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148 lines
5.8 KiB
148 lines
5.8 KiB
import matplotlib
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matplotlib.use('Qt5Agg') # or 'Qt5Agg', depending on your setup
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import matplotlib.pyplot as plt
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import numpy as np
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from numpy_da import DynamicArray
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import json
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import sys
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import argparse
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__author__ = 'm3x1m0m'
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class JsonSettingsExtractor:
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def __init__(self, fname):
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with open(fname, "r") as rf:
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settings = json.load(rf)
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self._currency = settings["currency"]
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# Kind of inefficient code. Does not matter only runs once at startup
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_technologies = settings["technologies"]
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self._label = [technology["label"] for technology in _technologies]
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self._installation_price = [technology["installation_price"] for technology in _technologies]
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self._reinstallation_price = [technology["reinstallation_price"] for technology in _technologies]
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self._kwh_expenditure = [technology["kwh_expenditure"] for technology in _technologies]
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self._kwh_price = [technology["kwh_price"] for technology in _technologies]
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self._percent_inflation = [technology["percent_inflation"] for technology in _technologies]
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self._years_lifespan = [technology["years_lifespan"] for technology in _technologies]
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self._amount_of_technologies = len(self.label)
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@property
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def currency(self):
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return self._currency
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@property
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def label(self):
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return self._label
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@property
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def installation_price(self):
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return np.array(self._installation_price)
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@property
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def reinstallation_price(self):
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return np.array(self._reinstallation_price)
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@property
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def kwh_expenditure(self):
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return np.array(self._kwh_expenditure)
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@property
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def percent_inflation(self):
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return np.array(self._percent_inflation)
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@property
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def years_lifespan(self):
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return self._years_lifespan
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@property
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def amount_of_technologies(self):
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return self._amount_of_technologies
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def main():
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parser = argparse.ArgumentParser(description='This script allows to calculate which heating technology makes sense financially for you.')
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parser.add_argument('-a','--settings', help='Settings file.', required=True, metavar=('FILENAME'))
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parser.add_argument('-b','--years', help='Amount of years for which to run the simulation.', required=True, type=int, metavar=('YEARS'))
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parser.add_argument('-c','--plot', help='Visualize the calculations done.', action='store_true')
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args = parser.parse_args()
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input_data = JsonSettingsExtractor(args.settings)
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# Basic settings
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currency = input_data.currency
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# Initializations
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year = 0 #first year
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#grand_total = DynamicArray(shape=(0, input_data._amount_of_technologies))
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grand_total = np.empty((0, input_data.amount_of_technologies))
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# Iteration for years in service
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for i in range(args.years):
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# Initializations
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increase_total = np.zeros(input_data.amount_of_technologies)
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increase_installation = np.zeros(input_data.amount_of_technologies)
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increase_expenditure = np.zeros(input_data.amount_of_technologies)
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# Handle possible replacement of heating system
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for j in range(len(input_data.years_lifespan)):
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if (year % input_data.years_lifespan[j]) == 0 and year == 0:
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increase_installation[j] = input_data.installation_price[j]
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elif (year % input_data.years_lifespan[j]) == 0 and year > 0:
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increase_installation[j] = input_data.reinstallation_price[j]
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else:
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increase_installation[j] = 0 # just to be clear
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# Calculate increase
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inflation_factor = np.array([1.0 + (x * year) / 100.0 for x in input_data.percent_inflation])
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increase_expenditure = input_data.kwh_expenditure * inflation_factor
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increase_total = increase_installation + increase_expenditure
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# Safe the yearly costs for plotting
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if year > 0:
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grand_total = np.vstack((grand_total, increase_total + grand_total[year - 1]))
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elif year == 0:
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grand_total = np.vstack((grand_total, increase_total))
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# Output data every year
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# Rounding
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increase_total = np.round(increase_total, decimals=0)
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increase_installation = np.round(increase_installation, decimals=0)
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increase_expenditure = np.round(increase_expenditure, decimals=0)
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print(f"Year {year:2.0f} \t\tInstallation [{currency}] \tExpenditure [{currency}] \tTotal [{currency}] \tGrand total [{currency}]")
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print("-------------------------------------------------------------------------------------------------------------")
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for j in range(input_data.amount_of_technologies):
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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}")
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print("\n")
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year += 1
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if args.plot:
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# Define colors for each line
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plt_colors = ["#8ecae6", "#219ebc", "#023047", "#ffb703", "#fb8500"];
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# Number of years and technologies
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years = grand_total.shape[0]
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technologies = grand_total.shape[1]
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# Create a figure and axis
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plt.figure(figsize=(10, 6))
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# Generate line plots for each technology
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for i in range(technologies):
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plt.plot(range(years), grand_total[:, i], marker='o', color=plt_colors[i], label=f'Technology {i+1}')
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# Adding titles and labels
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plt.title('Economical comparision of different heating technologies')
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plt.xlabel('Year')
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plt.ylabel(f'Cost {currency}')
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# plt.xticks(range(years), [f'Year {j+1}' for j in range(years)]) # Customize x-ticks if needed
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plt.legend(input_data.label)
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plt.grid()
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# Show the plot
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plt.tight_layout()
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plt.show()
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if __name__ == "__main__":
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main()
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