add hessigheim script

scripts/hessigheim.py

@@ -0,0 +1,58 @@
+"""Run on the Hessigheim benchmark"""
+import argparse
+import pathlib
+import time
+import numpy as np
+import matplotlib.pyplot as plt
+import concave_hull as ch
+
+def read_file_to_numpy(filename):
+    """
+    Reads a file and extracts the first two numbers (X, Y) from each line
+    (skipping the first line) into a 2D Nx2 numpy array.
+    """
+    with open(filename, 'r') as file:
+        lines = file.readlines()[1:]  # Skip the first line
+        data = [list(map(float, line.split()[0:2])) for line in lines]
+        return np.array(data)
+
+def main():
+    # Parsing the command line argument for the filename
+    parser = argparse.ArgumentParser(description='Process a txt file to extract X, Y coordinates.')
+    parser.add_argument('filename', type=str, help='The filename of the txt file')
+    args = parser.parse_args()
+    basename = pathlib.Path(args.filename).stem
+
+    # Process the file and get the numpy array
+    pts = read_file_to_numpy(args.filename)
+
+    # compute the hull
+    start = time.time()
+    hull = ch.concave_hull_2d(pts, 400, False)
+    end = time.time()
+    print(f"Compute Concave Hull Time [{len(pts)} Points]: {end - start: 2.4f} seconds")
+
+    # plot the hull
+    plt.fill(hull[:,0], hull[:, 1], linewidth=3, edgecolor='k', facecolor='w')
+
+    # plot the points
+    plt.scatter(*pts.T, alpha=0.02, s=1)
+
+    plt.title(f'{basename} Concave Hull Approximation [{len(pts)} points]')
+    plt.xlabel('$x$')
+    plt.ylabel('$y$')
+
+    xdiff = hull[:, 0].max() - hull[:, 0].min() 
+    ydiff = hull[:, 1].max() - hull[:, 1].min() 
+    plt.xlim(hull[:, 0].min() - xdiff * 0.05, hull[:, 0].max() + xdiff * 0.05)
+    plt.ylim(hull[:, 1].min() - ydiff * 0.05, hull[:, 1].max() + ydiff * 0.05)
+
+    # save to doc
+    plt.savefig(f'./doc/img/{basename}.png')
+
+    # show the plot
+    plt.show()
+
+if __name__ == "__main__":
+    main()
+