scikitplot.sp_logging

Logging utilities.

Inspired by “Tensorflow’s logging system”.

This module provides advanced logging utilities for Python applications, including support for singleton-based logging with customizable formatters, handlers, and thread-safety.

It extends Python’s standard logging library to enhance usability and flexibility for large-scale projects.

Scikit-plots logging helpers, supports vendoring.

Module Dependencies: - Python standard library: logging

User guide. See the Logging System section for further details.

Logging Levels

CRITICAL	int([x]) -> integer
DEBUG	int([x]) -> integer
ERROR	int([x]) -> integer
FATAL	int([x]) -> integer
INFO	int([x]) -> integer
NOTSET	int([x]) -> integer
WARN	int([x]) -> integer
WARNING	int([x]) -> integer

Functional Interface - get_logger

AlwaysStdErrHandler	A custom logging handler inherited from StreamHandler.
GoogleLogFormatter	A custom logging formatter inherited from Formatter.
critical	Log a message at the CRITICAL log level.
debug	Log a message at the DEBUG log level.
error	Log a message at the ERROR log level.
error_log	Empty helper method.
fatal	Log a message at the FATAL - CRITICAL log level.
getEffectiveLevel	Return how much logging output will be produced.
get_logger	Return SP (scikitplot) logger instance.
log_if	Log 'msg % args' at level 'level' only if condition is fulfilled.
setLevel	Set the threshold for what messages will be logged.
vlog	Log a message at the specified log level.
warn	Log a message at the WARN - WARNING log level.
warning	Log a message at the WARNING log level.