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# EstimatePeakErrors v1¶

## Summary¶

Calculates error estimates for peak parameters: centre, height, FWHM and intensity.

## Properties¶

Name

Direction

Type

Default

Description

Function

InOut

Function

Mandatory

Fitting function containing peaks. Must have a covariance matrix attached.

OutputWorkspace

Output

TableWorkspace

Mandatory

The name of the TableWorkspace with the output values and errors.

## Description¶

This algorithm takes a function after it has been optimized by the Fit algorithm and calculates peak parameter and associated errors for all peaks in this function. The peak parameters are its centre, height, FWHM and intensity. The output workspace is a table with three columns: parameter name, parameter value and parameter error.

## Usage¶

(At the moment the algorithm works properly if run from C++ only.):

import numpy as np

# Create a data set
x = np.linspace(-10,10,100)
y = 10 * 2.0 / (2.0**2 + (x+4)**2 ) + 10 * 3.0 / (3.0**2 + (x-3)**2 ) + 3.0
e = np.ones_like(x)
ws = CreateWorkspace(x,y,e)

# Define a fitting function.
fun = "name=Lorentzian,Amplitude=10,PeakCentre=-4,FWHM=2;"+\
"name=Lorentzian,Amplitude=10,PeakCentre=3,FWHM=3;"+\
"name=FlatBackground,A0=3"

# Fit the function.
Fit(fun,ws)

# Calculate peak parameter error estimates for the two Lorentzians.
params = EstimatePeakErrors(fun)


Categories: AlgorithmIndex | Optimization