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ScaleX v1

ScaleX dialog.

Summary

Scales the X-axis of an input workspace by the given factor, which can be either multiplicative or additive.

See Also

ChangeBinOffset, Scale

Properties

Name	Direction	Type	Default	Description
InputWorkspace	Input	MatrixWorkspace	Mandatory	Name of the input workspace
OutputWorkspace	Output	MatrixWorkspace	Mandatory	Name of the output workspace
Factor	Input	number	1	The value by which to scale the X-axis of the input workspace. Default is 1.0
Operation	Input	string	Multiply	Whether to multiply by, or add factor. Allowed values: [‘Multiply’, ‘Add’]
IndexMin	Input	number	0	The workspace index of the first spectrum to scale. Only used if IndexMax is set.
IndexMax	Input	number	Optional	The workspace index of the last spectrum to scale. Only used if explicitly set.
InstrumentParameter	Input	string		The name of an instrument parameter whose value is used to scale as the input factor
Combine	Input	boolean	False	If true, combine the value given in the Factor property with the value obtained from the instrument parameter. The factors are combined using the operation specified in the Operation parameter

Description

Scales the X axis, the X-coordinate of histograms in a histogram workspace, and the X-coordinate of events in an event workspace by the requested amount.

• The amount can be specified either as:

• an absolute numerical value via the “Factor” argument or

• an detector parameter name whose value is retrieved from the instrument.

Usage

Example - Modify the mean and standard deviation of a Gaussian via rescaling of the X-axis:

import numpy as np

# A Gaussian in the [-1, 1] range
DataX=np.arange(-1,1,0.01)
mean=0.3
sigma=0.2
DataY=np.exp( -(DataX-mean)**2/(2*sigma**2) )
ws = CreateWorkspace(DataX,DataY)

# Center the Gaussian by shifting the X-axis, then find its average
ws2 = ScaleX(ws, Factor=-mean, Operation='Add')
print('mean={:.2f}'.format(abs(np.sum( ws2.dataX(0) *ws2.dataY(0) ) / np.sum( ws2.dataY(0) ))))

# Decrease the standard deviation of the Gaussian by half via shrinkage of the X-axis,
# then find its standard deviation
ws3 = ScaleX(ws2, Factor=0.5, Operation='Multiply')
print('sigma={:.2f}'.format(np.sqrt( np.sum( ws3.dataX(0)**2 *ws3.dataY(0) ) / np.sum( ws3.dataY(0) ) )))

Output:

mean=0.00
sigma=0.10

Categories: AlgorithmIndex | Arithmetic | CorrectionFunctions

Source

C++ header: ScaleX.h

C++ source: ScaleX.cpp