Added smoothing for fov orientation change to make it less stressful to watch.

src/conversion_scripts/load_model_params_from_model_options.py

+from pathlib import Path
+import argparse
+import robofish.io
+from tqdm import tqdm
+
+def main(file_path):
+    files = list(file_path.glob("*.hdf5")) if file_path.is_dir() else [file_path]
+    
+    for f in tqdm(files):
+        with robofish.io.File(f, "r+") as iof:
+            model_options = iof.attrs["model_options"]
+            model_options = eval(model_options)["options"]
+            
+            neccessary_options = ["zor", "zoo", "zoa", "fov", "additive_zone_sizes"]
+            
+            # extracted_options
+            e_options = {no: model_options[no] for no in neccessary_options}
+            
+            if e_options["additive_zone_sizes"]:
+                e_options["zoo"] += e_options["zor"]
+                e_options["zoa"] += e_options["zoo"]
+                
+            for e in iof.entities:
+                e.attrs["category"] = "organism"
+                
+                if "model" in e:
+                    del e["model"]
+                
+                g = e.create_group("model")    
+                g.attrs["name"] = "couzin"
+                
+                p = g.create_group("parameters")
+                p.attrs["zoo"] = e_options["zoo"]
+                p.attrs["zoa"] = e_options["zoa"]
+                p.attrs["zor"] = e_options["zor"]
+                p.attrs["fov"] = e_options["fov"]
+                
+                    
+    
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Convert csv file from the socoro experiments to RoboFish track format.")
+    parser.add_argument("file_path", type=str, help="Path to the csv file.")
+    args = parser.parse_args()
+    main(Path(args.file_path))
\ No newline at end of file

src/robofish/io/app.py

@@ -267,6 +267,7 @@ def render(args: argparse.Namespace = None) -> None:
         "render_swarm_center": False,
         "highlight_switches": False,
         "figsize": 10,
+        "fov_smoothing_factor": 0.8
     }
 
     for key, value in default_options.items():

src/robofish/io/file.py

@@ -1026,6 +1026,7 @@ class File(h5py.File):
         custom_colors: bool = None,
         dpi: int = 200,
         figsize: int = 10,
+        fov_smoothing_factor: float = 0.8
     ) -> None:
         """Render a video of the file.
 
@@ -1116,9 +1117,9 @@ class File(h5py.File):
                 entity_zones = []
                 for zone_size in zone_sizes.values():
                     if fov >= 360:
-                        entity_zones.append(matplotlib.patches.Circle((0, 0), zone_size, color=fish_colors[ei], alpha=0.3, fill=False))
+                        entity_zones.append(matplotlib.patches.Circle((0, 0), zone_size, color=fish_colors[ei], alpha=0.2, fill=False))
                     else:
-                        entity_zones.append(matplotlib.patches.Wedge((0,0), zone_size, theta1=-fov/2, theta2=fov/2, color=fish_colors[ei], alpha=0.3, fill=False))
+                        entity_zones.append(matplotlib.patches.Wedge((0,0), zone_size, theta1=-fov/2, theta2=fov/2, color=fish_colors[ei], alpha=0.2, fill=False))
                 zones.append(entity_zones)
                 
         zones_flat = []
@@ -1319,6 +1320,8 @@ class File(h5py.File):
 
         pbar = tqdm(range(n_frames)) if video_path is not None else None
 
+        self.fov_orientations = [None] * n_entities
+        
         def update(frame):
             output_list = []
 
@@ -1395,15 +1398,22 @@ class File(h5py.File):
 
                 poses_trails = entity_poses[:, max(0, file_frame - trail) : file_frame]
                 for i_entity in range(n_entities):
-                    for zone in zones[i_entity]:
-    
+                    new_ori = this_pose[i_entity, 2]
+                    old_ori = self.fov_orientations[i_entity] if self.fov_orientations[i_entity] is not None else new_ori
+
+                    # Mix in complex space to handle the wrap around
+                    smoothed_ori = fov_smoothing_factor * np.exp(1j * old_ori) + (1-fov_smoothing_factor) * np.exp(1j * new_ori)
+                    self.fov_orientations[i_entity] = np.angle(smoothed_ori)
+                    
+                    ori_deg = np.rad2deg(self.fov_orientations[i_entity])
+                    
+                    for zone in zones[i_entity]:    
                         zone.set_center((
                             this_pose[i_entity, 0],
                             this_pose[i_entity, 1],
                         ))
                         if fovs[i_entity] < 360:
-                            ori_deg = np.rad2deg(this_pose[i_entity, 2])
-                            zone.theta1 = ori_deg  - fovs[i_entity] / 2
+                            zone.theta1 = ori_deg - fovs[i_entity] / 2
                             zone.theta2 = ori_deg + fovs[i_entity] / 2
 
                 if render_swarm_center: