3.12

SANSStitch v1

../_images/SANSStitch-v1_dlg.png

SANSStitch dialog.

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: [‘Both’, ‘None’, ‘ShiftOnly’, ‘ScaleOnly’]
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)}

Fit Modes

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 forumla 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.

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: Algorithms | SANS

Source

Python: SANSStitch.py (last modified: 2018-02-23)