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python-scripts/normal.py

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+import rasterio
+import numpy as np
+import matplotlib.pyplot as plt
+from scipy.stats import norm
+
+def read_raster_data(raster_path):
+    with rasterio.open(raster_path) as raster:
+        data = raster.read(1, masked=True)
+        if raster.nodata is not None:
+            data = data.filled(np.nan)
+    return data
+
+def flatten_data(data):
+    return data.flatten()
+
+# Replace 'traveltime.tif' with the path to your actual raster file
+x_axis = flatten_data(read_raster_data('traveltime.tif'))
+
+# Filter out NaN values
+x_axis = x_axis[~np.isnan(x_axis)]
+
+mean = np.mean(x_axis)
+sd = np.std(x_axis)
+
+# Plot histogram
+plt.hist(x_axis, bins=50, density=True, alpha=0.6, color='g', range=(mean-3*sd, mean+3*sd))
+xmin, xmax = plt.xlim()
+plt.xlim(0,5)
+plt.xlabel("Travel time in [h]")
+plt.ylabel("Quantaty in percentage")
+x = np.linspace(xmin, xmax, 100)
+
+
+# Calculate and plot the 0.85 quantile
+quantile_90 = np.quantile(x_axis, 0.90)
+plt.axvline(x=quantile_90, color='r', linestyle='--', label='90%')
+plt.legend()
+
+plt.savefig("traveltimedistribution.png", dpi=300)  # Save the plot