Colab-Live-Figures

Simple library to display live-updating images in Google Colaboratory

Installation

!pip install -q git+https://github.com/svenschultze/Colab-Live-Figures

Usage

import live

Create figure manually

To create new figure:

fig = live.Figure(width=40) # 40% width

To update the figure:

fig.imshow(img)

Matplotlib integration:

fig.figshow()

Create GIF of size (300, 300) with 10fps from previous imshow's and display:

fig.repeat(shape=(300, 300), fps=10)

Display video (4d numpy array) as GIF:

fig.vidshow(vid, fps=10)

Create figure automatically

live.imshow(img, width=30)

Matplotlib integration:

live.figshow(width=30)

Create GIF of size (300, 300) with 10fps from previous imshow's and display:

live.repeat(shape=(300, 300), fps=10)

Display video (4d numpy array) as GIF:

live.vidshow(vid, width=30, fps=10)

Examples

import live
import time
import numpy as np

for i in range(5):
    img = np.random.randint(0, 255, (15, 15, 3))
    live.imshow(img)
    time.sleep(.5)

import live
import time
import numpy as np
import matplotlib.pyplot as plt

for i in range(10):
    x, y = np.random.randint(0, 100, (2, 100))

    plt.figure(dpi=100)
    plt.scatter(x, y, c=np.random.rand(len(x), 3))

    live.figshow(width=50)
    time.sleep(1)

live.repeat(fps=5)

import live
import time
from skimage import io
import cv2
img = io.imread("https://upload.wikimedia.org/wikipedia/commons/thumb/e/ea/Van_Gogh_-_Starry_Night_-_Google_Art_Project.jpg/757px-Van_Gogh_-_Starry_Night_-_Google_Art_Project.jpg")

for i in range(1, 21, 2):
    blurred = cv2.GaussianBlur(img, (i, i), 0)
    live.imshow(blurred)
    time.sleep(.5)

import live
import time
import numpy as np

MAP_SIZE = 10
NEIGHBORS = np.array([
    (-1, -1), (-1, 0), (-1, 1),
    ( 0, -1),          ( 0, 1),
    ( 1, -1), ( 1, 0), ( 1, 1)
])
START = (
    (3, 4, 5, 5, 5), 
    (3, 4, 4, 3, 2)
)

def neighbors(c):
    n = (NEIGHBORS + c) % MAP_SIZE
    return {(x, y) for x, y in n}

map = np.zeros((MAP_SIZE, MAP_SIZE), dtype=np.uint8)
map[START] = 1

for i in range(35):
    live.imshow(map * 255)

    new_map = np.zeros_like(map)
    for c, alive in np.ndenumerate(map):
        population = sum(map[xn, yn] for xn, yn in neighbors(c))
        if alive and population in [2, 3]:
            new_map[c] = 1
        elif not alive and population == 3:
            new_map[c] = 1

    map = new_map    
    time.sleep(.5)

import live
import numpy as np

vid = np.random.randint(0, 255, (10, 15, 15, 3), dtype=np.uint8)
live.vidshow(vid)