def PyExec(self):
xvals, flat_yvals, baseline, errors = self._load_data()
# Find the index of the peaks given as input
# This is necessary as the FindPeakAlgorithm can introduce offset in the data
peakids = self._recentre_peak_position(xvals, flat_yvals)
# Fit all the peaks and find the best parameters
_, param = self.general_fit(xvals, flat_yvals, peakids)
# Create table of peak positions
peak_table = CreateEmptyTableWorkspace(StoreInADS=False)
refit_peak_table = CreateEmptyTableWorkspace(StoreInADS=False)
to_refit = self.parse_fit_table(param, peak_table, True)
# Refit all the bad peaks by adding stronger constraints
_, refitted_params = self.refit_peaks(to_refit)
self.parse_fit_table(refitted_params, refit_peak_table, False)
# Evaluate the total cost
fit_cost = self._evaluate_final_cost(xvals, flat_yvals, baseline,
errors, peak_table,
refit_peak_table)
self.setProperty('PeakProperties', peak_table)
self.setProperty('RefitPeakProperties', refit_peak_table)
self.setProperty('FitCost', fit_cost)
def PyExec(self):
self.workspace = self.getProperty("InputWorkspace").value
outws_name = self.getPropertyValue("OutputWorkspace")
# create table and columns
outws = CreateEmptyTableWorkspace(OutputWorkspace=outws_name)
columns = ["PeakCentre", "PeakCentreError", "Sigma", "SigmaError", "Height", "HeightError", "chiSq"]
nextrow = dict.fromkeys(["WorkspaceIndex"] + columns + ["FitStatus"])
outws.addColumn(type="int", name="WorkspaceIndex", plottype=1) # x
for col in columns:
outws.addColumn(type="double", name=col)
outws.addColumn(type="str", name="FitStatus")
nb_hist = self.workspace.getNumberHistograms()
for idx in range(nb_hist):
nextrow["WorkspaceIndex"] = idx
result = self.do_fit_gaussian(idx)
if not result:
for col in columns:
nextrow[col] = 0
nextrow["FitStatus"] = "failed"
else:
nextrow["FitStatus"] = result[0]
nextrow["chiSq"] = result[1]
ptable = result[3]
for num in range(ptable.rowCount() - 1):
row = ptable.row(num)
name = row["Name"]
nextrow[name] = row["Value"]
nextrow[name+"Error"] = row["Error"]
DeleteWorkspace(result.OutputParameters)
DeleteWorkspace(result.OutputNormalisedCovarianceMatrix)
outws.addRow(nextrow)
self.setProperty("OutputWorkspace", outws)
return
def live_monitor(self, input_ws, output_ws):
"""
:return:
"""
# Now get the data, read the first spectra
spectra = input_ws.readY(0)
# extract the first value from the array
count = spectra[0]
print(f'Total count: {count}')
count = input_ws.getNumberEvents()
# output it as a log message
logger.notice("Total counts so far " + str(count))
# if my ouput workspace has not been created yet, create it.
if not mtd.doesExist(output_ws):
table = CreateEmptyTableWorkspace(OutputWorkspace=output_ws)
table.setTitle("Event Rate History")
table.addColumn("str", "Time")
table.addColumn('str', 'EventsS')
table.addColumn("int", "Events")
table = mtd[output_ws]
assert table
def create_fit_tables(self):
wslist = [] # ws to be grouped
# extract fit parameters and errors
nruns = len(
self.get_loaded_ws_list()) # num of rows of output workspace
# get unique set of function parameters across all workspaces
func_params = set(
chain(*[
list(d['results'].keys()) for d in self._fit_results.values()
]))
for param in func_params:
# get max number of repeated func in a model (num columns of output workspace)
nfuncs = max([
len(d['results'][param]) for d in self._fit_results.values()
if param in d['results']
])
# make output workspace
ipeak = list(range(1, nfuncs + 1)) * nruns
ws = CreateWorkspace(OutputWorkspace=param,
DataX=ipeak,
DataY=ipeak,
NSpec=nruns)
# axis for labels in workspace
axis = TextAxis.create(nruns)
for iws, wsname in enumerate(self.get_active_ws_name_list()):
if wsname in self._fit_results and param in self._fit_results[
wsname]['results']:
fitvals = array(
self._fit_results[wsname]['results'][param])
data = vstack(
(fitvals, full((nfuncs - fitvals.shape[0], 2), nan)))
else:
data = full((nfuncs, 2), nan)
ws.setY(iws, data[:, 0])
ws.setE(iws, data[:, 1])
# label row
axis.setLabel(iws, wsname)
ws.replaceAxis(1, axis)
wslist += [ws]
# table for model summary/info
model = CreateEmptyTableWorkspace(OutputWorkspace='model')
model.addColumn(type="str", name="Workspace")
model.addColumn(type="float", name="chisq/DOF"
) # always is for LM minimiser (users can't change)
model.addColumn(type="str", name="status")
model.addColumn(type="str", name="Model")
for iws, wsname in enumerate(self.get_active_ws_name_list()):
if wsname in self._fit_results:
row = [
wsname, self._fit_results[wsname]['costFunction'],
self._fit_results[wsname]['status'],
self._fit_results[wsname]['model']
]
self.write_table_row(model, row, iws)
else:
self.write_table_row(model, ['', nan, ''], iws)
wslist += [model]
group_name = self._log_workspaces.name().split('_log')[0] + '_fits'
self._fit_workspaces = GroupWorkspaces(wslist,
OutputWorkspace=group_name)
def _get_output_peak(self, args, ideal_tube):
delete_peak_table_after = False
if self.OUTPUTPEAK in args:
output_peak = args[self.OUTPUTPEAK]
else:
output_peak = False
if isinstance(output_peak, ITableWorkspace):
if output_peak.columnCount() < len(ideal_tube.getArray()):
raise RuntimeError(
"Wrong argument {0}. "
"It expects a boolean flag, or a ITableWorksapce with columns (TubeId, Peak1,...,"
"PeakM) for M = number of peaks given in knownPositions".format(self.OUTPUTPEAK))
return output_peak, delete_peak_table_after
else:
if not output_peak:
delete_peak_table_after = True
# create the output peak table
output_peak = CreateEmptyTableWorkspace(OutputWorkspace="PeakTable")
output_peak.addColumn(type='str', name='TubeId')
for i in range(len(ideal_tube.getArray())):
output_peak.addColumn(type='float', name='Peak%d' % (i + 1))
return output_peak, delete_peak_table_after
def _get_calib_table(self, args):
if self.CALIBTABLE in args:
calib_table = args[self.CALIBTABLE]
# ensure the correct type is passed
# if a string was passed, transform it in mantid object
if isinstance(calib_table, str):
calib_table = mtd[calib_table]
# check that calibTable has the expected form
try:
if not isinstance(calib_table, ITableWorkspace):
raise 1
if calib_table.columnCount() != 2:
raise 2
colNames = calib_table.getColumnNames()
if colNames[0] != 'Detector ID' or colNames[1] != 'Detector Position':
raise 3
except:
raise RuntimeError(
"Invalid type for {0}."
"The expected type was ITableWorkspace with 2 columns(Detector ID and Detector Positions)".
format(self.CALIBTABLE))
else:
return calib_table
else:
calib_table = CreateEmptyTableWorkspace(OutputWorkspace="CalibTable")
# "Detector ID" column required by ApplyCalibration
calib_table.addColumn(type="int", name="Detector ID")
# "Detector Position" column required by ApplyCalibration
calib_table.addColumn(type="V3D", name="Detector Position")
return calib_table
def readCalibrationFile(table_name, in_path):
"""Read a calibration table from file
This loads a calibration TableWorkspace from a CSV file.
Example of usage:
.. code-block:: python
saveCalibration('CalibTable','/tmp/myCalibTable.txt')
:param table_name: name to call the TableWorkspace
:param in_path: the path to the calibration file
"""
DET = 'Detector ID'
POS = 'Detector Position'
re_float = re.compile(r"[+-]? *(?:\d+(?:\.\d*)?|\.\d+)(?:[eE][+-]?\d+)?")
calibTable = CreateEmptyTableWorkspace(OutputWorkspace=table_name)
calibTable.addColumn(type='int', name=DET)
calibTable.addColumn(type='V3D', name=POS)
with open(in_path, 'r') as file_p:
for line in file_p:
values = re.findall(re_float, line)
if len(values) != 4:
continue
nextRow = {
DET: int(values[0]),
POS: V3D(float(values[1]), float(values[2]), float(values[3]))
}
calibTable.addRow(nextRow)
def test_evaluate_cost_returns_the_expected_value_with_poisson(self):
peaks = [(35.2, 0.4), (25.03, 0.1), (10.03, 0.05)]
peaks += [(20.003, 0.004), (75.15, 0.2), (5.2, 0.05)]
params = [25.03, 35.2, 10.03, 75.15, 20.003, 5.2]
fit_table = self.simulate_fit_parameter_output(peaks, 100.034)
data_table = CreateEmptyTableWorkspace()
refit_data_table = CreateEmptyTableWorkspace()
_ = self.alg_instance.parse_fit_table(fit_table,
data_table,
refit=False)
cost = self.alg_instance.evaluate_cost(
self.x_values,
self.data_ws.readY(0),
self.data_ws.readY(1),
self.data_ws.readE(0),
peak_param=data_table,
refit_peak_param=refit_data_table,
use_poisson=True)
model = self.alg_instance.multi_peak(params, self.x_values,
np.zeros(len(self.x_values)))
expected = self.alg_instance.poisson_cost(
self.data_ws.readY(0) + self.data_ws.readY(1),
model + self.data_ws.readY(1))
self.assertAlmostEqual(cost, expected, 3)
def setUp(self):
# create sample workspace
self.xmin = 2123.33867005 + 4005.75
self.xmax = 2123.33867005 + 7995.75
self._input_ws = CreateSampleWorkspace(
Function="User Defined",
UserDefinedFunction="name=LinearBackground, \
A0=0.3;name=Gaussian, PeakCentre=8190, Height=5, Sigma=75",
NumBanks=2,
BankPixelWidth=1,
XMin=self.xmin,
XMax=self.xmax,
BinWidth=10.5,
BankDistanceFromSample=4.0,
SourceDistanceFromSample=1.4,
OutputWorkspace="ws")
lognames = "wavelength,TOF1"
logvalues = "6.0,2123.33867005"
AddSampleLogMultiple(self._input_ws, lognames, logvalues)
# create EPP table
self._table = CreateEmptyTableWorkspace(OutputWorkspace="epptable")
self._table.addColumn(type="double", name="PeakCentre")
table_row = {'PeakCentre': 8189.5}
for i in range(2):
self._table.addRow(table_row)
def mask_bank(bank_name: str, tubes_fit_success: np.ndarray, output_table: str) -> Optional[TableWorkspace]:
r"""
Creates a single-column `TableWorkspace` object containing the detector ID's of the
unsuccessfully fitted tubes
If all tubes were fit successfully, no `TableWorkspace` is created, and `None` is returned.
:param bank_name: a string of the form 'bankI' where 'I' is a bank number
:param tubes_fit_success: array of boolean containing a True/False entry for each tube, indicating wether
the tube was successfully calibrated.
:param output_table: name of the output TableWorkspace containing one column for detector ID from tubes
not successfully calibrated.
:raise AssertionError: the string bank_name does not follow the pattern 'bankI' where 'I' in an integer
:return: name of the mask TableWorkspace. Returns `None` if no TableWorkspace is created.
"""
assert re.match(r'^bank\d+$', bank_name), 'The bank name must be of the form "bankI" where "I" in an integer'
if False not in tubes_fit_success:
return None # al tubes were fit successfully
bank_number = bank_name[4:] # drop 'bank' from bank_name
tube_numbers = 1 + np.where(tubes_fit_success == False)[0] # noqa E712 unsuccessfully fitted tube numbers
tube_numbers = ','.join([str(n) for n in tube_numbers]) # failing tubes as a string
detector_ids = MaskBTP(Instrument='CORELLI', Bank=bank_number, Tube=tube_numbers)
table = CreateEmptyTableWorkspace(OutputWorkspace=output_table)
table.addColumn('long64', 'Detector ID')
[table.addRow([detector_id]) for detector_id in detector_ids.tolist()]
if AnalysisDataService.doesExist('CORELLIMaskBTP'):
DeleteWorkspaces(['CORELLIMaskBTP'])
return mtd[output_table]
def _createFakePeakPositionTable(self, peakPos):
"""Create a peak position TableWorkspace with a single column for peakPos."""
tableName = self._names.withSuffix('peak_position_table')
table = CreateEmptyTableWorkspace(OutputWorkspace=tableName,
EnableLogging=self._subalgLogging)
table.addColumn('double', 'PeakCentre')
table.addRow((peakPos,))
return table
def test_correct_number_of_rows_fetched_initially_batch(self):
ws = CreateEmptyTableWorkspace()
ws.addColumn("double", "l")
list(map(ws.addRow, ([i] for i in range(5 * BATCH_SIZE))))
presenter = TableWorkspaceDisplay(ws, batch=True)
# fetch more starting at index 0,0
index = presenter.view.model().index(0, 0)
presenter.view.model().fetchMore(index)
self.assertEqual(5 * BATCH_SIZE, presenter.view.model().max_rows())
self.assertEqual(BATCH_SIZE, presenter.view.model().rowCount())
presenter.close(ws.name())
def generateCropingTable(qmin, qmax):
mask_info = CreateEmptyTableWorkspace()
mask_info.addColumn("str", "SpectraList")
mask_info.addColumn("double", "XMin")
mask_info.addColumn("double", "XMax")
for (i, value) in enumerate(qmin):
mask_info.addRow([str(i), 0.0, value])
for (i, value) in enumerate(qmax):
mask_info.addRow([str(i), value, 100.0])
return mask_info
def test_window_deleted_correctly(self):
ws = CreateEmptyTableWorkspace()
p = TableWorkspaceDisplay(ws)
self.assert_window_created()
p.close(ws.name())
QApplication.processEvents()
self.assertEqual(None, p.ads_observer)
self.find_qt_widget("work")
self.assert_no_widgets()
def test_ion_table(self):
ws = DensityOfStates(File=self._file_name, SpectrumType='IonTable')
# Build the expected output
expected = CreateEmptyTableWorkspace()
expected.addColumn('str', 'Ion')
expected.addColumn('int', 'Count')
expected.addRow(['H', 4])
expected.addRow(['C', 8])
expected.addRow(['O', 8])
self.assertEquals(CheckWorkspacesMatch(ws, expected), 'Success!')
def _create_empty_table_workspace(name: str, num_rows: int) -> ITableWorkspace:
"""Create and empty table with the given number of rows
:param name: The name of the workspace in the ADS
:param num_rows: Number of rows for the new table
:return: A new tableworkspace
"""
# keep import here to avoid framework initialization in tests
from mantid.simpleapi import CreateEmptyTableWorkspace
table = CreateEmptyTableWorkspace(OutputWorkspace=name)
table.setRowCount(num_rows)
return table
def test_window_deleted_correctly(self):
ws = CreateEmptyTableWorkspace()
p = TableWorkspaceDisplay(ws)
self.assert_widget_created()
p.close(ws.name())
QApplication.processEvents()
self.assertEqual(None, p.ads_observer)
self.assert_widget_not_present("work")
self.assert_no_toplevel_widgets()
def test_window_deleted_correctly(self):
ws = CreateEmptyTableWorkspace()
p = TableWorkspaceDisplay(ws)
self.assert_widget_created()
p.close(ws.name())
QApplication.sendPostedEvents()
self.assertEqual(None, p.ads_observer)
self.assert_widget_not_present("work")
self.assert_no_toplevel_widgets()
def test_scrolling_updates_number_of_rows_fetched_batch(self):
ws = CreateEmptyTableWorkspace()
ws.addColumn("double", "l")
list(map(ws.addRow, ([i] for i in range(5 * BATCH_SIZE))))
presenter = TableWorkspaceDisplay(ws, batch=True)
# fetch more starting at index 0,0
index = presenter.view.model().index(0, 0)
presenter.view.model().fetchMore(index)
self.assertEqual(BATCH_SIZE, presenter.view.model().rowCount())
# scrolling should update our batch size to 2*BATCH_SIZE
presenter.view.scrollToBottom()
self.assertEqual(2 * BATCH_SIZE, presenter.view.model().rowCount())
presenter.close(ws.name())
def test_sample_tof(self):
# creates table workspace with mock elastic peak positions and widths:
table_ws = CreateEmptyTableWorkspace()
table_ws.addColumn("float", "PeakCentre")
table_ws.addColumn("float", "Sigma")
for row in range(132):
table_ws.addRow([1645.2, 15.0])
sampleProperties = {
'SampleMass': 2.93,
'FormulaUnitMass': 50.94,
'EPWidth': 15
}
yig_calibration_file = "D7_YIG_calibration_TOF.xml"
PolDiffILLReduction(Run='395639',
ProcessAs='Sample',
OutputWorkspace='sample_tof',
SampleAndEnvironmentProperties=sampleProperties,
SampleGeometry='None',
OutputTreatment='Individual',
InstrumentCalibration=yig_calibration_file,
ElasticChannelsWorkspace='table_ws',
MeasurementTechnique='TOF')
self._check_output(mtd['sample_tof'], 339, 132, 2, 'Energy transfer',
'DeltaE', 'Spectrum', 'Label')
self._check_process_flag(mtd['sample_tof'], 'Sample')
def trim_calibration_table(input_workspace: InputTable, output_workspace: Optional[str] = None) -> TableWorkspace:
r"""
Discard trim the X and Z pixel coordinates, since we are only interested in the calibrated Y-coordinate
:param input_workspace:
:param output_workspace:
:return: handle to the trimmed table workspace
"""
if output_workspace is None:
output_workspace = str(input_workspace) # overwrite the input table
# Extract detector ID's and Y-coordinates from the input table
table = mtd[str(input_workspace)]
detector_ids = table.column(0)
y_coordinates = [v.Y() for v in table.column(1)]
# create the (empty) trimmed table
table_trimmed = CreateEmptyTableWorkspace(OutputWorkspace=output_workspace)
table_trimmed.addColumn(type='int', name='Detector ID')
table_trimmed.addColumn(type='double', name='Detector Y Coordinate')
# fill the rows of the trimmed table
for detector_id, y_coordinate in zip(detector_ids, y_coordinates):
table_trimmed.addRow([detector_id, y_coordinate])
return table_trimmed
def test_ion_table(self):
ws = SimulatedDensityOfStates(File=self._file_name, SpectrumType='IonTable')
# Build the expected output
expected = CreateEmptyTableWorkspace()
expected.addColumn('str', 'Ion')
expected.addColumn('int', 'Count')
expected.addRow(['H', 4])
expected.addRow(['C', 8])
expected.addRow(['O', 8])
self.assertEquals(CheckWorkspacesMatch(ws, expected), 'Success!')
def test_that_show_normalised_covariance_matrix_will_not_raise_an_error(
self):
ws = CreateEmptyTableWorkspace()
wrapper = StaticWorkspaceWrapper("CovarianceMatrix", ws)
self.view.show_normalised_covariance_matrix(wrapper.workspace,
wrapper.workspace_name)
def test_that_current_normalised_covariance_matrix_will_return_a_statix_workspace_wrapper_when_a_fit_exists(self):
ws = CreateEmptyTableWorkspace()
wrapper = StaticWorkspaceWrapper("CovarianceMatrix", ws)
self.model._get_normalised_covariance_matrix_for = mock.Mock(return_value=wrapper)
covariance_wrapper = self.model.current_normalised_covariance_matrix()
self.assertEqual(covariance_wrapper, wrapper)
def update_calibration_params_table(params_table):
if len(params_table) == 0:
return
# Create blank, or clear rows from existing, params table.
if Ads.doesExist(CALIB_PARAMS_WORKSPACE_NAME):
workspace = Ads.retrieve(CALIB_PARAMS_WORKSPACE_NAME)
workspace.setRowCount(0)
else:
workspace = CreateEmptyTableWorkspace(OutputWorkspace=CALIB_PARAMS_WORKSPACE_NAME)
workspace.addColumn("int", "bankid")
workspace.addColumn("double", "difc")
workspace.addColumn("double", "difa")
workspace.addColumn("double", "tzero")
for row in params_table:
workspace.addRow(row)
def test_that_has_normalised_covariance_matrix_returns_true_when_there_is_not_a_covariance_matrix(
self):
ws = CreateEmptyTableWorkspace()
wrapper = StaticWorkspaceWrapper("CovarianceMatrix", ws)
self.model._get_normalised_covariance_matrix_for = mock.Mock(
return_value=wrapper)
self.assertTrue(self.model.has_normalised_covariance_matrix())
def test_has_valid_columns(self):
corelli_table = init_corelli_table()
self.assertEqual(has_valid_columns(corelli_table), True)
table_incomplete: TableWorkspace = CreateEmptyTableWorkspace()
table_incomplete.addColumn(type="int", name="Detector ID")
self.assertEqual(has_valid_columns(table_incomplete), False)
def test_update_match_table_when_table_has_less_than_3_rows(self):
correct_table_entries = ["9999", "Detector 3", "Al , C"]
# Create source tables
likelyhood_table = CreateEmptyTableWorkspace(OutputWorkspace="likelyhood")
likelyhood_table.addColumn("str", "Element")
likelyhood_table.addColumn("int", "Likelihood")
likelyhood_table.addRow(["Al", 20])
likelyhood_table.addRow(["C", 18])
# Run function
self.model.update_match_table("likelyhood", "9999; Detector 3")
# Assert statements
self.assertEqual(self.model.table_entries.get(), correct_table_entries)
# Delete tables from ADS
self.delete_if_present("likelyhood")
def init_corelli_table(name: Optional[str] = None,
table_type='calibration') -> TableWorkspace:
"""
Function that initializes a Corelli calibration TableWorkspace columns
"""
table: TableWorkspace = CreateEmptyTableWorkspace(
OutputWorkspace=name) if name else CreateEmptyTableWorkspace()
# expected columns declarations (column_type, column_name) for each type of calibration table
column_declarations = {
'calibration': [('int', 'Detector ID'),
('double', 'Detector Y Coordinate')],
'mask': [('int', 'Detector ID')]
}
for column_type, colum_name in column_declarations[table_type]:
table.addColumn(type=column_type, name=colum_name)
return table
def _createDiagnosticsReportTable(reportWSName, numberHistograms, algorithmLogging):
"""Return a table workspace for detector diagnostics reporting."""
if mtd.doesExist(reportWSName):
reportWS = mtd[reportWSName]
else:
reportWS = CreateEmptyTableWorkspace(OutputWorkspace=reportWSName,
EnableLogging=algorithmLogging)
existingColumnNames = reportWS.getColumnNames()
if 'WorkspaceIndex' not in existingColumnNames:
reportWS.addColumn('int', 'WorkspaceIndex', _PLOT_TYPE_X)
reportWS.setRowCount(numberHistograms)
for i in range(numberHistograms):
reportWS.setCell('WorkspaceIndex', i, i)
return reportWS
def _create_indexed_workspace(self, fractional_peaks, ndim, hklm):
# Create table with the number of columns we need
indexed = CreateEmptyTableWorkspace()
names = fractional_peaks.getColumnNames()
types = fractional_peaks.columnTypes()
# 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 < 5) | (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 test_that_handle_covariance_matrix_clicked_calls_the_expected_functions(self):
ws = CreateEmptyTableWorkspace()
wrapper = StaticWorkspaceWrapper("CovarianceMatrix", ws)
self.model.current_normalised_covariance_matrix = mock.Mock(return_value=wrapper)
self.presenter.handle_covariance_matrix_clicked()
self.model.current_normalised_covariance_matrix.assert_called_once_with()
self.view.show_normalised_covariance_matrix.assert_called_once_with(wrapper.workspace, wrapper.workspace_name)
def test_1d_plots_with_unplottable_type_raises_attributeerror(self):
table = CreateEmptyTableWorkspace()
_, ax = plt.subplots()
self.assertRaises(AttributeError, funcs.plot, ax, table, wkspIndex=0)
self.assertRaises(AttributeError,
funcs.errorbar,
ax,
table,
wkspIndex=0)
def setUpClass(cls):
cls.ws_widget = WorkspaceWidget(QMainWindow())
mat_ws = CreateSampleWorkspace()
table_ws = CreateEmptyTableWorkspace()
group_ws = GroupWorkspaces([mat_ws, table_ws])
cls.w_spaces = [mat_ws, table_ws, group_ws]
cls.ws_names = ['MatWS', 'TableWS', 'GroupWS']
for ws_name, ws in zip(cls.ws_names, cls.w_spaces):
cls.ws_widget._ads.add(ws_name, ws)
def createGoniometerTable(self):
"""
:return: Empty table workspace with columns Run, Chi, Phi and GonioAxis (unit vector)
"""
gonioTable = CreateEmptyTableWorkspace(StoreInADS=False)
# Add some columns, Recognized types are: int,float,double,bool,str,V3D,long64
gonioTable.addColumn(type="str", name="Run")
gonioTable.addColumn(type="float", name="Chi")
gonioTable.addColumn(type="float", name="Phi")
gonioTable.addColumn(type="V3D", name="GonioAxis")
return gonioTable
def generate_calitab(self):
"""Generate the calibration table with correct format"""
self.cali_table_name = "corelli_pd_cali_apl_tab"
CreateEmptyTableWorkspace(OutputWorkspace=self.cali_table_name)
_calitab = mtd[self.cali_table_name]
# fix headers
headers = [
"ComponentName",
"Xposition",
"Yposition",
"Zposition",
"XdirectionCosine",
"YdirectionCosine",
"ZdirectionCosine",
"RotationAngle",
]
datatypes = ["str"] + ["double"] * 7
for dt, hd in zip(datatypes, headers):
_calitab.addColumn(dt, hd)
# insert rows (components)
_cpts = [
["moderator", 0, 0, -19.9997, 0, 0, 0, 0],
["sample-position", 0, 0, 0, 0, 0, 0, 0],
[
"bank7/sixteenpack",
2.25637,
-0.814864,
-0.883485,
-0.0244456,
-0.99953,
-0.0184843,
69.4926,
],
[
"bank42/sixteenpack",
2.58643,
0.0725628,
0.0868798,
-0.011362,
-0.999935,
-0.000173303,
91.8796,
],
[
"bank57/sixteenpack",
0.4545,
0.0788326,
2.53234,
-0.0158497,
-0.999694,
0.0189818,
169.519,
],
]
for cp in _cpts:
_calitab.addRow(cp)
def _createDiagnosticsReportTable(reportWSName, numberHistograms, algorithmLogging):
"""Return a table workspace for detector diagnostics reporting."""
if mtd.doesExist(reportWSName):
reportWS = mtd[reportWSName]
else:
reportWS = CreateEmptyTableWorkspace(OutputWorkspace=reportWSName,
EnableLogging=algorithmLogging)
existingColumnNames = reportWS.getColumnNames()
if 'WorkspaceIndex' not in existingColumnNames:
reportWS.addColumn('int', 'WorkspaceIndex', _PLOT_TYPE_X)
reportWS.setRowCount(numberHistograms)
for i in range(numberHistograms):
reportWS.setCell('WorkspaceIndex', i, i)
return reportWS
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 ouput 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 = 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 = mtd['TubePlot']
fit_ws = 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': V3D(*pos)})
cleanUpFit()
return corrections_table
def _generate_props_table(self):
"""
Creates a table workspace with values calculated in algorithm.
"""
props_table = 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 setUp(self):
# create sample workspace
self.xmin = 2123.33867005 + 4005.75
self.xmax = 2123.33867005 + 7995.75
self._input_ws = CreateSampleWorkspace(Function="User Defined", UserDefinedFunction="name=LinearBackground, \
A0=0.3;name=Gaussian, PeakCentre=8190, Height=5, Sigma=75", NumBanks=2,
BankPixelWidth=1, XMin=self.xmin, XMax=self.xmax, BinWidth=10.5,
BankDistanceFromSample=4.0, SourceDistanceFromSample=1.4, OutputWorkspace="ws")
lognames = "wavelength,TOF1"
logvalues = "6.0,2123.33867005"
AddSampleLogMultiple(self._input_ws, lognames, logvalues)
# create EPP table
self._table = CreateEmptyTableWorkspace(OutputWorkspace="epptable")
self._table.addColumn(type="double", name="PeakCentre")
table_row = {'PeakCentre': 8189.5}
for i in range(2):
self._table.addRow(table_row)
def _create_indexed_workspace(self, fractional_peaks, ndim, hklm):
# 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']
indexed = CreateEmptyTableWorkspace()
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 < 5) | (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 create_output_table(centre1, centre2):
Centre_position = CreateEmptyTableWorkspace()
Centre_position.addColumn(type="double", name="X Centre Position")
Centre_position.addColumn(type="double", name="Y Centre Position")
Centre_position.addRow({"X Centre Position": centre1, "Y Centre Position": centre2})
def testAlignComponentsPositionXY(self):
CreateSampleWorkspace(OutputWorkspace='testWS', NumBanks=1,BankPixelWidth=4)
component='bank1'
MoveInstrumentComponent(Workspace='testWS',ComponentName=component,X=0.06,Y=0.04,Z=4.98,RelativePosition=False)
### Detector should move to [0.05,0.03,4.98]
### Calibration table generated with:
# CreateSampleWorkspace(OutputWorkspace='sample', NumBanks=1,BankPixelWidth=4)
# MoveInstrumentComponent(Workspace='sample',ComponentName='bank1',X=0.05,Y=0.03,Z=4.98,RelativePosition=False)
# CalculateDIFC(InputWorkspace='sample', OutputWorkspace='sample')
# d=mtd['sample'].extractY()
# for i in range(len(d)):
# print "calTable.addRow(["+str(i+16)+", "+str(d[i][0])+"])"
calTable = CreateEmptyTableWorkspace()
calTable.addColumn("int", "detid")
calTable.addColumn("double", "difc")
calTable.addRow([16, 44.3352831346])
calTable.addRow([17, 47.7503426493])
calTable.addRow([18, 51.6581064544])
calTable.addRow([19, 55.9553976608])
calTable.addRow([20, 49.6495672525])
calTable.addRow([21, 52.7214213944])
calTable.addRow([22, 56.285004349])
calTable.addRow([23, 60.2530897937])
calTable.addRow([24, 55.1227558338])
calTable.addRow([25, 57.9048914599])
calTable.addRow([26, 61.1671229038])
calTable.addRow([27, 64.8369848035])
calTable.addRow([28, 60.7118272387])
calTable.addRow([29, 63.2484968666])
calTable.addRow([30, 66.2480051141])
calTable.addRow([31, 69.650545037])
ws = mtd["testWS"]
startPos = ws.getInstrument().getComponentByName(component).getPos()
startRot = ws.getInstrument().getComponentByName(component).getRotation().getEulerAngles()
AlignComponents(CalibrationTable="calTable",
Workspace="testWS",
ComponentList=component,
Xposition=True,
Yposition=True)
ws = mtd["testWS"]
endPos = ws.getInstrument().getComponentByName(component).getPos()
endRot = ws.getInstrument().getComponentByName(component).getRotation().getEulerAngles()
self.assertAlmostEqual(endPos.getX(), 0.05)
self.assertAlmostEqual(endPos.getY(), 0.03)
self.assertEqual(startPos.getZ(), endPos.getZ())
self.assertEqual(startRot[0], endRot[0])
self.assertEqual(startRot[1], endRot[1])
self.assertEqual(startRot[2], endRot[2])
def PyExec(self):
""" Alg execution. """
instrument = self.getProperty(INSTRUMENT_PROP).value
run_number = self.getProperty(RUN_NUM_PROP).value
fit_deadtime = self.getProperty(FIT_DEADTIME_PROP).value
fix_phases = self.getProperty(FIX_PHASES_PROP).value
default_level = self.getProperty(DEFAULT_LEVEL).value
sigma_looseness = self.getProperty(SIGMA_LOOSENESS_PROP).value
groupings_file = self.getProperty(GROUPINGS_PROP).value
in_phases_file = self.getProperty(PHASES_PROP).value
in_deadtimes_file = self.getProperty(DEADTIMES_PROP).value
out_phases_file = self.getProperty(PHASES_RESULT_PROP).value
out_deadtimes_file = self.getProperty(DEADTIMES_RESULT_PROP).value
isis = config.getFacility('ISIS')
padding = isis.instrument(instrument).zeroPadding(0)
run_name = instrument + str(run_number).zfill(padding)
try:
run_number = int(run_number)
except:
raise RuntimeError("'%s' is not an integer run number." % run_number)
try:
run_file_path = FileFinder.findRuns(run_name)[0]
except:
raise RuntimeError("Unable to find file for run %i" % run_number)
if groupings_file == "":
groupings_file = DEFAULT_GROUPINGS_FILENAME % instrument
# Load data and other info from input files.
def temp_hidden_ws_name():
"""Generate a unique name for a temporary, hidden workspace."""
selection = string.ascii_lowercase + string.ascii_uppercase + string.digits
return '__temp_MaxEnt_' + ''.join(random.choice(selection) for _ in range(20))
input_data_ws_name = temp_hidden_ws_name()
LoadMuonNexus(Filename=run_file_path, OutputWorkspace=input_data_ws_name)
input_data_ws = mtd[input_data_ws_name]
if isinstance(input_data_ws, WorkspaceGroup):
Logger.get("MaxEnt").warning("Multi-period data is not currently supported. Just using first period.")
input_data_ws = input_data_ws[0]
groupings_ws_name = temp_hidden_ws_name()
LoadDetectorsGroupingFile(InputFile=groupings_file, OutputWorkspace=groupings_ws_name)
groupings_ws = mtd[groupings_ws_name]
def yield_floats_from_file(path):
"""Given a path to a file with a float on each line, will return
the floats one at a time. Throws otherwise. Strips whitespace
and ignores empty lines."""
with open(path, 'r') as f:
for i, line in enumerate(line.strip() for line in f):
if line == "":
continue
try:
yield float(line)
except:
raise RuntimeError("Parsing error in '%s': Line %d: '%s'." %
(path, i, line))
input_phases = np.array(list(yield_floats_from_file(in_phases_file)))
input_phases_size = len(input_phases)
input_deadtimes = np.array(list(yield_floats_from_file(in_deadtimes_file)))
input_deadtimes_size = len(input_deadtimes)
n_bins = input_data_ws.blocksize()
n_detectors = input_data_ws.getNumberHistograms()
def time_value_to_time_channel_index(value):
"""Given a time value, will return the index of the time channel in
which the value falls."""
bin_width = input_data_ws.readX(0)[1] - input_data_ws.readX(0)[0]
diff = value - input_data_ws.readX(0)[0]
return int(diff / bin_width)
# Mantid corrects for time zero on loading, so we want to find the actual channels
# where 0.0 occurs, and where we have values of 0.1 onwards.
time_zero_channel = time_value_to_time_channel_index(0.0)
first_good_channel = time_value_to_time_channel_index(0.1)
input_data = np.concatenate([input_data_ws.readY(i) for i in range(n_detectors)])
groupings = [groupings_ws.readY(row)[0] for row in range(groupings_ws.getNumberHistograms())]
groupings = map(int, groupings)
n_groups = len(set(groupings))
# Cleanup.
input_data_ws.delete()
groupings_ws.delete()
# We're faced with the problem of providing more than a dozen parameters to
# the Fortran, which can be a bit messy (especially on the Fortran side of
# things where we need to make "Cf2py" declarations). A cleaner way of
# doing this is to simply pass in a few callbacks -- one for each input
# type -- and have the Fortran provide the name of the variable it wants
# to the callback. The callback will then look up the corresponding value
# and feed it back to the Fortran.
#
# We also have a callback for printing to the results log.
self.int_vars = {
"RunNo" : run_number,
"frames" : FRAMES,
"res" : RES,
"Tzeroch" : time_zero_channel,
"firstgoodch" : first_good_channel,
"ptstofit" : POINTS_TO_FIT,
"histolen" : n_bins,
"nhisto" : n_detectors,
"n_groups" : n_groups,
}
self.float_vars = {
"deflevel" : default_level,
"sigloose" : sigma_looseness,
}
self.bool_vars = {
"fixphase" : fix_phases,
"fitdt" : fit_deadtime,
}
self._assert_map_values_are_of_expected_type()
def lookup(par_name, par_map, default):
"""The basis of the callbacks passed to the Fortran. Given a parameter
name it will consult the appropriate variable map, and return the
corresponding value of the parameter. Else return a default and log a
warning if a parameter with the name does not exist."""
par_name = par_name.strip()
if par_name in par_map:
return par_map[par_name]
msg = """WARNING: tried to find a value for parameter with name %s but
could not find one. Default of \"%s\" provided.""" % (par_name, default)
Logger.get("MaxEnt").warning(msg)
return default
def log(priority, message):
"""Log the given message with given priority."""
try:
logger = getattr(Logger.get("MaxEnt"), priority.lower())
except AttributeError:
# If we don't recognise the priority, use warning() as a default.
logger = getattr(Logger.get("MaxEnt"), "warning")
logger(message)
return True
# The Fortran expects arrays to be of a certain size, so any arrays that
# aren't big enough need to be padded.
input_phases = self._pad_to_length_with_zeros(input_phases, MAX_HISTOS)
input_deadtimes = self._pad_to_length_with_zeros(input_deadtimes, MAX_HISTOS)
input_data = self._pad_to_length_with_zeros(input_data, MAX_INPUT_DATA_SIZE)
groupings = self._pad_to_length_with_zeros(groupings, MAX_HISTOS)
# TODO: Return the contents of "NNNNN.max", instead of writing to file.
f_out, fchan_out, output_deadtimes, output_phases, chi_sq = maxent.mantid_maxent(
# Input data and other info:
input_data,
groupings,
input_deadtimes,
input_phases,
# Variable-lookup callbacks:
lambda par_name: lookup(par_name, self.int_vars, 0),
lambda par_name: lookup(par_name, self.float_vars, 0.0),
lambda par_name: lookup(par_name, self.bool_vars, False),
# Callback for logging:
log
)
def write_items_to_file(path, items):
"""Given a path to a file and a list of items, will write the items
to the file, one on each line."""
with open(path, 'w') as f:
for item in items:
f.write(str(item) + "\n")
# Chop the padded outputs back down to the correct size.
output_phases = output_phases[:input_phases_size]
output_deadtimes = output_deadtimes[:input_deadtimes_size]
input_phases = input_phases[:input_phases_size]
input_deadtimes = input_deadtimes[:input_deadtimes_size]
fchan_out = fchan_out[:n_bins]
f_out = f_out[:n_bins]
write_items_to_file(out_phases_file, output_phases)
write_items_to_file(out_deadtimes_file, output_deadtimes)
log_output = "\nDead times in:\n" + str(input_deadtimes) + "\n" +\
"\nDead times out:\n" + str(output_deadtimes) + "\n" +\
"\nPhases in:\n" + str(input_phases) + "\n" +\
"\nPhases out:\n" + str(output_phases) + "\n" + \
"\nGroupings:\n" + str(groupings) + "\n" +\
"\nChi Squared:\n" + str(chi_sq) + "\n" +\
"\nInput variables:\n"
for type_map in self.int_vars, self.float_vars, self.bool_vars:
for name, value in type_map.items():
log_output += str(name) + " = " + str(value) + "\n"
Logger.get("MaxEnt").notice(log_output)
# Generate our own output ws name if the user has not provided one.
out_ws_name = self.getPropertyValue(OUT_WS_PROP)
if out_ws_name == "":
out_ws_name = run_name + "; MaxEnt"
self.setPropertyValue(OUT_WS_PROP, out_ws_name)
out_ws = CreateWorkspace(OutputWorkspace=out_ws_name,
DataX=fchan_out[:n_bins],
DataY=f_out[:n_bins])
self.setProperty(OUT_WS_PROP, out_ws)
# MaxEnt inputs table.
input_table_name = run_name + "; MaxEnt Input"
input_table = CreateEmptyTableWorkspace(OutputWorkspace = input_table_name)
input_table.addColumn("str", "Name")
input_table.addColumn("str", "Value")
inputs = itertools.chain(self.int_vars.items(),
self.float_vars.items(),
self.bool_vars.items())
for name, value in inputs:
input_table.addRow([str(name), str(value)])
# Deadtimes and phases input/output table.
dead_phases_table_name = run_name + "; MaxEnt Deadtimes & Phases"
dead_phases_table = CreateEmptyTableWorkspace(OutputWorkspace = dead_phases_table_name)
for column_name in "Deadtimes In", "Deadtimes Out", "Phases In", "Phases Out":
dead_phases_table.addColumn("double", column_name)
for row in zip(input_deadtimes, output_deadtimes, input_phases, output_phases):
dead_phases_table.addRow(list(map(float, row)))
# Chi-squared output table.
chisq_table_name = run_name + "; MaxEnt Chi^2"
chisq_table = CreateEmptyTableWorkspace(OutputWorkspace = chisq_table_name)
chisq_table.addColumn("int", "Cycle")
for iteration in range(10):
chisq_table.addColumn("double", "Iter " + str(iteration + 1))
for cycle, data in enumerate(chi_sq):
chisq_table.addRow([cycle + 1] + list(map(float,data)))
all_output_ws = [input_table_name,
dead_phases_table_name,
chisq_table_name,
out_ws_name]
# The output workspaces of this algorithm belong in the same groups
# that are created by the muon interface. If the appropriate group
# doesn't exist already then it needs to be created.
if not run_name in mtd:
GroupWorkspaces(InputWorkspaces = all_output_ws,
OutputWorkspace = run_name)
else:
group = mtd[run_name]
for output_ws in all_output_ws:
if not group.contains(output_ws):
group.add(output_ws)
out_ws.getAxis(0).getUnit().setLabel("Field", "G")
out_ws.setYUnitLabel("P(B)")
if INSIDE_MANTIDPLOT:
mantidplot.plotSpectrum(out_ws, 0)
def testAlignComponentsRotationY(self):
CreateSampleWorkspace(OutputWorkspace='testWS', NumBanks=1,BankPixelWidth=4)
component='bank1'
MoveInstrumentComponent(Workspace='testWS',ComponentName=component,X=2.00,Y=0,Z=2.00,RelativePosition=False)
RotateInstrumentComponent(Workspace='testWS',ComponentName='bank1',X=0,Y=1,Z=0,Angle=50,RelativeRotation=False)
### Detector should rotate to +45deg around Y
### Calibration table generated with:
# CreateSampleWorkspace(OutputWorkspace='sample2', NumBanks=1,BankPixelWidth=4)
# MoveInstrumentComponent(Workspace='sample2',ComponentName='bank1',X=2.0,Y=0.0,Z=2.0,RelativePosition=False)
# RotateInstrumentComponent(Workspace='sample2',ComponentName='bank1',X=0,Y=1,Z=0,Angle=45,RelativeRotation=False)
# CalculateDIFC(InputWorkspace='sample2', OutputWorkspace='sample2')
# d=mtd['sample2'].extractY()
# for i in range(len(d)):
# print "calTable.addRow(["+str(i+16)+", "+str(d[i][0])+"])"
calTable = CreateEmptyTableWorkspace()
calTable.addColumn("int", "detid")
calTable.addColumn("double", "difc")
calTable.addRow([16, 2481.89300158])
calTable.addRow([17, 2481.90717397])
calTable.addRow([18, 2481.94969])
calTable.addRow([19, 2482.02054626])
calTable.addRow([20, 2490.36640334])
calTable.addRow([21, 2490.38050851])
calTable.addRow([22, 2490.42282292])
calTable.addRow([23, 2490.49334316])
calTable.addRow([24, 2498.83911141])
calTable.addRow([25, 2498.85314962])
calTable.addRow([26, 2498.89526313])
calTable.addRow([27, 2498.96544859])
calTable.addRow([28, 2507.31101837])
calTable.addRow([29, 2507.32498986])
calTable.addRow([30, 2507.36690322])
calTable.addRow([31, 2507.43675513])
ws = mtd["testWS"]
startPos = ws.getInstrument().getComponentByName(component).getPos()
startRot = ws.getInstrument().getComponentByName(component).getRotation().getEulerAngles("YZX") #YZX
AlignComponents(CalibrationTable="calTable",
Workspace="testWS",
ComponentList=component,
AlphaRotation=True)
ws = mtd["testWS"]
endPos = ws.getInstrument().getComponentByName(component).getPos()
endRot = ws.getInstrument().getComponentByName(component).getRotation().getEulerAngles("YZX") #YZX
self.assertEqual(startPos, endPos)
self.assertAlmostEqual(endRot[0],45.0,places=0)
self.assertEqual(startRot[1], endRot[1])
self.assertEqual(startRot[2], endRot[2])
def get_he3_log(path):
"""
Load the ³He log data into Mantid Table named "helium_log"
Parameters
----------
path
A string with the path to the ³He as a tsv file
"""
hetemp = load_helium_file(path)
my_table = CreateEmptyTableWorkspace()
my_table.addColumn("int", "Number")
my_table.addColumn("str", "Cell")
my_table.addColumn("float", "scale")
my_table.addColumn("str", "Start time")
my_table.addColumn("float", "fid")
my_table.addColumn("float", "Time Constant")
for run in hetemp:
my_table.addRow([run.run, run.cell,
run.scale,
run.dt.isoformat(),
run.fid, run.t1])
RenameWorkspace(my_table, "helium_log")
def get_log(runs):
"""
Uses the run journal to identify which run numbers are associated with
which samples and create a table for each sample, containing all the
information needed to analyse each run.
Parameters
----------
runs
A list of integer run numbers
"""
log_file = JPATH + "\\" + get_relevant_log(min(runs))
results = []
with open(log_file, "r") as infile:
journal = xml.etree.cElementTree.iterparse(infile)
for _, child in journal:
if "NXentry" in child.tag:
num = get_xml_run_number(child)
if num in runs:
for param in child:
if "title" in param.tag:
sample = param.text
elif "start_time" in param.tag:
start = datetime.datetime.strptime(
param.text,
"%Y-%m-%dT%H:%M:%S")
elif "end_time" in param.tag:
stop = datetime.datetime.strptime(
param.text,
"%Y-%m-%dT%H:%M:%S")
elif "duration" in param.tag:
duration = datetime.timedelta(
seconds=int(param.text))
elif "proton_charge" in param.tag:
proton_charge = float(param.text)
results.append(
QuickData(num, sample, start, stop, duration,
proton_charge))
child.clear()
if num > max(runs):
break
trans = [run for run in results
if re.match(RUN_IDENTIFIERS["trans"], run[1])]
csans = [run for run in results
if re.match(RUN_IDENTIFIERS["can_sans"], run[1])]
ctrans = [run for run in results
if re.match(RUN_IDENTIFIERS["can_trans"], run[1])]
dtrans = [run for run in results
if re.match(RUN_IDENTIFIERS["direct_trans"], run[1])]
temp = [convert_run(run, trans, csans, ctrans, dtrans)
for run in results
if (re.match(RUN_IDENTIFIERS["run"], run.sample) or
re.match(RUN_IDENTIFIERS["can_sans"], run.sample) or
re.match(RUN_IDENTIFIERS["direct_sans"], run.sample))
and run.charge/run.duration.seconds > 0.005]
d = {}
for run in temp:
if run.sample in d.keys():
d[run.sample].append(run)
else:
d[run.sample] = [run]
for k, v in d.items():
my_table = CreateEmptyTableWorkspace()
my_table.addColumn("int", "Run Number")
my_table.addColumn("str", "Sample")
my_table.addColumn("str", "Start time")
my_table.addColumn("str", "End time")
my_table.addColumn("int", "Trans run")
my_table.addColumn("int", "Can Sans run")
my_table.addColumn("int", "Can Trans run")
my_table.addColumn("int", "Direct Trans run")
for run in v:
my_table.addRow(
[run.number, run.sample,
run.start.isoformat(),
run.end.isoformat(),
run.trans, run.csans, run.ctrans,
run.direct])
RenameWorkspace(my_table, k+"_runs")
class CorrectTOFTest(unittest.TestCase):
def setUp(self):
# create sample workspace
self.xmin = 2123.33867005 + 4005.75
self.xmax = 2123.33867005 + 7995.75
self._input_ws = CreateSampleWorkspace(Function="User Defined", UserDefinedFunction="name=LinearBackground, \
A0=0.3;name=Gaussian, PeakCentre=8190, Height=5, Sigma=75", NumBanks=2,
BankPixelWidth=1, XMin=self.xmin, XMax=self.xmax, BinWidth=10.5,
BankDistanceFromSample=4.0, SourceDistanceFromSample=1.4, OutputWorkspace="ws")
lognames = "wavelength,TOF1"
logvalues = "6.0,2123.33867005"
AddSampleLogMultiple(self._input_ws, lognames, logvalues)
# create EPP table
self._table = CreateEmptyTableWorkspace(OutputWorkspace="epptable")
self._table.addColumn(type="double", name="PeakCentre")
table_row = {'PeakCentre': 8189.5}
for i in range(2):
self._table.addRow(table_row)
def tearDown(self):
for wsname in ['ws', 'epptable']:
if AnalysisDataService.doesExist(wsname):
run_algorithm("DeleteWorkspace", Workspace=wsname)
def testCorrection(self):
# tests that correction is done properly
OutputWorkspaceName = "outputws1"
alg_test = run_algorithm("CorrectTOF", InputWorkspace=self._input_ws, EPPTable=self._table, OutputWorkspace=OutputWorkspaceName)
self.assertTrue(alg_test.isExecuted())
wsoutput = AnalysisDataService.retrieve(OutputWorkspaceName)
velocity = h/(m_n*6.0e-10)
t_el = 4.0e+6/velocity
t_corr = np.arange(self.xmin, self.xmax + 1.0, 10.5) + t_el - (8189.5 - 2123.33867005)
self.assertTrue(np.allclose(t_corr, wsoutput.readX(0))) #sdd = 4
self.assertTrue(np.allclose(t_corr + t_el, wsoutput.readX(1))) #sdd = 8
run_algorithm("DeleteWorkspace", Workspace=wsoutput)
def testGroup(self):
# tests whether the group of workspaces is accepted as an input
ws2 = CloneWorkspace(self._input_ws)
group = GroupWorkspaces([self._input_ws, ws2])
OutputWorkspaceName = "output_wsgroup"
alg_test = run_algorithm("CorrectTOF", InputWorkspace='group', EPPTable=self._table, OutputWorkspace=OutputWorkspaceName)
self.assertTrue(alg_test.isExecuted())
wsoutput = AnalysisDataService.retrieve(OutputWorkspaceName)
self.assertTrue(isinstance(wsoutput, WorkspaceGroup))
self.assertEqual(2, wsoutput.getNumberOfEntries())
run_algorithm("DeleteWorkspace", Workspace=group)
run_algorithm("DeleteWorkspace", Workspace=wsoutput)
def testConvertUnits(self):
# test whether CorrectTof+ConvertUnits+ConvertToDistribution will give the same result as TOFTOFConvertTOFToDeltaE
OutputWorkspaceName = "outputws1"
alg_test = run_algorithm("CorrectTOF", InputWorkspace=self._input_ws, EPPTable=self._table, OutputWorkspace=OutputWorkspaceName)
self.assertTrue(alg_test.isExecuted())
wscorr = AnalysisDataService.retrieve(OutputWorkspaceName)
# convert units, convert to distribution
alg_cu = run_algorithm("ConvertUnits", InputWorkspace=wscorr, Target='DeltaE', EMode='Direct', EFixed=2.27, OutputWorkspace=OutputWorkspaceName+'_dE')
ws_dE = AnalysisDataService.retrieve(OutputWorkspaceName+'_dE')
alg_cd = run_algorithm("ConvertToDistribution", Workspace=ws_dE)
# create reference data for X axis
tof1 = 2123.33867005
dataX = self._input_ws.readX(0) - tof1
tel = 8189.5 - tof1
factor = m_n*1e+15/eV
newX = 0.5*factor*16.0*(1/tel**2 - 1/dataX**2)
# compare
# self.assertEqual(newX[0], ws_dE.readX(0)[0])
self.assertTrue(np.allclose(newX, ws_dE.readX(0), atol=0.01))
# create reference data for Y axis and compare to the output
tof = dataX[:-1] + 5.25
newY = self._input_ws.readY(0)*tof**3/(factor*10.5*16.0)
# compare
self.assertTrue(np.allclose(newY, ws_dE.readY(0), rtol=0.01))
run_algorithm("DeleteWorkspace", Workspace=ws_dE)
run_algorithm("DeleteWorkspace", Workspace=wscorr)
def calibrate(ws, tubeSet, knownPositions, funcForm, **kwargs):
"""
Define the calibrated positions of the detectors inside the tubes defined
in tubeSet.
Tubes may be considered a list of detectors alined that may be considered
as pixels for the analogy when they values are displayed.
The position of these pixels are provided by the manufactor, but its real
position depends on the electronics inside the tube and varies slightly
from tube to tube. The calibrate method, aims to find the real positions
of the detectors (pixels) inside the tube.
For this, it will receive an Integrated workspace, where a special
measurement was performed so to have a
pattern of peaks or through. Where gaussian peaks or edges can be found.
The calibration follows the following steps
1. Finding the peaks on each tube
2. Fitting the peaks agains the Known Positions
3. Defining the new position for the pixels(detectors)
Let's consider the simplest way of calling calibrate:
.. code-block:: python
from tube import calibrate
ws = Load('WISH17701')
ws = Integration(ws)
known_pos = [-0.41,-0.31,-0.21,-0.11,-0.02, 0.09, 0.18, 0.28, 0.39 ]
peaks_form = 9*[1] # all the peaks are gaussian peaks
calibTable = calibrate(ws,'WISH/panel03',known_pos, peaks_form)
In this example, the calibrate framework will consider all the
tubes (152) from WISH/panel03.
You may decide to look for a subset of the tubes, by passing the
**rangeList** option.
.. code-block:: python
# This code will calibrate only the tube indexed as number 3
# (usually tube0004)
calibTable = calibrate(ws,'WISH/panel03',known_pos,
peaks_form, rangeList=[3])
**Finding the peaks on each tube**
* Dynamically fitting peaks
The framework expects that for each tube, it will find a peak pattern
around the pixels corresponding to the known_pos positions.
The way it will work out the estimated peak position (in pixel) is
1. Get the length of the tube: distance(first_detector,last_detector) in the tube.
2. Get the number of detectors in the tube (nDets)
3. It will be assumed that the center of the tube correspond to the origin (0)
.. code-block:: python
centre_pixel = known_pos * nDets/tube_length + nDets/2
It will them look for the real peak around the estimated value as:
.. code-block:: python
# consider tube_values the array of counts, and peak the estimated
# position for the peak
real_peak_pos = argmax(tube_values[peak-margin:peak+margin])
After finding the real_peak_pos, it will try to fit the region around
the peak to find the best expected position of the peak in a continuous
space. It will do this by fitting the region around the peak to a
Gaussian Function, and them extract the PeakCentre returned by the
Fitting.
.. code-block:: python
centre = real_peak_pos
fit_start, fit_stop = centre-margin, centre+margin
values = tube_values[fit_start,fit_stop]
background = min(values)
peak = max(values) - background
width = len(where(values > peak/2+background))
# It will fit to something like:
# Fit(function=LinerBackground,A0=background;Gaussian,
# Height=peak, PeakCentre=centre, Sigma=width,fit_start,fit_end)
* Force Fitting Parameters
These dinamically values can be avoided by defining the **fitPar** for
the calibrate function
.. code-block:: python
eP = [57.5, 107.0, 156.5, 206.0, 255.5, 305.0, 354.5, 404.0, 453.5]
# Expected Height of Gaussian Peaks (initial value of fit parameter)
ExpectedHeight = 1000.0
# Expected width of Gaussian peaks in pixels
# (initial value of fit parameter)
ExpectedWidth = 10.0
fitPar = TubeCalibFitParams( eP, ExpectedHeight, ExpectedWidth )
calibTable = calibrate(ws, 'WISH/panel03', known_pos, peaks_form, fitPar=fitPar)
Different Function Factors
Although the examples consider only Gaussian peaks, it is possible to
change the function factors to edges by passing the index of the
known_position through the **funcForm**. Hence, considering three special
points, where there are one gaussian peak and thow edges, the calibrate
could be configured as:
.. code-block:: python
known_pos = [-0.1 2 2.3]
# gaussian peak followed by two edges (through)
form_factor = [1 2 2]
calibTable = calibrate(ws,'WISH/panel03',known_pos,
form_factor)
* Override Peaks
It is possible to scape the finding peaks position steps by providing the
peaks through the **overridePeaks** parameters. The example below tests
the calibration of a single tube (30) but scapes the finding peaks step.
.. code-block:: python
known_pos = [-0.41,-0.31,-0.21,-0.11,-0.02, 0.09, 0.18, 0.28, 0.39 ]
define_peaks = [57.5, 107.0, 156.5, 206.0, 255.5, 305.0, 354.5,
404.0, 453.5]
calibTable = calibrate(ws, 'WISH/panel03', known_pos, peaks_form,
overridePeaks={30:define_peaks}, rangeList=[30])
* Output Peaks Positions
Enabling the option **outputPeak** a WorkspaceTable will be produced with
the first column as tube name and the following columns with the position
where corresponding peaks were found. Like the table below.
+-------+-------+-----+-------+
|TubeId | Peak1 | ... | PeakM |
+=======+=======+=====+=======+
|tube0 | 15.5 | ... | 370.3 |
+-------+-------+-----+-------+
| ... | ... | ... | ... |
+-------+-------+-----+-------+
|tubeN | 14.9 | ... | 371.2 |
+-------+-------+-----+-------+
The signature changes to:
.. code-block:: python
calibTable, peakTable = calibrate(...)
It is possible to give a peakTable directly to the **outputPeak** option,
which will make the calibration to append the peaks to the given table.
.. hint::
It is possible to save the peakTable to a file using the
:meth:`savePeak` method.
**Find the correct position along the tube**
The second step of the calibration is to define the correct position of
pixels along the tube. This is done by fitting the peaks positions found
at the previous step against the known_positions provided.
::
known | *
positions | *
| *
| *
|________________
pixels positions
The default operation is to fit the pixels positions against the known
positions with a quadratic function in order to define an operation to
move all the pixels to their real positions. If necessary, the user may
select to fit using a polinomial of 3rd order, through the parameter
**fitPolyn**.
.. note::
The known positions are given in the same unit as the spacial position
(3D) and having the center of the tube as the origin.
Hence, this section will define a function that:
.. math:: F(pix) = RealRelativePosition
**Define the new position for the detectors**
Finally, the position of the detectors are defined as a vector operation
like
.. math::
\\vec{p} = \\vec{c} + v \\vec{u}
Where :math:`\\vec{p}` is the position in the 3D space, **v** is the
RealRelativePosition deduced from the last session, and finally,
:math:`\\vec{u}` is the unitary vector in the direction of the tube.
:param ws: Integrated workspace with tubes to be calibrated.
:param tubeSet: Specification of Set of tubes to be calibrated. If a string is passed, a TubeSpec will be created passing the string as the setTubeSpecByString.
This will be the case for TubeSpec as string
.. code-block:: python
self.tube_spec = TubeSpec(ws)
self.tube_spec.setTubeSpecByString(tubeSet)
If a list of strings is passed, the TubeSpec will be created with this list:
.. code-block:: python
self.tube_spec = TubeSpec(ws)
self.tube_spec.setTubeSpecByStringArray(tubeSet)
If a :class:`~tube_spec.TubeSpec` object is passed, it will be used as it is.
:param knownPositions: The defined position for the peaks/edges, taking the center as the origin and having the same units as the tube length in the 3D space.
:param funcForm: list with special values to define the format of the peaks/edge (peaks=1, edge=2). If it is not provided, it will be assumed that all the knownPositions are peaks.
Optionals parameters to tune the calibration:
:param fitPar: Define the parameters to be used in the fit as a :class:`~tube_calib_fit_params.TubeCalibFitParams`. If not provided, the dynamic mode is used. See :py:func:`~Examples.TubeCalibDemoMaps_All.provideTheExpectedValue`
:param margin: value in pixesl that will be used around the peaks/edges to fit them. Default = 15. See the code of :py:mod:`~Examples.TubeCalibDemoMerlin` where **margin** is used to calibrate small tubes.
.. code-block:: python
fit_start, fit_end = centre - margin, centre + margin
:param rangeList: list of tubes indexes that will be calibrated. As in the following code (see: :py:func:`~Examples.TubeCalibDemoMaps_All.improvingCalibrationSingleTube`):
.. code-block:: python
for index in rangelist:
do_calibrate(tubeSet.getTube(index))
:param calibTable: Pass the calibration table, it will them append the values to the provided one and return it. (see: :py:mod:`~Examples.TubeCalibDemoMerlin`)
:param plotTube: If given, the tube whose index is in plotTube will be ploted as well as its fitted peaks, it can receive a list of indexes to plot.(see: :py:func:`~Examples.TubeCalibDemoMaps_All.changeMarginAndExpectedValue`)
:param excludeShortTubes: Do not calibrate tubes whose length is smaller than given value. (see at: Examples/TubeCalibDemoMerlin_Adjustable.py)
:param overridePeaks: dictionary that defines an array of peaks positions (in pixels) to be used for the specific tube(key). (see: :py:func:`~Examples.TubeCalibDemoMaps_All.improvingCalibrationSingleTube`)
.. code-block:: python
for index in rangelist:
if overridePeaks.has_key(index):
use_this_peaks = overridePeaks[index]
# skip finding peaks
fit_peaks_to_position()
:param fitPolyn: Define the order of the polinomial to fit the pixels positions agains the known positions. The acceptable values are 1, 2 or 3. Default = 2.
:param outputPeak: Enable the calibrate to output the peak table, relating the tubes with the pixels positions. It may be passed as a boolean value (outputPeak=True) or as a peakTable value. The later case is to inform calibrate to append the new values to the given peakTable. This is usefull when you have to operate in subsets of tubes. (see :py:mod:`~Examples.TubeCalibDemoMerlin` that shows a nice inspection on this table).
.. code-block:: python
calibTable, peakTable = calibrate(ws, (omitted), rangeList=[1],
outputPeak=True)
# appending the result to peakTable
calibTable, peakTable = calibrate(ws, (omitted), rangeList=[2],
outputPeak=peakTable)
# now, peakTable has information for tube[1] and tube[2]
:rtype: calibrationTable, a TableWorkspace with two columns DetectorID(int) and DetectorPositions(V3D).
"""
FITPAR = 'fitPar'
MARGIN = 'margin'
RANGELIST = 'rangeList'
CALIBTABLE = 'calibTable'
PLOTTUBE = 'plotTube'
EXCLUDESHORT = 'excludeShortTubes'
OVERRIDEPEAKS = 'overridePeaks'
FITPOLIN = 'fitPolyn'
OUTPUTPEAK = 'outputPeak'
#check that only valid arguments were passed through kwargs
for key in kwargs.keys():
if key not in [FITPAR, MARGIN, RANGELIST, CALIBTABLE, PLOTTUBE,
EXCLUDESHORT, OVERRIDEPEAKS, FITPOLIN,
OUTPUTPEAK]:
msg = "Wrong argument: '%s'! This argument is not defined in the signature of this function. Hint: remember that arguments are case sensitive" % key
raise RuntimeError(msg)
# check parameter ws: if it was given as string, transform it in
# mantid object
if isinstance(ws,str):
ws = mtd[ws]
if not isinstance(ws,MatrixWorkspace):
raise RuntimeError("Wrong argument ws = %s. It must be a MatrixWorkspace" % (str(ws)))
# check parameter tubeSet. It accepts string or preferable a TubeSpec
if isinstance(tubeSet,str):
selectedTubes = tubeSet
tubeSet = TubeSpec(ws)
tubeSet.setTubeSpecByString(selectedTubes)
elif isinstance(tubeSet, list):
selectedTubes = tubeSet
tubeSet = TubeSpec(ws)
tubeSet.setTubeSpecByStringArray(selectedTubes)
elif not isinstance(tubeSet,TubeSpec):
raise RuntimeError("Wrong argument tubeSet. It must be a TubeSpec or a string that defines the set of tubes to be calibrated. For example: WISH/panel03")
# check the known_positions parameter
# for old version compatibility, it also accepts IdealTube, eventhough
# they should only be used internally
if not (isinstance(knownPositions, list) or
isinstance(knownPositions, tuple) or
isinstance(knownPositions, numpy.ndarray)):
raise RuntimeError("Wrong argument knownPositions. It expects a list of values for the positions expected for the peaks in relation to the center of the tube")
else:
idealTube = IdealTube()
idealTube.setArray(numpy.array(knownPositions))
#deal with funcForm parameter
try:
nPeaks = len(idealTube.getArray())
if len(funcForm) != nPeaks:
raise 1
for val in funcForm:
if val not in [1,2]:
raise 2
except:
raise RuntimeError("Wrong argument FuncForm. It expects a list of values describing the form of everysingle peaks. So, for example, if there are three peaks where the first is a peak and the followers as edge, funcForm = [1, 2, 2]. Currently, it is defined 1-Gaussian Peak, 2 - Edge. The knownPos has %d elements and the given funcForm has %d."%(nPeaks, len(funcForm)))
#apply the functional form to the ideal Tube
idealTube.setForm(funcForm)
# check the FITPAR parameter (optional)
# if the FITPAR is given, than it will just pass on, if the FITPAR is
# not given, it will create a FITPAR 'guessing' the centre positions,
# and allowing the find peaks calibration methods to adjust the parameter
# for the peaks automatically
if kwargs.has_key(FITPAR):
fitPar = kwargs[FITPAR]
#fitPar must be a TubeCalibFitParams
if not isinstance(fitPar, TubeCalibFitParams):
raise RuntimeError("Wrong argument %s. This argument, when given, must be a valid TubeCalibFitParams object"%FITPAR)
else:
# create a fit parameters guessing centre positions
# the guessing obeys the following rule:
#
# centre_pixel = known_pos * ndets/tube_length + ndets / 2
#
# Get tube length and number of detectors
tube_length = tubeSet.getTubeLength(0)
#ndets = len(wsp_index_for_tube0)
id1, ndets, step = tubeSet.getDetectorInfoFromTube(0)
known_pos = idealTube.getArray()
# position of the peaks in pixels
centre_pixel = known_pos * ndets/tube_length + ndets * 0.5
fitPar = TubeCalibFitParams(centre_pixel)
# make it automatic, it means, that for every tube,
# the parameters for fit will be re-evaluated, from the first
# guess positions given by centre_pixel
fitPar.setAutomatic(True)
# check the MARGIN paramter (optional)
if kwargs.has_key(MARGIN):
try:
margin = float(kwargs[MARGIN])
except:
raise RuntimeError("Wrong argument %s. It was expected a number!"%MARGIN)
fitPar.setMargin(margin)
#deal with RANGELIST parameter
if kwargs.has_key(RANGELIST):
rangeList = kwargs[RANGELIST]
if isinstance(rangeList,int):
rangeList = [rangeList]
try:
# this deals with list and tuples and iterables to make sure
# rangeList becomes a list
rangeList = list(rangeList)
except:
raise RuntimeError("Wrong argument %s. It expects a list of indexes for calibration"%RANGELIST)
else:
rangeList = range(tubeSet.getNumTubes())
# check if the user passed the option calibTable
if kwargs.has_key(CALIBTABLE):
calibTable = kwargs[CALIBTABLE]
#ensure the correct type is passed
# if a string was passed, transform it in mantid object
if isinstance(calibTable,str):
calibTable = mtd[calibTable]
#check that calibTable has the expected form
try:
if not isinstance(calibTable,ITableWorkspace):
raise 1
if calibTable.columnCount() != 2:
raise 2
colNames = calibTable.getColumnNames()
if colNames[0] != 'Detector ID' or colNames[1] != 'Detector Position':
raise 3
except:
raise RuntimeError("Invalid type for %s. The expected type was ITableWorkspace with 2 columns(Detector ID and Detector Positions)" % CALIBTABLE)
else:
calibTable = CreateEmptyTableWorkspace(OutputWorkspace="CalibTable")
# "Detector ID" column required by ApplyCalibration
calibTable.addColumn(type="int",name="Detector ID")
# "Detector Position" column required by ApplyCalibration
calibTable.addColumn(type="V3D",name="Detector Position")
#deal with plotTube option
if kwargs.has_key(PLOTTUBE):
plotTube = kwargs[PLOTTUBE]
if isinstance(plotTube, int):
plotTube = [plotTube]
try:
plotTube = list(plotTube)
except:
raise RuntimeError("Wrong argument %s. It expects an index (int) or a list of indexes" %PLOTTUBE)
else:
plotTube = []
#deal with minimun tubes sizes
if kwargs.has_key(EXCLUDESHORT):
excludeShortTubes = kwargs[EXCLUDESHORT]
try:
excludeShortTubes = float(excludeShortTubes)
except:
raise RuntimeError("Wrong argument %s. It expects a float value for the minimun size of tubes to be calibrated")
else:
#a tube with length 0 can not be calibrated, this is the minimun value
excludeShortTubes = 0.0
#deal with OVERRIDEPEAKS parameters
if kwargs.has_key(OVERRIDEPEAKS):
overridePeaks = kwargs[OVERRIDEPEAKS]
try:
nPeaks = len(idealTube.getArray())
# check the format of override peaks
if not isinstance(overridePeaks, dict):
raise 1
for key in overridePeaks.keys():
if not isinstance(key,int):
raise 2
if key < 0 or key >= tubeSet.getNumTubes():
raise 3
if len(overridePeaks[key]) != nPeaks:
raise 4
except:
raise RuntimeError("Wrong argument %s. It expects a dictionary with key as the tube index and the value as a list of peaks positions. Ex (3 peaks): overridePeaks = {1:[2,5.4,500]}"%OVERRIDEPEAKS)
else:
overridePeaks = dict()
# deal with FITPOLIN parameter
if kwargs.has_key(FITPOLIN):
polinFit = kwargs[FITPOLIN]
if polinFit not in [1, 2,3]:
raise RuntimeError("Wrong argument %s. It expects a number 1 for linear, 2 for quadratic, or 3 for 3rd polinomial order when fitting the pixels positions agains the known positions" % FITPOLIN)
else:
polinFit = 2
# deal with OUTPUT PEAK
deletePeakTableAfter = False
if kwargs.has_key(OUTPUTPEAK):
outputPeak = kwargs[OUTPUTPEAK]
else:
outputPeak = False
if isinstance(outputPeak, ITableWorkspace):
if outputPeak.columnCount() < len(idealTube.getArray()):
raise RuntimeError("Wrong argument %s. It expects a boolean flag, or a ITableWorksapce with columns (TubeId, Peak1,...,PeakM) for M = number of peaks given in knownPositions" % OUTPUTPEAK)
else:
if not outputPeak:
deletePeakTableAfter = True
# create the output peak table
outputPeak = CreateEmptyTableWorkspace(OutputWorkspace="PeakTable")
outputPeak.addColumn(type='str',name='TubeId')
for i in range(len(idealTube.getArray())):
outputPeak.addColumn(type='float',name='Peak%d'%(i+1))
getCalibration(ws, tubeSet, calibTable, fitPar, idealTube, outputPeak,\
overridePeaks, excludeShortTubes, plotTube, rangeList, polinFit)
if deletePeakTableAfter:
DeleteWorkspace(str(outputPeak))
return calibTable
else:
return calibTable, outputPeak
