warpTemplate is a Python package designed to provide warped supernova (SN) time series templates and correction models using the sncosmo package. It includes tools for loading existing templates, warping time series data, and calculating correction coefficients to match observed SN data. The package uses advanced techniques such as spline-based warping and template fitting to generate accurate, customized models for SN research.

Features

• Warped SN Time Series: Load existing SN templates and apply warping based on user-supplied data (phase, wavelength, flux).
• Template Correction: Correct templates to match observed SN data by calculating correction coefficients and interpolation.
• Easy-to-use Models: Create sncosmo.Model objects with the corrected templates.
• Data Handling: The package works directly with SN data tables (e.g., from ZTF, DES, or other surveys).
• Reproducible Results: Optional reproducibility with random seed for template selection.

Repository Layout

The repository separates two working layers:

• notebooks/template_creation/: historical notebooks and helper files for creating, validating, and colour-calibrating warp coefficient libraries.
• notebooks/template_usage/: current notebooks for loading existing coefficients, visualising templates, and creating light-curve tables.

The importable package code remains at the top level of warpTemplate/:

• loaders.py: load and sample stored coefficient files.
• models.py: wrap warped sources as sncosmo.Model objects.
• sources.py: implement the warped time-series source.
• lightcurves.py: convenience helpers for checking and plotting generated light curves.
• corrections.py: construction-side correction algorithm used by the historical template-building workflow.

For practical use, start with notebooks/template_usage/create_usable_template_lightcurves.ipynb.

Installation

From PyPI (if released)

pip install warp_templates

From GitHub (development version)

git clone https://github.com/your-username/warp_templates.git
cd warp_templates
pip install .

Requirements

• Python >= 3.10
• numpy
• scipy
• astropy
• sncosmo
• matplotlib

Usage

1. Load and Warp Time Series Model

from warp_templates import get_warpedTimeSeriesModel

warpdata = {
    'corrmodel': {
        'phase': [...],
        'wave': [...],
        'flux': [...],
    }
}

# Example of generating a warped time series model
model = get_warpedTimeSeriesModel(
    name="warped_Ia",
    original_template_name="template_name",
    warpdata=warpdata,
    z=0.05,
    hostr_v=3.1
)

# Use the model for further analysis or plotting

2. Load Templates and Generate Correction Coefficients

from warp_templates import WarpfitTemplateLoader, get_template_correction

# Load templates
loader = WarpfitTemplateLoader("warpcoeffs/")
templates = loader.get_templates("Ia")

# Compute correction for a template based on data (astropy Table)
tab = ...  # Your SN data table
correction = get_template_correction(tab, "Ia_template", z=0.05)

# Use the correction model for further work

3. Apply Template Correction

# The correction model is a 2D spline object that you can apply to your data
corrmodel = correction['corrmodel']
flux_corrected = corrmodel(some_phase_values, some_wave_values)

Functions

• get_warpedTimeSeriesModel(name: str, original_template_name: str, warpdata: dict, ...): Creates an sncosmo.Model using the warped template data.
• WarpfitTemplateLoader: A class for loading and filtering warp coefficients for different supernova templates.
• get_template_correction(tab: Table, templatename: str, z: float, ...): Computes the correction coefficients to match a template to observed SN data, including outlier rejection and interpolation.

Example Workflow

1. Load warp coefficients using WarpfitTemplateLoader.
2. Select a SN template and compute the correction to match observed SN data using get_template_correction.
3. Generate the warped model using get_warpedTimeSeriesModel.
4. Apply the correction model to the observed SN data for analysis or fitting.

Plotting

The package supports plotting of both the model and the corrected data. For example, you can save correction plots to disk:

import matplotlib.pyplot as plt

# Plot model data
fig = sncosmo.plot_lc(tab, model=model, errors=result.errors)
fig.savefig("model_plot.png")

Tests

This package includes a set of tests to ensure correct functionality. To run the tests:

pytest

License

MIT License. See LICENSE for more details.

Acknowledgments

This package leverages the sncosmo library for SN model fitting and flux computation.