Profiles use numpy arrays now

input_profile_processor/input_profile_processor.py

@@ -29,10 +29,11 @@ from Tooling.input_profile_processor.calc_irradiance import generate_g_t_series
 from Tooling.modifier import Modifier
 
 from datetime import datetime, timedelta
+import numpy as np
 import pandas as pd
 
 def process_input_profiles(input_profile_dict, t_start, dynamic):
-    d_step_min = min(dynamic.step_size(index) for index in dynamic.time_steps())
+    d_step_min = np.min(dynamic.step_lengths()) / 3600.0
     # 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
@@ -50,7 +51,7 @@ 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(index) for index in dynamic.time_steps())), input_profile.index[-1], freq = str(d_step_min) + 'H'), inplace = True)
+        input_profile.drop(pd.date_range(t_start + pd.Timedelta(hours = np.sum(dynamic.step_lengths()) / 3600.0), input_profile.index[-1], freq = str(d_step_min) + 'H'), inplace = True)
 
         if len(input_profile) != dynamic.number_of_steps():
             index = 0
@@ -75,6 +76,9 @@ def process_input_profiles(input_profile_dict, t_start, dynamic):
 
         input_profile = input_profile.squeeze()
         input_profile.set_axis(list(range(dynamic.number_of_steps())), inplace = True)
+        if isinstance(input_profile, pd.Series):
+            input_profiles[input_profile_name] = Profile(input_profile.values, dynamic)
+        else:
             input_profiles[input_profile_name] = Profile(input_profile, dynamic)
     return input_profiles
 
@@ -100,7 +104,7 @@ 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(index) for index in dynamic.time_steps()) - 1)
+    t_last = t_start + pd.Timedelta(hours = np.sum(dynamic.step_lengths()[:-1]) / 3600.0)
     profile = pd.read_csv(file, index_col = 0)
     try:
         file_start = pd.to_datetime(profile.index[0], format = '%d-%m-%Y %H:%M:%S')
@@ -156,7 +160,7 @@ 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(index) for index in list(dynamic.time_steps())[:-1]))
+    t_last = t_start + pd.Timedelta(hours = np.sum(dynamic.step_lengths()[:-1]) / 3600.0)
     years = range(t_start.year, t_last.year + 1)
 
     if profile_type == 'electricity_demand':