def CalibrateWish( run_per_panel_list):
'''
:param run_per_panel_list: is a list of tuples with the run number and the associated panel
run_per_panel_list = [ (17706, 'panel01'), (17705, 'panel02'), (17701, 'panel03'), (17702, 'panel04'), (17695, 'panel05')]
'''
# == Set parameters for calibration ==
previousDefaultInstrument = config['default.instrument']
config['default.instrument']="WISH"
# definition of the parameters static for the calibration
lower_tube = numpy.array([-0.41,-0.31,-0.21,-0.11,-0.02, 0.09, 0.18, 0.28, 0.39 ])
upper_tube = numpy.array(lower_tube+0.003)
funcForm = 9*[1] # 9 gaussian peaks
margin = 15
low_range = range(0,76)
high_range = range(76,152)
kwargs = {'margin':margin}
# it will copy all the data from the runs to have a single instrument with the calibrated data.
whole_instrument = LoadRaw(str(run_per_panel_list[0][0]))
whole_instrument = Integration(whole_instrument)
for (run_number, panel_name) in run_per_panel_list:
panel_name = str(panel_name)
run_number = str(run_number)
# load your data and integrate it
ws = LoadRaw(run_number, OutputWorkspace=panel_name)
ws = Integration(ws, 1, 20000, OutputWorkspace=panel_name)
# use the TubeSpec object to be able to copy the data to the whole_instrument
tube_set = TubeSpec(ws)
tube_set.setTubeSpecByString(panel_name)
# update kwargs argument before calling calibrate
kwargs['rangeList'] = low_range # calibrate only the lower tubes
calibrationTable = tube.calibrate(ws, tube_set, lower_tube, funcForm, **kwargs)
# update kwargs
kwargs['calibTable'] = calibrationTable # append calib to calibrationtable
kwargs['rangeList'] = high_range # calibrate only the upper tubes
calibrationTable = tube.calibrate(ws, tube_set, upper_tube, funcForm, **kwargs)
kwargs['calibTable'] = calibrationTable
ApplyCalibration(ws, calibrationTable)
# copy data from the current panel to the whole_instrument
for i in range(tube_set.getNumTubes()):
for spec_num in tube_set.getTube(i):
whole_instrument.setY(spec_num,ws.dataY(spec_num))
# calibrate the whole_instrument with the last calibrated panel which has the calibration accumulation
# of all the others
CopyInstrumentParameters(run_per_panel_list[-1][1],whole_instrument)
config['default.instrument'] = previousDefaultInstrument
def getCalibrationFromPeakFile(ws, calibTable, iTube, PeakFile):
"""
Get the results the calibration and put them in the calibration table provided.
@param ws: Integrated Workspace with tubes to be calibrated
@param calibTable: Calibration table into which the calibration results are placed
@param iTube: The ideal tube
@param PeakFile: File of peaks for calibration
"""
# Get Ideal Tube
ideal_tube = iTube.getArray()
# Read Peak File
peak_array = read_peak_file(PeakFile)
n_tubes = len(peak_array)
print("Number of tubes read from file =", n_tubes)
for i in range(n_tubes):
# Deal with (i+1)st tube got from file
tube_name = peak_array[i][0] # e.g. 'MERLIN/door3/tube_3_1'
tube = TubeSpec(ws)
tube.setTubeSpecByString(tube_name)
actual_tube = peak_array[i][1] # e.g. [2.0, 512.5, 1022.0]
wht, _ = tube.getTube(0)
print("Calibrating tube", i + 1, "of", n_tubes, tube_name)
if len(wht) < 1:
print(
"Unable to get any workspace indices for this tube. Calibration abandoned."
)
return
det_id_list, det_pos_list = getCalibratedPixelPositions(
ws, actual_tube, ideal_tube, wht)
if len(det_id_list) == len(wht): # We have corrected positions
for j in range(len(wht)):
next_row = {
'Detector ID': det_id_list[j],
'Detector Position': det_pos_list[j]
}
calibTable.addRow(next_row)
if n_tubes == 0:
return
# Delete temporary workspaces for getting new detector positions
DeleteWorkspace('PolyFittingWorkspace')
DeleteWorkspace('QF_NormalisedCovarianceMatrix')
DeleteWorkspace('QF_Parameters')
DeleteWorkspace('QF_Workspace')
def getCalibrationFromPeakFile ( ws, calibTable, iTube, PeakFile ):
"""
Get the results the calibration and put them in the calibration table provided.
@param ws: Integrated Workspace with tubes to be calibrated
@param calibTable: Calibration table into which the calibration results are placed
@param iTube: The ideal tube
@param PeakFile: File of peaks for calibtation
"""
# Get Ideal Tube
idealTube = iTube.getArray()
# Read Peak File
PeakArray = readPeakFile( PeakFile )
nTubes = len(PeakArray)
print "Number of tubes read from file =",nTubes
for i in range(nTubes):
# Deal with (i+1)st tube got from file
TubeName = PeakArray[i][0] # e.g. 'MERLIN/door3/tube_3_1'
tube = TubeSpec(ws)
tube.setTubeSpecByString(TubeName)
actualTube = PeakArray[i][1] # e.g. [2.0, 512.5, 1022.0]
wht, _ = tube.getTube(0)
print "Calibrating tube", i+1 ,"of", nTubes, TubeName #, " length", tubeSet.getTubeLength(i)
if len(wht) < 1 :
print "Unable to get any workspace indices for this tube. Calibration abandoned."
return
detIDList, detPosList = getCalibratedPixelPositions( ws, actualTube, idealTube, wht)
#print len(wht)
if len(detIDList) == len(wht): # We have corrected positions
for j in range(len(wht)):
nextRow = {'Detector ID': detIDList[j], 'Detector Position': detPosList[j] }
calibTable.addRow ( nextRow )
if nTubes == 0:
return
# Delete temporary workspaces for getting new detector positions
DeleteWorkspace('PolyFittingWorkspace')
DeleteWorkspace('QF_NormalisedCovarianceMatrix')
DeleteWorkspace('QF_Parameters')
DeleteWorkspace('QF_Workspace')
def CalibrateWish(run_per_panel_list):
'''
:param run_per_panel_list: is a list of tuples with the run number and the associated panel
run_per_panel_list = [ (17706, 'panel01'), (17705, 'panel02'), (17701, 'panel03'), (17702, 'panel04'), (17695, 'panel05')]
'''
# == Set parameters for calibration ==
previousDefaultInstrument = config['default.instrument']
config['default.instrument']="WISH"
# definition of the parameters static for the calibration
lower_tube = numpy.array([-0.41,-0.31,-0.21,-0.11,-0.02, 0.09, 0.18, 0.28, 0.39 ])
upper_tube = numpy.array(lower_tube+0.003)
funcForm = 9*[1] # 9 gaussian peaks
margin = 15
low_range = range(0,76)
high_range = range(76,152)
kwargs = {'margin':margin}
# it will copy all the data from the runs to have a single instrument with the calibrated data.
whole_instrument = LoadRaw(str(run_per_panel_list[0][0]))
whole_instrument = Integration(whole_instrument)
for (run_number, panel_name) in run_per_panel_list:
panel_name = str(panel_name)
run_number = str(run_number)
# load your data and integrate it
ws = LoadRaw(run_number, OutputWorkspace=panel_name)
ws = Integration(ws, 1, 20000, OutputWorkspace=panel_name)
# use the TubeSpec object to be able to copy the data to the whole_instrument
tube_set = TubeSpec(ws)
tube_set.setTubeSpecByString(panel_name)
# update kwargs argument before calling calibrate
kwargs['rangeList'] = low_range # calibrate only the lower tubes
calibrationTable = tube.calibrate(ws, tube_set, lower_tube, funcForm, **kwargs)
# update kwargs
kwargs['calibTable'] = calibrationTable # append calib to calibrationtable
kwargs['rangeList'] = high_range # calibrate only the upper tubes
calibrationTable = tube.calibrate(ws, tube_set, upper_tube, funcForm, **kwargs)
kwargs['calibTable'] = calibrationTable
ApplyCalibration(ws, calibrationTable)
# copy data from the current panel to the whole_instrument
for i in range(tube_set.getNumTubes()):
for spec_num in tube_set.getTube(i):
whole_instrument.setY(spec_num,ws.dataY(spec_num))
# calibrate the whole_instrument with the last calibrated panel which has the calibration accumulation
# of all the others
CopyInstrumentParameters(run_per_panel_list[-1][1],whole_instrument)
config['default.instrument'] = previousDefaultInstrument
def enforce_tube_spec(tube_set, ws):
if isinstance(tube_set, str):
selected_tubes = tube_set
tube_set = TubeSpec(ws)
tube_set.setTubeSpecByString(selected_tubes)
return tube_set
if isinstance(tube_set, list):
selected_tubes = tube_set
tube_set = TubeSpec(ws)
tube_set.setTubeSpecByStringArray(selected_tubes)
return tube_set
if isinstance(tube_set, TubeSpec):
return tube_set
raise RuntimeError(
"Wrong argument tubeSet. "
"It must be a TubeSpec or a string that defines the set of tubes to be calibrated. "
"For example: WISH/panel03")
def enforce_tube_spec(tube_set, ws):
if isinstance(tube_set, str):
selected_tubes = tube_set
tube_set = TubeSpec(ws)
tube_set.setTubeSpecByString(selected_tubes)
return tube_set
if isinstance(tube_set, list):
selected_tubes = tube_set
tube_set = TubeSpec(ws)
tube_set.setTubeSpecByStringArray(selected_tubes)
return tube_set
if isinstance(tube_set, TubeSpec):
return tube_set
raise RuntimeError("Wrong argument tubeSet. "
"It must be a TubeSpec or a string that defines the set of tubes to be calibrated. "
"For example: WISH/panel03")
CalibInstWS = mantid.Integration( rawCalibInstWS, RangeLower=1, RangeUpper=20000 )
mantid.DeleteWorkspace(rawCalibInstWS)
print("Created workspace (CalibInstWS) with integrated data from run and instrument to calibrate")
CalibratedComponent = 'WISH/panel03/tube038'
# Set fitting parameters
eP = [65.0, 113.0, 161.0, 209.0, 257.0, 305.0, 353.0, 401.0, 449.0]
ExpectedHeight = 2000.0 # Expected Height of Gaussian Peaks (initial value of fit parameter)
ExpectedWidth = 32.0 # Expected width of Gaussian peaks in pixels (initial value of fit parameter)
fitPar = TubeCalibFitParams( eP, ExpectedHeight, ExpectedWidth )
fitPar.setAutomatic(True)
print("Created objects needed for calibration.")
func_form = 9*[1]
# Use first tube as ideal tube
tube1 = TubeSpec(CalibInstWS)
tube1.setTubeSpecByString('WISH/panel03/tube038')
iTube = tube_calib.constructIdealTubeFromRealTube( CalibInstWS, tube1, fitPar, func_form)
known_pos = iTube.getArray()
print(known_pos)
# Get the calibration and put it into the calibration table
calibrationTable = tube.calibrate( CalibInstWS, 'WISH/panel03', known_pos, func_form, fitPar=fitPar)
print("Got calibration (new positions of detectors)")
#Apply the calibration
mantid.ApplyCalibration(Workspace=CalibInstWS, CalibrationTable=calibrationTable)
print("Applied calibration")
def runTest(self):
# This script calibrates WISH using known peak positions from
# neutron absorbing bands. The workspace with suffix "_calib"
# contains calibrated data. The workspace with suxxic "_corrected"
# contains calibrated data with known problematic tubes also corrected
ws = mantid.LoadNexusProcessed(Filename="WISH30541_integrated.nxs")
# This array defines the positions of peaks on the detector in
# meters from the center (0)
# For wish this is calculated as follows:
# Height of all 7 bands = 0.26m => each band is separated by 0.260 / 6 = 0.4333m
# The bands are on a cylinder diameter 0.923m. So we can work out the angle as
# (0.4333 * n) / (0.923 / 2) where n is the number of bands above (or below) the
# center band.
# Putting this together with the distance to the detector tubes (2.2m) we get
# the following: (0.4333n) / 0.4615 * 2200 = Expected peak positions
# From this we can show there should be 5 peaks (peaks 6 + 7 are too high/low)
# at: 0, 0.206, 0.413 respectively (this is symmetrical so +/-)
peak_positions = np.array([-0.413, -0.206, 0, 0.206, 0.413])
funcForm = 5 * [1] # 5 gaussian peaks
fitPar = TubeCalibFitParams([59, 161, 258, 353, 448])
fitPar.setAutomatic(True)
instrument = ws.getInstrument()
spec = TubeSpec(ws)
spec.setTubeSpecByString(instrument.getFullName())
idealTube = IdealTube()
idealTube.setArray(peak_positions)
# First calibrate all of the detectors
calibrationTable, peaks = tube.calibrate(ws, spec, peak_positions, funcForm, margin=15,
outputPeak=True, fitPar=fitPar)
self.calibration_table = calibrationTable
def findBadPeakFits(peaksTable, threshold=10):
""" Find peaks whose fit values fall outside of a given tolerance
of the mean peak centers across all tubes.
Tubes are defined as have a bad fit if the absolute difference
between the fitted peak centers for a specific tube and the
mean of the fitted peak centers for all tubes differ more than
the threshold parameter.
@param peakTable: the table containing fitted peak centers
@param threshold: the tolerance on the difference from the mean value
@return A list of expected peak positions and a list of indices of tubes
to correct
"""
n = len(peaksTable)
num_peaks = peaksTable.columnCount() - 1
column_names = ['Peak%d' % i for i in range(1, num_peaks + 1)]
data = np.zeros((n, num_peaks))
for i, row in enumerate(peaksTable):
data_row = [row[name] for name in column_names]
data[i, :] = data_row
# data now has all the peaks positions for each tube
# the mean value is the expected value for the peak position for each tube
expected_peak_pos = np.mean(data, axis=0)
# calculate how far from the expected position each peak position is
distance_from_expected = np.abs(data - expected_peak_pos)
check = np.where(distance_from_expected > threshold)[0]
problematic_tubes = list(set(check))
print("Problematic tubes are: " + str(problematic_tubes))
return expected_peak_pos, problematic_tubes
def correctMisalignedTubes(ws, calibrationTable, peaksTable, spec, idealTube, fitPar, threshold=10):
""" Correct misaligned tubes due to poor fitting results
during the first round of calibration.
Misaligned tubes are first identified according to a tolerance
applied to the absolute difference between the fitted tube
positions and the mean across all tubes.
The FindPeaks algorithm is then used to find a better fit
with the ideal tube positions as starting parameters
for the peak centers.
From the refitted peaks the positions of the detectors in the
tube are recalculated.
@param ws: the workspace to get the tube geometry from
@param calibrationTable: the calibration table output from running calibration
@param peaksTable: the table containing the fitted peak centers from calibration
@param spec: the tube spec for the instrument
@param idealTube: the ideal tube for the instrument
@param fitPar: the fitting parameters for calibration
@param threshold: tolerance defining is a peak is outside of the acceptable range
@return table of corrected detector positions
"""
table_name = calibrationTable.name() + 'Corrected'
corrections_table = mantid.CreateEmptyTableWorkspace(OutputWorkspace=table_name)
corrections_table.addColumn('int', "Detector ID")
corrections_table.addColumn('V3D', "Detector Position")
mean_peaks, bad_tubes = findBadPeakFits(peaksTable, threshold)
for index in bad_tubes:
print("Refitting tube %s" % spec.getTubeName(index))
tube_dets, _ = spec.getTube(index)
getPoints(ws, idealTube.getFunctionalForms(), fitPar, tube_dets)
tube_ws = mantid.mtd['TubePlot']
fit_ws = mantid.FindPeaks(InputWorkspace=tube_ws, WorkspaceIndex=0,
PeakPositions=fitPar.getPeaks(), PeaksList='RefittedPeaks')
centers = [row['centre'] for row in fit_ws]
detIDList, detPosList = getCalibratedPixelPositions(ws, centers, idealTube.getArray(), tube_dets)
for id, pos in zip(detIDList, detPosList):
corrections_table.addRow({'Detector ID': id, 'Detector Position': kernel.V3D(*pos)})
return corrections_table
corrected_calibration_table = correctMisalignedTubes(ws, calibrationTable, peaks, spec, idealTube, fitPar)
self.correction_table = corrected_calibration_table
tube.saveCalibration(self.correction_table.getName(), out_path=self.calibration_out_path)
tube.saveCalibration(self.calibration_table.getName(), out_path=self.correction_out_path)
# Get calibration raw file and integrate it
rawCalibInstWS = mantid.Load(
filename) # 'raw' in 'rawCalibInstWS' means unintegrated.
print("Integrating Workspace")
CalibInstWS = mantid.Integration(rawCalibInstWS,
RangeLower=rangeLower,
RangeUpper=rangeUpper)
mantid.DeleteWorkspace(rawCalibInstWS)
print(
"Created workspace (CalibInstWS) with integrated data from run and instrument to calibrate"
)
# == Create Objects needed for calibration ==
# Specify components to calibrate
thisTubeSet = TubeSpec(CalibInstWS)
thisTubeSet.setTubeSpecByString(CalibratedComponent1)
thisTubeSet.setTubeSpecByString(CalibratedComponent2)
# Specify the known positions
knownPos = [-0.50, -0.16, -0.00, 0.16, 0.50]
funcForm = [2, 1, 1, 1, 2]
print("Created objects needed for calibration.")
# == Get the calibration and put results into calibration table ==
calibrationTable, peakTable = tube.calibrate(CalibInstWS,
thisTubeSet,
knownPos,
funcForm,
# Set what we want to calibrate (e.g whole intrument or one door )
CalibratedComponent1 = 'C4_window' # Calibrate C4 window
CalibratedComponent2 = 'C3_window' # Calibrate C3 window
# Get calibration raw file and integrate it
rawCalibInstWS = Load(filename) #'raw' in 'rawCalibInstWS' means unintegrated.
print "Integrating Workspace"
CalibInstWS = Integration( rawCalibInstWS, RangeLower=rangeLower, RangeUpper=rangeUpper )
DeleteWorkspace(rawCalibInstWS)
print "Created workspace (CalibInstWS) with integrated data from run and instrument to calibrate"
# == Create Objects needed for calibration ==
# Specify components to calibrate
thisTubeSet = TubeSpec(CalibInstWS)
thisTubeSet.setTubeSpecByString(CalibratedComponent1)
thisTubeSet.setTubeSpecByString(CalibratedComponent2)
# Specify the known positions
knownPos = [-0.50,-0.16,-0.00, 0.16, 0.50 ]
funcForm = [2,1,1,1,2]
print "Created objects needed for calibration."
# == Get the calibration and put results into calibration table ==
calibrationTable, peakTable = tube.calibrate(CalibInstWS, thisTubeSet, knownPos, funcForm,
outputPeak=True)
print "Got calibration (new positions of detectors) "
def calibrate(ws, tubeSet, knownPositions, funcForm, **kwargs):
"""
Define the calibrated positions of the detectors inside the tubes defined
in tubeSet.
Tubes may be considered a list of detectors alined that may be considered
as pixels for the analogy when they values are displayed.
The position of these pixels are provided by the manufactor, but its real
position depends on the electronics inside the tube and varies slightly
from tube to tube. The calibrate method, aims to find the real positions
of the detectors (pixels) inside the tube.
For this, it will receive an Integrated workspace, where a special
measurement was performed so to have a
pattern of peaks or through. Where gaussian peaks or edges can be found.
The calibration follows the following steps
1. Finding the peaks on each tube
2. Fitting the peaks agains the Known Positions
3. Defining the new position for the pixels(detectors)
Let's consider the simplest way of calling calibrate:
.. code-block:: python
from tube import calibrate
ws = Load('WISH17701')
ws = Integration(ws)
known_pos = [-0.41,-0.31,-0.21,-0.11,-0.02, 0.09, 0.18, 0.28, 0.39 ]
peaks_form = 9*[1] # all the peaks are gaussian peaks
calibTable = calibrate(ws,'WISH/panel03',known_pos, peaks_form)
In this example, the calibrate framework will consider all the
tubes (152) from WISH/panel03.
You may decide to look for a subset of the tubes, by passing the
**rangeList** option.
.. code-block:: python
# This code will calibrate only the tube indexed as number 3
# (usually tube0004)
calibTable = calibrate(ws,'WISH/panel03',known_pos,
peaks_form, rangeList=[3])
**Finding the peaks on each tube**
* Dynamically fitting peaks
The framework expects that for each tube, it will find a peak pattern
around the pixels corresponding to the known_pos positions.
The way it will work out the estimated peak position (in pixel) is
1. Get the length of the tube: distance(first_detector,last_detector) in the tube.
2. Get the number of detectors in the tube (nDets)
3. It will be assumed that the center of the tube correspond to the origin (0)
.. code-block:: python
centre_pixel = known_pos * nDets/tube_length + nDets/2
It will them look for the real peak around the estimated value as:
.. code-block:: python
# consider tube_values the array of counts, and peak the estimated
# position for the peak
real_peak_pos = argmax(tube_values[peak-margin:peak+margin])
After finding the real_peak_pos, it will try to fit the region around
the peak to find the best expected position of the peak in a continuous
space. It will do this by fitting the region around the peak to a
Gaussian Function, and them extract the PeakCentre returned by the
Fitting.
.. code-block:: python
centre = real_peak_pos
fit_start, fit_stop = centre-margin, centre+margin
values = tube_values[fit_start,fit_stop]
background = min(values)
peak = max(values) - background
width = len(where(values > peak/2+background))
# It will fit to something like:
# Fit(function=LinerBackground,A0=background;Gaussian,
# Height=peak, PeakCentre=centre, Sigma=width,fit_start,fit_end)
* Force Fitting Parameters
These dinamically values can be avoided by defining the **fitPar** for
the calibrate function
.. code-block:: python
eP = [57.5, 107.0, 156.5, 206.0, 255.5, 305.0, 354.5, 404.0, 453.5]
# Expected Height of Gaussian Peaks (initial value of fit parameter)
ExpectedHeight = 1000.0
# Expected width of Gaussian peaks in pixels
# (initial value of fit parameter)
ExpectedWidth = 10.0
fitPar = TubeCalibFitParams( eP, ExpectedHeight, ExpectedWidth )
calibTable = calibrate(ws, 'WISH/panel03', known_pos, peaks_form, fitPar=fitPar)
Different Function Factors
Although the examples consider only Gaussian peaks, it is possible to
change the function factors to edges by passing the index of the
known_position through the **funcForm**. Hence, considering three special
points, where there are one gaussian peak and thow edges, the calibrate
could be configured as:
.. code-block:: python
known_pos = [-0.1 2 2.3]
# gaussian peak followed by two edges (through)
form_factor = [1 2 2]
calibTable = calibrate(ws,'WISH/panel03',known_pos,
form_factor)
* Override Peaks
It is possible to scape the finding peaks position steps by providing the
peaks through the **overridePeaks** parameters. The example below tests
the calibration of a single tube (30) but scapes the finding peaks step.
.. code-block:: python
known_pos = [-0.41,-0.31,-0.21,-0.11,-0.02, 0.09, 0.18, 0.28, 0.39 ]
define_peaks = [57.5, 107.0, 156.5, 206.0, 255.5, 305.0, 354.5,
404.0, 453.5]
calibTable = calibrate(ws, 'WISH/panel03', known_pos, peaks_form,
overridePeaks={30:define_peaks}, rangeList=[30])
* Output Peaks Positions
Enabling the option **outputPeak** a WorkspaceTable will be produced with
the first column as tube name and the following columns with the position
where corresponding peaks were found. Like the table below.
+-------+-------+-----+-------+
|TubeId | Peak1 | ... | PeakM |
+=======+=======+=====+=======+
|tube0 | 15.5 | ... | 370.3 |
+-------+-------+-----+-------+
| ... | ... | ... | ... |
+-------+-------+-----+-------+
|tubeN | 14.9 | ... | 371.2 |
+-------+-------+-----+-------+
The signature changes to:
.. code-block:: python
calibTable, peakTable = calibrate(...)
It is possible to give a peakTable directly to the **outputPeak** option,
which will make the calibration to append the peaks to the given table.
.. hint::
It is possible to save the peakTable to a file using the
:meth:`savePeak` method.
**Find the correct position along the tube**
The second step of the calibration is to define the correct position of
pixels along the tube. This is done by fitting the peaks positions found
at the previous step against the known_positions provided.
::
known | *
positions | *
| *
| *
|________________
pixels positions
The default operation is to fit the pixels positions against the known
positions with a quadratic function in order to define an operation to
move all the pixels to their real positions. If necessary, the user may
select to fit using a polinomial of 3rd order, through the parameter
**fitPolyn**.
.. note::
The known positions are given in the same unit as the spacial position
(3D) and having the center of the tube as the origin.
Hence, this section will define a function that:
.. math:: F(pix) = RealRelativePosition
**Define the new position for the detectors**
Finally, the position of the detectors are defined as a vector operation
like
.. math::
\\vec{p} = \\vec{c} + v \\vec{u}
Where :math:`\\vec{p}` is the position in the 3D space, **v** is the
RealRelativePosition deduced from the last session, and finally,
:math:`\\vec{u}` is the unitary vector in the direction of the tube.
:param ws: Integrated workspace with tubes to be calibrated.
:param tubeSet: Specification of Set of tubes to be calibrated. If a string is passed, a TubeSpec will be created passing the string as the setTubeSpecByString.
This will be the case for TubeSpec as string
.. code-block:: python
self.tube_spec = TubeSpec(ws)
self.tube_spec.setTubeSpecByString(tubeSet)
If a list of strings is passed, the TubeSpec will be created with this list:
.. code-block:: python
self.tube_spec = TubeSpec(ws)
self.tube_spec.setTubeSpecByStringArray(tubeSet)
If a :class:`~tube_spec.TubeSpec` object is passed, it will be used as it is.
:param knownPositions: The defined position for the peaks/edges, taking the center as the origin and having the same units as the tube length in the 3D space.
:param funcForm: list with special values to define the format of the peaks/edge (peaks=1, edge=2). If it is not provided, it will be assumed that all the knownPositions are peaks.
Optionals parameters to tune the calibration:
:param fitPar: Define the parameters to be used in the fit as a :class:`~tube_calib_fit_params.TubeCalibFitParams`. If not provided, the dynamic mode is used. See :py:func:`~Examples.TubeCalibDemoMaps_All.provideTheExpectedValue`
:param margin: value in pixesl that will be used around the peaks/edges to fit them. Default = 15. See the code of :py:mod:`~Examples.TubeCalibDemoMerlin` where **margin** is used to calibrate small tubes.
.. code-block:: python
fit_start, fit_end = centre - margin, centre + margin
:param rangeList: list of tubes indexes that will be calibrated. As in the following code (see: :py:func:`~Examples.TubeCalibDemoMaps_All.improvingCalibrationSingleTube`):
.. code-block:: python
for index in rangelist:
do_calibrate(tubeSet.getTube(index))
:param calibTable: Pass the calibration table, it will them append the values to the provided one and return it. (see: :py:mod:`~Examples.TubeCalibDemoMerlin`)
:param plotTube: If given, the tube whose index is in plotTube will be ploted as well as its fitted peaks, it can receive a list of indexes to plot.(see: :py:func:`~Examples.TubeCalibDemoMaps_All.changeMarginAndExpectedValue`)
:param excludeShortTubes: Do not calibrate tubes whose length is smaller than given value. (see at: Examples/TubeCalibDemoMerlin_Adjustable.py)
:param overridePeaks: dictionary that defines an array of peaks positions (in pixels) to be used for the specific tube(key). (see: :py:func:`~Examples.TubeCalibDemoMaps_All.improvingCalibrationSingleTube`)
.. code-block:: python
for index in rangelist:
if overridePeaks.has_key(index):
use_this_peaks = overridePeaks[index]
# skip finding peaks
fit_peaks_to_position()
:param fitPolyn: Define the order of the polinomial to fit the pixels positions agains the known positions. The acceptable values are 1, 2 or 3. Default = 2.
:param outputPeak: Enable the calibrate to output the peak table, relating the tubes with the pixels positions. It may be passed as a boolean value (outputPeak=True) or as a peakTable value. The later case is to inform calibrate to append the new values to the given peakTable. This is usefull when you have to operate in subsets of tubes. (see :py:mod:`~Examples.TubeCalibDemoMerlin` that shows a nice inspection on this table).
.. code-block:: python
calibTable, peakTable = calibrate(ws, (omitted), rangeList=[1],
outputPeak=True)
# appending the result to peakTable
calibTable, peakTable = calibrate(ws, (omitted), rangeList=[2],
outputPeak=peakTable)
# now, peakTable has information for tube[1] and tube[2]
:rtype: calibrationTable, a TableWorkspace with two columns DetectorID(int) and DetectorPositions(V3D).
"""
FITPAR = 'fitPar'
MARGIN = 'margin'
RANGELIST = 'rangeList'
CALIBTABLE = 'calibTable'
PLOTTUBE = 'plotTube'
EXCLUDESHORT = 'excludeShortTubes'
OVERRIDEPEAKS = 'overridePeaks'
FITPOLIN = 'fitPolyn'
OUTPUTPEAK = 'outputPeak'
#check that only valid arguments were passed through kwargs
for key in kwargs.keys():
if key not in [
FITPAR, MARGIN, RANGELIST, CALIBTABLE, PLOTTUBE, EXCLUDESHORT,
OVERRIDEPEAKS, FITPOLIN, OUTPUTPEAK
]:
msg = "Wrong argument: '%s'! This argument is not defined in the signature of this function. Hint: remember that arguments are case sensitive" % key
raise RuntimeError(msg)
# check parameter ws: if it was given as string, transform it in
# mantid object
if isinstance(ws, str):
ws = mtd[ws]
if not isinstance(ws, MatrixWorkspace):
raise RuntimeError(
"Wrong argument ws = %s. It must be a MatrixWorkspace" % (str(ws)))
# check parameter tubeSet. It accepts string or preferable a TubeSpec
if isinstance(tubeSet, str):
selectedTubes = tubeSet
tubeSet = TubeSpec(ws)
tubeSet.setTubeSpecByString(selectedTubes)
elif isinstance(tubeSet, list):
selectedTubes = tubeSet
tubeSet = TubeSpec(ws)
tubeSet.setTubeSpecByStringArray(selectedTubes)
elif not isinstance(tubeSet, TubeSpec):
raise RuntimeError(
"Wrong argument tubeSet. It must be a TubeSpec or a string that defines the set of tubes to be calibrated. For example: WISH/panel03"
)
# check the known_positions parameter
# for old version compatibility, it also accepts IdealTube, eventhough
# they should only be used internally
if not (isinstance(knownPositions, list)
or isinstance(knownPositions, tuple)
or isinstance(knownPositions, numpy.ndarray)):
raise RuntimeError(
"Wrong argument knownPositions. It expects a list of values for the positions expected for the peaks in relation to the center of the tube"
)
else:
idealTube = IdealTube()
idealTube.setArray(numpy.array(knownPositions))
#deal with funcForm parameter
try:
nPeaks = len(idealTube.getArray())
if len(funcForm) != nPeaks:
raise 1
for val in funcForm:
if val not in [1, 2]:
raise 2
except:
raise RuntimeError(
"Wrong argument FuncForm. It expects a list of values describing the form of everysingle peaks. So, for example, if there are three peaks where the first is a peak and the followers as edge, funcForm = [1, 2, 2]. Currently, it is defined 1-Gaussian Peak, 2 - Edge. The knownPos has %d elements and the given funcForm has %d."
% (nPeaks, len(funcForm)))
#apply the functional form to the ideal Tube
idealTube.setForm(funcForm)
# check the FITPAR parameter (optional)
# if the FITPAR is given, than it will just pass on, if the FITPAR is
# not given, it will create a FITPAR 'guessing' the centre positions,
# and allowing the find peaks calibration methods to adjust the parameter
# for the peaks automatically
if kwargs.has_key(FITPAR):
fitPar = kwargs[FITPAR]
#fitPar must be a TubeCalibFitParams
if not isinstance(fitPar, TubeCalibFitParams):
raise RuntimeError(
"Wrong argument %s. This argument, when given, must be a valid TubeCalibFitParams object"
% FITPAR)
else:
# create a fit parameters guessing centre positions
# the guessing obeys the following rule:
#
# centre_pixel = known_pos * ndets/tube_length + ndets / 2
#
# Get tube length and number of detectors
tube_length = tubeSet.getTubeLength(0)
#ndets = len(wsp_index_for_tube0)
id1, ndets, step = tubeSet.getDetectorInfoFromTube(0)
known_pos = idealTube.getArray()
# position of the peaks in pixels
centre_pixel = known_pos * ndets / tube_length + ndets * 0.5
fitPar = TubeCalibFitParams(centre_pixel)
# make it automatic, it means, that for every tube,
# the parameters for fit will be re-evaluated, from the first
# guess positions given by centre_pixel
fitPar.setAutomatic(True)
# check the MARGIN paramter (optional)
if kwargs.has_key(MARGIN):
try:
margin = float(kwargs[MARGIN])
except:
raise RuntimeError("Wrong argument %s. It was expected a number!" %
MARGIN)
fitPar.setMargin(margin)
#deal with RANGELIST parameter
if kwargs.has_key(RANGELIST):
rangeList = kwargs[RANGELIST]
if isinstance(rangeList, int):
rangeList = [rangeList]
try:
# this deals with list and tuples and iterables to make sure
# rangeList becomes a list
rangeList = list(rangeList)
except:
raise RuntimeError(
"Wrong argument %s. It expects a list of indexes for calibration"
% RANGELIST)
else:
rangeList = range(tubeSet.getNumTubes())
# check if the user passed the option calibTable
if kwargs.has_key(CALIBTABLE):
calibTable = kwargs[CALIBTABLE]
#ensure the correct type is passed
# if a string was passed, transform it in mantid object
if isinstance(calibTable, str):
calibTable = mtd[calibTable]
#check that calibTable has the expected form
try:
if not isinstance(calibTable, ITableWorkspace):
raise 1
if calibTable.columnCount() != 2:
raise 2
colNames = calibTable.getColumnNames()
if colNames[0] != 'Detector ID' or colNames[
1] != 'Detector Position':
raise 3
except:
raise RuntimeError(
"Invalid type for %s. The expected type was ITableWorkspace with 2 columns(Detector ID and Detector Positions)"
% CALIBTABLE)
else:
calibTable = CreateEmptyTableWorkspace(OutputWorkspace="CalibTable")
# "Detector ID" column required by ApplyCalibration
calibTable.addColumn(type="int", name="Detector ID")
# "Detector Position" column required by ApplyCalibration
calibTable.addColumn(type="V3D", name="Detector Position")
#deal with plotTube option
if kwargs.has_key(PLOTTUBE):
plotTube = kwargs[PLOTTUBE]
if isinstance(plotTube, int):
plotTube = [plotTube]
try:
plotTube = list(plotTube)
except:
raise RuntimeError(
"Wrong argument %s. It expects an index (int) or a list of indexes"
% PLOTTUBE)
else:
plotTube = []
#deal with minimun tubes sizes
if kwargs.has_key(EXCLUDESHORT):
excludeShortTubes = kwargs[EXCLUDESHORT]
try:
excludeShortTubes = float(excludeShortTubes)
except:
raise RuntimeError(
"Wrong argument %s. It expects a float value for the minimun size of tubes to be calibrated"
)
else:
#a tube with length 0 can not be calibrated, this is the minimun value
excludeShortTubes = 0.0
#deal with OVERRIDEPEAKS parameters
if kwargs.has_key(OVERRIDEPEAKS):
overridePeaks = kwargs[OVERRIDEPEAKS]
try:
nPeaks = len(idealTube.getArray())
# check the format of override peaks
if not isinstance(overridePeaks, dict):
raise 1
for key in overridePeaks.keys():
if not isinstance(key, int):
raise 2
if key < 0 or key >= tubeSet.getNumTubes():
raise 3
if len(overridePeaks[key]) != nPeaks:
raise 4
except:
raise RuntimeError(
"Wrong argument %s. It expects a dictionary with key as the tube index and the value as a list of peaks positions. Ex (3 peaks): overridePeaks = {1:[2,5.4,500]}"
% OVERRIDEPEAKS)
else:
overridePeaks = dict()
# deal with FITPOLIN parameter
if kwargs.has_key(FITPOLIN):
polinFit = kwargs[FITPOLIN]
if polinFit not in [1, 2, 3]:
raise RuntimeError(
"Wrong argument %s. It expects a number 1 for linear, 2 for quadratic, or 3 for 3rd polinomial order when fitting the pixels positions agains the known positions"
% FITPOLIN)
else:
polinFit = 2
# deal with OUTPUT PEAK
deletePeakTableAfter = False
if kwargs.has_key(OUTPUTPEAK):
outputPeak = kwargs[OUTPUTPEAK]
else:
outputPeak = False
if isinstance(outputPeak, ITableWorkspace):
if outputPeak.columnCount() < len(idealTube.getArray()):
raise RuntimeError(
"Wrong argument %s. It expects a boolean flag, or a ITableWorksapce with columns (TubeId, Peak1,...,PeakM) for M = number of peaks given in knownPositions"
% OUTPUTPEAK)
else:
if not outputPeak:
deletePeakTableAfter = True
# create the output peak table
outputPeak = CreateEmptyTableWorkspace(OutputWorkspace="PeakTable")
outputPeak.addColumn(type='str', name='TubeId')
for i in range(len(idealTube.getArray())):
outputPeak.addColumn(type='float', name='Peak%d' % (i + 1))
getCalibration(ws, tubeSet, calibTable, fitPar, idealTube, outputPeak,
overridePeaks, excludeShortTubes, plotTube, rangeList,
polinFit)
if deletePeakTableAfter:
DeleteWorkspace(str(outputPeak))
return calibTable
else:
return calibTable, outputPeak
CalibInstWS = mantid.Integration( rawCalibInstWS, RangeLower=1, RangeUpper=20000 )
mantid.DeleteWorkspace(rawCalibInstWS)
print("Created workspace (CalibInstWS) with integrated data from run and instrument to calibrate")
CalibratedComponent = 'WISH/panel03/tube038'
# Set fitting parameters
eP = [65.0, 113.0, 161.0, 209.0, 257.0, 305.0, 353.0, 401.0, 449.0]
ExpectedHeight = 2000.0 # Expected Height of Gaussian Peaks (initial value of fit parameter)
ExpectedWidth = 32.0 # Expected width of Gaussian peaks in pixels (initial value of fit parameter)
fitPar = TubeCalibFitParams( eP, ExpectedHeight, ExpectedWidth )
fitPar.setAutomatic(True)
print("Created objects needed for calibration.")
func_form = 9*[1]
# Use first tube as ideal tube
tube1 = TubeSpec(CalibInstWS)
tube1.setTubeSpecByString('WISH/panel03/tube038')
iTube = tube_calib.constructIdealTubeFromRealTube( CalibInstWS, tube1, fitPar, func_form)
known_pos = iTube.getArray()
print(known_pos)
# Get the calibration and put it into the calibration table
calibrationTable = tube.calibrate( CalibInstWS, 'WISH/panel03', known_pos, func_form, fitPar=fitPar)
print("Got calibration (new positions of detectors)")
#Apply the calibration
mantid.ApplyCalibration(Workspace=CalibInstWS, PositionTable=calibrationTable)
print("Applied calibration")
def calibrate(ws, tubeSet, knownPositions, funcForm, **kwargs):
"""
Define the calibrated positions of the detectors inside the tubes defined
in tubeSet.
Tubes may be considered a list of detectors alined that may be considered
as pixels for the analogy when they values are displayed.
The position of these pixels are provided by the manufactor, but its real
position depends on the electronics inside the tube and varies slightly
from tube to tube. The calibrate method, aims to find the real positions
of the detectors (pixels) inside the tube.
For this, it will receive an Integrated workspace, where a special
measurement was performed so to have a
pattern of peaks or through. Where gaussian peaks or edges can be found.
The calibration follows the following steps
1. Finding the peaks on each tube
2. Fitting the peaks agains the Known Positions
3. Defining the new position for the pixels(detectors)
Let's consider the simplest way of calling calibrate:
.. code-block:: python
from tube import calibrate
ws = Load('WISH17701')
ws = Integration(ws)
known_pos = [-0.41,-0.31,-0.21,-0.11,-0.02, 0.09, 0.18, 0.28, 0.39 ]
peaks_form = 9*[1] # all the peaks are gaussian peaks
calibTable = calibrate(ws,'WISH/panel03',known_pos, peaks_form)
In this example, the calibrate framework will consider all the
tubes (152) from WISH/panel03.
You may decide to look for a subset of the tubes, by passing the
**rangeList** option.
.. code-block:: python
# This code will calibrate only the tube indexed as number 3
# (usually tube0004)
calibTable = calibrate(ws,'WISH/panel03',known_pos,
peaks_form, rangeList=[3])
**Finding the peaks on each tube**
* Dynamically fitting peaks
The framework expects that for each tube, it will find a peak pattern
around the pixels corresponding to the known_pos positions.
The way it will work out the estimated peak position (in pixel) is
1. Get the length of the tube: distance(first_detector,last_detector) in the tube.
2. Get the number of detectors in the tube (nDets)
3. It will be assumed that the center of the tube correspond to the origin (0)
.. code-block:: python
centre_pixel = known_pos * nDets/tube_length + nDets/2
It will them look for the real peak around the estimated value as:
.. code-block:: python
# consider tube_values the array of counts, and peak the estimated
# position for the peak
real_peak_pos = argmax(tube_values[peak-margin:peak+margin])
After finding the real_peak_pos, it will try to fit the region around
the peak to find the best expected position of the peak in a continuous
space. It will do this by fitting the region around the peak to a
Gaussian Function, and them extract the PeakCentre returned by the
Fitting.
.. code-block:: python
centre = real_peak_pos
fit_start, fit_stop = centre-margin, centre+margin
values = tube_values[fit_start,fit_stop]
background = min(values)
peak = max(values) - background
width = len(where(values > peak/2+background))
# It will fit to something like:
# Fit(function=LinerBackground,A0=background;Gaussian,
# Height=peak, PeakCentre=centre, Sigma=width,fit_start,fit_end)
* Force Fitting Parameters
These dinamically values can be avoided by defining the **fitPar** for
the calibrate function
.. code-block:: python
eP = [57.5, 107.0, 156.5, 206.0, 255.5, 305.0, 354.5, 404.0, 453.5]
# Expected Height of Gaussian Peaks (initial value of fit parameter)
ExpectedHeight = 1000.0
# Expected width of Gaussian peaks in pixels
# (initial value of fit parameter)
ExpectedWidth = 10.0
fitPar = TubeCalibFitParams( eP, ExpectedHeight, ExpectedWidth )
calibTable = calibrate(ws, 'WISH/panel03', known_pos, peaks_form, fitPar=fitPar)
Different Function Factors
Although the examples consider only Gaussian peaks, it is possible to
change the function factors to edges by passing the index of the
known_position through the **funcForm**. Hence, considering three special
points, where there are one gaussian peak and thow edges, the calibrate
could be configured as:
.. code-block:: python
known_pos = [-0.1 2 2.3]
# gaussian peak followed by two edges (through)
form_factor = [1 2 2]
calibTable = calibrate(ws,'WISH/panel03',known_pos,
form_factor)
* Override Peaks
It is possible to scape the finding peaks position steps by providing the
peaks through the **overridePeaks** parameters. The example below tests
the calibration of a single tube (30) but scapes the finding peaks step.
.. code-block:: python
known_pos = [-0.41,-0.31,-0.21,-0.11,-0.02, 0.09, 0.18, 0.28, 0.39 ]
define_peaks = [57.5, 107.0, 156.5, 206.0, 255.5, 305.0, 354.5,
404.0, 453.5]
calibTable = calibrate(ws, 'WISH/panel03', known_pos, peaks_form,
overridePeaks={30:define_peaks}, rangeList=[30])
* Output Peaks Positions
Enabling the option **outputPeak** a WorkspaceTable will be produced with
the first column as tube name and the following columns with the position
where corresponding peaks were found. Like the table below.
+-------+-------+-----+-------+
|TubeId | Peak1 | ... | PeakM |
+=======+=======+=====+=======+
|tube0 | 15.5 | ... | 370.3 |
+-------+-------+-----+-------+
| ... | ... | ... | ... |
+-------+-------+-----+-------+
|tubeN | 14.9 | ... | 371.2 |
+-------+-------+-----+-------+
The signature changes to:
.. code-block:: python
calibTable, peakTable = calibrate(...)
It is possible to give a peakTable directly to the **outputPeak** option,
which will make the calibration to append the peaks to the given table.
.. hint::
It is possible to save the peakTable to a file using the
:meth:`savePeak` method.
**Find the correct position along the tube**
The second step of the calibration is to define the correct position of
pixels along the tube. This is done by fitting the peaks positions found
at the previous step against the known_positions provided.
::
known | *
positions | *
| *
| *
|________________
pixels positions
The default operation is to fit the pixels positions against the known
positions with a quadratic function in order to define an operation to
move all the pixels to their real positions. If necessary, the user may
select to fit using a polinomial of 3rd order, through the parameter
**fitPolyn**.
.. note::
The known positions are given in the same unit as the spacial position
(3D) and having the center of the tube as the origin.
Hence, this section will define a function that:
.. math:: F(pix) = RealRelativePosition
**Define the new position for the detectors**
Finally, the position of the detectors are defined as a vector operation
like
.. math::
\\vec{p} = \\vec{c} + v \\vec{u}
Where :math:`\\vec{p}` is the position in the 3D space, **v** is the
RealRelativePosition deduced from the last session, and finally,
:math:`\\vec{u}` is the unitary vector in the direction of the tube.
:param ws: Integrated workspace with tubes to be calibrated.
:param tubeSet: Specification of Set of tubes to be calibrated. If a string is passed, a TubeSpec will be created passing the string as the setTubeSpecByString.
This will be the case for TubeSpec as string
.. code-block:: python
self.tube_spec = TubeSpec(ws)
self.tube_spec.setTubeSpecByString(tubeSet)
If a list of strings is passed, the TubeSpec will be created with this list:
.. code-block:: python
self.tube_spec = TubeSpec(ws)
self.tube_spec.setTubeSpecByStringArray(tubeSet)
If a :class:`~tube_spec.TubeSpec` object is passed, it will be used as it is.
:param knownPositions: The defined position for the peaks/edges, taking the center as the origin and having the same units as the tube length in the 3D space.
:param funcForm: list with special values to define the format of the peaks/edge (peaks=1, edge=2). If it is not provided, it will be assumed that all the knownPositions are peaks.
Optionals parameters to tune the calibration:
:param fitPar: Define the parameters to be used in the fit as a :class:`~tube_calib_fit_params.TubeCalibFitParams`. If not provided, the dynamic mode is used. See :py:func:`~Examples.TubeCalibDemoMaps_All.provideTheExpectedValue`
:param margin: value in pixesl that will be used around the peaks/edges to fit them. Default = 15. See the code of :py:mod:`~Examples.TubeCalibDemoMerlin` where **margin** is used to calibrate small tubes.
.. code-block:: python
fit_start, fit_end = centre - margin, centre + margin
:param rangeList: list of tubes indexes that will be calibrated. As in the following code (see: :py:func:`~Examples.TubeCalibDemoMaps_All.improvingCalibrationSingleTube`):
.. code-block:: python
for index in rangelist:
do_calibrate(tubeSet.getTube(index))
:param calibTable: Pass the calibration table, it will them append the values to the provided one and return it. (see: :py:mod:`~Examples.TubeCalibDemoMerlin`)
:param plotTube: If given, the tube whose index is in plotTube will be ploted as well as its fitted peaks, it can receive a list of indexes to plot.(see: :py:func:`~Examples.TubeCalibDemoMaps_All.changeMarginAndExpectedValue`)
:param excludeShortTubes: Do not calibrate tubes whose length is smaller than given value. (see at: Examples/TubeCalibDemoMerlin_Adjustable.py)
:param overridePeaks: dictionary that defines an array of peaks positions (in pixels) to be used for the specific tube(key). (see: :py:func:`~Examples.TubeCalibDemoMaps_All.improvingCalibrationSingleTube`)
.. code-block:: python
for index in rangelist:
if overridePeaks.has_key(index):
use_this_peaks = overridePeaks[index]
# skip finding peaks
fit_peaks_to_position()
:param fitPolyn: Define the order of the polinomial to fit the pixels positions agains the known positions. The acceptable values are 1, 2 or 3. Default = 2.
:param outputPeak: Enable the calibrate to output the peak table, relating the tubes with the pixels positions. It may be passed as a boolean value (outputPeak=True) or as a peakTable value. The later case is to inform calibrate to append the new values to the given peakTable. This is usefull when you have to operate in subsets of tubes. (see :py:mod:`~Examples.TubeCalibDemoMerlin` that shows a nice inspection on this table).
.. code-block:: python
calibTable, peakTable = calibrate(ws, (omitted), rangeList=[1],
outputPeak=True)
# appending the result to peakTable
calibTable, peakTable = calibrate(ws, (omitted), rangeList=[2],
outputPeak=peakTable)
# now, peakTable has information for tube[1] and tube[2]
:rtype: calibrationTable, a TableWorkspace with two columns DetectorID(int) and DetectorPositions(V3D).
"""
FITPAR = 'fitPar'
MARGIN = 'margin'
RANGELIST = 'rangeList'
CALIBTABLE = 'calibTable'
PLOTTUBE = 'plotTube'
EXCLUDESHORT = 'excludeShortTubes'
OVERRIDEPEAKS = 'overridePeaks'
FITPOLIN = 'fitPolyn'
OUTPUTPEAK = 'outputPeak'
#check that only valid arguments were passed through kwargs
for key in kwargs.keys():
if key not in [FITPAR, MARGIN, RANGELIST, CALIBTABLE, PLOTTUBE,
EXCLUDESHORT, OVERRIDEPEAKS, FITPOLIN,
OUTPUTPEAK]:
msg = "Wrong argument: '%s'! This argument is not defined in the signature of this function. Hint: remember that arguments are case sensitive" % key
raise RuntimeError(msg)
# check parameter ws: if it was given as string, transform it in
# mantid object
if isinstance(ws,str):
ws = mtd[ws]
if not isinstance(ws,MatrixWorkspace):
raise RuntimeError("Wrong argument ws = %s. It must be a MatrixWorkspace" % (str(ws)))
# check parameter tubeSet. It accepts string or preferable a TubeSpec
if isinstance(tubeSet,str):
selectedTubes = tubeSet
tubeSet = TubeSpec(ws)
tubeSet.setTubeSpecByString(selectedTubes)
elif isinstance(tubeSet, list):
selectedTubes = tubeSet
tubeSet = TubeSpec(ws)
tubeSet.setTubeSpecByStringArray(selectedTubes)
elif not isinstance(tubeSet,TubeSpec):
raise RuntimeError("Wrong argument tubeSet. It must be a TubeSpec or a string that defines the set of tubes to be calibrated. For example: WISH/panel03")
# check the known_positions parameter
# for old version compatibility, it also accepts IdealTube, eventhough
# they should only be used internally
if not (isinstance(knownPositions, list) or
isinstance(knownPositions, tuple) or
isinstance(knownPositions, numpy.ndarray)):
raise RuntimeError("Wrong argument knownPositions. It expects a list of values for the positions expected for the peaks in relation to the center of the tube")
else:
idealTube = IdealTube()
idealTube.setArray(numpy.array(knownPositions))
#deal with funcForm parameter
try:
nPeaks = len(idealTube.getArray())
if len(funcForm) != nPeaks:
raise 1
for val in funcForm:
if val not in [1,2]:
raise 2
except:
raise RuntimeError("Wrong argument FuncForm. It expects a list of values describing the form of everysingle peaks. So, for example, if there are three peaks where the first is a peak and the followers as edge, funcForm = [1, 2, 2]. Currently, it is defined 1-Gaussian Peak, 2 - Edge. The knownPos has %d elements and the given funcForm has %d."%(nPeaks, len(funcForm)))
#apply the functional form to the ideal Tube
idealTube.setForm(funcForm)
# check the FITPAR parameter (optional)
# if the FITPAR is given, than it will just pass on, if the FITPAR is
# not given, it will create a FITPAR 'guessing' the centre positions,
# and allowing the find peaks calibration methods to adjust the parameter
# for the peaks automatically
if kwargs.has_key(FITPAR):
fitPar = kwargs[FITPAR]
#fitPar must be a TubeCalibFitParams
if not isinstance(fitPar, TubeCalibFitParams):
raise RuntimeError("Wrong argument %s. This argument, when given, must be a valid TubeCalibFitParams object"%FITPAR)
else:
# create a fit parameters guessing centre positions
# the guessing obeys the following rule:
#
# centre_pixel = known_pos * ndets/tube_length + ndets / 2
#
# Get tube length and number of detectors
tube_length = tubeSet.getTubeLength(0)
#ndets = len(wsp_index_for_tube0)
id1, ndets, step = tubeSet.getDetectorInfoFromTube(0)
known_pos = idealTube.getArray()
# position of the peaks in pixels
centre_pixel = known_pos * ndets/tube_length + ndets * 0.5
fitPar = TubeCalibFitParams(centre_pixel)
# make it automatic, it means, that for every tube,
# the parameters for fit will be re-evaluated, from the first
# guess positions given by centre_pixel
fitPar.setAutomatic(True)
# check the MARGIN paramter (optional)
if kwargs.has_key(MARGIN):
try:
margin = float(kwargs[MARGIN])
except:
raise RuntimeError("Wrong argument %s. It was expected a number!"%MARGIN)
fitPar.setMargin(margin)
#deal with RANGELIST parameter
if kwargs.has_key(RANGELIST):
rangeList = kwargs[RANGELIST]
if isinstance(rangeList,int):
rangeList = [rangeList]
try:
# this deals with list and tuples and iterables to make sure
# rangeList becomes a list
rangeList = list(rangeList)
except:
raise RuntimeError("Wrong argument %s. It expects a list of indexes for calibration"%RANGELIST)
else:
rangeList = range(tubeSet.getNumTubes())
# check if the user passed the option calibTable
if kwargs.has_key(CALIBTABLE):
calibTable = kwargs[CALIBTABLE]
#ensure the correct type is passed
# if a string was passed, transform it in mantid object
if isinstance(calibTable,str):
calibTable = mtd[calibTable]
#check that calibTable has the expected form
try:
if not isinstance(calibTable,ITableWorkspace):
raise 1
if calibTable.columnCount() != 2:
raise 2
colNames = calibTable.getColumnNames()
if colNames[0] != 'Detector ID' or colNames[1] != 'Detector Position':
raise 3
except:
raise RuntimeError("Invalid type for %s. The expected type was ITableWorkspace with 2 columns(Detector ID and Detector Positions)" % CALIBTABLE)
else:
calibTable = CreateEmptyTableWorkspace(OutputWorkspace="CalibTable")
# "Detector ID" column required by ApplyCalibration
calibTable.addColumn(type="int",name="Detector ID")
# "Detector Position" column required by ApplyCalibration
calibTable.addColumn(type="V3D",name="Detector Position")
#deal with plotTube option
if kwargs.has_key(PLOTTUBE):
plotTube = kwargs[PLOTTUBE]
if isinstance(plotTube, int):
plotTube = [plotTube]
try:
plotTube = list(plotTube)
except:
raise RuntimeError("Wrong argument %s. It expects an index (int) or a list of indexes" %PLOTTUBE)
else:
plotTube = []
#deal with minimun tubes sizes
if kwargs.has_key(EXCLUDESHORT):
excludeShortTubes = kwargs[EXCLUDESHORT]
try:
excludeShortTubes = float(excludeShortTubes)
except:
raise RuntimeError("Wrong argument %s. It expects a float value for the minimun size of tubes to be calibrated")
else:
#a tube with length 0 can not be calibrated, this is the minimun value
excludeShortTubes = 0.0
#deal with OVERRIDEPEAKS parameters
if kwargs.has_key(OVERRIDEPEAKS):
overridePeaks = kwargs[OVERRIDEPEAKS]
try:
nPeaks = len(idealTube.getArray())
# check the format of override peaks
if not isinstance(overridePeaks, dict):
raise 1
for key in overridePeaks.keys():
if not isinstance(key,int):
raise 2
if key < 0 or key >= tubeSet.getNumTubes():
raise 3
if len(overridePeaks[key]) != nPeaks:
raise 4
except:
raise RuntimeError("Wrong argument %s. It expects a dictionary with key as the tube index and the value as a list of peaks positions. Ex (3 peaks): overridePeaks = {1:[2,5.4,500]}"%OVERRIDEPEAKS)
else:
overridePeaks = dict()
# deal with FITPOLIN parameter
if kwargs.has_key(FITPOLIN):
polinFit = kwargs[FITPOLIN]
if polinFit not in [1, 2,3]:
raise RuntimeError("Wrong argument %s. It expects a number 1 for linear, 2 for quadratic, or 3 for 3rd polinomial order when fitting the pixels positions agains the known positions" % FITPOLIN)
else:
polinFit = 2
# deal with OUTPUT PEAK
deletePeakTableAfter = False
if kwargs.has_key(OUTPUTPEAK):
outputPeak = kwargs[OUTPUTPEAK]
else:
outputPeak = False
if isinstance(outputPeak, ITableWorkspace):
if outputPeak.columnCount() < len(idealTube.getArray()):
raise RuntimeError("Wrong argument %s. It expects a boolean flag, or a ITableWorksapce with columns (TubeId, Peak1,...,PeakM) for M = number of peaks given in knownPositions" % OUTPUTPEAK)
else:
if not outputPeak:
deletePeakTableAfter = True
# create the output peak table
outputPeak = CreateEmptyTableWorkspace(OutputWorkspace="PeakTable")
outputPeak.addColumn(type='str',name='TubeId')
for i in range(len(idealTube.getArray())):
outputPeak.addColumn(type='float',name='Peak%d'%(i+1))
getCalibration(ws, tubeSet, calibTable, fitPar, idealTube, outputPeak,\
overridePeaks, excludeShortTubes, plotTube, rangeList, polinFit)
if deletePeakTableAfter:
DeleteWorkspace(str(outputPeak))
return calibTable
else:
return calibTable, outputPeak
import tube
from tube_spec import TubeSpec
import numpy as np
run = 47301
ws = Load('CORELLI_' + str(run))
LoadInstrument(ws,
Filename='/SNS/users/rwp/CORELLI_Definition_91.07cm.xml',
RewriteSpectraMap='False')
ws = Integration(ws)
CloneWorkspace(InputWorkspace='ws', OutputWorkspace='ws2')
CloneWorkspace(InputWorkspace='ws', OutputWorkspace='ws1')
tubeSet = TubeSpec(ws)
tubeSet.setTubeSpecByStringArray(["bank33", "bank45", "bank57"])
a = (2 * 25.4 + 2) / 1000
knownPositions = np.arange(-7.5 * a, 8.5 * a, a)
funcForm = [1] * 16
calibTable = tube.calibrate(ws,
tubeSet,
knownPositions,
funcForm,
margin=6,
plotTube=True,
outputPeak=True)
ApplyCalibration(Workspace='ws2', PositionTable='CalibTable')
calibTable1 = tube.calibrate(ws,
tubeSet,
def runTest(self):
# This script calibrates WISH using known peak positions from
# neutron absorbing bands. The workspace with suffix "_calib"
# contains calibrated data. The workspace with suxxic "_corrected"
# contains calibrated data with known problematic tubes also corrected
ws = mantid.LoadNexusProcessed(Filename="WISH30541_integrated.nxs")
# This array defines the positions of peaks on the detector in
# meters from the center (0)
# For wish this is calculated as follows:
# Height of all 7 bands = 0.26m => each band is separated by 0.260 / 6 = 0.4333m
# The bands are on a cylinder diameter 0.923m. So we can work out the angle as
# (0.4333 * n) / (0.923 / 2) where n is the number of bands above (or below) the
# center band.
# Putting this together with the distance to the detector tubes (2.2m) we get
# the following: (0.4333n) / 0.4615 * 2200 = Expected peak positions
# From this we can show there should be 5 peaks (peaks 6 + 7 are too high/low)
# at: 0, 0.206, 0.413 respectively (this is symmetrical so +/-)
peak_positions = np.array([-0.413, -0.206, 0, 0.206, 0.413])
funcForm = 5 * [1] # 5 gaussian peaks
fitPar = TubeCalibFitParams([59, 161, 258, 353, 448])
fitPar.setAutomatic(True)
instrument = ws.getInstrument()
spec = TubeSpec(ws)
spec.setTubeSpecByString(instrument.getFullName())
idealTube = IdealTube()
idealTube.setArray(peak_positions)
# First calibrate all of the detectors
calibrationTable, peaks = tube.calibrate(ws, spec, peak_positions, funcForm, margin=15,
outputPeak=True, fitPar=fitPar)
self.calibration_table = calibrationTable
def findBadPeakFits(peaksTable, threshold=10):
""" Find peaks whose fit values fall outside of a given tolerance
of the mean peak centers across all tubes.
Tubes are defined as have a bad fit if the absolute difference
between the fitted peak centers for a specific tube and the
mean of the fitted peak centers for all tubes differ more than
the threshold parameter.
@param peakTable: the table containing fitted peak centers
@param threshold: the tolerance on the difference from the mean value
@return A list of expected peak positions and a list of indices of tubes
to correct
"""
n = len(peaksTable)
num_peaks = peaksTable.columnCount() - 1
column_names = ['Peak%d' % i for i in range(1, num_peaks + 1)]
data = np.zeros((n, num_peaks))
for i, row in enumerate(peaksTable):
data_row = [row[name] for name in column_names]
data[i, :] = data_row
# data now has all the peaks positions for each tube
# the mean value is the expected value for the peak position for each tube
expected_peak_pos = np.mean(data, axis=0)
# calculate how far from the expected position each peak position is
distance_from_expected = np.abs(data - expected_peak_pos)
check = np.where(distance_from_expected > threshold)[0]
problematic_tubes = list(set(check))
print("Problematic tubes are: " + str(problematic_tubes))
return expected_peak_pos, problematic_tubes
def correctMisalignedTubes(ws, calibrationTable, peaksTable, spec, idealTube, fitPar, threshold=10):
""" Correct misaligned tubes due to poor fitting results
during the first round of calibration.
Misaligned tubes are first identified according to a tolerance
applied to the absolute difference between the fitted tube
positions and the mean across all tubes.
The FindPeaks algorithm is then used to find a better fit
with the ideal tube positions as starting parameters
for the peak centers.
From the refitted peaks the positions of the detectors in the
tube are recalculated.
@param ws: the workspace to get the tube geometry from
@param calibrationTable: the calibration table output from running calibration
@param peaksTable: the table containing the fitted peak centers from calibration
@param spec: the tube spec for the instrument
@param idealTube: the ideal tube for the instrument
@param fitPar: the fitting parameters for calibration
@param threshold: tolerance defining is a peak is outside of the acceptable range
@return table of corrected detector positions
"""
table_name = calibrationTable.name() + 'Corrected'
corrections_table = mantid.CreateEmptyTableWorkspace(OutputWorkspace=table_name)
corrections_table.addColumn('int', "Detector ID")
corrections_table.addColumn('V3D', "Detector Position")
mean_peaks, bad_tubes = findBadPeakFits(peaksTable, threshold)
for index in bad_tubes:
print("Refitting tube %s" % spec.getTubeName(index))
tube_dets, _ = spec.getTube(index)
getPoints(ws, idealTube.getFunctionalForms(), fitPar, tube_dets)
tube_ws = mantid.mtd['TubePlot']
fit_ws = mantid.FindPeaks(InputWorkspace=tube_ws, WorkspaceIndex=0,
PeakPositions=fitPar.getPeaks(), PeaksList='RefittedPeaks')
centers = [row['centre'] for row in fit_ws]
detIDList, detPosList = getCalibratedPixelPositions(ws, centers, idealTube.getArray(), tube_dets)
for id, pos in zip(detIDList, detPosList):
corrections_table.addRow({'Detector ID': id, 'Detector Position': kernel.V3D(*pos)})
return corrections_table
corrected_calibration_table = correctMisalignedTubes(ws, calibrationTable, peaks, spec, idealTube, fitPar)
self.correction_table = corrected_calibration_table
tube.saveCalibration(self.correction_table.getName(), out_path=self.calibration_out_path)
tube.saveCalibration(self.calibration_table.getName(), out_path=self.correction_out_path)