Havin a rough idea of how the json file shoudl look like for now

example_1.json

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+{
+    "currency": "SEK",
+    "legend": ["Installation price", "Heating expenditure"],
+    "technologies": [	
+        {
+            "label": "Air water heat pump",
+            "installation_price": 120000,
+            "kwh_expenditure": 11000,
+            "kwh_price": 2,
+            "percent_inflation": 2,
+            "years_lifespan": 15
+        },
+        {
+            "label": "Geothermal heat pump",
+            "installation_price": 222788,
+            "kwh_expenditure": 5000,
+            "kwh_price": 2,
+            "percent_inflation": 2,
+            "years_lifespan": 25
+        },
+        {
+            "label": "District heating",
+            "installation_price": 95133,
+            "kwh_expenditure": 25000,
+            "kwh_price": 0.95,
+            "percent_inflation": 10,
+            "years_lifespan": 50
+        }
+    ]
+}

heatpump.py

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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()