def _convert_to_q(self, state, workspace, wavelength_adjustment_workspace, pixel_adjustment_workspace,
wavelength_and_pixel_adjustment_workspace):
"""
A conversion to momentum transfer is performed in this step.
The conversion can be either to the modulus of Q in which case the output is a 1D workspace, or it can
be a 2D reduction where the y axis is Qy, ie it is a numeric axis.
@param state: a SANSState object
@param workspace: the workspace to convert to momentum transfer.
@param wavelength_adjustment_workspace: the wavelength adjustment workspace.
@param pixel_adjustment_workspace: the pixel adjustment workspace.
@param wavelength_and_pixel_adjustment_workspace: the wavelength and pixel adjustment workspace.
@return: a reduced workspace
"""
output_dict = convert_workspace(workspace=workspace, state_convert_to_q=state.convert_to_q,
wavelength_adj_workspace=wavelength_adjustment_workspace,
pixel_adj_workspace=pixel_adjustment_workspace,
wavelength_and_pixel_adj_workspace=wavelength_and_pixel_adjustment_workspace,
output_summed_parts=True)
output_workspace = output_dict["output"]
sum_of_counts = output_dict["counts_summed"]
sum_of_norms = output_dict["norm_summed"]
return output_workspace, sum_of_counts, sum_of_norms
def test_that_converts_wavelength_workspace_to_q_for_2d(self):
workspace = self._get_workspace(is_adjustment=False)
adj_workspace = self._get_workspace(is_adjustment=True)
state = self._get_sample_state(q_xy_max=2., q_xy_step=0.5, q_xy_step_type=RangeStepType.LIN,
dim=ReductionDimensionality.TWO_DIM)
output_dict = convert_workspace(workspace=workspace, output_summed_parts=True,
state_convert_to_q=state.convert_to_q, wavelength_adj_workspace=adj_workspace)
output_workspace = output_dict["output"]
sum_of_counts = output_dict["counts_summed"]
sum_of_norms = output_dict["norm_summed"]
# We expect a q-based workspace with 8 histograms and 8 bins
self.assertEqual(output_workspace.getNumberHistograms(), 8)
self.assertEqual(len(output_workspace.dataX(0)), 9)
self.assertEqual(output_workspace.getAxis(0).getUnit().unitID(), "MomentumTransfer")
self.assertEqual(output_workspace.getAxis(1).getUnit().unitID(), "MomentumTransfer")
self.assertFalse(output_workspace.hasDx(0))
self.assertTrue(output_workspace.getAxis(0).isNumeric())
self.assertTrue(output_workspace.getAxis(1).isNumeric())
self.assertEqual(sum_of_counts.getAxis(0).getUnit().unitID(), "MomentumTransfer")
self.assertEqual(sum_of_norms.getAxis(0).getUnit().unitID(), "MomentumTransfer")
DeleteWorkspace(workspace)
DeleteWorkspace(adj_workspace)
DeleteWorkspace(output_workspace)
DeleteWorkspace(sum_of_counts)
DeleteWorkspace(sum_of_norms)
def test_that_converts_wavelength_workspace_to_q_for_1d_and_no_q_resolution(self):
# Arrange
workspace = self._get_workspace(is_adjustment=False)
adj_workspace = self._get_workspace(is_adjustment=True)
state = self._get_sample_state(q_min=1., q_max=2., q_step=0.1, q_step_type=RangeStepType.LIN)
# Act
output_dict = convert_workspace(workspace=workspace, output_summed_parts=True,
state_convert_to_q=state.convert_to_q, wavelength_adj_workspace=adj_workspace)
output_workspace = output_dict["output"]
sum_of_counts = output_dict["counts_summed"]
sum_of_norms = output_dict["norm_summed"]
# Assert
# We expect a q-based workspace with one histogram and 10 bins.
self.assertEqual(output_workspace.getNumberHistograms(), 1)
self.assertEqual(len(output_workspace.dataX(0)), 11)
self.assertEqual(output_workspace.getAxis(0).getUnit().unitID(), "MomentumTransfer")
self.assertFalse(output_workspace.hasDx(0))
self.assertTrue(output_workspace.getAxis(0).isNumeric())
self.assertTrue(output_workspace.getAxis(1).isSpectra())
self.assertEqual(sum_of_counts.getAxis(0).getUnit().unitID(), "MomentumTransfer")
self.assertEqual(sum_of_norms.getAxis(0).getUnit().unitID(), "MomentumTransfer")
DeleteWorkspace(workspace)
DeleteWorkspace(adj_workspace)
DeleteWorkspace(output_workspace)
DeleteWorkspace(sum_of_counts)
DeleteWorkspace(sum_of_norms)
def _convert_to_q(
self, state, workspaces: WsList,
adjustment_dict: Dict[WavRange, AdjustmentStruct]
) -> Dict[WavRange, SumsStruct]:
"""
A conversion to momentum transfer is performed in this step.
The conversion can be either to the modulus of Q in which case the output is a 1D workspace, or it can
be a 2D reduction where the y axis is Qy, ie it is a numeric axis.
@param state: a SANSState object
@param workspace: the workspace to convert to momentum transfer.
@param wavelength_adjustment_workspace: the wavelength adjustment workspace.
@param pixel_adjustment_workspace: the pixel adjustment workspace.
@param wavelength_and_pixel_adjustment_workspace: the wavelength and pixel adjustment workspace.
@return: a reduced workspace
"""
sums = {}
for wav_range in workspaces.keys():
adjustment = adjustment_dict[wav_range]
output_dict = \
convert_workspace(workspace=workspaces[wav_range], state_convert_to_q=state.convert_to_q,
wavelength_adj_workspace=adjustment.wavelength_adjustment,
wavelength_and_pixel_adj_workspace=adjustment.wavelength_and_pixel_adjustment,
pixel_adj_workspace=adjustment.pixel_adjustment, output_summed_parts=True)
sums[wav_range] = SumsStruct(counts=output_dict["counts_summed"],
norm=output_dict["norm_summed"])
workspaces[wav_range] = output_dict["output"]
return sums
