PyTables

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PyTables
Software Details:
Version: 3.2.0
Upload Date: 12 May 15
Developer: Francesc Alted
Distribution Type: Freeware
Downloads: 106

Rating: nan/5 (Total Votes: 0)

PyTables is built on top of the HDF5 library and the numarray Python package.

It holds an OO interface that, combined with C-generated code from Cython increases overall speed.

Features:

  • Easy to use
  • Support for the NaturalNaming scheme
  • Easy access to the data
  • Saves memory
  • Structure data in a natural way
  • Speedy I/O operations

What is new in this release:

  • Fixed a spurious unicode comparison warning.
  • Improved handling of empty string attributes. In previous versions of PyTables empty string were stored as scalar HDF5 attributes having size 1 and value '\0' (an empty null terminated string). Now empty string are stored as HDF5 attributes having zero size.
  • Added a new cookbook recipe and a couple of examples for simple threading with PyTables.
  • The redundant :func:`utilsextension.get_indices` function has been eliminated (replaced by :meth:`slice.indices`).
  • Allow negative indices in point selection.
  • Index wasn't being used if it claimed there were no results.
  • Atoms and Col types are no longer generated dynamically so now it is easier for IDEs and static analysis tool to handle them.
  • The keysort functions in idx-opt.c have been cythonised using fused types. The perfomance is mostly unchanged, but the code is much more simpler now.
  • Small unit tests re-factoring.

What is new in version 3.1.1:

  • Improvements:
  • Do not create a temporary array when the *obj* parameter is not specified in :meth:`File.create_array`.
  • Added two new utility functions (:func:`tables.nodes.filenode.read_from_filenode` and :func:`tables.nodes.filenode.save_to_filenode`) for the direct copy from filesystem to filenode and vice versa.
  • Removed the :file:`examples/nested-iter.py` considered no longer useful.
  • Better detection of the `-msse2` compiler flag.
  • Bugs fixed:
  • Fixed a critical bug that caused an exception at import time.
  • The internal Blosc_ library has been updated to version 1.3.5.

What is new in version 2.4.0:

  • Added support for the float16 data type. It is only available if numpy provides it as well (i.e. numpy >= 1.6).
  • Leaf nodes now have attributes for retrieving the size of data in memory and on disk. Data on disk can be compressed, so the new attributes make it easy to compute the data compression ration.

What is new in version 2.3.1:

  • Fixed a bug that prevented to read scalar datasets of UnImplemented types.
  • Fixed a bug in `setup.py` that caused installation of PyTables 2.3 to fail on hosts with multiple python versions installed.

What is new in version 2.3.1 RC1:

  • Fixed a bug that prevented to read scalar datasets of UnImplemented types.
  • Fixed a bug in `setup.py` that caused installation of PyTables 2.3 to fail on hosts with multiple python versions installed.

What is new in version 2.3:

  • OPSI is a powerful and innovative indexing engine allowing PyTables to perform fast queries on arbitrarily large tables. Moreover, it offers a wide range of optimization levels for its indexes so that the user can choose the best one that suits her needs (more or less size, more or less performance). Indexation code also takes advantage of the vectorization capabilities of the NumPy and Numexpr packages to ensure really short indexing and search times.
  • A fine-tuned LRU cache for both metadata (nodes) and regular data that lets you achieve maximum speed for intensive object tree browsing during data reads and queries. It complements the already efficient cache present in HDF5, although this is more geared towards high-level structures that are specific to PyTables and that are critical for achieving very high performance.

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