In [1]:
import numpy as np

In [2]:
# image has a line of intensity value 100 at col = 4, depth = 4
line = np.zeros((10, 10, 10))
line[:,2,2] = 100
line = np.single(line)

In [9]:
print line


[[[   0.    0.    0.    0.    0.    0.    0.    0.    0.    0.]
  [   0.    0.    0.    0.    0.    0.    0.    0.    0.    0.]
  [   0.    0.  100.    0.    0.    0.    0.    0.    0.    0.]
  [   0.    0.    0.    0.    0.    0.    0.    0.    0.    0.]
  [   0.    0.    0.    0.    0.    0.    0.    0.    0.    0.]
  [   0.    0.    0.    0.    0.    0.    0.    0.    0.    0.]
  [   0.    0.    0.    0.    0.    0.    0.    0.    0.    0.]
  [   0.    0.    0.    0.    0.    0.    0.    0.    0.    0.]
  [   0.    0.    0.    0.    0.    0.    0.    0.    0.    0.]
  [   0.    0.    0.    0.    0.    0.    0.    0.    0.    0.]]

 [[   0.    0.    0.    0.    0.    0.    0.    0.    0.    0.]
  [   0.    0.    0.    0.    0.    0.    0.    0.    0.    0.]
  [   0.    0.  100.    0.    0.    0.    0.    0.    0.    0.]
  [   0.    0.    0.    0.    0.    0.    0.    0.    0.    0.]
  [   0.    0.    0.    0.    0.    0.    0.    0.    0.    0.]
  [   0.    0.    0.    0.    0.    0.    0.    0.    0.    0.]
  [   0.    0.    0.    0.    0.    0.    0.    0.    0.    0.]
  [   0.    0.    0.    0.    0.    0.    0.    0.    0.    0.]
  [   0.    0.    0.    0.    0.    0.    0.    0.    0.    0.]
  [   0.    0.    0.    0.    0.    0.    0.    0.    0.    0.]]

 [[   0.    0.    0.    0.    0.    0.    0.    0.    0.    0.]
  [   0.    0.    0.    0.    0.    0.    0.    0.    0.    0.]
  [   0.    0.  100.    0.    0.    0.    0.    0.    0.    0.]
  [   0.    0.    0.    0.    0.    0.    0.    0.    0.    0.]
  [   0.    0.    0.    0.    0.    0.    0.    0.    0.    0.]
  [   0.    0.    0.    0.    0.    0.    0.    0.    0.    0.]
  [   0.    0.    0.    0.    0.    0.    0.    0.    0.    0.]
  [   0.    0.    0.    0.    0.    0.    0.    0.    0.    0.]
  [   0.    0.    0.    0.    0.    0.    0.    0.    0.    0.]
  [   0.    0.    0.    0.    0.    0.    0.    0.    0.    0.]]

 [[   0.    0.    0.    0.    0.    0.    0.    0.    0.    0.]
  [   0.    0.    0.    0.    0.    0.    0.    0.    0.    0.]
  [   0.    0.  100.    0.    0.    0.    0.    0.    0.    0.]
  [   0.    0.    0.    0.    0.    0.    0.    0.    0.    0.]
  [   0.    0.    0.    0.    0.    0.    0.    0.    0.    0.]
  [   0.    0.    0.    0.    0.    0.    0.    0.    0.    0.]
  [   0.    0.    0.    0.    0.    0.    0.    0.    0.    0.]
  [   0.    0.    0.    0.    0.    0.    0.    0.    0.    0.]
  [   0.    0.    0.    0.    0.    0.    0.    0.    0.    0.]
  [   0.    0.    0.    0.    0.    0.    0.    0.    0.    0.]]

 [[   0.    0.    0.    0.    0.    0.    0.    0.    0.    0.]
  [   0.    0.    0.    0.    0.    0.    0.    0.    0.    0.]
  [   0.    0.  100.    0.    0.    0.    0.    0.    0.    0.]
  [   0.    0.    0.    0.    0.    0.    0.    0.    0.    0.]
  [   0.    0.    0.    0.    0.    0.    0.    0.    0.    0.]
  [   0.    0.    0.    0.    0.    0.    0.    0.    0.    0.]
  [   0.    0.    0.    0.    0.    0.    0.    0.    0.    0.]
  [   0.    0.    0.    0.    0.    0.    0.    0.    0.    0.]
  [   0.    0.    0.    0.    0.    0.    0.    0.    0.    0.]
  [   0.    0.    0.    0.    0.    0.    0.    0.    0.    0.]]

 [[   0.    0.    0.    0.    0.    0.    0.    0.    0.    0.]
  [   0.    0.    0.    0.    0.    0.    0.    0.    0.    0.]
  [   0.    0.  100.    0.    0.    0.    0.    0.    0.    0.]
  [   0.    0.    0.    0.    0.    0.    0.    0.    0.    0.]
  [   0.    0.    0.    0.    0.    0.    0.    0.    0.    0.]
  [   0.    0.    0.    0.    0.    0.    0.    0.    0.    0.]
  [   0.    0.    0.    0.    0.    0.    0.    0.    0.    0.]
  [   0.    0.    0.    0.    0.    0.    0.    0.    0.    0.]
  [   0.    0.    0.    0.    0.    0.    0.    0.    0.    0.]
  [   0.    0.    0.    0.    0.    0.    0.    0.    0.    0.]]

 [[   0.    0.    0.    0.    0.    0.    0.    0.    0.    0.]
  [   0.    0.    0.    0.    0.    0.    0.    0.    0.    0.]
  [   0.    0.  100.    0.    0.    0.    0.    0.    0.    0.]
  [   0.    0.    0.    0.    0.    0.    0.    0.    0.    0.]
  [   0.    0.    0.    0.    0.    0.    0.    0.    0.    0.]
  [   0.    0.    0.    0.    0.    0.    0.    0.    0.    0.]
  [   0.    0.    0.    0.    0.    0.    0.    0.    0.    0.]
  [   0.    0.    0.    0.    0.    0.    0.    0.    0.    0.]
  [   0.    0.    0.    0.    0.    0.    0.    0.    0.    0.]
  [   0.    0.    0.    0.    0.    0.    0.    0.    0.    0.]]

 [[   0.    0.    0.    0.    0.    0.    0.    0.    0.    0.]
  [   0.    0.    0.    0.    0.    0.    0.    0.    0.    0.]
  [   0.    0.  100.    0.    0.    0.    0.    0.    0.    0.]
  [   0.    0.    0.    0.    0.    0.    0.    0.    0.    0.]
  [   0.    0.    0.    0.    0.    0.    0.    0.    0.    0.]
  [   0.    0.    0.    0.    0.    0.    0.    0.    0.    0.]
  [   0.    0.    0.    0.    0.    0.    0.    0.    0.    0.]
  [   0.    0.    0.    0.    0.    0.    0.    0.    0.    0.]
  [   0.    0.    0.    0.    0.    0.    0.    0.    0.    0.]
  [   0.    0.    0.    0.    0.    0.    0.    0.    0.    0.]]

 [[   0.    0.    0.    0.    0.    0.    0.    0.    0.    0.]
  [   0.    0.    0.    0.    0.    0.    0.    0.    0.    0.]
  [   0.    0.  100.    0.    0.    0.    0.    0.    0.    0.]
  [   0.    0.    0.    0.    0.    0.    0.    0.    0.    0.]
  [   0.    0.    0.    0.    0.    0.    0.    0.    0.    0.]
  [   0.    0.    0.    0.    0.    0.    0.    0.    0.    0.]
  [   0.    0.    0.    0.    0.    0.    0.    0.    0.    0.]
  [   0.    0.    0.    0.    0.    0.    0.    0.    0.    0.]
  [   0.    0.    0.    0.    0.    0.    0.    0.    0.    0.]
  [   0.    0.    0.    0.    0.    0.    0.    0.    0.    0.]]

 [[   0.    0.    0.    0.    0.    0.    0.    0.    0.    0.]
  [   0.    0.    0.    0.    0.    0.    0.    0.    0.    0.]
  [   0.    0.  100.    0.    0.    0.    0.    0.    0.    0.]
  [   0.    0.    0.    0.    0.    0.    0.    0.    0.    0.]
  [   0.    0.    0.    0.    0.    0.    0.    0.    0.    0.]
  [   0.    0.    0.    0.    0.    0.    0.    0.    0.    0.]
  [   0.    0.    0.    0.    0.    0.    0.    0.    0.    0.]
  [   0.    0.    0.    0.    0.    0.    0.    0.    0.    0.]
  [   0.    0.    0.    0.    0.    0.    0.    0.    0.    0.]
  [   0.    0.    0.    0.    0.    0.    0.    0.    0.    0.]]]

In [6]:
print line.shape[2]


10

In [8]:
print np.asarray(line[0])


[[   0.    0.    0.    0.    0.    0.    0.    0.    0.    0.]
 [   0.    0.    0.    0.    0.    0.    0.    0.    0.    0.]
 [   0.    0.  100.    0.    0.    0.    0.    0.    0.    0.]
 [   0.    0.    0.    0.    0.    0.    0.    0.    0.    0.]
 [   0.    0.    0.    0.    0.    0.    0.    0.    0.    0.]
 [   0.    0.    0.    0.    0.    0.    0.    0.    0.    0.]
 [   0.    0.    0.    0.    0.    0.    0.    0.    0.    0.]
 [   0.    0.    0.    0.    0.    0.    0.    0.    0.    0.]
 [   0.    0.    0.    0.    0.    0.    0.    0.    0.    0.]
 [   0.    0.    0.    0.    0.    0.    0.    0.    0.    0.]]

In [10]:
print np.asarray(line[0]).shape


(10, 10)

In [13]:
import scipy.misc

for plane in range(line.shape[2]):
    output = np.asarray(line[plane])
    ## Save as TIFF for testing
    scipy.misc.toimage(output).save('line' + str(plane) + '.tiff')

In [ ]: