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.
Installation via conda or pip. Both provide compiled binaries for Linux, Windows, and MacOS (x86_64 and arm64).
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conda install -c conda-forge deeptime |
pip install deeptime |
Documentation: deeptime-ml.github.io.
| Dimension reduction | Deep dimension reduction | SINDy |
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| Markov state models | Hidden Markov models | Datasets |
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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