def get_sub_splitters(self, split_start_index, split_stop_index, run_start_ns):
"""
chop splitters workspace to sub one
:param split_start_index:
:param split_stop_index:
:param run_start_ns: run start (epoch time) in nanoseconds
:return:
"""
# get splitting workspace
split_ws_name, info_ws_name = self._reductionSetup.get_splitters(throw_not_set=True)
split_ws = AnalysisDataService.retrieve(split_ws_name)
sub_split_ws_name = split_ws.name() + '_{0}'.format(split_start_index)
# split
if isinstance(split_ws, SplittersWorkspace):
# splitters workspace
mantidsimple.CreateEmptyTableWorkspace(OutputWorkspace=sub_split_ws_name)
sub_split_ws = AnalysisDataService.retrieve(sub_split_ws_name)
sub_split_ws.addColumn('float', 'start')
sub_split_ws.addColumn('float', 'stop')
sub_split_ws.addColumn('str', 'index')
num_rows = split_ws.rowCount()
for i_row in range(split_start_index, min(split_stop_index, num_rows)):
start_time = (split_ws.cell(i_row, 0) - run_start_ns) * 1.E-9
stop_time = (split_ws.cell(i_row, 1) - run_start_ns) * 1.E-9
target = str(split_ws.cell(i_row, 2))
sub_split_ws.addRow([start_time, stop_time, target])
# END-FOR
elif isinstance(split_ws, MatrixWorkspace):
# Matrix workspace
vec_x = split_ws.readX(0)[split_start_index:split_stop_index+1]
vec_y = split_ws.readY(0)[split_start_index:split_stop_index]
vec_e = split_ws.readE(0)[split_start_index:split_stop_index]
mantidsimple.CreateWorkspace(DataX=vec_x, DataY=vec_y, DataE=vec_e, NSpec=1,
OutputWorkspace=sub_split_ws_name)
elif isinstance(split_ws, ITableWorkspace):
# Table workspace
mantidsimple.CreateEmptyTableWorkspace(OutputWorkspace=sub_split_ws_name)
sub_split_ws = AnalysisDataService.retrieve(sub_split_ws_name)
sub_split_ws.addColumn('float', 'start')
sub_split_ws.addColumn('float', 'stop')
sub_split_ws.addColumn('str', 'index')
num_rows = split_ws.rowCount()
for i_row in range(split_start_index, min(split_stop_index, num_rows)):
start_time = split_ws.cell(i_row, 0)
stop_time = split_ws.cell(i_row, 1)
target = split_ws.cell(i_row, 2)
sub_split_ws.addRow([start_time, stop_time, target])
else:
# unsupported format
raise RuntimeError('Splitting workspace of type {0} is not supported.'.format(split_ws))
return sub_split_ws_name
def _build_output_table(self, parm_dict, tbl_prop_name):
tbl_name = self.getPropertyValue(tbl_prop_name)
if not tbl_name:
return
par_names = ['Center', 'Intensity', 'Alpha', 'Beta', 'Sigma', 'Gamma']
par_prefixes = ['pos', 'int', 'alp', 'bet', 'sig', 'gam']
table = msapi.CreateEmptyTableWorkspace(OutputWorkspace=tbl_name)
num_peaks = 0
while par_prefixes[0] + str(num_peaks) in parm_dict:
num_peaks += 1
for name in par_names:
table.addColumn('double', name)
for idx in range(0, num_peaks):
par_values = [
parm_dict[par_prefix + str(idx)] for par_prefix in par_prefixes
]
print("par_values: ", par_values)
table.addRow(par_values)
for parm in parm_dict:
self.log().debug("Parameters for output table: {0}".format(parm))
def test_issues_with_properties(self):
"""
Tests proper error handling when passing wrong properties or not passing required
ones.
"""
# No InputWorkspace property (required)
self.assertRaises(RuntimeError,
sapi.EnggCalibrate,
File='foo', Bank='1')
# Wrong (mispelled) InputWorkspace property
self.assertRaises(RuntimeError,
sapi.EnggCalibrate,
InputWorkpace='anything_goes', Bank='2')
# mispelled ExpectedPeaks
tbl = sapi.CreateEmptyTableWorkspace(OutputWorkspace='test_table')
self.assertRaises(RuntimeError,
sapi.EnggCalibrate,
Inputworkspace=self.__class__._data_ws, DetectorPositions=tbl, Bank='2', Peaks='2')
# mispelled DetectorPositions
self.assertRaises(RuntimeError,
sapi.EnggCalibrate,
InputWorkspace=self.__class__._data_ws, Detectors=tbl, Bank='2', Peaks='2')
# There's no output workspace
self.assertRaises(RuntimeError,
sapi.EnggCalibrate,
InputWorkspace=self.__class__._data_ws, Bank='1')
def PyExec(self):
in_ws = mtd[self.getPropertyValue('InputWorkspace')]
out_ws_name = self.getPropertyValue('OutputWorkspace')
out_ws = ms.CreateEmptyTableWorkspace(OutputWOrkspace=out_ws_name)
out_ws.addColumn('str', 'statistic')
stats = {
'standard_deviation': dict(),
'maximum': dict(),
'minimum': dict(),
'mean': dict(),
'median': dict(),
}
for name in in_ws.getColumnNames():
try:
col_stats = _stats_to_dict(
Stats.getStatistics(
np.array([float(v) for v in in_ws.column(name)])))
for statname in stats.keys():
stats[statname][name] = col_stats[statname]
out_ws.addColumn('float', name)
except ValueError:
logger.notice('Column \'%s\' is not numerical, skipping' %
name)
for name, stat in stats.items():
stat1 = dict(stat)
stat1['statistic'] = name
out_ws.addRow(stat1)
self.setProperty('OutputWorkspace', out_ws_name)
def _create_ion_table(self, unit_cell, ions):
"""
Creates an ion table from the ions and unit cell in the file_data object
populated when the phonon/castep file is parsed.
@param unit_cell :: The unit cell read from the castep/phonon file
@param ions :: The ion data obtained from the castep/phonon file
"""
ion_table = s_api.CreateEmptyTableWorkspace(
OutputWorkspace=self._out_ws_name)
ion_table.addColumn('str', 'Species')
ion_table.addColumn('int', 'FileIndex')
ion_table.addColumn('int', 'Number')
ion_table.addColumn('float', 'FractionalX')
ion_table.addColumn('float', 'FractionalY')
ion_table.addColumn('float', 'FractionalZ')
ion_table.addColumn('float', 'CartesianX')
ion_table.addColumn('float', 'CartesianY')
ion_table.addColumn('float', 'CartesianZ')
ion_table.addColumn('float', 'Isotope')
self._convert_to_cartesian_coordinates(unit_cell, ions)
for ion in ions:
ion_table.addRow([
ion['species'], ion['index'], ion['bond_number'],
ion['fract_coord'][0], ion['fract_coord'][1],
ion['fract_coord'][2], ion['cartesian_coord'][0],
ion['cartesian_coord'][1], ion['cartesian_coord'][2],
ion['isotope_number']
])
def generateSplitterWorkspace(self, fragment):
r"""
Create a table workspace with time intervals to keep
Parameters
----------
fragment: str
a-b start and end of time fragment to filter out
"""
inf = 172800 # a run two full days long
a, b = fragment.split('-')
b = inf if 'end' in b else float(b)
a = float(a)
splitter = sapi.CreateEmptyTableWorkspace(
OutputWorkspace=tws('splitter'))
splitter.addColumn('double', 'start')
splitter.addColumn('double', 'stop')
splitter.addColumn('str', 'target')
if a == 0.0:
splitter.addRow([b, inf, '0'])
elif b == inf:
splitter.addRow([0, a, '0'])
else:
splitter.addRow([0, a, '0'])
splitter.addRow([b, inf, '0'])
self._temps.extend('splitted_unfiltered', 'TOFCorrectWS')
def _calcIntegrationSpectra(self, vanWS):
"""
This does the real calculations behind _applySensitivityCorrection(), essentially a call to
the 'Integration' algorithm, for when we are given raw data from a Vanadium run.
@param vanWS :: workspace with data from a Vanadium run
@returns Integration workspace with Vanadium spectra integration values, as a table workspace
with one row per spectrum
"""
expectedDim = 'Time-of-flight'
dimType = vanWS.getXDimension().getName()
if expectedDim != dimType:
raise ValueError(
"This algorithm expects a workspace with %s X dimension, but "
"the X dimension of the input workspace is: '%s'" %
(expectedDim, dimType))
integWS = self._integrateSpectra(vanWS)
if 1 != integWS.blocksize() or integWS.getNumberHistograms(
) < vanWS.getNumberHistograms():
raise RuntimeError(
"Error while integrating vanadium workspace, the Integration algorithm "
"produced a workspace with %d bins and %d spectra. The workspace "
"being integrated has %d spectra." %
(integWS.blocksize(), integWS.getNumberHistograms(),
vanWS.getNumberHistograms()))
integTbl = sapi.CreateEmptyTableWorkspace(
OutputWorkspace='__vanIntegTbl')
integTbl.addColumn('double', 'Spectra Integration')
for i in range(integWS.getNumberHistograms()):
integTbl.addRow([integWS.readY(i)[0]])
return integTbl
def create_params_table(difc, tzero, difa):
"""
create the params table from the output from the calibration
@param difc :: the list of difc values to add to the table
@param tzero :: the list of tzero values to add to the table
@param difa :: the list of difa values to add to the table
"""
param_table = simple.CreateEmptyTableWorkspace(
OutputWorkspace="engg_calibration_banks_parameters")
# setup table
param_table.addColumn("int", "bankid")
param_table.addColumn("double", "difc")
param_table.addColumn("double", "difa")
param_table.addColumn("double", "tzero")
# add data to table
for i in range(len(difc)):
next_row = {
"bankid": i,
"difc": difc[i],
"difa": difa[i],
"tzero": tzero[i]
}
param_table.addRow(next_row)
def _create_fit_results_table(self, rows):
table = mantid.CreateEmptyTableWorkspace(OutputWorkspace=self.FIT_RESULTS_TABLE_NAME)
for col in self.FIT_PARAMS:
table.addColumn("double", col)
for row in rows:
table.addRow(row)
return table
def _build_output_lattice_table(self, lattice_params):
table_name = self.getPropertyValue(self.PROP_OUT_LATTICE_PARAMS)
table = mantid.CreateEmptyTableWorkspace(OutputWorkspace=table_name, StoreInADS=False)
for param in self.LATTICE_TABLE_PARAMS:
table.addColumn("double", param.split("_")[-1])
table.addRow([float(lattice_params[param]) for param in self.LATTICE_TABLE_PARAMS])
return table
def _create_lattice_params_table(self,
output_name=LATTICE_PARAMS_TABLE_NAME):
lattice_params = mantid.CreateEmptyTableWorkspace(
OutputWorkspace=output_name)
[
lattice_params.addColumn("double", param)
for param in self.LATTICE_PARAMS
]
lattice_params.addRow([random.random() for _ in self.LATTICE_PARAMS])
return lattice_params
def _createOutputWorkspaces(self):
"""
"""
self._myPixelInfoTableWS = api.CreateEmptyTableWorkspace(
OutputWorkspace=self.getPropertyValue('OutputWorkspace'))
self._myPixelInfoTableWS.addColumn("int", "DetectorID")
self._myPixelInfoTableWS.addColumn("double", "X")
self._myPixelInfoTableWS.addColumn("double", "Y")
self._myPixelInfoTableWS.addColumn("double", "Z")
self._myPixelInfoTableWS.addColumn("int", "OriginalDetID")
self._myScanPtFileTableWS = api.CreateEmptyTableWorkspace(
OutputWorkspace=self.getPropertyValue('DetectorTableWorkspace'))
self._myScanPtFileTableWS.addColumn("int", "Scan")
self._myScanPtFileTableWS.addColumn("int", "Pt")
self._myScanPtFileTableWS.addColumn("str", "Filename")
self._myScanPtFileTableWS.addColumn("int", "StartDetID")
return
def apply_vanadium_corrections(parent,
ws,
indices,
vanadium_ws,
van_integration_ws,
van_curves_ws,
progress_range=None):
"""
DEPRECATED: not used in UI, only in deprecated functions (EnggCalibrateFull, EnggVanadiumCorrections and EnggFocus)
Apply the EnggVanadiumCorrections algorithm on the workspace given, by using the algorithm
EnggVanadiumCorrections
@param parent :: parent (Mantid) algorithm that wants to run this
@param ws :: workspace to correct (modified in place)
@param indices :: workspace indices that are being processed (those not included will be ignored)
@param vanadium_ws :: workspace with data from a Vanadium run
@param van_integration_ws :: alternatively to vanWS, pre-calculated integration from Vanadium data
@param van_curves_ws :: alternatively to vanWS, pre-calculated bank curves from Vanadium data
@param progress_range :: pair for (startProgress, endProgress) with respect to the parent algorithm
"""
if vanadium_ws and vanadium_ws.getNumberHistograms() < len(indices):
raise ValueError(
"Inconsistency in inputs: the Vanadium workspace has less spectra (%d) than "
"the number of workspace indices to process (%d)" %
(vanadium_ws.getNumberHistograms(), len(indices)))
elif van_integration_ws and van_curves_ws:
# filter only indices from vanIntegWS (crop the table)
tbl = mantid.CreateEmptyTableWorkspace(
OutputWorkspace="__vanadium_integration_ws")
tbl.addColumn('double', 'Spectra Integration')
for i in indices:
tbl.addRow([van_integration_ws.cell(i, 0)])
van_integration_ws = tbl
# These corrections rely on ToF<->Dspacing conversions, so they're done after the calibration step
progress_params = dict()
if progress_range:
progress_params["startProgress"] = progress_range[0]
progress_params["endProgress"] = progress_range[1]
alg = parent.createChildAlgorithm('EnggVanadiumCorrections',
**progress_params)
if ws:
alg.setProperty('Workspace', ws)
if vanadium_ws:
alg.setProperty('VanadiumWorkspace', vanadium_ws)
if van_integration_ws:
alg.setProperty('IntegrationWorkspace', van_integration_ws)
if van_curves_ws:
alg.setProperty('CurvesWorkspace', van_curves_ws)
alg.execute()
def generateOutputParTable(name, difc, zero):
"""
Produces a table workspace with the two fitted calibration parameters
@param name :: the name to use for the table workspace that is created here
@param difc :: difc calibration parameter
@param zero :: zero calibration parameter
"""
tbl = sapi.CreateEmptyTableWorkspace(OutputWorkspace=name)
tbl.addColumn('double', 'difc')
tbl.addColumn('double', 'zero')
tbl.addRow([float(difc), float(zero)])
def _generateTableWS(self, vancorrdict):
""" Create table workspace
"""
tablews = api.CreateEmptyTableWorkspace(
OutputWorkspace="tempcorrtable")
tablews.addColumn('int', 'DetID')
tablews.addColumn('double', 'Correction')
for detid in sorted(vancorrdict.keys()):
tablews.addRow([detid, vancorrdict[detid]])
return tablews
def create_indexed_workspace(self, fractional_peaks, ndim, hklm):
"""Create a TableWorkepace that contains indexed peak data.
This produces a TableWorkepace that looks like a PeaksWorkspace but
with the additional index columns included. In future releases support
for indexing should be added to the PeaksWorkspace data type itself.
:param fractional_peaks: the peaks workspace containing peaks with
fractional HKL values.
:param ndim: the number of additional indexing columns to add.
:param hklm: the new higher dimensional miller indicies to add.
:returns: a table workspace with the indexed peak data
"""
# Create table with the number of columns we need
types = [
'int', 'long64', 'double', 'double', 'double', 'double', 'double',
'double', 'double', 'double', 'double', 'float', 'str', 'float',
'float', 'V3D', 'V3D'
]
name = self.getPropertyValue("OutputWorkspace")
indexed = api.CreateEmptyTableWorkspace(name)
names = fractional_peaks.getColumnNames()
# Insert the extra columns for the addtional indicies
for i in range(ndim - 3):
names.insert(5 + i, 'm{}'.format(i + 1))
types.insert(5 + i, 'double')
names = np.array(names)
types = np.array(types)
# Create columns in the table workspace
for name, column_type in zip(names, types):
indexed.addColumn(column_type, name)
# Copy all columns from original workspace, ignoring HKLs
column_data = []
idx = np.arange(0, names.size)
hkl_mask = (idx < 2) | (idx > 4 + (ndim - 3))
for name in names[hkl_mask]:
column_data.append(fractional_peaks.column(name))
# Insert the addtional HKL columns into the data
for i, col in enumerate(hklm.T.tolist()):
column_data.insert(i + 2, col)
# Insert the columns into the table workspace
for i in range(fractional_peaks.rowCount()):
row = [column_data[j][i] for j in range(indexed.columnCount())]
indexed.addRow(row)
return indexed
def _generate_props_table(self):
"""
Creates a table workspace with values calculated in algorithm.
"""
props_table = ms.CreateEmptyTableWorkspace(OutputWorkspace=self._props_output_workspace)
props_table.addColumn('int', 'NegativeXMinIndex')
props_table.addColumn('int', 'PositiveXMinIndex')
props_table.addColumn('int', 'PositiveXMaxIndex')
props_table.addRow([int(self._negative_min_index), int(self._positive_min_index),
int(self._positive_max_index)])
self.setProperty('OutputPropertiesTable', self._props_output_workspace)
def generate_output_param_table(name, difa, difc, tzero):
"""
Produces a table workspace with the two fitted calibration parameters
@param name :: the name to use for the table workspace that is created here
@param difa :: DIFA calibration parameter (GSAS parameter)
@param difc :: DIFC calibration parameter
@param tzero :: TZERO calibration parameter
"""
tbl = mantid.CreateEmptyTableWorkspace(OutputWorkspace=name)
tbl.addColumn('double', 'DIFA')
tbl.addColumn('double', 'DIFZ')
tbl.addColumn('double', 'TZERO')
tbl.addRow([float(difa), float(difc), float(tzero)])
def transposeFitParametersTable(params_table, output_table=None):
"""
Transpose the parameter table created from a multi domain Fit.
This function will make the output consistent with PlotPeakByLogValue.
@param params_table - the parameter table output from Fit.
@param output_table - name to call the transposed table. If omitted,
the output_table will be the same as the params_table
"""
params_table = s_api.mtd[params_table]
table_ws = '__tmp_table_ws'
table_ws = s_api.CreateEmptyTableWorkspace(OutputWorkspace=table_ws)
param_names = params_table.column(0)[:-1] # -1 to remove cost function
param_values = params_table.column(1)[:-1]
param_errors = params_table.column(2)[:-1]
# Find the number of parameters per function
func_index = param_names[0].split('.')[0]
num_params = 0
for i, name in enumerate(param_names):
if name.split('.')[0] != func_index:
num_params = i
break
# Create columns with parameter names for headers
column_names = [
'.'.join(name.split('.')[1:]) for name in param_names[:num_params]
]
column_error_names = [name + '_Err' for name in column_names]
column_names = list(zip(column_names, column_error_names))
table_ws.addColumn('double', 'axis-1')
for name, error_name in column_names:
table_ws.addColumn('double', name)
table_ws.addColumn('double', error_name)
# Output parameter values to table row
for i in range(0, params_table.rowCount() - 1, num_params):
row_values = param_values[i:i + num_params]
row_errors = param_errors[i:i + num_params]
row = [value for pair in zip(row_values, row_errors) for value in pair]
row = [i / num_params] + row
table_ws.addRow(row)
if output_table is None:
output_table = params_table.name()
s_api.RenameWorkspace(table_ws.name(), OutputWorkspace=output_table)
def generate_output_param_table(name, difa, difc, tzero):
"""
DEPRECATED: not used in UI, only in deprecated functions (EnggCalibrate, EnggFitTOFFromPeaks)
Produces a table workspace with the two fitted calibration parameters
@param name :: the name to use for the table workspace that is created here
@param difa :: DIFA calibration parameter (GSAS parameter)
@param difc :: DIFC calibration parameter
@param tzero :: TZERO calibration parameter
"""
tbl = mantid.CreateEmptyTableWorkspace(OutputWorkspace=name)
tbl.addColumn('double', 'DIFA')
tbl.addColumn('double', 'DIFZ')
tbl.addColumn('double', 'TZERO')
tbl.addRow([float(difa), float(difc), float(tzero)])
def write_diff_consts_to_table_from_prm(prm_filepath):
"""
read diff consntants from prm file and write in table workspace
:param prm_filepath: path to prm file
"""
diff_consts = read_diff_constants_from_prm(prm_filepath)
# make table
table = mantid.CreateEmptyTableWorkspace(
OutputWorkspace=DIFF_CONSTS_TABLE_NAME)
table.addColumn("int", "Index")
table.addColumn("double", "DIFA")
table.addColumn("double", "DIFC")
table.addColumn("double", "TZERO")
# add to row per spectrum to table
for ispec in range(len(diff_consts)):
table.addRow([ispec, *diff_consts[ispec, :]])
def _initialize_adjustments_table(self, table_name):
r"""Create a table with appropriate column names for saving the adjustments to each component"""
table = api.CreateEmptyTableWorkspace(OutputWorkspace=table_name)
item_types = [
'str', # component name
'double',
'double',
'double', # cartesian coordinates
'double',
'double',
'double', # direction cosines of axis of rotation
'double'
] # angle of rotation
for column_name, column_type in zip(self.adjustment_items, item_types):
table.addColumn(name=column_name, type=column_type)
return table
def _initialize_displacements_table(self, table_name):
r"""Create a table with appropriate column names for saving the relative displacements to each component"""
table = api.CreateEmptyTableWorkspace(OutputWorkspace=table_name)
item_types = [
'str', # component name
'double', # change in the distance between the component and the sample
'double',
'double',
'double', # relative displacement in cartesian coordinates
'double',
'double',
'double'
] # relative displacement in Euler angles
for column_name, column_type in zip(self.displacement_items,
item_types):
table.addColumn(name=column_name, type=column_type)
return table
def PyExec(self):
in_ws = mtd[self.getPropertyValue('InputWorkspace')]
indices_list = self.getPropertyValue('ColumnIndices')
out_ws_name = self.getPropertyValue('OutputWorkspace')
column_names = in_ws.getColumnNames()
# If column indices are not provided, then default to _ALL_ columns
if len(indices_list) > 0:
indices_list = [int(x) for x in indices_list.split(',')]
else:
indices_list = range(len(column_names))
out_ws = ms.CreateEmptyTableWorkspace(OutputWorkspace=out_ws_name)
out_ws.addColumn('str', 'Statistic')
stats = collections.OrderedDict([
('StandardDev', collections.OrderedDict()),
('Minimum', collections.OrderedDict()),
('Median', collections.OrderedDict()),
('Maximum', collections.OrderedDict()),
('Mean', collections.OrderedDict()),
])
for index in indices_list:
column_name = column_names[index]
try:
column_data = np.array([float(v) for v in in_ws.column(index)])
col_stats = _stats_to_dict(Stats.getStatistics(column_data))
for stat_name in stats:
stats[stat_name][column_name] = col_stats[stat_name]
out_ws.addColumn('float', column_name)
except RuntimeError:
logger.notice('Column \'%s\' is not numerical, skipping' %
column_name)
except:
logger.notice('Column \'%s\' is not numerical, skipping' %
column_name)
for index, stat_name in iteritems(stats):
stat = collections.OrderedDict(stat_name)
stat['Statistic'] = index
out_ws.addRow(stat)
self.setProperty('OutputWorkspace', out_ws)
def correctMisalignedTubes(ws, calibrationTable, peaksTable, spec, idealTube, fitPar, threshold=10):
""" Correct misaligned tubes due to poor fitting results
during the first round of calibration.
Misaligned tubes are first identified according to a tolerance
applied to the absolute difference between the fitted tube
positions and the mean across all tubes.
The FindPeaks algorithm is then used to find a better fit
with the ideal tube positions as starting parameters
for the peak centers.
From the refitted peaks the positions of the detectors in the
tube are recalculated.
@param ws: the workspace to get the tube geometry from
@param calibrationTable: the calibration table output from running calibration
@param peaksTable: the table containing the fitted peak centers from calibration
@param spec: the tube spec for the instrument
@param idealTube: the ideal tube for the instrument
@param fitPar: the fitting parameters for calibration
@param threshold: tolerance defining is a peak is outside of the acceptable range
@return table of corrected detector positions
"""
table_name = calibrationTable.name() + 'Corrected'
corrections_table = mantid.CreateEmptyTableWorkspace(OutputWorkspace=table_name)
corrections_table.addColumn('int', "Detector ID")
corrections_table.addColumn('V3D', "Detector Position")
mean_peaks, bad_tubes = findBadPeakFits(peaksTable, threshold)
for index in bad_tubes:
print("Refitting tube %s" % spec.getTubeName(index))
tube_dets, _ = spec.getTube(index)
getPoints(ws, idealTube.getFunctionalForms(), fitPar, tube_dets)
tube_ws = mantid.mtd['TubePlot']
fit_ws = mantid.FindPeaks(InputWorkspace=tube_ws, WorkspaceIndex=0,
PeakPositions=fitPar.getPeaks(), PeaksList='RefittedPeaks')
centers = [row['centre'] for row in fit_ws]
detIDList, detPosList = getCalibratedPixelPositions(ws, centers, idealTube.getArray(), tube_dets)
for id, pos in zip(detIDList, detPosList):
corrections_table.addRow({'Detector ID': id, 'Detector Position': kernel.V3D(*pos)})
return corrections_table
def _create_vulcan_binning_table(binning_table_name, binning_workspace_low_res, binning_workspace_high_res):
""" create a binning table for binning data into various resolution
:param binning_table_name:
:param binning_workspace_low_res:
:param binning_workspace_high_res:
:return:
"""
# create a TableWorkspace
api.CreateEmptyTableWorkspace(OutputWorkspace=binning_table_name)
bin_table_ws = AnalysisDataService.retrieve(binning_table_name)
bin_table_ws.addColumn('str', 'WorkspaceIndexes')
bin_table_ws.addColumn('str', 'BinningParameters')
# add a row for simple case
bin_table_ws.addRow(['0, 1', '{0}: {1}'.format(binning_workspace_low_res, 0)])
bin_table_ws.addRow(['2', '{0}: {1}'.format(binning_workspace_high_res, 0)])
return bin_table_ws
def _create_dummy_fit_parameters_no_hydrogen():
params = ms.CreateEmptyTableWorkspace(OutputWorkspace='__VesuvioCorrections_test_fit_params')
params.addColumn('str', 'Name')
params.addColumn('float', 'Value')
params.addColumn('float', 'Error')
params.addRow(['f0.Mass', 16.0, 0.0])
params.addRow(['f0.Width', 10.0, 0.0])
params.addRow(['f0.Intensity', 4.03064, 0.41762])
params.addRow(['f1.Mass', 27.0, 0.0])
params.addRow(['f1.Width', 13.0, 0.0])
params.addRow(['f1.Intensity', 3.23823, 0.447593])
params.addRow(['f2.Mass', 133.0, 0.0])
params.addRow(['f2.Width', 30.0, 0.0])
params.addRow(['f2.Intensity', 0.882613, 0.218913])
params.addRow(['Cost function value', 3.19573, 0.0])
return params
def applyVanadiumCorrections(parent, ws, indices, vanWS, vanIntegWS,
vanCurvesWS):
"""
Apply the EnggVanadiumCorrections algorithm on the workspace given, by using the algorithm
EnggVanadiumCorrections
@param parent :: parent (Mantid) algorithm that wants to run this
@param ws :: workspace to correct (modified in place)
@param indices :: workspace indices that are being processed (those not included will be ignored)
@param vanWS :: workspace with data from a Vanadium run
@param vanIntegWS :: alternatively to vanWS, pre-calculated integration from Vanadium data
@param vanIntegWS :: alternatively to vanWS, pre-calculated bank curves from Vanadium data
"""
if vanWS and vanWS.getNumberHistograms() < len(indices):
raise ValueError(
"Inconsistency in inputs: the Vanadium workspace has less spectra (%d) than "
"the number of workspace indices to process (%d)" %
(vanWS.getNumberHistograms(), len(indices)))
elif vanIntegWS and vanCurvesWS:
# filter only indices from vanIntegWS (crop the table)
tbl = sapi.CreateEmptyTableWorkspace(
OutputWorkspace="__vanadium_integration_ws")
tbl.addColumn('double', 'Spectra Integration')
for i in indices:
tbl.addRow([vanIntegWS.cell(i, 0)])
vanIntegWS = tbl
# These corrections rely on ToF<->Dspacing conversions, so they're done after the calibration step
# sapi.EnggVanadiumCorrections(Workspace=ws, VanadiumWorkspace=vanWS,
# IntegrationWorkspace=vanIntegWS,
# CurvesWorkspace=vanCurvesWS)
alg = parent.createChildAlgorithm('EnggVanadiumCorrections')
if ws:
alg.setProperty('Workspace', ws)
if vanWS:
alg.setProperty('VanadiumWorkspace', vanWS)
if vanIntegWS:
alg.setProperty('IntegrationWorkspace', vanIntegWS)
if vanCurvesWS:
alg.setProperty('CurvesWorkspace', vanCurvesWS)
alg.execute()
def _create_bond_table(self, bonds):
"""
Creates a bond table from the bond data obtained when the castep file is read
@param bonds :: The bond data read from the castep file
"""
if bonds is None or len(bonds) == 0:
raise RuntimeError('No bonds found in CASTEP file')
bond_table = s_api.CreateEmptyTableWorkspace(
OutputWorkspace=self._out_ws_name)
bond_table.addColumn('str', 'SpeciesA')
bond_table.addColumn('int', 'NumberA')
bond_table.addColumn('str', 'SpeciesB')
bond_table.addColumn('int', 'NumberB')
bond_table.addColumn('float', 'Length')
bond_table.addColumn('float', 'Population')
for bond in bonds:
bond_table.addRow([
bond['atom_a'][0], bond['atom_a'][1], bond['atom_b'][0],
bond['atom_b'][1], bond['length'], bond['population']
])
def _create_simple_binning_table(binning_table_name):
"""
create a binning table
:return:
"""
# TODO FIXME : more flexible!
"""
tof0 = 10000.
delta = 0.001
num_pts = 200
"""
# create a TableWorkspace
api.CreateEmptyTableWorkspace(OutputWorkspace=binning_table_name)
bin_table_ws = AnalysisDataService.retrieve(binning_table_name)
bin_table_ws.addColumn('str', 'WorkspaceIndexes')
bin_table_ws.addColumn('str', 'BinningParameters')
# add a row for simple case
bin_table_ws.addRow(['0', '10000, -0.002, 13000'])
return bin_table_ws
