hub/imports/energy_systems/xlsx_heat_pump_parameters.py

"""
XlsxHeatPumpParameters import the heat pump information
SPDX - License - Identifier: LGPL - 3.0 - or -later
Copyright © 2020 Project Author Peter Yefi peteryefi@gmail.com
Contributor Pilar Monsalvete Alvarez de Uribarri pilar.monsalvete@concordia.ca
"""

import pandas as pd
from typing import Dict
from typing import List
from city_model_structure.energy_systems.heat_pump import HeatPump
from city_model_structure.energy_system import EnergySystem


class XlsxHeatPumpParameters:
    """
  XlsxHeatPumpParameters class
  """

    def __init__(self, city, base_path):
        self._city = city
        self._base_path = (base_path / 'heat_pumps/Air source.xlsx')

    def _read_file(self) -> Dict:
        """
    reads xlsx file containing the heat pump information
    into a dictionary
    :return : Dict
    """
        xl_file = pd.ExcelFile(self._base_path)
        heat_pump_dfs = {sheet_name: xl_file.parse(sheet_name)
                         for sheet_name in xl_file.sheet_names}

        cooling_data = {}
        heating_data = {}

        for sheet, dataframe in heat_pump_dfs.items():
            if sheet == "Summary":
                continue
            # Remove nan rows and columns and extract cooling and heating data
            # for each sheet
            df = heat_pump_dfs[sheet].dropna(axis=1, how='all')
            cooling_df = df.iloc[4:34, 0:8]
            heating_df = df.iloc[4:29, 8:20]

            # extract the data into dictionaries each sheet is a key entry in the
            # dictionary
            cooling_data[sheet] = {}
            heating_data[sheet] = {}
            i = 0
            # for each sheet extract data for twout/Ta.RU temperatures. Thus, the twout
            # temp is the key for the values of pf,pa,qw data
            while i < 25:
                cooling_data[sheet][cooling_df.iloc[i][0]] = cooling_df.iloc[i + 1:i + 4, 2:8].values.tolist()
                heating_data[sheet][heating_df.iloc[i][0]] = heating_df.iloc[i + 1:i + 4, 2:8].values.tolist()
                i = i + 5
            # extract the last cooling data
            cooling_data[sheet][cooling_df.iloc[i][0]] = cooling_df.iloc[i + 1:i + 4, 2:8]
        return {"cooling": cooling_data, "heating": heating_data}

    def enrich_city(self):
        """
    Enriches the city with information from file
    """
        heap_pump_data = self._read_file()
        for (k_cool, v_cool), (k_heat, v_heat) in \
                zip(heap_pump_data["cooling"].items(), heap_pump_data["heating"].items()):
            heat_pump = HeatPump()
            heat_pump.model = k_cool
            h_data = self._extract_heat_pump_data(v_heat)
            c_data = self._extract_heat_pump_data(v_cool)
            heat_pump.cooling_pf = c_data[0]
            heat_pump.cooling_pa = c_data[1]
            heat_pump.cooling_qw = c_data[2]
            heat_pump.heating_pf = h_data[0]
            heat_pump.heating_pa = h_data[1]
            heat_pump.heating_qw = h_data[2]
            energy_system = EnergySystem('{} capacity heat pump'.format(heat_pump.model), 0, [], None)
            energy_system.heat_pump = heat_pump
            self._city.add_city_object(energy_system)
        return self._city

    def _extract_heat_pump_data(self, heat_pump_capacity_data) -> [List, List, List]:
        """
        Fetches a list of metric based data for heat pump for various temperature,
         eg. cooling capacity data for 12 capacity heat pump
         for 6,7,8,9,10 and 11 degree celsius
        :param heat_pump_capacity_data: the heat pump capacity data from the
        which the metric specific data is fetched: {List}
        :return: List
        """
        pf_data = []
        pa_data = []
        qw_data = []
        for _, metric_data in heat_pump_capacity_data.items():
            pf_data.append(metric_data[0])
            pa_data.append(metric_data[1])
            qw_data.append(metric_data[2])
        return [pf_data, pa_data, qw_data]