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LogAnalyzer: fixed XML output glitch in the case of a skipped (NA) test

This commit is contained in:
Andrew Chapman 2014-03-21 12:02:10 -07:00 committed by Andrew Tridgell
parent f122b1ba37
commit 0fe6c1ee3a
5 changed files with 129 additions and 4 deletions
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4
Tools/LogAnalyzer/LogAnalyzer.py
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@ -187,8 +187,8 @@ class TestSuite:
print >>xml, " <message>" + escape(test.result.statusMessage) + "</message>"
print >>xml, " <data>(test data will be embeded here at some point)</data>"
elif test.result.status == TestResult.StatusType.NA:
# skip any that aren't relevant for this vehicle/hardware/etc
continue
print >>xml, " <name>" + escape(test.name) + "</name>"
print >>xml, " <status>NA</status>"
else:
print >>xml, " <name>" + escape(test.name) + "</name>"
print >>xml, " <status>UNKNOWN</status>"

8
Tools/LogAnalyzer/example_output.xml
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@ -296,7 +296,8 @@
<result>
<name>Compass</name>
<status>PASS</status>
<message></message>
<message>No MAG data, unable to test mag_field interference
</message>
</result>
<result>
<name>Dupe Log Data</name>
@ -331,6 +332,11 @@
<message>14 slow loop lines found, max 18.56% on line 2983</message>
<data>(test data will be embeded here at some point)</data>
</result>
<result>
<name>Pitch/Roll</name>
<status>PASS</status>
<message></message>
</result>
<result>
<name>Underpowered</name>
<status>PASS</status>

2
Tools/LogAnalyzer/tests/TestCompass.py
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@ -89,7 +89,7 @@ class TestCompass(Test):
self.result.status = TestResult.StatusType.WARN
self.result.statusMessage = self.result.statusMessage + "Moderate change in mag_field (%.2f%%)\n" % (percentDiff*100)
else:
self.result.statusMessage = self.result.statusMessage + "Good mag_field (%.2f%%)\n" % (percentDiff*100)
self.result.statusMessage = self.result.statusMessage + "mag_field interference within limits (%.2f%%)\n" % (percentDiff*100)
if minMagField < minMagFieldThreshold:
self.result.statusMessage = self.result.statusMessage + "Min mag field length (%.2f) < recommended (%.2f)\n" % (minMagField,minMagFieldThreshold)
if maxMagField > maxMagFieldThreshold:

118
Tools/LogAnalyzer/tests/TestDualGyroDrift.py Normal file
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@ -0,0 +1,118 @@
from LogAnalyzer import Test,TestResult
import DataflashLog
# import scipy
# import pylab #### TEMP!!! only for dev
# from scipy import signal
class TestDualGyroDrift(Test):
'''test for gyro drift between dual IMU data'''
def __init__(self):
self.name = "Gyro Drift"
self.enable = False
def run(self, logdata, verbose):
self.result = TestResult()
self.result.status = TestResult.StatusType.PASS
# if "IMU" not in logdata.channels or "IMU2" not in logdata.channels:
# self.result.status = TestResult.StatusType.NA
# return
# imuX = logdata.channels["IMU"]["GyrX"].listData
# imu2X = logdata.channels["IMU2"]["GyrX"].listData
# # NOTE: weird thing about Holger's log is that the counts of IMU+IMU2 are different
# print "length 1: %.2f, length 2: %.2f" % (len(imuX),len(imu2X))
# #assert(len(imuX) == len(imu2X))
# # divide the curve into segments and get the average of each segment
# # we will get the diff between those averages, rather than a per-sample diff as the IMU+IMU2 arrays are often not the same length
# diffThresholdWARN = 0.03
# diffThresholdFAIL = 0.05
# nSamples = 10
# imu1XAverages, imu1YAverages, imu1ZAverages, imu2XAverages, imu2YAverages, imu2ZAverages = ([],[],[],[],[],[])
# imuXDiffAverages, imuYDiffAverages, imuZDiffAverages = ([],[],[])
# maxDiffX, maxDiffY, maxDiffZ = (0,0,0)
# sliceLength1 = len(logdata.channels["IMU"]["GyrX"].dictData.values()) / nSamples
# sliceLength2 = len(logdata.channels["IMU2"]["GyrX"].dictData.values()) / nSamples
# for i in range(0,nSamples):
# imu1XAverages.append(numpy.mean(logdata.channels["IMU"]["GyrX"].dictData.values()[i*sliceLength1:i*sliceLength1+sliceLength1]))
# imu1YAverages.append(numpy.mean(logdata.channels["IMU"]["GyrY"].dictData.values()[i*sliceLength1:i*sliceLength1+sliceLength1]))
# imu1ZAverages.append(numpy.mean(logdata.channels["IMU"]["GyrZ"].dictData.values()[i*sliceLength1:i*sliceLength1+sliceLength1]))
# imu2XAverages.append(numpy.mean(logdata.channels["IMU2"]["GyrX"].dictData.values()[i*sliceLength2:i*sliceLength2+sliceLength2]))
# imu2YAverages.append(numpy.mean(logdata.channels["IMU2"]["GyrY"].dictData.values()[i*sliceLength2:i*sliceLength2+sliceLength2]))
# imu2ZAverages.append(numpy.mean(logdata.channels["IMU2"]["GyrZ"].dictData.values()[i*sliceLength2:i*sliceLength2+sliceLength2]))
# imuXDiffAverages.append(imu2XAverages[-1]-imu1XAverages[-1])
# imuYDiffAverages.append(imu2YAverages[-1]-imu1YAverages[-1])
# imuZDiffAverages.append(imu2ZAverages[-1]-imu1ZAverages[-1])
# if abs(imuXDiffAverages[-1]) > maxDiffX:
# maxDiffX = imuXDiffAverages[-1]
# if abs(imuYDiffAverages[-1]) > maxDiffY:
# maxDiffY = imuYDiffAverages[-1]
# if abs(imuZDiffAverages[-1]) > maxDiffZ:
# maxDiffZ = imuZDiffAverages[-1]
# if max(maxDiffX,maxDiffY,maxDiffZ) > diffThresholdFAIL:
# self.result.status = TestResult.StatusType.FAIL
# self.result.statusMessage = "IMU/IMU2 gyro averages differ by more than %s radians" % diffThresholdFAIL
# elif max(maxDiffX,maxDiffY,maxDiffZ) > diffThresholdWARN:
# self.result.status = TestResult.StatusType.WARN
# self.result.statusMessage = "IMU/IMU2 gyro averages differ by more than %s radians" % diffThresholdWARN
# # pylab.plot(zip(*imuX)[0], zip(*imuX)[1], 'g')
# # pylab.plot(zip(*imu2X)[0], zip(*imu2X)[1], 'r')
# #pylab.plot(range(0,(nSamples*sliceLength1),sliceLength1), imu1ZAverages, 'b')
# print "Gyro averages1X: " + `imu1XAverages`
# print "Gyro averages1Y: " + `imu1YAverages`
# print "Gyro averages1Z: " + `imu1ZAverages` + "\n"
# print "Gyro averages2X: " + `imu2XAverages`
# print "Gyro averages2Y: " + `imu2YAverages`
# print "Gyro averages2Z: " + `imu2ZAverages` + "\n"
# print "Gyro averages diff X: " + `imuXDiffAverages`
# print "Gyro averages diff Y: " + `imuYDiffAverages`
# print "Gyro averages diff Z: " + `imuZDiffAverages`
# # lowpass filter using numpy
# # cutoff = 100
# # fs = 10000.0
# # b,a = scipy.signal.filter_design.butter(5,cutoff/(fs/2))
# # imuXFiltered = scipy.signal.filtfilt(b,a,zip(*imuX)[1])
# # imu2XFiltered = scipy.signal.filtfilt(b,a,zip(*imu2X)[1])
# #pylab.plot(imuXFiltered, 'r')
# # TMP: DISPLAY BEFORE+AFTER plots
# pylab.show()
# # print "imuX average before lowpass filter: %.8f" % logdata.channels["IMU"]["GyrX"].avg()
# # print "imuX average after lowpass filter: %.8f" % numpy.mean(imuXFiltered)
# # print "imu2X average before lowpass filter: %.8f" % logdata.channels["IMU2"]["GyrX"].avg()
# # print "imu2X average after lowpass filter: %.8f" % numpy.mean(imu2XFiltered)
# avg1X = logdata.channels["IMU"]["GyrX"].avg()
# avg1Y = logdata.channels["IMU"]["GyrY"].avg()
# avg1Z = logdata.channels["IMU"]["GyrZ"].avg()
# avg2X = logdata.channels["IMU2"]["GyrX"].avg()
# avg2Y = logdata.channels["IMU2"]["GyrY"].avg()
# avg2Z = logdata.channels["IMU2"]["GyrZ"].avg()
# avgRatioX = (max(avg1X,avg2X) - min(avg1X,avg2X)) / #abs(max(avg1X,avg2X) / min(avg1X,avg2X))
# avgRatioY = abs(max(avg1Y,avg2Y) / min(avg1Y,avg2Y))
# avgRatioZ = abs(max(avg1Z,avg2Z) / min(avg1Z,avg2Z))
# self.result.statusMessage = "IMU gyro avg: %.4f,%.4f,%.4f\nIMU2 gyro avg: %.4f,%.4f,%.4f\nAvg ratio: %.4f,%.4f,%.4f" % (avg1X,avg1Y,avg1Z, avg2X,avg2Y,avg2Z, avgRatioX,avgRatioY,avgRatioZ)

1
Tools/LogAnalyzer/tests/TestPitchRollCoupling.py
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@ -34,6 +34,7 @@ class TestPitchRollCoupling(Test):
orderedModes = collections.OrderedDict(sorted(logdata.modeChanges.items(), key=lambda t: t[0]))
isAuto = False # we always start in a manual control mode
prevLine = 0
mode = ""
for line,modepair in orderedModes.iteritems():
mode = modepair[0].upper()
if prevLine == 0: