Skip to main content

Water Network Tool for Resilience

Project description

[![build](https://github.com/USEPA/WNTR/workflows/build/badge.svg)](https://github.com/USEPA/WNTR/actions/workflows/build_tests.yml) [![Coverage Status](https://coveralls.io/repos/github/USEPA/WNTR/badge.svg?branch=main)](https://coveralls.io/github/USEPA/WNTR?branch=main) [![Documentation Status](https://readthedocs.org/projects/wntr/badge/?version=latest)](https://wntr.readthedocs.io/en/latest/?badge=latest)

The Water Network Tool for Resilience (WNTR) is a Python package designed to simulate and analyze resilience of water distribution networks. The software includes capability to:

  • Generate water network models

  • Modify network structure and operations

  • Add disruptive events including pipe leaks

  • Add response/repair strategies

  • Simulate pressure dependent demand and demand-driven hydraulics

  • Simulate water quality

  • Evaluate resilience

  • Visualize results

For more information, go to http://wntr.readthedocs.io

Installation

The latest release of WNTR can be installed from PyPI or Anaconda using one of the following commands in a command line or PowerShell prompt.

See [installation instructions](https://wntr.readthedocs.io/en/latest/installation.html) for more details.

Citing WNTR

To cite WNTR, use one of the following references:

  • Klise, K.A., Murray, R., Haxton, T. (2018). An overview of the Water Network Tool for Resilience (WNTR), In Proceedings of the 1st International WDSA/CCWI Joint Conference, Kingston, Ontario, Canada, July 23-25, 075, 8p.

  • Klise, K.A., Bynum, M., Moriarty, D., Murray, R. (2017). A software framework for assessing the resilience of drinking water systems to disasters with an example earthquake case study, Environmental Modelling and Software, 95, 420-431, doi: 10.1016/j.envsoft.2017.06.022

  • Klise, K.A., Hart, D.B., Moriarty, D., Bynum, M., Murray, R., Burkhardt, J., Haxton, T. (2017). Water Network Tool for Resilience (WNTR) User Manual, U.S. Environmental Protection Agency Technical Report, EPA/600/R-17/264, 47p.

License

WNTR is released under the Revised BSD license. See [LICENSE.md](https://github.com/USEPA/WNTR/blob/main/LICENSE.md) for more details.

Organization

Directories
  • wntr - Python package

  • documentation - User manual

  • examples - Examples and network files

Contact

EPA Disclaimer

The United States Environmental Protection Agency (EPA) GitHub project code is provided on an “as is” basis and the user assumes responsibility for its use. EPA has relinquished control of the information and no longer has responsibility to protect the integrity , confidentiality, or availability of the information. Any reference to specific commercial products, processes, or services by service mark, trademark, manufacturer, or otherwise, does not constitute or imply their endorsement, recommendation or favoring by EPA. The EPA seal and logo shall not be used in any manner to imply endorsement of any commercial product or activity by EPA or the United States Government.

Sandia Funding Statement

Sandia National Laboratories is a multimission laboratory managed and operated by National Technology and Engineering Solutions of Sandia, LLC., a wholly owned subsidiary of Honeywell International, Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA-0003525.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

wntr-1.0.0.tar.gz (3.2 MB view details)

Uploaded Source

Built Distributions

If you're not sure about the file name format, learn more about wheel file names.

wntr-1.0.0-cp310-cp310-win_amd64.whl (3.3 MB view details)

Uploaded CPython 3.10Windows x86-64

wntr-1.0.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.2 MB view details)

Uploaded CPython 3.10manylinux: glibc 2.17+ x86-64

wntr-1.0.0-cp310-cp310-macosx_10_15_x86_64.whl (3.3 MB view details)

Uploaded CPython 3.10macOS 10.15+ x86-64

wntr-1.0.0-cp39-cp39-win_amd64.whl (3.3 MB view details)

Uploaded CPython 3.9Windows x86-64

wntr-1.0.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.2 MB view details)

Uploaded CPython 3.9manylinux: glibc 2.17+ x86-64

wntr-1.0.0-cp39-cp39-macosx_10_15_x86_64.whl (3.3 MB view details)

Uploaded CPython 3.9macOS 10.15+ x86-64

wntr-1.0.0-cp38-cp38-win_amd64.whl (3.3 MB view details)

Uploaded CPython 3.8Windows x86-64

wntr-1.0.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.1 MB view details)

Uploaded CPython 3.8manylinux: glibc 2.17+ x86-64

wntr-1.0.0-cp38-cp38-macosx_10_15_x86_64.whl (3.3 MB view details)

Uploaded CPython 3.8macOS 10.15+ x86-64

wntr-1.0.0-cp37-cp37m-win_amd64.whl (3.3 MB view details)

Uploaded CPython 3.7mWindows x86-64

wntr-1.0.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.1 MB view details)

Uploaded CPython 3.7mmanylinux: glibc 2.17+ x86-64

wntr-1.0.0-cp37-cp37m-macosx_10_15_x86_64.whl (3.3 MB view details)

Uploaded CPython 3.7mmacOS 10.15+ x86-64

File details

Details for the file wntr-1.0.0.tar.gz.

File metadata

  • Download URL: wntr-1.0.0.tar.gz
  • Upload date:
  • Size: 3.2 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.11.2

File hashes

Hashes for wntr-1.0.0.tar.gz
Algorithm Hash digest
SHA256 6c45bf6c9e00015acae4a6d3494fb1303e1187f73345011ee7e8b16a7386f0cb
MD5 e175edc26573ea95530672dbe3b4b1b5
BLAKE2b-256 b2797b34ebb7f4990f4559446d8705f722b0c68a2fc80b89c4add6dd494f4834

See more details on using hashes here.

File details

Details for the file wntr-1.0.0-cp310-cp310-win_amd64.whl.

File metadata

  • Download URL: wntr-1.0.0-cp310-cp310-win_amd64.whl
  • Upload date:
  • Size: 3.3 MB
  • Tags: CPython 3.10, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.11.2

File hashes

Hashes for wntr-1.0.0-cp310-cp310-win_amd64.whl
Algorithm Hash digest
SHA256 c03717407d00e54aa7cc339d7c024b59830a88ab898cc2bc03dcce0cc5930a68
MD5 96f5d384d8833396609b4eb77ff18c56
BLAKE2b-256 5ed3572b0b01403b0e5ea1b1910409dee7593cba419e75ddc45c2baaa72f754a

See more details on using hashes here.

File details

Details for the file wntr-1.0.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for wntr-1.0.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 b080ea25ac024fb162cd405316ebac62e335ef9177fb60d966134995894b6b29
MD5 623ede47a4f63ef5dfa25b2f4a892a64
BLAKE2b-256 78a47660b25829e51222ee4d5d9d10935d34966f97fc0d7a54e2748b8ece0e95

See more details on using hashes here.

File details

Details for the file wntr-1.0.0-cp310-cp310-macosx_10_15_x86_64.whl.

File metadata

File hashes

Hashes for wntr-1.0.0-cp310-cp310-macosx_10_15_x86_64.whl
Algorithm Hash digest
SHA256 4b021cb17c87b250850cd64e96b38221a08816285bcbffd35739e55197e6c84d
MD5 ece52a43c442c10b02b3555b373984d9
BLAKE2b-256 6998d33c0649033e43c0b935f1be100626080ff5352b8cab576cd31ed4e8f464

See more details on using hashes here.

File details

Details for the file wntr-1.0.0-cp39-cp39-win_amd64.whl.

File metadata

  • Download URL: wntr-1.0.0-cp39-cp39-win_amd64.whl
  • Upload date:
  • Size: 3.3 MB
  • Tags: CPython 3.9, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.11.2

File hashes

Hashes for wntr-1.0.0-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 c74a984a4df90c026593a3f1e17794b0f1807775f68f3c2005635f1141044df7
MD5 a04909eeeb307a93f8181e87373f7f94
BLAKE2b-256 0304e2290423e9c69b36bc9a5210c87e351995a59ec7c46053d1df9851110de2

See more details on using hashes here.

File details

Details for the file wntr-1.0.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for wntr-1.0.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 49f61151a2bd77f31f28fae74f7020582b29e1903e30ae7e3f17dc48ac2a5bdd
MD5 23ed8cd4e47ad940070edc89a80c30ec
BLAKE2b-256 81e1ab50a654d9e525e2952854b4f2a4c246c02763b212e6b72a1f017c324891

See more details on using hashes here.

File details

Details for the file wntr-1.0.0-cp39-cp39-macosx_10_15_x86_64.whl.

File metadata

File hashes

Hashes for wntr-1.0.0-cp39-cp39-macosx_10_15_x86_64.whl
Algorithm Hash digest
SHA256 001050607283ab7fcd99ea681129d45eea3c9dfb3b2e359c73d4b346a5b6217b
MD5 4fcc4751a135a7fedf0402fd94e5df0e
BLAKE2b-256 b0714cd4b5c2c041ac55f57233485c0b6931260e955c6a694cccf90819fb1922

See more details on using hashes here.

File details

Details for the file wntr-1.0.0-cp38-cp38-win_amd64.whl.

File metadata

  • Download URL: wntr-1.0.0-cp38-cp38-win_amd64.whl
  • Upload date:
  • Size: 3.3 MB
  • Tags: CPython 3.8, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.11.2

File hashes

Hashes for wntr-1.0.0-cp38-cp38-win_amd64.whl
Algorithm Hash digest
SHA256 1fc59b4c4077ae9c65705a80d89ad9412d297c3ce648ca32caf4333b06f9f122
MD5 0f97fcbe243a57d70dd12f59f5c81c47
BLAKE2b-256 7e75453763cded6d3d8f7f04ef9c9e5e3f7399a3663066a46610b62aaf9a5b08

See more details on using hashes here.

File details

Details for the file wntr-1.0.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for wntr-1.0.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 7fe4def94205dd4ce60f87cc9ce45310a390aa4640e4cdf5bb87f2b5cf3ab29e
MD5 c6da6089db63b2c63c48e7b20c8b43ea
BLAKE2b-256 9bf7686fa9a89a879c942fad94a2600345ccda63fd7c205531c45c211b25917a

See more details on using hashes here.

File details

Details for the file wntr-1.0.0-cp38-cp38-macosx_10_15_x86_64.whl.

File metadata

File hashes

Hashes for wntr-1.0.0-cp38-cp38-macosx_10_15_x86_64.whl
Algorithm Hash digest
SHA256 f9d633c37ea241c367acad1735c919ebb0863bd82fd315b1d4e3458e64f59b76
MD5 a884a886f084af454ffac79a98544d32
BLAKE2b-256 c9e4e62820acc3d228bdbb8023ca47d4e89cbacd38bd21fccf41f370bcf6a73b

See more details on using hashes here.

File details

Details for the file wntr-1.0.0-cp37-cp37m-win_amd64.whl.

File metadata

  • Download URL: wntr-1.0.0-cp37-cp37m-win_amd64.whl
  • Upload date:
  • Size: 3.3 MB
  • Tags: CPython 3.7m, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.11.2

File hashes

Hashes for wntr-1.0.0-cp37-cp37m-win_amd64.whl
Algorithm Hash digest
SHA256 f5c5766b8d83c0e1b94313fdd8080d3a660ab35d57f7d1e40cb0c7b2795d1b54
MD5 79c744538af4b660069dadb0e48886eb
BLAKE2b-256 2cb556333c7679759b297271f716008be36417d53fa136ddc751cacff844a8be

See more details on using hashes here.

File details

Details for the file wntr-1.0.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for wntr-1.0.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 55e5f5b4e02e5b6ad2d209e14e700130e41aed3bfb1ab52f91361b2288d1c3d7
MD5 701589f831cc3c6d05b8a14206f1ab6c
BLAKE2b-256 9a26179c44adcbd4d87d7fb4e685577fc504f0b7dd4bec71f39860303dd3b9c3

See more details on using hashes here.

File details

Details for the file wntr-1.0.0-cp37-cp37m-macosx_10_15_x86_64.whl.

File metadata

File hashes

Hashes for wntr-1.0.0-cp37-cp37m-macosx_10_15_x86_64.whl
Algorithm Hash digest
SHA256 baa51303b16337509e6b7f3e8156c08e11fb05838ca3c394ec9fcece813657a3
MD5 673712f85c0dca231c8b49366508f744
BLAKE2b-256 aa1ff710e1440b6a6192352fbb9c923ac825f6a879123d8904ac21f63b030f38

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page