def _postAnalyze(self):
"""_postAnalyze doc..."""
ratios = []
for name, curve in DictUtils.iter(self.data):
segments = curve.segments
for i in ListUtils.rangeOn(segments):
segment = segments[i]
segmentLine = segment.line
# If this is an extrapolated segment, use the length from the neighboring segment
# instead of the artificial length of this segment.
if segment == segments[0]:
segmentLine = segments[i + 1].line
elif segment == segments[-1]:
segmentLine = segments[i - 1].line
for pairData in segment.pairs:
projectionLine = pairData["line"]
ratios.append(100.0 * projectionLine.length.raw / segmentLine.length.raw)
h = Histogram(
data=ratios,
binCount=50,
xLabel="Projection/Stride Ratio (%)",
title="Relative Stride to Projection Length Ratios",
)
h.shaveDataToXLimits()
self._paths.append(h.save(path=self.getTempFilePath(extension="pdf")))
self.mergePdfs(self._paths, "Curve-Projection.pdf")
def _histogramTrackwaySparseness(self, label, values):
""" Creates a histogram for each trackway entry in the values list. """
path = self.getTempFilePath(extension='pdf')
h = Histogram(data=values, isLog=True)
h.xLimits = [0, 200]
h.binCount = 20
h.xLabel = 'Relative Sparseness (%)'
h.title = 'Trackway Sparseness (%s)' % label
h.shaveDataToXLimits()
h.save(path=path)
return path
def _histogramSeriesSparseness(self, label, values):
"""_histogramSeriesSparseness doc..."""
path = self.getTempFilePath(extension='pdf')
h = Histogram(data=values, isLog=True)
h.xLimits = [0, 200]
h.binCount = 20
h.xLabel = 'Relative Sparseness (%)'
h.title = 'Track Series Sparseness (%s)' % label
h.shaveDataToXLimits()
h.save(path=path)
return path
def _processCurveDeviations(self, key, label):
"""_processCurveDeviations doc..."""
d = [data['deviations'][key]
for k, data in DictUtils.iter(self.trackwaysData)]
dCurved = []
dStraight = []
for item in d:
if item < 1.0:
dStraight.append(item)
else:
dCurved.append(min(10.0, item))
plot = Histogram(
data=dCurved,
title='%s Trackway Deviations' % label,
xLabel='Trackway Index')
self._paths.insert(0, plot.save(self.getTempFilePath(extension='pdf')))
d = [(index, d[index]) for index in ListUtils.rangeOn(d)]
dCurved = []
dStraight = []
for item in d:
if item[-1] < 1.0:
dStraight.append(item)
else:
dCurved.append((item[0], min(10.0, item[1])) )
self.logger.write('%s Trackways: %s/%s (%s%%)' % (
label,
len(dCurved),
len(dCurved) + len(dStraight),
100.0*len(dCurved)/(len(dCurved) + len(dStraight)) ))
plot = MultiScatterPlot(
{'data':dStraight, 'color':'blue'},
{'data':dCurved, 'color':'red'},
title='%s Trackway Deviations' % label,
xLabel='Trackway Index',
yLabel='Fractional Deviation')
self._paths.insert(1, plot.save(self.getTempFilePath(extension='pdf')))
def _plotTrackwayGauges(self, points, color, unit, heading):
histData = []
for p in points:
histData.append(p.yValue.value)
plot = Histogram(
data=histData,
title='%s Trackway Gauges' % heading,
xLabel='Averaged Trackway Gauge (%s)' % unit,
yLabel='Frequency',
color=color)
self._paths.insert(0, plot.save(self.getTempFilePath(extension='pdf')))
plot = ScatterPlot(
data=ListUtils.sortObjectList(points, 'y', inPlace=True),
title='%s Trackway Gauges' % heading,
xLabel='Trackway Pes Count (#)',
yLabel='Averaged Trackway Gauge (%s)' % unit,
color=color)
self._paths.insert(0, plot.save(self.getTempFilePath(extension='pdf')))
def _processAspectRatios(self):
"""_processAspectRatios doc..."""
aspects = []
pesAspects = []
manAspects = []
for entry in self.entries:
value = entry['aspect'].value
aspects.append(value)
if entry['track'].pes:
pesAspects.append(value)
else:
manAspects.append(value)
self.logger.write('%s\nASPECT RATIO' % ('='*80))
self.logger.write('Total: %s' %
NumericUtils.getMeanAndDeviation(aspects).label)
self.logger.write('Pes: %s' %
NumericUtils.getMeanAndDeviation(pesAspects).label)
self.logger.write('Manus: %s' %
NumericUtils.getMeanAndDeviation(manAspects).label)
h = Histogram(data=aspects, color='green')
h.title = 'Aspect Ratios'
h.yLabel = 'Count'
h.xLabel = 'Aspect Ratio (width/length)'
self._paths.append(h.save(self.getTempFilePath(extension='pdf')))
h = Histogram(data=pesAspects, color='green')
h.title = 'Aspect Ratios (Pes)'
h.yLabel = 'Count'
h.xLabel = 'Aspect Ratio (width/length)'
self._paths.append(h.save(self.getTempFilePath(extension='pdf')))
h = Histogram(data=manAspects, color='green')
h.title = 'Aspect Ratios (Manus)'
h.yLabel = 'Count'
h.xLabel = 'Aspect Ratio (width/length)'
self._paths.append(h.save(self.getTempFilePath(extension='pdf')))
def _postAnalyze(self):
h = Histogram(
data=self._uncs,
binCount=80,
xLimits=(0, max(*self._uncs)),
color="r",
title="Distribution of Rotational Uncertainties",
xLabel="Uncertainty Value (degrees)",
yLabel="Frequency",
)
p1 = h.save(self.getTempFilePath(extension="pdf"))
h.isLog = True
h.title += " (log)"
p2 = h.save(self.getTempFilePath(extension="pdf"))
self.mergePdfs([p1, p2], self.getPath("Rotational-Uncertainty-Distribution.pdf"))
average = NumericUtils.getMeanAndDeviation(self._uncs)
self.logger.write("Average rotational uncertainty: %s" % average.label)
# -------------------------------------------------------------------------------------------
# FIND LARGE UNCERTAINTY TRACKS
largeUncertaintyCount = 0
drawing = None
sitemap = None
# If track uncertainty is 2x average, add that track to the spreadsheet and map overlay
for t in self._tracks:
# if the tracksite has changed, save previous map and make a new one
if sitemap != t.trackSeries.trackway.sitemap:
# save the last site map drawing (if there was one)
if drawing:
drawing.save()
# then start a new drawing for this new site map
sitemap = t.trackSeries.trackway.sitemap
fileName = "%s-%s-ROTATION_UNC.svg" % (sitemap.name, sitemap.level)
path = self.getPath(self.DRAWING_FOLDER_NAME, fileName, isFile=True)
drawing = CadenceDrawing(path, sitemap)
# create a group to be instanced for the spreadsheet values
drawing.createGroup("rect1")
# create a rectangle of 100x100 cm that is to be scaled by fractional meters
drawing.rect((0, 0), 100, 100, scene=True, groupId="rect1")
# create another group to be instanced for the mapped values.
drawing.createGroup("rect2")
# create a rectangle of 100x100 cm that is to be scaled by fractional meters
drawing.rect((0, 0), 100, 100, scene=True, groupId="rect2")
# and place a grid and the federal coordinates in the drawing file
drawing.grid()
drawing.federalCoordinates()
# now examine the positional uncertainties for this track
rotation = t.rotationAngle.valueDegrees
if rotation.uncertainty <= 2.0 * average.uncertainty:
# then just indicate that this track has low uncertainty
self._drawLowUncertaintyMarker(drawing, t)
# label this track green
# drawing.text(
# t.name,
# (t.x - 20, t.z),
# scene=True,
# stroke='green',
# stroke_width='0.25',
# font_size='8px',
# font_family='Arial')
continue
# else, since the uncertainty is high, first write that track in the spreadsheet
largeUncertaintyCount += 1
self._largeUncCsv.createRow(uid=t.uid, fingerprint=t.fingerprint, r=rotation.label)
# if either the measured width or length is 0, mark with a yellow disk with red outline
if t.rotationMeasured == 0:
drawing.circle(
(t.x, t.z),
100 * (t.widthUncertainty + t.lengthUncertainty) / 2.0,
scene=True,
fill="yellow",
stroke="red",
)
# drawing.text(
# t.name,
# (t.x - 20, t.z),
# scene=True,
# stroke='black',
# stroke_width='0.25',
# font_size='6px',
# font_family='Arial')
continue
self._drawHighUncertaintyMarker(drawing, t)
# label this track with red
# drawing.text(
# t.name,
# (t.x - 20, t.z),
# scene=True,
# stroke='red',
# stroke_width='0.25',
# font_size='6px',
# font_family='Arial')
# and close off with a final save of the drawing file
if drawing:
drawing.save()
self.logger.write(
"%s Tracks with large rotational uncertainties found (%s%%)"
% (
largeUncertaintyCount,
NumericUtils.roundToOrder(100.0 * float(largeUncertaintyCount) / float(len(self._tracks)), -1),
)
)
self._largeUncCsv.save()
self._tracks = []
def _postAnalyze(self):
h = Histogram(
data=self._uncs,
binCount=80,
xLimits=(0, max(*self._uncs)),
color='r',
title='Distribution of Spatial (X, Z) Uncertainties',
xLabel='Uncertainty Value (m)',
yLabel='Frequency')
p1 = h.save(self.getTempFilePath(extension='pdf'))
h.isLog = True
h.title += ' (log)'
p2 = h.save(self.getTempFilePath(extension='pdf'))
self.mergePdfs([p1, p2], self.getPath('Spatial-Uncertainty-Distribution.pdf'))
average = NumericUtils.getMeanAndDeviation(self._uncs)
self.logger.write('Average spatial uncertainty: %s' % average.label)
#-------------------------------------------------------------------------------------------
# FIND LARGE UNCERTAINTY TRACKS
largeUncertaintyCount = 0
drawing = None
sitemap = None
# If track uncertainty is 2x average, add that track to the spreadsheet and map overlay
for t in self._tracks:
# if the tracksite has changed, save previous map and make a new one
if sitemap != t.trackSeries.trackway.sitemap:
# save the last site map drawing (if there was one)
if drawing:
drawing.save()
# then start a new drawing for this new site map
sitemap = t.trackSeries.trackway.sitemap
fileName = sitemap.name + "_" + sitemap.level + '_uncertainty.svg'
path = self.getPath(self.DRAWING_FOLDER_NAME, fileName, isFile=True)
drawing = CadenceDrawing(path, sitemap)
# create a group to be instanced for the spreadsheet values
drawing.createGroup('rect1')
# create a rectangle of 100x100 cm that is to be scaled by fractional meters
drawing.rect((0, 0), 100, 100, scene=True, groupId='rect1')
# create another group to be instanced for the mapped values.
drawing.createGroup('rect2')
# create a rectangle of 100x100 cm that is to be scaled by fractional meters
drawing.rect((0, 0), 100, 100, scene=True, groupId='rect2')
# and place a grid and the federal coordinates in the drawing file
drawing.grid()
drawing.federalCoordinates()
# now examine the positional uncertainties for this track
x = t.xValue
z = t.zValue
if x.uncertainty > 0.15 or z.uncertainty > 0.15:
# s = '%s%s %s%s: %s %s'% (
# t.site, t.level, t.trackwayType, t.trackwayNumber, t.name, t.uid)
# print('%s: (%s and %s)' % (s, x.uncertainty, z.uncertainty))
print('%s\t%s' % (t.uid, t.fingerprint))
if max(x.uncertainty, z.uncertainty) <= 2.0*average.uncertainty:
# then just indicate that this track has low uncertainty
self._drawLowUncertaintyMarker(drawing, t)
# label this track with green
drawing.text(
t.name,
(t.x - 20, t.z),
scene=True,
stroke='green',
stroke_width='0.25',
font_size='8px',
font_family='Arial')
continue
# else, since the uncertainty is high, first write that track in the spreadsheet
largeUncertaintyCount += 1
self._largeUncCsv.createRow(
uid=t.uid,
fingerprint=t.fingerprint,
x=x.label,
z=z.label)
# if either the measured width or length is 0, mark with a yellow disk with red outline
if t.widthMeasured == 0 or t.lengthMeasured == 0:
drawing.circle(
(t.x, t.z),
100*(t.widthUncertainty + t.lengthUncertainty)/2.0,
scene=True,
fill='yellow',
stroke='red')
drawing.text(
t.name,
(t.x - 20, t.z),
scene=True,
stroke='black',
stroke_width='0.25',
font_size='6px',
font_family='Arial')
continue
self._drawHighUncertaintyMarker(drawing, t)
# label this track with red
drawing.text(
t.name,
(t.x - 20, t.z),
scene=True,
stroke='red',
stroke_width='0.25',
font_size='6px',
font_family='Arial')
#
# # draw this track indicating it has high uncertainty
# drawing.use(
# 'rect1',
# (t.x, t.z),
# scene=True,
# rotation=t.rotation,
# opacity='0.5',
# scale=t.widthMeasured,
# scaleY=t.lengthMeasured,
# fill='red',
# stroke='red')
#
# # draw the map dimensions with an outline gray rectangle
# drawing.use(
# 'rect2',
# (t.x, t.z),
# scene=True,
# rotation=t.rotation,
# scale=t.width,
# scaleY=t.length,
# fill='none',
# stroke='gray')
# and close off with a final save of the drawing file
if drawing:
drawing.save()
self.logger.write('%s Tracks with large spatial uncertainties found (%s%%)' % (
largeUncertaintyCount, NumericUtils.roundToOrder(
100.0*float(largeUncertaintyCount)/float(len(self._tracks)), -1) ))
self._largeUncCsv.save()
self._tracks = []
