"""
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 city_model_structure.city import City
from monthly_demand_calculation import MonthlyDemandCalculation
from helpers.enrich_city import EnrichCity


parser = ArgumentParser(description='Monthly energy balance workflow v0.1.')
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_pickle_file', '-p', help='Use pickle file instead of importing geometry file', default=False,
                    required=True)
parser.add_argument('--populated_step_in_pickle', '-ps', help='Physics and usage parameters already in pickle file',
                    default=False, required=True)
parser.add_argument('--weather_step_in_pickle', '-ws', help='Weather parameters already in pickle file',
                    default=False, 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
pickle_file = ''
if ast.literal_eval(args.use_pickle_file):
  pickle_file = Path(args.input_geometry_file).resolve()
  city = City.load(pickle_file)
else:
  file = Path(args.input_geometry_file).resolve()
  pickle_file = Path(str(file).replace('.gml', '.pickle'))
  city = GeometryFactory(args.geometry_type, file).city
  print(len(city.buildings))
  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)
  city.save(pickle_file)

print('begin_populating_time', datetime.datetime.now())
# Step 2: Populate city adding thermal- and usage-related parameters
populated_step_in_pickle = ast.literal_eval(args.populated_step_in_pickle)
if populated_step_in_pickle:
  populated_city = city
else:
  populated_city = EnrichCity(city).enriched_city(construction_format='nrcan', usage_format='hft')
  pickle_file = Path(str(pickle_file).replace('.pickle', '_populated.pickle'))
  populated_city.save(pickle_file)
if populated_city.buildings is None:
  print('No building to be calculated')
  sys.exit()

print('begin_weather_time', datetime.datetime.now())
# Step 3: Populate city adding climate-related parameters
weather_format = 'epw'
weather_step_in_pickle = ast.literal_eval(args.weather_step_in_pickle)
if not weather_step_in_pickle:
  city.climate_reference_city = args.climate_reference_city
  path = (Path(args.project_folder) / 'tmp').resolve()
  city.climate_file = (path / f'{args.climate_reference_city}.cli').resolve()
  WeatherFactory(weather_format, populated_city, file_name=args.weather_file_name).enrich()
  for building in populated_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
  for building in city.buildings:
    for surface in building.surfaces:
      surface.swr = 0.2
  sra = SimplifiedRadiosityAlgorithm(city, Path(args.project_folder).resolve(), args.weather_file_name)
  if ast.literal_eval(args.use_cached_sra_file):
    sra.results()
    sra.set_irradiance_surfaces(populated_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(populated_city, building_name=building.name)
    else:
      sra.call_sra(weather_format, keep_files=keep_files)
      sra.set_irradiance_surfaces(populated_city)
  pickle_file = Path(str(pickle_file).replace('.pickle', '_weather.pickle'))
  populated_city.save(pickle_file)

print('begin_user_assignment_time', datetime.datetime.now())
# Step 4: Assign user defined parameters
for building in populated_city.buildings:
  building.attic_heated = 2
  building.basement_heated = 0

print('begin_insel_time', datetime.datetime.now())
# Step 5: Demand calculation calling INSEL
MonthlyDemandCalculation(city, args.project_folder, weather_format).monthly_demand()

print('begin_write_results_time', datetime.datetime.now())
# Step 6: Print results
print_results = None
file = 'city name: ' + city.name + '\n'
for building in populated_city.buildings:
  insel_file_name = building.name + '.insel'
  building_results = building.heating[cte.MONTH].rename(columns={'INSEL': building.name})
  if print_results is None:
    print_results = building_results
  else:
    print_results = pd.concat([print_results, building_results], axis='columns')
  file += '\n'
  file += 'name: ' + building.name + '\n'
  file += 'year of construction: ' + building.year_of_construction + '\n'
  file += 'function: ' + building.function + '\n'
  file += 'floor area: ' + str(building.thermal_zones[0].floor_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/heating_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)

pickle_file = Path(str(pickle_file).replace('.pickle', '_demand.pickle'))
populated_city.save(pickle_file)

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