The documentation is available in html here (generated by Sphinx). This module is a module designed to help deal with object-oriented logging.
When you are dealing with real-life object, it can be more convenient to have an object-oriented approach to keep all the underlying data when trying to log them.
Example of usecases:
- Logging all of the unavailable items whose manufacturing company shut down
- Logging the items that do not fit requirements
- Logging the items that have incoherent state with another database
Examples are available inside the example folder.
The module megalogger is centered around few submodules:
-
The submodule megalogged gives the abstract class for the item to be logged (the component).
-
The submodule megalogging gives the meta-logging manager.
-
The submodule blueprints gives abstract classes that are partially-implemented handlers for various usecases like Pandas Excel, Pandas ODS, Pandas SQL, Text Stream.
-
The submodule abstract_logger gives abstract loggers.
Short example
In this example, we will have 4 solar sensors objects represented by the class Solar_Sensor (child of the abstract class MegaLoggedItemTuple ) and the blueprint for Pandas Excel (we will build MyPandasLogger, child of PandasExcelLoggerBlueprint ).
We will iterate over each of the sensors to find those with power below 3 watts and log them out on an Excel sheet.
from src import megalogger
class Solar_Sensor(megalogger.MegaLoggedItemTuple):
def __init__(self,
power,
unique_id,
has_backsheet: bool):
self.power = power
self.life_expectancy = 10
self.unique_id = unique_id
self.has_backsheet: bool = has_backsheet
def to_tuple(self,
**kwargs):
return (self.power,
self.life_expectancy,
self.unique_id,
self.has_backsheet)
class MyPandasLogger(megalogger.PandasExcelLoggerBlueprint):
def add_item(self,
item,
message,
**kwargs):
if isinstance(item, Solar_Sensor):
tuple_equivalent = (*item.to_tuple(), message)
self.internal_list_tuple.append(tuple_equivalent)
else:
raise TypeError()
solar_sensor_a =\
Solar_Sensor(power=54,unique_id="1454", has_backsheet=False)
solar_sensor_b =\
Solar_Sensor(power=2,unique_id="1457", has_backsheet=True)
solar_sensor_c =\
Solar_Sensor(power=3.6,unique_id="1462", has_backsheet=False)
solar_sensor_d =\
Solar_Sensor(power=1.7,unique_id="1469", has_backsheet=False)
mylogger = MyPandasLogger()
for solar_iter in [solar_sensor_a,
solar_sensor_b,
solar_sensor_c,
solar_sensor_d]:
if solar_iter.power < 3:
mylogger.add_item(solar_iter,
f"Invalid power {solar_iter.power};"
f" must be above 3 Watts")
mylogger.yield_results(
dict_elements_arguments={
"EXCEL_LOCATION": "./test.xlsx",
"PANDAS_COLUMNS": [
"Power",
"Life expectancy",
"Unique ID",
"Has backsheet ?",
"Message"
]
}
)
