Metadata-Version: 2.4
Name: dsps
Version: 0.4.4
Summary: Differentiable Stellar Population Synthesis
Author-email: Andrew Hearin <ahearin@anl.gov>
License: BSD 3-Clause License
        
        Copyright (c) 2021, Andrew Hearin
        All rights reserved.
        
        Redistribution and use in source and binary forms, with or without
        modification, are permitted provided that the following conditions are met:
        
        1. Redistributions of source code must retain the above copyright notice, this
           list of conditions and the following disclaimer.
        
        2. Redistributions in binary form must reproduce the above copyright notice,
           this list of conditions and the following disclaimer in the documentation
           and/or other materials provided with the distribution.
        
        3. Neither the name of the copyright holder nor the names of its
           contributors may be used to endorse or promote products derived from
           this software without specific prior written permission.
        
        THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
        AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
        IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
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        FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
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Project-URL: home, https://github.com/ArgonneCPAC/dsps
Classifier: Programming Language :: Python :: 3
Requires-Python: >=3.11
Description-Content-Type: text/markdown
License-File: LICENSE.rst
Requires-Dist: numpy
Requires-Dist: jax
Requires-Dist: h5py
Dynamic: license-file

# dsps
[![tests](https://github.com/ArgonneCPAC/dsps/actions/workflows/tests.yml/badge.svg)](https://github.com/ArgonneCPAC/dsps/actions/workflows/tests.yml)

Differentiable Stellar Population Synthesis

## Installation

The latest release of DSPS is now available for installation with pip:
```
$ pip install dsps
```

Alternatively, the latest release is also available on conda-forge:
```
$ conda install -c conda-forge dsps
```

To install dsps from source, first clone the GitHub repository, and then:
```
$ pip install .
```

## Documentation

Online documentation for DSPS is available at [dsps.readthedocs.io](https://dsps.readthedocs.io/en/latest/).


## Citing DSPS
[The DSPS paper](https://arxiv.org/abs/2112.06830) has been published in [Monthly Notices of the Royal Astronomical Society](https://academic.oup.com/mnras/advance-article-abstract/doi/10.1093/mnras/stad456/7034352?utm_source=advanceaccess&utm_campaign=mnras&utm_medium=email). Citation information for the paper can be found at [this ADS link](https://ui.adsabs.harvard.edu/abs/2023MNRAS.521.1741H/abstract), copied below for convenience:

```
@ARTICLE{2023MNRAS.521.1741H,
       author = {{Hearin}, Andrew P. and {Chaves-Montero}, Jon{\'a}s and {Alarcon}, Alex and {Becker}, Matthew R. and {Benson}, Andrew},
        title = "{DSPS: Differentiable stellar population synthesis}",
      journal = {MNRAS},
     keywords = {software: data analysis, galaxies: stellar content, cosmology: large-scale structure of Universe, Astrophysics - Astrophysics of Galaxies, Astrophysics - Cosmology and Nongalactic Astrophysics},
         year = 2023,
        month = may,
       volume = {521},
       number = {2},
        pages = {1741-1756},
          doi = {10.1093/mnras/stad456},
archivePrefix = {arXiv},
       eprint = {2112.06830},
 primaryClass = {astro-ph.GA},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2023MNRAS.521.1741H},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
```
