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        "author": "Johan Niklasson, Oskar Hahr",
        "author_email": "jnikl@kth.se, ohahr@kth.se",
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            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Topic :: Scientific/Engineering :: Mathematics",
            "Topic :: System :: Distributed Computing"
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        "description": "# Extended networkx Tools\nPython Package for for visualizing and converting networkx graphs.\n\n## Introduction\n\nThis package was created for the purpose of examining bidirectional graphs with respect to its convergence rate and edge costs.\n\n## Installation\n\n```shell\npip install extended-networkx-tools\n```\n\n## Documentation\n\n[extended-networkx-tools.readthedocs.io](https://extended-networkx-tools.readthedocs.io/)\n\n## The package\n\nCurrently the package contains 3 main modules, `Creator`, `Analytics` and `Visual`.\n\n### Creator\n\nContains tools to create networkx graphs based on given parameters, such as randomly \ncreate an empty graph based on a number of nodes, or specify precisely the \ncoordinates of nodes and the edges between them.\n\n### Analytics\n\nHas tools for analysing the networkx object and extract useful information from it, such \nas convergence rate, neighbour matrix, its eigenvalues.\n\n### Solver\n\nUsed to find simple greedy solutions to a connected graph taken from graph theory. The current approaches are:\n\n- ``path``: Adds edges as a path from the start to end node\n- ``cycle``: Adds edges just like the path, but also one edge from the start to end node.\n- ``complete``: Adds edges between all nodes to all the other nodes, such as the maximum distance between every node is one.\n\n### Visual\n\nIs used to print a networkx graph to the screen, with its edges.\n\n[Example output graph][examplegraph]\n\n[examplegraph]: docs/source/_static/example-graph.png \"Example graph\"\n\n### AnalyticsGraph\n\nThe `AnalyticsGraph` class is a helper class that serves the purpose of a wrapper object\nthat can do all calculations based on changes done to the graph, rather\nthan recalculating every metric after simple changes. Such as the connectivity state\nwill stay the same after adding an edge.\n\nThere is also options to revert changes and keep previous calculations.\n\n**Example usage**:\n\n```python\nfrom extended_networkx_tools import Creator, Solver, AnalyticsGraph\n\n# Create a random graph with a path\ng = Creator.from_random(10)\ng = Solver.path(g)\n\n# Convert the graph to an AnalytcsGraph object\nag = AnalyticsGraph(g)\n\nconvergence_rate = ag.get_convergence_rate() # Calcualtes the convergence rate from scratch\nag.remove_edge(4, 5)    # Removes an edge\nag.revert()             # Revert the changes\nconvergence_rate = ag.get_convergence_rate() # Doesn't calculate it since it's saved from previous state\n```\n\n## Usage\n\n### Import\n\n\n```python\nfrom extended_networkx_tools import Creator, Analytics, Visual, Solver, AnalyticsGraph\n```\n\n\n\n",
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