"""
=======================================================
Visualize Interdisciplinary map of the journals network
=======================================================

The goal of this app is to show an overview of the journals network structure
as a complex network. Each journal is shown as a node and their connections
indicates a citation between two of them.
"""

###############################################################################
# First, let's import some useful functions

from os.path import join as pjoin

import numpy as np

import fury

###############################################################################
# Then let's download some available datasets.

files, folder = fury.data.fetch_viz_wiki_nw()
categories_file, edges_file, positions_file = sorted(files.keys())

###############################################################################
# We read our datasets

positions = np.loadtxt(pjoin(folder, positions_file))
categories = np.loadtxt(pjoin(folder, categories_file), dtype=str)
edges = np.loadtxt(pjoin(folder, edges_file), dtype=int)

###############################################################################
# We attribute a color to each category of our dataset which correspond to our
# nodes colors.

category2index = {category: i for i, category in enumerate(np.unique(categories))}

index2category = np.unique(categories)

categoryColors = fury.colormap.distinguishable_colormap(nb_colors=len(index2category))

colors = np.array([categoryColors[category2index[category]] for category in categories])

###############################################################################
# We define our node size

radii = 1 + np.random.rand(len(positions))

###############################################################################
# Lets create our edges now. They will indicate a citation between two nodes.
# OF course, the colors of each edges will be an interpolation between the two
# node that it connects.

edgesPositions = []
edgesColors = []
for source, target in edges:
    edgesPositions.append(np.array([positions[source], positions[target]]))
    edgesColors.append(np.array([colors[source], colors[target]]))

edgesPositions = np.array(edgesPositions)
edgesColors = np.average(np.array(edgesColors), axis=1)

###############################################################################
# Our data preparation is ready, it is time to visualize them all. We start to
# build 2 actors that we represent our data : sphere_actor for the nodes and
# lines_actor for the edges.

sphere_actor = fury.actor.sphere(
    centers=positions,
    colors=colors,
    radii=radii * 0.5,
    theta=8,
    phi=8,
)

lines_actor = fury.actor.line(
    edgesPositions,
    colors=edgesColors,
    opacity=0.1,
)

###############################################################################
# All actors need to be added in a scene, so we build one and add our
# lines_actor and sphere_actor.

scene = fury.window.Scene()

scene.add(lines_actor)
scene.add(sphere_actor)

###############################################################################
# The final step ! Visualize and save the result of our creation! Please,
# switch interactive variable to True if you want to visualize it.

interactive = False

if interactive:
    fury.window.show(scene=scene, size=(600, 600))

fury.window.record(scene=scene, out_path="journal_networks.png", size=(600, 600))

###############################################################################
# This example can be improved by adding some interactivy with slider,
# picking, etc. Play with it, improve it!
