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

    Click :ref:`here <sphx_glr_download_auto_examples_viz_network.py>` to download the full example code
.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_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.

First, let's import some useful functions


.. code-block:: default


    from os.path import join as pjoin
    from fury import actor, window, colormap as cmap
    import numpy as np







Then let's download some available datasets.


.. code-block:: default


    from fury.data.fetcher import fetch_viz_wiki_nw

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





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

 Out:

 .. code-block:: none

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



We read our datasets


.. code-block:: default


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







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


.. code-block:: default


    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])







We define our node size


.. code-block:: default


    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.


.. code-block:: default


    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.


.. code-block:: default


    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,
                             )







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


.. code-block:: default


    scene = 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.


.. code-block:: default


    interactive = False

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

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




.. image:: /auto_examples/images/sphx_glr_viz_network_001.png
    :class: sphx-glr-single-img




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  5.200 seconds)


.. _sphx_glr_download_auto_examples_viz_network.py:


.. only :: html

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



  .. container:: sphx-glr-download

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



  .. container:: sphx-glr-download

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


.. only:: html

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

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