.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/04_demos/viz_network.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_auto_examples_04_demos_viz_network.py>`
        to download the full example code

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_04_demos_viz_network.py:


=======================================================
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.

.. GENERATED FROM PYTHON SOURCE LINES 12-13

First, let's import some useful functions

.. GENERATED FROM PYTHON SOURCE LINES 13-23

.. code-block:: Python


    from os.path import join as pjoin

    import numpy as np

    from fury import actor
    from fury import colormap as cmap
    from fury import window
    from fury.data import fetch_viz_wiki_nw








.. GENERATED FROM PYTHON SOURCE LINES 24-25

Then let's download some available datasets.

.. GENERATED FROM PYTHON SOURCE LINES 25-30

.. code-block:: Python



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





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    Dataset is already in place. If you want to fetch it again please first remove the folder /Users/skoudoro/.fury/examples/wiki_nw 
    More information about complex networks can be found in this papers: https://arxiv.org/abs/0711.3199




.. GENERATED FROM PYTHON SOURCE LINES 31-32

We read our datasets

.. GENERATED FROM PYTHON SOURCE LINES 32-37

.. code-block:: Python


    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)








.. GENERATED FROM PYTHON SOURCE LINES 38-40

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

.. GENERATED FROM PYTHON SOURCE LINES 40-49

.. code-block:: Python


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

    index2category = np.unique(categories)

    categoryColors = cmap.distinguishable_colormap(nb_colors=len(index2category))

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








.. GENERATED FROM PYTHON SOURCE LINES 50-51

We define our node size

.. GENERATED FROM PYTHON SOURCE LINES 51-54

.. code-block:: Python


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








.. GENERATED FROM PYTHON SOURCE LINES 55-58

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.

.. GENERATED FROM PYTHON SOURCE LINES 58-68

.. code-block:: Python


    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)








.. GENERATED FROM PYTHON SOURCE LINES 69-72

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.

.. GENERATED FROM PYTHON SOURCE LINES 72-87

.. code-block:: Python


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

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








.. GENERATED FROM PYTHON SOURCE LINES 88-90

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

.. GENERATED FROM PYTHON SOURCE LINES 90-96

.. code-block:: Python


    scene = window.Scene()

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








.. GENERATED FROM PYTHON SOURCE LINES 97-99

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

.. GENERATED FROM PYTHON SOURCE LINES 99-107

.. code-block:: Python


    interactive = False

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

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




.. image-sg:: /auto_examples/04_demos/images/sphx_glr_viz_network_001.png
   :alt: viz network
   :srcset: /auto_examples/04_demos/images/sphx_glr_viz_network_001.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 108-110

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


.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (0 minutes 0.809 seconds)


.. _sphx_glr_download_auto_examples_04_demos_viz_network.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: viz_network.ipynb <viz_network.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: viz_network.py <viz_network.py>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_