pcraster

Environmental modeling software

https://github.com/pcraster/pcraster

Science Score: 26.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • CITATION.cff file
  • codemeta.json file
    Found codemeta.json file
  • .zenodo.json file
    Found .zenodo.json file
  • DOI references
  • Academic publication links
  • Committers with academic emails
  • Institutional organization owner
  • JOSS paper metadata
  • Scientific vocabulary similarity
    Low similarity (13.5%) to scientific vocabulary

Keywords

cpp earth-science hydrology modflow python simulation spatio-temporal-modeling

Keywords from Contributors

parallel-computing geospatial-data earth-observation
Last synced: 6 months ago · JSON representation

Repository

Environmental modeling software

Basic Info
  • Host: GitHub
  • Owner: pcraster
  • License: gpl-3.0
  • Language: C++
  • Default Branch: master
  • Homepage: http://www.pcraster.eu
  • Size: 42.2 MB
Statistics
  • Stars: 92
  • Watchers: 10
  • Forks: 27
  • Open Issues: 89
  • Releases: 0
Topics
cpp earth-science hydrology modflow python simulation spatio-temporal-modeling
Created over 10 years ago · Last pushed 6 months ago
Metadata Files
Readme Contributing License

README.md

PCRaster

Environmental modelling software

PCRaster is a collection of tools and software libraries tailored to the construction of spatio-temporal environmental models. Application domains are amongst others hydrology (rainfall-runoff, global water balance, groundwater (with Modflow)), ecology, or land use change. Two scripting languages (Python and PCRcalc) include a rich set of spatial operations for manipulating and analysing raster maps. A Python framework supports Monte Carlo simulations and data assimilation (Ensemble Kalman Filter and Particle Filter). The Aguila tool allows for the interactive visualisation of stochastic spatio-temporal data.

You can find more information about our research and development projects on our website. Information on PCRaster is given at the project website, and online documentation can be found here. For questions regarding the usage of PCRaster please use our mailing list, bugs can be reported via our issue tracker.

Installation

Conda Version Conda Platforms Conda Downloads

Packages are available for Linux, macOS and Windows via conda-forge. Install PCRaster e.g. with:

bash conda install -c conda-forge pcraster

More information on the installation of PCRaster is given in the documentation.

Build status

CI builds of our current development version:

Linux build status macOS build status Linux Conda status Windows build status

Owner

  • Name: PCRaster
  • Login: pcraster
  • Kind: organization
  • Email: info@pcraster.eu
  • Location: Utrecht, Netherlands

PCRaster R&D team, Department of Physical Geography, Utrecht University

GitHub Events

Total
  • Issues event: 22
  • Watch event: 4
  • Issue comment event: 36
  • Push event: 96
  • Pull request event: 4
  • Fork event: 1
  • Create event: 2
Last Year
  • Issues event: 22
  • Watch event: 4
  • Issue comment event: 36
  • Push event: 96
  • Pull request event: 4
  • Fork event: 1
  • Create event: 2

Committers

Last synced: 6 months ago

All Time
  • Total Commits: 1,233
  • Total Committers: 8
  • Avg Commits per committer: 154.125
  • Development Distribution Score (DDS): 0.168
Past Year
  • Commits: 213
  • Committers: 1
  • Avg Commits per committer: 213.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
Oliver Schmitz o****z@u****l 1,026
Kor de Jong k****1@u****l 187
Kor de Jong k****r@j****u 12
Kor de Jong k****g@g****u 4
Jürgen Fischer j****f@n****e 1
Katrin Leinweber k****i@p****e 1
Nicklas Larsson n****n@y****m 1
Oliver Schmitz s****9@s****l 1
Committer Domains (Top 20 + Academic)

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 414
  • Total pull requests: 6
  • Average time to close issues: 8 months
  • Average time to close pull requests: 1 day
  • Total issue authors: 38
  • Total pull request authors: 4
  • Average comments per issue: 0.5
  • Average comments per pull request: 0.33
  • Merged pull requests: 6
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 18
  • Pull requests: 2
  • Average time to close issues: 26 days
  • Average time to close pull requests: about 8 hours
  • Issue authors: 8
  • Pull request authors: 1
  • Average comments per issue: 1.5
  • Average comments per pull request: 0.0
  • Merged pull requests: 2
  • Bot issues: 0
  • Bot pull requests: 0
Top Authors
Issue Authors
  • OliverSchmitz (293)
  • kordejong (70)
  • geografin (4)
  • corentincarton (4)
  • nilason (4)
  • ghost (2)
  • openSourcerer9000 (2)
  • edwinkost (2)
  • hernanrr (2)
  • domeniconappo (2)
  • uttu90 (2)
  • BenjaminFTurner (1)
  • samtux (1)
  • esnyder-rve (1)
  • hajgato (1)
Pull Request Authors
  • OliverSchmitz (5)
  • nilason (2)
  • jef-n (1)
  • katrinleinweber (1)
Top Labels
Issue Labels
build (63) refactoring (51) bug (39) enhancement (31) modflow (21) port (13) documentation (8) windows (4) gdal (3) in progress (2) multicore (2) wontfix (1) help wanted (1)
Pull Request Labels

Packages

  • Total packages: 2
  • Total downloads: unknown
  • Total dependent packages: 2
    (may contain duplicates)
  • Total dependent repositories: 7
    (may contain duplicates)
  • Total versions: 11
proxy.golang.org: github.com/pcraster/pcraster
  • Versions: 6
  • Dependent Packages: 0
  • Dependent Repositories: 0
Rankings
Dependent packages count: 5.4%
Average: 5.6%
Dependent repos count: 5.8%
Last synced: 6 months ago
conda-forge.org: pcraster

PCRaster is a collection of tools and software libraries tailored to the construction of spatio-temporal environmental models. Application domains are amongst others hydrology (rainfall-runoff, global water balance, groundwater (with Modflow)), ecology, or land use change. PCRaster includes a rich set of spatial operations for manipulating and analysing raster maps. A Python framework supports Monte Carlo simulations and data assimilation (Ensemble Kalman Filter and Particle Filter). The Aguila tool allows for the interactive visualisation of stochastic spatio- temporal data.

  • Versions: 5
  • Dependent Packages: 2
  • Dependent Repositories: 7
Rankings
Dependent repos count: 12.8%
Dependent packages count: 19.6%
Average: 26.5%
Stargazers count: 35.6%
Forks count: 37.9%
Last synced: 6 months ago

Dependencies

environment/configuration/requirements.txt pypi
  • docopt ==0.6.2
  • numpy *
  • pycodestyle *
  • sphinx ==1.4.8
.github/workflows/linux-conda.yaml actions
  • actions/checkout v3 composite
  • conda-incubator/setup-miniconda v2 composite
.github/workflows/linux.yaml actions
  • actions/checkout v3 composite
.github/workflows/macos.yaml actions
  • actions/checkout v3 composite
.github/workflows/windows.yaml actions
  • actions/checkout v3 composite
  • conda-incubator/setup-miniconda v2 composite
source/pcraster_model_engine/csharp/PCRasterDotNetApi.csproj nuget