import matplotlib
matplotlib.use('Qt5Agg')  # or 'Qt5Agg', depending on your setup
import matplotlib.pyplot as plt
import numpy as np
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"]
            self._legend = settings["legend"]
            # 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._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]

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

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

    @property
    def installation_price(self):
        return self._installation_price

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

    @property
    def years_lifespan(self):
        self._years_lifespan

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('-g','--plot', help='Visualize costs over one or multiple years.', type=int, metavar=('YEARS'))
    args = parser.parse_args()

    input_data = JsonSettingsExtractor(args.settings)

    print("Inflations: {}".format(input_data.percent_inflation))
    print("Expend: {}".format(input_data.kwh_expenditure))


    # if args.plot != None:
    #     # Basic settings
    #     width = 0.3
    #     plt_colors = ["#8ecae6", "#219ebc", "#023047", "#ffb703", "#fb8500"];
    #     fig, ax = plt.subplots()
    #     ax.bar(input_data.label, input_data.installation_price, width, label = "Installation price", color = plt_colors[0])
        
    #     current_y = input_data.installation_price
    #     years = 0 #first year

    #     # Iteration for years in service
    #     for i in range(args.plot):
    #         inflation_factor = 1.0 + (input_data.percent_inflation * years) / 100.0
    #         increase = input_data.kwh_expenditure * inflation_factor
    #         ax.bar(input_data.label, increase, width, bottom = current_y, label = "Expenditure".format(y), color = plt_colors[1])
    #         years += 1
    #         current_y += increase

    #     ax.set_ylabel(input_data.currency)
    #     ax.set_title("Comparision of economics w/ different heating technologies")
    #     ax.legend(input_data.legend)

    #     plt.show()

if __name__ == "__main__":
    main()