rasteric 1.0.1

A Comprehensive Geospatial Library

Rasteric: A Comprehensive Geospatial Library

Rasteric is a comprehensive library for geospatial data preprocessing, analysis, and modeling. It provides a variety of functions for transforming, analyzing, and manipulating geospatial data, including raster and vector data.

Supported Data Formats

Rasteric supports multiple geospatial data formats, including:

• Raster data: GeoTIFF, TIFF
• Vector data: Shapefiles, GeoJSON
• Tabular data: CSV files with spatial attributes (e.g., latitude and longitude)

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Key Features and Functions

Data Handling

• convpath(): Standardizes file paths for cross-platform compatibility.
• stack(): Combines multiple raster files into a single multi-band raster.
• mergecsv(): Combines multiple CSV files into a single CSV.

Data Analysis

• zonalstats(): Computes zonal statistics for vector polygons based on raster values.
• stats(): Provides basic raster statistics (min, max, mean, std).
• ndvi(): Computes the Normalized Difference Vegetation Index.

Data Manipulation

• clip(): Clips a raster using a vector file.
• reproject(): Reprojects rasters to a specified coordinate reference system.
• resample(): Changes raster resolution using a specified scaling factor.

Data Visualization

• plot(): Displays a raster with customizable brightness and band selection.
• contour(): Overlays contour lines on a raster image.
• hist(): Plots a histogram of raster values.

Raster to Vector Conversion

• convras(): Converts a raster file to vector polygons.

Extraction and Integration

• extract(): Extracts raster values for vector features or CSV spatial points.
• align_to_shp(): Aligns a raster's CRS to match a shapefile's CRS.

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Example Usage

Visualizing a Raster

from rasteric import raster
from matplotlib import pyplot

# Plot raster bands with brightness adjustment
raster.plot('example.tif', bands=(3, 2, 1), title="Example Raster")

Stacking Multiple Rasters

from rasteric import raster

# Stack rasters into a single file
stacked_file = raster.stack("data_folder", "stacked_output.tif")

Extracting Data

from rasteric import raster

# Extract raster values for vector features
output_csv = raster.extract("example.tif", "vector.shp")

Computing NDVI

from rasteric import raster

# Calculate NDVI and save the result
ndvi_output = raster.ndvi("example.tif", "ndvi_output.tif", red_band=3, nir_band=4)

Function Descriptions

1. convpath(file_path) Description: Converts a file path to a cross-platform compatible format.

2. stack(input_folder, output_file) Description: Stacks multiple rasters into a single file with multi-band output.

3. mergecsv(path, outfile='combined.csv') Description: Merges all CSV files in a directory into one.

4. clip(raster_file, shapefile, output_file) Description: Clips a raster using a shapefile's geometry.

5. zonalstats(raster_file, vector_file, stats=['mean', 'max']) Description: Calculates statistics for vector polygons over raster values.

6. ndvi(raster_file, output_file, red_band, nir_band) Description: Computes the Normalized Difference Vegetation Index.

7. resample(input_raster, output_raster, scale_factor, resampling_method) Description: Resamples a raster to a new resolution.

8. extract(input_data, shp, output_csv, all_touched=False) Description: Extracts raster values for vector features or CSV points.

9. plot(file, bands, cmap, title, ax, brightness_factor) Description: Plots raster data with band selection and brightness control.

10. align_to_shp(input_tif, source_shp, output_tif) Description: Reprojects a raster to align its CRS with a shapefile.

Contributions and Issues

We welcome contributions and issue reports! Please submit pull requests or report bugs via the GitHub repository.