# pygeoglim
Fast Python package for extracting **geology attributes** (GLiM lithology + GLHYMPS hydrogeology) from Hugging Face datasets for any watershed or region.
## 🚀 Performance
- **Individual watersheds**: 1-5 seconds ⚡
- **Regional analysis**: 10-30 seconds
- **Large areas**: 1-2 minutes
- **Direct from Hugging Face**: No local downloads needed
## 📦 Installation
### From PyPI (Recommended)
```bash
pip install pygeoglim
```
### From GitHub
```bash
pip install git+https://github.com/galib9690/pygeoglim.git
```
### For Development
```bash
git clone https://github.com/galib9690/pygeoglim.git
cd pygeoglim
pip install -e .
```
## 🔧 Quick Start
```python
from pygeoglim import load_geometry, glim_attributes, glhymps_attributes
# Example 1: Using bounding box
geom = load_geometry(bbox=[-85.5, 39.5, -85.0, 40.0])
# Get GLiM lithology attributes
glim_attrs = glim_attributes(geom)
print("GLiM attributes:", glim_attrs)
# Get GLHYMPS hydrogeology attributes
glhymps_attrs = glhymps_attributes(geom)
print("GLHYMPS attributes:", glhymps_attrs)
# Example 2: Using shapefile
geom = load_geometry(shapefile="path/to/watershed.shp")
attrs = {**glim_attributes(geom), **glhymps_attributes(geom)}
```
## 📊 Output Attributes
### GLiM Lithology
- `geol_1st_class`: Dominant lithology class
- `glim_1st_class_frac`: Fraction of dominant class
- `geol_2nd_class`: Second most common class
- `glim_2nd_class_frac`: Fraction of second class
- `carbonate_rocks_frac`: Fraction of carbonate rocks
### GLHYMPS Hydrogeology
- `geol_permeability`: Area-weighted permeability (m²)
- `geol_porosity`: Area-weighted porosity (fraction)
## 🌍 Data Sources
- **GLiM**: Global Lithological Map from Hugging Face Hub
- **GLHYMPS**: Global Hydrogeology Maps from Hugging Face Hub (Parquet format)
- **Coverage**: Continental United States (CONUS)
## 🔄 Recent Updates
- ✅ Reverted to reliable .gpkg format for GLHYMPS data
- ✅ Simplified data loading with direct mask-based filtering
- ✅ Updated column mappings for actual dataset structure (`logK_Ice_x`, `Porosity_x`)
- ✅ Streamlined error handling
## 📋 Requirements
- Python >= 3.8
- geopandas >= 0.12.0
- shapely >= 1.8.0
- numpy >= 1.20.0
- pandas >= 1.3.0
## 🐛 Troubleshooting
If you encounter issues with GLHYMPS data loading, the package includes automatic fallback mechanisms and error reporting to help diagnose problems.
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"description": "# pygeoglim\n\nFast Python package for extracting **geology attributes** (GLiM lithology + GLHYMPS hydrogeology) from Hugging Face datasets for any watershed or region.\n\n## \ud83d\ude80 Performance\n\n- **Individual watersheds**: 1-5 seconds \u26a1\n- **Regional analysis**: 10-30 seconds\n- **Large areas**: 1-2 minutes\n- **Direct from Hugging Face**: No local downloads needed\n\n## \ud83d\udce6 Installation\n\n### From PyPI (Recommended)\n```bash\npip install pygeoglim\n```\n\n### From GitHub\n```bash\npip install git+https://github.com/galib9690/pygeoglim.git\n```\n\n### For Development\n```bash\ngit clone https://github.com/galib9690/pygeoglim.git\ncd pygeoglim\npip install -e .\n```\n\n## \ud83d\udd27 Quick Start\n\n```python\nfrom pygeoglim import load_geometry, glim_attributes, glhymps_attributes\n\n# Example 1: Using bounding box\ngeom = load_geometry(bbox=[-85.5, 39.5, -85.0, 40.0])\n\n# Get GLiM lithology attributes\nglim_attrs = glim_attributes(geom)\nprint(\"GLiM attributes:\", glim_attrs)\n\n# Get GLHYMPS hydrogeology attributes \nglhymps_attrs = glhymps_attributes(geom)\nprint(\"GLHYMPS attributes:\", glhymps_attrs)\n\n# Example 2: Using shapefile\ngeom = load_geometry(shapefile=\"path/to/watershed.shp\")\nattrs = {**glim_attributes(geom), **glhymps_attributes(geom)}\n```\n\n## \ud83d\udcca Output Attributes\n\n### GLiM Lithology\n- `geol_1st_class`: Dominant lithology class\n- `glim_1st_class_frac`: Fraction of dominant class\n- `geol_2nd_class`: Second most common class\n- `glim_2nd_class_frac`: Fraction of second class\n- `carbonate_rocks_frac`: Fraction of carbonate rocks\n\n### GLHYMPS Hydrogeology\n- `geol_permeability`: Area-weighted permeability (m\u00b2)\n- `geol_porosity`: Area-weighted porosity (fraction)\n\n## \ud83c\udf0d Data Sources\n\n- **GLiM**: Global Lithological Map from Hugging Face Hub\n- **GLHYMPS**: Global Hydrogeology Maps from Hugging Face Hub (Parquet format)\n- **Coverage**: Continental United States (CONUS)\n\n## \ud83d\udd04 Recent Updates\n\n- \u2705 Reverted to reliable .gpkg format for GLHYMPS data\n- \u2705 Simplified data loading with direct mask-based filtering\n- \u2705 Updated column mappings for actual dataset structure (`logK_Ice_x`, `Porosity_x`)\n- \u2705 Streamlined error handling\n\n## \ud83d\udccb Requirements\n\n- Python >= 3.8\n- geopandas >= 0.12.0\n- shapely >= 1.8.0\n- numpy >= 1.20.0\n- pandas >= 1.3.0\n\n## \ud83d\udc1b Troubleshooting\n\nIf you encounter issues with GLHYMPS data loading, the package includes automatic fallback mechanisms and error reporting to help diagnose problems.\n",
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