Rebase development branch on 1.1.1

simopt/gui/new_experiment_window.py

@@ -2306,7 +2306,10 @@ def __enable_exp_buttons(self, experiment_name: str) -> None:
             self.tk_buttons[button].configure(state="normal")
 
     def __update_action_button(
-        self, experiment_name: str, text: str, command: Callable | None = None
+        self,
+        experiment_name: str,
+        text: str,
+        command: Union[Callable, None] = None,
     ) -> None:
         name_base: Final[str] = "exp." + experiment_name
         action_bttn_name: Final[str] = name_base + ".action"

simoptlib.egg-info/PKG-INFO

@@ -1,6 +1,6 @@
 Metadata-Version: 2.1
 Name: simoptlib
-Version: 1.1.0
+Version: 1.1.1
 Summary: A testbed for simulation-optimization experiments.
 Author-email: David Eckman <eckman@tamu.edu>, Shane Henderson <sgh9@cornell.edu>, Sara Shashaani <sshasha2@ncsu.edu>, William Grochocinski <wagrocho@ncsu.edu>
 License: MIT License
@@ -76,7 +76,7 @@ Full documentation for the source code can be found on our **[readthedocs page](
 - Python >= 3.8
     - To check your Python version, open a terminal and run `python --version`. If you see a message along the lines of `Command not found`, then you likely don't have Python installed. If you know you have it installed but are getting a `Command not found` error, then you may need to [add Python to your PATH](https://realpython.com/add-python-to-path/).
     - For new installs, [Miniconda or Anaconda](https://www.anaconda.com/download) is recommended ([read about the differences between Miniconda and Anaconda](https://docs.anaconda.com/distro-or-miniconda/)). If you already have a compatible IDE (such as VS Code), we've found that Miniconda will work fine at 1/10 of the size of Anaconda. It is ***highly recommended*** to check the box during installation to add Python/Miniconda/Anaconda to your system PATH.
-- Ruby >= 3.0 (required for datafarming)
+- Ruby >= 2.5 (required for datafarming)
     - Included on MacOS, but Windows users will need to grab it from [here](https://rubyinstaller.org/).
 - `datafarming` gem < 2.0 (required for datafarming)
     - This can be installed via `gem install datafarming -v 1.4` once Ruby is installed/configured.

workshop/README.md

@@ -11,7 +11,7 @@ The most-up-to-date publication about this library is [Eckman et al. (2023)](htt
 ## Before Workshop
 Before attending the workshop please follow the instructions below:
 
-1. Install Python, Ruby, and required dependencies [as detailed in the README](https://github.com/simopt-admin/simopt/blob/master/README.md#getting-started)
+1. Install Python, Ruby, and required dependencies [as detailed in the README](https://github.com/simopt-admin/simopt/blob/master/README.md#getting-started). (Please note that the Ruby installation is only needed for a small portion of the workshop; if you encounter issues with installing Ruby, you can still fully follow along.)
 
 3. Install Microsoft's [Visual Studio Code (VS Code) IDE](https://code.visualstudio.com).
 

workshop/workshop.ipynb

@@ -140,11 +140,11 @@
     "### Exercise \\#2\n",
     "\n",
     "1. Open the file simopt/model/example.py in the VS Code editor.\n",
-    "2. Let's change how random search randomly samples solutions in R^2. For starters, uncomment Line 355\n",
+    "2. Let's change how random search randomly samples solutions in R^2. For starters, uncomment Line 430\n",
     "\n",
     "    `x = tuple([rand_sol_rng.uniform(-2, 2) for _ in range(self.dim)])`\n",
     "\n",
-    "    and comment out Line 356\n",
+    "    and comment out Lines 431-437\n",
     "    \n",
     "    `x = tuple(rand_sol_rng.mvnormalvariate(mean_vec=np.zeros(self.dim), cov=np.eye(self.dim), factorized=False))`\n",
     "\n",
@@ -225,31 +225,31 @@
     "### Exercise \\#3\n",
     "\n",
     "1. Open simopt/model/example.py again.\n",
-    "2. Change the noise in the objective function evaluations to create a slightly different 2D optimization problem. This can be done by changing Line 85: \n",
+    "2. Change the noise in the objective function evaluations to create a slightly different 2D optimization problem. This can be done by changing Line 99: \n",
     "    \n",
     "    `fn_eval_at_x = np.linalg.norm(x) ** 2 + noise_rng.normalvariate()`\n",
     "\n",
     "    where `x` is a numpy array of length two. For starters, try passing the argument `sigma=10` into the function call `noise_rng.normalvariate()`. The default value is `sigma=1`, so this has the effect of increasing the common variance of the noise from 1 to 100.\n",
     "3. Restart the kernel and run COMBO CODE CELL [0 + 1 + 3] below. *How have the plots changed? Why haven't they changed more?*\n",
     "\n",
-    "4. Next, change the underlying objective function by replacing `np.linalg.norm(x) ** 2` in Line 85 with some other two-dimensional function of `x`, e.g., `1 - np.exp(-np.linalg.norm(x) ** 2)`. (This objective function looks like an upside-down standard bivariate normal pdf, rescaled.)\n",
+    "4. Next, change the underlying objective function by replacing `np.linalg.norm(x) ** 2` in Line 99 with some other two-dimensional function of `x`, e.g., `1 - np.exp(-np.linalg.norm(x) ** 2)`. (This objective function looks like an upside-down standard bivariate normal pdf, rescaled.)\n",
     "5. Depending of your choice of new objective function, you MAY need to change other parts of the code, including:\n",
-    "    * The gradient of `f(x)` in Line 89. For the example given above, this would need to be changed from\n",
+    "    * The gradient of `f(x)` in Line 103. For the example given above, this would need to be changed from\n",
     "    \n",
     "        `gradients = {\"est_f(x)\": {\"x\": tuple(2 * x)}}`\n",
     "\n",
     "        to\n",
     "\n",
     "        `gradients = {\"est_f(x)\": {\"x\": tuple(2 * x * np.exp(-np.linalg.norm(x) ** 2))}}`\n",
-    "    * If you change the problem to a maxmization problem, you will need to change Line 173 from\n",
+    "    * If you change the problem to a maxmization problem, you will need to change Line 190 from\n",
     "    \n",
-    "        `self.minmax = (-1,)`\n",
+    "        `return (-1,)`\n",
     "        \n",
     "        to\n",
     "        \n",
-    "        `self.minmax = (1,)`.\n",
-    "    * The optimal solution in Line 204. (For the running example, this will not be necessary.)\n",
-    "    * The optimal objective function value in Line 203. (For the running example, this will not be necessary.)\n",
+    "        `return (1,)`.\n",
+    "    * The optimal solution in Line 214. (For the running example, this will not be necessary.)\n",
+    "    * The optimal objective function value in Line 208. (For the running example, this will not be necessary.)\n",
     "6. Restart the kernel and run COMBO CODE CELL [0 + 1 + 3] below. *How have the plots changed?*\n",
     "\n",
     "**Extra for Experts:** Change the dimension of the problem. To do this, you will need to change the dimension of the default initial solution, defined in Line 185."