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"""
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import Normalize
def normal_pdf(x, mean, var):
return np.exp((x  mean)**2 / (2*var))
xmin, xmax, ymin, ymax = (0, 100, 0, 100)
n_bins = 100
xx = np.linspace(xmin, xmax, n_bins)
yy = np.linspace(ymin, ymax, n_bins)
means_high = [20, 50]
means_low = [50, 60]
var = [150, 200]
gauss_x_high = normal_pdf(xx, means_high[0], var[0])
gauss_y_high = normal_pdf(yy, means_high[1], var[0])
gauss_x_low = normal_pdf(xx, means_low[0], var[1])
gauss_y_low = normal_pdf(yy, means_low[1], var[1])
weights = (np.outer(gauss_y_high, gauss_x_high)
 np.outer(gauss_y_low, gauss_x_low))
greys = np.full((*weights.shape, 3), 70, dtype=np.uint8)
vmax = np.abs(weights).max()
imshow_kwargs = {
'vmax': vmax,
'vmin': vmax,
'cmap': 'RdYlBu',
'extent': (xmin, xmax, ymin, ymax),
}
fig, ax = plt.subplots()
ax.imshow(greys)
ax.imshow(weights, **imshow_kwargs)
ax.set_axis_off()
alphas = np.ones(weights.shape)
alphas[:, 30:] = np.linspace(1, 0, 70)
fig, ax = plt.subplots()
ax.imshow(greys)
ax.imshow(weights, alpha=alphas, **imshow_kwargs)
ax.set_axis_off()
alphas = Normalize(0, .3, clip=True)(np.abs(weights))
alphas = np.clip(alphas, .4, 1) # alpha value clipped at the bottom at .4
fig, ax = plt.subplots()
ax.imshow(greys)
ax.imshow(weights, alpha=alphas, **imshow_kwargs)
ax.contour(weights[::1], levels=[.1, .1], colors='k', linestyles='')
ax.set_axis_off()
plt.show()
ax.contour(weights[::1], levels=[.0001, .0001], colors='k', linestyles='')
ax.set_axis_off()
plt.show()
