Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/markovmodel/pyemma_tutorials
How to analyze molecular dynamics data with PyEMMA
https://github.com/markovmodel/pyemma_tutorials
analysis kinetics markov-model md-simulations molecular-dynamics pyemma tutorial
Last synced: about 1 month ago
JSON representation
How to analyze molecular dynamics data with PyEMMA
- Host: GitHub
- URL: https://github.com/markovmodel/pyemma_tutorials
- Owner: markovmodel
- License: cc-by-4.0
- Created: 2018-04-19T12:12:54.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2019-05-29T14:02:58.000Z (over 5 years ago)
- Last Synced: 2024-03-15T14:11:22.580Z (9 months ago)
- Topics: analysis, kinetics, markov-model, md-simulations, molecular-dynamics, pyemma, tutorial
- Language: Jupyter Notebook
- Homepage:
- Size: 16 MB
- Stars: 68
- Watchers: 9
- Forks: 33
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Introduction to Markov state modeling with the PyEMMA software
[](https://circleci.com/gh/markovmodel/pyemma_tutorials)
[](https://mybinder.org/v2/gh/markovmodel/pyemma_tutorials/master)
[](http://creativecommons.org/licenses/by/4.0/)
This work is licensed under a Creative Commons Attribution 4.0 International License.
[PyEMMA](http://pyemma.org) (EMMA = Emma's Markov Model Algorithms) is an open source Python/C package for analysis of extensive molecular dynamics (MD) simulations.
### Content
The first [notebook 📓](notebooks/00-pentapeptide-showcase.ipynb) 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 [➜ 📓](notebooks/01-data-io-and-featurization.ipynb)
2. Dimension reduction and discretization [➜ 📓](notebooks/02-dimension-reduction-and-discretization.ipynb)
3. MSM estimation and validation [➜ 📓](notebooks/03-msm-estimation-and-validation.ipynb)
4. MSM analysis [➜ 📓](notebooks/04-msm-analysis.ipynb)
5. PCCA and TPT analysis [➜ 📓](notebooks/05-pcca-tpt.ipynb)
6. Expectations and observables [➜ 📓](notebooks/06-expectations-and-observables.ipynb)
7. Hidden Markov state models (HMMs) [➜ 📓](notebooks/07-hidden-markov-state-models.ipynb)
8. Common problems & bad data situations [➜ 📓](notebooks/08-common-problems.ipynb)
**Please note that this is a work in progress and we value any kind of feedback that helps us improving this tutorial.**
### Installation
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
#### Installing the tutorials as a package
After installing miniconda, you can install the tutorial either via
``` bash
conda create -n pyemma_tutorials -c conda-forge pyemma_tutorials
```
... or you can also install the tutorial in an existing environment by
``` bash
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:
``` bash
pip install git+https://github.com/markovmodel/pyemma_tutorials
```
#### Manual installation
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:
```bash
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:
```bash
jupyter nbextension enable toc2/main
jupyter nbextension enable exercise2/main
```
Now all remains is to clone the repository to get the tutorial notebooks:
```bash
git clone https://github.com/markovmodel/pyemma_tutorials.git
```
### Usage
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:
``` bash
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...
```bash
cd path_to_pyemma_tutorials/notebooks
```
... and start the notebook server there:
```bash
jupyter notebook
```
### Deinstallation
To uninstall you can remove the whole environment which will also uninstall the contained software again:
``` bash
conda env remove -n pyemma_tutorials
```
or if you have installed the package directly
``` bash
conda remove pyemma_tutorials
```