PyForecast

A statistical modeling tool used by Reclamation water managers and reservoir operators to train and build predictive models for seasonal inflows and streamflows.
https://github.com/doi-bor/pyforecast

Keywords

forecasting hydrology machine-learning python statistical-models

Keywords from Contributors

water-resources oracle-database

Last synced: 1 day ago
JSON representation

Acceptance Criteria

Repository metadata

PyForecast is a statistical modeling tool used by Reclamation water managers and reservoir operators to train and build predictive models for seasonal inflows and streamflows. PyForecast allows users to make current water-year forecasts using models developed with the program.

README.md

PyForecast

PyForecast is a statistical modeling tool useful in predicting monthly and seasonal inflows and streamflows. The tool collects meterological and hydrologic datasets, analyzes hundreds to thousands of predictor subsets, and returns statistical regressions between predictors and streamflows. Check out the Wiki for background information and a brief walkthrough for how to use the software. Beta testing is underway, you may download an installer at this link to install PyForecast on your machine.

Requirements

These packages can be installed automatically to your default python distribution by running the 'install_dependencies.bat' script.

Use

Run the software by downloading the source code and running the program via a Python IDE with main.py, via Visual Studio with NextFlow.pyproj, or by installing the program using the latest release at this link.

Disclaimer

The software as originally published constitutes a work of the United States Government and is not subject to domestic copyright protection under 17 USC ยค 105. Subsequent contributions by members of the public, however, retain their original copyright.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.


Owner metadata


GitHub Events

Total
Last Year

Committers metadata

Last synced: 11 days ago

Total Commits: 448
Total Committers: 8
Avg Commits per committer: 56.0
Development Distribution Score (DDS): 0.46

Commits in past year: 8
Committers in past year: 2
Avg Commits per committer in past year: 4.0
Development Distribution Score (DDS) in past year: 0.125

Name Email Commits
jrocha j****a@u****v 242
dloney d****y@u****v 104
Foley k****y@u****v 79
dependabot[bot] 4****] 13
jslanini 4****i 4
Beau Uriona b****a@u****v 4
Bob Lounsbury b****y@u****v 1
Beau Uriona b****a@g****m 1

Committer domains:


Issue and Pull Request metadata

Last synced: about 2 months ago

Total issues: 0
Total pull requests: 0
Average time to close issues: N/A
Average time to close pull requests: N/A
Total issue authors: 0
Total pull request authors: 0
Average comments per issue: 0
Average comments per pull request: 0
Merged pull request: 0
Bot issues: 0
Bot pull requests: 0

Past year issues: 0
Past year pull requests: 0
Past year average time to close issues: N/A
Past year average time to close pull requests: N/A
Past year issue authors: 0
Past year pull request authors: 0
Past year average comments per issue: 0
Past year average comments per pull request: 0
Past year merged pull request: 0
Past year bot issues: 0
Past year bot pull requests: 0

More stats: https://issues.ecosyste.ms/repositories/lookup?url=https://github.com/doi-bor/pyforecast

Top Issue Authors

Top Pull Request Authors


Top Issue Labels

Top Pull Request Labels


Dependencies

resources/GUI/WebMap/leaflet_shades/package-lock.json npm
  • 276 dependencies
resources/GUI/WebMap/leaflet_shades/package.json npm
  • browserify ^14.5.0 development
  • browserify-shim ^3.8.14 development
  • watchify ^3.11.0 development
  • cached-path-relative ^1.0.2
  • leaflet-editable ^1.1.0
  • leaflet.path.drag 0.0.6
environment.yml pypi
  • PyQt5 ==5.13.0
  • PyQtChart ==5.13.0
  • PyQtWebEngine ==5.13.0
  • fuzzywuzzy ==0.18.0
  • geojson ==2.5.0
  • isodate ==0.6.0
  • matplotlib ==3.3.2
  • numpy ==1.19.2
  • pandas ==1.1.2
  • pyqt5-tools ==5.13.0.1.5
  • pyqtgraph ==0.11.0
  • statsmodels ==0.12.0
  • zeep ==3.4.0

Score: 6.030685260261263