# -*- coding: utf-8 -*-
# SPDX-FileCopyrightText: 2015-2023 Tanguy Fardet
# SPDX-License-Identifier: GPL-3.0-or-later
# doc/examples/graph_properties/plot_attributes.py

"""
Plot various graph properties
=============================
"""

import nngt
import nngt.plot as nplt
from nngt.geometry import Shape

import matplotlib.pyplot as plt

plt.rcParams.update({
    'axes.edgecolor': 'grey', 'xtick.color': 'grey', 'ytick.color': 'grey',
    "figure.facecolor": (0, 0, 0, 0), "axes.facecolor": (0, 0, 0, 0),
    "axes.labelcolor": "grey", "text.color": "grey"
})


nngt.seed(0)


# %%
# Let's start by making a random exponential graph

shape = Shape.disk(100)

g = nngt.generation.distance_rule(5, shape=shape, nodes=1000, avg_deg=20)


# %%
# Let's plot the distances

nplt.edge_attributes_distribution(g, "distance", show=True)

# %%
# We then compute the betweenness and see how it correlates with the distance

nbetw, ebetw = nngt.analysis.betweenness(g)

g.new_edge_attribute("betweenness", "float", values=ebetw)

nplt.correlation_to_attribute(g, "distance", "betweenness",
                              attribute_type="edge", show=True)


# %%
# Let's check the correlations between various node properties and their degree

g.new_node_attribute("betweenness", "float", values=nbetw)

attr = ["betweenness", "clustering", "in-degree", "subgraph_centrality"]

nplt.correlation_to_attribute(g, "out-degree", attr, show=True)