[Numpy-discussion] Addressing arrays
Ted To
rainexpected@theo...
Mon Jan 30 10:57:12 CST 2012
Sure thing. To keep it simple suppose I have just a two dimensional
array (time,output):
[(1,2),(2,3),(3,4)]
I would like to look at all values of output for which, for example time==2.
My actual application has a six dimensional array and I'd like to look
at the contents using one or more of the first three dimensions.
Many thanks,
Ted
On 01/30/2012 10:50 AM, Zachary Pincus wrote:
> Ted, can you clarify what you're asking for? Maybe give a trivial example of an array and the desired output?
>
> I'm pretty sure this is a slicing question though:
>> If I have a three dimensional array a=(x,y,z), can I look at the values of z given particular values for x and y?
> Given that element values are scalars in this case, and indices are (x,y,z) triples, it seems likely that looking for "values of z" given an (x,y) pair is an slicing-by-index question, no?
>
> For indexing-by-value, "fancy indexing" with boolean masks is usually the way to go... again, Ted (or Chao), if you can describe your indexing needs in a bit more detail, it's often easy to find a compact slicing and/or fancy-indexing strategy that works well and reasonably efficiently.
>
> Zach
>
>
>
> On Jan 30, 2012, at 10:33 AM, Chao YUE wrote:
>
>> he is not asking for slicing. he is asking for how to index array by element value but not element index.
>>
>> 2012/1/30 Zachary Pincus <zachary.pincus@yale.edu>
>> a[x,y,:]
>>
>> Read the slicing part of the tutorial:
>> http://www.scipy.org/Tentative_NumPy_Tutorial
>> (section 1.6)
>>
>> And the documentation:
>> http://docs.scipy.org/doc/numpy/reference/arrays.indexing.html
>>
>>
>>
>> On Jan 30, 2012, at 10:25 AM, Ted To wrote:
>>
>>> Hi,
>>>
>>> Is there some straightforward way to access an array by values across a
>>> subset of its dimensions? For example, if I have a three dimensional
>>> array a=(x,y,z), can I look at the values of z given particular values
>>> for x and y?
>>>
>>> Thanks,
>>> Ted
>>> _______________________________________________
>>> NumPy-Discussion mailing list
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>>
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>>
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