log_calls — A decorator for debugging and profiling

(This document is a work in progress: an overly fat README that I'll be reducing alot [v0.2.2: though not before adding to it] and making more of a "quick intro". Complete documentation is here. Thanks for your patience/check this space! — BTO)

log_calls is a Python 3 decorator that can print much useful information about calls to a decorated function. It can write to stdout, to another stream, or to a logger. It can save you from writing, rewriting, copying, pasting and tweaking a lot of ad hoc, boilerplate code - and it can keep your codebase free of that clutter.

For each call of a decorated function, log_calls can show you:

  • the caller,
  • the arguments passed to the function, and any default values used,
  • the time the function took to execute,
  • the complete call chain back to another log_calls-decorated caller,
  • the number of the call,
  • indentation by call level,
  • the function's return value,
  • and more!

These and other features are optional and configurable settings, which can be specified for each decorated function via keyword parameters. You can also 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:

  • 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), optionally as text in CSV format or as a Pandas DataFrame.

The package contains another decorator, record_history, a stripped-down version of log_calls which only collects call history and statistics, and outputs no messages.

This document gives an overview of the decorator's features and their use. A thorough account, including many useful examples, can be found in the complete documentation for log_calls and record_history.


This document describes version 0.2.2 of log_calls.

What's new

  • 0.2.2
  • 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.


Dependencies and requirements

The log_calls package has no dependencies - it requires no other packages. All it does require is a standard distribution of Python 3.2+. (The software proper probably works with 3.0 and 3.1, but hasn't been tested with those earlier versions.)

NOTE: This package does require the CPython implementation, as it uses internals of stack frames which may well differ in other interpreters.


You have two simple options:

  1. Download the compressed repository, uncompress it into a directory, and run:

    $ python setup.py install

    in that directory, or

  2. run

    $ pip install log_calls

    to install log_calls from PyPI (the Python Package Index). Here and elsewhere, $ at the beginning of a line indicates your command prompt, whatever it may be.

Whichever you choose, ideally you'll do it in a virtual environment (a virtualenv).

Running the tests

Each *.py file in the log_calls directory has a corresponding test file test_*.py in the log_calls/tests/ directory; log_calls.py has a second, test_log_calls_more.py. The tests provide essentially 100% coverage (98% for log_calls.py, 100% for the others). All tests have passed on every tested platform + Python version; however, that's a sparse matrix :) If you encounter any turbulence, do let us know.

You can run the test suites either before or after installing log_calls.

Running the tests before installation

To do this, download the compressed repository, as in 1. above. After you uncompress the archive into a directory, and before you run the install command, first run one of the following commands:

$ python setup.py test [-q]

(-q for "quiet", recommended) or

$ python run_tests.py [-q | -v | -h]

which takes switches -q for "quiet", -v for "verbose", and -h for "help".

Running the tests after installation

You can run the tests for log_calls after installing it, by using the command:

$ python -m unittest discover log_calls.tests

What to expect

All the above commands run all tests in the log_calls/tests/ directory. If you run any of them, the output you see should end like so:

Ran 51 tests in 0.726s


indicating that all went well. If any test fails, it will say so.

Basic usage

log_calls has many features, and thus many, mostly independent, keyword parameters (14 in all). This section introduces all but four of them, one at a time, though of course you can use multiple parameters in any call to the decorator:

The two parameters that let you output log_calls messages to a Logger (logger and loglevel) are discussed in Using loggers. The two that determine whether call history is retained (record_history), and then how much of it (max_history), are discussed in Call history and statistics.

Every example in this document uses log_calls, so without further ado:

>>> from log_calls import log_calls

Using no parameters

First, let's see the simplest possible examples, using no parameters at all:

>>> @log_calls()
... def f(a, b, c):
...     pass
>>> f(1, 2, 3)
f <== called by <module>
    arguments: a=1, b=2, c=3
f ==> returning to <module>

Adding another decorated function to the call chain gives useful information too:

>>> @log_calls()
... def g(a):
...     f(a, 2*a, 3*a)
>>> g(3)
g <== called by <module>
    arguments: a=3
f <== called by g
    arguments: a=3, b=6, c=9
f ==> returning to g
g ==> returning to <module>

The enabled parameter (default – True)

The next most basic example:

>>> @log_calls(enabled=False)
... def f(a, b, c):
...     pass
>>> f(1, 2, 3)                # no output

The args_sep parameter (default – ', ')

The args_sep parameter specifies the character or string used to separate arguments. If the string ends in (or is) \n, additional whitespace is appended so that arguments line up nicely:

>>> @log_calls(args_sep='\\n')
... def f(a, b, c, **kwargs):
...     print(a + b + c)
>>> f(1, 2, 3, u='you')       # doctest: +NORMALIZE_WHITESPACE, +SKIP
f <== called by <module>
        [**]kwargs={'u': 'you'}
f ==> returning to <module>

NOTE: In all the doctest examples in this document, you'll see '\\n' where in actual code you'd write '\n'. This is a doctest quirk: all the examples herein work (as tests, they pass), and they would fail if '\n' were used. The only alternative would be to use raw character strings and write r'\n', which is not obviously better.

The log_args parameter (default – True)

When true, as seen above, arguments passed to the decorated function are logged. If the function's signature contains positional and/or keyword "varargs" (*args and/or **kwargs), these are included if they're nonempty. Any default values of keyword parameters with no corresponding argument are also logged, on a separate line.

>>> @log_calls()
... def f_a(a, *args, something='that thing', **kwargs): pass
>>> f_a(1, 2, 3, foo='bar')
f_a <== called by <module>
    arguments: a=1, [*]args=(2, 3), [**]kwargs={'foo': 'bar'}
    defaults:  something='that thing'
f_a ==> returning to <module>

Here, no argument information is logged at all:

>>> @log_calls(log_args=False)
... def f_b(a, *args, something='that thing', **kwargs): pass
>>> f_b(1, 2, 3, foo='bar')
f_b <== called by <module>
f_b ==> returning to <module>

If a function has no parameters, log_calls won't display any "arguments" section:

>>> @log_calls()
... def f(): pass
>>> f()
f <== called by <module>
f ==> returning to <module>

If a function has parameters but is passed no arguments, log_calls will display arguments: <none>, plus any default values used:

>>> @log_calls()
... def ff(*args, **kwargs): pass
>>> ff()
ff <== called by <module>
    arguments: <none>
ff ==> returning to <module>

>>> @log_calls()
... def fff(*args, kw='doh', **kwargs): pass
>>> fff()
fff <== called by <module>
    arguments: <none>
    defaults:  kw='doh'
fff ==> returning to <module>

The log_retval parameter (default – False)

When true, this parameter displays the value returned by the function:

>>> @log_calls(log_retval=True)
... def f(a, b, c):
...     return a + b + c
>>> _ = f(1, 2, 3)
f <== called by <module>
    arguments: a=1, b=2, c=3
    f return value: 6
f ==> returning to <module>

The log_exit parameter (default – True)

When false, this parameter suppresses the ... ==> returning to ... line that indicates the function's return to its caller.

>>> @log_calls(log_exit=False)
... def f(a, b, c):
...     return a + b + c
>>> _ = f(1, 2, 3)
f <== called by <module>
    arguments: a=1, b=2, c=3

The log_call_numbers parameter (default – False)

log_calls keeps a running tally of the number of times a decorated function is called. You can display this (1-based) number using the log_call_numbers parameter:

>>> @log_calls(log_call_numbers=True)
... def f(): pass
>>> for i in range(2): f()
f [1] <== called by <module>
f [1] ==> returning to <module>
f [2] <== called by <module>
f [2] ==> returning to <module>

The call number is also displayed when log_retval is true:

>>> @log_calls(log_call_numbers=True, log_retval=True)
... def f():
...     return 81
>>> _ = f()
f [1] <== called by <module>
    f [1] return value: 81
f [1] ==> returning to <module>

This is particularly valuable in the presence of recursion, for example. See the recursion example later, where the feature is used to good effect.

The log_elapsed parameter (default – False)

For performance profiling, you can measure the time it took a function to execute by using the log_elapsed keyword. When true, log_calls reports the time the decorated function took to complete, in seconds:

>>> @log_calls(log_elapsed=True)
... def f(n):
...     for i in range(n):
...         # do something time-critical
...         pass
>>> f(5000)                 # doctest: +ELLIPSIS
f <== called by <module>
    arguments: n=5000
    elapsed time: ... [secs]
f ==> returning to <module>

The indent parameter (default - False)

The indent parameter, when true, indents each new level of logged messages by 4 spaces, providing a visualization of the call hierarchy. (log_calls indents only when using print, not when using loggers.)

A decorated function's logged output is indented only as much as is necessary. Here, the even numbered functions don't indent, so the indented functions that they call are indented just one level more than their "inherited" indentation level:

>>> @log_calls(indent=True)
... def g1():
...     pass
>>> @log_calls()    # no extra indentation for g1
... def g2():
...     g1()
>>> @log_calls(indent=True)
... def g3():
...     g2()
>>> @log_calls()    # no extra indentation for g3
... def g4():
...     g3()
>>> @log_calls(indent=True)
... def g5():
...     g4()
>>> g5()
g5 <== called by <module>
g4 <== called by g5
    g3 <== called by g4
    g2 <== called by g3
        g1 <== called by g2
        g1 ==> returning to g2
    g2 ==> returning to g3
    g3 ==> returning to g4
g4 ==> returning to g5
g5 ==> returning to <module>

The prefix parameter (default - ''): decorating methods

Especially useful for clarity when decorating methods, the prefix keyword parameter lets you specify a string with which to prefix the name of the method. log_calls uses the prefixed name in its output: when logging a call to, and a return from, the method; when reporting the method's return value; and when the method is at the end of a call or return chain.

>>> import math
>>> class Point():
...     # NOTE: You can't decorate __init__ :D
...     def __init__(self, x, y):
...         self.x = x
...         self.y = y
...     @staticmethod
...     @log_calls(prefix='Point.')
...     def distance(pt1, pt2):
...         return math.sqrt((pt1.x - pt2.x)**2 + (pt1.y - pt2.y)**2)
...     @log_calls(log_retval=True, prefix='Point.')
...     def length(self):
...         return self.distance(self, Point(0, 0))
...     @log_calls(prefix='Point.')
...     def diag_reflect(self):
...         self.x, self.y = self.y, self.x
...         return self
...     def __repr__(self):
...         return "Point" + str((self.x, self.y))

>>> print("Point(1, 2).diag_reflect() =", Point(1, 2).diag_reflect())
Point.diag_reflect <== called by <module>
    arguments: self=Point(1, 2)
Point.diag_reflect ==> returning to <module>
Point(1, 2).diag_reflect() = Point(2, 1)

>>> print("length of Point(1, 2) =", round(Point(1, 2).length(), 2))  # doctest: +ELLIPSIS
Point.length <== called by <module>
    arguments: self=Point(1, 2)
Point.distance <== called by Point.length
    arguments: pt1=Point(1, 2), pt2=Point(0, 0)
Point.distance ==> returning to Point.length
    Point.length return value: 2.236...
Point.length ==> returning to <module>
length of Point(1, 2) = 2.24

The file parameter (default - sys.stdout)

The file parameter specifies a stream (an instance of io.TextIOBase) to which log_calls will print its messages. This value is supplied to the file keyword parameter of the print function, and, like that parameter, its default value is sys.stdout. This parameter is ignored if you've supplied a logger for output using the logger parameter.

If your program writes to the console a lot, you may not want log_calls messages interspersed with your real output: your understanding of both logically distinct streams can be compromised, so, better to make them two actually distinct streams. It can also be advantageous to gather all, and only all, of the log_calls messages in one place. You can use indent=True with a file, and the indentations will appear as intended, whereas that's not possible with loggers.

It's not possible to test this feature with doctest (in fact, there are subtleties to supporting this feature and using doctest at all), so we'll just give an example of writing to stderr, and reproduce the output:

>>> import sys
>>> @log_calls(file=sys.stderr, indent=True)
... def f(n):
...     if n <= 0:
...         return 'a'
...     return '(' + f(n-1) + ')'

Running >>> f(2) will return '((a))' and will write the following to stderr:

f <== called by <module>
    f <== called by f
        arguments: n=1
        f <== called by f
            arguments: n=0
        f ==> returning to f
    f ==> returning to f
f ==> returning to <module>

Using loggers

log_calls works well with loggers obtained from Python's logging module. First, we'll set up a logger with a single handler that writes to the console. Because doctest doesn't capture output written to stderr (the default stream to which console handlers write), we'll send the console handler's output to stdout, using the format <loglevel>:<loggername>:<message>.

>>> import logging
>>> import sys
>>> ch = logging.StreamHandler(stream=sys.stdout)
>>> c_formatter = logging.Formatter('%(levelname)8s:%(name)s:%(message)s')
>>> ch.setFormatter(c_formatter)
>>> logger = logging.getLogger('a_logger')
>>> logger.addHandler(ch)
>>> logger.setLevel(logging.DEBUG)

The logger parameter (default – None)

The logger keyword parameter tells log_calls to write its output using that logger rather than the print function:

>>> @log_calls(logger=logger)
... def somefunc(v1, v2):
...     logger.debug(v1 + v2)
>>> @log_calls(logger=logger)
... def anotherfunc():
...     somefunc(17, 19)
>>> anotherfunc()       # doctest: +NORMALIZE_WHITESPACE
DEBUG:a_logger:anotherfunc <== called by <module>
DEBUG:a_logger:somefunc <== called by anotherfunc
DEBUG:a_logger:    arguments: v1=17, v2=19
DEBUG:a_logger:somefunc ==> returning to anotherfunc
DEBUG:a_logger:anotherfunc ==> returning to <module>

The loglevel parameter (default – logging.DEBUG)

log_calls also takes a loglevel keyword parameter, whose value must be one of the logging module's constants - logging.DEBUG, logging.INFO, etc. – or a custom logging level if you've added any. log_calls writes output messages using logger.log(loglevel, …). Thus, if the logger's log level is higher than loglevel, no output will appear:

>>> logger.setLevel(logging.INFO)   # raise logger's level to INFO
>>> @log_calls(logger='logger_=', loglevel=logging.DEBUG)
... def f(x, y, z, **kwargs):
...     return y + z
>>> # No log_calls output from f
>>> # because loglevel for f < level of logger
>>> f(1,2,3, enable=True, sep_='\\n', logger_=logger)       # doctest: +NORMALIZE_WHITESPACE, +ELLIPSIS

The use of loggers, and of these parameters, is explored further in the main documentation, which contains an example of using a logger with multiple handlers that have different loglevels.

Call chains

log_calls does its best to chase back along the call chain to find the first enabled log_calls-decorated function on the stack. If there's no such function, it just displays the immediate caller. If there is such a function, however, it displays the entire list of functions on the stack up to and including that function when reporting calls and returns. Without this, you'd have to guess at what was called in between calls to functions decorated by log_calls. If you specified a prefix for the decorated caller on the end of a call chain, log_calls will use the prefixed name:

>>> @log_calls()
... def g1():
...     pass
>>> def g2():
...     g1()
>>> @log_calls(prefix='mid.')
... def g3():
...     g2()
>>> def g4():
...     g3()
>>> @log_calls()
... def g5():
...     g4()
>>> g5()
g5 <== called by <module>
mid.g3 <== called by g4 <== g5
g1 <== called by g2 <== mid.g3
g1 ==> returning to g2 ==> mid.g3
mid.g3 ==> returning to g4 ==> g5
g5 ==> returning to <module>

In the next example, g is log_calls-decorated but logging is disabled, so the reported call chain for f stops at its immediate caller:

>>> @log_calls()
... def f(): pass
>>> def not_decorated(): f()
>>> @log_calls(enabled=False, log_call_numbers=True)
... def g(): not_decorated()
>>> g()
f <== called by not_decorated
f ==> returning to not_decorated

Elaborating on the previous example, here are longer call chains with an intermediate decorated function that has logging disabled. We've also enabled indentation:

>>> @log_calls(indent=True)
... def e(): pass
>>> def not_decorated_call_e(): e()
>>> @log_calls(indent=True)
... def f(): not_decorated_call_e()
>>> def not_decorated_call_f(): f()
>>> @log_calls(enabled=False, log_call_numbers=True, indent=True)
... def g(): not_decorated_call_f()
>>> @log_calls(indent=True)
... def h(): g()
>>> h()
h <== called by <module>
    f <== called by not_decorated_call_f <== g <== h
        e <== called by not_decorated_call_e <== f
        e ==> returning to not_decorated_call_e ==> f
    f ==> returning to not_decorated_call_f ==> g ==> h
h ==> returning to <module>

log_calls chases back to the nearest enabled decorated function, so that there aren't gaps between call chains.

Indentation and call numbers with recursion

These features are especially useful in recursive and mutually recursive situations. We have to use OrderedDicts here because of doctest:

>>> from collections import OrderedDict
>>> @log_calls(log_call_numbers=True, log_retval=True, indent=True)
... def depth(d, key=None):
...     if not isinstance(d, dict):
...         return 0    # base case
...     elif not d:
...         return 1
...     else:
...         return max(map(depth, d.values(), d.keys())) + 1
>>> depth(
...     OrderedDict(
...         (('a', 0),
...          ('b', OrderedDict( (('c1', 10), ('c2', 11)) )),
...          ('c', 'text'))
...     )
... )
depth [1] <== called by <module>
    arguments: d=OrderedDict([('a', 0), ('b', OrderedDict([('c1', 10), ('c2', 11)])), ('c', 'text')])
    defaults:  key=None
    depth [2] <== called by depth [1]
        arguments: d=0, key='a'
        depth [2] return value: 0
    depth [2] ==> returning to depth [1]
    depth [3] <== called by depth [1]
        arguments: d=OrderedDict([('c1', 10), ('c2', 11)]), key='b'
        depth [4] <== called by depth [3]
            arguments: d=10, key='c1'
            depth [4] return value: 0
        depth [4] ==> returning to depth [3]
        depth [5] <== called by depth [3]
            arguments: d=11, key='c2'
            depth [5] return value: 0
        depth [5] ==> returning to depth [3]
        depth [3] return value: 1
    depth [3] ==> returning to depth [1]
    depth [6] <== called by depth [1]
        arguments: d='text', key='c'
        depth [6] return value: 0
    depth [6] ==> returning to depth [1]
    depth [1] return value: 2
depth [1] ==> returning to <module>

NOTE: The optional key parameter is for instructional purposes, so you can see the key that's paired with the value of d in the caller's dictionary. Typically the signature of this function would be just def depth(d), and the recursive case would return max(map(depth, d.values())) + 1.

The indent-aware writing method log_message(msg, indent_extra=4)

log_calls exposes the method it uses to write its messages, log_message. If a decorated function or method writes its own debugging messages, it can use can use log_message so that they align nicely with the messages written by log_calls. Even multiline messages will be properly aligned.

Consider the following function:

>>> @log_calls(indent=True, log_call_numbers=True)
... def f(n):
...     if n <= 0:
...         print("*** Base case n <= 0")
...     else:
...         print("*** n=%d is %s,\\n    but we knew that."
...               % (n, "odd" if n%2 else "even"))
...         f(n-1)
>>> f(2)
f [1] <== called by <module>
    arguments: n=2
*** n=2 is even,
    but we knew that.
    f [2] <== called by f [1]
        arguments: n=1
*** n=1 is odd,
    but we knew that.
        f [3] <== called by f [2]
            arguments: n=0
*** Base case n <= 0
        f [3] ==> returning to f [2]
    f [2] ==> returning to f [1]
f [1] ==> returning to <module>

The debugging messages written by f literally "stick out", and in a more complex situation with multiple functions and methods it could be difficult to figure out who actually wrote which message. If instead f uses log_message, all of its messages from each invocation align neatly within the context presented by log_calls:

>>> @log_calls(indent=True, log_call_numbers=True)
... def f(n):
...     if n <= 0:
...         f.log_message("*** Base case n <= 0")
...     else:
...         f.log_message("*** n=%d is %s,\\n    but we knew that."
...                       % (n, "odd" if n%2 else "even"))
...         f(n-1)
>>> f(2)
f [1] <== called by <module>
    arguments: n=2
    *** n=2 is even,
        but we knew that.
    f [2] <== called by f [1]
        arguments: n=1
        *** n=1 is odd,
            but we knew that.
        f [3] <== called by f [2]
            arguments: n=0
            *** Base case n <= 0
        f [3] ==> returning to f [2]
    f [2] ==> returning to f [1]
f [1] ==> returning to <module>

The indent_extra value is an offset from the column in which the entry and exit messages for the function begin. f uses the default value indent_extra=4, so its messages align with "arguments:". log_calls itself explicitly supplies indent_extra=0. Negative values are tolerated :), and do what you'd expect.

NOTE: In the example above, f accesses one of its attributes added by log_calls, namely, the log_message() method. (log_calls in fact adds two more attributes, discussed in subsequent sections: log_calls_settings and stats.) Indeed, any function, and any static method, can access its log_calls in the same syntactically straightforward way. Classmethods and instance methods decorated by log_calls can also use log_message(), but each of those kinds of methods requires its own approach (a little more syntax) to obtaining the log_calls wrapper which hosts the attributes. See the section Functions and methods accessing their own log_calls attributes for the not at all gory details.

Advanced Features

log_calls provides a number of features beyond those already described. We'll only give an overview of them here. For a full account, see the complete documentation.

Dynamic control of settings

Sometimes, you'll need or want to change a log_calls setting for a decorated function on the fly. The major impediment to doing so is that the values of the log_calls parameters are set once the decorated function is interpreted. Those values are established once and for all when the Python interpreter parses the definition of a decorated function and creates a function object.

The problem, and two log_calls solutions

Even if a variable is used as a parameter value, its value at the time Python processes the definition is "frozen" for the created function object. Subsequently changing the value of the variable will not affect the behavior of the decorator.

For example, suppose DEBUG is a module-level variable initialized to False:

>>> DEBUG = False

and you use this code:

>>> @log_calls(enabled=DEBUG)
... def foo(**kwargs):
...     pass
>>> foo()       # No log_calls output: DEBUG is False

If later you set Debug = True and call foo, that call won't be logged, because the decorated foo's enabled setting is bound to the original value of DEBUG, established when the definition was processed:

>>> DEBUG = True
>>> foo()       # Still no log_calls output

log_calls provides two ways to overcome this limitation and dynamically control the settings of a decorated function:

  • the log_calls_settings attribute, which provides a mapping interface and an attribute-based interface to settings, and
  • *indirect values.

The following two subsections give a brief introduction to these features, which [the main documentation]((http://www.pythonhosted.org/log_calls) presents in depth.

The log_calls_settings attribute

log_calls adds an attribute log_calls_settings to a decorated function, through which you can access the decorator settings for that function. This attribute is an object which lets you control the settings for a decorated function via a mapping (dict-like) interface, and equivalently, via attributes of the object. The mapping keys and the attribute names are simply the log_calls keywords. log_calls_settings also implements many of the standard dict methods for interacting with the settings in familiar ways.

The mapping interface and the attribute interface to settings

Once you've decorated a function with log_calls,

>>> @log_calls()
... def f(*args, **kwargs):
...     return 91

you can access and change its settings via the log_calls_settings attribute of the decorated function, which behaves like a dictionary. You can read and write settings using the log_calls keywords as keys:

>>> f.log_calls_settings['enabled']
>>> f.log_calls_settings['enabled'] = False
>>> _ = f()                   # no output (not even 91, because of "_ = ")
>>> f.log_calls_settings['enabled']
>>> f.log_calls_settings['log_retval']
>>> f.log_calls_settings['log_retval'] = True
>>> f.log_calls_settings['log_elapsed']
>>> f.log_calls_settings['log_elapsed'] = True

You can also use the same keywords as attributes of log_calls_settings instead of as keywords to the mapping interface; they're completely equivalent:

>>> f.log_calls_settings.log_elapsed
>>> f.log_calls_settings.log_call_numbers
>>> f.log_calls_settings.log_call_numbers = True
>>> f.log_calls_settings.enabled = True     # turn it back on!
>>> _ = f()                                 # doctest: +ELLIPSIS
f [1] <== called by <module>
    arguments: <none>
    f [1] return value: 91
    elapsed time: ... [secs]
f [1] ==> returning to <module>

>>> f.log_calls_settings.log_args = False
>>> f.log_calls_settings.log_elapsed = False
>>> f.log_calls_settings.log_retval = False
>>> _ = f()                                 # doctest: +ELLIPSIS
f [2] <== called by <module>
f [2] ==> returning to <module>

The log_calls_settings attribute has a length (14), its keys and items() can be iterated through, you can use in to test for key membership, and it has an update() method. As with an ordinary dictionary, attempting to access a nonexistent setting raises KeyError. Unlike an ordinary dictionary, you can't add new keys – the log_calls_settings dictionary is closed to new members, and attempts to add one will also raise KeyError.

The update(), as_OrderedDict() and as_dict() methods – and a typical use-case

The update() method of the log_calls_settings object lets you update several settings at once:

>>> f.log_calls_settings.update(
...     log_args=True, log_elapsed=False, log_call_numbers=False,
...     log_retval=False)
>>> _ = f()
f <== called by <module>
    arguments: <none>
f ==> returning to <module>

You can retrieve the entire collection of settings as either an OrderedDict using the as_OrderedDict() method, or as a dict using as_dict(). Either can serve as a snapshot of the settings, so that you can change settings temporarily, use the new settings, and then use update() restore settings from the snapshot. in addition to taking keyword arguments, as shown above, update() can take one or more dicts – in particular, a dictionary retrieved from one of the as_* methods. For example:

Retrieve settings (here, as an OrderedDict because those are more doctest-friendly, but using as_dict() suffices):

>>> od = f.log_calls_settings.as_OrderedDict()
>>> od                      # doctest: +NORMALIZE_WHITESPACE
OrderedDict([('enabled', True),           ('args_sep', ', '),
             ('log_args', True),          ('log_retval', False),
             ('log_elapsed', False),      ('log_exit', True),
             ('indent', False),           ('log_call_numbers', False),
             ('prefix', ''),              ('file', None),
             ('logger', None),            ('loglevel', 10),
             ('record_history', False),   ('max_history', 0)])

change settings temporarily:

>>> f.log_calls_settings.update(
...     log_args=False, log_elapsed=True, log_call_numbers=True,
...     log_retval=True)

use the new settings for f:

>>> _ = f()                     # doctest: +ELLIPSIS
f [4] <== called by <module>
    f [4] return value: 91
    elapsed time: ... [secs]
f [4] ==> returning to <module>

and restore original settings, this time passing the retrieved settings dictionary rather than keywords (we could pass **od, but that's unnecessary and a pointless expense):

>>> f.log_calls_settings.update(od)
>>> od == f.log_calls_settings.as_OrderedDict()

Indirect values

log_calls provides a second way to access and change settings on the fly. The decorator lets you specify any parameter except prefix or max_history with one level of indirection, by using indirect values: an indirect value is a string that names a keyword argument of the decorated function. It can be an explicit keyword argument present in the signature of the function, or an implicit keyword argument that ends up in **kwargs (if that's present in the function's signature). When the decorated function is called, the arguments passed by keyword, and the decorated function's explicit keyword parameters with default values, are both searched for the named parameter; if it is found and of the correct type, its value is used; otherwise the default value for the log_calls parameter is used.

To specify an indirect value for a parameter whose normal type is str (only args_sep, at present), append an '=' to the value. For consistency's sake, any indirect value can end in a trailing '=', which is stripped. Thus, enabled='enable_=' indicates an indirect value supplied by the keyword (argument or parameter) enable_ of the decorated function.

For example, in:

>>> @log_calls(args_sep='sep=', prefix="*** ")
... def f(a, b, c, sep='|'): pass

args_sep has an indirect value which names f's explicit keyword parameter sep, and prefix has a direct value (as it always does). A call can dynamically override the default value '|' in the signature of f by supplying a value:

>>> f(1, 2, 3, sep=' / ')
*** f <== called by <module>
    arguments: a=1 / b=2 / c=3 / sep=' / '
*** f ==> returning to <module>

or it can use f's default value by not supplying a sep argument:

>>> f(1, 2, 3)
*** f <== called by <module>
    arguments: a=1|b=2|c=3
    defaults:  sep='|'
*** f ==> returning to <module>

A decorated function doesn't have to explicitly declare the parameter named as an indirect value, if its signature includes **kwargs: the intermediate parameter can be an implicit keyword parameter, passed by a caller but not present in the function's signature. Consider:

>>> @log_calls(enabled='enable')
... def func1(a, b, c, **kwargs): pass
>>> @log_calls(enabled='enable')
... def func2(z, **kwargs): func1(z, z+1, z+2, **kwargs)

When the following statement is executed, the calls to both func1 and func2 will be logged:

>>> func2(17, enable=True)
func2 <== called by <module>
    arguments: z=17, [**]kwargs={'enable': True}
func1 <== called by func2
    arguments: a=17, b=18, c=19, [**]kwargs={'enable': True}
func1 ==> returning to func2
func2 ==> returning to <module>

whereas neither of the following two statements will trigger logging:

>>> func2(42, enable=False)     # no log_calls output
>>> func2(99)                   # no log_calls output

See the section in the full documentation on indirect values for several more examples and useful techniques involving indirect values. The test suite log_calls/tests/test_log_calls_more.py also contains further doctests/examples.

Call history and statistics

log_calls always collects a few basic statistics about calls to a decorated function. It can collect the entire history of calls to a function if asked to, or just the most recent n calls; the *_history parameters, discussed next, determine these settings. The statistics and history are accessible via the stats attribute which log_calls adds to a decorated function.

The record_history and max_history parameters

The two parameters we haven't yet discussed govern the recording of a decorated function's call history.

The record_history parameter (default – False)

When the record_history setting is true for a decorated function f, log_calls will retain a sequence of records holding the details of each logged call to that function. That history is accessible via attributes of the stats object.

Let's define a function f with record_history set to true:

>>> @log_calls(record_history=True, log_call_numbers=True, log_exit=False)
... def f(a, *args, x=1, **kwargs): pass

We'll call this function f in the following subsections, manipulate its settings, and examine its statistics.

The max_history parameter (default – 0)

The max_history parameter determines how many call history records are retained for a decorated function whose call history is recorded. If this value is 0 or negative, unboundedly many records are retained (unless or until you set the record_history setting to false, or call the stats.clear_history() method). If the value of max_history is > 0, log_calls will retain at most that many records, discarding the oldest records to make room for newer ones if the history reaches capacity.

You cannot change max_history using the mapping interface or the attribute of the same name; attempts to do so raise ValueError. The only way to change its value is with the stats.clear_history() method, discussed below.

The stats attribute and its attributes

The stats attribute of a decorated function is an object that provides statistics and data about calls to a decorated function:

The first three don't depend on the record_history setting at all.The last three yield empty results unless record_history is true.

The stats attribute also provides one method, stats.clear_history().

Let's call the function f defined above twice:

>>> f(0)
f [1] <== called by <module>
    arguments: a=0
    defaults:  x=1
>>> f(1, 100, 101, x=1000, y=1001)
f [2] <== called by <module>
    arguments: a=1, [*]args=(100, 101), x=1000, [**]kwargs={'y': 1001}

and look at its stats.

The num_calls_logged attribute

The stats.num_calls_logged attribute contains the number of the most recent logged call to a decorated function. Thus, f.stats.num_calls_logged will equal 2:

>>> f.stats.num_calls_logged

This counter gets incremented when a decorated function is called that has logging enabled, even if its log_call_numbers setting is false.

The num_calls_total attribute

The stats.num_calls_total attribute holds the total number of calls to a decorated function. This counter gets incremented even when logging is disabled for a function.

For example, let's now disable logging for f and call it 3 more times:

>>> f.log_calls_settings.enabled = False
>>> for i in range(3): f(i)

Now stats.num_calls_total will equal 5, but f.stats.num_calls_logged will still equal 2:

>>> f.stats.num_calls_total
>>> f.stats.num_calls_logged

Finally, let's re-enable logging for f and call it again. The displayed call number will be the number of the logged call, 3, the same value as f.stats.num_calls_logged after the call:

>>> f.log_calls_settings.enabled = True
>>> f(10, 20, z=5000)
f [3] <== called by <module>
    arguments: a=10, [*]args=(20,), [**]kwargs={'z': 5000}
    defaults:  x=1

>>> f.stats.num_calls_total
>>> f.stats.num_calls_logged

ATTENTION: Thus, log_calls has some overhead even when it's disabled, and somewhat more when it's enabled. So, ***comment it out in production code!*

The elapsed_secs_logged attribute

The stats.elapsed_secs_logged attribute holds the sum of the elapsed times of all logged calls to a decorated function, in seconds. Here's its value for the 3 logged calls to f above:

>>> f.stats.elapsed_secs_logged   # doctest: +SKIP
The history attribute

The stats.history attribute of a decorated function provides the call history of logged calls to the function as a tuple of records. Each record is a namedtupleof type CallRecord. Here's f's call history, in (almost) human-readable form:

>>> print('\\n'.join(map(str, f.stats.history)))   # doctest: +SKIP
CallRecord(call_num=1, argnames=['a'], argvals=(0,), varargs=(),
                       defaulted_kwargs=OrderedDict([('x', 1)]), implicit_kwargs={},
                       retval=None, elapsed_secs=2.1457672119140625e-06,
                       timestamp='10/28/14 15:56:13.733763',
                       prefixed_func_name='f', caller_chain=['<module>'])
CallRecord(call_num=2, argnames=['a'], argvals=(1,), varargs=(100, 101),
                       explicit_kwargs=OrderedDict([('x', 1000)]),
                       defaulted_kwargs=OrderedDict(), implicit_kwargs={'y': 1001},
                       retval=None, elapsed_secs=1.9073486328125e-06,
                       timestamp='10/28/14 15:56:13.734102',
                       prefixed_func_name='f', caller_chain=['<module>'])
CallRecord(call_num=3, argnames=['a'], argvals=(10,), varargs=(20,),
                       defaulted_kwargs=OrderedDict([('x', 1)]), implicit_kwargs={'z': 5000},
                       retval=None, elapsed_secs=2.1457672119140625e-06,
                       timestamp='10/28/14 15:56:13.734412',
                       prefixed_func_name='f', caller_chain=['<module>'])

The CSV representation pairs the argnames with their values in argvals (the argnames become column headings), making it even more human-readable, especially when viewed in a program that presents CSVs nicely.

The history_as_csv attribute

The value stats.history_as_csv attribute is a text representation in CSV format of a decorated function's call history. You can save this string and import it into the program or tool of your choice for further analysis. (Note: if your tool of choice is Pandas, you can use the stats attribute stats.history_as_DataFrame to directly obtain history in the representation you ultimately want.) The CSV representation breaks out each argument into its own column, throwing away information about whether an argument's value was passed or is a default.

The CSV separator is '|' rather than ',' because some of the fields – args, kwargs and caller_chain – use commas intrinsically. Let's examine one more history_as_csv for a function that has all of those fields:

>>> @log_calls(record_history=True, log_call_numbers=True,
...            log_exit=False, log_args=False)
... def f(a, *extra_args, x=1, **kw_args): pass
>>> def g(a, *args, **kwargs): f(a, *args, **kwargs)
>>> @log_calls(log_exit=False, log_args=False)
... def h(a, *args, **kwargs): g(a, *args, **kwargs)
>>> h(0)
h <== called by <module>
f [1] <== called by g <== h
>>> h(10, 17, 19, z=100)
h <== called by <module>
f [2] <== called by g <== h
>>> h(20, 3, 4, 6, x=5, y='Yarborough', z=100)
h <== called by <module>
f [3] <== called by g <== h

Here's the call history in CSV format:

>>> print(f.stats.history_as_csv)        # doctest: +NORMALIZE_WHITESPACE, +ELLIPSIS
1|0|()|1|{}|None|...|...|'f'|['g', 'h']
2|10|(17, 19)|1|{'z': 100}|None|...|...|'f'|['g', 'h']
3|20|(3, 4, 6)|5|{'y': 'Yarborough', 'z': 100}|None|...|...|'f'|['g', 'h']

Ellipses are for the elapsed_secs and timestamp fields. As usual, log_calls will use whatever names you use for varargs parameters (here, extra_args and kw_args). Whatever the name of the kwargs parameter, items within that field are guaranteed to be in sorted order.

The history_as_DataFrame attribute

The stats.history_as_DataFrame attribute returns the history of a decorated function as a Pandas DataFrame, if the Pandas library is installed. This saves you the intermediate step of calling DataFrame.from_csv with the proper arguments (and also saves you from having to know or care what those are).

If Pandas is not installed, the value of this attribute is None.

The clear_history(max_history=0) method

As you might expect, the stats.clear_history(max_history=0) method clears the call history of a decorated function. In addition, it resets all running sums: num_calls_total and num_calls_logged are reset to 0, and elapsed_secs_logged is reset to 0.0.

It is the only way to change the value of the max_history setting, via the optional keyword parameter for which you can supply any (integer) value, by default 0.

The function f has a nonempty history, as we just saw. Let's clear f's history, setting max_history to 33, and check that settings and stats tallies are reset:

>>> f.stats.clear_history(max_history=33)
>>> f.log_calls_settings.max_history
>>> f.stats.num_calls_logged
>>> f.stats.num_calls_total
>>> f.stats.elapsed_secs_logged

The record_history decorator

The record_history decorator is a stripped-down version of log_calls which records calls to a decorated function but writes no messages. You can think of it as log_calls with the record_history and log_call_numbers settings always true, and without any of the message-logging apparatus.

record_history has only three keyword parameters:

  • enabled
  • prefix
  • max_history

Just as the settings of log_calls for a decorated function are accessible dynamically through the log_calls_settings attribute, these settings of record_history are exposed via a record_history_settings attribute. record_history_settings is an object of the same type as log_calls_settings, so it has the same methods and behaviors described in the log_calls_settings section.

Functions decorated by record_history have a full-featured stats attribute, as described in the Call history and statistics section.

See the documentation for record_history for examples and tests.

ATTENTION: Like log_calls, record_history has some overhead. So, ***comment it out in production code!*

— Brian O'Neill, October-November 2014, NYC

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