def _processGaugeData(self, bundle, trackway, data):
pesCount = bundle.leftPes.count + bundle.rightPes.count
record = {'name':trackway.name, 'count':pesCount}
gaugeData = data['gauges']
try:
value = NumericUtils.getWeightedMeanAndDeviation(gaugeData.abs)
record['abs'] = value.value
record['absUnc'] = value.uncertainty
self._trackwayGauges.abs.append((pesCount, value))
except ZeroDivisionError:
return
widthValue = NumericUtils.getWeightedMeanAndDeviation(gaugeData.width)
record['width'] = widthValue.value
record['widthUnc'] = widthValue.uncertainty
self._trackwayGauges.width.append((pesCount, widthValue))
if gaugeData.pace:
value = NumericUtils.getWeightedMeanAndDeviation(gaugeData.pace)
record['pace'] = value.value
record['paceUnc'] = value.uncertainty
self._trackwayGauges.pace.append((pesCount, value))
else:
record['pace'] = ''
record['paceUnc'] = ''
if gaugeData.stride:
value = NumericUtils.getWeightedMeanAndDeviation(gaugeData.stride)
record['stride'] = value.value
record['strideUnc'] = value.uncertainty
self._trackwayGauges.stride.append((pesCount, value))
else:
record['stride'] = ''
record['strideUnc'] = ''
self._trackwayCsv.addRow(record)
plot = ScatterPlot(
data=data['points'],
title='%s Width-Normalized Gauges (%s)' % (trackway.name, widthValue.label),
xLabel='Track Position (m)',
yLabel='Gauge (AU)')
self._paths.append(plot.save(self.getTempFilePath(extension='pdf')))
analysisTrackway = trackway.getAnalysisPair(self.analysisSession)
analysisTrackway.simpleGauge = widthValue.raw
analysisTrackway.simpleGaugeUnc = widthValue.rawUncertainty
def _analyzeTrackway(self, trackway, sitemap):
entries = []
data = {'entries':entries}
self.trackwaysData[trackway.uid] = data
super(TrackHeadingStage, self)._analyzeTrackway(trackway, sitemap)
if len(entries) < 2:
del self.trackwaysData[trackway.uid]
return
d = [item.deviation for item in entries]
significantDeviations = []
for deviation in d:
if deviation >= 1.0:
significantDeviations.append(deviation)
devs = {
'global':d[-1],
'max':max(*d),
'fraction':len(significantDeviations)/len(d) }
data['deviations'] = devs
color = 'blue'
if devs['global'] >= 1.0:
color = 'red'
elif devs['max'] >= 1.0:
color ='green'
d = [item.point for item in entries]
plot = ScatterPlot(
data=d,
color=color,
title='%s Track Headings' % trackway.name,
yLabel='Angle (Degrees)',
xLabel='Trackway Curve Position (m)')
self._paths.append(plot.save(self.getTempFilePath(extension='pdf')))
def _plotTracks(self, tracks, trackway):
"""_plotTracks doc..."""
points = []
labels = []
for t in tracks:
at = t.getAnalysisPair(self.analysisSession)
if t.pes:
signal = 1 if t.left else 2
else:
signal = 3 if t.left else 4
p = PositionValue2D(x=at.curvePosition, y=signal)
points.append(p)
labels.append(t.shortFingerprint)
plot = ScatterPlot(
data=points,
title='%s Trackway Ordering' % trackway.name,
xLabel='Trackway Curve Position (m)',
yLabel='Classification',
yLimits=[0, 5],
yTickFunc=self._plotLabelChannelsFunc)
self._paths.append(plot.save(self.getTempFilePath(extension='pdf')))
