Building Stock. Cleaning code. This is a good version.

energy_demand/assumptions.py

@@ -1,7 +1,7 @@
 """ This file contains all assumptions of the energy demand model"""
 # pylint: disable=I0011,C0321,C0301,C0103, C0325
 
-def load_assumptions():
+def load_assumptions(data):
     """All assumptions
 
     Returns
@@ -16,15 +16,17 @@ def load_assumptions():
     assumptions_dict = {}
 
 
+    # Load assumptions from csv files
+    dwtype_floorarea = data['dwtype_floorarea']
 
     # ------------------------------------------------------------------------
     # Residential model
     # ------------------------------------------------------------------------
 
     # Building stock related
     change_floorarea_pp = 0.1 # [%]                                                                                  # Assumption of change in floor area up to end_year ASSUMPTION (if minus, check if new buildings are needed)
-    assump_dwtype_distr_ey = {'semi_detached': 20.0, 'terraced': 20, 'flat': 30, 'detached': 20, 'bungalow': 10}        # Assumption of distribution of dwelling types in end_year ASSUMPTION
-
+    assump_dwtype_distr_ey = {'semi_detached': 20.0, 'terraced': 20, 'flat': 30, 'detached': 20, 'bungalow': 10}     # Assumption of distribution of dwelling types in end_year ASSUMPTION
+    dwtype_floorarea = dwtype_floorarea # Average floor area per dwelling type (loaded from CSV)
 
 
 
@@ -33,5 +35,6 @@ def load_assumptions():
     # Add to dictionary
     assumptions_dict['change_floorarea_pp'] = change_floorarea_pp
     assumptions_dict['assump_dwtype_distr_ey'] = assump_dwtype_distr_ey
+    assumptions_dict['dwtype_floorarea'] = dwtype_floorarea
 
     return assumptions_dict

energy_demand/building_stock_functions.py

@@ -122,7 +122,7 @@ def get_sum_scenario_driver_lighting(self):
             sum_driver += dwelling.scenario_driver_lighting
         return sum_driver
 
-def get_floorarea_pp(reg_floorarea, reg_pop_by, glob_var, assump_final_diff_floorarea_pp):
+def calc_floorarea_pp(reg_floorarea, reg_pop_by, glob_var, assump_final_diff_floorarea_pp):
     """ Calculates future floor area per person depending on
     assumptions on final change and base year data
 
@@ -212,23 +212,17 @@ def get_dwtype_dist(dwtype_distr_by, assump_dwtype_distr_ey, glob_var):
 
             # iterate type
             for dtype in dwtype_distr_by:
-                val_by = dwtype_distr_by[dtype]               # base year value
-                sim_y = assump_dwtype_distr_ey[dtype]         # cur year value
-                diff_val = sim_y - val_by                           # Total difference
-                diff_y = diff_val / (len(sim_period)-1)             # Linear difference per year
-                y_distr[dtype] = val_by + (diff_y * sim_year_nr)    # Differene up to current year
+                val_by = dwtype_distr_by[dtype]                         # base year value
+                sim_y = assump_dwtype_distr_ey[dtype]                   # cur year value
+                diff_val = sim_y - val_by                               # Total difference
+                diff_y = diff_val / (len(sim_period)-1)                 # Linear difference per year
+                y_distr[dtype] = val_by + (diff_y * sim_year_nr) / 100   # Differene up to current year #TODO: Check procent
 
         dwtype_distr[sim_year] = y_distr
 
-    # test if not 100% all entries
-    for i in dwtype_distr:
-        n = 0
-        for e in dwtype_distr[i]:
-            n += dwtype_distr[i][e]
-        if round(n, 1) != 100:
-            print("ERROR")
-            import sys
-            sys.exit()
+    # Test if distribution is 100%
+    for y in dwtype_distr:
+        assert round(sum(dwtype_distr[y].values()), 1) == 100 # "The values in the dictionary do not sum to 100"
     return dwtype_distr
 
 def get_dwtype_age_distr(get_dwtype_age_distr_base):