6fqAq8kusO+nPgKrwmkmvcbEqe80VFsteExcKZfGOYtlFc7pE4QDkN9NIr5k03qXqG+8okjWouFgWrwrb449EStomKb1+zi/M9+EFTfUOhygQERERlXCPwdXr4Ytdu3bh9ttvR1RUFEJDQ9GjRw+sWrXK6/1TU1OhKEqFj507d+pSb21iDy4RERFRHZWamopBgwYhMDAQY8eORUREBNauXYsJEyYgMzMTs2fP9rqs+Ph49OvXr8z2K6+8Utd6awMTXCIiIqISdWkWBafTiSlTpkBRFGzduhVdu3YFACQmJqJXr15ITEzEmDFj0L59e6/K69evH5KSkvxeb23gEAUiIiKiOmjz5s04cuQIxo8f70kyASAsLAxz5syB0+nEsmXLDFNvTWIPLhEREVEJVdOg6tSFKy03NTUVAHDbbbeVec69bcuWLV6Xl56ejldffRX5+flo1aoVBg4ciOjoaN3rrQ1McImIiIj8yG63l/rbarXCai07Y096ejoAlDsUICoqCtHR0Z4Yb6xatarUTWLBwcFITk7GE088oWu9tYFDFIiIiIhKaKq+DwCIiYlBRESE55GSklJuW3JycgAAERER5T4fHh7uialMo0aNsHDhQhw4cAB5eXk4efIkVq5ciYYNG+LJJ5/EW2+9pUu9tYk9uERERER+lJWVhfDwcM/f5fXe1qS4uDjExcV5/g4JCcGECRPQuXNnXH/99UhMTMT9998Pk8k4/Z7GORIiIiKiatKgQdN0epQsvBMeHl7qUVGC6+5Brai31G63V9jL6o1rrrkGPXv2xK+//orDhw/7rV5/YIJLREREVAe5x8CWN971/PnzsNls1Z6qy32TWX5+vl/r1RsTXCIiIqISmnpxGWI9Hu4xuN6Kj48HAGzatKnMc+5t7hhfOJ1O7N69G4qioGXLln6r1x84BrcWFBe5YDJ7txa2ySz7H8QkXGJbFZbvUmVr2PvyP1SRcI12i1nWpmKnMN4liy9ymkXx0jOkKLLzo0DfNeml7fEHTZNdM1WTXQWn8H1QrMpeEw6nrD1Fwte0o1gUDpfwSxm4+GUuihe+jKSJgrR8afuLhZ9bTqcs3iX9bBdORyU9n77Q9Fo9oYS317jY6YeDrSEDBgxAmzZtsGrVKjz22GPo0qULACA3Nxfz5s2DxWJBQkKCJ95ms8FmsyE6OrrU9F87duzAjTfeCEX5/bPC6XTiiSeewLFjxzB48GA0bNjQ53rrIia4RERERCXc42X1KlvCYrFgyZIlGDRoEPr27Ytx48YhPDwca9euRUZGBubPn48OHTp44hcvXozk5GQkJiaWWrFs3LhxUBQFvXv3RosWLZCdnY2tW7fi0KFDaNmyJd58881q1VsXMcElIiIiKqFq8h5/SdlS/fv3R1paGhITE7F69WoUFRUhLi4O8+bNw4QJE7wq4+GHH8bGjRuRmpoKm80Gi8WCdu3a4amnnsJf//pXREVF6VJvbVI0vX8zIA/3XYeP/uMUrMHhVe8AH4YoCH/vNkvLFw4HCAyQD1EICpLtExIka1N4qCgcYSGy3waDA2TxQRZZPIcoVK2+D1EoLJa1J69QWH6RKJxDFLzgKJLtwCEKNc/ba+wosGPhw42Qk5NTaqou93f0zNfPev0dLeUosOMffylbN9U89uASERERldBUDZpOXbh6lUtlcRYFIiIiIjIU9uASERERldC0iw+9yib/YA8uERERERkKe3CJiIiISqiqBlWnsbJ6lUtlsQeXiIiIiAyFPbhEREREJerSQg/kO/bgEhEREZGhsAe3FhQUFMOlebcYvMkkm1BeuhCDdKGHS9ex9obDIv8fyumUTVrvcsniFelqGJCVrwVLFxmQxUsJL9llySVcuEG60IPTJYsvcMheo/kOUTgcwoUepIseAIDLJeupEi/0II6X7SBdiKGoSBZfXCw7qS7hahvSa1YXexb1mjPWUVD596+m6rfwhT8W1KCL2INLRERERIbCHlwiIiKiEqqmiZc6lpRN/sEeXCIiIiIyFPbgEhEREZXgLArGwB5cIiIiIjIU9uASERERleBKZsbAHlwiIiIiMhT24BIRERGV0DT5PMuSssk/2INLRERERIZiyAR3+fLlUBSl0seAAQO8Kis7Oxtz587Fddddh7CwMERHR+OGG27A4sWLUVhYqPOREBERkT9pmgZN1enBLly/MeQQhS5duiAxMbHc5z788EP89NNPGDRoUJXlZGdn4/rrr8fRo0fRp08fPPjgg3A4HPj0008xdepUrFu3Dp9//jlM4qVfiYiIqC7SdFzogQmu/xg2we3SpUuZ7UVFRVi8eDEsFgvuu+++Ksv517/+haNHj2LGjBlYtGhRqXL69OmDzZs3Iy0tDTfffLOofVfGhCEoJMyrWGnubDLJ1rw3m2Xlm4XlBwbIygeAYKssPsQq+8BoEFT5OuRl4gOLRPFhAQWieKtJVr4CLmZeFU3445QG2evapcnKd2qyj9pCV6AovsApe6MVuWRvfJcqOz+AfKyhpkmvgb7lFzll17iwSFa+0yUKh0v4tpeff1k8AEgnBNCExyAu38v4wnynrGCqly6rrsd169bht99+w7Bhw9CkSZMq448ePQoAuP3220ttDwwMxMCBAwEAZ86cqfmGEhERUa3QbXhCyYP847JKcJcuXQoAmDJlilfxcXFxAICNGzeW2l5cXIwvvvgCwcHB6NWrV802koiIiIiqxZBDFMpz7NgxfPnll2jRogUGDx7s1T5TpkzBihUr8OKLL+K7777DDTfcAIfDgY0bN+L8+fNYtWoVWrRoUeH+DocDDofD87fdbq/2cRAREZF+9OxpZQ+u/1w2Ce6yZcugqiomTpwIs5cDT4ODg5GamooHH3wQK1euxJYtWwAAJpMJjz76KPr06VPp/ikpKUhOTq5224mIiIjIe5fFEAVVVbFs2TIoioJJkyZ5vZ/NZsPAgQOxc+dOrF+/HtnZ2Th9+jTefPNNLFu2DD179sT58+cr3H/WrFnIycnxPLKysmricIiIiEgnqqbvg/zjsujB/fzzz3H8+HEMGDAArVu39nq/xx9/HNu3b8fevXtx3XXXAQAiIiJw//33w+Vy4eGHH8bLL79cYS+t1WqF1SqcEoCIiIiIquWySHClN5e5rV+/Hg0bNvQkt5e65ZZbAADff/999RtIREREdQLH4BqD4Yco/Pbbb/joo4/QsGFD3HnnnaJ9i4qKYLfbUVRUdp7Ss2fPAgB7aImIiIjqGMMnuCtWrEBRURH+9Kc/VZiM2mw2HDx4EDabrdT2m266CU6nE/PmzSu13eFweLb1799fn4YTERGR32mapuuD/MPwCa43wxMWL16MTp06YfHixaW2P//88wgLC8P8+fPRs2dPPP744/jLX/6Cq6++Gp999hmuv/568bAHIiIiItKXoRPcb7/9Fvv27UOPHj1w7bXXivfv0qULvv/+e0ycOBGnT5/G4sWLsXz5coSGhiI5ORlbt25FUFCQDi0nIiKi2qCqgKpqOj1q++guH4a+yaxHjx5e/RyQlJSEpKSkcp9r37493nnnnRptV3CQCUFB3v1voQiXgDcL/2XxckpgD5Ow/EAfXmEhVtlPOMGBskXdQwOLRfEhlrJjsCtjNcniAxVH1UGXUMBPyKpowv/dNcjeaKoiK9+sya6ZtD1SFpOsPU5V3hci/SVWeswuVXjNNFm8RfhhZxK+Jpyyjy3x8bqEHxO+/HKu9/1S4teQl/GK8NxT/WToBJeIiIhIQs+xshyD6z+GHqJARERERJcf9uASERERleA8uMbAHlwiIiIiMhT24BIRERGVYA+uMbAHl4iIiIgMhT24RERERCVUaFB1mu1ABXtw/YUJLhEREVEJDlEwBg5RICIiIiJDYQ8uERERUQku9GAM7MElIiIiIkNhDy7pypfhRtI11F3CNeY1Yby0fKdmFsWb6vnbUFHqXo+E9BprkMWrkF3jYk12jV2arO9B/BpVZeWrwvIBwKXqew2kHWHS9riE5Us/68TtEX4uSs+PL5/V0jr80aaaKFdTNagcg1vvsQeXiIiIiAylfncdEREREdUgzqJgDOzBJSIiIiJDYQ8uERERUQnOomAM7MElIiIiIkNhDy4RERFRCU1VoanCaSsEZZN/sAeXiIiIiAyFPbhEREREJVQd58HVq1wqiz24RERERGQo7MElIiIiKsFZFIyBPbhEREREZCjswa0FuRdcKFZdXsWahP+CKIpsfXOzWVa+ySQrP8AiX8Pe6ZLt43TJDsKEQFF8caDsIjgtsreVxWQVxQsvMXlB1WQnVdWErwlVFu9wyV5DDqfsPVDklLXH5cON35r4nErLl8W7VOnniqx8R7EsXlq+SxgvvVlf9UPPoiZukz7tKMyvvGCuZGYM7MElIiIiIkNhDy4RERFRCfbgGgN7cImIiIjIUNiDS0RERFRChQpVOmBYUDb5B3twiYiIiEpo6u/DFGr+4Vubdu3ahdtvvx1RUVEIDQ1Fjx49sGrVKp+Psbi4GF26dIGiKOjYsWO5MbGxsVAUpdzHQw895HPd/sIeXCIiIqI6KjU1FYMGDUJgYCDGjh2LiIgIrF27FhMmTEBmZiZmz54tLnPevHk4fPhwlXERERGYPn16me3du3cX1+lvTHCJiIiIStSlm8ycTiemTJkCRVGwdetWdO3aFQCQmJiIXr16ITExEWPGjEH79u29LnP37t1ISUnBokWL8Nhjj1UaGxkZiaSkJFGb6woOUSAiIiKqgzZv3owjR45g/PjxnuQWAMLCwjBnzhw4nU4sW7bM6/KKioqQkJCAG2+8EY8++qgeTa4z2INLREREVKIuLdWbmpoKALjtttvKPOfetmXLFq/LS0pKQnp6Ovbu3evVwlAOhwPvvvsuTp48iaioKPTu3RudO3f2ur7axASXiIiIyI/sdnupv61WK6zWsqtapqenA0C5QxCioqIQHR3tianKrl27sGDBAjz33HPo0KGDV/ucPn0aCQkJpbYNHjwYK1asQHR0tFdl1BYOUSAiIiIqoaqqrg8AiImJQUREhOeRkpJSbltycnIAXLzZqzzh4eGemMo4HA4kJCSga9eu+Otf/+rVeZg0aRJSU1Nx9uxZ2O127Ny5E0OGDMHGjRsxYsQI3Xq5awp7cGtBbm4xipzeLVxuEv4LYjbLdvDiF4o/lC/bwWKR/w/lKJLV4QiS1aFpZlF8kUvWnuJAWXsCzXwb1jYNsmvsUmXxTlX2migsFsYXyeKLnaJwOF2yeACQ3qMj/a6UxruExyA95qJiWYOKi2XzRUnbrwpPkC/TV0kTHL0X8fL2Bi5HgXffv3rKyspCeHi45+/yem9r0pw5c5Ceno7vv/8eZrN334Fz584t9XfPnj3xySefID4+HmlpadiwYQOGDh2qR3NrBHtwiYiIiEroNwfu77MzhIeHl3pUlOC6e24r6qW12+0V9u667d69G4sWLcJTTz2Fa6+9thpnBjCZTJg4cSIAYNu2bdUqS29McImIiIjqIPfY2/LG2Z4/fx42m63KKcJ++OEHuFwuJCUllVmwAQAOHToERVEQGRnpVZvcY2/z8/MFR+J//G2UiIiIqISmqdB0WqpXWm58fDxSUlKwadMmjB07ttRzmzZt8sRUpkOHDpg8eXK5zy1duhQREREYPXo0QkJCvGrTN998A+DiSmd1GRNcIiIiojpowIABaNOmDVatWoXHHnsMXbp0AQDk5uZi3rx5sFgspWY5sNlssNlsiI6O9vS09u7dG7179y63/KVLl6Jp06ZYsmRJqe379+9H8+bNy/TqpqWlYdGiRbBarbjrrrtq7Dj1wCEKRERERCX8MQbXWxaLBUuWLIGqqujbty8eeOABzJw5E507d8ZPP/2EpKSkUlN+LV68GJ06dcLixYurdQ5Wr16N5s2bY/jw4Zg6dSpmzpyJwYMH4+abb0ZxcTEWL16Mli1bVqsOvbEHl4iIiKiO6t+/P9LS0pCYmIjVq1ejqKgIcXFxmDdvHiZMmKBbnQcOHMDu3buxZcsWFBYWokmTJrjnnnswY8YM9OjRQ5d6axITXCIiIiI3H3paJWX7okePHvj000+rjEtKSkJSUpLX5VY01Vt8fHyVY3vrOg5RICIiIiJDYQ8uERERUQlVU6HqNIuCXuVSWezBJSIiIiJDYQ8uERERUQlfZjuQlE3+wQS3FjgcxdAU79bCNimyNe8VkyzeLIyXlm+xyH8kUDXv1sn2VWCAtE2yeFWVnaMAH85RXSJ8ifpFBfdNVBIvOwjpd5TTJSu/qFgWX1AkCodDGK/68KuqKrwI0l9upddA+ppwOmU7FBXLDqC4SBbvcgnPp/CAfbnG0jrE5euUDDoKnJXXq6nQfDkhXtBrAQkqq35/sxIRERER/QF7cImIiIhKcIiCMbAHl4iIiIgMhT24RERERCU0TdVtrCzH4PqPIXtwNU3D2rVr0b9/fzRr1gwhISG46qqr8OCDD+Lo0aNel3PixAk8+OCDaNmyJQIDA9G8eXNMnDgRWVlZOraeiIiIiKrDkD24M2fOxKJFi9CsWTOMHDkS4eHh2Lt3L95++238+9//xvbt23HNNddUWsaRI0fQu3dvnDlzBgMHDsQ999yD9PR0vPvuu9iwYQO2b9+Otm3b+umIiIiIyB9UFVB1Giur0+QMVA7DJbinT5/Gyy+/jNjYWOzduxfh4eGe515++WXMmDEDixYtwjvvvFNpOdOmTcOZM2fwyiuv4LHHHvNs/+CDD3D33XfjkUcewcaNG3U7DiIiIiLyjeGGKGRmZkJVVdx0002lklsAGDp0KADgzJkzlZZRWFiIzz77DE2aNMHUqVNLPTdmzBh06dIFn332mWi4AxEREdV9mqrq+iD/MFyC2759ewQGBmLbtm3Izc0t9dyGDRsAALfcckulZfz2229wOp1o1aoVlHJmsW/dujUA4KuvvqqhVhMRERFRTTHcEIUrrrgCzz77LJ544gl06tQJI0aMQFhYGH788Ud88cUXeOCBB8r0yv5RVFQUzGYzjh07Bk3TyiS5GRkZAICff/5Zt+MgIiIi/+M8uMZguAQXuHiTWfPmzfHggw/ijTfe8Gzv3bs3/vSnPyEgIKDS/UNCQhAfH4/Nmzfj9ddfxyOPPOJ5bu3atdizZw8AIDs7u9JyHA4HHA6H52+73S4/GCIiIiISMWSCO3/+fDzzzDNISkrCn//8Z0RFRWHPnj14/PHH0b9/f6xevRp33XVXpWUsWrQIffr0waOPPor//ve/uO6663D48GF89NFHuO666/DDDz/AbDZXWkZKSgqSk5PLbC/IK4bL5d1i8OUNkaiMySSMN8tGqcjLl8UDgMtV+T8gf6S6Kr8OfyQ9py5VFl/sksUHVPE6+iNh8+skvY9BE3aSSONdwmF0Tpcs3uHdx4NHgUN2AEVFsgNwCdsPAKrwpEqnB9WE5Us7zpzFsgYVF8lOktMpvQbC8yk9P37oWdS799LbYy4qdFT6POfBNQbDjcHdvHkz5syZg0cffRSzZ8/GlVdeidDQUNx000345JNPEBwcjBkzZlRZTufOnbFr1y7cfffd2L17N1555RUcOnQIb731Fu69914AQKNGjSotY9asWcjJyfE8OH8uERERkf4M14O7fv16AED//v3LPNeoUSNce+212LFjB2w2G6Kjoystq2PHjnj//ffLbE9ISAAAdO/evdL9rVYrrFarly0nIiKi2sYxuMZguB7coqKLv+2dPXu23Ofd231NPHNzc/Hf//4XDRs2xMCBA31rJBERERHpxnAJ7k033QTg4hjanJycUs+9++67OHz4MK6//nqEhYUBAGw2Gw4ePAibzVYqtqCgAE6ns9Q2h8OByZMn49y5c0hMTERQUJCOR0JERET+xnlwjcFwQxTGjBmDt956C6mpqWjfvj1GjBiBqKgo7N27F59//jmsVitefvllT/zixYuRnJyMxMREJCUlebZ///33uOuuuzBw4EDExMTAbrdj/fr1OH78OO6///4qpxojIiKi+sflzKuXZVNphktwzWYzNm7ciFdeeQXvv/8+/v3vf6OoqAhNmjTB+PHjMWvWLFxzzTVVltOyZUv069cPX3/9NX799VeEhISgW7duWLRoEUaNGuWHIyEiIiJ/CQwMRNOmTfHdl3frWk/Tpk0RGBioax0EKJp0LhHymd1uR0REBP485ygCg8K82udynCYsKEg2TVhwsGyarQYNZP/XhYbIjiFI+LkVIGs+pwnzAqcJqxynCasapwmreXVnmjA7liW1Rk5ODsLDw0s9V1hY6LmXRy+BgYEc4ugHhuvBJSIiIvJFUFAQk0+DMNxNZkRERER0eWOCS0RERESGwgSXiIiIiAyFCS4RERERGQpvMqsFzmIXTGbv7rhVTLL/QczCWQukd85KZ1GwQDhFAOR3FzudsnPkdMqO2emUHXOx8N9G6R38RphFQfgyEpPerK33LArSud2lsy5IX9PFwnhVeAc/4MM1EO4gvWbSzzr555C+8S5hfF2cRUFKr0mepDNSUP3EHlwiIiIiMhQmuERERERkKExwiYiIiMhQmOASERERkaEwwSUiIiIiQ2GCS0RERESGwgSXiIiIiAyFCS4RERERGQoTXCIiIiIyFCa4RERERGQoTHCJiIiIyFAstd2Ay5FLVeHycjF7k3h9cLMo2qRI139XRPGKIosHANUiXKPdy3P5e7zsmF3Ca+BSpedIFA6T/JTWObIrpj/p20wVHoDLJY2XNUgVHoAqfQ8I4wFAE59T6WeR9ByJwuXXQNgel1Pfzznp+fGFJv5+ktGreF9ez1T/sAeXiIiIiAyFCS4RERERGQoTXCIiIiIyFCa4RERERGQoTHCJiIiIyFCY4BIRERGRoTDBJSIiIiJDYYJLRERERIbCBJeIiIiIDIUJLhEREREZChNcIiIiIjIUS2034HKkqZrXa3ir0n9BnMJF7y1mUbhJuDi4dP30i/tI65CuYS8Kh0t4SjXhIWvCa1zXllFXlNpuQVnSayyNF74NIH0biMsXvkb1fo/5uo+EJrxo0vaowovmLJbFSz8bpe2RvoY0Vf5ZrTdvvyelVOkbhuol9uASERERkaEwwSUiIiIiQ2GCS0RERESGwgSXiIiIiAyFCS4RERERGQoTXCIiIiIyFCa4RERERGQoTHCJiIiIyFCY4BIRERGRoTDBJSIiIiJDYYJLRERERIZiqe0GXI4UkwLFpHgVa1K8i7u07LrE5EN7FOG/XYrwHEnVsVMKnQ/XEHQ/R5osXO/2SMsXf064hAcMwCR8H6uquAoR6SFrde2NrzNFesEAaDpfNOnrVFPlr1MyLvbgEhEREZGhMMElIiIiIkNhgktEREREhsIEl4iIiIgMhQkuERERERkKE1wiIiIiMhQmuERERERkKExwiYiIiMhQmOASERERkaEwwSUiIiIiQ2GCS0RERESGomiaVucXb165ciW+/vprfP/99/jxxx9RVFSEZcuWISEhodx4u92OpKQkrFmzBqdPn0bTpk0xatQoJCUlITw8XFT3rl27kJiYiB07dqCoqAhxcXGYPn06xo8fLz4Ou92OiIgI5OTkiNtBRERE1cfv4suDpbYb4I2nn34ax44dQ3R0NJo1a4Zjx45VGJuXl4f4+Hjs2bMHAwcOxLhx47B371689NJL+Oqrr5CWlobQ0FCv6k1NTcWgQYMQGBiIsWPHIiIiAmvXrsWECROQmZmJ2bNn19QhEhEREVENqRdDFJYsWYLMzEycPXsWDz30UKWxCxYswJ49e/Dkk09i06ZNeP755/Hpp59i7ty52LNnDxYsWOBVnU6nE1OmTIGiKNi6dSvefvtt/OMf/8DevXsRFxeHxMREpKen18ThEREREVENqhcJ7q233opWrVpVGadpGpYsWYIGDRpg7ty5pZ6bNWsWoqKisHTpUngzKmPz5s04cuQIxo8fj65du3q2h4WFYc6cOXA6nVi2bJn8YIiIiIhIV/UiwfVWeno6Tp06hZtuuqnMMISgoCDcfPPNOHnyJA4fPlxlWampqQCA2267rcxz7m1btmypfqOJiIiIqEbVizG43nIPGWjfvn25z7u3p6enVxjjTVlRUVGIjo6ucoiCw+GAw+Hw/J2TkwPg4gB3IiIi8j/3d3A9uMeeqsFQCa47gYyIiCj3effdku646pZ14sSJSstISUlBcnJyme0xMTFV1k9ERET6yc3NrfA7nuo/QyW4dc2sWbPw+OOPe/5WVRXnzp3DFVdcAUVRPNvtdjtiYmKQlZV1WUxZcrkdL3D5HfPldrzA5XfMPF7jM+oxa5qG3NxcNG/evLabQjoyVILr/k+soh5a988S3vzH5k1ZVZVjtVphtVpLbYuMjKwwPjw83FAfIlW53I4XuPyO+XI7XuDyO2Yer/EZ8ZjZc2t8hrrJ7NIxtuWpaoyut2WdP38eNpvNq3KIiIiIyL8Ml+A2b94c27ZtQ15eXqnnCgsLsXXrVjRv3hzt2rWrsqz4+HgAwKZNm8o8597mjiEiIiKiusNQCa6iKJgyZQouXLiAZ555ptRzKSkpOH/+vGfxBrfi4mIcPHgQR44cKRU/YMAAtGnTBqtWrcKePXs823NzczFv3jxYLJYKlwqWslqtSExMLDOcwagut+MFLr9jvtyOF7j8jpnHa3yX4zGTcShaPZgnY8mSJUhLSwMA/Pjjj9i9ezduuukmT0/syJEjMXLkSAAXl+rt06ePZ6ne66+/Hnv37sWnn36KLl26lFmqNzMzE61bt0arVq2QmZlZqt6vvvoKgwYNgtVqxbhx4xAeHo61a9ciIyMD8+fPx1NPPeWX4yciIiIi79WLBDchIQHvvvtuhc8nJiYiKSnJ83dOTg6Sk5Px4Ycf4vTp02jatClGjx6NxMTEMgPLK0twAeDbb79FYmIiduzYgaKiIsTFxWH69OmYMGFCTR0eEREREdWgepHgEhERERF5y1BjcImIiIiImOASERERkaEwwa1Fu3btwu23346oqCiEhoaiR48eWLVqVW03SxexsbFQFKXcx0MPPVTbzfPZypUr8eCDD6J79+6wWq1QFAXLly+vMN5ut+Pxxx9Hq1atYLVa0apVKzz++OOeRUjqA8kxJyUlVXjdg4KC/NtwH508eRIvv/wybrvtNrRs2RKBgYFo2rQpRo0ahW+++abcferzdZYeb32/xtnZ2XjsscfQq1cvNG3aFFarFS1atMAtt9yCNWvWoLxRfPX5+gLyY67v15guT4Zayaw+SU1NxaBBgxAYGIixY8ciIiICa9euxYQJE5CZmYnZs2fXdhNrXEREBKZPn15me/fu3f3fmBry9NNP49ixY4iOjkazZs1w7NixCmPz8vIQHx/vmeFj3Lhx2Lt3L1566SV89dVXZWb4qKskx+x23333ITY2ttQ2i6V+fPy89tpreOGFF9C2bVsMHDgQjRs3Rnp6Ov7zn//gP//5D/7973/j7rvv9sTX9+ssPV63+nqNbTYb3nnnHdx4440YOXIkGjZsiDNnzuC///0vRo8ejfvvvx//+te/PPH1/foC8mN2q6/XmC5TGvldcXGx1rZtW81qtWq7d+/2bLfb7VpcXJxmsVi0n3/+uRZbWPNatWqltWrVqrabUeM+//xzLTMzU9M0TUtJSdEAaMuWLSs3du7cuRoA7cknnyx3+9y5c/Vubo2QHHNiYqIGQPvqq6/818AatmbNGm3r1q1ltm/dulULCAjQGjZsqBUWFnq21/frLD3e+n6NnU6nVlxcXGa73W7Xrr76ag2Atm/fPs/2+n59NU1+zPX9GtPliUMUasHmzZtx5MgRjB8/Hl27dvVsDwsLw5w5c+B0OrFs2bJabCF569Zbb0WrVq2qjNM0DUuWLEGDBg0wd+7cUs/NmjULUVFRWLp0abk/h9Y13h6zUdx1113o27dvme19+/ZF//79ce7cOfz4448AjHGdJcdrBGazudxeyLCwMAwaNAgAcPjwYQDGuL6A7JiJ6iv+tlALUlNTAQC33XZbmefc27Zs2eLPJvmFw+HAu+++i5MnTyIqKgq9e/dG586da7tZfpGeno5Tp05h0KBBZX6+DAoKws0334yPPvoIhw8fRvv27Wuplfr5+uuv8e2338JsNqNjx4649dZbDbE6UkBAAIDff6Y1+nX+4/FeymjXuLCwEJs3b4aiKLj66qsBGP/6lnfMlzLaNSZjY4JbC9LT0wGg3A/AqKgoREdHe2KM5PTp02WWNx48eDBWrFiB6Ojo2mmUn1R2zS/dnp6eXi+/GKvyx96uZs2a4d1338XAgQNrqUXVd/z4cXzxxRdo2rQprr32WgDGvs7lHe+l6vs1zs7OxssvvwxVVXHmzBls2LABWVlZSExMLHXdAONcX2+O+VL1/RrT5YVDFGpBTk4OAJRZVc0tPDzcE2MUkyZNQmpqKs6ePQu73Y6dO3diyJAh2LhxI0aMGFHnf9KrLm+u+aVxRtGlSxe8++67yMzMREFBAdLT0zFv3jxkZ2djxIgR2Lt3b2030SfFxcW499574XA4sGDBApjNZgDGvc4VHS9gnGucnZ2N5ORkzJs3D2+99RZOnz6NhQsXIjEx0RNjtOvrzTEDxrnGdHlhDy75xR//8+/Zsyc++eQTxMfHIy0tDRs2bMDQoUNrqXWkl5EjR5b6u127dnj66afRpEkTPPDAA5g/fz4++OCD2mmcj1RVxaRJk7B161bcf//9uPfee2u7Sbqq6niNco1jY2OhaRpcLheysrLw3nvv4amnnsL27duxevVqQ84W4O0xG+Ua0+WFPbi1wP3ff0X/5dvt9gp7CIzEZDJh4sSJAIBt27bVcmv05c01vzTO6O677z5YLJZ6d901TcP999+PlStX4k9/+hPefPPNUs8b7TpXdbyVqa/X2Gw2IzY2Fn//+98xf/58rFu3Dm+//TYA411ft8qOuTL19RrT5YEJbi3443iuS50/fx42m61ejN+qCe6xt/n5+bXcEn1Vds0v3X65XPfAwECEhYXVq+uuqiomT56Md955B+PGjcPy5cthMpX+CDXSdfbmeCtTH6/xH7lv+nXfGGyk61uRPx5zZYxwjcm4mODWgvj4eADApk2byjzn3uaOMTr3ykh/nDzcaNq3b4/mzZtj27ZtyMvLK/VcYWEhtm7diubNm6Ndu3a11EL/Sk9Px/nz5+vNdVdVFVOmTMGyZctwzz33YMWKFaXGoboZ5Tp7e7yVqW/XuDynTp0C8PusEUa5vpX54zFXxgjXmIyLCW4tGDBgANq0aYNVq1Zhz549nu25ubmYN28eLBZLmdkG6rP9+/cjOzu7zPa0tDQsWrQIVqsVd911l/8b5keKomDKlCm4cOECnnnmmVLPpaSk4Pz585gyZQoURamlFta83Nxc/PDDD2W2nz9/HpMnTwYAjBs3zt/NEnP3ZC5btgxjxozBypUrK0z2jHCdJcdrhGu8Z8+ecoccnDt3zrOi5JAhQwAY4/oCsmM2wjWmy5OiGf329Trqq6++wqBBg2C1WjFu3DiEh4dj7dq1yMjIwPz58/HUU0/VdhNrTFJSEhYsWIABAwYgNjYWVqsV+/btw6ZNm2AymfDmm29iypQptd1MnyxZsgRpaWkAgB9//BG7d+/GTTfd5OnBGTlypOcGjby8PPTp08ezxOf111+PvXv34tNPP0WXLl3qxRKfgPfHnJmZidatW6N79+649tpr0bhxY5w8eRKffvopfvvtNwwcOBCffPIJAgMDa/NwqpSUlITk5GQ0aNAA06ZNK7dna+TIkejSpQuA+n+dJcdrhGs8ffp0LFmyBP3790erVq0QGhqKY8eOYf369bhw4QJGjRqF1atXe4Zn1PfrC8iO2QjXmC5TtbJ+GmmapmnffPONNnjwYC0iIkILDg7Wunfvrq1cubK2m1XjUlNTtbvvvltr166dFhYWpgUEBGhXXnmlNnbsWO2bb76p7eZVy3333acBqPCRmJhYKj47O1ubMWOGFhMTowUEBGgxMTHajBkztOzs7No5AB94e8w5OTnaI488ol1//fVadHS0ZrFYtIiICK1Pnz7am2++qTmdzto9EC9VdbwoZ6ni+nydJcdrhGv89ddfawkJCVrHjh218PBwzWKxaI0bN9YGDx6srVq1SlNVtcw+9fn6aprsmI1wjenyxB5cIiIiIjIUjsElIiIiIkNhgktEREREhsIEl4iIiIgMhQkuERERERkKE1wiIiIiMhQmuERERERkKExwiYiIiMhQmOASERERkaEwwSWieis2NhaKoiAzM9Mv9SmKAkVR/FKXL+p6+4iI/IUJLhEREREZChNcIiIiIjIUJrhEREREZChMcImIiIjIUJjgEhnEsWPH8OCDD6JNmzawWq0ICwtDmzZtcOedd+K9994rFetyufDRRx9h0qRJiIuLQ0REBEJCQtCpUyc8+eSTsNls5dbRr18/KIqC1NRU/PDDD7jjjjsQHR2N8PBw3Hrrrfjuu+88sV9//TUGDx6Mhg0bIiwsDEOHDsXBgwfLlJmZmQlFURAbGwtN0/Daa6/h2muvRUhICBo3box7770Xx48f9+mcfPbZZxgxYgSaNGkCq9WKK6+8EhMnTsSRI0cq3Md9XFFRUWjQoAF69uxZ5vx54/z587BarbBYLPj1118rjBs1ahQURcGrr75aantxcTFee+019OjRA+Hh4QgNDUXnzp3x7LPPIj8/X9SWqm4+q+hmvUu3b9myBbfeeisiIyPRsGFD3HnnnUhPT/fEfvzxx+jbty/Cw8MRFRWFcePG4dSpUxXWee7cOTz11FO45pprEBoairCwMNx44414++23oaqq6PiIiMrQiKjey8jI0KKjozUAWkhIiHbttddqXbp00Ro2bKgB0Dp37lwqPisrSwOgmUwmrVmzZlq3bt20jh07akFBQRoALTY2Vjt9+nSZeuLj4zUA2vPPP68FBwdrkZGR2vXXX69FRERoALSwsDBt37592urVqzWLxaI1btxY69atmxYSEqIB0Bo1alSm3IyMDA2A1qpVK+3hhx/WAGgtW7bUrr/+ek97GjVqpB08eLBMe1q1aqUB0DIyMso8N23aNA2ABkBr3Lix1rVrVy08PFwDoIWHh2vbtm0rs8+WLVu04OBgT0z37t21pk2bagC0BQsWeMrz1vDhwzUA2quvvlru8zk5OVpQUJBmNpu1X375xbM9Pz9fu+WWWzz1derUSbvuuus0k8mkAdC6dOmi2Wy2MuVV1L6q2l3ReXRvX7RokWY2mz3XMzQ0VAOgNWvWTPvll1+0RYsWaQC0K6+8UuvcubNmtVo1ANpVV12lFRQUlKlv3759WosWLTQAWmBgoHb11Vdrbdu21RRF0QBoo0eP1lRVrbC9RERVYYJLZACPPvqoBkC77777tNzc3FLPHThwQHvrrbdKbcvOztaWL1+u/fbbb6W2nz9/3lNWQkJCmXrcCW5AQID2+OOPaw6HQ9M0TSssLNTuuOMODcD/b+/uY6os+wCOf09yhh7AXkBiCCLQHOACREBtR5MlUdOwMi1Fa2VbI1uuXMkfrZq5ZE1WsGK56VI2dla2tLYSamKxTjST4hSCL+lMZJVodRZQgfR7/uC574fDuQ/Hg0nbeX6fzc1d13293Pf1z4/rXC+yePFiue6666SqqkqGhobMegsLCwWQZ5991qdOI8CNiIgQu90uLpfLzLtw4YIsWbJEACksLPQLegIFZm+++aYAkpqaKocOHTLTL126JFu3bjWDsZHBV29vryQlJQkgDz74oPT19YmIyNDQkFRVVYndbg85wHW5XALIggULLPN3794tgCxZssQnfdOmTQJIYmKitLa2muknT56UjIwMAWTVqlV+9V2tANdut/uN5/z58wWQpUuXisPhkPr6erPc2bNnJS0tTQCpra31qbO3t1fS09MFkCeffFK8Xq+Zd/ToUZk9e7YA8vrrrwfsr1JKBaMBrlJhoKSkRADxeDz/SH3JycnicDhkcHDQJ90IcOfMmeMXbB4/ftwMpJYvX+5XZ0NDgwCSnZ3tk24EuEbAM9rPP/9szuQ2NTX55FkFZn/99ZckJCTIpEmT5Ouvv7Z8vxUrVgggdXV1ZtrOnTsFkOnTp8vAwIBfmdLS0pAD3L6+PomOjg44y2yM265du8w0r9drznjv27fPr8zhw4cFEJvNJt9//71P3tUKcK3Gs7Gx0ax348aNfvnGHxmlpaU+6TU1NQLIPffcY9kXj8cjNptN0tLSAvZXKaWC0TW4SoWB5ORkAN59911E5LLLNTU18dRTT7F06VIWLVqE0+nE6XTi9Xrp7+/3WWM50sMPP+y3pnPWrFk4HA4A1q9f71dmzpw5AJw+fTpgfzZs2OCXFh8fz3333QcMr6kNpqWlhZ9++om8vDyzzdFKS0sB+Oyzz8w0o+7169djt9v9yjz++ONB2x7N4XCwfPlyAL91vD09PRw8eJDIyEhWrFhhpn/++ef09/czY8YMs+xIBQUFLFiwABHhk08+CblP42E1nrm5uWPmBxrv9957D4BHH33Usq3s7GxmzpzJ6dOnOXfu3Hi7rJT6Pxfxb3dAKXXlNmzYwJ49e3jppZeoq6vjjjvuYOHChRQVFZGYmOj3/MDAAPfffz/79+8fs95ffvnFMj09Pd0yPS4ujrNnz1rmT5s2DYDe3l7Lsna7nZtuuskyLzMzE4ATJ06M2V+A7777DhjevOZ0Oi2f+e233wDo7u4204y6jbYC9SFUa9asob6+HpfLRUVFhZm+d+9eLl26xLJly7j22mv9+pGRkRFwY9js2bNpaWm5rO/xTxhrPIPljx5vY3yef/55Xn75Zcv2jE2O3d3dJCUlja/TSqn/axrgKhUGcnNzaW5u5oUXXqCpqYkdO3awY8cObDYbxcXFvPbaaz4BWmVlJfv37ychIYFXXnmFRYsWkZCQQGRkJABOpxO3283g4KBle8ZM7WhGQGaVH+wK2djYWK65xvpHpRtvvBGA33//fcw6ALxeLzA8Q9rT0zPms3/88Yf5fyMQGxm4WfUhVLfffjtxcXF8++23dHR0kJWVBYDL5QKGA+CRjH7Ex8cHrDOU7/FPCDaeY+WP/kXBGJ/W1tag7Y4cH6WUCoUuUVAqTMyfP5/GxkZ+/fVXGhoa2Lx5M0lJSXz88ccUFxebs5YA9fX1AOzevZt169aRkpJiBrcAXV1dE919Ll68GPB4qPPnzwMQExMTtJ7o6GgAysrKkOF9BgH/ffrpp37lAgXFRh9CFRERYS6xMILarq4u3G43MTExLFu2zLL/Y7VnHDt2Od9jpEDLV/r6+kKq50oY73fy5Mmg47N48eIJ65dSKrxogKtUmImOjqakpITKykqOHTtGeno63d3dHDhwwHzGOO/0lltu8St/8eJFn5/uJ8rg4GDA82k7OzuB4XW+wRgzpO3t7SG1b9RtdVbvyD6MhzFLawS4LpcLEeHuu+9mypQplv3o7OwMGJAePXrU59lgoqKiAOvg3ev1Bjz3+GoY7/gopVQoNMBVKow5HA5uvvlmAJ9D942gyuoCgqqqKoaGhiamg6PU1tb6pfX09LB3715g+Of+YBYuXEhcXBwej8dnhjYYo+5du3ZZLs2w6tvlcjqdzJgxg1OnTnH48GEz0F29erXlsw6Hg66uLt5//32//CNHjtDS0mIuP7kcaWlpAHz11Vd+eTt37gzlVa7YvffeC0BNTU1IGyKVUioUGuAqFQbKy8t5++23/W64am5u5uDBgwDk5eWZ6cbmq02bNplrPkWEuro6tm/fzuTJkyeo5/8TERFBbW2tGczC8Ca3tWvX8ueff5Kfn09RUVHQeiZPnsyWLVsAWLlyJfv27fMLpNrb29m8eTNut9tMW716NdOnT+fcuXM89thj5vpPEaG6upqPPvpo3O9ms9l44IEHAHjxxRdpa2sjLi7OMkCdOnUq5eXlADzxxBN88803Zt6pU6d46KGHAFi1alXAzX6j3XnnnQA899xzPn/UNDQ0sGXLFiIiJm47hnHb3qFDhygrK+PHH3/0ye/t7eWdd97h6aefnrA+KaXC0ESdR6aUunpycnLMyxIyMzOlsLDQPMMUkLVr1/o8f+TIEfO2qalTp8rcuXMlMTFRAFm3bp153u3ISxJEJGC6YaybxUSsz2O1usksJSVF8vPzzVvFYmNjpaOjI6T2KioqzPZuuOEGKSgokLy8PPN2N0AOHDjgU6apqcnnuxQUFFzRTWYjtbW1meUBKS8vD/hsf3+/FBUVmc9mZWVJTk6OTJo0ybyZLpSbzM6fP2++R2RkpOTm5srMmTMFkIqKiqDn4IYynoaR4zpaZ2enpKamCv+9TS8zM1PmzZsns2bNMt9x3rx5Ab+PUkoFozO4SoWBV199lY0bN5Kdnc2FCxdoa2sDoKSkhA8++IC6ujqf5+fOnUtzczPFxcX8/fffHDt2jPj4eGpqatizZ8+/8AbD3njjDaqrq4mJiaG9vZ2oqCjKyspobW0N+Ziubdu24Xa7WbNmDVFRUXg8Hs6cOUNSUhKPPPIIH374IbfddptPmaKiIr788kvuuusubDYbHR0dJCcn43K5eOaZZ67o3XJycsz1p+B/esJIU6ZMobGxkerqavLz8/nhhx84ceIEWVlZbN26lS+++ILY2NjLbnvatGm43W5WrlyJw+Hg+PHjXH/99bz11lts27btit5rPDIyMvB4PFRWVlJQUEB3dzdtbW0MDAxw6623sn37dr9zg5VSKhQ2EV0EpZT695w5c4bU1FRSUlLMzW9KKaXUldAZXKWUUkopFVY0wFVKKaWUUmFFA1yllFJKKRVWNMBVSimllFJhRTeZKaWUUkqpsKIzuEoppZRSKqxogKuUUkoppcKKBrhKKaWUUiqsaICrlFJKKaXCiga4SimllFIqrGiAq5RSSimlwooGuEoppZRSKqxogKuUUkoppcKKBrhKKaWUUiqs/AfUnS/TxotvJAAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# step 3: plot the correlation matrix\n", + "fig, ax = plt.subplots(figsize=(7, 7))\n", + "ax.imshow(correlation_matrix, cmap=\"coolwarm\")\n", + "# change the xtick labels for latents\n", + "# ax.set_xticks(np.arange(len(volumes)))\n", + "# ax.set_xticklabels(\n", + "# [np.round(r, 2) for r in latent_coordinates], rotation=90, fontsize=8)\n", + "# set the y-ticklabels to the frames in the pdb_dir\n", + "conformation_names = [float(r.split(\"_\")[1].strip(\".pdb\")) for r in frames]\n", + "yticks = np.linspace(0, len(frames), 10)\n", + "yticklabels = np.round(np.linspace(0, 10, 10, dtype=float), 1)\n", + "ax.set_yticks(yticks)\n", + "ax.set_yticklabels(yticklabels)\n", + "ax.set_ylabel(\"time (\\u03BCs)\", fontsize=16)\n", + "ax.set_xlabel(\"sampled volume\", fontsize=16)\n", + "# ax.set_yticklabels(np.round(np.linspace(0, 10, len(frames)), 2))\n", + "# ax.set_yticklabels(conformation_names)\n", + "# ax.set_yticklabels([np.round(r*1.2/1000, 1) for r in conformation_names])\n", + "# make a colorbar with the same size as the image\n", + "cbar = ax.figure.colorbar(ax.get_images()[0], ax=ax, orientation=\"vertical\", pad=0.01, shrink=0.8)\n", + "cbar.ax.tick_params(labelsize=14)\n", + "cbar.ax.set_ylabel(\"Correlation\", fontsize=16, rotation=270, labelpad=20)\n", + "ax.tick_params(axis=\"both\", which=\"major\", labelsize=14)\n", + "fig.tight_layout()\n", + "\n", + "# fig.savefig(os.path.join(figures_dir, f\"{os.path.basename(vseries_dir)}_correlation_matrix.png\"), dpi=300, bbox_inches=\"tight\")\n", + "# fig.savefig(os.path.join(figures_dir, f\"{os.path.basename(vseries_dir)}_correlation_matrix.pdf\"), bbox_inches=\"tight\")" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# save the correlation matrix\n", + "np.save(os.path.join(project_dir, \"cryosparc_P51_J696_series_000\", \"correlation_matrix.npy\"), correlation_matrix)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "ensemble_refinement", + "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": "3.10.13" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/paper_fig_ipynbs/plot_correlation_matrix_mixed.ipynb b/paper_fig_ipynbs/plot_correlation_matrix_mixed.ipynb new file mode 100644 index 0000000..e61fcab --- /dev/null +++ b/paper_fig_ipynbs/plot_correlation_matrix_mixed.ipynb @@ -0,0 +1,501 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# computing correlation matrix between 3D classes and frames of the MD trajectory" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## making an ensemble of structures and aligning them to the first volume\n", + "This part of the script pulls 50 structures from the pdb_dir that stores the pdb file frames of the MD trajectory and bundles them in a single .pdb file. Then the ensemble can be aligned to the first 3D class in ChimeraX by using the \"fit volume\" tool.\n", + "\n", + "From the aligned ensemble, a mask should be made in ChimeraX using the molmap command." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "# imports\n", + "import os\n", + "import numpy as np\n", + "import gemmi\n", + "import mrcfile\n", + "import warnings\n", + "\n", + "from tqdm import tqdm\n", + "from emmer.pdb.convert.convert_pdb_to_map import convert_pdb_to_map\n", + "from emmer.ndimage.filter.low_pass_filter import low_pass_filter\n" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "# functions\n", + "def filter_gemmi_model(model_gemmi, remove_sidechains=False):\n", + " # remove ligand residues and all sidechain atoms\n", + " list_of_residue_names = [\n", + " \"F2A\", \"H2A\", \"NLM\", \"NM5\", \"NM7\", \"NME\"\n", + " ]\n", + " new_model_gemmi = gemmi.Model(model_gemmi.name)\n", + " for chn in model_gemmi:\n", + " new_chain_gemmi = gemmi.Chain(chn.name)\n", + " for res in chn:\n", + " if res.name not in list_of_residue_names:\n", + " new_res_gemmi = gemmi.Residue()\n", + " new_res_gemmi.name = res.name\n", + " \n", + " if remove_sidechains:\n", + " for atm in res:\n", + " if atm.name == \"CA\":\n", + " new_atm_gemmi = gemmi.Atom()\n", + " new_atm_gemmi.name = atm.name\n", + " new_atm_gemmi.pos = atm.pos\n", + " new_atm_gemmi.element = gemmi.Element(atm.element.name)\n", + " new_res_gemmi.add_atom(new_atm_gemmi)\n", + "\n", + " else:\n", + " for atm in res:\n", + " new_atm_gemmi = gemmi.Atom()\n", + " new_atm_gemmi.name = atm.name\n", + " new_atm_gemmi.pos = atm.pos\n", + " new_atm_gemmi.element = gemmi.Element(atm.element.name)\n", + " new_res_gemmi.add_atom(new_atm_gemmi) \n", + "\n", + " new_chain_gemmi.add_residue(new_res_gemmi)\n", + " new_model_gemmi.add_chain(new_chain_gemmi)\n", + " return new_model_gemmi\n", + "\n", + "# functions\n", + "def compute_correlation(modelmap, targetmap, mask):\n", + " \"\"\"Compute the correlation between two maps\"\"\"\n", + " \n", + " warnings.filterwarnings(\"ignore\", category=RuntimeWarning, message=\"invalid value encountered in scalar divide\")\n", + " # apply mask to both maps\n", + " modelmap_masked = modelmap * mask\n", + " targetmap_masked = targetmap * mask\n", + " # compute the mean of the two maps\n", + " mean_modelmap = np.mean(modelmap_masked)\n", + " mean_targetmap = np.mean(targetmap_masked)\n", + " # compute the standard deviation of the two maps\n", + " std_modelmap = np.std(modelmap_masked)\n", + " std_targetmap = np.std(targetmap_masked)\n", + " # compute the correlation\n", + " correlation = np.mean((modelmap_masked - mean_modelmap) * (targetmap_masked - mean_targetmap)) / (std_modelmap * std_targetmap)\n", + " return correlation\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "project_dir = \"/home/mjoosten1/projects/roodmus/data/DE-Shaw_covid_spike_protein/DESRES-Trajectory_sarscov2-11021566-11021571-mixed\"\n", + "pdb_dir = \"/home/mjoosten1/projects/roodmus/data/DE-Shaw_covid_spike_protein/mnt/parakeet_storage4/ConformationSampling/DESRES-Trajectory_sarscov2-11021566-11021571-mixed\"\n", + "output_dir = os.path.join(project_dir, \"aligned_ensembles\")\n", + "\n", + "pdb_files = [os.path.join(pdb_dir, f) for f in os.listdir(pdb_dir) if f.endswith(\".pdb\")]\n", + "split_1 = [f for f in pdb_files if int(f.strip(\".pdb\").split(\"_\")[-1]) <= 8334]\n", + "split_2 = [f for f in pdb_files if int(f.strip(\".pdb\").split(\"_\")[-1]) > 8334]\n", + "\n", + "N = 25\n", + "# sample N files from the total number of files, evenly spaced\n", + "split_1_sample = split_1[::int(np.ceil(len(split_1)/N))]\n", + "split_2_sample = split_2[::int(np.ceil(len(split_2)/N))]\n", + "\n", + "ensemble_gemmi = gemmi.Structure()\n", + "ensemble_gemmi_CA = gemmi.Structure()\n", + "for filename in split_1_sample:\n", + " pdb_path = os.path.join(pdb_dir, filename)\n", + " frame_gemmi = gemmi.read_structure(pdb_path)\n", + " frame_gemmi_filtered = filter_gemmi_model(frame_gemmi[0], remove_sidechains=False)\n", + " ensemble_gemmi.add_model(frame_gemmi_filtered)\n", + " frame_gemmi_filtered_CA = filter_gemmi_model(frame_gemmi[0], remove_sidechains=True)\n", + " ensemble_gemmi_CA.add_model(frame_gemmi_filtered_CA)\n", + "\n", + "# write the ensemble to a pdb file\n", + "ensemble_gemmi.write_pdb(os.path.join(output_dir, \"ensemble_closed_state.pdb\"))\n", + "ensemble_gemmi_CA.write_pdb(os.path.join(output_dir, \"ensemble_closed_state_CA.pdb\"))\n", + "\n", + "ensemble_gemmi = gemmi.Structure()\n", + "ensemble_gemmi_CA = gemmi.Structure()\n", + "for filename in split_2_sample:\n", + " pdb_path = os.path.join(pdb_dir, filename)\n", + " frame_gemmi = gemmi.read_structure(pdb_path)\n", + " frame_gemmi_filtered = filter_gemmi_model(frame_gemmi[0], remove_sidechains=False)\n", + " ensemble_gemmi.add_model(frame_gemmi_filtered)\n", + " frame_gemmi_filtered_CA = filter_gemmi_model(frame_gemmi[0], remove_sidechains=True)\n", + " ensemble_gemmi_CA.add_model(frame_gemmi_filtered_CA)\n", + "\n", + "# write the ensemble to a pdb file\n", + "ensemble_gemmi.write_pdb(os.path.join(output_dir, \"ensemble_open_state.pdb\"))\n", + "ensemble_gemmi_CA.write_pdb(os.path.join(output_dir, \"ensemble_open_state_CA.pdb\"))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## compute modelmaps for each frame of the aligned ensmebles" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# define the data for each classification run\n", + "# 2 class data set\n", + "classes_data = {\n", + " \"2_classes\": {\n", + " \"class1\": {\n", + " \"job\": \"job044\",\n", + " \"map\": \"run_class001.mrc\",\n", + " },\n", + " \"class2\": {\n", + " \"job\": \"job050\",\n", + " \"map\": \"run_class001.mrc\",\n", + " },\n", + " },\n", + " \"3_classes\": {\n", + " \"class1\": {\n", + " \"job\": \"job018\",\n", + " \"map\": \"run_class001.mrc\",\n", + " },\n", + " \"class2\": {\n", + " \"job\": \"job031\",\n", + " \"map\": \"run_class001.mrc\",\n", + " },\n", + " \"class3\": {\n", + " \"job\": \"job032\",\n", + " \"map\": \"run_class001.mrc\",\n", + " },\n", + " },\n", + " \"10_classes\": {\n", + " \"class1\": {\n", + " \"job\": \"job093\",\n", + " \"map\": \"run_class001.mrc\",\n", + " \"resolution\": 3,\n", + " },\n", + " \"class2\": {\n", + " \"job\": \"job094\",\n", + " \"map\": \"run_class001.mrc\",\n", + " \"resolution\": 3,\n", + " },\n", + " \"class3\": {\n", + " \"job\": \"job095\",\n", + " \"map\": \"run_class001.mrc\",\n", + " \"resolution\": 3,\n", + " },\n", + " \"class4\": {\n", + " \"job\": \"job096\",\n", + " \"map\": \"run_class001.mrc\",\n", + " \"resolution\": 3,\n", + " },\n", + " \"class5\": {\n", + " \"job\": \"job097\",\n", + " \"map\": \"run_class001.mrc\",\n", + " \"resolution\": 3,\n", + " },\n", + " \"class6\": {\n", + " \"job\": \"job098\",\n", + " \"map\": \"run_class001.mrc\",\n", + " \"resolution\": 3,\n", + " },\n", + " \"class7\": {\n", + " \"job\": \"job099\",\n", + " \"map\": \"run_class001.mrc\",\n", + " \"resolution\": 3,\n", + " },\n", + " \"class8\": {\n", + " \"job\": \"job100\",\n", + " \"map\": \"run_class001.mrc\",\n", + " \"resolution\": 3,\n", + " },\n", + " \"class9\": {\n", + " \"job\": \"job101\",\n", + " \"map\": \"run_class001.mrc\",\n", + " \"resolution\": 3,\n", + " },\n", + " \"class10\": {\n", + " \"job\": \"job102\",\n", + " \"map\": \"run_class001.mrc\",\n", + " \"resolution\": 3,\n", + " },\n", + " },\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 25/25 [12:15<00:00, 29.43s/it]\n", + "100%|██████████| 25/25 [12:14<00:00, 29.37s/it]\n" + ] + } + ], + "source": [ + "open_ensemble = os.path.join(project_dir, \"aligned_ensembles\", \"ensemble_open_state_aligned.pdb\")\n", + "closed_ensemble = os.path.join(project_dir, \"aligned_ensembles\", \"ensemble_closed_state_aligned.pdb\")\n", + "open_map = os.path.join(project_dir, \"aligned_ensembles\", \"ensemble_open_modelmap.mrc\")\n", + "closed_map = os.path.join(project_dir, \"aligned_ensembles\", \"ensemble_closed_modelmap.mrc\")\n", + "\n", + "resolution = 3\n", + "for classification_job in [\"2_classes\", \"3_classes\", \"10_classes\"]:\n", + " n_classes = len(classes_data[classification_job])\n", + "\n", + " # load the class maps\n", + " for class_name, class_data in classes_data[classification_job].items():\n", + " class_map = os.path.join(project_dir, \"Refine3D\", class_data[\"job\"], class_data[\"map\"])\n", + " classes_data[classification_job][class_name][\"map_data\"] = mrcfile.open(class_map).data\n", + "\n", + "# get the gemmi structure of the first class\n", + "class1_gemmi = gemmi.read_ccp4_map(os.path.join(project_dir, \"Refine3D\", classes_data[classification_job][\"class1\"][\"job\"], classes_data[classification_job][\"class1\"][\"map\"]))\n", + "unitcell = class1_gemmi.grid.unit_cell\n", + "size = class1_gemmi.grid.shape\n", + "vsize = class1_gemmi.grid.spacing[0]\n", + "\n", + "# load the ensemble modelmaps and threshold them at 2sigma below the mean\n", + "open_map_mrc = mrcfile.open(open_map)\n", + "closed_map_mrc = mrcfile.open(closed_map)\n", + "threshold_open = np.mean(open_map_mrc.data) + 1 * np.std(open_map_mrc.data)\n", + "threshold_closed = np.mean(closed_map_mrc.data) + 2 * np.std(closed_map_mrc.data)\n", + "\n", + "# print(f\"mean of open map: {np.mean(open_map_mrc.data)}\")\n", + "# print(f\"mean of closed map: {np.mean(closed_map_mrc.data)}\")\n", + "# print(f\"threshold of open map: {threshold_open}\")\n", + "# print(f\"threshold of closed map: {threshold_closed}\")\n", + "\n", + "open_mask = open_map_mrc.data > threshold_open\n", + "closed_mask = closed_map_mrc.data > threshold_closed\n", + "\n", + "# load the ensembles\n", + "ensemble_open_gemmi = gemmi.read_structure(open_ensemble)\n", + "ensemble_closed_gemmi = gemmi.read_structure(closed_ensemble)\n", + "n_frames = len(ensemble_open_gemmi) + len(ensemble_closed_gemmi)\n", + "\n", + "modelmaps_open = []\n", + "for frame_model in tqdm(ensemble_open_gemmi):\n", + " frame_gemmi = gemmi.Structure()\n", + " frame_gemmi.add_model(frame_model)\n", + " map_from_model_unfiltered = convert_pdb_to_map(\n", + " input_pdb=frame_gemmi,\n", + " unitcell=unitcell,\n", + " size=size,\n", + " return_grid=False,\n", + " )\n", + " map_from_model_zyx = low_pass_filter(\n", + " map_from_model_unfiltered, resolution, vsize\n", + " )\n", + " map_from_model = np.rot90(\n", + " np.flip(map_from_model_zyx, axis=0), axes=(2, 0)\n", + " )\n", + " modelmaps_open.append(map_from_model)\n", + "\n", + "modelmaps_closed = []\n", + "for frame_model in tqdm(ensemble_closed_gemmi):\n", + " frame_gemmi = gemmi.Structure()\n", + " frame_gemmi.add_model(frame_model)\n", + " map_from_model_unfiltered = convert_pdb_to_map(\n", + " input_pdb=frame_gemmi,\n", + " unitcell=unitcell,\n", + " size=size,\n", + " return_grid=False,\n", + " )\n", + " map_from_model_zyx = low_pass_filter(\n", + " map_from_model_unfiltered, resolution, vsize\n", + " )\n", + " map_from_model = np.rot90(\n", + " np.flip(map_from_model_zyx, axis=0), axes=(2, 0)\n", + " )\n", + " modelmaps_closed.append(map_from_model)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Computing correlation matrix for 2_classes\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 25/25 [01:12<00:00, 2.88s/it]\n", + "100%|██████████| 25/25 [01:05<00:00, 2.64s/it]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Computing correlation matrix for 3_classes\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 25/25 [01:48<00:00, 4.34s/it]\n", + "100%|██████████| 25/25 [01:39<00:00, 3.97s/it]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Computing correlation matrix for 10_classes\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 25/25 [05:59<00:00, 14.36s/it]\n", + "100%|██████████| 25/25 [05:25<00:00, 13.04s/it]\n" + ] + } + ], + "source": [ + "# compute the correlation\n", + "for classification_job in [\"2_classes\", \"3_classes\", \"10_classes\"]:\n", + " print(f\"Computing correlation matrix for {classification_job}\")\n", + " n_classes = len(classes_data[classification_job])\n", + " correlation_matrix = np.zeros((n_frames, n_classes))\n", + " for i, modelmap in enumerate(tqdm(modelmaps_open)):\n", + " for j, class_name in enumerate(classes_data[classification_job].keys()):\n", + " correlation_matrix[i, j] = compute_correlation(modelmap, classes_data[classification_job][class_name][\"map_data\"], open_mask)\n", + "\n", + " for i, modelmap in enumerate(tqdm(modelmaps_closed)):\n", + " for j, class_name in enumerate(classes_data[classification_job].keys()):\n", + " correlation_matrix[i+len(modelmaps_open), j] = compute_correlation(modelmap, classes_data[classification_job][class_name][\"map_data\"], closed_mask)\n", + "\n", + " # save the correlation matrix as a .npy file in the aligned_ensembles directory\n", + " np.save(os.path.join(project_dir, \"aligned_ensembles\", f\"correlation_matrix_{classification_job}.npy\"), correlation_matrix)\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.3674143905601764\n" + ] + }, + { + "data": { + "text/plain": [ + "Text(-0.65, 12, 'open state')" + ] + }, + "execution_count": 36, + "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", + "print(correlation_matrix.max())\n", + "fig, ax = plt.subplots(figsize=(3.5, 3.5))\n", + "# show the heatmap as a square (~50x3)\n", + "ax.imshow(correlation_matrix, cmap=\"coolwarm\", aspect=\"auto\", origin=\"lower\")\n", + "ticklabels = [f\"class {i+1}\" for i in range(n_classes)]\n", + "ax.set_xticks(range(n_classes))\n", + "ax.set_xticklabels(ticklabels, fontsize=12, rotation=45)\n", + "ax.set_yticks([0, n_frames//2-2, n_frames//2+2, n_frames-1])\n", + "ax.set_yticklabels([\"0 \\u03BCs\", \"10 \\u03BCs\", \"0 \\u03BCs\", \"10 \\u03BCs\"], fontsize=12)\n", + "ax.axhline(y=n_frames//2, color=\"black\", linestyle=\"solid\", linewidth=2)\n", + "# add colorbar\n", + "cbar = ax.figure.colorbar(ax.get_images()[0], ax=ax, orientation=\"vertical\")\n", + "ax.text(\n", + " -0.65,\n", + " 3*n_frames//4,\n", + " \"closed state\",\n", + " ha='right',\n", + " va='bottom',\n", + " fontsize=12,\n", + " fontweight='bold',\n", + ")\n", + "ax.text(\n", + " -0.65,\n", + " n_frames//4,\n", + " \"open state\",\n", + " ha='right',\n", + " va='bottom',\n", + " fontsize=12,\n", + " fontweight='bold',\n", + ")\n", + "\n", + "# save the figure\n", + "# fig.savefig(os.path.join(project_dir, \"figures\", f\"correlation_matrix_{n_classes}classes.pdf\"), bbox_inches=\"tight\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "roodmus", + "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": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/paper_fig_ipynbs/plot_correlation_matrix_spike_trimer.ipynb b/paper_fig_ipynbs/plot_correlation_matrix_spike_trimer.ipynb new file mode 100644 index 0000000..2170ba8 --- /dev/null +++ b/paper_fig_ipynbs/plot_correlation_matrix_spike_trimer.ipynb @@ -0,0 +1,1151 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# plotting correlation between heterogeneous reconstruction and MD trajectory\n", + "This notebook computes the correlation between frames of the MD trajectory and volumes reconstructed by cryoDRGN / cryoSPARC" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# making an ensemble of structures and aligning them to the first volume\n", + "This part of the script pulls 50 structures from the pdb_dir and bundles them in a single .pdb file. Then the ensemble can be aligned to the first volume of the cryoDRGN reconstruction in ChimeraX by hand. The aligned ensemble should be saved as a .pdb file in the same directory as the cryoDRGN reconstruction." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# imports\n", + "import os\n", + "import numpy as np\n", + "import gemmi\n", + "import warnings\n", + "import itertools\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "\n", + "from joblib import Parallel, delayed\n", + "from tqdm import tqdm\n", + "from emmer.pdb.convert.convert_pdb_to_map import convert_pdb_to_map\n", + "from emmer.ndimage.filter.low_pass_filter import low_pass_filter\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# functions\n", + "def filter_gemmi_model(model_gemmi, remove_sidechains=False):\n", + " # remove ligand residues and all sidechain atoms\n", + " list_of_residue_names = [\n", + " \"F2A\", \"H2A\", \"NLM\", \"NM5\", \"NM7\", \"NME\"\n", + " ]\n", + " new_model_gemmi = gemmi.Model(model_gemmi.name)\n", + " for chn in model_gemmi:\n", + " new_chain_gemmi = gemmi.Chain(chn.name)\n", + " for res in chn:\n", + " if res.name not in list_of_residue_names:\n", + " new_res_gemmi = gemmi.Residue()\n", + " new_res_gemmi.name = res.name\n", + " new_res_gemmi.seqid.num = res.seqid.num\n", + " \n", + " if remove_sidechains:\n", + " for atm in res:\n", + " if atm.name == \"CA\":\n", + " new_atm_gemmi = gemmi.Atom()\n", + " new_atm_gemmi.name = atm.name\n", + " new_atm_gemmi.pos = atm.pos\n", + " new_atm_gemmi.element = gemmi.Element(atm.element.name)\n", + " new_res_gemmi.add_atom(new_atm_gemmi)\n", + "\n", + " else:\n", + " for atm in res:\n", + " new_atm_gemmi = gemmi.Atom()\n", + " new_atm_gemmi.name = atm.name\n", + " new_atm_gemmi.pos = atm.pos\n", + " new_atm_gemmi.element = gemmi.Element(atm.element.name)\n", + " new_res_gemmi.add_atom(new_atm_gemmi) \n", + "\n", + " new_chain_gemmi.add_residue(new_res_gemmi)\n", + " new_model_gemmi.add_chain(new_chain_gemmi)\n", + " return new_model_gemmi\n", + "\n", + "# functions\n", + "def compute_correlation(modelmap, targetmap, mask, i, j):\n", + " \"\"\"Compute the correlation between two maps\"\"\"\n", + " \n", + " warnings.filterwarnings(\"ignore\", category=RuntimeWarning, message=\"invalid value encountered in scalar divide\")\n", + " # apply mask to both maps\n", + " modelmap_masked = modelmap * mask\n", + " targetmap_masked = targetmap * mask\n", + " # compute the mean of the two maps\n", + " mean_modelmap = np.mean(modelmap_masked)\n", + " mean_targetmap = np.mean(targetmap_masked)\n", + " # compute the standard deviation of the two maps\n", + " std_modelmap = np.std(modelmap_masked)\n", + " std_targetmap = np.std(targetmap_masked)\n", + " # compute the correlation\n", + " correlation = np.mean((modelmap_masked - mean_modelmap) * (targetmap_masked - mean_targetmap)) / (std_modelmap * std_targetmap)\n", + " return correlation, i, j\n" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [], + "source": [ + "# create an ensemble structure\n", + "project_dir = \"/home/mjoosten1/projects/roodmus/data/DE-Shaw_covid_spike_protein/20231116_DESRES-Trajectory_sarscov2-11021571-all-glueCA/\"\n", + "pdb_dir = \"/home/mjoosten1/projects/roodmus/data/DE-Shaw_covid_spike_protein/mnt/parakeet_storage4/ConformationSampling/DESRES-Trajectory_sarscov2-11021571-all-glueCA/even_sampling_8334\"\n", + "output_dir = os.path.join(project_dir, \"cryoDRGN\", \"analyze_320\", \"aligned_ensemble\")\n", + "if not os.path.exists(output_dir):\n", + " os.makedirs(output_dir)\n", + "\n", + "pdb_files = [os.path.join(pdb_dir, f) for f in os.listdir(pdb_dir) if f.endswith(\".pdb\")]\n", + "pdb_files.sort()\n", + "N = 50\n", + "# sample N files from the total number of files, evenly spaced\n", + "pdb_files = pdb_files[::len(pdb_files)//N]\n", + "\n", + "ensemble_gemmi = gemmi.Structure()\n", + "ensemble_CA_gemmi = gemmi.Structure()\n", + "for filename in pdb_files:\n", + " pdb_path = os.path.join(pdb_dir, filename)\n", + " frame_gemmi = gemmi.read_structure(pdb_path)\n", + " frame_gemmi_filtered = filter_gemmi_model(frame_gemmi[0], remove_sidechains=False)\n", + " ensemble_gemmi.add_model(frame_gemmi_filtered)\n", + " frame_gemmi_CA = filter_gemmi_model(frame_gemmi[0], remove_sidechains=True)\n", + " ensemble_CA_gemmi.add_model(frame_gemmi_CA)\n", + "\n", + "# write the ensemble to a pdb file\n", + "ensemble_gemmi.write_pdb(os.path.join(output_dir, \"ensemble.pdb\"))\n", + "ensemble_CA_gemmi.write_pdb(os.path.join(output_dir, \"ensemble_CA.pdb\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "conformation_000000.pdb\n", + "conformation_000166.pdb\n", + "conformation_000332.pdb\n", + "conformation_000498.pdb\n", + "conformation_000664.pdb\n", + "conformation_000830.pdb\n", + "conformation_000996.pdb\n", + "conformation_001162.pdb\n", + "conformation_001328.pdb\n", + "conformation_001494.pdb\n", + "conformation_001660.pdb\n", + "conformation_001826.pdb\n", + "conformation_001992.pdb\n", + "conformation_002158.pdb\n", + "conformation_002324.pdb\n", + "conformation_002490.pdb\n", + "conformation_002656.pdb\n", + "conformation_002822.pdb\n", + "conformation_002988.pdb\n", + "conformation_003154.pdb\n", + "conformation_003320.pdb\n", + "conformation_003486.pdb\n", + "conformation_003652.pdb\n", + "conformation_003818.pdb\n", + "conformation_003984.pdb\n", + "conformation_004150.pdb\n", + "conformation_004316.pdb\n", + "conformation_004482.pdb\n", + "conformation_004648.pdb\n", + "conformation_004814.pdb\n", + "conformation_004980.pdb\n", + "conformation_005146.pdb\n", + "conformation_005312.pdb\n", + "conformation_005478.pdb\n", + "conformation_005644.pdb\n", + "conformation_005810.pdb\n", + "conformation_005976.pdb\n", + "conformation_006142.pdb\n", + "conformation_006308.pdb\n", + "conformation_006474.pdb\n", + "conformation_006640.pdb\n", + "conformation_006806.pdb\n", + "conformation_006972.pdb\n", + "conformation_007138.pdb\n", + "conformation_007304.pdb\n", + "conformation_007470.pdb\n", + "conformation_007636.pdb\n", + "conformation_007802.pdb\n", + "conformation_007968.pdb\n", + "conformation_008134.pdb\n", + "conformation_008300.pdb\n" + ] + } + ], + "source": [ + "for pdb_file in pdb_files:\n", + " print(os.path.basename(pdb_file))" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# save a specific frame without the ligand residues\n", + "project_dir = \"/home/mjoosten1/projects/roodmus/data/DE-Shaw_covid_spike_protein/20231116_DESRES-Trajectory_sarscov2-11021571-all-glueCA/\"\n", + "pdb_dir = \"/home/mjoosten1/projects/roodmus/data/DE-Shaw_covid_spike_protein/mnt/parakeet_storage4/ConformationSampling/DESRES-Trajectory_sarscov2-11021571-all-glueCA/even_sampling_8334\"\n", + "output_dir = os.path.join(project_dir, \"cryoDRGN\", \"analyze_320\", \"aligned_ensemble\")\n", + "\n", + "conformation = 163\n", + "\n", + "conformation_filename = f\"conformation_{conformation:06d}.pdb\"\n", + "conformation_path = os.path.join(pdb_dir, conformation_filename)\n", + "conformation_gemmi = gemmi.read_structure(conformation_path)\n", + "conformation_gemmi_filtered = filter_gemmi_model(conformation_gemmi[0], remove_sidechains=False)\n", + "conformation_struct_gemmi = gemmi.Structure()\n", + "conformation_struct_gemmi.add_model(conformation_gemmi_filtered)\n", + "conformation_struct_gemmi.write_pdb(os.path.join(output_dir, conformation_filename))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# plotting Correlation matrix" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# inputs and parameters\n", + "n_frames = 51\n", + "resolution = 3\n", + "n_jobs = 12\n", + "\n", + "project_dir = \"/home/mjoosten1/projects/roodmus/data/DE-Shaw_covid_spike_protein/20231116_DESRES-Trajectory_sarscov2-11021571-all-glueCA\"\n", + "pdb_dir = \"/home/mjoosten1/projects/roodmus/data/DE-Shaw_covid_spike_protein/mnt/parakeet_storage4/ConformationSampling/DESRES-Trajectory_sarscov2-11021571-all-glueCA/even_sampling_8334\"\n", + "vseries_dir = os.path.join(project_dir, \"cryoDRGN\", \"analyze_320\")\n", + "mask_filename = os.path.join(project_dir, \"cryoDRGN\", \"analyze_320\", \"aligned_ensemble\", \"ensemble_map.mrc\")\n", + "ensemble_filename = os.path.join(project_dir, \"cryoDRGN\", \"analyze_320\", \"aligned_ensemble\", \"ensemble_aligned.pdb\")\n", + "figures_dir = os.path.join(project_dir, \"figures\")\n", + "latent_coordinates = np.arange(0, 50)\n", + "\n", + "frames = [r for r in os.listdir(pdb_dir) if r.endswith(\".pdb\")]\n", + "frames.sort()\n", + "frames = frames[::int(len(frames)/n_frames)]\n", + "\n", + "volumes = [r for r in os.listdir(vseries_dir) if r.endswith(\".mrc\") and not r.endswith(\"inverted.mrc\") and \"mask\" not in r]\n", + "volumes.sort()\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 51/51 [22:15<00:00, 26.18s/it]\n" + ] + } + ], + "source": [ + "# step 1: load each frame and compute the modelmap\n", + "vseries_vol0 = gemmi.read_ccp4_map(os.path.join(vseries_dir, volumes[0]))\n", + "unitcell = vseries_vol0.grid.unit_cell\n", + "size = vseries_vol0.grid.shape\n", + "vsize = vseries_vol0.grid.spacing[0]\n", + "\n", + "# create list of frames\n", + "frames_fit = []\n", + "ensemble_fit_gemmi = gemmi.read_structure(ensemble_filename)\n", + "for mdl in ensemble_fit_gemmi:\n", + " tmp_struc = gemmi.Structure()\n", + " tmp_struc.add_model(mdl)\n", + " frames_fit.append(tmp_struc)\n", + "\n", + "modelmaps = []\n", + "for frame_gemmi in tqdm(frames_fit):\n", + " map_from_model_unfiltered = convert_pdb_to_map(\n", + " input_pdb=frame_gemmi,\n", + " unitcell=unitcell,\n", + " size=size,\n", + " return_grid=False,\n", + " )\n", + " map_from_model_zyx = low_pass_filter(\n", + " map_from_model_unfiltered, resolution, vsize\n", + " )\n", + " map_from_model = np.rot90(\n", + " np.flip(map_from_model_zyx, axis=0), axes=(2, 0)\n", + " )\n", + " modelmaps.append(map_from_model)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n", + "[Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 8.2s\n", + "[Parallel(n_jobs=12)]: Done 8 tasks | elapsed: 9.9s\n", + "[Parallel(n_jobs=12)]: Done 17 tasks | elapsed: 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finished\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "correlation average: 0.6517528325390709\n", + "correlation std: 0.08834393954066774\n", + "correlation min: 0.42234519507562934\n", + "correlation max: 0.8088763163768885\n" + ] + } + ], + "source": [ + "# step 2: correlate each modelmap with the vseries'\n", + "\n", + "# load vseries\n", + "vseries_stack = []\n", + "for volume in volumes:\n", + " vseries_vol = gemmi.read_ccp4_map(os.path.join(vseries_dir, volume))\n", + " vseries_stack.append(np.array(vseries_vol.grid))\n", + "\n", + "indecies = itertools.product(range(n_frames), range(len(volumes)))\n", + "mask = gemmi.read_ccp4_map(mask_filename)\n", + "mask_data = np.array(mask.grid, copy=False)\n", + "# threshold = mask_data[mask_data > 0].mean() - 2 * mask_data[mask_data > 0].std()\n", + "threshold = 0.1\n", + "mask_data[mask_data < threshold] = 0\n", + "mask_data[mask_data >= threshold] = 1\n", + "# mask_data = np.rot90(np.flip(mask_data, axis=0), axes=(2, 0))\n", + "\n", + "correlations = Parallel(n_jobs=n_jobs, verbose=10)(\n", + " delayed(compute_correlation)(modelmaps[i], vseries_stack[j], mask_data, i, j)\n", + " for i, j in indecies\n", + ")\n", + "correlation_matrix = np.zeros((n_frames, len(volumes)))\n", + "for correlation, i, j in correlations:\n", + " correlation_matrix[i, j] = correlation\n", + "\n", + "print(f\"correlation average: {np.mean(correlation_matrix)}\")\n", + "print(f\"correlation std: {np.std(correlation_matrix)}\")\n", + "print(f\"correlation min: {np.min(correlation_matrix)}\")\n", + "print(f\"correlation max: {np.max(correlation_matrix)}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# save the correlation matrix\n", + "np.save(os.path.join(project_dir, \"cryoDRGN\", \"analyze_320\", \"correlation_matrix.npy\"), correlation_matrix)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# optionally, load the correlation matrix\n", + "correlation_matrix = np.load(os.path.join(project_dir, \"cryoDRGN\", \"analyze_320\", \"correlation_matrix.npy\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# step 3: plot the correlation matrix\n", + "fig, ax = plt.subplots(figsize=(7, 7))\n", + "ax.imshow(correlation_matrix, cmap=\"coolwarm\")\n", + "# change the xtick labels for latents\n", + "# ax.set_xticks(np.arange(len(volumes)))\n", + "# ax.set_xticklabels(\n", + "# [np.round(r, 2) for r in latent_coordinates], rotation=90, fontsize=8)\n", + "# set the y-ticklabels to the frames in the pdb_dir\n", + "conformation_names = [float(r.split(\"_\")[1].strip(\".pdb\")) for r in frames]\n", + "yticks = np.linspace(0, len(frames), 10)\n", + "yticklabels = np.round(np.linspace(0, 10, 10, dtype=float), 1)\n", + "ax.set_yticks(yticks)\n", + "ax.set_yticklabels(yticklabels)\n", + "ax.set_ylabel(\"time (\\u03BCs)\", fontsize=16)\n", + "ax.set_xlabel(\"sampled volume\", fontsize=16)\n", + "# ax.set_yticklabels(np.round(np.linspace(0, 10, len(frames)), 2))\n", + "# ax.set_yticklabels(conformation_names)\n", + "# ax.set_yticklabels([np.round(r*1.2/1000, 1) for r in conformation_names])\n", + "# make a colorbar with the same size as the image\n", + "cbar = ax.figure.colorbar(ax.get_images()[0], ax=ax, orientation=\"vertical\", pad=0.01, shrink=0.8)\n", + "cbar.ax.tick_params(labelsize=14)\n", + "cbar.ax.set_ylabel(\"Correlation\", fontsize=16, rotation=270, labelpad=20)\n", + "ax.tick_params(axis=\"both\", which=\"major\", labelsize=14)\n", + "fig.tight_layout()\n", + "\n", + "# fig.savefig(os.path.join(figures_dir, f\"{os.path.basename(vseries_dir)}_correlation_matrix.png\"), dpi=300, bbox_inches=\"tight\")\n", + "# fig.savefig(os.path.join(figures_dir, f\"{os.path.basename(vseries_dir)}_correlation_matrix.pdf\"), bbox_inches=\"tight\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "[np.round(r*1.2/1000, 1) for r in conformation_names]" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.39121689 0.420966 0.50136312 0.523121 0.40999361 0.40465066\n", + " 0.44328147 0.5539773 0.41890325 0.43338811 0.4321999 0.4360047\n", + " 0.44503964 0.38351482 0.40006533 0.40079894 0.41409157 0.52286852\n", + " 0.42237042 0.53041432 0.40143174 0.54328068 0.4456324 0.39507519\n", + " 0.38682515 0.44674016 0.42214454 0.5110771 0.54018202 0.38117455\n", + " 0.38200183 0.39620609 0.38392792 0.4548163 0.51809608 0.50461308\n", + " 0.38403738 0.38672585 0.44839081 0.548738 0.40122026 0.54731468\n", + " 0.41194836 0.46102111 0.54404973 0.41435055 0.42214121 0.41770316\n", + " 0.38136079 0.40479955 0.41657247]\n" + ] + }, + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# print the values in the last column of the correlation matrix\n", + "print(correlation_matrix[:,0])\n", + "fig, ax = plt.subplots()\n", + "ax.plot(correlation_matrix[:,0])" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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volumebest_framebest_correlationworst_frameworst_correlation
0vol_000.mrcconformation_000489.pdb0.716774conformation_008150.pdb0.554730
1vol_001.mrcconformation_000489.pdb0.723066conformation_008150.pdb0.523048
2vol_002.mrcconformation_000489.pdb0.736966conformation_005868.pdb0.522214
3vol_003.mrcconformation_000652.pdb0.748178conformation_005868.pdb0.523335
4vol_004.mrcconformation_000652.pdb0.748676conformation_005868.pdb0.522523
5vol_005.mrcconformation_000652.pdb0.748349conformation_005868.pdb0.531752
6vol_006.mrcconformation_001141.pdb0.760226conformation_005868.pdb0.547736
7vol_007.mrcconformation_001304.pdb0.768888conformation_005868.pdb0.563817
8vol_008.mrcconformation_001304.pdb0.763573conformation_000000.pdb0.556793
9vol_009.mrcconformation_001630.pdb0.768273conformation_000000.pdb0.548102
10vol_010.mrcconformation_002119.pdb0.784942conformation_000000.pdb0.535036
11vol_011.mrcconformation_002119.pdb0.796089conformation_000000.pdb0.528688
12vol_012.mrcconformation_002119.pdb0.799061conformation_000163.pdb0.524465
13vol_013.mrcconformation_002119.pdb0.798659conformation_000163.pdb0.513286
14vol_014.mrcconformation_002119.pdb0.798715conformation_000163.pdb0.512752
15vol_015.mrcconformation_002119.pdb0.791834conformation_000163.pdb0.501615
16vol_016.mrcconformation_002608.pdb0.792473conformation_000163.pdb0.495965
17vol_017.mrcconformation_002771.pdb0.787484conformation_000163.pdb0.486204
18vol_018.mrcconformation_003097.pdb0.796788conformation_000163.pdb0.478887
19vol_019.mrcconformation_003423.pdb0.796643conformation_000163.pdb0.458746
20vol_020.mrcconformation_003423.pdb0.796206conformation_000163.pdb0.446482
21vol_021.mrcconformation_003423.pdb0.800533conformation_000163.pdb0.457418
22vol_022.mrcconformation_003423.pdb0.797428conformation_000163.pdb0.451932
23vol_023.mrcconformation_003912.pdb0.802977conformation_000163.pdb0.442790
24vol_024.mrcconformation_003912.pdb0.808557conformation_000163.pdb0.451470
25vol_025.mrcconformation_003912.pdb0.804797conformation_000163.pdb0.457436
26vol_026.mrcconformation_004401.pdb0.799692conformation_000163.pdb0.459473
27vol_027.mrcconformation_004401.pdb0.796841conformation_000163.pdb0.451976
28vol_028.mrcconformation_004727.pdb0.798042conformation_000163.pdb0.457758
29vol_029.mrcconformation_004890.pdb0.793104conformation_000163.pdb0.484361
30vol_030.mrcconformation_004890.pdb0.799667conformation_000163.pdb0.505812
31vol_031.mrcconformation_004890.pdb0.792795conformation_000163.pdb0.491123
32vol_032.mrcconformation_005705.pdb0.788915conformation_000163.pdb0.461918
33vol_033.mrcconformation_005705.pdb0.793584conformation_000163.pdb0.458720
34vol_034.mrcconformation_005705.pdb0.796815conformation_000163.pdb0.449673
35vol_035.mrcconformation_005705.pdb0.792232conformation_000163.pdb0.451396
36vol_036.mrcconformation_006520.pdb0.792123conformation_000163.pdb0.442331
37vol_037.mrcconformation_006520.pdb0.794150conformation_000163.pdb0.445536
38vol_038.mrcconformation_006520.pdb0.795016conformation_000163.pdb0.441351
39vol_039.mrcconformation_006520.pdb0.793952conformation_000163.pdb0.435375
40vol_040.mrcconformation_006520.pdb0.792386conformation_000163.pdb0.431339
41vol_041.mrcconformation_006846.pdb0.796745conformation_000163.pdb0.425643
42vol_042.mrcconformation_006846.pdb0.797578conformation_000163.pdb0.422812
43vol_043.mrcconformation_006846.pdb0.796907conformation_000163.pdb0.423541
44vol_044.mrcconformation_006846.pdb0.791391conformation_000163.pdb0.426640
45vol_045.mrcconformation_007498.pdb0.787916conformation_000163.pdb0.429592
46vol_046.mrcconformation_007824.pdb0.791748conformation_000163.pdb0.427036
47vol_047.mrcconformation_007824.pdb0.798896conformation_000163.pdb0.428204
48vol_048.mrcconformation_007824.pdb0.800068conformation_000163.pdb0.429599
49vol_049.mrcconformation_007824.pdb0.801101conformation_000163.pdb0.436141
\n", + "
" + ], + "text/plain": [ + " volume best_frame best_correlation \\\n", + "0 vol_000.mrc conformation_000489.pdb 0.716774 \n", + "1 vol_001.mrc conformation_000489.pdb 0.723066 \n", + "2 vol_002.mrc conformation_000489.pdb 0.736966 \n", + "3 vol_003.mrc conformation_000652.pdb 0.748178 \n", + "4 vol_004.mrc conformation_000652.pdb 0.748676 \n", + "5 vol_005.mrc conformation_000652.pdb 0.748349 \n", + "6 vol_006.mrc conformation_001141.pdb 0.760226 \n", + "7 vol_007.mrc conformation_001304.pdb 0.768888 \n", + "8 vol_008.mrc conformation_001304.pdb 0.763573 \n", + "9 vol_009.mrc conformation_001630.pdb 0.768273 \n", + "10 vol_010.mrc conformation_002119.pdb 0.784942 \n", + "11 vol_011.mrc conformation_002119.pdb 0.796089 \n", + "12 vol_012.mrc conformation_002119.pdb 0.799061 \n", + "13 vol_013.mrc conformation_002119.pdb 0.798659 \n", + "14 vol_014.mrc conformation_002119.pdb 0.798715 \n", + "15 vol_015.mrc conformation_002119.pdb 0.791834 \n", + "16 vol_016.mrc conformation_002608.pdb 0.792473 \n", + "17 vol_017.mrc conformation_002771.pdb 0.787484 \n", + "18 vol_018.mrc conformation_003097.pdb 0.796788 \n", + "19 vol_019.mrc conformation_003423.pdb 0.796643 \n", + "20 vol_020.mrc conformation_003423.pdb 0.796206 \n", + "21 vol_021.mrc conformation_003423.pdb 0.800533 \n", + "22 vol_022.mrc conformation_003423.pdb 0.797428 \n", + "23 vol_023.mrc conformation_003912.pdb 0.802977 \n", + "24 vol_024.mrc conformation_003912.pdb 0.808557 \n", + "25 vol_025.mrc conformation_003912.pdb 0.804797 \n", + "26 vol_026.mrc conformation_004401.pdb 0.799692 \n", + "27 vol_027.mrc conformation_004401.pdb 0.796841 \n", + "28 vol_028.mrc conformation_004727.pdb 0.798042 \n", + "29 vol_029.mrc conformation_004890.pdb 0.793104 \n", + "30 vol_030.mrc conformation_004890.pdb 0.799667 \n", + "31 vol_031.mrc conformation_004890.pdb 0.792795 \n", + "32 vol_032.mrc conformation_005705.pdb 0.788915 \n", + "33 vol_033.mrc conformation_005705.pdb 0.793584 \n", + "34 vol_034.mrc conformation_005705.pdb 0.796815 \n", + "35 vol_035.mrc conformation_005705.pdb 0.792232 \n", + "36 vol_036.mrc conformation_006520.pdb 0.792123 \n", + "37 vol_037.mrc conformation_006520.pdb 0.794150 \n", + "38 vol_038.mrc conformation_006520.pdb 0.795016 \n", + "39 vol_039.mrc conformation_006520.pdb 0.793952 \n", + "40 vol_040.mrc conformation_006520.pdb 0.792386 \n", + "41 vol_041.mrc conformation_006846.pdb 0.796745 \n", + "42 vol_042.mrc conformation_006846.pdb 0.797578 \n", + "43 vol_043.mrc conformation_006846.pdb 0.796907 \n", + "44 vol_044.mrc conformation_006846.pdb 0.791391 \n", + "45 vol_045.mrc conformation_007498.pdb 0.787916 \n", + "46 vol_046.mrc conformation_007824.pdb 0.791748 \n", + "47 vol_047.mrc conformation_007824.pdb 0.798896 \n", + "48 vol_048.mrc conformation_007824.pdb 0.800068 \n", + "49 vol_049.mrc conformation_007824.pdb 0.801101 \n", + "\n", + " worst_frame worst_correlation \n", + "0 conformation_008150.pdb 0.554730 \n", + "1 conformation_008150.pdb 0.523048 \n", + "2 conformation_005868.pdb 0.522214 \n", + "3 conformation_005868.pdb 0.523335 \n", + "4 conformation_005868.pdb 0.522523 \n", + "5 conformation_005868.pdb 0.531752 \n", + "6 conformation_005868.pdb 0.547736 \n", + "7 conformation_005868.pdb 0.563817 \n", + "8 conformation_000000.pdb 0.556793 \n", + "9 conformation_000000.pdb 0.548102 \n", + "10 conformation_000000.pdb 0.535036 \n", + "11 conformation_000000.pdb 0.528688 \n", + "12 conformation_000163.pdb 0.524465 \n", + "13 conformation_000163.pdb 0.513286 \n", + "14 conformation_000163.pdb 0.512752 \n", + "15 conformation_000163.pdb 0.501615 \n", + "16 conformation_000163.pdb 0.495965 \n", + "17 conformation_000163.pdb 0.486204 \n", + "18 conformation_000163.pdb 0.478887 \n", + "19 conformation_000163.pdb 0.458746 \n", + "20 conformation_000163.pdb 0.446482 \n", + "21 conformation_000163.pdb 0.457418 \n", + "22 conformation_000163.pdb 0.451932 \n", + "23 conformation_000163.pdb 0.442790 \n", + "24 conformation_000163.pdb 0.451470 \n", + "25 conformation_000163.pdb 0.457436 \n", + "26 conformation_000163.pdb 0.459473 \n", + "27 conformation_000163.pdb 0.451976 \n", + "28 conformation_000163.pdb 0.457758 \n", + "29 conformation_000163.pdb 0.484361 \n", + "30 conformation_000163.pdb 0.505812 \n", + "31 conformation_000163.pdb 0.491123 \n", + "32 conformation_000163.pdb 0.461918 \n", + "33 conformation_000163.pdb 0.458720 \n", + "34 conformation_000163.pdb 0.449673 \n", + "35 conformation_000163.pdb 0.451396 \n", + "36 conformation_000163.pdb 0.442331 \n", + "37 conformation_000163.pdb 0.445536 \n", + "38 conformation_000163.pdb 0.441351 \n", + "39 conformation_000163.pdb 0.435375 \n", + "40 conformation_000163.pdb 0.431339 \n", + "41 conformation_000163.pdb 0.425643 \n", + "42 conformation_000163.pdb 0.422812 \n", + "43 conformation_000163.pdb 0.423541 \n", + "44 conformation_000163.pdb 0.426640 \n", + "45 conformation_000163.pdb 0.429592 \n", + "46 conformation_000163.pdb 0.427036 \n", + "47 conformation_000163.pdb 0.428204 \n", + "48 conformation_000163.pdb 0.429599 \n", + "49 conformation_000163.pdb 0.436141 " + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# for each volume, print the frame with the highest and lowest correlation\n", + "results = {\n", + " \"volume\": [],\n", + " \"best_frame\": [],\n", + " \"best_correlation\": [],\n", + " \"worst_frame\": [],\n", + " \"worst_correlation\": [],\n", + "}\n", + "for i, volume in enumerate(volumes):\n", + " max_correlation = np.max(correlation_matrix[:, i])\n", + " max_correlation_index = np.argmax(correlation_matrix[:, i])\n", + " min_correlation = np.min(correlation_matrix[:, i])\n", + " min_correlation_index = np.argmin(correlation_matrix[:, i])\n", + " results[\"volume\"].append(volume)\n", + " results[\"best_frame\"].append(frames[max_correlation_index])\n", + " results[\"best_correlation\"].append(max_correlation)\n", + " results[\"worst_frame\"].append(frames[min_correlation_index])\n", + " results[\"worst_correlation\"].append(min_correlation)\n", + "\n", + "results_df = pd.DataFrame(results)\n", + "results_df.head(n=len(volumes))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "roodmus", + "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": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/paper_fig_ipynbs/supplementary_figure_1.ipynb b/paper_fig_ipynbs/supplementary_figure_1.ipynb new file mode 100644 index 0000000..8749e39 --- /dev/null +++ b/paper_fig_ipynbs/supplementary_figure_1.ipynb @@ -0,0 +1,371 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# plotting functions of supplmentary figure 1 in the manuscript\n", + "This figure shows the following:\n", + "\n", + "- Panel showing CTF estimation for the covid spike trimer\n", + "- Panel showing ground-truth and estimated poses for covid spike trimer\n", + "\n", + "- Panel showing consensus reconstruction of covid spike monomer\n", + "- Panel showing particle picking for covid spike monomer\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# imports\n", + "import os\n", + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "# roodmus\n", + "from roodmus.analysis.utils import load_data\n", + "from roodmus.analysis.plot_ctf import plot_defocus_scatter\n", + "from roodmus.analysis.plot_alignment import true_pose_distribution_plot, picked_pose_distribution_plot\n", + "from roodmus.analysis.plot_picking import plot_recall, plot_precision" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# loading covid spike trimer data\n", + "project_dir = \"/tudelft/mjoosten1/staff-umbrella/ajlab/MJ/projects/Roodmus/data/DE-Shaw_covid_spike_protein/20231116_DESRES-Trajectory_sarscov2-11021571-all-glueCA\"\n", + "config_dir = \"/tudelft/mjoosten1/staff-umbrella/ajlab/MJ/projects/Roodmus/data/DE-Shaw_covid_spike_protein/DESRES-Trajectory_sarscov2-11021571-all-glueCA/Micrographs\"\n", + "# config_dir = os.path.join(project_dir, \"Micrographs\")\n", + "figures_dir = os.path.join(project_dir, \"figures\")\n", + "meta_files = [\n", + " os.path.join(project_dir, \"Refine3D\", \"job008\", \"run_it011_data.star\"),\n", + " os.path.join(project_dir, \"Refine3D\", \"job039\", \"run_it014_data.star\"),\n", + "]\n", + "\n", + "jobtypes = {\n", + " os.path.join(project_dir, \"Refine3D\", \"job008\", \"run_it011_data.star\"): \"Refine3D_8000\",\n", + " os.path.join(project_dir, \"Refine3D\", \"job039\", \"run_it014_data.star\"): \"Refine3D_236079\",\n", + "}\n", + "\n", + "particle_diameter = 100 # approximate particle diameter in Angstroms\n", + "ugraph_shape = (4000, 4000) # shape of the micrograph in pixels. Only needs to be given if the metadata file is a .star file\n", + "verbose = True # prints out progress statements\n", + "ignore_missing_files = True # if .mrc files are missing, the analysis will still be performed\n", + "enable_tqdm = True # enables tqdm progress bars\n", + "\n", + "for i, meta_file in enumerate(meta_files):\n", + " if i == 0:\n", + " analysis = load_data(meta_file, config_dir, particle_diameter, ugraph_shape=ugraph_shape, verbose=verbose, enable_tqdm=enable_tqdm, ignore_missing_files=ignore_missing_files) # creates the class\n", + " else:\n", + " analysis.add_data(meta_file, config_dir, verbose=verbose) # updates the class with the next metadata file\n", + " \n", + "df_picked = pd.DataFrame(analysis.results_picking)\n", + "df_truth = pd.DataFrame(analysis.results_truth)\n", + "# df_precision, df_picked = analysis.compute_precision(df_picked, df_truth, verbose=verbose)\n", + "p_match, _, p_unmatched, t_unmatched, closest_truth_index = analysis._match_particles(\n", + " meta_files,\n", + " df_picked,\n", + " df_truth,\n", + " verbose=False,\n", + " enable_tqdm=True,\n", + ")\n", + "df_precision = analysis.compute_1to1_match_precision(\n", + " p_match,\n", + " p_unmatched,\n", + " t_unmatched,\n", + " df_truth,\n", + " verbose=False,\n", + ")\n", + "print(f\"mean precision: {df_precision['precision'].mean()}\")\n", + "print(f\"mean recall: {df_precision['recall'].mean()}\")\n", + "df_precision[\"jobtype\"] = df_precision[\"metadata_filename\"].map(jobtypes)\n", + "df_truth[\"pdb_index\"] = df_truth[\"pdb_filename\"].apply(lambda x: int(x.strip(\".pdb\").split(\"_\")[-1]))\n", + "df_picked[\"closest_truth_index\"] = closest_truth_index\n", + "df_picked[\"TP\"] = df_picked[\"closest_truth_index\"].apply(lambda x: np.isnan(x) == False)\n", + "df_picked[\"closest_pdb_index\"] = df_picked[\"closest_truth_index\"].apply(lambda x: df_truth.loc[x, \"pdb_index\"] if np.isnan(x) == False else np.nan)\n", + "df_picked.head(n=10)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## panel A\n", + "plotting the CTF estimation for the covid spike trimer" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from importlib import reload\n", + "import roodmus.analysis.plot_ctf\n", + "reload(roodmus.analysis.plot_ctf)\n", + "from roodmus.analysis.plot_ctf import plot_defocus_scatter\n", + "\n", + "fig, (ax_l, ax_r) = plot_defocus_scatter(\n", + " df_picked=df_picked,\n", + " metadata_filename=meta_files[0],\n", + " df_truth=df_truth,\n", + " palette=\"BuGn\",\n", + ")\n", + "fig.set_size_inches(4.5*2, 4.5)\n", + "# change the xticklabels from Angstrom to um\n", + "ax_l.set_xticklabels([f\"{-x/1000:.1f}\" for x in ax_l.get_xticks()])\n", + "ax_r.set_xticklabels([f\"{-x/1000:.1f}\" for x in ax_r.get_xticks()])\n", + "ax_l.set_yticklabels([f\"{-x/1000:.1f}\" for x in ax_l.get_yticks()])\n", + "ax_r.set_aspect(\"equal\")\n", + "ax_l.set_aspect(\"equal\")\n", + "ax_l.set_xlabel(\"Ground-truth\")\n", + "ax_l.set_ylabel(\"Estimated\")\n", + "ax_r.set_xlabel(\"Ground-Truth\")\n", + "fig.tight_layout()\n", + "\n", + "fig.savefig(os.path.join(figures_dir, \"defocus_scatter.pdf\"), bbox_inches=\"tight\")\n", + "print(f\"saved figure to: {os.path.join(figures_dir, 'defocus_scatter.pdf')}\")\n", + "\n", + "# print the correlation coefficient between the true and estimated defocus values\n", + "print(f\"correlation coefficient: {df_picked['defocusU'].corr(df_truth['defocus'])}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# compute the correlation between the estimated and ground truth defocus values\n", + "df_picked_defocus = df_picked.groupby(\"ugraph_filename\").agg({\"defocusU\": \"mean\"}).reset_index()\n", + "print(len(df_picked_defocus))\n", + "df_truth_defocus = df_truth.groupby(\"ugraph_filename\").agg({\"defocus\": \"mean\"}).reset_index()\n", + "print(len(df_truth_defocus))\n", + "\n", + "# print the correlation coefficient between the true and estimated defocus values\n", + "# print(f\"correlation coefficient: {np.corrcoef(defocus_truth, defocus_picked)[0, 1]}\")\n", + "print(f\"correlation coefficient: {df_picked_defocus['defocusU'].corr(df_truth_defocus['defocus'])}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## panel B\n", + "plotting distribution of estimated poses for covid spike trimer" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from importlib import reload\n", + "import roodmus.analysis.plot_alignment\n", + "reload(roodmus.analysis.plot_alignment)\n", + "from roodmus.analysis.plot_alignment import picked_pose_distribution_plot\n", + "\n", + "\n", + "grid, vmin, vmax = picked_pose_distribution_plot(\n", + " df_picked=df_picked,\n", + " metadata_filename=meta_files[0],\n", + ")\n", + "outfilename = os.path.join(figures_dir, f\"picked_pose_distribution_{os.path.basename(meta_files[0])}.pdf\")\n", + "grid.savefig(outfilename, bbox_inches=\"tight\")\n", + "print(f\"saved figure to: {outfilename}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## panel C\n", + "plotting ground-truth poses for covid spike trimer" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from importlib import reload\n", + "import roodmus.analysis.plot_alignment\n", + "reload(roodmus.analysis.plot_alignment)\n", + "from roodmus.analysis.plot_alignment import true_pose_distribution_plot\n", + "\n", + "grid, _, _ = true_pose_distribution_plot(\n", + " df_truth=df_truth,\n", + " vmin=vmin,\n", + " vmax=vmax,\n", + ")\n", + "outfilename = os.path.join(figures_dir, \"true_pose_distribution.pdf\")\n", + "grid.savefig(outfilename, bbox_inches=\"tight\")\n", + "print(f\"saved figure to: {outfilename}\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## panel E\n", + "plotting particle picking for covid spike monomer" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# data loading for spike monomer\n", + "### data loading (6xm5 steered MD)\n", + "project_dir = \"/home/mjoosten1/projects/roodmus/data/6xm5_steered_Roodmus_2\"\n", + "config_dir = os.path.join(project_dir, \"mrc\")\n", + "figures_dir = os.path.join(project_dir, \"figures\")\n", + "meta_files = [\n", + " os.path.join(project_dir, \"cryoSPARC\", \"J508_picked_particles.cs\"), # blob picker\n", + " os.path.join(project_dir, \"cryoSPARC\", \"J509_extracted_particles.cs\"), # filtering\n", + " os.path.join(project_dir, \"cryoSPARC\", \"J513_passthrough_particles_selected.cs\"), # 2D class selection\n", + " os.path.join(project_dir, \"cryoSPARC\", \"J515_topaz_picked_particles.cs\"), # topaz picker\n", + " os.path.join(project_dir, \"cryoSPARC\", \"J518_050_particles.cs\"), # 2D classification 2\n", + " os.path.join(project_dir, \"cryoSPARC\", \"J519_passthrough_particles_class_0.cs\"), # ab initio class 0\n", + " os.path.join(project_dir, \"cryoSPARC\", \"J519_passthrough_particles_class_1.cs\"), # ab initio class 1\n", + " os.path.join(project_dir, \"cryoSPARC\", \"J519_passthrough_particles_class_2.cs\"), # ab initio class 2\n", + " os.path.join(project_dir, \"cryoSPARC\", \"J519_passthrough_particles_class_3.cs\"), # ab initio class 3\n", + " os.path.join(project_dir, \"cryoSPARC\", \"J523_passthrough_particles.cs\"), # homogneous refinement\n", + "]\n", + "\n", + "jobtypes = {\n", + " os.path.join(project_dir, \"cryoSPARC\", \"J508_picked_particles.cs\"): \"blob_picker\",\n", + " os.path.join(project_dir, \"cryoSPARC\", \"J509_extracted_particles.cs\"): \"filtering\",\n", + " os.path.join(project_dir, \"cryoSPARC\", \"J513_passthrough_particles_selected.cs\"): \"2D_class_selection\",\n", + " os.path.join(project_dir, \"cryoSPARC\", \"J515_topaz_picked_particles.cs\"): \"topaz_picker\",\n", + " os.path.join(project_dir, \"cryoSPARC\", \"J518_050_particles.cs\"): \"2D_classification_2\",\n", + " os.path.join(project_dir, \"cryoSPARC\", \"J519_passthrough_particles_class_0.cs\"): \"ab_initio_class_0\",\n", + " os.path.join(project_dir, \"cryoSPARC\", \"J519_passthrough_particles_class_1.cs\"): \"ab_initio_class_1\",\n", + " os.path.join(project_dir, \"cryoSPARC\", \"J519_passthrough_particles_class_2.cs\"): \"ab_initio_class_2\",\n", + " os.path.join(project_dir, \"cryoSPARC\", \"J519_passthrough_particles_class_3.cs\"): \"ab_initio_class_3\",\n", + " os.path.join(project_dir, \"cryoSPARC\", \"J523_passthrough_particles.cs\"): \"homogeneous_refinement\",\n", + "}\n", + "\n", + "particle_diameter = 100 # approximate particle diameter in Angstroms\n", + "ugraph_shape = (4000, 4000) # shape of the micrograph in pixels. Only needs to be given if the metadata file is a .star file\n", + "verbose = True # prints out progress statements\n", + "ignore_missing_files = True # if .mrc files are missing, the analysis will still be performed\n", + "enable_tqdm = True # enables tqdm progress bars\n", + "\n", + "for i, meta_file in enumerate(meta_files):\n", + " if i == 0:\n", + " analysis = load_data(meta_file, config_dir, particle_diameter, ugraph_shape=ugraph_shape, verbose=verbose, enable_tqdm=enable_tqdm, ignore_missing_files=ignore_missing_files) # creates the class\n", + " else:\n", + " analysis.add_data(meta_file, config_dir, verbose=verbose) # updates the class with the next metadata file\n", + "df_picked = pd.DataFrame(analysis.results_picking)\n", + "df_truth = pd.DataFrame(analysis.results_truth)\n", + "df_precision, df_picked = analysis.compute_precision(df_picked, df_truth, verbose=verbose)\n", + "# p_match, _, p_unmatched, t_unmatched, closest_truth_index = analysis._match_particles(\n", + "# meta_files,\n", + "# df_picked,\n", + "# df_truth,\n", + "# verbose=False,\n", + "# enable_tqdm=True,\n", + "# )\n", + "# df_precision = analysis.compute_1to1_match_precision(\n", + "# p_match,\n", + "# p_unmatched,\n", + "# t_unmatched,\n", + "# df_truth,\n", + "# verbose=False,\n", + "# )\n", + "# df_truth[\"pdb_index\"] = df_truth[\"pdb_filename\"].apply(lambda x: int(x.strip(\".pdb\").split(\"_\")[-1]))\n", + "# df_picked[\"closest_truth_index\"] = closest_truth_index\n", + "# df_picked[\"TP\"] = df_picked[\"closest_truth_index\"].apply(lambda x: np.isnan(x) == False)\n", + "# df_picked[\"closest_pdb_index\"] = df_picked[\"closest_truth_index\"].apply(lambda x: df_truth.loc[x, \"pdb_index\"] if np.isnan(x) == False else np.nan)\n", + "print(f\"mean precision: {df_precision['precision'].mean()}\")\n", + "print(f\"mean recall: {df_precision['recall'].mean()}\")\n", + "df_picked.head(n=10)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "### plot boxplot for precision and recall\n", + "from importlib import reload\n", + "import roodmus.analysis.plot_picking\n", + "reload(roodmus.analysis.plot_picking)\n", + "from roodmus.analysis.plot_picking import plot_recall, plot_precision\n", + "\n", + "order = []\n", + "for r in meta_files:\n", + " if type(r) == str:\n", + " order.append(r)\n", + " else:\n", + " order.append(r[0]) \n", + "fig, ax = plot_precision(df_precision, jobtypes, order)\n", + "xticklabels = ax.get_xticklabels()\n", + "ax.set_xticklabels(xticklabels, fontsize=18)\n", + "ax.set_yticklabels(ax.get_yticklabels(), fontsize=16)\n", + "ax.set_title(\"\")\n", + "ax.set_ylabel(\"Precision\", fontsize=21)\n", + "fig.set_size_inches(7, 7)\n", + "\n", + "fig.savefig(os.path.join(figures_dir, \"precision.pdf\"), bbox_inches=\"tight\")\n", + "print(f\"saved figure to: {os.path.join(figures_dir, 'precision.pdf')}\")\n", + "\n", + "\n", + "fig, ax = plot_recall(df_precision, jobtypes, order)\n", + "xticklabels = ax.get_xticklabels()\n", + "ax.set_xticklabels(xticklabels, fontsize=16)\n", + "ax.set_yticks([0.2, 0.4, 0.6, 0.8, 1.0])\n", + "ax.set_yticklabels(\n", + " [f\"{x:.1f}\" for x in ax.get_yticks()],\n", + " fontsize=16,\n", + ")\n", + "ax.set_title(\"\")\n", + "ax.set_ylabel(\"Recall\", fontsize=21)\n", + "fig.set_size_inches(7, 7)\n", + "\n", + "fig.savefig(os.path.join(figures_dir, \"recall.pdf\"), bbox_inches=\"tight\")\n", + "print(f\"saved figure to: {os.path.join(figures_dir, 'recall.pdf')}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "roodmus", + "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": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/paper_fig_ipynbs/supplementary_figure_2.ipynb b/paper_fig_ipynbs/supplementary_figure_2.ipynb new file mode 100644 index 0000000..2658dab --- /dev/null +++ b/paper_fig_ipynbs/supplementary_figure_2.ipynb @@ -0,0 +1,584 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# plotting functions of supplmentary figure 2 in the manuscript\n", + "This figure shows the following:\n", + "- example micrograph of a dataset with radiation damage\n", + "- example micrograph of a dataset without radiation damage\n", + "- 3D recsonstruction of spike protein, dataset simulated with radiation damage\n", + "- boxplot showing the particle picking precision as a function of exposure\n", + "- boxplot showing the particle picking recall as a function of exposure\n", + "- plot showing the decreased precision of picked particle locations as a function of exposure" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# imports\n", + "import os\n", + "import mrcfile\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "import numpy as np\n", + "from pipeliner.mrc_image_tools import mrc_thumbnail\n", + "from gemmi import cif\n", + "from roodmus.analysis.utils import load_data\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## panel A\n", + "plotting the micrograph with radiation damage" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "project_dir = \"/home/mjoosten1/projects/roodmus/data/DESRES-Trajectory_sarscov2-11021571-all-glueCA_fractionated\"\n", + "figures_dir = os.path.join(project_dir, \"figures\")\n", + "ugraph_dir = os.path.join(project_dir, \"MotionCorr\", \"job007\", \"Movies\")\n", + "movies_dir = os.path.join(project_dir, \"Movies\")\n", + "ugraph_file = os.path.join(ugraph_dir, \"000000.mrc\")\n", + "print(f\"found micrograph: {ugraph_file}\")\n", + "\n", + "ugraph_thumbnail = mrc_thumbnail(ugraph_file, 512, os.path.join(figures_dir, f\"{ugraph_file.replace('.mrc', '_thumbnail')}.png\"))\n", + "\n", + "fig, ax = plt.subplots(figsize=(7, 7))\n", + "ax.imshow(ugraph_thumbnail, cmap=\"gray\")\n", + "ax.axis(\"off\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## panel B\n", + "plotting the micrograph without radiation damage" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "project_dir = \"/tudelft/mjoosten1/staff-umbrella/ajlab/MJ/projects/Roodmus/data/DE-Shaw_covid_spike_protein/DESRES-Trajectory_sarscov2-11021571-all-glueCA\"\n", + "figures_dir = os.path.join(project_dir, \"figures\")\n", + "ugraph_dir = os.path.join(project_dir, \"Micrographs\")\n", + "ugraph_file = os.path.join(ugraph_dir, \"000000.mrc\")\n", + "print(f\"found micrograph: {ugraph_file}\")\n", + "\n", + "ugraph_thumbnail = mrc_thumbnail(ugraph_file, 512, os.path.join(figures_dir, f\"{ugraph_file.replace('.mrc', '_thumbnail')}.png\"))\n", + "\n", + "fig, ax = plt.subplots(figsize=(7, 7))\n", + "ax.imshow(ugraph_thumbnail, cmap=\"gray\")\n", + "ax.axis(\"off\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## panel C\n", + "need to flip the handedness of the map and the local resolution mask" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "project_dir = \"/home/mjoosten1/projects/roodmus/data/DESRES-Trajectory_sarscov2-11021571-all-glueCA_fractionated\"\n", + "refined_map = os.path.join(project_dir, \"Refine3D\", \"job048\", \"run_class001.mrc\")\n", + "locres_mask = os.path.join(project_dir, \"LocalRes\", \"job061\", \"relion_locres.mrc\")\n", + "postprocess_map = os.path.join(project_dir, \"PostProcess\", \"job050\", \"postprocess.mrc\")\n", + "\n", + "with mrcfile.open(refined_map, mode='r') as mrc:\n", + " refined_map_data = mrc.data\n", + "\n", + "with mrcfile.new(refined_map.replace(\".mrc\", \"_flipped.mrc\"), overwrite=True) as mrc:\n", + " mrc.set_data(np.flip(refined_map_data, axis=0).astype(np.float32))\n", + "\n", + "\n", + "with mrcfile.open(locres_mask, mode='r') as mrc:\n", + " locres_mask_data = mrc.data\n", + "\n", + "with mrcfile.new(locres_mask.replace(\".mrc\", \"_flipped.mrc\"), overwrite=True) as mrc:\n", + " mrc.set_data(np.flip(locres_mask_data, axis=0).astype(np.float32))\n", + "\n", + "with mrcfile.open(postprocess_map, mode='r') as mrc:\n", + " postprocess_map_data = mrc.data\n", + "\n", + "with mrcfile.new(postprocess_map.replace(\".mrc\", \"_flipped.mrc\"), overwrite=True) as mrc:\n", + " mrc.set_data(np.flip(postprocess_map_data, axis=0).astype(np.float32))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## panel D\n", + "plotting the boxplot of particle picking precision as a function of exposure" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "project_dir = \"/home/mjoosten1/projects/roodmus/data/20231017_EMPIAR_SNR_comparison\"\n", + "figures_dir = os.path.join(project_dir, \"figures\")\n", + "if not os.path.exists(figures_dir):\n", + " os.makedirs(figures_dir)\n", + "particle_diameter = 100 # approximate particle diameter in Angstroms\n", + "ugraph_shape = (4000, 4000) # shape of the micrograph in pixels. Only needs to be given if the metadata file is a .star file\n", + "verbose = True # prints out progress statements\n", + "ignore_missing_files = True # if .mrc files are missing, the analysis will still be performed\n", + "enable_tqdm = True # enables tqdm progress bars\n", + "\n", + "data = {\n", + " 0: {\n", + " \"exposure\": 45,\n", + " \"LoG\": \"job004\",\n", + " \"Class2D\": \"job005\",\n", + " \"topaz\": \"job010\",\n", + " \"homogeneous\": \"job016\"\n", + " },\n", + " 1: {\n", + " \"exposure\": 35,\n", + " \"LoG\": \"job037\",\n", + " \"Class2D\": \"job038\",\n", + " \"topaz\": \"job042\",\n", + " \"homogeneous\": \"job048\"\n", + " },\n", + " 2: {\n", + " \"exposure\": 25,\n", + " \"LoG\": \"job054\",\n", + " \"Class2D\": \"job055\",\n", + " \"topaz\": \"job059\",\n", + " \"homogeneous\": \"job065\"\n", + " },\n", + " 3: {\n", + " \"exposure\": 15,\n", + " \"LoG\": \"job071\",\n", + " \"Class2D\": \"job072\",\n", + " \"topaz\": \"job076\",\n", + " \"homogeneous\": \"job082\"\n", + " },\n", + " 4: {\n", + " \"exposure\": 5,\n", + " \"LoG\": \"job088\",\n", + " \"Class2D\": \"job089\",\n", + " \"topaz\": None,\n", + " \"homogeneous\": None\n", + " },\n", + " 5: {\n", + " \"exposure\": 10,\n", + " \"LoG\": \"job093\",\n", + " \"Class2D\": \"job094\",\n", + " \"topaz\": None,\n", + " \"homogeneous\": None\n", + " },\n", + " 6: {\n", + " \"exposure\": 12,\n", + " \"LoG\": \"job098\",\n", + " \"Class2D\": \"job099\",\n", + " \"topaz\": None,\n", + " \"homogeneous\": None\n", + " },\n", + " 7: {\n", + " \"exposure\": 8,\n", + " \"LoG\": \"job114\",\n", + " \"Class2D\": \"job115\",\n", + " \"topaz\": None,\n", + " \"homogeneous\": None,\n", + " },\n", + "}\n", + "\n", + "for key, item in data.items():\n", + " exposure = f\"{item['exposure']}\".zfill(2)\n", + " config_dir = os.path.join(project_dir, f\"mrc_epa_{exposure}\")\n", + " print(config_dir)\n", + " meta_files = [\n", + " os.path.join(project_dir, \"Extract\", item[\"LoG\"], \"particles.star\"),\n", + " # os.path.join(project_dir, \"Class2D\", item[\"Class2D\"], \"run_it025_data.star\"),\n", + " ]\n", + " jobtypes = {\n", + " os.path.join(project_dir, \"Extract\", item[\"LoG\"], \"particles.star\"): \"LoG\",\n", + " os.path.join(project_dir, \"Class2D\", item[\"Class2D\"], \"run_it025_data.star\"): \"Class2D\",\n", + " }\n", + " if item[\"topaz\"]:\n", + " meta_files.append(os.path.join(project_dir, \"Extract\", item[\"topaz\"], \"particles.star\"))\n", + " jobtypes[os.path.join(project_dir, \"Extract\", item[\"topaz\"], \"particles.star\")] = \"topaz\"\n", + " if item[\"homogeneous\"]:\n", + " meta_files.append(os.path.join(project_dir, \"Refine3D\", item[\"homogeneous\"], \"run_data.star\"))\n", + " jobtypes[os.path.join(project_dir, \"Refine3D\", item[\"homogeneous\"], \"run_data.star\")] = \"homogeneous\"\n", + "\n", + " for i, meta_file in enumerate(meta_files):\n", + " if i == 0:\n", + " analysis = load_data(meta_file, config_dir, particle_diameter, ugraph_shape=ugraph_shape, verbose=verbose, enable_tqdm=enable_tqdm, ignore_missing_files=ignore_missing_files) # creates the class\n", + " else:\n", + " analysis.add_data(meta_file, config_dir, verbose=verbose) # updates the class with the next metadata file\n", + " \n", + " df_picked = pd.DataFrame(analysis.results_picking)\n", + " df_truth = pd.DataFrame(analysis.results_truth)\n", + "\n", + " # compute precision and recall\n", + " # df_precision, df_picked = analysis.compute_precision(df_picked, df_truth, verbose=verbose)\n", + " p_match, _, p_unmatched, t_unmatched, _ = analysis._match_particles(\n", + " meta_files,\n", + " df_picked,\n", + " df_truth,\n", + " verbose=False,\n", + " enable_tqdm=True,\n", + " )\n", + " df_precision = analysis.compute_1to1_match_precision(\n", + " p_match,\n", + " p_unmatched,\n", + " t_unmatched,\n", + " df_truth,\n", + " verbose=False,\n", + " )\n", + "\n", + " # add a column to the picked data frame that indicates exposure\n", + " df_picked[\"exposure\"] = item[\"exposure\"]\n", + " df_precision[\"exposure\"] = item[\"exposure\"]\n", + "\n", + " df_overlap = analysis.compute_overlap(df_picked, df_truth, verbose=verbose)\n", + "\n", + " # add a column to the overlap data frame that indicates exposure\n", + " df_overlap[\"exposure\"] = item[\"exposure\"]\n", + "\n", + " if key == 0:\n", + " df_precision_all = df_precision\n", + " df_picked_all = df_picked\n", + " df_overlap_all = df_overlap\n", + " df_truth_all = df_truth\n", + " else:\n", + " df_precision_all = pd.concat([df_precision_all, df_precision])\n", + " df_picked_all = pd.concat([df_picked_all, df_picked])\n", + " df_overlap_all = pd.concat([df_overlap_all, df_overlap])\n", + " df_truth_all = pd.concat([df_truth_all, df_truth])\n", + "\n", + "jobtypes_all = {}\n", + "for value in data.values():\n", + " jobtypes_all[value[\"LoG\"]] = \"LoG\"\n", + " # jobtypes_all[value[\"Class2D\"]] = \"Class2D\"\n", + " if value[\"topaz\"]:\n", + " jobtypes_all[value[\"topaz\"]] = \"topaz\"\n", + " if value[\"homogeneous\"]:\n", + " jobtypes_all[value[\"homogeneous\"]] = \"homogeneous\"\n", + "df_precision_all[\"job\"] = df_precision_all[\"metadata_filename\"].apply(lambda x: x.split(\"/\")[-2])\n", + "df_precision_all[\"jobtype\"] = df_precision_all[\"job\"].map(jobtypes_all)\n", + "df_picked_all[\"job\"] = df_picked_all[\"metadata_filename\"].apply(lambda x: x.split(\"/\")[-2])\n", + "df_picked_all[\"jobtype\"] = df_picked_all[\"job\"].map(jobtypes_all)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df_precision_LoG = df_precision_all.groupby(\"jobtype\").get_group(\"LoG\")\n", + "\n", + "fig, ax = plt.subplots(figsize=(7, 3.5))\n", + "# create stripplot for precision per exposure, with different columns for different metadata files\n", + "sns.stripplot(x=\"exposure\", y=\"precision\", hue=\"defocus\", data=df_precision_LoG, ax=ax, jitter=0.1, dodge=False, alpha=0.5, legend=False, palette=\"RdYlBu\")\n", + "sns.boxplot(x=\"exposure\", y=\"precision\", hue=\"exposure\", data=df_precision_LoG, ax=ax, dodge=False, palette=\"Blues\")\n", + "\n", + "ax.set_xlabel(\"Fluence ($e^-$/$\\AA^2$)\", fontsize=16)\n", + "ax.set_ylabel(\"Precision\", fontsize=16)\n", + "sm = plt.cm.ScalarMappable(\n", + " cmap=\"RdYlBu\",\n", + " norm=plt.Normalize(\n", + " vmin=df_precision[\"defocus\"].min()/10000,\n", + " vmax=df_precision[\"defocus\"].max()/10000,\n", + " ),\n", + ")\n", + "sm._A = []\n", + "cbar = fig.colorbar(sm, ax=ax)\n", + "cbar.set_label(\"Defocus (Å)\", rotation=270, labelpad=20, fontsize=16)\n", + "cbar.ax.tick_params(labelsize=14)\n", + "ax.legend().remove()\n", + "\n", + "# fig.savefig(os.path.join(figures_dir, \"precision_vs_exposure.pdf\"), bbox_inches=\"tight\")\n", + "print(f\"saved figure to: {os.path.join(figures_dir, 'precision_vs_exposure.pdf')}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## panel E\n", + "plotting the boxplot of particle picking recall as a function of exposure" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# sort df_precision_all by exposure\n", + "df_precision_LoG = df_precision_all.groupby(\"jobtype\").get_group(\"LoG\")\n", + "\n", + "fig, ax = plt.subplots(figsize=(7, 3.5))\n", + "# create stripplot for precision per exposure, with different columns for different metadata files\n", + "sns.stripplot(x=\"exposure\", y=\"recall\", hue=\"defocus\", data=df_precision_LoG, ax=ax, jitter=0.1, dodge=False, alpha=0.5, legend=False, palette=\"RdYlBu\")\n", + "sns.boxplot(x=\"exposure\", y=\"recall\", hue=\"exposure\", data=df_precision_LoG, ax=ax, dodge=False, palette=\"Blues\")\n", + "\n", + "ax.set_xlabel(\"Fluence ($e^-$/$\\AA^2$)\", fontsize=16)\n", + "ax.set_ylabel(\"Recall\", fontsize=16)\n", + "sm = plt.cm.ScalarMappable(\n", + " cmap=\"RdYlBu\",\n", + " norm=plt.Normalize(\n", + " vmin=df_precision[\"defocus\"].min()/10000,\n", + " vmax=df_precision[\"defocus\"].max()/10000,\n", + " ),\n", + ")\n", + "sm._A = []\n", + "cbar = fig.colorbar(sm, ax=ax)\n", + "cbar.set_label(\"Defocus (\\u03BCm)\", rotation=270, labelpad=20, fontsize=16)\n", + "cbar.ax.tick_params(labelsize=14)\n", + "ax.legend().remove()\n", + "\n", + "# fig.savefig(os.path.join(figures_dir, \"recall_vs_exposure.pdf\"), bbox_inches=\"tight\")\n", + "print(f\"saved plot to {os.path.join(figures_dir, 'recall_vs_exposure.pdf')}\")\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## panel F\n", + "Plot of the average distance between a picked particle and the closest truth particle. This acts as a measurement of the accuracy of the particle picking. The data gets filtered to only include TP particles (i.e. particles that are within 1 particle radius of a truth particle). Because of this, the increase in average distance cannot be explained by more FP particle picks, and only by less precise picking of good particles." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "project_dir = \"/home/mjoosten1/projects/roodmus/data/20231017_EMPIAR_SNR_comparison\"\n", + "figures_dir = os.path.join(project_dir, \"figures\")\n", + "if not os.path.exists(figures_dir):\n", + " os.makedirs(figures_dir)\n", + "particle_diameter = 100 # approximate particle diameter in Angstroms\n", + "ugraph_shape = (4000, 4000) # shape of the micrograph in pixels. Only needs to be given if the metadata file is a .star file\n", + "verbose = True # prints out progress statements\n", + "ignore_missing_files = True # if .mrc files are missing, the analysis will still be performed\n", + "enable_tqdm = True # enables tqdm progress bars\n", + "\n", + "data = {\n", + " 0: {\n", + " \"exposure\": 45,\n", + " \"LoG\": \"job004\",\n", + " \"Class2D\": \"job005\",\n", + " \"topaz\": \"job010\",\n", + " \"homogeneous\": \"job016\"\n", + " },\n", + " 1: {\n", + " \"exposure\": 35,\n", + " \"LoG\": \"job037\",\n", + " \"Class2D\": \"job038\",\n", + " \"topaz\": \"job042\",\n", + " \"homogeneous\": \"job048\"\n", + " },\n", + " 2: {\n", + " \"exposure\": 25,\n", + " \"LoG\": \"job054\",\n", + " \"Class2D\": \"job055\",\n", + " \"topaz\": \"job059\",\n", + " \"homogeneous\": \"job065\"\n", + " },\n", + " 3: {\n", + " \"exposure\": 15,\n", + " \"LoG\": \"job071\",\n", + " \"Class2D\": \"job072\",\n", + " \"topaz\": \"job076\",\n", + " \"homogeneous\": \"job082\"\n", + " },\n", + " 4: {\n", + " \"exposure\": 5,\n", + " \"LoG\": \"job088\",\n", + " \"Class2D\": \"job089\",\n", + " \"topaz\": None,\n", + " \"homogeneous\": None\n", + " },\n", + " 5: {\n", + " \"exposure\": 10,\n", + " \"LoG\": \"job093\",\n", + " \"Class2D\": \"job094\",\n", + " \"topaz\": None,\n", + " \"homogeneous\": None\n", + " },\n", + " 6: {\n", + " \"exposure\": 12,\n", + " \"LoG\": \"job098\",\n", + " \"Class2D\": \"job099\",\n", + " \"topaz\": None,\n", + " \"homogeneous\": None\n", + " },\n", + " 7: {\n", + " \"exposure\": 8,\n", + " \"LoG\": \"job114\",\n", + " \"Class2D\": \"job115\",\n", + " \"topaz\": None,\n", + " \"homogeneous\": None,\n", + " },\n", + "}\n", + "\n", + "for key, item in data.items():\n", + " exposure = f\"{item['exposure']}\".zfill(2)\n", + " config_dir = os.path.join(project_dir, f\"mrc_epa_{exposure}\")\n", + " print(config_dir)\n", + " meta_files = [\n", + " os.path.join(project_dir, \"Extract\", item[\"LoG\"], \"particles.star\"),\n", + " # os.path.join(project_dir, \"Class2D\", item[\"Class2D\"], \"run_it025_data.star\"),\n", + " ]\n", + " jobtypes = {\n", + " os.path.join(project_dir, \"Extract\", item[\"LoG\"], \"particles.star\"): \"LoG\",\n", + " os.path.join(project_dir, \"Class2D\", item[\"Class2D\"], \"run_it025_data.star\"): \"Class2D\",\n", + " }\n", + " if item[\"topaz\"]:\n", + " meta_files.append(os.path.join(project_dir, \"Extract\", item[\"topaz\"], \"particles.star\"))\n", + " jobtypes[os.path.join(project_dir, \"Extract\", item[\"topaz\"], \"particles.star\")] = \"topaz\"\n", + " if item[\"homogeneous\"]:\n", + " meta_files.append(os.path.join(project_dir, \"Refine3D\", item[\"homogeneous\"], \"run_data.star\"))\n", + " jobtypes[os.path.join(project_dir, \"Refine3D\", item[\"homogeneous\"], \"run_data.star\")] = \"homogeneous\"\n", + "\n", + " for i, meta_file in enumerate(meta_files):\n", + " if i == 0:\n", + " analysis = load_data(meta_file, config_dir, particle_diameter, ugraph_shape=ugraph_shape, verbose=verbose, enable_tqdm=enable_tqdm, ignore_missing_files=ignore_missing_files) # creates the class\n", + " else:\n", + " analysis.add_data(meta_file, config_dir, verbose=verbose) # updates the class with the next metadata file\n", + " \n", + " df_picked = pd.DataFrame(analysis.results_picking)\n", + " df_truth = pd.DataFrame(analysis.results_truth)\n", + "\n", + " # compute precision and recall\n", + " df_precision, df_picked = analysis.compute_precision(df_picked, df_truth, verbose=verbose)\n", + " # p_match, _, p_unmatched, t_unmatched, closest_truth_index = analysis._match_particles(\n", + " # meta_files,\n", + " # df_picked,\n", + " # df_truth,\n", + " # verbose=False,\n", + " # enable_tqdm=True,\n", + " # )\n", + " # df_precision = analysis.compute_1to1_match_precision(\n", + " # p_match,\n", + " # p_unmatched,\n", + " # t_unmatched,\n", + " # df_truth,\n", + " # verbose=False,\n", + " # )\n", + " # df_truth[\"pdb_index\"] = df_truth[\"pdb_filename\"].apply(lambda x: int(x.strip(\".pdb\").split(\"_\")[-1]))\n", + " # df_picked[\"closest_truth_index\"] = closest_truth_index\n", + " # df_picked[\"TP\"] = df_picked[\"closest_truth_index\"].apply(lambda x: np.isnan(x) == False)\n", + " # df_picked[\"closest_pdb_index\"] = df_picked[\"closest_truth_index\"].apply(lambda x: df_truth.loc[x, \"pdb_index\"] if np.isnan(x) == False else np.nan)\n", + "\n", + " # add a column to the picked data frame that indicates exposure\n", + " df_picked[\"exposure\"] = item[\"exposure\"]\n", + " df_precision[\"exposure\"] = item[\"exposure\"]\n", + "\n", + " df_overlap = analysis.compute_overlap(df_picked, df_truth, verbose=verbose)\n", + "\n", + " # add a column to the overlap data frame that indicates exposure\n", + " df_overlap[\"exposure\"] = item[\"exposure\"]\n", + "\n", + " if key == 0:\n", + " df_precision_all = df_precision\n", + " df_picked_all = df_picked\n", + " df_overlap_all = df_overlap\n", + " df_truth_all = df_truth\n", + " else:\n", + " df_precision_all = pd.concat([df_precision_all, df_precision])\n", + " df_picked_all = pd.concat([df_picked_all, df_picked])\n", + " df_overlap_all = pd.concat([df_overlap_all, df_overlap])\n", + " df_truth_all = pd.concat([df_truth_all, df_truth])\n", + "\n", + "jobtypes_all = {}\n", + "for value in data.values():\n", + " jobtypes_all[value[\"LoG\"]] = \"LoG\"\n", + " # jobtypes_all[value[\"Class2D\"]] = \"Class2D\"\n", + " if value[\"topaz\"]:\n", + " jobtypes_all[value[\"topaz\"]] = \"topaz\"\n", + " if value[\"homogeneous\"]:\n", + " jobtypes_all[value[\"homogeneous\"]] = \"homogeneous\"\n", + "df_precision_all[\"job\"] = df_precision_all[\"metadata_filename\"].apply(lambda x: x.split(\"/\")[-2])\n", + "df_precision_all[\"jobtype\"] = df_precision_all[\"job\"].map(jobtypes_all)\n", + "df_picked_all[\"job\"] = df_picked_all[\"metadata_filename\"].apply(lambda x: x.split(\"/\")[-2])\n", + "df_picked_all[\"jobtype\"] = df_picked_all[\"job\"].map(jobtypes_all)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# plot the average distance to the closest particle as a function of exposure\n", + "df_picked_all[\"job\"] = df_picked_all[\"metadata_filename\"].apply(lambda x: x.split(\"/\")[-2])\n", + "df_picked_all[\"jobtype\"] = df_picked_all[\"job\"].map(jobtypes_all)\n", + "df_picked_LoG = df_picked_all[df_picked_all[\"jobtype\"] == \"LoG\"]\n", + "df_picked_LoG_TP = df_picked_LoG.groupby(\"TP\").get_group(True)\n", + "fig, ax = plt.subplots(figsize=(3.5, 3.5))\n", + "# sns.violinplot(x=\"exposure\", y=\"closest_dist\", data=df_picked_LoG_TP, ax=ax, inner=\"quartile\", palette=\"Blues\", legend=False)\n", + "sns.boxplot(x=\"exposure\", y=\"closest_dist\", hue=\"exposure\", data=df_picked_LoG_TP, ax=ax, palette=\"Blues\", dodge=False)\n", + "# sns.stripplot(x=\"exposure\", y=\"closest_dist\", data=df_picked_LoG_TP, ax=ax, jitter=0.1, dodge=True, alpha=0.5, color=\"k\")\n", + "ax.set_xlabel(\"Fluence ($e^-$/$\\AA^2$)\", fontsize=16)\n", + "ax.set_ylabel(\"Distance ($\\AA$)\", fontsize=16)\n", + "ax.tick_params(labelsize=14)\n", + "ax.legend().remove()\n", + "\n", + "# fig.savefig(os.path.join(figures_dir, \"distance_to_closest_particle_vs_exposure.pdf\"), bbox_inches=\"tight\")\n", + "print(f\"saved image to {os.path.join(figures_dir, 'distance_to_closest_particle_vs_exposure.pdf')}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "roodmus", + "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": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/paper_fig_ipynbs/supplementary_figure_4.ipynb b/paper_fig_ipynbs/supplementary_figure_4.ipynb new file mode 100644 index 0000000..79fa5b8 --- /dev/null +++ b/paper_fig_ipynbs/supplementary_figure_4.ipynb @@ -0,0 +1,443 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# plotting functions of supplmentary figure 3 in the manuscript\n", + "This figure shows the 3D classification of the covid spike monomer steered MD simulation and c3c3b steered MD simulation.\n", + "\n", + "his figure shows the following:\n", + "\n", + "- Panel showing distribution of particles in each 3D class over the frames of the steered MD simulation\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# imports\n", + "import os\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import numpy as np\n", + "import mrcfile\n", + "\n", + "# roodmus\n", + "from roodmus.analysis.utils import load_data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## panel A\n", + "plotting the unnormalised correlation matrices of the 3D classification of the spike glycoprotein" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "project_dir = \"/tudelft/mjoosten1/staff-umbrella/ajlab/MJ/projects/Roodmus/data/DE-Shaw_covid_spike_protein/DESRES-Trajectory_sarscov2-11021566-11021571-mixed\"\n", + "for classification_job in [\"2_classes\", \"3_classes\", \"10_classes\"]:\n", + " print(f\"loading data for {classification_job}\")\n", + " correlation_matrix_filename = os.path.join(project_dir, \"aligned_ensembles\", f\"correlation_matrix_{classification_job}.npy\")\n", + " if os.path.exists(correlation_matrix_filename):\n", + " correlation_matrix = np.load(correlation_matrix_filename)\n", + " else:\n", + " print(f\"correlation matrix for {classification_job} does not exist yet. Compute it first.\")\n", + " n_classes = correlation_matrix.shape[1]\n", + " n_frames = correlation_matrix.shape[0]\n", + " print(f\"number of classes: {n_classes}\")\n", + " print(f\"number of frames: {n_frames}\")\n", + "\n", + " fig, ax = plt.subplots(figsize=(3.5, 3.5))\n", + " # show the heatmap as a square (~50x3)\n", + " ax.imshow(correlation_matrix, cmap=\"coolwarm\", aspect=\"auto\", origin=\"lower\")\n", + " ticklabels = [f\"class {i+1}\" for i in range(n_classes)]\n", + " ax.set_xticks(range(n_classes))\n", + " ax.set_xticklabels(ticklabels, fontsize=12, rotation=45)\n", + " ax.set_yticks([0, n_frames//2-2, n_frames//2+2, n_frames-1])\n", + " ax.set_yticklabels([\"0 \\u03BCs\", \"10 \\u03BCs\", \"0 \\u03BCs\", \"10 \\u03BCs\"], fontsize=12)\n", + " ax.axhline(y=n_frames//2, color=\"black\", linestyle=\"solid\", linewidth=2)\n", + " # add colorbar\n", + " cbar = ax.figure.colorbar(ax.get_images()[0], ax=ax, orientation=\"vertical\")\n", + " ax.text(\n", + " -0.65,\n", + " 3*n_frames//4,\n", + " \"Closed state\",\n", + " ha='right',\n", + " va='bottom',\n", + " fontsize=12,\n", + " fontweight='bold',\n", + " )\n", + " ax.text(\n", + " -0.65,\n", + " n_frames//4,\n", + " \"Open state\",\n", + " ha='right',\n", + " va='bottom',\n", + " fontsize=12,\n", + " fontweight='bold',\n", + " )\n", + "\n", + " # save the figure\n", + " outfilename = os.path.join(project_dir, \"figures\", f\"correlation_matrix_{n_classes}classes_unnormalised.pdf\")\n", + " # fig.savefig(outfilename, bbox_inches=\"tight\")\n", + " print(f\"saved figure to: {outfilename}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## panel B\n", + "plotting the distribution of particles in each 3D class" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "### data loading (6xm5 steered MD)\n", + "project_dir = \"/home/mjoosten1/projects/roodmus/data/6xm5_steered_Roodmus_2\"\n", + "config_dir = os.path.join(project_dir, \"mrc\")\n", + "figures_dir = os.path.join(project_dir, \"figures\")\n", + "meta_files = [\n", + " os.path.join(project_dir, \"cryoSPARC\", \"J519_passthrough_particles_class_0.cs\"),\n", + " os.path.join(project_dir, \"cryoSPARC\", \"J519_passthrough_particles_class_1.cs\"),\n", + " os.path.join(project_dir, \"cryoSPARC\", \"J519_passthrough_particles_class_2.cs\"),\n", + " os.path.join(project_dir, \"cryoSPARC\", \"J519_passthrough_particles_class_3.cs\"),\n", + "]\n", + "\n", + "jobtypes = {\n", + " os.path.join(project_dir, \"cryoSPARC\", \"J519_passthrough_particles_class_0.cs\"): \"ab initio class 0\",\n", + " os.path.join(project_dir, \"cryoSPARC\", \"J519_passthrough_particles_class_1.cs\"): \"ab initio class 1\",\n", + " os.path.join(project_dir, \"cryoSPARC\", \"J519_passthrough_particles_class_2.cs\"): \"ab initio class 2\",\n", + " os.path.join(project_dir, \"cryoSPARC\", \"J519_passthrough_particles_class_3.cs\"): \"ab initio class 3\",\n", + "}\n", + "\n", + "particle_diameter = 100 # approximate particle diameter in Angstroms\n", + "ugraph_shape = (4000, 4000) # shape of the micrograph in pixels. Only needs to be given if the metadata file is a .star file\n", + "verbose = True # prints out progress statements\n", + "ignore_missing_files = True # if .mrc files are missing, the analysis will still be performed\n", + "enable_tqdm = True # enables tqdm progress bars\n", + "\n", + "for i, meta_file in enumerate(meta_files):\n", + " if i == 0:\n", + " analysis = load_data(meta_file, config_dir, particle_diameter, ugraph_shape=ugraph_shape, verbose=verbose, enable_tqdm=enable_tqdm, ignore_missing_files=ignore_missing_files) # creates the class\n", + " else:\n", + " analysis.add_data(meta_file, config_dir, verbose=verbose) # updates the class with the next metadata file\n", + "df_picked = pd.DataFrame(analysis.results_picking)\n", + "df_truth = pd.DataFrame(analysis.results_truth)\n", + "# df_precision, df_picked = analysis.compute_precision(df_picked, df_truth, verbose=verbose)\n", + "p_match, _, p_unmatched, t_unmatched, closest_truth_index = analysis._match_particles(\n", + " meta_files,\n", + " df_picked,\n", + " df_truth,\n", + " verbose=False,\n", + " enable_tqdm=True,\n", + ")\n", + "df_precision = analysis.compute_1to1_match_precision(\n", + " p_match,\n", + " p_unmatched,\n", + " t_unmatched,\n", + " df_truth,\n", + " verbose=False,\n", + ")\n", + "print(f\"mean precision: {df_precision['precision'].mean()}\")\n", + "print(f\"mean recall: {df_precision['recall'].mean()}\")\n", + "df_truth[\"pdb_index\"] = df_truth[\"pdb_filename\"].apply(lambda x: int(x.strip(\".pdb\").split(\"_\")[-1]))\n", + "df_picked[\"closest_truth_index\"] = closest_truth_index\n", + "df_picked[\"TP\"] = df_picked[\"closest_truth_index\"].apply(lambda x: np.isnan(x) == False)\n", + "df_picked[\"closest_pdb_index\"] = df_picked[\"closest_truth_index\"].apply(lambda x: df_truth.loc[x, \"pdb_index\"] if np.isnan(x) == False else np.nan)\n", + "df_picked[\"jobtype\"] = df_picked[\"metadata_filename\"].map(jobtypes)\n", + "print(f\"total number of particles loaded: {len(df_picked)}\")\n", + "print(f\"number of particles in class 0: {len(df_picked[df_picked['jobtype'] == 'ab initio class 0'])}\")\n", + "df_picked.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# add a column to the df_picked data frame that indicates if the particles originates from the open or closed state of the spike protein\n", + "df_truth[\"pdb_index\"] = df_truth[\"pdb_filename\"].apply(lambda x: int(x.strip(\".pdb\").split(\"_\")[-1]))\n", + "# df_picked[\"jobtype\"] = df_picked[\"metadata_filename\"].apply(lambda x: jobtypes[x])\n", + "df_picked_grouped = df_picked.groupby(\"TP\").get_group(True)\n", + "print(f\"number of TP particles : {len(df_picked_grouped)}\")\n", + "num_classes = len(df_picked_grouped[\"jobtype\"].unique())\n", + "\n", + "dt = 0.01 # ps timeinterval between frames\n", + "\n", + "fig, ax = plt.subplots(figsize=(7, 3.5))\n", + "# make kde plot out of the data\n", + "kde_picked = sns.kdeplot(\n", + " data=df_picked_grouped,\n", + " x=\"closest_pdb_index\",\n", + " ax=ax,\n", + " hue=\"jobtype\",\n", + " fill=False,\n", + " label=\"jobtype\",\n", + " linewidth=10,\n", + " alpha=1,\n", + " palette=\"RdYlBu\",\n", + " legend=True,\n", + ")\n", + "# add an extra black line over the second class\n", + "kde_extra = sns.kdeplot(\n", + " data=df_picked_grouped,\n", + " x=\"closest_pdb_index\",\n", + " ax=ax,\n", + " hue=\"jobtype\",\n", + " palette={f\"{r}\": \"black\" for r in df_picked_grouped[\"jobtype\"].unique()},\n", + " label=\"picked_particles\",\n", + " linewidth=3,\n", + " alpha=1,\n", + " fill=False,\n", + " legend=False,\n", + ")\n", + "# add kdeplot of the True particles\n", + "ax_truth = ax.twinx()\n", + "kde_truth = sns.kdeplot(\n", + " data=df_truth,\n", + " x=\"pdb_index\",\n", + " ax=ax_truth,\n", + " color=\"black\",\n", + " linestyle=\"--\",\n", + " label=\"GT\",\n", + " linewidth=3,\n", + " fill=False,\n", + " alpha=1,\n", + " legend=True,\n", + ")\n", + "\n", + "# get the legend handles from kde_picked\n", + "handles, labels = ax.get_legend_handles_labels()\n", + "h = [handles[r] for r in range(len(handles)) if labels[r] == \"jobtype\"]\n", + "h = h[::-1]\n", + "h.extend([handles[r] for r in range(len(handles)) if labels[r] == \"GT\"])\n", + "l = [f\"class {r+1}\" for r in range(num_classes)] + [\"GT\"]\n", + "# add the legend\n", + "ax.legend(\n", + " handles=h,\n", + " labels=l,\n", + " loc='upper right',\n", + " bbox_to_anchor=(1.50, 1.0),\n", + " ncol=1,\n", + " fontsize=14,\n", + " frameon=True,\n", + ")\n", + "ax_truth.set_ylabel(\"GT\", fontsize=16, rotation=270, labelpad=20)\n", + "if num_classes == 10:\n", + " ylim = ax.get_ylim()\n", + " ax.set_ylim(ylim[0], ylim[1]*1.1)\n", + "else:\n", + " ylim = ax_truth.get_ylim()\n", + " ax.set_ylim(ylim[0], ylim[1])\n", + "\n", + "# change the xticks to the time in us\n", + "ax.set_xticks(np.linspace(0, df_truth[\"pdb_index\"].max(), 5))\n", + "ax.set_xticklabels(np.linspace(0, df_truth[\"pdb_index\"].max()*dt, 5).round(2), fontsize=14)\n", + "ax.set_xlabel(\"Time (ps)\", fontsize=16)\n", + "ax.set_ylabel(\"Density\", fontsize=16)\n", + "ax.tick_params(axis='both', which='major', labelsize=14)\n", + "ax_truth.tick_params(axis='both', which='major', labelsize=14)\n", + "\n", + "# save the figure\n", + "# fig.savefig(os.path.join(project_dir, \"figures\", f\"frame_distribution.pdf\"), bbox_inches=\"tight\")\n", + "print(f\"saved figure to: {os.path.join(project_dir, 'figures', 'frame_distribution.pdf')}\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## panel C\n", + "plotting the precision per 3D class and the fraction of particles in each 3D class" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# plot the preicison per 3D class\n", + "df_precision[\"jobtype\"] = df_precision[\"metadata_filename\"].apply(lambda x: jobtypes[x].replace(\"ab initio\", \"\"))\n", + "# change the numbering in the classes from 0-n to 1-n+1\n", + "df_precision[\"jobtype\"] = df_precision[\"jobtype\"].apply(lambda x: f\"class {int(x.split()[-1])+1}\")\n", + "num_classes = len(df_precision[\"jobtype\"].unique())\n", + "colors = sns.color_palette(\"RdYlBu\", n_colors=num_classes)\n", + "\n", + "fig, ax = plt.subplots(figsize=(3.5, 3.5))\n", + "sns.barplot(\n", + " data=df_precision,\n", + " x=\"jobtype\",\n", + " y=\"precision\",\n", + " hue=\"jobtype\",\n", + " palette=colors,\n", + " edgecolor='black',\n", + " ax=ax,\n", + " dodge=0,\n", + " linewidth=1,\n", + " errorbar=None,\n", + ")\n", + "ax.set_ylim((0, 1))\n", + "ax.set_ylabel(\"Precision\", fontsize=16)\n", + "ax.set_xlabel(\"\")\n", + "ax.tick_params(axis='both', which='major', labelsize=14)\n", + "ax.legend().remove()\n", + "\n", + "# fig.savefig(os.path.join(project_dir, \"figures\", f\"precision_per_class.pdf\"), bbox_inches=\"tight\")\n", + "print(f\"saved figure to {os.path.join(project_dir, 'figures', 'precision_per_class.pdf')}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "results = {\n", + " \"class\": [],\n", + " \"picked_fraction\": [],\n", + "}\n", + "for jobtype in df_picked[\"jobtype\"].unique():\n", + " results[\"class\"].append(jobtype.replace(\"ab initio\", \"\"))\n", + " results[\"picked_fraction\"].append(len(df_picked[df_picked[\"jobtype\"] == jobtype]) / len(df_picked))\n", + "df_results = pd.DataFrame(results)\n", + "df_results[\"class\"] = df_results[\"class\"].apply(lambda x: f\"class {int(x.split()[-1])+1}\")\n", + "\n", + "fig, ax = plt.subplots(figsize=(3.5, 3.5))\n", + "sns.barplot(\n", + " data=df_results,\n", + " x=\"class\",\n", + " y=\"picked_fraction\",\n", + " hue=\"class\",\n", + " palette=colors,\n", + " edgecolor='black',\n", + " ax=ax,\n", + " dodge=0,\n", + " linewidth=1,\n", + ")\n", + "ax.set_ylim((0, 1))\n", + "ax.set_ylabel(\"Picked fraction\", fontsize=16)\n", + "ax.set_xlabel(\"\")\n", + "ax.tick_params(axis='both', which='major', labelsize=14)\n", + "ax.legend().remove()\n", + "\n", + "# fig.savefig(os.path.join(project_dir, \"figures\", f\"particle_fraction_per_class.pdf\"), bbox_inches=\"tight\")\n", + "print(f\"saved figure to {os.path.join(project_dir, 'figures', 'particle_fraction_per_class.pdf')}\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## panel D\n", + "For each class, the density map is plotted before refinement" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# flipping volume\n", + "volumes_to_flip = [\n", + " # \"/home/mjoosten1/projects/roodmus/data/6xm5_steered_Roodmus_2/cryosparc_P51_J519/cryosparc_P51_J519_class_00_final_volume.mrc\",\n", + " \"/home/mjoosten1/projects/roodmus/data/6xm5_steered_Roodmus_2/cryosparc_P51_J520/cryosparc_P51_J520_003_volume_map_sharp.mrc\",\n", + "]\n", + "\n", + "for volume_to_flip in volumes_to_flip:\n", + " with mrcfile.open(volume_to_flip, mode='r', permissive=True) as mrc:\n", + " volume = mrc.data\n", + " vsize = mrc.voxel_size\n", + " with mrcfile.new(volume_to_flip.replace(\".mrc\", \"_flipped.mrc\"), overwrite=True) as mrc:\n", + " mrc.set_data(np.flip(volume, axis=0))\n", + " mrc.voxel_size = vsize\n", + " \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# print the colors for the 4 classes as hex values\n", + "colors = sns.color_palette(\"RdYlBu\", n_colors=num_classes)\n", + "for color in colors:\n", + " # convert the rgb values to hex\n", + " color_hex = '#%02x%02x%02x' % tuple([int(r*255) for r in color])\n", + " print(color_hex)\n", + " \n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## in text\n", + "print some stats about the distribution of the particles in the 3D classes" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# print the total number of TP and FP particles in the dataset\n", + "print(f\"total number of TP particles: {len(df_picked_grouped)}\")\n", + "print(f\"total number of FP particles: {len(df_picked[df_picked['TP'] == False])}\")\n", + "print(f\"total number of particles: {len(df_picked)}\")\n", + "\n", + "# print the precision and recall per class\n", + "df_precision = df_precision.groupby(\"jobtype\").agg({\"precision\": \"mean\", \"recall\": \"mean\"})\n", + "df_precision.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "roodmus", + "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": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/paper_fig_ipynbs/supplementary_figure_5.ipynb b/paper_fig_ipynbs/supplementary_figure_5.ipynb new file mode 100644 index 0000000..e13e939 --- /dev/null +++ b/paper_fig_ipynbs/supplementary_figure_5.ipynb @@ -0,0 +1,202 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Plotting functions for supplementary figure 5 of the manuscript\n", + "In this figure we show a hexbin plot of the latent space of the cryoDRGN training of the SARS-CoV-2 spike protein dataset\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# imports\n", + "import os\n", + "import pandas as pd\n", + "\n", + "# roodmus\n", + "from roodmus.analysis.utils import load_data\n", + "from roodmus.heterogeneity.hetRec import HetRec\n", + "from roodmus.heterogeneity.plot_heterogeneous_reconstruction import plot_latent_space_hexbin\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## panel A\n", + "hexbin plot of the latent space for the covid spike trimer cryodrgn training" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# data loading for DE-Shaw covid spike partially open set\n", + "project_dir = \"/tudelft/mjoosten1/staff-umbrella/ajlab/MJ/projects/Roodmus/data/DE-Shaw_covid_spike_protein/20231116_DESRES-Trajectory_sarscov2-11021571-all-glueCA\"\n", + "ugraph_dir = \"/tudelft/mjoosten1/staff-umbrella/ajlab/MJ/projects/Roodmus/data/DE-Shaw_covid_spike_protein/DESRES-Trajectory_sarscov2-11021571-all-glueCA\"\n", + "config_dir = os.path.join(ugraph_dir, \"Micrographs\")\n", + "figures_dir = os.path.join(project_dir, \"figures\")\n", + "meta_file = os.path.join(project_dir, \"cryoDRGN\", \"run_data.star\")\n", + "jobtypes = {\n", + " os.path.join(project_dir, \"cryoDRGN\", \"run_data.star\"): \"cryoDRGN\",\n", + "}\n", + "latent_file = os.path.join(project_dir, \"cryoDRGN\", \"train_320\", \"z.19.pkl\")\n", + "\n", + "particle_diameter = 100 # approximate particle diameter in Angstroms\n", + "ugraph_shape = (4000, 4000) # shape of the micrograph in pixels. Only needs to be given if the metadata file is a .star file\n", + "verbose = True\n", + "ignore_missing_files = True\n", + "enable_tqdm = True\n", + "\n", + "analysis = load_data(meta_file, config_dir, particle_diameter, ugraph_shape=ugraph_shape, verbose=verbose, enable_tqdm=enable_tqdm, ignore_missing_files=ignore_missing_files) # creates the class\n", + "df_picked = pd.DataFrame(analysis.results_picking)\n", + "df_truth = pd.DataFrame(analysis.results_truth)\n", + "df_precision, df_picked = analysis.compute_precision(df_picked, df_truth, verbose=verbose)\n", + "\n", + "df_picked, ndim = HetRec.add_latent_space_coordinates(\n", + " latent_file=latent_file,\n", + " df_picked=df_picked,\n", + ")\n", + "df_picked, pca = HetRec.compute_PCA(\n", + " df_picked=df_picked,\n", + " ndim=ndim,\n", + ")\n", + "\n", + "df_picked.tail()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# make a hexbin plot of the latent space\n", + "dim1=0\n", + "dim2=1\n", + "\n", + "grid = plot_latent_space_hexbin(\n", + " df_picked,\n", + " dim_1=dim1,\n", + " dim_2=dim2,\n", + " pca=True,\n", + " palette=\"coolwarm\"\n", + ")\n", + "grid.set_axis_labels(f\"PCA{dim1}\", f\"PCA{dim2}\", fontsize=16)\n", + "grid.figure.get_axes()[0].tick_params(labelsize=14)\n", + "grid.figure.get_axes()[0].set_xlim((-12, 12))\n", + "grid.figure.get_axes()[0].set_ylim((-12, 12))\n", + "\n", + "# grid.savefig(os.path.join(figures_dir, f\"{os.path.basename(latent_file)}_pca_{dim1}_{dim2}_hexbin.pdf\"), bbox_inches=\"tight\")\n", + "print(f\"saved figure to {os.path.join(figures_dir, f'{os.path.basename(latent_file)}_pca_{dim1}_{dim2}_hexbin.pdf')}\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## panel D\n", + "hexbin plot of the latent space for the covid RTC" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "### data loading covid RTC DE-Shaw data set\n", + "project_dir = \"/home/mjoosten1/projects/roodmus/data/DE-Shaw_covid_RTC/20240124_DESRES-Trajectory_sarscov2-13795965-no-water-movies\"\n", + "config_dir = os.path.join(project_dir, \"Movies\")\n", + "figures_dir = os.path.join(project_dir, \"figures\")\n", + "meta_file = os.path.join(project_dir, \"cryoDRGN\", \"run_data.star\")\n", + "jobtypes = {\n", + " os.path.join(project_dir, \"cryoDRGN\", \"run_data.star\"): \"cryoDRGN\",\n", + "}\n", + "latent_file = os.path.join(project_dir, \"cryoDRGN\", \"train_320\", \"z.24.pkl\")\n", + "\n", + "particle_diameter = 100 # approximate particle diameter in Angstroms\n", + "ugraph_shape = (4000, 4000) # shape of the micrograph in pixels. Only needs to be given if the metadata file is a .star file\n", + "verbose = True\n", + "ignore_missing_files = True\n", + "enable_tqdm = True\n", + "\n", + "analysis = load_data(meta_file, config_dir, particle_diameter, ugraph_shape=ugraph_shape, verbose=verbose, enable_tqdm=enable_tqdm, ignore_missing_files=ignore_missing_files) # creates the class\n", + "df_picked = pd.DataFrame(analysis.results_picking)\n", + "df_truth = pd.DataFrame(analysis.results_truth)\n", + "df_precision, df_picked = analysis.compute_precision(df_picked, df_truth, verbose=verbose)\n", + "\n", + "df_picked, ndim = HetRec.add_latent_space_coordinates(\n", + " latent_file=latent_file,\n", + " df_picked=df_picked,\n", + ")\n", + "df_picked, pca = HetRec.compute_PCA(\n", + " df_picked=df_picked,\n", + " ndim=ndim,\n", + ")\n", + "df_picked.tail()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# make a hexbin plot of the latent space\n", + "dim1=0\n", + "dim2=1\n", + "\n", + "grid = plot_latent_space_hexbin(\n", + " df_picked,\n", + " dim_1=dim1,\n", + " dim_2=dim2,\n", + " pca=True,\n", + " palette=\"coolwarm\"\n", + ")\n", + "grid.set_axis_labels(f\"PCA{dim1}\", f\"PCA{dim2}\", fontsize=16)\n", + "grid.figure.get_axes()[0].tick_params(labelsize=14)\n", + "grid.figure.get_axes()[0].set_xlim((-12, 12))\n", + "grid.figure.get_axes()[0].set_ylim((-12, 12))\n", + "\n", + "grid.savefig(os.path.join(figures_dir, f\"{os.path.basename(latent_file)}_pca_{dim1}_{dim2}_hexbin.pdf\"), bbox_inches=\"tight\")\n", + "print(f\"saved figure to {os.path.join(figures_dir, f'{os.path.basename(latent_file)}_pca_{dim1}_{dim2}_hexbin.pdf')}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "roodmus", + "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": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/paper_fig_ipynbs/supplementary_figure_6.ipynb b/paper_fig_ipynbs/supplementary_figure_6.ipynb new file mode 100644 index 0000000..af30618 --- /dev/null +++ b/paper_fig_ipynbs/supplementary_figure_6.ipynb @@ -0,0 +1,461 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# plotting functions for supplementary figure 6\n", + "This figure shows the results of 2 3DFlex models trained on the covid spike monomer steered MD dataset. The panels are largely the same as figure 5 in the manunscript\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# imports\n", + "import os\n", + "import gemmi\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import numpy as np\n", + "\n", + "# roodmus\n", + "from roodmus.analysis.utils import load_data\n", + "from roodmus.analysis.utils import IO\n", + "from roodmus.heterogeneity.plot_heterogeneous_reconstruction import plot_latent_space_scatter" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 3DFlex model with zdim = 1\n", + "This model's latent space must be visualised differently then the usual cases where zdim >= 2.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# data loading\n", + "project_dir = \"/home/mjoosten1/projects/roodmus/data/6xm5_steered_Roodmus_2\"\n", + "config_dir = os.path.join(project_dir, \"mrc\")\n", + "figures_dir = os.path.join(project_dir, \"figures\")\n", + "meta_file = os.path.join(project_dir, \"cryoSPARC\", \"J526_passthrough_particles.cs\")\n", + "jobtypes = {\n", + " os.path.join(project_dir, \"cryoSPARC\", \"J526_passthrough_particles.cs\"): \"3DFlex zdim=1\"\n", + "}\n", + "latent_file = os.path.join(project_dir, \"cryoSPARC\", \"J526_latents_019446.cs\")\n", + "dt = 0.01 # ps interval between frames\n", + "\n", + "particle_diameter = 100 # approximate particle diameter in Angstroms\n", + "ugraph_shape = (4000, 4000) # shape of the micrograph in pixels. Only needs to be given if the metadata file is a .star file\n", + "verbose = True\n", + "ignore_missing_files = True\n", + "enable_tqdm = True\n", + "\n", + "analysis = load_data(meta_file, config_dir, particle_diameter, ugraph_shape=ugraph_shape, verbose=verbose, enable_tqdm=enable_tqdm, ignore_missing_files=ignore_missing_files) # creates the class\n", + "df_picked = pd.DataFrame(analysis.results_picking)\n", + "df_truth = pd.DataFrame(analysis.results_truth)\n", + "df_precision, df_picked = analysis.compute_precision(df_picked, df_truth, verbose=verbose)\n", + "print(f\"mean precision: {df_precision['precision'].mean()}\")\n", + "print(f\"mean recall: {df_precision['recall'].mean()}\")\n", + "\n", + "latent_space, ndim = IO.get_latents_cs(latent_file)\n", + "print(f\"latent space dimensionality: {ndim}\")\n", + "print(latent_space.shape)\n", + "for i in range(ndim):\n", + " df_picked[\"latent_{}\".format(i)] = latent_space[:, i]\n", + "df_picked.tail()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## panel A\n", + "plotting the 1-dimensional latent space as a histogram" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(3.5, 3.5))\n", + "sns.histplot(\n", + " data=df_picked,\n", + " x=\"latent_0\",\n", + " color=\"black\",\n", + " kde=True,\n", + " bins=20,\n", + " ax=ax,\n", + ")\n", + "ax.set_xlabel(\"Z0\", fontsize=16)\n", + "ax.set_ylabel(\"Count\", fontsize=16)\n", + "ax.tick_params(axis=\"both\", which=\"major\", labelsize=14)\n", + "\n", + "outfilename = os.path.join(figures_dir, f\"histogram_z0_{os.path.basename(latent_file)}.pdf\")\n", + "# fig.savefig(outfilename, bbox_inches=\"tight\")\n", + "print(f\"saved figure to: {outfilename}\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## panel B\n", + "kdeplot of the 1-dimensional latent space versus the time in the MD trajectory of each particle" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df_picked[\"time\"] = df_picked[\"closest_pdb_index\"].apply(lambda x: x * dt)\n", + "print(df_picked[\"time\"].max())\n", + "print(df_picked[\"latent_0\"].max())\n", + "\n", + "fig, ax = plt.subplots(figsize=(3.5, 3.5))\n", + "sns.histplot(\n", + " data=df_picked,\n", + " x=\"time\",\n", + " y=\"latent_0\",\n", + " cbar=True,\n", + " cbar_kws={\"label\": \"\"},\n", + " bins=50,\n", + " cmap=\"Oranges\",\n", + " ax=ax,\n", + ")\n", + "ax.set_xlabel(\"Time (ps)\", fontsize=16)\n", + "ax.set_ylabel(\"Z0\", fontsize=16)\n", + "ax.tick_params(axis=\"both\", which=\"major\", labelsize=14)\n", + "# change the tick labels of the colourbar to fontsize 14\n", + "cbar = ax.collections[0].colorbar\n", + "cbar.ax.tick_params(labelsize=14)\n", + "outfilename = os.path.join(figures_dir, f\"z0_vs_time_{os.path.basename(latent_file)}.pdf\")\n", + "# fig.savefig(outfilename, bbox_inches=\"tight\")\n", + "print(f\"saved figure to: {outfilename}\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 3DFlex model with zdim = 2" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# data loading\n", + "project_dir = \"/home/mjoosten1/projects/roodmus/data/6xm5_steered_Roodmus_2\"\n", + "config_dir = os.path.join(project_dir, \"mrc\")\n", + "figures_dir = os.path.join(project_dir, \"figures\")\n", + "meta_file = os.path.join(project_dir, \"cryoSPARC\", \"J577_passthrough_particles.cs\")\n", + "jobtypes = {\n", + " os.path.join(project_dir, \"cryoSPARC\", \"J577_passthrough_particles.cs\"): \"3DFlex zdim=2\"\n", + "}\n", + "latent_file = os.path.join(project_dir, \"cryoSPARC\", \"J577_latents_022224.cs\")\n", + "dt = 0.01 # ps interval between frames\n", + "\n", + "particle_diameter = 100 # approximate particle diameter in Angstroms\n", + "ugraph_shape = (4000, 4000) # shape of the micrograph in pixels. Only needs to be given if the metadata file is a .star file\n", + "verbose = True\n", + "ignore_missing_files = True\n", + "enable_tqdm = True\n", + "\n", + "analysis = load_data(meta_file, config_dir, particle_diameter, ugraph_shape=ugraph_shape, verbose=verbose, enable_tqdm=enable_tqdm, ignore_missing_files=ignore_missing_files) # creates the class\n", + "df_picked = pd.DataFrame(analysis.results_picking)\n", + "df_truth = pd.DataFrame(analysis.results_truth)\n", + "df_precision, df_picked = analysis.compute_precision(df_picked, df_truth, verbose=verbose)\n", + "# p_match, _, p_unmatched, t_unmatched, closest_truth_index = analysis._match_particles(\n", + "# meta_file,\n", + "# df_picked,\n", + "# df_truth,\n", + "# verbose=False,\n", + "# enable_tqdm=True,\n", + "# )\n", + "# df_precision = analysis.compute_1to1_match_precision(\n", + "# p_match,\n", + "# p_unmatched,\n", + "# t_unmatched,\n", + "# df_truth,\n", + "# verbose=False,\n", + "# )\n", + "# df_picked[\"TP\"] = ~np.isnan(closest_truth_index)\n", + "print(f\"mean precision: {df_precision['precision'].mean()}\")\n", + "print(f\"mean recall: {df_precision['recall'].mean()}\")\n", + "\n", + "latent_space, ndim = IO.get_latents_cs(latent_file)\n", + "print(f\"latent space dimensionality: {ndim}\")\n", + "print(latent_space.shape)\n", + "for i in range(ndim):\n", + " df_picked[\"latent_{}\".format(i)] = latent_space[:, i]\n", + "df_picked.tail()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## panel C\n", + "scatter plot of the 2-dimensional latent space" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# latent space scatter plot\n", + "dim1=0\n", + "dim2=1\n", + "\n", + "grid = plot_latent_space_scatter(\n", + " df_picked,\n", + " dim_1=dim1,\n", + " dim_2=dim2,\n", + " pca=False\n", + ")\n", + "df_FP = df_picked[df_picked[\"TP\"]==0]\n", + "print(f\"number of FP: {len(df_FP)}\")\n", + "print(f\"number of TP: {len(df_picked)-len(df_FP)}\")\n", + "ax = grid.figure.get_axes()[0]\n", + "# sns.scatterplot(\n", + "# data=df_FP,\n", + "# x=f\"latent_{dim1}\",\n", + "# y=f\"latent_{dim2}\",\n", + "# color=\"red\",\n", + "# s=10,\n", + "# ax=ax,\n", + "# label=\"FP\",\n", + "# )\n", + "grid.set_axis_labels(f\"Z{dim1}\", f\"Z{dim2}\", fontsize=16)\n", + "grid.figure.get_axes()[0].tick_params(axis=\"both\", which=\"major\", labelsize=14)\n", + "\n", + "outfilename = os.path.join(figures_dir, f\"{os.path.basename(latent_file)}_{dim1}_{dim2}.png\")\n", + "# grid.figure.savefig(outfilename, dpi=600, bbox_inches=\"tight\")\n", + "print(f\"saved figure to: {outfilename}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## panel D\n", + "scatterplot of the 2-dimensional latent space with the time in the MD trajectory of each particle as the color" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# latent space scatter plot, coloured by ground truth frames\n", + "dim1=0\n", + "dim2=1\n", + "\n", + "fig, ax = plot_latent_space_scatter(\n", + " df_picked,\n", + " dim_1=dim1,\n", + " dim_2=dim2,\n", + " color_by=\"closest_pdb_index\",\n", + " palette=\"RdYlBu\",\n", + " pca=False,\n", + ")\n", + "# remove legend and add colorbar for the closest_pdb_index\n", + "ax.legend_.remove()\n", + "S_m = plt.cm.ScalarMappable(cmap=\"RdYlBu\")\n", + "S_m.set_array(df_picked[\"closest_pdb_index\"])\n", + "cbar = plt.colorbar(S_m)\n", + "cbar.set_label(\"Time (ps)\", rotation=270, labelpad=15, fontsize=16) # time in ps\n", + "# change the tick labels on the colorbar to go from 0 to 10 us\n", + "cbar.set_ticks(np.linspace(1, df_picked[\"closest_pdb_index\"].max(), 10))\n", + "xticklabels = [np.round(r, 1) for r in np.linspace(1, df_picked[\"closest_pdb_index\"].max(), 10)*dt]\n", + "cbar.set_ticklabels(xticklabels, fontsize=14)\n", + "ax.set_xlabel(f\"Z{dim1}\", fontsize=16)\n", + "ax.set_ylabel(f\"Z{dim2}\", fontsize=16)\n", + "ax.tick_params(labelsize=14)\n", + "\n", + "# add trajectory to the plot\n", + "trajectory_file = os.path.join(project_dir, \"cryoSPARC\", \"J577_custom_path_2.csv\")\n", + "trajectory = np.loadtxt(trajectory_file, delimiter=\",\", skiprows=1)\n", + "\n", + "ax.scatter(trajectory[:, 0], trajectory[:, 1], s=5, c=\"black\", zorder=10)\n", + "ax.plot(trajectory[:, 0], trajectory[:, 1], c=\"black\", zorder=10, linewidth=0.5)\n", + "ax.set_aspect(\"equal\")\n", + "\n", + "# add trajectory to the plot\n", + "# N_volumes = 50\n", + "# pdb_indices = np.unique(df_picked[\"closest_pdb_index\"])\n", + "# d_pdbs = len(pdb_indices) // N_volumes\n", + "\n", + "# trajectory = np.zeros((N_volumes, ndim))\n", + "# for i in range(N_volumes):\n", + "# pdb_group = pdb_indices[i*d_pdbs:(i+1)*d_pdbs]\n", + "# mean_latent = df_picked[df_picked[\"closest_pdb_index\"].isin(pdb_group)].agg(\n", + "# {f\"latent_{i}\": \"mean\" for i in range(ndim)}\n", + "# )\n", + "# trajectory[i] = mean_latent.values\n", + "\n", + "# ax.scatter(trajectory[:, 0], trajectory[:, 1], s=5, c=\"black\", zorder=10)\n", + "# ax.plot(trajectory[:, 0], trajectory[:, 1], c=\"black\", zorder=10, linewidth=0.5)\n", + "# ax.set_aspect(\"equal\")\n", + "\n", + "outfilename = os.path.join(figures_dir, f\"{os.path.basename(latent_file)}_latent_space_scatter_colored_by_closest_pdb_index_pca.png\")\n", + "# fig.savefig(outfilename, bbox_inches=\"tight\", dpi=600)\n", + "print(f\"saved figure to: {outfilename}\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## panel E\n", + "plotting the correlation between the MD trajectory and the 1-dimensional latent space\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# plot the correlation matrix\n", + "project_dir = \"/home/mjoosten1/projects/roodmus/data/6xm5_steered_Roodmus_2\"\n", + "figures_dir = os.path.join(project_dir, \"figures\")\n", + "correlation_matrix_file = os.path.join(project_dir, \"cryosparc_P51_J527_series_000\", \"correlation_matrix.npy\")\n", + "correlation_matrix = np.load(correlation_matrix_file)\n", + "latent_file = os.path.join(project_dir, \"cryoSPARC\", \"J526_latents_019446.cs\")\n", + "\n", + "frames = correlation_matrix.shape[0]\n", + "\n", + "fig, ax = plt.subplots(figsize=(3.5, 3.5))\n", + "ax.imshow(correlation_matrix, cmap=\"coolwarm\")\n", + "yticks = np.linspace(0, frames, 10)\n", + "yticklabels = np.round(np.linspace(0, 10, 10, dtype=float), 1)\n", + "ax.set_yticks(yticks)\n", + "ax.set_yticklabels(yticklabels)\n", + "ax.set_ylabel(\"Time (ps)\", fontsize=16)\n", + "ax.set_xlabel(\"Sampled volume\", fontsize=16)\n", + "cbar = ax.figure.colorbar(ax.get_images()[0], ax=ax, orientation=\"vertical\", pad=0.01, shrink=0.9)\n", + "cbar.ax.tick_params(labelsize=14)\n", + "cbar.ax.set_ylabel(\"Correlation\", fontsize=16, rotation=270, labelpad=20)\n", + "ax.tick_params(axis=\"both\", which=\"major\", labelsize=14)\n", + "\n", + "outfilename = os.path.join(figures_dir, f\"{os.path.basename(latent_file)}_correlation_matrix.pdf\")\n", + "fig.savefig(outfilename, bbox_inches=\"tight\")\n", + "print(f\"saved figure to: {outfilename}\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## panel F\n", + "plotting the correlation between the MD trajectory and the 2-dimensional latent space" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# plot the correlation matrix\n", + "project_dir = \"/home/mjoosten1/projects/roodmus/data/6xm5_steered_Roodmus_2\"\n", + "figures_dir = os.path.join(project_dir, \"figures\")\n", + "correlation_matrix_file = os.path.join(project_dir, \"cryosparc_P51_J641_series_000\", \"correlation_matrix.npy\")\n", + "correlation_matrix = np.load(correlation_matrix_file)\n", + "latent_file = os.path.join(project_dir, \"cryoSPARC\", \"J577_latents_022224.cs\")\n", + "\n", + "frames = correlation_matrix.shape[0]\n", + "\n", + "fig, ax = plt.subplots(figsize=(3.5, 3.5))\n", + "ax.imshow(correlation_matrix, cmap=\"coolwarm\")\n", + "yticks = np.linspace(0, frames, 10)\n", + "yticklabels = np.round(np.linspace(0, 10, 10, dtype=float), 1)\n", + "ax.set_yticks(yticks)\n", + "ax.set_yticklabels(yticklabels)\n", + "ax.set_ylabel(\"Time (ps)\", fontsize=16)\n", + "ax.set_xlabel(\"Sampled volume\", fontsize=16)\n", + "cbar = ax.figure.colorbar(ax.get_images()[0], ax=ax, orientation=\"vertical\", pad=0.01, shrink=0.9)\n", + "cbar.ax.tick_params(labelsize=14)\n", + "cbar.ax.set_ylabel(\"Correlation\", fontsize=16, rotation=270, labelpad=20)\n", + "ax.tick_params(axis=\"both\", which=\"major\", labelsize=14)\n", + "\n", + "outfilename = os.path.join(figures_dir, f\"{os.path.basename(latent_file)}_correlation_matrix.pdf\")\n", + "fig.savefig(outfilename, bbox_inches=\"tight\")\n", + "print(f\"saved figure to: {outfilename}\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## in-text\n", + "compute and print the total mass of the first conformation\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pdb_file = \"/home/mjoosten1/projects/roodmus/data/6xm5_steered_Roodmus_1/pdb/conformation_000000.pdb\"\n", + "pdb_gemmi = gemmi.read_structure(pdb_file)\n", + "mass = 0\n", + "for chn in pdb_gemmi[0]:\n", + " for res in chn:\n", + " for atm in res:\n", + " mass += atm.element.weight\n", + "\n", + "print(f\"total mass: {mass/1000} kDa\")\n", + "print(f\"mass of entire protein: {mass/1000*3} kDa\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "roodmus", + "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": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/paper_fig_ipynbs/supplementary_figure_7.ipynb b/paper_fig_ipynbs/supplementary_figure_7.ipynb new file mode 100644 index 0000000..0ea1ec1 --- /dev/null +++ b/paper_fig_ipynbs/supplementary_figure_7.ipynb @@ -0,0 +1,590 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# plotting functions for supplementary figure 7\n", + "This figure shows the results of a 3DFlex model trained on the C3-C3b steered MD dataset. The panels are largely the same as figure 5 in the manunscript\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# imports\n", + "import os\n", + "import gemmi\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import numpy as np\n", + "\n", + "# roodmus\n", + "from roodmus.analysis.utils import load_data\n", + "from roodmus.analysis.utils import IO\n", + "from roodmus.heterogeneity.plot_heterogeneous_reconstruction import plot_latent_space_scatter" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## panel C\n", + "plotting the latent space of the C3-C3b steered MD dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loading metadata from /home/mjoosten1/projects/roodmus/data/c3c3b/cryoSPARC/J685_passthrough_particles.cs...\n", + "loaded metadata from /home/mjoosten1/projects/roodmus/data/c3c3b/cryoSPARC/J685_passthrough_particles.cs. determined file type: cs\n", + "\n", + "\n", + "Dictionaries now contain 96000 reconstructed particles\n", + "added 96000 particles from /home/mjoosten1/projects/roodmus/data/c3c3b/cryoSPARC/J685_passthrough_particles.cs\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "loading truth data: 100%|██████████| 786/786 [12:45<00:00, 1.03it/s, micrograph=000793.mrc]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loaded ground-truth particle positions from config files\n", + "Dictionaries now contain 96000 particles and 196500 true particles\n", + "Added 196500 particles from /home/mjoosten1/projects/roodmus/data/c3c3b/mrc\n", + "For each micrograph, for each metadata file, compute the precision, recall and multiplicity\n", + "Speed of computation depends on the number of particles in the micrograph. progressbar is not accurate\n", + "Total number of groups to loop over: 786\n", + "Number of micgrographs: 786\n", + "Number of metadata files: 1\n", + "Starting loop over groups\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "computing precision: 100%|██████████| 786/786 [00:59<00:00, 13.13it/s, precision=0.989, recall=0.382, multiplicity=0.392]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "time taken to compute precision: 60.29331994056702\n", + "mean precision: 0.9952549365508312\n", + "mean recall: 0.4981609435816187\n", + "latent space dimensionality: 2\n", + "(96000, 2)\n" + ] + }, + { + "data": { + "text/html": [ + "
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Only needs to be given if the metadata file is a .star file\n", + "verbose = True\n", + "ignore_missing_files = True\n", + "enable_tqdm = True\n", + "\n", + "analysis = load_data(meta_file, config_dir, particle_diameter, ugraph_shape=ugraph_shape, verbose=verbose, enable_tqdm=enable_tqdm, ignore_missing_files=ignore_missing_files) # creates the class\n", + "df_picked = pd.DataFrame(analysis.results_picking)\n", + "df_truth = pd.DataFrame(analysis.results_truth)\n", + "df_precision, df_picked = analysis.compute_precision(df_picked, df_truth, verbose=verbose)\n", + "print(f\"mean precision: {df_precision['precision'].mean()}\")\n", + "print(f\"mean recall: {df_precision['recall'].mean()}\")\n", + "\n", + "latent_space, ndim = IO.get_latents_cs(latent_file)\n", + "print(f\"latent space dimensionality: {ndim}\")\n", + "print(latent_space.shape)\n", + "for i in range(ndim):\n", + " df_picked[\"latent_{}\".format(i)] = latent_space[:, i]\n", + "df_picked.tail()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "number of FP: 359\n", + "number of TP: 95641\n", + "saved figure to: /home/mjoosten1/projects/roodmus/data/c3c3b/figures/J685_latents_011072.cs_0_1.png\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# latent space scatter plot\n", + "dim1=0\n", + "dim2=1\n", + "\n", + "grid = plot_latent_space_scatter(\n", + " df_picked,\n", + " dim_1=dim1,\n", + " dim_2=dim2,\n", + " pca=False\n", + ")\n", + "df_FP = df_picked[df_picked[\"TP\"]==0]\n", + "print(f\"number of FP: {len(df_FP)}\")\n", + "print(f\"number of TP: {len(df_picked)-len(df_FP)}\")\n", + "ax = grid.figure.get_axes()[0]\n", + "# sns.scatterplot(\n", + "# data=df_FP,\n", + "# x=f\"latent_{dim1}\",\n", + "# y=f\"latent_{dim2}\",\n", + "# color=\"red\",\n", + "# s=10,\n", + "# ax=ax,\n", + "# label=\"FP\",\n", + "# )\n", + "grid.set_axis_labels(f\"Z{dim1}\", f\"Z{dim2}\", fontsize=16)\n", + "grid.figure.get_axes()[0].tick_params(axis=\"both\", which=\"major\", labelsize=14)\n", + "\n", + "outfilename = os.path.join(figures_dir, f\"{os.path.basename(latent_file)}_{dim1}_{dim2}.png\")\n", + "# grid.figure.savefig(outfilename, dpi=600, bbox_inches=\"tight\")\n", + "print(f\"saved figure to: {outfilename}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## panel D\n", + "plotting the latent space with each point coloured by the timepoint in the MD trajectory" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "saved figure to: /home/mjoosten1/projects/roodmus/data/c3c3b/figures/J685_latents_011072.cs_latent_space_scatter_colored_by_closest_pdb_index_pca.png\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# latent space scatter plot, coloured by ground truth frames\n", + "dim1=0\n", + "dim2=1\n", + "\n", + "fig, ax = plot_latent_space_scatter(\n", + " df_picked,\n", + " dim_1=dim1,\n", + " dim_2=dim2,\n", + " color_by=\"closest_pdb_index\",\n", + " palette=\"RdYlBu\",\n", + " pca=False,\n", + ")\n", + "# remove legend and add colorbar for the closest_pdb_index\n", + "ax.legend_.remove()\n", + "S_m = plt.cm.ScalarMappable(cmap=\"RdYlBu\")\n", + "S_m.set_array(df_picked[\"closest_pdb_index\"])\n", + "cbar = plt.colorbar(S_m)\n", + "cbar.set_label(\"Time (ps)\", rotation=270, labelpad=15, fontsize=16) # time in ps\n", + "# change the tick labels on the colorbar to go from 0 to 10 us\n", + "cbar.set_ticks(np.linspace(1, df_picked[\"closest_pdb_index\"].max(), 10))\n", + "xticklabels = [np.round(r, 1) for r in np.linspace(1, df_picked[\"closest_pdb_index\"].max(), 10)*dt]\n", + "cbar.set_ticklabels(xticklabels, fontsize=14)\n", + "ax.set_xlabel(f\"Z{dim1}\", fontsize=16)\n", + "ax.set_ylabel(f\"Z{dim2}\", fontsize=16)\n", + "ax.tick_params(labelsize=14)\n", + "\n", + "# add trajectory to the plot\n", + "# trajectory_file = os.path.join(project_dir, \"cryosparc_P51_J696_series_000\", \"trajectory.txt\")\n", + "# trajectory = np.loadtxt(trajectory_file)\n", + "\n", + "# ax.scatter(trajectory[:, 0], trajectory[:, 1], s=5, c=\"black\", zorder=10)\n", + "# ax.plot(trajectory[:, 0], trajectory[:, 1], c=\"black\", zorder=10, linewidth=0.5)\n", + "# ax.set_aspect(\"equal\")\n", + "\n", + "# add trajectory to the plot\n", + "N_volumes = 500\n", + "pdb_indices = np.unique(df_picked[\"closest_pdb_index\"])\n", + "d_pdbs = len(pdb_indices) // N_volumes\n", + "\n", + "trajectory = np.zeros((N_volumes, ndim))\n", + "for i in range(N_volumes):\n", + " pdb_group = pdb_indices[i*d_pdbs:(i+1)*d_pdbs]\n", + " mean_latent = df_picked[df_picked[\"closest_pdb_index\"].isin(pdb_group)].agg(\n", + " {f\"latent_{i}\": \"mean\" for i in range(ndim)}\n", + " )\n", + " trajectory[i] = mean_latent.values\n", + "\n", + "ax.scatter(trajectory[:, 0], trajectory[:, 1], s=5, c=\"black\", zorder=10)\n", + "ax.plot(trajectory[:, 0], trajectory[:, 1], c=\"black\", zorder=10, linewidth=0.5)\n", + "ax.set_aspect(\"equal\")\n", + "\n", + "outfilename = os.path.join(figures_dir, f\"{os.path.basename(latent_file)}_latent_space_scatter_colored_by_closest_pdb_index_pca.png\")\n", + "# fig.savefig(outfilename, bbox_inches=\"tight\", dpi=600)\n", + "print(f\"saved figure to: {outfilename}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot only the latent space for the first 1/500 th of pdb indices\n", + "pdb_indices = np.unique(df_picked[\"closest_pdb_index\"])\n", + "N_volumes = 500\n", + "d_pdbs = len(pdb_indices) // N_volumes\n", + "\n", + "i = 499\n", + "\n", + "pdb_group = pdb_indices[i*d_pdbs:(i+1)*d_pdbs]\n", + "df_picked_subset = df_picked[df_picked[\"closest_pdb_index\"].isin(pdb_group)]\n", + "grid = plot_latent_space_scatter(\n", + " df_picked_subset,\n", + " dim_1=dim1,\n", + " dim_2=dim2,\n", + " pca=False,\n", + ")\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## panel E\n", + "plotting the correlation matrix between the MD trajectory and sampled volumes from the 3DFlex model" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "saved figure to: /home/mjoosten1/projects/roodmus/data/c3c3b/figures/J685_latents_011072.cs_correlation_matrix.pdf\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot the correlation matrix\n", + "project_dir = \"/home/mjoosten1/projects/roodmus/data/c3c3b\"\n", + "figures_dir = os.path.join(project_dir, \"figures\")\n", + "correlation_matrix_file = os.path.join(project_dir, \"cryosparc_P51_J696_series_000\", \"correlation_matrix.npy\")\n", + "correlation_matrix = np.load(correlation_matrix_file)\n", + "latent_file = os.path.join(project_dir, \"cryoSPARC\", \"J685_latents_011072.cs\")\n", + "\n", + "frames = correlation_matrix.shape[0]\n", + "\n", + "fig, ax = plt.subplots(figsize=(3.5, 3.5))\n", + "ax.imshow(correlation_matrix, cmap=\"coolwarm\")\n", + "yticks = np.linspace(0, frames, 10)\n", + "yticklabels = np.round(np.linspace(0, 10, 10, dtype=float), 1)\n", + "ax.set_yticks(yticks)\n", + "ax.set_yticklabels(yticklabels)\n", + "ax.set_ylabel(\"Time (ps)\", fontsize=16)\n", + "ax.set_xlabel(\"Sampled volume\", fontsize=16)\n", + "cbar = ax.figure.colorbar(ax.get_images()[0], ax=ax, orientation=\"vertical\", pad=0.01, shrink=0.9)\n", + "cbar.ax.tick_params(labelsize=14)\n", + "cbar.ax.set_ylabel(\"Correlation\", fontsize=16, rotation=270, labelpad=20)\n", + "ax.tick_params(axis=\"both\", which=\"major\", labelsize=14)\n", + "\n", + "outfilename = os.path.join(figures_dir, f\"{os.path.basename(latent_file)}_correlation_matrix.pdf\")\n", + "# fig.savefig(outfilename, bbox_inches=\"tight\")\n", + "print(f\"saved figure to: {outfilename}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "roodmus", + "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": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/pyproject.toml b/pyproject.toml index 20d4692..9a58085 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,14 +4,14 @@ build-backend = "setuptools.build_meta" [project] name = "roodmus" -version = "0.0.35" +version = "0.0.47" authors = [ { name="Joel Greer", email="joel.greer@stfc.ac.uk" }, { name="Maarten Joosten", email="m.j.joosten@tudelft.nl" }, ] description = "Synthetic SP micrograph creation and analysis" readme = "README.md" -requires-python = ">=3.10" +requires-python = ">= 3.10" license = {file = "LICENSE"} keywords = ["parakeet", "cryo-em", "micrographs", "analysis", "roodmus", "single-particle"] classifiers = [ @@ -19,14 +19,55 @@ classifiers = [ "Operating System :: OS Independent", ] dependencies = [ - "lxml_html_clean", - "mdtraj", - "glob2", - "seaborn", + "appdirs==1.4.4", + "beautifulsoup4==4.11.2", + "gemmi==0.5.4", + "glob2==0.7", + "matplotlib==3.5.2", "MDAnalysis", + "mdtraj", + "mrcfile==1.5.4", + "numpy<2.0.0", + "pandas", + "pandas-stubs", + "Pillow", + "pydantic", + "PyWavelets", + "PyYAML", + "requests", + "requests-html", + "scikit-image", + "scikit-learn>=1.3.4", + "scipy", + "seaborn==0.12.2", + "setuptools==80.0", + "starfile==0.5.2", + "tqdm==4.64.0", + "types-pytz==2022.7.1.2", + "types-tabulate==0.9.0.1", + "typing_extensions", "umap-learn", - "scikit-learn>=1.3", - "python-parakeet==0.5.1", +] + +[project.optional-dependencies] +dev = [ + "pre-commit>=4.0.0", + "pytest", + "black==26.3.1", + "flake8==7.3.0", + "mypy==1.20.0", + "types-tabulate" +] + +docs = [ + "sphinx", + "sphinx-rtd-theme>=1.2.0" +] + +gpu = [ + "guanaco==0.3.3", + "python-multem==0.4.0", + "python-parakeet==0.6.5", ] [project.scripts] @@ -36,6 +77,3 @@ roodmus = "roodmus.cli:main" "Homepage" = "https://github.com/ccpem/roodmus" "Bug Tracker" = "https://github.com/ccpem/roodmus/issues" -[tool.licensecheck] -using = "requirements:requirements.txt;requirements-dev.txt" -format = "json" diff --git a/requirements-dev.txt b/requirements-dev.txt index dfbb4a2..34f3732 100644 --- a/requirements-dev.txt +++ b/requirements-dev.txt @@ -3,7 +3,6 @@ astunparse==1.6.3 beautifulsoup4==4.11.2 bs4==0.0.1 ccpem-utils==0.0.1.dev8 -certifi @ file:///croot/certifi_1671487769961/work/certifi cfgv==3.3.1 charset-normalizer==3.0.1 click==8.1.3 @@ -20,7 +19,7 @@ fonttools==4.38.0 fsspec==2023.1.0 gemmi==0.5.4 glob2==0.7 -guanaco==0.3.0 +guanaco==0.3.3 h5py==3.8.0 HeapDict==1.0.1 identify==2.5.19 @@ -30,59 +29,62 @@ importlib-metadata==6.0.0 Jinja2==3.1.2 joblib==1.2.0 kiwisolver==1.4.4 +licensecheck locket==1.0.0 lxml==4.9.2 maptools==0.3.5 MarkupSafe==2.1.2 matplotlib==3.5.2 -mdtraj==1.9.7 -mrcfile==1.4.2 +MDAnalysis +mdtraj +mrcfile==1.5.4 msgpack==1.0.4 networkx==3.0 nodeenv==1.7.0 -numpy==1.23.1 +numpy packaging==23.0 pandas==1.5.3 pandas-stubs==1.5.3.230304 parse==1.19.0 partd==1.3.0 Pillow==9.3.0 --e git+ssh://git@gitlab.com/ccpem/ccpem-pipeliner.git@adf40ed2c55c39200924ef39c2d2fe3d6f712885#egg=pipeliner platformdirs==3.1.0 pre-commit==3.1.1 profet==0.0.1 psutil==5.9.4 -pydantic==1.10.4 +pydantic pyee==8.2.2 pyparsing==3.0.9 pypdb==2.2 pyppeteer==1.0.2 pyquery==2.0.0 +pytest python-dateutil==2.8.2 -python-multem==0.3.3 --e git+ssh://git@github.com/rosalindfranklininstitute/parakeet.git@024b86ebf55adf737c1b1116b8adbb59ee7db491#egg=python_parakeet -pytz==2022.7.1 +python-multem==0.4.0 +python-parakeet==0.6.5 PyWavelets==1.4.1 PyYAML==6.0 requests==2.28.2 requests-html==0.10.0 scikit-image==0.19.3 -scikit-learn==1.2.1 +scikit-learn>=1.3.4 scipy==1.9.3 seaborn==0.12.2 six==1.16.0 sortedcontainers==2.4.0 soupsieve==2.3.2.post1 -starfile==0.4.11 +starfile==0.5.2 tblib==1.7.0 threadpoolctl==3.1.0 tifffile==2023.2.3 toolz==0.12.0 tornado==6.2 tqdm==4.64.0 +twine types-pytz==2022.7.1.2 types-tabulate==0.9.0.1 -typing_extensions==4.4.0 +typing_extensions +umap-learn urllib3==1.26.14 virtualenv==20.20.0 w3lib==2.1.1 diff --git a/requirements-noGPU.txt b/requirements-noGPU.txt new file mode 100644 index 0000000..1c87381 --- /dev/null +++ b/requirements-noGPU.txt @@ -0,0 +1,88 @@ +appdirs==1.4.4 +astunparse==1.6.3 +beautifulsoup4==4.11.2 +bs4==0.0.1 +ccpem-utils==0.0.1.dev8 +cfgv==3.3.1 +charset-normalizer==3.0.1 +click==8.1.3 +cloudpickle==2.2.1 +cssselect==1.2.0 +cycler==0.11.0 +dask==2023.1.1 +dask-jobqueue==0.8.1 +distlib==0.3.6 +distributed==2023.1.1 +fake-useragent==1.1.1 +filelock==3.9.0 +fonttools==4.38.0 +fsspec==2023.1.0 +gemmi==0.5.4 +glob2==0.7 +h5py==3.8.0 +HeapDict==1.0.1 +identify==2.5.19 +idna==3.4 +imageio==2.25.0 +importlib-metadata==6.0.0 +Jinja2==3.1.2 +joblib==1.2.0 +kiwisolver==1.4.4 +locket==1.0.0 +lxml==4.9.2 +maptools==0.3.5 +MarkupSafe==2.1.2 +matplotlib==3.5.2 +MDAnalysis +mdtraj +mrcfile==1.5.4 +msgpack==1.0.4 +networkx==3.0 +nodeenv==1.7.0 +numpy +packaging==23.0 +pandas==1.5.3 +pandas-stubs==1.5.3.230304 +parse==1.19.0 +partd==1.3.0 +Pillow==9.3.0 +platformdirs==3.1.0 +pre-commit==3.1.1 +profet==0.0.1 +psutil==5.9.4 +pydantic +pyee==8.2.2 +pyparsing==3.0.9 +pypdb==2.2 +pyppeteer==1.0.2 +pyquery==2.0.0 +pytest +python-dateutil==2.8.2 +PyWavelets==1.4.1 +PyYAML==6.0 +requests==2.28.2 +requests-html==0.10.0 +scikit-image==0.19.3 +scikit-learn>=1.3.4 +scipy==1.9.3 +seaborn==0.12.2 +six==1.16.0 +sortedcontainers==2.4.0 +soupsieve==2.3.2.post1 +starfile==0.5.2 +tblib==1.7.0 +threadpoolctl==3.1.0 +tifffile==2023.2.3 +toolz==0.12.0 +tornado==6.2 +tqdm==4.64.0 +types-pytz==2022.7.1.2 +types-tabulate==0.9.0.1 +typing_extensions +umap-learn +urllib3==1.26.14 +virtualenv==20.20.0 +w3lib==2.1.1 +websockets==10.4 +zict==2.2.0 +zipp==3.12.1 diff --git a/requirements.txt b/requirements.txt index dfbb4a2..6898036 100644 --- a/requirements.txt +++ b/requirements.txt @@ -3,7 +3,6 @@ astunparse==1.6.3 beautifulsoup4==4.11.2 bs4==0.0.1 ccpem-utils==0.0.1.dev8 -certifi @ file:///croot/certifi_1671487769961/work/certifi cfgv==3.3.1 charset-normalizer==3.0.1 click==8.1.3 @@ -20,7 +19,7 @@ fonttools==4.38.0 fsspec==2023.1.0 gemmi==0.5.4 glob2==0.7 -guanaco==0.3.0 +guanaco==0.3.3 h5py==3.8.0 HeapDict==1.0.1 identify==2.5.19 @@ -35,45 +34,44 @@ lxml==4.9.2 maptools==0.3.5 MarkupSafe==2.1.2 matplotlib==3.5.2 -mdtraj==1.9.7 -mrcfile==1.4.2 +MDAnalysis +mdtraj +mrcfile==1.5.4 msgpack==1.0.4 networkx==3.0 nodeenv==1.7.0 -numpy==1.23.1 +numpy packaging==23.0 pandas==1.5.3 pandas-stubs==1.5.3.230304 parse==1.19.0 partd==1.3.0 Pillow==9.3.0 --e git+ssh://git@gitlab.com/ccpem/ccpem-pipeliner.git@adf40ed2c55c39200924ef39c2d2fe3d6f712885#egg=pipeliner platformdirs==3.1.0 pre-commit==3.1.1 profet==0.0.1 psutil==5.9.4 -pydantic==1.10.4 +pydantic pyee==8.2.2 pyparsing==3.0.9 pypdb==2.2 pyppeteer==1.0.2 pyquery==2.0.0 python-dateutil==2.8.2 -python-multem==0.3.3 --e git+ssh://git@github.com/rosalindfranklininstitute/parakeet.git@024b86ebf55adf737c1b1116b8adbb59ee7db491#egg=python_parakeet -pytz==2022.7.1 +python-multem==0.4.0 +python-parakeet==0.6.5 PyWavelets==1.4.1 PyYAML==6.0 requests==2.28.2 requests-html==0.10.0 scikit-image==0.19.3 -scikit-learn==1.2.1 +scikit-learn>=1.3.4 scipy==1.9.3 seaborn==0.12.2 six==1.16.0 sortedcontainers==2.4.0 soupsieve==2.3.2.post1 -starfile==0.4.11 +starfile==0.5.2 tblib==1.7.0 threadpoolctl==3.1.0 tifffile==2023.2.3 @@ -82,7 +80,8 @@ tornado==6.2 tqdm==4.64.0 types-pytz==2022.7.1.2 types-tabulate==0.9.0.1 -typing_extensions==4.4.0 +typing_extensions +umap-learn urllib3==1.26.14 virtualenv==20.20.0 w3lib==2.1.1 diff --git a/src/roodmus/analysis/plot_classes.py b/src/roodmus/analysis/plot_classes.py index 9b00eb7..e51f875 100644 --- a/src/roodmus/analysis/plot_classes.py +++ b/src/roodmus/analysis/plot_classes.py @@ -32,7 +32,11 @@ import seaborn as sns from scipy.ndimage import zoom -from roodmus.analysis.utils import load_data, plotDataFrame +from roodmus.analysis.utils import ( + load_data, + plotDataFrame, + convert_truth_idxs_to_df, +) def add_arguments(parser): @@ -136,8 +140,16 @@ def __init__( self.dpi = dpi self.pdf = pdf - def setup_plot_data(self, df_picked: pd.DataFrame): + def setup_plot_data( + self, + df_picked: pd.DataFrame, + unique_truth_pdb_idxs: list[str] = [], + ): self.plot_data = {"plot_classes": {"df_picked": df_picked}} + if unique_truth_pdb_idxs: + self.plot_data["frame_distribution"][ + "truth_pdb_idxs" + ] = convert_truth_idxs_to_df(unique_truth_pdb_idxs) def setup_plot_data_empty(self): self.plot_data = {"plot_classes": {"df_picked": None}} @@ -379,7 +391,7 @@ def main(args): print(f"meta_files in df: {df_picked['metadata_filename'].unique()}") # compute the precision for the picked particles - _, df_picked = analysis.compute_precision( + _, df_picked, unique_truth_pdb_idxs = analysis.compute_precision( df_picked, df_truth, verbose=args.verbose ) diff --git a/src/roodmus/analysis/plot_ctf.py b/src/roodmus/analysis/plot_ctf.py index a30a6db..3e522d7 100644 --- a/src/roodmus/analysis/plot_ctf.py +++ b/src/roodmus/analysis/plot_ctf.py @@ -386,7 +386,7 @@ def plot_defocus_scatter( # plot the results plt.rcParams["font.size"] = 24 - plt.style.use("seaborn-whitegrid") + # plt.style.use("seaborn-whitegrid") fig, ax = plt.subplots(1, 2, figsize=(16, 8), sharey=True) sns.scatterplot( x="defocus_truth", @@ -442,7 +442,7 @@ def plot_defocus_scatter( ), ) sm._A = [] - cbar = plt.colorbar(sm) + cbar = plt.colorbar(sm, ax=ax) cbar.set_label("Micrograph") fig.tight_layout() return fig, ax diff --git a/src/roodmus/analysis/plot_frames.py b/src/roodmus/analysis/plot_frames.py index 6917fbb..95e4650 100644 --- a/src/roodmus/analysis/plot_frames.py +++ b/src/roodmus/analysis/plot_frames.py @@ -29,7 +29,11 @@ import pandas as pd import seaborn as sns -from roodmus.analysis.utils import load_data, plotDataFrame +from roodmus.analysis.utils import ( + load_data, + plotDataFrame, + convert_truth_idxs_to_df, +) def add_arguments(parser): @@ -118,10 +122,15 @@ def setup_plot_data( self, df_truth: pd.DataFrame, df_picked: pd.DataFrame, + unique_truth_pdb_idxs: list[str] = [], ): self.plot_data = {"frame_distribution": {}} self.plot_data["frame_distribution"]["df_truth"] = df_truth self.plot_data["frame_distribution"]["df_picked"] = df_picked + if unique_truth_pdb_idxs: + self.plot_data["frame_distribution"][ + "truth_pdb_idxs" + ] = convert_truth_idxs_to_df(unique_truth_pdb_idxs) def setup_plot_data_empty( self, @@ -279,7 +288,7 @@ def main(args): ) # data frame containing the ground-truth particles # compute the precision for the picked particles - _, df_picked = analysis.compute_precision( + _, df_picked, unique_truth_pdb_idxs = analysis.compute_precision( df_picked, df_truth, verbose=args.verbose ) @@ -290,7 +299,11 @@ def main(args): dpi=args.dpi, pdf=args.pdf, ) - frame_distribution.setup_plot_data(df_truth, df_picked) + frame_distribution.setup_plot_data( + df_truth, + df_picked, + unique_truth_pdb_idxs=unique_truth_pdb_idxs, + ) frame_distribution.make_and_save_plots(overwrite_data=True) diff --git a/src/roodmus/analysis/plot_picking.py b/src/roodmus/analysis/plot_picking.py index 937c932..731c622 100644 --- a/src/roodmus/analysis/plot_picking.py +++ b/src/roodmus/analysis/plot_picking.py @@ -2630,7 +2630,7 @@ def main(args): if plot_type == "precision": # first need to compute the precision statistics - df_precision, _ = analysis.compute_precision( + df_precision, _, _ = analysis.compute_precision( df_picked, df_truth, verbose=args.verbose ) diff --git a/src/roodmus/analysis/utils.py b/src/roodmus/analysis/utils.py index 5570051..e0f07e0 100644 --- a/src/roodmus/analysis/utils.py +++ b/src/roodmus/analysis/utils.py @@ -33,8 +33,7 @@ from tqdm import tqdm import pandas as pd import pickle - -from pipeliner.starfile_handler import DataStarFile +import gemmi class IO(object): @@ -71,7 +70,9 @@ def get_ugraph_cs(self, metadata_cs: np.recarray) -> List[str]: """ if "location/micrograph_path" in metadata_cs.dtype.names: - ugraph_paths = metadata_cs["location/micrograph_path"].tolist() + ugraph_paths: List[np.bytes_] = metadata_cs[ + "location/micrograph_path" + ].tolist() # Cryosparc may add suffixes to the micrograph # name, such as _patch_aligned_doseweighted.mrc. # We remove these suffixes to match the micrograph @@ -249,29 +250,31 @@ def get_latents_cs(self, latent_file: str): # Loading .star files and parsing the ctf parameters, # the particle positions and orientations @classmethod - def load_star(self, star_path): + def load_star(self, star_path: str) -> gemmi.cif.Document: """Load metadata from .star file. Args: star_path (str): Relion metadata. Returns: - DataStarFile: Loaded Relion metadata. + gemmi.cif.Document: Loaded Relion metadata. """ - return DataStarFile(star_path) + return gemmi.cif.read(star_path) @classmethod - def get_ugraph_star(self, metadata_star): + def get_ugraph_star(self, metadata_star: gemmi.cif.Document) -> List[str]: """Grab micrograph file paths from Relion metadata. Args: - metadata_star (DataStarFile): Loaded Relion metadata. + metadata_star (gemmi.cif.Block): Loaded Relion metadata. Returns: ugraph_paths (List[str]): Micrograph file paths. """ - ugraph_paths = metadata_star.column_as_list( - "particles", "_rlnMicrographName" + ugraph_paths = list( + metadata_star.find_block("particles").find_loop( + "_rlnMicrographName" + ) ) # convert to basename and remove index ugraph_paths = [ @@ -282,38 +285,40 @@ def get_ugraph_star(self, metadata_star): @classmethod def get_ctf_star( self, - metadata_star, + metadata_star: gemmi.cif.Document, ) -> np.ndarray: """Grab ctf information from Relion metadata. Args: - metadata_star (DataStarFile): Loaded Relion metadata. + metadata_star (gemmi.cif.Document): Loaded Relion metadata. Returns: np.ndarray: ctf data. """ kV = [ float(r) - for r in metadata_star.column_as_list("optics", "_rlnVoltage") + for r in metadata_star.find_block("optics").find_loop( + "_rlnVoltage" + ) ] Cs = [ float(r) - for r in metadata_star.column_as_list( - "optics", "_rlnSphericalAberration" + for r in metadata_star.find_block("optics").find_loop( + "_rlnSphericalAberration" ) ] amp = [ float(r) - for r in metadata_star.column_as_list( - "optics", "_rlnAmplitudeContrast" + for r in metadata_star.find_block("optics").find_loop( + "_rlnAmplitudeContrast" ) ] defocusU = np.array( [ float(r) - for r in metadata_star.column_as_list( - "particles", "_rlnDefocusU" + for r in metadata_star.find_block("particles").find_loop( + "_rlnDefocusU" ) ], dtype=float, @@ -321,8 +326,8 @@ def get_ctf_star( defocusV = np.array( [ float(r) - for r in metadata_star.column_as_list( - "particles", "_rlnDefocusV" + for r in metadata_star.find_block("particles").find_loop( + "_rlnDefocusV" ) ] ).tolist() @@ -340,45 +345,53 @@ def get_ctf_star( ) @classmethod - def get_positions_star(self, metadata_star) -> np.ndarray: + def get_positions_star( + self, metadata_star: gemmi.cif.Document + ) -> np.ndarray: """Grab reconstructed particle positions from Relion metadata. Args: - metadata_star (DataStarFile): Loaded Relion metadata. + metadata_star (gemmi.cif.Document): Loaded Relion metadata. Returns: ps (np.ndarray): particle position data. """ x = [ float(r) - for r in metadata_star.column_as_list( - "particles", "_rlnCoordinateX" + for r in metadata_star.find_block("particles").find_loop( + "_rlnCoordinateX" ) ] y = [ float(r) - for r in metadata_star.column_as_list( - "particles", "_rlnCoordinateY" + for r in metadata_star.find_block("particles").find_loop( + "_rlnCoordinateY" ) ] pos = np.stack([x, y], axis=1) return pos @classmethod - def get_orientations_star(self, metadata_star) -> np.ndarray: + def get_orientations_star( + self, metadata_star: gemmi.cif.Document + ) -> np.ndarray: """Grab reconstructed orientations from Relion metadata. Args: - metadata_star (DataStarFile): Loaded Relion metadata. + metadata_star (gemmi.cif.Document): Loaded Relion metadata. Returns: euler (np.ndarray): particle orientation data. """ - euler_phi = metadata_star.column_as_list("particles", "_rlnAngleRot") - euler_theta = metadata_star.column_as_list( - "particles", "_rlnAngleTilt" + euler_phi = list( + metadata_star.find_block("particles").find_loop("_rlnAngleRot") + ) + euler_theta = list( + metadata_star.find_block("particles").find_loop("_rlnAngleTilt") + ) + euler_psi = list( + metadata_star.find_block("particles").find_loop("_rlnAnglePsi") ) - euler_psi = metadata_star.column_as_list("particles", "_rlnAnglePsi") num_particles = np.max( [len(euler_phi), len(euler_theta), len(euler_psi)] @@ -399,19 +412,21 @@ def get_orientations_star(self, metadata_star) -> np.ndarray: return euler @classmethod - def get_class2D_star(self, metadata_star): + def get_class2D_star( + self, metadata_star: gemmi.cif.Document + ) -> np.ndarray | None: """Grab 2D class from Relion metadata. Args: - metadata_star (DataStarFile): Loaded Relion metadata. + metadata_star (gemmi.cif.Document): Loaded Relion metadata. Returns: - class2d (np.ndarray): 2D class data. + class2d (np.ndarray | None): 2D class data. """ class2d = [ int(r) - for r in metadata_star.column_as_list( - "particles", "_rlnClassNumber" + for r in metadata_star.find_block("particles").find_loop( + "_rlnClassNumber" ) ] if class2d: @@ -518,6 +533,78 @@ def rot2euler(self, r): return alpha, beta, gamma +def get_closest_pdb_index( + closest_pdbs: pd.core.series.Series, + index_prefix=False, +) -> Tuple[pd.core.series.Series, List[str]]: + """Get the index of the truth particle which is closest to the given + picked particle. + + Args: + closest_pdbs (pandas.core.series.Series): picked particle + column with the closest + + Raises: + ValueError: Try/except catches truth conformation filenames not + of format filename_XXX.pdb + + Returns: + Tuple[List[int], List[str]]: the indices which map each picked + particle to the closest truth particle and the sorted list of truth + particle pdb filenames if we needed to create a list of indices. + + """ + # empty list if we don't need to create indices for each truth particle + # pdb + unique_pdbs = [] + + try: + closest_pdb_index = closest_pdbs.apply( + lambda x: int(os.path.basename(x).split("_")[-1].split(".")[0]) + ) + if index_prefix: + # if using prefix as index, instead do the following + closest_pdb_index = closest_pdbs.apply( + lambda x: int(os.path.basename(x).split("_")[0].split(".")[0]) + ) + # ValueError: invalid literal for int() with base 10: '6ttf' + except ValueError: + # find all pdb filenames and sort into a list + unique_pdbs = sorted(np.unique(closest_pdbs).tolist()) + # iterate through closest_pdb filenames and grab the index + # from the sorted list of pdb filenames for each + closest_pdb_index = pd.Series( + int(unique_pdbs.index(closest_pdb)) for closest_pdb in closest_pdbs + ) + + return closest_pdb_index, unique_pdbs + + +def convert_truth_idxs_to_df(truth_idxs: List[str]) -> pd.DataFrame: + """Convert unique_truth_pdb_idxs to pd.DataFrame to allow it to be + saved as csv by default along with any plots made using the indexes. + + Args: + truth_idxs (List[str]): sorted list of truth particle pdb filenames + + Returns: + pd.DataFrame: dataframe allowing truth_idxs to be saved to csv in + plotDataFrame class along with the dataframes used to create plots. + Allows easy interpretation of plots from indices. + """ + if len(truth_idxs) < 1: + raise ValueError( + "Truth particles have not been assigned indices and must" + " therefore already have suitable pdb filename formats." + " No need to call this function!" + ) + idxs = [str(x) for x in list(range(0, len(truth_idxs), 1))] + truth_pdb_idxs = {} + truth_pdb_idxs["truth_particle_filename"] = truth_idxs + truth_pdb_idxs["truth_particle_index"] = idxs + return pd.DataFrame(truth_pdb_idxs) + + class load_data(object): def __init__( self, @@ -1442,6 +1529,7 @@ def _extract_from_config( for instance in molecules["instances"]: position = instance["position"] orientation = instance["orientation"] # rotation vector + orientation = -np.array(orientation) # invert the orientation # convert to euler angles # euler = geom.rot2euler(geom.expmap(np.array(orientation))) euler = R.from_rotvec(orientation).as_euler("ZYZ") @@ -1787,12 +1875,16 @@ def _match_particles( matched_truth_dfs.append(ugraph_truth.iloc[t_match]) # Extract the unmatched picked particles - p_list = np.arange(len(picked_pos_x), dtype=int).tolist() + p_list: List[int] = np.arange( + len(picked_pos_x), dtype=int + ).tolist() p_unmatched = list(set(p_list).difference(p_match)) unmatched_picked_dfs.append(ugraph_picked.iloc[p_unmatched]) # Extract the unmatched truth particles - t_list = np.arange(len(truth_pos_x), dtype=int).tolist() + t_list: List[int] = np.arange( + len(truth_pos_x), dtype=int + ).tolist() t_unmatched = list(set(t_list).difference(t_match)) unmatched_truth_dfs.append(ugraph_truth.iloc[t_unmatched]) @@ -1953,6 +2045,7 @@ def compute_precision( self, results_picking: pd.DataFrame, results_truth: pd.DataFrame, + index_prefix=False, verbose: bool = False, ): """This function produces another data frame containing the number @@ -1970,8 +2063,13 @@ def compute_precision( calculation. Defaults to False. Returns: - pandas.DataFrame: a data frame containing the precision, recall - and multiplicity for each micrograph. + Tuple[pandas.DataFrame, pandas.DataFrame, List[str]]: a data frame + containing the precision, recall and multiplicity for each + micrograph. Followed by an updated version of results_picking with + new fields relating the picked particle and closest truth particle + as well as a alphanumeric sorted list of truth pdb filenames and + so that results_picking["losest_pdb_index"] indices can be mapped + to the corresponding truth pdb filename. """ # define results data frame @@ -2079,7 +2177,9 @@ def compute_precision( 0 # the number of false positives in the current micrograph ) for particle in range(len(pos_picked_in_ugraph)): - TP += float(np.any(sdm[particle] < self.particle_diameter / 2)) + TP += float( + np.any(sdm[particle] <= self.particle_diameter / 2) + ) FP += float(np.all(sdm[particle] > self.particle_diameter / 2)) TP_all.append( @@ -2153,9 +2253,13 @@ def compute_precision( results_picking["closest_dist"] = closest_dist_all results_picking["closest_particle"] = closest_particle_all results_picking["closest_pdb"] = closest_pdb_all - results_picking["closest_pdb_index"] = results_picking[ - "closest_pdb" - ].apply(lambda x: int(x.split("_")[-1].split(".")[0])) + ( + results_picking["closest_pdb_index"], + unique_pdbs, + ) = get_closest_pdb_index( + results_picking["closest_pdb"], + index_prefix=index_prefix, + ) # set the closest_pdb_index to np.nan if the particle is not # closer to a truth particle thatn the particle diameter results_picking.loc[ @@ -2164,7 +2268,11 @@ def compute_precision( ] = np.nan # convert the results data frame to a pandas data frame - return pd.DataFrame(results_precision), results_picking + return ( + pd.DataFrame(results_precision), + results_picking, + unique_pdbs, + ) def compute_overlap( self, diff --git a/src/roodmus/heterogeneity/het_ensemblecomparison.py b/src/roodmus/heterogeneity/het_ensemblecomparison.py index 7052efb..5df1273 100644 --- a/src/roodmus/heterogeneity/het_ensemblecomparison.py +++ b/src/roodmus/heterogeneity/het_ensemblecomparison.py @@ -760,7 +760,8 @@ def main(args): ) # creates the class df_picked = pd.DataFrame(analysis.results_picking) df_truth = pd.DataFrame(analysis.results_truth) - _, df_picked = analysis.compute_precision( + # TODO update to _, df_picked, truth_idx if ever required + _, df_picked, _ = analysis.compute_precision( df_picked, df_truth, verbose=args.verbose, diff --git a/src/roodmus/simulation/configuration.py b/src/roodmus/simulation/configuration.py index 92d1978..ae3db89 100644 --- a/src/roodmus/simulation/configuration.py +++ b/src/roodmus/simulation/configuration.py @@ -24,6 +24,7 @@ import os +import re from typing import Any import yaml @@ -192,6 +193,15 @@ def _set_config(self, args): args.margin_z, ) + # sample->motion ISN'T INCLUED IN PARAKEET VERSION + self.config.sample.motion = config.SampleMotion() + self.config.sample.motion.global_drift = [ + float(r) for r in args.global_drift_vector + ] + self.config.sample.motion.interaction_range = args.interaction_range + self.config.sample.motion.velocity = args.velocity + self.config.sample.motion.noise_magnitude = args.noise_magnitude + # sample->sputter (not yet supported as user input) # self.config.sample.sputter.element = # self.config.sample.sputter.thickness = @@ -348,18 +358,42 @@ def add_molecules( self._save_config() - def update_config(self, sample): + def update_config(self, sample, verbose=False): """ Updates the configuration file with the generated positions and orientations sample (parakeet.sample.Sample): Sample object from Parakeet """ - frame_idx = 0 - for molecule in sample.molecules: - _, positions, orientations = sample.get_molecule(molecule) + mciter = sample.iter_molecules() + for i in mciter: + print(i) + + for frame_idx, molecule in enumerate(sample.molecules): + assert compare_roodmus_parakeet_fname( + molecule, + self.config.sample.molecules.local[frame_idx].filename, + ), ( + "Molecule being updated in YAML is not the same as that " + "retrieved from Parakeet's sample!\n{}\n{}".format( + self.config.sample.molecules.local[frame_idx].filename, + molecule, + ) + ) + _, positions, orientations = sample.get_molecule(molecule) self.config.sample.molecules.local[frame_idx].instances = [] + if verbose: + print( + "Check mol {}: {}\n{} vs {}\n pos: {}\nori: {}".format( + frame_idx, + molecule, + molecule, + self.config.sample.molecules.local[frame_idx].filename, + positions, + orientations, + ) + ) for position, orientation in zip(positions, orientations): self.config.sample.molecules.local[frame_idx].instances.append( @@ -371,6 +405,25 @@ def update_config(self, sample): self.config.sample.molecules.local[frame_idx].instances[ -1 ].orientation = [float(o) for o in orientation] - - frame_idx += 1 self._save_config() + + +def compare_roodmus_parakeet_fname( + parakeet: str, + roodmus: str, + verbose: bool = True, +) -> bool: + # parakeet sample fnames are stored with / and . replaced by _ + # so just compare the basename and put back the . with reference + # to the filename stored by roodmus' config class + r_fname = os.path.basename(roodmus) + p_fname = parakeet[-len(r_fname) :] + # put the .'s back in + for c in [match.start() for match in re.finditer(r"\.", r_fname)]: + p_fname_temp = list(p_fname) + p_fname_temp[c] = "." + p_fname = "".join(p_fname_temp) + if verbose: + print(r_fname) + print(p_fname) + return p_fname == r_fname diff --git a/src/roodmus/simulation/run_parakeet.py b/src/roodmus/simulation/run_parakeet.py index 25314d0..852baf6 100644 --- a/src/roodmus/simulation/run_parakeet.py +++ b/src/roodmus/simulation/run_parakeet.py @@ -94,6 +94,12 @@ def add_arguments( default=False, action="store_true", ) + run_parakeet_parser.add_argument( + "--delete_hdf", + help="delete hdf5 images after ugraph generated", + default=False, + action="store_true", + ) run_parakeet_parser.add_argument( "--orientations", help="how to generate the orientations \ @@ -118,6 +124,13 @@ def add_arguments( default=False, action="store_true", ) + run_parakeet_parser.add_argument( + "--from_yaml", + action="store_true", + help="use .yaml files in mrc dir to generate micrographs instead of \ + generating new ones", + required=False, + ) options_microscope_beam = run_parakeet_parser.add_argument_group( "microscope_beam" @@ -699,6 +712,43 @@ def add_arguments( required=False, ) + options_sample_motion = options_sample.add_argument_group("motion") + options_sample_motion.add_argument( + "--global_drift_magnitude", + help="magnitude of global drift in A/frame", + type=float, + default=0, + required=False, + ) + options_sample_motion.add_argument( + "--global_drift_std", + help="standard deviation on direction of global drift in rad", + type=float, + default=0, + required=False, + ) + options_sample_motion.add_argument( + "--interaction_range", + help="radius within which to average particle directions", + type=float, + default=700, + required=False, + ) + options_sample_motion.add_argument( + "--velocity", + help="local velocity magnitude of particles in A/frame", + type=float, + default=1, + required=False, + ) + options_sample_motion.add_argument( + "--noise_magnitude", + help="noise on directional alilgnment of particles", + type=float, + default=0, + required=False, + ) + # scan args options_scan = run_parakeet_parser.add_argument_group("scan") options_scan.add_argument( @@ -978,15 +1028,29 @@ def sample_defocus(c_10: float, c_10_stddev: float) -> float: return np.random.normal(c_10, c_10_stddev) +def sample_global_drift_vector( + global_drift_magnitude: float, global_drift_std: float +) -> np.ndarray: + """ + based one the specified magnitude and standard deviation sample + a random vector for global drift + """ + + random_direction = np.random.normal(0, global_drift_std) + rx = np.cos(random_direction) + ry = np.sin(random_direction) + return global_drift_magnitude * np.array([rx, ry]) + + def get_pdb_files(pdb_dir: str) -> List[str]: """Grab a list of molecule/structure definition files (such as PDBs) to add - to micrographs + to micrographs and alphanumerically sort them Args: pdb_dir (str): Path to directory containing all molecules to use Returns: - list[str]: List of molecules file paths + list[str]: List of molecules file paths in alphanumerical order """ pdb_dir = os.path.abspath(pdb_dir) pdb_files = [] @@ -1000,7 +1064,22 @@ def get_pdb_files(pdb_dir: str) -> List[str]: elif file.endswith(".cif.gz"): # untested as to whether parakeet can unpack .cif.gz pdb_files.append(os.path.join(pdb_dir, file)) - return pdb_files + + # ensure list is not empty to avoid zero divs in get_instances + if len(pdb_files) == 0: + raise ValueError( + "Found 0 atomic structure (pdb/cif/mmcif/cif.gz) files in" + " directory {}!".format(pdb_dir) + ) + + """ + # check that single filetype is used for structures + ftype = lambda x: os.path.basename(x).split(".")[-1] + assert len( + np.unique([ftype(x) for x in pdb_files]) + )==1, "Multiple structure filetypes provided" + """ + return sorted(pdb_files) def get_instances( @@ -1053,7 +1132,13 @@ def get_instances( def simulate_image( - config, mrc_dir, mrc_filename, write_mtf=False, overwrite_mtf=False + config, + mrc_dir, + mrc_filename, + write_mtf=False, + overwrite_mtf=False, + delete_hdf=False, + verbose=False, ): """Call parakeet through the Python API and simulate a new micrograph based on the provided config file. @@ -1079,7 +1164,7 @@ def simulate_image( # run_parakeet session or if overwrite requested if write_mtf: metadata_exporter = parakeet.metadata.RelionMetadataExporter( - config.config, sample, mrc_dir + config.config, sample, None, mrc_dir ) if not os.path.exists( os.path.join( @@ -1125,23 +1210,30 @@ def simulate_image( ) # remove the intermediate files - os.system( - "rm {} {} {} {}".format( - config.sample_filename, - config.exit_wave_filename, - config.optics_filename, - config.image_filename, + if delete_hdf: + os.system( + "rm {} {} {} {}".format( + config.sample_filename, + config.exit_wave_filename, + config.optics_filename, + config.image_filename, + ) ) - ) # update the config from the sample and save/overwrite it - config.update_config(sample) + config.update_config(sample, verbose=verbose) return def simulate_image_parallel( - config, mrc_dir, leading_zeros=6, write_mtf=False, overwrite_mtf=False + config, + mrc_dir, + leading_zeros=6, + write_mtf=False, + overwrite_mtf=False, + delete_hdf=False, + verbose=False, ): mrc_filename = os.path.join( mrc_dir, f"{config.image_index}".zfill(leading_zeros) + ".mrc" @@ -1152,6 +1244,8 @@ def simulate_image_parallel( mrc_filename, write_mtf=write_mtf, overwrite_mtf=overwrite_mtf, + delete_hdf=delete_hdf, + verbose=verbose, ) return @@ -1183,9 +1277,23 @@ def main(args): # the script will look at the files in the output directory # and continue with the numbering from the last image - images_in_directory = len( + images = sorted( [r for r in os.listdir(args.mrc_dir) if r.endswith(".mrc")] ) + images_in_directory = len(images) + # check last of alphanumeric ordered mrc fnames is same + # index as the len of list of fnames + # relies on XXXXXX.mrc convention + if images_in_directory > 0: + last_index = os.path.basename(images[-1]).split(".")[0] + assert images_in_directory - 1 == int(last_index), ( + "Ugraph names are not labelled with base-0 indices." + "{} ugraphs with last index {}".format( + images_in_directory, last_index + ) + ) + + # get alphanumeric ordered structures to simulate frames = get_pdb_files(args.pdb_dir) # loop over the number of images to generate config files @@ -1203,6 +1311,11 @@ def main(args): args.mrc_dir, f"{n_image}".zfill(args.leading_zeros) + ".yaml" ) + # sample a drift vector for the micrograph + args.global_drift_vector = sample_global_drift_vector( + args.global_drift_magnitude, args.global_drift_std + ) + # initialise the configuration # the .h5 files will be saved to _sample.h5 in the mrc_dir config = Configuration( @@ -1254,6 +1367,8 @@ def main(args): mrc_filename, write_mtf=i == 0, overwrite_mtf=args.overwrite_metadata, + delete_hdf=args.delete_hdf, + verbose=args.verbose, ) progressbar.update(1) @@ -1275,6 +1390,8 @@ def main(args): args.leading_zeros, write_mtf=i == 0, overwrite_mtf=args.overwrite_metadata, + delete_hdf=args.delete_hdf, + verbose=args.verbose, ) for i, config in enumerate(list_of_configs) ) diff --git a/src/roodmus/simulation/write_starfile.py b/src/roodmus/simulation/write_starfile.py index 567834d..b5dbe34 100644 --- a/src/roodmus/simulation/write_starfile.py +++ b/src/roodmus/simulation/write_starfile.py @@ -88,6 +88,13 @@ def add_arguments( help="Pixel size of micrographs (in Angstroms)", ) + write_starfile_parser.add_argument( + "--pp_defoci", + action="store_true", + help="Allow truth particles to include particle ground truth \ + z position in defocus to get per-particle defoci", + ) + write_starfile_parser.add_argument( "--tqdm", action="store_true", @@ -380,6 +387,7 @@ def __init__( pixel_size, optics_group, extract_dir=None, + per_particle_defoci=False, enable_progressbar=False, ): self.cif_document = cif.Document() @@ -392,8 +400,8 @@ def __init__( "_rlnImageName", "_rlnMicrographName", "_rlnOpticsGroup", - "_rlnCtfMaxResolution", - "_rlnCtfFigureOfMerit", + # "_rlnCtfMaxResolution", + # "_rlnCtfFigureOfMerit", "_rlnDefocusU", "_rlnDefocusV", "_rlnDefocusAngle", @@ -405,11 +413,10 @@ def __init__( "_rlnAngleTilt", "_rlnOriginXAngst", "_rlnOriginYAngst", - "_rlnNormCorrection", - "_rlnLogLikeliContribution", - "_rlnMaxValueProbDistribution", - "_rlnNrOfSignificantSamples", - "_rlnRandomSubset", + # "_rlnNormCorrection", + # "_rlnLogLikeliContribution", + # "_rlnMaxValueProbDistribution", + # "_rlnNrOfSignificantSamples", ] self.particles = self.cif_document.add_new_block("particles") self.loop = self.particles.init_loop(prefix="", tags=self.tags) @@ -419,6 +426,7 @@ def __init__( self.pixel_size = pixel_size self.optics_group = optics_group self.extract_dir = extract_dir + self.per_particle_defoci = per_particle_defoci self.enable_progressbar = enable_progressbar def parse_df(self, df_particles): @@ -431,6 +439,41 @@ def parse_df(self, df_particles): euler_phi, euler_psi, euler_theta, defocusU, defocusV, Class2D """ + # check if the columns defocusU and defocusV are present + # if the columns are not present, but defocus is present, + # make a new column for defocusU and defocusV and copy the + # values from defocus + if ( + "defocusU" not in df_particles.columns + and "defocus" in df_particles.columns + ): + df_particles["defocusU"] = df_particles["defocus"] + if self.per_particle_defoci: + # Adjust defocus - should only occur if ground truth + # is utilised. Assumes defocus plane is centre sample z + # and sample and microscope z axes co-align. + df_particles["defocusU"] = df_particles["defocusU"] - ( + df_particles["position_z"] + - (df_particles["ice_thickness"] / 2.0) + ) + if ( + "defocusV" not in df_particles.columns + and "defocus" in df_particles.columns + ): + df_particles["defocusV"] = df_particles["defocus"] + if self.per_particle_defoci: + # Adjust defocus - should only occur if ground truth + # is utilised. Assumes defocus plane is centre sample z + # and sample and microscope z axes co-align. + df_particles["defocusV"] = df_particles["defocusV"] - ( + df_particles["position_z"] + - (df_particles["ice_thickness"] / 2.0) + ) + + # by convention it appears that defocus is +ve in RELION + df_particles["defocusU"] = df_particles["defocusU"].abs() + df_particles["defocusV"] = df_particles["defocusV"].abs() + progressbar = tqdm( total=len(df_particles), desc="Writing starfiles", @@ -459,12 +502,12 @@ def parse_df(self, df_particles): str(row["position_y"] * self.pixel_size), "1", # figure of merit not used str(row.get("Class2D", "0")), - str(np.rad2deg(row.get("euler_phi", "0"))), + str(np.rad2deg(row.get("euler_psi", 0))), # corr to psi image_name, micrograph_filename, str(self.optics_group), - "0", # CTF max resolution not used - "0", # CTF figure of merit not used + # "0", # CTF max resolution not used + # "1", # CTF figure of merit not used str(row.get("defocusU", "0")), str(row.get("defocusV", "0")), "0", # defocus angle not used @@ -472,15 +515,14 @@ def parse_df(self, df_particles): "1", # CTF scalefactor defaults to 1 "0", # phase shift not used "1", # group number not used - str(np.rad2deg(row.get("euler_psi", "0"))), - str(np.rad2deg(row.get("euler_theta", "0"))), + str(np.rad2deg(row.get("euler_phi", 0))), # corr to phi + str(np.rad2deg(row.get("euler_theta", 0))), "0", # origin x not used "0", # origin y not used - "0", # norm correction not used - "0", # log likelihood contribution not used - "0", # max value prob distribution not used - "1", # nr of significant samples not used - "1", # random subset not used + # "0", # norm correction not used + # "0", # log likelihood contribution not used + # "0", # max value prob distribution not used + # "1", # nr of significant samples not used ] ) progressbar.update(1) @@ -672,6 +714,7 @@ def main(args): args.pixel_size, args.optics_group, args.extract_dir, + args.pp_defoci, args.tqdm, ) starfile.parse_df(df_particles) diff --git a/tests/integration/fixtures/analysis_test_outputs/alignment_star/particles_picked_pose_distribution.png b/tests/integration/fixtures/analysis_test_outputs/alignment_star/particles_picked_pose_distribution.png index 40e34b2..5bca8d5 100644 Binary files a/tests/integration/fixtures/analysis_test_outputs/alignment_star/particles_picked_pose_distribution.png and b/tests/integration/fixtures/analysis_test_outputs/alignment_star/particles_picked_pose_distribution.png differ diff --git a/tests/integration/fixtures/analysis_test_outputs/alignment_star/plot_picked_pose_distribution/df_picked.csv b/tests/integration/fixtures/analysis_test_outputs/alignment_star/plot_picked_pose_distribution/df_picked.csv index e07a785..528d3e1 100644 --- a/tests/integration/fixtures/analysis_test_outputs/alignment_star/plot_picked_pose_distribution/df_picked.csv +++ b/tests/integration/fixtures/analysis_test_outputs/alignment_star/plot_picked_pose_distribution/df_picked.csv @@ -1,263 +1,263 @@ ,metadata_filename,ugraph_filename,position_x,position_y,euler_phi,euler_theta,euler_psi,ugraph_shape,defocusU,defocusV,class2D -0,tests/integration/fixtures/analysis_test_inputs/relion_subset/Extract/job013/particles.star,000000.mrc,3503.0,2772.0,-2.951633810329534,0.8871101926171462,2.829301065063373,"(4000, 4000)",15287.149414,15261.893555,1 -1,tests/integration/fixtures/analysis_test_inputs/relion_subset/Extract/job013/particles.star,000000.mrc,1105.0,2396.0,0.4140657514674891,0.4731530355357162,-1.4172424337373608,"(4000, 4000)",15287.149414,15261.893555,1 -2,tests/integration/fixtures/analysis_test_inputs/relion_subset/Extract/job013/particles.star,000000.mrc,1375.0,1903.0,2.3863471474156492,2.7675906733024465,0.9045458049517668,"(4000, 4000)",15287.149414,15261.893555,1 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b/tests/integration/fixtures/analysis_test_outputs/frames_star/frame_distribution/df_picked.csv index 44e5ae6..1de4229 100644 --- a/tests/integration/fixtures/analysis_test_outputs/frames_star/frame_distribution/df_picked.csv +++ b/tests/integration/fixtures/analysis_test_outputs/frames_star/frame_distribution/df_picked.csv @@ -1,263 +1,263 @@ ,metadata_filename,ugraph_filename,position_x,position_y,euler_phi,euler_theta,euler_psi,ugraph_shape,defocusU,defocusV,class2D,TP,closest_dist,closest_particle,closest_pdb,closest_pdb_index -0,tests/integration/fixtures/analysis_test_inputs/relion_subset/Select/job009/particles.star,000000.mrc,1946.0,938.0,0.0,0.0,2.126303962269734,"(4000, 4000)",15287.149414,15261.893555,93,True,8.917601176375856,112,/mnt/parakeet_storage4/ConformationSampling/DESRES-Trajectory_sarscov2-11021571-all-glueCA/even_sampling_8334/conformation_003142.pdb,3142.0 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b/tests/integration/fixtures/analysis_test_outputs/picking_star/label_truth_and_picked/df_truth.csv index b72f60e..54e6965 100644 --- a/tests/integration/fixtures/analysis_test_outputs/picking_star/label_truth_and_picked/df_truth.csv +++ b/tests/integration/fixtures/analysis_test_outputs/picking_star/label_truth_and_picked/df_truth.csv @@ -1,301 +1,301 @@ ,ugraph_filename,ice_thickness,pdb_filename,position_x,position_y,position_z,euler_phi,euler_theta,euler_psi,defocus -0,000000.mrc,500.0,/mnt/parakeet_storage4/ConformationSampling/DESRES-Trajectory_sarscov2-11021571-all-glueCA/even_sampling_8334/conformation_000019.pdb,2034.7222900390625,328.1455383300781,103.19911193847656,-0.6081667393607689,2.0811144299635704,-1.7532485793976984,-15309.73434082482 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a/tests/integration/fixtures/analysis_test_outputs/picking_star/precision_and_recall.png and b/tests/integration/fixtures/analysis_test_outputs/picking_star/precision_and_recall.png differ diff --git a/tests/integration/fixtures/analysis_test_outputs/picking_star/recall.png b/tests/integration/fixtures/analysis_test_outputs/picking_star/recall.png index e338675..82f58bf 100644 Binary files a/tests/integration/fixtures/analysis_test_outputs/picking_star/recall.png and b/tests/integration/fixtures/analysis_test_outputs/picking_star/recall.png differ diff --git a/trace_PreP_through_latent.ipynb b/trace_PreP_through_latent.ipynb new file mode 100644 index 0000000..7c41bad --- /dev/null +++ b/trace_PreP_through_latent.ipynb @@ -0,0 +1,1771 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/dvi41342/miniforge3/envs/roodmus/lib/python3.10/site-packages/pipeliner/__init__.py:1: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " from pkg_resources import get_distribution\n" + ] + } + ], + "source": [ + "# imports\n", + "import os\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import html\n", + "\n", + "from sklearn.decomposition import PCA\n", + "\n", + "# roodmus\n", + "from roodmus.analysis.utils import load_data\n", + "from roodmus.heterogeneity.plot_heterogeneous_reconstruction import (\n", + " plot_latent_space_scatter\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# functions\n", + "def get_latents_cryodrgn(latent_file):\n", + " latents = np.load(latent_file, allow_pickle=True)\n", + " print(\"latents: {}, latents.shape[1]: {}\".format(latents, latents.shape[1]))\n", + " ndim = latents.shape[1]\n", + " return latents, ndim" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/mnt/dinaMISMO/synthetic_datasets/MapReconstruction/PreP/extract_frames_6xou_unsteered_0-200ns/Import/job007/particles.star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not 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+ "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "\n", + "loading metadata from /mnt/dinaMISMO/synthetic_datasets/MapReconstruction/PreP/extract_frames_6xou_unsteered_0-200ns/Import/job007/particles.star...\n", + "loaded metadata from /mnt/dinaMISMO/synthetic_datasets/MapReconstruction/PreP/extract_frames_6xou_unsteered_0-200ns/Import/job007/particles.star. determined file type: star\n", + "\n", + "\n", + "Dictionaries now contain 20100 reconstructed particles\n", + "added 20100 particles from /mnt/dinaMISMO/synthetic_datasets/MapReconstruction/PreP/extract_frames_6xou_unsteered_0-200ns/Import/job007/particles.star\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "loading truth data: 100%|██████████| 67/67 [00:16<00:00, 4.18it/s, micrograph=000066.mrc]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loaded ground-truth particle positions from config files\n", + "Dictionaries now contain 20100 particles and 20100 true particles\n", + "Added 20100 particles from /mnt/dinaMISMO/synthetic_datasets/MapReconstruction/PreP/extract_frames_6xou_unsteered_0-200ns/Micrographs\n", + "For each micrograph, for each metadata file, compute the precision, recall and multiplicity\n", + "Speed of computation depends on the number of particles in the micrograph. progressbar is not accurate\n", + "Total number of groups to loop over: 67\n", + "Number of micgrographs: 67\n", + "Number of metadata files: 1\n", + "Starting loop over groups\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "computing precision: 100%|██████████| 67/67 [00:05<00:00, 12.75it/s, precision=1, recall=1, multiplicity=1.07]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "time taken to compute precision: 5.275198459625244\n", + "latent space dimensionality: 8\n", + "(20100, 8)\n", + "[0.1691661 0.14170709 0.1332838 0.13199815 0.1179916 0.11128086\n", + " 0.10226911 0.09230328]\n", + "[315.66614 288.913 280.19473 278.8401 263.63123 256.0245 245.439\n", + " 233.17387]\n" + ] + }, + { + "data": { + "text/html": [ + "
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metadata_filenameugraph_filenameposition_xposition_yeuler_phieuler_thetaeuler_psiugraph_shapedefocusUdefocusV...latent_6latent_7PCA_0PCA_1PCA_2PCA_3PCA_4PCA_5PCA_6PCA_7
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" + ], + "text/plain": [ + " metadata_filename ugraph_filename \\\n", + "20095 /mnt/dinaMISMO/synthetic_datasets/MapReconstru... 000058.mrc \n", + "20096 /mnt/dinaMISMO/synthetic_datasets/MapReconstru... 000058.mrc \n", + "20097 /mnt/dinaMISMO/synthetic_datasets/MapReconstru... 000058.mrc \n", + "20098 /mnt/dinaMISMO/synthetic_datasets/MapReconstru... 000058.mrc \n", + "20099 /mnt/dinaMISMO/synthetic_datasets/MapReconstru... 000058.mrc \n", + "\n", + " position_x position_y euler_phi euler_theta euler_psi \\\n", + "20095 3167.964355 1419.746216 2.342370 1.593567 -1.644509 \n", + "20096 1463.147827 3179.908447 -2.946752 0.961545 -2.343948 \n", + "20097 3613.651123 2322.348145 0.240971 0.008801 -0.354462 \n", + "20098 223.331451 1715.293823 1.181207 1.805982 -1.914031 \n", + "20099 3200.553223 2070.339355 1.607324 0.033726 -1.573615 \n", + "\n", + " ugraph_shape defocusU defocusV ... latent_6 latent_7 \\\n", + "20095 (4000, 4000) -26199.68613 -26199.68613 ... -0.828033 1.786695 \n", + "20096 (4000, 4000) -26199.68613 -26199.68613 ... -1.796655 -0.934579 \n", + "20097 (4000, 4000) -26199.68613 -26199.68613 ... -0.887409 0.101615 \n", + "20098 (4000, 4000) -26199.68613 -26199.68613 ... 1.216764 1.698741 \n", + "20099 (4000, 4000) -26199.68613 -26199.68613 ... -2.041573 -1.436153 \n", + "\n", + " PCA_0 PCA_1 PCA_2 PCA_3 PCA_4 PCA_5 PCA_6 \\\n", + "20095 0.332256 0.235065 1.201625 -3.870411 -1.259568 -0.443173 1.842189 \n", + "20096 -1.437623 2.116327 -0.455647 -0.179700 -0.230144 -0.902039 -1.555978 \n", + "20097 0.465229 1.156685 -3.490604 0.290977 -1.391266 -0.340226 -0.308574 \n", + "20098 0.124830 -2.038110 -1.329813 -0.297920 -2.432164 2.702004 -0.274995 \n", + "20099 -1.146854 1.869835 -2.405998 0.340546 1.267649 0.497503 -2.125551 \n", + "\n", + " PCA_7 \n", + "20095 -2.043319 \n", + "20096 0.190705 \n", + "20097 -0.401093 \n", + "20098 -0.628627 \n", + "20099 -0.129020 \n", + "\n", + "[5 rows x 32 columns]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# data loading for DE-Shaw covid spike partially open set\n", + "project_dir = \"/mnt/dinaMISMO/synthetic_datasets/MapReconstruction/PreP/extract_frames_6xou_unsteered_0-200ns\"\n", + "config_dir = os.path.join(project_dir, \"Micrographs\")\n", + "figures_dir = os.path.join(project_dir, \"figures\")\n", + "# meta_file = os.path.join(project_dir, \"cryoDRGN\", \"run_data.star\")\n", + "meta_file = os.path.join(project_dir, \"Import/job007/particles.star\")\n", + "jobtypes = {\n", + " # os.path.join(project_dir, \"cryoDRGN\", \"run_data.star\"): \"CryoDRGN\",\n", + " os.path.join(project_dir, \"Import/job007/particles.star\"): \"CryoDRGN\",\n", + "}\n", + "\n", + "# latent_file = os.path.join(project_dir, \"CryoDRGN\", \"train_320\", \"z.19.pkl\")\n", + "latent_file = os.path.join(project_dir, \"CryoDRGN\", \"job009\", \"train_128\", \"z.24.pkl\")\n", + "\n", + "particle_diameter = 100 # approximate particle diameter in Angstroms\n", + "ugraph_shape = (4000, 4000) # shape of the micrograph in pixels. Only needs to be given if the metadata file is a .star file\n", + "verbose = True\n", + "ignore_missing_files = True\n", + "enable_tqdm = True\n", + "\n", + "print(meta_file)\n", + "for file in meta_file:\n", + " if file.endswith(\".star\"):\n", + " print(\"is star\")\n", + " else:\n", + " print(\"not star\")\n", + "print(type(meta_file))\n", + "\n", + "analysis = load_data(meta_file, config_dir, particle_diameter, ugraph_shape=ugraph_shape, verbose=verbose, enable_tqdm=enable_tqdm, ignore_missing_files=ignore_missing_files) # creates the class\n", + "df_picked = pd.DataFrame(analysis.results_picking)\n", + "df_truth = pd.DataFrame(analysis.results_truth)\n", + "df_precision, df_picked, pdb_labels = analysis.compute_precision(df_picked, df_truth, verbose=verbose)\n", + "\n", + "# latent_space, ndim = IO.get_latents_cs(latent_file)\n", + "latent_space, ndim = get_latents_cryodrgn(latent_file)\n", + "print(f\"latent space dimensionality: {ndim}\")\n", + "print(latent_space.shape)\n", + "for i in range(ndim):\n", + " df_picked[\"latent_{}\".format(i)] = latent_space[:, i]\n", + "\n", + "# perform PCA on latent space and add PCA coordinates to the dataframe\n", + "pca = PCA(n_components=ndim)\n", + "pca.fit(latent_space)\n", + "print(pca.explained_variance_ratio_)\n", + "print(pca.singular_values_)\n", + "latent_space_pca = pca.transform(latent_space)\n", + "for i in range(ndim):\n", + " df_picked[\"PCA_{}\".format(i)] = latent_space_pca[:, i]\n", + "df_picked.tail()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## FIG 4 Panel A (FP picks), 128 pixel ds\n", + "\n", + "plotting latent space of epoch 25 of cryoDRGN with the FP particle picks plotted in red" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "number of FP: 0\n", + "number of TP: 20100\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# latent space scatter plot\n", + "dim1=0\n", + "dim2=1\n", + "\n", + "grid = plot_latent_space_scatter(\n", + " df_picked,\n", + " dim_1=dim1,\n", + " dim_2=dim2,\n", + " # color_by=\"TP\", # \"#4eb3d3\",\n", + " pca=True,\n", + " palette=\"Set1\"\n", + ")\n", + "df_FP = df_picked[df_picked[\"TP\"]==0]\n", + "print(f\"number of FP: {len(df_FP)}\")\n", + "print(f\"number of TP: {len(df_picked)-len(df_FP)}\")\n", + "# ax = grid.fig.get_axes()[0] DEPRECATED, HAVENT FOUND REPLACEMENT YET\n", + "sns.scatterplot(\n", + " data=df_FP,\n", + " x=f\"PCA_{dim1}\",\n", + " y=f\"PCA_{dim2}\",\n", + " color=\"red\",\n", + " s=10,\n", + " # ax=ax,\n", + " label=\"FP\",\n", + ")\n", + "# grid.set_axis_labels(f\"PCA{dim1}\", f\"PCA{dim2}\", fontsize=16)\n", + "grid.figure.get_axes()[0].tick_params(labelsize=14)\n", + "grid.figure.get_axes()[0].set_xlim((-12, 15))\n", + "grid.figure.get_axes()[0].set_ylim((-12, 12))\n", + "\n", + "grid.savefig(os.path.join(figures_dir, f\"{os.path.basename(latent_file)}_pca_{dim1}_{dim2}_FP.pdf\"), bbox_inches=\"tight\")\n", + "grid.savefig(os.path.join(figures_dir, f\"{os.path.basename(latent_file)}_pca_{dim1}_{dim2}_FP.png\"), bbox_inches=\"tight\", dpi=600)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## FIG 4, Panel B (track MD trajectory through latent space), 128 \n", + "\n", + "plot of the latent space with each point coloured by its corresponding frame from the MD trajectory" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.clf()\n", + "\n", + "# latent space scatter plot, coloured by ground truth frames\n", + "dim1=0\n", + "dim2=1\n", + "dt = 1.2e-3# time between frames in microseconds\n", + "\n", + "fig, ax = plot_latent_space_scatter(\n", + " df_picked,\n", + " dim_1=dim1,\n", + " dim_2=dim2,\n", + " color_by=\"closest_pdb_index\",\n", + " palette=\"RdYlBu\",\n", + " pca=True,\n", + ")\n", + "# remove legend and add colorbar for the closest_pdb_index\n", + "ax.legend_.remove()\n", + "\n", + "S_m = plt.cm.ScalarMappable(cmap=\"RdYlBu\")\n", + "S_m.set_array(df_picked[\"closest_pdb_index\"])\n", + "\"\"\"\n", + "cbar = plt.colorbar(S_m)\n", + "cbar.set_label(\"time [\\u03BCs]\", rotation=270, labelpad=15, fontsize=16) # time in ps\n", + "# change the tick labels on the colorbar to go from 0 to 10 us\n", + "cbar.set_ticks(np.linspace(1, df_picked[\"closest_pdb_index\"].max(), 10))\n", + "xticklabels = [np.round(r, 1) for r in np.linspace(1, df_picked[\"closest_pdb_index\"].max(), 10)*dt]\n", + "cbar.set_ticklabels(xticklabels, fontsize=14)\n", + "ax.set_xlabel(f\"PCA{dim1}\", fontsize=16)\n", + "ax.set_ylabel(f\"PCA{dim2}\", fontsize=16)\n", + "ax.tick_params(labelsize=14)\n", + "#ax.set_xlim((-12, 16))\n", + "#ax.set_ylim((-12, 12))\n", + "ax.set_xlim((-20, 20))\n", + "ax.set_ylim((-20, 20))\n", + "#plt.xticks([-20,-10,0,10,20])\n", + "#plt.yticks([])\n", + "\"\"\"\n", + "\n", + "# add trajectory to the plot\n", + "N_volumes = 50\n", + "pdb_indices = np.unique(df_picked[\"closest_pdb_index\"])\n", + "d_pdbs = len(pdb_indices) // N_volumes\n", + "\n", + "trajectory = np.zeros((N_volumes, ndim))\n", + "trajectory_pca = np.zeros((N_volumes, ndim))\n", + "for i in range(N_volumes):\n", + " pdb_group = pdb_indices[i*d_pdbs:(i+1)*d_pdbs]\n", + " mean_latent = df_picked[df_picked[\"closest_pdb_index\"].isin(pdb_group)].agg(\n", + " {f\"latent_{i}\": \"mean\" for i in range(ndim)}\n", + " )\n", + " trajectory[i] = mean_latent.values\n", + " mean_pca = df_picked[df_picked[\"closest_pdb_index\"].isin(pdb_group)].agg(\n", + " {f\"PCA_{i}\": \"mean\" for i in range(ndim)}\n", + " )\n", + " trajectory_pca[i] = mean_pca.values\n", + "\n", + "ax.scatter(trajectory_pca[:, 0], trajectory_pca[:, 1], s=5, c=\"black\", zorder=10)\n", + "ax.plot(trajectory_pca[:, 0], trajectory_pca[:, 1], c=\"black\", zorder=10, linewidth=0.5)\n", + "ax.set_aspect(\"equal\")\n", + "\n", + "fig.savefig(os.path.join(figures_dir, f\"{os.path.basename(latent_file)}_latent_space_scatter_colored_by_closest_pdb_index_pca.png\"), dpi=600, bbox_inches=\"tight\")\n", + "fig.savefig(os.path.join(figures_dir, f\"{os.path.basename(latent_file)}_latent_space_scatter_colored_by_closest_pdb_index_pca.pdf\"), bbox_inches=\"tight\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Track MD trajectory through latent space using CV instead of time" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/mnt/dinaMISMO/synthetic_datasets/MapReconstruction/PreP/extract_frames_6xou_unsteered_0-200ns/Refine3D/job038/run_data.star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not star\n", + "not 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+ "name": "stdout", + "output_type": "stream", + "text": [ + "Loaded ground-truth particle positions from config files\n", + "Dictionaries now contain 9707 particles and 20100 true particles\n", + "Added 20100 particles from /mnt/dinaMISMO/synthetic_datasets/MapReconstruction/PreP/extract_frames_6xou_unsteered_0-200ns/Micrographs\n", + "For each micrograph, for each metadata file, compute the precision, recall and multiplicity\n", + "Speed of computation depends on the number of particles in the micrograph. progressbar is not accurate\n", + "Total number of groups to loop over: 67\n", + "Number of micgrographs: 67\n", + "Number of metadata files: 1\n", + "Starting loop over groups\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "computing precision: 100%|██████████| 67/67 [00:02<00:00, 25.98it/s, precision=1, recall=0.564, multiplicity=0.593] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "time taken to compute precision: 2.5940940380096436\n", + "latents: [[-2.19428778e+00 -2.72671223e-01 2.15191984e+00 ... -2.26414537e+00\n", + " -3.77899599e+00 -7.30133891e-01]\n", + " [-6.95316374e-01 -1.40665615e+00 -1.14162683e-01 ... 1.11378670e+00\n", + " -3.94871116e+00 -8.15742254e-01]\n", + " [-1.71029675e+00 -1.15372825e+00 -1.09810650e+00 ... -9.98205543e-01\n", + " -1.35369539e+00 -2.13364196e+00]\n", + " ...\n", + " [-1.59409797e+00 -1.15052891e+00 1.21966088e+00 ... -1.72624707e+00\n", + " -1.87069118e-01 1.92315280e+00]\n", + " [-1.96756780e+00 3.18276525e-01 -3.10060084e-01 ... 7.27799416e-01\n", + " -1.41837192e+00 -1.86001372e+00]\n", + " [-3.87802887e+00 1.21402726e-01 1.72536820e-03 ... 1.81111479e+00\n", + " 2.93009043e-01 -3.24379349e+00]], latents.shape[1]: 8\n", + "latent space dimensionality: 8\n", + "(9707, 8)\n", + "[0.20699814 0.12541926 0.1198101 0.11734328 0.11378741 0.108757\n", + " 0.10600974 0.10187504]\n", + "[218.62451 170.17578 166.32684 164.60565 162.09242 158.46898 156.45467\n", + 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metadata_filenameugraph_filenameposition_xposition_yeuler_phieuler_thetaeuler_psiugraph_shapedefocusUdefocusV...latent_6latent_7PCA_0PCA_1PCA_2PCA_3PCA_4PCA_5PCA_6PCA_7
9702/mnt/dinaMISMO/synthetic_datasets/MapReconstru...000066.mrc1432.01032.0-0.4430040.789031-0.852520(4000, 4000)18719.0312518697.150391...-0.3118230.6595811.1431491.133475-0.033617-0.689148-0.142706-0.476242-0.5594890.729532
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9704/mnt/dinaMISMO/synthetic_datasets/MapReconstru...000066.mrc1057.0133.0-0.2239701.6702640.152480(4000, 4000)18719.0312518697.150391...-0.1870691.9231531.5858800.376755-0.888762-2.2239592.318026-1.1066930.812318-0.320980
9705/mnt/dinaMISMO/synthetic_datasets/MapReconstru...000066.mrc1846.0482.0-0.3918911.999002-2.133000(4000, 4000)18719.0312518697.150391...-1.418372-1.860014-1.524564-1.469406-1.2982370.056593-1.186507-1.221899-0.2594410.809966
9706/mnt/dinaMISMO/synthetic_datasets/MapReconstru...000066.mrc1466.01203.0-2.9613722.8858572.588374(4000, 4000)18719.0312518697.150391...0.293009-3.243793-0.345150-3.636068-3.0334890.874840-0.4504170.0707661.6169711.772781
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5 rows × 32 columns

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" + ], + "text/plain": [ + " metadata_filename ugraph_filename \\\n", + "9702 /mnt/dinaMISMO/synthetic_datasets/MapReconstru... 000066.mrc \n", + "9703 /mnt/dinaMISMO/synthetic_datasets/MapReconstru... 000066.mrc \n", + "9704 /mnt/dinaMISMO/synthetic_datasets/MapReconstru... 000066.mrc \n", + "9705 /mnt/dinaMISMO/synthetic_datasets/MapReconstru... 000066.mrc \n", + "9706 /mnt/dinaMISMO/synthetic_datasets/MapReconstru... 000066.mrc \n", + "\n", + " position_x position_y euler_phi euler_theta euler_psi ugraph_shape \\\n", + "9702 1432.0 1032.0 -0.443004 0.789031 -0.852520 (4000, 4000) \n", + "9703 679.0 3565.0 -2.475374 0.833369 -0.516780 (4000, 4000) \n", + "9704 1057.0 133.0 -0.223970 1.670264 0.152480 (4000, 4000) \n", + "9705 1846.0 482.0 -0.391891 1.999002 -2.133000 (4000, 4000) \n", + "9706 1466.0 1203.0 -2.961372 2.885857 2.588374 (4000, 4000) \n", + "\n", + " defocusU defocusV ... latent_6 latent_7 PCA_0 PCA_1 \\\n", + "9702 18719.03125 18697.150391 ... -0.311823 0.659581 1.143149 1.133475 \n", + "9703 18719.03125 18697.150391 ... 0.265405 -0.297168 -1.221051 -0.256351 \n", + "9704 18719.03125 18697.150391 ... -0.187069 1.923153 1.585880 0.376755 \n", + "9705 18719.03125 18697.150391 ... -1.418372 -1.860014 -1.524564 -1.469406 \n", + "9706 18719.03125 18697.150391 ... 0.293009 -3.243793 -0.345150 -3.636068 \n", + "\n", + " PCA_2 PCA_3 PCA_4 PCA_5 PCA_6 PCA_7 \n", + "9702 -0.033617 -0.689148 -0.142706 -0.476242 -0.559489 0.729532 \n", + "9703 0.749162 1.400694 0.437830 1.028751 -1.562045 -1.027869 \n", + "9704 -0.888762 -2.223959 2.318026 -1.106693 0.812318 -0.320980 \n", + "9705 -1.298237 0.056593 -1.186507 -1.221899 -0.259441 0.809966 \n", + "9706 -3.033489 0.874840 -0.450417 0.070766 1.616971 1.772781 \n", + "\n", + "[5 rows x 32 columns]" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# reload the PreP trajectory and get analysis precision alg\n", + "# to use prefix (index in CV sweep) for indices instead suffix (index in MD trajectory)\n", + "project_dir = \"/mnt/dinaMISMO/synthetic_datasets/MapReconstruction/PreP/extract_frames_6xou_unsteered_0-200ns\"\n", + "config_dir = os.path.join(project_dir, \"Micrographs\")\n", + "figures_dir = os.path.join(project_dir, \"figures\")\n", + "# meta_file = os.path.join(project_dir, \"cryoDRGN\", \"run_data.star\")\n", + "\n", + "\"\"\"\n", + "# for ground truth extracted picked particles\n", + "meta_file = os.path.join(project_dir, \"Import/job007/particles.star\")\n", + "jobtypes = {\n", + " # os.path.join(project_dir, \"cryoDRGN\", \"run_data.star\"): \"CryoDRGN\",\n", + " os.path.join(project_dir, \"Import/job007/particles.star\"): \"CryoDRGN\",\n", + "}\n", + "latent_file = os.path.join(project_dir, \"CryoDRGN\", \"job009\", \"train_128\", \"z.24.pkl\")\n", + "\"\"\"\n", + "\n", + "# for autopicked (topaz) extracted picked particles\n", + "meta_file = os.path.join(project_dir, \"Refine3D/job038/run_data.star\")\n", + "jobtypes = {\n", + " os.path.join(project_dir, \"Refine3D/job038/run_data.star\"): \"CryoDRGN\",\n", + "}\n", + "latent_file = os.path.join(project_dir, \"CryoDRGN\", \"job039\", \"train_128\", \"z.24.pkl\")\n", + "\n", + "particle_diameter = 100 # approximate particle diameter in Angstroms\n", + "ugraph_shape = (4000, 4000) # shape of the micrograph in pixels. Only needs to be given if the metadata file is a .star file\n", + "verbose = True\n", + "ignore_missing_files = True\n", + "enable_tqdm = True\n", + "\n", + "print(meta_file)\n", + "for file in meta_file:\n", + " if file.endswith(\".star\"):\n", + " print(\"is star\")\n", + " else:\n", + " print(\"not star\")\n", + "print(type(meta_file))\n", + "\n", + "analysis = load_data(\n", + " meta_file,\n", + " config_dir,\n", + " particle_diameter,\n", + " ugraph_shape=ugraph_shape,\n", + " verbose=verbose,\n", + " enable_tqdm=enable_tqdm,\n", + " ignore_missing_files=ignore_missing_files\n", + ") # creates the class\n", + "df_picked = pd.DataFrame(analysis.results_picking)\n", + "df_truth = pd.DataFrame(analysis.results_truth)\n", + "df_precision, df_picked, pdb_labels = analysis.compute_precision(\n", + " df_picked,\n", + " df_truth,\n", + " verbose=verbose,\n", + " index_prefix=True, # NEW: USES PREFIX INSTEAD OF SUFFIX FOR ORDERING\n", + ")\n", + "\n", + "# latent_space, ndim = IO.get_latents_cs(latent_file)\n", + "latent_space, ndim = get_latents_cryodrgn(latent_file)\n", + "print(f\"latent space dimensionality: {ndim}\")\n", + "print(latent_space.shape)\n", + "for i in range(ndim):\n", + " df_picked[\"latent_{}\".format(i)] = latent_space[:, i]\n", + "\n", + "# perform PCA on latent space and add PCA coordinates to the dataframe\n", + "pca = PCA(n_components=ndim)\n", + "pca.fit(latent_space)\n", + "print(pca.explained_variance_ratio_)\n", + "print(pca.singular_values_)\n", + "latent_space_pca = pca.transform(latent_space)\n", + "for i in range(ndim):\n", + " df_picked[\"PCA_{}\".format(i)] = latent_space_pca[:, i]\n", + "df_picked.tail()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "
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iqCqxl57CesHVSNnpvmM0vC4ixv9gvP+gkPMr4cKvoQ90IE049s3jA23P8W7A+0koFKK1tZVLLrnkmK/Pnj2b559/ni9+8YuvPbdq1Spmz579AbXwxEQQRHRBAn8TusGNaskmqUWQBAMGyQyCRJNWzFgSxkaSFLs1FElE13W07l2w7R/pEykWyM6D5Ag46xEU51u+Z3jLZkLXfY6rnU4++61vUD7NC8CqLZ209QY5fVEZNovCrAl5OOQkyc4e5PwiZKsVLXWY2LrnME6Zi2h9XdgEQYCGj4G/E7EvhXZgK2LlZLw2M0ZFIqlqxKcuw56MYJg0GzHL99qxsybk8bPrF6CjM60+503t/U/o8WGI9YKl6G0/97tFrp4K1VPfs/P9N5xQ4nP99ddzxhlnUFJSQk9PD9/5zneQJImVK1cCcOmll1JQUMCtt94KwHXXXcfChQv52c9+xmmnncY999zD1q1b+dOf/nQ8P8aHEi0WJbJmNXoyge2kMxAMbz8K0vs3QsczIJlI1l1GVAEQsIZjyGaRIlVjX0okz6y85gIQSkXRZSNWUU4X3VMM0PUQoNPVMww5Cyn0mtEHW1A1hdAzTyM6nNjPuIDI3m1ooRBaKESuvwNB0DncF+THd+9A1+GseWXccs0c9HgM/89uJL7lFeyXfQHT7EUEbvkCeiyCuvKzqJEYUnYOlqWnIUoygsGKkFOL4aKvQzyKYHVQD/zuKwtIplIU5/XDonNAqIQ3uBAIgsC0hncvOgC6loTuxyHeB/YaKDzn/3SeDzsnlPh0dXWxcuVKhoeHyc7OZt68eWzcuJHs7GwAOjo6EN/QQebMmcPdd9/NjTfeyDe/+U2qqqp45JFHPhI+PuFEiu39Y6iazuQcB07Tf/dT6rE+iBwCcxGC+c1TkPDzTzP619+gBfykBvvRG+Yy+MoWcpbNxTWh7s0nVBOv/RV1FZBAAzm5DzE5hKR5sGGjtT/IrKL0tCuaSqC6i9AXfRWbrCDYs9BaNyHqUfoDOp0DveTnHEDb9E90m4/49n7izftQisqwz2wgccZJSBYjlnE1IFpwWGKU5Ng53DdGQXZ6+qaGxohvS6fGSOzfjWnm/NdK5yQOtxF+9lEAxJEmjHNOQSqeBIAgKyC/7ntTkmsHPQ5aF6CD3g8U/le/wevooB8xYGsp9FQSbagL0ZWNYPoPXud6ALQWwApiBQjHzhL5YSCTQP494HgkkD8ciLKpJ73EPyXHTpXnvzMg6r33QawbJCsUXo4gHj2yCT52H/7f/BA0DfPSM9jxz42ED3XgnjWJhavvRpSONpTqiSD68B4EUxa6q4p4MsqGV/axsH4Pg8I4Wg6JRGUzhXYTObs3YV+ylJTbSVRNYBINWJS0jWqofT9bGg/zzHaNT003UmtvR2t6DgSBcKqeyN59ZH3hBgylxTCwHtDBN/u1qcqAP8pwIEp1kQvpyAgr8vLTJPZux7zoVIwNk0k0bkft7USNJ/H/5gcIdjves2YjF5UjL/zU23xpOuidaeERSkD0vfW+7/b3iPZBtBsspaQ2PEdq9T8Q62ZhuOjrCMrb2BvVg8Dh9P/CVBDf3yyP/04mgfz/AFkmhSyTjKqB1/Ie3N1kN9ANStYxq23aTjoDPRIm0d1N2/o2ZIcde10JdV9awdieVzD6ahl8+DFMJUVkn34KgsGBkJdOUyEAZoONhOrk3heqmOvqo+iX3wF3FrELPk3v928m0d1J7pe/hlk++sJySwrT1t/DVEHEHp6FMOMUBAQETy7OCjNOfT56KI6g2KHgzSt2PrcZn/tow7dl4SlYFr6+MmpomAINU9A1Dbm4FGGgCbF3E0Lpm20j+7tGaW4fZUqVl2KfDYRi4NgrWZGmJoLr1mKfOQvrhAn/4Qc4GsGcC+a08VrrTNcm0w5shXgU3k58BA/oXYAdhA/WQ/vdkhGfjyh2o8ziUg/ovKUn7FsRO3SIwNq1WCdNwvbqReGZD7YaULIQjoiPruukVB1dgIAuYT//UrQ9+xj4+ZXknLaUsovqyWqIAS2MNgc4/MOfgyBgLi3BNu71QNGRp54kvHM306bPwnvxYrru+AOqpsHwINamHQTgLfMjS4Xl2M//FE19cbxV1RSGBtGbVyGecSWCmJ7a6Yb3ZnlaEEVM46cB09DVlQjS0aIejaf48b276BuJMrMmm29f+taGW13X6fzeLUR27GCkooLahx5GTybTAmd/+0wBuq4TTmooooBRFpHnn4/q8CKWT0Swud7+Q4hZoM8DxA99yeaM+HyEkQQhPax4l3T9/BcEVq3CUFBA3WOPIttsCJIJzK/fwcfCCX75zx00tQ5zyfnjiLpM5FgU5kyqZ8mmR9BSKsT2A22AgGg0Yakuwzl/OpL9dbtEdF8zvTd+HVIpoodasY+vx7zkFOIDvQiSgmnWImzTZ2Ob89ZZAp8L5PCLh3aQ5dzOrz7hw6draM07EGYvAEFBMFe+6+8glkhx38uH6B4Kc/78Mqr+LYj2jcITPdSGZLcjOt3YTAoQxfIfbGyCIKAcsTUqvhxiXd20XvtFkqMBqn/7K2yTJ77lsYPRJB2hOEZRoMZtwVA2Hqls/Dv/cB9iO88byYjP/yCSNT0clxwOEEWSO9ejRUMYJs9HOJKgqr03yOoN6dCUPc1DVM4uxB9PoWo6lsJ0oitdz4VYGYgm7FM9THoyC8k4xls5zksWK4JRwRIO0vPP+0HTcCdFQpdeQ4nJQmjdegRRxDFr5lHOiQP+dNKv4UCcMWsJuTMuRhON+BMNWE0mjJJEYmCInnsfwuD1kHfhOQjS29/1W3uC3PvSIQBy3ZY3ic+rDD/1DC3XfRlTaSk1d/6Zr100kZaeIONK/q3KqZ4AUul67Uco+n834jnzLCz19Yxt30FkX9pLemznrrcVn9iRQOC4ppPSdN6jgd2Hjoz4nCCkNJ2eUAyDJJJrfXsHyIIvfwnngvlYGhoI9nayMepAE5zM3LuT7GlpH6fSfAenzCuh+ZCfeZPzcLosZFuVoyLjBUEGc3l6IzaMaEyHsejqAPRug+x5mGvrKP7N74k078c2qwG58ZfohhqU/AKSXZ30ynZ+/cBullc7qb7ualBVam7/M66F8197n9PmlSFLIvleKzUVPgThdBr7gqxvGqLQYWJ5VTa99z/Coe//DABzcSHuubPe9jvIz7LSUOLiYHeQ+n8XkjcQad4Hmkbs0CES3T0UzCgge/AAyRdWkZo6F7m4Ii082k4gAEIDiOn0p4rPh2vpUgDs02W855+LGgrhWvD2uaB9ZgVBALMkYf63hGaRRIruQAyfzYjT/NEY4bwVGfE5QegIRtkxGAJgYaEL79vUxDLk5OA57TQAWg62c1BNd+JCq0z2kX1sFgPf+OQMVE1/3aakq6AOgWAG0Qp6ClItoI2RbBoGNYIyrhBCfRBsBHMOmLKwz52Pfe58tIOPQCKIktiCcuON7N7azxZbAYQ1eoJJao1GtEgENRI+qr15XiuXn1FPMBznwdUHsZoVjDnp0VtXMEY8pWHwpVsuWq3s6UswPhDG7Xxrg6vbbuTmy6YRS6i4rCJ67wsQH4TsuQjW15fMvWefSWpsDGNBPraJE0mOjRH42f9DD/pJtjTh+uoPgRgQOPIdBYG0+OjJCGgagtGG4jRSdvU8kM0IRW+/JG+SJYpsxx7uvHhwkC0doxS5zVwyvRjDO0yT8mEkIz4nOLquQ6wrbRsxvTlsJNcl4/Gn0BDIy8t607FH2bJTbaDuA6xohln4IwGy5BYSLTGCf78fQkHMp59JsKwAn9GFIHoIbd2G5HQgl1cw6JqJI5HCKutsO+Th901DLJylsKzWRv3eF6m49jxSxmyirW0Et2zDMf1og+7qjZ389p7dAHz7c7OozbZSawhgbXsS6/wyAn/9M/dv7OOV54e5qbSL+tpCnBbLMePLhl9YQ+8/78OzeD6uc+bC0JGyOAYPWAtfy5ljriin7KZvATASS9IfSpCd5UMP+hFdry5j29ATFSTb24h7LLQFuvEqUXIP/x2SYZj2OfTRdjjwWHp3cxbkTCCWUhmMJjFJIl6z8rbBpGON+whs3Iy/IT0iHA4n0kb7jPhk+KA4PBJiKJyk0mvF9YbRTbHDnK5pLolHj3rCLTD4BCCi516AYD66IoJ7dBcXantJKm66IitIBaIUOM1EOw/R8+iT9K7ezcQffxN7dRnor0aKRxiOqjT7YWaWk94xFwc+9XOMyQSeliYu+1Q71109geWdf2J4RyuSw07ouz+hcUzDY57PyRXZzPJEcJdkY1ckpmQrRLtHMCfbGZx4BqrXx/C6tVgb6pAsr9tQXPb051JkkTy3mbqyLLTtj0HXBhAVnBP/H2ufGubs5VWIBXnsG1OpFJPHHAV23/lP/M+/zPBzL+Bd/hyKrQxiQ+imAkJ/+THJg43YLr0uvQx/hEA8RVgyIF71LbwvPIRUc8RuI4iEHllN6ME78fzkFiaUawSDLkgEIRVDHz2MYPGiI4BkBJMLSBuW+6NpZ0KzIr1tatbWb91CYNM2qj77GQovWEmJ14ZZ+Whfvh/t1v+PMRxJ8HzLMKGEykg0yfLq153aZFGgyHGMQE7t1VSgGmhvLkY3trsPS5ZOs2UGa9uTSEIPKyd4ScojcPZcivPtDK3bir26jKBWSnswF4dRwmMyoqlRXh6owaEP4BdkMMiEJR/Qzovre1lR50YMDKIOdBFKpAAIxFRiwRChgWGy2g5iaNnH4QXLceaXIHinMFpcga6DY9lJ9PljFLxBfBZNK8LjNGE2KdQcsdMkDC4MQNKSgzvLya8/W48maq9Ogl4r5fMqupaAVIyspYvwv7CGnPPPRnb5UEfrST59O0K+TmLHerThARLb1h4lPm6TTDiZwhAJEX1pFYrn9e8/2d6CaHdiyFOAOA5HiGTZyUjRfgTfeASrD+beAJIBwZYegZqOjFqMooByZNSj6zqD0SQJVcNnMbw2rZLd6c9r3LmdGd/4PMLbZOj8qPDR/wT/Q2iaTjSZXgl5NTXqf8Rak7bViApY3py+Iryrld5VzxC+YRq4behAKJJgzUAeOjChYiYVhrRNqG1MpyWgIesp6h/5B/Ld91N5x+9psdmxqCpWXUUcHWTBjFwmLKshNRJAHU7XoSrrOQjl41EkgX3/eISeb/+E6vPmITRt5s5oNf1BJ1+5tIa4ekQtFIiM9QCve+iKosCkmtcv+EAwxk6mkl1VjF/2UBlTyfX3Y6qpJWg3oelpZ8xX0dU4dDwCoRbyzzgD7/IXUTwedElmZMOLWA83w+FmLGdfReThe1AmHm20dhkVnCKENrcydPU3aC9voDKRwmyQsZ1zKdG1q0iOOsGdJJrw4qwqfe3YZEcboUf+geT2YjvvMkSLFa9ZwaJIKKLwmsgEEyoHRtM3jKSm09k+SoHXQuX3biTn3DOwjW84IYQHMuLzkSLbZmRxZRYDY3Hq3mFJG0EygnPym57X+vah7XmKrEUlyBNuILvUh9fnxRyPEX/wAZh3JgCR9l5sK+aBFsQmp5368pJROn/5e9A0OjUjL/SkDcSnlFgpHmlkxcWn0G92ERx3Ml7/KFo4jFRcwkhShSRk5+SgxRNEIxo2j5fFoX0MR1Ks2VXI0hU5oGt4g2uR7QbgrePs2jsDrH62g+nLKrDpIvG//JGeO27HNms2FX/8I6LBQCoUove+BxBkmexzliCGDgKQGDtMOKcOj1Fh3Qvb6Y/nc5LFQbyonnjlZPK+V4BotYCugfCGFT5FQV9yBg+sayPcPML8uMBCj07kxScRFAVRyUc2ZmP4twXH2MYXiL34JADK+Cn05dbjtBrw2I9Opi+LIArpEdvwWJy9fUHuev4g379yOtmnvXXOpY8iGfH5iNGQ4+CNwdKRaAizUUEQ3355XY2nhUMypu0fWss6usxl7JHrMR9uJuv2h5j0i28T3tRI+19vY7KqkSytxNrYjHTmEtSBzXie385sowth7mKkP3yHw39+GNHlQOgbRgfsHhfeL3yFrP4ghkQKX44D75evR1dVOrc3UWyTiFsteGUN62Xn477oNEzb11LRuIOcQBJTqhyvoRLnyAsI0cOQnxbAkZEIv/rtRob8ES6+Yiozx+WmsyuWubGlYjzyxw18bmU98cZGMJoQrFbUWAzRYGD0pZcYevBB+mpn0uXo5dzJCzFpfQyaaglFEuhxFa21iX+0eVjj+QyLJldSabKTpQ9gSraAUvamMAU16CeROjIC1TQizz5M9NmHAAgrFgxnXYbXefQUWKlqAKMJOSeftbFsbvvTJoqyrdz0iSlkvUGArIrMhCwbh4bDPN3cR1TVqCxxo6onXghmRnw+wqzaeYh/vdLD8olOLpxXhSgeuyRNoLmFDZd8HU1TmXnnj4iWFGNzlbEzUUx7RESsmcCkjZtZs66F/EPdKAtOwaWnsHmsmK86BX34ZaLr95N69B4UwFDgxLnchn3J99kyamRxQw4GVcX+7EMoF5zKjIKjU0kc/uNdNH/zB8jTp7L3is/z2blGjOd+j7ZN2xj75x0AOGcvxJZtR9y2i66mERRXIbmVpQDs2tPP40+m45sKq70Ul7opMKqY9j/GF+sPMRroQNzUjPWrX2Xg0Sfp/svfED2/ovT6z2Eb20LVGYXcLy1i9+4Rnm2W+P5FsxnWHZSaFEhppOwOfjI/gt9Xj6vASiwY4srbx7jwpDLOXnq0iKQa12G654dcOO9yhitmU1eUhdSVB6KIYDDSi51927r5+JKjva5Nk2eR/fsHERUjXet7GW9PcnrnM0Tv2oF66eWvOX4C2AwSgq4TS2lYFImTan3k/peBwx9GMuLzIUYd6EEwGBFdWcd8/ZVGP9GEytM7Rjl9ahSrxQDJYZDs6XCJIwxt3oNssyDPmEyTbEUOxuiT6slx6rRHohTpcYoqbLQ1bqfvBzcDUH71RfR96TM4r/kMpvnFBC0+zIIIZjPCWAywY5C6ycLFiCLj2buVoR/+CD3bSMnyUxEMLgC0VIqRHXsASG3bQeEnU2w/oDDu0H3Y2ocIFZaQ6mrHUFPNyB9+SexgK+a5i2l74kVMBYUEiutwOoyMG+djoD9MSbU37eg4tBeh7TkUwH3yNCL9DizjxhG8OR153/ev+yi4+AyEzt0giJRXGtndG6XGnER4/BEmzcxFEECqnceUnAiWA49Q0gmhljn8ITab666ZjSDAju4gVoNMtS8dMqL1tIKmUrjmr5RXVSGZ8+Gks0lYXOxtH+Xn+y18tj49Ck0kVbYeGMRuURhfakd2mUGwsmySwEjTKsyrHiACROtqsS1aetRvOz7fSZbVgEmW8FjfYQbJjxgZ8fmQkty9kfBtNyK4stA/90Pk3EJs/1bL6eQpuSS29LCowYXN4qT9zr8xsmkHRedNJ2vpha9NxRJDfgbWbKHgJzfSIygQSlDtMTMcTTG+0ICsJ3EN72LEXktOWTnS8BDJpt2gqhDw03l/jKBmQvzkD3C2HqT/spuZdO/3cc/Kw3d4I/274oR/fxPOs07G2WAnMbgWg28+gcZOutZuYVvhVApP0XGfvJhYYSG9oSgz++9ANpcy6C1D8JYilxYQa2tPf7BEDMlmZXDdRg7e9QwPhss57eIZbN83RCoSx3voaXSDOb1knYwg59lxTJoBQMFVl9NnNuE5+SRS/gBi3gRENcxp+Sq5z6/F+MJO3JfNQV93NzrA2AAmVwlICjoCks3BsmoHqkFKl0UeGyP8+CMEZ0zDMXUm8sRFEAkiuHyIRbXE2tuJHGzBPnUqpdUyt8xJUFecXpl6fmcPv32kEVEU+OlVedQU9oMwjiJfHp6ZE+m+V0G021EKj53/O9957DS0JwoZ8fmQkupqBTWFPtxPc9N+9g2InDcuH8cbXOrn1BUwpy5d1zvc3s22z/8YNI1UNIlj/hl0hlNkWRQG1mwBXUc42AaTJ+MxyYRTGoosIgKRnhhNp12PUXeQ95ObiI2OYUyMoT/6EMN+A4N/+DMA0ne+zejeFtA0+vtV9AN+jC8/ybjzr2eX59eoRXEqDG0ARP0HiEaCeEpD3LfLQdI0i+snTiIoCGA30l/7FfILAtSdZqR3axibI0DRjV8j2jEAbh++3GL8f/4jXuCaW37Er9Z30NkfovGwn3OWbccy1oYw9yrQ2xH0ITiSiiNrxTI8y5fi7+ggtOZ5wv96HlGUKLzja0yYM56gRcVUUgwjm0A2gJZEyilELZ6DmlQx20XyXryX0ZWfw2SW8Q7uIzzUy+Af/ghXqthmzmZbxfmkUhrTInEOfOYaYgdayLn8Ekq/9f8oyn7Db3jELqTrOkntSHI1fQzIwzp7HqX3PoqgGFDyjva9OlGKAv4nMuLzIcUwfTF6YISQYmWfo4zRWIqxRAqHWUEb85PatwuxsBQ5rzS9v8dF9sLpDL64Cdfk8WzqSbG1e5gsi8LiT69EkCTyIiEmlHkwSiKH/BHGokmCI1F+cft2VqyoYpZjlL7cGrpscQCqc5sR84sRjiy1W73ZtDiKcH3lRpyTZmNqXE1yuB/ppXuwzboELdBDSrMgSSLm6EHUPDtCVOPmZWGsmhmSAwwbcpDRkYQkghxHkuPE1jyPv6wa7+mn4HJnE+3sQo1HCJhNaPEEXmOAupIcAqEE8ybmYIn2gGIGewmCqRgdiaCYixAOo4wcZkTxou9vJvXwPZhPOw8tFCM+OETOipmMHWhm68+eoeGKFYzk1fPkQSMlooklQZCtdkbXN2OvqCLPdRjJGCTlFlC9NhzldehDTWzbV8G3/pKu8fWNC8fh6hsAIDnsf9NvuGScl/rtrci9HfhiZ5AccqOORpCzhyAex1Bc+qZjQuvWMPirn2Cbt5CsT1+L+C4KFRwYGKPTH6U+107eR2DUlBGfDylSdj6WldcSiyQo6w3iMMnkHlkViT1xF5F9u+ibfTbZRjc5HieK3crMf/6GaHcfjrpKWlvS/jVjsRSeZXMoOnXBa+fuD8U5PBIiy2EGt4lvfXUBA63deId7GRLS5WbsigiyhFpTRdalKxHzi/nOEwFy8suIBURq3S40/yB6cARpyyqazNM4rcJIct0IenEBiuswNl0lnDeZyUIf0UfvR12TQFt4JYF1O4mMd6N/chKJIY3Alv1YFi4gKodR9CwM0RZyqsH1i+th+CCq4GfGs3cw7vxLWNc8zNB5V+ErKUZwpKPrR6JJDh7xjcmxFjGaEohPmEP2N7OJPvggg/c9RP/DT1H5+1+y6wf/JPei0+m0TeRfa2Ns3N+OLIlMnGfA9tQ/kQrLkCZNRDKOACA6rUQeu5+IruM+/xR8ZSOvfY+CHqXqe58jdMiPe+ZMRn58A4I3B9uFn0Sx2RHbW1HvvA0VGHI4WDf9DHKHOqk7cD+JDauwn3cp5mkzGX3kQWItrWR/4TpCTz9Esq0Vf1srjjPOwVhW8Y76Syie4v4d3SQ1nZFIggsmv1cpXd8/MuLzISWV0hBEAY/FwKIK72vPjyVSJMNhDi69kl2mAgyHApxvteAwKhjcTgzudPrQOfkSCx2tiLIRg5TPG39qaypGw2gn26RSAEwmmYl71xLr7sFZWEx+bi6FoR48tW4OPfggQ/c9hCBJNJx7A89uC/LVry2gfSjMoGM8jikygqjSHLdy4bbHUJu3oooiwhe/gOrMpf+urfQYbFTWzSXZuJf8kUMIkQH8m0YZXb0VyeZAXnEa3dmVVCkW4rFBxKZX0Ft3IMtGlItvJKlKTM+pw+x/jlOvWoDoKyQxEiccaEN1O0kJr9/lLYkRcjtXM5I9jVBeMUabFfvk8UQPdyF7spn6jx8jTh5P1OtjhXcEQ5aFSFLFHNoHkRDqgT0ED/YAFsylVqJDpNOlAhomPAc28/2rTsIz2EKRugmDsBlbjZXApjHiG9LldVLjppM9Yy5yXj6m8ROJNe0lUFLDvpjCPls5xdmd+K68EGO5jZS/leHNO2msXkpZ+xDVRXbCniysc+eh5B49HXs7FFHAZzfSHYjhfqcljo4zGfH5ENLS4eeHt2/FYpT5+ienUfAGh8L+cILeqWegy0aIgsZr18ZR2LV2iDWmN8x5HHqgkb4XNlB22Tm4ijwID99P4ae/RL+u4IsGGBo3gdTCpRRZVarEdUT+fi8xWza9cikmQCwoQHTYOeOULKrpYM9OO48+N0ZXl4PPXlDBJybnYAjOIbFvG/Lsk5FLZ6HIhwnV1tDkKsPy828T27IVR88o47++nGg4RvCAlzGliOGsHAp3vILmmUrEbcNmywKLE0wOBKsfo1lFHNGgYAlSXhJoQteziWelk5+ZW3ZTJ4yimrw4W+9GSATxJcNYfKdgrHYiqBraBSsJvPQy8ZfXMHbjN/B+/3tkzVzIUCRBe3+Itgk11FYeQiosYWjCDOK7dnOAMuKqxowrrsPYd5hY036syxuY0L+F0J9/RMhqx75yBSmjFzErCmYrQn4xCV8hLxwYRNU1Zv30d5gSUXbFTdDUT7VZx1VagLV4H+h9SAVFbJz7Sf6yMYrSHuLP57op/fRi5KWfRTS/86mTUZG4aHIhw5EE+c5ju1x82MiIz4eQ3QeGONg+CkBTq/8o8fGaJWL5biIpA7NFhWyrEecbQwh0HVWHhJxPp7Icq5QiO2pg01X/D11VSQyPMv/emzCX5eP+4pXYp8yBFafTWVaDQVdxdbWQ5UwhFpShWNy8qEyk4Iob8FSVMm5WDUo0im/XKpYf3MKUQh8/SNXhDgzA31cxuGAihq//GXduAYJ8GAjjmpKH0KajJ9MBlLoqgLcEW/YQthIXPYP5iNd9mVgigdF/BYarL2Rf2ekcLjgbMzpz5WHM+El1diHaDChF6amW4jNiTgjo27YTfeDvWKbW4pjqQPdNROh6BU3KxpYbRm3bgh6PIrZt5rmiT9Ne52FhTCSxdTv/ihThsikEwgZSLh+mudOJlUzD5nSSe9ICrNs2MoaMvncjuq5hO/MsTNMaSKxejW3JAuLdQzzdU8ML/UYuDGyn4ebfk8jKZSCi80pzLxdMkjDbR5GlAqYKbip9NiyKjCJWoXeEEKLtCI5i5EIFOICm62i5k1Cm1iE48951v3GYlaMWJD7sZMTnQ8ikWh8Tqr2YjRLjKo/28XFJXbiczeiCF+TJJDa/RCw4imHOCjSrg12DIXrDcfIsVlrGRARgaZGDogtOpuOeJ8k/bQFisg3naR52Vt/CI+uClLeoNOSA74kn6fvBTwhUlFB08myMgX4+U3KYVxyzKan2kie24Q0cINHWDG1NeGnikiVTUR58lENrNtH+jyeY8OIv2TygcHK1FcluJDfLQMGgmdDnv0L2vq0UTbYgKFGIB9BTEQKmGmSDARIJECXswV6iQi4jiXSE967eAiYLEuGn/oDodCKWX43ik4iknER1HeHpR0js2UVi724sE69D8OaTEs5HLCpAtg3D0pNJrXuFYc8E7t42iqrplMxYQbci0twxSkW2me9kd8M/nqGlowP0u6j8xXdRt96Dy56Fy5FFPD8Xwe7EPMWI1tmEvvNpAKTxJ/GPvQqxZIpW1cr4YJScKicpKUqOw0Bddg+CEAM9iiDkvSELgQiFZ0MyCMYszlrch3OoCZ8SoVyUEZxLPrjOdhz56CYDOYEpL3Ty628s4sdfXkBe9r8lxNLT8dqCPkRq/y527z3ME90yndt3EE5qHA7GiKs62pG5mCwKKIrCrL98mzO2/Iq80CYij6/jvrXlfP8fhwlGEuxuGsY95sfY1QlA9FAHY/sP0/XoK7iFBK9s7iDc2kRlz/242Il5QiWCw0Ns8ikIexuxzZgEQPaiSUQlkdyXnmDr7LPo/PUOArFCNnWM8kLCRs6ppdiz+tCD3aiCD0FPMiwouH7+R0xfu5nYnBlIqVGKrDEssoDbJNMyFCZhzCEYNNDXEmTPhhA9fg9dw0a6u6NIM+aComA75WR0dwlSvA2pwE18wxZSYxIxxUH3aV9ig9ZAntuE0yJRaNHpEF1UZ9uY5FHQ16xCECVSg4OkhoZIHd6NEBxE794HkQAGr5GhRefyyvAMBsNOyMoHUUQvn0hNrpVxPiPV1hQiKoE1a5Du/QfnFZlJ6fmkk2wXgiCix0bRE+lsj6mNzxD/189IbX0WZ0Ehp587h+nzGlBmnvG+9i1d11H3PEPy0e+gtm15X9/rP3FCjXxuvfVWHnroIfbt24fZbGbOnDn86Ec/oqam5i2PufPOO7niiiuOes5oNBKLvTn9xPuOOgqpZhAsCEodsaRE43CYlKZTn2XFbpRBKgVkED0EzCluC9cRV3WGA04WP7+GkvJK/CYreVYDPosBh1HGYZTpHgmzsQkmJIy4qqfhs5VxqdvHvx7cy23fqKVPcJA/oQz5otORnS6CD9+PkpNDaEzms92P49WXoQsKgp5AcFsZvuCbvLK5g0GPzOxUO7Me/SkWs8iOZDGe5DYSqSS6Nobb2MVnZxl4ujmFJNuIdjvp/OGDIMnkff2TVNWV4cty0bNzO968MkRfinz6OMMGz7baqdEjJB55mOyzTucPzR7u/XETJ22LIfvMrN01wMrT6jn3F39EsoPifwqAUKqHlNlI8E+r6fzdPRgnTWL6Nz7HJG2A3oeeJXX7JiZe93VWx33UVGQjr7wUYfN6nDk+NNnIcPFUckMdiGYHWC2IM09hz5ABw1gQe40VY8WFRHcMkti8m69X1dL101+Q6OwgNaOa2CtPo8YSJAcHUL75/0gXEjSgDx9A3/o7MFjRKi4kteNZGO4iNdCOPGExUnEtFNeiayqpHc+gj/YiTViOmFWAHhuCkY3pPpI1B8H4X9TiigbRNv4DNBVdUqBs+n/XZ/8LTijxefnll7nmmmuYPn06qVSKb37zmyxfvpympias1rdOqelwONi/f/9r28fNwUsbAH04/dDyGIjYafGnl5BdJpkaowySJ/0AjLlJPLZ+egMxDBYzPVd/CQGo/dvv2JGqIqHDzDwHbhR+dlcL25rGGF++nEtz6wglJdwVBn745QZqClphzEJg1mJaZ5xGTXgAi99PuKWD9j/+g8LFk4mNwVCHGWtVJX5XKY1iHncd6gHA2lDDvIEHweJm0rh6Np5/EdkN4yg6LR9R6iTXbeLykiGCHXH8W0ZJdHQBkNyxB++MM9neOUTDqdXII83oUh66x8jAiIb31lvILzIgZiv4p89CbUkwZ24xPn83WZLCLpOVg4dG2PzVG5l07/exeWoQQkH29bi5e5OFK3rSd/b47t0YEwM8aS5HWXY+tUDSZeeLsRaGHnwZ1QoWr4GBwmJ+OljBlcZy1EgtkQeexL1gFr7qMAdfClPsDWCtD4MEuiGJkX7kDS+Rd93l7LDVk6u24ZIPgVMmXjMbBIF4ew+CKCKnOkCNERbz2DRqR5v2Wabt/gdWXwEYXzcsa32tqKv+mN4QZcSFl0BgDwT2pp+TneB7Pb/1q6hjYwhGI+J/KGONyYZYvxytaRVC2cz/Y0d9bzihxOeZZ545avvOO+/E5/Oxbds2FixY8BZHpcUmN/fNKUY/cMQsUE3oggWSMZyKBZsiklT1o/LSvIrdrHDekko6hiOY7QbsF5xD4vkX0eLDJPQqAILdXcTNItFgugJEOKaiS0ZIpnDKGuPzh1CxYxRsHADiqkbA68VlVgjs2INz0jiSHYexXZCFwV5G8vF/IblqKalfSFGOle6BMLVVVhhJQmgIRYtQ6DQTKCxAJ51ATBBitJnmsNkZp3x+Jb6ubkRRwLLsXMLbnyXH6UYeeQniI4weKKJ5o0qLu5iDKy7nS8vNSMFWeh21nHKpiLyniebTvoaeSnHL9VdgCSaR/vZj1lrKWTTix3vgZSYbdrK96Hz6i0+i1GbCXu5jbTCLVftDgJmxj11LU3eILw3s5eDtj7H0D5/C5ouRlRhjhRTjwO4Osu65l0R3N8kRP1ZGEM1nE9NtRGI6ZqNKoqIO85rHIZWg1eDhlTGZ3FQIF4CawjqhgbGN6xn5219IdbXju+EGJL2MZsM81nbHAQPeuZ9lUlX+UTc70eYGdx74exE9ae91jL50ORxBAOMbXKiPEFr7ModeWsvApHlUTxtPce5bj4wEUUKcfQni1HMRTO8sLcv7xQklPv9OIJC2j3g8bz9MDYVClJSUoGkaU6ZM4Qc/+AENDQ1vuX88Hicej7+2HQwG35sGS1kgLoTAVgg/icOQy7LS09ERMcnHTrHpdZroiqewGmTyLr8Y5dSlGDY/SL3ZQcLhZdCag3n3Fj7e/ywHc2rI86UY7guixtLZ/jbqZUw0DRHYv4/WwzIWr5OcCU4Kv7qIrDl1xJp7iHT0cvhTX2PyL64l6Knm4O3PAs/y1Z//BGYvoNrQidC1EC2rlJTFjH0ojuFAI8OGYmyV2QRiFtqCGjFVp8mYjf3sC+j53l9J3fYnwi+9gOeic0jM9GKU/bQ80ErfA8/jMBmx/+Q3hDQLzmAU+xP/RMnzYYn2oafSoqYNDuHfswXL/AVogHTEniKaLEyoLkCSdQ4XFPP8+j4m5noxygN4LDKl5hTF1XZafv4cAJZyK0r0IJjhjNoaevOycX7h84SeeALXx89id8kUamUT/SMRtvZpLKjtwa8WIi66FGX/BswlNUgHw6yzTMA5x4Ar24tSUEPvzZ8ivH0H7mVLCO/cx/7v34Hp5lKMxV4QILsgD/HVUY8eAH0Ewe7B8LFb0CMBBF8ZAIKrAd3gAkQEy5tXwaJ7d7Fp9tl0J0T27x/m0z438tsUkhREEY6z8MAJLD6apvHFL36RuXPnMm7cWyekqqmp4fbbb2fChAkEAgF++tOfMmfOHBobGyksPLaX6K233srNN9/83zVQ7UlXgpAK0qLzKoICqeH0/6lRDKKGIB57+TSR0sgxySwrdWORJcxKFtRX0ZXtJOvgLnamzDQHxyCnnFwtgG/nQ1hO/S4PNfXw8Jr0lOmLn5qCVdrG1gMuHt6oIkndNBjKUA80I86ZzpaSxSixGAUxHbW8mviQjCC9gK6qWNsPUFTTizzaTMw3G+PQdsyBfpqv/iOJvkGy/vobunIauGtLBzW5Ctk2BY9Rpefme0kOB4jtOABAZGcjujwF2eLB4EvHQ5kqSpg33o1bC9Dx8AH2//JeEATm/vKTTPz6Skb3tpNq3I69oQ5Hz2HGWRXGbDU4ynS2CpO48dc78XnMTMqV+UxFOyl/KxOnN9BRWIk5FcOWSOC5agX9oxp+v0iuCTTRxsiGRlIzczk8ZwlTp3jpi8TpUWVQUzisBg75x1ggJEhqAl01S8ifPIMiIcIF9hzWbe/nymcNrDwph4vzI4R3p6P5NVXDddoZFI0liZ+ylBWSgWgwzrObOphenc3EMjdojUAYdAeCdTqCzZke6bzaLSwFx+5HehLb3GqcfhjSROxmA++ygO1x44QVn2uuuYa9e/eydu3at91v9uzZzJ49+7XtOXPmUFdXxx//+EduueWWYx5zww038OUvf/m17WAwSFHRsSOTj4keh+ROQEsnZZf+LWWGYybEcsCQf1SSMDWeoHlLMyMGE+WV+dz7Sjs720b47Mk1zKl9PYeOGA1h6DrArMgoZZYcOsctZt9XbiXPLCG77FQJfbCmh7J8O5M8Y2itwxTarYiCyE8/42VyQSNjvgq2dwlUJBuxNO3GeP7pDKS89OvtWP7fVwlrRtS2rchBgcbIeJ553kdSzaXYIzPlwouwhg5hcoFveB/5ZgsH+8dYtmkbE6vjtMysYd/zmzBcdzaSvxtx4VJCO3di27ke76XXIi5aQK5PZuT+uwnNmY7Bls5l45o6ju7tgyhmE44SL64lVTjG59JurSPZ0kkqx0nXxFMIHBxE18EfiPOpxcOod6RzBpkuuIycg89zaOrpuMsqMNWUYtCtHPCr2Gx1pHQTIxufx3TWx3FISUIjHXj0KHnOOqKighwcY+LIfvx73LjKUowEo7hM+6C3F6MhjzZzPpqeLssTbd9Kxa++g2iWSHYNY/BlUbLyZLrEFHHRyN+f2Uf/aIztrcP85jOzkTABYXTVgBZtRLKNgFAP4punWUeTYsjnZs/WMWRZZv54MyJJwIAWixLZtgUlNx9jRSVq7yG09kak8omIvmPXl/8gOSHF5/Of/zxPPPEEa9asecvRy1uhKAqTJ0+mpaXlLfcxGo0Y30XA35uR051K6wfR9aZXBUM2GN7c6brXbGG9JZdoQmCobYQ97X5Sqk5jx+hR4mNb+xja3o1ookRuRQNxey6dMR+lOQKO/k6W1eZSdsNcMFuIJZIEhVKmuPv4/mXTmFBwGLQwthyNsnghkdv+TGTzJtSXinCfcjL14VYOGktJTlpM2cfy0JMqT9wepTsQZcuO9Gjq1gt8LMjdiN56L6asEj5uKqXxyb2ozz5D4A/fp7E0C+cdM/nZbj+njZ9IqAfmNK9HR0B47lEsn7iWVOsuEnGVvn89Rul3PsWsYh/+liG67n8O59xp+K76ONmBBxDHNlCi9fOEvAhLSSXxYR2vw8G5S0pxt+9H0RKogpCO6g8HiU+cj6OgENFgIDZpLvY/3UahG8K7NwBg+NbvaJTtzCOBoprYEanCrvdTmuumy25CjAzBY8/SM/M8Hu+RuXFGB0LrWgyChMjF/PAzM3GbFcw7tqOsmItgUNEm5pPa8Tzqc3/GVzoe27k3UFfspn+0l/piF5Ikgl6HOtZL8K+3E9+8FudnrsA8zwe6HYgDjqNGQq93FjM7D7nY3pLOJrCgQafMJ4JQyOj9/2Lo1z9FdHso/Pt96Pf/FPoPo1ZMxnTl9/+L/vvecEKJj67rXHvttTz88MO89NJLlJWVvetzqKrKnj17OPXUU9+HFh5BkECZCHoUhHc+9xZFEXMyQVQ2ImoqKybl0zEUZm5dWnii7Z2EDx7CUjWOxN6NSKU16KJO+fCzbG0dT/jJv7Gn5RCV15xNWX2KbfWfBrMXmnvpq5lK84iBurEcXA6Vx1sm8vD2UZaffBm1u3YiGI04LFFa5EpuaCxBPdCOaHCztK6NuspKBnenU0ZYLTLZLiACQiqGICqosQBetx3x+uvYaKtjTNToC8QYCPbj8kbZ055khjkbUUsh109l2GLl8B+fJLBxJ87xNZSGkwjb1+ERJJS5ZSQSgwjZbvSEHWIDhOMKtHVSWGKmxZXP5n0jbDzk5+KabOLWFKYLPwEJjZQgcrhsKgY9RfGe+1G6dqB/bCZjXXH8SQmnXaTL5sOmSAxhxhWNUZsbx9d2B3rAhuI+g2BOOa6W3cR3buGV3hpGJ9pwA6poZCSocVKJnS5/CtVTjHJk/iOQQlRUGD+dEd2DGElw9Sk1nDq9iEJvelSXGI0TffQJxjp7EFw+Ytv3YZ5/MmjbgTAIdSAc+0ZaV5JLsW8AowJVBRqQtiOpgVEAtGCAYcmESzamHfuU/+bG+d5xQonPNddcw913382jjz6K3W6nr68PAKfTiflInMyll15KQUEBt956KwDf/e53mTVrFpWVlYyOjvKTn/yE9vZ2rrrqqve3sYIh/XgH6FoKbe8z5EodzDJMpjFgZGcnXL60mpVHMuwlg2PsvfJaQk37KL72Skq+ciVawABtGxGCh1jQMJPDv27D3lCDHgU1LONKjhC0ZfPC9NNI6nByfpjU2t0k5o/j+eYw8aTGmqCJ+V/8DEaXG2HVn4hUn/VaPuFwDECgyhMh29nNKcvN6Ns2of2jg/hXzkWIxxlLaFirqvCNNxNKZSMk7GRlQX62lfKRHpzbXmJZQQnJF9P2kfjE2TTFDBSa0helZDUz9q/70PdsA0Apn4R/7UZ8O15EcseI5czlVw+ZyQoM09D5DxaesxSbwUH5RI1zxEcgBFHTRAb//gCOyz+JR42hpMYwNKW9lJOdLfxiQy3Pbaniaxd4mOqEUcVAefOjGLqaUFwyoCMkxzC++DDyoy+hX/oJKl55kq9Oc7MlNItij5PWAYlRycHhxx+j/MKP0ZlaQV5/K4IUQ+9uItkdolNz890DdcSbdvCdS6YyrsyDmggy2rGWsYSBHiGbn1suwOsV+NrsLNyC+UitNBfoR1dxfSOV+U5++dk5CEQwKDoIDgBc538cyelEKS5j0GCme/k12AdacVePf1dd9f3ihBKf3//+9wAsWrToqOfvuOMOLr/8cgA6OjoQxdcdu/1+P1dffTV9fX243W6mTp3K+vXrqa+v/6Ca/R/RhzvRNv8TgKxCnaa+GSybWkjpEeEB0tUgOtIeyvGeIaSiswk5Y2jxCLpswlc/EeMvbyXx8jOEnniUxJ4SykpqiGghtuhlaDp0WfIYPvkKJqlbOW+KgeeaBJbUGsgvLkMd0dgdu4bgcJyvnOajMW5HyTWzOZCP29yLHotTfHgrA2tfJpLtZb9zFmOYKTEPoXvs7Itn4zYJqIn0KqHFIDJhrop1zgLa5Ukopdmozz1Byu0lqemEvvIFqk+agdR+iOhQD9aCYkSzlYjBgW3aVOz2AIy2Ywp2M6tgBXJZLvud9Vif+gdT+w5i/PJXoDu9KmYqNGD67R0k/34b+ZFRUskEmqcOIdZHIJXNM5vS6Uc2dhhY9vLDGOadiaZYCfmNSI8+h7RiNmpSJvDCX9HjcaQCL4YVy1mco6LlD9IdNTN9cAMTjGZGDFXE/vINcnzFqOVTib2wmmSfH8npILniE1xQkUWvP0X3SIhxZR5SI7txhjfhROClrGUM7+hmOASHDHkUBeMItjwEuiG9iM9IMIY/FKcsz4H4hmmY0SABR4+ildxcPJd8EgA5pTIsFWIqLMF0DLeN44Gg68eKic7wbggGgzidTgKBAA6H4/98Hl1LQWIIFCeC9AbHs/AI6uofw1AnwvRTeVRYjNcos6Aq+ygfkZFXNhDctovsU5ZhralkV/cw2w/6MetJTnHtx2px0fOvVYw99CByro/SK6YjRvrYt/D7eJKHcae6GXBOwiEkcEWbwCyCNkbikIlm2yx+97dmTCMDnDdeR1y6kFbNyERrgsme5wklC4geNkAoTnduJYHcAkba/MxnPQPeGQzas5EAnxxlJAL1psM4Ai/T7TqTFjU/nVExGCWncz++XBcpRaTr1IvwLFtEcngI+aSTcdpkun/zZ8wVpdTdcDYED7M/XkbEUsX/+1sLiZTGx5cVsYB9NNXN5VRpF3JshJhvMu22Ojyj+3Fvf4LQqg2IdRMxzlpIOKXwVJuZbRt7OaU4wlxpPYMrv02faMUWC1Lw5+8g109Gad1KxFVDwh8mlhLIn+PFGNgO+fXoih197ysAxKtPR1p9NxjNSOd+ncDD99NiLSH7jEVUlPWTUA083pRLXY6PKmGMtpEBzMogBS6NjoSHvz6dwKgYWFAiM3PXHzFd+wnSNh8jo9EZfPsf2+kYDHPTyTnUxQ6h27IxTZn7f+5z7zXv5lo4oUY+H0UiSZXuUAyrIpGX3AGjG8FUjJ5z5mtL7ILFhThjCUJ0gKApl5HuFCOxJOOiCbIsr8/fPfNn45n/+spdIpDkH3ft5FtL4lg70tMMFl6AIa8Q0WFCDD9HJH8q5kSMvL5HAZ18xYw0sA1SYfBNQMjJZyA3m1/c1YZil7iw8SmSLzTh6drHglu+TLhb5cA6Kz99NkJ79yDfOTnJuHITLaMyrzT5cT36PJprB54vXYni8zLY1cv4nEEciQGQrKSEdBfUAI/NxB5bCYu2rcJaX4nvq19gaF8HB84+j51tOobGQb5w49exemVGDxzGUQQ7u4wUNbgQj9hXIpKJsZmnUp30Y9j9JKCjaCJFbg0xFUBJtuA8fQqpnFoMrf8i5plC8bCX8md+jB5P4P/4MhKyCTSImh24zl+KNLoLNW8qvc0KI3f9HQBz3VfITSsmQmEpmsEKJjsJTynxs76KacMLJH5+E8rF1/KD5zS+nQwjCBpGOUaFF/JdZpoaO3hkyIwoFLNynI2eaJTTlgo4xSTGrZvRh3tI7R9GqipAkIroHYliMaa/r6L96+hfuwHZ6cbS2YPrrAve1376fpARn+NMWzDKvpEIkgBn2AbSBsF4N6vXt2KxOpg7OR/18EEi9z6MXFpEx/QFSCIUOC2s7w2yrFDCLI2A4AXJha5pJBvTYQWKUsq8UgNV0c70Ly0ItMWtbHbOoq7MxahrDokXdiC89BiOZfV45UZSogPRnAdqLL24ovZxyO+jo2+MKp8RgqMAxIaDmAMBcrQwW/1etu09DMCuHgMTBmJUirtZMnUyoUdkhv/1ONG9B2j9ya2MpEyU2m3YpUZUUzHOSJSKrDhJ1cjG3hii00SytIzg0y+jzVjEIedE9h0Y45UX2jhrmpN9tz1MtLuP3EoP7oYShElGZE3jM1dOZWRojDk5oyjxbnRrNqrVhxTuR7XnsyOSTWOHhXkVn8OhD1EQ2AWAK7CHkPkMst0ukv5RjLMXIIbbsNpycViSSMNbIBVDtIxgnbiQYF4uosuFYdIUhLxq9GCc+LYmxLiDsfFnsD87bU8pLuxFeek59J3rkaU5PL8tQV1xNtGkRCxkweroJhqPAnY0HVr8Apu6VACmOCVOynKieisZe2IrqWKd2KJabv3XNkLRJBfMLyP68D3Etm8FQcBQWfWB9df3koz4HGeUI9UwRUEgZZuKQXGwp9PEr+5u4qYlYRL+AIK9DK2lmURLM8OhfGqXnUF/UsUsgknfi5YIsWaPn2DUwwJ7N/z+2wBUffpmPpPfxZbr76T60oWwZDF/bjagakEKipzYWobo+dx3AIhFPsHQWR9jTtMhvGO7QVdRx5+DPppCyMripAUS8Ugc4403ou/bx+DEqTzx4BCL3EGKhw9x1pJSegajTMwKEFWD9OZNwNB0CJ/PgOuKc2DKRLapApIo4A8opO7aiy4fpOibOTgObqQ7dwGa7qLUqVFSY0dYuJw961SqhvtYfGEVl5xZTOfj6+hftQ4Ad7GXUOMBtqmz+ee/XuHXfzodU06U7E33ICQiNE25nqhpJlKOm/j2Hdy2D0ZCSQ5XZ3FNtZXW/GWUWL3EsmqZ48jBNvViVM2LYEhgavwDCXsx4fqPES9ehKFnM3G/FaezF+W27yDYPWQVD7Npr43mgyLzlFwKO59DHbcMACmlIZZWIOUWIk9fwHcw4hdsfOY3I/hDCUp8o0w/a4DyvQdYmjcFMa8AVRIQAEkEQySIllXIzt+/ghqOEr/jIvo2NDMcTNvLrCYF6/zFRDZvxDJ7HpZZH55p17shIz7HmXKXGZtBwqKIGI0K2ArZvraRxbVDTBx9CkZBqJJRln+Mfn+Y3px6Uof9TK7JomXtXp5rV/BmubnpT+1AO8mTs3m1AlTCJCLkZlNx/gL6NndgnGd9LdVGIJpijj3JkMdBYiSIt9CFQ40hH9iC4EmHKQw9+TzNf3wZ1x1/pKjSy3B3gKDDxtgpNQT6w+zetJUzTK8w+tJLnD1rOrYzT6MnlU17fhXrB62QbWd6SQnK7j2EH+/ntAtkCksV5NZejFdcBpIf4eAu9EMbyT24g3lzvoI1GUBS0hdZWfIw/Q8/AE2lWHduwr34Ulg+D8IhCq46H7MDPuu10xiqpzcpgpyLXHsWWTvvwtXWTEpKEU+NoQ8HKLALjITAa9IxTRtHf1ShMbsOm0HCJKhkmYZAHCUVStsplEgfjo33ExZ93Nx3Op9ONhL52304GwqwLVrKgK2Ib93WQSKlMTAtmy+sOJd2nHQeHGJ4aJBut4FxE04jNjJEQ/+jDBQsotA7CX8oQWGegwE1QVaDnZKsAga8JQjARd2NKNEwpeU6giuPSY/eg5ZI8Kd9CT6h76G4wEK3p5DZtVm45lyMbclJSE73fw4m/ZCSEZ/jjCwK5NuO9rv42Mk1tLXZUVt2I/U1IuTWYl24iNHWIZ58tBFN7yYVTHDf84Msy0syZ1oukiSgqjqCCpEFF6JPGM/T4Xym9B8mqzwX96mLCbZv42PVS+mOytR6TTj1MEt/uIRYWECcW0OLqZhAdx0WowPBZqfn/nWo0ThDhwe4fUO6ptaMC300b48ij46yvEFG3p5OqB7r7SNWXISUHODQHj+GIhspTUe2mDBOmkTuglqyPDDyyz/h/uTJGC3rUJXqI050INg8JH7zO2KTZmA35yOZFQKrtqBkeUhuXQu6TlH/LspvuQy/bmG9rQCLmmCSEECSXze6d0dy2BY+nYTiZE65wuCvbye0YSOf+mIOfdUWCrY9iumsmym0G4ilFKKaRlQTiTsXIg230KkXkWPRMWoR9AMbsGSVsMBTQ+juVRTdcBVmn0B8UCL0zF7yPV5qxQAL9r/IcHY5+vQczvCFsJkUVM1Pn+ZC1AxgdjFCLpUEaFhYQq8ock97Pud4ZUS7k2K9HUULoQU7UDeuY3DKVwkrHryuGKbBYT5WpNH+pXsQOrqYcd7JZJ1UhSDYUbKPrgz7USMjPh9CTEYZS142L3EZnqoY5UXFWBIpXFZDenVL1zEYJD7n66biwduQN5fwh1t+Q8u+LsY+/w1eHg1Q/sTtzHP04ji4gbFnXsQwawF/8p6OGouw+8AQ2Zv7+OZnJ1JTP4QUiNDWnmRvsYQwYQXzbXGa+mI4F9qwXvJJyixBvrDQTVGkh8pAK5tDcxkKJLninBI8Kz6B2ruPtlgRxb4u7MZRMHsp1OJEe3rpLG9g8ZQITrPIwQMaqyd9kvEJEwvwI+m9aE47Kd9cAmE7B6oc3LZJ48YdL+LuOoClMAepehxKWQl0tyJXVPLIPhhXa0LTISQa2LY/isutky17YCzCtd/ZxHAgyRWX5/Ps7hg1W7YCIDbtperaa/AsKMbQ+Chmo5lDJeeAwYAuCOhDrUhd68ixzCWwqhFpyjQeF1ZSl+VhYUmI3opSXBMVIImSY6T9y3/hM2UNGHSRwOoX6T3QwMxzKxGEOGBFEhUsDom/7PEyHPw4l0YOseCv36Tjsz+g31jIdOcY3kMHCU3LIXvkCQDiFaXEaz/HQUM6VEdXJazfug7TSRcQ7+sHIBkYQx1pQvZ403GAH2Ey4vMhJaFqBHSFgK5gCMZ4pXWIiT47Z80owm0zMKfGR9/2EFEg1dlOmdSB7m9n6/AIwtKlNLYPcdnyNoSrajHkuOjXCqj0ZDMUPuL7YpAISVaaKs4hcuf9xOxh9OJ0MnrJPIac5WTP/JOod4nkhDewIhHlwK+f4bGPfY2iSS6WVdUxrELuzqc5VHc2CcVN1Bjk6c2D/PWpfkrzwxhliW+dE8Edb4Q4PH5wNk/sGuKhvXH+ft0UnIEI4f1+DMERUv4eCpct5vZJnXSGpmPtr6FvJE6no5hJ1m7KJwxDcj+5ei3mgRjzd9+HXljOXQPlTBtfRmsoSWFKJxLXKSxw8MLWXgZHInzlomsZlzxM4uQzGbZ5sSWjGBzZiPufo1C2EiuagFELYxhqpid7AcleGdPBbWitO+is+TJ3re/lnzfkkTW7Hj0mIZiSqANJ3PNmYB0dI664CQC620MsZcOsxNESBqIpJ50mL6c5NmF2xugcy8aVm88/uy10Dvew02XkxwOrceXloTsVBD1J3FOMtWAKjqEAQUFB3teIVDOZ5JonqLnpS4ztbsQ6fTID2RMo1FWEjPhkeD8oOVL0TZFENralo9w3Nvazo3UYl9XArGof+R+7EL8ChiIHVHgozjeRkm9iN9l4nUFEUiDA8LRZ/OElK1lmKM42c/3ZBeTXFBJXRDoCMXKWzEb8/i+ZvnQqsY4BtJc2s+DkEsryJuEM9xH47Z8QzBbKL74AtXsP8R/dhXbFJTxXOYu80mXsIR+SoMgGyvas4qpcG3cNS/z8mgk482M8/ZKZbfsT+IrTVRVKcwyMPLuP3d/7HYLFTOCXv2Vh+SAN9sNIYwcZVWu4N1LH07u7mFqu4R3noTyRzkdkFOI4d2wi9ezjACz/+m9o14GkxqjFzGc/M5Wte4cxWGT6BsNsMpQz++r5DLcFSX1iJZ3ZXsbOPYvyuo/jiA6TNfAI6CoHSj/GPa1GJKvAuUsvwnFwJ51+lUKfmej6newunIB9i8Z0Z5LubfuxH96EbcYi+gdkSv/6W2KV1Yyt2U68cy/RbbuJfOpGCs0R7CPPApBfehLxb32PuoMKncPd1DtTiAeHoX+IREccweWgU45T7k3g27UR49o1JF54Fk49Fds5n0Aor2Po1PMZ1QXkpEBuKIHi+GhUqXgrMuLzAaDpOuGkikWWkN5hvgODJFLlSWdfTBQ42ds3Riga4wpXJ7LZioCOqbyC3C99hb7gMHtGzbgNGvVXzcYTjLN6VSPdUSsJTePXz2ssW14JXjMuNUnBaAdBqxk9FccsQ3GuhOGLp6C98Bjx3kFG20ZomTaL9QYNUfBxyqJzca6+ByGRIP7XfxLv7Yc/3471J7Nol4vJklRGUhKWrk5S//gzVYLAzV+9CcEQ5GCvxE/u7OWK3BEaerdQP3c8FTWjjD2V9sYWTWZKw/2o/j60bA+65OSn91uQi9NG771dIZbMKCNSdCoGUaehJYHicxIEDKVlFCbbGIuXU+cewCiCv9bHaNMgdl3nkxeO5567dtE1PYWnaS9j3d2kurtpqlvK9pTMtNtuY9wPL8PsjBNUDOhAStOJT5pDauFJrByS2NQ0zOb8Ym5/MR00e/0iDy/GHXzrpgZMso6lp4+91dOJaAK2IT/x+x8GUULQNRKWLHSjDSERQXbm0l5ST2lqhI9n21jiGsY55QoYHEJ95QXEwkpqZttRAxZs02eROtSK7cpP47rwYgSDkY4rV+JeeTlKw2QsHQeIx0Ioy898z/vqB0lGfD4AGgfDNA2HqXSZmJLreMdpWpOaRttoFEESObMhj+jQNtQNfwFAz9d5ZKSMpVl7CBTPBcWMPyESV3VEUUAr9HL/sIcsEU5eMIIr18pAUmNEVCC3ht4WP9FOP7MSzSi+FM6CHGLbH02HJGY30NLWCTXZaDpoFfWYoksRRrrJPv90Alv34P7c1eRlWfGm/ExSBlAtYUJrdhMEBIuFoC+fG3/ehqbprLxoHFO2PEqWMkT4/l8Tn7eI/EtORcorQzTbSLz4BJHWXbw89iVGKcVtk0lEUsyq8TIvO0HZU3cxuGwR0fpiSqe3kXhxCO8NN5LIyeHmZxKcamgkR+9E9VQwvzyHiV+cQ0gVoK2LmdfmkZ9vYKzDjXXKFDSPl0RVHVarQsnZ82i/8yXKrj2PhrweYmIBsiBRsX8X0X1RipraqSksYMNIOrGcKECXkMV5JzvpzHYiJFPEDa2YUykiokK0cjLGs65AqKzC7yzAHusjPuNyko8/RPyVTQzYpnD36oMAuBZ4OXd+Dgd3V5AnGuHgRlLP3o9ivARp2nTCNZMJKRZ0VWLV4810V32chY89S0njThLbXkT5zi+O3Wn0EKC/q4Dl40VGfD4AukLpZPQdY3Em+HQU6c3io0ZjDDz5LKaiItwzpwDQPRZnz2AYARB1MIcTWAAEAaV/Dx6XD1kN4evdjFY0n7hs49BQGCniZ5rYiZZfSKF7FEHwsKUrhcMgYZdFZD3Gr3+/iRVKF7vu+iuCJDHhO58mFa8iLzeJqIHl8UeomTodX3yEHCGCXj+J2KZ1mBrGoX7qswQRmDwySPdvHyP7kmk4HK2Y59mQHV8kbCtkW8JKSk3XMdfGxtjlqWRJpYf8+ePZu64Pc0c/ygv3osxajp6bS0flDP7fo2F0HT5zQTnTxwkUOOI0Xfotev2jmPccouuXv0OzyhSNrEU90EigeCrN7dl8fXqM1OpnwFuEfMqXCIqQ0HSUskLUPA9PjaoUldlQD/4L6fRprO6IcXp1CntNPmOjOvuu+ynFX72C+ScFSbRppKweep95gLJPzELGwKKOTVinTMPqc0DzDlzjFhFUBRAV1veZ6d/QSG6WjYoyB01Z8+jvSHKafwehwf3sy6mnoH0Q5dBL5C5diUkRiSU1XKkog9sEvrkqwsrSQk4JDqa/q9EIw489RtuuAzzsmUlxzz7WrW6jbyCMsXoyJWIfub/6B1J2LqOPPECqtwfnWeeh5BcQ696P0dZF8qXd6CkdafqppHKreHh9GwOjMc6aVUJp7odHlDLi8wEwIdtORzBGgc2AIh27WtHhX/6evn2tDH36GuS93RR57cTjKcJ7Wqk8vB21vpJfNtuYP/5y5hZFkZwpZvT/EzkVRq88CfoPskkrp9hmYM7Bf2Id3Is+NpWRKeewbtCGSYQDvUGMosCSXA2bzYAUfrWQn8rwhl0MvbgO9ZqPEzzjbEadbmSjgW1tEo902rm8YIT7rKdiDbmYS1o8VUnC2NXK1getTLlsLvq6J/E/9iTmSRNZ4q1A9NnA6cLYP4C7eQcH7FOZn7uTyKIzeKQDipdeh+3nN5NzwVlgsjC+RCDLKlBepDPOtR09lcAxaxIjT7+EZ/ki6DqAkOUiGVQxTp6J87nHuL7uTJRACBBIaR7UyBjuLA+DUQGvUWDzkaSQQ4XFDJ13DY9sCTF9qRmXMYFQUIr/a78GYPjpTSj6VOIH21EWLKbyilmkxCzk3Q/hAuqKa+nAjrTxJeQZVYh2J5KqkWWIcNnpozx0wMFtz7TyufPGMS/PgS0whHbT78l3buevVSuZUBtg8qqn+JJixTyxkIp1f0AwmPjj2VdjMUbAdz5COII8ZTna40/T6Kmh3Z+g3T/M+IYcBobaKM23Yl92OnJuAdFdOxj4ftqZFEkk64pPEdq3B0kJom5eBYAgGzk40ce9a9K5frIcxoz4/K9RYDdSYH/7HCqJwSESs2bTK1sgkkIfCDE0HKFhy1NYnr6flNnMN//2Y779cB6lE+vZEBPIyVnEhOa/EhAsHLbXk6NqOAQdUywdpU1ohKGYTkoHgyIyodCFIEBXOMr8lROgr4T6qU7s5hT7f/sYiCLDVePwu734o0kSkQi+LCvxrjG6RyIoMYWlT/4Z+6SbiWb5MIaC2K//HH7ZSWtjK1ltKRKd3RiravDv3Ej285uQbFaWvPJzDJ6zGXxuP1H7JLbsSvHAU4eRZZGbZy5kZPXL5C1NcdWhbcS7eqhb+CVQ44h6jLxPLiQ1ZxGpv/4M2x23oZx6HsyejOG0aUguOzO7e+mZcQ6DU8/A8ZtbMQQewXzK6VTkFhERReqzDBzojyEFY/zlmQE0TWekbYTl4wcQsisY//vPEz48SjJuo//W9FRGHE5yeP6p9LX2cQECiBIvUs+ufaOEJ3yMxfvGePnuF1GTKT79qRr6bdOorBKZ0JvE47GQ0nVGHVlYp0xHCoc4qUGjZG8no3fejnv5aRRl90MiCmoShzqElGxDL3XR97SG1ZFDbMmpTNi5n21JkdoiJ4unFLLyjFrqCqJoB/ehHm5E8mQhebNRhwaRZJXYz67AMP5UEooNUVZAlMGTS57HTEWunfbBENWFzve7q78rMuLzIaHg8o+jbd/LoAyaJLF2XTuJeIrx5vSKhmQxY7UluPjsKrpTBjrDMTpx4C//PCXZMgfaQ+SaREwbV9E++Sy8Yj9BSzGuQJQixYDFbKE1ko4dytKSRI0ylspsKubn4ScX39Ql+P1R9jmysSVUjLKI16LgtevcONWCnqyndIef6NPtjPn9DLhyMfoKaekNMBJV6S6qYcaTj5J7y/eIrXkBx7R6Ah391HzrKuw1OqCTe2o9wb0jKIa0EDvtBuSJkyg+dRrRti5ih9KOjP41u5C6DZgnVhJv6iNfURhR07md5UQYc70NgxRGy4nT55nMqq0BQmNxJi+6mqef62TlnV/AUJiH5+xTcZ0+iz/8rhldh3NOLWfd5j5W1AiY800Ig3sgtgdTnkTUsYLQER+q1NAw67f4eaE5iTbnfMpqi3nkxQ4kUaC+1I3DDTu2t7D8ggZ6dQcp3YZgg0XTCzlweIT6mmxsqRjuHAvmUgf5qUYO790NgDbUz2goH2ftPMTCMkSXDmERjTzkrAD9oRgDihVh+hR+vEijIy6hIWA1QWr746SeuB9kA6GVP8BzycVIsox0eA2M+TH07iGWfQGWi7+LKKhI5RPwSjLfvXQq0YSK90O2OpYRnw8JjgkNNExooEbTeezFVrbu6GHW5Hw8l12Jc0Y95hIbAUc+HSNGSl3QEQSbKPLUrmG+tsLJco+MeNOXUfc3YzjzHA6eeSWNXXEcJFkafQI9z0lnzlJSgkhp+3ZkaxlrNgcZOLmIzYk8AqJGcVkO1cExskMDpIpKiI/4qYwewp6XAgkCE6sZuO1fZKtpG5aq6+SbVUaikB0OYCwsYHhnE/eYl/PimkG++oMfIs4uQg0fQLLGie/vQdy3n/zs5Zy+uJSB0TjmOWZo2oz/hbXkXnQ2eiqJSUrQsXuIaKIM4U9/AiD/so+hdnWgTBrHur5sinq6Kcxy0hWx8dhzaSOuMDGPkCoxeOVXKFh1L676HMyGBD//go+OXTGKN69i8dAOEj/sRl/2XQQ5DL7x6IkoJmcK749/QbJ/EP/a9UwqMtAZ9zDmcjMQiBJLpIW7JMfK7LpRbr62hIMpmXA4QTYgouNLhqipCZHn8rN+t5mewrlMaChAvu93uKt9mKouxFtrRIj1oI5bRNxbTKplB2obRJufwnbSUlJKhIGYEadBwm614yJOMKniNKhwpGrHtspzUZ9YTfW6dI4n5xWfQTjwMvK0FbgmTX1T37KaFKwfkhw+byQjPh8yZFFg8YwiPHk29ofi7IkJnDR3FsbAbuIRgSU5g3j0XeBYwOPbxlhQa8K8bxuh2+5F11RUIJVbzYFoiqIykZnOAUxD2STaQ0x19yAM+zEMdlLbtROnnM+m/fPIKkuX0ZFCY3h/9D1iHZ0k//V3LLt2s7+smMFBF3XuLobiFrpFhXBEQhoI0ODqxxbeS1XMgxLTSdZWk5hUyeOf3ATA1o2dzE1uIlnmI7DtENHtO/CcPp9Fh1dTfs9DxCbPoSzrbLoe20DsUBuJvn6yzjuHgfZBeqafzljvABWvfi/jx2Fetoj9cQPewWaqwi+i9xhxFl9BjsfCcCCK121it8/M79sS/O3PP8DkUBGSUUpcZgzCAGJVNYl/PYJksxL3J8BTi2l4NUKwC0bbEXcKuKcW4509hsPTzYKPLcJp7qd3XYKBCTYEo4mz6xL4Coc4vRA6WiVe3H6Y1FA3whNPEZIUvD+5iA37NX76WLow4ne8RorOvQ73wEFSkkzshfsY60tgtg9yOGceNY4Bhh+9m1R3F5F9rZQuKMbldWGQCpAlkVKLRHLXWoTW7YjTFqGc5aOlp5Ds0XTmR4wm5HFzUE6/LF0S5yNERnyOE4mUhkE+dmdx241IFgN6KE7PWJzQcAfGgZdwSGYSZUsgpTPNuQZ/+WwUi5XGeCX5C5bgqasl7MphcE87SVFioq0dR7IJnBDPKWErudQrMXKmpxAEM6aQg/jtf8KuRfFefR3K9l2Eu7uouPWzJKVhembUs2vMBO2g+YvoNCpYEyr3PrGP1o4AF59VTa2YR/7oYUarp5Hj7MXnS/Cnc1T8wxGMviCxoTCd63dQNd+KbUkugttK8PGtqKEwyiur6C4tRfMWYapM4l44h50/vQctniDP6eEfholceMkXmTYhB/nk+YiDo0Tbxni5v5xBr4ulyacYp6/ns3Pr6YkUkGUM88BYOpe0YpVJSAJIdgz+ITyJnVizA7h+dz2JtgEMYz38/EUP18wrxxLsQrWUo0UbEePdAPhizfSnTmVtWzXFeaNc2fg9hEAYoW0emr0KRCOuPRs53WamdU0fA5t2IF9yEV0jFtDThnxRFECVMSS7kCP7sO5up/n+RgTALbhhSgoxEcFcVsJYdxfmcQ2IcgQLYyC4ADtafzP6tqfRug7QUzyDNu8cJtjjvDwwStbZX6auxIXstH/khAcy4nNc2NThZ0d3gNklbibmH9sIWO21kkipZFkNuBPN6SeteWAsI5BM4k+4qM1L8ni3ytyN69jm1zFnG/nxT7bhdho4xzTKqMuEzwa6bqPNN4lYXELKyyVCNaZ4B7rDinnDKlKA4YJLGFyyggkrKjDJUUJJAbsYBkxIAkh7d5OzfS/PMA5PbhYBl4mU28rqUAnZJVWc5eknu+sFAquKMd71J3IB58qP05U/k+G2p0kEgjR8YTFGtx331DJi3SM4T1tKS7dO713PYcrNxnmKG2txLqFD3RRPtPDziS4S3QmyO7aiP9hJv7Oc1YdsbNs/wiuSwPTLTsY1sAqHlk1OjkLR4BouX3o+/f44kbgBmyGKpItEOkbwZJnQYyJmWwrJrBKLOZhR7+THz2jMmHAFMxkmWGLkZbWBSvcotaYRDg1LjCU1GnFQNuVsjIFuhqVsXB0J1IFOlIYSRgbMyDeuxHTlFXQPR9mwdphAMMZnp9nwFDjxdWxC2dxEeOpcbAuyaJhUzeDvnyD+7AMU+OwoeWHsjhjWc5dhmDEFcAJmdM0Bagz2vYAkD6HPPYVn1SJ6W4Zwm2XMdjMmQUBZewfxFyIYLrkJqWrKB9OB3yPeU/EJh8P/sTTx/zqJlMaO7gBxVWNPX5Bx5gT62ChSUeVRzoc5NiM5lenyOXpqEbgqwezDZHBgshfgTI6gjaxlfPZEOqNO5OadxK05jI0lGBtL4Otvxr/LTHPdHMoKUuSKQdqTFvplD5vHZlJtn0GhIYR43sUIikRvUSE+2jGaTByW6kkYJOzmMGdHdxHtSTHa0s3usIPaxUXgNvPFM0Si0ij3HLRikEUUNZ3gXFJEkCTQNKINk9ieX4v9q5XMl9dhlvbBmIRzQQ3uOfno8VHy1ARD475EctRPLBZj0l++hjW0F5tzCNXYTuTgdhI7t4Io4a6YQt20q9i2f5h5k3JRtF5C3gUUFlfRH7PzYvwCJq26j/DDDxFauwTvd7/AoJiFnFtE6pk7EZIxhKpZ7Ks5DTnfi6yJLElFCVgdXPxUgAmFk9iyqQ+7WeLPE9oodfcwaC4kz2YgJRehjYSw6CmGfvEH9GAQx2e+SLyonGAgxu8e34+m64wrciHoKRYvTDE6bMPz6AMAqC4f4kQNgzmLxMF0scRESwucchnGgh5QTEjFbvSYHWIBGP4TumU6kdZhIq0mrA6BrBwTvWNJzLLE2u4E+dk6NdF0tdxE8zbM/8vi09LSwuLFi1FV9b087QmFQRaZVeJmb2+QeS6d0M++gt7Xgfnyr2Gcly7XE0mmGEtoeEwyiiQiyCZwlNIfjtI1MkqRGsbSfQhZGma6aytP3vYnEiOj2Nra+NilX4KhPiaMvIjdUgAF+ZjUPZhUmJO1kP3RPDyaxs7GIKPZOrNnGenLnct86xYMai+pVAmyUkFCBSGlIz75MurDD+KYPIOS676L2WmjUmomL7wWUvCJ+nMYTSqEzNU4xDhWV5Sy6y9As3rZVj8BRiEhK+hiOlZNx0yid4yxdc0YC5w4amL4GjwQDPFw+yTWv5zkm6flEVBdmBIS4bLxRKIK7uggiUSS+TOLmDYpnzyvhBovRehsJhyz8LVfHiCaULmoYgqTeYh4SwvCgS0EnTPoM1iYZ3EjBHpRBZlHNwdZclEJVoNEuKGe9c+3omk6FlO6JLWkSCQqZhH97W00OD0YLr6MF2wl2EtzmN72CvqR8tj6aD/u0jBBXUUSBTRVp6HQyCmTglgNIaScXIQJM9GadzKWV81AykiZvAv7175CrPkw+vwlaCXTMVUmUZseRX3iPoTyWaDHEXxWdL2NwYdeRh0ZJtrRxyl//xSTCl20dI4SjCQJ5tSiyUvQhvoYffhZfA0LUQqLQdUQLZbj0r/fDZlp13FgUr6TSflOUof3E+rrAEDrTS8zJ1WNDb1BRuMpqt1mxnvtoEfRE9vxSUHGlDqCB7oZufdf6Lu3Yrz1JrynL6bn7w+TtWIBy04roPrxB9F7ujHkTk6v0kigYSCqiyRSGtpTz1C7fzuhSXMRC4bJ6dyLYpdAAF2LoYXjKIcOs6cvzoTedPkhmnaBniAlicQ1Ezu6aqiI95OVXMPaRD0Pb/LzyfESUxwajqpZxGMC4y1+chwFmI06fUOTsAazeXJniul7n8P88uMgCMg/uR7cZeAqo3OPyNYDw7xYnE+OOYnbEeOmDSnCsSxunG/A29FEUpLI8QrIsh9NhqCngr4HVpPS0lVfpRwvRV+4HKGng9HV28g5dxzmnbvRKqagRPrZHS/jyrkxxKceYOSljRROm4IzayqfmJ/HWBymjM9BFwQO9Y5geTGdNTFn6lTya8bTbcsmEErg/fy1qP2HUaaWM0aMSSNPcvOp9YyE/EzN2oShy0ISC0KuhnL2QtSTl/HcZhOjTSqiOoNsXWXRdDshReL5Hd2cNqOQ2MAoRl8V+oFXAB30aegTphEdNwPDmqcRxk/F2LqDispayrOymVKehctmIPSvbQzddTvmmTPQ4hE6r74ELRgg9/s/w9zw4SiR81a8K/HxeDxv+3pmxPPukIqrMF/yFbS+DpS56VGPquuMJdJLqtGUhq7FQQsg6H4QQEBhW3Ylhs9/m7Ida9k4VEDOFZdhP+csBksLsBiMGD72TapGdyNu/SfGDQfpmPdpdFkhx9JP/6gN+b7fowaDFAy0k1hWC7QSGzIil/k44KtjeKSHu1cPsa8rxOU1J7GiwENiyjx6ZSsNcpInn05iPzxE1Uh6SpFfdS7DgRw2HxbJ3vMM9vIC/M+/wKSfXEpVYD/DqTqEeYt49P+zd95hdpRl///MzOl1T9veezab3U3vpJBKCD10CB2kKKCogA0LRVFUBBUREAQFFJAmLSGEJKT3bHazu9neT9lz9vRzZub3x4kgP8FXFN4Xle915cpOeWaemXnOd+65n/v+3pv0/PrJDgobXZQDmqISdo1VMXvsT4jjQ1xVuwCPqZqZ0h68ciE9VDAazEyj9xrcmBYsJhxP4zG993kaMZiQwmN89YwphAe6mR19Hq3Zg29zppqE2DAH45xZDLe2sGm8hibPOEW+N9jx2H5iR7sJH2ph7RftdDy0gfTqVbTgxmrUstuvYenkyVgri9H++TcUPu6j6NqbsbjtmMoTqFUuFEbJ6h9ASMWo8v8eccJMBLMNnPmoXYOknn2S1KzJdCXszBG62CDV4lFSzHUFcNiiZOlSOOUA/tEkW6UmZtiOYIuHSY1DdE8YQQpyj/V4Vl8xn+OHnkfp2Y5UFUJUdWTbp4AgkbXmXEzTG9FXxBhfv5PE4UOZsbN/738W+SQSCT73uc8xadIHX1R3dze33Xbbx9Kx/wYIooh+wer3rTNoJGbl2fHHU5SaouD9M4gGVFsFqHG84xpUIIFAX/UMHrlvGxUlWZy2shw1EuVoUsXj0aGxh0hUziOiczCsONGlRYrlIGU2P95Fiwn/6Tks1aWkD+1Ge/zppI62EjKVMSyo5D73CPmu46lbWYM924hdjaNvsHC60oJBGWG930EqpQeNFtIpnB4XE8tsrCwJIXaZiHUcxTlzIrrBjOVg1g7gP9JPw8RivnTrQhSjhHjCfHq7R0hoDQjRTNyQqCQ43fcrWvJXotY0UmY0cNayNKl0jFlz87DatUiMIKeMyGGRhAi5B14jPb2Ebck0xw88yl3rWtk8rNCYTnNNXRXZjji6lmdI+APM8UzBqJURon4cs5qIHe3GPq2J0OuvMH5kmOgXvsYXnruXO/amaTiuDnnN/ZgObyZ5dyZdQWxvYUfeDCaMwr3bc/GNK1wxR8vktp8DKvHhKKKhBKPLi+IxMDRkIPlKF+XznAgGC6eEt5LsH8CYlYdoE0gcjGGrs5EIDpGwL+KgrYqSqqXofvRdIm+9gPDCS9z9+MN09SYQD/Uh5i9HEBQgnkkgFcyIBgOG6kJQ92NsLMC6YiVyOIZ51pz/xZH8z+EjkU9TUxNFRUWsXbv2A7fv27fvU0E+9913Hz/4wQ8YGhqisbGRe++9lxkzZnzo/k8//TRf//rX6erqoqqqirvuuuuTLZf8/yGlKPSHEugkgXyrgVyznlyzHjXaD0o48y9dg2CazvhoLxKQSsr0DWVkJ7LjIdQrbiTVM8CEX97BiGkCnQeSOGZOIZ1Ikx/tJmzJY3QoysZQPuGTLmHS/JU8eDhO/aQTmZ6rJaehHFGTosKXIBVPcc5UA4P1xahAoNVFzuA4kijR/9JRznDk8oalhP66myhzR7G/0cmPLnGjF7zEpixh+O0xYq0tJPJK0Y81E07lIReV8NuHHubttzcxYdpMbrrmc0gJmfJJbiTN6aj9AyQO9bO9aC3feyWEZv0Bvvy5WaxeXIxh1xaUrQNYT2xEqxkGI4S9Nkzv/AnN0H5arPPYNmjjoe06/vRaZmbwTUCYv5RbK+rQ/PF2tECWOZevPWnnzJmzKVjiIm/qbOL79mEsm44nvgnLqll4+ndx9Ymn0i4ZaQkmKHGa0S9ZjBJP0p+VzXdf8vOlVR52tYfw2LQUi6OIVVNIxc0Efv0EaiSM6+ar8e0fpv/XvwPAXfUVNsWzOKqWsuTklaR6W7Hu6UcZHsDWIGHKn8RqnZf+iBalqx+NI/OFoa8sw541QpUtlwOn30V+KkmRYkIQ9ahpM2p0BDErGwQHqEVoPUnyvv1tEMz/a2P3X8FHIp9Vq1YxNjb2odudTicXXnjhv9qnfwlPPvkkN954I7/4xS+YOXMmP/7xj1m+fDmtra1kZ2f/zf5btmzhnHPO4Y477uDEE0/kiSee4JRTTmH37t3U19f/r/S5ayzO7uEMkSwuFvGYjwmC6wrBWAMomb+BUW+EHQeHcWcZmDrRxcrjSpkzsJe+/S0ASDv3IFQU0//yXvJyQth7t9JTtYY3vG5K7OX0xeJIoobHDyvsPhzi7RaYsHcb9oU1vJ6/mOGUwKTjVlGelYXn4B7IdiPnFBP74z30xYoZfewpEATOfu4n2KdqASsmzyr06iZQwOCMIA8PMr5pM7vzKsg/4fNE9SZ+/tMf8Yv7f4yqqhx4ZyMeSeHzl16Kyx5GNAPZ+Qj7O7EW5nLnDWWM+lIk4il0sSj+3e2YJ9XS1hqjbiKoqg5jiZ60v5Gxnv3c9FQ723f/hvFgy7v3VAW2vLOVLeGvsLB4Mtr+Ayh6C0e6o9x0MM3ihSY2bOxCEHK515Mmvm0LsW3gLj0Xe6AT3I0IQNiUgzvbQtxo46lWB5fNUpkafYvVDfO4oHgAe2/GKpJ1TajHygqluvzoPHkgSYhmE/GSeuqUBFO1CsNuJ0PuuRj3vYktx4Cq1SBEQkjaJHmCTOrXN6O4CtBeczXWFVWIRplnXo/x5Nuj5LtM3F0zGZtGZvjlx9kdNFFSW8XEWdNBqv3kB+rHjP+4iqUzZ85k+vTp/OxnPwNAURSKioq47rrr+OpXv/o3+5911llEIhFefPHFd9fNmjWLpqYmfvGLX/xD5/xXK5a2B6LsGhpHABaXOHCbPrwawT2P7ea59R1oJIF7b16ETYoQ+95tjA5DKg3VDXqMbiPmS89D7W5H8nWyM2cVW8dMmLQi5dok4wkZMZyib3gEQzjBokMvocnLI3DuqVQY+wknHAR++zbN33kAY0E2k1/4GWOuYqRf/YqB+x9C63ZR+7OvY5vsZFy0s7HDzGxtB3bNMKEdA8R7wgjVdRz9zev0/Wk95Z+/kOt2vMrOd97+wGvSajUY9DqKs6w4yyvwB/xMmzGZs/LqYepMqhuy8KXM3PfdH9HXvY9pNaVkBxJs8PloHxhkbFwhEtEwHmp733FvXDSf2fc9jSsV40hLL1O0XSQEDz29McrnT+bAriEmDexkJKXDLqYQHvkFdbeuxWhOM1i/kqTZjtDRAuMjGBua6NjWzSSxnazhTVDYiDI6iKbAhhAdJlWwmOjhIQQ5gmXJZGSniz/+MYQtx0lTBWT/+fsA+JZ/kf6cSeQ/dBeepSVoLYFMZ0vmkhpUSP3kOwBEzFVYzz0LQ16U+9608+e94+g0Ig/cMB+XHODhlw/wQqeISSfy/SVuHPIwhkn5SIZ8EP++b/aTxKemYumqVat48MEHycvL+yRP8y6SySS7du3i5ptvfnedKIosWbKEd9555wPbvPPOO9x4443vW7d8+XKee+65Dz1PIpEgkUi8uxw6NvX6z6LUbkQnCehE8UOJR02MQnAnJ81w4RvLZXl+EvGxn5MqLaHgi5eR3XEUfH768irQZDvIVyMkh9J0P9ZOVvULNKw8Ea3OSP5AM+E0FE23kVXXhu+NBMNvrce46HgmmDvRyMOYdP1EjqkoxgZGkYKDRN0VeOqrMc2YSWo8SuxoM+qEU2iJ2Nn2p308e3iAgjwTV5fL2OfUoa0vYeulu5j25TPRDB1kvphilyCgqioCcOPqZVx444Xcev+LvPj070ml0hwKR6EvI5R+6EALv/mQ+7X+7e3/4z11OKx86YozkZ+4C+Xii+jKzqNV9XDC6B+ZMnUi/jwzOeUybxwUebjTgNlg5p5v3EC8fhIRl5ORS6+n9/RLOMn/MKgKkj2C+rsnYOYUaJwOxiwUb4DUYJS4ay7N19xP5Teux2yKoskDrTDKScuz+NMhI2J67N1+jXuDeBJHUDa+wuiqH5Av7CYk5JM6nCb28wdwn7AGdEYMhmz0VbNA8XHKnDTZnjQVdgX7lsdQiuqQTFYgglYUCD71GLk3r0LSD4LqBXXuv4W4/CdKPhs3biQWi32Sp3gfvF4vsiyTk/P+kiI5OTm0tLR8YJuhoaEP3H9oaOhDz3PHHXd8rL4tjShQbDN+4DZVVWnxR/AkD+OKt1CuEzh9/gKSX/oWAwdbMFZVMhI6m5q1pxL0dVGT04dG8jE8ls/wc9sYemkjvLSRaXI/vvxavNUz6HPnMVHYAkD8cDsAsQ3ridx8AXaGkdPZmLMC1HzxHCzlBRjlBGXhTsZmTiYVUBDTKdLLZ3IgnoVeK9CU7mXVgV8gBysIORowCiaEvYeZedF0dG4j3lf2slZR4LTTeOeN11BsTt585W2SooVhv+9ju49/jUBgnHse/B1XxkPkTC9lramPlLmQtz1n4EgmiMs6sq1GElluIE4kLiPrJZKdXZCbQ851V7KzOYaq1SMkYyBocF5yNoJ/GGFob+aeueazSdvIrGg7jttvQ9zwBHJFEdu6JnGwV2FOboqFoe2ksuqIzb0IfxxCvgSFD3wJ23d+wLNHTfTG5tI9muSqOpHCeYuQGuciKUn0xRMQ9QaggEITnFkAySfvQt7/FrL0IqtXfhlHaxsFBglTTxtKIA42DWAB/j1SLT6L8/kncPPNN7/PWgqFQhQVFf3Lx+0LxgjEUpQ5jVh0mTdXXFZo9kdpMHswSHlsj04nqdXjtmZEoUS9geHRKNnDBzDpNGikTLiDVpSxn34yI+8cwlboRjUaeDGQi3FDCxet9iFaciFpxby6lJRqIF5Tz85oGY16B/033EblSaWkayoxh3pIP/w4Un4x6ue+w+DceQDY0wKiAIRCFHYcIFVQiykZJFUxgWBYi/rz35Lq7UZtbCLvC1czvr+VG+omgd/HPW9uAGD3n575u+Eba1edSFtgiC1bdr5vvSDAzWevRluSx7fv+lXGmhIgy6QjEEm+u987B4/whcvORyougsEWiAzzwK938IXrZqECWdteZmXKh2X2TPIrsykXWkhqjazbM8jguJPZs2U03lqwuxEtGnR6C2JZKezvR1Vk9I2VTHt1I8qbf8K5/Ayk3DwCg2F+9EKIZFphsNDAtboEgQfu5pXJp6OpqcNmDBE//5tMfuMJ5pVPZ126hPoCEePd38Xv9zJuMPKOayI2XytLciOIe3ejWbAG0ZGDkF2cuf7CWpxTprLc7UGyWlFPXUFqZABNXgmiwQmC9C+Pxf8N/EeRj9vtRpIkhoeH37d+eHiY3NzcD2yTm5v7kfYH0Ov16PV/XxzsoyLob+PPHfp343zmFGd+lAZJpNphpDfiYVRZxL7RMJBg3sUXUzC1kYjBwUF9MfP1PkzeON4xC6JFx4jk4VBpCfnP/Zb8jre45S0bHQMRLj2liVhiBN7pxye5OCqWkF1chxhNM9fRgzErjP2HV+J9ZR/9P/kp1edmUmXUWATTkTaKXRWY9x1AfuMlXKKAOH0ee36bqSRRceGJHLIVMzo0xkmz5qNFwZxjQH7td9gXrCYYCLGtf+B9191Ul0cNTby9aSszJtThqCti5+FmFswq5svXX8l3ntrEO+/s4i+uyfIcCxcurOKm69ciKzqigSi7du9lXpmTOHZ+8NQLqIAAzJ0xhfDnvkyPaKIstxF7pJemGh+9BztYpO9H48rG9vbLnFQ8QlopY6y1hYEpy7h/X8Zy0M+1MmVSPcL4fgTfYbSWcoLOFdhWXYYWP1qtQmTvZhRA2LsF+cRzSdXOpOadEQ50Bsh2WhjxGWiZtIrHRj0wOsqUajcHugN8M6eJCdEhLiwFweNkpLGa8JtedpsreeqdjDWYd6qHuh0vE8qfyGi2lvKZp6CrnIzoyEXQGzFU1bx7H3VFJR/rePwgqJEAamwMwVWCIPzr1tV/FPnodDqmTp3KunXrOOWUU4CMw3ndunVce+21H9hm9uzZrFu3juuvv/7dda+//jqzZ8/+WPumxqPEX30SNRxCv/QMpOyC97aFjyBGDqETpxCTQftXGcqCIDDJbWVrJEUkGiLLIBFPKySTMpuck6ieUsbVlTH8SRNH1BR529/E2N+Kb+3XABiSRdzlszknV4c/FKMyOoTQso/01hew6YxUNq6g7Tv3A+CuuAXDokq04U2k+oZQ43FGmkfIP+lkwjENW1Zcg3bRApzzp6LZ+BYArpIijAU5pMbGEatreLktxOK6bNLTz8A9cxqpX2UcqKp/mNCBMZoCPt6BdwliatUkbjh1Aalbb6Lvpm8ib2vm+i9fjsWRwDC+l5uuOBPDeDeb39zIvBmN3LzMg+CspHf9UXo3HmbmjjauueV88qsE4sEAY94kB4cHmBkd5XPTp9IrmECFqNbJoNXFKdeojAdSdHa2IspOJp25hnQ4TuCBJwCwTllAocdIvzdGmTmEGhtF1ZiQ8KEYLHQYSsgSoUDXQVo20n3CZeS27UCpaWSXpYEHf7GflXNK+OppNbgPvYWvZjrRqAFhYxeCIKDViJS5DIiVk4gXOjBr9iAlO8i+YinmUjfD5jgCYDZocAhp4nm13P6OyP7OTVy8soZzltUQjCSxpGXESADBbEfQ/JV/R1VA6Qb8IJSA6M6sTqdQQz4EuwdB+uiWkRoNkH75dvD3Is6/DKluyUc+xv+P/yjyAbjxxhtZu3Yt06ZNY8aMGfz4xz8mEolw8cUXA3DhhRdSUFDAHXfcAcAXvvAFFixYwA9/+ENWrVrF73//e3bu3MkDx0SsPi6kWveReCHjQhWcHowrz31vo6DBoo5ygqeFkKaCwuy/1dmtFEaw7fsxaWsug1Vn8shuC2/vHsB44BD3XlrAOsWBip5owSTsP/sV7rPDKKYseofG2SmJZGdJuGSBzWoOeZKEBhCUNDp7xsEtSBJJv0I6okGDgHtRKXLqJIw1BZhOmcbob5sxzphC+uYbSCRjGLbUg3cE58wCiseXkR6LI2pETvCkmVlqgm9/mcFQiNwzL4D+DqKSC61ujMs8LvQzZ7Hp7S1MsjnR5Z/GE74y5rjHkceCAMT7R1DiLkLjRvLcB/nmZY2olywhHDPQrBQzvqMb8/bX6P9zRjeod1svb+nnMWXaMq57+EYMkSDWdc+SWPcyBef6CBpsqNEECZ2VBAIGo4bOsRgTNaMMGMvwWJLoGyeTHvWyVc6hsNDG15clqRx9kSOJ5RxITqXMFMBe1Eg8lubJ9T2MR1KsbLLzlrkWoamWi42tHGrzkUorPL+xk6aiSbTvD/D9Z5qRZYWLLppKhTVG/dgOjIObSOrPpM0xBYeQR1ngSVIaA76JDdRZ43z/9EoMdis54WGCZ9xC8w8ykyU9I2H++HYnv3vrKKdPzuK0nd9Dajoe7erP/RWhxICMHw9VD7hRFYXUyw8gb3sJzfHnoV183kcev2o0BP5MuSMCfR+5/QfhP458zjrrLEZHR/nGN77B0NAQTU1NvPLKK+86lXt6ehD/yrKYM2cOTzzxBF/72te45ZZbqKqq4rnnnvvYY3zEnEIEVy5q0I+msPL9G01loC7ELUdxW4o+8M3kTHlRUmF0/naO7tyBSOYY2VYRbX8XusJcEjJkVeRQ/PB3eXJMwBaJcMR7rNieTsIXz9Sl2jphOY1RAaWkiPJpURp/8w2G2hJ4o0Z0N38by1e/guj0UvK1BUimAJBEK8jov/5FWtCBTseCiy4nNzWEEtHSevdjAOSfdzLTVi9CGguQHB1h3XGf4+k3BKbV13L+dAsuQYttxmRuKfDQqxoJWTx8ZUeICyfrGa4soPBrt2Ie6iW6bz9Row/vurdxzrwTjVZmDAtH96XpKrLSNmZiiZyi5OQFBDuHGJg0m4d2aXDOdJGl0YHdg33tmbwgTKV2Xx+zWh8jPuckxmuPQ68kiPuCzEptwzm8G1WjY2zZl4lefROj+9p5YrsEBDjOIeOa+0XChgLKYykI92GVIzR7ZbYeynymV+cYWJIv4U50UtD/EvPLr2A4aKckx8LWjUeYMq2B+KFM7p4uHGBGZAPq4YyjX9u9G3PdYtKYkK11BGIaRsQ8PL2HyU/1oYa8hA4exLj0VG65YA4tPX5OP87Nzb9pQ1FV3jgS4aSsXNjzOtqlF4L5L9PaBiAXGAbhmD8tlUDel7FU5UNb/inyEVzFiMddAYE+hNpFH7n9B+FjJR9FUXj55Zc58cQTAbjlllv+x3ywTwLXXnvth35mbdiw4W/WrVmzhjVr1nyifdLkFmG99eeoyQSSO+NP8o3F+MPr7eh1IicsLMeblrFFVQo/oMCAUNCIMOkkUskUe3d5WBBtYdGibAqjXThchczN1xBJJqjXbmfUVILcnETnkLDqNWhUhQkWhbBFT6s/iTMZp7NiGm9u8nG+NZ+qSi+WCTr+dKQAc3IxTvMQommcQV8CW8qFzuvFPrAb8+TjIQxaVNKt7eiG3yKh91Bx1hLikRTWC0+jVTDTkRBYds1X6DyoAQIcPOLDPN+Fb2icg0krnv2HqK6rwOT1ce0JE+gfGidnci76U1dj2L4JqaiYkXvuA2DguWbyTp6N/5nXiT36LCXnXYCw7Cz2lOYx2ZrAaLaxv80HBFGTGREvSQCTIUquU0U31oHh5CUMp7IY27WXN/sNjIzFuPWYXEnKXoCVIFnrHsRpLeScOaeQjMeZ4u6jV59HQgFBryV6MM5kd4AZvoNsLmggEEkzyR2nVu/D0PMGQ2WnUN1xkOtN42xLTWRWzj4Kgh1cftr5jASSzM2Pom47hFg9HWWkm1TlTHzxTA6fR8zC6evFsW8djHYj5zYiGIwYS/JI9xxlZt1U5pToCWsHuWipm1e2BpjFCJKpBs28UzL5ZO8OFAnEiUA1CBm/pKA3oj3papSWbUgNC/6p8SsIAtKExf9U2w/Dx0I+7e3tPPTQQzzyyCOMjo6SSmUGwV/H23wGEG2O9y1v3N3PEy9nQgAsDiPaAitpRcWq02DXv//RCAYr0pQzkYALlG34rvp85piXXMFQ3TLSKZkOf5TSvCKaBy1sOfZ2Pn9xBRFJIHR0gB0mNxYNVMV3IDbMpqMryZ2PDHP52SbmVHcwOc/MmDKHzriTcDTBjXcfQZIE7ppwFIN7Mprbv8vMBUtIHT6CMDyAUGlGVzWBxLMPk3vBGmiqoqc9E/O0x13BqceLePIc1Fa5EJ0Sb3um8MfmOFZjGYsrs2nKO8IpFYPEy6eTtms42h/jxUgu10w3UfDinbT2aOmNq5SOPkfJbJlU/3TikSB4TOhSDlS3Sl6WBsdIjC+sUJmi78IkJ9FYwdcfo4FDlKa2IG81UbDyKnb26dh8zBJ5qaiBpTNKybN70XoPIKdiWP1tnJuzE60rBfEIoXSSpMaAv38c9dB+1NlTyG2s4duR11CSSXQdYYTKKoZmXk6+rQ8l20Xw3ldxW9PIJy6Cngjn5o8iRXoQzBZi89cw/tYWAr0mlLl/5fMTbIi+HuTBzOeSqMZRMSGabYiOXMZuuxZ1zlIeLV5Ojl3DtUV9jN3yLZQf/gzttMUM+qNoJAHPsRLbCCKoUZA7M6kXYg6axoXQuPDjHM7/Mv5p8onFYjz99NM8+OCDbN68mfnz5/ONb3yDU0899ePs3380SvKs6DQiBr1ErttEUith1Ih8QE3B98FVUUq4dgKJtiNECko54ouwuMiBx1yMTnGRo+9HIwlIApi3vkWv7EbfWIasqASTEHBPJj6k0hOAidUONu5IMadGg0a2ES/MY1/ShEkHRXmDdPWH8DvKcMQjxLdvw12QS2xoAMJjSCsvxlnjZ+aC7yJGjiKO/pZwehYDZg9lWpUqtlK9ZBIxoxlddxeKxQbESSsqHaMJQqYSvPtTzAy3sCHl4c3OCGvnOrFOygiT5drM3PNoCPvEmUxJvorrpIUcdk2m9eAw67f1YtRLPHRzDga7yKZWLSee4iL5wrP0+1ykjg5StCAHQmRKAgl5lNUamNoVobUvRK1TJm/dvYiVkwgsvAxjQCDVO4DiT2G1jqMY7XSFIzgHOsnav5uaGgOqIUHcVIq2xA/7X0GpX4YUO0KWYRxRiCK6QN84CcbzCOVUMVp0LdmdL6OGhlBGtBgaC+iZvATV001ekQaXNYAkKEiylYTFga5pYSZFI28CGjWNZu5qIj/8BkrAC39+kuJrFtHih7G4jqwzz8PQMJn9nT6++7u96LUSt50/hfK8Y1aQ0g2MgtoPata7VtCnCR+ZfHbs2MGDDz7I73//eyoqKjjvvPPYsmUL999/P3V1dZ9EH/9jMWVCDg9/dxmSJNKfTjM8niCQgInu9++nDDajRnyIRVMQ9GbiWS5cD/yG+MgI2wUrukCcQ2IQrVaiPrSbWn8rty2aRuiZ19gslPJ07zgTI/0sO76cdCyJ4A2gFJTimleMgyQz9r5NYIeJ+ManEG/4KsggqAqnzMkmMTTODMMO5IZFtF59PSSCJF99GV39JEz5GX+S1hZDSCVAiVOp6aK2RAHJhfZQK27vDlSDm6PGM7AYksxuzMVu0fPalm6uWFnMqVXbEdPjnO4voM7loGg8jCp7EKQwgbCERhIIC/m0JBeSbxNpsvWx35uJdzEbJTSSis0kUFPhIjYuEcyaRmxgiPGWfuJ2F6Wz1xAurCGqc+KIBDnHPURuWQCHIKMASsch/PM1hKZdQKFtJ4ahbsZ+/wxCWT3FtR6SZdWIs2fgrO8loDppHstBU7CcCQkzyY1bSVdPRnx5G+aTq0knTfSXLkAxliDoJBIakW7yKenfi9y6E7VnhIqzzyMyOkpsKEi2K8JorICIbEY36WQsRb3Y2t5Gbt0KeVVgsmGYvxx5qA95ygJkg5WpTj05lSuxn3oKAL1HekimFZJphQF/9D3ywQqMAg4+ra7dj9SrhoYGQqEQ5557Llu2bGHixEwN6w/KmfoM/xgKczIOnvR4nN5wgnyzHqMkoia8HO4L0eNNM2XoOZxjzShTziJYuZyN/UEUVWVuQRGaPQP0huJErXr06TSNUgxhvIuiwFH89VNxt2f8Cja9lnvu346qwhkLcqnOkQCZADri3lH83iT3WxaxNhZlkhAgyz9EzG4jf/gFCCcJBYJUqt0kg2PoTzge8dqLwZlCTgxztM+OQU5j1ktYqxxoE3tB0BAvWIYm0knSNQGHPZemH97JzvI1dA0JfOv8KrJjHUjxARBEuqwlhNETazlE10/uxbRsOeklS1lY6eY7Tx3CqDfzw4V9VAy+xKn1F1OVW0OlJ02fXyC31EOp1UAw0Ifj6As4bHn41VaGp53L7twa8sJRgqkQ+niCSY6DaNODKJIHGheTsBeytTnGc6/s47S5uSyTZZorTqAo5iV2x3fQFuSTvvjzxAMDCCVO1Kx8isfaiTz8EOm+bsShMQZfWsfo48XY6msQLEa2FJ9BTpef0mILqVQx2UkJHSDEY/ie3szwg48iFm2g+Rs/pWqiBYsuCQKk3EWEdo3QOe1snKUTSe84QOAHD2G/5FJ+0u8g8nY3vlCC0XmlXLIyk0g6qzab0WAcnVakqcL13sASSwE3YPzUBh1+JPJpbW3lrLPOYtGiRZ9ZOR8ziqwGso3ajGyqHGW0fzs/eNFCPK0yUjGHc2lGVlSO9AeRjwXcjSVSCFkGyh1GQgLMc/SgT/WhTm5E3ncIQWOmOtzGF8tziNnTdDoNDPriOB0aCroPIeWUYx4ZxFVRxHjNFKa3hzA89huUqhr23/0ASixO/L5vY6wrx/LMwyh730JrNOG8/ib0BVFIp4gnXDz2yxaOc41jCw5Qff4sjBYLst5Nv9cAjtlYQknUQ7vJPekEPr9nG+PDPrS988mbnoMi1zBCLn/ozUZRYVZuBRVdRwk//SS2Ravo84dQVIjEZUZTdnJMhdyzUWV/TyvXzzcyV+qha9Ya/L4o9z3ZD+nlfKkpgLWuAUt3K50FtTjdLlRBQLYbUUN2CA+ioicwYQljxmxe/OlO/KEE/u5+UgyS57GiC2QK7KmpNBP8m1Cbm9Ht3kjl2u/jfPNnhCtLSfd1o5aVYbriKnR9XUiaBLbiAkLBOFv3D5Fl0XHXpaU837yA5TMLcWp9JDZ2AJn69et29GLxOCgtzMTpaOUk23JXoehzCPhTmMoqsV16MermzVx6zRe588lM4cFxnw+5uxmppA6XzcBFS6v/dkAJEhkx+k8vPhL5HD16lEceeYTPfe5zxGIxzjnnHM4777z3CZ9/hn8eek3mDaUio1Fj6LU24mkZvT0HccY19ATivL3hINWTKykvspNKKrSNZXLnpmdbMKdHARCkMLJnJuFtPfzBOIG9/SkuVPv5wUXljKfT5BU5Cd32G5xOPbpLz2df6njGEhqa1FaiExuJRmWSgxmHdb9fZN3LXq7UZWEEJKcbiXEIjoE8jk4q4AxtC+pAiO6iJsR3RlEf+An6onzM37iBjR0CNd/5CsmePkq/eAnjT2UkOTy1RRhDaUawobFp0IoCCVlFp9UhWm2YlyzDlCsxq8SAZUEJHmOKhqoxujtmsq8787nX4hWw3/cQrrsq6Jfyae8dA2DvnCbKPn8SrmSE2tajKJXloDcip1XG9HXYtR4UczbtliK6x+IsW1jGure6OMHQgrT+ZeyA9aqLMWSfgWwtITl8JPNDMTrQPP871Kw8zAyhu/563ixahG8ozu9aDjC10saNiVepsE7EXOvBYhQxRltYWaQS3DiMptSKc2olamEFwSDMlkcp3/cUrZHlVHiM6Pa+RcI1i8deOAyoXHLyRNw2K54zTiIrV+Tnc4L0j6vYh7Yg73Yhlfx7GwAfiXwKCgq49dZbufXWW1m/fj0PPfQQc+fOJZ1O88gjj3DZZZdRXf0BLPwZPhIEjQ1H/nS+emKEgTFoyhtHiO6lzBTnnDllXPKT7fzmlrlEOg8hacvRiFCZb0cvNaJETci42K6CodLOpj9k6rZ3OIqY86tfU3LeUvRyjJCSIOeSqYjiTqaYJ/JGsgaTx05IMjNmcZJ92QUYUwnWp+0E/GP8WJnI9RdV4xZCyL5+pNwcBCAZS0FkHP8r67BOHCHVVI9OryPe3Ud251FKLAZS/YMARAeDCFdcQz5jWLrWE+2RMJ9xObroAS7KD+JXi3EeOor2/LVoZjWgT/uYUjJO2dEuuq6+g7ZTTsT65Ru4XBrlaI+PhY4xRidMJfbQU9SfdiIzJ+WgKiq5FdmoQFgRcfUcwdS7Be/0E7CnRnC89fNMX066FUVWEQWompTHglwZ3dExECWEvCJ0uTr05VX42x0MHvHhrj4ZNT8P9aU/kjhvDanBfqSQF6dBw/6uMeJJmc3NAdbMnsAqWxfXbc8llVYocbip/dkPiBxqxZuTTfnsPIwaAweePoIh24H2humY1Dhyy2F05jhZ8jDKMctWCUeoqNAgVc4k9eIDWLa/RI3RiuDMRSh6T6kwmVbY3u0nnlaYXuzAbvz0Z7TDv+CJWrx4MYsXLyYYDPL444/z0EMPcffdd1NfX8/+/fs/zj7+V0IwFlOSF6JE/jUkPUDGKtLrdFy5pgpXZADNL7/ByTOWom+YTralDH/Uw94eBTXkxeiwMyG3nfOXutjfKdNQbkFultC47WhsKVwXnIwgZiwlkxqjWgzSqzFQ3foWmqnLMZ51InkPfYuz5p5IgXUAX9xDWNvExmGJ9sFxjt/VibvYTUw0o0YzQmhmvYijqgC3aTq489nxs9cZ73yMibd/A6HtEGlBx+aGRSwI7cPStg4BGAvJJP6wHdcqE0VZUUz1UcL+HOTePvSaLvSAbmIxydMWU3xGPYlQK4VTpzF/aBuHRtzcNlyPXitypzufc0x+EkYHOWKMlKBFHxxF8CgYixrJFiOYhg4DArLFSUTvpMiQoMCoZTAtIeflEHn9dexXfQFttpG0ZCfw7KtsmnE2wRX11BVlgUaicuJE1u/r5aGdpXyuUYvVO8Z00zgjZVaqNSFMzz6G5YpLMWhFUmkFNaFiKikgcqgVc0Ux6vgIgt2JpbqU/IX1bK87ja0jYNNWc+nYXmaU6Dlr9kQGghpsiQDKnnWgt8JfAmM1WrSnXo+UV/buWOn0RXjjyLFnqZOYU/ZXvp9PMf5lN7jdbufqq6/m6quvZu/evTz00EMfR78+A4BkAGMZxDrBsQA0RtyGYk6vNKOEAuimzUc8egDrstWoqso7XQGOhFNMzMunxB7BEu3mstn9pE9dgNYwTrT4VHxPbsKxZgHaIgeq5CadirNv9zidkV5MLhe+UfCsex3vrsNIpSZy0kdAiePS9TKQqOU3G3rQaUSK975D6zN/Yup911N+6RRsjcWEeyLYunYhR8eIjsmUHVfLvvY+NP5h4oEQktXC7I599JdUkzPndJIIHMyeSOU3J+Cq8KEoInt+3Ulnf4AqUwzvulHylhYj1eRTuqoWcd+LSDoT5cs9SJOrad4gk0p5SaUUhkbGqXrzD2iuv5F+k52hYJIGs46SZB/qO29jdBWSqF2Of9cgqmKia0QlEeqiaGKmILNPNNBw/nJsziNAmnQwwaizhMMxLTNsIRqUw6SEHISgj5P4E8qUE2kfSLMwdpjkl27murNOQN37Noa6Ohyh7dw5uYoBSz0T6cY2B3KnnMpg0Tx8/T049BKGd36HpXA+aaMeSJBUQJV0iLKGvV0JDnaNsF0vMckYxOpoRrPgTMSCKoS8svcRD0CWSYtRK5JIK7j+jhDdpw0fiXxisRivv/46ixYtwmp9fxhuKBSip6eHH/zgBx9rB/+bIYg6VM9KkCNEsTIUTaGPCeSaVYiOo62px7joBHQTp5JMK/QEM/6fwVCSpGjFKk0lRopsfRhIYqqzMuC4BKfchizp0NuHaOkr5ZbngqTlAHe63sH76GN4RZGiW29iY9pOdp+TWc5RUpKH4Y5MtnUyrZCwZSElU0THFAonx4iXziV8x/Ok3eUkB3vxvpiRF5122w103fkTVFnGPn8uY8VN2DZt4KhvlLdypuIu11JjTQIK4Ziee4466RuNc0Kxg/pf/4yofzn9Vyxh6V/uiZxE4/PRfN5N1BWUctrSS9BYTUw2jxDo60PyZDMYiKMC/VIWJYIAsSBCXxCxeDbezftwf+urPL9rmFhK4Rx3GE92mizC6McPkHZkoxHGiccMaMeSlNeIzMrqwxo7CLEDqDEbqAp1zgTlig6ryY5Xkuj5w6uU3nAVzjorgnc7ZYKfAosXVatH6vUjmXUcits4rJ3Ckp9+mcTAEN33/g7TnBMpd0lMdKgYRvIJFk2mYEjmYFeQWgdoh4chlUS0uRCnfHAyZ47VwBVzykjJKtnWD4/nUVUZov2gsSDo/+/UDv+Cj0Q+DzzwAM8//zwnnXTS32yz2Wz89Kc/pbe3l2uuueZj6+B/OwRJD5KewFiA/f0xFARmFzvI2vA8yfXPktRqkXKK0BWUMaswi+5gDItRh4qCP2bgxXYDJxmtFOcE6A95OBDQUuEcIJ6yIu/uIsckYdBrCEdTCJaMeqHWbiVrej533tjF2vkxnK80U7Isj6mRYaKVc1GshRT4BIxfvIzgCy9zeGw6fTNdVE5tou32H1N0USZVRbBYSWY5EE1G5PEwWpeDiYVh2u58EBmYfILE0f5SyNER05iJjMgExjPR8XGdEUQRc76L5YPPMNxwFgbJTsSYg6H7EHI4jNR6kPNW78ZxwjySUgHJ6dMRd26lqmk6o9Ekxre2MJ49AWutGUUVSY5Hyf/1PXglN+cUKSAKBLqHqD7yCkL6KKGSlQQ2phj//cuIBhP+9Rup6RlG/uppkGpHxYAsmPE5F+AeHGe0fDo2U4IJ3/sCIxv20f/zX2O45lxcExpRtr2I1LyTgzOuodB1HK/Eanl8UwCzUcPSq69B99OfknvZOfyuN4qqqtSYBHSuIJ7gS6yceR6z6+x4/H1og6egmX78u+NBDQ6BICHYPO8bJ45/xOIJ7IHhN0BjRS05H0H3fzsb9pHI5/HHH+frX//6h26//vrr+fa3v/0Z+fwdjEYSDIbiFGUZcRj/MRNZTY4wHhxgOJJJz/BHkxizy3iy/gaCkpmTghrcxig/+NlW+kfCrF1Tz1lTBzEZWlk7OZddD4wyePpyumSJyc4QCW0JUutu1O6tZAnb+P4lX2SwpZkarZ/4PV9ELSvHmh3kG+fkUfiHnxId6Cc952pskS6WZon8oH8Zz+TM5KaeF4kdbEENhVgwWaB9fx95P/8x6bw8tCtXc9ibwGPVUfebn7BvfTObXGVcbNvJQHUpkfYeaqsczNDsIfWOjp4Nb5EMx7nlwmvpGFeZ4d9G1g2rsZdGQZeNNt+NnH8SWWoSW1cLqa9fjhzwYVk+jW5XI/64Qu1NJeQI2+j45QP0/PCPhAJBcr+xGvOcQl4NHcfCkgRj2iwEBRQp40MpkCP07wyR31SIHLMQeeIX+NZlkj+z5s5kcOJ0fvFIgLKcGaxZmMPg0S4Mu3bi7G3GuljGVKCyZ/LxjNYvp/YKP5oHbyXtmJPREjTaaB1N86d4IyaLEQiTTCp4qtwUfmU16bJKJg2PUGsJUfL0r0iV1aCZVUaBJUiJQ2S/bSJ221SMhowDWRk4hPzynSDpkE68FdFT/tEGXyrjmyM9nqkD/388Ff+RyKetrY3GxsYP3d7Q0EBbW9uHbv9vR1pWeKV1GG8kRanDyKn1+R+6rxKPkxwcpMfsYigsU6yXyDeDgoYCm4GO7Cm0xFsZ9kXJPhJivsXKaCCKUS8x6o2glTOzXHpNmuJiMyaHl1qjmRE1nyNyEbX6jKhX2lVNVW6EWlMKpUeHXo7Rv3EXRxafQll8PfGxjMD5wDu9uK+8DoMlwfwBLcF9Mo6pxYijc8le3Eg0LeA693QGJs4EwCEmsbf5KDOPYlIiBAsr2Hs0yeiEKiZ9N5vxAYHWm+8hUFFC3pQKOLgda0MTxpF28ox63CVuhNdeQbNgLUrPQezeXtKCin5gL151IuMlcexLHehKaikSopTadKj9/SjZdvJW1xDqWoK0YC4jE8tQPS6mG7WE0wLmoW5+/Uc/q89uQK9Vkba+Q8pRgFfJRvP8k1jzHIyKIu5Fc9CuWEqPPp/ew4P0jkRYXqMw0ZVCOnU64WQTVjlAr6WYtwdSQAo8LkpKClBS0DrnBn7XbcIs6qgqs+ADjp9bwiRdiBKtF6lhKoqpjNwbryDvnKmQTqG0HYRFS9FpRwnLNeQYtVh17/1E1cAAyCmQUxkL6K/IZ1fbKNtaRple42F69futonfhaAJRCzongjHng/f5X8RHIp90Os3o6CjFxcUfuH10dJR0Ov2xdOw/EgKIx/R1xb8TG6UqCr2330EoEGTLBTegAvEsFycWDoK5FlGjQxBF9rZmCGbYH6UiT+JPX9ejyAl2t8XY2FtNiS3EtsMCpzUFMKrbIQqCNIPYkVEC29tJjuchL1pDSfzPoANxwiR8z3cQaesmpt2BT9VjvfzzBCqqsRc7cDqbEdIBFhYZaLTVYu0I4PryGtqPJgjsP8KUpm70wlQSqgazHMByyXkMV5TjV2Vy9h3iyxefy/gjPdivvwDvY79HTaYwTplGojszO5oeHMT3+NNEWjsw/ubHOK64lv5XDxL16dCHt3NUtNJgD5NufYue4hkcTNu4tCQCKgiCyPYuiZceHOeihW7KrjuDLRTgTyr0eqHapeGwP06uNodt7+xjw7qjnHV2Aysa6vB9/gv40mmyb/8O9uEW6r+yllBzJzYlwDRHjJ3ZBspcApX6Ucaf24CgKOSdOxvtyEaStlPwmLMZjaTw6GXM8wuQg2Z2+vS0DYYBWDDBhCZtwGQRaOrch1zeiG5SI/qAl4ZH7kWM+Ejt0eGzl3BkMIdaYz7F2W6y/r9xIZZOg/AoSFrEoveMAFlRefCVIwwGohzuCTAWSRKOJVncWIDd/J51Legc4Jn3r4/jjwkfiXwmTpzIG2+8wdSpUz9w+2uvvfZuysVn+FtoRJEVNR4GxxMU2j9YMB4yVk/gtVfB4SRbjjEsGfFYzIj29+57bZmTFXNK2NfmZcWUHCLNWzDbMlZn6UAHg3s9bJt5HCEpRjId5i9n06XiDJpziPeOk3Rl03NonKI6K6IyTlLj5DG/QH1PM8qzP8Tzxas4PGMFI3GZnohEcUE+nd06eoatjFqysHkuYGbzBnZsDDOmd9GwbxvldUUoWkgPhJCDIeTdezHOmQmKghIKUHLVCjS5blxnLEe1ORj3xgiGTNhrZqCdMYPIXT9FNOiJmewk2kP03/84AMnzLuUlXR7lz/8cxvyUzwyxqelCYjE9JmMKNSXxw1+3EIqkmJBrwLL1TWxnXIgfHY7EOLruXrAW45U1XHXtbA7u6WfC5BzUjp2oyYzuc8wbwGq00nX3gwgaDdlnL2NqvcpP3QmCA0GkrihGXxtKOER6aApS+SJc/W9welUu4aRIafdDiOkIQuVM6g4Mssngpq7QwDTjJszaFMO/OcL4q6+RcHso+PH9+L7zJZRgAPdt92A59RR6+6LU5GYh600MRRLkmHTvC+AVzA6kmX8lQncMkigwpdLFSzuiTK32cN8LzQCIgsjJsz95edV/Fh+JfC655BJuvPFGJk6c+K5mz1/wwgsv8L3vfY8f/ehHH2sH/9PgMutxmf9+hrFkMlH8zW8S3PAW88QQTCzHIQ6jBNtQu/pQOndgaDyZGy+Yis8Xpe87d7Nz+VymT3KiE5IE+7xIsSAFP7qLCqsB7Renk9JMoNdrYE88H6+skn/x54m09/GLp7rY11TIRQstjHTpyVd6SW3cBECquwdTLAyCEYteYDBg5KqvDhMM9bL6bA1CjRPPn3fQmFfCek0RT4knsuJQghzNCAm9iezTV2CsKsdnyEaunIjtwvkYbX0Q7SQd8LP/R0+T1ViLobKUqMFKyYmrmDC9FtFqRDBbGfrNJjTZHuTxCKOuEsZ9KoItC3XMj0mvY/73b2Xw4GKqbzibcW8vjTVZOD0Wls5zMPatFvKUX1NQ7MHQuhft9NmUzi3HpKqka2wUT86jbtP9xIqaSF/1OWRUtI4szAefpepL5xLNzsFs6Eft6OdN3yze7nZzmnOEUqMFxnzIRw+hhpOEFl1C4HA71bYQmIpQPeWIWpizQEP59hFcDUZ0SR8oRkRj5rmLZgvDBw4iejNR5MnWQxgKyqhiGL/PTK/bAQkZXTqF0275wDES278H7/0/Rl9Vg+uqL3DhkiqWTSkknpJ5eUcvybSCw/Lpnnb/SORzxRVXsHHjRk466SRqa2upqckIWLe0tHDkyBHOPPNMrrjiik+ko/9tcK5ciXPlSgBG3t7K0Z3rKFpei2bHHwEVtfVN7n4qxuYt3VzZso0jTQt52V+NQStycu4ADPYTeDWjXmefV8WEZV7e3D+FSsNR9CYzY+4cyvKykKRB2rrjKNvbyMsrRRC9iCcuIDzgxeEyYuk7grl8EqrXT1IJEYlkPqu14QgLOzvRJJOIB3dy0hwR6cyzSOkkEm1vY3L4USPjjL3xNt1nXEzu2lV4XL3Q5Udt/hMeTzVT7r6OuM9L7fXzkW0TiPvTCNMzcTfpjhG8L76G+6rL2O9p5M3WOCangfCKC8na9xYj/Umi7d0EX9+AYWYE42gb31hxOomGBhBS2H5wE33Xf49SSxzFbESqm0Bx7176rv0aos2OfdWJ9PYOYJtdyNgzL6CrrCLXLYOqkogGeGFwOmdLR9AnQ+wbA413EPGl3zIwHqTwzDPQ2QYhmUBBZuNwNtXJrQhTT0XMiqLKBhgZxu4R6N0HY7lNTKx24rwiiKaggLjZw7ebrVwybw1OIUZ2QxM4KlDevB+x0IY+p5qEDOGd27DPmoVk/tvyx+EN64jt2kFs1w6sxy/H2DSVkpwMUd11yQyiiTQTirM+6WH6L+EjBxn+9re/5eSTT+bxxx/nyJEjmWnCmhpuu+02zjzzzE+ij//ViPYP89bqa0kFx4nedCb1Jx+H2r6NVOE0hp46ygV14xinncWk0S6Olk9g4qQiptRNZc+Lb6MrKUI1mWjRV1AYFJg81EX3LbejsVuZ/Mg92NMjfN+4B6PZgqFtGOVQC/lVkxhq3UVWTg6p5csYLSwDScQ1Ekd6aQvfXltPj1embMMjpA7uIXjhlTgmV5I7tYpBaxYARnc5iUNBfK9tBmDK4gOs6ysiFClmyfg+ACRvB8nVZ1Oen0TQGlDiEZSxINK63SiiQEg2o832INRW01Qk0j+WorrUQIE6hGZaKUmxnNzyKvJyTQi+NwEQAv2AAoDsdtNx1hcodB5l8K5fozkwhG35CkzVVYzv2k1gw1Z6X9tOTUJDoqeP7JPmIhw3jz2Wcp7dnuDkWTFMg4dRfb1MnLSEYPsoSkszSSAgG7FOmEnMlcPLe2RcJUUcsF+OJipRY+1DKyVQdVnE+46iMU9E9/Tb9BtEzFaF+I4d2K+/AE2Xjlv7J3LevBwumjAFQRSJ5U3AWW3GbdhEQqggsOMNlPq6DyQf0/RZBF94BkPdJLQl7w86LMv9ACnMTyE+EvnIsszdd9/N888/TzKZ5MQTT+Rb3/oWRuOH+y8+w78GQRQQ9RnzWVUNiLPWIky7AK3ezHdW/wJdPEi88whvP3iI3ITMzNY/czSUJF5Xx+ZLv0rvcJQzc+w0//T3SJpj8h3BcayxIUJhkSM/+wPVJ8+Eo5kqoCZRh+3CC+mbt4RenY2KZIKc559G8HtxFmqYu/dHTJlzPoeaMyQSiyVp2evHvOdJrNdeSVKnx+KxQlkR9rlTSY0GsE0tY/vBMNFEmhknLsSkyWLAWs3emBmhQ6Gy6znEWBx51ET0uUcz17rqPH53+q1cUFvNDPVZ6o73kTYWIBjNxHRFHErOJNk0h1z/fgbHV5Ob6kFn9xA52sHBeC5b9waQkikqD3WhDA+jsdkJPvcsic5Ocs46m/YXtyFotRiK8sm/9EzKvjQfQQzR5CqFZAuTdM2gTSLWzGCF5jAtx9ViHF2AEg6j1tfTVtBASqNhSoOMmgjyhxYFFYWT0oWUZSsIIS/abC/Jl9YTeOp5ggY9U5+5k+wzG5AH43zdtpcOSc+kxhqEY6kTg5UzyTJtAxUM4hCu809Fm/PB1X7Ns+dR9syrCHo9ou7T/Xn1YfhI5HP77bfzrW99iyVLlmA0GvnpT3/K6OjoZykVnyCMedkseuVXhJo7yF02D1EyggRqOoS1SUDAwljdVAqdC8iryGdc1LB3NAgIXHRmKWK4gwpXGyPpekRnEbaLTsVgFChwteJdN0rN+cuIDvmx5uQix2MkfQH8RbW0iRZIK1gOH2b8l78EIHnFFfRXnEGNKGD6zu14e4fZTCELB3eixr10zT8J16JpGL60AvHA76i78XiSeeW09RmJJdNM8GgZe203Q4MBji5145TGsBzZxegr60n19WI7/7JMDpOiYNUoVJU4uf3Bg1x6/FSWlvWysSObpfVdxAQrSTXjiI1qzcS7W7DlW7CGetg/4uY32zrpGYpwyckVlE2sxzs+D3XBCoaO9GJOv4oaDVF98QpMTujTF+Mz51ApRACVREpmgj2BoX83AKqliNCjD1Fx4Vp0tUEQJPyWOBERdFoNZkOCBGOoZPw54aDMyx0qksbFQk8txvJM1LltehNSziRUbxupZ+7CKghMzK4l0nOQQ4KdAqcFo8FGTC3FIA6DZEdXUPk34yGRkjnYFSDHYaDQnXmZqIkYgv7fzwD4SOTz6KOPcv/993PllVcC8MYbb7xbj/2vK0J8hr9FIiVzsM1LrttMQbYFNT6OcnQrWFxIxVM+uJESBiSckyfinPz/zSLKMQQy0hJjcpp3sqq4fHEDsUQQowQxGXRmK/mGcVBVVF02yapK0mIW+eYjSEI+GmsEgxxiTNDTly4keN3VjCRlKnRxavRJkqJEdq6Fkfx8ZFHiwXgd+7vGmZxwkBv2o+RM4NLFBYi/2kBsLE5qLMjQs+soW1WPxWBFbd3Ki0fc2F59mRsvPhmXI4k7nCTi1MOm1xGjQVK9PSh9mZIs8fFxTKech6ikeFE7kde2DRCNp3lhV5SlmkFwTOeo10ShJ0aTPkhM0eEKjTE8aS6xEjveoVFuv7+P0nwbpy+ppGJqKcZEBznXHsevB+vxmycye+Y8SkJtjAdlNNkRXm2z8fKOPnpieUwsM/DQ7/tZXOnhpIJpiJFhEgNBpPppREQduvxaVFGHEpGIqQJyUsZtTFBg7eacSaUoaSPPbwmw8VCAefXZLDQGyFtsIXfZPYh9W6D7eVIBO8jH/Gb5uWyw1rK3LYBBO0a1x8zhYRPTixpYVpsPShroArQgZTSfX9rew6Pr2nFadNx1yQyy9j5PetMf0Sw+D+2ckz/+gfsJ4iORT09PDyeccMK7y0uWLEEQBAYGBigsLPzYO/efhGffaOfnT+0nz23i3lsW4+xeh7LzSRBEhJO/i+h+/3c78hCkdgF60M0CMeNMTMkK0ViKcNiI276Ezq5u2pO11EzPYtdgkLF4ilJdjBc29JNySsyd0ohqzkIU9hORs3Dow2iFMQBEI0iFpQz/7vdY6qs5ohjZNxxiIzrutKzDEx9i3DMPjc2KdeZ0YqmMPyUVi7Ni8330r7gIr2EC7tNPR7NhI5YhP5rGBnSVZUSrT0BVRSZ0xIm8+Cr+vApqlDfQa4fQlwiItnpCm/sZUyVSl9yEYaiHnqbjmWbo59ArRzBpBlgwrYmDbV5OaRDYGa7hnbYBcgzdGBeMUJncRVIpZdcVd5C1agXmq04lpbMwe6KLoD+FRa9BFOBopBSXLUUwkbmtMXMWCV0hpjll6A+8yuWe3USrKtiyO8SGjX6OHB1jllvDaFcfkTGZ3Bl5KMX5XLfBwXeuvIRiVwIhaee5P3VwuNPP1adWMc3lIXuom6RoZduRDLHE4ik8Ti9iegTkAKrBAr4DJPtz0M45GcluRXIl0Gs0gIJJK5JKy6Rklf5Q+lhRxU6gGxBBFkDKJxTNpJ8EoynkZJz05mchFkbe+gKa2Sf9W2lrfeQgQ4PB8L51Wq323WoVn+HDMRrImN8j/hjRWAqndOw7XZRA+gD9FWWcTF3PeKYSARZGo0m2D4UQ0govPdvMSfNLKSqYg0FW8CVk/EkZl1FL10ial9/up6bcQaNlgOpiJ4dzcoj7Igg6K0m9Bw0pzFNcaHoh79ZrSc+dxYQsPfv6oditI6XksfH2lyk6yYJm5YmEN73DhZPc9M8rIe/tl5GHhymK9qPqVZTSCrqaBNy1DcSGfHTZaiiUBAxSEkexAf0tlzOIgF9fTN74YZJSMbqCLPLWVJJcrxCcXEtQraGq2IleFMl5fTvCbx9lwn2/YPHxsxg80If+4Ba0OhOHbcXUo0cTH4OIQsMNSwmH9YyI+dgYplAN8efX+2ne1sPX9m9Bt3YVvQYLS6oSeDv6yY/KMGsS+u79iAf+jBn4fCmExiT6lRyGPSoLjHsQyyaSpY6h9+7CFDvIzSffRHVuL6IQxai10zM4zkggzp4jQ2QdfJ3m7z1BwzfP5vPHL2B3n5HpZQawFkFgBOwVKLKEOpTE0FSH5DYjFsxF0Fk5LpmmOC9AttmLoqqUu7MpcZmPkchfflcKkCI9MsjqcgmrsZIij5ncbAep489D3vYS0rzT/62IBz4i+aiqykUXXfS+OuXxeJyrrroK81955J955pmPr4f/ILq6uvjOd77D+vXrGRoaIj8/n/PPP59bb70V3d9xyC1cuJC33nrrfeuuvPJKfvGLX3ys/TtlcQVWs5ayQjsl+TZUz2KwuBBMTkTHB1iNUj6oyUzVAdHFWDxFWyBKXFZAgPIyB95Qgl7NOOVuCwKQa9RglmLEGOOaJdkEywrYgIDOP4jY00l/bQOxsIgg1FKuO4De4SN7ZROD8XwKXUaKaKXemcBpN6FJmDHedAqBLW0kPXnEs1yoT/yGJd+8gmS1k4g4l8SBPegf/Dl7qxdQ9uRPkdqasV5+Ldu64lQVS+ikAQrtIoPtQ1Rt/iMDp52B7DqHsd++TMdDj1J8ykzKz1lMZeI5cNUiuEsBldwrluN78S00+bkoBg3Fs8rQjx3mlIW19B4eQNP2JKopF2X3RqR4BLu7mH6Hi6iaRTyWSRuJxNJoykuJmq1E0ioY9ARUE7/6Uws/q85Dl5WLzp4DsRA6TyGOyADG1ADR/nF+n7OaKiXJ/Lxh8O5C1VvIy3MikHmBCCisnlvIM2/1MaPGgNNYgajX0fOn3cxbNZUZDRo2hl34DLl4yo9DVdKom15B7WtF0IqIFgn8EuQuxazTMDHPA2RSInLsoCgqL2/roWtYYcWEPMoKNaT6o/hvuww1EWfVrT/EUJOxlLWzVqOdtfofGoNqKklk6xYkhwNj/YenSf1v4SORz9q1a/9m3fnnn/+xdeZfQUtLC4qi8Mtf/pLKykoOHjzI5ZdfTiQS4e677/67bS+//HK+/e1vv7tsMpk+9v4V5Vq56OSM30YJ9KL6ehDzJyKYsj64gWgG3Xt+nsFwjISsYNdJaBQVty1OkTDIgUQJhwZlJrjNGAZHaKwcR8mOsfFIhOZmPxabDl+tg4L0biqGsugIuxky29A//xpqOkrg1c3MeuwLDKVno46M4lH0WAy7EJQY+gYHw3e+irhoAdsmfY66U0/Hu+5lnGctwz45gL+5i5ayyaihBFL7YQCkwV7eOuhjaX0WRR4AlfFdexh94jmcvX0E4hGc555O7owJyL44iZ4uqFAhMYYq6xCkBKpqpPSnd5Auy0ZNCVi1oD9xBm5HFwUGA/J2K2JsFLFqCsqBt0nUzCWtqNj8Xk6cpMOuOCgS/ZTNLOFwhxd7iQsROHB4lFA4xWuPbmH5qZPxnfBNkNNYFQVTwSiW5o3sq1/IC28PI4kCjcuSGG3TwZjFWEqLKVmIRRMgmrbiqXBxVlpg/Z/bufGyUk7Y8i006hBJr59Yp5d5hs2MSafgjXkhFCFLYwAExOx8YJiMVZt5oYeSMioqdp0GQRDoHApx3/PHopRHFS427kGtmIMaztRECz37e1RBi7Fh8kcag8EXnmPkjm8haLUUPfx7DDUTPlL7jxsfiXwefvjhT6of/zJWrFjBihUr3l0uLy+ntbWVn//85/8j+ZhMJnJzcz/pLgKgxseRX/0+hL1QtxxpzkX/UDuPSUebP4bdIDHNIZB8616E0T6qJp1AqOE0Nu0dpMhlolhbh+wf5fBIMxsOZArkza8ox5otMXDTF8k3mwl9+ZsEQxrUgSD246YiSX5SO7ayOZ1DoW6cCrcNjRJDjsgA+HRmJuXABJcFnWMB/p2HcZw6n7HcKroOjzGAntIrbsTS14G/fi43JDvRDVcxsneIVCDB6KtbEfU69Pk5aDv2IrQdRjz0DqqiECrOwj5pOnprCsYCRIYE2q76BrbjFpI12kNBcRnjZXXYlT5AxeGJoU5ZCYOtKBTSLNexaSgfzVgn+TY99XOPY+lkP9ajWwknA3iKJyBLAmpaZlmdleI8M1NL9HhyBxkS3ChYSIsyieaN2NPj5BszzqFSlwax9yijA1Fi/jasHeOMnHASD21NMalJR3mWn0MDAXyKgW1bUsypjuEN1TDgHWHEZ2fS9Aa686sRqKLR7kWTtkNyOkpIjxxyIWbNRACCyTTtwTgAFTYDDoMWp9VAeZ6Vfm+EmdmQPtyJYelajKvOItnZQaylHXHr5o9MPko0k2umplKo8dhHavtJ4NNZ0OdjQjAY/IfKNT/++OP89re/JTc3l9WrV/P1r3/971o/iUSCRCLx7nIoFPrHO6UqkMq0VdPJf7iZ26RjZYULQRAQx/0kQxlhL30qSjqaJpFIk+0yMvjLhwivOgFLXSXiwQPkuk1kG4NEt4+ihEIQCpG/dxuHf/Y4giQx9+mvEx1Nsz5Sye6xJHlGC+en6tGbYsiDPgxrziPiyGFZwTB60YcyQaL11ldQU2lCUxczRQ0wN19PlcFL0mAg3N5Mf/kk1j05QN0vvouaTlP2ubMpWTMJ2Z9g4MsvIHQXgpJxXismGwOv9eFq0KPN0dP57UdRqicSmzUH+5I6lGic3dv6mVqYJFcEVZeFMvgOI7uDaHIK+W2bFlUbJpFIMXF5Nffe+TY3NLRhGNiAQdSQOuNO/BoPtscfRHr0EVZefS2KsQzthCKi0XF2ezPi9ScbZNR9uznJcJSJa87A49YjdFXTd8/3AJCjMazZuSzLc5O3+3V8VfU89nqGNFySmfJuLx7DOlzpGHuLlxDKrqDIGCIqG4jKWZilFPJYmvjPf5rJYF8YQM6dgDLlPQJRjllDDque71w0Df+vfs3oHc/SM20ylVorltPWMnznbaiCgHnm3H98zB2DbfVpCHoDGqcbw0ckrk8C/7Hk097ezr333vs/Wj3nnnsuJSUl5Ofns3//fr7yla/Q2tr6d/1Wd9xxB7fddts/1S/BaEda9iVU71HED5ti/xBoJZFoWiYoWTBd8W18+/YSKJ7KBfOrCe57FWvP84wuWMC2qBbRoeX6S6eyucXLr3bCydXHoVkVRTQYEVQNk+64jMLTGkB08uqcLzF85z0EIkkCkSRH/tCGZDbQndKRWz+PPWMyc1Ma9HpIxyAZCKJNxJFiQcKVZVR6N2IK78ekg/Ipy4m8up1JA8NUf/k84sEEeUtKMZq6UArnYL/gYuSxICVf+wqC3ohrYQ5SugPvQQfhAzGsp53FrprpjCoSU3w6qt0qyycOIeQ4ScVFBr0SPc8mccfTBO77ERfWVtO95koO9qdoPjDMli09XFAhkg+oRgsOswmLEmXg2T+CLJN4/jkKv/FNUuZakokoBZYUpGWQIpCVgzY4Sl1BgviRdjST6zHXVRM53IYmy45ZH0d74G2MpQK58V3MqpnBjrYoU7L8OPPMiIOZl4ldJ5NfHMEi7iMhWrj9jxaWTWtkptQB6YwTObxrF21P3M3kV5+hrLgEVVFx6N+beMiy6Dn65kbi3b3E+wYovPwirJPqyL/rJyDLCJq//9NNpmVe2NrN0FiME6cXU5JjRWPPwrHmbxNT/6/wqSefr371q9x1111/d5/Dhw9TW1v77nJ/fz8rVqxgzZo1XH755X+37V/nok2aNIm8vDyOP/54Ojo6qKio+MA2N998MzfeeOO7y6FQiKKion/kcgAQc6oh55+r8tEeiOFPpLE4yshLHqRxcgWSHMd24GlIxXBojFTX1NATUukcDNPjz8QCeSvLGTl+LSdWG2m76HKmv3wrWnMUGKHgklMIGrUM2/SUOkyM7znCC6NWukaj1PnHKJ+fxUstWSyucmPYvYuqJROw9m/g8ORFNIdFHCEjbiChd4HDwfCv7qD0vOOxzGug5+4XiI+l0IsRXPMP4FzTwDvtbkweN01VTqT+X4OgxdKYz8DabxDv7KXkuusZnbOSaDIjJRt1FWCVYmjsXkrs0BGXSQ70kxgcRjKbqI53488y0R2wU1/j5PFDMNZwFjuPyly2dz3Wlj9jXXshic1bMS86Htvs2ci9h8lTNdhEHXaPA51aiqoVwZqHUO4kddTHtnaJKfd9geTBUYyRo5gCrxCoW01qqBWzJcDXFvoJuzrJ6tmOoplBS+l5aFIhlJIZGLR+kEEvRTEbrbzTGqL+hBmYzruQ2OEhBh5el5ECScs4A4dQm5+E3MmotachHJv99KxcQryzG9eKJZhrqwAyM1r/A/EAdAyGeGx9pkaY1aDlwpxPX8rFp558vvjFL3LRRRf93X3Ky98TVRoYGGDRokXMmTOHBx544COfb+bMjBhWe3v7h5KPXq9/34zfx4XQuteJNR/CvnIVhsqqD9xHPfa/IssMP/4E+twcXKtWItYtRTn4Z8JDkC238rvdCucvMHDp0n6S+gIsJW50hlJiuzfjbKwi2j6GvVGHmjIjrDmdPHTM1kl0tvrIt2qwJXUwGsOiUVhRpOdgVMOfj6SZmN3IJMMLJGvn409o0OkVOnW1WGQBndOJcdiLfUI59V9dSWBfD0Ovbcc4v4Lg/oN4X97AjA0/JH+Sm+6QldaAygTbTAQlhNztI97dD4BxeJC6LC35dhFZjYBGRElpEDWgygJqfR0mj5GsIgdS5x6M3e9w4SIHwdzViMunsf3FrdjTXhZWGDGOdLHRuoLb3zaQk3Mmn5syGe9wCPdYjO5bfoIx20H28gI6mtbwSrORsiwB/eU/pmDNYm57uxetRuKWtQUMJ8zU5RRgbemmOXcWHmcuRaUG3JIR1ZzC66jlqcEsplRkow2nmagtQxEMtPSL9HoDrJzvYighUDN9GdQEKDCVoy8oxFJXi7LvYUiMQfebULIQLDmo8Qge9QCuixvRNtYgaj9aOZxsu5HSHAs9IxEq823/c4P/A3zqycfj8eDxfIgy2/+H/v5+Fi1axNSpU3n44Yf/qajrvXv3ApCX98E5NZ8UEn199N50A6TTyAE/+d/49gfuV5llwNs/zPivHiAeDKGNjDDgH2OgcCUe/SR2zL8IRfs8k++5l9mOvWi8BzGKOoSsFKlADsmD28k39SEebCZdMJfRg6NEJjYyHk+hr3azwCrSVFGF0Bmk4qiPBmeKvrEkI8cCDIdSMsaLvk9Iq2eCKOIRQHnoXiK/fwzZYkF/ycUUza1Ga7HhnJpP1uRqdHm5RPcfxDq5gfiIjNsyxD7ViC+eJi25yH79LaSkQvbSBaSiMXZryyh4bQPGUxtRjE5kVSI+lCTWE6bvD29iG/BimleJLgti3hEib6zDOutCnGYFobSc4/wHELf8ASQt8UkXMNyiB4JYrXqMuVYiikraU8lYSx8Dr27D4l7DC94uXtjppTxPx/e/fz56jYVr8qwc7IqwfthIc28Si6GIm/ydVO69D1JJ1G98laHSaejL52IIjfGNReNotQGSaUAYZNDn4ns/bee66+aQK/qxjQ4RNpZiyZpA7pnvzTQJuVNQ/e2QOwWMGR+lmkygjvQgpBKo/sGPPJ5cNgPfvmAa0USaXMenM/XiU08+/yj6+/tZuHAhJSUl3H333YyOjr677S8zWf39/Rx//PE8+uijzJgxg46ODp544glOOOEEXC4X+/fv54YbbuC4446joaHhf7X/ksWMvqycRNsRNHnvyaumFYV9o2GCiTTldiOldiPm0gJia88iYfIiHnid3vmnEldUUu4iHLMacUyo4NSTqhG7DmYOojUyEHKS3fYsRvko8nHLiIZSJGJ24rF+/hKaZhYhdNsd7LCZGTE4mHrdKaRHw9ibW6msqUIry2RbDSgaDbmdrQiHDxCfNhe72UgEkApKiPR6UcbDtN7yCNVr5zHjnBrSkgND4QXo58xg3xXfQyNC9X13c9hahJqUab3zcSovPpXk1g2oWS5GilezbEoQc98zbDo8l6S1gmXTqzAXlaLTu5G0oFbWEt69E82hZgxTpyBVzQJnpoKnLqeQlMkJ2hy0bz5P/YwLOX2pBzGlYEjJyBoRdzxEfyxG8WVrEK0mip2ZyOQvL4xgH+9CJMqKBZcRNIcJxTPbChwGXKUTUPc+j6agAE0qhW34KL2505GtZlzSAdSUiHS0D9nqIiEn6ekLsuvFzVzKUwjJKMPT1xJpWk7OXxGCkNMI2ZMQhPdelqLNCWfejO/QXjqzJlE/nsDxdypTfBBsJi0206e3gOB/DPm8/vrrtLe3097e/jepHuqxCpCpVIrW1lai0YwfRKfT8cYbb/DjH/+YSCRCUVERp59+Ol/72tc++Q6rSVACINhANKLJclD8s1+Q7O/DVP8e8XljKbpCmVmVgXCC0mMKiIbCfBSrEWXwCPnJAL06B4UWPQUv/BxJp0OOJ9i214yrfAHNI2ZiI17OnlyDPNxOurAISyqAePD32HImkfZ2YNW6CPzkYSK+MSKBIHN/uJy88v1QDh3brQwdfzIU56M8+xveePYw+w4Oc/pxVZS3dyAsXo0lGGG8d5T+B36H1pmFsbKC/ldaEMb9RBMDeG/6GkfiAhW334Vy5eVYdu2iKrofw6r5hB/6Df5kCG33ALHcUk5sMmM39bJxoJ7vPhcAdmLTa5nblE/WnJnv3hvbggbU6Z9DEFMcaNNi/dND6HQS+rnHYWtaQfJ3mUDRnPa3yZlwNsdPK8AR8xHWmAmYXMxseYWBKIRVlTljg0wqUikeOsLYUy+gKa/EVxhlZU0ER2o/s40VJH9+PyNqjPzjVpI1qwpp1+/RmByQM4WUKuKNluDYv570K78DnR7zmi/zzC1WdGoSYacRklH8Q8O8+WY7N5w2CUVVGYokSSsquWYdOun9Q2RrLJ/vbR4CRrnePMiJc0o/yRH5v47/GPK56KKL/kffUGlp6btEBFBUVPQ30c3/a0gdBqUXBCfoZoIgocvLR5f3flF5u07CY9Tij6dwHSuDq4ZaYfBljKfOQUmdjyU3lwKdHkl8L7xeMugpzM3mqT1JIhqJtQtCYBBIVy5AFkWkjg2ZaX+dkbJQJ/GyVRzOMqHcdzu5WzeiCQ3xlyqpxmwbCAJZF6zBv34bf3whE1qwo8zJrqI8etYPsSRnCo2H/pg5uSDiXrGQ8W278L2+GcfKpfSlJUDBm53PzFu+wMF7nsZy0VmMYKPbJDFsczG3pAJTaJy8mY2koh4SwYw+j0YSsLXtZrR3Jxit+F54BdeK43HPKwBZC2ISV0sz47/+KQkgVVaBOG0h+kl7UY42s09bSaEJtO37kd/5CcOn3ouc8KOQxmnJpiMQo0VxMMUeI731VQDSR9sJ+AYp8QRQjJW4hzoY3LObtptu45Bdy2p9GrOcQgwO4hnYTy95pJP9qMFjYRepFJrxURy+VwAIH3cW3Yd6+cNoEZ58gURaYSiaYDya4khLH7OPbsCQjGA55Ty0uZkxkOc2Y9RLpGWVAs/favr8u+M/hnz+7aAeC/JSo2Ryd6QP3M2o1TAn3048pWDRH3tckU5QEqSSfjqyFiOPp6jM0mIU33+M4uULuLaiDdRhVJ8X75s9RCctZ1xJMSG3HrztCKWzEKsXY9HqKbuxBL/eiKYsm9BTT5CYciJ6vQjldvrv+hHpkhxcd9/B4oZT2D0sUFefw/ouPwBjVidKKknZV76A47ip2HL2EG2ah90lYtTHMWhDtI8r6F7fRndnH4aLz+HIwoUIoTiFWSbEgT6iu3ZT/qPvEHWZMbhqqfjd61yX76ByQg7+G7+MX1GwXHoJ8XVvYZtYQPzAEQyOMLFYHcldf1WiOxEjpjGQOOcGxrwpCnxBhq7+Iutb2pjx9ZOwJkZx7H4UNaUlPnkVo7FMxrhX46Ao3wlzFuF1VVCe7cdFB8TbaZ0wD8+3bqWoMEpZYB2x8YmEak9FkhN8+9EBDnR387MvVpI9sQk0KmgNqK5s8IsgiPQLHmzHzWLRYIj6HD1DQ0OM6qwgCrjjQdJPPUgYkFw5aNdcCEBtiYNf3rQQRVEozP70zVb9q/iMfP6voJ0A8jCILhD+/ne5RhSx6P/KeW6rhYSXEdMcDgcysSVGjUSl473AyLF4im29ATRiFhP7B/Fdl4l3yv/eVOITFuNPleCympFs2e+2SSgylrY9eEz9DJx/PqOqHhGV8rF+3KVZ7PWnOevCMzh53UYuvOB0dLOLqanxcORwL8U7NqCkZezTp2JrqIWRZkzF4+ivXIMcMzLS00vw5BtRZZmSi0/DtnA6R8nM3lWKYSwHNpLQxOm/4Dzc3/8+8sxpVH79K5Sm0gyvf4PAsaBEv6KnYHoTvXfdz1COh7ofn43sG2Tkza0UXHg5mtJSlCkzMKKQlCRKCi207ThIeN8hAIZ7FGr8R/Gv6ya6cw+2C800zV2JDx0lbZvRVJSjmZlDclBLSo6ABIo2j8NDEotWL8J5+NnM/dbp0A1uJ1EwmXPWFHGlTaQ63IuypwM1JaMcfgVjKMTO2s8j6YwYLA5KcqyU5FiJvPgkCUcuVE1Bg0ocEdXhQQiPoS17v4ZPvvs/z+L5Cz4jn/8riPbMv38CgrkUzKVY4il042OkVRXr/+cw6BuL0Xksk76wqp7sL9wIgoBt4WIcViuQeZMmxjsIRUawYib0pTtJtLVhuXApLGsAVxGSnEJ99RGm98g0zTuexLRluAtdDLkLGO0aZUIqQE3Ux69HC7CX5HJVEhwaG6p7NchhpLx8nn+5nR/ef5hLr7qGicIgpSctofcr1zP1y1/Bkq2l/MgbRAxG+jvaAUjt3k3W3DmoQE8sxdjkuWT/+Afs2XoUz3ELMb7RwziQGvES6xJRBRN5F52P8+TV+DdsonXyfAylRUx56lek6EeuKkM6+2w03lECs5cjOMuJ7s4oMSY7unAfJ5N/8A3SBRWohlFGcbLOp2VBTy/mgmpa20PMrzeik8eR9SUk7SpiMokY6MEQ6GHWmmsQglGSTz+KrKqIZY0gCCRSCu6cIvxphQQQTspYdBKC3YHuV3dScNFNxN94DVc8guXWH2Fx2dHm/fdI03xGPv+GaO3y4w/GmVKXw+JiB4oCVv37H2W2RY9VJ6EVBbIdFhyX/q2wv6yorO8ROBLIYYnBR3zvXky11agdB7H94acYFpyCzj+EGorQtfJSHt4TZM3oJsoPvMWO068GFAR0jEW07DmayRtaofVwdP8AoWiKRfW5ZIlaRr1R0mmFXx4x8NA9V2IzpmEsQNGeF3BlZyo4mKafjmPNWSQ7jyJ396Ab9qLkF2WSLgUBjl/M0tkz0fV3E62qxTYvgMZuRypuxDZrDn9Joon3Zaal1VQSYftj6PqbqT/vRup/cSIDh9OEghIhRw7OL3+T8O6dRBaewKjWiSOox5Alopt+OmFfEr+ujw5TDaYXHqdsej2dxaUMqAI5RRZydm4h7fNjqS0BnYn4qBnxmd8gukshOIhizSVZl8v+0uOo0kmQVlCAhCwjpcE4dwmSw4334V8j+XzIyQShH30P0ze/+wmNmE8nPiOffzN0DQS5/vtvEUvIXH/eZE5Z/MGBkNkWPWvq8xGETFrGByGlKHSFBEClRZfDwjvvwL9zD+np1WgH2rBLIocffY3xXfvRuuZSb3BQHuxBPnSA6nNTtCW0lFhkyorHKMgxku3UYTBr+OPLRzDa9WgkgUX2NMeLPeReWIs+L4e6ukLC+/ZhWHoyvqiIPb8ULSHSHg8Ji53o3j2QTmOZNwX3aSdQanMRTKTJElS6r70OncWARhIIbdoKgG3WDGyz5rx7TfnnnYGg02BvrIWWRxDK6tE6QoBKfrGG8H07CDXWk56zHGHOcrzxNNnPPcnQD3+UqQ3/9O+pbWrgsjmlaCURh/04ku27UNSMvJeSTqHVhkn0dRNYeB320WYMIR/JwS4UyxQScgHK9n1o8vKxNlrJNuneLRA5Ek0SkVWy2w6hO7gPy7JVhDe8QfyNjFM6vnc3+pKPWAL53xifkc+/GWJxmXgyk20ejqVQ0xEIHwbJApYaBEFAjQVR2jYhmeyIFXM+9FgGjcTSKg/dgSjlLjOtrgWEJ83DkWfFZtQxtu8goa27AMjqaGW1AaTNb+A+92ImDP+ReVojpq7DbJ76Va7/cpombTu/3TjI+mebMeg1rJyay55Lb2D8QAsla06k6Vc/JDU8hG/rDobe2kFofwsW51kULq9ml1xFyWwTxuYDCFo9gXf241pZg9g6iLB5O8rkBiKHmtEuOZ5INI39jLMxWHQ4li973zUZS4ooufFausai6ItM6HxtKGknosZHuNmHZeoUwsqxBE6dhjyznojZRAAQJAlBp8UbjKMDgikZjd+Lqe8gVUdeJequxLrvFdBb0M6Yh6ZzM2rPPtTCSnRn30j0aAeJ9b8FQKmbSr4rB60okmfWE07K9EeOFScUJHw/voeaZ57BfeV1yN4REAQMTR8t1+/fHZ+Rz78ZJpQ7ue1zsxn1R1k8swjG9zM0HiGipCkSh9Cb81COvIWy43cACGY3Ql7thx6v0m2h0m1hMJygdzjz6TQUTuI26hArKsi+8ToSh1uwn3QCHNxFcvYy2n/wCxpvvxCTbydy0Uzi/QEsNRX0mQooKR9Cp5NYMcVMuV3k8HgEgHQ0439KGA3oLzydutXL6Ljm22jduaS9UWZkH0aZNJ228iaG2vtpmFeO3D9C92+2kgyNM/rSq9Q+8DOO/vYFep76M4JWy5JdL6BzuVBVldauADazjvxsC0e8Yd7q8iNQyMS8KpqG4hhzq7DUpchyOBhLpomlFEYDMTa1+7AVTWP6fT9B63bTbcrmmz95m6p8K1PnleEpmEeN1og7rwRjMoy2woGYlYPOXoe8dTuKdgrDOU10RHRMml6BsGMrajLBQGETTa73nMXGVIwCIU4sBckXnsM6dx66/AI0djtFv8xU7BD+y3TQPyOff0McN7Xg3b99/mxeHBaQVZil1dJkBvTHpmU1BlIaI81DIURBoNZlQq/52yn9eFpBIwpUOgwMRZLYtCKDwSgeqxHj5ZcipBXcFj3rYxbynv8m6bEge7/0IJW//yXda6+m4KIUvgmVjMTTVE7O5bGLzSj3fY9kZCqN999OcH8rnsUZCQjZoIV0AsFhpfami0isexZJHeHAiERHSxZPH8yms0vHTXNKWEYWradchmTRUdO9H+eiBYxsy4hsabNsKDodqpzmje39fO/BHThteq5zDGOMBTGfdDIRSUsMEe+WbZSeswbpWICwQ6/FH40wLgkU1Lg5vLOPJScsRaMR6XzrCNGETDCaJlsjkjJk488/mRyXhf5v3k7WBB1aB2grnZhOvYFkWuYnj+ykfSDErFoPN931ayKxBI1Z78+nkjc8QdaW53B4ShCuuQWN04V0TLblv410/oLPyOffHIouF5WM01Y59jjF6uMQ7HmgM9AteDjiHQfAbtC8GyH9F6QTCbo37SGZk42jMIccMcLTu/sJJdIsn5DNrFIXXf1B/AmZ0hIPwtKlyOEwloXHMdoxgByOkNy/H9MFEFXAqlUQ+/cRVFViu3bi+Vwc51UXvHs+vagjKSiIgki8oxdVFQgWLeBbzzgpzw3S2TUGwKZmmbhRz733vYHHY+Zb3zsegNLPXUC8rARdQTaJp37OaPcR+uZ+CQB/KMHAaB/C479j4sRqElMb0W/diC6SCfxTVZVk89v8+ZCKX3BQNa0AQS/hyTIiyTHURJqpjYV8s8SJkpQZScpEkjJ6UQAXZM2bxagvSHTSQtwmPcWqiqpCMpX5DE6mFSSDAdsxnXPF14vasxeyc0CXeTbquA99lpWYqEWTltF9wMvgvwWfkc+/OTwWAyursgkn0xgFGAnGcQ1tRTn0KuLEZdiL8tGKAiJg0/3t4z76i8c59NW7MNVUUPmH+xkWEgRimR9T/1icbQcGufnHm9FpRX78lYU4LjofedVS+u59kC1DekpWn0sqt4hlNiManQZTbBeJJbWoiTj6gjw0nS+gVlQj2HNREknau4fZGRMRgOULF6HEE+iK8slxpGjpG2PtBU00N48yHkly+PAIAKOjETSpNMrQViRrGakF8zB3HkTe8joAc6VO0qvqsIQCaL/6BmKum7KicWz2XsJVRVgbTgRAHTnKxh2DfP+5BNDNhQmZ4xeXM7lGS+rJW2Dcz/hZ95LUaTEYdaQDMVBUgsfIxb10MV5fhFgiTW80RbZFDwJcvaYB0imytO+3YJTdLyMWahFChxCqyhAsVyHmlrFvMMXtj62nNNfKl8+bjNv+6Uz8/KTxGfn8B6Aoy8i2IyN858XD1ORZuEV9GqJ+lF1/wF01n+XlTkRBwPgBb9loZ6ZmVrStE8tAO7lVUaYUTiaaVGnIt3Ho4DCyohJLyDT3B/nDMwdoyDUyZdseSupmMTh/NTVlbrIsmW8aVclFX9ZCzjWLiAxLdObPR6fqyZdlYtv/TFLKA3NBRhrE6aToiotRev7Ed04L0RnIIjswyPChGM1jKuecXE1JsYPCAhtNlu2oPQdJ5J5Mzu5uNAXFaOctI93ZSsiTx3EN5dS4zIwU34nJPoRZ1wK+o1gbruPZt7vZvHeAMxYUkdbbqa1RyMq2gEGiMs+G0tGK7OsDwJQcB2xoBJEqm4FQWqbCmfHdqLEQTklhTBBxG7XoJZGuUBxvUgZECkKtqLaJCNpjvh67GyQfyCCkRtHMvBJB1LL35Rb8oQT+UIKeofBn5PMZ/r0x4I+hqtAxHCE253iMh19AnLgMQdRg/jsuhewF04hOaULSaQiv30LO9HM5OScbQcyQiXu6juB4Ar1Ow1AojgrsG4px/M038eT2CO3b27n89PfKKQmGYvCchTLUxnieg7Cgg5RKVjSA3XWYaK8G2lPMmFVG7jHCSusmYexdR2nzJtR4im9+biJq0ano7e9J4CrtB4nrmxh65CWGf/04usJ8yv/wJNsCacYVaJIzEdDZi+eh+HbCQAvY6/FFJH72+32oKhj1Gr56ZiP7kn28sWeQ/b1jLJ5ZRFXhBJSJiyDsJ7t/K7aGk0EQOBpMYNCIGDQiajxM+pUf4BjtYMrxX0CbOwNBEDBImWl0vZKCt/+MstiIVDgJAKlxJYrvIAJjYKtCEDOR7LPrczjY6afQY6Kq8J8LNP1PwGfk8x+CeROyybHJ1BeGMBsKERuWgt7yofvHk2kC4STjEyayw5v54S7IdiIY3x/e77DqWXus6sbmtlHGkzJ5bhPmUgedL60DoH8k/L42gqRHKqjHlpTxj8fRSSJCdydbNuTy5ReHkZUhHKKA9s2NZDVNILp1Cz0/+xX5551G4SklaHOrwOZ4/zGLluF7+HHkYOZccmgcqwRTCh1EU2lKjlkP/liKnaESCt0XU5plIEtrYtX8Ml7Z3MWMSR7M2dlMrIzzxp5B3HYDNpMOwWBGiPlQva3gPYy5dBohWyGpY0nIMVnBlgyBt5Oe6VcyKhRQ5h8gWxcn11yGoecAmp2voOnYAyveq/Ai/L/27jw+qupu/Pjn3tkyk8xkXwkQwhJ22SMoKBgFtIotbggIlGIX0T5AFelPK9ri8vh6lNanYn1Mte0PxWJV1CIKAetChDxoUBaRLRJCNhgyWWc/zx8TBtOsEwI3y3m/Xvf1ImfuvfO9Q+abc8499xyTBV3KhEaf/aB4I2t6H0BUlGNwREJ4v0b79AQy+XQT8ZFm4i1u8J0MPKdqmAjNLCLn9vhYt+UQuw+fZsn1g4HA7XDzsGFN7n/OyL7RGK1G9KpCv+hwfvPTyzl47AxZE/s0uX+EUceQGAsqcPwPOVR/W4ZONxKf34c1ZzN5r2zEGBNF/2uH46+tpXzLDtJ/s7XJuz+KKRJz34Gc2bGbuB/eRHhqPMaEeM5NXusuOIr9k4+otdioHXYl+90GbJZaUs2x/OKODG6/MZ7wcIHLX8P1l/clLclKjM1MUowJhAd6j4SKkyi9RoAtAatBT69wIx6/IMZkQDHF4LruQfJq4hBO8KheEur+gdLndqL7JuKr64cy+TJ0cYFaYM23R7B/9Cm2caOJHBNYI0tUnUZ4v0P4Bb4DnyFOFaLG90KXIpOP1NWpVvABWEBpvh+hxuUl78hpAA4cO8P1mX0QAvrHtvwQo9WoZ3zS+WbC1eNTuXp8y88i6eoTYFh6GpaX/sKvb0tBn3U9ti2llACKUY/xuumEnT1L8sK5Ld52jr12Kv6TR6n9eCsRV5yf88iz5yMcb/6dsx9+BEDG7/7A2WEjiDEH+rgMej3h4YHanYoOnU5lRP84Ck6Ws/mT/YwdYiJx9DT8AyejmG0o+sAik8nh9f1Yp/OgdAemuCkkK704Ve0iTldNIMsrKGYz+lGJ9dEErvfY489g37odU2oK43LeRR8Rjqjcj5oUGGhomHUz7uz/QU0b2uLn153J5NOd6HqBYgUMgYXYmxEdYeKn12VwoLCCKcMSGRjXfPOsNR6/H72iNLtUb6GjDofLS79ZMxjZNw5jah/M/QZRNyqemDFDcCT2Ys2+OqyzfsXNg/rQdB0qQNHriV+0GN8Pf4Qu9vzUur6Cb1BNgYShGAykpcYzpJcVCHwGBl0YViUZIfwY1EDNxOXy8tuXvmRm1kC+88VQecaHd8ce6j7Lo89t1xM36Xujjc/mg9+N/vTHXD5wFE6fhQi3A6JuRQnvA0IA9SuAKnGB96wf52OIiUGpf7xF+BQCyUlwmFRMi5+j/5DGtR7h8+I/8QWoOtQ+oxvMcNidyOTT3ahtmyz8quFJXDX8whZKLKlxcaTCSYLFwIAoc/AZpnPO1nmC8/2k1W4n/NgHUDkAkboKc1I86Xf9kO/Kqgg/spcal5eUmNZXilX0BvRxCQ3K9BOyMHtcJD3wAIahYzAPazwFrkFtOAWpAExGPal9o3D7ocytw/vGZkrf3EL14eNM+Wf2+Z3jJsLpXIgehckYRmlZNUXfnKJf4TYM/cegm3Azitowpn6rlhNzzVVEDB+KzhxIgmrCaOzfHeTIaR+PvHKCh5Yk09STef6C3fi3/SFwvTNXovTRfo2ti0EmH6ndzji9eIXgVI2b3lZTo1v5elXBoCr4hMBQG2jmUVEEPjcYAsmgb4KVNfPG4fH66RXbMPn4nS6Ksv+Cs7CQlEXzCc9ouNyQT3jw+33oU/tjueOXwXKny0tljZuE7yUzUWtHlO5FsaagxGUQZtLzq7vGUOUTuAwKNfZahDcwnidqzPAG76NED4fo82VvfXqcW+p2oxYdwFd0AHXQJJSYhgsOmBITSLhxZsPzhFkJSxlN8Xcn+OXcZDJHJIHfAaIMlChQ62tz/vOzbYr6eYy6I5l8pHZLtBip8/qCY17+ndWkZ0b/OKo9PnQpP0SJ6Y2SkIES1nBWvoTIsEbHAlR99TUn/vMZAAxRUYQ/uCL4ml/4qHaX4cODWReNWR/oi6p1eljz4m4+yz/FAwvHcf2UQLNGHNsGx7YidCaYuhrFEkd6ahQAjhoXFX5IevG31Kz6KbZhDe/4/btYWxhHvf2I038BgyahWFtfFfccS5iBWVPP13f8zqP4nJUYrMXgjwDVjJqeCaoCih61m9Z6QCYf6QLEmQ3EmQ2UVjv5uqyK3tYwYizGBvvEWIz1c+2kQVxaSOcP652KZehg6r49QsTIhrURIfz4CKz+6ROeYPmZCieffnkKgL3flgeTD/VjbNAZQWlYQ4sMNxFZ37lsim14ix+goLiKOreXIX0Dr90yuR8HC6OpmTqN6JgoFF37vkYeeznlr+VQvOljUpcvIuxqsJoEqk6ProXZCLoLRXx/RnWpXSorK4mMjMThcGCzdc4F2prj9vgwGtr/fJHL62Pz4dM43F56WU1cmx7XgdGB+2wFvqoqzH0arwjr8lXj87sx6sLR1/fp+P2Ct7Yf4evDp7llWjpDTcdA1UNCBsrpbyAiCSWqpW7tho6cdLDsuU9xun08vGAsU0b1av2gNhBC4Nn6HMrxndSFDeTEUQvOpUvpFW4M3mXrikL5LsiaTw+2+ZPjvPTmPm6e1p/5PxiCoij46uqCHaRt4RcCT32/hNff9r9jwu9FUVv/9TNGR+EKt/LBZwUYDTqmjEsNrtJh0kU0mndfVRVmZw1kdtZA/AU7ETsDy+cw6haUjBkoOiOhsFc6qXMF+oLKK5whHdsinwel7BsAwgxVmK69ESfgCeEz7Oq65z08qU3e/ddx7JUu1m8+RFWtm9L1r5E/ZRpF6/7U5nOYDXrGp0SRERvOZW1cYcF38EN8/1iO7+CHbdp/R14hj7+Ux+p1n7P7q+LAkj/CAcLV8oHfb16dPQqnD7Tp/b5v1KA47rtlBD++fjDTxnbc/MqK3og6eQnq0GvRT15E3PjRpFiMJJg77yJ/Ha1bJZ+0tDSU+jEn57Ynn3yyxWOcTif33HMPsbGxREREMHv2bEpLSy9RxNq6eWo6SbEWFtw4BKvFSOn/fxWv/SzFL2bjralp83n6RZuZmBpFiq3pjuPvE34fYt97UGtH7HuX6mMnWj3m+81CvUEFCoEvgK8Ciy82Q+k9DmXcPEgbA7WnIKxxf06r763XcdOV/bjzukEhrxjaGl3f0egmL0btfRnRYQZSIkyE9aApNrpVn09aWhqLFy9myZIlwTKr1Up4ePMjd3/+85/zz3/+k1deeYXIyEiWLl2Kqqp89tlnbX5frft8ap0eCour6J1sxRJmwJ6zA3dRETE3zMQYG9visX6/QK1vxpS/8SZF6/5E4rw7SV60oMF+FfsPIzweokcFRuR6fX6+OFCKJczA8IGh9fPUfbYRw6lPKCuPx342juH/7+ct7u/1+dn1dQlGvcq4YYkoHAICE8W73RP4cPtJ/H7BdVkDCDM1bsoJRyHojCgRiY1ekzpWSN8F0Y307dtXPPvss23ev6KiQhgMBrFx48Zg2cGDBwUgcnNz23weh8MhAOFwOEIJt8M89XKeuOrHG8VTL+eJqgMHxecDhojP0zNE4R/+O+Rz+f1+8a99xeLRDV+KT/aXCCGEKM/NFxvCRohXdUNE0eaPhBBCfPBZgZiy8O9i6uKNYt/h8pDeo/JwgcjJWiA2mC8ThZu2NXitxukWLo+vlSArhfAfFsJfIrbmHBZjr1gnxl6xTmzZ+m1IcUgdL5TvQrdqdgE8+eSTxMbGMnr0aJ5++mm8Xm+z++7ZswePx0NWVlawbPDgwfTp04fc3Nxmj3O5XFRWVjbYtOL1+ck/VA5A/qFyRFgYav1MeroWanzN8fkFGz49zsGTDl775Bg+v6C2qASf04Xw+3GWBUYs19TV3+b2CWqdzX/GTbEO6MvEvz3N9V9tIvWma4Ll+0+cZVn2bp76x1fYq1voz1GsoAwAJRHz9/pIzD2ov6Q76FZ3u+677z7GjBlDTEwMO3fuZNWqVRQXF/PMM880uX9JSQlGo5GoqKgG5YmJiZSUlDT7Pk888QSPPvpoR4bebnqdyrK5o9m5t5hJlyVj7ZfEkFf/iruklMgrQx8r4i4p44p4He/XKlwzMhmdqpA8YzKjn12FWnWW03/7C2F6L9f+aBaqqhBuNjBmaOjNGXNSfKOyA4UVVNZ5OXDSwcnTNcREtN7HMunyPqz7/Y0IIRj/vbmtfWfPIFx16JN6ziJ8Xc7Fr4hdmJUrVwoCj+I0ux08eLDJY7Ozs4VerxdOp7PJ19evXy+MRmOj8vHjx4sHHnig2ZicTqdwOBzBrbCwUNNmV0c6sOY58Xb8GPHBDT8RZZ9/ESz3e73i07FXi5zEQeKTEVcIn8vVpvPZq5zi7S15IueNf4qTp1punh0trhSPb9wrXtp6SFTVudt9DZ6TBaLk7pvFqVuuEHV5n7b7PFLoQml2dfqaz4oVK1i4cGGL+6SnN73QWmZmJl6vl4KCAjIyMhq9npSUhNvtpqKiokHtp7S0lKSk5h+6NJlMmExddyBYS8wpifiqqnF/+RVhMVHBckWnI3XRXAqeeZ7UxfNQjQ3Hy5x1ejhQXoPZoGNYXDgmfaBFX1RUTubHzxNWegyn8zaY+4tm3zs9ycqqWxo/FBoqb0kR/rLAKGfvd0dg3BUXfM6L6aOvTvG/h09z1Yhkxg9qXCPsrjp98omPjyc+vn3/Ifn5+aiqSkJCQpOvjx07FoPBQE5ODrNnzwbg0KFDnDhxgokTJ7Y75s7A6/Xj9fsJa2LS+Jb0nf8jIgakYYyPxTqw4XQPfX6xmF7zbkNnazyep6jKxcn6fprkCCPJ9U2mXlYdij0wP7K52t6eSwmZcdgoIubcjb+yAtOka1o/oAlltW7sLg+JZiPRYR3Tl+R49y2qtm0hctZsrNMCix3WOL38afM31Ll9lNhrZfLpinJzc9m1axdTp07FarWSm5vLsmXLmDdvHtHRgfEdRUVFXHPNNfz1r39lwoQJREZGsnjxYpYvX05MTAw2m417772XiRMncvnll2t8Re3jt5dxxq3nqfX7OFlSxa9/MoGRGW3/hVZ0OuKuHN/0a4qCPrLp26eJYQr4i3CZY7GZzo9ViU5JxvXzx/Ae3Y9p/NUhXUt7qWEWrLctbvfxHp+fwmpXYJJ73B2SfHy1NZSv/U/8lQ68pcXB5BNm1DFlRDIf7DlJZkbTfyS7q26TfEwmExs2bGD16tW4XC769evHsmXLWL58eXAfj8fDoUOHqK2tDZY9++yzqKrK7NmzcblcTJ8+neeff16LS7hgni/+hTP7dxwadRd5+wJ3vL44WBZS8mkPR7WLqKNbid77BiKmH/peD/L9Xy3TyMsxjew6yVyvKsSb9ZTXeYkxdUytRzVbiLzpR5xd/wq2GTcGy3WqwuLpg5h9RRqxtu7ZlG9OtxpkqBWtBxme43xjHZ6cjVQlDODlpAWUnHVzzx0jGZze8kDDC7H762IeWfc5f/1BIdHlu0HRofvRf6FEdOwDppeaEAKvX6BXm5+lMeRzer34ztrRxcZ121VK5YOlPZR+/DWI6gpik/rw62vGoxhCe4iyPfIPnaa2zsvfDvdhydhownsP7vKJBwJNTIOuY5JO8Jx6Pfr4ntW0aolMPt2Ivu8g9AtXXdL3nDK2FwVFDsKSrKhjh6J2gYF+QgjKnR5cXh9RJgPWEDvlpY4hP3XpggzuF8Pjv7xS6zBCUufxU14/QlvgaTL5+B12hMeNLu7C5rmWmtc9G55Sh6v55ltO/PeLOPK+0DqUJnntp6na9Bp1uz9tdV+dCqb6JpWxib4Xb/F3VK35GVUP3YVnf16HxyoFyJqP1CbHnlqL/cPtGBMTGPfRexg62YyN1e/+narXXgKdnsQ/voYxral1IQJMeh3JFiNun5/wJmZx9JeeRNjLAPAVHsUwrOmhB9KFkclHahNjbGAmZlNyIqqh8/XrqObAShVKmBnF2HpHe7hBT3gzl2HIGIXpxrsQNVUYxk7pyDCl75G32jtAZ7nVfjG5T5/B8b9fEjFsMObene9hTb+zDucXuegTkjEOGNLkPgJBYB4gJ5CMQtuni5XaRt5qlzqcMS6W+BlZre+oETXMjGXStFb2qgQOnzsCSLuoMUktkx3O0gXpWhVnA3CuSdb6lK/SxSWTj9RupZs2s+uKGRSsfaFLrKypYAFGAaMBOaWq1mTykdqt+LV/UHf8O44//Qfcpy/NE+sXSsGMgg2Fjh29LIVO9vlI7ZY4+0aq9x0kee6tGOtX+hR1x6ByN0JNwvvFQdSENPSjZ7ZyJqknkslHarfkW28m4QfTGy4yWHsYvGdROAtuO76PtqOmXYYanaJdoFKnJJtd0gVptLqpuR+oYQiRiig6ghKVDKbQJ7LvUkQd+PLB9yWItq931tPJmo/UoRTLIERYP6iuRHdlOGpif1RLpNZhXVzCDpTX/zsOlG6ebDuITD5Sh1NUA4otFnX4+XE3Pr+gzusj3KDrsPlxOg0lEoQV8Af+LbWJTD7SRSeE4NPjZ8gvruTKtBjGpkZpHVLHUiJArX/+S+k5yx1fKNnn0wMds9ewo+AMB8qrL8n7uXx+9pdVAXD49KV5z0tO0cnEEyJZ8+mBvrHXUFbjoajKRarNhK2D5iluTphex1XpsRw9U8PQhO757JsUOpl8eiCrUU9ZjYdIkx6j7sL/WheUVrFjXzFpCVauHp7UZJ/OsEQbwxJl4pHOk8mnBxqXbCPVGkasWU+Y/sJb3jlfF7NtbzFQzKAUG71i5d0eqXUy+fRAZoOeftEd91/fOy6QbAYkW7FZOt9cP1LnJJOPdMGyLkthcK9IosKNWM0Xf8UMqXvoNne7PvroIxRFaXLLy2t+Ht6rr7660f4/+9nPLmHkXZ+qKPSJj8BmkYlHartuU/OZNGkSxcXFDcoefvhhcnJyGDduXIvHLlmyhMceeyz4s8ViuSgxSpJ0XrdJPkajkaSk88uceDweNm3axL333tvqiFqLxdLgWEmSLr5u0+z6d++88w5nzpxh0aJFre67fv164uLiGD58OKtWrWqwlntTXC4XlZWVDTZJkkLTbWo+/y47O5vp06eTmtryZOd33nknffv2JSUlha+++oqVK1dy6NAh3nzzzWaPeeKJJ3j00Uc7OmRJ6llEJ7dy5UoBtLgdPHiwwTGFhYVCVVXxxhtvhPx+OTk5AhBHjhxpdh+n0ykcDkdwKywsFIBwOBwhv58kdScOh6PN34VOX/NZsWIFCxcubHGf9PT0Bj+//PLLxMbGctNNN4X8fpmZmQAcOXKE/v2bXnjOZDJhMplCPrckSed1+uQTHx9PfHx8m/cXQvDyyy9z1113YWjH4nb5+fkAJCcnh3ysJElt1+06nLdv387x48f5yU9+0ui1oqIiBg8ezO7duwE4evQov/3tb9mzZw8FBQW888473HXXXUyZMoWRI0de6tAlqUfp9DWfUGVnZzNp0iQGDx7c6DWPx8OhQ4eCd7OMRiPbtm1j7dq11NTU0Lt3b2bPns1DDz10qcOWpB5HLpfcAXrCcsmS1BahfBe6XbNLkqSuQSYfSZI0IZOPJEmakMlHkiRNyOQjSZImZPKRLjq/x4PzVAnC59M6FKkTkclHuqiEEBz93X+RO/Zqvnvuf7QOR+pEZPKRLipfbR0lG98GoPSt97QNRupUut0IZ6lz0YdbGPjYrynf/CGJt8zSOhypE5EjnDuAHOEsSQFyhLMkSZ2eTD6SJGlCJh9JkjQhk48kSZqQyUeSJE3I5CNJkiZk8pEkSRMy+UiSpAmZfCRJ0oRMPpIkaUImH0m6AG6PD59fPqHUHvLBUklqpwMnzvLcO/tJjraw9KahxFjDtA6pS5HJR5La6esCO8X2OortdRwvrZbJJ0Rdptm1Zs0aJk2ahMViISoqqsl9Tpw4wQ033IDFYiEhIYH7778fr9fb4nntdjtz587FZrMRFRXF4sWLqa6uvghXIHU3Y/rHMaiXjcnDk+ifLGczCFWXqfm43W5uvfVWJk6cSHZ2dqPXfT4fN9xwA0lJSezcuZPi4uLgeu2PP/54s+edO3cuxcXFbN26FY/Hw6JFi7j77rt59dVXL+blSN3AwF6RPPXjCSiKonUoXZPoYl5++WURGRnZqHzz5s1CVVVRUlISLFu3bp2w2WzC5XI1ea4DBw4IQOTl5QXL3n//faEoiigqKmpzTA6HQwDC4XC0/UIkqRsK5bvQZZpdrcnNzWXEiBEkJiYGy6ZPn05lZSX79+9v9pioqCjGjRsXLMvKykJVVXbt2tXse7lcLiorKxtskiSFptskn5KSkgaJBwj+XFJS0uwxCQkJDcr0ej0xMTHNHgPwxBNPEBkZGdx69+59gdFLUs+jafJ58MEHURSlxe2bb77RMsQmrVq1CofDEdwKCwu1DkmSuhxNO5xXrFjBwoULW9wnPT29TedKSkpi9+7dDcpKS0uDrzV3TFlZWYMyr9eL3W5v9hgAk8mEyWRqU1ySJDVN0+QTHx9PfHx8h5xr4sSJrFmzhrKysmBTauvWrdhsNoYOHdrsMRUVFezZs4exY8cCsH37dvx+P5mZmR0SlyRJTesyfT4nTpwgPz+fEydO4PP5yM/PJz8/Pzgm57rrrmPo0KHMnz+fvXv38sEHH/DQQw9xzz33BGspu3fvZvDgwRQVFQEwZMgQZsyYwZIlS9i9ezefffYZS5cu5Y477iAlJUWza5WkHuES3H3rEAsWLBBAo23Hjh3BfQoKCsTMmTOF2WwWcXFxYsWKFcLj8QRf37FjhwDE8ePHg2VnzpwRc+bMEREREcJms4lFixaJqqqqkGKTt9olKSCU74Jct6sDyHW7JCkglO9Clxnh3Jmdy99yvI/U0537DrSlTiOTTweoqqoCkON9JKleVVUVkZGRLe4jm10dwO/3c+rUKaxWa4c851NZWUnv3r0pLCyUzTjk5/HvOvPnIYSgqqqKlJQUVLXl+1my5tMBVFUlNTW1w89rs9k63S+XluTn0VBn/Txaq/Gc02VutUuS1L3I5CNJkiZk8umETCYTjzzyiHyEo578PBrqLp+H7HCWJEkTsuYjSZImZPKRJEkTMvlIkqQJmXwkSdKETD6dXFpaWqPZHZ988kmtw7pk/vjHP5KWlkZYWBiZmZmNJozrSVavXt3od2Hw4MFah9VucoRzF/DYY4+xZMmS4M9Wq1XDaC6d119/neXLl/PCCy+QmZnJ2rVrmT59OocOHWo093ZPMWzYMLZt2xb8Wa/vul9hWfPpAqxWK0lJScEtPDxc65AuiWeeeYYlS5awaNEihg4dygsvvIDFYuHPf/6z1qFpRq/XN/hdiIuL0zqkdpPJpwt48skniY2NZfTo0Tz99NOtrsLaHbjdbvbs2UNWVlawTFVVsrKyyM3N1TAybR0+fJiUlBTS09OZO3cuJ06c0Dqkduu6dbYe4r777mPMmDHExMSwc+dOVq1aRXFxMc8884zWoV1Up0+fxufzNbkcUmdc0eRSyMzM5JVXXiEjI4Pi4mIeffRRJk+ezL59+7pmU/ziTagoNWflypVNTgn7/e3gwYNNHpudnS30er1wOp2XOOpLq6ioSABi586dDcrvv/9+MWHCBI2i6lzOnj0rbDabeOmll7QOpV1kzUcDF7JkUGZmJl6vl4KCAjIyMi5CdJ1DXFwcOp0uuPzROaWlpS0ua9STREVFMWjQII4cOaJ1KO0ik48GLmTJoPz8fFRV7fZ3e4xGI2PHjiUnJ4ebb74ZCEzalpOTw9KlS7UNrpOorq7m6NGjzJ8/X+tQ2kUmn04sNzeXXbt2MXXqVKxWK7m5uSxbtox58+YRHR2tdXgX3fLly1mwYAHjxo1jwoQJrF27lpqaGhYtWqR1aJr41a9+xY033kjfvn05deoUjzzyCDqdjjlz5mgdWrvI5NOJmUwmNmzYwOrVq3G5XPTr149ly5axfPlyrUO7JG6//XbKy8v5zW9+Q0lJCaNGjWLLli2NOqF7ipMnTzJnzhzOnDlDfHw8V155JZ9//nmHLbx5qckpNSRJ0oQc5yNJkiZk8pEkSRMy+UiSpAmZfCRJ0oRMPpIkaUImH0mSNCGTjyRJmpDJR5IkTcjkI0mSJmTykTSzcOHC4FzERqORAQMG8NhjjwUnSxNC8OKLL5KZmUlERARRUVGMGzeOtWvXUltb2+BcJ0+exGg0Mnz48JDjsNvtzJ07F5vNRlRUFIsXL6a6urpDrlFqnkw+kqZmzJhBcXExhw8fZsWKFaxevZqnn34agPnz5/Mf//EfzJo1ix07dpCfn8/DDz/Mpk2b+PDDDxuc55VXXuG2226jsrKSXbt2hRTD3Llz2b9/P1u3buW9997j448/5u677+6wa5SaofF8QlIPtmDBAjFr1qwGZddee624/PLLxeuvvy4A8fbbbzc6zu/3i4qKigY/p6eniy1btoiVK1eKJUuWtDmGAwcOCEDk5eUFy95//32hKIooKioK/aKkNpM1H6lTMZvNuN1u1q9fT0ZGBrNmzWq0j6IoREZGBn/esWMHtbW1ZGVlMW/ePDZs2EBNTU2b3i83NzfYnDsnKysLVVVDrkFJoZHJR+oUhBBs27aNDz74gGnTpnH48OE2z9SYnZ3NHXfcgU6nY/jw4aSnp7Nx48Y2HVtSUtJoYja9Xk9MTAwlJSUhX4fUdjL5SJp67733iIiIICwsjJkzZ3L77bezevVqRBtneqmoqODNN99k3rx5wbJ58+aRnZ19sUKWOoicTEzS1NSpU1m3bh1Go5GUlJTgIniDBg1q0yoVr776Kk6nk8zMzGCZEAK/38+3337LoEGDWjw+KSmJsrKyBmVerxe73S7nir7IZM1H0lR4eDgDBgygT58+DVbfvPPOO/n222/ZtGlTo2OEEDgcDiDQ5FqxYgX5+fnBbe/evUyePLlNiwtOnDiRiooK9uzZEyzbvn07fr+/QUKTLgJt+7ulnqypu13n+P1+cfvttwuz2SzWrFkj8vLyREFBgXj33XfFtGnTxFtvvSW+/PLLZpcZev7550VSUpLweDytxjFjxgwxevRosWvXLvHpp5+KgQMHijlz5lzo5UmtkMlH0kxLyUcIIXw+n1i3bp0YP368sFgswmazibFjx4rf//73ora2VixdulQMHTq0yWOLi4uFqqpi06ZNrcZx5swZMWfOHBERESFsNptYtGiRqKqqau9lSW0k53CWJEkTss9HkiRNyOQjdWuPP/44ERERTW4zZ87UOrweTTa7pG7Nbrdjt9ubfM1sNtOrV69LHJF0jkw+kiRpQja7JEnShEw+kiRpQiYfSZI0IZOPJEmakMlHkiRNyOQjSZImZPKRJEkT/weCg1Vp6rNKWAAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.clf()\n", + "\n", + "# latent space scatter plot, coloured by ground truth frames\n", + "dim1=0\n", + "dim2=1\n", + "dt = 1.2e-3# time between frames in microseconds\n", + "\n", + "fig, ax = plot_latent_space_scatter(\n", + " df_picked,\n", + " dim_1=dim1,\n", + " dim_2=dim2,\n", + " color_by=\"closest_pdb_index\",\n", + " palette=\"RdYlBu\",\n", + " pca=True,\n", + ")\n", + "# remove legend and add colorbar for the closest_pdb_index\n", + "ax.legend_.remove()\n", + "\n", + "S_m = plt.cm.ScalarMappable(cmap=\"RdYlBu\")\n", + "S_m.set_array(df_picked[\"closest_pdb_index\"])\n", + "\"\"\"\n", + "cbar = plt.colorbar(S_m)\n", + "cbar.set_label(\"time [\\u03BCs]\", rotation=270, labelpad=15, fontsize=16) # time in ps\n", + "# change the tick labels on the colorbar to go from 0 to 10 us\n", + "cbar.set_ticks(np.linspace(1, df_picked[\"closest_pdb_index\"].max(), 10))\n", + "xticklabels = [np.round(r, 1) for r in np.linspace(1, df_picked[\"closest_pdb_index\"].max(), 10)*dt]\n", + "cbar.set_ticklabels(xticklabels, fontsize=14)\n", + "ax.set_xlabel(f\"PCA{dim1}\", fontsize=16)\n", + "ax.set_ylabel(f\"PCA{dim2}\", fontsize=16)\n", + "ax.tick_params(labelsize=14)\n", + "#ax.set_xlim((-12, 16))\n", + "#ax.set_ylim((-12, 12))\n", + "ax.set_xlim((-20, 20))\n", + "ax.set_ylim((-20, 20))\n", + "#plt.xticks([-20,-10,0,10,20])\n", + "#plt.yticks([])\n", + "\"\"\"\n", + "\n", + "# add trajectory to the plot\n", + "N_volumes = 50\n", + "pdb_indices = np.unique(df_picked[\"closest_pdb_index\"])\n", + "d_pdbs = len(pdb_indices) // N_volumes\n", + "\n", + "trajectory = np.zeros((N_volumes, ndim))\n", + "trajectory_pca = np.zeros((N_volumes, ndim))\n", + "for i in range(N_volumes):\n", + " pdb_group = pdb_indices[i*d_pdbs:(i+1)*d_pdbs]\n", + " mean_latent = df_picked[df_picked[\"closest_pdb_index\"].isin(pdb_group)].agg(\n", + " {f\"latent_{i}\": \"mean\" for i in range(ndim)}\n", + " )\n", + " trajectory[i] = mean_latent.values\n", + " mean_pca = df_picked[df_picked[\"closest_pdb_index\"].isin(pdb_group)].agg(\n", + " {f\"PCA_{i}\": \"mean\" for i in range(ndim)}\n", + " )\n", + " trajectory_pca[i] = mean_pca.values\n", + "\n", + "ax.scatter(trajectory_pca[:, 0], trajectory_pca[:, 1], s=5, c=\"black\", zorder=10)\n", + "ax.plot(trajectory_pca[:, 0], trajectory_pca[:, 1], c=\"black\", zorder=10, linewidth=0.5)\n", + "ax.set_aspect(\"equal\")\n", + "\n", + "fig.savefig(os.path.join(figures_dir, f\"{os.path.basename(latent_file)}_latent_space_scatter_colored_by_closest_pdb_index_pca.png\"), dpi=600, bbox_inches=\"tight\")\n", + "fig.savefig(os.path.join(figures_dir, f\"{os.path.basename(latent_file)}_latent_space_scatter_colored_by_closest_pdb_index_pca.pdf\"), bbox_inches=\"tight\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## FIG 2A - Micrograph, PS thumbnail and radially averaged PS " + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "# imports\n", + "import os\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import seaborn as sns\n", + "import mrcfile\n", + "\n", + "# from emmer.ndimage.filter.smoothen_mask import smoothen_mask\n", + "from pipeliner.mrc_image_tools import mrc_thumbnail\n", + "# from gemmi import cif\n", + "\n", + "# roodmus\n", + "from roodmus.analysis.utils import load_data" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.clf()\n", + "from pipeliner.mrc_image_tools import mrc_thumbnail\n", + "# load the input micrograph and plot a thumbnail\n", + "project_dir = \"/home/dvi41342/MapReconstruction/DESRES-Trajectory_sarscov2-11021571-all-glueCA_fractionated\"\n", + "figures_dir = os.path.join(project_dir, \"figures\")\n", + "input_mrc_filename = os.path.join(project_dir, \"MotionCorr\", \"job007\", \"Movies\", \"000000.mrc\")\n", + "input_mrc_thumbnail = mrc_thumbnail(\n", + " input_mrc_filename,\n", + " 500,\n", + " os.path.join(figures_dir, \"motioncorr_ugraph_000000.png\"),\n", + ")\n", + "\n", + "fig, ax = plt.subplots()\n", + "ax.imshow(input_mrc_thumbnail, cmap='gray')\n", + "ax.set_xticks([])\n", + "ax.set_yticks([])\n", + "fig.savefig(os.path.join(figures_dir, \"motioncorr_ugraph_000000.png\"), dpi=600, bbox_inches=\"tight\")" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.clf()\n", + "# plot the power specrtrum\n", + "from pipeliner.mrc_image_tools import mrc_thumbnail\n", + "diagnostic_filename = os.path.join(project_dir, \"CtfFind\", \"job008\", \"Movies\", \"000000_PS.mrc\")\n", + "mrc_PS = mrc_thumbnail(\n", + " diagnostic_filename,\n", + " 500,\n", + " os.path.join(figures_dir, \"PS_ugraph_000000.png\"),\n", + ")\n", + "\n", + "fig, ax = plt.subplots()\n", + "ax.imshow(mrc_PS, cmap='gray')\n", + "ax.set_xticks([])\n", + "ax.set_yticks([])\n", + "fig.savefig(os.path.join(figures_dir, \"PS_ugraph_000000.png\"), dpi=600, bbox_inches=\"tight\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## FIG 1A - LocalRes and FSC" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [], + "source": [ + "# imports\n", + "import os\n", + "import mrcfile\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import numpy as np\n", + "import seaborn as sns\n", + "\n", + "from gemmi import cif\n", + "\n", + "# roodmus\n", + "from roodmus.analysis.utils import load_data" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "project_dir = \"/home/dvi41342/MapReconstruction/DESRES-Trajectory_sarscov2-11021571-all-glueCA_fractionated\"\n", + "figures_dir = os.path.join(project_dir, \"figures\")\n", + "job = \"job029\"\n", + "fsc_postprocess_filename = os.path.join(project_dir, \"PostProcess\", job, \"postprocess.star\")\n", + "resolution = 3.44\n", + "\n", + "\n", + "fsc_cif = cif.read(fsc_postprocess_filename).find_block(\"fsc\")\n", + "rlnResolution = fsc_cif.find_loop(\"_rlnResolution\")\n", + "rlnFourierShellCorrelationCorrected = fsc_cif.find_loop(\"_rlnFourierShellCorrelationCorrected\")\n", + "rlnFourierShellCorrelationParticleMaskFraction = fsc_cif.find_loop(\"_rlnFourierShellCorrelationParticleMaskFraction\")\n", + "rlnFourierShellCorrelationUnmaskedMaps = fsc_cif.find_loop(\"_rlnFourierShellCorrelationUnmaskedMaps\")\n", + "rlnFourierShellCorrelationMaskedMaps = fsc_cif.find_loop(\"_rlnFourierShellCorrelationMaskedMaps\")\n", + "rlnCorrectedFourierShellCorrelationPhaseRandomizedMaskedMaps = fsc_cif.find_loop(\"_rlnCorrectedFourierShellCorrelationPhaseRandomizedMaskedMaps\")\n", + "\n", + "df = pd.DataFrame({\n", + " \"resolution\": rlnResolution,\n", + " \"fsc\": rlnFourierShellCorrelationCorrected,\n", + " \"fsc_masked\": rlnFourierShellCorrelationMaskedMaps,\n", + " \"fsc_unmasked\": rlnFourierShellCorrelationUnmaskedMaps,\n", + " \"fsc_masked_random\": rlnCorrectedFourierShellCorrelationPhaseRandomizedMaskedMaps,\n", + " \"fsc_masked_fraction\": rlnFourierShellCorrelationParticleMaskFraction,\n", + "})\n", + "\n", + "# convert all columns to float\n", + "for column in df.columns:\n", + " df[column] = df[column].astype(float)\n", + "\n", + "fig, ax = plt.subplots(figsize=(3.5, 3.5))\n", + "sns.lineplot(x=\"resolution\", y=\"fsc\", data=df, ax=ax, legend=True, label=\"corrected\", linewidth=3)\n", + "sns.lineplot(x=\"resolution\", y=\"fsc_masked\", data=df, ax=ax, legend=True, label=\"masked\", linewidth=3)\n", + "sns.lineplot(x=\"resolution\", y=\"fsc_unmasked\", data=df, ax=ax, legend=True, label=\"unmasked\", linewidth=3)\n", + "ax.axhline(0.143, color=\"black\", linestyle=\"--\")\n", + "ax.axhline(0.5, color=\"black\", linestyle=\"-.\")\n", + "ax.axvline(1/resolution, color=\"black\", linestyle=\"dotted\")\n", + "ax.set_xlabel(\"Spatial Frequency ($\\AA^{-1}$)\", fontsize=21)\n", + "ax.set_ylabel(\"FSC\", fontsize=21)\n", + "# ax.set_ylim((-0.1, 1.25))\n", + "# change the fontsize of the ticks and the legend\n", + "ax.tick_params(axis='both', which='major', labelsize=20)\n", + "ax.legend(fontsize=18, bbox_to_anchor=(0.65, 1.1), loc=2, borderaxespad=0.)\n", + "fig.savefig(os.path.join(figures_dir, f\"{job}_FSC.pdf\"), bbox_inches=\"tight\")\n", + "fig.savefig(os.path.join(figures_dir, f\"{job}_FSC.png\"), bbox_inches=\"tight\", dpi=600)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## FIG 1F - Precision and Recall" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loading metadata from /home/dvi41342/MapReconstruction/DESRES-Trajectory_sarscov2-11021571-all-glueCA_fractionated/Extract/job013/particles.star...\n", + "loaded metadata from /home/dvi41342/MapReconstruction/DESRES-Trajectory_sarscov2-11021571-all-glueCA_fractionated/Extract/job013/particles.star. determined file type: star\n", + "\n", + "\n", + "Dictionaries now contain 17501 reconstructed particles\n", + "added 17501 particles from /home/dvi41342/MapReconstruction/DESRES-Trajectory_sarscov2-11021571-all-glueCA_fractionated/Extract/job013/particles.star\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "loading truth data: 100%|██████████| 67/67 [00:19<00:00, 3.49it/s, micrograph=000066.mrc]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loaded ground-truth particle positions from config files\n", + "Dictionaries now contain 17501 particles and 20100 true particles\n", + "Added 20100 particles from /home/dvi41342/MapReconstruction/DESRES-Trajectory_sarscov2-11021571-all-glueCA_fractionated/Movies\n", + "loading metadata from /home/dvi41342/MapReconstruction/DESRES-Trajectory_sarscov2-11021571-all-glueCA_fractionated/Class2D/job014/run_it200_data.star...\n", + "loaded metadata from /home/dvi41342/MapReconstruction/DESRES-Trajectory_sarscov2-11021571-all-glueCA_fractionated/Class2D/job014/run_it200_data.star. determined file type: star\n", + "checking if ugraphs exist...\n", + "\n", + "\n", + "Dictionaries now contain 35002 reconstructed particles\n", + "added 17501 particles from /home/dvi41342/MapReconstruction/DESRES-Trajectory_sarscov2-11021571-all-glueCA_fractionated/Class2D/job014/run_it200_data.star\n", + "loading metadata from /home/dvi41342/MapReconstruction/DESRES-Trajectory_sarscov2-11021571-all-glueCA_fractionated/Select/job020/particles.star...\n", + "loaded metadata from /home/dvi41342/MapReconstruction/DESRES-Trajectory_sarscov2-11021571-all-glueCA_fractionated/Select/job020/particles.star. determined file type: star\n", + "checking if ugraphs exist...\n", + "\n", + "\n", + "Dictionaries now contain 50848 reconstructed particles\n", + "added 15846 particles from /home/dvi41342/MapReconstruction/DESRES-Trajectory_sarscov2-11021571-all-glueCA_fractionated/Select/job020/particles.star\n", + "loading metadata from /home/dvi41342/MapReconstruction/DESRES-Trajectory_sarscov2-11021571-all-glueCA_fractionated/Select/job041/particles.star...\n", + "loaded metadata from /home/dvi41342/MapReconstruction/DESRES-Trajectory_sarscov2-11021571-all-glueCA_fractionated/Select/job041/particles.star. determined file type: star\n", + "checking if ugraphs exist...\n", + "\n", + "\n", + "Dictionaries now contain 53534 reconstructed particles\n", + "added 2686 particles from /home/dvi41342/MapReconstruction/DESRES-Trajectory_sarscov2-11021571-all-glueCA_fractionated/Select/job041/particles.star\n", + "loading metadata from /home/dvi41342/MapReconstruction/DESRES-Trajectory_sarscov2-11021571-all-glueCA_fractionated/Select/job042/particles.star...\n", + "loaded metadata from /home/dvi41342/MapReconstruction/DESRES-Trajectory_sarscov2-11021571-all-glueCA_fractionated/Select/job042/particles.star. determined file type: star\n", + "checking if ugraphs exist...\n", + "\n", + "\n", + "Dictionaries now contain 57056 reconstructed particles\n", + "added 3522 particles from /home/dvi41342/MapReconstruction/DESRES-Trajectory_sarscov2-11021571-all-glueCA_fractionated/Select/job042/particles.star\n", + "loading metadata from /home/dvi41342/MapReconstruction/DESRES-Trajectory_sarscov2-11021571-all-glueCA_fractionated/Select/job043/particles.star...\n", + "loaded metadata from /home/dvi41342/MapReconstruction/DESRES-Trajectory_sarscov2-11021571-all-glueCA_fractionated/Select/job043/particles.star. determined file type: star\n", + "checking if ugraphs exist...\n", + "\n", + "\n", + "Dictionaries now contain 61929 reconstructed particles\n", + "added 4873 particles from /home/dvi41342/MapReconstruction/DESRES-Trajectory_sarscov2-11021571-all-glueCA_fractionated/Select/job043/particles.star\n", + "loading metadata from /home/dvi41342/MapReconstruction/DESRES-Trajectory_sarscov2-11021571-all-glueCA_fractionated/Select/job044/particles.star...\n", + "loaded metadata from /home/dvi41342/MapReconstruction/DESRES-Trajectory_sarscov2-11021571-all-glueCA_fractionated/Select/job044/particles.star. determined file type: star\n", + "checking if ugraphs exist...\n", + "\n", + "\n", + "Dictionaries now contain 66694 reconstructed particles\n", + "added 4765 particles from /home/dvi41342/MapReconstruction/DESRES-Trajectory_sarscov2-11021571-all-glueCA_fractionated/Select/job044/particles.star\n", + "For each micrograph, for each metadata file, compute the precision, recall and multiplicity\n", + "Speed of computation depends on the number of particles in the micrograph. progressbar is not accurate\n", + "Total number of groups to loop over: 469\n", + "Number of micgrographs: 67\n", + "Number of metadata files: 7\n", + "Starting loop over groups\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "computing precision: 100%|██████████| 469/469 [00:17<00:00, 26.24it/s, precision=1, recall=0.22, multiplicity=0.23] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "time taken to compute precision: 18.138648748397827\n", + "mean precision: 0.9952617416568891\n", + "mean recall: 0.5052637739298599\n" + ] + } + ], + "source": [ + "project_dir = \"/home/dvi41342/MapReconstruction/DESRES-Trajectory_sarscov2-11021571-all-glueCA_fractionated\"\n", + "config_dir = os.path.join(project_dir, \"Movies\")\n", + "figures_dir = os.path.join(project_dir, \"figures\")\n", + "meta_files = [\n", + " os.path.join(project_dir, \"Extract\", \"job013\", \"particles.star\"),\n", + " os.path.join(project_dir, \"Class2D\", \"job014\", \"run_it200_data.star\"),\n", + " os.path.join(project_dir, \"Select\", \"job020\", \"particles.star\"),\n", + " os.path.join(project_dir, \"Select\", \"job041\", \"particles.star\"),\n", + " os.path.join(project_dir, \"Select\", \"job042\", \"particles.star\"),\n", + " os.path.join(project_dir, \"Select\", \"job043\", \"particles.star\"),\n", + " os.path.join(project_dir, \"Select\", \"job044\", \"particles.star\"),\n", + " # os.path.join(project_dir, \"Refine3D\", \"job014\", \"run_it015_data.star\"),\n", + " # os.path.join(project_dir, \"Refine3D\", \"job020\", \"run_it017_data.star\"),\n", + " # os.path.join(project_dir, \"Refine3D\", \"job008\", \"run_it011_data.star\"),\n", + "]\n", + "\n", + "jobtypes = {\n", + " os.path.join(project_dir, \"Extract\", \"job013\", \"particles.star\"): \"topaz picker\",\n", + " os.path.join(project_dir, \"Class2D\", \"job014\", \"run_it200_data.star\"): \"2D classification\",\n", + " os.path.join(project_dir, \"Select\", \"job020\", \"particles.star\"): \"2D class selection\",\n", + " os.path.join(project_dir, \"Select\", \"job041\", \"particles.star\"): \"3D class 0\",\n", + " os.path.join(project_dir, \"Select\", \"job042\", \"particles.star\"): \"3D class 1\",\n", + " os.path.join(project_dir, \"Select\", \"job043\", \"particles.star\"): \"3D class 2\",\n", + " os.path.join(project_dir, \"Select\", \"job044\", \"particles.star\"): \"3D class 3\",\n", + " # os.path.join(project_dir, \"Refine3D\", \"job014\", \"run_it015_data.star\"): \"Homogeneous refinement 1\",\n", + " # os.path.join(project_dir, \"Refine3D\", \"job020\", \"run_it017_data.star\"): \"Homogeneous refinement 2\",\n", + " # os.path.join(project_dir, \"Refine3D\", \"job008\", \"run_it011_data.star\"): \"Homogeneous refinement 3\",\n", + "}\n", + "\n", + "particle_diameter = 100 # approximate particle diameter in Angstroms\n", + "ugraph_shape = (4000, 4000) # shape of the micrograph in pixels. Only needs to be given if the metadata file is a .star file\n", + "verbose = True # prints out progress statements\n", + "ignore_missing_files = True # if .mrc files are missing, the analysis will still be performed\n", + "enable_tqdm = True # enables tqdm progress bars\n", + "\n", + "for i, meta_file in enumerate(meta_files):\n", + " if i == 0:\n", + " analysis = load_data(meta_file, config_dir, particle_diameter, ugraph_shape=ugraph_shape, verbose=verbose, enable_tqdm=enable_tqdm, ignore_missing_files=ignore_missing_files) # creates the class\n", + " else:\n", + " analysis.add_data(meta_file, config_dir, verbose=verbose) # updates the class with the next metadata file\n", + " \n", + "df_picked = pd.DataFrame(analysis.results_picking)\n", + "df_truth = pd.DataFrame(analysis.results_truth)\n", + "df_precision, df_picked = analysis.compute_precision(df_picked, df_truth, verbose=verbose)\n", + "print(f\"mean precision: {df_precision['precision'].mean()}\")\n", + "print(f\"mean recall: {df_precision['recall'].mean()}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_153676/1497854792.py:20: UserWarning: FixedFormatter should only be used together with FixedLocator\n", + " ax.set_yticklabels(ax.get_yticklabels(), fontsize=18)\n", + "/tmp/ipykernel_153676/1497854792.py:32: UserWarning: FixedFormatter should only be used together with FixedLocator\n", + " ax.set_yticklabels(ax.get_yticklabels(), fontsize=18)\n" + ] + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from importlib import reload\n", + "import roodmus.analysis.plot_picking\n", + "reload(roodmus.analysis.plot_picking)\n", + "from roodmus.analysis.plot_picking import (\n", + " plot_precision, plot_recall,\n", + ")\n", + "\n", + "order = []\n", + "for r in meta_files:\n", + " if type(r) == str:\n", + " order.append(r)\n", + " else:\n", + " order.append(r[0]) \n", + "fig, ax = plot_precision(df_precision, jobtypes, order)\n", + "xticklabels = ax.get_xticklabels()\n", + "ax.set_xticklabels(xticklabels, fontsize=12)\n", + "ax.set_title(\"\")\n", + "fig.set_size_inches(7, 7)\n", + "ax.set_xticklabels(ax.get_xticklabels(), fontsize=18)\n", + "ax.set_yticklabels(ax.get_yticklabels(), fontsize=18)\n", + "ax.set_ylabel(\"Precision\", fontsize=21)\n", + "\n", + "fig.savefig(os.path.join(figures_dir, \"precision.pdf\"), bbox_inches=\"tight\")\n", + "fig.savefig(os.path.join(figures_dir, \"precision.png\"), bbox_inches=\"tight\", dpi=600)\n", + "\n", + "fig, ax = plot_recall(df_precision, jobtypes, order)\n", + "xticklabels = ax.get_xticklabels()\n", + "ax.set_xticklabels(xticklabels, fontsize=12)\n", + "ax.set_title(\"\")\n", + "fig.set_size_inches(7, 7)\n", + "ax.set_xticklabels(ax.get_xticklabels(), fontsize=18)\n", + "ax.set_yticklabels(ax.get_yticklabels(), fontsize=18)\n", + "ax.set_ylabel(\"Recall\", fontsize=21)\n", + "\n", + "fig.savefig(os.path.join(figures_dir, \"recall.pdf\"), bbox_inches=\"tight\")\n", + "fig.savefig(os.path.join(figures_dir, \"recall.png\"), bbox_inches=\"tight\", dpi=600)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Get LocalRes Spread" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mean rad dmg dataset: 3.33144211769104\n", + "variance rad dmg dataset: 1.5853596925735474\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# rad dmg dataset\n", + "locres_sc_file = \"/home/dvi41342/MapReconstruction/DESRES-Trajectory_sarscov2-11021571-all-glueCA_fractionated/LocalRes/job039/relion_locres.mrc\"\n", + "mask_sc_file = \"/home/dvi41342/MapReconstruction/DESRES-Trajectory_sarscov2-11021571-all-glueCA_fractionated/MaskCreate/job034/mask.mrc\"\n", + "\n", + "# load the mrc files\n", + "locres_sc = mrcfile.open(locres_sc_file, mode='r')\n", + "mask_sc = mrcfile.open(mask_sc_file, mode='r')\n", + "locres_sc_data = locres_sc.data * mask_sc.data\n", + "locres_sc_data[locres_sc_data == 0] = np.nan\n", + "\n", + "# plot the local resolution\n", + "fig, ax = plt.subplots(1, 1, figsize=(4.5, 4.5))\n", + "ax.hist(locres_sc_data.flatten(), bins=100, range=(0, 10), density=True, color=\"blue\", alpha=0.5);\n", + "ax.set_xlabel(\"Local resolution ($\\AA$)\", fontsize=21)\n", + "ax.set_ylabel(\"Density\", fontsize=21)\n", + "ax.set_title(\"Rad dmg dataset\", fontsize=21)\n", + "ax.tick_params(axis='both', which='major', labelsize=18)\n", + "\n", + "fig.tight_layout()\n", + "\n", + "# print the mean and variance of the local resolution\n", + "print(f\"mean rad dmg dataset: {np.nanmean(locres_sc_data)}\")\n", + "print(f\"variance rad dmg dataset: {np.nanvar(locres_sc_data)}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "roodmus", + "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": "3.10.18" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}