{
    "info": {
        "author": "Renato Fabbri",
        "author_email": "renato.fabbri@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 2 - Pre-Alpha",
            "Intended Audience :: Developers",
            "Intended Audience :: Education",
            "Intended Audience :: Information Technology",
            "Intended Audience :: Other Audience",
            "Intended Audience :: Religion",
            "Intended Audience :: Science/Research",
            "License :: Public Domain",
            "Programming Language :: Python :: 3",
            "Topic :: Artistic Software",
            "Topic :: Communications :: Email",
            "Topic :: Multimedia :: Graphics :: Presentation",
            "Topic :: Multimedia :: Sound/Audio :: Sound Synthesis",
            "Topic :: Multimedia :: Video :: Display",
            "Topic :: Other/Nonlisted Topic",
            "Topic :: Scientific/Engineering :: Information Analysis",
            "Topic :: Scientific/Engineering :: Physics",
            "Topic :: Scientific/Engineering :: Visualization",
            "Topic :: Text Processing",
            "Topic :: Text Processing :: Linguistic"
        ],
        "description": "==================================================================\nPython utilities for the analysis of the GMANE email list database\n==================================================================\n\nThis project delivers helper classes for the analysis of the GMANE\nemail database. Install with:\n\n    $ pip install gmaneLegacy\n\nor\n\n    $ python setup.py install\n\nFor greater control of customization (and debugging), clone the repo and install with pip with -e:\n\n    $ git clone https://github.com/ttm/gmaneLegacy.git\n\n    $ pip install -e <path_to_repo>\n\nThis install method is especially useful with\nreload function from IPython.lib.deepreload and the standard importlib.\n\nFunctionalities are based on physics articles on interaction networks:\n[1] Stability in human interaction networks: primitive typology of vertex, prominence of measures and activity statistics: http://arxiv.org/abs/1310.7769\n[2] A connective differentiation of textual production in interaction networks: http://arxiv.org/abs/1412.7309\n[3] Versinus: a visualization method for graphs in evolution: http://arxiv.org/abs/1412.7311\n\nWith core concepts of 1) analysis of topological structure; 2) analysis of textual production; 3) visualization of evolving structures. Activity distribution along time and among participants are also approached through specific routines and indirectly through 1), 2) and 3).\n\nIdeally, this package should ease:\n- Downloading GMANE email list data.\n- Building elementary data structures with downloaded data.\n- Analysis of data through complex networks and NLP criteria.\n- Visualization through diverse layout methods.\n\nPS.\nImplemented measures of symmetry in network agents activity by hand (not found in network and numeric packages) according to [1].\nPS2.\nongoing research in \ntests/newTextTables.py and\ntests/makeOverallTextAnalysis.py\nPS.\nAlso check the gmane Python package https://github.com/ttm/gmane\n\n\nUsage example\n=================\nDownload messages from one GMANE list:\n\n.. code:: python\n\n    import gmane as g\n    dl=g.DownloadGmaneData() # saves into ~/.gmane/\n    dl.downloadListsIDS() # acquires all GMANE list_ids\n    dl.downloadListMessages(dl.list_ids[100])\n    dl.cleanDownloadedLists() # remove empty messages for coherence\n    dl.downloadedStats() # creates ~/.gmane/stats.txt\n\n    # to load message contents to Python objects:\n    # load 10 messages from list with list_id gmane.ietf.rfc822\n    lm=g.LoadMessages(\"gmane.ietf.rfc822\",10)\n\n    # or access the structures downloaded to your filesystem\n    dl=g.DownloadGmaneData()\n    dl.getDownloadedLists()\n    lms=[]\n    # and download all messages from 5 lists\n    for list_id in dl.downloaded_lists[:5]:\n        lms.append(g.LoadMessages(list_id))\n\n    # to load first three lists with the greated number\n    # of downloaded messages:\n    dl.downloadedStats() # might take a while\n    load_msgs=[]\n    for list_stat in dl.lists[:3]:\n        list_id=list_stat[0]\n        load_msgs.append(g.LoadMessages(list_id))\n\n    # to make basic datastructures of a list with\n    # greatest number of messages:\n    ds=g.MessageDataStructures(load_msgs[0])\n    mm=ds.messages\n    ids=ds.message_ids\n    print(\"first: \", mm[ids[0]][2], \"last:\", mm[ids[-1]][2])\n\n    # circular (directional) statistics for activity along time\n    # (hours of the day, days of the week, days of the month, etc):\n    # mean_vec, mean_angle, size_mean_vec, circular_mean,\n    # circular_variance, circular dispersion\n    # and histograms\n    ts=g.TimeStatistics(ds)\n    print(\"made overall circular activity statistics along time\")\n\n    # make latex tables to observe distributions within bins of interest\n    hi=100*ts.hours[\"histogram\"]/ts.hours[\"histogram\"].sum()\n    row_labels=list(range(24))\n    tstring=g.parcialSums(row_labels,data=[hi],partials=[1,2,3,4,6,12],\n                partial_labels=[\"h\",\"2h\",\"3h\",\"4h\",\"6h\",\"12h\"],datarow_labels=[\"APACHE\"])\n    g.writeTex(tstring,\"here.tex\")\n\n    ps=g.AgentStatistics(ds)\n    print(\"made overall activity statistics among participants\")\n    \n    # build the interaction network of the messages:\n    nw=g.InteractionNetwok(ds)\n\n    print(\"number of nodes: {}, number of edges: {}\".format(\n    nw.g.number_of_nodes(), nw.g.number_of_edges()))\n\n    nm=g.NetworkMeasures(nw) # take measures, including symmetry related measures\n    np=g.NetworkPartitioning(nm) # partition in primitive typology\n    sa=np.sectorialized_agents # get members of each sector\n    print(\"{} agents in periphery, {} are intermediary and {} hubs\".format(sa[0],sa[1],sa[2]))\n    sa=np.sectorialized_agents__ # smoothed histogram for classification\n    print(\"{} agents in periphery, {} are intermediary and {} hubs\".format(sa[0],sa[1],sa[2]))\n\n    # draw\n    nd=g.NetworkDrawer()\n    print(\"drawer started\")\n    nd.makeLayout(nm)\n    print(\"gave (x,y) for each author with 5-15-80\")\n    nd2=g.NetworkDrawer()\n    print(\"drawer two started\")\n    nd2.makeLayout(nm,np)\n    print(\"gave (x,y) for each author with \\\n    sectors by comparison with Erdos-Renyi\")\n    nd.drawNetwork( iN,nm ,\"test.png\")\n    nd2.drawNetwork( iN,nm,\"test2.png\")\n\n    # make basic PCA plots of network measures:\n    npca=g.NetworkPCA(nm)\n    # Plot PCA with a colored primitive sectors \n    npca=g.NetworkPCA(nm,np)\n\n    # Evolves network with measures, partitions,\n    # PCA, principal components and Versinus plots saved to disk\n    lm=lms[0] # loaded messages from list with most messages\n    ne=g.NetworkEvolution(step_size=10)\n    ne.evolveRaw(lm.messages,imagerate=4,erdos_sectors=True)\n    # ne.makeVideo() use this to avoid evolving again just to make video\n    # see testDrawer.py or g.NetworkEvolution to make movies:\n    # https://www.youtube.com/watch?v=iS8NwEy291g\n\n    # after making network evolution measurements and video,\n    # you can both make music:\n    em=g.EvolutionMusic()\n    print(\"music is done\")\n    # avconv -i mixY.wav -i evo[..<depends on the evolution done>..].avi final.avi\n    # delivers you the final.avi animation with a soundtrack relative to network measures\n    # currently it is the 'four hubs dance' by default:\n    # https://www.youtube.com/watch?v=YxDiwzAUPeU\n\n    # and further analysis of measures and Erdos sectors:\n    et=g.EvolutionTimelines()\n    print(\"Written png files with network measures along evolution timeline\")\n\n    # Enjoy!\n\nFurther documentation is in tests/ folder and object docstrings.",
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        "keywords": "complexity networks human interaction physics data mining analysis visualization",
        "license": "Public Domain",
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