Feature/fix type errors

simopt/input_models.py

@@ -3,9 +3,9 @@
 import bisect
 import itertools
 import math
-import random
 from abc import abstractmethod
 from collections.abc import Sequence
+from random import Random
 from typing import ParamSpec, Protocol, TypeVar
 
 P = ParamSpec("P")
@@ -15,9 +15,9 @@
 class InputModel(Protocol[P, R]):
     """Abstract base for input models used by simulations."""
 
-    rng: random.Random | None = None
+    rng: Random | None = None
 
-    def set_rng(self, rng: random.Random) -> None:
+    def set_rng(self, rng: Random) -> None:
         """Attach a Python RNG to the input model.
 
         Args:

simopt/models/amusementpark.py

@@ -478,9 +478,10 @@ def set_completion(i: int, new_time: float) -> None:
         cumulative_util = [cumulative_util[i] / time_open for i in attraction_range]
 
         # Calculate responses from simulation data.
+        percent_departed = total_departed / total_visitors if total_visitors else 0
         responses = {
             "total_departed": total_departed,
-            "percent_departed": total_departed / total_visitors,
+            "percent_departed": percent_departed,
             "average_number_in_system": time_average / time_open,
             "attraction_utilization_percentages": cumulative_util,
         }

simopt/models/chessmm.py

@@ -2,7 +2,7 @@
 
 from __future__ import annotations
 
-import random
+from random import Random
 from typing import Annotated, ClassVar, Final
 
 import numpy as np
@@ -111,7 +111,7 @@ class ChessAvgDifferenceConfig(BaseModel):
 class EloInputModel(InputModel):
     """Input model for player Elo ratings."""
 
-    rng: random.Random | None = None
+    rng: Random | None = None
 
     def random(
         self, mean: float, std: float, min_rating: float, max_rating: float

simopt/models/cntnv.py

@@ -2,7 +2,7 @@
 
 from __future__ import annotations
 
-import random
+from random import Random
 from typing import Annotated, ClassVar, Self
 
 import numpy as np
@@ -128,7 +128,7 @@ class CntNVMaxProfitConfig(BaseModel):
 class DemandInputModel(InputModel):
     """Input model for Burr Type XII demand."""
 
-    rng: random.Random | None = None
+    rng: Random | None = None
 
     def random(self, burr_c: float, burr_k: float) -> float:  # noqa: D102
         # Generate random demand according to Burr Type XII distribution.

simopt/models/dualsourcing.py

@@ -2,7 +2,7 @@
 
 from __future__ import annotations
 
-import random
+from random import Random
 from typing import Annotated, ClassVar, Self
 
 import numpy as np
@@ -166,7 +166,7 @@ class DualSourcingMinCostConfig(BaseModel):
 class DemandInputModel(InputModel):
     """Input model for daily demand."""
 
-    rng: random.Random | None = None
+    rng: Random | None = None
 
     def random(self, mu: float, sigma: float) -> int:  # noqa: D102
         def round_and_clamp_non_neg(x: float | int) -> int:

simopt/models/dynamnews.py

@@ -3,7 +3,7 @@
 from __future__ import annotations
 
 import math
-import random
+from random import Random
 from typing import Annotated, ClassVar, Final, Self
 
 import numpy as np
@@ -155,7 +155,7 @@ class DynamNewsMaxProfitConfig(BaseModel):
 class Utility(InputModel):
     """Input model for customer utility sampling."""
 
-    rng: random.Random | None = None
+    rng: Random | None = None
 
     def _gumbelvariate(self, mu: float, beta: float) -> float:
         assert self.rng is not None

simopt/models/facilitysizing.py

@@ -2,7 +2,7 @@
 
 from __future__ import annotations
 
-import random
+from random import Random
 from typing import Annotated, ClassVar, Final, Self
 
 import numpy as np
@@ -204,7 +204,7 @@ def _validate_problem(self) -> Self:
 class DemandInputModel(InputModel):
     """Input model for multivariate normal demand at facilities."""
 
-    rng: random.Random | None = None
+    rng: Random | None = None
 
     def _mvnormalvariate(
         self,

simopt/models/hotel.py

@@ -307,7 +307,7 @@ def replicate(self) -> tuple[dict, dict]:
         total_revenue = 0
 
         # Generate interarrival times
-        arr_bound = 10 * round(168 * np.sum(f_lambda))
+        arr_bound = 10 * round(168 * sum(f_lambda))
         arr_time = np.array(
             [
                 [self.arrival_model.random(f_lambda[i]) for _ in range(arr_bound)]

simopt/models/ironore.py

@@ -6,8 +6,8 @@
 
 from __future__ import annotations
 
-import random
 from math import copysign, sqrt
+from random import Random
 from typing import Annotated, ClassVar, Self
 
 import numpy as np
@@ -202,7 +202,7 @@ class IronOreMaxRevConfig(BaseModel):
 class MovementInputModel(InputModel):
     """Input model for mining movement and price shocks."""
 
-    rng: random.Random | None = None
+    rng: Random | None = None
 
     def random(self, mean: float, std: float) -> float:  # noqa: D102
         assert self.rng is not None

simopt/models/network.py

@@ -2,8 +2,8 @@
 
 from __future__ import annotations
 
-import random
 from enum import IntEnum
+from random import Random
 from typing import Annotated, ClassVar, Final, Self
 
 import numpy as np
@@ -215,7 +215,7 @@ class NetworkMinTotalCostConfig(BaseModel):
 class RouteInputModel(InputModel):
     """Input model for routing choices in the network."""
 
-    rng: random.Random | None = None
+    rng: Random | None = None
 
     def random(self, choices: list[int], weights: list[float], k: int) -> list[int]:  # noqa: D102
         assert self.rng is not None

simopt/models/tableallocation.py

@@ -5,7 +5,7 @@
 import bisect
 import itertools
 from collections.abc import Sequence
-from typing import Annotated, ClassVar, Self
+from typing import Annotated, ClassVar, Self, cast
 
 import numpy as np
 from pydantic import BaseModel, Field, model_validator
@@ -256,9 +256,12 @@ def fast_weighted_choice(
         # Pass through all arrivals of groups to the restaurants.
         for n in range(n_arrivals):
             # Determine group size.
-            group_size = self.group_size_model.random(
-                population=group_size_options,
-                weights=f_lambda,
+            group_size = cast(
+                int,
+                self.group_size_model.random(
+                    population=group_size_options,
+                    weights=f_lambda,
+                ),
             )
 
             # Find smallest table size to start search.

ty.toml

@@ -19,15 +19,6 @@ exclude = [
     "simopt/gui/new_experiment_window.py",
     "simopt/gui/plot_window.py",
     "simopt/model.py",
-    "simopt/models/amusementpark.py",
-    "simopt/models/chessmm.py",
-    "simopt/models/cntnv.py",
-    "simopt/models/dualsourcing.py",
-    "simopt/models/dynamnews.py",
-    "simopt/models/facilitysizing.py",
-    "simopt/models/ironore.py",
-    "simopt/models/network.py",
-    "simopt/models/tableallocation.py",
     "simopt/plot_type.py",
     "simopt/plots",
     "simopt/problem.py",