[h5py] 290/455: Reshuffle readme
Ghislain Vaillant
ghisvail-guest at moszumanska.debian.org
Thu Jul 2 18:19:43 UTC 2015
This is an automated email from the git hooks/post-receive script.
ghisvail-guest pushed a commit to annotated tag 1.3.0
in repository h5py.
commit 195c396bcd96c60c6e279721f6ae7dfc63307a0d
Author: andrewcollette <andrew.collette at gmail.com>
Date: Sun Jun 21 04:10:42 2009 +0000
Reshuffle readme
---
ANN.txt | 163 --------------------------------------------------
README.txt | 199 +++++++++++++++++++++++++++++++++++++++++++------------------
2 files changed, 142 insertions(+), 220 deletions(-)
diff --git a/ANN.txt b/ANN.txt
deleted file mode 100644
index a94be97..0000000
--- a/ANN.txt
+++ /dev/null
@@ -1,163 +0,0 @@
-
-Announcing HDF5 for Python (h5py) 1.2
-=====================================
-
-I'm pleased to announce the availability of HDF5 for Python 1.2 final!
-This release represents a significant update to the h5py feature set.
-Some of the new new features are:
-
- - Support for variable-length strings!
- - Use of built-in Python exceptions (KeyError, etc), in addition to H5Error
- - Top-level support for HDF5 CORE, SEC2, STDIO, WINDOWS and FAMILY drivers
- - Support for ENUM and ARRAY types
- - Support for Unicode file names
- - Huge speedup (3x) when slicing into a compressed/chunked dataset
-
-Main site: http://h5py.alfven.org
-Google code: http://h5py.googlecode.com
-
-
-What is h5py?
--------------
-
-HDF5 for Python (h5py) is a general-purpose Python interface to the
-Hierarchical Data Format library, version 5. HDF5 is a versatile,
-mature scientific software library designed for the fast, flexible
-storage of enormous amounts of data.
-
-From a Python programmer's perspective, HDF5 provides a robust way to
-store data, organized by name in a tree-like fashion. You can create
-datasets (arrays on disk) hundreds of gigabytes in size, and perform
-random-access I/O on desired sections. Datasets are organized in a
-filesystem-like hierarchy using containers called "groups", and
-accesed using the tradional POSIX /path/to/resource syntax.
-
-In addition to providing interoperability with existing HDF5 datasets
-and platforms, h5py is a convienient way to store and retrieve
-arbitrary NumPy data and metadata.
-
-
-List of new features in 1.2
----------------------------
-
- - Variable-length strings are now supported! They are mapped to native
- Python strings via the NumPy "object" type. VL strings may be read,
- written and created from h5py, and are allowed in all HDF5 contexts,
- even as members of compound or array types.
-
- - HDF5 exceptions now inherit from common Python built-ins like TypeError
- and ValueError (in addition to current HDF5 error hierarchy), freeing
- the user from knowledge of the HDF5 error system. Existing code which
- uses H5Error will continue to work.
-
- - Many different low-level HDF5 drivers can now be used when creating
- a file, which allows purely in-memory ("core") files, multi-volume
- ("family") files, and files which use low-level buffered I/O.
-
- - Groups and attributes now support the standard Python dictionary
- interface methods, including keys(), values() and friends. The existing
- methods (listnames(), listobjects(), etc.) remain and will not be
- removed until at least h5py 1.4 or equivalent.
-
- - Workaround for an HDF5 bug has sped up reading/writing of chunked
- datasets. When using a slice with fewer dimensions than the dataset,
- there can be as much as a 3x improvement in write times over h5py 1.1.
-
- - Enumerated types are now fully supported; they can be used in NumPy
- anywhere integer types are allowed, and are stored as native HDF5
- enums. Conversion between integers and enums is supported.
-
- - The NumPy "array" dtype is now allowed as a top-level type when
- creating a dataset, not just as a member of a compound type.
-
- - Unicode file names are now supported
-
- - It's now possible to explicitly set the type of an attribute, and to
- preserve the type of an attribute while modifying it.
-
- - High-level objects now have .parent and .file attributes, to make the
- navigation of HDF5 files more convenient.
-
-
-Design revisions since 1.1
---------------------------
-
- - The role of the "name" attribute on File objects has changed. "name"
- now returns the HDF5 path of the File object ('/'); the file name on
- disk is available at File.filename.
-
- - Dictionary-interface methods for Group and AttributeManager objects have
- been renamed to follow the standard Python convention (keys(), values(),
- etc). The old method names are still available but deprecated.
-
- - The HDF5 shuffle filter is no longer automatically activated when
- GZIP or LZF compression is used; many datasets "in the wild" do not
- benefit from shuffling.
-
-
-Standard features
------------------
-
- - Supports storage of NumPy data of the following types:
-
- * Integer/Unsigned Integer
- * Float/Double
- * Complex/Double Complex
- * Compound ("recarray")
- * Strings
- * Boolean
- * Array
- * Enumeration (integers)
- * Void
-
- - Random access to datasets using the standard NumPy slicing syntax,
- including a subset of fancy indexing and point-based selection
-
- - Transparent compression of datasets using GZIP, LZF or SZIP,
- and error-detection using Fletcher32
-
- - "Pythonic" interface supporting dictionary and NumPy-array metaphors
- for the high-level HDF5 abstrations like groups and datasets
-
- - A comprehensive, object-oriented wrapping of the HDF5 low-level C API
- via Cython, in addition to the NumPy-like high-level interface.
-
- - Supports many new features of HDF5 1.8, including recursive iteration
- over entire files and in-library copy operations on the file tree
-
- - Thread-safe
-
-
-Where to get it
----------------
-
-* Main website, documentation: http://h5py.alfven.org
-
-* Downloads, bug tracker: http://h5py.googlecode.com
-
-
-Requires
---------
-
-* Linux, Mac OS-X or Windows
-
-* Python 2.5 (Windows), Python 2.5 or 2.6 (Linux/Mac OS-X)
-
-* NumPy 1.0.3 or later
-
-* HDF5 1.6.5 or later (including 1.8); HDF5 is included with
- the Windows version.
-
-
-Thanks
-------
-
-Thanks to D. Dale, E. Lawrence and other for their continued support
-and comments. Also thanks to the Francesc Alted and the PyTables project,
-for inspiration and generously providing their code to the community. Thanks
-to everyone at the HDF Group for creating such a useful piece of software.
-
-
-
-
-
diff --git a/README.txt b/README.txt
index 7a917a6..f07ab35 100644
--- a/README.txt
+++ b/README.txt
@@ -1,78 +1,163 @@
-README for the "h5py" Python/HDF5 interface
-===========================================
-Copyright (c) 2008 Andrew Collette
-Version 1.2.0 *BETA*
+Announcing HDF5 for Python (h5py) 1.2
+=====================================
-* http://h5py.alfven.org Main site, docs, quick-start guide
-* http://h5py.googlecode.com Downloads, FAQ and bug tracker
+I'm pleased to announce the availability of HDF5 for Python 1.2 final!
+This release represents a significant update to the h5py feature set.
+Some of the new new features are:
-* mail: "h5py" at the domain "alfven dot org"
+ - Support for variable-length strings!
+ - Use of built-in Python exceptions (KeyError, etc), alongside H5Error
+ - Top-level support for HDF5 CORE, SEC2, STDIO, WINDOWS and FAMILY drivers
+ - Support for ENUM and ARRAY types
+ - Support for Unicode file names
+ - Big speedup (~3x) when using single-index slicing on a chunked dataset
-The h5py package provides both a high- and low-level interface to the
-HDF5 library from Python. The low-level interface is
-intended to be a complete wrapping of the HDF5 1.6 API, while the high-
-level component supports Python-style object-oriented access to HDF5 files,
-datasets and groups.
+Main site: http://h5py.alfven.org
+Google code: http://h5py.googlecode.com
-The goal of this package is not to provide yet another scientific data
-model. It is an attempt to create as straightforward a binding as possible
-to the existing HDF5 API and abstractions, so that Python programs can
-easily deal with HDF5 files and exchange data with other HDF5-aware
-applications.
-Installation
-------------
+What is h5py?
+-------------
-See the file INSTALL.txt.
+HDF5 for Python (h5py) is a general-purpose Python interface to the
+Hierarchical Data Format library, version 5. HDF5 is a versatile,
+mature scientific software library designed for the fast, flexible
+storage of enormous amounts of data.
+
+From a Python programmer's perspective, HDF5 provides a robust way to
+store data, organized by name in a tree-like fashion. You can create
+datasets (arrays on disk) hundreds of gigabytes in size, and perform
+random-access I/O on desired sections. Datasets are organized in a
+filesystem-like hierarchy using containers called "groups", and
+accesed using the tradional POSIX /path/to/resource syntax.
+
+In addition to providing interoperability with existing HDF5 datasets
+and platforms, h5py is a convienient way to store and retrieve
+arbitrary NumPy data and metadata.
+
+
+Full list of new features in 1.2
+--------------------------------
+
+ - Variable-length strings are now supported! They are mapped to native
+ Python strings via the NumPy "object" type. VL strings may be read,
+ written and created from h5py, and are allowed in all HDF5 contexts,
+ even as members of compound or array types.
+
+ - HDF5 exceptions now inherit from common Python built-ins like TypeError
+ and ValueError (in addition to current HDF5 error hierarchy), freeing
+ the user from knowledge of the HDF5 error system. Existing code which
+ uses H5Error will continue to work.
+
+ - Many different low-level HDF5 drivers can now be used when creating
+ a file, which allows purely in-memory ("core") files, multi-volume
+ ("family") files, and files which use low-level buffered I/O.
+
+ - Groups and attributes now support the standard Python dictionary
+ interface methods, including keys(), values() and friends. The existing
+ methods (listnames(), listobjects(), etc.) remain and will not be
+ removed until at least h5py 1.4 or equivalent.
+
+ - Workaround for an HDF5 bug has sped up reading/writing of chunked
+ datasets. When using a slice with fewer dimensions than the dataset,
+ there can be as much as a 3x improvement in write times over h5py 1.1.
+
+ - Enumerated types are now fully supported; they can be used in NumPy
+ anywhere integer types are allowed, and are stored as native HDF5
+ enums. Conversion between integers and enums is supported.
+
+ - The NumPy "array" dtype is now allowed as a top-level type when
+ creating a dataset, not just as a member of a compound type.
+
+ - Unicode file names are now supported
+
+ - It's now possible to explicitly set the type of an attribute, and to
+ preserve the type of an attribute while modifying it.
+
+ - High-level objects now have .parent and .file attributes, to make the
+ navigation of HDF5 files more convenient.
+
+
+Design revisions since 1.1
+--------------------------
+
+ - The role of the "name" attribute on File objects has changed. "name"
+ now returns the HDF5 path of the File object ('/'); the file name on
+ disk is available at File.filename.
+
+ - Dictionary-interface methods for Group and AttributeManager objects have
+ been renamed to follow the standard Python convention (keys(), values(),
+ etc). The old method names are still available but deprecated.
+
+ - The HDF5 shuffle filter is no longer automatically activated when
+ GZIP or LZF compression is used; many datasets "in the wild" do not
+ benefit from shuffling.
+
+
+Standard features
+-----------------
+
+ - Supports storage of NumPy data of the following types:
+
+ * Integer/Unsigned Integer
+ * Float/Double
+ * Complex/Double Complex
+ * Compound ("recarray")
+ * Strings
+ * Boolean
+ * Array
+ * Enumeration (integers)
+ * Void
+
+ - Random access to datasets using the standard NumPy slicing syntax,
+ including a subset of fancy indexing and point-based selection
+
+ - Transparent compression of datasets using GZIP, LZF or SZIP,
+ and error-detection using Fletcher32
+
+ - "Pythonic" interface supporting dictionary and NumPy-array metaphors
+ for the high-level HDF5 abstrations like groups and datasets
+
+ - A comprehensive, object-oriented wrapping of the HDF5 low-level C API
+ via Cython, in addition to the NumPy-like high-level interface.
+
+ - Supports many new features of HDF5 1.8, including recursive iteration
+ over entire files and in-library copy operations on the file tree
+
+ - Thread-safe
+
+
+Where to get it
+---------------
+
+* Main website, documentation: http://h5py.alfven.org
+
+* Downloads, bug tracker: http://h5py.googlecode.com
-Documentation
--------------
-Extensive documentation is available through docstrings, as well as in
-HTML format on the web and in the "docs/" directory in the
-distribution. This document is an overview of some of the package's
-features and highlights.
-Features
-========
+Requires
+--------
-High-Level
-----------
+* Linux, Mac OS-X or Windows
-- Numpy-style access to HDF5 datasets, with automatic conversion between
- datatypes. Slice into an HDF5 dataset and get a Numpy array back.
- Create and use datasets with chunking, compression, or other filters
- transparently.
+* Python 2.5 (Windows), Python 2.5 or 2.6 (Linux/Mac OS-X)
-- Dictionary-style access to HDF5 groups and attributes, including
- iteration.
+* NumPy 1.0.3 or later
-Low-Level
----------
+* HDF5 1.6.5 or later (including 1.8); HDF5 is included with
+ the Windows version.
-- Low-level wrappings for most of the HDF5 1.6 C API, divided in an
- intuitive fashion across modules like h5a, h5d, h5s, etc.
-- No more micro-managing of identifiers; a minimal object layer on top of
- HDF5 integer identifiers means you don't need to remember to close
- every single identifier you create.
+Thanks
+------
-- Most API functions are presented as methods on object identifiers, rather
- than functions. In addition to being more Pythonic, this makes programs
- less verbose and objects easier to inspect from the command line.
+Thanks to D. Dale, E. Lawrence and other for their continued support
+and comments. Also thanks to the Francesc Alted and the PyTables project,
+for inspiration and generously providing their code to the community. Thanks
+to everyone at the HDF Group for creating such a useful piece of software.
-- Automatic exception handling; using the HDF5 error callback mechanism,
- Python exceptions are raised by the library itself when errors occur.
- Many new exception classes are provided, based on the HDF5 major error
- codes.
-- Minimal changes to the HDF5 API:
- - A near-1:1 mapping between HDF5 functions and h5py functions/methods
- - Constants have their original names; H5P_DEFAULT becomes h5p.DEFAULT
- - Python extensions are provided only when not doing so would be
- obviously wrong; you don't need to learn a totally new API
- - The majority of the HDF5 C-API documentation is still valid for the
- h5py interface
--
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