Metadata-Version: 2.1
Name: pystorms
Version: 0.1.1
Summary: Simulation Sandbox for the development and evaluation of stormwater control algorithms
Home-page: https://github.com/kLabUM/pystorms
Author: Abhiram Mullapudi, Sara C. Troutman, Sara Rimer, Branko Kerkez
Author-email: abhiramm@umich.edu, stroutm@umich.edu
License: UNKNOWN
Description: # Open-Storm's Benchmarking Library Smarter Stormwater Networks
        [![Build Status](https://travis-ci.com/kLabUM/Benchmarking.svg?token=T5sBokVeGg3qH8pp1ceB&branch=master)](https://travis-ci.com/kLabUM/Benchmarking)
        [![License: GPL v3](https://img.shields.io/badge/License-GPLv3-blue.svg)](https://www.gnu.org/licenses/gpl-3.0)
        [![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/python/black)
        
        ## Overview 
        
        This repo has been developed in an effort to systematize quantitative analysis of stormwater control algorithms. It is a natural extension of the the Open-Storm mission to open up and ease access into the technical world of smart stormwater systems. Our initial efforts allowed us to develop open source and free tools for anyone to be able to deploy flood sensors, measure green infrastructure, or even control storm or sewer systems. Now we have developed a tool to be able to test the performance of algorithms used to coordinate these different sensing and control technologies that have been deployed throughout urban water systems.    
        
        For reference on this `Benchmarking` we refer the reader to our manuscript describing the motivation behind the `Benchmarking` effort and details on the specifics behind this corresponding repo. In general, this repo provides three components:
        
        1. A library of `scenarios` that are built to allow for systematic quantitative evaluation of stormwater control algorithms, 
        2. A stormwater hydraulic simulator named `pyswmm_lite` and forked heavily from OWA's [SWMM](https://github.com/kLabUM/Stormwater-Management-Model.git) and [pyswmm](https://github.com/kLabUM/pyswmm_lite.git), and
        3. An `environment` script that links the `pyswmm_lite` simulator to the `scenarios`, and can be edited/updated by users who might want to interface the `scenarios` with other stormwater simulator software (the `environment` script is included in `pyswmm-lite`).
        
        ## Installation 
        
        To use the scenarios we have developed with our default stormwater simulator, two installations must occur: (i) an installation of `pyswmm_lite` and (ii) the corresponding `Benchmarking` library. Additionally, both of these also require `python3` and `numpy`.
        
        Before installation, we encourage users to consider setting up their Python environments using the `virtualenv`/`venv` packages for beetter control and organization of various Python packages and libraries. Details on these packages can be viewed [here](virtualenv.pypa.io) and [here](https://docs.python.org/3/library/venv.html), with an additional tool called `virtualenvwrapper` available [here](https://virtualenvwrapper.readthedocs.io/en/latest/command_ref.html).
        
        The *Easy Installation* instructions below attempts to install both `pyswmm_lite`, the `Benchmarking` library, and the corresponding dependencies (if not yet installed) altogether, but if you run into any errors or would like demarcated installation instructions, follow the additional instructions presented in the *Alternative Installation* section.
        
        ### Easy Installation 
        
        The easiest way to install the `Benchmarking` library and its dependencies is to run the following:
        
        ```bash
        pip3 install git+https://github.com/kLabUM/Benchmarking
        ```
        
        In case you encounter trouble in installing `pyswmm_lite`, then you can use the following set of commands instead:
        
        ```bash
        git clone https://github.com/kLabUM/pyswmm_lite.git
        cd pyswmm_lite
        pip3 install .
        ```
        
        ### Alternative Installation 
        
        An alternative method to install the library is the following:
        
        1. Clone/download the zip file of the `Benchmarking` repository from https://github.com/kLabUM/Benchmarking.git
        2. Move the downloaded `Benchmarking` to your desired directory
        2. Open your python client (Anaconda or generic python) from within the `Benchmarking` folder and run `pip3 install .`
        
        Repeat the same method for `pyswmm_lite` if needed. 
        
        **NOTE** : You could also you `pip` instead of `pip3`
        
        Raise an issue, if you run into any issues installing or working with the library.
        
        **TODO** : The beta (uva's) references tides and hurricane Matthew... If we wanted to be thorough, we should change those.
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: GNU Lesser General Public License v3 (LGPLv3)
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
