.. 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_animated.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_animated.py>`
to download the full example code.
.. rst-class:: sphx-glr-example-title
.. _sphx_glr_auto_examples_04_demos_viz_network_animated.py:
=======================================================
Visualize Networks (Animated version)
=======================================================
The goal of this demo is to show how to visualize a
complex network and use an force directed algorithm to
layout the network. A simpler animation of the network
made by adding some random displacements to nodes
positions is also demoed.
.. GENERATED FROM PYTHON SOURCE LINES 14-15
First, let's import some useful functions
.. GENERATED FROM PYTHON SOURCE LINES 15-23
.. code-block:: Python
import math
from os.path import join as pjoin
import numpy as np
import fury
.. GENERATED FROM PYTHON SOURCE LINES 24-31
This demo has two modes.
Use `mode = 0` to visualize a randomly generated geographic network by
iterating it using a force-directed layout heuristic.
Use `mode = 1` to visualize a large network being animated with random
displacements
.. GENERATED FROM PYTHON SOURCE LINES 31-34
.. code-block:: Python
mode = 0
.. GENERATED FROM PYTHON SOURCE LINES 35-36
Then let's download some available datasets. (mode 1)
.. GENERATED FROM PYTHON SOURCE LINES 36-43
.. code-block:: Python
if mode == 1:
from fury.data.fetcher import fetch_viz_wiki_nw
files, folder = fetch_viz_wiki_nw()
categories_file, edges_file, positions_file = sorted(files.keys())
.. GENERATED FROM PYTHON SOURCE LINES 44-45
We read our datasets (mode 1)
.. GENERATED FROM PYTHON SOURCE LINES 45-52
.. code-block:: Python
if mode == 1:
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)
vertices_count = len(positions)
.. GENERATED FROM PYTHON SOURCE LINES 53-54
Generate a geographic random network, requires networkx package (mode 0)
.. GENERATED FROM PYTHON SOURCE LINES 54-66
.. code-block:: Python
if mode == 0:
import networkx as nx
vertices_count = 100
view_size = 100
network = nx.random_geometric_graph(vertices_count, 0.2)
positions = view_size * np.random.random((vertices_count, 3)) - view_size / 2.0
categories = np.arange(0, vertices_count)
edges = np.array(network.edges())
positions = view_size * np.random.random((vertices_count, 3)) - view_size / 2.0
.. GENERATED FROM PYTHON SOURCE LINES 67-69
We attribute a color to each category of our dataset which correspond to our
nodes colors.
.. GENERATED FROM PYTHON SOURCE LINES 69-80
.. code-block:: Python
category2index = {category: i for i, category in enumerate(np.unique(categories))}
index2category = np.unique(categories)
category_colors = fury.colormap.distinguishable_colormap(nb_colors=len(index2category))
colors = np.array(
[category_colors[category2index[category]] for category in categories]
)
.. GENERATED FROM PYTHON SOURCE LINES 81-82
We define our node size
.. GENERATED FROM PYTHON SOURCE LINES 82-85
.. code-block:: Python
radii = 1 + np.random.rand(len(positions))
.. GENERATED FROM PYTHON SOURCE LINES 86-88
Let's create our edges now. They will indicate a citation between two nodes.
The colors of each edge are interpolated between the two endpoints.
.. GENERATED FROM PYTHON SOURCE LINES 88-95
.. code-block:: Python
edges_colors = []
for source, target in edges:
edges_colors.append(np.array([colors[source], colors[target]]))
edges_colors = np.average(np.array(edges_colors), axis=1)
.. GENERATED FROM PYTHON SOURCE LINES 96-99
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.
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.. code-block:: Python
sphere_actor = fury.actor.sphere(
centers=np.zeros(positions.shape), colors=colors, radii=radii * 0.5, theta=8, phi=8
)
lines_actor = fury.actor.line(
np.zeros((len(edges), 2, 3)),
colors=edges_colors,
lod=False,
fake_tube=True,
linewidth=3,
)
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Defining timer callback and layout iterator
.. GENERATED FROM PYTHON SOURCE LINES 115-222
.. code-block:: Python
def new_layout_timer(
showm,
edges_list,
vertices_count,
max_iterations=1000,
vertex_initial_positions=None,
):
view_size = 500
viscosity = 0.10
alpha = 0.5
a = 0.0005
b = 1.0
deltaT = 1.0
sphere_geometry = np.array(fury.utils.vertices_from_actor(sphere_actor))
geometry_length = sphere_geometry.shape[0] / vertices_count
if vertex_initial_positions is not None:
pos = np.array(vertex_initial_positions)
else:
pos = view_size * np.random.random((vertices_count, 3)) - view_size / 2.0
velocities = np.zeros((vertices_count, 3))
def iterate(iterationCount):
nonlocal pos, velocities
for _ in range(iterationCount):
forces = np.zeros((vertices_count, 3))
# repulstive forces
for vertex1 in range(vertices_count):
for vertex2 in range(vertex1):
x1, y1, z1 = pos[vertex1]
x2, y2, z2 = pos[vertex2]
distance = (
math.sqrt(
(x2 - x1) * (x2 - x1)
+ (y2 - y1) * (y2 - y1)
+ (z2 - z1) * (z2 - z1)
)
+ alpha
)
rx = (x2 - x1) / distance
ry = (y2 - y1) / distance
rz = (z2 - z1) / distance
Fx = -b * rx / distance / distance
Fy = -b * ry / distance / distance
Fz = -b * rz / distance / distance
forces[vertex1] += np.array([Fx, Fy, Fz])
forces[vertex2] -= np.array([Fx, Fy, Fz])
# attractive forces
for vFrom, vTo in edges_list:
if vFrom == vTo:
continue
x1, y1, z1 = pos[vFrom]
x2, y2, z2 = pos[vTo]
distance = math.sqrt(
(x2 - x1) * (x2 - x1)
+ (y2 - y1) * (y2 - y1)
+ (z2 - z1) * (z2 - z1)
)
Rx = x2 - x1
Ry = y2 - y1
Rz = z2 - z1
Fx = a * Rx * distance
Fy = a * Ry * distance
Fz = a * Rz * distance
forces[vFrom] += np.array([Fx, Fy, Fz])
forces[vTo] -= np.array([Fx, Fy, Fz])
velocities += forces * deltaT
velocities *= 1.0 - viscosity
pos += velocities * deltaT
pos[:, 0] -= np.mean(pos[:, 0])
pos[:, 1] -= np.mean(pos[:, 1])
pos[:, 2] -= np.mean(pos[:, 2])
counter = 0
def _timer(_obj, _event):
nonlocal counter, pos
counter += 1
if mode == 0:
iterate(1)
else:
pos[:] += (np.random.random(pos.shape) - 0.5) * 1.5
spheres_positions = fury.utils.vertices_from_actor(sphere_actor)
spheres_positions[:] = sphere_geometry + np.repeat(pos, geometry_length, axis=0)
edges_positions = fury.utils.vertices_from_actor(lines_actor)
edges_positions[::2] = pos[edges_list[:, 0]]
edges_positions[1::2] = pos[edges_list[:, 1]]
fury.utils.update_actor(lines_actor)
fury.utils.compute_bounds(lines_actor)
fury.utils.update_actor(sphere_actor)
fury.utils.compute_bounds(lines_actor)
showm.scene.reset_clipping_range()
showm.render()
if counter >= max_iterations:
showm.exit()
return _timer
.. GENERATED FROM PYTHON SOURCE LINES 223-225
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 225-234
.. code-block:: Python
scene = fury.window.Scene()
camera = scene.camera()
scene.add(lines_actor)
scene.add(sphere_actor)
.. GENERATED FROM PYTHON SOURCE LINES 235-239
The final step! Visualize the result of our creation! Also, we need to move
the camera a little bit farther from the network. you can increase the
parameter max_iteractions of the timer callback to let the animation run for
more time.
.. GENERATED FROM PYTHON SOURCE LINES 239-264
.. code-block:: Python
showm = fury.window.ShowManager(
scene=scene,
reset_camera=False,
size=(900, 768),
order_transparent=True,
multi_samples=8,
)
scene.set_camera(position=(0, 0, -300))
timer_callback = new_layout_timer(
showm, edges, vertices_count, max_iterations=200, vertex_initial_positions=positions
)
# Run every 16 milliseconds
showm.add_timer_callback(True, 16, timer_callback)
showm.start()
fury.window.record(
scene=showm.scene, size=(900, 768), out_path="viz_animated_networks.png"
)
.. _sphx_glr_download_auto_examples_04_demos_viz_network_animated.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_animated.ipynb <viz_network_animated.ipynb>`
.. container:: sphx-glr-download sphx-glr-download-python
:download:`Download Python source code: viz_network_animated.py <viz_network_animated.py>`
.. container:: sphx-glr-download sphx-glr-download-zip
:download:`Download zipped: viz_network_animated.zip <viz_network_animated.zip>`
.. only:: html
.. rst-class:: sphx-glr-signature
`Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_