import math
import subprocess
import xmltodict

import geopandas as gpd
from shapely.geometry import Point

from matsim_activity_to_matsim_schedule import MatsimActivityToMatsimSchedule
from hub_function_to_matsim_activity import HubFunctionToMatsimActivity


# TODO: Please check this https://github.com/matsim-org/matsim-code-examples/issues/63 for the CRS in matsim should match the city

class MatSimEngine:
  def __init__(self, city, output_file_path):
    self._city = city
    self._output_file_path = output_file_path

    facilities_dict = {
      'facilities': {
        '@name': 'Montreal Facilities',
        'facility': []
      }
    }

    hub_function_to_matsim = HubFunctionToMatsimActivity()
    matsim_schedule = MatsimActivityToMatsimSchedule()

    buildings_shape_data = {
      'id': [],
      'geometry': []
    }

    # 1- Facilities
    # TODO: this should come from the factories, please check idf generation
    for building in city.buildings:
      activity = hub_function_to_matsim.dictionary[building.function].split(',')

      for surface in building.grounds:
        for coord in surface.solid_polygon.coordinates:
          buildings_shape_data['id'].append(f"{building.name}")
          buildings_shape_data['geometry'].append(Point(coord[0], coord[1]))

      facility = {
        '@id': building.name,
        '@x': str(building.centroid[0]),
        '@y': str(building.centroid[1]),
        'activity': []
      }

      if len(building.thermal_zones_from_internal_zones) > 1:
        raise NotImplementedError("multi-zone buildings aren't yet supported")

      building_schedules = []

      capacity = 0
      for thermal_zone in building.thermal_zones_from_internal_zones:
        capacity = thermal_zone.occupancy.occupancy_density * building.floor_area * building.storeys_above_ground
        for schedule in thermal_zone.occupancy.occupancy_schedules:
          building_schedules.append(schedule)

      for new_activity in activity:
        activity_info = {
          '@type': new_activity,
          'capacity': {
            '@value': math.ceil(capacity)
          },
          'opentime': []
        }

        for schedule in building_schedules:
          opening_hour = 0
          closing_hour = 0

          # Find opening hour (first hour > 0)
          for i, value in enumerate(schedule.values):
            if value > 0:
              opening_hour = i
              break

          for i, value in reversed(list(enumerate(schedule.values))):
            if value > 0:
              closing_hour = i
              break

          for day in schedule.day_types:
            if day[0:3] != 'hol':
              activity_info['opentime'].append({
                  '@day': day[0:3],
                  '@start_time': opening_hour,
                  '@end_time': closing_hour
                })

        facility['activity'].append(activity_info)

      facilities_dict['facilities']['facility'].append(facility)

    gdf = gpd.GeoDataFrame(
      buildings_shape_data,
      crs=city.srs_name
    )

    gdf.to_file("input_files/buildings_shapefile.shp")

    # Convert the Python dictionary to an XML string
    xml_content = xmltodict.unparse(facilities_dict, pretty=True, short_empty_elements=True)

    # Write the XML to the file
    with open(f"{output_file_path}/{city.name}_facilities.xml", 'w') as file:
      file.write(xml_content)

    # 2- Network
    # First get only the part of the network necessary for the simulation
    java_path = "java"
    jar_path = "matsim-network-from-osm.jar"
    command = [
      java_path,
      "-jar", jar_path,
      "input_files/merged-network.osm.pbf",
      "input_files/buildings_shapefile.shp",
      f"{output_file_path}/network.xml.gz"
    ]
    subprocess.run(command)

    # 3- Population

    # 3.1 - Public Transport

    # 4- Config Generation

  def run(self):
    java_path = "java"
    jar_path = "matsim.jar"
    command = [java_path, "-jar", jar_path]

    # Must generate this config file first.
    # command.append(config_file_path)

    subprocess.run(command)