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%load_ext watermark

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%watermark -u -d -v -p matplotlib,numpy

Last updated: 21/08/2014 

CPython 3.4.1
IPython 2.0.0

matplotlib 1.3.1
numpy 1.8.2

More information about the watermark magic command extension.

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%matplotlib inline

Heatmaps in matplotlib¶

Sections¶

Simple heat maps¶

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import numpy as np

# Sample from a bivariate Gaussian distribution
mean = [0,0]
cov = [[0,1],[1,0]] 
x, y = np.random.multivariate_normal(mean, cov, 10000).T

Using NumPy's histogram2d¶

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from matplotlib import pyplot as plt

hist, xedges, yedges = np.histogram2d(x,y)
X,Y = np.meshgrid(xedges,yedges)
plt.imshow(hist)
plt.grid(True)
plt.colorbar()
plt.show()

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# changing the interpolation

plt.imshow(hist, interpolation='nearest')
plt.grid(True)
plt.colorbar()
plt.show()

Using hist2d from matplotlib¶

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plt.hist2d(x, y, bins=10)
plt.colorbar()
plt.grid()
plt.show()

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# changing the bin-size

plt.hist2d(x, y, bins=40)
plt.colorbar()
plt.grid()
plt.show()

Using pcolor from matplotlib¶

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plt.pcolor(hist)
plt.colorbar()
plt.grid()
plt.show()

Using matshow from matplotlib¶

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import numpy as np
import matplotlib.pyplot as plt

columns = ['A', 'B', 'C', 'D']
rows = ['1', '2', '3', '4']

data = np.random.random((4,4))

fig = plt.figure()

ax = fig.add_subplot(111)

cax = ax.matshow(data, interpolation='nearest')
fig.colorbar(cax)

ax.set_xticklabels([''] + columns)
ax.set_yticklabels([''] + rows)

plt.show()

Using different color maps¶

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Available color maps¶

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More color maps are available at http://wiki.scipy.org/Cookbook/Matplotlib/Show_colormaps

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from math import ceil
import numpy as np

# Sample from a bivariate Gaussian distribution
mean = [0,0]
cov = [[0,1],[1,0]] 
x, y = np.random.multivariate_normal(mean, cov, 10000).T


size = len(plt.cm.datad.keys())
all_maps = list(plt.cm.datad.keys())

fig, ax = plt.subplots(ceil(size/4), 4, figsize=(12,100))

counter = 0
for row in ax:
    for col in row:
        try:
            col.imshow(hist, cmap=all_maps[counter])
            col.set_title(all_maps[counter])
        except IndexError:
           break
        counter += 1

plt.tight_layout()
plt.show()

New colormaps¶

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New colormaps designed by Stéfan van der Walt and Nathaniel Smith were included in matplotlib 1.5, and the viridis colormap will be the new default colormap in matplotlib 2.0.

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from math import ceil
import numpy as np
from matplotlib import pyplot as plt

# Sample from a bivariate Gaussian distribution
mean = [0,0]
cov = [[0,1],[1,0]] 
x, y = np.random.multivariate_normal(mean, cov, 10000).T


size = len(plt.cm.datad.keys())
all_maps = list(plt.cm.datad.keys())

new_maps = ['viridis', 'inferno', 'magma', 'plasma']

counter = 0
for i in xrange(4):
    plt.subplot(1, 4, counter + 1)
    
    plt.imshow(hist, cmap=new_maps[counter])
    plt.title(new_maps[counter])
    counter += 1

plt.tight_layout()
plt.show()

To use 'viridis' in matplotlib 1.4, it's possible to include the colormap from the cmaps package.

import colormaps as cmaps
plt.register_cmap(name='viridis', cmap=cmaps.viridis)
plt.set_cmap(cmaps.viridis)

img=mpimg.imread('stinkbug.png')
lum_img = np.flipud(img[:,:,0])
imgplot = plt.pcolormesh(lum_img)