{
    "info": {
        "author": "Kent Kawashima",
        "author_email": "i@kentwait.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 3 - Alpha",
            "Environment :: Console",
            "Intended Audience :: Developers",
            "Intended Audience :: Education",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: Apache Software License",
            "Natural Language :: English",
            "Operating System :: OS Independent",
            "Programming Language :: Python",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: Implementation :: CPython",
            "Topic :: Scientific/Engineering"
        ],
        "description": "`nxsim`_\r\n========\r\n\r\nNxsim is a Python package for simulating agents connected by any type of\r\nnetwork using SimPy and Networkx in Python 3.4.\r\n\r\nInstall\r\n-------\r\n\r\n::\r\n\r\n    pip3 install nxsim  # from PyPI\r\n    pip3 install git+git://github.com/kentwait/nxsim.git  # from GitHub\r\n\r\n\r\nQuickstart\r\n----------\r\n\r\nNxsim provides a framework for doing forward-time simulations of events\r\noccurring in a network. It uses Networkx to create a network and SimPy 3\r\nto create agents over each node in the network.\r\n\r\nTo create a simulation, nxsim requires a graph generated by Networkx and\r\nan \u201cagent\u201d class to populate each node of the network.\r\n\r\nFirst, create a graph using Networkx.\r\n\r\n::\r\n\r\n    import networkx as nx\r\n\r\n    number_of_nodes = 10\r\n    G = nx.complete_graph(number_of_nodes)\r\n\r\nThen, subclass *BaseNetworkAgent* to create your own agent based on your\r\nneeds.\r\n\r\n::\r\n\r\n    from nxsim import BaseNetworkAgent\r\n\r\n    # Just like subclassing a process in SimPy\r\n    class MyAgent(BaseNetworkAgent):\r\n        def __init__(self, environment=None, agent_id=0, state=()):  # Make sure to have these three keyword arguments\r\n            super().__init__(environment=environment, agent_id=agent_id, state=state)\r\n            # Add your own attributes here\r\n\r\n        def run(self):\r\n            # Add your behaviors here\r\n\r\nNotice that \u201cagents\u201d in nxsim use the same concepts as \u201cprocesses\u201d in\r\nSimPy 3 except that their interactions can be limited by the graph in\r\nthe simulation environment. For more information about SimPy, they have\r\na great introduction posted on their `website`_.\r\n\r\nHere is a graph-based example:\r\n\r\n::\r\n\r\n    import random\r\n    from nxsim import BaseNetworkAgent\r\n\r\n    class ZombieOutbreak(BaseNetworkAgent):\r\n        def __init__(self, environment=None, agent_id=0, state=()):\r\n            super().__init__(environment=environment, agent_id=agent_id, state=state)\r\n            self.bite_prob = 0.05\r\n\r\n        def run(self):\r\n            while True:\r\n                if self.state['id'] == 1:\r\n                    self.zombify()\r\n                    yield self.env.timeout(1)\r\n                else:\r\n                    yield self.env.event()\r\n\r\n        def zombify(self):\r\n            normal_neighbors = self.get_neighboring_agents(state_id=0)\r\n            for neighbor in normal_neighbors:\r\n                if random.random() < self.bite_prob:\r\n                    neighbor.state['id'] = 1  # zombie\r\n                    print(self.env.now, self.id, neighbor.id, sep='\\t')\r\n                    break\r\n\r\nYou can now set-up your simulation by creating a *NetworkSimulation*\r\ninstance.\r\n\r\n::\r\n\r\n    from nxsim import NetworkSimulation\r\n\r\n    # Initialize agent states. Let's assume everyone is normal.\r\n    # Add keys as as necessary, but \"id\" must always refer to that state category\r\n    init_states = [{'id': 0, } for _ in range(number_of_nodes)]\r\n\r\n    # Seed a zombie\r\n    init_states[5] = {'id': 1}\r\n    sim = NetworkSimulation(topology=G, states=init_states, agent_type=ZombieOutbreak,\r\n                            max_time=30, dir_path='sim_01', num_trials=1, logging_interval=1.0)\r\n\r\nAnd finally, start it up.\r\n\r\n::\r\n\r\n    sim.run_simulation()\r\n\r\nRunning the simulation saves pickled dictionaries into the *dir\\_path*\r\nfolder, in this case to \u201csim\\_01\u201d. Now, let\u2019s retrieve the history and\r\nstates of the trial\r\n\r\n::\r\n\r\n    trial = BaseLoggingAgent.open_trial_state_history(dir_path='sim_01', trial_id=0)\r\n\r\nAnd plot the number of zombies per time interval using matplotlib:\r\n\r\n::\r\n\r\n    from matplotlib import pyplot as plt\r\n    zombie_census = [sum([1 for node_id, state in g.items() if state['id'] == 1]) for t,g in trial.items()]\r\n    plt.plot(zombie_census)\r\n\r\nAnd that\u2019s it!\r\n\r\nNote\r\n====\r\n\r\nThis package is still under development. If you encounter a bug, please\r\nfile an issue at https://github.com/kentwait/nxsim/issues to get it\r\nresolved.\r\n\r\nAcknowledgment\r\n==============\r\n\r\nThanks to Jo\u00e9 Schaul for bringing `ComplexNetworkSim`_ to the world.\r\nThis project is a SimPy 3- and Python 3.4-compatible fork of\r\nComplexNetworkSim.\r\n\r\nLinks\r\n=====\r\n\r\n-  `nxsim - Github`_\r\n-  `nxsim - PyPI`_\r\n-  `SimPy v3.0 Documentation`_\r\n-  `Networkx v1.9 Documentation`_\r\n-  `matplotlib v1.4.3 Documentation`_\r\n-  `ComplexNetworkSim Documentation`_\r\n\r\n.. _nxsim: https://github.com/kentwait/nxsim\r\n.. _website: https://simpy.readthedocs.org/en/latest/simpy_intro/index.html\r\n.. _ComplexNetworkSim: https://github.com/jschaul/ComplexNetworkSim\r\n.. _nxsim - Github: https://github.com/kentwait/nxsim\r\n.. _nxsim - PyPI: https://pypi.python.org/pypi/nxsim\r\n.. _SimPy v3.0 Documentation: https://simpy.readthedocs.org/en/latest/contents.html\r\n.. _Networkx v1.9 Documentation: http://networkx.github.io/documentation/networkx-1.9.1\r\n.. _matplotlib v1.4.3 Documentation: http://matplotlib.org/users/\r\n.. _ComplexNetworkSim Documentation: https://pythonhosted.org/ComplexNetworkSim",
        "description_content_type": null,
        "docs_url": null,
        "download_url": "https://github.com/kentwait/nxsim/archive/master.zip",
        "downloads": {
            "last_day": -1,
            "last_month": -1,
            "last_week": -1
        },
        "home_page": "http://github.com/kentwait/nxsim",
        "keywords": "networks, simulation, complex networks, discrete event simulation, forward simulation, temporal networks",
        "license": "Apache License, Version 2.0",
        "maintainer": "",
        "maintainer_email": "",
        "name": "nxsim",
        "package_url": "https://pypi.org/project/nxsim/",
        "platform": "",
        "project_url": "https://pypi.org/project/nxsim/",
        "project_urls": {
            "Download": "https://github.com/kentwait/nxsim/archive/master.zip",
            "Homepage": "http://github.com/kentwait/nxsim"
        },
        "release_url": "https://pypi.org/project/nxsim/0.1.2/",
        "requires_dist": null,
        "requires_python": null,
        "summary": "nxsim is a Python package for simulating agents in a network.",
        "version": "0.1.2"
    },
    "last_serial": 1623557,
    "releases": {
        "0.1.2": [
            {
                "comment_text": "",
                "digests": {
                    "md5": "59ee54470768a6b5693857634252841f",
                    "sha256": "b8bb8a03b21bac69b7de51435b37835629b3affa7994357346a8ca6f9819616f"
                },
                "downloads": -1,
                "filename": "nxsim-0.1.2.tar.gz",
                "has_sig": false,
                "md5_digest": "59ee54470768a6b5693857634252841f",
                "packagetype": "sdist",
                "python_version": "source",
                "requires_python": null,
                "size": 5199,
                "upload_time": "2015-07-03T00:03:40",
                "url": "https://files.pythonhosted.org/packages/1a/9b/54dc90f75a1a1bb0b6ca1e7de668859925b99fa15dd34bb44d6cda415085/nxsim-0.1.2.tar.gz"
            }
        ]
    },
    "urls": [
        {
            "comment_text": "",
            "digests": {
                "md5": "59ee54470768a6b5693857634252841f",
                "sha256": "b8bb8a03b21bac69b7de51435b37835629b3affa7994357346a8ca6f9819616f"
            },
            "downloads": -1,
            "filename": "nxsim-0.1.2.tar.gz",
            "has_sig": false,
            "md5_digest": "59ee54470768a6b5693857634252841f",
            "packagetype": "sdist",
            "python_version": "source",
            "requires_python": null,
            "size": 5199,
            "upload_time": "2015-07-03T00:03:40",
            "url": "https://files.pythonhosted.org/packages/1a/9b/54dc90f75a1a1bb0b6ca1e7de668859925b99fa15dd34bb44d6cda415085/nxsim-0.1.2.tar.gz"
        }
    ]
}