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Pull Request — dev (#1193)
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01:46
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nested_subnetwork_example.main()   B

Complexity

Conditions 3

Size

Total Lines 210
Code Lines 106

Duplication

Lines 0
Ratio 0 %

Importance

Changes 0
Metric Value
eloc 106
dl 0
loc 210
rs 7
c 0
b 0
f 0
cc 3
nop 0

How to fix   Long Method   

Long Method

Small methods make your code easier to understand, in particular if combined with a good name. Besides, if your method is small, finding a good name is usually much easier.

For example, if you find yourself adding comments to a method's body, this is usually a good sign to extract the commented part to a new method, and use the comment as a starting point when coming up with a good name for this new method.

Commonly applied refactorings include:

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import logging
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import os
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import matplotlib.pyplot as plt
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import pandas as pd
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from oemof.tools import logger
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from oemof.network import SubNetwork
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from oemof.network.network.hierachical_nodes import HierachicalLabel
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from oemof import solph
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from oemof.solph import EnergySystem
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from oemof.solph import Model
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from oemof.solph import buses
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from oemof.solph import components
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from oemof.solph import create_time_index
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from oemof.solph import flows
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from oemof.solph import helpers
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from oemof.solph import Results
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STORAGE_LABEL = "battery_storage"
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def get_data_from_file_path(file_path: str) -> pd.DataFrame:
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    file_dir = os.path.dirname(os.path.abspath(__file__))
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    data = pd.read_csv(file_dir + "/" + file_path)
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    return data
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def plot_figures_for(element: dict) -> None:
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    figure, axes = plt.subplots(figsize=(10, 5))
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    element["sequences"].plot(ax=axes, kind="line", drawstyle="steps-post")
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    plt.legend(
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        loc="upper center",
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        prop={"size": 8},
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        bbox_to_anchor=(0.5, 1.25),
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        ncol=2,
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    )
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    figure.subplots_adjust(top=0.8)
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    plt.show()
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class Volatile(solph.Facade):
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    def __init__(
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        self,
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        label: str,
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        output_bus: solph.Bus,
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        timeseries: float | list[float],
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        nominal_capacity: float,
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    ):
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        self.output_bus = output_bus
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        self.timeseries = timeseries
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        self.nominal_capacity = nominal_capacity
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        super().__init__(label=label, facade_type=type(self))
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    def define_subnetwork(self):
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        self.subnode(
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            solph.components.Source,
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            label="source",
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            outputs={
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                self.output_bus: solph.Flow(
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                    max=self.timeseries, nominal_capacity=self.nominal_capacity
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                ),
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            },
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        )
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def main():
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    # For models that need a long time to optimise, saving and loading the
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    # EnergySystem might be advised. By default, we do not do this here. Feel
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    # free to experiment with this once you understood the rest of the code.
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    # *************************************************************************
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    # ********** PART 1 - Define and optimise the energy system ***************
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    # *************************************************************************
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    # Read data file
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    file_name = "subnetwork_example.csv"
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    data = get_data_from_file_path(file_name)
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    solver = "cbc"  # 'glpk', 'gurobi',....
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    debug = False  # Set number_of_timesteps to 3 to get a readable lp-file.
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    number_of_time_steps = len(data)
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    solver_verbose = False  # show/hide solver output
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    # initiate the logger (see the API docs for more information)
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    logger.define_logging(
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        logfile="oemof_example.log",
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        screen_level=logging.INFO,
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        file_level=logging.INFO,
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    )
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    logging.info("Initialize the energy system")
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    date_time_index = create_time_index(2012, number=number_of_time_steps)
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    # create the energysystem and assign the time index
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    energysystem = EnergySystem(
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        timeindex=date_time_index, infer_last_interval=False
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    )
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    ##########################################################################
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    # Create oemof objects
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    ##########################################################################
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    logging.info("Create oemof objects")
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    # The bus objects were assigned to variables which makes it easier to
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    # connect components to these buses (see below).
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    # create natural gas bus
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    bus_gas = buses.Bus(label=HierachicalLabel("natural_gas"))
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    # create electricity bus
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    bus_electricity = buses.Bus(label=HierachicalLabel("electricity"))
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    # adding the buses to the energy system
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    energysystem.add(bus_gas, bus_electricity)
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    # create excess component for the electricity bus to allow overproduction
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    energysystem.add(
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        components.Sink(
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            label="excess_bus_electricity",
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            inputs={bus_electricity: flows.Flow()},
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        )
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    )
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    # create source object representing the gas commodity
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    energysystem.add(
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        components.Source(
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            label="rgas",
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            outputs={bus_gas: flows.Flow()},
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        )
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    )
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    # *** SUB-NETWORK ***************************
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    # Add a subnetwork for Renewable Energies. This not a Facade it just meant
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    # to group components
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    renewables = SubNetwork("renewables")
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    re_bus = renewables.subnode(buses.Bus, "re_elec")
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    # create fixed source object representing wind power plants
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    renewables.subnode(
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        Volatile,
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        label="wind",
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        output_bus=re_bus,
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        timeseries=data["wind"],
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        nominal_capacity=1000000,
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    )
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    # create fixed source object representing pv power plants
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    renewables.subnode(
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        Volatile,
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        label="pv",
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        output_bus=re_bus,
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        timeseries=data["pv"],
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        nominal_capacity=582000,
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    )
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    renewables.subnode(
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        components.Converter,
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        label="connection",
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        outputs={bus_electricity: flows.Flow()},
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        inputs={re_bus: flows.Flow()},
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    )
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    energysystem.add(renewables)  # Subnetwork to Energysystem
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    # *************************************************************
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    # create simple sink object representing the electrical demand
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    # nominal_value is set to 1 because demand_el is not a normalised series
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    energysystem.add(
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        components.Sink(
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            label="demand",
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            inputs={
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                bus_electricity: flows.Flow(
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                    fix=data["demand_el"], nominal_capacity=1
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                )
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            },
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        )
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    )
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    # create simple converter object representing a gas power plant
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    energysystem.add(
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        components.Converter(
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            label="pp_gas",
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            inputs={bus_gas: flows.Flow()},
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            outputs={
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                bus_electricity: flows.Flow(
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                    nominal_capacity=10e10, variable_costs=50
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                )
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            },
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            conversion_factors={bus_electricity: 0.58},
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        )
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    )
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    # create storage object representing a battery
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    nominal_capacity = 10077997
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    nominal_value = nominal_capacity / 6
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    battery_storage = components.GenericStorage(
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        nominal_capacity=nominal_capacity,
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        label=STORAGE_LABEL,
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        inputs={bus_electricity: flows.Flow(nominal_capacity=nominal_value)},
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        outputs={
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            bus_electricity: flows.Flow(
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                nominal_capacity=nominal_value, variable_costs=0.001
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            )
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        },
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        loss_rate=0.00,
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        initial_storage_level=None,
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        inflow_conversion_factor=1,
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        outflow_conversion_factor=0.8,
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    )
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    energysystem.add(battery_storage)
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    ##########################################################################
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    # Optimise the energy system and plot the results
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    ##########################################################################
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    logging.info("Optimise the energy system")
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    # initialise the operational model
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    energysystem_model = Model(energysystem)
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    # This is for debugging only. It is not(!) necessary to solve the problem
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    # and should be set to False to save time and disc space in normal use. For
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    # debugging the timesteps should be set to 3, to increase the readability
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    # of the lp-file.
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    if debug:
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        file_path = os.path.join(
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            helpers.extend_basic_path("lp_files"), "basic_example.lp"
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        )
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        logging.info(f"Store lp-file in {file_path}.")
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        io_option = {"symbolic_solver_labels": True}
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        energysystem_model.write(file_path, io_options=io_option)
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    # if tee_switch is true solver messages will be displayed
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    logging.info("Solve the optimization problem")
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    energysystem_model.solve(
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        solver=solver, solve_kwargs={"tee": solver_verbose}
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    )
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    results = Results(energysystem_model)
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    # ToDO Implement a filter methode for the Result object to exclude
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    #  subcomponents of a facade/sub-network
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    # The following lines are meant to show how the result should look like
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    # in case the subcomponents should be exclude. There should not be a
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    # postprocessing it is better to filter the nodes directly
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    # Filter columns that are internal only
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    keep_columns = [
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        c
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        for c in results.flow.columns
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        if getattr(c[1].label, "parent", None)
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        != getattr(c[0].label, "parent", None)
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        or (
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            getattr(c[0].label, "parent", True) is True
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            and getattr(c[1].label, "parent", True) is True
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        )
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    ]
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    flow_results_filtered = results.flow[keep_columns].copy()
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    # Replace subcomponent with facade object
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    for level in [0, 1]:
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        flow_results_filtered.rename(
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            columns={
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                c[level]: getattr(c[level].label, "parent", c[level])
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                for c in flow_results_filtered.columns
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            },
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            level=level,
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            inplace=True,
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        )
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    print("**** All results ****")
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    print(results.flow.sum())
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    print("**** Filtered results ****")
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    print(flow_results_filtered.sum())
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if __name__ == "__main__":
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    main()
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