#-*- coding:utf-8 -*-
#
# This file is part of the NNGT project to generate and analyze
# neuronal networks and their activity.
# Copyright (C) 2015-2020  Tanguy Fardet
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program.  If not, see <http://www.gnu.org/licenses/>.

"""
Hive plot panel
===============
"""

import os

import matplotlib.pyplot as plt

import nngt


dirpath  = os.path.abspath(os.getcwd())
rootpath = os.path.abspath(dirpath + "/../../..")


# load graph

g = nngt.load_from_file(rootpath + "/testing/Networks/rat_brain.graphml",
                        attributes=["weight"], cleanup=True,
                        attributes_types={"weight": float})


# prepare attributes

cc = nngt.analysis.local_clustering(g, weights="weight")

g.new_node_attribute("cc", "double", values=cc)

g.new_node_attribute("strength", "double",
                     values=g.get_degrees(weights="weight"))

flux = g.get_degrees("out") - g.get_degrees("in")

g.new_node_attribute("flux", "double", values=flux)


# figure parameters

cc_bins = [0, 0.1, 0.25, 0.6]

todo = ["strength", "cc", "flux"]
bins = [3, cc_bins, 3]


# make plot

fig, axes = plt.subplots(len(todo), len(todo), figsize=(10, 9))

for i in range(len(todo)):
    radial = todo[i]

    for j in range(len(todo)):
        ax_name = todo[j]
        ax_bins = bins[j]

        ax = axes[i, j]

        if i == 0:
            ax.set_title(ax_name)

        if j == 0:
            ax.set_ylabel(radial)

        size = todo[list(set([0, 1, 2]).difference([i, j]))[0]]

        nngt.plot.hive_plot(
            g, radial, axes=ax_name, edge_alpha=0.1, nsize=size, max_nsize=50,
            axes_bins=ax_bins, axes_units="native", axis=ax, show_names=False)

for i in range(len(todo)):
    fig.text(0.03, 0.8 - i*0.33, todo[i], rotation=90, fontsize="large")

plt.tight_layout()

plt.show()
