test.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from basic_tools import *\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import scipy.stats as st"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(497, 7622)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h2_total=pd.read_csv(h2_total_par_filtered_path,index_col='phenotype')\n",
    "h2_total.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "pheno_select=h2_total.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "497"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(pheno_select)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading h2 results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "h2_neale_result=pd.read_csv(h2_path,sep='\\t')\n",
    "h2_neale_result=h2_neale_result.set_index('phenotype')\n",
    "h2_neale_sorted=h2_neale_result.sort_values('h2_observed',ascending=False)\n",
    "h2_neale_sorted.columns = h2_neale_sorted.columns.map(lambda x: 'neale.'+str(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "h2_neale2_result=pd.read_csv(h2_v2_path,sep='\\t')\n",
    "h2_neale2_result=h2_neale2_result.set_index('phenotype')\n",
    "h2_neale2_sorted=h2_neale2_result.sort_values('h2_observed',ascending=False)\n",
    "h2_neale2_sorted.columns = h2_neale2_sorted.columns.map(lambda x: 'neale2.'+str(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## from my ldsc result of univariate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>uni.filename</th>\n",
       "      <th>uni.Total Observed scale h2</th>\n",
       "      <th>uni.Total Observed scale h2_se</th>\n",
       "      <th>uni.Total Liability scale h2</th>\n",
       "      <th>uni.Total Liability scale h2_se</th>\n",
       "      <th>uni.Lambda GC</th>\n",
       "      <th>uni.Mean Chi^2</th>\n",
       "      <th>uni.Intercept</th>\n",
       "      <th>uni.Intercept_se</th>\n",
       "      <th>uni.Ratio</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>phenotype</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>50_irnt</th>\n",
       "      <td>data/out_final/uni.50_irnt.ldsc.log</td>\n",
       "      <td>0.3772</td>\n",
       "      <td>0.0197</td>\n",
       "      <td>0.3772</td>\n",
       "      <td>0.0197</td>\n",
       "      <td>2.5074</td>\n",
       "      <td>4.7497</td>\n",
       "      <td>1.6108</td>\n",
       "      <td>0.0373</td>\n",
       "      <td>0.1629 (0.0099)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20153_irnt</th>\n",
       "      <td>data/out_final/uni.20153_irnt.ldsc.log</td>\n",
       "      <td>0.3293</td>\n",
       "      <td>0.0209</td>\n",
       "      <td>0.3293</td>\n",
       "      <td>0.0209</td>\n",
       "      <td>1.5696</td>\n",
       "      <td>2.1139</td>\n",
       "      <td>1.2127</td>\n",
       "      <td>0.0188</td>\n",
       "      <td>0.1909 (0.0169)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5097_irnt</th>\n",
       "      <td>data/out_final/uni.5097_irnt.ldsc.log</td>\n",
       "      <td>0.2878</td>\n",
       "      <td>0.0233</td>\n",
       "      <td>0.2878</td>\n",
       "      <td>0.0233</td>\n",
       "      <td>1.3169</td>\n",
       "      <td>1.5435</td>\n",
       "      <td>1.1186</td>\n",
       "      <td>0.0134</td>\n",
       "      <td>0.2183 (0.0246)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5098_irnt</th>\n",
       "      <td>data/out_final/uni.5098_irnt.ldsc.log</td>\n",
       "      <td>0.2825</td>\n",
       "      <td>0.0223</td>\n",
       "      <td>0.2825</td>\n",
       "      <td>0.0223</td>\n",
       "      <td>1.3203</td>\n",
       "      <td>1.5455</td>\n",
       "      <td>1.1236</td>\n",
       "      <td>0.0135</td>\n",
       "      <td>0.2266 (0.0247)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5133_irnt</th>\n",
       "      <td>data/out_final/uni.5133_irnt.ldsc.log</td>\n",
       "      <td>0.2771</td>\n",
       "      <td>0.0221</td>\n",
       "      <td>0.2771</td>\n",
       "      <td>0.0221</td>\n",
       "      <td>1.3068</td>\n",
       "      <td>1.5356</td>\n",
       "      <td>1.1237</td>\n",
       "      <td>0.0135</td>\n",
       "      <td>0.231 (0.0252)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      uni.filename  \\\n",
       "phenotype                                            \n",
       "50_irnt        data/out_final/uni.50_irnt.ldsc.log   \n",
       "20153_irnt  data/out_final/uni.20153_irnt.ldsc.log   \n",
       "5097_irnt    data/out_final/uni.5097_irnt.ldsc.log   \n",
       "5098_irnt    data/out_final/uni.5098_irnt.ldsc.log   \n",
       "5133_irnt    data/out_final/uni.5133_irnt.ldsc.log   \n",
       "\n",
       "            uni.Total Observed scale h2  uni.Total Observed scale h2_se  \\\n",
       "phenotype                                                                 \n",
       "50_irnt                          0.3772                          0.0197   \n",
       "20153_irnt                       0.3293                          0.0209   \n",
       "5097_irnt                        0.2878                          0.0233   \n",
       "5098_irnt                        0.2825                          0.0223   \n",
       "5133_irnt                        0.2771                          0.0221   \n",
       "\n",
       "            uni.Total Liability scale h2  uni.Total Liability scale h2_se  \\\n",
       "phenotype                                                                   \n",
       "50_irnt                           0.3772                           0.0197   \n",
       "20153_irnt                        0.3293                           0.0209   \n",
       "5097_irnt                         0.2878                           0.0233   \n",
       "5098_irnt                         0.2825                           0.0223   \n",
       "5133_irnt                         0.2771                           0.0221   \n",
       "\n",
       "            uni.Lambda GC  uni.Mean Chi^2  uni.Intercept  uni.Intercept_se  \\\n",
       "phenotype                                                                    \n",
       "50_irnt            2.5074          4.7497         1.6108            0.0373   \n",
       "20153_irnt         1.5696          2.1139         1.2127            0.0188   \n",
       "5097_irnt          1.3169          1.5435         1.1186            0.0134   \n",
       "5098_irnt          1.3203          1.5455         1.1236            0.0135   \n",
       "5133_irnt          1.3068          1.5356         1.1237            0.0135   \n",
       "\n",
       "                  uni.Ratio  \n",
       "phenotype                    \n",
       "50_irnt     0.1629 (0.0099)  \n",
       "20153_irnt  0.1909 (0.0169)  \n",
       "5097_irnt   0.2183 (0.0246)  \n",
       "5098_irnt   0.2266 (0.0247)  \n",
       "5133_irnt    0.231 (0.0252)  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h2_myuni_sorted=read_ldsc('uni',False)\n",
    "h2_myuni_sorted.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## from my ldsc result of various BP scales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bp300 was loaded\n",
      "bp128 was loaded\n",
      "bp64 was loaded\n",
      "bp32 was loaded\n",
      "bp16 was loaded\n",
      "bp8 was loaded\n"
     ]
    }
   ],
   "source": [
    "suffix_bp_list_power=\"bp300 bp128 bp64 bp32 bp16 bp8\".split()\n",
    "suffix_bp_list_full=\"bp300 bp128 bp64 bp32 bp16 bp8\".split()\n",
    "\n",
    "h2_bp_sorted_list=[]\n",
    "for suffix in suffix_bp_list_full:\n",
    "    h2_bp_sorted_list.append(read_ldsc(suffix,verbose=True))\n",
    "    print(suffix,\"was loaded\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## merging data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "h2_total=h2_bp_sorted_list[0].copy()\n",
    "for h2_bp_sorted in h2_bp_sorted_list[1:]:\n",
    "    h2_total=h2_total.join(h2_bp_sorted)\n",
    "\n",
    "h2_total=h2_total.join(h2_myuni_sorted)\n",
    "h2_total=h2_total.join(h2_neale_sorted)\n",
    "h2_total=h2_total.join(h2_neale2_sorted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "h2_total['uni.Total h2 z']=h2_total['uni.Total Observed scale h2']/h2_total['uni.Total Observed scale h2_se']\n",
    "h2_total['uni.Total h2 p']=st.norm.sf(h2_total['uni.Total Observed scale h2']/h2_total['uni.Total Observed scale h2_se'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(561, 7677)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h2_total.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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0tnHx/YtpWlhoR0D/oaixMLMNZna3mZ0MHEqU/Ojx1CVzHMepIKbNWkpbe0ensrb2DqbNWpqnRf8iycgCSR+WdD2wgGhj3idTlcpxHKfCWNnaVlJ5fyPJDu5XgIXA3cAkM1tfuIXjOE7/Y/eGelpyGIbdG+p7QZryk2RkcbCZfcLM7nRD4ThOtTJp/Cjq62o7ldXX1TJp/Khekqi8FE2rClwpaZvr5mlVHcepIjKrnqp1NVSStKoLyiGI4zhOpTNhdGPVGIdsCqZVDX9/Xj5xHMdxnEokiYN7P+DrwPB4fTM7Jj2xHMdxnEoiyQ7ue4CfADcCHUXqOo7jOP2QJMZik5n9OHVJHMdxnIolydLZByV9QdJuknbOvFKXzHEcx6kYkowsMgmJJsXKDI9A6ziOUzUUNRZmNqIcgjiO4ziVS5LVUP+Wq9zMfpGg7XHAj4jSqt5oZlOzrp9PFM22A1gHTDSzZ8O1i4HPhmtfMrNZxZ7nOI7jpEOSaaj3x44HAv8CPAUUNBaSaoHrgI8ArwHzJM3IGIPAHWb2k1D/ROAa4DhJBwBnAO8FdgcelbRfSMLkOI7jlJkk01BfjJ9LagCmJ7j34cCLZrYstJsOnARsMRZm9o9Y/cFEvhBCvelm9g5R8qUXw/3+lOC5juM4Tg+TZGSRzXogiR+jEVgRO38NOCK7kqQLgK8BA4DMRr9GYG5W22322EuaCEwEGDZsGM3NzQnE6h7r1q0ry3MqgWrSFapL32rSFapL37R0TeKzeJCtv/hrgAOIwpX3CGZ2HXCdpLOAKWxdfZWk7Q3ADQBjxoyxsWPH9pRYeWlubqYcz6kEqklXqC59q0lXqC5909I1ycjie7HjTcCrZvZagnYtwJ6x8z1CWT6mA5nNf6W2dRzHcVKkUIhyWUTeFKqZOnkuzwNGShpB1NGfAZyV1X6kmb0QTo8HMsczgDskXUPk4B4JPJlEIcdxHKfnKTSyeEzSfcAvzWx5plDSAOCDRNNFjwG35GpsZpskXQjMIlo6e5OZLZF0BTDfzGYAF0o6FmgHVod7EurdTeQM3wRc4CuhHMfpbZoWtng+ixwcB3wGuDOMDlqJls7WAg8DPzSzhYVubmYzgZlZZZfEjr9coO1VwFXFFHAcxykHTQtbuPj+xbS1R79bW1rbuPj+xQBVYTAK5bN4G7geuF5SHbAL0GZmreUSznEcp1wUGzVMm7V0i6HI0NbewbRZS6vCWCQJJAjRktfjzKxV0i5hpOE4jtMvyIwaWlrbMLaOGpoWbl1Xs7K1LWfbfOX9jaLGQtKlwDeBi0PRAOC2NIVyHMcpJ4VGDRl2b6jP2TZfeX8jydLZTwCjiUJ8YGYrJe2QqlSO4zgpkG+qKd/ooKW1jX0vnkmHGUMH1VFXI9o3b10AWl9Xy6Txo8olfq+SxFhsNDOTZACSBqcsk+M4To9TyEG9e0M9LXkMRkfYHbB6Qzt1taKhvo41be2+GioHd0v6KdAg6fNEK6R+lq5YjuM4pVPISV1oqmnS+FGdDEk+2juMwdtvx9OXfjQ1HSqVJIEEvyfpI8A/gFHAJWb2SOqSOY7jFCFuHBoG1bHu7U1bponiI4cGCjuo4wYl3wgjXr8aSbQaKhiH7wD/BSzwtKqO4/Q22SuYVm9o7+RPgM5O6mIO6gmjG5kz+Rgaizisq8WhnU2S1VD/Lul14BlgPrAg/HUcx+k1ck0r5SIzEpg0fhT1dbWdruVyUOeqV6h+tZDEZ/F14EAz+3vawjiO4yQl6XRQfOQAFA3XkT0lVSvRYUZjlTm0s0liLF4CNqQtiOM4TikUWsGUYctIYE0Uo3TC6MZEnX3SetVEEmNxMfBHSU8A72QKzexLqUnlOE6/Ia3ge7lWMNXVisEDtttmaWtz8wsF7uQkIYmx+CkwG1gMbE5XHMdx+hNpBd/LGKC29o5UpomqObpsPpIYizoz+1rqkjiO0+9II/hetgHqMEPAuP137TFDUc3RZfORZOnsryVNlLSbpJ0zr9Qlcxynz5NG8L1cBsiA2+cu7xT4ryfvnx0nqhpJYizOJPgtiJbN+tJZx3ESkUbwvXyGxqBHOvRqjy6bj6LGwsxG5HjtUw7hHMfp2yTd21AKhQxNT3To1R5dNh9J81l0CUnHSVoq6UVJk3Nc/5qkZyU9I+m3kvaOXeuQ9HR4zUhTTsdx0mHC6EauPvkgGhvqEdDYUM/VJx/Urbn/SeNHoTzXeqJDT8PA9QeSOLi7hKRa4DrgI8BrwDxJM8zs2Vi1hcAYM9sg6T+A7wKnh2ttZnZoWvI5jlMeenrPwoTRjcx/dRW3z11OPLhHT3XoSTfvVRupGQvgcOBFM1sGIGk6cBKwxViY2WOx+nOBc1KUx3GcfsKVEw5izN47p9ahp70pry8uzU0SG+p+ScdLKnXKqhFYETt/LZTl47PAr2PnAyXNlzRX0oQSn+04Tj9nwuhGJo0fxe4N9axsbWParKU9shoqbZKkcK1EZGaFK0jHAp8GjgTuAW42s6JLDiSdSpS3+3Ph/FPAEWZ2YY665wAXAh82s3dCWaOZtUjah2hT4L+Y2UtZ7SYCEwGGDRt22PTp04uJ1W3WrVvHkCFDUn9OJVBNukJ16dsfdG1ta6dldRubY31YjUTj0Hoa6us61a0kfZe+vpaNHdvubx5QW8Ood3c/CWmpuo4bN26BmY0pVi9JPotHgUcl7US0jPZRSSuIEiDdZmbteZq2AHvGzvcIZZ0IxuhbxAxFeG5L+LtMUjNRatdOxsLMbgBuABgzZoyNHTu2mDrdprm5mXI8pxKoJl2huvTtD7oePXU2La3bRodtbKhlzuSxncoqSd9PT34IyzGpI+DlqWO7ff+0dE00tSTpXcB5wOeInNI/At4HFEqCNA8YKWmEpAHAGUCnVU2SRhOFEznRzP4WKx8qaftwvAtwNDFfh+M4Tl/dD9FXl+Ym8Vk8APweGAScYGYnmtldZvZFIO9Yx8w2EU0tzQKeA+42syWSrpB0Yqg2Ldzjnqwlsv8MzJe0CHgMmJq1ispxnCqnr3a6fXVpbpLVUP+TtWppC8XmucxsJjAzq+yS2PGxedr9ETgogWyO4/QSU5oWc8cTy8kkp6uvq+Hqkw8u26qeXFFn+0Kn21eX5iYxFn+S9DXgg0Q76v8A/NjM3k5VMsdxKpYpTYu5be7yTmVt7Zv5yl1PM//VVVw5If3fen2104W+mS8jibH4BbAW+N9wfhZwK3BaWkI5jlPZ3PnEirzXbp+7nDF771yWzrAvdrp9lSTG4kAzOyB2/pgk9x84Th9mZWsb+148kw4zaiXOPGLPkkYDHQWW3GcC+nkn3r9IshrqKUlHZk4kHYFHnXWcPsuUpsW8tX7jlg6/w4zb5i5nStPiHntGpa9Ickon78hC0mKiHwl1RGlVl4fzvYHnyyOe4zg9QdPCFi5/cAmrN0Tboi7KMYi484kVXDnhIKY0LebOJ1Z0Gj2UmoWu0lckOaVTaBrq42WTwnGcHiFXzCGAi+5ZRMfmwtEaOsxyOq4hCkkx6d5FQOQnyKQyzUVfWJHklE5eY2Fmr5ZTEMdxuke+dKDCihqKDLkMRYb2DuPyB5cwYXQjZx6xZ8660fLZ7oUgdyqTNKPOOo5TRvKlA+1JMtNYGWd4ZrqqK05yp2/hxsJx+gnldipfOeEgNw5VRKqZ8hzHKQ9NC1uoUe78cXmKu0R2NFeneii0GmotkHei08x2TEUix3FKIp9TGiJn8ymHNXLXkytoT+i3yEddjfj4Ibtx6OUP09oWTUcNHVTHpSe8t+J9FK1t7Rw9dXaf2+ldSRRycO8AIOk7wF+Jdm0LOBvYrSzSOY5TkKaFLXkNhWCLszmeVS7fCKQQjQ31jNt/122MzuoN7Z1WSVUiTQtbaFndtiWcecbxD5UrcyWSZBrqRDO73szWmtk/zOzHROlRHcfpZabNyp+HzNjaGU4Y3cicycfw8tTj+f4nD9nGYCjrb4b6ulp+ePqhzJl8DI89/2bO0Ul7hxWUo7eZNmtppwRJEDn+K1nmSiSJsVgv6WxJtZJqJJ0NrE9bMMdxitMVp/aE0Y00Dq2nsaEeEY0afnD6obwy9Xh+cPqhncrjy2ALPauSd2z31bwXlUaS1VBnESU7+hHRj5U5ocxxnF4g1w7rfBw9dfaW+fn4hr3Jh25m0vj3bTMNUygw3+4N9bTk6WArecd2JNvaPOVOUpKkVX0Fn3ZynF6hFMOQi5bWNibds4hvPbCY9Ru37rnY2LG55Hn7SeNHMemeRdtMRdXVqqJ3bE8aP4qW5xZ0KvNd5qWTJFPefpJ+K+nP4fxgSVPSF81xqpvMKqeuGooM7Zutk6HIUOq8/YTRjUw77ZBOy2eHDqpj2qmHVLSjONe0m+8yL50k01A/AyYR5crGzJ6RdAdwZZqCOU41kCuWU6YTK5QzIhevTD2eEZMfyr/ePQelztv31fwRDfV1zJk8trfF6NMkcXAPMrMns8o2Jbm5pOMkLZX0oqTJOa5/TdKzkp4Jo5e9Y9fOlfRCeJ2b5HmO05fIxHJqaW3D2Lqks2lhC1A4Z0Q2tWF1U6nz8D5v7yQlibH4u6R9CRv0JJ1KtO+iIJJqgeuAjwEHAGdKOiCr2kJgjJkdDNwLfDe03Rm4FDgCOBy4VNLQRBo5Th8hXyynzNRQbQn7Ic48Yk8gmp+vr6tN1Mbn7Z1SSGIsLiCagtpfUgvwFeD8BO0OB140s2VmthGYTpaj3MweM7MN4XQusEc4Hg88YmarzGw18AhwXIJnOk6fYErT4rwrizJTQxkDUAgB5xy515YYTRNGN3L1yQdtmZ9vqK+jrnZbo1NbI5+3d0pCVmSoK2mEmb0saTBQY2ZrM2VF2p0KHGdmnwvnnwKOMLML89S/FnjdzK6U9HVgoJldGa59G2gzs+9ltZkITAQYNmzYYdOnT0+ic7dYt24dQ4YMSf05lUA16Qrl03dlaxtvrd+Y9/qA2hpGvXsHAF7++3rWvZN71vddgwckmkZqbWvnjTVvs7FjMwNqaxi200C263jHP9t+Sqm6jhs3boGZjSlWL4mD+z7gfWYW34h3L3BYYmmKIOkcYAzw4VLamdkNwA0AY8aMsbFjx/aUSHlpbm6mHM+pBKpJVyifvlHu69z/evV1tVx98kGMDb/4x7JtlruG+jouO7F78Zj8s+2/pKVroUCC+wPvBXaSdHLs0o7AwAT3bgHi4+g9Qln2c44FvgV82MzeibUdm9W2OcEzHafiKeS4PuWwbVcb9dUVSE7/opDPYhRRatUG4ITY633A5xPcex4wUtIISQOAM4AZ8QqSRhP5Q040s7/FLs0CPippaHBsfzSUOU6fp5Db+r4FLVtWQzlOJVEo6uwvgV9K+oCZ/anUG5vZJkkXEnXytcBNZrZE0hXAfDObAUwDhgD3KFr5sdzMTjSzVSHa7bxwuyvMbFWpMjhOuSm0byLDoAG1OTfJwdbVUH1tJJFEb6dvk8RnsVDSBURTUlumn8zsM8UamtlMYGZW2SWx42MLtL0JuCmBfI5TEeTKgT3p3kVcNmMJa9rat3SiG/IYigx9LcBdvtzf4CHA+xNJls7eCrybaDnr40T+g22jcjlOlZNr30R7h9Ha1r5l092kexYxsK7wv11f2yhXbL+I0z9IMrJ4j5mdJukkM/t5CPXx+7QFc5xy091saklGBO2brWjGug0bNzFi8kNln87p6lSShwCvDpKMLNrD31ZJBwI7Af+UnkiOU362ZlPLHXojSfvuhfvbyuoN7V2SoTsUCz1SiHwjob42QnIKk8RY3BBWJH2baDXTs4SwHI7TX+hONrVMR5sG5ZrO6c5UUq4QIx5KpP+RJJ/FjeHwcWCfdMVxnN5hZWtb511B8fIi5Opoe5JyTOd0ZyopM1Xlq6H6N4U25X2tUEMzu6bnxXGc3qGr2dSaFrbkjfHUU5RjOidfFrykz/aNg/2fQtNQOxR5OU6/YdL4UdRkRXnNN5XStLCFkf/5EMMnP8RX7nq6y8+sr6vlhyHndaE65ZjO8akkpxiFNuVdXk5BHKc3mTC6kabXn6WxobbgVErTwpZuGYgMjVn3j+9TyDB0UB2XntC9GFBJ8akkpxhFfRaSboZtF3ok2ZTnOH2JYtnUmhbHvkxhAAAf4klEQVS28NUeMBQAr695m6/c9TQX3b2IM4/Yk6tPPqjXO+q+OJXkO8fLR5J9Fr+KHQ8EPgGsTEccx6lMmha2MOmeRYmXx9ZKBQMGZq51mHHb3OUAzJl8THfFrCp853h5Kbp01szui71uBz5JFE7ccaqGabOWFt1Ml6GutrChyEWp+bYd3zlebpLss8hmJL4pz6kySlm+2t5R+va8Uo2L4zvHy00Sn8VaOvssXge+mZpEjlMBZCcc6gkaG+pZGXZIZ1NKvm0norvLfZ3SSLIpz5fJOv2aKU2LufOJFXzlwHY+c/FD1AraN/fsMxob6pkz+RjO/tmfmPPSttH2k+TbdjozafyobVaR+XLf9Cg6DSXpE5J2ip03SJqQrliOUx6mNC3mtrnLt0wDbbaeNxSZDqxpYQtPLV+zzfWj992ZKycc1LMPrQImjG7k6pMPorGhHhEZ5KtPPsid2ymRZDXUpWb2QObEzFolXQo0pSeW45SHrjqWRY715DmolbZ0YEdPnZ0zLMgrb/kce1fpi8t9+ypJHNy56iQxMo5T8XTVsbxTfV3i+2c6M3fIOn2ZJMZivqRrJO0bXtcAC5LcXNJxkpZKelHS5BzXPyTpKUmbJJ2ada1D0tPhNSO7reP0BF11LK/fuInBA2qL1hNsCfPtobydvkwSY/FFYCNwV3i9A1xQrJGkWuA64GPAAcCZkg7IqrYcOA+4I8ct2szs0PA6MYGcjlMyXXUst3cYdbU11NYUNjYGXHT3omhTXw/GX2pa2MLRU2czYvJDHD11dllyXjjVTZJNeevNbLKZjQmvi81sfYJ7Hw68aGbLzGwjMB04Kever5jZM0APuxQdJxlXTjiIc47cq0sjjDVt7Xz/tEMYOqjwlFSH2ZadxT3hkO1OoiLH6SqyPHO2kh6kgA+v2K/9MK10nJl9Lpx/CjjCzC7MUfcW4Fdmdm+sbBPwNLAJmGpm2zjUJU0EJgIMGzbssOnTpxcSqUdYt24dQ4YMSf05lUB/1LW1rZ031rzNxo7NDKitYdhOA2kI/oe/rVrDGyW4DwbU1jDq3VtXli99fS0bO/L/7smu31XyPaeU+/fHz7YQ1aRvqbqOGzdugZkVjcpRyFH9vcRPS4e9zaxF0j7AbEmLzeyleAUzuwG4AWDMmDE2duzY1IVqbm6mHM+pBPqbrlOaFnP73OUYNWQG1fV1HVx98gFMGN3ItbfP4PuLi/shIPJF/OD0QxkbGxW0ZsUqytXm5alju6cE8OnJDwUdun7//vbZFqOa9E1L10Ihyh/v5r1b6Jx7bI9Qlggzawl/l0lqBkYDLxVs5Dh5aFrYEgxFZzKxhCaMbmTnwXUknRE1tg1Wlzm/6O5FOVdZ9ZQj23cuO71Bkk15IyXdK+lZScsyrwT3ngeMlDRC0gDgDKIc3kWRNFTS9uF4F+BootzfjlMyTQtbuOju/BFjMx3v7g31if0X+RIWTRjdyPc/eUiqiYQ8UZHTGyRZDXUz8GMi38E44BfAbcUamdkm4EJgFvAccLeZLZF0haQTASS9X9JrwGnATyUtCc3/mWjJ7iLgMSKfhRsLp2QyzuBC+ynixuHKCQfx0tX/yg9PP3SbDjlDsY457Z3FvnPZ6Q2SbK6rN7PfSpKZvQpcJmkBcEmxhmY2E5iZVXZJ7Hge0fRUdrs/Ah7/wOk2ucJYZ5PLkMQzx7W0tm3JT5Gd4S4fae8s9p3LTrlJYizekVQDvCDpQiK/Q3UsK3D6PEl2RxeaUuqJDtmzuTn9gSTG4svAIOBLwHeIpqLOTVMox+kp8jmDM6Q9158rm9ukexdx2YwlrGlrd+Ph9BmSbMqbZ2brzOw1M/u0mZ1iZnPLIZzjdJdczuA4A+u6kv8rObmmwdo7jNa2dt9Q5/QpkiQ/egQ4zcxaw/lQYLqZjU9bOMfJJntKZ9z+u/LY82/mnOLJ1I131jUhXGxmgezqDe1bdlc3pCBvkmmw+PJdx6lUkkxD7ZIxFABmtlqSp1V1yk6uKZ3b5i7fcj3zKz1Drg1yudJoZzrrq47s+VFGsWmwDB551ql0kvx3bJa0V+ZE0t4kC+XvOD1GZq9EsZVNmY4/ySqoOGl11sWmwTL4hjqn0kkysvgW8AdJjxNFFPh/hHhMjlMqXVkZ1LSwhUn35N4VnYuudPxpddbxJbgrW9toGFTHurc30R4b4viGOqcvkCQH928kvQ84MhR9xcz+nq5YTn8k1zRSZtqokMG4bMaSTp1rMRoG1TFowHaJpn8g1lmveSHxM0ohewmuL6V1+iKJMt4F4/CrlGVx+jm5poaynbvxjrRhUB1mUaTYUlj39iaOP3g37lvQkmgqKu0VUdn4hjqnL1Le/xKnqimWVjQ7T8PqDe0lGwqA9s3GY8+/uSUkRjEyK6K68izHqRY8l7ZTNopFSy3VKV2Ila1tnX7BZ0+BZdPW3sEba3reWHR3ysmnrJxKIe/IQtLOhV7lFNLpHxSLlprUx5CEbId1PPhePgolLuoK3c1o5xnxnEqi0DTUAmB++Psm8BfghXC8IH3RnP7GhNGNnHJYY6corxmfRdPCFkpPbBqR/SXOt7powuhG5kw+Jq/BGFDbs7OyhXw05WjvOD1J3v8OMxthZvsAjwInmNkuZvYu4OPAw+US0Ok/NC1s4b4FLdssgc38Yu7q5p3aWtFQX5c4XHe+Ec6wnQZ2UYLcFPPRpN3ecXqSJD6LI83s85kTM/u1pO+mKJPTTynkk+iOr6K9wxi8/XY8felHE9XP3vuQ8QU09PDS2e5mtPOMeE4lkcRYrJQ0ha0Jj84GVqYnktNfSfMXcan+jlzLV5ube9ZYTBo/ahunel2tWP/OJkZMfqiowzpXe9/A5/QWSSZpzwR2BR4A7g/HZ6YplNM/SfsX8ZSmxcUrlZHsjHZDB9VB2DeSxGHtGfGcSiLJDu5VwJclDTaz9WWQyemnTBo/ikn3LCppN3Yp3DZ3OXc9uZxNm6mYZabxEczRU2ezekPn5bnFIs76Bj6nUig6spB0lKRnifJoI+kQSdcnubmk4yQtlfSipMk5rn9I0lOSNkk6NevauZJeCC9PttQPmDC6kSED093a076Zil1m6g5rpy+TZBrqB8B44C0AM1sEfKhYI0m1wHXAx4ADgDMlHZBVbTlwHnBHVtudgUuBI4DDgUtDHg2ngmla2MLRU2czYvJDHD11ds6OunVD+XZJV9oy03zTcO6wdvoCSWNDrZA6rYJPsnTlcOBFM1sGIGk6cBLwbOy+r4Rr2buhxgOPhCmwTAKm44A7k8jrlJ9cQQK/etfTfOWup2mMJSkqd2z7SvrV7g5rpy+TxFiskHQUYJLqiHJyP5egXSOwInb+GtFIIQm52m4zcStpIiFc+rBhw2hubk54+66zbt26sjynEkiqa2tbO6+tauML++czBWth/VrO2BPYsyclLM6A2prEn1fan20DcPVRtbyxpp2NHZsZUFvDsJ0G0LDmhR5fiVWMavoeQ3Xpm5auSYzF+cCPiDrrFqINeRf0uCRdwMxuAG4AGDNmjI0dOzb1ZzY3N1OO51QCSXQ9+2d/Ys5L64HiCX7KTX1dLVeffBBjEzqI/bPtv1STvmnpmsRYyMzO7sK9W+j8O3KPUJa07dists1dkMFJkSlNi5nz0qreFoNaiQ4zhoaQ5mva2itmNZTj9BeSGIs5kl4B7gLui+fjLsI8YKSkEUSd/xnAWQnbzgL+K+bU/ihwccK2TopkoqD2ZNC/rtLYUM+cycf0thiOUxUk2Wexn6TDiTr7b4VltNPN7LYi7TZJupCo468FbjKzJZKuAOab2QxJ7yfa7DcUOEHS5Wb2XjNbJek7RAYH4IqMs9vpGsVCXee63pB1jylNi7l97vKKSMDujmHHKS9JV0M9CTwp6b+Aa4CfszX8R6F2M4GZWWWXxI7nEU0x5Wp7E3BTEvmcwhRLZ5rv+tVH1W5pf/mDS7bZUFZOJMjEHxw6qI5LT3ivTzE5Thkpaiwk7Qh8gmhksS/RSODwlOVyepBi6UzzXV+x6h2GT36onKLmJR6o9u32ns074ThOcZJsylsEHEo0FbSfmX3TzDyfRR+i2M7hSvA/lEKlbbZznGqg4Mgi7MK+38wuKpM8TgrkC3VdI1XMyKFUKmmzneNUAwWNhZl1hA15Th8j7rBuGFRHXY22CeCXnYSoEhHkdKjXSInCfDuO0zMkcXA/LWkGcA+wJeqsmd2fmlROt8h2WK/e0E5draivq6GtQuf76+tqt/GbNNTX8fFDduO+BS3bXMsYumxnveM46ZDEWAwkCiIYX9BuRLktnAokl8O6vcNo7+i9kcTQQXUAOVdUNYbRQb6lvWP23nnLtZqwAS9OsTDfjuN0nyT7LD5dDkGcnqPSHNYN9dFSVyBvIL1CeRvi10bk8bG4D8Nx0iVJPov9JP1W0p/D+cEhzapTgTQtbEHFq5WV1rb2LVNF3c385mG+Had3SDIN9TNgEvBTADN7RtIdwJVpCuZ0jWmzllbEDutsMlNFcyYf063pIg/z7Ti9QxJjMcjMnszKZ7EpJXmcblLJ0zErW9uKhh0pRqZud+7hOE7pJDEWf5e0L2EFY0h/+tdUpXISk9351tfVsKFCVzztVF9XMEFS0k7f81I7TvlJYiwuIMoZsb+kFuBl4JxUpXISkSumU6UiovhO2au0MlNmvgTWcSqbog5uM1tmZscCuwL7m9kHM+lQnd4l1xLZSkTA2UfuVTT/tofxcJzKJUkgwS8DNwNrgZ9Jeh8w2cweTls4Zyu55vor2T+RIT699NjzbxYd/fQFnRynGkkSSPAzZvYPogRE7wI+BUxNVapepmlhC0dPnc2IyQ9x9NTZNC1MmuAvPXkuvn8xLa1tGFunbBrCRrdKRdBp9dOk8aOoryucftWXwDpOZZIorWr4+6/AL0ICo0pbyt9jFMv90BvkCyGeO2pSusRTl+baTR0nu+OPr2RqaW3bJu6TL4F1nMolibFYIOlhYARwsaQdgMpcbtMDFMv9UE6KpTAtd5yn7DSm2YY1Tr6OP76SqbvLaB3HKR9JjMVnifJZLDOzDZLeBSQKASLpOOBHRGlVbzSzqVnXtwd+ARxGFH/qdDN7RdJw4Dkg4+2ca2bnJ3lmdymW+6FcFOqIe4vszj97pFAbRhpJl8H6EljH6TskiQ21OXTe50gy4A9m9kCxdiEXxnXAR4DXgHmSZpjZs7FqnwVWm9l7JJ0B/Ddwerj2kpkdWpI2PUC+3A/lnkuvtJVO9XU1OTt27/AdpzpIshrqeuA9wJ2h6N8lHWtmFxRpejjwopktC/eZDpwExI3FScBl4fhe4Nre9oeUO5xEvqmYSlsVdPXJB29TFp8mK3VU4ThO30JWJAGOpOeBf7ZQUVIN8KyZ7V+k3anAcWb2uXD+KeAIM7swVufPoc5r4fwl4AhgCLAE+AvwD2CKmf0+xzMmAhMBhg0bdtj06dMTKV2M1rZ23ljzNhs7NjOgtoZhOw2koT5aebRu3TqGDBnSY895bVUbFnPzCrHHzvWsbG2jY3PvRnkaVg9vBJt1UONOna61trXTsrqNzTm+PzUSjUPrt7xnfYWe/GwrnWrSFapL31J1HTdu3AIzG1OsXhKfxYvAXsCr4XxP4IXEknSNvwJ7mdlbkg4DmiS9Nyzh3YKZ3UC0u5wxY8bY2LFjUxYLmpub6annHHr5w7S2bbuUVGxkwHZ1vLOpd9cRXHTQJr6/OPqKNK7Y3GnEcPTU2bS05l8G29hQy5zJY8shZo/Rk59tpVNNukJ16ZuWrnmNhaQHiVY27gA8J+nJcOlw4Ml87WK0EBmWDHuEslx1XpO0HbAT8FYYxbwDYGYLwohjP2B+guf2GVrbcu9oNuh1Q5FN9hLiYtNklTaN5jhO9yg0svheN+89DxgpaQSRUTgDOCurzgzgXOBPwKnAbDMzSbsCq0IO8H2AkcCybsrjdJP4EuJ8CwEy+OY6x+lf5DUWZvZ45ljSMOD94fRJM/tbsRub2SZJFwKziJbO3hQ29F0BzDezGcD/AbdKehFYRWRQAD4EXCGpnWhPx/lmtqp09bpHLudzQw/es0bQy26JksmMGHItBMjgm+scp/+RZDXUJ4FpQDPRbu7/lTTJzO4t1tbMZgIzs8ouiR2/DZyWo919wH3F7p8m+XZyX31U4XAVpdyzyNqCXkEqLFdmxNDdPRaO4/Qtkji4vwW8PzOaCFNEjxItde235NvJ/caawpFTc1FsJ3alUF9XyymHNXLfgpZEIwbfY+E41UMSY1GTNe30FskCEPZp8jloN3ZsZsTkhxKFp2ha2MLlDy5hdZHQ3JVArbQlH/aYvXcOocLX+ojBcRwgmbH4jaRZbN2UdzpZU0v9kUIO3HjkV8gdYLASw3Xko76udouhgK0jhubmZl46e2zvCuc4TkWQJPnRJOCnwMHhdYOZfTNtwXqbJOG0CyXrqbRwHdkMHVSHiIIDxg2F4zhOLpKMLDCz+4H7U5aloog7cFeGPBK5qJTAg6WQHT3WcRynGImMRbUSd+BGO5aTBxgstg+hHAweUMuGjR2eM8JxnG7T7x3VPUWuaalCHW+SaayeYuQ/DSY7+mJ9XS1XfeIgfnD6oTQ21PuUk+M43cJHFgnJdLBvLH0KQdHVUJnytFZDDR4QGYMkiYTcODiO010KxYbaEbiYKKbTr83sjti1683sC2WQr6KYMLqR5jUv8PLUsYnrTxjdWLAjj19riKUsLTVznO95cBwnTQqNLG4mii57H/AZSacAZ5nZO8CR5RCuv1CoI/dO3nGcvkAhn8W+ZjbZzJrM7ETgKWB2SKvqOI7jVBGFRhbbS6oxs80AZnaVpBbgd0TJiRzHcZwqodDI4kGg02J8M7sFuAjYmKJMjuM4ToVRKET5N/KU/4Yov4TjOI5TJSQJUb49cAowPF7fzK5ITyzHcRynkkiyz+KXwBpgASHVqeM4jlNdJDEWe5jZcalL4jiO41QsScJ9/FHSQV25uaTjJC2V9KKkyTmuby/prnD9CUnDY9cuDuVLJY3vyvMdx3GcnqHQDu7FRKkbtgM+LWkZ0TSUADOzgwvdWFItcB3wEeA1YJ6kGWb2bKzaZ4HVZvYeSWcA/w2cLukAonzc7wV2Bx6VtJ+ZVW7Mb8dxnH5MoWmoj3fz3ocDL5rZMgBJ04GTgLixOAm4LBzfC1wrSaF8etgt/rKkF8P9/tRNmRzHcZwuUGjp7KvdvHcjsCJ2/hpwRL46ZrZJ0hrgXaF8blZbj4nhOI7TS/TpqLOSJgITAYYNG0Zzc3Pqz1y3bl1ZnlMJVJOuUF36VpOuUF36pqVrmsaiBdgzdr5HKMtV5zVJ2wE7AW8lbIuZ3QDcACDpzXHjxnV3NJSEXYC/l+E5lUA16QrVpW816QrVpW+puu6dpFKaxmIeMFLSCKKO/gzgrKw6M4BziXwRpwKzzcwkzQDukHQNkYN7JPBkoYeZ2a49LH9OJM03szHleFZvU026QnXpW026QnXpm5auqRmL4IO4EJgF1AI3mdkSSVcA881sBvB/wK3Bgb2KyKAQ6t1N5AzfBFzgK6Ecx3F6j1R9FmY2E5iZVXZJ7Pht4LQ8ba8CrkpTPsdxHCcZnoO7dG7obQHKSDXpCtWlbzXpCtWlbyq6yszSuK/jOI7Tj/CRheM4jlMUNxaO4zhOUdxY5KE7QRD7Ggl0/ZCkpyRtknRqb8jYkyTQ92uSnpX0jKTfSkq0Dr0SSaDr+ZIWS3pa0h9CXLY+SzF9Y/VOkWSS+uxy2gSf7XmS3gyf7dOSPtetB5qZv7JeREt9XwL2AQYAi4ADsup8AfhJOD4DuKu35U5R1+HAwcAvgFN7W+Yy6DsOGBSO/6Off7Y7xo5PBH7T23KnqW+otwPwO6KQQmN6W+4UP9vzgGt76pk+ssjNliCIZrYRyARBjHMS8PNwfC/wLyEIYl+jqK5m9oqZPQNs7g0Be5gk+j5mZhvC6VyiCAJ9kSS6/iN2Opgo0nRfJcn/LcB3iCJcv11O4XqYpLr2GG4scpMrCGJ2IMNOQRCJsgm+qyzS9SxJdO1PlKrvZ4FfpypReiTSVdIFkl4Cvgt8qUyypUFRfSW9D9jTzB4qp2ApkPR7fEqYTr1X0p45rifGjYXj5EHSOcAYYFpvy5ImZnadme0LfBOY0tvypIWkGuAa4KLelqVMPAgMtyj30CNsnQnpEm4sclNKEESygiD2NRIFbexHJNJX0rHAt4ATLcqr0hcp9bOdDkxIVaJ0KabvDsCBQLOkV4AjgRl91Mld9LM1s7di390bgcO680A3FrnZEgRR0gAiB/aMrDqZIIgQC4JYRhl7iiS69ieK6itpNPBTIkPxt16QsadIouvI2OnxwAtllK+nKaivma0xs13MbLiZDSfyR51oZvN7R9xukeSz3S12eiLwXLee2Nte/Up9Af8K/IVoxcG3QtkVRF8ugIHAPcCLRBFx9+ltmVPU9f1Ec6LriUZPS3pb5pT1fRR4A3g6vGb0tswp6vojYEnQ8zHgvb0tc5r6ZtVtpo+uhkr42V4dPttF4bPdvzvP83AfjuM4TlF8GspxHMcpihsLx3EcpyhuLBzHcZyiuLFwHMdxiuLGwnEcxymKG4t+jqThkv6c59o0Sc+HcAAPSGoo4b5jJf2q5yTtGSS9ImmXbt6jy7pJukzS14vUmdCV6K6S1uUoy/v5lpNKkcNJDzcW1c0jwIEWhQP4C3BxbwoTdsJXAxOAPh0KvFxU0Xei4nFjUR3USvqZpCWSHpZUD2BmD1sUBBHyRFdVxDRJfw55D06PXd5R0kMhpv5PJNVIqpV0S6z+V8N99pX0G0kLJP1e0v6h/JbQ9gngu2Fk0BB7/guShknaVdJ9kuaF19Hh+ruCTksk3QjkjPwbYv8/JWmRpN+GssMl/UnSQkl/lDQqR7shkm4Oujwj6ZRQvi5W51RJt+Ro+/kg66Ig+yBJRxHtpp0WcgzsW+C9GRHkWyzpynwfLrCdpNslPacoYNwgScdIaorJ8hFJD+SQ8RVJl4f3ZnHs2YMl3STpyfD+nBTKhwcZnwqvo3LcszZ8Z+aF9+zf83wm3w7fnT9IujMzIpPULOmHkuYDXw7PnK2t+UX2CvVuUSy/SuYzUTQy/F32d7PA++ckobd3Ifor3RdRLopNwKHh/G7gnBz1HsxTfgrRCKQWGAYsB3YDxhKFeN4nXHuEKOzJYcAjsfYN4e9vgZHh+Aii8CgAtwC/AmrD+Y+AT8fqPRqO7wA+GI73Ap4Lx/8DXBKOjycKsb1Llg67EkXoHBHOdw5/dwS2C8fHAveF47HAr8LxfwM/jN1raPi7LlZ2KnBLOL4M+Ho4fleszpXAF2M6nxq7lu+9mQH8Wzi+IP7MrM/XgKPD+U3A14mM5vPArrH374Qc7V+JyfUF4MZw/F+Z7wPQQDTyHAwMAgaG8pHA/Jgcfw7HE4Ep4Xh7YH7mvY899/1Eu8YHEsVseiH2vjUD12d9N88Nx58BmvK8j+tin982383e/l/s6y8f4lUHL5vZ0+F4AdE/9hYkfYvIoNyeo+0HgTvNrAN4Q9LjRP/o/wCeNLNl4R53hrq/BfaR9L/AQ8DDkoYARwH3aGvKj+1jz7gn3B/gLuAS4GZCUqlQfixwQKz9juG+HwJOBjCzhyStzqHDkcDvzOzlUG9VKN8J+Lmi+EgG1OVoe2yQg9A21/3zcWAYETQAQ4BZ2RWKvDdHExlrgFuJDFcuVpjZnHB8G/AlM/uepFuBcyTdDHwA+Lc87e8PfxcQ3kvgo8CJ2up/GUhkpFcC10o6FOgA9stxv48CB8d+9e9EZFhejtU5Gvilmb0NvC3pwax73BU7/kBMrluJQqkXI9d3894E7Zw8uLGoDuJRUzuA+syJpPOAjwP/YuFnWQlk1zczWy3pEGA8cD7wSeArQKuZHZrnPutjx38C3iNpV6K5/cz0Sw1wZOhctqDu5Zv6DvCYmX1CUVrc5hLaxnUfmKfOLcAEM1sU3uexOerUUPi9SfKZbPM5hL83E/0qf5vIIG8iN5nvRwdb+wQBp5jZ0nhFSZcRxc06JMieK4GQiEYr2xjHElhfvAqbggyZ8OMDYtfyvSdOF/F5vCpG0nHAN4gCj23IU+33wOlhHnpXol/yT4Zrh4d59RrgdOAPilYi1ZjZfUS5Ed5nUTa2lyWdFp6rYFC2IRisB4jyDjxnZpmw7w8DX4zJnulcfwecFco+BgzNcdu5wIckjQj1dg7lO7E1rPN5efR/hGgKKPPczP3fkPTPQfdP5Gm7A/BXSXXA2bHyteEaRd6bOWwd1cTbZ7OXpA+E47OAP4R7ryQaCUwhMhylMAv4ooI1VhSJF6L37K9mthn4FNE0T662/xH0RtJ+kgZn1ZkDnCBpYBhdfbyALH+k8/vw+3D8ClvDbp9I55HhNt/NQso6xXFjUd1cS9RpPaLI2fqTHHUeAJ4hilw5G/iGmb0ers0L93iOaIrhAaJsXc2SniaaEsmssDob+KykRUSRMAulgLwLOIfOUxFfAsYEJ+ezRKMWgMuJDMESoqmK5dk3M7M3iebR7w/Pz9z3u8DVkhaSf5R9JTBUkcN+EVF+boDJRL6WPwJ/zdP228ATRB3j87Hy6cCk4Djel/zvzZeBCyQtpnA2v6Wh3nNExvLHsWu3E01TlRqe+jtEne8z4b39Tii/Hjg3yLo/uUcANwLPAk8pWk77U7LeXzObR+STeYYoE+FiomyTufgi8GlJzxAZqC+H8p8BHw6yfCBLllzfTacbeNRZx+nHSLoWWGhm/9fbsmQjaYiZrZM0iGiEONHMnuqB+44lcpYXGq04JeI+C8fpp0haQPRru1LTiN6gaHPiQODnPWEonPTwkYXjOI5TFPdZOI7jOEVxY+E4juMUxY2F4ziOUxQ3Fo7jOE5R3Fg4juM4Rfn/PPGZ2KkvH2YAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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KKeVGubl5LHjvq6JEAXBwbyx/b95RjVEdO00WSinlRgX2AtKS00pNT0tOr4Zojp8mC6WUciNfPx/+786ri03z8fOhw8Xtqymi46NtFkop5WbNTm/Cs7Mn8vn7XxMYHMDN9/wfobWovQI0WSillNsFBPpz1vltadmuOR4ego+vT3WHdMw0WSilVBXx8/et7hCOm7ZZKKWUqpQmC6WUUpXSZKGUUqpSmiyUUkpVSpOFUkqpSmmyUEopVSlNFkoppSqlyUIppVSlNFkopZSqlCYLpZRSldJkoZRSqlKaLJRSSlVKk4VSSqlKVZgsROQiEXlDRP4QkTgR2S8iS0RkhIjUrs7YlVKqBrDn20lKTCEzI7O6Qzkm5XZRLiJLgX+BL4GngaOAL3A60B34UkReNsZ8VRWBKqVUbZecmMLSRSv49ssfaNS0PsPHDqZh4+jqDsslFY1ncYcxJr7EtHRgo+PvJRGJdFtkSilVh+Tl5fPFvCXMevUjAP7ZtptNa//iw2/eqObIXFNuNVTJRCEiwSJyvoiElbdMSSLSS0S2i8hOERlfxvyHRGSro5prhYg0c5p3l4j84/i769h2Syl1IpITUzl88CjxhxPISKtd1SU1VWpyGou/WFZsWnJSKkcPV/g1WmOUmyxE5KPCMwcRuQr4C3ge2CQiN1a2YRGxAW8AvYEzgFtE5IwSi/0OdDTGtAf+B7zgWDccmARcCFwATHJOUkop90mMS+LJ+57n3msf5u4+DzL/nS9IS06r7rBqPU9PGxFR4aWmBwYFVkM0x66iBu5znM4cJgGXGWN6AOcDj7uw7QuAncaY3caYXGA+0Nd5AWPMD8aYwp8ta4DGjsdXAcuMMYnGmCRgGdDLpT1SSh23vNw8Fs5ZzN4d+wEwxvD1x98RfySxmiOr/ULCgnl44r14+3gXTevV73KCQ2tHsqiozcJDRIKNMalAAbAfrKonEXFl7O5GwAGn5wexzhTKMwRYWsG6jUquICLDgGEA0dHRxMTEuBBW9UpPT68VcbqblkPNLIMCewG+kTauHXZ5selb/v6LfbF7Tvrr1cQycCdjDI+/MoKc7Fw8vTzx9PTk900ba0U5VPSlPxn4QUTeAH4BPhORr7CuhPr2ZAYhIrcDHYGux7KeMWYmMBOgY8eOplu3biczLLeIiYmhNsTpbloONbMM7HY738z7ns9mziuaJiK8+eU0GjQ5+Vft1MQyqA61oRwqauBeAAwA2mBdLusNdAY+McY87MK2DwFNnJ43dkwrRkR6ABOA64wxOceyrlLq5LLZbHS75hJ63XgFXt5ehEWFMvaF+wkJD67u0FQ1q7A6yRizE3jkOLe9DmgtIi2wvuhvBm51XkBEzgPeAXoZY446zfoOeMapUftK4NHjjEMpdQxCwoIZ+MDNDBjWDxCCwwKx2WzVHZaqZsfV3YeIPFHZMsaYfGAk1hf/NmCBMWaLiEwRkesci00DArGquDY5qrkwxiQCT2ElnHXAFMc0pVQV8PX3JSwylLDIEE0UCqjkzKICQ4EplS1kjFkCLCkx7Qmnxz0qWHc2MPs441NKuUFyUiq7tu/ht583cmGXDrRs04LQMK2iOhVU1N1HanmzAD/3hKOUqqkyM7L48J0FzHvvCwA+fOczbhvan8Ejb8U/QL8S6rqKqqGSgdbGmOASf0FAbBXFp5SqIkkJyRw+dJT4o4nk5uSWmp+RnsmCucW7gvt0zpdkpOsd3qeCiqqh5gLNgCNlzJtXxjSlVC11JDaO8fdOZeffe/AP8GPcUyO5uHtH/PydzxgMBfaCYutZz02VxqqqR0WXzj5ujFlbzrzjvUJKKVXDZKRnMmPqTHb+bd10l5mRxVNjXyItNaPYcn7+fvTqW/xmvd79Li+RUKpecmIKB/fF8u+Bw6QklVd7rk6USw3cInI90AXrJ8TPxpiFbo1KKXVSGWNIS0xj8/KNZKVm0qF3J0IiQ/Dy9SY7M5stm7YXW95uLyDhaCL16v/XsXRgUAD3jx9Cp0vOZXXMOi7u1onOl55PYFBAVe9OkcT4ZCbc9ww7tuwCoGOXcxn/7CjCInS4nZOt0mQhIm8CrYBPHJPuEZEexpgRbo1MKXVS5GRkk52ZzXsPvk1GsnW2sPbr37h7xn1Et6iPr78v515wFiuX/Fy0jqeXJ5HREaW2FRoeQq++l9OjT1c8var3klpjDCsXrypKFADrf97E1k3bueSKC6oxsrrJlfssLgeuMsa8b4x5H+jjmKaUquHysnLYv2EHO9duL0oUAKaggFWfxpCXnUtAoD8jxw+h/flWp9Ch4SE8/fqjBAWX38FddScKsEac+/vPnaWm79iyuxqiqftcqYbaCTQF9jmeN3FMU0rVcHnZuez48U/qd2hVap6IgAgAUdERPPPGY+Tm5OLh4UFIWDCeXsd7G1bV8PTy5Mp+3fjB6YwI4NIrK+qvVB0vV84sgoBtIhIjIjHAViBYRL4qvONaKVUz2fMLSNgTS6PWjQgM++9MwcPmQZcBXfHy8SqaFhIWTFT9SCLqhdf4RFGo7VmtGD5+EBFRYUQ3jOKxF0ZTv2G96g6rTnLliKi0aw+lVM1ht9vJycrB188XLx8vwhpHse7jFdzyxO3sWLudnKwcOvTuRHBk7W8EDg4Nou+tveje+xIQK+Fp9yTuUWmyMMb8KCL1sQYzMsA6Y8xht0emlDpmKYmprPxyFdvWb+fcS87m0qs7c8WD/dn81Wo2zFtJ254daHJuK7z9fao71JPG09OT8CgdSNPdXLkaaijW2cVKrK4+XhORKY6+m5RSNURaSjpvPvkef67ZCsBf67ax5+/9DBp3K51u6U5+Th5eft7YPGtHFZOqWVxpsxgLnGeMGWiMuQtrWFW9KU+pGiYnK5c/12zFy8fLarwGfl22lpysHDy9vfAN8tdEoY6bK0dOAuA8WnuaY5pSqgaxeXnw2FsPkZ2dQ2BQAJt/+Ytln/1g1QcodYIq6nX2IcfDncBvIvIlVptFX+CPKohNKXUMEuKTePieSaSmpCMiDLzvZh54/r5qvcNa1R0VVUMFOf52AYv4r7ewL4GTP3K7UqqUtNR0Yg8eYdvmHSQcTSQ/L7/M5ZKTUnhh4uukpqQD1t3NH7w1n8iGEcUuj1XqeJV7ZmGMmVyVgSh1KkpOTCE1JY38vHxCw0MIj/zvqp601HQ+nbWIT2ZZXbH5B/gx/cOptGzbvNR28vPs7N9bfJh6YwzpaRmlllXqeJR7ZiEi74rIWeXMCxCRwSJym/tCU6pus9vtTHzwOW7oOYSb+9zDvbeNJf7of6MHZ6RnFiUKsHqDnT75nTJ7Vg0I9Kdrz4uLTQsOCSQ0vPbfS6FqhooauN8AnhCRs4G/gDjAF2gNBGMNefqx2yNUqo7Kyszmt182Fj3fu+sAiz5dyqDhN2Oz2UhPKX1WEHvoKPn59lLT/fx9uefBOwD4cdlqmjRryPinRxGmyUKdJBVVQ20CbhKRQKAj0ADIArYZY7aXt55SyjU5ZYxG98/fu8nPy8dmsxEWGUpwSGBROwRA996XEBjkX+b2wiPDeODxYdw9+nY8vWyEhmmiUCePK3dwp4vIb0BTTRJKnTxBZVyldE3/K/Hxte6uDg0LZsbHT/Pq1Fkc2hdL16suZsCQfkXzy+Ln54ufn6/bYlanLlfu4L4OmAZ4Ay1E5FxgijHmOncHp1Rd5unlyUszJ/P6C++RnZXD7UNvoH2HdkXzbZ42mp7WmEmvjCEvN4/A4AC8fbyrMWJ1KnPlprxJWP1CxYBVPSUiLdwZlFKnAg8PD7p0vZAzz2mDKTCEhoVg8yzdCV5QSPnjSihVVVxJFnnGmJTC7gMcdIR2pU4CESE8QjvBUzWfK8lii4jcCthEpDUwCljt3rCUUkrVJK50JHg/cCaQgzUOdyrwgDuDUupUYM+3s3/PQY7ExpGWml75CkpVo0qThTEm0xgzwRjTyRjT0fE4uyqCU6quij10hL27DtC/x2CuvfR2Pnh7PikpaZWvqFQ1qTRZiEhHEflCRDaKyB+Ff1URnFJ1UWZGJm9Mm01uXh5gdcsx950FJCckV3NkSpXPlTaLj7HGtPgTKHBvOErVfdlZOezcvodGrSKLTT904DDNTmtSTVEpVTFXkkWcMeYrt0ei1CkiMDiQ7lddQgH/3cFt87TR8vTm1ReUUpVw6T4LEZkFrMBq5AbAGPOF26JSqg7z9vbipjv68t133+Mf4EdUdCSPTh1FSGhwdYemVLlcSRaDgLaAF/9VQxlAk4VSxyksIpToBlF8vvx9xEMIjwilxL1MStUoriSLTsaYNm6PRKlaKic7B08vT2y20ndfV0REiKwX7qaolDq5XEkWq0XkDGPMVrdHo1QtkpKUysY1f7Jy6SrOPKcNV/btTnhkaHWHpZRbuJIsOgObRGQPVpuFAMYY096tkSlVg+Xm5PL5R98w980FAKxatoaflq3hmTcf0wGHVJ3kSrLo5fYolKpl0lMz+N+cr4tN27p5O1mZ2ZosVJ3kyngW+6oiEKVqG18/HzIzsopN8/BwpQcdpWoftx7ZItJLRLaLyE4RGV/G/Mscd4bni8gNJebZRWST40/v81A1SnBoEHc7hjEtdHmfS/Hz14GHVN3kSjXUcRERG9Y43j2Bg8A6EfmqREP5fmAgMKaMTWQZY851V3xKnQhPL08u7dGZ089syZof19P2rNa0bNuc4NCg6g5NKbdwZaS8+4GPjDFJx7jtC4Cdxpjdju3MB/oCRcnCGLPXMU+7EVG1TmBwAIHBAbRs07y6Q1HK7VyphorGOitY4KhWcvXOoUbAAafnBx3TXOUrIutFZI2I9DuG9ZQ6LsmJKcQdSSAx/lh/FylV97nSwP24iEwErsS6m/t1EVkAvGeM2eXG2JoZYw6JyGnAShH5s+TricgwYBhAdHQ0MTExbgzn5EhPT68VcbpbTSuHvLx8Du2PJSsrGx8fbxo1bYCPm8e7rmllUB20DCy1oRxcarMwxhgROQwcBvKBMOB/IrLMGDOunNUOAc5daDZ2THOJMeaQ4/9uEYkBzgN2lVhmJjAToGPHjqZbt26ubr7axMTEUBvidLeaVA5JicmMHjyBbX/+UzStXv1I5i56nYgo991hXZPKoLpoGVhqQzm4Mp7FaBHZALwA/AKcbYy5Dzgf6F/BquuA1iLSQkS8gZsBl65qEpEwEfFxPI4ELsGprUOpkykvN79YogA4ejie7KycctZQ6tTjyplFOHB9yfstjDEFInJNeSsZY/JFZCTwHWADZhtjtojIFGC9MeYrEekELMQ6U7lWRCYbY84E2gHvOBq+PYDntLsR5S42TxtNmzdi/97/TnyDQ4Lw8XW9Gio3J5eU+FR++3YtASEBdOh2LsERwdo5oKozXGmzmCQiHUSkL1Zvs78YYzY65m2rZN0lwJIS055werwOq3qq5HqrgbNd2gOlTlB4RCjPvDaB0YMmkBCfRFBwIM+9/jghYa53GZ4Qm8hzQ6Zhz7cDsHzeCsa9O4aQCO12XNUNrlw6OxG4if+6JH9fRD4zxkx1a2RKVRERoeXpLfjo67fIzs7Gx9eH0LBgvLy8XFo/NyeX7z78vihRAKQkpPLP7//Qscf57gpbqSrlSjXU7cA5xphsABF5DtgEaLJQNYrdbichLolvF60kNyeXPv17EFUvAi/vsr/0k+KTWR2znt079nJl3240atLg+LoMN2C3l75VqKBAbx9SdYcryeJfwBfIdjz34RiualKqqiTGJzPwulGkpaQD8Ml7i5j7zWs0atqg1LJJCSmMu2cK/2zdDcDnH37D5OnjuOzKi465ncHb15urbu/J5p/+oMCRNAJCAmjT4fQT3COlao5yk4WIvIbVRpECbBGRZY7nPYG1VROeUq778bvVRYkCrOqhBR98yajH7sbmWXxgouTEZP7ZupvAoAAGDOnH2R3aIR5CSlIKoeHHPiZFVONIHps9jp8WriIgJJCLr7mIoHDt+kPVHRWdWax3/N+AdcVSoRi3RaPUCSir2qegwJS5rDEQ3SCKae9N4s2X3+fVae/i6+fDiDGD6dOvB8Ehx/ZF7+PrQ/3m9bnxgRv0CihVJ5WbLIwxc6oyEKWORV5ePtlZ2QQE+hd1C96t1yW8//p80tN6QYzlAAAgAElEQVQyAPDy8uSmgX2xedrIycklLSWNuCMJhEeGERIWxIuzJ7Nk0TKWLf4RgMyMLKZNfoMLLu5wzMmikCYKVVe5rddZpdwl/mgin3zwBX9t2kaPPl3p0acrYeEhRESF8cHXr/L1gu/Jyc6h3y29iYyOoKCggK2bt/Pg4Ink5OTi4eHB2MkjOP2Mlmz47Y9S2//j962c1rpZNezZiUtPyyAjPZO01HRCw0IICw8pVQWn1PHQZKFqlcSEZEYNfoy/t1h3XK/7dRN7dx/g/rFD8PXzJbpBFENH31ZsnYS4JJ565GVycnIBq7rqlafe5v1Fr3LWOW3Z8NvmYsufcXbtbJhOT8vgi0+W8PoLszDGEBwSxDufvEirNi2qOzRVB+iwXqpWyczIKkoUhRZ+spj09Mxy1zHGcPjQ0WLTcnPzsOfb6dbzEi7s0gEAL28v7hl9J9ENok5+4FUgIz2zKFEApKak8cyE6SQnpVZzZKouqOhqqK+xrn4qkzHmOrdEpFQFPD1tiEjRFyJAYFAAFbUU+Ph6c3G3Tvzyw38X8TVqWp+Q0CC++HAxV/bqzvAHBxEUEkhoWAghobXzruu01PRi5QKwb/dB8vPyqikiVZdUdGbxIvASsAfIAt51/KVTovdXpapCQUEBaanpXHfDVcWmP/T4fYSGh5S7XlBwIOOnjqJ3v8sJiwil86XnM+ODZ6jXIIrh4wfR5YoLaNAomoaN6x9TFx81TWhYSKmG+W5XXkxAoH81RaTqkoquhvoRQEReMsZ0dJr1tYisL2c1pdwmKSGZMfdNYsiI2+l+VRd2/7OPs89rR+OmDbHZKm7EjawXzpgnR5CZkYWPjzdBIYGAlUiCggOrIny3CwsP4Z1PXuSZCdPZt/sg3a68mOFjBuPn71fdoak6wJUG7gAROc1peNQWQIB7w1KqNGMg9tBRnhz7AlHRkTRoWI/Zb85j3tdvu7S+f4Af/gF194vT5mmjVZsWvPzuU+Tn5REQ6K+JQp00riSLB4EYEdkNCNAMuMetUalTWmZGFulpGaQkpxIWHkJwaDDe3l74BfjSu+8VfP2/74g7Ek/ckXian9YEP3/f6g65RgmtxVVpquZypYvyb0WkNdDWMelvY4yOCqPcIjs7h5jvf2Hqo69gz7fj5+/LjPefpn2HMwgI8Gf0+LuJio7gp+W/0vbMVgx/eHClo9klxCXy2y8bSUpIpvtVXYiMCsfbzUOmKlXXuNJFuT/wENaY2HeLSGsRaWOM+cb94alTTVpKGs89/mpRd99Zmdk8OWYasxa8QkRUGOERYdwz+k5uHXg9vv6++FdSzZIQl8iQAQ9ywDGw0WvT3uPjr96i5enN3b0rStUprtxn8T6QC1zkeH4I7Z5cuYm9wBTdPFco9uAR8vLyijoJ9PLyIjwyrFSiSE5MIfbQUY7GxpOSaN1bsO2vf4oSBUBebh7vzJhLZmaWm/dEqbrFlWTR0hjzApAHYIzJhAova1eqTFlZ2SQnpWK324tNT0/NYOe2PXz41gL2bt/Hax88U+xyzwu6nMeKJT/x6vPvkpSYUua2Yw8dYeqjrzDw/+5n2uTXOfxvHMnxKWRlZZdaNjsrG1NOB4NKqbK50sCdKyJ+OG7QE5GWgLZZqGNy+NBRZk6fy77dB+l5TVeimlqNsLm5eezfc4jFC5bx03erycnO5eLLOzHx+Yd5+tFXOP+ic7j97hsYd98U/Px9Obg/lrAS91QkJSQz7r4p7Nhq3f7z0/I1JCakcO/9d3JOhzMJCQ0mJfm/u5gH3XeL3nug1DFyJVk8CXwLNBGRj4FLgEHuDErVLQlxSdxz8xiOHo4HYOsfO3j46aEc+TeOlV+vYuMvf9D6zNN44b1JTB49jdUr1zH04TuY/fl0vvv6B9577WOee/1xDu2PJTsrm8SEZMIj/htzIjs7tyhRFPrr920EBPvj7e3NvG/eYt7sL0iIT+LWwf1pdlqpYd+VUpVw5Wqo70VkA9AZq/pptDEm3u2RqTojOTGlKFEUstk8eP/lefyyzOqC4+/N/7Dr773cOWIA0ye/Q3JiCj+v/I3MrEyGjLyVh4ZNIsXRx9HZ57Vj2luTCI8MA8DLy0ZgUEBR1+Rg3aBms9nw8/clNDyYUeOHUVBgx9tbr4JS6nhU2mYhIiuMMQnGmMXGmG+MMfEisqIqglN1g19A6fsg/Pz9+HXFumLTtm7cTrNWTWjbvhWxB4/w9f++5/pbruGLT5YUJQqAP3/fxq4de4ueB4cG8/hzDxZ1xe3p5cnYJ0dSv3E9fHyt5ODpadNEodQJqKgjQV/AH4gUkTD+a9QOBhpVQWyqjggIDODq/j1Z/PkywPoyt9k88AvwIyPtv95iPb08CQzy577xgxh33xRCw4IJCPInIS6x1DaPxMYVPfb29qLzpR1ZFDOHhLhEwiPD8PPzIbiWdgioVE1UUTXUPcADQEOsoVULk0Uq8Lqb41J1SEhoECMfGcxNd/Vl7879NGxSn527tzNgWD9mvzSvaLn+g67B5uXJA0Mmkpmeyfipo8BAvwF9WLNqQ9Fy3t5enN/5nGKv4efvi5+/L/XqR1bZfil1KqmoI8EZwAwRud8Y81oVxqTqoNCwEELDQoiuH8GWzTtIT8+kcXRT3vjiBf7asJX6TaIpEENOdg6jxg+lSYtGfPP592zZ/Ddntm/L4888yKIFSwkMDuSuYTfx/eIfuKLXZTRq0kCHMlWqCrjSwP2aiJwFnAH4Ok2f687AVN2Sm5NLVmY2Odl5tG57GmnZSXh5eHH40FFan9XS6tojwJdFc5bQpF1jht8+jpycXE47vRktWzfHnm9n2Og72Pz7ViY/+iL79x7iw1n/Y/43M4msV3F3H0qpE+dKdx+TgG5YyWIJ0Bv4GdBkoSqVmpLOP3/vJjQ0mHeem8tfG7bh6eXJXY9cT75deP9FqxoqODSIMS+OoGf/bjx4tzVW9sXdOjFizGAO7DlEy7YtSIhLZO67C8hwjIoXH5dIQkKSJgulqoAr91ncAJwD/G6MGSQi0cBH7g1L1QXGGH5btYE1qzYQFR7BXxu2ISL0H3gNAUH+NG3cjCffHcei2Uvoc0tPGjStz6zXPyQxIZnGzRpy17AB3HvzWLIyrbuwO1/WkcenPsijDzxd9BoBAXpznVJVwZVkkWWMKRCRfBEJBo4CTdwcl6oDkhNTWPjJEjpfej5//LoVgJuG9iUjJ4M9u1J4bPgLBIcE8e78l2nWojHePt7cPeoObrjtWgoKDK8/P7soUQCs+Wk9Awb2JTgkiNSUNK6/uU+dGbhIqZrOlWSxXkRCsYZU3YA1rOqvbo1K1XppKelkZWQz8N6bCY8KpVmzxnS67Fwu7NaB/j0HM/TBmwBITUnjmcen88q7U/H28S5qCE9KTC7zklmAl95+kvDwMCKiwggJDSpzGaXUyeVKA/dwx8O3ReRbINgY84d7w1I1SXZmNtnZORWOc+0sLSWdj978H4vnfw9AQFAAz82eyA8rf6ZF26YYU7wTv317DpKfl1dsWnBIEP0G9GLak28WTQsMCqBVm+ZE1os4wT1SSh2rim7K61DRPGPMRveEpGqSw4eOMm/WFxzaH0vP67rR6eJziYgKq3CdtJT0okQBkJGWwXsvfUSrc0/D08uzVNVR9ysvISCo+Ei9NpuNK/pchs3Tky8/XUq9+lEMHzuQMKc+oZRSVaeiM4uXKphngMtPciyqhjl6OJ6Rtz/KkX+tu6XX/vw7w8cN4obbr8HLx6toObvdTnJiCgUFBh8fb9JSM0pt68ihOC66shMzZ8zlxbeeZMu2vwgJDebyXl0Y8fBg/PxKdwkSEhbMtTdeSdeeF+Hl7aU9xSpVjSq6Ka97VQaiap74IwlFiaLQl/O/pdtVF9OgcTQAOdk5bN6wlafHv0LC0UTO73wOE557gHoNozjqtO6F3c9n7a+/s3b178THJTLw/v7MWfg6UfXC8atgtDsPDw+Xq7+UUu7jyn0Wd5Y1XW/Kq/t8/Uv/2g8I8ic1NQ3/ZD9CQoNJTUnnkXsmk5eXD8CGNZt5c9oHPD3zMWZMeocj/8bTtfdFdOx6Hu8OeACAxPgkvLw8KbAXVJgolFI1hytXQ3VyeuwLXAFsRG/Kq/OCQ4K4oMu5rP15E2B1K37vw3fy2+rf+b+begMQdzi+KFEU2vjbZrx9vJjwykPk5ebx+/q/OLj/X2a89zSpKWlE1YvgUOx+QsL0SialagtXroa63/m54zLa+W6LSNUYfv6+PPL0KPbtOsDB/bF0vvR8EMjLy2PRJ0u5pHsnwqPCsNk8sNsLitZrd/bp+Ph6E+hoyD6j/enc2W8EOTm5+Pr5EhwSyODRN5Kbk1feSyulahhXxuAuKQNocbIDUTVLUmIKL095i+u7DyI+LonzOp3Frr/3kpaczsY1f/LOy3O489qRbN28ndc/eh5fPx8AmrdswqjH7iY3M4/UxBQK8nLx9fXmxbcnM+7JkYybNIIxE0fw78HDpS6hVUrVXK60WXyNY/xtrORyBrDAlY2LSC9gBmADZhljnisx/zJgOtAeuNkY8z+neXcBjzueTjXGzHHlNdWJs9vtLFm4giULVzBw+ADycnIZfN0DGGMQEUZPvJvLe1/KyqWreOfluUx/fyrvLngZT29PPGweHNzzL6EhwWQmZxLVPIrsrBxWrVzDD9/+TG5uHkdi4xg18S78ymgTUUrVTK60Wbzo9Dgf2GeMOVjZSiJiA94AegIHgXUi8pUxZqvTYvuBgcCYEuuGA5OAjliJaoNj3SQX4lUnKDM9i62btjHj/akIwtHYeF6b9yxvT5vDXxu38d6MeUyY9gArl64iKzMLMMTuP8rXX37Hd9/8AIB/gB9vf/QiY++ZTOzBI0x6cQxtzmjFT8t/pcOF7QmO8iFEBydSqtZwJVnsB2KNMdkAIuInIs2NMXsrWe8CYKcxZrdjvflAX6AoWRRuQ0QKSqx7FbDMGJPomL8M6AV84kK86gT5+vsyeORtPDNuOrt37AMgKCSQ5955nPH3TCU9NQNfX+us4P9uvZoNa//g3I5nFyUKgMyMLF6fNourr+/B2y/NYeLo57igSwcmvTSGkNBgYmJiqmPXlFLHyZU2i88A5y9zu2NaZRoBB5yeH8T14VhPZF11gry8PPn3wJGiRAGOu7L/t5yuV17EhZd1ID4ukYkvPETXnhfz3OMzyM7KLjUI0eHYuGL3SKz75XfycrVRW6naSCprZBSRTcaYc0tM22yMOae8dRzL3AD0MsYMdTy/A7jQGDOyjGU/AL4pbLMQkTGArzFmquP5RKzeb18ssd4wYBhAdHT0+fPn1/yLtNLT0wkMrPk9paYkpZa6IS8wKIDg0CB8fL3JzcklLS2D5MQUAFq2acHuf/ZS4HRVVGS9CPLz8klNSStav0HjaDw8PGpNObiTloGWQaHqLIfu3btvMMZ0rGw5V6qh4kTkOmPMVwAi0heId2G9QxTvyryxY5orDmENuOS8bkzJhYwxM4GZAB07djTdunUruUiNExMTQ02Ps6CggIN7/2Xokw8VOxOYNmsSnjYbxgajB08gOysHgEH33cIZbc6kQWQj3nl1Lv8ePMLV/9eTyy67jHenf8iWzdu54JLzuHFAL8IjrX6lakM5uJuWgZZBodpQDq4ki3uBj0XkdUCwqofKvKu7hHVAaxFpgfXlfzNwq4txfQc8IyKFPdZdCTzq4rrqBCUlpvDph1/y7NsT+OLDxWRmZNG7/+WERYXyzx+7+Ofv3bw593kSE5IJjwhj4+rNbNm4ndXL1vLgo/eSlZWNv58vn02Zz8ipQygwBQQEBuDj613du6aUOk6u3JS3C+gsIoGO5+mubNgYky8iI7G++G3AbGPMFhGZAqw3xnwlIp2AhUAYcK2ITDbGnGmMSRSRp7ASDsCUwsZu5X4CrF3zOzErVtP7usvx8/Nl7vufMWrM3Rw5FEdaSgYP3fEEQcGBZGZmYc+389KcKYRHhZIUl8yh2MO0O70Vt0++g5Dw4FJtGUqp2qeiLsofKmc6AMaYlyvbuDFmCda43c7TnnB6vA6riqmsdWcDsyt7DXXyhUWEMuKhQTwwbCKz3vgYgHZntSYoOJClX6xk0qtj2PX3Hg7s+RcPmwcDhvQjLSWd/7vzavKy8zj97Fb4+fng6eXKiatSqjao6NOsHfecokSE8y88h08Xv8viRcto1aYFF3XpiM3Dg+kfTyUnJ5dnZ04kNyfXWh4hJSGV7z5cRo8BlxMUHFDJKyilapuKuiifXJWBKNekJKeSlJBM7KGjtDy9GSGhISetLaCgoICU5DS8HAMUtTkjkDZntCQhLokXHnuNbX/8w3kXnMXICUOJqh9BZnomW9dt5+v3FgPCdUP6UK9x1EmJRSlVs7jS3YcvMAQ4E6vXWQCMMYPdGJcqQ2pKGrNe/YhP534JgJe3F2999ALtO5xxwttOSU4l5vvVfDHvGyLqhXP/uKE0adaQtJR0xgyaRFhEKL37X0HsgSM888h0Jr74EOFRYZzf7VxOP6cVCASFBmr7hFJ1lCs35X0I1Me6q/pHrDaGNHcGpcqWkZ5ZlCgA8nLzeH7SayQlJB/TdrIys0hKSCYn26pGKigo4Kflv/L0Y6+w7a9/2L/7IBt+20xCfBI5ubk89eajXHZ1Z7bt2EG9ZpEMHHUz+blWt+QiQnB4EMFhQZoolKrDXGmBbGWMuVFE+hpj5ojIPGCVuwNTpWVlZJWaFnckgcTEFH5a+Sudu3QkIjKswobluMMJ7Pp7D/n5duo3rkdkdDieXjZ2bNuNj483p53enAcfu4c/Nm4l5vtfiD10hKzsbObPXQjAj8tXs37NJp6dPsFt+6mUqnlcSRaFd2Uli8hZwGGgnvtCUuUJDgsmKjqCuCMJRdN69unK/DlfsOCjL/EP8GPBklk0aVZ2zyjxRxJ5dOhU/t1/GIDO3c9n6Lg7+HH5akIjQvh48dt4iPDGi+9zy6D/497bxjJ91lOMHDy+2HZ+X/8n9oKS3XkppeoyV6qhZjpujnsc+AqrI8AX3BqVKlNEZBgz57/MFX0u47TWzRh6/+1c2uNCvvxsKWB13jfrjY/IcVylVNIvy9cWJQqbp40bBl/LoJtGs2fnflq3PY2/t/xDXl4+va67HJvNg7zcPHLz8vD1K96VuIjg6Wlz784qpWoUV27Km+V4+BNwmnvDURURERo1qc/jzz5ITnYu/2zfxfC7HiHfaVjTlOQ07HZ70fOszGwS45M4uP9fDh88UjT9rA5t+W3170x46gFW//gbjz0wleCQIB4Yfw85Wbm0bNOc0PAQli5cwdARt/HS028Vrdvvpj74B/hXzU4rpWqESs8sROQZx1Cqhc/DRGSqe8NSFQkI8KfAbieqXiReXl7F5t0x9EZsHv+9rTu27uLmq4Yx9ZFX6NS1Q1EjtM3TRr0GkezYtpNP5iwkLTWdQwdiGTdyMmd3aAcYXv/gWfbtOYgpgNmfzuCB8ffw3qfTGf3I3QSH6G04Sp1KXGmz6G2MeazwiTEmSUT68N8odqqKJSUk89LUt0hNSePtudP47OMvSU/P5I4hN/L7uj9Zsmg5wx8ahM3myRsvzMZuLyAhLokNazcz9Z1H2b/7IG3PaY1/oD8BgX5EN4jiSKzVw6wxhlU/rCHmu5+58+4BvDJzCngI/v6+nH9hhR0NK6XqMFeShU1EfIwxOWANfgT4uDcsVZGcnFyWL/kRu72A7Vt30bNPV+pFR9GwcX0GDxgNQL7dzqixd2O3F3DzoH50uLA9/gF+BPj5EXv0CFP6vUR+Xj5nn9uOF9+czLDbHnaMegfNWjRm/55DTHjwGRaumENUvYjq3F2lVA3gSgP3x8AKERkiIkOAZYCOh13N/Pz9AEiMT+LTuYv4+L3/UeB0hdLyJT9SUFDAEy8+TEJiIo+MnsKMaTMpsMFb0z8oauf4c9M2vv78O3pfdzkA11x/JUkJKSTEJ5GZkUV2VnbV75xSqsapNFkYY54HpgLtHH9PGWP0aqhqFBIaxIgxxW+gv33ojeTm5tGnXw9sNhtNmzfGw0P4/JOvWfHdKs4+tx1R9SLZtOHPUtvb+ud2Bt5zC58ufpc2bVvx7OPTAYiMCi9KSkqpU5tL3YIaY74FvnVzLMpFfv5+9Oh9GedfeA6/r/uTM9q3webhwdh7J9Pz6q689NZkgoKD2LF1N76+vrw66xnWrFpPo6YNOevctqW2d/FlF7B00XIu6XYhP61YTW5uHi1Pb84zMyYUGxZVKXXq0j6ka6mIqHAiosKJjArn3tvHsmv7XgDee/1jHp06mv27D3LeBe3pdd3l/LTiV775YhmH/z3KmCeGM/Xlx3jxqTdIS02nR+/LuP7mPqSlpmPz9OSRyaMIDgnEy8uraFQ7pZRypc1C1WBrft5QlCgKrf91Ez2u7sp7b3zMwOvvZ+0vG3nutYk0P60JL055k/bntWPO568z+9MZXHLZhYy663Huum4Uu7bvZfYb8/D28dFEoZQqRs8sarnGzRqWmnbdjb14bPTT/LXpbwDWrNpA/NFE7h51BxMeeIbkhBQaNq7Pz8t/Y87bn1JgChg04mbycvO4bUh/wrTqSSlVQkUj5QVjjXvdGFhqjJnnNO9NY8zwKohPVaJRkwZcflUXVn73MwDRDaJo3KxhUaIotHP7HqIbWJfXRjesx549Bzn/4vZc1K0jPj7eFNgL8PPzpaDAkJKYSkh4cHXsjlKqhqrozOJ94B/gc2CwiPQHbnXcb9G5KoI71eTl5ZMYl8T3X8Zg87TR89rLCI8Mw+Zpo6CggKzMbHx8vPH08qSgoICEo0lkpmdyx7AbuW1ofzxsNnx8vMnMyCQiMoyE+KSibQcFBxIQGMDsz2YQEOhHu7NakZyYQlpqBqGhwfz521befe5DsrNyaNGmKeOnP0BkdHg1loZSqiapKFm0NMb0dzxeJCITgJUicl0VxFXnpSancvRIAls2/83Z57Ujql4EGWmZDLpuNNlZOfj6+7J/90GGPzIIBJITU9mycRte3l6cc8FZpCWl8uYzHzBk7O0MvvkB3p33MhMefJq9uw/QpduFjJ00gkljXiAnJxcvby8mvziO5i2b4OPz36h6QUGBgNVt+RuT36OgwACwZ/t+5rw8n+FPDMIvQC+dVUpVnCx8RMTDGFMAYIx5WkQOYXUoGFgl0dVRmZnZfD5vMW++9H7RtBmzn2bTmr/IzsrhnE5nMmzMnSxf/CMfvP0pN91xHWMHPlk0yFFU/Qhe+fApuva5hIBAX24bdD32/Hx69unG0q9W8HPMbwQGB/DBF68hIoSGhRAUElgsUThLOJxYlCgK7fhzJ9lZOZoslFJAxVdDfQ1c7jzBGPMB8DBQdh/YyiXpqenMeu2jYtNWfrsKe76dtme34sEn72XH1p3s3L6Hg/v+5ZsF3xcbDS/ucAI/L/+NCy47l4S4ZPre0IdDB46wc/sennh2DFde3Y1vv1rJzNc+JLp+FPXqR+JXoptxZ1ENIrCV6HL8rE7t9IY8pVSRcpOFMWacMWZ5GdO/Nca0dm9YdZspKCA3N6/YtJjvV9P/zmu4Y8RNTH5kGvPnLqLLFRfS/9ZrSE/JKLWNpIQU3n55DiNuH8+tve9l9/Z9nNm+DSMHP8odQ2/C39+PO4bcyO5d+4p1WV6WgOAAxk4bSXCY1ZPsWZ3acdvIG/H11y7AlFIWV7oo9xGRW0XkMRF5ovCvKoKra+x2OwUFhoAgfy7uekGxeRd17US+3c7oIY+xaf1f7Ni2i+eeeJX09HS6X9MFD6dux22eNi7u3onVMeuLpn36wSI6d+mIPd/OxrV/sGDpLL5fGkN+Xj5JiSkVxuXr58N5l7Tn5U+fYua3rzDmhRGER4VWuI5S6tTiyn0WXwIpwAYgx73h1F0J8Ul8/fl32HztLIv7ickvjWXL5u3s232A0PBQunS/kMVffI/dXny40m8WLuOaflfy8odPMf/dhdhsHtwy7HoWf7GMtNT0YstmpGfg7e1FeEQo+fl2vv1qJZd264yHYwyLinh62giL1AShlCqbK8misTGml9sjqcOSE1MYP+op1q7eyL1jb6Nxw2b8umodzU9rip+/LxjIz8+jXoOoUus2bFyfM9qfzpHYOM67+GxatGpKTm4uuXnFq7FCwoLx9PKiUdMGtDmjFSu/W8WQ+24lODhQ78ZWSp0wV7r7WC0iZ7s9kjosMzOLtas34u3jTb3oSDZv2ELTZo3BwK+r1rPw08Vs2byds85py9nntitar179SPrd1JuhNz3E3l0HiPn2F54Y/TwRkWFceW03+t3Sm3r1Izm/c3tem/MMiOGND54nKyuLjp3PpVvPS2jesmk17rlSqq6o6A7uPwHjWGaQiOzGqoYSwBhj2ldNiLWfh82Dvjf0ov+t17J3/y669byEsIgwht85jv17DgKweOFynnxhLFNeHk96WgZHD8cTHBzIlLEvkRiXxNKFK+ja4yK2/fEPdrudcSMmc/lVlzJ28nBatmnBzFfn0qV7Z+pfGEX9Ms5QlFLqRFRUDXVNlUVRy9ntBZiCAjy9ihdnTnYOaakZeHt706dfDwbfNIohowfw4qR3efHNyUWJotDHsz/HFEDM0l/4v9v6MPKOR4vmhYQFk5GeRZ/+V7Bpw18899pE6tWPYtNvf/H1JzO5c+SNNGgcXSX7q5Q69ZSbLIwx+6oykNooP99O3JF4dmzbRcPG9fHx8cY/wJ/IeuEkJSTzwdvzWbJwOWMmjWD22x+Tn29dwmqMIT8/v9T2PD1t5Ofns2HNHwx7+M6i6b5+Ptx5700c2Psv/oG+PDx8Enm5eQwdfjtxBxJY/8smBt5/M2Hh2kCtlHIP7XX2BCTGJ/Hb6o38f3t3HldVnT9+/PW+sgoKsigqmRqm5hKuZYtZllsNallaTqmpM2M0Tc5k1rQvM6PZON9xafppmkuapaaSmS2WZQGGPnkAACAASURBVE3mNrimQS4JrqCICFyW+/n9cQ8EiN5rXLgI7+fjwYNzPvdzznmfD5f7vp+zfM6+ncmsWLIGh8NB2/at+Oesl/hw2ae8+/YKwHncruTlq2czs8jLy6Nt+1b8sCvJWUeEh343lIR3PwEgtEF9pi/4O6fSMujY5Rqys3NYtiSBLRsTi9dz+nQGwfWCAMgvc9+GUkp5kj7P4hLk5uRy8ngah/ankHbiFAeSDxHZMJzli1cXP//6h11JLJ77ATklnl3936+3cOfgO0qta9b0hUye+QLP/O1xRo27n/+36HWSfzjAzm0/0G/Qbfz3680sfns5V7RoAsaQfS6H/23+5ZGoIsKgewew9bsdREaF0/TKxlXTCEqpWkl7Fm7KzbHz9bqNvPTk6+Tn5VOvfjBvLn6NrRt3nFd39/a93D38N8Xzn3z4Jf835xXatmvF0eMpxA3px73D4xj/+2cJCPRnxNihhIWHEhQUxCvTniIgwJ8jR49x8kQ6vr6+nMvOYcV7HzFtzt95d94HOBwOhj40iNAG9ekzqBcDh/UjTO+RUEpVIk0WbjqbeZZXnppafLjnbGYWrz79L56b9Jfz6t5yx4106d6Bho0jsYnw3OQ/czr9DLm5eTS5ojE+Upf4kRPJPHMWgIl/fJlVX7xDi1bNmP36Qo6mHGfemul0v6Ezi+ct4/b+t7Bq6Vq2fr+DPnf2QmzCf/41n2lz/saoR4dhq6MdRKVU5dJk4abcHDv23NI3sO/dlUy9kGBemDKB6ZNmc/bsOe66+w76D7wNP38/pr/9d/z8fXl8zLPsT3JeL/C7J4Zx00034VNi4L4+d97KR8s/paCggB63deXzVV/h6+vDpo3/o0PsNTSJjmLYiMG8M2cZs6YvBGD4w/cQHBykiUIpVSU0WbgpsG4gkY3COXk8vbis+42dCAwMoN9vbuX6mzpjDNQNCqRuUCD79iSTduIUZzOzihMFgMPhYOk7H/LMq+N5Z+4yuvfoRLfrOzFh3Evk5xfwxoLJtLqmJWcyz5KwbC1TZr5Ig7AQxsQP58ZbuvPdhi30uLkrbdu3Iii4rjeaQilVC2mycFNYRCgz5k/ipSdfJ2nvfrrf2JmnX32M+iHOkVrDI8PIOH2G7Kxs7HY79evX44edSeTnn3+VUsapM3TodA09b+3B3l3JLJy1jELrstqIRmF8vuYrIqLCuKnXddSzrnYKbRBCj5u70uPmrlW300opZdFk4SabzUaLmGb831uvUFBYiL+/H/XqB2OM4XRaBmcyMiksdBBYNxAfh4Nxv53A8WNpzF8xnYDAAHJLXB111z19OJD8M0k/HGD9p98Wl19/cxcSt+yi+02dOXs2i9sH3HLejX5KKeUNlfpJJCL9gH8DdYC3jDGTyrzuDywAugDpwFBjzEERaQ78AOyzqm40xvyhMmN1V2hYSKn5Y6kneOy3fy0+PPXgH+6lc4+OpB4+BsDiOcuZ/8F05sxcRNbZc0Q3a8JPPx6gTbsYxv1lBC1bXcm2TTvo3K0Ddw3pw6n00/y4J5nVH3zOjbd04+H44VW+j0opVValJQsRqQPMBO4AUoDNIpJgjNlTotpo4LQxJkZEhgGTgaHWaz8ZY2IrK76KcDgcnDmdCcB/pswrThQ2m41zWdlERoUzY/4/+Oqz7/hgyUe0u7Y1g4b2Z/eOfZxKz8A/0J/6ofX56ceD7NubxIDBvWndLoZBt40odXPduD+P9MbuKaXUeSqzZ9EdSDbG7AcQkSXAQKBkshgIvGhNLwNmiLjx8AUvyMvL59zZc9jq2Pjv+s0snvsBD8c/wKGfDtP1xlh6D7iZlq2vZP3n33Jv/9EYh+He4XG8+PoE3vznPP711qtE3B5OUvI+OrS/lulTZvHpR19RWFhI5pksrr+pC12vu5bvNmxBRLj7/ju56uoW3t5tpZQCQIwxlbNikSFAP2PMGGv+QeA6Y8yjJerssuqkWPM/AdcBwcBu4EcgE3jWGLOhnG38DvgdQKNGjbosWbKkUvalsKCQ3Fw7tjo2CgsK8Q/wI+NUJv4BfgQE+GNwPlfbz9+fw4dSSy17ZYtofH19OHbkJHa7nfCGDahXrx77kw4WP+ioecsrqBsUWPwkPcE5Um3Jp+PVNFlZWQQHB3s7DK/SNtA2KOLNdrj11lu3GmNcXjlTXc+eHgWaGWPSRaQLsFJE2hljMktWMsbMAmYBdO3a1fTq1cvjgZzLyubrLzaSvCeF+f/vPQD8/Hx5Y+FrNIyKxMenDvt/PEjza65i9YpPWPDW+6WWX7jiDSbGv8zxoycBGPvnoXy/byd33dOHjxPWMXzkPTRreUXxVU+unMnIJC8vn8DAgOJxoS5H69evpzL+XpcTbQNtgyKXQztU5lfXVOCKEvPRVlm5dUTEBwgB0o0xdmNMOoAxZivwE3B1JcZaLofDQXZ2DjFXN2fh7KXF5Xl5+fz92f9DBL77egtvzVjEK0//k1ZtWp63joAA/+JEUeTLT76hbbtWvPTak7S7to1bicIYw6EDh3n8d88y8LYHeX7CJE4cT6v4TiqllBsqs2exGWglIi1wJoVhwANl6iQAI4DvgCHAF8YYIyKRwCljTKGItARaAfsrMVbS006Tm5OLr68v9eoHkZubx+cffcXXn3/HI0+MKh4osMiRlOPk2fNo0z6GFq2a8d/1mwkNDaHvXbfyyeovAejZuwd1gwKtoccLi5eNbtYEsdnw9fV1P76Tp/j9b5/gaOpxANat3UBOjp3J054rvtdDKaUqS6X1LIwxBcCjwCc4L4N93xizW0ReFpE4q9ocIFxEkoE/A09Z5T2BHSKSiPPE9x+MMacqK9ZjR04w9v7xDLj5fu685QFWLv2YH3cnE9KgPiMfuZ+g4Lo0KvP0udv79yRh2Sf8Nu4R9u1OptcdNzD9tbe4+747+XjDEhavepN7h8dx8nAaj04YQ9F5e7HZeHHKBMIv8bnYOTm5xYmiyHdfb8Zuz6vYziullBsq9QyqMWaNMeZqY8xVxpi/WWXPG2MSrOlcY8y9xpgYY0z3oiunjDHLjTHtjDGxxpjOxpgPKyvGc1nZTP37f/gp6SAAefY8Pln9JcEhwbzz9jLEBps3JjJ11sv07N2DK65swtCHBjLwvn4sW/Qhxhhmvj4XA3TodA0RDcM4dy6bBwb+gT+NeYaQyPr0j+vNh1+/w8JVbxDTugVt2l/6ETV/f3/8/f1KlUU3a4LNVi0vHlNK1TA193IbN+Xk5LJ7x75SZfF/eZifD6bwwqQnMMZwcP9hFsxeSrvYNjw36S+0i23LH0c+zbmsbACyzp7Dz9+X4Q/fTWDdQOJHTQTAZhPq1qtLZFQ4TaKjaNexNT4+dfDzc//wU5H6IcE8P+mJ4ju66wYF8rd//ZWw8EvroSil1K9RXa+GqjLBwXXpcVMXli4+AkDDqEgaRUUy9W9vsmfnPsIiGvDCP54gJyeXJtFRLFmwkmEPDiK3xAi0nbt3IDg4iCMpx3i476ji8qEPDqJePc9cDhcQGMBtfW6m+w2dycw4S0iD+oSG1qea3pailKphan2yCAgMYMyjD3LieBpff7GRh/9wP5NemMaenc7exqm00zz56EvMWjyVMfeP580FU4hsFMGdg29nZ+JeOnVrz9g/PsiRlGP4+vjw5sLX+W7DZnrc3I027Tw7MmzRiLYNG0V4bJ1KKeWOWp8sAHx86tD1uk6M/P39hIaFMP31t/Dx9aEgvwAAuz2P3Bw7jsJClixYwR8njGXYyLsZHVyXM6czeeNfbxP/54cJDAwgNCxER4ZVStU4tf6cBYB/oD8NoyL40+hnOLT/MHPf+zcz5k5i/vIZ9ItzPsgosG4ABQWF5ObY2fDlRv405q+s/uBTIhqGcW2ndvj6+Z43yKBSStUU2rMAss/lYs/OY877/ybl51QevDuePHse/v5+vDr1aQbfN4D3F64E4J4H7uLfk2Zx8kQ6s2csxM/Pl6yz56hTg4fmUEqpWv8Jl3nmLK+9OJ1O13XgzOlMXnjyNfKsexfs9jz+8cI0wiPCcDgM85ZOY9umHexP/uXJd1+t+y9DHvgNDcJDvbULSilV6Wp9ssg6m012VjZbv0+kcXRDMs+cLfX6qbTT1PGpw8Ah/QFh/qz3Sr3eIqYZEQ3DqjBipZSqerU+WZw+ncHYxx7km682kZpylJjWpYcFb39tGwIDA7iq1ZU0vaIxcUP6Fb/WqHEkj4x/mLpB+ixspVTNVqvPWRQWFJKfl8/oYY/jcDhI/vEAr0yZyBv/epudiT8Q26U9f33lcYLr1aVefef4S088+wi/f+whcrJzaRAWQkTDcC/vhVJKVb5anSwQWLX04+JBAg/+9DMTHn2Jl6dMpFnzaBwOB9s27qBX3xuLFwkJrU9IaH1vRayUUl5Rqw9DiQihDUp/8B87coIDyT/jKCjk3Nlset7eg3r19eEsSqnarVYnC5vNxtAHB5fqKTSMiqR3/540vbIJV19zFSENtBehlFK1+zAUENkonOWfzOX7b7fh6+dDl+7X6nkIpZQqo9Ynizp16hDZKIK77u7j7VCUUqraqtWHoZRSSrlHk4VSSimXNFkopZRySZOFUkoplzRZKKWUckmThVJKKZc0WSillHJJjDHejsEjROQkcMhlRe+LANK8HUQ1oO2gbQDaBkW82Q5XGmMiXVWqMcniciEiW4wxtf4h3doO2gagbVDkcmgHPQyllFLKJU0WSimlXNJkUfVmeTuAakLbQdsAtA2KVPt20HMWSimlXNKehVJKKZc0WSillHJJk4UHiUg/EdknIski8lQ5r/uLyHvW69+LSHOrvLmI5IhIovXzZlXH7ilutEFPEdkmIgUiMqTMayNEJMn6GVF1UXtWBdugsMT7IKHqovY8N9rhzyKyR0R2iMg6EbmyxGu15b1wsTaoXu8FY4z+eOAHqAP8BLQE/IDtwDVl6jwCvGlNDwPes6abA7u8vQ9V1AbNgY7AAmBIifIwYL/1u4E13cDb+1SVbWC9luXtfajCdrgVqGtNjyvx/1Cb3gvltkF1fC9oz8JzugPJxpj9xpg8YAkwsEydgcB8a3oZ0FtEpApjrGwu28AYc9AYswNwlFm2L/CZMeaUMeY08BnQryqC9rCKtEFN4k47fGmMybZmNwLR1nRtei9cqA2qHU0WntMUOFxiPsUqK7eOMaYAOAMUPfC7hYj8T0S+EpGbKzvYSuJOG1TGstVJRfcjQES2iMhGERnk2dCq1KW2w2jg41+5bHVVkTaAavZeqPXP4K4mjgLNjDHpItIFWCki7Ywxmd4OTFW5K40xqSLSEvhCRHYaY37ydlCVSUR+C3QFbvF2LN5ygTaoVu8F7Vl4TipwRYn5aKus3Doi4gOEAOnGGLsxJh3AGLMV53HOqys9Ys9zpw0qY9nqpEL7YYxJtX7vB9YDnTwZXBVyqx1E5HbgGSDOGGO/lGUvAxVpg+r3XvD2SZOa8oOzl7YfaMEvJ7PalakTT+kT3O9b05FAHWu6Jc43VJi396ky2qBE3Xmcf4L7AM4Tmg2s6drWBg0Af2s6AkiizAnRy+XHzf+HTji/GLUqU15r3gsXaYNq917weoPWpB9gAPCj9cd/xip7Gec3BoAAYCmQDGwCWlrl9wC7gURgG/Abb+9LJbZBN5zHbs8B6cDuEss+bLVNMjDK2/tS1W0A3ADstD5UdgKjvb0vldwOnwPHrfd9IpBQC98L5bZBdXwv6HAfSimlXNJzFkoppVzSZKGUUsolTRZKKaVc0mShlFLKJU0WSimlXNJkodxijYy76wKvTRGRvdbImStEJNQD23tRRJ74lcuuF5GuLuo8LiJ1L3G9vURkdTnlI0VkxqXG6Ski0klE5lRg+ZetG8M8EYvLdVnt1cSNdb0uIrd5Ii5VcZoslCd8BrQ3xnTEeU35016Oxx2PA5eULKqxvwLTfu3CxpjnjTGfVzQIEanj5rpGAi6TBTAdOG9Yb+UdmizUpagjIrNFZLeIfCoigQDGmE+Nc2BEuMjImSIyUUR2ish2EZlklY0Vkc1W2fLyvu2LSIyIfG7V2SYiV5X9li8iM0RkZDnL/scajG23iLxklT2G88PqSxH50irrIyLfWetfKiLBVnk/q9e0Dbj7Im1zhdWjSRKRF6xlXxaRx0vE8jcR+VOZ+Jpb658nIj+KyCIRuV1EvrXW1d2qFyQic0VkkzXg5ECrvB7Q0Riz3Zov1SMTkV3WNpqLyA/l/f2sbQ+x9nVpiWWL27i8drTKD4rIZKt97i1al/Xa89bfdpeIzBKnITjHQFokzuc0BIpIF3EOoLlVRD4RkcYAxphDQLiIRF2k3VVV8fZdgfpzefzgfAZDARBrzb8P/Laceh9eoLw/8F9+Gbs/zPodXqLOq8AfrekXgSes6e+BwdZ0AM4eQS9gdYllZwAjren1QNcy26ljlXe05g8CEdZ0BPA1EGTNTwSet7Z1GGgFiLXPq8vZt5E4B4MMBwKBXTg/EJsD26w6Npx38YZfoF07WHW2AnOt7Q0EVlr1/l7UrkAozh5cEM7nISwvsb7idrPmd1nbuODfD2vYEZzDU/xcoh3+U6LOxdrxyRLbm4c1hAklhugAFmKNTFDm7+OL830Rac0PBeaWWG42cI+33//6o8+zUJfmgDEm0ZreivMDqJiIPIPzA2lROcveDrxtrLH7jTGnrPL2IrJBRHYCw4F2ZdZZD2hqjFlhLZdrfhn/3x33Wd96/2et+5py6lxvlX8rIonACOBKoI21z0nG+cn1zkW285kxJt0YkwN8ANxkjDkIpItIJ6AP8D9jDRhZxgFjzE5jjAPnsC/rrO3t5Jc27gM8ZcW3HmciawY0Bk661xQX//sZZ+9wLfAbcQ50eSewynr5Yu343gW2d6s4nwi5E7iNMn9bS2ugPfCZtW/PUrpnegL3DlmpSqZDlKtLYS8xXYjzWzTgPGkJ3AX0tj7o3DUPGGSM2W6to5ebyxVQ+jBqQNkKItICeALoZow5LSLzyquH81v8Z8aY+8ssH+tmLABl97lo/i2cPY8onD2G8pRsV0eJeQe//I8Kzm/Y+8rE2IbS+3Sxdrng36+EJcCjwClgizHmrBvteK7sSkQkAHgDZw/isIi8yIXbfrcxpkc5rxXFn3OB11QV0p6FqjAR6Qc8iXNwtAt96/8MGFV0TkJEwqzyesBREfHF2bMoxRhzFkgR6+Ev4nyOeV3gEHCNNR8K9C5nm/VxfpCdEZFGOA+FFTlrbRuc51luFJEYaxtBInI1sBdoLiJXWfVKJZMy7hCRMOs8wCDgW6t8Bc6nvHUDPrnI8q58AvxRxPlkRau3AvADEFOi3kGgs1WnM84RTy/FV9byY3EmDrh4O15IUWJIs87/lHzWeMm23wdEikgPK2ZfESnZA7ka56E05WWaLJQnzMD5z/+ZddLyzbIVjDFrgQRgi3W4oegk7HM4z0l8i/PDuTwPAo+JyA6cx7ejjDGHcR5332X9/l8529xule8FFvPLBzjALGCtiHxpjDmJ89v/u9Y2vgPaGGNygd8BH1mHYE5cpA02AcuBHTjPIWyxYsgDvsQ5HH3hRZZ35RWcx/d3iMhuax5jzF4gxDpchxVDmFXnUZznNtxmxbgaZ0JYbZVdrB0vtJ4MnOcbduFMdJtLvDwPeNN6H9TBmUgmi8h2nCOv3gDOxIEzEW65lH1QlUNHnVWqEomIDeew8/caY5IqaRvjgbPGmLcqY/3eIiKDgc7GmOe8HYvSnoVSlUZErsH5PIZ1lZUoLP+h9PmImsIH+Ke3g1BO2rNQSinlkvYslFJKuaTJQimllEuaLJRSSrmkyUIppZRLmiyUUkq5pMN9qFpp69at0Tab7VOHw9EG55ATNYWx2Wx7HQ5Hny5duqR4OxhVc2iyULWSzWb7NCoqqlWjRo3EZqs5HWyHwyFHjx5tffjw4e/i4uJiEhISauL9F8oLas5/iVKXwOFwtGnUqJFPTUoUADabjcaNG9t8fHyigXFxcXE1qdekvKhm/aco5b4a1aMoyWazYY032BEI8XI4qoaomf8tSl2G1q9fT2xsLO3ateOWW24pt86BAwe47rrriImJYejQoeTl5V1slQ6cgw8qVWGaLJSqBjIyMnjkkUdISEhg9+7dLF26tNx6EydOZPz48SQnJ9OgQQPmzJlTxZGq2kqThVJusJ9OJ+OHHZzasYWMH3ZgP13eA+9+vcWLF3P33XfTrFkzABo2bHheHWMMX3zxBUOGOB8NMWLECFauXOnROJS6EE0WSrlgP53OuZRDOPKdh3wc+XmcSznk0YTx448/cvr0aXr16kWXLl1YsGDBeXXS09MJDQ3Fx8d5EWN0dDSpqakei0Gpi9FLZ5VyIedYKhhH6ULjIOdYKv4Nwj2yjYKCArZu3cq6devIycmhR48eXH/99Vx99dUeWb9SFaU9C6VcKOpRuFvurpkzZxIbG0tsbCxNmjShb9++BAUFERERQc+ePdm+fXup+uHh4WRkZFBQUABASkoKTZs2rVAMSrlLk4VSLth8/S6p3F3x8fEkJiaSmJjI4MGD+eabbygoKCA7O5vvv/+etm3blqovItx6660sW7YMgPnz5zNw4MAKxaCUuzRZKOVCYFRTkDL/KmJzlntI27Zt6devHx07dqR79+6MGTOG9u3bAzBgwACOHDkCwOTJk5k6dSoxMTGkp6czevRoj8Wg1MXoOQulXCg6L5FzLBVHfh42Xz8Co5p67HxFkQkTJjBhwoTzytesWVM83bJlSzZt2uTR7SrlDk0WSrnBv0G4x5ODUpcTPQyllFLKJU0WSimlXNJkoZRSyiVNFkoppVzSZKGUUsolTRZKVSObN2/Gx8en+Ma7snr16kXr1q2L7/w+ceJEFUeoaiu9dFapaqKwsJCJEyfSp0+fi9ZbtGgRXbt2raKolHLSZKGUG3LTTpKd+jOOPDs2P3/qNm1GQESkR7cxffp07rnnHjZv3uzR9SrlCXoYSikXctNOknXoJxx5dgAceXayDv1EbtpJj20jNTWVFStWMG7cOJd1R40aRWxsLK+88grGGI/FoNTFaLJQyoXs1J/BUWaIcofDWe4hjz/+OJMnT8bVc8EXLVrEzp072bBhAxs2bGDhwoUei0Gpi9HDUEq5UNSjcLfcXTNnzmT27NkAnDlzhmHDhgGQlpbGmjVr8PHxYdCgQaWWKRqSvF69ejzwwANs2rSJhx56qEJxKOUOTRZKuWDz8y83Mdj8/Cu03vj4eOLj488rHzlyJHfdddd5iaKgoICMjAwiIiLIz89n9erV3H777RWKQSl36WEopVyo27QZlD08ZLM5y6tAbGwsAHa7nb59+9KxY0diY2Np2rQpY8eOrZIYlNKehVIuFF31VNlXQxWZN29eqfnExEQAgoKC2Lp1a6VsUylXNFko5YaAiMhKSw5KXQ70MJRSSimXNFkopZRySZOFUkoplzRZKKWUckmThVJKKZc0WShVDaxatar4/omuXbvyzTfflFtv69atdOjQgZiYGB577DEdG0pVGU0WSlUDvXv3Zvv27SQmJjJ37lzGjBlTbr1x48Yxe/ZskpKSSEpKYu3atVUcqaqt9D4LpdxwfHsyB9dtxn4mC/+QYJr37kaja2M8tv7g4ODi6XPnziEi59U5evQomZmZXH/99QA89NBDrFy5kv79+3ssDqUuRJOFUi4c355M0ocbcOQXAGA/k0XShxsAPJowVqxYwdNPP82JEyf46KOPzns9NTWV6Ojo4vno6GhSU1M9tn2lLkYPQynlwsF1m4sTRRFHfgEH13n2IUWDBw9m7969rFy5kueee86j61aqojRZKOWC/UzWJZW7a+bMmcXP0j5y5Ehxec+ePdm/fz9paWml6jdt2pSUlJTi+ZSUlOIhy5WqbJoslHLBPyT4ksrdFR8fT2JiIomJiWRnZxdf2bRt2zbsdjvh4eGl6jdu3Jj69euzceNGjDEsWLCAgQMHVigGpdylyUIpF5r37obNt/TpPZuvD817d/PYNpYvX0779u2JjY0lPj6e9957r/gkd9EQ5QBvvPEGY8aMISYmhquuukpPbqsqoye4lXKh6CR2ZV4NNXHiRCZOnFjua0VDlAN07dqVXbt2eWy7SrlLk4VSbmh0bYxHk4NSlxs9DKWUUsolTRZKKaVc0mShlFLKJU0WSimlXNJkoZRSyiVNFkpVA3v37qVHjx74+/vz+uuvX7DeyJEjadGiRfGd3yUvq1WqMumls0pVA2FhYUybNo2VK1e6rDtlyhSGDBlSBVEp9QtNFkq5Ydu6bXw8Zy0ZJzMIjQyl/+h+dO7d2WPrb9iwIQ0bNix3tFmlqgM9DKWUC9vWbWPZ1OVknMgAAxknMlg2dTnb1m3zSjzPPPMMHTt2ZPz48djtdq/EoGofTRZKufDxnLXk2/NLleXb8/l4TtU/pe4f//gHe/fuZfPmzZw6dYrJkydXeQyqdtJkoZQLGSczLqncXRcaovxiGjdujIjg7+/PqFGj2LRpU4ViUMpdmiyUciE0MvSSyt1VcojyJk2auLXM0aNHATDGsHLlStq3b1+hGJRylyYLpVzoP7ofvv6+pcp8/X3pP7qfx7Zx7NgxoqOjmTp1Kq+++irR0dFkZmYCMGDAgOKex/Dhw+nQoQMdOnQgLS2NZ5991mMxKHUxejWUUi4UXfVUmVdDRUVFlXoKXklr1qwpnv7iiy88tk2lLoUmC6Xc0Ll3Z48mB6UuN3oYSimllEuaLJRSSrmkyUIppZRLmiyUUkq5pMlCKaWUS5oslPKitWvX0rp1a2JiYpg0adJ5r9vtdoYOHUpMTAzXXXcdBw8erPoglUKThVJeU1hYSHx8PB9//DF79uzh3XffZc+ePaXqzJkzhwYNGpCcnMz48eOZOHGil6JVtZ0mC6Xc8NHKz+h7w31c27wXfW+4j49WflbhdW7atImYmBhatmyJn58fw4YNhgo2/wAAAi5JREFUY9WqVaXqrFq1ihEjRgAwZMgQ1q1bhzGmwttW6lJpslDKhY9WfsZLT03haOpxjDEcTT3OS09NqXDCSE1N5Yorriiej46OJjU19YJ1fHx8CAkJIT09vULbVerX0GShlAvTXptNbk7p50bk5tiZ9tpsL0WkVNXTZKGUC8eOnLikcnc1bdqUw4cPF8+npKTQtGnTC9YpKCjgzJkzhIeHV2i7Sv0amiyUciGqScNLKndXt27dSEpK4sCBA+Tl5bFkyRLi4uJK1YmLi2P+/PkALFu2jNtuuw0RqdB2lfo1NFko5cJjT44lINC/VFlAoD+PPTm2Quv18fFhxowZ9O3bl7Zt23LffffRrl07nn/+eRISEgAYPXo06enpxMTEMHXq1HIvr1WqKuios0q5cOegOwDnuYtjR04Q1aQhjz05tri8IgYMGMCAAQNKlb388svF0wEBASxdurTC21GqojRZKOWGOwfd4ZHkoNTlSg9DKaWUckmThVJKKZc0WajayjgcDm/HUCkcDofe5a08TpOFqpVsNtveY8eOFda0hOFwODh69KgjNzc3DdBrbJXH6AluVSs5HI4+qampXx05cqRlTbpvwRhDbm7uqfnz578P+ANZ3o5J1Qyi3VVVW8XFxfkAI4EbgULvRuNxAsxOSEjY6O1AVM2gyULVanFxcTYgGgj0diweZIDTCQkJJ70diKo5NFkopZRySU9wK6WUckmThVJKKZc0WSillHLp/wOrBeQXkNAttQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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jTFyWLIwxxsRlycIYY0xcliyMMcbEZcnCGGNMXJYsjDHGxGXJwhhjTFxJTRYiMlFEFojIYhG5uoXxF4rINyLypYjMEJHhbnl/EQm55V+KyP3JjNMYY0zbktYHt4h4gfuAQ4GVwCwRmaqqc2Mme1pV73enPxq4E5jojvtOVfdIVnzGGGMSl8wzi1HAYlVdoqp1wBTgmNgJVLUiZjAD0CTGY4wxZiuJanK+n0XkBGCiqp7nDk8CRqvq5GbT/Rq4HEgBDlbVRSLSH5gDLAQqgOtU9YMW1nE+cD5AcXHx3lOmTEnKtmyJYDBIZmZmR4eRkK4UK1i8ydSVYoWuFW9nj3XcuHGzVXVk3AlVNSl/wAnAwzHDk4B725j+NOBx93UqUOC+3htYAWS3tb69995bO4N33323o0NIWFeKVdXiTaauFKtq14q3s8cKfKYJfKcn8zLUKqBPzHBvt6w1U4CfAahqrapucl/PBr4DhiQpTmOMaRSJRCjfWM6mtSVUlFTEn2EH0eYNbhHZBzgDOADoAYSAb4FXgSdVtbyN2WcBg0VkAE6SOAXn7CF2+YNVdZE7eASwyC0vAkpUNSIiOwGDgSVbuG3GGLNFwvVhlsxZyoPXP0pFSSXFfbox+fYLKO7TraND63CtnlmIyGvAecAbODWUegDDgeuAAPCSW4OpRaoaBia7888DnlXVOSJyU8x8k0Vkjoh8iXPf4ky3/EDga7f8eeBCVS3Zhu00xpi4guVV3Hf1g1SUVAKwbsV6Hv7D41SWBTs4so7X1pnFJFXd2KwsCHzu/v1VRArbWriqTgOmNSu7Pub1Ja3M9x/gP20t2xhj2lttTS2hYKhJ2bL5y4lGoh0UUefRarJoSBQiUgz0cotXqeq65tMYY8yPQWoglfSsNKorf0gYA4b3w+O1xi7augy1h4jMBKYDt7t/74nITBHZazvFZ4wx201mTgaTb7+Q3MIcAHoM6M65159JVm7nrfq6vbR1Geox4AJV/SS2UETGAP8ERiQxLmOM2e58fh87De/PtY9cSSQcwZfiJzsvq6PD6hTaShYZzRMFgKrOFJGMJMZkjDEdxuP1kFOQ09FhdDptJYvXRORV4F84D8WB89zEz4HXkx2YMcaYzqOtG9wXi8hhOO05Nd7gBu5zazkZY4zZQbT5UJ6qvga8tp1iMcYY00m1VRsqR0RuE5F5IlIiIpvc17eJSO72DNIYY0zHaqvy8LNAKTBOVfNVtQAYB5S544wxxuwg2koW/VX1z6q6tqFAVdeq6m1Av+SHZowxprNoK1ksE5Hfuk9wA87T3CJyFT/UjjLGGLMDaCtZnAwU4Dy1XSIiJThPc+cDJ22H2IwxxnQSbVWdLQWucv+MMcbswFpNFiIiwIk4/WI/DxyM88zFfOB+VbVmGI0xZgfR1nMW9wHdcPrGPganq9OpOJ0UDQVabF7cGGPMj09byeIAVd1NRPzAWqCHqtaJyDM4/VkYY4zZQbR1gzsMoKr1wCxVrXOHw4BdgjLGmB1IW8lirYhkAqjqxIZCEekO1CWycBGZKCILRGSxiFzdwvgLReQbEflSRGaIyPCYcde48y0QkQmJb5Ixxpj21mqyUNXDVLWljmcrgSPjLVhEvDj3PQ7D6bv71Nhk4HpaVXdT1T1wOle60513OHAKsAtO/99/d5dnjDGmA7TZkGADETkO2B+nZtQMVX0hgdlGAYtVdYm7jCk4N8rnNkygqhUx02e4y8edboqq1gLfi8hid3kfJxKvMcaY9hU3WYjI34FBwDNu0QUiMl5Vfx1n1l40fdJ7JTC6heX/Grgcp9bVwTHzzmw2by+MMR0mEolQVVbF+hUbyMzNICsvi4wc6wdtR5HImcXBwDBVVQAReZyYs4Ntpar3AfeJyGnAdcCZic4rIucD5wMUFxczffr09gprqwWDwU4RRyK6Uqxg8SZTIrGG68OsX7aeaNSp35KWlU5et1w83rZufSbHj23fdgWJJIvFQF9gmTvcB1iUwHyr3Gkb9HbLWjMF+MeWzKuqDwIPAowcOVLHjh2bQFjJNX36dDpDHInoSrGCxZtM8WKtqarhqT89w9yPm/5OvOKhy+mxU48kR7e5H9O+7SoS+UmQBcwTkekiMh3nrCJbRKaKyNQ25psFDBaRASKSgnPDusn0IjI4ZvAIfkhCU4FTRCRVRAYAg4FPE9oiY0y7q68PU7qudLPyipLKDojGdIREziyu35oFq2pYRCYDbwBe4FFVnSMiNwGfqepUYLKIjAfqcfrOONOdd46IPIuTmMLAr1U1sjVxGGO2XXpWGqMm/oSX/v7D773UtFS69y9uY67E1NbUUlVRTSQSITWQSnZe1jYv07S/uMlCVd9zn60YhVNbaVZsHxdx5p0GTGtWdn3M61abDFHVW4FbE1mPMSa5vF4ve4/fC1Xlk2mfkluUw9G/PIrM3MxtWm5VsJoPXvuYx+58htqaOnYeMZgr77iI/CLrjLOzSaQ21Hk4ZxfvAAL8n4jcpKqPJjs4Y0znkZGTwf7H7sdeh+yF1+8lPTNtm5dZWRbkgT8+3jg8/6tFPPfQi5x1+WmkBlK2efmm/SRyGepKYE9V3QQgIgXAR4AlC2N2MF6vl6y8rT+bqKutI1hexfo1G8kvyqMqWL3ZNHM/X0CoOmTJopNJJFlswnlqu0GlW2aMMQlTVRZ9u4Sbf/1X6uvqERHOvPwUjjxtAq88/UbjdLuP3oX0jG0/azHtq63+LC53Xy4GPhGRl3DuWRwDfL0dYjPGdHGV5ZVUVYbwer2kpqVw3x8eob6uHnCSx5N3P8tdz93Cuy9/QFVlNXvssxvHnX0UKal2VtHZtHVm0VAl4Tv3r8FLyQvHGPNjsXHtJj54YyZv/Xc6aekBTvvV8Uy65CT+cuV9jdOEwxHE4+Ge//wJjSopgRSycrbtprlJjra6Vf3D9gzEGPPjEaqq4ZtZ8/jX3/7dWPbHS+/i7uf/RGZOBsHyKgCKexURSAuQV5jTUaGaBLX6UJ6IPCQiu7YyLkNEzhGR05MXmjGmq6oJ1TD/q0VNzhKiUeWLD7/m1F8eR2H3fEYeuAc33P9bSxRdRLxuVa8Xkd2Ab4ENQADnaepsnNpQTyU9QmNMl1IdrEZEGDFmV8YcMpKS9aU8eNu/qKupo9eAnuw6cmdGH7w3KakpZGSld3S4JkFtXYb6EjjJ7QBpJNADCAHzVHXBdorPGNNFBCurWDJ3Ka/9+21yC3I4+Jj9eeT2pyjqUcDZl5/Kx2/PYsDQfqSkptgN7C4okSe4gyLyCdDXkoQxpjVzZs3njivvbRz+5N3ZXPmXyfz+3D9x0gXHss8hPyEnP7sDIzTbIm5DgiJyNPAl8Lo7vEecBgSNMTuYyvIgU594vUlZRWkl61dtpLh3EXU1tZYourhEWp29AaddqDJovDw1IJlBGWO6Fo/HQyA9dbPyQHoqgbRU8rvldUBUpj0lkizqVbW8WZm2OKUxZoeUkZXO6ZNPxOfzNpYN2LkfuQU5/PavF9lZxY9AIs19zHF7sfO6/U9cjNM2lDGmg9WFatFolGhUSevgmkW9B/Tgnhdu4/MZX1HYvYCdhvUjPTONQFqgQ+My7SORZHERcC1Qi9MP9xvAzckMyhjTtnBtPZUby5nz1uf4UvwM2nc49bV1ZBXkICIdElNKIIVuPQuZeNIhHbJ+k1yJ1IaqxkkW1yY/HGNMa2qCIWqra6kL1ZKSlsqbd71A+ZoSABZM/5rDrzkZn99Pek5Gu62zsjxI6YYyvp09nwFD+1LYvYDcgmz8fn+7rcN0DYn0ZzES+B3QP3Z6Vd09eWEZYxqoKsGSShbOnE96Tjo9B/eirirExCuO582/vUjpig3UVtWw6tulDDmgxUYXtkp9XT0fvvlJk/4mjp40kSNP/SlFPQrbbT2ma0jkBvdTwGPA8cBRMX9xichEEVkgIotF5OoWxl8uInNF5GsReVtE+sWMi4jIl+6fVdU1O6yq0iBPXPEQy75aQnpaKm/f8Rxv3voMHz7wKgf/8giyipzmMjxeDx5PIh/pxAQrqnjinmeblE175i2qgiHC4XC7rcd0DYncs9jg9pe9RUTEi9NkyKHASmCWiExV1bkxk30BjFTVahH5JXA7cLI7LqSqe2zpeo35sZn/4RyqyqoY/bN9+fAfU6kP1QFQumIDn0+Zzm6HjeTzFz+m/8gh+Fuovrq1VJWaUG2TsnA4gkajVh9yB5TQcxYi8rCInCoixzX8JTDfKGCxqi5R1TpgCk5fGI1U9V33ngjATKD3FkVvzA6gtsr5whahMVE02LB4Nb126c9xt5xJem4m3piqq9sqkBZg/5+OblK2y947k56Zjs+fyO9M82OSyDt+NrAz4AeibpkC/40zXy9gRczwSmB0K9MCnAu8FjMcEJHPgDBwm6q+mECsxvzo7DJudz757wwQwZfqJ1xb3ziuYKfupOVkEMhq/57l0jPTOPuK0xgwtC+zZ3zNkN0GcthJh5CVmxV/ZvOjI6ptn0+KyAJVHbrFCxY5AZioque5w5OA0ao6uYVpzwAmAwepaq1b1ktVV4nITsA7wCGq+l2z+c4HzgcoLi7ee8qUKVsaZrsLBoNkZnaNzlu6UqywY8Wrqo3PT3g8HlSV2qoafH4fVZsq0GgUr99HVrdcPO1wNhEv1mg0ioh0WLXc5rrSsdDZYx03btxsVR0Zb7pEziw+EpHhze41JGIV0CdmuLdb1oSIjMepltuYKABUdZX7f4mITAf2pGmPfajqg8CDACNHjtSxY8duYYjtb/r06XSGOBLRlWKFHSfeulAd3374Lf+96z9E6iNk5mZy3u3nUdCrEI0o4ZpaIuEI3hQfadntU012R9m3HaErxdqWRO5ZjAG+dGs1fS0i34hIIn1wzwIGi8gAEUkBTgGa3CgXkT2BB4CjVXV9THmeiKS6rwuB/YAtTVbGdBk1wWqqNlUQ3FhOpL4ej9BYs6m6sprZb84mVBnC4/eQlptJZmFOuyUKYxKRyJnFxK1ZsKqGRWQyzhPfXuBRVZ0jIjcBn7k1rO4AMoHn3NPb5ap6NDAMeEBEojgJ7batOLMxpksIlVcR3FjO1/+dQdWmCvqOHMLgQ/bkuEuP5aX7XmbSDZP4/qslPH/HswzccxB7/XRvMnM772UN8+OUyBPcy7Z24ao6DZjWrOz6mNfjW5nvI2C3rV2vMV1JfaiW9+5+gXCNU9Np/puz8ab6GbjfcM64/nQWfraQGc+9D8B3X3zHirnLOe43J5CWhJvaxrSm/Z7gMcbEVV0WJLixnOCmCupCtYTrwlSuL2tMFAA5PQvoNrwf7z/7Ph88P4P8Hvkcd/nxjTeX538yn7qautZWYUxSWGVpY7aTUHkVq+csZdF737Dx+7XsduRoho4bQXpe06qoux1/AI/f+ARl68sAWDhrARPOncjO+wxj3kdz8fq9iKdz1EoyO45Eesq7SESs5xJjtlI0HKFqUwU1FVVEw1EG7juc/c87jK9e+ojghnICWWkMPNC56upPT6W2LtyYKBp89vpnDBszDICxp40jkGHNfpvtK5Ezi2Kcpjo+Bx4F3tB4D2cYYwCoraph2WcL+ealjxlyyB50H9ob8XpZ8cVidtpnOGsXrGS3w0exy5FjGDp+L2qra4m2cNYQSE+lsE8hv77vInK65ZASSOmArTE7srhnFqp6HTAYeAQ4C1gkIn8UkYFJjs2YLq+6NMiSj+dyxPWnUdQjm8rFy/B7lJ3G7EyfPQdRPLgXAOm5maTlZ5HVPZ/sohwG7TWocRkej4fDzj+cvsP60WNgD9I7uJMjs2NK6J6FqqqIrAXW4jS/kQc8LyJvqepvkxmgMV2ZhsMcdMHhlC5awYr3ZhOpq2fd5/MZMHFfCvp2xxfwo6qUbiijZG0J333zPQN334njLj+ejSs3snHlRoaMHExmF25io7I8iNfrIT3TklxXlkh/FpcAPwc2Ag8DV6pqvYh4gEWAJQtjmomEI0Trwyx5dQY1ZZUU7tyfoSeOZ/6zbxGtD7Pqo6/YZVJvAtkZVJRWMvO1T3n5kR9qmU84fTwTzhjPoD0HtbGWzi1YWcWc2fP572OvkJaZxs8vOpne/XuQkmqX0LqiRM4s8oHjmj9voapRETkyOWEZ07WKKitEAAAgAElEQVSFq2uo3liOd53Tk926rxbh8fso2m0Q6z6fDzityALUhmp58+n/NZn/f/9+lwN/tj9pGV33WYrv5n7PzZf8tXH4y5nf8sDUv1Lcs6gDozJbK5F7FjcABSJysVszaq+YcfOSGp0xXVRteRCa1QMpWbSczF7dAOi17wj8Gc5lGRGhLqYlWXDOTLa2Hkk4HKF0YxkVZZVbNX97qAnVMPXpN5qUhevDzJ7xZQdFZLZVIlVnfw88DhQAhcA/ReS6ZAdmTFfmz9z8jCC9MBdvio/hpx9G/tB+eLzOx8+f4mevsU37+dpl9DACW9iRUXUwROnGMso2lVFZHuTZh19i/leLqK2pjT9zO/P6fBQW529WXtBt8zLTNSRyGeoMYISq1gCIyG3Al8AtyQzMmK7MH0glJTsdpBRUSclMY9CR+5NekLPZtDkF2Zw4+Vj6D+vH3E/nMWTPwexz+Ggy2mgosL6uHp/f1/hUd6g6RHVViA/f/IRoNMruo3bhmNMn8per7+M3f/o13Xq2Xw96ifD7fRx/9lF88OZMKkqdM5wBQ/sxdLeuew9mR5dIslgNBIAadziVFpoaN2ZHFa6pIVIdor6yktSCfMTvx5eWSkpGGqMvP5VoXRhfIAV/G/cfcgpzOPiEg9jvyH1ICfjxen/ooyJcFyZYWdU4XFdXT2VZkLUr17P7T4ZTW1PLt7PnkxpIZfS4vbnvD4/wwmOvctODV3PG5JOY+8UCuvUsTOo+aElhcT73Pv9nvpv3PekZafTq34Pc/M2TpekaWk0WIvJ/OD3ilQNzROQtd/hQ4NPtE54xnVukppaKBQupXrW6sSx/zz2QbkUgQiAn8dZhPV4Pac2ezA6WV/H+Kx/x8uOvOc9bnD4eX6qfmdNnc+HvzqK2tpbLTrmOyvIgAD37dufGf/yWC474DR+8NpMJJx6MP6VjWvXxeDzkF+aSf8CeHbJ+077aumfxGTAbeAH4HfAuMB2no6KXkh6ZMZ1UTVWosSE/jUaaJAqA8gUL0br2aehv2cLlPHPP8wTLq6goreTf975AQVEea1es494bH6KqoroxUQCsXr6Wb2bN4+CjDqA2VEu4PkyPvsXtEovZsbX6k0NVH9+egRjT2YWCISo3lhOuraOmupa8noWkp/s3my5aX9/C3Fvn4zdmbVY259N5DNplJ2a8MbPFm+BlJeXOZa2j9icrN5OsbOv7wmw7a6LcmDZE6uqJ1NVTX1tHtKYOP0qKCBVzllCzqZy6uii+Zv0rZ/Tuhfja59LP4N03b1Wn18CerFu1gZ59uwNN25Hyp/jZd/woxh25H0U9Ci1RmHZjTZQb04JoOEx9tVOnI7RuA0QjBIqKWDBtJtWbKtjz5xP46um32OucIygcNZLK75ZQX1lJWnEx6T264/VvfsahqlQHQ6Sk+vGnbD6+JXsesDvD9h7KvNkLAKdKbVGvIlYvW8Pv/+8K0jPT+MMDV/PSv6aRGkjh5POPJTMnkyzrctW0M0sWxjRTX1NLuCpEaM06akvLyB2yE+WLl1A2dwG7HX8gM/72H5Z98DXFu+5EOFSLrzifnKFD0EgE8fsb+86OVVFWyaz3v+Dtlz+g/+A+HHfmkS0+h9Bcdl4WF/3xfGqqa1BVPD4vNdU13P3sH8nKyyI1NYURo3ah/+A+eL1eMi1JmCRpqzbUyzi1n1rk9pXdJhGZCNyN0wf3w6p6W7PxlwPn4TROuAE4p6FZERE5E2h4+O8Wu4ditofaympqKqpIzUojrW8vsgb2pfTr+eQOHcSatesJrVlD0bB+BDeUkduvmID75ezx+aCVS0/h+jCv/+cdHrt7Cj37dmfYiMF8+PanjDt8P7ITaCAwKzeTrDh9bufkZW/5xhqzBdo6s/iL+/84oDvwpDt8KrAu3oJFxAvch1PVdiVOnxhTVXVuzGRfACNVtVpEfgncDpwsIvnADcBInIQ12523NPFNM2bL1AVDiEdIz0lvbGpDI1GyBvenriJIoLAAjUTw+Dx0G9aPQF4m/gSesq4oD/LylDcZe/h+HPqzg3jpydeoramjd78e7Lz7YDKsyXHTBbR6g1tV31PV94D9VPVkVX3Z/TsNOCCBZY8CFqvqElWtA6YAxzRbx7uqWu0OzgR6u68nAG+paombIN4CJm7ZphmTmGgkQqS2FvGA1tWx+t2PWPbSm2yY9RUajuBLTSWQn0s0GiG9V0/SC3Lou8+uFA7qgz8QP1l4PMJ+40dxxq9PwOfzMeH4g6kOVvP7X97G8iUrt8MWGrPtJF5jZSIyDzhCVZe4wwOAaao6LM58JwATVfU8d3gSMFpVJ7cy/b3AWlW9RUSuAAKqeos77vdASFX/0mye84HzAYqLi/eeMmVK3A1OtmAwSGZm16iB0pVihSTFq0qkthYNh0EEb2oq4VAN0fowAB6/H1/Dk9cKCIh4mldCajNeVaWmuoY1K9cTiUTw+/0U9ypiw5pNpAT8FPcsamy2o6PYsZA8nT3WcePGzVbVkfGmS+QG92XAdBFZgvMR6QdcsI3xNSEiZ+BccjpoS+ZT1QeBBwFGjhypY8eObc+wtsr06dPpDHEkoivFCu0fbzQSoWLRIqpXrAC3eQ3xeMjbZ19WvPauM+xV+o49CI/fhy+wZf1eN8RbsqGU84/5DVWV1Y3j+g/py/E/P4Kli1dywskH4vN521hS8u3ox0IydaVY2xI3Wajq6yIyGNjZLZqvqok0Y7kK6BMz3JsW2pQSkfE4T4UfFLPcVcDYZvNOT2CdxiQkUldHtL6eQEEhgaIiKhcvpr6iAo1E0EgYj99HtD5MSm4O4nHOOLZWTai2SaIAWLpwOT36FDNi9K4dniiMSUQiTZSnA1cCk1X1K6Bvgp0ezQIGi8gAEUkBTgGmNlv2nsADwNGquj5m1BvAT0UkT0TygJ+6ZcZstXBtHbUVVdSUB9FoFESoWLKUDZ/MJmvwYLzumYPH7ycajuANpFI8Zi98aWnbdJkokJZKTn7T2kpDdh1It55FFHTL26ZtMmZ7SeQJ7n8CdcA+7vAqEmieXFXDwGScL/l5wLOqOkdEbhKRhmq3dwCZwHMi8qWITHXnLQFuxkk4s4Cb3DJjtlg0HKGusoqy71fz5ePT+PqJ1wmu2UTVytXkj9gVX2YG5QsWk9azJ5k77YQ3LUC/ow6lzxEHu2cW29bQQU5eNjf9/SqKezk9xO00tB9X334xRd0LWnwmw5jOKJF7FgNV9WQRORXAreaa0M8sVZ0GTGtWdn3M6/FtzPso8Ggi6zGmNeFQDZu+nkdw+SpSsjPZ5bgDWTjtE7564g1Gnn8UVctXkj9iVzZ++jnpI3bF4/fj8fuhHXsz9fq8DB6+E3c9eTPhcAR/ip/cfHsuwnQtifysqRORNNwH9ERkILD9u94yZgtFw2E2fTWX8gXfEQnVEFq3kbUzPmHQhFFEauupD9VRs2EDHp+P9F498AQCTqJIAhEhrzCXou4FlihMl5TImcWNwOtAHxF5CtgPODuZQRnTHqL19QSXNX2OIVpbB0Tx+L3401KJZmeBKtkDB9glIWPakEhtqDdFZDYwBqfq7CWqujHpkRmzjUQ8+LMyidQ6t7sChfkE+vWFQBrDTzqE+spysgcNxJOSgsfbeo0kVaW8pIL6ujB+v4+cguwOfy7CmO0tbrIQkbdV9RDg1RbKjOkUVJVQeTXh+no8Xi+pGQH8gVS6jdmLlW9MJ9CjO5pbyJuPvk11WRXDxu7O3keNwZfW9s0JVWXFd6u47fK7WbdyA8W9ivjtXy+m76BediZidiitHu0iEnDbaCp0q7Dmu3/9gV7bK0BjElG5sYLVi1ay/JulVGwoY8W3S6kL1ZGak03/YyaQPXxnXvzzs2xavoFQRTWfT53J3OlfE41E21xueUkFt13mJAqAdas2cNtld1NeUrE9NsuYTqOtM4sLgEuBnjjdqzacd1cA9yY5LmMSVl1RzYpvv2fVgpXMef8bPF4PJ153OnWhWlLSUvClp1G2aA3RcNPEsPCjuQw/eA/Ss39oyK90Uzkb127C5/eRX5hLOBxh3aoNTebbsGYjYbc5EGN2FG01JHi3qg4ArlDVnVR1gPs3QlUtWZhOIVJTS83KVWTXlrHHmAH84p5f0XtYX754fRaRSITykgqWLliOBnwce/MkCvv/0B91QZ8i/CnO7yVVZePaTaxfuYF5Xyzk5sl/4dZL70RE6NazsMk6C7vn40uw1lR9fT0lG0qZ+/kC1qxYR7C8qv023pjtKJEb3P8nIrsCw4FATPm/khmYMfFEIxFK5y6idO5CCsaMpHRdBfNe+pi9Dt0bT4qPKLBiwXIqy6pYvnglw/YawqEXH8Nz1zxKWlY6+5xyEP5ACuH6MMsWruCBG//JuhXrGf6Tnbnmr5dw62V3sfDbxfz+viu46Vd3sGHNJgq753P1nZeQkx+/HwqA5YtXcu05t1JbUwfAz848nOPPOco6KTJdTiI3uG/AaadpOM4DdocBMwBLFqZDRWvrKFu0hOxhQ/j6nW+Z9+5XAMz93xfsOmFvBueN4INpM5n55iwApj35JidPPo5Jd/8Kr9dDhtuhULC8ijsuvptQldON6txZ8/H7fRx5yk9ZvWwto8fuzW2PX09dXT3+FD85+VkJ3dyORqLcf8tjjYkC4MXHp3H4yeMtWZguJ5HqHCcAh+A0H342MALISWpUxiRCBG9qCqlFhcx/7+smo+b+7wu8Xm9jomjw8mOvEYlGGxMFQHUw1JgoGsybvYCBw/oz5uCReDwecgtz6NazkLzCnIRrQakqG9ZsXsu8OhhKdAuN6TQSOepDqhoFwiKSDaynaWuyxnQIbyCV/DEjCft8TLzmZHY/cjQe7w+HtLq9Amdkp5PXLRdwujht/oxEWkYAn7/pSXbfIX0o6lFAfmHuVsfn8Xo4YOI+Tcpy8rPJzkvsEpYxnUkiT3B/JiK5wEM4taKCwMdJjcqYBATLgzz70KusW7mew0//Kf3GDKXvnoN45ZanGHXyQXi8Xv70zA14vB5K1pfi9XlZsWgVaZlNn61Iz0zjF9efySO3/Iu62nryu+Vx7rU/p2f/7tsUn4hw/LlHMWzvnXnh0ZfJzsvi3CvP2KwFWmO6gkRucP/KfXm/iLwOZKvq123NY8y2iEQiBEuDLFuwgoycDAqK86ncWE5tdS3B0koycjMREb79ZB75xXkcfdZhfP3xt8z/YiGjx4/kpDvPp6oqxLXn3MLGtSVkZmdw/jU/Z9aMLzj1guNIDaQ0WV9qWip7HTiCnf9zC/X19YgIXv+29TERrKwiVF3DPTc+RG5+Npf96Zfk5GVbf9umy2rroby9mv8B+YDPfW1MUlRsqmDDyg1k52SwavEqnrpjCtWV1ZSu3sQjl9xP5aYKIpEIc2cvYP/DxnDvtQ/x5J3P8sYzb3PTuX9m2cKVfPy/z9i41mnmI1hRxf23Psb4Yw7i2YenNrnh3CAlNYWoRvn7n/7JpAm/5qpzbmLht99Rv5XPUyz6dgmrlq7hsw++5H8vvc/lp/2empC1v2m6rrbOLP7axjgFDm7nWIxxHrD76ns+euod6qpr6b1LP446eyIl68vw+n1UlQWZ/eqnHDTpEPabOJrykgqWL1zRZBkvPvIKJ/7q2KbLDYYQEdatWk99Xf1mZxfVVSHuv/1xPnrHuSG+7LuVXHXuTTz8yt8oKNqyDoqqgtW88K9X6bvbD890VAdDzP1iAQdMGLNFyzKms2g1WajquO0ZiDEA9aFapj/8GiLCXsftS58ROxEV6D6gO0vXLgGgfEM50UiU3gN7sWbZ2s2WEY1E8TbrqjQ7N4twOMKE48e1WG21prqGz2Z82aSsuipEZVnlFicLr9fT4jqsuqzpyhJ5zuLnLZXbQ3kmGcrWlIDC6FMPYv73K7n7L0+gqhT1KODYyT8lIy+TkUeMxuf3kZnjo6B7Pj36FbNm2brGZRx11mH06N+NgcP68928pRT3LuLCa8+ipraOPfbZrcX1+vw++g/qw5wvFjSWebweMrMzW5y+LYG0AKddeDxvvvFWY1m/Qb0ZMKTvFi/LmM4ikdpQP4l5HcB55uJzEngoT0QmAncDXuBhVb2t2fgDgb8BuwOnqOrzMeMiwDfu4HJVPRrzo5fbswBfqp/8AcVMvfnhxvINazZRuqmcE284nfzi/Mby/G55XHXvZXz0+iesWbqWA47aj94De5KRlc619/7GacNJlZRAChlZ6a0+I5Gdm8VlN13Ib8+5iZINpfh8Xn597blkZG7dDeni3kX0HdSbi//wC3Lyshm8y07kFtjjSabrSqQ21EWxw2412inx5hMRL3AfcCiwEpglIlNVdW7MZMuBs4ArWlhESFX3iLce0/VVlVdRV1OHx+PBl+Jj/K+OYMPapl2upwRSSEsP4E9LoaqqmqhGSXe/yPOKcjli0gSi0WiTZJCzhc8z9OrXg/ueu41QVQ2pgVQystJJSw/En7EFfr8fn8/L2GMO2qr5jelsEjmzaK4KGJDAdKOAxaq6BEBEpgDHAI3JQlWXuuPabifa/GgFSyt55tZn+P6b7/F4POx77L4ceNJB5IVq8fm8hMMRAC699QKWrl7CBUdeDiIceuxYTv/VCY0PuNXXhxFhm/qY8Hg85BfmQWH8aY3Z0cT9ZInIyyIy1f17BVgAvJDAsnsBsdVUVrJl/WAEROQzEZkpIj/bgvlMFxEJR/h02qd8/833AESjUWb8ZwZl68rIycvi+n9cSc/+3Rm2x2CqglVUllcSjSrRSJQ3nn+HBd8spipYzapla/jnnU/z6F+fZtWyNYSsiqox7U5Ute0JRGLPo8PAMlVd2dr0MfOdAExU1fPc4UnAaFWd3MK0jwGvNLtn0UtVV4nITsA7wCGq+l2z+c4HzgcoLi7ee8qUuFfHki4YDJKZueU3RTtCR8eqUaVkzSZqqpt+ued2yyUjx6k5FIlEEBE2rStBfEL5xh86HcovyiczO4MVS1YRjTonpx6P0Gen3o1Nj3ekjt6/W6IrxQpdK97OHuu4ceNmq+rIeNMl8olaDqxR1RoAEUkTkf4Nl5DasIqmbUj1dssSoqqr3P9LRGQ6sCfwXbNpHgQeBBg5cqSOHTs20cUnzfTp0+kMcSSio2ONRqN88spMXv7Xy03KL3nwUor7Oc8oRMIRKsoqqQ6G+HjmR7z2z/cbOx564JU7ee25t3nxsbeazH/smUdw9mWnbp+NaENH798t0ZViha4Vb1eKtS2JXOB9Doi9pxBxy+KZBQwWkQEikgKcAkxNJCi3G9dU93UhsB8x9zrMj4PH42H3g0Yw8rCf4PV5CWQG+Nklx5Jd4LSdVFVZzYw3Z3L5qddx+am/R0S46o6L8fm8HHXaBLJzs/D6Nj+EvV7rG9uY9pbImYVPVRvbR1DVOvfLv02qGhaRycAbOFVnH1XVOSJyE/CZqk4VkZ/g3P/IA44SkT+o6i7AMOAB98a3B7itWS0q8yORkZPBERccwfhJ4xER0rLSGluALdlQyl3X3t84bVlJOTmBCh558x5SUlJIywhw6LFjeXXKW43NfqdlpDHheGtcwJj2lkiy2CAiR6vqVAAROQbYvJH+FqjqNJwOk2LLro95PQvn8lTz+T4CWn56yvzopKalkpqWuln515/O2azso7c+Zb/xo0jLcKq05hfl8vcXbmfjuhLq68P06F28xVVmjTHxJZIsLgSeEpF7AcGp4dTiU91mx1VZUUlNTR0iQn5B7jZVYW0wYGi/zcqG7DaIlNQfTmxrqmp57fl3eO35t8nOzeLCa84kPXPgZs19GGO2TdxPtKp+p6pjcLpVHaaq+6rq4uSHZrqKjes38bvL/siho09g0rG/4vNPv6a2Zturr/bq35NDjjmwcTg1kMqE48c1JoJoNMoHb87kqX88T8mGUpYuWs7vfnEr5aUVrS3SGLOVWj2zEJHLWykHQFXvTFJMpguprgrxwN2PM378fvz2qvMB4ZOZX9BvQG+KAqlEI9EmvddtiZy8LM6+/DROufA4opEI387/lryYnuuqKqt5d9qHTeaJRqLM+2ohxT2LtmWzjDHNtHUZyi78mriqq6o54aTD8SxZxabXZgDwkxFDEBWee/glVi1by1GnTaCgWx5VVdXUh8MEAqnk5uUQaOE+RXOZ2RmNrbXOW9Q06aQGUug3sDdzYxr/A+jZd9t6uDPGbK6tJsr/sD0DMV1TWnoa6XX1bFq+urEspUcxV59zM+tWbcDjEY44eTyrV63lqotvYtn3K0lLC3DtrZczfuIBpGdsfc9xKakpnHL+scz+6CvWr3bqXIw7Yj87qzAmCRJpojwAnAvsgtPqLACqek4S4zKdXCQcobIiSLguTHrvnqQcnkvFN/Op3VTOxk0VrFu1AYARY3alMljNg/c9zrLvnQf/Q6Eabrjyz4zeb69tShYARd0LuOvJmykvrSSQlkp6ZjrZuZ33aVljuqpEakM9AcwHJgA3AacD85IZlOncIuEIK79fTVp6gA9e/JB3n51OTmEOp/3mRDKyMqiOuUeRV5CLz+9l7jcLmy4jEqFkYynF3bf9LCCvMLfJvQxjTPtL5M7jIFX9PVClqo8DRwCjkxuW6WxqKqup3FjOivnLWTp/OX6/j//+61VSc9P49V8vpL62nodueIzMQf3p1qOQfoOcll7mfD6ftPQAP9mnaWvzqakpFBTlt7QqY0wnlEiyqHf/l4nIrkAO0C15IZnOprqkks+efIvFH3+LepS07ABrV6xnt72H8/BfnuTfj7zEkecfQbguTFWonoKeRdz84DVcevMFHHT4vmTnZHHpVRcwer+9Aejesxv3PfZncnKzO3jLjDGJSuQy1IMikgdch9O2UyZwfduzmB+D2lAtVZXVhOvDDDl6X9575yPOPex6IpEI4w7dj6tuuIi0jABfzvyW0391Ah6vh5zCHDw+L3kFORxy9A/PSKgqf7zrWiKRCD6/r90e3DPGbB+J9JTX0Lfl+8BOyQ3HdBahqhAVpZW88/IMpk+bQUZWBhff+AvG7L83H773Ke++9SGj9t2LPffZjY/+NwvxCKdfdSppmWktLk9EKCou2KIYqqtC1NXWk5WTgddrT2Qb05ESqQ31R+B2VS1zh/OA36jqdckOzmw/tVUh6mvq0WgUf2oKNXV1LF+4Em8YfvGbSZRsKuPGi+7gmjsv4cP3PgVg1swv2GXQUEYdtCf5RXn0HdSHlFT/NscSjUZZu2o9D9/1FKuXr+XQow9i/FEHxp/RGJM0iVyGOkxVf9cwoKqlInI4zmUp8yNQU1nNrKffZekn8wHI71/MmHMnUl1ZzZjDRuHxeug5oAdnpZ1Ciu+HZLDvAaMYNnQwfQb2Iie//e4/lG0q56JTr6GsxGm2Y9HcJdTW1NJzyJadmRhj2k8iF429DX1LgNP5ERD/0VvTZZSt2tSYKADqQ3WEamp59b9vM/mEq7nj6nsp2VhKVVU1qemp+HxejjvlCMYffiC7/mRYuyYKgPVrNzYmigbTnn+biNsftzFm+0vkzOIp4G0R+ac7fDbwePJCMttbybJ1TYaHTNiLv173D76btxSA7xcs52+/f4Bzrzydkg1lvPHxc6SlBcjI3LYH6lqTmZWxWVl+UW5ju2TGmO0vkVZn/wzcgtMh0TDgZlW9PdmBme2naLf+HHDZsew16WCyu+eTXpjdmCgarF+9kcysDL6Y+TWFRflJSxQAWTlZHHzEAY3DKakpTP7duVvdIKExZtsl1Ku9qr4OvJ7kWMx2oqrUhmpRhe8XLuO2K+5h9bK1DBzWn0uuP49AWoDinkWsW72hcZ6snEzC4Qi7jxye9Phy8rKY/LtzOOW8n7F+zUYG7TyAnLxsVm9YkfR1G2NaZj/VdjCb1pfw5aff8srzb1FXW4ff56M25PSa+928pdxz8yMEstP57e2TyXLbWErPTOPC353FqmVr6D+473aJMycvm4FD+7PP2JEUdS9ol1pWxpitl9CZxdYSkYnA3Th9cD+sqrc1G38g8Ddgd+AUVX0+ZtyZ/FDj6ha3qRGzDUo3lXPfn//JO9M+AOC0yUeRohlcevMF/P6CPwGweO73RCIRBuzcn/977jZC1SFSUvx4vF4CaalkZCXv8pMxpvNK2pmFiHiB+4DDcHrZO1VEml/DWA6cBTzdbN584AacNqhGATe4z3eYbRCqDjUmigaP3PMUhcU/tNHUe0BPvF4vPp+X/KJcevXrQVGPQgq65VmiMGYH1mqyEJFsEfmTiDwhIqc1G/f3BJY9ClisqktUtQ6YAhwTO4GqLlXVr4Fos3knAG+paomqlgJvARMTWKdxRcMRQuVVhMqC1FbVEIk41U7veuxmzrn4VLJznb6tKsuDiHvjuKBbPlfdfhG5BTkdFrcxpnMSVW15hMh/gEXATOAcnAYFT1PVWhH5XFX3anPBIicAE1X1PHd4EjBaVSe3MO1jwP+3d+fxVZTnAsd/zzlJTgIJIQkkrBIw7IIgQZBaK1AEt2gREZdWrWirUqt1414vVOkmam2rcmtVVNpyjaitpC4sImBdWQTCIhRkTQxbyL6d7b1/zBBPQuCckJws5Pl+PufDzHvemXnOhMyTmffMM+8cvwwlIg8A0caYX9vzs4AKY8xTtZa7A7gDICUlZURmZmbIHzxcSktLiY1t+ucpGJ+f6p+lCMbnx1Ppxu/x4oiMwOeAvNxDeD1e4jrEktgpgbKyMvALCZ3iMX6DiFQ/37olaq59e7paU7ytKVZoXfG29FjHjh273hiTHqzfqcYszjbGXGNPvy0ijwAfikhGo0TYCIwxLwAvAKSnp5uLL764eQMCVq1aRVPHUVFUyu6Pt5A4sCfOyEgiXBFUVXk4ejCfgoMFDB45gB9d8zOOHMqvXuaaGy5nyKi+jD5/NImdW8cVvubYtw3RmuJtTbFC64q3NcV6Kqcas3CJSPX7xpjfAC9iFRQMpe5CLtAzYL6H3c5hhEgAACAASURBVBaKhizbZvh8Po4ePsahw/l0Gt6HfV8d4OWHXuKfT71F8dEi5r/wGvmlRaz/9yZu/9kPayy75pMNuFxRrSZRKKWa16nOLP4FjAM+ON5gjHlVRA4Cz4aw7rVAXxHpjXWgnwbccOpFqi0FfhswqH0J8F8hLtsm+P1+jhzM584bH+Rg7hGioiK5f9adpI3oy8YPNnBo32Fuvf86br3u5yx441kiImr+qAcO7YdoiXClVIhOerQwxjxkjPmgjvYlxpi+wVZsjPECM7AO/F8Bi4wxW0VkzvFLWSIyUkRygGuBv4jIVnvZY8CvsBLOWmCO3dbmVVW6+WbvQbJeeZ+dG7/mDy/+mnPTB+N2e3j6188zdIL1RLryojIiHU4iIiPYmr2dhE7xRERaCaN32lnMePA2HA4tn6GUCk0oJcpdwDVAamB/Y8ycYMsaY94D3qvVNjtgei3WJaa6ln0ZeDnYNtqa4vwiXJERXPj9dCJckbj9Xh79/YNsXLOFZx5/CY9dbE9EiIqOwuvxMnBof2Lj2rN41QLcbg8xMdEkdkpg+3+2NfOnUUq1FqHclLcYKALWA1XhDUfVpaysnNLiUgqOFdExrgPlJeXkZO+l64jePP+HBRQcKyLj2on8JfNJKgrLERHGTLmQlSs+5abbptD9rK7ExscSG99yv5GhlGrZQkkWPYwxeo9DM/H5fFSWVeIpdVOWX0b7yGh2rdvF4O+ew7N/fJmVyz4BYOO6Lcyeez9jLhrJfS//Ar/AecZP+9h2dVZxra28rILiohL278mhR69udIiPC2k5pVTbEMoI56ciMiTskag6uYvLObpmG4VfbCEx0snB/Yc4a1gfFv02k1tun4YzoBLrv95chtfnI7FrEp26JJHStXNIB3yPx8OnH63liu/eyE9vepArL7qJ5e+uprJSTySVUpaTnlmIyGbA2H1uFZHdWJehBDDGmKFNE2LbUlpaRnlpOR6Pl/YxMRxcvYHD2bsAOLbzAN2/dx7bt3xN/9EDOLL3EKlnn8XX/9kLQEJSRyJO46a6woJifvvIH/D7rRvpjTE8OWceF449n+hofc6VUurUl6GuaLIoFABFRSW89vJb5OUe4iczfsjhr3NxndWNc0YMYPd7n1J+6Bj5m3dx9gVDyPtPHh2TO9I5JYmxEy/E5/Vy2eQJJCR1rPd2/X4/hQU1n0xXWVGJ1x4sN8Zw9Mgx1n62Aa/Hy+gL00nqnIDT2XLv9lZKNa6TJgtjzL6mDESBr8LNtddejiAc2r6fdgmxlB0r5fO/rWDS/ZPZsuBdIlyRxCfFs6dkN8mpKVx/yw/IXPA2cfGxYKwxDkc975+IjnYx+rsj+Pzf66vbBg3tX31WcfRwPtdf+ROOHLbuAI/v2IFF779El67JjffhlVItmt6V1UIUHynC6/bixk9pZQUdeifzTfYe/B4PXQf0ZM/a/5DY7yzOGpdOTIf2XHzTePbuPsC902fx+b/Xs/yd1fzo6rs5ll9Y723Hd+zAnKdmcs31V9AztTtXXnMJT/9lTvVZyrL3VlcnCoCiwmLe/HvWt7WolFJnvLA+z0KdWsGxQirKK4nEQWRkBDt27GbWvY9zbvo5TLv1avpMPI8DH2+l9+gB5GbvYfA13yHCFYUzKpLysgr+7+V/1FhfZUUVaz/ZwBXXXFLvWDolJ3L/rDspKy0npn0M7drFVL9XXFRyQv/CohKMMfpcbKXaCE0WzaT4WDEuZyRRCZFUllXhiIygT5dknl/4BKVlFRw+eJTPV6/DgXBWcjwDxw/HFfDNJmeEk46JJ5YSr6stVNEx0UTHRJ/QfuXkS5j/vwvxuD3Wtp1Obrhlcr0vdymlWi/9bW9ixhhK84tw+A3uskqO7jyI0wgHcw8TkdSBTsmJHD16jNf+9g/++cZ7xHSIITf3EO0710wCLlcUP777etrHfvtAorP7pTJwSL9Gj7lzchKL3nuJy6/+PpOuHMdr77xA124pjb4dpVTLpWcWTaiooASv243D4cB4fGx64yP6jh3G6leWMeaGsezetZ+0/qk8ct9vq8cDHv7Zr5i/6I+4PR6ioqNqrK9rtxQWLX2Jjeu20CE+lrQBfUjq1PhVZF3RLvqk9eKXjz+IMabOsw+l1JlNzyyaSMGRQqrKq/B6fLir3Kz7cguDr7uIXauzGXHFKL54499UlFZwOC+fs/ulVi9njGHl0o/rvN/BGeGkc0oSEy7/HqMuHBGWRBHIFe3SRKFUG6XJIsxKi8s4eiifwsJilr31IQ/fOIf1H2Vzzjn9+fV/P03/q0fjcDooLywlrkMsUa4oyssqaqwj9eye1RVjlVKqOegRKEyqKqvwuD143V5iol3gNaR/bzijxqfzm7t+z6Dz+pHWrzdut4coh4Ohl4wgpks8sfGxNUqH9+nbi4vGX9CMn0QppTRZhEV5STmVpZW4K6pwRkbgrfLiiHQS3zGWBX96ndv/+0fs3bGf1N49EIfg9fnoMqAHOB10TOzAK28+Q86BPJwOJ117pIT98pJSSgWjyaIR+f1+jN/grfRw6Os8lr+ylOIjRVww+ULOGTcU3D6i20XTISGOjglxLP7nUkZ/byTJvTvVWE9S50SSOic206dQSqkT6ZhFIyguLKGooJjiwhJ8Ph8F+YUk9ezE9bNuIDYhlk/e+Ajj9SNREQwYfDaVlVW4YqO57uYfkNylU/ANKKVUMwtrshCRSSKyQ0R2icjMOt53icjr9vtfiEiq3Z4qIhUistF+PR/OOBuisKAIr9tDZUkFuZv3IQiudi4W/HYhRoQLfvAdAI4eOIrT4eBQ3lH6DOhFctfOdE5JaubolVIqNGG7DCUiTmAeMAHIAdaKSJYxJvBZnrcBBcaYNBGZBswFrrPf+9oYMyxc8TVEcWEJbreHyKgIq4i704FxOuiQksC2z7+iPLGE7984ns/eX8OoCekgQte+3XA6HVwyZRwdk07/LmullGoO4TyzOB/YZYzZbYxxA5nAVbX6XAUssKffBMZLCy425PX6yD2QR+6BPFyuSCKcTozPj8fjAfy0T4qlfWIcq15ZRs+zu3M45zCV5ZVc8/BUomKicMXF0PWsFC3trZRqdcKZLLoDBwLmc+y2OvsYY7xYz/o+fm2mt4hsEJHVIvLdMMYZkrKyco4eyScuLpauPVIor6iktLQcr9+Pz+tjxdJ/U+V242rnoqKoDIdDGH3pKJJTUxgwZiBxCXHEtNMb2pRSrZOEq8y0iEwBJhljptvzPwRGGWNmBPTZYvfJsee/BkYBJUCsMSZfREYAbwODjTHFtbZxB3AHQEpKyojMzMywfBbrIUAGh8OJMQZjDA6HUFJcisPhwO32EB3toqrKTWSUE3EbXHExOCOcLbrYXmlpKbGxsc0dRsg03vBpTbFC64q3pcc6duzY9caY9GD9wvnV2VygZ8B8D7utrj45IhIBxAP5xspgVQDGmPV2EukHrAtc2BjzAvACQHp6urn44osb/UOUlpThQHB7PHjcXjweDx6vB1e0CydOnvzVc0y+4QpmPziX3//5UQ4d+YZR3zmf9vGxOJwtN1EArFq1inDss3DReMOnNcUKrSve1hTrqYTzaLYW6CsivUUkCpgGZNXqkwXcbE9PAT40xhgR6WwPkCMifYC+wO4wxlqtqqqK3AN5fLkmm+LCYnw+H8UlpXz64Vq+yTnInq/3c/2VP+GOG+/H7XVz5z03s3r5p5w3cgjbt+7E4XAQl9ihxScKpZSqj7Ad0ewxiBnAUuArYJExZquIzBGRDLvbfCBJRHYBvwCOf732IiBbRDZiDXz/1BhzLFyxBioqKKZduxjS+qfi9fjwef3ERLsYPnoIzggnLlcUTzz3KHu/3s8fH3+B2I6xdE5OorCgiAGD+2mSUEqdkcJ6B7cx5j3gvVptswOmK4Fr61juLeCtcMZWl8KCIkSEb3IOUVFeTo9e3XG73Yg48Pt93PfTWUREOJnz5MNcmjGerdnbKSosZsToc9m79wA9zupKfuGhpg5bKaXCTst9ABXlFVRVuWnXPgZHXHtcMS58Pj/lJeVUVFTxyH2/ITraxR/+MocfT/05M26dyYv/9zTvLf6A+MR44uLjuP+RO4nv2KG5P4pSSoVFm79mUlxUSlWFm+gYFz6Pj5KiEnxeH5XllYjDgSsqgkd+dR/ZG7axJOtDLrn8YioqKqmsrOL2GT+kc3ISKV07a6JQSp3R2nSyKCstx+/zUVpWRs6+PA4dPIrHHqeIiIig8FghOQcOEhvXnti49mzN3k7P1O44nU7OSu2uxf6UUm1Gm04Wfo+P0tIynprzv1x36e1cf/kdvLkwy74jG7Zl76BHz65ER7vwuD2MvGA4u3bs4ZePP0BcfFwzR6+UUk2nTY9ZeHw+3v3Hcj5a8RkAVVVuXnpuIReNH0O3nilEx0Tj9xvKykqYlDGea66/EnEIcXGxeje2UqpNadvJwuth4/qtJ7Rv3rCNnqndOLt/b2I7tKNjYjwzH72H9rHtmiFKpZRqfm36MlTHhHjSR597Qvu56eeAEZK7dCI5pTOJSR01USil2rQ2nSxcriiuunYSYydeiIgQHe3i7gd+THJKEvEJcfo4U6WUsrXpy1AAnZKT+OXcB3nol3cjDgdxHWKJidHxCKWUCtTmkwVAh/hYOsS33KqQSinV3Nr0ZSillFKh0WShlFIqKE0WSimlgtJkoZRSKihNFkoppYLSZKGUUioosR533fqJyBFgX3PHAXQCjjZ3ECFqTbGCxhtOrSlWaF3xtvRYexljOgfrdMYki5ZCRNYZY9KbO45QtKZYQeMNp9YUK7SueFtTrKeil6GUUkoFpclCKaVUUJosGt8LzR1APbSmWEHjDafWFCu0rnhbU6wnpWMWSimlgtIzC6WUUkFpslBKKRWUJosQicgkEdkhIrtEZGYd77tE5HX7/S9EJNVuTxWRChHZaL+ebyHxXiQiX4qIV0Sm1HrvZhHZab9ubgXx+gL2b1YLiPUXIrJNRLJFZIWI9Ap4ryXu21PF29L27U9FZLMdz8ciMijgvf+yl9shIhPDHWtD4m2u40KDGGP0FeQFOIGvgT5AFLAJGFSrz13A8/b0NOB1ezoV2NIC400FhgJ/BaYEtCcCu+1/E+zphJYar/1eaQvbt2OBdvb0nQH/F1rqvq0z3ha6bzsETGcAS+zpQXZ/F9DbXo+zBcfb5MeFhr70zCI05wO7jDG7jTFuIBO4qlafq4AF9vSbwHgRkSaMMVDQeI0xe40x2YC/1rITgeXGmGPGmAJgOTCpBcfb1EKJdaUxptye/RzoYU+31H17snibWiixFgfMtgeOf0PnKiDTGFNljNkD7LLX11LjbXU0WYSmO3AgYD7HbquzjzHGCxQBSfZ7vUVkg4isFpHvhjtYQos3HMueroZuM1pE1onI5yJydeOGdoL6xnob8P5pLtsYGhIvtMB9KyJ3i8jXwBPAPfVZtpE1JF5o+uNCg+hjVcMvDzjLGJMvIiOAt0VkcK2/OFTD9DLG5IpIH+BDEdlsjPm6uYMSkZuAdOB7zR1LKE4Sb4vbt8aYecA8EbkB+B+gScZ+TtdJ4m11xwU9swhNLtAzYL6H3VZnHxGJAOKBfPu0OB/AGLMe6xpnvxYQbziWPV0N2qYxJtf+dzewChjemMHVElKsIvJ94BEgwxhTVZ9lG1lD4m2R+zZAJnD8bKfF7tsA1fE203GhYZp70KQ1vLDOwHZjDZwdH8gaXKvP3dQc4F5kT3fGHmjDGgjLBRKbO96Avq9y4gD3HqwB2AR7uiXHmwC47OlOwE5qDTI2w/+F4Vi//H1rtbfIfXuKeFvivu0bMH0lsM6eHkzNAe7dhH+AuyHxNvlxocGft7kDaC0v4DLgP/Yv1SN22xysv8QAooE3sAbW1gB97PZrgK3ARuBL4MoWEu9IrGusZUA+sDVg2R/bn2MXcGtLjhcYA2y2f1E3A7e1gFg/AA7ZP/ONQFYL37d1xttC9+2fAn6fVgYenLHOjL4GdgCXtpB9W2e8zXVcaMhLy30opZQKSscslFJKBaXJQimlVFCaLJRSSgWlyUIppVRQmiyUUkoFpclChcSukrnlJO89KSLb7aql/xSRjo2wvUdF5IHTXHaViKQH6XOviLSr53ovFpF36mi/RUSeq2+cjUVEhovI/AYsP8e+Ka8xYgm6Lnt/dQthXU+JyLjGiEs1nCYL1RiWA+cYY4Zifef8v5o5nlDcC9QrWbRg/w08c7oLG2NmG2M+aGgQIuIMcV23AEGTBfAscELZb9U8NFmo+nCKyIsislVElolIDIAxZpmxiifCKaqWisjDdm3/TSLyuN12u4istdvequuvfRFJE5EP7D5fisjZtf/KF5HnROSWOpb9s10Ib6uIPGa33YN1sFopIivttktE5DN7/W+ISKzdPsk+a/oSmHyKfdPTPqPZKSK/tJedIyL3BsTyGxH5ea34Uu31vyoi/xGRhSLyfRH5xF7X+Xa/9iLysoissYvPXWW3xwFDjTGb7PkaZ2QissXeRqqIfFXXz8/e9hT7s74RsGz1Pq5rP9rte0Vkrr1/rj2+Lvu92fbPdouIvCCWKVj1pxaK9RyHGBEZIVYxvfUislREugIYY/YBSSLS5RT7XTWV5r4rUF+t44VVf98LDLPnFwE31dHvXydpvxT4lG+fm5Bo/5sU0OfXwM/s6UeBB+zpL4Af2NPRWGcEFwPvBCz7HHCLPb0KSK+1HafdPtSe3wt0sqc7AR8B7e35h4HZ9rYOAH0BsT/zO3V8tluwCsMlATHAFqwDYirwpd3HgXWXb9JJ9usQu8964GV7e1cBb9v9fnt8vwIdsc7g2mM9i+KtgPVV7zd7fou9jZP+/LBLqGCVr9gfsB/+HNDnVPvxoYDtvYpdjoWA8hXA37DvUq7184nE+n/R2Z6/Dng5YLkXgWua+/+/vvR5Fqp+9hhjNtrT67EOQNVE5BGsA9LCOpb9PvCKsZ+bYIw5ZrefIyL/FpHNwI1YNX4C1xkHdDfG/NNertJ8++yFUEy1/+rdYK97UB19Rtvtn4jIRqyqoL2AAfZn3mmsI9ffT7Gd5caYfGNMBfAP4EJjzF4gX0SGA5cAG4xdPK6WPcaYzcYYP1YJiBX29jbz7T6+BJhpx7cKK5GdBXQFjoS2K0798zPW2eES4EqximFeDiy23z7Vfnz9JNsbK9ZTIzcD46j1s7X1B84Bltuf7X+oeWZ6mNAuWakw0xLlqj6qAqZ9WH9FA9agJXAFMN4+0IXqVeBqY8wmex0Xh7icl5qXUaNrdxCR3sADwEhjTIGIvFpXP6y/4pcbY66vtfywEGOBEx9qc3z+Jawzjy5YZwx1Cdyv/oB5P9/+jgrWX9g7asU4gJqf6VT75aQ/vwCZwAzgGFbRu5IQ9mNZ7ZWISDTwv1hnEAdE5FFOvu+3GmMuqOO94/FXnOQ91YT0zEI1mIhMAh7CKp52sr/6lwO3Hh+TEJFEuz0OyBORSKwzixqMMSVAjtgP3hHrWeftgH3AIHu+IzC+jm12wDqQFYlICtalsONK7G2DNc7yHRFJs7fRXkT6AduBVBE52+5XI5nUMkFEEu1xgKuBT+z2f2I9DW8ksPQUywezFPiZiPX0RftsBeArIC2g317gPLvPeVgVUetjtb387ViJA069H0/meGI4ao//BD43PXDf7wA6i8gFdsyRIhJ4BtIP61KaamaaLFRjeA7rl3+5nOTh88aYJUAWsM6+3HB8EHYW1pjEJ1gH57r8ELhHRLKxrm93McYcwLruvsX+d0Md29xkt28H/o9vD+AALwBLRGSlMeYI1l//r9nb+AwYYIypBO4A3rUvwRw+xT5YA7wFZGONIayzY3BjVRtdZIzxnWL5YH6FdX0/W0S22vMYY7YD8fblOuwYEu0+M7DGNkJmx/gOVkJ4x2471X482XoKscYbtmAlurUBb78KPG//P3BiJZK5IrIJqwrrGLASB1YiXFefz6DCQ6vOKhVGIuLAKkF9rTFmZ5i2cR9QYox5KRzrby4i8gPgPGPMrOaORemZhVJhIyKDsJ5bsSJcicL2Z2qOR5wpIoDfN3cQyqJnFkoppYLSMwullFJBabJQSikVlCYLpZRSQWmyUEopFZQmC6WUUkFpuQ91Rlu/fn0Ph8OxzO/3D8AqLdFWGYfDsd3v918yYsSInOYORrU+mizUGc3hcCzr0qVL35SUFHE42u6JtN/vl7y8vP779+//PCMjY3hWVlaoxQeVAvQylDrD+f3+ASkpKRFtOVEAOBwOunbt6oiMjOwOPJyRkRHf3DGp1qVt/waptqBNn1EEcjgc2HUIE6l/gUHVxulvkVJNZOHChQwdOpQhQ4YwZswYNm3aVON9n8/H8OHDueKKK6rb9uzZw6hRo0hLS+O6667D7XbXue7f/e53pKWl0b9/f5YuDVrc1mAVJVQqZJoslGoivXv3ZvXq1WzevJlZs2Zxxx131Hj/T3/6EwMHDqzR9vDDD3Pfffexa9cuEhISmD9//gnr3bZtG5mZmWzdupUlS5Zw11134fM1pMCtUifSZKFUgKqCfAq/yuZY9joKv8qmqqCuB9udnjFjxpCQkADA6NGjycn59ktJOTk5vPvuu0yfPr26zRjDhx9+yJQp1qMgbr75Zt5+++0T1rt48WKmTZuGy+Wid+/epKWlsWbNmkaLWynQZKFUtaqCfMpy9uH3WJd6/B43ZTn7GjVhHDd//nwuvfTbZwjde++9PPHEEwSOr+Tn59OxY0ciIqwvLfbo0YPc3NwT1pWbm0vPnj2r50/WT6mG0GShlK3iYC4Yf81G47faG9HKlSuZP38+c+fOBeCdd94hOTmZESNGNOp2lGpMmiyUsh0/owi1PRTz5s1j2LBhDBs2jG+++Ybs7GymT5/O4sWLSUpKAuCTTz4hKyuL1NRUpk2bxocffshNN91EUlIShYWFeL1ewLpU1b179xO20b17dw4cOFA9f7J+SjWEJgulbI7IqHq1h+Luu+9m48aNbNy4Ea/Xy+TJk/nb3/5Gv379qvv87ne/Iycnh71795KZmcm4ceP4+9//jogwduxY3nzzTQAWLFjAVVdddcI2MjIyyMzMpKqqij179rBz507OP//8045ZqbposlDKFtOlO0itXwlxWO2NYM6cOeTn53PXXXcxbNgw0tPTgy4zd+5cnn76adLS0sjPz+e2224DICsri9mzZwMwePBgpk6dyqBBg5g0aRLz5s3D6XQ2SsxKHadPylNntPXr15v6jAVUFeRTcTAXv8eNIzKKmC7dcSUkhTHCprV+/Xoee+yxl4Hns7Ky1jZ3PKr10NpQSgVwJSSdUclBqcail6GUUkoFpclCKaVUUJoslFJKBaXJQimlVFCaLJRSSgWlyUKpJrJ9+3YuuOACXC4XTz31VI33CgsLmTJlCgMGDGDgwIF89tlnABw7dowJEybQt29fJkyYQEFBQZ3rXrBgAX379qVv374sWLAg7J9FtT2aLJRqIomJiTzzzDM88MADJ7z385//nEmTJrF9+3Y2bdpUXar88ccfZ/z48ezcuZPx48fz+OOPn7DssWPHeOyxx/jiiy9Ys2YNjz322EmTilKnS5OFUgGKdu1jZ+a/+Gr+6+zM/BdFu/Y12rqTk5MZOXIkkZE1nztUVFTERx99VH13dlRUFB07dgSs8uM333wzcPIS5UuXLmXChAkkJiaSkJDAhAkTWLJkSaPFrRRoslCqWtGufeR9vBZvWTkA3rJy8j5e26gJoy579uyhc+fO3HrrrQwfPpzp06dTVlYGwKFDh+jatSsAXbp04dChQycsryXKVVPQZKGU7fC6bEytJ8wZn4/D67LDul2v18uXX37JnXfeyYYNG2jfvn2dl5tE5PgztJVqcposlLIdP6MItT0UtUuU16VHjx706NGDUaNGATBlyhS+/PJLAFJSUsjLywMgLy+P5OTkE5bXEuWqKWiyUMoW0b5dvdpDEViivFu3bnX26dKlCz179mTHjh0ArFixgkGDBgFW+fHj3246WYnyiRMnsmzZMgoKCigoKGDZsmVMnDjxtGNWqi5aSFApW3L6UPI+XlvjUpQ4nSSnD22U9R88eJD09HSKi4txOBz88Y9/ZNu2bXTo0IFnn32WG2+8EbfbTZ8+fXjllVcAmDlzJlOnTmX+/Pn06tWLRYsWAbBu3Tqef/55XnrpJRITE5k1axYjR44EYPbs2SQmJjZKzEodpyXK1RmtviXKi3bt4/C6bLxl5US0b0dy+lDi03qFMcKmpSXK1enSMwulAsSn9TqjkoNSjUXHLJRSSgWlyUIppVRQmiyUUkoFpclCKaVUUJoslFJKBaXJQqkmUlRUxJVXXsm5557L4MGDq++lgNBKjIdarlypcNBkoVQTmTdvHoMGDWLTpk2sWrWK+++/H7fbHXKJ8VDKlSsVLposlArw1UebefEnz/D0lF/x4k+e4auPNjfaukWEkpISjDGUlpaSmJhIREREyCXGQylXrlS46E15Stm++mgzy59/F2+VB4CSo0Usf/5dAAZeNKTB658xYwYZGRl069aNkpISXn/9dRwOR8glxkMpV65UuOiZhVK2jxeurE4Ux3mrPHy8cGWjrH/p0qXV1Wc3btzIjBkzKC4uPq11ably1dQ0WShlK8kvqld7KAJLlM+bN4/JkycjIqSlpdG7d2+2b98econxUMqVKxUumiyUssUlxderPRSBJcoHDBjAihUrAOuS0o4dO+jTp0/IJcZDKVeuVLhoslDKduGNY4lw1Xw+doQrkgtvHNso6581axaffvopQ4YMYfz48cydO5dOnTrVKDE+cuTIGiXGp0+fzrp16wCrXPny5cvp27cvH3zwATNnzmyUuJQKhZYoV2e0+pYo/+qjzXy8cCUl+UXEJcVz4Y1jG2Vwu6XQEuXqdOm3oZQKMPCiIWdUclCqsehlKKWUUkFpslBKKRWUeLQdTAAAAqZJREFUJgullFJBabJQSikVlCYLpZRSQWmyUCrMlixZQv/+/UlLS6uzUmxVVRXXXXcdaWlpjBo1ir179zZ9kEoFoclCqTDy+XzcfffdvP/++2zbto3XXnuNbdu21egzf/58EhIS2LVrF/fddx8PP/xwM0Wr1MlpslAqwLtvL2fimKmcm3oxE8dM5d23lzdofWvWrCEtLY0+ffoQFRXFtGnTWLx4cY0+gaXHp0yZwooVK9CbZVVLo8lCKdu7by/nsZlPkpd7CGMMebmHeGzmkw1KGKGUHw/sExERQXx8PPn5+ae9TaXCQZOFUrZnnniRyoqqGm2VFVU888SLzRSRUi2HJgulbAe/OVyv9lCEUn48sI/X66WoqIikpKTT3qZS4aDJQilbl251Px/iZO2hGDlyJDt37mTPnj243W4yMzPJyMio0Sew9Pibb77JuHHj9MFGqsXRZKGU7Z6Hbic6xlWjLTrGxT0P3X7a64yIiOC5555j4sSJDBw4kKlTpzJ48GBmz55NVlYWALfddhv5+fmkpaXx9NNP1/n1WqWam1adVcp2+dUTAGvs4uA3h+nSLZl7Hrq9uv10XXbZZVx22WU12ubMmVM9HR0dzRtvvNGgbSgVbposlApw+dUTGpwclDoT6WUopZRSQWmyUEopFZQmC3WmM36/v7ljaBH8fr/eGa5OmyYLdUZzOBzb8/Ly/G09Yfj9fvLy8vyVlZVHAf1erqo3HeBWZzS/33/J/v37P8/Ly+velu9dMMZQWVl57K9//evfgUSguLljUq2LJgt1RhsxYkRORkbGYOABoBvQ1q/DJAAfADubOxDVuohew1RtQUZGRjRWsohs7liaWRnwTVZWVtu+LqfqTZOFUkqpoHSAWymlVFCaLJRSSgWlyUIppVRQ/w/zqcQH9f/jEQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "#basic settings for plt\n",
    "\"\"\"\n",
    "plt.rcParams['font.size']=22\n",
    "plt.rcParams[\"figure.figsize\"] = (5,5)\n",
    "plt.rcParams['lines.linewidth'] = 2\n",
    "plt.rcParams['lines.color'] = 'r'\n",
    "\"\"\"\n",
    "plt.rcParams['axes.grid'] = True\n",
    "\n",
    "plt.title(\"h2 value from different source\")\n",
    "plt.scatter(h2_total['neale.h2_observed'], h2_total['neale2.h2_observed'])\n",
    "plt.xlabel(\"h2 observed calculated by neale group\")\n",
    "plt.ylabel(\"h2 observed calculated by me(univariate)\")\n",
    "plt.show()\n",
    "\n",
    "plt.title(\"h2 value from different source\")\n",
    "plt.scatter(h2_total['neale.h2_liability'], h2_total['neale2.h2_liability'])\n",
    "plt.xlabel(\"h2 observed calculated by neale group\")\n",
    "plt.ylabel(\"h2 observed calculated by me(univariate)\")\n",
    "plt.show()\n",
    "\n",
    "plt.title(\"h2 value from different source\")\n",
    "plt.scatter(h2_total['neale.h2_liability'], h2_total['uni.Total Liability scale h2'])\n",
    "plt.xlabel(\"h2 observed calculated by neale group\")\n",
    "plt.ylabel(\"h2 observed calculated by me(univariate)\")\n",
    "plt.show()\n",
    "\n",
    "plt.title(\"h2 value from different source\")\n",
    "plt.scatter(h2_total['neale2.h2_liability'], h2_total['uni.Total Liability scale h2'])\n",
    "plt.xlabel(\"h2 observed calculated by neale group\")\n",
    "plt.ylabel(\"h2 observed calculated by me(univariate)\")\n",
    "plt.show()\n",
    "\n",
    "\n",
    "def plot_compare_h2(suffix='cm300',mode='all'):\n",
    "    if mode=='all':\n",
    "        idx=h2_total.index\n",
    "    elif mode=='continuous':\n",
    "        idx=h2_total[h2_total['uni.Total Liability scale h2']==h2_total['uni.Total Observed scale h2']].index\n",
    "    elif mode=='binary':\n",
    "        idx=h2_total[h2_total['uni.Total Liability scale h2']!=h2_total['uni.Total Observed scale h2']].index\n",
    "        \n",
    "    x_data=h2_total.loc[idx]['uni.Total Liability scale h2']\n",
    "    y_data=h2_total.loc[idx]['{}.Total Liability scale h2'.format(suffix)]\n",
    "    #x_data=h2_total[idx]['uni.Total Observed scale h2']\n",
    "    #y_data=h2_total[idx]['{}.Total Observed scale h2'.format(suffix)]\n",
    "    #c=h2_total[idx]['cm300.Total Liability scale h2']/h2_total[idx]['cm300.Total Observed scale h2']\n",
    "    #x_data=h2_total[idx]['uni.Total Observed scale h2']*c\n",
    "    #y_data=h2_total[idx]['{}.Total Observed scale h2'.format(suffix)]*c\n",
    "    \n",
    "    hue_data=np.log10(st.norm.sf(h2_total.loc[idx]['{}.Total Observed scale h2'.format(suffix)]/h2_total.loc[idx]['{}.Total Observed scale h2_se'.format(suffix)]))\n",
    "    \n",
    "    plt.title(\"h2 values of {} traits from different source\".format(mode))\n",
    "    sns.scatterplot(x_data,y_data,hue=hue_data)\n",
    "    plt.xlabel(\"h2 calculated by me(univariate)\")\n",
    "    plt.ylabel(\"h2 calculated by me({})\".format(suffix))\n",
    "    plt.legend(loc='upper center', bbox_to_anchor=(0.5, -0.15), fontsize=10,fancybox=True, shadow=True)\n",
    "    plt.show()\n",
    "\n",
    "    \n",
    "\n",
    "    \n",
    "for suffix in suffix_bp_list_full:\n",
    "    plot_compare_h2(suffix,mode='binary')\n",
    "\n",
    "    \n",
    "for suffix in suffix_bp_list_full:\n",
    "    plot_compare_h2(suffix,mode='continuous')\n",
    "#plot_compare_h2(\"cm15\")\n",
    "#plot_compare_h2(\"cm10\")\n",
    "#plot_compare_h2(\"cm2\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "a=pd.read_pickle('23115_irnt.pickle')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(1, 249230914),\n",
       " (2, 243082521),\n",
       " (3, 197900375),\n",
       " (4, 190922257),\n",
       " (5, 180736061),\n",
       " (6, 170934414),\n",
       " (7, 159128550),\n",
       " (8, 146303560),\n",
       " (9, 141104957),\n",
       " (10, 135503523),\n",
       " (11, 134946451),\n",
       " (12, 133840247),\n",
       " (13, 115109852),\n",
       " (14, 107289436),\n",
       " (15, 102461014),\n",
       " (16, 90238127),\n",
       " (17, 81109537),\n",
       " (18, 78017073),\n",
       " (19, 59117442),\n",
       " (20, 62963628),\n",
       " (21, 48119634),\n",
       " (22, 51237712)]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "[(chr,a[a.CHR==chr]['BP'].iloc[-1]) for chr in range(1,22+1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bp300.filename</th>\n",
       "      <th>bp300.Total Observed scale h2</th>\n",
       "      <th>bp300.Total Observed scale h2_se</th>\n",
       "      <th>bp300.Total Liability scale h2</th>\n",
       "      <th>bp300.Total Liability scale h2_se</th>\n",
       "      <th>bp300.Lambda GC</th>\n",
       "      <th>bp300.Mean Chi^2</th>\n",
       "      <th>bp300.Intercept</th>\n",
       "      <th>bp300.Intercept_se</th>\n",
       "      <th>bp300.Ratio</th>\n",
       "      <th>...</th>\n",
       "      <th>neale2.isBadPower</th>\n",
       "      <th>neale2.isLowNeff</th>\n",
       "      <th>neale2.isMidNeff</th>\n",
       "      <th>neale2.isExtremeSE</th>\n",
       "      <th>neale2.isHighSE</th>\n",
       "      <th>neale2.isSexBias</th>\n",
       "      <th>neale2.isBadOrdinal</th>\n",
       "      <th>neale2.isNumericOrdinal</th>\n",
       "      <th>uni.Total h2 z</th>\n",
       "      <th>uni.Total h2 p</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>phenotype</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>50_irnt</th>\n",
       "      <td>data/out_final/bp300.50_irnt.ldsc.log</td>\n",
       "      <td>0.4004</td>\n",
       "      <td>0.0243</td>\n",
       "      <td>0.4004</td>\n",
       "      <td>0.0243</td>\n",
       "      <td>2.5027</td>\n",
       "      <td>4.5198</td>\n",
       "      <td>1.4446</td>\n",
       "      <td>0.0673</td>\n",
       "      <td>0.1263 (0.0191)</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>19.147208</td>\n",
       "      <td>5.105832e-82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20153_irnt</th>\n",
       "      <td>data/out_final/bp300.20153_irnt.ldsc.log</td>\n",
       "      <td>0.3398</td>\n",
       "      <td>0.0234</td>\n",
       "      <td>0.3398</td>\n",
       "      <td>0.0234</td>\n",
       "      <td>1.5696</td>\n",
       "      <td>2.0516</td>\n",
       "      <td>1.1714</td>\n",
       "      <td>0.0236</td>\n",
       "      <td>0.163 (0.0224)</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>15.755981</td>\n",
       "      <td>3.124117e-56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20015_irnt</th>\n",
       "      <td>data/out_final/bp300.20015_irnt.ldsc.log</td>\n",
       "      <td>0.2949</td>\n",
       "      <td>0.0185</td>\n",
       "      <td>0.2949</td>\n",
       "      <td>0.0185</td>\n",
       "      <td>2.1545</td>\n",
       "      <td>3.5864</td>\n",
       "      <td>1.3002</td>\n",
       "      <td>0.0471</td>\n",
       "      <td>0.1161 (0.0182)</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>17.315789</td>\n",
       "      <td>1.787921e-67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5097_irnt</th>\n",
       "      <td>data/out_final/bp300.5097_irnt.ldsc.log</td>\n",
       "      <td>0.2869</td>\n",
       "      <td>0.0246</td>\n",
       "      <td>0.2869</td>\n",
       "      <td>0.0246</td>\n",
       "      <td>1.3169</td>\n",
       "      <td>1.5267</td>\n",
       "      <td>1.1100</td>\n",
       "      <td>0.0158</td>\n",
       "      <td>0.2088 (0.0299)</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>12.351931</td>\n",
       "      <td>2.377794e-35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5098_irnt</th>\n",
       "      <td>data/out_final/bp300.5098_irnt.ldsc.log</td>\n",
       "      <td>0.2816</td>\n",
       "      <td>0.0239</td>\n",
       "      <td>0.2816</td>\n",
       "      <td>0.0239</td>\n",
       "      <td>1.3203</td>\n",
       "      <td>1.5293</td>\n",
       "      <td>1.1153</td>\n",
       "      <td>0.0160</td>\n",
       "      <td>0.2179 (0.0302)</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>12.668161</td>\n",
       "      <td>4.438533e-37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5134_irnt</th>\n",
       "      <td>data/out_final/bp300.5134_irnt.ldsc.log</td>\n",
       "      <td>0.2776</td>\n",
       "      <td>0.0238</td>\n",
       "      <td>0.2776</td>\n",
       "      <td>0.0238</td>\n",
       "      <td>1.3068</td>\n",
       "      <td>1.5187</td>\n",
       "      <td>1.1156</td>\n",
       "      <td>0.0154</td>\n",
       "      <td>0.2229 (0.0297)</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>12.144737</td>\n",
       "      <td>3.058956e-34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5133_irnt</th>\n",
       "      <td>data/out_final/bp300.5133_irnt.ldsc.log</td>\n",
       "      <td>0.2770</td>\n",
       "      <td>0.0237</td>\n",
       "      <td>0.2770</td>\n",
       "      <td>0.0237</td>\n",
       "      <td>1.3068</td>\n",
       "      <td>1.5207</td>\n",
       "      <td>1.1154</td>\n",
       "      <td>0.0162</td>\n",
       "      <td>0.2216 (0.0311)</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>12.538462</td>\n",
       "      <td>2.299180e-36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5096_irnt</th>\n",
       "      <td>data/out_final/bp300.5096_irnt.ldsc.log</td>\n",
       "      <td>0.2704</td>\n",
       "      <td>0.0227</td>\n",
       "      <td>0.2704</td>\n",
       "      <td>0.0227</td>\n",
       "      <td>1.3374</td>\n",
       "      <td>1.5677</td>\n",
       "      <td>1.1146</td>\n",
       "      <td>0.0170</td>\n",
       "      <td>0.2018 (0.03)</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>12.772093</td>\n",
       "      <td>1.173779e-37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5099_irnt</th>\n",
       "      <td>data/out_final/bp300.5099_irnt.ldsc.log</td>\n",
       "      <td>0.2691</td>\n",
       "      <td>0.0225</td>\n",
       "      <td>0.2691</td>\n",
       "      <td>0.0225</td>\n",
       "      <td>1.3374</td>\n",
       "      <td>1.5701</td>\n",
       "      <td>1.1191</td>\n",
       "      <td>0.0170</td>\n",
       "      <td>0.2088 (0.0299)</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>12.849057</td>\n",
       "      <td>4.353277e-38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23101_irnt</th>\n",
       "      <td>data/out_final/bp300.23101_irnt.ldsc.log</td>\n",
       "      <td>0.2583</td>\n",
       "      <td>0.0128</td>\n",
       "      <td>0.2583</td>\n",
       "      <td>0.0128</td>\n",
       "      <td>2.2425</td>\n",
       "      <td>3.2016</td>\n",
       "      <td>1.2580</td>\n",
       "      <td>0.0326</td>\n",
       "      <td>0.1172 (0.0148)</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>22.851485</td>\n",
       "      <td>7.062672e-116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23102_irnt</th>\n",
       "      <td>data/out_final/bp300.23102_irnt.ldsc.log</td>\n",
       "      <td>0.2576</td>\n",
       "      <td>0.0128</td>\n",
       "      <td>0.2576</td>\n",
       "      <td>0.0128</td>\n",
       "      <td>2.2425</td>\n",
       "      <td>3.1987</td>\n",
       "      <td>1.2582</td>\n",
       "      <td>0.0325</td>\n",
       "      <td>0.1175 (0.0148)</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>22.782178</td>\n",
       "      <td>3.443984e-115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5132_irnt</th>\n",
       "      <td>data/out_final/bp300.5132_irnt.ldsc.log</td>\n",
       "      <td>0.2556</td>\n",
       "      <td>0.0216</td>\n",
       "      <td>0.2556</td>\n",
       "      <td>0.0216</td>\n",
       "      <td>1.3238</td>\n",
       "      <td>1.5427</td>\n",
       "      <td>1.1180</td>\n",
       "      <td>0.0170</td>\n",
       "      <td>0.2174 (0.0314)</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>12.940299</td>\n",
       "      <td>1.332958e-38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5135_irnt</th>\n",
       "      <td>data/out_final/bp300.5135_irnt.ldsc.log</td>\n",
       "      <td>0.2554</td>\n",
       "      <td>0.0219</td>\n",
       "      <td>0.2554</td>\n",
       "      <td>0.0219</td>\n",
       "      <td>1.3169</td>\n",
       "      <td>1.5372</td>\n",
       "      <td>1.1133</td>\n",
       "      <td>0.0166</td>\n",
       "      <td>0.211 (0.0309)</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>12.783251</td>\n",
       "      <td>1.016931e-37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23129_irnt</th>\n",
       "      <td>data/out_final/bp300.23129_irnt.ldsc.log</td>\n",
       "      <td>0.2551</td>\n",
       "      <td>0.0137</td>\n",
       "      <td>0.2551</td>\n",
       "      <td>0.0137</td>\n",
       "      <td>2.1895</td>\n",
       "      <td>3.1840</td>\n",
       "      <td>1.2607</td>\n",
       "      <td>0.0342</td>\n",
       "      <td>0.1194 (0.0157)</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>21.495238</td>\n",
       "      <td>8.625402e-103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23130_irnt</th>\n",
       "      <td>data/out_final/bp300.23130_irnt.ldsc.log</td>\n",
       "      <td>0.2536</td>\n",
       "      <td>0.0136</td>\n",
       "      <td>0.2536</td>\n",
       "      <td>0.0136</td>\n",
       "      <td>2.1851</td>\n",
       "      <td>3.1718</td>\n",
       "      <td>1.2594</td>\n",
       "      <td>0.0340</td>\n",
       "      <td>0.1194 (0.0157)</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>21.586538</td>\n",
       "      <td>1.201776e-103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23105_irnt</th>\n",
       "      <td>data/out_final/bp300.23105_irnt.ldsc.log</td>\n",
       "      <td>0.2528</td>\n",
       "      <td>0.0119</td>\n",
       "      <td>0.2528</td>\n",
       "      <td>0.0119</td>\n",
       "      <td>2.2558</td>\n",
       "      <td>3.1413</td>\n",
       "      <td>1.2446</td>\n",
       "      <td>0.0308</td>\n",
       "      <td>0.1142 (0.0144)</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>23.666667</td>\n",
       "      <td>3.975746e-124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3143_irnt</th>\n",
       "      <td>data/out_final/bp300.3143_irnt.ldsc.log</td>\n",
       "      <td>0.2436</td>\n",
       "      <td>0.0146</td>\n",
       "      <td>0.2436</td>\n",
       "      <td>0.0146</td>\n",
       "      <td>1.7530</td>\n",
       "      <td>2.2533</td>\n",
       "      <td>1.1932</td>\n",
       "      <td>0.0238</td>\n",
       "      <td>0.1542 (0.019)</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>17.484127</td>\n",
       "      <td>9.464485e-69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23117_irnt</th>\n",
       "      <td>data/out_final/bp300.23117_irnt.ldsc.log</td>\n",
       "      <td>0.2400</td>\n",
       "      <td>0.0111</td>\n",
       "      <td>0.2400</td>\n",
       "      <td>0.0111</td>\n",
       "      <td>2.2115</td>\n",
       "      <td>3.0309</td>\n",
       "      <td>1.2296</td>\n",
       "      <td>0.0290</td>\n",
       "      <td>0.113 (0.0143)</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>23.758242</td>\n",
       "      <td>4.515333e-125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23113_irnt</th>\n",
       "      <td>data/out_final/bp300.23113_irnt.ldsc.log</td>\n",
       "      <td>0.2398</td>\n",
       "      <td>0.0113</td>\n",
       "      <td>0.2398</td>\n",
       "      <td>0.0113</td>\n",
       "      <td>2.1983</td>\n",
       "      <td>3.0474</td>\n",
       "      <td>1.2395</td>\n",
       "      <td>0.0295</td>\n",
       "      <td>0.117 (0.0144)</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>23.402174</td>\n",
       "      <td>2.030616e-121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23114_irnt</th>\n",
       "      <td>data/out_final/bp300.23114_irnt.ldsc.log</td>\n",
       "      <td>0.2395</td>\n",
       "      <td>0.0113</td>\n",
       "      <td>0.2395</td>\n",
       "      <td>0.0113</td>\n",
       "      <td>2.1983</td>\n",
       "      <td>3.0454</td>\n",
       "      <td>1.2390</td>\n",
       "      <td>0.0294</td>\n",
       "      <td>0.1169 (0.0144)</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>23.391304</td>\n",
       "      <td>2.619843e-121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23118_irnt</th>\n",
       "      <td>data/out_final/bp300.23118_irnt.ldsc.log</td>\n",
       "      <td>0.2392</td>\n",
       "      <td>0.0111</td>\n",
       "      <td>0.2392</td>\n",
       "      <td>0.0111</td>\n",
       "      <td>2.2115</td>\n",
       "      <td>3.0269</td>\n",
       "      <td>1.2301</td>\n",
       "      <td>0.0290</td>\n",
       "      <td>0.1135 (0.0143)</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>23.955556</td>\n",
       "      <td>4.043505e-127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4100_irnt</th>\n",
       "      <td>data/out_final/bp300.4100_irnt.ldsc.log</td>\n",
       "      <td>0.2389</td>\n",
       "      <td>0.0141</td>\n",
       "      <td>0.2389</td>\n",
       "      <td>0.0141</td>\n",
       "      <td>1.4566</td>\n",
       "      <td>1.6942</td>\n",
       "      <td>1.1086</td>\n",
       "      <td>0.0151</td>\n",
       "      <td>0.1565 (0.0218)</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>16.962121</td>\n",
       "      <td>7.829400e-65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4119_irnt</th>\n",
       "      <td>data/out_final/bp300.4119_irnt.ldsc.log</td>\n",
       "      <td>0.2351</td>\n",
       "      <td>0.0139</td>\n",
       "      <td>0.2351</td>\n",
       "      <td>0.0139</td>\n",
       "      <td>1.4637</td>\n",
       "      <td>1.6888</td>\n",
       "      <td>1.1129</td>\n",
       "      <td>0.0150</td>\n",
       "      <td>0.1639 (0.0218)</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>16.556391</td>\n",
       "      <td>7.198758e-62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23121_irnt</th>\n",
       "      <td>data/out_final/bp300.23121_irnt.ldsc.log</td>\n",
       "      <td>0.2340</td>\n",
       "      <td>0.0113</td>\n",
       "      <td>0.2340</td>\n",
       "      <td>0.0113</td>\n",
       "      <td>2.1589</td>\n",
       "      <td>2.9937</td>\n",
       "      <td>1.2287</td>\n",
       "      <td>0.0296</td>\n",
       "      <td>0.1147 (0.0149)</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>22.857143</td>\n",
       "      <td>6.204494e-116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23122_irnt</th>\n",
       "      <td>data/out_final/bp300.23122_irnt.ldsc.log</td>\n",
       "      <td>0.2339</td>\n",
       "      <td>0.0113</td>\n",
       "      <td>0.2339</td>\n",
       "      <td>0.0113</td>\n",
       "      <td>2.1545</td>\n",
       "      <td>2.9912</td>\n",
       "      <td>1.2264</td>\n",
       "      <td>0.0295</td>\n",
       "      <td>0.1137 (0.0148)</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>22.890110</td>\n",
       "      <td>2.914687e-116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23125_irnt</th>\n",
       "      <td>data/out_final/bp300.23125_irnt.ldsc.log</td>\n",
       "      <td>0.2313</td>\n",
       "      <td>0.0110</td>\n",
       "      <td>0.2313</td>\n",
       "      <td>0.0110</td>\n",
       "      <td>2.1545</td>\n",
       "      <td>2.9719</td>\n",
       "      <td>1.2294</td>\n",
       "      <td>0.0284</td>\n",
       "      <td>0.1163 (0.0144)</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>23.465909</td>\n",
       "      <td>4.547791e-122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23126_irnt</th>\n",
       "      <td>data/out_final/bp300.23126_irnt.ldsc.log</td>\n",
       "      <td>0.2300</td>\n",
       "      <td>0.0110</td>\n",
       "      <td>0.2300</td>\n",
       "      <td>0.0110</td>\n",
       "      <td>2.1502</td>\n",
       "      <td>2.9647</td>\n",
       "      <td>1.2321</td>\n",
       "      <td>0.0286</td>\n",
       "      <td>0.1182 (0.0145)</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>23.306818</td>\n",
       "      <td>1.890436e-120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21002_irnt</th>\n",
       "      <td>data/out_final/bp300.21002_irnt.ldsc.log</td>\n",
       "      <td>0.2275</td>\n",
       "      <td>0.0091</td>\n",
       "      <td>0.2275</td>\n",
       "      <td>0.0091</td>\n",
       "      <td>2.2203</td>\n",
       "      <td>2.9147</td>\n",
       "      <td>1.2004</td>\n",
       "      <td>0.0242</td>\n",
       "      <td>0.1046 (0.0126)</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>26.461538</td>\n",
       "      <td>1.343854e-154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23098_irnt</th>\n",
       "      <td>data/out_final/bp300.23098_irnt.ldsc.log</td>\n",
       "      <td>0.2273</td>\n",
       "      <td>0.0091</td>\n",
       "      <td>0.2273</td>\n",
       "      <td>0.0091</td>\n",
       "      <td>2.1983</td>\n",
       "      <td>2.8873</td>\n",
       "      <td>1.1976</td>\n",
       "      <td>0.0241</td>\n",
       "      <td>0.1047 (0.0128)</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>26.474359</td>\n",
       "      <td>9.566917e-155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5084_irnt</th>\n",
       "      <td>data/out_final/bp300.5084_irnt.ldsc.log</td>\n",
       "      <td>0.2257</td>\n",
       "      <td>0.0151</td>\n",
       "      <td>0.2257</td>\n",
       "      <td>0.0151</td>\n",
       "      <td>1.3409</td>\n",
       "      <td>1.4894</td>\n",
       "      <td>1.1077</td>\n",
       "      <td>0.0116</td>\n",
       "      <td>0.22 (0.0237)</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>15.076923</td>\n",
       "      <td>1.148641e-51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COPD_EXCL</th>\n",
       "      <td>data/out_final/bp300.COPD_EXCL.ldsc.log</td>\n",
       "      <td>0.0099</td>\n",
       "      <td>0.0016</td>\n",
       "      <td>0.0346</td>\n",
       "      <td>0.0056</td>\n",
       "      <td>1.0926</td>\n",
       "      <td>1.1029</td>\n",
       "      <td>1.0171</td>\n",
       "      <td>0.0071</td>\n",
       "      <td>0.1666 (0.0692)</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>6.400000</td>\n",
       "      <td>7.768848e-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ASTHMA_MEDICATIO_COMORB</th>\n",
       "      <td>data/out_final/bp300.ASTHMA_MEDICATIO_COMORB.l...</td>\n",
       "      <td>0.0098</td>\n",
       "      <td>0.0016</td>\n",
       "      <td>0.0411</td>\n",
       "      <td>0.0067</td>\n",
       "      <td>1.0833</td>\n",
       "      <td>1.1059</td>\n",
       "      <td>1.0213</td>\n",
       "      <td>0.0069</td>\n",
       "      <td>0.2011 (0.0654)</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>6.846154</td>\n",
       "      <td>3.793095e-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20110_6</th>\n",
       "      <td>data/out_final/bp300.20110_6.ldsc.log</td>\n",
       "      <td>0.0098</td>\n",
       "      <td>0.0016</td>\n",
       "      <td>0.0391</td>\n",
       "      <td>0.0065</td>\n",
       "      <td>1.0679</td>\n",
       "      <td>1.0891</td>\n",
       "      <td>1.0141</td>\n",
       "      <td>0.0065</td>\n",
       "      <td>0.1579 (0.0731)</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>5.800000</td>\n",
       "      <td>3.315746e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M13_MENISCUSDERANGEMENTS</th>\n",
       "      <td>data/out_final/bp300.M13_MENISCUSDERANGEMENTS....</td>\n",
       "      <td>0.0096</td>\n",
       "      <td>0.0019</td>\n",
       "      <td>0.0601</td>\n",
       "      <td>0.0118</td>\n",
       "      <td>1.0772</td>\n",
       "      <td>1.0956</td>\n",
       "      <td>1.0172</td>\n",
       "      <td>0.0076</td>\n",
       "      <td>0.1794 (0.0794)</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>5.866667</td>\n",
       "      <td>2.223217e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20003_1140860696</th>\n",
       "      <td>data/out_final/bp300.20003_1140860696.ldsc.log</td>\n",
       "      <td>0.0095</td>\n",
       "      <td>0.0014</td>\n",
       "      <td>0.0630</td>\n",
       "      <td>0.0090</td>\n",
       "      <td>1.0957</td>\n",
       "      <td>1.1085</td>\n",
       "      <td>1.0257</td>\n",
       "      <td>0.0074</td>\n",
       "      <td>0.2371 (0.0679)</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>6.357143</td>\n",
       "      <td>1.027704e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X_RESPIRATORY</th>\n",
       "      <td>data/out_final/bp300.X_RESPIRATORY.ldsc.log</td>\n",
       "      <td>0.0093</td>\n",
       "      <td>0.0016</td>\n",
       "      <td>0.0333</td>\n",
       "      <td>0.0058</td>\n",
       "      <td>1.0926</td>\n",
       "      <td>1.1000</td>\n",
       "      <td>1.0197</td>\n",
       "      <td>0.0073</td>\n",
       "      <td>0.1967 (0.0729)</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>9.865876e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PULMONARYDG</th>\n",
       "      <td>data/out_final/bp300.PULMONARYDG.ldsc.log</td>\n",
       "      <td>0.0093</td>\n",
       "      <td>0.0016</td>\n",
       "      <td>0.0333</td>\n",
       "      <td>0.0058</td>\n",
       "      <td>1.0926</td>\n",
       "      <td>1.1000</td>\n",
       "      <td>1.0197</td>\n",
       "      <td>0.0073</td>\n",
       "      <td>0.1967 (0.0729)</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>9.865876e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20003_1141146234</th>\n",
       "      <td>data/out_final/bp300.20003_1141146234.ldsc.log</td>\n",
       "      <td>0.0091</td>\n",
       "      <td>0.0018</td>\n",
       "      <td>0.0570</td>\n",
       "      <td>0.0115</td>\n",
       "      <td>1.0864</td>\n",
       "      <td>1.1219</td>\n",
       "      <td>1.0415</td>\n",
       "      <td>0.0093</td>\n",
       "      <td>0.3407 (0.0766)</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>6.066667</td>\n",
       "      <td>6.529610e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1150_3</th>\n",
       "      <td>data/out_final/bp300.1150_3.ldsc.log</td>\n",
       "      <td>0.0090</td>\n",
       "      <td>0.0017</td>\n",
       "      <td>0.0337</td>\n",
       "      <td>0.0066</td>\n",
       "      <td>1.0833</td>\n",
       "      <td>1.0867</td>\n",
       "      <td>1.0190</td>\n",
       "      <td>0.0076</td>\n",
       "      <td>0.2197 (0.0871)</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.866516e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PULM_MEDICATIO_COMORB</th>\n",
       "      <td>data/out_final/bp300.PULM_MEDICATIO_COMORB.lds...</td>\n",
       "      <td>0.0089</td>\n",
       "      <td>0.0016</td>\n",
       "      <td>0.0354</td>\n",
       "      <td>0.0063</td>\n",
       "      <td>1.0864</td>\n",
       "      <td>1.1026</td>\n",
       "      <td>1.0250</td>\n",
       "      <td>0.0073</td>\n",
       "      <td>0.2436 (0.0707)</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>6.153846</td>\n",
       "      <td>3.781302e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D12</th>\n",
       "      <td>data/out_final/bp300.D12.ldsc.log</td>\n",
       "      <td>0.0089</td>\n",
       "      <td>0.0013</td>\n",
       "      <td>0.0642</td>\n",
       "      <td>0.0095</td>\n",
       "      <td>1.0833</td>\n",
       "      <td>1.0989</td>\n",
       "      <td>1.0243</td>\n",
       "      <td>0.0070</td>\n",
       "      <td>0.2456 (0.0712)</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>7.166667</td>\n",
       "      <td>3.842292e-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6154_6</th>\n",
       "      <td>data/out_final/bp300.6154_6.ldsc.log</td>\n",
       "      <td>0.0084</td>\n",
       "      <td>0.0013</td>\n",
       "      <td>0.0542</td>\n",
       "      <td>0.0083</td>\n",
       "      <td>1.0802</td>\n",
       "      <td>1.0867</td>\n",
       "      <td>1.0164</td>\n",
       "      <td>0.0067</td>\n",
       "      <td>0.1893 (0.0774)</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>4.016001e-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20002_1138</th>\n",
       "      <td>data/out_final/bp300.20002_1138.ldsc.log</td>\n",
       "      <td>0.0081</td>\n",
       "      <td>0.0013</td>\n",
       "      <td>0.0407</td>\n",
       "      <td>0.0066</td>\n",
       "      <td>1.0895</td>\n",
       "      <td>1.0938</td>\n",
       "      <td>1.0251</td>\n",
       "      <td>0.0069</td>\n",
       "      <td>0.2677 (0.0738)</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>6.230769</td>\n",
       "      <td>2.320752e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2159</th>\n",
       "      <td>data/out_final/bp300.2159.ldsc.log</td>\n",
       "      <td>0.0081</td>\n",
       "      <td>0.0014</td>\n",
       "      <td>0.0465</td>\n",
       "      <td>0.0079</td>\n",
       "      <td>1.0772</td>\n",
       "      <td>1.0836</td>\n",
       "      <td>1.0225</td>\n",
       "      <td>0.0063</td>\n",
       "      <td>0.2696 (0.0751)</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>5.571429</td>\n",
       "      <td>1.263295e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>K21</th>\n",
       "      <td>data/out_final/bp300.K21.ldsc.log</td>\n",
       "      <td>0.0079</td>\n",
       "      <td>0.0013</td>\n",
       "      <td>0.0502</td>\n",
       "      <td>0.0080</td>\n",
       "      <td>1.0710</td>\n",
       "      <td>1.0712</td>\n",
       "      <td>1.0087</td>\n",
       "      <td>0.0061</td>\n",
       "      <td>0.1226 (0.0852)</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>7.090909</td>\n",
       "      <td>6.661692e-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20003_1140923346</th>\n",
       "      <td>data/out_final/bp300.20003_1140923346.ldsc.log</td>\n",
       "      <td>0.0077</td>\n",
       "      <td>0.0012</td>\n",
       "      <td>0.0580</td>\n",
       "      <td>0.0092</td>\n",
       "      <td>1.0926</td>\n",
       "      <td>1.0900</td>\n",
       "      <td>1.0215</td>\n",
       "      <td>0.0066</td>\n",
       "      <td>0.2388 (0.0734)</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>9.865876e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6147_4</th>\n",
       "      <td>data/out_final/bp300.6147_4.ldsc.log</td>\n",
       "      <td>0.0076</td>\n",
       "      <td>0.0014</td>\n",
       "      <td>0.0517</td>\n",
       "      <td>0.0095</td>\n",
       "      <td>1.0802</td>\n",
       "      <td>1.0803</td>\n",
       "      <td>1.0203</td>\n",
       "      <td>0.0067</td>\n",
       "      <td>0.2529 (0.0832)</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>5.214286</td>\n",
       "      <td>9.226344e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H26</th>\n",
       "      <td>data/out_final/bp300.H26.ldsc.log</td>\n",
       "      <td>0.0073</td>\n",
       "      <td>0.0016</td>\n",
       "      <td>0.0448</td>\n",
       "      <td>0.0100</td>\n",
       "      <td>1.0679</td>\n",
       "      <td>1.0830</td>\n",
       "      <td>1.0173</td>\n",
       "      <td>0.0067</td>\n",
       "      <td>0.2087 (0.0807)</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>3.397673e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>K29</th>\n",
       "      <td>data/out_final/bp300.K29.ldsc.log</td>\n",
       "      <td>0.0073</td>\n",
       "      <td>0.0013</td>\n",
       "      <td>0.0410</td>\n",
       "      <td>0.0073</td>\n",
       "      <td>1.0864</td>\n",
       "      <td>1.0795</td>\n",
       "      <td>1.0181</td>\n",
       "      <td>0.0062</td>\n",
       "      <td>0.2277 (0.0785)</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>5.750000</td>\n",
       "      <td>4.462172e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6147_2</th>\n",
       "      <td>data/out_final/bp300.6147_2.ldsc.log</td>\n",
       "      <td>0.0071</td>\n",
       "      <td>0.0013</td>\n",
       "      <td>0.0357</td>\n",
       "      <td>0.0064</td>\n",
       "      <td>1.0926</td>\n",
       "      <td>1.1035</td>\n",
       "      <td>1.0452</td>\n",
       "      <td>0.0070</td>\n",
       "      <td>0.4365 (0.0672)</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>5.166667</td>\n",
       "      <td>1.191529e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I84</th>\n",
       "      <td>data/out_final/bp300.I84.ldsc.log</td>\n",
       "      <td>0.0071</td>\n",
       "      <td>0.0014</td>\n",
       "      <td>0.0414</td>\n",
       "      <td>0.0082</td>\n",
       "      <td>1.0741</td>\n",
       "      <td>1.0855</td>\n",
       "      <td>1.0237</td>\n",
       "      <td>0.0072</td>\n",
       "      <td>0.2774 (0.0847)</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>4.928571</td>\n",
       "      <td>4.141653e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6179_4</th>\n",
       "      <td>data/out_final/bp300.6179_4.ldsc.log</td>\n",
       "      <td>0.0070</td>\n",
       "      <td>0.0013</td>\n",
       "      <td>0.0363</td>\n",
       "      <td>0.0067</td>\n",
       "      <td>1.0679</td>\n",
       "      <td>1.0660</td>\n",
       "      <td>1.0038</td>\n",
       "      <td>0.0061</td>\n",
       "      <td>0.0578 (0.0922)</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>5.583333</td>\n",
       "      <td>1.179759e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20002_1474</th>\n",
       "      <td>data/out_final/bp300.20002_1474.ldsc.log</td>\n",
       "      <td>0.0066</td>\n",
       "      <td>0.0012</td>\n",
       "      <td>0.0497</td>\n",
       "      <td>0.0088</td>\n",
       "      <td>1.0895</td>\n",
       "      <td>1.0913</td>\n",
       "      <td>1.0376</td>\n",
       "      <td>0.0064</td>\n",
       "      <td>0.4122 (0.0701)</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>5.181818</td>\n",
       "      <td>1.098667e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20002_1452</th>\n",
       "      <td>data/out_final/bp300.20002_1452.ldsc.log</td>\n",
       "      <td>0.0062</td>\n",
       "      <td>0.0019</td>\n",
       "      <td>0.0433</td>\n",
       "      <td>0.0134</td>\n",
       "      <td>1.0527</td>\n",
       "      <td>1.0768</td>\n",
       "      <td>1.0240</td>\n",
       "      <td>0.0070</td>\n",
       "      <td>0.3119 (0.0915)</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>3.294118</td>\n",
       "      <td>4.936558e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N39</th>\n",
       "      <td>data/out_final/bp300.N39.ldsc.log</td>\n",
       "      <td>0.0060</td>\n",
       "      <td>0.0013</td>\n",
       "      <td>0.0382</td>\n",
       "      <td>0.0085</td>\n",
       "      <td>1.0618</td>\n",
       "      <td>1.0648</td>\n",
       "      <td>1.0149</td>\n",
       "      <td>0.0067</td>\n",
       "      <td>0.2303 (0.1033)</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>4.750000</td>\n",
       "      <td>1.017083e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>K11_OTHGASTR</th>\n",
       "      <td>data/out_final/bp300.K11_OTHGASTR.ldsc.log</td>\n",
       "      <td>0.0059</td>\n",
       "      <td>0.0012</td>\n",
       "      <td>0.0376</td>\n",
       "      <td>0.0079</td>\n",
       "      <td>1.0618</td>\n",
       "      <td>1.0637</td>\n",
       "      <td>1.0127</td>\n",
       "      <td>0.0061</td>\n",
       "      <td>0.1997 (0.0963)</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>4.666667</td>\n",
       "      <td>1.530627e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6144_1</th>\n",
       "      <td>data/out_final/bp300.6144_1.ldsc.log</td>\n",
       "      <td>0.0053</td>\n",
       "      <td>0.0011</td>\n",
       "      <td>0.0371</td>\n",
       "      <td>0.0079</td>\n",
       "      <td>1.0557</td>\n",
       "      <td>1.0547</td>\n",
       "      <td>1.0077</td>\n",
       "      <td>0.0059</td>\n",
       "      <td>0.1412 (0.1087)</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>4.909091</td>\n",
       "      <td>4.574978e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6179_6</th>\n",
       "      <td>data/out_final/bp300.6179_6.ldsc.log</td>\n",
       "      <td>0.0052</td>\n",
       "      <td>0.0013</td>\n",
       "      <td>0.0382</td>\n",
       "      <td>0.0094</td>\n",
       "      <td>1.0557</td>\n",
       "      <td>1.0486</td>\n",
       "      <td>1.0070</td>\n",
       "      <td>0.0064</td>\n",
       "      <td>0.1439 (0.1323)</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>3.833333</td>\n",
       "      <td>6.320923e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>680_4</th>\n",
       "      <td>data/out_final/bp300.680_4.ldsc.log</td>\n",
       "      <td>0.0046</td>\n",
       "      <td>0.0011</td>\n",
       "      <td>0.0312</td>\n",
       "      <td>0.0075</td>\n",
       "      <td>1.0527</td>\n",
       "      <td>1.0497</td>\n",
       "      <td>1.0114</td>\n",
       "      <td>0.0062</td>\n",
       "      <td>0.2302 (0.1252)</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>3.397673e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Z09</th>\n",
       "      <td>data/out_final/bp300.Z09.ldsc.log</td>\n",
       "      <td>0.0046</td>\n",
       "      <td>0.0013</td>\n",
       "      <td>0.0344</td>\n",
       "      <td>0.0097</td>\n",
       "      <td>1.0405</td>\n",
       "      <td>1.0323</td>\n",
       "      <td>0.9968</td>\n",
       "      <td>0.0067</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>3.750000</td>\n",
       "      <td>8.841729e-05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>561 rows × 7677 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                             bp300.filename  \\\n",
       "phenotype                                                                     \n",
       "50_irnt                               data/out_final/bp300.50_irnt.ldsc.log   \n",
       "20153_irnt                         data/out_final/bp300.20153_irnt.ldsc.log   \n",
       "20015_irnt                         data/out_final/bp300.20015_irnt.ldsc.log   \n",
       "5097_irnt                           data/out_final/bp300.5097_irnt.ldsc.log   \n",
       "5098_irnt                           data/out_final/bp300.5098_irnt.ldsc.log   \n",
       "5134_irnt                           data/out_final/bp300.5134_irnt.ldsc.log   \n",
       "5133_irnt                           data/out_final/bp300.5133_irnt.ldsc.log   \n",
       "5096_irnt                           data/out_final/bp300.5096_irnt.ldsc.log   \n",
       "5099_irnt                           data/out_final/bp300.5099_irnt.ldsc.log   \n",
       "23101_irnt                         data/out_final/bp300.23101_irnt.ldsc.log   \n",
       "23102_irnt                         data/out_final/bp300.23102_irnt.ldsc.log   \n",
       "5132_irnt                           data/out_final/bp300.5132_irnt.ldsc.log   \n",
       "5135_irnt                           data/out_final/bp300.5135_irnt.ldsc.log   \n",
       "23129_irnt                         data/out_final/bp300.23129_irnt.ldsc.log   \n",
       "23130_irnt                         data/out_final/bp300.23130_irnt.ldsc.log   \n",
       "23105_irnt                         data/out_final/bp300.23105_irnt.ldsc.log   \n",
       "3143_irnt                           data/out_final/bp300.3143_irnt.ldsc.log   \n",
       "23117_irnt                         data/out_final/bp300.23117_irnt.ldsc.log   \n",
       "23113_irnt                         data/out_final/bp300.23113_irnt.ldsc.log   \n",
       "23114_irnt                         data/out_final/bp300.23114_irnt.ldsc.log   \n",
       "23118_irnt                         data/out_final/bp300.23118_irnt.ldsc.log   \n",
       "4100_irnt                           data/out_final/bp300.4100_irnt.ldsc.log   \n",
       "4119_irnt                           data/out_final/bp300.4119_irnt.ldsc.log   \n",
       "23121_irnt                         data/out_final/bp300.23121_irnt.ldsc.log   \n",
       "23122_irnt                         data/out_final/bp300.23122_irnt.ldsc.log   \n",
       "23125_irnt                         data/out_final/bp300.23125_irnt.ldsc.log   \n",
       "23126_irnt                         data/out_final/bp300.23126_irnt.ldsc.log   \n",
       "21002_irnt                         data/out_final/bp300.21002_irnt.ldsc.log   \n",
       "23098_irnt                         data/out_final/bp300.23098_irnt.ldsc.log   \n",
       "5084_irnt                           data/out_final/bp300.5084_irnt.ldsc.log   \n",
       "...                                                                     ...   \n",
       "COPD_EXCL                           data/out_final/bp300.COPD_EXCL.ldsc.log   \n",
       "ASTHMA_MEDICATIO_COMORB   data/out_final/bp300.ASTHMA_MEDICATIO_COMORB.l...   \n",
       "20110_6                               data/out_final/bp300.20110_6.ldsc.log   \n",
       "M13_MENISCUSDERANGEMENTS  data/out_final/bp300.M13_MENISCUSDERANGEMENTS....   \n",
       "20003_1140860696             data/out_final/bp300.20003_1140860696.ldsc.log   \n",
       "X_RESPIRATORY                   data/out_final/bp300.X_RESPIRATORY.ldsc.log   \n",
       "PULMONARYDG                       data/out_final/bp300.PULMONARYDG.ldsc.log   \n",
       "20003_1141146234             data/out_final/bp300.20003_1141146234.ldsc.log   \n",
       "1150_3                                 data/out_final/bp300.1150_3.ldsc.log   \n",
       "PULM_MEDICATIO_COMORB     data/out_final/bp300.PULM_MEDICATIO_COMORB.lds...   \n",
       "D12                                       data/out_final/bp300.D12.ldsc.log   \n",
       "6154_6                                 data/out_final/bp300.6154_6.ldsc.log   \n",
       "20002_1138                         data/out_final/bp300.20002_1138.ldsc.log   \n",
       "2159                                     data/out_final/bp300.2159.ldsc.log   \n",
       "K21                                       data/out_final/bp300.K21.ldsc.log   \n",
       "20003_1140923346             data/out_final/bp300.20003_1140923346.ldsc.log   \n",
       "6147_4                                 data/out_final/bp300.6147_4.ldsc.log   \n",
       "H26                                       data/out_final/bp300.H26.ldsc.log   \n",
       "K29                                       data/out_final/bp300.K29.ldsc.log   \n",
       "6147_2                                 data/out_final/bp300.6147_2.ldsc.log   \n",
       "I84                                       data/out_final/bp300.I84.ldsc.log   \n",
       "6179_4                                 data/out_final/bp300.6179_4.ldsc.log   \n",
       "20002_1474                         data/out_final/bp300.20002_1474.ldsc.log   \n",
       "20002_1452                         data/out_final/bp300.20002_1452.ldsc.log   \n",
       "N39                                       data/out_final/bp300.N39.ldsc.log   \n",
       "K11_OTHGASTR                     data/out_final/bp300.K11_OTHGASTR.ldsc.log   \n",
       "6144_1                                 data/out_final/bp300.6144_1.ldsc.log   \n",
       "6179_6                                 data/out_final/bp300.6179_6.ldsc.log   \n",
       "680_4                                   data/out_final/bp300.680_4.ldsc.log   \n",
       "Z09                                       data/out_final/bp300.Z09.ldsc.log   \n",
       "\n",
       "                          bp300.Total Observed scale h2  \\\n",
       "phenotype                                                 \n",
       "50_irnt                                          0.4004   \n",
       "20153_irnt                                       0.3398   \n",
       "20015_irnt                                       0.2949   \n",
       "5097_irnt                                        0.2869   \n",
       "5098_irnt                                        0.2816   \n",
       "5134_irnt                                        0.2776   \n",
       "5133_irnt                                        0.2770   \n",
       "5096_irnt                                        0.2704   \n",
       "5099_irnt                                        0.2691   \n",
       "23101_irnt                                       0.2583   \n",
       "23102_irnt                                       0.2576   \n",
       "5132_irnt                                        0.2556   \n",
       "5135_irnt                                        0.2554   \n",
       "23129_irnt                                       0.2551   \n",
       "23130_irnt                                       0.2536   \n",
       "23105_irnt                                       0.2528   \n",
       "3143_irnt                                        0.2436   \n",
       "23117_irnt                                       0.2400   \n",
       "23113_irnt                                       0.2398   \n",
       "23114_irnt                                       0.2395   \n",
       "23118_irnt                                       0.2392   \n",
       "4100_irnt                                        0.2389   \n",
       "4119_irnt                                        0.2351   \n",
       "23121_irnt                                       0.2340   \n",
       "23122_irnt                                       0.2339   \n",
       "23125_irnt                                       0.2313   \n",
       "23126_irnt                                       0.2300   \n",
       "21002_irnt                                       0.2275   \n",
       "23098_irnt                                       0.2273   \n",
       "5084_irnt                                        0.2257   \n",
       "...                                                 ...   \n",
       "COPD_EXCL                                        0.0099   \n",
       "ASTHMA_MEDICATIO_COMORB                          0.0098   \n",
       "20110_6                                          0.0098   \n",
       "M13_MENISCUSDERANGEMENTS                         0.0096   \n",
       "20003_1140860696                                 0.0095   \n",
       "X_RESPIRATORY                                    0.0093   \n",
       "PULMONARYDG                                      0.0093   \n",
       "20003_1141146234                                 0.0091   \n",
       "1150_3                                           0.0090   \n",
       "PULM_MEDICATIO_COMORB                            0.0089   \n",
       "D12                                              0.0089   \n",
       "6154_6                                           0.0084   \n",
       "20002_1138                                       0.0081   \n",
       "2159                                             0.0081   \n",
       "K21                                              0.0079   \n",
       "20003_1140923346                                 0.0077   \n",
       "6147_4                                           0.0076   \n",
       "H26                                              0.0073   \n",
       "K29                                              0.0073   \n",
       "6147_2                                           0.0071   \n",
       "I84                                              0.0071   \n",
       "6179_4                                           0.0070   \n",
       "20002_1474                                       0.0066   \n",
       "20002_1452                                       0.0062   \n",
       "N39                                              0.0060   \n",
       "K11_OTHGASTR                                     0.0059   \n",
       "6144_1                                           0.0053   \n",
       "6179_6                                           0.0052   \n",
       "680_4                                            0.0046   \n",
       "Z09                                              0.0046   \n",
       "\n",
       "                          bp300.Total Observed scale h2_se  \\\n",
       "phenotype                                                    \n",
       "50_irnt                                             0.0243   \n",
       "20153_irnt                                          0.0234   \n",
       "20015_irnt                                          0.0185   \n",
       "5097_irnt                                           0.0246   \n",
       "5098_irnt                                           0.0239   \n",
       "5134_irnt                                           0.0238   \n",
       "5133_irnt                                           0.0237   \n",
       "5096_irnt                                           0.0227   \n",
       "5099_irnt                                           0.0225   \n",
       "23101_irnt                                          0.0128   \n",
       "23102_irnt                                          0.0128   \n",
       "5132_irnt                                           0.0216   \n",
       "5135_irnt                                           0.0219   \n",
       "23129_irnt                                          0.0137   \n",
       "23130_irnt                                          0.0136   \n",
       "23105_irnt                                          0.0119   \n",
       "3143_irnt                                           0.0146   \n",
       "23117_irnt                                          0.0111   \n",
       "23113_irnt                                          0.0113   \n",
       "23114_irnt                                          0.0113   \n",
       "23118_irnt                                          0.0111   \n",
       "4100_irnt                                           0.0141   \n",
       "4119_irnt                                           0.0139   \n",
       "23121_irnt                                          0.0113   \n",
       "23122_irnt                                          0.0113   \n",
       "23125_irnt                                          0.0110   \n",
       "23126_irnt                                          0.0110   \n",
       "21002_irnt                                          0.0091   \n",
       "23098_irnt                                          0.0091   \n",
       "5084_irnt                                           0.0151   \n",
       "...                                                    ...   \n",
       "COPD_EXCL                                           0.0016   \n",
       "ASTHMA_MEDICATIO_COMORB                             0.0016   \n",
       "20110_6                                             0.0016   \n",
       "M13_MENISCUSDERANGEMENTS                            0.0019   \n",
       "20003_1140860696                                    0.0014   \n",
       "X_RESPIRATORY                                       0.0016   \n",
       "PULMONARYDG                                         0.0016   \n",
       "20003_1141146234                                    0.0018   \n",
       "1150_3                                              0.0017   \n",
       "PULM_MEDICATIO_COMORB                               0.0016   \n",
       "D12                                                 0.0013   \n",
       "6154_6                                              0.0013   \n",
       "20002_1138                                          0.0013   \n",
       "2159                                                0.0014   \n",
       "K21                                                 0.0013   \n",
       "20003_1140923346                                    0.0012   \n",
       "6147_4                                              0.0014   \n",
       "H26                                                 0.0016   \n",
       "K29                                                 0.0013   \n",
       "6147_2                                              0.0013   \n",
       "I84                                                 0.0014   \n",
       "6179_4                                              0.0013   \n",
       "20002_1474                                          0.0012   \n",
       "20002_1452                                          0.0019   \n",
       "N39                                                 0.0013   \n",
       "K11_OTHGASTR                                        0.0012   \n",
       "6144_1                                              0.0011   \n",
       "6179_6                                              0.0013   \n",
       "680_4                                               0.0011   \n",
       "Z09                                                 0.0013   \n",
       "\n",
       "                          bp300.Total Liability scale h2  \\\n",
       "phenotype                                                  \n",
       "50_irnt                                           0.4004   \n",
       "20153_irnt                                        0.3398   \n",
       "20015_irnt                                        0.2949   \n",
       "5097_irnt                                         0.2869   \n",
       "5098_irnt                                         0.2816   \n",
       "5134_irnt                                         0.2776   \n",
       "5133_irnt                                         0.2770   \n",
       "5096_irnt                                         0.2704   \n",
       "5099_irnt                                         0.2691   \n",
       "23101_irnt                                        0.2583   \n",
       "23102_irnt                                        0.2576   \n",
       "5132_irnt                                         0.2556   \n",
       "5135_irnt                                         0.2554   \n",
       "23129_irnt                                        0.2551   \n",
       "23130_irnt                                        0.2536   \n",
       "23105_irnt                                        0.2528   \n",
       "3143_irnt                                         0.2436   \n",
       "23117_irnt                                        0.2400   \n",
       "23113_irnt                                        0.2398   \n",
       "23114_irnt                                        0.2395   \n",
       "23118_irnt                                        0.2392   \n",
       "4100_irnt                                         0.2389   \n",
       "4119_irnt                                         0.2351   \n",
       "23121_irnt                                        0.2340   \n",
       "23122_irnt                                        0.2339   \n",
       "23125_irnt                                        0.2313   \n",
       "23126_irnt                                        0.2300   \n",
       "21002_irnt                                        0.2275   \n",
       "23098_irnt                                        0.2273   \n",
       "5084_irnt                                         0.2257   \n",
       "...                                                  ...   \n",
       "COPD_EXCL                                         0.0346   \n",
       "ASTHMA_MEDICATIO_COMORB                           0.0411   \n",
       "20110_6                                           0.0391   \n",
       "M13_MENISCUSDERANGEMENTS                          0.0601   \n",
       "20003_1140860696                                  0.0630   \n",
       "X_RESPIRATORY                                     0.0333   \n",
       "PULMONARYDG                                       0.0333   \n",
       "20003_1141146234                                  0.0570   \n",
       "1150_3                                            0.0337   \n",
       "PULM_MEDICATIO_COMORB                             0.0354   \n",
       "D12                                               0.0642   \n",
       "6154_6                                            0.0542   \n",
       "20002_1138                                        0.0407   \n",
       "2159                                              0.0465   \n",
       "K21                                               0.0502   \n",
       "20003_1140923346                                  0.0580   \n",
       "6147_4                                            0.0517   \n",
       "H26                                               0.0448   \n",
       "K29                                               0.0410   \n",
       "6147_2                                            0.0357   \n",
       "I84                                               0.0414   \n",
       "6179_4                                            0.0363   \n",
       "20002_1474                                        0.0497   \n",
       "20002_1452                                        0.0433   \n",
       "N39                                               0.0382   \n",
       "K11_OTHGASTR                                      0.0376   \n",
       "6144_1                                            0.0371   \n",
       "6179_6                                            0.0382   \n",
       "680_4                                             0.0312   \n",
       "Z09                                               0.0344   \n",
       "\n",
       "                          bp300.Total Liability scale h2_se  bp300.Lambda GC  \\\n",
       "phenotype                                                                      \n",
       "50_irnt                                              0.0243           2.5027   \n",
       "20153_irnt                                           0.0234           1.5696   \n",
       "20015_irnt                                           0.0185           2.1545   \n",
       "5097_irnt                                            0.0246           1.3169   \n",
       "5098_irnt                                            0.0239           1.3203   \n",
       "5134_irnt                                            0.0238           1.3068   \n",
       "5133_irnt                                            0.0237           1.3068   \n",
       "5096_irnt                                            0.0227           1.3374   \n",
       "5099_irnt                                            0.0225           1.3374   \n",
       "23101_irnt                                           0.0128           2.2425   \n",
       "23102_irnt                                           0.0128           2.2425   \n",
       "5132_irnt                                            0.0216           1.3238   \n",
       "5135_irnt                                            0.0219           1.3169   \n",
       "23129_irnt                                           0.0137           2.1895   \n",
       "23130_irnt                                           0.0136           2.1851   \n",
       "23105_irnt                                           0.0119           2.2558   \n",
       "3143_irnt                                            0.0146           1.7530   \n",
       "23117_irnt                                           0.0111           2.2115   \n",
       "23113_irnt                                           0.0113           2.1983   \n",
       "23114_irnt                                           0.0113           2.1983   \n",
       "23118_irnt                                           0.0111           2.2115   \n",
       "4100_irnt                                            0.0141           1.4566   \n",
       "4119_irnt                                            0.0139           1.4637   \n",
       "23121_irnt                                           0.0113           2.1589   \n",
       "23122_irnt                                           0.0113           2.1545   \n",
       "23125_irnt                                           0.0110           2.1545   \n",
       "23126_irnt                                           0.0110           2.1502   \n",
       "21002_irnt                                           0.0091           2.2203   \n",
       "23098_irnt                                           0.0091           2.1983   \n",
       "5084_irnt                                            0.0151           1.3409   \n",
       "...                                                     ...              ...   \n",
       "COPD_EXCL                                            0.0056           1.0926   \n",
       "ASTHMA_MEDICATIO_COMORB                              0.0067           1.0833   \n",
       "20110_6                                              0.0065           1.0679   \n",
       "M13_MENISCUSDERANGEMENTS                             0.0118           1.0772   \n",
       "20003_1140860696                                     0.0090           1.0957   \n",
       "X_RESPIRATORY                                        0.0058           1.0926   \n",
       "PULMONARYDG                                          0.0058           1.0926   \n",
       "20003_1141146234                                     0.0115           1.0864   \n",
       "1150_3                                               0.0066           1.0833   \n",
       "PULM_MEDICATIO_COMORB                                0.0063           1.0864   \n",
       "D12                                                  0.0095           1.0833   \n",
       "6154_6                                               0.0083           1.0802   \n",
       "20002_1138                                           0.0066           1.0895   \n",
       "2159                                                 0.0079           1.0772   \n",
       "K21                                                  0.0080           1.0710   \n",
       "20003_1140923346                                     0.0092           1.0926   \n",
       "6147_4                                               0.0095           1.0802   \n",
       "H26                                                  0.0100           1.0679   \n",
       "K29                                                  0.0073           1.0864   \n",
       "6147_2                                               0.0064           1.0926   \n",
       "I84                                                  0.0082           1.0741   \n",
       "6179_4                                               0.0067           1.0679   \n",
       "20002_1474                                           0.0088           1.0895   \n",
       "20002_1452                                           0.0134           1.0527   \n",
       "N39                                                  0.0085           1.0618   \n",
       "K11_OTHGASTR                                         0.0079           1.0618   \n",
       "6144_1                                               0.0079           1.0557   \n",
       "6179_6                                               0.0094           1.0557   \n",
       "680_4                                                0.0075           1.0527   \n",
       "Z09                                                  0.0097           1.0405   \n",
       "\n",
       "                          bp300.Mean Chi^2  bp300.Intercept  \\\n",
       "phenotype                                                     \n",
       "50_irnt                             4.5198           1.4446   \n",
       "20153_irnt                          2.0516           1.1714   \n",
       "20015_irnt                          3.5864           1.3002   \n",
       "5097_irnt                           1.5267           1.1100   \n",
       "5098_irnt                           1.5293           1.1153   \n",
       "5134_irnt                           1.5187           1.1156   \n",
       "5133_irnt                           1.5207           1.1154   \n",
       "5096_irnt                           1.5677           1.1146   \n",
       "5099_irnt                           1.5701           1.1191   \n",
       "23101_irnt                          3.2016           1.2580   \n",
       "23102_irnt                          3.1987           1.2582   \n",
       "5132_irnt                           1.5427           1.1180   \n",
       "5135_irnt                           1.5372           1.1133   \n",
       "23129_irnt                          3.1840           1.2607   \n",
       "23130_irnt                          3.1718           1.2594   \n",
       "23105_irnt                          3.1413           1.2446   \n",
       "3143_irnt                           2.2533           1.1932   \n",
       "23117_irnt                          3.0309           1.2296   \n",
       "23113_irnt                          3.0474           1.2395   \n",
       "23114_irnt                          3.0454           1.2390   \n",
       "23118_irnt                          3.0269           1.2301   \n",
       "4100_irnt                           1.6942           1.1086   \n",
       "4119_irnt                           1.6888           1.1129   \n",
       "23121_irnt                          2.9937           1.2287   \n",
       "23122_irnt                          2.9912           1.2264   \n",
       "23125_irnt                          2.9719           1.2294   \n",
       "23126_irnt                          2.9647           1.2321   \n",
       "21002_irnt                          2.9147           1.2004   \n",
       "23098_irnt                          2.8873           1.1976   \n",
       "5084_irnt                           1.4894           1.1077   \n",
       "...                                    ...              ...   \n",
       "COPD_EXCL                           1.1029           1.0171   \n",
       "ASTHMA_MEDICATIO_COMORB             1.1059           1.0213   \n",
       "20110_6                             1.0891           1.0141   \n",
       "M13_MENISCUSDERANGEMENTS            1.0956           1.0172   \n",
       "20003_1140860696                    1.1085           1.0257   \n",
       "X_RESPIRATORY                       1.1000           1.0197   \n",
       "PULMONARYDG                         1.1000           1.0197   \n",
       "20003_1141146234                    1.1219           1.0415   \n",
       "1150_3                              1.0867           1.0190   \n",
       "PULM_MEDICATIO_COMORB               1.1026           1.0250   \n",
       "D12                                 1.0989           1.0243   \n",
       "6154_6                              1.0867           1.0164   \n",
       "20002_1138                          1.0938           1.0251   \n",
       "2159                                1.0836           1.0225   \n",
       "K21                                 1.0712           1.0087   \n",
       "20003_1140923346                    1.0900           1.0215   \n",
       "6147_4                              1.0803           1.0203   \n",
       "H26                                 1.0830           1.0173   \n",
       "K29                                 1.0795           1.0181   \n",
       "6147_2                              1.1035           1.0452   \n",
       "I84                                 1.0855           1.0237   \n",
       "6179_4                              1.0660           1.0038   \n",
       "20002_1474                          1.0913           1.0376   \n",
       "20002_1452                          1.0768           1.0240   \n",
       "N39                                 1.0648           1.0149   \n",
       "K11_OTHGASTR                        1.0637           1.0127   \n",
       "6144_1                              1.0547           1.0077   \n",
       "6179_6                              1.0486           1.0070   \n",
       "680_4                               1.0497           1.0114   \n",
       "Z09                                 1.0323           0.9968   \n",
       "\n",
       "                          bp300.Intercept_se      bp300.Ratio  ...  \\\n",
       "phenotype                                                      ...   \n",
       "50_irnt                               0.0673  0.1263 (0.0191)  ...   \n",
       "20153_irnt                            0.0236   0.163 (0.0224)  ...   \n",
       "20015_irnt                            0.0471  0.1161 (0.0182)  ...   \n",
       "5097_irnt                             0.0158  0.2088 (0.0299)  ...   \n",
       "5098_irnt                             0.0160  0.2179 (0.0302)  ...   \n",
       "5134_irnt                             0.0154  0.2229 (0.0297)  ...   \n",
       "5133_irnt                             0.0162  0.2216 (0.0311)  ...   \n",
       "5096_irnt                             0.0170    0.2018 (0.03)  ...   \n",
       "5099_irnt                             0.0170  0.2088 (0.0299)  ...   \n",
       "23101_irnt                            0.0326  0.1172 (0.0148)  ...   \n",
       "23102_irnt                            0.0325  0.1175 (0.0148)  ...   \n",
       "5132_irnt                             0.0170  0.2174 (0.0314)  ...   \n",
       "5135_irnt                             0.0166   0.211 (0.0309)  ...   \n",
       "23129_irnt                            0.0342  0.1194 (0.0157)  ...   \n",
       "23130_irnt                            0.0340  0.1194 (0.0157)  ...   \n",
       "23105_irnt                            0.0308  0.1142 (0.0144)  ...   \n",
       "3143_irnt                             0.0238   0.1542 (0.019)  ...   \n",
       "23117_irnt                            0.0290   0.113 (0.0143)  ...   \n",
       "23113_irnt                            0.0295   0.117 (0.0144)  ...   \n",
       "23114_irnt                            0.0294  0.1169 (0.0144)  ...   \n",
       "23118_irnt                            0.0290  0.1135 (0.0143)  ...   \n",
       "4100_irnt                             0.0151  0.1565 (0.0218)  ...   \n",
       "4119_irnt                             0.0150  0.1639 (0.0218)  ...   \n",
       "23121_irnt                            0.0296  0.1147 (0.0149)  ...   \n",
       "23122_irnt                            0.0295  0.1137 (0.0148)  ...   \n",
       "23125_irnt                            0.0284  0.1163 (0.0144)  ...   \n",
       "23126_irnt                            0.0286  0.1182 (0.0145)  ...   \n",
       "21002_irnt                            0.0242  0.1046 (0.0126)  ...   \n",
       "23098_irnt                            0.0241  0.1047 (0.0128)  ...   \n",
       "5084_irnt                             0.0116    0.22 (0.0237)  ...   \n",
       "...                                      ...              ...  ...   \n",
       "COPD_EXCL                             0.0071  0.1666 (0.0692)  ...   \n",
       "ASTHMA_MEDICATIO_COMORB               0.0069  0.2011 (0.0654)  ...   \n",
       "20110_6                               0.0065  0.1579 (0.0731)  ...   \n",
       "M13_MENISCUSDERANGEMENTS              0.0076  0.1794 (0.0794)  ...   \n",
       "20003_1140860696                      0.0074  0.2371 (0.0679)  ...   \n",
       "X_RESPIRATORY                         0.0073  0.1967 (0.0729)  ...   \n",
       "PULMONARYDG                           0.0073  0.1967 (0.0729)  ...   \n",
       "20003_1141146234                      0.0093  0.3407 (0.0766)  ...   \n",
       "1150_3                                0.0076  0.2197 (0.0871)  ...   \n",
       "PULM_MEDICATIO_COMORB                 0.0073  0.2436 (0.0707)  ...   \n",
       "D12                                   0.0070  0.2456 (0.0712)  ...   \n",
       "6154_6                                0.0067  0.1893 (0.0774)  ...   \n",
       "20002_1138                            0.0069  0.2677 (0.0738)  ...   \n",
       "2159                                  0.0063  0.2696 (0.0751)  ...   \n",
       "K21                                   0.0061  0.1226 (0.0852)  ...   \n",
       "20003_1140923346                      0.0066  0.2388 (0.0734)  ...   \n",
       "6147_4                                0.0067  0.2529 (0.0832)  ...   \n",
       "H26                                   0.0067  0.2087 (0.0807)  ...   \n",
       "K29                                   0.0062  0.2277 (0.0785)  ...   \n",
       "6147_2                                0.0070  0.4365 (0.0672)  ...   \n",
       "I84                                   0.0072  0.2774 (0.0847)  ...   \n",
       "6179_4                                0.0061  0.0578 (0.0922)  ...   \n",
       "20002_1474                            0.0064  0.4122 (0.0701)  ...   \n",
       "20002_1452                            0.0070  0.3119 (0.0915)  ...   \n",
       "N39                                   0.0067  0.2303 (0.1033)  ...   \n",
       "K11_OTHGASTR                          0.0061  0.1997 (0.0963)  ...   \n",
       "6144_1                                0.0059  0.1412 (0.1087)  ...   \n",
       "6179_6                                0.0064  0.1439 (0.1323)  ...   \n",
       "680_4                                 0.0062  0.2302 (0.1252)  ...   \n",
       "Z09                                   0.0067                0  ...   \n",
       "\n",
       "                         neale2.isBadPower neale2.isLowNeff neale2.isMidNeff  \\\n",
       "phenotype                                                                      \n",
       "50_irnt                              False            False            False   \n",
       "20153_irnt                           False            False            False   \n",
       "20015_irnt                           False            False            False   \n",
       "5097_irnt                            False            False            False   \n",
       "5098_irnt                            False            False            False   \n",
       "5134_irnt                            False            False            False   \n",
       "5133_irnt                            False            False            False   \n",
       "5096_irnt                            False            False            False   \n",
       "5099_irnt                            False            False            False   \n",
       "23101_irnt                           False            False            False   \n",
       "23102_irnt                           False            False            False   \n",
       "5132_irnt                            False            False            False   \n",
       "5135_irnt                            False            False            False   \n",
       "23129_irnt                           False            False            False   \n",
       "23130_irnt                           False            False            False   \n",
       "23105_irnt                           False            False            False   \n",
       "3143_irnt                            False            False            False   \n",
       "23117_irnt                           False            False            False   \n",
       "23113_irnt                           False            False            False   \n",
       "23114_irnt                           False            False            False   \n",
       "23118_irnt                           False            False            False   \n",
       "4100_irnt                            False            False            False   \n",
       "4119_irnt                            False            False            False   \n",
       "23121_irnt                           False            False            False   \n",
       "23122_irnt                           False            False            False   \n",
       "23125_irnt                           False            False            False   \n",
       "23126_irnt                           False            False            False   \n",
       "21002_irnt                           False            False            False   \n",
       "23098_irnt                           False            False            False   \n",
       "5084_irnt                            False            False            False   \n",
       "...                                    ...              ...              ...   \n",
       "COPD_EXCL                            False            False            False   \n",
       "ASTHMA_MEDICATIO_COMORB              False            False            False   \n",
       "20110_6                              False            False            False   \n",
       "M13_MENISCUSDERANGEMENTS             False            False            False   \n",
       "20003_1140860696                     False            False             True   \n",
       "X_RESPIRATORY                          NaN              NaN              NaN   \n",
       "PULMONARYDG                          False            False            False   \n",
       "20003_1141146234                     False            False            False   \n",
       "1150_3                               False            False            False   \n",
       "PULM_MEDICATIO_COMORB                False            False            False   \n",
       "D12                                  False            False             True   \n",
       "6154_6                               False            False            False   \n",
       "20002_1138                           False            False            False   \n",
       "2159                                 False            False            False   \n",
       "K21                                  False            False            False   \n",
       "20003_1140923346                     False            False             True   \n",
       "6147_4                               False            False             True   \n",
       "H26                                  False            False            False   \n",
       "K29                                  False            False            False   \n",
       "6147_2                               False            False            False   \n",
       "I84                                  False            False            False   \n",
       "6179_4                               False            False            False   \n",
       "20002_1474                           False            False             True   \n",
       "20002_1452                           False            False             True   \n",
       "N39                                  False            False            False   \n",
       "K11_OTHGASTR                         False            False            False   \n",
       "6144_1                               False            False             True   \n",
       "6179_6                               False            False             True   \n",
       "680_4                                False            False             True   \n",
       "Z09                                  False            False             True   \n",
       "\n",
       "                         neale2.isExtremeSE neale2.isHighSE neale2.isSexBias  \\\n",
       "phenotype                                                                      \n",
       "50_irnt                               False           False            False   \n",
       "20153_irnt                            False           False            False   \n",
       "20015_irnt                            False           False            False   \n",
       "5097_irnt                             False           False            False   \n",
       "5098_irnt                             False           False            False   \n",
       "5134_irnt                             False           False            False   \n",
       "5133_irnt                             False           False            False   \n",
       "5096_irnt                             False           False            False   \n",
       "5099_irnt                             False           False            False   \n",
       "23101_irnt                            False           False            False   \n",
       "23102_irnt                            False           False            False   \n",
       "5132_irnt                             False           False            False   \n",
       "5135_irnt                             False           False            False   \n",
       "23129_irnt                            False           False            False   \n",
       "23130_irnt                            False           False            False   \n",
       "23105_irnt                            False           False            False   \n",
       "3143_irnt                             False           False            False   \n",
       "23117_irnt                            False           False            False   \n",
       "23113_irnt                            False           False            False   \n",
       "23114_irnt                            False           False            False   \n",
       "23118_irnt                            False           False            False   \n",
       "4100_irnt                             False           False            False   \n",
       "4119_irnt                             False           False            False   \n",
       "23121_irnt                            False           False            False   \n",
       "23122_irnt                            False           False            False   \n",
       "23125_irnt                            False           False            False   \n",
       "23126_irnt                            False           False            False   \n",
       "21002_irnt                            False           False            False   \n",
       "23098_irnt                            False           False            False   \n",
       "5084_irnt                             False           False            False   \n",
       "...                                     ...             ...              ...   \n",
       "COPD_EXCL                             False           False            False   \n",
       "ASTHMA_MEDICATIO_COMORB               False           False            False   \n",
       "20110_6                               False           False            False   \n",
       "M13_MENISCUSDERANGEMENTS              False           False            False   \n",
       "20003_1140860696                      False           False            False   \n",
       "X_RESPIRATORY                           NaN             NaN              NaN   \n",
       "PULMONARYDG                           False           False            False   \n",
       "20003_1141146234                      False           False            False   \n",
       "1150_3                                False           False            False   \n",
       "PULM_MEDICATIO_COMORB                 False           False            False   \n",
       "D12                                   False           False            False   \n",
       "6154_6                                False           False             True   \n",
       "20002_1138                            False           False            False   \n",
       "2159                                  False           False            False   \n",
       "K21                                   False           False            False   \n",
       "20003_1140923346                      False           False            False   \n",
       "6147_4                                False           False            False   \n",
       "H26                                   False           False            False   \n",
       "K29                                   False           False            False   \n",
       "6147_2                                False           False            False   \n",
       "I84                                   False           False            False   \n",
       "6179_4                                False           False            False   \n",
       "20002_1474                            False           False            False   \n",
       "20002_1452                            False           False            False   \n",
       "N39                                   False           False            False   \n",
       "K11_OTHGASTR                          False           False            False   \n",
       "6144_1                                False           False            False   \n",
       "6179_6                                False           False            False   \n",
       "680_4                                 False           False            False   \n",
       "Z09                                   False           False            False   \n",
       "\n",
       "                         neale2.isBadOrdinal neale2.isNumericOrdinal  \\\n",
       "phenotype                                                              \n",
       "50_irnt                                False                   False   \n",
       "20153_irnt                             False                   False   \n",
       "20015_irnt                             False                   False   \n",
       "5097_irnt                              False                   False   \n",
       "5098_irnt                              False                   False   \n",
       "5134_irnt                              False                   False   \n",
       "5133_irnt                              False                   False   \n",
       "5096_irnt                              False                   False   \n",
       "5099_irnt                              False                   False   \n",
       "23101_irnt                             False                   False   \n",
       "23102_irnt                             False                   False   \n",
       "5132_irnt                              False                   False   \n",
       "5135_irnt                              False                   False   \n",
       "23129_irnt                             False                   False   \n",
       "23130_irnt                             False                   False   \n",
       "23105_irnt                             False                   False   \n",
       "3143_irnt                              False                   False   \n",
       "23117_irnt                             False                   False   \n",
       "23113_irnt                             False                   False   \n",
       "23114_irnt                             False                   False   \n",
       "23118_irnt                             False                   False   \n",
       "4100_irnt                              False                   False   \n",
       "4119_irnt                              False                   False   \n",
       "23121_irnt                             False                   False   \n",
       "23122_irnt                             False                   False   \n",
       "23125_irnt                             False                   False   \n",
       "23126_irnt                             False                   False   \n",
       "21002_irnt                             False                   False   \n",
       "23098_irnt                             False                   False   \n",
       "5084_irnt                              False                   False   \n",
       "...                                      ...                     ...   \n",
       "COPD_EXCL                              False                   False   \n",
       "ASTHMA_MEDICATIO_COMORB                False                   False   \n",
       "20110_6                                False                   False   \n",
       "M13_MENISCUSDERANGEMENTS               False                   False   \n",
       "20003_1140860696                       False                   False   \n",
       "X_RESPIRATORY                            NaN                     NaN   \n",
       "PULMONARYDG                            False                   False   \n",
       "20003_1141146234                       False                   False   \n",
       "1150_3                                 False                   False   \n",
       "PULM_MEDICATIO_COMORB                  False                   False   \n",
       "D12                                    False                   False   \n",
       "6154_6                                 False                   False   \n",
       "20002_1138                             False                   False   \n",
       "2159                                   False                   False   \n",
       "K21                                    False                   False   \n",
       "20003_1140923346                       False                   False   \n",
       "6147_4                                 False                   False   \n",
       "H26                                    False                   False   \n",
       "K29                                    False                   False   \n",
       "6147_2                                 False                   False   \n",
       "I84                                    False                   False   \n",
       "6179_4                                 False                   False   \n",
       "20002_1474                             False                   False   \n",
       "20002_1452                             False                   False   \n",
       "N39                                    False                   False   \n",
       "K11_OTHGASTR                           False                   False   \n",
       "6144_1                                 False                   False   \n",
       "6179_6                                 False                   False   \n",
       "680_4                                  False                   False   \n",
       "Z09                                    False                   False   \n",
       "\n",
       "                         uni.Total h2 z uni.Total h2 p  \n",
       "phenotype                                               \n",
       "50_irnt                       19.147208   5.105832e-82  \n",
       "20153_irnt                    15.755981   3.124117e-56  \n",
       "20015_irnt                    17.315789   1.787921e-67  \n",
       "5097_irnt                     12.351931   2.377794e-35  \n",
       "5098_irnt                     12.668161   4.438533e-37  \n",
       "5134_irnt                     12.144737   3.058956e-34  \n",
       "5133_irnt                     12.538462   2.299180e-36  \n",
       "5096_irnt                     12.772093   1.173779e-37  \n",
       "5099_irnt                     12.849057   4.353277e-38  \n",
       "23101_irnt                    22.851485  7.062672e-116  \n",
       "23102_irnt                    22.782178  3.443984e-115  \n",
       "5132_irnt                     12.940299   1.332958e-38  \n",
       "5135_irnt                     12.783251   1.016931e-37  \n",
       "23129_irnt                    21.495238  8.625402e-103  \n",
       "23130_irnt                    21.586538  1.201776e-103  \n",
       "23105_irnt                    23.666667  3.975746e-124  \n",
       "3143_irnt                     17.484127   9.464485e-69  \n",
       "23117_irnt                    23.758242  4.515333e-125  \n",
       "23113_irnt                    23.402174  2.030616e-121  \n",
       "23114_irnt                    23.391304  2.619843e-121  \n",
       "23118_irnt                    23.955556  4.043505e-127  \n",
       "4100_irnt                     16.962121   7.829400e-65  \n",
       "4119_irnt                     16.556391   7.198758e-62  \n",
       "23121_irnt                    22.857143  6.204494e-116  \n",
       "23122_irnt                    22.890110  2.914687e-116  \n",
       "23125_irnt                    23.465909  4.547791e-122  \n",
       "23126_irnt                    23.306818  1.890436e-120  \n",
       "21002_irnt                    26.461538  1.343854e-154  \n",
       "23098_irnt                    26.474359  9.566917e-155  \n",
       "5084_irnt                     15.076923   1.148641e-51  \n",
       "...                                 ...            ...  \n",
       "COPD_EXCL                      6.400000   7.768848e-11  \n",
       "ASTHMA_MEDICATIO_COMORB        6.846154   3.793095e-12  \n",
       "20110_6                        5.800000   3.315746e-09  \n",
       "M13_MENISCUSDERANGEMENTS       5.866667   2.223217e-09  \n",
       "20003_1140860696               6.357143   1.027704e-10  \n",
       "X_RESPIRATORY                  6.000000   9.865876e-10  \n",
       "PULMONARYDG                    6.000000   9.865876e-10  \n",
       "20003_1141146234               6.066667   6.529610e-10  \n",
       "1150_3                         5.000000   2.866516e-07  \n",
       "PULM_MEDICATIO_COMORB          6.153846   3.781302e-10  \n",
       "D12                            7.166667   3.842292e-13  \n",
       "6154_6                         6.500000   4.016001e-11  \n",
       "20002_1138                     6.230769   2.320752e-10  \n",
       "2159                           5.571429   1.263295e-08  \n",
       "K21                            7.090909   6.661692e-13  \n",
       "20003_1140923346               6.000000   9.865876e-10  \n",
       "6147_4                         5.214286   9.226344e-08  \n",
       "H26                            4.500000   3.397673e-06  \n",
       "K29                            5.750000   4.462172e-09  \n",
       "6147_2                         5.166667   1.191529e-07  \n",
       "I84                            4.928571   4.141653e-07  \n",
       "6179_4                         5.583333   1.179759e-08  \n",
       "20002_1474                     5.181818   1.098667e-07  \n",
       "20002_1452                     3.294118   4.936558e-04  \n",
       "N39                            4.750000   1.017083e-06  \n",
       "K11_OTHGASTR                   4.666667   1.530627e-06  \n",
       "6144_1                         4.909091   4.574978e-07  \n",
       "6179_6                         3.833333   6.320923e-05  \n",
       "680_4                          4.500000   3.397673e-06  \n",
       "Z09                            3.750000   8.841729e-05  \n",
       "\n",
       "[561 rows x 7677 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h2_total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "suffix_bp_list_power=\"bp300 bp128 bp64 bp32 bp16 bp8\".split()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "regression_result_bp_list_dict=dict([make_regression_result_list(h2_total,phenotype,suffix_bp_list_power) for phenotype in phenotypes_par_filtered.index])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "h2_bp8_df=pd.DataFrame([[phenotype]+(regression_result_list[-1]['h2']/regression_result_list[-1]['h2'].sum()).tolist() for phenotype,regression_result_list in regression_result_bp_list_dict.items()])\n",
    "h2_bp8_df=h2_bp8_df.set_index(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "375235"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum([regression_result_bp_list_dict['23115_irnt'][i].shape[0] for i in range(len(regression_result_bp_list_dict['23115_irnt']))])*497"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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