| Name | dive-EPR JSON |
| Version |
0.2.1
JSON |
| download |
| home_page | None |
| Summary | Python package for Bayesian analysis of dipolar EPR spectroscopy data through Markov chain Monte Carlo sampling with PyMC. |
| upload_time | 2024-08-14 16:46:36 |
| maintainer | None |
| docs_url | None |
| author | Sarah Sweger, Lukas Zha |
| requires_python | >=3.9 |
| license | MIT License Copyright (c) 2024 Sarah Sweger, Julian Cheung, Lukas Zha, Stephan Pribitzer, Stefan Stoll Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. |
| keywords |
epr
deer
bayesian
mcmc
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# dive
### About
`dive` is a Python package for Bayesian analysis of dipolar EPR (electron paramagnetic resonance) spectroscopy data through Markov chain Monte Carlo (MCMC) sampling with the Python package [PyMC](https://www.pymc.io).
### Requirements
`dive` is available for Windows, Mac and Linux systems and requires **Python 3.9** or later and **PyMC 5.0** or later.
### Features
`dive`'s features include:
- An output InferenceData object containing many random posterior samples for each parameter
- Full uncertainty quantification for all model parameters, including the distance distribution
- Visualizations for ensembles of fitted signals and residuals
- Visualizations for ensembles of fitted distance distributions
- Histograms for margnialized posteriors of other parameters such as modulation depth and background decay rate
### Setup
You can install `dive` using `pip`. Please note that the PyPI package name is `dive-EPR`.
pip install dive-EPR
You can also directly clone the `dive` directory. Please make sure to also import the necessary packages.
pip install pymc deerlab scipy matplotlib numpy pandas mkl-service h5netcdf pytest
git clone https://github.com/StollLab/dive
`dive` can then be used by importing the package as usual.
import dive
### Documentation
See the [documentation](https://stolllab.github.io/dive) for a detailed guide on how to use `dive`. An IPython Notebook guide on using `dive` can also be found under the `examples/` directory.
### Citation
When you use `dive` in your work, please cite the following publication:
**Bayesian Probabilistic Analysis of DEER Spectroscopy Data Using Parametric Distance Distribution Models** <br>
Sarah R. Sweger, Stephan Pribitzer, and Stefan Stoll <br>
*J. Phys. Chem. A* 2020, 124, 30, 6193–6202 <br>
<a href="https://doi.org/10.1021/acs.jpca.0c05026"> doi.org/10.1021/acs.jpca.0c05026</a>
### License
`dive` is licensed under the [MIT License](LICENSE).
Copyright © 2024: Sarah Sweger, Julian Cheung, Lukas Zha, Stephan Pribitzer, Stefan Stoll
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"description": "# dive\n\n### About\n`dive` is a Python package for Bayesian analysis of dipolar EPR (electron paramagnetic resonance) spectroscopy data through Markov chain Monte Carlo (MCMC) sampling with the Python package [PyMC](https://www.pymc.io).\n\n### Requirements\n\n`dive` is available for Windows, Mac and Linux systems and requires **Python 3.9** or later and **PyMC 5.0** or later.\n \n### Features\n\n`dive`'s features include:\n- An output InferenceData object containing many random posterior samples for each parameter\n- Full uncertainty quantification for all model parameters, including the distance distribution\n- Visualizations for ensembles of fitted signals and residuals\n- Visualizations for ensembles of fitted distance distributions\n- Histograms for margnialized posteriors of other parameters such as modulation depth and background decay rate\n\n### Setup\n\nYou can install `dive` using `pip`. Please note that the PyPI package name is `dive-EPR`.\n\n pip install dive-EPR\n\nYou can also directly clone the `dive` directory. Please make sure to also import the necessary packages.\n\n pip install pymc deerlab scipy matplotlib numpy pandas mkl-service h5netcdf pytest\n git clone https://github.com/StollLab/dive\n\n`dive` can then be used by importing the package as usual.\n\n import dive\n\n### Documentation\n\nSee the [documentation](https://stolllab.github.io/dive) for a detailed guide on how to use `dive`. An IPython Notebook guide on using `dive` can also be found under the `examples/` directory.\n\n### Citation\n\nWhen you use `dive` in your work, please cite the following publication:\n\n **Bayesian Probabilistic Analysis of DEER Spectroscopy Data Using Parametric Distance Distribution Models** <br>\nSarah R. Sweger, Stephan Pribitzer, and Stefan Stoll <br>\n *J. Phys. Chem. A* 2020, 124, 30, 6193\u20136202 <br>\n <a href=\"https://doi.org/10.1021/acs.jpca.0c05026\"> doi.org/10.1021/acs.jpca.0c05026</a>\n\n\n### License\n\n`dive` is licensed under the [MIT License](LICENSE).\n\nCopyright \u00a9 2024: Sarah Sweger, Julian Cheung, Lukas Zha, Stephan Pribitzer, Stefan Stoll\n",
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