flixOpt 1.0.1

[FBumann]

flixOpt 1.0.1

This patch includes the following changes:

• a config file for flixOpt, which the user can overwrite
• default values being stored as empty dicts or empty lists instead of None whoever possible
• default values in network visualization improved
• typo in attribute of class CoolingTower fixed

Adding a CONFIG to flixOpt

To control central behaviour of flixOpt without hard coding it, we introduce a CONFIG.
a default config is provided and loaded when importing the package.
The values are validated on import.
CUrrently there are Configs for:

Logging

• logging_level
• logging_file
• rich: use the rich logging (for terminal formatting)

modeling

• BIG
• EPSILON
• BIG_BINARY_BOUND

If the user desires to change these values, he has to provide his own config_file.

import flixOpt as fx

import numpy as np

if __name__ == '__main__':
    fx.CONFIG.load_config('new_config.yaml')
    # --- Define Thermal Load Profile ---
    # Load profile (e.g., kW) for heating demand over time
    thermal_load_profile = np.array([30., 0., 20.])
    ...

This has to be done at the beginning of your script, to work as expected.
This is currently the only way to reliably change the config values!

To change the logging level without a config file, use the existing function:

fx.change_logging_level('DEBUG')

Default values changed: {} / [] instead of None

Instead of using None to represent the absence of certain values, empty Lists and Dictionaries are now used (when applicable).
This enables iterating over them easier.

LinearConverter

• conversion_factors
• segmented_conversion_factors

Flow

• effects_per_flow_hour

InvestParameters

• fix_effects
• specific_effects
• divest_effects

OnOffParameters

• effects_per_switch_on
• effects_per_running_hour

aggregation.py

AggregationParameters

• time_series_for_high_peaks
• time_series_for_low_peaks

Aggregation

• weights
• time_series_for_high_peaks
• time_series_for_low_peaks

Other improvements:

Improved an Exception message in CalculationResults.to_dataframe(), regarding not found Elements