luh2_scratch-backup.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "41d2d831-86c0-4259-84d0-5c6e916424dd",
   "metadata": {},
   "source": [
    "# Import module"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "dff6c858-9e68-437b-94f9-5ba3475130c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from luh2mod import PrepDataSet, ImportData, SetMaskLUH2, SetMaskSurfData, RegridConservative"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "15e2cd24-f56d-44fe-bdb6-4211a84504ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "# import xarray as xr\n",
    "# import numpy as np\n",
    "# import xesmf as xe"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ab589c8-4bc6-4566-bf63-171e83e8523e",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Import and Prep Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "5c4cfdcf-060b-447d-814b-ecac93d4e322",
   "metadata": {},
   "outputs": [],
   "source": [
    "state_fn = \"/home/glemieux/Data/luh2/states_modified.nc\"\n",
    "static_fn = \"/home/glemieux/Data/luh2/staticData_quarterdeg.nc\"\n",
    "surf_fn = \"/home/glemieux/Data/luh2/orig/surfdata_4x5_16pfts_Irrig_CMIP6_simyr2000_c170824.nc\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8199e025-54c6-43d5-b8d6-52f4816063ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input file dataset opened: /home/glemieux/Data/luh2/states_modified.nc\n",
      "LUH2\n",
      "PrepDataSet: LUH2\n",
      "LUH2 dataset lat/lon boundary variables formatted and added as new variable for xESMF\n",
      "data set updated for xESMF\n",
      "\n",
      "Input file dataset opened: /home/glemieux/Data/luh2/orig/surfdata_4x5_16pfts_Irrig_CMIP6_simyr2000_c170824.nc\n",
      "Surface\n",
      "PrepDataSet: SurfData\n",
      "Surface dataset dimensions renamed for xESMF\n",
      "data set updated for xESMF\n",
      "\n",
      "Input file dataset opened: /home/glemieux/Data/luh2/staticData_quarterdeg.nc\n"
     ]
    }
   ],
   "source": [
    "ds_luh2 = PrepDataSet(state_fn)\n",
    "ds_regrid_target = PrepDataSet(surf_fn)\n",
    "ds_luh2_static = ImportData(static_fn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "id": "c22adb7b-c600-45c9-9eb4-2224cb402f1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7feefb708340>"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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NRWxsbM0EREREVEOUQgalgYuRDX2/pbKYRIeIiMhWGWONja2u0bHNXhMREZFN4IgOERGRmVPB8GdVcTEyERERmSVhhLuuBBMdIiIiMkfGePq4rT69nGt0iIiIyGpxRIfIAthaXRuyXNbwWzXHejm860p/THSIiIjMHKeu9Geb6R0RERHZBI7oEBERmTk+60p/THSIiIjMHKeu9MepKyIiIrJaOo3o/PTTT5Ibbtu2LerU4YARERGRoTiioz+dMpGOHTtCJpNBCKFTo3Z2drh48SICAwMNCo6IiIiY6BhC5yGX06dPo1GjRtWeJ4RAUFCQQUER2SprqEFClsvSfn9S6t1YWt/IeHRKdHr16oUWLVqgfv36OjXas2dPODs7GxIXERER/Y0jOvrTKdE5dOiQpEb37NmjVzBERERUkYDht4frtvjE+nC1MBERkZnjiI7+JCc6SqUSmzdvxn//+1/k5uZCpVJpHD948KDRgiMiIiIyhOREZ9q0adi8eTOefvppBAUFQSazzQyRiIiopnBER3+SE50vvvgCX375JQYNGmSKeIiIiOgRTHT0J7kysqOjI1q0aGGKWIiIiIiMSvKIzowZM/DJJ59g5cqVnLYiIqumrfaKlNot1kJbn82hLo0xYrCU68kRHf3plOg899xzGq8PHjyI7777Dk888QQcHBw0jiUlJRkvOiIiIoIQMggDExVD32+pdEp03N3dNV4PGzbMJMEQERERGZNOiU5iYqKp4yAiIqJKqCAzuGCgoe+3VJIXI/ft2xe3b9+usL+wsBB9+/Y1RkxERET0kPI1OoZutkhyopOamoqSkpIK++/fv4+jR48aJSgiIiIiY9D5rquffvpJ/efz588jJydH/VqpVGLv3r1o0qSJcaMjIiIiLkY2gM6JTseOHSGTySCTybROUTk7O2PFihVGDY6IiIh4e7khdE50MjMzIYRAYGAgvv/+ezRq1Eh9zNHREY0bN4a9vb1JgiSyZJXV+rCU+h3VMdcaK1Q1W7tGlv73jSM6+tM50fH390dpaSlGjx6NBg0awN/f35RxERERERlM0mJkBwcHfPPNN6aKhYiIiLQQRrjjylZHdCTfdTV06FDs3LnTBKEQERGRNgKAEAZutd2JWiL5WVctWrTAggULcOLECQQHB6NevXoax6dOnWq04IiIiIgMITnR2bhxI+rXr4+0tDSkpaVpHJPJZEx0iIiIjEwFGWSsjKwXyYlOZmamKeIgIiKiSvCuK/1JXqPzMCEEhLDVWT8iIiIyd5JHdABgy5YtWLp0KS5dugQAaNWqFWbNmoVXXnnFqMERkWWqrGaJNdRuMYe6SMaIQUr9I2u4bpZOJWSQsWCgXiQnOsuXL8d7772HKVOmoHv37hBC4Pjx45g4cSLy8vIwffp0U8RJRERks8rvnDK0DVskeepqxYoVWLNmDT788EM8++yzGDJkCJYsWYLVq1fj008/NUWMAIC4uDg8+eSTcHV1RePGjTF06FBcuHBB45zIyEj1YyrKt27dupksJiIiIjJvkhMdhUKBsLCwCvvDwsKgUCiMEpQ2hw8fxuTJk3Hq1CmkpKSgrKwM4eHhuHv3rsZ5AwcOhEKhUG979uwxWUxEREQ1oXwxsqGbLdKrjs6XX36Jd955R2P/9u3b0bJlS6MF9qi9e/dqvE5MTETjxo2RlpaGnj17qvfL5XJ4e3ubLA4iIqKaxruu9Cc50Zk3bx6GDx+OI0eOoHv37pDJZDh27Bj++9//4ssvvzRFjFoVFBQAABo0aKCxPzU1FY0bN0b9+vXRq1cvLFq0CI0bN660neLiYhQXF6tfFxYWmiZgIiIiPXExsv4kT109//zzOH36NDw9PbFz504kJSXB09MT33//PYYNG2aKGCsQQiA6OhpPPfUUgoKC1PsjIiKwbds2HDx4EMuWLcOZM2fQt29fjUTmUXFxcXB3d1dvfn5+NdEFIiIiqgF63V4eHByMf/3rX8aORWdTpkzBTz/9hGPHjmnsHz58uPrPQUFBCAkJgb+/P7799ls899xzWtuKiYlBdHS0+nVhYSGTHSIiMiu860p/eiU6KpUKly9fRm5uLlQqlcaxh9fLmMKbb76JXbt24ciRI2jatGmV5/r4+MDf319d70cbuVwOuVxu7DDJRhlab8QS68+Yqs+m+CxT0habqWrr1HS75vy968oc6h8Z4kGiY+gaHSMFY2EkJzqnTp3CiBEjcP369QpVkWUyGZRKpdGCe5gQAm+++SaSk5ORmpqKgICAat+Tn5+P7Oxs+Pj4mCQmIiIiMm+SE52JEyciJCQE3377LXx8fCCT1czipsmTJ+Pzzz/HN998A1dXV+Tk5AAA3N3d4ezsjDt37iA2NhbPP/88fHx8cO3aNbzzzjvw9PSssbVDREREpsC7rvQnOdG5dOkSduzYgRYtWpginkqtWbMGANC7d2+N/YmJiYiMjIS9vT3OnTuHLVu24Pbt2/Dx8UGfPn2wfft2uLq61misRERExiT+3gxtwxZJTnS6du2Ky5cv13iiU93DQ52dnbFv374aioaIiIgsgeRE580338SMGTOQk5ODdu3awcHBQeN4+/btjRYcERERcerKEJITneeffx4AMHbsWPU+mUwGIYRJFyMTERHZLBucuyoqKtKYzbGzs4OLi4vkdiQXDMzMzKywXb16Vf1fIiIiMjJjPOdKjxGd1atXIyAgAE5OTggODsbRo0erPL+4uBhz5syBv78/5HI5HnvsMWzatEmnzzp79iyefvpp9WtfX194eHiot/r16+PMmTOS+yB5RMff31/yh5D1kFJPwxj1KWqyNklNq+m+aWvbVPVRpNRjsfT6JuUsLV4pLK3WUU3+1q3Z9u3bERUVhdWrV6N79+5Yt24dIiIicP78eTRr1kzre1588UX8/vvvSEhIQIsWLZCbm4uysjKdPm/FihV46qmnNPZt3boVTZo0gRACmzZtwqeffoqtW7dK6odeBQOJiIio5tRGZeTly5dj3LhxGD9+PAAgPj4e+/btw5o1axAXF1fh/L179+Lw4cO4evWq+jmUzZs31/nzjh8/jsjISI193bp1Q2BgIIAHNx29+OKL0joBPaauiIiIqGYZOm318GLmwsJCjU3b8yBLSkqQlpaG8PBwjf3h4eE4ceKE1hh37dqFkJAQLFmyBE2aNEGrVq0wc+ZM/PXXXzr1MTs7W2OkaP78+fD09FS/9vHxwe+//65TWw9jokNERGRD/Pz8NB5mrW10Ji8vD0qlEl5eXhr7vby81AV7H3X16lUcO3YMP//8M5KTkxEfH48dO3Zg8uTJOsUll8vx22+/qV9Pnz4dbm5u6tfZ2dmoW7euTm09jFNXRERE5k7PxcQV2sCDhOHhBKKq5z0++vSD8justVGpVJDJZNi2bRvc3d0BPJj++uc//4lVq1bB2dm5yvA6deqEnTt3onv37lqPJyUloVOnTlW2oY3kEZ3s7GyNjOv7779HVFQU1q9fL/nDiYiIqHrla3QM3QDAzc1NY9OW6Hh6esLe3r7C6E1ubm6FUZ5yPj4+aNKkiTrJAYA2bdpACKGRN1Rm0qRJiI+Px6pVqzQeGK5UKrFixQqsWLECb7zxhi5flwbJic6IESNw6NAhAEBOTg769++P77//Hu+88w7mz58vOQAiIiIyL46OjggODkZKSorG/pSUFISFhWl9T/fu3XHz5k3cuXNHve/ixYuws7ND06ZNq/3M559/HtHR0XjzzTfh4eGBTp06oXPnzmjQoAGioqIwbdo0/POf/5TcF8mJzs8//4wuXboAAL788ksEBQXhxIkT+Pzzz7F582bJARAREVE1hJE2CaKjo7Fx40Zs2rQJGRkZmD59OrKysjBx4kQAQExMDEaPHq0+f8SIEWjYsCFeffVVnD9/HkeOHMGsWbMwduzYaqetyn344Yc4ceIEIiMj4ePjA29vb0RGRuL48eNYunSptA78TfIandLSUvUw14EDB/Dss88CAFq3bg2FQqFXEGSearLuhDnUuLCWei6VkVK3x9DrYYzraQ6/CSkMjbemf2eG/h4s7e+FpcX7qNp4BMTw4cORn5+P+fPnQ6FQICgoCHv27FHX01MoFMjKylKf7+LigpSUFLz55psICQlBw4YN8eKLL2LhwoWSPrdbt27o1q2bpPdURfKIzhNPPIG1a9fi6NGjSElJwcCBAwEAN2/eRMOGDY0WGBEREdWuSZMm4dq1ayguLkZaWhp69uypPrZ582akpqZqnN+6dWukpKTg3r17yM7OxrJly3QezVmyZInGrehHjhzRuPW9qKgIkyZNktwHyYnOhx9+iHXr1qF37954+eWX0aHDg0x/165d6iktIiIiMrIanLaqDTExMSgqKlK/fuaZZ3Djxg3163v37mHdunWS25U8ddW7d2/k5eWhsLAQHh4e6v2vvfaaXve3ExERUdVs4enl4pHSzY++1pdeBQOFEEhLS8O6devU2ZejoyMTHSIiIlOohcXI1kLyiM7169cxcOBAZGVlobi4GP3794erqyuWLFmC+/fvY+3ataaIk4iIiEgyyYnOtGnTEBISgv/9738ai4+HDRumfvAXERERGZPs783QNszbxo0b4eLiAgAoKyvD5s2b1c+7enj9jhSSE51jx47h+PHjcHR01Njv7++vsWiIiIiIjMQYU09mPnXVrFkzbNiwQf3a29sbW7durXCOVJITHZVKBaVSWWH/b7/9BldXV8kBkHUwVY2Kmqx9YQ59kFLLx9rr/lgDKXVpjFE3SMq1N9W55kDKd2lpfbNm165dM0m7khcj9+/fH/Hx8erXMpkMd+7cwdy5czFo0CBjxkZERESATSxGjo+Pxx9//GH0diUnOh9//DEOHz6Mtm3b4v79+xgxYgSaN2+OGzdu4MMPPzR6gERERDav/Onlhm5mbN68efD19cWLL76I/fv3197t5b6+vjh79ixmzpyJ119/HZ06dcLixYuRnp6Oxo0bGyUoIiIisi05OTlISEjAH3/8gYiICPj7+2Pu3LnIzMw0qF3Ja3QAwNnZGWPHjsXYsWMN+nAiIiKqnhAPNkPbMGdyuRwjR47EyJEjce3aNSQmJmLLli1YtGgRevfujfHjx2PYsGHq523qSq+CgVu3bsVTTz0FX19fXL9+HcCDKa1vvvlGn+aIiIioKjawRudhzZs3x7x585CZmYm9e/fCy8sL48aNg6+vr+S2JCc6a9asQXR0NCIiIvDnn3+q78Dy8PDQWKRMREREZCg7OzvIZDIIIaBSqaS/X+obVqxYgQ0bNmDOnDmoU+f/Z75CQkJw7tw5yQEQERFRNWxgMfLDrl+/jnnz5iEgIADh4eG4efMmNmzYAIVCIbktyWt0MjMz0alTpwr75XI57t69KzkAqn3GqN+hrQ1j1KcwVbumYmjNHGuubVIZKbVmLI019EEqY9SNoopk4sFmaBvm7P79+/j666+xadMmHD58GD4+PhgzZgzGjh2LwMBAvduVnOgEBATg7Nmz8Pf319j/3XffoW3btnoHQkRERJWwgcrI3t7euH//Pp555hns3r0bAwYMgJ2dXkuJNUhOdGbNmoXJkyfj/v37EELg+++/x7///W/ExcVh48aNBgdEREREtuf999/HqFGjjF6qRnKi8+qrr6KsrAyzZ8/GvXv3MGLECDRp0gSffPIJXnrpJaMGR0RERDDOGhszX6Mza9YsjBo1yujtSkp0ysrKsG3bNgwePBgTJkxAXl4eVCoVCwUSERGZkg1MXRmrEvKjJE1+1alTB2+88QaKi4sBAJ6enkxyiIiIyChkMuOPOkmeuuratSvS09MrLEYmIiIiE7GBER0AeO+991C3bt0qz1m+fLmkNiUnOpMmTcKMGTPw22+/ITg4GPXq1dM43r59e6lNEhERUVVsJNE5d+4cHB0dKz2uz4iP5ERn+PDhAICpU6dqfLAQAjKZTF0p2dINa9UOdWQO6tfWUrPEHNha7Qwp/TWX70bb772y2KSca6oYbI2U2kP8t6tqhvymykQpgKvGC4aQnJxc+3ddGfoUUSIiIpLIBu66MsX6HECPRIdrc4iIiGqWLVRGru6uq/z8fGzduhVRUVGS2pWc6OzatUvrfplMBicnJ7Ro0QIBAQFSmyUiIiIblpiYCHd3d419Qgjs378fCQkJ+Oabb+Dm5mb6RGfo0KHqNTkPe3idzlNPPYWdO3fCw8NDavNGsXr1aixduhQKhQJPPPEE4uPj0aNHj1qJhYiIyGA2sBh5zJgx6j9fu3YNmzZtwubNm3Hjxg2MHDkS3377Lfr06SO5XckPkUhJScGTTz6JlJQUFBQUoKCgACkpKejSpQv+85//4MiRI8jPz8fMmTMlB2MM27dvR1RUFObMmYP09HT06NEDERERyMrKqpV4iIiIqHrFxcX497//jX79+qFNmzb4+eefsXz5ctjZ2eHtt9/GP/7xD9jb20tuV/KIzrRp07B+/XqEhYWp9/Xr1w9OTk547bXX8MsvvyA+Ph5jx46VHIwxLF++HOPGjcP48eMBAPHx8di3bx/WrFmDuLi4WomJiIjIEDIYYY2OUSIxnSZNmqBt27YYNWoUduzYoZ4Vevnllw1qV/KIzpUrV+Dm5lZhv5ubG65efXCbXcuWLZGXl2dQYPooKSlBWloawsPDNfaHh4fjxIkTWt9TXFyMwsJCjY2IiIhqllKphEwmg0wm02vkpjKSR3SCg4Mxa9YsbNmyBY0aNQIA3Lp1C7Nnz8aTTz4JALh06RKaNm1qtCB1lZeXB6VSCS8vL439Xl5eyMnJ0fqeuLg4zJs3rybCMwtS6myYa80SKfVczEFNx2UO34+U35Q5xEu1Q8o1toZ/uwxiA7eXKxQKfP3110hISMC0adMQERGBUaNGGXzbueQRnYSEBGRmZqJp06Zo0aIFWrZsiaZNm+LatWvYuHEjAODOnTt47733DArMEI9+KeWLpLWJiYlRrzUqKChAdnZ2TYRIRESkO2GkzYw5OTlh5MiROHjwIM6dO4c2bdpg6tSpKCsrw6JFi5CSkqJXUWLJIzqPP/44MjIysG/fPly8eBFCCLRu3Rr9+/eHnd2DvGno0KGSAzEGT09P2NvbVxi9yc3NrTDKU04ul0Mul9dEeERERKSDxx57DAsXLsT8+fOxb98+JCQk4JlnnoGrq6vkpTGSEx3gwYjJwIED0bt3b8jlcpNVM5TK0dERwcHBSElJwbBhw9T7U1JSMGTIkFqMjIiIyAA2cHu5NnZ2doiIiEBERARu3bqFrVu3Sm9D6htUKhUWLFiAJk2awMXFRf1IiPfeew8JCQmSAzC26OhobNy4EZs2bUJGRgamT5+OrKwsTJw4sbZDIyIi0kt5ZWRDN0vWqFEjREdHS36f5BGdhQsX4rPPPsOSJUswYcIE9f527drh448/xrhx4yQHYUzDhw9Hfn4+5s+fD4VCgaCgIOzZs4ePriAiIjJjAQEB1c4QyWQyXLlyRVK7khOdLVu2YP369ejXr5/GKEn79u3x66+/Sm3OJCZNmoRJkybVdhhERETGYQNTV1U92uHatWtYt24diouLJbcrOdG5ceMGWrRoUWG/SqVCaWmp5ACIiIioGjaQ6EybNq3Cvj/++AMLFizAmjVr0LVrV3z44YeS25Wc6DzxxBM4evRohamgr776Cp06dZIcgKWwtFofNV1HQtv3YJW1LCyAMWqLaGvDXH/rlbHmGivW0AepTPVvDP/tMk9//fUXli9fjqVLl6J58+ZISkrCoEGD9GpLcqIzd+5cvPLKK7hx4wZUKhWSkpJw4cIFbNmyBf/5z3/0CoKIiIgqZ4zFxJawGFmpVGLDhg2YN28enJycsGLFCoOLBkpOdAYPHozt27fjgw8+gEwmw/vvv4/OnTtj9+7d6N+/v96BEBERUSVsoDLyl19+iXfffRcFBQV455138MYbb8DR0dHgdvWqozNgwAAMGDDA4A8nIiIiHdjAGp2XXnoJzs7OePnll3H9+nW8/fbbWs9bvny5pHb1SnSIiIiIjKlnz57V3j6uzxSWTomOh4eHzo3/8ccfkoMgIiKiytnCGp3U1FSTtKtTohMfH6/+c35+PhYuXIgBAwYgNDQUAHDy5Ens27evVh/kSUREZLVsYOpKKjc3N5w9exaBgYFVnqdTojNmzBj1n59//nnMnz8fU6ZMUe+bOnUqVq5ciQMHDmD69Ol6hkxERESkGyF0y9wkr9HZt2+f1oI9AwYMqHThkDXTVm/BHOqN1HQMNVl3Qkp9FHO4FuZCSr0QU32XUtqQEgPrnpC+LOa3Y4xnVVnZiI6uJD/Us2HDhkhOTq6wf+fOnWjYsKFRgiIiIqKHCCNtNkjyiM68efMwbtw4pKamqtfonDp1Cnv37sXGjRuNHiARERGRviQnOpGRkWjTpg0+/fRTJCUlQQiBtm3b4vjx4+jataspYiQiIrJtXIxcga53g+tVR6dr167Ytm2bPm8lIiIiiWzh9nKpdF2MrNMancLCQkkfXlRUJOl8IiIism2BgYHIz8/X+fzvvvsOTZo0qfY8nRIdDw8P5Obm6vzhTZo0wdWrV3U+n4iIiGzbtWvXoFQqdT7/qaeeglwur/Y8naauhBDYuHEjXFxcdPrw0tJSnc4jIiIiHXCNjt50SnSaNWuGDRs26Nyot7c3HBwc9A7KklhznRYpdVfMlanitcQ6RdpiNud+SInNmv8eGqqy79zSvjNz+Lfn0e+ssEgJj1Y189m2skbn/PnzyMnJqfKc9u3bS2pTp0Tn2rVrkholIiIikqpfv35aFxnLZDIIISCTySRNbwF8ejkREZFlsIARGUOdPn0ajRo1MmqbTHSIiIjMnY2s0WnWrBkaN25s1DYlPwKCiIiIyFJwRIeIiMjM2cJi5F69esHR0dHo7XJEh4iIyNzZwEM9k5KSsHXrVq1FigsKCrBixQrJBYwBPROdo0ePYtSoUQgNDcWNGzcAAFu3bsWxY8f0aY6IiIhs3KpVq3DkyBG4ublVOObu7o6jR49ixYoVktuVPHX19ddf45VXXsHIkSORnp6O4uJiAA8e+/DBBx9gz549koMgy1FZ7Y2arHFR0/U0zKHeiDXUNAL4XRLpyxamrnbs2IFly5ZVevz111/HzJkzMWfOHEntSh7RWbhwIdauXYsNGzZoFAUMCwvDjz/+KLU5IiIiqk4tTV2tXr0aAQEBcHJyQnBwMI4eParT+44fP446deqgY8eOOn/WlStX0LJly0qPt2zZEleuXNG5vXKSE50LFy6gZ8+eFfa7ubnh9u3bkgMgIiIi87N9+3ZERUVhzpw5SE9PR48ePRAREYGsrKwq31dQUIDRo0ejX79+kj7P3t4eN2/erPT4zZs3YWcnfcWN5Hf4+Pjg8uXLFfYfO3YMgYGBkgMgIiKiatTCiM7y5csxbtw4jB8/Hm3atEF8fDz8/PywZs2aKt/3+uuvY8SIEQgNDZX0eZ06dcLOnTsrPZ6cnIxOnTpJahPQI9F5/fXXMW3aNJw+fRoymQw3b97Etm3bMHPmTEyaNElyAERERFS18jU6hm4AUFhYqLGVr7V9WElJCdLS0hAeHq6xPzw8HCdOnKg0zsTERFy5cgVz586V3McpU6Zg2bJlWLlypcZjHpRKJVasWIGPP/4YkydPltyu5MXIs2fPRkFBAfr06YP79++jZ8+ekMvlmDlzJqZMmSI5ACIiIqqGESsj+/n5aeyeO3cuYmNjNfbl5eVBqVTCy8tLY7+Xl1elD928dOkS3n77bRw9ehR16kgv0/f8889j9uzZmDp1KubMmYPAwEDIZDJcuXIFd+7cwaxZs/DPf/5Tcrt6FQxctGgR5syZg/Pnz0OlUqFt27ZwcXHRpykiIiKqQdnZ2Rq3cMvl8krPlclkGq/LH6z5KKVSiREjRmDevHlo1Ur/R7ovWrQIQ4cOxbZt23Dp0iUIIdCzZ0+MGDECXbp00atNvSsj161bFyEhIfq+nYiIiHRlxBEdNzc3rbVqHubp6Ql7e/sKoze5ubkVRnmAByVmfvjhB6Snp6tnd1QqFYQQqFOnDvbv34++fftW+Zn37t3DrFmzsHPnTpSWlqJfv35YsWIFPD09JXSyIp0Sneeee07nBpOSkvQOxhJpq79hDrVCTMWa641Y83WTytDftZTfiTG+d0PjNYf6UDXNGv7tqunr9mi7ZaIUwFWTfNajarqOjqOjI4KDg5GSkoJhw4ap96ekpGDIkCEVzndzc8O5c+c09q1evRoHDx7Ejh07EBAQUO1nzp07F5s3b8bIkSPh7OyMzz//HG+88Qa++uor3QPXQqdEx93dXf1nIQSSk5Ph7u6uHtFJS0vD7du3JSVEREREZL6io6PxyiuvICQkBKGhoVi/fj2ysrIwceJEAEBMTAxu3LiBLVu2wM7ODkFBQRrvb9y4MZycnCrsr0xSUhISEhLw0ksvAQBGjhyJ7t27Q6lUwt7eXu9+6JToJCYmqv/81ltv4cUXX8TatWvVH6xUKjFp0qRqh8KIiIhID0acutLV8OHDkZ+fj/nz50OhUCAoKAh79uyBv78/AEChUFRbU0eK7Oxs9OjRQ/26S5cuqFOnDm7evFlhAbUUktfobNq0CceOHdPIruzt7REdHY2wsDAsXbpU72CIiIiootp6BMSkSZMqLR2zefPmKt8bGxtb4W6uqiiVygpPL69Tpw7Kysp0bkMbyYlOWVkZMjIy8Pjjj2vsz8jIgEqlMigYIiIisk1CCERGRmrcBXb//n1MnDgR9erVU++TuhZYcqLz6quvYuzYsbh8+TK6desGADh16hQWL16MV199VWpzREREVJ1amLqqaWPGjKmwb9SoUQa3KznR+eijj+Dt7Y2PP/4YCoUCwIPHQsyePRszZswwOCBtrl27hgULFuDgwYPIycmBr68vRo0ahTlz5mgMc2m7t3/NmjXqhVNEREQWyQYSnYfXAxuT5ETHzs4Os2fPxuzZs1FYWAgAJl+E/Ouvv0KlUmHdunVo0aIFfv75Z0yYMAF3797FRx99pHFuYmIiBg4cqH798B1jREREZFv0LhgImD7BKTdw4ECN5CUwMBAXLlzAmjVrKiQ69evXh7e3d43EZS6k1MOw5rogtkbqNTb02tdUbRJ9aPsujFHLx1Tt1uTfQ3OIQQopcZlrH0xB9vdmaBu2SHKiExAQoHWKqNzVqzVTPKmgoAANGjSosH/KlCkYP348AgICMG7cOLz22mtVPta9uLhY44Fm5aNUREREZsMGpq5MRXKiExUVpfG6tLQU6enp2Lt3L2bNmmWsuKp05coVrFixAsuWLdPYv2DBAvTr1w/Ozs7473//ixkzZiAvLw/vvvtupW3FxcVh3rx5pg6ZiIhIb7V1e7k1kJzoTJs2Tev+VatW4YcffpDUVmxsbLVJxpkzZzSeqXXz5k0MHDgQL7zwAsaPH69x7sMJTceOHQEA8+fPrzLRiYmJQXR0tPp1YWGhQYWJiIiIyHwYtEbnYREREYiJiZG0anrKlCnqUs+Vad68ufrPN2/eRJ8+fdSlqKvTrVs3FBYW4vfff9f6EDLgwVNbq3pyKxERUa3j1JXejJbo7NixQ+uamap4enrq/FTSGzduoE+fPggODkZiYmKV627Kpaenw8nJCfXr15cUFxERkdmx0UTFUJITnU6dOmksRhZCICcnB7du3cLq1auNGly5mzdvonfv3mjWrBk++ugj3Lp1S32s/A6r3bt3IycnB6GhoXB2dsahQ4cwZ84cvPbaaxyxISIislGSE50hQ4ZoJDp2dnZo1KgRevfujdatWxs1uHL79+/H5cuXcfnyZTRt2lTjmBAPUlwHBwesXr0a0dHRUKlUCAwMxPz58zF58mSTxERERFRTuBhZfzJRnikQgAeLkd3d3dEbQ1BH5qBXG5XVrTAVW6olYUo1fd1qmqH1liytHgtJx3+7pCkTpUjFNygoKDBZXbny/ycFTfgA9o5OBrWlLLmPnze8Y9J4zVH1C10eYW9vj9zc3Ar78/PzNZ5oTkRERFTbJE9dVTYAVFxcXOHx6kRERGQ4Tl3pT+dE59NPPwXw4MGZGzduhIuLi/qYUqnEkSNHTLZGh4iIyKbx9nK96ZzofPzxxwAejOisXbtWY5rK0dERzZs3x9q1a40fIREREZGedE50MjMzAQB9+vRBUlISPDw8TBYUERER/T9OXelP8hqdQ4cOmSIOIiIiqgynrvSmU6ITHR2NBQsWoF69ehrPhdJm+fLlRgmMiIiI/sZER286JTrp6ekoLS0FAPz4448aBQOpospqQxhao8LSa05QzTDG70Tbb5W/P8tk7fWhiKqjU6Lz8HRVamqqqWIhIiIiLbhGR3+SCwaOHTsWRUVFFfbfvXsXY8eONUpQRERE9BBhpM0GSU50PvvsM/z1118V9v/111/YsmWLUYIiIiIiMgad77oqLCyEEAJCCBQVFcHJ6f+fuaFUKrFnzx40btzYJEESERHZMpkQkBn4aEpD32+pdE506tevD5lMBplMhlatWlU4LpPJMG/ePKMGR0REROBdVwbQOdE5dOgQhBDo27cvvv76azRo0EB9zNHREf7+/vD19TVJkERERET60DnR6dWrF4AHFZL9/PxgZyd5eQ8RERHpgXdd6U9yZWR/f38AwL1795CVlYWSkhKN4+3btzdOZFaIdUgeMNcaLaaKwVR1TIwRr5SaT5X1wxyuHVXOVHW9jBGDOZDyPdRqPzh1pTfJic6tW7fw6quv4rvvvtN6XKlUGhwUERERkTFInn+KiorCn3/+iVOnTsHZ2Rl79+7FZ599hpYtW2LXrl2miJGIiMimlU9dGbrZIskjOgcPHsQ333yDJ598EnZ2dvD390f//v3h5uaGuLg4PP3006aIk4iIyHZx6kpvkkd07t69q66X06BBA9y6dQsA0K5dO/z444/GjY6IiIg4omMAyYnO448/jgsXLgAAOnbsiHXr1uHGjRtYu3YtfHx8jB4gERERkb4kT11FRUVBoVAAAObOnYsBAwZg27ZtcHR0xObNm40dHxEREXHqSm+SE52RI0eq/9ypUydcu3YNv/76K5o1awZPT0+jBkdEREQP2OrUk6EkJzqPqlu3Ljp37myMWMhGmHNNDVOQ0t+arG0i9fOk1GOxtWtM/8/Srr22eGv67yGZlk6JTnR0tM4NLl++XO9giIiISAshHmyGtmGDdEp00tPTdWpMJpMZFAwRERFVxEdA6E+nROfQoUOmjoOIiIjI6Axeo0NEREQmxruu9MZEh4iIyMzJVA82Q9uwRZILBhIRERFZCo7oEBERmTtOXemNiY4VkFLHhDVPzJspr4UxauYQlbPm34g59o13XemPiQ4REZG5Yx0dvXGNDhEREVktjugQERGZOU5d6Y+JDhERkbnjYmS9ceqKiIiIrBZHdIiIiMwcp670x0SHiIjI3PGuK71ZTKLTvHlzXL9+XWPfW2+9hcWLF6tfZ2VlYfLkyTh48CCcnZ0xYsQIfPTRR3B0dKzpcGuUlJoPpqoPwfo8lknKNaqsDg+vs23i74EshcUkOgAwf/58TJgwQf3axcVF/WelUomnn34ajRo1wrFjx5Cfn48xY8ZACIEVK1bURrhERERGwakr/VlUouPq6gpvb2+tx/bv34/z588jOzsbvr6+AIBly5YhMjISixYtgpubW02GSkREZDy860pvFnXX1YcffoiGDRuiY8eOWLRoEUpKStTHTp48iaCgIHWSAwADBgxAcXEx0tLSKm2zuLgYhYWFGhsRERFZB4sZ0Zk2bRo6d+4MDw8PfP/994iJiUFmZiY2btwIAMjJyYGXl5fGezw8PODo6IicnJxK242Li8O8efNMGjsREZEhOHWlv1od0YmNjYVMJqty++GHHwAA06dPR69evdC+fXuMHz8ea9euRUJCAvLz89XtyWSyCp8hhNC6v1xMTAwKCgrUW3Z2tvE7SkREZAiVMM5mg2p1RGfKlCl46aWXqjynefPmWvd369YNAHD58mU0bNgQ3t7eOH36tMY5f/75J0pLSyuM9DxMLpdDLpdLC5yIiKgmcY2O3mo10fH09ISnp6de701PTwcA+Pj4AABCQ0OxaNEiKBQK9b79+/dDLpcjODjYOAETERGRRbGINTonT57EqVOn0KdPH7i7u+PMmTOYPn06nn32WTRr1gwAEB4ejrZt2+KVV17B0qVL8ccff2DmzJmYMGGCUe64Ys2Iqmn7Hvid1Q4p37uUc3ndqCZU9pvUxpZ+kzIYYY2OUSKxPBaR6Mjlcmzfvh3z5s1DcXEx/P39MWHCBMyePVt9jr29Pb799ltMmjQJ3bt31ygYSEREZNFYGVlvFpHodO7cGadOnar2vGbNmuE///lPDURERERElsCi6ugQERHZovLbyw3dpFq9ejUCAgLg5OSE4OBgHD16tNJzk5KS0L9/fzRq1Ahubm4IDQ3Fvn37DOi1cTDRISIiMnfCSJsE27dvR1RUFObMmYP09HT06NEDERERyMrK0nr+kSNH0L9/f+zZswdpaWno06cPBg8erL55qLYw0SEiIqIKli9fjnHjxmH8+PFo06YN4uPj4efnhzVr1mg9Pz4+HrNnz8aTTz6Jli1b4oMPPkDLli2xe/fuGo5cExMdIiIiMycTwigbgAqPPSouLq7weSUlJUhLS0N4eLjG/vDwcJw4cUKnmFUqFYqKitCgQQPDvwADMNEhIiIydyojbQD8/Pzg7u6u3uLi4ip8XF5eHpRKZYWCu15eXlU+Vulhy5Ytw927d/Hiiy9K7a1RWcRdV+bAluo1GAu/s9oh5XvnNbJd5nrtpcTFmjv6yc7O1qgvV9XTAR59hFJ1j1Uq9+9//xuxsbH45ptv0LhxY/2DNQImOkRERGbu4aknQ9oAADc3t2oL6Xp6esLe3r7C6E1ubm6Vj1UCHixiHjduHL766iv84x//MChmY+DUFRERkbmr4buuHB0dERwcjJSUFI39KSkpCAsLq/R9//73vxEZGYnPP/8cTz/9tO4faEIc0SEiIjJ3tVAZOTo6Gq+88gpCQkIQGhqK9evXIysrCxMnTgQAxMTE4MaNG9iyZQuAB0nO6NGj8cknn6Bbt27q0SBnZ2e4u7sbFrsBmOgQERFRBcOHD0d+fj7mz58PhUKBoKAg7NmzB/7+/gAAhUKhUVNn3bp1KCsrw+TJkzF58mT1/jFjxmDz5s01Hb4aEx0iIiIzp29l40fbkGrSpEmYNGmS1mOPJi+pqanSP6AGMNEhIiIyd3yop964GJmIiIisFkd0iIioSlLq1WhTWQ0bbe2yDpR2MtWDzdA2bBETHSIiInPHqSu9ceqKiIiIrBZHdIiIiMydxIJ/lbZhg5joEBERmTljPgLC1nDqioiIiKwWR3SIiIjMHRcj642JDhERkbkTAAy9Pdw28xwmOtbK0LoXxiCldgZVTUq9ECnfry3VIaHq1fTfTf7+dMc1OvrjGh0iIiKyWhzRISIiMncCRlijY5RILA4THSIiInPHxch649QVERERWS2O6BAREZk7FQCZEdqwQUx0iIiIzBzvutIfp66IiIjIanFEh4iIyNxxMbLemOhUIvniObi52td2GHozVSEuKQXFWLjOeExVyK2ydnk9al5N/30x1W/K0n47hnwPhUVKeLQyYjBVYaKjN05dERERkdXiiA4REZG544iO3pjoEBERmTveXq43JjpERERmjreX649rdIiIiMhqcUSHiIjI3HGNjt6Y6BAREZk7lQBkBiYqKiY69JBhrdqhjszB5J9jznVMDK2zYQ59sBZSvktT1UcxB9bcNylqugaOuX7vxojLkH+nykQpgKsGx0CmZRFrdFJTUyGTybRuZ86cUZ+n7fjatWtrMXIiIiIjKJ+6MnSzQRYxohMWFgaFQqGx77333sOBAwcQEhKisT8xMREDBw5Uv3Z3d6+RGImIiEzHGIkKEx2z5ejoCG9vb/Xr0tJS7Nq1C1OmTIFMpllYoH79+hrnEhERke2yiKmrR+3atQt5eXmIjIyscGzKlCnw9PTEk08+ibVr10KlstEKSUREZD04daU3ixjReVRCQgIGDBgAPz8/jf0LFixAv3794OzsjP/+97+YMWMG8vLy8O6771baVnFxMYqLi9WvCwsLTRY3ERGRXlQCBk892ehdV7U6ohMbG1vpIuPy7YcfftB4z2+//YZ9+/Zh3LhxFdp79913ERoaio4dO2LGjBmYP38+li5dWmUMcXFxcHd3V2+PJk9ERERkuWp1RGfKlCl46aWXqjynefPmGq8TExPRsGFDPPvss9W2361bNxQWFuL333+Hl5eX1nNiYmIQHR2tfl1YWMhkh4iIzItQPdgMbcMG1Wqi4+npCU9PT53PF0IgMTERo0ePhoND9TVu0tPT4eTkhPr161d6jlwuh1wu1zkGYzPnWjPmHBuZb22TqtRkzLb4+9X2/RrjezC0DSnX3Ri1fKzy2rMyst4sao3OwYMHkZmZqXXaavfu3cjJyUFoaCicnZ1x6NAhzJkzB6+99lqtJjJEREQG4xodvVlUopOQkICwsDC0adOmwjEHBwesXr0a0dHRUKlUCAwMxPz58zF58uRaiJSIiIjMgUUlOp9//nmlxwYOHKhRKJCIiMhqcOpKbxaV6BAREdkkASMkOkaJxOJYZMFAIiIiIl1wRIeIiMjccepKb0x0iIiIzJ1KBcDAOjg2+kgkJjpEVGOssr6JGTGH79dca/mQ7WKiQ0REZO44daU3JjpERETmjomO3njXFREREVktjugQERGZOz4CQm9MdIiIiMycECoIA58+buj7LRUTHSIiInMnhOEjMlyjQ0RERGRdOKJDZKEqqyuirY6JVMZowxSMUUvF0L7ZYj0XU9XGIQmEEdbo2OiIDhMdIiIic6dSATID19jY6BodTl0RERGR1eKIDhERkbnj1JXemOgQERGZOaFSQRg4dWWrt5dz6oqIiIisFkd0iIiIzB2nrvTGRIeIiMjcqQQgY6KjDyY6RFaG9U2qxu9HOinfGb9fMjdMdIiIiMydEAAMraPDER0iIiIyQ0IlIAycuhJMdIiIiMgsCRUMH9Hh7eVEREREaqtXr0ZAQACcnJwQHByMo0ePVnn+4cOHERwcDCcnJwQGBmLt2rU1FGnlmOgQERGZOaESRtmk2L59O6KiojBnzhykp6ejR48eiIiIQFZWltbzMzMzMWjQIPTo0QPp6el45513MHXqVHz99dfG+Ar0xkSHiIjI3AmVcTYJli9fjnHjxmH8+PFo06YN4uPj4efnhzVr1mg9f+3atWjWrBni4+PRpk0bjB8/HmPHjsVHH31kjG9Ab1yj84jyxVplKDW4NhMREVmvMpQCqJlFvsb4f1J5vIWFhRr75XI55HK5xr6SkhKkpaXh7bff1tgfHh6OEydOaG3/5MmTCA8P19g3YMAAJCQkoLS0FA4ODoZ1QE9MdB5RVFQEADiGPbUcCRERWYKioiK4u7ubpG1HR0d4e3vjWI5x/p/k4uICPz8/jX1z585FbGysxr68vDwolUp4eXlp7Pfy8kJOTo7WtnNycrSeX1ZWhry8PPj4+BjeAT0w0XmEr68vsrOzIYRAs2bNkJ2dDTc3t9oOy+gKCwvh5+dnlf1j3ywT+2aZbLlvQggUFRXB19fXZDE4OTkhMzMTJSUlRmlPCAGZTKax79HRnIc9eq6291d3vrb9NYmJziPs7OzQtGlT9dCem5ub1f3lfZg19499s0zsm2Wy1b6ZaiTnYU5OTnBycjL55zzM09MT9vb2FUZvcnNzK4zalPP29tZ6fp06ddCwYUOTxVodLkYmIiIiDY6OjggODkZKSorG/pSUFISFhWl9T2hoaIXz9+/fj5CQkFpbnwMw0SEiIiItoqOjsXHjRmzatAkZGRmYPn06srKyMHHiRABATEwMRo8erT5/4sSJuH79OqKjo5GRkYFNmzYhISEBM2fOrK0uAODUVaXkcjnmzp1b5dylJbPm/rFvlol9s0zsm/UaPnw48vPzMX/+fCgUCgQFBWHPnj3w9/cHACgUCo2aOgEBAdizZw+mT5+OVatWwdfXF59++imef/752uoCAEAmbPXhF0RERGT1OHVFREREVouJDhEREVktJjpERERktZjoEBERkdViolMJqY+mN0exsbGQyWQam7e3t/q4EAKxsbHw9fWFs7MzevfujV9++aUWI67ckSNHMHjwYPj6+kImk2Hnzp0ax3XpS3FxMd588014enqiXr16ePbZZ/Hbb7/VYC+0q65vkZGRFa5jt27dNM4x177FxcXhySefhKurKxo3boyhQ4fiwoULGudY6rXTpW+Weu3WrFmD9u3bqwvlhYaG4rvvvlMft9RrBlTfN0u9ZlQ5JjpaSH00vTl74oknoFAo1Nu5c+fUx5YsWYLly5dj5cqVOHPmDLy9vdG/f3/1877Myd27d9GhQwesXLlS63Fd+hIVFYXk5GR88cUXOHbsGO7cuYNnnnkGSqWyprqhVXV9A4CBAwdqXMc9ezSfe2OufTt8+DAmT56MU6dOISUlBWVlZQgPD8fdu3fV51jqtdOlb4BlXrumTZti8eLF+OGHH/DDDz+gb9++GDJkiDqZsdRrBlTfN8AyrxlVQVAFXbp0ERMnTtTY17p1a/H222/XUkT6mTt3rujQoYPWYyqVSnh7e4vFixer992/f1+4u7uLtWvX1lCE+gEgkpOT1a916cvt27eFg4OD+OKLL9Tn3LhxQ9jZ2Ym9e/fWWOzVebRvQggxZswYMWTIkErfYyl9E0KI3NxcAUAcPnxYCGFd1+7RvglhXdfOw8NDbNy40aquWbnyvglhXdeMHuCIziPKH03/6KPmq3o0vTm7dOkSfH19ERAQgJdeeglXr14FAGRmZiInJ0ejn3K5HL169bK4furSl7S0NJSWlmqc4+vri6CgIIvob2pqKho3boxWrVphwoQJyM3NVR+zpL4VFBQAABo0aADAuq7do30rZ+nXTqlU4osvvsDdu3cRGhpqVdfs0b6Vs/RrRppYGfkR+jya3lx17doVW7ZsQatWrfD7779j4cKFCAsLwy+//KLui7Z+Xr9+vTbC1ZsufcnJyYGjoyM8PDwqnGPu1zUiIgIvvPAC/P39kZmZiffeew99+/ZFWloa5HK5xfRNCIHo6Gg89dRTCAoKAmA9105b3wDLvnbnzp1DaGgo7t+/DxcXFyQnJ6Nt27bq/5lb8jWrrG+AZV8z0o6JTiWkPpreHEVERKj/3K5dO4SGhuKxxx7DZ599pl5cZw39LKdPXyyhv8OHD1f/OSgoCCEhIfD398e3336L5557rtL3mVvfpkyZgp9++gnHjh2rcMzSr11lfbPka/f444/j7NmzuH37Nr7++muMGTMGhw8fVh+35GtWWd/atm1r0deMtOPU1SP0eTS9pahXrx7atWuHS5cuqe++soZ+6tIXb29vlJSU4M8//6z0HEvh4+MDf39/XLp0CYBl9O3NN9/Erl27cOjQITRt2lS93xquXWV908aSrp2joyNatGiBkJAQxMXFoUOHDvjkk0+s4ppV1jdtLOmakXZMdB6hz6PpLUVxcTEyMjLg4+ODgIAAeHt7a/SzpKQEhw8ftrh+6tKX4OBgODg4aJyjUCjw888/W1x/8/PzkZ2dDR8fHwDm3TchBKZMmYKkpCQcPHgQAQEBGsct+dpV1zdtLOnaPUoIgeLiYou+ZpUp75s2lnzN6G81vvzZAnzxxRfCwcFBJCQkiPPnz4uoqChRr149ce3atdoOTZIZM2aI1NRUcfXqVXHq1CnxzDPPCFdXV3U/Fi9eLNzd3UVSUpI4d+6cePnll4WPj48oLCys5cgrKioqEunp6SI9PV0AEMuXLxfp6eni+vXrQgjd+jJx4kTRtGlTceDAAfHjjz+Kvn37ig4dOoiysrLa6pYQouq+FRUViRkzZogTJ06IzMxMcejQIREaGiqaNGliEX174403hLu7u0hNTRUKhUK93bt3T32OpV676vpmydcuJiZGHDlyRGRmZoqffvpJvPPOO8LOzk7s379fCGG510yIqvtmydeMKsdEpxKrVq0S/v7+wtHRUXTu3FnjllFLMXz4cOHj4yMcHByEr6+veO6558Qvv/yiPq5SqcTcuXOFt7e3kMvlomfPnuLcuXO1GHHlDh06JABU2MaMGSOE0K0vf/31l5gyZYpo0KCBcHZ2Fs8884zIysqqhd5oqqpv9+7dE+Hh4aJRo0bCwcFBNGvWTIwZM6ZC3ObaN239AiASExPV51jqtauub5Z87caOHav+969Ro0aiX79+6iRHCMu9ZkJU3TdLvmZUOZkQQtTc+BERERFRzeEaHSIiIrJaTHSIiIjIajHRISIiIqvFRIeIiIisFhMdIiIislpMdIiIiMhqMdEhIiIiq8VEh6xW7969ERUVZVWfGxkZiaFDhxrURvPmzSGTySCTyXD79u1Kz9u8eTPq169v0GeRptTUVPV3b+h1JCLdMNEhMrKkpCQsWLBA/bp58+aIj4+vvYC0mD9/PhQKBdzd3Ws7FKul7bqHhYVBoVDgxRdfrJ2giGxQndoOgMjaNGjQoLZDqJarq6v6KdS1rbS0FA4ODrUdRo1wdHSEt7c3nJ2dK32IJBEZF0d0yGb8+eefGD16NDw8PFC3bl1ERETg0qVL6uPlUzX79u1DmzZt4OLigoEDB0KhUKjPKSsrw9SpU1G/fn00bNgQb731FsaMGaMxDfHw1FXv3r1x/fp1TJ8+XT1lAQCxsbHo2LGjRnzx8fFo3ry5+rVSqUR0dLT6s2bPno1Hn9gihMCSJUsQGBgIZ2dndOjQATt27NDr+9m8eTOaNWuGunXrYtiwYcjPz69wzu7duxEcHAwnJycEBgZi3rx5KCsrUx//9ddf8dRTT8HJyQlt27bFgQMHIJPJsHPnTgDAtWvXIJPJ8OWXX6J3795wcnLCv/71LwBAYmIi2rRpAycnJ7Ru3RqrV6/W+OwbN25g+PDh8PDwQMOGDTFkyBBcu3ZNfTw1NRVdunRBvXr1UL9+fXTv3h3Xr1+vtL/VtXfmzBn0798fnp6ecHd3R69evfDjjz9qtBEbG4tmzZpBLpfD19cXU6dOBVD5dSeimsdEh2xGZGQkfvjhB+zatQsnT56EEAKDBg1CaWmp+px79+7ho48+wtatW3HkyBFkZWVh5syZ6uMffvghtm3bhsTERBw/fhyFhYXq/4lrk5SUhKZNm6qnih5OmqqzbNkybNq0CQkJCTh27Bj++OMPJCcna5zz7rvvIjExEWvWrMEvv/yC6dOnY9SoUTh8+LDuXwyA06dPY+zYsZg0aRLOnj2LPn36YOHChRrn7Nu3D6NGjcLUqVNx/vx5rFu3Dps3b8aiRYsAACqVCkOHDkXdunVx+vRprF+/HnPmzNH6eW+99RamTp2KjIwMDBgwABs2bMCcOXOwaNEiZGRk4IMPPsB7772Hzz77DMCD69KnTx+4uLjgyJEjOHbsmDoRLSkpQVlZGYYOHYpevXrhp59+wsmTJ/Haa69VmmBU1x4AFBUVYcyYMTh69ChOnTqFli1bYtCgQSgqKgIA7NixAx9//DHWrVuHS5cuYefOnWjXrh0Aw647ERlZrT5SlMiEevXqJaZNmyaEEOLixYsCgDh+/Lj6eF5ennB2dhZffvmlEEKIxMREAUBcvnxZfc6qVauEl5eX+rWXl5dYunSp+nVZWZlo1qyZGDJkiNbPFUIIf39/8fHHH2vENnfuXNGhQweNfR9//LHw9/dXv/bx8RGLFy9Wvy4tLRVNmzZVf9adO3eEk5OTOHHihEY748aNEy+//HKl34u2eF5++WUxcOBAjX3Dhw8X7u7u6tc9evQQH3zwgcY5W7duFT4+PkIIIb777jtRp04doVAo1MdTUlIEAJGcnCyEECIzM1MAEPHx8Rrt+Pn5ic8//1xj34IFC0RoaKgQQoiEhATx+OOPC5VKpT5eXFwsnJ2dxb59+0R+fr4AIFJTUyvt98Oqa0+bsrIy4erqKnbv3i2EEGLZsmWiVatWoqSkROv52r7ncmPGjNH4zRCR6XBEh2xCRkYG6tSpg65du6r3NWzYEI8//jgyMjLU++rWrYvHHntM/drHxwe5ubkAgIKCAvz+++/o0qWL+ri9vT2Cg4ONHm9BQQEUCgVCQ0PV++rUqYOQkBD16/Pnz+P+/fvo378/XFxc1NuWLVtw5coVSZ+XkZGh8VkAKrxOS0vD/PnzNT5rwoQJUCgUuHfvHi5cuAA/Pz+NtT8Pf1cPe7gft27dQnZ2NsaNG6fR9sKFC9X9SEtLw+XLl+Hq6qo+3qBBA9y/fx9XrlxBgwYNEBkZiQEDBmDw4MH45JNPqhxFqa49AMjNzcXEiRPRqlUruLu7w93dHXfu3EFWVhYA4IUXXsBff/2FwMBATJgwAcnJyRrTeERkHrgYmWyCeGRty8P7H57eeHRRrEwmq/DeR6dDKmu7KnZ2dhXe9/AUmi5UKhUA4Ntvv0WTJk00jsnlcklt6dIHlUqFefPm4bnnnqtwzMnJqcJ3WZV69epptAsAGzZs0EhEgQeJZPk5wcHB2LZtW4W2GjVqBODBGp+pU6di79692L59O959912kpKSgW7duWvtSXXuRkZG4desW4uPj4e/vD7lcjtDQUPXUlp+fHy5cuICUlBQcOHAAkyZNwtKlS3H48GGbWVxNZAmY6JBNaNu2LcrKynD69GmEhYUBAPLz83Hx4kW0adNGpzbc3d3h5eWF77//Hj169ADwYMFwenp6hYXFD3N0dIRSqdTY16hRI+Tk5GgkB2fPntX4LB8fH5w6dQo9e/YE8GAhdFpaGjp37qzuk1wuR1ZWFnr16qVTHyrTtm1bnDp1SmPfo687d+6MCxcuoEWLFlrbaN26NbKysvD777/Dy8sLwIMFvdXx8vJCkyZNcPXqVYwcOVLrOZ07d8b27dvRuHFjuLm5VdpWp06d0KlTJ8TExCA0NBSff/651kRHl/aOHj2K1atXY9CgQQCA7Oxs5OXlaZzj7OyMZ599Fs8++ywmT56M1q1b49y5c+jcubPW605ENY9TV2QTWrZsiSFDhmDChAk4duwY/ve//2HUqFFo0qQJhgwZonM7b775JuLi4vDNN9/gwoULmDZtGv78888qRzKaN2+OI0eO4MaNG+r/Ufbu3Ru3bt3CkiVLcOXKFaxatQrfffedxvumTZuGxYsXIzk5Gb/++ismTZqkUeDP1dUVM2fOxPTp0/HZZ5/hypUrSE9Px6pVq9SLeHVVPhKyZMkSXLx4EStXrsTevXs1znn//fexZcsWxMbG4pdffkFGRoZ65AQA+vfvj8ceewxjxozBTz/9hOPHj6sXI1c30hMbG4u4uDh88sknuHjxIs6dO4fExEQsX74cADBy5Eh4enpiyJAhOHr0KDIzM3H48GFMmzYNv/32GzIzMxETE4OTJ0/i+vXr2L9/f5VJbHXtAUCLFi2wdetWZGRk4PTp0xg5ciScnZ3VbWzevBkJCQn4+eefcfXqVWzduhXOzs7w9/cHoP26E1EtqL3lQUSm9eii4D/++EO88sorwt3dXTg7O4sBAwaIixcvqo8nJiZqLL4VQojk5GTx8F+T0tJSMWXKFOHm5iY8PDzEW2+9JV544QXx0ksvVfq5J0+eFO3btxdyuVyjrTVr1gg/Pz9Rr149MXr0aLFo0SKNxcilpaVi2rRpws3NTdSvX19ER0eL0aNHayxiValU4pNPPhGPP/64cHBwEI0aNRIDBgwQhw8frvR7qWyRbEJCgmjatKlwdnYWgwcPFh999FGF72Pv3r0iLCxMODs7Czc3N9GlSxexfv169fGMjAzRvXt34ejoKFq3bi12794tAIi9e/cKIf5/MXJ6enqFz9+2bZvo2LGjcHR0FB4eHqJnz54iKSlJfVyhUIjRo0cLT09PIZfLRWBgoJgwYYIoKCgQOTk5YujQocLHx0c4OjoKf39/8f777wulUlnp91BVe0II8eOPP4qQkBAhl8tFy5YtxVdffaXx3SUnJ4uuXbsKNzc3Ua9ePdGtWzdx4MABdfuVXXchuBiZqCbJhNBjgQERAXiw1qNNmzZ48cUXNaohm7PmzZsjKiqqRh6Pcfz4cTz11FO4fPmyxiJvWxcZGYnbt29XWZqAiIyDU1dEEly/fh0bNmxQT6+88cYbyMzMxIgRI2o7NEneeustuLi4oKCgwKjtJicnIyUlBdeuXcOBAwfw2muvoXv37kxy/nb06FG4uLhoXQRNRKbBxchEEtjZ2WHz5s2YOXMmhBAICgrCgQMHdF7QbA4OHz6svsPL1dXVqG0XFRVh9uzZyM7OhqenJ/7xj39g2bJlRv0MSxYSEqJedO7i4lK7wRDZCE5dERERkdXi1BURERFZLSY6REREZLWY6BAREZHVYqJDREREVouJDhEREVktJjpERERktZjoEBERkdViokNERERWi4kOERERWa3/A4LtuEKWD+CHAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(ds_regrid_target.PCT_NATVEG > 0).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "id": "77ebca4c-fe4b-4678-9574-c7a8f882ceeb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['mxsoil_color',\n",
       " 'SOIL_COLOR',\n",
       " 'PCT_SAND',\n",
       " 'PCT_CLAY',\n",
       " 'ORGANIC',\n",
       " 'FMAX',\n",
       " 'natpft',\n",
       " 'cft',\n",
       " 'LANDFRAC_PFT',\n",
       " 'PFTDATA_MASK',\n",
       " 'PCT_NATVEG',\n",
       " 'PCT_CROP',\n",
       " 'PCT_NAT_PFT',\n",
       " 'PCT_CFT',\n",
       " 'MONTHLY_LAI',\n",
       " 'MONTHLY_SAI',\n",
       " 'MONTHLY_HEIGHT_TOP',\n",
       " 'MONTHLY_HEIGHT_BOT',\n",
       " 'time',\n",
       " 'AREA',\n",
       " 'LONGXY',\n",
       " 'LATIXY',\n",
       " 'EF1_BTR',\n",
       " 'EF1_FET',\n",
       " 'EF1_FDT',\n",
       " 'EF1_SHR',\n",
       " 'EF1_GRS',\n",
       " 'EF1_CRP',\n",
       " 'CANYON_HWR',\n",
       " 'EM_IMPROAD',\n",
       " 'EM_PERROAD',\n",
       " 'EM_ROOF',\n",
       " 'EM_WALL',\n",
       " 'HT_ROOF',\n",
       " 'THICK_ROOF',\n",
       " 'THICK_WALL',\n",
       " 'T_BUILDING_MIN',\n",
       " 'WIND_HGT_CANYON',\n",
       " 'WTLUNIT_ROOF',\n",
       " 'WTROAD_PERV',\n",
       " 'ALB_IMPROAD_DIR',\n",
       " 'ALB_IMPROAD_DIF',\n",
       " 'ALB_PERROAD_DIR',\n",
       " 'ALB_PERROAD_DIF',\n",
       " 'ALB_ROOF_DIR',\n",
       " 'ALB_ROOF_DIF',\n",
       " 'ALB_WALL_DIR',\n",
       " 'ALB_WALL_DIF',\n",
       " 'TK_ROOF',\n",
       " 'TK_WALL',\n",
       " 'TK_IMPROAD',\n",
       " 'CV_ROOF',\n",
       " 'CV_WALL',\n",
       " 'CV_IMPROAD',\n",
       " 'NLEV_IMPROAD',\n",
       " 'peatf',\n",
       " 'zbedrock',\n",
       " 'abm',\n",
       " 'gdp',\n",
       " 'SLOPE',\n",
       " 'STD_ELEV',\n",
       " 'binfl',\n",
       " 'Ws',\n",
       " 'Dsmax',\n",
       " 'Ds',\n",
       " 'LAKEDEPTH',\n",
       " 'F0',\n",
       " 'P3',\n",
       " 'ZWT0',\n",
       " 'PCT_WETLAND',\n",
       " 'PCT_LAKE',\n",
       " 'PCT_GLACIER',\n",
       " 'GLACIER_REGION',\n",
       " 'GLC_MEC',\n",
       " 'PCT_GLC_MEC',\n",
       " 'PCT_GLC_MEC_GIC',\n",
       " 'PCT_GLC_MEC_ICESHEET',\n",
       " 'PCT_GLC_GIC',\n",
       " 'PCT_GLC_ICESHEET',\n",
       " 'TOPO_GLC_MEC',\n",
       " 'PCT_URBAN',\n",
       " 'URBAN_REGION_ID',\n",
       " 'CONST_HARVEST_VH1',\n",
       " 'CONST_HARVEST_VH2',\n",
       " 'CONST_HARVEST_SH1',\n",
       " 'CONST_HARVEST_SH2',\n",
       " 'CONST_HARVEST_SH3',\n",
       " 'CONST_GRAZING',\n",
       " 'CONST_FERTNITRO_CFT',\n",
       " 'UNREPRESENTED_PFT_LULCC',\n",
       " 'UNREPRESENTED_CFT_LULCC',\n",
       " 'lon',\n",
       " 'lat',\n",
       " 'mask']"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(ds_regrid_target.variables)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "acb8ecd6-0464-42d8-b5b1-29648df9e286",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ds_luh2_static.icwtr.plot()\n",
    "static_mask_test_where =  ds_luh2_static.icwtr.where(ds_luh2_static.icwtr != 1)\n",
    "static_mask_test = ds_luh2_static.icwtr != 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "84bd4e37-c643-44aa-a399-0dc9806dfec5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7ff020ea1e40>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x400 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(ncols=2,figsize=(16,4))\n",
    "static_mask_test_where.plot(ax=axs[0])\n",
    "static_mask_test.plot(ax=axs[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ccdcdaa-790b-449d-81f4-243b39c4aae4",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Calculate land fraction and mask to normalize against"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "dc8d683e-bc20-49cd-b77d-1733d0080171",
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_luh2_static[\"landfrac\"] = 1 - ds_luh2_static.icwtr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "1b89455c-0220-4522-9eac-2cfb0a133910",
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_luh2 = SetMaskLUH2(ds_luh2, ds_luh2_static)\n",
    "ds_luh2_static = SetMaskLUH2(ds_luh2_static,ds_luh2_static)\n",
    "ds_regrid_target = SetMaskSurfData(ds_regrid_target)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1aa832ee-0b06-4ff5-b79e-4d18e08b351c",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Regrid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "9a41be98-e847-46b6-a475-d0a6d1d0d4fe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Defining regridder\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/glemieux/local/conda/miniconda3/envs/jupyter-luh2/lib/python3.10/site-packages/xesmf/backend.py:56: UserWarning: Latitude is outside of [-90, 90]\n",
      "  warnings.warn('Latitude is outside of [-90, 90]')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Regridding\n",
      "skipping variable 1/22: time\n",
      "skipping variable 2/22: lat\n",
      "skipping variable 3/22: lon\n",
      "regridding variable 4/22: primf\n",
      "regridding variable 5/22: primn\n",
      "regridding variable 6/22: secdf\n",
      "regridding variable 7/22: secdn\n",
      "regridding variable 8/22: urban\n",
      "regridding variable 9/22: c3ann\n",
      "regridding variable 10/22: c4ann\n",
      "regridding variable 11/22: c3per\n",
      "regridding variable 12/22: c4per\n",
      "regridding variable 13/22: c3nfx\n",
      "regridding variable 14/22: pastr\n",
      "regridding variable 15/22: range\n",
      "regridding variable 16/22: secmb\n",
      "regridding variable 17/22: secma\n",
      "skipping variable 18/22: lat_bounds\n",
      "skipping variable 19/22: lon_bounds\n",
      "skipping variable 20/22: lat_b\n",
      "skipping variable 21/22: lon_b\n"
     ]
    }
   ],
   "source": [
    "regrid_luh2, regridder_luh2 = RegridConservative(ds_luh2, ds_regrid_target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "f94f716e-bb5c-405d-83f0-7568605490e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "regrid_land_fraction = regridder_luh2(ds_luh2_static)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "ca7e2f16-3014-4389-9a81-34d813e145b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "regrid_luh2_landfrac = regrid_luh2 / regrid_land_fraction.landfrac"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6bbc1b21-128d-45f5-95f2-f50a66439b9f",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Sum the states"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "id": "9dda8c5c-fa36-4204-a433-b2617b9656f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Avoid summing non-vegetated variables\n",
    "# coords_avoid = [\"time\",\"lat\",\"lat_b\",\"lon\",\"lon_b\",\"secma\",\"secmb\",\"urban\"]\n",
    "coords_avoid = [\"time\",\"lat\",\"lat_b\",\"lon\",\"lon_b\",\"secma\",\"secmb\"]\n",
    "ds_varnames = list(regrid_luh2.variables.keys())\n",
    "state_sum = 0.0\n",
    "state_sum_landfrac = 0.0\n",
    "for i in range(len(regrid_luh2.variables)):\n",
    "    if (not(ds_varnames[i] in coords_avoid)):\n",
    "        state_sum = state_sum + regrid_luh2[ds_varnames[i]]\n",
    "        state_sum_landfrac = state_sum_landfrac + regrid_luh2_landfrac[ds_varnames[i]]\n",
    "        # state_sum = state_sum + regrid_luh2[ds_varnames[i]].isel(time=0)\n",
    "        # state_sum_landfrac = state_sum_landfrac + regrid_luh2_landfrac[ds_varnames[i]].isel(time=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "id": "50ce5035-a9b5-4c44-b155-dda7e89dddb2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x400 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(ncols=3,figsize=(12,4))\n",
    "state_sum_landfrac.where(state_sum_landfrac.mean(dim='time') == 1.0).mean(dim='time').plot(ax=axs[0])\n",
    "axs[0].set_title('LUH2 state sum == 1.0')\n",
    "state_sum_landfrac.where(state_sum_landfrac.mean(dim='time') > 1.0).mean(dim='time').plot(ax=axs[1])\n",
    "axs[1].set_title('LUH2 state sum > 1.0')\n",
    "state_sum_landfrac.where(state_sum_landfrac.mean(dim='time') < 1.0).mean(dim='time').plot(ax=axs[2])\n",
    "axs[2].set_title('LUH2 state sum < 1.0')\n",
    "fig.tight_layout()\n",
    "# fig.suptitle('time = 0850-01-01')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "a3b82382-f44a-4138-9998-17ad6635eb30",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
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       "\n",
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       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
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       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
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       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
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       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
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       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
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       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
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       "\n",
       ".xr-preview {\n",
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       "\n",
       ".xr-array-preview,\n",
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       "\n",
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       "\n",
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       "\n",
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       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
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       "\n",
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       "\n",
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       "\n",
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       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
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       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
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       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
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       "  vertical-align: middle;\n",
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       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray ()&gt;\n",
       "array(1161)\n",
       "Coordinates:\n",
       "    time     object 0850-01-01 00:00:00</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-eccbb953-3afb-46ba-88b9-495f66740736' class='xr-array-in' type='checkbox' checked><label for='section-eccbb953-3afb-46ba-88b9-495f66740736' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>1161</span></div><div class='xr-array-data'><pre>array(1161)</pre></div></div></li><li class='xr-section-item'><input id='section-053f1826-30b5-4bce-aa7f-408b06030821' class='xr-section-summary-in' type='checkbox'  checked><label for='section-053f1826-30b5-4bce-aa7f-408b06030821' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>0850-01-01 00:00:00</div><input id='attrs-f83f3aaf-f902-47c8-b2a5-a1e7d5d428ec' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f83f3aaf-f902-47c8-b2a5-a1e7d5d428ec' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fbd8d6d6-5611-4289-8735-4d20f7f07ea4' class='xr-var-data-in' type='checkbox'><label for='data-fbd8d6d6-5611-4289-8735-4d20f7f07ea4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(cftime.DatetimeNoLeap(850, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
       "      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-79e8e783-5e55-4226-bcaa-c258321c6e66' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-79e8e783-5e55-4226-bcaa-c258321c6e66' class='xr-section-summary'  title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-5c89b779-bcb3-4ddb-9092-cb9a1f8f2bca' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-5c89b779-bcb3-4ddb-9092-cb9a1f8f2bca' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray ()>\n",
       "array(1161)\n",
       "Coordinates:\n",
       "    time     object 0850-01-01 00:00:00"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "state_sum_landfrac.isel(time=0).count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "ea9e245c-56c7-46bc-8750-cef0b656db1d",
   "metadata": {},
   "outputs": [
    {
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       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray ()&gt;\n",
       "array(0.99996804)\n",
       "Coordinates:\n",
       "    time     object 0850-01-01 00:00:00</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-9b480354-3a99-4a7a-8b56-a1692b03f996' class='xr-array-in' type='checkbox' checked><label for='section-9b480354-3a99-4a7a-8b56-a1692b03f996' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>1.0</span></div><div class='xr-array-data'><pre>array(0.99996804)</pre></div></div></li><li class='xr-section-item'><input id='section-3a8f8792-5d0e-4f61-9244-64e576fe7f09' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3a8f8792-5d0e-4f61-9244-64e576fe7f09' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>0850-01-01 00:00:00</div><input id='attrs-42bfeb9b-771e-4a8e-a890-c72934be1beb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-42bfeb9b-771e-4a8e-a890-c72934be1beb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7c5a7695-a1cf-4812-b012-c5dbf725137e' class='xr-var-data-in' type='checkbox'><label for='data-7c5a7695-a1cf-4812-b012-c5dbf725137e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(cftime.DatetimeNoLeap(850, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
       "      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-70874a46-7a86-4c57-8525-520ae6d90251' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-70874a46-7a86-4c57-8525-520ae6d90251' class='xr-section-summary'  title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-807bb31c-f40a-4ea8-8d58-a0a5efb6b884' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-807bb31c-f40a-4ea8-8d58-a0a5efb6b884' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray ()>\n",
       "array(0.99996804)\n",
       "Coordinates:\n",
       "    time     object 0850-01-01 00:00:00"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "state_sum_landfrac.isel(time=0).sum() / state_sum_landfrac.isel(time=0).count()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f54f9d35-b1ab-4fe6-9054-ec244372f534",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Alternate summation method to check against"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "ce95606e-d175-4ecb-a647-8751c67e58d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "regrid_luh2_landfrac_array = regrid_luh2_landfrac.to_array().isel(variable=slice(0,12))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "id": "68a679cf-577e-4580-9afa-2da2d955f90d",
   "metadata": {},
   "outputs": [],
   "source": [
    "state_sum_array = regrid_luh2_landfrac_array.sum(dim='variable')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "id": "38fccbe6-e119-497f-a627-e1a51dd33b67",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(ncols=3,figsize=(12,4))\n",
    "state_sum_array.where(state_sum_array == 1.0).isel(time=0).plot(ax=axs[0])\n",
    "axs[0].set_title('LUH2 state sum == 1.0')\n",
    "state_sum_array.where(state_sum_array > 1.0).isel(time=0).plot(ax=axs[1])\n",
    "axs[1].set_title('LUH2 state sum > 1.0')\n",
    "state_sum_array.where(state_sum_array < 1.0).isel(time=0).plot(ax=axs[2])\n",
    "axs[2].set_title('LUH2 state sum < 1.0')\n",
    "fig.tight_layout()\n",
    "# fig.suptitle('time = 0850-01-01')"
   ]
  },
  {
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   "execution_count": 174,
   "id": "78b5c740-b16a-4a3b-99f8-30d21128e3b5",
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   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray ()&gt;\n",
       "array(0.99996804)\n",
       "Coordinates:\n",
       "    time     object 0850-01-01 00:00:00</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-c1477290-c398-4e48-8b73-cd4f31faeb3b' class='xr-array-in' type='checkbox' checked><label for='section-c1477290-c398-4e48-8b73-cd4f31faeb3b' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>1.0</span></div><div class='xr-array-data'><pre>array(0.99996804)</pre></div></div></li><li class='xr-section-item'><input id='section-d8d52773-1769-422f-bc89-b53f14ef115b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d8d52773-1769-422f-bc89-b53f14ef115b' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>0850-01-01 00:00:00</div><input id='attrs-fd4ac258-8373-43b9-b8b8-cf21278ef1ed' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fd4ac258-8373-43b9-b8b8-cf21278ef1ed' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c6470b85-13ca-432a-a6e3-7922a3548495' class='xr-var-data-in' type='checkbox'><label for='data-c6470b85-13ca-432a-a6e3-7922a3548495' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(cftime.DatetimeNoLeap(850, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
       "      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ac8192b7-a53d-4d6b-a2c6-c72891a56ec9' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ac8192b7-a53d-4d6b-a2c6-c72891a56ec9' class='xr-section-summary'  title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-b1dd9312-7aec-4b41-ab88-93acf8aa9453' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b1dd9312-7aec-4b41-ab88-93acf8aa9453' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray ()>\n",
       "array(0.99996804)\n",
       "Coordinates:\n",
       "    time     object 0850-01-01 00:00:00"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "state_sum_array.isel(time=0).sum() / state_sum_array.isel(time=0).where(state_sum_array.isel(time=0) != 0).count()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f54cdca-5053-4751-8108-a420126c9a2e",
   "metadata": {},
   "source": [
    "**Temporary fix: normalize the states to unity and record the value to apply to transitions**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "id": "ca901e8c-7462-4a06-9345-7ae6ac9327c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 1., 1.])"
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.array([[0.8, 0.6, 0.3],[0.21, 0.39, 0.7]])\n",
    "(a / a.sum(axis=0)).sum(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "id": "a4963b21-a9ed-4190-886b-5308c5871069",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 1., 1.])"
      ]
     },
     "execution_count": 218,
     "metadata": {},
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    }
   ],
   "source": [
    "corrb = 1.0 / a.sum(axis=0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "id": "4494b34e-d107-4d5f-a0bd-e235f3f7e13b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# regrid_luh2_landfrac[\"tcorr\"] = 1.0 / state_sum_array\n",
    "regrid_luh2_landfrac[\"tcorr\"] = 1.0 / state_sum_landfrac"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "id": "466aac3e-7c1b-475e-ba42-0e0349ff0866",
   "metadata": {},
   "outputs": [
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       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "</symbol>\n",
       "</defs>\n",
       "</svg>\n",
       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
       " *\n",
       " */\n",
       "\n",
       ":root {\n",
       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body[data-theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (lat: 46, lon: 72)&gt;\n",
       "array([[0.       , 0.       , 0.       , ..., 0.       , 0.       ,\n",
       "        0.       ],\n",
       "       [0.       , 0.       , 0.       , ..., 0.       , 0.       ,\n",
       "        0.       ],\n",
       "       [0.       , 0.       , 0.       , ..., 0.       , 0.       ,\n",
       "        0.       ],\n",
       "       ...,\n",
       "       [0.       , 0.       , 0.       , ..., 1.0000006, 0.       ,\n",
       "        0.       ],\n",
       "       [0.       , 0.       , 0.       , ..., 0.       , 0.       ,\n",
       "        0.       ],\n",
       "       [0.       , 0.       , 0.       , ..., 0.       , 0.       ,\n",
       "        0.       ]], dtype=float32)\n",
       "Coordinates:\n",
       "  * lon      (lon) float64 0.0 5.0 10.0 15.0 20.0 ... 340.0 345.0 350.0 355.0\n",
       "  * lat      (lat) float64 -90.0 -86.0 -82.0 -78.0 -74.0 ... 78.0 82.0 86.0 90.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 46</li><li><span class='xr-has-index'>lon</span>: 72</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-f490ac69-adce-43fb-91bf-acd0bc8ada35' class='xr-array-in' type='checkbox' checked><label for='section-f490ac69-adce-43fb-91bf-acd0bc8ada35' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0</span></div><div class='xr-array-data'><pre>array([[0.       , 0.       , 0.       , ..., 0.       , 0.       ,\n",
       "        0.       ],\n",
       "       [0.       , 0.       , 0.       , ..., 0.       , 0.       ,\n",
       "        0.       ],\n",
       "       [0.       , 0.       , 0.       , ..., 0.       , 0.       ,\n",
       "        0.       ],\n",
       "       ...,\n",
       "       [0.       , 0.       , 0.       , ..., 1.0000006, 0.       ,\n",
       "        0.       ],\n",
       "       [0.       , 0.       , 0.       , ..., 0.       , 0.       ,\n",
       "        0.       ],\n",
       "       [0.       , 0.       , 0.       , ..., 0.       , 0.       ,\n",
       "        0.       ]], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-39f779dc-78bb-47e4-9396-4ba14988fe5b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-39f779dc-78bb-47e4-9396-4ba14988fe5b' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 5.0 10.0 ... 345.0 350.0 355.0</div><input id='attrs-591d22ee-493a-4fc2-b333-2a4638f5ec07' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-591d22ee-493a-4fc2-b333-2a4638f5ec07' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-472ad0f4-5946-4998-ac87-61ac5c6a2e6c' class='xr-var-data-in' type='checkbox'><label for='data-472ad0f4-5946-4998-ac87-61ac5c6a2e6c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees east</dd></dl></div><div class='xr-var-data'><pre>array([  0.,   5.,  10.,  15.,  20.,  25.,  30.,  35.,  40.,  45.,  50.,  55.,\n",
       "        60.,  65.,  70.,  75.,  80.,  85.,  90.,  95., 100., 105., 110., 115.,\n",
       "       120., 125., 130., 135., 140., 145., 150., 155., 160., 165., 170., 175.,\n",
       "       180., 185., 190., 195., 200., 205., 210., 215., 220., 225., 230., 235.,\n",
       "       240., 245., 250., 255., 260., 265., 270., 275., 280., 285., 290., 295.,\n",
       "       300., 305., 310., 315., 320., 325., 330., 335., 340., 345., 350., 355.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-90.0 -86.0 -82.0 ... 86.0 90.0</div><input id='attrs-12572d2f-4964-4d95-b6ea-7729af0e08a8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-12572d2f-4964-4d95-b6ea-7729af0e08a8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f9936bb5-4426-4ed3-b6db-5340ca103e94' class='xr-var-data-in' type='checkbox'><label for='data-f9936bb5-4426-4ed3-b6db-5340ca103e94' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees north</dd></dl></div><div class='xr-var-data'><pre>array([-90., -86., -82., -78., -74., -70., -66., -62., -58., -54., -50., -46.,\n",
       "       -42., -38., -34., -30., -26., -22., -18., -14., -10.,  -6.,  -2.,   2.,\n",
       "         6.,  10.,  14.,  18.,  22.,  26.,  30.,  34.,  38.,  42.,  46.,  50.,\n",
       "        54.,  58.,  62.,  66.,  70.,  74.,  78.,  82.,  86.,  90.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0e6568e3-99f5-405c-ae36-3d1114d13b33' class='xr-section-summary-in' type='checkbox'  ><label for='section-0e6568e3-99f5-405c-ae36-3d1114d13b33' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-1124a0d2-7689-4133-8063-c2471300c4f9' class='xr-index-data-in' type='checkbox'/><label for='index-1124a0d2-7689-4133-8063-c2471300c4f9' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([               0.0,                5.0,               10.0,\n",
       "                            15.0,               20.0,               25.0,\n",
       "                            30.0,               35.0,               40.0,\n",
       "                            45.0,               50.0,               55.0,\n",
       "                            60.0,               65.0,               70.0,\n",
       "                            75.0,               80.0,               85.0,\n",
       "                            90.0,               95.0,              100.0,\n",
       "                           105.0,              110.0, 115.00000000000001,\n",
       "                           120.0,              125.0,              130.0,\n",
       "                           135.0,              140.0,              145.0,\n",
       "                           150.0,              155.0,              160.0,\n",
       "                           165.0,              170.0,              175.0,\n",
       "                           180.0,              185.0,              190.0,\n",
       "                           195.0,              200.0,              205.0,\n",
       "                           210.0,              215.0,              220.0,\n",
       "                           225.0, 230.00000000000003,              235.0,\n",
       "                           240.0, 244.99999999999997,              250.0,\n",
       "              254.99999999999997,              260.0,              265.0,\n",
       "                           270.0,              275.0,              280.0,\n",
       "                           285.0,              290.0,              295.0,\n",
       "                           300.0,              305.0,              310.0,\n",
       "                           315.0,              320.0,              325.0,\n",
       "                           330.0,              335.0,              340.0,\n",
       "                           345.0,              350.0,              355.0],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-91ca69fe-b367-46e5-b6d0-13ba429066ee' class='xr-index-data-in' type='checkbox'/><label for='index-91ca69fe-b367-46e5-b6d0-13ba429066ee' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([              -90.0,               -86.0,  -81.99999999999999,\n",
       "                            -78.0,               -74.0,               -70.0,\n",
       "                            -66.0,               -62.0,  -58.00000000000001,\n",
       "                            -54.0,               -50.0,  -45.99999999999999,\n",
       "                            -42.0,  -37.99999999999999,               -34.0,\n",
       "              -29.999999999999996,               -26.0,               -22.0,\n",
       "                            -18.0,               -14.0,               -10.0,\n",
       "                             -6.0,                -2.0,                 2.0,\n",
       "                              6.0,                10.0,                14.0,\n",
       "                             18.0,                22.0,                26.0,\n",
       "               29.999999999999996,                34.0,   37.99999999999999,\n",
       "                             42.0,   45.99999999999999,                50.0,\n",
       "                             54.0,   58.00000000000001,                62.0,\n",
       "                             66.0,                70.0,                74.0,\n",
       "                             78.0,   81.99999999999999,                86.0,\n",
       "                             90.0],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-dad82931-a1e7-4b75-a9f1-c7f2fc04b395' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-dad82931-a1e7-4b75-a9f1-c7f2fc04b395' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray (lat: 46, lon: 72)>\n",
       "array([[0.       , 0.       , 0.       , ..., 0.       , 0.       ,\n",
       "        0.       ],\n",
       "       [0.       , 0.       , 0.       , ..., 0.       , 0.       ,\n",
       "        0.       ],\n",
       "       [0.       , 0.       , 0.       , ..., 0.       , 0.       ,\n",
       "        0.       ],\n",
       "       ...,\n",
       "       [0.       , 0.       , 0.       , ..., 1.0000006, 0.       ,\n",
       "        0.       ],\n",
       "       [0.       , 0.       , 0.       , ..., 0.       , 0.       ,\n",
       "        0.       ],\n",
       "       [0.       , 0.       , 0.       , ..., 0.       , 0.       ,\n",
       "        0.       ]], dtype=float32)\n",
       "Coordinates:\n",
       "  * lon      (lon) float64 0.0 5.0 10.0 15.0 20.0 ... 340.0 345.0 350.0 355.0\n",
       "  * lat      (lat) float64 -90.0 -86.0 -82.0 -78.0 -74.0 ... 78.0 82.0 86.0 90.0"
      ]
     },
     "execution_count": 233,
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   ],
   "source": [
    "(regrid_luh2_landfrac / state_sum_landfrac).drop_vars({\"secma\",\"secmb\",\"tcorr\"}).to_array().sum(dim='variable').mean(dim='time').where("
   ]
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray ()&gt;\n",
       "array(True)\n",
       "Coordinates:\n",
       "    time     object 0850-01-01 00:00:00</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-b15ccfb0-fd7f-4061-9eec-96c74369c129' class='xr-array-in' type='checkbox' checked><label for='section-b15ccfb0-fd7f-4061-9eec-96c74369c129' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>True</span></div><div class='xr-array-data'><pre>array(True)</pre></div></div></li><li class='xr-section-item'><input id='section-f9e63d7d-8ee4-4a4f-821a-66950986ea70' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f9e63d7d-8ee4-4a4f-821a-66950986ea70' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>0850-01-01 00:00:00</div><input id='attrs-46b4f729-5e5c-4e36-ad3d-74ff3d039e61' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-46b4f729-5e5c-4e36-ad3d-74ff3d039e61' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4ae8de74-6a8f-4d48-9fbb-c8e44ccbdd8b' class='xr-var-data-in' type='checkbox'><label for='data-4ae8de74-6a8f-4d48-9fbb-c8e44ccbdd8b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(cftime.DatetimeNoLeap(850, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
       "      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a20118d6-b0cd-47b9-83ff-41c30b7de831' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a20118d6-b0cd-47b9-83ff-41c30b7de831' class='xr-section-summary'  title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-feff7ea7-d577-4d96-b343-96d8947eef8f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-feff7ea7-d577-4d96-b343-96d8947eef8f' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray ()>\n",
       "array(True)\n",
       "Coordinates:\n",
       "    time     object 0850-01-01 00:00:00"
      ]
     },
     "execution_count": 191,
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   ],
   "source": [
    "checksum = (regrid_luh2_landfrac_array.isel(time=0) * regrid_luh2_landfrac.tcorr.isel(time=0)).sum(dim='variable')\n",
    "checksum.sum() / checksum.where(checksum != 0).count() == 1.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "id": "ce20316a-66dd-4cb4-8ee4-a37ba579fd1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# regrid_luh2_landfrac[\"tcorr\"] = 1.0 / state_sum_landfrac\n",
    "for i in range(len(regrid_luh2.variables)):\n",
    "    if (not(ds_varnames[i] in coords_avoid)):\n",
    "        regrid_luh2_landfrac[ds_varnames[i]] = regrid_luh2_landfrac[ds_varnames[i]] * regrid_luh2_landfrac[\"tcorr\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "id": "ecf11581-cf94-463e-ac7b-3f32fbdebdd5",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_state_sum = 0.0\n",
    "new_state_sum_landfrac = 0.0\n",
    "for i in range(len(regrid_luh2.variables)):\n",
    "    if (not(ds_varnames[i] in coords_avoid)):\n",
    "        new_state_sum = new_state_sum + regrid_luh2[ds_varnames[i]]\n",
    "        new_state_sum_landfrac = new_state_sum_landfrac + regrid_luh2_landfrac[ds_varnames[i]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "id": "87c61fc9-3ecb-49fb-bf99-b69ea42f14e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(ncols=3,figsize=(12,4))\n",
    "state_sum_landfrac.where(new_state_sum_landfrac == 1.0).isel(time=0).plot(ax=axs[0])\n",
    "axs[0].set_title('LUH2 state sum == 1.0')\n",
    "state_sum_landfrac.where(new_state_sum_landfrac > 1.0).isel(time=0).plot(ax=axs[1])\n",
    "axs[1].set_title('LUH2 state sum > 1.0')\n",
    "state_sum_landfrac.where(new_state_sum_landfrac < 1.0).isel(time=0).plot(ax=axs[2])\n",
    "axs[2].set_title('LUH2 state sum < 1.0')\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f37a5bb-9944-475f-aeb8-9cf0977dcaaf",
   "metadata": {},
   "source": [
    "## Exploration below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "4fb10151-f7ab-424d-95c9-9014849aca7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "state_sum_zeronans = state_sum.where(~np.isnan(state_sum),0)\n",
    "state_sum_landfrac_zeronans = state_sum_landfrac.where(~np.isnan(state_sum_landfrac),0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "7dcbae4f-0270-4cc6-b4d6-07a326ad3b89",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'primf'"
      ]
     },
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    "ds_varnames[0]"
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   "cell_type": "code",
   "execution_count": 87,
   "id": "6dfb2565-9384-4585-a9e2-00086fcc5225",
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       ".xr-attrs dd {\n",
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       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
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       "\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;primf&#x27; (lat: 46, lon: 72)&gt;\n",
       "array([[nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       ...,\n",
       "       [nan, nan, nan, ...,  0., nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan]], dtype=float32)\n",
       "Coordinates:\n",
       "    time     object 0850-01-01 00:00:00\n",
       "  * lon      (lon) float64 0.0 5.0 10.0 15.0 20.0 ... 340.0 345.0 350.0 355.0\n",
       "  * lat      (lat) float64 -90.0 -86.0 -82.0 -78.0 -74.0 ... 78.0 82.0 86.0 90.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'primf'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 46</li><li><span class='xr-has-index'>lon</span>: 72</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-f2aeed53-94c0-498d-b68d-1f62a46ad21f' class='xr-array-in' type='checkbox' checked><label for='section-f2aeed53-94c0-498d-b68d-1f62a46ad21f' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>nan nan nan nan nan nan nan nan ... nan nan nan nan nan nan nan nan</span></div><div class='xr-array-data'><pre>array([[nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       ...,\n",
       "       [nan, nan, nan, ...,  0., nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan]], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-53621684-b54f-4d62-b621-72dc169656b1' class='xr-section-summary-in' type='checkbox'  checked><label for='section-53621684-b54f-4d62-b621-72dc169656b1' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>0850-01-01 00:00:00</div><input id='attrs-bda0c80b-7dea-4731-a85a-0021f763917a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bda0c80b-7dea-4731-a85a-0021f763917a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4c11b5f8-d5e1-4e96-bd03-3e965e8136c6' class='xr-var-data-in' type='checkbox'><label for='data-4c11b5f8-d5e1-4e96-bd03-3e965e8136c6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(cftime.DatetimeNoLeap(850, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 5.0 10.0 ... 345.0 350.0 355.0</div><input id='attrs-4a153625-7c04-44f0-b6f8-8551ebec6abe' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4a153625-7c04-44f0-b6f8-8551ebec6abe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9bfd71ee-ef4a-4e5b-9be6-b1c36ae331d1' class='xr-var-data-in' type='checkbox'><label for='data-9bfd71ee-ef4a-4e5b-9be6-b1c36ae331d1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees east</dd></dl></div><div class='xr-var-data'><pre>array([  0.,   5.,  10.,  15.,  20.,  25.,  30.,  35.,  40.,  45.,  50.,  55.,\n",
       "        60.,  65.,  70.,  75.,  80.,  85.,  90.,  95., 100., 105., 110., 115.,\n",
       "       120., 125., 130., 135., 140., 145., 150., 155., 160., 165., 170., 175.,\n",
       "       180., 185., 190., 195., 200., 205., 210., 215., 220., 225., 230., 235.,\n",
       "       240., 245., 250., 255., 260., 265., 270., 275., 280., 285., 290., 295.,\n",
       "       300., 305., 310., 315., 320., 325., 330., 335., 340., 345., 350., 355.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-90.0 -86.0 -82.0 ... 86.0 90.0</div><input id='attrs-134a3b07-4365-49fa-85de-18a40965c2f7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-134a3b07-4365-49fa-85de-18a40965c2f7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-681959b8-21d2-4b99-a133-4414bfd98a58' class='xr-var-data-in' type='checkbox'><label for='data-681959b8-21d2-4b99-a133-4414bfd98a58' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees north</dd></dl></div><div class='xr-var-data'><pre>array([-90., -86., -82., -78., -74., -70., -66., -62., -58., -54., -50., -46.,\n",
       "       -42., -38., -34., -30., -26., -22., -18., -14., -10.,  -6.,  -2.,   2.,\n",
       "         6.,  10.,  14.,  18.,  22.,  26.,  30.,  34.,  38.,  42.,  46.,  50.,\n",
       "        54.,  58.,  62.,  66.,  70.,  74.,  78.,  82.,  86.,  90.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-16c2949f-6746-4abc-b87a-3913f6ed4340' class='xr-section-summary-in' type='checkbox'  ><label for='section-16c2949f-6746-4abc-b87a-3913f6ed4340' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-04124823-9a80-44ca-9504-760a557ad0a3' class='xr-index-data-in' type='checkbox'/><label for='index-04124823-9a80-44ca-9504-760a557ad0a3' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([               0.0,                5.0,               10.0,\n",
       "                            15.0,               20.0,               25.0,\n",
       "                            30.0,               35.0,               40.0,\n",
       "                            45.0,               50.0,               55.0,\n",
       "                            60.0,               65.0,               70.0,\n",
       "                            75.0,               80.0,               85.0,\n",
       "                            90.0,               95.0,              100.0,\n",
       "                           105.0,              110.0, 115.00000000000001,\n",
       "                           120.0,              125.0,              130.0,\n",
       "                           135.0,              140.0,              145.0,\n",
       "                           150.0,              155.0,              160.0,\n",
       "                           165.0,              170.0,              175.0,\n",
       "                           180.0,              185.0,              190.0,\n",
       "                           195.0,              200.0,              205.0,\n",
       "                           210.0,              215.0,              220.0,\n",
       "                           225.0, 230.00000000000003,              235.0,\n",
       "                           240.0, 244.99999999999997,              250.0,\n",
       "              254.99999999999997,              260.0,              265.0,\n",
       "                           270.0,              275.0,              280.0,\n",
       "                           285.0,              290.0,              295.0,\n",
       "                           300.0,              305.0,              310.0,\n",
       "                           315.0,              320.0,              325.0,\n",
       "                           330.0,              335.0,              340.0,\n",
       "                           345.0,              350.0,              355.0],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-35cf87cf-f584-4f9f-b27f-ef04b2473b7e' class='xr-index-data-in' type='checkbox'/><label for='index-35cf87cf-f584-4f9f-b27f-ef04b2473b7e' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([              -90.0,               -86.0,  -81.99999999999999,\n",
       "                            -78.0,               -74.0,               -70.0,\n",
       "                            -66.0,               -62.0,  -58.00000000000001,\n",
       "                            -54.0,               -50.0,  -45.99999999999999,\n",
       "                            -42.0,  -37.99999999999999,               -34.0,\n",
       "              -29.999999999999996,               -26.0,               -22.0,\n",
       "                            -18.0,               -14.0,               -10.0,\n",
       "                             -6.0,                -2.0,                 2.0,\n",
       "                              6.0,                10.0,                14.0,\n",
       "                             18.0,                22.0,                26.0,\n",
       "               29.999999999999996,                34.0,   37.99999999999999,\n",
       "                             42.0,   45.99999999999999,                50.0,\n",
       "                             54.0,   58.00000000000001,                62.0,\n",
       "                             66.0,                70.0,                74.0,\n",
       "                             78.0,   81.99999999999999,                86.0,\n",
       "                             90.0],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0069eff0-0be9-45ea-a99e-3d937c5e0ba1' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-0069eff0-0be9-45ea-a99e-3d937c5e0ba1' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'primf' (lat: 46, lon: 72)>\n",
       "array([[nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       ...,\n",
       "       [nan, nan, nan, ...,  0., nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan]], dtype=float32)\n",
       "Coordinates:\n",
       "    time     object 0850-01-01 00:00:00\n",
       "  * lon      (lon) float64 0.0 5.0 10.0 15.0 20.0 ... 340.0 345.0 350.0 355.0\n",
       "  * lat      (lat) float64 -90.0 -86.0 -82.0 -78.0 -74.0 ... 78.0 82.0 86.0 90.0"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regrid_luh2[ds_varnames[0]][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "46701465-8c6b-4239-848b-7d2870fc97e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "maxstates = state_sum_zeronans.where(state_sum_zeronans == state_sum_zeronans.max(), drop=True)\n",
    "maxstates_sum = maxstates.sum(dim='time')\n",
    "maxstates_count = maxstates.count(dim='time')\n",
    "# maxstates.sum(dim='time') / maxstates.count(dim='time') == 1.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "36210ec7-eb46-4f46-9714-85e4eb13fe75",
   "metadata": {},
   "outputs": [
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       "               nan,        nan,        nan],\n",
       "       [       nan,        nan,        nan,        nan,        nan,\n",
       "        1.00000007,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan,        nan],\n",
       "       [       nan, 1.00000007, 1.00000012, 1.00000008,        nan,\n",
       "        1.00000009,        nan,        nan,        nan,        nan,\n",
       "               nan, 1.00000007,        nan],\n",
       "       [1.00000008,        nan,        nan,        nan,        nan,\n",
       "        1.0000001 ,        nan, 1.00000008,        nan,        nan,\n",
       "               nan, 1.00000016, 1.00000009],\n",
       "       [       nan,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan, 1.00000012],\n",
       "       [       nan,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan,        nan, 1.00000007,        nan,\n",
       "               nan,        nan,        nan]])\n",
       "Coordinates:\n",
       "  * lon      (lon) float64 0.0 5.0 10.0 15.0 20.0 ... 120.0 125.0 350.0 355.0\n",
       "  * lat      (lat) float64 -26.0 -18.0 -14.0 -10.0 2.0 ... 18.0 22.0 26.0 34.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 11</li><li><span class='xr-has-index'>lon</span>: 13</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-32eeb11c-1d00-460b-a147-33fe5aafd608' class='xr-array-in' type='checkbox' checked><label for='section-32eeb11c-1d00-460b-a147-33fe5aafd608' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>nan nan nan nan nan nan nan nan ... nan nan nan 1.0 nan nan nan nan</span></div><div class='xr-array-data'><pre>array([[       nan,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan,        nan,        nan, 1.00000012,\n",
       "        1.00000012,        nan,        nan],\n",
       "       [       nan,        nan,        nan,        nan, 1.00000012,\n",
       "               nan,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan,        nan],\n",
       "       [       nan,        nan,        nan,        nan, 1.00000011,\n",
       "               nan,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan,        nan],\n",
       "       [       nan,        nan,        nan,        nan, 1.0000001 ,\n",
       "               nan,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan,        nan],\n",
       "       [       nan,        nan,        nan,        nan,        nan,\n",
       "               nan, 1.00000007,        nan,        nan,        nan,\n",
       "               nan,        nan,        nan],\n",
       "       [       nan,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan, 1.00000012,        nan,        nan,\n",
       "               nan,        nan,        nan],\n",
       "       [       nan,        nan,        nan,        nan,        nan,\n",
       "        1.00000007,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan,        nan],\n",
       "       [       nan, 1.00000007, 1.00000012, 1.00000008,        nan,\n",
       "        1.00000009,        nan,        nan,        nan,        nan,\n",
       "               nan, 1.00000007,        nan],\n",
       "       [1.00000008,        nan,        nan,        nan,        nan,\n",
       "        1.0000001 ,        nan, 1.00000008,        nan,        nan,\n",
       "               nan, 1.00000016, 1.00000009],\n",
       "       [       nan,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan, 1.00000012],\n",
       "       [       nan,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan,        nan, 1.00000007,        nan,\n",
       "               nan,        nan,        nan]])</pre></div></div></li><li class='xr-section-item'><input id='section-2606d2b0-8371-4656-9278-db5f5bc8352c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2606d2b0-8371-4656-9278-db5f5bc8352c' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 5.0 10.0 ... 125.0 350.0 355.0</div><input id='attrs-59a040ce-cf59-4d31-a40d-64eb3300ff65' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-59a040ce-cf59-4d31-a40d-64eb3300ff65' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dca2f40c-d1e4-451a-b044-81f6914c61ab' class='xr-var-data-in' type='checkbox'><label for='data-dca2f40c-d1e4-451a-b044-81f6914c61ab' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees east</dd></dl></div><div class='xr-var-data'><pre>array([  0.,   5.,  10.,  15.,  20.,  25.,  40.,  45.,  60., 120., 125., 350.,\n",
       "       355.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-26.0 -18.0 -14.0 ... 26.0 34.0</div><input id='attrs-cb626ec2-6651-402a-a28e-f1f4be593c54' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cb626ec2-6651-402a-a28e-f1f4be593c54' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3e2eabba-aa97-4cc5-ab47-0c6f51a1ed1f' class='xr-var-data-in' type='checkbox'><label for='data-3e2eabba-aa97-4cc5-ab47-0c6f51a1ed1f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees north</dd></dl></div><div class='xr-var-data'><pre>array([-26., -18., -14., -10.,   2.,   6.,  10.,  18.,  22.,  26.,  34.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-306ea59a-9952-4da4-b485-9f95b8c908ec' class='xr-section-summary-in' type='checkbox'  ><label for='section-306ea59a-9952-4da4-b485-9f95b8c908ec' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-4207dfad-fe83-4749-b065-123f6dc839ea' class='xr-index-data-in' type='checkbox'/><label for='index-4207dfad-fe83-4749-b065-123f6dc839ea' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([0.0, 5.0, 10.0, 15.0, 20.0, 25.0, 40.0, 45.0, 60.0, 120.0, 125.0,\n",
       "              350.0, 355.0],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-95b3d4b8-5f3c-4257-9411-97a21e166f94' class='xr-index-data-in' type='checkbox'/><label for='index-95b3d4b8-5f3c-4257-9411-97a21e166f94' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([-26.0, -18.0, -14.0, -10.0, 2.0, 6.0, 10.0, 18.0, 22.0, 26.0,\n",
       "              34.0],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5f6711e2-4c4c-4d7e-a38b-c92e6cd5e26a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-5f6711e2-4c4c-4d7e-a38b-c92e6cd5e26a' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
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       "array([[       nan,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan,        nan,        nan, 1.00000012,\n",
       "        1.00000012,        nan,        nan],\n",
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       "               nan,        nan,        nan,        nan,        nan,\n",
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       "               nan,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan,        nan],\n",
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       "               nan,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan,        nan],\n",
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       "               nan, 1.00000007,        nan,        nan,        nan,\n",
       "               nan,        nan,        nan],\n",
       "       [       nan,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan, 1.00000012,        nan,        nan,\n",
       "               nan,        nan,        nan],\n",
       "       [       nan,        nan,        nan,        nan,        nan,\n",
       "        1.00000007,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan,        nan],\n",
       "       [       nan, 1.00000007, 1.00000012, 1.00000008,        nan,\n",
       "        1.00000009,        nan,        nan,        nan,        nan,\n",
       "               nan, 1.00000007,        nan],\n",
       "       [1.00000008,        nan,        nan,        nan,        nan,\n",
       "        1.0000001 ,        nan, 1.00000008,        nan,        nan,\n",
       "               nan, 1.00000016, 1.00000009],\n",
       "       [       nan,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan, 1.00000012],\n",
       "       [       nan,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan,        nan, 1.00000007,        nan,\n",
       "               nan,        nan,        nan]])\n",
       "Coordinates:\n",
       "  * lon      (lon) float64 0.0 5.0 10.0 15.0 20.0 ... 120.0 125.0 350.0 355.0\n",
       "  * lat      (lat) float64 -26.0 -18.0 -14.0 -10.0 2.0 ... 18.0 22.0 26.0 34.0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "maxstates_sum / maxstates_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "299c918a-9b69-4bdb-9552-6728c30bf7c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "maxstates_landfrac = state_sum_landfrac_zeronans.where(state_sum_landfrac_zeronans == state_sum_landfrac_zeronans.max(), drop=True)\n",
    "maxstates_landfrac_sum = maxstates_landfrac.sum()\n",
    "maxstates_landfrac_count = maxstates_landfrac.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5168875d-2a16-4471-9b7d-d602d328c9cc",
   "metadata": {},
   "outputs": [
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       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (time: 1166, lat: 1, lon: 1)&gt;\n",
       "array([[[1.0000132]],\n",
       "\n",
       "       [[1.0000132]],\n",
       "\n",
       "       [[1.0000132]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[1.0000132]],\n",
       "\n",
       "       [[1.0000132]],\n",
       "\n",
       "       [[1.0000132]]], dtype=float32)\n",
       "Coordinates:\n",
       "  * time     (time) object 0850-01-01 00:00:00 ... 2015-01-01 00:00:00\n",
       "  * lon      (lon) float64 260.0\n",
       "  * lat      (lat) float64 82.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1166</li><li><span class='xr-has-index'>lat</span>: 1</li><li><span class='xr-has-index'>lon</span>: 1</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-3299e3c4-3823-4484-bb2f-04b780050f9c' class='xr-array-in' type='checkbox' checked><label for='section-3299e3c4-3823-4484-bb2f-04b780050f9c' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 ... 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0</span></div><div class='xr-array-data'><pre>array([[[1.0000132]],\n",
       "\n",
       "       [[1.0000132]],\n",
       "\n",
       "       [[1.0000132]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[1.0000132]],\n",
       "\n",
       "       [[1.0000132]],\n",
       "\n",
       "       [[1.0000132]]], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-918856bc-c0bd-4282-ba7c-f376bb79de44' class='xr-section-summary-in' type='checkbox'  checked><label for='section-918856bc-c0bd-4282-ba7c-f376bb79de44' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>0850-01-01 00:00:00 ... 2015-01-...</div><input id='attrs-0136c3ea-2f63-466f-97af-5bbf36f630df' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0136c3ea-2f63-466f-97af-5bbf36f630df' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4c07cb9a-81b5-4b1f-8d29-2fd17fcdfbab' class='xr-var-data-in' type='checkbox'><label for='data-4c07cb9a-81b5-4b1f-8d29-2fd17fcdfbab' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(850, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(851, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(852, 1, 1, 0, 0, 0, 0, has_year_zero=True), ...,\n",
       "       cftime.DatetimeNoLeap(2013, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2014, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2015, 1, 1, 0, 0, 0, 0, has_year_zero=True)],\n",
       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>260.0</div><input id='attrs-3481842f-5529-4d42-ba07-2c4f7145e243' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3481842f-5529-4d42-ba07-2c4f7145e243' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-69120fac-32d7-4ff8-89b9-0dbb455138c1' class='xr-var-data-in' type='checkbox'><label for='data-69120fac-32d7-4ff8-89b9-0dbb455138c1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees east</dd></dl></div><div class='xr-var-data'><pre>array([260.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>82.0</div><input id='attrs-ead2ab19-77fe-4bc7-bcf4-fd8316c304eb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ead2ab19-77fe-4bc7-bcf4-fd8316c304eb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-13891791-ceb8-45d9-a0a6-90d9143fd02e' class='xr-var-data-in' type='checkbox'><label for='data-13891791-ceb8-45d9-a0a6-90d9143fd02e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees north</dd></dl></div><div class='xr-var-data'><pre>array([82.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0fe5d4d7-54c1-4767-90bf-1a26b67406d2' class='xr-section-summary-in' type='checkbox'  ><label for='section-0fe5d4d7-54c1-4767-90bf-1a26b67406d2' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-32103976-80d5-4317-9478-975785c64b80' class='xr-index-data-in' type='checkbox'/><label for='index-32103976-80d5-4317-9478-975785c64b80' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(CFTimeIndex([0850-01-01 00:00:00, 0851-01-01 00:00:00, 0852-01-01 00:00:00,\n",
       "             0853-01-01 00:00:00, 0854-01-01 00:00:00, 0855-01-01 00:00:00,\n",
       "             0856-01-01 00:00:00, 0857-01-01 00:00:00, 0858-01-01 00:00:00,\n",
       "             0859-01-01 00:00:00,\n",
       "             ...\n",
       "             2006-01-01 00:00:00, 2007-01-01 00:00:00, 2008-01-01 00:00:00,\n",
       "             2009-01-01 00:00:00, 2010-01-01 00:00:00, 2011-01-01 00:00:00,\n",
       "             2012-01-01 00:00:00, 2013-01-01 00:00:00, 2014-01-01 00:00:00,\n",
       "             2015-01-01 00:00:00],\n",
       "            dtype=&#x27;object&#x27;, length=1166, calendar=&#x27;noleap&#x27;, freq=&#x27;AS-JAN&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-8b3e0117-ba17-409d-bdcf-b701eec5640b' class='xr-index-data-in' type='checkbox'/><label for='index-8b3e0117-ba17-409d-bdcf-b701eec5640b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([260.0], dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-2e7e02a0-419f-4911-a10e-53fd085d766b' class='xr-index-data-in' type='checkbox'/><label for='index-2e7e02a0-419f-4911-a10e-53fd085d766b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([81.99999999999999], dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9aced491-5a68-4930-b25e-34032c6fe702' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-9aced491-5a68-4930-b25e-34032c6fe702' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray (time: 1166, lat: 1, lon: 1)>\n",
       "array([[[1.0000132]],\n",
       "\n",
       "       [[1.0000132]],\n",
       "\n",
       "       [[1.0000132]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[1.0000132]],\n",
       "\n",
       "       [[1.0000132]],\n",
       "\n",
       "       [[1.0000132]]], dtype=float32)\n",
       "Coordinates:\n",
       "  * time     (time) object 0850-01-01 00:00:00 ... 2015-01-01 00:00:00\n",
       "  * lon      (lon) float64 260.0\n",
       "  * lat      (lat) float64 82.0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "maxstates_landfrac"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1aab4aca-738b-4295-af94-7d83af0980ec",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
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   "codemirror_mode": {
    "name": "ipython",
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