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



class Producer:
    def __init__(self, quantity):
        self.quantity = quantity
        self.consumption_profile = self.create_consumption_profile()
        self.production_profile = self.create_production_profile()
        self.final_consumption = self.calculate_final_consumption()
        self.final_production = self.calculate_final_production()

    # Vebrauchsprofil aus CSV-Datei in Dataframe einlesen und die Leistungs-Series extrahieren
    def create_consumption_profile(self):
        consumption_profile = pd.read_csv('Lastprofil_final_H0.csv',delimiter=';')
        return consumption_profile['Leistung']
    
    # Verbrauchsprofil mit der Anzahl der Haushalte multiplizieren   
    def calculate_final_consumption(self):
        final_consumption = self.consumption_profile.mul(self.quantity)
        return final_consumption
    
    # Erzegungsprofil über PV_Input.py erstelln, in .csv schreiben und von dort dann die Leistungs-Series extrahieren
    def create_production_profile(self):
        production_profile = pv.create_production_profile()
        production_profile.to_csv('production_profile.csv', sep=';',header=['Leistung'])
        production_profile = pd.read_csv('production_profile.csv',delimiter=';')
        return production_profile['Leistung']

    # Erzeugungsprofil mit der Anzahl der Haushalte multiplizieren   
    def calculate_final_production(self):
        final_production = self.production_profile.mul(self.quantity)
        return final_production
    


class Consumer:
    def __init__(self, quantity):
        self.quantity = quantity
        self.consumption_profile = self.create_consumption_profile()
        self.final_consumption = self.calculate_final_consumption()

    # Vebrauchsprofil aus CSV-Datei in Dataframe einlesen und die Leistungs-Series extrahieren, Werte in Watt umrechnen und mit 4 multiplizieren, da Originalwerte 15-minütlich waren
    def create_consumption_profile(self):
        consumption_profile = pd.read_csv('Lastprofil_final_H0.csv',delimiter=';')
        return consumption_profile['Leistung']*1000*4
    
    # Verbrauchsprofil mit der Anzahl der Haushalte multiplizieren
    def calculate_final_consumption(self):
        final_consumption = self.consumption_profile.mul(self.quantity)
        return final_consumption
    


class Neighborhood:
    def __init__(self, producer, consumer):
        self.producer = producer
        self.consumer = consumer

    # Gesamterzeugung, Gesamtverbrauch und Nettoverbrauch plotten
    def plot_consumption(self):
        total_consumption = -1*(self.consumer.final_consumption + self.producer.final_consumption)
        total_production = self.producer.final_production
        net_value = total_consumption + total_production

        # X-Werte anlegen, einen Wert löschen da 8761 Werte vorhanden sind, aber nur 8760 benötigt werden
        x = pd.date_range(start='2018-12-31', end ='2019-12-31', freq='1h')
        x = x[:-1]
        # Plot dimensionieren
        plt.figure(figsize=(10, 6))
        # y-Werte den x-Werten zuordnen
        plt.bar(x, total_consumption)
        plt.bar(x, total_production)
        plt.bar(x, net_value)
        #plt.plot(x, net_value, '-')
        # Titel und Achsenbeschriftungen anlegen
        plt.xlabel('Stunde')
        plt.ylabel('Strom (kWh)')
        plt.title('Produktion/Verbrauch Nachbarschaft')
        # X-Ticks nur monatlich plotten
        monthly_ticks = pd.date_range(start='2018-12-31', end ='2019-12-31', freq='1MS')
        plt.xticks(monthly_ticks)
        # Legende anlegen
        plt.legend(['Verbrauch', 'Produktion', 'Netto-Wert'], loc='upper right')
        # X-Labels rotieren
        plt.xticks(rotation=45)
        # Grid anlegen
        plt.grid(color='black', axis='y', linestyle='--', linewidth=0.5)
        plt.show()