final commit

src/conversion_scripts/convert_marc.py

@@ -19,7 +19,7 @@ import robofish.io
 
 def convertTrajectory(path, save_path, categories):
     ar = pd.read_csv(path, sep=";").to_numpy()
-    new = robofish.io.File(world_size_cm=[100, 100], frequency_hz=20)
+    new = robofish.io.File(world_size_cm=[100, 100], frequency_hz=25)
 
     # convert x,y from m to cm
     ar[:, [0, 1, 3, 4]] = ar[:, [0, 1, 3, 4]] * 100

src/robofish/evaluate/evaluate.py

@@ -87,7 +87,7 @@ def evaluate_speed(
     if labels is None:
         labels = paths
 
-    plt.hist(speeds, bins=20, label=labels, density=True, range=[0, 50])
+    plt.hist(speeds, bins=20, label=labels, density=True, range=[0, 25])
     plt.title("Agent speeds")
     plt.xlabel("Speed [cm/s]")
     plt.ylabel("Frequency")
@@ -147,7 +147,7 @@ def evaluate_turn(
         labels = paths
 
     # TODO: Quantil range
-    plt.hist(turns, bins=40, label=labels, density=True, range=[-30, 30])
+    plt.hist(turns, bins=41, label=labels, density=True, range=[-30, 30])
     plt.title("Agent turns")
     plt.xlabel("Change in orientation [Degree / timestep at %dhz]" % frequency)
     plt.ylabel("Frequency")
@@ -193,8 +193,8 @@ def evaluate_orientation(
                 world_size[0] / 2,
                 world_size[1] / 2,
             ]
-            xbins = np.linspace(world_bounds[0], world_bounds[2], 10)
-            ybins = np.linspace(world_bounds[1], world_bounds[3], 10)
+            xbins = np.linspace(world_bounds[0], world_bounds[2], 11)
+            ybins = np.linspace(world_bounds[1], world_bounds[3], 11)
             ret_1 = stats.binned_statistic_2d(
                 poses[:, 0], poses[:, 1], poses[:, 2], "mean", bins=[xbins, ybins]
             )
@@ -226,13 +226,13 @@ def evaluate_orientation(
 
         plot = ax[i].pcolormesh(
             xx,
-            yy * (-1),
+            yy,
             np.arctan2(s_2, s_1).T,
             vmin=-np.pi,
             vmax=np.pi,
             cmap="twilight",
         )
-        # cbar = plt.colorbar(plot, ax=ax[i], pad=0.015, aspect=10)
+        cbar = plt.colorbar(plot, ax=ax[i], pad=0.015, aspect=10)
         show_values(plot)
 
     if save_path is None:
@@ -419,7 +419,10 @@ def evaluate_tankpositions(
         ax[i].set_xlim(-world_bounds[i][0] / 2, world_bounds[i][0] / 2)
         ax[i].set_ylim(-world_bounds[i][1] / 2, world_bounds[i][1] / 2)
 
-        sns.kdeplot(x=x_pos[i], y=y_pos[i] * (-1), n_levels=25, shade=True, ax=ax[i])
+        ax[i].set_xlabel("x [cm]")
+        ax[i].set_ylabel("y [cm]")
+
+        sns.kdeplot(x=x_pos[i], y=y_pos[i], n_levels=25, shade=True, ax=ax[i])
 
     if save_path is None:
         plt.show()
@@ -450,21 +453,26 @@ def evaluate_trajectories(
     pos = []
     world_bounds = []
     for k, files in enumerate(files_per_path):
+        path_poses = []
         for p, file in files.items():
             poses = file.select_entity_poses(
                 None if predicate is None else predicate[k]
             )
             world_bounds.append(file.attrs["world_size_cm"])
-            path_pos = {
-                fish: pd.DataFrame({"x": poses[fish, :, 0], "y": poses[fish, :, 1]})
-                for fish in range(len(poses))
-            }
-            combined = pd.concat(
-                [
-                    path_pos[fish].assign(Agent=f"Agent {fish}")
-                    for fish in path_pos.keys()
-                ]
-            )
+            path_poses.append(poses[:, :, :2])
+        poses = np.concatenate(path_poses, axis = 1)
+
+        path_pos = {
+            fish: pd.DataFrame({"x": poses[fish, :, 0], "y": poses[fish, :, 1]})
+            for fish in range(len(poses))
+        }
+        combined = pd.concat(
+            [
+                path_pos[fish].assign(Agent=f"Agent {fish}")
+                for fish in path_pos.keys()
+            ]
+        )
+
         pos.append((path_pos, combined))
 
     fig, ax = plt.subplots(1, len(pos), figsize=(len(pos) * 8, 8))
@@ -481,7 +489,7 @@ def evaluate_trajectories(
         ax[i].set_title("Trajectories (%s)" % labels[i])
         ax[i].set_xlim(-world_bounds[i][0] / 2, world_bounds[i][0] / 2)
         ax[i].set_ylim(-world_bounds[i][1] / 2, world_bounds[i][1] / 2)
-        ax[i].invert_yaxis()
+        # ax[i].invert_yaxis()
         ax[i].xaxis.set_ticks_position("top")
         ax[i].xaxis.set_label_position("top")
         ax[i].yaxis.set_ticks_position("left")
@@ -557,11 +565,10 @@ def evaluate_positionVec(
     worldBoundsX, worldBoundsY = max(worldBoundsX), max(worldBoundsY)
 
     for i in range(len(posVec)):
-        df = pd.DataFrame({"x": posVec[i][:, 0], "y": posVec[i][:, 1] * (-1)})
+        df = pd.DataFrame({"x": posVec[i][:, 0], "y": posVec[i][:, 1]})
         grid = sns.displot(df, x="x", y="y", binwidth=(10, 10), cbar=True)
         grid.axes[0, 0].set_xlabel("x [cm]")
         grid.axes[0, 0].set_ylabel("y [cm]")
-        # print(worldBoundsX, worldBoundsY)
         grid.set(xlim=(-worldBoundsX, worldBoundsX))
         grid.set(ylim=(-worldBoundsY, worldBoundsY))
         grids.append(grid)
@@ -636,12 +643,12 @@ def evaluate_follow_iid(
             }
         )
 
+        plt.rcParams["lines.markersize"] = 1
         grid = sns.jointplot(
             x="IID [cm]",
             y="Follow",
             data=follow_iid_data,
             linewidth=0,
-            s=1,
             kind="scatter",
             xlim=(0, maxDist),
             ylim=(-5, 5),
@@ -682,7 +689,7 @@ def evaluate_all(
         predicate: a lambda function, selecting entities
             (example: lambda e: e.category == "fish")
     """
-    save_folder = Path(save_folder)
+    # save_folder = Path(save_folder)
     evaluate_speed(paths, labels, save_folder + "speed.png", predicate)
     evaluate_turn(paths, labels, save_folder + "turn.png", predicate)
     evaluate_orientation(paths, labels, save_folder + "orientation.png", predicate)