def load_workspace_from_filename(filename,
input_properties=DEFAULT_INPUTS,
output_properties=DEFAULT_OUTPUTS):
try:
alg, psi_data = create_load_algorithm(filename, input_properties)
alg.execute()
except:
alg, psi_data = create_load_algorithm(
filename.split(os.sep)[-1], input_properties)
alg.execute()
workspace = AnalysisDataService.retrieve(
alg.getProperty("OutputWorkspace").valueAsStr)
if is_workspace_group(workspace):
# handle multi-period data
load_result = _get_algorithm_properties(alg, output_properties)
load_result["OutputWorkspace"] = [
MuonWorkspaceWrapper(ws) for ws in workspace.getNames()
]
run = get_run_from_multi_period_data(workspace)
if not psi_data:
load_result["DataDeadTimeTable"] = AnalysisDataService.retrieve(
load_result["DeadTimeTable"]).getNames()[0]
for index, deadtime_table in enumerate(
AnalysisDataService.retrieve(
load_result["DeadTimeTable"]).getNames()):
if index == 0:
load_result["DataDeadTimeTable"] = deadtime_table
else:
DeleteWorkspace(Workspace=deadtime_table)
load_result["FirstGoodData"] = round(
load_result["FirstGoodData"] - load_result['TimeZero'], 2)
UnGroupWorkspace(load_result["DeadTimeTable"])
load_result["DeadTimeTable"] = None
UnGroupWorkspace(workspace.name())
else:
load_result["DataDeadTimeTable"] = None
load_result["FirstGoodData"] = round(load_result["FirstGoodData"],
2)
else:
# single period data
load_result = _get_algorithm_properties(alg, output_properties)
load_result["OutputWorkspace"] = [
MuonWorkspaceWrapper(load_result["OutputWorkspace"])
]
run = int(workspace.getRunNumber())
if not psi_data:
load_result["DataDeadTimeTable"] = load_result["DeadTimeTable"]
load_result["DeadTimeTable"] = None
load_result["FirstGoodData"] = round(
load_result["FirstGoodData"] - load_result['TimeZero'], 2)
else:
load_result["DataDeadTimeTable"] = None
load_result["FirstGoodData"] = round(load_result["FirstGoodData"],
2)
return load_result, run, filename, psi_data
def load_workspace_from_filename(filename,
input_properties=DEFAULT_INPUTS,
output_properties=DEFAULT_OUTPUTS):
try:
alg, psi_data = create_load_algorithm(filename, input_properties)
alg.execute()
except:
alg, psi_data = create_load_algorithm(
filename.split(os.sep)[-1], input_properties)
alg.execute()
# The filename given to the loading algorithm can be different to the file that was actually loaded.
# Pulling the filename back out of the algorithm after loading ensures that the path is accurate.
filename = alg.getProperty("Filename").value
workspace = AnalysisDataService.retrieve(
alg.getProperty("OutputWorkspace").valueAsStr)
if is_workspace_group(workspace):
# handle multi-period data
load_result = _get_algorithm_properties(alg, output_properties)
load_result["OutputWorkspace"] = [
MuonWorkspaceWrapper(ws) for ws in workspace.getNames()
]
run = get_run_from_multi_period_data(workspace)
deadtime_tables = AnalysisDataService.retrieve(
load_result["DeadTimeTable"]).getNames()
load_result["DataDeadTimeTable"] = deadtime_tables[0]
for table in deadtime_tables[1:]:
DeleteWorkspace(Workspace=table)
load_result["FirstGoodData"] = round(
load_result["FirstGoodData"] - load_result['TimeZero'], 3)
print("hiii", )
UnGroupWorkspace(load_result["DeadTimeTable"])
load_result["DeadTimeTable"] = None
UnGroupWorkspace(workspace.name())
else:
# single period data
load_result = _get_algorithm_properties(alg, output_properties)
load_result["OutputWorkspace"] = [
MuonWorkspaceWrapper(load_result["OutputWorkspace"])
]
run = int(workspace.getRunNumber())
load_result["DataDeadTimeTable"] = load_result["DeadTimeTable"]
load_result["DeadTimeTable"] = None
load_result["FirstGoodData"] = round(
load_result["FirstGoodData"] - load_result['TimeZero'], 3)
return load_result, run, filename, psi_data
def __call__(self, vesuvio_input, iteration, verbose_output=False):
vesuvio_output = VesuvioTOFFitOutput(
lambda index: vesuvio_input.sample_data.getSpectrum(
index).getSpectrumNo())
if vesuvio_input.using_back_scattering_spectra:
fit_profile_collection = self._mass_profile_collection.filter(
ignore_hydrogen_filter)
else:
fit_profile_collection = self._mass_profile_collection
all_mass_values = self._mass_profile_collection.masses
fit_mass_values = fit_profile_collection.masses
for index in range(vesuvio_input.spectra_number):
self._fit_namer.set_index(index)
all_profiles = ";".join(
self._mass_profile_collection.functions(index))
fit_profiles = ";".join(fit_profile_collection.functions(index))
# Calculate pre-fit to retrieve parameter approximations for corrections
prefit_result = self._prefit(vesuvio_input.sample_data, index,
fit_mass_values, fit_profiles)
# Calculate corrections
corrections_result = self._corrections(
vesuvio_input.sample_data, vesuvio_input.container_data, index,
all_mass_values, all_profiles, prefit_result[1],
verbose_output)
# Calculate final fit
fit_result = self._final_fit(corrections_result[-1],
fit_mass_values, fit_profiles)
# Update output with results from fit
_update_output(vesuvio_output, prefit_result, corrections_result,
fit_result)
# Clear ADS of intermediate workspaces and workspace group
if verbose_output:
UnGroupWorkspace(corrections_result[0])
UnGroupWorkspace(corrections_result[1])
mtd.remove(prefit_result[1].getName())
mtd.remove(corrections_result[-1].getName())
mtd.remove(fit_result[1].getName())
return vesuvio_output
def calibrate_tube(workspace: WorkspaceTypes,
tube_name: str,
output_peak_table: str = 'PeakTable',
output_parameters_table: str = 'ParametersTable',
output_peak_y_table: str = 'PeakYTable',
shadow_height: float = 1000,
shadow_width: float = 4,
fit_domain: float = 7) -> TableWorkspace:
r"""
Calibration table for one tube of CORELLI
This function creates TableWorkspace 'CalibTable', TableWorkspace 'PeakTable',
and WorkspaceGroup 'ParametersTable' containing TableWorkspace 'ParametersTableI'
where 'I' is the tube number.
:param workspace: string or handle to ~mantid.dataobjects.Workspace2D
:param tube_name: string uniquely representing one tube e.g. 'bank88/sixteenpack/tube3'
:param output_peak_table:
:param output_parameters_table:
:param output_peak_y_table:
:param shadow_height: estimated dip in the background intensity. Dips typical of Cd-wire runs are around 1000
neutron counts.
:param shadow_width: estimated width of the shadow cast by the wire, in pixel units. The Cd-wire typically
cast a shadow over four pixels.
:param fit_domain: estimated range, in pixel units, over which to carry out the fit. An appropriate value
is about twice the shadow width
:return: table containing detector ID and position vector
"""
message = f'Cannot process workspace {workspace}. Pass the name of an existing workspace or a workspace handle'
assert isinstance(workspace, (str, Workspace2D)), message
assert shadow_height > 0, 'shadow height must be positive'
for marker in ('bank', 'sixteenpack', 'tube'):
assert marker in tube_name, f'{tube_name} does not uniquely specify one tube'
peak_height, peak_width = -shadow_height, shadow_width
# Initial guess for the peak positions, assuming:
# - the center of the the wire mesh coincides with the center ot the tube_calib_fit_params
# - wires cast a shadow on a perfectly calibrated tube
fit_extent = (fit_domain / PIXELS_PER_TUBE) * TUBE_LENGTH # fit domain in meters
assert fit_extent < WIRE_GAP, 'The fit domain cannot be larger than the distance between consecutive wires'
wire_pixel_positions = wire_positions(units='pixels')[1: -1]
fit_par = TubeCalibFitParams(wire_pixel_positions, height=peak_height, width=peak_width, margin=fit_domain)
fit_par.setAutomatic(True)
# Generate the calibration table, the peak table, and the parameters table
peaks_form = [1] * len(wire_pixel_positions) # signals we'll be fitting dips (peaks with negative heights)
calibration_table, _ = tube.calibrate(workspace, tube_name, wire_positions(units='meters')[1: -1], peaks_form,
fitPar=fit_par, outputPeak=True,
parameters_table_group='ParametersTableGroup')
calibration_table = trim_calibration_table(calibration_table) # discard X and Z coordinates
# Additional workspaces
# Table with shadow positions along the tube, in pixel units
if output_peak_table != 'PeakTable': # 'PeakTable' is output by tube.calibrate
RenameWorkspace(InputWorkspace='PeakTable', OutputWorkspace=output_peak_table)
# Table with shadow positions along the vertical axis
calculate_peak_y_table(output_peak_table, 'ParametersTableGroup', output_workspace=output_peak_y_table)
# Table with optimized parameters for the polynomial coefficients A0, A1, A2, and chi-square
RenameWorkspace(InputWorkspace=mtd['ParametersTableGroup'].getItem(0), OutputWorkspace=output_parameters_table)
UnGroupWorkspace(InputWorkspace='ParametersTableGroup')
return calibration_table
def setUpClass(cls):
LoadNexusProcessed(Filename="VULCAN_192227_diagnostics.nxs",
OutputWorkspace="diagtest")
UnGroupWorkspace("diagtest")
cls.workspaces = [
"diag_dspacing", "diag_fitted", "diag_fitparam", "strain",
"single_strain", "difference", "single_diff", "center_tof"
]
def test_ungrouping_in_ads_calls_any_change_handle(self):
CreateSampleWorkspace(OutputWorkspace="ws1")
CreateSampleWorkspace(OutputWorkspace="ws2")
GroupWorkspaces(InputWorkspaces="ws1,ws2", OutputWorkspace="NewGroup")
self.project.anyChangeHandle = mock.MagicMock()
UnGroupWorkspace(InputWorkspace="NewGroup")
# 1 for removing old group and 1 for something else but 2 seems right
self.assertEqual(2, self.project.anyChangeHandle.call_count)
def remove_unwanted_workspaces(workspace_name, temp_workspace_name, period):
# Delete all entries except for the period which is requested
workspaces_to_keep = temp_workspace_name + "_" + str(period)
group_workspace = mtd[temp_workspace_name]
workspace_names_to_remove = [element.name() for element in group_workspace if element.name() != workspaces_to_keep]
for to_remove in workspace_names_to_remove:
DeleteWorkspace(to_remove)
# We need to ungroup the group workspace which only contains now a single workspace
UnGroupWorkspace(group_workspace)
RenameWorkspace(InputWorkspace=workspaces_to_keep, OutputWorkspace=workspace_name)
def test_observeUnGroup_calls_unGroupHandle_when_set_on_ads_and_a_group_is_ungrouped(self):
CreateSampleWorkspace(OutputWorkspace="ws1")
CreateSampleWorkspace(OutputWorkspace="ws2")
GroupWorkspaces(InputWorkspaces="ws1,ws2", OutputWorkspace="NewGroup")
self.fake_class.observeUnGroup(True)
self.fake_class.unGroupHandle = mock.MagicMock()
UnGroupWorkspace(InputWorkspace="NewGroup")
self.assertEqual(self.fake_class.unGroupHandle.call_count, 1)
def test_observeAll_calls_anyChangeHandle_when_set_on_ads_ungroup_performed(self):
self.fake_class.observeAll(True)
CreateSampleWorkspace(OutputWorkspace="ws1")
expected_count = 1
CreateSampleWorkspace(OutputWorkspace="ws2")
expected_count += 1
GroupWorkspaces(InputWorkspaces="ws1,ws2", OutputWorkspace="NewGroup")
# One for grouping the workspace
expected_count += 1
# One for adding it to the ADS
expected_count += 1
# Will replace first workspace
UnGroupWorkspace(InputWorkspace="NewGroup")
# One for ungrouping the workspace
expected_count += 1
# One for removing the grouped workspace object from the ADS
expected_count += 1
self.assertEqual(self.fake_class.anyChangeHandle.call_count, expected_count)
def __load_cached_data(cache_files, sha1, abs_method="", prefix_name=""):
"""try to load cached data from memory and disk
:param abs_method: absorption calculation method
:param sha1: SHA1 that identify cached workspace
:param cache_files: list of cache file names to search
:param prefix_name: prefix to add to wkspname for caching
return found_abs_wksp_sample, found_abs_wksp_container
abs_wksp_sample, abs_wksp_container, cache_files[ 0 ]
"""
# init
abs_wksp_sample, abs_wksp_container = "", ""
found_abs_wksp_sample, found_abs_wksp_container = False, False
# step_0: depending on the abs_method, suffix will be different
if abs_method == "SampleOnly":
abs_wksp_sample = f"{prefix_name}_ass"
found_abs_wksp_container = True
elif abs_method == "SampleAndContainer":
abs_wksp_sample = f"{prefix_name}_ass"
abs_wksp_container = f"{prefix_name}_acc"
elif abs_method == "FullPaalmanPings":
abs_wksp_sample = f"{prefix_name}_assc"
abs_wksp_container = f"{prefix_name}_ac"
else:
raise ValueError(
"Unrecognized absorption correction method '{}'".format(
abs_method))
# step_1: check memory
if mtd.doesExist(abs_wksp_sample):
found_abs_wksp_sample = mtd[abs_wksp_sample].run(
)["absSHA1"].value == sha1
if mtd.doesExist(abs_wksp_container):
found_abs_wksp_container = mtd[abs_wksp_container].run(
)["absSHA1"].value == sha1
# step_2: load from disk if either is not found in memory
if (not found_abs_wksp_sample) or (not found_abs_wksp_container):
for candidate in cache_files:
if os.path.exists(candidate):
wsntmp = "tmpwsg"
Load(Filename=candidate, OutputWorkspace=wsntmp)
wstype = mtd[wsntmp].id()
if wstype == "Workspace2D":
RenameWorkspace(InputWorkspace=wsntmp,
OutputWorkspace=abs_wksp_sample)
elif wstype == "WorkspaceGroup":
UnGroupWorkspace(InputWorkspace=wsntmp)
else:
raise ValueError(
f"Unsupported cached workspace type: {wstype}")
break
# step_3: check memory again
if mtd.doesExist(abs_wksp_sample):
found_abs_wksp_sample = mtd[abs_wksp_sample].run(
)["absSHA1"].value == sha1
if mtd.doesExist(abs_wksp_container):
found_abs_wksp_container = mtd[abs_wksp_container].run(
)["absSHA1"].value == sha1
return found_abs_wksp_sample, found_abs_wksp_container, abs_wksp_sample, abs_wksp_container, cache_files[
0]
