Converters
We currently provide 4 types of converters. Here, we discuss their basic structure and guidelines for building a custom converter.
Use the examples notebooks to understand how to convert data from the respective formats.
PGM JSON Converter: Refer to the PGM JSON Example
VisonExcelConverter Refer to the Vision Example
Pandapower Converter: Converts pandapower network (a dictionary of dataframes) to power-grid-model data.
Refer to converters in API documentation for more details
Structure
All converters are derived from the base power_grid_model_io.converters.base_converter
.
The usable functions for loading, saving and converting the data are located in the base class.
The private functions (_load_data
, _parse_data
and _serialize_data
) are overloaded based on the specific type of converter (i.e., excel, json or pandapower).
The VisonExcelConverter
extends the tabular converters for Excel exports of Vision.
It is recommended that custom converters be created in a similar way.
Instantiation
Converter objects can be instantiated in the following way. For instance, for a PgmJsonConverter
:
from power_grid_model_io.converters.pgm_json_converter import PgmJsonConverter
converter = PgmJsonConverter(source_file=source, destination_file=destination)
Common methods for converters
The common methods across different converters, including data IO and log level configurations, are described below. Converter specific methods are presented in corresponding sections.
Loading data
Methods load_input_data(), load_update_data(), load_sym_output_data() and load_asym_output_data() are for loading the relevant data to the converter.
Input data can be configured at converter initialisation by passing the path to source data to parameter source_file
. Alternatively, the data can also be provided as an argument to the load function after initialisation.
In addition to the input data that will be used in calculations by power-grid-model, other miscellaneous information in the source file is stored in extra_info
:
input_data, extra_info = converter.load_input_data(data=example_data)
Saving data
It is possible to save the data in the format of the converter.
The Converter can be instantiated with a path given to destination_file
.
Alternatively, the destination path can be provided in the save function.
You can also add additional information about each component in the form of extra_info
generated by Load data to be saved along with it.
converter.save(example_data, extra_info=example_extra_info, destination=destination_path)
Configuring the log output
An interface is provided to configure the log level of converters.
This configuration can be done at converter initialisation or after.
Notice that this log level only belongs to the logger within the converter.
Users need to set their basic configuration of the logging
module to a level that is below what is configuired for the converters.
# Examplary usage in your script
import logging
from power_grid_model_io.converters import VisionExcelConverter
logging.basicConfig(level=logging.INFO) # Only levels INFO and above will be logged
converter_warning = VisionExcelConverter(input_file, log_level=logging.WARNING) # If there is any logs above WARNING, they will be logged
converter_info = VisionExcelConverter(input_file) # Uses default INFO level
logging.basicConfig(level=logging.WARNING) # Only levels WARNING and above will be logged
assert converter_info.get_log_level() == logging.INFO # Previously created converters will retain their original log level, regardless of system wide configuration
converter_ = VisionExcelConverter(input_file, log_level=logging.DEBUG) # Any logs on DEBUG and INFO level will not be logged
converter_.set_log_level(logging.WARNING) # Now the converter's log level is set to WARNING