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        "author": "Arnour Sabino",
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        "description": "Almetro Library\n===============================\n\nversion number: 1.0.7\nauthor: Arnour Sabino\n\nOverview\n--------\n\nA python library to measure algorithms execution time and compare with its theoretical complexity.\n\n[![Build Status](https://travis-ci.org/arnour/almetro.svg?branch=master)](https://travis-ci.org/arnour/almetro)\n\nInstallation / Usage\n--------------------\n\nTo install use pip:\n\n    $ pip install almetro\n\n\nOr clone the repo:\n\n    $ git clone https://github.com/arnour/almetro.git\n    $ python setup.py install\n\nInformation\n--------------------\n\nAlmetro uses timeit module from python to time your algorithms.\n\nSee more [here](https://docs.python.org/3/library/timeit.html)\n\n\nExamples\n--------------------\n\nApplying Almetro to a quadratic algorithm:\n\n```python\nimport almetro\nfrom almetro.algorithms import loop_n_quadratic\nfrom almetro.complexity import cn_quadratic\nfrom almetro.instance import growing\n\nmetro = almetro\\\n            .new()\\\n            .with_execution(trials=5)\\\n            .with_instances(instances=20, provider=growing(initial_size=100, growth_size=100))\\\n            .metro(algorithm=loop_n_quadratic, complexity=cn_quadratic)\n\nchart = metro.chart()\n\nchart.show()\n```\n\n![Chart Almetro n quadratic](https://raw.githubusercontent.com/arnour/almetro/master/images/chart_almetro_n_quadratic.png)\n\n---\n\nApplying Almetro to a lg n algorithm:\n\n```python\nimport almetro\nfrom almetro.algorithms import loop_n_log\nfrom almetro.complexity import clog_n\nfrom almetro.instance import growing\n\nmetro = almetro\\\n            .new()\\\n            .with_execution(trials=100)\\\n            .with_instances(instances=20, provider=growing(initial_size=10000, growth_size=10000))\\\n            .metro(algorithm=loop_n_log, complexity=clog_n)\n\nchart = metro.chart()\n\nchart.show()\n```\n\n![Chart Almetro lg n](https://github.com/arnour/almetro/blob/master/images/chart_almetro_lg_n.png?raw=true)\n\n\n---\n\nCustomazing execution:\n\n```python\nimport almetro\nfrom almetro.complexity import Complexity\nfrom almetro.instance import generator\n\nmy_custom_complexity = Complexity(\n    theoretical=lambda v=1, e=1, c=1: v * v,\n    experimental=lambda v=1, e=1, c=1: v + e,\n    text='O(v^2)',\n    latex=r'$\\mathcal{O}(v^2)$'\n)\n\n# You need to provide instances as dict: {'name': '', 'size': {}, 'value': {}}\n# Size must contains all needed theoretical complexity arguments\n# Value must contain all needed algorithms arguments\n\ndef my_custom_instances(n):\n    g = create_some_graph()\n    for _ in range(n):\n        yield {\n            'name': 'my instance name',\n            'size': {'v': len(g.nodes()), 'e': len(g.edges())}, 'c': some_order_value(),\n            'value': {\n                'graph': g,\n                'v': len(g.nodes())\n            }\n        }\n\ndef my_custom_algorithm(graph, v):\n    # Do some stuff\n    pass\n\nN = 50\n\ninstances_generator = my_custom_instances(N)\n\n# Trials determine how many times each instance will be repeated for Almetro to pick the min time.\nmetro = almetro\\\n            .new()\\\n            .with_execution(trials=5)\\\n            .with_instances(instances=N, provider=generator(instances_generator)\\\n            .metro(algorithm=my_custom_algorithm, complexity=my_custom_complexity)\n\nmetro.chart().show()\n```\n\n![Chart Almetro v^2](https://github.com/arnour/almetro/blob/master/images/chart_v_quadratic_graph.png?raw=true)\n\n\n```python\nmetro.table().show()\n```\n\n![Table Almetro v^2](https://github.com/arnour/almetro/blob/master/images/table_v_quadratic_graph.png?raw=true)\n\n---\n\n",
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