How to output color to transparent using hexbin and matplotlib?
I am creating a "Heatmap" using hexbin and I want this heatmap to be placed on top of the image. However, I would like the graph coloring to fade out to transparency, like a frequency (i.e. when a color fades to white it fades out). I tried changing the alpha value, but that doesn't give the desired effect.
My code:
n = 100000
x = np.random.standard_normal(n)
img = imread("soccer.jpg")
y = 2.0 + 3.0 * x + 4.0 * np.random.standard_normal(n)
plt.hexbin(x,y, bins='log', cmap=plt.cm.Reds, alpha = 0.3)
plt.imshow(img,zorder=0, extent=[-10, 10, -20, 20])
#plt.show()
plt.savefig('map.png')
I am open to using 2d histogram or any other plotting function. Even just being transparent when there are no values in that hex would be great, since many of my areas have data nulls.
Image of my current code:
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1 answer
Rough example:
n = 100000
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.colors import LinearSegmentedColormap
x = np.random.standard_normal(n)
img = plt.imread("soccer.jpg")
y = 2.0 + 3.0 * x + 4.0 * np.random.standard_normal(n)
red_high = ((0., 0., 0.),
(.3, .5, 0.5),
(1., 1., 1.))
blue_middle = ((0., .2, .2),
(.3, .5, .5),
(.8, .2, .2),
(1., .1, .1))
green_none = ((0,0,0),(1,0,0))
cdict3 = {'red': red_high,
'green': green_none,
'blue': blue_middle,
'alpha': ((0.0, 0.0, 0.0),
(0.3, 0.5, 0.5),
(1.0, 1.0, 1.0))
}
dropout_high = LinearSegmentedColormap('Dropout', cdict3)
plt.register_cmap(cmap = dropout_high)
plt.hexbin(x,y, bins='log', cmap=dropout_high)
plt.imshow(img,zorder=0, extent=[-10, 10, -20, 20])
plt.show()
#plt.savefig('map.png')
(I'm afraid my soccer field is sideways. I usually play as if it were.)
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