deeptime

Name	deeptime JSON
Version	0.4.4 JSON
	download
home_page
Summary	Python library for analysis of time series data including dimensionality reduction, clustering, and Markov model estimation.
upload_time	2022-12-20 13:26:38
maintainer
docs_url	None
author	Moritz Hoffmann, Martin Scherer, Tim Hempel, Andreas Mardt, Maaike Galama, Brian de Silva, Brooke Husic, Stefan Klus, Hao Wu, Nathan Kutz, Steven Brunton, Frank Noé
requires_python	>=3.8
license
keywords	markov-model hidden-markov-model time-series-analysis covariance-estimation koopman-operator coherent-set-detection
VCS
bugtrack_url
requirements	No requirements were recorded.
Travis-CI	No Travis.
coveralls test coverage	No coveralls.

            # deeptime

[![License: LGPL v3](https://img.shields.io/badge/License-LGPL%20v3-blue.svg)](https://www.gnu.org/licenses/lgpl-3.0) [![Build Status](https://dev.azure.com/clonker/deeptime/_apis/build/status/deeptime-ml.deeptime?branchName=main)](https://dev.azure.com/clonker/deeptime/_build/latest?definitionId=1&branchName=main) [![codecov](https://codecov.io/gh/deeptime-ml/deeptime/branch/main/graph/badge.svg?token=MgQZqDM4sK)](https://codecov.io/gh/deeptime-ml/deeptime) [![DOI](https://img.shields.io/badge/DOI-10.1088%2F2632--2153%2Fac3de0-blue)](https://doi.org/10.1088/2632-2153/ac3de0)

Deeptime is a general purpose Python library offering various tools to estimate dynamical models 
based on time-series data including conventional linear learning methods, such as Markov State 
Models (MSMs), Hidden Markov Models (HMMs) and Koopman models, as well as kernel and 
deep learning approaches such as VAMPnets and deep MSMs. The library is largely compatible 
with scikit-learn, having a range of Estimator classes for these different models, but in 
contrast to scikit-learn also provides Model classes, e.g., in the case of an MSM, 
which provide a multitude of analysis methods to compute interesting thermodynamic, kinetic 
and dynamical quantities, such as free energies, relaxation times and transition paths.

Releases:

Installation via `conda` recommended, `pip` compiles the library locally.

|  [![conda-forge](https://img.shields.io/conda/v/conda-forge/deeptime?color=brightgreen&label=conda-forge)](https://github.com/conda-forge/deeptime-feedstock) 	|   [![PyPI](https://badge.fury.io/py/deeptime.svg)](https://pypi.org/project/deeptime)	|
|:-:	|:-:	|
|  `conda install -c conda-forge deeptime` |  `pip install deeptime`  	|

Documentation: [deeptime-ml.github.io](https://deeptime-ml.github.io/).

## Building the latest trunk version of the package:

Using pip with a local clone and pulling dependencies:
```
git clone https://github.com/deeptime-ml/deeptime.git

cd deeptime
pip install .
```

Or using pip directly on the remote:
```
pip install git+https://github.com/deeptime-ml/deeptime.git@main
```

            

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