umami.configuration package#

Submodules#

umami.configuration.base module#

Base modul for process configuration (preprocessing, training, …).

class umami.configuration.base.Configuration(yaml_config: str)#

Bases: object

Configuration base class.

property config_path: str#

Return config path.

Returns:

Config path.

Return type:

str

load_config_file() None#

Load config file from disk.

umami.configuration.configuration module#

Configuration for logger of umami and tensorflow as well as reading global config.

class umami.configuration.configuration.CustomFormatter(fmt=None, datefmt=None, style='%', validate=True)#

Bases: Formatter

Logging Formatter to add colors and count warning / errors using implementation from https://stackoverflow.com/questions/384076/how-can-i-color-python-logging-output

FORMATS = {10: '\x1b[38;21m%(asctime)s - %(levelname)s:%(name)s: %(message)s (%(filename)s:%(lineno)d)\x1b[0m', 20: '\x1b[32;21m%(levelname)s:%(name)s: %(message)s\x1b[0m', 30: '\x1b[33;21m%(levelname)s:%(name)s: %(message)s\x1b[0m', 40: '\x1b[31;21m%(asctime)s - %(levelname)s:%(name)s: %(message)s (%(filename)s:%(lineno)d)\x1b[0m', 50: '\x1b[31;1m%(asctime)s - %(levelname)s:%(name)s: %(message)s (%(filename)s:%(lineno)d)\x1b[0m'}#
bold_red = '\x1b[31;1m'#
date_format = '%(levelname)s:%(name)s: %(message)s'#
debugformat = '%(asctime)s - %(levelname)s:%(name)s: %(message)s (%(filename)s:%(lineno)d)'#
format(record)#

Format the specified record as text.

The record’s attribute dictionary is used as the operand to a string formatting operation which yields the returned string. Before formatting the dictionary, a couple of preparatory steps are carried out. The message attribute of the record is computed using LogRecord.getMessage(). If the formatting string uses the time (as determined by a call to usesTime(), formatTime() is called to format the event time. If there is exception information, it is formatted using formatException() and appended to the message.

green = '\x1b[32;21m'#
grey = '\x1b[38;21m'#
red = '\x1b[31;21m'#
reset = '\x1b[0m'#
yellow = '\x1b[33;21m'#
class umami.configuration.configuration.GlobalConfiguration#

Bases: object

This is a global configuration to allow certain settings which are hardcoded so far.

get_configuration()#

Assign configuration from file to class variables.

Raises:

KeyError – if required config is not present in passed config file

load_config_file()#

Load config file from disk.

set_logging_level() object#

Set DebugLevel for logging.

Returns:

Umami logger.

Return type:

object

set_mpl_plotting_backend()#

Setting the plotting backend of matplotlib.

set_tf_debug_level()#

Setting the Debug level of tensorflow. For reference see https://stackoverflow.com/questions/35869137/avoid-tensorflow-print-on-standard-error

umami.configuration.configuration.set_log_level(umami_logger, log_level: str)#

Setting log level

Parameters:
  • umami_logger (logger) – logger object

  • log_level (str) – logging level corresponding CRITICAL, ERROR, WARNING, INFO, DEBUG, NOTSET

Module contents#