log_calls — What Has Been New

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

• 0.2.5
  Performance timing/profiling enhancements & additions.
  
  • Both elapsed (wall) and CPU (process) time are now both reported.
    Python 3.3+ enhances the time module (see PEP 418; also see the Python 3 docs for the new functions perf_counter and process_time), and we take advantage of the new functionaility when it's available.
    (Under Py < 3.3 log_calls reports elapsed and CPU times as the same number, so as not to further complicate user experience, docs and tests with special appearance and behavior for older Python versions.)
    
    • Use time.perf_counter, time.process_time if available (Python 3.3+), otherwise use time.time as in earlier versions.
      
    • Added stats.CPU_secs_logged attribute.
    • Added CPU_secs column to call history (new field for CallRecords).
    • log_elapsed reports both elapsed and CPU 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 here 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 here 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 function 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 here.

• 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 for log_message().
  • Documentation 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, browsable as HTML here) 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.