Re: [Pytables-users] File content other than data

2010-03-23 Thread Matt Calder
Thanks Maarten and Francesc, The "sleep" experiment seems definitive. In fact, I found the following in the HDF5 User Guide, Name: H5Pset_obj_track_times Signature: herr_t H5Pset_obj_track_times( hid_t ocpl_id, hbool_t track_times ) Purpose: Sets the recording of times a

Re: [Pytables-users] File content other than data

2010-03-23 Thread Francesc Alted
A Tuesday 23 March 2010 14:17:29 Matt Calder escrigué: > Maarten, > > Thanks for the pointer to h5diff. Unfortunately, it shows no > difference in the files. Any idea how a timestamp might find its way > into the file? Or where I might look for an answer? I'm guessing this > is happening at the hd

Re: [Pytables-users] File content other than data

2010-03-23 Thread Maarten Sneep
On Tue, 23 Mar 2010 09:17:29 -0400, Matt Calder wrote: > Thanks for the pointer to h5diff. Unfortunately, it shows no > difference in the files. Any idea how a timestamp might find its way > into the file? Or where I might look for an answer? I'm guessing this > is happening at the hdf5 rather th

Re: [Pytables-users] File content other than data

2010-03-23 Thread Matt Calder
Maarten, Thanks for the pointer to h5diff. Unfortunately, it shows no difference in the files. Any idea how a timestamp might find its way into the file? Or where I might look for an answer? I'm guessing this is happening at the hdf5 rather than the pytable level. Matt On Tue, Mar 23, 2010 at 9:

Re: [Pytables-users] File content other than data

2010-03-23 Thread Maarten Sneep
On Tue, 23 Mar 2010 08:50:37 -0400, Matt Calder wrote: > $ diff /tmp/file1.h5 /tmp/file2.h5 > Binary files /tmp/file1.h5 and /tmp/file2.h5 differ The HDF-5 library comes with a h5diff tool, which will give you far more details. It probably is just a timestamp. Maarten

[Pytables-users] File content other than data

2010-03-23 Thread Matt Calder
Hello, I have a question about data in hdf5 files aside from the data proper. Specifically, I have the following example: >>> import tables >>> import numpy >>> # Define simple table >>> table1 = numpy.array([[(0,0,0), (1,0,0)], [(0,1,0), (0,0,1)]], {'names': >>> ('r','g','b'), 'formats': ('f4',