How wateraccounting/wapormooc works
WaPORMOOC is a set of self-paced course materials, not a single application: learners use notebooks to learn Python and apply geospatial methods to WaPOR data. Its principal practical workflow is obtaining WaPOR data, then learning spatial-data handling and analysis, including temporal aggregation, farm-level seasonal AETI and water productivity, and zonal-statistics plotting. The package also implements a downloader and preprocessing functions. Notebook internals were not sampled, so links between course lessons are not inferred; documented capabilities are represented at their course-directory paths.
Main entry points: 1_WaPOR_download_colab Download lessons · download_WaPOR_v3_data.py Data downloader · 2_Introduction_python Python introduction · N01_Intro_Pandas_and_Geopandas.ipynb Spatial data basics
Components 10
WaPOR downloads
- Download lessons
1_WaPOR_download_colab - Data downloader
package/download_WaPOR_v3_data.py
Python foundations
- Python introduction
2_Introduction_python
Geospatial analysis
- Spatial data basics
3_Python_for_WaPOR/N01_Intro_Pandas_and_Geopandas.ipynb - Temporal aggregation
3_Python_for_WaPOR/N02_Temporal_Aggregation.ipynb - Farm seasonal AETI
3_Python_for_WaPOR/N03_Average_Seasonal_AETI_per_farm.ipynb - Seasonal WP
3_Python_for_WaPOR/N04_Seasonal_Aggregation_and_WP.ipynb - Zonal-stat plots
3_Python_for_WaPOR/N05_Plotting_Zonal_Statistics.ipynb
Other components
- Learner
- WaPOR service
Connections 9
- Learner → Download lessons: studies
README.md - Learner → Python introduction: studies
README.md - Learner → Spatial data basics: studies
README.md - Learner → Temporal aggregation: studies
README.md - Learner → Farm seasonal AETI: studies
README.md - Learner → Seasonal WP: studies
README.md - Learner → Zonal-stat plots: studies
README.md - Download lessons → Data downloader: uses
README.md - Data downloader → WaPOR service: requests data
package/download_WaPOR_v3_data.py