181 lines
8.9 KiB
Python
181 lines
8.9 KiB
Python
"""
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HeatPumpExport exports heatpump coefficient into several formats
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SPDX - License - Identifier: LGPL - 3.0 - or -later
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Copyright © 2021 Project Author Peter Yefi peteryefi@gmail.com
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"""
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import os
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from typing import List, Tuple, Union, Dict
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import yaml
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from string import Template
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import pandas as pd
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class HeatPumpExport:
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"""
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Exports heat pump values as coefficients
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of some defined function
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"""
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def __init__(self, base_path, city):
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self._template_path = (base_path / 'heat_pumps/template.txt')
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self._constants_path = (base_path / 'heat_pumps/constants.yaml')
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# needed to compute max demand.
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self._demand_path = (base_path / 'heat_pumps/demand.txt')
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self._city = city
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self._input_data = None
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self._base_path = base_path
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def run_insel(self, user_input: Dict, hp_model: str, data_type: str) -> None:
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"""
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Runs insel and write the necessary files
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:param user_input: a dictionary containing the user
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values necessary to run insel
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:param hp_model: a string that indicates the heat
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pump model to be used e.g. 012, 015
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:param data_type: a string that indicates whether
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insel should run for heat or cooling performance
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:return:
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"""
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self._input_data = user_input
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# update input data with other data necessary to run insel
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capacity_coff, comp_power_coff = self._extract_model_coff(hp_model, data_type)
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self._update_input_data_with_coff(capacity_coff, comp_power_coff)
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# update input data with constants
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self._update_input_data_with_constants()
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# update input data with input and output files for insel
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self._update_input_data_with_files()
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insel_file_handler = None
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insel_template_handler = None
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try:
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# run insel
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insel_template_handler = open(self._template_path, "r")
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insel_template_handler = insel_template_handler.read()
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insel_template = Template(insel_template_handler).substitute(self._input_data)
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# create the insel file and write the template with substituted values into it
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insel_file = (self._base_path / 'heat_pumps/dompark_heat_pump.insel')
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insel_file_handler = open(insel_file, "w")
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insel_file_handler.write(insel_template)
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# Now run insel
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os.system('insel {}'.format(insel_file))
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# Writer headers to csv output files generated by insel
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self._write_insel_output_headers()
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except IOError as err:
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print("I/O exception: {}".format(err))
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finally:
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insel_file_handler.close()
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insel_template_handler.close()
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def _write_insel_output_headers(self):
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"""
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Write headers to the various csv file generated by insel
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:return:
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"""
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header_data = {
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self._input_data['fileOut1']: ['Year', ' Month', ' Day', 'Hour', 'Minute', 'HP Heat Output (kW)',
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'HP Electricity Consumption (kW)', 'HP COP', 'TES Charging Rate (kg/s)',
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'TES Discharging Rate (kg/s)', 'TES Node 1 Temperature', 'TES Node 2 Temperature',
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'TES Node 3 Temperature', 'TES Node 4 Temperature', 'TES Energy Content (J)',
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'TES Energy Content (kWh)', 'TES Energy Content Variation (kWh)',
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'Auxiliary Heater Fuel Flow Rate (kg/s)', 'Auxiliary Heater Energy Input (kW)',
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'HP Operational Cost (CAD)', 'Auxiliary Heater Operational Cost (CAD)',
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'Operational CO2 Emissions of HP (g)',
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'Operational CO2 Emissions of Auxiliary Heater (g)',
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'Return Temperature', 'Demand (kW)'],
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self._input_data['fileOut2']: ['Day', 'Operational Daily Emissions from Heat Pumps (g)',
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'Operational Daily Emissions from Auxiliary Heater (g)'],
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self._input_data['fileOut3']: ['Month', 'Monthly Operational Costs of Heat Pumps (CAD)',
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'Monthly Operational Costs of Auxiliary Heater (CAD)'],
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self._input_data['fileOut4']: ['Month', 'Monthly Fuel Consumption of Auxiliary Heater (m3)'],
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self._input_data['fileOut5']: ['Month', 'Operational Monthly Emissions from Heat Pumps (g)',
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'Operational Monthly Emissions from Auxiliary Heater (g)'],
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self._input_data['fileOut6']: ['Day', 'Daily HP Electricity Demand (kWh)'],
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self._input_data['fileOut7']: ['Day', 'Daily Operational Costs of Heat Pumps (CAD)',
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'Daily Operational Costs of Auxiliary Heater (CAD)'],
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self._input_data['fileOut8']: ['Month', 'Monthly HP Electricity Demand (kWh)'],
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self._input_data['fileOut9']: ['Day', 'Daily Fuel Consumption of Auxiliary Heater (m3)'],
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self._input_data['fileOut10']: ['Year', 'Month', 'Day', 'Hour', 'HP Electricity Demand (kWh)']
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}
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for file_path, header in header_data.items():
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file_path = file_path.strip("'")
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df = pd.read_csv(file_path, sep='\s+')
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df.to_csv(file_path, header=header)
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def _update_input_data_with_files(self):
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"""
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Updates input data for insel with some files that will
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be written to after insel runs. Also specifies and input file
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which is the Heating Demand (demand.txt) file
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:return:
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"""
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self._input_data["HeatingDemand"] = f"'{str(self._demand_path)}'"
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self._input_data["fileOut1"] = f"'{str((self._base_path / 'heat_pumps/technical_performance.csv'))}'"
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self._input_data["fileOut2"] = f"'{str((self._base_path / 'heat_pumps/system_daily_emissions.csv'))}'"
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self._input_data["fileOut3"] = f"'{str((self._base_path / 'heat_pumps/monthly_operational_costs.csv'))}'"
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self._input_data["fileOut4"] = f"'{str((self._base_path / 'heat_pumps/monthly_fossil_fuel_consumptions.csv'))}'"
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self._input_data["fileOut5"] = f"'{str((self._base_path / 'heat_pumps/system_monthly_emissions.csv'))}'"
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self._input_data["fileOut6"] = f"'{str((self._base_path / 'heat_pumps/daily_hp_electricity_demand.csv'))}'"
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self._input_data["fileOut7"] = f"'{str((self._base_path / 'heat_pumps/daily_operational_costs.csv'))}'"
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self._input_data["fileOut8"] = f"'{str((self._base_path / 'heat_pumps/monthly_hp_electricity_demand.csv'))}'"
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self._input_data["fileOut9"] = f"'{str((self._base_path / 'heat_pumps/daily_fossil_fuel_consumption.csv'))}'"
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self._input_data["fileOut10"] = f"'{str((self._base_path / 'heat_pumps/hp_hourly_electricity_demand.csv'))}'"
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def _compute_max_demand(self):
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"""
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Retrieves the maximum demand value from
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the demands text file
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:return: float
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"""
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max_demand = -1
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with open(self._demand_path) as file_handler:
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for demand in file_handler.readlines():
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if float(demand) > max_demand:
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max_demand = float(demand)
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return max_demand
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def _update_input_data_with_constants(self):
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with open(self._constants_path) as file:
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constants_dict = yaml.load(file, Loader=yaml.FullLoader)
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for key, value in constants_dict.items():
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self._input_data[key] = value
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# compute maximum demand. TODO: This should come from catalog in the future
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max_demand = self._compute_max_demand()
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# compute TESCapacity
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self._input_data["TESCapacity"] = self._input_data["HoursOfStorageAtMaxDemand"] * (max_demand * 3.6) / (
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(self._input_data["Cp"] / 1000) * self._input_data["TemperatureDifference"])
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def _update_input_data_with_coff(self, capacity_coff: List, comp_power_coff: List):
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"""
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Updates the user data with coefficients derived from imports
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:param capacity_coff: heat or cooling capacity coefficients
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:param comp_power_coff: heat or cooling comppressor power coefficients
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:return:
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"""
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self._input_data["a1"] = capacity_coff[0]
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self._input_data["a2"] = capacity_coff[1]
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self._input_data["a3"] = capacity_coff[2]
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self._input_data["a4"] = capacity_coff[3]
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self._input_data["a5"] = capacity_coff[4]
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self._input_data["a6"] = capacity_coff[5]
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self._input_data["b1"] = comp_power_coff[0]
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self._input_data["b2"] = comp_power_coff[1]
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self._input_data["b3"] = comp_power_coff[2]
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self._input_data["b4"] = comp_power_coff[3]
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self._input_data["b5"] = comp_power_coff[4]
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self._input_data["b6"] = comp_power_coff[5]
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def _extract_model_coff(self, hp_model: str, data_type='heat') -> Union[Tuple[List, List], None]:
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"""
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Extracts heat pump coefficient data for a specific
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model. e.g 012, 140
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:param hp_model: the model type
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:param data_type: indicates whether we're extracting cooling
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or heating perfarmcn coefficients
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:return:
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"""
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for energy_system in self._city.energy_systems:
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if energy_system.heat_pump.model == hp_model:
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if data_type == 'heat':
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return energy_system.heat_pump.heating_capacity_coff, energy_system.heat_pump.heating_comp_power_coff
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return energy_system.heat_pump.cooling_capacity_coff, energy_system.heat_pump.cooling_comp_power_coff
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return None
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