Refactored handling of profiles with AggregatedDynamic dynamics

dynamics.py

@@ -1048,10 +1048,10 @@ class AggregatedDynamic(Dynamic):
             np.full(number_of_time_steps_per_period, step_length, dtype=int)
         )
         root_dynamic = self.dynamic_tree.root()
-        self.shape_ = sum(len(period) for period in periods)
-        self.state_shape_ = sum(len(period) + 1 for period in periods)
         self.period_dynamics = []
         self.n_p = []
+        running_index = 0
+        self.offsets_ = []
         for i, period in enumerate(periods):
             index = 0
             period_indices = [0]
@@ -1066,8 +1066,12 @@ class AggregatedDynamic(Dynamic):
             self.n_p.append(
                 sum(1 for period_index in period_order if period_index == i)
             )
+            self.offsets_.append((running_index, running_index + len(period)))
+            running_index += len(period)
         self.period_order = period_order
         self.n = len(period_order)
+        self.shape_ = running_index
+        self.state_shape_ = running_index + len(periods)
 
         # sanity check 5: all elements in period order are valid period indices (above n_p[i] is the number of times that period index i appears in period order -> sum(n_p) == len(period_order))
         if sum(n_p for n_p in self.n_p) != len(period_order):
@@ -1075,6 +1079,12 @@ class AggregatedDynamic(Dynamic):
                 "All elements in period order have to be valid period indices!"
             )
 
+    def number_of_periods(self) -> int:
+        return len(self.period_dynamics)
+
+    def offsets(self, period_index) -> Tuple[int]:
+        return self.offsets_[period_index]
+
     def number_of_steps(self) -> int:
         return self.dynamic.number_of_steps()
 
@@ -1129,7 +1139,7 @@ class PeriodDynamic(Dynamic):
 
 def resample(
     values, dynamic: Dynamic, target_dynamic: Dynamic
-):  # values: type hinting Union[np-array, dict[int, np-array]], type hinting Union[np-array, dict[int, np-array]]
+):  # values: type hinting a np-array, type hinting a np-array
     if dynamic == target_dynamic:
         return values
     elif isinstance(dynamic, TreeDynamic) and isinstance(target_dynamic, TreeDynamic):
@@ -1168,7 +1178,8 @@ def resample_aggregation_down(
         raise ValueError(
             f"The period dynamic has to be part of the aggregated dynamic!"
         )
-    return values[target_dynamic.period_index]
+    offset = dynamic.offsets(target_dynamic.period_index)
+    return values[offset[0] : offset[1]]
 
 
 def resample_variable(variable, dynamic: Dynamic, target_dynamic: Dynamic):
@@ -1196,9 +1207,7 @@ def resample_variable_tree(variable, dynamic: TreeDynamic, target_dynamic: TreeD
 
 
 class Profile:
-    def __init__(
-        self, values, dynamic: Dynamic
-    ):  # values: type hinting Union[np-array, dict[int, np-array]]
+    def __init__(self, values, dynamic: Dynamic):  # values: type hinting np-array
         self.values = values
         self.dynamic = dynamic
 
@@ -1217,18 +1226,20 @@ class Profile:
             }
         elif isinstance(dynamic, AggregatedDynamic):
             df = pd.read_csv(path, index_col=[0, 1])
-            profiles = {column: dict() for column in df.columns}
-            for period_index in df.index.levels[0]:
-                if (
-                    len(df.loc[period_index])
-                    != dynamic.period_dynamics[period_index].number_of_steps()
+            if len(df.index.levels[0]) != dynamic.number_of_periods():
+                raise ValueError(
+                    f"The number of periods in the csv file and the number of periods in the dynamic have the be the same!"
+                )
+            if any(
+                len(df.loc[period_index]) != period_dynamic.number_of_steps()
+                for period_index, period_dynamic in enumerate(dynamic.period_dynamics)
             ):
                 raise ValueError(
-                        f"The number of rows in the csv file for a period and the number of steps in the period dynamic have to be the same!"
+                    f"The number of rows in the csv file for a period and the number of segments in that period have the be the same!"
                 )
-                for column in df.columns:
-                    profiles[column][period_index] = df.loc[period_index][column].values
-            return {column: Profile(profiles[column], dynamic) for column in df.columns}
+            return {
+                column: Profile(df[column].values, dynamic) for column in df.columns
+            }
         else:
             raise ValueError(f"Invalid dynamic type {type(dynamic)}!")
 

optimization_model.py

@@ -152,9 +152,9 @@ class EntityResult:
                 
         self.u_result = np.empty(n_u_vars, dtype=float)
 
-        self.i_result = np.empty((n_i_vars, dynamic.number_of_steps()), dtype=float)
+        self.i_result = np.empty((n_i_vars, dynamic.shape()), dtype=float)
 
-        self.i_prime_result = np.empty((n_i_prime_vars, dynamic.number_of_steps() + 1), dtype=float)
+        self.i_prime_result = np.empty((n_i_prime_vars, dynamic.state_shape()), dtype=float)
 
         self.dynamic = dynamic
 

topology.py

@@ -325,7 +325,7 @@ class Topology:
             for name in objectives:
                 scaled_expression = 0.0
                 one_time_expression = 0.0
-                for period_index in range(len(block.dynamic.period_dynamics)):
+                for period_index in range(block.dynamic.number_of_periods()):
                     (
                         period_scaled_expression,
                         period_one_time_expression,
@@ -559,7 +559,7 @@ class Topology:
         elif isinstance(block.dynamic, (AggregatedDynamic)):
             self._results[key].extract_result(block)
 
-            for period_index in range(len(block.dynamic.period_dynamics)):
+            for period_index in range(block.dynamic.number_of_periods()):
                 period_block = block.blocks[str(period_index)]
 
                 self._results[key].sub_results[period_index].extract_result(