{ "info": { "author": "Michal Krassowski", "author_email": "krassowski.michal+pypi@gmail.com", "bugtrack_url": null, "classifiers": [ "Development Status :: 4 - Beta", "Intended Audience :: Developers", "Intended Audience :: Science/Research", "License :: OSI Approved :: MIT License", "Operating System :: Microsoft :: Windows", "Operating System :: POSIX :: Linux", "Programming Language :: Python :: 3.6", "Programming Language :: Python :: 3.7", "Topic :: Software Development :: Libraries :: Python Modules", "Topic :: Utilities" ], "description": "nbpipeline\n==========\n\n|Build Status|\n\nSnakemake-like pipelines for Jupyter Notebooks, producing interactive\npipeline reports like this:\n\nInstall & general remarks\n~~~~~~~~~~~~~~~~~~~~~~~~~\n\nThese are still early days of this software so please bear in mind that\nit is not ready for production yet. Note: for simplicity I assume that\nyou are using a recent Ubuntu with git installed.\n\n.. code:: bash\n\n pip install nbpipeline\n\nGraphiz is required for static SVG plots:\n\n.. code:: bash\n\n sudo apt-get install graphviz libgraphviz-dev graphviz-dev\n\nDevelopment install\n^^^^^^^^^^^^^^^^^^^\n\nTo install the latest development version you may use:\n\n.. code:: bash\n\n git clone https://github.com/krassowski/nbpipeline\n cd nbpipeline\n pip install -r requirements.txt\n ln -s $(pwd)/nbpipeline/nbpipeline.py ~/bin/nbpipeline\n\nQuickstart\n~~~~~~~~~~\n\nCreate ``pipeline.py`` file with list of rules for your pipeline. For\nexample:\n\n.. code:: python\n\n from nbpipeline.rules import NotebookRule\n\n\n NotebookRule(\n 'Extract protein data', # a nice name for the step\n input={'protein_data_path': 'data/raw/data_from_wetlab.xlsx'},\n output={'output_path': 'data/clean/protein_levels.csv'},\n notebook='analyses/Data_extraction.ipynb',\n group='Proteomics' # this is optional\n )\n\n NotebookRule(\n 'Quality control and PCA on proteins',\n input={'protein_levels_path': 'data/clean/protein_levels.csv'},\n output={'qc_report_path': 'reports/proteins_failing_qc.csv'},\n notebook='analyses/Exploration_and_quality_control.ipynb',\n group='Proteomics'\n )\n\nthe keys of the input and output variables should correspond to\nvariables in one of the first cells in the corresponding notebook, which\nshould be tagged as \"parameters\". You will be warned if your notebook\nhas no cell tagged as \"parameters\".\n\nFor more details, please see the example\n`pipeline `__\nand\n`notebooks `__\nin the\n`examples `__\ndirectory.\n\nRun the pipeline:\n^^^^^^^^^^^^^^^^^\n\n::\n\n nbpipepline\n\nOn any consecutive run the notebooks which did not change will not be\nrun again. To disable this cache, use ``--disable_cache`` switch.\n\nTo generate an interactive diagram of the rules graph, together with\nreproducibility report add ``-i`` switch:\n\n::\n\n nbpipepline -i\n\nThe software defaults to ``google-chrome`` for graph visualization\ndisplay, which can be changed with a CLI option.\n\nIf you named your definition files differently (e.g. ``my_rules.py``\ninstead of ``pipeline.py``), use:\n\n::\n\n nbpipepline --definitions_file my_rules.py\n\nTo display all command line options use:\n\n::\n\n nbpipepline -h\n\n.. |Build Status| image:: https://travis-ci.org/krassowski/nbpipeline.svg?branch=master\n :target: https://travis-ci.org/krassowski/nbpipeline", "description_content_type": "", "docs_url": null, "download_url": "", "downloads": { "last_day": -1, "last_month": -1, "last_week": -1 }, "home_page": "https://github.com/krassowskinbpipeline", "keywords": "snakemake,pipeline,reproducible,jupyter,notebooks", "license": "MIT", "maintainer": "", 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