{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "24e71e1d-e45e-4ebe-adfb-8ef52d66018e",
   "metadata": {},
   "source": [
    "# Figure 2b. Representative simulation examples.\n",
    "\n",
    "Representative simulation examples illustrating limitations of competing methods: (i) methods under “one-causal” assumption fail to detect causal signals with heterogeneous effects and (ii) spurious detection of non-causal variants with strongest marginal effect; (iii) COLOC (V5) shows reduced sensitivity to weak causal effects in GWAS."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5fdcc9d6-5752-4a9b-998e-d38cc949b43d",
   "metadata": {},
   "source": [
    "## 1. Heterogeneous effects for two causal variants\n",
    "\n",
    "When analyzing multiple causal variants, it's important to consider that they may exhibit heterogeneous effects across different contexts, populations, or individuals. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ef61800-a274-40f1-ae9e-d80cf7b24789",
   "metadata": {},
   "source": [
    "### ColocBoost identify two CoS including two true causal variants."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e62724ce-8683-4d6b-84c6-c6a23ddecf91",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "── \u001b[1mAttaching core tidyverse packages\u001b[22m ──────────────────────── tidyverse 2.0.0 ──\n",
      "\u001b[32m✔\u001b[39m \u001b[34mdplyr    \u001b[39m 1.1.4     \u001b[32m✔\u001b[39m \u001b[34mreadr    \u001b[39m 2.1.5\n",
      "\u001b[32m✔\u001b[39m \u001b[34mforcats  \u001b[39m 1.0.0     \u001b[32m✔\u001b[39m \u001b[34mstringr  \u001b[39m 1.5.1\n",
      "\u001b[32m✔\u001b[39m \u001b[34mggplot2  \u001b[39m 3.5.1     \u001b[32m✔\u001b[39m \u001b[34mtibble   \u001b[39m 3.2.1\n",
      "\u001b[32m✔\u001b[39m \u001b[34mlubridate\u001b[39m 1.9.4     \u001b[32m✔\u001b[39m \u001b[34mtidyr    \u001b[39m 1.3.1\n",
      "\u001b[32m✔\u001b[39m \u001b[34mpurrr    \u001b[39m 1.0.4     \n",
      "── \u001b[1mConflicts\u001b[22m ────────────────────────────────────────── tidyverse_conflicts() ──\n",
      "\u001b[31m✖\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mfilter()\u001b[39m masks \u001b[34mstats\u001b[39m::filter()\n",
      "\u001b[31m✖\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mlag()\u001b[39m    masks \u001b[34mstats\u001b[39m::lag()\n",
      "\u001b[36mℹ\u001b[39m Use the conflicted package (\u001b[3m\u001b[34m<http://conflicted.r-lib.org/>\u001b[39m\u001b[23m) to force all conflicts to become errors\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>622</li><li>1275</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 622\n",
       "\\item 1275\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 622\n",
       "2. 1275\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1]  622 1275"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "library(tidyverse)\n",
    "library(colocboost)\n",
    "data(Heterogeneous_Effect)\n",
    "data <- Heterogeneous_Effect\n",
    "X <- data$X\n",
    "Y <- data$Y\n",
    "data$variant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1c43f09f-20f5-49cd-8c4d-fd206d4e2f1d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Starting checking the input data.\n",
      "\n",
      "Starting gradient boosting algorithm.\n",
      "\n",
      "Boosting iterations for outcome 1 converge after 51 iterations!\n",
      "\n",
      "Boosting iterations for outcome 2 converge after 56 iterations!\n",
      "\n",
      "Starting assemble analyses and results summary.\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<dl>\n",
       "\t<dt>$`cos1:y1_y2`</dt>\n",
       "\t\t<dd>622</dd>\n",
       "\t<dt>$`cos2:y1_y2`</dt>\n",
       "\t\t<dd><style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>1293</li><li>1270</li><li>1288</li><li>1287</li><li>1305</li><li>1300</li><li>1275</li><li>1271</li><li>1273</li><li>1290</li><li>1291</li></ol>\n",
       "</dd>\n",
       "</dl>\n"
      ],
      "text/latex": [
       "\\begin{description}\n",
       "\\item[\\$`cos1:y1\\_y2`] 622\n",
       "\\item[\\$`cos2:y1\\_y2`] \\begin{enumerate*}\n",
       "\\item 1293\n",
       "\\item 1270\n",
       "\\item 1288\n",
       "\\item 1287\n",
       "\\item 1305\n",
       "\\item 1300\n",
       "\\item 1275\n",
       "\\item 1271\n",
       "\\item 1273\n",
       "\\item 1290\n",
       "\\item 1291\n",
       "\\end{enumerate*}\n",
       "\n",
       "\\end{description}\n"
      ],
      "text/markdown": [
       "$`cos1:y1_y2`\n",
       ":   622\n",
       "$`cos2:y1_y2`\n",
       ":   1. 1293\n",
       "2. 1270\n",
       "3. 1288\n",
       "4. 1287\n",
       "5. 1305\n",
       "6. 1300\n",
       "7. 1275\n",
       "8. 1271\n",
       "9. 1273\n",
       "10. 1290\n",
       "11. 1291\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "$`cos1:y1_y2`\n",
       "[1] 622\n",
       "\n",
       "$`cos2:y1_y2`\n",
       " [1] 1293 1270 1288 1287 1305 1300 1275 1271 1273 1290 1291\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res <- colocboost(X = X, Y = Y)\n",
    "res$cos_details$cos$cos_index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1e590c83-5831-4132-837a-d46f7e9015a3",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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YEHDx6MqfJbu+XatWvHY9MAAAAAQEXWPYewdevWM2fOFELk5eU9/vjjZWVljqkK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AAIANrkkIJZ06derUqdOsWbPMZvPJkydzcnIKC5fjFkwAACAASURBVAuNRmNISEhY\nWFhkZGTz5s39/Kptkxw4cKALgwEAAAAA2ObKhPD/VxoQIE9HAQAAAADQJhfcQwgA9mPgFgAA\nAO1wfQthcXHxwYMHDx48mJ2dnZ+fX1paWqdOnYiIiLZt23bu3Ll+/fou3yKAyoJ69izPyVY7\nCvVJk0+Y4uP9z2WaGzdWOxwAAABtcWVCuGnTpiVLlqxfv95kMlVXpmvXro8//viDDz5Yq1Yt\nF24aQAX1589TOwQNKRo6pGjoENtlaLoEAAA+yDVdRvPy8u6///4BAwZ8/fXXNrJBIcTevXvH\njRvXrVu3I0eOuGTTAHTDTSkZmR4AAEB1XJAQGo3GoUOHWk8qWKM//vjjtttuO3HihPKtA/A6\nZGgAAAAa4YIuo7Nnz962bdv/VRcQ0K9fv379+rVt2zY6Ojo0NLRWrVrXrl27cuVKenr6L7/8\nkpKSkp2dLYTIzs4eNmzYgQMHbExEAUCvpFv7rP/15OYAAAAgMVgsFiXrFxQUNGvWLC8vTwjR\nv3//jz76KCYmxkb54uLiOXPmvPrqq+Xl5UKI5cuXP/TQQ0oCcM6CBQvS09OFELNmzapdu7a7\nNxcbGyuEkLYIQCYnaR5rM7SRFtJuCQAAvEVycrIQYtmyZcqrUtpCmJqaKmWDt9xyyw8//FDj\nUDEhISEvv/xyRETEs88+K4RYsWKFKgnht99+K7VqvvTSSx5ICAHPM+0/YCkuDureTe1AqlYh\nMZP+VZ6SVahWrtB85mzpwT9DkpIU1g8AAKAzSrtrbt++XVp4/fXX7R84dOLEiW3atBFC/PLL\nL0ajUWEMACq78tTE7LEPqx2FR1Vu/UtLS5MezJ89O2f8BHP19y3TPAgAAHyT0oQwMzNTCOHn\n59e3b18HturnN3jwYCFESUmJVAMA17IYjRat/tpSXb9N993mJ+0Ki9FI4gcAAGBNaZfRK1eu\nCCHq168fGBjo0IrNmzeXFrKzs1u1auXc1qdNm3bw4EEnVpTXGjNmTFBQkPVTs2bN6tChg3Px\nANA+KSf0/O2LAAAAGqQ0IZTywJKSEkdXlAaVEUIoGWV09+7d8gCnztm8eXOFR55//nklFQJQ\nhY3WxbS0tLq5uUH/+yB5IAAAgFDeZbRBgwZCiMLCQkd7fspDbjZq1EhhDAAqSEtLM5lMFovF\nd+ZasCfBO3LkiAciAQAA8CJKWwjj4uKkhS+++OKFF16wc63CwsLvvvtOCFG7du2oqCint24w\nGOTlFi1a9O/f384VU1JSLl68KIQYPXp0SEiI9VORkZFOxwOorsqBVbTWGmZjVsDqHq/wEpye\nw9BVw5kCAADog9KE8Pbbb586daoQ4o033hg0aJA9d9+Zzebx48dfvnxZCNGvX7+AAOdjWLhw\nYXJy8u+//y6EyMjIOHHixJw5czp37lzjiomJiVJCOHfu3CZNmjgdAOAVNJUTOtdoaf0Sqpuy\nwv7Z56ubnQIAAMDXKO0yevPNN0sJWH5+fq9evebPn19YWGij/Pbt2xMTE1etWiX9+8QTTyjZ\nevv27ffs2TNz5kxpYJht27Z17dr1+eefLy4uVlKtQ4xG4981KS0ttVgsHgsJPkvjHUTlSSAq\nsDMfk9a1/RqdS+00vt8AAADcR2kLoRDinXfe6devX3l5eV5e3uTJk6dOndqzZ8/4+PjmzZuH\nh4cHBAQUFRVlZ2cfO3Zsx44d58+fl1ccMmTIgAEDFG7d399/2rRpd999d3Jy8t69e8vLy+fO\nnbtu3bpPP/20Z8+eCiu3x4wZM956660ai9WrV88DwQAyU6dOfnm5akdhF/vzMXtKVtlOaIqP\n9z+XaW7c2OHgAAAAdM3gkparxYsXO9rW17Vr182bN4eHhyvfuqSsrGzevHmvvPKKNOSpn5/f\n008/PXPmzNDQ0MqFExMTpeFJL1y4oLDLqNForHFAnb59+wYEBJyoflJswCVs5Eta6BXp7oY4\n69fo6La0sH8AAADslJycLIRYtmyZ8qqUdhmVTJgwISUlxc7hYfz8/J577rnU1FQXZoNCCH9/\n/xdeeGH//v09evQQQpSXly9YsKBjx44K56WoUVBQUKua1KpVy3r8G8BNfDmr8eXXDgAA4DTX\nJIRCiMGDB6enp69YsWLgwIF169atXCAgIODGG298+eWX//7777lz51YY29NV2rZtu2PHjnnz\n5kkNgydPnuzTp8/EiRNt39kI6JvGkyX7w9P4CwEAAPA6LriHUBYSEjJmzJgxY8YIIU6dOnXp\n0qW8vLzS0tI6derUrVs3NjY2ODjYhZurjp+f3+TJk++6665HHnlk+/btFovl/fffT0lJ+eST\nT/r16+eBAAAVSSmT07MyaJ/9Q4kCAACgRq5MCK3FxMTExMS4qXJ7tGnT5ueff37//fenTp16\n7dq106dP9+/ff/z48XPmzKldu7aKgQEeoMEk0PYEg/akefKLqlBY+YvV4O4CAADwDHclhFpg\nMBgmTpyYlJT0yCOPpKamCiE++uijjRs3rly5Uu3QALcz7T9gKS4O6t5N7UDs5VBWVmPhCkmj\nf9alWidPlPToUWVhTc3TCAAA4El6TgglMTExW7ZsWbJkyT//+c+CgoLTp0/37t3btePZABp0\n5amJZZcvRx0/qnYgNfBM79bwFSuCd+w0f7DIHB3tpk0AAAB4I3sTwsOHD+fn57sjgnbt2tWp\nU8cdNcsMBsOECRMGDx786KOPbtq0qaysLC8vz61bBFRnMRotRqPaUThGTg5dnhkaSkvlvwAA\nAJDZmxA++eSTbpq/ITU1NTEx0R01V9C8efONGzd+8skn//jHP9yU3AJwCfpwAgAAeIb+u4xW\n8Oijjw4aNGj+/PnS/PVhYWFqRwSgCuSEALSjwqhXnJ0A6InPJYRCiOjo6Dlz5qgdBeBbnJgu\nwpM5IfkngOpUPndxxgCgJ/YmhLNmzcrJyXFHBB06dHBHtQA0xfOTBzqagnKFB8B+nDEA6Ia9\nCWH37t3dGgcAHfNMNli5Txc5IQCFPP9jFgB4mC92GQXgSR64nKpyE1J250RXVVepvF2yTQAA\noDUkhICuyElInbi4enE3qBuMcF02qHBEB1N8vP+5THPjxi4Jxh42clSPxQAAAFAj9yaEL7/8\ncnFxsRDiiSeeaN26tVu3BcA6Ccmf9Ey+EBnqZSBuSgXlB+15XXKxoqFDioYOqbE82RqAClTs\nZQDAOfTQcZR7E8KFCxdKU8DfeeedJISAW1V3yeK9rVKuug7z/PUcl4+Ailw+RUSV5xAvPa8C\nukcPHSf4qR0AALfzfH7iki3arkThJqr8YkhLSyOXA7xalVNEKK/W+owh3ZysvE4AXiHNitqx\nuAsJIQA1Kb+uqjFvrO7HQucqBKBZNjpKKK884b+UVwXATVz+DV6hQr1eIZAQAvBW7vu5Tkm1\nev22AADApzj6g7L3IiEE9KlW+onAvw6pHUUNPPZbu3/WpeBduzywIXu+J/Td7QQAALV45rpC\nf1/iTDsB6FPEnLl+ubmX1qzy/KbtPFHKZ20PDPoSvmJF8I6d5g8WmaOj3bcVO7PBCgsaGQaW\njnAAAPgmWggBnah4c4vJJEwm+SmPhWFPUuT5+3AMpaXyX3voPjty08AbAACoqLrvMt1/rStE\nCyGgK9Ipz/qEqLWToByPljMQT45P7fmmQv3NUAIAgMfo77uShBDQFflaPzAwsMxgsD5neSBL\ndKjPpMZ5PjsiHwMAwGm2xw934hvW8/MYq8W9XUY3b968e/fu3bt333zzzW7dEADxv6dCk8lk\nsVjkx31k3OTKlKRYvrOXAH2o7njnpxYAzqlw9tDr3DPubSHs3LmzW+sHIKtucOTqzlzuaI/S\n2m9pujxrQ0XSx5vPlZZp7SwEr8OAW6jA+lYXvX4L0GUU0AOmWa+S8teu5bsxncYVs6OqbGDX\nzedBf+RbqXmP4KgqB9yy/4NEMqlXuv8WYJRRAK6kp/NjBbaTKDelWGmVuGMrEh2/d46y3tU2\nxuDxYERwGJ9nOErJQV3l+ZmzhKbwdthAQgh4vSrPcaZOnUw3dvJ8MEKT12Gm+HhzdLS5cWM3\n1e/JrxmXbEuvd0EoVyEV5AICQI3nAU4U+uYLPwvSZRTQkMonF6ev2vMnPaM4HP0oGjqkaOgQ\n5fV4uBOaW+eHUNgzSpcc/XbXX68hwHup1V2zxs4jnCI8Q0/pmeeRECq1ffv2H374wXaZnJyc\n0NBQz8QD7+XoqDA21pKpdbeYvu9Sc99lh8d2mstTTc/PpuhyTu98LvjgCzR+Q7Vav3Dp+JvO\nK6i+/3Xw3SchIVTq/PnzNX4cjUZjUFCQZ+IBrKl1rnTHdjWSZHKXSAX6uNVe4ZtITggbvH2g\nEe33JnBrZwpR/Vvm4yd/V3Hutwat7Xwv/e6TkRAqdf/9999///22y8TGxnomGOiS7akjnK7W\nJactF/ZxtU0j2aA+eGBPau16sYIK1x98tOA+LsmmPHamtWfT8uNaPsad446zgf72kmt58idF\np6v1ha8JEkJA/yqfy9z3FaX7k6YWaOcKQ0k3S3lZ3dsy+cTCfVzy6bL/bgJvb4rUggpfly65\nW5s3ojLlnTKcW8vdOaH3vt0khEBFWusR7lwYtdJPGIxGU/t44eBP1Pb/FO2ZK2mXvAv+WZdq\nnTxR0qOH8qq0wH1fOZ75zFf+SdjOz5hGDkl4EW1en1WOqrokxMZp1s6hLzX48l3FTd9Bdu4x\n5T+H6fitqY6dO007h60UiUPvtXaCdwgJITxExR4v9nN51wV3X87aOE9FzJnrl5t7ac0qG7HV\n+AOzjZIe45Kv/PAVK4J37DR/sMgcHa28Nm9n42Pjgffa6eaO6oop3DT0Sn67tX/9Xd1PJG5q\nYKxyP6g+ZoyjX5fqHs5Ot1BV/tfzI9+4vBXUuTBUYeeXiHWoWgjbA0gI4S7WR111l4BCw9/Q\nsupeiBNfVx7NrEwmYTJ5aFuaZygtlf/CKziXsqp+Uatjrs3GVfndQZtsfD96ZouVfyGy89vZ\n/sOtxgod/bq0c/9UboZ1083zrq3NtUdHjf3kPXNl4u2HpKM1eN0XEAkhXMzdP6vYrtN9rQc2\nTqk1/tpq/1rVrWKjsJJ7ne2/RPPGsxuqY+MD4+i7XN3Hz4lPi/LPmFq/vsub0+UxUvmU7tyb\nW/kRz9/J7NBGbV+mK/zpQVMfFVe1Q9b4Vejo56fK3noO7brKba1KmosV7ij7V/f8ycTdW3R0\n11VuIdfUIaNLJIRwGad7ULhwlGFVrsmc26gGG0i96Ac8aIcnhyyywZ5Pr7tHitNfTujWPuRa\n3l02Pic1trdIpJdW40dOy2ddR9+gyl9qdn5+HN3bysdodbQe5amgEzF7uDHTVdckburn7KqO\n3xrviqwiEkIo5QsHj/sSUZe3irhPhd/qfOF9t93nWQu0kIm5artOX5zV2FfNuoCPfHTdzZMX\n057nqgbGKj9sFWrQ+AfSidg0ledrp4Oolt9la0rePs+8Rq/Yk9o5BOznp3YA8G6ePDJd3mtf\n5sJqfYSP7DRX/STpDlV+dO18X9R9OS7funOdkaAFXncmcSjg6hqmdP+9Y+erc3o/KH8XPLa6\nN/LAm6I1XBAKWghRJTt7dHjvdVWFlyO9Xo3/UgtVaOrXbnvYGXB1n3bPvF57epk6dDx675Fb\n3Q7XSNuvN7LxYdDUbvS6c4tD7O8oq7WDV2vxKOSZCxtv2Wl2jqzum0gIfZQ9fSClBdsj1Ls8\nGIcGaFG+OesHleeESn5Xc2jrrurrDzvp7zvD9k07bvpoOZrkuORSxsNXAPbvPTmMyrulyrZf\nTR3vDgVj+3107UvzlmxQor8Ti9Y4uod1+Y7wY7f4705gP9hAl1Ff5MnuFo5yuhecOzbt4dVt\nXzNVLmZ7FVOnTqYbOykJSU9M8fHm6Ghz48ZqB+IJGvnOq9ADx7lDO+G/XBiVq6pSwp6D3f5V\nNM7rItdg6gh30HgvQY2H511Uv5TVPloIfY72P6bWPxg7Gq23DNDiHIfCy5/0jPsi8TpFQ4cU\nDR3i3Loa/1RUybqF38azNlZ3dCi2yqOE27O5GnsH1FhDjbT29rm7v4NrRzy2Z1gU9/FYv9nK\nx4vHGlVovdECjb8FWusgAF0iIYRb1DjwtFu3qEsa/8aCV1DYO8CtHcirS0v09MlX3o/AiTH9\n7Qymuv1f+X5yz+Rp7u43a+PHC91/m0CXqjsw9XQKhfvQZRRuYaOfmKM1uKk84Ausjwv7jxEb\nQ864Jqzq1djRVOPcnUvYs2dctdPs7OVbY6822/vEA/eNy3XKodqu35P99Gj8gT3s6dbh6CqA\nNVoI4S5Kvufs+c52Gl10fJCNDo32rOtrHxgtvF4txOBC7u4p6qoa0v539jzbfY8rr+hoGMpn\nGHe6i6x2aC0eaFPlQ8x910juqBYaR0IIN9Ls95wPXuLbSU97hm81qMtNh5K7j1CnG4erzB7d\n3ddDvkrWzYkLqI71IebQIencVoTbbu2GBpEQQj/sHAlDTzmPDbXSTxiMRlP7eIfW0seeqfwx\nMJ85W3rwT9GkiSrxwHdo5wiqrjHNM/1+7S/pkhvOtbPbAQ9QvUszR5z+GCwWi9ox6F9sbKwQ\nIj09XZWt++Bxq+9xKezU8PEn/HJzL61ZpXYgapI/CVeefKpo7bom21IP5OWpGxKACqQf6Xzk\npzrA63imq6rOeKaPUnJyshBi2bJlyquihVDPfPagtR4pzmd3gjCZhMmkdhAqk3/dtBiNQojD\n+/eLmBi1gwLwP9x60zgAV+Eg1TESQt3iuGUPQPz3Y9BS7TAAAPBGXE05yhuHMGDaCX3i6AWs\n5ebmqh0CAACAFumwhfDo0aM///zz0aNHjx07duHChWvXrhUWFprN5rCwsPDw8Hr16rVt2zYu\nLq5Lly49evTw9/dXO14AAAAAUId+EsK8vLz58+evXLnyxIkTVRbIzs6WFnbu3CktNGrUaPjw\n4VOmTInR121FNA8CAAAAnqdkIm616KTL6LJly2JiYl599dXqssEqXb58ecmSJTfccMM//vGP\n0tJS94UHAAAAABqkhxbCl1566c0336zwYFhYWHR0dFRUVGhoaHBwcEBAQElJSXFxcVZWVkZG\nxtWrV+WSJpNp3rx5Bw4c+Oabb+rUqePZ2AEAAABANV6fEK5du1bOBg0GQ79+/R544IFbb71V\nmvqvOllZWTt27Pj222+/+uork8kkhNiyZcv48eNXr17tiaDdif6ikJg6dfLLYySV/2OKj/c/\nl2lu3FjtQAAAALTFuyemLysra9GiRWZmphCiZcuWX3zxRffu3R2qITMz85FHHtmwYYP077p1\n6+666y6Xx+nJielJCAEbfH12SgAA4GZeNzG9d99DmJqaKmWDderU2bhxo6PZoBAiKioqJSVl\nwIAB0r+ffPKJi0MEAAAAAK3y7oRQbtl79NFHbfcRtcHf33/RokUGg0EI8dNPPxmNRpfFB0BL\n5F/svG74LwAAADfx7oRQah4UQgwcOFBJPbGxse3atRNCmEym8+fPuyAyABpTIQkkJwQAABDe\nPqiMPLVgkyZNFFYVHR196NAhIURWVpZD0xK+8cYb8+bNs10mPz+/bt26iuID4CxyPwAAgOp4\nd0IoZ1k5OTkKq7py5Yq04OjME4888ki3bt1sl3nzzTeZ0AIeViv9hMFoNLWPVzsQTTCfOVt6\n8M+QpCS1AwEAANAW704I5YbB3bt39+nTx+l68vLypOZBIUTTpk0dWjcyMjIyMtJ2mc8//9zJ\nyByXkJDACIoQQkTMmeuXm3tpzSq1A9GE/Nmzi9aua7ItNaBNG/lBDhYAAADvvoewZ8+e0sKS\nJUvy8vKcrmfevHlFRUVCiE6dOumgbycd5JyWkJCg4t5z8aZNJmEyubJCT7H/XbCnmFTGYjTK\nfx2tAahM3XMFoHs6O76083IS/kvtQPTMG3evd7cQJiUlhYaGFhUVnTlzZuTIkWvWrKlXr56j\nlXz44YdvvPGGtDx69GhXx6gOhU0f1a1uf7U1Hgxaa5mxDlhedjpIeya7q3IXOTdLXuX3JSEh\n4UJgYJnBoPAts7FuhfL2f2CqjFaqofK7IJW08XGy/QLtzxirjFNaqBCYNfmp6l5+dU+5hIfn\nVPSpKRxtnwTsGa62xh3lvgbqGs9g1mHXeIjZrqpyhdXVrO4nx6ETReXjWl698oJ1DWq9RnvO\nxg7VZudLq/LNtf1Zsic26+OrxpoderF2HnSVN+TQ57/KL7gqq61uLQ+cHITrjs0K9dj4VFTe\nM/afo2xUJdXjxMtx7YFTXbXewrsnphdCvPnmmy+99JK0HBUV9fTTTz/wwAPR0dE1rnjt2rUf\nf/xx4cKF27dvlx5p06bNgQMHQkNDXR6kCyeOBOx0oXOXssuXozNOqR2IJuQ88mjxTxuu2/hT\nrXhuqgQAAF7PhfmFd7cQCiGmTJmyYcOGHTt2CCEyMzOnTp06derUFi1a3Hjjjc2bN2/atGlY\nWFhwcLC/v39JSUlJScnly5fPnDlz7NixAwcOmM1muZ6IiIgvvvjCHdkgAAAAAGiT1yeEAQEB\nGzduHDt27H/+8x/5wYyMjIyMDPsradOmzbp16+Li4twQIAAAAABolHcPKiMJDg5es2bNxo0b\nu3fv7ui60dHRCxcuPHToENkgAAAAAF/j9fcQVnD69On169dv37792LFj6enpJSUlFQoYDIbm\nzZvfcMMNCQkJQ4YM6dKli8FgcHdUycnJu3btSkxMdPeGrO3fv//8+fO1a9f25EbhcpcvX65X\nr15AgMON+Z/nF9YX4o464e6Iyuu8WlTco9T8RHjoSX9/z2/dZDIVFhbWr1/f85uGC+Xm5gYF\nBYWEhKgdCJxXXl6enZ3dqFEjD3z1w32uXbtWVlbGDM/eLjs7u1evXg0bNlQ7EG/1888/9+jR\ng3sIq9CyZcunn3766aefFkKUl5fn5eVdu3atsLDQbDaHhoaGh4dHREQEBQV5OKp77rknMDDQ\nwxvNysrKz88nIfR2OTk5oaGhTiSED5AKWnk1VM2L+KKioitXrpAQervc3Nzw8HASQq9WWlqa\nk5PToEEDfzV+G4KrFBYWlpaWkhB6u5ycnKtXr5IQOi0xMXHIkCEuqUpvLYSQPfnkk1euXFm9\nerXagUCRWrVq/fjjj/3791c7EDhv2bJl//rXv/7++2+1A4Eit956a1JS0osvvqh2IHDeoUOH\n2rdvf+nSpUaNGqkdC5w3efLk06dPf/vtt2oHAkXCw8PXrFmTlJSkdiDQxT2EAAAAAAAnkBAC\nAAAAgI8iIQQAAAAAH0VCCAAAAAA+ioQQAAAAAHwUCSEAAAAA+CgSQgAAAADwUSSEAAAAAOCj\nSAgBAAAAwEeREAIAAACAj/J/9dVX1Y4BbhEYGPj/2jvzqKiO7I9XN0uz2ixiaJDI5hohJsEl\niiOoCUbUOJJjjJLRRCfRaBzJ5oLJaNTo0dFJ9CQaR40aXEiioJioR6MIKHjUAYIB1GkFBAFl\na2k2obt/f9Tv3FOnl9eNbdL1quvzV/V7t75dr6rv63vfUtW/f/8BAwbYuiEcq9DpdJMmTXJ3\nd7d1QziPj4eHh4+Pz4gRI2zdEI5VSKXSkSNHBgQE2LohnMfH3d1dq9XGx8dLpfyCuIiRyWTh\n4eGDBg2ydUM4VqHVaidOnOjp6WnrhnCQRKfT2boNHA6Hw+FwOBwOh8OxAfwKGYfD4XA4HA6H\nw+HYKY62bgDnCVNbW5uWllZYWFhTU9Pe3q5QKEJDQ1999dWIiAhbN40dsrOzL1y4UFxc3NDQ\nIJVKvb29BwwYMGrUqJiYGAsfQyouLk5PT1cqlTU1NY6OjgqFYvDgwdOmTbP8aTQaFBgjJSVl\n165duLxgwYLXX39d2L61tfWXX37Jycmprq5uamry8/MLDAx8+eWXY2JiHBwcLPlGGhQYoLCw\nMC0traysrLq6WiaTBQYGDh069NVXX/X19bWkOg2uZOfOWFVVdfLkyf/+97/V1dVqtdrNza1n\nz56RkZGxsbGRkZGWKGi12uzs7FOnTlVWVj548MDT01OhUIwcOXLy5MkWPm9Pg4IoKCoqWr58\nuVqtRghNmTLlgw8+sLyu9fEJGwo0cOTIka+//lqr1SKEPvvss7Fjx1pel4dAbKLjsEJ9ff2b\nb75pyhuHDx/++++/27qNouf48eMCr2WGhIQcOnRIWOH69eum3iWTSqWJiYkNDQ30K7BHRUVF\njx49oB82bdokYNzZ2bl+/Xo3NzejfRgYGHjs2DHhr6NBgQGKioqGDh1qtAdcXFyWL1/+6NEj\ngeo0uJKdO2N5eXlCQoLAxYuoqKisrCxhkfT09N69exut7urq+sUXX3R1ddGvQD9arfZf//qX\nTCaDQ3v33XctrGt9fMKGAg2oVKrZs2eTLTcbtwA8BGIYnhAywu3bt81e1ZDJZD///LOtWypi\nPvzwQ+EexnzwwQemFI4fP+7s7CxcvXfv3mVlZTQrMElcXBzZAwIJYXt7e0xMjNmfwcqVK2lW\nYIDU1FQXFxfhHnjppZdM5YQ0uJKdO2NWVhZ5FQYh5OjoKJfLDftk8+bNpkSWLVsm3IEIoXHj\nxnV0dNCsQD8VFRWxsbF6B2VhQmh9fMKGAg1kZWUFBwfrNdvChJCHQGzDE0IWUKlU5DWbqKio\nHTt2ZGdnX7t27ciRIzNmzIALWh4eHr/99put2ytK1qxZAz0slUoTEhL279+fnZ2dmZn5n//8\nZ9y4ceTZZNeuXYYK+fn55INDf/3rXw8fPnzlypXc3Nw9e/aMGjUKdg0ePLi5uZlOBSbZvXu3\n3v+BQEI4c+ZMMAsKClqzZs3Zs2cLCgpOnTqVlJREBrjfffcdtQpiJycnB+5UuLm5LVy48Jdf\nfikoKDh79uy6desCAwOhB5YtW2ZYnQZXsnNnrKiogKkFHR0dk5KSfvvtN41Gg/cqlcoNGzaQ\nv2Sjd7x37twJBu7u7gsXLvz555/z8/MzMzM3btwYcW7/MAAAFA5JREFUEhICe2fPnm20GTQo\n0E9KSopcLsdH0aNHD0fH/3/byJKE0Pr4hA0Fm9PR0bF06VJoJ/lEvSUJIQ+BmIcnhCywcuVK\n+BF//PHH8J8KnDhxAq6IjB071iaNFDUlJSVOTk64A+VyeWZmpqHNjh07YBT8/f0N70uMHj0a\n73Vycjp69KjeXq1WS47j559/bvgVNCiwR2VlJY513N3d4T/SVEJ49uxZ6J9x48Y1NjbqGdy4\ncQOuv3p7ezc1NVGoIHba29v79OmDDzA0NLS4uFjPoK6u7vnnn8cGzs7O1dXVegY0uJKdO+Nb\nb70FR/fTTz8ZtSkoKIC0v3///np7GxoaIEsJCgoyfFrv4cOH5J1/w0dPaVCgnJaWFvJt6uHD\nhyuVSjhkSxJC6+MTNhRsy//+978hQ4bAIcyZMycnJwc+mk0IeQhkD/CEUPQ0NDTAdda4uDit\nVmvUbP369fArN+rMHAEWLlwIvZeenm7KjHwu/9y5c+SuM2fOwK7169ebUpgwYQK28fLy0ovj\naVBgkvj4eHy8n376aVhYGC6bSgjhCqJCoaivrzdqc+nSJYlEgs1WrVpFoYLY2bJlCz40Nze3\n69evG7UpLCx0dnbu27fvpEmTLl26RO6iwZW4M0JSMWrUKAGzefPmQUeVlpaSu5KTk/F2iURy\n8eJFo9UbGhoUCgU2i4mJ0dtLgwLllJSU4JZLpdJly5Z1dnbqiLEzmxBaH5+woWBztm/fjhsm\nl8tx+pefnw+tNZsQ8hDIHuAJoejZu3cv/MRzc3NNmbW0tMBJfN68eX9mCxkAbkcMGjRIwOz8\n+fMwFnoZxZw5c+AU09raakqBvGiXkpJCmwJ77Nu3Dx9pQEBAc3OzcEJYVlZmyd+JTqcbP348\nNgsPD6dNQexotVqYveOjjz4SsDQ1jQcNrmTnztjQ0ADH9cknnwhYko9znzx5ktwFt8Hj4uIE\nFNauXQsKd+/epU2BcnBCGBAQ8Ouvv8JGyxNC6+MTNhRsDk4IX3zxxTt37uAt3UoIeQhkD/CE\nUPQkJCTgH25YWJiwJVy88ff3N3WJi2NIV1fX4sWLp0+fPmbMGOFHCO7duwfnkaVLl8J2jUbj\n5+eHt8+dO1f46+DM+/rrr1OlwB7V1dXe3t74SI8fP67T6YQTwm3btsH4Ckd13333HViWlJRQ\npSB28vLy4LgqKiq6W50GV+LO2NjYCIMoPPVRSkoKWJIJYVFREWz//vvvBRTu3LkDltu3b6dK\ngX5KSkqmTJlSV1dHbrQ8IbQ+PmFDwebs3LkzOTkZ3+DFWJ4Q8hDITuAL04uerKwsXIiOjha2\nhCfNampqbty48cc2iyEcHBy++uqr1NTUzMzMTz/9VMCyvb0dyuR0CKWlpQ8ePMBly4cpMzOT\nKgX2mD9/Pg5MExMTJ0+ebNYefK1Pnz6mppjHkO+mk31Ig4LYOXbsGC4MGTIkKCiou9VpcCXu\njF5eXvAU5c2bNwUsyVviAwcOhDI4AjLXh8HBwTA5pFFXsqEC/YSHhx87dszCJT0NsT4+YUPB\n5syZM2ft2rUwG1C34CGQncATQnHT0NAAP3HyjWGjPPfcc1Cm6lTFDGRkQ85IVlpaCmXLh6m2\ntlalUtGjwBgHDx7EqYW/v/9XX31lSRXoQ7Md2LdvX5jKjPQ1GhTEzrVr13Bh2LBhsPHGjRvf\nf//9F198sW7dut27d8OLT4bQ4ErcGRFCM2bMwIUTJ05UVVUZtdFqtYcPH8blESNGwGV7RPSh\nXC43nEZfD+hDo65kQwX6ebwUAmN9fMKGAg3AlDB/KDwEEjU8IRQ35Bnn6aefFjYm7yeQvsF5\nUuzfvx8XPDw84L1k9CSGiQYFlrh///7ixYtxefv27T4+Pmar6HS6W7du4bLZDkREH0IH0qDA\nAMXFxbiAA47c3Nzo6OgBAwb87W9/S05OXrly5bx58wYNGhQZGXny5EnD6jS4EndGhFBycrK/\nvz9CqLW1NT4+Hn7YQEtLy9y5c69fv44QcnR0hJmEMNCH3XKEmzdv6nQ6ehTYhgZPoUHBfuAh\nkKjhCaG4qampgbLZJVOfeuopuNpXXV39BzbLLrl8+XJqaiouL1682M3NDXbBMMlkMrO5B7mE\nGgwTDQos8d5779XX1yOEZsyYMXXqVEuqNDU1wfMwZn0NEX0IHUiDgthpbW2trKzE5aeeeurb\nb78dM2bMxYsXDS2LioomTpy4atUqve00uBJ3RoSQr6/vyZMn8dEVFhYOGjRowoQJK1eu3Lx5\n85o1axITE4OCgvBkHm5ubgcOHHjxxRfJ6tCH3XKE1tZWuN5PgwLbWB+fsKFgJ/AQSOw8/sMA\nHBpoaWmBMrnaplEkEomLi4tardaryLGeBw8ezJo1S6PRIISCg4NXrFhB7oXeNjtGCCHyNAoV\naVBghh9++OHIkSMIoV69epFztAjTLV9DRB8aDoENFcQOORlJYWHh5s2bNRpNZGTkrFmzBg4c\n6OTkdOfOnbS0NJhefPXq1f7+/vPnz4daNLgSd0bMkCFDioqK/v3vfx88eFCpVJ4+ffr06dOk\nQWBgYEJCwpIlS8jl3THW9KGXlxclCmxjfXzChoI9wEMgBuAJobjB5x2Mi4uLWXs4VZEVOVbS\n2Nj4yiuvKJVKhJCrq+vRo0f1zjjQ2xaOkWFFGhTYoK6ubtGiRbj89ddf9+zZ08KKj+FrehVp\nUBA75IHgbHDDhg2ffPIJLLqIEFqwYMGRI0cSExPx3dSkpKQpU6bApX0aXIk7I1BWVnbr1i1y\nFQqS2traK1euZGdnBwcHk0OM6BgFPo7CWB+fsKHAPDwEYgOeEIqbrq4uKDs4OJi1h4cZyIoc\na7h79+6ECRPwe01OTk6HDx8mXyvHQG93a4zIijQosMGiRYvwDAEJCQmvvfaa5RWt9zUaFMQO\n+d+s0WiSkpKWLl1qaJaQkHD//v333nsPIdTe3r5t2zZYM5oGV+LOiPnyyy8//vhjfFAKhSIx\nMTEqKsrHx0etVhcXFx89evTatWu5ubm5ubkpKSlpaWlklEnDKPBxFIaGMx4NCmzDQyBm4O8Q\nihvyvjY54a8pwMaSm+Ycs1y8eHHo0KH4VOjq6pqenj5lyhRDMximbo0RIoaJBgUGSEtLwy85\n+Pr6fvPNN92qa72v0aAgdsj/cplMJjAH+jvvvAOTUsJKFYgOV+LOiBA6cOBAUlISDrZmz559\n+/btjRs3Tp8+ffz48VOnTl2xYsXVq1f37t2LZ0c8c+bMrFmzyOo0jAIfR2FoOOPRoMAwPARi\nCZ4QihsPDw8oW/Irb2trM6zIeTz27ds3duzY2tpahJCfn9+5c+cmTpxo1BJ6u1tjRFakQUHs\nNDQ04FtGCKFt27b16tWrW9Wt9zUaFMQOeSDDhg3z9vY2Zeng4BAfH4/LpaWlTU1NegrcGW2I\nWq1+//33cTk2NnbPnj1Gn+OaPXv2hg0bcPnYsWOnTp2CXTSMAh9HYWg449GgwCo8BGIMnhCK\nGzIegqVyTKFSqTo6OgwrcrqLVqtdunTpnDlzHj16hBAaOHBgXl7eiBEjTNlDb6vVarMnI3x6\n1atIg4LYWbx4MZ5nbNq0aW+88UZ3q8vlcniLyayvIaIPoQNpUBA7np6eUCZnBjfK4MGDcUGn\n08EUczS4EnfG1NRUmB9o1apVUqnJUGTRokUw6Hv27IHt0BXdcgSpVArrZdOgwDbWxydsKLAH\nD4GYhCeE4qZv375QrqioEDYuLy+Hcv/+/f+oNrFOe3v7tGnTNm7ciD9OnDgxLy8vNDRUoAoM\nk06nu3v3rrC+0WGiQUHUZGRkHDhwACEUEBCwc+fOx1CQyWSweJFZX0NEH0IH0qAgdvz8/Fxd\nXXHZ7Ksd5B/5w4cPcYEGV7JzZ0QIZWVl4YJMJhs5cqSApbOz87Bhw3A5NzcXtkMfdssRQkJC\nnJ2d6VFgG+vjEzYUGIOHQKzCE0Jx06dPHwiPyGU3jVJSUgLlgQMH/oHNYpeWlpb4+Hh4Jen9\n99/PyMgwe7kXr6CNsXyYPD094R4IDQqiZvXq1bjg5OSUkJAQY4Kqqipstn37dth44cIFvBH6\n0GwH1tbWwg0Q0tdoUBA1UqkUjuX+/fvCxkYf+6HBlezcGRFC9+7dwwVfX19y/gajwFTA5Ipw\n0Ic1NTWQ7ZsC+tCoK9lQgW2sj0/YUGAJHgIxDE8IxY1EIoEJnS5duiRsnJ2djQtubm7PPPPM\nH9syFuns7Jw6deq5c+cQQhKJZMuWLVu3bhV42AmIiIiAS8KWD1NUVBRVCqIGHhEpLy+/YBow\nu337NmyEx4ReeOEFXCgsLBRemwg6ECE0dOhQKNOgIHaeffZZXMjPz9dqtQKWsIQ9IpIKGlzJ\nzp0RIQSnTUve54HfOZk6giNotdq8vDyB6g8ePCgtLcVlo65kQwW2sT4+YUOBGXgIxDY8IRQ9\nMKfT1atXyQuoemg0moyMDFyOi4uzZCUWjh5z5849e/YsQkgikezYsSMpKcnCiu7u7mPHjsXl\nEydOCFjevHkTT9iFEJo6dSpVChzwta6uLnJ+C0PS0tJwwc/Pb9SoUVQpiJ3JkyfjwsOHD3/9\n9VcBy5ycHFwIDAyEOYRocCXujDAcjY2NphYhBO7cuaNXCyE0ZswYuC8Bf21GSU9PhzLZhzQo\nMI/18QkbCmzAQyDG0XFEDnnv+5///Kcpsx9//BHMDh48+Cc2kBF2794NHbh58+buViffWzt/\n/rwps4ULF2IbBweHiooK2hTES11dXbUFBAcH42P/7LPPYGN7ezsW0Wq1gYGB2CAmJsbUd1VW\nVsKVyHfeeYfcRYOC2Glra4NZRmJjY02ZKZVKWKNi1qxZ5C4aXMmenVGn023duhUOPyUlRcCy\nsrISbkFMnz6d3DVz5ky8XS6XNzU1Ga2u0WgiIyOxWb9+/fT20qAgRuRyOT6cd999V9jS+viE\nDQUKyc/Ph9YeOnTIrD0PgZiHJ4QsMG3aNPzzdXFxKSgoMDS4f/9+QEAA/CF1dnb++Y0UNeXl\n5fAO0syZMx9Doa2tDeL4fv36PXz40NDmwoULEPq8/fbbFCowT1hYGD72TZs2GTUgA9kdO3YY\nGnR1dcXFxWEDJycnpVJJoYLYSU5Ohh7YsmWLocGjR4/gYjBCKCcnh9xLgyvZuTOWl5fD85/9\n+/dXq9WmLOfPnw/jePjwYXJXYWEhTLr7xhtvGK2+du1aqL5//369vTQoiBHLE0Ldk4hP2FCg\njW4lhDwEsgd4QsgCxcXFcDfAy8vr0KFD5Mno/Pnz5ARQaWlpNmyqSJk7dy7uPblcXldX93gi\n5AW2wYMH5+Xlwa6Ojo7du3fDwqkeHh5Gr0vRoMA2ZhPCjo6O8PBwbCOVSletWqVSqWDvzZs3\nX375Zejhf/zjH3QqiJ3m5maIvSQSyYoVK8iMQqlUjh8/HnogPj7eUIEGV7JzZ1ywYAEcfnR0\n9O3bt/UM2trali9fDjaRkZEajUbP5s033wSDhISEsrIy2FVfX//hhx/C3ueff96wOiUKoqNb\nCaH18QkbCrTRrYSQh0D2gESn0yGO+Dlw4EBiYiJ87NWr14ABA5ydnW/dukXOn7tixYp169bZ\nooEipqKiIiwsDM9x7+XlBTmDMI6OjobTDPz973/ftWsXfAwLCwsNDW1vby8uLq6vr8cbHRwc\nMjIyXnnlFaOyNCgwTHh4uFKpRAht2rTpo48+MmpTXFw8cuRIlUqFP7q5uUVERHh7e1dWVv7+\n++9wRo2JiTl9+rTR+eVpUBA7V69ejY2NVavV+KOHh8fw4cM9PT0rKiry8/OhB/r06XPlyhU/\nPz9DBRpcyZ6dsaWlZfz48XCSdHBwiImJeeGFF7y9vdva2m7dunXmzJm6ujq819fX99KlS/36\n9dMTUavVo0ePLigowB+lUmlERIRCoaivry8qKoIZaxQKRV5eHizZQpsCzfzlL39pbW3V21hQ\nUKDRaBBCfn5+hkf05ZdfRkdHk1usj0/YULAVJSUl5GULTFtbG7wpFxoaarjaXnZ2Np5elYdA\n9oJN01HOk2Tv3r3kqs16ODo6rl69WqvV2rqZ4oOcrdFyHBwcDKU6OzuXLFkCjxgZ0rNnz/T0\ndIHG0KDAMGbvEGIuX74M9+iM8tprr5l6oYgeBbFz8eLFkJAQgR6IioqqqqoyVZ0GV7JzZ1Sp\nVIZxqiHPPfdcSUmJKZHq6uqXXnpJoPqzzz6Lr5LQrEAt7u7uZgdIj4yMDEMd6+MTNhRswpUr\nVywfPqC5uRlX5yGQncATQqYoKytbsmQJPEyF8fLyevvtt/FVc85j8ATPhpicnJyZM2fq/dGG\nhIQsX768pqbGkibRoMAkFiaEOp1OrVZv2LAhIiKC7EAnJ6dJkyZZ+F9Cg4LYaWlpWb9+PczY\ngXF0dIyOjt63b58lj+fR4Ep27oyXL19+6623DBf76tGjR1xcXGpqqtnXsbRabUpKyrhx42Aa\nIcywYcO2bt3a0dFhtg00KNDJk0oIdU8iPmFD4c+HnoQQQ8M5087Pukbhj4wyiE6nq62tvXfv\nnkajUSgUCoVC7y+K0y1UKhX5tL2FSCSSMWPGCBh0dnZWV1fX1NTIZDKFQkHOqG4hNCgwxuXL\nl/Fq5mFhYUFBQZZUaWxsvHfvXnNzc69evQICAh5jqnEaFMQO7gGVSuXj49O7d2+Y/8BCaHAl\n7oy1tbW1tbXNzc2urq4+Pj5PP/20JUuckbS2tlZVVdXV1Xl5eQUGBppdL5tOBarIzs7GT4da\nTkREhK+vr6m91scnbCj8mTQ3N1+7dq27tUaPHo0PiodAdgJPCDkcDofD4XA4HA7HTuEL03M4\nHA6Hw+FwOByOncITQg6Hw+FwOBwOh8OxU3hCyOFwOBwOh8PhcDh2yv8BZ4cNp/WhKhUAAAAA\nSUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 360,
       "width": 600
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width = 10, repr.plot.height = 6)\n",
    "colocboost_plot(res, plot_cols = 1, add_vertical = T, add_vertical_idx = c(data$variant))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1da736ef-aac5-4c07-90b2-56c4910263b9",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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HAIhcMs7csJOT0H2IRZRpU6derUL7/8Yr1MWVlZUFCQc+IBoKGji99vf/tg/+Rk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\nePlyyP5Ul3eXY7Bm/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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 360,
       "width": 600
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "colocboost_plot(res, plot_cols = 1, add_vertical = T, add_vertical_idx = c(data$variant), grange = c(0:2000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c8bf1b18-945e-44c8-8a61-5586983800a2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A matrix: 2 × 2 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>cos1:y1_y2</th><th scope=col>cos2:y1_y2</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>cos1:y1_y2</th><td>0.9869088</td><td>0.0125171</td></tr>\n",
       "\t<tr><th scope=row>cos2:y1_y2</th><td>0.0125171</td><td>0.9840258</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A matrix: 2 × 2 of type dbl\n",
       "\\begin{tabular}{r|ll}\n",
       "  & cos1:y1\\_y2 & cos2:y1\\_y2\\\\\n",
       "\\hline\n",
       "\tcos1:y1\\_y2 & 0.9869088 & 0.0125171\\\\\n",
       "\tcos2:y1\\_y2 & 0.0125171 & 0.9840258\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A matrix: 2 × 2 of type dbl\n",
       "\n",
       "| <!--/--> | cos1:y1_y2 | cos2:y1_y2 |\n",
       "|---|---|---|\n",
       "| cos1:y1_y2 | 0.9869088 | 0.0125171 |\n",
       "| cos2:y1_y2 | 0.0125171 | 0.9840258 |\n",
       "\n"
      ],
      "text/plain": [
       "           cos1:y1_y2 cos2:y1_y2\n",
       "cos1:y1_y2 0.9869088  0.0125171 \n",
       "cos2:y1_y2 0.0125171  0.9840258 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res$cos_details$cos_purity$min_abs_cor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "aec46069-9972-40ca-9055-ff1a446cd5be",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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wBQk0zLhJwvCgAWlD09pDA6xvfMKY2/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\n/Ef5H98eZ/1Wv08PAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 360,
       "width": 600
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "colocboost_plot(res, y = \"vcp\", plot_cols = 1, add_vertical = T, add_vertical_idx = c(data$variant), \n",
    "                grange = c(0:2000))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29d179a5-2014-4cbc-a897-b2c1b97e0647",
   "metadata": {},
   "source": [
    "### HyPrColoc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "eafb23b0-35e9-442d-b880-754f341fac1f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "“replacing previous import ‘Matrix::tcrossprod’ by ‘Rmpfr::tcrossprod’ when loading ‘hyprcoloc’”\n",
      "Warning message:\n",
      "“replacing previous import ‘Matrix::crossprod’ by ‘Rmpfr::crossprod’ when loading ‘hyprcoloc’”\n",
      "Warning message:\n",
      "“replacing previous import ‘Matrix::.__C__atomicVector’ by ‘Rmpfr::.__C__atomicVector’ when loading ‘hyprcoloc’”\n"
     ]
    }
   ],
   "source": [
    "library(hyprcoloc)\n",
    "betas = sebetas = matrix(NA, nrow = ncol(X), ncol = 2) \n",
    "for (i in 1:2){\n",
    "    x <- scale(X)\n",
    "    y <- scale(Y[,i])\n",
    "    rr <- susieR::univariate_regression(x, y)\n",
    "    betas[,i] = rr$betahat\n",
    "    sebetas[, i] <- rr$sebetahat\n",
    "}\n",
    "traits <- c(1:2)\n",
    "rsid <- paste0(\"snp\", c(1:ncol(X)))\n",
    "colnames(betas) <- colnames(sebetas) <- traits\n",
    "rownames(betas) <- rownames(sebetas) <- rsid\n",
    "res_hyprcoloc <- hyprcoloc(betas, sebetas, trait.names=traits, snp.id=rsid, snpscores = TRUE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "36936a3b-3c5a-42be-93d9-403aeb3349d9",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 1 × 7</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>iteration</th><th scope=col>traits</th><th scope=col>posterior_prob</th><th scope=col>regional_prob</th><th scope=col>candidate_snp</th><th scope=col>posterior_explained_by_snp</th><th scope=col>dropped_trait</th></tr>\n",
       "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;lgl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;lgl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>1</td><td>None</td><td>NA</td><td>1</td><td>NA</td><td>NA</td><td>1</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 1 × 7\n",
       "\\begin{tabular}{lllllll}\n",
       " iteration & traits & posterior\\_prob & regional\\_prob & candidate\\_snp & posterior\\_explained\\_by\\_snp & dropped\\_trait\\\\\n",
       " <dbl> & <chr> & <lgl> & <dbl> & <lgl> & <dbl> & <chr>\\\\\n",
       "\\hline\n",
       "\t 1 & None & NA & 1 & NA & NA & 1\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 1 × 7\n",
       "\n",
       "| iteration &lt;dbl&gt; | traits &lt;chr&gt; | posterior_prob &lt;lgl&gt; | regional_prob &lt;dbl&gt; | candidate_snp &lt;lgl&gt; | posterior_explained_by_snp &lt;dbl&gt; | dropped_trait &lt;chr&gt; |\n",
       "|---|---|---|---|---|---|---|\n",
       "| 1 | None | NA | 1 | NA | NA | 1 |\n",
       "\n"
      ],
      "text/plain": [
       "  iteration traits posterior_prob regional_prob candidate_snp\n",
       "1 1         None   NA             1             NA           \n",
       "  posterior_explained_by_snp dropped_trait\n",
       "1 NA                         1            "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res_hyprcoloc$results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5f565441-7498-4cf8-824b-7a92cb91407d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "List of 1\n",
      " $ results:'data.frame':\t1 obs. of  7 variables:\n",
      "  ..$ iteration                 : num 1\n",
      "  ..$ traits                    : chr \"None\"\n",
      "  ..$ posterior_prob            : logi NA\n",
      "  ..$ regional_prob             : num 1\n",
      "  ..$ candidate_snp             : logi NA\n",
      "  ..$ posterior_explained_by_snp: num NA\n",
      "  ..$ dropped_trait             : chr \"1\"\n",
      " - attr(*, \"class\")= chr \"hyprcoloc\"\n"
     ]
    }
   ],
   "source": [
    "res_hyprcoloc %>% str"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3c53f76-99a9-47d4-b1e1-94fccfad710c",
   "metadata": {},
   "source": [
    "### COLOC (one causal assumption)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "840d7777-d28f-400b-8031-233b6dcbcddc",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "This is coloc version 5.2.3\n",
      "\n",
      "Warning message in adjust_prior(p1, nrow(df1), \"1\"):\n",
      "“p1 * nsnps >= 1, setting p1=1/(nsnps + 1)”\n",
      "Warning message in adjust_prior(p2, nrow(df2), \"2\"):\n",
      "“p2 * nsnps >= 1, setting p2=1/(nsnps + 1)”\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
      " 1.76e-21  4.33e-09  4.04e-13  9.93e-01  6.84e-03 \n",
      "[1] \"PP abf for shared variant: 0.684%\"\n"
     ]
    }
   ],
   "source": [
    "library(coloc)\n",
    "LD <- get_cormat(X)\n",
    "colnames(LD) <- rownames(LD) <- 1:ncol(X)\n",
    "MAF <- colMeans(X)/2 %>% as.numeric\n",
    "D1 <- list(\"beta\" = betas[,1], \"varbeta\" = sebetas[,1]^2,\n",
    "           \"N\" = nrow(X), \"sdY\" = sd(unlist(data$Y[,1])),\n",
    "           \"type\" = 'quant', \"MAF\" = MAF, \"LD\" = LD,\n",
    "           \"snp\" = 1:ncol(X), \"position\" = 1:ncol(X))\n",
    "D2 <- list(\"beta\" = betas[,2], \"varbeta\" = sebetas[,2]^2,\n",
    "           \"N\" = nrow(X), \"sdY\" = sd(unlist(data$Y[,2])),\n",
    "           \"type\" = 'quant', \"MAF\" = MAF, \"LD\" = LD,\n",
    "           \"snp\" = 1:ncol(X), \"position\" = 1:ncol(X))\n",
    "my.res <- coloc.abf(dataset1=D1, dataset2=D2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a0408cd9-10dc-48ae-a91c-745166885772",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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EPbK16sSvZIS5O0CCwAcJTS2Rpai9r8b8ote9bM2m4eIn12wl9WyiGwAECGa4bm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\nDYIFQA2CBUANggVADYIFQA2CBUCN/wDddiplZlp91AAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 360,
       "width": 600
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(my.res$results$SNP.PP.H4[c(1500:4000)])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5aae07bb-fc7e-4425-bfdd-7b2f2f811a8c",
   "metadata": {},
   "source": [
    "### COLOC (V5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7cae5691-0f04-42a1-9f82-0aba3439e263",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "running max iterations: 100\n",
      "\n",
      "\tconverged: TRUE\n",
      "\n",
      "running max iterations: 100\n",
      "\n",
      "\tconverged: TRUE\n",
      "\n"
     ]
    }
   ],
   "source": [
    "library(susieR)\n",
    "out_p <- runsusie(D1)\n",
    "out_e <- runsusie(D2)\n",
    "out_coloc = coloc.susie(out_e, out_p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8fb55563-c509-4616-b969-f5f55e6ec791",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.table: 4 × 10</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>nsnps</th><th scope=col>hit1</th><th scope=col>hit2</th><th scope=col>PP.H0.abf</th><th scope=col>PP.H1.abf</th><th scope=col>PP.H2.abf</th><th scope=col>PP.H3.abf</th><th scope=col>PP.H4.abf</th><th scope=col>idx1</th><th scope=col>idx2</th></tr>\n",
       "\t<tr><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>12900</td><td>3495</td><td>2256</td><td>4.890181e-21</td><td>1.140795e-12</td><td>4.286644e-09</td><td>0.999999996</td><td>2.924359e-11</td><td>1</td><td>1</td></tr>\n",
       "\t<tr><td>12900</td><td>2256</td><td>2256</td><td>9.904029e-19</td><td>4.439301e-15</td><td>8.681693e-07</td><td>0.001895203</td><td>9.981039e-01</td><td>2</td><td>1</td></tr>\n",
       "\t<tr><td>12900</td><td>3495</td><td>3408</td><td>1.921831e-17</td><td>4.483300e-09</td><td>5.255216e-10</td><td>0.120836772</td><td>8.791632e-01</td><td>1</td><td>2</td></tr>\n",
       "\t<tr><td>12900</td><td>2256</td><td>3408</td><td>8.156886e-12</td><td>3.656176e-08</td><td>2.230487e-04</td><td>0.999775096</td><td>1.818970e-06</td><td>2</td><td>2</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.table: 4 × 10\n",
       "\\begin{tabular}{llllllllll}\n",
       " nsnps & hit1 & hit2 & PP.H0.abf & PP.H1.abf & PP.H2.abf & PP.H3.abf & PP.H4.abf & idx1 & idx2\\\\\n",
       " <int> & <chr> & <chr> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <int> & <int>\\\\\n",
       "\\hline\n",
       "\t 12900 & 3495 & 2256 & 4.890181e-21 & 1.140795e-12 & 4.286644e-09 & 0.999999996 & 2.924359e-11 & 1 & 1\\\\\n",
       "\t 12900 & 2256 & 2256 & 9.904029e-19 & 4.439301e-15 & 8.681693e-07 & 0.001895203 & 9.981039e-01 & 2 & 1\\\\\n",
       "\t 12900 & 3495 & 3408 & 1.921831e-17 & 4.483300e-09 & 5.255216e-10 & 0.120836772 & 8.791632e-01 & 1 & 2\\\\\n",
       "\t 12900 & 2256 & 3408 & 8.156886e-12 & 3.656176e-08 & 2.230487e-04 & 0.999775096 & 1.818970e-06 & 2 & 2\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.table: 4 × 10\n",
       "\n",
       "| nsnps &lt;int&gt; | hit1 &lt;chr&gt; | hit2 &lt;chr&gt; | PP.H0.abf &lt;dbl&gt; | PP.H1.abf &lt;dbl&gt; | PP.H2.abf &lt;dbl&gt; | PP.H3.abf &lt;dbl&gt; | PP.H4.abf &lt;dbl&gt; | idx1 &lt;int&gt; | idx2 &lt;int&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|\n",
       "| 12900 | 3495 | 2256 | 4.890181e-21 | 1.140795e-12 | 4.286644e-09 | 0.999999996 | 2.924359e-11 | 1 | 1 |\n",
       "| 12900 | 2256 | 2256 | 9.904029e-19 | 4.439301e-15 | 8.681693e-07 | 0.001895203 | 9.981039e-01 | 2 | 1 |\n",
       "| 12900 | 3495 | 3408 | 1.921831e-17 | 4.483300e-09 | 5.255216e-10 | 0.120836772 | 8.791632e-01 | 1 | 2 |\n",
       "| 12900 | 2256 | 3408 | 8.156886e-12 | 3.656176e-08 | 2.230487e-04 | 0.999775096 | 1.818970e-06 | 2 | 2 |\n",
       "\n"
      ],
      "text/plain": [
       "  nsnps hit1 hit2 PP.H0.abf    PP.H1.abf    PP.H2.abf    PP.H3.abf  \n",
       "1 12900 3495 2256 4.890181e-21 1.140795e-12 4.286644e-09 0.999999996\n",
       "2 12900 2256 2256 9.904029e-19 4.439301e-15 8.681693e-07 0.001895203\n",
       "3 12900 3495 3408 1.921831e-17 4.483300e-09 5.255216e-10 0.120836772\n",
       "4 12900 2256 3408 8.156886e-12 3.656176e-08 2.230487e-04 0.999775096\n",
       "  PP.H4.abf    idx1 idx2\n",
       "1 2.924359e-11 1    1   \n",
       "2 9.981039e-01 2    1   \n",
       "3 8.791632e-01 1    2   \n",
       "4 1.818970e-06 2    2   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "out_coloc$summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "3481ad5a-874a-40a3-a85f-a052ba6480f8",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "'2256'"
      ],
      "text/latex": [
       "'2256'"
      ],
      "text/markdown": [
       "'2256'"
      ],
      "text/plain": [
       "[1] \"2256\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "o <- order(out_coloc$results$SNP.PP.H4.row2,decreasing=TRUE)\n",
    "cs <- cumsum(out_coloc$results$SNP.PP.H4.row2[o])\n",
    "w <- which(cs > 0.95)[1]\n",
    "cos1 <- out_coloc$results[o,][1:w,]$snp\n",
    "cos1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d0d067a6-f383-45aa-b2be-b51e26067e59",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>'3425'</li><li>'3426'</li><li>'3457'</li><li>'3521'</li><li>'3419'</li><li>'3480'</li><li>'3453'</li><li>'3437'</li><li>'3495'</li><li>'3416'</li><li>'3484'</li><li>'3523'</li><li>'3410'</li><li>'3411'</li><li>'3414'</li><li>'3415'</li><li>'3417'</li><li>'3420'</li><li>'3421'</li><li>'3422'</li><li>'3423'</li><li>'3424'</li><li>'3432'</li><li>'3435'</li><li>'3439'</li><li>'3440'</li><li>'3449'</li><li>'3450'</li><li>'3451'</li><li>'3452'</li><li>'3454'</li><li>'3455'</li><li>'3456'</li><li>'3458'</li><li>'3472'</li><li>'3476'</li><li>'3479'</li><li>'3481'</li><li>'3482'</li><li>'3485'</li><li>'3486'</li><li>'3504'</li><li>'3508'</li><li>'3510'</li><li>'3511'</li><li>'3512'</li><li>'3516'</li><li>'3447'</li><li>'3446'</li><li>'3494'</li><li>'3428'</li><li>'3438'</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item '3425'\n",
       "\\item '3426'\n",
       "\\item '3457'\n",
       "\\item '3521'\n",
       "\\item '3419'\n",
       "\\item '3480'\n",
       "\\item '3453'\n",
       "\\item '3437'\n",
       "\\item '3495'\n",
       "\\item '3416'\n",
       "\\item '3484'\n",
       "\\item '3523'\n",
       "\\item '3410'\n",
       "\\item '3411'\n",
       "\\item '3414'\n",
       "\\item '3415'\n",
       "\\item '3417'\n",
       "\\item '3420'\n",
       "\\item '3421'\n",
       "\\item '3422'\n",
       "\\item '3423'\n",
       "\\item '3424'\n",
       "\\item '3432'\n",
       "\\item '3435'\n",
       "\\item '3439'\n",
       "\\item '3440'\n",
       "\\item '3449'\n",
       "\\item '3450'\n",
       "\\item '3451'\n",
       "\\item '3452'\n",
       "\\item '3454'\n",
       "\\item '3455'\n",
       "\\item '3456'\n",
       "\\item '3458'\n",
       "\\item '3472'\n",
       "\\item '3476'\n",
       "\\item '3479'\n",
       "\\item '3481'\n",
       "\\item '3482'\n",
       "\\item '3485'\n",
       "\\item '3486'\n",
       "\\item '3504'\n",
       "\\item '3508'\n",
       "\\item '3510'\n",
       "\\item '3511'\n",
       "\\item '3512'\n",
       "\\item '3516'\n",
       "\\item '3447'\n",
       "\\item '3446'\n",
       "\\item '3494'\n",
       "\\item '3428'\n",
       "\\item '3438'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. '3425'\n",
       "2. '3426'\n",
       "3. '3457'\n",
       "4. '3521'\n",
       "5. '3419'\n",
       "6. '3480'\n",
       "7. '3453'\n",
       "8. '3437'\n",
       "9. '3495'\n",
       "10. '3416'\n",
       "11. '3484'\n",
       "12. '3523'\n",
       "13. '3410'\n",
       "14. '3411'\n",
       "15. '3414'\n",
       "16. '3415'\n",
       "17. '3417'\n",
       "18. '3420'\n",
       "19. '3421'\n",
       "20. '3422'\n",
       "21. '3423'\n",
       "22. '3424'\n",
       "23. '3432'\n",
       "24. '3435'\n",
       "25. '3439'\n",
       "26. '3440'\n",
       "27. '3449'\n",
       "28. '3450'\n",
       "29. '3451'\n",
       "30. '3452'\n",
       "31. '3454'\n",
       "32. '3455'\n",
       "33. '3456'\n",
       "34. '3458'\n",
       "35. '3472'\n",
       "36. '3476'\n",
       "37. '3479'\n",
       "38. '3481'\n",
       "39. '3482'\n",
       "40. '3485'\n",
       "41. '3486'\n",
       "42. '3504'\n",
       "43. '3508'\n",
       "44. '3510'\n",
       "45. '3511'\n",
       "46. '3512'\n",
       "47. '3516'\n",
       "48. '3447'\n",
       "49. '3446'\n",
       "50. '3494'\n",
       "51. '3428'\n",
       "52. '3438'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1] \"3425\" \"3426\" \"3457\" \"3521\" \"3419\" \"3480\" \"3453\" \"3437\" \"3495\" \"3416\"\n",
       "[11] \"3484\" \"3523\" \"3410\" \"3411\" \"3414\" \"3415\" \"3417\" \"3420\" \"3421\" \"3422\"\n",
       "[21] \"3423\" \"3424\" \"3432\" \"3435\" \"3439\" \"3440\" \"3449\" \"3450\" \"3451\" \"3452\"\n",
       "[31] \"3454\" \"3455\" \"3456\" \"3458\" \"3472\" \"3476\" \"3479\" \"3481\" \"3482\" \"3485\"\n",
       "[41] \"3486\" \"3504\" \"3508\" \"3510\" \"3511\" \"3512\" \"3516\" \"3447\" \"3446\" \"3494\"\n",
       "[51] \"3428\" \"3438\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "o <- order(out_coloc$results$SNP.PP.H4.row3,decreasing=TRUE)\n",
    "cs <- cumsum(out_coloc$results$SNP.PP.H4.row3[o])\n",
    "w <- which(cs > 0.95)[1]\n",
    "cos2 <- out_coloc$results[o,][1:w,]$snp\n",
    "cos2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "decdc192-ca4a-4988-b683-1ff9aa150200",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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oZgATDwEqx1ddouSy/ntnR96fmaIlgA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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 360,
       "width": 600
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "vcp_coloc <- 1 - (1-out_coloc$results$SNP.PP.H4.row1)*(1-out_coloc$results$SNP.PP.H4.row4)\n",
    "plot(vcp_coloc[c(1500:4000)])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2a79947-2b91-4771-aa72-c086b69ad144",
   "metadata": {},
   "source": [
    "### MOLOC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "f859af6f-f5c8-474a-9ef8-d193a9155d31",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Use default priors: 1e-04,1e-05\n",
      "\n",
      "Mean estimated sdY from data1: 0.560918402515816\n",
      "\n",
      "Mean estimated sdY from data2: 0.561003724043599\n",
      "\n",
      "Use prior variances of 0.01 0.1 0.5\n",
      "\n",
      "Use prior variances of 0.01 0.1 0.5\n",
      "\n",
      "Best SNP per trait a: 2256\n",
      "\n",
      "Probability that trait a colocalizes with at least one other trait = 0.0067\n",
      "\n",
      "Best SNP per trait b: 2256\n",
      "\n",
      "Probability that trait b colocalizes with at least one other trait = 0.0067\n",
      "\n",
      "Best SNP per trait ab: 2256\n",
      "\n",
      "Probability that trait ab colocalizes with at least one other trait = 0.0067\n",
      "\n"
     ]
    }
   ],
   "source": [
    "library(moloc)\n",
    "moloc_input <- lapply(1:2, function(cnt){\n",
    "    tibble(SNP = 1:ncol(data$X), BETA = betas[,cnt],\n",
    "           SE = sebetas[,cnt], N = nrow(data$X), MAF = MAF)\n",
    "})\n",
    "res_moloc <- moloc_test(listData = moloc_input)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "4589d04c-9515-4f92-885b-6c36332122ce",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 5 × 4</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>prior</th><th scope=col>sumbf</th><th scope=col>logBF_locus</th><th scope=col>PPA</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>a</th><td>1.00e-04</td><td>37.56061</td><td>28.09563</td><td>4.648440e-09</td></tr>\n",
       "\t<tr><th scope=row>a,b</th><td>1.00e-08</td><td>65.95099</td><td>47.02103</td><td>9.933272e-01</td></tr>\n",
       "\t<tr><th scope=row>b</th><td>1.00e-04</td><td>28.39039</td><td>18.92540</td><td>4.838694e-13</td></tr>\n",
       "\t<tr><th scope=row>ab</th><td>1.00e-05</td><td>54.04022</td><td>44.57524</td><td>6.672820e-03</td></tr>\n",
       "\t<tr><th scope=row>zero</th><td>9.99e-01</td><td> 0.00000</td><td> 0.00000</td><td>2.264332e-21</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 5 × 4\n",
       "\\begin{tabular}{r|llll}\n",
       "  & prior & sumbf & logBF\\_locus & PPA\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\ta & 1.00e-04 & 37.56061 & 28.09563 & 4.648440e-09\\\\\n",
       "\ta,b & 1.00e-08 & 65.95099 & 47.02103 & 9.933272e-01\\\\\n",
       "\tb & 1.00e-04 & 28.39039 & 18.92540 & 4.838694e-13\\\\\n",
       "\tab & 1.00e-05 & 54.04022 & 44.57524 & 6.672820e-03\\\\\n",
       "\tzero & 9.99e-01 &  0.00000 &  0.00000 & 2.264332e-21\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 5 × 4\n",
       "\n",
       "| <!--/--> | prior &lt;dbl&gt; | sumbf &lt;dbl&gt; | logBF_locus &lt;dbl&gt; | PPA &lt;dbl&gt; |\n",
       "|---|---|---|---|---|\n",
       "| a | 1.00e-04 | 37.56061 | 28.09563 | 4.648440e-09 |\n",
       "| a,b | 1.00e-08 | 65.95099 | 47.02103 | 9.933272e-01 |\n",
       "| b | 1.00e-04 | 28.39039 | 18.92540 | 4.838694e-13 |\n",
       "| ab | 1.00e-05 | 54.04022 | 44.57524 | 6.672820e-03 |\n",
       "| zero | 9.99e-01 |  0.00000 |  0.00000 | 2.264332e-21 |\n",
       "\n"
      ],
      "text/plain": [
       "     prior    sumbf    logBF_locus PPA         \n",
       "a    1.00e-04 37.56061 28.09563    4.648440e-09\n",
       "a,b  1.00e-08 65.95099 47.02103    9.933272e-01\n",
       "b    1.00e-04 28.39039 18.92540    4.838694e-13\n",
       "ab   1.00e-05 54.04022 44.57524    6.672820e-03\n",
       "zero 9.99e-01  0.00000  0.00000    2.264332e-21"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res_moloc$priors_lkl_ppa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "5dc32a0e-a7a4-46d5-8fda-e1657cb4fd68",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 3 × 2</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>coloc_ppas</th><th scope=col>best.snp.coloc</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>a</th><td>0.00667282</td><td>2256</td></tr>\n",
       "\t<tr><th scope=row>b</th><td>0.00667282</td><td>2256</td></tr>\n",
       "\t<tr><th scope=row>ab</th><td>0.00667282</td><td>2256</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 3 × 2\n",
       "\\begin{tabular}{r|ll}\n",
       "  & coloc\\_ppas & best.snp.coloc\\\\\n",
       "  & <dbl> & <chr>\\\\\n",
       "\\hline\n",
       "\ta & 0.00667282 & 2256\\\\\n",
       "\tb & 0.00667282 & 2256\\\\\n",
       "\tab & 0.00667282 & 2256\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 3 × 2\n",
       "\n",
       "| <!--/--> | coloc_ppas &lt;dbl&gt; | best.snp.coloc &lt;chr&gt; |\n",
       "|---|---|---|\n",
       "| a | 0.00667282 | 2256 |\n",
       "| b | 0.00667282 | 2256 |\n",
       "| ab | 0.00667282 | 2256 |\n",
       "\n"
      ],
      "text/plain": [
       "   coloc_ppas best.snp.coloc\n",
       "a  0.00667282 2256          \n",
       "b  0.00667282 2256          \n",
       "ab 0.00667282 2256          "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res_moloc$best_snp"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e909b6b-fa14-4775-a4b0-09194dd6f246",
   "metadata": {},
   "source": [
    "## 2. Non-causal strongest marginal effect"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "30c7a5f2-08b4-485d-ae79-337f7d2fd003",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>654</li><li>858</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 654\n",
       "\\item 858\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 654\n",
       "2. 858\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 654 858"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "library(colocboost)\n",
    "library(tidyverse)\n",
    "data(Non_Causal_Strongest_Marginal)\n",
    "data <- Non_Causal_Strongest_Marginal\n",
    "X <- data$X\n",
    "Y <- data$Y\n",
    "data$variant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "3ab0bb00-5a6c-4e2c-a9f4-dee7ec46777f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Starting checking the input data.\n",
      "\n",
      "Starting gradient boosting algorithm.\n",
      "\n",
      "Boosting iterations for outcome 1 converge after 50 iterations!\n",
      "\n",
      "Boosting iterations for outcome 2 converge after 56 iterations!\n",
      "\n",
      "Starting assemble analyses and results summary.\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<dl>\n",
       "\t<dt>$`cos1:y1_y2`</dt>\n",
       "\t\t<dd><style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>654</li><li>721</li><li>761</li><li>797</li><li>673</li><li>677</li><li>756</li><li>720</li><li>740</li><li>678</li><li>773</li><li>777</li></ol>\n",
       "</dd>\n",
       "\t<dt>$`cos2:y1_y2`</dt>\n",
       "\t\t<dd><style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>858</li><li>865</li><li>851</li><li>886</li><li>876</li><li>854</li><li>885</li></ol>\n",
       "</dd>\n",
       "</dl>\n"
      ],
      "text/latex": [
       "\\begin{description}\n",
       "\\item[\\$`cos1:y1\\_y2`] \\begin{enumerate*}\n",
       "\\item 654\n",
       "\\item 721\n",
       "\\item 761\n",
       "\\item 797\n",
       "\\item 673\n",
       "\\item 677\n",
       "\\item 756\n",
       "\\item 720\n",
       "\\item 740\n",
       "\\item 678\n",
       "\\item 773\n",
       "\\item 777\n",
       "\\end{enumerate*}\n",
       "\n",
       "\\item[\\$`cos2:y1\\_y2`] \\begin{enumerate*}\n",
       "\\item 858\n",
       "\\item 865\n",
       "\\item 851\n",
       "\\item 886\n",
       "\\item 876\n",
       "\\item 854\n",
       "\\item 885\n",
       "\\end{enumerate*}\n",
       "\n",
       "\\end{description}\n"
      ],
      "text/markdown": [
       "$`cos1:y1_y2`\n",
       ":   1. 654\n",
       "2. 721\n",
       "3. 761\n",
       "4. 797\n",
       "5. 673\n",
       "6. 677\n",
       "7. 756\n",
       "8. 720\n",
       "9. 740\n",
       "10. 678\n",
       "11. 773\n",
       "12. 777\n",
       "\n",
       "\n",
       "\n",
       "$`cos2:y1_y2`\n",
       ":   1. 858\n",
       "2. 865\n",
       "3. 851\n",
       "4. 886\n",
       "5. 876\n",
       "6. 854\n",
       "7. 885\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "$`cos1:y1_y2`\n",
       " [1] 654 721 761 797 673 677 756 720 740 678 773 777\n",
       "\n",
       "$`cos2:y1_y2`\n",
       "[1] 858 865 851 886 876 854 885\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res <- colocboost(X = X, Y = Y)\n",
    "res$cos_details$cos$cos_index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "0714afe0-3790-4aa7-bdaf-bfebb821ae39",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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/vtt98OHDiwc+fO3377zXgF45YtW7Zv\n3/76669PnTrVjs40jUazYMGCNWvWZGZmlpaWhoSEtGjRIj09/bnnnqtbt66cFj7//HPpu1T3\n7t1tCoQA4DJS/DMGP6s3+ax2wSE3BQUAANXYEAgNBsO0adOmTp2q0+mkR0JCQtLT00ePHt2j\nRw+VynpTgYGBjRo1atSo0cCBA4UQeXl5a9as+fzzz0+cOCGEqKysnDVr1pYtW1asWHH77bfL\nLywnJ6d///5nzpwxPqLVarOzs7Ozsz/55JPp06ePHTtWfmsA4OHsznWWbwpqeY/mLiMkZAIA\n4NXkTkxfWlo6aNCgyZMnS2kwPDz87bffvnDhwrJly3r37i0nDdaUkJDw8ssv5+Tk/Pjjj507\nd5YePHz4cNeuXbdskXsxzOXLl3v06FE1DVZ148aNcePGDR8+XK1W21EhAHgRp2Yzk40bH8z8\nL+cVAAAAnEFukBs5cuTatWul5SeffPKDDz5ISEhwSAUBAQH3339/3759V61a9dprr+Xl5RUV\nFfXv3z8zM7N9+/ZWN3/55Zf/+usvaVmlUqWlpbVo0UKtVp84ceLAgQPSkNTly5cXFBRs2LDB\nE+a6AADnsdCV55DGaz5YbXfSP+k2BADAW8jtISwsLBRCNGjQYNOmTUuWLHFUGjQKCAgYMmTI\n0aNHR4wYIYSorKy8fv261a3+/PPPNWvWSMsdO3b8448/du/ePX/+/CVLlvz88885OTkPPvig\n9OyOHTvS09ONg10BwLFCu6eFpqWJoCB3F+IRMjMzA0JCQlO7hqV1c3ctAADAErmBUAhxzz33\nZGVl9evXz3nVxMTELF269Ouvv5bZlffTTz9VVlYKIW655ZaffvqpWbNmVZ9t3rz5hg0b/vGP\nf0h3HN20adNzzz3njLIBIPbDWXVXfevuKlzNcm9k3TWrY2Z84LJiAACAHeQGwueee27Lli1x\ncXFOrUYyfPjwjIyMW2+91eqav/32m7TwzDPPmLub6Guvvfbll19Ky4sWLZo5c6aj6gQAAAAA\nryY3ED7++OP23TnGPh07dmzVqpXV1S5fviwtWJ7X/umnn541a5a0/M477/zwww/KKwQAz2Th\n+j0u7QMAANU4PuP9+eefx44dO3ny5I0bN0pKSiorK6OiomrXrp2UlNSyZcvk5OTAQBvGqVpW\nUlIiLdSpU8fymm+++ebx48eXLFliMBiGDx++b9++Dh06OKoMAPAo1aYrrPqgK7m9AAAAYJXD\nAmFRUdGcOXNWrlx5/PhxC6vVr1//4Ycffvnll1u2bKl8p5GRkdJCcXGx1ZXnz5+fm5v7yy+/\nlJSUPPjgg7/88kvDhg2V1wAAnskFAczcTU1NPm73LIgAAMB5HNNZt3nz5mbNmr333nuW06AQ\noqCgYP78+W3btn311Vf1er3C/cbHx0sLBw8etLpyaGjounXrEhMThRB5eXn9+vW7evWqwgIA\nQKLNytbs3+/uKtygZsYzpsGQI0eCzUwSCwAAPIQDAuG2bdseeeQRaV4KmSorK//5z38OHz5c\n4a67dOkiLSxatEjO1PP16tVbv359VFSUECI7O7tPnz5kQgAOcX38+MLhTwrF57ncy77J5Tv9\nL+nBgMrK2L9PrT3n02rtO6ZQAADgIEqHjKrV6jFjxlRUVEj/jIiI6Nev3913392sWbP4+Pjw\n8HCVSlVeXl5cXHzu3LmsrKyffvrpyJEj0sorVqwYMGDA448/bvfeH3zwwZCQEK1We+7cuTFj\nxnz11VdWL1Bs3779ypUrH3nkkYqKisOHD6ekpKxdu7Zt27Z21wAAQgiDWmPQaoXB4O5C7FQ1\nqjlkcnmDXi8qK4VWq7QyAADgTEp7CH/88cdz585Jy0888cSFCxdWr1794osvPvDAAx06dGjR\nokXTpk3btGnTrVu3J554YubMmYcPH962bVv9+vWlTWbPnq1k73Xr1n3yySel5a+//rpr165r\n1qyx2lfZr1+/lStXBgcHCyFOnz6dkpIyadKk8vJyJZUAgPcy2XHnpN48OgkBAPAoSgPh9u3b\npYU+ffosW7ZMzkSFffr02bJlS0hIiBDi4MGD165dU1LAhx9+mJCQIC3/+uuv6enpDzzwgNWt\nBg4cuGHDBmnsaHl5+bRp06xe/QgAPsn1CY1MCACA51AaCPPy8qSFt956KyAgQOZW7dq1e+ih\nh4QQBoPhzz//VFJAbGxs1S5HIUS9evXkbNi3b9+MjIwWLVoo2TsAoCarw03JhAAAeAilgVCj\n0UgLHTt2tGlD49cFOTeDsaxly5ZZWVnDhw9XqVRCiKrh0LL27dv//vvvL730kjR8FAAAAAD8\nitJAaExfQUFBNm0YHh4uLTRo0EBhDUKIunXrfv311xcuXFi8ePGgQYPkbxgRETFnzpzc3Ny/\n/e1vvXv37t27d2xsrPJ6AMBbOGluQFvPEgIAALdQepfR5ORkaeHChQs23avz4sWLQoioqCj5\nHXpWxcfHjxo1yo4Nk5KSFixY4KgyAMAHMIk8AAD+QGkP4dChQ6VLB9evXy9/K4PBsHnzZiHE\n4MGDpXGeAODVQrunhaalCRvHSngIk5PLK2wzICQkNLVrTO97FLYDAACcSmkYS0pKevzxx1es\nWDF79uzBgwcbOwwt+/zzz3NyciIiIl599VWFBQCAJ4j9cJa7S1DEGf2BddesFkII7h8DAIAH\nU9pDKIRYsGDBPffcU1hYeNddd33zzTeVlZUWVi4pKXnvvfdeeumlkJCQJUuWtG7dWnkBAACP\nVTVqNsjubPwv/6uA/K/k3psaAAA4SYDBYFCyfWVlZVFR0Y0bN2bNmvXll18aDIZGjRo98MAD\n7du3b9SoUXR0tEqlUqvVRUVFp06d+vXXX7ds2VJcXJyQkDBz5swWLVro9XpzBbRq1ap27dpK\navMcUsfpyZMn3V0IALiBnOAXP0rRHyMAAPzK6NGjhRCLFy9W3pTSQNirV6/du3crr6OmXbt2\n9ezZ0xktux6BEIBfqTrNYIPszjK3IhMCACCTAwOh0iGjCvMkAMDH2JcGhbyORAAA4FgOuIYQ\nAKDNytbs3+/uKtzP7jQoIRMCAOBiSu8y+sYbbzz33HOhoaEqlUqaf8JRbJrVEADc6/r48bqz\n5xJOnxSBnGgTwq40KMn/KoCxowAAuIzSQNi/f3+H1AEAXs2g1hi0WsEoegAA4FXknsl2/bWC\nXJ0IAF7K7u5BCQNHAQBwGbmB8IUXXjh16pRTSzEyGAzTpk3bt2+fa3YHAFBImlQw/6sAaY5B\nd5cDAADkkhsIjx071qVLl82bNzu1GiFEUVFRenr6pEmT9Hq9s/cFAFCI+eUBAPBqNtz84Pr1\n6/379x8zZszNmzedVM3WrVvbtGnz3XffOal9AIADEQUBAPB2cgPh/fffLy18+eWXrVq1+uqr\nrxzbg3fmzJmhQ4f27dv3zz//FEI0bdq0efPmDmwfAOBYpEEAAHyA3EA4YcKEpUuXhoaGCiH+\n/PPP0aNHd+jQYenSpRqNRmEFx48ff/7551u2bPntt99Kj/To0ePAgQMNGjRQ2DIAwElIgwAA\n+AYbhoyOGDHi3//+d8uWLaV/Zmdnjxw5slGjRi+99NLOnTt1Op1NO7548eJnn33Ws2fP1q1b\nz5s3T6vVCiGCgoImTpy4bdu2uLg4m1oDAPcK7Z4WmpYmgoLcXQgAAIANAmyd3UGtVk+aNGnO\nnDkVFRVVH69Vq9add97ZpUuXli1bNmnSpFGjRlFRUREREcHBwWVlZWVlZVeuXDl37tzZs2cz\nMzMPHDhw8uTJai23bdt2/vz5qampSl+Ta/3xxx9Wb4g6adKksLCw8+fPu6YkAHAql3UPMkM9\nAAAmjR49WgixePFi5U3ZPDF9WFjYRx99NGbMmLfeemvdunXGPHnz5s2dO3fu3LnTjiIaNGgw\nefLkZ555JsgLT65v37599uzZlte5fv16TEyMa+oBAAAAAJls7iGsKjc3d/bs2d9+++2NGzfs\na6FLly7jxo0bOnRocHCw3WV4vuTkZCFEzU5RAPBGrryAkE5CAABqcmcPYVUtWrT44osv5syZ\ns3379k2bNv3888/Z2dlWLyasV69eSkpKr169Bg4c2LhxYyUFAAB8W/5XAWRCAACcR1EglISE\nhPTr169fv35CiPLy8tOnT587dy4vL+/mzZvl5eU6nS48PDwiIuKWW25p3LhxUlIStw8FAAAA\nAE/ggEBYVXh4eJs2bdq0aePYZgHAw2mzsg03i0PT0txdCAAAgA1smHYCAGDO9fHjC4c/KfR6\ndxfig5jzEAAA5yEQAoADGNQag1YrFNymCwAAwPUcMGTUYDDk5ubm5OTk5ubm5+eXlpaWlJTo\ndLrIyMioqKjY2NgWLVq0bNmydevWYWFhyncHAAAAAHAIRYFwz549y5Yt27hxY0FBgdWVIyIi\n7r///vT09PT0dG+cbxAAIIkfZWAYJwAAvsHOIaPZ2dm9e/fu2bPnwoUL5aRBIURZWdn3338/\ndOjQNm3arF+/3r79AoCXyvwvk/8EAABwC3t6CDdt2vT444+XlJSYaE6lCgsLCwsLU6lUarW6\nvLxco9FUWycnJ+fhhx+eNGnS1KlT7SkZALxN1eBXLQRK/+zUqZOcbWuuZvlZ53FlJyHzEAIA\n4Dw2B8Jjx44NGjTIGPOio6MfeeSRtLS0O+64o2HDhvXq1QsI+J+vCEVFRefPn//jjz/27du3\nfv36ixcvSo+///77oaGhEydOVP4aAMCT2d0NWHPDzMzMqqnPZLY0cnY+ZOAoAAA+wOYho089\n9ZSUBkNCQmbMmHHp0qWvvvrqb3/7W+fOnevXr18tDQohoqOj27VrN2TIkLlz5549e3b16tXx\n8fHSU1OmTMnKylL+GgDAY8lMg/JDY9VBp45qEwAA+C3bAmFWVtbBgweFEIGBgd9///2ECRMi\nIiLkbx4UFPTYY48dPnw4KSlJCFFRUfHpp5/aVAAAeKbQ7mmhaWnCcXfMIs4BAAAXsC0Qrlu3\nTloYMmRI//797dvlrbfeumjRImn5u+++q6ystK8dAPAcsR/OqrvqW3dXUZ2zU6ULru7jAkIA\nAJzKtkB45swZaSE9PV3JXnv16pWYmCiEuH79+uXLl5U0BQCeTOaFfK68HwwAAICRbTeVMYa3\nJk2aKNxxcnLyhQsXhBCXLl1q0KCBwtYAwMd06tRJef+elDNN3onU8oPyA6pTby1D9yAAAM5m\nWyAMDQ2VFsrLyxXuWKvV/qcClT1TXwCAt5AT7axOPlG1NfnNCmt3IjX3oPx6BLcbBQDAm9k2\nZLR+/frSwtGjR5XsVafT5eTkSMt0DwLweZ3+y/JqVueNqPaIrQ3aKvN/WVjTGV15dA8CAOAC\ntvXOtWvXTlpYtGjRM888Exho86wVklWrVl25ckUIkZCQULduXfsaAQDPoc3KNtwsDk1Ls7ya\n1W69ajMNWo18Jldw0r1kLHcbyuknlN+XSBoEAMA1bEt0AwcOlELgwYMH7Z5T/vDhw2PHjpWW\nBw8eXHPqQgDwOtfHjy8c/qTQ662uKaerUAln31nUQvvxowzSf+aeEjKSnrkWAACAM9jWQxgf\nHz906NDly5cLIWbOnJmVlTV58uSUlBSZm+fn58+bN2/WrFnS1Pa1atV6+eWXba0YADyQQa0x\naLXCQJIRQoj4UQaDVpuX1FTVtGn9vbtrPmtcljoMSYAAALiLzTd0mTNnzo4dOwoKCoQQmzdv\n3rx5c7NmzdLS0jp06JCYmNigQYPIyMiwsLCgoCC1Wq1Wq69cuXLhwoXc3Nz9+/f/9ttvOp3O\n2NTcuXMbN27syFcDAPAqREEAANzL5kAYFxeXkZHRr1+/EydOSI+cOnXq1KlTS5Yskd9IaGjo\nokWLhg0bZuveAcC7mJzdQThoVgkAAACF7LkrTNOmTQ8ePPjOO+9ERUXZum1AQMCgQYMOHTpE\nGgTg8+RM+VCN8ssLXTDHPVEWAACfYedtQqOjo6dPn37+/PnFixc/+uijt9xyi+X1w8PDu3Xr\nNnPmzJycnDVr1rRs2dK+/QKAt7A641/Nu8u4IMsBAABUpWhS+Dp16owaNWrUqFFCiGvXruXm\n5hYUFJSWlpaUlOh0uoiIiKioqNjY2OTk5MaNG3M3UQA+w5jrPLNDT2qzWvikWwmb3U8AACAA\nSURBVA8AANSkKBBWVadOndTUVEe1BgCeqeYoUClr3aLRBAnx+++/GwIDLWe8ajMNOo/M7keC\nIgAA/sxhgRAAfJ6FUaDa9u2Cbr3VEBgoXBj5HMJYqmOTYUBISGhq1+DkZAe2CQAAHM5tgTAj\nI+PGjRtCiNTU1Li4OHeVAQAOUTxubNV/emO3m/xhpTLjbt01q5VVBAAAnM5tgXD8+PHSN49d\nu3b17NnTXWUAgJ+TPoodngYBAIBXYMgoAPipqgnQG7s0AQCAcgRCAHApd/WwOSryedcVkgAA\nwDI75yEEAD/kmZNMyOHYDkC6EwEA8BkEQgCwgblEpzp1OiQry/KGvpEG5dNkZFQcPeqWXQMA\nAJkYMgoAtqmW66S4FT17tiq/4PLqlSLQxIk23xtjaX3gqE5XOGyEqnnzetu2uKooAABgM9sC\n4SeffHLixAmH7PjChQsOaQcAPEGAtkJUVAQIYXB3JdW4q3vQoNcbdDqDRuOWvQMAAJlsC4Tr\n16/fs2ePk0rxXtevX7e8gl6vDzTVaQAAziZ/PgkAAOCHGDKq1DvvvDNjxgyrq8XGxrqgGAAu\n5rdZy/cGwQIA4J9sC4RRUVH/2UylioyMVLLjkpKSyspKJS14iDfffHPQoEGW1xk4cGBwcLBr\n6gHgMt4yk7sdk877bdAFAMDf2BYIP/jgg+3bt2s0moiIiEOHDiUlJdm9486dO/vGF46YmBir\n3/ZCQ0NdUwwAl5HzCeb2KGhk68BRqXJpk2rbes6LAgAAytkWCNu1azdt2rQ33nijuLh46NCh\nGRkZKhWDTgH4HavhygNTk9VMWLNm4yMe+HIAAIBD2Hynk1dffbVnz55CiAMHDkyePNnxFQGA\nN3PjfINWmSvMk2sGAABOZXP/XmBg4JIlS9q1a1dUVDRr1qx77723V69ezqgMALyItn27oFtv\nNXj8/YRdFvwCQkJCU7sGJye7ZncAAMA+9gz4TExMnDt37ogRI/R6/fDhw7OysuLi4hxeGQB4\nJpMDL4vHjXV9JR6u7prV7i4BAABYYefJ7OHDhw8ePFgIcenSpaeeesqhJQGA57L1MjwAAABP\nZv8tYRYvXjxlyhRpWafT2Xp3mYiICGniiqCgILtrAADPQRoEAABex/5AGBERcfvtt9u9+d69\ne+3eFgAAAACgHJNGAIDXqDZglT5JAACgkKffEA8AvELb4BDN/v1O3UXNyxdtmmve9TQZGRVH\nj7q7CgAAYAmBEABsYK5T7vr48YXDnxR6vR1tZv6X5XVsetz9dLrCYSOuvfKau+sAAACWMGQU\nAGzTqVOnqjFMiogFao1BqxUGg01NVYtz0j99YyCoQa836HQGjcbdhQAAAEuUBsLp06dfvHgx\nLCwsKCgoICDA1s1DQ0NjYmISEhK6du162223KSwGAFzDIZnNczr3uDQRAAC/pTQQbtu2bc+e\nPQ4ppXXr1q+//vqoUaMc0hoAeKnMzExXRjKTlyaSCQEA8BMedA3hsWPHRo8e/fDDD5eXl7u7\nFgDwLOYSmpOSm+f0XgIAAKdSGgg7dux4xx13JCcnN2zYMDg4uHrrgYERERE1t6pdu3Z0dHR0\ndHStWrWqDTRdv3793/72N4VVAYDvcUb2I/gBAODnlA4Z/eSTT4QQN2/enDhx4meffSaE6NSp\n02OPPdarV6/k5OSYmJjAwECtVltYWHjw4MHt27cvXbr05s2brVu3/vLLL1u3bi2E0Ov1p06d\n2rJlyyeffHLu3DkhxPLly8eMGXP33XfbV5LBYMjNzc3JycnNzc3Pzy8tLS0pKdHpdJGRkVFR\nUbGxsS1atGjZsmXr1q3DwsIUvnwAcDgLwU96iiGdAADAUQIMNt4Tr6aSkpK777770KFDcXFx\nCxcufOSRRyysfPXq1TfffPP//u//IiIiNm7c2KtXr6rtPPjgg9IViYMGDVqzZo2tlezZs2fZ\nsmUbN24sKCiwunJERMT999+fnp6enp4eFBRk675skpycLIQ4efKkU/cCwI0KunXXnT/f8MI5\nYcvnickOOheHPXOdhArLMGi1eUlNVU2b1t+7W0k7AACgptGjRwshFi9erLwpB1xDOHLkyEOH\nDtWpU2fv3r2W06AQIi4ubtGiRS+++GJZWdmgQYPy8vKMT0VFRS1btkzqtdu1a5dNSTU7O7t3\n7949e/ZcuHChnDQohCgrK/v++++HDh3apk2b9evXy98XANQU2j0tNC3NpjQoTIUun+n6CwgJ\nCU3tGpbWzd2FAAAAS5T2EP76669dunQRQvzzn/8cP368zK3Ky8uTk5Pz8vLGjh0rDTQ16t+/\n/+bNm4UQ58+fT0xMlNPapk2bHn/88ZKSkppPqVSqsLCwsLAwlUqlVqvLy8s1ZibFmjRp0tSp\nU2XWbyt6CAF4spqdhD6TSwEA8Eke1EO4du1aaeGJJ56Qv1V4ePjAgQOFECtWrNDr9VWfatWq\nlbRw9epVOU0dO3Zs0KBBxjQYHR09cuTIBQsW/Prrr/n5+Vqt9ubNm1euXMnPz79+/bparb5x\n48aRI0e+/fbbcePGNWrUyNjO+++/P336dPkvAQB8RrX4RxoEAMB/KL2pzLFjx4QQkZGRt956\nq00bStPQX79+/fTp01IHmiQkJERaKCsrk9POU089JXX6hYSETJky5aWXXjJ5X1Oj6Ojodu3a\ntWvXbsiQIXPmzFm7du1LL72Un58vhJgyZcpDDz3Url07m14IAPgAQiAAAP5JaQ+hFNvKysq0\nWq1NG968eVNauHLlStXHjVcVRkVFWW0kKyvr4MGDQojAwMDvv/9+woQJltNgNUFBQY899tjh\nw4eTkpKEEBUVFZ9++qn8zQEAAADAqykNhLfccosQwmAwbNu2zaYN9+3bJy1UDX6VlZU7d+6U\nluVcQLhu3TppYciQIf3797epAKNbb7110aJF0vJ3331XWVlpXzsAAAAA4F2UBsIWLVpIC2+9\n9ZbJ27qYtHPnzh07dkjLDRo0MD4+b968ixcvCiFuu+222NhYq+2cOXNGWkhPT5dfc029evWS\n8uf169cvX76spCkA/kmbla3Zv9/WrTL/yxkluZ0mI6Pi6FF3VwEAACxRGgiN80wcO3asa9eu\n27dvt7y+RqOZN2/egAEDpLubdurUSepjLCwsnDhx4ksvvSSt9thjj8nZuzG8NWnSxK7y/z/j\ndYyXLl1S2BQAP3R9/PjC4U+K/71LlgXVcqAPxkKdrnDYiGuvvObuOgAAgCVKbyrToUOHhx9+\n+IcffhBCHDt27N57723UqFGPHj2aN2/esGHDiIiI4OBgjUZTXFx84cKF7OzsvXv3FhUVGTd/\n8cUXpYUdO3Z88MEH0nJkZOQrr7wiZ++hoaHSQnl5ucIXYrwGUqVS+p4A8EMGtcag1Qp5E/mY\ny36ZmZk+c3MXg15v0OkMZmb6AQAAHsIB4eeLL77Izc3NycmR/nnx4sVly5bJ2XDQoEEjR46U\nlps1ayYtBAYGLlq0qH79+nJaMK529OjRbt3sn/5Yp9MZ6686hBUAAAAAfJjSIaNCiPj4+H37\n9g0ZMiQgIEDmJiEhIW+88cbKlSuNj0gjNhMTE9evXz9kyBCZ7RiniFi0aJFe9kitmlatWiXd\n7DQhIaFu3bp2twPAf2RW4e5aAAAA7OSAQCiEuOWWW7799ttDhw5NmDChbdu2wcHBJlcLCwtL\nSUl5//33T548+eGHHwYFBRmfql279k8//XTy5EmbbhY6cODAwMBAIcTBgwcnTpxoX/GHDx8e\nO3astDx48GD5sRaAf6oZAjMzMzWMjQQAAF7IkdfLtW/fvn379jNmzNDpdKdPn7569WpJSYlG\nowkPD4+MjIyPj09MTJTym0l9+/a1dY/x8fFDhw5dvny5EGLmzJlZWVmTJ09OSUmRuXl+fv68\nefNmzZolfZOrVavWyy+/bGsNAPwK/YEAAMCXOOUGKiqVyjgdhbPNmTNnx44dBQUFQojNmzdv\n3ry5WbNmaWlpHTp0SExMbNCgQWRkZFhYWFBQkFqtVqvVV65cuXDhQm5u7v79+3/77TedTmds\nau7cuY0bN3ZN2QC8jgOjYKdOnUy25jN3lAEAAN7C6++oGRcXl5GR0a9fvxMnTkiPnDp16tSp\nU0uWLJHfSGho6KJFi4YNG+acGgF4PZlp8Pfff+94551y1qyZCaumwapPkRIBAIDzOD4QlpeX\nZ2dnZ2dnFxYWFhcXV1RU1K5dOzo6ukWLFp07d65Tp47D99i0adODBw9++OGH//rXv0pKSmza\nNiAg4NFHH33//fdbtmzp8MIA+A9t+3ZBt95qMD8qviZzSa/mBYoWVvZYASEhoaldg/87xSsA\nAPBMjgyE27Ztmz9//oYNG4xz+tWUkpLy7LPPDh8+3NyNZ+wTHR09ffr01157bf369Rs2bNi7\nd29hYaGF9cPDw++4444BAwYMHDiwefPmDqwEgH8qHjfWIe340jWKddesdncJAADACscEwqKi\nomeffbbqNBLmHDhw4MCBA3Pnzl22bJnDO+Xq1KkzatSoUaNGCSGuXbuWm5tbUFBQWlpaUlKi\n0+kiIiKioqJiY2OTk5MbN27M3UQBAAAA+DkHBEKNRvPwww/v2bNH/ia///773Xff/fPPPxvn\no3e4OnXqpKamOqnxqtasWbNq1SrL6xQUFERFRbmgGADu5byBnZmZmV43ahQAAHg+BwTCmTNn\nGtOgSqXq3bt37969W7Ro0bBhw4iIiODg4NLS0mvXrp08eXL//v2bNm2SBnMWFhYOHDjwyJEj\nFiai8Ap16tSJjY21vE5gYGDVSRcBeB1z9wWtto5TC3Be4wAAwG8FGAwGJdvfvHmzUaNGRUVF\nQog+ffosWLAgKSnJwvrl5eX/+Mc//v73v+v1eiHEkiVLnnzySSUFOEpGRsaNGzeEEKmpqXFx\ncY5tPDk5WQhx8uRJxzYLwMUs3BfUSbtw6r4AAICXGj16tBBi8eLFyptS2kO4a9cuKQ3eeeed\nmzdvtnqrmPDw8EmTJkVHR0tTwC9dutRDAuH48eOl72G7du3q2bOnu8sB4IkspDJtVrbhZnFo\nWpryXdTMhF6aBjUZGYExMcFt2ri7EAAAYJbS4Zp79+6VFqZOnSr/xqEvvPCCdPXg/v37NRqN\nwhoAwO2ujx9fOPxJodcrb6pa/PPSNCh0usJhI6698pq76wAAAJYo7SHMy8sTQgQGBt5zzz3y\ntwoMDOzXr9+//vUvtVqdl5d32223KSwDANzLoNYYtFqhbBC+kbeGwCoMer1BpzNwyg8AAM+m\ntIfw2rVrQog6deqEhITYtGFiYqK0YHnCQAAAAACAkygNhFIOVKvVtm6o/+/AKm+/yygAAAAA\neCmlYUy6IWdJSYk0dlQ+4y0369atq7AGAAAAAIAdlF5D2LJlS2nhm2++eeONN2RuVVJSsm7d\nOiFErVq1EhIS7N77J598cuLECbs3r+rChQsOaQcAAAAAvIXSQHjvvfdOmDBBCDFt2rT777+/\nbdu2VjfR6XRjxoy5cuWKEKJ3794qlf01rF+/fs+ePXZvDgAAAAD+TOmQ0Y4dO3bu3FkIUVxc\nfNddd82ePbukpMTC+nv37u3Zs+eKFSukfz7//PMKCwAAAAAA2EdpD6EQ4qOPPurdu7dery8q\nKnrllVcmTJjQvXv31q1bJyYmRkVFqVSqsrKywsLC3Nzcffv2Xbp0ybjhgAED7rvvPiW7joqK\nkhZUKlVkZKSSpkpKSiorK5W0AMCfhXZPC2rYUAQFubsQTxEQEhKa2jU4OdndhQAAAEsCDI6Y\nNWvevHm29vWlpKRs377dmOjsk5WV1aVLF41GU7t27cOHDyclJdndVOfOnTMzM4UQu3bt6tmz\np5KqakpOThZV7qMDAAAAAHYbPXq0EGLx4sXKm3LMlA/PPffcpk2bZN4eJjAw8NVXX921a5fC\nNCiEaNeu3bRp04QQxcXFQ4cO1el0ChsEAAAAAP/hsDkA+/Xrd/LkyaVLl/bt2zcmJqbmCiqV\nqkOHDpMmTTpz5szHH38cHh7ukP2++uqrUofegQMHJk+e7JA2AQAAAMAfOOAaQqPw8PARI0aM\nGDFCCHH27Nm//vqrqKiooqKidu3aMTExycnJYWFhDtydJDAwcMmSJe3atSsqKpo1a9a9997b\nq1cvh+8FAAAAAHyPIwNhVUlJSUqu6LNJYmLi3LlzR4wYodfrhw8fnpWVFRcX55pdAwAAAID3\nctiQUfcaPnz44MGDhRCXLl166qmn3F0OAL+jzcrW7N/v7io8iyYjo+LoUXdXAQAALHFWD6Hr\nLV68eMqUKdKyTqezdb77iIgIaeKKIO4aD8B218eP1509l3D6pAj0kRNtSul0hcNGqJo3r7dt\ni7tLAQAAZslNTcePHy8uLnZGBa1atapdu7bydiIiIm6//Xa7N9+7d6/yGgD4LYNaY9BqhSMm\n8vENBr3eoNMZNBp3FwIAACyRGwjHjh27Z88eZ1TgjHn/AAAAAABWMbQJAAAAAPwUgRAAAAAA\n/JTcIaMzZsy4evWqMypo27atM5oFAAAAAFgmNxCmpqY6tQ4AAAAAgIsxZBQAAAAA/JTvzEMI\nAG4U2j0tqGFDwUSm/xUQEhKa2jU4OdndhQAAAEucGwgnTZpUXl4uhHj++eebNm3q1H0BgBvF\nfjjL3SV4nLprVru7BAAAYIVzA+Gnn35aVFQkhHjwwQcJhAAAAADgUbiGEAAAAAD8FNcQAoD7\nZWZmVnukU6dObqkEAAD4FXoIAcDNaqZBcw8CAAA4Fj2EAGCJMZhZ7rLTZmUbbhaHpqW5pCjv\noMnICIyJCW7Txt2FAAAAswiEAGBatT466Z/mYuH18eN1Z88lnD4pAhl5IYQQQqcrHDZC1bx5\nvW1b3F0KAAAwi0AIACbYOmLToNYYtFphMNjUbKdOnSzsKDMzU+aVhDK7MV3JoNcbdDqDRuPu\nQgAAgCUEQgD4DzkBTH5IM7mt1UdMrlAzN0o1mGtQToVVt/WcGAkAAFyMQAjA31WNRvIDlZK9\nKN/WcmtWOwxtGg0LAAB8mHMD4fbt23U6nRCiVatWTt2RG124cOHAgQOW1ykpKQkLC3NNPQCU\n85k7fJrsz/SZVwcAAJRzbiDs3LmzU9v3BMuXL//oo48sr1NUVBQbG+uaegDYxOfTkU0XItJJ\nCACAv2HIqFJvv/3222+/bXmd5ORk1xQDwNnkRyYPvNELAABANdweHQBMqxnk5He1Vbsu0b39\nkPL37vP9pQAAoBp6CAHALPmde6Hd08qiozMPH3ZqPXazKRM6pEszICQkNLVrMOMjAADwbARC\nAH7N8kyA8p0ZMlgMGay8HU/gqExYd81q5Y0AAACnIhAC8HEmZ/Az96xDdgEAAOAtuIYQgC+z\nYy54AAAA/0EPIQCfZS77Mb+CVSbfOt40AAB8Dz2EAPyah4ccjyrP7bdLBQAADkcgBAATbE1i\nqlOnQ7KyHFuAVINHZUJhy5hbTUZGxdGjTi0GAAAoxJBRAF7P8m1jrKp5o1E7Wjj3wouq/ILL\nq1eKwMCqj9csz+TmtlYosyppwaZt5exL1phbna5w2AhV8+b1tm2Rv3cAAOBiBEIA3s2+28ZU\nizRKeuGk3d2irRAVFQFCGBQ3aJKSBq1mPGd0Qhr0eoNOZ9BoHN4yAABwIAIhANjPws1XjE85\naqpDc/uVE+dk9lUCAAB/QyAE4MUUJhw5scrCaFILdzG18E/lTLZvdy+fo7oHq1XVsW1bq+t7\n2uWRAAD4IW4qA8BP1YxVVh+puZV85sKPraFISbw03qjG8t7llGQ19P7+++/mNjS+sdy2FAAA\ntyMQAoBt7MswUoeYnDxmH8tVVesLNTK3vuVnbdq1szcHAABKEAgBeDGHjzl0djiR0wnp8D1W\n7Y6z9Y6jFt5hOyqXOcgWAAC4DNcQAvAvMm+v4oyI4vrYY3KPrrx4T61WE/YAr6NwJh4A3oVA\nCMC7yb+Hp8zvNDJnraj2iLZ9u6BbbzUE2jPswiEJrWYLzk5iVmoODta2aV3ZqJHM1rjHDOAa\nVsOeyWun+fUEfBiBEPAOnK81x0LsceW7VDxurOUVHDX5hDMmsVDIXEnXP5ju+mIcS+EdXAFP\nYzXsWRjXbTk68msCeC8CIWC/mn84nfQX0Y3nawmiHqhmAHPqz8Vk3nPGHj2qF6LqSyYWKuS3\nHyMe9cKVn0iyHB2lZ132ZxGAAwUYDAZ31+D7kpOThRAnT550dyF+oeps4K7ZUTUuu82JC/7K\nWph13XNY/pYj58y3a1ju2XPZqQQlu5OTixzVC6q8ESUUvm98J67KKz5GnEHO74Ir3weZH5VW\ny5Z5GbbJxgE40OjRo4UQixcvVt4UPYTwKSZP6rv4y5nLTgk7uzvFpoFDHsujRleay4Qufj/d\nPoW9Ve49xpwxi4Z3/dbYx+T5At/4GLGDzKPIXWM97FjBgXv37R+9J+NcFcwhEMKDKPzTaO57\nmLkHq347d2pss6l9P78kw1f/XBlfhclM6Lzj0MWDSx3I5wODp1FynNg3vNbTfsQKBwa7a1yx\nnD8Zjg17DmnNz//SuYXfnqtyHl/6xsKQUVdgyKhlDvmNUv4nyu4vQI5q3FzfkcPHGcp8w71o\nfKMndAOqTp0OLCvVtmtXbTyVrfP4mRu45UV/ZoyVhxw5YqhVq+K22+xuyi2vWuGR78Yx3nZT\n0nFt+fXKH9HtRna/fAuv3TUjKuV8mHvCx6NVHnIk+DZPG8Ps1Tzkc54ho/Bctp7285xTVtW+\nwZt7IS7+42rrOB/7spO3nCO0MAzY7aJnz1blFzQ8c0r6p90h1tyNGbzlZySMr12n+3PgIF1i\n4tU5/3R3RdVZ7szxzAPMedw4qrPmLlx2EXjNPdZ83O7LZZ3RIycz6XnRB0VV3lK2x45md1RV\n7uro9iKWf7u95UiuiUAIBzD3Nd34i+FdHzEWRvQpb9OOIT0Wvp6aK9WOBCv/Z+TeMVE1/+kh\nArQVoqLi98zMjnfeqbAp37jsyqDXi8pKodVWe9y9cavm4EYh+5B2yJtf80yTJ3cFKznkbP0p\nm/w1d/FghJqrufeuVL4xFtdWdo8mrfoWOfb4se8H4UDyTw3LvzmQ5dZcf/8Fn+Glv4AEQihi\n8tuV5RXkD0Pyxt8oq+x7XbZ+h7b7L0HV8uTs1Op3Wfl/UTwz5tVkdSCciw9dj40Tv//+ez1T\nj9vUzevY/iI5Sdt5A4Fknmly9vFjxyAOzzmoqrL72LDjo8bZn04mf4uVHB7edRK2KvnnBeSc\nJ7X7DKmw9hnlynfYNX/95bTjxr9udv+a231OwWpJltvxut8+AiH+Q87h65APGm/8PXE2j8pC\ntoZAm7YVZg4Aj3oH5PCQUYVyuohdtmuJwgvPLKxvIbNZ/fst58PN3M/U8hhyk+ubrFD+S5b/\nISm/Kqsn7BxSj62s5nA5HxfO7kt0+5gIp67jOZwXezL/d7CSA3eh8NizvAvP/KakJKTZvSMh\n4622fILApu+3jj0R6S18MBDm5OTs3r07JycnNzc3Pz+/tLS0pKREp9NFRkZGRUXFxsa2aNGi\nZcuWXbp06datW1BQkLvrdQULfQgWzqU572+JnD+xdpzU8VW+/eqEH7xAkyzED/lf0x1dlFwK\nTwo4fNdWY4OSb88WevYsXIVYbX373hbLOzL5hckFHLgjxzal5DYwjt3EqTytHhdQ/uXEqW+a\n1c8fcxfKWm225rYexRnjU5x0XkDOMBDLW9lam9fxnUBYVFQ0e/bsZcuWnTp1yuQKhYWF0kJG\nRoa0ULdu3UcfffStt95KSkpyUZUuoaSHR87mDmH1j7d958i9gtcV7HD2fTR7HYUZwIFtKud1\nPyZP+BxTUoOFs+Nu+Vm49+yDP3xc+A+7h1d4ckCqdu6m5u+vwlftFSyUKmd8h/wRRna/J/ad\nSnDjB68r+ci0E4sXL37ttdeuX79ux7YhISEvvPDCzJkzg4ODHV6YxJXTTnjRISvnt9o1o1jh\net77CWvufOEtY54LKij4a933hsBAd9T1P8wVad93KTt/TBUV9Qal6xISrn7xmT2b12D1mHHX\nqdxqX2i899j2AURHwBt5yFUYDuSaMxdMO/E/3n333enTp1d7MDIysmHDhgkJCREREWFhYSqV\nSq1Wl5eXX758+fz581Wjo1ar/eSTT44cOfL999/Xrl3btbX7Nflng+zeHB7LS3980h8t4/+V\nXy3mVCbHOnrm+XU5PHYkrbuGbqIm3nzAG/Gb63ZeHwh/+OEHYxoMCAjo3bv3sGHD0tLSpE45\ncy5fvrxv3761a9euWbNGq9UKIXbs2DFmzJhvv/3WFUXDFnxMwHNIR2PV/4v/JkBt+3YxTZt2\nvPNODz9ibc2E9r+c4GBtm9aVjRqZe97hPWke/s4DAOCZvHvIaGVlZePGjfPy8oQQTZo0+eab\nb1JTU21qIS8v7+mnn96yZYv0z/Xr1z/00EMOr5Mho4Bv86jrzm0aWmmVk14O4yoBAD7JZSNx\nGDL6H7t27ZLSYO3atbdu3Wq5V9CkhISETZs29evXb+vWrUKIhQsXOiMQAoBreOyIUB+YawQA\nAJ/k/psfKGHs2XvmmWfsSIOSoKCguXPnBgQECCF++uknjUbjsPoA+B+PjWT2ccjL8bH3BAAA\nX+LdgVDqHhRC9O3bV0k7ycnJrVq1EkJotdpLly45oDL34YsX4Leq/vp7+EcB3YMAAHgI7w6E\nxqkF69evr7Cphg0bSguXL1+2acNp06bVsebs2bP2TYlhH+nmhy7bHYBq3PILKHOndtSm8CPF\n5LZ8RgEA4CG8+xrCmJgYaeHq1asKm7p27Zq0YOvME08//XTXrl0trzN9+nTXT2jhUXe5AHye\nNivbcLM4NC1N+mfV+dBqTkRheWoKR811XvPGLQ7JdfKnD9VkZAT+91MaAAB4Ju8OhMaOwZ9/\n/rlXr152t1NUVHTs2DFpuUGDBjZtGx8fHx8fb3md5cuX21mZg3BDPws8e9UWWwAAIABJREFU\nZ9Y4eDILx4n0+3V9/Hjd2XMJp0+K/52YXnq2ZhKzL63Zcbg6vC+uWoM1s+5/lnS6wmEjVM2b\n19u2xbEFAIAvcdmXNL4NwhzvDoTdu3f/9NNPhRDz588fN25cdHS0fe188sknZWVlQoj27dvH\n+O75bAsfBHaHImObXh2rrH5jtpwELKygkFe/q8pZzmCWfyjy3zerb7Lxp2xuGnppwaDWGLRa\nYeNEPvYN4LRQhuuZ27VBrzfodAbzt+ky90IceMzz7ccTWD6D4FFc85HrseN3TP4uO/Cz1Nmq\n/b57xa9/tQu/FVZb89CyPGLfw98cOdx+1NXkpRdEePc8hKWlpbfeequU5e69996VK1fGxsba\n2sgXX3zxwgsv6PV6IcSsWbPefPNNh9fpwHlC3MtsV4DFNav+ujr2Y8jWMWzy61eo5gdx1dnA\nTZZR8y2q1lq1Z6vtwvKIRJlvlEP+fJrs+JLTpsnjxCoLP1M574DMd9vkOtWeLejWXXf+fMML\n50RQkMzilXDUQFDnMWi1eUlNVU2b1t+72+5G5AysVfJ7bS5amztxZrkkC2s6LwPY99M396bZ\n+mZaeKPs2NDc5hY+Tyy/sSZfV80Wqn4+W65NPnMx2MKnirlqLVRl62wuVj8h5bBjQ5nHmxK2\nVmLyrZP/fio5YCyXKn9MvpBxXMln4cy+/NBV7VqJakXWXNOmFyuT1V8BC1HZ6sUdJj9/3Pjn\n2IH5wrsDoRBi+vTp7777rrSckJDw4osvDhs2zHiHGAtKS0t//PHHTz/9dO/evdIjzZo1O3Lk\nSEREhMOL9JlACMAcFwdCz+eQQAgAAExiYvr/76233tqyZcu+ffuEEHl5eRMmTJgwYULjxo07\ndOiQmJjYoEGDyMjIsLCwoKAgtVqtVquvXLly4cKF3NzcI0eO6HQ6YzvR0dHffPONM9IgAAAA\nAHgmrw+EKpVq69atI0eOXLVqlfHB8+fPnz9/Xn4jzZo1W79+fcuWLZ1QIAAAAAB4KO+eh1AS\nFha2cuXKrVu3pqam2rptw4YNP/3002PHjpEGAQAAAPgbr7+GsJpz585t2LBh7969ubm5J0+e\nVKvV1VYICAhITEy8/fbbO3XqNGDAgC5dugQEBDi7qtGjR//73//u2bOns3dU1caNGyMjI1Uq\nr+8Ehq+6du1aVFRUSEiIuwtxjCU3S+P1+geia1W6uxIPESzEpqKbFwMDn64V6e5a7HTjxo2w\nsLCwsDB3FwKYVlxcrFKpuNoFHqukpKRu3bqdO3d2dyG+affu3d26deMaQhOaNGny4osvvvji\ni0IIvV5fVFRUWlpaUlKi0+kiIiKioqKio6NDQ0NdXNVjjz3m+m+9BQUFCQkJUVFRLt4vINO1\na9eCgoJ8JhCO9NrY4yQVQtwXXcvdVShy48aNWrVqEQjhsYqKikJCQgiE8Fg3b96srOQ0qbP0\n7NlzwIABDmnK13oIYRQREfHdd9898MAD7i4EMK1JkyZTpkwZOXKkuwsBTEtJSRk0aJAz5iIC\nHOKhhx66/fbbP/roI3cXApj29NNP63S6JUuWuLsQWOEL1xACAAAAAOxAIAQAAAAAP0UgBAAA\nAAA/RSAEAAAAAD9FIAQAAAAAP0UgBAAAAAA/RSAEAAAAAD9FIAQAAAAAP0UgBAAAAAA/RSAE\nAAAAAD8V9Pe//93dNcAp9Hp9v379atWq5e5CANMMBkOfPn3q1Knj7kIA0wICAtLS0uLj491d\nCGBaUFBQx44dmzRp4u5CANOCg4NbtWrVvHlzdxcCKwIMBoO7awAAAAAAuAFDRgEAAADAT6nc\nXQAc7Pjx4+vWrTt9+nRBQYFKpYqPj2/Tps2jjz7aoEEDd5cGb9W/f//S0lKZK3fp0uXDDz+0\nsILyQ5SDHN99991nn32m1+uFEJMnT77nnnvkb+sJRyDHsM/Lzs5+++23S0pKhBADBgx49dVX\nLa8/ceLE/fv3y2w8JCRk69atFla4fPny2rVrjxw5UlBQoFar4+Pjb7vttocffrht27Yyd6G8\nBXisnJycrVu3Hjp0qLCwUKvVRkdHN27cOCUl5YEHHoiMjJTTgiccYByiDmaArzh69GjXrl1N\n/pQDAwOHDx9+7do1d9cI76PX6wMDbRhK0LdvX3NNKT9EOchRVFQ0cuTIqj/6FStWyNzWE45A\njmGfp9fr//GPf4SGhhp/ss8++6zVrXr06GHyqDApNDTUXDtXr14dMWKEuQ/tlJSUY8eOWa5E\neQvwWMePH+/Tp4+54yo6Ovq9997TarUWWvCEA4xD1BkIhD5i/fr1ISEh5n7JJQ0bNjx37py7\nK4WXuX79uuXjqhpzgVD5IcpBjr1799a8f4bMQOgJRyDHsM+7cOFCr169qv1M5QTC9u3bWz4w\nqjIXCM+cOWO1kzk0NHTTpk3mylDeAjzWqlWrqp6nMKd79+6lpaUmW/CEA4xD1Em4qYwvOHz4\nsPQLLP1z4MCBQ4YMadq0qU6n++OPPxYtWmQciNKmTZuff/45KirKfcXCy5w7dy4pKUlanjp1\nqtXBGPXq1UtNTa32oPJDlIPcz2m12smTJ3/00UfSMNG4uLirV69KT61YseLxxx+3vLknHIEc\nwz5v+fLl48aNKyoqEkLUrl27rKxMp9MJIZ599tl58+ZZ3rZJkybnz58XQgwePHjo0KGWVw4K\nCnrooYeqPVhcXJySkpKTkyP9s3Pnzs8880zr1q0jIiLOnTu3evXqVatWSb8+UVFR//73v2t+\nmCtvAR5r9+7d9957r3RACiFSUlJGjBjRqlWr0NDQixcv/vjjj998801FRYX07PDhw7/++utq\nLXjCAcYh6kTuTqRwgLvuukv6aQYHB3///ffVntXr9e+++67xJz516lS3FAkvdejQIePBc/Dg\nQfsaUX6IcpD7s1OnTnXo0MH48x01alRGRobxn3J6CD3hCOQY9mGlpaVDhgwx/vhSUlJOnz4d\nHR0t/VNOD6Fx5Q8//NC+GqoeP2+88UZlZWW1FTZu3GjsoL7nnnuc0QI8k1arNZ7YDQgI+Oc/\n/1lznQMHDhgPwoCAgCNHjlRbwRMOMA5R5yEQer1t27YZfz1mzJhhbrX7779fWicmJubGjRuu\nrBBebdeuXcYD7MSJE3a0oPwQ5SD3c1988YX0k42OjpbiX9XzFFYDoSccgRzDvu2PP/6QfnCB\ngYETJkyoqKgwVMl4VgNh1Uu1FyxYYEcB165dM0473LdvX71eb3K1GTNmGI/D3bt3O7YFeKzV\nq1cbf2rjx483t9rixYuNq02ePLnqU55wgHGIOhWB0OuNGjXK+AWirKzM3GpVT6gvW7bMlRXC\nq61bt8545Fy+fNmOFpQfohzkfk4KhKmpqWfPnpUesSkQesIRyDHs26RA2KBBgx07dhgflB8I\nb9y4Yfy5r1y50o4CvvrqK2MLP//8s7nVSktLjVU988wzjm0BHst4I66goKD8/Hxzq5WXl4eF\nhUlr9u/fv+pTnnCAcYg6FfMQeje9Xr9p0yZpedCgQeHh4ebWTEtLa9y4sbS8YcMGVxQHn1D1\nm0pMTIytmys/RDnIERQUNHHiRJN3lLHKE45AjmF/MGDAgKysLJtmQDFS+DErqhwtTZs2NXcb\nWyFERETEI488Ii1v3LjRUOUuEspbgMfq2LHj8OHD+/Tp8+CDD9avX9/camFhYYmJidJyYWFh\n1ac84QDjEHUqAqF3y8nJuXLlirTcvXt3yyunpaVJC7t373ZqVfAlxm8qYWFhVm+QWJPyQ5SD\nHKNGjZo2bZpKZc/EuZ5wBHIM+7xmzZr98MMPcXFx9m1eNRAaOzdssnfvXmlB/gFWUFCQm5vr\nwBbgsV566aWvv/5627ZtVYf8mKRWq6WF2rVrV33cEw4wDlGnIhB6N+OtloQQVW+6YNIdd9wh\nLVy+fFm6DRpglfGbin3nrZUfohzkCA4OtntbTzgCOYZ9nn1nK4wU9hBeu3bNeMZB/gEmhDB+\nV1beAnxAaWlpXl6etHz77bcbH/eEA4xD1NkIhN6t6oFu7Og3p2HDhsblql9QAAuqBUK1Wr16\n9erx48cPHDjwvvvuGzx48Ouvv75hw4by8nKTmys/RDnIoYQnHIEcw7CsWiA0GAy7du2aPHny\n4MGD77vvvoEDBz7//PNLly7966+/TG7OIQqH+OabbyorK6Xl9PR04+OecIBxiDqbonNacLuC\nggJpITQ0tE6dOpZXTkhIMC7n5+c7sSz4EOM3lVq1as2ePfv999+/du1atXU+/vjjevXqvfvu\nu+PGjQsICKj6lPJDlIMcSnjCEcgxDMuqBsJffvnlzTffPHHiRLV15s2bFxYWNmbMmOnTp1eb\no9J4gAkhrM7ZXa9ePZVKJc1HV/MQtbsFeLuioqKpU6dKy2lpacZpcoRnHGAcov+PvTuPb6La\n/z9+2qYLXSgFCnShUKTsmxShUJQKyKoIIiqbLF5RQRFUBMUNL7K4oriBcnFBEEVBFgHZKShb\nQSggpSBrocja0tImTZvfH3O/+eV2SSeZSTJJXs+HDx9DMnPm0/Y0zTvnzBxHY4TQvZnXOA4J\nCal05+Dg4LIHAtaZ36ns3bt34sSJZdOg5NKlS88888yDDz5oMBgsH1feRenkUEILPZA+DOss\nA2H//v3LpkFJYWHhRx99lJSUdPbsWcvHLftJpX3Mx8fHfCfJsl3U7hbg1kpKSkaNGnX+/Hkh\nREBAwNy5cy2f1UIHo4s6GiOE7i0vL0/aMHd9Kyz3MR8IWFfqKqb69esPHz68Q4cOkZGRt27d\nOnLkyNKlS1NTU6Vnf/7553Hjxn3xxRfm/ZV3UTo5lNBCD6QPw7pSL7NhYWHDhw9PSUmJjY0t\nKSk5derUr7/++sMPP0jT+Y4cOdKvX7+dO3ea3xZb9hOZfUw6pGwXtbsFuK+SkpInnnhi+fLl\n0j/nzp1reQ2e0EYHo4s6GoHQvUkD4kIIPz+/Sne2vOrdfCBgnfmja51O9+9///uFF16w7Egp\nKSnjxo1bvHjx6NGj9Xq9EOLLL78cOnRoSkqKtIPyLkonhxJa6IH0YVhnOUI4ePDgTz75JCIi\nwvxIcnLysGHDXnzxxfvvv18aGzx48ODbb789bdo0aQfLfmJTHyvbRe1uAW6qsLDw0UcfNa9c\nP3ny5DFjxpTaRwsdjC7qaARC92aeXGS+U7AVlvvImbkECCFWrFghzQINDQ2taP2iIUOGGAyG\nUaNGSf+cOXOmORAq76J0ciihhR5IH4Z1L7300tixY4UQfn5+8fHx5e7Tpk2btWvXtmvXTrqD\n15w5c15++eXAwEDxv9OMbepjZbuo3S3AHV24cGHAgAF79uyR/vnaa6+ZP2WwpIUORhd1NK4h\ndG/mK8vl/HpY3gey1CXpQEXi4uIaNmzYsGFDK6vZCiFGjhxpvhP01q1bb926JW0r76J0ciih\nhR5IH4Z1kZGR0stsRWlQ0qxZsyeeeELazs3N3bFjh7Rt2U9s6mNlu6jdLcDtpKWl3XHHHVIa\n1Ol0n3/+eblpUGijg9FFHY1A6N7Ms0ry8vIq/Q25dOlS2QMBtfTv31/aMBgM5hs9K++idHIo\noYUeSB+GWswvs0KIgwcPShuW/cS8VltFcnJypOn9orwuancLcC/Lli278847L1y4IISoWrXq\n6tWrzZ81lKWFDkYXdTQCoXtLSEiQNkwm07lz56zvfObMGfN248aNHVgWvJLlZ9vm12vlXZRO\nDiW00APpw1CL9ZdZIUSpG5CWZb2L2t0C3MiMGTMeeughaQytYcOGu3fv7tmzp5X9tdDB6KKO\nRiB0b02aNDFvW67aWa6//vpL2ggLC7NctRNQnb+/v7ShvIvSyaGEFnogfRiOYH6ZrVevXpUq\nVaRt+R1MCNG0aVO1WoC7mDJlytSpU00mkxCic+fOu3fvtnyBKpcWOhhd1NEIhO6tZcuWAQEB\n0vbvv/9ufWfz2gDt2rVzbFnwStISRpKaNWtKG8q7KJ0cSmihB9KHoZZyX2Z9fHzM6wTI72DB\nwcHNmzdXqwW4hddff3327NnS9sMPP7xx48bq1atXepQWOhhd1NEIhO4tJCSka9eu0vbq1aut\n7Hn8+PGjR49K25YXIQBW/P777zNnzhw7dmz//v3N94mpyNatW6WNoKCgRo0aSdvKuyidHEpo\noQfSh2HF6dOnP/jgg4kTJz744IPm+z1WxPwyK4Ro3bq1ebtfv37Sxr59+7Kzsys6vLi4eNWq\nVdJ2z549LddzU94CNG7BggVvvvmmtD1s2LDFixdLd6mVQwsdjC7qWCa4ufnz55t/mlu2bKlo\nt3Hjxkn7+Pn5nT171okFwo199tln5t41b948K3ump6f7+PhIe95zzz2WTynvonRylHLgwAFz\nl1iyZIn1nbXQA+nDXig8PFz6aT7xxBNWdrOc3jZ48GArexYUFNStW1faMzQ0VK/Xm5+ynET3\n+uuvV9SCebk5IcTixYstn1LeArTsyJEj5mjUv39/o9Fo0+Fa6GB0UYciELq9goKCmJgYqes3\natQoNze37D7btm3z9f3vaPDo0aOdXyTcVHZ2tvlPSI0aNU6cOFHubteuXWvZsqX5JXj16tWW\nzyrvonRylGJTINRCD6QPeyGZgdBkMpnnwvn4+Pz000/l7lNcXDxixAhzt3/hhRdK7fDAAw9I\nTwUFBf35559lW/jnn3+io6PNnbCoqEj1FqBNxcXF5j7WuHHjcl9/KqWFDkYXdRwCoSdYsGCB\n+Y9EixYtdu3aZX5Kr9cvWLDAvKBnaGgonzrDJpMmTTL3rlq1an377beWH0sXFxevWbPGPEFU\nCNGjR4+yjSjvonRyWLIpEJq00QPpw95GfiBcs2aNuW/odLo33njj2rVrljscPHjQ8j6Q0dHR\nly9fLtXI0aNHzZeqVqtWbcmSJZbvhrds2dKgQQNzC8uXLy9bhvIWoE3ffvut+Qe3YcMG+xrR\nQgejizqOj8lkEnB/jz/++Jdffmn+52233dagQYPCwsKjR49evXpVetDPz2/VqlW9e/d2UY1w\nSwaDoWvXrjt37jQ/EhYW1rJly4iIiJs3bx4+fPjatWvmp5o0afL777+Xu+yP8i5KJ/dOf/31\n1/Dhw0s9WFBQYL7WrkGDBmW7XGpqqvmWdBIt9ED6sKe66667yl5l/eeffxYXFwshIiMj4+Li\nSj07Z86czp07m//54osvvvPOO+Z/+vv7t2jRIioqSq/Xnzx58vTp0+anQkNDN2/efMcdd5Qt\n47vvvhs2bJj5n7Vq1WrSpElAQEBmZqbljfhffvnlt956q9wvRHkL0KBGjRplZmYKIfz8/Nq0\naSPzqAULFlhepyq00cHooo7i6kQKdRQVFU2YMMF8EVdZNWvWXLFihavLhFu6detW2XfkZT3w\nwAM3btyoqBHlXZRO7p327t1bad8r6+bNm6Xa0UIPpA97qpCQEFu76KpVq0o18s4771R6A4wW\nLVocPXrUSiVfffVVWFhYRYfrdLpp06aVlJQ4tAVojZ+fn639UwiRmppatiktdDC6qCMQCD3K\njh07hgwZUuovU3x8/EsvvZSdne3q6uDe/vjjj5EjR9auXbvUi29UVNTQoUP/+OMPOY0o76J0\ncm+jViCUaKEH0oc9jyqB0GQynT59evLkyQkJCaU+NQgLC+vZs+f3339fXFxcaTGnT5+eMGGC\n+UoqSbVq1UaPHn3gwAE5X47yFqApKgZCkzY6GF1UdUwZ9UBFRUUXL17Mzs4ODAyMioqqVauW\nqyuCR5F6V35+flhYWGRkZKlXZDmUd1E6ufe4efNmWlqarUfdeeedVt4DaaEH0oc9SWpqqjQ7\nVL6WLVvWqFGjomdzcnLOnj2bm5sbFBRUo0aNunXr2vqe3mQyXbp06cKFC8XFxVFRUVFRUc5v\nARqxbds2O97t33777ebrYMvSQgeji6qIQAgAAAAAXoqF6QEAAADASxEIAQAAAMBLEQgBAAAA\nwEsRCAEAAADASxEIAQAAAMBLEQgBAAAAwEsRCAEAAADASxEIAQAAAMBLEQgBAAAAwEsRCAEA\nAADASxEIAQAAAMBLEQgBAAAAwEsRCAEAAADASxEIAQAAAMBLEQgBAAAAwEsRCAEAAADASxEI\nAQAAAMBLEQgBAAAAwEsRCAEAAADASxEIAQAAAMBLEQgBAAAAwEsRCAEAAADASxEIAQAAAMBL\nEQgBAAAAwEvpXF0AAACeb968eRcvXmzduvWAAQMq2ufnn38+dOhQ7dq1n3rqKfODRUVFW7Zs\nOXz4cGFhYZ06de68886EhASnlAwA8AoEQgAAHK569epPPvlkUFDQ4cOHb7vttrI7nDx5cujQ\noYWFhUuWLDE/uHbt2scffzwrK8tyz0GDBi1YsCAsLMzhRQMAvICPyWRydQ0AAHi+fv36rVq1\nqkePHuvXry/7bK9evdavX9+3b9/Vq1dLj2zcuLF3795Go7FPnz6DBw8ODw/fu3fvBx98kJeX\nN2DAgJ9//tm55QMAPBOBEAAAZzh//nyzZs1u3ry5ZMmSRx55xPKppUuXPvLII2FhYUeOHKlb\nt670YMuWLQ8fPjxmzJh58+aZ99y0aVP37t2FEMePH2fuKABAOW4qAwCAM8TGxs6cOVMIMXHi\nxBs3bpgfz83NnThxohBixowZ5jR44cKFU6dOCSGmTp1q2Ui3bt1iY2OFEPv373da5QAAD0Yg\nBADASZ566qmOHTtmZ2e/9NJL5genTp168eLFjh07jh071vxgdHR0Xl7ezZs34+LiSjVSrVo1\nJ5ULAPACTBkFAMB5jhw5cvvttxuNxt9//z0pKSktLa19+/Y6ne7AgQPNmjWr9PCrV6/GxMQY\nDIa///67fv36jq8XAODhGCEEAMB5mjdvPmXKFJPJ9MQTT+j1+ieeeKKkpOSll16SkwaFEK+8\n8operx84cCBpEACgCkYIAQBwKr1e36ZNm2PHjnXo0GH37t3NmjU7cOBAQEBApQfOmTNn4sSJ\nUVFR+/fvr1OnjhNKBQB4PAIhAADOlpqa2qVLF5PJ5Ovrm5qa2qlTJ+v7m0ymyZMnv/POOzVr\n1ty0aVOrVq2cUycAwOMxZRQAAGe78847+/XrJ4QYMGBApWmwoKDgoYceeuedd+rXr79jxw7S\nIABARTpXFwAAgDeSbhZa6S1Dr1692rt377179yYlJf3yyy+1atVySnUAAG/BCCEAABqVl5cn\npcFBgwZt2bKFNAgAUB2BEAAALSouLu7fv//evXtHjhy5dOnSoKAgV1cEAPBABEIAALTo7bff\n3rRpU5cuXb788ksfHx9XlwMA8ExcQwgAgOZcuXJlxowZQoj77rtv7dq1ZXeIjY1t06aN0+sC\nAHgaAiEAAJqze/fuvLw8IcQLL7xQ7g5Dhw5dtGiRc4sCAHggAiEAAC7Qv3//+vXrt23bttxn\nExISXn/9dSuHs/gEAEAVLEwPAAAAAF6Km8oAAAAAgJciEAIAAACAlyIQAgAAAICXIhACAAAA\ngJciEAIAAACAlyIQAgAAAICXIhACAAAAgJdiYXpnWLNmzcqVK11dBQAAAAAP0a9fv759+ypv\nhxFCZ1i2bNnWrVtdXQUAAAAAT7B169Zly5ap0hQjhE7SqVOnefPmuboKAAAAAG5v1KhRajXF\nCCEAAAAAeCkCIQCowHAoXb9zp6ur0Bb9jh1Fhw+7ugoAAGANU0YBQAXXJ0wwnjodczJT+PJB\nmxBCCKPxytDhukaNam9Y7+pSAABAhXjjAgAqMBXqTQaDMJlcXYhWmEpKTEajSa93dSEAAMAa\nAiEAAAAAeCkCIQAAAAB4KQIhAAAAAHgpbioDANqV9Pr/3JFl17SerqoEAAB4JAIhAGhOqRxY\n6nFiIQAAUAuBEAA0pKIoWHYfYiEAAFCOawgBQAWBnZMDk5OFn5+SRuSkQft2dj6fgIDAjklB\nyZ1cXQgAALCGEUIAUEHE27MVtmBHwEt6fb2Wxwkjl/3o6hIAAEAlGCEEADem8XFCAACgcQRC\nAHA9ch0AAHAJAiEAuDfCJAAAsBuBEABcjEQHAABchUAIACowHErX79zp6iq0Rb9jR9Hhw66u\nAgAAWMNdRgFABdcnTDCeOh1zMlP48kGbEEIIo/HK0OG6Ro1qb2D8EwAA7SIQAoAKTIV6k8Eg\nTCZXF+JiF7/yMW/7ThMl4rT5kaiR3v7NAQBAg/gkGwCgDss0WO6z1ncAAADORyAEACglP+yR\nCQEA0BQCIQBAEVszHpkQAADtIBACgIvtmtbT1SU4G5kQAACNIBACgHtzbZ60O9qRCQEA0AIC\nIQC4nhcOEgIAAC0gEAKACgI7JwcmJws/P7tbsC8TuunwoCqHAwAA5ViHUKnU1NQ1a9ZY32f7\n9u21atVyTj0AXCLi7dnOPynjigAAQCECoVLnz59PS0uzvk92dnZ+fr5z6gHgvqSAl/T6evk7\nAwAAKEEgVGrw4MGDBw+2vk9CQoJzigHgAXZN62k9ExIFAQCAWgiEAKA55shnmQzJgQAAQHUE\nQgDQLkIgAABwKO4yCgAqMBxK1+/c6eoqAAAAbMMIIQCo4PqECcZTp2NOZgpfPmiTK2qkydUl\nAADg7XjjAgAqMBXqTQaDMHlXwiHRAQDg7giEAAAAAOClmDIKAJ7D+XcljRppuviVj30Hql4M\nAACwFYEQANxeuesWOi0c2pEJSYMAAGgEU0YBwLGkYGZ9rXkljctpWc5uSiq0KeCRBgEA0A4f\nk5fdAsElEhIShBCZmZmuLgSAo2R36mw8cyb27Gnh5ydkhKtKh+zKbaHcBettZXlqK+3YMago\nZ5yQNAgAgHKjRo0SQixcuFB5UwRCZyAQAh7PHAiT3two/6hyQ5eDxhLto1YsJAcCAKAiFQMh\nU0YBQAWBnZMDk5NtSoOivOynqTQo7KonaqRJ+k+3+sEqGS9J246oDQAAKEcgBAAVRLw9+76m\no+w40DJxaS0NSuyuKnLZj9VmzlC3GAAAoC4vusvoH3/8sWbNmkOPHFB3AAAgAElEQVSHDl2+\nfNnf379mzZqtW7fu0aNHx44dXV0aAHelSoRLen39rmk9tZkGJVKFrq4CAACoz70D4dGjR3Nz\nc4UQjRs3joiIqGi3I0eOPPXUU6mpqaUeX758+RtvvNG+ffv33nuvc+fOjq0VgGdRN79pOQ0C\nAAAP5t5TRseOHduxY8eOHTtu27aton1+++23pKSksmnQbM+ePXfffff8+fMdUyMAD+SF+c0L\nv2QAALyBewfCSh0/fnzQoEF5eXnmR4KCguLj4+Pj46tUqWJ+0Gg0Pvnkk2vWrHFFjQDcDNEI\nAAB4DA8PhBMnTpTmlAohWrRosWLFihs3bvz9999///13bm7uzp07Bw0aJD1rMplGjhxpGR0B\noCzSoHz6HTuKDh92dRUAAMAaNa8hPHPmzO7du48dO3bmzJnz58/fvHnz1q1bRqOxSpUqISEh\nNWrUqFevXnx8/O23356YmGg5QOcgf/3116+//iptd+/e/ZdffgkODjY/q9PpOnXq1KlTpwUL\nFjz++OMmk+nKlSvz589/7rnnHF0YADfl5WnQtlvLGI1Xhg7XNWpUe4NXf9MAANA4pYHQaDRu\n3rz5p59+Wr169YULF2Qe5e/v36FDhwceeGDgwIFxcXEKa6jI+vX/fRdSrVq1pUuXWqZBS489\n9tihQ4c++ugjIcSyZcsIhACgnKmkxGQ0mvR6VxcCAACssX/K6PXr12fPnh0fH9+zZ8/58+fL\nT4NCiKKioh07djz33HPx8fH9+/e3cscXJQ4cOCBtjB49unr16lb2fOmll3x8fIQQ+/btKygo\ncEQxANydlw8PynTxKx/pv+zFga6uBQAAVM6eEUKDwfDJJ5+8+eabN27cKHeHGjVq1K1bNyws\nLDg42N/f/9atW7du3frnn3/OnTtXVFRkuWdJSckvv/zyyy+/9OzZ87333mvevLk9X0QFrly5\nIm106dLF+p516tRp3rz54cOHi4qKsrKyGjZsqGIZAODxLn7lU/ZBv7fOm8T5i1/5RI00Ob8k\nAAAgh82BcO/evcOHD8/IyLB8MCIiolevXp06derQoUPTpk1DQ0PLPbakpCQrK2vfvn27d+/e\nvHnzvn37TKb/vktYv379xo0bX3jhhTfffDMgIMCOr6Ss4uJiaaNOnTqV7hwTE3P48GEhxLV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EkoCAgD59+vTp00cIUVBQcPLkydOnT2dl\nZd28ebOgoMBoNFapUiU4OLhmzZr16tWLj4/n9qEA4HxOu3ivbCI1P0IyBABAa1QIhJaqVKnS\nokWLFi1aqNssAGic4VC66WZuYHKyqwtxMcs0GHDwoCksrKhBA8tnyYQAAGiKDctOAAAqcn3C\nhCvDHhUlJa4uRCt8iosj3niz6odzSz2uhRmtAADAjEAIACowFepNBoNQ48bLjuPMMGYqKRHF\nxcJgcNoZAQCAHVSYMmoymTIyMo4dO5aRkXHx4sX8/Py8vDyj0RgSEhIaGhoREdG4ceOmTZs2\nb948KChI+ekAQLP2799fam1658+QTExMZBQOAADIpCgQbtu2bdGiRatXr87Ozq505+Dg4F69\neg0aNGjQoEGeut4gAK+l1+vLfV3ztqvm5MdR7kQKAIAW2DllND09vVu3bikpKV9++aWcNCiE\nuHXr1s8//zx48OAWLVqsXLnSvvMCgNthvK4sK3ciBQAAzmTPCOGaNWseeeSRvLy8cprT6YKC\ngoKCgnQ6XWFhYUFBgV6vL7XPsWPH7r///ldfffXNN9+0p2SNOXXq1L59+6zvk5eXx3RZwIOl\npaXVrGwHVUbAyqYmDQ6sSSWZDIasCp6tKPt522gqAABaYHMgPHLkyMCBA80xLzw8vH///snJ\nybfffntsbGzt2rV9fHws98/JyTlz5sxff/2Vmpq6cuXKc+fOSY//+9//DgwMnDp1qvKvwbWW\nLVs2c+ZM6/vk5ORUr17dOfUA0Cbla/GVm6PKDVGavYyQvAcAgNbYHAhHjx4tpcGAgIBp06aN\nHz8+ODjYyv7h4eGtWrVq1arVww8//OGHHy5fvnz8+PEXL14UQkybNu2+++5r1aqV3dVrwaRJ\nkyZNmmR9n4SEBOcUAwCaEhQURAgEAEDLbLuG8NChQ3v27BFC+Pr6/vzzz1OmTLGeBkvx8/N7\n8MEH//zzz/j4eCFEUVHR3Lmll6gCALeTmJhoaN3K0KpVqVuMlkv1sbtyGyw3hjkzm/kEBAR2\nTApK7uS0MwIAADvYNkK4YsUKaePhhx/u27evfaesVavWggULunbtKoT46aefPv/8c246CsDd\n5Y4b6+oSSpPinxQXXTJMF7nsx3If1+yMVgAAvJBtI4R///23tDFo0CAlZ7377rvj4uKEENev\nX7906ZKSpgDAy1kPe4mJie4yadNd6gQAwJPYFgjN4a1+/foKT2y+rO7ChQsKmwIAuJdSMdWN\nUisAAB7GtimjgYGB0kZBQYHCExsMhv9WoLNn6QsAcFPm5GPryuzlzrR06xzl1sUDAOAZbAtj\nderUkTYOHz7cqZP9twowGo3Hjh2TtqOjo+1uBwDcjrRQRLkrs8vJhA6rCwAAeCPbpoyal4hY\nsGBBSUmJ3Wf94YcfLl++LISIiYmJjIy0ux0A0AjdiZMBhw7J3NnKyuzqVWSztP+lvEH9jh1F\nhw8rbwcAADiObYFwwIABvr6+Qog9e/bYvab8n3/+OXbsf2/H99BDD5VayB4A3FH4nDkR0/4t\nZHxSps1RvnJHLBW1aDReGTr82sTnFTUCAAAczLZAGBUVNXjwYGl71qxZffv23b17t/zDL168\n+PrrryclJeXk5AghwsLCnn32WZsKAABt8jEUiaIiD/t8S0kmNJWUmIxGk16vYj0AAEB1Nt/Q\n5cMPP9y0aVN2drYQ4tdff/31118bNmyYnJzcpk2buLi46OjokJCQoKAgPz+/wsLCwsLCy5cv\nnz17NiMjY+fOnfv27TMajeamPv7443r16qn51QCAiwQGBhor38tthgcBAICXsDkQ1qhRY8eO\nHX369Dl+/Lj0yIkTJ06cOPH111/LbyQwMHDBggVDhw619ewA4F7KTYCszA4AADTCtimjkttu\nu23Pnj0vv/xyaGiorcf6+PgMHDjwwIEDpEEAnqdt27ZSAkz8PzYdrs3xQwAA4MHsCYRCiPDw\n8LfeeuvMmTMLFy584IEHatasaX3/KlWqdOrUadasWceOHVu2bFnTpk3tOy8AaJ+cXKepldkJ\nogAAeC1Fi8JXr1595MiRI0eOFEJcu3YtIyMjOzs7Pz8/Ly/PaDQGBweHhoZGREQkJCTUq1eP\nu4kCQCkaT2IaLw8AACinKBBaql69eseOHdVqDQDgTFL2M1/ZSBQEAMBLqBYIAcCbBXZO9ouN\nFX5+ri5EERVzoE9AQGDHJP+EBLUaBAAAjuCyQLhjx44bN24IITp27FijRg1XlQEAqoh4e7ar\nS9CcyGU/uroEAABQCZcFwgkTJkhzk7Zs2ZKSkuKqMgAAAADAa9l5l1EAAAAAgLsjEAIAAACA\nlyIQAgAAAICXIhACgAoMh9L1O3e6ugpt0e/YUXT4sKurAAAA1rDsBACo4PqECcZTp2NOZgpf\nPmgTQghhNF4ZOlzXqFHtDetdXQoAAKiQbYHw/fffP378uConPnv2rCrtAIAWmAr1JoNBmEyu\nLkQrTCUlJqPRpNe7uhAAAGCNbYFw5cqV27Ztc1ApAAAAAABnYsooANhJWkxVEuPCOgAAAOxl\nWyAMDQ3972E6XUhIiJIT5+XlFRcXK2kBAFzIMg0KIfR6vZ+rSgEAALCXbYFwxowZGzdu1Ov1\nwcHBBw4ciI+Pt/vE7dq1K/V2CgDcBS9fAADAM9h2N7xWrVpNnz5dCJGbmzt48GCj0eiYqgDA\nLe3fv9/VJQAAANjA5tujP/fccykpKUKI3bt3v/baa+pXBAAAAABwCpsDoa+v79dffx0eHi6E\nmD179pYtWxxQFQC4GUPrVoZWrUwsQvh/fAICAjsmBSV3cnUhAADAGnvuMhoXF/fxxx8PHz68\npKRk2LBhhw4dqlGjhuqVAYA2lXsBYe64sUKIxMREp5ejXZHLfnR1CQAAoBJ2fpg9bNiwhx56\nSAhx4cKF0aNHq1oSAAAAAMAZ7F+HcOHChdOmTZO2jUajTmdbU8HBwdLCFX5+7n2r9m+++ebb\nb7+1vs+FCxfCwsKcUw8AAAAAyGR/IAwODm7SpIndh2/fvt3uYzWlUaNGlU4SS0tL8/f3d049\nAAAAACCT/YEQkqSkpKSkJOv7/PTTT84pBoDjVLr2IBcQAgAAt8MN8QBABboTJ9MXLnR1Fdqi\n37Gj6PBhV1cBAACsYYQQACpX6fBg+Jw5/tmXxIgRgpUnJEbjlaHDdY0a1d6w3tWlAACACvHG\nBQBU4GMoMhkMwmRydSFaYSopMRmNJr3e1YUAAABrlI4QvvXWW+fOnQsKCvLz8/Px8bH18MDA\nwGrVqsXExCQlJTVo0EBhMQDgbUoNXXIdIwAAsInSQLhhw4Zt27apUkrz5s1feOGFkSNHqtIa\nADjf/v37295xh33HWkY7mbmu7ETWtLQ0MiEAAJBPQ1NGjxw5MmrUqPvvv7+goMDVtQDA/3B0\nyioV7Sq9ZNHKPnKOBQAAkCgNhG3btr399tsTEhJiY2PLLrXn6+sbHBxc9qiqVauGh4eHh4eH\nhYWVmmi6cuXKxx9/XGFVAKA6x2XCciMcuQ4AADiB0kD4/vvv79+/Py0tbcCAAcXFxUKIxMTE\nmTNn7tq16+rVq0VFRfn5+Xq9Pisra/ny5ePGjQsLCxNCNG/efOfOnTdu3MjNzTUajRkZGR99\n9FH9+vWlNr/77juPWbYeAAAAADRLhSmjeXl5Xbp0mTt3bkRExPLly/ft2zdlypQOHTpUr17d\n19dXCBEQEBAdHd2/f/+PP/741KlTo0eP/uOPP9q3b79lyxYhhK+vb6NGjZ555pn09PQuXbpI\nbX700UfKCwMAt8YgIQAAcDQVAuGIESMOHDhQvXr17du39+/f3/rONWrUWLBgwTPPPHPr1q2B\nAwdmZWWZnwoNDV20aFFQUJAQYsuWLSbu3g7AfRhatzK0amVSdRFC+yapauSmMj4BAYEdk4KS\nO7m6EAAAYI3S9y579+79+eefhRCvvvpqs2bNZB41e/bsmJiY69evz5gxw/Lx2NjYrl27CiGu\nXbt27tw5hbUBgNPkjht7ffqbzgxjGgl+VkQu+7HazBmV7wcAAFxHaSBcvny5tDFkyBD5R1Wp\nUmXAgAFCiCVLlpSUlFg+ZU6VV69eVVgbADhNYmKi3Qmt3APltFbqpEpqAAAA3knpOoRHjhwR\nQoSEhNSqVcumA6Vl6K9fv37y5MmEhATz4wEBAdLGrVu3FNZmMplycnLy8/Pz8vKMRmNISEho\naGjVqlXNpwAAmyQmJpZ7XZ/yGFaqZZsaJAQCAAC7KQ2EUmy7deuWwWCwKWjdvHlT2rh8+bJl\nIDRfVRgaGmpHPefPn1+9evXWrVuPHTt2/Pjxsksa+vr6xsfHN23atH379v3792/ZsqUdZwHg\ntZQkt0pbVqspAAAAmZQGwpo1awohTCbThg0b+vbtK//A1NRUacMy+BUXF2/evFnajouLs6mS\n7du3v/7661u3brW+W0lJycmTJ0+ePLl69erXXnutcePGr7766pAhQ0othwgAFSG5AQAAj6H0\nGsLGjRtLG5MnT87Ly5N51ObNmzdt2iRtR0dHmx///PPPpXvJNGjQICIiQmZrBoNh5MiRXbp0\nqTQNlpWRkTFs2LD27dufP3/e1mMBwMxwKF2/c6erq9AW/Y4dRYcPu7oKAABgjdIRwv79+0+b\nNk0IceTIkaSkpDlz5nTv3t3K/nq9fuHChS+88IK0qkRiYqI0xnjlypUPPvhg1qxZ0m4PPvig\nzAKKi4t79+5tHleUREdH33777bGxsTExMcHBwUFBQTqdrrCwsKCg4NKlS2fOnPnrr7+OHTtm\n3n/fvn0dOnTYtGlTkyZNbPnqAeC/rk+YYDx1OuZkplB15Qk3ZjReGTpc16hR7Q3rXV0KAACo\nkNJA2KZNm/vvv/+XX34RQhw5cuSee+6pW7duly5dGjVqFDtN4wkAACAASURBVBsbGxwc7O/v\nr9frc3Nzz549m56evn379pycHPPhzzzzjLSxadMm8xIUISEhEydOlFnArFmzzGkwKipq3Lhx\nQ4cOrV+/fqUHXr58ecWKFR9//PGhQ4eEEBcuXBgyZMju3bv9/f3lfvEA8H9MhXqTwSBYQPX/\nmEpKTEajSa93dSEAAMAapYFQCPHZZ59lZGSYB9zOnTu3aNEiOQcOHDhwxIgR0nbDhg2lDV9f\n3wULFtSpU0dOC3l5eW+99Za03bNnzyVLlsifaBoZGfn444+PHj165syZr776qhDiwIED8+fP\nHzdunMwWAAAAAMCtqTC1KSoqKjU19eGHH5Z/X5aAgIBJkyYtXbrU/Ih0o9G4uLiVK1c+/PDD\nMttZuXKldB/R+Pj4H3/8UX4aNPPz83vllVeee+456Z8yoywAAAAAeAB1rnWpWbPm999/f+DA\ngSlTprRs2bKiWZdBQUEdOnT497//nZmZ+fbbb/v5+Zmfqlq16rp16zIzM226Vekff/whbYwd\nOzYsLMzu+qdOnRoUFCSE2LVrl3k9DAAAAADwbCpMGTVr3bp169atZ86caTQaT548efXq1by8\nPL1eX6VKlZCQkKioqLi4ON+Kb7fQs2dPW8948eJFaaN9+/b21y1E9erVW7RosW/fPiHEhQsX\nzLdOBYBylVqe3jMWopC+KM/4WgAAgExqBsL/36hO55xMZR7NCw8PV9iUdLNTIcS1a9cUNgXA\ng5WKguYHY5xfinosvyhiIQAAXsUhgdBpIiMjpY0LFy60bt1aSVMXLlyQNmrVqqW0LACQzTKM\nuSSGVRRxyYQAAHgD9QNhQUFBenp6enr6lStXcnNzi4qKqlatGh4e3rhx43bt2lWvXl3Fc8XE\n/PdD+bVr1/bu3dvudk6fPn348GEhhJ+fX3R0tDrFAfAmN5s0rhYbKywujZajVBjzpNE5n4CA\nwI5J/gkJri4EAABYo2Yg3LBhw7x581atWmUwGCrap0OHDk888cSwYcNUWe6vV69eb7/9thDi\nP//5z9ixY+1bVt5kMj333HMlJSVCiK5du1apUkV5YQC8Te64sQk2Brlyh+aczKE1RC770XGN\nAwAAVahzl9GcnJxHHnmkR48eP/30k5U0KITYvXv36NGjk5KS/vrrL+Xnveuuu6RBwvz8/F69\neu3atcvWFm7cuDF48ODly5dL/xw5cqTyqgB4IRWH9bQQFAEAgJdQIRDq9fr777/fclHBSu3f\nv/+uu+46ceKEwlP7+fnNnTtX2j5z5kynTp169+793XffnT592vqBubm569evf/rpp+vVq2eu\nvEePHkOGDFFYEgC4Ec+YngoAAOymwpTRWbNmbdu27b/N6XTdunXr1q1b48aNY2Njg4OD/f39\n8/Pzr127lpmZuXPnzjVr1ly5ckUIceXKlQEDBhw8eNDKQhRyDBgwYNKkSe+8844QwmQyrVu3\nbt26dUKI8PDwuLi46OjokJCQoKAgPz+/wsLCwsLCy5cvnz17Njs7W5ojanb77bd/8803SioB\n4A0SExPLjuA5LVZ55HIXAADAhXxMJpOS42/evFm3bt2cnBwhRPfu3efPnx8fH29l/4KCgnff\nffeNN96Q8tjXX3/96KOPKilA8vnnn0+ePDk3N9eOY318fIYNG/bZZ5+FhIQor6RcCQkJQojM\nzEwHtQ/A+czZzO5UVtHU0IoaLHd/VTIhORMAAPcyatQoIcTChQuVN6U0EK5cufL+++8XQtxx\nxx07d+6UeauYjz766NlnnxVCdOvWbePGjUoKMLt69ep77723ePHiM2fOyDykatWq995775Qp\nU1q2bKlKDRUhEAIez3Ao3XQzNzA52aajZF4uWO6wpOWzNp3UafQ7dvhWq+bfooWrCwEAwNNo\nKBC+8MIL7733nhBi7dq1vXr1knlUSUlJ48aNT5w4ERQUdOPGjcDAQCU1lJKenr59+/aMjIyM\njIzs7Oz8/Py8vDyj0RgcHBwaGhoREZGQkNCkSZPExMSUlBRVbnZaKQIh4PEude1mPHU65mSm\nsHEafKl1CO24o4xGA6HRmHVbgq5Ro9ob1ru6FAAAPI2KgVDpNYRZWVlCCF9f365du8o/ytfX\nt0+fPh999FFhYWFWVlaDBg0UlmGpZcuWjh7xs1RQUHDx4kXr+xQVFel06i/5CEA7TIV6k8Eg\nbP+ITaNxTjFTSYnJaDTp9a4uBAAAWKM0pVy7dk0IUb169YCAAJsOjIuLkzauXLmibiB0smnT\nps2ePbvS3SIiIpxQDAAAAADIpzQQSjmwsLDQ1gPNN/lUeJdRl5s+ffrkyZOt79OuXTsfHx/n\n1AMAAAAAMikNhDVq1BBC5OXlZWVlSWvEy2S+oC4yMlJhDap49NFHjx49KoSYN2+eTTO4dDpd\npaN/7h56AbhcRZcXeuqMUwAA4BxKg0rTpk2ljcWLF8s/Ki8vb8WKFUKIsLAwm2Kk4xw9ejQt\nLS0tLe3mzZuurgUAypGYmGgZ/0r9EwAAwA5KA+E999wjbUyfPj09PV3OIUajccyYMZcvXxZC\ndOvWjbutAIBZuRmvbA4kCgIAAFUoDYRt27Zt166dECI3N/fOO++cM2dOXl6elf23b9+ekpKy\nZMkS6Z9PPfWUwgIAwMNY5j2yHwAAcCgVRufeeeedbt26lZSU5OTkTJw4ccqUKZ07d27evHlc\nXFxoaKhOp7t169aVK1cyMjJSU1MvXLhgPrBfv349evRQXgAAuFxg52S/2Fjh56dWg+6eA30C\nAgI7JvknJLi6EAAAYI0KgTAlJeWTTz4xj/Xp9fpNmzZt2rTJ+lEdOnT47rvvlJ8dALQg4u3K\nl5/xNpHLfnR1CQAAoBLqXL/35JNPxsXFjRkzRlqn3jpfX98JEyZMnz69SpUqCs977do1g8Gg\nsBGJ0WhUpR0AAAAAcBeq3dClT58+mZmZy5Yt++6773bv3n3jxo3SZ9LpWrRocd999z322GP1\n6tVT5aQPPPDAtm3bVGkKAAAAALyNmnf4rFKlyvDhw4cPHy6EOHXq1D///JOTk1NUVFS1atVq\n1aolJCQEBQWpeDoAgN1KrWro7pcsAgAA+zhqyYf4+Pj4+HgHNQ4AUKLsGvdpaWlkQgAAvJB7\nrwHYtWtXacpojRo1pk6d6uPjY3dT77///rlz59QrDYB3MRxKN93MDUxOdnUhlSubBs2Pq5sJ\n9Tt2+Far5t+ihYptAgAAdbl3IHz55ZfXrFmzZ8+eq1ev+vj4TJgwwe6mFi1aRCAEYLfrEyYY\nT52OOZkpfJWu7+ohjMYrQ4frGjWqvWG9q0sBAAAVkhsIjx49mpub64gKmjVrVrVqVfuO1el0\nixYtatOmza1btyZPnpySktKmTRt1ywMAOUyFepPBIEwmVxeiFaaSEpPRaNLrXV0IAACwRm4g\nHDt2rIPu57lly5aUlBS7D09ISHjvvfeeeuopg8EwePDgtLS04OBg9aoDAAAAAI/lCVObnnzy\nyb59+wohjh07Nn78eFeXAwBuiZvKAADghTwhEAohFixYULNmTWnjxx9/dHU5AKBdBD8AAGAm\nd8rozJkzr1696ogKWrZsqbyR2rVrf/vtt1999ZUQYvPmzf379/f391feLAB4JCkTmm83SkQE\nAMBryQ2EHTt2dGgdyvXq1atXr152Hz5hwoTs7GwhxG233aZeUQDcm2ev3u5hXw4AALCDey87\noaJhw4a5ugQA2sLq7QAAwON5yDWEAKAuK6u3l/t4YOfkwORk4efnyKLciU9AQGDHpKDkTq4u\nBAAAWOPYEcJXX321oKBACPHUU08xFROAB4t4e7arS9CcyGXc4gsAAK1zbCCcO3duTk6OEOLe\ne+8lEAIAAACApjBlFAAAAAC8FIEQAMpR0c1juKkMAADwJARCAChf2exHGgQAAB6GZScAoELm\nBdwrjYKGQ+mmm7mByclOqcs96Hfs8K1Wzb9FC1cXAgAAKkQgBIBKyBkYvD5hgvHU6ZiTmcKX\nmRdCCCGMxitDh+saNaq9Yb2rSwEAABXijQsAqMBUqDcZDMJkcnUhWmEqKTEZjSa93tWFAAAA\naxghBAD3k5aWZt7mykYAAGA3RggBwM1YpkHpn6UeAQAAkMmxI4QbN240Go1CiGbNmjn0RADg\nJch+AABARY4NhO3atXNo+wAAiZxboQIAAJTClFEAAAAA8FLcVEaprVu3rlu3zvo+V69eDQ4O\ndk49ADSCITsAAKB9BEKl/vnnn7///tv6PkVFRcXFxc6pB4BLBHZO9ouNFX5+ltf4SdvOiYVa\nC58+AQGBHZP8ExJcXQgAALDGx8SqWY6XkJAghMjMzHR1IQAcrtybvqib1pxwCgAAoGWjRo0S\nQixcuFB5U1xDCAD/n8IlHCo6Vt1bg5bNfqRBAABgH6aMAoAQ/5vZnDnV0z5arg0AALgRRggB\noPwRPFb8AwAAHo9ACAAAAABeiimjALydlZFA81OVTtE0HEo33cxNTE5W5Y4v8s+rZfodO3yr\nVfNv0cLVhQAAgAoRCAGgchUtKmhObjWefkZ3MfvSj0sT77ijVCYsdaCV/JmYmFjqWe1fzVgh\no/HK0OG6Ro1qb1jv6lIAAECFCIQAvJr8CwXLZkLLY30MRaKoyKey9eitn87KTUrdLhOaSkpM\nRqNJr3d1IQAAwBoCIQDYw45bznCXmnKV/ba4XfoFAMB9cVMZAIDLcH9XAABcixFCAF6HvKER\n/CAAAHA5RggBeBdCiEZUejklPykAAJyAQAjAi8jJGDIvYHPmdW5ee00dmRDQsrT/5epyANiJ\nKaNwhooWVbN+d37APvbdpETOPqXu9ll2lQjr7ZS7v92FVdSUC3+PLEtq27KlWm3yygBoUNmX\nIH5bATdFIIRjWVlUjb8lcAQlNylRktaud+tqPH2m7R13qNu+qODzFCtNOfn3qKJK9qenR7Ro\nHtq2rdMqARyEzy7L8qQ1cqBN/N45E4EQDmT9PWtFjyv5nedPkUNp/9VZyZwlhfOdIt6e7dD2\nbWL5i+DQn5r1L+r6jLcaqD1eWva8GuyH8CR8dmmHcn9DWWBGpkpfGD34+6adzzq9DYEQLmDT\n2twyJ/KV3eaFQ128KxKu7lR2fMNd/lNzxOmszDsA1MU4mB3K/Q2taE++jaXI+ZjMU79vXIbq\nQgRCWKOFcKXuCx8DC/bhXZEnUeunpvyPd0Wzx+04KV0RquPtaUX4zkhs/fy60pcp+d9YLbw9\nU5c3J2EtIBB6PvsmjJX9hE+zmVD+G0QGFqCQ/A8UHPSGyY63C9pnfe6o/N9QfqNtpf1J4K7i\nRr8+cBX58y8cOomJjARVEAg9nH0Txiq6LYdbv+i4xR94LrFQSMllabay8hvhFp3NaZR8KC7z\nPjo2nQ4Sl08nLheTOOAWNDVVQYOjhRosCdYRCD2ZM98Zm7crvSm/1mjhPZDEI3O4Z7P86RgO\npZtu5gYmJ7u2JE0JOHjQFBZW1KCBlTcHlV5R7BYvI25Hm99SjcxMkfPNcbuXZVvHz8v9Jrjd\nV60FzoyOVl4wnfazkzMVy+7XH3qg4xAIvZHdL0Ny3pxp830GHESDr86uyg/XJ0wwnjodczIz\n7cAB55/d0ez4lvoUF0e88aYxLu7qhx+UakeD3aZcXjhi78JPoPhEzEHKzle0b5aQ0PyPw+1+\nYR3xgZeVn51QdZ6qedumJXx5i6hZBEKoc0cHF3L+52E2Xe+kfAaUFv4GM0pTEfNPx1SoNxkM\nwmRydUWVq+gqF3VnwJpKSkRxsTAYrJxL5l0E7Di7HNZ/N+nwsIkG04h9uU7OoL1afxEcdMc4\nywdd/lOwzsmvM6p8Q6yMAXrtgIH2e1qlCIQey8N+La3/spX942TTb2ZFx9rxXSpVp5wZUO7y\nsyj35Z5pReJ/f6z79+8Xvr6uradS1j9CFk75CTq029v3mytk/O5r6q++g4ZbXXLxj7u8DFak\nojQibbjwojJACZkvjPKbUqLSt09O/kWr6O2Qdv5G2IRA6Jm88I9BRTPUzY/L/0RT4XdPzser\ntp7I8stx3FvSSgczbfoGKixSyQu9S8Yz9Xq9n5NP6Uhe+BoikxZmvVqfBFhphU77RVb97Zqj\n3//J/M7YekGUk9+22vrLa98vu6amjTi/EvlTkxR+o0r9ajvu217qDUDZF5lKD3dtHnNyDdrp\n/GrxMbnDBCebHDt2bOvWrceOHcvIyLh48WJ+fn5eXp7RaAwJCQkNDY2IiGjcuHHTpk3bt2/f\nqVMnPz9nvIVLSEgQQmRmZjrhXEKlbqqp13ozORMyrb9Ga+SLUmUyvcJx0VKsfN/sfrtg/Sx2\nX2Vu/c6TrrpQoeaYJ/2ys/9Z8bNJ8yOETlJUVHvgIGNMzNXPPnF1KTawtf/Y9NGJzItt7G5c\nzqxFO341bH1hsSNWyZmHL6dZdT+HknMWu19qKp2hbffX4uhXP1W+fJntV6rSApzw+WmllGdC\ndb/Pat3ipWyzTut7cl4xHMdVva6sUaNGCSEWLlyovCnPCYQ5OTlz5sxZtGjRiRMnZB4SGRn5\nwAMPTJ48OT4+3qG1OTMQaiTwOI7drzjajLiqc9BbN/tU+ldHYeDU1A+UQFiaewZCYVfXsukt\nsq0hTXlXV/gOXsl8AZktO+I7IPPF0IUvI/Lfl1f0E3ThNQjOfAVWeHmzrfnH1g9u5Negqb9Z\nnkrdC1PLvkbJ6fkEQpdZuHDh888/f/36dTuODQgIePrpp2fNmuXv7696YRICIZzJoTHM7ko8\nvmcSCEtz20BoHzt+rZz8HlHJp2nmbdWrdWiuKDvdrtQOrn1dUmvqitamwDiCCwOVWh+IaO1D\nTE8lc7Z8qR+HuqOmBELXeOWVV956661SD4aEhMTGxsbExAQHBwcFBel0usLCwoKCgkuXLp05\nc6ZsdOzWrdvPP/9ctWpVR1RIIIR3IhB6NS8LhB7PQW9neZcMQF2Om9SgsAbVqRgI3f6mMr/8\n8os5Dfr4+HTr1m3o0KHJyclSBqvIpUuXUlNTly9fvmzZMoPBIITYtGnTmDFjvv/+e2cUDXgH\nr3qfZ2jdyq9WLdLg/+fvb2jRvLhuXVfXAXU44VYWAKAcryp2cO8RwuLi4nr16mVlZQkh6tev\nv3jx4o4dO9rUQlZW1mOPPbZ+/XrpnytXrrzvvvtUr5MRQgDughEbAACUYITQqbZs2SKlwapV\nq/7222/WRwXLFRMTs2bNmj59+vz2229CiC+//NIRgRAA7GD3pUE2XW9jx33SCY0AAHgM9w6E\n5pG9f/3rX3akQYmfn9/HH3/cuHFjk8m0bt06vV4fGBioXo3Oxhs1wN2pvpJVRU1Z+QjTShaV\nnnLQjcsBAICTuXcglIYHhRA9e/ZU0k5CQkKzZs2OHDliMBguXLjg6FUoHI27GwPuq6KQJv8G\n/Zbb5a5UadNUFpk781EUAABuyr0D4ZUrV6SNOnXqKGwqNjb2yJEjQohLly7ZFAinT5/+/vvv\nW98nNze3WrVqiuoD4Facf4fVcpOb025+LRRMcAUAAC7k3oHQnLKuXr2qsKlr165JG7auPPHY\nY48lJSVZ3+ett95y0IIWADRCd+Kk7618Q6tWokwMK3e9I7tTU7ljcc4MfpUyVxhw8KApLKyo\nQQNXVwQAACrk3oHQPDD4xx9/3H333Xa3k5OTIw0PCiGio6NtOjYqKioqKsr6Pt99952dlSmg\n2Rlcmi0MHswJvS58zhzdxexLPy5NvOOOcgtQ8Vyain/lSkxM3L9nT8Qbbxrj4q5++EGlOwvG\nFaFV9E8ANtH+3+iy3HvJrM6dO0sb8+bNy8nJsbud999//9atW0KI1q1be9LcTrV6ZOL/sWnn\ncg8p9xIm+Y1btmDlLNA+OT81VX6+5hbsa0d+AT6GIlFUlNi2rR1n8Ui3t2kjiouFwVDRDqV+\nvjK/1fJ3s+lx2K3s63nZHTzghdoDvgQ72PrXX87jntEfgIq4ad9273UI8/Pza9WqJWW5e+65\nZ+nSpREREbY28tlnnz399NMlJSVCiNmzZ7/44ouq16niOiGqkz71LNV9y33Q8tmKro9S/deg\n3FtiKGnBurLtW36xVoqp6CwVHWt+qtwDE8ubYVjqXLZ+aF3u/uV+vZXWo5xN3zrru5XaX0mH\nUTgPM7tTZ+OZM7FnTws/P/tO6qZ/RSpiMhiy4m/T3XZbne1bhS1fqfwfhPl3oaLGK/2dlfMr\nUO7ZrbwyVFqGqPiVoaJzWX+qVIOlTm3luy3/BkWlzmX9b4QVNr0SKqT8hct6x5N5oBOGFkt1\nJzt+v+R3e+sFlNqt0h7i0G+Og7pWpd8u698lO96TVPoKY/28ZQ+xb0UiK1+XlQorfXG2tfNI\nr2ml3gip+C7FStky+78zqZgv3DsQCiHeeuutV155RdqOiYl55plnhg4dGhsbW+mB+fn5a9eu\nnTt37vbt26VHGjZsePDgweDgYNWL1HIgBKAK+wKhBysVCAEAgIpYmP7/mzx58vr161NTU4UQ\nWVlZU6ZMmTJlSr169dq0aRMXFxcdHR0SEhIUFOTn51dYWFhYWHj58uWzZ89mZGQcPHjQaDSa\n2wkPD1+8eLEj0iAAAAAAaJPbB0KdTvfbb7+NGDHihx9+MD945syZM2fOyG+kYcOGK1eubNq0\nqQMKBAAAAACNcu+bykiCgoKWLl3622+/dezY0dZjY2Nj586de+TIEdIgAAAAAG/j9tcQlnL6\n9OlVq1Zt3749IyMjMzOzsLCw1A4+Pj5xcXFNmjRJTEzs169f+/btfXx8HF3VqFGjfv/995SU\nFEefyNLq1atDQkJ0OrcfBIanunbtWmhoaEBAgKsLUcfXN/OjSkp6h4cVu7oSjfAXYk3OzXO+\nvo+Fhbi6FjvduHEjKCgoKCjI1YUA5cvNzdXpdFztAs3Ky8uLjIxs166dqwvxTFu3bu3UqRPX\nEJajfv36zzzzzDPPPCOEKCkpycnJyc/Pz8vLMxqNwcHBoaGh4eHhgYGBTq7qwQcfdP673uzs\n7JiYmNDQUCefF5Dp2rVrfn5+HhMIR7ht7HGQIiF6hIe5ugpFbty4ERYWRiCEZuXk5AQEBBAI\noVk3b94sLuZj0v/X3r0HRVX+fwB/ll0uAisoIi3eEBFJYbyMhg5YxKhkJoIapkKimUwyNdh4\n10HFjElqdHIczWSySEhNpYCc0QoUBS85SAqYl1yxdPFCgnJb2N3vH89vnjm/vbHs2XXP7r5f\nfz275zlPT3PeHPdzrtYSExMTHx9vkaEc7QwhMJ6enkePHp0+fbqtJwKgX1BQ0JYtWxYtWmTr\niQDoFxkZOWfOHGu8iwjAImbOnBkWFpaTk2PriQDo995773V1dX377be2ngh0wxHuIQQAAAAA\nAAAzoCAEAAAAAABwUigIAQAAAAAAnBQKQgAAAAAAACeFghAAAAAAAMBJoSAEAAAAAABwUigI\nAQAAAAAAnBQKQgAAAAAAACeFghAAAAAAAMBJoSAEAAAAAABwUuLNmzfbeg5gFWq1+s0335RK\npbaeCIB+Go1mypQpffv2tfVEAPQTiURRUVEymczWEwHQTywWjxs3LigoyNYTAdDP1dV15MiR\noaGhtp4IdEOk0WhsPQcAAAAAAACwAVwyCgAAAAAA4KQktp4AWFhtbW1hYeHt27cVCoVEIpHJ\nZOHh4bNnzw4MDLT11MBezZgxo6WlxcTOr7zyyvbt24104B9RhByOHj26e/dutVpNCMnMzIyN\njTV9XSEkEBl2eFevXl23bt3z588JIfHx8R9//LHx/hs2bDh37pyJg7u5uZ08edJIh4aGhuPH\nj1dXVysUivb2dplMFhwcPGvWrIiICBP/E/xHAMG6fv36yZMnq6qqHj9+rFQqfXx8hgwZEhkZ\nOX36dC8vL1NGEELAEFEL04CjuHbt2sSJE/VuZRcXl+Tk5MbGRlvPEeyPWq12cenBpQRxcXGG\nhuIfUYQcmpqaFi1axN30BQUFJq4rhAQiww5PrVZ//vnn7u7ubMumpaV1u9Zrr72mNxV6ubu7\nGxrnyZMnKSkphnbakZGRNTU1xmfCfwQQrNra2ilTphjKlY+Pz6ZNm5RKpZERhBAwRNQaUBA6\niJ9//tnNzc3QHzk1cOBAuVxu65mCnfnvv/+M50qLoYKQf0QRcjhz5ozu8zNMLAiFkEBk2OHV\n19e//vrrWtvUlIJw9OjRxoPBZagg/Pvvv7s9yezu7l5SUmJoGvxHAME6fPgw9ziFIdHR0S0t\nLXpHEELAEFErwUNlHMGVK1foHzD9mJiYOG/evGHDhnV1ddXV1eXm5rILUcLDwysrK729vW03\nWbAzcrl86NChtJ2VldXtxRgBAQGTJk3S+pJ/RBFyJ6dUKjMzM3Nycuhlon5+fk+ePKGLCgoK\n3nnnHeOrCyGByLDDO3jwYHp6elNTEyGkd+/era2tXV1dhJC0tLS9e/caXzcoKOju3buEkKSk\npPnz5xvvLBaLZ86cqfVlc3NzZGTk9evX6cfx48cvXbp01KhRnp6ecrn8yJEjhw8fpn8+3t7e\nFRUVujtz/iOAYJWVlU2dOpUGkhASGRmZkpIycuRId3f3e/funThxIj8/v7Ozky5NTk7Oy8vT\nGkEIAUNErcjWFSlYwOTJk+nWdHV1PXbsmNZStVq9ceNGtsWzsrJsMkmwU1VVVSw8Fy9eNG8Q\n/hFFyJ3ZrVu3xowZw7Zvamrq2bNn2UdTzhAKIYHIsANraWmZN28e23yRkZG3b9/28fGhH005\nQ8g6b9++3bw5cPOzatUqlUql1aG4uJidoI6NjbXGCCBMSqWSHdgViUQ7duzQ7XPhwgUWQpFI\nVF1drdVBCAFDRK0HBaHdO3XqFPvzyM7ONtTtjTfeoH18fX2fPn36ImcIdq20tJQF7MaNG2aM\nwD+iCLmT27NnD92yPj4+tPzjHqfotiAUQgKRYcdWV1dHN5yLi8vatWs7Ozs1nBqv24KQe6v2\nvn37zJhAY2Mje+1wXFycWq3W2y07O5vlsKyszLIjKIhvagAADL5JREFUgGAdOXKEbbWMjAxD\n3b755hvWLTMzk7tICAFDRK0KBaHdS01NZT8gWltbDXXjHlD//vvvX+QMwa4VFhay5DQ0NJgx\nAv+IIuROjhaEkyZNunPnDv2mRwWhEBKIDDs2WhAGBgb+9ttv7EvTC8KnT5+y7X7o0CEzJnDg\nwAE2QmVlpaFuLS0tbFZLly617AggWOxBXGKx+MGDB4a6tbW1eXh40J4zZszgLhJCwBBRq8J7\nCO2bWq0uKSmh7Tlz5vTq1ctQz6ioqCFDhtB2UVHRi5gcOATuLxVfX9+ers4/ogg5iMXiDRs2\n6H2iTLeEkEBk2BnEx8f/+eefPXoDCsNzN0s4aRk2bJihx9gSQjw9PRMSEmi7uLhYw3mKBP8R\nQLDGjRuXnJw8ZcqUt95666WXXjLUzcPDY/DgwbT9+PFj7iIhBAwRtSoUhPbt+vXrjx49ou3o\n6GjjnaOiomijrKzMqrMCR8J+qXh4eHT7gERd/COKkENqauonn3wikZjz4lwhJBAZdnghISE/\n/fSTn5+featzC0J2cqNHzpw5QxumB0yhUPz1118WHAEE66OPPsrLyzt16hT3kh+92tvbaaN3\n797c74UQMETUqlAQ2jf2qCVCCPehC3qNHTuWNhoaGuhj0AC6xX6pmHfcmn9EEXJwdXU1e10h\nJBAZdnjmHa1geJ4hbGxsZEccTA8YIYT9VuY/AjiAlpaWf//9l7bDwsLY90IIGCJqbSgI7Rs3\n6OxEvyEDBw5kbe4PFAAjtArC9vb2I0eOZGRkJCYmTps2LSkpaeXKlUVFRW1tbXpX5x9RhBz4\nEEICkWEwTqsg1Gg0paWlmZmZSUlJ06ZNS0xM/OCDD7777ruHDx/qXR0RBYvIz89XqVS0/fbb\nb7PvhRAwRNTaeB3TAptTKBS04e7u3rdvX+OdBwwYwNoPHjyw4rTAgbBfKlKpdOfOnVu3bm1s\nbNTq88UXXwQEBGzcuDE9PV0kEnEX8Y8oQg58CCGByDAYxy0Iz58/v3r16hs3bmj12bt3r4eH\nx7Jly7Zt26b1jkoWMEJIt+/sDggIkEgk9H10uhE1ewSwd01NTVlZWbQdFRXFXpNDhBEwRNTa\ncIbQvrF3HHt5eXXb2dPTU3dFAOPYL5VLly6tWLFCtxqkGhoaPvzww7lz5yqVSu73/COKkAMf\nQkggMgzGcQvChIQE3WqQam9v//LLLydOnFhfX8/9npuTbjMmEonYkyR1I2r2CGDX1Gr14sWL\n//nnH0KIm5vbrl27uEuFEDBE1NpwhtC+PX/+nDZY9I3g9mErAhindRdTUFBQSkpKZGSkv79/\na2trTU3NoUOHysvL6dJjx46lp6d//fXXrD//iCLkwIcQEogMg3Fau1mpVJqSkhITEzNw4EC1\nWn3nzp1ffvnl8OHD9HK+mpqa+Pj4c+fOsZ/F3JyYmDG6im5EzR4B7JdarU5LSzt+/Dj9uGvX\nLu49eEQYAUNErQ0FoX2jJ8QJIWKxuNvO3Lve2YoAxrFD1xKJZOvWrStXruQGKSYmJj09PT8/\nf8mSJR0dHYSQ/fv3L1y4MCYmhnbgH1GEHPgQQgKRYTCOe4Zw/vz5u3fv7tOnD/smKioqOTl5\n9erVs2bNoucGq6urt2/fvmXLFtqBm5MeZUw3omaPAHaqvb393XffZW+uX7NmzbJly7T6CCFg\niKi1oSC0b+ziIvakYCO4fUy5cgmAEFJYWEivAvX29jb0/qIFCxYolcrFixfTj9nZ2awg5B9R\nhBz4EEICkWEwbt26dcuXLyeEiMXioUOH6u0zZsyYEydOjB8/nj7Ba+fOnevXr3d3dyf//zLj\nHmVMN6JmjwD26P79+4mJiRcvXqQfMzMz2VEGLiEEDBG1NtxDaN/YneWm/HlwnwOpdUs6gCGD\nBw8OCQkJCQkx8jZbQkhqaip7EnRZWVlraytt848oQg58CCGByDAY5+/vT3ezhqpBauTIkWlp\nabTd3Nx89uxZ2ubmpEcZ042o2SOA3bl8+fKECRNoNSiRSPbu3au3GiTCCBgiam0oCO0bu6rk\n+fPn3f6FNDQ06K4IYCkJCQm0oVQq2YOe+UcUIQc+hJBAZBgshe1mCSHV1dW0wc0Je1ebIU1N\nTfTyfqIvomaPAPblxx9/nDx58v379wkhvXv3Li4uZscadAkhYIiotaEgtG/Dhw+nDY1Gc+/e\nPeOd7969y9ojRoyw4rTAKXGPbbP9Nf+IIuTAhxASiAyDpRjfzRJCtB5Aqst4RM0eAezIp59+\nmpSURM+hhYSEXLhwIS4uzkh/IQQMEbU2FIT2LSwsjLW5b+3Uq66ujjakUin3rZ0AFufq6kob\n/COKkAMfQkggMgzWwHazQ4YM6dWrF22bHjBCyMsvv2ypEcBerF27dsOGDRqNhhASHR194cIF\n7g5KLyEEDBG1NhSE9i0iIsLNzY22KyoqjHdm7wYYP368dacFTom+wojq168fbfCPKEIOfAgh\ngcgwWIre3axIJGLvCTA9YJ6enqNGjbLUCGAXNm3a9Nlnn9H2vHnzfv311759+3a7lhAChoha\nGwpC++bl5RUbG0vbxcXFRnreuHGjtraWtrk3IQAYUVFRkZ2dvXz58oSEBPacGEPKyspow8PD\nIzQ0lLb5RxQhBz6EkEBkGIyQy+U7duxYsWLF3Llz2fMeDWG7WULI6NGjWTs+Pp42/vjjD4VC\nYWh1lUpVVFRE23Fxcdz3ufEfAQQuNzc3KyuLtpOTk/Pz8+lTak0hhIAhotalATu3b98+tjVL\nS0sNdUtPT6d9xGJxfX39C5wg2LE9e/awdH311VdGel69elUkEtGeU6dO5S7iH1GEHLRUVVWx\nSBQUFBjvLIQEIsNOyMfHh27NtLQ0I924l7fNnz/fSM+2trZBgwbRnt7e3h0dHWwR9yK6TZs2\nGRqBvW6OEJKfn89dxH8EELKamhpWGiUkJHR1dfVodSEEDBG1KhSEdq+trW3AgAE0+qGhoc3N\nzbp9Tp8+7eLyf2eDlyxZ8uInCXZKoVCwf0L8/Pxu3bqlt1tjY2NERATbBRcXF3OX8o8oQg5a\nelQQCiGByLATMrEg1Gg07Fo4kUh09OhRvX1UKtWiRYtY7FeuXKnVYfbs2XSRh4fHlStXdEd4\n+PBhYGAgC2FnZ6fFRwBhUqlULGMjRozQu//plhAChohaDwpCR5Cbm8v+kQgPDz9//jxb1NHR\nkZuby17o6e3tjaPO0COrVq1i6erfv39eXh73sLRKpSopKWEXiBJCpk2bpjsI/4gi5MDVo4JQ\nI4wEIsPOxvSCsKSkhGVDIpFs3ry5sbGR26G6upr7HMjAwMBHjx5pDVJbW8tuVfX19S0oKOD+\nGi4tLQ0ODmYjHD9+XHca/EcAYcrLy2Mb7tSpU+YNIoSAIaLWI9JoNATs3/vvv79//372cdiw\nYcHBwe3t7bW1tU+ePKFfisXioqKi6dOn22iOYJeUSmVsbOy5c+fYN1KpNCIiok+fPs+ePbt2\n7VpjYyNbFBYWVlFRofe1P/wjipA7p7q6upSUFK0v29ra2L12wcHBupErLy9nj6SjhJBAZNhR\nvfrqq7p3WV+5ckWlUhFC/P39Bw8erLV0586d0dHR7OPq1atzcnLYR1dX1/DwcJlM1tHRcfv2\nbblczhZ5e3v//vvvEyZM0J3GwYMHk5OT2cf+/fuHhYW5ubndvHmT+yD+9evXb9u2Te//CP8R\nQIBCQ0Nv3rxJCBGLxWPGjDFxrdzcXO59qkQYAUNErcXWFSlYRmdnZ0ZGBruJS1e/fv0KCwtt\nPU2wS62trbq/yHXNnj376dOnhgbhH1GE3DldunSp2+zpevbsmdY4QkggMuyovLy8ehrRoqIi\nrUFycnK6fQBGeHh4bW2tkZkcOHBAKpUaWl0ikWzZskWtVlt1BBAasVjc03wSQsrLy3WHEkLA\nEFFrQEHoUM6ePbtgwQKtf5mGDh26bt06hUJh69mBfausrExNTQ0ICNDa+cpksoULF1ZWVpoy\nCP+IIuTOxlIFISWEBCLDjsciBaFGo5HL5WvWrBk+fLjWUQOpVBoXF/fDDz+oVKpuJyOXyzMy\nMtidVJSvr++SJUuqqqpM+d/hPwIIigULQo0wAoaIWhwuGXVAnZ2dDx48UCgU7u7uMpmsf//+\ntp4ROBSarpaWFqlU6u/vr7VHNgX/iCLkzuPZs2eXL1/u6VqTJ0828htICAlEhh1JeXk5vTrU\ndBEREX5+foaWNjU11dfXNzc3e3h4+Pn5DRo0qKe/6TUaTUNDw/3791UqlUwmk8lkL34EEIjT\np0+b8Wt/7Nix7D5YXUIIGCJqQSgIAQAAAAAAnBReTA8AAAAAAOCkUBACAAAAAAA4KRSEAAAA\nAAAATup/hJ8TaS9xWM0AAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 360,
       "width": 600
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width = 10, repr.plot.height = 6)\n",
    "colocboost_plot(res, plot_cols = 1, add_vertical = T, add_vertical_idx = c(data$variant))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "69d95913-b451-41a8-bdca-ab98c1c5345b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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//www8//fTTpEmTIiIiqprm5+c3YMCAtWvXZmRkjBs3zppXrL7Ro0enpaUNGzZM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vrlixwiGFAnB1vj1ifGNihEajdCF2Vllo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\nAAAAAOCm/j/RsLq5pOaLfQAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 360,
       "width": 600
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "colocboost_plot(res, y = \"vcp\", plot_cols = 1, add_vertical = T, add_vertical_idx = c(data$variant))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "62c2faa0-3c33-4387-a88a-34c7cd2f43a1",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A matrix: 2 × 2 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>cos1:y1_y2</th><th scope=col>cos2:y1_y2</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>cos1:y1_y2</th><td>0.8666675</td><td>0.2562119</td></tr>\n",
       "\t<tr><th scope=row>cos2:y1_y2</th><td>0.2562119</td><td>0.7613753</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A matrix: 2 × 2 of type dbl\n",
       "\\begin{tabular}{r|ll}\n",
       "  & cos1:y1\\_y2 & cos2:y1\\_y2\\\\\n",
       "\\hline\n",
       "\tcos1:y1\\_y2 & 0.8666675 & 0.2562119\\\\\n",
       "\tcos2:y1\\_y2 & 0.2562119 & 0.7613753\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A matrix: 2 × 2 of type dbl\n",
       "\n",
       "| <!--/--> | cos1:y1_y2 | cos2:y1_y2 |\n",
       "|---|---|---|\n",
       "| cos1:y1_y2 | 0.8666675 | 0.2562119 |\n",
       "| cos2:y1_y2 | 0.2562119 | 0.7613753 |\n",
       "\n"
      ],
      "text/plain": [
       "           cos1:y1_y2 cos2:y1_y2\n",
       "cos1:y1_y2 0.8666675  0.2562119 \n",
       "cos2:y1_y2 0.2562119  0.7613753 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res$cos_details$cos_purity$min_abs_cor"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0344867-44a9-4390-9e1a-766b0c4fa68a",
   "metadata": {},
   "source": [
    "### HyPrColoc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "b711c956-6627-4800-aa90-5658dc7e2914",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "library(hyprcoloc)\n",
    "betas = sebetas = matrix(NA, nrow = ncol(X), ncol = 2) \n",
    "for (i in 1:2){\n",
    "    x <- scale(X)\n",
    "    y <- scale(Y[,i])\n",
    "    rr <- susieR::univariate_regression(x, y)\n",
    "    betas[,i] = rr$betahat\n",
    "    sebetas[, i] <- rr$sebetahat\n",
    "}\n",
    "traits <- c(1:2)\n",
    "rsid <- paste0(\"snp\", c(1:ncol(X)))\n",
    "colnames(betas) <- colnames(sebetas) <- traits\n",
    "rownames(betas) <- rownames(sebetas) <- rsid\n",
    "res_hyprcoloc <- hyprcoloc(betas, sebetas, trait.names=traits, snp.id=rsid, snpscores = TRUE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "b4e592c2-bf9f-44fc-974f-c9c66ea84668",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 1 × 7</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>iteration</th><th scope=col>traits</th><th scope=col>posterior_prob</th><th scope=col>regional_prob</th><th scope=col>candidate_snp</th><th scope=col>posterior_explained_by_snp</th><th scope=col>dropped_trait</th></tr>\n",
       "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;lgl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>1</td><td>1, 2</td><td>0.9995</td><td>1</td><td>snp721</td><td>1</td><td>NA</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 1 × 7\n",
       "\\begin{tabular}{lllllll}\n",
       " iteration & traits & posterior\\_prob & regional\\_prob & candidate\\_snp & posterior\\_explained\\_by\\_snp & dropped\\_trait\\\\\n",
       " <dbl> & <chr> & <dbl> & <dbl> & <chr> & <dbl> & <lgl>\\\\\n",
       "\\hline\n",
       "\t 1 & 1, 2 & 0.9995 & 1 & snp721 & 1 & NA\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 1 × 7\n",
       "\n",
       "| iteration &lt;dbl&gt; | traits &lt;chr&gt; | posterior_prob &lt;dbl&gt; | regional_prob &lt;dbl&gt; | candidate_snp &lt;chr&gt; | posterior_explained_by_snp &lt;dbl&gt; | dropped_trait &lt;lgl&gt; |\n",
       "|---|---|---|---|---|---|---|\n",
       "| 1 | 1, 2 | 0.9995 | 1 | snp721 | 1 | NA |\n",
       "\n"
      ],
      "text/plain": [
       "  iteration traits posterior_prob regional_prob candidate_snp\n",
       "1 1         1, 2   0.9995         1             snp721       \n",
       "  posterior_explained_by_snp dropped_trait\n",
       "1 1                          NA           "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res_hyprcoloc$results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "21b3d4b4-a907-4cc7-a0c2-d080fa42f7ab",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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BYADT+fmvOSet5xpqk33PX9yuu17QkW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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 360,
       "width": 600
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(res_hyprcoloc$snpscores[[1]])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15a4417e-4c65-465d-bd11-0b1424f8e264",
   "metadata": {},
   "source": [
    "### COLOC (one causal assumption)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "5bc55518-1011-46b4-b16c-599a5a069be8",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
      " 6.41e-51  2.21e-28  3.44e-26  1.86e-04  1.00e+00 \n",
      "[1] \"PP abf for shared variant: 100%\"\n"
     ]
    }
   ],
   "source": [
    "library(coloc)\n",
    "LD <- get_cormat(data$X)\n",
    "colnames(LD) <- rownames(LD) <- 1:ncol(data$X)\n",
    "MAF <- colMeans(data$X)/2 %>% as.numeric\n",
    "D1 <- list(\"beta\" = betas[,1], \"varbeta\" = sebetas[,1]^2,\n",
    "           \"N\" = nrow(data$X), \"sdY\" = sd(unlist(data$Y[,1])),\n",
    "           \"type\" = 'quant', \"MAF\" = MAF, \"LD\" = LD,\n",
    "           \"snp\" = 1:ncol(data$X), \"position\" = 1:ncol(data$X))\n",
    "D2 <- list(\"beta\" = betas[,2], \"varbeta\" = sebetas[,2]^2,\n",
    "           \"N\" = nrow(data$X), \"sdY\" = sd(unlist(data$Y[,2])),\n",
    "           \"type\" = 'quant', \"MAF\" = MAF, \"LD\" = LD,\n",
    "           \"snp\" = 1:ncol(data$X), \"position\" = 1:ncol(data$X))\n",
    "my.res <- coloc.abf(dataset1=D1, dataset2=D2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "1fd5be5c-ec82-40bf-861a-3ecd93b1af7d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "'721'"
      ],
      "text/latex": [
       "'721'"
      ],
      "text/markdown": [
       "'721'"
      ],
      "text/plain": [
       "[1] \"721\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "o <- order(my.res$results$SNP.PP.H4,decreasing=TRUE)\n",
    "cs <- cumsum(my.res$results$SNP.PP.H4[o])\n",
    "w <- which(cs > 0.95)[1]\n",
    "my.res$results[o,][1:w,]$snp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "25159a62-3b97-40bb-ac5f-b9c37800b1d0",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "pph4.snp <- my.res$results$SNP.PP.H4[order(my.res$results$position,decreasing=FALSE)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "bcdc7ab9-fb71-4f0a-90f5-00512c6c0856",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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MGta8tHe7KG1Wob1zQdVX0lk56u1JdH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\n4/8ADe6nqSf5um0AAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 360,
       "width": 600
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(pph4.snp[c(400:1200)])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad1ed1a7-ab5c-48ab-b52c-a2c18efe1b19",
   "metadata": {},
   "source": [
    "### COLOC (V5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "89c7bf22-1083-4c07-95a9-4c2476d9c642",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "running max iterations: 100\n",
      "\n",
      "\tconverged: TRUE\n",
      "\n",
      "running max iterations: 100\n",
      "\n",
      "\tconverged: TRUE\n",
      "\n"
     ]
    }
   ],
   "source": [
    "library(susieR)\n",
    "out_p <- runsusie(D1)\n",
    "out_e <- runsusie(D2)\n",
    "out_coloc = coloc.susie(out_e, out_p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "dd27a726-dd78-4ccf-baab-0377f4bffc81",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "out_coloc$summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "c45bf4db-89a3-45d5-8ab7-4a82de87f97a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>'654'</li><li>'721'</li><li>'761'</li><li>'673'</li><li>'677'</li><li>'797'</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item '654'\n",
       "\\item '721'\n",
       "\\item '761'\n",
       "\\item '673'\n",
       "\\item '677'\n",
       "\\item '797'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. '654'\n",
       "2. '721'\n",
       "3. '761'\n",
       "4. '673'\n",
       "5. '677'\n",
       "6. '797'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] \"654\" \"721\" \"761\" \"673\" \"677\" \"797\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "o <- order(out_coloc$results$SNP.PP.H4.row2,decreasing=TRUE)\n",
    "cs <- cumsum(out_coloc$results$SNP.PP.H4.row2[o])\n",
    "w <- which(cs > 0.95)[1]\n",
    "cos1 <- out_coloc$results[o,][1:w,]$snp\n",
    "cos1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "124546ad-a31e-4ce0-b5e8-eac173ef6e68",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "'858'"
      ],
      "text/latex": [
       "'858'"
      ],
      "text/markdown": [
       "'858'"
      ],
      "text/plain": [
       "[1] \"858\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "o <- order(out_coloc$results$SNP.PP.H4.row3,decreasing=TRUE)\n",
    "cs <- cumsum(out_coloc$results$SNP.PP.H4.row3[o])\n",
    "w <- which(cs > 0.95)[1]\n",
    "cos2 <- out_coloc$results[o,][1:w,]$snp\n",
    "cos2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "0f6f90d2-f94c-4fd1-b2fd-166ce8f5e393",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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DUHgBheBav0vBfdT6YKwQKg4VWwUjo1\nJ8mHJXXOJvx6RZU7CBaAuoIK1ql11v9MJdvc4FkByHhugrV8arWXGhis8Wc8V/uhL/kKC4Aj\nN8EqrvE1UbGHs+I9LAAaboL1rBoyttJoggUg3Vy9hzVkz+8qhym+hzXjhsF9D+h38s1znVcj\nWAA0XAVrw+5DKocpBeuLQ8xvHWMFMfP2+A1OKxIsABrufko4+8rKU5i3Z4+u93ml+2dd+651\naPx3My4KHeF0sDvBAqDh55Hu09RTVeP71ZsOaxIsABp+BuvOSFnVeFvoboc1CRYADT+DlbhC\nqW2zmuCwJsECoOEyWKtKVsYHK+uealNH1QX/TLeqEoc1CRYADZfBKlaj4oNRKfyUsPwX6qxn\nl679bs3ip45XAysc1iRYADRcBuu5MybFB5PqnmpT18bTKw+LD1+4xWlFggVAw+frYX1y/4gh\nJwy54uEvnVcjWAA0uIAfADFcBivFU20aimAB0HAVrJRPtWkoggVAw02wUj/VpqEIFgANN8FK\n/VSbhiJYADTcBCv1U20aimAB0HATrNRPtWkoggVAw02wUj/VpqEIFgANN8FK/VSbhiJYADRc\nHdaQ8qk2DUWwAGi4PHA0xVNtGopgAdDg1BwAYnBqDgAxODUHgBicmgNADE7NASAGp+YAEINT\ncwCIwak5AMTg1BwAYnBqDgAxODUHgBjenJpTtsOLuVQjWAA0vAnWxX29mEs1ggVAw02wNq2u\ndM5Bq1dv8nBWBAuAhptgFatkxR7OimAB0HATrHtUzrBRtgPajxr1uoezIlgANFy9h/VE28LH\n7QHvYQHwgbs33b89Vx3ziUGwAPjC7U8Jp++V8+dSggXAD64Pa9hyTfhn8wgWAB94cBzWvF7R\nzgQLQPp5ceBo2dgcggUg/bw50n37T5rVXCBYADTcBOuV2gum3Od2OgkEC4CGm2CpK79Nfnjd\n5eoBL6ZkECwAWm6C9WAs79r5iV+Ws3PeyNzYg17NimAB0HD1HtbiwUoVDrzgumsvGNhKqcGL\nPZsVwQKg4fJN97kje8RPfe5xtZe//JlgAdBw/1PCHz55553lP3g1nziCBUDDm8MavEawAGi4\nDVb5+Kusu/7jvPxN9QQLgI7bYA1V/a27bupCr2ZkIVgANFwG6z01Ypt1X361WuTZnAgWAC2X\nwXooui0+2Bp+2KMZWQgWAA2XwRqXm3jvqiJ3vEczshAsABougzVFvRYfTFavejQjC8ECoOEy\nWNuLWt48ff68ly+PdfPyd6kSLAAabn9K+GHiSPeey72bE8ECoOX6wNGyqTcOHXrj1DLPZmQh\nWAA0ONIdgBhugjWo9oIT7nI7nQSCBUDDTbDqXMj9sGKXs6lEsABouAlW22G1tCv2aFYEC4CG\nq0sk11Hs0awIFgANN8FaX8dWj2ZFsABo8FNCAGJwPSwAYnA9LABicD0sAGJwPSwAYnA9LABi\ncD0sAGJwPSwAYnA9LABicD0sAGJwpDsAMVwHa81dZx199G8e+N6zGVkIFgANt8GaVhB/D6vD\nnIZsYUw9x8UTLAAaLoP1Q+uODy5dt+79Ce06b2vAFi6uc+2/mggWAA2XwXpOLYgP5qtX6n3e\nfV0rtczu2vU+hzUJFgANl8Eau3vlqEv9R7r/SeX0imuT26uX06k8BAuAhstg3dmqctS5/l9A\nsfFC9csP7RHfEgJoBJfBek29GB/8S01P4Zn/7Rorto6IJ1gAGsFlsEq75o56Zf68KVfldC9N\n5ak//T7as4RgAWgUt4c1LNu3gafmLOoXGvEDwQLQCK4PHC2d8vsLht44LfVTc3ZOyN/zEIIF\noOGCODXn80GKYAFoODfBuriG+o/Dqja7xHk5wQKg4d0vUi32cFYEC4CGm2B9VMN6D2dFsABo\nBHV5mSGR22o98sXuhVXy1GYPXgNAhgnq8jIXtK19Kk/Zy89XuZSvsADUFczlZerDt4QANIK5\nvEx9CBYADT8vL2PaMm389VfeMOE/9fyKHYIFQMPPy8sYO27ITRwD0XpchdOKBAuAhp+XlzFO\nV33GvPLuwndfurW7usxpRYIFQMPPy8vMUddVfl1VNlwtdViTYAHQ8PPyMveGNlWNv1UPOKxJ\nsABo+Hl5mXHR8qpxWXicw5oEC4CGn5eXeVG9VjV+Xk1xWJNgAdDw89ScrZ0LH1prj74cl991\nu8OaBAuAhttglY+/yrrrP658FysnW9BJqbY9DtqvtVLdP3JakWAB0HAbrKGqv3XXTdXzu5zj\ntj42pEebvDb7n/1357foCRYADZfBek+NsE/JKb9aLfJsTgQLgJbLYD0UTZxCuDXs9ItRG4pg\nAdBwGaxxuYn3ripyUzg1J2UEC4CGy2BNqTxSYbJ61aMZWQgWAA2Xwdpe1PLm6fPnvXx5rFs9\nF2BoEIIFQMPtTwk/7NHAX6SaEoIFQMP1gaNlU28cOvTGqan/ItVUECwAGp4e6T5muKu5VCNY\nADQ8DdaoAa7mUo1gAdAgWADEIFgAxCBYAMQgWADEIFgAxCBYAMQgWADEIFgAxCBYAMTwNFgT\nrnA1l2oEC4CG62C9f/OpRx59xh2feTYjC8ECoOE2WLeF4peXif7FuzkRLABaLoP1fqj385+s\nX/fBX7uEP/RwVgQLgIbLYN1T+H188FnIyy+xCBYADZfBGn9M5WjvOz2ZTxzBAqDhMlivdEsM\ntrf4tzcTshEsABoug1V6xC32L5/Y9tuBqfyu+lQRLAAaLoM1Y0SszaDzhw1qXXDhJRdffPEr\nHs2KYAHQcBmsYlVDsUezIlgANFwGa82yj5Kt92hWBAuAhqen5niGYAHQcBus8vFXWXf9x3n5\nnjvBAqDjNlhDVX/rrpu60KsZWQgWAA2XwXpPjdhm3ZdfrRZ5NieCBUDLZbAeim6LD7aGH/Zo\nRhaCBUDDZbDG5Sbeu6rIHe/RjCwEC4CGy2BNUa/FB5PVqx7NyEKwAGi4DNb2opY3T58/7+XL\nY912eDgrggVAw+1PCT/sET/Gvedy7+ZEsABouT5wtGzqjUOH3ji1zLMZWQgWAA2XwVq61sO5\nVCNYADTcnvwcOf7vWzycTgLBAqDhMlhvHhFS+ef/a6eHM7IQLAAart/DWn2v2ayO1yzwbEYW\nggVAw4urNay+9+ch1WO6NxOyESwAGh5dXubT0z27eJ+FYAHQ8CJY6x8fnKPaPuvNhGwEC4CG\n62Ct+b8BEdXi3Gmlnk3JIFgAtNz+lPDIsIqd8sxWD2dkIVgANNwehxU+9q/fezidBIIFQMNl\nsJZ84+FcqhEsABr8EgoAYhAsAGIQLABiECwAYhAsAGIQLABiECwAYhAsAGIQLABiECwAYhAs\nAGIQLABiECwAYhAsAGIQLABiECwAYhAsAGIQLABiBBWsis+dLgVPsABo+Bqsn246cO+zl9rD\nbY6/eJVgAdDwNVhnKtVSxZ6yhgQLQIP5GazFatC3xoeHhv5pECwAjeBnsCaqFebt9hNiJQQL\nQCP4GaxxoZ3W3Y+9dltBsAA0nJ/Beka9Z9+v6tB9DcEC0GB+BmtNXt+V9mB+YdFsggWgoXz9\nKeEEpd61B0v2UAQLQEP5e+DoCwOWxwfrh0eKHdYjWAA0ODUHgBgEC4AYBAuAGEEF69YBT9Z6\nZN1Jx1XpqTZ78BoAMkxQwTq1zk8JN99yY5VBfIUFoK6ggrVq6TqHpXxLCECD97AAiOF3sDbN\nnTp52sKt9axFsABo+BusaUdFlCXrBOdXJVgANHwN1liVPfDaMRPGjDwqEp7otCLBAqDhZ7BW\nRo6tfKd9Zb+cbxzWJFgANPwM1iNqVdX4I+X0JRbBAqDhZ7DGZFWPK8JjHNYkWAA0/AzWJPVB\n1XiBesJhTYIFQMPPYK3NPXhZYjine8F6hzUJFgANX39KOCmiepwxYuRlpxWp7BedViRYADT8\nPQ5r4blt7eOwOl6y3HE9ggVAw/dTc9Yumbfk2/pWIlgANDiXEIAYBAuAGAQLgBgEC4AYBAuA\nGAQLgBgEC4AYBAuAGAQLgBgEC4AYBAuAGAQLgBgEC4AYBAuAGAQLgBgEC4AYBAuAGAQLgBgE\nC4AYBAuAGAQLgBgEC4AYBAuAGAQLgBgEC4AYBAuAGAQLgBgEC4AYBAuAGAQLgBgEC4AYBAuA\nGAQLgBgEC4AYBAuAGAQLgBgEC4AYBAuAGAQLgBgEC4AYBAuAGAQLgBgEC4AYBAuAGAQLgBgE\nC4AYBAuAGAQLgBgEC4AYBAuAGAQLgBgEC4AYBAuAGAQLgBgEC4AYBAuAGAQLgBgEC4AYBAuA\nGAQLgBgEC4AYBAuAGAQLgBgEC65UPPfrA/tdPKcBz/h22n0vf5W2+SCzESy4UTokb/hDd54S\nGZfqE8pvyW7xs9bRK7anc1bIWAQLbvyxw0fW3UvRN1J8wnWFkysM441O56RxUshcBAsu7Gg9\nKT64eFBqT/gk8m/7fnH07fTMCJnN72Btmjt18rSFW+tZi2AJsUStiw9ebJ3aE+7pmRgc+7u0\nTCgFaz6vCOql4Zq/wZp2VERZsk5wflWCJcRclfi3541Yak+4/qTE4LfBfE/40/9rp1TBResD\neXG452uwxqrsgdeOmTBm5FGR8ESnFQmW78pWl5l7w+oGfvGxNjQ/Prh3v9SecPvPE4MzRjTs\nlbyxrX+XiR+vnHxw0dogXh3u+RmslZFj11UO++V847AmwfLZzCNjKvuAfaMq/6QPGvTEX57+\n1cyPyoxNRX9M8YWyVtn337f+Zwprr7ymf9fj767vDYQGGLO7vdv91PcC77YJP/kZrEfUqqrx\nR8rpSyyC5a8nI5f897Nh4fBtyx4dnNegd8MfCauIanVJ7/03pbZ+Rf/DvzXvNp/QY0f9K09v\ncfgdE3/XqefXDZmRo/3uit9Py/GwgvCRn8Eak1U9rgiPcViTYPlqTf5fDOO98KsjIlEVbtuh\nNPVnTomec5DKiai91tW/rmGsf+PR/3zct9Vvbrlgt+6f1r/6NwU3W9+hbup/TOoTcrYzMis+\nWKeWebVN+MrPYE1S1d9uLFBPOKxJsHz1l6Jywxh57IvRvBu+nnO1ujzlJ27raH4nuOr1mTOz\n6h6GtWXevC01Hij9XSx3v+y8W5/+7XEXP/ZTClu/bf9y+/7T0MKUZ+SsPPrf+OBr9XGtRRXT\nTu+25zF/5t34Js7PYK3NPbjy37U53Qucdg2C5avLrJ/Y/er6tqNPG2UOukRS+Oonbnpuokln\nDq+15Jszw6FQ+MzkNyovaj9lp1H2j9YpH89w4nWJQfeHU31KfXon3mr7Z8ta35F+c0go2rVz\nqGC3d716KaSFrz8lnBRRPc4YMfKy04pU9otOKxIsX40427wZeHK70lOuNQcHdr4t1Sc+cGBi\ncPuR1Q++M+q4E6/udPibP/745uF7V3+rOC9snXBY/vbV4YmJ9492frK01Ch9bexN//zyxdv+\n9EKNP/JPX3zujd4jEz+z7HOP4yzKHZeWJX+H+2jLpdbd2qJRlY/Ew7Wzb+uDzKm+v3e33b5z\n3BoC5u9xWAvPbWsfh9XxkuWO66UUrDmDWyg0WCgS0j9u/S+/MKRf6rC9gnZZtZ5jbyMUjtRd\nOZxYXuMJocqnNGC2jRBJvEY4rH+lcGGhfgl8EWoxeG79f+t9PzVn7ZJ5S76tb6VUgvV4JCsW\n9Wxvbi521QX7plVjNmg9tUZV7HFY82p2mFQrzZI6aUss9i4gocrbXe4xSdNGAMLRrKyI49GZ\nNrHnEi7PatOlc6tIbrb1lyUcCcdvrX+SIzWHiQei4aSheRuJhHXLMnxDLaP2srzWNTfUwuqO\nWY1cc8cJ7R5OfUbRWCz+QKRyRqGItbIyV7EeMG+rNpSVHTH/u7BD1FwhlF0Q30TI/ELMem3r\nK6lQqPr1VJZ521LF/0XKc/jQovZcIvV8jlrn2g/E9gjHNxSNxadftaFotv0ieTmRJvin1gw2\nlJ0badW5S9uY83dehuBg3dA9P9S+V+/OERXJzTE/5lheLKTCObnmDhfNyzb3/uy8LJVYlmUt\nC+XkRq1l5gOhmLUsnGutnFV75UzeULsuiQ3lxpKXtexoZSFifZURCqtouHWqM4pmhUJZ1jeE\nu/XerXJZtrVM5bcMWTPKV1UbyrM62DKv0Jyt9UCbttbKkZZm8FR+fPp5KvEioRbZ5gNZu+1r\n/9MbUjlZu/rQoql9jvIKEp+jcfvGN5QTq7mh7JxQfOVwTk6T+1NrDhuKdu7dq32oRfcb6vt7\nH1Swbh3wZK1HthTfWGVQ/cEa8IteLVSnw47opkK5OeZOHTV3XnPXzrX/ncw2h7E86xsNc5n5\n2TE/RSGVbS0L5VrLsuxlOdYXFOG8mL2y1fmM31BRbuWGIskbamdtqKi9Ncy2lu2R+oyKQvZ3\nUS0OOaTGjLKjeVZqcqPZVRvazXogz3rbsaW9od2tDYXzo/aLaD+0rvnWNHKsZS4/R/mVn6O7\nu8Y3FO1Rc0NtVXxDpo5N7k+tOWyo2xGHdVYtevU/rr6/90EF61RVXOuRtSccV+Vgta2+DRzV\nv0+h6njoz609z/q8RHIj9ifK3vOsz0uW9V2Aysmxl1mfl5j1SVT2smh8ZfuvTG6WvbK1LOM3\nVJRduaFw8obaWsu6Wbd2fcK7pzyjUI/4jPL6HV45o7A1oy7K+l5RZasuVRvazX6fwupTgb2n\nd7D3dPNbsFAoFv/QssyXt2cUsl810i23etd3mFFu/Z+j3MrP0V37JDZ0YI0NxTolPjTzq4Ci\nJven1hw21OPnh+6uWvfpf3R9f++DCtaqpU7HRr+j6j1x49KD24cKDjy0R56K5lvJzm5hfXWZ\nZ/2LnZVvfSmak2995ZlnLYu1yLE+W9ayaL75uQ2ZK9vLzPbH8nNCiZUzf0OdOiY2FMlO3lCb\n1ubKnXvmqBatzGXhPdunOqMs899Me0N7HLpXYkaRiDWj9lnK+kZAZbWv2lDbQvPZ4ayY+d1i\nfiSkOhdaG2oRMzcUi+WHzf04Pysr8SKx7HzrQ+vc1tydrdmGY/V+aDHHz1F+XuJzNGG/+IZy\nC2psKLfDnqH4W7+RfTs1uT+15rCh3P0OPbCl2u3gS+v7e98038NKIVizI/md9u2cF7HeybXf\nrTU/8kjIHobMz4p9G661TIUTy2qunFjWHDa0e3xZrGPNDXWJmss6dcvtbD6Q1XXP1GfUoTBs\nr7xHfuWyLBV/O9UOQCgcqVB/niEAAArZSURBVF55T/sN2M72i0SjRarqRax/nWPxF0m8lLmH\nh623P6L2O/JZHXf9oUWsf6J3+TkKVc52zyx7+tFDs+Mrh2LJGzJvO3e1vvYLZXXr1AT/1JrB\nhnIieZ27dcqPzK7v733TvIBfCsEyrswJZ9nv3CWEVC11HmjssgzaUPwIqeqDnkJJ96HoIGtZ\nOFRrmeOLhGLWn0A4Vv3nEB9F7a/744c8JW3Q/F+hvU4o8WgoaYVQ7QfCu5it44z0D1S+WnxG\nmkOxQvacQuG6B4QF/6fWHDYUzsmJhnKurPevfdO8gF8qwap4sJ3DZwKN4rRzefukJihTPg6x\n2j1Y/+XYmuYF/FIJlmnt27Nnz541e5ZldvzO/C9r9Nas2fYDs2fNtFeYHX848f/4EnNZfPXZ\nVbfNYUNL1s/Tbei9De/NnrW0dPvihs5o7g8VK96qMaO3v15ZMnvWW19++VbtDS3auPCtFWsW\nb1xsP/zW51+9bQ4+W/POrJJVq0tqzKjk86/et2e0YW59H1rJ2+bz33prF5+jkvcWlZS8/e6s\n2Qs2xD+0kpVfv129obnz5lRu6P3tO5Y02T+1ZrCht1O6qGLTvIBfisEC0Lw0zQv4ESwAGk3z\nAn4EC4BG07yAH8ECoNE0L+BHsABoNM0L+BEsABpN8wJ+BAuARtO8gB/BAqAh9lxCAM0PwQIg\nBsECIAbBAiBG0wzW/IBPGwfQRM1vcE7SHyxj8YIUDN//6eZmjHo46Cn4bo8Lg56B705vjnv2\nm6n8nV+wYHHDa+JDsFJy67FBz8B3S9SGoKfgu57/F/QMfMee7SmCFRiC1SywZ3uKYAWGYDUL\n7NmeIliBIVjNAnu2pwhWYAhWs8Ce7SmCFRiC1SywZ3uKYAWGYDUL7NmeIliBIVjNAnu2pwhW\nYAhWs8Ce7SmCFRiC1SywZ3uqqQRr9PFBz8B3H4c2BT0F3/V6POgZ+I4921NNJVhbUvpVsZll\nRdAT8N/q5nflDvZsTzWVYAFAvQgWADEIFgAxCBYAMQgWADEIFgAxCBYAMQgWADEIFgAxCBYA\nMQgWADEIFgAxCBYAMQgWADEIFgAxmkiwtr43/4eg5+CLtXM/35kYfv7ucqPuMBOVzZoTH6yf\nu7jyglhJwwy04n9fJkZJe3ZG7+QVX835wo89u0kEq/wPeUplXbot6HmkXUkfpVT7R6zhot7m\nsOuMWsPMNF51te7WDwkr1fr+WsMM9N7B5h/oEdZV7JL27MzeyV/oZn7Iu9sXwE7vnt0kgnWT\nOvn1GRepoUHPI90W5bWd8ObEvdVEw/imXesH3n6yc+4HNYaZ6bNcO1gV/cPXz361v/pbjWEG\nWlnY8fGSCTn7ltXYszN6J5+iDpm5+p1fqgfTvmc3hWCtz+5Xbt6dGloW9EzS7Gw107x9Xx1u\nGNepV83hIvXrGsPMNKDoACtYr6rrzNstnfbYmTzMQL+JvG/ePvbrFcl7dmbv5Merj83b9aGD\n075nN4VgPansb5JeU6ODnkmaPT3WvivoYhj77F5hDQ/J25E8zEiT1NReVrCG2zu18TtVkjzM\nPJuzT6scJu3Zmb2T91f2t7otuqV9z24KwbpWzbfu1qkzgp6JLzaEBhib1In2+HK1NGkY5KzS\n5ts2Zxt2sHq3sP/7OfVA8jDzvKnuM95/efZ2o8aendk7+e/Va4b1670uTPue3RSCdZb62rqr\niB4a9Ex8cYmaYixTl9rj0Wp60jDIWaXNOYVr48HabV/7v0vU75OHmedR9Wh/6x3o/9bYszN7\nJ9/Yu9XNT4/t1P2rtO/ZTSFYJ6qN9n2LAwKeiC/GqRGGMV9dbf/H3Wpy0jDIaaXLdOtHDHaw\n8n9mP7BAXZk8zDzjVburPl37RMsWXyTv2Rm+ky/oYTZ6z+np37ObQrBOVevs+5zeAU/EB2WX\nqcvKrTciL7f/c5x6JWkY5MTSZMtexxiJYLXqaT8yR12bPMw849WvrLuH1E3Je3Zm7+SvxgYv\n2vzxcHV32vfsphCsi+JvwW5VA4KeSdp9d2x4nHX/pfqN/d+/VyVJw+DmlTbX5H5iJIJV1NF+\nZLoanTzMPA/HP6rP1InJe3Zm7+R7ddhq3lYcnLsp3Xt2UwjW+HiBF6qrgp5Juv3QJ+9le1CR\nH/+H9mS1IWkY2LzS5oPw4KdNXTo8/S9jcMg+znuCeiF5mHlmqBusu2/U8cl7dkbv5OvjX1Qa\nw9X8dO/ZTSFYC9Vl1t2f1NSgZ5JmZUfmzU4MT46sNm83F/StMcw4U1Wlw8w8PWM9dEz0u+Rh\n5vkpv5f10/ypZpqS9uyM3sk3hQ607werD9K9ZzeFYBlHxGYaxqKC7mVBTyTNxqgnKodvqJO3\nGTuHq6dqDDPOzh9tPyv68SdjfUGRuf9OVBcZycMMdI263TDWHKTerbFnZ/RO3l89b97Oz9qn\nIt17dpMI1vIOoX5HRFovDHoe6dZKHRb3jXWIzm7HdFLDrH+Lk4YZyn4Py5iclXPkAarX9zWH\nmWfLEWrvIwvUH40ae3ZG7+TLOqpfDBsUbWW9WZXePbtJBMtYf9tJJ97yddCzSLujK60x/+P1\nYQPPfyn+eNIwM118rn33wchBQ+7fVnuYeUofO/NXl8RP+U3aszN6J//u3rMGnP7nb+xxWvfs\nphEsAEgBwQIgBsECIAbBAiAGwQIgBsECIAbBAiAGwQIgBsECIAbBAiAGwQIgBsECIAbBAiAG\nwQIgBsECIAbBAiAGwQIgBsECIAbBAiAGwQIgBsECIAbBAiAGwQIgBsECIAbBAiAGwQIgBsEC\nIAbBAiAGwQIgBsECIAbBAiAGwQIgBsGCr76YuTHoKUAwgoX0WDZzk+7h0ep1v2eCDEKwkB5n\nq//pHiZYcINgIT0IFtKAYCE97GBtnfmFsXXBB9vtRyqWzdlYGawtC+assx6a9Y69aPZbgc0T\nohAspIcdrJXqDw+2yFFtXjYf+KCHUtFRt1vB2nZFTCk14HPDuEy9Yi56Ul0T8GwhBMFCetjB\n+lLtf+xKY1n7Fj8apd3Cd3/29jFFVrDOio7+8LNHCrpuNX7cu8sW4/v23X8KerqQgWAhPexg\nrVb535rjUWq2MU1dZo5+6mAGa4G63lrjfvU3w3gz9Dvj8vA7wc4VYhAspEciWEdb47vUS8bN\n6kVrONQM1mh14zOmu9Vw84EropPCNwQ5UUhCsJAeiWCdY43vVZONC9Uca3iLGazhKmGw+cCW\nfVSPbYHOFIIQLKRHIljnWWMrWOephdaw2AzW+eqNbbZS84Hyw1X/ikBnCkEIFtKjdrBGqBnW\n8EozWNeof1Svd5caqv4SzBQhD8FCetQO1gR1vzXsYwbr7+p8a7jmP2WG8XHO6cagvE+DnCkE\nIVhIj9rB+lD12GAYz0XNYP3YLnueYZSeoeYa5Ue0/MpYmde/POjpQgaChfSoHSzjUtX+lP5t\nb1DTDeNfudmnnbeXusUw7lYPmcvHq3sCni2EIFhIj9uOXmYY3x59hzWefPQswyj/25Djr/38\n9aOtY66+uPmkE64oMYwfBp9vfW1VdtbgH4KdLYQgWADEIFgAxCBYAMQgWADEIFgAxCBYAMQg\nWADEIFgAxCBYAMQgWADEIFgAxCBYAMQgWADEIFgAxCBYAMQgWADEIFgAxCBYAMQgWADEIFgA\nxCBYAMQgWADEIFgAxCBYAMQgWADEIFgAxCBYAMQgWADEIFgAxCBYAMQgWADE+P8+4L/oa8Y3\nhwAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 360,
       "width": 600
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "vcp_coloc <- 1 - (1-out_coloc$results$SNP.PP.H4.row2)*(1-out_coloc$results$SNP.PP.H4.row3)\n",
    "plot(vcp_coloc[c(400:1200)])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc7269a1-ed10-411d-9b82-c516dced148b",
   "metadata": {},
   "source": [
    "### MOLOC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "ad434793-04f9-45a0-90d5-67dd9c091184",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Use default priors: 1e-04,1e-05\n",
      "\n",
      "Mean estimated sdY from data1: 0.553434902059988\n",
      "\n",
      "Mean estimated sdY from data2: 0.553187001637554\n",
      "\n",
      "Use prior variances of 0.01 0.1 0.5\n",
      "\n",
      "Use prior variances of 0.01 0.1 0.5\n",
      "\n",
      "Best SNP per trait a: 721\n",
      "\n",
      "Probability that trait a colocalizes with at least one other trait = 1\n",
      "\n",
      "Best SNP per trait b: 721\n",
      "\n",
      "Probability that trait b colocalizes with at least one other trait = 1\n",
      "\n",
      "Best SNP per trait ab: 721\n",
      "\n",
      "Probability that trait ab colocalizes with at least one other trait = 1\n",
      "\n"
     ]
    }
   ],
   "source": [
    "library(moloc)\n",
    "moloc_input <- lapply(1:2, function(cnt){\n",
    "    tibble(SNP = 1:ncol(data$X), BETA = betas[,cnt],\n",
    "           SE = sebetas[,cnt], N = nrow(data$X), MAF = MAF)\n",
    "})\n",
    "res_moloc <- moloc_test(listData = moloc_input)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "c3a1479b-74cc-47c4-8a22-d3e19da315e5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 5 × 4</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>prior</th><th scope=col>sumbf</th><th scope=col>logBF_locus</th><th scope=col>PPA</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>a</th><td>1.00e-04</td><td> 60.59171</td><td> 52.90784</td><td>2.492328e-28</td></tr>\n",
       "\t<tr><th scope=row>a,b</th><td>1.00e-08</td><td>125.07877</td><td>109.71104</td><td>2.529180e-04</td></tr>\n",
       "\t<tr><th scope=row>b</th><td>1.00e-04</td><td> 65.56983</td><td> 57.88597</td><td>3.618911e-26</td></tr>\n",
       "\t<tr><th scope=row>ab</th><td>1.00e-05</td><td>126.45320</td><td>118.76934</td><td>9.997471e-01</td></tr>\n",
       "\t<tr><th scope=row>zero</th><td>9.99e-01</td><td>  0.00000</td><td>  0.00000</td><td>1.207708e-50</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 5 × 4\n",
       "\\begin{tabular}{r|llll}\n",
       "  & prior & sumbf & logBF\\_locus & PPA\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\ta & 1.00e-04 &  60.59171 &  52.90784 & 2.492328e-28\\\\\n",
       "\ta,b & 1.00e-08 & 125.07877 & 109.71104 & 2.529180e-04\\\\\n",
       "\tb & 1.00e-04 &  65.56983 &  57.88597 & 3.618911e-26\\\\\n",
       "\tab & 1.00e-05 & 126.45320 & 118.76934 & 9.997471e-01\\\\\n",
       "\tzero & 9.99e-01 &   0.00000 &   0.00000 & 1.207708e-50\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 5 × 4\n",
       "\n",
       "| <!--/--> | prior &lt;dbl&gt; | sumbf &lt;dbl&gt; | logBF_locus &lt;dbl&gt; | PPA &lt;dbl&gt; |\n",
       "|---|---|---|---|---|\n",
       "| a | 1.00e-04 |  60.59171 |  52.90784 | 2.492328e-28 |\n",
       "| a,b | 1.00e-08 | 125.07877 | 109.71104 | 2.529180e-04 |\n",
       "| b | 1.00e-04 |  65.56983 |  57.88597 | 3.618911e-26 |\n",
       "| ab | 1.00e-05 | 126.45320 | 118.76934 | 9.997471e-01 |\n",
       "| zero | 9.99e-01 |   0.00000 |   0.00000 | 1.207708e-50 |\n",
       "\n"
      ],
      "text/plain": [
       "     prior    sumbf     logBF_locus PPA         \n",
       "a    1.00e-04  60.59171  52.90784   2.492328e-28\n",
       "a,b  1.00e-08 125.07877 109.71104   2.529180e-04\n",
       "b    1.00e-04  65.56983  57.88597   3.618911e-26\n",
       "ab   1.00e-05 126.45320 118.76934   9.997471e-01\n",
       "zero 9.99e-01   0.00000   0.00000   1.207708e-50"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res_moloc$priors_lkl_ppa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "2619631c-36b3-48c3-86bd-4abca612cfdb",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 3 × 2</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>coloc_ppas</th><th scope=col>best.snp.coloc</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>a</th><td>0.9997471</td><td>721</td></tr>\n",
       "\t<tr><th scope=row>b</th><td>0.9997471</td><td>721</td></tr>\n",
       "\t<tr><th scope=row>ab</th><td>0.9997471</td><td>721</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 3 × 2\n",
       "\\begin{tabular}{r|ll}\n",
       "  & coloc\\_ppas & best.snp.coloc\\\\\n",
       "  & <dbl> & <chr>\\\\\n",
       "\\hline\n",
       "\ta & 0.9997471 & 721\\\\\n",
       "\tb & 0.9997471 & 721\\\\\n",
       "\tab & 0.9997471 & 721\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 3 × 2\n",
       "\n",
       "| <!--/--> | coloc_ppas &lt;dbl&gt; | best.snp.coloc &lt;chr&gt; |\n",
       "|---|---|---|\n",
       "| a | 0.9997471 | 721 |\n",
       "| b | 0.9997471 | 721 |\n",
       "| ab | 0.9997471 | 721 |\n",
       "\n"
      ],
      "text/plain": [
       "   coloc_ppas best.snp.coloc\n",
       "a  0.9997471  721           \n",
       "b  0.9997471  721           \n",
       "ab 0.9997471  721           "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res_moloc$best_snp"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0520b35a-29f2-4179-8110-d3e6ec800e00",
   "metadata": {},
   "source": [
    "## 3. Weaker causal effect for target traits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "add231d5-a5c8-4483-9f79-92982f163c84",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>1926</li><li>2271</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 1926\n",
       "\\item 2271\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 1926\n",
       "2. 2271\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 1926 2271"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "library(colocboost)\n",
    "library(tidyverse)\n",
    "data(Weaker_GWAS_Effect)\n",
    "data <- Weaker_GWAS_Effect\n",
    "X <- data$X\n",
    "Y <- data$Y\n",
    "data$variant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "a480a884-5f2e-4cd6-b76e-c4c1b1a04e0d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Starting checking the input data.\n",
      "\n",
      "Starting gradient boosting algorithm.\n",
      "\n",
      "Boosting iterations for outcome 1 converge after 23 iterations!\n",
      "\n",
      "Boosting iterations for outcome 2 converge after 33 iterations!\n",
      "\n",
      "Starting assemble analyses and results summary.\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<dl>\n",
       "\t<dt>$`cos1:y1_y2`</dt>\n",
       "\t\t<dd>1926</dd>\n",
       "\t<dt>$`cos2:y1_y2`</dt>\n",
       "\t\t<dd>2271</dd>\n",
       "</dl>\n"
      ],
      "text/latex": [
       "\\begin{description}\n",
       "\\item[\\$`cos1:y1\\_y2`] 1926\n",
       "\\item[\\$`cos2:y1\\_y2`] 2271\n",
       "\\end{description}\n"
      ],
      "text/markdown": [
       "$`cos1:y1_y2`\n",
       ":   1926\n",
       "$`cos2:y1_y2`\n",
       ":   2271\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "$`cos1:y1_y2`\n",
       "[1] 1926\n",
       "\n",
       "$`cos2:y1_y2`\n",
       "[1] 2271\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res <- colocboost(X = X, Y = Y)\n",
    "res$cos_details$cos$cos_index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "a14f4cfb-7af9-42b4-9872-02db7e4737a5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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c8880xFRYXK5Z47d04IERAQcMMNN1Q5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\nTqfRAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 360,
       "width": 600
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width = 10, repr.plot.height = 6)\n",
    "colocboost_plot(res, plot_cols = 1, add_vertical = T, add_vertical_idx = c(data$variant))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "6328c815-982b-4928-ab33-0a7c350b60f2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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rRp0+aHH374+eefn3vuubCwsPt18/b27tev37Jlyy5cuDB69Gh7tlh+w4cPP378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\n+KX+ANXSS7w2srbJAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 360,
       "width": 600
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "colocboost_plot(res, y = \"vcp\", plot_cols = 1, add_vertical = T, add_vertical_idx = c(data$variant))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "912341d7-d151-4b3a-acfc-a0348f150429",
   "metadata": {},
   "source": [
    "### HyPrColoc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "ea3c218e-2823-440e-8113-566218a6d5ed",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "library(hyprcoloc)\n",
    "betas = sebetas = matrix(NA, nrow = ncol(X), ncol = 2) \n",
    "for (i in 1:2){\n",
    "    x <- scale(X)\n",
    "    y <- scale(Y[,i])\n",
    "    rr <- susieR::univariate_regression(x, y)\n",
    "    betas[,i] = rr$betahat\n",
    "    sebetas[, i] <- rr$sebetahat\n",
    "}\n",
    "traits <- c(1:2)\n",
    "rsid <- paste0(\"snp\", c(1:ncol(X)))\n",
    "colnames(betas) <- colnames(sebetas) <- traits\n",
    "rownames(betas) <- rownames(sebetas) <- rsid\n",
    "res_hyprcoloc <- hyprcoloc(betas, sebetas, trait.names=traits, snp.id=rsid, snpscores = TRUE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "5a600ff4-c8cf-4d1e-943f-4425b030cc59",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 1 × 7</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>iteration</th><th scope=col>traits</th><th scope=col>posterior_prob</th><th scope=col>regional_prob</th><th scope=col>candidate_snp</th><th scope=col>posterior_explained_by_snp</th><th scope=col>dropped_trait</th></tr>\n",
       "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;lgl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>1</td><td>1, 2</td><td>0.9616</td><td>1</td><td>snp1926</td><td>0.9823</td><td>NA</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 1 × 7\n",
       "\\begin{tabular}{lllllll}\n",
       " iteration & traits & posterior\\_prob & regional\\_prob & candidate\\_snp & posterior\\_explained\\_by\\_snp & dropped\\_trait\\\\\n",
       " <dbl> & <chr> & <dbl> & <dbl> & <chr> & <dbl> & <lgl>\\\\\n",
       "\\hline\n",
       "\t 1 & 1, 2 & 0.9616 & 1 & snp1926 & 0.9823 & NA\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 1 × 7\n",
       "\n",
       "| iteration &lt;dbl&gt; | traits &lt;chr&gt; | posterior_prob &lt;dbl&gt; | regional_prob &lt;dbl&gt; | candidate_snp &lt;chr&gt; | posterior_explained_by_snp &lt;dbl&gt; | dropped_trait &lt;lgl&gt; |\n",
       "|---|---|---|---|---|---|---|\n",
       "| 1 | 1, 2 | 0.9616 | 1 | snp1926 | 0.9823 | NA |\n",
       "\n"
      ],
      "text/plain": [
       "  iteration traits posterior_prob regional_prob candidate_snp\n",
       "1 1         1, 2   0.9616         1             snp1926      \n",
       "  posterior_explained_by_snp dropped_trait\n",
       "1 0.9823                     NA           "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res_hyprcoloc$results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "759eb30f-eefd-412a-84b2-cd6ec2581f01",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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c9nS4IFwCKZwZogKyrWi+QJny0JFgCLZAZrY/bpBfHl/FYNt/hsSbAAWCT1VcIJ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\nZHuhxSVNAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 360,
       "width": 600
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(res_hyprcoloc$snpscores[[1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "32545ccd-b62c-402b-b057-b63bd374a6a0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "b658bbe0-7cf8-482a-89ce-fec93f8f6ed2",
   "metadata": {},
   "source": [
    "### COLOC (one causal assumption)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "2f03c6bd-3342-4687-a0a5-45d3c692543f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
      " 3.94e-13  2.65e-09  2.69e-06  1.71e-02  9.83e-01 \n",
      "[1] \"PP abf for shared variant: 98.3%\"\n"
     ]
    }
   ],
   "source": [
    "library(coloc)\n",
    "LD <- get_cormat(data$X)\n",
    "colnames(LD) <- rownames(LD) <- 1:ncol(data$X)\n",
    "MAF <- colMeans(data$X)/2 %>% as.numeric\n",
    "D1 <- list(\"beta\" = betas[,1], \"varbeta\" = sebetas[,1]^2,\n",
    "           \"N\" = nrow(data$X), \"sdY\" = sd(unlist(data$Y[,1])),\n",
    "           \"type\" = 'quant', \"MAF\" = MAF, \"LD\" = LD,\n",
    "           \"snp\" = 1:ncol(data$X), \"position\" = 1:ncol(data$X))\n",
    "D2 <- list(\"beta\" = betas[,2], \"varbeta\" = sebetas[,2]^2,\n",
    "           \"N\" = nrow(data$X), \"sdY\" = sd(unlist(data$Y[,2])),\n",
    "           \"type\" = 'quant', \"MAF\" = MAF, \"LD\" = LD,\n",
    "           \"snp\" = 1:ncol(data$X), \"position\" = 1:ncol(data$X))\n",
    "my.res <- coloc.abf(dataset1=D1, dataset2=D2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "f30e1c97-4dd3-4e27-b269-30b8d2387e6c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "'1926'"
      ],
      "text/latex": [
       "'1926'"
      ],
      "text/markdown": [
       "'1926'"
      ],
      "text/plain": [
       "[1] \"1926\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "o <- order(my.res$results$SNP.PP.H4,decreasing=TRUE)\n",
    "cs <- cumsum(my.res$results$SNP.PP.H4[o])\n",
    "w <- which(cs > 0.95)[1]\n",
    "my.res$results[o,][1:w,]$snp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "9ad20e97-4643-4db8-bfbf-6e486bd9f256",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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5VOadqaBAvI1WCwTl4ty+5P3+8YrXzS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\nRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 360,
       "width": 600
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pph4.snp <- my.res$results$SNP.PP.H4[order(my.res$results$position,decreasing=FALSE)]\n",
    "plot(pph4.snp)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f76d1417-e582-4160-9a75-d88660b57714",
   "metadata": {},
   "source": [
    "### COLOC (V5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "0647e6d9-40e1-43aa-b169-af8f2f188a76",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "running max iterations: 100\n",
      "\n",
      "\tconverged: TRUE\n",
      "\n",
      "running max iterations: 100\n",
      "\n",
      "\tconverged: TRUE\n",
      "\n"
     ]
    }
   ],
   "source": [
    "library(susieR)\n",
    "out_p <- runsusie(D1)\n",
    "out_e <- runsusie(D2)\n",
    "out_coloc = coloc.susie(out_e, out_p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "10042eb7-7286-4d66-ab2d-0c123dfe4293",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "out_coloc$summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "a480668f-d47d-4f3b-88e0-76220ec999f9",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "'1926'"
      ],
      "text/latex": [
       "'1926'"
      ],
      "text/markdown": [
       "'1926'"
      ],
      "text/plain": [
       "[1] \"1926\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "o <- order(out_coloc$results$SNP.PP.H4.row2,decreasing=TRUE)\n",
    "cs <- cumsum(out_coloc$results$SNP.PP.H4.row2[o])\n",
    "w <- which(cs > 0.95)[1]\n",
    "cos1 <- out_coloc$results[o,][1:w,]$snp\n",
    "cos1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "b93015d4-b1cb-40d8-8e06-8afca69803d5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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WWAMFxeBgjD5WWAMFxeBgjD5WWAMFxe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\nAq9cG8C8xwAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 360,
       "width": 600
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "vcp_coloc <- out_coloc$results$SNP.PP.H4.row2\n",
    "plot(vcp_coloc)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3195976f-649a-4914-97a2-66294b237d96",
   "metadata": {},
   "source": [
    "### MOLOC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "a2a61e76-3f0c-4ac4-a964-b1805574378f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Use default priors: 1e-04,1e-05\n",
      "\n",
      "Mean estimated sdY from data1: 0.537763795330779\n",
      "\n",
      "Mean estimated sdY from data2: 0.537609105700604\n",
      "\n",
      "Use prior variances of 0.01 0.1 0.5\n",
      "\n",
      "Use prior variances of 0.01 0.1 0.5\n",
      "\n",
      "Best SNP per trait a: 1926\n",
      "\n",
      "Probability that trait a colocalizes with at least one other trait = 0.99\n",
      "\n",
      "Best SNP per trait b: 1926\n",
      "\n",
      "Probability that trait b colocalizes with at least one other trait = 0.99\n",
      "\n",
      "Best SNP per trait ab: 1926\n",
      "\n",
      "Probability that trait ab colocalizes with at least one other trait = 0.99\n",
      "\n"
     ]
    }
   ],
   "source": [
    "library(moloc)\n",
    "moloc_input <- lapply(1:2, function(cnt){\n",
    "    tibble(SNP = 1:ncol(data$X), BETA = betas[,cnt],\n",
    "           SE = sebetas[,cnt], N = nrow(data$X), MAF = MAF)\n",
    "})\n",
    "res_moloc <- moloc_test(listData = moloc_input)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "4e713dc6-17ac-4e02-9d44-19936b569c34",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 5 × 4</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>prior</th><th scope=col>sumbf</th><th scope=col>logBF_locus</th><th scope=col>PPA</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>a</th><td>1.00e-04</td><td>17.99025</td><td> 9.456204</td><td>1.984014e-09</td></tr>\n",
       "\t<tr><th scope=row>a,b</th><td>1.00e-08</td><td>42.71907</td><td>25.650974</td><td>1.089268e-02</td></tr>\n",
       "\t<tr><th scope=row>b</th><td>1.00e-04</td><td>24.81574</td><td>16.281686</td><td>1.827317e-06</td></tr>\n",
       "\t<tr><th scope=row>ab</th><td>1.00e-05</td><td>40.32003</td><td>31.785978</td><td>9.891055e-01</td></tr>\n",
       "\t<tr><th scope=row>zero</th><td>9.99e-01</td><td> 0.00000</td><td> 0.000000</td><td>3.051242e-13</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 5 × 4\n",
       "\\begin{tabular}{r|llll}\n",
       "  & prior & sumbf & logBF\\_locus & PPA\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\ta & 1.00e-04 & 17.99025 &  9.456204 & 1.984014e-09\\\\\n",
       "\ta,b & 1.00e-08 & 42.71907 & 25.650974 & 1.089268e-02\\\\\n",
       "\tb & 1.00e-04 & 24.81574 & 16.281686 & 1.827317e-06\\\\\n",
       "\tab & 1.00e-05 & 40.32003 & 31.785978 & 9.891055e-01\\\\\n",
       "\tzero & 9.99e-01 &  0.00000 &  0.000000 & 3.051242e-13\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 5 × 4\n",
       "\n",
       "| <!--/--> | prior &lt;dbl&gt; | sumbf &lt;dbl&gt; | logBF_locus &lt;dbl&gt; | PPA &lt;dbl&gt; |\n",
       "|---|---|---|---|---|\n",
       "| a | 1.00e-04 | 17.99025 |  9.456204 | 1.984014e-09 |\n",
       "| a,b | 1.00e-08 | 42.71907 | 25.650974 | 1.089268e-02 |\n",
       "| b | 1.00e-04 | 24.81574 | 16.281686 | 1.827317e-06 |\n",
       "| ab | 1.00e-05 | 40.32003 | 31.785978 | 9.891055e-01 |\n",
       "| zero | 9.99e-01 |  0.00000 |  0.000000 | 3.051242e-13 |\n",
       "\n"
      ],
      "text/plain": [
       "     prior    sumbf    logBF_locus PPA         \n",
       "a    1.00e-04 17.99025  9.456204   1.984014e-09\n",
       "a,b  1.00e-08 42.71907 25.650974   1.089268e-02\n",
       "b    1.00e-04 24.81574 16.281686   1.827317e-06\n",
       "ab   1.00e-05 40.32003 31.785978   9.891055e-01\n",
       "zero 9.99e-01  0.00000  0.000000   3.051242e-13"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res_moloc$priors_lkl_ppa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "98eaf415-b2cb-4bd1-975e-927b624b1657",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 3 × 2</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>coloc_ppas</th><th scope=col>best.snp.coloc</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>a</th><td>0.9891055</td><td>1926</td></tr>\n",
       "\t<tr><th scope=row>b</th><td>0.9891055</td><td>1926</td></tr>\n",
       "\t<tr><th scope=row>ab</th><td>0.9891055</td><td>1926</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 3 × 2\n",
       "\\begin{tabular}{r|ll}\n",
       "  & coloc\\_ppas & best.snp.coloc\\\\\n",
       "  & <dbl> & <chr>\\\\\n",
       "\\hline\n",
       "\ta & 0.9891055 & 1926\\\\\n",
       "\tb & 0.9891055 & 1926\\\\\n",
       "\tab & 0.9891055 & 1926\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 3 × 2\n",
       "\n",
       "| <!--/--> | coloc_ppas &lt;dbl&gt; | best.snp.coloc &lt;chr&gt; |\n",
       "|---|---|---|\n",
       "| a | 0.9891055 | 1926 |\n",
       "| b | 0.9891055 | 1926 |\n",
       "| ab | 0.9891055 | 1926 |\n",
       "\n"
      ],
      "text/plain": [
       "   coloc_ppas best.snp.coloc\n",
       "a  0.9891055  1926          \n",
       "b  0.9891055  1926          \n",
       "ab 0.9891055  1926          "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res_moloc$best_snp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8d5e0cee-61cd-4a82-894e-d7bac771d76b",
   "metadata": {},
   "outputs": [],
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