From b4054e3247c552226911aa8c588b50c5d97bd030 Mon Sep 17 00:00:00 2001 From: Antonio Valentino Date: Sat, 17 Aug 2024 10:37:57 +0200 Subject: [PATCH] Update to PyTables v3.10.1 --- .buildinfo | 2 +- FAQ.html | 133 +- MIGRATING_TO_2.x.html | 49 +- MIGRATING_TO_3.x.html | 39 +- _modules/index.html | 21 +- _modules/tables/array.html | 61 +- _modules/tables/atom.html | 192 +- _modules/tables/attributeset.html | 8 +- _modules/tables/carray.html | 87 +- _modules/tables/description.html | 8 +- _modules/tables/earray.html | 31 +- _modules/tables/exceptions.html | 128 +- _modules/tables/expression.html | 8 +- _modules/tables/file.html | 8 +- _modules/tables/filters.html | 8 +- _modules/tables/flavor.html | 8 +- _modules/tables/group.html | 103 +- _modules/tables/index.html | 41 +- _modules/tables/indexes.html | 8 +- _modules/tables/leaf.html | 115 +- _modules/tables/link.html | 8 +- _modules/tables/misc/enum.html | 8 +- _modules/tables/node.html | 8 +- _modules/tables/nodes/filenode.html | 8 +- _modules/tables/table.html | 588 +- _modules/tables/tests/common.html | 26 +- _modules/tables/tests/test_suite.html | 26 +- _modules/tables/unimplemented.html | 8 +- _modules/tables/vlarray.html | 66 +- _sources/release_notes.rst.txt | 1 + .../_sphinx_javascript_frameworks_compat.js | 134 - _static/basic.css | 27 +- _static/doctools.js | 2 +- _static/documentation_options.js | 5 +- _static/graphviz.css | 2 +- _static/jquery-3.6.0.js | 10881 ---------------- _static/jquery.js | 2 - _static/language_data.js | 4 +- _static/searchtools.js | 196 +- _static/sphinx_highlight.js | 16 +- _static/underscore-1.13.1.js | 2042 --- _static/underscore.js | 6 - cookbook/custom_data_types.html | 25 +- cookbook/hints_for_sql_users.html | 85 +- cookbook/index.html | 27 +- cookbook/inmemory_hdf5_files.html | 39 +- cookbook/no_root_install.html | 39 +- cookbook/py2exe_howto.html | 27 +- cookbook/simple_table.html | 25 +- cookbook/tailoring_atexit_hooks.html | 25 +- cookbook/threading.html | 33 +- dev_team.html | 25 +- development.html | 25 +- downloads.html | 29 +- genindex.html | 21 +- index.html | 29 +- objects.inv | Bin 8224 -> 8224 bytes other_material.html | 33 +- project_pointers.html | 25 +- py-modindex.html | 21 +- release-notes/RELEASE_NOTES_v0.7.1.html | 25 +- release-notes/RELEASE_NOTES_v0.7.2.html | 25 +- release-notes/RELEASE_NOTES_v0.8.html | 25 +- release-notes/RELEASE_NOTES_v0.9.1.html | 25 +- release-notes/RELEASE_NOTES_v0.9.html | 33 +- release-notes/RELEASE_NOTES_v1.0.html | 33 +- release-notes/RELEASE_NOTES_v1.1.1.html | 33 +- release-notes/RELEASE_NOTES_v1.1.html | 33 +- release-notes/RELEASE_NOTES_v1.2.1.html | 35 +- release-notes/RELEASE_NOTES_v1.2.2.html | 35 +- release-notes/RELEASE_NOTES_v1.2.3.html | 35 +- release-notes/RELEASE_NOTES_v1.2.html | 35 +- release-notes/RELEASE_NOTES_v1.3.1.html | 35 +- release-notes/RELEASE_NOTES_v1.3.2.html | 35 +- release-notes/RELEASE_NOTES_v1.3.3.html | 35 +- release-notes/RELEASE_NOTES_v1.3.html | 35 +- release-notes/RELEASE_NOTES_v1.4.html | 35 +- release-notes/RELEASE_NOTES_v2.0.x-pro.html | 53 +- release-notes/RELEASE_NOTES_v2.0.x.html | 51 +- release-notes/RELEASE_NOTES_v2.1.x-pro.html | 37 +- release-notes/RELEASE_NOTES_v2.1.x.html | 37 +- release-notes/RELEASE_NOTES_v2.2.x-pro.html | 59 +- release-notes/RELEASE_NOTES_v2.2.x.html | 59 +- release-notes/RELEASE_NOTES_v2.3.x.html | 53 +- release-notes/RELEASE_NOTES_v2.4.x.html | 89 +- release-notes/RELEASE_NOTES_v3.0.x.html | 123 +- release-notes/RELEASE_NOTES_v3.1.x.html | 107 +- release-notes/RELEASE_NOTES_v3.10.x.html | 129 +- release-notes/RELEASE_NOTES_v3.2.x.html | 149 +- release-notes/RELEASE_NOTES_v3.3.x.html | 41 +- release-notes/RELEASE_NOTES_v3.4.x.html | 87 +- release-notes/RELEASE_NOTES_v3.5.x.html | 41 +- release-notes/RELEASE_NOTES_v3.6.x.html | 39 +- release-notes/RELEASE_NOTES_v3.7.x.html | 55 +- release-notes/RELEASE_NOTES_v3.8.x.html | 39 +- release-notes/RELEASE_NOTES_v3.9.x.html | 117 +- release_notes.html | 199 +- search.html | 21 +- searchindex.js | 2 +- usersguide/bibliography.html | 25 +- usersguide/condition_syntax.html | 25 +- usersguide/datatypes.html | 31 +- usersguide/file_format.html | 75 +- usersguide/filenode.html | 43 +- usersguide/index.html | 31 +- usersguide/installation.html | 39 +- usersguide/introduction.html | 41 +- usersguide/libref.html | 25 +- usersguide/libref/declarative_classes.html | 243 +- usersguide/libref/expr_class.html | 61 +- usersguide/libref/file_class.html | 177 +- usersguide/libref/filenode_classes.html | 149 +- usersguide/libref/helper_classes.html | 179 +- usersguide/libref/hierarchy_classes.html | 207 +- usersguide/libref/homogenous_storage.html | 121 +- usersguide/libref/link_classes.html | 71 +- usersguide/libref/structured_storage.html | 271 +- usersguide/libref/top_level.html | 61 +- usersguide/optimization.html | 125 +- usersguide/parameter_files.html | 149 +- usersguide/tutorials.html | 115 +- usersguide/usersguide.html | 35 +- usersguide/utilities.html | 39 +- 123 files changed, 3687 insertions(+), 16440 deletions(-) delete mode 100644 _static/_sphinx_javascript_frameworks_compat.js delete mode 100644 _static/jquery-3.6.0.js delete mode 100644 _static/jquery.js delete mode 100644 _static/underscore-1.13.1.js delete mode 100644 _static/underscore.js diff --git a/.buildinfo b/.buildinfo index 5e5f265..cad61f4 100644 --- a/.buildinfo +++ b/.buildinfo @@ -1,4 +1,4 @@ # Sphinx build info version 1 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done. -config: 418fccdf9c73067ab1e1f0a09e54b921 +config: 2fdf1fd3f80e419371699124137c3e59 tags: 645f666f9bcd5a90fca523b33c5a78b7 diff --git a/FAQ.html b/FAQ.html index 9b5562b..5b61462 100644 --- a/FAQ.html +++ b/FAQ.html @@ -1,25 +1,22 @@ - + - + - FAQ — PyTables 3.10.0 documentation - - - + FAQ — PyTables 3.10.1 documentation + + + - - - - - - + + + @@ -52,7 +49,7 @@
- 3.10.0 + 3.10.1
@@ -141,11 +138,11 @@
-

FAQ

+

FAQ

-

General questions

+

General questions

-

What is PyTables?

+

What is PyTables?

PyTables is a package for managing hierarchical datasets designed to efficiently cope with extremely large amounts of data.

It is built on top of the HDF5 [1] library, the Python language [2] and the @@ -156,12 +153,12 @@

What is PyTables? -

What are PyTables’ licensing terms?

+

What are PyTables’ licensing terms?

PyTables is free for both commercial and non-commercial use, under the terms of the BSD 3-Clause License.

-

I’m having problems. How can I get support?

+

I’m having problems. How can I get support?

The most common and efficient way is to subscribe (remember you need to subscribe prior to send messages) to the PyTables users mailing list [4], and send there a brief description of your issue and, if possible, a short script @@ -172,7 +169,7 @@

I’m having problems. How can I get support? -

Why HDF5?

+

Why HDF5?

HDF5 [1] is the underlying C library and file format that enables PyTables to efficiently deal with the data. It has been chosen for the following reasons:

    @@ -185,7 +182,7 @@

    Why HDF5? -

    Why Python?

    +

    Why Python?

    1. Python is interactive.

      People familiar with data processing understand how powerful command line @@ -210,7 +207,7 @@

      Why Python? -

      Why NumPy?

      +

      Why NumPy?

      NumPy [3] is a Python package to efficiently deal with large datasets in-memory, providing containers for homogeneous data, heterogeneous data, and string arrays. @@ -219,7 +216,7 @@

      Why NumPy? -

      Where can PyTables be applied?

      +

      Where can PyTables be applied?

      In all the scenarios where one needs to deal with large datasets:

      • Industrial applications

        @@ -245,7 +242,7 @@

        Where can PyTables be applied? -

        Is PyTables safe?

        +

        Is PyTables safe?

        Well, first of all, let me state that PyTables does not support transactional features yet (we don’t even know if we will ever be motivated to implement this!), so there is always the risk that you can lose your data in case of an @@ -255,7 +252,7 @@

        Is PyTables safe? -

        Can PyTables be used in concurrent access scenarios?

        +

        Can PyTables be used in concurrent access scenarios?

        It depends. Concurrent reads are no problem at all. However, whenever a process (or thread) is trying to write, then problems will start to appear. First, PyTables doesn’t support locking at any level, so several process writing @@ -292,10 +289,10 @@

        Can PyTables be used in concurrent access scenarios?

        Using an IPv4 socket.

-

See also the discussion in gh-790.

+

See also the discussion in gh-790.

-

What kind of containers does PyTables implement?

+

What kind of containers does PyTables implement?

PyTables does support a series of data containers that address specific needs of the user. Below is a brief description of them:

@@ -324,17 +321,17 @@

What kind of containers does PyTables implement?Library Reference for more specific information.

-

Cool! I’d like to see some examples of use.

+

Cool! I’d like to see some examples of use.

Sure. Go to the HowToUse section to find simple examples that will help you getting started.

-

Can you show me some screenshots?

+

Can you show me some screenshots?

Well, PyTables is not a graphical library by itself. However, you may want to check out ViTables [8], a GUI tool to browse and edit PyTables & HDF5 [1] files.

-

Is PyTables a replacement for a relational database?

+

Is PyTables a replacement for a relational database?

No, by no means. PyTables lacks many features that are standard in most relational databases. In particular, it does not have support for relationships (beyond the hierarchical one, of course) between datasets and it @@ -352,7 +349,7 @@

Is PyTables a replacement for a relational database?

-

How can PyTables be fast if it is written in an interpreted language like Python?

+

How can PyTables be fast if it is written in an interpreted language like Python?

Actually, all of the critical I/O code in PyTables is a thin layer of code on top of HDF5 [1], which is a very efficient C library. Cython [9] is used as the glue language to generate “wrappers” around HDF5 calls so that they can be @@ -362,7 +359,7 @@

How can PyTables be fast if it is written in an interpreted language like Py properly, allows to generate code that runs at almost pure C speeds).

-

If it is designed to deal with very large datasets, then PyTables should consume a lot of memory, shouldn’t it?

+

If it is designed to deal with very large datasets, then PyTables should consume a lot of memory, shouldn’t it?

Well, you already know that PyTables sits on top of HDF5, Python and NumPy [3], and if we add its own logic (~7500 lines of code in Python, ~3000 in Cython and ~4000 in C), then we should conclude that PyTables isn’t effectively a paradigm @@ -378,7 +375,7 @@

If it is designed to deal with very large datasets, then PyTables should con of memory (on a 32-bit platform). All in all, it is not that much, is it?.

-

Why was PyTables born?

+

Why was PyTables born?

Because, back in August 2002, one of its authors (Francesc Alted [10]) had a need to save lots of hierarchical data in an efficient way for later post-processing it. After trying out several approaches, he found that they presented distinct @@ -396,7 +393,7 @@

Why was PyTables born?

-

How does PyTables compare with the h5py project?

+

How does PyTables compare with the h5py project?

Well, they are similar in that both packages are Python interfaces to the HDF5 [1] library, but there are some important differences to be noted. h5py [13] is an attempt to map the HDF5 [1] feature set to NumPy [3] as closely as possible. In @@ -422,20 +419,20 @@

How does PyTables compare with the h5py project?FAQ of h5py [18].

-

I’ve found a bug. What do I do?

+

I’ve found a bug. What do I do?

The PyTables development team works hard to make this eventuality as rare as possible, but, as in any software made by human beings, bugs do occur. If you find any bug, please tell us by file a bug report in the issue tracker [19] on GitHub [20].

-

Is it possible to get involved in PyTables development?

+

Is it possible to get involved in PyTables development?

Indeed. We are keen for more people to help out contributing code, unit tests, documentation, and helping out maintaining this wiki. Drop us a mail on the users mailing list and tell us in which area do you want to work.

-

How can I cite PyTables?

+

How can I cite PyTables?

The recommended way to cite PyTables in a paper or a presentation is as following:

    @@ -456,15 +453,15 @@

    How can I cite PyTables? -

    PyTables 2.x issues

    +

    PyTables 2.x issues

    -

    I’m having problems migrating my apps from PyTables 1.x into PyTables 2.x. Please, help!

    +

    I’m having problems migrating my apps from PyTables 1.x into PyTables 2.x. Please, help!

    Sure. However, you should first check out the Migrating from PyTables 1.x to 2.x document. It should provide hints to the most frequently asked questions on this regard.

    -

    For combined searches like table.where(‘(x<5) & (x>3)’), why was a & operator chosen instead of an and?

    +

    For combined searches like table.where(‘(x<5) & (x>3)’), why was a & operator chosen instead of an and?

    Search expressions are in fact Python expressions written as strings, and they are evaluated as such. This has the advantage of not having to learn a new syntax, but it also implies some limitations with logical and and or @@ -483,7 +480,7 @@

    For combined searches like table.where(‘(x<5) & (x>3)’)< 335 [23] and this thread [24] for more details).

    -

    I can not select rows using in-kernel queries with a condition that involves an UInt64Col. Why?

    +

    I can not select rows using in-kernel queries with a condition that involves an UInt64Col. Why?

    This turns out to be a limitation of the numexpr [17] package. Internally, numexpr [17] uses a limited set of types for doing calculations, and unsigned integers are always upcasted to the immediate signed integer that can fit the @@ -501,7 +498,7 @@

    I can not select rows using in-kernel queries with a condition that involves platforms, where the values will be converted to Python long values).

    -

    I’m already using PyTables 2.x but I’m still getting numarray objects instead of NumPy ones!

    +

    I’m already using PyTables 2.x but I’m still getting numarray objects instead of NumPy ones!

    This is most probably due to the fact that you are using a file created with PyTables 1.x series. By default, PyTables 1.x was setting an HDF5 attribute FLAVOR with the value ‘numarray’ to all leaves. Now, PyTables 2.x sees @@ -530,11 +527,11 @@

    I’m already using PyTables 2.x but I’m still getting numarray objects in

-

Installation issues

+

Installation issues

-

Windows

+

Windows

-

Error when importing tables

+

Error when importing tables

You have installed the binary installer for Windows and, when importing the tables package you are getting an error like:

The command in "0x6714a822" refers to memory in "0x012011a0". The
@@ -556,104 +553,104 @@ 
Error when importing tables