rasteric 1.2.0

A Python geospatial library for raster preprocessing, analysis, visualization, and modeling

Rasteric

Rasteric is a Python geospatial library designed for raster preprocessing, analysis, visualization, and modeling. It simplifies common GIS workflows with easy-to-use functions built on top of Rasterio and GeoPandas.

Installation

pip install rasteric

Dependencies are installed automatically:

• rasterio
• geopandas
• shapely
• numpy
• pandas
• matplotlib
• scikit-learn
• rasterstats
• scipy (optional, for gap filling)

Requirements

• Python >= 3.8

Supported Data Formats

• Raster: GeoTIFF, TIFF
• Vector: Shapefiles, GeoJSON
• Tabular: CSV with spatial attributes (latitude/longitude)

Typical Use Cases

• Satellite image preprocessing (Sentinel-2, Landsat)
• NDVI and vegetation analysis
• Land-use / land-cover mapping
• Agricultural monitoring
• Raster-vector data extraction
• Geospatial machine learning data preparation
• Time-series crop monitoring

Quick Start

from rasteric import plot, clip, ndvi, evi, stack, extract, mosaic, calc

# Visualize a raster with RGB bands
plot('sentinel2.tif', bands=(4, 3, 2), brightness_factor=4)

# Clip raster to study area
clip('raster.tif', 'boundary.shp', 'clipped.tif')

# Compute NDVI
ndvi('sentinel2.tif', 'ndvi_output.tif', red_band=3, nir_band=4)

# Compute Enhanced Vegetation Index
evi('sentinel2.tif', 'evi_output.tif', nir_band=4, red_band=3, blue_band=2)

# Stack band files into a single multi-band raster
stack('bands_folder/', 'stacked.tif')

# Merge tiles into one raster
mosaic('tiles/', 'merged.tif')

# Band math with custom expression
calc('(B8 - B4) / (B8 + B4)', inputs={'B8': 'nir.tif', 'B4': 'red.tif'}, output='ndvi.tif')

# Extract raster values at vector points
extract('raster.tif', 'points.shp', 'extracted.csv')

Functions

Vegetation Indices

Function	Description
ndvi(raster_file, output_file, red_band, nir_band)	Normalized Difference Vegetation Index
evi(raster_file, output_file, nir_band, red_band, blue_band)	Enhanced Vegetation Index
savi(raster_file, output_file, nir_band, red_band, L)	Soil-Adjusted Vegetation Index
ndwi(raster_file, output_file, green_band, swir_band)	Normalized Difference Water Index
gndvi(raster_file, output_file, nir_band, green_band)	Green NDVI (chlorophyll sensitivity)
ndmi(raster_file, output_file, nir_band, swir_band)	Normalized Difference Moisture Index

Data Handling

Function	Description
stack(input_folder, output_file)	Stack multiple rasters into a single multi-band file
mosaic(input_folder, output_file, method)	Merge raster tiles into one continuous raster
mergecsv(path, outfile)	Merge all CSV files in a directory
clip(raster_file, shapefile, output_file)	Clip a raster using a shapefile geometry
reproject(input_raster, output_raster, target_crs)	Reproject a raster to a different CRS
resample(input_raster, output_raster, scale_factor)	Resample raster to a new resolution

Raster Calculator

Function	Description
calc(expression, inputs, output_file)	Evaluate band math expression with named inputs
bandmath(input_file, equation, output_file)	Evaluate band math using B1, B2, ... band numbers

Analysis

Function	Description
zonalstats(raster_file, vector_file, stats)	Zonal statistics for vector polygons
stats(raster_file)	Basic raster statistics (min, max, mean, std)
fillnodata(input_raster, output_file)	Fill NoData pixels via interpolation

Extraction

Function	Description
extract(input_data, shp, output_csv)	Extract raster values for vector features or CSV points
align_to_shp(input_tif, source_shp, output_tif)	Reproject raster to match a shapefile's CRS

Visualization

Function	Description
plot(file, bands, cmap, title, ax, brightness_factor)	Display raster with band selection and brightness
contour(file)	Overlay contour lines on a raster
hist(file, bin, title)	Plot histogram of raster pixel values

Conversion

Function	Description
convras(raster_file, output_shapefile)	Convert raster to vector polygons

Utilities

Function	Description
convpath(file_path)	Convert backslashes to forward slashes
bandnames(input_raster, band_names)	Update band descriptions
haversine(lon1, lat1, lon2, lat2)	Great-circle distance between two points

Sentinel-2 Support

Rasteric includes dedicated Sentinel-2 processing functions:

from rasteric import process_sen2

# Convert JP2 to TIFF, resample bands, stack, and remove clouds
process_sen2('source_folder/', 'output_folder/')

Roadmap

v1.3 (planned)

• change_detect() — Detect changes between two rasters
• timeseries() — Time-series analysis of raster folder
• cloudmask() — Cloud masking for Sentinel-2

v2.0 (planned)

• classify() — Image classification (Random Forest, K-Means)
• texture() — GLCM texture features
• sample() — ML training data preparation

Contributing

We welcome contributions and issue reports!

• Repository: github.com/tnmthai/rasteric
• Issues: github.com/tnmthai/rasteric/issues
• PyPI: pypi.org/project/rasteric

License

MIT License