def test_Q(self):
ConvertWANDSCDtoQTest_out=ConvertWANDSCDtoQ('ConvertWANDSCDtoQTest_data',BinningDim0='-8.08,8.08,101',
BinningDim1='-0.88,0.88,11',BinningDim2='-8.08,8.08,101',NormaliseBy='None')
self.assertTrue(ConvertWANDSCDtoQTest_out)
s = ConvertWANDSCDtoQTest_out.getSignalArray()
self.assertAlmostEqual(np.nanmax(s), 8.97233331213612)
self.assertAlmostEqual(np.nanargmax(s), 22780)
self.assertEquals(ConvertWANDSCDtoQTest_out.getNumDims(), 3)
self.assertEquals(ConvertWANDSCDtoQTest_out.getNPoints(), 112211)
d0 = ConvertWANDSCDtoQTest_out.getDimension(0)
self.assertEquals(d0.name, 'Q_sample_x')
self.assertEquals(d0.getNBins(), 101)
self.assertAlmostEquals(d0.getMinimum(), -8.08, 5)
self.assertAlmostEquals(d0.getMaximum(), 8.08, 5)
d1 = ConvertWANDSCDtoQTest_out.getDimension(1)
self.assertEquals(d1.name, 'Q_sample_y')
self.assertEquals(d1.getNBins(), 11)
self.assertAlmostEquals(d1.getMinimum(), -0.88, 5)
self.assertAlmostEquals(d1.getMaximum(), 0.88, 5)
d2 = ConvertWANDSCDtoQTest_out.getDimension(2)
self.assertEquals(d2.name, 'Q_sample_z')
self.assertEquals(d2.getNBins(), 101)
self.assertAlmostEquals(d2.getMinimum(), -8.08, 5)
self.assertAlmostEquals(d2.getMaximum(), 8.08, 5)
self.assertEqual(ConvertWANDSCDtoQTest_out.getNumExperimentInfo(), 1)
ConvertWANDSCDtoQTest_out.delete()
def test_Q_norm(self):
ConvertWANDSCDtoQTest_out = ConvertWANDSCDtoQ('ConvertWANDSCDtoQTest_data',NormalisationWorkspace='ConvertWANDSCDtoQTest_norm',
BinningDim0='-8.08,8.08,101',BinningDim1='-0.88,0.88,11',BinningDim2='-8.08,8.08,101')
s = ConvertWANDSCDtoQTest_out.getSignalArray()
self.assertAlmostEqual(np.nanmax(s), 7.476944426780101)
self.assertAlmostEqual(np.nanargmax(s), 22780)
ConvertWANDSCDtoQTest_out.delete()
def test_Q_norm(self):
ConvertWANDSCDtoQTest_out = ConvertWANDSCDtoQ('ConvertWANDSCDtoQTest_data',NormalisationWorkspace='ConvertWANDSCDtoQTest_norm',
BinningDim0='-8.08,8.08,101',BinningDim1='-0.88,0.88,11',BinningDim2='-8.08,8.08,101')
s = ConvertWANDSCDtoQTest_out.getSignalArray()
self.assertAlmostEqual(np.nanmax(s), 7.476944426780101)
self.assertAlmostEqual(np.nanargmax(s), 22780)
ConvertWANDSCDtoQTest_out.delete()
def test_HKL_norm_and_KeepTemporary(self):
ConvertWANDSCDtoQTest_out = ConvertWANDSCDtoQ('ConvertWANDSCDtoQTest_data',NormalisationWorkspace='ConvertWANDSCDtoQTest_norm',
Frame='HKL',KeepTemporaryWorkspaces=True,BinningDim0='-8.08,8.08,101',
BinningDim1='-8.08,8.08,101',BinningDim2='-8.08,8.08,101',
Uproj='1,1,0',Vproj='1,-1,0',Wproj='0,0,1')
self.assertTrue(ConvertWANDSCDtoQTest_out)
self.assertTrue(mtd.doesExist('ConvertWANDSCDtoQTest_out'))
self.assertTrue(mtd.doesExist('ConvertWANDSCDtoQTest_out_data'))
self.assertTrue(mtd.doesExist('ConvertWANDSCDtoQTest_out_normalization'))
s = ConvertWANDSCDtoQTest_out.getSignalArray()
self.assertAlmostEqual(np.nanmax(s), 4.646855396509936)
self.assertAlmostEqual(np.nanargmax(s), 443011)
self.assertEquals(ConvertWANDSCDtoQTest_out.getNumDims(), 3)
self.assertEquals(ConvertWANDSCDtoQTest_out.getNPoints(), 101**3)
d0 = ConvertWANDSCDtoQTest_out.getDimension(0)
self.assertEquals(d0.name, '[H,H,0]')
self.assertEquals(d0.getNBins(), 101)
self.assertAlmostEquals(d0.getMinimum(), -8.08, 5)
self.assertAlmostEquals(d0.getMaximum(), 8.08, 5)
d1 = ConvertWANDSCDtoQTest_out.getDimension(1)
self.assertEquals(d1.name, '[H,-H,0]')
self.assertEquals(d1.getNBins(), 101)
self.assertAlmostEquals(d1.getMinimum(), -8.08, 5)
self.assertAlmostEquals(d1.getMaximum(), 8.08, 5)
d2 = ConvertWANDSCDtoQTest_out.getDimension(2)
self.assertEquals(d2.name, '[0,0,L]')
self.assertEquals(d2.getNBins(), 101)
self.assertAlmostEquals(d2.getMinimum(), -8.08, 5)
self.assertAlmostEquals(d2.getMaximum(), 8.08, 5)
self.assertEqual(ConvertWANDSCDtoQTest_out.getNumExperimentInfo(), 1)
ConvertWANDSCDtoQTest_out.delete()
def test_COP(self):
ConvertWANDSCDtoQTest_out = ConvertWANDSCDtoQ(
'ConvertWANDSCDtoQTest_data',
BinningDim0='-8.08,8.08,101',
BinningDim1='-1.68,1.68,21',
BinningDim2='-8.08,8.08,101',
NormaliseBy='None')
ConvertWANDSCDtoQTest_cop = ConvertWANDSCDtoQ(
'ConvertWANDSCDtoQTest_data',
BinningDim0='-8.08,8.08,101',
BinningDim1='-1.68,1.68,21',
BinningDim2='-8.08,8.08,101',
NormaliseBy='None',
ObliquityParallaxCoefficient=1.5)
self.assertTrue(ConvertWANDSCDtoQTest_out)
self.assertTrue(ConvertWANDSCDtoQTest_cop)
Test_out = ConvertWANDSCDtoQTest_out.getSignalArray().copy()
Test_cop = ConvertWANDSCDtoQTest_cop.getSignalArray().copy()
x, y, z = np.meshgrid(np.linspace(-8, 8, 101),
np.linspace(-1.6, 1.6, 21),
np.linspace(-8, 8, 101),
indexing='ij')
Test_out_max_Qy = y[~np.isnan(Test_out)].max()
Test_cop_max_Qy = y[~np.isnan(Test_cop)].max()
# Test whether Qy is scaled by ObliquityParallaxCoefficient correctly
proportion = Test_cop_max_Qy / Test_out_max_Qy
self.assertAlmostEquals(proportion, 1.5, 5)
ConvertWANDSCDtoQTest_out.delete()
ConvertWANDSCDtoQTest_cop.delete()
def test_Q(self):
ConvertWANDSCDtoQTest_out = ConvertWANDSCDtoQ(
'ConvertWANDSCDtoQTest_data',
BinningDim0='-8.08,8.08,101',
BinningDim1='-0.88,0.88,11',
BinningDim2='-8.08,8.08,101',
NormaliseBy='None')
self.assertTrue(ConvertWANDSCDtoQTest_out)
s = ConvertWANDSCDtoQTest_out.getSignalArray()
self.assertAlmostEqual(np.nanmax(s), 8.97233331213612)
self.assertAlmostEqual(np.nanargmax(s), 22780)
self.assertEqual(ConvertWANDSCDtoQTest_out.getNumDims(), 3)
self.assertEqual(ConvertWANDSCDtoQTest_out.getNPoints(), 112211)
d0 = ConvertWANDSCDtoQTest_out.getDimension(0)
self.assertEqual(d0.name, 'Q_sample_x')
self.assertEqual(d0.getNBins(), 101)
self.assertAlmostEquals(d0.getMinimum(), -8.08, 5)
self.assertAlmostEquals(d0.getMaximum(), 8.08, 5)
d1 = ConvertWANDSCDtoQTest_out.getDimension(1)
self.assertEqual(d1.name, 'Q_sample_y')
self.assertEqual(d1.getNBins(), 11)
self.assertAlmostEquals(d1.getMinimum(), -0.88, 5)
self.assertAlmostEquals(d1.getMaximum(), 0.88, 5)
d2 = ConvertWANDSCDtoQTest_out.getDimension(2)
self.assertEqual(d2.name, 'Q_sample_z')
self.assertEqual(d2.getNBins(), 101)
self.assertAlmostEquals(d2.getMinimum(), -8.08, 5)
self.assertAlmostEquals(d2.getMaximum(), 8.08, 5)
self.assertEqual(ConvertWANDSCDtoQTest_out.getNumExperimentInfo(), 1)
ConvertWANDSCDtoQTest_out.delete()
def test_HKL_norm_and_KeepTemporary(self):
ConvertWANDSCDtoQTest_out = ConvertWANDSCDtoQ(
'ConvertWANDSCDtoQTest_data',
NormalisationWorkspace='ConvertWANDSCDtoQTest_norm',
Frame='HKL',
KeepTemporaryWorkspaces=True,
BinningDim0='-8.08,8.08,101',
BinningDim1='-8.08,8.08,101',
BinningDim2='-8.08,8.08,101',
Uproj='1,1,0',
Vproj='1,-1,0',
Wproj='0,0,1')
self.assertTrue(ConvertWANDSCDtoQTest_out)
self.assertTrue(mtd.doesExist('ConvertWANDSCDtoQTest_out'))
self.assertTrue(mtd.doesExist('ConvertWANDSCDtoQTest_out_data'))
self.assertTrue(
mtd.doesExist('ConvertWANDSCDtoQTest_out_normalization'))
s = ConvertWANDSCDtoQTest_out.getSignalArray()
self.assertAlmostEqual(np.nanmax(s), 4.646855396509936)
self.assertAlmostEqual(np.nanargmax(s), 443011)
self.assertEqual(ConvertWANDSCDtoQTest_out.getNumDims(), 3)
self.assertEqual(ConvertWANDSCDtoQTest_out.getNPoints(), 101**3)
d0 = ConvertWANDSCDtoQTest_out.getDimension(0)
self.assertEqual(d0.name, '[H,H,0]')
self.assertEqual(d0.getNBins(), 101)
self.assertAlmostEquals(d0.getMinimum(), -8.08, 5)
self.assertAlmostEquals(d0.getMaximum(), 8.08, 5)
d1 = ConvertWANDSCDtoQTest_out.getDimension(1)
self.assertEqual(d1.name, '[H,-H,0]')
self.assertEqual(d1.getNBins(), 101)
self.assertAlmostEquals(d1.getMinimum(), -8.08, 5)
self.assertAlmostEquals(d1.getMaximum(), 8.08, 5)
d2 = ConvertWANDSCDtoQTest_out.getDimension(2)
self.assertEqual(d2.name, '[0,0,L]')
self.assertEqual(d2.getNBins(), 101)
self.assertAlmostEquals(d2.getMinimum(), -8.08, 5)
self.assertAlmostEquals(d2.getMaximum(), 8.08, 5)
self.assertEqual(ConvertWANDSCDtoQTest_out.getNumExperimentInfo(), 1)
ConvertWANDSCDtoQTest_out.delete()
def runTest(self):
S = np.random.random(32 * 240 * 100)
ConvertWANDSCDtoQTest_data = CreateMDHistoWorkspace(
Dimensionality=3,
Extents='0.5,32.5,0.5,240.5,0.5,100.5',
SignalInput=S.ravel('F'),
ErrorInput=np.sqrt(S.ravel('F')),
NumberOfBins='32,240,100',
Names='y,x,scanIndex',
Units='bin,bin,number')
ConvertWANDSCDtoQTest_dummy = CreateSingleValuedWorkspace()
LoadInstrument(ConvertWANDSCDtoQTest_dummy,
InstrumentName='WAND',
RewriteSpectraMap=False)
ConvertWANDSCDtoQTest_data.addExperimentInfo(
ConvertWANDSCDtoQTest_dummy)
log = FloatTimeSeriesProperty('s1')
for t, v in zip(range(100), np.arange(0, 50, 0.5)):
log.addValue(t, v)
ConvertWANDSCDtoQTest_data.getExperimentInfo(0).run()['s1'] = log
ConvertWANDSCDtoQTest_data.getExperimentInfo(0).run().addProperty(
'duration', [60.] * 100, True)
ConvertWANDSCDtoQTest_data.getExperimentInfo(0).run().addProperty(
'monitor_count', [120000.] * 100, True)
ConvertWANDSCDtoQTest_data.getExperimentInfo(0).run().addProperty(
'twotheta', list(np.linspace(np.pi * 2 / 3, 0, 240).repeat(32)),
True)
ConvertWANDSCDtoQTest_data.getExperimentInfo(0).run().addProperty(
'azimuthal', list(np.tile(np.linspace(-0.15, 0.15, 32), 240)),
True)
peaks = CreatePeaksWorkspace(NumberOfPeaks=0,
OutputType='LeanElasticPeak')
SetUB(ConvertWANDSCDtoQTest_data,
5,
5,
7,
90,
90,
120,
u=[-1, 0, 1],
v=[1, 0, 1])
SetGoniometer(ConvertWANDSCDtoQTest_data,
Axis0='s1,0,1,0,1',
Average=False)
CopySample(InputWorkspace=ConvertWANDSCDtoQTest_data,
OutputWorkspace=peaks,
CopyName=False,
CopyMaterial=False,
CopyEnvironment=False,
CopyShape=False,
CopyLattice=True)
Q = ConvertWANDSCDtoQ(InputWorkspace=ConvertWANDSCDtoQTest_data,
UBWorkspace=peaks,
Wavelength=1.486,
Frame='HKL',
Uproj='1,1,0',
Vproj='-1,1,0',
BinningDim0='-6.04,6.04,151',
BinningDim1='-6.04,6.04,151',
BinningDim2='-6.04,6.04,151')
data_norm = ConvertHFIRSCDtoMDE(ConvertWANDSCDtoQTest_data,
Wavelength=1.486,
MinValues='-6.04,-6.04,-6.04',
MaxValues='6.04,6.04,6.04')
HKL = ConvertQtoHKLMDHisto(data_norm,
PeaksWorkspace=peaks,
Uproj='1,1,0',
Vproj='-1,1,0',
Extents='-6.04,6.04,-6.04,6.04,-6.04,6.04',
Bins='151,151,151')
for i in range(HKL.getNumDims()):
print(HKL.getDimension(i).getUnits(), Q.getDimension(i).getUnits())
np.testing.assert_equal(
HKL.getDimension(i).getUnits(),
Q.getDimension(i).getUnits())
hkl_data = mtd["HKL"].getSignalArray()
Q_data = mtd["Q"].getSignalArray()
print(np.isnan(Q_data).sum())
print(np.isclose(hkl_data, 0).sum())
xaxis = mtd["HKL"].getXDimension()
yaxis = mtd["HKL"].getYDimension()
zaxis = mtd["HKL"].getZDimension()
x, y, z = np.meshgrid(
np.linspace(xaxis.getMinimum(), xaxis.getMaximum(),
xaxis.getNBins()),
np.linspace(yaxis.getMinimum(), yaxis.getMaximum(),
yaxis.getNBins()),
np.linspace(zaxis.getMinimum(), zaxis.getMaximum(),
zaxis.getNBins()),
indexing="ij",
copy=False,
)
print(
x[~np.isnan(Q_data)].mean(),
y[~np.isnan(Q_data)].mean(),
z[~np.isnan(Q_data)].mean(),
)
print(
x[~np.isclose(hkl_data, 0)].mean(),
y[~np.isclose(hkl_data, 0)].mean(),
z[~np.isclose(hkl_data, 0)].mean(),
)
np.testing.assert_almost_equal(x[~np.isnan(Q_data)].mean(),
x[~np.isclose(hkl_data, 0)].mean(),
decimal=2)
np.testing.assert_almost_equal(y[~np.isnan(Q_data)].mean(),
y[~np.isclose(hkl_data, 0)].mean(),
decimal=2)
np.testing.assert_almost_equal(z[~np.isnan(Q_data)].mean(),
z[~np.isclose(hkl_data, 0)].mean(),
decimal=1)
def PyExec(self):
load_van = not self.getProperty("VanadiumFile").isDefault
load_files = not self.getProperty("Filename").isDefault
output = self.getProperty("OutputType").value
if load_files:
datafiles = self.getProperty("Filename").value
else:
datafiles = list(
map(str.strip,
self.getProperty("InputWorkspaces").value.split(",")))
prog = Progress(self, 0.0, 1.0, len(datafiles) + 1)
vanadiumfile = self.getProperty("VanadiumFile").value
vanws = self.getProperty("VanadiumWorkspace").value
height = self.getProperty("DetectorHeightOffset").value
distance = self.getProperty("DetectorDistanceOffset").value
wslist = []
out_ws = self.getPropertyValue("OutputWorkspace")
out_ws_name = out_ws
if load_van:
vanws = LoadMD(vanadiumfile, StoreInADS=False)
has_multiple = len(datafiles) > 1
for in_file in datafiles:
if load_files:
scan = LoadMD(in_file,
LoadHistory=False,
OutputWorkspace="__scan")
else:
scan = mtd[in_file]
# Make sure the workspace has experiment info, otherwise SetGoniometer will add some, causing issues.
if scan.getNumExperimentInfo() == 0:
raise RuntimeError(
"No experiment info was found in '{}'".format(in_file))
prog.report()
self.log().information("Processing '{}'".format(in_file))
SetGoniometer(Workspace=scan,
Axis0='omega,0,1,0,-1',
Axis1='chi,0,0,1,-1',
Axis2='phi,0,1,0,-1',
Average=False)
# If processing multiple files, append the base name to the given output name
if has_multiple:
if load_files:
out_ws_name = out_ws + "_" + os.path.basename(
in_file).strip(',.nxs')
else:
out_ws_name = out_ws + "_" + in_file
wslist.append(out_ws_name)
exp_info = scan.getExperimentInfo(0)
self.__move_components(exp_info, height, distance)
# Get the wavelength from experiment info if it exists, or fallback on property value
wavelength = self.__get_wavelength(exp_info)
# set the run number to be the same as scan number, this will be used for peaks
if not exp_info.run().hasProperty('run_number') and exp_info.run(
).hasProperty('scan'):
try:
exp_info.mutableRun().addProperty(
'run_number',
int(exp_info.run().getProperty('scan').value), True)
except ValueError:
# scan must be a int
pass
# Use ConvertHFIRSCDtoQ (and normalize van), or use ConvertWANDSCtoQ which handles normalization itself
if output == "Q-sample events":
norm_data = self.__normalization(scan, vanws, load_files)
minvals = self.getProperty("MinValues").value
maxvals = self.getProperty("MaxValues").value
merge = self.getProperty("MergeInputs").value
ConvertHFIRSCDtoMDE(InputWorkspace=norm_data,
Wavelength=wavelength,
MinValues=minvals,
MaxValues=maxvals,
OutputWorkspace=out_ws_name)
DeleteWorkspace(norm_data)
elif output == 'Q-sample histogram':
bin0 = self.getProperty("BinningDim0").value
bin1 = self.getProperty("BinningDim1").value
bin2 = self.getProperty("BinningDim2").value
# Convert to Q space and normalize with from the vanadium
ConvertWANDSCDtoQ(
InputWorkspace=scan,
NormalisationWorkspace=vanws,
Frame='Q_sample',
Wavelength=wavelength,
NormaliseBy=self.getProperty("NormaliseBy").value,
BinningDim0=bin0,
BinningDim1=bin1,
BinningDim2=bin2,
OutputWorkspace=out_ws_name)
if load_files:
DeleteWorkspace(scan)
else:
norm_data = self.__normalization(scan, vanws, load_files)
RenameWorkspace(norm_data, OutputWorkspace=out_ws_name)
if has_multiple:
out_ws_name = out_ws
if output == "Q-sample events" and merge:
MergeMD(InputWorkspaces=wslist, OutputWorkspace=out_ws_name)
DeleteWorkspaces(wslist)
else:
GroupWorkspaces(InputWorkspaces=wslist,
OutputWorkspace=out_ws_name)
# Don't delete workspaces if they were passed in
if load_van:
DeleteWorkspace(vanws)
self.setProperty("OutputWorkspace", out_ws_name)
