"""
This project aims to assign energy systems archetype names to Montreal buildings.
The random assignation is based on statistical information extracted from different sources, being:
- For residential buildings:
  - SHEU 2015: https://oee.nrcan.gc.ca/corporate/statistics/neud/dpa/menus/sheu/2015/tables.cfm
- For non-residential buildings:
  - Montreal dataportal: https://dataportalforcities.org/north-america/canada/quebec/montreal
  - https://www.eia.gov/consumption/commercial/data/2018/
"""
import json
import random

from hub.city_model_structure.building import Building

energy_systems_format = 'montreal_future'

# parameters:
residential_systems_percentage = {
  'Central Hydronic Air and Gas Source Heating System with Unitary Split Cooling and Air Source HP DHW and Grid Tied PV': 100,
  'Central Hydronic Air and Electricity Source Heating System with Unitary Split Cooling and Air Source HP DHW and Grid Tied PV': 0,
  'Central Hydronic Ground and Gas Source Heating System with Unitary Split Cooling and Air Source HP DHW and Grid Tied PV': 0,
  'Central Hydronic Ground and Electricity Source Heating System with Unitary Split Cooling and Air Source HP DHW '
  'and Grid Tied PV': 0,
  'Central Hydronic Water and Gas Source Heating System with Unitary Split Cooling and Air Source HP DHW and Grid Tied PV': 0,
  'Central Hydronic Water and Electricity Source Heating System with Unitary Split Cooling and Air Source HP DHW '
  'and Grid Tied PV': 0,
  'Central Hydronic Air and Gas Source Heating System with Unitary Split and Air Source HP DHW': 0,
  'Central Hydronic Air and Electricity Source Heating System with Unitary Split and Air Source HP DHW': 0,
  'Central Hydronic Ground and Gas Source Heating System with Unitary Split and Air Source HP DHW': 0,
  'Central Hydronic Ground and Electricity Source Heating System with Unitary Split and Air Source HP DHW': 0,
  'Central Hydronic Water and Gas Source Heating System with Unitary Split and Air Source HP DHW': 0,
  'Central Hydronic Water and Electricity Source Heating System with Unitary Split and Air Source HP DHW': 0,
  'Grid Tied PV System': 0,
  'system 1 gas': 0,
  'system 1 gas grid tied pv': 0,
  'system 1 electricity': 0,
  'system 1 electricity grid tied pv': 0,
  'system 2 gas': 0,
  'system 2 gas grid tied pv': 0,
  'system 2 electricity': 0,
  'system 2 electricity grid tied pv': 0,
  'system 3 and 4 gas': 0,
  'system 3 and 4 gas grid tied pv': 0,
  'system 3 and 4 electricity': 0,
  'system 3 and 4 electricity grid tied pv': 0,
  'system 6 gas': 0,
  'system 6 gas grid tied pv': 0,
  'system 6 electricity': 0,
  'system 6 electricity grid tied pv': 0,
  'system 8 gas': 0,
  'system 8 gas grid tied pv': 0,
  'system 8 electricity': 0,
  'system 8 electricity grid tied pv': 0,
}

non_residential_systems_percentage = {'system 1 gas': 0,
                                      'system 1 electricity': 0,
                                      'system 2 gas': 0,
                                      'system 2 electricity': 0,
                                      'system 3 and 4 gas': 39,
                                      'system 3 and 4 electricity': 36,
                                      'system 5 gas': 0,
                                      'system 5 electricity': 0,
                                      'system 6 gas': 13,
                                      'system 6 electricity': 12,
                                      'system 8 gas': 0,
                                      'system 8 electricity': 0}


def _retrieve_buildings(path, year_of_construction_field=None,
                        function_field=None, function_to_hub=None, aliases_field=None):
  _buildings = []
  with open(path, 'r', encoding='utf8') as json_file:
    _geojson = json.loads(json_file.read())
    for feature in _geojson['features']:
      _building = {}
      year_of_construction = None
      if year_of_construction_field is not None:
        year_of_construction = int(feature['properties'][year_of_construction_field])
      function = None
      if function_field is not None:
        function = feature['properties'][function_field]
        if function_to_hub is not None:
          # use the transformation dictionary to retrieve the proper function
          if function in function_to_hub:
            function = function_to_hub[function]
      building_name = ''
      building_aliases = []
      if 'id' in feature:
        building_name = feature['id']
      if aliases_field is not None:
        for alias_field in aliases_field:
          building_aliases.append(feature['properties'][alias_field])
      _building['year_of_construction'] = year_of_construction
      _building['function'] = function
      _building['building_name'] = building_name
      _building['building_aliases'] = building_aliases
      _buildings.append(_building)
  return _buildings


def call_random(_buildings: [Building], _systems_percentage):
  _buildings_with_systems = []
  _systems_distribution = []
  _selected_buildings = list(range(0, len(_buildings)))
  random.shuffle(_selected_buildings)
  total = 0
  maximum = 0
  add_to = 0
  for _system in _systems_percentage:
    if _systems_percentage[_system] > 0:
      number_of_buildings = round(_systems_percentage[_system] / 100 * len(_selected_buildings))
      _systems_distribution.append({'system': _system, 'number': _systems_percentage[_system],
                                    'number_of_buildings': number_of_buildings})
      if number_of_buildings > maximum:
        maximum = number_of_buildings
        add_to = len(_systems_distribution) - 1
      total += number_of_buildings
  missing = 0
  if total != len(_selected_buildings):
    missing = len(_selected_buildings) - total
  if missing != 0:
    _systems_distribution[add_to]['number_of_buildings'] += missing
  _position = 0
  for case in _systems_distribution:
    for i in range(0, case['number_of_buildings']):
      _buildings[_selected_buildings[_position]].energy_systems_archetype_name = case['system']
      _position += 1
  return _buildings