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python-scripts/correlation.py

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+import rasterio
+import numpy as np
+from scipy.stats import pearsonr
+import matplotlib.pyplot as plt
+
+def read_raster_data(raster_path, target_width, target_height):
+    with rasterio.open(raster_path) as raster:
+        data = raster.read(1, masked=True)  # Reads the first band
+        if data.shape != (target_height, target_width):
+            data = data[:target_height, :target_width]
+        if raster.nodata is not None:
+            data = data.filled(np.nan)  # Fill masked values with NaN
+    return data
+
+def flatten_data(data):
+    return data.flatten()
+
+def calculate_correlation(data1, data2):
+    mask = ~np.isnan(data1) & ~np.isnan(data2)
+    
+    filtered_data1 = data1[mask]
+    filtered_data2 = data2[mask]
+    
+    correlation, _ = pearsonr(filtered_data1, filtered_data2)
+    return correlation, filtered_data1, filtered_data2  # Return the filtered data for plotting
+
+def plot_correlation(data1, data2, correlation, file_path):
+    plt.scatter(data1, data2, alpha=0.5)
+    plt.xlim(0,7)
+    #plt.title(f'Correlation: {correlation:.2f}')
+    plt.xlabel('Raster 1 Values')
+    plt.ylabel('Raster 2 Values')
+    plt.savefig(file_path, dpi=300)
+    plt.close()
+    
+
+if __name__ == "__main__":
+    raster_path1 = 'friction surface.tif'
+    raster_path2 = 'ghsl.tif'
+    save_path = 'plot.png'
+
+    # Common dimensions
+    target_width = 275  # Choose based on your requirements
+    target_height = 254
+
+    data1 = flatten_data(read_raster_data(raster_path1, target_width, target_height))
+    data2 = flatten_data(read_raster_data(raster_path2, target_width, target_height))
+
+    correlation, filtered_data1, filtered_data2 = calculate_correlation(data1, data2)
+    plot_correlation(filtered_data1, filtered_data2, correlation, save_path)
+