pyflowline

pyflowline: a mesh-independent river network generator for hydrologic models - Published in JOSS (2023)

https://github.com/changliao1025/pyflowline

Science Score: 98.0%

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  • CITATION.cff file
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  • DOI references
    Found 10 DOI reference(s) in README and JOSS metadata
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    Published in Journal of Open Source Software

Keywords

earth-system-model gis graph hydrology river

Keywords from Contributors

mesh

Scientific Fields

Artificial Intelligence and Machine Learning Computer Science - 40% confidence
Last synced: 4 months ago · JSON representation ·

Repository

A mesh independent river network generator for hydrologic models

Basic Info
Statistics
  • Stars: 23
  • Watchers: 1
  • Forks: 5
  • Open Issues: 55
  • Releases: 45
Topics
earth-system-model gis graph hydrology river
Created over 4 years ago · Last pushed 5 months ago
Metadata Files
Readme Changelog Contributing License Citation Authors

README.md

PyFlowline

DOI DOI Downloads

PyFlowline: a mesh-independent river network generator for hydrologic models.

Quickstart

Please refer to the quickstart documentation for details on how to get started using the PyFlowline package.

PyFlowline is mesh independent, meaning you can apply it to both structured

  1. traditional rectangle projected mesh
  2. latitude-longitude
  3. hexagon
  4. dggs (dggrid)

and unstructured mesh systems

  1. Model for Prediction Across Scales mesh (MPAS)
  2. Triangulated Irregular Network (TIN) mesh

This package generates the mesh cell-based conceptual river networks using the following steps:

  1. Flowline simplification: PyFlowline checks the vector dataset and corrects undesired flowlines, such as braided rivers.
  2. Mesh generation: PyFlowline generates structured meshes (e.g., rectangle, hexagon) or imports user-provided unstructured meshes into the PyFlowline-compatible GEOJSON format.
  3. Topological relationship reconstruction: PyFlowline reconstructs the topological relationship using the mesh and flowline intersections.

Dependency

PyFlowline depends on the following packages

  1. numpy
  2. gdal
  3. netCDF4

PyFlowline also has three optional dependency packages

  1. cython for performance
  2. matplotlib for visualization
  3. cartopy for visulization
  4. simplekml for Google Earth KML support

Installation

Please refer to the official documentation for details on how to install the PyFlowline package.

Application

We provide several examples in the examples folder to demonstrate the model capability. We also recommend starting with the notebooks/mpas_example.ipynb notebook, after following the Quickstart and Installation instructions.

Acknowledgment

This work was supported by the Earth System Model Development program areas of the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research as part of the multi-program, collaborative Integrated Coastal Modeling (ICoM) project and the Interdisciplinary Research for Arctic Coastal Environments (InteRFACE) project.

This research was supported as part of the Next Generation Ecosystem Experiments-Tropics, funded by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research at Pacific Northwest National Laboratory. The study was also partly supported by U.S. Department of Energy Office of Science Biological and Environmental Research through the Earth and Environmental System Modeling program as part of the Energy Exascale Earth System Model (E3SM) project.

This research was supported by the Next Generation Ecosystem Experiments-Tropics project, funded by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research at Pacific Northwest National Laboratory.

License

BSD 3-Clause License

Copyright © 2022, Battelle Memorial Institute

  1. Battelle Memorial Institute (hereinafter Battelle) hereby grants permission to any person or entity lawfully obtaining a copy of this software and associated documentation files (hereinafter “the Software”) to redistribute and use the Software in source and binary forms, with or without modification. Such person or entity may use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software and may permit others to do so, subject to the following conditions:
  • Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimers.

  • Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.

  • Other than as used herein, neither the name Battelle Memorial Institute or Battelle may be used in any form whatsoever without the express written consent of Battelle.

  1. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL BATTELLE OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

References

Several publications describe the algorithms used in PyFlowline in detail. If you make use of PyFlowline in your work, please consider including a reference to the following:

  • Liao et al., (2023). pyflowline: a mesh-independent river network generator for hydrologic models. Journal of Open Source Software, 8(91), 5446, https://doi.org/10.21105/joss.05446

  • Liao. C. Cooper, M (2022) Pyflowline: a mesh-independent river network generator for hydrologic models. Zenodo. https://doi.org/10.5281/zenodo.6407298

  • Liao, C., Zhou, T., Xu, D., Cooper, M. G., Engwirda, D., Li, H.-Y., & Leung, L. R. (2023). Topological relationship-based flow direction modeling: Mesh-independent river networks representation. Journal of Advances in Modeling Earth Systems, 15, e2022MS003089. https://doi.org/10.1029/2022MS003089

Owner

  • Name: Chang Liao
  • Login: changliao1025
  • Kind: user
  • Location: Richland, WA
  • Company: Pacific Northwest National Laboratory

工欲善其事,必先利其器。

JOSS Publication

pyflowline: a mesh-independent river network generator for hydrologic models
Published
November 07, 2023
Volume 8, Issue 91, Page 5446
Authors
Chang Liao ORCID
Atmospheric, Climate, and Earth Sciences, Pacific Northwest National Laboratory, Richland, WA, USA
Matt G. Cooper ORCID
Atmospheric, Climate, and Earth Sciences, Pacific Northwest National Laboratory, Richland, WA, USA
Editor
Hauke Schulz ORCID
Tags
Python hydrologic model river networks mesh geographic information system

Citation (CITATION.cff)

cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
  - family-names: Liao
    given-names: Chang
    orcid: https://orcid.org/0000-0002-7348-8858  
  - family-names: Cooper
    given-names: Matt
    orcid: https://orcid.org/0000-0002-0165-209X
title: "Pyflowline: a mesh-independent river networks generator for hydrologic models"
version: 0.1.22
doi: 10.5281/zenodo.6407299
date-released: 2022-03-31

GitHub Events

Total
  • Issues event: 4
  • Watch event: 3
  • Delete event: 1
  • Issue comment event: 1
  • Push event: 18
  • Pull request review event: 4
  • Pull request review comment event: 10
  • Pull request event: 6
  • Fork event: 1
Last Year
  • Issues event: 4
  • Watch event: 3
  • Delete event: 1
  • Issue comment event: 1
  • Push event: 18
  • Pull request review event: 4
  • Pull request review comment event: 10
  • Pull request event: 6
  • Fork event: 1

Committers

Last synced: 5 months ago

All Time
  • Total Commits: 760
  • Total Committers: 4
  • Avg Commits per committer: 190.0
  • Development Distribution Score (DDS): 0.03
Past Year
  • Commits: 17
  • Committers: 1
  • Avg Commits per committer: 17.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
changliao1025 c****5@o****m 737
mgcooper m****r@g****m 21
dependabot[bot] 4****] 1
Hauke Schulz 4****s 1

Issues and Pull Requests

Last synced: 4 months ago

All Time
  • Total issues: 38
  • Total pull requests: 155
  • Average time to close issues: about 2 months
  • Average time to close pull requests: 5 days
  • Total issue authors: 5
  • Total pull request authors: 4
  • Average comments per issue: 0.79
  • Average comments per pull request: 0.01
  • Merged pull requests: 142
  • Bot issues: 0
  • Bot pull requests: 1
Past Year
  • Issues: 5
  • Pull requests: 5
  • Average time to close issues: N/A
  • Average time to close pull requests: 2 minutes
  • Issue authors: 1
  • Pull request authors: 1
  • Average comments per issue: 0.0
  • Average comments per pull request: 0.0
  • Merged pull requests: 4
  • Bot issues: 0
  • Bot pull requests: 0
Top Authors
Issue Authors
  • changliao1025 (31)
  • smchartrand (2)
  • donghuix (1)
  • mgcooper (1)
  • andres-patrignani (1)
Pull Request Authors
  • changliao1025 (153)
  • mgcooper (8)
  • dependabot[bot] (1)
  • observingClouds (1)
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dependencies (1)

Packages

  • Total packages: 2
  • Total downloads:
    • pypi 48 last-month
  • Total dependent packages: 2
    (may contain duplicates)
  • Total dependent repositories: 1
    (may contain duplicates)
  • Total versions: 50
  • Total maintainers: 1
pypi.org: pyflowline

A mesh-independent river network generator for hydrologic models

  • Versions: 42
  • Dependent Packages: 1
  • Dependent Repositories: 1
  • Downloads: 48 Last month
Rankings
Dependent packages count: 4.8%
Stargazers count: 15.6%
Average: 16.3%
Downloads: 16.7%
Dependent repos count: 21.5%
Forks count: 22.6%
Maintainers (1)
Last synced: 4 months ago
conda-forge.org: pyflowline
  • Versions: 8
  • Dependent Packages: 1
  • Dependent Repositories: 0
Rankings
Dependent packages count: 28.8%
Dependent repos count: 34.0%
Average: 43.4%
Stargazers count: 49.6%
Forks count: 61.1%
Last synced: 4 months ago

Dependencies

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