Metadata-Version: 1.1
Name: resim
Version: 0.1.1
Summary: chemoREsistance SIMulator
Home-page: http://github.com/zeochoy/resim
Author: Zeo Choy
Author-email: zeochoy@gmail.com
License: MIT
Description-Content-Type: UNKNOWN
Description: ReSim: chemoREsistance SIMulator
        ================================
        ReSim (chemoREsistance SIMulator) is a python package that simulates cancer
        chemoresistance dynamics based on a stochastic model. The model describes the
        interaction between chemotherapeutics and various cancer cell subpopulations
        (including sensitive, primary resistant, acquired resistant and quiescent
        cancer cells).
        
        ReSim provides both GUI (powered by `Flask <http://flask.pocoo.org>`) and CUI.
        
        **Remarks**: Simulation result is greatly depended on input params, therefore
        ReSim is intended to use in parallel with *in vitro* assay. Procedures or
        details in identifying input params *in vitro* is out of scope here and will be
        documented in elsewhere (hope to get it publish soon).
        
        Dependencies
        ____________
        * flask
        * Flask-WTF
        * numpy
        * scipy
        * pandas
        * matplotlib
        * seaborn
        * plotly
        * sdeint
        
        Installation
        ------------
        We **strongly** recommend everyone to install resim within a virtual
        environment. Setup the venv as usual and run ::
        
          $ pip install resim
        
        Otherwise, install it locally.
        
        Getting Start - GUI
        -------------------
        Open your command prompt, simply run ::
        
          $ resim-flask
        
        ReSim is now available at http://localhost:5000/resim .
        
        **Note** GUI provides a simplified & handy version of resim with interactive
        plotly plots. Please use CUI if you need more control of input params.
        
        Advanced Usage
        --------------
        Using defaults params (calibrated from the case of sorafenib and liver cancer)::
        
          >>> import resim
        
          >>> model = resim.Simulator()
          >>> res = model.simulate()
        
          >>> snsplt_cells = resim.plot_cells(res, subplot=True)
          >>> snsplt_drug = resim.plot_drug(res)
          >>> snsplt_fht = resim.plot_fht(res)
        
          >>> snsplt_cells.figure.savefig('resim_cells.png')
          >>> snsplt_drug.figure.savefig('resim_drug.png')
          >>> snsplt_fht.figure.savefig('resim_fht.png')
        
        You may set your own params. For examples, input the growth rate of sensitive cells and resistant cells determined *in vitro*::
        
          >>> import resim
        
          >>> model = resim.Simulator(gr=[0.2, 0.1, 0.1])
          >>> res = model.simulate()
        
        `model.simulate()` returns a dict of dictionary containing `pd.DataFrame` or
        `np.array`. To extract the result,::
        
          >>>  df_cells = res['case']['cells']
          >>>  df_drugs = res['case']['drugs']
        
          >>> l_fht_control = res['control']['fht'])
          >>> l_fht_treatment = res['case']['fht'])
        
        **Notes** GUI outputs are generated by `Plotly <https://plot.ly/>`. But both
        `plot_cells`, `plot_drug` and `plot_fht` in CUI return `seaborn
        <https://seaborn.pydata.org/>` plot object. You may customize your own theme,
        title, layout.
        
        Documentation
        -------------
        Available at https://resim.readthedocs.io/.
        
        TODO
        ----
        * Add class methods for conventional first-line/second-line chemos in HCC, PC.
        * Add tool for converting tumor's diameter/volume to number of cells.
        * Add tool for converting doubling time to growth rate.
        * Add tool for converting half-life to k.
        * Add command line module.
        * Integrate parallel programming sde solver module `nsim
          <https://github.com/mattja/nsim>` to speed up the computation.
        
Keywords: chemoresistance cancer outcome simulations modeling
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Topic :: Scientific/Engineering :: Medical Science Apps.
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Environment :: Console
