Updated files to comply with style standards

.gitignore

@@ -1,3 +1,4 @@
 *.DS_Store
 *.pyc
 *build/
+*.spyproject/

mcmcplot/mcseaborn.py

@@ -10,96 +10,106 @@
 import seaborn as sns
 from .utilities import generate_names, check_settings
 
-def plot_joint_distributions(chains, names = None, sns_style = 'white', settings = None):
+
+def plot_joint_distributions(chains, names=None, sns_style='white',
+                             settings=None):
     """
     Plot joint distribution for each parameter set.
-    
+
     https://seaborn.pydata.org/generated/seaborn.jointplot.html
 
     Args:
-        * **chains** (:class:`~numpy.ndarray`): Sampling chain for each parameter
-        * **names** (:py:class:`list`): List of strings - name of each parameter
-        * **sns_style** (:py:class:`str`): Style for seaborn plot.  Default is `white`.
-        * **settings** (:py:class:`dict`): Settings for features of this method.
-
+        * **chains** (:class:`~numpy.ndarray`): Sampling chain
+        for each parameter
+        * **names** (:py:class:`list`): List of strings - name
+        of each parameter
+        * **sns_style** (:py:class:`str`): Style for seaborn plot.
+        Default is `white`.
+        * **settings** (:py:class:`dict`): Settings for features
+        of this method.
+
     Returns:
-        * (:py:class:`tuple`): (figure handle, settings actually used in program)
+        * (:py:class:`tuple`): (figure handle, settings actually
+        used in program)
     """
     default_settings = {
-        'skip': 1,
-        'sns_style': sns_style,
-        'sns': sns.axes_style(style = sns_style),
-        'jointplot': dict(kind='kde', data = None, height = 6.0, space=0)
-        }
-
-    settings = check_settings(default_settings = default_settings, user_settings = settings)
+            'skip': 1,
+            'sns_style': sns_style,
+            'sns': sns.axes_style(style=sns_style),
+            'jointplot': dict(kind='kde', data=None, height=6.0, space=0)
+            }
+    settings = check_settings(
+            default_settings=default_settings, user_settings=settings)
     sns.set_style(settings['sns_style'], settings['sns'])
-
-    nsimu, nparam = chains.shape # number of rows, number of columns
-    names = generate_names(nparam = nparam, names = names)
+    nsimu, nparam = chains.shape  # number of rows, number of columns
+    names = generate_names(nparam=nparam, names=names)
     inds = range(0, nsimu, settings['skip'])
-
     g = []
-    for jj in range(2,nparam+1):
-        for ii in range(1,jj):
-            chain1 = pd.Series(chains[inds,ii-1], name=names[ii-1])
-            chain2 = pd.Series(chains[inds,jj-1], name=names[jj-1])
-
+    for jj in range(2, nparam+1):
+        for ii in range(1, jj):
+            chain1 = pd.Series(chains[inds, ii-1], name=names[ii-1])
+            chain2 = pd.Series(chains[inds, jj-1], name=names[jj-1])
             # Show the joint distribution using kernel density estimation
-            a = sns.jointplot(x = chain1, y = chain2, **settings['jointplot'])
+            a = sns.jointplot(x=chain1, y=chain2, **settings['jointplot'])
             g.append(a)
-
     return g, settings
 
-def plot_paired_density_matrix(chains, names = None, sns_style = 'white', index = None, settings = None):
+
+def plot_paired_density_matrix(chains, names=None, sns_style='white',
+                               index=None, settings=None):
     """
     Plot paired density matrix.
-    
+
     https://seaborn.pydata.org/generated/seaborn.PairGrid.html
 
     Args:
-        * **chains** (:class:`~numpy.ndarray`): Sampling chain for each parameter
-        * **names** (:py:class:`list`): List of strings - name of each parameter
-        * **sns_style** (:py:class:`str`): Style for seaborn plot.  Default is `white`.
-        * **settings** (:py:class:`dict`): Settings for features of this method.
-
+        * **chains** (:class:`~numpy.ndarray`): Sampling chain
+        for each parameter
+        * **names** (:py:class:`list`): List of strings - name
+        of each parameter
+        * **sns_style** (:py:class:`str`): Style for seaborn plot.
+        Default is `white`.
+        * **settings** (:py:class:`dict`): Settings for features
+        of this method.
+
     Returns:
-        * (:py:class:`tuple`): (figure handle, settings actually used in program)
+        * (:py:class:`tuple`): (figure handle, settings actually
+        used in program)
     """
     default_settings = {
-    'skip': 1,
-    'sns_style': sns_style,
-    'sns': sns.axes_style(style = sns_style),
-    'pairgrid': dict(diag_sharey = False, height=2.5),
-    'ld_type': sns.kdeplot,
-    'ld': {},
-    'md_type': sns.kdeplot,
-    'md': {},
-    'ud_type': sns.scatterplot,
-    'ud': {},
-    }
-    settings = check_settings(default_settings = default_settings, user_settings = settings)
+            'skip': 1,
+            'sns_style': sns_style,
+            'sns': sns.axes_style(style=sns_style),
+            'pairgrid': dict(diag_sharey=False, height=2.5),
+            'ld_type': sns.kdeplot,
+            'ld': {},
+            'md_type': sns.kdeplot,
+            'md': {},
+            'ud_type': sns.scatterplot,
+            'ud': {},
+            }
+    settings = check_settings(
+            default_settings=default_settings, user_settings=settings)
     sns.set_style(settings['sns_style'], settings['sns'])
-
-    nsimu, nparam = chains.shape # number of rows, number of columns
-    names = generate_names(nparam = nparam, names = names)
-
-    df = pd.DataFrame(chains, columns = names)
-    if index is not None: # a valid categorical column must be appended in order to use hue
-        df = df.assign(index = index)
-
+    nsimu, nparam = chains.shape  # number of rows, number of columns
+    names = generate_names(nparam=nparam, names=names)
+    df = pd.DataFrame(chains, columns=names)
+    # A valid categorical column must be appended in order to use hue
+    if index is not None:
+        df = df.assign(index=index)
     g = sns.PairGrid(df, **settings['pairgrid'])
     g.map_lower(settings['ld_type'], **settings['ld'])
     g.map_upper(settings['ud_type'], **settings['ud'])
     g.map_diag(settings['md_type'], **settings['md'])
     return g, settings
 
+
 class Plot:
     '''
     Wrapper routines for analyzing/plotting sampling chains from MCMC process.
-    
+
     Uses methods from the `seaborn` package:
-    
+
     https://seaborn.pydata.org/
 
     Attributes:
@@ -108,4 +118,4 @@ class Plot:
     '''
     def __init__(self):
         self.plot_joint_distributions = plot_joint_distributions
-        self.plot_paired_density_matrix = plot_paired_density_matrix
+        self.plot_paired_density_matrix = plot_paired_density_matrix