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

Author: pre-commit-ci[bot]

doc/manual/source/extending.rst

@@ -27,14 +27,14 @@ subpackges, as shown below.
 * .. py:class:: BaseFunction
 
     + .. py:class:: BaselineFunction
-    
+
         - .. py:class:: FromSequence
         - .. py:class:: NanoSpherical
         - .. py:class:: Polynomial
         - *etc.*
-        
+
     + .. py:class:: PeakFunction
-    
+
         - .. py:class:: Gaussian
         - .. py:class:: GaussianOverR
         - *etc.*
@@ -57,7 +57,7 @@ for examples.
 .. py:method:: estimate_parameters(r, y)
 
     Return a Numpy array of parameters estimated from the data.
-    
+
     :param r: Grid on which the data are defined.
     :param y: The data.
     :type r: `Sequence`
@@ -69,9 +69,9 @@ for examples.
 
 
 .. py:method:: _jacobian_raw(pars, r, free)
-   
+
     Return Jacobian for parameters evaluated over `r`.
-    
+
     :param pars: The parameters of the baseline.
     :param r: Scalar or grid on which to calculate the Jacobian.
     :param free: Boolean values indicating if corresponding parameter is free (True) or fixed (False).
@@ -84,7 +84,7 @@ for examples.
 .. py:method:: _transform_derivativesraw(pars, in_format, out_format)
 
     Return the gradient matrix of `pars` represented in format 'out_format'.
-    
+
     :param pars: The parameters of the baseline.
     :param in_format: The format of `pars`.
     :param out_format: The desired format of `pars`.
@@ -97,7 +97,7 @@ for examples.
 .. py:method:: _transform_parametersraw(pars, in_format, out_format)
 
     Return parameters transformed into format 'out_format'.
-    
+
     :param pars: The parameters of the baseline.
     :param in_format: The format of `pars`.
     :param out_format: The desired format of `pars`.
@@ -106,11 +106,11 @@ for examples.
     :type out_format: `str`
     :returns: The transformed parameters.
     :rtype: `numpy.ndarray`
-    
+
 .. py:method:: _valueraw(pars, r)
 
     Return value of baseline with given parameters at r.
-    
+
     :param pars: The parameters of the baseline.
     :param r: Scalar or grid on which to calculate the baseline.
     :type pars: `Sequence(float)`
@@ -130,12 +130,12 @@ following differences:
 1) The ``estimate_parameters`` method is required.
 2) The "position" key must be defined in the ``parameterdict`` class member.
 3) Peak functions must implement the additional method ``scale_at``.
-   
+
 .. py:method:: scale_at(pars, r, scale)
 
     Return peak parameters such that the value at ``r`` is scaled by ``scale``
     while the position of the peak's maxima remains unchanged.
-    
+
     :param pars: The parameters of the peak.
     :param r: Position where the peak will be rescaled.
     :param scale: A scale factor > 0.

doc/manual/source/index.rst

@@ -10,38 +10,38 @@ Last updated |today|.
 
 Tool for unbiased peak extraction from atomic pair distribution functions.
 
-The diffpy.srmise package is an implementation of the `ParSCAPE algorithm  
-<https://dx.doi.org/10.1107/S2053273315005276>`_ for peak extraction from 
-atomic pair distribution functions (PDFs).  It is designed to function even 
-when *a priori* knowledge of the physical sample is limited, utilizing the 
-Akaike Information Criterion (AIC) to estimate whether peaks are 
-statistically justified relative to alternate models.  Three basic use cases 
-are anticipated for diffpy.srmise.  The first is peak fitting a user-supplied 
-collections of peaks.  The second is peak extraction from a PDF with no (or 
-only partial) user-supplied peaks.  The third is an AIC-driven multimodeling 
-analysis where the output of multiple diffpy.srmise trials are ranked. 
-
-The framework for peak extraction defines peak-like clusters within the data, 
-extracts a single peak within each cluster, and iteratively combines nearby 
-clusters while performing a recursive search on the residual to identify 
-occluded peaks.  Eventually this results in a single global cluster 
-containing many peaks fit over all the data.  Over- and underfitting are 
-discouraged by use of the AIC when adding or removing (during a pruning step) 
-peaks.  Termination effects, which can lead to physically spurious peaks in 
-the PDF, are incorporated in the mathematical peak model and the pruning step 
-attempts to remove peaks which are fit better as termination ripples due to 
-another peak. 
-
-Where possible, diffpy.srmise provides physically reasonable default values 
-for extraction parameters.  However, the PDF baseline should be estimated by 
-the user before extraction, or by performing provisional peak extraction with 
-varying baseline parameters.  The package defines a linear (crystalline) 
-baseline, arbitrary polynomial baseline, a spherical nanoparticle baseline, 
-and an arbitrary baseline interpolated from a list of user-supplied values.  
-In addition, PDFs with accurate experimentally-determined uncertainties are 
-necessary to provide the most reliable results, but historically such PDFs 
-are rare.  In the absence of accurate uncertainties an ad hoc uncertainty 
-must be specified. 
+The diffpy.srmise package is an implementation of the `ParSCAPE algorithm
+<https://dx.doi.org/10.1107/S2053273315005276>`_ for peak extraction from
+atomic pair distribution functions (PDFs).  It is designed to function even
+when *a priori* knowledge of the physical sample is limited, utilizing the
+Akaike Information Criterion (AIC) to estimate whether peaks are
+statistically justified relative to alternate models.  Three basic use cases
+are anticipated for diffpy.srmise.  The first is peak fitting a user-supplied
+collections of peaks.  The second is peak extraction from a PDF with no (or
+only partial) user-supplied peaks.  The third is an AIC-driven multimodeling
+analysis where the output of multiple diffpy.srmise trials are ranked.
+
+The framework for peak extraction defines peak-like clusters within the data,
+extracts a single peak within each cluster, and iteratively combines nearby
+clusters while performing a recursive search on the residual to identify
+occluded peaks.  Eventually this results in a single global cluster
+containing many peaks fit over all the data.  Over- and underfitting are
+discouraged by use of the AIC when adding or removing (during a pruning step)
+peaks.  Termination effects, which can lead to physically spurious peaks in
+the PDF, are incorporated in the mathematical peak model and the pruning step
+attempts to remove peaks which are fit better as termination ripples due to
+another peak.
+
+Where possible, diffpy.srmise provides physically reasonable default values
+for extraction parameters.  However, the PDF baseline should be estimated by
+the user before extraction, or by performing provisional peak extraction with
+varying baseline parameters.  The package defines a linear (crystalline)
+baseline, arbitrary polynomial baseline, a spherical nanoparticle baseline,
+and an arbitrary baseline interpolated from a list of user-supplied values.
+In addition, PDFs with accurate experimentally-determined uncertainties are
+necessary to provide the most reliable results, but historically such PDFs
+are rare.  In the absence of accurate uncertainties an ad hoc uncertainty
+must be specified.
 
 
 ===================
@@ -81,7 +81,7 @@ Where next?
 
    tutorial/index.rst
    extending.rst
-   
+
 ======================================
 API
 ======================================

doc/source/license.rst

@@ -9,9 +9,9 @@ OPEN SOURCE LICENSE AGREEMENT
 =============================
 BSD 3-Clause License
 
-Copyright (c) 2024, The Trustees of Columbia University in 
+Copyright (c) 2024, The Trustees of Columbia University in
 the City of New York.
-All Rights Reserved. 
+All Rights Reserved.
 
 Redistribution and use in source and binary forms, with or without
 modification, are permitted provided that the following conditions are met: