Use print function instead of statement.

pavoljuhas · pavoljuhas · commit b4d47a90ee7f · 2017-07-21T16:59:34.000-04:00
Make this more compatible with Python 3.
diff --git a/cmi_plugins/ipy_gaussianfit.py b/cmi_plugins/ipy_gaussianfit.py
@@ -46,6 +46,7 @@
 This extension is based on the SrFit example gaussianrecipe.py
 """
 
+from __future__ import print_function
 from diffpy.srfit.fitbase import FitContribution, FitRecipe, Profile, FitResults
 
 
@@ -89,7 +90,7 @@ def __init__(self, x, y, dy=None, A=None, sig=None, x0=None):
         if A is not None:  self.A = A
         if sig is not None:  self.sig = sig
         if x0 is not None:  self.x0 = x0
-        print 'Initial parameter values:'
+        print('Initial parameter values:')
         self.printValues()
         return
 
@@ -178,9 +179,9 @@ def _makeRecipe(self, x, y, dy):
     def printValues(self):
         '''Print out values of Gaussian parameters
         '''
-        print 'A =', self.A
-        print 'sig =', self.sig
-        print 'x0 =', self.x0
+        print('A =', self.A)
+        print('sig =', self.sig)
+        print('x0 =', self.x0)
         return
 
 
@@ -203,8 +204,8 @@ def refine(self):
         from scipy.optimize.minpack import leastsq
         leastsq(self.recipe.residual, self.recipe.values)
         self.results = FitResults(self.recipe)
-        print "Fit results:\n"
-        print self.results
+        print("Fit results:\n")
+        print(self.results)
         return
 
 # end of class GaussianFit
diff --git a/cmi_scripts/calcbvsnacl/calcbvs.py b/cmi_scripts/calcbvsnacl/calcbvs.py
@@ -5,6 +5,7 @@
 DiffPy-CMI.
 '''
 
+from __future__ import print_function
 from diffpy.Structure import loadStructure
 from diffpy.srreal.bvscalculator import BVSCalculator
 
@@ -16,12 +17,12 @@
 
 # calculate BVS and print the expected and actual valences
 vsim = bvsc(nacl)
-print 'Calculate bond valence sums for NaCl "bvsc(nacl)"'
-print 'expected "bvsc.valences":\n ', bvsc.valences
-print 'calculated "bvsc.value":\n ' , vsim
-print 'difference "bvsc.bvdiff":\n ', bvsc.bvdiff
-print 'root mean square difference "bvsc.bvrmsdiff":', bvsc.bvrmsdiff
-print
+print('Calculate bond valence sums for NaCl "bvsc(nacl)"')
+print('expected "bvsc.valences":\n ', bvsc.valences)
+print('calculated "bvsc.value":\n ' , vsim)
+print('difference "bvsc.bvdiff":\n ', bvsc.bvdiff)
+print('root mean square difference "bvsc.bvrmsdiff":', bvsc.bvrmsdiff)
+print()
 
 # create new BVS calculator with a custom bond valence parameters
 bvsc2 = BVSCalculator()
@@ -31,21 +32,21 @@
 # These parameters have 6A cutoff.
 bvsc2.bvparamtable.setCustom('Na', +1, 'Cl', -1, Ro=1.6833, B=0.608)
 bvsc2.rmax = 6
-print "BVS in NaCl with alternate parameters:\n ", bvsc2(nacl)
-print
+print("BVS in NaCl with alternate parameters:\n ", bvsc2(nacl))
+print()
 
 # Lookup table of bond valence parameters can be used as separate object.
 from diffpy.srreal.bvparameterstable import BVParametersTable
 
 table = BVParametersTable()
 bp = table.lookup('Na+', 'Cl-')
 bp2 = table.lookup('Na', +1, 'Br', -1)
-print "Standard lookup of bond valence parameters:"
-print " ", bp
-print " ", bp2
-print
-
-print "Handling of unknown or invalid ion pairs:"
-print "  table.lookup('A+', 'X-'):", table.lookup('A+', 'X-')
-print "  table.lookup('A+', 'X-') == table.none():", (
-        table.lookup('A+', 'X-') == table.none())
+print("Standard lookup of bond valence parameters:")
+print(" ", bp)
+print(" ", bp2)
+print()
+
+print("Handling of unknown or invalid ion pairs:")
+print("  table.lookup('A+', 'X-'):", table.lookup('A+', 'X-'))
+print("  table.lookup('A+', 'X-') == table.none():", (
+        table.lookup('A+', 'X-') == table.none()))
diff --git a/cmi_scripts/fitCdSeNP/fitCdSeNP.py b/cmi_scripts/fitCdSeNP/fitCdSeNP.py
@@ -1,6 +1,8 @@
 #!/usr/bin/env python
 # -*- coding: utf-8 -*-
 
+from __future__ import print_function
+
 # We'll need numpy and matplotlib for plotting our results
 import numpy as np
 import matplotlib.pyplot as plt
@@ -89,17 +91,17 @@
 cdseFit.clearFitHooks()
 
 # We can now execute the fit using scipy's least square optimizer.
-print "Refine PDF using scipy's least-squares optimizer:"
-print "  variables:", cdseFit.names
-print "  initial values:", cdseFit.values
+print("Refine PDF using scipy's least-squares optimizer:")
+print("  variables:", cdseFit.names)
+print("  initial values:", cdseFit.values)
 leastsq(cdseFit.residual, cdseFit.values)
-print "  final values:", cdseFit.values
-print
+print("  final values:", cdseFit.values)
+print()
 
 # Obtain and display the fit results.
 cdseResults = FitResults(cdseFit)
-print "FIT RESULTS\n"
-print cdseResults
+print("FIT RESULTS\n")
+print(cdseResults)
 
 # Plot the observed and refined PDF.
 
diff --git a/cmi_scripts/mpdf/example_corefinement1.py b/cmi_scripts/mpdf/example_corefinement1.py
@@ -7,6 +7,7 @@
 '''
 
 # Import necessary functions
+from __future__ import print_function
 import numpy as np
 import matplotlib.pyplot as plt
 from scipy.optimize.minpack import leastsq
@@ -94,9 +95,9 @@ def mpdf(parascale, ordscale):
 # parameters according to the CIF-loaded space group.
 from diffpy.srfit.structure import constrainAsSpaceGroup
 sgpars = constrainAsSpaceGroup(nucpdf.phase, pcif.spacegroup.short_name)
-print "Space group parameters are:",
-print ', '.join([p.name for p in sgpars])
-print
+print("Space group parameters are:", end=' ')
+print(', '.join([p.name for p in sgpars]))
+print()
 
 # We can now cycle through the parameters and activate them in the recipe as
 # variables
@@ -122,16 +123,16 @@ def mpdf(parascale, ordscale):
 mnofit.clearFitHooks()
 
 # Initial structural fit
-print "Refine PDF using scipy's least-squares optimizer:"
-print "  variables:", mnofit.names
-print "  initial values:", mnofit.values
+print("Refine PDF using scipy's least-squares optimizer:")
+print("  variables:", mnofit.names)
+print("  initial values:", mnofit.values)
 leastsq(mnofit.residual, mnofit.values)
-print "  final values:", mnofit.values
-print
+print("  final values:", mnofit.values)
+print()
 # Obtain and display the fit results.
 mnoresults = FitResults(mnofit)
-print "FIT RESULTS\n"
-print mnoresults
+print("FIT RESULTS\n")
+print(mnoresults)
 
 
 # Get the experimental data from the recipe
diff --git a/cmi_scripts/mpdf/example_corefinement2.py b/cmi_scripts/mpdf/example_corefinement2.py
@@ -9,6 +9,7 @@
 '''
 
 # Import necessary functions
+from __future__ import print_function
 import numpy as np
 import matplotlib.pyplot as plt
 from scipy.optimize import leastsq
@@ -65,9 +66,9 @@
 # parameters according to the CIF-loaded space group.
 from diffpy.srfit.structure import constrainAsSpaceGroup
 sgpars = constrainAsSpaceGroup(nucpdf.phase, pcif.spacegroup.short_name)
-print "Space group parameters are:",
-print ', '.join([p.name for p in sgpars])
-print
+print("Space group parameters are:", end=' ')
+print(', '.join([p.name for p in sgpars]))
+print()
 
 # We can now cycle through the parameters and activate them in the recipe as
 # variables
@@ -89,16 +90,16 @@
 mnofit.clearFitHooks()
 
 # Initial structural fit
-print "Refine PDF using scipy's least-squares optimizer:"
-print "  variables:", mnofit.names
-print "  initial values:", mnofit.values
+print("Refine PDF using scipy's least-squares optimizer:")
+print("  variables:", mnofit.names)
+print("  initial values:", mnofit.values)
 leastsq(mnofit.residual, mnofit.values)
-print "  final values:", mnofit.values
-print
+print("  final values:", mnofit.values)
+print()
 # Obtain and display the fit results.
 mnoresults = FitResults(mnofit)
-print "FIT RESULTS\n"
-print mnoresults
+print("FIT RESULTS\n")
+print(mnoresults)
 
 # Get the experimental data from the recipe
 r = mnofit.totpdf.profile.x
@@ -147,7 +148,7 @@ def magresidual(p, yexp, mcalc):
 
 p0=[5.0,3.0] # initial parameter values (paraScale, ordScale)
 pOpt=leastsq(magresidual, p0, args=(gdiff, mc))
-print pOpt
+print(pOpt)
 
 magfit = mc.calc(both=True)[2]
 magdiff = gdiff - magfit
@@ -189,15 +190,15 @@ def mpdf(parascale, ordscale):
 mnofit.addVar(totpdf.ordscale,value=mc.ordScale)
 
 # do the co-refinement
-print "Refine PDF using scipy's least-squares optimizer:"
-print "  variables:", mnofit.names
-print "  initial values:", mnofit.values
+print("Refine PDF using scipy's least-squares optimizer:")
+print("  variables:", mnofit.names)
+print("  initial values:", mnofit.values)
 leastsq(mnofit.residual, mnofit.values)
-print "  final values:", mnofit.values
-print
+print("  final values:", mnofit.values)
+print()
 mnoresults=FitResults(mnofit)
-print "FIT RESULTS\n"
-print mnoresults
+print("FIT RESULTS\n")
+print(mnoresults)
 
 # Get the calculated PDF and compute the difference between the calculated and
 # measured PDF
diff --git a/cmi_scripts/mpdf/example_fromPDFgui.py b/cmi_scripts/mpdf/example_fromPDFgui.py
@@ -7,6 +7,7 @@
 '''
 
 # Import necessary functions
+from __future__ import print_function
 import numpy as np
 import matplotlib.pyplot as plt
 from scipy.optimize import leastsq
@@ -48,7 +49,7 @@ def residual(p, yexp, mcalc):
 
 p0 = [5.0,3.0] # initial parameter values (paraScale, ordScale)
 pOpt = leastsq(residual, p0, args=(Drexp,mc))
-print pOpt
+print(pOpt)
 
 fit=mc.calc(both=True)[2]
 
diff --git a/cmi_scripts/mpdf/example_fromSrfit.py b/cmi_scripts/mpdf/example_fromSrfit.py
@@ -7,6 +7,7 @@
 '''
 
 # Import necessary functions
+from __future__ import print_function
 import numpy as np
 import matplotlib.pyplot as plt
 from scipy.optimize import leastsq
@@ -60,9 +61,9 @@
 # parameters according to the H-3m space group.
 from diffpy.srfit.structure import constrainAsSpaceGroup
 spaceGroupParams = constrainAsSpaceGroup(MnOPDF.MnO.phase, spaceGroup)
-print "Space group parameters are:",
-print ', '.join([p.name for p in spaceGroupParams])
-print
+print("Space group parameters are:", end=' ')
+print(', '.join([p.name for p in spaceGroupParams]))
+print()
 
 # We can now cycle through the parameters and activate them in the recipe as
 # variables
@@ -84,17 +85,17 @@
 MnOFit.clearFitHooks()
 
 # We can now execute the fit using scipy's least square optimizer.
-print "Refine PDF using scipy's least-squares optimizer:"
-print "  variables:", MnOFit.names
-print "  initial values:", MnOFit.values
+print("Refine PDF using scipy's least-squares optimizer:")
+print("  variables:", MnOFit.names)
+print("  initial values:", MnOFit.values)
 leastsq(MnOFit.residual, MnOFit.values)
-print "  final values:", MnOFit.values
-print
+print("  final values:", MnOFit.values)
+print()
 
 # Obtain and display the fit results.
 MnOResults = FitResults(MnOFit)
-print "FIT RESULTS\n"
-print MnOResults
+print("FIT RESULTS\n")
+print(MnOResults)
 
 # Get the experimental data from the recipe
 r = MnOFit.MnO.profile.x
@@ -116,7 +117,7 @@ def residual(p, yexp, mcalc):
 
 p0 = [5.0, 3.0] # initial parameter values (parScale, ordScale)
 pOpt = leastsq(residual, p0, args=(gdiff,mc))
-print pOpt
+print(pOpt)
 
 fit=mc.calc(both=True)[2]
 
diff --git a/cmi_scripts/pdfrectprofile/nirectpdf.py b/cmi_scripts/pdfrectprofile/nirectpdf.py
@@ -1,6 +1,7 @@
 #!/usr/bin/env python
 # -*- coding: utf-8 -*-
 
+from __future__ import print_function
 import matplotlib.pyplot as plt
 from diffpy.Structure import loadStructure
 from diffpy.srreal.pdfcalculator import PDFCalculator
@@ -13,15 +14,15 @@
 # Calculate PDF with default profile function
 pc1 = PDFCalculator()
 r1, g1 = pc1(ni)
-print "standard peakprofile:\n    " + repr(pc1.peakprofile)
+print("standard peakprofile:\n    " + repr(pc1.peakprofile))
 
 # Create new calculator that uses the custom profile function
 pc2 = PDFCalculator()
 pc2.peakprofile = RectangleProfile()
 # Note:  pc2.peakprofile = 'rectangleprofile'
 # would do the same, because RectangleProfile class was registered
 # under its 'rectangleprofile' identifier.
-print "custom peakprofile:\n    " + repr(pc1.peakprofile)
+print("custom peakprofile:\n    " + repr(pc1.peakprofile))
 r2, g2 = pc2(ni)
 
 # compare both simulated curves
