# satfarm
[](https://badge.fury.io/py/satfarm)
[](https://pypi.org/project/satfarm/)
[](https://opensource.org/licenses/MIT)
A Python package for satellite image processing with a fluent interface, built on top of rasterio and xarray.
## 🌟 Features
- **Raster data I/O**: Support for GeoTIFF, PNG, and other common formats
- **Coordinate reference system**: Transformation and resampling capabilities
- **Band operations**: Spectral index calculations and band manipulations
- **Image rendering**: Visualization with customizable color maps
- **Geometric operations**: Clipping, shrinking, and spatial transformations
- **Fluent interface**: Chain operations together for readable, maintainable code
## 📦 Installation
```bash
pip install satfarm
```
### Development Installation
```bash
git clone https://github.com/yourusername/satfarm.git
cd satfarm
pip install -e ".[dev]"
```
## 🚀 Quick Start
```python
from satfarm import SatImage
import numpy as np
# Basic usage
processor = SatImage()
# Load and process satellite imagery
result = (processor
.read_tif("path/to/satellite_image.tif")
.change_pixel_dtype("float32")
.change_nodata(new_nodata=np.nan, old_nodata=0)
.reproject("EPSG:4326")
.shrink(distance=30)
.set_band_alias(["red", "green", "blue", "nir"])
.to_tif("processed_image.tif")
)
print(result)
```
## 📖 Examples
### Spectral Index Calculation
```python
from satfarm import SatImage
# Load image and set band aliases
simage = (SatImage()
.read_tif("multispectral_image.tif")
.set_band_alias(["blue", "green", "red", "nir"])
)
# Calculate vegetation indices
equations = {
"NDVI": "(B[4] - B[3]) / (B[4] + B[3])",
"NDRE": "(B[4] - B[2]) / (B[4] + B[2])",
"SAVI": "1.5 * (B[4] - B[3]) / (B[4] + B[3] + 0.5)"
}
# Process multiple indices
index_images = list(simage.calculate_index(equations))
for idx_img in index_images:
print(f"Index: {idx_img.get_band_alias()}")
stats = idx_img.calculate_band_stats()
print(f"Statistics: {stats}")
```
### Image Rendering and Visualization
```python
# Render index with custom colormap
rendered = (index_images[0] # NDVI
.render_index(vmin=0, vmax=1, cmap="viridis")
.rescale(0.5) # Reduce resolution by 50%
.to_png("ndvi_visualization.png")
)
```
### Image Merging and Analysis
```python
# Merge multiple index images
merged = SatImage().merge(index_images)
# Get image boundary
boundary = merged.get_boundary()
# Calculate comprehensive statistics
stats = merged.calculate_band_stats()
```
## 🏗️ API Reference
### Core Class
#### `SatImage`
The main class providing a fluent interface for satellite image processing.
**Key Methods:**
- **I/O Operations**
- `read_tif(path)`: Load GeoTIFF files
- `to_tif(path)`: Save as GeoTIFF
- `to_png(path)`: Save as PNG
- **Data Manipulation**
- `change_pixel_dtype(dtype)`: Convert pixel data type
- `change_nodata(new_nodata, old_nodata)`: Update nodata values
- `reproject(crs)`: Reproject to different coordinate system
- `rescale(factor)`: Resize image by scaling factor
- `shrink(distance)`: Reduce image extent by specified distance
- **Band Operations**
- `set_band_alias(aliases)`: Assign names to bands
- `extract_band(bands)`: Select specific bands
- `apply_scale_factor(factors)`: Apply scaling factors to bands
- `calculate_index(equations)`: Compute spectral indices
- **Analysis**
- `calculate_band_stats()`: Compute statistical metrics
- `get_boundary()`: Extract image boundary geometry
- **Visualization**
- `render_index(vmin, vmax, cmap)`: Render with color mapping
- **Utilities**
- `copy()`: Create deep copy
- `merge(images)`: Combine multiple images
- `is_empty()`: Check if image data exists
## 🔧 Dependencies
- **numpy** (>=1.21.0): Numerical computing
- **rioxarray** (>=0.13.0): Rasterio integration with xarray
- **geopandas** (>=0.12.0): Geospatial data handling
- **Pillow** (>=9.0.0): Image processing
- **matplotlib** (>=3.5.0): Plotting and visualization
- **typeguard** (>=4.0.0): Runtime type checking
## 🤝 Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
1. Fork the project
2. Create your feature branch (`git checkout -b feature/AmazingFeature`)
3. Commit your changes (`git commit -m 'Add some AmazingFeature'`)
4. Push to the branch (`git push origin feature/AmazingFeature`)
5. Open a Pull Request
## 📄 License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## 🙏 Acknowledgments
- Built on top of the excellent [rasterio](https://rasterio.readthedocs.io/) and [xarray](https://xarray.pydata.org/) libraries
- Inspired by modern geospatial processing workflows
- Thanks to the open source geospatial community
## 📞 Support
If you encounter any issues or have questions, please [open an issue](https://github.com/yourusername/satfarm/issues) on GitHub.
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"description": "# satfarm\n\n[](https://badge.fury.io/py/satfarm)\n[](https://pypi.org/project/satfarm/)\n[](https://opensource.org/licenses/MIT)\n\nA Python package for satellite image processing with a fluent interface, built on top of rasterio and xarray.\n\n## \ud83c\udf1f Features\n\n- **Raster data I/O**: Support for GeoTIFF, PNG, and other common formats\n- **Coordinate reference system**: Transformation and resampling capabilities \n- **Band operations**: Spectral index calculations and band manipulations\n- **Image rendering**: Visualization with customizable color maps\n- **Geometric operations**: Clipping, shrinking, and spatial transformations\n- **Fluent interface**: Chain operations together for readable, maintainable code\n\n## \ud83d\udce6 Installation\n\n```bash\npip install satfarm\n```\n\n### Development Installation\n\n```bash\ngit clone https://github.com/yourusername/satfarm.git\ncd satfarm\npip install -e \".[dev]\"\n```\n\n## \ud83d\ude80 Quick Start\n\n```python\nfrom satfarm import SatImage\nimport numpy as np\n\n# Basic usage\nprocessor = SatImage()\n\n# Load and process satellite imagery\nresult = (processor\n .read_tif(\"path/to/satellite_image.tif\")\n .change_pixel_dtype(\"float32\")\n .change_nodata(new_nodata=np.nan, old_nodata=0)\n .reproject(\"EPSG:4326\")\n .shrink(distance=30)\n .set_band_alias([\"red\", \"green\", \"blue\", \"nir\"])\n .to_tif(\"processed_image.tif\")\n)\n\nprint(result)\n```\n\n## \ud83d\udcd6 Examples\n\n### Spectral Index Calculation\n\n```python\nfrom satfarm import SatImage\n\n# Load image and set band aliases\nsimage = (SatImage()\n .read_tif(\"multispectral_image.tif\")\n .set_band_alias([\"blue\", \"green\", \"red\", \"nir\"])\n)\n\n# Calculate vegetation indices\nequations = {\n \"NDVI\": \"(B[4] - B[3]) / (B[4] + B[3])\",\n \"NDRE\": \"(B[4] - B[2]) / (B[4] + B[2])\",\n \"SAVI\": \"1.5 * (B[4] - B[3]) / (B[4] + B[3] + 0.5)\"\n}\n\n# Process multiple indices\nindex_images = list(simage.calculate_index(equations))\n\nfor idx_img in index_images:\n print(f\"Index: {idx_img.get_band_alias()}\")\n stats = idx_img.calculate_band_stats()\n print(f\"Statistics: {stats}\")\n```\n\n### Image Rendering and Visualization\n\n```python\n# Render index with custom colormap\nrendered = (index_images[0] # NDVI\n .render_index(vmin=0, vmax=1, cmap=\"viridis\")\n .rescale(0.5) # Reduce resolution by 50%\n .to_png(\"ndvi_visualization.png\")\n)\n```\n\n### Image Merging and Analysis\n\n```python\n# Merge multiple index images\nmerged = SatImage().merge(index_images)\n\n# Get image boundary\nboundary = merged.get_boundary()\n\n# Calculate comprehensive statistics\nstats = merged.calculate_band_stats()\n```\n\n## \ud83c\udfd7\ufe0f API Reference\n\n### Core Class\n\n#### `SatImage`\n\nThe main class providing a fluent interface for satellite image processing.\n\n**Key Methods:**\n\n- **I/O Operations**\n - `read_tif(path)`: Load GeoTIFF files\n - `to_tif(path)`: Save as GeoTIFF\n - `to_png(path)`: Save as PNG\n\n- **Data Manipulation**\n - `change_pixel_dtype(dtype)`: Convert pixel data type\n - `change_nodata(new_nodata, old_nodata)`: Update nodata values\n - `reproject(crs)`: Reproject to different coordinate system\n - `rescale(factor)`: Resize image by scaling factor\n - `shrink(distance)`: Reduce image extent by specified distance\n\n- **Band Operations**\n - `set_band_alias(aliases)`: Assign names to bands\n - `extract_band(bands)`: Select specific bands\n - `apply_scale_factor(factors)`: Apply scaling factors to bands\n - `calculate_index(equations)`: Compute spectral indices\n\n- **Analysis**\n - `calculate_band_stats()`: Compute statistical metrics\n - `get_boundary()`: Extract image boundary geometry\n\n- **Visualization**\n - `render_index(vmin, vmax, cmap)`: Render with color mapping\n\n- **Utilities**\n - `copy()`: Create deep copy\n - `merge(images)`: Combine multiple images\n - `is_empty()`: Check if image data exists\n\n## \ud83d\udd27 Dependencies\n\n- **numpy** (>=1.21.0): Numerical computing\n- **rioxarray** (>=0.13.0): Rasterio integration with xarray\n- **geopandas** (>=0.12.0): Geospatial data handling\n- **Pillow** (>=9.0.0): Image processing\n- **matplotlib** (>=3.5.0): Plotting and visualization\n- **typeguard** (>=4.0.0): Runtime type checking\n\n## \ud83e\udd1d Contributing\n\nContributions are welcome! 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