def test_that_can_load_isis_nexus_file_with_event_data_and_multi_period(self):
# Arrange
state = SANSLoadTest._get_simple_state(sample_scatter="LARMOR00013065.nxs",
calibration="80tubeCalibration_18-04-2016_r9330-9335.nxs")
# Act
output_workspace_names = {"SampleScatterWorkspace": "sample_scatter",
"SampleScatterMonitorWorkspace": "sample_monitor_scatter"}
load_alg = self._run_load(state, publish_to_cache=True, use_cached=True, move_workspace=False,
output_workspace_names=output_workspace_names)
# Assert
expected_number_of_workspaces = [4, 0, 0, 0, 0, 0]
expected_number_on_ads = 1
workspace_type = [EventWorkspace, None, None, None, None, None]
self._do_test_output(load_alg, expected_number_of_workspaces, expected_number_on_ads, workspace_type)
# Check that calibration is added
self.assertTrue(SANSLoadTest._has_calibration_been_applied(load_alg))
# Confirm that the ADS workspace contains the calibration file
try:
AnalysisDataService.retrieve("80tubeCalibration_18-04-2016_r9330-9335")
on_ads = True
except RuntimeError:
on_ads = False
self.assertTrue(on_ads)
# Cleanup
remove_all_workspaces_from_ads()
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 test_DNSFRSelfCorrection(self):
outputWorkspaceName = "DNSFlippingRatioCorrTest_Test4"
# consider normalization=1.0 as set in self._create_fake_workspace
dataws_sf = self.__sf_nicrws - self.__sf_bkgrws
dataws_nsf = self.__nsf_nicrws - self.__nsf_bkgrws
alg_test = run_algorithm("DNSFlippingRatioCorr", SFDataWorkspace=dataws_sf,
NSFDataWorkspace=dataws_nsf, SFNiCrWorkspace=self.__sf_nicrws.getName(),
NSFNiCrWorkspace=self.__nsf_nicrws.getName(), SFBkgrWorkspace=self.__sf_bkgrws.getName(),
NSFBkgrWorkspace=self.__nsf_bkgrws.getName(), SFOutputWorkspace=outputWorkspaceName+'SF',
NSFOutputWorkspace=outputWorkspaceName+'NSF')
self.assertTrue(alg_test.isExecuted())
# check whether the data are correct
ws_sf = AnalysisDataService.retrieve(outputWorkspaceName + 'SF')
ws_nsf = AnalysisDataService.retrieve(outputWorkspaceName + 'NSF')
# dimensions
self.assertEqual(24, ws_sf.getNumberHistograms())
self.assertEqual(24, ws_nsf.getNumberHistograms())
self.assertEqual(2, ws_sf.getNumDims())
self.assertEqual(2, ws_nsf.getNumDims())
# data array: spin-flip must be zero
for i in range(24):
self.assertAlmostEqual(0.0, ws_sf.readY(i)[0])
# data array: non spin-flip must be nsf - sf^2/nsf
nsf = np.array(dataws_nsf.extractY())
sf = np.array(dataws_sf.extractY())
refdata = nsf + sf
for i in range(24):
self.assertAlmostEqual(refdata[i][0], ws_nsf.readY(i)[0])
run_algorithm("DeleteWorkspace", Workspace=outputWorkspaceName + 'SF')
run_algorithm("DeleteWorkspace", Workspace=outputWorkspaceName + 'NSF')
run_algorithm("DeleteWorkspace", Workspace=dataws_sf)
run_algorithm("DeleteWorkspace", Workspace=dataws_nsf)
return
def edit_matrix_workspace(sq_name, scale_factor, shift, edited_sq_name=None):
"""
Edit the matrix workspace of S(Q) by scaling and shift
:param sq_name: name of the SofQ workspace
:param scale_factor:
:param shift:
:param edited_sq_name: workspace for the edited S(Q)
:return:
"""
# get the workspace
if AnalysisDataService.doesExist(sq_name) is False:
raise RuntimeError('S(Q) workspace {0} cannot be found in ADS.'.format(sq_name))
if edited_sq_name is not None:
simpleapi.CloneWorkspace(InputWorkspace=sq_name, OutputWorkspace=edited_sq_name)
sq_ws = AnalysisDataService.retrieve(edited_sq_name)
else:
sq_ws = AnalysisDataService.retrieve(sq_name)
# get the vector of Y
sq_ws = sq_ws * scale_factor
sq_ws = sq_ws + shift
if sq_ws.name() != edited_sq_name:
simpleapi.DeleteWorkspace(Workspace=edited_sq_name)
simpleapi.RenameWorkspace(InputWorkspace=sq_ws, OutputWorkspace=edited_sq_name)
assert sq_ws is not None, 'S(Q) workspace cannot be None.'
print('[DB...BAT] S(Q) workspace that is edit is {0}'.format(sq_ws))
def test_that_can_find_can_reduction_if_it_exists(self):
# Arrange
test_director = TestDirector()
state = test_director.construct()
tagged_workspace_names = {None: "test_ws",
OutputParts.Count: "test_ws_count",
OutputParts.Norm: "test_ws_norm"}
SANSFunctionsTest._prepare_workspaces(number_of_workspaces=4,
tagged_workspace_names=tagged_workspace_names,
state=state,
reduction_mode=ISISReductionMode.LAB)
# Act
workspace, workspace_count, workspace_norm = get_reduced_can_workspace_from_ads(state, output_parts=True,
reduction_mode=ISISReductionMode.LAB) # noqa
# Assert
self.assertTrue(workspace is not None)
self.assertTrue(workspace.name() == AnalysisDataService.retrieve("test_ws").name())
self.assertTrue(workspace_count is not None)
self.assertTrue(workspace_count.name() == AnalysisDataService.retrieve("test_ws_count").name())
self.assertTrue(workspace_norm is not None)
self.assertTrue(workspace_norm.name() == AnalysisDataService.retrieve("test_ws_norm").name())
# Clean up
SANSFunctionsTest._remove_workspaces()
def _check_if_all_multi_period_workspaces_have_the_same_position(self, base_name, number_of_workspaces):
reference_name = base_name + str(1)
reference_workspace = AnalysisDataService.retrieve(reference_name)
reference_position, reference_rotation = self._get_position_and_rotation(reference_workspace)
for index in range(2, number_of_workspaces + 1):
ws_name = base_name + str(index)
workspace = AnalysisDataService.retrieve(ws_name)
position, rotation = self._get_position_and_rotation(workspace)
self.assertEqual(position, reference_position)
self.assertEqual(rotation, reference_rotation)
def calculate_peak_center(self):
""" Calculate peak's center by averaging the peaks found and stored in PeakWorkspace
:return:
"""
# Go through the peak workspaces to calculate peak center with weight (monitor and counts)
peak_ws = AnalysisDataService.retrieve(self._myPeakWorkspaceName)
# spice table workspace
spice_table_name = get_spice_table_name(self._myExpNumber, self._myScanNumber)
spice_table_ws = AnalysisDataService.retrieve(spice_table_name)
pt_spice_row_dict = build_pt_spice_table_row_map(spice_table_ws)
det_col_index = spice_table_ws.getColumnNames().index('detector')
monitor_col_index = spice_table_ws.getColumnNames().index('monitor')
num_found_peaks = peak_ws.rowCount()
q_sample_sum = numpy.array([0., 0., 0.])
weight_sum = 0.
for i_peak in xrange(num_found_peaks):
# get peak
peak_i = peak_ws.getPeak(i_peak)
run_number = peak_i.getRunNumber()
# get Pt. number
pt_number = run_number % self._myScanNumber
# get row number and then detector counts and monitor counts
if pt_number not in pt_spice_row_dict:
# skip
print '[Error] Scan %d Peak %d Pt %d cannot be located.' % (self._myScanNumber, i_peak, pt_number)
continue
row_index = pt_spice_row_dict[pt_number]
det_counts = spice_table_ws.cell(row_index, det_col_index)
monitor_counts = spice_table_ws.cell(row_index, monitor_col_index)
if monitor_counts < 1.:
# skip zero-count
continue
# convert q sample from V3D to ndarray
q_i = peak_i.getQSampleFrame()
q_array = numpy.array([q_i.X(), q_i.Y(), q_i.Z()])
# calculate weight
weight_i = float(det_counts)/float(monitor_counts)
# contribute to total
weight_sum += weight_i
q_sample_sum += q_array * weight_i
# set non-normalized peak intensity as detector counts (roughly)
peak_i.setIntensity(det_counts)
# END-FOR (i_peak)
self._avgPeakCenter = q_sample_sum/weight_sum
return
def test_genHKLList(self):
""" Test to load a .hkl file
"""
# Set up
alg_test = run_algorithm("CreateLeBailFitInput",
ReflectionsFile = "",
MaxHKL = "12,12,12",
FullprofParameterFile = "2011B_HR60b2.irf",
Bank = 2,
LatticeConstant = 4.66,
GenerateBraggReflections = True,
InstrumentParameterWorkspace = "PG3_Bank2_Foo2",
BraggPeakParameterWorkspace = "Arb_Peaks"
)
# Execute
self.assertTrue(alg_test.isExecuted())
# Verify some values
# Profile parameter workspace
paramws = AnalysisDataService.retrieve("PG3_Bank2_Foo2")
paramname0 = paramws.cell(0, 0)
if paramname0.lower() == "bank":
numrowgood = 28
else:
numrowgood = 27
#print "Parameter name of first line = ", paramname0
#self.assertEqual(numrowgood, paramws.rowCount())
paramnames = []
for i in range(paramws.rowCount()):
paramname = paramws.cell(i, 0)
paramnames.append(paramname)
self.assertEqual(paramnames.count("LatticeConstant"), 1)
# Bragg peak list
braggws = AnalysisDataService.retrieve("Arb_Peaks")
self.assertEqual(braggws.rowCount() > 20, True)
# 4. Delete the test hkl file
AnalysisDataService.remove("PG3_Bank2_Foo2")
AnalysisDataService.remove("Arb_Peaks")
return
def test_LoadPartiallyValidFilesMultipleLogValues(self):
outputWorskapceName = "LoadLogPropertyTableTest_Test2"
alg_test = run_algorithm(
"LoadLogPropertyTable",
FirstFile="emu00006473.nxs",
LastFile="emu00006475.nxs",
LogNames="Temp_Sample,dur",
OutputWorkspace=outputWorskapceName,
)
self.assertTrue(alg_test.isExecuted())
# Verify some values
tablews = AnalysisDataService.retrieve(outputWorskapceName)
self.assertEqual(2, tablews.rowCount())
self.assertEqual(3, tablews.columnCount())
self.assertEqual(6473, tablews.cell(0, 0))
self.assertAlmostEqual(200.078, tablews.cell(0, 1), 2)
self.assertEqual("8697", tablews.cell(0, 2))
self.assertEqual(6475, tablews.cell(1, 0))
self.assertAlmostEqual(283.523, tablews.cell(1, 1), 2)
self.assertEqual("5647", tablews.cell(1, 2))
run_algorithm("DeleteWorkspace", Workspace=outputWorskapceName)
return
def test_LoadValidFilesComments(self):
outputWorskapceName = "LoadLogPropertyTableTest_Test1"
alg_test = run_algorithm(
"LoadLogPropertyTable",
FirstFile="MUSR00015189.nxs",
LastFile="MUSR00015193.nxs",
LogNames="comment",
OutputWorkspace=outputWorskapceName,
)
self.assertTrue(alg_test.isExecuted())
# Verify some values
tablews = AnalysisDataService.retrieve(outputWorskapceName)
self.assertEqual(5, tablews.rowCount())
self.assertEqual(2, tablews.columnCount())
self.assertEqual("18.95MHz 100W", tablews.cell(0, 1))
self.assertEqual(15189, tablews.cell(0, 0))
self.assertEqual(15193, tablews.cell(4, 0))
run_algorithm("DeleteWorkspace", Workspace=outputWorskapceName)
return
def test_temperature_log_is_time_series(self):
outputWorkspaceName = "output_ws"
EditInstrumentGeometry(self._input_ws, L2="4,8", Polar="0,15",
Azimuthal="0,0", DetectorIDs="1,2")
AddTimeSeriesLog(
self._input_ws,
'temperature',
'2010-09-14T04:20:12',
Value='0.0')
AddTimeSeriesLog(
self._input_ws,
'temperature',
'2010-09-14T04:20:13',
Value='0.0')
AddTimeSeriesLog(
self._input_ws,
'temperature',
'2010-09-14T04:20:14',
Value='0.0')
alg_test = run_algorithm("ComputeCalibrationCoefVan",
VanadiumWorkspace=self._input_ws,
EPPTable=self._table,
OutputWorkspace=outputWorkspaceName)
self.assertTrue(alg_test.isExecuted())
wsoutput = AnalysisDataService.retrieve(outputWorkspaceName)
self._checkDWF(wsoutput, 0.0)
def convert_fit_parameter_table_to_dict(table_name):
"""
:param table_name:
:return:
"""
# table workspace
assert isinstance(table_name, str), 'blabla'
table_ws = AnalysisDataService.retrieve(table_name)
# create dictionary
fit_param_dict = dict()
params_list = table_ws.getColumnNames()
# ['wsindex', 'peakindex', 'Height', 'PeakCentre', 'Sigma', 'A0', 'A1', 'chi2']
# go through all lines
num_rows = table_ws.rowCount()
for irow in range(num_rows):
value_dict = dict()
ws_index = None
for iparam, par_name in enumerate(params_list):
value_i = table_ws.cell(irow, iparam)
if par_name == 'wsindex':
ws_index = int(value_i)
else:
value_dict[par_name] = float(value_i)
# END-FOR (column)
assert ws_index is not None
fit_param_dict[ws_index] = value_dict
# END-FOR (row)
return fit_param_dict
def test_LoadTOF(self):
outputWorkspaceName = "LoadDNSLegacyTest_Test7"
filename = "dnstof.d_dat"
tof1 = 424.668 # must be changed if L1 will change
alg_test = run_algorithm("LoadDNSLegacy", Filename=filename, Normalization='no',
OutputWorkspace=outputWorkspaceName)
self.assertTrue(alg_test.isExecuted())
# Verify some values
ws = AnalysisDataService.retrieve(outputWorkspaceName)
# dimensions
self.assertEqual(24, ws.getNumberHistograms())
self.assertEqual(100, ws.getNumberBins())
# data array
self.assertEqual(8, ws.readY(19)[37]) # must be changed after comissioning will be finished
self.assertAlmostEqual(tof1, ws.readX(0)[0], 3)
self.assertAlmostEqual(tof1+40.1*100, ws.readX(0)[100], 3)
# sample logs
run = ws.getRun()
self.assertEqual(-7.5, run.getProperty('deterota').value)
self.assertEqual(100, run.getProperty('tof_channels').value)
self.assertEqual(51428, run.getProperty('mon_sum').value)
self.assertEqual('z', run.getProperty('polarisation').value)
self.assertEqual(33, run.getProperty('EPP').value) # check that EPP is taken from file
self.assertEqual('7', str(run.getProperty('polarisation_comment').value))
self.assertEqual('no', run.getProperty('normalized').value)
# check whether detector bank is rotated
det = ws.getDetector(0)
self.assertAlmostEqual(7.5, ws.detectorSignedTwoTheta(det)*180/pi)
run_algorithm("DeleteWorkspace", Workspace=outputWorkspaceName)
return
def test_DNSMomentumTransfer(self):
outputWorkspaceName = "DNSMergeRunsTest_Test4"
alg_test = run_algorithm("DNSMergeRuns", WorkspaceNames=self.workspaces,
OutputWorkspace=outputWorkspaceName, HorizontalAxis='|Q|')
self.assertTrue(alg_test.isExecuted())
# check whether the data are correct
ws = AnalysisDataService.retrieve(outputWorkspaceName)
# dimensions
self.assertEqual(96, ws.blocksize())
self.assertEqual(2, ws.getNumDims())
self.assertEqual(1, ws.getNumberHistograms())
# data array
# reference values
ttheta = np.round(np.radians(self.angles), 4)
qarr = np.sort(4.0*np.pi*np.sin(0.5*ttheta)/4.2)
# read the merged values
dataX = ws.extractX()[0]
for i in range(len(self.angles)):
self.assertAlmostEqual(qarr[i], dataX[i])
# check that the intensity has not been changed
dataY = ws.extractY()[0]
for i in range(len(dataY)):
self.assertAlmostEqual(1.0, dataY[i])
run_algorithm("DeleteWorkspace", Workspace=outputWorkspaceName)
return
def test_TwoTheta(self):
# check whether the 2theta angles the same as in the data workspace
outputWorkspaceName = "DNSDetCorrVanaTest_Test5"
# rotate detector bank to different angles
api.LoadInstrument(self.__dataws, InstrumentName='DNS')
api.LoadInstrument(self.__vanaws, InstrumentName='DNS')
api.LoadInstrument(self.__bkgrws, InstrumentName='DNS')
api.RotateInstrumentComponent(self.__dataws, "bank0", X=0, Y=1, Z=0, Angle=-7.53)
api.RotateInstrumentComponent(self.__vanaws, "bank0", X=0, Y=1, Z=0, Angle=-8.02)
api.RotateInstrumentComponent(self.__bkgrws, "bank0", X=0, Y=1, Z=0, Angle=-8.54)
# run correction
alg_test = run_algorithm("DNSDetEffCorrVana", InputWorkspace=self.__dataws.getName(),
OutputWorkspace=outputWorkspaceName, VanaWorkspace=self.__vanaws.getName(),
BkgWorkspace=self.__bkgrws.getName())
self.assertTrue(alg_test.isExecuted())
# check dimensions and angles
ws = AnalysisDataService.retrieve(outputWorkspaceName)
# dimensions
self.assertEqual(24, ws.getNumberHistograms())
self.assertEqual(2, ws.getNumDims())
# angles
tthetas = np.array([7.53 + i*5 for i in range(24)])
for i in range(24):
det = ws.getDetector(i)
self.assertAlmostEqual(tthetas[i], np.degrees(ws.detectorSignedTwoTheta(det)))
run_algorithm("DeleteWorkspace", Workspace=outputWorkspaceName)
return
def test_LoadWavelength(self):
outputWorkspaceName = "LoadDNSLegacyTest_Test8"
filename = "dn134011vana.d_dat"
alg_test = run_algorithm("LoadDNSLegacy", Filename=filename, Normalization='no',
OutputWorkspace=outputWorkspaceName, CoilCurrentsTable=self.curtable,
Wavelength=5.7)
self.assertTrue(alg_test.isExecuted())
# Verify some values
ws = AnalysisDataService.retrieve(outputWorkspaceName)
# dimensions
self.assertEqual(24, ws.getNumberHistograms())
self.assertEqual(2, ws.getNumDims())
# data array
self.assertEqual(31461, ws.readY(1))
self.assertEqual(13340, ws.readY(23))
self.assertAlmostEqual(5.7, ws.readX(1)[0], 3)
self.assertAlmostEqual(5.7, ws.readX(23)[0], 3)
# sample logs
run = ws.getRun()
self.assertEqual(5.7, run.getProperty('wavelength').value)
self.assertAlmostEqual(2.51782, run.getProperty('Ei').value, 3)
run_algorithm("DeleteWorkspace", Workspace=outputWorkspaceName)
return
def save_workspaces(self, workspaces_to_save=None):
"""
Use the private method _get_workspaces_to_save to get a list of workspaces that are present in the ADS to save
to the directory that was passed at object creation time, it will also add each of them to the output_list
private instance variable on the WorkspaceSaver class.
:param workspaces_to_save: List of Strings; The workspaces that are to be saved to the project.
"""
# Handle getting here and nothing has been given passed
if workspaces_to_save is None:
return
for workspace_name in workspaces_to_save:
# Get the workspace from the ADS
workspace = ADS.retrieve(workspace_name)
place_to_save_workspace = os.path.join(self.directory, workspace_name)
from mantid.simpleapi import SaveMD, SaveNexusProcessed
try:
if isinstance(workspace, MDHistoWorkspace) or isinstance(workspace, IMDEventWorkspace):
# Save normally using SaveMD
SaveMD(InputWorkspace=workspace_name, Filename=place_to_save_workspace + ".nxs")
else:
# Save normally using SaveNexusProcessed
SaveNexusProcessed(InputWorkspace=workspace_name, Filename=place_to_save_workspace + ".nxs")
except Exception:
logger.warning("Couldn't save workspace in project: " + workspace)
self.output_list.append(workspace_name)
def retrieve_hkl_from_spice_table(self):
""" Get averaged HKL from SPICE table
HKL will be averaged from SPICE table by assuming the value in SPICE might be right
:return:
"""
# get SPICE table
spice_table_name = get_spice_table_name(self._myExpNumber, self._myScanNumber)
assert AnalysisDataService.doesExist(spice_table_name), 'Spice table for exp %d scan %d cannot be found.' \
'' % (self._myExpNumber, self._myScanNumber)
spice_table_ws = AnalysisDataService.retrieve(spice_table_name)
# get HKL column indexes
h_col_index = spice_table_ws.getColumnNames().index('h')
k_col_index = spice_table_ws.getColumnNames().index('k')
l_col_index = spice_table_ws.getColumnNames().index('l')
# scan each Pt.
hkl = numpy.array([0., 0., 0.])
num_rows = spice_table_ws.rowCount()
for row_index in xrange(num_rows):
mi_h = spice_table_ws.cell(row_index, h_col_index)
mi_k = spice_table_ws.cell(row_index, k_col_index)
mi_l = spice_table_ws.cell(row_index, l_col_index)
hkl += numpy.array([mi_h, mi_k, mi_l])
# END-FOR
self._spiceHKL = hkl/num_rows
return
def get_average_omega(exp_number, scan_number):
"""Get average omega (omega-theta)
:param exp_number:
:param scan_number:
:return:
"""
# get table workspace
spice_table_name = util4.get_spice_table_name(exp_number, scan_number)
spice_table = AnalysisDataService.retrieve(spice_table_name)
# column index
col_omega_index = spice_table.getColumnNames().index('omega')
col_2theta_index = spice_table.getColumnNames().index('2theta')
# get the vectors
vec_size = spice_table.rowCount()
vec_omega = numpy.ndarray(shape=(vec_size, ), dtype='float')
vec_2theta = numpy.ndarray(shape=(vec_size, ), dtype='float')
for i_row in range(vec_size):
vec_omega[i_row] = spice_table.cell(i_row, col_omega_index)
vec_2theta[i_row] = spice_table.cell(i_row, col_2theta_index)
# END-FOR
vec_omega -= vec_2theta * 0.5
return numpy.sum(vec_omega)
def test_LoadPRFFile(self):
""" Test to load a .prf file
"""
# 1. Create test .prf file
prffilename = "test.prf"
self._createPrfFile(prffilename)
# 2. Execute the algorithm
alg_test = run_algorithm("LoadFullprofFile",
Filename = prffilename,
OutputWorkspace = "Data",
PeakParameterWorkspace = "Info")
self.assertTrue(alg_test.isExecuted())
# 3. Check data
dataws = AnalysisDataService.retrieve("Data")
self.assertEqual(dataws.getNumberHistograms(), 4)
self.assertEqual(len(dataws.readX(0)), 36)
# value
self.assertEqual(dataws.readX(0)[13], 5026.3223)
self.assertEqual(dataws.readY(1)[30], 0.3819)
# 4. Clean
os.remove(prffilename)
AnalysisDataService.remove("Data")
AnalysisDataService.remove("Info")
return
def test_saveGSS(self):
""" Test to Save a GSAS file to match V-drive
"""
# Create a test data file and workspace
binfilename = "testbin.dat"
self._createBinFile(binfilename)
datawsname = "TestInputWorkspace"
self._createDataWorkspace(datawsname)
# Execute
alg_test = run_algorithm("SaveVulcanGSS",
InputWorkspace = datawsname,
BinFilename = binfilename,
OutputWorkspace = datawsname+"_rebinned",
GSSFilename = "tempout.gda")
self.assertTrue(alg_test.isExecuted())
# Verify ....
outputws = AnalysisDataService.retrieve(datawsname+"_rebinned")
#self.assertEqual(4, tablews.rowCount())
# Delete the test hkl file
os.remove(binfilename)
AnalysisDataService.remove("InputWorkspace")
AnalysisDataService.remove(datawsname+"_rebinned")
return
def get_weighted_peak_centres(self):
""" Get the peak centers found in peak workspace.
Guarantees: the peak centers and its weight (detector counts) are exported
:return: 2-tuple: list of 3-tuple (Qx, Qy, Qz)
list of double (Det_Counts)
"""
# get PeaksWorkspace
if AnalysisDataService.doesExist(self._myPeakWorkspaceName) is False:
raise RuntimeError('PeaksWorkspace %s does ot exit.' % self._myPeakWorkspaceName)
peak_ws = AnalysisDataService.retrieve(self._myPeakWorkspaceName)
# get peak center, peak intensity and etc.
peak_center_list = list()
peak_intensity_list = list()
num_peaks = peak_ws.getNumberPeaks()
for i_peak in xrange(num_peaks):
peak_i = peak_ws.getPeak(i_peak)
center_i = peak_i.getQSampleFrame()
intensity_i = peak_i.getIntensity()
peak_center_list.append((center_i.X(), center_i.Y(), center_i.Z()))
peak_intensity_list.append(intensity_i)
# END-FOR
return peak_center_list, peak_intensity_list
def decode(self, obj_dic, project_path=None):
"""
Decode a InstrumentView Dictionary from project Save and return the object created
:param obj_dic: Dict; A dictionary containing the information for an InstrumentView
:param project_path: String; The location of the project save location
:return: InstrumentView's View; The View object with correct state is returned.
"""
load_mask = True
if obj_dic is None:
return None
if project_path is None:
project_path = ""
load_mask = False
# Make the widget
ws = ADS.retrieve(obj_dic["workspaceName"])
instrument_view = InstrumentViewPresenter(ws).container
instrument_widget = instrument_view.widget
# Then 'decode' set the values from the dictionary
self.widget_decoder.decode(obj_dic, instrument_widget, project_path, load_mask)
# Show the end result
return instrument_view
def get_moving_motor_information(spice_table_name):
"""
:param spice_table_name:
:return:
"""
table = AnalysisDataService.retrieve(spice_table_name)
col_names = table.getColumnNames()
pt_index = col_names.index('Pt.')
omega_index = col_names.index('omega')
chi_index = col_names.index('chi')
phi_index = col_names.index('phi')
col_tup_dict = {'omega': omega_index, 'phi': phi_index, 'chi': chi_index}
std_list = list()
motor_vector_dict = dict()
for motor in col_tup_dict:
motor_index = col_tup_dict[motor]
motor_vector = numpy.array(table.column(motor_index))
motor_vector_dict[motor] = motor_vector
std_list.append((motor_vector.std(), motor))
std_list.sort()
moving_motor = std_list[-1][1]
pt_list = table.column(pt_index)
motor_pos_dict = dict()
for i_m in range(len(pt_list)):
motor_pos_dict[pt_list[i_m]] = motor_vector_dict[moving_motor][i_m]
return moving_motor, motor_pos_dict
def test_updateDouble(self):
""" Test for update a double value
"""
# tablews = self.create_TableWorkspace()
alg_init = run_algorithm("CreateEmptyTableWorkspace", OutputWorkspace="TestTableWorkspace")
self.assertTrue(alg_init.isExecuted())
tablews = AnalysisDataService.retrieve("TestTableWorkspace")
tablews.addColumn("str", "Name")
tablews.addColumn("double", "Value")
tablews.addColumn("str", "FitOrTie")
tablews.addRow(["A", 1.34, "Fit"])
tablews.addRow(["B", 2.34, "Tie"])
tablews.addRow(["S", 3.34, "Tie"])
alg_test = run_algorithm("UpdatePeakParameterTableValue", InputWorkspace=alg_init.getPropertyValue("OutputWorkspace"),
Column="Value", ParameterNames=["A"], NewFloatValue=1.00)
self.assertTrue(alg_test.isExecuted())
newvalue_A = tablews.cell(0, 1)
self.assertEqual(newvalue_A, 1.00)
return
def __init__(self, data_file, workspace_name=None):
self.errors = []
if HAS_MANTID:
try:
if workspace_name is None:
self.data_ws = "__raw_data_file"
else:
self.data_ws = str(workspace_name)
api.HFIRLoad(Filename=str(data_file), OutputWorkspace=self.data_ws)
ws = AnalysisDataService.retrieve(self.data_ws)
x = ws.dataX(0)
self.wavelength = (x[0]+x[1])/2.0
self.wavelength_spread = x[1]-x[0]
self.sample_detector_distance = ws.getRun().getProperty("sample-detector-distance").value
self.sample_detector_distance_offset = ws.getRun().getProperty("sample-detector-distance-offset").value
self.sample_si_window_distance = ws.getRun().getProperty("sample-si-window-distance").value
self.sample_detector_distance_moved = ws.getRun().getProperty("sample_detector_distance").value
self.sample_thickness = ws.getRun().getProperty("sample-thickness").value
self.beam_diameter = ws.getRun().getProperty("beam-diameter").value
logger.notice("Loaded data file: %s" % data_file)
except:
logger.error("Error loading data file:\n%s" % sys.exc_value)
self.errors.append("Error loading data file:\n%s" % sys.exc_value)
def test_LoadHKLFile(self):
""" Test to load a .hkl file
"""
# 1. Create a test file
hklfilename = "test.hkl"
self._createHKLFile(hklfilename)
# 2.
alg_test = run_algorithm("LoadFullprofFile", Filename = hklfilename,
OutputWorkspace = "Foo", PeakParameterWorkspace = "PeakParameterTable")
self.assertTrue(alg_test.isExecuted())
# 3. Verify some values
tablews = AnalysisDataService.retrieve("PeakParameterTable")
self.assertEqual(4, tablews.rowCount())
# alpha of (11 5 1)/Row 0
self.assertEqual(0.34252, tablews.cell(0, 3))
# 4. Delete the test hkl file
os.remove(hklfilename)
AnalysisDataService.remove("PeakParameterTable")
AnalysisDataService.remove("Foo")
return
def test_calculation_fitsample(self):
OutputWorkspaceName = "outputws"
# generate EPP table
table = FindEPP(self._input_ws)
alg_test = run_algorithm("TOFTOFConvertTofToDeltaE", InputWorkspace=self._input_ws,
EPPTable=table, OutputWorkspace=OutputWorkspaceName)
self.assertTrue(alg_test.isExecuted())
wsoutput = AnalysisDataService.retrieve(OutputWorkspaceName)
# create reference data for X axis
dataX = np.linspace(4005.75, 7995.75, 381)
tel = 6005.25
factor = m_n*1e+15/eV
newX = 0.5*factor*16.0*(1/tel**2 - 1/dataX**2)
# compare
self.assertTrue(np.allclose(newX, wsoutput.readX(0))) # sdd = 4.0
self.assertTrue(np.allclose(4.0*newX, wsoutput.readX(1))) # sdd = 8.0
# 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)
self.assertTrue(np.allclose(newY, wsoutput.readY(0))) # sdd = 4.0
self.assertTrue(np.allclose(newY/4.0, wsoutput.readY(1))) # sdd = 8.0
DeleteWorkspace(wsoutput)
DeleteWorkspace(table)
def test_LoadNormalizeToDuration(self):
outputWorkspaceName = "LoadDNSLegacyTest_Test1"
filename = "dn134011vana.d_dat"
alg_test = run_algorithm("LoadDNSLegacy", Filename=filename, Normalization='duration',
OutputWorkspace=outputWorkspaceName, CoilCurrentsTable=self.curtable)
self.assertTrue(alg_test.isExecuted())
# Verify some values
ws = AnalysisDataService.retrieve(outputWorkspaceName)
# dimensions
self.assertEqual(24, ws.getNumberHistograms())
self.assertEqual(2, ws.getNumDims())
# data array
self.assertAlmostEqual(31461.0/600.0, ws.readY(1))
self.assertAlmostEqual(13340.0/600.0, ws.readY(23))
# sample logs
run = ws.getRun()
self.assertEqual(-8.54, run.getProperty('deterota').value)
self.assertEqual(8332872, run.getProperty('mon_sum').value)
self.assertEqual('duration', run.getProperty('normalized').value)
# check whether detector bank is rotated
det = ws.getDetector(0)
self.assertAlmostEqual(8.54, ws.detectorSignedTwoTheta(det)*180/pi)
run_algorithm("DeleteWorkspace", Workspace=outputWorkspaceName)
return
def _hasWorkspaceID(workspace_name, workspace_id):
"""Check that a workspace has the given type"""
workspace = AnalysisDataService.retrieve(workspace_name)
if isinstance(workspace, WorkspaceGroup):
return workspace[0].id() == workspace_id
else:
return workspace.id() == workspace_id
def display_workspace(self, name):
from mantidqt.widgets.workspacedisplay.matrix.presenter import MatrixWorkspaceDisplay
from mantidqt.widgets.workspacedisplay.table.presenter import TableWorkspaceDisplay
if AnalysisDataService.doesExist(name):
ws = AnalysisDataService.retrieve(name)
if isinstance(ws, MatrixWorkspace):
presenter = MatrixWorkspaceDisplay(ws, plot=plot)
presenter.show_view()
elif isinstance(ws, ITableWorkspace):
presenter = TableWorkspaceDisplay(ws, plot=matplotlib.pyplot)
presenter.show_view()
def test_input_run_is_loaded_if_not_in_ADS(self):
args = self._default_options
args['InputRunList'] = '13460'
outputs = ['IvsQ_13460', 'IvsQ_binned_13460', 'TOF_13460', 'TOF']
self._assert_run_algorithm_succeeds(args, outputs)
history = [
'ReflectometryISISPreprocess', 'ReflectometryReductionOneAuto',
'GroupWorkspaces'
]
self._check_history(AnalysisDataService.retrieve('IvsQ_binned_13460'),
history)
def testTable(self):
# tests that correct table is created
OutputWorkspaceName = "outputws1"
alg_test = run_algorithm("FindEPP", InputWorkspace=self._input_ws, OutputWorkspace=OutputWorkspaceName)
self.assertTrue(alg_test.isExecuted())
wsoutput = AnalysisDataService.retrieve(OutputWorkspaceName)
self.assertEqual(2, wsoutput.rowCount())
self.assertEqual(9, wsoutput.columnCount())
columns = ['WorkspaceIndex', 'PeakCentre', 'PeakCentreError', 'Sigma', 'SigmaError', 'Height', 'HeightError', 'chiSq', 'FitStatus']
self.assertEqual(columns, wsoutput.getColumnNames())
DeleteWorkspace(wsoutput)
def runTest(self):
enginx = EnginX(vanadium_run="ENGINX236516",
focus_runs=["ENGINX299080"],
save_dir=CWDIR,
full_inst_calib_path=FULL_CALIB,
ceria_run="ENGINX193749",
group=GROUP.TEXTURE20)
enginx.main(plot_cal=False, plot_foc=False)
# store workspaces for validation
self._ws_foc = ADS.retrieve(
"299080_engggui_focusing_output_ws_Texture20")
def test_debug_option_outputs_extra_workspaces_with_overridden_names(self):
self._create_workspace(13460, 'TOF_')
args = self._default_options
args['InputRunList'] = '13460'
args['Debug'] = True
args.update(self._all_output_names)
outputs = ['testIvsQ', 'testIvsQBin', 'testIvsLam', 'TOF_13460', 'TOF']
self._assert_run_algorithm_succeeds(args, outputs)
history = ['ReflectometryReductionOneAuto', 'GroupWorkspaces']
self._check_history(AnalysisDataService.retrieve('testIvsQBin'),
history)
def update_fit(self, fit_props):
for fit_prop in fit_props:
wsname = fit_prop['properties']['InputWorkspace']
self._fit_results[wsname] = {
'model': fit_prop['properties']['Function'],
'status': fit_prop['status']
}
self._fit_results[wsname]['results'] = defaultdict(
list) # {function_param: [[Y1, E1], [Y2,E2],...] }
fnames = [
x.split('=')[-1] for x in findall(
'name=[^,]*', fit_prop['properties']['Function'])
]
# get num params for each function (first elem empty as str begins with 'name=')
# need to remove ties and constraints which are enclosed in ()
nparams = [
s.count('=')
for s in sub(r'=\([^)]*\)', '', fit_prop['properties']
['Function']).split('name=')[1:]
]
params_dict = ADS.retrieve(fit_prop['properties']['Output'] +
'_Parameters').toDict()
# loop over rows in output workspace to get value and error for each parameter
istart = 0
for ifunc, fname in enumerate(fnames):
for iparam in range(0, nparams[ifunc]):
irow = istart + iparam
key = '_'.join([
fname, params_dict['Name'][irow].split('.')[-1]
]) # funcname_param
self._fit_results[wsname]['results'][key].append([
params_dict['Value'][irow], params_dict['Error'][irow]
])
if key in fit_prop['peak_centre_params']:
# param corresponds to a peak centre in TOF which we also need in dspacing
# add another entry into the results dictionary
key_d = key + "_dSpacing"
try:
dcen = self._convert_TOF_to_d(
params_dict['Value'][irow], wsname)
dcen_er = self._convert_TOFerror_to_derror(
params_dict['Error'][irow], dcen, wsname)
self._fit_results[wsname]['results'][key_d].append(
[dcen, dcen_er])
except (ValueError, RuntimeError) as e:
logger.warning(
f"Unable to output {key_d} parameters for TOF={params_dict['Value'][irow]}: "
+ str(e))
istart += nparams[ifunc]
# append the cost function value (in this case always chisq/DOF) as don't let user change cost func
# always last row in parameters table
self._fit_results[wsname]['costFunction'] = params_dict['Value'][
-1]
self.create_fit_tables()
def split_to_single_bank(self, gss_ws_name):
"""
Split a multiple-bank GSAS workspace to a set of single-spectrum MatrixWorkspace
Parameters
----------
gss_ws_name
Returns
-------
Name of grouped workspace and list
"""
# check
assert isinstance(gss_ws_name, str)
assert AnalysisDataService.doesExist(gss_ws_name)
# get workspace
gss_ws = AnalysisDataService.retrieve(gss_ws_name)
ws_list = list()
angle_list = list()
if gss_ws.getNumberHistograms() == 1:
# input is already a single-spectrum workspace
ws_list.append(gss_ws_name)
else:
num_spec = gss_ws.getNumberHistograms()
for i_ws in range(num_spec):
# split this one to a single workspace
out_ws_name = '%s_bank%d' % (gss_ws_name, i_ws + 1)
# also can use ExtractSpectra()
simpleapi.CropWorkspace(InputWorkspace=gss_ws_name,
OutputWorkspace=out_ws_name,
StartWorkspaceIndex=i_ws,
EndWorkspaceIndex=i_ws)
assert AnalysisDataService.doesExist(out_ws_name)
ws_list.append(out_ws_name)
# END-FOR
# END-IF
# calculate bank angles
for ws_name in ws_list:
bank_angle = calculate_bank_angle(ws_name)
angle_list.append(bank_angle)
# group all the workspace
ws_group_name = gss_ws_name + '_group'
simpleapi.GroupWorkspaces(InputWorkspaces=ws_list,
OutputWorkspace=ws_group_name)
self._braggDataDict[ws_group_name] = (gss_ws_name, ws_list)
return ws_group_name, ws_list, angle_list
def test_loading_run_with_instrument_prefix_in_name(self):
args = self._default_options
args['InputRunList'] = 'INTER13460'
outputs = ['IvsQ_13460', 'IvsQ_binned_13460', 'TOF_13460', 'TOF']
self._assert_run_algorithm_succeeds(args, outputs)
history = [
'ReflectometryISISPreprocess', 'ReflectometryReductionOneAuto',
'GroupWorkspaces'
]
self._check_history(AnalysisDataService.retrieve('IvsQ_binned_13460'),
history)
def _add_workspace_to_group(group_name, workspace_name):
if AnalysisDataService.doesExist(group_name):
workspaces_to_group = AnalysisDataService.retrieve(
group_name).getNames()
else:
workspaces_to_group = []
if workspace_name not in workspaces_to_group:
workspaces_to_group.append(workspace_name)
WorkspaceGroupDefinition().add_workspaces_to_group(
group_name, workspaces_to_group)
WorkspaceGroupDefinition().execute_grouping()
def _expand_groups(self):
"""expand workspace groups"""
workspaces = self.getProperty("InputWorkspace").value
input_workspaces = []
for wsname in workspaces:
wks = AnalysisDataService.retrieve(wsname)
if isinstance(wks, WorkspaceGroup):
input_workspaces.extend(wks.getNames())
else:
input_workspaces.append(wsname)
return input_workspaces
def runTest(self):
enginx = EnginX(vanadium_run="ENGINX236516",
focus_runs=["ENGINX299080"],
save_dir=CWDIR,
full_inst_calib_path=FULL_CALIB,
ceria_run="ENGINX193749",
group=GROUP.CROPPED,
spectrum_num="1-1200") # North
enginx.main(plot_cal=False, plot_foc=False)
# store workspaces for validation
self._ws_foc = ADS.retrieve(
"299080_engggui_focusing_output_ws_Cropped")
def process_vanadium(vanadium_path, calibration, full_calib):
van_run = path_handling.get_run_number_from_path(
vanadium_path, calibration.get_instrument())
van_foc_name = CURVES_PREFIX + calibration.get_group_suffix()
if ADS.doesExist(van_foc_name):
ws_van_foc = ADS.retrieve(van_foc_name)
else:
if ADS.doesExist(van_run):
ws_van = ADS.retrieve(
van_run) # will exist if have only changed the ROI
else:
ws_van = _load_run_and_convert_to_dSpacing(
vanadium_path, calibration.get_instrument(), full_calib)
if not ws_van:
raise RuntimeError(
f"vanadium run {van_run} has no proton_charge - "
f"please supply a valid vanadium run to focus.")
ws_van_foc = _focus_run_and_apply_roi_calibration(
ws_van, calibration, ws_foc_name=van_foc_name)
ws_van_foc = _smooth_vanadium(ws_van_foc)
return ws_van_foc, van_run
def testFitOutputWorkspacesAreDeleted(self):
OutputWorkspaceName = "outputws1"
alg_test = run_algorithm("FindEPP",
InputWorkspace=self._input_ws,
OutputWorkspace=OutputWorkspaceName,
Version=1)
wsoutput = AnalysisDataService.retrieve(OutputWorkspaceName)
DeleteWorkspace(wsoutput)
oldOption = mantid.config['MantidOptions.InvisibleWorkspaces']
mantid.config['MantidOptions.InvisibleWorkspaces'] = '1'
self.assertEqual(mtd.size(), 1) # Only self._input_ws exists.
mantid.config['MantidOptions.InvisibleWorkspaces'] = oldOption
def test_key_operator_does_same_as_retrieve(self):
wsname = 'ADSTest_test_key_operator_does_same_as_retrieve'
self._run_createws(wsname)
ws_from_op = AnalysisDataService[wsname]
ws_from_method = AnalysisDataService.retrieve(wsname)
self.do_check_for_matrix_workspace_type(ws_from_op)
self.do_check_for_matrix_workspace_type(ws_from_method)
self.assertEquals(ws_from_op.name(), ws_from_method.name())
self.assertEquals(ws_from_op.getMemorySize(),
ws_from_method.getMemorySize())
def test_update_plot_guess_will_use_tmp_workspace_with_data_range_if_plot_range_type_is_selected(self):
guess_workspace_name = "__frequency_domain_analysis_fitting_guessName1"
self.model.dataset_names = self.dataset_names
self.model.single_fit_functions = self.single_fit_functions
self.model.start_xs = [0.0, 1.0]
self.model.end_xs = [10.0, 11.0]
self.model.current_dataset_index = 0
self.model.plot_guess = True
self.model.plot_guess_type = X_FROM_FIT_RANGE
self.model.context = mock.Mock()
self.model._double_pulse_enabled = mock.Mock(return_value=False)
self.model._get_plot_guess_name = mock.Mock(return_value=guess_workspace_name)
self.model._get_guess_parameters = mock.Mock(return_value=['func', 'ws'])
self.model.update_plot_guess()
guess_workspace = AnalysisDataService.retrieve(guess_workspace_name)
data_workspace = AnalysisDataService.retrieve(self.dataset_names[0])
self.assertEqual(guess_workspace.dataX(0).min(), 0.0)
self.assertEqual(guess_workspace.dataX(0).max(), 10.0)
self.assertEqual(guess_workspace.dataX(0).size, data_workspace.blocksize())
def get_region_of_interest(mask_ws_name):
"""
get region of interest from mask workspace
:param mask_ws_name:
:return:
"""
# check input
assert isinstance(mask_ws_name, str), 'Mask workspace name {0} must be an integer but not a {1}' \
''.format(mask_ws_name, type(mask_ws_name))
mask_ws = ADS.retrieve(mask_ws_name)
# construct a 2D matrix
size_x = size_y = int(math.sqrt(mask_ws.getNumberHistograms()))
mask_matrix = numpy.ndarray(shape=(size_x, size_y), dtype='int')
# mask or unmask all the matrix element according to mask workspace
for iws in range(mask_ws.getNumberHistograms()):
det_id = mask_ws.getDetector(iws).getID()
pixel_2d_id = det_id / size_y, det_id % size_y
mask_matrix[pixel_2d_id] = int(mask_ws.isMasked(iws))
# END-FOR
# find lower left corner
lower_left_corner = None
for ir in range(size_y):
if mask_matrix[ir].min() == 0:
ll_row = ir
ret_value = numpy.where(mask_matrix[ir] == 0)
ll_col = ret_value[0][0]
lower_left_corner = ll_row, ll_col
break
# END-IF
# END-FOR
# find upper right corner
upper_right_corner = None
for ir in range(size_y - 1, -1, -1):
if mask_matrix[ir].min() == 0:
ur_row = ir
ret_value = numpy.where(mask_matrix[ir] == 0)
ur_col = ret_value[0][-1]
upper_right_corner = ur_row, ur_col
break
# END-IF
# END-FOR
# check before return
if lower_left_corner is None or upper_right_corner is None:
raise RuntimeError(
'It is impossible not to find either lower left corner {0} or upper right corner {1}'
''.format(lower_left_corner, upper_right_corner))
return lower_left_corner, upper_right_corner
def test_sum(self):
outputWorkspaceName = "output_ws"
alg_test = run_algorithm("ComputeCalibrationCoefVan", VanadiumWorkspace=self._input_ws,
EPPTable=self._table, OutputWorkspace=outputWorkspaceName)
self.assertTrue(alg_test.isExecuted())
wsoutput = AnalysisDataService.retrieve(outputWorkspaceName)
# check whether sum is calculated correctly, for theta=0, dwf=1
y_sum = sum(self._input_ws.readY(0)[27:75])
self.assertAlmostEqual(y_sum, wsoutput.readY(0)[0])
DeleteWorkspace(wsoutput)
def _getRunNumberAsString(workspace_name):
"""Get the run number for a workspace. If it's a workspace group, get
the run number from the first child workspace."""
try:
workspace = AnalysisDataService.retrieve(workspace_name)
if not isinstance(workspace, WorkspaceGroup):
return str(workspace.getRunNumber())
# Get first child in the group
return str(workspace[0].getRunNumber())
except:
raise RuntimeError('Could not find run number for workspace ' +
workspace_name)
def test_overriding_output_names_includes_all_specified_outputs(self):
self._create_workspace(13460, 'TOF_')
args = self._default_options
args['InputRunList'] = '13460'
args.update(self._all_output_names)
# Current behaviour is that the optional workspaces are output even if
# Debug is not set
outputs = ['testIvsQ', 'testIvsQBin', 'testIvsLam', 'TOF_13460', 'TOF']
self._assert_run_algorithm_succeeds(args, outputs)
history = ['ReflectometryReductionOneAuto', 'GroupWorkspaces']
self._check_history(AnalysisDataService.retrieve('testIvsQBin'),
history)
def test_setGetMonitorWS(self):
run_algorithm('CreateWorkspace', OutputWorkspace='ws1', DataX=[
1., 2., 3.], DataY=[2., 3.], DataE=[2., 3.], UnitX='TOF')
run_algorithm('CreateWorkspace', OutputWorkspace='ws_mon', DataX=[
1., 2., 3.], DataY=[2., 3.], DataE=[2., 3.], UnitX='TOF')
ws1 = AnalysisDataService.retrieve('ws1')
try:
monWs = ws1.getMonitorWorkspace()
GotIt = True
except RuntimeError:
GotIt = False
self.assertFalse(GotIt)
monWs = AnalysisDataService.retrieve('ws_mon')
ws1.setMonitorWorkspace(monWs)
monWs.setTitle("My Fake Monitor workspace")
monWs1 = ws1.getMonitorWorkspace()
self.assertEquals(monWs.getTitle(), monWs1.getTitle())
ws1.clearMonitorWorkspace()
try:
monWs1 = ws1.getMonitorWorkspace()
GotIt = True
except RuntimeError:
GotIt = False
self.assertFalse(GotIt)
# Check weak pointer issues
ws1.setMonitorWorkspace(monWs)
wms = ws1.getMonitorWorkspace()
allFine = False
try:
ws1.setMonitorWorkspace(wms)
allFine = True
except ValueError:
pass
self.assertTrue(allFine)
def test_DNSFRVanaCorrection(self):
outputWorkspaceName = "DNSFlippingRatioCorrTest_Test6"
# create fake vanadium data workspaces
dataY = np.array([1811., 2407., 3558., 3658., 3352., 2321., 2240., 2617., 3245., 3340., 3338., 3310.,
2744., 3212., 1998., 2754., 2791., 2509., 3045., 3429., 3231., 2668., 3373., 2227.])
__sf_vanaws = create_fake_dns_workspace('__sf_vanaws', dataY=dataY/58.0, flipper='ON')
self.workspaces.append('__sf_vanaws')
dataY = np.array([2050., 1910., 2295., 2236., 1965., 1393., 1402., 1589., 1902., 1972., 2091., 1957.,
1593., 1952., 1232., 1720., 1689., 1568., 1906., 2001., 2051., 1687., 1975., 1456.])
__nsf_vanaws = create_fake_dns_workspace('__nsf_vanaws', dataY=dataY/58.0, flipper='OFF')
self.workspaces.append('__nsf_vanaws')
# consider normalization=1.0 as set in self._create_fake_workspace
dataws_sf = __sf_vanaws - self.__sf_bkgrws
dataws_nsf = __nsf_vanaws - self.__nsf_bkgrws
alg_test = run_algorithm("DNSFlippingRatioCorr", SFDataWorkspace=dataws_sf,
NSFDataWorkspace=dataws_nsf, SFNiCrWorkspace=self.__sf_nicrws.getName(),
NSFNiCrWorkspace=self.__nsf_nicrws.getName(), SFBkgrWorkspace=self.__sf_bkgrws.getName(),
NSFBkgrWorkspace=self.__nsf_bkgrws.getName(), SFOutputWorkspace=outputWorkspaceName+'SF',
NSFOutputWorkspace=outputWorkspaceName+'NSF')
self.assertTrue(alg_test.isExecuted())
# check whether the data are correct
ws_sf = AnalysisDataService.retrieve(outputWorkspaceName + 'SF')
ws_nsf = AnalysisDataService.retrieve(outputWorkspaceName + 'NSF')
# dimensions
self.assertEqual(24, ws_sf.getNumberHistograms())
self.assertEqual(24, ws_nsf.getNumberHistograms())
self.assertEqual(2, ws_sf.getNumDims())
self.assertEqual(2, ws_nsf.getNumDims())
# data array: for vanadium ratio sf/nsf must be around 2
ws = ws_sf/ws_nsf
for i in range(24):
self.assertAlmostEqual(2.0, np.around(ws.readY(i)))
run_algorithm("DeleteWorkspace", Workspace=outputWorkspaceName + 'SF')
run_algorithm("DeleteWorkspace", Workspace=outputWorkspaceName + 'NSF')
run_algorithm("DeleteWorkspace", Workspace=dataws_sf)
run_algorithm("DeleteWorkspace", Workspace=dataws_nsf)
run_algorithm("DeleteWorkspace", Workspace=ws)
return
def test_DNSFRSelfCorrection(self):
outputWorkspaceName = "DNSFlippingRatioCorrTest_Test4"
# consider normalization=1.0 as set in self._create_fake_workspace
dataws_sf = self.__sf_nicrws - self.__sf_bkgrws
dataws_nsf = self.__nsf_nicrws - self.__nsf_bkgrws
alg_test = run_algorithm("DNSFlippingRatioCorr",
SFDataWorkspace=dataws_sf,
NSFDataWorkspace=dataws_nsf,
SFNiCrWorkspace=self.__sf_nicrws.getName(),
NSFNiCrWorkspace=self.__nsf_nicrws.getName(),
SFBkgrWorkspace=self.__sf_bkgrws.getName(),
NSFBkgrWorkspace=self.__nsf_bkgrws.getName(),
SFOutputWorkspace=outputWorkspaceName + 'SF',
NSFOutputWorkspace=outputWorkspaceName +
'NSF')
self.assertTrue(alg_test.isExecuted())
# check whether the data are correct
ws_sf = AnalysisDataService.retrieve(outputWorkspaceName + 'SF')
ws_nsf = AnalysisDataService.retrieve(outputWorkspaceName + 'NSF')
# dimensions
self.assertEqual(24, ws_sf.getNumberHistograms())
self.assertEqual(24, ws_nsf.getNumberHistograms())
self.assertEqual(2, ws_sf.getNumDims())
self.assertEqual(2, ws_nsf.getNumDims())
# data array: spin-flip must be zero
for i in range(24):
self.assertAlmostEqual(0.0, ws_sf.readY(i)[0])
# data array: non spin-flip must be nsf - sf^2/nsf
nsf = np.array(dataws_nsf.extractY())
sf = np.array(dataws_sf.extractY())
refdata = nsf + sf
for i in range(24):
self.assertAlmostEqual(refdata[i][0], ws_nsf.readY(i)[0])
run_algorithm("DeleteWorkspace", Workspace=outputWorkspaceName + 'SF')
run_algorithm("DeleteWorkspace", Workspace=outputWorkspaceName + 'NSF')
run_algorithm("DeleteWorkspace", Workspace=dataws_sf)
run_algorithm("DeleteWorkspace", Workspace=dataws_nsf)
return
def test_that_can_load_isis_nexus_file_with_event_data_and_multi_period(
self):
# Arrange
state = SANSLoadTest._get_simple_state(
sample_scatter="LARMOR00013065.nxs",
calibration="80tubeCalibration_18-04-2016_r9330-9335.nxs")
# Act
output_workspace_names = {
"SampleScatterWorkspace": "sample_scatter",
"SampleScatterMonitorWorkspace": "sample_monitor_scatter"
}
load_alg = self._run_load(
state,
publish_to_cache=True,
use_cached=True,
move_workspace=False,
output_workspace_names=output_workspace_names)
# Assert
expected_number_of_workspaces = [4, 0, 0, 0, 0, 0]
expected_number_on_ads = 1
workspace_type = [EventWorkspace, None, None, None, None, None]
self._do_test_output(load_alg, expected_number_of_workspaces,
expected_number_on_ads, workspace_type)
# Check that calibration is added
self.assertTrue(SANSLoadTest._has_calibration_been_applied(load_alg))
# Confirm that the ADS workspace contains the calibration file
try:
AnalysisDataService.retrieve(
"80tubeCalibration_18-04-2016_r9330-9335")
on_ads = True
except RuntimeError:
on_ads = False
self.assertTrue(on_ads)
# Cleanup
remove_all_workspaces_from_ads()
def test_slicing_loads_input_run_if_not_in_ADS(self):
args = self._default_options
args.update(self._default_slice_options_real_run)
args['InputRunList'] = '38415'
outputs = self._expected_real_time_sliced_outputs
self._assert_run_algorithm_succeeds(args, outputs)
history = [
'ReflectometryReductionOneAuto', 'ReflectometryReductionOneAuto',
'ReflectometryReductionOneAuto', 'GroupWorkspaces'
]
self._check_history(
AnalysisDataService.retrieve('IvsQ_binned_38415_sliced_0_210'),
history, False)
def test_debug_option_outputs_extra_workspaces(self):
self._create_workspace(13460, 'TOF_')
args = self._default_options
args['InputRunList'] = '13460'
args['Debug'] = True
outputs = [
'IvsQ_13460', 'IvsQ_binned_13460', 'IvsLam_13460', 'TOF_13460',
'TOF'
]
self._assert_run_algorithm_succeeds(args, outputs)
history = ['ReflectometryReductionOneAuto', 'GroupWorkspaces']
self._check_history(AnalysisDataService.retrieve('IvsQ_binned_13460'),
history)
def test_input_run_is_reloaded_if_in_ADS_with_unknown_prefix(self):
self._create_workspace(13460, 'TEST_')
args = self._default_options
args['InputRunList'] = '13460'
outputs = [
'IvsQ_13460', 'IvsQ_binned_13460', 'TEST_13460', 'TOF_13460', 'TOF'
]
self._assert_run_algorithm_succeeds(args, outputs)
history = [
'LoadNexus', 'ReflectometryReductionOneAuto', 'GroupWorkspaces'
]
self._check_history(AnalysisDataService.retrieve('IvsQ_binned_13460'),
history)
def _create_flat_background_test_workspace(workspace_name):
LoadNexusProcessed(Filename="LOQ48127", OutputWorkspace=workspace_name)
workspace = AnalysisDataService.retrieve(workspace_name)
# Rebin to only have four values at 11, 31, 51, 70.5
workspace = Rebin(workspace, "1,20,80")
# For each spectrum we set the first two entries to 2 and the other two entries to 4.
for index in range(workspace.getNumberHistograms()):
data_y = workspace.dataY(index)
data_y[0] = 2.
data_y[1] = 2.
data_y[2] = 4.
data_y[3] = 4.
return workspace
def _setUniformNumberOfSlices(self, alg, workspace_name):
"""If slicing by a specified number of slices is requested, find the time
interval to use to give this number of even time slices and set the relevant
property on the given slicing algorithm"""
if self.getProperty(Prop.NUMBER_OF_SLICES).isDefault:
return
number_of_slices = self.getProperty(Prop.NUMBER_OF_SLICES).value
run=AnalysisDataService.retrieve(workspace_name).run()
total_duration = (run.endTime() - run.startTime()).total_seconds()
slice_duration = total_duration / number_of_slices
alg.setProperty("TimeInterval", slice_duration)
self.log().information('Slicing ' + workspace_name + ' into ' + str(number_of_slices)
+ ' even slices of duration ' + str(slice_duration))
def _removeWorkspace(workspace_name):
"""Remove the workspace with the given name, including any child workspaces if it
is a group. If a corresponding monitors workspace exists, remove that too."""
if AnalysisDataService.doesExist(workspace_name):
workspace = AnalysisDataService.retrieve(workspace_name)
if isinstance(workspace, WorkspaceGroup):
# Remove child workspaces first
while workspace.getNumberOfEntries():
_removeWorkspace(workspace[0].name())
AnalysisDataService.remove(workspace_name)
# If a corresponding monitors workspace also exists, remove that too
if AnalysisDataService.doesExist(_monitorWorkspace(workspace_name)):
_removeWorkspace(_monitorWorkspace(workspace_name))
def clear_fit_result_lines(self):
"""
Delete the fit curves.
"""
for line in self.get_lines():
if line.get_label() == self.sequential_fit_line:
line.remove()
if self.fit_result_ws_name:
ws = AnalysisDataService.retrieve(self.fit_result_ws_name)
self.get_axes().remove_workspace_artists(ws)
self.fit_result_ws_name = ""
self.update_legend()