Merge pull request #114 from sbillinge/codespell

chore: codespell

Author: sbillinge

.codespell/ignore_words.txt

@@ -6,3 +6,9 @@ mater
 
 ;; Frobenius norm used in np.linalg.norm
 fro
+
+;; nin is a legit variable in builder
+nin
+
+;; highT is used for high Temperature in examples/debymodelII
+highT

.travis.yml

@@ -101,7 +101,7 @@ def makeRecipe(stru1, stru2, datname):
     recipe.constrain(generator_zns.scale, "1 - scale_CdS")
     # We also want the resolution factor to be the same on each.
 
-    # Vary the gloabal scale as well.
+    # Vary the global scale as well.
     recipe.addVar(contribution.scale, 0.3)
 
     # Now we can configure the structural parameters. We tag the different

doc/examples/coreshellnp.py

@@ -16,7 +16,7 @@
 
 This is example of fitting the fcc nickel structure to measured PDF
 data. The purpose of this example is to demonstrate and describe the
-classes in configuraiton options involved with setting up a fit in this
+classes in configuration options involved with setting up a fit in this
 way. The main benefit of using SrFit for PDF refinement is the
 flexibility of modifying the PDF profile function for specific needs,
 adding restraints to a fit and the ability to simultaneously refine a

doc/examples/crystalpdf.py

@@ -132,7 +132,7 @@ def makeRecipe(
     recipe.constrain(xgenerator_sini_si.scale, "1 - pscale_sini_ni")
 
     # The qdamp parameters are too correlated to vary so we fix them based on
-    # previous measurments.
+    # previous measurements.
     xgenerator_ni.qdamp.value = 0.055
     xgenerator_si.qdamp.value = 0.051
     ngenerator_ni.qdamp.value = 0.030

doc/examples/crystalpdfall.py

@@ -117,7 +117,7 @@ def makeRecipe(ciffile, xdatname, ndatname):
     recipe.addVar(ngenerator.scale, 1, "nscale")
     recipe.addVar(xgenerator.qdamp, 0.01, "xqdamp")
     recipe.addVar(ngenerator.qdamp, 0.01, "nqdamp")
-    # delta2 is a non-structual material propery. Thus, we constrain together
+    # delta2 is a non-structual material property. Thus, we constrain together
     # delta2 Parameter from each PDFGenerator.
     delta2 = recipe.newVar("delta2", 2)
     recipe.constrain(xgenerator.delta2, delta2)

doc/examples/crystalpdftwodata.py

@@ -96,7 +96,7 @@ def makeRecipe(niciffile, siciffile, datname):
     recipe.constrain(generator_ni.qdamp, "qdamp")
     recipe.constrain(generator_si.qdamp, "qdamp")
 
-    # Vary the gloabal scale as well.
+    # Vary the global scale as well.
     recipe.addVar(contribution.scale, 1)
 
     # Now we can configure the structural parameters. Since we're using

doc/examples/crystalpdftwophase.py

@@ -145,7 +145,7 @@ def makeRecipe():
     # We would like to 'suggest' that the offset should remain positive. This
     # is somethine that we know about the system that might help the refinement
     # converge to a physically reasonable result.  We will do this with a soft
-    # contraint, or restraint. Here we restrain the offset variable to between
+    # constraint, or restraint. Here we restrain the offset variable to between
     # 0 and infinity. We tell the recipe that we want to scale the penalty for
     # breaking the restraint by the point-average chi^2 value so that the
     # restraint is roughly as significant as any other data point throughout

doc/examples/debyemodel.py

@@ -71,7 +71,7 @@ def makeRecipe(datname):
 
     ## 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
+    # attributes of the model. These vary from model to model, but are adopted
     # as SrFit Parameters within the generator. Whereas the dispersion
     # parameters are accessible as, e.g. "radius.width", within the
     # SASGenerator these are named like "radius_width".

doc/examples/ellipsoidsas.py

@@ -31,7 +31,7 @@
 
 Extensions
 
-After reading through the code, try to perform the folowing tasks. The process
+After reading through the code, try to perform the following tasks. The process
 will leave you with a much better understanding of how SrFit works.
 
 - Play around with setting the values of the Variables and Parameters. What

doc/examples/gaussianrecipe.py

@@ -508,7 +508,7 @@ def makeData(strufile, q, datname, scale, a, Uiso, sig, bkgc, nl=1):
 
     y += bkgd
 
-    # Multipy by a scale factor
+    # Multiply by a scale factor
     y *= scale
 
     # Calculate the uncertainty

doc/examples/npintensity.py

@@ -52,7 +52,7 @@ def makeRecipe(niciffile, siciffile, datname):
     # for free by the PDFContribution. We simply need to add it to the recipe.
     recipe.addVar(contribution.qdamp, 0.03)
 
-    # Vary the gloabal scale as well.
+    # Vary the global scale as well.
     recipe.addVar(contribution.scale, 1)
 
     # Now we can configure the structural parameters. Since we're using

doc/examples/simplepdftwophase.py

@@ -368,7 +368,7 @@ def _getUndefinedArgs(self, eqstr):
         # builders. These will be treated as arguments that need to be
         # generated.
         for tok in set(args):
-            # Move genuine varibles to the eqargs dictionary
+            # Move genuine variables to the eqargs dictionary
             if (
                 # Check registered builders
                 tok in self.builders
@@ -427,13 +427,13 @@ def __evalBinary(self, other, OperatorClass, onleft=True):
         Other can be an BaseBuilder or a constant.
 
         onleft  --  Indicates that the operator was passed on the left side
-                    (defualt True).
+                    (default True).
         """
         # Create the Operator
         op = OperatorClass()
 
         # onleft takes care of non-commutative operators, and assures that the
-        # ordering is perserved.
+        # ordering is preserved.
         if onleft:
             # Add the literals to the operator
             op.addLiteral(self.literal)

src/diffpy/srfit/equation/builder.py

@@ -164,7 +164,7 @@ def setRoot(self, root):
     def __call__(self, *args, **kw):
         """Call the equation.
 
-        New Argument values are acceped as arguments or keyword
+        New Argument values are accepted as arguments or keyword
         assignments (or both).  The order of accepted arguments is given
         by the args attribute. The Equation will remember values set in
         this way.

src/diffpy/srfit/equation/equationmod.py

@@ -14,7 +14,7 @@
 ##############################################################################
 """Argument class.
 
-Arguments are the leaves of an equation tree, in essense a variable or a
+Arguments are the leaves of an equation tree, in essence a variable or a
 constant.
 """
 

src/diffpy/srfit/equation/literals/argument.py

@@ -34,7 +34,7 @@ def __init__(self, getconsts=True):
         """Initialize.
 
         Arguments
-        getconsts   --  Flag indicating whether to grap constant arguments
+        getconsts   --  Flag indicating whether to parse constant arguments
                         (default True).
         """
         self.args = []

src/diffpy/srfit/equation/visitors/argfinder.py

@@ -26,8 +26,8 @@ class Configurable(object):
     A Configurable has state of which a FitRecipe must be aware.
 
     Attributes
-    _configobjs     --  Set of Configureables in a hierarcy or instances.
-                        Messasges get passed up the hierarcy to a FitReciple
+    _configobjs     --  Set of Configureables in a hierarchy or instances.
+                        Messages get passed up the hierarchy to a FitReciple
                         via these objects.
     """
 

src/diffpy/srfit/fitbase/configurable.py

@@ -19,7 +19,7 @@
 optionally one or more ProfileGenerators or Calculators that help in
 this, and a Profile that holds the observed and calculated signals.
 
-See the examples in the documention for how to use a FitContribution.
+See the examples in the documentation for how to use a FitContribution.
 """
 
 __all__ = ["FitContribution"]
@@ -36,7 +36,7 @@ class FitContribution(ParameterSet):
 
     FitContributions organize an Equation that calculates the signal, and a
     Profile that holds the signal. ProfileGenerators and Calculators can be
-    used as well.  Contraints and Restraints can be created as part of a
+    used as well.  Constraints and Restraints can be created as part of a
     FitContribution.
 
     Attributes

src/diffpy/srfit/fitbase/fitcontribution.py

@@ -517,7 +517,7 @@ def newVar(self, name, value=None, fixed=False, tag=None, tags=[]):
         This method lets new variables be created that are not tied to a
         Parameter.  Orphan variables may cause a fit to fail, depending on the
         optimization routine, and therefore should only be created to be used
-        in contraint or restraint equations.
+        in constraint or restraint equations.
 
         name    --  The name of the variable. The variable will be able to be
                     used by this name in restraint and constraint equations.

src/diffpy/srfit/fitbase/fitrecipe.py

@@ -155,7 +155,7 @@ def update(self):
         self.residual = numpy.dot(res, res)
         self._calculateMetrics()
 
-        # Calcualte the restraints penalty
+        # Calculate the restraints penalty
         w = self.chi2 / len(res)
         self.penalty = sum([r.penalty(w) for r in recipe._restraintlist])
 
@@ -600,7 +600,7 @@ def _init(self, con, weight, fitres):
 
     # FIXME: factor rw, chi2, cumrw, cumchi2 to separate functions.
     def _calculateMetrics(self):
-        """Calculte chi2 and Rw of the recipe."""
+        """Calculate chi2 and Rw of the recipe."""
         # We take absolute values in case the signal is complex
         num = numpy.abs(self.y - self.ycalc)
         y = numpy.abs(self.y)

src/diffpy/srfit/fitbase/fitresults.py

@@ -278,7 +278,7 @@ def setCalculationPoints(self, x):
             if (self.dyobs == 1).all():
                 self.dy = numpy.ones_like(self.x)
             else:
-                # FIXME - This does not follow error propogation rules and it
+                # FIXME - This does not follow error propagation rules and it
                 # introduces (more) correlation between the data points.
                 self.dy = rebinArray(self.dyobs, self.xobs, self.x)
 

src/diffpy/srfit/fitbase/profile.py

@@ -46,7 +46,7 @@ class ProfileParser(object):
                     dy      --  A numpy array containing the uncertainty read
                                 from the file. This is None if the uncertainty
                                 cannot be read.
-    _x          --  Indpendent variable from the chosen bank
+    _x          --  Independent variable from the chosen bank
     _y          --  Profile from the chosen bank
     _dx         --  Uncertainty in independent variable from the chosen bank
     _dy         --  Uncertainty in profile from the chosen bank

src/diffpy/srfit/fitbase/profileparser.py

@@ -479,7 +479,7 @@ def registerFunction(self, f, name=None, argnames=None):
         passed in the equation string, as this will be handled automatically.
 
         f           --  The callable to register. If this is an Equation
-                        instance, then all that needs to be provied is a name.
+                        instance, then all that needs to be provided is a name.
         name        --  The name of the function to be used in equations. If
                         this is None (default), the method will try to
                         determine the name of the function automatically.
@@ -722,7 +722,7 @@ def unconstrain(self, *pars):
     def getConstrainedPars(self, recurse=False):
         """Get a list of constrained managed Parameters in this object.
 
-        recurse --  Recurse into managed objects and retrive their constrained
+        recurse --  Recurse into managed objects and retrieve their constrained
                     Parameters as well (default False).
         """
         const = self._getConstraints(recurse)
@@ -762,7 +762,7 @@ def restrain(self, res, lb=-inf, ub=inf, sig=1, scaled=False, ns={}):
 
         The penalty is calculated as
         (max(0, lb - val, val - ub)/sig)**2
-        and val is the value of the calculated equation.  This is multipled by
+        and val is the value of the calculated equation.  This is multiplied by
         the average chi^2 if scaled is True.
 
         Raises ValueError if ns uses a name that is already used for a

src/diffpy/srfit/fitbase/recipeorganizer.py

@@ -43,7 +43,7 @@ class Restraint(Validatable):
 
     The penalty is calculated as
     (max(0, lb - val, val - ub)/sig)**2
-    and val is the value of the calculated equation.  This is multipled by the
+    and val is the value of the calculated equation.  This is multiplied by the
     average chi^2 if scaled is True.
     """
 

src/diffpy/srfit/fitbase/restraint.py

@@ -89,7 +89,7 @@ def __init__(self, name="pdf"):
     _parnames = ["delta1", "delta2", "qbroad", "scale", "qdamp"]
 
     def _setCalculator(self, calc):
-        """Set the SrReal calulator instance.
+        """Set the SrReal calculator instance.
 
         Setting the calculator creates Parameters from the variable
         attributes of the SrReal calculator.

src/diffpy/srfit/pdf/basepdfgenerator.py

@@ -193,7 +193,7 @@ def lognormalSphericalCF(r, psize, psig):
     psize  --  The mean particle diameter
     psig   --  The log-normal width of the particle diameter
 
-    Here, r is the independent variable, mu is the mean of the distrubution
+    Here, r is the independent variable, mu is the mean of the distribution
     (not of the particle size), and s is the width of the distribution. This is
     the characteristic function for the lognormal distribution of particle
     diameter: