def _calibration_processing(calibration_dir, calibration_runs, cross_correlate_params, get_det_offset_params,
grouping_file_name, input_ws, instrument, offset_file, rebin_1_params, rebin_2_params):
calibration_ws = input_ws
if calibration_ws.getAxis(0).getUnit().unitID() != WORKSPACE_UNITS.d_spacing:
calibration_ws = mantid.Rebin(InputWorkspace=input_ws, Params=rebin_1_params)
calibration_ws = mantid.ConvertUnits(InputWorkspace=calibration_ws, Target="dSpacing")
spectrum_list = []
for i in range(0, calibration_ws.getNumberHistograms()):
try:
calibration_ws.getDetector(i)
except RuntimeError:
pass
else:
spectrum_list.append(i)
calibration_ws = mantid.ExtractSpectra(InputWorkspace=calibration_ws, WorkspaceIndexList=spectrum_list)
rebinned = mantid.Rebin(InputWorkspace=calibration_ws, Params=rebin_2_params)
cross_correlated = mantid.CrossCorrelate(InputWorkspace=rebinned, **cross_correlate_params)
# Offsets workspace must be referenced as string so it can be deleted, as simpleapi doesn't recognise it as a ws
offsets_ws_name = "offsets"
mantid.GetDetectorOffsets(InputWorkspace=cross_correlated, GroupingFileName=offset_file,
OutputWorkspace=offsets_ws_name, **get_det_offset_params)
rebinned_tof = mantid.ConvertUnits(InputWorkspace=rebinned, Target="TOF")
aligned = mantid.AlignDetectors(InputWorkspace=rebinned_tof, CalibrationFile=offset_file)
grouping_file = os.path.join(calibration_dir, grouping_file_name)
focused = mantid.DiffractionFocussing(InputWorkspace=aligned, GroupingFileName=grouping_file,
OutputWorkspace=instrument._generate_output_file_name(calibration_runs)
+ "_grouped")
print("Saved cal file to " + offset_file)
common.remove_intermediate_workspace([calibration_ws, rebinned, cross_correlated, rebinned_tof,
offsets_ws_name])
return focused
def _generate_grouped_ts_pdf(focused_ws, q_lims):
focused_ws = mantid.ConvertUnits(InputWorkspace=focused_ws,
Target="MomentumTransfer",
EMode='Elastic')
min_x = np.inf
max_x = -np.inf
num_x = -np.inf
for ws in focused_ws:
x_data = ws.dataX(0)
min_x = min(np.min(x_data), min_x)
max_x = max(np.max(x_data), max_x)
num_x = max(x_data.size, num_x)
binning = [min_x, (max_x - min_x) / num_x, max_x]
focused_ws = mantid.Rebin(InputWorkspace=focused_ws, Params=binning)
focused_data_combined = mantid.ConjoinSpectra(InputWorkspaces=focused_ws)
mantid.MatchSpectra(InputWorkspace=focused_data_combined,
OutputWorkspace=focused_data_combined,
ReferenceSpectrum=5)
if type(q_lims) == str:
q_min = []
q_max = []
try:
with open(q_lims, 'r') as f:
line_list = [line.rstrip('\n') for line in f]
for line in line_list[1:]:
value_list = line.split()
q_min.append(float(value_list[2]))
q_max.append(float(value_list[3]))
q_min = np.array(q_min)
q_max = np.array(q_max)
except IOError:
raise RuntimeError("q_lims is not valid")
elif type(q_lims) == list or type(q_lims) == np.ndarray:
q_min = q_lims[0, :]
q_max = q_lims[1, :]
else:
raise RuntimeError("q_lims is not valid")
bin_width = np.inf
for i in range(q_min.size):
pdf_x_array = focused_data_combined.readX(i)
tmp1 = np.where(pdf_x_array >= q_min[i])
tmp2 = np.amin(tmp1)
q_min[i] = pdf_x_array[tmp2]
q_max[i] = pdf_x_array[np.amax(np.where(pdf_x_array <= q_max[i]))]
bin_width = min(pdf_x_array[1] - pdf_x_array[0], bin_width)
focused_data_combined = mantid.CropWorkspaceRagged(
InputWorkspace=focused_data_combined, XMin=q_min, XMax=q_max)
focused_data_combined = mantid.Rebin(
InputWorkspace=focused_data_combined,
Params=[min(q_min), bin_width, max(q_max)])
focused_data_combined = mantid.SumSpectra(
InputWorkspace=focused_data_combined,
WeightedSum=True,
MultiplyBySpectra=False)
pdf_output = mantid.PDFFourierTransform(
Inputworkspace=focused_data_combined,
InputSofQType="S(Q)",
PDFType="G(r)",
Filter=True)
return pdf_output
def create_calibration(calibration_runs, instrument, offset_file_name,
grouping_file_name, calibration_dir, rebin_1_params,
rebin_2_params, cross_correlate_params,
get_det_offset_params):
"""
Create a calibration file from (usually) a ceria run
:param calibration_runs: Run number(s) for this run
:param instrument: The PEARL instrument object
:param offset_file_name: Name of the file to write detector offset information to
:param grouping_file_name: Name of grouping calibration file
:param calibration_dir: Path to directory containing calibration information
:param rebin_1_params: Parameters for the first rebin step (as a string in the usual format)
:param rebin_2_params: Parameters for the second rebin step (as a string in the usual format)
:param cross_correlate_params: Parameters for CrossCorrelate (as a dictionary PropertyName: PropertyValue)
:param get_det_offset_params: Parameters for GetDetectorOffsets (as a dictionary PropertyName: PropertyValue)
"""
input_ws_list = common.load_current_normalised_ws_list(
run_number_string=calibration_runs,
instrument=instrument,
input_batching=INPUT_BATCHING.Summed)
input_ws = input_ws_list[0]
calibration_ws = mantid.Rebin(InputWorkspace=input_ws,
Params=rebin_1_params)
if calibration_ws.getAxis(
0).getUnit().unitID() != WORKSPACE_UNITS.d_spacing:
calibration_ws = mantid.ConvertUnits(InputWorkspace=calibration_ws,
Target="dSpacing")
rebinned = mantid.Rebin(InputWorkspace=calibration_ws,
Params=rebin_2_params)
cross_correlated = mantid.CrossCorrelate(InputWorkspace=rebinned,
**cross_correlate_params)
offset_file = os.path.join(calibration_dir, offset_file_name)
# Offsets workspace must be referenced as string so it can be deleted, as simpleapi doesn't recognise it as a ws
offsets_ws_name = "offsets"
mantid.GetDetectorOffsets(InputWorkspace=cross_correlated,
GroupingFileName=offset_file,
OutputWorkspace=offsets_ws_name,
**get_det_offset_params)
rebinned_tof = mantid.ConvertUnits(InputWorkspace=rebinned, Target="TOF")
aligned = mantid.AlignDetectors(InputWorkspace=rebinned_tof,
CalibrationFile=offset_file)
grouping_file = os.path.join(calibration_dir, grouping_file_name)
focused = mantid.DiffractionFocussing(
InputWorkspace=aligned,
GroupingFileName=grouping_file,
OutputWorkspace=instrument._generate_output_file_name(calibration_runs)
+ "_grouped")
common.remove_intermediate_workspace([
calibration_ws, rebinned, cross_correlated, rebinned_tof, aligned,
offsets_ws_name
])
return focused
def _generate_flux_spectrum(self, run_set, sam_ws):
r"""
Retrieve the aggregate flux and create an spectrum of intensities
versus wavelength such that intensities will be similar for any
of the possible flux normalization types.
Parameters
----------
sam_ws: str
Name of aggregated sample workspace
Returns
-------
str
Name of aggregated flux workspace (output workspace)
"""
flux_binning = [1.5, 0.0005, 7.5] # wavelength binning
suffix = re.sub('[^0-9a-zA-Z]+', '_', self._flux_normalization_type)
flux_ws = tws(self._make_run_name(run_set[0]) + '_' + suffix)
if self._MonNorm:
self._sum_monitors(run_set, flux_ws)
rpf = self._elucidate_reflection_parameter_file(sam_ws)
sapi.LoadParameterFile(Workspace=flux_ws, Filename=rpf)
sapi.ModeratorTzeroLinear(InputWorkspace=flux_ws,
OutputWorkspace=flux_ws)
sapi.Rebin(InputWorkspace=flux_ws,
OutputWorkspace=flux_ws,
Params='10', # 10 microseconds TOF bin width
PreserveEvents=False)
sapi.ConvertUnits(InputWorkspace=flux_ws,
OutputWorkspace=flux_ws,
Target='Wavelength')
sapi.OneMinusExponentialCor(InputWorkspace=flux_ws,
OutputWorkspace=flux_ws,
C='0.20749999999999999',
C1='0.001276')
sapi.Scale(InputWorkspace=flux_ws, OutputWorkspace=flux_ws,
Factor='1e-06')
sapi.Rebin(InputWorkspace=flux_ws, OutputWorkspace=flux_ws,
Params=flux_binning)
else:
ws = mtd[sam_ws].getRun()
if self._flux_normalization_type == 'Proton Charge':
aggregate_flux = ws.getProtonCharge()
elif self._flux_normalization_type == 'Duration':
aggregate_flux = ws.getProperty('duration').value
# These factors ensure intensities typical of flux workspaces
# derived from monitor data
f = {'Proton Charge': 0.00874, 'Duration': 0.003333}
x = np.arange(flux_binning[0], flux_binning[2], flux_binning[1])
y = f[self._flux_normalization_type] * \
aggregate_flux * np.ones(len(x) - 1)
_flux_ws = sapi.CreateWorkspace(OutputWorkspace=flux_ws, DataX=x,
DataY=y, UnitX='Wavelength')
_flux_ws.setYUnit(mtd[sam_ws].YUnit())
return flux_ws
def extract_roi(workspace, step='0.01', roi=[162, 175, 112, 145]):
"""
Returns a spectrum (Counts/proton charge vs lambda) given a filename
or run number and the lambda step size and the corner of the ROI.
:param str workspace: Mantid workspace name
:param float step: wavelength bin width for rebinning
:param list roi: [x_min, x_max, y_min, y_max] pixels
"""
_workspace = str(workspace)
if mantid.mtd[_workspace].getRun()['gd_prtn_chrg'].value > 0:
api.NormaliseByCurrent(InputWorkspace=_workspace,
OutputWorkspace=_workspace)
api.ConvertUnits(InputWorkspace=_workspace,
Target='Wavelength',
OutputWorkspace=_workspace)
api.Rebin(InputWorkspace=_workspace,
Params=step,
OutputWorkspace=_workspace)
api.RefRoi(InputWorkspace=_workspace,
NXPixel=304,
NYPixel=256,
SumPixels=True,
XPixelMin=roi[0],
XPIxelMax=roi[1],
YPixelMin=roi[2],
YPixelMax=roi[3],
IntegrateY=True,
ConvertToQ=False,
OutputWorkspace=_workspace)
api.SumSpectra(InputWorkspace=_workspace, OutputWorkspace=_workspace)
return _workspace
def _group_and_SofQW(self, wsName, etRebins, isSample=True):
""" Transforms from wavelength and detector ID to S(Q,E)
@param wsName: workspace as a function of wavelength and detector id
@return: S(Q,E)
"""
api.ConvertUnits(InputWorkspace=wsName,
OutputWorkspace=wsName,
Target='DeltaE',
EMode='Indirect')
api.CorrectKiKf(InputWorkspace=wsName,
OutputWorkspace=wsName,
EMode='Indirect')
api.Rebin(InputWorkspace=wsName,
OutputWorkspace=wsName,
Params=etRebins)
if self._groupDetOpt != "None":
if self._groupDetOpt == "Low-Resolution":
grp_file = "BASIS_Grouping_LR.xml"
else:
grp_file = "BASIS_Grouping.xml"
# If mask override used, we need to add default grouping file
# location to search paths
if self._overrideMask:
config.appendDataSearchDir(DEFAULT_MASK_GROUP_DIR)
api.GroupDetectors(InputWorkspace=wsName,
OutputWorkspace=wsName,
MapFile=grp_file,
Behaviour="Sum")
wsSqwName = wsName + '_divided_sqw' if isSample and self._doNorm else wsName + '_sqw'
api.SofQW3(InputWorkspace=wsName,
OutputWorkspace=wsSqwName,
QAxisBinning=self._qBins,
EMode='Indirect',
EFixed='2.0826')
return wsSqwName
def test_unit_conversion(self):
import mantid.simpleapi as mantid
eventWS = self.base_event_ws
ws = mantid.Rebin(eventWS, 10000, PreserveEvents=False)
tmp = scn.mantid.convert_Workspace2D_to_data_array(ws)
target_tof = tmp.coords['tof']
ws = mantid.ConvertUnits(InputWorkspace=ws,
Target="Wavelength",
EMode="Elastic")
converted_mantid = scn.mantid.convert_Workspace2D_to_data_array(ws)
da = scn.mantid.convert_EventWorkspace_to_data_array(
eventWS, load_pulse_times=False)
da = sc.histogram(da, bins=target_tof)
d = sc.Dataset(data={da.name: da})
converted = scn.convert(d, 'tof', 'wavelength', scatter=True)
self.assertTrue(
np.all(np.isclose(converted_mantid.values, converted[""].values)))
self.assertTrue(
np.all(
np.isclose(
converted_mantid.coords['wavelength'].values,
converted.coords['wavelength'].values,
)))
def test_Workspace2D_not_common_bins_masks(self):
import mantid.simpleapi as mantid
eventWS = self.base_event_ws
ws = mantid.Rebin(eventWS, 10000, PreserveEvents=False)
ws = mantid.ConvertUnits(ws,
"Wavelength",
EMode="Direct",
EFixed=0.1231)
# these X values will mask different number of bins
masked_ws = self._mask_bins_and_spectra(ws,
-214,
-192,
num_spectra=3,
indices='0-40')
self.assertFalse(masked_ws.isCommonBins())
ds = scn.mantid.convert_Workspace2D_to_data_array(masked_ws)
# bin with 3 masks
np.testing.assert_array_equal(ds.masks["bin"].values[0],
[True, True, False])
# bin with only 2
np.testing.assert_array_equal(ds.masks["bin"].values[31],
[True, True, False])
np.testing.assert_array_equal(ds.masks["spectrum"].values[0:3],
[True, True, True])
def convert_to_y_space_and_symmetrise(ws_name,mass):
# phenomenological roule-of-thumb to define the y-range for a given mass
max_Y = np.ceil(2.5*mass+27)
rebin_parameters = str(-max_Y)+","+str(2.*max_Y/120)+","+str(max_Y)
# converting to y-space, rebinning, and defining a normalisation matrix to take into account the kinetic cut-off
sapi.ConvertToYSpace(InputWorkspace=ws_name,Mass=mass,OutputWorkspace=ws_name+"_JoY",QWorkspace=ws_name+"_Q")
ws = sapi.Rebin(InputWorkspace=ws_name+"_JoY", Params = rebin_parameters,FullBinsOnly=True, OutputWorkspace= ws_name+"_JoY")
tmp=sapi.CloneWorkspace(InputWorkspace=ws_name+"_JoY")
for j in range(tmp.getNumberHistograms()):
for k in range(tmp.blocksize()):
tmp.dataE(j)[k] =0.
if np.isnan( tmp.dataY(j)[k] ) :
ws.dataY(j)[k] =0.
tmp.dataY(j)[k] =0.
if (tmp.dataY(j)[k]!=0):
tmp.dataY(j)[k] =1.
tmp=sapi.SumSpectra('tmp')
sapi.SumSpectra(InputWorkspace=ws_name+"_JoY",OutputWorkspace=ws_name+"_JoY_sum")
sapi.Divide(LHSWorkspace=ws_name+"_JoY_sum", RHSWorkspace="tmp", OutputWorkspace =ws_name+"_JoY_sum")
#rewriting the temporary workspaces ws and tmp
ws=sapi.mtd[ws_name+"_JoY_sum"]
tmp=sapi.CloneWorkspace(InputWorkspace=ws_name+"_JoY_sum")
for k in range(tmp.blocksize()):
tmp.dataE(0)[k] =(ws.dataE(0)[k]+ws.dataE(0)[ws.blocksize()-1-k])/2.
tmp.dataY(0)[k] =(ws.dataY(0)[k]+ws.dataY(0)[ws.blocksize()-1-k])/2.
sapi.RenameWorkspace(InputWorkspace="tmp",OutputWorkspace=ws_name+"_JoY_sym")
normalise_workspace(ws_name+"_JoY_sym")
return max_Y
def _calibData(self, sam_ws, mon_ws):
api.MaskDetectors(Workspace=sam_ws, DetectorList=self._dMask)
#MaskedWorkspace='BASIS_MASK')
api.ModeratorTzeroLinear(InputWorkspace=sam_ws,\
OutputWorkspace=sam_ws)
api.LoadParameterFile(Workspace=sam_ws,
Filename=config.getInstrumentDirectory() +
'BASIS_silicon_111_Parameters.xml')
api.ConvertUnits(InputWorkspace=sam_ws,
OutputWorkspace=sam_ws,
Target='Wavelength',
EMode='Indirect')
if not self._noMonNorm:
api.ModeratorTzeroLinear(InputWorkspace=mon_ws,\
OutputWorkspace=mon_ws)
api.Rebin(InputWorkspace=mon_ws,
OutputWorkspace=mon_ws,
Params='10')
api.ConvertUnits(InputWorkspace=mon_ws,
OutputWorkspace=mon_ws,
Target='Wavelength')
api.OneMinusExponentialCor(InputWorkspace=mon_ws,
OutputWorkspace=mon_ws,
C='0.20749999999999999',
C1='0.001276')
api.Scale(InputWorkspace=mon_ws,
OutputWorkspace=mon_ws,
Factor='9.9999999999999995e-07')
api.RebinToWorkspace(WorkspaceToRebin=sam_ws,
WorkspaceToMatch=mon_ws,
OutputWorkspace=sam_ws)
api.Divide(LHSWorkspace=sam_ws,
RHSWorkspace=mon_ws,
OutputWorkspace=sam_ws)
def test_Workspace2D(self):
# This is from the Mantid system-test data
filename = 'CNCS_51936_event.nxs'
eventWS = mantid.LoadEventNexus(filename)
ws = mantid.Rebin(eventWS, -0.001, PreserveEvents=False)
d = mantidcompat.to_dataset(ws)
print(d)
def _calibData(self, sam_ws, mon_ws):
sapi.MaskDetectors(Workspace=sam_ws,
DetectorList=self._dMask)
sapi.ModeratorTzeroLinear(InputWorkspace=sam_ws,
OutputWorkspace=sam_ws)
sapi.LoadParameterFile(Workspace=sam_ws,
Filename=pjoin(DEFAULT_CONFIG_DIR,
self._reflection["parameter_file"]))
sapi.ConvertUnits(InputWorkspace=sam_ws,
OutputWorkspace=sam_ws,
Target='Wavelength',
EMode='Indirect')
if self._MonNorm:
sapi.ModeratorTzeroLinear(InputWorkspace=mon_ws,
OutputWorkspace=mon_ws)
sapi.Rebin(InputWorkspace=mon_ws,
OutputWorkspace=mon_ws,
Params='10')
sapi.ConvertUnits(InputWorkspace=mon_ws,
OutputWorkspace=mon_ws,
Target='Wavelength')
sapi.OneMinusExponentialCor(InputWorkspace=mon_ws,
OutputWorkspace=mon_ws,
C='0.20749999999999999',
C1='0.001276')
sapi.Scale(InputWorkspace=mon_ws,
OutputWorkspace=mon_ws,
Factor='1e-06')
sapi.RebinToWorkspace(WorkspaceToRebin=sam_ws,
WorkspaceToMatch=mon_ws,
OutputWorkspace=sam_ws)
sapi.Divide(LHSWorkspace=sam_ws,
RHSWorkspace=mon_ws,
OutputWorkspace=sam_ws)
def generate_ts_pdf(run_number, focus_file_path, merge_banks=False, q_lims=None, cal_file_name=None,
sample_details=None, delta_r=None, delta_q=None, pdf_type="G(r)", lorch_filter=None,
freq_params=None, debug=False):
focused_ws = _obtain_focused_run(run_number, focus_file_path)
focused_ws = mantid.ConvertUnits(InputWorkspace=focused_ws, Target="MomentumTransfer", EMode='Elastic')
raw_ws = mantid.Load(Filename='POLARIS'+str(run_number)+'.nxs')
sample_geometry = common.generate_sample_geometry(sample_details)
sample_material = common.generate_sample_material(sample_details)
self_scattering_correction = mantid.TotScatCalculateSelfScattering(
InputWorkspace=raw_ws,
CalFileName=cal_file_name,
SampleGeometry=sample_geometry,
SampleMaterial=sample_material,
CrystalDensity=sample_details.material_object.crystal_density)
ws_group_list = []
for i in range(self_scattering_correction.getNumberHistograms()):
ws_name = 'correction_' + str(i)
mantid.ExtractSpectra(InputWorkspace=self_scattering_correction, OutputWorkspace=ws_name,
WorkspaceIndexList=[i])
ws_group_list.append(ws_name)
self_scattering_correction = mantid.GroupWorkspaces(InputWorkspaces=ws_group_list)
self_scattering_correction = mantid.RebinToWorkspace(WorkspaceToRebin=self_scattering_correction,
WorkspaceToMatch=focused_ws)
focused_ws = mantid.Subtract(LHSWorkspace=focused_ws, RHSWorkspace=self_scattering_correction)
if delta_q:
focused_ws = mantid.Rebin(InputWorkspace=focused_ws, Params=delta_q)
if merge_banks:
q_min, q_max = _load_qlims(q_lims)
merged_ws = mantid.MatchAndMergeWorkspaces(InputWorkspaces=focused_ws, XMin=q_min, XMax=q_max,
CalculateScale=False)
fast_fourier_filter(merged_ws, freq_params=freq_params)
pdf_output = mantid.PDFFourierTransform(Inputworkspace="merged_ws", InputSofQType="S(Q)-1", PDFType=pdf_type,
Filter=lorch_filter, DeltaR=delta_r,
rho0=sample_details.material_object.crystal_density)
else:
for ws in focused_ws:
fast_fourier_filter(ws, freq_params=freq_params)
pdf_output = mantid.PDFFourierTransform(Inputworkspace='focused_ws', InputSofQType="S(Q)-1", PDFType=pdf_type,
Filter=lorch_filter, DeltaR=delta_r,
rho0=sample_details.material_object.crystal_density)
pdf_output = mantid.RebinToWorkspace(WorkspaceToRebin=pdf_output, WorkspaceToMatch=pdf_output[4],
PreserveEvents=True)
if not debug:
common.remove_intermediate_workspace('self_scattering_correction')
# Rename output ws
if 'merged_ws' in locals():
mantid.RenameWorkspace(InputWorkspace='merged_ws', OutputWorkspace=run_number + '_merged_Q')
mantid.RenameWorkspace(InputWorkspace='focused_ws', OutputWorkspace=run_number+'_focused_Q')
if isinstance(focused_ws, WorkspaceGroup):
for i in range(len(focused_ws)):
mantid.RenameWorkspace(InputWorkspace=focused_ws[i], OutputWorkspace=run_number+'_focused_Q_'+str(i+1))
mantid.RenameWorkspace(InputWorkspace='pdf_output', OutputWorkspace=run_number+'_pdf_R')
if isinstance(pdf_output, WorkspaceGroup):
for i in range(len(pdf_output)):
mantid.RenameWorkspace(InputWorkspace=pdf_output[i], OutputWorkspace=run_number+'_pdf_R_'+str(i+1))
return pdf_output
def align_and_focus(run_number, nexus_file_name, target_unit, binning_parameters, convert_to_matrix):
"""
align and focus a run
:param run_number:
:param nexus_file_name:
:param target_unit:
:param binning_parameters:
:param convert_to_matrix:
:return:
"""
# check inputs ... blabla
# load data
output_ws_name = 'VULCAN_{0}_events'.format(run_number)
mantidapi.Load(Filename=nexus_file_name, OutputWorkspace=output_ws_name)
mantidapi.CompressEvents(InputWorkspace=output_ws_name,
OutputWorkspace=output_ws_name,
Tolerance='0.01')
# TODO FIXME - the calibration file name shall be set from user configuration through function method
VULCAN_FOCUS_CAL = '/SNS/VULCAN/shared/CALIBRATION/2017_8_11_CAL/VULCAN_calibrate_2017_08_17.h5'
VULCAN_FOCUS_CAL_GEN1 = '/SNS/VULCAN/shared/CALIBRATION/2011_1_7_CAL/vulcan_foc_all_2bank_11p.cal'
# calibration file
if output_ws_name.endswith('h5'):
cal_file_name = VULCAN_FOCUS_CAL
else:
cal_file_name = VULCAN_FOCUS_CAL_GEN1
# align and focus
final_ws_name = 'VULCAN_{0}'.format(run_number)
print(output_ws_name)
print(final_ws_name)
print(cal_file_name)
# output is TOF
mantidapi.AlignAndFocusPowder(InputWorkspace=output_ws_name,
OutputWorkspace=final_ws_name,
CalFileName=cal_file_name,
Params='-0.001',
DMin='0.5', DMax='3.5',
PreserveEvents=not convert_to_matrix)
# clean
mantidapi.DeleteWorkspace(Workspace=output_ws_name)
# convert unit
if target_unit == 'dSpacing':
mantidapi.ConvertUnits(InputWorkspace=final_ws_name,
OutputWorkspace=final_ws_name,
Target='dSpacing',
EMode='Elastic')
# binning
mantidapi.Rebin(InputWorkspace=final_ws_name,
OutputWorkspace=final_ws_name,
Params=numpy.array(binning_parameters))
return final_ws_name
def save_mantid_gsas(gsas_ws_name, gda_file_name, binning_parameters):
"""
Save temporary GSAS file
:param gsas_ws_name:
:param gda_file_name:
:param binning_parameters:
:return:
"""
temp1_ws = ADS.retrieve(gsas_ws_name)
print('[DB...BAT] Before aligned {0}.. vec x: {1}... is histograms? {2}'
''.format(type(temp1_ws),
temp1_ws.readX(2)[0], temp1_ws.isHistogramData()))
aligned_gss_ws_name = '{0}_temp'.format(gsas_ws_name)
if isinstance(binning_parameters, numpy.ndarray):
# align to VDRIVE
align_to_vdrive_bin(gsas_ws_name, binning_parameters,
aligned_gss_ws_name)
elif binning_parameters is not None:
api.Rebin(InputWorkspace=gsas_ws_name,
OutputWorkspace=aligned_gss_ws_name,
Params=binning_parameters)
# END-IF (rebin)
aws = ADS.retrieve(aligned_gss_ws_name)
print('[DB...INFO] Save Mantid GSS: {} is histogram: {}'.format(
aligned_gss_ws_name, aws.isHistogramData()))
# Convert from PointData to Histogram
api.ConvertToHistogram(InputWorkspace=aligned_gss_ws_name,
OutputWorkspace=aligned_gss_ws_name)
# Save
print('[DB...VERY IMPORTANT] Save to GSAS File {0} as a temporary output'.
format(gda_file_name))
curr_ws = ADS.retrieve(aligned_gss_ws_name)
print(
'[DB...INFO] Into SaveGSS: number of histograms = {}, Bank 1/2 size = {}, Bank3 size = {}'
''.format(curr_ws.getNumberHistograms(), len(curr_ws.readX(0)),
len(curr_ws.readX(2))))
print('[DB...INFO] B1[0] = {}, B1[-1] = {}, B3[0] = {}, B3[-1] = {}'
''.format(
curr_ws.readX(0)[0],
curr_ws.readX(0)[-1],
curr_ws.readX(2)[0],
curr_ws.readX(2)[-1]))
api.SaveGSS(InputWorkspace=aligned_gss_ws_name,
Filename=gda_file_name,
SplitFiles=False,
Append=False,
Format="SLOG",
MultiplyByBinWidth=False,
ExtendedHeader=False,
UseSpectrumNumberAsBankID=True)
return gda_file_name
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 align_to_vdrive_bin(input_ws, vec_ref_tof, output_ws_name):
"""
Rebin input workspace (to histogram data)
in order to to match VULCAN's VDRIVE-generated GSAS file
:param input_ws: focused workspace
:param vec_ref_tof: vector of TOF bins
:param output_ws_name:
:return:
"""
# Create a complicated bin parameter
params = []
dx = None
for ibin in range(len(vec_ref_tof) - 1):
x0 = vec_ref_tof[ibin]
xf = vec_ref_tof[ibin + 1]
dx = xf - x0
params.append(x0)
params.append(dx)
# last bin
assert dx is not None, 'Vector of refT has less than 2 values. It is not supported.'
x0 = vec_ref_tof[-1]
xf = 2 * dx + x0
params.extend([x0, 2 * dx, xf])
# Rebin
tempws = api.Rebin(InputWorkspace=input_ws,
Params=params,
PreserveEvents=True)
# Map to a new workspace with 'vdrive-bin', which is the integer value of log bins
numhist = tempws.getNumberHistograms()
newvecx = []
newvecy = []
newvece = []
for iws in range(numhist):
vecx = tempws.readX(iws)
vecy = tempws.readY(iws)
vece = tempws.readE(iws)
for i in range(len(vecx) - 1):
newvecx.append(int(vecx[i] * 10) / 10.)
newvecy.append(vecy[i])
newvece.append(vece[i])
# ENDFOR (i)
# ENDFOR (iws)
api.DeleteWorkspace(Workspace=tempws)
gsaws = api.CreateWorkspace(DataX=newvecx,
DataY=newvecy,
DataE=newvece,
NSpec=numhist,
UnitX="TOF",
ParentWorkspace=input_ws,
OutputWorkspace=output_ws_name)
return gsaws
def _group_and_SofQW(self, wsName, etRebins, isSample=True):
""" Transforms from wavelength and detector ID to S(Q,E)
@param wsName: workspace as a function of wavelength and detector id
@param etRebins: final energy domain and bin width
@param isSample: discriminates between sample and vanadium
@return: S(Q,E)
"""
sapi.ConvertUnits(InputWorkspace=wsName,
OutputWorkspace=wsName,
Target='DeltaE',
EMode='Indirect')
sapi.CorrectKiKf(InputWorkspace=wsName,
OutputWorkspace=wsName,
EMode='Indirect')
sapi.Rebin(InputWorkspace=wsName,
OutputWorkspace=wsName,
Params=etRebins)
if self._groupDetOpt != "None":
if self._groupDetOpt == "Low-Resolution":
grp_file = "BASIS_Grouping_LR.xml"
else:
grp_file = "BASIS_Grouping.xml"
# If mask override used, we need to add default grouping file
# location to search paths
if self._overrideMask:
config.appendDataSearchDir(DEFAULT_MASK_GROUP_DIR)
sapi.GroupDetectors(InputWorkspace=wsName,
OutputWorkspace=wsName,
MapFile=grp_file,
Behaviour="Sum")
wsSqwName = wsName + '_divided_sqw' if isSample and self._doNorm else wsName + '_sqw'
sapi.SofQW3(InputWorkspace=wsName,
QAxisBinning=self._qBins,
EMode='Indirect',
EFixed=self._reflection["default_energy"],
OutputWorkspace=wsSqwName)
# Rebin the vanadium within the elastic line
if not isSample:
sapi.Rebin(InputWorkspace=wsSqwName,
OutputWorkspace=wsSqwName,
Params=self._reflection["vanadium_bins"])
return wsSqwName
def rebin_time(bin_param, wsname):
"""
rebin by time
@param bin_param:: the parameters to rebin to
@param wsname:: the workspace to rebin
@return: the rebinned workspace
"""
output = "engg_focus_input_ws"
ws = simple.Rebin(InputWorkspace=wsname, Params=bin_param, OutputWorkspace=output)
return ws
def filter_cross_sections(file_path, events=True, histo=False):
"""
Filter events according to polarization state.
:param str file_path: file to read
:param bool events: if True, an event nexus file will be written
:param bool histo: if True, a histo nexus file will be written
"""
cross_sections = {}
cross_sections_histo = {}
xs_list = api.MRFilterCrossSections(file_path,
PolState=POL_STATE,
AnaState=ANA_STATE,
PolVeto=POL_VETO,
AnaVeto=ANA_VETO)
if len(xs_list) > 0:
tof_min, tof_max = get_tof_range(xs_list[0])
for workspace in xs_list:
if "cross_section_id" in workspace.getRun():
entry = workspace.getRun().getProperty("cross_section_id").value
else:
entry = 'Off_Off'
api.AddSampleLog(Workspace=workspace,
LogName='cross_section_id',
LogText=entry)
if workspace.getNumberEvents() < 5:
logging.warn("No events in %s", entry)
continue
run_number = workspace.getRunNumber()
if events:
events_file = "/tmp/filtered_%s_%s_%s.nxs" % (run_number, entry,
"events")
api.SaveNexus(InputWorkspace=workspace,
Filename=events_file,
Title='entry_%s' % entry)
cross_sections['entry-%s' % entry] = events_file
if histo:
#tof_min = workspace.getTofMin()
#tof_max = workspace.getTofMax()
ws_binned = api.Rebin(InputWorkspace=workspace,
Params="%s, %s, %s" %
(tof_min, TOF_BIN, tof_max),
PreserveEvents=False)
histo_file = "/tmp/filtered_%s_%s_%s.nxs" % (run_number, entry,
"histo")
api.SaveNexus(InputWorkspace=ws_binned,
Filename=histo_file,
Title='entry_%s' % entry)
cross_sections_histo['entry-%s' % entry] = histo_file
return cross_sections, cross_sections_histo
def _do_silicon_calibration(self, runs_to_process, cal_file_name, grouping_file_name):
create_si_ws = common._read_ws(number=runs_to_process, instrument=self)
cycle_details = self._get_cycle_information(runs_to_process)
instrument_version = cycle_details["instrument_version"]
if instrument_version == "new" or instrument_version == "new2":
create_si_ws = mantid.Rebin(InputWorkspace=create_si_ws, Params="100,-0.0006,19950")
create_si_d_spacing_ws = mantid.ConvertUnits(InputWorkspace=create_si_ws, Target="dSpacing")
if instrument_version == "new2":
create_si_d_spacing_rebin_ws = mantid.Rebin(InputWorkspace=create_si_d_spacing_ws, Params="1.71,0.002,2.1")
create_si_cross_corr_ws = mantid.CrossCorrelate(InputWorkspace=create_si_d_spacing_rebin_ws,
ReferenceSpectra=20, WorkspaceIndexMin=9,
WorkspaceIndexMax=1063, XMin=1.71, XMax=2.1)
elif instrument_version == "new":
create_si_d_spacing_rebin_ws = mantid.Rebin(InputWorkspace=create_si_d_spacing_ws, Params="1.85,0.002,2.05")
create_si_cross_corr_ws = mantid.CrossCorrelate(InputWorkspace=create_si_d_spacing_rebin_ws,
ReferenceSpectra=20, WorkspaceIndexMin=9,
WorkspaceIndexMax=943, XMin=1.85, XMax=2.05)
elif instrument_version == "old":
create_si_d_spacing_rebin_ws = mantid.Rebin(InputWorkspace=create_si_d_spacing_ws, Params="3,0.002,3.2")
create_si_cross_corr_ws = mantid.CrossCorrelate(InputWorkspace=create_si_d_spacing_rebin_ws,
ReferenceSpectra=500, WorkspaceIndexMin=1,
WorkspaceIndexMax=1440, XMin=3, XMax=3.2)
else:
raise NotImplementedError("The instrument version is not supported for creating a silicon calibration")
common.remove_intermediate_workspace(create_si_d_spacing_ws)
common.remove_intermediate_workspace(create_si_d_spacing_rebin_ws)
calibration_output_path = self.calibration_dir + cal_file_name
create_si_offsets_ws = mantid.GetDetectorOffsets(InputWorkspace=create_si_cross_corr_ws,
Step=0.002, DReference=1.920127251, XMin=-200, XMax=200,
GroupingFileName=calibration_output_path)
create_si_aligned_ws = mantid.AlignDetectors(InputWorkspace=create_si_ws,
CalibrationFile=calibration_output_path)
grouping_output_path = self.calibration_dir + grouping_file_name
create_si_grouped_ws = mantid.DiffractionFocussing(InputWorkspace=create_si_aligned_ws,
GroupingFileName=grouping_output_path)
del create_si_offsets_ws, create_si_grouped_ws
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 _create_calibration(self, calibration_runs, offset_file_name, grouping_file_name):
input_ws = common._read_ws(number=calibration_runs, instrument=self)
cycle_information = self._get_cycle_information(calibration_runs)
# TODO move these hard coded params to instrument specific
if cycle_information["instrument_version"] == "new" or cycle_information["instrument_version"] == "new2":
input_ws = mantid.Rebin(InputWorkspace=input_ws, Params="100,-0.0006,19950")
d_spacing_cal = mantid.ConvertUnits(InputWorkspace=input_ws, Target="dSpacing")
d_spacing_cal = mantid.Rebin(InputWorkspace=d_spacing_cal, Params="1.8,0.002,2.1")
if cycle_information["instrument_version"] == "new2":
cross_cor_ws = mantid.CrossCorrelate(InputWorkspace=d_spacing_cal, ReferenceSpectra=20,
WorkspaceIndexMin=9, WorkspaceIndexMax=1063, XMin=1.8, XMax=2.1)
elif cycle_information["instrument_version"] == "new":
cross_cor_ws = mantid.CrossCorrelate(InputWorkspace=d_spacing_cal, ReferenceSpectra=20,
WorkspaceIndexMin=9, WorkspaceIndexMax=943, XMin=1.8, XMax=2.1)
else:
cross_cor_ws = mantid.CrossCorrelate(InputWorkspace=d_spacing_cal, ReferenceSpectra=500,
WorkspaceIndexMin=1, WorkspaceIndexMax=1440, XMin=1.8, XMax=2.1)
if self._old_api_uses_full_paths: # Workaround for old API setting full paths
grouping_file_path = grouping_file_name
offset_file_path = offset_file_name
else:
offset_file_path = self.calibration_dir + offset_file_name
grouping_file_path = self.calibration_dir + grouping_file_name
# Ceo Cell refined to 5.4102(3) so 220 is 1.912795
offset_output_path = mantid.GetDetectorOffsets(InputWorkspace=cross_cor_ws, Step=0.002, DReference=1.912795,
XMin=-200, XMax=200, GroupingFileName=offset_file_path)
del offset_output_path # This isn't used so delete it to keep linters happy
aligned_ws = mantid.AlignDetectors(InputWorkspace=input_ws, CalibrationFile=offset_file_path)
cal_grouped_ws = mantid.DiffractionFocussing(InputWorkspace=aligned_ws, GroupingFileName=grouping_file_path)
common.remove_intermediate_workspace(d_spacing_cal)
common.remove_intermediate_workspace(cross_cor_ws)
common.remove_intermediate_workspace(aligned_ws)
common.remove_intermediate_workspace(cal_grouped_ws)
def test_EventWorkspace(self):
# This is from the Mantid system-test data
filename = 'CNCS_51936_event.nxs'
eventWS = mantid.LoadEventNexus(filename)
ws = mantid.Rebin(eventWS, -0.001, PreserveEvents=False)
binned_mantid = mantidcompat.to_dataset(ws)
tof = sp.Variable(binned_mantid[sp.Coord.Tof])
d = mantidcompat.to_dataset(eventWS)
binned = sp.histogram(d, tof)
delta = sp.sum(binned_mantid - binned, sp.Dim.Position)
print(delta)
def calculate_mantid_resolutions(ws_name, mass):
max_Y = np.ceil(2.5*mass+27)
rebin_parameters = str(-max_Y)+","+str(2.*max_Y/240)+","+str(max_Y) # twice the binning as for the data
ws= sapi.mtd[ws_name]
for index in range(ws.getNumberHistograms()):
sapi.VesuvioResolution(Workspace=ws,WorkspaceIndex=index,Mass=mass,OutputWorkspaceYSpace="tmp")
tmp=sapi.Rebin("tmp",rebin_parameters)
if index == 0:
sapi.RenameWorkspace(tmp,"resolution")
else:
sapi.AppendSpectra("resolution", tmp, OutputWorkspace= "resolution")
sapi.SumSpectra(InputWorkspace="resolution",OutputWorkspace="resolution")
normalise_workspace("resolution")
safe_delete_ws(tmp)
def _rebinVdrive(self, inputws, vec_refT, outputwsname):
""" Rebin to match VULCAN's VDRIVE-generated GSAS file
Arguments:
- inputws : focussed workspace
- vec_refT: list of TOF bins
"""
# Create a complicated bin parameter
params = []
for ibin in range(len(vec_refT) - 1):
x0 = vec_refT[ibin]
xf = vec_refT[ibin + 1]
dx = xf - x0
params.append(x0)
params.append(dx)
# last bin
x0 = vec_refT[-1]
xf = 2 * dx + x0
params.extend([x0, 2 * dx, xf])
# Rebin
tempws = api.Rebin(InputWorkspace=inputws,
Params=params,
PreserveEvents=False)
# Map to a new workspace with 'vdrive-bin', which is the integer value of log bins
numhist = tempws.getNumberHistograms()
newvecx = []
newvecy = []
newvece = []
for iws in range(numhist):
vecx = tempws.readX(iws)
vecy = tempws.readY(iws)
vece = tempws.readE(iws)
for i in range(len(vecx) - 1):
newvecx.append(int(vecx[i] * 10) / 10.)
newvecy.append(vecy[i])
newvece.append(vece[i])
# ENDFOR (i)
# ENDFOR (iws)
api.DeleteWorkspace(Workspace=tempws)
gsaws = api.CreateWorkspace(DataX=newvecx,
DataY=newvecy,
DataE=newvece,
NSpec=numhist,
UnitX="TOF",
ParentWorkspace=inputws,
OutputWorkspace=outputwsname)
return gsaws
def test_Workspace2D(self):
import mantid.simpleapi as mantid
eventWS = self.base_event_ws
ws = mantid.Rebin(eventWS, 10000, PreserveEvents=False)
d = scn.mantid.convert_Workspace2D_to_data_array(ws)
self.assertEqual(
d.attrs["run_start"].value,
"2012-05-21T15:14:56.279289666",
)
self.assertEqual(d.data.unit, sc.units.counts)
for i in range(ws.getNumberHistograms()):
assert np.all(np.equal(d.values[i], ws.readY(i)))
assert np.all(np.equal(d.variances[i], ws.readE(i) * ws.readE(i)))
self.assertEqual(d.coords['spectrum'].dtype, sc.DType.int32)
self.assertEqual(d.coords['tof'].dtype, sc.DType.float64)
def test_EventWorkspace(self):
import mantid.simpleapi as mantid
eventWS = self.base_event_ws
ws = mantid.Rebin(eventWS, 10000)
binned_mantid = scn.mantid.convert_Workspace2D_to_data_array(ws)
target_tof = binned_mantid.coords['tof']
d = scn.mantid.convert_EventWorkspace_to_data_array(
eventWS, load_pulse_times=False)
binned = sc.histogram(d, bins=target_tof)
delta = sc.sum(binned_mantid - binned, 'spectrum')
delta = sc.sum(delta, 'tof')
self.assertLess(np.abs(delta.value), 1e-5)
def absorption_correction(filename,
lambda_binning=(0.7, 10.35, 5615),
**mantid_args):
"""
This method is a straightforward wrapper exposing CylinderAbsorption
through scipp
CylinderAbsorption calculates an approximation of the
attenuation due to absorption and single scattering in a 'cylindrical'
shape.
Requirements:
- The instrument associated with the workspace must be fully defined.
(This being a WISH-centric implementation is done with the predefined
instr file)
Parameters
----------
filename: Path to the file with data
lambda_binning: min, max and number of steps for binning in wavelength
mantid_args: additional arguments to be passed to Mantid's
CylinderAbsorption method.
Returns
-------
Scipp dataset containing absorption correction in Wavelength units.
"""
# Create empty workspace with proper dimensions.
workspace = simpleapi.LoadEventNexus(filename,
MetaDataOnly=True,
LoadMonitors=False,
LoadLogs=False)
workspace.getAxis(0).setUnit('Wavelength')
# Rebin the resulting correction based on default WISH binning
lambda_min, lambda_max, number_bins = lambda_binning
bin_width = (lambda_max - lambda_min) / number_bins
workspace = simpleapi.Rebin(workspace,
params=[lambda_min, bin_width, lambda_max],
FullBinsOnly=True)
correction = simpleapi.CylinderAbsorption(workspace, **mantid_args)
return scn.from_mantid(correction)
def test_function_attached_as_workpace_method_does_the_same_as_the_free_function(self):
# Use Rebin as a test
ws1 = simpleapi.CreateWorkspace(DataX=[1.5,2.0,2.5,3.0],DataY=[1,2,3],NSpec=1,UnitX='Wavelength')
self.assertTrue(hasattr(ws1, "rebin"))
ws2 = simpleapi.Rebin(ws1,Params=[1.5,1.5,3])
ws3 = ws1.rebin(Params=[1.5,1.5,3])
ws4 = ws1.rebin([1.5,1.5,3])
result = simpleapi.CompareWorkspaces(ws2,ws3)
self.assertTrue(result[0])
result = simpleapi.CompareWorkspaces(ws2,ws4)
self.assertTrue(result[0])
simpleapi.DeleteWorkspace(ws1)
simpleapi.DeleteWorkspace(ws2)
simpleapi.DeleteWorkspace(ws3)