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 = []
