def focus_whole(run_number, van_curves, van_int, full_inst_calib, focus_directory, focus_general, do_pre_process, params, time_period):
"""
focus a whole run with no cropping
@param run_number :: the run nuumber to focus
@param van_curves :: the path to the vanadium curves file
@param van_int :: the path to the integrated vanadium file
@param full_inst_calib :: workspace containing the full instrument calibration
@param focus_directory :: the user specific focus directory to save to
@param focus_general :: the general focus directory to save to
@param do_pre_process :: whether or not to pre-process the run before focussing it
@param params :: the rebin parameters for pre-processing
@param time_period :: the time period for pre-processing
"""
van_curves_ws, van_integrated_ws, ws_to_focus = _prepare_focus(run_number, van_curves, van_int, do_pre_process,
params, time_period)
# loop through both banks, focus and save them
for bank_no in range(1, 3):
sample_ws_clone = simple.CloneWorkspace(ws_to_focus)
curves_ws_clone = simple.CloneWorkspace(van_curves_ws)
tof_output_name = "engg_focus_output_bank_{}".format(bank_no)
dspacing_output_name = tof_output_name + "_dSpacing"
cal_file = NORTH_BANK_CAL if bank_no == 1 else SOUTH_BANK_CAL
region_calib = 'engg_calibration_bank_1' if bank_no == 1 else 'engg_calibration_bank_2'
df_kwarg = {"GroupingFileName": cal_file}
_run_focus(input_workspace=sample_ws_clone, tof_output_name=tof_output_name, region_calib=region_calib,
vanadium_integration_ws=van_integrated_ws, vanadium_curves_ws=curves_ws_clone, df_kwarg=df_kwarg,
full_calib=full_inst_calib)
_save_out(run_number, focus_directory, focus_general, tof_output_name, "ENGINX_{}_{}{{}}", str(bank_no))
_save_out(run_number, focus_directory, focus_general, dspacing_output_name, "ENGINX_{}_{}{{}}", str(bank_no))
simple.DeleteWorkspace(sample_ws_clone)
simple.DeleteWorkspace(curves_ws_clone)
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 _convert_units_wavelength(self, unit, input_ws, output_ws, target):
if unit != 'Wavelength':
# Configure conversion
if unit == 'dSpacing':
emode = 'Elastic'
efixed = 0.0
elif unit == 'DeltaE':
emode = 'Indirect'
from IndirectCommon import getEfixed
efixed = getEfixed(input_ws)
else:
s_api.CloneWorkspace(InputWorkspace=input_ws,
OutputWorkspace=output_ws)
#raise ValueError('Unit %s in sample workspace is not supported' % unit)
if unit == 'dSpacing' or unit == 'DeltaE':
# Do conversion
# Use temporary workspace so we don't modify data
s_api.ConvertUnits(InputWorkspace=input_ws,
OutputWorkspace=output_ws,
Target=target,
EMode=emode,
EFixed=efixed)
else:
# No need to convert
s_api.CloneWorkspace(InputWorkspace=input_ws,
OutputWorkspace=output_ws)
def _focus_mode_trans(output_file_paths, atten, instrument,
calibrated_spectra):
summed_ws = mantid.CloneWorkspace(InputWorkspace=calibrated_spectra[0])
for i in range(1, 9): # Add workspaces 2-9 to workspace 1
summed_ws = mantid.Plus(LHSWorkspace=summed_ws,
RHSWorkspace=calibrated_spectra[i])
summed_ws = mantid.Scale(InputWorkspace=summed_ws,
Factor=0.111111111111111)
if atten:
# Clone a workspace which is not attenuated
no_att = output_file_paths["output_name"] + "_noatten"
mantid.CloneWorkspace(InputWorkspace=summed_ws, OutputWorkspace=no_att)
summed_ws = mantid.ConvertUnits(InputWorkspace=summed_ws,
Target="dSpacing")
summed_ws = instrument._attenuate_workspace(summed_ws)
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 focus_cropped(run_number, van_curves, van_int, full_inst_calib, focus_directory, focus_general, do_pre_process, params, time_period,
crop_on,
use_spectra):
"""
focus a partial run, cropping either on banks or on specific spectra
@param van_curves :: the path to the vanadium curves file
@param van_int :: the path to the integrated vanadium file
@param full_inst_calib :: workspace containing the full instrument calibration
@param run_number :: the run nuumber to focus
@param focus_directory :: the user specific focus directory to save to
@param focus_general :: the general focus directory to save to
@param do_pre_process :: whether or not to pre-process the run before focussing it
@param params :: the rebin parameters for pre-processing
@param time_period :: the time period for pre-processing
@param crop_on :: the bank or spectra to crop on
@param use_spectra :: whether to focus by spectra or banks
"""
van_curves_ws, van_integrated_ws, ws_to_focus = _prepare_focus(run_number, van_curves, van_int, do_pre_process,
params, time_period)
tof_output_name = "engg_focus_output{0}{1}"
sample_ws_clone = simple.CloneWorkspace(ws_to_focus)
curves_ws_clone = simple.CloneWorkspace(van_curves_ws)
# check whether to crop on bank or spectra
if not use_spectra:
# get the bank to crop on, focus and save it out
bank = {"North": "1",
"South": "2"}
bank_no = bank.get(crop_on)
cal_file = NORTH_BANK_CAL if bank_no == 1 else SOUTH_BANK_CAL
region_calib = 'engg_calibration_bank_1' if bank_no == 1 else 'engg_calibration_bank_2'
df_kwarg = {"GroupingFileName": cal_file}
tof_output_name = tof_output_name.format("_bank_", bank_no)
dspacing_output_name = tof_output_name + "_dSpacing"
_run_focus(input_workspace=sample_ws_clone, tof_output_name=tof_output_name, vanadium_integration_ws=van_integrated_ws,
vanadium_curves_ws=curves_ws_clone, df_kwarg=df_kwarg, full_calib=full_inst_calib,
region_calib=region_calib)
_save_out(run_number, focus_directory, focus_general, tof_output_name, "ENGINX_{}_{}{{}}", crop_on)
_save_out(run_number, focus_directory, focus_general, dspacing_output_name, "ENGINX_{}_{}{{}}", crop_on)
else:
# crop on the spectra passed in, focus and save it out
tof_output_name = tof_output_name.format("_", "cropped")
dspacing_output_name = tof_output_name + "_dSpacing"
grp_ws = Utils.create_grouping_workspace_from_spectra_list(crop_on, ws_to_focus)
df_kwarg = {"GroupingWorkspace": grp_ws}
region_calib = 'engg_calibration_cropped'
_run_focus(input_workspace=sample_ws_clone, tof_output_name=tof_output_name, vanadium_integration_ws=van_integrated_ws,
vanadium_curves_ws=curves_ws_clone, df_kwarg=df_kwarg, region_calib=region_calib,
full_calib=full_inst_calib)
_save_out(run_number, focus_directory, focus_general, tof_output_name, "ENGINX_{}_bank_{}{{}}", "cropped")
_save_out(run_number, focus_directory, focus_general, dspacing_output_name, "ENGINX_{}_bank_{}{{}}", "cropped")
simple.DeleteWorkspace(sample_ws_clone)
simple.DeleteWorkspace(curves_ws_clone)
def stitch_reflectivity(reduction_list,
xs=None,
normalize_to_unity=True,
q_cutoff=0.01):
"""
Stitch and normalize data sets
:param string xs: name of the cross-section to use
:param bool normalize_to_unity: if True, the specular ridge will be normalized to 1
"""
if not reduction_list:
return []
# Select the cross-section we will use to determine the scaling factors
if xs is None:
xs = reduction_list[0].cross_sections.keys()[0]
# First, determine the overall scaling factor as needed
scaling_factor = 1.0
if normalize_to_unity:
idx_list = reduction_list[0].cross_sections[xs].q < q_cutoff
total = 0
weights = 0
for i in range(len(reduction_list[0].cross_sections[xs]._r)):
if idx_list[i]:
w = 1.0 / float(reduction_list[0].cross_sections[xs]._dr[i])**2
total += w * float(reduction_list[0].cross_sections[xs]._r[i])
weights += w
if weights > 0 and total > 0:
scaling_factor = weights / total
reduction_list[0].set_parameter("scaling_factor", scaling_factor)
else:
scaling_factor = reduction_list[0].cross_sections[
xs].configuration.scaling_factor
# Stitch the data sets together
_previous_ws = None
running_scale = scaling_factor
scaling_factors = [running_scale]
for i in range(len(reduction_list)):
n_total = len(reduction_list[i].cross_sections[xs].q)
p_0 = reduction_list[i].cross_sections[
xs].configuration.cut_first_n_points
p_n = n_total - reduction_list[i].cross_sections[
xs].configuration.cut_last_n_points
ws = api.CreateWorkspace(
DataX=reduction_list[i].cross_sections[xs].q[p_0:p_n],
DataY=reduction_list[i].cross_sections[xs]._r[p_0:p_n],
DataE=reduction_list[i].cross_sections[xs]._dr[p_0:p_n])
ws.setDistribution(True)
ws = api.ConvertToHistogram(ws)
if _previous_ws is not None:
_, scale = api.Stitch1D(_previous_ws, ws)
running_scale *= scale
scaling_factors.append(running_scale)
reduction_list[i].set_parameter("scaling_factor", running_scale)
_previous_ws = api.CloneWorkspace(ws)
return scaling_factors
def PyExec(self):
input_ws = self.getProperty('InputWorkspace').value
output_ws = self.getPropertyValue('OutputWorkspace')
num_specs = input_ws.getNumberHistograms()
api.CloneWorkspace(InputWorkspace=input_ws, OutputWorkspace=output_ws)
for i in range(0, num_specs):
x_data = input_ws.readX(i)
y_data = input_ws.readY(i)
e_data = input_ws.readE(i)
indexes = x_data.argsort()
x_ordered = x_data[indexes]
if input_ws.isHistogramData():
max_index = np.argmax(indexes)
indexes = np.delete(indexes, max_index)
y_ordered = y_data[indexes]
e_ordered = e_data[indexes]
mtd[output_ws].setX(i, x_ordered)
mtd[output_ws].setY(i, y_ordered)
mtd[output_ws].setE(i, e_ordered)
self.setProperty('OutputWorkspace', output_ws)
def _pre_process_corrections(self):
"""
If the sample is not in wavelength then convert the corrections to
whatever units the sample is in.
"""
unit_id = s_api.mtd[self._sample_ws_wavelength].getAxis(
0).getUnit().unitID()
logger.information('x-unit is ' + unit_id)
factor_types = ['ass']
if self._use_can:
factor_types.extend(['acc', 'acsc', 'assc'])
for factor_type in factor_types:
input_name = self._get_correction_factor_ws_name(factor_type)
output_name = self._corrections + '_' + factor_type
s_api.CloneWorkspace(InputWorkspace=input_name,
OutputWorkspace=output_name)
# Group the temporary factor workspaces (for easy removal later)
s_api.GroupWorkspaces(InputWorkspaces=[
self._corrections + '_' + f_type for f_type in factor_types
],
OutputWorkspace=self._corrections)
def _run_focus(input_workspace,
tof_output_name,
vanadium_integration_ws,
vanadium_curves_ws,
df_kwarg,
full_calib,
region_calib):
simple.NormaliseByCurrent(InputWorkspace=input_workspace, OutputWorkspace=input_workspace)
input_workspace /= vanadium_integration_ws
simple.ReplaceSpecialValues(InputWorkspace=input_workspace, OutputWorkspace=input_workspace, NaNValue=0,
InfinityValue=0)
simple.ApplyDiffCal(InstrumentWorkspace=input_workspace, CalibrationWorkspace=full_calib)
ws_d = simple.ConvertUnits(InputWorkspace=input_workspace, Target='dSpacing')
focused_sample = simple.DiffractionFocussing(InputWorkspace=ws_d, **df_kwarg)
curves_rebinned = simple.RebinToWorkspace(WorkspaceToRebin=vanadium_curves_ws, WorkspaceToMatch=focused_sample)
normalised = simple.Divide(LHSWorkspace=focused_sample, RHSWorkspace=curves_rebinned,
AllowDifferentNumberSpectra=True)
simple.ApplyDiffCal(InstrumentWorkspace=normalised, CalibrationWorkspace=region_calib)
dspacing_output_name = tof_output_name + "_dSpacing"
simple.CloneWorkspace(InputWorkspace=normalised, OutputWorkspace=dspacing_output_name)
simple.ConvertUnits(InputWorkspace=normalised, OutputWorkspace=tof_output_name, Target='TOF')
simple.DeleteWorkspace(curves_rebinned)
simple.DeleteWorkspace(focused_sample)
simple.DeleteWorkspace(normalised)
simple.DeleteWorkspace(ws_d)
def calculate_ub_matrix(self, peak_info_list, a, b, c, alpha, beta, gamma):
"""
Calculate UB matrix
Set Miller index from raw data in Workspace2D.
:param peakws:
:param a:
:param b:
:param c:
:param alpha:
:param beta:
:param gamma:
:return:
"""
# Check
assert isinstance(peak_info_list, list)
for peak_info in peak_info_list:
if isinstance(peak_info, PeakInfo) is False:
raise NotImplementedError('Input PeakList is of type %s.' %
str(type(peak_info_list[0])))
assert isinstance(peak_info, PeakInfo)
if len(peak_info_list) < 2:
return False, 'Too few peaks are input to calculate UB matrix. Must be >= 2.'
# Construct a new peak workspace by combining all single peak
ub_peak_ws_name = 'Temp_UB_Peak'
ub_peak_ws = api.CloneWorkspace(
InputWorkspace=peak_info_list[0].get_peak_workspace(),
OutputWorkspace=ub_peak_ws_name)
for i_peak_info in xrange(1, len(peak_info_list)):
# Set HKL as optional
peak_ws = peak_info_list[i_peak_info].get_peak_workspace()
# Combine peak workspace
ub_peak_ws = api.CombinePeaksWorkspaces(
LHSWorkspace=ub_peak_ws,
RHSWorkspace=peak_ws,
CombineMatchingPeaks=False,
OutputWorkspace=ub_peak_ws_name)
# END-FOR(i_peak_info)
# Calculate UB matrix
try:
api.CalculateUMatrix(PeaksWorkspace=ub_peak_ws_name,
a=a,
b=b,
c=c,
alpha=alpha,
beta=beta,
gamma=gamma)
except ValueError as val_err:
return False, str(val_err)
ub_matrix = ub_peak_ws.sample().getOrientedLattice().getUB()
self._myLastPeakUB = ub_peak_ws
return True, ub_matrix
def edit_matrix_workspace(sq_name,
scale_factor,
shift,
edited_sq_name=None):
"""
Edit the matrix workspace of S(Q) by scaling and shift
:param sq_name: name of the SofQ workspace
:param scale_factor:
:param shift:
:param edited_sq_name: workspace for the edited S(Q)
:return:
"""
# get the workspace
if AnalysisDataService.doesExist(sq_name) is False:
raise RuntimeError(
'S(Q) workspace {0} cannot be found in ADS.'.format(sq_name))
if edited_sq_name is not None:
simpleapi.CloneWorkspace(InputWorkspace=sq_name,
OutputWorkspace=edited_sq_name)
sq_ws = AnalysisDataService.retrieve(edited_sq_name)
else:
sq_ws = AnalysisDataService.retrieve(sq_name)
# get the vector of Y
sq_ws = sq_ws * scale_factor
sq_ws = sq_ws + shift
if sq_ws.name() != edited_sq_name:
simpleapi.DeleteWorkspace(Workspace=edited_sq_name)
simpleapi.RenameWorkspace(InputWorkspace=sq_ws,
OutputWorkspace=edited_sq_name)
assert sq_ws is not None, 'S(Q) workspace cannot be None.'
print('[DB...BAT] S(Q) workspace that is edit is {0}'.format(sq_ws))
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 makePhaseQuadTable(self, inputs):
"""
generates a phase table from CalMuonDetectorPhases
"""
cloned_workspace_CalMuon = mantid.CloneWorkspace(
InputWorkspace=inputs["InputWorkspace"], StoreInADS=False)
mantid.MaskDetectors(Workspace=cloned_workspace_CalMuon,
DetectorList=inputs['MaskedDetectors'],
StoreInADS=False)
self.alg = mantid.AlgorithmManager.create("CalMuonDetectorPhases")
self.alg.initialize()
self.alg.setAlwaysStoreInADS(False)
self.alg.setRethrows(True)
self.alg.setProperty("FirstGoodData", inputs["FirstGoodData"])
self.alg.setProperty("LastGoodData", inputs["LastGoodData"])
self.alg.setProperty("InputWorkspace", cloned_workspace_CalMuon)
self.alg.setProperty("DetectorTable", "PhaseTable")
self.alg.setProperty("DataFitted", "fits")
self.alg.execute()
mantid.AnalysisDataService.addOrReplace(
"PhaseTable",
self.alg.getProperty("DetectorTable").value)
self.alg = None
def _sum_groups_of_three_ws(calibrated_spectra, output_file_names):
workspace_list = []
output_list = []
for outer_loop_count in range(0, 3):
# First clone workspaces 1/4/7
pass_multiplier = (outer_loop_count * 3)
workspace_names = "focus_mode_groups-" + str(pass_multiplier + 1)
workspace_list.append(
mantid.CloneWorkspace(
InputWorkspace=calibrated_spectra[pass_multiplier],
OutputWorkspace=workspace_names))
# Then add workspaces 1+2+3 / 4+5+6 / 7+8+9
for i in range(1, 3):
input_ws_index = i + pass_multiplier # Workspaces 2/3 * n
inner_workspace_names = "focus_mode_groups-" + str(input_ws_index)
workspace_list[outer_loop_count] = mantid.Plus(
LHSWorkspace=workspace_list[outer_loop_count],
RHSWorkspace=calibrated_spectra[input_ws_index],
OutputWorkspace=inner_workspace_names)
# Finally scale the output workspaces
mod_first_number = str((outer_loop_count * 3) + 1) # Generates 1/4/7
mod_last_number = str((outer_loop_count + 1) * 3) # Generates 3/6/9
workspace_names = output_file_names[
"output_name"] + "_mod" + mod_first_number + '-' + mod_last_number
output_list.append(
mantid.Scale(InputWorkspace=workspace_list[outer_loop_count],
OutputWorkspace=workspace_names,
Factor=0.333333333333))
for ws in workspace_list:
remove_intermediate_workspace(ws)
return output_list
def create_indexed_peaksworkspace(self, fractional_peaks, qs, hklm):
"""Create a PeaksWorkspace that contains indexed peak data.
:param fractional_peaks: the peaks workspace containing peaks with
fractional HKL values.
:param qs: The set of modulation vectors determined
:param hklm: the new higher dimensional miller indices to add.
:returns: a peaks workspace with the indexed peak data
"""
# pad to 6 columns so we can assume a (hkl) (mnp) layout
hklm = np.pad(hklm,
pad_width=(0, 6 - hklm.shape[1]),
mode='constant',
constant_values=0)
indexed = api.CloneWorkspace(fractional_peaks, StoreInADS=False)
# save modulation vectors. ensure qs has 3 rows
qs = np.pad(qs,
pad_width=((0, 3 - qs.shape[0]), (0, 0)),
mode='constant',
constant_values=0)
lattice = fractional_peaks.sample().getOrientedLattice()
lattice.setModVec1(V3D(*qs[0]))
lattice.setModVec2(V3D(*qs[1]))
lattice.setModVec3(V3D(*qs[2]))
# save indices
for row, peak in enumerate(indexed):
row_indices = hklm[row]
peak.setHKL(*row_indices[:3])
peak.setIntMNP(V3D(*row_indices[3:]))
return indexed
def test_convert_array_run_log_to_attrs(self):
# Given a Mantid workspace with a run log
import mantid.simpleapi as mantid
target = mantid.CloneWorkspace(self.base_event_ws)
log_name = "SampleTemp"
self.assertTrue(
target.run().hasProperty(log_name),
f"Expected input workspace to have a {log_name} run log")
# When the workspace is converted to a scipp data array
d = scn.mantid.convert_EventWorkspace_to_data_array(target, False)
# Then the data array contains the run log as an unaligned coord
self.assertTrue(
np.allclose(target.run()[log_name].value,
d.attrs[log_name].values.data.values),
"Expected values in the unaligned coord to match "
"the original run log from the Mantid workspace")
self.assertEqual(d.attrs[log_name].values.unit, sc.units.K)
self.assertTrue(
np.array_equal(
target.run()[log_name].times.astype('datetime64[ns]'),
d.attrs[log_name].values.coords["time"].values),
"Expected times in the unaligned coord to match "
"the original run log from the Mantid workspace")
def test_alg_produces_correct_workspace_in_APS_from_python(self):
dataX = numpy.linspace(start=1, stop=3, num=11)
dataY = numpy.linspace(start=1, stop=3, num=10)
workspace1_test = simpleapi.CreateWorkspace(DataX=dataX,
dataY=dataY,
NSpec=1)
workspace2_test = simpleapi.CloneWorkspace(workspace1_test)
ws1_name = "workspace1_test"
ws2_name = "workspace2_test"
self.assertTrue(ws1_name in mtd)
self.assertTrue(ws2_name in mtd)
outputWs_name = "message"
if outputWs_name in mtd:
simpleapi.DeleteWorkspace(outputWs_name)
result, message = simpleapi.CompareWorkspaces(workspace1_test,
workspace2_test)
self.assertTrue(outputWs_name in mtd)
simpleapi.DeleteWorkspace(message)
simpleapi.DeleteWorkspace(ws1_name)
simpleapi.DeleteWorkspace(ws2_name)
def test_function_call_returns_tuple_when_a_single_argument_is_provided(
self):
dataX = numpy.linspace(start=1, stop=3, num=11)
dataY = numpy.linspace(start=1, stop=3, num=10)
workspace1_test = simpleapi.CreateWorkspace(DataX=dataX,
dataY=dataY,
NSpec=1)
workspace2_test = simpleapi.CloneWorkspace(workspace1_test)
ws1_name = "workspace1_test"
ws2_name = "workspace2_test"
self.assertTrue(ws1_name in mtd)
self.assertTrue(ws2_name in mtd)
outputWs_name = "message"
if outputWs_name in mtd:
simpleapi.DeleteWorkspace(outputWs_name)
result = simpleapi.CompareWorkspaces(workspace1_test, workspace2_test)
self.assertTrue(isinstance(result, tuple))
simpleapi.DeleteWorkspace(ws1_name)
simpleapi.DeleteWorkspace(ws2_name)
def makePhaseQuadTable(self, inputs):
"""
generates a phase table from CalMuonDetectorPhases
"""
mantid.CloneWorkspace(InputWorkspace=inputs["InputWorkspace"],
OutputWorkspace="__tmp__")
mantid.MaskDetectors(Workspace="__tmp__",
DetectorList=inputs['MaskedDetectors'],
StoreInADS=False)
self.alg = mantid.AlgorithmManager.create("CalMuonDetectorPhases")
self.alg.initialize()
self.alg.setRethrows(True)
self.alg.setProperty("FirstGoodData", inputs["FirstGoodData"])
self.alg.setProperty("LastGoodData", inputs["LastGoodData"])
self.alg.setProperty("InputWorkspace", "__tmp__")
self.alg.setProperty("DetectorTable", "PhaseTable")
self.alg.setProperty("DataFitted", "fits")
self.alg.execute()
mantid.DeleteWorkspace("__tmp__")
mantid.DeleteWorkspace("fits")
self.alg = None
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 convertToElasticQ(input_ws, output_ws=None):
"""
Helper function to convert the spectrum axis of a sample to ElasticQ.
@param input_ws - the name of the workspace to convert from
@param output_ws - the name to call the converted workspace
"""
if output_ws is None:
output_ws = input_ws
axis = s_api.mtd[input_ws].getAxis(1)
if axis.isSpectra():
e_fixed = getEfixed(input_ws)
s_api.ConvertSpectrumAxis(input_ws, Target='ElasticQ', EMode='Indirect', EFixed=e_fixed,
OutputWorkspace=output_ws)
elif axis.isNumeric():
# Check that units are Momentum Transfer
if axis.getUnit().unitID() != 'MomentumTransfer':
raise RuntimeError('Input must have axis values of Q')
s_api.CloneWorkspace(input_ws, OutputWorkspace=output_ws)
else:
raise RuntimeError('Input workspace must have either spectra or numeric axis.')
def test_subtract_summed_runs(self):
# Load a vanadium workspace for this test
sample_empty_number = "100"
ws_file_name = "POL" + sample_empty_number
original_ws = mantid.Load(ws_file_name)
mantid.AddSampleLog(Workspace=original_ws,
LogName='gd_prtn_chrg',
LogText="10.0",
LogType='Number')
no_scale_ws = mantid.CloneWorkspace(
InputWorkspace=original_ws,
OutputWorkspace="test_subtract_sample_empty_ws")
# Subtracting from self should equal 0
returned_ws = common.subtract_summed_runs(
ws_to_correct=no_scale_ws,
instrument=ISISPowderMockInst(),
empty_sample_ws_string=sample_empty_number)
y_values = returned_ws.readY(0)
for i in range(returned_ws.blocksize()):
self.assertAlmostEqual(y_values[i], 0)
# Check what happens when we specify scale as a half
scaled_ws = common.subtract_summed_runs(
ws_to_correct=original_ws,
instrument=ISISPowderMockInst(),
scale_factor=0.75,
empty_sample_ws_string=sample_empty_number)
scaled_y_values = scaled_ws.readY(0)
self.assertAlmostEqual(scaled_y_values[2], 0.20257424)
self.assertAlmostEqual(scaled_y_values[4], 0.31700152)
self.assertAlmostEqual(scaled_y_values[7], 0.35193970)
mantid.DeleteWorkspace(returned_ws)
mantid.DeleteWorkspace(scaled_ws)
def _focus_mode_groups(cycle_information, output_file_paths, save_range,
calibrated_spectra):
output_list = []
to_save = _sum_groups_of_three_ws(calibrated_spectra, output_file_paths)
workspaces_4_to_9_name = output_file_paths["output_name"] + "_mods4-9"
workspaces_4_to_9 = mantid.Plus(LHSWorkspace=to_save[1],
RHSWorkspace=to_save[2])
workspaces_4_to_9 = mantid.Scale(InputWorkspace=workspaces_4_to_9,
Factor=0.5,
OutputWorkspace=workspaces_4_to_9_name)
to_save.append(workspaces_4_to_9)
append = False
index = 1
for ws in to_save:
if cycle_information["instrument_version"] == "new":
mantid.SaveGSS(InputWorkspace=ws,
Filename=output_file_paths["gss_filename"],
Append=append,
Bank=index)
elif cycle_information["instrument_version"] == "new2":
mantid.SaveGSS(InputWorkspace=ws,
Filename=output_file_paths["gss_filename"],
Append=False,
Bank=index)
workspace_names = ws.name()
dspacing_ws = mantid.ConvertUnits(InputWorkspace=ws,
OutputWorkspace=workspace_names,
Target="dSpacing")
remove_intermediate_workspace(ws)
output_list.append(dspacing_ws)
mantid.SaveNexus(Filename=output_file_paths["nxs_filename"],
InputWorkspace=dspacing_ws,
Append=append)
append = True
index += 1
for i in range(0, save_range):
monitor_ws_name = output_file_paths["output_name"] + "_mod" + str(i +
10)
monitor_ws = calibrated_spectra[i + 9]
to_save = mantid.CloneWorkspace(InputWorkspace=monitor_ws,
OutputWorkspace=monitor_ws_name)
mantid.SaveGSS(InputWorkspace=to_save,
Filename=output_file_paths["gss_filename"],
Append=True,
Bank=i + 5)
to_save = mantid.ConvertUnits(InputWorkspace=to_save,
OutputWorkspace=monitor_ws_name,
Target="dSpacing")
mantid.SaveNexus(Filename=output_file_paths["nxs_filename"],
InputWorkspace=to_save,
Append=True)
output_list.append(to_save)
return output_list
def block_fit_ncp(par,first_spectrum,last_spectrum, masses,ws_name,fit_arguments, verbose,IPFile, g_log):
g_log.notice( "\n"+ "Fitting Workspace: "+ str(ws_name))
g_log.debug("Fitting parameters are given as: [Intensity Width Centre] for each NCP")
widths=np.zeros((len(masses),last_spectrum-first_spectrum+1))
intensities=np.zeros((len(masses),last_spectrum-first_spectrum+1))
centres=np.zeros((len(masses),last_spectrum-first_spectrum+1))
spectra=np.zeros((last_spectrum-first_spectrum+1))
tof_fit_ws = sapi.CloneWorkspace(InputWorkspace=str(ws_name),OutputWorkspace=str(ws_name)+"_fit")
j=0
for j, spectrum in enumerate(range(first_spectrum,last_spectrum+1)):
data_x, data_y,data_e = load_workspace(ws_name , spectrum)
ncp, fitted_par, result = fit_ncp(par, spectrum, masses, data_x, data_y, data_e, fit_arguments, IPFile, g_log)
for bin in range(len(data_x)-1):
tof_fit_ws.dataY(j)[bin] = ncp[bin]*(data_x[bin+1]-data_x[bin])
tof_fit_ws.dataE(j)[bin] = 0.
# Calculate the reduced chi2 from the fitting Cost function:
reduced_chi2 = result.fun/(len(data_x) - len(par))
if (reduced_chi2 > 1.e-3):
g_log.debug( spectrum, fitted_par, "%.4g" % reduced_chi2)
else:
g_log.debug( spectrum, " ... skipping ...")
npars = len(par)/len(masses)
for m in range(len(masses)):
if (reduced_chi2>1.e-3):
index = int(npars*m)
intensities[m][j]=float(fitted_par[index])
widths[m][j]=float(fitted_par[index+1])
centres[m][j]=float(fitted_par[index+2])
else:
widths[m][j]=None
intensities[m][j]=None
centres[m][j]=None
spectra[j]=spectrum
return spectra, widths, intensities, centres
def _attenuate_workspace(output_file_paths, attenuated_ws,
attenuation_filepath):
# Clone a workspace which is not attenuated
no_att = output_file_paths["output_name"] + "_noatten"
mantid.CloneWorkspace(InputWorkspace=attenuated_ws, OutputWorkspace=no_att)
return pearl_algs.attenuate_workspace(
attenuation_file_path=attenuation_filepath,
ws_to_correct=attenuated_ws)
def merge_reflectivity(reduction_list, xs, q_min=0.001, q_step=-0.01):
"""
Combine the workspaces for a given cross-section into a single workspace.
TODO: trim workspaces
trim_first = [item.cross_sections[pol_state].configuration.cut_first_n_points for item in self.data_manager.reduction_list]
trim_last = [item.cross_sections[pol_state].configuration.cut_last_n_points for item in self.data_manager.reduction_list]
"""
ws_list = []
scaling_factors = []
q_max = q_min
for i in range(len(reduction_list)):
# If we couldn't calculate the reflectivity, we won't have a workspace available
if reduction_list[i].cross_sections[xs].reflectivity_workspace is None:
continue
_, _q_max = reduction_list[i].get_q_range()
q_max = max(q_max, _q_max)
ws_name = str(
reduction_list[i].cross_sections[xs].reflectivity_workspace)
# Stitch1DMany only scales workspaces relative to the first one
if i == 0:
api.Scale(InputWorkspace=ws_name,
OutputWorkspace=ws_name + '_histo',
factor=reduction_list[i].cross_sections[xs].
configuration.scaling_factor,
Operation='Multiply')
api.ConvertToHistogram(InputWorkspace=ws_name + '_histo',
OutputWorkspace=ws_name + '_histo')
else:
scaling_factors.append(reduction_list[i].cross_sections[xs].
configuration.scaling_factor)
api.ConvertToHistogram(InputWorkspace=ws_name,
OutputWorkspace=ws_name + '_histo')
ws_list.append(ws_name + '_histo')
params = "%s, %s, %s" % (q_min, q_step, q_max)
if len(ws_list) > 1:
merged_ws, _ = api.Stitch1DMany(InputWorkspaces=ws_list,
Params=params,
UseManualScaleFactors=True,
ManualScaleFactors=scaling_factors,
OutputWorkspace=ws_name + "_merged")
elif len(ws_list) == 1:
merged_ws = api.CloneWorkspace(ws_list[0],
OutputWorkspace=ws_name + "_merged")
else:
return None
# Remove temporary workspaces
for ws in ws_list:
api.DeleteWorkspace(ws)
api.SaveAscii(InputWorkspace=merged_ws, Filename="/tmp/test.txt")
return merged_ws
def _clone(self, workspace):
"""
Clones the specified workspace.
:param workspace: The workspace to clone.
:return: A clone of the specified workspace.
"""
return s_api.CloneWorkspace(InputWorkspace=workspace,
OutputWorkspace="cloned",
StoreInADS=False)
def focus_texture_mode(run_number, van_curves, van_int, full_inst_calib, focus_directory, focus_general, do_pre_process, params,
time_period, dg_file):
"""
perform a texture mode focusing using the grouping csv file
@param run_number :: the run number to focus
@param van_curves :: the path to the vanadium curves file
@param van_int :: the path to the integrated vanadium file
@param full_inst_calib :: workspace containing the full instrument calibration
@param focus_directory :: the user specific focus directory to save to
@param focus_general :: the general focus directory to save to
@param do_pre_process :: whether or not to pre-process the run before focussing it
@param params :: the rebin parameters for pre-processing
@param time_period :: the time period for pre-processing
@param dg_file :: the grouping file to use for texture mode
"""
van_curves_ws, van_integrated_ws, ws_to_focus = _prepare_focus(run_number, van_curves, van_int, do_pre_process,
params, time_period)
banks = {}
# read the csv file to work out the banks
# ensure csv reading works on python 2 or 3
with open(dg_file, 'r', newline='', encoding='utf-8') as grouping_file:
group_reader = csv.reader(_decomment_csv(grouping_file), delimiter=',')
for row in group_reader:
banks.update({row[0]: ','.join(row[1:])})
# loop through the banks described in the csv, focusing and saving them out
for bank in banks:
sample_ws_clone = simple.CloneWorkspace(ws_to_focus)
curves_ws_clone = simple.CloneWorkspace(van_curves_ws)
tof_output_name = "engg_focusing_output_ws_texture_bank_{}"
tof_output_name = tof_output_name.format(bank)
dspacing_output_name = tof_output_name + "_dSpacing"
grp_ws = Utils.create_grouping_workspace_from_spectra_list(banks[bank], ws_to_focus)
df_kwarg = {"GroupingWorkspace": grp_ws}
_run_focus(input_workspace=sample_ws_clone, tof_output_name=tof_output_name, region_calib=full_inst_calib,
vanadium_curves_ws=curves_ws_clone, full_calib=full_inst_calib, df_kwarg=df_kwarg,
vanadium_integration_ws=van_integrated_ws)
_save_out(run_number, focus_directory, focus_general, tof_output_name, "ENGINX_{}_texture_{}{{}}", bank)
_save_out(run_number, focus_directory, focus_general, dspacing_output_name, "ENGINX_{}_texture_{}{{}}", bank)
simple.DeleteWorkspace(sample_ws_clone)
simple.DeleteWorkspace(curves_ws_clone)
def subtract_other_masses(ws_name, widths, intensities, centres, spectra, masses, IPFile,g_log):
hydrogen_ws = sapi.CloneWorkspace(InputWorkspace=ws_name)
for index in range(len(spectra)): # for each spectrum
data_x, data_y, data_e = load_workspace(ws_name , spectra[index]) # get the experimental data after the last correction
for m in range(len(masses)-1): # for all the masses but the first (generally H)
other_par = (intensities[m+1, index], widths[m+1, index], centres[m+1,index]) # define the input parameters to get the NCPs
ncp = calculate_ncp(other_par, spectra[index], [masses[m+1]], data_x, IPFile, g_log)
for bin in range(len(data_x)-1):
hydrogen_ws.dataY(index)[bin] -= ncp[bin]*(data_x[bin+1]-data_x[bin])
return hydrogen_ws
def test_set_sample(self):
import mantid.simpleapi as mantid
target = mantid.CloneWorkspace(self.base_event_ws)
d = scn.mantid.convert_EventWorkspace_to_data_array(target, False)
d.attrs["sample"].value.setThickness(3)
# before
self.assertNotEqual(3, target.sample().getThickness())
target.setSample(d.attrs["sample"].value)
# after
self.assertEqual(3, target.sample().getThickness())
