| Name | epaswmm JSON |
| Version |
5.3.0.dev8
JSON |
| download |
| home_page | None |
| Summary | A python package for EPA SWMM5 preprocessing, solver, and post-processing. |
| upload_time | 2025-04-07 13:31:07 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.8 |
| license | Creative Commons Legal Code
CC0 1.0 Universal
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LEGAL SERVICES. DISTRIBUTION OF THIS DOCUMENT DOES NOT CREATE AN
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| keywords |
swmm
stormwater
modeling
water
hydrology
hydraulics
wastewater
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
EPA ORD Stormwater Management Model (SWMM)
==========================================
Stormwater Management Model (SWMM) computational engine and output post-processing codebase
## Build Status
[](https://github.com/USEPA/Stormwater-Management-Model/actions/workflows/unit_testing.yml)
[](https://github.com/USEPA/Stormwater-Management-Model/actions/workflows/regression_testing.yml)
[](https://github.com/USEPA/Stormwater-Management-Model/actions/workflows/build_docs.yml)
[](https://github.com/USEPA/Stormwater-Management-Model/actions/workflows/deploy.yml)
[](https://github.com/USEPA/Stormwater-Management-Model/actions/workflows/build-and-test.yml)
[](https://github.com/USEPA/Stormwater-Management-Model/issues)
## Python Binding
[](https://pypi.org/project/epaswmm)
[](https://pypi.org/project/epaswmm)
[](https://pypi.org/project/epaswmm)
[](https://pepy.tech/project/epaswmm)
[](https://pepy.tech/project/epaswmm)
[](https://pepy.tech/project/epaswmm)
## Introduction
This is the official SWMM source code repository maintained by US EPA Office of Research and Development, Center For Environmental Solutions & Emergency Response, Water Infrastructure Division located in Cincinnati, Ohio.
SWMM is a dynamic hydrology-hydraulic water quality simulation model. It is used for single event or long-term (continuous) simulation of runoff quantity and quality from primarily urban areas. SWMM source code is written in the C Programming Language and released in the Public Domain.
## Build Instructions
The 'src' folder of this repository contains the C source code for
version of Storm Water Management Model's computational
engine. Consult the included 'Roadmap.txt' file for an overview of
the various code modules. The code can be compiled into both a shared
object library and a command line executable. Under Windows, the
library file (swmm5.dll) is used to power SWMM's graphical user
interface.
Also included is a python interface for the SWMM computational engine and output
post-processing application programming interfaces located in the python folder.
The 'CMakeLists.txt' file is a script used by CMake (https://cmake.org/)
to build the SWMM binaries. CMake is a cross-platform build tool
that generates platform native build systems for many compilers. To
check if the required version is installed on your system, enter from
a console window and check that the version is 3.5 or higher.
```bash
cmake --version
```
To build the SWMM engine library and its command line executable
using CMake and the Microsoft Visual Studio C compiler on Windows:
1. Open a console window and navigate to the directory where this
Readme file resides (which should have 'src' as a sub-directory
underneath it).
2. Use the following command to create the directory for storing the built binaries:
```bash
mkdir build
```
3. Then the following CMake commands to build the binaries:
``` bash
cmake -G <compiler> -B build
cmake --build ./build --config Release
```
where `<compiler>` is the name of the compiler being used
in double quotes (e.g., "Visual Studio 17 2022" for windows, "Ninja" for linux, or "Xcode" for macos). The resulting engine shared libraries (i.e., swmm5.dll), command line executable (i.e., runswmm.exe), and output processing libraries (i.e., swmm-output.dll)
will appear in the build\Release directory.
### Python Bindings (Experimental)
Experimental python bindings for the SWMM API are being developed to support regression and benchmark testing as well as for other applications. _**These bindings are still under development and testing and has yet to be cleared through US EPA ORD's official quality assurance review process**_. The exprimental python bindings can be built and installed locally using the following command.
```bash
cd python
python -m pip install -r requirements.txt
python -m pip install .
```
Users may also build python wheels for installation or distribution. Once the python bindings
have been validated and cleared through EPA's quality assuracnce clearance process, they will be available for installation via package indexing repositories such as pypi.
Example usage of python bindings can be found below. More extensive documentation will be provided once cleared.
```python
from epaswmm import solver
from epaswmm.solver import Solver
from epaswmm.output import Output
# Alternative 1 to run SWMM
with Solver(inp_file="input_file.inp") as swmm_solver:
# Open swmm file and starts the simulation
swmm_solver.start()
# Set initialization parameters e.g., time step stride, start date, end date etc.
swmm_solver.time_stride = 600
for elapsed_time, current_datetime in swmm_solver:
# Get and set attributes per timestep
print(current_datetime)
swmm_solver.set_value(
object_type=solver.SWMMObjects.RAIN_GAGE,
property_type=solver.SWMMRainGageProperties.GAGE_RAINFALL,
index="RG1",
value=3.6
)
# Alternative 2 to run SWMM
swmm_solver = Solver(inp_file="input_file.inp")
swmm_solver.initialize()
for elapsed_time, current_datetime in swmm_solver:
# Get and set attributes per timestep
print(current_datetime)
swmm_solver.finalize()
# or
# swmm_solver.end()
# swmm_solver.report()
# swmm_solver.close()
# Alternative 3 to run SWMM
swmm_solver = Solver(inp_file="input_file.inp")
swmm_solver.execute()
# To read output file
swmm_output = Output(output_file='output_file.out')
# Dict[datetime, float]
link_timeseries = swmm_output.get_link_timeseries(
element_index="C1",
attribute=output.LinkAttribute.FLOW_RATE,
)
```
## Unit and Regression Testing
Unit tests and regression tests have been developed for both the natively compiled SWMM computational engine and output toolkit as well as their respective python bindings. Unit tests for the natively compiled toolkits use the Boost 1.67.0 library and can be compiled by adding DBUILD_TESTS=ON flag during the cmake build phase as shown below:
```bash
ctest --test-dir . -DBUILD_TESTS=ON --config Debug --output-on-failure
```
Unit testing on the python bindings may be executed using the following command after installation.
```bash
cd python\tests
pytest .
```
Regression tests are executed using the python bindings using the pytest and pytest-regressions extension using the following commands.
```bash
cd ci
pytest --data-dir <path-to-regression-testing-files> --atol <absolute-tolerance> --rtol <relative-tolerance> --benchmark-compare --benchmark-json=PATH
```
## Find Out More
The source code distributed here is identical to the code found at the official [SWMM website](https://www.epa.gov/water-research/storm-water-management-model-swmm).
The SWMM website also hosts the official manuals and installation binaries for the SWMM software.
A live web version of the SWMM documentation of the API and user manuals can be found on the [SWMM GitHub Pages website](https://usepa.github.io/Stormwater-Management-Model). Note that this is an experimental version that is still under development and has yet to go through EPA'S official quality assurance review process.
## 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.
Raw data
{
"_id": null,
"home_page": null,
"name": "epaswmm",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.8",
"maintainer_email": "Caleb Buahin <buahin.caleb@epa.gov>",
"keywords": "SWMM, stormwater, modeling, water, hydrology, hydraulics, wastewater",
"author": null,
"author_email": "Caleb Buahin <buahin.caleb@epa.gov>",
"download_url": "https://files.pythonhosted.org/packages/61/89/fd54ae4bb3d726c5cc0bf1983242dba853c2942e38cf71963e649299163b/epaswmm-5.3.0.dev8.tar.gz",
"platform": null,
"description": "EPA ORD Stormwater Management Model (SWMM)\r\n==========================================\r\n\r\nStormwater Management Model (SWMM) computational engine and output post-processing codebase\r\n\r\n## Build Status\r\n[](https://github.com/USEPA/Stormwater-Management-Model/actions/workflows/unit_testing.yml)\r\n[](https://github.com/USEPA/Stormwater-Management-Model/actions/workflows/regression_testing.yml)\r\n[](https://github.com/USEPA/Stormwater-Management-Model/actions/workflows/build_docs.yml)\r\n[](https://github.com/USEPA/Stormwater-Management-Model/actions/workflows/deploy.yml)\r\n[](https://github.com/USEPA/Stormwater-Management-Model/actions/workflows/build-and-test.yml)\r\n[](https://github.com/USEPA/Stormwater-Management-Model/issues)\r\n\r\n## Python Binding\r\n[](https://pypi.org/project/epaswmm)\r\n[](https://pypi.org/project/epaswmm)\r\n[](https://pypi.org/project/epaswmm)\r\n[](https://pepy.tech/project/epaswmm)\r\n[](https://pepy.tech/project/epaswmm)\r\n[](https://pepy.tech/project/epaswmm)\r\n\r\n## Introduction\r\nThis is the official SWMM source code repository maintained by US EPA Office of Research and Development, Center For Environmental Solutions & Emergency Response, Water Infrastructure Division located in Cincinnati, Ohio.\r\n\r\nSWMM is a dynamic hydrology-hydraulic water quality simulation model. It is used for single event or long-term (continuous) simulation of runoff quantity and quality from primarily urban areas. SWMM source code is written in the C Programming Language and released in the Public Domain.\r\n\r\n## Build Instructions\r\n\r\nThe 'src' folder of this repository contains the C source code for\r\nversion of Storm Water Management Model's computational\r\nengine. Consult the included 'Roadmap.txt' file for an overview of\r\nthe various code modules. The code can be compiled into both a shared\r\nobject library and a command line executable. Under Windows, the \r\nlibrary file (swmm5.dll) is used to power SWMM's graphical user\r\ninterface.\r\n\r\nAlso included is a python interface for the SWMM computational engine and output \r\npost-processing application programming interfaces located in the python folder.\r\n\r\nThe 'CMakeLists.txt' file is a script used by CMake (https://cmake.org/)\r\nto build the SWMM binaries. CMake is a cross-platform build tool\r\nthat generates platform native build systems for many compilers. To\r\ncheck if the required version is installed on your system, enter from \r\na console window and check that the version is 3.5 or higher.\r\n\r\n```bash\r\ncmake --version\r\n```\r\n\r\nTo build the SWMM engine library and its command line executable\r\nusing CMake and the Microsoft Visual Studio C compiler on Windows:\r\n\r\n1. Open a console window and navigate to the directory where this\r\n Readme file resides (which should have 'src' as a sub-directory\r\n underneath it).\r\n\r\n2. Use the following command to create the directory for storing the built binaries:\r\n\r\n```bash\r\nmkdir build\r\n```\r\n\r\n3. Then the following CMake commands to build the binaries:\r\n\r\n``` bash\r\ncmake -G <compiler> -B build\r\ncmake --build ./build --config Release\r\n```\r\n\r\nwhere `<compiler>` is the name of the compiler being used\r\nin double quotes (e.g., \"Visual Studio 17 2022\" for windows, \"Ninja\" for linux, or \"Xcode\" for macos). The resulting engine shared libraries (i.e., swmm5.dll), command line executable (i.e., runswmm.exe), and output processing libraries (i.e., swmm-output.dll)\r\nwill appear in the build\\Release directory.\r\n\r\n### Python Bindings (Experimental)\r\n\r\nExperimental python bindings for the SWMM API are being developed to support regression and benchmark testing as well as for other applications. _**These bindings are still under development and testing and has yet to be cleared through US EPA ORD's official quality assurance review process**_. The exprimental python bindings can be built and installed locally using the following command.\r\n\r\n```bash\r\ncd python\r\npython -m pip install -r requirements.txt\r\npython -m pip install . \r\n```\r\nUsers may also build python wheels for installation or distribution. Once the python bindings\r\nhave been validated and cleared through EPA's quality assuracnce clearance process, they will be available for installation via package indexing repositories such as pypi.\r\n\r\nExample usage of python bindings can be found below. More extensive documentation will be provided once cleared.\r\n\r\n```python\r\n\r\nfrom epaswmm import solver\r\nfrom epaswmm.solver import Solver \r\nfrom epaswmm.output import Output\r\n\r\n# Alternative 1 to run SWMM\r\n\r\nwith Solver(inp_file=\"input_file.inp\") as swmm_solver:\r\n \r\n # Open swmm file and starts the simulation\r\n swmm_solver.start()\r\n\r\n # Set initialization parameters e.g., time step stride, start date, end date etc.\r\n swmm_solver.time_stride = 600 \r\n\r\n for elapsed_time, current_datetime in swmm_solver:\r\n\r\n # Get and set attributes per timestep\r\n print(current_datetime)\r\n\r\n swmm_solver.set_value(\r\n object_type=solver.SWMMObjects.RAIN_GAGE,\r\n property_type=solver.SWMMRainGageProperties.GAGE_RAINFALL,\r\n index=\"RG1\",\r\n value=3.6\r\n )\r\n\r\n# Alternative 2 to run SWMM\r\nswmm_solver = Solver(inp_file=\"input_file.inp\")\r\nswmm_solver.initialize()\r\n\r\nfor elapsed_time, current_datetime in swmm_solver:\r\n # Get and set attributes per timestep\r\n print(current_datetime)\r\n\r\nswmm_solver.finalize()\r\n# or\r\n# swmm_solver.end()\r\n# swmm_solver.report()\r\n# swmm_solver.close()\r\n\r\n# Alternative 3 to run SWMM\r\nswmm_solver = Solver(inp_file=\"input_file.inp\")\r\nswmm_solver.execute()\r\n\r\n# To read output file\r\n\r\nswmm_output = Output(output_file='output_file.out')\r\n\r\n# Dict[datetime, float]\r\nlink_timeseries = swmm_output.get_link_timeseries(\r\n element_index=\"C1\",\r\n attribute=output.LinkAttribute.FLOW_RATE,\r\n)\r\n\r\n```\r\n\r\n## Unit and Regression Testing\r\n\r\nUnit tests and regression tests have been developed for both the natively compiled SWMM computational engine and output toolkit as well as their respective python bindings. Unit tests for the natively compiled toolkits use the Boost 1.67.0 library and can be compiled by adding DBUILD_TESTS=ON flag during the cmake build phase as shown below:\r\n\r\n```bash\r\nctest --test-dir . -DBUILD_TESTS=ON --config Debug --output-on-failure\r\n```\r\n\r\nUnit testing on the python bindings may be executed using the following command after installation.\r\n\r\n```bash\r\ncd python\\tests\r\npytest .\r\n```\r\n\r\nRegression tests are executed using the python bindings using the pytest and pytest-regressions extension using the following commands.\r\n\r\n```bash\r\ncd ci\r\npytest --data-dir <path-to-regression-testing-files> --atol <absolute-tolerance> --rtol <relative-tolerance> --benchmark-compare --benchmark-json=PATH\r\n```\r\n\r\n## Find Out More\r\nThe source code distributed here is identical to the code found at the official [SWMM website](https://www.epa.gov/water-research/storm-water-management-model-swmm).\r\nThe SWMM website also hosts the official manuals and installation binaries for the SWMM software. \r\n\r\nA live web version of the SWMM documentation of the API and user manuals can be found on the [SWMM GitHub Pages website](https://usepa.github.io/Stormwater-Management-Model). Note that this is an experimental version that is still under development and has yet to go through EPA'S official quality assurance review process.\r\n\r\n## Disclaimer \r\nThe 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.\r\n\r\n",
"bugtrack_url": null,
"license": "Creative Commons Legal Code\r\n \r\n CC0 1.0 Universal\r\n \r\n CREATIVE COMMONS CORPORATION IS NOT A LAW FIRM AND DOES NOT PROVIDE\r\n LEGAL SERVICES. DISTRIBUTION OF THIS DOCUMENT DOES NOT CREATE AN\r\n ATTORNEY-CLIENT RELATIONSHIP. CREATIVE COMMONS PROVIDES THIS\r\n INFORMATION ON AN \"AS-IS\" BASIS. CREATIVE COMMONS MAKES NO WARRANTIES\r\n REGARDING THE USE OF THIS DOCUMENT OR THE INFORMATION OR WORKS\r\n PROVIDED HEREUNDER, AND DISCLAIMS LIABILITY FOR DAMAGES RESULTING FROM\r\n THE USE OF THIS DOCUMENT OR THE INFORMATION OR WORKS PROVIDED\r\n HEREUNDER.\r\n \r\n Statement of Purpose\r\n \r\n The laws of most jurisdictions throughout the world automatically confer\r\n exclusive Copyright and Related Rights (defined below) upon the creator\r\n and subsequent owner(s) (each and all, an \"owner\") of an original work of\r\n authorship and/or a database (each, a \"Work\").\r\n \r\n Certain owners wish to permanently relinquish those rights to a Work for\r\n the purpose of contributing to a commons of creative, cultural and\r\n scientific works (\"Commons\") that the public can reliably and without fear\r\n of later claims of infringement build upon, modify, incorporate in other\r\n works, reuse and redistribute as freely as possible in any form whatsoever\r\n and for any purposes, including without limitation commercial purposes.\r\n These owners may contribute to the Commons to promote the ideal of a free\r\n culture and the further production of creative, cultural and scientific\r\n works, or to gain reputation or greater distribution for their Work in\r\n part through the use and efforts of others.\r\n \r\n For these and/or other purposes and motivations, and without any\r\n expectation of additional consideration or compensation, the person\r\n associating CC0 with a Work (the \"Affirmer\"), to the extent that he or she\r\n is an owner of Copyright and Related Rights in the Work, voluntarily\r\n elects to apply CC0 to the Work and publicly distribute the Work under its\r\n terms, with knowledge of his or her Copyright and Related Rights in the\r\n Work and the meaning and intended legal effect of CC0 on those rights.\r\n \r\n 1. Copyright and Related Rights. A Work made available under CC0 may be\r\n protected by copyright and related or neighboring rights (\"Copyright and\r\n Related Rights\"). Copyright and Related Rights include, but are not\r\n limited to, the following:\r\n \r\n i. the right to reproduce, adapt, distribute, perform, display,\r\n communicate, and translate a Work;\r\n ii. moral rights retained by the original author(s) and/or performer(s);\r\n iii. publicity and privacy rights pertaining to a person's image or\r\n likeness depicted in a Work;\r\n iv. rights protecting against unfair competition in regards to a Work,\r\n subject to the limitations in paragraph 4(a), below;\r\n v. rights protecting the extraction, dissemination, use and reuse of data\r\n in a Work;\r\n vi. database rights (such as those arising under Directive 96/9/EC of the\r\n European Parliament and of the Council of 11 March 1996 on the legal\r\n protection of databases, and under any national implementation\r\n thereof, including any amended or successor version of such\r\n directive); and\r\n vii. other similar, equivalent or corresponding rights throughout the\r\n world based on applicable law or treaty, and any national\r\n implementations thereof.\r\n \r\n 2. Waiver. To the greatest extent permitted by, but not in contravention\r\n of, applicable law, Affirmer hereby overtly, fully, permanently,\r\n irrevocably and unconditionally waives, abandons, and surrenders all of\r\n Affirmer's Copyright and Related Rights and associated claims and causes\r\n of action, whether now known or unknown (including existing as well as\r\n future claims and causes of action), in the Work (i) in all territories\r\n worldwide, (ii) for the maximum duration provided by applicable law or\r\n treaty (including future time extensions), (iii) in any current or future\r\n medium and for any number of copies, and (iv) for any purpose whatsoever,\r\n including without limitation commercial, advertising or promotional\r\n purposes (the \"Waiver\"). Affirmer makes the Waiver for the benefit of each\r\n member of the public at large and to the detriment of Affirmer's heirs and\r\n successors, fully intending that such Waiver shall not be subject to\r\n revocation, rescission, cancellation, termination, or any other legal or\r\n equitable action to disrupt the quiet enjoyment of the Work by the public\r\n as contemplated by Affirmer's express Statement of Purpose.\r\n \r\n 3. Public License Fallback. Should any part of the Waiver for any reason\r\n be judged legally invalid or ineffective under applicable law, then the\r\n Waiver shall be preserved to the maximum extent permitted taking into\r\n account Affirmer's express Statement of Purpose. 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