Appendix II: What Has Been New¶

This document collects the full What’s New sections of all earlier log_calls releases.

• 0.2.5.post3

  • Later binding for prefix, though it’s still not dynamically changeable.

• 0.2.5.post1 & 0.2.5.post2

  • Silly fixups (release-bungling)

• 0.2.5

  Performance timing/profiling enhancements & additions

  • Both elapsed time and process time are both reported now. Python 3.3+ enhances the time module (see PEP 418), and we take advantage of the new functions perf_counter and process_time.

    • Use time.perf_counter, time.process_time (Python 3.3+).
    • Added stats.process_secs_logged attribute.
    • Added process_secs column to call history (new field for CallRecords).
    • log_elapsed reports both elapsed and process times.

  • Optimized the decorator wrapper, ~15% speedup

    (still trivial with ~big data, see the IPython notebook history_to_pandas-and-profiling).

  • Added a “true bypass” feature: when enabled < 0, adjourn to the decorated function immedately, with no further processing. Again, not a speed speed demon – see the IPython notebook referenced above.

  • Deprecation warning issued if settings_path parameter used.

    (You’ll see this only if you run the Python interpreter with the -W <action> option, where <action> is any valid action string other than ignore, e.g. default.)

  • Updated tests and docs to reflect these changes.

• 0.2.4.post4

  • (docs & description changes only, no code changes)

• 0.2.4.post3

  • (never existed)

• 0.2.4.post2

  • The settings parameter (formerly settings_path) lets you specify default values for multiple settings, either as a dictionary or as a file. The settings_path parameter is deprecated, as settings is a superset. See the documentation http://www.pythonhosted.org/log_calls#settings-parameter for details, discussion and examples.

• 0.2.4.post1

  • settings_path feature: allow file=sys.stderr in settings files, under IPython too; neater internals of settings file parsing.

• 0.2.4

  • The new settings_path parameter lets you specify a file containing default values for multiple settings. See the documentation http://www.pythonhosted.org/log_calls#settings-parameter for details, discussion and examples.

  • You can now use a logger name (something you’d pass to logging.getLogger()) as the value of the logger setting.

  • The indent setting now works with loggers too. See examples:

    • using log_message as a general output method that works as expected, whatever the destination – stdout, another stream, a file, or a logger [in tests/test_log_calls_more.py, docstring of main__log_message__all_possible_output_destinations()];
    • setting up a logger with a minimal formatter that looks just like the output of print [in tests/test_log_calls_more.py, docstring of main__logging_with_indent__minimal_formatters()].

  • Added the decorator used_unused_keywords to support the settings_path feature, and made it visible (you can import it from the package) because it’s more broadly useful. This decorator lets a function obtain, on a per-call basis, two dictionaries of its explicit keyword arguments and their values: those which were actually passed by the caller, and those which were not and received default values. For examples, see the docstring of main() in used_unused_kwds.py.

  • When displaying returned values (log_retval setting is true), the maximum displayed length of values is now 77, up from 60, not counting trailing ellipsis.

  • The deprecated indent_extra parameter to log_message is gone.

  • Little bug fixes, improvements.

• 0.2.3 and 0.2.3.post N

  • A better signature for “the indent-aware writing method log_message()”, and more, better examples of it — full docs http://www.pythonhosted.org/log_calls#log_message.

• 0.2.2

  • “The indent-aware writing method log_message()”, which decorated functions and methods can use to write extra debugging messages that align nicely with log_calls messages.
  • Documentation http://www.pythonhosted.org/log_calls#log_message for log_message().
  • Documentation http://www.pythonhosted.org/log_calls#accessing-own-attrs for how functions and methods can access the attributes that log_calls adds for them, within their own bodies.

• 0.2.1

  • The stats.history_as_DataFrame attribute, whose value is the call history of a decorated function as a Pandas DataFrame (if Pandas is installed; else None).
  • An IPython notebook (log_calls/docs/history_to_pandas.ipynb, which compares the performance of using record_history vs a vectorized approach using numpy to amass medium to large datasets, and which concludes that if you can vectorize, by all means do so.

• 0.2.0

  • Initial public release.