def test_dx_histogram_ascending(self):
dataX = [1., 2., 3., 4.]
dataY = [1., 2., 3.]
dx = [1., 2., 3.]
unsortedws = CreateWorkspace(DataX=dataX, DataY=dataY, Dx=dx, UnitX='TOF', Distribution=False)
# Run the algorithm
sortedws = SortXAxis(InputWorkspace=unsortedws)
sortedDx = sortedws.readDx(0)
# Check the resulting data values. Sorting operation should have resulted in no changes
self.assertEqual(dx, sortedDx.tolist())
DeleteWorkspace(unsortedws)
DeleteWorkspace(sortedws)
def test_sort_descending(self):
dataX = [1., 2., 3., 4.]
dataY = [1., 2., 3.]
unsortedws = CreateWorkspace(DataX=dataX, DataY=dataY, UnitX='TOF', Distribution=False)
# Run the algorithm
sortedws = SortXAxis(InputWorkspace=unsortedws, Ordering="Descending")
sortedX = sortedws.readX(0)
sortedY = sortedws.readY(0)
# Check the resulting data values. Sorting operation should have resulted in no changes
self.assertEqual([4., 3., 2., 1.], sortedX.tolist())
self.assertEqual([3., 2., 1.], sortedY.tolist())
DeleteWorkspace(unsortedws)
DeleteWorkspace(sortedws)
def test_on_multiple_spectrum(self):
dataX = [3., 2., 1., 3., 2., 1.
] # In descending order, so y and e will need to be reversed.
dataY = [1., 2., 3., 1., 2., 3.]
dataE = [1., 2., 3., 1., 2., 3.]
unsortedws = CreateWorkspace(DataX=dataX,
DataY=dataY,
DataE=dataE,
UnitX='TOF',
Distribution=True,
NSpec=2)
dataY.reverse()
dataE.reverse()
# Run the algorithm
sortedws = SortXAxis(InputWorkspace=unsortedws)
# Check the resulting data values for 1st spectrum.
sortedX = sortedws.readX(0)
sortedY = sortedws.readY(0)
sortedE = sortedws.readE(0)
self.assertEqual(sorted(dataX[0:3]), sortedX.tolist())
self.assertEqual(dataY[0:3], sortedY.tolist())
self.assertEqual(dataE[0:3], sortedE.tolist())
# Check the resulting data values for 2nd spectrum.
sortedX = sortedws.readX(1)
sortedY = sortedws.readY(1)
sortedE = sortedws.readE(1)
self.assertEqual(sorted(dataX[3:]), sortedX.tolist())
self.assertEqual(dataY[3:], sortedY.tolist())
self.assertEqual(dataE[3:], sortedE.tolist())
DeleteWorkspace(unsortedws)
DeleteWorkspace(sortedws)
def rebin_reduction(workspace_name, rebin_string, multi_frame_rebin_string, num_bins):
"""
@param workspace_name Name of workspace to rebin
@param rebin_string Rebin parameters
@param multi_frame_rebin_string Rebin string for multiple frame rebinning
@param num_bins Max number of bins in input frames
"""
from mantid.simpleapi import (Rebin, RebinToWorkspace, SortXAxis)
if rebin_string is not None:
if multi_frame_rebin_string is not None and num_bins is not None:
# Multi frame data
if mtd[workspace_name].blocksize() == num_bins:
Rebin(InputWorkspace=workspace_name,
OutputWorkspace=workspace_name,
Params=rebin_string)
else:
Rebin(InputWorkspace=workspace_name,
OutputWorkspace=workspace_name,
Params=multi_frame_rebin_string)
else:
# Regular data
SortXAxis(InputWorkspace=workspace_name,
OutputWorkspace=workspace_name)
Rebin(InputWorkspace=workspace_name,
OutputWorkspace=workspace_name,
Params=rebin_string)
else:
try:
# If user does not want to rebin then just ensure uniform binning across spectra
RebinToWorkspace(WorkspaceToRebin=workspace_name,
WorkspaceToMatch=workspace_name,
OutputWorkspace=workspace_name)
except RuntimeError:
logger.warning('Rebinning failed, will try to continue anyway.')
def test_x_ascending(self):
dataX = [1., 2., 3.] # In ascending order, so y and e will need to be reversed.
dataY = [1., 2., 3.]
dataE = [1., 2., 3.]
unsortedws = CreateWorkspace(DataX=dataX,DataY=dataY,DataE=dataE,UnitX='TOF',Distribution=True)
# Run the algorithm
sortedws = SortXAxis(InputWorkspace=unsortedws)
sortedX = sortedws.readX(0)
sortedY = sortedws.readY(0)
sortedE = sortedws.readE(0)
# Check the resulting data values. Sorting operation should have resulted in no changes
self.assertEqual(dataX, sortedX.tolist())
self.assertEqual(dataY, sortedY.tolist())
self.assertEqual(dataE, sortedE.tolist())
DeleteWorkspace(unsortedws)
DeleteWorkspace(sortedws)
def test_dx_multiple_spectrum(self):
dataX = [3, 2, 1, 3, 2, 1] # In descending order, so y and e will need to be reversed.
dataY = [1, 2, 3, 1, 2, 3]
dx = [1, 2, 3, 1, 2, 3]
unsortedws = CreateWorkspace(DataX=dataX, DataY=dataY, Dx=dx, UnitX='TOF', Distribution=True, NSpec=2)
dx.reverse()
# Run the algorithm
sortedws = SortXAxis(InputWorkspace=unsortedws)
# Check the resulting data values for 1st spectrum.
sortedDx = sortedws.readDx(0)
self.assertEqual(dx[0:3], sortedDx.tolist())
# Check the resulting data values for 2nd spectrum.
sortedDx = sortedws.readDx(1)
self.assertEqual(dx[3:], sortedDx.tolist())
DeleteWorkspace(unsortedws)
DeleteWorkspace(sortedws)
def test_sort_descending(self):
dataX = [1., 2., 3., 4.]
dataY = [1., 2., 3.]
unsortedws = CreateWorkspace(DataX=dataX,
DataY=dataY,
UnitX='TOF',
Distribution=False)
# Run the algorithm
sortedws = SortXAxis(InputWorkspace=unsortedws, Ordering="Descending")
sortedX = sortedws.readX(0)
sortedY = sortedws.readY(0)
# Check the resulting data values. Sorting operation should have resulted in no changes
self.assertEqual([4., 3., 2., 1.], sortedX.tolist())
self.assertEqual([3., 2., 1.], sortedY.tolist())
DeleteWorkspace(unsortedws)
DeleteWorkspace(sortedws)
def test_dx_histogram_ascending(self):
dataX = [1., 2., 3., 4.]
dataY = [1., 2., 3.]
dx = [1., 2., 3.]
unsortedws = CreateWorkspace(DataX=dataX,
DataY=dataY,
Dx=dx,
UnitX='TOF',
Distribution=False)
# Run the algorithm
sortedws = SortXAxis(InputWorkspace=unsortedws)
sortedDx = sortedws.readDx(0)
# Check the resulting data values. Sorting operation should have resulted in no changes
self.assertEqual(dx, sortedDx.tolist())
DeleteWorkspace(unsortedws)
DeleteWorkspace(sortedws)
def test_on_multiple_spectrum(self):
dataX = [3., 2., 1., 3., 2., 1.] # In descending order, so y and e will need to be reversed.
dataY = [1., 2., 3., 1., 2., 3.]
dataE = [1., 2., 3., 1., 2., 3.]
unsortedws = CreateWorkspace(DataX=dataX,DataY=dataY,DataE=dataE,UnitX='TOF',Distribution=True, NSpec=2)
dataY.reverse()
dataE.reverse()
# Run the algorithm
sortedws = SortXAxis(InputWorkspace=unsortedws)
# Check the resulting data values for 1st spectrum.
sortedX = sortedws.readX(0)
sortedY = sortedws.readY(0)
sortedE = sortedws.readE(0)
self.assertEqual(sorted(dataX[0:3]), sortedX.tolist())
self.assertEqual(dataY[0:3], sortedY.tolist())
self.assertEqual(dataE[0:3], sortedE.tolist())
# Check the resulting data values for 2nd spectrum.
sortedX = sortedws.readX(1)
sortedY = sortedws.readY(1)
sortedE = sortedws.readE(1)
self.assertEqual(sorted(dataX[3:]), sortedX.tolist())
self.assertEqual(dataY[3:], sortedY.tolist())
self.assertEqual(dataE[3:], sortedE.tolist())
DeleteWorkspace(unsortedws)
DeleteWorkspace(sortedws)
def test_sorts_x_histogram_descending(self):
dataX = [4., 3., 2., 1.]
dataY = [1., 2., 3.]
dataE = [1., 2., 3.]
unsortedws = CreateWorkspace(DataX=dataX,DataY=dataY,DataE=dataE,UnitX='TOF',Distribution=False)
# Run the algorithm
sortedws = SortXAxis(InputWorkspace=unsortedws)
sortedX = sortedws.readX(0)
sortedY = sortedws.readY(0)
sortedE = sortedws.readE(0)
# Check the resulting data values. Sorting operation should have resulted in no changes
self.assertEqual(sorted(dataX), sortedX.tolist())
dataY.reverse()
dataE.reverse()
self.assertEqual(dataY, sortedY.tolist())
self.assertEqual(dataE, sortedE.tolist())
DeleteWorkspace(unsortedws)
DeleteWorkspace(sortedws)
def test_x_ascending(self):
dataX = [1., 2., 3.
] # In ascending order, so y and e will need to be reversed.
dataY = [1., 2., 3.]
dataE = [1., 2., 3.]
unsortedws = CreateWorkspace(DataX=dataX,
DataY=dataY,
DataE=dataE,
UnitX='TOF',
Distribution=True)
# Run the algorithm
sortedws = SortXAxis(InputWorkspace=unsortedws)
sortedX = sortedws.readX(0)
sortedY = sortedws.readY(0)
sortedE = sortedws.readE(0)
# Check the resulting data values. Sorting operation should have resulted in no changes
self.assertEqual(dataX, sortedX.tolist())
self.assertEqual(dataY, sortedY.tolist())
self.assertEqual(dataE, sortedE.tolist())
DeleteWorkspace(unsortedws)
DeleteWorkspace(sortedws)
def test_dx_multiple_spectrum(self):
dataX = [3, 2, 1, 3, 2, 1
] # In descending order, so y and e will need to be reversed.
dataY = [1, 2, 3, 1, 2, 3]
dx = [1, 2, 3, 1, 2, 3]
unsortedws = CreateWorkspace(DataX=dataX,
DataY=dataY,
Dx=dx,
UnitX='TOF',
Distribution=True,
NSpec=2)
dx.reverse()
# Run the algorithm
sortedws = SortXAxis(InputWorkspace=unsortedws)
# Check the resulting data values for 1st spectrum.
sortedDx = sortedws.readDx(0)
self.assertEqual(dx[0:3], sortedDx.tolist())
# Check the resulting data values for 2nd spectrum.
sortedDx = sortedws.readDx(1)
self.assertEqual(dx[3:], sortedDx.tolist())
DeleteWorkspace(unsortedws)
DeleteWorkspace(sortedws)
def test_sorts_x_histogram_descending(self):
dataX = [4., 3., 2., 1.]
dataY = [1., 2., 3.]
dataE = [1., 2., 3.]
unsortedws = CreateWorkspace(DataX=dataX,
DataY=dataY,
DataE=dataE,
UnitX='TOF',
Distribution=False)
# Run the algorithm
sortedws = SortXAxis(InputWorkspace=unsortedws)
sortedX = sortedws.readX(0)
sortedY = sortedws.readY(0)
sortedE = sortedws.readE(0)
# Check the resulting data values. Sorting operation should have resulted in no changes
self.assertEqual(sorted(dataX), sortedX.tolist())
dataY.reverse()
dataE.reverse()
self.assertEqual(dataY, sortedY.tolist())
self.assertEqual(dataE, sortedE.tolist())
DeleteWorkspace(unsortedws)
DeleteWorkspace(sortedws)
def rebin_reduction(workspace_name, rebin_string, multi_frame_rebin_string,
num_bins):
"""
@param workspace_name Name of workspace to rebin
@param rebin_string Rebin parameters
@param multi_frame_rebin_string Rebin string for multiple frame rebinning
@param num_bins Max number of bins in input frames
"""
from mantid.simpleapi import (Rebin, SortXAxis, RemoveSpectra)
if rebin_string is not None:
if multi_frame_rebin_string is not None and num_bins is not None:
# Multi frame data
if mtd[workspace_name].blocksize() == num_bins:
Rebin(InputWorkspace=workspace_name,
OutputWorkspace=workspace_name,
Params=rebin_string)
else:
Rebin(InputWorkspace=workspace_name,
OutputWorkspace=workspace_name,
Params=multi_frame_rebin_string)
else:
# Regular data
RemoveSpectra(InputWorkspace=workspace_name,
OutputWorkspace=workspace_name,
RemoveSpectraWithNoDetector=True)
SortXAxis(InputWorkspace=workspace_name,
OutputWorkspace=workspace_name)
Rebin(InputWorkspace=workspace_name,
OutputWorkspace=workspace_name,
Params=rebin_string)
else:
try:
# If user does not want to rebin then just ensure uniform binning across spectra
# extract the binning parameters from the first spectrum.
# there is probably a better way to calculate the binning parameters, but this
# gets the right answer.
xaxis = mtd[workspace_name].readX(0)
params = []
for i, x in enumerate(xaxis):
params.append(x)
if i < len(xaxis) - 1:
params.append(xaxis[i + 1] - x) # delta
Rebin(InputWorkspace=workspace_name,
OutputWorkspace=workspace_name,
Params=params)
except RuntimeError:
logger.warning('Rebinning failed, will try to continue anyway.')
def _sort_x_axis(workspace):
return SortXAxis(InputWorkspace=workspace,
OutputWorkspace="__sorted",
StoreInADS=False,
EnableLogging=False)
def PyExec(self):
self._setup()
# Fit line to each of the spectra
function = 'name=LinearBackground, A0=0, A1=0'
input_params = [
self._input_ws + ',i%d' % i
for i in range(self._spec_range[0], self._spec_range[1] + 1)
]
input_params = ';'.join(input_params)
PlotPeakByLogValue(Input=input_params,
OutputWorkspace=self._output_msd_ws,
Function=function,
StartX=self._x_range[0],
EndX=self._x_range[1],
FitType='Sequential',
CreateOutput=True)
delete_alg = self.createChildAlgorithm("DeleteWorkspace",
enableLogging=False)
delete_alg.setProperty(
"Workspace", self._output_msd_ws + '_NormalisedCovarianceMatrices')
delete_alg.execute()
delete_alg.setProperty("Workspace",
self._output_msd_ws + '_Parameters')
delete_alg.execute()
rename_alg = self.createChildAlgorithm("RenameWorkspace",
enableLogging=False)
rename_alg.setProperty("InputWorkspace", self._output_msd_ws)
rename_alg.setProperty("OutputWorkspace", self._output_param_ws)
rename_alg.execute()
params_table = mtd[self._output_param_ws]
# MSD value should be positive, but the fit output is negative
msd = params_table.column('A1')
for i, value in enumerate(msd):
params_table.setCell('A1', i, value * -1)
# Create workspaces for each of the parameters
parameter_ws_group = []
# A0 workspace
ws_name = self._output_msd_ws + '_A0'
parameter_ws_group.append(ws_name)
ConvertTableToMatrixWorkspace(self._output_param_ws,
OutputWorkspace=ws_name,
ColumnX='axis-1',
ColumnY='A0',
ColumnE='A0_Err',
EnableLogging=False)
xunit = mtd[ws_name].getAxis(0).setUnit('Label')
xunit.setLabel('Temperature', 'K')
SortXAxis(InputWorkspace=ws_name,
OutputWorkspace=ws_name,
EnableLogging=False)
# A1 workspace
ws_name = self._output_msd_ws + '_A1'
parameter_ws_group.append(ws_name)
ConvertTableToMatrixWorkspace(self._output_param_ws,
OutputWorkspace=ws_name,
ColumnX='axis-1',
ColumnY='A1',
ColumnE='A1_Err',
EnableLogging=False)
xunit = mtd[ws_name].getAxis(0).setUnit('Label')
xunit.setLabel('Temperature', 'K')
SortXAxis(InputWorkspace=ws_name,
OutputWorkspace=ws_name,
EnableLogging=False)
AppendSpectra(InputWorkspace1=self._output_msd_ws + '_A0',
InputWorkspace2=self._output_msd_ws + '_A1',
ValidateInputs=False,
OutputWorkspace=self._output_msd_ws,
EnableLogging=False)
delete_alg.setProperty("Workspace", self._output_msd_ws + '_A0')
delete_alg.execute()
delete_alg.setProperty("Workspace", self._output_msd_ws + '_A1')
delete_alg.execute()
# Create a new vertical axis for the Q and Q**2 workspaces
y_axis = NumericAxis.create(2)
for idx in range(1):
y_axis.setValue(idx, idx)
mtd[self._output_msd_ws].replaceAxis(1, y_axis)
# Rename fit workspace group
original_fit_ws_name = self._output_msd_ws + '_Workspaces'
if original_fit_ws_name != self._output_fit_ws:
rename_alg.setProperty("InputWorkspace",
self._output_msd_ws + '_Workspaces')
rename_alg.setProperty("OutputWorkspace", self._output_fit_ws)
rename_alg.execute()
# Add sample logs to output workspace
copy_alg = self.createChildAlgorithm("CopyLogs", enableLogging=False)
copy_alg.setProperty("InputWorkspace", self._input_ws)
copy_alg.setProperty("OutputWorkspace", self._output_msd_ws)
copy_alg.execute()
copy_alg.setProperty("InputWorkspace", self._input_ws)
copy_alg.setProperty("OutputWorkspace", self._output_fit_ws)
copy_alg.execute()
self.setProperty('OutputWorkspace', self._output_msd_ws)
self.setProperty('ParameterWorkspace', self._output_param_ws)
self.setProperty('FitWorkspaces', self._output_fit_ws)