Numpy 2.0 changes

int-brain-lab · Nov 8, 2024 · 14fee7d · 14fee7d
1 parent 241d8bc
commit 14fee7d
Show file tree

Hide file tree

Showing 2 changed files with 19 additions and 14 deletions.
diff --git a/brainbox/video.py b/brainbox/video.py
@@ -5,7 +5,8 @@
 
 def frame_diff(frame1, frame2):
     """
-    Outputs pythagorean distance between two frames
+    Outputs pythagorean distance between two frames.
+
     :param frame1: A numpy array of pixels with a shape of either (m, n, 3) or (m, n)
     :param frame2: A numpy array of pixels with a shape of either (m, n, 3) or (m, n)
     :return: An array with a shape equal to the input frames
@@ -14,17 +15,21 @@ def frame_diff(frame1, frame2):
         raise ValueError('Frames must have the same shape')
     diff32 = np.float32(frame1) - np.float32(frame2)
     if frame1.ndim == 3:
-        norm32 = (np.sqrt(diff32[:, :, 0] ** 2 + diff32[:, :, 1] ** 2 + diff32[:, :, 2] ** 2) /
-                  np.sqrt(255 ** 2 * 3))
+        norm32 = np.float32(
+            np.sqrt(diff32[:, :, 0] ** 2 + diff32[:, :, 1] ** 2 + diff32[:, :, 2] ** 2) /
+            np.sqrt(255 ** 2 * 3)
+        )
     else:
-        norm32 = np.sqrt(diff32 ** 2 * 3) / np.sqrt(255 ** 2 * 3)
-    return np.uint8(norm32 * 255)
+        norm32 = np.float32(np.sqrt(diff32 ** 2 * 3) / np.sqrt(255 ** 2 * 3))
+    return np.uint8(np.round(norm32 * 255))
 
 
 def frame_diffs(frames, diff=1):
     """
-    Return the difference between frames.  May also take difference between more than 1 frames.
-    Values are normalized between 0-255.
+    Return the difference between frames.
+
+    May also take difference between more than 1 frames. Values are normalized between 0-255.
+
     :param frames: Array or list of frames, where each frame is either (y, x) or (y, x, 3).
     :param diff: Take difference between frames N and frames N + diff.
     :return: uint8 array with shape (n-diff, y, x).
@@ -35,9 +40,9 @@ def frame_diffs(frames, diff=1):
     diff32 = frames[diff:] - frames[:-diff]
     # Normalize
     if frames.ndim == 4:
-        norm32 = np.sqrt((diff32 ** 2).sum(axis=3)) / np.sqrt(255 ** 2 * 3)
+        norm32 = np.sqrt((diff32 ** 2).sum(axis=3)) / np.sqrt(255 ** 2 * 3).astype(np.float32)
     else:
-        norm32 = np.sqrt(diff32 ** 2 * 3) / np.sqrt(255 ** 2 * 3)
+        norm32 = np.sqrt(diff32 ** 2 * 3) / np.sqrt(255 ** 2 * 3).astype(np.float32)
     return np.uint8(norm32 * 255)
 
 

diff --git a/ibllib/io/extractors/ephys_passive.py b/ibllib/io/extractors/ephys_passive.py
@@ -426,10 +426,10 @@ def _extract_passiveAudio_intervals(audio: dict, rig_version: str) -> Tuple[np.a
         noiseOff_times = soundOff_times[diff > 0.3]
 
         # append with nans
-        toneOn_times = np.r_[toneOn_times, np.full((NTONES - len(toneOn_times)), np.NAN)]
-        toneOff_times = np.r_[toneOff_times, np.full((NTONES - len(toneOff_times)), np.NAN)]
-        noiseOn_times = np.r_[noiseOn_times, np.full((NNOISES - len(noiseOn_times)), np.NAN)]
-        noiseOff_times = np.r_[noiseOff_times, np.full((NNOISES - len(noiseOff_times)), np.NAN)]
+        toneOn_times = np.r_[toneOn_times, np.full((NTONES - len(toneOn_times)), np.nan)]
+        toneOff_times = np.r_[toneOff_times, np.full((NTONES - len(toneOff_times)), np.nan)]
+        noiseOn_times = np.r_[noiseOn_times, np.full((NNOISES - len(noiseOn_times)), np.nan)]
+        noiseOff_times = np.r_[noiseOff_times, np.full((NNOISES - len(noiseOff_times)), np.nan)]
 
     else:
         # Get all sound onsets and offsets
@@ -676,7 +676,7 @@ def _extract(self, sync_collection: str = 'raw_ephys_data', task_collection: str
         else:
             # If we don't have task replay then we set the treplay intervals to NaN in our passivePeriods_df dataset
             passiveGabor_df, passiveStims_df = (None, None)
-            passivePeriods_df.taskReplay = np.NAN
+            passivePeriods_df.taskReplay = np.nan
 
         if plot:
             f, ax = plt.subplots(1, 1)