PyTables comes with a couple of utilities that make the life easier to the user. One is called ptdump and lets you see the contents of a PyTables file (or generic HDF5 file, if supported). The other one is named ptrepack that allows to (recursively) copy sub-hierarchies of objects present in a file into another one, changing, if desired, some of the filters applied to the leaves during the copy process.
Normally, these utilities will be installed somewhere in your PATH during the process of installation of the PyTables package, so that you can invoke them from any place in your filesystem after the installation has successfully finished.
As has been said before, ptdump utility allows you look into the contents of your PyTables files. It lets you see not only the data but also the metadata (that is, the structure and additional information in the form of attributes).
$ ptdump -hto see the message usage:
usage: ptdump [-R start,stop,step] [-a] [-h] [-d] [-v] file[:nodepath] -R RANGE -- Select a RANGE of rows in the form "start,stop,step" -a -- Show attributes in nodes (only useful when -v or -d are active) -c -- Show info of columns in tables (only useful when -v or -d are active) -i -- Show info of indexed columns (only useful when -v or -d are active) -d -- Dump data information on leaves -h -- Print help on usage -v -- Dump more metainformation on nodes
$ ptdump vlarray1.h5 Filename: 'vlarray1.h5' Title: '' , Last modif.: 'Fri Feb 6 19:33:28 2004' , rootUEP='/', filters=Filters(), Format version: 1.2 / (Group) '' /vlarray1 (VLArray(4,), shuffle, zlib(1)) 'ragged array of ints'we can see that the file contains a just a leaf object called vlarray1, that is an instance of VLArray, has 4 rows, and two filters has been used in order to create it: shuffle and zlib (with a compression level of 1).
$ ptdump -v vlarray1.h5 / (Group) '' children := ['vlarray1' (VLArray)] /vlarray1 (VLArray(4,), shuffle, zlib(1)) 'ragged array of ints' atom = Atom(type=Int32, shape=1, flavor='Numeric') nrows = 4 flavor = 'Numeric' byteorder = 'little'so we can see more info about the atoms that are the components of the vlarray1 dataset, i.e. they are scalars of type Int32 and with Numeric flavor.
$ ptdump -va vlarray1.h5 / (Group) '' children := ['vlarray1' (VLArray)] /._v_attrs (AttributeSet), 5 attributes: [CLASS := 'GROUP', FILTERS := None, PYTABLES_FORMAT_VERSION := '1.2', TITLE := '', VERSION := '1.0'] /vlarray1 (VLArray(4,), shuffle, zlib(1)) 'ragged array of ints' atom = Atom(type=Int32, shape=1, flavor='Numeric') nrows = 4 flavor = 'Numeric' byteorder = 'little' /vlarray1.attrs (AttributeSet), 4 attributes: [CLASS := 'VLARRAY', FLAVOR := 'Numeric', TITLE := 'ragged array of ints', VERSION := '1.0']
$ ptdump -d vlarray1.h5 / (Group) '' /vlarray1 (VLArray(4,), shuffle, zlib(1)) 'ragged array of ints' Data dump: [array([5, 6]), array([5, 6, 7]), array([5, 6, 9, 8]), array([ 5, 6, 9, 10, 12])]we see here a data dump of the 4 rows in vlarray1 object, in the form of a list. Because the object is a VLA, we see a different number of integers on each row.
ptdump -R2,4 -d vlarray1.h5:/vlarray1 /vlarray1 (VLArray(4,), shuffle, zlib(1)) 'ragged array of ints' Data dump: [array([5, 6, 9, 8]), array([ 5, 6, 9, 10, 12])]Here, we have specified the range of rows between 2 and 4 (the upper limit excluded, as usual in Python). See how we have selected only the /vlarray1 object for doing the dump (vlarray1.h5:/vlarray1).
$ ptdump -R2,4 -vad vlarray1.h5:/vlarray1 /vlarray1 (VLArray(4,), shuffle, zlib(1)) 'ragged array of ints' atom = Atom(type=Int32, shape=1, flavor='Numeric') nrows = 4 flavor = 'Numeric' byteorder = 'little' /vlarray1.attrs (AttributeSet), 4 attributes: [CLASS := 'VLARRAY', FLAVOR := 'Numeric', TITLE := 'ragged array of ints', VERSION := '1.0'] Data dump: [array([5, 6, 9, 8]), array([ 5, 6, 9, 10, 12])]
This utility is a very powerful one and let's you to copy any leaf, group or complete subtree into another file. During the copy process you are allowed to change the filter properties if you want so. Also, in the case of duplicated pathnames, you can decide if you want to overwrite already existing nodes on the destination file. Generally speaking, ptrepack can be useful in may situations, like replicating a subtree in another file, change the filters in objects and see how affect this to the compression degree or I/O performance, consolidating specific data in repositoris or even importing generic HDF5 files and create true PyTables counterparts.
$ ptrepack -hto see the message usage:
usage: ptrepack [-h] [-v] [-o] [-R start,stop,step] [--non-recursive] [--dest-title=title] [--dont-copyuser-attrs] [--overwrite-nodes] [--complevel=(0-9)] [--complib=lib] [--shuffle=(0|1)] [--fletcher32=(0|1)] [--keep-source-filters] sourcefile:sourcegroup destfile:destgroup -h -- Print usage message. -v -- Show more information. -o -- Overwite destination file. -R RANGE -- Select a RANGE of rows (in the form "start,stop,step") during the copy of *all* the leaves. --non-recursive -- Do not do a recursive copy. Default is to do it. --dest-title=title -- Title for the new file (if not specified, the source is copied). --dont-copy-userattrs -- Do not copy the user attrs (default is to do it) --overwrite-nodes -- Overwrite destination nodes if they exist. Default is to not overwrite them. --complevel=(0-9) -- Set a compression level (0 for no compression, which is the default). --complib=lib -- Set the compression library to be used during the copy. lib can be set to "zlib", "lzo" or "ucl". Defaults to "zlib". --shuffle=(0|1) -- Activate or not the shuffling filter (default is active if complevel>0). --fletcher32=(0|1) -- Whether to activate or not the fletcher32 filter (not active by default). --keep-source-filters -- Use the original filters in source files. The default is not doing that if any of --complevel, --complib, --shuffle or --fletcher32 option is specified.
$ ptdump tutorial1.h5 Filename: 'tutorial1.h5' Title: 'Test file' , Last modif.: 'Fri Feb 6 19:33:28 2004' , rootUEP='/', filters=Filters(), Format version: 1.2 / (Group) 'Test file' /columns (Group) 'Pressure and Name' /columns/name (Array(3,)) 'Name column selection' /columns/pressure (Array(3,)) 'Pressure column selection' /detector (Group) 'Detector information' /detector/readout (Table(10L,)) 'Readout example'Now, copy the /columns to other non-existing file. That's easy:
$ ptrepack tutorial1.h5:/columns reduced.h5That's all. Let's see the contents of the newly created reduced.h5 file:
$ ptdump reduced.h5 Filename: 'reduced.h5' Title: '' , Last modif.: 'Fri Feb 20 15:26:47 2004' , rootUEP='/', filters=Filters(), Format version: 1.2 / (Group) '' /name (Array(3,)) 'Name column selection' /pressure (Array(3,)) 'Pressure column selection'so, you have copied the childs of /columns group into the root of the reduced.h5 file.
$ ptrepack tutorial1.h5:/columns reduced.h5:/columns ptdump reduced.h5 Filename: 'reduced.h5' Title: '' , Last modif.: 'Fri Feb 20 15:39:15 2004' , rootUEP='/', filters=Filters(), Format version: 1.2 / (Group) '' /name (Array(3,)) 'Name column selection' /pressure (Array(3,)) 'Pressure column selection' /columns (Group) '' /columns/name (Array(3,)) 'Name column selection' /columns/pressure (Array(3,)) 'Pressure column selection'Ok. Much better. But you want to get rid of the existing nodes on the new file. You can achieve this by adding the -o flag:
$ ptrepack -o tutorial1.h5:/columns reduced.h5:/columns $ ptdump reduced.h5 Filename: 'reduced.h5' Title: '' , Last modif.: 'Fri Feb 20 15:41:57 2004' , rootUEP='/', filters=Filters(), Format version: 1.2 / (Group) '' /columns (Group) '' /columns/name (Array(3,)) 'Name column selection' /columns/pressure (Array(3,)) 'Pressure column selection'where you can see how the old contents of the reduced.h5 file has been overwritten.
$ ptrepack tutorial1.h5:/detector/readout reduced.h5:/rawdata $ ptdump reduced.h5 Filename: 'reduced.h5' Title: '' , Last modif.: 'Fri Feb 20 15:52:22 2004', rootUEP='/', filters=Filters(), Format version: 1.2 / (Group) '' /rawdata (Table(10L,)) 'Readout example' /columns (Group) '' /columns/name (Array(3,)) 'Name column selection' /columns/pressure (Array(3,)) 'Pressure column selection'where the /detector/readout has been copied to /rawdata in destination.
$ ptrepack --complevel=1 tutorial1.h5:/detector/readout reduced.h5:/rawdata Problems doing the copy from 'tutorial1.h5:/detector/readout' to 'reduced.h5:/rawdata' The error was --> exceptions.ValueError: The destination (/rawdata (Table(10L,)) 'Readout example') already exists. Assert the overwrite parameter if you really want to overwrite it. The destination file looks like: Filename: 'reduced.h5' Title: ''; Last modif.: 'Fri Feb 20 15:52:22 2004'; rootUEP='/'; filters=Filters(), Format version: 1.2 / (Group) '' /rawdata (Table(10L,)) 'Readout example' /columns (Group) '' /columns/name (Array(3,)) 'Name column selection' /columns/pressure (Array(3,)) 'Pressure column selection' Traceback (most recent call last): File "../utils/ptrepack", line 358, in ? start=start, stop=stop, step=step) File "../utils/ptrepack", line 111, in copyLeaf raise RuntimeError, "Please, check that the node names are not duplicated in destination, and if so, add the --overwrite-nodes flag if desired." RuntimeError: Please, check that the node names are not duplicated in destination, and if so, add the --overwrite-nodes flag if desired.ooops!. We ran into problems: we forgot that /rawdata pathname already existed in destination file. Let's add the --overwrite-nodes, as the verbose error suggested:
$ ptrepack --overwrite-nodes --complevel=1 tutorial1.h5:/detector/readout reduced.h5:/rawdata $ ptdump reduced.h5 Filename: 'reduced.h5' Title: ''; Last modif.: 'Fri Feb 20 16:02:20 2004'; rootUEP='/'; filters=Filters(), Format version: 1.2 / (Group) '' /rawdata (Table(10L,), shuffle, zlib(1)) 'Readout example' /columns (Group) '' /columns/name (Array(3,)) 'Name column selection' /columns/pressure (Array(3,)) 'Pressure column selection'you can check how the filter properties has been changed for the /rawdata table. Check as the other nodes still exists.
$ ptrepack -R1,8,3 tutorial1.h5:/detector/readout reduced.h5:/slices/aslice $ ptdump reduced.h5 Filename: 'reduced.h5' Title: ''; Last modif.: 'Fri Feb 20 16:17:13 2004'; rootUEP='/'; filters=Filters(); Format version: 1.2 / (Group) '' /rawdata (Table(10L,), shuffle, zlib(1)) 'Readout example' /columns (Group) '' /columns/name (Array(3,)) 'Name column selection' /columns/pressure (Array(3,)) 'Pressure column selection' /slices (Group) '' /slices/aslice (Table(3L,)) 'Readout example'note how only 3 rows of the original readout table has been copied to the new aslice destination. Note as well how the previously inexistent slices group has been created in the same operation.
This tool is able to convert a file in NetCDF format to a PyTables file (and hence, to a HDF5 file). However, for this to work, you will need the NetCDF interface for Python that comes with the excellent Scientific Python (see ) package. This script was initially contributed by Jeff Whitaker. It has been updated to support selectable filters from the command line and some other small improvements.
If you want other file formats to be converted to PyTables, have a look at the SciPy (see ) project (subpackage io), and look for different methods to import them into Numeric/numarray objects. Following the SciPy documentation, you can read, among other formats, ASCII files (read_array), binary files in C or Fortran (fopen) and MATLAB (version 4 or 5) files (loadmat). Once you have the content of your files as Numeric/numarray objects, you can save them as regular (E)Arrays in PyTables files. Remember, if you end with a nice conversor, do not forget to contribute it back to the community. Thanks!.
$ nctoh5 -hto see the message usage:
usage: nctoh5 [-h] [-v] [-o] [--complevel=(0-9)] [--complib=lib] [--shuffle=(0|1)] [--fletcher32=(0|1)] [--unpackshort=(0|1)] [--quantize=(0|1)] netcdffilename hdf5filename -h -- Print usage message. -v -- Show more information. -o -- Overwite destination file. --complevel=(0-9) -- Set a compression level (0 for no compression, which is the default). --complib=lib -- Set the compression library to be used during the copy. lib can be set to "zlib", "lzo" or "ucl". Defaults to "zlib". --shuffle=(0|1) -- Activate or not the shuffling filter (default is active if complevel>0). --fletcher32=(0|1) -- Whether to activate or not the fletcher32 filter (not active by default). --unpackshort=(0|1) -- unpack short integer variables to float variables using scale_factor and add_offset netCDF variable attributes (not active by default). --quantize=(0|1) -- quantize data to improve compression using least_significant_digit netCDF variable attribute (not active by default). See http://www.cdc.noaa.gov/cdc/conventions/cdc_netcdf_standard.shtml for further explanation of what this attribute means.If you have followed the small tutorial on the ptrepack utility (see B.2), you should easily realize what most of the different flags would mean.