def convert(self, wavelength_min, wavelength_max, detector_workspace_indexes, monitor_workspace_index,
correct_monitor=False, bg_min=None, bg_max=None):
"""
Run the conversion
Arguments:
workspace_ids: Start and end ranges. Ids to be considered as workspaces. Nested list syntax supported
wavelength_min: min wavelength in x for monitor workspace
wavelength_max: max wavelength in x for detector workspace
detector_workspace_indexes: Tuple of workspace indexes (or tuple of tuple min, max ranges to keep)
monitor_workspace_index: The index of the monitor workspace
correct_monitor: Flag indicating that monitors should have a flat background correction applied
bg_min: x min background in wavelength
bg_max: x max background in wavelength
Returns:
_monitor_ws: A workspace of monitors
"""
# Sanity check inputs.
if wavelength_min >= wavelength_max:
raise ValueError("Wavelength_min must be < wavelength_max min: %s, max: %s" % (wavelength_min, wavelength_max))
if correct_monitor and not all((bg_min, bg_max)):
raise ValueError("Either provide ALL, monitors_to_correct, bg_min, bg_max or none of them")
if all((bg_min, bg_max)) and bg_min >= bg_max:
raise ValueError("Background min must be < Background max")
sum = ConvertToWavelength.sum_workspaces(self.__ws_list)
sum_wavelength= msi.ConvertUnits(InputWorkspace=sum, Target="Wavelength", AlignBins='1')
logger.debug("Monitor detector index %s" % str(monitor_workspace_index))
# Crop out the monitor workspace
_monitor_ws = msi.CropWorkspace(InputWorkspace=sum_wavelength,
StartWorkspaceIndex=monitor_workspace_index,EndWorkspaceIndex=monitor_workspace_index)
# Crop out the detector workspace then chop out the x-ranges of interest.
_detector_ws = ConvertToWavelength.crop_range(sum_wavelength, detector_workspace_indexes)
_detector_ws = msi.CropWorkspace(InputWorkspace=_detector_ws, XMin=wavelength_min, XMax=wavelength_max)
# Apply a flat background
if correct_monitor and all((bg_min, bg_max)):
_monitor_ws = msi.CalculateFlatBackground(InputWorkspace=_monitor_ws,WorkspaceIndexList=0,StartX=bg_min, EndX=bg_max)
msi.DeleteWorkspace(Workspace=sum_wavelength.name())
return (_monitor_ws, _detector_ws)
def _crop_single_ws_in_tof(ws_to_rebin, x_max, x_min):
"""
Implementation of cropping the single workspace in TOF. First converts to TOF, crops then converts
back to the original unit.
:param ws_to_rebin: The workspace to crop
:param x_max: (Optional) The minimum TOF values to crop to
:param x_min: (Optional) The maximum TOF values to crop to
:return: The cropped workspace with the original units
"""
previous_units = ws_to_rebin.getAxis(0).getUnit().unitID()
if previous_units != "TOF":
ws_to_rebin = mantid.ConvertUnits(InputWorkspace=ws_to_rebin,
Target="TOF",
OutputWorkspace=ws_to_rebin)
if x_min > x_max:
raise ValueError("XMin is larger than XMax. (" + str(x_min) + " > " +
str(x_max) + ")")
if x_max <= 1 and x_min <= 1: # If <= 1, cropping by fractions, not absolutes
x_axis = ws_to_rebin.dataX(0)
x_min = x_axis[0] * (1 + x_min)
x_max = x_axis[-1] * x_max
cropped_ws = mantid.CropWorkspace(InputWorkspace=ws_to_rebin,
OutputWorkspace=ws_to_rebin,
XMin=x_min,
XMax=x_max)
if previous_units != "TOF":
cropped_ws = mantid.ConvertUnits(InputWorkspace=cropped_ws,
Target=previous_units,
OutputWorkspace=cropped_ws)
return cropped_ws
def ProcessVana(rnum, cycle):
# Preparation of the V/Nd sphere run for SX normalization
mantid.LoadRaw(Filename='/archive/NDXWISH/Instrument/data/cycle_' + cycle + '/WISH000' + str(rnum) + '.raw',
OutputWorkspace='Vana', LoadMonitors='Separate')
mantid.CropWorkspace(InputWorkspace='Vana', OutputWorkspace='Vana', XMin=6000, XMax=99000)
mantid.NormaliseByCurrent(InputWorkspace='Vana', OutputWorkspace='Vana')
mantid.ConvertUnits(InputWorkspace='Vana', OutputWorkspace='Vana', Target='Wavelength')
# create Abs Correction for V
shape = '''<sphere id="V-sphere">
<centre x="0.0" y="0.0" z="0.0" />
<radius val="0.0025"/>
</sphere>'''
mantid.CreateSampleShape('Vana', shape)
mantid.SetSampleMaterial('Vana', SampleNumberDensity=0.0719, ScatteringXSection=5.197, AttenuationXSection=4.739,
ChemicalFormula='V0.95 Nb0.05')
mantid.AbsorptionCorrection(InputWorkspace='Vana', OutputWorkspace='Abs_corr', ElementSize=0.5)
# SphericalAbsorption(InputWorkspace='WISH00038428', OutputWorkspace='Abs_corr_sphere', SphericalSampleRadius=0.25)
# correct Vanadium run for absorption
mantid.Divide(LHSWorkspace='Vana', RHSWorkspace='Abs_corr', OutputWorkspace='Vana_Abs')
mantid.DeleteWorkspace('Vana')
mantid.DeleteWorkspace('Abs_corr')
# Smoot data with redius 3
mantid.SmoothNeighbours(InputWorkspace='Vana_Abs', OutputWorkspace='Vana_smoot', Radius=3)
mantid.DeleteWorkspace('Vana_Abs')
# SmoothData38428
mantid.SmoothData(InputWorkspace='Vana_smoot', OutputWorkspace='Vana_smoot1', NPoints=300)
mantid.DeleteWorkspace('Vana_smoot')
def normalise_ws_current(ws_to_correct, monitor_ws, spline_coeff, lambda_values, integration_range, ex_regions):
processed_monitor_ws = mantid.ConvertUnits(InputWorkspace=monitor_ws, Target="Wavelength")
processed_monitor_ws = mantid.CropWorkspace(InputWorkspace=processed_monitor_ws,
XMin=lambda_values[0], XMax=lambda_values[-1])
for reg in range(0, 4):
processed_monitor_ws = mantid.MaskBins(InputWorkspace=processed_monitor_ws, XMin=ex_regions[0, reg],
XMax=ex_regions[1, reg])
splined_monitor_ws = mantid.SplineBackground(InputWorkspace=processed_monitor_ws,
WorkspaceIndex=0, NCoeff=spline_coeff)
normalised_ws = mantid.ConvertUnits(InputWorkspace=ws_to_correct, Target="Wavelength",
OutputWorkspace=ws_to_correct)
normalised_ws = mantid.NormaliseToMonitor(InputWorkspace=normalised_ws, MonitorWorkspace=splined_monitor_ws,
IntegrationRangeMin=integration_range[0],
IntegrationRangeMax=integration_range[-1],
OutputWorkspace=normalised_ws)
normalised_ws = mantid.ConvertUnits(InputWorkspace=normalised_ws, Target="TOF", OutputWorkspace=normalised_ws)
common.remove_intermediate_workspace(processed_monitor_ws)
common.remove_intermediate_workspace(splined_monitor_ws)
return normalised_ws
def execute(self, reducer, workspace):
"""
Trips leading and trailing Nan values from workspace
@param reducer: unused
@param workspace: the workspace to convert
"""
result_ws = mtd[workspace]
if result_ws.getNumberHistograms() != 1:
#Strip zeros is only possible on 1D workspaces
return
y_vals = result_ws.readY(0)
length = len(y_vals)
# Find the first non-zero value
start = 0
for i in range(0, length):
if not self._isNan(y_vals[i]):
start = i
break
# Now find the last non-zero value
stop = 0
length -= 1
for j in range(length, 0,-1):
if not self._isNan(y_vals[j]):
stop = j
break
# Find the appropriate X values and call CropWorkspace
x_vals = result_ws.readX(0)
startX = x_vals[start]
# Make sure we're inside the bin that we want to crop
endX = 1.001*x_vals[stop + 1]
api.CropWorkspace(InputWorkspace=workspace, OutputWorkspace=workspace, XMin=startX, XMax=endX)
def PhaseQuad(self, inputs):
"""
do the phaseQuad algorithm
groups data into a single set
"""
cloned_workspace = mantid.CloneWorkspace(
InputWorkspace=inputs["InputWorkspace"], StoreInADS=False)
mantid.MaskDetectors(Workspace=cloned_workspace,
DetectorList=inputs['MaskedDetectors'],
StoreInADS=False)
mantid.CropWorkspace(InputWorkspace=cloned_workspace,
XMin=inputs['FirstGoodData'],
XMax=inputs['LastGoodData'],
OutputWorkspace='cropped_workspace_pre_phasequad')
self.alg = mantid.AlgorithmManager.create("PhaseQuad")
self.alg.initialize()
self.alg.setRethrows(True)
self.alg.setProperty("InputWorkspace",
'cropped_workspace_pre_phasequad')
self.alg.setProperty("PhaseTable", "PhaseTable")
self.alg.setProperty("OutputWorkspace", "__phaseQuad__")
self.alg.execute()
mantid.DeleteWorkspace("cropped_workspace_pre_phasequad")
self.alg = None
def crop_range(cls, ws, rng):
"""
Given a range of workspace indexes to keep (rng), Crop the workspace such that these are kept.
Arguments:
ws : Workspace to crop spectrum from
rng : Tuple of range tuples. syntax may be (start, stop) or ((start0, stop0), (start1, stop1), ...)
returns a new copy of the workspace with spectra cropped out.
"""
_in_rng = msi.CloneWorkspace(ws)
if not isinstance(rng, tuple):
raise ValueError("Elements must be tuples.")
def is_actual_range(rng):
return len(rng) == 2 and isinstance(rng[0], int) and isinstance(
rng[1], int)
if is_actual_range(rng):
start, stop = rng[0], rng[1]
_in_rng = msi.CropWorkspace(InputWorkspace=_in_rng,
StartWorkspaceIndex=start,
EndWorkspaceIndex=stop)
else:
for subrng in rng:
_in_rng = ConvertToWavelength.crop_range(ws, subrng)
return _in_rng
def _focus_mode_trans(output_file_paths, attenuation_filepath,
calibrated_spectra):
summed_ws = mantid.MergeRuns(InputWorkspaces=calibrated_spectra[:9])
xList = summed_ws.readX(0)
summed_ws = mantid.CropWorkspace(InputWorkspace=summed_ws,
XMin=xList[1],
Xmax=xList[-2])
summed_ws = mantid.Scale(InputWorkspace=summed_ws,
Factor=0.111111111111111)
if attenuation_filepath:
summed_ws = _attenuate_workspace(
output_file_paths=output_file_paths,
attenuated_ws=summed_ws,
attenuation_filepath=attenuation_filepath)
summed_ws = mantid.ConvertUnits(InputWorkspace=summed_ws, Target="TOF")
mantid.SaveGSS(InputWorkspace=summed_ws,
Filename=output_file_paths["gss_filename"],
Append=False,
Bank=1)
mantid.SaveFocusedXYE(InputWorkspace=summed_ws,
Filename=output_file_paths["tof_xye_filename"],
Append=False,
IncludeHeader=False)
summed_ws = mantid.ConvertUnits(InputWorkspace=summed_ws,
Target="dSpacing")
# Rename to user friendly name:
summed_ws_name = output_file_paths["output_name"] + "_mods1-9"
summed_ws = mantid.RenameWorkspace(InputWorkspace=summed_ws,
OutputWorkspace=summed_ws_name)
mantid.SaveFocusedXYE(InputWorkspace=summed_ws,
Filename=output_file_paths["dspacing_xye_filename"],
Append=False,
IncludeHeader=False)
mantid.SaveNexus(InputWorkspace=summed_ws,
Filename=output_file_paths["nxs_filename"],
Append=False)
output_list = [summed_ws]
for i in range(0, 9):
workspace_name = output_file_paths["output_name"] + "_mod" + str(i + 1)
to_save = mantid.ConvertUnits(InputWorkspace=calibrated_spectra[i],
Target="dSpacing",
OutputWorkspace=workspace_name)
output_list.append(to_save)
mantid.SaveNexus(Filename=output_file_paths["nxs_filename"],
InputWorkspace=to_save,
Append=True)
return output_list
def calc_calibration_without_vanadium(focused_ws, index, instrument):
focus_spectrum = mantid.ExtractSingleSpectrum(InputWorkspace=focused_ws,
WorkspaceIndex=index)
focus_spectrum = mantid.ConvertUnits(InputWorkspace=focus_spectrum,
Target="TOF")
focus_spectrum = mantid.Rebin(InputWorkspace=focus_spectrum,
Params=instrument.tof_binning)
focus_calibrated = mantid.CropWorkspace(InputWorkspace=focus_spectrum,
XMin=0.1)
return focus_calibrated
def split_to_single_bank(self, gss_ws_name):
"""
Split a multiple-bank GSAS workspace to a set of single-spectrum MatrixWorkspace
Parameters
----------
gss_ws_name
Returns
-------
Name of grouped workspace and list
"""
# check
assert isinstance(gss_ws_name, str)
assert AnalysisDataService.doesExist(gss_ws_name)
# get workspace
gss_ws = AnalysisDataService.retrieve(gss_ws_name)
ws_list = list()
angle_list = list()
if gss_ws.getNumberHistograms() == 1:
# input is already a single-spectrum workspace
ws_list.append(gss_ws_name)
else:
num_spec = gss_ws.getNumberHistograms()
for i_ws in range(num_spec):
# split this one to a single workspace
out_ws_name = '%s_bank%d' % (gss_ws_name, i_ws + 1)
# also can use ExtractSpectra()
simpleapi.CropWorkspace(InputWorkspace=gss_ws_name,
OutputWorkspace=out_ws_name,
StartWorkspaceIndex=i_ws,
EndWorkspaceIndex=i_ws)
assert AnalysisDataService.doesExist(out_ws_name)
ws_list.append(out_ws_name)
# END-FOR
# END-IF
# calculate bank angles
for ws_name in ws_list:
bank_angle = calculate_bank_angle(ws_name)
angle_list.append(bank_angle)
# group all the workspace
ws_group_name = gss_ws_name + '_group'
simpleapi.GroupWorkspaces(InputWorkspaces=ws_list,
OutputWorkspace=ws_group_name)
self._braggDataDict[ws_group_name] = (gss_ws_name, ws_list)
return ws_group_name, ws_list, angle_list
def load_and_crop_data(runs, spectra, ip_file, diff_mode='single',
fit_mode='spectra', rebin_params=None):
"""
@param runs The string giving the runs to load
@param spectra A list of spectra to load
@param ip_file A string denoting the IP file
@param diff_mode Either 'double' or 'single'
@param fit_mode If bank then the loading is changed to summing each bank to a separate spectrum
@param rebin_params Rebin parameter string to rebin data by (no rebin if None)
"""
instrument = VESUVIO()
load_banks = (fit_mode == 'bank')
output_name = _create_tof_workspace_suffix(runs, spectra)
if load_banks:
sum_spectra = True
if spectra == "forward":
bank_ranges = instrument.forward_banks
elif spectra == "backward":
bank_ranges = instrument.backward_banks
else:
raise ValueError("Fitting by bank requires selecting either 'forward' or 'backward' "
"for the spectra to load")
bank_ranges = ["{0}-{1}".format(x, y) for x, y in bank_ranges]
spectra = ";".join(bank_ranges)
else:
sum_spectra = False
if spectra == "forward":
spectra = "{0}-{1}".format(*instrument.forward_spectra)
elif spectra == "backward":
spectra = "{0}-{1}".format(*instrument.backward_spectra)
if diff_mode == "double":
diff_mode = "DoubleDifference"
else:
diff_mode = "SingleDifference"
kwargs = {"Filename": runs,
"Mode": diff_mode, "InstrumentParFile": ip_file,
"SpectrumList": spectra, "SumSpectra": sum_spectra,
"OutputWorkspace": output_name}
full_range = ms.LoadVesuvio(**kwargs)
tof_data = ms.CropWorkspace(InputWorkspace=full_range,
XMin=instrument.tof_range[0],
XMax=instrument.tof_range[1],
OutputWorkspace=output_name)
if rebin_params is not None:
tof_data = ms.Rebin(InputWorkspace=tof_data,
OutputWorkspace=output_name,
Params=rebin_params)
return tof_data
def PyExec(self):
""" Main execution body
"""
inputws = self.getProperty("InputWorkspace").value
run = inputws.getRun()
channel_width = float(run.getLogData('channel_width').value)
full_channels = float(run.getLogData('full_channels').value)
tof1 = float(run.getLogData('TOF1').value)
outputws = api.CropWorkspace(inputws, XMin=0., XMax=full_channels*channel_width + tof1, StoreInADS=False)
self.setProperty("OutputWorkspace", outputws)
def save_gda(d_spacing_group, output_path, gsas_calib_filename, grouping_scheme,raise_warning):
if raise_warning:
raise RuntimeWarning("Could not save gda file, as GSAS calibration file was not found. "
"It should be present at " + gsas_calib_filename)
return
input_ws = mantid.CropWorkspace(InputWorkspace=d_spacing_group,
XMax=10,
StoreInADS=False)
mantid.SaveGDA(InputWorkspace=input_ws,
GSASParamFile=gsas_calib_filename,
GroupingScheme=grouping_scheme,
OutputFilename=output_path)
def Norm_data(rnum, cycle):
# Load and normalize SX data
mantid.LoadRaw(
Filename='/archive/Instruments$/NDXWISH/Instrument/data/cycle_' +
cycle + '/WISH000' + str(rnum) + '.raw',
OutputWorkspace='WISH000' + str(rnum),
LoadMonitors='Separate')
# ConvertToEventWorkspace(InputWorkspace='WISH000'+str(rnum), OutputWorkspace='WISH000'+str(rnum))
mantid.CropWorkspace(InputWorkspace='WISH000' + str(rnum),
OutputWorkspace='WISH000' + str(i),
XMin=6000,
XMax=99000)
mantid.ConvertUnits(InputWorkspace='WISH000' + str(rnum),
OutputWorkspace='WISH000' + str(rnum),
Target='Wavelength')
mantid.NormaliseByCurrent(InputWorkspace='WISH000' + str(rnum),
OutputWorkspace='WISH000' + str(rnum))
# normalize By vanadium PredictPeaks
mantid.CropWorkspace(InputWorkspace='WISH000' + str(rnum),
OutputWorkspace='WISH000' + str(rnum),
XMin=0.75,
XMax=9.3)
mantid.RebinToWorkspace(WorkspaceToRebin='Vana_smoot1',
WorkspaceToMatch='WISH000' + str(rnum),
OutputWorkspace='Vana_smoot1')
mantid.Divide(LHSWorkspace='WISH000' + str(rnum),
RHSWorkspace='Vana_smoot1',
OutputWorkspace='WISH000' + str(rnum))
# remove spike in the data above 1e15 and -1e15
mantid.ReplaceSpecialValues(InputWorkspace='WISH000' + str(rnum),
OutputWorkspace='WISH000' + str(rnum),
NaNValue=0,
InfinityValue=0,
BigNumberThreshold=1e15,
SmallNumberThreshold=-1e15)
# Convert to Diffraction MD and Lorentz Correction
mantid.ConvertToDiffractionMDWorkspace(
InputWorkspace='WISH000' + str(rnum),
OutputWorkspace='WISH000' + str(rnum) + '_MD',
LorentzCorrection=True)
def _crop_spline_to_percent_of_max(spline, input_ws, output_workspace):
spline_spectrum = spline.readY(0)
y_val = numpy.amax(spline_spectrum)
y_val = y_val / 100
x_list = input_ws.readX(0)
small_spline_indecies = numpy.nonzero(spline_spectrum > y_val)[0]
x_max = x_list[small_spline_indecies[-1]]
x_min = x_list[small_spline_indecies[0]]
output = mantid.CropWorkspace(inputWorkspace=input_ws,
XMin=x_min,
XMax=x_max,
OutputWorkspace=output_workspace)
return output
def extract_ws_spectra(ws_to_split):
"""
Extracts individual spectra from the workspace into a list of workspaces. Each workspace will contain
one of the spectra from the workspace and will have the form of "<ws_name>_<spectrum number>".
:param ws_to_split: The workspace to split into individual workspaces
:return: A list of extracted workspaces - one per spectra in the original workspace
"""
num_spectra = ws_to_split.getNumberHistograms()
spectra_bank_list = []
for i in range(0, num_spectra):
output_name = ws_to_split.getName() + "-" + str(i + 1)
# Have to use crop workspace as extract single spectrum struggles with the variable bin widths
spectra_bank_list.append(mantid.CropWorkspace(InputWorkspace=ws_to_split, OutputWorkspace=output_name,
StartWorkspaceIndex=i, EndWorkspaceIndex=i))
return spectra_bank_list
def PyExec(self):
import mantid.simpleapi as ms
self.log().warning(
'FindReflectometryLines algorithm version 1 has been deprecated and will be removed in a future release.'
)
in_ws = self.getPropertyValue("InputWorkspace")
min_wavelength = self.getPropertyValue("StartWavelength")
keep_workspaces = self.getPropertyValue("KeepIntermediateWorkspaces")
# Crop off lower wavelengths where the signal is also lower.
cropped_ws = ms.CropWorkspace(InputWorkspace=in_ws,
XMin=float(min_wavelength))
# Integrate over the higher wavelengths after cropping.
summed_ws = ms.Integration(InputWorkspace=cropped_ws)
# Loop through each histogram, and fetch out each intensity value from the single bin to generate a list of all values.
n_histograms = summed_ws.getNumberHistograms()
y_data = np.empty([n_histograms])
for i in range(0, n_histograms):
intensity = summed_ws.readY(i)[0]
y_data[i] = intensity
#Remove the background
y_data = self.__remove_background(y_data)
#Find the peaks
peak_index_list = self.__find_peak_spectrum_numbers(y_data, summed_ws)
#Reverse the order so that it goes from high spec number to low spec number
peak_index_list.reverse()
n_peaks_found = len(peak_index_list)
output_ws = WorkspaceFactory.createTable("TableWorkspace")
output_ws.addColumn("int", "Reflected Spectrum Number")
if n_peaks_found > 2:
raise PeakFindingException("Found more than two peaks.")
elif n_peaks_found == 0:
raise PeakFindingException("No peaks found")
elif n_peaks_found == 1:
output_ws.addRow(peak_index_list)
elif n_peaks_found == 2:
output_ws.addColumn("int", "Transmission Spectrum Number")
output_ws.addRow(peak_index_list)
if int(keep_workspaces) == 0:
ms.DeleteWorkspace(Workspace=cropped_ws)
ms.DeleteWorkspace(Workspace=summed_ws)
self.setProperty("OutputWorkspace", output_ws)
def read_event_nexus(self, number, output, panel):
simple.RenameWorkspace("{}_monitors".format(output),
"w{}_monitors".format(number))
simple.Rebin(InputWorkspace=output,
OutputWorkspace=output,
Params='6000,-0.00063,110000')
simple.ConvertToMatrixWorkspace(output, output)
spectra_min, spectra_max = self.return_panel.get(panel)
simple.CropWorkspace(InputWorkspace=output,
OutputWorkspace=output,
StartWorkspaceIndex=spectra_min - 6,
EndWorkspaceIndex=spectra_max - 6)
simple.MaskBins(InputWorkspace=output,
OutputWorkspace=output,
XMin=99900,
XMax=106000)
def _crop_spline_to_percent_of_max(spline, input_ws, output_workspace, min_value, max_value):
spline_spectrum = spline.readY(0)
if not spline_spectrum.any():
return mantid.CloneWorkspace(inputWorkspace=input_ws, OutputWorkspace=output_workspace)
x_list = input_ws.readX(0)
min_index = x_list.searchsorted(min_value)
max_index = x_list.searchsorted(max_value)
sliced_spline_spectrum = spline_spectrum[min_index:max_index:1]
y_val = numpy.amax(sliced_spline_spectrum)
y_val = y_val / 100
small_spline_indecies = numpy.nonzero(spline_spectrum > y_val)[0]
x_max = x_list[small_spline_indecies[-1]]
x_min = x_list[small_spline_indecies[0]]
output = mantid.CropWorkspace(inputWorkspace=input_ws, XMin=x_min, XMax=x_max, OutputWorkspace=output_workspace)
return output
def _crop_single_ws_in_tof(ws_to_rebin, x_max, x_min):
"""
Implementation of cropping the single workspace in TOF. First converts to TOF, crops then converts
back to the original unit.
:param ws_to_rebin: The workspace to crop
:param x_max: (Optional) The minimum TOF values to crop to
:param x_min: (Optional) The maximum TOF values to crop to
:return: The cropped workspace with the original units
"""
previous_units = ws_to_rebin.getAxis(0).getUnit().unitID()
if previous_units != "TOF":
ws_to_rebin = mantid.ConvertUnits(InputWorkspace=ws_to_rebin, Target="TOF", OutputWorkspace=ws_to_rebin)
cropped_ws = mantid.CropWorkspace(InputWorkspace=ws_to_rebin, OutputWorkspace=ws_to_rebin,
XMin=x_min, XMax=x_max)
if previous_units != "TOF":
cropped_ws = mantid.ConvertUnits(InputWorkspace=cropped_ws, Target=previous_units, OutputWorkspace=cropped_ws)
return cropped_ws
def normalise_ws_current(ws_to_correct, monitor_ws, spline_coeff,
lambda_values, integration_range):
processed_monitor_ws = mantid.ConvertUnits(InputWorkspace=monitor_ws,
Target="Wavelength")
processed_monitor_ws = mantid.CropWorkspace(
InputWorkspace=processed_monitor_ws,
XMin=lambda_values[0],
XMax=lambda_values[-1])
# TODO move these masks to the adv. config file
ex_regions = numpy.zeros((2, 4))
ex_regions[:, 0] = [3.45, 3.7]
ex_regions[:, 1] = [2.96, 3.2]
ex_regions[:, 2] = [2.1, 2.26]
ex_regions[:, 3] = [1.73, 1.98]
for reg in range(0, 4):
processed_monitor_ws = mantid.MaskBins(
InputWorkspace=processed_monitor_ws,
XMin=ex_regions[0, reg],
XMax=ex_regions[1, reg])
splined_monitor_ws = mantid.SplineBackground(
InputWorkspace=processed_monitor_ws,
WorkspaceIndex=0,
NCoeff=spline_coeff)
normalised_ws = mantid.ConvertUnits(InputWorkspace=ws_to_correct,
Target="Wavelength",
OutputWorkspace=ws_to_correct)
normalised_ws = mantid.NormaliseToMonitor(
InputWorkspace=normalised_ws,
MonitorWorkspace=splined_monitor_ws,
IntegrationRangeMin=integration_range[0],
IntegrationRangeMax=integration_range[-1],
OutputWorkspace=normalised_ws)
normalised_ws = mantid.ConvertUnits(InputWorkspace=normalised_ws,
Target="TOF",
OutputWorkspace=normalised_ws)
common.remove_intermediate_workspace(processed_monitor_ws)
common.remove_intermediate_workspace(splined_monitor_ws)
return normalised_ws
def make_workspace(coord: str,
emode: str = 'Elastic',
efixed: Optional[sc.Variable] = None) -> 'sapi.Workspace':
ws = sapi.CreateSampleWorkspace(XMin=1000, NumBanks=1, StoreInADS=False)
# Crop out spectra index 0 as has two_theta=0, gives inf d-spacing
ws = sapi.CropWorkspace(ws, StartWorkspaceIndex=1, StoreInADS=False)
ws = sapi.ConvertUnits(InputWorkspace=ws,
Target=mantid_coord(coord),
EMode=emode,
EFixed=efixed.value if efixed is not None else None,
StoreInADS=False) # start in origin units
ws.mutableRun().addProperty(
'deltaE-mode', kernel.StringPropertyWithValue('deltaE-mode', emode),
'', True)
if efixed is not None:
ws.mutableRun().addProperty(
'Ei', kernel.FloatPropertyWithValue('Ei', efixed.value),
str(efixed.unit), False)
return ws
def focus_onepanel(self, work, focus, panel):
cal = "WISH_diff{}"
if cal.format("_cal") not in simple.mtd:
simple.LoadDiffCal(filename=self.get_cal(),
InstrumentName="WISH",
WorkspaceName=cal.format(""))
simple.AlignDetectors(InputWorkspace=work,
OutputWorkspace=work,
CalibrationWorkspace=cal.format("_cal"))
simple.DiffractionFocussing(InputWorkspace=work,
OutputWorkspace=focus,
GroupingWorkspace=cal.format("_group"))
if self.deleteWorkspace:
simple.DeleteWorkspace(work)
if panel == 5 or panel == 6:
simple.CropWorkspace(InputWorkspace=focus,
OutputWorkspace=focus,
XMin=0.3)
return focus
def _sum_groups_of_three_ws(calibrated_spectra, output_file_names):
output_list = []
for i in range(3):
ws_name = output_file_names["output_name"] + "_mods{}-{}".format(
i * 3 + 1, (i + 1) * 3)
summed_spectra = mantid.MergeRuns(
InputWorkspaces=calibrated_spectra[i * 3:(i + 1) * 3],
OutputWorkspace=ws_name)
xList = summed_spectra.readX(0)
summed_spectra = mantid.CropWorkspace(InputWorkspace=summed_spectra,
XMin=xList[1],
Xmax=xList[-2])
scaled = mantid.Scale(InputWorkspace=summed_spectra,
Factor=1. / 3,
OutputWorkspace=ws_name)
output_list.append(scaled)
return output_list
def _run_get_monitor(self, run_number, input_dir, spline_terms):
load_monitor_ws = common._load_monitor(run_number, input_dir=input_dir, instrument=self)
get_monitor_ws = mantid.ConvertUnits(InputWorkspace=load_monitor_ws, Target="Wavelength")
common.remove_intermediate_workspace(load_monitor_ws)
lmin, lmax = self._get_lambda_range()
get_monitor_ws = mantid.CropWorkspace(InputWorkspace=get_monitor_ws, XMin=lmin, XMax=lmax)
ex_regions = numpy.zeros((2, 4))
ex_regions[:, 0] = [3.45, 3.7]
ex_regions[:, 1] = [2.96, 3.2]
ex_regions[:, 2] = [2.1, 2.26]
ex_regions[:, 3] = [1.73, 1.98]
# ConvertToDistribution(works)
for reg in range(0, 4):
get_monitor_ws = mantid.MaskBins(InputWorkspace=get_monitor_ws, XMin=ex_regions[0, reg],
XMax=ex_regions[1, reg])
monitor_ws = mantid.SplineBackground(InputWorkspace=get_monitor_ws, WorkspaceIndex=0, NCoeff=spline_terms)
common.remove_intermediate_workspace(get_monitor_ws)
return monitor_ws
def _apply_vanadium_norm(sample_ws_foc, van_ws_foc):
# divide by curves - automatically corrects for solid angle, det efficiency and lambda dep. flux
sample_ws_foc = mantid.CropWorkspace(InputWorkspace=sample_ws_foc,
OutputWorkspace=sample_ws_foc.name(),
XMin=0.45)
van_ws_foc_rb = mantid.RebinToWorkspace(
WorkspaceToRebin=van_ws_foc,
WorkspaceToMatch=sample_ws_foc,
OutputWorkspace=VAN_CURVE_REBINNED_NAME) # copy so as not to lose data
sample_ws_foc = mantid.Divide(LHSWorkspace=sample_ws_foc,
RHSWorkspace=van_ws_foc_rb,
OutputWorkspace=sample_ws_foc.name(),
AllowDifferentNumberSpectra=False)
sample_ws_foc = mantid.ReplaceSpecialValues(
InputWorkspace=sample_ws_foc,
OutputWorkspace=sample_ws_foc.name(),
NaNValue=0,
NaNError=0.0,
InfinityValue=0,
InfinityError=0.0)
return sample_ws_foc
def focus_onepanel(self, work, focus, panel):
cal = "WISH_diff{}"
if cal.format("_cal") not in simple.mtd:
simple.LoadDiffCal(filename=self.get_cal(),
InstrumentName="WISH",
WorkspaceName=cal.format(""))
simple.AlignAndFocusPowder(InputWorkspace=work,
OutputWorkspace=focus,
GroupingWorkspace=cal.format("_group"),
CalibrationWorkspace=cal.format("_cal"),
Dspacing=True,
params="-0.00063")
simple.ConvertUnits(InputWorkspace=focus,
OutputWorkspace=focus,
Target="dSpacing")
if self.deleteWorkspace:
simple.DeleteWorkspace(work)
if panel == 5 or panel == 6:
simple.CropWorkspace(InputWorkspace=focus,
OutputWorkspace=focus,
XMin=0.3)
return focus
def calc_calibration_with_vanadium(focused_ws, index, vanadium_ws, instrument):
data_ws = mantid.ExtractSingleSpectrum(InputWorkspace=focused_ws,
WorkspaceIndex=index)
data_ws = mantid.ConvertUnits(InputWorkspace=data_ws, Target="TOF")
data_ws = mantid.Rebin(InputWorkspace=data_ws,
Params=instrument._get_focus_tof_binning())
data_processed = "van_processed" + str(
index) # Workaround for Mantid overwriting the WS in a loop
mantid.Divide(LHSWorkspace=data_ws,
RHSWorkspace=vanadium_ws,
OutputWorkspace=data_processed)
mantid.CropWorkspace(InputWorkspace=data_processed,
XMin=0.1,
OutputWorkspace=data_processed)
mantid.Scale(InputWorkspace=data_processed,
Factor=10,
OutputWorkspace=data_processed)
remove_intermediate_workspace(data_ws)
return data_processed
def PyExec(self):
workflow_prog = Progress(self, start=0.0, end=0.3, nreports=4)
workflow_prog.report('Setting up algorithm')
self._setup()
input_ws = mtd[self._sample]
min_spectrum_index = input_ws.getIndexFromSpectrumNumber(
int(self._spectra_range[0]))
max_spectrum_index = input_ws.getIndexFromSpectrumNumber(
int(self._spectra_range[1]))
# Crop to the required spectra range
workflow_prog.report('Cropping Workspace')
cropped_input = ms.CropWorkspace(
InputWorkspace=input_ws,
OutputWorkspace='__symm',
StartWorkspaceIndex=min_spectrum_index,
EndWorkspaceIndex=max_spectrum_index)
# Find the smallest data array in the first spectra
len_x = len(cropped_input.readX(0))
len_y = len(cropped_input.readY(0))
len_e = len(cropped_input.readE(0))
sample_array_len = min(len_x, len_y, len_e)
sample_x = cropped_input.readX(0)
# Get slice bounds of array
try:
workflow_prog.report('Calculating array points')
self._calculate_array_points(sample_x, sample_array_len)
except Exception as exc:
raise RuntimeError(
'Failed to calculate array slice boundaries: %s' % exc.message)
max_sample_index = sample_array_len - 1
centre_range_len = self._positive_min_index + self._negative_min_index
positive_diff_range_len = max_sample_index - self._positive_max_index
output_cut_index = max_sample_index - self._positive_min_index - positive_diff_range_len - 1
new_array_len = 2 * max_sample_index - centre_range_len - 2 * positive_diff_range_len - 1
logger.information('Sample array length = %d' % sample_array_len)
logger.information(
'Positive X min at i=%d, x=%f' %
(self._positive_min_index, sample_x[self._positive_min_index]))
logger.information(
'Negative X min at i=%d, x=%f' %
(self._negative_min_index, sample_x[self._negative_min_index]))
logger.information(
'Positive X max at i=%d, x=%f' %
(self._positive_max_index, sample_x[self._positive_max_index]))
logger.information('New array length = %d' % new_array_len)
logger.information('Output array LR split index = %d' %
output_cut_index)
# Create an empty workspace with enough storage for the new data
workflow_prog.report('Creating OutputWorkspace')
out_ws = WorkspaceFactory.Instance().create(
cropped_input, cropped_input.getNumberHistograms(),
int(new_array_len), int(new_array_len))
# For each spectrum copy positive values to the negative
pop_prog = Progress(self,
start=0.3,
end=0.95,
nreports=out_ws.getNumberHistograms())
for idx in range(out_ws.getNumberHistograms()):
pop_prog.report('Populating data in workspace %i' % idx)
# Strip any additional array cells
x_in = cropped_input.readX(idx)[:sample_array_len]
y_in = cropped_input.readY(idx)[:sample_array_len]
e_in = cropped_input.readE(idx)[:sample_array_len]
# Get some zeroed data to overwrite with copies from sample
x_out = np.zeros(new_array_len)
y_out = np.zeros(new_array_len)
e_out = np.zeros(new_array_len)
# Left hand side (reflected)
x_out[:output_cut_index] = -x_in[self._positive_max_index -
1:self._positive_min_index:-1]
y_out[:output_cut_index] = y_in[self._positive_max_index -
1:self._positive_min_index:-1]
e_out[:output_cut_index] = e_in[self._positive_max_index -
1:self._positive_min_index:-1]
# Right hand side (copied)
x_out[output_cut_index:] = x_in[self._negative_min_index:self.
_positive_max_index]
y_out[output_cut_index:] = y_in[self._negative_min_index:self.
_positive_max_index]
e_out[output_cut_index:] = e_in[self._negative_min_index:self.
_positive_max_index]
# Set output spectrum data
out_ws.setX(idx, x_out)
out_ws.setY(idx, y_out)
out_ws.setE(idx, e_out)
logger.information('Symmetrise spectrum %d' % idx)
end_prog = Progress(self, start=0.95, end=1.0, nreports=3)
end_prog.report('Deleting temp workspaces')
ms.DeleteWorkspace(cropped_input)
if self._props_output_workspace != '':
end_prog.report('Generating property table')
self._generate_props_table()
self.setProperty('OutputWorkspace', out_ws)
end_prog.report('Algorithm Complete')
def _load_and_sum_runs(self, spectra):
"""Load the input set of runs & sum them if there
is more than one.
@param spectra :: The list of spectra to load
@returns a tuple of length 2 containing (main_detector_ws, monitor_ws)
"""
isis = config.getFacility("ISIS")
inst_prefix = isis.instrument("VESUVIO").shortName()
runs = self._get_runs()
self.summed_ws, self.summed_mon = "__loadraw_evs", "__loadraw_evs_monitors"
for index, run in enumerate(runs):
run = inst_prefix + str(run)
self._raise_error_period_scatter(run, self._back_scattering)
if index == 0:
out_name, out_mon = SUMMED_WS, SUMMED_MON
else:
out_name, out_mon = SUMMED_WS + 'tmp', SUMMED_MON + 'tmp'
# Load data
raw_filepath = FileFinder.findRuns(run)[0]
ms.LoadRaw(Filename=raw_filepath,
SpectrumList=spectra,
OutputWorkspace=out_name,
LoadMonitors='Exclude',
EnableLogging=_LOGGING_)
ms.LoadRaw(Filename=raw_filepath,
SpectrumList=self._mon_spectra,
OutputWorkspace=out_mon,
EnableLogging=_LOGGING_)
# Sum
if index > 0:
ms.Plus(LHSWorkspace=SUMMED_WS,
RHSWorkspace=out_name,
OutputWorkspace=SUMMED_WS,
EnableLogging=_LOGGING_)
ms.Plus(LHSWorkspace=SUMMED_MON,
RHSWorkspace=out_mon,
OutputWorkspace=SUMMED_MON,
EnableLogging=_LOGGING_)
ms.DeleteWorkspace(out_name, EnableLogging=_LOGGING_)
ms.DeleteWorkspace(out_mon, EnableLogging=_LOGGING_)
# Check to see if extra data needs to be loaded to normalise in data
x_max = self._tof_max
if self._foil_out_norm_end > self._tof_max:
x_max = self._foil_out_norm_end
self._crop_required = True
ms.CropWorkspace(Inputworkspace= SUMMED_WS,
OutputWorkspace=SUMMED_WS,
XMax=x_max,
EnableLogging=_LOGGING_)
ms.CropWorkspace(Inputworkspace= SUMMED_MON,
OutputWorkspace=SUMMED_MON,
XMax=self._mon_tof_max,
EnableLogging=_LOGGING_)
summed_data, summed_mon = mtd[SUMMED_WS], mtd[SUMMED_MON]
self._load_diff_mode_parameters(summed_data)
return summed_data, summed_mon
