"""
Monthly energy balance main
SPDX - License - Identifier: LGPL - 3.0 - or -later
Copyright © 2020 Project Author Pilar Monsalvete pilar_monsalvete@yahoo.es
"""

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

from helpers import monthly_values as mv
from populate import Populate
from insel.insel import Insel
from insel.templates.monthly_energy_balance import MonthlyEnergyBalance as templates
from simplified_radiosity_algorithm.simplified_radiosity_algorithm import SimplifiedRadiosityAlgorithm
from factories.geometry_factory import GeometryFactory
from factories.weather_factory import WeatherFactory
from city_model_structure.city import City

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('--cli_weather_file', '-c',  help='weather cli file', required=True)
required.add_argument('--city_name', '-n',  help='city name for dat file', required=True)

try:
  args = parser.parse_args()
except SystemExit:
  sys.exit()
keep_files = True
# 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:
  city = GeometryFactory(args.geometry_type, Path(args.input_geometry_file).resolve()).city

# 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 = Populate(city).populated_city
  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()

# Step 3: Populate city adding climate-related parameters
weather_step_in_pickle = ast.literal_eval(args.weather_step_in_pickle)
if not weather_step_in_pickle:
  city_name = args.city_name
  WeatherFactory('dat', populated_city, city_name)
  for building in populated_city.buildings:
    if 'hour' not in building.external_temperature:
      print('No external temperature found')
      sys.exit()

    if 'month' not in building.external_temperature:
      building.external_temperature['month'] = mv.MonthlyValues().\
        get_mean_values(building.external_temperature['hour'][['inseldb']])

  max_buildings_handled_by_sra = 500
  sra_file_name = 'SRA'
  sra = SimplifiedRadiosityAlgorithm(Path(args.project_folder).resolve(), sra_file_name,
                                     Path(args.cli_weather_file).resolve(),
                                     city.city_objects, lower_corner=city.lower_corner)
  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 = 100
      for building in city.buildings:
        new_city = city.region(building.location, radius)
        sra_file_name = 'SRA'

        sra_new = SimplifiedRadiosityAlgorithm(Path(args.project_folder).resolve(), sra_file_name,
                                               Path(args.cli_weather_file).resolve(),
                                               new_city.city_objects, lower_corner=new_city.lower_corner)
        sra_new.call_sra(keep_files=True)
        sra_new.set_irradiance_surfaces(city, building_name=building.name)
    else:
      sra.call_sra(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)

# Step 4: Demand calculation (one model per building)
print_results = None
file = 'city name: ' + city.name + '\r\n'
i = 0
for building in populated_city.buildings:
  if 3000 < i and building.name != 'BLD122177':
    print(building.name)
    full_path_out = Path(args.project_folder + '/outputs/' + building.name + '_insel.out').resolve()
    full_path_out.parent.mkdir(parents=True, exist_ok=True)
    insel_file_name = building.name + '.insel'
    try:
      content = templates.generate_meb_template(building, full_path_out)
      insel = Insel(Path(args.project_folder).resolve(), insel_file_name, content, mode=2, keep_files=keep_files).run()
      building.heating['month'], building.cooling['month'] = templates.demand(full_path_out)
      new_building = building.heating['month'].rename(columns={'INSEL': building.name})
      if print_results is None:
        print_results = new_building
      else:
        print_results = pd.concat([print_results, new_building], axis='columns')
      file += '\r\n'
      file += 'name: ' + building.name + '\r\n'
      file += 'year of construction: ' + building.year_of_construction + '\r\n'
      file += 'function: ' + building.function + '\r\n'

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


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)