kdes work but scale is wrong

code/modules/behaviour_handling.py

@@ -1,13 +1,10 @@
 import numpy as np
-
 import os
-
 from IPython import embed
 
-
 from pandas import read_csv
 from modules.logger import makeLogger
-from modules.datahandling import causal_kde1d, acausal_kde1d
+from modules.datahandling import causal_kde1d, acausal_kde1d, flatten
 
 
 logger = makeLogger(__name__)
@@ -43,7 +40,7 @@ class Behavior:
 
         # csv_filename = [f for f in os.listdir(
         #     folder_path) if f.endswith('.csv')][0]
-        logger.info(f'CSV file: {csv_filename}')
+        # logger.info(f'CSV file: {csv_filename}')
         self.dataframe = read_csv(os.path.join(folder_path, csv_filename))
 
         self.chirps = np.load(os.path.join(
@@ -92,6 +89,12 @@ def correct_chasing_events(
 
     wrong_bh = np.arange(len(category))[
         category != 2][:-1][np.diff(category[category != 2]) == 0]
+
+    if category[category != 2][-1] == 0:
+        wrong_bh = np.append(
+            wrong_bh,
+            np.arange(len(category))[category != 2][-1])
+
     if onset_ids[0] > offset_ids[0]:
         offset_ids = np.delete(offset_ids, 0)
         help_index = offset_ids[0]
@@ -106,51 +109,61 @@ def correct_chasing_events(
         len(category))[category == 1]
 
     # Check whether on- or offset is longer and calculate length difference
+
     if len(new_onset_ids) > len(new_offset_ids):
-        len_diff = len(onset_ids) - len(offset_ids)
-        logger.info(f'Onsets are greater than offsets by {len_diff}')
+        embed()
+        logger.warning('Onsets are greater than offsets')
     elif len(new_onset_ids) < len(new_offset_ids):
-        len_diff = len(offset_ids) - len(onset_ids)
-        logger.info(f'Offsets are greater than onsets by {len_diff}')
+        logger.warning('Offsets are greater than onsets')
     elif len(new_onset_ids) == len(new_offset_ids):
-        logger.info('Chasing events are equal')
+        # logger.info('Chasing events are equal')
+        pass
 
     return category, timestamps
 
 
-def event_triggered_chirps(
-    event: np.ndarray,
+def center_chirps(
+    events: np.ndarray,
     chirps: np.ndarray,
     time_before_event: int,
     time_after_event: int,
-    dt: float,
-    width: float,
+    # dt: float,
+    # width: float,
 ) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
 
     event_chirps = []   # chirps that are in specified window around event
     # timestamps of chirps around event centered on the event timepoint
     centered_chirps = []
 
-    for event_timestamp in event:
+    for event_timestamp in events:
+
         start = event_timestamp - time_before_event
         stop = event_timestamp + time_after_event
         chirps_around_event = [c for c in chirps if (c >= start) & (c <= stop)]
-        event_chirps.append(chirps_around_event)
+
         if len(chirps_around_event) == 0:
             continue
-        else:
-            centered_chirps.append(chirps_around_event - event_timestamp)
 
-    time = np.arange(-time_before_event, time_after_event, dt)
+        centered_chirps.append(chirps_around_event - event_timestamp)
+        event_chirps.append(chirps_around_event)
 
-    # Kernel density estimation with some if's
-    if len(centered_chirps) == 0:
-        centered_chirps = np.array([])
-        centered_chirps_convolved = np.zeros(len(time))
-    else:
-        # convert list of arrays to one array for plotting
-        centered_chirps = np.concatenate(centered_chirps, axis=0)
-        centered_chirps_convolved = (acausal_kde1d(
-            centered_chirps, time, width)) / len(event)
+    centered_chirps = np.sort(flatten(centered_chirps))
+    event_chirps = np.sort(flatten(event_chirps))
 
-    return event_chirps, centered_chirps, centered_chirps_convolved
+    if len(centered_chirps) != len(event_chirps):
+        raise ValueError(
+            'Non centered chirps and centered chirps are not equal')
+
+    # time = np.arange(-time_before_event, time_after_event, dt)
+
+    # # Kernel density estimation with some if's
+    # if len(centered_chirps) == 0:
+    #     centered_chirps = np.array([])
+    #     centered_chirps_convolved = np.zeros(len(time))
+    # else:
+    #     # convert list of arrays to one array for plotting
+    #     centered_chirps = np.concatenate(centered_chirps, axis=0)
+    #     centered_chirps_convolved = (acausal_kde1d(
+    #         centered_chirps, time, width)) / len(event)
+
+    return centered_chirps