{"id":20915652,"url":"https://github.com/markovmodel/pyemma_tutorials","last_synced_at":"2025-05-13T10:33:23.260Z","repository":{"id":37686802,"uuid":"130210474","full_name":"markovmodel/pyemma_tutorials","owner":"markovmodel","description":"How to analyze molecular dynamics data with PyEMMA","archived":false,"fork":false,"pushed_at":"2019-05-29T14:02:58.000Z","size":16800,"stargazers_count":68,"open_issues_count":2,"forks_count":33,"subscribers_count":9,"default_branch":"master","last_synced_at":"2024-03-15T14:11:22.580Z","etag":null,"topics":["analysis","kinetics","markov-model","md-simulations","molecular-dynamics","pyemma","tutorial"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"cc-by-4.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/markovmodel.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2018-04-19T12:12:54.000Z","updated_at":"2024-01-12T06:50:22.000Z","dependencies_parsed_at":"2022-09-15T10:00:36.810Z","dependency_job_id":null,"html_url":"https://github.com/markovmodel/pyemma_tutorials","commit_stats":null,"previous_names":[],"tags_count":6,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/markovmodel%2Fpyemma_tutorials","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/markovmodel%2Fpyemma_tutorials/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/markovmodel%2Fpyemma_tutorials/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/markovmodel%2Fpyemma_tutorials/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/markovmodel","download_url":"https://codeload.github.com/markovmodel/pyemma_tutorials/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":225206750,"owners_count":17438185,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["analysis","kinetics","markov-model","md-simulations","molecular-dynamics","pyemma","tutorial"],"created_at":"2024-11-18T16:16:58.982Z","updated_at":"2024-11-18T16:17:00.564Z","avatar_url":"https://github.com/markovmodel.png","language":"Jupyter Notebook","readme":"# Introduction to Markov state modeling with the PyEMMA software\n\n[![CircleCI](https://circleci.com/gh/markovmodel/pyemma_tutorials.svg?style=svg)](https://circleci.com/gh/markovmodel/pyemma_tutorials)\n[![Binder](https://mybinder.org/badge.svg)](https://mybinder.org/v2/gh/markovmodel/pyemma_tutorials/master)\n[![CCA](https://img.shields.io/github/license/markovmodel/pyemma_tutorials.svg)](http://creativecommons.org/licenses/by/4.0/)\n![Conda](https://img.shields.io/conda/dn/conda-forge/pyemma_tutorials.svg)\n![Conda (channel only)](https://img.shields.io/conda/vn/conda-forge/pyemma_tutorials.svg)\n\nThis work is licensed under a \u003ca rel=\"license\" href=\"http://creativecommons.org/licenses/by/4.0/\"\u003eCreative Commons Attribution 4.0 International License\u003c/a\u003e.\n\n[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.\n\n### Content\n\nThe first [notebook 📓](notebooks/00-pentapeptide-showcase.ipynb) in this tutorial guides through the basic analysis workflow using real MD data of a pentapeptide:\n\n\u003cimg src=\"notebooks/static/pentapeptide-structure.png\" width=\"320\" height=\"171\" /\u003e\n\nWe keep the details minimal throughout the showcase but point to the more specialized notebooks which allow you to go in-depth on selected topics.\n\nIn 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:\n\n1. Data-I/O and featurization [➜ 📓](notebooks/01-data-io-and-featurization.ipynb)\n2. Dimension reduction and discretization [➜ 📓](notebooks/02-dimension-reduction-and-discretization.ipynb)\n3. MSM estimation and validation [➜ 📓](notebooks/03-msm-estimation-and-validation.ipynb)\n4. MSM analysis [➜ 📓](notebooks/04-msm-analysis.ipynb)\n5. PCCA and TPT analysis [➜ 📓](notebooks/05-pcca-tpt.ipynb)\n6. Expectations and observables [➜ 📓](notebooks/06-expectations-and-observables.ipynb)\n7. Hidden Markov state models (HMMs) [➜ 📓](notebooks/07-hidden-markov-state-models.ipynb)\n8. Common problems \u0026 bad data situations [➜ 📓](notebooks/08-common-problems.ipynb)\n\n**Please note that this is a work in progress and we value any kind of feedback that helps us improving this tutorial.**\n\n### Installation\nWe 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.\n\nIf you do not have conda, please follow the instructions here:\n\nhttps://conda.io/miniconda.html\n\n#### Installing the tutorials as a package\n\nAfter installing miniconda, you can install the tutorial either via\n\n``` bash\nconda create -n pyemma_tutorials -c conda-forge pyemma_tutorials\n```\n\n... or you can also install the tutorial in an existing environment by\n\n``` bash\nconda install -c conda-forge pyemma_tutorials\n```\n\nIf you intend to install with pip, for which can not give any support, you feel free to run:\n\n``` bash\npip install git+https://github.com/markovmodel/pyemma_tutorials\n```\n\n#### Manual installation\n\nIf you wish to install the tutorial manually, you will need the following packages (including all their dependencies):\n\n- `pyemma`\n- `mdshare`\n- `nglview`\n- `nbexamples`\n- `jupyter_contrib_nbextensions`\n\nThis can be done, for example, with conda:\n\n```bash\nconda install -c conda-forge pyemma mdshare nglview nbexamples jupyter_contrib_nbextensions\n```\n\nAfter installing `jupyter_contrib_nbextensions`, you need to activate the `toc2` and `exercise2` extensions:\n\n```bash\njupyter nbextension enable toc2/main\njupyter nbextension enable exercise2/main\n```\n\nNow all remains is to clone the repository to get the tutorial notebooks:\n\n```bash\ngit clone https://github.com/markovmodel/pyemma_tutorials.git\n```\n\n### Usage\nNow 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:\n\n``` bash\nconda activate pyemma_tutorials  # skip this, if you have installed in your root environment or used pip to install.\npyemma_tutorials\n```\n\nThe 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.\n\nIf you have a manual installation, move to the repository's notebook directory...\n\n```bash\ncd path_to_pyemma_tutorials/notebooks\n```\n\n... and start the notebook server there:\n\n```bash\njupyter notebook\n```\n\n### Deinstallation\n\nTo uninstall you can remove the whole environment which will also uninstall the contained software again:\n``` bash\nconda env remove -n pyemma_tutorials\n```\n\nor if you have installed the package directly\n\n``` bash\nconda remove pyemma_tutorials\n```\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmarkovmodel%2Fpyemma_tutorials","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmarkovmodel%2Fpyemma_tutorials","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmarkovmodel%2Fpyemma_tutorials/lists"}