"""
===========
Hiveplotlib
===========

`Hiveplotlib <https://hiveplotlib.readthedocs.io/>`_ is a network visualization
library for hive plots, a layout in which nodes are partitioned onto a small
number of radial axes and positioned along each axis by a sorting variable.
Since positions are determined by the data rather than a layout algorithm, hive
plots are reproducible and make structural properties of a graph directly legible.

Below, we visualize Zachary's Karate Club with both a conventional layout and a
hive plot.
"""

import matplotlib.pyplot as plt
import networkx as nx
from hiveplotlib import HivePlot

# sphinx_gallery_thumbnail_number = 2

G = nx.karate_club_graph()

# %%
# Conventional View: Circular Layout
# ----------------------------------
#
# Zachary's Karate Club is usually drawn as a circular layout. That view shows
# every connection, but the relationship between the two factions is
# hard to read off the resulting alignment of edges.

# color nodes by faction
faction_color = [
    "royalblue" if data["club"] == "Mr. Hi" else "darkorange"
    for _, data in G.nodes(data=True)
]

fig, ax = plt.subplots(figsize=(8, 8))
nx.draw_circular(
    G, with_labels=True, node_color=faction_color, font_color="white", ax=ax
)
plt.show()

# %%
# Hive Plot View
# --------------
#
# We pass the NetworkX graph directly to the ``HivePlot`` class, partitioning nodes
# onto axes by their ``club`` membership and sorting each axis by node ``degree``
# (requesting ``degree`` as a computed metric at construction time).
#
# Using repeat axes lets us see within-faction edges, while coloring
# distinguishes within-faction connections (blue/orange) from between-faction
# ones (gray).
hp = HivePlot(
    graph=G,
    partition_variable="club",
    sorting_variables="degree",
    node_graph_metrics="degree",
    repeat_axes=True,
    non_repeat_edge_kwargs={"color": "darkgray"},
)
hp.update_edges("Mr. Hi", "Mr. Hi", color="royalblue")
hp.update_edges("Officer", "Officer", color="darkorange")

# only two partition groups, so drop one of the two redundant sets of
# between-faction edges
hp.reset_edges(axis_id_1="Mr. Hi_repeat", axis_id_2="Officer")

fig, ax = hp.plot()
plt.show()