def test_fixed_window_scan(self):
out_ws = ApplyPaalmanPingsCorrection(
SampleWorkspace=mtd['fapi'][0],
CorrectionsWorkspace='fws_corrections',
OutputWorkspace='wsfapicorr')
self.assertTrue(out_ws)
self.assertTrue(isinstance(out_ws, MatrixWorkspace))
self.assertEquals(out_ws.blocksize(), mtd['fapi'][0].blocksize())
self.assertEquals(out_ws.getNumberHistograms(),
mtd['fapi'][0].getNumberHistograms())
self.assertEquals(
out_ws.getAxis(0).getUnit().unitID(),
mtd['fapi'][0].getAxis(0).getUnit().unitID())
def test_sample_and_can_corrections(self):
corr = ApplyPaalmanPingsCorrection(
SampleWorkspace=self._sample_ws,
CorrectionsWorkspace=self._corrections_ws,
CanWorkspace=self._can_ws)
self._verify_workspace(corr, 'sample_and_can_corrections')
def test_container_rebinning_enabled(self):
xs = numpy.array([0.0, 1.0, 0.0, 1.1])
ys = numpy.array([2.2, 3.3])
sample_1 = CreateWorkspace(DataX=xs, DataY=ys, NSpec=2,
UnitX='Wavelength')
xs = numpy.array([-1.0, 0.0, 1.0, 2.0, -1.0, 0.0, 1.0, 2.0])
ys = numpy.array([0.101, 0.102, 0.103, 0.104, 0.105, 0.106])
container_1 = CreateWorkspace(DataX=xs, DataY=ys, NSpec=2,
UnitX='Wavelength')
corrected = ApplyPaalmanPingsCorrection(SampleWorkspace=sample_1,
CanWorkspace=container_1,
RebinCanToSample=True)
self.assertTrue(numpy.all(sample_1.extractY() > corrected.extractY()))
DeleteWorkspace(sample_1)
DeleteWorkspace(container_1)
DeleteWorkspace(corrected)
def test_sample_and_can_corrections_with_can_shift(self):
corr = ApplyPaalmanPingsCorrection(SampleWorkspace=self._sample_ws,
CorrectionsWorkspace=self._corrections_ws,
CanWorkspace=self._can_ws,
CanShiftFactor = 0.03)
self._verify_workspace(corr, 'sample_and_can_corrections')
def test_container_rebinning_enabled(self):
xs = numpy.array([0.0, 1.0, 0.0, 1.1])
ys = numpy.array([2.2, 3.3])
sample_1 = CreateWorkspace(DataX=xs, DataY=ys, NSpec=2,
UnitX='Wavelength')
xs = numpy.array([-1.0, 0.0, 1.0, 2.0, -1.0, 0.0, 1.0, 2.0])
ys = numpy.array([0.101, 0.102, 0.103, 0.104, 0.105, 0.106])
container_1 = CreateWorkspace(DataX=xs, DataY=ys, NSpec=2,
UnitX='Wavelength')
corrected = ApplyPaalmanPingsCorrection(SampleWorkspace=sample_1,
CanWorkspace=container_1,
RebinCanToSample=True)
self.assertTrue(numpy.all(sample_1.extractY() > corrected.extractY()))
DeleteWorkspace(sample_1)
DeleteWorkspace(container_1)
DeleteWorkspace(corrected)
def test_sample_and_can_corrections_with_can_scale(self):
corr = ApplyPaalmanPingsCorrection(
SampleWorkspace='sample_ws',
CorrectionsWorkspace='corrections_ws',
CanWorkspace='can_ws',
CanScaleFactor=0.9)
self._verify_workspace(corr, 'sample_and_can_corrections')
def test_two_factor_approximation(self):
two_corrections = self._create_group_of_factors(self._corrections_ws, ['acc', 'ass'])
corr = ApplyPaalmanPingsCorrection(SampleWorkspace=self._sample_ws,
CorrectionsWorkspace=two_corrections,
CanWorkspace=self._can_ws,
CanShiftFactor = 0.03)
DeleteWorkspace(two_corrections)
self._verify_workspace(corr, 'sample_and_can_corrections')
def test_fixed_window_scan(self):
Load(Filename='ILL/IN16B/mc-abs-corr-q.nxs',
OutputWorkspace='fws_corrections_ass')
GroupWorkspaces(InputWorkspaces=['fws_corrections_ass'],
OutputWorkspace='fws_corrections')
in_ws = Load(Filename='ILL/IN16B/fapi-fws-q.nxs',
OutputWorkspace='fapi')
out_ws = ApplyPaalmanPingsCorrection(
SampleWorkspace=in_ws[0],
CorrectionsWorkspace='fws_corrections',
OutputWorkspace='wsfapicorr')
self.assertTrue(out_ws)
self.assertTrue(isinstance(out_ws, MatrixWorkspace))
self.assertEquals(out_ws.blocksize(), in_ws[0].blocksize())
self.assertEquals(out_ws.getNumberHistograms(),
in_ws[0].getNumberHistograms())
self.assertEquals(
out_ws.getAxis(0).getUnit().unitID(),
in_ws[0].getAxis(0).getUnit().unitID())
def test_container_input_workspace_not_unintentionally_rebinned(self):
xs = numpy.array([0.0, 1.0, 0.0, 1.1])
ys = numpy.array([2.2, 3.3])
sample_1 = CreateWorkspace(DataX=xs, DataY=ys, NSpec=2,
UnitX='Wavelength')
ys = numpy.array([0.11, 0.22])
container_1 = CreateWorkspace(DataX=xs, DataY=ys, NSpec=2,
UnitX='Wavelength')
corrected = ApplyPaalmanPingsCorrection(SampleWorkspace=sample_1,
CanWorkspace=container_1)
numHisto = container_1.getNumberHistograms()
for i in range(numHisto):
container_xs = container_1.readX(i)
for j in range(len(container_xs)):
self.assertEqual(container_xs[j], xs[i * numHisto + j])
DeleteWorkspace(sample_1)
DeleteWorkspace(container_1)
DeleteWorkspace(corrected)
def test_can_subtraction(self):
corr = ApplyPaalmanPingsCorrection(SampleWorkspace=self._sample_ws,
CanWorkspace=self._can_ws)
self._verify_workspace(corr, 'can_subtraction')
def test_sample_corrections_only(self):
corr = ApplyPaalmanPingsCorrection(
SampleWorkspace=self._sample_ws,
CorrectionsWorkspace=self._corrections_ws)
self._verify_workspace(corr, 'sample_corrections_only')
def test_can_subtraction_with_can_scale(self):
corr = ApplyPaalmanPingsCorrection(SampleWorkspace=self._sample_ws,
CanWorkspace=self._can_ws,
CanScaleFactor=0.9)
self._verify_workspace(corr, 'can_subtraction')
def test_can_subtraction_with_can_shift(self):
corr = ApplyPaalmanPingsCorrection(SampleWorkspace='sample_ws',
CanWorkspace='can_ws',
CanShiftFactor=0.03)
self._verify_workspace(corr, 'can_subtraction')
