Hi Andre,
Oh OK, I think I understand a little better. What I would do would be to
make "for i,file in enumerate(hdf5_files)" the outer most loop and then use
the File.walkNodes() method [1] to walk each file and pick out only the
data sets that you want to copy, skipping over all others. This should
allow you to only open each of the 400 files once. Hope this helps.
Be Well
Anthony
1.
http://pytables.github.com/usersguide/libref/file_class.html?highlight=walknodes#tables.File.walkNodes
On Wed, Aug 15, 2012 at 7:25 PM, Andre' Walker-Loud <walksl...@gmail.com>wrote:
> Hi Anthony,
>
> > I am a little confused. Let me verify. You have 400 hdf5 file (re and
> im) buried in an a unix directory tree. You want to make a single file
> which concatenates this data. Is this right?
>
> Sorry for my description - that is not quite right.
> The "unix directory tree" is the group tree I have made in each individual
> hdf5 file. So I have 400 hdf5 files, each with the given directory tree.
> And I basically want to copy that directory tree, but "merge" all of them
> together.
> However, there are bits in each of the small files that I do not want to
> merge - I only want to grab the average data sets, while the little files
> contains many different samples (which I have already averaged into the
> "avg" group.
>
> Is this clear?
>
>
> Thanks,
>
> Andre
>
>
>
> >
> > Be Well
> > Anthony
> >
> > On Wed, Aug 15, 2012 at 6:52 PM, Andre' Walker-Loud <walksl...@gmail.com>
> wrote:
> > Hi All,
> >
> > Just a strategy question.
> > I have many hdf5 files containing data for different measurements of the
> same quantities.
> >
> > My directory tree looks like
> >
> > top description [ group ]
> > sub description [ group ]
> > avg [ group ]
> > re [ numpy array shape = (96,1,2) ]
> > im [ numpy array shape = (96,1,2) ] - only exists for know subset
> of data files
> >
> > I have ~400 of these files. What I want to do is create a single file,
> which collects all of these files with exactly the same directory
> structure, except at the very bottom
> >
> > re [ numpy array shape = (400,96,1,2) ]
> >
> >
> > The simplest thing I came up with to do this is loop over the two levels
> of descriptive group structures, and build the numpy array for the final
> set this way.
> >
> > basic loop structure:
> >
> > final_file = tables.openFile('all_data.h5','a')
> >
> > for d1 in top_description:
> > final_file.createGroup(final_file.root,d1)
> > for d2 in sub_description:
> > final_file.createGroup(final_file.root+'/'+d1,d2)
> > data_re = np.zeros([400,96,1,2])
> > for i,file in enumerate(hdf5_files):
> > tmp = tables.openFile(file)
> > data_re[i] = np.array(tmp.getNode('/d1/d2/avg/re')
> > tmp.close()
> >
> final_file.createArray(final_file.root+'/'+d1+'/'+d2,'re',data_re)
> >
> >
> > But this involves opening and closing the individual 400 hdf5 files many
> times.
> > There must be a smarter algorithmic way to do this - or perhaps built in
> pytables tools.
> >
> > Any advice is appreciated.
> >
> >
> > Andre
> >
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