def _group_spectra(self, ws):
"""
Groups the spectrum axis by summing spectra
@param ws : the input workspace
"""
new_axis = []
start_index = 0
axis = mtd[ws].getAxis(1).extractValues()
grouped = self._hide('grouped')
name = grouped
while start_index < len(axis):
end = axis[start_index] + self._rebin_width
end_index = np.argwhere(axis < end)[-1][0]
SumSpectra(InputWorkspace=ws, OutputWorkspace=name,
StartWorkspaceIndex=int(start_index), EndWorkspaceIndex=int(end_index))
count = end_index - start_index + 1
Scale(InputWorkspace=name, OutputWorkspace=name, Factor=1./count)
new_axis.append(np.sum(axis[start_index:end_index + 1]) / count)
if name != grouped:
AppendSpectra(InputWorkspace1=grouped, InputWorkspace2=name, OutputWorkspace=grouped)
DeleteWorkspace(name)
start_index = end_index + 1
name = self._hide('ws_{0}'.format(start_index))
spectrum_axis = NumericAxis.create(len(new_axis))
for i in range(len(new_axis)):
spectrum_axis.setValue(i, new_axis[i])
mtd[grouped].replaceAxis(1, spectrum_axis)
RenameWorkspace(InputWorkspace=grouped, OutputWorkspace=ws)
def _group_spectra(self, ws):
"""
Groups the spectrum axis by summing spectra
@param ws : the input workspace
"""
new_axis = []
start_index = 0
axis = mtd[ws].getAxis(1).extractValues()
grouped = self._hide('grouped')
name = grouped
while start_index < len(axis):
end = axis[start_index] + self._rebin_width
end_index = np.argwhere(axis < end)[-1][0]
SumSpectra(InputWorkspace=ws, OutputWorkspace=name,
StartWorkspaceIndex=int(start_index), EndWorkspaceIndex=int(end_index))
count = end_index - start_index + 1
Scale(InputWorkspace=name, OutputWorkspace=name, Factor=1./count)
new_axis.append(np.sum(axis[start_index:end_index + 1]) / count)
if name != grouped:
AppendSpectra(InputWorkspace1=grouped, InputWorkspace2=name, OutputWorkspace=grouped)
DeleteWorkspace(name)
start_index = end_index + 1
name = self._hide('ws_{0}'.format(start_index))
spectrum_axis = NumericAxis.create(len(new_axis))
for i in range(len(new_axis)):
spectrum_axis.setValue(i, new_axis[i])
mtd[grouped].replaceAxis(1, spectrum_axis)
RenameWorkspace(InputWorkspace=grouped, OutputWorkspace=ws)
def _format_width_workspace(width_workspace, temperatures, run_numbers,
x_axis_is_temperature):
number_of_temperatures = len(temperatures)
axis = NumericAxis.create(number_of_temperatures)
for index in range(number_of_temperatures):
# The slice here is to make the plot versus number less cluttered/messy when using 5 or more digits.
value = float(
temperatures[index]) if x_axis_is_temperature else float(
run_numbers[index][-3:])
axis.setValue(index, value)
width_workspace.replaceAxis(1, axis)
width_workspace.setYUnitLabel("Temperature")
def _create_workspace_for_group_plot(
plot_type: SpectraSelection, workspaces: List[Workspace],
plot_index: int, log_name: str,
custom_log_values: List[float]) -> MatrixWorkspace:
_validate_workspace_choices(workspaces, plot_index)
number_of_workspaces = len(workspaces)
first_ws = workspaces[0]
first_blocksize = first_ws.blocksize()
if plot_type == SpectraSelection.Contour:
x_size = first_blocksize + 1
else:
x_size = first_blocksize
matrix_ws = WorkspaceFactory.Instance().create(
parent=first_ws,
NVectors=number_of_workspaces,
XLength=x_size,
YLength=first_blocksize)
matrix_ws.setYUnitLabel(first_ws.YUnitLabel())
log_values = []
for i in range(number_of_workspaces):
ws = workspaces[i]
if isinstance(ws, MatrixWorkspace):
if plot_type == SpectraSelection.Contour:
matrix_ws.applyBinEdgesFromAnotherWorkspace(ws, plot_index, i)
else:
matrix_ws.applyPointsFromAnotherWorkspace(ws, plot_index, i)
matrix_ws.setY(i, ws.readY(plot_index))
matrix_ws.setE(i, ws.readE(plot_index))
if log_name == "Custom":
log_values.append(
get_single_workspace_log_value(
i, log_values=custom_log_values))
else:
log_values.append(
get_single_workspace_log_value(i,
matrix_ws=ws,
log_name=log_name))
log_values_axis = NumericAxis.create(len(log_values))
for i in range(len(log_values)):
log_values_axis.setValue(i, log_values[i])
matrix_ws.replaceAxis(1, log_values_axis)
return matrix_ws
def test_replace_axis(self):
x_axis = NumericAxis.create(1)
x_axis.setValue(0, 0)
ws1 = WorkspaceCreationHelper.create2DWorkspaceWithFullInstrument(
2, 1, False)
ws1.replaceAxis(0, x_axis)
ws2 = WorkspaceCreationHelper.create2DWorkspaceWithFullInstrument(
2, 1, False)
ws2.replaceAxis(0, x_axis)
try:
del ws1, ws2
except:
self.fail(
"Segmentation violation when deleting the same axis twice")
def _fill_s_2d_workspace(self,
s_points=None,
workspace=None,
protons_number=None,
nucleons_number=None):
from mantid.api import NumericAxis
from abins.constants import MILLI_EV_TO_WAVENUMBER
if protons_number is not None:
s_points = s_points * self.get_cross_section(
scattering=self._scale_by_cross_section,
protons_number=protons_number,
nucleons_number=nucleons_number)
n_q_values, n_freq_bins = s_points.shape
n_q_bins = self._q_bins.size
assert n_q_values + 1 == n_q_bins
if self._energy_units == 'meV':
energy_bins = self._bins / MILLI_EV_TO_WAVENUMBER
else:
energy_bins = self._bins
wrk = WorkspaceFactory.create("Workspace2D",
NVectors=n_freq_bins,
XLength=n_q_bins,
YLength=n_q_values)
freq_axis = NumericAxis.create(n_freq_bins)
freq_offset = (energy_bins[1] - energy_bins[0]) / 2
for i, freq in enumerate(energy_bins[1:]):
wrk.setX(i, self._q_bins)
wrk.setY(i, s_points[:, i].T)
freq_axis.setValue(i, freq + freq_offset)
wrk.replaceAxis(1, freq_axis)
AnalysisDataService.addOrReplace(workspace, wrk)
self.set_workspace_units(workspace,
layout="2D",
energy_units=self._energy_units)
def replace_workspace_axis(wsName, new_values):
ax1 = NumericAxis.create(len(new_values))
for i in range(len(new_values)):
ax1.setValue(i, new_values[i])
ax1.setUnit('MomentumTransfer')
mtd[wsName].replaceAxis(1, ax1) #axis=1 is vertical
def PyExec(self):
workflow_prog = Progress(self, start=0.0, end=1.0, nreports=20)
self._setup()
workflow_prog.report('Validating input')
input_workspace = mtd[self._input_ws]
num_spectra, num_w = self._CheckHistZero(self._input_ws)
logger.information('Sample %s has %d Q values & %d w values' %
(self._input_ws, num_spectra, num_w))
self._CheckElimits([self._energy_min, self._energy_max],
self._input_ws)
workflow_prog.report('Cropping Workspace')
input_ws = '__temp_sqw_moments_cropped'
crop_alg = self.createChildAlgorithm("CropWorkspace",
enableLogging=False)
crop_alg.setProperty("InputWorkspace", input_workspace)
crop_alg.setProperty("XMin", self._energy_min)
crop_alg.setProperty("XMax", self._energy_max)
crop_alg.setProperty("OutputWorkspace", input_ws)
crop_alg.execute()
mtd.addOrReplace(input_ws,
crop_alg.getProperty("OutputWorkspace").value)
logger.information('Energy range is %f to %f' %
(self._energy_min, self._energy_max))
if self._factor > 0.0:
workflow_prog.report('Scaling Workspace by factor %f' %
self._factor)
scale_alg = self.createChildAlgorithm("Scale", enableLogging=False)
scale_alg.setProperty("InputWorkspace", input_ws)
scale_alg.setProperty("Factor", self._factor)
scale_alg.setProperty("Operation", 'Multiply')
scale_alg.setProperty("OutputWorkspace", input_ws)
scale_alg.execute()
logger.information('y(q,w) scaled by %f' % self._factor)
# calculate delta x
workflow_prog.report('Converting to point data')
convert_point_alg = self.createChildAlgorithm("ConvertToPointData",
enableLogging=False)
convert_point_alg.setProperty("InputWorkspace", input_ws)
convert_point_alg.setProperty("OutputWorkspace", input_ws)
convert_point_alg.execute()
mtd.addOrReplace(
input_ws,
convert_point_alg.getProperty("OutputWorkspace").value)
x_data = np.asarray(mtd[input_ws].readX(0))
workflow_prog.report('Creating temporary data workspace')
x_workspace = "__temp_sqw_moments_x"
create_alg = self.createChildAlgorithm("CreateWorkspace",
enableLogging=False)
create_alg.setProperty("DataX", x_data)
create_alg.setProperty("DataY", x_data)
create_alg.setProperty("UnitX", "DeltaE")
create_alg.setProperty("OutputWorkspace", x_workspace)
create_alg.execute()
mtd.addOrReplace(x_workspace,
create_alg.getProperty("OutputWorkspace").value)
# calculate moments
multiply_alg = self.createChildAlgorithm("Multiply",
enableLogging=False)
workflow_prog.report('Multiplying Workspaces by moments')
moments_0 = self._output_ws + '_M0'
moments_1 = self._output_ws + '_M1'
multiply_alg.setProperty("LHSWorkspace", x_workspace)
multiply_alg.setProperty("RHSWorkspace", input_ws)
multiply_alg.setProperty("OutputWorkspace", moments_1)
multiply_alg.execute()
mtd.addOrReplace(moments_1,
multiply_alg.getProperty("OutputWorkspace").value)
moments_2 = self._output_ws + '_M2'
multiply_alg.setProperty("LHSWorkspace", x_workspace)
multiply_alg.setProperty("RHSWorkspace", moments_1)
multiply_alg.setProperty("OutputWorkspace", moments_2)
multiply_alg.execute()
mtd.addOrReplace(moments_2,
multiply_alg.getProperty("OutputWorkspace").value)
moments_3 = self._output_ws + '_M3'
multiply_alg.setProperty("LHSWorkspace", x_workspace)
multiply_alg.setProperty("RHSWorkspace", moments_2)
multiply_alg.setProperty("OutputWorkspace", moments_3)
multiply_alg.execute()
mtd.addOrReplace(moments_3,
multiply_alg.getProperty("OutputWorkspace").value)
moments_4 = self._output_ws + '_M4'
multiply_alg.setProperty("LHSWorkspace", x_workspace)
multiply_alg.setProperty("RHSWorkspace", moments_3)
multiply_alg.setProperty("OutputWorkspace", moments_4)
multiply_alg.execute()
mtd.addOrReplace(moments_4,
multiply_alg.getProperty("OutputWorkspace").value)
workflow_prog.report('Converting to Histogram')
convert_hist_alg = self.createChildAlgorithm("ConvertToHistogram",
enableLogging=False)
convert_hist_alg.setProperty("InputWorkspace", input_ws)
convert_hist_alg.setProperty("OutputWorkspace", input_ws)
convert_hist_alg.execute()
workflow_prog.report('Integrating result')
integration_alg = self.createChildAlgorithm("Integration",
enableLogging=False)
integration_alg.setProperty(
"InputWorkspace",
convert_hist_alg.getProperty("OutputWorkspace").value)
integration_alg.setProperty("OutputWorkspace", moments_0)
integration_alg.execute()
mtd.addOrReplace(moments_0,
integration_alg.getProperty("OutputWorkspace").value)
moments = [moments_1, moments_2, moments_3, moments_4]
divide_alg = self.createChildAlgorithm("Divide", enableLogging=False)
for moment_ws in moments:
workflow_prog.report('Processing workspace %s' % moment_ws)
convert_hist_alg.setProperty("InputWorkspace", moment_ws)
convert_hist_alg.setProperty("OutputWorkspace", moment_ws)
convert_hist_alg.execute()
integration_alg.setProperty(
"InputWorkspace",
convert_hist_alg.getProperty("OutputWorkspace").value)
integration_alg.setProperty("OutputWorkspace", moment_ws)
integration_alg.execute()
divide_alg.setProperty(
"LHSWorkspace",
integration_alg.getProperty("OutputWorkspace").value)
divide_alg.setProperty("RHSWorkspace", moments_0)
divide_alg.setProperty("OutputWorkspace", moment_ws)
divide_alg.execute()
mtd.addOrReplace(moment_ws,
divide_alg.getProperty("OutputWorkspace").value)
workflow_prog.report('Deleting Workspaces')
delete_alg = self.createChildAlgorithm("DeleteWorkspace",
enableLogging=False)
delete_alg.setProperty("Workspace", input_ws)
delete_alg.execute()
delete_alg.setProperty("Workspace", x_workspace)
delete_alg.execute()
# create output workspace
extensions = ['_M0', '_M1', '_M2', '_M3', '_M4']
transpose_alg = self.createChildAlgorithm("Transpose",
enableLogging=False)
convert_hist_alg = self.createChildAlgorithm("ConvertToHistogram",
enableLogging=False)
convert_units_alg = self.createChildAlgorithm("ConvertUnits",
enableLogging=False)
for ext in extensions:
ws_name = self._output_ws + ext
workflow_prog.report('Processing Workspace %s' % ext)
transpose_alg.setProperty("InputWorkspace", ws_name)
transpose_alg.setProperty("OutputWorkspace", ws_name)
transpose_alg.execute()
convert_hist_alg.setProperty(
"InputWorkspace",
transpose_alg.getProperty("OutputWorkspace").value)
convert_hist_alg.setProperty("OutputWorkspace", ws_name)
convert_hist_alg.execute()
convert_units_alg.setProperty(
"InputWorkspace",
convert_hist_alg.getProperty("OutputWorkspace").value)
convert_units_alg.setProperty("Target", 'MomentumTransfer')
convert_units_alg.setProperty("EMode", 'Indirect')
convert_units_alg.setProperty("OutputWorkspace", ws_name)
convert_units_alg.execute()
mtd.addOrReplace(
ws_name,
convert_units_alg.getProperty("OutputWorkspace").value)
workflow_prog.report('Adding Sample logs to %s' % ws_name)
copy_alg = self.createChildAlgorithm("CopyLogs",
enableLogging=False)
copy_alg.setProperty("InputWorkspace", input_workspace)
copy_alg.setProperty("OutputWorkspace", ws_name)
copy_alg.execute()
add_sample_log_alg = self.createChildAlgorithm("AddSampleLog",
enableLogging=False)
add_sample_log_alg.setProperty("Workspace", ws_name)
add_sample_log_alg.setProperty("LogName", "energy_min")
add_sample_log_alg.setProperty("LogType", "Number")
add_sample_log_alg.setProperty("LogText", str(self._energy_min))
add_sample_log_alg.execute()
add_sample_log_alg.setProperty("Workspace", ws_name)
add_sample_log_alg.setProperty("LogName", "energy_max")
add_sample_log_alg.setProperty("LogType", "Number")
add_sample_log_alg.setProperty("LogText", str(self._energy_max))
add_sample_log_alg.execute()
add_sample_log_alg.setProperty("Workspace", ws_name)
add_sample_log_alg.setProperty("LogName", "scale_factor")
add_sample_log_alg.setProperty("LogType", "Number")
add_sample_log_alg.setProperty("LogText", str(self._factor))
add_sample_log_alg.execute()
# Group output workspace
workflow_prog.report('Appending moments')
append_alg = self.createChildAlgorithm("AppendSpectra",
enableLogging=False)
append_alg.setProperty("InputWorkspace1", self._output_ws + '_M0')
append_alg.setProperty("InputWorkspace2", self._output_ws + '_M1')
append_alg.setProperty("ValidateInputs", False)
append_alg.setProperty("OutputWorkspace", self._output_ws)
append_alg.execute()
append_alg.setProperty("InputWorkspace1",
append_alg.getProperty("OutputWorkspace").value)
append_alg.setProperty("InputWorkspace2", self._output_ws + '_M2')
append_alg.setProperty("ValidateInputs", False)
append_alg.setProperty("OutputWorkspace", self._output_ws)
append_alg.execute()
append_alg.setProperty("InputWorkspace1",
append_alg.getProperty("OutputWorkspace").value)
append_alg.setProperty("InputWorkspace2", self._output_ws + '_M3')
append_alg.setProperty("ValidateInputs", False)
append_alg.setProperty("OutputWorkspace", self._output_ws)
append_alg.execute()
append_alg.setProperty("InputWorkspace1",
append_alg.getProperty("OutputWorkspace").value)
append_alg.setProperty("InputWorkspace2", self._output_ws + '_M4')
append_alg.setProperty("ValidateInputs", False)
append_alg.setProperty("OutputWorkspace", self._output_ws)
append_alg.execute()
mtd.addOrReplace(self._output_ws,
append_alg.getProperty("OutputWorkspace").value)
delete_alg.setProperty("Workspace", self._output_ws + '_M0')
delete_alg.execute()
delete_alg.setProperty("Workspace", self._output_ws + '_M1')
delete_alg.execute()
delete_alg.setProperty("Workspace", self._output_ws + '_M2')
delete_alg.execute()
delete_alg.setProperty("Workspace", self._output_ws + '_M3')
delete_alg.execute()
delete_alg.setProperty("Workspace", self._output_ws + '_M4')
delete_alg.execute()
# Create a new vertical axis for the Q and Q**2 workspaces
y_axis = NumericAxis.create(5)
for idx in range(5):
y_axis.setValue(idx, idx)
mtd[self._output_ws].replaceAxis(1, y_axis)
self.setProperty("OutputWorkspace", self._output_ws)
def _integrate_iq(self):
"""
Produces I(Q) or I(Phi,Q) using Q1DWeighted
"""
if self._resolution == 'MildnerCarpenter':
self._setup_mildner_carpenter()
run = mtd[self._input_ws].getRun()
q_min = run.getLogData('qmin').value
q_max = run.getLogData('qmax').value
self.log().information('Using qmin={0:.2f}, qmax={1:.2f}'.format(q_min, q_max))
pixel_height = run.getLogData('pixel_height').value
pixel_width = run.getLogData('pixel_width').value
pixel_size = pixel_height if pixel_height >= pixel_width else pixel_width
binning_factor = self.getProperty('BinningFactor').value
wavelength = 0. # for TOF mode there is no wavelength
if run.hasProperty('wavelength'):
wavelength = run.getLogData('wavelength').value
l2 = run.getLogData('l2').value
beamY = 0.
if run.hasProperty('BeamCenterY'):
beamY = run.getLogData('BeamCenterY').value
q_binning = self._get_iq_binning(q_min, q_max, pixel_size, wavelength, l2, binning_factor, -beamY)
n_wedges = self.getProperty('NumberOfWedges').value
pixel_division = self.getProperty('NPixelDivision').value
gravity = wavelength == 0.
if self._output_type == 'I(Q)':
wedge_ws = self.getPropertyValue('WedgeWorkspace')
wedge_angle = self.getProperty('WedgeAngle').value
wedge_offset = self.getProperty('WedgeOffset').value
asymm_wedges = self.getProperty('AsymmetricWedges').value
Q1DWeighted(InputWorkspace=self._input_ws, OutputWorkspace=self._output_ws,
NumberOfWedges=n_wedges, OutputBinning=q_binning, AccountForGravity=gravity,
WedgeWorkspace=wedge_ws, WedgeAngle=wedge_angle, WedgeOffset=wedge_offset,
AsymmetricWedges=asymm_wedges, NPixelDivision=pixel_division)
if self._resolution == 'MildnerCarpenter':
x = mtd[self._output_ws].readX(0)
mid_x = (x[1:] + x[:-1]) / 2
res = self._deltaQ(mid_x)
mtd[self._output_ws].setDx(0, res)
if n_wedges != 0:
for wedge in range(n_wedges):
mtd[wedge_ws].getItem(wedge).setDx(0, res)
if n_wedges != 0:
self.setProperty('WedgeWorkspace', mtd[wedge_ws])
elif self._output_type == 'I(Phi,Q)':
wedge_ws = '__wedges' + self._input_ws
iq_ws = '__iq' + self._input_ws
wedge_angle = 360./n_wedges
azimuth_axis = NumericAxis.create(n_wedges)
azimuth_axis.setUnit("Degrees")
for i in range(n_wedges):
azimuth_axis.setValue(i, i * wedge_angle)
Q1DWeighted(InputWorkspace=self._input_ws, OutputWorkspace=iq_ws, NumberOfWedges=n_wedges,
NPixelDivision=pixel_division, OutputBinning=q_binning, WedgeWorkspace=wedge_ws,
WedgeAngle=wedge_angle, AsymmetricWedges=True, AccountForGravity=gravity)
DeleteWorkspace(iq_ws)
ConjoinSpectra(InputWorkspaces=wedge_ws, OutputWorkspace=self._output_ws)
mtd[self._output_ws].replaceAxis(1, azimuth_axis)
DeleteWorkspace(wedge_ws)
if self._resolution == 'MildnerCarpenter':
x = mtd[self._output_ws].readX(0)
mid_x = (x[1:] + x[:-1]) / 2
res = self._deltaQ(mid_x)
for i in range(mtd[self._output_ws].getNumberHistograms()):
mtd[self._output_ws].setDx(i, res)
def _fit_bragg_peaks(self, ws, yig_peaks):
""" Fits peaks defined in the yig_peaks argument
returns a workspace with fitted peak positions
on the Y axis and the expected positions on the X axis"""
fitting_method = self.getPropertyValue('FittingMethod')
max_n_peaks = len(max(yig_peaks, key=len))
conjoined_peak_fit_name = 'conjoined_peak_fit_{}'.format(self.getPropertyValue('FitOutputWorkspace'))
# if the name exists in ADS, delete it
if conjoined_peak_fit_name in mtd:
DeleteWorkspace(Workspace=conjoined_peak_fit_name)
ws_names = []
background = 'name=FlatBackground, A0=1e-4'
function = "name=Gaussian, PeakCentre={0}, Height={1}, Sigma={2}"
constraints = "f{0}.Height > 0, f{0}.Sigma < {1}, {2} < f{0}.PeakCentre < {3}"
for pixel_no in range(mtd[ws].getNumberHistograms()):
# create the needed columns in the output workspace
results_x = np.zeros(max_n_peaks)
results_y = np.zeros(max_n_peaks)
results_e = np.zeros(max_n_peaks)
single_spectrum_peaks = yig_peaks[pixel_no]
ws_name = 'pixel_{}'.format(pixel_no)
fit_function = [background]
if len(single_spectrum_peaks) >= 1:
if fitting_method == 'Individual':
ws_name += '_peak_{}'
peak_no = 0
function_no = 0
fit_constraints = []
for peak_intensity, peak_centre_guess, peak_centre_expected in single_spectrum_peaks:
function_no += 1
fit_function.append(function.format(float(peak_centre_guess), peak_intensity, 0.5*self._peakWidth))
fit_constraints.append(constraints.format(function_no, self._peakWidth,
peak_centre_guess - self._minDistance,
peak_centre_guess + self._minDistance))
if fitting_method == 'Individual':
name = ws_name.format(peak_no)
ws_names.append(name)
results_x, results_y, results_e = self._call_fit(ws, name, fit_function, fit_constraints,
peak_no, pixel_no, single_spectrum_peaks,
results_x, results_y, results_e,
startX=peak_centre_expected - self._minDistance,
endX=peak_centre_expected + self._minDistance)
fit_function = [background]
fit_constraints = []
function_no = 0
peak_no += 1
if fitting_method == 'Global':
ws_names.append(ws_name)
results_x, results_y, results_e = self._call_fit(ws, ws_name, fit_function, fit_constraints,
0, pixel_no, single_spectrum_peaks,
results_x, results_y, results_e)
if fitting_method != 'None':
CreateWorkspace(OutputWorkspace='ws',
DataX=results_x,
DataY=results_y,
DataE=results_e,
UnitX='degrees',
NSpec=1)
try:
ConjoinWorkspaces(InputWorkspace1=conjoined_peak_fit_name, InputWorkspace2='ws',
CheckOverlapping=False,
YAxisLabel='TwoTheta_fit',
YAxisUnit='degrees')
except ValueError:
RenameWorkspace(InputWorkspace='ws', OutputWorkspace=conjoined_peak_fit_name)
else:
ws_names.append(ws_name)
single_spectrum_name = '{}_single_spectrum'.format(ws_name)
ExtractSpectra(InputWorkspace=ws, OutputWorkspace=single_spectrum_name,
WorkspaceIndexList=[pixel_no])
EvaluateFunction(Function=';'.join(fit_function),
InputWorkspace=single_spectrum_name,
OutputWorkspace=ws_name)
DeleteWorkspace(Workspace=single_spectrum_name)
if fitting_method in ['Individual', 'Global']:
y_axis = NumericAxis.create(self._D7NumberPixels)
for pixel_no in range(self._D7NumberPixels):
y_axis.setValue(pixel_no, pixel_no+1)
mtd[conjoined_peak_fit_name].replaceAxis(1, y_axis)
# clean up after fitting:
DeleteWorkspaces(['out_Parameters', 'out_NormalisedCovarianceMatrix'])
single_peak_fit_results_name = 'peak_fits_{}'.format(self.getPropertyValue('FitOutputWorkspace'))
GroupWorkspaces(InputWorkspaces=ws_names, OutputWorkspace=single_peak_fit_results_name)
return conjoined_peak_fit_name, single_peak_fit_results_name
def _integrate_iq(self, ws_in, ws_out, panel=None):
"""
Produces I(Q) or I(Phi,Q) using Q1DWeighted
"""
if self._resolution == 'MildnerCarpenter':
self._setup_mildner_carpenter()
elif self._resolution == 'DirectBeam':
self._setup_directbeam_resolution()
run = mtd[ws_in].getRun()
q_min_name = 'qmin'
q_max_name = 'qmax'
if panel:
q_min_name += ('_' + panel)
q_max_name += ('_' + panel)
q_min = run.getLogData(q_min_name).value
q_max = run.getLogData(q_max_name).value
self.log().information(
'From sample logs qmin={0:.5f}, qmax={1:.5f}'.format(q_min, q_max))
instrument = mtd[self._input_ws].getInstrument()
pixel_width = instrument.getNumberParameter('x-pixel-size')[0] / 1000
pixel_height = instrument.getNumberParameter('y-pixel-size')[0] / 1000
pixel_size = pixel_height if pixel_height >= pixel_width else pixel_width
binning_factor = self.getProperty('BinningFactor').value
wavelength = 0. # for TOF mode there is no wavelength
if run.hasProperty('wavelength'):
wavelength = run.getLogData('wavelength').value
l2 = run.getLogData('l2').value
q_binning = self._get_iq_binning(q_min, q_max, pixel_size, wavelength,
l2, binning_factor)
n_wedges = self.getProperty('NumberOfWedges').value
pixel_division = self.getProperty('NPixelDivision').value
gravity = wavelength == 0.
shape_table = self.getProperty('ShapeTable').value
if self._output_type == 'I(Q)':
if panel:
# do not process wedges for panels
n_wedges = 0
shape_table = ''
wedge_ws = self.getPropertyValue('WedgeWorkspace')
wedge_angle = self.getProperty('WedgeAngle').value
wedge_offset = self.getProperty('WedgeOffset').value
asymm_wedges = self.getProperty('AsymmetricWedges').value
Q1DWeighted(InputWorkspace=ws_in,
OutputWorkspace=ws_out,
NumberOfWedges=n_wedges,
OutputBinning=q_binning,
AccountForGravity=gravity,
WedgeWorkspace=wedge_ws,
WedgeAngle=wedge_angle,
WedgeOffset=wedge_offset,
AsymmetricWedges=asymm_wedges,
NPixelDivision=pixel_division,
ShapeTable=shape_table)
if shape_table:
# if there is a shape table, the final number of wedges cannot be known beforehand
# (because of possible symmetry choices)
n_wedges = mtd[wedge_ws].size()
if self._resolution != 'None':
res = self._set_resolution(ws_out)
for wedge in range(n_wedges):
self._set_resolution(mtd[wedge_ws][wedge].name(), res)
if n_wedges != 0:
self.setProperty('WedgeWorkspace', mtd[wedge_ws])
elif self._output_type == 'I(Phi,Q)':
wedge_ws = '__wedges' + ws_in
iq_ws = '__iq' + ws_in
wedge_angle = 360. / n_wedges
azimuth_axis = NumericAxis.create(n_wedges)
azimuth_axis.setUnit("Phi")
for i in range(n_wedges):
azimuth_axis.setValue(i, i * wedge_angle)
Q1DWeighted(InputWorkspace=ws_in,
OutputWorkspace=iq_ws,
NumberOfWedges=n_wedges,
NPixelDivision=pixel_division,
OutputBinning=q_binning,
WedgeWorkspace=wedge_ws,
WedgeAngle=wedge_angle,
AsymmetricWedges=True,
AccountForGravity=gravity)
DeleteWorkspace(iq_ws)
ConjoinSpectra(InputWorkspaces=wedge_ws, OutputWorkspace=ws_out)
mtd[ws_out].replaceAxis(1, azimuth_axis)
DeleteWorkspace(wedge_ws)
self._set_resolution(ws_out)
def test_constructor_methods_return_the_correct_type(self):
self.assertTrue(isinstance(NumericAxis.create(2), NumericAxis))
self.assertTrue(isinstance(SpectraAxis.create(2), SpectraAxis))
self.assertTrue(isinstance(TextAxis.create(2), TextAxis))
def test_constructor_methods_return_the_correct_type(self):
self.assertTrue(isinstance(NumericAxis.create(2), NumericAxis))
self.assertTrue(
isinstance(SpectraAxis.create(self._test_ws), SpectraAxis))
self.assertTrue(isinstance(TextAxis.create(2), TextAxis))
def PyExec(self):
workflow_prog = Progress(self, start=0.0, end=1.0, nreports=20)
self._setup()
workflow_prog.report('Validating input')
input_workspace = mtd[self._input_ws]
num_spectra, num_w = self._CheckHistZero(self._input_ws)
logger.information('Sample %s has %d Q values & %d w values' % (self._input_ws, num_spectra, num_w))
self._CheckElimits([self._energy_min, self._energy_max], self._input_ws)
workflow_prog.report('Cropping Workspace')
input_ws = '__temp_sqw_moments_cropped'
crop_alg = self.createChildAlgorithm("CropWorkspace", enableLogging=False)
crop_alg.setProperty("InputWorkspace", input_workspace)
crop_alg.setProperty("XMin", self._energy_min)
crop_alg.setProperty("XMax", self._energy_max)
crop_alg.setProperty("OutputWorkspace", input_ws)
crop_alg.execute()
mtd.addOrReplace(input_ws, crop_alg.getProperty("OutputWorkspace").value)
logger.information('Energy range is %f to %f' % (self._energy_min, self._energy_max))
if self._factor > 0.0:
workflow_prog.report('Scaling Workspace by factor %f' % self._factor)
scale_alg = self.createChildAlgorithm("Scale", enableLogging=False)
scale_alg.setProperty("InputWorkspace", input_ws)
scale_alg.setProperty("Factor", self._factor)
scale_alg.setProperty("Operation", 'Multiply')
scale_alg.setProperty("OutputWorkspace", input_ws)
scale_alg.execute()
logger.information('y(q,w) scaled by %f' % self._factor)
# calculate delta x
workflow_prog.report('Converting to point data')
convert_point_alg = self.createChildAlgorithm("ConvertToPointData", enableLogging=False)
convert_point_alg.setProperty("InputWorkspace", input_ws)
convert_point_alg.setProperty("OutputWorkspace", input_ws)
convert_point_alg.execute()
mtd.addOrReplace(input_ws, convert_point_alg.getProperty("OutputWorkspace").value)
x_data = np.asarray(mtd[input_ws].readX(0))
workflow_prog.report('Creating temporary data workspace')
x_workspace = "__temp_sqw_moments_x"
create_alg = self.createChildAlgorithm("CreateWorkspace", enableLogging=False)
create_alg.setProperty("DataX", x_data)
create_alg.setProperty("DataY", x_data)
create_alg.setProperty("UnitX", "DeltaE")
create_alg.setProperty("OutputWorkspace", x_workspace)
create_alg.execute()
mtd.addOrReplace(x_workspace, create_alg.getProperty("OutputWorkspace").value)
# calculate moments
multiply_alg = self.createChildAlgorithm("Multiply", enableLogging=False)
workflow_prog.report('Multiplying Workspaces by moments')
moments_0 = self._output_ws + '_M0'
moments_1 = self._output_ws + '_M1'
multiply_alg.setProperty("LHSWorkspace", x_workspace)
multiply_alg.setProperty("RHSWorkspace", input_ws)
multiply_alg.setProperty("OutputWorkspace", moments_1)
multiply_alg.execute()
mtd.addOrReplace(moments_1, multiply_alg.getProperty("OutputWorkspace").value)
moments_2 = self._output_ws + '_M2'
multiply_alg.setProperty("LHSWorkspace", x_workspace)
multiply_alg.setProperty("RHSWorkspace", moments_1)
multiply_alg.setProperty("OutputWorkspace", moments_2)
multiply_alg.execute()
mtd.addOrReplace(moments_2, multiply_alg.getProperty("OutputWorkspace").value)
moments_3 = self._output_ws + '_M3'
multiply_alg.setProperty("LHSWorkspace", x_workspace)
multiply_alg.setProperty("RHSWorkspace", moments_2)
multiply_alg.setProperty("OutputWorkspace", moments_3)
multiply_alg.execute()
mtd.addOrReplace(moments_3, multiply_alg.getProperty("OutputWorkspace").value)
moments_4 = self._output_ws + '_M4'
multiply_alg.setProperty("LHSWorkspace", x_workspace)
multiply_alg.setProperty("RHSWorkspace", moments_3)
multiply_alg.setProperty("OutputWorkspace", moments_4)
multiply_alg.execute()
mtd.addOrReplace(moments_4, multiply_alg.getProperty("OutputWorkspace").value)
workflow_prog.report('Converting to Histogram')
convert_hist_alg = self.createChildAlgorithm("ConvertToHistogram", enableLogging=False)
convert_hist_alg.setProperty("InputWorkspace", input_ws)
convert_hist_alg.setProperty("OutputWorkspace", input_ws)
convert_hist_alg.execute()
workflow_prog.report('Integrating result')
integration_alg = self.createChildAlgorithm("Integration", enableLogging=False)
integration_alg.setProperty("InputWorkspace", convert_hist_alg.getProperty("OutputWorkspace").value)
integration_alg.setProperty("OutputWorkspace", moments_0)
integration_alg.execute()
mtd.addOrReplace(moments_0, integration_alg.getProperty("OutputWorkspace").value)
moments = [moments_1, moments_2, moments_3, moments_4]
divide_alg = self.createChildAlgorithm("Divide", enableLogging=False)
for moment_ws in moments:
workflow_prog.report('Processing workspace %s' % moment_ws)
convert_hist_alg.setProperty("InputWorkspace", moment_ws)
convert_hist_alg.setProperty("OutputWorkspace", moment_ws)
convert_hist_alg.execute()
integration_alg.setProperty("InputWorkspace", convert_hist_alg.getProperty("OutputWorkspace").value)
integration_alg.setProperty("OutputWorkspace", moment_ws)
integration_alg.execute()
divide_alg.setProperty("LHSWorkspace", integration_alg.getProperty("OutputWorkspace").value)
divide_alg.setProperty("RHSWorkspace", moments_0)
divide_alg.setProperty("OutputWorkspace", moment_ws)
divide_alg.execute()
mtd.addOrReplace(moment_ws, divide_alg.getProperty("OutputWorkspace").value)
workflow_prog.report('Deleting Workspaces')
delete_alg = self.createChildAlgorithm("DeleteWorkspace", enableLogging=False)
delete_alg.setProperty("Workspace", input_ws)
delete_alg.execute()
delete_alg.setProperty("Workspace", x_workspace)
delete_alg.execute()
# create output workspace
extensions = ['_M0', '_M1', '_M2', '_M3', '_M4']
transpose_alg = self.createChildAlgorithm("Transpose", enableLogging=False)
convert_hist_alg = self.createChildAlgorithm("ConvertToHistogram", enableLogging=False)
convert_units_alg = self.createChildAlgorithm("ConvertUnits", enableLogging=False)
for ext in extensions:
ws_name = self._output_ws + ext
workflow_prog.report('Processing Workspace %s' % ext)
transpose_alg.setProperty("InputWorkspace", ws_name)
transpose_alg.setProperty("OutputWorkspace", ws_name)
transpose_alg.execute()
convert_hist_alg.setProperty("InputWorkspace", transpose_alg.getProperty("OutputWorkspace").value)
convert_hist_alg.setProperty("OutputWorkspace", ws_name)
convert_hist_alg.execute()
convert_units_alg.setProperty("InputWorkspace", convert_hist_alg.getProperty("OutputWorkspace").value)
convert_units_alg.setProperty("Target", 'MomentumTransfer')
convert_units_alg.setProperty("EMode", 'Indirect')
convert_units_alg.setProperty("OutputWorkspace", ws_name)
convert_units_alg.execute()
mtd.addOrReplace(ws_name, convert_units_alg.getProperty("OutputWorkspace").value)
workflow_prog.report('Adding Sample logs to %s' % ws_name)
copy_alg = self.createChildAlgorithm("CopyLogs", enableLogging=False)
copy_alg.setProperty("InputWorkspace", input_workspace)
copy_alg.setProperty("OutputWorkspace", ws_name)
copy_alg.execute()
add_sample_log_alg = self.createChildAlgorithm("AddSampleLog", enableLogging=False)
add_sample_log_alg.setProperty("Workspace", ws_name)
add_sample_log_alg.setProperty("LogName", "energy_min")
add_sample_log_alg.setProperty("LogType", "Number")
add_sample_log_alg.setProperty("LogText", str(self._energy_min))
add_sample_log_alg.execute()
add_sample_log_alg.setProperty("Workspace", ws_name)
add_sample_log_alg.setProperty("LogName", "energy_max")
add_sample_log_alg.setProperty("LogType", "Number")
add_sample_log_alg.setProperty("LogText", str(self._energy_max))
add_sample_log_alg.execute()
add_sample_log_alg.setProperty("Workspace", ws_name)
add_sample_log_alg.setProperty("LogName", "scale_factor")
add_sample_log_alg.setProperty("LogType", "Number")
add_sample_log_alg.setProperty("LogText", str(self._factor))
add_sample_log_alg.execute()
# Group output workspace
workflow_prog.report('Appending moments')
append_alg = self.createChildAlgorithm("AppendSpectra", enableLogging=False)
append_alg.setProperty("InputWorkspace1", self._output_ws + '_M0')
append_alg.setProperty("InputWorkspace2", self._output_ws + '_M1')
append_alg.setProperty("ValidateInputs", False)
append_alg.setProperty("OutputWorkspace", self._output_ws)
append_alg.execute()
append_alg.setProperty("InputWorkspace1", append_alg.getProperty("OutputWorkspace").value)
append_alg.setProperty("InputWorkspace2", self._output_ws + '_M2')
append_alg.setProperty("ValidateInputs", False)
append_alg.setProperty("OutputWorkspace", self._output_ws)
append_alg.execute()
append_alg.setProperty("InputWorkspace1", append_alg.getProperty("OutputWorkspace").value)
append_alg.setProperty("InputWorkspace2", self._output_ws + '_M3')
append_alg.setProperty("ValidateInputs", False)
append_alg.setProperty("OutputWorkspace", self._output_ws)
append_alg.execute()
append_alg.setProperty("InputWorkspace1", append_alg.getProperty("OutputWorkspace").value)
append_alg.setProperty("InputWorkspace2", self._output_ws + '_M4')
append_alg.setProperty("ValidateInputs", False)
append_alg.setProperty("OutputWorkspace", self._output_ws)
append_alg.execute()
mtd.addOrReplace(self._output_ws, append_alg.getProperty("OutputWorkspace").value)
delete_alg.setProperty("Workspace", self._output_ws + '_M0')
delete_alg.execute()
delete_alg.setProperty("Workspace", self._output_ws + '_M1')
delete_alg.execute()
delete_alg.setProperty("Workspace", self._output_ws + '_M2')
delete_alg.execute()
delete_alg.setProperty("Workspace", self._output_ws + '_M3')
delete_alg.execute()
delete_alg.setProperty("Workspace", self._output_ws + '_M4')
delete_alg.execute()
# Create a new vertical axis for the Q and Q**2 workspaces
y_axis = NumericAxis.create(5)
for idx in range(5):
y_axis.setValue(idx, idx)
mtd[self._output_ws].replaceAxis(1, y_axis)
self.setProperty("OutputWorkspace", self._output_ws)
def _integrate_iq(self):
"""
Produces I(Q) or I(Phi,Q) using Q1DWeighted
"""
if self._resolution == 'MildnerCarpenter':
self._setup_mildner_carpenter()
run = mtd[self._input_ws].getRun()
q_min = run.getLogData('qmin').value
q_max = run.getLogData('qmax').value
self.log().information('Using qmin={0:.2f}, qmax={1:.2f}'.format(q_min, q_max))
pixel_height = run.getLogData('pixel_height').value
pixel_width = run.getLogData('pixel_width').value
pixel_size = pixel_height if pixel_height >= pixel_width else pixel_width
binning_factor = self.getProperty('BinningFactor').value
wavelength = 0. # for TOF mode there is no wavelength
if run.hasProperty('wavelength'):
wavelength = run.getLogData('wavelength').value
l2 = run.getLogData('l2').value
beamY = 0.
if run.hasProperty('BeamCenterY'):
beamY = run.getLogData('BeamCenterY').value
q_binning = self._get_iq_binning(q_min, q_max, pixel_size, wavelength, l2, binning_factor, -beamY)
n_wedges = self.getProperty('NumberOfWedges').value
pixel_division = self.getProperty('NPixelDivision').value
gravity = wavelength == 0.
if self._output_type == 'I(Q)':
wedge_ws = self.getPropertyValue('WedgeWorkspace')
wedge_angle = self.getProperty('WedgeAngle').value
wedge_offset = self.getProperty('WedgeOffset').value
asymm_wedges = self.getProperty('AsymmetricWedges').value
Q1DWeighted(InputWorkspace=self._input_ws, OutputWorkspace=self._output_ws,
NumberOfWedges=n_wedges, OutputBinning=q_binning, AccountForGravity=gravity,
WedgeWorkspace=wedge_ws, WedgeAngle=wedge_angle, WedgeOffset=wedge_offset,
AsymmetricWedges=asymm_wedges, NPixelDivision=pixel_division)
if self._resolution == 'MildnerCarpenter':
x = mtd[self._output_ws].readX(0)
mid_x = (x[1:] + x[:-1]) / 2
res = self._deltaQ(mid_x)
mtd[self._output_ws].setDx(0, res)
if n_wedges != 0:
for wedge in range(n_wedges):
mtd[wedge_ws].getItem(wedge).setDx(0, res)
if n_wedges != 0:
self.setProperty('WedgeWorkspace', mtd[wedge_ws])
elif self._output_type == 'I(Phi,Q)':
wedge_ws = '__wedges' + self._input_ws
iq_ws = '__iq' + self._input_ws
wedge_angle = 360./n_wedges
azimuth_axis = NumericAxis.create(n_wedges)
azimuth_axis.setUnit("Degrees")
for i in range(n_wedges):
azimuth_axis.setValue(i, i * wedge_angle)
Q1DWeighted(InputWorkspace=self._input_ws, OutputWorkspace=iq_ws, NumberOfWedges=n_wedges,
NPixelDivision=pixel_division, OutputBinning=q_binning, WedgeWorkspace=wedge_ws,
WedgeAngle=wedge_angle, AsymmetricWedges=True, AccountForGravity=gravity)
DeleteWorkspace(iq_ws)
ConjoinSpectra(InputWorkspaces=wedge_ws, OutputWorkspace=self._output_ws)
mtd[self._output_ws].replaceAxis(1, azimuth_axis)
DeleteWorkspace(wedge_ws)
if self._resolution == 'MildnerCarpenter':
x = mtd[self._output_ws].readX(0)
mid_x = (x[1:] + x[:-1]) / 2
res = self._deltaQ(mid_x)
for i in range(mtd[self._output_ws].getNumberHistograms()):
mtd[self._output_ws].setDx(i, res)
def _q_rebin(self, ws):
"""
Rebins the single crystal omega scan measurement output onto 2D Qx-Qy grid.
:param ws: Output of the cross-section separation and/or normalisation.
:return: WorkspaceGroup containing 2D distributions on a Qx-Qy grid.
"""
DEG_2_RAD = np.pi / 180.0
fld = self._sampleAndEnvironmentProperties['fld'].value if 'fld' in self._sampleAndEnvironmentProperties else 1
nQ = self._sampleAndEnvironmentProperties['nQ'].value if 'nQ' in self._sampleAndEnvironmentProperties else 80
omega_shift = self._sampleAndEnvironmentProperties['OmegaShift'].value \
if 'OmegaShift' in self._sampleAndEnvironmentProperties else 0
wavelength = mtd[ws][0].getRun().getLogData('monochromator.wavelength').value
ki = 2 * np.pi / wavelength
dE = 0.0 # monochromatic data
const_val = 2.07194 # hbar^2/2m
kf = np.sqrt(ki * ki - dE / const_val)
twoTheta = -mtd[ws][0].getAxis(1).extractValues() * DEG_2_RAD # detector positions in radians
omega = mtd[ws][0].getAxis(0).extractValues() * DEG_2_RAD # omega scan angle in radians
ntheta = len(twoTheta)
nomega = len(omega)
omega = np.matrix(omega) + omega_shift * DEG_2_RAD
Qmag = np.sqrt(ki * ki + kf * kf - 2 * ki * kf * np.cos(twoTheta))
# beta is the angle between ki and Q
beta = (twoTheta / np.abs(twoTheta)) * np.arccos((ki * ki - kf * kf + Qmag * Qmag) / (2 * ki * Qmag))
alpha = -np.pi/2 + omega.T * np.ones(shape=(1, ntheta)) + np.ones(shape=(nomega, 1)) * beta
Qx = np.multiply((np.ones(shape=(nomega, 1)) * Qmag), np.cos(alpha)).T
Qy = np.multiply((np.ones(shape=(nomega, 1)) * Qmag), np.sin(alpha)).T
Qmax = 1.1 * np.max(Qmag)
dQ = Qmax / nQ
output_names = []
for entry in mtd[ws]:
w_out = np.zeros(shape=((fld + 1) * nQ, (fld + 1) * nQ))
e_out = np.zeros(shape=((fld + 1) * nQ, (fld + 1) * nQ))
n_out = np.zeros(shape=((fld + 1) * nQ, (fld + 1) * nQ))
w_in = entry.extractY()
e_in = entry.extractE()
for theta in range(ntheta):
for omega in range(nomega):
if fld == 1:
ix = int(((Qx[theta, omega] + dQ / 2.) / dQ) + nQ)
iy = int(((Qy[theta, omega] + dQ / 2.) / dQ) + nQ)
if Qx[theta, omega] > 0.99 * Qmax or Qy[theta, omega] > 0.99 * Qmax:
continue
else:
ix = int(abs((Qx[theta, omega]) + dQ / 2.) / dQ)
iy = int(abs((Qy[theta, omega]) + dQ / 2.) / dQ)
w_out[ix, iy] += w_in[theta, omega]
e_out[ix, iy] += e_in[theta, omega]**2
n_out[ix, iy] += 1.
w_out /= n_out
e_out = np.sqrt(e_out / n_out)
w_out_name = entry.name() + '_qxqy'
output_names.append(w_out_name)
data_x = [(val-(fld*nQ)) * dQ for val in range((fld+1)*nQ)]
y_axis = NumericAxis.create(int((fld+1)*nQ))
for q_index in range(int((fld+1)*nQ)):
y_axis.setValue(q_index, (q_index-(fld*nQ))*dQ)
CreateWorkspace(DataX=data_x, DataY=w_out, DataE=e_out, NSpec=int((fld+1)*nQ),
OutputWorkspace=w_out_name)
mtd[w_out_name].replaceAxis(1, y_axis)
mtd[w_out_name].getAxis(0).setUnit('Label').setLabel('Qx', r'\AA^{-1}')
mtd[w_out_name].getAxis(1).setUnit('Label').setLabel('Qy', r'\AA^{-1}')
ReplaceSpecialValues(InputWorkspace=w_out_name, OutputWorkspace=w_out_name, NaNValue=0,
NaNError=0, InfinityValue=0, InfinityError=0)
DeleteWorkspace(Workspace=ws)
GroupWorkspaces(InputWorkspaces=output_names, OutputWorkspace=ws)
return ws
def _get_scan_data(self, ws_name, progress):
""" Loads YIG scan data, removes monitors, and prepares
a workspace for Bragg peak fitting"""
# workspace indices for monitors
monitor_indices = "{0}, {1}".format(self._D7NumberPixels, self._D7NumberPixels+1)
scan_data_name = "scan_data_{}".format(ws_name)
self._created_ws_names.append(scan_data_name)
progress.report(0, 'Loading YIG scan data')
LoadAndMerge(Filename=self.getPropertyValue("Filenames"), OutputWorkspace=scan_data_name, LoaderName='LoadILLPolarizedDiffraction',
startProgress=0.0, endProgress=0.6)
progress.report(6, 'Conjoining the scan data')
# load the group into a single table workspace
nfiles = mtd[scan_data_name].getNumberOfEntries()
# new vertical axis
x_axis = NumericAxis.create(nfiles)
# Fill the intensity and position tables with all the data from scans
conjoined_scan_name = "conjoined_input_{}".format(ws_name)
# if the name exists in ADS, delete it
if conjoined_scan_name in mtd:
DeleteWorkspace(Workspace=conjoined_scan_name)
self._created_ws_names.append(conjoined_scan_name)
masking_criteria = self._prepare_masking_criteria()
name_list = []
for entry_no, entry in enumerate(mtd[scan_data_name]):
# normalize to monitor1 as monitor2 is sometimes empty:
monitor1_counts = entry.readY(self._D7NumberPixels)[0]
if monitor1_counts != 0:
monitor_name = '__monitor_' + entry.name()
CreateSingleValuedWorkspace(DataValue=monitor1_counts, ErrorValue=np.sqrt(monitor1_counts),
OutputWorkspace=monitor_name)
Divide(LHSWorkspace=entry,
RHSWorkspace=monitor_name,
OutputWorkspace=entry)
DeleteWorkspace(Workspace=monitor_name)
# remove Monitors:
RemoveSpectra(InputWorkspace=entry, WorkspaceIndices=monitor_indices, OutputWorkspace=entry)
# prepare proper label for the axes
x_axis_label = entry.run().getProperty('2theta.requested').value
x_axis.setValue(entry_no, x_axis_label)
# convert the x-axis to signedTwoTheta
ConvertAxisByFormula(InputWorkspace=entry,
Axis='X',
Formula='-180.0 * signedtwotheta / pi',
OutputWorkspace=entry)
# mask bins using predefined ranges
for criterion in masking_criteria:
MaskBinsIf(InputWorkspace=entry,
Criterion=criterion,
OutputWorkspace=entry)
# append the new row to a new MatrixWorkspace
ConvertToPointData(InputWorkspace=entry, OutputWorkspace=entry)
name_list.append(entry.name())
ConjoinXRuns(InputWorkspaces=name_list, OutputWorkspace=conjoined_scan_name)
#replace axis and corrects labels
x_axis.setUnit("Label").setLabel('TwoTheta', 'degrees')
mtd[conjoined_scan_name].replaceAxis(0, x_axis)
y_axis = NumericAxis.create(self._D7NumberPixels)
for pixel_no in range(self._D7NumberPixels):
y_axis.setValue(pixel_no, pixel_no+1)
mtd[conjoined_scan_name].replaceAxis(1, y_axis)
return conjoined_scan_name
