def setUp(self):
input_ws = CreateSampleWorkspace(
Function="User Defined",
UserDefinedFunction="name=LinearBackground, " +
"A0=0.3;name=Gaussian, PeakCentre=5, Height=10, Sigma=0.3",
NumBanks=2,
BankPixelWidth=1,
XMin=0,
XMax=10,
BinWidth=0.1,
BankDistanceFromSample=4.0)
self._input_ws = input_ws
self._table = FindEPP(input_ws, OutputWorkspace="table")
AddSampleLog(self._input_ws,
LogName='wavelength',
LogText='4.0',
LogType='Number',
LogUnit='Angstrom')
for i in range(input_ws.getNumberHistograms()):
y = input_ws.dataY(i)
y.fill(0.)
y[51] = 100.
e = input_ws.dataE(i)
e.fill(0.)
e[51] = 10.
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 setUp(self):
input_ws = CreateSampleWorkspace(Function="User Defined",
UserDefinedFunction="name=LinearBackground, A0=0.3;name=Gaussian, \
PeakCentre=5, Height=10, Sigma=0.3", NumBanks=2, BankPixelWidth=1,
XMin=0, XMax=10, BinWidth=0.1, BankDistanceFromSample=4.0)
self._input_ws = input_ws
self._table = FindEPP(input_ws, OutputWorkspace="table")
AddSampleLog(self._input_ws, LogName='wavelength', LogText='4.0', LogType='Number', LogUnit='Angstrom')
def _fitEPP(ws, wsType, wsNames, algorithmLogging):
"""Return a fitted EPP table for a workspace."""
if wsType == common.WS_CONTENT_DETS:
eppWSName = wsNames.withSuffix('epp_detectors')
else:
eppWSName = wsNames.withSuffix('epp_monitors')
eppWS = FindEPP(InputWorkspace=ws,
OutputWorkspace=eppWSName,
EnableLogging=algorithmLogging)
return eppWS
def setUp(self):
input_ws = CreateSampleWorkspace(
Function="User Defined",
UserDefinedFunction="name=LinearBackground, " +
"A0=0.3;name=Gaussian, PeakCentre=5, Height=10, Sigma=0.3",
NumBanks=2, BankPixelWidth=1, XMin=0, XMax=10, BinWidth=0.1,
BankDistanceFromSample=4.0)
self._input_ws = input_ws
self._table = FindEPP(input_ws, OutputWorkspace="table")
AddSampleLog(self._input_ws, LogName='wavelength', LogText='4.0',
LogType='Number', LogUnit='Angstrom')
# These ranges correspond to 6*FWHM of the gaussian above,
# the integration ranges of ComputeCalibrationCoefVan.
self._lowerBoundRange = slice(28, 73)
self._upperBoundRange = slice(27, 74)
def _fitElasticChannel(ys, wsNames, wsCleanup, algorithmLogging):
"""Return index to the peak position of ys."""
xs = np.array([i for i in range(len(ys))])
l2SumWSName = wsNames.withSuffix('summed_detectors_at_l2')
l2SumWS = CreateWorkspace(OutputWorkspace=l2SumWSName,
DataX=xs,
DataY=ys,
EnableLogging=algorithmLogging)
fitWSName = wsNames.withSuffix('summed_detectors_at_l2_fit_results')
fitWS = FindEPP(InputWorkspace=l2SumWS,
OutputWorkspace=fitWSName,
EnableLogging=algorithmLogging)
peakCentre = float(fitWS.cell('PeakCentre', 0))
wsCleanup.cleanup(l2SumWS)
wsCleanup.cleanup(fitWS)
return peakCentre
def _EPPTable(self, ws, eppWSName):
eppWS = FindEPP(InputWorkspace=ws,
OutputWorkspace=eppWSName,
EnableLogging=False)
return eppWS
