This work is licensed under a Creative Commons Attribution 4.0 International License.

PyEMMA (EMMA = Emma's Markov Model Algorithms) is an open source Python/C package for analysis of extensive molecular dynamics (MD) simulations.

The first notebook 📓 in this tutorial guides through the basic analysis workflow using real MD data of a pentapeptide:

We keep the details minimal throughout the showcase but point to the more specialized notebooks which allow you to go in-depth on selected topics.

In detail, the remaining eight notebooks revisit all aspects shown in the showcase, provide additional details and variants, and contain exercises (and solutions) to self-check your learning progress:

1. Data-I/O and featurization ➜ 📓
2. Dimension reduction and discretization ➜ 📓
3. MSM estimation and validation ➜ 📓
4. MSM analysis ➜ 📓
5. PCCA and TPT analysis ➜ 📓
6. Expectations and observables ➜ 📓
7. Hidden Markov state models (HMMs) ➜ 📓
8. Common problems & bad data situations ➜ 📓

Please note that this is a work in progress and we value any kind of feedback that helps us improving this tutorial.

We recommended to install the PyEMMA tutorials with conda. The following command will create a new environment that comes with all the dependencies of the tutorial.

If you do not have conda, please follow the instructions here:

https://conda.io/miniconda.html

After installing miniconda, you can install the tutorial either via

conda create -n pyemma_tutorials -c conda-forge pyemma_tutorials

... or you can also install the tutorial in an existing environment by

conda install -c conda-forge pyemma_tutorials

If you intend to install with pip, for which can not give any support, you feel free to run:

pip install git https://github.com/markovmodel/pyemma_tutorials

If you wish to install the tutorial manually, you will need the following packages (including all their dependencies):

• pyemma
• mdshare
• nglview
• nbexamples
• jupyter_contrib_nbextensions

This can be done, for example, with conda:

conda install -c conda-forge pyemma mdshare nglview nbexamples jupyter_contrib_nbextensions

After installing jupyter_contrib_nbextensions, you need to activate the toc2 and exercise2 extensions:

jupyter nbextension enable toc2/main
jupyter nbextension enable exercise2/main

Now all remains is to clone the repository to get the tutorial notebooks:

git clone https://github.com/markovmodel/pyemma_tutorials.git

Now we have a fresh conda environment containing the notebooks and the software to run them. We can now just activate the environment and run the notebook server by invoking:

conda activate pyemma_tutorials  # skip this, if you have installed in your root environment or used pip to install.
pyemma_tutorials

The last command will start the notebook server and your browser should pop up pointing to a list of notebooks. You can choose either to preview or to create your own copy of the notebook. The latter will create a copy of the chosen notebook in your home directory, so your changes will not be lost after shutting down the notebook server.

If you have a manual installation, move to the repository's notebook directory...

cd path_to_pyemma_tutorials/notebooks

... and start the notebook server there:

jupyter notebook

To uninstall you can remove the whole environment which will also uninstall the contained software again:

conda env remove -n pyemma_tutorials

or if you have installed the package directly

conda remove pyemma_tutorials