Source: pytables Maintainer: Debian Science Maintainers Uploaders: Antonio Valentino , Yaroslav Halchenko Section: python Priority: optional Build-Depends: debhelper (>= 12), dh-python, locales, libhdf5-dev, python-all-dev, python-all-dbg, python3-all-dev, python3-all-dbg, python-setuptools, python3-setuptools, python-six, python3-six, python-numpy, python-numpy-dbg, python3-numpy, python3-numpy-dbg, python-numexpr, python-numexpr-dbg, python3-numexpr, python3-numexpr-dbg, python-mock, cython, cython-dbg, cython3, cython3-dbg, zlib1g-dev, liblzo2-dev, libblosc-dev, liblz4-dev (>= 0.0~r122), libsnappy-dev, libbz2-dev, libzstd-dev, python3-sphinx, python3-sphinx-rtd-theme, python3-ipython, python3-numpydoc, texlive-generic-extra, texlive-latex-recommended, texlive-latex-extra, texlive-fonts-recommended, libjs-jquery-cookie, libjs-mathjax, latexmk Standards-Version: 4.3.0 Vcs-Browser: https://salsa.debian.org/science-team/pytables Vcs-Git: https://salsa.debian.org/science-team/pytables.git Homepage: http://www.pytables.org Package: python-tables Architecture: all Depends: python-tables-lib (>= ${source:Version}), python-tables-lib (<< ${source:Version}.1~), python-tables-data (= ${source:Version}), python-numexpr, python-mock, ${python:Depends}, ${misc:Depends} Suggests: python-tables-doc (>= 3.4.2-3), python-netcdf4, vitables Description: hierarchical database for Python 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 2 version of the package. Package: python-tables-lib Architecture: any Depends: ${python:Depends}, ${shlibs:Depends}, ${misc:Depends} Recommends: python-tables (= ${source:Version}) Description: hierarchical database for Python 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 2 interpreter. Package: python-tables-dbg Architecture: any Section: debug Depends: python-tables (= ${source:Version}), python-tables-lib (= ${binary:Version}), python-dbg, python-numpy-dbg, python-numexpr-dbg, ${python:Depends}, ${shlibs:Depends}, ${misc:Depends} Suggests: python-tables-doc, python-netcdf4 Description: hierarchical database for Python based on HDF5 (debug 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 2 debug interpreter. Package: python3-tables Architecture: all Depends: python3-tables-lib (>= ${source:Version}), python3-tables-lib (<< ${source:Version}.1~), python-tables-data (= ${source:Version}), python3-numexpr, ${python3:Depends}, ${misc:Depends} Suggests: python-tables-doc (>> 3.4.2-3), python3-netcdf4, vitables Replaces: python-tables (<< 3.4.2-1) Breaks: python-tables (<< 3.4.2-1) 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: ${python3:Depends}, ${shlibs:Depends}, ${misc: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: python3-tables-dbg Architecture: any Section: debug Depends: python3-tables (= ${source:Version}), python3-tables-lib (= ${binary:Version}), python3-dbg, python3-numpy-dbg, python3-numexpr-dbg, ${python3:Depends}, ${shlibs:Depends}, ${misc:Depends} Suggests: python-tables-doc, python3-netcdf4 Description: hierarchical database for Python 3 based on HDF5 (debug 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 debug 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 Replaces: python-tables (<< 3.4.2-3) Breaks: python-tables (<< 3.4.2-3) 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.