Project Scaffolding¶

SudaPy includes a project scaffolding command that creates a standardized folder structure for geomatics projects.

Create a new project¶

sudapy init my_survey_project

This creates the following structure:

my_survey_project/
    README.md
    data_raw/          # Original source data (do not modify)
    data_clean/        # Cleaned / processed data
    outputs/           # Analysis results, tables, statistics
    maps/              # Exported maps (PNG, HTML)
    scripts/
        process.py     # Starter processing script

Folder conventions¶

Folder	Purpose
`data_raw/`	Store original datasets here. Treat this as read-only to preserve data provenance.
`data_clean/`	Processed, cleaned, or reprojected data ready for analysis.
`outputs/`	Analysis results: CSVs, statistics, exported tables.
`maps/`	Generated map images (PNG) and interactive maps (HTML).
`scripts/`	Python processing scripts. A starter `process.py` is included.

The starter script¶

The generated scripts/process.py contains a minimal template:

"""Processing script -- edit this for your workflow."""

from sudapy.crs.registry import suggest_utm_zone
from sudapy.vector.ops import reproject

# Example: suggest CRS for Khartoum
print(suggest_utm_zone(lon=32.5, lat=15.6))

Edit this file to build your processing pipeline.

Typical workflow¶

# 1. Create project
sudapy init khartoum_analysis

cd khartoum_analysis

# 2. Copy raw data
cp ~/Downloads/parcels.gpkg data_raw/

# 3. Process
sudapy vector reproject --in data_raw/parcels.gpkg --out data_clean/parcels_utm.gpkg --to 32636
sudapy vector area --in data_clean/parcels_utm.gpkg --field area_m2 --out data_clean/parcels_area.gpkg

# 4. Generate report
sudapy report --in data_clean/parcels_area.gpkg

# 5. Export map
sudapy map quick --in data_clean/parcels_area.gpkg --out maps/parcels.html