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SANSStitch v1¶

../_images/ImageNotFound.png — Enable screenshots using DOCS_SCREENSHOTS in CMake¶

Summary ¶

Stitch the high angle and low angle banks of a workspace together

Properties ¶

Name	Direction	Type	Default	Description
HABCountsSample	Input	MatrixWorkspace	Mandatory	High angle bank sample workspace in Q
HABNormSample	Input	MatrixWorkspace	Mandatory	High angle bank normalization workspace in Q
LABCountsSample	Input	MatrixWorkspace	Mandatory	Low angle bank sample workspace in Q
LABNormSample	Input	MatrixWorkspace	Mandatory	Low angle bank normalization workspace in Q
ProcessCan	Input	boolean	False	Process the can
HABCountsCan	Input	MatrixWorkspace		High angle bank sample workspace in Q
HABNormCan	Input	MatrixWorkspace		High angle bank normalization workspace in Q
LABCountsCan	Input	MatrixWorkspace		Low angle bank sample workspace in Q
LABNormCan	Input	MatrixWorkspace		Low angle bank normalization workspace in Q
Mode	Input	string	None	What to fit. Free parameter(s). Allowed values: [‘ShiftOnly’, ‘ScaleOnly’, ‘Both’, ‘None’]
ScaleFactor	Input	number	Optional	Optional scaling factor
ShiftFactor	Input	number	Optional	Optional shift factor
FitMin	Input	number	0	Optional minimum q for fit
FitMax	Input	number	1000	Optional maximum q for fit
MergeMask	Input	boolean	False	Controls whether the user has manually specified the merge region
MergeMin	Input	number	0	The minimum of the merge region in q
MergeMax	Input	number	1000	The maximum of the merge region in q
OutputWorkspace	Output	MatrixWorkspace	Mandatory	Stitched high and low Q 1-D data
OutScaleFactor	Output	number		Applied scale factor
OutShiftFactor	Output	number		Applied shift factor

Description ¶

This algorithm is used to stitch together reduced data typically provided by Q1D v2. Data from high-angle and low-angle banks in a SANS experiment can be stitched together using this algorithm.

Merging of inputs is achieved using the following foruma, where C denotes counts, N denotes normalization and f and r relate to forward (high-angle) and rear (low-angle) respectively:

\[\frac{C_f(Q)+(shift\cdot N_f(Q))+C_r(Q)}{\frac{N_f(Q)}{scale} + N_r(Q)}\]

There are 4 available fit modes used to scale and shift the high angle bank data so that it can be stitched together with the low angle bank data. Where fitting is required Fit v1 is used with a composite function comprised of a FlatBackground and TabulatedFunction. In all cases the shift and scale are used to alter the counts and errors for the high angle bank.

None is the mode for no fit determined scaling or shifting. In this case the ScaleFactor and ShiftFactor properties must both be provided. With Both, fitting is used to establish optimum parameters for both the scaling and shifting of the high angle bank data. ScaleOnly mode ties the shift, so ShiftFactor must be provided. ShiftOnly mode ties the scale so ScaleFactor must be provided.

Can Runs ¶

When can runs are provided they are processed separately using the same merge formula above, and then subtracted from the processed sample run. If can runs are provided as inputs then the scale and shift factors are determined as part of fitting by operating on a workspace calculated from:

\[\frac{C_{sample}(Q)}{N_{sample}(Q)} - \frac{C_{can}(Q)}{N_{can}(Q)}\]

This is analogous to how Q1D v2 operates on input workspaces.

Merging of front and rear banks for the can is achieved using a different form from that above.

\[\frac{C_f(Q)+C_r(Q)}{\frac{N_f(Q)}{scale} + N_r(Q)}\]

where C denotes counts, N denotes normalization and f and r relate to forward (high-angle) and rear (low-angle) respectively. The can workspace is subtracted from the merged sample workspace to generate the output.

Workflow ¶

Usage ¶

Example - Simple shift:

hab_counts = CreateWorkspace(DataX=range(4,10), DataY=[1]*5, UnitX='MomentumTransfer')
hab_norm = CreateWorkspace(DataX=range(4,10), DataY=[1]*5, UnitX='MomentumTransfer')
lab_counts = CreateWorkspace(DataX=range(0,6), DataY=[6]*5, UnitX='MomentumTransfer')
lab_norm = CreateWorkspace(DataX=range(0,6), DataY=[1]*5, UnitX='MomentumTransfer')

uniform_binning = [0, 1, 10]
hab_counts = Rebin(hab_counts, Params=uniform_binning)
hab_norm = Rebin(hab_norm, Params=uniform_binning)
lab_counts = Rebin(lab_counts, Params=uniform_binning)
lab_norm = Rebin(lab_norm, Params=uniform_binning)

stitched, scale, shift = SANSStitch(HABCountsSample=hab_counts,
    HABNormSample=hab_norm,
    LABCountsSample=lab_counts,
    LABNormSample=lab_norm,
    Mode='ShiftOnly', ScaleFactor=1.0 )

print("{:.1f}".format(scale))
print("{:.1f}".format(shift))

Output:

1.0
6.0

Categories: AlgorithmIndex | SANS

Source ¶

Python: SANSStitch.py