Contributing#

Thank you for your interest !

Steps for contributing#

Master branch in the repository houses the code that has been completely tested and is bug free to the best of out knowledge. If you encounter a bug in the code, please raise an issue in the repository.

Instructions on raising a issue

Addressing bug fix#

  1. Fork the repository and clone it into your local machine

git clone https://github.com/kLabUM/pystorms
cd pystorms

  1. Create a new branch with your or just

For example, I am fixing a issue with the pollutant, I would create a new branch using this command.

git checkout -b abhiramm7_pollutantfix

  1. Add you fixes and push the changes into your fork of the repository.

git add <your changed files>
git push origin abhiramm7_pollutantfix

More details

  1. Once you confident on the changes, you can create a pull request on the benchmarking repository.

Raising a pull request

We can then work though the pull request.

Testing#

Please create a unit test for the code addition or any contribution you wish make to the library. This repository uses pytest for testing. Unit tests can be found in the /pystorms/tests/

Details on where to include what#

networks.py#

This file provides access to the input files available in the network. So if you were to add a new network to the library, you would do the following.

  1. Add your processed input file (refer to building scenario on how to process your input file) to the networks folder. Once you add your network to the library, it would be public. So please be carefull on what you upload

  2. Once the file has been updated to the networks folder, add the reference to the name in the networks.py

...
elif name="<your network>"
    path = os.path.join(HERE, 'networks/<your network>.inp')

  1. Add test to the test/tests_networks.py

network = benchmarking.networks.load_network("<your name>")
assert("inp"=  == network[-3:])

utilities.py#

Any general function you might need in developing scenarios would be in the utilities.py.

For example, we use append_rainfall function for adding rainfall timeseries to input files. This can be used on any input file and is not specific to a particular scenario. Hence, this function would be in utilities.py.

scenario.py#

This contains the main scenarios used for testing control algorithms. Refer to building scenarios for more details on how to build a scenario.

Anything that is specific to a network or scenario would go here.

config#

All the configuration files, like the input file, state and action space, and performance metrics are placed in this file. Create a new file for the scenario you are creating.

# Configuration file for scenario theta

# swmm inp file
swmm_input: theta
# state definitions
states:
        - !!python/tuple
          - P1
          - depthN
        - !!python/tuple
          - P2
          - depthN
# Action space
action_space:
        - "1"
        - "2"
# Performance Targets
performance_targets:
        - !!python/tuple
          - "8"
          - flow
        - !!python/tuple
          - P1
          - flooding
        - !!python/tuple
          - P2
          - flooding

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