"""
Monthly energy balance main
SPDX - License - Identifier: LGPL - 3.0 - or -later
Copyright © 2020 Project Author Pilar Monsalvete Álvarez de Uribarri pilar.monsalvete@concordia.ca
"""

import sys
from pathlib import Path
from argparse import ArgumentParser
import ast
import pandas as pd
import datetime

import helpers.constants as cte
from helpers import monthly_values as mv
from simplified_radiosity_algorithm import SimplifiedRadiosityAlgorithm
from imports.geometry_factory import GeometryFactory
from imports.weather_factory import WeatherFactory
from imports.construction_factory import ConstructionFactory
from imports.usage_factory import UsageFactory
from exports.energy_building_exports_factory import EnergyBuildingsExportsFactory
from insel.monthly_demand_calculation import MonthlyDemandCalculation


parser = ArgumentParser(description='Monthly energy balance workflow v1.0.')
required = parser.add_argument_group('required arguments')
parser.add_argument('--geometry_type', '-g', help='Geometry type {citygml}', default='citygml')
required.add_argument('--input_geometry_file', '-i', help='Input geometry file', required=True)
parser.add_argument('--use_cached_sra_file', '-u', help='Use sra files from cache, instead of freshly calculated sra '
                                                        'files', default=False)
required.add_argument('--project_folder', '-f', help='Project folder', required=True)
required.add_argument('--weather_file_name', '-w',  help='Weather file', required=True)
required.add_argument('--climate_reference_city', '-c', help='Closest city with climate weather', required=True)

try:
  args = parser.parse_args()
except SystemExit:
  sys.exit()
keep_files = True

print('begin_time', datetime.datetime.now())
# Step 1: Initialize the city model
file = Path(args.input_geometry_file).resolve()
city = GeometryFactory(args.geometry_type, file).city
for building in city.buildings:
  volume = building.volume
  if str(volume) == 'inf':
    sys.stderr.write(f'Building {building.name} has geometry errors. It has been removed from the city\n')
    city.remove_city_object(building)

print('begin_populating_time', datetime.datetime.now())
# Step 2: Populate city adding thermal- and usage-related parameters
for building in city.buildings:
  building.year_of_construction = 2006
  if building.function is None:
    building.function = 'large office'
  building.attic_heated = 0
  building.basement_heated = 1
ConstructionFactory('nrel', city).enrich()
UsageFactory('comnet', city).enrich()

print('begin_weather_time', datetime.datetime.now())
# Step 3: Populate city adding climate-related parameters
weather_format = 'epw'
city.climate_reference_city = args.climate_reference_city
tmp_path = (Path(args.project_folder) / 'tmp').resolve()
city.climate_file = (tmp_path / f'{args.climate_reference_city}.cli').resolve()
WeatherFactory(weather_format, city, file_name=args.weather_file_name).enrich()
for building in city.buildings:
  if cte.HOUR not in building.external_temperature:
    print('No external temperature found')
    sys.exit()

  if cte.MONTH not in building.external_temperature:
    building.external_temperature[cte.MONTH] = mv.MonthlyValues().\
      get_mean_values(building.external_temperature[cte.HOUR][[weather_format]])

max_buildings_handled_by_sra = 500
sra = SimplifiedRadiosityAlgorithm(city, Path(args.project_folder).resolve(), args.weather_file_name)
if ast.literal_eval(args.use_cached_sra_file):
  sra.set_irradiance_surfaces(city)
else:
  total_number_of_buildings = len(city.buildings)
  if total_number_of_buildings > max_buildings_handled_by_sra:
    radius = 80
    for building in city.buildings:
      new_city = city.region(building.centroid, radius)
      sra_new = SimplifiedRadiosityAlgorithm(new_city, Path(args.project_folder).resolve(), args.weather_file_name)
      sra_new.call_sra(weather_format, keep_files=True)
      sra_new.set_irradiance_surfaces(city, building_name=building.name)
  else:
    sra.call_sra(weather_format, keep_files=keep_files)
    sra.set_irradiance_surfaces(city)

print('begin_insel_time', datetime.datetime.now())
# Step 5: Demand calculation calling INSEL
EnergyBuildingsExportsFactory('insel_monthly_energy_balance', city, tmp_path).export()
insel = MonthlyDemandCalculation(city, tmp_path, weather_format)
insel.run()
insel.results()

print('begin_write_results_time', datetime.datetime.now())
# Step 6: Print results
print_results = None
file = 'city name: ' + city.name + '\n'
for building in city.buildings:
  insel_file_name = building.name + '.insel'
  heating_results = building.heating[cte.MONTH].rename(columns={'INSEL': f'{building.name} heating Wh'})
  cooling_results = building.cooling[cte.MONTH].rename(columns={'INSEL': f'{building.name} cooling Wh'})
  if print_results is None:
    print_results = heating_results
  else:
    print_results = pd.concat([print_results, heating_results], axis='columns')
  print_results = pd.concat([print_results, cooling_results], axis='columns')
  file += '\n'
  file += 'name: ' + building.name + '\n'
  file += 'year of construction: ' + str(building.year_of_construction) + '\n'
  file += 'function: ' + building.function + '\n'
  file += 'floor area: ' + str(building.internal_zones[0].area) + '\n'
  file += 'storeys: ' + str(building.storeys_above_ground) + '\n'
  file += 'heated_volume: ' + str(building.volume) + '\n'
  file += 'volume: ' + str(building.volume) + '\n'

full_path_results = Path(args.project_folder + '/outputs/demand.csv').resolve()
print_results.to_csv(full_path_results)
full_path_metadata = Path(args.project_folder + '/outputs/metadata.csv').resolve()
with open(full_path_metadata, 'w') as metadata_file:
  metadata_file.write(file)

print('end_time', datetime.datetime.now())