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        "author": "Dileep Kishore, Srikiran Chandrasekaran",
        "author_email": "k.dileep1994@gmail.com",
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            "Development Status :: 4 - Beta",
            "Intended Audience :: Developers",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: Apache Software License",
            "Natural Language :: English",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7"
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        "description": "# pyssa : Python package for stochastic simulations\n\n[![Build Status](https://travis-ci.com/Heuro-labs/pyssa.svg?token=qCMKydrUTvcJ87J6czex&branch=master)](https://travis-ci.com/Heuro-labs/pyssa)\n[![Build Status](https://dev.azure.com/srikiranc/pyssa/_apis/build/status/Heuro-labs.pyssa?branchName=master)](https://dev.azure.com/srikiranc/pyssa/_build/latest?definitionId=1?branchName=master)\n[![codecov](https://img.shields.io/codecov/c/github/Heuro-labs/pyssa.svg)](https://codecov.io/gh/Heuro-labs/pyssa)\n[![Updates](https://pyup.io/repos/github/Heuro-labs/pyssa/shield.svg)](https://pyup.io/repos/github/Heuro-labs/pyssa/)\n[![Documentation Status](https://readthedocs.org/projects/pyssa/badge/?version=latest)](https://pyssa.readthedocs.io/en/latest/?badge=latest)\n[![pypi](https://img.shields.io/pypi/v/pyssa.svg)](https://pypi.python.org/pypi/pyssa)\n![License](https://img.shields.io/badge/license-Apache%202-blue.svg)\n[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/ambv/black)\n\n\n\n## Introduction\n\n`pyssa` is a Python package for stochastic simulations. It offers a simple api to define models, perform stochastic simulations on them and visualize the results in a convenient manner.\n\n\n## Install\n\nInstall with `pip`:\n\n```bash\n$ pip install pyssa\n```\n\n\n## Documentation\n\n  - General: <https://pyssa.readthedocs.io>.\n\n\n## Usage\n\n```python\nfrom pyssa.simulation import Simulation\nV_r = np.array([[1, 0], [0, 1], [0, 0]])  # Reactant matrix\nV_p = np.array([[0, 0], [1, 0], [0, 1]])  # Product matrix\nX0 = np.array([100, 0, 0])  # Initial state\nk = np.array([1.0, 1.0])  # Rate constants\nsim = Simulation(V_r, V_p, X0, k)  # Declare the simulation object\n# Run the simulation\nsim.simulate(max_t=150, max_iter=1000, chem_flag=True, n_rep=10)\n```\n\nYou can change the algorithm used to perform the simulation by changing the `algorithm` parameter\n\n```python\nsim.simulate(max_t=150, max_iter=1000, chem_flag=True, n_rep=10, algorithm=\"tau_adaptive\")\n```\n\nYou can run the simulations on multiple cores by specifying the `n_procs` parameter\n\n```python\nsim.simulate(max_t=150, max_iter=1000, chem_flag=True, n_rep=10, n_procs=4)\n```\n\n\n### Plotting\n\n```python\nsim.plot()\n```\n\n![Plot of species A, B and C](https://raw.githubusercontent.com/Heuro-labs/pyssa/master/docs/images/plot_basic.png)\n\n### Accessing the results\n\n```python\nresults = sim.results\n```\n\nYou can also access the final states of all the simulation runs by\n\n```python\nfinal_times, final_states = results.final\n```\n\n## Benchmarks\n\nWe chose `numba` after extensive testing and benchmarking against `python` and `cython` implementations.\n\nName (time in ms) | Min | Max | Mean | StdDev | Median | IQR | Outliers | OPS | Rounds | Iterations |\n| --- |:---:| :---:|:---:| :---:|:---:| :---:|:---:| :---:|:---:| :---:|\ntest_numba_benchmark | 314.1758 (1.0) | 342.9915 (1.0) | 322.9318 (1.0) |  11.4590 (1.0) |   318.7983 (1.0) |  9.1533 (1.0) |     1;1 | 3.0966 (1.0) |     5 |     1 |\ntest_cy_benchmark |  17,345.7698 (55.21)  |  19,628.3931 (57.23)  |  18,255.3931 (56.53)  | 862.4711 (75.27) |  18,148.9358 (56.93)  |  1,030.3676 (112.57) |   2;0 | 0.0548 (0.02) |    5 |     1 |\ntest_py_benchmark |  27,366.3681 (87.11) |  28,417.8333 (82.85) |   27,782.2482 (86.03)  |  387.2758 (33.80)  |  27,728.4224 (86.98)  |  347.3891 (37.95) |   2;0 | 0.0360 (0.01) |    5 |     1 |\n\n## License\n\nCopyright (c) 2018-2019, Dileep Kishore, Srikiran Chandrasekaran. Released under: Apache Software License 2.0\n\n## Credits\n\n- [Numba](https://numba.pydata.org/)\n- [pytest](https://docs.pytest.org)\n- [Cookiecutter](https://github.com/audreyr/cookiecutter)\n- [audreyr/cookiecutter-pypackage](https://github.com/audreyr/cookiecutter-pypackage)\n- [black](https://github.com/ambv/black)",
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