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Re: [Pytables-users] Pytables-users Digest, Vol 80, Issue 3
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From: David R. <dav...@gm...> - 2013-01-03 20:17:28
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Thanks a lot for the help so far guys!
Looking at itertools, I found what I believe to be the perfect function for
what I need, itertools.combinations. This appears to be a valid replacement
to the method proposed.
There is a small problem that I didn't mention is that my compare function
actually takes as inputs 2 columns from the table. Like so:
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['element1'][ii],
data['element1'][jj],data['element2'][ii],
data['element2'][jj])
Is there an efficient way of using itertools with this structure?
On Thu, Jan 3, 2013 at 1:29 PM, <
pyt...@li...> wrote:
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> 1. Re: Nested Iteration of HDF5 using PyTables (Josh Ayers)
>
>
> ----------------------------------------------------------------------
>
> Message: 1
> Date: Thu, 3 Jan 2013 10:29:33 -0800
> From: Josh Ayers <jos...@gm...>
> Subject: Re: [Pytables-users] Nested Iteration of HDF5 using PyTables
> To: Discussion list for PyTables
> <pyt...@li...>
> Message-ID:
> <
> CAC...@ma...>
> Content-Type: text/plain; charset="iso-8859-1"
>
> 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 <sc...@gm...> 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 <dav...@gm...>
> 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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