The geometry module

The demosys.geometry module currently provides some simple functions to generate VAOs for simple things.


from demosys import geometry
# Create a fullscreen quad for overing the entire screen
vao = geometry.quad_fs()
# Create a 1 x 1 quad on the xy plane
vao = geometry.quad_2f(with=1.0, height=1.0)
# Create a unit cube
vao = geometry.cube(1.0, 1.0, 1.0)
# Create a bounding box
vao = geometry.bbox()
# Create a sphere
vao = geometry.sphere(radius=0.5, sectors=32, rings=16)
# Random 10.000 random points in 3d
vao = geometry.points_random_3d(10_000)


Improvements or suggestions can be made by through pull requests or issues on github.

See the geometry reference for more info.

Scene/Mesh File Formats

The demosys.scene.loaders currently support loading wavefront obj files and gltf.

You can create your own scene loader by adding the loader class to SCENE_LOADERS.


Generating Custom Geometry

To efficiently generate geometry in Python we must avoid as much memory allocation as possible. If performance doesn’t matter, then take this section lightly. Lbraries like numpy can also be used to generate geometry.

The naive way of generating geometry would probably look something like this:

import numpy
import moderngl
from pyrr import Vector3

def random_points(count):
    points = []
    for p in range(count):
        # Let's pretend we calculated random values for x, y, z
        points.append(Vector3([x, y, x]))

    # Create VBO enforcing float32 values with numpy
    points_data = numpy.array(points, dtype=numpy.float32)

    vao = VAO("random_points", mode=moderngl.POINTS)
    vao.buffer(points_data, 'f4', "in_position")
    return vao

This works perfectly fine, but we allocate a new list for every iteration and pyrr internally creates a numpy array. The points list will also have to dynamically expand. This gets exponentially more ugly as the count value increases.

We move on to version 2:

def random_points(count):
    # Pre-allocate a list containing zeros of length count * 3
    points = [0] * count * 3
    # Loop count times incrementing by 3 every frame
    for p in range(0, count * 3, 3):
        # Let's pretend we calculated random values for x, y, z
        points[p] = x
        points[p + 1] = y
        points[p + 2] = z

  points_data = numpy.array(points, dtype=numpy.float32)

This version is orders of magnitude faster because we don’t allocate memory in the loop. It has one glaring flaw. It’s not a very pleasant read even for such simple task, and it will not get any better if we add more complexity.

Let’s move on to version 3:

def random_points(count):
    def generate():
        for p in range(count):
            # Let's pretend we calculated random values for x, y, z
            yield x
            yield y
            yield z

    points_data = numpy.fromiter(generate(), count=count * 3, dtype=numpy.float32)

Using generators in Python like this is much a cleaner way. We also take advantage of numpy’s fromiter() that basically slurps up all the numbers we emit with yield into its internal buffers. By also telling numpy what the final size of the buffer will be using the count parameter, it will pre-allocate this not having to dynamically increase its internal buffer.

Generators are extremely simple and powerful. If things get complex we can easily split things up in several functions because Python’s yield from can forward generators.

Imagine generating a single VBO with interleaved position, normal and uv data:

def generate_stuff(count):
    # Returns a distorted position of x, y, z
    def pos(x, y, z):
        # Calculate..
        yield x
        yield y
        yield x

    def normal(x, y, z):
        # Calculate
        yield x
        yield y
        yield z

    def uv(x, y, x):
        # Calculate
        yield u
        yield v

    def generate(count):
        for i in range(count):
            # resolve current x, y, z pos
            yield from pos(x, y, z)
            yield from normal(x, y, z)
            yield from uv(x, y, z)

    interleaved_data = numpy.fromiter(generate(), count=count * 8, dtype=numpy.float32)