From de99762570398e282a87e0acd17b8c9f025d0b35 Mon Sep 17 00:00:00 2001
From: Sam Dotson <sgd2@illinois.edu>
Date: Mon, 23 Dec 2024 12:35:43 -0600
Subject: [PATCH] removes notebook

---
 .../examples/parallelization_tutorial.ipynb   | 655 ------------------
 1 file changed, 655 deletions(-)
 delete mode 100644 docs/source/examples/parallelization_tutorial.ipynb

diff --git a/docs/source/examples/parallelization_tutorial.ipynb b/docs/source/examples/parallelization_tutorial.ipynb
deleted file mode 100644
index d9ff443..0000000
--- a/docs/source/examples/parallelization_tutorial.ipynb
+++ /dev/null
@@ -1,655 +0,0 @@
-{
- "cells": [
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Parallelization Tutorial\n",
-    "\n",
-    "`osier` is the first multi- and many-objective energy system optimization\n",
-    "platform. This notebook offers a guide to parallelization with `osier`.\n",
-    "\n",
-    "You can run this notebook in interactive mode with Binder by clicking the badge below.\n",
-    "\n",
-    "[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/samgdotson/osier/env-instructions?labpath=docs%2Fsource%2Fexamples%2Fparallelization_tutorial.ipynb)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# basic imports\n",
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "from unyt import MW, GW, km\n",
-    "\n",
-    "# osier imports\n",
-    "from osier import CapacityExpansion\n",
-    "from osier.tech_library import nuclear_adv, wind, battery, natural_gas\n",
-    "from osier import total_cost, annual_emission\n",
-    "\n",
-    "# pymoo imports\n",
-    "from pymoo.algorithms.moo.nsga2 import NSGA2\n",
-    "from pymoo.optimize import minimize\n",
-    "from pymoo.visualization.pcp import PCP\n",
-    "\n",
-    "from multiprocessing.pool import ThreadPool\n",
-    "from pymoo.core.problem import StarmapParallelization"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Preparing input data\n",
-    "\n",
-    "Users only need to supply relevant timeseries data to `osier`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "n_hours = 24  # hours per day\n",
-    "n_days = 2  # days to model\n",
-    "N = n_hours*n_days  # total number of time steps\n",
-    "phase_shift = 0  # horizontal shift [radians]\n",
-    "base_shift = 2  # vertical shift [units of demand]\n",
-    "hours = np.linspace(0,N,N)\n",
-    "total_demand = 185  # [MWh], sets the total demand [units of energy]\n",
-    "\n",
-    "demand = (np.sin((hours*np.pi/n_hours*2+phase_shift))*-1+np.ones(N)*(base_shift+1))\n",
-    "\n",
-    "np.random.seed(1234) # sets the seed for repeatability\n",
-    "\n",
-    "noise = np.random.random(N)\n",
-    "demand += noise\n",
-    "\n",
-    "demand = demand/demand.sum() * total_demand \n",
-    "wind_speed = np.random.weibull(a=2.5,size=N)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Set up the problem\n",
-    "\n",
-    "`osier` comes pre-loaded with technology data from the `osier.tech_library`. Users simply need to pass the data to a `CapacityExpansion` problem and run it using a `pymoo.minimize` runner."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "problem = CapacityExpansion(technology_list=[wind, natural_gas, nuclear_adv, battery],\n",
-    "                            demand=demand*MW,\n",
-    "                            wind=wind_speed,\n",
-    "                            upper_bound = 1/wind.capacity_credit,\n",
-    "                            objectives=[total_cost, annual_emission],\n",
-    "                            model_engine='logical',\n",
-    "                            solver='appsi_highs')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "==========================================================\n",
-      "n_gen  |  n_eval  | n_nds  |      eps      |   indicator  \n",
-      "==========================================================\n",
-      "     1 |       20 |      5 |             - |             -\n",
-      "     2 |       40 |     10 |  0.0900438922 |         nadir\n",
-      "     3 |       60 |     12 |  0.0865541462 |         ideal\n",
-      "     4 |       80 |     13 |  0.0628297417 |         ideal\n",
-      "     5 |      100 |     16 |  0.0075832178 |         ideal\n",
-      "     6 |      120 |     18 |  0.1945920416 |         nadir\n",
-      "     7 |      140 |     20 |  0.0574346489 |         ideal\n",
-      "     8 |      160 |     20 |  0.0452822361 |         ideal\n",
-      "     9 |      180 |     19 |  0.0457673861 |         nadir\n",
-      "    10 |      200 |     20 |  0.0339976358 |         nadir\n",
-      "CPU times: user 4min 42s, sys: 2.1 s, total: 4min 44s\n",
-      "Wall time: 4min 46s\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "res = minimize(problem,\n",
-    "               NSGA2(pop_size=20),\n",
-    "               termination=('n_gen', 10),\n",
-    "               seed=1,\n",
-    "               save_history=True,\n",
-    "               verbose=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 600x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "with plt.style.context('dark_background'):\n",
-    "    fig, ax = plt.subplots(1,1,figsize=(6,4))\n",
-    "\n",
-    "    ax.scatter(res.F[:,0], res.F[:,1], edgecolors='red', facecolors='k')\n",
-    "    ax.set_ylabel(r\"Lifecycle CO$_2$ Emissions [MT/MWh]\")\n",
-    "    ax.set_xlabel(r\"Total Cost [M\\$]\")\n",
-    "    ax.grid(alpha=0.2)\n",
-    "\n",
-    "    plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/6h/g412p7x53jbcqr_x5sy9z8th0000gn/T/ipykernel_92122/1527621391.py:5: MatplotlibDeprecationWarning: The 'labels' parameter of boxplot() has been renamed 'tick_labels' since Matplotlib 3.9; support for the old name will be dropped in 3.11.\n",
-      "  bplot = ax.boxplot(res.X,\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 600x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from osier import get_tech_names\n",
-    "with plt.style.context('dark_background'):\n",
-    "    fig, ax = plt.subplots(1,1,figsize=(6,4))\n",
-    "\n",
-    "    bplot = ax.boxplot(res.X,\n",
-    "                       patch_artist=True,\n",
-    "                       labels=get_tech_names(problem.technology_list))\n",
-    "    ax.set_ylabel(\"Fraction of Peak Demand\")\n",
-    "\n",
-    "    # fill with colors\n",
-    "    colors = ['tab:blue', 'tab:orange', 'tab:green']\n",
-    "    for patch, color in zip(bplot['boxes'], colors):\n",
-    "        patch.set_facecolor(color)\n",
-    "\n",
-    "    for median in bplot['medians']:\n",
-    "        median.set_color('red')\n",
-    "\n",
-    "    ax.yaxis.grid(True, alpha=0.2)\n",
-    "    plt.show()"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Threads"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Exception ignored in: <function capture_output.__del__ at 0x14a3d1c60>\n",
-      "Traceback (most recent call last):\n",
-      "  File \"/Users/samdotson/miniforge3/envs/osier-test04/lib/python3.12/site-packages/pyomo/common/tee.py\", line 208, in __del__\n",
-      "    self.__exit__(None, None, None)\n",
-      "  File \"/Users/samdotson/miniforge3/envs/osier-test04/lib/python3.12/site-packages/pyomo/common/tee.py\", line 196, in __exit__\n",
-      "    self.tee.__exit__(et, ev, tb)\n",
-      "  File \"/Users/samdotson/miniforge3/envs/osier-test04/lib/python3.12/site-packages/pyomo/common/tee.py\", line 430, in __exit__\n",
-      "    self.close(et is not None)\n",
-      "  File \"/Users/samdotson/miniforge3/envs/osier-test04/lib/python3.12/site-packages/pyomo/common/tee.py\", line 413, in close\n",
-      "    raise RuntimeError(\n",
-      "RuntimeError: TeeStream: deadlock observed joining reader threads\n",
-      "Exception ignored in: <function capture_output.__del__ at 0x14a3d1c60>\n",
-      "Traceback (most recent call last):\n",
-      "  File \"/Users/samdotson/miniforge3/envs/osier-test04/lib/python3.12/site-packages/pyomo/common/tee.py\", line 208, in __del__\n",
-      "    self.__exit__(None, None, None)\n",
-      "  File \"/Users/samdotson/miniforge3/envs/osier-test04/lib/python3.12/site-packages/pyomo/common/tee.py\", line 196, in __exit__\n",
-      "    self.tee.__exit__(et, ev, tb)\n",
-      "  File \"/Users/samdotson/miniforge3/envs/osier-test04/lib/python3.12/site-packages/pyomo/common/tee.py\", line 430, in __exit__\n",
-      "    self.close(et is not None)\n",
-      "  File \"/Users/samdotson/miniforge3/envs/osier-test04/lib/python3.12/site-packages/pyomo/common/tee.py\", line 413, in close\n",
-      "    raise RuntimeError(\n",
-      "RuntimeError: TeeStream: deadlock observed joining reader threads\n"
-     ]
-    }
-   ],
-   "source": [
-    "# initialize the thread pool and create the runner\n",
-    "n_threads = 4\n",
-    "pool = ThreadPool(n_threads)\n",
-    "runner = StarmapParallelization(pool.starmap)\n",
-    "\n",
-    "problem = CapacityExpansion(technology_list=[wind, natural_gas, nuclear_adv, battery],\n",
-    "                            demand=demand*MW,\n",
-    "                            wind=wind_speed,\n",
-    "                            upper_bound = 1/wind.capacity_credit,\n",
-    "                            objectives=[total_cost, annual_emission],\n",
-    "                            model_engine='lp',\n",
-    "                            solver='appsi_highs',\n",
-    "                            elementwise_runner=runner)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Running HiGHS 1.8.1 (git hash: 4a7f24a): Copyright (c) 2024 HiGHS under MIT licence terms\n"
-     ]
-    },
-    {
-     "ename": "KeyboardInterrupt",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[11], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m res \u001b[39m=\u001b[39m minimize(problem,\n\u001b[1;32m      2\u001b[0m                NSGA2(pop_size\u001b[39m=\u001b[39;49m\u001b[39m20\u001b[39;49m),\n\u001b[1;32m      3\u001b[0m                termination\u001b[39m=\u001b[39;49m(\u001b[39m'\u001b[39;49m\u001b[39mn_gen\u001b[39;49m\u001b[39m'\u001b[39;49m, \u001b[39m10\u001b[39;49m),\n\u001b[1;32m      4\u001b[0m                seed\u001b[39m=\u001b[39;49m\u001b[39m1\u001b[39;49m,\n\u001b[1;32m      5\u001b[0m                save_history\u001b[39m=\u001b[39;49m\u001b[39mTrue\u001b[39;49;00m,\n\u001b[1;32m      6\u001b[0m                verbose\u001b[39m=\u001b[39;49m\u001b[39mTrue\u001b[39;49;00m)\n\u001b[1;32m      7\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39m'\u001b[39m\u001b[39mThreads:\u001b[39m\u001b[39m'\u001b[39m, res\u001b[39m.\u001b[39mexec_time)\n\u001b[1;32m      9\u001b[0m pool\u001b[39m.\u001b[39mclose()\n",
-      "File \u001b[0;32m~/miniforge3/envs/osier-test04/lib/python3.12/site-packages/pymoo/optimize.py:67\u001b[0m, in \u001b[0;36mminimize\u001b[0;34m(problem, algorithm, termination, copy_algorithm, copy_termination, **kwargs)\u001b[0m\n\u001b[1;32m     64\u001b[0m     algorithm\u001b[39m.\u001b[39msetup(problem, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[1;32m     66\u001b[0m \u001b[39m# actually execute the algorithm\u001b[39;00m\n\u001b[0;32m---> 67\u001b[0m res \u001b[39m=\u001b[39m algorithm\u001b[39m.\u001b[39;49mrun()\n\u001b[1;32m     69\u001b[0m \u001b[39m# store the deep copied algorithm in the result object\u001b[39;00m\n\u001b[1;32m     70\u001b[0m res\u001b[39m.\u001b[39malgorithm \u001b[39m=\u001b[39m algorithm\n",
-      "File \u001b[0;32m~/miniforge3/envs/osier-test04/lib/python3.12/site-packages/pymoo/core/algorithm.py:138\u001b[0m, in \u001b[0;36mAlgorithm.run\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    136\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mrun\u001b[39m(\u001b[39mself\u001b[39m):\n\u001b[1;32m    137\u001b[0m     \u001b[39mwhile\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mhas_next():\n\u001b[0;32m--> 138\u001b[0m         \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mnext()\n\u001b[1;32m    139\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mresult()\n",
-      "File \u001b[0;32m~/miniforge3/envs/osier-test04/lib/python3.12/site-packages/pymoo/core/algorithm.py:158\u001b[0m, in \u001b[0;36mAlgorithm.next\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    156\u001b[0m \u001b[39m# call the advance with them after evaluation\u001b[39;00m\n\u001b[1;32m    157\u001b[0m \u001b[39mif\u001b[39;00m infills \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m--> 158\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mevaluator\u001b[39m.\u001b[39;49meval(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mproblem, infills, algorithm\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m)\n\u001b[1;32m    159\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39madvance(infills\u001b[39m=\u001b[39minfills)\n\u001b[1;32m    161\u001b[0m \u001b[39m# if the algorithm does not follow the infill-advance scheme just call advance\u001b[39;00m\n\u001b[1;32m    162\u001b[0m \u001b[39melse\u001b[39;00m:\n",
-      "File \u001b[0;32m~/miniforge3/envs/osier-test04/lib/python3.12/site-packages/pymoo/core/evaluator.py:69\u001b[0m, in \u001b[0;36mEvaluator.eval\u001b[0;34m(self, problem, pop, skip_already_evaluated, evaluate_values_of, count_evals, **kwargs)\u001b[0m\n\u001b[1;32m     65\u001b[0m \u001b[39m# evaluate the solutions (if there are any)\u001b[39;00m\n\u001b[1;32m     66\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mlen\u001b[39m(I) \u001b[39m>\u001b[39m \u001b[39m0\u001b[39m:\n\u001b[1;32m     67\u001b[0m \n\u001b[1;32m     68\u001b[0m     \u001b[39m# do the actual evaluation - call the sub-function to set the corresponding values to the population\u001b[39;00m\n\u001b[0;32m---> 69\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_eval(problem, pop[I], evaluate_values_of, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m     71\u001b[0m \u001b[39m# update the function evaluation counter\u001b[39;00m\n\u001b[1;32m     72\u001b[0m \u001b[39mif\u001b[39;00m count_evals:\n",
-      "File \u001b[0;32m~/miniforge3/envs/osier-test04/lib/python3.12/site-packages/pymoo/core/evaluator.py:90\u001b[0m, in \u001b[0;36mEvaluator._eval\u001b[0;34m(self, problem, pop, evaluate_values_of, **kwargs)\u001b[0m\n\u001b[1;32m     87\u001b[0m X \u001b[39m=\u001b[39m pop\u001b[39m.\u001b[39mget(\u001b[39m\"\u001b[39m\u001b[39mX\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m     89\u001b[0m \u001b[39m# call the problem to evaluate the solutions\u001b[39;00m\n\u001b[0;32m---> 90\u001b[0m out \u001b[39m=\u001b[39m problem\u001b[39m.\u001b[39;49mevaluate(X, return_values_of\u001b[39m=\u001b[39;49mevaluate_values_of, return_as_dictionary\u001b[39m=\u001b[39;49m\u001b[39mTrue\u001b[39;49;00m, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m     92\u001b[0m \u001b[39m# for each of the attributes set it to the problem\u001b[39;00m\n\u001b[1;32m     93\u001b[0m \u001b[39mfor\u001b[39;00m key, val \u001b[39min\u001b[39;00m out\u001b[39m.\u001b[39mitems():\n",
-      "File \u001b[0;32m~/miniforge3/envs/osier-test04/lib/python3.12/site-packages/pymoo/core/problem.py:257\u001b[0m, in \u001b[0;36mProblem.evaluate\u001b[0;34m(self, X, return_values_of, return_as_dictionary, *args, **kwargs)\u001b[0m\n\u001b[1;32m    254\u001b[0m     only_single_value \u001b[39m=\u001b[39m \u001b[39mnot\u001b[39;00m (\u001b[39misinstance\u001b[39m(X, \u001b[39mlist\u001b[39m) \u001b[39mor\u001b[39;00m \u001b[39misinstance\u001b[39m(X, np\u001b[39m.\u001b[39mndarray))\n\u001b[1;32m    256\u001b[0m \u001b[39m# this is where the actual evaluation takes place\u001b[39;00m\n\u001b[0;32m--> 257\u001b[0m _out \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mdo(X, return_values_of, \u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m    259\u001b[0m out \u001b[39m=\u001b[39m {}\n\u001b[1;32m    260\u001b[0m \u001b[39mfor\u001b[39;00m k, v \u001b[39min\u001b[39;00m _out\u001b[39m.\u001b[39mitems():\n\u001b[1;32m    261\u001b[0m \n\u001b[1;32m    262\u001b[0m     \u001b[39m# copy it to a numpy array (it might be one of jax at this point)\u001b[39;00m\n",
-      "File \u001b[0;32m~/miniforge3/envs/osier-test04/lib/python3.12/site-packages/pymoo/core/problem.py:297\u001b[0m, in \u001b[0;36mProblem.do\u001b[0;34m(self, X, return_values_of, *args, **kwargs)\u001b[0m\n\u001b[1;32m    295\u001b[0m \u001b[39m# do the function evaluation\u001b[39;00m\n\u001b[1;32m    296\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39melementwise:\n\u001b[0;32m--> 297\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_evaluate_elementwise(X, out, \u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m    298\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m    299\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_evaluate_vectorized(X, out, \u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n",
-      "File \u001b[0;32m~/miniforge3/envs/osier-test04/lib/python3.12/site-packages/pymoo/core/problem.py:315\u001b[0m, in \u001b[0;36mProblem._evaluate_elementwise\u001b[0;34m(self, X, out, *args, **kwargs)\u001b[0m\n\u001b[1;32m    312\u001b[0m f \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39melementwise_func(\u001b[39mself\u001b[39m, args, kwargs)\n\u001b[1;32m    314\u001b[0m \u001b[39m# execute the runner\u001b[39;00m\n\u001b[0;32m--> 315\u001b[0m elems \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49melementwise_runner(f, X)\n\u001b[1;32m    317\u001b[0m \u001b[39m# for each evaluation call\u001b[39;00m\n\u001b[1;32m    318\u001b[0m \u001b[39mfor\u001b[39;00m elem \u001b[39min\u001b[39;00m elems:\n\u001b[1;32m    319\u001b[0m \n\u001b[1;32m    320\u001b[0m     \u001b[39m# for each key stored for this evaluation\u001b[39;00m\n",
-      "File \u001b[0;32m~/miniforge3/envs/osier-test04/lib/python3.12/site-packages/pymoo/core/problem.py:42\u001b[0m, in \u001b[0;36mStarmapParallelization.__call__\u001b[0;34m(self, f, X)\u001b[0m\n\u001b[1;32m     41\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__call__\u001b[39m(\u001b[39mself\u001b[39m, f, X):\n\u001b[0;32m---> 42\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mlist\u001b[39m(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mstarmap(f, [[x] \u001b[39mfor\u001b[39;49;00m x \u001b[39min\u001b[39;49;00m X]))\n",
-      "File \u001b[0;32m~/miniforge3/envs/osier-test04/lib/python3.12/multiprocessing/pool.py:375\u001b[0m, in \u001b[0;36mPool.starmap\u001b[0;34m(self, func, iterable, chunksize)\u001b[0m\n\u001b[1;32m    369\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mstarmap\u001b[39m(\u001b[39mself\u001b[39m, func, iterable, chunksize\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m):\n\u001b[1;32m    370\u001b[0m \u001b[39m    \u001b[39m\u001b[39m'''\u001b[39;00m\n\u001b[1;32m    371\u001b[0m \u001b[39m    Like `map()` method but the elements of the `iterable` are expected to\u001b[39;00m\n\u001b[1;32m    372\u001b[0m \u001b[39m    be iterables as well and will be unpacked as arguments. Hence\u001b[39;00m\n\u001b[1;32m    373\u001b[0m \u001b[39m    `func` and (a, b) becomes func(a, b).\u001b[39;00m\n\u001b[1;32m    374\u001b[0m \u001b[39m    '''\u001b[39;00m\n\u001b[0;32m--> 375\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_map_async(func, iterable, starmapstar, chunksize)\u001b[39m.\u001b[39;49mget()\n",
-      "File \u001b[0;32m~/miniforge3/envs/osier-test04/lib/python3.12/multiprocessing/pool.py:768\u001b[0m, in \u001b[0;36mApplyResult.get\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    767\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mget\u001b[39m(\u001b[39mself\u001b[39m, timeout\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m):\n\u001b[0;32m--> 768\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mwait(timeout)\n\u001b[1;32m    769\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mready():\n\u001b[1;32m    770\u001b[0m         \u001b[39mraise\u001b[39;00m \u001b[39mTimeoutError\u001b[39;00m\n",
-      "File \u001b[0;32m~/miniforge3/envs/osier-test04/lib/python3.12/multiprocessing/pool.py:765\u001b[0m, in \u001b[0;36mApplyResult.wait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    764\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mwait\u001b[39m(\u001b[39mself\u001b[39m, timeout\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m):\n\u001b[0;32m--> 765\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_event\u001b[39m.\u001b[39;49mwait(timeout)\n",
-      "File \u001b[0;32m~/miniforge3/envs/osier-test04/lib/python3.12/threading.py:655\u001b[0m, in \u001b[0;36mEvent.wait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    653\u001b[0m signaled \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_flag\n\u001b[1;32m    654\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m signaled:\n\u001b[0;32m--> 655\u001b[0m     signaled \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_cond\u001b[39m.\u001b[39;49mwait(timeout)\n\u001b[1;32m    656\u001b[0m \u001b[39mreturn\u001b[39;00m signaled\n",
-      "File \u001b[0;32m~/miniforge3/envs/osier-test04/lib/python3.12/threading.py:355\u001b[0m, in \u001b[0;36mCondition.wait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    353\u001b[0m \u001b[39mtry\u001b[39;00m:    \u001b[39m# restore state no matter what (e.g., KeyboardInterrupt)\u001b[39;00m\n\u001b[1;32m    354\u001b[0m     \u001b[39mif\u001b[39;00m timeout \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m--> 355\u001b[0m         waiter\u001b[39m.\u001b[39;49macquire()\n\u001b[1;32m    356\u001b[0m         gotit \u001b[39m=\u001b[39m \u001b[39mTrue\u001b[39;00m\n\u001b[1;32m    357\u001b[0m     \u001b[39melse\u001b[39;00m:\n",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
-     ]
-    }
-   ],
-   "source": [
-    "res = minimize(problem,\n",
-    "               NSGA2(pop_size=20),\n",
-    "               termination=('n_gen', 10),\n",
-    "               seed=1,\n",
-    "               save_history=True,\n",
-    "               verbose=True)\n",
-    "print('Threads:', res.exec_time)\n",
-    "\n",
-    "pool.close()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import psutil\n",
-    "\n",
-    "threads_count = psutil.cpu_count() / psutil.cpu_count(logical=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2.0"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "threads_count"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Create new objectives and modify technology data\n",
-    "\n",
-    "`osier` allows users to modify the problem formulation on the fly. Both by adding new data fields to technologies or by creating a new objective.\n",
-    "\n",
-    "Users can create new objectives for any quantifiable metric. Here we add a parameter called `land_use` to the modeled technologies."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "nuclear_adv.land_use = 4.4*1e-3 * (km**2/GW)\n",
-    "natural_gas.land_use = 3.2*1e-3 * (km**2/GW)\n",
-    "wind.land_use = 12.3e3*1e-3 * (km**2/GW)\n",
-    "battery.land_use = 6.0*1e-3 * (km**2/GW)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Then, we create a function that calculates the total land use. The minimum required format is\n",
-    "\n",
-    "```py\n",
-    "def objective(technology_list, solved_dispatch_model):\n",
-    "    # some calculation\n",
-    "    return objective\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def land_use(technology_list, solved_dispatch_model):\n",
-    "    \"\"\"\n",
-    "    Calculates land use intensity.\n",
-    "    \"\"\"\n",
-    "    \n",
-    "    obj_value = np.array([t.capacity.to_value() * t.land_use for t in technology_list]).sum()\n",
-    "    \n",
-    "    return obj_value"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now, we re-initialize the problem with our new objective and updated technologies."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "==========================================================\n",
-      "n_gen  |  n_eval  | n_nds  |      eps      |   indicator  \n",
-      "==========================================================\n",
-      "     1 |       20 |      6 |             - |             -\n",
-      "     2 |       40 |     11 |  0.0823881536 |         ideal\n",
-      "     3 |       60 |     18 |  0.0589579800 |         ideal\n",
-      "     4 |       80 |     20 |  0.0063982942 |         ideal\n",
-      "     5 |      100 |     20 |  0.0311724049 |         ideal\n",
-      "     6 |      120 |     20 |  0.0106202339 |         ideal\n",
-      "     7 |      140 |     20 |  0.0269553868 |         nadir\n",
-      "     8 |      160 |     20 |  0.0102049651 |         ideal\n",
-      "     9 |      180 |     20 |  0.1287526163 |         nadir\n",
-      "    10 |      200 |     20 |  0.0397818793 |         ideal\n",
-      "CPU times: user 4min 45s, sys: 12.2 s, total: 4min 57s\n",
-      "Wall time: 5min 10s\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "problem = CapacityExpansion(technology_list=[wind, natural_gas, nuclear_adv, battery],\n",
-    "                            demand=demand*MW,\n",
-    "                            wind=wind_speed,\n",
-    "                            upper_bound = 1/wind.capacity_credit,\n",
-    "                            objectives=[total_cost, annual_emission, land_use],\n",
-    "                            solver='cbc')\n",
-    "\n",
-    "res1 = minimize(problem,\n",
-    "               NSGA2(pop_size=20),\n",
-    "               termination=('n_gen', 10),\n",
-    "               seed=1,\n",
-    "               save_history=True,\n",
-    "               verbose=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "==========================================================\n",
-      "n_gen  |  n_eval  | n_nds  |      eps      |   indicator  \n",
-      "==========================================================\n",
-      "     1 |       20 |      6 |             - |             -\n",
-      "     2 |       40 |     11 |  0.0823881536 |         ideal\n",
-      "     3 |       60 |     18 |  0.0589579800 |         ideal\n",
-      "     4 |       80 |     20 |  0.0063982942 |         ideal\n",
-      "     5 |      100 |     20 |  0.0311724049 |         ideal\n",
-      "     6 |      120 |     20 |  0.0106202339 |         ideal\n",
-      "     7 |      140 |     20 |  0.0269553868 |         nadir\n",
-      "     8 |      160 |     20 |  0.0102049651 |         ideal\n",
-      "     9 |      180 |     20 |  0.1287526163 |         nadir\n",
-      "    10 |      200 |     20 |  0.0397818793 |         ideal\n",
-      "CPU times: user 4min 54s, sys: 3.38 s, total: 4min 58s\n",
-      "Wall time: 5min 4s\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "problem = CapacityExpansion(technology_list=[wind, natural_gas, nuclear_adv, battery],\n",
-    "                            demand=demand*MW,\n",
-    "                            wind=wind_speed,\n",
-    "                            upper_bound = 1/wind.capacity_credit,\n",
-    "                            objectives=[total_cost, annual_emission, land_use],\n",
-    "                            solver='appsi_highs')\n",
-    "\n",
-    "res2 = minimize(problem,\n",
-    "               NSGA2(pop_size=20),\n",
-    "               termination=('n_gen', 10),\n",
-    "               seed=1,\n",
-    "               save_history=True,\n",
-    "               verbose=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "==========================================================\n",
-      "n_gen  |  n_eval  | n_nds  |      eps      |   indicator  \n",
-      "==========================================================\n",
-      "     1 |       20 |      7 |             - |             -\n",
-      "     2 |       40 |     10 |  0.1400175567 |         ideal\n",
-      "     3 |       60 |     16 |  0.1098694596 |         ideal\n",
-      "     4 |       80 |     20 |  0.1229353182 |         ideal\n",
-      "     5 |      100 |     20 |  0.0348904059 |             f\n",
-      "     6 |      120 |     20 |  0.0218664153 |         ideal\n",
-      "     7 |      140 |     20 |  0.0059155786 |         nadir\n",
-      "     8 |      160 |     20 |  0.0057877007 |         ideal\n",
-      "     9 |      180 |     20 |  0.0806871345 |         nadir\n",
-      "    10 |      200 |     20 |  0.0079582816 |         ideal\n",
-      "CPU times: user 3min, sys: 1.88 s, total: 3min 2s\n",
-      "Wall time: 3min 6s\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "problem = CapacityExpansion(technology_list=[wind, natural_gas, nuclear_adv, battery],\n",
-    "                            demand=demand*MW,\n",
-    "                            wind=wind_speed,\n",
-    "                            upper_bound = 1/wind.capacity_credit,\n",
-    "                            objectives=[total_cost, annual_emission, land_use],\n",
-    "                            model_engine='logical',\n",
-    "                            solver='appsi_highs')\n",
-    "\n",
-    "res3 = minimize(problem,\n",
-    "               NSGA2(pop_size=20),\n",
-    "               termination=('n_gen', 10),\n",
-    "               seed=1,\n",
-    "               save_history=True,\n",
-    "               verbose=True)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Visualization\n",
-    "\n",
-    "Below are some example visualizations using the `pymoo` visualization module and `matplotlib`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1300x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "res = res1\n",
-    "obj_labels=['Total Cost', 'CO2eq', 'Land Use']\n",
-    "with plt.style.context('dark_background'):\n",
-    "    plot = PCP(title=(\"Objective Space\", {'pad': 30, 'fontsize':20}),\n",
-    "            n_ticks=10,\n",
-    "            legend=(True, {'loc': \"upper left\"}),\n",
-    "            labels=obj_labels,\n",
-    "            figsize=(13,6),\n",
-    "            )\n",
-    "\n",
-    "    plot.set_axis_style(color=\"grey\", alpha=0.5)\n",
-    "    plot.tight_layout = True\n",
-    "    plot.add(res.F, color=\"grey\", alpha=0.3)\n",
-    "\n",
-    "    plot.add(res.F[3], linewidth=5, color=\"tab:green\", label=r\"Least CO$_2$\")\n",
-    "    plot.add(res.F[6], linewidth=5, color=\"tab:blue\", label=\"Least Cost\")\n",
-    "    plot.show()\n",
-    "    plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 600x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "with plt.style.context('dark_background'):\n",
-    "    fig, ax = plt.subplots(1,1,figsize=(6,4))\n",
-    "\n",
-    "    bplot = ax.boxplot(res.X,\n",
-    "                       patch_artist=True,\n",
-    "                       tick_labels=get_tech_names(problem.technology_list))\n",
-    "    ax.set_ylabel(\"Fraction of Peak Demand\")\n",
-    "\n",
-    "    # fill with colors\n",
-    "    colors = ['tab:blue', 'tab:orange', 'tab:green']\n",
-    "    for patch, color in zip(bplot['boxes'], colors):\n",
-    "        patch.set_facecolor(color)\n",
-    "\n",
-    "    for median in bplot['medians']:\n",
-    "        median.set_color('red')\n",
-    "\n",
-    "    ax.yaxis.grid(True, alpha=0.2)\n",
-    "    plt.show()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "osier-env",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.6"
-  },
-  "orig_nbformat": 4
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}