felling 0.0.2

A simple package to easily create consistent logs

felling :evergreen_tree:

felling verb: to cut down
Synonyms: Logging

Felling easily improves repeatability and debugging of code by always initially logging some runtime metadata and ensuring logs are always written to a file

import felling
felling.configure()
# Done!

[TOC]

Usage

1. Install the package with pip install felling

2. The package only needs to be imported once per runtime, in __main__ run the following code

   import felling
   felling.configure()
   

   Logs will saved in ./logs/{time_ran}_{file_name}.log where file_name will be the name of the file __main__ unless a file_name is passed into felling.configure At this point your logs are now configured for this run time and you can log as normal.

Logging in Python :snake:

This is a basic explanation of logging in python, none of this is specific too felling.

First, you must import logging and name your logger, this is usually done by:

import logging
logger = logging.getLogger(__name__)

Now you can write logs, there are a few different levels which you can use. They are, in ascending order of severity:

logger.debug('lowest level')
logger.info(message)
logger.warning(message)
logger.error(message)
logger.critical(message)

If you wish for execution information to be put into the log it can be done by:

logger.error('Everything has failed', exc_info = True)

Try and excepts can also be handled properly, for example:

try:
    float('this cannot be a float') 
except ValueError as e:
    logger.exception('your message here')
    raise
except Exception as e:
    logger.exception(e.args)
    raise

Line 3 logger.exception('your message here') will log your message, the error message and its full traceback.

Line 6 will then catch all other exceptions and log them, Note catching specific error is generally preferred.

Testing

When running tests logging can be easily turned off if desired. In the file tests/__init__.py write:

logging.disable()

By default this disables all logs of level critical and lower, felling is capable of reading this status and won't generate any empty log files.

Now go and write some beautiful logs :sunrise_over_mountains:

felling additional features

felling.configure() has a lot more features than outlined in the basic summary above, they can be seen below

Initial logs

For repeatability it can be useful to have some metadata about each run time. When configuring felling it will log:

1. The users username
2. The most recent git commit hash for __main__s repo, its fine if it isn’t a git repo.
3. The git remote status from git remote show origin

log_path

import felling
felling.configure(log_path = './logs')

When configuring a pathlib.Path or a str path can be provide as the directory to save logs to.

log_file_name

import felling
felling.configure(log_file_name = 'logs_for_foo')

When configuring a custom file name for the log file can be passed

file_log_level and std_log_level

import felling
felling.configure(file_log_level ='ERROR', std_out_log_level = 40)

Log levels can be provided either as a string (“ERROR”) or integer (40) as defined here. file_log_level defines the minimum log level which will be written to the log file, while std_out_log_level defines the minimum log level which will be written to the std output (your terminal)

error_only_modules

import felling
import pandas
felling.configure(error_only_modules = pandas)

If some packages are writing lots of logs you’re not interested in a specific error_only handler can be set up. In the above example only pandas error logs will be used. Some packages have control for their verbosity of logging, however, setting it up with felling works for all packages and is comprehensive.

modules_to_debug

import felling
import pandas
felling.configure(modules_to_debug = pandas)

Similar to [error_only_modules](## error_only_modules) you can specify modules which you would like debug logs to be interpreted

package_versions_to_log

import felling
import pandas
felling.configure(package_versions_to_log = pandas)

For repeatability it can be helpful to log package versions, packages past to package_versions_to_log will have their version numbers logged while running initial logs