diff --git a/docs/source/algorithms/Fit-v1.rst b/docs/source/algorithms/Fit-v1.rst
index e8cafc709b5b69bc70ee8d06b66280cecac3f4c3..20673be76be7afef59625939c52d5a405d8fd377 100644
--- a/docs/source/algorithms/Fit-v1.rst
+++ b/docs/source/algorithms/Fit-v1.rst
@@ -382,7 +382,7 @@ Usage
.. testcode:: ExFitPeak
- from __future__ import print_function
+ from __future__ import print_function
# create a workspace with a gaussian peak sitting on top of a linear (here flat) background
ws = CreateSampleWorkspace(Function="User Defined", UserDefinedFunction="name=LinearBackground, \
A0=0.3;name=Gaussian, PeakCentre=5, Height=10, Sigma=0.7", NumBanks=1, BankPixelWidth=1, XMin=0, XMax=10, BinWidth=0.1)
@@ -463,90 +463,92 @@ Output:
.. testoutput:: simFit
- Constant 1: 2.00
- Constant 2: 5.00
+ Constant 1: 2.00
+ Constant 2: 5.00
**Example - Fit to two data sets with shared parameter:**
.. testcode:: shareFit
-
- import math
- import numpy as np
-
- # create data
- xData=np.linspace(start=0,stop=10,num=22)
- yData=[]
- for x in xData:
- yData.append(2.0)
- yData2=[]
- for x in xData:
- yData2.append(5.0)
- # create workspaces
- input = CreateWorkspace(xData,yData)
- input2 = CreateWorkspace(xData,yData2)
- # create function
- myFunc=';name=FlatBackground,$domains=i,A0=0'
- multiFunc='composite=MultiDomainFunction,NumDeriv=1'+myFunc+myFunc+';ties=(f0.A0=f1.A0)'
- # do fit
- fitStatus, chiSq, covarianceTable, paramTable, fitWorkspace = Fit( Function=multiFunc,\
- InputWorkspace=input, WorkspaceIndex=0, \
- InputWorkspace_1=input2, WorkspaceIndex_1=0, \
- StartX = 0.1, EndX=9.5, StartX_1 = 0.1, EndX_1=9.5,Output='fit' )
- # print results
- print "Constant 1: {0:.2f}".format(paramTable.column(1)[0])
- print "Constant 2: {0:.2f}".format(paramTable.column(1)[1])
+
+ from __future__ import print_function
+ import math
+ import numpy as np
+
+ # create data
+ xData=np.linspace(start=0,stop=10,num=22)
+ yData=[]
+ for x in xData:
+ yData.append(2.0)
+ yData2=[]
+ for x in xData:
+ Data2.append(5.0)
+ # create workspaces
+ input = CreateWorkspace(xData,yData)
+ input2 = CreateWorkspace(xData,yData2)
+ # create function
+ myFunc=';name=FlatBackground,$domains=i,A0=0'
+ multiFunc='composite=MultiDomainFunction,NumDeriv=1'+myFunc+myFunc+';ties=(f0.A0=f1.A0)'
+ # do fit
+ fit_output = Fit(Function=multiFunc, InputWorkspace=input, WorkspaceIndex=0, \
+ InputWorkspace_1=input2, WorkspaceIndex_1=0, \
+ StartX = 0.1, EndX=9.5, StartX_1 = 0.1, EndX_1=9.5,Output='fit')
+ paramTable = fit_output.OutputParameters # table containing the optimal fit parameters
+ # print results
+ print("Constant 1: {0:.2f}".format(paramTable.column(1)[0]))
+ print("Constant 2: {0:.2f}".format(paramTable.column(1)[1]))
Output:
.. testoutput:: shareFit
- Constant 1: 3.50
- Constant 2: 3.50
-
+ Constant 1: 3.50
+ Constant 2: 3.50
+
**Example - Fit to two data sets with one shared parameter:**
.. testcode:: shareFit2
-
- import math
- import numpy as np
-
- # create data
- xData=np.linspace(start=0,stop=10,num=22)
- yData=[]
- for x in xData:
- yData.append(2.0*x+10.)
- yData2=[]
- for x in xData:
- yData2.append(5.0*x+7.)
- # create workspaces
- input = CreateWorkspace(xData,yData)
- input2 = CreateWorkspace(xData,yData2)
- # create function
- myFunc=';name=LinearBackground,$domains=i,A0=0,A1=0'
- multiFunc='composite=MultiDomainFunction,NumDeriv=1'+myFunc+myFunc+';ties=(f0.A1=f1.A1)'
- # do fit
- fitStatus, chiSq, covarianceTable, paramTable, fitWorkspace = Fit( Function=multiFunc,\
- InputWorkspace=input, WorkspaceIndex=0, \
- InputWorkspace_1=input2, WorkspaceIndex_1=0, \
- StartX = 0.1, EndX=9.5, StartX_1 = 0.1, EndX_1=9.5,Output='fit' )
- # print results
- print 'Gradients (shared):'
- print "Gradient 1: {0:.2f}".format(paramTable.column(1)[3])
- print "Gradient 2: {0:.2f}".format(paramTable.column(1)[1])
- print 'offsets:'
- print "Constant 1: {0:.2f}".format(paramTable.column(1)[0])
- print "Constant 2: {0:.2f}".format(paramTable.column(1)[2])
-
+
+ from __future__ import print_function
+ import math
+ import numpy as np
+
+ # create data
+ xData=np.linspace(start=0,stop=10,num=22)
+ yData=[]
+ for x in xData:
+ yData.append(2.0*x+10.)
+ yData2=[]
+ for x in xData:
+ yData2.append(5.0*x+7.)
+ # create workspaces
+ input = CreateWorkspace(xData,yData)
+ input2 = CreateWorkspace(xData,yData2)
+ # create function
+ myFunc=';name=LinearBackground,$domains=i,A0=0,A1=0'
+ multiFunc='composite=MultiDomainFunction,NumDeriv=1'+myFunc+myFunc+';ties=(f0.A1=f1.A1)'
+ # do fit
+ fit_output = Fit(Function=multiFunc, InputWorkspace=input, WorkspaceIndex=0, \
+ InputWorkspace_1=input2, WorkspaceIndex_1=0, \
+ StartX = 0.1, EndX=9.5, StartX_1 = 0.1, EndX_1=9.5,Output='fit')
+ paramTable = fit_output.OutputParameters # table containing the optimal fit parameters
+ # print results
+ print('Gradients (shared):')
+ print("Gradient 1: {0:.2f}".format(paramTable.column(1)[3]))
+ print("Gradient 2: {0:.2f}".format(paramTable.column(1)[1]))
+ print('offsets:')
+ print("Constant 1: {0:.2f}".format(paramTable.column(1)[0]))
+ print("Constant 2: {0:.2f}".format(paramTable.column(1)[2]))
+
Output:
.. testoutput:: shareFit2
- Gradients (shared):
- Gradient 1: 3.50
- Gradient 2: 3.50
- offsets:
- Constant 1: 2.86
- Constant 2: 14.14
+ Gradients (shared):
+ Gradient 1: 3.50
+ Gradient 2: 3.50
+ offsets:
+ Constant 1: 2.86
+ Constant 2: 14.14
.. categories::
diff --git a/docs/source/algorithms/LoadSassena-v1.rst b/docs/source/algorithms/LoadSassena-v1.rst
index 6209e30f95c515a5951bddb66ab72cc80e6ce8e8..49dbebc86262b2c55d8379b3e7aa510f3d952277 100644
--- a/docs/source/algorithms/LoadSassena-v1.rst
+++ b/docs/source/algorithms/LoadSassena-v1.rst
@@ -56,7 +56,7 @@ Usage
from __future__ import print_function
ws = LoadSassena("loadSassenaExample.h5", TimeUnit=1.0)
- print 'workspaces instantiated: ', ', '.join(ws.getNames())
+ print('workspaces instantiated: ', ', '.join(ws.getNames()))
fqtReal = ws[1] # Real part of F(Q,t)
# Let's fit it to a Gaussian. We start with an initial guess
intensity = 0.5
diff --git a/docs/source/algorithms/SassenaFFT-v1.rst b/docs/source/algorithms/SassenaFFT-v1.rst
index fe370667e0b1f44f2aa7c0df96da2a16e7e993bf..1c960cfb2a711104ea0cd41e75508e3e77e0e4b6 100644
--- a/docs/source/algorithms/SassenaFFT-v1.rst
+++ b/docs/source/algorithms/SassenaFFT-v1.rst
@@ -70,7 +70,7 @@ Usage
ws = LoadSassena("loadSassenaExample.h5", TimeUnit=1.0)
SassenaFFT(ws, FFTonlyRealPart=1, Temp=1000, DetailedBalance=1)
- print 'workspaces instantiated: ', ', '.join(ws.getNames())
+ print('workspaces instantiated: ', ', '.join(ws.getNames()))
sqt = ws[3] # S(Q,E)
# I(Q,t) is a Gaussian, thus S(Q,E) is a Gaussian too (at high temperatures)
diff --git a/docs/source/fitfunctions/TabulatedFunction.rst b/docs/source/fitfunctions/TabulatedFunction.rst
index 09771f6365b09300564df17c0f50e9c896b1a74c..dccc781ceb156138fb1ddf5c9e10d86ade94c86d 100644
--- a/docs/source/fitfunctions/TabulatedFunction.rst
+++ b/docs/source/fitfunctions/TabulatedFunction.rst
@@ -43,9 +43,6 @@ Usage
# Call the Fit algorithm and perform the fit
myFunc='name=TabulatedFunction,Workspace=ws1,WorkspaceIndex=0,Scaling=1.0,Shift=0.0'
fit_output = Fit(Function=myFunc, InputWorkspace=ws2, Output='fit')
-
- fitStatus, chiSq, covarianceTable, paramTable, fitWorkspace =\
- Fit(Function=myFunc, InputWorkspace=ws2, Output='fit')
paramTable = fit_output.OutputParameters # table containing the optimal fit parameters
fitWorkspace = fit_output.OutputWorkspace