Merge branch 'development' into feature_add_nbstripout

Author: Grochocinski

.github/workflows/ty.yml

@@ -0,0 +1,28 @@
+name: "ty CI"
+
+on:
+  push:
+    branches: [ "master", "development" ]
+  pull_request:
+    branches: [ "master" , "development" ]
+
+jobs:
+  type_checker:
+    runs-on: ubuntu-latest 
+    steps:
+    - name: Checkout code
+      uses: actions/checkout@v5
+
+    - name: Set up Python
+      uses: actions/setup-python@v4
+      with:
+        python-version: "3.12"
+
+    - name: Install uv
+      run: pip install uv
+
+    - name: Install Dependencies
+      run: uv sync --all-extras
+
+    - name: ty Type Checking
+      run: uv run ty check

CITATION.cff

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

README.md

@@ -1,4 +1,4 @@
-![SimOpt Logo](.github/resources/logo_full_magnifying_glass.png)
+![SimOpt Logo](https://raw.githubusercontent.com/simopt-admin/simopt/master/.github/resources/logo_full_magnifying_glass.png)
 
 ## About the Project
 SimOpt is a testbed of simulation-optimization problems and solvers. Its purpose is to encourage the development and constructive comparison of simulation-optimization (SO) solvers (algorithms). We are particularly interested in the finite-time performance of solvers, rather than the asymptotic results that one often finds in related literature.
@@ -36,7 +36,7 @@ Full documentation for the source code can be found on our **[readthedocs page](
     - It is ***highly recommended*** to check the box during installation to add Python/Miniconda/Anaconda to your system PATH.
     - If you know you have Python installed but are getting a `Command not found` error when trying to use Python commands, then you may need to [add Python to your PATH](https://realpython.com/add-python-to-path/).
 - [VS Code](https://code.visualstudio.com/download) (optional)
-    - This is a lightweight IDE that is compatible with Miniconda and is recommended for running the GUI
+    - This is a lightweight IDE that is compatible with Miniconda.
 - [Git](https://git-scm.com/downloads) (optional)
     - If you don't have Git installed, you can download the code as a zip file instead
 
@@ -49,7 +49,7 @@ There are two ways to download a copy of the source code onto your machine:
 
 If you do not need the source code for SimOpt, you may install the library as a Python package instead. See the [Package](#package) and [Basic Example](#basic-example) sections for more details about this option.
 
-The `demo` folder contains a handful of useful scripts that can be easily modified, as directed in the comments:
+The `notebooks` folder includes several useful Jupyter notebooks and scripts that are easy to customize. You can either run the scripts as standalone programs or open the notebooks in JupyterLab or VS Code. A description of the contents is provided below:
 
 | File                                     | Description                                                                                                                                                                                                        |
 | ---------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
@@ -190,33 +190,37 @@ from simopt import models, solvers, experiment_base
 ```
 
 The following snippet of code will run 10 macroreplications of the Random Search solver ("RNDSRCH") on the Continuous Newsvendor problem ("CNTNEWS-1"):
-```
+```python
 myexperiment = simopt.experiment_base.ProblemSolver("RNDSRCH", "CNTNEWS-1")
 myexperiment.run(n_macroreps=10)
 ```
 
 The results will be saved to a .pickle file in a folder called `experiments/outputs`. To post-process the results, by taking, for example 200 postreplications at each recommended solution, run the following:
-```
+```python
 myexperiment.post_replicate(n_postreps=200)
 simopt.experiment_base.post_normalize([myexperiment], n_postreps_init_opt=200)
 ```
 
 A .txt file summarizing the progress of the solver on each macroreplication can be produced:
-```    
+```python
 myexperiment.log_experiment_results()
 ```
 
 A .txt file called `RNDSRCH_on_CNTNEWS-1_experiment_results.txt` will be saved in a folder called `experiments/logs`.
 
 One can then plot the mean progress curve of the solver (with confidence intervals) with the objective function values shown on the y-axis:
-```
-simopt.experiment_base.plot_progress_curves(experiments=[myexperiment], plot_type="mean", normalize=False)
+```python
+simopt.experiment_base.plot_progress_curves(
+    experiments=[myexperiment],
+    plot_type=simopt.experiment_base.PlotType.MEAN,
+    normalize=False,
+)
 ```
 
-The Python scripts in the `demo` folder provide more guidance on how to run common experiments using the library.
+The Python scripts in the `notebooks` folder provide more guidance on how to run common experiments using the library.
 
 One can also use the SimOpt graphical user interface by running the following from the terminal:
-```
+```bash
 python -m simopt
 ```
 
@@ -230,6 +234,7 @@ The core development team currently consists of
 - [**Sara Shashaani**](https://shashaani.wordpress.ncsu.edu) (North Carolina State University)
 - [**Shane Henderson**](https://people.orie.cornell.edu/shane/) (Cornell University)
 - [**William Grochocinski**](https://github.com/Grochocinski) (North Carolina State University)
+- [**Cen Wang**](https://cenwangumass.github.io/) (Texas A&M University)
 
 ## Citation
 To cite this work, please use the `CITATION.cff` file or use the built-in citation generator:

docs/source/conf.py

@@ -27,7 +27,7 @@
 project = "SimOpt"
 copyright = "2025, simopt-admin"  # noqa: A001
 author = "simopt-admin"
-release = "1.2.0"
+release = "1.2.1"
 
 # -- General configuration ---------------------------------------------------
 

docs/source/models/dualsourcing.rst

@@ -17,8 +17,8 @@ Regular orders arrive after :math:`l_r` periods while expedited orders arrive af
 :math:`l_e < l_r`. Let the difference in lead times be :math:`l = l_r − l_e ≥ 1`. 
 
 If there is remaining on-hand inventory at the end of period :math:`n` (after demand :math:`d_n` is satisfied), 
-these items are carried over to the next period (i.e., :math:`I_n+1 > 0`) at a holding cost per unit. 
-However, if there is a stock-out (i.e., :math:`In + 1 < 0`), there is a penalty cost per unit 
+these items are carried over to the next period (i.e., :math:`I_{n+1} > 0`) at a holding cost per unit. 
+However, if there is a stock-out (i.e., :math:`I_{n+1} < 0`), there is a penalty cost per unit 
 of unsatisfied demand.
 
 We will let the period :math:`n` expediting order be based on the on-hand inventory plus the orders that 

docs/source/models/san.rst

@@ -108,70 +108,79 @@ Optimal Objective Function Value
 Unknown
 
 Optimization Problem: Minimize Longest Path Plus Penalty with Stochastic Constraints (SAN-2)
-============================================================================================
+--------------------------------------------------------------------------------------------
 
 Decision Variables
--------------------
+^^^^^^^^^^^^^^^^^^
+
 * **arc_means**
 
 Objectives
------------
-Suppose that we can select :math:`\theta_i > 0` for each :math:`i`, but there is an associated cost.  
+^^^^^^^^^^
+
+Suppose that we can select :math:`\theta_i > 0` for each :math:`i`, but there is an associated cost.
 In particular, we want to minimize:
 
 .. math::
 
     \mathbb{E}[T(\theta)] + f(\theta),
 
-where :math:`T(\theta)` is the (random) duration of the longest path from node :math:`a` to node :math:`i`, and  
+where :math:`T(\theta)` is the (random) duration of the longest path from node :math:`a` to node :math:`i`, and
 
 .. math::
 
     f(\theta) = \sum_{i=1}^{n} \theta_i^{-1},
 
 where :math:`n` is the number of arcs.
 
-The objective function is convex in :math:`\theta`.  
+The objective function is convex in :math:`\theta`.
 
 Constraints
-------------
-We require that :math:`\theta_i > 0` for each :math:`i`.  
+^^^^^^^^^^^
+
+We require that :math:`\theta_i > 0` for each :math:`i`.
 Additionally, we allow :math:`n` stochastic constraints that restrict the expected time to reach node :math:`i`, of the form:
 
 .. math::
 
     \mathbb{E}[T_i(\theta)] \leq a_i.
 
 Problem Factors
-----------------
-* **budget**: Maximum number of replications the solver is allowed to take.  
-  *Default:* ``10000``
+^^^^^^^^^^^^^^^
 
-* **arc_costs**: Cost associated with each arc.  
-  *Default:* ``(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1)``
+* **budget**: Maximum number of replications the solver is allowed to take.
+    *Default:* ``10000``
 
-* **constraint_nodes**: Nodes with corresponding stochastic constraints.  
-  *Default:* ``[6, 8]``
+* **arc_costs**: Cost associated with each arc.
+    *Default:* ``(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1)``
 
-* **length_to_node_constraint**: Maximum expected length to corresponding constraint nodes.  
-  *Default:* ``[5, 5]``
+* **constraint_nodes**: Nodes with corresponding stochastic constraints.
+    *Default:* ``[6, 8]``
+
+* **length_to_node_constraint**: Maximum expected length to corresponding constraint nodes.
+    *Default:* ``[5, 5]``
 
 Fixed Model Factors
---------------------
+^^^^^^^^^^^^^^^^^^^
+
 * **N/A**
 
 Starting Solution
-------------------
+^^^^^^^^^^^^^^^^^
+
 * **initial_solution**: ``(8,) * 13``
 
 Random Solutions
------------------
+^^^^^^^^^^^^^^^^
+
 Each arc mean is sampled independently from a lognormal distribution with 2.5th and 97.5th percentiles equal to 0.1 and 10, respectively.
 
 Optimal Solution
------------------
+^^^^^^^^^^^^^^^^
+
 * **Unknown**
 
 Optimal Objective Function Value
----------------------------------
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
 * **Unknown**

pyproject.toml

@@ -2,17 +2,23 @@
 requires = ["setuptools>=61.0"]
 build-backend = "setuptools.build_meta"
 
-[tool.setuptools]
-packages = ["simopt", "simopt.gui", "simopt.models", "simopt.solvers"]
+[tool.setuptools.packages.find]
+where = ["."]
+include = ["simopt*"]
+# Ignore the build directory so we don't circularly include built distributions.
+exclude = ["build*"]
+
+[tool.setuptools.package-data]
+"simopt.data_farming" = ["*.npz"]
 
 [project]
 name = "simoptlib"
-version = "1.2.0"
+version = "1.2.2.dev0"
 authors = [
   { name = "David Eckman", email = "eckman@tamu.edu" },
   { name = "Shane Henderson", email = "sgh9@cornell.edu" },
   { name = "Sara Shashaani", email = "sshasha2@ncsu.edu" },
-  { name = "William Grochocinski", email = "wagrocho@ncsu.edu" },
+  { name = "William Grochocinski", email = "williamgrochocinski@gmail.com" },
 ]
 description = "A testbed for simulation-optimization experiments."
 readme = "README.md"
@@ -38,9 +44,16 @@ dependencies = [
 ]
 
 [project.optional-dependencies]
-dev = ["ruff>=0.14.2", "jupytext", "nbstripout"]
+dev = [
+    "nbstripout>=0.8.0",
+    "jupytext>=1.18.1",
+    "pytest-xdist>=3.8.0",
+    "pytest>=9.0.1",
+    "ruff>=0.14.2",
+    "ty>=0.0.1a26",
+]
 docs = ["sphinx>=8.2.3", "sphinx-autoapi>=3.6.1", "sphinx-rtd-theme>=3.0.2"]
-notebooks = ["ipykernel"]
+notebooks = ["ipykernel>=7.1.0"]
 
 [project.urls]
 "Homepage" = "https://github.com/simopt-admin/simopt"