Rendering

`Canvas.draw_image`

Renders the given image onto the canvas.

You can optionally specify a position (x, y, by default both 0) for the top-left corner, as well as a target size (width, height, by default equal to the input image's size).
An opacity value may also be specified for the composition.

Acceptable types for the input image include:

A PIL image instance
A uint8 NumPy array representing an RGB (h, w, 3) or RGBA (h, w, 4) image.

from scripton.canvas import Canvas
from PIL import Image

canvas = Canvas(width=640, height=480)
canvas.draw_image(
    image=Image.open('overlay.png'),
    opacity=0.5
)

`Canvas.from_image`

Creates a canvas instance containing the given image.

This is equivalent to creating a canvas with width and height matching the image and then calling draw_image.

from scripton.canvas import Canvas
from PIL import Image

canvas = Canvas.from_image(Image.open('background.png'))

`Canvas.draw_matrix`

Renders a colormapped version of the given matrix (either a NumPy array or a PyTorch tensor) onto the canvas.

The optional target bounds (x, y, width, height) and opacity behave as described in draw_image
The optional colormap keyword argument can be set to one of the values displayed in the IDE.
The optional range keyword argument can specify the (min, max) ranges corresponding to the extremes of the colormap. By default, the matrix's min/max values are used.
If you just want to display a colormapped version of the matrix with no further changes, consider using the simpler show function.

from scripton.canvas import Canvas

import numpy as np

x = np.arange(100)
matrix = np.abs(x[:, None] - x)

canvas = Canvas(width=640, height=480)
canvas.draw_matrix(
    matrix,
    colormap='Magma',
    x=270,
    y=190,
)

`Canvas.from_matrix`

Creates a canvas instance containing a colormapped version of the given matrix.

This is equivalent to creating a canvas with width and height matching the matrix and then calling draw_matrix.

from scripton.canvas import Canvas

import numpy as np

x = np.arange(100)
matrix = np.abs(x[:, None] - x)
canvas = Canvas.from_matrix(matrix)