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"""
.. _tutorials-visual-style:
Visual styling
===========================
This example shows how to change the visual style of network plots.
"""
import igraph as ig
import matplotlib.pyplot as plt
import random
# %%
# To configure the visual style of a plot, we can create a dictionary with the
# various setting we want to customize:
visual_style = {
"edge_width": 0.3,
"vertex_size": 15,
"palette": "heat",
"layout": "fruchterman_reingold"
}
# %%
# Let's see it in action! First, we generate four random graphs:
random.seed(1)
gs = [ig.Graph.Barabasi(n=30, m=1) for i in range(4)]
# %%
# Then, we calculate a color colors between 0-255 for all nodes, e.g. using
# betweenness just as an example:
betweenness = [g.betweenness() for g in gs]
colors = [[int(i * 255 / max(btw)) for i in btw] for btw in betweenness]
# %%
# Finally, we can plot the graphs using the same visual style for all graphs:
fig, axs = plt.subplots(2, 2)
axs = axs.ravel()
for g, color, ax in zip(gs, colors, axs):
ig.plot(g, target=ax, vertex_color=color, **visual_style)
plt.show()
# %%
# .. note::
# If you would like to set global defaults, for example, always using the
# Matplotlib plotting backend, or using a particular color palette by
# default, you can use igraph's `configuration instance
# :class:`igraph.configuration.Configuration`. A quick example on how to use
# it can be found here: :ref:`tutorials-configuration`.
# %%
# In the matplotlib backend, igraph creates a special container
# :class:`igraph.drawing.matplotlib.graph.GraphArtist` which is a matplotlib Artist
# and the first child of the target Axes. That object can be used to customize
# the plot appearance after the initial drawing, e.g.:
g = ig.Graph.Barabasi(n=30, m=1)
fig, ax = plt.subplots()
ig.plot(g, target=ax)
artist = ax.get_children()[0]
# Option 1:
artist.set(vertex_color="blue")
# Option 2:
artist.set_vertex_color("blue")
plt.show()
# %%
# .. note::
# The :meth:`igraph.drawing.matplotlib.graph.GraphArtist.set` method can
# be used to change multiple properties at once and is generally more
# efficient than multiple calls to specific ``artist.set_...`` methods.
# %%
# In the matplotlib backend, you can also specify the size of self-loops,
# either as a number or a sequence of numbers, e.g.:
g = ig.Graph(n=5)
g.add_edge(2, 3)
g.add_edge(0, 0)
g.add_edge(1, 1)
fig, ax = plt.subplots()
ig.plot(
g,
target=ax,
vertex_size=20,
edge_loop_size=[
0, # ignored, the first edge is not a loop
30, # loop for vertex 0
80, # loop for vertex 1
],
)
plt.show()
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