[Numpy-discussion] fast access and normalizing of ndarray slices

[Val Kalatsky]

Hi Wolfgang,

I thought maybe there is a trick for your specific operation.
Your array stacking is a simple case of the group-by operation and
normalization is aggregation followed by update.
I believe group-by and aggregation are on the NumPy todo-list.
You may have to write a small extension module to speed up your operations.
Val

On Thu, May 31, 2012 at 8:27 AM, Wolfgang Kerzendorf
<wkerzendorf at gmail.com>wrote:

> Hey Val,
>
> Well it doesn't matter what I do, but specifically I do factor =
> sum(data_array[start_point:start_point+length_data]) and then
> data[array[start_point:start_point+length_data]) /= factor. and that for
> every star_point and length data.
>
> How to do this fast?
>
> Cheers
>    Wolfgang
> On 2012-05-31, at 1:43 AM, Val Kalatsky wrote:
>
> What do you mean by "normalized it"?
> Could you give the output of your procedure for the sample input data.
> Val
>
> On Thu, May 31, 2012 at 12:36 AM, Wolfgang Kerzendorf <
> wkerzendorf at gmail.com> wrote:
>
>> Dear all,
>>
>> I have an ndarray which consists of many arrays stacked behind each other
>> (only conceptually, in truth it's a normal 1d float64 array).
>> I have a second array which tells me the start of the individual data
>> sets in the 1d float64 array and another one which tells me the length.
>> Example:
>>
>> data_array = (conceptually) [[1,2], [1,2,3,4], [1,2,3]] = in reality
>> [1,2,1,2,3,4,1,2,3, dtype=float64]
>> start_pointer = [0, 2, 6]
>> length_data = [2, 4, 3]
>>
>> I now want to normalize each of the individual data sets. I wrote a
>> simple for loop over the start_pointer and length data grabbed the data and
>> normalized it and wrote it back to the big array. That's slow. Is there an
>> elegant numpy way to do that? Do I have to go the cython way?
>>
>> Cheers
>>   Wolfgang
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>
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