Release notes for PyTables 2.0 series¶
|Author:||Francesc Alted i Abad|
|Author:||Ivan Vilata i Balaguer|
Changes from 2.0.3 to 2.0.4¶
- Selections in tables works now in threaded environments. The problem was in the Numexpr package – the solution has been reported to the upstream authors too. Fixes #164.
- PyTables had problems importing native HDF5 files with gaps in nested compound types. This has been solved. Fixes #173.
- In order to prevent a bug existing in HDF5 1.6 series, the
EArray.truncate()method refused to accept a 0 as parameter (i.e. truncate an existing EArray to have zero rows did not work). As this has been fixed in the recent HDF5 1.8 series, this limitation has been removed (but only if the user has one of these installed). Fixes #171.
- Small fixes for allowing the test suite to pass when using the new NumPy 1.1. However, it remains a small issue with the way the new NumPy represents complex numbers. I’m not fixing that in the PyTables suite, as there are chances that this can be fixed in NumPy itself (see ticket #841).
Changes from 2.0.2 to 2.0.3¶
- Replaced the algorithm for computing chunksizes by another that is more general and useful for a larger range of expected dataset sizes. The outcome of the new calculation is the same than before for dataset sizes <= 100 GB. For datasets between 100 GB <= size < 10 TB, larger values are returned. For sizes >= 10 TB a maximum value of 1 MB is always returned.
- Fixed a problem when updating multidimensional cells using the Row.update() method in the middle of table iterators . Fixes #149.
- Added support for the latest 1.8.0 version of the HDF5 library. Fixes ticket #127.
- PyTables compiles now against latest versions of Pyrex (0.9.6.4). For the first time, the extensions do compile without warnings! Fixes #159.
- Numexpr module has been put in sync with the version in SciPy sandbox.
- Added a couple of warnings in User’s Guide so as to tell the user that it is not safe to use methods that can change the number of rows of a table in the middle of a row iterator. Fixes #153.
Changes from 2.0.1 to 2.0.2¶
File; fixes #113. With this, and if using Python 2.5 you can do things like:
- with tables.openFile(“test.h5”) as h5file:
Carefully preserve type when converting NumPy scalar to numarray; fixes #125.
Fixed a nasty bug that appeared when moving or renaming groups due to a bad interaction between
Group._g_updateChildrenLocation()and the LRU cache. Solves #126.
Return 0 when no rows are given to
Table.modifyRows(); fixes #128.
Added an informative message when the
nctoh5utility is run without the NetCDF interface of ScientificPython bening installed.
Now, a default representation of closed nodes is provided; fixes #129.
Changes from 2.0 to 2.0.1¶
Table.readCoords()was not checked for contiguousness, raising fatal errors when it was discontiguous. This has been fixed.
There is an inconsistency in the way used to specify the atom shape in
Atomconstructors. When the shape is specified as
shape=()it means a scalar atom and when it is specified as
shape=Nit means an atom with
shape=(N,). But when the shape is specified as
shape=1(i.e. in the default case) then a scalar atom is obtained instead of an atom with
shape=(1,). This is inconsistent and not the behavior that NumPy exhibits.
Changing this will require a migration path which includes deprecating the old behaviour if we want to make the change happen before a new major version. The proposed path is:
- In PyTables 2.0.1, we are changing the default value of the
(), and issue a
DeprecationWarningwhen someone uses
shape=1stating that, for the time being, it is equivalent to
(), but in near future versions it will become equivalent to
(1,), and recommending the user to pass
shape=()if a scalar is desired.
- In PyTables 2.1, we will remove the previous warning and take
shape=(N,)for any value of N.
See ticket #96 for more info.
- In PyTables 2.0.1, we are changing the default value of the
The info about the
chunkshapeattribute of a leaf is now printed in the
__repr__()of chunked leaves (all except
After some scrupulous benchmarking job, the size of the I/O buffer for
Tableobjects has been reduced to the minimum that allows maximum performance. This represents more than 10x of reduction in size for that buffer, which will benefit those programs dealing with many tables simultaneously (#109).
--shufflewere specified at the same time, the ‘shuffle’ filter was always set to ‘off’. This has been fixed (#104).
An ugly bug related with the integrated Numexpr not being aware of all the variations of data arrangements in recarray objects has been fixed (#103). We should stress that the bug only affected the Numexpr version integrated in PyTables, and not the original one.
When passing a record array to a table at creation time, its real length is now used instead of the default value for
expectedrows. This allows for better performance (#97).
Added some workarounds so that NumPy scalars can be successfully converted to numarray objects. Fixes #98.
PyTables is now able to access table rows beyond 2**31 in 32-bit Python. The problem was a limitation of
xrangeand we have replaced it by a new
lrangeclass written in Pyrex. Moreover,
lrangehas been made publicly accessible as a safe 64-bit replacement for
xrangefor 32-bit platforms users. Fixes #99.
If a group and a table are created in a function, and the table is accessed through the group, the table can be flushed now. Fixes #94.
It is now possible to directly assign a field in a nested record of a table using the natural naming notation (#93).
Changes from 2.0rc2 to 2.0¶
- Added support for recognizing native HDF5 files with datasets compressed with szip compressor.
- Fixed a problem when asking for the string representation (str()) of closed files. Fixes ticket #79.
- Do not take LZO as available when its initialisation fails.
- Fixed a glitch in ptrepack utility. When the user wants a copy of a group, and a group is to be created in destination, the attributes of the original group are copied. If it is not to be created, the attributes will not be copied. I think this should be what the user would expect most of the times.
- Fixed the check for creating intermediate groups in ptrepack utility. Solves ticket #83.
- Before, when reading a dataset with an unknown CLASS id, a warning was
issued and the dataset mapped to
UnImplemented. This closed the door to have the opportunity to try to recognize the dataset and map it to a supported CLASS. Now, when a CLASS attribute is not recognized, an attempt to recognize its associated dataset is made. If it is recognized, the matching class is associated with the dataset. If it is not recognized, then a warning is issued and the dataset becomes mapped to
- Always pass verbose and heavy values in the common test module to test(). Fixes ticket #85.
- Now, the
--heavyflag passed to test_all.py are honored.
- All the DLL’s of dependencies are included now in Windows binaries. This should allow for better portability of the binaries.
- Fixed the description of Node._v_objectID that was misleading.
Changes from 2.0rc1 to 2.0rc2¶
- The “Optimization tips” chapter of the User’s Guide has been completely updated to adapt to PyTables 2.0 series. In particular, new benchmarks on the much improved indexed queries have been included; you will see that PyTables indexing is competitive (and sometimes much faster) than that of traditional relational databases. With this, the manual should be fairly finished for 2.0 final release.
- Large refactoring done on the
Rowclass. The most important change is that
Table.rowis now a single object. This allows to reuse the same
Rowinstance even after
Table.flush()calls, which can be convenient in many situations.
- I/O buffers unified in the
Rowclass. That allows for bigger savings in memory space whenever the
Rowextension is used.
- Improved speed (up to a 70%) with unaligned column operations (a quite
common scenario when dealing with
Tableobjects) through the integrated Numexpr. In-kernel searches take advantage of this optimization.
VLUnicodeAtomfor storing variable-length Unicode strings in
VLArrayobjects regardless of encoding. Closes ticket #51.
- Added support for
timedatatypes to be portable between big-endian and low-endian architectures. This feature is not currently supported natively by the HDF5 library, so the support for such conversion has been added in PyTables itself. Fixes #72.
- Added slice arguments to
Table.getWhereList(). Although API changes are frozen, this may still be seen as an inconsistency with other query methods. The patch is backwards-compatible anyway.
- Added missing overwrite argument to
Node._f_rename(). Fixes ticket #66.
tables.test()no longer exits the interpreter session. Fixes ticket #67.
- Fix comparing strings where one is a prefix of the other in integrated Numexpr. Fixes ticket #76.
- Added a check for avoiding an ugly HDF5 message when copying a file over
itself (for both
File.copyFile()). Fixes ticket #73.
- Corrected the appendix E, were it was said that PyTables doesn’t support compounds of compounds (it does since version 1.2!).
Changes from 2.0b2 to 2.0rc1¶
- The API Reference section of the User’s Manual (and the matching docstrings) has been completely reviewed, expanded and corrected. This process has unveiled some errors and inconsistencies which have also been fixed.
VLArray.__getitem__()to behave as expected in Python when using slices, instead of following the semantics of PyTables’
read()methods (e.g. reading just one element when no stop is provided). Fixes ticket #50.
- Removed implicit UTF-8 encoding from
vlstringatoms. Now a variable-length string is stored as is, which lets users use any encoding of their choice, or none of them. A
vlunicodeatom will probably be added to the next release so as to fix ticket #51.
- Allow non-sequence objects to be passed to
VLArray.append()when using an
objectatom. This was already possible in 1.x but stopped working when the old append syntax was dropped in 2.0. Fixes ticket #63.
Cols.__len__()to return the number of rows of the table or nested column (instead of the number of fields), like its counterparts in
- Python scalars cached in
AttributeSetinstances are now kept as NumPy objects instead of Python ones, because they do become NumPy objects when retrieved from disk. Fixes ticket #59.
- Avoid HDF5 error when appending an empty array to a
Table(ticket #57) or
EArray(ticket #49) dataset.
- Fix wrong implementation of the top-level
table.description._v_dfltsmap, which was also including the pathnames of columns inside nested columns. Fixes ticket #45.
- Optimized the access to unaligned arrays in Numexpr between a 30% and a 70%.
- Fixed a die-hard bug that caused the loading of groups while closing a file.
This only showed with certain usage patterns of the LRU cache (e.g. the one
- Avoid copious warnings about unused functions and variables when compiling Numexpr.
- Several fixes to Numexpr expressions with all constant values. Fixed tickets #53, #54, #55, #58. Reported bugs to mainstream developers.
- Solved an issue when trying to open one of the included test files in append mode on a system-wide installation by a normal user with no write privileges on it. The file isn’t being modified anyway, so the test is skipped then.
- Added a new benchmark to compare the I/O speed of
EArrayobjects with that of
- The old
Row.__call__()is no longer available as a public method. It was not documented, anyway. Fixes ticket #46.
Cols._f_close()is no longer public. Fixes ticket #47.
Attributes._f_close()is no longer public. Fixes ticket #52.
- The undocumented
Description.classdictattribute has been completely removed. Fixes ticket #44.
Changes from 2.0b1 to 2.0b2¶
- A very exhaustive overhauling of the User’s Manual is in process. The chapters 1 (Introduction), 2 (Installation), 3 (Tutorials) have been completed (and hopefully, the lines of code are easier to copy&paste now), while chapter 4 (API Reference) has been done up to (and including) the Table class. During this tedious (but critical in a library) overhauling work, we have tried hard to synchronize the text in the User’s Guide with that which appears on the docstrings.
- Removed the
Group._f_walkNodes(). Using it with a false value was redundant with
Group._f_iterNodes(). Fixes ticket #42.
- Removed the
Table.read(). It was undocumented and redundant with
Table.readCoordinates(). Fixes ticket #41.
- Fixed the signature of
Group.__iter__()(by removing its parameters).
- Added new
- Added a couple of new attributes for leaves:
nrowsinbuf: the number of rows that fit in the internal buffers.
chunkshape: the chunk size for chunked datasets.
- Fixed setuptools so that making an egg out of the PyTables 2 package is possible now.
- Added a new
tables.restrict_flavors()function allowing to restrict available flavors to a given set. This can be useful e.g. if you want to force PyTables to get NumPy data out of an old,
numarray-flavored PyTables file even if the
numarraypackage is installed.
- Fixed a bug which caused filters of unavailable compression libraries to be
loaded as using the default Zlib library, after issuing a warning. Added a
Important changes from 1.4.x to 2.0¶
Column.createIndex()has received a couple of new parameters:
filters. The first one sets the desired quality level of the index, while the second one allows the user to specify the filters for the index.
Table.indexpropshas been split into
Table.autoIndex. The later groups the functionality of the old
- The new
Table.colpathnamesis a sequence which contains the full pathnames of all bottom-level columns in a table. This can be used to walk all
Columnobjects in a table when used with
- The new
Table.colinstancesdictionary maps column pathnames to their associated
Colsobject for simple or nested columns, respectively. This is similar to
Table.cols._f_col(), but faster.
Rowhas received a new
Row.fetch_all_fields()method in order to return all the fields in the current row. This returns a NumPy void scalar for each call.
tables.test(verbose=False, heavy=False)high level function for interactively running the complete test suite from the Python console.
- Added a
tables.print_versions()for easily getting the versions for all the software on which PyTables relies on.
- You can no longer mark a column for indexing in a
Coldeclaration. The only way of creating an index for a column is to invoke the
createIndex()method of the proper column object after the table has been created.
- Now the
Table.colnamesattribute is just a list of the names of top-level columns in a table. You can still get something similar to the old structure by using
Table.description._v_nestedNames. See also the new
File.groupsdictionaries have been removed. If you still need this functionality, please use the
Table.removeIndex()is no longer available; to remove an index on a column, one must use the
removeIndex()method of the associated
Column.dirtyis no longer available. If you want to check column index dirtiness, use
complevelparameters have been removed from
File.createVLArray(). They were already deprecated in PyTables 1.x.
atomparameters have been swapped in
File.createCArray(). This has been done to be consistent with
Atom()definitions (i.e. type comes before and shape after).
Node._v_rootgrouphas been removed. Please use
Leaf.isOpen()methods have been removed. Please use the
Node._v_isopenattribute instead (it is much faster).
File.delAttrNode()methods have been removed. Please use
File.copyAttrs()has been removed. Please use
table[colname]idiom is no longer supported. You can use
table.cols._f_col(column)for doing the same.
File.createEArray()received a new
shapeparameter. This allows to not have to use the shape of the atom so as to set the shape of the underlying dataset on disk.
- All the leaf constructors have received a new
chunkshapeparameter that allows specifying the chunk sizes of datasets on disk.
Leafnodes have received a new
byteorderparameter that allows the user to specify the byteorder in which data will be written to disk (data in memory is now always handled in native order).
—The PyTables Team