fix: use bounding box instead of a grid

FoldOptLib/examples/misc_functions.py

@@ -1,16 +1,43 @@
 import numpy as np
 
 
-def sample_random_dataset(grid, sample_size=2, seed=180):
+def sample_random_dataset(bounding_box, sample_size=2, seed=180):
+    """
+    Generate a random dataset of 3D coordinates within a specified model bounding box.
+
+    Parameters
+    ----------
+    bounding_box : ndarray
+        A 3x3 array where each row represents the minimum and maximum values of x and y coordinates.
+    sample_size : int, optional
+        The number of random samples to generate. Default is 2.
+    seed : int, optional
+        The seed for the random number generator for reproducibility. Default is 180.
+
+    Returns
+    -------
+    random_xyz : ndarray
+        A sample_size x 3 array of random 3D coordinates within the specified bounding box.
+    """
+    # Set the seed for the random number generator for reproducible results
     np.random.seed(seed)
-    # select all xy points with z = 0
-    xyz = grid[grid[:, 2] == 0]
-    xmax = xyz[:, 0].max()
-    ymax = xyz[:, 1].max()
+
+    # Extract the maximum x and y coordinates from the bounding box
+    xmax, ymax = bounding_box[1, 0], bounding_box[1, 1]
+
+    # Define the maximum z coordinate (fixed at 0 to only sample model's surface)
     zmax = 0.
-    xn = np.random.uniform(low=0, high=xmax, size=sample_size)
-    yn = np.random.uniform(low=0, high=ymax, size=sample_size)
+
+    # Generate 'sample_size' number of random x-coordinates
+    xn = np.random.uniform(low=bounding_box[0, 0], high=xmax, size=sample_size)
+
+    # Generate 'sample_size' number of random y-coordinates
+    yn = np.random.uniform(low=bounding_box[0, 1], high=ymax, size=sample_size)
+
+    # Create an array of z-coordinates, all set to 'zmax' (fixed z-coordinate for all points)
     zn = np.tile(zmax, sample_size)
+
+    # Combine the x, y, and z coordinates into a single 2D array (shape: sample_size x 3)
     random_xyz = np.array([xn, yn, zn]).T
 
     return random_xyz