Refactored dynamics

input_profile_processor/input_profile_processor.py

@@ -31,7 +31,7 @@ from datetime import datetime, timedelta
 import pandas as pd
 
 def process_input_profiles(input_profile_dict, t_start, dynamic):
-    d_step_min = min(dynamic.step_size_p(position) for position in range(dynamic.number_of_steps()))
+    d_step_min = min(dynamic.step_size(index) for index in dynamic.time_steps())
     # This entire process assumes four things:
     # 1. d_step_min devides a day into an integer amount of steps
     # 2. d_step_min devides the intervall from YYYY.MM.DD 00:00:00 of the day containing t_start to t_start into an integer amount of steps
@@ -49,15 +49,15 @@ def process_input_profiles(input_profile_dict, t_start, dynamic):
 
     for input_profile_name, (input_profile_type, input_profile) in input_profiles.items():
         input_profile.drop(pd.date_range(input_profile.index[0], t_start - timedelta(hours = d_step_min), freq = str(d_step_min) + 'H'), inplace = True)
-        input_profile.drop(pd.date_range(t_start + pd.Timedelta(hours = sum(dynamic.step_size_p(position) for position in range(dynamic.number_of_steps()))), input_profile.index[-1], freq = str(d_step_min) + 'H'), inplace = True)
+        input_profile.drop(pd.date_range(t_start + pd.Timedelta(hours = sum(dynamic.step_size(index) for index in dynamic.time_steps())), input_profile.index[-1], freq = str(d_step_min) + 'H'), inplace = True)
 
         if len(input_profile) != dynamic.number_of_steps():
             index = 0
             drop = []
-            for position in range(dynamic.number_of_steps()):
-                if dynamic.step_size_p(position) != d_step_min:
-                    number_of_steps = int(dynamic.step_size_p(position) / d_step_min)
-                    input_profile.iloc[index] = sum(d_step_min * input_profile.iloc[index + i] for i in range(number_of_steps)) / dynamic.step_size_p(position)
+            for index in dynamic.time_steps():
+                if dynamic.step_size(index) != d_step_min:
+                    number_of_steps = int(dynamic.step_size(index) / d_step_min)
+                    input_profile.iloc[index] = sum(d_step_min * input_profile.iloc[index + i] for i in range(number_of_steps)) / dynamic.step_size(index)
                     drop.extend(index + i + 1 for i in range(number_of_steps - 1))
                     index += number_of_steps
                 else:
@@ -99,7 +99,7 @@ def expand_profile_to_year(profile, year, t_step):
     return profile
 
 def load_profile(name, file, t_start, dynamic, d_step_min):
-    t_last = t_start + pd.Timedelta(hours = sum(dynamic.step_size_p(position) for position in range(dynamic.number_of_steps() - 1)))
+    t_last = t_start + pd.Timedelta(hours = sum(dynamic.step_size(index) for index in dynamic.time_steps()) - 1)
     profile = pd.read_csv(file, index_col = 0)
     try:
         file_start = pd.to_datetime(profile.index[0], format = '%d-%m-%Y %H:%M:%S')
@@ -155,7 +155,7 @@ def load_profile(name, file, t_start, dynamic, d_step_min):
     return resample_profile(name, profile, t_start, dynamic, d_step_min, t_last)
 
 def generate_profile(name, profile_type, parameters, input_profiles, t_start, dynamic, d_step_min):
-    t_last = t_start + pd.Timedelta(hours = sum(dynamic.step_size_p(position) for position in range(dynamic.number_of_steps() - 1)))
+    t_last = t_start + pd.Timedelta(hours = sum(dynamic.step_size(index) for index in list(dynamic.time_steps())[:-1]))
     years = range(t_start.year, t_last.year + 1)
 
     if profile_type == 'elec_demand':