{
"info": {
"author": "Vincenzo Arcidiacono",
"author_email": "vinci1it2000@gmail.com",
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"License :: OSI Approved :: European Union Public Licence 1.1 (EUPL 1.1)",
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"Programming Language :: Python :: 3.6",
"Programming Language :: Python :: 3.7",
"Programming Language :: Python :: Implementation :: CPython",
"Topic :: Scientific/Engineering",
"Topic :: Scientific/Engineering :: Information Analysis",
"Topic :: Software Development :: Libraries :: Python Modules",
"Topic :: Utilities"
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"description": ".. _start-intro:\n\n\nWhat is schedula?\n*****************\n\nSchedula implements a intelligent function scheduler, which selects\nand executes functions. The order (workflow) is calculated from the\nprovided inputs and the requested outputs. A function is executed when\nall its dependencies (i.e., inputs, input domain) are satisfied and\nwhen at least one of its outputs has to be calculated.\n\nNote: Schedula is performing the runtime selection of the\n **minimum-workflow** to be invoked. A workflow describes the\n overall process - i.e., the order of function execution - and it is\n defined by a directed acyclic graph (DAG). The **minimum-workflow**\n is the DAG where each output is calculated using the shortest path\n from the provided inputs. The path is calculated on the basis of a\n weighed directed graph (data-flow diagram) with a modified Dijkstra\n algorithm.\n\n\nInstallation\n************\n\nTo install it use (with root privileges):\n\n::\n\n $ pip install schedula\n\nOr download the last git version and use (with root privileges):\n\n::\n\n $ python setup.py install\n\n\nInstall extras\n==============\n\nSome additional functionality is enabled installing the following\nextras:\n\n* plot: enables the plot of the Dispatcher model and workflow (see\n ``plot()``).\n\n* web: enables to build a dispatcher Flask app (see ``web()``).\n\n* sphinx: enables the sphinx extension directives (i.e., autosummary\n and dispatcher).\n\n* parallel: enables the parallel execution of Dispatcher model.\n\nTo install schedula and all extras, do:\n\n::\n\n $ pip install schedula[all]\n\nNote: ``plot`` extra requires **Graphviz**. Make sure that the directory\n containing the ``dot`` executable is on your systems\u2019 path. If you\n have not you can install it from its `download page\n `_.\n\n.. _end-quick:\n\n\nWhy may I use schedula?\n***********************\n\nImagine we have a system of interdependent functions - i.e. the inputs\nof a function are the output for one or more function(s), and we do\nnot know which input the user will provide and which output will\nrequest. With a normal scheduler you would have to code all possible\nimplementations. I\u2019m bored to think and code all possible combinations\nof inputs and outputs from a model.\n\n\nSolution\n========\n\nSchedula allows to write a simple model (``Dispatcher``) with just the\nbasic functions, then the ``Dispatcher`` will select and execute the\nproper functions for the given inputs and the requested outputs.\nMoreover, schedula provides a flexible framework for structuring code.\nIt allows to extract sub-models from a bigger one and to run your\nmodel asynchronously or in parallel without extra coding.\n\nNote: A successful `application `_\n is CO_2MPAS, where schedula has been used to model an entire\n `vehicle `_.\n\n\nVery simple example\n*******************\n\nLet\u2019s assume that we have to extract some filesystem attributes and we\ndo not know which inputs the user will provide. The code below shows\nhow to create a ``Dispatcher`` adding the functions that define your\nsystem. Note that with this simple system the maximum number of inputs\ncombinations is 31 ((2^n - 1), where *n* is the number of data).\n\n..\n\n >>> import schedula as sh\n >>> import os.path as osp\n >>> dsp = sh.Dispatcher()\n >>> dsp.add_data(data_id='dirname', default_value='.', initial_dist=2)\n 'dirname'\n >>> dsp.add_function(function=osp.split, inputs=['path'],\n ... outputs=['dirname', 'basename'])\n 'split'\n >>> dsp.add_function(function=osp.splitext, inputs=['basename'],\n ... outputs=['fname', 'suffix'])\n 'splitext'\n >>> dsp.add_function(function=osp.join, inputs=['dirname', 'basename'],\n ... outputs=['path'])\n 'join'\n >>> dsp.add_function(function_id='union', function=lambda *a: ''.join(a),\n ... inputs=['fname', 'suffix'], outputs=['basename'])\n 'union'\n\n [graph]\n\nTip: You can explore the diagram by clicking on it.\n\nNote: For more details how to created a ``Dispatcher`` see:\n ``add_data()``, ``add_func()``, ``add_function()``,\n ``add_dispatcher()``, ``SubDispatch``, ``SubDispatchFunction``,\n ``SubDispatchPipe``, ``DispatchPipe``, and ``DFun``.\n\nThe next step to calculate the outputs would be just to run the\n``dispatch()`` method. You can invoke it with just the inputs, so it\nwill calculate all reachable outputs:\n\n..\n\n >>> inputs = {'path': 'schedula/_version.py'}\n >>> o = dsp.dispatch(inputs=inputs)\n >>> o\n Solution([('path', 'schedula/_version.py'),\n ('basename', '_version.py'),\n ('dirname', 'schedula'),\n ('fname', '_version'),\n ('suffix', '.py')])\n\n [graph]\n\nor you can set also the outputs, so the dispatch will stop when it\nwill find all outputs:\n\n..\n\n >>> o = dsp.dispatch(inputs=inputs, outputs=['basename'])\n >>> o\n Solution([('path', 'schedula/_version.py'), ('basename', '_version.py')])\n\n [graph]\n\n\n",
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