Python library for analysis of time series data including dimensionality reduction, clustering, and Markov model estimation.
Project description
deeptime
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.
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conda install -c conda-forge deeptime |
pip install deeptime |
Documentation: 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 -r tests/requirements.txt
pip install -e .
Or using pip directly on the remote:
pip install git+https://github.com/deeptime-ml/deeptime.git@main
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