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        "author": "Trang Tran",
        "author_email": "ttdtrang@gmail.com",
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        "description": "PyGeneNet\n---------\n\nThis package implements GeneNet algorithm for learning causal genetic network from time series data. The original implementation is described here\n\nN. A. Barker, C. J. Myers, and H. Kuwahara, \u201cLearning Genetic Regulatory Network Connectivity from Time Series Data,\u201d IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 8, no. 1, pp. 152\u2013165, Jan. 2011.\n\nInstallation\n------------\n\nThe program requires python 2.7 and packages below\n    * numpy 1.8.2\n    * pandas 0.15.2\n    * graphviz 0.4.3\n    * matplotlib (only required for timing in ``examples``)\n\nTo use this program, please have python installed on your system, and run:\n    python setup.py install\n\nThe package will be installed in Python ``site-packages`` directory by default and will be available under the name pygenenet. \n\nTo check your installation:\n    python\n    >>> import pygenenet\n\nshould not return an error\n\n\nTo remove the package, run (with appropriate permission)\n    pip uninstall pygenenet\n\nOr manually remove the files in the location installed above. The exact locations of each files can be obtained by running installation again with the --record option\n    python setup.py install --record files.txt\nLocation of files will be written to `files.txt`.\n\nUsage\n-----\nExample use can be found in pygenenet/examples\n    Data files:\n        ``net3_ssa_10``  : synthetic data from stochastic simulation of a 3-species network, for 2000s, trajectories written every 10s\n        ``net3_ssa_100`` : synthetic data from stochastic simulation of a 3-species network, for 2000s, trajectories written every 100s\n        ``net4_ssa_10``  : synthetic data from stochastic simulation of a 4-species network, for 2000s, trajectories written every 10s\n        ``net4_ssa_100`` : synthetic data from stochastic simulation of a 4-species network, for 2000s, trajectories written every 100s\n\n    Example scripts:\n        1. ``demo_net3.py`` to learn the network from ``net3_ssa_10``. The result should look like this\n            Learned causal network in 23.927177906s\n                  CI  LacI  TetR\n            CI     0     0    -1\n            LacI  -1     0     0\n            TetR   0    -1     0\n\n        2. ``demo_net4.py`` to learn the network from ``net4_ssa_10``. The result should look like this\n            Learned causal network in 23.927177906s\n                  CI  GFP  LacI  TetR\n            CI     0    0    -1     0\n            GFP    0    0     1     0\n            LacI   0    0    -1     0\n            TetR   0    0    -1     0\n\n        3. ``performance_net3.py`` and ``performance_net4.py`` will time the learning algorithm with various input subsets and input data frequencies and report a plot. These scripts require ``matplotlib``.\n\n\n",
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