Adding a dimension to each numpy.array element

Question

I am trying to convert each element of a numpy array into the array itself (say, to interpret the grayscale image as a color image). In other words:

>>> my_ar = numpy.array((0,5,10))
[0, 5, 10]
>>> transformed = my_fun(my_ar)  # In reality, my_fun() would do something more useful
array([
      [ 0,  0, 0], 
      [ 5, 10, 15], 
      [10, 20, 30]])
>>> transformed.shape
(3, 3)

I tried:

def my_fun_e(val):
    return numpy.array((val, val*2, val*3))

my_fun = numpy.frompyfunc(my_fun_e, 1, 3)

but we get:

my_fun(my_ar)
(array([[0 0 0], [ 5 10 15], [10 20 30]], dtype=object), array([None, None, None], dtype=object), array([None, None, None], dtype=object))

and I tried:

my_fun = numpy.frompyfunc(my_fun_e, 1, 1)

but we get:

>>> my_fun(my_ar)
array([[0 0 0], [ 5 10 15], [10 20 30]], dtype=object)

This is close, but not entirely correct - I get an array of objects, not an array of ints.

Update 3! OK. I realized that my example was too simple in advance - I do not just want to replicate my data in the third dimension, I would like to convert it at the same time. Maybe this is clearer?

Answer 1

Use a map to apply your transform function to each my_ar element:

import numpy

my_ar = numpy.array((0,5,10))
print my_ar

transformed = numpy.array(map(lambda x:numpy.array((x,x*2,x*3)), my_ar))
print transformed

print transformed.shape

Answer 2

numpy.dstack , ? , - "".

>>> import numpy as N
>>> a = N.array([[1,2,3],[4,5,6],[7,8,9]])
>>> a
array([[1, 2, 3],
       [4, 5, 6],
       [7, 8, 9]])
>>> b = N.dstack((a,a,a))
>>> b
array([[[1, 1, 1],
        [2, 2, 2],
        [3, 3, 3]],

       [[4, 4, 4],
        [5, 5, 5],
        [6, 6, 6]],

       [[7, 7, 7],
        [8, 8, 8],
        [9, 9, 9]]])
>>> b[1,1]
array([5, 5, 5])

Answer 3

:

 numpy.resize(my_ar, (3,3)).transpose()

, , (my_ar.shape[0],)*2 -

Answer 4

Does this do what you want:

tile(my_ar, (1,1,3))