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Source: pytables
Maintainer: Debian Science Maintainers <debian-science-maintainers@lists.alioth.debian.org>
Uploaders: Antonio Valentino <antonio.valentino@tiscali.it>,
Yaroslav Halchenko <debian@onerussian.com>
Section: python
Priority: optional
Build-Depends: cython3,
debhelper-compat (= 12),
dh-python,
latexmk,
libblosc-dev,
libbz2-dev,
libhdf5-dev,
libjs-jquery-cookie,
libjs-mathjax,
liblz4-dev,
liblzo2-dev,
libsnappy-dev,
libzstd-dev,
locales,
pybuild-plugin-pyproject,
python3-all-dev,
python3-ipython,
python3-numexpr,
python3-numpy,
python3-numpydoc,
python3-packaging,
python3-sphinx,
python3-sphinx-rtd-theme,
python3-setuptools,
texlive-fonts-recommended,
texlive-latex-recommended,
texlive-latex-extra,
texlive-plain-generic,
tex-gyre,
zlib1g-dev
Standards-Version: 4.6.1
Vcs-Browser: https://salsa.debian.org/science-team/pytables
Vcs-Git: https://salsa.debian.org/science-team/pytables.git
Homepage: https://www.pytables.org
Rules-Requires-Root: no
Package: python3-tables
Architecture: all
Depends: ${misc:Depends},
${python3:Depends},
python3-numexpr,
python-tables-data (= ${source:Version}),
python3-tables-lib (>= ${source:Version}),
python3-tables-lib (<< ${source:Version}.1~)
Suggests: python3-netcdf4,
python-tables-doc,
vitables
Description: hierarchical database for Python3 based on HDF5
PyTables is a package for managing hierarchical datasets and designed
to efficiently cope with extremely large amounts of data.
.
It is built on top of the HDF5 library and the NumPy package. It
features an object-oriented interface that, combined with C extensions
for the performance-critical parts of the code (generated using
Cython), makes it a fast, yet extremely easy to use tool for
interactively save and retrieve very large amounts of data. One
important feature of PyTables is that it optimizes memory and disk
resources so that they take much less space (between a factor 3 to 5,
and more if the data is compressible) than other solutions, like for
example, relational or object oriented databases.
.
- Compound types (records) can be used entirely from Python (i.e. it
is not necessary to use C for taking advantage of them).
- The tables are both enlargeable and compressible.
- I/O is buffered, so you can get very fast I/O, specially with
large tables.
- Very easy to select data through the use of iterators over the
rows in tables. Extended slicing is supported as well.
- It supports the complete set of NumPy objects.
.
This is the Python 3 version of the package.
Package: python3-tables-lib
Architecture: any
Depends: ${misc:Depends},
${python3:Depends},
${shlibs:Depends}
Recommends: python3-tables (= ${source:Version})
Description: hierarchical database for Python3 based on HDF5 (extension)
PyTables is a package for managing hierarchical datasets and designed
to efficiently cope with extremely large amounts of data.
.
It is built on top of the HDF5 library and the NumPy package. It
features an object-oriented interface that, combined with C extensions
for the performance-critical parts of the code (generated using
Cython), makes it a fast, yet extremely easy to use tool for
interactively save and retrieve very large amounts of data. One
important feature of PyTables is that it optimizes memory and disk
resources so that they take much less space (between a factor 3 to 5,
and more if the data is compressible) than other solutions, like for
example, relational or object oriented databases.
.
- Compound types (records) can be used entirely from Python (i.e. it
is not necessary to use C for taking advantage of them).
- The tables are both enlargeable and compressible.
- I/O is buffered, so you can get very fast I/O, specially with
large tables.
- Very easy to select data through the use of iterators over the
rows in tables. Extended slicing is supported as well.
- It supports the complete set of NumPy objects.
.
This package contains the extension built for the Python 3 interpreter.
Package: python-tables-doc
Architecture: all
Section: doc
Depends: ${misc:Depends},
${sphinxdoc:Depends},
libjs-mathjax,
libjs-jquery-cookie
Suggests: xpdf | pdf-viewer,
www-browser
Description: hierarchical database for Python based on HDF5 - documentation
PyTables is a package for managing hierarchical datasets and designed
to efficiently cope with extremely large amounts of data.
.
It is built on top of the HDF5 library and the NumPy package. It
features an object-oriented interface that, combined with C extensions
for the performance-critical parts of the code (generated using
Cython), makes it a fast, yet extremely easy to use tool for
interactively save and retrieve very large amounts of data. One
important feature of PyTables is that it optimizes memory and disk
resources so that they take much less space (between a factor 3 to 5,
and more if the data is compressible) than other solutions, like for
example, relational or object oriented databases.
.
- Compound types (records) can be used entirely from Python (i.e. it
is not necessary to use C for taking advantage of them).
- The tables are both enlargeable and compressible.
- I/O is buffered, so you can get very fast I/O, specially with
large tables.
- Very easy to select data through the use of iterators over the
rows in tables. Extended slicing is supported as well.
- It supports the complete set of NumPy objects.
.
This package includes the manual in PDF and HTML formats.
Package: python-tables-data
Architecture: all
Multi-Arch: foreign
Depends: ${misc:Depends}
Description: hierarchical database for Python based on HDF5 - test data
PyTables is a package for managing hierarchical datasets and designed
to efficiently cope with extremely large amounts of data.
.
It is built on top of the HDF5 library and the NumPy package. It
features an object-oriented interface that, combined with C extensions
for the performance-critical parts of the code (generated using
Cython), makes it a fast, yet extremely easy to use tool for
interactively save and retrieve very large amounts of data. One
important feature of PyTables is that it optimizes memory and disk
resources so that they take much less space (between a factor 3 to 5,
and more if the data is compressible) than other solutions, like for
example, relational or object oriented databases.
.
- Compound types (records) can be used entirely from Python (i.e. it
is not necessary to use C for taking advantage of them).
- The tables are both enlargeable and compressible.
- I/O is buffered, so you can get very fast I/O, specially with
large tables.
- Very easy to select data through the use of iterators over the
rows in tables. Extended slicing is supported as well.
- It supports the complete set of NumPy objects.
.
This package includes daya fils used for unit testing.
|
