Logging entry, exit and runtime of functions with a decorator using loguru module

main.py

import time
from loguru import logger
from more_loguru import logger_wraps

logger.add('test.log', mode='w')

@logger_wraps(debug_io=True)
def my_func(a, b, c=1, d="Text"):
    """A test function"""
    time.sleep(1)
    return str([a, b, c, d])

func = my_func

my_func("hello", "logger", c="print this", d="keyword arg")

# Logging Existing library functions
import numpy as np
logger_wraps(debug_io=True)(np.random.rand)(10,10)
logger_wraps(log_full_doc=True)(np.sum)(np.random.rand(10,10))

more_loguru.py

from datetime import datetime
import functools

from loguru import logger

def dict2str(a_dict, indent=4):
    """Return formatted string version of dict"""
    if len(a_dict) == 0:
        return "{}"
    
    max_key_len = max(len(key) for key in a_dict)
    line_indent = "\n"+" "*indent
    str_items = line_indent.join(f"{key}"+ " "*(max_key_len-len(key)) +f": {value},"
                                       for key, value in a_dict.items())
    return "{" + line_indent + str_items + line_indent + "}"

def iter2str(a_list, indent=4, open_bracket="(", close_bracket=")"):
    """Return formatted string version of an iterator"""
    if len(a_list) == 0:
        return open_bracket+close_bracket
    
    line_indent = "\n"+" "*indent
    str_items = line_indent.join(str(value)+',' for value in a_list)
    
    return open_bracket + line_indent + str_items + line_indent + close_bracket

def get_func_name(func):
    """Return the name of the given function including module name"""
    if hasattr(func, '__module__') and func.__module__ != None:
        func_name = '.'.join([func.__module__, func.__qualname__])
    else:
        func_name = func.__qualname__
    return func_name

def get_func_small_doc(func):
    """Return first two lines from the function documentation"""
    try:
        split_doc = func.__doc__.split("\n")
        if len(split_doc) < 2:
            doc = ' '.join(split_doc).strip()
        else:
            doc = ' '.join(split_doc[:2]).strip()
    except:
        doc = None
    return doc


def logger_wraps(*, func_entry=True, func_exit=True, level="DEBUG", 
                 debug_io=False, debug_args=False, debug_kwargs=False, 
                 debug_result=False, log_full_doc=False):
    """
    A wrapper function to log the function entry, exit and runtime outputting arguments and results

    Parameters
    ----------
    func_entry : bool, optional
        Whether to log the entry of function. The default is True.
    func_exit : bool, optional
        Whether to log the exit of function. The default is True.
    level : str, optional
        logging level. The default is "DEBUG".
    debug_io : bool, optional
        Whether to log function inputs and output parameters. The default is False.
    debug_args : bool, optional
        Whether to log function arguments. The default is False.
    debug_kwargs : bool, optional
        Whether to log function keyword arguments. The default is False.
    debug_result : bool, optional
        Whether to log function result. The default is False.
    is_full_doc : bool, optional
        Whether to log full documentation of function. 
        If False, first two lines of function documentation are logged. 
        The default is False.

    Returns
    -------
    decorator:
        An object that can be used to decorate a function.

    """
    def wrapper(func):
        
        name = get_func_name(func)
        doc = func.__doc__ if log_full_doc else get_func_small_doc(func)
        
        @functools.wraps(func)
        def wrapped(*args, **kwargs):
            logger_ = logger.opt(depth=1)
            if func_entry:
                entry_dict =  {
                    "State": "ENTRY",
                    "Function": name,
                    "Description": doc,
                    }
                if debug_args or debug_io:
                    entry_dict["Args"] = iter2str(args, indent=22)
                if debug_kwargs or debug_io:
                    entry_dict["Kwargs"] = dict2str(kwargs, indent=22)
                    
                entry_str = dict2str(entry_dict)
                logger_.log(level, entry_str)
                
            start = datetime.now()
            result = func(*args, **kwargs)
            end = datetime.now()
            runtime = end - start
            
            if func_exit:
                exit_dict = {
                    "State": "EXIT",
                    "Function": name,
                    "Runtime": runtime,
                    }
                if debug_result or debug_io:
                    exit_dict["Result"] = str(result)
                    
                exit_str = dict2str(exit_dict)
                logger_.log(level, exit_str)
                
            return result
        
        return wrapped
    
    return wrapper

test.log

2022-03-17 13:35:57.119 | DEBUG    | __main__:<module>:15 - {
    State      : ENTRY
    Function   : __main__.my_func
    Description: A test function
    Args       : (
                      hello
                      logger
                      )
    Kwargs     : {
                      c: print this
                      d: keyword arg
                      }
    }
2022-03-17 13:35:58.136 | DEBUG    | __main__:<module>:15 - {
    State   : EXIT
    Function: __main__.my_func
    Runtime : 0:00:01.013995
    Result  : ['hello', 'logger', 'print this', 'keyword arg']
    }
2022-03-17 13:35:58.138 | DEBUG    | __main__:<module>:19 - {
    State      : ENTRY
    Function   : RandomState.rand
    Description: rand(d0, d1, ..., dn)
    Args       : (
                      10
                      10
                      )
    Kwargs     : {}
    }
2022-03-17 13:35:58.141 | DEBUG    | __main__:<module>:19 - {
    State   : EXIT
    Function: RandomState.rand
    Runtime : 0:00:00.001005
    Result  : [[0.15421369 0.10137913 0.91046302 0.43190587 0.29174799 0.71519251
  0.52935884 0.06936744 0.26859926 0.73353876]
 [0.42048735 0.49197483 0.63040623 0.28178557 0.068379   0.06677066
  0.26446492 0.1479684  0.73640894 0.67122753]
 [0.73802108 0.93509084 0.25294261 0.05860547 0.65150791 0.18761106
  0.48256235 0.2612798  0.31041145 0.2340653 ]
 [0.52375654 0.68371864 0.30928983 0.87638674 0.70446178 0.2054611
  0.26661087 0.52606977 0.88117853 0.80641446]
 [0.87759751 0.1872547  0.73896022 0.2782295  0.40346596 0.78815338
  0.88895959 0.31498619 0.12840834 0.8945755 ]
 [0.48283961 0.16861856 0.66444653 0.76056871 0.96547068 0.71580742
  0.44246482 0.86323614 0.47825779 0.54422304]
 [0.26766282 0.29110596 0.2603545  0.50927323 0.32419506 0.0094917
  0.05879885 0.03719602 0.49640424 0.12116557]
 [0.9772078  0.37718526 0.64472585 0.14811863 0.94161187 0.86663402
  0.39839245 0.77827622 0.1294619  0.95028546]
 [0.56660615 0.94056333 0.83124345 0.62046998 0.37744329 0.7341235
  0.4192672  0.3835191  0.53189709 0.76989918]
 [0.83758544 0.57230601 0.62635092 0.02452058 0.78512488 0.28017589
  0.35995325 0.75439324 0.43808403 0.73764402]]
    }
2022-03-17 13:35:58.142 | DEBUG    | __main__:<module>:20 - {
    State      : ENTRY
    Function   : numpy.sum
    Description: 
    Sum of array elements over a given axis.

    Parameters
    ----------
    a : array_like
        Elements to sum.
    axis : None or int or tuple of ints, optional
        Axis or axes along which a sum is performed.  The default,
        axis=None, will sum all of the elements of the input array.  If
        axis is negative it counts from the last to the first axis.

        .. versionadded:: 1.7.0

        If axis is a tuple of ints, a sum is performed on all of the axes
        specified in the tuple instead of a single axis or all the axes as
        before.
    dtype : dtype, optional
        The type of the returned array and of the accumulator in which the
        elements are summed.  The dtype of `a` is used by default unless `a`
        has an integer dtype of less precision than the default platform
        integer.  In that case, if `a` is signed then the platform integer
        is used while if `a` is unsigned then an unsigned integer of the
        same precision as the platform integer is used.
    out : ndarray, optional
        Alternative output array in which to place the result. It must have
        the same shape as the expected output, but the type of the output
        values will be cast if necessary.
    keepdims : bool, optional
        If this is set to True, the axes which are reduced are left
        in the result as dimensions with size one. With this option,
        the result will broadcast correctly against the input array.

        If the default value is passed, then `keepdims` will not be
        passed through to the `sum` method of sub-classes of
        `ndarray`, however any non-default value will be.  If the
        sub-class' method does not implement `keepdims` any
        exceptions will be raised.
    initial : scalar, optional
        Starting value for the sum. See `~numpy.ufunc.reduce` for details.

        .. versionadded:: 1.15.0

    where : array_like of bool, optional
        Elements to include in the sum. See `~numpy.ufunc.reduce` for details.

        .. versionadded:: 1.17.0

    Returns
    -------
    sum_along_axis : ndarray
        An array with the same shape as `a`, with the specified
        axis removed.   If `a` is a 0-d array, or if `axis` is None, a scalar
        is returned.  If an output array is specified, a reference to
        `out` is returned.

    See Also
    --------
    ndarray.sum : Equivalent method.

    add.reduce : Equivalent functionality of `add`.

    cumsum : Cumulative sum of array elements.

    trapz : Integration of array values using the composite trapezoidal rule.

    mean, average

    Notes
    -----
    Arithmetic is modular when using integer types, and no error is
    raised on overflow.

    The sum of an empty array is the neutral element 0:

    >>> np.sum([])
    0.0

    For floating point numbers the numerical precision of sum (and
    ``np.add.reduce``) is in general limited by directly adding each number
    individually to the result causing rounding errors in every step.
    However, often numpy will use a  numerically better approach (partial
    pairwise summation) leading to improved precision in many use-cases.
    This improved precision is always provided when no ``axis`` is given.
    When ``axis`` is given, it will depend on which axis is summed.
    Technically, to provide the best speed possible, the improved precision
    is only used when the summation is along the fast axis in memory.
    Note that the exact precision may vary depending on other parameters.
    In contrast to NumPy, Python's ``math.fsum`` function uses a slower but
    more precise approach to summation.
    Especially when summing a large number of lower precision floating point
    numbers, such as ``float32``, numerical errors can become significant.
    In such cases it can be advisable to use `dtype="float64"` to use a higher
    precision for the output.

    Examples
    --------
    >>> np.sum([0.5, 1.5])
    2.0
    >>> np.sum([0.5, 0.7, 0.2, 1.5], dtype=np.int32)
    1
    >>> np.sum([[0, 1], [0, 5]])
    6
    >>> np.sum([[0, 1], [0, 5]], axis=0)
    array([0, 6])
    >>> np.sum([[0, 1], [0, 5]], axis=1)
    array([1, 5])
    >>> np.sum([[0, 1], [np.nan, 5]], where=[False, True], axis=1)
    array([1., 5.])

    If the accumulator is too small, overflow occurs:

    >>> np.ones(128, dtype=np.int8).sum(dtype=np.int8)
    -128

    You can also start the sum with a value other than zero:

    >>> np.sum([10], initial=5)
    15
    
    }
2022-03-17 13:35:58.144 | DEBUG    | __main__:<module>:20 - {
    State   : EXIT
    Function: numpy.sum
    Runtime : 0:00:00
    }