from pathlib import Path
from scripts.ep_workflow import energy_plus_workflow
from hub.helpers.monthly_values import MonthlyValues
from hub.imports.geometry_factory import GeometryFactory
from hub.helpers.dictionaries import Dictionaries
from hub.imports.construction_factory import ConstructionFactory
from hub.imports.usage_factory import UsageFactory
from hub.imports.weather_factory import WeatherFactory
import hub.helpers.constants as cte
from hub.imports.energy_systems_factory import EnergySystemsFactory
from hub.helpers.peak_loads import PeakLoads
from pathlib import Path
import subprocess
from hub.imports.results_factory import ResultFactory
from hub.imports.energy_systems_factory import EnergySystemsFactory
from scripts.energy_system_sizing_and_simulation_factory import EnergySystemsSimulationFactory
from scripts.solar_angles import CitySolarAngles
import hub.helpers.constants as cte
from hub.exports.exports_factory import ExportsFactory
from scripts.pv_sizing_and_simulation import PVSizingSimulation
import pandas as pd
import geopandas as gpd
import json

#%% # -----------------------------------------------
# Specify the GeoJSON file path
#%% # -----------------------------------------------
input_files_path = (Path(__file__).parent / 'input_files')
output_path = (Path(__file__).parent / 'out_files').resolve()
output_path.mkdir(parents=True, exist_ok=True)
energy_plus_output_path = output_path / 'energy_plus_outputs'
energy_plus_output_path.mkdir(parents=True, exist_ok=True)
simulation_results_path = (Path(__file__).parent / 'out_files' / 'simulation_results').resolve()
simulation_results_path.mkdir(parents=True, exist_ok=True)
sra_output_path = output_path / 'sra_outputs'
sra_output_path.mkdir(parents=True, exist_ok=True)
cost_analysis_output_path = output_path / 'cost_analysis'
cost_analysis_output_path.mkdir(parents=True, exist_ok=True)

#%%-----------------------------------------------
#"""add geojson paths and create city for Baseline"""
#%% # -----------------------------------------------

geojson_file_path_baseline = output_path / 'updated_buildings_with_all_data_baseline.geojson'
geojson_file_path_2024 = output_path / 'updated_buildings_with_all_data_2024.geojson'
with open(geojson_file_path_baseline , 'r') as f:
    building_type_data = json.load(f)
with open(geojson_file_path_2024, 'r') as f:
    building_type_data_2024 = json.load(f)

# Create city object from GeoJSON file
city = GeometryFactory('geojson',
                       path=geojson_file_path_baseline,
                       height_field='maximum_roof_height',
                       year_of_construction_field='year_built',
                       function_field='building_type',
                       function_to_hub=Dictionaries().montreal_function_to_hub_function).city
#%%----------------------------------------------
# Enrich city data
#%% # ----------------------------------------------
ConstructionFactory('nrcan', city).enrich()
UsageFactory('nrcan', city).enrich()
WeatherFactory('epw', city).enrich()
# #energy plus is not going to be processed here, as demand has been obtained before
# energy_plus_workflow(city)
#%% # -----------------------------------------------
#"""Enrich city with geojson file data"""
#%% # -----------------------------------------------
percentage_data = {
    1646: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 2672.550473, "total_floor_area": 26725.50473},
    1647: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 2653.626087, "total_floor_area": 26536.26087},
    1648: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 1056.787496, "total_floor_area": 10567.87496},
    1649: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 1906.620746, "total_floor_area": 19066.20746},
    1650: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 659.1119416, "total_floor_area": 5272.895533},
    1651: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 1167.208109, "total_floor_area": 9337.664871},
    1652: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 1193.251653, "total_floor_area": 9546.013222},
    1653: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 1491.722543, "total_floor_area": 11933.78035},
    1654: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 1168.005028, "total_floor_area": 9344.040224},
    1655: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 1264.906961, "total_floor_area": 10119.25569},
    1656: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 1281.768818, "total_floor_area": 10254.15054},
    1657: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 290.3886018, "total_floor_area": 2323.108814},
    1658: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 847.5095193, "total_floor_area": 6780.076155},
    1659: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 1115.319153, "total_floor_area": 8922.553224},
    1660: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 469.2918062, "total_floor_area": 3754.33445},
    1661: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 1292.298346, "total_floor_area": 10338.38677},
    1662: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 625.7828863, "total_floor_area": 5006.263091},
    1663: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 1876.02897, "total_floor_area": 15008.23176},
    1664: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 1118.224781, "total_floor_area": 22364.49562},
    1665: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 1502.787808, "total_floor_area": 30055.75617},
    1666: {"type1_%": 0.891045711, "type2_%": 0.108954289, "type3_%": 0, "roof_area": 3038.486076, "total_floor_area": 30384.86076},
    1667: {"type1_%": 0.8, "type2_%": 0.2, "type3_%": 0, "roof_area": 1343.832818, "total_floor_area": 13438.32818},
    1668: {"type1_%": 0.7, "type2_%": 0.3, "type3_%": 0, "roof_area": 961.0996956, "total_floor_area": 4805.498478},
    1669: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 489.1282111, "total_floor_area": 1956.512845},
    1673: {"type1_%": 0.7, "type2_%": 0.3, "type3_%": 0, "roof_area": 1693.141465, "total_floor_area": 5079.424396},
    1674: {"type1_%": 0.7, "type2_%": 0.3, "type3_%": 0, "roof_area": 3248.827576, "total_floor_area": 9746.482729},
    1675: {"type1_%": 0.7, "type2_%": 0.3, "type3_%": 0, "roof_area": 4086.842191, "total_floor_area": 12260.52657},
    1676: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 2786.114146, "total_floor_area": 11144.45658},
    1677: {"type1_%": 1, "type2_%": 0, "type3_%": 0, "roof_area": 5142.784184, "total_floor_area": 15428.35255},
    1678: {"type1_%": 0.7, "type2_%": 0.3, "type3_%": 0, "roof_area": 6068.664574, "total_floor_area": 18205.99372},
    1679: {"type1_%": 0.7, "type2_%": 0.3, "type3_%": 0, "roof_area": 5646.751407, "total_floor_area": 16940.25422},
    1680: {"type1_%": 0.7, "type2_%": 0.3, "type3_%": 0, "roof_area": 1601.765953, "total_floor_area": 4805.297859},
    1681: {"type1_%": 0.7, "type2_%": 0.3, "type3_%": 0, "roof_area": 9728.221797, "total_floor_area": 29184.66539},
    1687: {"type1_%": 0.606611029, "type2_%": 0.28211422, "type3_%": 0.11127475, "roof_area": 4268.608743, "total_floor_area": 59760.52241},
    1688: {"type1_%": 0.92, "type2_%": 0.08, "type3_%": 0, "roof_area": 2146.654828, "total_floor_area": 38639.7869},
    1689: {"type1_%": 0.96, "type2_%": 0.04, "type3_%": 0, "roof_area": 2860.270711, "total_floor_area": 57205.41421},
    1690: {"type1_%": 0.94, "type2_%": 0.06, "type3_%": 0, "roof_area": 2189.732519, "total_floor_area": 28466.52275},
    1691: {"type1_%": 0.75, "type2_%": 0.25, "type3_%": 0, "roof_area": 3159.077523, "total_floor_area": 31590.77523},
}

def enrich_buildings_with_geojson_data (building_type_data, city):
      for building in city.buildings:
          for idx, feature in enumerate(building_type_data['features']):
              if feature['properties']['id'] == str(building.name):
                  building.heating_demand[cte.HOUR] = [x *1000* cte.WATTS_HOUR_TO_JULES for x in building_type_data['features'][idx]['properties'].get('heating_demand_kWh', [0])]
                  building.cooling_demand[cte.HOUR] = [x *1000* cte.WATTS_HOUR_TO_JULES for x in building_type_data['features'][idx]['properties'].get('cooling_demand_kWh', [0])]
                  building.domestic_hot_water_heat_demand[cte.HOUR] =    [x *1000* cte.WATTS_HOUR_TO_JULES for x in building_type_data['features'][idx]['properties'].get('domestic_hot_water_heat_demand_kWh', [0])]
                  building.appliances_electrical_demand[cte.HOUR] =   [x *1000* cte.WATTS_HOUR_TO_JULES for x in building_type_data['features'][idx]['properties'].get('appliances_electrical_demand_kWh', [0])]
                  building.lighting_electrical_demand[cte.HOUR] =   [x *1000* cte.WATTS_HOUR_TO_JULES for x in building_type_data['features'][idx]['properties'].get('lighting_electrical_demand_kWh', [0])]
                  building.heating_demand[cte.MONTH] = MonthlyValues.get_total_month(building.heating_demand[cte.HOUR])
                  building.cooling_demand[cte.MONTH] = MonthlyValues.get_total_month(building.cooling_demand[cte.HOUR])
                  building.domestic_hot_water_heat_demand[cte.MONTH] = (MonthlyValues.get_total_month(building.domestic_hot_water_heat_demand[cte.HOUR]))
                  building.appliances_electrical_demand[cte.MONTH] = (MonthlyValues.get_total_month(building.appliances_electrical_demand[cte.HOUR]))
                  building.lighting_electrical_demand[cte.MONTH] = (MonthlyValues.get_total_month(building.lighting_electrical_demand[cte.HOUR]))
                  building.heating_demand[cte.YEAR] = [sum(building.heating_demand[cte.MONTH])]
                  building.cooling_demand[cte.YEAR] = [sum(building.cooling_demand[cte.MONTH])]
                  building.domestic_hot_water_heat_demand[cte.YEAR] = [sum(building.domestic_hot_water_heat_demand[cte.MONTH])]
                  building.appliances_electrical_demand[cte.YEAR] = [sum(building.appliances_electrical_demand[cte.MONTH])]
                  building.lighting_electrical_demand[cte.YEAR] = [sum(building.lighting_electrical_demand[cte.MONTH])]



enrich_buildings_with_geojson_data (building_type_data, city)
print('test')

#%%-----------------------------------------------
# """ADD energy systems"""
#%% # -----------------------------------------------

for building in city.buildings:
  building.energy_systems_archetype_name = 'system 1 electricity'

EnergySystemsFactory('montreal_custom', city).enrich()

def baseline_to_dict(building):
    return {
        'heating_consumption_kWh': [x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in building.heating_consumption[cte.HOUR]],
        'cooling_consumption_kWh':[x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in  building.cooling_consumption[cte.HOUR]],
        'domestic_hot_water_consumption_kWh': [x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in building.domestic_hot_water_consumption[cte.HOUR]],
        'appliances_consumption_kWh':[x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in  building.appliances_electrical_demand[cte.HOUR]],
        'lighting_consumption_kWh': [x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in building.lighting_electrical_demand[cte.HOUR]]
    }
buildings_dic={}
for building in city.buildings:
    buildings_dic[building.name]=baseline_to_dict(building)
scenario={}

scenario['baseline']=buildings_dic
print("Scenario 1: Baseline is performed successfully")



del city
del buildings_dic
del building_type_data

#%%-----------------------------------------------
# Scenario 2
#%% # -----------------------------------------------

# Create city object from GeoJSON file
city = GeometryFactory('geojson',
                       path=geojson_file_path_2024,
                       height_field='maximum_roof_height',
                       year_of_construction_field='year_built',
                       function_field='building_type',
                       function_to_hub=Dictionaries().montreal_function_to_hub_function).city
#%%-----------------------------------------------
# Enrich city data
#%% # -----------------------------------------------
ConstructionFactory('nrcan', city).enrich()
UsageFactory('nrcan', city).enrich()
WeatherFactory('epw', city).enrich()

enrich_buildings_with_geojson_data (building_type_data_2024, city)

def to_dict(building,hourly_pv):
    return {
        'heating_consumption_kWh': [x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in building.heating_consumption[cte.HOUR]],
        'cooling_consumption_kWh':[x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in  building.cooling_consumption[cte.HOUR]],
        'domestic_hot_water_consumption_kWh': [x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in building.domestic_hot_water_consumption[cte.HOUR]],
        'appliances_consumption_kWh':[x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in  building.appliances_electrical_demand[cte.HOUR]],
        'lighting_consumption_kWh': [x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in building.lighting_electrical_demand[cte.HOUR]],
        'hourly_pv_kWh': [x /(cte.WATTS_HOUR_TO_JULES * 1000) for x in hourly_pv]
    }
buildings_dic={}


for building in city.buildings:
  building.energy_systems_archetype_name = 'system 1 electricity pv'

EnergySystemsFactory('montreal_custom', city).enrich()
# #%%-----------------------------------------------
# # """SRA"""
# #%% # -----------------------------------------------
ExportsFactory('sra', city, output_path).export()
sra_path = (output_path / f'{city.name}_sra.xml').resolve()
subprocess.run(['sra', str(sra_path)])
ResultFactory('sra', city, output_path).enrich()
solar_angles = CitySolarAngles(city.name,
                               city.latitude,
                               city.longitude,
                               tilt_angle=45,
                               surface_azimuth_angle=180).calculate
df = pd.DataFrame()
df.index = ['yearly lighting (kWh)', 'yearly appliance (kWh)', 'yearly heating (kWh)', 'yearly cooling (kWh)',
            'yearly dhw (kWh)', 'roof area (m2)', 'used area for pv (m2)', 'number of panels', 'pv production (kWh)']
for building in city.buildings:
  ghi = [x / cte.WATTS_HOUR_TO_JULES for x in building.roofs[0].global_irradiance[cte.HOUR]]
  pv_sizing_simulation = PVSizingSimulation(building,
                                            solar_angles,
                                            tilt_angle=45,
                                            module_height=1,
                                            module_width=2,
                                            ghi=ghi)
  pv_sizing_simulation.pv_output()
  yearly_lighting = building.lighting_electrical_demand[cte.YEAR][0] / 1000
  yearly_appliance = building.appliances_electrical_demand[cte.YEAR][0] / 1000
  yearly_heating = building.heating_demand[cte.YEAR][0] / (3.6e6 * 3)
  yearly_cooling = building.cooling_demand[cte.YEAR][0] / (3.6e6 * 4.5)
  yearly_dhw = building.domestic_hot_water_heat_demand[cte.YEAR][0] / 1000
  roof_area = building.roofs[0].perimeter_area
  used_roof = pv_sizing_simulation.available_space()
  number_of_pv_panels = pv_sizing_simulation.total_number_of_panels
  yearly_pv = building.onsite_electrical_production[cte.YEAR][0] / (3.6e6)
  hourly_pv = building.onsite_electrical_production[cte.HOUR]
  df[f'{building.name}'] = [yearly_lighting, yearly_appliance, yearly_heating, yearly_cooling, yearly_dhw, roof_area,
                            used_roof, number_of_pv_panels, yearly_pv]
  buildings_dic[building.name]=to_dict(building,hourly_pv)

# %%-----------------------------------------------
# """South facing facades"""
# %% # -----------------------------------------------
# Function to convert radians to degrees
import math
def radians_to_degrees(radians):
    return radians * (180 / math.pi)
# Step 1: Create the walls_id dictionary
walls_id={}

for building in city.buildings:
    ids = {}
    for walls in building.walls:
        id=walls.id
        azimuth_degree=radians_to_degrees(float(walls.azimuth))
        if azimuth_degree>90.0 or azimuth_degree <float(-90.0):
            ids[id]= {
                'azimuth': azimuth_degree,
                'global_irradiance': walls.global_irradiance[cte.HOUR],
                'area': walls.perimeter_area
            }
    walls_id[building.name] = ids

# Step 2: Calculate pv_on_facade for each wall
for building_id, ids in walls_id.items():
    for wall_id, wall_data in ids.items():
        if 'global_irradiance' in wall_data:
            ghi = [x / cte.WATTS_HOUR_TO_JULES/1000 for x in wall_data['global_irradiance']]
            wall_data['pv_on_facade'] = [x * 0.6 * wall_data['area']*0.22 for x in ghi]




walls_dic = output_path / 'walls_id.json'
with open(walls_dic , 'w') as json_file:
    json.dump(walls_id, json_file, indent=4)

import pandas as pd
#### EXPORT
# Convert walls_id dictionary to a DataFrame
# Convert walls_id dictionary to DataFrames for static and hourly data
# def convert_walls_id_to_dfs(walls_id):
#     static_data = {}
#     hourly_data = {}
#
#     for building_id, ids in walls_id.items():
#         for wall_id, wall_data in ids.items():
#             # Static data
#             static_data[f"{building_id}_{wall_id}_azimuth"] = wall_data.get('azimuth', None)
#             static_data[f"{building_id}_{wall_id}_area"] = wall_data.get('area', None)
#
#             if 'pv_on_facade' in wall_data:
#                 hourly_data[f"{building_id}_{wall_id}_pv_on_facade"] = wall_data['pv_on_facade']
#
#     # Create DataFrames
#     static_df = pd.DataFrame([static_data])
#     hourly_df = pd.DataFrame(hourly_data)
#
#     return static_df, hourly_df


# output_path_walls_id_dic =output_path / 'walls_id_data.xlsx'
#
# static_df, hourly_df = convert_walls_id_to_dfs(walls_id)
# with pd.ExcelWriter(output_path_walls_id_dic) as writer:
#     static_df.to_excel(writer, sheet_name='Static Data', index=False)
#     hourly_df.to_excel(writer, sheet_name='Hourly Data', index=False)

# print(f"Data successfully exported to {output_path}")
# # Save the DataFrame to an Excel file



df.to_csv(output_path / 'pv.csv')

scenario['efficient with PV']=buildings_dic
print("Scenario 2: efficient with PV run successfully")

# #%%-----------------------------------------------
# # Scenario 3
# #%% # -----------------------------------------------
#
# for building in city.buildings:
#   building.energy_systems_archetype_name = 'PV+4Pipe+DHW'
# EnergySystemsFactory('montreal_future', city).enrich()
# buildings_dic = {}
# for building in city.buildings:
#   EnergySystemsSimulationFactory('archetype13', building=building, output_path=simulation_results_path).enrich()
#   buildings_dic[building.name] = to_dict(building, hourly_pv)
# scenario['efficient with PV+4Pipe+DHW']=buildings_dic
# print("Scenario 3: efficient with PV+4Pipe+DHW run successfully")
#
# def extract_HP_size(building):
#     dic={
#         # Heat Pump Rated Heating and Cooling Output
#         'hp_heat_size': building.energy_systems[1].generation_systems[1].nominal_heat_output/1000,
#         'hp_cooling_output':  building.energy_systems[1].generation_systems[1].nominal_cooling_output/1000,
#         # Boiler Rated Heat Output
#         'boiler_heat_output': building.energy_systems[1].generation_systems[0].nominal_heat_output/1000,
#         # TES characteristics
#         'tes_volume':building.energy_systems[1].generation_systems[0].energy_storage_systems[0].volume,
#         'tes_height':building.energy_systems[1].generation_systems[0].energy_storage_systems[0].height,
#         # DHW HP
#         'dhw_hp_heat_output': building.energy_systems[-1].generation_systems[0].nominal_heat_output/1000,
#         # DHW TES Characteristics
#         'dhw_tes_volume':  building.energy_systems[-1].generation_systems[0].energy_storage_systems[0].volume,
#         'dhw_tes_height':  building.energy_systems[-1].generation_systems[0].energy_storage_systems[0].height,
#     }
#
#
#     return dic
# HPs={}
# for building in city.buildings:
#     HPs[building.name]=extract_HP_size(building)

clusters=pd.read_csv(output_path/'clusters.csv')


# Step 2: Extract the demand data for each building
def extract_building_demand(city):
    building_demand = {}
    for building in city.buildings:
        demands = {
            'heating_demand': [x / (1000 * cte.WATTS_HOUR_TO_JULES) for x in building.heating_demand[cte.HOUR]],
            'cooling_demand': [x / (1000 * cte.WATTS_HOUR_TO_JULES) for x in building.cooling_demand[cte.HOUR]],
            'domestic_hot_water_demand': [x / (1000 * cte.WATTS_HOUR_TO_JULES) for x in building.domestic_hot_water_heat_demand[cte.HOUR]],
            'appliances_electrical_demand': [x / (1000 * cte.WATTS_HOUR_TO_JULES) for x in building.appliances_electrical_demand[cte.HOUR]],
            'lighting_electrical_demand': [x / (1000 * cte.WATTS_HOUR_TO_JULES) for x in building.lighting_electrical_demand[cte.HOUR]]
        }
        building_demand[building.name] = demands
    return building_demand

# Step 3: Sum the demand types for each cluster
def sum_demands_by_cluster(building_demand, clusters, demand_types):
    cluster_demands = {cluster: {demand_type: [0] * 8760 for demand_type in demand_types} for cluster in clusters['cluster'].unique()}

    for _, row in clusters.iterrows():
        building_id = str(row['id'])
        cluster = row['cluster']
        if building_id in building_demand:
            for demand_type in demand_types:
                cluster_demands[cluster][demand_type] = [sum(x) for x in zip(cluster_demands[cluster][demand_type], building_demand[building_id][demand_type])]

    return cluster_demands


def plot_demands_by_cluster(cluster_demands, demand_types, output_folder):
    import os
    os.makedirs(output_folder, exist_ok=True)

    for cluster, demands in cluster_demands.items():
        plt.figure(figsize=(15, 10))
        for demand_type in demand_types:
            plt.plot(demands[demand_type], label=demand_type)

        plt.title(f'Summed Demands for Cluster {cluster}')
        plt.xlabel('Hour of the Year')
        plt.ylabel('Demand (kWh)')
        plt.legend(loc='upper right')
        plt.grid(True)
        plt.tight_layout()
        plt.savefig(os.path.join(output_folder, f'cluster_{cluster}_summed_demands.png'))
        plt.close()


# Example usage
demand_types = [
    'heating_demand',
    'cooling_demand',
    'domestic_hot_water_demand',
    'appliances_electrical_demand',
    'lighting_electrical_demand'
]

# Extract the building demand data
building_demand = extract_building_demand(city)
cluster_demands = sum_demands_by_cluster(building_demand, clusters, demand_types)
# Create a DataFrame to export the results
cluster_demands_df = {f"{cluster}_{demand_type}": data for cluster, demands in cluster_demands.items() for
                      demand_type, data in demands.items()}
cluster_demands_df = pd.DataFrame(cluster_demands_df)

# Save the results to an Excel file

cluster_demands_df.to_excel(output_path/'cluster_demands.xlsx', index=False)

print(f"Clustered demand data successfully exported to {output_path}")



#%%-----------------------------------------------
# Scenario 4
#%% # -----------------------------------------------

del city
del buildings_dic


geojson_file_path_clusters= output_path / 'new.geojson'

with open(geojson_file_path_clusters , 'r') as f:
    building_type_data_new = json.load(f)

# Create city object from GeoJSON file
city = GeometryFactory('geojson',
                       path=geojson_file_path_clusters,
                       height_field='maximum_roof_height',
                       year_of_construction_field='year_built',
                       function_field='building_type',
                       function_to_hub=Dictionaries().montreal_function_to_hub_function).city
#%%-----------------------------------------------
# Enrich city data
#%% # -----------------------------------------------
ConstructionFactory('nrcan', city).enrich()
UsageFactory('nrcan', city).enrich()
WeatherFactory('epw', city).enrich()

buildings_clusters={
    1651: 4,
    1662: 0,
    1667: 1,
    1674: 2,
    1688: 3
}

for building_id in buildings_clusters:
    cluster=buildings_clusters[building_id]
    for idx, feature in enumerate(building_type_data_new['features']):
        if feature['properties']['id'] == str(building_id):
            building_type_data_new['features'][idx]['properties']['heating_demand_kWh']=cluster_demands[cluster]['heating_demand']
            building_type_data_new['features'][idx]['properties']['cooling_demand_kWh'] = cluster_demands[cluster]['cooling_demand']
            building_type_data_new['features'][idx]['properties']['domestic_hot_water_heat_demand_kWh'] = cluster_demands[cluster]['domestic_hot_water_demand']
            building_type_data_new['features'][idx]['properties']['appliances_electrical_demand_kWh'] = cluster_demands[cluster]['appliances_electrical_demand']
            building_type_data_new['features'][idx]['properties']['lighting_electrical_demand_kWh'] = cluster_demands[cluster]['lighting_electrical_demand']

enrich_buildings_with_geojson_data (building_type_data_new, city)


for building in city.buildings:
  building.energy_systems_archetype_name = 'PV+4Pipe+DHW'
EnergySystemsFactory('montreal_future', city).enrich()
buildings_dic = {}
for building in city.buildings:
  EnergySystemsSimulationFactory('archetype13', building=building, output_path=simulation_results_path).enrich()
  buildings_dic[building.name] = to_dict(building, hourly_pv)
scenario['efficient with PV+4Pipe+DHW']=buildings_dic
print("Scenario 4: efficient with PV+4Pipe+DHW run successfully for Clusters")

def extract_HP_size(building):
    dic={
        # Heat Pump Rated Heating and Cooling Output
        'hp_heat_size': building.energy_systems[1].generation_systems[1].nominal_heat_output/1000,
        'hp_cooling_output':  building.energy_systems[1].generation_systems[1].nominal_cooling_output/1000,
        # Boiler Rated Heat Output
        'boiler_heat_output': building.energy_systems[1].generation_systems[0].nominal_heat_output/1000,
        # TES characteristics
        'tes_volume':building.energy_systems[1].generation_systems[0].energy_storage_systems[0].volume,
        'tes_height':building.energy_systems[1].generation_systems[0].energy_storage_systems[0].height,
        # DHW HP
        'dhw_hp_heat_output': building.energy_systems[-1].generation_systems[0].nominal_heat_output/1000,
        # DHW TES Characteristics
        'dhw_tes_volume':  building.energy_systems[-1].generation_systems[0].energy_storage_systems[0].volume,
        'dhw_tes_height':  building.energy_systems[-1].generation_systems[0].energy_storage_systems[0].height,
    }


    return dic
HPs={}
for building in city.buildings:
    HPs[building.name]=extract_HP_size(building)

#%%-------------------------------------------------------
#""""EXPORTERS"""
#%%-------------------------------------------------------


# Convert the dictionary to a DataFrame
df = pd.DataFrame.from_dict(HPs, orient='index')

# Save the DataFrame to an Excel file
output_path_HPs =output_path/ 'HPs_data_sc4.xlsx'
df.to_excel(output_path_HPs, index_label='building_id')

print(f"Data successfully exported to {output_path}")



#%%-------------------------------------------------------
#""""EXPORTERS"""
#%%-------------------------------------------------------


# Convert the dictionary to a DataFrame
df = pd.DataFrame.from_dict(HPs, orient='index')

# Save the DataFrame to an Excel file
output_path_HPs =output_path/ 'HPs_data.xlsx'
df.to_excel(output_path_HPs, index_label='building_id')

print(f"Data successfully exported to {output_path}")

import pandas as pd

districts_demands={}
def extract_and_sum_demand_data(scenario, demand_types):


    # Conversion factor constant
    conversion_factor = 1 / (cte.WATTS_HOUR_TO_JULES * 1000)

    # Loop through each scenario
    for scenario_key, buildings in scenario.items():
        # Loop through each building in the scenario
        # Initialize an empty dictionary to store the district demand sums
        district_demand = {demand_type: [0] * 8760 for demand_type in demand_types}
        district_demand['hourly_pv_kWh']= [0] * 8760
        for building_id, building_data in buildings.items():
            # Loop through each demand type and sum up the data
            for demand_type in demand_types:
                if demand_type in building_data:
                    district_demand[demand_type] = [sum(x) for x in zip(district_demand[demand_type], building_data[demand_type])]

                # If PV data is available and relevant
                if scenario_key == "efficient with PV":
                    district_demand['hourly_pv_kWh'] = [sum(x) for x in zip(district_demand['hourly_pv_kWh'], building_data['hourly_pv_kWh'])]
            if scenario_key == 'efficient with PV+4Pipe+DHW':
                district_demand['hourly_pv_kWh'] = districts_demands["efficient with PV"]['hourly_pv_kWh']
            districts_demands[scenario_key]=district_demand

    return districts_demands

# Example usage
# Assuming 'scenario' is a dictionary with the required structure and 'cte' is defined somewhere with WATTS_HOUR_TO_JULES constant
demand_types = [
    'heating_consumption_kWh',
    'cooling_consumption_kWh',
    'domestic_hot_water_consumption_kWh',
    'appliances_consumption_kWh',
    'lighting_consumption_kWh',
    # 'hourly_pv_kWh'  # Include this only if you want to consider PV data
]

# # Call the function with your scenario data
district_demand = extract_and_sum_demand_data(scenario, demand_types)
#
# """"EXPORTERS"""
# import pandas as pd
#
#
# Export the DataFrame to an Excel file
excel_file_path = r'C:\Users\a_gabald\PycharmProjects\summer_course_2024\out_files\districts_balance.xlsx'
# df.to_excel(excel_file_path, index=True, index_label='Building')

# Create an Excel writer object
with pd.ExcelWriter(excel_file_path, engine='xlsxwriter') as writer:
    for scenarios,demands in district_demand.items():
        # Convert demands to a DataFrame
        df_demands = pd.DataFrame(demands)
        # Convert building_id to string and check its length
        sheet_name = str(scenarios)
        if len(sheet_name) > 31:
            sheet_name = sheet_name[:31]  # Truncate to 31 characters if necessary
        # Write the DataFrame to a specific sheet named after the building_id
        df_demands.to_excel(writer, sheet_name=sheet_name, index=False)


print("district balance data is exported successfully")


import pandas as pd

# Assuming your scenario dictionary is already defined as follows:
# scenario = {
#     'baseline': { ... },
#     'efficient with PV': { ... }
# }


def dict_to_df_col_wise(building_data):
    """
    Converts a dictionary of building data to a DataFrame.

    Args:
        building_data (dict): Dictionary containing building data where keys are building ids and values are dictionaries
                              with hourly data for various demand types.

    Returns:
        pd.DataFrame: DataFrame with columns for each building and demand type.
    """
    # Create a dictionary to hold DataFrames for each demand type
    df_dict= {}

    # Loop over each building
    for building_id, data in building_data.items():
        # Create a DataFrame for this building's data
        building_df = pd.DataFrame(data)

        # Rename columns to include building_id
        building_df.columns = [f"{building_id}_{col}" for col in building_df.columns]

        # Add this DataFrame to the dictionary
        df_dict[building_id] = building_df

    # Concatenate all building DataFrames column-wise
    result_df = pd.concat(df_dict.values(), axis=1)

    return result_df

# Create DataFrames for each scenario
baseline_df = dict_to_df_col_wise(scenario['baseline'])
efficient_with_pv_df = dict_to_df_col_wise(scenario['efficient with PV'])
efficient_with_pv_hps = dict_to_df_col_wise(scenario['efficient with PV+4Pipe+DHW'])


# Write the DataFrames to an Excel file with two separate sheets
with pd.ExcelWriter(r'C:\Users\a_gabald\PycharmProjects\summer_course_2024\out_files\scenario_data.xlsx') as writer:
    baseline_df.to_excel(writer, sheet_name='baseline', index=True)
    efficient_with_pv_df.to_excel(writer, sheet_name='efficient with PV', index=True)
    efficient_with_pv_hps.to_excel(writer, sheet_name='efficient with HPs_2', index=True)

print("hourly data has been successfully exported per building to scenario_data.xlsx")



import pandas as pd
import matplotlib.pyplot as plt
import numpy as np


def convert_hourly_to_monthly(hourly_data):
    """
    Converts hourly data to monthly data by summing up the values for each month.

    Args:
        hourly_data (list): List of hourly data (length 8760).

    Returns:
        list: List of monthly data (length 12).
    """
    hourly_series = pd.Series(hourly_data, index=pd.date_range(start='1/1/2023', periods=8760, freq='H'))
    monthly_data = hourly_series.resample('M').sum()
    return monthly_data.tolist()

import os
def plot_stacked_demands_vs_pv(district_demand, demand_types, output_path, pv_type='hourly_pv_kWh'):
    """
    Plots the stacked monthly demand for each scenario and compares it to the PV data.

    Args:
        district_demand (dict): Dictionary with scenario keys and demand data.
        demand_types (list): List of demand types to plot.
        output_path (str): Path to save the plots.
        pv_type (str): The PV data type to compare against.
    """
    os.makedirs(output_path, exist_ok=True)

    for scenario_key, demand_data in district_demand.items():
        # Convert hourly data to monthly data for each demand type
        monthly_data = {demand_type: convert_hourly_to_monthly(demand_data[demand_type]) for demand_type in
                        demand_types}
        monthly_pv = convert_hourly_to_monthly(demand_data.get(pv_type, [0] * 8760))

        # Create a DataFrame for easier plotting
        combined_data = pd.DataFrame(monthly_data)
        combined_data['Month'] = range(1, 13)
        combined_data['PV'] = monthly_pv

        # Plotting
        fig, ax1 = plt.subplots(figsize=(14, 8))

        # Plot stacked demands
        combined_data.set_index('Month', inplace=True)
        combined_data[demand_types].plot(kind='bar', stacked=True, ax=ax1, colormap='tab20')

        ax1.set_xlabel('Month')
        ax1.set_ylabel('Energy Demand (kWh)')
        ax1.set_title(f'Monthly Energy Demand and PV Generation for {scenario_key}')

        # Plot PV data on the secondary y-axis
        ax2 = ax1.twinx()
        ax2.plot(combined_data.index, combined_data['PV'], color='black', linestyle='-', marker='o',
                 label='PV Generation')
        ax2.set_ylabel('PV Generation (kWh)')

        # Add legends
        ax1.legend(loc='upper left')
        ax2.legend(loc='upper right')

        ax1.set_xticks(combined_data.index)
        ax1.set_xticklabels(['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec'])

        # Save the plot
        plt.savefig(os.path.join(output_path, f'{scenario_key}_monthly_demand_vs_pv.png'))
        plt.close()


# Example usage
# district_demand = extract_and_sum_demand_data(scenario, demand_types)

# Specify the demand types and PV type
demand_types = [
    'heating_consumption_kWh',
    'cooling_consumption_kWh',
    'domestic_hot_water_consumption_kWh',
    'appliances_consumption_kWh',
    'lighting_consumption_kWh'
]

# Plot the data
plot_stacked_demands_vs_pv(district_demand, demand_types, output_path)
# Plot the data
print('test')
import csv