displayarray

Display arrays, and any updates to those arrays

from displayarray import display
import numpy as np

arr = np.random.normal(0.5, 0.1, (100, 100, 3))

with display(arr) as d:
    while d:
        arr[:] += np.random.normal(0.001, 0.0005, (100, 100, 3))
        arr %= 1.0

Get webcams and videos at 60fps, and run functions on the data:

from displayarray import display
import math as m

def forest_color(arr):
    forest_color.i += 1
    arr[..., 0] = (m.sin(forest_color.i*(2*m.pi)*4/360)*255 + arr[..., 0]) % 255
    arr[..., 1] = (m.sin((forest_color.i * (2 * m.pi) * 5 + 45) / 360) * 255 + arr[..., 1]) % 255
    arr[..., 2] = (m.cos(forest_color.i*(2*m.pi)*3/360)*255 + arr[..., 2]) % 255

forest_color.i = 0

display("fractal test.mp4", callbacks=forest_color, blocking=True, fps_limit=120)

Display tensors as they're running in tensorflow, like this denoising autoencoder:

# see test_display_tensorflow in test_simple_apy for full code.

...

autoencoder.compile(loss="mse", optimizer="adam")

while displayer:
    grab = tf.convert_to_tensor(
        displayer.FRAME_DICT["fractal test.mp4frame"][np.newaxis, ...].astype(np.float32)
        / 255.0
    )
    grab_noise = tf.convert_to_tensor(
        (((displayer.FRAME_DICT["fractal test.mp4frame"][np.newaxis, ...].astype(
            np.float32) + np.random.uniform(0, 255, grab.shape)) / 2) % 255)
        / 255.0
    )
    displayer.update((grab_noise.numpy()[0] * 255.0).astype(np.uint8), "uid for grab noise")
    autoencoder.fit(grab_noise, grab, steps_per_epoch=1, epochs=1)
    output_image = autoencoder.predict(grab, steps=1)
    displayer.update((output_image[0] * 255.0).astype(np.uint8), "uid for autoencoder output")

Handle input

Mouse events captured whenever the mouse moves over the window:

event:0
x,y:133,387
flags:0
param:None

Code:

from displayarray.input import mouse_loop
from displayarray import display

@mouse_loop
def print_mouse_thread(mouse_event):
    print(mouse_event)

display("fractal test.mp4", blocking=True)

Installation

displayarray is distributed on [PyPI] (https://pypi.org) as a universal wheel in Python 3.6+ and PyPy.

$ pip install displayarray

Usage

See tests for more example code. API will be generated soon.

License

displayarray is distributed under the terms of both

• MIT License <https://choosealicense.com/licenses/mit>_
• Apache License, Version 2.0 <https://choosealicense.com/licenses/apache-2.0>_

at your option.