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"""
.. _tutorials-connected-components:
=====================
Connected Components
=====================
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
# %%
# First, we generate a randomized geometric graph with random vertex sizes. The
# seed is set to the example is reproducible in our manual: you don't really
# need it to understand the concepts.
random.seed(0)
g = ig.Graph.GRG(50, 0.15)
# %%
# 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')
# %%
# Finally, we can visualize the distinct connected components of the graph:
fig, ax = plt.subplots()
ig.plot(
components,
target=ax,
palette=ig.RainbowPalette(),
vertex_size=7,
vertex_color=list(map(int, ig.rescale(components.membership, (0, 200), clamp=True))),
edge_width=0.7
)
plt.show()
# %%
# .. note::
#
# We use the integers from 0 to 200 instead of 0 to 255 in our vertex
# colors, since 255 in the :class:`igraph.drawing.colors.RainbowPalette`
# corresponds to looping back to red. This gives us nicely distinct hues.
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