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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()