1. GCN10Py

GCN10py is a Python interface to the high-performance C-based gcn10 executable for generating high-resolution (10m) Global Curve Number (GCN) rasters using MPI. It enables block-based, parallel generation of CN maps from global soil and land cover datasets.

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2. Installation

2.1.Prerequisites

Ensure the following dependencies are installed and available in your system PATH:

• gcn10 (compiled C executable with MPI and GDAL)
• MPI implementation (e.g., MPICH, OpenMPI)
• GDAL runtime libraries (CLI + C bindings)

2.2. pip

Install via pip:

pip install gcn10py

Note: This package requires a working build of the gcn10 executable, compiled with MPI and GDAL support.

2.3. Building from Source

1. Compile the C binary (following the instructions here) and add it to your PATH.

   src/gcn10py/gcn10      # or gcn10.exe on Windows
   

2. Install the build tools:

   pip install hatchling
   

3. Build the wheel package:

   python -m build
   

4. Install locally for testing:

   pip install dist/gcn10py-0.1.1-py3-none-any.whl
   

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3. Usage

3.1. command-line (required -c, optional -l and -o)

• -c <path>: required. path to the config.txt used by the C backend.
• -l <path>: optional. text file containing block ids (one per line).
• -o: optional. overwrite existing outputs.

basic examples:

# minimal: run using config only
gcn10py -c config.txt

# run and overwrite any existing outputs
gcn10py -c config.txt -o

# run a specific block list
gcn10py -c config.txt -l blocks.txt

# run a specific block list and overwrite outputs
gcn10py -c config.txt -l blocks.txt -o

mpi examples:

# 10 mpi processes, config only
mpirun -n 10 gcn10py -c config.txt

# 10 mpi processes, block list + overwrite
mpirun -n 10 gcn10py -c config.txt -l blocks.txt -o

(optional) call the C binary directly:

mpirun -n 10 ./gcn10 -c config.txt -l blocks.txt -o

3.2. Python API

from gcn10py import run

# run with config only
run(["-c", "config.txt"])

# run with overwrite
run(["-c", "config.txt", "-o"])

# run with a block list
run(["-c", "config.txt", "-l", "blocks.txt"])

# run with a block list and overwrite
run(["-c", "config.txt", "-l", "blocks.txt", "-o"])

you can wrap this in your own scripts, add logging, or integrate with job schedulers.

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4. Application

The CN rasters are usually required and analysed on a watershed scale.

A ready-to-use driver and example config live in the testing/ directory:

• testing/gcn10_driver.py — orchestrates the full pipeline (discover blocks for your aoi, launch mpi, mosaic, clip)
• testing/config.yaml — example configuration (aoi, blocks, mpi settings, mosaic patterns, gdal options)
• test data under testing/ for quick validation

4.1 Run for a Vector Area of Interest (yaml-driven execution)

run it like this:

# from the project root
cd testing
python gcn10_driver.py -y config.yaml

# or make it executable
chmod a+x gcn10_driver.py
./gcn10_driver.py -y testing/config.yaml

what it does:

• reads your aoi (gpkg/shp)
• finds intersecting block ids from esa_extent_blocks.shp (or uses a provided list)
• launches gcn10 via mpi (or uses the python api) to generate per-block cn rasters
• mosaics each of the 18 combinations (hc × arc × drainage) and clips to the aoi
• writes all 18 final geotiffs to the output directory defined in the yaml

notes:

• the driver normalizes paths to absolute and runs with cwd set to the directory of config.txt so relative paths inside the config resolve consistently
• set run.cli_cmd: gcn10 or copy the gcn10 in testing/config.yaml if you prefer calling the C binary directly

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5. About the Package

5.1 Project Structure

gcn10py/
├── LICENSE
├── README.md
├── pyproject.toml
├── src/
│   └── gcn10py/
│       ├── __init__.py
│       ├── cli.py
│       └── gcn10         # compiled C executable (excluded from repo)
└── testing/
    └── gcn10_driver.py
    

5.2. Testing & Benchmarks

• functional check:
  • edit testing/config.yaml to point to your AOI and inputs
  • run testing/gcn10_driver.py -y testing/config.yaml
  • verify 18 outputs appear in io.output_dir

measured on:

OS: Slackware 15.0+, x86_64
Kernel: 6.12.39
CPU: Intel(R) Core(TM) Ultra 7 165U
CPU(s): 14
Memory: 64 GiB

• gcn10 C binary: 4 blocks, 4 MPI processes: 4 min 15 s
• gcn10py CLI (python wrapper only): same 4 blocks: 5 min 40 s
• YAML driver (same 4 blocks) + build VRTs + 18 mosaics clipped to AOI: 6 min 34 s
  (GDAL threads: GDAL_NUM_THREADS=ALL_CPUS, creation options: TILED=YES, COMPRESS=LZW, BIGTIFF=YES)

performance will vary with I/O bandwidth, MPI binding, GDAL config, OS and most importantly internet connection. For best performance, use linux native utilities.

5.3. License

Copyright (C) 2025
Abdullah Azzam (mabdazzam)

Released under the GNU General Public License v3.0.
See the LICENSE file for full terms.

5.4. Contact

Questions, bug reports, or contributions? Open an issue or pull request on GitHub.