Product Exponential Quadratic Fit Function

[Owen Arnold]

Following #5626 we need functions specific to detector efficiency corrections. These need to be named functions with named parameters so that they can be configured in the IDF xml.

[Owen Arnold]

refs #5634. Template for new Product function.

Changeset: 83b754d91c04370fa02b79adb5823007ddea8ac3

[Owen Arnold]

Wikipage now at http://www.mantidproject.org/ProductQuadraticExp

[Owen Arnold]

refs #5634. Working with tests.

Changeset: e708079ff54f8ce3a35dea87151ec14387b67a9b

[Owen Arnold]

refs #5633 #5634. Documentation.

Changeset: 3b1821e1920f87f9756d2accbf92cb8ba758c52f

[Owen Arnold]

refs #5634. Fix GCC errors and warnings.

Changeset: 4c90aa9955ff44a4439992db3804f85357ca136f

[Owen Arnold]

refs #5634. Fix GCC errors and warnings.

Changeset: db267153faf6adf20bfd6c6043de63f02b7c29be

[Owen Arnold]

Tester: The following demonstrates this fit function working with the NormaliseByDetector alg, for which it was written. The demo.xml file is attached.

import math 

inWS = Load(Filename='POLREF00004699.nxs')
inWS = ConvertSpectrumAxis(InputWorkspace=inWS,Target='signed_theta')
inWS = inWS[0]
inWS = ConvertUnits(InputWorkspace=inWS,Target='Wavelength',EMode='Indirect')

# Overwrite the
for i in range(0, inWS.getNumberHistograms()):
        Y = inWS.dataY(i)
        for j in range(0, len(Y)):
                Y[j] = 1

# Parameter file sets a global ProductLinearExp fit function for the whole instrument.
LoadParameterFile(Workspace=inWS,Filename=r'demo.xml')
normalised = NormaliseByDetector(InputWorkspace=inWS)

# It's enough just to use just the first histogram and compare the expected Y values with the Actual ones to determine if the ProductLinearExp fit function is being used.
for k in range(0, 1):
        Xout = normalised.dataX(i)
        Yout = normalised.dataY(i)
        Yin = inWS.dataY(i)
        for l in range(0, len(Xout)-1):
                x = (Xout[l]  + Xout[l + 1])  / 2
                yExpected= 1/( (x*x + x + 1) * math.exp(-x/1)) # because we set A1 = 1 A0 = 1. A2=1, Height=1 and Lifetime = 1 in the demo.xml file.
                yActual = Yout[l]
                if((math.fabs(yExpected) - math.fabs(yActual)) > 0.0001):
                        print math.fabs(yExpected) , math.fabs(yActual)
                        raise RuntimeError("Error, NormaliseByDetector is not using ProductLinearExp fit function properly.")

[Owen Arnold]

refs #5633 #5634. Test via FunctionFactory

Quite a simple but important test, because these functions would not work via NormaliseByDetector unless they were factory registered. Also provides checking that they are types of IFunction, since the test downcasts the boost shared_ptr.

Changeset: 76e97bb7f0c883f83d6a97f19d0ac2b1f91ab3fe

[Stuart Campbell]

This ticket has been transferred to github issue ​6480