Position sampling in Environments with holes.

ratinabox/Environment.py

@@ -554,7 +554,6 @@ def sample_positions(self, n=10, method="uniform_jitter"):
         Args:
             n (int): number of features
             method: "uniform", "uniform_jittered" or "random" for how points are distributed
-            true_random: if True, just randomly scatters point
         Returns:
             array: (n x dimensionality) of positions
         """
@@ -581,33 +580,44 @@ def sample_positions(self, n=10, method="uniform_jitter"):
                 positions[:, 1] = np.random.uniform(
                     self.extent[2], self.extent[3], size=n
                 )
+                if (self.is_rectangular is False) or (self.has_holes is True):
+                # in this case, the positions you have sampled within the extent of the environment may not actually fall within it's legal area (i.e. they could be outside the polygon boundary or inside a hole). Brute force this by randomly resampling these points until all fall within the env.
+                    for i, pos in enumerate(positions):
+                        if self.check_if_position_is_in_environment(pos) == False:
+                            pos = self.sample_positions(n=1, method="random").reshape(
+                                -1
+                            )  # this recursive call must pass eventually, assuming the env is sufficiently large. this is why we don't need a while loop
+                            positions[i] = pos
             elif method[:7] == "uniform":
                 ex = self.extent
                 area = (ex[1] - ex[0]) * (ex[3] - ex[2])
+                if (self.has_holes is True): 
+                    area -= sum(shapely.geometry.Polygon(hole).area for hole in self.holes)
                 delta = np.sqrt(area / n)
                 x = np.linspace(ex[0] + delta /2, ex[1] - delta /2, int((ex[1] - ex[0])/delta))
                 y = np.linspace(ex[2] + delta /2, ex[3] - delta /2, int((ex[3] - ex[2])/delta))
                 positions = np.array(np.meshgrid(x, y)).reshape(2, -1).T
+
+                if (self.is_rectangular is False) or (self.has_holes is True):
+                    # in this case, the positions you have sampled within the extent of the environment may not actually fall within it's legal area (i.e. they could be outside the polygon boundary or inside a hole).
+                    delpos = [i for (i,pos) in enumerate(positions) if self.check_if_position_is_in_environment(pos) == False]
+                    positions = np.delete(positions,delpos,axis=0) # this will delete illegal positions
+
                 n_uniformly_distributed = positions.shape[0]
                 if method[7:] == "_jitter":
                     positions += np.random.uniform(
                         -0.45 * delta, 0.45 * delta, positions.shape 
-                    )
+                    ) 
                 n_remaining = n - n_uniformly_distributed
                 if n_remaining > 0:
-                    positions_remaining = self.sample_positions(
-                        n=n_remaining, method="random"
+                    # sample remaining from available positions with further jittering (delta = delta/2)
+                    positions_remaining = np.array([positions[i] for i in np.random.choice(range(len(positions)),n_remaining, replace=False)])
+                    delta /= 2
+                    positions_remaining += np.random.uniform(
+                        -0.45 * delta, 0.45 * delta, positions_remaining.shape
                     )
                     positions = np.vstack((positions, positions_remaining))
 
-            if (self.is_rectangular is False) or (self.has_holes is True):
-                # in this case, the positions you have sampled within the extent of the environment may not actually fall within it's legal area (i.e. they could be outside the polygon boundary or inside a hole). Brute force this by randomly resampling these points until all fall within the env.
-                for i, pos in enumerate(positions):
-                    if self.check_if_position_is_in_environment(pos) == False:
-                        pos = self.sample_positions(n=1, method="random").reshape(
-                            -1
-                        )  # this recursive call must pass eventually, assuming the env is sufficiently large. this is why we don't need a while loop
-                        positions[i] = pos
             return positions
 
     def discretise_environment(self, dx=None):