To help you get started, we've collected the most common ways that vispy is being used within popular public projects.

vispy / vispy / examples / demo / gloo / realtime_signals.py View on Github

void main() {
    gl_FragColor = v_color;

    // Discard the fragments between the signals (emulate glMultiDrawArrays).
    if ((fract(v_index.x) > 0.) || (fract(v_index.y) > 0.))
        discard;

    // Clipping test.
    vec2 test = abs((v_position.xy-v_ab.zw)/v_ab.xy);
    if ((test.x > 1) || (test.y > 1))
        discard;
}
"""


class Canvas(app.Canvas):
    def __init__(self):
        app.Canvas.__init__(self, title='Use your wheel to zoom!',
                            keys='interactive')
        self.program = gloo.Program(VERT_SHADER, FRAG_SHADER)
        self.program['a_position'] = y.reshape(-1, 1)
        self.program['a_color'] = color
        self.program['a_index'] = index
        self.program['u_scale'] = (1., 1.)
        self.program['u_size'] = (nrows, ncols)
        self.program['u_n'] = n

        gloo.set_viewport(0, 0, *self.physical_size)

        self._timer = app.Timer('auto', connect=self.on_timer, start=True)

        gloo.set_state(clear_color='black', blend=True,

View more ways to use vispy

VisPy: interactive scientific visualization in Python

Main website:

VisPy is a high-performance interactive 2D/3D data visualization library. VisPy leverages the computational power of modern Graphics Processing Units (GPUs) through the OpenGL library to display very large datasets. Applications of VisPy include:

• High-quality interactive scientific plots with millions of points.
• Direct visualization of real-time data.
• Fast interactive visualization of 3D models (meshes, volume rendering).
• OpenGL visualization demos.
• Scientific GUIs with fast, scalable visualization widgets (Qt or IPython notebook with WebGL).

Releases

See CHANGELOG.md.

Announcements

See the VisPy Website.

Using VisPy

VisPy is a young library under heavy development at this time. It targets two categories of users:

1. Users knowing OpenGL, or willing to learn OpenGL, who want to create beautiful and fast interactive 2D/3D visualizations in Python as easily as possible.
2. Scientists without any knowledge of OpenGL, who are seeking a high-level, high-performance plotting toolkit.

If you're in the first category, you can already start using VisPy. VisPy offers a Pythonic, NumPy-aware, user-friendly interface for OpenGL ES 2.0 called gloo. You can focus on writing your GLSL code instead of dealing with the complicated OpenGL API - VisPy takes care of that automatically for you.

If you're in the second category, we're starting to build experimental high-level plotting interfaces. Notably, VisPy now ships a very basic and experimental OpenGL backend for matplotlib.

Installation

Please follow the detailed installation instructions on the VisPy website.

Structure of VisPy

Currently, the main subpackages are:

• app: integrates an event system and offers a unified interface on top of many window backends (Qt4, wx, glfw, jupyter notebook, and others). Relatively stable API.
• gloo: a Pythonic, object-oriented interface to OpenGL. Relatively stable API.
• scene: this is the system underlying our upcoming high level visualization interfaces. Under heavy development and still experimental, it contains several modules.
  • Visuals are graphical abstractions representing 2D shapes, 3D meshes, text, etc.
  • Transforms implement 2D/3D transformations implemented on both CPU and GPU.
  • Shaders implements a shader composition system for plumbing together snippets of GLSL code.
  • The scene graph tracks all objects within a transformation graph.
• plot: high-level plotting interfaces.

The API of all public interfaces are subject to change in the future, although app and gloo are relatively stable at this point.

Code of Conduct

The VisPy community requires its members to abide by the Code of Conduct. In this CoC you will find the expectations of members, the penalties for violating these expectations, and how violations can be reported to the members of the community in charge of enforcing this Code of Conduct.

Governance

The VisPy project maintainers make decisions about the project based on a simple consensus model. This is described in more detail on the governance page of the vispy website as well as the list of maintainers.

In addition to decisions about the VisPy project, there is also a steering committee for the overall VisPy organization. More information about this committee can also be found on the steering committee page of the vispy website, along with the organization's charter and other related documents (linked in the charter).

Genesis

VisPy began when four developers with their own visualization libraries decided to team up: Luke Campagnola with PyQtGraph, Almar Klein with Visvis, Cyrille Rossant with Galry, Nicolas Rougier with Glumpy.

Now VisPy looks to build on the expertise of these developers and the broader open-source community to build a high-performance OpenGL library.

External links

• User mailing list
• Dev mailing list
• Chat room
• Developer chat room
• Wiki
• Gallery
• Documentation