David,
The change in issue 27 was only for iteration over a tables.Column
instance. To use it, tweak Anthony's code as follows. This will iterate
over the "element" column, as in your original example.
Note also that this will only work with the development version of PyTables
available on github. It will be very slow using the released v2.4.0.
from itertools import izip
with tb.openFile(...) as f:
data = f.root.data.cols.element
data_i = iter(data)
data_j = iter(data)
data_i.next() # throw the first value away
for i, j in izip(data_i, data_j):
compare(i, j)
Hope that helps,
Josh
On Thu, Jan 3, 2013 at 9:11 AM, Anthony Scopatz <scop...@gmail.com> wrote:
> HI David,
>
> Tables and table column iteration have been overhauled fairly recently
> [1]. So you might try creating two iterators, offset by one, and then
> doing the comparison. I am hacking this out super quick so please forgive
> me:
>
> from itertools import izip
>
> with tb.openFile(...) as f:
> data = f.root.data
> data_i = iter(data)
> data_j = iter(data)
> data_i.next() # throw the first value away
> for i, j in izip(data_i, data_j):
> compare(i, j)
>
> You get the idea ;)
>
> Be Well
> Anthony
>
> 1. https://github.com/PyTables/PyTables/issues/27
>
>
> On Thu, Jan 3, 2013 at 9:25 AM, David Reed <david.ree...@gmail.com> wrote:
>
>> I was hoping someone could help me out here.
>>
>> This is from a post I put up on StackOverflow,
>>
>> I am have a fairly large dataset that I store in HDF5 and access using
>> PyTables. One operation I need to do on this dataset are pairwise
>> comparisons between each of the elements. This requires 2 loops, one to
>> iterate over each element, and an inner loop to iterate over every other
>> element. This operation thus looks at N(N-1)/2 comparisons.
>>
>> For fairly small sets I found it to be faster to dump the contents into a
>> multdimensional numpy array and then do my iteration. I run into problems
>> with large sets because of memory issues and need to access each element of
>> the dataset at run time.
>>
>> Putting the elements into an array gives me about 600 comparisons per
>> second, while operating on hdf5 data itself gives me about 300 comparisons
>> per second.
>>
>> Is there a way to speed this process up?
>>
>> Example follows (this is not my real code, just an example):
>>
>> *Small Set*:
>>
>>
>> with tb.openFile(h5_file, 'r') as f:
>> data = f.root.data
>>
>> N_elements = len(data)
>> elements = np.empty((N_irises, 1e5))
>>
>> for ii, d in enumerate(data):
>> elements[ii] = data['element']
>>
>> D = np.empty((N_irises, N_irises)) for ii in xrange(N_elements):
>> for jj in xrange(ii+1, N_elements):
>> D[ii, jj] = compare(elements[ii], elements[jj])
>>
>> *Large Set*:
>>
>>
>> with tb.openFile(h5_file, 'r') as f:
>> data = f.root.data
>>
>> N_elements = len(data)
>>
>> D = np.empty((N_irises, N_irises))
>> for ii in xrange(N_elements):
>> for jj in xrange(ii+1, N_elements):
>> D[ii, jj] = compare(data['element'][ii], data['element'][jj])
>>
>>
>>
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