Merge pull request #75 from bobleesj/cookie-flake8-E266

Fix flake8 error of multiple # (E266)

Author: sbillinge

doc/examples/crystalpdf.py

@@ -32,13 +32,13 @@
 from diffpy.srfit.pdf import PDFGenerator, PDFParser
 from diffpy.structure import Structure
 
-####### Example Code
+# Example Code
 
 
 def makeRecipe(ciffile, datname):
     """Create a fitting recipe for crystalline PDF data."""
 
-    ## The Profile
+    # The Profile
     # This will be used to store the observed and calculated PDF profile.
     profile = Profile()
 
@@ -53,7 +53,7 @@ def makeRecipe(ciffile, datname):
     profile.loadParsedData(parser)
     profile.setCalculationRange(xmax=20)
 
-    ## The ProfileGenerator
+    # The ProfileGenerator
     # The PDFGenerator is for configuring and calculating a PDF profile. Here,
     # we want to refine a Structure object from diffpy.structure. We tell the
     # PDFGenerator that with the 'setStructure' method. All other configuration
@@ -65,18 +65,18 @@ def makeRecipe(ciffile, datname):
     stru.read(ciffile)
     generator.setStructure(stru)
 
-    ## The FitContribution
+    # The FitContribution
     # Here we associate the Profile and ProfileGenerator, as has been done
     # before.
     contribution = FitContribution("nickel")
     contribution.addProfileGenerator(generator)
     contribution.setProfile(profile, xname="r")
 
-    ## Make the FitRecipe and add the FitContribution.
+    # Make the FitRecipe and add the FitContribution.
     recipe = FitRecipe()
     recipe.addContribution(contribution)
 
-    ## Configure the fit variables
+    # Configure the fit variables
 
     # The PDFGenerator class holds the ParameterSet associated with the
     # Structure passed above in a data member named "phase". (We could have

doc/examples/crystalpdfall.py

@@ -26,7 +26,7 @@
 from diffpy.srfit.fitbase import FitContribution, FitRecipe, FitResults, Profile
 from diffpy.srfit.pdf import PDFGenerator, PDFParser
 
-####### Example Code
+# Example Code
 
 
 def makeProfile(datafile):
@@ -50,14 +50,14 @@ def makeContribution(name, generator, profile):
 def makeRecipe(ciffile_ni, ciffile_si, xdata_ni, ndata_ni, xdata_si, xdata_sini):
     """Create a fitting recipe for crystalline PDF data."""
 
-    ## The Profiles
+    # The Profiles
     # We need a profile for each data set.
     xprofile_ni = makeProfile(xdata_ni)
     xprofile_si = makeProfile(xdata_si)
     nprofile_ni = makeProfile(ndata_ni)
     xprofile_sini = makeProfile(xdata_sini)
 
-    ## The ProfileGenerators
+    # The ProfileGenerators
     # We create one for each phase and share the phases.
     xgenerator_ni = PDFGenerator("xG_ni")
     stru = loadCrystal(ciffile_ni)
@@ -78,7 +78,7 @@ def makeRecipe(ciffile_ni, ciffile_si, xdata_ni, ndata_ni, xdata_si, xdata_sini)
     xgenerator_sini_si = PDFGenerator("xG_sini_si")
     xgenerator_sini_si.setPhase(phase_si)
 
-    ## The FitContributions
+    # The FitContributions
     # We one of these for each data set.
     xcontribution_ni = makeContribution("xnickel", xgenerator_ni, xprofile_ni)
     xcontribution_si = makeContribution("xsilicon", xgenerator_si, xprofile_si)

doc/examples/crystalpdfobjcryst.py

@@ -27,13 +27,13 @@
 from diffpy.srfit.fitbase import FitContribution, FitRecipe, FitResults, Profile
 from diffpy.srfit.pdf import PDFGenerator, PDFParser
 
-####### Example Code
+# Example Code
 
 
 def makeRecipe(ciffile, datname):
     """Create a fitting recipe for crystalline PDF data."""
 
-    ## The Profile
+    # The Profile
     # This will be used to store the observed and calculated PDF profile.
     profile = Profile()
 
@@ -46,7 +46,7 @@ def makeRecipe(ciffile, datname):
     profile.loadParsedData(parser)
     profile.setCalculationRange(xmax=20)
 
-    ## The ProfileGenerator
+    # The ProfileGenerator
     # This time we use the CreateCrystalFromCIF method of pyobjcryst.crystal to
     # create a Crystal object. That object is passed to the PDFGenerator as in
     # the previous example.
@@ -55,7 +55,7 @@ def makeRecipe(ciffile, datname):
     generator.setStructure(stru)
     generator.setQmax(40.0)
 
-    ## The FitContribution
+    # The FitContribution
     contribution = FitContribution("nickel")
     contribution.addProfileGenerator(generator)
     contribution.setProfile(profile, xname="r")
@@ -64,7 +64,7 @@ def makeRecipe(ciffile, datname):
     recipe = FitRecipe()
     recipe.addContribution(contribution)
 
-    ## Configure the fit variables
+    # Configure the fit variables
 
     # As before, we get a handle to the structure parameter set. In this case,
     # it is a ObjCrystCrystalParSet instance that was created when we called

doc/examples/crystalpdftwodata.py

@@ -28,13 +28,13 @@
 from diffpy.srfit.fitbase import FitContribution, FitRecipe, FitResults, Profile
 from diffpy.srfit.pdf import PDFGenerator, PDFParser
 
-####### Example Code
+# Example Code
 
 
 def makeRecipe(ciffile, xdatname, ndatname):
     """Create a fitting recipe for crystalline PDF data."""
 
-    ## The Profiles
+    # The Profiles
     # We need a profile for each data set. This means that we will need two
     # FitContributions as well.
     xprofile = Profile()
@@ -51,7 +51,7 @@ def makeRecipe(ciffile, xdatname, ndatname):
     nprofile.loadParsedData(parser)
     nprofile.setCalculationRange(xmax=20)
 
-    ## The ProfileGenerators
+    # The ProfileGenerators
     # We need one of these for the x-ray data.
     xgenerator = PDFGenerator("G")
     stru = loadCrystal(ciffile)
@@ -76,7 +76,7 @@ def makeRecipe(ciffile, xdatname, ndatname):
     ngenerator = PDFGenerator("G")
     ngenerator.setPhase(xgenerator.phase)
 
-    ## The FitContributions
+    # The FitContributions
     # We associate the x-ray PDFGenerator and Profile in one FitContribution...
     xcontribution = FitContribution("xnickel")
     xcontribution.addProfileGenerator(xgenerator)

doc/examples/crystalpdftwophase.py

@@ -28,13 +28,13 @@
 from diffpy.srfit.fitbase import FitContribution, FitRecipe, FitResults, Profile
 from diffpy.srfit.pdf import PDFGenerator, PDFParser
 
-####### Example Code
+# Example Code
 
 
 def makeRecipe(niciffile, siciffile, datname):
     """Create a fitting recipe for crystalline PDF data."""
 
-    ## The Profile
+    # The Profile
     profile = Profile()
 
     # Load data and add it to the profile
@@ -43,7 +43,7 @@ def makeRecipe(niciffile, siciffile, datname):
     profile.loadParsedData(parser)
     profile.setCalculationRange(xmax=20)
 
-    ## The ProfileGenerator
+    # The ProfileGenerator
     # In order to fit two phases simultaneously, we must use two PDFGenerators.
     # PDFGenerator is designed to take care of as little information as it
     # must. (Don't do too much, and do it well.) A PDFGenerator can generate
@@ -63,7 +63,7 @@ def makeRecipe(niciffile, siciffile, datname):
     stru = loadCrystal(siciffile)
     generator_si.setStructure(stru)
 
-    ## The FitContribution
+    # The FitContribution
     # Add both generators to the FitContribution. Add the Profile. This will
     # send the metadata to the generators.
     contribution = FitContribution("nisi")
@@ -81,7 +81,7 @@ def makeRecipe(niciffile, siciffile, datname):
     recipe = FitRecipe()
     recipe.addContribution(contribution)
 
-    ## Configure the fit variables
+    # Configure the fit variables
     # Start by configuring the scale factor and resolution factors.
     # We want the sum of the phase scale factors to be 1.
     recipe.newVar("scale_ni", 0.1)

doc/examples/debyemodel.py

@@ -53,7 +53,7 @@
 500.0 0.03946 0.00250
 """
 
-####### Example Code
+# Example Code
 
 
 def makeRecipe():
@@ -80,7 +80,7 @@ def makeRecipe():
 
     """
 
-    ## The Profile
+    # The Profile
     # Create a Profile to hold the experimental and calculated signal.
     profile = Profile()
 
@@ -90,7 +90,7 @@ def makeRecipe():
     x, y, dy = numpy.hsplit(xydy, 3)
     profile.setObservedProfile(x, y, dy)
 
-    ## The FitContribution
+    # The FitContribution
     # The FitContribution associates the profile with the Debye function.
     contribution = FitContribution("pb")
     # Tell the contribution about the Profile. We will need to use the
@@ -123,7 +123,7 @@ def makeRecipe():
     # can specify that as well.
     contribution.setEquation("debye(T, 207.2, abs(thetaD)) + offset")
 
-    ## The FitRecipe
+    # The FitRecipe
     # The FitRecipe lets us define what we want to fit. It is where we can
     # create variables, constraints and restraints. If we had multiple profiles
     # to fit simultaneously, the contribution from each could be added to the

doc/examples/debyemodelII.py

@@ -38,7 +38,7 @@
 
 from diffpy.srfit.fitbase import FitRecipe, FitResults
 
-####### Example Code
+# Example Code
 
 
 def makeRecipeII():

doc/examples/ellipsoidsas.py

@@ -21,13 +21,13 @@
 from diffpy.srfit.fitbase import FitContribution, FitRecipe, FitResults, Profile
 from diffpy.srfit.sas import SASGenerator, SASParser
 
-####### Example Code
+# Example Code
 
 
 def makeRecipe(datname):
     """Create a fitting recipe for ellipsoidal SAS data."""
 
-    ## The Profile
+    # The Profile
     # This will be used to store the observed and calculated I(Q) data.
     profile = Profile()
 
@@ -37,7 +37,7 @@ def makeRecipe(datname):
     parser.parseFile(datname)
     profile.loadParsedData(parser)
 
-    ## The ProfileGenerator
+    # The ProfileGenerator
     # The SASGenerator is for configuring and calculating a SAS profile. We use
     # a sas model to configure and serve as the calculation engine of the
     # generator. This allows us to use the full sas model creation
@@ -49,7 +49,7 @@ def makeRecipe(datname):
     model = EllipsoidModel()
     generator = SASGenerator("generator", model)
 
-    ## The FitContribution
+    # The FitContribution
     # Here we associate the Profile and ProfileGenerator, as has been done
     # before.
     contribution = FitContribution("ellipsoid")
@@ -62,11 +62,11 @@ def makeRecipe(datname):
     # will have on the estimated parameter uncertainties.
     contribution.setResidualEquation("log(eq) - log(y)")
 
-    ## Make the FitRecipe and add the FitContribution.
+    # Make the FitRecipe and add the FitContribution.
     recipe = FitRecipe()
     recipe.addContribution(contribution)
 
-    ## Configure the fit variables
+    # Configure the fit variables
     # The SASGenerator uses the parameters from the params and dispersion
     # attribues of the model. These vary from model to model, but are adopted
     # as SrFit Parameters within the generator. Whereas the dispersion

doc/examples/gaussiangenerator.py

@@ -43,7 +43,7 @@
 
 from diffpy.srfit.fitbase import FitContribution, FitRecipe, Profile, ProfileGenerator
 
-####### Example Code
+# Example Code
 
 
 class GaussianGenerator(ProfileGenerator):
@@ -124,20 +124,20 @@ def makeRecipe():
 
     """
 
-    ## The Profile
+    # The Profile
     # Create a Profile to hold the experimental and calculated signal.
     profile = Profile()
 
     # Load data and add it to the profile. This uses the loadtxt function from
     # numpy.
     profile.loadtxt("data/gaussian.dat")
 
-    ## The ProfileGenerator
+    # The ProfileGenerator
     # Create a GaussianGenerator named "g". This will be the name we use to
     # refer to the generator from within the FitContribution equation.
     generator = GaussianGenerator("g")
 
-    ## The FitContribution
+    # The FitContribution
     # Create a FitContribution that will associate the Profile with the
     # GaussianGenerator.  The GaussianGenerator will be accessible as an
     # attribute of the FitContribution by its name ("g"). Note that this will
@@ -146,7 +146,7 @@ def makeRecipe():
     contribution.addProfileGenerator(generator)
     contribution.setProfile(profile)
 
-    ## The FitRecipe
+    # The FitRecipe
     # Now we create the FitRecipe and add the FitContribution.
     recipe = FitRecipe()
     recipe.addContribution(contribution)

doc/examples/gaussianrecipe.py

@@ -48,7 +48,7 @@
 
 from diffpy.srfit.fitbase import FitContribution, FitRecipe, FitResults, Profile
 
-####### Example Code
+# Example Code
 
 
 def main():
@@ -92,15 +92,15 @@ def makeRecipe():
 
     """
 
-    ## The Profile
+    # The Profile
     # Create a Profile to hold the experimental and calculated signal.
     profile = Profile()
 
     # Load data and add it to the profile. This uses the loadtxt function from
     # numpy.
     profile.loadtxt("data/gaussian.dat")
 
-    ## The FitContribution
+    # The FitContribution
     # The FitContribution associates the Profile with a fitting equation. The
     # FitContribution also stores the parameters of the fitting equation. We
     # give our FitContribution then name "g1". We will be able to access the
@@ -128,7 +128,7 @@ def makeRecipe():
     # attribute.  Parameters also have a 'name' attribute.
     contribution.A.value = 1.0
 
-    ## The FitRecipe
+    # The FitRecipe
     # The FitRecipe lets us define what we want to fit. It is where we can
     # create variables, constraints and restraints.
     recipe = FitRecipe()