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Small improvements to sai-cli diagnose #72

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Merged
merged 3 commits into from
Jul 7, 2025
Merged

Small improvements to sai-cli diagnose #72

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77e483c
Use symlog instead of log axis (to avoid hiding values near 0)
kaatrasa Jul 7, 2025
65f0c5e
Add isOptionalSensor flag to timestamp check
kaatrasa Jul 7, 2025
9fa43e8
Simplify IMU noise check
kaatrasa Jul 7, 2025
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26 changes: 13 additions & 13 deletions python/cli/diagnose/sensors.py
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Original file line number Diff line number Diff line change
Expand Up @@ -103,7 +103,8 @@ def analyzeTimestamps(
minFrequencyHz,
maxFrequencyHz,
plotArgs,
allowDataGaps=False):
allowDataGaps=False,
isOptionalSensor=False):
WARNING_RELATIVE_DELTA_TIME = 0.2
DATA_GAP_RELATIVE_DELTA_TIME = 10
MIN_DATA_GAP_SECONDS = 0.25
Expand Down Expand Up @@ -152,7 +153,7 @@ def toPercent(value):
plottype="scatter",
xLabel="Time (s)",
yLabel="Time diff (ms)",
yScale="log" if dataGaps > 0 else None,
yScale="symlog" if dataGaps > 0 else None,
s=10,
**plotArgs))

Expand All @@ -168,7 +169,7 @@ def toPercent(value):
)

if dataGaps > 0 and not allowDataGaps:
self.__addIssue(DiagnosisLevel.ERROR,
self.__addIssue(DiagnosisLevel.WARNING if isOptionalSensor else DiagnosisLevel.ERROR,
f"Found {dataGaps} gaps in the data longer than {SECONDS_TO_MILLISECONDS*thresholdDataGap:.1f}ms.")

if badDeltaTimes > MAX_BAD_DELTA_TIME_RATIO * total and not allowDataGaps:
Expand All @@ -180,12 +181,12 @@ def toPercent(value):

frequency = 1.0 / medianDeltaTime
if minFrequencyHz is not None and frequency < minFrequencyHz:
self.__addIssue(DiagnosisLevel.ERROR,
self.__addIssue(DiagnosisLevel.WARNING if isOptionalSensor else DiagnosisLevel.ERROR,
f"Minimum required frequency is {minFrequencyHz:.1f}Hz but data is {frequency:.1f}Hz"
)

if maxFrequencyHz is not None and frequency > maxFrequencyHz:
self.__addIssue(DiagnosisLevel.ERROR,
self.__addIssue(DiagnosisLevel.WARNING if isOptionalSensor else DiagnosisLevel.ERROR,
f"Maximum allowed frequency is {maxFrequencyHz:.1f}Hz but data is {frequency:.1f}Hz"
)

Expand Down Expand Up @@ -233,16 +234,15 @@ def analyzeSignalNoise(
signal,
timestamps,
samplingRate,
cutoffFrequency,
noiseThreshold,
sensorName,
measurementUnit):
WINDOW_SIZE_SECONDS = 1.0
count = np.shape(timestamps)[0]
windowSize = int(WINDOW_SIZE_SECONDS * samplingRate)
cutoffFrequency = samplingRate / 4.0
if windowSize <= 0: return
if count < windowSize: return
if cutoffFrequency >= 2.0 * samplingRate: return

def highpass(signal, fs, cutoff, order=3):
from scipy.signal import butter, filtfilt
Expand Down Expand Up @@ -428,7 +428,6 @@ def diagnoseAccelerometer(data, output):
ACC_MIN_FREQUENCY_HZ = 50.0
ACC_MAX_FREQUENCY_HZ = 1e4
ACC_NOISE_THRESHOLD = 2.5 # m/s²
ACC_CUTOFF_FREQUENCY_HZ = 50.0
ACC_UNIT_CHECK_THRESHOLD = 200.0 # m/s²

sensor = data["accelerometer"]
Expand All @@ -447,7 +446,6 @@ def diagnoseAccelerometer(data, output):
return

samplingRate = computeSamplingRate(deltaTimes)
cutoffThreshold = min(samplingRate / 4.0, ACC_CUTOFF_FREQUENCY_HZ)

status = Status()
status.analyzeTimestamps(
Expand All @@ -470,7 +468,6 @@ def diagnoseAccelerometer(data, output):
signal,
timestamps,
samplingRate,
cutoffThreshold,
ACC_NOISE_THRESHOLD,
sensorName="Accelerometer",
measurementUnit="m/s²")
Expand Down Expand Up @@ -572,7 +569,8 @@ def diagnoseMagnetometer(data, output):
MAGN_MAX_FREQUENCY_HZ,
plotArgs={
"title": "Magnetometer time diff"
})
},
isOptionalSensor=True)
status.analyzeSignalDuplicateValues(signal)
status.analyzeSignalUnit(
signal,
Expand Down Expand Up @@ -622,7 +620,8 @@ def diagnoseBarometer(data, output):
BARO_MAX_FREQUENCY_HZ,
plotArgs={
"title": "Barometer time diff"
})
},
isOptionalSensor=True)
status.analyzeSignalDuplicateValues(signal, BARO_DUPLICATE_VALUE_THRESHOLD)
status.analyzeSignalUnit(
signal,
Expand Down Expand Up @@ -670,7 +669,8 @@ def diagnoseGps(data, output):
plotArgs={
"title": "GPS time diff"
},
allowDataGaps=True)
allowDataGaps=True,
isOptionalSensor=True)
status.analyzeSignalDuplicateValues(signal)

output["gps"] = {
Expand Down