Added a method to read heat pump data into dictionary

city_model_structure/energy_system.py

@@ -12,6 +12,7 @@ class EnergySystem(CityObject):
     """
   EnergySystem(CityObject) class
   """
+
     def __init__(self, name, lod, surfaces, city_lower_corner):
         super().__init__(name, lod, surfaces, city_lower_corner)
         self._heat_pump = None
@@ -19,4 +20,3 @@ class EnergySystem(CityObject):
     @property
     def heat_pump(self) -> HeatPump:
         return self._heat_pump
-

imports/energy_systems/xlsx_heat_pump_parameters.py

@@ -5,19 +5,55 @@ Copyright © 2020 Project Author Peter Yefi peteryefi@gmail.com
 Contributor Pilar Monsalvete Alvarez de Uribarri pilar.monsalvete@concordia.ca
 """
 
+import pandas as pd
+from typing import Dict
+
 
 class XlsxHeatPumpParameters:
     """
   XlsxHeatPumpParameters class
   """
+
     def __init__(self, city, base_path):
         self._city = city
         self._base_path = (base_path / 'heat_pumps/Air source.xlsx')
 
-  def _read_file(self):
+    def _read_file(self) -> Dict:
         """
     reads xlsx file containing the heat pump information
+    into a dictionary
+    :return : Dict
     """
+        xl_file = pd.ExcelFile(self._base_path)
+        heat_pump_dfs = {sheet_name: xl_file.parse(sheet_name)
+                         for sheet_name in xl_file.sheet_names}
+
+        cooling_data = {}
+        heating_data = {}
+
+        for sheet, dataframe in heat_pump_dfs.items():
+            if sheet == "Summary":
+                continue
+            # Remove nan rows and columns and extract cooling and heating data
+            # for each sheet
+            df = heat_pump_dfs[sheet].dropna(axis=1, how='all')
+            cooling_df = df.iloc[4:34, 0:8]
+            heating_df = df.iloc[4:29, 8:20]
+
+            # extract the data into dictionaries each sheet is a key entry in the
+            # dictionary
+            cooling_data[sheet] = {}
+            heating_data[sheet] = {}
+            i = 0
+            # for each sheet extract data for twout/Ta.RU temperatures. Thus, the twout
+            # temp is the key for the values of pf,pa,qw data
+            while i < 25:
+                cooling_data[sheet][cooling_df.iloc[i][0]] = cooling_df.iloc[i + 1:i + 4, 2:8].values.tolist()
+                heating_data[sheet][heating_df.iloc[i][0]] = cooling_df.iloc[i + 1:i + 4, 2:8].values.tolist()
+                i = i + 5
+            # extract the last cooling data
+            cooling_data[sheet][cooling_df.iloc[i][0]] = cooling_df.iloc[i + 1:i + 4, 2:8]
+        return {"cooling": cooling_data, "heating": heating_data}
 
     def enrich_city(self):
         """