OSIER capacity expansion.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9966863a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Solver set: cplex_direct\n"
     ]
    }
   ],
   "source": [
    "# basic imports\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from unyt import kW, minute, hour, day, MW\n",
    "import sys\n",
    "\n",
    "# osier imports\n",
    "from osier import CapacityExpansion\n",
    "import osier.tech_library as lib\n",
    "\n",
    "# pymoo imports\n",
    "from pymoo.algorithms.moo.nsga2 import NSGA2\n",
    "from pymoo.optimize import minimize\n",
    "\n",
    "\n",
    "# set the solver based on operating system -- assumes glpk or cbc is installed.\n",
    "if \"win32\" in sys.platform:\n",
    "    solver = 'cplex'\n",
    "elif \"linux\" in sys.platform:\n",
    "    solver = \"cbc\"\n",
    "elif \"darwin\" in sys.platform:\n",
    "    solver = 'cplex_direct'\n",
    "else:\n",
    "    solver = \"cbc\"\n",
    "\n",
    "print(f\"Solver set: {solver}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "df97ee86",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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tqAV6nX02bXNzE/qedCVTr/3tZWV82dBA/wgTLJ345BN2vPQS+Vu2kB3h2gjs+7FWragBeowYQef16zFyclh/551kr1xJ782byfJ9hrFyJRuqq+H+++nz+edkNTVx4MIL2QL0PnKE1gleh7s/+oidP/0pfc89l9xwVlCSSfh3NwzDruX3hmHUGYax0TCMbYZhHDIM4/kY52w0DKNbtGMWL15sIPeNuJeysrKEz3XDkvL+d+0qP9rPf57Y+XPmGCxebHDbbfI57drZ2/fSUvme73zH8d8q43/7ZC1//7tBTU3y+t6ihcGxYwa//a28//735TefNOnkc7/xDdl3xRXy/he/kPft2yfentNPl8+44YaU/+6GYSyxev+205V0OzLGcBrwLeBd4KqQY3oCphOvAnFt7baxTUoqGTFC1vGOL5gsWQJnnCEm/L59EqJoJ+pKSj/CJbc1h6YmcWsWFUlm/R13SKDDm2+efOxLL8Hq1XDXXXJsWZlcIwcOJP79n3+eEUX1nMhjuNG3AEwD1iBjDA8hAmI40CbFDsyKquZ4QbwsWSIRTRMm2D++AJL89uWXGpmUToQW0EsGZsjqlVdKbaxIoamGIVFKZWVybHMikgIxi+o1t/y3jaRKGOYjYw4Aj/kWgIeRAedy4CzgoxS1R0kGnTtH3z9yJKxdG7uiaiRMQendOzXCABqymm6E1klKBrW1Unjvv/4LVqyQMNJIzJwpswqaApEsYWjZUh540hTNfFYSo7hY/mHvuCPyMVYrqkZi0yYpPgbJyWGwgia5pQ9ZWdC9uz3CUFwsdY8iWQsmptXQr5/kPiRDGD7+WCzT889v/mfZhAqDkhhf/7rEdd9zj0yaEkrfvpITEG9iWyim1aAWg/fo3FmusWSOMYA/ZHX1arEIYvHaa/7rMBnCcOKEJOT17t38z7IJFQYlMSZNkkG05cslISh0HoN4K6pGwpzxLZXC0LNnxlTBdDXRktuaQ02NrO+9l6BaWdH42c/ELRprBkKrJGv+aZtQYVDip00b+NrXxDd72WVSznjmzODBtJEjJYpo7drmfVeqLYYtW6TGf8+eqfk+JTJmcluyheHtt+Hcc+Hf/7Z+zvvvS7n2SOVU4kWFQXEd558vg2dvvQWbN0sJ67IyePpp/zHxVlSNxOuvw003+edLsBtTgNSd5Dx2WQxNTbBgQXI/M15qa8VVFjrNbJoQSxi6WFg62dg+JR2ZNEmsAfOf69134Re/gG98A37+cwkxHTKk+eMLINbIo4/KOhWoMKQPdglDOpCs+adtIpYjdatvyYpyTDYQPl9dcScTJ4oYHD3q3/bAA1LX6Pe/l3oyLVs2f3zBCTTJLX3Iz5eB2t0uzHk1haGoKDkD2kkmljB8DpwZ45jlSWqLkgn07y+lg//7v0/ed911MGCAzJAGmSkMe/bIhCpqMThPjx4Srtxcd2Q6kuYWQyxX0igLn2HlGMUtmKGpb7118r5Dh2RGrb175ck7VbkHyUZDVtMDO5Lb0oWtW0Xw0lQYYlkM9wMfIhnJkf7LjyS1RUp6M3GizN28eXP4/evXS7p/x46pbVcy0SS39MDNwmDOMZ6mwhDLYqgGpiLisBH4B/AjxL2kEU1eo21bCfMLV3AskBUrJLwvU1GLIT1IdgG9dCONQ1Zj3dwfBr6NVEgdBbwC9AX+DXxpZ8OUNOSCC/xhqm6mrk4shqxoMReK7dhRQC+dyGBhAIlIOgOY7Fu+hlgSD9jYLiUdmThRwlQXLnS6JfZSVycht926Od0S79KqFXTooMLgELHGGOYAHYAVwCfA75BIJcWLTJwIc+cGh6m6EXPQvKgo8pSSir107y5rtwtD+/YyHrdvn9OtCSKWxbAemR+hxLf0A/QxyouUlUmpYre7kUCT3NIBNye3maRxyGosi+EG37oDMl/C2cjgc3dkgp3v2dc0Ja2IFqbqNlQYnMcUBrcPPoMIw5o1zrYlBKslJBuBQ8Bh3+siIM+uRilpyMSJcvGaF7Ob2bFDSnBoyKpz2FVAL51IY4shlivp/4BPgXrgHqA98DjQHxhsb9OUtMEMU/WCtQDB8wIrzuAFV1J9vTyApKEwxLIYNgAvIGUvTtjfHCUtufBCidLxijBA83MZhg2TQext25LXJi+Rny9Twh465HRL7MN8AMlAYTDjEssj7F+WxLYo6crEiXDggPvDVAPZsgUGJ2gUt2wJ8+fD9Olw/fVJbZZncHtym0mahqzGEoYlwFrAjNkLzPgxgAvsaJSSZphhqseOOd2S1FFXJ/1OhDFjZNKigQOT2yYv4fbkNpPaWrEu04xYYww/BfYhg85/A74OnO9bVBS8wIABMjetl9xIIMLQrl1iNZ/Gj5d1WVly2+Ql3FwnKZDa2rQcy7Iy+DwG+H9AL2Ae8C9giL3NUtIG86nZa8LQnHkZJkyQdZcumj2dKF4Shtat0+46sVoIbwMwC5gNVACltrVISS/OOQeqqlI353K6kGguwymnyNjEvHnyvn//5LbLC2RlSeazV4QB0m6cIZYwFAO/QkJW7wZWAmWI1aB4gfJyWObBGINEhcF0Iz34oKzVnRQ/nTvLLIBeGXyGtBOGWIPP1cAqxFrYj0zheVPA/v+1qV1KOtCpk5TBePRRp1uSerZulXW8rqTx48UN9cYbcOSIWgyJ4IUcBpMMFYZ7kOgjgHY2t0VJN844Q9YrVzrbDicwJ1KJx2Jo0QLGjYOZMyVG/Ysv1GJIBC8Jw86d0NiYccJwVxK+IxsJe90CXBKyLwt4EJiElNz4PpobkT4MGSLrFSucbIVzxJvkNmKEDDi/8468r6oSV5wSH14SBsOQ6yzNhCHWGIOV7JxYx9xM5FLdE/FXbr0e8KDPIo0pL5enZi/4esMR7xSfEybAiROS8wFQWQnFxeIvV6zjhQJ6gaRhklssi+GXwK4o+7OQG/8TEfYXARcD9wE/CbN/MvAc4q76BOgEFCC1mRSnGTLEu9YCyJPc6NHWj58wARYvhj175H1VFeTkQN++IhKKNXr0EFfc7t1OtyQ11NZKUmQaEUsY3keS2qIxJ8q+PwE/R4rvhaMQCCzXWefbFioM1/sWCgoKKEvQb1tcXJzQeW4hnv4bOTl8MWgQnf/+d/Jd4CdP5Lff1djIrq5dKS0vp0VjY9RjT3TqxLoRI+j66KN09/29Dh85wiagcOzYiP8AqSKTrv1tpaUc2LuXktLkRMWne993HjrE7sJC+p9+OlmGEfuEOEi077GE4ZqEPlW4BNgBLAXOi3BMuEl1w/1lnvAt1NfXG5XNePpqzrluwHL/Bw2CvDz2zJvHHpf8zeL+7X3W0hcHD0JNTfRjv/lNyM5m9wsvsNv8Hl+S3JZ27dLCYkiba/+aa2Ts5t57w+/Py4P6+qS2N236Ho6VKyEvj6q9e20puphI360muCXCaOBSYCPwT6SExvMhx9QhGdUmRcBWG9ukWMUcNPViRJJJPLkM48eLC2nxYv+2AwdEHDRkNZi774bf/MY/50IoXimgZ5KGIat2CsPtyI3+NOBbwLvAVSHHvApcjVgOZyF1mXR8IR0YMkTi8KuqnG6Jc8QrDHPnyuBzIFVVGrIayPDhcgPMyYGrQm8HPrxSQM/EY8IQiRt9C8CbyLzS1cCTBCfPKU5SXi4ztoXe6LyE1XpJgwbJMW+/ffK+ykq1GAK57DKZnGbVKnEphcMrdZJM0lAYYo0xhIskCsRq5vN83wLwWMB2A5lDWkk3hgyBWbOcboWzNDTAl1/GthjMonmzZ5+8r7JSSjx47WYXialTZa6KF1+EJ5+U3I9A91vLllLR1kt/qz17ZEKiNBKGWBZDe98yHPghEjFUiDzxD7C3aYpjFBRIETMvjy+YWElyGz8eVq/2WxiBmK44tRrEpVZWBq+8Av/6l9wMQ62G7t1l7SVhgLTLZYglDHf7lm7AUGR+hp8Cw5DxA8WNeD3jOZC6uuiupDZtpAKtme0cihkRouMM4kYCsUT37xeBuPJKaNXKf4zXkttMrAjDihXwk1hOnORgdYzhVOBowPujyKCy4kbMiKRVq5xtRzqwZUt0i+G888T9EW58AeQf/tAhtRhA3EiffOIvUPi3v0mhxilT/MeYkUpqMQQzbJj8X6Yo6c+qMPwdWITUTroTKcP9nE1tUpymvBzWr5enOq9TVwc9e0oUTTgmTJAbf6T5sA1Di+mB3PSGDxcrweS992DjxmB3kpfqJAVSWysu3Ozs8PunTZPCjq++mpLmWBWG+4Brgb3Al0ji2+9sapPiNEOG6PiCSV2dVE0tKAi/f8IEucFFy4yuqlKLYepUWc+Y4d9mGPDsszB2rN8q87IwZGfLRE/hmDYN3n0X9u5NSXPiCVddAfwbmAHsRtxLitto0wZKS3V8wSRayGqfPlBSEnl8waSyUo7Ny0t++zKFqVMl/Lm6Onj7M8+I8F59tbzPz5dosIaGlDfRUaKFrJaXQ79+8NJLKWuOVWH4T2A7UhfpdeAN31pxG4MGyT+qWgxCuCS3rl3hV78S99GJE7Hnw66qkqfBfv3sa2c6062bDNAHupFMNm4Ui+v735f3XktuM4kmDNOmyXU2c2bKmmNVGG4G+gMDgTOAwb614jbMgWe1GIRAYRg4EJ54Qv6J77tPnoDHjz/5KTgUr0cmXXqpCGOgGymQv/1NLK8xY7yb7xFLGObPh13RCl0nF6vCUIuUq1DczpAhktS1aZPTLUkP9u6Fw4fhjjtECK66Cp57TkRi/HiYNy/2Z3zxhay9Os4wdSps2BD5YePll6Wu1DXXeFcYDhyAfftOFoYBA+SBIoVuJIid+WyyHslcfgMIHGXTOZ/dRnm5upFCWbYMeveG228Xi8Gcb8EqDQ3yROhFi6FdO5nu9JFHIh9z6JAkvF1xhQziL/PoJI7hQlanTZO5KSJZWzZhVRg2+5Y836K4kawsEYann3a6JenFuefKuqkp8c/wamTSpEmS5xHrxva3v8F114mQeC25zSSSMCxYkPK/iVVhuNvWVijpQXGx/GPq+EIwzREEk8pK+O53m/85mcbUqXJT++ij6Md9+KG43EpLvelKAhGGoUP97/v3h8GD4cc/TnlTrI4xdAf+B6mG+m7AorgJsxSGupKST1WVFIeLNAeBG2nZEi6+WEpgWBHXZ56RtZeFoUcPf1jz5ZfLOlw0l81YFYYXgEqgD2I9bAQWRztByUDKy6Uk8tq1TrfEfXgxMunCC6F9e+v+8aeektDfDz+0t13pihmZZIZGT5smlla44ow2Y1UYugJPAceQeaCvRSbWUdzEkCFyA4sxv7GSAF6ssjp1qkTavGvRubBjh4xJbN5sb7vSlcCQ1eJiOPPMlEcjmVgdYzjmW9cDFyPTb2p1VbdRXg4ffOB0K9xJXZ1EJ3nFYsjOhsmT4Y034OjR2McrwRZDRYW8fvllR5piVRh+C3RESm7/GegA3GpXoxQH6NwZTj1VxxfswmvF9MrKZG6FN990uiWZg5lM2auXWFuLFjlmPVl1Jb2OJLitAc5H5mNITZk/u8nKgjlz4Ne/drolzqIZz/bjpWk+TT/5hg3OtiOTOHxYspvHjBGLwSFrAawLQx8kme0VRBDMJfOZPFmqO06e7HRLnEUjkuynshJOOy14Yhq3YhYddGDgNKOpq5NILnBUGKy6kmYig8+vAUkI6k4TsrLgzjvl9aBBkJsrNc+9SHm5TKCyc6fTLXEvVVVSoLBfPymv4WbM8tH19c62I9OorZWHtOXLoabGsWZYtRiOAA8B7yFRSeaS2UyeLD/C669LzPUAD09jraUw7MdLIauFhfKQoQPP8WEOQDsUjWRiVRgeRGZuG4XM/WwumYtpLXzxBdx2m2w780xn2+QUublSFE7HF+xl3TpZe2GcobBQ3UiJYI7JOOhGAuuupMHAd4EL8LuSDN/7zMS0Fr77XTHxDxyQdHQz+9JLDBok2ZYqDPZy6JBUrfWCxXDKKSoMifDkk2K5m3kvDmFVGKYCxYA77MJAa2H6dAklXLHCuxbDiBGy/vRTZ9vhBbxSTK+w0LtVUpvDvn0SJekwVl1JK4FONrYjtZjWwr33ysxIIBfxkCEyOOg1Kiok61TnYLCfykr3Www5OTKvgloMGYtVi6EHUitpMcHzMVya9BbZTai1YLJ8uVQW7dfPP7GKV6iokGQaxX4qK6V+kJt98D17ygOWW/vnAawKw522tiKVBI4tmNYC+M3eoUO9JQzt2snAs8NREJ7BDFMdONC9N04zh2HrVmfboSSMVb/J+0hF1Vzf68VALAdiK2AR4oZaS/g5Hc5DMqpX+JbfWGxPQhiRrAWAzz+HI0eC66F7gaFD5elOLYbUYFauHTzY2XbYiSa3ZTxWLYYfANcDXYC+QCHwGHBhlHMakailg4igLATeAj4JOW4BcIn1JifOwQsvDG8tgJSbXr3aHQPQ2dkn9y8SZrGuxVpFPSXs2SNP0oMGOd0S+zCT21QYMharFsOPgNHAft/7dUB+jHMMRBRAhCHXt80ZsrLYddNN4a0Fk+XLM99iOPtsqbdy1VXWjq+okAzL3bvtbZfiZ80adwtDYaEktuk1lbFYtRgaCQ5VzcHaTT4bWAr0Ax4BwsVDjkLcTVuBnyFup1Cu9y0UFBRQlkBUx4GxY9ly+ukU/OIXdCwpCXvM3q1b2d6lC30vuIDcDPSPHiktZfNzz9HUsSNtf/ADei1ZErS/uLj4pHOqR4+m9fLlFLo8UiZc351i+9atfHnOOZQOGEBWMqYNtUAq+791wAAO7dxJvzQJy02n3z7VJNx3wzCsLH8wDONXhmFUGoYxzjCMGYZh3GfxXAzD6GQYxnuGYQwK2d7BMIx2vteTDMNYF+uzFi9ebCCiFN/Sq5fR9dFHDbKzIx9TUSFfMmVKYt/h5FJcbLB1q0FtrcFLLxk0NBi0ahV0TFlZWfA5PXpIf2+5xfn227yc1Hcnl2uukb97377u7P/cuQYLFzr/d07H397BvhuGscTqPduqK+mXwE5gNXADMvfzf1k8F+BLYD4wIWT7fvzupjcRd1O3OD7XOrW1dH/wwei+91WrZKwh08YZevaE2bMle/mii+Cvf4U2beCcc6KfZya26cBzajEjk9zqTios1IikDMeqMDQhFVZvAqYBTyIqFI3u+JPiWgNjkVyIQHoCWb7XFb72OOeYPHJE4swzaZyhUycRhR49YOJEia56/33py/jx0c+tqBAhXL48JU1VfHz2mazdLAw68JzRxBKGLOAuYBdyU69CLAcrYaUFSDXWVUh46xxkwp8bfQuIyKxBxhgeAr5FbMGxl2XLMsdiaNNGKsOWlsKUKf7IosOHYcECa8KwerUcr6SOhgZYv96dwtCunSTwqTBkNLGE4RYkGmkE0BUJVx3p2xZras9VwJnAGcAg4B7f9sd8C8DDwECgHDgL+Ciu1tvBsmXyxJMfK+jKYXJzJSntrLPg29+GefOC97/9ttx4iqJMzV1RoWGqTuHWyCRNbnMFsYThauBKYEPAtvXAVb597sN0q6S71fCLX4jr6IYb4JVXTt7/zjuyvuii8Of36yfzPOv4gjOsWSPF9HJznW5JctHkNlcQSxhyETdSKDt9+9yHWXo63ccZrrgC3nsPnnoq/P61a2WawEjuJB14dpY1a0QUSkudbkly0eQ2VxBLGKKV2XZHCe5Q9u+H6ur0FobiYnFDzJoV/bh33oFx4yQTOpSKCvF1mwOhSmpxa2SSupJcQSxhKEdCSkOXA8jkPe7EygD0qadC69apaU8okyfL2oowdO7stw4CqaiApUutl85QkktVlUSEuVEYvvxSJiVSMpZYwpANdAiztMetriSQcYa+faFjx/D7i4rEVeNURdIpU2SWp40box83d67c+CeEpI/k5IhFpG4k5zh6VMqzuE0YdOY2V+DBWWksYJbgHjIk/P4HHpCwvEmTYNSolDULgK5dYfTo2NYCwN69cvMPHWcYPBhatVJhcJpMikxq2dLacZrc5gpUGMJhRiaFG2cYOxa++U34/e9h+3a4O1w1cRu55BIZM7AiDCDupBEjoEsX/zazoqoKg7OsWSPjRW3aON2S6HTtKoUZTRdmNDS5zRWoMIRj506J6AkdZ8jLg4cflsHpu++G+++Xwd1YpSeSyZQpUFtrfT7dd94RIRk71r9Np/JMD9askbkwTj/d6ZZEp7xcLOTzzot+XFYWFBSoMLgAFYZIhCvBfeutEnv+n/8JjY3w2GNQXw/33BP+M5JN69aSl2DVWgBJYNuzJ9idpIlt6UGmRCYNGCDrM86Iflx+voxfqSsp41FhiMSyZTJpuxl51KsX/PrXMGOGZBWD1CP63e/kSer88+1v09ix4naIRxhOnJBB6PHjpdZIu3byj65uJOepqZFryC3CoMltrkGFIRLLl4sLxvxneOABMZVvDakE8uST4tpJhdUwZQrs2ydF8uLh7behsJDGkhIYNkyn8kwXmpokjySWMIwfDy+/LNefE5iurm7d/Als4dDkNtegwhAJ04d/5pkyjvCNb8B9953sl29slO1jxshxdtGihQw8v/EGHDsW37mzZwPQMGaMTuWZbqxeHVsY7rkHLrtM/PdOMGCA5F1AdKtBk9tcgwpDJGprZWrCUaPgz3+Gdevgj38Mf+zTT4tg2Gk1jBolPtx43EgmW7bAmjU0nHOOTuWZbqxZI3kxnTqF33/mmX4x79s3Zc36im7d5Lp78UV5H0sYTpyQaD0lo1FhiMayZTJ3sjngfDRCFZBjx+Dee6XS6cSJ9rRlyhT5/rfeSuz8t9/m8LBhYtmoGyl9MAegBw4Mv/+GG/zZ6f36paZNgZhupA8/hM2bYwvDtm2aTe8CVBiisWyZuHBeftlfrTQSzz4rT+J2WQ2TJ8O778KBA4md/847GHl5MtubCkP6EC0yqV07Kan+j3/Iw4cTFoM58Pz555JtX14e+dhTTlE3kktQYYjGm29K2YKf/CT2scePiygMHw6XXprcdpx+OpSUJOZGMlmwgCxzQh4VhvShrk4CCsIJw7e/LZPePPKIlD9xymI4cEBcq6tWSaReXl74YzW5zTWoMETjgw/EjbR5s7XjX3hBhCTZ2dBTpsj61VcT/4zGRtosXqxTeaYjkUpj3HCDlIH/9FNJqnTKYvj8c3m9apXkKURKyFNhcA0qDMnkxAl46CGpsXTqqcn73MmTJYqomWZ6t4cfhh/9SKfyTDfCCcOIEZJg+fjj8r6mxhmLYcAAf2n2VatkHW6coVUrKbuiriRXoMKQbNaulXWynu4KCmDkSJg5s9kf1Xr1anjiiea3SUkua9ZI9E/Pnv5tN9wABw+KFQpiMXTqFFzzKhHathWRmTo19rEdO4oVYFoM69bJQ0U4YdAcBlehwpBsampknaynO3O8ojnjC0p6EzoA3aEDfOtbMH26P9ggWdfV174mhfsuuyz2sabLyLQYTpyQB59wA9Ca9ewqVBiSzZYtkvSWLIth8mS5KZiWiOI+QoXhqqvkyf6xx/zHVFfLurnXlZmEOWZM7GNDhQHEnRTNYlBXkitQYUg2TU2wfn1yhCEnBy64AF57rfmfpaQvu3ZJUpgpDDfeCEuWBFfQ3bBBrq3mWgzjxsnnnHaaJNZFY8AAcR0FTgi1ahX06CFJb4GoxeAqVBjsoKYmOcLQp49MkKJRRO7HHIAeNUomUjIHnU0aG+Wm25zrqqBAEunMLOZYVsOAAVBZKUJisnKlrEOthsJCmUN8377E26ekDSoMdpCsCJLSUll/8UXzP0tJb9askZv2D38I+/fL+EIo1dXNu67MOTkeeEDGLqwIgznwbLJ6taxDxxk0uc1VqDDYQXW1JCZ17968zykpkfW6dc1vk5LerFnjz3R+/nl5+g6luZbouHEyQdOyZfDRR9GFoW1bcTcFji+A1NjasiW8xaBuJNegwmAHZgRJc91JJSUyb7MWvHM/5gB0dvbJbiST6moJaW3XLu6PN0AshnnzwDBg4UJxWXXsGP6E/v1lHSoMEH4AWoXBVdgpDK2ARcBKYC0QLh04C3gIqAZWAWEmWc5AkhVaWFqqbiSvYEadffKJP5EsFPO6Ki6O++OPlpTIGMOcObJh4UKpAzZqVPgTzBpJ4YRh5UrZn5Pj36auJFdhpzA0AhcA5cAQYAJwVsgxE4ES33I98KiN7UkdGzfKgF0yLAZ1I3mDAwfgzjvhttsiH2OGrCbwwNFw9tnyYu5cWS9aJIX5IrmTBgyQ/aYYBbJqldRLKiuT9126SOazWgyuwU5hMICDvte5vsUIOWYy8Jxv+ydAJ8Ch2UiSyNGjUl+pOcLQqhX07q0Wg5e45x55ko9EM1yUDaNGyWQ7tbWy4dAhGWs455zwJwwYINfe8eMn7wstjaGhqq4jJ/YhzSIbWAr0Ax4BPg3ZXwjUBryv822rDznuet9CQUEBZeaTSpwUJ2CCJ8rm+nqaBg/mtATb2lhSwgbglIYGOiT4GaGksv/phlv6vm73btoNH05BHNeEkZtLdUUFnV55hZ4B523/7DO+/Pa3KRk8mBYhswLWlJfTqrKSwjDfY2Rl8cXRo3Q+/3zyly3j4MiR1AGn5ubSJknXajJxy2+fCAn33TCMVCydDMN4zzCMQSHb3zAMY0zA+3mGYQyL9lmLFy82EAsj7qWsrCzhc+NeHn/cYPv2xM+fMkU6PGxY0tqU0v6n2eKavn/0kcHcufGdc+65ci1demnwdvMaGzUqeHvLlgbHjxvcdVfkz1y2zODNN+X1tdfK5/Tu7fzfx82/fTP7bhjGEqv37FRFJX0JzEfGGQKpA3oFvC8C3DGCVV0t2aHt2yd2vpnDoGMMSiCJ5DKMHSsuofnzg7ebbqvQcYbSUomOCjfwbBIYmWS6kupDDX0lU7FTGLojYwYArYGxQGXIMa8CVyPRSWcB+zjZjZSZNDdktaREyiTs35+8NimZT00N9OoVebKccIwbJ5V1Q6+lXbskszlUGAJnbYvEqlUiCF27SkTSzp2Rp75VMg47haEAeA8JQ10MzAFeB270LQBvAuuRcNUngZtsbE9qSYYwqLWghFJdLWGmffpYO75TJxgxgjYffRR+/8KFMHo0ZGX5tw0YIJVUowU+BA5Aaw6D67Bz8HkVcGaY7QElIzGAH9nYBudorjCUlsJbbyWvPYo7CLyuqqpiH3/++ZCdTduPPyZsmuTChfAf/yGhp6aFcPrp8j2NjZE/V4XB1Wjms10cPCiuoESEoV07SUZSi0EJJd5chrFj4cABWpvF70IxxxkCw1YDZ22LxI4dsG2bCIMmt7kOFQY7SbSYnlkjSXMYlFB27ZKxAqsPHOPGwfz5ZIXLRwC5Rrdt848z5OSItRptfMFk1SoYPlyCLNRicBUqDHaSaNEzLZ6nRMNqZFLv3nItmWUwIrFwoV8Y+vWD3NzYFgP4I5NatFBhcBkqDHZSXR1/BAn4hcF0GyhKIFYfOMwy22YZjEgsWCCD2YWF0WskhRJY00mFwVWoMNhJTU18ESQmpaVSuuDwYXvapWQ21dVyTbWI8e87bpzcsGO5hcxxhtGj/dN5VoZGlochcNxCxxhchQqDnSQamaShqko0amrECu3VK/IxWVlw4YWx3UggN/iDB8WdNGCAFIE8dCj2eZWVUmgP1GJwGSoMdpLoBO4qDEo0rEQmDRkC3brFdiOB5Cx8/LFfGKy4kUAS2iorZb1rl7VzlIxAhcFOzAiSeCKTOneWf2iNSFIiYcUSneCrPmNFGEDcSeXl4kqyKgwg80dYyadQMgq7q6sq8UYmaUSSEostW+DIkcgPHNnZcP318P77kktjBXPinpYt4xOGW2+FNm2sH69kBGox2E28wmAWz1OLQYmEYcD69ZGvq8mTZb7mP/3J+md++ql/7oV4hKGhQeokKa5ChcFuqqtlKsZYESQmJSXi812/3t52KZlNtFyGW26R6+fVV61/XkODTNwD1pLbFFejwmA3ZgRJUZG140tKYNMmf7SHooQjkiU6bJiUt3joIZleNh5eflnGDLSir+dRYbCbeENWS0vVjaTEproa2raFnj2Dt99yi9zYn346/s/8wx9g1KikNE/JbFQY7CbeomcaqqpYIdwDR0EBXHEFPPUUHDjgTLsUV6DCYDdbtkj5YisWQ48e0KGDWgxKbMI9cNx0k0Qk/fnPzrRJcQ0qDHbT1AQbNlgTBg1VVayyaZNEEZnXVatWcOONMGuWXG+K0gxUGFKB1ZBVFQbFKsePiziYFsNVV0li5P/9n7PtUlyBCkMqsFomubRUygts2mR/m5TMJ/CB45ZbJNx0wQJHm6S4AxWGVFBTA+3bQ/fu0Y8rKZH48xMnUtMuJbMxHzjGjYOBA+NLaFOUKKgwpAKrIasakaTEQ00NdOkCd90F9fXw4otOt0hxCSoMqcBKyGpWlgiDRiQpVjGvq7PPhr/8RdyQipIEVBhSwcaNEp0UzWIoLITWrdViUKxjWqJHjsDjjzvbFsVVqDCkgqNHZUa2aMKgxfOUeFm/Xq6t55/XQnZKUtGy26kiVmSShqoq8XL4sNRFiqcaqqJYQC2GVBErl6GkRP7RdYpEJR4WLZJpORUliagwpIqaGsjPl7DVcJSWirVgGKltl6IoSggqDKkiVsiqhqoqipIm2CkMvYD3gM+BtcDNYY45D9gHrPAtv7GxPc5ihhaGE4bsbNmuwqAoShpg5+DzceCnwDKgPbAUmAOEjpQtAC6xsR3pgWkxXH45VFbC2rX+fb17Q26uRiQpipIW2CkM9b4F4ABiORRysjB4g4MHYfZsuPJKWbZulfezZ/un/VSLQVGUNCBV4aqnAWcCn4bZNwpYCWwFfoa4nUK53rdQUFBAWVlZQo0oLi5O6LykcfPNHOvZk4azz6ZhzBgapkyh6fvf/2p3P8MgJ8G+WcHx/juIl/sO3u6/9j1+UiEM7YCXgVuA0MlklwG9gYPAJGAmUBLmM57wLdTX1xuVlZUJN6Y55yaFykqYP19et2gBQ4fCRRdBXh7VH36Ygq93uP8O4uW+g7f7r32PD7uFIRcRhReAV8LsDxSKN4G/AN2AXTa3Kz1oaoIlS2RRFEVJE+yMSsoCnkLGFv43wjE9fccBVPjas9vGNimKoigxsNNiGA18F1iNhKIC/Ao41ff6MWAa8EMkgukw8C1AM7wURVEcxE5hWIjfGojEw75FURRFSRM081lRFEUJQoVBURRFCUKFQVEURQlChUFRFEUJQoVBURRFCSLLyLz6/zuBTQme653kufB4uf9e7jt4u//ad6E30N3KSZkoDM1hCTDc6UY4iJf77+W+g7f7r32PE3UlKYqiKEGoMCiKoihBeE0YnnC6AQ7j5f57ue/g7f5r3+PEa2MMiqIoSgy8ZjEoiqIoMVBhUBRFUYLwkjBMAKqAauCXDrclFTwN7ADWBGzrAswB1vnWnR1oVyroBbyHzAWyFrjZt90L/W8FLEKmy10L3O3b7oW+m2QDy4HXfe+91PeN+Kc6MGcAi7v/XhGGbOARYCIwALjSt3YzzyBiGMgvgXnI9KnzcK9AHgd+CpwOnAX8CPm9vdD/RuACoBwYglwDZ+GNvpvcjDwUmHip7wDnI7+9mb8Qd/+9IgwViKWwHjgK/BOY7GiL7OcDYE/ItsnAs77XzwJTUtmgFFKPzCcOcAC5SRTijf4byBzqIFPr5vq2eaHvAEXAxcBfA7Z5pe+RiLv/XhGGQqA24H2db5vX6IHcNPGt8x1sS6o4DTgT+BTv9D8bcSXsQFwHXur7n4CfA00B27zSd5CHgNnAUuB637a4+2/nDG7pRLiZ5DRO1/20A14GbgH2O9uUlHICcSV0AmYAg5xsTAq5BBHDpcB5zjbFMUYDW5Gb/xygMpEP8YrFUIcMSJoUIX88r7EdKPC9LkD+idxKLiIKLwCv+LZ5qf8AXwLzkXEGL/R9NHApMgD7T2Ss5Xm80XcT8762A3koqCCB/ntFGBYjAy99gDzgW8CrjrbIGV4Fvud7/T1gloNtsZMs4ClkbOF/A7Z7of/dEUsBoDUwFnlq9ELfb0ce+k5D/sffBa7CG30HaAu0D3h9ERKVGHf/vZT5PAnxP2YjoZz3Odoa+5mOmNPdkCeGO4GZwL+AU4HNwDc4eYDaDYwBFiBhe6av+VeIr93t/T8DGWDMRh78/gXcA3TF/X0P5DzgZ4h7ySt9L0asBJBhgn8g97m4++8lYVAURVEs4BVXkqIoimIRFQZFURQlCBUGRVEUJQgVBkVRFCUIFQZFURQlCBUGRVEUJQgVBsWLdEVqCa0AtgFbfK8PAn+x4fueATYANwa8n2bhvNa+dh1F8lEUJSV4pVaSogSyG6klBHAXIgh/tPk7bwNeiuP4bOAw0s6NNrRHUSKiFoOi+DkP/+QudyEZxLORG/NlwB+QbOq3kVpMAMOA95HCbe/gr0kTi3OBj5BS8Kb1cB4ywdA/fN+jKI6gwqAokemL1PafjBRjew8YjDzJX4yIw5+RG/sw4iu1UoCU7rgEuD9gewVwB+6fSEpJY9SVpCiReQs4hjy9ZyOWAr73pwH9kZLWc3zbs/HXvY/FTKSO02dIvXyTRch4hKI4hgqDokSm0bduQgTCCHifg1RxXQuMasZnQ/B8IQ0JfJaiJBV1JSlK4lQhZa5NYcgFBjrXHEVJDioMipI4R5Hxhf8GViKhpWc72SBFSQZadltR7OcZJNopnnDVQDYCw4FdSWqPokRFLQZFsZ99wL34E9ysYia45RI8ub2i2IpaDIqiKEoQajEoiqIoQagwKIqiKEGoMCiKoihBqDAoiqIoQfx/51aTQeOnkR0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#demand profile\n",
    "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  # rescale\n",
    "\n",
    "with plt.style.context(\"dark_background\"):\n",
    "    plt.plot(hours, demand, color='cyan')\n",
    "    plt.ylabel('Demand [MW]')\n",
    "    plt.xlabel('Time [hr]')\n",
    "    plt.grid(alpha=0.2)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9ed4ca71",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#wind profile\n",
    "np.random.seed(123)\n",
    "shape_factor = 2.5\n",
    "wind_speed = np.random.weibull(a=shape_factor,size=N)\n",
    "\n",
    "with plt.style.context('dark_background'):\n",
    "    plt.plot(wind_speed, color='lime')\n",
    "    plt.grid(alpha=0.2)\n",
    "    plt.ylabel('Demand [MW]')\n",
    "    plt.xlabel('Time [hr]')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8368b708",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with plt.style.context('dark_background'):\n",
    "    plt.plot(hours, demand-wind_speed, color='white', linestyle='--', label='Net Demand')\n",
    "    plt.plot(hours, demand, color='cyan', label='Demand')\n",
    "    plt.plot(hours, wind_speed, color='lime', label='Wind')\n",
    "    plt.grid(alpha=0.2)\n",
    "    plt.ylabel('Demand [MW]')\n",
    "    plt.xlabel('Time [hr]')\n",
    "    plt.legend()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "01fdeb49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Battery: 815.3412599999999 MW, WindTurbine: 0.0 MW]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "technologies = [lib.battery, lib.wind]\n",
    "technologies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "dd6b671e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from osier import total_cost\n",
    "problem = CapacityExpansion(technology_list = [lib.natural_gas, lib.battery],\n",
    "                            demand=demand*MW,\n",
    "                            objectives = [total_cost],\n",
    "                            solver=solver)  # the objectives must be passed as a LIST of functions!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "69bfca55",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Compiled modules for significant speedup can not be used!\n",
      "https://pymoo.org/installation.html#installation\n",
      "\n",
      "To disable this warning:\n",
      "from pymoo.config import Config\n",
      "Config.warnings['not_compiled'] = False\n",
      "\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-7-0a3bf26e3930>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mstart\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mperf_counter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m res = minimize(problem,\n\u001b[0m\u001b[1;32m      6\u001b[0m                \u001b[0malgorithm\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m                \u001b[0mtermination\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'n_gen'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.8/site-packages/pymoo/optimize.py\u001b[0m in \u001b[0;36mminimize\u001b[0;34m(problem, algorithm, termination, copy_algorithm, copy_termination, **kwargs)\u001b[0m\n\u001b[1;32m     65\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     66\u001b[0m     \u001b[0;31m# actually execute the algorithm\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 67\u001b[0;31m     \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0malgorithm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     68\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     69\u001b[0m     \u001b[0;31m# store the deep copied algorithm in the result object\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.8/site-packages/pymoo/core/algorithm.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    136\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    137\u001b[0m         \u001b[0;32mwhile\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhas_next\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 138\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    139\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    140\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.8/site-packages/pymoo/core/algorithm.py\u001b[0m in \u001b[0;36mnext\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    156\u001b[0m         \u001b[0;31m# call the advance with them after evaluation\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    157\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0minfills\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 158\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0meval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mproblem\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minfills\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malgorithm\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    159\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madvance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minfills\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minfills\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    160\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.8/site-packages/pymoo/core/evaluator.py\u001b[0m in \u001b[0;36meval\u001b[0;34m(self, problem, pop, skip_already_evaluated, evaluate_values_of, count_evals, **kwargs)\u001b[0m\n\u001b[1;32m     67\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     68\u001b[0m             \u001b[0;31m# do the actual evaluation - call the sub-function to set the corresponding values to the population\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 69\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_eval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mproblem\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpop\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mI\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mevaluate_values_of\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     70\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     71\u001b[0m         \u001b[0;31m# update the function evaluation counter\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.8/site-packages/pymoo/core/evaluator.py\u001b[0m in \u001b[0;36m_eval\u001b[0;34m(self, problem, pop, evaluate_values_of, **kwargs)\u001b[0m\n\u001b[1;32m     88\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     89\u001b[0m         \u001b[0;31m# call the problem to evaluate the solutions\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 90\u001b[0;31m         \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mproblem\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreturn_values_of\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mevaluate_values_of\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreturn_as_dictionary\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     91\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     92\u001b[0m         \u001b[0;31m# for each of the attributes set it to the problem\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.8/site-packages/pymoo/core/problem.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(self, X, return_values_of, return_as_dictionary, *args, **kwargs)\u001b[0m\n\u001b[1;32m    255\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    256\u001b[0m         \u001b[0;31m# this is where the actual evaluation takes place\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 257\u001b[0;31m         \u001b[0m_out\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreturn_values_of\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    258\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    259\u001b[0m         \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.8/site-packages/pymoo/core/problem.py\u001b[0m in \u001b[0;36mdo\u001b[0;34m(self, X, return_values_of, *args, **kwargs)\u001b[0m\n\u001b[1;32m    295\u001b[0m         \u001b[0;31m# do the function evaluation\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    296\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0melementwise\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 297\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_evaluate_elementwise\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    298\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    299\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_evaluate_vectorized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.8/site-packages/pymoo/core/problem.py\u001b[0m in \u001b[0;36m_evaluate_elementwise\u001b[0;34m(self, X, out, *args, **kwargs)\u001b[0m\n\u001b[1;32m    313\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    314\u001b[0m         \u001b[0;31m# execute the runner\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 315\u001b[0;31m         \u001b[0melems\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0melementwise_runner\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    316\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    317\u001b[0m         \u001b[0;31m# for each evaluation call\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.8/site-packages/pymoo/core/problem.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, f, X)\u001b[0m\n\u001b[1;32m     30\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     31\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 32\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     33\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     34\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.8/site-packages/pymoo/core/problem.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m     30\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     31\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 32\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     33\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     34\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.8/site-packages/pymoo/core/problem.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m     23\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     24\u001b[0m         \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 25\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mproblem\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_evaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     26\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     27\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.8/site-packages/osier/models/capacity_expansion.py\u001b[0m in \u001b[0;36m_evaluate\u001b[0;34m(self, x, out, *args, **kwargs)\u001b[0m\n\u001b[1;32m    187\u001b[0m             \u001b[0;34m-\u001b[0m \u001b[0msolar_gen\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    188\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 189\u001b[0;31m         model = DispatchModel(technology_list=self.dispatchable_techs,\n\u001b[0m\u001b[1;32m    190\u001b[0m                               \u001b[0mnet_demand\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnet_demand\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    191\u001b[0m                               \u001b[0mpower_units\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpower_units\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.8/site-packages/osier/models/dispatch.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, technology_list, net_demand, time_delta, solver, lower_bound, oversupply, undersupply, verbosity, penalty, power_units, curtailment, allow_blackout, **kwargs)\u001b[0m\n\u001b[1;32m    244\u001b[0m             \u001b[0msync_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtechnology_list\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    245\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 246\u001b[0;31m         self.technology_list = synchronize_units(\n\u001b[0m\u001b[1;32m    247\u001b[0m             \u001b[0msync_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    248\u001b[0m             \u001b[0munit_power\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpower_units\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.8/site-packages/osier/utils.py\u001b[0m in \u001b[0;36msynchronize_units\u001b[0;34m(tech_list, unit_power, unit_time)\u001b[0m\n\u001b[1;32m     43\u001b[0m     \"\"\"\n\u001b[1;32m     44\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 45\u001b[0;31m     \u001b[0msynced_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mt\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtech_list\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     46\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     47\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mt\u001b[0m \u001b[0;32min\u001b[0m \u001b[0msynced_list\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.8/site-packages/osier/utils.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m     43\u001b[0m     \"\"\"\n\u001b[1;32m     44\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 45\u001b[0;31m     \u001b[0msynced_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mt\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtech_list\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     46\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     47\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mt\u001b[0m \u001b[0;32min\u001b[0m \u001b[0msynced_list\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.8/copy.py\u001b[0m in \u001b[0;36mdeepcopy\u001b[0;34m(x, memo, _nil)\u001b[0m\n\u001b[1;32m    170\u001b[0m                     \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    171\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 172\u001b[0;31m                     \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_reconstruct\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mrv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    173\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    174\u001b[0m     \u001b[0;31m# If is its own copy, don't memoize.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.8/copy.py\u001b[0m in \u001b[0;36m_reconstruct\u001b[0;34m(x, memo, func, args, state, listiter, dictiter, deepcopy)\u001b[0m\n\u001b[1;32m    268\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mstate\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    269\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mdeep\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 270\u001b[0;31m             \u001b[0mstate\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    271\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'__setstate__'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    272\u001b[0m             \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__setstate__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.8/copy.py\u001b[0m in \u001b[0;36mdeepcopy\u001b[0;34m(x, memo, _nil)\u001b[0m\n\u001b[1;32m    144\u001b[0m     \u001b[0mcopier\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_deepcopy_dispatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    145\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mcopier\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 146\u001b[0;31m         \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcopier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    147\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    148\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0missubclass\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.8/copy.py\u001b[0m in \u001b[0;36m_deepcopy_dict\u001b[0;34m(x, memo, deepcopy)\u001b[0m\n\u001b[1;32m    228\u001b[0m     \u001b[0mmemo\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    229\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 230\u001b[0;31m         \u001b[0my\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    231\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    232\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdict\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_deepcopy_dict\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.8/copy.py\u001b[0m in \u001b[0;36mdeepcopy\u001b[0;34m(x, memo, _nil)\u001b[0m\n\u001b[1;32m    151\u001b[0m             \u001b[0mcopier\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"__deepcopy__\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mcopier\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 153\u001b[0;31m                 \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcopier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    154\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    155\u001b[0m                 \u001b[0mreductor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdispatch_table\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.8/site-packages/unyt/array.py\u001b[0m in \u001b[0;36m__deepcopy__\u001b[0;34m(self, memodict)\u001b[0m\n\u001b[1;32m   2101\u001b[0m         \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0munyt_array\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__deepcopy__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmemodict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2102\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2103\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mret\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munits\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2104\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2105\u001b[0m             \u001b[0;31m# subclasses might not take name as a kwarg\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.8/copy.py\u001b[0m in \u001b[0;36mdeepcopy\u001b[0;34m(x, memo, _nil)\u001b[0m\n\u001b[1;32m    151\u001b[0m             \u001b[0mcopier\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"__deepcopy__\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mcopier\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 153\u001b[0;31m                 \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcopier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    154\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    155\u001b[0m                 \u001b[0mreductor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdispatch_table\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.8/site-packages/unyt/unit_object.py\u001b[0m in \u001b[0;36m__deepcopy__\u001b[0;34m(self, memodict)\u001b[0m\n\u001b[1;32m    522\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    523\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__deepcopy__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemodict\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 524\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdeep\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    525\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    526\u001b[0m     \u001b[0;31m#\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.8/site-packages/unyt/unit_object.py\u001b[0m in \u001b[0;36mcopy\u001b[0;34m(self, deep)\u001b[0m\n\u001b[1;32m    516\u001b[0m         \u001b[0mdimensions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdimensions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    517\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mdeep\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 518\u001b[0;31m             \u001b[0mregistry\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mregistry\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    519\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    520\u001b[0m             \u001b[0mregistry\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mregistry\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.8/copy.py\u001b[0m in \u001b[0;36mdeepcopy\u001b[0;34m(x, memo, _nil)\u001b[0m\n\u001b[1;32m    151\u001b[0m             \u001b[0mcopier\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"__deepcopy__\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mcopier\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 153\u001b[0;31m                 \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcopier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    154\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    155\u001b[0m                 \u001b[0mreductor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdispatch_table\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.8/site-packages/unyt/unit_registry.py\u001b[0m in \u001b[0;36m__deepcopy__\u001b[0;34m(self, memodict)\u001b[0m\n\u001b[1;32m    274\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    275\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__deepcopy__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemodict\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 276\u001b[0;31m         \u001b[0mlut\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlut\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    277\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlut\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlut\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    278\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.8/copy.py\u001b[0m in \u001b[0;36mdeepcopy\u001b[0;34m(x, memo, _nil)\u001b[0m\n\u001b[1;32m    144\u001b[0m     \u001b[0mcopier\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_deepcopy_dispatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    145\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mcopier\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 146\u001b[0;31m         \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcopier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    147\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    148\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0missubclass\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.8/copy.py\u001b[0m in \u001b[0;36m_deepcopy_dict\u001b[0;34m(x, memo, deepcopy)\u001b[0m\n\u001b[1;32m    228\u001b[0m     \u001b[0mmemo\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    229\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 230\u001b[0;31m         \u001b[0my\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    231\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    232\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdict\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_deepcopy_dict\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.8/copy.py\u001b[0m in \u001b[0;36mdeepcopy\u001b[0;34m(x, memo, _nil)\u001b[0m\n\u001b[1;32m    144\u001b[0m     \u001b[0mcopier\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_deepcopy_dispatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    145\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mcopier\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 146\u001b[0;31m         \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcopier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    147\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    148\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0missubclass\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.8/copy.py\u001b[0m in \u001b[0;36m_deepcopy_tuple\u001b[0;34m(x, memo, deepcopy)\u001b[0m\n\u001b[1;32m    208\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    209\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_deepcopy_tuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdeepcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdeepcopy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 210\u001b[0;31m     \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    211\u001b[0m     \u001b[0;31m# We're not going to put the tuple in the memo, but it's still important we\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    212\u001b[0m     \u001b[0;31m# check for it, in case the tuple contains recursive mutable structures.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.8/copy.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    208\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    209\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_deepcopy_tuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdeepcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdeepcopy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 210\u001b[0;31m     \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    211\u001b[0m     \u001b[0;31m# We're not going to put the tuple in the memo, but it's still important we\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    212\u001b[0m     \u001b[0;31m# check for it, in case the tuple contains recursive mutable structures.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.8/copy.py\u001b[0m in \u001b[0;36mdeepcopy\u001b[0;34m(x, memo, _nil)\u001b[0m\n\u001b[1;32m    126\u001b[0m \u001b[0;32mdel\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    127\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 128\u001b[0;31m \u001b[0;32mdef\u001b[0m \u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_nil\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    129\u001b[0m     \"\"\"Deep copy operation on arbitrary Python objects.\n\u001b[1;32m    130\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "algorithm = NSGA2(pop_size=20)\n",
    "\n",
    "import time\n",
    "start = time.perf_counter()\n",
    "res = minimize(problem,\n",
    "               algorithm,\n",
    "               termination=('n_gen', 10),\n",
    "               seed=1,\n",
    "               save_history=True,\n",
    "               verbose=True)\n",
    "end = time.perf_counter()\n",
    "print(f\"The simulation took {(end-start)/60:.3f} minutes.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "85bcf9cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.25723553])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([0.70666673, 0.31068083])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(res.F, res.X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ef4e9b77",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[NaturalGas_Conv: 3.7188949914308496 MW, Battery: 1.6349848215023173 MW]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from osier import DispatchModel\n",
    "lib.battery.capacity =  res.X[1]*demand.max()*MW\n",
    "lib.natural_gas.capacity = res.X[0]*demand.max()*MW\n",
    "\n",
    "technologies = [lib.natural_gas, lib.battery]\n",
    "display(technologies)\n",
    "\n",
    "model = DispatchModel(technology_list=technologies,\n",
    "                      net_demand=demand)\n",
    "model.solve(solver=solver)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f03ed7c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with plt.style.context(\"dark_background\"):\n",
    "    fig, ax = plt.subplots(figsize=(8,4))\n",
    "    ax.grid(alpha=0.2)\n",
    "    ax.minorticks_on()\n",
    "    ax.fill_between(hours,\n",
    "                    y1=0,\n",
    "                    y2=model.results['NaturalGas_Conv'].values,\n",
    "                    color='tab:orange',\n",
    "                    label='Natural Gas')\n",
    "    ax.fill_between(hours,\n",
    "                    y1=model.results['NaturalGas_Conv'].values,\n",
    "                    y2=model.results['Battery'].values+model.results['NaturalGas_Conv'].values,\n",
    "                    color='tab:green',\n",
    "                    label='Battery discharge')\n",
    "    ax.fill_between(hours,\n",
    "                    y1=0,\n",
    "                    y2=model.results['Battery_charge'].values,\n",
    "                    color='tab:pink',\n",
    "                    label='Battery charge')\n",
    "    ax.plot(hours, model.net_demand, color='cyan', label='Net Demand')\n",
    "    ax.set_xlim(0,48)\n",
    "    # ax.set_ylim(0,5.5)\n",
    "    ax.legend(loc='upper left')\n",
    "    ax.set_ylabel(\"Demand [MW]\")\n",
    "    ax.set_xlabel(\"Time [hr]\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7de1c0ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "problem = CapacityExpansion(technology_list = [lib.wind, lib.battery],\n",
    "                            demand=demand*MW,\n",
    "                            wind=wind_speed,\n",
    "                            upper_bound= 1 / lib.wind.capacity_credit,\n",
    "                            objectives = [total_cost],\n",
    "                            solver=solver)  # the objectives must be passed as a LIST of functions!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d4bcbb0e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==========================================================\n",
      "n_gen  |  n_eval  | n_nds  |      eps      |   indicator  \n",
      "==========================================================\n",
      "     1 |       20 |      1 |             - |             -\n",
      "     2 |       40 |      1 |  0.000000E+00 |             f\n",
      "     3 |       60 |      1 |  0.0731198079 |         ideal\n",
      "     4 |       80 |      1 |  0.000000E+00 |             f\n",
      "     5 |      100 |      1 |  0.0023483205 |             f\n",
      "     6 |      120 |      1 |  0.0255449274 |         ideal\n",
      "     7 |      140 |      1 |  0.000000E+00 |             f\n",
      "     8 |      160 |      1 |  0.000000E+00 |             f\n",
      "     9 |      180 |      1 |  0.000000E+00 |             f\n",
      "    10 |      200 |      1 |  0.0002146070 |             f\n",
      "The simulation took 2.495 minutes.\n"
     ]
    }
   ],
   "source": [
    "algorithm = NSGA2(pop_size=20)\n",
    "\n",
    "import time\n",
    "start = time.perf_counter()\n",
    "res = minimize(problem,\n",
    "               algorithm,\n",
    "               termination=('n_gen', 10),\n",
    "               seed=1,\n",
    "               save_history=True,\n",
    "               verbose=True)\n",
    "end = time.perf_counter()\n",
    "print(f\"The simulation took {(end-start)/60:.3f} minutes.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "344a8e23",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[WindTurbine: 10.135967781702929 MW, Battery: 2.7950442069865686 MW]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Max wind production: 10.135967781702929 MW'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "technologies = []\n",
    "for X,tech in zip(res.X,problem.technology_list):\n",
    "    tech.capacity = X*problem.max_demand\n",
    "    technologies.append(tech)\n",
    "display(technologies)\n",
    "# normalize the wind speed\n",
    "wind_speed = (wind_speed / wind_speed.max()) * res.X[0]*problem.max_demand\n",
    "net_dem = demand*MW - wind_speed\n",
    "display(f\"Max wind production: {wind_speed.max()}\")\n",
    "\n",
    "model = DispatchModel(technology_list=[technologies[1]],\n",
    "                      net_demand=net_dem)\n",
    "model.solve(solver=solver)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "39b614c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with plt.style.context(\"dark_background\"):\n",
    "    fig, ax = plt.subplots(figsize=(8,4))\n",
    "    ax.grid(alpha=0.2)\n",
    "    ax.minorticks_on()\n",
    "    ax.fill_between(hours,\n",
    "                    y1=0,\n",
    "                    y2=np.array(wind_speed),\n",
    "                    color='tab:blue',\n",
    "                    label='Wind Turbine')\n",
    "    ax.fill_between(hours,\n",
    "                    y1=np.array(wind_speed),\n",
    "                    y2=model.results['Battery'].values+np.array(wind_speed),\n",
    "                    color='tab:green',\n",
    "                    label='Battery discharge')\n",
    "    ax.fill_between(hours,\n",
    "                    y1=0,\n",
    "                    y2=model.results['Battery_charge'].values,\n",
    "                    color='tab:pink',\n",
    "                    label='Battery charge')\n",
    "    ax.fill_between(hours,\n",
    "                    y1=model.results['Battery_charge'].values,\n",
    "                    y2=model.results['Battery_charge'].values+model.results['Curtailment'].values,\n",
    "                    color='gray',\n",
    "                    label='Curtailment')\n",
    "    ax.plot(hours, demand, color='cyan', label='Net Demand')\n",
    "    ax.set_xlim(0,48)\n",
    "    # ax.set_ylim(0,5.5)\n",
    "    ax.legend(loc='upper left')\n",
    "    ax.set_ylabel(\"Demand [MW]\")\n",
    "    ax.set_xlabel(\"Time [hr]\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d1e37022",
   "metadata": {},
   "outputs": [],
   "source": [
    "from osier import annual_emission\n",
    "\n",
    "# the default emission is `lifecycle_co2_rate`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "bb9419c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "problem = CapacityExpansion(technology_list = [lib.wind, lib.natural_gas, lib.battery],\n",
    "                            demand=demand*MW,\n",
    "                            wind=wind_speed,\n",
    "                            upper_bound= 1 / lib.wind.capacity_credit,\n",
    "                            objectives = [total_cost, annual_emission],\n",
    "                            solver=solver)  # the objectives must be passed as a LIST of functions!\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "fd19b247",
   "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 |      4 |  0.0158631600 |         nadir\n",
      "     3 |       60 |      6 |  0.0046803095 |         nadir\n",
      "     4 |       80 |     13 |  0.0075483249 |         ideal\n",
      "     5 |      100 |     11 |  0.0286189131 |         ideal\n",
      "     6 |      120 |     16 |  0.0143108624 |             f\n",
      "     7 |      140 |     19 |  0.0038096294 |         ideal\n",
      "     8 |      160 |     20 |  0.0123935102 |             f\n",
      "     9 |      180 |     20 |  0.0488020532 |         nadir\n",
      "    10 |      200 |     20 |  0.0176305030 |             f\n",
      "The simulation took 4.723 minutes.\n"
     ]
    }
   ],
   "source": [
    "algorithm = NSGA2(pop_size=20)\n",
    "\n",
    "import time\n",
    "start = time.perf_counter()\n",
    "res = minimize(problem,\n",
    "               algorithm,\n",
    "               termination=('n_gen', 10),\n",
    "               seed=1,\n",
    "               save_history=True,\n",
    "               verbose=True)\n",
    "end = time.perf_counter()\n",
    "print(f\"The simulation took {(end-start)/60:.3f} minutes.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "24d8c91b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.19945119e+00, 4.48339838e-06],\n",
       "       [5.20590010e-01, 4.22836313e-05],\n",
       "       [5.94956650e-01, 3.31273855e-05],\n",
       "       [6.39785553e-01, 2.43974512e-05],\n",
       "       [7.08193933e-01, 1.88392322e-05],\n",
       "       [1.03741440e+00, 5.01230980e-06],\n",
       "       [6.76877772e-01, 2.19705621e-05],\n",
       "       [7.63720969e-01, 1.58503836e-05],\n",
       "       [8.60297364e-01, 1.15062221e-05],\n",
       "       [5.53008596e-01, 3.79200939e-05],\n",
       "       [8.09557178e-01, 1.54093261e-05],\n",
       "       [9.93427512e-01, 7.06494074e-06],\n",
       "       [8.48928279e-01, 1.35553105e-05],\n",
       "       [9.43233259e-01, 1.05890851e-05],\n",
       "       [9.84786540e-01, 8.96392716e-06],\n",
       "       [1.03492980e+00, 5.01576659e-06],\n",
       "       [5.23407779e-01, 4.19378638e-05],\n",
       "       [9.56761295e-01, 1.01734674e-05],\n",
       "       [5.33459304e-01, 3.95948803e-05],\n",
       "       [5.43811675e-01, 3.82503908e-05]])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(res.F)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e77e220f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "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": 23,
   "id": "2db56943",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2.53341702, 0.04144996, 0.55710663],\n",
       "       [0.81401452, 0.41946283, 0.30094145],\n",
       "       [0.97601979, 0.40287869, 0.35417943],\n",
       "       [1.1384407 , 0.39580232, 0.25387131],\n",
       "       [1.30377163, 0.39488571, 0.25098929],\n",
       "       [2.11218424, 0.09849739, 0.56117257],\n",
       "       [1.21045201, 0.42561055, 0.25098929],\n",
       "       [1.42508911, 0.41533998, 0.24747279],\n",
       "       [1.66520048, 0.39488571, 0.25098929],\n",
       "       [0.8920409 , 0.41946283, 0.30094145],\n",
       "       [1.47365324, 0.50706447, 0.25557499],\n",
       "       [2.03385808, 0.29352286, 0.26963251],\n",
       "       [1.56593795, 0.50884959, 0.25559555],\n",
       "       [1.75907345, 0.48251036, 0.34948459],\n",
       "       [1.88970608, 0.50711056, 0.25422291],\n",
       "       [2.10823443, 0.09849739, 0.55725319],\n",
       "       [0.82023035, 0.42580387, 0.29455337],\n",
       "       [1.79151691, 0.48189384, 0.34948459],\n",
       "       [0.86380735, 0.41930255, 0.26382456],\n",
       "       [0.88775931, 0.41930255, 0.26573897]])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(res.X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "8c209cfc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "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()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e22f9df3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "with plt.style.context('dark_background'):\n",
    "    fig, ax = plt.subplots(1,1,figsize=(6,4))\n",
    "\n",
    "    ax.scatter(res.X[:,0], res.X[:,1], edgecolors='yellow', facecolors='k')\n",
    "    ax.set_ylabel(r\"Natural Gas\")\n",
    "    ax.set_xlabel(r\"Wind Turbines\")\n",
    "    ax.set_title(\"Fraction of Peak Demand\")\n",
    "    ax.grid(alpha=0.2)\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c90d60cd",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}