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(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 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 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)
