def absoluteHeightTask(sensor_id, infile, dtype='OGP', zoffset=0,
pickle_file=None):
sensorData = md_factory.create(infile, dtype=dtype)
if dtype == 'OGP':
#
# Fit and set the reference plane to the gauge blocks.
#
sensorData.set_ref_plane(sensorData.reference.xyzPlane_fit(),
zoffset=zoffset)
elif dtype == 'ITL':
#
# Set reference plane at znom=12.998 mm
#
sensorData.set_ref_plane(XyzPlane(0, 0, 12998.))
else:
raise RuntimeError("%s not supported for absolute height analysis"
% dtype)
#
# Write surface height data points.
#
outfile = '%s_abs_height_residuals.txt' % sensor_id
sensorData.write_residuals(outfile)
#
# Make a histogram of residual heights.
#
sensorData.plot_statistics(title='Sensor Absolute Height, %s' % infile)
metData.plot.save('%s_abs_height_hist.png' % sensor_id)
#
# Box and whisker plot of residual heights
#
sensorData.resids_boxplot()
metData.plot.save('%s_abs_height_boxplot.png' % sensor_id)
#
# Quantile table
#
sensorData.quantile_table(outfile='%s_abs_height_quantile_table.txt'
% sensor_id)
#
# Surface plots
#
azims = (10, 45)
for azim in azims:
sensorData.absolute_height_plot(azim=azim)
metData.plot.save('%s_abs_height_point_cloud_azim_%i.png'
% (sensor_id, azim))
if pickle_file is not None:
sensorData.persist(pickle_file)
def flatnessTask(sensor_id, infile, dtype='OGP', pickle_file=None):
sensorData = md_factory.create(infile, dtype=dtype)
#
# Fit and set the reference plane to the LSF to the sensor surface
# points.
#
sensorData.set_ref_plane(sensorData.sensor.xyzPlane_fit(), zoffset=0)
#
# Write residual points relative to LSF surface.
#
outfile = '%s_flatness_residuals.txt' % sensor_id
sensorData.write_residuals(outfile)
#
# Make a histogram of residual heights.
#
sensorData.plot_statistics(title='Flatness, %s' % sensor_id)
metData.plot.save('%s_flatness_hist.png' % sensor_id)
#
# Box and whisker plot of residual heights
#
sensorData.resids_boxplot(title='Residuals Box Plot, %s' % sensor_id)
metData.plot.save('%s_flatness_boxplot.png' % sensor_id)
#
# Quantile table
#
sensorData.quantile_table(outfile='%s_flatness_quantile_table.txt'
% sensor_id)
#
# Surface plots
#
azims = (10, 45)
for azim in azims:
sensorData.flatness_plot(azim=azim, title='Surface Plot, %s' % sensor_id)
metData.plot.save('%s_flatness_point_cloud_azim_%i.png'
% (sensor_id, azim))
if pickle_file is not None:
sensorData.persist(pickle_file)
def flatnessTask_delta(raft_id, infiles, dtype='OGP', pickle_file=None):
# This is modified from flatnessTask to accept a list of data files as
# input. # The script processes each file to evaluate the temperature
# and standard deviation of the residuals, # then look the differences
# of the results for warm, cold, and warm again
# This routine implicitly assumes that the scan grids are exactly
# the same for the different data sets. It does keep track of
# which scan points are missing in the various data sets.
# From Peter: "This step would take the files from the previous steps,
# subtract them, and look at the difference residuals: histogram,
# quantiles, plots."
# Process each file, evaluating temperature, time, and standard
# deviation of flatness
stdev = []
temperature = []
nsigma = 4
for infile in infiles:
raftData = md_factory.create([infile], dtype=dtype)
# Fit and set the reference plane to the LSF to the raft surface
# points.
#
raftData.set_ref_plane(raftData.sensor.xyzPlane_fit(), zoffset=0)
dz = raftData.resids
# Apply nsigma clipping to evaluate the standard deviation
mean = np.mean(dz)
std = np.std(dz)
index = np.where((dz > mean-nsigma*std) & (dz < mean+nsigma*std))
stdev.append(np.std(dz[index]))
# Parse the file name to get the temperature and time stamp
pieces = infile.split('_')
nelem = len(pieces)
loc = pieces[nelem-1].find('C')
temperature.append(pieces[nelem-1][0:loc])
# Find the 'best' (least standard deviation, after clipping) scans at the warmest and
# coldest temperatures
temp = np.array(temperature).astype(float)
loc = np.where((temp == np.max(temp)))[0]
std = np.array(stdev)
pos = np.where(std[loc] == np.min(std[loc]))[0]
locWarm = loc[pos[0]]
loc = np.where((temp == np.min(temp)))[0]
pos = np.where(std[loc] == np.min(std[loc]))[0]
locCold = loc[pos[0]]
# print("locWarm: ", locWarm)
# print("locCold: ", locCold)
raftDataDelta = md_factory.create([infiles[locWarm], infiles[locCold]], dtype=dtype)
# For positions that are in common in the scans evaluate the difference in z
raftDataDelta.set_ref_plane(raftDataDelta.sensor.xyzPlane_fit(), zoffset=0)
dzDelta = raftDataDelta.resids
# print("flatness resid: ", np.std(dzDelta))
#
# Write residual points relative to LSF surface.
#
outfile = '%s_flatness_delta_residuals.txt' % raft_id
raftDataDelta.write_residuals(outfile)
#
# Make a histogram of residual heights.
#
raftDataDelta.plot_statistics(title='Raft Flatness, %s' % raft_id)
metData.plot.save('%s_flatness_delta_hist.png' % raft_id)
#
# Box and whisker plot of residual heights
#
raftDataDelta.resids_boxplot(title='Residual Box Plot, %s' % raft_id)
metData.plot.save('%s_flatness_delta_boxplot.png' % raft_id)
#
# Quantile table
#
raftDataDelta.quantile_table(outfile='%s_flatness_delta_quantile_table.txt'
% raft_id)
#
# Surface plots
#
azims = (10, 45)
for azim in azims:
raftDataDelta.flatness_plot(azim=azim, title='Surface Plot (Warm-Cold)')
metData.plot.save('%s_flatness_delta_point_cloud_azim_%i.png'
% (raft_id, azim))
if pickle_file is not None:
raftDataDelta.persist(pickle_file)
