style: code formatting

Author: ntamas

doc/examples_sphinx-gallery/articulation_points.py

@@ -8,6 +8,7 @@
 This example shows how to compute and visualize the `articulation points <https://en.wikipedia.org/wiki/Biconnected_component>`_ in a graph using :meth:`igraph.GraphBase.articulation_points`. For an example on bridges instead, see :ref:`tutorials-bridges`.
 
 """
+
 import igraph as ig
 import matplotlib.pyplot as plt
 
@@ -30,9 +31,9 @@
     vertex_size=30,
     vertex_color="lightblue",
     vertex_label=range(g.vcount()),
-    vertex_frame_color = ["red" if v in articulation_points else "black" for v in g.vs],
-    vertex_frame_width = [3 if v in articulation_points else 1 for v in g.vs],
+    vertex_frame_color=["red" if v in articulation_points else "black" for v in g.vs],
+    vertex_frame_width=[3 if v in articulation_points else 1 for v in g.vs],
     edge_width=0.8,
-    edge_color='gray'
+    edge_color="gray",
 )
 plt.show()

doc/examples_sphinx-gallery/betweenness.py

@@ -24,14 +24,14 @@
 # :meth:`igraph.utils.rescale` to rescale the betweennesses in the interval
 # ``[0, 1]``.
 def plot_betweenness(g, vertex_betweenness, edge_betweenness, ax, cax1, cax2):
-    '''Plot vertex/edge betweenness, with colorbars
+    """Plot vertex/edge betweenness, with colorbars
 
     Args:
         g: the graph to plot.
         ax: the Axes for the graph
         cax1: the Axes for the vertex betweenness colorbar
         cax2: the Axes for the edge betweenness colorbar
-    '''
+    """
 
     # Rescale betweenness to be between 0.0 and 1.0
     scaled_vertex_betweenness = ig.rescale(vertex_betweenness, clamp=True)
@@ -45,7 +45,7 @@ def plot_betweenness(g, vertex_betweenness, edge_betweenness, ax, cax1, cax2):
 
     # Plot graph
     g.vs["color"] = [cmap1(betweenness) for betweenness in scaled_vertex_betweenness]
-    g.vs["size"]  = ig.rescale(vertex_betweenness, (10, 50))
+    g.vs["size"] = ig.rescale(vertex_betweenness, (10, 50))
     g.es["color"] = [cmap2(betweenness) for betweenness in scaled_edge_betweenness]
     g.es["width"] = ig.rescale(edge_betweenness, (0.5, 1.0))
     ig.plot(
@@ -58,8 +58,8 @@ def plot_betweenness(g, vertex_betweenness, edge_betweenness, ax, cax1, cax2):
     # Color bars
     norm1 = ScalarMappable(norm=Normalize(0, max(vertex_betweenness)), cmap=cmap1)
     norm2 = ScalarMappable(norm=Normalize(0, max(edge_betweenness)), cmap=cmap2)
-    plt.colorbar(norm1, cax=cax1, orientation="horizontal", label='Vertex Betweenness')
-    plt.colorbar(norm2, cax=cax2, orientation="horizontal", label='Edge Betweenness')
+    plt.colorbar(norm1, cax=cax1, orientation="horizontal", label="Vertex Betweenness")
+    plt.colorbar(norm2, cax=cax2, orientation="horizontal", label="Edge Betweenness")
 
 
 # %%

doc/examples_sphinx-gallery/bipartite_matching.py

@@ -7,6 +7,7 @@
 
 This example demonstrates an efficient way to find and visualise a maximum biparite matching using :meth:`igraph.Graph.maximum_bipartite_matching`.
 """
+
 import igraph as ig
 import matplotlib.pyplot as plt
 
@@ -16,7 +17,7 @@
 #  - nodes 5-8 to the other side
 g = ig.Graph.Bipartite(
     [0, 0, 0, 0, 0, 1, 1, 1, 1],
-    [(0, 5), (1, 6), (1, 7), (2, 5), (2, 8), (3, 6), (4, 5), (4, 6)]
+    [(0, 5), (1, 6), (1, 7), (2, 5), (2, 8), (3, 6), (4, 5), (4, 6)],
 )
 
 # %%

doc/examples_sphinx-gallery/bipartite_matching_maxflow.py

@@ -9,6 +9,7 @@
 
 .. note::  :meth:`igraph.Graph.maximum_bipartite_matching` is usually a better way to find the maximum bipartite matching. For a demonstration on how to use that method instead, check out :ref:`tutorials-bipartite-matching`.
 """
+
 import igraph as ig
 import matplotlib.pyplot as plt
 
@@ -17,7 +18,7 @@
 g = ig.Graph(
     9,
     [(0, 4), (0, 5), (1, 4), (1, 6), (1, 7), (2, 5), (2, 7), (2, 8), (3, 6), (3, 7)],
-    directed=True
+    directed=True,
 )
 
 # %%
@@ -62,6 +63,6 @@
     vertex_size=30,
     vertex_label=range(g.vcount()),
     vertex_color=["lightblue" if i < 9 else "orange" for i in range(11)],
-    edge_width=[1.0 + flow.flow[i] for i in range(g.ecount())]
+    edge_width=[1.0 + flow.flow[i] for i in range(g.ecount())],
 )
 plt.show()

doc/examples_sphinx-gallery/bridges.py

@@ -7,6 +7,7 @@
 
 This example shows how to compute and visualize the `bridges <https://en.wikipedia.org/wiki/Bridge_(graph_theory)>`_ in a graph using :meth:`igraph.GraphBase.bridges`. For an example on articulation points instead, see :ref:`tutorials-articulation-points`.
 """
+
 import igraph as ig
 import matplotlib.pyplot as plt
 
@@ -37,7 +38,7 @@
     target=ax,
     vertex_size=30,
     vertex_color="lightblue",
-    vertex_label=range(g.vcount())
+    vertex_label=range(g.vcount()),
 )
 plt.show()
 
@@ -72,9 +73,9 @@
     vertex_size=30,
     vertex_color="lightblue",
     vertex_label=range(g.vcount()),
-    edge_background="#FFF0",    # transparent background color
-    edge_align_label=True,      # make sure labels are aligned with the edge
+    edge_background="#FFF0",  # transparent background color
+    edge_align_label=True,  # make sure labels are aligned with the edge
     edge_label=g.es["label"],
-    edge_label_color="red"
+    edge_label_color="red",
 )
 plt.show()

doc/examples_sphinx-gallery/cluster_contraction.py

@@ -7,6 +7,7 @@
 
 This example shows how to find the communities in a graph, then contract each community into a single node using :class:`igraph.clustering.VertexClustering`. For this tutorial, we'll use the *Donald Knuth's Les Miserables Network*, which shows the coapperances of characters in the novel *Les Miserables*.
 """
+
 import igraph as ig
 import matplotlib.pyplot as plt
 
@@ -106,7 +107,10 @@
 # Finally, we can assign colors to the clusters and plot the cluster graph,
 # including a legend to make things clear:
 palette2 = ig.GradientPalette("gainsboro", "black")
-g.es["color"] = [palette2.get(int(i)) for i in ig.rescale(cluster_graph.es["size"], (0, 255), clamp=True)]
+g.es["color"] = [
+    palette2.get(int(i))
+    for i in ig.rescale(cluster_graph.es["size"], (0, 255), clamp=True)
+]
 
 fig2, ax2 = plt.subplots()
 ig.plot(
@@ -123,7 +127,8 @@
 legend_handles = []
 for i in range(num_communities):
     handle = ax2.scatter(
-        [], [],
+        [],
+        [],
         s=100,
         facecolor=palette1.get(i),
         edgecolor="k",
@@ -133,7 +138,7 @@
 
 ax2.legend(
     handles=legend_handles,
-    title='Community:',
+    title="Community:",
     bbox_to_anchor=(0, 1.0),
     bbox_transform=ax2.transAxes,
 )

doc/examples_sphinx-gallery/complement.py

@@ -7,6 +7,7 @@
 
 This example shows how to generate the `complement graph <https://en.wikipedia.org/wiki/Complement_graph>`_ of a graph (sometimes known as the anti-graph) using :meth:`igraph.GraphBase.complementer`.
 """
+
 import igraph as ig
 import matplotlib.pyplot as plt
 import random
@@ -49,27 +50,27 @@
     layout="circle",
     vertex_color="black",
 )
-axs[0, 0].set_title('Original graph')
+axs[0, 0].set_title("Original graph")
 ig.plot(
     g2,
     target=axs[0, 1],
     layout="circle",
     vertex_color="black",
 )
-axs[0, 1].set_title('Complement graph')
+axs[0, 1].set_title("Complement graph")
 
 ig.plot(
     g_full,
     target=axs[1, 0],
     layout="circle",
     vertex_color="black",
 )
-axs[1, 0].set_title('Union graph')
+axs[1, 0].set_title("Union graph")
 ig.plot(
     g_empty,
     target=axs[1, 1],
     layout="circle",
     vertex_color="black",
 )
-axs[1, 1].set_title('Complement of union graph')
+axs[1, 1].set_title("Complement of union graph")
 plt.show()

doc/examples_sphinx-gallery/configuration.py

@@ -7,6 +7,7 @@
 
 This example shows how to use igraph's :class:`configuration instance <igraph.Configuration>` to set default igraph settings. This is useful for setting global settings so that they don't need to be explicitly stated at the beginning of every igraph project you work on.
 """
+
 import igraph as ig
 import matplotlib.pyplot as plt
 import random

doc/examples_sphinx-gallery/connected_components.py

@@ -7,6 +7,7 @@
 
 This example demonstrates how to visualise the connected components in a graph using :meth:`igraph.GraphBase.connected_components`.
 """
+
 import igraph as ig
 import matplotlib.pyplot as plt
 import random
@@ -21,7 +22,7 @@
 # %%
 # Now we can cluster the graph into weakly connected components, i.e. subgraphs
 # that have no edges connecting them to one another:
-components = g.connected_components(mode='weak')
+components = g.connected_components(mode="weak")
 
 # %%
 # Finally, we can visualize the distinct connected components of the graph:

doc/examples_sphinx-gallery/delaunay-triangulation.py

@@ -8,6 +8,7 @@
 This example demonstrates how to calculate the `Delaunay triangulation <https://en.wikipedia.org/wiki/Delaunay_triangulation>`_ of an input graph. We start by generating a set of points on a 2D grid using random ``numpy`` arrays and a graph with those vertex coordinates and no edges.
 
 """
+
 import numpy as np
 from scipy.spatial import Delaunay
 import igraph as ig
@@ -20,8 +21,8 @@
 np.random.seed(0)
 x, y = np.random.rand(2, 30)
 g = ig.Graph(30)
-g.vs['x'] = x
-g.vs['y'] = y
+g.vs["x"] = x
+g.vs["y"] = y
 
 # %%
 # Because we already set the `x` and `y` vertex attributes, we can use