game of life and other scipy stuff

scipy.py

# %%
from skimage import io
import numpy as np
import matplotlib.pyplot as plt

url_astronaut = ('https://raw.githubusercontent.com/scikit-image/scikit-image/'
                 'master/skimage/data/astronaut.png')
astro = io.imread(url_astronaut)
print("Type:", type(astro), "Shape:", astro.shape, "Data type:", astro.dtype)
plt.imshow(astro)

# %%
def overlay_grid(image, spacing=128):
    """Return an image with a grid overlay, using the provided spacing.

    Parameters
    ----------
    image : array, shape (M, N, 3)
        The input image.
    spacing : int
        The spacing between the grid lines.

    Returns
    -------
    image_gridded : array, shape (M, N, 3)
        The original image with a blue grid superimposed.
    """
    image_gridded = image.copy()
    sq_mask = np.zeros(astro.shape[:2], bool)
    sq_mask[0:-1:spacing, 0:-1] = True
    sq_mask[0:-1, 0:-1:spacing] = True
    image_gridded[sq_mask] = [0, 255, 0]
    return image_gridded


fig, ax = plt.subplots(figsize=(30, 30))
ax.imshow(overlay_grid(astro, 128), interpolation='nearest')
# %%
from scipy import ndimage as ndi
from skimage import morphology
def tax(prices):
    return 10000 + 0.05 * np.percentile(prices, 90)
house_price_map = (0.5 + np.random.rand(100, 100)) * 1e6
footprint = morphology.disk(radius=10)
tax_rate_map = ndi.generic_filter(house_price_map, tax, footprint=footprint)
plt.imshow(tax_rate_map)
plt.colorbar()

# %%
#figure = np.zeros((20,20))
#x = figure.shape[0] // 2
#y = figure.shape[1] // 2
#figure[x:x+3, y] = 1
#figure[x, y:y+3] = 1
figure = np.random.randint(0, 2, size=(100, 100))
fig, ax = plt.subplots(figsize=(10, 10))
ax.imshow(figure)

frame_count = 500
frames = []
# %%
def convey(тeighbours):
    тeighbours = тeighbours.reshape(3,3)
    i = тeighbours[1,1]
    
    count = np.sum(тeighbours) - i
    #return count
    if i == 0 and count == 3:
        return 1
    if i == 1 and count in [2,3]:
        return 1
    return 0
footprint = np.ones((3,3))
 
frames.append(figure.copy())
for i in range(0,frame_count):
    figure = ndi.generic_filter(figure, convey, footprint=footprint)
    frames.append(figure.copy())
video = np.array(frames)
fig, ax = plt.subplots(figsize=(20, 20))
ax.imshow(figure)


# %%

from matplotlib import pyplot as plt
from matplotlib import animation
from IPython.display import HTML

# np array with shape (frames, height, width, channels)


fig = plt.figure(figsize=(20, 20))
im = plt.imshow(video[0,:,:])

plt.close() # this is required to not display the generated image

def init():
    im.set_data(video[0,:,:])

def animate(i):
    im.set_data(video[i,:,:])
    return im

anim = animation.FuncAnimation(fig, animate, init_func=init, frames=video.shape[0],
                               interval=200)
HTML(anim.to_html5_video())
# %%