diff --git a/dynamics.py b/dynamics.py
index 02d438ef47bb755d4d7ab31a6bb760cc203f4734..5e011f85f23a842e6cddabbc1cc73a2ef04315ff 100644
--- a/dynamics.py
+++ b/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()
- ):
- 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!"
- )
- 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}
+ 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 segments in that period have the be the same!"
+ )
+ return {
+ column: Profile(df[column].values, dynamic) for column in df.columns
+ }
else:
raise ValueError(f"Invalid dynamic type {type(dynamic)}!")
diff --git a/optimization_model.py b/optimization_model.py
index 2df8cd6c7fba81c3051863cc6773efe3eb91f1dd..316a2feaf6de221b8aebe6290fdcd40c5cd7e11e 100644
--- a/optimization_model.py
+++ b/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
diff --git a/topology.py b/topology.py
index 9fdd0eeda950344868b1f7dcb0998280e825f3c9..37d67c2cc8fc5aefbcacfe4fbdc0c7ffe0d79897 100644
--- a/topology.py
+++ b/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(