tap(value, label=None)

Prints value and then returns it. Useful to tap into some functional pipeline for debugging:

fields = (f for f in fields_for(category) if section in tap(tap(f).sections))
# ... do something with fields

If label is specified then it’s printed before corresponding value:

squares = {tap(x, 'x'): tap(x * x, 'x^2') for x in [3, 4]}
# x: 3
# x^2: 9
# x: 4
# x^2: 16
# => {3: 9, 4: 16}
@log_calls(print_func, errors=True, stack=True)

Will log or print all function calls, including arguments, results and raised exceptions. Can be used as decorator or tapped into call expression:

sorted_fields = sorted(fields, key=print_calls(lambda f: f.order))

If errors is set to False then exceptions are not logged. This could be used to separate channels for normal and error logging:

@log_calls(, errors=False)
def some_suspicious_function(...):
    # ...
    return result

print_calls() always prints everything, including error stack traces.

@log_exits(print_func, errors=True, stack=True)

Will log or print every time execution enters or exits the function. Should be used same way as log_calls() and print_calls() when you need to track only one event per function call.

@log_errors(print_func, label=None, stack=True)

Will log or print all function errors providing function arguments causing them. If stack is set to False then each error is reported with simple one line message.

Can be combined with silent() or ignore() to trace occasionally misbehaving function:

def guess_user_id(username):
    initial = first_guess(username)
    # ...

Can also be used as context decorator:

with print_errors('initialization', stack=False):
    # ...
# SomeException: a bad thing raised in initialization
@log_durations(print_func, label=None)

Will time each function call and log or print its duration:

def do_hard_work(n):
    samples = range(n)
    # ...

# 121 ms in do_hard_work(10)
# 143 ms in do_hard_work(11)
# ...

A block of code could be timed with a help of context manager:

with print_durations('Creating models'):
    # ...

# 10.2 ms in Creating models
log_iter_durations(seq, print_func, label=None)

Wraps iterable seq into generator logging duration of processing of each item:

for item in print_iter_durations(seq, label='hard work'):

# 121 ms in iteration 0 of hard work
# 143 ms in iteration 1 of hard work
# ...