import matplotlib
matplotlib.use('Qt5Agg')  # or 'Qt5Agg', depending on your setup
import matplotlib.pyplot as plt
import numpy as np
from numpy_da import DynamicArray
import json
import sys
import argparse
__author__ = 'm3x1m0m'

class JsonSettingsExtractor:

    def __init__(self, fname):
        with open(fname, "r") as rf:
            settings = json.load(rf)
            self._currency = settings["currency"]
            # Kind of inefficient code. Does not matter only runs once at startup
            _technologies = settings["technologies"]
            self._label = [technology["label"] for technology in _technologies]
            self._installation_price = [technology["installation_price"] for technology in _technologies]
            self._reinstallation_price = [technology["reinstallation_price"] for technology in _technologies]
            self._kwh_expenditure = [technology["kwh_expenditure"] for technology in _technologies]
            self._kwh_price = [technology["kwh_price"] for technology in _technologies]
            self._percent_inflation = [technology["percent_inflation"] for technology in _technologies]
            self._years_lifespan = [technology["years_lifespan"] for technology in _technologies]
            self._amount_of_technologies = len(self.label)

    @property
    def currency(self):
        return self._currency

    @property
    def label(self):
        return self._label

    @property
    def installation_price(self):
        return np.array(self._installation_price)
    
    @property
    def reinstallation_price(self):
        return np.array(self._reinstallation_price)

    @property
    def kwh_expenditure(self):
        return np.array(self._kwh_expenditure)
    
    @property
    def percent_inflation(self):
        return np.array(self._percent_inflation)

    @property
    def years_lifespan(self):
        return self._years_lifespan

    @property
    def amount_of_technologies(self):
        return self._amount_of_technologies

def main():

    parser = argparse.ArgumentParser(description='This script allows to calculate which heating technology makes sense financially for you.')
    parser.add_argument('-a','--settings', help='Settings file.', required=True, metavar=('FILENAME'))
    parser.add_argument('-b','--years', help='Amount of years for which to run the simulation.', required=True, type=int, metavar=('YEARS'))
    parser.add_argument('-c','--plot', help='Visualize the calculations done.', action='store_true')
    args = parser.parse_args()

    input_data = JsonSettingsExtractor(args.settings)

    # Basic settings
    currency = input_data.currency

    # Initializations
    year = 0 #first year
    #grand_total = DynamicArray(shape=(0, input_data._amount_of_technologies))
    grand_total = np.empty((0, input_data.amount_of_technologies))

    # Iteration for years in service
    for i in range(args.years):
        # Initializations
        increase_total = np.zeros(input_data.amount_of_technologies)
        increase_installation = np.zeros(input_data.amount_of_technologies)
        increase_expenditure = np.zeros(input_data.amount_of_technologies)

        # Handle possible replacement of heating system
        for j in range(len(input_data.years_lifespan)): 
            if (year % input_data.years_lifespan[j]) == 0 and year == 0:
                increase_installation[j] = input_data.installation_price[j]
            elif (year % input_data.years_lifespan[j]) == 0 and year > 0:
                increase_installation[j] = input_data.reinstallation_price[j]
            else: 
                increase_installation[j] = 0 # just to be clear
        
        # Calculate increase
        inflation_factor = np.array([1.0 + (x * year) / 100.0 for x in input_data.percent_inflation])
        increase_expenditure = input_data.kwh_expenditure * inflation_factor
        increase_total = increase_installation + increase_expenditure

        # Safe the yearly costs for plotting
        if year > 0:
            grand_total = np.vstack((grand_total, increase_total + grand_total[year - 1]))
        elif year == 0:
            grand_total = np.vstack((grand_total, increase_total))

        # Output data every year

        # Rounding
        increase_total = np.round(increase_total, decimals=0)
        increase_installation = np.round(increase_installation, decimals=0)
        increase_expenditure = np.round(increase_expenditure, decimals=0)

        print(f"Year {year:2.0f} \t\tInstallation [{currency}] \tExpenditure [{currency}] \tTotal [{currency}] \tGrand total [{currency}]")
        print("-------------------------------------------------------------------------------------------------------------")
        for j in range(input_data.amount_of_technologies):
            print(f"{input_data.label[j]} \t{increase_installation[j]:8.0f} \t\t{increase_expenditure[j]:8.0f} \t\t{increase_total[j]:8.0f} \t{grand_total[year][j]:8.0f}")
        print("\n")

        year += 1

    if args.plot:
        # Define colors for each line
        plt_colors = ["#8ecae6", "#219ebc", "#023047", "#ffb703", "#fb8500"];

        # Number of years and technologies
        years = grand_total.shape[0]
        technologies = grand_total.shape[1]

        # Create a figure and axis
        plt.figure(figsize=(10, 6))

        # Generate line plots for each technology
        for i in range(technologies):
            plt.plot(range(years), grand_total[:, i], marker='o', color=plt_colors[i], label=f'Technology {i+1}')

        # Adding titles and labels
        plt.title('Economical comparision of different heating technologies')
        plt.xlabel('Year')
        plt.ylabel(f'Cost {currency}')
        # plt.xticks(range(years), [f'Year {j+1}' for j in range(years)])  # Customize x-ticks if needed
        plt.legend(input_data.label)
        plt.grid()

        # Show the plot
        plt.tight_layout()
        plt.show()

if __name__ == "__main__":
    main()