"""
Monthly demand calculation using the monthly energy balance methodology based on the norm...
SPDX - License - Identifier: LGPL - 3.0 - or -later
Copyright © 2020 Project Author Pilar Monsalvete Alvarez de Uribarri pilar.monsalvete@concordia.ca
"""

from pathlib import Path
import pandas as pd
import sys
import csv
from insel.insel import Insel


class MonthlyDemandCalculation(Insel):
  def __init__(self, city, main_path, weather_format):
    super().__init__(main_path)
    self._city = city
    self._weather_format = weather_format

  def results(self):
    for building in self._city.buildings:
      file_name = building.name + '.out'
      full_path_out = Path(self._path / file_name).resolve()
      full_path_out.parent.mkdir(parents=True, exist_ok=True)
      try:
        building.heating['month'], building.cooling['month'] = self._demand(full_path_out)
        heating_year = 0
        for value in building.heating['month']['INSEL']:
          if value == 'NaN':
            value = '0'
          heating_year += float(value)
        yearly_heating = pd.DataFrame([heating_year], columns=['INSEL'])
        building.heating['year'] = yearly_heating

        cooling_year = 0
        for value in building.cooling['month']['INSEL']:
          if value == 'NaN':
            value = '0'
          cooling_year += float(value)
        yearly_cooling = pd.DataFrame([cooling_year], columns=['INSEL'])
        building.cooling['year'] = yearly_cooling

      except ValueError:
        print(sys.exc_info()[1])
        print('Building ' + building.name + ' could not be processed')
        continue

  @staticmethod
  def _demand(insel_outputs_path):
    heating = []
    cooling = []
    with open(Path(insel_outputs_path).resolve()) as csv_file:
      csv_reader = csv.reader(csv_file)
      for line in csv_reader:
        demand = str(line).replace("['", '').replace("']", '').split()
        for i in range(0, 2):
          if demand[i] != 'NaN':
            aux = float(demand[i])*1000     # kWh to Wh
            demand[i] = str(aux)
        heating.append(demand[0])
        cooling.append(demand[1])
    monthly_heating = pd.DataFrame(heating, columns=['INSEL'])
    monthly_cooling = pd.DataFrame(cooling, columns=['INSEL'])
    return monthly_heating, monthly_cooling