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logwrap 3.0.1

Decorator for logging function arguments and return value by human-readable way

logwrap

logwrap is a helper for logging in human-readable format function arguments and call result on function call. Why? Because logging of *args, **kwargs become useless with project grow and you need more details in call log.

Cons:

  • Log records are not single line.

Pros:

  • Log records are not single 100500 symbols length line. (Especially actual for testing/development environments and for Kibana users).
  • Service free: job is done by this library and it’s dependencies. It works at virtualenv
  • Free software: Apache license
  • Open Source: https://github.com/penguinolog/logwrap
  • PyPI packaged: https://pypi.python.org/pypi/logwrap
  • Self-documented code: docstrings with types in comments
  • Tested: see bages on top
  • Support multiple Python versions:
Python 2.7
Python 3.4
Python 3.5
Python 3.6
PyPy
PyPy3 3.5+

This package includes helpers:

  • logwrap - main helper
  • LogWrap - class with logwrap implementation. May be used directly.
  • pretty_repr
  • pretty_str
  • PrettyFormat

Usage

logwrap

The main decorator. Could be used as not argumented (@logwrap.logwrap) and argumented (@logwrap.logwrap()). Not argumented usage simple calls with default values for all positions. Argumented usage with arguments from signature:

@logwrap.logwrap(
    log=logging.getLogger(__name__),  # __name__ = 'logwrap'
    log_level=logging.DEBUG,
    exc_level=logging.ERROR,
    max_indent=20,  # forwarded to the pretty_repr
    spec=None,  # use target callable function for spec
    blacklisted_names=None,  # list argument names, which should be dropped from log
    blacklisted_exceptions=None,  # Exceptions to skip in log
    log_call_args=True,  # Log call arguments before call
    log_call_args_on_exc=True,  # Log call arguments if exception happens
    log_result_obj=True,  # Log result object
)

Usage examples:

@logwrap.logwrap()
def foo():
    pass

is equal to:

@logwrap.logwrap
def foo():
    pass

Get decorator for use without parameters:

get_logs = logwrap.logwrap()  # set required parameters via arguments

type(get_logs) == LogWrap  # All logic is implemented in LogWrap class starting from version 2.2.0

@get_logs
def foo():
    pass

Call example:

import logwrap

@logwrap.logwrap
def example_function1(
        arg1: str,
        arg2: str='arg2',
        *args,
        kwarg1: str,
        kwarg2: str='kwarg2',
        **kwargs
) -> tuple():
    return (arg1, arg2, args, kwarg1, kwarg2, kwargs)

example_function1('arg1', kwarg1='kwarg1', kwarg3='kwarg3')

This code during execution will produce log records:

Calling:
'example_function1'(
    # POSITIONAL_OR_KEYWORD:
    'arg1'=u'''arg1''',
    'arg2'=u'''arg2''',
    # VAR_POSITIONAL:
    'args'=(),
    # KEYWORD_ONLY:
    'kwarg1'=u'''kwarg1''',
    'kwarg2'=u'''kwarg2''',
    # VAR_KEYWORD:
    'kwargs'=
         dict({
            'kwarg3': u'''kwarg3''',
         }),
)
Done: 'example_function1' with result:

 tuple((
    u'''arg1''',
    u'''arg2''',
    (),
    u'''kwarg1''',
    u'''kwarg2''',
     dict({
        'kwarg3': u'''kwarg3''',
     }),
 ))

Limitations:

  • nested wrapping (@logwrap @deco2 …) is not parsed under python 2.7: functools.wraps limitation. Please set logwrap as the first level decorator.

LogWrap

May be used as logwrap with possibility to read and change several parameters later.

Example construction and read from test:

log_call = logwrap.LogWrap()
log_call.log_level == logging.DEBUG
log_call.exc_level == logging.ERROR
log_call.max_indent == 20
log_call.blacklisted_names == []
log_call.blacklisted_exceptions == []
log_call.log_call_args == True
log_call.log_call_args_on_exc == True
log_call.log_result_obj == True

On object change, variable types is validated.

pretty_repr

This is specified helper for making human-readable repr on complex objects. Signature is self-documenting:

def pretty_repr(
    src,  # object for repr
    indent=0,  # start indent
    no_indent_start=False,  # do not indent the first level
    max_indent=20,  # maximum allowed indent level
    indent_step=4,  # step between indents
    py2_str=False,  # use bytes for python 2 __repr__ and __str__
)

Limitation: Dict like objects is always marked inside {} for readability, even if it is collections.OrderedDict (standard repr as list of tuples).

pretty_str

This is specified helper for making human-readable str on complex objects. Signature is self-documenting:

def pretty_str(
    src,  # object for __str__
    indent=0,  # start indent
    no_indent_start=False,  # do not indent the first level
    max_indent=20,  # maximum allowed indent level
    indent_step=4,  # step between indents
    py2_str=False,  # use bytes for python 2 __repr__ and __str__
)
Limitations:

Dict like objects is always marked inside {} for readability, even if it is collections.OrderedDict (standard repr as list of tuples).

Iterable types is not declared, only brackets is used.

String and bytes looks the same (its __str__, not __repr__).

PrettyFormat

PrettyFormat is the main formatting implementation class. on pretty_repr instance of this class is created and executed. This class is mostly exposed for typing reasons. Object signature:

def __init__(
    self,
    keyword='repr',  # Currently 'repr' is supported, will be extended in the future
    max_indent=20,  # maximum allowed indent level
    indent_step=4,  # step between indents
    py2_str=False,  # use bytes for python 2 __repr__ and __str__
)

Callable object (PrettyFormat instance) signature:

def __call__(
    self,
    src,  # object for repr
    indent=0,  # start indent
    no_indent_start=False  # do not indent the first level
)

Adopting your code

pretty_repr behavior could be overridden for your classes by implementing specific magic method:

def __pretty_repr__(
    self,
    parser  # PrettyFormat class instance,
    indent  # start indent,
    no_indent_start  # do not indent the first level
):
    return ...

This method will be executed instead of __repr__ on your object.

Testing

The main test mechanism for the package logwrap is using tox. Test environments available:

pep8
py27
py34
py35
py36
pypy
pypy3
pylint
docs

CI systems

For code checking several CI systems is used in parallel:

  1. Travis CI: is used for checking: PEP8, pylint, bandit, installation possibility and unit tests. Also it’s publishes coverage on coveralls.
  2. coveralls: is used for coverage display.

CD system

Travis CI: is used for package delivery on PyPI.

 
File Type Py Version Uploaded on Size
logwrap-3.0.1-cp34-cp34m-manylinux1_i686.whl (md5) Python Wheel cp34 2017-09-12 541KB
logwrap-3.0.1-cp34-cp34m-manylinux1_x86_64.whl (md5) Python Wheel cp34 2017-09-12 593KB
logwrap-3.0.1-cp35-cp35m-manylinux1_i686.whl (md5) Python Wheel cp35 2017-09-12 533KB
logwrap-3.0.1-cp35-cp35m-manylinux1_x86_64.whl (md5) Python Wheel cp35 2017-09-12 580KB
logwrap-3.0.1-cp36-cp36m-manylinux1_i686.whl (md5) Python Wheel cp36 2017-09-12 546KB
logwrap-3.0.1-cp36-cp36m-manylinux1_x86_64.whl (md5) Python Wheel cp36 2017-09-12 597KB
logwrap-3.0.1.tar.gz (md5) Source 2017-09-12 227KB