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# ScaleX v1¶

## Summary¶

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

## 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

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
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