# Adding a dimension to every element of a numpy.array

I'm trying to transform each element of a numpy array into an array itself (say, to interpret a greyscale 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've tried:

def my_fun_e(val): return numpy.array((val, val*2, val*3)) my_fun = numpy.frompyfunc(my_fun_e, 1, 3)

but 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've tried:

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

but get:

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

This is close, but not quite right -- I get an array of objects, not an array of ints.

**Update 3!** OK. I've realized that my example was too simple beforehand -- I don't just want to replicate my data in a third dimension, I'd like to transform it at the same time. Maybe this is clearer?

## Answers

Use map to apply your transformation function to each element in my_ar:

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

Does numpy.dstack do what you want? The first two indexes are the same as the original array, and the new third index is "depth".

>>> 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])

I propose:

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

You can of course adapt the shape (my_ar.shape[0],)*2 or whatever

Does this do what you want:

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