Merge branch 'master' into develop

src/conversion_scripts/convert_socoro.py

+from pathlib import Path
+import argparse
+
+import numpy as np
+import robofish.io
+from tqdm import tqdm
+
+from robofish.socoro.preprocessing.stats import load_trial
+
+
+def convert(input_path: Path, output_path: Path):
+    """
+    Convert from csv format to RoboFish track format.
+
+    robofish.socoro is used for the load_trial function.
+    This is needed because the csv files do not have headers.
+    Unused columns: "frame_number", "datetime_local".
+    These columns could be used if needed, e.g. convert datetime_local to
+    calendar_time_points or include frame_number if files will be split into segments.
+
+    TODO: filter warning about unbounded radians
+    """
+    trial_df = load_trial(input_path)
+    robot_poses = np.stack(
+        [
+            np.clip(trial_df.robot_position_x_cm, 0, 100) - 50,
+            np.clip(trial_df.robot_position_y_cm, 0, 100) - 50,
+            -trial_df.robot_orientation_rad,
+        ]
+    ).T
+    guppy_poses = np.stack(
+        [
+            np.clip(trial_df.fish_position_x_cm, 0, 100) - 50,
+            np.clip(trial_df.fish_position_y_cm, 0, 100) - 50,
+            -trial_df.fish_orientation_rad,
+        ]
+    ).T
+
+    f = robofish.io.File(world_size_cm=[100, 100], frequency_hz=25.0)
+
+    f.create_entity(category="robot", poses=robot_poses, name="robot")
+    f.create_entity(category="organism", poses=guppy_poses, name="guppy")
+
+    for robot_dataset in [
+        trial_df.robot_mode.astype(bytes),
+        trial_df.avoidance_score,
+        trial_df.follow_value,
+        trial_df.carefulness_variable,
+    ]:
+        f["entities"]["robot"].create_dataset(robot_dataset.name, data=robot_dataset)
+    f["samplings"]["25 hz"].create_dataset(
+        "monotonic_time_points_us",
+        data=(trial_df.monotonic_time_ms - trial_df.monotonic_time_ms[0]) * 1000,
+    )
+    f.save_as(output_path, no_warning=True)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description="Convert csv file from the socoro experiments to RoboFish track format."
+    )
+    parser.add_argument(
+        "input", type=str, help="Single folder of files to be converted"
+    )
+    parser.add_argument("output", type=str, help="Output path")
+    args = parser.parse_args()
+
+    output_path = Path(args.output)
+    output_path.mkdir(exist_ok=True)
+    for path in tqdm(list(Path(args.input).glob("*.csv"))):
+        convert(input_path=path, output_path=output_path / f"{path.stem}.hdf5")

src/robofish/evaluate/evaluate.py

@@ -494,7 +494,7 @@ def evaluate_quiver(
 
     if poses_from_paths is None:
         poses_from_paths, file_settings = utils.get_all_poses_from_paths(
-            paths, predicate
+            paths, predicate, predicate
         )
     if speeds_turns_from_paths is None:
         speeds_turns_from_paths, file_settings = utils.get_all_data_from_paths(

src/robofish/io/file.py

@@ -934,15 +934,6 @@ class File(h5py.File):
             # Plotting outside of the figure to have the label
             ax.plot([550, 600], [550, 600], lw=5, c=this_c, label=fish_id)
 
-        # ax.scatter(
-        #     [poses[:, skip_timesteps, 0]],
-        #     [poses[:, skip_timesteps, 1]],
-        #     marker="h",
-        #     c="black",
-        #     s=ms,
-        #     label="Start",
-        #     zorder=5,
-        # )
         ax.scatter(
             [poses[:, -1, 0]],
             [poses[:, -1, 1]],
@@ -1108,9 +1099,12 @@ class File(h5py.File):
         )
         xv, yv = np.meshgrid(x, y)
 
-        grid_points = plt.scatter(xv, yv, c="gray", s=1.5)
+        points = [
+            plt.scatter([], [], marker="x", color="k"),
+            plt.plot([], [], linestyle="dotted", alpha=0.5, color="k", zorder=0)[0],
+            plt.scatter(xv, yv, c="gray", s=1.5),
+        ]
 
-        # border = plt.plot(border_vertices[0], border_vertices[1], "k")
         border = patches.Polygon(border_vertices.T, facecolor="w", zorder=-1)
 
         def title(file_frame: int) -> str:
@@ -1201,7 +1195,7 @@ class File(h5py.File):
                         [options["view_size"], min_view + options["margin"]]
                     )
 
-                    if not np.isnan(min_view).any() and not new_view_size is np.nan:
+                    if not np.isnan(min_view).any() and new_view_size is not np.nan:
                         self.middle_of_swarm = options[
                             "slow_view"
                         ] * self.middle_of_swarm + (1 - options["slow_view"]) * np.mean(