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        "author": "Tommaso Belluzzo",
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            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Natural Language :: English",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Programming Language :: Python :: 3.8",
            "Programming Language :: Python :: 3 :: Only",
            "Topic :: Education",
            "Topic :: Scientific/Engineering :: Artificial Intelligence",
            "Topic :: Scientific/Engineering :: Information Analysis",
            "Topic :: Scientific/Engineering :: Mathematics",
            "Topic :: Scientific/Engineering :: Physics",
            "Topic :: Software Development :: Libraries"
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        "description": "PyDTMC is a full-featured, lightweight library for discrete-time Markov chains analysis. It provides classes and functions for creating, manipulating and simulating markovian stochastic processes.\n\n## Requirements\n\nPyDTMC supports only `Python 3` and the minimum required version is `3.6`. In addition, the environment must include the following libraries:\n\n* [Matplotlib](https://matplotlib.org/)\n* [NetworkX](https://networkx.github.io/)\n* [Numpy](https://www.numpy.org/)\n\nFor a better user experience, it's recommended to install [Graphviz](https://www.graphviz.org/) and [PyDot](https://pypi.org/project/pydot/) before using the `plot_graph` function.\n\n## Installation & Upgrade\n\nVia PyPI:\n\n```sh\n$ pip install PyDTMC\n$ pip install --upgrade PyDTMC\n```\n\nVia GitHub:\n\n```sh\n$ pip install git+https://github.com/TommasoBelluzzo/PyDTMC.git@master#egg=PyDTMC\n$ pip install --upgrade git+https://github.com/TommasoBelluzzo/PyDTMC.git@master#egg=PyDTMC\n```\n\n## Usage\n\nThe core element of the library is the `MarkovChain` class, which can be instantiated as follows:\n\n```console\n>>> import numpy as np\n>>> p = np.array([[0.2, 0.7, 0.0, 0.1], [0.0, 0.6, 0.3, 0.1], [0.0, 0.0, 1.0, 0.0], [0.5, 0.0, 0.5, 0.0]])\n>>> mc = MarkovChain(p, ['A', 'B', 'C', 'D'])\n>>> print(mc)\n\nDISCRETE-TIME MARKOV CHAIN\n SIZE:         4\n CLASSES:      2\n  - RECURRENT: 1\n  - TRANSIENT: 1\n ABSORBING:    YES\n APERIODIC:    YES\n IRREDUCIBLE:  NO\n ERGODIC:      NO\n```\n\nStatic values of `MarkovChain` instances can be retrieved through their properties:\n\n```console\n>>> print(mc.recurrent_states)\n['C']\n\n>>> print(mc.transient_states)\n['A', 'B', 'D']\n\n>>> print(mc.steady_states)\n[array([0., 0., 1., 0.])]\n\n>>> print(mc.fundamental_matrix)\n[[1.50943396 2.64150943 0.41509434]\n [0.18867925 2.83018868 0.30188679]\n [0.75471698 1.32075472 1.20754717]]\n\n>>> print(mc.absorption_times)\n[4.56603774 3.32075472 3.28301887]\n\n>>> print(mc.topological_entropy)\n0.6931471805599457\n```\n\nDynamic computations on `MarkovChain` instances can be performed through their methods:\n\n```console\n>>> print(mc.expected_rewards(10, [2, -3, 8, -7]))\n[-2.76071635 -12.01665113  23.23460025  -8.45723276]\n\n>>> print(mc.expected_transitions(2))\n[[0.085  0.2975 0.     0.0425]\n [0.     0.345  0.1725 0.0575]\n [0.     0.     0.7    0.    ]\n [0.15   0.     0.15   0.    ]]\n \n>>> print(mc.walk(10))\n['D', 'A', 'B', 'C', 'C', 'C', 'C', 'C', 'C', 'C']\n```\n\nPlotting functions can provide a visual representation of a `MarkovChain` instance; in order to display function outputs immediately, the [interactive mode](https://matplotlib.org/faq/usage_faq.html#what-is-interactive-mode) of `Matplotlib` must be turned on:\n\n```console\n>>> plot_eigenvalues(mc)\n```\n\n![Eigenplot](https://i.imgur.com/ARWWG7z.png)\n\n```console\n>>> plot_graph(mc)\n```\n\n![Graphplot](https://i.imgur.com/looxKRO.png)\n\n```console\n>>> plot_walk(mc, 10, 'sequence')\n```\n\n![Walkplot](https://i.imgur.com/oxjDYr3.png)",
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