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    "info": {
        "author": "Zeo Choy",
        "author_email": "zeochoy@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 4 - Beta",
            "Environment :: Console",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 2.7",
            "Topic :: Scientific/Engineering :: Medical Science Apps."
        ],
        "description": "ReSim: chemoREsistance SIMulator\n================================\nReSim (chemoREsistance SIMulator) is a python package that simulates cancer\nchemoresistance dynamics based on a stochastic model. The model describes the\ninteraction between chemotherapeutics and various cancer cell subpopulations\n(including sensitive, primary resistant, acquired resistant and quiescent\ncancer cells).\n\nReSim provides both GUI (powered by `Flask <http://flask.pocoo.org>`) and CUI.\n\n**Remarks**: Simulation result is greatly depended on input params, therefore\nReSim is intended to use in parallel with *in vitro* assay. Procedures or\ndetails in identifying input params *in vitro* is out of scope here and will be\ndocumented in elsewhere (hope to get it publish soon).\n\nDependencies\n____________\n* flask\n* Flask-WTF\n* numpy\n* scipy\n* pandas\n* matplotlib\n* seaborn\n* plotly\n* sdeint\n\nInstallation\n------------\nWe **strongly** recommend everyone to install resim within a virtual\nenvironment. Setup the venv as usual and run ::\n\n  $ pip install resim\n\nOtherwise, install it locally.\n\nGetting Start - GUI\n-------------------\nOpen your command prompt, simply run ::\n\n  $ resim-flask\n\nReSim is now available at http://localhost:5000/resim .\n\n**Note** GUI provides a simplified & handy version of resim with interactive\nplotly plots. Please use CUI if you need more control of input params.\n\nAdvanced Usage\n--------------\nUsing defaults params (calibrated from the case of sorafenib and liver cancer)::\n\n  >>> import resim\n\n  >>> model = resim.Simulator()\n  >>> res = model.simulate()\n\n  >>> snsplt_cells = resim.plot_cells(res, subplot=True)\n  >>> snsplt_drug = resim.plot_drug(res)\n  >>> snsplt_fht = resim.plot_fht(res)\n\n  >>> snsplt_cells.figure.savefig('resim_cells.png')\n  >>> snsplt_drug.figure.savefig('resim_drug.png')\n  >>> snsplt_fht.figure.savefig('resim_fht.png')\n\nYou may set your own params. For examples, input the growth rate of sensitive cells and resistant cells determined *in vitro*::\n\n  >>> import resim\n\n  >>> model = resim.Simulator(gr=[0.2, 0.1, 0.1])\n  >>> res = model.simulate()\n\n`model.simulate()` returns a dict of dictionary containing `pd.DataFrame` or\n`np.array`. To extract the result,::\n\n  >>>  df_cells = res['case']['cells']\n  >>>  df_drugs = res['case']['drugs']\n\n  >>> l_fht_control = res['control']['fht'])\n  >>> l_fht_treatment = res['case']['fht'])\n\n**Notes** GUI outputs are generated by `Plotly <https://plot.ly/>`. But both\n`plot_cells`, `plot_drug` and `plot_fht` in CUI return `seaborn\n<https://seaborn.pydata.org/>` plot object. You may customize your own theme,\ntitle, layout.\n\nDocumentation\n-------------\nAvailable at https://resim.readthedocs.io/.\n\nTODO\n----\n* Add class methods for conventional first-line/second-line chemos in HCC, PC.\n* Add tool for converting tumor's diameter/volume to number of cells.\n* Add tool for converting doubling time to growth rate.\n* Add tool for converting half-life to k.\n* Add command line module.\n* Integrate parallel programming sde solver module `nsim\n  <https://github.com/mattja/nsim>` to speed up the computation.\n\n\n",
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        "home_page": "http://github.com/zeochoy/resim",
        "keywords": "chemoresistance cancer outcome simulations modeling",
        "license": "MIT",
        "maintainer": "",
        "maintainer_email": "",
        "name": "resim",
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