def PyExec(self):
workspace = get_input_workspace_as_copy_if_not_same_as_output_workspace(
self)
wavelength_pairs: List[Tuple[float, float]] = json.loads(
self.getProperty(self.WAV_PAIRS).value)
progress = Progress(self,
start=0.0,
end=1.0,
nreports=1 +
len(wavelength_pairs)) # 1 - convert units
# Convert the units into wavelength
progress.report("Converting workspace to wavelength units.")
workspace = self._convert_units_to_wavelength(workspace)
# Get the rebin option
output_group = WorkspaceGroup()
for pair in wavelength_pairs:
rebin_string = self._get_rebin_string(workspace, *pair)
progress.report(f"Converting wavelength range: {rebin_string}")
# Perform the rebin
rebin_options = self._get_rebin_params(rebin_string, workspace)
out_ws = self._perform_rebin(rebin_options)
append_to_sans_file_tag(out_ws, "_toWavelength")
output_group.addWorkspace(out_ws)
self.setProperty("OutputWorkspace", output_group)
def _create_matrix_workspaces_for_parameter_combination(
self, workspace_group: WorkspaceGroup, x_parameter_name: str,
y_parameter_name: str) -> None:
"""Creates the matrix workspace for a specific x and y parameter, and adds it to the workspace group."""
if x_parameter_name != y_parameter_name and x_parameter_name in self.x_parameters() \
and y_parameter_name in self.y_parameters():
x_values = self._convert_str_column_values_to_int(
self.fitting_context.x_parameters[x_parameter_name])
x_errors = self.fitting_context.x_parameter_errors[
x_parameter_name]
y_values = self._convert_str_column_values_to_int(
self.fitting_context.y_parameters[y_parameter_name])
y_errors = self.fitting_context.y_parameter_errors[
y_parameter_name]
# Sort the data based on the x_values being in ascending order
x_values, x_errors, y_values, y_errors = zip(
*sorted(zip(x_values, x_errors, y_values, y_errors)))
output_name = self.parameter_combination_workspace_name(
x_parameter_name, y_parameter_name)
if not self._parameter_combination_workspace_exists(
output_name, x_values, y_values, y_errors):
self._create_workspace(x_values, x_errors, x_parameter_name,
y_values, y_errors, y_parameter_name,
output_name)
workspace_group.add(output_name)
def test_plot_default_case_with_detector_3_not_present(self):
grpws = WorkspaceGroup()
ws_detector1 = '9999; Detector 1'
grpws.addWorkspace(CreateSampleWorkspace(OutputWorkspace=ws_detector1))
ws_detector2 = '9999; Detector 2'
grpws.addWorkspace(CreateSampleWorkspace(OutputWorkspace=ws_detector2))
ws_detector3 = '9999; Detector 4'
grpws.addWorkspace(CreateSampleWorkspace(OutputWorkspace=ws_detector3))
ws_detector4 = '9998; Detector 1'
grpws.addWorkspace(CreateSampleWorkspace(OutputWorkspace=ws_detector4))
run = [9999, 9998]
self.context.data_context._loaded_data.add_data(run=run,
workspace=grpws)
loadedData = self.context.data_context._loaded_data
self.context.group_context.reset_group_to_default(loadedData)
self.presenter.add_group_to_view(self.context.group_context._groups[0],
False)
self.presenter.add_group_to_view(self.context.group_context._groups[1],
False)
self.presenter.add_group_to_view(self.context.group_context._groups[2],
False)
self.presenter.add_group_to_view(self.context.group_context._groups[3],
False)
self.assertEqual(self.view.num_rows(), 4)
self.assertEqual(len(self.model.groups), 4)
analyse_checkbox = self.view.get_table_item(0, 4)
self.assertEqual(analyse_checkbox.checkState(), 0)
self.presenter.plot_default_case()
self.assertCountEqual(self.context.group_context.selected_groups,
['9999; Detector 1', '9998; Detector 1'])
def _create_matrix_workspaces_for_parameter_combinations(
self, workspace_group: WorkspaceGroup) -> list:
"""Creates a MatrixWorkspace for each parameter combination. These are the workspaces that will be fitted."""
workspace_names = []
for x_parameter_name in self.x_parameters():
for y_parameter_name in self.y_parameters():
if x_parameter_name != y_parameter_name:
x_values = self._convert_str_column_values_to_int(
x_parameter_name, self.fitting_context.x_parameters)
x_errors = self.fitting_context.x_parameter_errors[
x_parameter_name]
y_values = self._convert_str_column_values_to_int(
y_parameter_name, self.fitting_context.y_parameters)
y_errors = self.fitting_context.y_parameter_errors[
y_parameter_name]
# Sort the data based on the x_values being in ascending order
x_values, x_errors, y_values, y_errors = zip(
*sorted(zip(x_values, x_errors, y_values, y_errors)))
output_name = self.parameter_combination_workspace_name(
x_parameter_name, y_parameter_name)
if not self._parameter_combination_workspace_exists(
output_name, x_values, y_values, y_errors):
self._create_workspace(
x_values, x_errors, x_parameter_name, y_values,
y_errors, self._create_y_label(y_parameter_name),
output_name)
workspace_group.add(output_name)
workspace_names.append(output_name)
return workspace_names
def _create_sample_ws_group(ws_name):
# This has to be done as two steps or the simple API can't figure out the output name
ws_group = WorkspaceGroup()
ws_group.addWorkspace(CreateSampleWorkspace(OutputWorkspace=ws_name, Function='Flat background',
NumBanks=1, BankPixelWidth=1, NumEvents=1,
XMin=1, XMax=14, BinWidth=2))
return ws_group
def add_directory_structure(dirs):
"""
create the nested WorkspaceGroup structure in the ADS specified by the
stored directory attribute.
dirs = ["dir1", "dir2"] eg. ['Muon Data', 'MUSR72105', 'MUSR72105 Raw Data']
"""
if not dirs:
return
if len(dirs) > len(set(dirs)):
raise ValueError("Group names must be unique")
for directory in dirs:
if not AnalysisDataService.doesExist(directory):
workspace_group = WorkspaceGroup()
AnalysisDataService.addOrReplace(directory, workspace_group)
elif not isinstance(AnalysisDataService.retrieve(directory),
WorkspaceGroup):
AnalysisDataService.remove(directory)
workspace_group = WorkspaceGroup()
AnalysisDataService.addOrReplace(directory, workspace_group)
else:
# exists and is a workspace group
pass
# Create the nested group structure in the ADS
previous_dir = ""
for i, directory in enumerate(dirs):
if i == 0:
previous_dir = directory
continue
if not AnalysisDataService.retrieve(previous_dir).__contains__(
directory):
AnalysisDataService.retrieve(previous_dir).add(directory)
previous_dir = directory
def loaded_workspace_as_group(self, run):
if self.is_multi_period():
workspace_group = WorkspaceGroup()
for workspace_wrapper in self._loaded_data.get_data(run=run, instrument=self.instrument)['workspace']['OutputWorkspace']:
workspace_group.addWorkspace(workspace_wrapper.workspace)
return workspace_group
else:
return self._loaded_data.get_data(run=run, instrument=self.instrument)['workspace']['OutputWorkspace'][0].workspace
def test_that_can_add_workspaces_to_WorkspaceGroup_when_not_in_ADS(self):
ws1 = WorkspaceFactory.create("Workspace2D", 2, 2, 2)
ws2 = WorkspaceFactory.create("Workspace2D", 2, 2, 2)
ws_group = WorkspaceGroup()
ws_group.addWorkspace(ws1)
ws_group.addWorkspace(ws2)
self.assertEqual(ws_group.size(), 2)
def test_that_can_add_workspaces_to_WorkspaceGroup_when_not_in_ADS(self):
ws1 = WorkspaceFactory.create("Workspace2D", 2, 2, 2)
ws2 = WorkspaceFactory.create("Workspace2D", 2, 2, 2)
ws_group = WorkspaceGroup()
ws_group.addWorkspace(ws1)
ws_group.addWorkspace(ws2)
self.assertEqual(ws_group.size(), 2)
def test_that_can_add_workspaces_to_WorkspaceGroup_when_in_ADS(self):
self.create_matrix_workspace_in_ADS("ws1")
self.create_matrix_workspace_in_ADS("ws2")
ws_group = WorkspaceGroup()
mtd.add("group1", ws_group)
ws_group.add("ws1")
ws_group.add("ws2")
self.assertTrue("ws1" in mtd["group1"])
self.assertTrue("ws2" in mtd["group1"])
def test_that_can_add_workspaces_to_WorkspaceGroup_when_in_ADS(self):
self.create_matrix_workspace_in_ADS("ws1")
self.create_matrix_workspace_in_ADS("ws2")
ws_group = WorkspaceGroup()
mtd.add("group1", ws_group)
ws_group.add("ws1")
ws_group.add("ws2")
self.assertTrue("ws1" in mtd["group1"])
self.assertTrue("ws2" in mtd["group1"])
def set_reduced_can_workspace_on_output(self, completed_event_bundled):
"""
Sets the reduced can workspaces on the output properties.
The reduced can workspaces can be either LAB or HAB
:param completed_event_bundled: a list containing output bundles
"""
# Find the LAB Can and HAB Can entries if they exist
lab_groups = WorkspaceGroup()
hab_groups = WorkspaceGroup()
for bundle in completed_event_bundled:
if bundle.output_bundle.data_type is DataType.CAN:
reduction_mode = bundle.output_bundle.reduction_mode
output_workspace = bundle.output_bundle.output_workspace
# Make sure that the output workspace is not None which can be the case if there has never been a
# can set for the reduction.
if output_workspace is not None and not does_can_workspace_exist_on_ads(
output_workspace):
if reduction_mode is ReductionMode.LAB:
lab_groups.addWorkspace(output_workspace)
elif reduction_mode is ReductionMode.HAB:
hab_groups.addWorkspace(output_workspace)
else:
raise RuntimeError(
"SANSSingleReduction: The reduction mode {0} should not"
" be set with a can.".format(reduction_mode))
self._set_prop_if_group_has_data("OutputWorkspaceLABCan", lab_groups)
self._set_prop_if_group_has_data("OutputWorkspaceHABCan", hab_groups)
def create_run_workspaces(self, run):
detectors = ['Detector 1', 'Detector 2', 'Detector 3', 'Detector 4']
grpws = WorkspaceGroup()
ws_detector1 = CreateSampleWorkspace()
grpws.addWorkspace(ws_detector1)
ws_detector2 = CreateSampleWorkspace()
grpws.addWorkspace(ws_detector2)
ws_detector3 = CreateSampleWorkspace()
grpws.addWorkspace(ws_detector3)
ws_detector4 = CreateSampleWorkspace()
grpws.addWorkspace(ws_detector4)
run_results = RunObject(run, detectors, grpws)
self.model._loaded_data_store.add_data(run=[run], workspace=grpws)
self.model._data_context.run_info_update(run_results)
def test_finalise_groupworkspace(self):
"""
The test will fail if it cannot delete a workspace because it does not exist
"""
grpws = WorkspaceGroup()
ws_detector1 = CreateSampleWorkspace()
grpws.addWorkspace(ws_detector1)
grpws_name = 'New Group Workspace'
ws1 = CreateSampleWorkspace()
ws2 = CreateSampleWorkspace()
ws3 = CreateSampleWorkspace()
ws_list = [ws1, ws2, ws3]
load_utils_ea.finalise_groupworkspace(self.model, grpws, grpws_name,
ws_list)
self.assertTrue(AnalysisDataService.doesExist('New Group Workspace'))
def setUp(self) -> None:
# create a mock database
# tests save_bank_table and load_bank_table, save_manifest
self.database_path: str = TestCorelliDatabase.test_dir.name
date: str = datetime.now().strftime('%Y%m%d') # format YYYYMMDD
calibrated_ws = init_corelli_table()
calibrated_ws.addRow([28672, -1.2497636826045173])
calibrated_ws.addRow([28673, -1.2462425728938251])
calibrated_ws.addRow([28674, -1.2427213977528369])
calibrated_ws.addRow([28675, -1.2392001571797284])
save_bank_table(calibrated_ws, 10, self.database_path, date)
calibrated_ws = init_corelli_table()
calibrated_ws.addRow([28676, -1.2597636826045173])
calibrated_ws.addRow([28677, -1.2562425728938251])
calibrated_ws.addRow([28678, -1.2527213977528369])
calibrated_ws.addRow([28679, -1.2492001571797284])
save_bank_table(calibrated_ws, 20, self.database_path, date)
calibrated_ws = init_corelli_table()
calibrated_ws.addRow([28700, -1.1511478720770645])
calibrated_ws.addRow([28701, -1.1476249296284657])
calibrated_ws.addRow([28702, -1.2427213977528369])
calibrated_ws.addRow([28703, -1.2392001571797284])
save_bank_table(calibrated_ws, 30, self.database_path, date)
calibrated_ws = init_corelli_table()
calibrated_ws.addRow([28704, -1.1611478720770645])
calibrated_ws.addRow([28705, -1.1776249296284657])
calibrated_ws.addRow([28706, -1.2827213977528369])
calibrated_ws.addRow([28707, -1.2992001571797284])
save_bank_table(calibrated_ws, 40, self.database_path, '20200601') # use different date
calibrated_ws = init_corelli_table('calibration_' + str(40))
calibrated_ws.addRow([28704, -1.1711478720770645])
calibrated_ws.addRow([28705, -1.1876249296284657])
calibrated_ws.addRow([28706, -1.2927213977528369])
calibrated_ws.addRow([28707, -1.3092001571797284])
save_bank_table(calibrated_ws, 40, self.database_path, '20200101') # use different date
# placeholder to read from the database
self.ws_group = WorkspaceGroup()
date: str = datetime.now().strftime('%Y%m%d') # format YYYYMMDD
self.ws_group.addWorkspace(load_bank_table(10, self.database_path, date))
self.ws_group.addWorkspace(load_bank_table(20, self.database_path, date))
self.ws_group.addWorkspace(load_bank_table(30, self.database_path, date))
self.ws_group.addWorkspace(load_bank_table(40, self.database_path, '20200601'))
def test_grouped_workspaces_not_in_ads(self):
fig = plt.figure()
plt.plot([0, 1], [0, 1])
num_plots = 3
ws_list = []
ws_group = WorkspaceGroup()
for i in range(num_plots):
ws = CloneWorkspace(self._test_ws, StoreInADS=False)
ws_list.append(ws)
ws_group.addWorkspace(ws)
plot([ws_group], wksp_indices=[1], fig=fig, overplot=True)
ax = plt.gca()
self.assertEqual(len(ws_group) + 1, len(ax.lines))
self.assertEqual(len(ws_group) + 1, len(ax.lines))
def combine_loaded_runs(model, run_list):
"""
As a result of this function there should be:-
- a groupworkspace named after the range of runs used e.g. '1234-1237'
- a workspace for each detector named [detector]_[groupworkspace]
e.g. 'Detector 1_1234-1237'
"""
co_add_workspace_name = str(run_list[0]) + "-" + str(run_list[-1])
co_add_workspace = WorkspaceGroup()
finished_detectors = []
ws_remove = []
for run in run_list:
run_detectors = get_detectors(model, run)
detectors_to_check = check_for_unused_detectors(
run_detectors, finished_detectors)
if detectors_to_check:
for detector in run_detectors:
new_ws_name = detector
new_ws = find_ws_to_use(model, run_detectors, detector, run)
for current_run in run_list:
if current_run != run:
all_detectors = get_detectors(model, current_run)
if detector in all_detectors:
workspace_to_add = find_ws_to_use(
model, run_detectors, detector, current_run)
new_ws = new_ws + workspace_to_add
add_detector_workspace_to_group(co_add_workspace, new_ws,
new_ws_name, detector,
finished_detectors, ws_remove)
finalise_groupworkspace(model, co_add_workspace, co_add_workspace_name,
ws_remove)
def set_transmission_workspaces_on_output(self, completed_event_slices,
fit_state):
calc_can, calc_sample = WorkspaceGroup(), WorkspaceGroup()
unfit_can, unfit_sample = WorkspaceGroup(), WorkspaceGroup()
output_hab_or_lab = None
for bundle in completed_event_slices:
if output_hab_or_lab is not None and output_hab_or_lab != bundle.output_bundle.reduction_mode:
continue # The transmission workspace for HAB/LAB is the same, so only output one
output_hab_or_lab = bundle.output_bundle.reduction_mode
calculated_transmission_workspace = bundle.transmission_bundle.calculated_transmission_workspace
unfitted_transmission_workspace = bundle.transmission_bundle.unfitted_transmission_workspace
if bundle.transmission_bundle.data_type is DataType.CAN:
if does_can_workspace_exist_on_ads(
calculated_transmission_workspace):
# The workspace is cloned here because the transmission runs are diagnostic output so even though
# the values already exist they need to be labelled separately for each reduction.
calculated_transmission_workspace = CloneWorkspace(
calculated_transmission_workspace, StoreInADS=False)
if does_can_workspace_exist_on_ads(
unfitted_transmission_workspace):
unfitted_transmission_workspace = CloneWorkspace(
unfitted_transmission_workspace, StoreInADS=False)
if calculated_transmission_workspace:
calc_can.addWorkspace(calculated_transmission_workspace)
if unfitted_transmission_workspace:
unfit_can.addWorkspace(unfitted_transmission_workspace)
elif bundle.transmission_bundle.data_type is DataType.SAMPLE:
if calculated_transmission_workspace:
calc_sample.addWorkspace(calculated_transmission_workspace)
if unfitted_transmission_workspace:
unfit_sample.addWorkspace(unfitted_transmission_workspace)
else:
raise RuntimeError(
"SANSSingleReduction: The data type {0} should be"
" sample or can.".format(
bundle.transmission_bundle.data_type))
self._set_prop_if_group_has_data(
"OutputWorkspaceCalculatedTransmission", calc_sample)
self._set_prop_if_group_has_data("OutputWorkspaceUnfittedTransmission",
unfit_sample)
self._set_prop_if_group_has_data(
"OutputWorkspaceCalculatedTransmissionCan", calc_can)
self._set_prop_if_group_has_data(
"OutputWorkspaceUnfittedTransmissionCan", unfit_can)
def merge_and_crop_workspaces(self, workspaces):
""" where workspaces is a tuple of form:
(filepath, ws name)
"""
workspace_name = self.getPropertyValue('GroupWorkspace')
# detectors is a dictionary of {detector_name : [names_of_workspaces]}
detectors = {
f"{workspace_name}; Detector {x}": []
for x in range(1, 5)
}
# fill dictionary
for workspace in workspaces:
detector_number = workspace[0]
detectors[f"{workspace_name}; Detector {detector_number}"].append(
workspace)
# initialise a group workspace
overall_ws = WorkspaceGroup()
# merge each workspace list in detectors into a single workspace
for detector, workspace_list in detectors.items():
if workspace_list:
# sort workspace list according to type_index
sorted_workspace_list = [None] * NUM_FILES_PER_DETECTOR
# sort workspace list according to type_index
for workspace in workspace_list:
data_type = workspace.rsplit("_")[1]
sorted_workspace_list[SPECTRUM_INDEX[data_type] -
1] = workspace
workspace_list = sorted_workspace_list
# create merged workspace
merged_ws = self.create_merged_workspace(workspace_list)
ConvertToHistogram(InputWorkspace=merged_ws,
OutputWorkspace=detector)
minX, maxX = [], []
ws = AnalysisDataService.retrieve(detector)
for i in range(ws.getNumberHistograms()):
xdata = ws.readX(i)
minX.append(xdata[0])
if i == 2:
maxX.append(xdata[-1])
else:
maxX.append(xdata[-1] - 1)
CropWorkspaceRagged(InputWorkspace=detector,
OutputWorkspace=detector,
xmin=minX,
xmax=maxX)
overall_ws.addWorkspace(AnalysisDataService.retrieve(detector))
self.setProperty("GroupWorkspace", overall_ws)
def test_add_detector_workspace_to_group(self):
"""
The test will fail if it cannot delete a workspace because it does not exist
"""
grp_ws = WorkspaceGroup()
new_ws = CreateSampleWorkspace()
new_ws_name = 'Test Workspace Name'
detector = 'Detector 3'
finished_detectors = []
ws_list = []
load_utils_ea.add_detector_workspace_to_group(grp_ws, new_ws,
new_ws_name, detector,
finished_detectors,
ws_list)
self.assertEqual(ws_list, ['Test Workspace Name'])
self.assertEqual(finished_detectors, ['Detector 3'])
self.assertEqual(grp_ws.getNames()[0], 'Test Workspace Name')
def set_output_workspaces(self,
workflow_outputs: SANSWorkflowAlgorithmOutputs):
"""
Sets the output workspaces which can be HAB, LAB or Merged.
At this step we also provide a workspace name to the sample logs which can be used later on for saving
:param workflow_outputs: collection of wavelength sliced and reduced workspaces
"""
# Note that this breaks the flexibility that we have established with the reduction mode. We have not hardcoded
# HAB or LAB anywhere which means that in the future there could be other detectors of relevance. Here we
# reference HAB and LAB directly since we currently don't want to rely on dynamic properties. See also in PyInit
merged, lab, hab, scaled = WorkspaceGroup(), WorkspaceGroup(
), WorkspaceGroup(), WorkspaceGroup()
for ws in workflow_outputs.lab_output:
lab.addWorkspace(ws)
for ws in workflow_outputs.hab_output:
hab.addWorkspace(ws)
for ws in workflow_outputs.merged_output:
merged.addWorkspace(ws)
for ws in workflow_outputs.scaled_hab_output:
scaled.addWorkspace(ws)
self._set_prop_if_group_has_data("OutputWorkspaceLAB", lab)
self._set_prop_if_group_has_data("OutputWorkspaceHAB", hab)
self._set_prop_if_group_has_data("OutputWorkspaceHABScaled", scaled)
self._set_prop_if_group_has_data("OutputWorkspaceMerged", merged)
def set_reduced_can_workspace_on_output(self, output_bundles):
"""
Sets the reduced can group workspaces on the output properties.
The reduced can workspaces can be:
LAB Can or
HAB Can
:param output_bundles: a list of output bundles
"""
workspace_group_lab_can = WorkspaceGroup()
workspace_group_hab_can = WorkspaceGroup()
# Find the LAB Can and HAB Can entries if they exist
for component_bundle in output_bundles:
for output_bundle in component_bundle:
if output_bundle.data_type is DataType.Can:
reduction_mode = output_bundle.reduction_mode
output_workspace = output_bundle.output_workspace
# Make sure that the output workspace is not None which can be the case if there has never been a
# can set for the reduction.
if output_workspace is not None and not does_can_workspace_exist_on_ads(
output_workspace):
name = self._get_output_workspace_name(
output_bundle.state,
output_bundle.reduction_mode,
can=True)
AnalysisDataService.addOrReplace(
name, output_workspace)
if reduction_mode is ISISReductionMode.LAB:
workspace_group_lab_can.addWorkspace(
output_workspace)
elif reduction_mode is ISISReductionMode.HAB:
workspace_group_hab_can.addWorkspace(
output_workspace)
else:
raise RuntimeError(
"SANSSingleReduction: The reduction mode {0} should not"
" be set with a can.".format(reduction_mode))
if workspace_group_lab_can.size() > 0:
# LAB group workspace is non-empty, so we want to set it as output
self.setProperty("OutputWorkspaceLABCan", workspace_group_lab_can)
if workspace_group_hab_can.size() > 0:
self.setProperty("OutputWorkspaceHABCan", workspace_group_hab_can)
def create_group_workspace_and_load(self):
grpws = WorkspaceGroup()
ws_detector1 = '9999; Detector 1'
grpws.addWorkspace(CreateSampleWorkspace(OutputWorkspace=ws_detector1))
ws_detector2 = '9999; Detector 2'
grpws.addWorkspace(CreateSampleWorkspace(OutputWorkspace=ws_detector2))
ws_detector3 = '9999; Detector 3'
grpws.addWorkspace(CreateSampleWorkspace(OutputWorkspace=ws_detector3))
run = 9999
self.context.data_context._loaded_data.add_data(run=[run],
workspace=grpws)
loadedData = self.context.data_context._loaded_data
self.context.group_context.reset_group_to_default(loadedData)
def set_reduced_can_count_and_norm_on_output(self, completed_event_slices):
"""
Sets the reduced can count and norm group workspaces on the output properties.
This includes the HAB/LAB counts and Norms
:param completed_event_slices: a list containing a single list of output bundle parts
"""
# Find the partial output bundles fo LAB Can and HAB Can if they exist
lab_can_counts, hab_can_counts = WorkspaceGroup(), WorkspaceGroup()
lab_can_norms, hab_can_norms = WorkspaceGroup(), WorkspaceGroup()
for bundle in completed_event_slices:
if bundle.output_bundle.data_type is DataType.CAN:
reduction_mode = bundle.parts_bundle.reduction_mode
output_workspace_count = bundle.parts_bundle.output_workspace_count
output_workspace_norm = bundle.parts_bundle.output_workspace_norm
# Make sure that the output workspace is not None which can be the case if there has never been a
# can set for the reduction.
if output_workspace_norm is not None and output_workspace_count is not None and \
not does_can_workspace_exist_on_ads(output_workspace_norm) and \
not does_can_workspace_exist_on_ads(output_workspace_count):
if reduction_mode is ReductionMode.LAB:
lab_can_counts.addWorkspace(output_workspace_count)
lab_can_norms.addWorkspace(output_workspace_norm)
elif reduction_mode is ReductionMode.HAB:
hab_can_counts.addWorkspace(output_workspace_count)
hab_can_norms.addWorkspace(output_workspace_norm)
else:
raise RuntimeError(
"SANSSingleReduction: The reduction mode {0} should not"
" be set with a partial can.".format(
reduction_mode))
self._set_prop_if_group_has_data("OutputWorkspaceLABCanCount",
lab_can_counts)
self._set_prop_if_group_has_data("OutputWorkspaceLABCanNorm",
lab_can_norms)
self._set_prop_if_group_has_data("OutputWorkspaceHABCanCount",
hab_can_counts)
self._set_prop_if_group_has_data("OutputWorkspaceHABCanNorm",
hab_can_norms)
def _create_workspace_group_to_store_combination_workspaces(self) -> WorkspaceGroup:
"""Return the Workspace Group used to store the different parameter combination matrix workspaces."""
group_name = self.parameter_combination_group_name()
if group_name is None:
return None
if check_if_workspace_exist(group_name):
workspace_group = retrieve_ws(group_name)
else:
workspace_group = WorkspaceGroup()
add_ws_to_ads(group_name, workspace_group)
return workspace_group
def set_output_workspaces(self, reduction_mode_vs_output_workspaces,
reduction_mode_vs_workspace_names):
"""
Sets the output workspaces which can be HAB, LAB or Merged.
At this step we also provide a workspace name to the sample logs which can be used later on for saving
:param reduction_mode_vs_output_workspaces: map from reduction mode to output workspace
:param reduction_mode_vs_workspace_names: an unused dict. Required for version 2 compatibility
"""
# Note that this breaks the flexibility that we have established with the reduction mode. We have not hardcoded
# HAB or LAB anywhere which means that in the future there could be other detectors of relevance. Here we
# reference HAB and LAB directly since we currently don't want to rely on dynamic properties. See also in PyInit
merged, lab, hab = WorkspaceGroup(), WorkspaceGroup(), WorkspaceGroup()
for reduction_mode, output_workspace_list in reduction_mode_vs_output_workspaces.items(
):
for output_workspace in output_workspace_list:
if reduction_mode is ReductionMode.MERGED:
merged.addWorkspace(output_workspace)
elif reduction_mode is ReductionMode.LAB:
lab.addWorkspace(output_workspace)
elif reduction_mode is ReductionMode.HAB:
hab.addWorkspace(output_workspace)
else:
raise RuntimeError(
"SANSSingleReduction: Cannot set the output workspace. The selected reduction "
"mode {0} is unknown.".format(reduction_mode))
self._set_prop_if_group_has_data("OutputWorkspaceLAB", lab)
self._set_prop_if_group_has_data("OutputWorkspaceHAB", hab)
self._set_prop_if_group_has_data("OutputWorkspaceMerged", merged)
def combine_spatial_banks(tables: WorkspaceGroup,
table_type: str = 'calibration',
name: Optional[str] = None) -> TableWorkspace:
"""
Function that inputs a GroupWorkspace of TableWorkspace .
:param tables: input GroupWorkspace with independent bank TableWorkspace for Corelli
:param table_type: input type of table. One of ('calibration', 'mask')
:param name: input name for the TableWorkSpace output
:return unified TableWorkspace for all banks
"""
message = f'{"Cannot process Corelli combine_spatial_banks, input is not of type WorkspaceGroup"}'
assert isinstance(tables, WorkspaceGroup), message
combined_table: TableWorkspace = init_corelli_table(table_type=table_type, name=name)\
if name else init_corelli_table(table_type=table_type)
for i in range(tables.getNumberOfEntries()):
table = tables.getItem(i)
# check type
message = f'Cannot process Corelli combine_spatial_banks, table ' + str(
i) + ' is not of type TableWorkspace'
assert isinstance(table, TableWorkspace), message
# check column names
if not has_valid_columns(table, table_type=table_type):
message = f'Table index {i} of type {table_type }is not a valid Corelli TableWorkspace'
raise RuntimeError(message)
table_dict = table.toDict()
# loop through rows
column_names = table.getColumnNames(
) # 'Detector ID' and 'Detector Y Coordinate' for 'calibration' table_type
for r in range(0, table.rowCount()):
combined_table.addRow(
[table_dict[name][r] for name in column_names])
return combined_table
def set_can_and_sam_on_output(self, completed_event_slices):
"""
Sets the reduced can and sample workspaces.
These is the LAB/HAB can and sample
Cans are also output for optimization, so check for double output.
:param output_bundles: a list containing a single list of output_bundles
"""
lab_cans, hab_cans = WorkspaceGroup(), WorkspaceGroup()
lab_samples, hab_samples = WorkspaceGroup(), WorkspaceGroup()
for bundle in completed_event_slices:
reduction_mode = bundle.output_bundle.reduction_mode
output_workspace = bundle.output_bundle.output_workspace
if bundle.output_bundle.data_type is DataType.CAN:
if output_workspace is not None and not does_can_workspace_exist_on_ads(
output_workspace):
if reduction_mode is ReductionMode.LAB:
lab_cans.addWorkspace(output_workspace)
elif reduction_mode is ReductionMode.HAB:
hab_cans.addWorkspace(output_workspace)
else:
raise RuntimeError(
"SANSSingleReduction: The reduction mode {0} should not"
" be set with a can.".format(reduction_mode))
elif bundle.output_bundle.data_type is DataType.SAMPLE:
if output_workspace is not None and not does_can_workspace_exist_on_ads(
output_workspace):
if reduction_mode is ReductionMode.LAB:
lab_samples.addWorkspace(output_workspace)
elif reduction_mode is ReductionMode.HAB:
hab_samples.addWorkspace(output_workspace)
else:
raise RuntimeError(
"SANSSingleReduction: The reduction mode {0} should not"
" be set with a sample.".format(reduction_mode))
self._set_prop_if_group_has_data("OutputWorkspaceLABCan", lab_cans)
self._set_prop_if_group_has_data("OutputWorkspaceHABCan", hab_cans)
self._set_prop_if_group_has_data("OutputWorkspaceLABSample",
lab_samples)
self._set_prop_if_group_has_data("OutputWorkspaceHABSample",
hab_samples)
def test_find_ws_to_use(self):
run = 5555
detector = 'Detector 3'
run_detectors = [
'Detector 1', 'Detector 2', 'Detector 3', 'Detector 4'
]
grpws = WorkspaceGroup()
ws_detector1 = CreateSampleWorkspace()
grpws.addWorkspace(ws_detector1)
ws_detector2 = CreateSampleWorkspace()
grpws.addWorkspace(ws_detector2)
ws_detector3 = CreateSampleWorkspace()
grpws.addWorkspace(ws_detector3)
ws_detector4 = CreateSampleWorkspace()
grpws.addWorkspace(ws_detector4)
self.model._loaded_data_store.add_data(run=[run], workspace=grpws)
ws = load_utils_ea.find_ws_to_use(self.model, run_detectors, detector,
run)
self.assertEqual(ws.name(), 'ws_detector3')
def create_group_workspace_and_load(self):
grpws = WorkspaceGroup()
ws_detector1 = '9999; Detector 1'
grpws.addWorkspace(CreateSampleWorkspace(OutputWorkspace=ws_detector1))
ws_detector2 = '9999; Detector 2'
grpws.addWorkspace(CreateSampleWorkspace(OutputWorkspace=ws_detector2))
run = 9999
self.loadedData.add_data(run=[run], workspace=grpws)
def add_copy_to_ads(self) -> None:
"""Adds the output workspaces for this fit to the ADS. Must be a copy so the fit history remains a history."""
workspace_group = WorkspaceGroup()
add_ws_to_ads(self.output_group_name(), workspace_group)
for output_workspace in self.output_workspaces:
add_ws_to_ads(output_workspace.workspace_name,
output_workspace.workspace_copy())
workspace_group.add(output_workspace.workspace_name)
add_ws_to_ads(self.parameter_workspace.workspace_name,
self.parameter_workspace.workspace_copy())
workspace_group.add(self.parameter_workspace.workspace_name)
add_ws_to_ads(self.covariance_workspace.workspace_name,
self.covariance_workspace.workspace_copy())
workspace_group.add(self.covariance_workspace.workspace_name)
def create_test_workspacegroup(group_name=None, size=None, items=None):
if size is not None and items is not None:
raise ValueError("Provide either size or items not both.")
group_name = group_name if group_name is not None else 'fitting_context_testgroup'
group = WorkspaceGroup()
if size is not None:
for i in range(size):
ws_name = '{}_{}'.format(group_name, i)
fake_ws = create_test_workspace(ws_name)
group.addWorkspace(fake_ws)
elif items is not None:
for item in items:
group.addWorkspace(item)
ads = AnalysisDataService.Instance()
ads.addOrReplace(group_name, group)
return group