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 hdf5 rather than the pytable level.
This is my guess too. The script below generates identical files: --------------------------------------- import tables import numpy import time # Define simple table table1 = numpy.array([[(0,0,0), (1,0,0)], [(0,1,0), (0,0,1)]], {'names': ('r','g','b'), 'formats': ('f4', 'f4', 'f4')}) # Write table to a file h5file = tables.openFile("/tmp/file1.h5", "w", PYTABLES_SYS_ATTRS=False) h5file.createTable("/", "table1", description = table1) h5file.close() #time.sleep(1) # Write same table to another file h5file = tables.openFile("/tmp/file2.h5", "w", PYTABLES_SYS_ATTRS=False) h5file.createTable("/", "table1", description = table1) h5file.close() --------------- [notice how I've disabled pytables' system attributes, just in case] $ diff /tmp/file1.h5 /tmp/file2.h5 $ but if I uncomment the `time.sleep(1)` line, the files differ: $ diff /tmp/file1.h5 /tmp/file2.h5 Els fitxers /tmp/file1.h5 i /tmp/file2.h5 difereixen $ Object IDs are also identical: $ h5ls -i /tmp/file1.h5 HDF5 "/tmp/file1.h5" { GROUP "/" { OBJECTID { 33554433 } DATASET "table1" { DATATYPE H5T_COMPOUND { H5T_IEEE_F32LE "r"; H5T_IEEE_F32LE "g"; H5T_IEEE_F32LE "b"; } DATASPACE SIMPLE { ( 4 ) / ( H5S_UNLIMITED ) } OBJECTID { 83886081 } [...] $ h5ls -i /tmp/file2.h5 HDF5 "/tmp/file2.h5" { GROUP "/" { OBJECTID { 33554433 } DATASET "table1" { DATATYPE H5T_COMPOUND { H5T_IEEE_F32LE "r"; H5T_IEEE_F32LE "g"; H5T_IEEE_F32LE "b"; } DATASPACE SIMPLE { ( 4 ) / ( H5S_UNLIMITED ) } OBJECTID { 83886081 } [...] Mmh, I am afraid that you will need to use another way to check whether the files are identical or not. -- Francesc Alted ------------------------------------------------------------------------------ Download Intel® Parallel Studio Eval Try the new software tools for yourself. Speed compiling, find bugs proactively, and fine-tune applications for parallel performance. See why Intel Parallel Studio got high marks during beta. http://p.sf.net/sfu/intel-sw-dev _______________________________________________ Pytables-users mailing list Pytables-users@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/pytables-users