rasteric 1.0.1
A Comprehensive Geospatial Library
License: MIT
Requires Python: >=3.6

# 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)

---

## 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.

---

## Example Usage

### Visualizing a Raster
```python
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.