# image
import matplotlib.pyplot as plt
import numpy as np

#image data
a = np.array([0.313660827978,0.365348418405,0.423733120134,0.365348418405,
0.439599930621,0.525083754405,0.423733120134,0.525083754405,0.651536351379]).reshape(3,3)

'''
for the value of "interpolation", check this:
    http://matplotlib.org/examples/images_contours_and_fields/interpolation_methods.html
'''
#origin 调整按数值排序的顺序
plt.imshow(a,interpolation='nearest',cmap = 'bone',origin = 'upper')
plt.colorbar(shrink = 0.9)

plt.xticks(())
plt.yticks(())
plt.show()
5ca9d12b5d2d2d74e20573ddf0b64a357a1e8860
<mask token>

<mask token>
plt.imshow(a, interpolation='nearest', cmap='bone', origin='upper')
plt.colorbar(shrink=0.9)
plt.xticks(())
plt.yticks(())
plt.show()

<mask token>
a = np.array([0.313660827978, 0.365348418405, 0.423733120134, 
    0.365348418405, 0.439599930621, 0.525083754405, 0.423733120134, 
    0.525083754405, 0.651536351379]).reshape(3, 3)
<mask token>
plt.imshow(a, interpolation='nearest', cmap='bone', origin='upper')
plt.colorbar(shrink=0.9)
plt.xticks(())
plt.yticks(())
plt.show()

import matplotlib.pyplot as plt
import numpy as np
a = np.array([0.313660827978, 0.365348418405, 0.423733120134, 
    0.365348418405, 0.439599930621, 0.525083754405, 0.423733120134, 
    0.525083754405, 0.651536351379]).reshape(3, 3)
<mask token>
plt.imshow(a, interpolation='nearest', cmap='bone', origin='upper')
plt.colorbar(shrink=0.9)
plt.xticks(())
plt.yticks(())
plt.show()

# image
import matplotlib.pyplot as plt
import numpy as np

#image data
a = np.array([0.313660827978,0.365348418405,0.423733120134,0.365348418405,
0.439599930621,0.525083754405,0.423733120134,0.525083754405,0.651536351379]).reshape(3,3)

'''
for the value of "interpolation", check this:
    http://matplotlib.org/examples/images_contours_and_fields/interpolation_methods.html
'''
#origin 调整按数值排序的顺序
plt.imshow(a,interpolation='nearest',cmap = 'bone',origin = 'upper')
plt.colorbar(shrink = 0.9)

plt.xticks(())
plt.yticks(())
plt.show()