# 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?

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])
```