semvec/pypi-packages huggingface dataset:
db4sphinx 0.1.3
Docbook parser for docutils and sphinx
License: MIT
Keywords: sphinx docbook xml

A Docbook parser for docutils and Sphinx
========================================

This project provides a simple Docbook converter for use with docutils and
sphinx.  When used with Sphinx, it can read docbook assembly files to produce
toctrees automatically, or it can be used to incorporate individual docbook
files in documentation (together with rST toctree).

Assembly handling is designed to work with the ``topic-maker-chunk.xsl`` style
from the Docbook distribution:
::

   xsltproc --xinclude \
      --stringparam chunk.section.depth 1  \
      --stringparam assembly.filename index.xml \
      --stringparam base.dir topics/ \
      /usr/share/sgml/docbook/xsl-ns-stylesheets-1.79.1/assembly/topic-maker-chunk.xsl -
log_calls 0.3.2
Debugging and profiling decorator for functions and classes that logs caller name(s), args+values, execution time, and more. Eliminates reams of boilerplate code.
License: MIT
Keywords: decorator debugging profiling logging function method class call caller stack recursion exploring learning teaching

`log_calls` is a Python 3.3+ decorator that can print a lot of useful information
about calls to decorated functions, methods and properties. The decorator can
write to ``stdout``, to another stream or file, or to a logger. `log_calls`
provides methods for writing your own debug messages, and for easily "dumping"
variables and expressions paired with their values. It can decorate individual
functions, methods and properties; but it can also programmatically decorate
callable members of entire classes and class hierarchies, even of entire modules,
with just a single line — which can greatly expedite learning a new codebase.

In short, `log_calls` can save you from writing, rewriting, copying, pasting and
tweaking a lot of ad hoc, debug-only, boilerplate code — and it can keep *your*
codebase free of that clutter.

For each call to a decorated function or method, `log_calls` can show you:

* the caller (in fact, the complete call chain back to another `log_calls`-decorated
  caller so there are no gaps in chains displayed)
* the arguments passed to the function or method, and any default values used
* nesting of calls, using indentation
* the number of the call (whether it's the 1\ :superscript:`st` call,
  the 2\ :superscript:`nd`, the 103\ :superscript:`rd`, ...)
* the return value
* the time it took to execute
* and more!

These and other features are optional and configurable settings, which can be
specified for each decorated callable via keyword parameters, as well as *en
masse* for a group of callables all sharing the same settings. You can examine
and change these settings on the fly using attributes with the same names as
the keywords, or using a dict-like interface whose keys are the keywords.

`log_calls` can also collect profiling data and statistics, accessible at runtime,
such as:

* the number of calls to a function
* total time taken by the function
* the function's entire call history (arguments, time elapsed, return values, callers,
  and more), available as text in CSV format and, if `Pandas <http://pandas.pydata.org>`_
  is installed, as a `DataFrame <http://pandas.pydata.org/pandas-docs/stable/dsintro.html#dataframe>`_.

The package contains two other decorators:

* `record_history`, a stripped-down version of `log_calls`,
  only collects call history and statistics, and outputs no messages;
* `used_unused_keywords` lets a function or method easily determine, per-call,
  which of its keyword parameters were actually supplied by the caller,
  and which received their default values.

NOTE: This package requires the CPython implementation, as it makes assumptions
about stack frame internals which may not be valid in other interpreters.

See the documentation online at http://www.pythonhosted.org/log_calls/,
or in the log_calls/docs/ directory, for usage, details, examples and *tips und tricks*.