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Minor fix to frame arrival time check in sai-cli diagnose #80

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Merged
merged 1 commit into from
Aug 11, 2025
Merged

Minor fix to frame arrival time check in sai-cli diagnose #80

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2 changes: 1 addition & 1 deletion python/cli/diagnose/diagnose.py
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Original file line number Diff line number Diff line change
Expand Up @@ -122,7 +122,7 @@ def addMeasurement(type, t, v):
if measurementType == "gyroscope":
latestGyroTime = t
for f in framesMissingNextGyroTime:
gyroTimeDeltas["next"] = abs(latestGyroTime - gyroTimeDeltas["t"])
f["next"] = abs(latestGyroTime - f["t"])
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Bug was that gyroTimeDeltas doesn't refer to f when there are multiple frames on one line.

framesMissingNextGyroTime.clear()

elif barometer is not None:
Expand Down
19 changes: 9 additions & 10 deletions python/cli/diagnose/sensors.py
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Original file line number Diff line number Diff line change
Expand Up @@ -234,9 +234,9 @@ def toPercent(value):
"timestamps that don't overlap with IMU")


def analyzeDiscardedFrames(self, start_time, end_time, timestamps):
def analyzeDiscardedFrames(self, startTime, endTime, timestamps):
if len(timestamps) == 0: return
self.images.append(plotDiscardedFrames(start_time, end_time, timestamps))
self.images.append(plotDiscardedFrames(startTime, endTime, timestamps))
self.__addIssue(DiagnosisLevel.WARNING,
f"Found {len(timestamps)} discarded frames. Typically this happens when disk I/O "
f"is too slow and prevents recording frames leading them to be discarded. You can "
Expand All @@ -263,7 +263,6 @@ def analyzeArrivalTimes(

total = len(deltaTimes)
if total == 0: return

medianDeltaTime = np.nanmedian(deltaTimes)
warningThreshold = min(.5, medianDeltaTime * 4)
errorThreshold = 1
Expand Down Expand Up @@ -299,11 +298,11 @@ def analyzeArrivalTimes(
**plotArgs))

self.__addIssue(DiagnosisLevel.ERROR if maxError > errorThreshold else DiagnosisLevel.WARNING,
f"Found {badDeltaTimes} {dataName} where the neibhgouring gyroscope samples "
f"Found {badDeltaTimes} {dataName} where the neighbouring gyroscope samples "
f"have time difference (expected {medianDeltaTime*SECONDS_TO_MILLISECONDS:.1f}ms) "
f"larger than {warningThreshold*SECONDS_TO_MILLISECONDS:.1f}ms, up to {maxError*SECONDS_TO_MILLISECONDS:.1f}ms. "
f"This indicates that gyroscope and {dataName} for same moment of time are fed "
f"large time appart which may lead to data being dropped and higher latency."
f"large time apart which may lead to data being dropped and higher latency."
)

def analyzeSignalDuplicateValues(
Expand Down Expand Up @@ -687,17 +686,17 @@ def diagnoseCamera(data, output):
sensor = data["cameras"]
output["cameras"] = []

start_time = None
end_time = None
startTime = None
endTime = None
for ind in sensor.keys():
camera = sensor[ind]
timestamps = np.array(camera["t"])
deltaTimes = np.array(camera["td"])

if len(timestamps) == 0: continue

if start_time == None or start_time > timestamps[0]: start_time = timestamps[0]
if end_time == None or end_time < timestamps[-1]: end_time = timestamps[-1]
if startTime == None or startTime > timestamps[0]: startTime = timestamps[0]
if endTime == None or endTime < timestamps[-1]: endTime = timestamps[-1]

status = Status()
status.analyzeTimestamps(
Expand Down Expand Up @@ -742,7 +741,7 @@ def diagnoseCamera(data, output):

if len(data["discardedFrames"]) > 0:
status = Status()
status.analyzeDiscardedFrames(start_time, end_time, data["discardedFrames"])
status.analyzeDiscardedFrames(startTime, endTime, data["discardedFrames"])
output["discardedFrames"] = {
"diagnosis": status.diagnosis.toString(),
"issues": status.serializeIssues(),
Expand Down