Fixed follow_iid plots. Fixed validation to allow np.nan

src/robofish/evaluate/evaluate.py

@@ -504,12 +504,20 @@ def evaluate_follow_iid(
                             calculate_follow(poses[i, :, 0:2], all_poses[j, :, 0:2])
                         )
             file.close()
-        follow.append(path_follow)
-        iid.append(path_iid)
+        follow.append(np.array(path_follow))
+        iid.append(np.array(path_iid))
 
     grids = []
     worldBoundsX, worldBoundsY = max(worldBoundsX), max(worldBoundsY)
-    maxDist = (worldBoundsX ** 2 + worldBoundsY ** 2) ** (1 / 2)
+
+    follow_flat = np.concatenate([f.flatten() for f in follow])
+    iid_flat = np.concatenate([i.flatten() for i in iid])
+
+    # Find the 0.5%/ 99.5% quantiles as min and max for the axis
+    follow_range = np.max(
+        [-1 * np.quantile(follow_flat, 0.005), np.quantile(follow_flat, 0.995)]
+    )
+    max_iid = np.quantile(iid_flat, 0.995)
 
     for i in range(len(follow)):
         follow_iid_data = pd.DataFrame(
@@ -524,10 +532,12 @@ def evaluate_follow_iid(
             x="IID [cm]",
             y="Follow",
             data=follow_iid_data,
-            linewidth=0,
-            kind="scatter",
-            xlim=(0, maxDist),
-            ylim=(-5, 5),
+            kind="hist",
+            xlim=(0, max_iid),
+            ylim=(-follow_range, follow_range),
+            cbar=True,
+            legend=True,
+            joint_kws={"bins": 50},
         )
         grid.fig.set_figwidth(9)
         grid.fig.set_figheight(6)

src/robofish/io/validation.py

@@ -304,6 +304,8 @@ def validate_positions_range(world_size, positions, e_name):
     # positions which are just a bit over the world edge are fine
     error_allowance = 1.01
 
+    positions = np.array([p for p in positions if not any(np.isnan(p))])
+
     allowed_x = [
         -1 * world_size[0] * error_allowance / 2,
         world_size[0] * error_allowance / 2,
@@ -332,12 +334,9 @@ def validate_positions_range(world_size, positions, e_name):
 
 
 def validate_orientations_length(orientations, e_name):
-    ori_lengths = np.linalg.norm(orientations, axis=1)
+    orientations = np.array([o for o in orientations if not any(np.isnan(o))])
 
-    # import matplotlib.pyplot as plt
-    #
-    # plt.plot(ori_lengths)
-    # plt.show()
+    ori_lengths = np.linalg.norm(orientations, axis=1)
 
     # Check if all orientation lengths are all 1. Different lengths cause warnings.
     assert_validate(