replace motion activity resample apply/agg lambdas with vectorized max() and first() (#23383)
CI / AMD64 Build (push) Waiting to run
CI / Assemble and push default build (push) Blocked by required conditions
CI / ARM Build (push) Waiting to run
CI / Jetson Jetpack 6 (push) Waiting to run
CI / AMD64 Extra Build (push) Blocked by required conditions
CI / ARM Extra Build (push) Blocked by required conditions
CI / Synaptics Build (push) Blocked by required conditions

This commit is contained in:
Josh Hawkins
2026-06-01 15:51:43 -06:00
committed by GitHub
parent 570e21340a
commit db9e64c598
+9 -10
View File
@@ -605,9 +605,10 @@ def motion_activity(
if not filtered:
return JSONResponse(content=[])
camera_list = list(filtered)
clauses.append((Recordings.camera << camera_list))
else:
clauses.append((Recordings.camera << allowed_cameras))
camera_list = list(allowed_cameras)
clauses.append((Recordings.camera << camera_list))
data: list[Recordings] = (
Recordings.select(
@@ -635,14 +636,12 @@ def motion_activity(
df.set_index(["start_time"], inplace=True)
# normalize data
motion = (
df["motion"]
.resample(f"{scale}s")
.apply(lambda x: max(x, key=abs, default=0.0))
.fillna(0.0)
.to_frame()
)
cameras = df["camera"].resample(f"{scale}s").agg(lambda x: ",".join(set(x)))
motion = df["motion"].resample(f"{scale}s").max().fillna(0.0).to_frame()
if len(camera_list) == 1:
cameras = df["camera"].resample(f"{scale}s").first().fillna("")
else:
cameras = df["camera"].resample(f"{scale}s").agg(lambda x: ",".join(set(x)))
df = motion.join(cameras)
length = df.shape[0]