Merge branch 'develop'

Author: cenwangumass

.github/workflows/codeql.yml

@@ -13,9 +13,9 @@ name: "CodeQL"
 
 on:
   push:
-    branches: [ "master", "development" ]
+    branches: [ "master", "development", "develop" ]
   pull_request:
-    branches: [ "master" , "development" ]
+    branches: [ "master" , "development", "develop" ]
   schedule:
     - cron: '30 13 * * 1'
 

.github/workflows/ruff.yml

@@ -2,9 +2,9 @@ name: "Ruff CI"
 
 on:
   push:
-    branches: [ "master", "development" ]
+    branches: [ "master", "development", "develop" ]
   pull_request:
-    branches: [ "master" , "development" ]
+    branches: [ "master" , "development", "develop" ]
 
 jobs:
   linter:

.github/workflows/ty.yml

@@ -2,9 +2,9 @@ name: "ty CI"
 
 on:
   push:
-    branches: [ "master", "development" ]
+    branches: [ "master", "development", "develop" ]
   pull_request:
-    branches: [ "master" , "development" ]
+    branches: [ "master" , "development", "develop" ]
 
 jobs:
   type_checker:

CITATION.cff

@@ -3,7 +3,7 @@
 
 cff-version: 1.2.0
 title: SimOpt
-version: 1.2.1
+version: 1.2.2
 message: >-
   If you use this software, please cite it using the
   metadata from this file

docs/source/conf.py

@@ -27,7 +27,7 @@
 project = "SimOpt"
 copyright = "2025, simopt-admin"  # noqa: A001
 author = "simopt-admin"
-release = "1.2.1"
+release = "1.2.2"
 
 # -- General configuration ---------------------------------------------------
 

pyproject.toml

@@ -11,9 +11,12 @@ exclude = ["build*"]
 [tool.setuptools.package-data]
 "simopt.data_farming" = ["*.npz"]
 
+[tool.uv.sources]
+simopt-extensions = { git = "https://github.com/cenwangumass/simopt-extensions", rev = "2bcf002" }
+
 [project]
 name = "simoptlib"
-version = "1.2.1"
+version = "1.2.2"
 authors = [
   { name = "David Eckman", email = "eckman@tamu.edu" },
   { name = "Shane Henderson", email = "sgh9@cornell.edu" },
@@ -53,6 +56,9 @@ dev = [
 ]
 docs = ["sphinx>=8.2.3", "sphinx-autoapi>=3.6.1", "sphinx-rtd-theme>=3.0.2"]
 notebooks = ["ipykernel>=7.1.0"]
+ext = [
+    "simopt-extensions",
+]
 
 [project.urls]
 "Homepage" = "https://github.com/simopt-admin/simopt"

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/_ext.py

@@ -0,0 +1,56 @@
+import importlib
+import os
+from collections.abc import Callable
+from types import ModuleType
+
+from simopt.model import Model
+
+
+def find_model_patches(module: ModuleType) -> list[Callable]:
+    patches: list[Callable] = []
+    for attr in dir(module):
+        if not attr.startswith("patch_model"):
+            continue
+        candidate = getattr(module, attr)
+        if callable(candidate):
+            patches.append(candidate)
+    return patches
+
+
+def patch(model_class: type[Model], patch_function: Callable) -> None:
+    # Ask the extension what class to patch and with what method
+    class_name, method = patch_function()
+    # Patch the method into the model_class if it matches
+    full_name = model_class.__module__ + "." + model_class.__qualname__
+    if full_name == class_name:
+        model_class.replicate = method
+
+
+def load_module(module_name: str, model_class: type[Model]) -> None:
+    # Import the specified library
+    try:
+        module = importlib.import_module(module_name)
+    except ImportError as e:
+        raise ImportError(f"SimOpt failed to load extension '{module_name}'") from e
+
+    # Find all patch_model* functions in the library
+    patches = find_model_patches(module)
+    if not patches:
+        raise ImportError(f"'{module_name}' does not have any 'patch_model*' functions")
+
+    # Apply each patch to the model_class
+    for p in patches:
+        patch(model_class, p)
+
+
+def patch_model(model_class: type[Model]) -> None:
+    env_var = os.environ.get("SIMOPT_EXT")
+    if not env_var:
+        return
+
+    # Assume that the user has specified a comma-separated list of libraries to import.
+    # For example, SIMOPT_EXT="simopt_extension_a,simopt_extension_b"
+    for part in env_var.split(","):
+        module_name = part.strip()
+        if module_name:
+            load_module(module_name, model_class)

simopt/models/amusementpark.py

@@ -17,6 +17,7 @@
     VariableType,
 )
 from simopt.input_models import Exp, Gamma, WeightedChoice
+from simopt.models._ext import patch_model
 
 INF = float("inf")
 
@@ -478,9 +479,10 @@ 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,
         }
@@ -543,3 +545,6 @@ def get_random_solution(self, rand_sol_rng: MRG32k3a) -> tuple:  # noqa: D102
             n_elements=num_elements, summation=summation, with_zero=False
         )
         return tuple(vector)
+
+
+patch_model(AmusementPark)

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