<- [back] to the matplotlib-gallery repository
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%load_ext watermark
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%watermark -u -d -v -p matplotlib,numpy
[More information](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/ipython_magic/watermark.ipynb) about the `watermark` magic command extension.
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%matplotlib inline
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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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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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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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plt.pcolor(hist)
plt.colorbar()
plt.grid()
plt.show()
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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()
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 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)
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import matplotlib.pyplot as plt
from matplotlib.colors import LogNorm
import numpy as np
np.random.seed(1)
a = np.random.random((25, 25))
plt.subplot(1, 1, 1)
plt.pcolor(a, norm=LogNorm(vmin=a.min() / 1.2, vmax=a.max() * 1.2), cmap='PuBu_r')
plt.colorbar()
plt.show()