Metadata-Version: 2.4
Name: cait
Version: 1.3.0
Summary: Cryogenic Artificial Intelligence Tools - A Python Package for the Data Analysis of Rare Event Search Experiments with Machine Learning.
Author-email: Philipp Schreiner <philipp.schreiner@oeaw.ac.at>, Felix Wagner <felix.wagner@oeaw.ac.at>
Project-URL: Repository, https://github.com/fewagner/cait
Project-URL: Documentation, https://cait.readthedocs.io/en/latest/index.html
Project-URL: Changelog, https://cait.readthedocs.io/en/latest/changelog.html
Classifier: Development Status :: 5 - Production/Stable
Classifier: Topic :: Scientific/Engineering :: Physics
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Operating System :: OS Independent
Requires-Python: >=3.8
Description-Content-Type: text/x-rst
License-File: LICENSE
Requires-Dist: numpy>=1.22
Requires-Dist: scipy>=1.6
Requires-Dist: pandas>=1.1
Requires-Dist: h5py>=3.2
Requires-Dist: uproot>=4.1
Requires-Dist: ipywidgets>=7.5
Requires-Dist: plotly<6.0
Requires-Dist: dash>=2.0
Requires-Dist: matplotlib>=3.4
Requires-Dist: multiprocess
Requires-Dist: numba>=0.54
Requires-Dist: tqdm>=4.62
Requires-Dist: scikit-learn>=0.24
Requires-Dist: deprecation
Provides-Extra: nn
Requires-Dist: pytorch-lightning==1.9.4; extra == "nn"
Requires-Dist: torch>=1.8; extra == "nn"
Provides-Extra: clplot
Requires-Dist: uniplot>=0.21.2; extra == "clplot"
Provides-Extra: test
Requires-Dist: pytest; extra == "test"
Provides-Extra: remfiles
Requires-Dist: webdav4>=0.9.8; extra == "remfiles"
Requires-Dist: xrootd>=5.7.0; extra == "remfiles"
Provides-Extra: docs
Requires-Dist: Sphinx==8.2.3; extra == "docs"
Requires-Dist: sphinx-rtd-theme==3.0.2; extra == "docs"
Requires-Dist: rinohtype==0.5.5; extra == "docs"
Requires-Dist: myst-nb==1.2.0; extra == "docs"
Dynamic: license-file

.. -*- mode: rst -*-

|PyPi|_ |DOI|_

.. |PyPi| image:: https://badge.fury.io/py/cait.svg
.. _PyPi: https://badge.fury.io/py/cait

.. |DOI| image:: https://zenodo.org/badge/DOI/10.5281/zenodo.5091415.svg
.. _DOI: https://doi.org/10.5281/zenodo.5091415

.. image:: https://raw.githubusercontent.com/fewagner/cait/master/docs/source/logo/logo_wide.png
  :width: 256
  :target: https://github.com/fewagner/cait

**Cait** (Cryogenic Artificial Intelligence Tools) is a Python 3 software package for raw data analysis of cryogenic solid-state detectors. It is tailored to the needs of the CRESST, COSINUS and NUCLEUS experiment, but applicable to all analyses whose raw data consists of pulses.

**Documentation:** https://cait.readthedocs.io/

**Source Code:** https://github.com/fewagner/cait

**Bug Report:** https://github.com/fewagner/cait/issues


Installation
============

Cait is hosted on the Python package index.

.. code:: console

    $ pip install cait

Have a look at the Installation page for more installation options/tips. 
You can now import the library in Python:

.. code:: python

    import cait as ai

Citations
===============

If you use Cait in your research work, please reference the package accordingly.

Cait uses a number of Python packages. If you use methods that are based on those packages, please consider
referencing them: h5py, numpy, matplotlib, scipy, numba, sklearn, uproot, torch, pytorch-lightning, plotly.

``cait`` implements methods that were used in prior research work. Please consider
referencing them:

- 2020, F. Wagner, Machine Learning Methods for the Raw Data Analysis of cryogenic Dark Matter Experiments", https://doi.org/10.34726/hss.2020.77322 (accessed on the 9.7.2021)
- 2019, D. Schmiedmayer, Calculation of dark-matter exclusions-limits using a maximum Likelihood approach, https://repositum.tuwien.at/handle/20.500.12708/5351 (accessed on the 9.7.2021)
- 2019, CRESST Collaboration et. al., First results from the CRESST-III low-mass dark matter program, doi 10.1103/PhysRevD.100.102002
- 2020, M. Stahlberg, Probing low-mass dark matter with CRESST-III : data analysis and first results, available via https://doi.org/10.34726/hss.2021.45935 (accessed on the 9.7.2021)
- 2019, M. Mancuso et. al., A method to define the energy threshold depending on noise level for rare event searches" (arXiv:1711.11459)
- 2018, N. Ferreiro Iachellini, Increasing the sensitivity to low mass dark matter in cresst-iii witha new daq and signal processing, doi 10.5282/edoc.23762
- 2016, F. Reindl, Exploring Light Dark Matter With CRESST-II Low-Threshold Detectors", available via http://mediatum.ub.tum.de/?id=1294132 (accessed on the 9.7.2021)
- 1995, F. Pröbst et. al., Model for cryogenic particle detectors with superconducting phase transition thermometers, doi 10.1007/BF00753837

We want you ...
===============

... to contribute! We are always happy about any contributions to our software. To coordinate
efforts, please get in touch with felix.wagner(at)oeaw.ac.at such that we can include your
features in the upcoming release. If you have any troubles with the current release, please open an issue in the Bug Report.
