WIP: fix TrodesPosVideo

src/spyglass/position/v1/dlc_utils.py

@@ -434,7 +434,7 @@ def find_mp4(
         .rsplit(video_filepath.parent.as_posix(), maxsplit=1)[-1]
         .split("/")[-1]
     )
-    return _convert_mp4(video_file, video_path, output_path, videotype="mp4")
+    return _convert_mp4(video_file, video_path, output_path, videotype="mp4", count_frames=True)
 
 
 def _convert_mp4(

src/spyglass/position/v1/dlc_utils_makevid.py

@@ -80,10 +80,16 @@ def __init__(
         if not Path(video_filename).exists():
             raise FileNotFoundError(f"Video not found: {video_filename}")
 
+        try:
+            position_mean = position_mean["DLC"]
+            orientation_mean = orientation_mean["DLC"]
+        except IndexError:
+            pass  # trodes data provides bare arrays
+
         self.video_filename = video_filename
         self.video_frame_inds = video_frame_inds
-        self.position_mean = position_mean["DLC"]
-        self.orientation_mean = orientation_mean["DLC"]
+        self.position_mean = position_mean
+        self.orientation_mean = orientation_mean
         self.centroids = centroids
         self.likelihoods = likelihoods
         self.position_time = position_time
@@ -163,7 +169,7 @@ def _get_input_stats(self, video_filename=None) -> Tuple[int, int]:
                 "stream=width,height,r_frame_rate",
                 "-of",
                 "csv=p=0:s=x",
-                video_filename,
+                str(video_filename),
             ],
             stdout=subprocess.PIPE,
             stderr=subprocess.PIPE,

src/spyglass/position/v1/position_trodes_position.py

@@ -13,6 +13,7 @@
 from spyglass.position.v1.dlc_utils_makevid import make_video
 from spyglass.settings import test_mode
 from spyglass.utils import SpyglassMixin, logger
+from spyglass.utils.position import fill_nan
 
 schema = dj.schema("position_v1_trodes_position")
 
@@ -327,19 +328,35 @@ def make(self, key):
             pos_df = pos_df[:min_len]
             video_time = video_time[:min_len]
 
+        centroids = {
+            "red": np.asarray(adj_df[["xloc", "yloc"]]),
+            "green": np.asarray(adj_df[["xloc2", "yloc2"]]),
+        }
+        position_mean = np.asarray(pos_df[["position_x", "position_y"]])
+        orientation_mean = np.asarray(pos_df[["orientation"]])
+        position_time = np.asarray(pos_df.index)
+        if np.any(video_time):
+            centroids = {
+                color: fill_nan(
+                    variable=data,
+                    video_time=video_time,
+                    variable_time=position_time,
+                )
+                for color, data in centroids.items()
+            }
+            position_mean = fill_nan(position_mean, video_time, position_time)
+            orientation_mean = fill_nan(
+                orientation_mean, video_time, position_time
+            )
+
         make_video(
-            processor="opencv-trodes",
             video_filename=video_path,
-            centroids={
-                "red": np.asarray(adj_df[["xloc", "yloc"]]),
-                "green": np.asarray(adj_df[["xloc2", "yloc2"]]),
-            },
-            position_mean=np.asarray(pos_df[["position_x", "position_y"]]),
-            orientation_mean=np.asarray(pos_df[["orientation"]]),
+            centroids=centroids,
             video_time=video_time,
-            position_time=np.asarray(pos_df.index),
+            position_mean=position_mean,
+            orientation_mean=orientation_mean,
+            position_time=position_time,
             output_video_filename=output_video_filename,
             cm_to_pixels=meters_per_pixel * M_TO_CM,
-            disable_progressbar=False,
         )
         self.insert1(dict(**key, has_video=True))