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.

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