.. 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 ` 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 13-20 .. code-block:: Python from os.path import join as pjoin import numpy as np import fury .. GENERATED FROM PYTHON SOURCE LINES 21-22 Then let's download some available datasets. .. GENERATED FROM PYTHON SOURCE LINES 22-26 .. code-block:: Python files, folder = fury.data.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 27-28 We read our datasets .. GENERATED FROM PYTHON SOURCE LINES 28-33 .. 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 34-36 We attribute a color to each category of our dataset which correspond to our nodes colors. .. GENERATED FROM PYTHON SOURCE LINES 36-45 .. code-block:: Python 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]) .. GENERATED FROM PYTHON SOURCE LINES 46-47 We define our node size .. GENERATED FROM PYTHON SOURCE LINES 47-50 .. code-block:: Python radii = 1 + np.random.rand(len(positions)) .. GENERATED FROM PYTHON SOURCE LINES 51-54 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 54-64 .. 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 65-68 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 68-83 .. code-block:: Python 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, ) .. GENERATED FROM PYTHON SOURCE LINES 84-86 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 86-92 .. code-block:: Python scene = fury.window.Scene() scene.add(lines_actor) scene.add(sphere_actor) .. GENERATED FROM PYTHON SOURCE LINES 93-95 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 95-103 .. code-block:: Python interactive = False if interactive: fury.window.show(scene, size=(600, 600)) fury.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 .. rst-class:: sphx-glr-script-out .. code-block:: none /opt/homebrew/Caskroom/miniforge/base/envs/py39/lib/python3.9/site-packages/sphinx_gallery/gen_rst.py:722: UserWarning: We'll no longer accept the way you call the record function in future versions of FURY. Here's how to call the Function record: record(scene='value', cam_pos='value', cam_focal='value', cam_view='value', out_path='value', path_numbering='value', n_frames='value', az_ang='value', magnification='value', size='value', reset_camera='value', screen_clip='value', stereo='value', verbose='value') exec(self.code, self.fake_main.__dict__) .. GENERATED FROM PYTHON SOURCE LINES 104-106 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 0.803 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 ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: viz_network.py ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_