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        "description": "====\nPywr\n====\n\nPywr is a generalised network resource allocation model written in Python. It aims to be fast, free, and extendable.\n\n.. image:: https://travis-ci.org/pywr/pywr.svg?branch=master\n   :target: https://travis-ci.org/pywr/pywr\n\n.. image:: https://ci.appveyor.com/api/projects/status/j1llo3j6o4ww9t1t/branch/master?svg=true\n   :target: https://ci.appveyor.com/project/snorfalorpagus/pywr/branch/master\n\n.. image:: https://img.shields.io/badge/chat-on%20gitter-blue.svg\n   :target: https://gitter.im/pywr/pywr\n\n.. image:: https://codecov.io/gh/pywr/pywr/branch/master/graph/badge.svg\n  :target: https://codecov.io/gh/pywr/pywr\n\nOverview\n========\n\nPywr is a tool for solving network resource allocation problems at discrete timesteps using a linear programming approach. It's principal application is in resource allocation in water supply networks, although other uses are conceivable. A network is represented as a directional graph using `NetworkX <https://networkx.github.io/>`__. Nodes in the network can be given constraints (e.g. minimum/maximum flows) and costs, and can be connected as required. Parameters in the model can vary time according to boundary conditions (e.g. an inflow timeseries) or based on states in the model (e.g. the current volume of a reservoir).\n\nModels can be developed using the Python API, either in a script or interactively using `IPython <https://ipython.org/>`__/`Jupyter <https://jupyter.org/>`__. Alternatively, models can be defined in a rich `JSON-based document format <https://pywr.github.io/pywr-docs/json.html>`__.\n\n.. image:: https://raw.githubusercontent.com/pywr/pywr/master/docs/source/_static/pywr_d3.png\n   :width: 250px\n   :height: 190px\n\nNew users are encouraged to read the `Pywr Tutorial <https://pywr.github.io/pywr-docs/tutorial.html>`__.\n\nDesign goals\n============\n\nPywr is a tool for solving network resource allocation problems. It has many similarities with other software packages such as WEAP, Wathnet, Aquator and MISER, but also has some significant differences. Pywr\u00e2\u20ac\u2122s principle design goals are that it is:\n\n- Fast enough to handle large stochastic datasets and large numbers of scenarios and function evaluations required by advanced decision making methodologies;\n- Free to use without restriction \u00e2\u20ac\u201c licensed under the GNU General Public Licence;\n- Extendable \u00e2\u20ac\u201c uses the Python programming language to define complex operational rules and control model runs.\n\nInstallation\n============\n\nPywr should work on Python 3.6 (or later) on Windows, Linux or OS X.\n\nSee the documentation for `detailed installation instructions <https://pywr.github.io/pywr-docs/install.html>`__.\n\nProvided that you have the required `dependencies <https://pywr.github.io/pywr-docs/install.html#dependencies>`__ already installed, it's as simple as:\n\n.. code-block:: console\n\n    python setup.py install --with-glpk --with-lpsolve\n\nFor most users it will be easier to install the `binary packages made available for the Anaconda Python distribution <https://anaconda.org/pywr/pywr>`__. See install docs for more information. Note that these packages may lag behind the development version.\n\nLicense\n=======\n\nCopyright (C) 2014-19 Joshua Arnott, James E. Tomlinson, Atkins, University of Manchester\n\n\nThis program is free software; you can redistribute it and/or modify\nit under the terms of the GNU General Public License as published by\nthe Free Software Foundation; either version 1, or (at your option)\nany later version.\n\nThis program is distributed in the hope that it will be useful,\nbut WITHOUT ANY WARRANTY; without even the implied warranty of\nMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\nGNU General Public License for more details.\n\nYou should have received a copy of the GNU General Public License\nalong with this program; if not, write to the Free Software\nFoundation, Inc., 51 Franklin Street, Fifth Floor, Boston MA  02110-1301 USA.\n\n\n",
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