swmm-api 0.4.52

API for reading, manipulating and running US-EPA-SWMM-Projects

© Institute of Urban Water Management and Landscape Water Engineering, Graz University of Technology and Markus Pichler

💡 Getting started

With this package you can read INP-files, manipulate them and write new ones. You can run swmm within the python api. And you can read the RPT- and OUT-files as a pandas DataFrame for further analysis.

This package is based on the command line SWMM syntax. (see Appendix D in the SWMM User Manual 5.2)

Introduction

The swmm-api package is a powerful tool for modellers and researchers who use the Storm Water Management Model (SWMM). This software enables the manipulation and analysis of SWMM models, both in terms of the input data and the simulation results. The package is written in Python, making it an attractive option for those who use this language for data management and advanced analysis.

One of the key features of swmm-api is its ability to read and write SWMM import-files (.inp), allowing the user to manipulate the model structure and input data. The package also has the capability to run the SWMM model within the Python environment, providing users with quick access to simulation results. Furthermore, swmm-api can read both the report (.rpt) and binary output-files (.out), presenting the results as a Pandas DataFrame for easy analysis. The ability to read binary hotstart-files (.hst) is also included, which enables the acceleration of simulation time by using initial values stored in the file.

The swmm-api package is designed to be flexible and user-friendly, with an object-oriented structure that is lightweight and fast. The package is based on the SWMM command line syntax, making it easy to use for those familiar with this model. Additionally, swmm-api has the ability to interact with GIS data, making it a valuable tool for modellers working with spatial data.

Install the package

pip install swmm-api

... to install the package with all dependencies for macros use:

pip install swmm-api[macros]

... to install the package with all dependencies for GIS I/O use (for Linux or for Windows using python >= 3.10):

pip install swmm-api[gis]

... and to install the package with all dependencies for macros and GIS I/O use (for Linux or for Windows using python >= 3.10):

pip install swmm-api[full]

To add the GIS functionality on Windows, I recommend using python version >= 3.10 or with Mamba (or miniconda or Anaconda) and run mamba install geopandas to install the GIS dependencies (see GeoPandas).

Here you can see which packages are getting installed:

packages	required	macros	gis	full	docs
pandas	x	x	x	x	x
tqdm	x	x	x	x	x
networkx		x		x	x
fastparquet		x		x	x
matplotlib		x		x	x
SWMM_xsections_shape_generator		x		x	x
pyswmm		x		x	x
Shapely			x	x	x
pyproj			x	x	x
Rtree			x	x	x
geopandas			x	x	x
sphinx					x
nbsphinx					x
recommonmark					x
pydata_sphinx_theme					x

📖 Documentation

Link to the documentation of the api and some example jupyter notebook.

Here are example files for other use-cases.

Read, manipulate and write the INP-File

Read the INP-File

from swmm_api import read_inp_file, SwmmInput

inp = read_inp_file('inputfile.inp')  # type: swmm_api.SwmmInput
# or 
inp = SwmmInput.read_file('inputfile.inp')
# or
inp = SwmmInput('inputfile.inp')

Getting information

from swmm_api.input_file.section_labels import TIMESERIES

# getting a specific section of the inp-file

sec_timeseries = inp[TIMESERIES]  # type: swmm_api.input_file.helpers.InpSection
# or
sec_timeseries = inp.TIMESERIES  # type: swmm_api.input_file.helpers.InpSection

# getting a specific timeseries as pandas.Series
ts = inp[TIMESERIES]['regenseries'].pandas  # type: pandas.Series

Manipulate the INP-File

from swmm_api.input_file.section_labels import JUNCTIONS

# setting the elevation of a specific node to a new value

inp[JUNCTIONS]['J01'].elevation = 210
# or
inp.JUNCTIONS['J01'].elevation = 210
# or
inp.JUNCTIONS['J01']['elevation'] = 210

Write the manipulated INP-File

inp.write_file('new_inputfile.inp')

see examples/inp_file_reader.ipynb

see examples/inp_file_structure.ipynb

see examples/inp_file_macros.ipynb

Run SWMM

Run SWMM with a specified executable.

from swmm_api import swmm5_run
swmm5_run('new_inputfile.inp', swmm_lib_path=r'C:\path\to\runswmm.exe')

Or run SWMM with pyswmm. This would be platform independent as pyswmm is compiled for all platforms. Additionally, you gain the advantage of a progress bar.

from swmm_api import swmm5_run
swmm5_run('new_inputfile.inp', progress_size=100)

swmm5 C:\path\to\new_inputfile.inp:  77%|███████▋  | 77/100 [00:03<00:01, 22.36it/s, 2007-02-16 08:46:27]

Read the OUT-File

from swmm_api import read_out_file, SwmmOutput

out = read_out_file('new_inputfile.out')   # type: swmm_api.SwmmOut
# or
out = SwmmOutput('new_inputfile.out')

df = out.to_frame()  # type: pandas.DataFrame

# or if only a single timeseries of the results is needed
ts = out.get_part('node', 'J1', 'depth')  # type: pandas.Series

see examples/out_file_reader.ipynb

Read the RPT-File

from swmm_api import read_rpt_file, SwmmReport

rpt = read_rpt_file('new_inputfile.rpt')  # type: swmm_api.SwmmReport
# or
rpt = SwmmReport('new_inputfile.rpt')

node_flooding_summary = rpt.node_flooding_summary  # type: pandas.DataFrame

see examples/rpt_file_reader.ipynb

🗺️ GIS interactions

geopandas and its requirements (Shapely, pyproj, Rtree) must be installed! (Use python version >3.10 or conda (Anaconda|miniconda) on Windows)

from swmm_api import SwmmInput
from swmm_api.input_file.macros.gis import write_geo_package, gpkg_to_swmm, complete_vertices

inp = SwmmInput('inputfile.inp')

coords = inp.COORDINATES.geo_series  # type: geoandas.GeoSeries with points for all nodes

complete_vertices(inp)  # this will insert the start and end node points into the link vertices.
# this function is automatically called in `write_geo_package`, but is needed if the geo-series of vertices is used directly.

vertices = inp.VERTICES.geo_series  # type: geoandas.GeoSeries with lines for all links
polygons = inp.POLYGONS.geo_series  # type: geoandas.GeoSeries with polygons for all subcatchments

# create geopackage of all objects in inp file
write_geo_package(inp, gpkg_fn='geopackage.gpkg', driver='GPKG', label_sep='.', crs="EPSG:32633")

# read above written geopackage and convert it to inp-data
inp_new = gpkg_to_swmm('geopackage.gpkg', label_sep='.')
inp_new.write_file('new_inputfile.inp')

⚠️ Be Aware!

As python is case-sensitive this API is also case-sensitive, but SWMM is case-insensitive. This is important for the naming of the objects. For example, you could create a junction 'a' and 'A' with this API, but SWMM would only consider one and ignore the other.

AND

This package uses utf-8 as default encoding for the file I/O (reading and writing inp, rpt and out files.) Every function to read a file has the option to define a custom encoding (for example Windows uses this as default for german encoding='iso-8859-1').

But one can set a default encoding for the package using:

from swmm_api import CONFIG
CONFIG['encoding'] = 'iso-8859-1'

You can also set a default SWMM exe for the package using:

CONFIG['exe_path'] = r'C:\path\to\runswmm.exe'

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This documentation will be continuously extended and enhanced. If you have any question, don't hesitate to write the author and email or create an issue on GitLab or GitHub.

MORE INFORMATION COMING SOON

Cite as

Pichler, Markus. (2022). swmm_api: API for reading, manipulating and running SWMM-Projects with python (0.3). Zenodo. https://doi.org/10.5281/zenodo.7054804

Publications using or mentioning swmm-api

1. Baumann, H., Ravn, N. H., & Schaum, A. (2022). Efficient hydrodynamic modelling of urban stormwater systems for real-time applications. Modelling, 3(4), 464–480. https://doi.org/10.3390/modelling3040030
2. Farina, A., Di Nardo, A., Gargano, R., & Greco, R. (2022). Assessing the environmental impact of combined sewer overflows through a parametric study. EWaS5, 8. https://doi.org/10.3390/environsciproc2022021008
3. Schilling, J., & Tränckner, J. (2022). Generate_swmm_inp: An open-source qgis plugin to import and export model input files for swmm. Water, 14(14), 2262. https://doi.org/10.3390/w14142262
4. Wicki, T. (2022). Effekt der Einzugsgebietsmodellierung auf die Abflusssimulation im urbanen Gebiet. Master thesis. Paris Lodron-Universität Salzburg. https://unigis.at/files/Mastertheses/Full/106726.pdf
5. Zhang, Z., Tian, W., & Liao, Z. (2023). Towards coordinated and robust real-time control: A decentralized approach for combined sewer overflow and urban flooding reduction based on multi-agent reinforcement learning. Water Research, 229, 119498. https://doi.org/10.1016/j.watres.2022.119498
6. van der Werf, J. A., Kapelan Z., Langeveld, J. G. (2023). Predictive heuristic control: inferring risks from heterogeneous nowcast accuracy. Water Sci Technol 2023; 87 (4): 1009–1028. https://doi.org/10.2166/wst.2023.027
7. Farina, A., Di Nardo, A., Gargano, R., Van Der Werf, J. A., & Greco, R. (2023). A simplified approach for the hydrological simulation of urban drainage systems with SWMM. Journal of Hydrology, 623, 129757. https://doi.org/10.1016/j.jhydrol.2023.129757
8. Ryrfors Wien, C. (2023). Nature-based solution retrofit in an urban catchment for CSO reduction (Master's thesis, NTNU). https://hdl.handle.net/11250/3108487
9. Van Der Werf, J. A., Kapelan, Z., & Langeveld, J. G. (2023). Happy to control: A heuristic and predictive policy to control large urban drainage systems. Water Resources Research, 59(8), e2022WR033854. https://doi.org/10.1029/2022WR033854
10. Pritsis, S., Pons, V., Rokstad, M. M., Clemens-Meyer, F. H. L. R., Kleidorfer, M., & Tscheikner-Gratl, F. (2024). The role of hyetograph shape and designer subjectivity in the design of an urban drainage system. Water Science & Technology, 90(3), 920–934. https://doi.org/10.2166/wst.2024.261
11. Pichler, M., König, A. W., Reinstaller, S., & Muschalla, D. (2024). Fully automated simplification of urban drainage models on a city scale. Water Science & Technology. https://doi.org/10.2166/wst.2024.337
12. Zhang, Z., Tian, W., Lu, C., Liao, Z., & Yuan, Z. (2024). Graph neural network-based surrogate modelling for real-time hydraulic prediction of urban drainage networks. Water Research, 263, 122142. https://doi.org/10.1016/j.watres.2024.122142

Packages or repositories using swmm-api (on GitHub)

MarkusPic / swmm-model-simplification

• alBartig / PacketSWMM | swmmRouting | SWMMpulse
• Zhiyu014 / GNN-UDS | MARL-UDS
• QianyangWang / PyCUP
• Ahad-Hasan-Tanim10 / Bayes_opt-SWMM
• zhentaoumich / pyswmm_viz | pyswmm / pyswmm_viz

Alternative packages

• swmmr / R-language / GitHub / cran
• MatSWMM / Matlab / GitHub
• swmmNode / TypeScript / GitHub

---

• swmmio / docs / pypi / GitHub / simular to this package but more high-level approach (= slower for specific tasks)
• GisToSWMM5 / GitHub / converting gis data to swmm model (also possible with swmm_api: swmm_api.input_file.macro_snippets.gis_standard_import and swmm_api.input_file.macro_snippets.gis_export)
• swmmtoolbox / GitHub / Thanks to Tim Cera for this package! I used his package to understand the .out-files but completely rewrote the reading process in this package.
• swmmnetwork / GitHub / create graph network from swmm model (see swmm_api.input_file.macros.inp_to_graph)
• SWMMOutputAPI / GitHub / read the output file (see swmm_api.output_file.out) / (OpenWaterAnalytics)
• swmm-pandas / pypi / equal functionalities to this package, but not feature complete
• swmmout / pypi / docs / simular to swmmtoolbox and SWMMOutputAPI
• swmmtonetcdf / pypi / GitHub
• hymo / GitHub Input and Report Reader (Lucas Nguyen)
• shmm / GitHub Input Reader (Lucas Nguyen)
• swmmreport / GitHub Report Reader (Lucas Nguyen)
• swmmdoodler / GitHub

Other SWMM-related python-packages

• pyswmm / pypi / GitHub / Website / RTC, etc. / based on swmm-toolkit (OpenWaterAnalytics)
• swmm-toolkit / pypi / GitHub / by Michael Tryby (OpenWaterAnalytics)
• SWMM5 / pypi / GitHub / simular approach to swmm-toolkit (by Assela Pathirana)
• SWMM-xsections-shape-generator / pypi / tool to generate custom shapes (by me)
• SWMM_EA / pypi / usage of genetic algorithms with SWMM (by Assela Pathirana)
• OSTRICH-SWMM / GitHub / OSTRICH optimization software toolkit with SWMM