Sebastian Raschka

<- [back] to the matplotlib-gallery repository

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

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Last updated: 21/08/2014

CPython 3.4.1
IPython 2.0.0

matplotlib 1.3.1
numpy 1.8.2

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%matplotlib inline

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# 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

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### 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()

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### 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()

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### Using pcolor from matplotlib

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

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### 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()

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

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

plt.show()

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# Using different color maps

### Available color maps

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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

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()

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