[pre-commit.ci] auto fixes from pre-commit hooks

Author: pre-commit-ci[bot]

doc/examples/fit_initial.py

@@ -12,12 +12,11 @@
 ##############################################################################
 """Example of peak fitting C60 PDF (unnormalized) with unreliable uncertainties.
 
-Peak fitting in SrMise means fitting a model of initial peaks, which may be
-specified manually or estimated with a clustering-based convenience function,
-just as with specifying initial peaks for peak extraction.  Unlike peak
-extraction, it does not attempt to add or remove peaks, apply termination
-ripples, or otherwise do anything beyond chi-square fitting using the specified
-grid."""
+Peak fitting in SrMise means fitting a model of initial peaks, which may be specified manually or estimated with a
+clustering-based convenience function, just as with specifying initial peaks for peak extraction.  Unlike peak
+extraction, it does not attempt to add or remove peaks, apply termination ripples, or otherwise do anything beyond
+chi-square fitting using the specified grid.
+"""
 
 import matplotlib.pyplot as plt
 

doc/examples/multimodel_known_dG1.py

@@ -10,8 +10,8 @@
 # See LICENSE.txt for license information.
 #
 ##############################################################################
-"""Extract multiple models from crystalline PDF with known uncertainties for
-use in later AIC-driven multimodeling analysis.
+"""Extract multiple models from crystalline PDF with known uncertainties for use in later AIC-driven multimodeling
+analysis.
 
 The multimodel approach generates many models of varying complexity by assuming
 a range of experimental uncertainties are physically plausible.  This example

doc/examples/multimodel_unknown_dG1.py

@@ -10,8 +10,8 @@
 # See LICENSE.txt for license information.
 #
 ##############################################################################
-"""Extract multiple models from nanoparticle PDF with unknown uncertainties for
-use in later AIC-driven multimodeling analysis.
+"""Extract multiple models from nanoparticle PDF with unknown uncertainties for use in later AIC-driven
+multimodeling analysis.
 
 The multimodel approach generates many models of varying complexity by assuming
 a range of experimental uncertainties are physically plausible.  This example

doc/examples/multimodel_unknown_dG2.py

@@ -10,8 +10,7 @@
 # See LICENSE.txt for license information.
 #
 ##############################################################################
-"""AIC-driven multimodel analysis of nanoparticle PDF with unknown
-uncertainties.
+"""AIC-driven multimodel analysis of nanoparticle PDF with unknown uncertainties.
 
 The multimodel approach generates many models of varying complexity by assuming
 a range of experimental uncertainties are physically plausible.  This example

doc/examples/parameter_summary.py

@@ -25,7 +25,8 @@
 nyquist (whether to use Nyquist sampling)
 supersample (minimum amount to oversample during initial stages)
 cres (clustering resolution)
-initial_peaks (peaks already assumed to exist during extraction)"""
+initial_peaks (peaks already assumed to exist during extraction)
+"""
 
 import matplotlib.pyplot as plt
 

src/diffpy/__init__.py

@@ -12,7 +12,6 @@
 # See LICENSE.rst for license information.
 #
 ##############################################################################
-
 """Blank namespace package for module diffpy."""
 
 

src/diffpy/srmise/__init__.py

@@ -12,7 +12,6 @@
 # See LICENSE.rst for license information.
 #
 ##############################################################################
-
 """Peak extraction and peak fitting tool for atomic pair distribution functions."""
 
 # package version

src/diffpy/srmise/applications/plot.py

@@ -11,7 +11,7 @@
 # See LICENSE.txt for license information.
 #
 ##############################################################################
-"""plot extracted peaks and comparison to ideal distances (if given)"""
+"""Plot extracted peaks and comparison to ideal distances (if given)"""
 
 import optparse
 import sys
@@ -175,7 +175,7 @@ def labelallsubplots():
 
 
 def makeplot(ppe_or_stability, ip=None, **kwds):
-    """Plot stuff"""
+    """Plot stuff."""
     if isinstance(ppe_or_stability, PeakStability):
         stability = ppe_or_stability
         ppe = stability.ppe

src/diffpy/srmise/basefunction.py

@@ -76,7 +76,7 @@ def __init__(
         base=None,
         Cache=None,
     ):
-        """Set parameterdict defined by subclass
+        """Set parameterdict defined by subclass.
 
         Parameters
         ----------
@@ -159,32 +159,50 @@ def __init__(
     # "Virtual" class methods ####
 
     def actualize(self, *args, **kwds):
-        """Create ModelPart instance of self with given parameters.  ("Virtual" method)"""
+        """Create ModelPart instance of self with given parameters.
+
+        ("Virtual" method)
+        """
         emsg = "actualize() must be implemented in a BaseFunction subclass."
         raise NotImplementedError(emsg)
 
     def estimate_parameters(self, *args, **kwds):
-        """Estimate BaseFunction parameters from supplied data. ("Virtual" method)"""
+        """Estimate BaseFunction parameters from supplied data.
+
+        ("Virtual" method)
+        """
         emsg = "estimate_parameters() must be implemented in a BaseFunction subclass."
         raise NotImplementedError(emsg)
 
     def _jacobianraw(self, *args, **kwds):
-        """Calculate the jacobian. ("Virtual" method)"""
+        """Calculate the jacobian.
+
+        ("Virtual" method)
+        """
         emsg = "_jacobianraw() must be implemented in a BaseFunction subclass."
         raise NotImplementedError(emsg)
 
     def _transform_derivativesraw(self, *args, **kwds):
-        """Convert BaseFunction parameters to another form. ("Virtual" method)"""
+        """Convert BaseFunction parameters to another form.
+
+        ("Virtual" method)
+        """
         emsg = "transform_parameters() must be implemented in a BaseFunction subclass."
         raise NotImplementedError(emsg)
 
     def _transform_parametersraw(self, *args, **kwds):
-        """Convert BaseFunction parameters to another form. ("Virtual" method)"""
+        """Convert BaseFunction parameters to another form.
+
+        ("Virtual" method)
+        """
         emsg = "transform_parameters() must be implemented in a BaseFunction subclass."
         raise NotImplementedError(emsg)
 
     def _valueraw(self, *args, **kwds):
-        """Calculate value of function. ("Virtual" method)"""
+        """Calculate value of function.
+
+        ("Virtual" method)
+        """
         emsg = "_valueraw must() be implemented in a BaseFunction subclass."
         raise NotImplementedError(emsg)
 
@@ -373,7 +391,7 @@ def pgradient(self, p, format):
         return
 
     def getmodule(self):
-        """Return 'diffpy.srmise.basefunction'"""
+        """Return 'diffpy.srmise.basefunction'."""
         return "diffpy.srmise.basefunction"
 
     def writestr(self, baselist):
@@ -480,7 +498,8 @@ def safefunctionlist(fs):
         is guaranteed to be safe for saving/reinstantiating peak functions.
 
         Parameters
-        fs: List of BaseFunction instances."""
+        fs: List of BaseFunction instances.
+        """
         fsafe = []
         for f in fs:
             BaseFunction.safefunction(f, fsafe)

src/diffpy/srmise/baselines/arbitrary.py

@@ -26,17 +26,12 @@
 class Arbitrary(BaselineFunction):
     """Methods for evaluating a baseline from an arbitrary function.
 
-    Supports baseline calculations with arbitrary functions.  These functions,
-    if implemented, must have the following signatures and return values:
-    valuef(pars, x) ==> numpy.array of length x if x is a sequence
-                  ==> number if x is a number
-    jacobianf(pars, x, free) ==> list, each element a numpy.array of length x if
-                                x is a sequence or None if value of free for
-                                that parameter is False.
-                            ==> list, each element a number if x is a number
-                                or None if value of free for that parameter is
-                                False
-    estimatef(x, y) ==> numpy.array of length npars
+    Supports baseline calculations with arbitrary functions.  These functions, if implemented, must have the
+    following signatures and return values: valuef(pars, x) ==> numpy.array of length x if x is a sequence
+    ==> number if x is a number jacobianf(pars, x, free) ==> list, each element a numpy.array of length x if
+    x is a sequence or None if value of free for                             that parameter is False.
+    ==> list, each element a number if x is a number                             or None if value of free for that
+    parameter is                             False estimatef(x, y) ==> numpy.array of length npars
     """
 
     def __init__(self, npars, valuef, jacobianf=None, estimatef=None, Cache=None):
@@ -117,7 +112,8 @@ def estimate_parameters(self, r, y):
         The numpy array of parameters in the default internal format.
 
         we raise NotImplementedError if no estimation routine is defined, and
-        SrMiseEstimationError if parameters cannot be estimated for any other."""
+        SrMiseEstimationError if parameters cannot be estimated for any other.
+        """
         if self.estimatef is None:
             emsg = "No estimation routine provided to Arbitrary."
             raise NotImplementedError(emsg)