Add loading lightning pose data from nwb files

src/dynamic_routing_analysis/data_utils.py

@@ -7,6 +7,11 @@
 import pandas as pd
 import pynwb
 
+vid_angle_npc_names={
+    'behavior':'side',
+    'face':'front',
+    'eye':'eye',
+}
 
 def load_trials_or_units(session, table_name):
     # convenience function to load trials or units from cache if available, 
@@ -43,15 +48,14 @@ def load_trials_or_units(session, table_name):
     return table
 
 
-def load_facemap_data(session,session_info,trials,vid_angle,keep_n_SVDs=500,use_s3=True):
+def load_facemap_data(session,session_info=None,trials=None,vid_angle=None,keep_n_SVDs=500,use_s3=True):
     # function to load facemap data from s3 or local cache
-    vid_angle_npc_names={
-            'behavior':'side',
-            'face':'front',
-            'eye':'eye',
-            }
+    if not vid_angle:
+        raise ValueError("vid_angle must be specified")
 
     if isinstance(session, pynwb.NWBFile):
+        if trials is None:
+            trials = session.trials[:]
         if not any("facemap" in k for k in session.processing["behavior"].data_interfaces.keys()):
             raise AttributeError(
                 f"Facemap data not found in {session.session_id} NWB file"
@@ -206,8 +210,10 @@ def load_facemap_data(session,session_info,trials,vid_angle,keep_n_SVDs=500,use_
     return mean_trial_behav_SVD #mean_trial_behav_motion
 
 
-def load_LP_data(session, trials, vid_angle, LP_parts_to_keep=None):
-
+def load_LP_data(session, trials=None, vid_angle=None, LP_parts_to_keep=None):
+    if not vid_angle:
+        raise ValueError("vid_angle must be specified")
+
     def zscore(x):
         return (x - np.nanmean(x)) / np.nanstd(x)
 
@@ -237,22 +243,48 @@ def part_info(part, df, temp_error, pca_error):
     if LP_parts_to_keep is None:
         LP_parts_to_keep = ['ear_base_l', 'jaw', 'nose_tip', 'whisker_pad_l_side']
 
-    vid_angle_npc_names = {
+    vid_angle_idx = {
         'behavior': 0,
         'face': 3,
     }
-
-    df = session._LPFaceParts[vid_angle_npc_names[vid_angle]][:]
-    df_temp_error = session._LPFaceParts[vid_angle_npc_names[vid_angle] + 1][:]
-    df_pca_error = session._LPFaceParts[vid_angle_npc_names[vid_angle] + 2][:]
-    cam_frames = df['timestamps'].values.astype('float')
-
-    LP_traces = []
-    for part_no, part_name in enumerate(LP_parts_to_keep):
-        x, y = part_info(part_name, df, df_temp_error[part_name].values.astype('float'),
-                         df_pca_error[part_name].values.astype('float'))
-        LP_traces.append(x)
-        LP_traces.append(y)
+    camera_idx = vid_angle_idx[vid_angle]
+    if isinstance(session, pynwb.NWBFile):
+        if trials is None:
+            trials = session.trials[:]
+        if not any(
+            k.startswith('lp_')
+            for k in session.processing["behavior"].data_interfaces.keys()
+        ):
+            raise AttributeError(
+                f"lightning_pose data not found in {session.session_id} NWB file"
+            )
+        df = session.processing["behavior"][
+            f"lp_{vid_angle_npc_names[vid_angle]}_camera"
+        ][:]
+        cam_frames = df.timestamps.values
+
+        LP_traces = []
+        for part_no, part_name in enumerate(LP_parts_to_keep):
+            if f"{part_name}_x" not in df.columns:
+                continue
+            x, y = part_info(part_name, df, df[f"{part_name}_error"].values.astype('float'),
+                            df[f"{part_name}_temporal_norm"].values.astype('float'))
+            LP_traces.append(x)
+            LP_traces.append(y)
+        if not LP_traces:
+            raise ValueError(f"None of requested LP parts found for {vid_angle} camera: {LP_parts_to_keep}")
+    else:
+        df = session._LPFaceParts[camera_idx][:]
+        df_temp_error = session._LPFaceParts[camera_idx + 1][:]
+        df_pca_error = session._LPFaceParts[camera_idx + 2][:]
+        cam_frames = df['timestamps'].values.astype('float')
+
+        LP_traces = []
+        for part_no, part_name in enumerate(LP_parts_to_keep):
+            x, y = part_info(part_name, df, df_temp_error[part_name].values.astype('float'),
+                            df_pca_error[part_name].values.astype('float'))
+            LP_traces.append(x)
+            LP_traces.append(y)
 
     LP_info = {
         'LP_traces': np.array(LP_traces).T