slicing multi-dimensional numpy arrays with arrays [duplicate]


This question already has an answer here:

<ul><li> <a href="/questions/30917753/subsetting-a-2d-numpy-array" dir="ltr" rel="nofollow">Subsetting a 2D numpy array</a> <span class="question-originals-answer-count"> 4 answers </span> </li> </ul>

I have a numpy array (we'll call it test) of ~286 x 181 x 360, and need to extract a 3-D array from it. The ranges needed for the three dimensions are defined as other numpy arrays (a_dim, b_dim, and c_dim) (based ultimately on user input). Naively, I had hoped I'd be able to do something like big_array[a_dim,b_dim,c_dim]. That ran happily when the b_dim and c_dim contained only a single value (which happened to occur in my main test case), but doesn't work when they're bigger than 1.

Traceback (most recent call last): File "<pyshell#541>", line 1, in <module> test[a_dim,b_dim,c_dim] ValueError: shape mismatch: objects cannot be broadcast to a single shape

To simplify, given the following 4 arrays:

test=arange(125).reshape((5,5,5)) a_dim=[0,1] b_dim=[1,2] c_dim=[2,3]

What I'd like to get as output from that combination above is:

array([[[ 7, 8], [12, 13]], [[32, 33], [37, 38]]])

Or, a 3D array containing the all of the rows, columns, and bands (or whatever you'd like to call a third dimension) that are defined in a_dim, b_dim, and c_dim. I've tried using ix_ for this, but am clearly missing something from the examples I've seen:

>>> test[ix_((a_dim,b_dim,c_dim))] Traceback (most recent call last): File "<pyshell#517>", line 1, in <module> test[ix_((a_dim,b_dim,c_dim))] File "C:\Python27\lib\site-packages\numpy\lib\index_tricks.py", line 73, in ix_ raise ValueError, "Cross index must be 1 dimensional" ValueError: Cross index must be 1 dimensional

Any suggestions? Thanks!


So close -- just remove one pair of parens in the call to numpy.ix_():

>>> test[ix_(a_dim,b_dim,c_dim)] array([[[ 7, 8], [12, 13]], [[32, 33], [37, 38]]])


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