logfunc - @logf()

@logf() is a Python decorator designed for uncomplicated and immediate addition of logging to functions. Its main goal is to provide developers with a tool that can be added quickly to any function and left in place without further adjustments.

I originally made @logf() for my own use, but I hope it can be useful to others as well.

Highlights

• Async Support: Incorporated from version 1.6 onwards.
• Broad Python 3 Compatibility: Designed to work seamlessly across multiple Python 3 versions,
• Effortless Logging: Implement logging without disrupting the flow of your code.
• Leave-and-Forget: Once integrated, no further adjustments are needed.
• Encourages Logic Compartmentalization.
• Customizable: Numerous settings available for tailoring logging behavior to specific needs.
• Environment Variables: Overriding default settings made easy with environment variables.
• Log Exceptions: Option to log exceptions before they are raised.

Usage

Installation

To integrate @logf() into your projects:

pip install logfunc

Importing

Simply import the decorator to start using it:

from logfunc import logf

Basic Usage

Apply the @logf() decorator to functions you intend to log:

from logfunc import logf

@logf()
def concatenate_strings(str1: str, str2: str) -> str:
    return str1 + str2

This setup ensures automatic logging of function name, parameters, return values, and execution time.

@logf() args

• level: Set the log level (DEBUG, INFO, WARNING, etc.).
• log_args & log_return: Control whether to log arguments and return values.
• max_str_len: Limit the length of logged strings.
• log_exec_time: Option to log the execution time.
• single_msg: Consolidate all log data into a single message.
• use_print: Choose to print() log messages instead of using standard logging.
• log_stack_info: Pass stack_info=$x to .log() but not print
• use_logger: Pass a logger name or logger object to use instead of logging.log
• identifier: Add a unique identifier to enter/exit log messages.

print_all used to be an env var, now just unset LOGF_LEVEL and set USE_PRINT=True for the same effect.

Environment Variable Overrides

Modify the behavior of @logf() using environment variables:

Env Var	Example Values
LOGF_LEVEL	DEBUG, INFO, WARNING
LOGF_MAX_STR_LEN	10, 50, 10000000
LOGF_SINGLE_MSG	True, False
LOGF_USE_PRINT	True, False
LOGF_STACK_INFO	True, False
LOGF_LOG_EXEC_TIME	True, False
LOGF_LOG_ARGS	True, False
LOGF_LOG_RETURN	True, False
LOGF_USE_LOGGER	'logger_name'
LOGF_LOG_LEVEL	DEBUG, INFO, WARNING
LOGF_IDENTIFIER	True, False

See the following output for an example of how an env var will affect @logf() behaviour:

With LOGF_USE_PRINT=True:

mym2@Carys-MacBook-Pro logf % gitpoll ~/test
Running once...
-> __init__()[CwKVbK] | (<CmdExec >, 'git rev-parse --abbrev-ref HEAD') {}
<- __init__()[CwKVbK] 0.0048s | None
-> __init__()[BIimGf] | (<CmdExec >, 'git config --get branch.test.remote') {}
<- __init__()[BIimGf] 0.0040s | None
-> __init__()[ED1XW0] | (<CmdExec >, 'git config --get branch.test.merge') {}
<- __init__()[ED1XW0] 0.0039s | None
-> __init__()[dsPXjJ] | (<CmdExec >, 'git rev-parse refs/remotes//') {}
<- __init__()[dsPXjJ] 0.0044s | None
-> __init__()[5rkgc9] | (<CmdExec >, 'git rev-parse HEAD') {}
<- __init__()[5rkgc9] 0.0037s | None
-> __init__()[GDti62] | (<CmdExec >, 'git fetch') {}
<- __init__()[GDti62] 1.1160s | None

With LOGF_SINGLE_MSG=True:

mym2@Carys-MacBook-Pro logf % gitpoll ~/test
Running once...
__init__() 0.0050s | (<CmdExec >, 'git rev-parse --abbrev-ref HEAD') {} | None
__init__() 0.0041s | (<CmdExec >, 'git config --get branch.test.remote') {} | None
__init__() 0.0041s | (<CmdExec >, 'git config --get branch.test.merge') {} | None
__init__() 0.0041s | (<CmdExec >, 'git rev-parse refs/remotes//') {} | None
__init__() 0.0038s | (<CmdExec >, 'git rev-parse HEAD') {} | None
__init__() 1.0993s | (<CmdExec >, 'git fetch') {} | None

Real-world Examples

Here are a couple of real-world examples of @logf() usage:

from logfunc import logf


# Database operations
@logf(level='ERROR')
def db_insert(item):
    # Insert item into database
    pass

# Asynchronous tasks in an application
@logf()
async def fetch_data(url):
    # Fetch data from URL asynchronously
    return data

Testing

Activate/create your venv with python3 -m venv venv and source venv/bin/activate if you haven't already.

Run pip install -r requirements_dev.txt to install the testing dependencies.

Run pytest tests.py to run the tests.

Output should look like this:

---------- coverage: platform darwin, python 3.11.5-final-0 ----------
Name                  Stmts   Miss  Cover   Missing
---------------------------------------------------
logfunc/__init__.py       2      0   100%
logfunc/config.py        59      0   100%
logfunc/defaults.py       2      0   100%
logfunc/main.py          69      0   100%
logfunc/msgs.py           8      0   100%
logfunc/utils.py         35      0   100%
logfunc/version.py        1      0   100%
---------------------------------------------------
TOTAL                   176      0   100%


==================================== 25 passed in 0.06s

You can also just run the tests.py file directly.

Contributing

Contributions are welcome! Please feel free to submit a pull request or open an issue.

License

MIT

Contact

[email protected]