Setting Transparency Based on Pixel Values in Matplotlib

Just mask your "river" array.

e.g.

rivers = np.ma.masked_where(rivers == 0, rivers)

As a quick example of overlaying two plots in this manner:

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.cm as cm

# Generate some data...
gray_data = np.arange(10000).reshape(100, 100)

masked_data = np.random.random((100,100))
masked_data = np.ma.masked_where(masked_data < 0.9, masked_data)

# Overlay the two images
fig, ax = plt.subplots()
ax.imshow(gray_data, cmap=cm.gray)
ax.imshow(masked_data, cmap=cm.jet, interpolation='none')
plt.show()

Also, on a side note, imshow will happily accept floats for its RGBA format. It just expects everything to be in a range between 0 and 1.

An alternate way to do this with out using masked arrays is to set how the color map deals with clipping values below the minimum of clim (shamelessly using Joe Kington's example):

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.cm as cm

# Generate some data...
gray_data = np.arange(10000).reshape(100, 100)

masked_data = np.random.random((100,100))

my_cmap = cm.jet
my_cmap.set_under('k', alpha=0)


# Overlay the two images
fig, ax = plt.subplots()
ax.imshow(gray_data, cmap=cm.gray)
im = ax.imshow(masked_data, cmap=my_cmap, 
          interpolation='none', 
          clim=[0.9, 1])
plt.show()

There as also a set_over for clipping off the top and a set_bad for setting how the color map handles 'bad' values in the data.

An advantage of doing it this way is you can change your threshold by just adjusting clim with im.set_clim([bot, top])