def setUpClass(cls):
def gaussian(x,y,z,x0,y0,z0,ox,oy,oz,A):
return A*np.exp(-(x-x0)**2/(2*ox**2)-(y-y0)**2/(2*oy**2)-(z-z0)**2/(2*oz**2))
def peaks(i,j,k):
return gaussian(i,j,k,16,100,50,2,2,2,20)+gaussian(i,j,k,16,150,50,1,1,1,10)
S=np.fromfunction(peaks,(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()
ConvertWANDSCDtoQTest_data.addExperimentInfo(ConvertWANDSCDtoQTest_dummy)
ConvertWANDSCDtoQTest_data.getExperimentInfo(0).run().addProperty('s1', list(np.arange(0,50,0.5)), True)
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)
SetUB(ConvertWANDSCDtoQTest_data, 5,5,7,90,90,120,u=[-1,0,1],v=[1,0,1])
# Create Normalisation workspace
S=np.ones((32,240,1))
ConvertWANDSCDtoQTest_norm=CreateMDHistoWorkspace(Dimensionality=3,Extents='0.5,32.5,0.5,240.5,0.5,1.5',SignalInput=S,ErrorInput=S,
NumberOfBins='32,240,1',Names='y,x,scanIndex',Units='bin,bin,number')
ConvertWANDSCDtoQTest_dummy2 = CreateSingleValuedWorkspace()
ConvertWANDSCDtoQTest_norm.addExperimentInfo(ConvertWANDSCDtoQTest_dummy2)
ConvertWANDSCDtoQTest_norm.getExperimentInfo(0).run().addProperty('monitor_count', [100000.], True)
def setUpClass(cls):
def gaussian(x,y,z,x0,y0,z0,ox,oy,oz,A):
return A*np.exp(-(x-x0)**2/(2*ox**2)-(y-y0)**2/(2*oy**2)-(z-z0)**2/(2*oz**2))
def peaks(i,j,k):
return gaussian(i,j,k,16,100,50,2,2,2,20)+gaussian(i,j,k,16,150,50,1,1,1,10)
S=np.fromfunction(peaks,(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()
ConvertWANDSCDtoQTest_data.addExperimentInfo(ConvertWANDSCDtoQTest_dummy)
ConvertWANDSCDtoQTest_data.getExperimentInfo(0).run().addProperty('s1', list(np.arange(0,50,0.5)), True)
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)
SetUB(ConvertWANDSCDtoQTest_data, 5,5,7,90,90,120,u=[-1,0,1],v=[1,0,1])
# Create Normalisation workspace
S=np.ones((32,240,1))
ConvertWANDSCDtoQTest_norm=CreateMDHistoWorkspace(Dimensionality=3,Extents='0.5,32.5,0.5,240.5,0.5,1.5',SignalInput=S,ErrorInput=S,
NumberOfBins='32,240,1',Names='y,x,scanIndex',Units='bin,bin,number')
ConvertWANDSCDtoQTest_dummy2 = CreateSingleValuedWorkspace()
ConvertWANDSCDtoQTest_norm.addExperimentInfo(ConvertWANDSCDtoQTest_dummy2)
ConvertWANDSCDtoQTest_norm.getExperimentInfo(0).run().addProperty('monitor_count', [100000.], True)
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)